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Through regulation of the extracellular fluid volume , the kidneys provide important long-term regulation of blood pressure . At the level of the individual functional unit ( the nephron ) , pressure and flow control involves two different mechanisms that both produce oscillations . The nephrons are arranged in a complex branching structure that delivers blood to each nephron and , at the same time , provides a basis for an interaction between adjacent nephrons . The functional consequences of this interaction are not understood , and at present it is not possible to address this question experimentally . We provide experimental data and a new modeling approach to clarify this problem . To resolve details of microvascular structure , we collected 3D data from more than 150 afferent arterioles in an optically cleared rat kidney . Using these results together with published micro-computed tomography ( μCT ) data we develop an algorithm for generating the renal arterial network . We then introduce a mathematical model describing blood flow dynamics and nephron to nephron interaction in the network . The model includes an implementation of electrical signal propagation along a vascular wall . Simulation results show that the renal arterial architecture plays an important role in maintaining adequate pressure levels and the self-sustained dynamics of nephrons .
An important feature of the vascular system is its highly branched structure . It is assumed that the branching geometry of blood vessels is governed by defined principles [1–3] . Tree-like structures of arterial systems have been a subject of experimental and theoretical studies related to vascular topology and flow [4–6] . Structural connectivity in vascular networks directly influences their functional state , defining how much oxygen and nutrients are delivered to different regions of the organism . The network experiences local responses affecting the local blood flow and altering the overall state of the network [7–9] . The topology of the vascular network is also an important determinant of the consequences of pathological processes in the vessels [10] . Nephrons are arranged in a complex branching structure , the nephro-arterial network , that delivers blood to each nephron and , at the same time , provides the basis for an interaction between each nephron and neighboring ones . The interaction between nephrons across the arterial network results in complex spatial patterns of synchronization between tubular pressure oscillations of the individual nephrons . This was demonstrated in several recent studies employing measurements of renal cortical blood flow using laser speckle flowmetry [11–14] . At present , both the functional significance of spatial patterns and the mechanisms leading to their formation are unknown . An important step in the investigation of the mechanisms and the functional significance of the spatial synchronization of cortical blood flow is a better understanding of the topology of the renal circulation . This requires a detailed mapping of the entire renal vascular tree , a task that has become feasible due to the development of micro-computed tomography ( μCT ) [15] and optical clearance methods [16 , 17] . Such data provide a statistical basis from which computational models of the renal circulation can be generated . Two main features of the nephron-vascular network are ( i ) a significant pressure drop between the origin of an afferent arteriole and its insertion into the glomerulus , and ( ii ) the presence of the tubulo-glomerular feedback mechanism . Experimental results [18 , 19] show that neighboring nephrons adjust their TGF-mediated tubular pressure oscillations to attain a synchronized regime . Neighboring nephrons interact by vascular propagation of electrical signals , initiated by the TGF of each nephron , which travel via endothelial cells in the arteriolar wall and can reach ( the branch point with ) the neighboring afferent arteriole . This coupling tends to produce simultaneous constriction of the afferent arterioles leading to in-phase synchronization of the oscillations . Another type of interaction , hemodynamic coupling , occurs due to the topology of the renal vasculature . Sharing of blood flow at each branch point of the arterial tree provides an interdependence of the local perfusion pressures , and , hence , affects the hemodynamics of all nephrons located downstream of the branch point . Due to a significant time lag in the system , this type of interaction can lead to anti-phase synchronization betweeen nephrons . Depending on the precise topology of the arteriolar network the first of these mechanisms may be more important for the local coupling of nephrons , with the other mechanism more important for global coupling . Several recent modeling studies have investigated features of nephron-nephron interactions [20–22] . The complex renal arterial network in these studies was approximated by a simple bifurcating tree-like structure and included a group of 10–20 nephrons . Postnov et al . [20] showed that such a topology induces desynchronization between originally identical nephron models ( coupling-induced inhomogeneity ) . Marsh et al . [21] demonstrated the presence of steady state , quasiperiodic and chaotic dynamics in an ensemble of cortical and medullary nephrons depending on the interaction strengths and the arterial blood pressure . Bayram et al . [22] suggested that several nephrons originating from the same small artery are more likely to be in an oscillatory state than a single nephron will . We recently demonstrated [23] that ( i ) neighboring nephrons at the same branch level were synchronized in-phase due to the vascular propagated electrical coupling , ( ii ) nephrons separated by one branch point tended to display a phase-shifted pattern due to hemodynamic coupling , and ( iii ) distantly located groups of nephrons showed asynchronous behavior . Marsh et al . [24] introduced heterogeneity into the nephro-arterial network , and observed similar dynamical patterns . However , all of the studies cited used an overly simplified representation of the renal arterial network , and it is not clear to what extent the observed dynamics might be a consequence of the simplification . Further progress requires the use of more realistic models of the renal arterial network . In this study we focus on understanding how renal arterial structure affects blood flow dynamics . To address this question: ( i ) we perform experiments using optical tissue clearance to resolve details of microvascular structure not previously available; ( ii ) we develop an algorithm for generating a realistic model of the renal arterial network using the data obtained from our experiments and from micro-computed tomography study by Nordsletten et al [15]; ( iii ) we present a mathematical model that describes blood flow dynamics and nephron to nephron interaction in the renal nephro-arterial network including a new implementation of electrical signal propagation along a vascular wall; and ( iv ) we simulate how renal specific vascular structure can affect renal blood flow patterns and nephron-to-nephron interactions .
We suggest a general algorithm to create an asymmetrical bifurcating tree based on experimental data , with modifications that are specific for the renal microcirculation: In both cases the structure of the vascular networks is simulated using a probability-based bifurcating algorithm together with Murray’s law [1] . Main features and assumptions of the suggested algorithm are described below: A more detailed description can be found in Materials and Methods . The available data were collected by means of μCT with 20-μm and 4-μm resolution and published by Nordsletten et al . [15] . For the distribution of daughter vessel diameters as a function of the parent vessel diameter we use a cubic approximation for mean values and a linear approximation for standard deviations ( Fig 2 , left panel ) . For simplicity , it is assumed that branch level corresponds to a Strahler order . For a given parent vessel diameter , we use the cubic approximation to find the mean and standard deviation for the distribution of daughter vessel diameters . The diameter for the first daughter vessel is then drawn at random from this distribution . The diameter of the second daughter vessel is calculated so that it complies with Murray’s law . For the length distribution as a function of the vessel diameter , a linear approximation is used for both mean and standard deviations for the best fit in the range of [20;100] μm ( Fig 2 , right panel ) . Based on these approximations we build a vascular tree with gradually decreasing diameter and with afferent arterioles located only at the end of the tree ( ABT ) . Nordsletten et al . found only a small deviation ( ≈1% ) between a regression analysis performed on all daughter-parent diameter relationships and Murray’s law [15] , a finding that justifies our use of it . Accordingly , we designed our algorithm to ensure that the vascular tree it generates also follows Murray’s law . Because vessel diameters at a bifurcation fulfill Murray’s law , they are not statistically independent . This reflects the fact that knowing the diameters of two of the three vessels in a bifurcation , the diameter of the last vessel can be calculated from Murray’s law . It should also be noted that the assumption that Strahler order corresponds to branching order may introduce some errors . Two daughter vessels need not have the same Strahler order , as this depends on the number of bifurcations that occur downstream from the respective daughter vessels [25–27] . Thus , the Strahler ordering scheme categorizes the vessels starting from the smallest vessels going upstream to the larger vessels . In principle it is therefore not possible to assign a Strahler order to a given vessel before the downstream branching process has come to an end . Since our algorithm “works” from the larger vessels towards the smaller ones , the Strahler ordering scheme is incompatible with the bifurcating algorithm , since it , in a sense , only can be applied retrospectively . However , the effect of this is expected only to be of minor significance . The data obtained by μCT provide important information about the dimensions of the larger renal vessels . However , due to the limited spatial resolution , important information on afferent arterioles is missing . Different studies show that ( i ) afferent arterioles ( AA ) can branch not only from small vessels , but also from larger arteries [28 , 29] and ( ii ) two , three , or four afferent arterioles can originate together from a single site on an artery [30] . These features make the renal vascular structure significantly different from the vascular structure of other organs [31 , 32] . We assume that the distances between the origins of AAs on the vascular tree can be described by an exponential distribution . This assumption is based on several observations: The simplest possible distribution that satisfies observations 1 and 2 is an exponential distribution . Finally , we assumed that the distribution of the distance between neighboring AAs is independent of the diameter of the feeding vessel . To provide experimental support for using an exponential distribution for the distances between the origins of afferent arterioles , we performed experimental studies to visualize renal vascular architecture using confocal microscopic imaging of chemically cleared renal tissue ( see details in Materials and Methods ) [16 , 17] . We obtained two 3D stacks of renal vascular structure , from which we measured the distances between the origins of neighboring AAs . An example of the observed structures is shown in Fig 3 , right panel . The data set contains information on distances between origins of more than 150 afferent arterioles branching from the feeding vessels with diameters of 25–100 μm . An exponential function ( red ) provides a good fit to the data ( blue ) ( Fig 3 , left panel ) . The exponential distribution is used to generate a vascular tree specific to the kidney ( KSABT ) where afferent arterioles may branch off any vessel on the arterial structure . Using the algorithm described above and the statistical data on renal vascular morphology , a renal vascular structure is generated in the form of a connectivity matrix that maps the connections between vessels as well as the vessel length and diameter . The matrix is used as the input for full-scale computer simulations . A hemodynamic interaction occurs when one nephron is stimulated by its TGF-mechanism to contract its afferent arteriole , causing the hydrostatic pressure to rise at the upstream branch node , increasing the blood flow to the second nephron . Half a period later when the increased blood flow activates the TGF-mechanism in the second nephron and causes its afferent arteriole to contract , the blood flow to this nephron is reduced , and the blood flow to the first nephron increases . Due to the time lag in the response , this type of interaction tends to induce anti-phase synchronization between nephrons . To describe the flow in each vessel of the vascular tree , one needs the hemodynamic resistance Rhdr calculated from the Poiseuille relationship: R h d r = 128 η L π D 4 . ( 1 ) Here L is the length of the vessel , η is the viscosity of blood . The viscosity depends on vessel diameter , and we adopt the expression for viscosity from Ref . [33] , assuming an invariant hematocrit of 0 . 45: η = 1 + 6 e x p - 0 . 085 D + 2 . 2 - 2 . 44 e x p - 0 . 06 D 0 . 645 D D - 1 . 1 2 D D - 1 . 1 2 , ( 2 ) where η is in units of centipose and D is the inner diameter of the vessel in micrometers . The pressure variation in each node , derived from expressions for conservation of flow in each node ( Fig 4 ) , is given by: afferent : C h d r d P j d t = P p n j - P j R h d r v j - F n e p h j , ( 3 ) other : C h d r d P j d t = P p n j - P j R h d r v j - P j - P d n 1 j R h d r d v 1 j - P j - P d n 2 j R h d r d v 2 j . ( 4 ) Here Chdr is the vessel compliance . Ppnj , Pdn1j , Pdn2j , and Pj denote pressure in the parent node , the first daughter node , the second daughter node , and the current node , respectively . Rhdrvj , Rhdrdv1j , and Rhdrdv2j are the hemodynamic resistance of the current vessel , the first daughter vessel and the second daughter vessel , respectively . Pressure in the root of the tree and blood flow to the nephrons Fnephj serve as boundary conditions , where Fnephj is calculated for each nephron as the difference between the pressure in the node giving rise to the afferent arteriole and the glomerular capillary pressure divided by the hemodynamic resistance of the afferent arteriole . The hemodynamic resistance is determined from the nephron model ( Eq ( 16 ) . Activation of TGF in one nephron causes membrane depolarization and contraction of the vascular smooth muscle cells of the corresponding afferent arteriole . Because the cells of the renal vessels are coupled electrically by gap junctions [34] , the membrane depolarization spreads , through electrotonic conduction , into the vascular smooth cells of the neighboring AAs causing them to contract [35] . Thus , the cells of the renal vasculature constitute a well-coupled syncytium in which the endothelial cells form the path of least resistance [36] . The deflection in the membrane potential generated by the macula densa decays exponentially with the distance between the site of contact between the macula densa and the AA [35 , 37] . The electrotonic spread between the vascular cells is fast , and it acts to adjust the TGF-mediated tubular pressure oscillations to attain a state of in-phase synchronization [18] . Fig 5 shows a schematic representation of the electrical coupling . Electrotonic propagation of the electric current originating from the macula densa through vascular segments is described by the following equations: For segment sites that are connected to nephrons: C u d V d t = I n + G g ( V p r e v - V ) - G u ( V - V r e s t ) , ( 5 ) where In stands for the current induced by the macula densa signal and transmitted to the vessel . Vprev describes the voltage in the previous ( downstream ) adjacent unit of the same segment . Vrest is the resting membrane potential , Cu is the unit capacitance . Gu and Gg are the unit and gap junction conductance , respectively . For inner units of the segment: C u d V d t = G u ( V p r e v + V n e x t - 2 V ) - G u ( V - V r e s t ) , ( 6 ) where Vprev and Vnext denote potentials for downstream and upstream adjacent units , respectively . At the branch point ( downstream of the vessel ) : C u d V d t = G g 1 ( V 1 - V ) + G g 2 ( V 2 - V ) + G g ( V n e x t - V ) - G u ( V - V r e s t ) , ( 7 ) where V1 , 2 , and Gg1 , 2 represent the voltages at the ends of connected vessel segments and gap junction conductances , respectively . If the branch point is upstream relative to the current segment then Vprev should be used instead of Vnext . If the current segment has branch points from both sides then Gg ( Vnext − V ) should be replaced with the corresponding conductances and voltages for the connected vessels . For the root segment Gg1 ( V1 − V ) + Gg2 ( V2 − V ) should be replaced with specific values for G and resting values for V1 and V2 . The model of the individual nephron consists of six coupled ordinary differential equations , each representing an essential physiological relation and a number of algebraic functions . A sketch of the main components of the nephron model is given in Fig 6 . The six coupled differential equations are ( see Material and Methods , [38] for details ) : d P t d t = F f i l t - F r e a b - F H e n / C t u b ( 8 ) d r d t = v r ( 9 ) d v r d t = P a v - P e q ω - K × v r ( 10 ) d X 1 d t = F H e n - 3 T × X 1 ( 11 ) d X 2 d t = 3 T × ( X 1 - X 2 ) ( 12 ) d X 3 d t = 3 T × ( X 2 - X 3 ) ( 13 ) The first equation of the model ( eq 8 ) represents the pressure variations in the proximal tubule in terms of the in- and outgoing fluid flows . Here , Ffilt is the single-nephron glomerular filtration rate and Ctub is the elastic compliance of the tubule . The flow into the loop of Henle is determined by the difference ( Pt − Pd ) between the proximal and the distal tubular pressures and by the flow resistance RHenle . The reabsorption in the proximal tubule Freab is assumed to be constant . Eqs 9 and 10 describe the dynamics of the afferent arteriole . Here , r represents the inner radius of the vessel and v is its rate of change . ω is a measure of the mass relative to the elastic compliance of the arteriolar wall , Pav denotes the average pressure in the arteriole , and Peq is the value of this pressure for which the arteriole is in equilibrium with its present radius and level of muscular activation . The expressions for Ffilt , Pav and Peq involve a number of algebraic equations that must be solved along with the integration . The remaining equations ( eqs 11–13 ) in the single-nephron model describe the delay T in the TGF regulation . This delay arises both from the transit time through the loop of Henle and from the cascaded enzymatic processes between the macula densa cells and the smooth muscle cells that control the contraction of the afferent arteriole . Although the model is significantly simplified and does not contain a detailed description of all physiological mechanisms , its dynamical features include TGF oscillations , the response of afferent arteriole to increasing inlet pressure and , as a consequence , the autoregulation of efferent arteriolar blood flow ( Fig 7 ) .
We used the algorithms described above to construct models of both the renal vascular tree and a simple asymmetric bifurcating tree . Simple asymmetric bifurcating tree . Fig 8 shows an example of a simulation of the ABT structure based on the experimental data described by Nordsletten et al . [15] . The plots in the bottom panel show a high correlation of the simulated structure with the experimental data . Note , however , the divergence in the relation between daughter and parent vessel diameter in the simulated and the experimental data . As noted above , the experimental data are grouped according to Strahler order , and two daughter vessels will not necessarily belong to the same Strahler order . This is especially likely if the two daughter vessels have a large difference in diameter . In this case the larger daughter vessel will , on average , be expected to have a greater Strahler order than the one with the smaller diameter , since there is a greater likelihood for further downstream bifurcations in the branch coming from the larger daughter vessel . It is therefore not to be expected that the simulated and experimental distributions will be identical . Bifurcating tree with exponentially distributed afferent arterioles . An example of a simulation of a KSABT structure is shown in Fig 9 . Note that there is an even more pronounced difference in the dependence of daughter vessel diameter on parent vessel diameter compared to the experimental data [15] . The reason is that KSABT structure allows afferent arterioles to branch from any vessel ( except the renal artery ) and recalculates the vessel diameter after each bifurcation in accordance with Murray’s law . This algorithm leads to segmentation of large vessels so that at the end of each segment the vessel divides into an afferent arteriole and a segment with a slightly reduced diameter . The distribution of the distances between afferent arterioles ( Fig 9 , bottom panel ) is based on the experimental data obtained from optical clearing methods [16 , 17] . Notice that the number of afferent arterioles in the ABT and KSABT structures differ even though the parameters were similar in the two cases ( see Table 1 ) , and that in the KSABT structure the vessels feeding the afferent arterioles are , on average , wider than for the ABT structure where afferent arterioles appear only at the top of the tree . The larger diameter of the feeding vessels in the KSABT structure reduces the hemodymamic resistance of the vessels and , consequently , the pressure drop from the renal artery to afferent arterioles will be smaller when compared to that in the ABT structure . As discussed above , nephrons can interact with each other via hemodynamic and/or electrical coupling . These interactions can lead to different dynamical patterns , and can affect the physiological properties of the whole kidney . The vascular structure is important for the characteristics of both types of interactions . It affects the electrical properties of the vascular tree in the following ways: To estimate the effect of vessel dimensions on the electrical coupling on a pair of nephrons , we performed simulations for a minimal branching structure consisting of one branch point and three vessels , a root vessel and two afferent arterioles . The lengths of the root vessel and the first afferent arteriole are fixed at 300 μm and 50 μm , respectively , and the diameter of the afferent arteriole is fixed at 20 μm . The length and diameter of the second afferent arteriole are varied from 50 to 250 μm and from 20 to 40 μm , respectively . The root vessel diameter is adjusted according to Murray’s law . Fig 10 illustrates how the coupling strength depends on the length and diameter of the second afferent arteriole . Inspection of the figure shows that electrical interaction in a tree with short afferent arterioles leads to synchronization of nephron dynamics ( right bottom panel ) , while in case of long afferent arterioles the nephrons are desynchronized ( right top panel ) . A characteristic feature of the renal vascular tree is that the pressure drop from the renal artery to the start of the afferent arteriole is small , whereas there is a relatively large pressure drop across the afferent arteriole [40] . This allows for efficient regulation of the glomerular filtration pressure through regulation of the resistance of the afferent arteriole . Simulations on the two types of vascular structures show that the vascular structure with exponentially distributed AAs ( KSABT ) has much lower pressure drop compared to the ABT . Simulations for a small branch of 40 μm of initial diameter ( Fig 11 ) with realistic hemodynamic resistances and root feeding pressure Proot clearly show that in the case of KSABT ( right panel ) the tubular pressure Pt of all nephrons is quite high and TGF oscillations can be observed . For the ABT , however , the root feeding pressure for the nephrons is low and outside the working range of the single nephron model that leads to negative values of Pt ( left panel ) . In the nephron model that we use the negative values of tubular pressure are observed when the root feeding pressure is low and cannot balance flows and pressures on the venous side . The higher pressures in the KSABT structure can be explained by two related factors: ( i ) the total number of branching levels is smaller in the KSABT than in the ABT network , and , ( ii ) the average diameter of feeding vessels is larger in KSABT , reducing the pressure drop between the renal artery and the afferent arterioles and providing higher pressure to the nephrons . The working pressure interval is depicted on the bottom panel of Fig 11 . All nephrons in the KSABT work properly from 8 kPa in the root artery . This allows us to use a realistic pressure ( around 13–13 . 5 kPa ) in the renal artery . In the case of ABT there is no oscillatory behavior even at “hypertensive” values of the pressure . Autoregulation properties of the two types of structures differ . Nephrons in ABT are perfused at inadequate pressure to support autoregulation of total efferent flow or glomerular filtration rate ( Fig 12 , left ) . In contrast , the structure of KSABT provides nephrons with a higher pressure , supporting autoregulation at the level of individual nephrons and net flow ( Fig 12 , right ) . A significant shift in nephron feed pressure in KSABT leads to prolonged and smoothed range of autoregulation for net flows .
The systemic circulatory system plays a central role in supplying organs and tissues with oxygen and nutrients and removing metabolic waste products . On a large scale , the circulatory system can be viewed as a branching network where larger vessels on the arterial side branch into smaller and smaller vessels until the capillary level is reached . It is clear however that there are large differences in the structure of the vascular system among different tissues and organs , and that this difference relates to the specific functions of the tissue and organ . In other words , the vascular topology is optimized to subserve the specific needs of a given organ or tissue . In skeletal muscle , the main need is to allow for large variations in total blood flow so as to match the supply with demand during muscle work . In other organs , like the brain , there is no need for large variations in overall flow , but there is a need to redirect flow to local areas where the metabolic activity is high . In the kidney , the critical aspect of the circulation is not only to supply the tissues with oxygen and nutrients , but rather to support the filtration of plasma at the glomerulus of each nephron . For example , in the human kidneys an amount of fluid corresponding to approximately 3 times the body weight is filtered every 24 hours . This difference in function poses restrictions on the design of the vascular system in the specific organs and tissues . Many diseases , e . g . hypertension and diabetes , affect the vascular system , and the morbidity and mortality of these diseases are to a large extent associated with the diseases’ effects on the vascular system . The effect of the disease in a given organ is not only the result of the disease process itself , but also depends on the specific topology of the vascular system in the organ [10] . In recent years several new imaging modalities , e . g . μCT , and confocal microscopy , have made it possible to obtain images of the entire microcirculation in a given organ [9 , 15] . This has paved the way for a better understanding of the complex interrelationship between organ function and vascular topology . To obtain a thorough understanding of the role of vascular topology in organ function it is necessary to combine experimental and modeling approaches , since many questions cannot be addressed experimentally . In this connection , a major task is to create realistic models of the vascular tree in a given organ as a basis for further model studies . In this study we present new experimental findings of the 3D arterial and afferent arteriolar structure and we develop a probability-based algorithm for generating such structures from the data . To study the role of kidney specific vascular structure we also introduce a mathematical model that describes blood flow dynamics and nephron-to-nephron interactions in the arterio-nephron network . The simulation results indicate that the arterial structure in the kidney minimizes the pressure drop between the main renal artery and glomeruli , and it affects nephron-nephron interactions . One of the main results is that the distances between individual AAs are exponentially distributed . This conclusion is the result of the analysis of 3D data sets with more than 150 afferent arterioles obtained with the optical clearance method . This result differs from that in most other organs where the interbranch distance seems to follow a lognormal distribution [31 , 32] . Exponential distributions are typically seen in Poisson processes where the time or distance between events are independent of each other . The genetic/molecular mechanisms that underlies such a branching pattern are unknown , and an interesting subject for further theoretical work . Statistical distributions derived from our data and from the literature [15] form the basis for our structure generating algorithm . The microcirculation of the kidney is unusual because it has 2 capillary networks , the glomerular capillaries and the peritubular capillaries , connected through the efferent arteriole . Compared to capillary networks in other organs , the glomerular capillaries operate with high intravascular pressure , a condition required to to drive the filtration of fluid across the capillary wall into the lumen of the proximal tubule . The high filtration rate is a prerequisite for the ability of the kidneys to regulate the volume and composition of the extracellular fluid . When we used a simple bifurcating tree ( the ABT algorithm ) , it was apparent that with the vessel dimensions reported by Norsletten et al . [15] , the pressure drop from the renal artery to the glomerular capillaries exceeds the value found experimentally [40] . This is not surprising , since in the ABT the AAs only appear at the terminal branch points of the tree , an assumption that maximizes the hemodynamic resistance between the renal artery and the glomerulus . In contrast , when afferent arterioles are distributed exponentially across the vascular tree and allowed to branch from any arterial segment , the resulting pressure in the glomerular capillaries is significantly greater , and in a range that is compatible with normal nephron function . It is of interest to note that only the KSABT algorithm resulted in a realistic number of nephrons for the whole kidney ( Table 1 ) , i . e . around 33 , 000 [41 , 42] . Because the ABT only has glomeruli at the terminal branches , the number of nephrons becomes much lower than in the KSABT , where AAs , and thus nephrons , also originates from the larger vessels . For the ABT to give a realistic number of nephrons , it will be necessary to start it with a vessel of an unrealistically large diameter . The topology of the arterial network influences not only the pressure drop along the network , but also electrically mediated nephron-nephron interactions . As shown in Fig 10 signal propagation depends on the dimensions of the branching vessels . A signal from the macula densa is conducted through the nephron’s afferent arteriole and , at the next bifurcation , propagates preferentially to the largest diameter branch . A nephron whose AA originates from a larger vessel will therefore be less efficient in synchronizing with its neighbors than will a nephron at the distal end of the arterial tree , where the vessels tend to be of a similar scale . Previous studies on nephron-nephron interactions have used a simple bifurcating tree when investigating the network properties of the renal circulation [23 , 24] . Such simplifications , by ignoring significant structural asymmetries , could therefore overestimate synchronization . Another important feature is that asymmetrical connections of endothelial cells can lead to anisotropic propagation of electrical signal between two afferent arterioles and can affect nephron-to-nephron interaction . Although our algorithm results in architecturally realistic arterial networks for the renal circulation , the tubular pressure distribution between nephrons is exaggerated , cf . Fig 11 . Experimental data has shown that the proximal tubular pressure is quite uniform in nephrons on the surface of the kidney [43] . This suggests the presence of additional processes that acts to homogenize the pressure distribution between nephrons . One possibility is that the length , and thereby the resistance , of the AA may vary according to the branch site , i . e . if the AA branches from a larger vessel with a high intravascular pressure it may be longer compared to a similar vessel that from a smaller vessel . At present , there is no data on renal vascular morphology to test this possibility , and it has therefore not been included in the present algorithm . Another possibility is that the individual vessels actively adjust their radius to compensate for differences in the feeding pressures . The AA has a myogenic response [44] that allows it to adjust its radius in response to changes in the intravascular pressure . An increase in pressure will provoke vasoconstriction , increasing the hemodynamic resistance of vessel , reducing glomerular pressure towards the control value . This mechanism plays an important role in renal autoregulation of blood flow [45] and is implemented in our model in a simplified way . An anatomical feature not explicitly included in the present algorithm is the presence of triplets and quadruplets at the branch sites , as reported in [30] . Such triplets or quadruplets of AAs mostly occur at the top of the tree , since the small arteries will always split onto two AAs . We do not explicitly model the population of juxtamedullary nephrons in the present work . However , it is known that this subpopulation of nephrons predominantly derive their afferent arterioles from the larger vessels in the arterial network , e . g . the arcuate and subarcuate arteries [28] . However , the available data does not allow specific modeling of this subpopulation . In the present work we have chosen to use a simple nephron model [38] . The model , detailed below , has primitive representations of TGF and the afferent arteriole that cause a single combined effect on afferent arteriolar diameter in response to arterial pressure change . When several copies are combined in a network configuration the model generates interactions [20 , 21 , 23] similar to those produced with more detailed representations of TGF and the afferent arteriole [24] . Because of the complexity of the network a minimal model such as the one we use here best serves the purposes of the study by providing the clearest opportunity for understanding the contributions of the vascular structure . To test stability of the model we ran simulations for different values of parameters Chdr ( 0 . 3–5nL/kPa ) , β ( 0 . 4–0 . 67 ) , α ( 12–20 ) . We found minor quantitative changes but the dynamics of the model with ABT and KSABT structures remained qualitatively unchanged . In conclusion , we have demonstrated that the renal vasculature has specific characteristics that were not taken into account in previous modeling studies . We have developed a new algorithm for generating a renal arterial network using experimental data on the length and radius distribution of renal vessels . The resultant network topology closely resembles that found in normal rats . We have found that in the contrast to the simpler known algorithm , the kidney specific vascular tree ( based on our data and from the literature ) contained a realistic number of nephrons , displayed adequate pressure levels in the nephrons , and tended to weaken interactions between nephrons whose afferent arterioles originated from larger vessels compared to the nephrons on top of the tree .
All experimental protocols were approved by the Danish National Animal Experiments Inspectorate and were conducted in accordance with guidelines of the American Physiological Society . A 12 week old Sprague-Dawley rat was anesthetized ( intraperitoneal injection of pentobarbital ) , a catheter was inserted into the aorta , and the vena cava was opened . The blood was removed by perfusing the animal with PBS with nifedipine and HEPES to prevent blood clotting . Thereafter the animal was perfused with biotin ( 2 mg/ml ) that binds to endothelial cells , washed with PBS and perfused with Alexa-647 Streptavidin ( 20 mg/ml ) . The animal was then fixed with 4% paraformaldehyde in PBS using a pressure of 90 mmHg . The kidneys were removed and a section of one of the kidneys was clarified according to Erturk et al . [17] , using dehydration steps in THF and final clearing in DBE [16] . Tissue dehydrated by this method shrinks approximately 20% in each dimension . The kidney slice was scanned with a 633 nm laser in a Zeiss LSM 710 equipped with a 10X/0 . 3 NA objective . Two 3D stacks were recorded , covering 850 x 850 x 497 μm and 850 x 850 x 1018 μm . The second image stack was obtained with spectral imaging to obtain both green autofluorescence from tubules and the Alexa-633 signal from both vessels and tubuli . The combined image showing tubuli in one channel and vessels and tubuli in the other was used as input for the segmentation protocol . The dual channel image was then automatically segmented in the open source image processing package “FiJi” , using pixel-based segmentation plugin “Trainable Weka Segmentation” . After segmentation the 150 images in the stack were manually corrected in FiJi for missing features and holes in vessels . The 3D representations ( Figs 3 and 13 ) were performed with open platform for bioimage informatics “Icy” . To build a vascular structure using the present algorithm four distributions are needed: Gddp ( daughter-parent diameter distribution ) , Gvlvd ( vessel length–vessel diameter distribution ) , Eaad ( distribution of distances between two neighboring AAs ) and Gaad ( distribution for afferent arterioles diameter ) , where Gddp , Gvlvd and Gaad are assumed to be Gaussian distributions and Eaad is an exponential distribution . All distributions were obtained from experimental data . Two parameters are used to control the structure size: Dinitial describes the diameter of the first vessel and Dstop is the vessel diameter where bifurcations stop . This algorithm combines a recursively bifurcating algorithm ( for a simple bifurcating structure ) and recursively bifurcating algorithm with additional segmentation ( for a structure with exponentially distributed distances between AAs ) ( Fig 14 ) . We simplified the model described in Ref . [46] with some additional assumptions: This allows us to build a computationally simple , but physiologically relevant , model for the propagation of the electrical signals generated by the nephrons . Fig 15 represents the main notations of the coupling . Each endothelial cell is described by the whole-cell conductance Gc which can be linear or nonlinear , and instantaneous ( very fast activated ) or inertial ( governed by the dynamics of gating variables ) . We use the simplest representation in the form of an instantaneous and linear whole-cell conductance Gc ( assumptions 1 and 2 ) being connected in parallel with the whole-cell capacitance Cc and coupled with neighbors by means of gap junction conductances Ggj . There is considerable variability with regard to the reported morphological and electrical parameters of endothelial cells . For the present work we chose parameters to be in accordance with those in Ref . [46] . We assume that a vessel segment of diameter D contains N = πD/Wc endothelial cells in its cross-section , where Wc ≈ 5μm is a typical width of an individual cell . Similarly , we assume that the length L of the vessel segment is composed of M = L/Lc endothelial cells , where Lc ≈ 50μm is a typical effective length of an endothelial cell . The length of an endothelial cell is around 100 μm . However , due to overlap of the ECs in the vessel wall , the effective length is approximately half the the real length of the cell [47] . We ignore possible spatial inhomogeneity of voltage distribution in a vessel cross-section . Thus , we describe each unit ( piece of vessel with length Lc ) with its unit conductance Gu = NGc , unit capacitance Cu = NCc , and total gap junction conductance Gg = NGgj . A vessel segment is represented by a chain of M such coupled units . At the branch ( bifurcation ) point three vessel segments are connected to each other . At this point we assume a triangle-shaped coupling geometry as shown in Fig 15 . To calculate Gg1 , Gg2 , Gg3 we use assumption 3 listed above . Cells from the i-th segment connected to the j-th and k-th segments are split in two parts according to circumference ( or equivalently—diameters ) of the j-th and k-th segments . So , Ni = Nj + Nk , where Nj/Nk = Dj/Dk and N is the number of cells which is always an integer . However , this rule gives different number of cells for different vessels at the same branch point . For example , the number of cells Nj−k from j-th to k-th segment will differ from the number of cells Nk−j from the k-th to j-th segment . The existence and value of this mismatch can not be predicted in advance . Thus , we assume that the number of gap junctions between two segments is equal to the arithmetic average between the number of cells in the connected vessels . In this way we have some intermediate , but symmetric value for inter-segment coupled cells: Njk = Nkj = ( Nj−k + Nk−j ) /2 . The conductance of such coupling is Ggjk = Ggkj = NjkGgj . At the root of the tree the same rule for the conductance is applied . However , we assume that the diameter of vessels downstream from the root is large enough , so that each endothelial cell from the root segment will be connected to two endothelial cells from downstream vessels . This gives us Ggroot = 2NiGgj for each connected vessel , where Ni is the number of cells in the circumference of the root segment . Physiological justification for all equations and expressions are given in Ref [38] . Here we focus on computational implementation of the model . Example of initial condition is: Pt = 1 . 7923 kPa , r = 1 . 0221 , vr = −0 . 0149 , X1 = 0 . 9177 , X2 = 0 . 8903 , and X3 = 0 . 8695 . The functions that are used in the model are described below . Flow in the loop of Henle: F H e n = P t - P d R H e n ( 14 ) Preglomerular resistance R a = R a 0 × ( β + 1 - β r 4 ) ( 15 ) R = R a 0 R a ( 16 ) We solve the third-order equation A C e 3 + B C e 2 + C C e + D = 0 to find Ce , the efferent arteriolar plasma protein concentration . For appropriate parameter valse the equation has a single , positive solution . This solution is determined numerically for each integration step . A , B , C , D are found from the model equations: A = b + R × b × H a ( 17 ) B = a + R × b × C a × ( 1 - H a ) + R × a × H a ( 18 ) C = P t - P v + R × a × C a × ( 1 - H a ) + R × ( P t - P a ) × H a ( 19 ) D = ( P t - P a ) × R × C a × ( 1 - H a ) ( 20 ) Functions of plasma protein concentrations: P g = b × C e 2 + a × C e + P t ( 21 ) P a v = 1 2 × ( P a - ( P a - P g ) × β × R a 0 R a + P g ) ( 22 ) F F i l t = ( 1 - H a ) × ( 1 - C a C e ) × P a - P g R a ( 23 ) The tubuloglomerular feedback function Ψ: Ψ = Ψ m a x - Ψ m a x - Ψ m i n 1 + Ψ e q - Ψ m i n Ψ m a x - Ψ e q × e x p ( α × ( 3 × X 3 T × F H e n 0 - 1 ) ) ( 24 ) The depolarisation of the cells in the afferent arteriole due to the TGF signal from the macula densa is assumed to be directly proportional to Ψ . Equilibrium pressure: P e l = 1 . 6 × ( r - 1 ) + 2 . 4 × e ( 10 × ( r - 1 . 4 ) ) ( 25 ) P a c t = 4 . 7 1 + e ( 13 × ( 0 . 4 - r ) ) + 7 . 2 × r + 6 . 3 ( 26 ) P e q = P e l + Ψ × P a c t ( 27 ) Note that the parameter Pa in the single nephron-model becomes a variable in the nephro-arterial network .
|
By maintaining the volume and composition of the body fluids within narrow ranges , and by producing a set of hormones that affect the blood vessels , the kidneys provide important long-term regulation of blood pressure . Disturbances of kidney function can cause hypertension , a prevalent disease in modern societies . The kidneys protect their own function against short-term variations in blood pressure at the level of the individual unit ( the nephron ) . In recent years , it has become clear that there is an interaction between nephrons , and that this interaction is mediated through the arterial network of the kidney . The renal vacular network has a complex topology , and at present there are no computational models of this topology , precluding a computational assessment of the consequences of nephron-nephron interactions for renal blood flow control . In this work we focus on understanding how kidney specific vascular structure affects blood flow patterns and nephron-to-nephron interaction in kidney . The paper presents an approach to constructing realistic models of the renal vascular architecture . We developed a computational approach to reproduce the architecture and to examine its consequences for the operating regime of the nephrons .
|
[
"Abstract",
"Introduction",
"Model",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"medicine",
"and",
"health",
"sciences",
"body",
"fluids",
"cardiovascular",
"anatomy",
"endothelial",
"cells",
"renal",
"arteries",
"epithelial",
"cells",
"hemodynamics",
"arterioles",
"arteries",
"nephrons",
"kidneys",
"blood",
"vessels",
"animal",
"cells",
"biological",
"tissue",
"blood",
"pressure",
"hematology",
"blood",
"flow",
"blood",
"anatomy",
"cell",
"biology",
"physiology",
"epithelium",
"biology",
"and",
"life",
"sciences",
"cellular",
"types",
"renal",
"system",
"vascular",
"medicine"
] |
2016
|
Modeling of Kidney Hemodynamics: Probability-Based Topology of an Arterial Network
|
Invadopodia are specialized membrane protrusions composed of F-actin , actin regulators , signaling proteins , and a dynamically trafficked invadopodial membrane that drive cell invasion through basement membrane ( BM ) barriers in development and cancer . Due to the challenges of studying invasion in vivo , mechanisms controlling invadopodia formation in their native environments remain poorly understood . We performed a sensitized genome-wide RNAi screen and identified 13 potential regulators of invadopodia during anchor cell ( AC ) invasion into the vulval epithelium in C . elegans . Confirming the specificity of this screen , we identified the Rho GTPase cdc-42 , which mediates invadopodia formation in many cancer cell lines . Using live-cell imaging , we show that CDC-42 localizes to the AC-BM interface and is activated by an unidentified vulval signal ( s ) that induces invasion . CDC-42 is required for the invasive membrane localization of WSP-1 ( N-WASP ) , a CDC-42 effector that promotes polymerization of F-actin . Loss of CDC-42 or WSP-1 resulted in fewer invadopodia and delayed BM breaching . We also characterized a novel invadopodia regulator , gdi-1 ( Rab GDP dissociation inhibitor ) , which mediates membrane trafficking . We show that GDI-1 functions in the AC to promote invadopodia formation . In the absence of GDI-1 , the specialized invadopodial membrane was no longer trafficked normally to the invasive membrane , and instead was distributed to plasma membrane throughout the cell . Surprisingly , the pro-invasive signal ( s ) from the vulval cells also controls GDI-1 activity and invadopodial membrane trafficking . These studies represent the first in vivo screen for genes regulating invadopodia and demonstrate that invadopodia formation requires the integration of distinct cellular processes that are coordinated by an extracellular cue .
Basement membrane ( BM ) is a dense , highly cross-linked extracellular matrix that surrounds most tissues and acts as a barrier to migrating cells [1] . During development and immune function , specialized cells acquire the ability to cross BM barriers to facilitate cell movement into new tissues [2–4] . Misregulation of cell invasion also underlies the pathology of numerous human diseases , including cancer , where BM invasion initiates metastasis and is associated with poor prognosis [5 , 6] . Invadopodia are protrusive , F-actin rich , membrane associated structures that were identified over twenty years ago in vitro within transformed cells and highly metastatic cancer cell lines [7–9] . The formation and regulation of invadopodia have been examined extensively in cancer cell lines and tumor models because these structures are thought to facilitate tumor cell invasion across BM barriers [9–13] . Invadopodia are complex structures whose function requires the coordinated activity of actin regulators , signaling proteins , and membrane trafficking [8 , 14–17] . Due to the difficulty of examining the dynamic interactions between invasive cells , BM , and tissue being invaded in native physiological settings , the mechanisms that control invadopodia formation and activity in vivo remain largely unknown [18–20] . Anchor cell ( AC ) invasion into the vulval epithelium in C . elegans is a genetically and visually tractable model to dissect invadopodia formation and activity during BM invasion in vivo [20 , 21] . The AC is a specialized uterine cell that invades through the underlying BM to initiate contact with the vulval cells during uterine-vulval attachment . AC invasion is initiated by dynamic F-actin rich invadopodia that localize to the AC-BM interface ( the invasive cell membrane ) and breach the BM during a precise 15-minute window in the early-to-mid L3 larval stage . The netrin receptor UNC-40 ( DCC ) traffics to the breach site , where it promotes formation of a large protrusion that contacts the vulval tissue and clears a single large opening in the BM . Formation of the invasive protrusion also shuts down the production of invadopodia [22] . AC invadopodia are composed of F-actin and a number of actin regulators , including the ADF/cofilin ortholog UNC-60 and the Ena/VASP ortholog UNC-34 . In addition , we have found that invadopodia are constructed from a specialized invadopodial membrane , enriched for the phospholipid PI ( 4 , 5 ) P2 and the membrane associated Rac GTPases MIG-2 and CED-10 [23] . The invadopodial membrane is dynamically recycled through the endolysosome during invadopodia formation and breakdown [23] . The mechanisms that control and coordinate the assembly of F-actin and the trafficking of the invadopodial membrane to invadopodia during their formation are poorly understood . We performed a sensitized genome-wide RNAi screen and identified 13 putative regulators of invadopodia . Using quantitative live cell imaging , genetic analysis , and site of action studies , we have characterized two of these genes: the Rho GTPase cdc-42 , which promotes invadopodia formation in a number of cancers; and the Rab GDP dissociation inhibitor gdi-1 , a newly identified invadopodia regulator that mediates intracellular membrane trafficking . We find that both cdc-42 and gdi-1 are expressed and function in the AC to mediate distinct aspects of invadopodia formation . CDC-42 is localized and activated in puncta at the invasive cell membrane , where it promotes F-actin formation and initiates invadopodia generation through its effector WSP-1 ( N-WASP ) . In contrast , GDI-1 is localized in the cytosol and regulates the proper targeting of the invadopodial membrane to the invasive cell membrane where invadopodia form . Strikingly , the activity of both GDI-1 and CDC-42 are controlled by the vulval cells , which secrete an unidentified cue ( s ) that stimulates AC invasion . Loss of the vulval cells led to a dramatic decrease in the rate of invadopodia formation and mislocalization of F-actin and the invadopodial membrane throughout the cell . Together , our findings have identified new regulators of invadopodia in vivo and show that an extrinsic signal ( s ) generated by the tissue being invaded coordinates distinct cellular aspects of invadopodia formation in the invading cell to promote BM breaching and invasion .
During AC cell invasion , F-actin rich , membrane associated invadopodia mediate the initial BM breach ( Fig 1A ) . The netrin receptor UNC-40/DCC localizes to the BM breach site and directs formation of a large protrusion that widens the BM breach and shuts down additional invadopodia formation [22] . In the absence of UNC-40 , BM invasion is driven solely by invadopodia , which create numerous BM breaches under the AC ( Fig 1A ) . This inefficient form of BM clearing delays invasion in unc-40 mutants [24 , 25] . To identify genes required for invadopodia BM breaching , we performed a sensitized genome-wide RNAi screen in an unc-40/DCC mutant background . Complete blocks in AC invasion disrupt uterine-vulval attachment and frequently result in an easily observed protruded vulva ( Pvl ) and egg-laying defective ( Egl ) phenotype . These phenotypes occur at low penetrance in unc-40 ( e271 ) mutants [24 , 25] . To identify regulators of invadopodia and BM breaching , we thus screened for genes whose RNAi-mediated knockdown enhanced the Pvl and Egl phenotypes in unc-40 ( e271 ) mutants . From the nearly 11 , 000 genes in the C . elegans ORF-RNAi library ( approximately 55% of protein-coding genes in C . elegans ) [26] , we identified 722 genes that enhanced the unc-40 Pvl and Egl phenotypes ( S1 Table ) . To identify genes that directly control invadopodia and BM breaching , we performed a secondary screen of 150 candidate genes that are membrane associated , components of cell signaling pathways , or are predicted to be secreted ( S2 Table; see Methods ) . Using differential interference contrast ( DIC ) microscopy to examine AC invasion , we found that RNAi-mediated knockdown of 13 genes significantly enhanced the unc-40 ( e271 ) invasion defect at the mid P6 . p four-cell stage ( the time when AC invasion is completed in wild-type animals; Tables 1 and S2 ) . Confirming the rigor of this screen , we identified four genes previously characterized as regulators of AC invasion: lin-3 , an EGF-like ligand required for specification of the vulval precursor cells , which generate a secreted cue ( s ) that promotes invasion [21]; pat-4 , the C . elegans integrin-linked kinase [24]; mig-6 , the C . elegans ortholog of the extracellular matrix protein Papilin [27]; and cdc-42 , the C . elegans ortholog of the Cdc42 Rho GTPase [28] . Further , no known dedicated components of the UNC-40/DCC signaling pathway in the AC were identified [29 , 30] . RNAi knockdown of the Rho GTPase cdc-42 showed one of the strongest enhancements of the unc-40 ( e271 ) AC invasion defect ( Table 1 ) . Further , vertebrate Cdc42 has been implicated in stimulating invadopodia in cancer cell lines , but its function in regulating invadopodia in vivo is unknown [31 , 32] . We thus decided to examine the role of CDC-42 during AC invasion . We confirmed that homozygous cdc-42 ( gk388 ) mutants showed a penetrant disruption in AC invasion using DIC optics ( 34% defect; see Table 1 ) . Further , we examined a functional GFP fusion of the major BM component laminin ( laminin::GFP ) , which revealed that many cdc-42 ( gk388 ) mutants failed to breach the BM at the normal time ( Fig 1B; 6/25 cdc-42 ( gk388 ) animals had no BM breach at the mid P6 . p four-cell stage whereas 25/25 of wild type animals breached the BM; p < 0 . 001 , Fisher’s exact test ) . We have recently shown that selective removal of the CDC-42 protein from the AC disrupts invasion to a degree similar to cdc-42 ( gk388 ) mutant animals [33] . In addition , RNAi targeting of cdc-42 blocked AC invasion and did not further enhance the cdc-42 ( gk388 ) mutant invasion phenotype ( Table 1 ) . These observations offer compelling evidence that the cdc-42 ( gk388 ) mutant has a significant , and likely complete loss of cdc-42 function in the AC . Based on experiments using a cell-specific protein degradation system , we previously proposed that CDC-42 functions in the AC to promote invasion [33] . Confirming that CDC-42 promotes invasion cell-autonomously , we found that AC-specific expression of a GFP-tagged CDC-42 ( cdh-3>GFP::cdc-42 ) in cdc-42 null animals fully rescued AC invasion in homozygous cdc-42 ( gk388 ) worms ( Table 1 ) . As we previously reported , a rescuing cdc-42 translational reporter ( cdc-42>GFP::cdc-42 ) is expressed broadly in the developing uterine and vulval cells , including the AC ( Fig 1C ) [33] . When GFP::CDC-42 was expressed under its endogenous promoter , the signal from the neighboring uterine and vulval cells made it difficult to assess the subcellular distribution of GFP::CDC-42 in the AC ( see Fig 1C ) . Therefore we examined AC-expressed GFP::CDC-42 ( cdh3>GFP::cdc-42 ) at the same development stage . Ventral views revealed that GFP::CDC-42 was localized in punctate structures at the AC-BM interface prior to invasion ( Fig 1D ) . Indicating that CDC-42 localizes to AC invadopodia , the GFP::CDC-42 puncta colocalized with F-actin , a marker for invadopodia ( Fig 1D ) [22] . We next wanted to determine if CDC-42 regulates invadopodia formation , dynamics , or function . As cdc-42 mutants are sterile and lethal [34] , we knocked down cdc-42 using RNAi beginning at the early L1 larval stage , which resulted in an invasion defect similar to cdc-42 null mutants from heterozygous mothers ( Table 1 ) . Quantitative live-cell imaging revealed that invadopodia size and lifetime were not affected by loss of cdc-42; however , the rate of invadopodia formation was nearly cut in half ( 1 . 72 per minute compared to 2 . 75 per minute in wild type ) and there were fewer invadopodia ( Table 2; Fig 1E and 1F; S1 and S2 Movies ) . The F-actin puncta that did form after cdc-42 knockdown colocalized with the invadopodia components UNC-34 ( Ena/VASP orthologue ) and the phospholipid ( PI ( 4 , 5 ) P2 ) [22] ( S1 Fig ) . These observations suggest that CDC-42 stimulates invadopodia construction and that loss of cdc-42 significantly reduces , but does not appear to eliminate invadopodia formation . We next wanted to determine the effects of decreased invadopodia due to loss of cdc-42 on the ability of the AC to breach BM . AC invasion is tightly coordinated with the divisions of the underlying vulval precursor cell P6 . p . The P6 . p initiates division in the early-to-mid L3 larval stage ( P6 . p two-cell stage ) , the P6 . p daughters divide again in the mid-L3 larval stage ( P6 . p four-cell stage ) , followed by a last division cycle in early L4 stage ( P6 . p eight-cell stage ) [21] . AC invadopodia first breach the BM in a 15-minute window during the mid-L3 stage coinciding with the late P6 . p two-cell stage and early P6 . p four-cell stage [22] . We carefully examined BM breaching using ventral views of the AC-BM interface and found that loss of cdc-42 delayed BM breaching by approximately one hour ( Fig 1H ) . Furthermore , we observed fewer invadopodia mediated BM breaching events after loss of cdc-42 . In wild type animals over 65% of animals had two or more BM breaches , whereas less than 25% of animals had multiple BM breaches after loss of cdc-42 ( Fig 1G ) . Taken together , these data suggest that loss of cdc-42 reduces the number of invadopodia formed , resulting in fewer , and delayed invadopodia-mediated BM breaching events . The molecular mechanism by which vertebrate Cdc42 stimulates invadopodia formation in cancer cell lines is poorly understood [35] . Depletion of the actin regulator N-WASP , a direct effector of Cdc42 [36] , and depletion of Cdc42 have similar effects on invadopodia formation in cultured rat mammary MTln3 adenocarcinoma cells [32 , 37] . These results suggest that N-WASP may act downstream of Cdc42 to direct invadopodia formation in mammary carcinoma cells . The gene wsp-1 encodes the sole C . elegans WASP-like protein [38] . A deletion mutant of wsp-1 ( wsp-1 ( gm324 ) ) results in a loss of detectable protein and is thought to be a null or strong loss of function allele . Despite the severe disruption in wsp-1 , wsp-1 ( gm324 ) animals are homozygous viable [38] . We examined AC invasion by DIC in wsp-1 ( gm324 ) mutants and found a highly penetrant delay or block in AC invasion at the mid P6 . p four-cell stage ( Table 1 ) . Suggesting a possible cell autonomous role in AC invasion , a transcriptional reporter for wsp-1 ( wsp-1>GFP ) was expressed in the AC ( Fig 2A ) . Uterine-specific RNAi-mediated knockdown of wsp-1 resulted in AC invasion defects where the BM ( viewed with laminin::GFP ) was not breached ( n = 12/25 animals with no BM breach at the mid P6 . p four-cell stage; Fig 2B , Table 1 ) . Because uterine cells neighboring the AC do not contribute to invasion [21] , an AC invasion defect in this background indicates that wsp-1 functions in the AC to promote invasion . We next determined the localization of an AC-specific , functional GFP-tagged WSP-1 translational fusion protein ( cdh-3>GFP::wsp-1 ) . Similar to GFP::CDC-42 , GFP::WSP-1 was found in punctate structures at the ACs invasive cell membrane that colocalized with F-actin puncta , indicating that WSP-1 localizes to AC invadopodia ( Fig 2C ) . As N-WASP is localized in part by directly binding active Cdc42 ( GTP-bound ) [39] , we next examined if loss of CDC-42 altered WSP-1 localization . The number of GFP::WSP-1 puncta at the invasive cell membrane was significantly reduced after RNAi-mediated knockdown of cdc-42 ( Fig 2D ) , indicating that WSP-1 localization is CDC-42 dependent . Furthermore , we found that similar to loss of cdc-42 , wsp-1 mutants had normal invadopodia lifetimes but significantly fewer invadopodia , delayed BM breaching , and fewer BM breaching events relative to wild type animals ( Fig 2E–2H ) . Together , these results suggest that wsp-1 functions downstream of cdc-42 to promote invadopodia formation in the AC . The Cdc42 GTPase switches between an active GTP-bound form and an inactive GDP-bound state . Cdc42 interacts with and activates effectors such as N-WASP when bound to GTP [36 , 40] . To determine the subcellular site of CDC-42 activation , we created transgenic animals that express an AC-specific genetically encoded sensor for active , GTP-bound CDC-42 ( cdh-3>GFP::GDBwsp-1 ) ( [41]; see Methods ) . Lateral imaging revealed that GTP-CDC-42 was polarized to the invasive membrane and the probe also showed signal in the nucleus ( Fig 3A ) . We suspect that the nuclear concentration does not represent active GTP-CDC-42 , as rescuing GFP::CDC-42 was not localized in the nucleus ( see Fig 1C ) . We thus focused on GTP-CDC-42 at the invasive membrane . Examining ventral views of the AC , we found that GTP-CDC-42 localized to puncta that overlapped with invadopodial F-actin ( Fig 3B ) . Furthermore , we found that GTP-CDC-42 was enriched at 50% of small BM breaches ( < 4 um2; present in 20/40 breaches ) , indicating that CDC-42 is often active at the site of initial BM penetration . When the total area of BM cleared was larger than 4 um2 , however , GTP-CDC-42 was no longer enriched at breaches ( present in 4/18 breaches ) . In addition , GTP-CDC-42 was not localized to the large invasive protrusion that forms after BM breaching ( Fig 3C; detected in 1/7 protrusions ) . Most ACs rapidly formed an invasive protrusion after BM breach in cdc-42 mutants ( n = 6/8 animals ) , consistent with invasive protrusion generation being independent of CDC-42 activity . Taken together , these observations suggest that CDC-42 is activated at the invasive membrane where it promotes invadopodia formation , but that it is not required for invasive protrusion formation . A number of extracellular signals have been implicated in stimulating invadopodia in cell culture , mouse , and zebrafish cancer models [42–46] . It has been proposed that these signals activate Cdc42 [31]; however , direct confirmation of this notion is lacking for most invadopodia induction events . We have previously shown that a diffusible chemotactic cue ( s ) from the primary fated vulval precursor cells ( the tissue that the AC invades ) stimulates and targets invasion [21] . To determine if the vulval cue regulates invadopodia formation and CDC-42 activation , we examined animals lacking the vulval precursor cells ( vulvaless animals; lin-3 RNAi treatment; see Methods ) and observed fewer invadopodia ( Fig 3D; S3 Movie; Table 2 ) . Notably , loss of the vulval precursor cells did not affect the distribution of GFP::CDC-42 at the invasive cell membrane ( Fig 3F ) , but the number of activated GTP-CDC-42 puncta was reduced nearly three fold ( 1 . 14 ± 0 . 36 versus 2 . 67 ± 0 . 39 in wild-type; Fig 3F ) . To genetically test if CDC-42 functions in the vulval cue pathway , we performed epistasis analysis . If CDC-42 functioned solely in the vulval cue pathway , loss of cdc-42 activity in vulvaless animals should not enhance the invasion defect of animals lacking vulval precursor cells . Supporting this idea , loss of cdc-42 did not augment the AC invasion defect of vulvaless animals ( Table 1 ) . However , unlike loss of cdc-42 , the invadopodia in animals lacking vulval cells were larger and longer-lived ( Table 2; Fig 3E ) , suggesting that in addition to promoting CDC-42 activation , the vulval cells also regulate other aspects of invadopodia . Together , these data offer compelling evidence that CDC-42 is activated downstream of a pro-invasive cue from the primary vulval precursor cells to promote invadopodia formation . We were next interested in determining whether our sensitized screen identified new regulators of invadopodia formation , dynamics , or function . A gene whose loss strongly enhanced the unc-40 invasion defect was gdi-1 , which encodes a Rab GDP-dissociation inhibitor ( GDI ) ( Table 1 ) . Rab GDIs have high affinity for GDP-bound Rab proteins and are thought to deliver Rabs to specific membrane compartments to direct membrane trafficking [47] . To determine where gdi-1 is expressed we built a transcriptional reporter for gdi-1 ( gdi-1>GFP ) and found that gdi-1 is upregulated in the AC and the vulval precursor cells prior to and throughout invasion ( Figs 4A and S2 ) . RNAi targeting gdi-1 results in embryonic lethality [48] . Consistent with this observation , we found that a putative null deletion allele of gdi-1 ( tm660 ) was embryonic lethal ( see Methods ) . Thus , to avoid embryonic lethality , we used RNAi targeting gdi-1 beginning at the L1 stage , which resulted in a highly penetrant AC invasion defect in the L3 stage ( Table 1 ) . Further , uterine-specific RNAi knockdown of gdi-1 also disrupted AC invasion ( n = 33/100 AC failed to breach BM at the mid P6 . p four-cell stage; Fig 4B; Table 1 ) , while vulval specific RNAi did not alter invasion ( Table 1 ) . Given that neighboring uterine cells do not regulate invasion , these observations strongly suggest that GDI-1 functions in the AC . We next generated an AC-specific N-terminal GFP fusion to GDI-1 ( cdh-3>GFP::gdi-1 ) . Full length GFP::GDI-1 localized to the cytosol , similar to vertebrate GDIs ( S2 Fig ) [49 , 50] . Demonstrating that the RNAi construct efficiently targets gdi-1 , the level of AC-specific GFP::GDI-1 was reduced by 73 . 6% in gdi-1 RNAi treated animals relative to controls ( S2 Fig ) . To further confirm the specificity of gdi-1 RNAi , we generated a construct in which gdi-1 from the related nematode C . briggsae was specifically expressed in the AC ( cdh-3>GFP::Cbrgdi-1 ) . The C . briggsae and C . elegans genes share 86% identity , which is below the 95% threshold required for RNAi targeting [51] . Expression of GFP::Cbrgdi-1 in the AC restored invasion in animals treated with C . elegans gdi-1 RNAi ( n = 52/57 ( 91% ) normal invasion , 3/57 ( 5% ) partial invasion , 2/57 ( 4% ) no invasion; Table 1 ) . Taken together , these data confirm the specificity of the C . elegans gdi-1 RNAi and indicate that gdi-1 functions within the AC cytosol to promote invasion . We next examined whether GDI-1 was required for invadopodia formation or activity . Similar to cdc-42 , we found that loss of gdi-1 decreased the rate of invadopodia formation by nearly 50% ( 1 . 60 per minute compared with 2 . 75 per minute in wild type; Table 2 ) , reduced the number of invadopodia , and delayed BM breaching ( Table 2; Fig 4C and 4D; S4 Movie ) . However , unlike loss of cdc-42 , RNAi-mediated reduction of gdi-1 resulted in larger invadopodia that had longer lifetimes ( Table 2; Fig 4E ) . We conclude that GDI-1 is necessary for proper invadopodia formation . We next tested the interaction between gdi-1 and cdc-42 in regulating invadopodia and invasion . Loss of gdi-1 did not affect the punctate localization of total GFP::CDC-42 or activated GTP-CDC-42 at the invasive membrane ( Fig 4F and 4G ) , suggesting that gdi-1 does not regulate CDC-42 . Furthermore , RNAi mediated knockdown of gdi-1 in homozygous cdc-42 ( gk388 ) mutants significantly enhanced the cdc-42 ( gk388 ) invasion defect ( Table 1 ) , indicating that gdi-1 has functions outside of cdc-42 that promote invasion . Due to the difficulty of expressing probes to view invadopodia in cdc-42 mutants , which were unhealthy from pleiotropic effects due to loss of cdc-42 , we performed double RNAi targeting both cdc-42 and gdi-1 ( cdc-42; gdi-1 RNAi ) to examine invasion and invadopodia dynamics . This treatment slightly enhanced the invasion defect associated with loss of either gdi-1 or cdc-42 alone ( Table 1 ) , but it was not significant , indicating we did not achieve efficient knockdown of each gene . Notably , however , the combined cdc-42; gdi-1 RNAi treated worms showed a trend of further reducing invadopodia formation rate ( Table 2 ) and mispolarization of F-actin compared to loss of cdc-42 or gdi-1 alone ( Fig 5A–5D and 5F ) . Although our data do not formally rule out that cdc-42 and gdi-1 act together in some aspects of invadopodia regulation , the genetic interaction and cell biological observations support the idea that gdi-1 and cdc-42 control distinct aspects of invadopodia formation . Loss of the vulval precursor cells lead to a more severe invasion defect than loss of cdc-42 alone ( Table 1 ) , suggesting the vulval cue ( s ) controls another pathway ( s ) that promotes invasion . Thus , we wanted to next determine if gdi-1 is a component of the vulval cue pathway with functions separate from cdc-42 . Loss of gdi-1 did not enhance the AC invasion defect of vulvaless animals ( Table 1 ) , indicating that GDI-1 promotes invasion through a pathway regulated by the vulval cue . We next examined invadopodia formation in vulvaless animals . If the vulval cue controls separate pathways ( such as one controlling CDC-42 and an independent pathway involving GDI-1 ) that converge to promote invadopodia formation , loss of the vulval precursor cells should more severely perturb invadopodia formation relative to loss of cdc-42 or gdi-1 alone . Consistent with this notion , vulvaless animals showed a greater reduction in rate of invadopodia formation than loss of either of gdi-1 or cdc-42 ( 0 . 86 per minute versus 1 . 60 per minute and 1 . 72 per minute , respectively; Table 2 ) . Further , vulvaless animals showed an increase in the proportion of invadopodia with lifetimes greater than five minutes relative to loss of gdi-1 or cdc-42 ( see Figs 1F , 3E and 4E ) . Finally , loss of the vulval cells resulted in a more severe mispolarization of F-actin throughout the cell compared to loss of cdc-42 or gdi-1 ( Fig 5B–5F ) . These data are consistent with the vulval cue controlling the CDC-42 and GDI-1 pathways ( and possibly other pathways ) to promote invadopodia formation at the invasive cell membrane . We have recently shown that a unique invadopodial membrane is dynamically recycled through the endolysosome to the invasive cell membrane to form invadopodia [23] . Given the role of Rab GDIs in membrane trafficking , we examined whether GDI-1 is required for proper trafficking of the invadopodial membrane . Examination of the invadopodial membrane components PI ( 4 , 5 ) P2 ( mCherry::PLCδPH ) and the RAC GTPases MIG-2 and CED-10 revealed that RNAi-mediated knockdown of gdi-1 led to inappropriate localization of the invadopodial membrane in apical and lateral plasma membrane domains ( S3 Fig ) . In addition , the endolysosome markers CUP-5 and LMP-1 , which normally polarize to the invasive membrane and colocalize with invadopodial membrane components [23 , 52 , 53] , were less polarized following loss of gdi-1 ( S3 Fig ) . Time-lapse imaging of the invadopodial membrane marker PI ( 4 , 5 ) P2 in wild type animals revealed that the invadopodial membrane was dynamically trafficked to invadopodia at the invasive membrane and remained polarized over time ( Fig 6A; S5 Movie ) . Following loss of gdi-1 , the invadopodial membrane was still actively trafficked , but it was dynamically mis-targeted to lateral and apical plasma membrane domains ( Fig 6B; S6 Movie ) . Notably , the invadopodial membrane was severely mis-targeted to apical and lateral plasma membrane domains in vulvaless animals ( Fig 6C; S7 Movie ) , consistent with the idea that GDI-1 is a component of a pathway regulated by the vulval cue . In contrast to loss of gdi-1 and vulvaless animals , loss of cdc-42 did not alter the polarization or targeting of the invadopodial membrane ( Figs 6D and S3; S8 Movie ) , further supporting the notion that CDC-42 and GDI-1 regulate distinct aspects of invadopodia formation . Taken together , these observations suggest that GDI-1 acts downstream of the vulval cue to target trafficking of the invadopodial membrane to the site of invadopodia formation at AC’s invasive cell membrane . Lastly , we wanted to determine if the disruption of invadopodial membrane trafficking was a specific defect of the invadopodial membrane compartment or a more general perturbation of intracellular membrane trafficking and cell polarization . We thus examined other markers of AC secretion and polarization . The AC secretes the EGF-like ligand LIN-3 , which induces vulval formation and expression of egl-17 [54] . Induction of egl-17 expression in the vulval cells was normal after loss of gdi-1 , indicating that the AC properly traffics and secretes LIN-3 ( Fig 7A ) . In addition , loss of gdi-1 did not affect AC secretion and deposition of the extracellular matrix protein hemicentin into the BM ( Fig 7B ) [55] . Finally , RNAi targeting of gdi-1 did not alter the polarization of the integrin receptor INA-1/PAT-3 to the AC invasive cell membrane ( Fig 7C ) [24] . These results strongly suggest that loss of gdi-1 does not globally disrupt membrane transport or cell polarity , but instead specifically perturbs invadopodial membrane trafficking .
The Rho GTPase Cdc42 has been implicated as a central molecule in stimulating invadopodia in numerous cancer cell lines , including melanoma , glioblastoma , breast , and pancreatic tumor derived cells [31 , 58] . Through our screen we independently confirmed that the C . elegans ortholog of the Rho GTPase Cdc42 is a key molecule in promoting invadopodia formation in vivo . CDC-42 is expressed in the AC and is localized to invadopodia along the invasive cell membrane . Furthermore , we found that CDC-42 was activated at the invasive membrane and that CDC-42 activation was dependent on the vulval tissue that the AC invades . We have previously shown that the primary vulval precursor cells stimulate and target AC invasion with a diffusible cue ( s ) [21] . Our new observations support the idea that the vulval cue induces invadopodia formation through activation of CDC-42 . This is consistent with observations in cancer cell lines , where external cues such as the EGF growth factor and collagen I fibers are thought to stimulate activation of Cdc42 and invadopodia formation [32 , 45] . We also find that in the AC , similar to mammary adenocarcinoma cells , the actin regulator and CDC-42 effector WSP-1 ( N-WASP ) , functions downstream of CDC-42 to promote F-actin formation [32] . WSP-1 was localized to invadopodia and this localization was dependent on CDC-42 . It is likely that this interaction is direct , as vertebrate Cdc42 activates and localizes N-WASP [36 , 39] . Notably , loss of cdc-42 led to a greater reduction in invadopodia than loss of wsp-1 , suggesting that CDC-42 acts through other downstream effectors in addition to WSP-1 . A similar result was seen in mammary adenocarcinoma cells [37] , suggesting that WASP proteins are not the sole effector of Cdc42 responsible for invadopodia formation . In breast and pancreatic tumor cell lines , depletion of Cdc42 function results in a complete or near complete loss of invadopodia formation and matrix degradation [32 , 37 , 45 , 58] . In contrast , the loss of CDC-42 in the AC resulted in only an approximate 50% reduction in the rate and number of invadopodia formed and a one-hour invasion delay . Although we cannot rule out that there was some limited CDC-42 activity in the AC in our studies , these observations suggest that invadopodia are also stimulated through a CDC-42 independent mechanism . The apparent redundancies in the mechanisms that induce invadopodia in C . elegans might reflect the robustness of the genetic networks underlying AC invasion . The process of AC invasion is highly conserved across nematode species and is under intense evolutionary selective pressure , as defects in invasion perturb the egg-laying apparatus and decrease fecundity [28 , 59 , 60] . Transformed cancer cell lines may not possess these robust networks . Tumors in vivo , however , are heterogeneous and robust cell populations , and have many alternative molecular pathways to promote , growth , survival , and dispersal [61] . Thus , while our results support the notion that the Rho GTPase Cdc42 is a central initiator of invadopodia formation , they also suggest that multiple mechanisms for invadopodia induction exist and that these could be used in other contexts , including cancer cell invasion . A combination of ultrastructural and live-cell imaging studies have revealed that the invadopodial membrane is dynamic and has unique structural and lipid properties [15 , 17 , 62] . We recently found that the invadopodial membrane of the AC is a unique membrane compartment , harboring lipid anchored Rac GTPases and the phospholipid PI ( 4 , 5 ) P2 , that is actively recycled through the endolysosome during invadopodia formation and turnover [23] . The membrane associated matrix metalloproteinase MT1-MMP is actively recycled through the endolysosome to invadopodia in several tumor cell lines , suggesting this recycling pathway is a shared feature of invadopodia [63 , 64] . The invadopodial membrane may be crucial for targeted delivering of proteases such as MT1-MMP . In addition , the invadopodial membrane might be important in providing new membrane to allow for the rapid growth of protrusions through extracellular matrices . Here we show that the Rab GDP dissociation inhibitor GDI-1 is a key regulator of invadopodial membrane trafficking and invadopodia formation . Using live-cell imaging we found that loss of GDI-1 resulted in invadopodial membrane that was still actively trafficked; however , it was not properly targeted to the invasive cell membrane where invadopodia form . This is in contrast to loss of UNC-60 ( ADF/Cofilin ) , which leads to a severe reduction in invadopodial membrane trafficking to the cell surface and accumulation of static invadopodial membrane vesicles within the cell [23] . These results suggest that the correct targeting of invadopodial membrane depends on GDI-1 , while UNC-60 ( ADF/cofilin ) is necessary for active trafficking or docking of invadopodial membrane to the plasma membrane . Importantly , neither loss of UNC-60 or GDI-1 altered other trafficking and polarization events in the AC [23] , suggesting both are specific regulators of the invadopodial membrane . Rab GTPases are crucial mediators of membrane trafficking that direct vesicle budding , vesicle movement , and vesicle fusion [65] . Rab GDIs are thought to regulate membrane trafficking by facilitating recycling of Rab proteins between target and donor membrane compartments [50] . Work in Saccharomyces cerevisiae has shown that amino acid residues required for Rab recognition in vitro are also required for the function of GDI in vivo , highlighting the dedication of GDI function to Rab regulation [66] . Rab GDIs show preferential interaction with different Rabs , signifying an ability to regulate specific membrane trafficking events [65 , 67] . However , we have not yet identified a Rab protein that promotes AC invasion in our screens [25 , 28] , suggesting that GDI-1 interacts with multiple Rabs or a Rab essential for viability . Notably , the human ortholog of gdi-1 , GDI-1β , is upregulated in numerous carcinomas , including pancreatic , esophageal , gastric , thyroid and gallbladder [68–72] . Further , GDI-1β is highly expressed in human medulloblastomas and stimulates invasion in a medulloblastoma cell line in vitro [73] . Thus , the function of GDI-1 in promoting invadopodial membrane trafficking and invasion may be conserved . Work from cell culture indicates that invadopodia are highly dynamic superstructures that require the coordinated activities of signaling proteins , F-actin generation , membrane trafficking , proteases , and cell adhesion [15 , 16] . Further , invadopodia form in response to numerous cues including growth factors , the extracellular matrix , and metabolic and hypoxia-induced factors [42] . How independent pathways within the cell are controlled and coordinated to form invadopodia in response to extracellular cues is poorly understood . We have previously shown that the vulval precursor cells secrete an unidentified , diffusible cue ( s ) that promotes invasion [21] . Our genetic epistasis and cell biological observations indicate that this cue ( s ) from the vulval precursor cells regulates both CDC-42 directed F-actin generation and GDI-1 mediated invadopodial membrane trafficking to coordinate invadopodia formation . Other local interactions may link these independent cellular activities to build functional invadopodia . For example , the actin regulators Cdc42 and N-WASP , which promote F-actin formation , also have functions in directing vesicle fusion and concentration of MT1-MMP at invadopodia [64 , 74] . Our findings that combined loss of gdi-1 and cdc-42 enhanced the loss of invadopodia and the mislocalization of F-actin throughout the cell , suggests that CDC-42 and GDI-1 pathways do not simply combine one-to-one to form invadopodia . Rather , they may each link with additional unidentified pathways that promote invadopodia formation . For example , GDI-1 likely promotes invadopodial membrane trafficking to AC invadopodia that are initiated by CDC-42 as well as those formed independently of CDC-42 activity . As loss of the vulval cells led to dramatic defects in invadopodia formation and mislocalization of invadopodial components , we suspect that the vulval cue might regulate other aspects of invadopodia formation and that extracellular cues may generally function to coordinate distinct aspects of invadopodia formation to spatiotemporally control invadopodia construction and cell invasion .
Culturing and handling of C . elegans was done as previously described [75] . Wild type animals were strain N2 . In the text and figures , we designated linkage to a promoter with a greater than symbol ( > ) and used a double colon ( :: ) for linkages that fuse open reading frames . The alleles and transgenes used in this study were as follows: qyIs8[lam-1>lam-1::GFP]; qyIs17[cdh-3>mCherry]; qyIs43[pat-3>pat-3::GFP; ina-1>ina-1]; qyEx45[cdc-42>GFP::cdc-42]; qyIs46[emb-9>emb-9::mCherry]; qyIs57[cdh-3>mCherry::moeABD]; qyIs61[cdh-3>GFP::unc-34]; qyIs204[wsp-1>GFP]; qyIs211[cdh-3>lmp-1::GFP]; qyIs212[cdh-3>GFP::wsp-1]; qyIs219[cdh-3>GFP::PLCδPH]; qyIs220[cdh-3>mig-2::GFP]; qyIs221[cdh-3>ced-10::GFP]; qyIs409[cdh-3>GFP::cup-5]; qyIs410[cdh-3>GFP::cdc-42]; qyIs412[cdh-3>GFP::GBDwsp-1]; qyIs427[lam-1>lam-1::mCherry]; qyEx507[cdh-3>GFP::gdi-1]; qyEx515[gdi-1>GFP]; qyEx533[cdh3>GFP::Cbrgdi-1]; urIs[rol-6 ( 1006 ) ; lam-1>lam-1::GFP]; LGI , unc-40 ( e271 ) ; ayIs4[egl-17>GFP]; LGII , cdc-42 ( gk388 ) ; mIn1; LGIII , unc-119 ( ed4 ) ; rhIs23[him-4>him-4::GFP]; LGIV , wsp-1 ( ng324 ) ; qyIs10[lam-1>lam-1:GFP]; gdi-1 ( tm660 ) ; nT1; LGV , qyIs50[cdh-3>mCherry::moeABD]; LGX , qyIs7[lam-1>lam-1::GFP]; qyIs24[cdh-3>mCherry:: PLCδPH] . The deletion allele gdi-1 ( tm660 ) was obtained from the National Bioresource Project ( NBRP; http://www . shigen . nig . ac . jp/c . elegans ) . The gdi-1 ( tm660 ) allele is a 541 bp deletion in the first exon that shifts the frame of the open reading such that only the first 37 amino acids of the protein are predicted to be made ( out of 549 amino acids in the full protein ) . The gdi-1 ( tm660 ) allele is thus likely a null for gdi-1 activity . The gdi-1 ( tm660 ) allele was balanced using the nT1 translocation by creating the strain , gdi-1 ( tm660 ) IV/nT1[qIs51] ( IV;V ) . The qIs51 transgene , which is linked to nT1 , expresses GFP in the pharynx . The presence of gdi-1 in the balanced strains was confirmed using the published NBRP primers: ExtRev:TCAAGGAGTGCATCATCTCG , ExtFwd:CCTGATCATTCAACGACAAG , IntFwd:GTGAGTGATGTTGGTGAAGT , IntRev:CTCGGGAATGCTGTCGGTTT . Balanced gdi-1/nT1 mothers never segregated non-fluorescent gdi-1 homozyogous progeny ( n = 0/54 examined ) in timed egg-lays . Dead embryos were observed on the plates , but dead larvae were not seen . These results suggest that gdi-1 is required for embryonic viability . Reporter constructs were generated by PCR fusion [76] . AC-specific promoter fusions were generated with the cdh-3 ( mk62-62 ) AC-specific regulatory element [76 , 77] . Transgenic worms were created by transformation with co-injection markers of pPDMM016B ( unc-119+ ) into the germline of unc-119 ( ed4 ) worms . These expression constructs were injected with EcoRI-digested salmon sperm DNA and pBSSK DNA , along with serial dilutions of the fusion construct to optimize expression levels and avoid toxicity . Stably expressed extrachromosomal lines were established and selected lines were integrated by gamma irradiation . See S3 Table for primers used in generation of new transgenic strains . The GTP-CDC-42 biosensor was built by fusing the GTP-CDC-42 binding domain of WSP-1 to GFP under the AC-specific cdh-3 promoter [41] . The wsp-1>GFP transcriptional reporter construct was built by fusing 7 . 3 kb of the 5’ cis-regulatory element of wsp-1 to GFP . The AC-specific regulatory of the cdh-3 promoter and GFP were fused to the open reading from of wsp-1 to create the AC-specific GFP:WSP-1 construct . 2 . 5 kb of the putative 5’ cis-regulatory for gdi-1 was fused to GFP to generate the transcriptional reporter strain . The AC-specific C . elegans and C . briggsae gdi-1 translational reporters were built by fusing the cdh-3> AC-specific regulatory element to the species-specific full-length gdi-1 genes and their respective 3’ UTRs . Anchor cell invasion was scored as previously described using DIC microscopy [21 , 76] . Briefly , animals were scored for invasion at the primary vulval precursor P6 . p four-cell stage when BM invasion is completed in wild type animals . Anchor cells were scored as “normal” invasion if there was a visible breach in the phase dense line at the mid-P6 . p four-cell stage , “partial” if the breach in the phase dense line was smaller than the nucleus , and “blocked” invasion if the phase dense line remained intact . For timing of breach experiments , the vulval precursor cells divisions , the distal tip cell migrations , and the divisions of the ventral uterine cells were used to determine “early” and “late” two-cell and “early , ” “mid , ” and “late” four-cell stages . RNA interference ( RNAi ) targeting lin-3 or lin-3 mutants ( lin-3 ( n378 ) , let-59 ( s49 ) , unc-22 ( s7 ) , unc-31 ( e169 ) /lin-3 ( n1059 ) , unc-24 ( 138 ) , dpy-20 ( e128 ) ; qyIs24[cdh-3>mCherry:: PLCδPH] ) result in vulvaless worms that lack specification of primary vulval precursor cells and , therefore , do not generate the vulval cue ( s ) [21 , 28] . Staging of these worms was based on the distal tip cell migration and divisions of the ventral uterine cells . RNAi targeting open reading frames of genes or using an empty vector control ( L4440 ) was delivered by feeding synchronized L1-arrested worms E . coli expressing double stranded RNA , as previously described [28] . Synchronized L1-arrested larvae were plated on fields of the RNAi-expressing bacteria , except worms homozygous for cdc-42 ( gk388 ) were plated on RNAi as freshly harvested eggs because homozygous cdc-42 mutants do not survive L1 arrest well . All RNAi clones after the initial genome-wide screen were sequenced prior to use . Tissue-specific RNAi experiments were preformed as previously described using rrf-3 ( pk1426 ) ; qyIs102[fos-1a>rde-1; myo-2>GFP]; qyIs10[lam-1>lam-1:GFP]; rde-1 ( ne219 ) ; qyIs24[cdh-3>mCherry:: PLCδPH] worms for uterine-specific RNAi and rrf-3 ( pk1426 ) ; qyIs138[unc-62>rde-1]; rde-1 ( ne219 ) worms for vulval-specific RNAi [24 , 27 , 28] . Briefly , those experiments use animals possessing a null mutation of rde-1 , a gene required for RNAi sensitivity [78] . A functional copy of RDE-1 is expressed specifically in a tissue of interest , thus restoring RNAi in those cells and allowing for determination of site-of-action . In the primary genome-wide RNAi screen in the unc-40 ( e271 ) mutant background , 50–100 synchronized L1-arrested worms were plated on RNAi for 70 hours at 20°C . Egg production was assayed by counting the number of laid eggs at this time point . Worms on control plates laid an average of 100 eggs and experimental worms were scored as ( - ) when no eggs were laid , ( + ) when there was a moderate reduction in the number of eggs laid , and ( ++ ) when the number of eggs laid was decreased by more than 50% . Worms were also scored for the protruding vulval phenotype in three categories: ( # ) when 10% of the worms had protruding vulvas , ( ## ) when 20% of worms had protruding vulvas , and ( ### ) when more than 50% of worms exhibited this phenotype . Candidate genes for the secondary screen for AC invasion defects were identified using AmiGO v1 . 8 [79] . These genes were selected using the GO terms “extracellular space” ( GOID:0005615 ) , “integral component of membrane” ( GO:0016021 ) , “GPCR signaling” ( GOID:0007186 ) , and “GTPase regulator activity” ( GOID:0030695 ) . Images were acquired using an EM-CCD camera ( Hamamatsu Photonics ) and a spinning disk confocal ( CSU-10; Yokogawa ) mounted on a microscope ( AxioImager; Carl Zeiss ) with a Plan-APOCHROMAT 100x/1 . 4 oil differential objected controlled by MicroManager software [80] . Acquired images were processed using ImageJ 1 . 40g and Photoshop ( CS3 Extended , Adobe ) and smoothened using a 0 . 5 pixel radius Gaussian blur filer . 3D reconstructions were built from confocal z-stacks , analyzed , and exported . mov files using IMARIS 7 . 4 ( Bitplane , Inc . ) . Figures were constructed using Illustrator ( CS3 Extended , Adobe ) . Quantitative analyses of AC invadopodia and BM breach were done using ImageJ , Imaris , or both . For time-lapse microscopy , worms were anesthetized in 0 . 2% tricaine and 0 . 02% levamosile in M9 and then transferred to 5% noble agar pads , sealed with VALAP , and imaged at 23°C at one minute time intervals [22] . For consistent measurements , isosurface renderings , built in place of polymerized F-actin at the invasive membrane , were used to determine a threshold for assigning the spots that were used to quantify AC invadopodia number , size , and dynamics on blinded time-lapses [22] . Average invadopodia number and size were blindly calculated from more than five 24-minute time-lapses taken at one-minute intervals for each group . Rate of invadopodia formation was calculated by determining the number of new invadopodia appearing in each frame during the first five minutes of each time-lapse . Invadopodia lifetimes were measured by determining the number of frames in which each invadopodium was present during a ten-minute portion of each time-lapse . Quantification of GFP::CDC-42 , GTP-CDC-42 , and GFP::WSP-1 foci was performed in a blinded manner using the manual threshold and particle counting with the watershed filter functions in ImageJ on max projections of the two confocal z-planes that comprised the invasive membrane . The size of BM breach was calculated by using the threshold and measurement functions in ImageJ . Colocalization measurements were performed using the Imaris coloclaization module on blinded images . The enrichment of invadopodial membrane components and PAT-3::GFP at the invasive membrane were performed using ImageJ . For markers of the invadopodial membrane compartment , polarity measurements were calculated from sum projections that were thresholded manually . The proportion of thresholded signal in a region of interest ( ROI ) containing only the invasive membrane relative to the entire AC was then calculated . PAT-3::GFP calculations were performed as previously described [23 , 24] . Briefly , the average intensity from a five pixel wide line scan of the AC invasive membrane was divided by the average intensity from a line scan of the apicolateral AC membrane . The fluorescence intensities of GFP::GDI-1 and GFP::CbrGDI-1 were measured from sum projections of confocal z-stacks acquired with the same exposure times using ImageJ . The integrated density was measured in an ROI containing the AC and the average was calculated from at least 13 animals in each group . Statistical analysis was preformed using JMP version 9 . 0 ( SAS Institute ) or Graphpad PRISM v . 5 , using two-tailed Fisher’s Exact test , a two-tailed unpaired Student’s t-test , or nonparametric Wilcoxon rank-sum test . Figure legends specify when each test was used .
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During animal development specialized cells acquire the ability move and invade into other tissues to form complex organs and structures . Understanding this cellular behavior is important medically , as cancer cells can hijack the developmental program of invasion to metastasize throughout the body . One of the most formidable barriers invasive cells face is basement membrane–-a thin , dense , sheet-like assembly of proteins and carbohydrates that surrounds most tissues . Cells deploy small , protrusive , membrane associated structures called invadopodia ( invasive feet ) to breach basement membranes . How invadopodia are formed and controlled during invasion has been challenging to understand , as it is difficult to examine these dynamic structures in live animals . Using the nematode worm Caenorhabditis elegans , we have conducted the first large-scale screen to isolate genes that control invadopodia in live animals . Our screen isolated 13 genes and we confirmed two are key invadopodia regulators: the Rho GTPase CDC-42 that promotes F-actin polymerization at invadopodia to generate the force to breach basement membranes , and the Rab GDI-1 that promotes membrane addition at invadopodia that may allow invadopodia to extend through basement membranes . This work provides new insights into invadopodia construction and identifies potential novel targets for anti-metastasis therapies .
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[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[] |
2016
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A Sensitized Screen for Genes Promoting Invadopodia Function In Vivo: CDC-42 and Rab GDI-1 Direct Distinct Aspects of Invadopodia Formation
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The genus Orthopoxviridae contains a diverse group of human pathogens including monkeypox , smallpox and vaccinia . These viruses are presumed to be less dependent on host functions than other DNA viruses because they have large genomes and replicate in the cytoplasm , but a detailed understanding of the host factors required by orthopoxviruses is lacking . To address this topic , we performed an unbiased , genome-wide pooled RNAi screen targeting over 17 , 000 human genes to identify the host factors that support orthopoxvirus infection . We used secondary and tertiary assays to validate our screen results . One of the strongest hits was heat shock factor 1 ( HSF1 ) , the ancient master regulator of the cytoprotective heat-shock response . In investigating the behavior of HSF1 during vaccinia infection , we found that HSF1 was phosphorylated , translocated to the nucleus , and increased transcription of HSF1 target genes . Activation of HSF1 was supportive for virus replication , as RNAi knockdown and HSF1 small molecule inhibition prevented orthopoxvirus infection . Consistent with its role as a transcriptional activator , inhibition of several HSF1 targets also blocked vaccinia virus replication . These data show that orthopoxviruses co-opt host transcriptional responses for their own benefit , thereby effectively extending their functional genome to include genes residing within the host DNA . The dependence on HSF1 and its chaperone network offers multiple opportunities for antiviral drug development .
The Poxviridae family is comprised of several human pathogens in the Orthopoxvirus genus , including monkeypox ( MPXV ) and smallpox ( Variola ) , which was eradicated through vaccination with vaccinia ( VACV ) . With a dramatic increase in human MPXV cases in Africa , the rise of VACV-like orthopoxvirus infection in South America , and concerns about the weaponization of smallpox , it is important to design new strategies for the treatment and prevention of these diseases [1] , [2] . To this end , one valuable method to understand the mechanism of disease is to determine the virus-host interactions necessary for orthopoxvirus infection . Orthopoxviruses are large double-stranded DNA viruses with a unique lifecycle in the cytoplasm of the host cell . The viruses enact a cascade of transcriptional responses , with early gene expression occurring from the stages of viral entry to uncoating , intermediate gene expression after DNA replication , followed by late gene expression until the end of the virus lifecycle [3] , [4] . Early in infection , orthopoxviruses express factors that cleave host mRNAs , effectively preventing the expression of most host genes [4] , [5] . Poxviruses are also known to use host proteins during their lifecycle . This includes the use of the proteasome to facilitate viral uncoating and DNA replication , the ribosome to translate mRNAs , and specific host factors to help drive late viral transcription events [6]–[9] . Several RNAi screens have been performed in recent years and have expanded our knowledge of the host proteins involved in orthopoxvirus replication . Moser et al . performed a screen of kinase genes in Drosophila cells and found that modulation of the actin cytoskeleton by AMPK is important for VACV entry [10] . Mercer et al . screened the 7 , 000 genes comprising the ‘druggable genome’ and revealed the role of the proteasome in viral uncoating and of the Cullin3 ubiquitin ligase in initiating viral DNA replication [9] . Finally , Sivan et al . performed two siRNA screens targeting over 18 , 000 genes to reveal the importance of nuclear pore genes in viral morphogenesis [11] . All of these important new insights were based on an arrayed RNAi screen format . Notably , these screens generated hit lists with some overlap on the protein or functional level , but also significant numbers of unique hits . This is presumably due to substantial false negative rates , false positive rates , and the distinct model systems and readouts used to assess VACV infection , suggesting that more host protein factors remain to be discovered . Here , we used two complimentary and unbiased assays to identify host proteins necessary for orthopoxvirus infection . First , we developed a pooled-cell lentiviral shRNA screen in human cells based on screening formats previously utilized to determine pathways important in cancer biology [12] , [13] . Strengths of the pooled screen format are the ease of scaling to larger screening sets and the ability to enable multiple screening paradigms . Furthermore , cells are cultured in standard low-throughput format , rather than in multiwell plates , and thus can be easily passaged and otherwise manipulated . As a second assay , we used RNASeq to analyze the host transcriptional responses elicited by poxviral infection . These data identified host mRNAs that were upregulated during infection , suggesting that they may facilitate virus infection . By comparing the data from these two orthogonal datasets , we identified proteins involved in the heat shock response as critical factors for orthopoxvirus infection . In particular , we found that heat shock factor 1 ( HSF1 ) , the ancient master regulator of the cytoprotective heat shock response , is necessary for orthopoxvirus infection . We find that depletion and knockout of HSF1 or its pharmacologic inhibition significantly reduces VACV infection . Moreover , the principal targets of HSF1 transcription are upregulated during VACV infection , even as global host gene expression is suppressed . Our findings define a set of host factors that are necessary for orthopoxvirus infection and suggest that poxviruses have evolved to utilize host stress responses to their own advantage .
To identify host factors necessary for orthopoxvirus infection we completed a whole-genome scale , pooled RNAi screen using lentiviral vectors . This method delivered ∼90 , 000 short hairpin RNAs ( shRNAs ) with 5 or more independent shRNAs targeting ∼17 , 000 human genes to our target cells ( Figure 1A ) [14] . Human A549 cells were transduced in four replicates with the shRNA lentivirus library at a multiplicity of infection ( MOI ) of ≤1 to generate cell populations with predominantly no more than one shRNA expressed in each cell . Non-transduced cells were eliminated following selection with puromycin . Each replicate was then infected at an MOI of 5 with a modified vaccinia virus ( VACV ) that expressed a fusion of the core protein A4L and Venus yellow fluorescent protein [15] . This dose of VACV infected 100% of control cells at 12 hours ( data not shown ) . At 12 hours post infection ( hpi ) , cells were fixed and sorted for Venus-negative cells , with gates set on uninfected cells to collect Venus-negative pools . This population was selected to enrich for cells in which a host protein essential for orthopoxvirus replication , but not essential for host cell survival , had been suppressed . To determine which host genes were being suppressed in the Venus-negative cells , the cell population was analyzed to resolve hairpin sequences that were enriched in abundance , in essence using the hairpin sequence as a barcode to indicate shRNA treatment . The abundance of each hairpin was assessed by next generation sequencing [16] using the Illumina GAIIx system . For each replicate , hairpins with fewer than 15 raw reads were not considered . The remaining hairpins were normalized to the total read depth for each individual replicate to eliminate variation in read depth across replicates . The fold-change enrichment of each hairpin within the Venus-negative sorted cells was determined by comparison to the initial abundance of each hairpin observed in the plasmid DNA pool used to generate the pooled lentivirus library . These fold changes were used to rank the enrichment of each hairpin in the Venus-negative cell population in each replicate . Using the RIGER [12] algorithm within the Gene-E software tool ( http://www . broadinstitute . org/cancer/software/GENE-E/ ) , the weighted-second-best metric was used to rank the enriched target genes within each replicate . This method uses the pre-calculated ranked hairpin lists for each replicate , and then ranks the candidate genes based on the first and second most enriched hairpin for each gene in each replicate . Therefore , at least two hairpins against each gene were enriched in the original screen , providing evidence for the specificity of the target gene in VACV infection . The target genes identified in the top 500 genes in each replicate were considered candidate hits ( Table S1 ) . To better understand the candidate hits from our screen , we categorized the cellular pathways represented in our dataset for both functional pathways and biological process gene ontology ( GO ) terms . First , we used Ingenuity Pathway Analysis ( IPA ) to analyze the functional and signaling pathways associated with these host genes . We found that genes associated with molecular transport , ion transport and apoptosis were significantly overrepresented ( Figure 1B ) . The top categories overrepresented in our dataset correlate well with those identified in other screens for host genes important during vaccinia infection , with cell death and survival ( p = 1 . 27E-06 ) and cell morphology ( p = 3 . 17E-05 ) being significantly overrepresented in both screens analyzed using these parameters [11] . Using a second gene ontology classifier , Panther , we determined the Biological Process GO terms represented in the dataset , represented in a pie chart in Figure 1C ( see Table S2 for more information ) . This classification highlighted the wide range of cellular processes represented in the initial hit list . Together , these data indicate that the candidate host factors necessary for orthopoxvirus infection are varied , and that several host biological processes act to promote orthopoxvirus infection . From the initial list of candidate genes identified in our primary screen , a subset of 172 genes was selected for a secondary screen in arrayed format with a different VACV reporter virus system ( see Table S1 and Figure S1 for details ) . In the secondary screen , 5–7 distinct shRNAs targeting each gene of interest were used to assess the effect of decreased host protein expression on VACV early and late gene expression . The shRNA lentiviral vectors were arrayed in a 96 well plate format , with a different shRNA in each well . A549 cells were transduced at an MOI of ∼1 with the lentivirus vectors expressing each shRNA , selected with puromycin for lentiviral integration , and then infected with a modified vaccinia virus that expressed soluble Venus under an early promoter and soluble mCherry under a late promoter ( VACV-LREV ) [15] , [17] . Cells were fixed 20 hours post VACV-LREV infection and fluorescence was measured from each well ( Figure 2A ) . The secondary screen was carried out with 3 independent biological replicates ( Table S3 ) . Genes were considered hits if shRNA expression led to at least a 50% decrease in either early or late virus promoter-dependent fluorescence production ( 1 ) with more than one hairpin in a replicate or ( 2 ) in at least 2 replicates of the secondary screen without significant toxicity to the cells , as determined independently by cell viability assay ( CellTiterGlo; data not shown ) . In most cases , knocking down host factors with shRNA blocked VACV-LREV late gene expression and not early expression , indicating that the host genes were not necessary for VACV entry and early gene expression . We compiled a list of 34 genes that validated in the secondary screen ( 20% of those tested; Table S4 ) . There were 7 genes that were positive hits in all replicates of the secondary screen: transcriptions factors HSF1 and SKI , the integrin binding protein ITGB1BP1 , the aminophospholipid transporter ATP8B1 , the Notch ligand JAG1 , the nuclear transporter TNPO3 and the chemokine receptor CCR9 ( Figure 2B ) . We considered these seven positives the highest-confidence hits emerging from the initial pooled RNAi screen . Among the pooled RNAi screen hits , as well as previously published RNAi screen hits , were a large number of proteins that localize to the nucleus , including transcription factors , suggesting that VACV requires host systems that operate in the nucleus for its own replication [9] , [11] . To investigate the effects of VACV infection on transcriptional responses , we analyzed host mRNA expression 6 hours post VACV infection using RNASeq ( Figure 3A ) . For each gene , we calculated the difference in normalized read counts ( from the Illumina sequencing ) between the pre-infection and the 6 hpi samples and compared it with the average number of Illumina read counts across these samples . Consistent with prior reports of a profound suppression of host mRNA following VACV infection , we also saw an overall decrease in the amount of host mRNA [4] , [5] , [8] . After the normalization protocol , most genes showed a decrease or no difference in expression ( genes in gray; Figure 3B ) . In contrast , 611 host genes were upregulated during VACV infection , as defined by at least a two-fold change in transcript abundance at 6 hours ( genes in black; Figure 3B , Table S5 ) . We consider these genes to be actively expressed during VACV infection to counteract the nonspecific decay of host mRNA during poxvirus infection [4] , [5] , [18] , [19] . We examined the upregulated genes for transcription factor targets ( TFT ) and GO biological process terms using the Molecular Signatures Database ( MSigDB ) [20] . Strikingly , the set of 611 upregulated genes was very strongly enriched for genes regulated by HSF1 ( p = 3 . 39E-15 ) and the stress response ( p = 1 . 44E-14 ) . Because HSF1 was also one of the high-confidence hits from the RNAi screen , we assessed the set of upregulated host genes during VACV infection for enrichment of HSF1-responsive genes ( using a specifically defined set of 61 genes that have at least a two-fold increase in expression and have HSF1 bound to their promoters in multiple cell lines following a 42°C heat shock ( Table S6 ) ) [21] . Remarkably , there were 25 HSF1-regulated genes enriched at least two-fold at 6 hpi in our VACV dataset , which encompassed 41% of the HSF1-regulated gene list ( genes in red , selected genes labeled; Figure 3B ) . HSF1-regulated genes highly expressed during VACV infection include 83% of the HSPs regulated by HSF1 . This included HSPA6 , which is not upregulated in cancer cells addicted to HSF1 , but is strongly upregulated during a bona fide heat shock response [21] . A number of HSF1-regulated HSP activators and cochaperones ( AHSA1 , BAG3 , CHORDC1 , STIP1 ) were also expressed ( Table S7 ) . To determine whether the increase in HSF1-regulated gene transcription was observed during VACV infection in other next generation sequencing datasets , we analyzed the data described by Yang et al [4] . In that study , HeLa cells were infected with a high MOI of VACV and total polyadenylated RNA was collected at 0 and 4 hpi ( termed the Whole Transcriptome Analysis ( WTA ) dataset A ) . We analyzed the WTA-A dataset , and found 981 genes upregulated over two-fold at 4 hours post VACV infection ( genes in black; Figure 3C ) , with an approximately 30% overlap with the genes upregulated at least two-fold in our dataset . Of the 61 HSF1-responsive genes we previously reported , 28 were upregulated ( 46% ) in the Yang WTA-A dataset ( genes in red with selected genes labeled; Figure 3C ) . This correlates well with our data at 6 hpi , with 19 of the 25 HSF1-regulated genes expressed in both datasets ( Table S7 ) . The overlap has good representation of the HSP70/HSP110 superfamily and HSF1-regulated HSP cochaperones and activators . Together , these data indicate that HSF1-transcribed genes are upregulated during VACV infection . Previously published HSP data and a retrospective analysis of microarray experiments tracking host gene expression following poxvirus infection showed an association with the maintenance or upregulation of HSF1-regulated genes [5] , [22]–[27] . These findings establish that the HSF1-regulated gene expression program is a dominant host transcriptional event stimulated by VACV infection . The integrated analysis of both the pooled RNAi screen and the RNAseq host transcription data indicated that HSF1 was an important host factor . Therefore , we began to investigate the potential role of HSF1 in controlling orthopoxvirus infection . HSF1 , the master transcriptional regulator of the heat shock response , controls the expression of most heat shock genes both under basal conditions and following proteotoxic cellular stress [21] , [28]–[33] . The heat shock protein family is comprised of a large number of heat shock proteins ( HSPs ) with a broad range of chaperone functions . They are often designated by their molecular weight: HSPB ( small HSPs ) , DNAJ ( HSP40 ) , HSPD , HSPA ( HSP70 ) , HSPC ( HSP90 ) and HSPH , with most families containing multiple isoforms [34]–[38] . Our RNAseq data supported the hypothesis that these genes were actively transcribed during VACV infection . Together with the RNAi data , our results suggested that HSF1 is critical for orthopoxvirus replication; thus , we investigated the role of HSF1 during orthopoxvirus infection more rigorously . We used five shRNA lentiviral vectors to create five independent stable cell lines with depleted HSF1 . The knockdown efficacy of the shRNAs targeting HSF1 varied , with 23–61% of HSF1 remaining after selection . A representative immunoblot is shown in Figure 4A; the percent of HSF1 remaining after shRNA knockdown was quantified using four distinct anti-HSF1 antibodies ( Figure 4B ) . The stable knockdown cells were infected with a VACV expressing Venus under an early promoter and TagBFP under a late promoter ( Figure 4C ) [17] . Hairpins that reduced HSF1 levels inhibited VACV gene expression , significantly decreasing both early and late gene expression when compared to a control hairpin ( p<0 . 002 ) . To validate this finding , cell lines with HSF1 knocked down at least 50% were infected with VACV at MOI 0 . 01 to measure viral growth in the absence of HSF1 . We observed a ∼1 log10 decrease in viral titer ( 90% inhibition of viral growth ) at 24 hpi in the knockdown cells compared to control shRNA cells ( Figure 4D ) . These data strongly support the HSF1 target specificity of the phenotype , indicating shRNA knockdown of HSF1 is limiting viral gene expression and viral growth . We further confirmed the importance of HSF1 for VACV replication by analyzing virus infection in knockout mouse embryonic fibroblasts ( MEFs ) lacking HSF1 [39] , [40] . Infecting at MOI 0 . 01 with VACV-TrpV expressing Venus under an early promoter , mCherry under an intermediate promoter , and TagBFP under a late promoter , we observed that early , intermediate and late viral gene expression was inhibited in HSF1 null MEFs ( Figure 5A ) . Images in Figure 5B show the expected cytopathic effects ( CPE ) induced by vaccinia virus infection in wild type Hsf1+/+ MEFs , but no CPE in the absence of HSF1 ( Hsf1−/− MEFs ) . This effect was specific to HSF1 , as knockout of HSF2 had equivalent levels of infection and corresponding CPE to WT counterparts ( data not shown ) . In both the Hsf1−/− MEFs and the shRNA-knockdown cells , the depletion of HSF1 reduces VACV early , intermediate and late gene expression . This indicates that HSF1 is necessary for the entire VACV lifecycle , which is unexpected since orthopoxviruses package most of the viral factors necessary for early gene expression within the virion . Orthopoxviruses may need HSF1 directly or may activate its transcriptional activity to enhance production of an HSF1-regulated target that is necessary for infection . Our results demonstrating a role for HSF1 in vaccinia replication suggested that HSF1 was being activated following infection . In unstressed cells , HSF1 has been shown to exist as an inactive monomer in the cytoplasm , often in complexes with chaperone proteins . HSF1 undergoes an extensive set of posttranslational modifications , including phosphorylation , acetylation and sumoylation [33] . Upon activation , HSF1 is hyperphosphorylated and translocates to the nucleus to promote transcription of target genes [30] , [41]–[43] . We investigated whether HSF1 was activated during VACV infection in a manner similar to its activation by heat shock . When cells were exposed to elevated temperatures ( 42°C ) , an increase in the phosphorylated form of HSF1 is observed ( Figure 6A , lane 1 ) , when compared to the basal level of phosphorylated HSF1 in cells grown at 37°C ( lane 2 ) . Basal levels of HSF1 phosphorylation are seen in VACV-infected cells at 30 minutes post infection ( lane 3 ) suggesting that there is no immediate change in HSF1 activation during virus entry . However , at later times in infection , levels of phosphorylated HSF1 strongly increased , similar to that seen following heat-shock ( Figure 6A and 6B ) . This demonstrated that VACV infection results in HSF1 phosphorylation , an established marker of HSF1 activation [42] . To determine if phosphorylated HSF1 is relocating to the nucleus upon infection , we undertook immunofluorescence analysis of HSF1 . In cells grown at 37°C , the HSF1 antibody recognizing phosphorylation at S326 showed HSF1 located in the cytoplasm of primary human foreskin fibroblast ( HFF-1 ) cells and A549 cells ( white arrows , Figure 6C and Figure S2A , respectively ) . During heat shock , phosphorylated HSF1 signal increases as HSF1 localizes to the nucleus ( Figure 6C ) . Similarly , during VACV infection , phosphorylated HSF1 translocated to the nucleus , indicating that VACV is activating HSF1 in a manner similar to heat shock . A different HSF1 antibody , recognizing pS303 , shows a distinct staining pattern , with HSF1 in the nucleus in cells grown at 37°C and the development of nuclear stress granules as evidenced by bright foci in the nucleus , upon heat shock or VACV infection ( Figure S2B ) . These data demonstrate that phosphorylated HSF1 is in the nucleus during VACV infection , with staining patterns similar to heat shock , consistent with activation of this transcription factor . In A549 cells with HSF1 depleted by shRNA knockdown , lower levels of total HSF1 correspond to a decrease in phosphorylated HSF1 during VACV infection ( Figure 6D , quantitated in Figure 6E ) . The decrease in HSF1 activation following VACV infection corresponded with a decrease in expression of HSF1-transcribed genes , including HSP27 ( Figure 6D and 6E ) . The lack of HSF1 activation and downstream effectors led to a decrease in VACV gene expression as measured by fluorophores expressed from VACV promoters ( Figure 4C ) , as well as expression of native VACV proteins measured by immunoblot . Here , the early viral protein I3 and a late protein recognized by a polyclonal antibody raised to virions , which are composed of predominantly late viral proteins , both show a decrease in protein levels when HSF1 is knocked down ( Figure 6F , quantitated in Figure 6G ) . We see an inhibition of both early and late gene expression , with more inhibition of late gene expression than early gene expression . These data indicate that HSF1 activation , and perhaps transcription of downstream HSPs , is necessary for viral protein expression during VACV infection . When HSF1 is depleted from the cell , the cellular milieu may be altered such that it is non-permissive for orthopoxvirus infection , or alternatively the virus may directly require active HSF1 transcription during infection . To differentiate between these options , we pharmacologically inhibited HSF1 activity coincident with virus infection , for acute inhibition of HSF1 activity . We treated cells with several reported HSF1 inhibitors , including triptolide [44] , KNK437 [45]–[47] , quercetin [48]–[50] , and KRIBB11 [51] . The first three compounds do not bind HSF1 directly and likely influence HSF1 activity indirectly , along with the activity of other cellular systems [52] , while KRIBB11 has been reported to bind HSF1 directly [51] . One hour after drug treatment , the cells were infected with VACV expressing Venus from an early promoter and mCherry from a late promoter ( VACV-LREV ) . All four drugs reduced viral gene expression from both early and late promoters in A549 cells ( Figure 7A ) . More inhibition of late gene expression was observed compared to early gene expression; this may be due to the cascade transcription mechanism employed by poxviruses or HSF1 may be more important for late stages of infection than early . All four HSF1-inhibitory drugs also blocked virus replication as measured by viral titer . The compounds inhibited viral growth by 2 to 3 log10 in both A549 and HeLa cells ( Figure 7B ) . Although the inhibitors each have off target effects , the drugs all function to block HSF1 with different mechanisms , strengthening the conclusion that active HSF1 transcription is necessary for orthopoxvirus replication , and that inhibition of HSF1 has antiviral effects . We also tested pharmacologic inhibitors of some of the heat shock proteins transcriptionally controlled by HSF1 and expressed during VACV infection , including HSP90 , HSP70 and HSP27 . PFTμ interacts with HSP70 and prevents its activity [53] , KRIBB3 prevents the phosphorylation of HSP27 [54] , [55] , Ganetespib ( STA-9090 ) binds to the ATP-binding domain in the N-terminus of HSP90 [56] , [57] , while myricetin may block the interaction between members of the HSP40 and HSP70 families [58] . Acute inhibition of heat shock protein activity significantly decreased VACV-LREV infection as determined by fluorophore expression from early and late gene promoters ( Figure 7C ) . Similar to the HSF1-inhibitory drugs , late gene expression was more inhibited than early gene expression . Together , these data suggest that not only is HSF1 important for VACV infection , but that several major HSF1-regulated targets are important as well . We next evaluated whether HSF1 could be a potential therapeutic target for other orthopoxviruses , in particular monkeypox , which currently leads to outbreaks in the human population [1] . Knocking down HSF1 protein levels over 50% in A549 cells using four different shRNA sequences ( Figure 4B ) significantly inhibited MPXV early and late gene expression at 48 hpi . The expression of both eGFP , driven by an early MPXV promoter , and dsTomato Red , driven by a late MPXV promoter [59] , were significantly decreased when HSF1 levels were decreased ( p<0 . 01; Figure 8A ) . These data strongly correlate with the efficacy of HSF1 knockdown for each shRNA , showing a clear relationship between the level of HSF1 present in the cell ( Figure 4A and 4B ) and the ability of MPXV to express dsTomato Red from a late gene promoter ( Figure 8B ) . For example , when HSF1 is knocked down with 48% protein remaining , MPXV late gene expression is 44 . 4% , while HSF1 knockdown with only 24% remaining results in 14 . 5% MPXV late gene expression . These data position HSF1 as a conserved host requirement of orthopoxvirus replication and as a potential pan-orthopoxvirus target for future therapeutic development .
Here we identify HSF1 , the master regulator of the host transcriptional response to proteotoxic cellular stress , as a critical host factor for orthopoxvirus infection . Identifying HSF1 as important resulted from the combined use of two unbiased experimental approaches: RNASeq and pooled shRNA screening . Pooled shRNA libraries have not yet been widely used to identify host factors important for virus replication , but may be a useful tool for probing the virus-host interaction on a genomic scale . While pooled screening is subject to the same false hit rates and cell toxicity issues as comparable arrayed format screens , our results show that both screening approaches identify components of similar cellular pathways . Of our high-confidence hits , half ( HSF1 , JAG1 , TNPO3 , SKI ) have a nuclear function or signal to transcription factors , which is interesting for a cytoplasmic virus . We also have a strong correlation with recently published host factors necessary for orthopoxvirus infection . Sivan et al recently described the importance of nuclear pore proteins in viral morphogenesis; we identified the nuclear import protein TNPO3 in our screen . JAG1 , a Notch signaling molecule , is regulated by the Wnt pathway . The Wnt pathway was recently published to be important for Myxoma leporipoxvirus infection [60] . We also identified ITGB1BP1 , or ICAP1 , which specifically binds to the cytoplasmic domain of beta1 integrin . Beta1 integrin was recently shown to interact with VACV on the cell surface and signal through PI3K/Akt to facilitate VACV entry [61] . Previously published RNAi screens for necessary host factors during poxvirus infection of mammalian cells all also identified members of the heat shock response pathway , however the candidate genes were not validated or further developed in those publications [9] , [11] , [60] . Our screen was the only one to identify the master regulator of the pathway , HSF1 , as important . The reason that our screen identified HSF1 and other screens did not is not immediately apparent , but it is notable that all of the screens enforce the idea that HSPs are important for viral replication . The activation of HSF1 helps unify a mechanism for how poxviruses control the expression of many host proteins that they utilize . Earlier reports established that several heat shock proteins associate with VACV proteins during infection . HSP90 interacts with VACV core protein 4a ( A10L ) , and colocalizes with the viral factory during specific stages of the virus lifecycle [62] . Evidence suggests several viral proteins are bound by HSP70/72 [63] . HSP27 ( HSPB1 ) binds to three VACV proteins in protein-interaction studies: a truncated TNF-α receptor-like protein ( VACV-WR002 ) , C2L kelch-like protein ( VACV-WR026 ) and I4L ribonucleoside-diphosphate reductase large subunit ( VACV-WR073 ) [64] . Upregulation of HSF1 transcription provides a mechanism for how VACV and other poxviruses ensure sufficient levels of multiple chaperones through the activation of a single host protein . While other studies have shown that poxviruses can activate host transcription processes [65] , our study suggests that the activation of HSF1 aids orthopoxvirus replication on multiple levels . In addition to the involvement of HSPs as mentioned in the previous paragraph , HSC70 , HSP72 and HSP90 have been shown to be packaged within virions [66]–[68] , suggesting an importance for chaperones in early stages of virus infection . The continuous usage of host cell chaperones at multiple ( if not all ) stages of the virus lifecycle shows that poxviruses use the heat shock response to extend their genome , activating HSF1 to transcribe essential factors that are encoded by the host . A prediction of this genome extension hypothesis would be that some members of the family Poxviridae will have evolved to include one or more HSF1-stimulated genes in their own genome , reducing their dependence on host-production of HSPs . Consistent with this , genus Mulluscipoxvirus ( Molluscum contagiosum; accession number AAC55141 ) and genus Crocodylipoxvirus ( Nile crocodilepox virus; accession number YP_784220 ) encode proteins with homology to the DNAJ/HSP40 chaperone family . Interestingly , other large , cytoplasmically replicating DNA viruses encode HSPs in their genome . This is most striking in the case of mimiviruses , which express HSP70 ( MIMI_L254 ) , HSP40/DNAJ ( MIMI_R269 , MIMI_gp0838 ) and DNAK ( MIMI_L393 ) homologs , suggesting the requirement for large amounts of these HSPs during viral replication is consistent across cytoplasmic large DNA viruses [69] , [70] . How orthopoxviruses activate HSF1 is an interesting question for future study . In unstressed cells , HSF1 is in an inactive form in the cytoplasm , bound to several proteins including HSP90 and HSP70 . There are several proposed mechanisms of activation of HSF1 , including the idea that recruitment of HSP90 and/or HSP70 away from HSF1 in the cytoplasm allows the free HSF1 to become post-translationally modified and translocate to the nucleus to begin transcribing genes [30] , [41]–[43] . HSF1 may be activated during VACV infection when cytosolic HSP90 is recruited to the viral factory , as has been previously shown [62] . Poxviruses also encode kinases , which may act to directly phosphorylate and activate HSF1 during VACV infection . Finally , orthopoxviruses may indirectly activate HSF1 by stimulating the MAPK signaling pathway [71] , which in turn strongly drives HSF1 activity [72] . Understanding these mechanisms may provide insight into how an invading virion can manipulate the levels of a selective set of host proteins while also deploying proteins that reduce the general level of host mRNAs . Together , these data unify previously disparate observations regarding individually identified heat shock proteins and poxvirus infection . Earlier studies had illustrated the importance of individual members of the heat-shock response , but had not established whether poxviruses co-opted existing proteins or whether a heat-shock response was activated . Both our shRNA screening data and transcriptomic analysis implicate HSF1 activation as an important aspect of the orthopoxvirus lifecycle . This has implications not only for understanding viral evolution but also offers potential antiviral targets . Furthermore , our studies underscore that the HSF1 pathway is a viable target for broad-spectrum antiviral development , as it is a core cellular process used by multiple viruses , including HIV and EBV [73] , [74] . A more complete understanding of how perturbing cellular homeostasis benefits viral replication will be important for illuminating the biology of virus-host interactions and for recognizing new therapeutic possibilities .
A549 cells ( CCL-85 ) , HFF-1 ( SCRC-1041 ) and HeLa ( CCL-2 ) cells were obtained from the ATCC . The VACV used in this study was strain Western Reserve or a derivative thereof [15] , [17] . MPXV experiments were completed with modified MPXV Zaire 1979 at USAMRIID under appropriate containment conditions [59] . A549 cells were seeded in 96 wells plates at low density the previous day . The lentivirus vectors were added to each well to achieve an MOI ∼1 . Infection was allowed to proceed overnight , then puromycin selection was applied for 5 days . The knockdown cells were infected with VACV-LREV at MOI 1 or 0 . 01 . Cells were fixed with 4% formaldehyde 16–19 hpi , then read on a Tecan infinite M1000 for Venus ( excitation: 515 nm and emission: 528 nm ) and mCherry ( excitation: 587 nm and emission: 610 nm ) . The secondary screen was completed with 3 independent experiments . Each plate was background corrected by subtracting the average of empty wells and normalized to 100% by the total RFU across the plate for early and late . An example plate had an average early Venus signal of 20 , 000 RLU , with an average background around 700 RLU and a reading of the GFP shRNA of 4 , 000 RLU . The average late mCherry signal was 3 , 200 RLU , with an average background signal around 75 RLU and an example positive hit of 1300 RLU . The hits were determined by comparing the normalized data across all three replicates for hairpins that decreased fluorescence more than 50% . A549 cells were seeded in 96-well plates the previous day and infected with the arrayed lentiviral vectors at MOI ∼1 . After 5 days of puromycin selection , the cells were lysed and luciferase read according to manufacturer's instructions using CellTiter-Glo Luminescent Cell Viability Assay system ( Promega ) on a LUMIstar Omega luminometer ( BMG Labtech ) for 1 second/well . A549 cells were infected at an MOI specified in text . At times indicated , cells were lysed in RIPA buffer with protease and phosphatase inhibitors ( 1 mM PMSF , 1 mM benzamidine , 100 nM okadaic acid , 100 nM microcysin and 100 nM sodium fluoride ) . 20 µg of total lysate were separated on a 4–15% SDS-PAGE gel and transferred to PVDF ( Bio-Rad 162-0177 ) . Blots were probed with polyclonal antibodies specific to HSP27 ( abcam [G3 . 1] antibody ab2790 ) , Virostat anti-VACV virion ( Virostat 8101 ) , VACV I3L ( mAb 10D2 , generous gift of Dr . David Evans , University of Alberta , Edmonton ) , HSF1 , including anti-HSF1 ( phospho S326 ) ( HSF1-PS326; abcam [EP1713Y] ab76076 ) , anti-HSF1-J7F9 ( abcam [J7F9] ab115303 ) , anti-HSF1 ( phospho S303 ) ( abcam ab47369 ) , and anti-HSF1 #4356 ( Cell Signaling ) . A549 or HeLa cells were seeded in 96-well plates the previous day and infected with modified VACV viruses as specified in the text at an MOI of 1 ( fluorescence readout ) or 0 . 1 ( viral titer ) . For drug treatment , compounds were added at specified concentrations prior to virus addition . Inhibitor compounds: Triptolide , Quercetin ( Tocris Bioscience ) , KRIBB11 ( EMD Millipore ) , KRIBB3 , Pifithrin-μ , Myricetin ( Sigma Aldrich ) , and Heat Shock Protein Inhibitor I/KNK437 ( Santa Cruz Biotechnology , Inc . ) For fluorophore assays , cells were fixed at 18 hpi with 4% formaldehyde . Plates were read on a Tecan infinite M1000 for Venus ( excitation: 515 nm and emission: 528 nm ) and mCherry ( excitation: 587 nm and emission: 610 nm ) . For plaque assays , virus was collected 24 hpi and titered by plaque assay . Virus was collected at specified timepoints post infection . Virus was freeze/thawed and sonicated 3× . Viruses were then serially diluted in 10 fold dilutions and added to confluent BSC-40 cells . 24–48 hours post infection , cells were fixed and stained with crystal violet to visualize plaques . HFF-1 or A549 cells were seeded on coverslips the previous day and infected with VACV at MOI 1 , mock infected or heat shocked for 2 hours ( HFF-1 ) or 1 hour ( A549 ) at 42°C . VACV and mock cells were fixed at 5 hpi ( HFF-1 ) or 24 hpi ( A549 ) with 4% formaldehyde . Heat shocked cells were recovered at 37°C for 30 minutes ( HFF-1 ) or 1 hour ( A549 ) , then fixed . Cells were stained for HSF1 with HSF1-phospho-S326 or HSF1-phospho-S303 and DAPI to delineate nuclei . Coverslips were mounted using ProLong Gold antifade reagent with DAPI ( Invitrogen ) and imaged on Axiovert 200M microscope ( Zeiss ) . HeLa cells were grown to 90–100% confluency in 6-well plates , then inoculated with VACV-WR ( MOI 10 ) in DMEM+2% FBS , and incubated at 37°C for 1 hour . After the 1 hour incubation , virus was removed and cells were washed three times with PBS before fresh media ( with 2% FBS ) was added ( 0 hpi time point ) and returned to 37°C . At each time point ( 0 , 0 . 5 , 2 , 6 , 18 hpi ) , cells from two wells were harvested for nucleic acid extraction . RNA isolation: Total RNA was extracted from cells at each time point with Trizol , following the manufacturer's instructions . cDNA Library preparation: We used the Illumina mRNA seq V2 protocol ( April 2008 ) to generate the cDNA library for next-generation sequencing . In short , mRNA was purified from the total RNA samples and fragmented before proceeding with reverse transcription . Adapters were ligated to the resulting cDNA fragments . Templates were size-selected through gel purification , then enriched by PCR , and the resulting libraries were validated on an Agilent Bioanalyzer DNA 1000 chip . A549 cells were seeded in 96 well plates the previous day . Cells were infected with shRNA lentiviruses at MOI ∼1 overnight , then selected with puromycin for 3 days . Cells were infected with modified MPXV [59] at MOI 1 for 48 hours . Cells were fixed with 10% neutral buffered formalin , then read on a SpectraMax M5 .
|
Orthopoxviruses bring in many of the factors they need for replication and impair the host cell by preventing the expression of host proteins . Although orthopoxviruses are less reliant on the host than some viruses , host factors are still required for infection . Here , we report results from two genome-scale approaches that identify host proteins used by orthopoxviruses during infection . These approaches showed that the master regulator of the heat shock response , heat shock factor 1 ( HSF1 ) , is a critical host factor for orthopoxvirus replication . HSF1-regulated genes are some of the only host genes with expression maintained or increased following virus infection . Our studies show that orthopoxviruses enter the cell and activate a host transcription pathway as part of its own replication process . These proteins are then utilized by the virus during infection and packaged into the virion , essentially extending the viral genome to include genes co-opted from the host nuclear DNA . This is supported by the existence of heat shock proteins in the viral genome of non-orthopoxvirus genera . We further show that small-molecule inhibitors of HSF1 and HSF1-transcribed genes are effective inhibitors of orthopoxvirus replication , suggesting a new avenue for antiviral development .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"host",
"cells",
"virulence",
"factors",
"and",
"mechanisms",
"dna",
"viruses",
"viral",
"transmission",
"and",
"infection",
"viral",
"classification",
"virology",
"biology",
"microbiology",
"viral",
"replication"
] |
2014
|
The Master Regulator of the Cellular Stress Response (HSF1) Is Critical for Orthopoxvirus Infection
|
Genetic-modification strategies are currently being developed to reduce the transmission of vector-borne diseases , including African trypanosomiasis . For tsetse , the vector of African trypanosomiasis , a paratransgenic strategy is being considered: this approach involves modification of the commensal symbiotic bacteria Sodalis to express trypanosome-resistance-conferring products . Modified Sodalis can then be driven into the tsetse population by cytoplasmic incompatibility ( CI ) from Wolbachia bacteria . To evaluate the effectiveness of this paratransgenic strategy in controlling African trypanosomiasis , we developed a three-species mathematical model of trypanosomiasis transmission among tsetse , humans , and animal reservoir hosts . Using empirical estimates of CI parameters , we found that paratransgenic tsetse have the potential to eliminate trypanosomiasis , provided that any extra mortality caused by Wolbachia colonization is low , that the paratransgene is effective at protecting against trypanosome transmission , and that the target tsetse species comprises a large majority of the tsetse population in the release location .
African trypanosomiasis infects 30 , 000 people in sub-Saharan Africa . Without treatment , infection is almost always fatal [1] . In addition , losses in livestock production due to African trypanosomiasis are estimated at US$1 billion annually [2] . Currently , control efforts primarily target the tsetse vector by insect traps , insecticide spraying of land and livestock , and sterile-insect technique [3] . Transgenesis of infectious-disease vectors is being widely considered as a possible strategy for controlling the burden of vector-borne disease [4]–[7] . For tsetse , instead of trypanosomiasis-refractory genes being incorporated into the tsetse genome directly , these genes may be encoded in Sodalis , commensal bacteria that colonize the gut of tsetse [8] . This mode of gene expression is termed paratransgenesis , where changes in the insect are induced by modifying genes of a commensal organism . Wolbachia , bacteria that are transmitted by female insects to their progeny , could be harnessed to drive pathogen-refractory transgenes into insect vector populations [9] , [10] . Some strains of Wolbachia cause cytoplasmic incompatibility ( CI ) , where the sperm of Wolbachia-colonized males is less competent than the sperm of non-colonized males at fertilizing non-colonized eggs [11] . Because of CI , when Wolbachia is at high frequency in the population , Wolbachia-colonized females have higher reproductive success than non-colonized females because mates are likely to be colonized males . At low frequency , Wolbachia-colonized females may have a fitness disadvantage if Wolbachia reduces egg count or increases mortality . Thus , a threshold in Wolbachia frequency may exist above which Wolbachia increases tsetse fitness and is therefore driven to fixation [9] , as has been observed in natural and laboratory populations [12] , [13] . Moreover , tsetse laboratory lines and some wild populations are naturally colonized by CI-inducing Wolbachia [14] , [15] , suggesting the feasibility of using Wolbachia as a gene driver . Wolbachia-induced CI gives paratransgenesis a powerful potential advantage to over existing vector-control methods: CI could prevent trypanosomiasis-tolerant tsetse from re-invading treated areas and could allow paratransgenic tsetse to invade and replace neighboring populations . We evaluated the effectiveness of a paratransgenic strategy , transgenic Sodalis driven by Wolbachia , to control African trypanosomiasis . To do this , we developed a mathematical model combining the population genetics of CI-inducing Wolbachia in tsetse with the transmission dynamics of Trypanosoma brucei gambiense , the parasite responsible for 95% of reported human cases [1] , among tsetse , humans , and non-human animal reservoir hosts . Previous mathematical models have examined the invasion of Wolbachia into populations of fruit flies [9] , mosquitoes [16] , and tsetse [17] , but the analysis we present here is the first to incorporate the epidemiology of the trypanosome disease system with the population genetics of CI dynamics . Parametrizing this model with empirical estimates of CI , we assess the potential effectiveness of a trypanosome-refractory paratransgenic intervention to control trypanosomiasis .
Cytoplasmic incompatibility has been modeled using discrete-time models , with both non-overlapping generations [18]–[21] and overlapping generations [16] , [22] . We extended our previous continuous-time model of Wolbachia–tsetse dynamics [17] , based on reproductive rates of tsetse mating pairs ( Table 1 ) , to include age structure in the tsetse population ( Text S1 ) . Tsetse were divided into 10-day age classes [23] and mating was assumed to occur in the first adult age class [24] . This age-structured tsetse model enabled us to examine the effects of tsetse remating by allowing a proportion of both paratransgenic , Wolbachia-colonized and wild-type , non-colonized female tsetse to mate a second time [25] . We explicitly tracked the Wolbachia status of mating partners . For a given tsetse mating pair , reduction in reproductive rate was reduced by four parameters: first , the proportion of nonviable zygotes of non-colonized eggs by Wolbachia-colonized sperm , ( e . g . indicates that all incompatible fertilizations fail , which is perfect CI ) ; second , the fecundity benefit of Wolbachia-colonized females relative to non-colonized females , ( e . g . implies there is no fecundity benefit associated with Wolbachia colonization ) ; third , the proportion of Wolbachia-colonization females having non-colonized offspring , termed ( e . g . implies perfect transmission from females to their offspring ) ; and last , an increased mortality rate for Wolbachia-colonized tsetse , ( e . g . implies no extra mortality cost ) . In our empirical scenario we set , , , and [17] , but varied these parameters in the sensitivity analysis . We assumed that the paratransgenic tsetse are all released at one time into the target area , not continually released over several years . We also assumed that the paratransgenic tsetse released were newly emerged adults with a sex ratio equal to that of the wild population . We constructed a three-species SEIR differential-equation model for T . b . gambiense trypanosome infection among tsetse , humans , and animal reservoir hosts based on a previously published model [26] . Tsetse were modeled by combining our age-structured model for Wolbachia with a dynamic model of trypanosome infection ( Figure 1 and Text S1 ) . Our model assumptions are consistent with the modeling study of Rogers [26] , unless otherwise specified ( Table 2 ) . We assumed that tsetse are only susceptible to trypanosome infection during their first blood-meal and only within 24 hours after emergence from pupa to adult . Should susceptible tsetse become infected after feeding on an infectious vertebrate , the tsetse enter the exposed state during which the trypanosome infection incubates . After incubation , tsetse enter the infectious state and can transmit infection to humans and the animal reservoir . We further assumed that tsetse do not clear trypanosomes . Susceptible tsetse that do not become infected enter the resistant state after their first blood-meal or 24 hours after emergence , whichever comes first . The dynamics of humans and animal reservoir trypanosomiasis infection each follow a standard vector-transmission model , in which individuals are divided into susceptible , exposed , infectious , and recovered states ( Figure 1 ) . Susceptible vertebrate hosts may become exposed after being bitten by an infectious tsetse . After incubating the infection , exposed vertebrates enter an infectious state in which they are capable of transmission to tsetse . Vertebrate hosts clear the infection and enter a recovered state in which they are immune to re-infection . After this immunity wanes , vertebrates return to being susceptible to trypanosomiasis . Both human and animal populations are of constant size with no births or deaths . Our base-case model assumed that paratransgenic tsetse are completely resistant to trypanosomiasis , that there is no tsetse remating , and that only one tsetse species inhabits the intervention location . We explore the sensitivity of results to these three assumptions .
Trypanosomiasis is transmitted by multiple tsetse species [24] , between which Wolbachia-CI parameter values may vary . Therefore , we calculated the effect of varying the CI parameters on the effectiveness of a paratransgenic release . Depending on the parameters , the model can exhibit a threshold for the size of the paratransgene release such that if the abundance of paratransgenic tsetse released are below this threshold , paratransgenic tsetse are driven out of the population , whereas if the release is larger than the threshold , paratransgenic tsetse are driven to fixation . These dynamics arise because Wolbachia-induced CI generates a frequency-dependent fitness effect: at low frequencies , the incompatibility of Wolbachia-colonized females with the predominantly non-colonized males imposes a fitness cost to colonization , while at high frequencies , the incompatibility of non-colonized males with the predominantly colonized females confers a fitness advantage to the colonized tsetse . This frequency-dependent fitness is in addition to the frequency-independent effects of the fecundity benefit ( ) and mortality cost ( ) of Wolbachia colonization . ( See also Text S1 . ) For our empirical parameters , the fecundity benefit is sufficient for the fitness of Wolbachia to be positive even at low frequencies , such that there is no threshold: a paratransgenic release of any size will eventually lead to the fixation of the paratransgene . As a sensitivity analysis , we varied the CI parameters and examined the resulting threshold release size required to drive the paratransgene to fixation ( Figure 3 ) . Around the empirical parameter values , the threshold size is sensitive to transmission failure ( ) , fecundity benefit ( ) and mortality cost ( ) , and highly insensitive to incompatibility ( ) . However , when transmission failure is high ( ) , fecundity cost is high ( ) , or mortality cost is high ( ) , paratransgenic fixation is not feasible and , consequently , trypanosomiasis is not eliminated . It is plausible that the paratransgene may only confer imperfect immunity against trypanosomes . To determine the impact of imperfect paratransgenic immunity against trypanosomiasis on disease prevalence , we considered the possibility that a proportion of Wolbachia-colonized tsetse are immune to trypanosome infection , and a proportion are susceptible but are still able to transmit Wolbachia colonization . We found that imperfect immunity does not affect population replacement by paratransgenic tsetse . However , imperfect immunity impacts trypanosomiasis prevalence among tsetse , humans , and the animal reservoirs , because trypanosome transmission is not as effectively suppressed by the paratransgene ( Figure 4A ) . Our results suggest that imperfect immunity has little effect on the dynamics of trypanosomiasis elimination provided that the efficacy of paratransgenic immunity is above . Conversely , efficacy below 85% reduces the efficacy of paratransgenic control ( Figure 4A ) . Multiple species of tsetse can inhabit the same region . For example , a maximum monthly proportion of any single species of tsetse ( G . pallicera ) over several years was found to be 79% at a sampling site in Côte d'Ivoire [27] . Because intra-species mating among tsetse species results in either no offspring or sterile offspring [27] , paratransgenic tsetse releases can be assumed to be species-specific . We found that when the targeted tsetse species comprises 85% or more of the tsetse population , trypanosomiasis is eliminated ( Figure 4C ) . However , when the targeted species is a smaller fraction of the tsetse population , trypanosomiasis is reduced to a lower endemic prevalence without being eliminated . For example , if the target species is 79% , trypanosomiasis long-term prevalence is reduced more than 20 fold in humans and 6 fold in livestock . Evidence for remating of female tsetse is mixed . For example , one study supports the hypothesis that female tsetse typically mate only once soon after their emergence as adults and store the sperm from this mating to fertilize eggs throughout their lives [24] , [28] . Conversely , the proportion of female tsetse mating more than once has been estimated to be as high as 38% [25] , [29] , [30] . In addition , there may be differences in remating rates between species [31] . Thus we compared our baseline scenario with a scenario where all female tsetse can remate . We also considered the possibility that failure to produce offspring due to incompatible first matings may affect the likelihood of remating among female tsetse . We found that differences in remating rates [29] can impact the effectiveness of a paratransgenic control strategy ( Figure 4C ) . When remating occurs equally for all females , remating does not substantially affect CI dynamics . However , if only females that have incompatible first matings can remate , Wolbachia-colonized tsetse invade the population more slowly than when no remating takes place and may even be unsuccessful in invading . When only incompatible females remate , if fewer than 27% remate , Wolbachia is driven to fixation , but more slowly than with no remating . If 27% or more of the incompatible females remate , Wolbachia is driven out of the population .
To evaluate the potential for using CI as a mechanism to drive trypanosome-refractory paratransgenes into tsetse populations , we integrated the population genetics of CI for Wolbachia-colonized tsetse into a dynamic model of trypanosomiasis in tsetse , humans , and an animal reservoir . Based on empirical data for CI in tsetse , we found that a one-time release of paratransgenic tsetse could eliminate trypanosomiasis , provided that extra mortality due to Wolbachia colonization is low , that the paratransgene is effective at protecting against trypanosome transmission , and that the target tsetse species comprises a large majority of the tsetse population in the release location . Due to the relatively slow transient dynamics of paratransgenic population replacement , it is crucial to understand not only if trypanosomiasis can be eliminated , but also how quickly elimination occurs . Specifically , the size of the paratransgenic release determines whether the time scale of elimination is a year or a decade . For example , a release of 20% of the wild-type population would eliminate trypanosomiasis in about 4 years , while a release of 10% would take 6 years eliminate trypanosomiasis . The best available parameter estimates are for Wolbachia in the tsetse G . m . morsitans and the trypanosome T . b . gambiense . The empirical Wolbachia CI parameters were derived from laboratory experiments on the tsetse G . m . morsitans [17] . Parametrizing our model with these empirical values , only a large mortality cost of Wolbachia colonization , insufficient immunity of the paratransgenic tsetse to trypanosomiasis transmission , or the cohabitation of multiple tsetse species could prevent the elimination of trypanosomiasis . The empirical parameter estimates include a fecundity benefit of for Wolbachia colonization . Similar frequency-independent fitness benefits of Wolbachia colonization have been observed in Drosophila melanogaster [12] and the mosquito Aedes albopictus [13] . However , there are no empirical estimates of the effect of Wolbachia on tsetse mortality or other tsetse life-history traits . Therefore it is conceivable that Wolbachia colonization could have an overall negative impact on tsetse fitness when all components of fitness are incorporated . For example , D . melanogaster from wild populations showed weaker CI than in laboratory populations [12] . If this were true for tsetse , reduced transmission of Wolbachia to offspring could generate a threshold below which a release would fail , even in the presence of a frequency-independent fitness benefit of Wolbachia colonization . Moreover , Wolbachia-induced CI remains to be investigated in the many other species of tsetse that transmit trypanosomiasis to humans and livestock [24] . Our analysis shows that a multi-species tsetse population can be effectively manipulated to control trypanosomiasis provided that the target species is in the majority . A single-species paratransgenic release has the potential to prevent trypanosomiasis transmission among this species . However , if the non-targeted tsetse species in the control location are abundant , they alone may be sufficient to support trypanosomiasis endemicity , albeit at a lower prevalence . Different feeding preferences of different tsetse species cohabiting the same area may create separate epidemiological systems that maintain trypanosomes: in this setting , eliminating trypanosomes from one tsetse species would not eliminate trypanosomes from the whole area , leaving humans at risk . Moreover , differences in trapping efficiency of tsetse species may result in unreliable estimates of the relative size of the species' populations . Controlling multiple tsetse species by paratransgenesis could be difficult as Wolbachia drivers and transgenic Sodalis must be developed for each species . In contrast , differences in competence for trypanosome transmission to humans and livestock may mitigate the importance of many tsetse species . More research is needed to understand the control of trypanosomes in areas with multiple tsetse species . It is possible that the trypanosome-refractory transgene in the symbiont Sodalis could become unlinked from the Wolbachia used to drive the paratransgene into the tsetse population [9] , [32] . In addition , some wild populations of tsetse already harbor Wolbachia [15] , which may interfere with the colonization of a new strain of Wolbachia . However , if the new strain exhibits bidirectional CI with the established resident strain , whereby eggs colonized by the each strain are incompatible with sperm from males colonized by the other strain , then it could drive paratransgenic tsetse into the population , provided a sufficiently large release [33] . In addition , a potential anti-trypanosome effect of Wolbachia colonization , which we have not included in our model , would increase the effectiveness of the paratransgene and perhaps also mitigate for the paratransgene becoming unlinked Wolbachia . This possibility is supported by the observation that Wolbachia colonization in Aedes aegypti mosquitoes activates an immune response that protects the mosquitoes against infection with dengue and other vector-borne pathogens [34]–[37] . A similar immune response to Wolbachia has also been seen in Drosophila [38] . Trypanosome infection has been observed to have a fitness cost to tsetse [39] , but the very low prevalence of trypanosomes in tsetse populations means that trypanosomes would not play a substantial role in selection for resistant paratransgenes in the few generations until establishment of a successful introduction . Indeed , we made the conservative assumption that there was no tsetse fitness cost from trypanosome infection , although the results would likely be very similar using e . g . the estimate of a 30% fecundity cost from trypanosome infection [39] . Trypanosomiasis control policies have focused on reducing tsetse density with insecticide and , in some cases , sterile-insect technique . Given that seasonal fluctuations in the abundance of different tsetse species could compromise the speed or viability of tsetse population replacement [27] , combinations of controls measures , such as treatment of animal reservoir hosts as well as control of wild game or habitat disruption [24] , could synergistically enhance the efficacy of a paratransgene release and speed the eradication of trypanosomes from a target area . Unlike conventional tsetse control , Wolbachia-induced CI could prevent trypanosomiasis-tolerant tsetse from re-invading treated areas and could allow paratransgenic tsetse to invade and replace neighboring populations . Indeed , it might be possible to release paratransgenic tsetse into a target area and then , once the paratransgene is driven to fixation , capture paratransgenic tsetse from this area to release in the next target area , saving on-going costs of colony breeding . In our modeling , we have assumed that once trypanosome-refractory tsetse are established , they do not lose effectiveness over time . Effectiveness would wane if the transgenic Sodalis becomes unlinked from the Wolbachia driver , e . g . if maternal transmission of Sodalis is not perfect . Over time , transgenic Sodalis could shed the trypanosome-resistant genes or trypanosomes could develop resistance . Should effectiveness wane , the control area may be able to again sustain trypanosome transmission . Our analysis has only examined whether paratransgenic tsetse can be effective without waning effectiveness: in future work we will closely examine the potential for waning , the time until the tsetse population reverts to transmission potential , and the resulting potential effectiveness over time of paratransgenic tsetse . Like sterile-insect technique , where irradiated males are released to decrease females' mating success , this paratransgenic strategy would require breeding large numbers of tsetse in a colony for release . Unlike irradiated males , we believe that paratransgenesis is unlikely to cause high mortality or low mobility as both Wolbachia and Sodalis occur in wild tsetse populations , although this will have to be established empirically in laboratory and wild populations . Efficient colony breeding may require releasing more males than females , while , in our analysis , we assumed that paratransgenic releases have a balanced sex ratio . Of course , paratransgenic releases must contain some females as only they transmit Wolbachia to their offspring , which in turn drives the trypanosome-resistant transgene into the population . If breeding necessitates male-biased paratransgeneic releases , a balanced sex ratio would be reestablished after the first generation provided that the releases were not too heavily biased towards males , and our model predictions would not change . In addition , ethical concerns over releasing tsetse should be less for paratransgenesis than for sterile-insect technique as the paratransgenic tsetse are resistant to trypanosome transmission . We developed a mathematical model that integrates Wolbachia population genetics and trypanosomiasis epidemiology , parametrized by recent empirical studies of Wolbachia in tsetse . We used this modeling framework to evaluate a novel paratransgenic control strategy to eliminate trypanosomiasis and provide predictions of feasibility and speed of trypanosomiasis elimination . We find that paratransgenesis has the potential to be a feasible strategy to control trypanosomiasis rapidly .
|
African sleeping sickness is a fatal disease occurring in sub-Saharan Africa . The parasites that cause African sleeping sickness are transmitted between humans and livestock by the tsetse fly . Controlling the spread of the parasite by tsetse flies has been proposed as a promising strategy for reducing the incidence of sleeping sickness . One potential control method relies on releasing genetically modified tsetse that are resistant to carrying the sleeping sickness parasite . For this strategy to be successful , resistant tsetse must be able to invade the susceptible tsetse population . Here , we used a mathematical model to assess the feasibility of such a strategy and the implications for sleeping sickness prevalence in humans and livestock . We found that the strategy has the potential to eliminate sleeping sickness , provided that the genetic modification is effective at protecting against trypanosome transmission and provided that the target tsetse species comprises a large majority of the tsetse population in the release location .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"medicine",
"infectious",
"disease",
"epidemiology",
"epidemiology",
"population",
"biology",
"biology"
] |
2013
|
Evaluating Paratransgenesis as a Potential Control Strategy for African Trypanosomiasis
|
Giardiasis is widely acknowledged to be a neglected disease in need of new therapeutics to address toxicity and resistance issues associated with the limited available treatment options . We examined seven protein kinases in the Giardia lamblia genome that are predicted to share an unusual structural feature in their active site . This feature , an expanded active site pocket resulting from an atypically small gatekeeper residue , confers sensitivity to “bumped” kinase inhibitors ( BKIs ) , a class of compounds that has previously shown good pharmacological properties and minimal toxicity . An initial phenotypic screen for biological activity using a subset of an in-house BKI library found that 5 of the 36 compounds tested reduced trophozoite growth by at least 50% at a concentration of 5 μM . The cellular localization and the relative expression levels of the seven protein kinases of interest were determined after endogenously tagging the kinases . Essentiality of these kinases for parasite growth and infectivity were evaluated genetically using morpholino knockdown of protein expression to establish those that could be attractive targets for drug design . Two of the kinases were critical for trophozoite growth and attachment . Therefore , recombinant enzymes were expressed , purified and screened against a BKI library of >400 compounds in thermal stability assays in order to identify high affinity compounds . Compounds with substantial thermal stabilization effects on recombinant protein were shown to have good inhibition of cell growth in wild-type G . lamblia and metronidazole-resistant strains of G . lamblia . Our data suggest that BKIs are a promising starting point for the development of new anti-giardiasis therapeutics that do not overlap in mechanism with current drugs .
Giardia lamblia is the most commonly reported intestinal protozoan parasite and the cause of giardiasis , a gastrointestinal illness resulting in diarrhea , nutrient malabsorption , vomiting , and weight loss [1] . It infects approximately 280 million people worldwide [2 , 3 , 4] . This disease contributes to the global health burden of diarrheal diseases that collectively constitute the second-leading cause of death in children under five years old [3 , 4] . Infection can also cause developmental delays and failure to thrive [5]; as few as 3 occurrences ( >2 weeks duration ) of diarrheal disease per year during the first 2 years of life is associated with reduced height ( approximately 10 cm ) and intelligence quotient score ( 10 points ) by 7–9 years of age [6] . G . lamblia has a simple life cycle consisting of two forms , the binucleate flagellated trophozoites and the tetranucleate infective cysts . Cysts are the environmentally resistant forms responsible for transmission of the disease [1] . First choice therapeutic options are limited to metronidazole and chemically related nitroimidazole drugs . These compounds are prodrugs whose reduction to reactive radicals is mediated intracellularly by pyruvate: ferredoxin oxidoreductase and other enzymes involved in anaerobic metabolism . Resistance can occur in up to 20% of clinical presentations , primarily due to down-regulation or mutation of these activating enzymes [7 , 8] . The toxic intermediates cause DNA damage in Giardia trophozoites [9] , and attack protein sulfhydryl groups non-specifically . Even when infection is cleared , pathophysiological changes in the gut may persist , severely impacting quality of life [3 , 8] . Consequently , there is an increasing need to develop alternative drugs to treat giardiasis . To address this need , we have combined a structure-based approach with targeted phenotypic screening to jointly identify and validate a class of potential protein targets in Giardia and a corresponding class of drug-like molecules that attack them . This approach takes advantage of an in-house library of protein kinase inhibitors based on a limited number of chemical scaffolds , developed in the course of previous work to optimize potency , pharmacological properties , and selectivity for inhibition of CDPK ( Calcium Dependent Protein Kinase ) homologs in several apicomplexan pathogens [10 , 11] . A primary structural determinant of target selectivity in this library is the fortuitous presence of an atypically small gatekeeper residue in the active site of the target CDPKs [12 , 13] . The presence of a small amino acid at the gatekeeper position creates a much larger effective pocket than is found in the majority of protein kinases [14] , allowing inhibition by compounds that are too large to be accommodated in a typical kinase active site . Compounds from this library have been shown to have minimal cytotoxicity against human cells , consistent with selective activity disfavoring inhibition of human kinases . Several have shown promise in animal trials for anticoccidial efficacy [15 , 16] . While design of the 400+ compounds in our BKI ( bumped kinase inhibitor ) library was biased toward optimal selectivity for CDPK homologs , all library compounds are expected to preferentially inhibit small gatekeeper kinases . Protein kinases in general constitute an attractive class of molecular targets for drug discovery , distinct from the targets of existing anti-giardiasis drugs . Of the 278 protein kinases identified in the G . lamblia genome ( strain WB ) , 80 kinases form a core kinome while the remaining 198 constitute a massively expanded family of NEK kinases [17] . The core kinome contains 80 kinases from 49 families that are recognizably also present in higher eukaryotes . It contains 19 families that have no recognized homologs outside of Giardia [17] . Where direct homologs can be identified , the average sequence identity between Giardia and human homologs is roughly 40% [17] . Thus , individual Giardia kinases are in general expected to show extensive sequence and structural differences to their human homologs , if any , facilitating development of selective inhibitors . Furthermore , the greatly reduced number of core kinome classes , each containing no more than three members in the G . lamblia genome , suggests that gene loss in these parasites has pared the remaining core kinome down to a near-minimal set of essential proteins . The huge expansion of NEK kinase sequences in the Giardia genome stands in contrast to the reduction in size of the core kinome . Similar , though less extreme , expansions of the NEK kinase group have been found in ciliates and excavates [18 , 19] . The total of 198 NEK homologs in the G . lamblia genome may be compared to a single NEK homolog in yeast and 11 in humans . Many of the Giardia NEK sequences are highly variable across strains , and roughly two-thirds are inferred to be catalytically inactive due to the loss of conserved catalytic residues in the canonical active site [17] . Nevertheless , among the catalytically active Giardia NEK kinases some are likely to carry out biologically essential phosphorylation and hence to constitute valid drug targets . In other eukaryotes , NEK kinases are involved in cell cycle control [20 , 21] . Two Giardia NEK kinases have been shown to be active in mitosis and excystation [22] . It is worth noting that two trypanosomal NEK kinases , one of them coincidentally possessing a small gatekeeper residue , have been suggested as drug targets in Trypanosoma brucei [23] . While the G . lamblia kinome contains no CDPK homologs , it does contain multiple genes encoding kinases with small gatekeeper amino acid residues in their ATP binding sites , creating a potential opportunity to design specific ATP-competitive inhibitors that are highly selective for one or more parasite kinases relative to all human kinases . Using reverse genetics , we show that some of these kinases are essential for Giardia trophozoite proliferation . In addition to providing insight into the role of these newly described kinases , we also show that they are targets of some BKIs . Our results constitute the first steps toward further development of BKIs into an effective alternative treatment for giardiasis .
The sequence of the TgCDPK1 kinase domain core was used as a BLASTP probe of Giardia strain WB sequences in GiardiaDB . This search returned 232 hits in total . Of these , 121 had E scores < 2x10-4 and within this set the initial alignment for eight Giardia kinases indicated a glycine , alanine , serine , or threonine gatekeeper residue . These eight , all with E scores < 4x10-24 , were selected for the initial evaluation presented here , as their substantial sequence similarity to TgCDPK1 was expected to indicate higher likelihood of sensitivity to the existing BKI library compounds . Subsequently , ClustalW/HMMER multisequence alignment was used to match all 232 sequences against TgCDPK1 residues 110–133 , corresponding to the β-hairpin containing the gatekeeper residue itself and residues contributing to the active site pocket . Four additional putative small-gatekeeper kinases were identified from this search and earmarked for eventual characterization paralleling the work reported here . Finally , we noted that 15 putative kinase ORFs annotated as active in the tabulation by Manning [17] were not recovered by either of our probes . These were matched individually to the nearest homolog with a representative structure in the PDB in order to identify the gatekeeper residues with certainty . G . lamblia wild-type strains WBC6 ( ATCC 50803 ) , 713 and metronidazole-resistant 713-M3 cells [24] ( supplied by the L . Eckmann lab , UC San Diego School of Medicine ) , were grown in TYI-S-33 medium supplemented with 10% bovine serum and 0 . 05 mg/mL bovine bile [25] . Cells were cultured at 37° under hypoxic conditions using 15 mL polystyrene screw-cap tubes ( Corning Life Sciences DL ) . Preliminary phenotypic screening for inhibitory effects of 36 compounds from our focused BKI library [26] against the trophozoite stage of G . lamblia was carried out at a final concentration of 5 μM . This library subset was chosen to include multiple chemical scaffolds and both large and small substituents at the R1 and R2 scaffold positions [10 , 27 , 28] . Compounds were screened in 48-hour growth assays in 96-well microtiter plates . Each compound ( 150 μL ) was added to 150 μL of well-suspended diluted parasites ( ~20 , 000 cells/mL ) and incubated in anaerobic BD GasPak Bio-Bags ( Becton Dickinson , San Jose , California ) . After 48 hours , 96-well plates were placed on ice for 30 minutes , fixed with 0 . 64% paraformaldehyde , and thoroughly resuspended and counted with a MoxiZ Coulter counter ( Orflo Technologies , Hailey , ID . ) To generate endogenously tagged small gatekeeper kinases , the putative kinase-coding genes were amplified from genomic DNA by PCR and cloned into the pKS_3HA_Neo vector [29] . Primer sequences and restriction enzymes are shown in S1 Table . PCR amplifications were performed using iProof DNA polymerase ( Bio-Rad ) . Typically , an amplicon of ~ I kb in length that lacked the start codon was cloned in frame to a C-terminal triple-hemagglutinin epitope tag ( 3xHA ) into the pKS_3HA_Neo plasmid . The plasmids were linearized by the enzyme reported in S1 Table and ~5 μg of DNA was used to transform wild-type Giardia . Transformants were selected with G418 at 40 μg/mL . For protein expression , the complete coding region of each protein kinase was PCR amplified from Giardia genomic DNA . PCR amplicons were cloned into the ligation independent cloning ( LIC ) site of ( MBP ) -AVA0421 expression vector and validated by sequencing [30] . Recombinant proteins were expressed in E . coli BL21 ( DE3 ) , Invitrogen , Carlsbad , CA ) using Studier auto-induction protocols at 20°C [31] . Recombinant protein purifications were performed as previously described [26] . Giardia trophozoites were harvested after chilling cultures on ice for 30 minutes . After detachment , cells were pelleted at 700xg , washed once in HBS ( HEPES buffered saline ) , then resuspended in 300 μL of lysis buffer ( 50 mM Tris pH 7 . 5 , 150 mM NaCl , 7 . 5% Glycerol , 0 . 25 mM CaCl2 , 0 . 25 mM ATP , 0 . 5 mM DTT , 0 . 5 mM PMSF , 0 . 1% Triton X-100 , Halt 100X Protease Inhibitor Cocktail ( ThermoFisher Scientific ) , then sonicated . The lysate was cleared by centrifugation at 10 , 000xg for 10 minutes at 4°C and then boiled in 2x Laemmli Sample Buffer ( Bio-Rad ) . After SDS-PAGE , samples were transferred to PVDF membrane ( Immobilon-FL ) following the manufacturers’ directions . Primary polyclonal rabbit anti-giActin 28PB+1 [32] and monoclonal anti-HA mouse HA7 antibodies ( IgG1; Sigma-Aldrich ) were diluted 1:2500 in blocking solution ( 5% dry milk , 0 . 05% Tween-20 in TBS ) . Secondary anti-mouse Alexa-555 and anti-rabbit Alexa-647 antibodies were used . Horseradish peroxidase-linked anti-mouse or anti-rabbit antibodies ( Bio-Rad ) were used at 1:7 , 000 . Multiplexed immunoblots were imaged on a Chemidoc MP ( Bio-Rad ) and signals were quantitated using ImageJ [33] . G . lamblia cells were pelleted at 500xg at room temperature , the pellet and remaining attached cells were fixed in PME ( 100 mM Pipes pH 7 . 0 , 5 mM EGTA , 10 mM MgSO4 ) plus 0 . 025% Triton X-100 , 100 μM MBS , and 100 μM EGS for 30 minutes at 37°C . Cells were again pelleted , washed , resuspended with PME , and adhered to poly-L-lysine ( Sigma-Aldrich ) coated coverslips . Cells were permeabilized in PME + 0 . 1% Triton X-100 for 10 minutes then washed 2X with PME + 0 . 1% Triton X-100 and blocked for 30 minutes in PMEBALG ( PME + 1% BSA , 0 . 1% NaN3 , 100 mM lysine , 0 . 5% cold water fish skin gelatin ( Sigma Aldrich , St . Louis , MO ) [32] . Cells were stained with rabbit anti-giActin antibody 28PB+1 [32] and mouse monoclonal anti-HA ( Clone HA7 , Sigma-Aldrich ) both diluted 1:125 in PMEBALG and incubated overnight . After three subsequent washes with PME + 0 . 05% Triton X-100 , cells were incubated in secondary antibodies Alexa-488 goat anti-mouse and Alexa-555 goat anti-α-rabbit ( Sigma-Aldrich , St . Louis , MO ) ( diluted 1:125 in PMEBALG ) for 1 hour [32] . Cells were washed three times with PME + 0 . 05% Triton X-100 . The coverslips were mounted with ProLong Gold anti-fade plus DAPI , ( Thermo Fisher Scientific , Rockford , IL ) . Fluorescence deconvolution microscopy images were collected as described [34] . A minimum of 150 cells were examined and 30 imaged per experiment . Trophozoites were cultured to confluency , iced for 30 minutes to detach , spun down ( 500xg for 5 minutes ) and media was replaced with 1 . 0 mL fresh Giardia growth medium . Cells and cuvettes were chilled on ice . Lyophilized morpholinos listed in S1 Table ( Gene Tools , LLC , Philomath , OR ) were resuspended in sterile water and 30 μL of a 100 mM morpholino stock was added to 300 μL of cells in a 4 mm cuvette . We used Gene Tools , LLC standard morpholino as a negative control . Cells were electroporated ( 375V , 1000 μF , 750 Ohms , GenePulser Xcell , Bio-Rad , Hercules , CA ) . Cells were transferred to fresh media and incubated 4 hours at 37°C to allow cells to recover . Cells were then iced for 30 minutes , counted and diluted to 20 , 000 cells/mL . Aliquots were counted every 12 hours over 48 hours . All cell counting was done using a Coulter counter ( MoxiZ ) . Three independent replicates of each cell line and control were analyzed for each time point . Quantification of protein expression was determined at the 24-hour time point by the Western blot assay described above . Trophozoites were cultured and treated with morpholinos as described above . After 4 hours of recovery , cells were iced for 30 minutes , counted , and then diluted to 20 , 000 cells/mL . After 48 hours , the media was decanted into a fresh tube and replaced with 1XPBS ( phosphate buffered saline ) . Both tubes were placed on ice to detach or prevent attachment . The cells from each group were then counted , using a Coulter counter ( MoxiZ ) , as above . Thermal shift assays on purified E . coli expressed Gl50803_8445 and Gl50803_16034 proteins were performed as described previously [35] . EC50 assays were performed on wild type Giardia trophozoites to determine the potency of each BKI that elicited a temperature shift in the thermal shift assays . Giardia trophozoites were harvested after chilling cultures on ice for 30 minutes . Three-fold serial dilutions of compounds from 10 μM to 1 . 524 nM concentrations were created in fresh Giardia growth media and growth assays were set up in 96-well microtiter plates as above . Growth was assayed after 48-hours and EC50 values were determined using gnuplot .
BKIs originated conceptually with the observation that most protein kinases will not tolerate ATP analogs containing a “bump” on the ATP purine ring . This restriction arises because the sidechain of a specific residue , the gatekeeper , limits the volume available in the active site to accommodate such chemical modification of the substrate . This observation was exploited , notably by the Shokat group [26 , 36] , to probe the function of individual kinases in vivo by introducing an engineered variant in which the naturally occurring gatekeeper was replaced by a smaller residue , making the engineered kinase uniquely competent to recognize bumped ATP analogs . The precision of this technique is possible because naturally occurring kinases with gatekeepers smaller than threonine ( i . e . glycine , serine , alanine ) are extremely rare [14] . Threonine is less rare as a gatekeeper residue although still much less common in the human kinome than larger gatekeepers , particularly methionine . Notwithstanding their rarity in the human kinome , small gatekeeper kinases are found naturally in the genomes of various protozoa . Individual small gatekeeper kinases have been characterized as potential targets for drug development against eukaryotic pathogens such as T . brucei [23] , T . gondii [37] , C . parvum , and P . falciparum [12 , 26 , 27] . In the case of apicomplexan targets , BKIs have been designed for high selectivity relative to all human kinases , including human threonine gatekeeper kinases , by simultaneously exploiting the gatekeeper-mediated restriction ( Fig 1 ) and the geometry of the ribose binding pocket in the specific target kinase [38] . A genome wide search of the Giardia lamblia genome for genes encoding kinases with small amino acid gatekeeper residues was performed using the core kinase domain of TgCDPK1 as the sequence template . TgCDPK1 was chosen because it was a primary target guiding the assembly of the BKI library used in this study [13] . BLASTP results identified eight putative kinase-coding sequences with small gatekeeper residues . The gatekeeper residues included a threonine in kinases Gl50803_8445 , Gl50803_9665 , Gl50803_11364 , Gl50803_16034 and Gl50803_17368 , a glycine in Gl50803_13215 , an alanine in Gl50803_12148 and a serine in Gl50803_9421 . The Ala-gatekeeper sequence ( Gl50803_12148 ) belongs to a catalytically inactive NEK kinase [17] and was not pursued further . Of the seven small gatekeeper kinases predicted to be sensitive to BKI inhibition , four ( Gl50803_8445 , Gl50803_9421 , Gl50803_9665 , and Gl50803_13215 ) are Giardia-specific NEK kinases . Based on sequence homology , kinase Gl50803_17368 is inferred to be a member of the ULK family . Gl50803_16034 is inferred to be a member of the CAMKL/AMPK family ( Fig 2 ) . Gl50803_11364 is one of the few G . lamblia kinases that have been previously investigated . This AKT family kinase is differentially expressed in encystation compared to the trophozoite stage but its regulatory function is unknown [39] . Our identification of genes encoding small gatekeeper kinases suggested the possibility that BKIs might affect Giardia trophozoites , the stage that colonizes the intestine . As a reference point , we assayed sensitivity of Giardia trophozoites to staurosporine , a broad spectrum kinase inhibitor that acts on small and large gatekeeper kinases . As shown in Fig 3 , treatment with 5 μM staurosporine in 0 . 1% DMSO resulted in a 95% reduction in growth compared to vehicle alone or untreated cells . Next , we tested 36 compounds selected from a previously reported BKI library [26] , for effects on growth at 5 μM concentration . Five of these compounds reduced growth by at least 50% compared to control cells treated with 0 . 1% DMSO . Compound 1213 reduced growth by 79% ( Fig 3 ) . Although , this BKI library was originally developed to explore selectivity for TgCDPK1 and CpCDPK1 over mammalian kinases [27] , these results indicate that this class of compounds can effectively inhibit growth of Giardia trophozoites . The presence of multiple kinases with small gatekeeper residues and the ability of BKIs to inhibit Giardia growth suggest that BKIs could be developed as an alternative basis for treatment of giardiasis . Finding that BKIs can inhibit Giardia trophozoite growth established the priority to determine which kinases are critical for trophozoite proliferation and attachment . To accomplish this , we used an epitope tag to facilitate detection of each kinase [29] . We established seven cell lines , each with one specific kinase gene endogenously epitope-tagged with 3xHA [29] . Our integration constructs lack promoters and start codons; therefore , detection of protein products indicated successful integration into the genome with expression driven by the native promoter . Western blot analysis showed that five of the seven lines expressed detectable levels of proteins of the predicted molecular weight ( Fig 4a ) . Attempts to integrate two of the seven kinase genes ( Gl50803_9665 and Gl50803_17368 , ) in six separate trials , failed to yield lines that expressed detectable levels of the tagged protein as assayed by Western blotting . To assess further whether successful integration had actually occurred , we used PCR to assay the kinase locus for integration . As shown in S1 Fig , successful integration of constructs was achieved for both Gl50803_9665 and Gl50803_17368 . The absence of detectable protein expression may be due to low level of endogenous protein expression or more likely developmental regulation; therefore , these two were not carried forward for genetic analyses . Using the same cell lines that were established for Western blotting , we analyzed cellular localization as a secondary method for detecting relative abundance . Using identical exposure and scaling conditions , the five 3xHA-tagged kinases: Gl50803_8445 , Gl50803_9421 , Gl50803_11364 , Gl50803_13215 and Gl50803_16034 were localized by immunofluorescence microscopy . We observed strong signals for kinase Gl50803_8445 around nuclei and the cell perimeter and lower levels distributed throughout the cytosol . Strong signals for kinases Gl50803_9421 and Gl50803_11364 were detected throughout the cytosol . Cytosolic signals for kinase Gl50803_9421 were also observed but were relatively weak . Cytosolic and nuclear signals were enriched for kinase Gl50803_13215 . Finally , punctate signals for kinase Gl50803_16034 were uniformly dispersed throughout the cytosol ( Fig 4b ) . These data document differences in the overall distribution of small gatekeeper kinases , with three distinct patterns resolved by our microscopy thus far . The differences may indicate different cellular roles in the trophozoite stage . Following successful 3xHA integrated tagging of kinases we pursued a reverse genetics approach to test the relative importance of each kinase for trophozoite proliferation . Previous attempts to use RNAi for gene knockdown have been unsuccessful and gene knockouts in Giardia are yet to be accomplished due to the tetraploid nature of trophozoites [22] . Therefore , we chose to target the remaining kinases with anti-sense translation blocking morpholino oligomers . Treatment with 100 μM gene specific antisense-morpholino oligomers reduced protein levels by 89%±12 ( Gl50803_8445 ) , 42%±9 ( Gl50803_9421 ) , 56%±11 ( Gl50803_11364 ) , 23%±7 ( Gl50803_13215 ) , and 80%±3 ( Gl50803_16034 ) compared to levels observed in cells with control morpholino oligomers ( Fig 5a ) . For kinases Gl50803_9421 and Gl50803_11364 , we did not detect observable changes in growth despite appreciable depletion of protein levels , indicating that they likely may not be essential for trophozoite proliferation ( Fig 5b and S2 Fig ) . Gl50803_13215 showed a significant , yet modest decrease in growth ( Fig 5b ) . In contrast , depleting Gl50803_8445 and Gl50803_16034 resulted in 70% and 50% reduction in growth , respectively , compared to the control , 48 hours after morpholino treatment ( Fig 5b ) . Thermal shift assays ( TSAs ) quantify the change in the denaturing temperature of a protein due to the stabilizing presence of a bound small molecule , in this case bumped kinase inhibitors bound at the kinase active site . Denaturation is conveniently tracked by following increased fluorescence from a dye that associates with hydrophobic regions that are exposed as the protein denatures [40 , 41] . For a given protein target , the magnitude of the thermal shift induced by individual small molecules is roughly correlated with the binding affinity of that molecule [35] . This obviates the need to determine activation requirements and suitable substrates for in vitro kinase activity assays of individual target kinases . We used TSAs to rapidly screen the BKI library in order to prioritize a subset of them for individual characterization of anti-giardial activity . After cloning , expressing and purifying both Gl50803_8445 and Gl50803_16034 proteins , we were able to assess ~400 BKIs for interaction with each purified kinase ( Fig 6 ) . Two compounds ( 1264 , 1244 ) induced large shifts ( ΔTm > 5° ) in the stability of target Gl50803_16034 . No equivalently large shifts were observed for target Gl50803_8445 , although several compounds gave ΔTm ≈ 3° . Notably the two compounds with the largest effect on Gl50803_16034 showed minimal effect on GL50803_8445 , confirming the expectation that the BKI library compounds can exhibit specificity even among small-gatekeeper kinases . Library compound 1213 , previously shown to be potent in suppressing trophozoite growth , induced a moderate ΔTm ( 2°–3° ) in both targets . To see if the implied binding to an essential kinase would translate into in vivo activity , we selected 7 compounds with relatively large ΔTm for assessment of their phenotypic suppression of growth or attachment . Any or all of the small-gatekeeper kinases we have identified may constitute a target for new drugs against giardiasis . All are likely to be susceptible to bumped kinase inhibitors , and indeed a single compound may act on more than one of these kinases . However , compounds are not expected to be uniformly potent against all of the kinases . While rigorous identification of the specific target kinases for all library compounds found to have anti-Giardia activity is beyond the scope of the current report , we performed an initial evaluation of the in vivo activity of seven BKIs for which a large thermal shift implied binding to either Gl50803_8445 or Gl50803_16034 ( Figs 6d and S3 ) . EC50 values for these compounds were determined from dose-response curves for trophozoite growth in culture , based on the total number of cells present 48 hours after introduction of inhibitor . Compound 1264 stands out as having both a sub-micromolar EC50 ( 0 . 9 μM ) against Giardia trophozoites and a large induced thermal shift ( 6 . 5° ) for kinase Gl50803_16034 . We infer that GI50803_16034 is a primary target , although not necessarily the sole target , for 1264 . The lower in vivo potency ( EC50 4 . 6 μM ) for compound 1244 , which also shows large ΔTm ( 5 . 2° ) for this same target kinase , is unexpected but may be due to poor cellular absorption or metabolic degradation . By contrast , compound 1213 shows somewhat greater in vivo activity ( EC50 0 . 80 μM ) but shows only modest stabilization of either of the target kinases tested ( ΔTm ≈ 3° ) . This suggests either that the primary target of 1213 is a different kinase or that its potency arises by modest inhibition of multiple targets . Our initial search for small gatekeeper kinases was focused on identifying kinases most similar to TgCDPK1 due to an available library that could be used for this proof of concept study . Considering the variability of EC50 values relative to thermal shifts , we performed a more exhaustive search for small gatekeeper kinases , this time setting no threshold on similarity to TgCDPK1 . Three additional NEK kinases were identified ( GL50803_8152 , GL50803_112518 , GL50803_40904 ) and a putative threonine-gatekeeper CDC7 homolog ( GL50803_112076 ) . Although initial alignment of active site residues was less certain for sequences in this wider search , we confirmed the identity of the gatekeeper residue in GL50803_112076 by comparison with the known gatekeeper for CDC7 homologs with structures in the PDB . These four hits from the exhaustive search constitute possible additional targets for the activity of BKI compounds reported here . Note that whether the BKIs act through inhibition of a single target or through inhibition of multiple kinases , this mechanism is distinct from that of the existing anti-giardiasis drug metronidazole and chemically related alternatives . Therefore , cross-resistance is unlikely . This is confirmed by the observed equal potency of compound 1213 against both wild-type and metronidazole-resistant strains of Giardia ( Fig 7 ) . Given the critical role of kinase Gl50803_8445 and Gl50803_16034 in Giardia trophozoite growth , we analyzed the terminal phenotype of these kinases and found that depleting either kinase resulted in multinucleate cells indicating a block in cytokinesis ( Fig 8 ) . Quantification indicated 68% of l50803_8445 and 70% of Gl50803_16034 cells were blocked in cytokinesis . Compound 1213 induced a similar defective cytokinesis phenotype with slightly higher efficacy than knockdown of either kinase alone . This may reflect incomplete knockdown by morpholino treatment or an additive effect of chemical inhibition acting on multiple kinases . Clinical giardiasis is caused by Giardia trophozoites attaching to the intestinal microvilli , colonizing the intestines and creating a barrier against nutrient absorption by the host . Additionally , histological tissue samples have indicated an increased production of mucus by host goblet cells and vacuolated epithelial cells in the mucosa [42 , 43] , that likely reflect an effort to dislodge attached parasites . Therefore , kinases with a role in attachment , even indirectly , would be promising targets , as infection could be cleared without necessarily killing the parasites . An example of this pharmacological strategy is the demonstration that the anti-giardial activity of the isoflavone formononetin occurs by inducing rapid detachment of trophozoites from the intestine of infected mice [44] . In cell culture , Giardia attaches to the surface of the culture tube , with a small fraction of cells detached and freely swimming . Therefore , we assessed the ability of trophozoites to remain attached 48 hours post-knockdown for the two kinases validated to be essential for growth . This was assayed by monitoring the ratio of cells that were free-swimming to those attached to the culture tube . We observed that the ability of cells to attach to the culture tube was substantially reduced following knockdown of Gl50803_8445 and Gl50803_16034 cells . We observed an 80% and 56% decrease in parasite attachment ( Fig 8c ) when we knocked down Gl50803_8445 and Gl50803_16034 , respectively . In this assay , we simply poured off detached cells; maintaining attachment during intestinal peristalsis would likely present a greater challenge to the cells . These genetic experiments , performed at the population level with incomplete knockdown indicate that targeting kinases Gl50803_8445 and Gl50803_16304 with BKIs could be an effective strategy to clear a Giardia infection due to reduced attachment and proliferation . Indeed , treatment of trophozoites with compound 1213 led to an 87% decrease in parasite attachment .
Several efforts to identify potential new anti-giardial therapeutics by phenotypic screening against large libraries of drugs or drug-like molecules have been reported previously . Tejman-Yarden et al [45] screened a library of 910 bioactive compounds including ~750 approved drugs . Of these , 56 compounds exhibited inhibition of G . lamblia growth and attachment at 10 μM , including 15 compounds with known anti-giardial activity and most notably the approved anti-arthritis drug auranofin . Auranofin was reported to be active against Giardia trophozoites with an EC50 of 4–6 μM . Galkin , et al [46] found the most active compound in the LOPAC1280 library of pharmaceutically active compounds to be disulfiram ( tetraethylthiuram disulfide ) , previously used in long-term treatment of alcoholism . They reported an EC50 of 0 . 9 μM against Giardia trophozoites . Disulfiram covalently attacks protein sulfhydryls in general , but the primary target in this case is a non-catalytic cysteine near the active site of G . lamblia carbamate kinase . Both of these potential leads , disulfiram and auranofin , overlap in part with the chemical action of existing nitroimidazole drugs including metronidazole in attacking protein sulfhydryl groups . Thus , they share a profile of side-effects and potential toxicity , although the specific sets of intracellular targets differ [47] . The use of phenotypic screening is generally viewed in contradistinction to molecular target-based approaches to drug discovery [48] . Identifying off-label activity of an approved drug through phenotypic screening has the obvious benefit of immediate clinical applicability if the compound has sufficient anti-parasite activity . On the other hand , it is not clear that either of the previously reported hits from anti-giardial screening offer much scope for follow-on chemical modification of the existing drug to improve selectivity or anti-giardial potency . This illustrates a common drawback of phenotypic screening , which is further exacerbated if the molecular target of a cell-active compound found by screening is not known , precluding structure-guided optimization of selectivity or potency . We have been able to combine the strengths of phenotypic screening and a structural approach to target-based lead discovery . Preliminary examination of the G . lamblia genome established that it contained eight coding sequences for kinase homologs with an atypically small ( Gly/Ala/Ser/Thr ) active site residue at the gatekeeper position . It is important to note that these sequences were not selected on the basis of sequence homology either to each other or to previously characterized drug targets . Instead they were selected because they are predicted to share a specific unusual structural feature at the putative active site . We then conducted an initial targeted phenotypic screen for anti-giardia activity in a representative subset of compounds drawn from a library designed to target exactly this shared structural feature , confirming that the library was rich in cell-active compounds . Follow-up TSA screening of the entire library against purified kinases Gl50803_8445 and Gl50803_16034 highlighted specific library compounds with high implied affinity for those targets . Next , we showed that library compounds with high implied affinity for Gl50803_16034 exhibited EC50 ≤ 1 μM against Giardia trophozoites , confirming the success of this approach to lead discovery . It is quite possible that the library compounds selected for high-implied affinity for Gl50803_16034 also bind others of the identified small-gatekeeper kinases not yet individually characterized . This does not diminish their potential as leads for anti-giardia drug design . It is formally possible that they also hit some unknown protein target , but we consider this unlikely based on prior characterization of compounds from this BKI library as being non-cytotoxic to mammalian cell lines and having low activity against a panel of larger gatekeeper kinases [26 , 31 , 44] . Here we have shown that a set of kinases in Giardia possess an unusual structural feature , a small gatekeeper residue , which is a promising target for the development of highly selective inhibitors . Knockdown of target proteins identified two kinases with strong growth , attachment and cytokinesis defects; the cells appear to have completed nuclear division but never completely divided . As a result of the cytokinesis defect , growth is inhibited and a high percentage of cells are unable to maintain attachment presumably due to gross morphological defects impeding the function of the ventral adhesive disc [1 , 49] . Compounds from a BKI library originally designed to target T . gondii CDPK1 were able to phenocopy the results observed after kinase knockdown . In particular , exposure to BKI 1213 induced defects in cytokinesis similar to those produced by kinase knockdown as observed by fluorescence microscopy . Such a severe defect hinders the cells ability to attach and creates the potential to clear an infection from the intestines . Collectively , our results suggest that in vivo use of BKIs may provide an alternative treatment for giardiasis . Importantly , we have shown that BKIs can inhibit the growth of metronidazole-resistant Giardia due to their entirely different mechanism of action . Moreover , BKIs are not expected to have the problematic side effects associated with the free radical production by metronidazole and related drugs . Therefore , BKIs are an attractive starting point to find an alternative treatment for giardiasis .
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The eukaryotic protozoan Giardia lamblia is the most commonly reported intestinal parasite worldwide . Current treatments used to treat giardiasis include metronidazole and other nitroimidazole derivatives . However , emergence of metronidazole-resistance strains and adverse reactions to the treatments suggest that alternative therapies against giardiasis are necessary . Here we identify a set of protein kinases in the Giardia genome that have an atypically small amino acid residue , called the gatekeeper residue , in the ATP binding pocket . Small gatekeeper residues are rare in mammalian kinases . We investigated whether this subset of kinases is necessary for parasite growth and proliferation and , if so , could they be targeted with a class of compounds called bumped kinase inhibitors ( BKIs ) , designed to exploit the enlarged active site pocket made accessible by the small gatekeeper amino acid . Morpholino knockdown of two of the small gatekeeper kinases produced a distinctive phenotype characterized by defective cytokinesis . This phenotype was mimicked in Giardia cells treated with our most potent BKI . These results suggest that BKIs may be developed to selectively target small gatekeeper kinases in Giardia lamblia to provide a novel treatment option for giardiasis .
|
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2016
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Identification and Validation of Small-Gatekeeper Kinases as Drug Targets in Giardia lamblia
|
Plasmodium spp . and helminths are co-endemic in many parts of the tropics; hence , co-infection is a common phenomenon . Interactions between Plasmodium and helminth infections may alter the host’s immune response and susceptibility and thus impact on morbidity . There is little information on the direction and magnitude of such interactions and results are conflicting . This study aimed at shedding new light on the potential interactions of Plasmodium and helminth co-infections on anemia and splenomegaly in different population groups in Côte d’Ivoire . Parasitologic and clinical data were obtained from four cross-sectional community-based studies and a national school-based survey conducted between 2011 and 2013 in Côte d’Ivoire . Six scenarios of co-infection pairs defined as Plasmodium infection or high parasitemia , combined with one of three common helminth infections ( i . e . , Schistosoma mansoni , S . haematobium , and hookworm ) served for analysis . Adjusted logistic regression models were built for each scenario and interaction measures on additive scale calculated according to Rothman et al . , while an interaction term in the model served as multiplicative scale measure . All identified significant interactions were of antagonistic nature but varied in magnitude and species combination . In study participants aged 5–18 years from community-based studies , Plasmodium-hookworm co-infection showed an antagonistic interaction on additive scale on splenomegaly , while Plasmodium-Schistosoma co-infection scenarios showed protective effects on multiplicative scale for anemia and splenomegaly in participants aged 5–16 years from a school-based study . No exacerbation from co-infection with Plasmodium and helminths was observed , neither in participants aged 5–18 years nor in adults from the community-based studies . Future studies should unravel underlying mechanisms of the observed interactions , as this knowledge might help shaping control efforts against these diseases of poverty .
Anemia is multifactorial . However , in low- and middle-income countries it is largely attributable to parasitic diseases , such as malaria and helminth infections [1] . Yet , the etiology of anemia differs between malaria and helminthiases . Plasmodium spp . , the malaria causing parasite , affects iron and hemoglobin ( Hb ) levels during different stages of its development within the human host . An increased red blood cell ( RBC ) death related to Plasmodium asexual replication , reduced iron absorption , and decrease in recycling of RBC iron in individuals with inflammation are potential causes of anemia during a malaria episode [2–5] . Helminth infections are less important with regard to systemic inflammation [5] . However , helminth infections may lead to anemia through blood loss from ingestion of blood cells and lacerations on digestive mucosa by hookworms and through Schistosoma eggs passing through blood vessels and bladder and intestinal tissues that lead to hematuria or blood in stool [6 , 7] . In addition to anemia , splenomegaly is a frequent clinical manifestation of chronic malaria [4] and advanced Schistosoma mansoni infections [8] . Helminths and Plasmodium spp . are co-endemic in many parts of the tropics . Hence , helminth-Plasmodium co-infection is a common phenomenon [9] . In Côte d’Ivoire , for example , a national school-based survey showed a prevalence of 13 . 5% for Plasmodium-soil-transmitted helminth co-infection and 5 . 6% for Plasmodium-Schistosoma co-infection [10] . The impact of such co-infections on the pathophysiology is poorly understood . Effects on the host’s health and immune response due to concomitant infection of Plasmodium and helminths are complex and depend on various factors ( e . g . , stage , intensity , force of transmission , sociodemographic characteristics , immune status , and co-morbidities ) [9 , 11] . To effectively combat Plasmodium infection , a timely and strong pro-inflammatory and type 1 immune response is induced in infected humans . Concomitant chronic helminth infection may , however , alter the immune response by down regulating the Th1 pathway toward anti-inflammatory and type 2 responses [11 , 12] . This shift may prevent individuals from clinical immunopathology due to inflammation but may , on the other hand , affect malaria parasite clearance [13] . Apart from immunomodulation , other mechanisms such as cohabitation and competition for resources may also influence clinical manifestations in the co-infected host [11] . Potential interactions on pathophysiology , such as anemia and splenomegaly , might be bidirectional , showing antagonism and synergism in co-infected individuals , which might explain conflicting results of recent studies [14] . While several studies conducted in West Africa identified antagonistic effects or reduced odds for malaria-related pathology in Plasmodium-helminth co-infected individuals [15–18] , synergism with potential exacerbation of anemia and splenomegaly was reported from studies in Kenya and Zimbabwe [19–21] . Interactions on outcomes in individuals with co-morbidities may be determined on additive or multiplicative scales . Most studies looking at co-morbidities do not look at actual interactions but rather compare estimates from regression analysis between morbidity groups and the few studies exploring actual interactions mainly looked at statistical interaction on multiplicative scale introducing a product term into regression analysis and may thus have missed out potential interaction on additive scale . In clinical research , biologic interactions are often assessed within case-control studies through interaction measures on additive scale , as proposed by Rothman et al . [22] . It is further argued that in etiologic epidemiologic research focus should be put on the assessment of biologic interactions expressed as departure form additivity rather than statistical interaction looking at departure from multiplicativity [23] . The measures proposed by Rothman et al . not only identify the direction but also the magnitude of potential departure from additivity ( i . e . , antagonism or synergism ) and are expressed as synergy index ( SI ) , attributable proportion ( AP ) , and relative excess due to interaction ( RERI ) . In practical use a RERI of 0 , an AP of 0 and a SI of 1 would indicate no biologic interaction and exact additivity of the individual effects of Plasmodium and helminth infection on the disease outcome meaning Plasmodium and helminth infections are independent in causing a certain disease outcome . Yet , if the combined effect of Plasmodium and helminth infection is larger ( or smaller ) than the sum of the individual effects there is interaction on an additive scale or at least departure from additivity that would then translate into a RERI>0 , AP>0 and SI>1 or a RERI<0 , AP<0 and SI<1 , respectively , in case of an antagonistic interaction . This approach has found only limited application in infectious disease epidemiology , although methodologies for cross-sectional designs adjusting for other influencing factors by use of logistic regression analysis have been proposed [24] . With regard to helminth co-infections , a former study revealed significant synergistic effects on anemia among S . japonicum-hookworm co-infected individuals in The Philippines [25] . To date , such interaction measures for helminth and Plasmodium co-infections are lacking . The aim of the current study was to shed new light on the potential interactions on additive and multiplicative scale for Plasmodium and helminth co-infections on anemia and splenomegaly among two population groups ( school-aged children/adolescents and adults ) in Côte d’Ivoire .
The study protocol was approved by the institutional research commissions of the Swiss Tropical and Public Health Institute ( Swiss TPH; Basel , Switzerland ) and the Centre Suisse de Recherches Scientifiques en Côte d’Ivoire ( CSRS; Abidjan , Côte d’Ivoire ) . Ethical clearance was received from the ethics committees of Basel ( EKBB , reference no . 30/11 ) and Côte d’Ivoire ( reference no . 09-2011/MSHP/CNER-P ) . In addition , permission for conduct of the national school-based survey was obtained from the Ministry of National Education in Côte d’Ivoire . Directors and teachers of the schools , village chiefs of the study communities , and all concerned district , health , and education authorities were informed about the purpose and procedures of the study . Written informed consent was obtained from each participant , while parents/guardians signed on behalf of children aged below 18 years . Children assented orally . Participation was voluntary; hence , individuals could withdraw from the study at any time without further obligation . All participants benefitted from treatment against soil-transmitted helminths with albendazole ( single oral dose of 200 mg for children aged 1–2 years and 400 mg for all participants >2 years ) given free of charge . Participants diagnosed with Schistosoma eggs were treated with praziquantel ( 40 mg/kg ) . In schools where the prevalence of Schistosoma infection was above 25% , all school children received praziquantel . Individuals with clinical malaria ( i . e . , tympanic temperature ≥38°C and a positive malaria rapid diagnostic test ( RDT ) ) and/or severe anemia were offered artemisinin-based combination therapy together with paracetamol and an anti-anemic treatment , respectively , if the RDT result was negative . Four community-based surveys and a national school-based survey were carried out . Surveys followed a cross-sectional design , including parasitologic and clinical examination and questionnaire interviews for sociodemographic information . The four communities surveyed were situated in southern Côte d’Ivoire and had less than 1 , 000 inhabitants each with main economic and livelihood activity in subsistence farming and cash crop production ( i . e . , cacao , coffee , and rubber ) . The village Sahoua ( geographic coordinates: 6°19’20” N latitude , 5°10’30” W longitude ) and Ancien Carrefour ( 5°37’40” N , 4°01’15” W ) were visited in August and September 2011 , as described in more detail elsewhere [17] . Two rural communities , namely La Scierie ( 6°49’23” N , 3°24’53” W ) and Azaguié CNRA ( 5°36’10” N , 4°00’51” W ) , were surveyed between May and August 2013 . For recruitment , all community members were invited for participation . For the national school-based study , 94 school localities were visited between November 2011 and February 2012 ( i . e . , dry season ) , whereof 93 participated in the survey . In one school the children received deworming shortly before the current study; all analyses were thus based on the results from 92 schools . The localities were selected applying a lattice plus close pairs design [26] and taking into account the ecologic zones , residential area ( i . e . , a minimum of 20% urban settings ) , population density , and the presence of a primary school with a minimum of 60 children attending grades 3 to 5 [10 , 27 , 28] . In each school , a subsample of 60 children was asked to participate in the study . Côte d’Ivoire is highly endemic for malaria with perennial transmission of Plasmodium infections [28 , 29] , while infections with soil-transmitted helminths ( predominantly hookworm ) and Schistosoma spp . are more focally distributed [10] . In all surveyed communities overall Plasmodium prevalence is expected to be high ( ≥60% ) and prevalence of soil-transmitted helminths to be of moderate endemicity ( range: 10–50% ) [17] . Sahoua has been identified as endemic zone for S . haematobium infection , while the two communities situated in the Azaguié district are considered to be S . mansoni endemic [17 , 30] . The study procedures were described elsewhere [10 , 17 , 27 , 28] . In brief , every participant was invited to provide a single stool , urine , and finger-prick blood sample . Duplicate Kato-Katz thick smears [31] were prepared from each stool sample and microscopically examined for the presence of eggs of S . mansoni and soil-transmitted helminths ( i . e . , Ascaris lumbricoides , hookworm , and Trichuris trichiura ) . Urine samples were either processed by a filtration technique for detection of S . haematobium eggs or , in case of the national school-based study , using reagent strips ( Hemastix; Siemens Healthcare Diagnostics GmbH , Eschborn , Germany ) to detect microhematuria [32] . From finger-prick blood samples , thin and thick blood films were prepared on microscope slides . Slides were stained with 10% Giemsa and examined under a microscope ( x100 ) with immersion oil by experienced laboratory technicians . Parasitemia ( parasites/μl of blood ) of the three endemic Plasmodium species ( i . e . , P . falciparum , P . malariae , and P . ovale ) were recorded [33] . Additionally , finger-prick blood samples were subjected to an RDT for P . falciparum ( ICT ML01 Malaria Pf kit; ICT Diagnostics , Cape Town , South Africa ) . Ten percent of the Kato-Katz thick smears , urine filters , and blood films were subjected to quality control and re-examined by a senior laboratory technician . In case of discrepancies of parasite counts , the slides were re-examined by another senior technician and results discussed until agreement was reached . Clinical examination involved palpation of spleen to assess organ enlargement . Hb was measured using a HemoCue device ( HemoCue Hb 301 system; Angelholm , Sweden ) . Anthropometric measurements , including height and weight , were taken from each participant for subsequent analysis of nutritional status . Sociodemographic information of each participant was collected through administration of pre-tested individual rapid appraisal [34] or a household-based questionnaire [35] for school children and community members , respectively . Data were double-entered and cross-checked using EpiInfo version 3 . 5 . 3 ( Centers for Disease Control and Prevention; Atlanta , United States of America ) . Statistical analyses were performed in Stata version 14 . 0 ( Stata Corp . ; College Station , United States of America ) . Data from the community surveys and the national school-based survey were analyzed separately . Within the community data set , we divided the sample into school-aged children/adolescents ( 5–18 years ) and adults ( >18 years ) . All school-aged children/adolescents and adults with complete parasitologic data and Hb measurement were considered for the main analysis and , depending on the outcome , further stratified into participants with anemia and splenomegaly records , respectively . According to the World Health Organization ( WHO ) , anemia was defined as Hb levels below 11 . 5 g/dl and 12 . 0 g/dl in children aged 5–11 years and children aged 12–14 years , respectively , while pregnant women ( ≥15 years ) , non-pregnant women ( ≥15 years ) , and males ( ≥15 years ) had anemia cut-offs of 11 . 0 , 12 . 0 , and 13 . 0 g/dl , respectively [36] . Participants having a palpable spleen of grade 1 or higher using a Hackett’s scale [37] were considered to have splenomegaly . Malnutrition in school-aged children/adolescents was defined as a Z-score <-2 in any of the calculated indicators ( i . e . , height-for-age , body mass index ( BMI ) -for-age , or weight-for-age ) based on STATA macros from WHO child growth standards [38] . In adults , BMI and mid-upper arm circumference ( MUAC ) were used to define malnutrition according to cut-offs reported by Eddleston et al . [39] . Schools participating in the national school-based survey were classified into different endemicity profiles to address heterogeneity according to the following criteria: ( i ) Plasmodium-endemic: any positive case per school from microscopy or RDT; ( ii ) S . haematobium-endemic: any positive case for microhematuria defined as reagent strip positive with an intensity of ≥1+; ( iii ) S . mansoni-endemic: any positive case per school detected by microscopy of Kato-Katz thick smears; and ( iv ) hookworm-endemic: schools with a minimum hookworm infection prevalence of 10% or any case of moderate- or heavy-intensity infection ( ≥2 , 000 eggs per gram of stool ( EPG ) ) [7] . Since school-aged children/adolescents from the community-based and the school-based surveys showed very high rates of Plasmodium infection ( 84 . 4% and 75 . 0% , respectively ) , a two-scenario approach was adopted for analysis of interaction measures of the different Plasmodium-helminth co-infection categories . The first scenario used “presence or absence” of Plasmodium infection , while the second scenario classified participants into “no or lightly-infected” versus participants with high Plasmodium parasitemia . Cut-offs for high parasitemia were set at 1 , 000 and 1 , 500 parasites/μl of blood for the school-based and the community-based surveys , respectively , taking into account the relationship between Plasmodium parasitemia and prevalence of anemia ( S1 Fig ) . Sample size used for interaction analysis thus varied depending on the number of participants with available information of the two clinical outcomes ( i . e . , anemia and splenomegaly ) and belonging to a school endemic to the investigated pair of parasites . Univariate logistic regression analysis was applied to calculate crude odds ratios ( ORs ) for anemia and splenomegaly and different explanatories , including sociodemographic , parasitologic ( i . e . , ( co- ) infection status ) , and clinical indicators . For interaction analysis , multivariable logistic regression models were built for each Plasmodium-helminth pair , including all major helminth species identified from parasitologic examination , namely Plasmodium-S . haematobium , Plasmodium-S . mansoni , and Plasmodium-hookworm co-infection . To adjust for concurrent helminth infections , infection status with soil-transmitted helminths or Schistosoma , respectively , was introduced in the multivariable models as a covariate . All models were adjusted for sex , age group , socioeconomic status , and malnutrition that may have an influence on infection status and clinical outcome [34 , 40] . Specifically , for schistosomiasis that is caused by two species in Côte d’Ivoire ( S . mansoni and S . haematobium ) , we excluded these school-aged children/adolescents from the sample who were infected with S . haematobium in case interaction between S . mansoni and Plasmodium was assessed and vice versa to obtain species-specific estimates . For all Plasmodium-helminth pairs , interaction measures on multiplicative scale were assessed through an interaction term in the respective logistic regression model . RERI , AP , and SI and their respective 95% confidence intervals ( CIs ) and p-values were calculated to assess departure from additivity using the following formulas incorporated in a readily available calculation mask [23]: RERI=eβ^1+β^2+β^3-eβ^1-eβ^2+1 ( 1 ) AP=RERIORA+B+ ( 2 ) and SI=ORA+B--1 ( ORA+B--1 ) + ( ORA-B+-1 ) ( 3 ) where eβ^1 stands for the OR of having condition A ( A+B- ) relative to the reference category of not having either condition ( A-B- ) , eβ^2 stands for the OR of having condition B ( A-B+ ) relative to the reference category ( A-B- ) and eβ^3 is the OR of having both conditions ( A+B+ ) relative to the reference ( A-B- ) . Interaction measures on additive scale are calculated on the assumption that all infection categories ( i . e . , single and co-infection categories ) present an increased risk for an outcome compared to the reference category . In case ORs were lower than 1 for one or several of these risk categories within a model , AP and SI were considered as non-applicable [41] . Significant antagonistic and synergistic interactions on additive scale were defined as SI <1 and SI >1 , respectively , with a p-value below 0 . 05 and a 95% CI not including 1 . The same was applied for interactions on multiplicative scale but using the OR of the respective product term .
Fig 1 depicts the participation in the community- and the school-based studies . From 2 , 224 invited community members 1 , 307 ( 58 . 8% ) participants had written informed consent , complete information on socioeconomic status , parasitologic infection , nutritional indicators , and anemia and were older than 4 years . The community sample for interaction analysis on anemia and splenomegaly consisted of 601 and 592 individuals aged 5–18 years , respectively , while there were 706 and 659 adults , respectively . Among the 5 , 491 school-aged children/adolescents invited for participation during the national school-based survey 4 , 938 ( 89 . 9% ) and 4 , 870 ( 88 . 6% ) fulfilled all inclusion criteria for subsequent analysis of interactions on anemia and splenomegaly , respectively . Adults , in general , had a lower prevalence of Plasmodium and helminth infections than school-aged children/adolescents , with the exception of hookworm that was more often found in older individuals ( 31 . 4% in adults compared to 26 . 0% and 17 . 2% in school-aged children/adolescents in the community and the school survey , respectively ) ( Table 1 ) . Anemia among adults ( 28 . 3% ) from the four communities was comparable to the national school-based survey ( 28 . 8% ) . Splenomegaly showed a considerably lower prevalence in the adult age group ( 2 . 7% ) compared to school-aged children/adolescents in the school-based ( 11 . 7% ) and community-based surveys ( 22 . 5% ) . Subsequent interaction analysis for adults was restricted to anemia as a major outcome . From all three investigated population strata ( i . e . , adults , school-aged children/adolescents from the community-based surveys , and school-aged children/adolescents from the school-based survey ) , school-aged children/adolescents from the community-based surveys showed the highest rates in terms of infection ( prevalence of Plasmodium , 84 . 4%; Schistosoma , 42 . 6%; and soil-transmitted helminths , 28 . 6% ) and clinical outcomes ( anemia , 40 . 4%; splenomegaly , 22 . 5% ) . Malnutrition , assessed as a secondary outcome and included in the interaction models as an explanatory factor , was found in 4 . 7% , 26 . 0% , and 28 . 4% of the adults , school-aged children/adolescents from the community-based surveys , and school-aged children/adolescents from the school-based survey , respectively . The national school-based sample revealed a Plasmodium infection prevalence of 75 . 0% . Helminth infections in the school survey were lower compared to the community surveys . While all 92 ( 100% ) schools were classified as Plasmodium-endemic , 65 ( 70 . 7% ) , 55 ( 59 . 8% ) , and 36 ( 39 . 1% ) fulfilled the defined endemicity criteria for S . haematobium , hookworm , and S . mansoni infection , respectively . Univariate relationship analysis between clinical morbidity and sociodemographic , parasitologic , and nutritional factors revealed that the contribution from Plasmodium and helminth infections on anemia in adults was low ( Table 2 ) . Other factors such as older age ( >60 years; OR 1 . 71 , 95% CI 1 . 01 , 2 . 91 ) and sex ( male sex; OR 0 . 63 , 95% CI 0 . 45 , 0 . 88 ) ranked much higher among the investigated determinants of anemia . Adult individuals infected with Schistosoma ( OR 0 . 67 , 95% CI 0 . 45 , 0 . 99 ) or co-infected with Plasmodium and S . mansoni ( OR 0 . 49 , 95% CI 0 . 25 , 0 . 95 ) showed lower crude ORs for anemia . Within school-aged children/adolescents from the community- and school-based surveys , sociodemographic determinants were much more important for school-aged children/adolescents from the school-based sample , which captures a wider range of living conditions , including urban and rural environments and areas with a great variety in ethnic groups and cultures . School children from wealthier households showed lower crude ORs for both anemia ( OR 0 . 74 , 95% CI 0 . 61 , 0 . 91 ) and splenomegaly ( OR 0 . 50 , 95% CI 0 . 37 , 0 . 67 ) . Anemia was positively associated with male sex ( OR 1 . 32 , 95% CI 1 . 16 , 1 . 49 ) in the school-based sample . With regard to single parasite infections , Plasmodium infection and high parasitemia showed the highest ORs for anemia , and hence , was the main contributor to splenomegaly with 2-fold and 3-fold higher ORs among school-aged children/adolescents from the school- and community-based samples , respectively . In school-aged children/adolescents from the community-based surveys , no significant positive association between clinical morbidity and helminth infections could be shown from univariate analysis . On the contrary , infection with Schistosoma and hookworm both showed lower ORs for splenomegaly . In school-aged children/adolescents from the school-based survey , however , infection with S . haematobium ( OR 1 . 62 , 95% CI 1 . 26 , 2 . 07 ) , S . mansoni ( OR 1 . 73 , 95% CI 1 . 28 , 2 . 35 ) , and A . lumbricoides ( OR 1 . 83 , 95% CI 1 . 20 , 2 . 79 ) showed higher odds for anemia . We found higher odds of anemia and splenomegaly for Plasmodium-helminth ( S . haematobium , S . mansoni , and hookworm ) co-infection compared to school children not infected with Plasmodium and the respective helminth species . In school-aged children/adolescents enrolled in the community surveys , only the Plasmodium-S . haematobium co-infection showed a significant positive relationship with splenomegaly . Malnutrition showed a positive association with clinical morbidity among school-aged children/adolescents . Fig 2 depicts in detail the different multiple species infections observed in the three population cohorts ( i . e . , adults from communities , school-aged children/adolescents from communities , and school-aged children/adolescents from the school-based survey ) and how samples were drawn within to serve for interaction analysis of the different co-infection scenarios . Highest proportions of Plasmodium-helminth co-infections were observed in school-aged children/adolescents from the community-based surveys . Excluded individuals in the communities showed differences in mean age and socioeconomic status but were similar with regard to the outcome variables and other influencing factors . Most individuals excluded from the heterogeneous national school-based repository were eliminated during harmonization for specific helminth species endemicity , while only a small proportion ( <3% ) was dropped for being co-infected with the second Schistosoma species . While for the Plasmodium-S . mansoni scenario excluded individuals were of older age , lower socioeconomic status and more affected by co-morbidities ( here: splenomegaly and malnutrition ) the opposite was observed for the Plasmodium-hookworm scenario . Individuals from hookworm non-endemic areas had higher proportions of females , were younger , wealthier , and less affected by anemia and malnutrition . In all three Plasmodium-helminth co-infection models of the adult sample , at least one co-infection had a lower odds ( OR <1 ) compared to the reference category ( S1 Table ) . Consequently , measures of interaction on additive scale were not reliable and their calculation not applicable . We identified a significant antagonistic effect on anemia on multiplicative scale in adult participants co-infected with Plasmodium and S . mansoni ( OR 0 . 39 , 95% CI 0 . 16 , 0 . 95 ) . Similarly to the findings from univariate regression analysis , single and co-infection categories of Plasmodium and Schistosoma showed significantly higher ORs for anemia in the multivariable models compared to non-infected school-aged children/adolescents from the school-based survey , while no such relationships could be shown for hookworm infection ( Table 3 ) . ORs of the co-infection categories remained slightly below the expected sum of the ORs for the single infection categories , thus showing a tendency for antagonism ( SI <1 ) . Indeed , for the scenario of high Plasmodium parasitemia and concomitant S . haematobium infection ( OR 0 . 26 , 95% CI 0 . 13 , 0 . 52 ) , we identified a significant antagonistic effect on multiplicative scale for anemia among school-aged children/adolescents from the school-based sample . This finding was underlined by a negative RERI of -1 . 86 ( 95% CI -2 . 75 , -0 . 96 ) . No significant antagonism or synergism on anemia on additive scale ( indicated by SI and AP ) for Plasmodium-helminth co-infection was identified . Interaction analysis in school-aged children/adolescents from the communities revealed no significant interactions , neither on additive nor on multiplicative scale , for anemia as an outcome using six different co-infection scenarios ( Table 4 ) . Of note , certain co-infection categories showed significantly higher ORs for anemia compared to the corresponding reference categories such as school-aged children/adolescents positive for Plasmodium and S . haematobium ( OR 2 . 39 , 95% CI 1 . 08 , 5 . 32 ) and school-aged children/adolescents with high Plasmodium parasitemia and concomitant hookworm infection ( OR 2 . 35 , 95% CI 1 . 19 , 4 . 62 ) . Within the national school-based survey , co-infection with Plasmodium and either of the two Schistosoma species showed antagonistic effects on splenomegaly ( Table 3 ) . For the scenarios of high Plasmodium parasitemia and S . haematobium co-infection ( OR 0 . 33 , 95% CI 0 . 13 , 0 . 86 ) and Plasmodium-S . mansoni ( OR 0 . 32 , 95% CI 0 . 10 , 0 . 99 ) co-infection , these negative interactions were found to be on multiplicative level . Both single-infection categories of the two scenarios showed significantly higher ORs for splenomegaly; the co-infection categories , however , were below the expected sum or non-significant . No significant antagonism or synergism on splenomegaly for Plasmodium-hookworm co-infection was observed in the school-based sample . Table 4 summarizes the six scenarios applied for testing interaction on splenomegaly in Plasmodium-helminth co-infection in school-aged children/adolescents from the community-based surveys . Neither of the Plasmodium-Schistosoma pairs showed any significant interaction measures or higher ORs for single and co-infection categories compared to the reference categories for splenomegaly . For Plasmodium-hookworm co-infection , however , we could identify a significant antagonism for splenomegaly on additive scale ( SI 0 . 25 , 95% CI 0 . 07 , 0 . 91 ) .
Unlike studies conducted in East Africa [45 , 46] and what is generally expected from two parasite species that have been shown to be positively associated [47] and having different mechanistic pathways of causing anemia [2 , 14] , we could not identify any exacerbation in anemia due to Plasmodium-hookworm co-infection in the school-aged population . What we observed is basically no departure from additivity . This may partly be explained by generally low hookworm infection intensities found in our study samples [10 , 17] . Our results are in line with earlier findings from Côte d’Ivoire [16] and from rural communities in central Ghana [47] that showed a comparable epidemiologic profile with high Plasmodium rates ( ≥75% of school-aged children/adolescents infected ) and mainly low-intensity hookworm infections . In settings with such a high force of Plasmodium transmission , contribution to anemia may thus be largely attributable to malaria , while the effect on anemia from light-intensity hookworm infection seems negligible . Nutritional studies assessing potential contribution of Plasmodium-hookworm co-infection to iron-deficiency anemia undertaken in rural Côte d’Ivoire further emphasize this assumption [5] . Of note , numerous studies on Plasmodium-helminth co-infection and its relationship with anemia reported highest odds of anemia in Plasmodium single-infected individuals , followed by helminth ( co- ) infected individuals compared to the uninfected reference group [48–50] . However , these prior studies did not have a closer look at potential ( antagonistic ) interactions , which indeed seem to be present , considering lower risks of anemia in people co-infected compared to Plasmodium single-infected individuals . Within the community-based survey , Plasmodium-hookworm co-infection showed protective effects against splenomegaly , whereas this negative interaction could not be confirmed in the national school-based sample . Other studies on potential interaction from Plasmodium-hookworm co-infection on splenomegaly are still scarce . Mboera and colleagues investigated the effect of concurrent hookworm and Plasmodium infection in Tanzanian school children and revealed enhanced Plasmodium parasitemia in Plasmodium-hookworm co-infected individuals but no exacerbating effect on splenomegaly [49] . In the community-based sample where S . haematobium infection was three times more prevalent than in the national school-based sample , S . haematobium single or Plasmodium- S . haematobium co-infection were negatively associated with anemia , whereas Plasmodium infection was the main driver of splenomegaly . For all four scenarios of Plasmodium-S . haematobium co-infection on anemia and splenomegaly , we found no significant interactions among school-aged children/adolescents from the four communities . Yet , we identified a significant antagonism on multiplicative scale on both morbidity indicators for the scenario of S . haematobium plus concomitant high Plasmodium parasitemia from the national school-based survey . Earlier studies from Tanzania and Zimbabwe identified Plasmodium and S . haematobium single- and co-infections as important predictors of anemia and splenomegaly , while only little information was available on the presence and magnitude of potential interactions from co-infection [20 , 49 , 50] . Findings from Mali and Senegal corroborate the assumption of a protective effect of Plasmodium-S . haematobium co-infection on malaria pathology , such as a reduced number of febrile malaria episodes and lower Plasmodium parasitemia in co-infected individuals [15 , 18 , 51 , 52] . While earlier studies conducted in East Africa reported exacerbation of ( hepato- ) splenomegaly in Plasmodium-S . mansoni co-infection [8 , 19] , we observed the contrary . Although all infection categories ( single and co-infection ) were significantly positively associated with splenomegaly in school-aged children/adolescents from the national school-based sample , interaction measures clearly indicated an antagonism on multiplicative scale in Plasmodium-S . mansoni co-infected school-aged children/adolescents . Among school-aged children/adolescents from the four communities , we could not identify a significant antagonism , but odds of splenomegaly seemed to be reduced both in school-aged children/adolescents with low and high Plasmodium parasitemia and concomitant S . mansoni infection , compared to school-aged children/adolescents infected with Plasmodium singly ( low and high parasitemia ) . The odds of anemia were highest among Plasmodium-S . mansoni co-infected , compared to all other infection categories , in school-aged children/adolescents from the school-based sample . Interaction analysis , however , did not show any protective effect nor any exacerbation to the expected risk from concomitant infection . Interestingly , both antagonism as well as synergism in Plasmodium-S . mansoni co-infection might be explained by immunological interactions by down- or up-regulating , respectively , of a pro-inflammatory immune response that is related with the development of ( hepato- ) splenomegaly and that may also contribute to anemia due to inflammation [1 , 53 , 54] . The direction in which the immune response and thus the potential morbidity is induced may depend on the clinical phase of the disease , the duration since infection , the infection intensity of both , Plasmodium and Schistosoma , and the disposition of the host’s immune system [53] . In our study , we mainly focused on school-aged children/adolescents , part of whom might not yet have acquired full immunity against these infectious diseases . All S . mansoni-positive individuals were identified as egg excreters and their infection stage thus may be considered as chronic but with mainly low egg counts . Although we mainly identified antagonistic effects on anemia and splenomegaly in Plasmodium-helminth co-infected individuals , our data indicate considerable burden due to these infections . Especially on national scale , multiparasitism represented a double burden among school-aged children/adolescents , yet an exacerbation from concomitant infection was not evident in our setting . Burden assessment due to parasitic infections for co-endemic countries thus warrants adaption as demanded and discussed over the last decade [55 , 56] . Of note are also the apparent differences in interactions of Plasmodium-helminth co-infections between African sub-regions . While data from East African settings highlight potential exacerbation [19–21] the opposite was found in studies investigating etiopathology of malaria in helminth co-infected individuals in West Africa [15 , 16 , 18 , 51 , 52] . Some of these differences from one country to another may be explained through a different force of Plasmodium transmission ( e . g . , mostly higher in West Africa ) , but also varying helminth species strains , related virulence and , to what extent they cause clinical morbidity , may play a role whether Plasmodium-helminth co-infection results in a more antagonistic effect rather than in synergism [9 , 57] . Moreover , the presence of a second helminth co-infection may steer potential interactions on malaria pathology in the opposite direction or enhance them [58 , 59] . In the light of national control programs and increased financial means and control efforts targeting helminthiases , a deeper understanding about potential effects on susceptibility to malaria in co-endemic areas should be gained . Removing helminth infections may alter immune responses to Plasmodium infection in previously helminth-infected individuals and thus also influence the number of malaria attacks and severity [58] . Malaria control should thus pay increased attention to the school-aged population that is the major target group of preventive chemotherapy against helminthiases and already neglected in terms of malaria treatment and use of preventive measures compared to other populations ( e . g . , pregnant women and children under five years of age ) . Furthermore , for enhancing ( cost- ) efficiency , concerted efforts against both types of infectious diseases in co-endemic areas are desirable and could for example include education campaigns against malaria during helminth mass drug administration and against helminthiases during mass distributions of insecticide-treated bed nets , respectively . Our study has several strengths and limitations . The diagnostic approach , particularly for detection of helminth infections , lacks sensitivity , and hence infection rates might be underestimated . Of note , mainly light-intensity infections were missed , thus our results might still be reliable for relationship and interaction analysis between infection status and clinical morbidity [7] . In Côte d’Ivoire , helminthiases other than schistosomiasis and soil-transmitted helminthiasis exist , such as enterobiasis , strongyloidiasis , onchocerciasis , and lymphatic filariasis [60 , 61] . These diseases were not investigated but they might influence malaria pathology by altering the immune response to Plasmodium [62 , 63] . With regard to the two clinical outcomes considered in our study—anemia and splenomegaly—especially the former is multifactorial and may be caused by hematological diseases , inflammation , and nutritional deficiencies , in addition to helminth infection and malaria . However , we conjecture that a major contribution to these clinical manifestations are parasitic infections [1] with Plasmodium and helminth infections ranking among the most important ones in terms of prevalence and burden [55] . With regard to methodological and analytical limitations it is worth highlighting that especially the concept of biologic interaction on additive scale , as proposed by Rothman et al . [22] , is designed for case-control studies with matched groups and investigating risk exposures ( relative risk ( RR ) or OR >1 ) ; conditions that are usually not met in observational studies . Cross-sectional studies may thus complicate analysis or produce a high uncertainty and variance in the interaction estimates through ( i ) unbalanced numbers within the different exposure categories ( e . g . , low number of Plasmodium-uninfected school-aged children/adolescents in our samples ) ; ( ii ) relationships between exposure and outcome may not always reveal an actual risk but rather being protective ( RR or OR <1 ) compared to the defined reference category; ( iii ) the investigated outcomes may be influenced by other covariates ( e . g . , age , sex , and socioeconomic status ) that are potentially not equally distributed among the comparison groups; and ( iv ) splenomegaly and anemia are manifestations due to chronic infection and longitudinal study designs may better capture relationships with ( earlier ) infections that may even be missed during cross-sectional surveys . We addressed some of these points by the introduction of low- and high-parasitemia Plasmodium infection scenarios to avoid small reference groups and for investigation of the relationship with parasite burden . Additionally , effects on estimates from unbalanced numbers in each category may be less pronounced in large data sets such as the national school-based study sample in the current analysis . Interaction measures on additive scale for preventive factors were not considered and recoding of exposure variables , as proposed by Knol and colleagues [41] , avoided due to complication of subsequent interpretation . Furthermore , we built logistic regression models that allowed for adjustment for potential confounders and Schistosoma species-specific models by excluding individuals with concomitant infections with the second Schistosoma species . To reassure that only individuals from actual co-endemic areas are compared within interaction analysis , we subsampled school-aged children/adolescents from the national cohort according to helminth endemicity . This may have helped to harmonize partly as seen in several differences in parameters between excluded and included individuals . We could , however , not change the study design; a longitudinal design looking at infection status at different time points and thus allowing for discrimination of repeated infections may be more appropriate in future studies assessing clinical outcomes that are related to chronic exposure with the investigated parasites .
|
Malaria ( due to infection with Plasmodium spp . ) and parasitic worms ( for example soil-transmitted helminths and Schistosoma spp . ) are common in the tropics . Hence , people are often co-infected , depending on various factors . Interactions between Plasmodium and helminth infections may alter immune response and susceptibility of the infected host , and thus impact on morbidity by either making it worse ( synergism ) or by reducing it ( antagonism ) . Although these co-infections are common , little is known about the direction and magnitude of such interactions . To deepen the understanding of how co-infection could affect morbidity in infected people , we looked at clinical data ( i . e . , anemia and splenomegaly ) in different population groups in Côte d’Ivoire . We did not observe any exacerbation from co-infection with Plasmodium and helminths; all identified significant interactions were of antagonistic nature but varied in magnitude and parasite combination . In the light of enhanced control efforts targeting helminthiases , a better understanding about potential effects on susceptibility to malaria in co-endemic areas should be gained and intervention strategies against the two type of diseases be planned in a more integrative manner .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
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"splenomegaly",
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2019
|
Antagonistic effects of Plasmodium-helminth co-infections on malaria pathology in different population groups in Côte d’Ivoire
|
Diarrheal diseases are a major cause of morbidity and mortality worldwide . In many cases , antibiotic therapy is either ineffective or not recommended due to concerns about emergence of resistance . The pathogenesis of several of the most prevalent infections , including cholera and enteroxigenic Escherichia coli , is dominated by enterotoxins produced by lumen-dwelling pathogens before clearance by intestinal defenses . Toxins gain access to the host through critical host receptors , making these receptors attractive targets for alternative antimicrobial strategies that do not rely on conventional antibiotics . Here , we developed a new nanotechnology strategy as a countermeasure against cholera , one of the most important and prevalent toxin-mediated enteric infections . The key host receptor for cholera toxin , monosialotetrahexosylganglioside ( GM1 ) , was coated onto the surface of polymeric nanoparticles . The resulting GM1-polymer hybrid nanoparticles were shown to function as toxin decoys by selectively and stably binding cholera toxin , and neutralizing its actions on epithelial cells in vitro and in vivo . Furthermore , the GM1-coated nanoparticle decoys attenuated epithelial 3’ , 5’-cyclic adenosine monophosphate production and fluid responses to infection with live Vibrio cholera in cell culture and a murine infection model . Together , these studies illustrate that the new nanotechnology-based platform can be employed as a non-traditional antimicrobial strategy for the management of enteric infections with enterotoxin-producing pathogens .
Diarrheal diseases are a major cause of morbidity and mortality in developing regions , with an estimated 3–5 million cases and over 100 , 000 deaths per year [1 , 2] . Diarrhea accounts for 1 in 9 childhood deaths worldwide , making it the second leading cause of death among children under the age of five . As an example , in diarrheal patients attending a hospital in Mirpur , Bangladesh , Vibrio cholerae was found to be the causative agent of diarrheal disease in 23% of patients [3] . Current treatments involve rehydration with oral or intravenous replacement electrolyte solutions [4 , 5] . While this method has reduced mortality rates in children with acute diarrheal diseases , in general , stool volume and diarrheal durations are not decreased [6] . Administration of antibiotics in conjunction with electrolyte solutions can reduce the volume and duration of diarrhea [7] , but extensive use of antibiotics may lead to the emergence of antibiotic-resistant strains of bacteria , threatening the utility of existing antibiotics [8] . Therefore , an urgent need exists to develop alternative treatments . V . cholerae is usually contracted through ingestion of contaminated water or food in which the bacterium is present [5] . While bacterial colonization is limited to the lumen and epithelial surface of the intestinal tract , the disease symptoms are primarily caused by bacterially produced toxins . Most prominently , V . cholerae secretes cholera toxin ( CT ) , which is composed of an A subunit responsible for toxicity and a pentameric B subunit ( CTB ) responsible for receptor binding [9] . CTB binds to GM1 gangliosides on the surface of intestinal epithelial cells , which subsequently leads to endocytosis of the entire protein complex [10] . The A1 subunit ( CTA1 ) is cleaved from the rest of the toxin through the reduction of a disulfide bond [11] . CTA1 then catalyzes ADP-ribosylation of the GSα protein [12] , leading to its activation , stimulation of adenylyl cyclases , and a sustained increase in epithelial cyclic AMP levels [13] . This series of events culminates in a massive efflux of chloride ions and an inhibition of sodium absorption by the epithelium , which leads to the rapid outflow of water into the intestinal lumen , and the attending severe diarrhea and dehydration [14] . Since the GM1 ganglioside host receptors play a key role in the CT-mediated pathogenesis of cholera , it constitutes an attractive target for novel antimicrobial strategies [15] . The recent emergence of nanotechnology is beginning to have a profound impact on modern medicine [16] . Nanoparticle systems have shown to be superior in facilitating drug solubility , systemic circulation , and drug release , and in their ability for differential cell targeting compared to free drugs [17 , 18] . In addition , nanoparticles can be engineered to serve as decoys or sinks for microbial toxins , opening up new possibilities for treating toxin-mediated diseases [19 , 20] . To determine whether this concept can be applied to cholera as a prototypic model for intestinal diseases caused by enterotoxins , we set out to develop a nanotechnology-based strategy for CT neutralization and treatment of cholera . The work described here demonstrates that nanoparticle decoys are a promising new therapeutic avenue for toxin-mediated diarrheal diseases .
Laboratory mice were used for parts of the study . Anesthesia was done with isoflurane inhalation , and buprenorphine was given before surgery for preventive pain management . Mice were euthanized by CO2 inhalation and cervical dislocation . All animal studies were reviewed and approved by the UC San Diego Institutional Animal Care and Use Committee . GM1 ganglioside-coated poly ( lactic-co-glycolic acid ) ( PLGA ) hybrid nanoparticles ( GM1-NPs ) were prepared by nanoprecipitation as previously described [18 , 21] with several modifications . Briefly , a PLGA stock solution was prepared by dissolving PLGA pellets ( LACTEL Absorbable Polymers , Pelham , AL ) in acetonitrile at a concentration of 2 . 5 mg/mL . A GM1 ganglioside stock solution was prepared by dissolving GM1 ( Carbosynth , San Diego , CA ) in deionized water at 10 mg/mL . To prepare the aqueous phase for GM1-NP synthesis , the desired amount of GM1 stock solution was added into deionized water to yield a final GM1 concentration of 10% ( w/v ) of the PLGA polymer . A predetermined volume of the PLGA solution was then added dropwise ( 1 ml/min ) into the aqueous GM1 solution under gentle stirring . The nanoparticles were allowed to self-assemble for 2 h with continuous stirring while the organic solvent was allowed to evaporate under vacuum . To remove the remaining free molecules and organic solvent , the nanoparticle suspensions were washed in deionized water three times using an Amicon Ultra centrifuge filter ( Millipore , Billerica , MA ) with a molecular weight cut-off of 100 kDa . Nanoparticles were resuspended in deionized water and used immediately or stored at 4°C ( up to 4 weeks ) for later use . As a control , PLGA nanoparticle cores ( PLGA-NPs ) were prepared with the nanoprecipitation method described above , but without GM1 in the aqueous solution . As another control , polyethylene glycol ( PEG ) -modified PLGA nanoparticles ( PEG-NPs ) were fabricated with a coat of 1 , 2-distearoyl-sn-glycero-3-phosphoethanolamine-N-[methoxy ( polyethylene glycol ) -2000] ( DSPE-mPEG2000; average molecular weight 2 . 8 kDa , Laysan Bio , Inc . , AL ) through nanoprecipitation as previously described [21] . The aqueous phase contained a DSPE-mPEG2000 concentration of 10% ( w/v ) of the PLGA polymer . All stated concentrations for nanoparticles refer to the concentration of the PLGA polymer in the respective formulation . Nanoparticle stability was analyzed in deionized water and phosphate-buffered saline ( PBS ) . For stability in water , nanoparticles were synthesized as described above at a final polymer concentration of 1 mg/mL . To test the stability in PBS , nanoparticles at 2 mg/mL in water were added to an equal volume of 2 × PBS . Particle size distribution and zeta-potential were measured by dynamic light scattering using a Malvern ZEN 3600 Zetasizer . Transmission electron microscopy of nanoparticles was done by depositing a suspension ( 2 mg/mL ) on a glow-discharged , carbon-coated 400-mesh copper grid . The grid was washed with distilled water and stained with 1% ( w/v ) uranyl acetate . Imaging was carried out on a Zeiss Libra 120 PLUS energy filter transmission electron microscope . Binding of FITC-labeled CTB ( Sigma-Aldrich , St . Louis , MO ) was tested by incubating the different nanoparticle suspensions with 10 μg/mL CTB in in 400 μl of PBS ( pH 7 . 2 ) for 30 min . Each sample was transferred to an Amicon Ultra centrifuge filter and centrifuged at 8 , 000 rpm in a Beckman Coulter microfuge 22R centrifuge for 5 min . Fluorescence was determined using a Synergy Mx fluorescent spectrophotometer ( Biotek , Winooski , VT ) . Bound CTB was calculated with the formula: CTB bound ( % ) = ( 1- CTB in supernatant/total CTB input ) × 100% . All experiments were performed in triplicate . Bound CTB was plotted against nanoparticle concentrations , and a curve was fitted with the binding-saturation equation in GraphPad Prism . To investigate the CT binding and neutralization capability , 400 μl of PBS solution containing 1 or 0 . 25 mg/mL of nanoparticles was mixed with 5 μl of different concentrations of FITC-CTB , and incubated for 30 min at 37°C . Each sample was processed as described above , and bound CTB was calculated , plotted against CTB input concentrations , and fitted with a binding-saturation equation . To determine the binding capacity of different nanoparticle formulations , 400 μl of PBS solution containing 1 mg/mL of GM1-NPs or PEG-NPs were incubated with 10 μg/mL CTB for 30 min at 37°C . CTB incubated in PBS solution was used as the negative control . Each sample was processed and analyzed as described above , and the bound CTB was calculated . To determine stability of toxin binding to the nanoparticles , 1 ml of a PBS solution containing 1 mg/mL of GM1-NPs was incubated with 10 μg/mL FITC-CTB for 30 min at 37°C . The sample were transferred to an Amicon Ultra centrifuge filter and centrifuged at 8 , 000 rpm for 5 min , and the CTB-loaded nanoparticles were resuspended in 1 ml of a pool of undiluted luminal content obtained from the small intestine of several male and female adult C57BL/6 mice . After 24 h incubation at 37°C , particles were dialyzed for 24 h against a PBS solution using a PTFE Dialyzer ( Harvard Apparatus ) and Nucleopore hydrophilic membrane ( Whatman ) with a molecular weight cut-off of 200 kDa . Retention of FITC-CTB on the nanoparticles was measured by fluorescence spectroscopy . GM1-NPs incubated in PBS and free CTB incubated in luminal content were used as positive and negative ( background ) control , respectively . Data are expressed as bound FITC-CTB after 24 h relative to the initial amount bound before incubation . Experiments were done in triplicate . To visualize nanoparticles and toxin colocalization , fluorescently-labeled GM1-NPs and PEG-NPs were prepared using 1 , 1’-dioctadecyl-3 , 3 , 3’ , 3’-tetramethylindo-dicarbocyanine perchlorate fluorescent dye ( DiD; excitation/emission 644/665 nm; Life Technologies , Carlsbad , CA ) for incorporation into the polymer solution at a concentration of 10 μg/mL during the preparation . Labeled GM1-NPs and PEG-NPs ( 1 mg/mL ) were then incubated with 10 μg/mL CTB as described for the toxin binding studies . After 30 min incubation , nanoparticle solutions were washed three times in deionized water using an Amicon Ultra centrifuge filter , and the samples were visualized by confocal fluorescence microscopy using an Olympus FV1000 microscope with a 100x oil objective . To obtain stable images , the particles were dispersed in glycerol to significantly decrease their spontaneous movements . Vibrio cholerae strain N16961 ( serogroup O1 , biovar El Tor; ATCC ) was grown overnight at 37°C in Luria Bertani broth supplemented with trimethylamine N-oxide without agitation at 37°C in an atmosphere of 5% CO2 and 95% air . These conditions have been shown to induce CT expression [22] . Human HCA7 colon cancer cells ( ATCC ) were grown in 75-cm2 culture flasks in Dulbecco’s modified Eagle’s medium ( DMEM ) supplemented with 10% fetal bovine serum and 1% penicillin-streptomycin at 37°C in 5% CO2 and 95% air . Cells were plated into 48-well plates at 5x105 cells/well , and monolayers were grown overnight before experiments . For toxin neutralization , we mixed different concentrations of CT ( List Biological Laboratories , Campbell , CA ) and nanoparticles , incubated for 1 h , and added the mixture to the cell monolayers . After 2 h , supernatants were collected and assayed for cAMP by enzyme immunoassay ( Cyclic AMP ELISA Kit , Cayman Chemical Co . , Ann Arbor , MI ) . All cAMP measurements were done without additional acetylation . For neutralization experiments with live bacteria , nanoparticles were added to epithelial cell monolayers , which were then immediately inoculated with V . cholerae at a multiplicity-of-infection of 30 , as determined by measuring optical density at 600 nm ( OD600 ) . After 2 h of infection , cAMP levels were determined in the supernatants by enzyme immunoassay . Ligated loops of the mid-distal small intestine were prepared in anesthetized adult C57BL/6 mice as described previously [23] . Briefly , mice were fasted for 4–6 h before anesthesia and surgery , and given 0 . 1% buprenorphine for preventive pain management . After shaving and disinfection of the abdomen , a small abdominal incision was made , and a small intestinal loop was identified and ligated with two small surgical clips placed 2–3 cm apart . Agents were injected into the loop with a 30G needle in a 200 μl volume , and the abdominal cavity was closed with sutures . Loops were excised at different times , and the luminal loop volume was determined and related to the length of the loop . For CT tests , a solution of 12 . 5 μg/mL CT was incubated with and without 250 μg/mL GM1-NPs or PEG-NPs for 1 h at room temperature , and the mixture , or PBS as a control , was injected into the ligated loops . For tests with live bacteria , V . cholerae were prepared as described above , and injected in modified Luria Bertani broth at 105 bacteria per loop , either alone or with 1 . 8 mg/loop of GM1-NPs or PEG-NPs . Broth alone was used as a control . Data were analyzed using Prism 5 ( GraphPad Software , La Jolla , USA ) . Means were compared with Student’s t-test or analysis of variance ( Anova ) . P values <0 . 05 were considered as significant .
Hybrid nanoparticles , comprised of a polymeric core and a lipid shell , combine the merits of both polymeric nanoparticles and liposomes while avoiding some of their limitations [18] . Compared to the aqueous cores of conventional liposomes , a solid polymeric core provides better control over the mechanical stability , particle morphology , size distribution , and drug release kinetics [21] . Therefore , we applied hybrid nanoparticle fabrication to the formulation of GM1-NPs as schematically outlined in Fig 1A . Briefly , an organic solution of PLGA as the polymeric core constituent was added dropwise under gentle stirring to a solution of GM1 in water to yield a final 1:2 volume ratio of organic to aqueous solution . The mixture was vortexed vigorously for 3 min followed by solvent evaporation under reduced pressure . The remaining organic solvent and free molecules were removed by centrifugation . As controls , two other types of nanoparticles were prepared: PLGA-NPs and PEG-NPs . PEG-NPs have a PLGA core coated with DSPE-mPEG2000 [24] , a lipid modified with polyethylene glycol that is not expected to bind CT . PLGA-NPs are comprised of only a PLGA core without a lipid shell . Analysis of the nanoparticles by dynamic light scattering revealed a narrow size distribution of the GM1-NPs , with a measured hydrodynamic diameter of ~100 nm ( Fig 1B ) , which was similar to the previously reported size of the control PEG-NPs [21] . By comparison , the bare PLGA core had a slightly smaller diameter of ~75 nm ( Fig 1B ) , suggesting that the increased size of the GM1-NPs was due to the additional GM1 gangliosides coated as an exterior layer onto the PLGA core . Furthermore , GM1-NPs as well as the control PEG-NPs , had a significantly more negative surface zeta-potential of -40 to -50 mV compared to -25 mV of PLGA-NPs ( Fig 1C ) , which is also indicative of a differences in the nanoparticle surface characteristics due to the lipid coating of the GM1-NPs compared to the bare PLGA cores . To test the stability of the nanoparticles in aqueous solution , they were incubated in water or PBS for up to two weeks and analyzed by dynamic light scattering . All three nanoparticle preparations were stable in water for the entire test period , but only the two lipid-coated preparations were stable in PBS , while the bare PLGA-NPs rapidly formed aggregates ( Fig 1D ) . These findings clearly distinguished GM1-NPs from the uncoated PLGA-NPs and suggested that the GM1 layer surrounding the GM1-NPs can provide steric and electronic repulsion to prevent detrimental particle aggregation that would interfere with in vitro and in vivo studies . Transmission electron microscopy confirmed that the GM1-NPs were dispersed as single particles with a core/shell structure characteristic of a unilamellar membrane coating around a nanoparticle core ( Fig 1E ) . To determine the ability of GM1-NPs to bind CT , we incubated them , as well as control PEG-NPs , with fluorescently ( FITC ) -labeled CTB . Unbound CTB was removed by centrifugal filtration , and bound FITC was assayed by fluorescence spectroscopy . Over 95% of input CTB was bound to GM1-NPs , whereas the control PEG-NPs had only background levels of fluorescence ( Fig 2A ) , showing the specificity of CTB binding to the GM1-NPs . We also constructed GM1-NPs and PEG-NPs in which a far-red fluorescent dye , DiD , was encapsulated in the PLGA core , and incubated them with FITC-CTB under the same conditions . Fluorescence imaging revealed co-localization of DiD and FITC in the GM1-NPs , whereas no FITC staining was observed in the PEG-NPs ( Fig 2B ) . These results confirm that CTB binds specifically to GM1-NPs . Toxin binding was concentration-dependent in regard to CTB and GM1-NPs ( Fig 2C and 2D ) . Fitting of a one-site specific binding model revealed a maximal binding capacity ( Bmax ) of ~10−7 mol CTB per mg GM1-NP ( equivalent to ~1 mg CTB/mg NP ) . Taken together , these results show that GM1-NPs are stable in a physiologically relevant salt solution , and can bind CTB in a specific and a high-capacity manner . Having shown specific CTB binding to GM1-NPs , we next investigated whether the particles could block the functional impact of CT holotoxin on intestinal epithelial cells . A fixed concentration of CT was mixed with different concentrations of GM1-NPs or PEG-NPs , and the mixtures were added to monolayers of human HCA7 intestinal epithelial cells . As a functional read-out for CT bioactivity , we determined levels of secreted cAMP in the supernatants , which correlate closely with intracellular cAMP levels [25] . GM1-NPs neutralized the ability of CT to activate cAMP production and secretion in a concentration-dependent fashion , while the GM1-free control PEG-NPs had no effect ( Fig 3A ) . Half-maximal neutralization of 10 ng/mL CT was achieved at 28 ng/mL GM1-NPs ( as measured by their PLGA content ) . As a further demonstration of the specificity and saturability of the CT/GM1-NP interaction , we observed that increased CT concentrations could overwhelm the neutralizing capacity of GM1-NPs , so a CT concentration of 361 ng/mL was required in the presence of 1 , 000 ng/mL of GM1-NPs to recover 50% of the maximal CT response seen in the absence of nanoparticles ( Fig 3B ) . Control PEG-NPs had no neutralizing effect under these conditions ( although decreasing CT concentrations led to the expected diminishment of the epithelial cAMP response ) . Neutralization of purified CT was a necessary precondition for practical utility of GM1-NPs , but the particles must be effective against CT produced by live bacteria to have therapeutic potential . Therefore , we infected HCA7 epithelial monolayers with live , CT-secreting V . cholerae in the absence or presence of nanoparticles , and measured cAMP secretion in the culture supernatants . GM1-NPs significantly attenuated the cAMP response compared to PEG-NPs , although attenuation was incomplete ( Fig 3C ) . Nanoparticles alone without bacteria had no effect on cAMP production . Given the intense exposure of the epithelial monolayers to high numbers of bacteria without physiological mixing that occur with normal intestinal motility and the absence of a normal mucus layer , these data strongly suggest that the GM1-NPs can significantly neutralize CT produced by live bacteria in close contact with epithelial cells . To be effective in the intestinal lumen , where V . cholerae resides and secretes CT , nanoparticles have to be stable and functional in the presence of the relevant physiological factors at that site . Of particular importance are bile acids whose amphoteric nature promotes lipid solubilization and digestive enzymes that can break down lipids and other complex molecules . Therefore , we tested whether GM1-NPs remained intact and active upon exposure to these luminal factors . Incubation of GM1-NPs in a solution containing concentrated porcine bile had no impact on particle size or their ability to bind FITC-labeled CTB ( Fig 4A and 4B ) . Furthermore , incubation of CTB-loaded GM1-NPs for 24 h with luminal fluid ( containing bile acids , various digestive enzymes , and some commensal bacteria ) from the small intestine of mice did not detach the toxin , indicating that toxin binding to the nanoparticles was stable and not affected by luminal factors ( Fig 4C; similar observations were made after 48 h of incubation ) . This conclusion was confirmed by the observation that exposure of the nanoparticles to fecal homogenates ( which also contain bile acids and digestive enzymes , as well as large numbers of commensal bacteria and their enzymatic products ) did not compromise the ability of GM1-NPs to functionally neutralize CT in respect to epithelial cAMP induction ( Fig 4D ) . Together , these results demonstrate that the GM1-NPs display stable and prolonged functionality in the presence of intestinal luminal factors , suggesting that they are suitable for in vivo applications to neutralize CT . To evaluate the therapeutic efficacy of the nanoparticles in vivo , we utilized ligated intestinal loops in adult mice as a model . Constructed in the distal small intestine , these loops allow undisturbed exposure of the intestine to defined microbial stimuli and therapeutic interventions in the physiologically relevant environment without confounding variables related to intestinal motility or variable susceptibility of adult mice to sustained infection with the target microbe . In a first test , we injected the loops with CT in the absence or presence of GM1-NPs or PEG-NPs . CT alone induced a robust fluid response in the lumen of the loops , which was not affected by the control PEG-NPs ( Fig 5A ) . In contrast , GM1-NPs completely blocked the fluid response to CT , indicating that the nanoparticles were as effective in vivo as they were in vitro . Subsequently , we infected the loops with live V . cholerae with and without nanoparticles . Increased fluid secretion was observed after infection , which was significantly attenuated by treatment with GM1-NPs but not with control PEG-NPs ( Fig 5B and 5C ) . In parallel to the attenuated fluid response , levels of secreted cAMP in the intestinal lumen were significantly decreased with GM1-NP treatment compared to control PEG-NPs after V . cholerae infection ( Fig 5D ) . Neither of the nanoparticles had an impact on baseline fluid secretion without infection .
Cholera continues to be a major public health challenge in many regions of the world [5] . Medical strategies to combat this scourge can be divided into preventive approaches , which seek to protect individuals from infection , and therapeutic approaches , which attenuate disease symptoms in infected persons . For prevention , the FDA recently approved the first cholera vaccine , Vaxchora , composed of attenuated live bacteria , but the vaccine is currently only effective for V . cholerae serogroup 01 and , as a live agent , has the potential to cause disease itself , either in attenuated form in predisposed individuals or potentially upon reversion to a more virulent form [26] . In this regard , quality controls of live microbial agents as therapeutic agents can be challenging . As an alternative , attenuating medical strategies employ well-controlled interventions to ameliorate symptoms and assure survival while allowing mucosal immune defenses to clear the infection . The classical treatment is oral or intravenous rehydration [4] , in which bacterially-induced diarrheal processes proceed unhindered , but the devastating systemic consequences of dehydration are prevented by providing sufficient electrolytes and water during the acute disease stage . Although usually effective for promoting survival , severe disease symptoms can still occur for days . As an alternative attenuation strategy , we show here that a nanotechnology-based intervention can be effective at targeting the bacterially-produced CT , which is the primary cause of diarrheal symptoms in cholera [5] . By coating nanoparticles with the CT-binding lipid , GM1 , the particles were able to bind and neutralize the toxin , thereby preventing its effects on epithelial electrolyte and fluid secretion in vitro and in vivo ( Fig 6 ) . This strategy represents a novel interventional approach whose mechanisms of action are physiologically distinct from vaccination , rehydration , or antibiotics , thus significantly broadening the medical armamentarium against cholera . Neutralization strategies for microbial toxins have been proposed and implemented with different technological means , including antibodies and live bacteria . For cholera , Escherichia coli has been engineered to produce GM1 lipid on its surface [27] . The recombinant E . coli was shown to bind CT and attenuate V . cholerae-induced disease in animal models [15 , 27] , thus underlining our findings . However , live microbes as therapeutic agents are problematic due to the difficulties in performing quality controls in manufacturing [28] . Furthermore , genetically engineered live bacteria , particularly those that belong to the normal intestinal microbiota , may colonize the intestine permanently [29] , raising concerns about long-term microecological consequences of such interventions . In contrast , our nanoparticles were constructed exclusively from tractable reagents that can be readily quality-controlled . The fabrication process is effective and high-yield , thereby minimizing potentially offensive by-products and the accompanying need for extensive purification schemes . Furthermore , the nanoparticles do not replicate and are thus not retained for extended periods in the intestine beyond the treatment period . In the nanoparticle design , we considered that many glycosylated lipid derivatives , such as gangliosides , contain sialic acid residues on their sugar chains that are prominently positioned and critical for attachment of cholera toxin [30] . Consequently , it was important that the GM1-oligosaccharides were located on the outside of the nanoparticle core in the proper orientation , so the lipid arrangement would resemble as much as possible the naturally occurring cell-surface location and affinity of GM1 to which CT normally binds . We were able to achieve the desired lipid location and orientation on the polymeric nanoparticle surface through a modified nanoprecipitation method [18 , 21] , in which GM1-gangliosides with their inherent amphiphilic property self-assembled in a single-step synthesis on the hydrophobic nanoparticle surface to produce a lipid monolayer on the interface of the nanoparticle core and the hydrophilic GM1-oligosaccharides present on the outer shell . The fabricated GM1-nanoparticles represent a novel class of core-shell structured lipid-polymer hybrid nanoparticles , which are known for combining the strengths of both liposomes and polymeric nanoparticles [18] . The lipid shell incasing the core mimics biological membranes and can mediate specific interactions with the environment , such as the reported interaction between the shell GM1 and soluble CT . Meanwhile , the nanoparticle core serves as structural support that provides controlled morphology , size tunability , and narrow size distribution [31] . In addition , lipid-polymeric nanoparticles have excellent physical stability [21 , 32 , 33] , making GM1-NPs promising for their use in tropical environments . In terms of safety , PLGA polymer , which makes up the GM1-NP core , is a safe and FDA-approved biodegradable polymer [34] , and GM1-ganglioside is extracted from natural cell membranes . Hence , it is likely that the GM1-nanoparticles are biocompatible and safe for prospective clinical translation . Since cholera is often a disease affecting poor people in developing countries , cost-effective manufacturing is critical for clinical utility . PLGA has long been produced for pharmaceutical applications [35 , 36] , and a variety of manufacturing processes have been used in industry for nanoparticle formulations [37–39] . Such processes could be adapted to large-scale GM1-NP production , facilitating downstream translational development . Optimal formulation will require further development , but the platform technology has great flexibility . For example , nanoparticles can be administered directly , as shown for formulations such as poly ( lactic-co-glycolic acid ) nanoparticles and polyacrylic acid nanoparticles to treat colitis or hypercalcemia [40 , 41] . Alternatively , nanoparticles could be loaded into capsules [42 , 43] or a pH-responsive polymer matrix for targeting to specific sections of the intestinal tract [44] . Our nanoparticle strategy has implications for treating other enteric infections in which microbially-produced toxins play an important and central pathophysiological role . For example , the heat-labile enterotoxin of enterotoxigenic E . coli , the most common cause of traveler’s diarrhea , also binds GM1 ganglioside [45] . Structural analysis of CTB and the binding subunit of LT bound to GM1-pentasaccharide revealed that the residues contacting the terminal galactose sugar are conserved between the two toxins [46] , suggesting that nanoparticle-based intervention would predictably also be effective in enterotoxigenic E . coli-induced disease . For other toxin-mediated enteric diseases , such as those caused by shiga toxin from Shigella dysenteriae [47] , similar nanotechnology strategies using the appropriate lipids may also be a promising new therapeutic avenue .
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Diarrheal diseases are a major cause of suffering and death in the world , particularly in tropical regions with limited health care resources . Many of the most important diarrhea-causing microbes produce toxins that activate fluid secretion in the gut . A prototype pathogen in this category is the cause of cholera , Vibrio cholerae , which is characterized by profuse diarrhea and severe electrolyte disturbances due to the release of cholera toxin . Although treatment with fluids by mouth or injection can save patients from death , they still experience the devastating symptoms of the disease . In the present study , we have developed a new intervention strategy with engineered nanoparticles , particulates than are smaller than one millionth of a meter , which can neutralize cholera toxin in the gut before it can cause the characteristic disease manifestations . This strategy represents a novel interventional approach whose mechanism of action is different from currently existing therapies , thus significantly broadening the medical armamentarium against cholera and perhaps other gut infections that cause diseases dominated by toxin production .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
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2018
|
Neutralization of cholera toxin with nanoparticle decoys for treatment of cholera
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As pattern recognition receptor on dendritic cells ( DCs ) , DC-SIGN binds carbohydrate structures on its pathogen ligands and essentially determines host pathogen interactions because it both skews T cell responses and enhances pathogen uptake for cis infection and/or T cell trans-infection . How these processes are initiated at the plasma membrane level is poorly understood . We now show that DC-SIGN ligation on DCs by antibodies , mannan or measles virus ( MV ) causes rapid activation of neutral and acid sphingomyelinases followed by accumulation of ceramides in the outer membrane leaflet . SMase activation is important in promoting DC-SIGN signaling , but also for enhancement of MV uptake into DCs . DC-SIGN-dependent SMase activation induces efficient , transient recruitment of CD150 , which functions both as MV uptake receptor and microbial sensor , from an intracellular Lamp-1+ storage compartment shared with acid sphingomyelinase ( ASM ) within a few minutes . Subsequently , CD150 is displayed at the cell surface and co-clusters with DC-SIGN . Thus , DC-SIGN ligation initiates SMase-dependent formation of ceramide-enriched membrane microdomains which promote vertical segregation of CD150 from intracellular storage compartments along with ASM . Given the ability to promote receptor and signalosome co-segration into ( or exclusion from ) ceramide enriched microdomains which provide a favorable environment for membrane fusion , DC-SIGN-dependent SMase activation may be of general importance for modes and efficiency of pathogen uptake into DCs , and their routing to specific compartments , but also for modulating T cell responses .
Their interaction with myeloid dendritic cells ( DCs ) is believed to be central to the understanding of immunomodulation by viruses also including measles virus [1] , [2] , [3] , [4] . In the hematopoetic system , MV replication segregates with expression of CD150 , an Ig-like domain containing molecule , expression of which is usually low on lymphocytes and immature DCs , where it is upregulated on activation by TLR ligation or inflammatory stimuli [5] , [6] , [7] . CD150 is sufficient to support MV binding , fusion and cell entry in vitro and in vivo [8] . In DCs , in common with other viruses , DC-SIGN enhances entry , and this is important in viral spread to secondary lymphatics and transmission to T cells , but also for modulation of DC viability and function and thereby determine T cell activation in quantitative and qualitative terms [9] , [10] , [11] , [12] , [13] . DC-SIGN is a C-type lectin receptor which functions to regulate adhesion by interaction with integrins , but also , as a pattern recognition receptor ( PRR ) , to recognize carbohydrate structures on pathogens , thereby targeting them for endocytic uptake , processing and subsequent presentation [13] , [14] , [15] . It is enriched in nanoclusters at the leading edge on the DC plasma membrane , where ligands are acquired and then transported rearward to mid-lamellar sites for subsequent endocytosis [16] , [17] , [18] , [19] . On differential recognition of carbohydrates , DC-SIGN signals and its signalosome involves a scaffolding complex containing lymphocyte specific protein 1 ( LSP1 ) , kinase suppressor of Ras1 ( KSR1 ) and connector enhancer of ksr ( CNK ) as required for Raf-1 recruitment [20] . DC-SIGN-induced Raf-1 kinase activation was linked to modulation of TLR signaling at the level of NF-κB activation by promoting activation of its p65 subunit and thereby increasing initiation and duration of cytokine gene transcription [11] , [21] , [22] . By unknown mechanisms , viruses can escape lysosomal degradation thereby avoiding immune surveillance , and rather exploit DC-SIGN to gain entry to DCs [12] , [13] , [23] , [24] . Similarly , how DC-SIGN enhances viral uptake for infection ( referred to as ‚cis-infection' ) or internalization into and storage in non-lysosomal compartments for subsequent transfer to conjugating T cells ( referred to as ’trans-infection' ) is mechanistically not well understood , however , co-segregation or concentration of virions or their respective low level expressed uptake receptors has been proposed to contribute [1] , [25] . Local enrichment of ceramides is known to promote biophysical alterations of the membrane which can support fusion and negative curvature , but also segregation of membrane receptors and signalosome components thereby regulating a large variety of cellular processes [26] , [27] , [28] , [29] . In response to a variety of stimuli also including ligation of TNF-R family members and Fcγ receptors , neutral and acid sphingomyelinases ( SMases: NSM or ASM ) are activated to generate membrane ceramides , which , on ASM activation , cause formation of outer membrane ceramide–enriched platforms [30] , [31] , [32] . In contrast to NSM , ASM is compartimentalized in non-lysosomal vesicles from where , on activation , it is recruited to the cell surface to catalyze breakdown of sphingomyelin ( SM ) into phospho-choline and ceramide . Ceramides act to convey and modulate receptor signaling by segregating or concentrating signaling components and this also includes KSR1 , which catalyzes c-Raf-1 activation thereby enhancing its activity towards ERK1/2 [33] , [34] , [35] , [36] , [37] . As they promote receptor clustering and formation of membrane invaginations , ceramides can enhance endocytic uptake of viruses entering their target cells by this route [38] , [39] . Ceramides can , however , also enhance intracellular vesicle fusion [40] . Thus , regulation of lateral segregation and concentration of receptors by ceramide-enriched platforms ( or interference with that , as evidence for HIV [40] and of membrane fusion may be key to understanding the role of ceramides in viral uptake . We now show that DC-SIGN ligation causes transient activation of both NSM and ASM within 3 to 15 mins . and this is accompanied by membrane ceramide accumulation . DC-SIGN signaling accounting for c-Raf-1 and ERK activation is abrogated on pharmacological interference with ASM activation indicating that activation of this enzyme is essential in this process . SMase activation also accounted for enhancement of MV uptake into DCs and this was promoted by DC-SIGN dependent surface recruitment of the MV binding and uptake receptor CD150 , which was surface recruited from an intracellular storage compartment containing ASM . These data , for the first time , describe and mechanistically link regulated membrane lipid dynamics to modulation of PRR-dependent uptake into DCs , which may be relevant for viral and general entry processes into these cells .
Membrane ceramide platforms segregate receptors and signalosomes both of which can affect viral entry . DC-SIGN may act to trap or concentrate virions ( also including MV ) for receptor interaction , and we thus analysed whether MV interaction with this molecule promoted membrane ceramide accumulation on DCs by employing an assay based on immunodetection of an a-ceramide antibody bound to intact cells ( spot assay ) . On MV exposure , DCs responded by an about twofold increase in extrafacial ceramides which peaked at 15 mins and subsequently returned to baseline levels ( Fig . 1A , left panel ) . Ceramide accumulation occurred DC-SIGN dependently , since it was efficiently abrogated upon pre-exposure of DCs with a DC-SIGN-specific antibody ( AZ-D1 ) or EGTA , which prevents Ca2+-dependent DC-SIGN ligand binding ( Fig . 1A , left panel and middle panel ) . In contrast , antibodies blocking MV interaction with its entry receptor , CD150 , did not prevent , yet even slightly enhanced MV ceramide induction ( Fig . 1A , right panel ) . Similar to MV , the DC-SIGN binding antibody AZ-D1 increased membrane ceramide display when crosslinked , while this was not observed with the antibody alone nor an isotype control antibody alone ( Fig . 1B , left panel; for further experiments , DC-SIGN-specific antibodies were thus used crosslinked ) . Ceramide production in response to DC-SIGN ligation was sensitive to the ASM inhibitor amitriptyline indicating that activation of this enzyme was involved ( Fig . 1B , right panel ) . To assess activation of SMases directly , we determined their activity after exposure of DCs to mannan , a well-defined DC-SIGN agonist . In line with amitriptyline sensitivity of ceramide generation , ASM surface display raised about 1 . 8 fold almost immediately following mannan addition , and this was paralleled by a rise in extrafacial ceramides both of which were EGTA sensitive ( Fig . 1C , and not shown ) . Mannan-dependent activation of ASM was further confirmed using a commercial detection assay ( Fig . 1D , left panel ) which essentially mirrored kinetics and magnitude of the response determined by spot assays . Using the same experimental approach , a rapid , very efficient activation of NSM was also measured ( about 5-fold ) which peaked after 3 mins and then vanished , and this was entirely prevented upon RNAi mediated silencing of NSM expression ( Fig . 1D , right panel ) . Importantly , ASM activation also occurred on MV exposure of DCs in a dose dependent manner , and this relied on the presence of the MV glycoproteins because it was not observed when a recombinant MV expressing the VSV G protein instead was used ( Fig . 1E ) . Altogether these findings indicate that ligation of DC-SIGN by antibodies , mannan or MV promotes rapid activation of SMases , and ASM-dependent ceramide accumulation in the outer membrane leaflet . DC-SIGN signaling includes activation of c-Raf-1 and ERK [11] , [22] , [41] . To asses if DC-SIGN signaling involves SMase activation , cells were pre-exposed to amitriptyline which per se did not affect DC viability ( not shown ) or LPS-induced upregulation of CD86 or CD83 after 24 hrs ( Fig . 2A ) . PMA/ionomycin-dependent activation of c-Raf-1 or ERK as determined by detection of p-c-Raf-1 or pERK within 30 mins was unaffected on pre-exposure of DCs to amitriptyline ( Fig . 2B , upper panels ) . In line with earlier findings obtained upon ManLam- , antibody or HIV exposure [11] , [22] , [41] , DC-SIGN ligation by crosslinked AZ-D1 caused c-Raf-1 and ERK activation ( Fig . 2B , bottom panels ) . In contrast to that induced by PMA/ionomycin , however , α-DC-SIGN induced c-Raf-1 and ERK phosphorylation was sensitive to amitriptyline indicating that DC-SIGN signaling involves ASM activation ( Fig . 2B , bottom panels ) . DC-SIGN signaling does not confer NF-κB activation , yet apparently modulates that induced upon TLR ligation [11] . As revealed both by an ELISA kit based detection system or nuclear translocation of p65 , TLR4 ligation by LPS indeed promoted NF-κB activation after 60 mins . ( Fig . 3A ) . Mannan exposure did , however , reduce magnitude of NF-κB activation measured by either method indicating that DC-SIGN ligation interferes with TLR4 signaling ( Fig . 3A and B ) . Interestingly , however , ablation of DC-SIGN signaling by amitriptyline or an NSM inhibitor , GW4896 , apparently enhanced LPS-induced NF-κB activation as reflected by efficient nuclear accumulation of p65 , and enhanced levels of activation as determined by ELISA ( Fig . 3B and C ) . Overall , these findings support the interpretation that DC-SIGN membrane signaling essentially involves ceramide generation , and may act to dampen rather than to enhance TLR-dependent NF-κB activation and thereby production of pro-inflammatory cytokines . To assess the overall impact of SMase activation on MV uptake into DCs , these were exposed to amitriptyline prior to infection . Thereby , intracellular accumulation levels of MV N protein 12 hrs following infection were reduced by about 50% ( Fig . 4A , left panel ) indicating that SMase activation is beneficial for viral DC infection . Consistent with this hypothesis , GFP levels produced from a tagged MV wild-type recombinant virus only on replication ( IC323-eGFP ) were reduced by about 50% upon siRNA mediated ablation of ASM expression ( Fig . 4A , right panel ) . Pre-exposure to amitriptyline did not affect MV binding to DCs as determined by detection of MV F protein positive cells after 2 hrs at 4°C ( Fig . 4B , left panel and graph ) , yet efficiently reduced intracellular GFP-accumulation after 24 hrs ( with FIP added following infection to prevent MV spread ) ( Fig . 4B , right panel and graph ) . Amitriptyline did , however , not affect uptake or replication of a recombinant attenuated MV strain into T or epithelial cells ( Fig . 4C ) ( which cannot be infected with IC323-eGFP due to the absence of CD150 ) indicating that SMase activation or ceramide elevation alone do not necessarily enhance MV infection as occurring in DCs . MV binding to DC-SIGN causes SMase activation on DCs which , in turn , promotes MV infection . We thus analysed whether this might relate to DC-SIGN-dependent alterations of membrane distribution of CD150 . Because expression levels of this protein are low on DCs ( [9] , [10] and see below ) , we initially analysed the impact of DC-SIGN ligation on CD150 expression in Raji cells expressing high levels of endogenous CD150 , and on stable transfection , DC-SIGN ( Raji-DC-SIGN ) ( Fig . 5A , upper and second row ) . In untreated Raji-DC-SIGN cells , both molecules revealed a punctate expression pattern overall covering the cell surface ( Fig . 5 ) . DC-SIGN-ligation caused enhanced co-clustering of DC-SIGN and CD150 in large platforms ( after 5 mins , Fig . 5A , third row ) , which subsequently protruded from the cell surface ( after 10 mins , Fig . 5A , fourth row ) , revealing that DC-SIGN signaling indeed promotes redistribution of CD150 . Suggesting a role of SMase activation in this process , DC-SIGN enriched protrusions emanating from the cell surface ( prominent formation of which may relate to very low phospholipid scramblase levels of Raji cells [42] ) were locally also enriched for ceramides ( Fig . 5A , bottom row ) . As reported erlier , efficient MV uptake into DCs relies on both DC-SIGN ( for trapping ) and CD150 ( particularly for fusion ) [9] , [10] . In line with earlier findings , interference of DC-SIGN interaction by mannan , an antibody or EGTA reduced MV binding to DCs by approximately 50% ( [9] and Fig . S1 ) , and blocking of either DC-SIGN and CD150 alone or in combination strongly interferes with MV uptake and replication ( Fig . 5B ) . On immature DCs , expression of endogenous CD150 was generally low as reported [9] , [10] , yet increased surface display ligation within 15 mins after DC-SIGN was detectable by flow cytometry ( Fig . 5C ) . To follow CD150 redistribution in response to DC-SIGN ligation in DCs in detail , we generated C-terminally HA-tagged CD150 which , when overexpressed in HeLa cells , did not differ with regard to subcellular distribution , surface expression level , glycosylation and DRM association from the unmodified protein ( not shown ) . When transfected into DCs ( CD150-HA-DCs ) , transgenic CD150 , similar as the endogenous in DCs , mainly localized to intracellular compartments , while DC-SIGN , expectedly appeared in clusters at the cell surface [17] , [19] , [43] with little detectable overlap of both molecules ( Fig . 6A , upper panels and right graph ) . Mirroring our findings in Raji-DC-SIGN cells , DC-SIGN ligation by mannan promoted both surface translocation , clustering of CD150-HA and co-clustering with DC-SIGN in DCs peaking between 10 and 15 mins after exposure ( Fig . 6A , middle and bottom panels , and right graph ) indicating that DC-SIGN-signaling indeed causes clustering and surface recruitment of this molecule . The latter was further confirmed by using a surface biotinylation/streptavidin precipitation approach with CD150-HA-DCs where exposure to mannan substantially increased the amounts of CD150 pulled down by streptavidin-beads ( Fig . 6B , right lanes ) . The amounts of cytosolic CD150 were also slightly elevated on mannan exposure indicating that the CD150 storage compartment might reveal differential sensitivity to detergent lysis on DC-SIGN signaling ( Fig . 6B , left lanes ) . DC-SIGN-dependent CD150-HA surface recruitment involved transport and membrane fusion of exocytic vesicles in a SNARE-dependent manner as indicated by its sensitivity to N-ethylmaleimide ( Fig . 6C ) , and , also ASM activation as it was essentially abolished on pre-exposure to amitriptyline ( Fig . 6D ) . Importantly , ASM inhibition also interfered with MV-induced CD150 surface recruitment as determined by WGA/CD150 co-segregation levels ( Fig . 7 ) . To gain insight into the nature of the translocating CD150 compartment , we performed marker analyses in DCs transfected to overexpress CD150-HA . Expectedly , CD150-HA was co-detected with the trans-Golgi marker GM130 ( Fig . 8A , upper row ) . CD150-HA does not accumulate in the MIIC loading compartment , as there is little co-segregation with oligomerized MHCII ( detected by the FN1 antibody ) ( Fig . 8A , second row ) , yet rather in a Lamp-1 positive compartment that also contained ASM ( Fig . 8A , third row ) . CD150 substantially colocalized with ASM in intracellular compartments in unstimulated DCs ( Fig . 8B , upper row , first three panels ) , and both were redistributed to the cell surface on DC-SIGN ligation ( Fig . 8B bottom row ) . Confirming co-segration of both molecules , the degree of colocalization remained identical prior to and after surface recruitment ( an example for unstimulated DCs is shown in the pseudo-coloured scatter plot in Fig . 8B , upper row , right panel ) . These data indicate that CD150 shares an intracytoplasmic lysosomal compartment with ASM from which it is recruited to and displayed at the cell surface on DC-SIGN-mediated ASM activation to enhance MV entry into DCs .
As professional antigen-presenting cells , DCs operate at the interface of innate and adaptive immunity . Their location in the mucosa coins them first cells encountering invading pathogens also including viruses which , occasionally , exploit these cells as Trojan horses for transfer to secondary lymphoid tissues . They display a plethora of pattern recognition receptors ( PRRs ) , and amongst those , the C-type lectin receptor DC-SIGN containing a mannose-binding domain has received particular attention with regard to its extraordinary pathogen–recognition capability which involves a broad panel of microorganisms and viruses also including HIV and MV . This interaction , especially for viruses , does not promote sorting into degradative compartments , but rather , DC-SIGN mediated enhancement of DC cis-infection or trans-infection of T cells have been described , and these involve enhanced access to the DC cytoplasm ( as for MV [9] , [10] , or surface trapping of virions ( as for HIV and CMV [44] , [45] , [46] ) whereby they are concentrated and stored in invaginated compartments with plasma membrane continuity for subsequent transfer to conjugating target cells . In addition to these , signaling properties have been ascribed to DC-SIGN , which , though not able to initiate signaling pathways leading to regulated gene expression , modulates signals evoked after TLR ligation to stimulate NF-kB activation , and Rho-GTPase dependent activation of Raf-1 was found to be central [11] , [21] , [22] . SMase activation and subsequent ceramide accumulation were directly linked to c-Raf-1 and ERK activation in response to DC-SIGN ligation ( Fig . 2B ) and this in line with previous observations made in other cell types . Thus , the ability of KSR1 , identified as essential component of the DC-SIGN signalosome [21] to activate Raf-1 and to enhance its activity towards ERK1/2 requires recruitment and specific binding to ceramide-enriched platforms in Cos-7 and intestinal epithelial cells [33] , [34] , [35] , [36] , [37] . In addition , Raf-1 activation in response to ceramide activation and physical interaction of c-Raf-1 with ceramides in response to IL-1β in mesangial cells were described [47] , [48] . Though our findings with regard to the importance of DC-SIGN signal initiation are in agreement with these observations , those elaborating on the role of DC-SIGN modulation of TLR signaling are obviously not . As shown by two independent methods , we were unable to confirm an enhancing effect of DC-SIGN signaling on TLR-induced NF-κB activation [11] using either antibodies ( the ability of which to promote c-Raf or ERK activation has been documented by us ( Fig . 2B ) and others [11] , [22] , [41] or mannan at any time point analysed ( Fig . 3 ) . The reasons for this discrepancy remain unknown , yet are not likely to include donor dependent variations , or any obvious technical problems since the reagents used gave reliably the expected positive results in control experiments ( e . g . NκB DNA binding and nuclear translocation in response to TLR signaling alone ( Fig . 3 ) or the ability of antibodies or mannan to promote DC-SIGN activation ( Figs . 2B ) ) . Because abrogation of SMase activation rather promoted LPS-induced NκB activation , our data suggest that DC-SIGN signaling may weaken rather than enhance TLR4 signaling ( Fig . 3 A , B ) , thereby downregulating inflammatory responses . This hypothesis is in line with ASM dependent downregulation of LPS-induced TNF-α production in macrophages [49] , and suggest that SMase activation in response to a PRR such as DC-SIGN might be an efficient regulator of systemic inflammatory responses . SMase induction and subsequent membrane ceramide accumulation on DC-SIGN ligation reveals a kinetics typically observed for other receptors activating this pathway as well [27] . Both NSM and ASM are activated by DC-SIGN ( Fig . 1C and D ) , and interestingly , this , though DC-SIGN-independently , also occurred in T cells exposed to MV [50] or on ligation of TNF-R or IL-1β-R [51] , [52] . As evidenced from their kinetics of induction and the ability of both amitriptyline and GW4896 to interfere with DC-SIGN-dependent NF-κB modulation ( Fig . 1 and 3B ) , NSM and ASM might be induced sequentially as reported by us in T cells previously [50] which , was , however , not further addressed in the present study . It is , however , tempting to speculate that NSM activation might promote redistribution and plasma membrane fusion of a lysosomal Lamp1+ compartment containing both ASM and CD150 ( Fig . 8 ) in an exocytic process which , due to its NEM sensitivity , involves as yet unidentified SNAREs ( Fig . 6C ) . Their activation by an established DC-SIGN agonist ( and sensitivity to EGTA inhibition ) and cross-linked specific antibodies clearly links SMases and ceramides to DC-SIGN ligation ( Fig . 1 ) . MV is known to interact with other signaling surface receptors on DCs which , theoretically , could also elicit these responses . CD46 is unlikely to be involved , since wild-type MVs as that used in this study do not interact with this receptor [53] . MV binding to CD150 , which is expressed to low levels on DCs ( Fig . 5B and [9] , [10] ) , is not known to depend on divalent cations ( neutralisation of which abrogate ceramide induction by MV ( Fig 1A ) ) nor do antibodies directed against this molecule prevent their MV-induced activation ( Fig . 1A ) indicating that CD150 also does not contribute . MV is known to act as TLR2 , but not TLR4 agonist on monocytes , and LPS or PamCSK dependent upregulation of CD150 has been proposed to support MV infection of these cells [54] , [55] . Though LPS signaling in various cell types also including DCs can involve ASM , this needs , however , tight regulation as it results in massive DC apoptosis if high doses of LPS are applied [56] or , on standard dose LPS application ( 100 ng/ml ) , if ceramide turnover is prevented [57] , [58] . Ceramide generation , however , is insufficient to promote most responses to LPS including NF-κB activation [59] , and in line with these observations , DC-SIGN ligation alone does not activate NF-κB [11] and SMase inhibition by amitriptyline did not interfere with LPS-driven upregulation of CD83 and CD86 in our system as well ( Fig . 2A ) . Ligation of TLR2 by MV , not yet directly shown to occur on DCs so far , is unlikely to contribute to early ASM induction which is EGTA sensitive ( Fig . 1 ) , may , however , contribute to late upregulation of CD150 ( as measured in monocytes late ( 12 or 24 hrs ) after TLR2 stimulation [54] , [55] ) coincided with that of proinflammatory cytokines and may thus occur secondary to IL-1R ligation by IL-1β [7] , a well established SMase activator [51] , [60] . SMase activation as induced upon DC-SIGN ligation is beneficial for MV uptake into DCs ( Fig . 4 ) , and this may also be supported by biophysical properties of ceramide-enriched domains such as promotion of negative membrane curvature which can favor receptor mediated endocytosis [28] , [39] , or their gel-like phase supporting membrane fusion in general [27] , [40] . As these also apply to SMase activators other than DC-SIGN , recruitment and segregation of specific receptors into these platforms may be decisive for their respective role in pathogen uptake . For MV , DC-SIGN clearly enhances DC cis-infection [9] , [10] , and this is greatly aided by rapid surface recruitment of CD150 which co-clusters with DC-SIGN . Thereby , SMase activation promotes surface translocation and compartimentalization of a receptor promoting MV fusion ( Fig . 6 and 7 ) . Interestingly , MV binding to DCs is not strengthened on SMase activation ( Fig . 4B ) indicating that major trapping of MV relies on DC-SIGN . This corroborates our earlier observations that DC maturation ( known to upregulate CD150 [9] , [10] ) does not increase MV binding to these cells [61] . In line with DC-SIGN acting as a major trapping factor for MV it is not surprising that pre-ligation of this molecule by crosslinked DC-SIGN antibodies or mannan blocks rather enhances MV uptake into these cells because they render DC-SIGN inaccessible to MV binding ( Fig . 5B ) . Surface recruitment and compartimentalization of CD150 may act in concert with the fusion promoting membrane environment provided by SMase activation [40] to support viral entry . If entry receptors are , however , highly abundant ( such as CD46 on Molt-4 or A549 cells ) , SMase activation may not have substantial effects on viral uptake ( Fig . 4C ) . For HIV , however , ceramide induction elevation even acts antivirally , since it shifted the virus into endocytic , degradative uptake route in phagocytic cells , and prevented lateral co-segregation of CD4 and CXCR4 and thereby membrane fusion [62] , [63] , [64] . In DCs , where HIV is not routed into a degradative compartment on DC-SIGN interaction , ceramide interference with receptor co-segregation would be consistent with compartimentalization and storage of virus for trans-infection of T cells which is efficiently prevented on ablation of DC-SIGN interaction . It will thus be interesting to determine if segregation of receptors promoting uptake and routing of pathogens in response to SMase activation in DCs by DC-SIGN and other stimuli follows a common or counter-regulatory role which may essentially decide the outcome of the interaction of a given pathogen with these cells . Most interestingly , CD150 has recently been identified as microbial sensor on macrophages essentially promoting phagosome routing and recruitment of the cellular machinery for bacterial killing [65] . If this would apply to DCs , upregulation of CD150 in an SMase dependent manner might directly impact on routing of pathogens other than MV in DCs .
Primary human cells were obtained from the Department of Transfusion Medicine , University of Würzburg , and analysed anonymously . All experiments involving human material were conducted according to the principles expressed in the Declaration of Helsinki and ethically approved by the Ethical Committee of the Medical Faculty of the University of Würzburg . CD150-HA was generated by PCR mediated HA-tag insertion at the C-terminus of CD150 full-length cDNA and cloning under CMV promoter in pCG vector . 15 µg of plasmid were nucleofected into 2×106 DCs following the manufacturer's protocol ( Amaxa ) . For silencing of NSM2 , DCs were transfected with 100 nM siRNA targeting human SMPD3 ( NSM2 ) specific [68] or , for control , a scrambled siRNA ( Eurogentec , Belgium ) using transfection reagent DF4 ( Dharmacon , Lafayette , CO ) , according to the manufacturer's protocol . Before cells were recruited into the respective experiments ( after 72 hrs ) , aliquots were harvested for RNA extraction ( Qiagen , RNAeasy Kit ) and subsequent RT-PCR analyses . Forward 5′-GCCCTTATCTTTCCATGCTACTG-3′ and reverse 5′-ACAGAGGCTGTCCTCTTAATGCT-3′ primers were used for specific SMPD3 amplification .
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Dendritic cells ( DCs ) bear receptors specialized on recognition of patterns specific to pathogens ( such as carbohydrates ) , which can either promote functional activation of these cells ( such as TLRs ) , which renders them capable of efficiently presenting antigens to T cells , or , as DC-SIGN , endocytic uptake as essential for loading MHC molecules . Viruses such as HIV and measles virus ( MV ) exploit DC-SIGN for both their uptake into DCs and modulation of TLR signaling , yet how this is mechanistically exerted is poorly understood . We now show that DC-SIGN activates sphingomyelinases ( SMases ) which convert their sphingomyelin substrate into ceramides , thereby catalysing the formation of membrane platforms able to recruit and concentrate receptors and associated signaling components . We found DC-SIGN-dependent SMase activation as essential for DC-SIGN and thereby modulation of TLR signaling , but also for enhancement of MV uptake . This is mediated by a fast , transient recruitment of its entry receptor , CD150 , from an intracellular storage compartment to the cell surface where it co-clusters in ceramide enriched platforms with DC-SIGN . The ability to segregate viral receptors into ( or exclude them from ) membrane microdomains , which , based on their biophysical properties , facilitate membrane fusion , proposes DC-SIGN-mediated SMAse activation as a central regulator of pathogen uptake into DCs .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"immunology/cellular",
"microbiology",
"and",
"pathogenesis",
"immunology/antigen",
"processing",
"and",
"recognition",
"cell",
"biology/leukocyte",
"signaling",
"and",
"gene",
"expression",
"cell",
"biology/cell",
"signaling",
"immunology/immunomodulation",
"cell",
"biology/membranes",
"and",
"sorting",
"infectious",
"diseases/viral",
"infections",
"virology/host",
"invasion",
"and",
"cell",
"entry",
"immunology/leukocyte",
"activation"
] |
2011
|
DC-SIGN Mediated Sphingomyelinase-Activation and Ceramide Generation Is Essential for Enhancement of Viral Uptake in Dendritic Cells
|
Tetrahydrobiopterin ( BH4 ) is a co-factor required for catalytic activity of nitric oxide synthase ( NOS ) and amino acid-monooxygenases , including phenylalanine hydroxylase . BH4 is unstable: during oxidative stress it is non-enzymatically oxidized to dihydrobiopterin ( BH2 ) , which inhibits NOS . Depending on BH4 availability , NOS oscillates between NO synthase and NADPH oxidase: as the BH4/BH2 ratio decreases , NO production falls and is replaced by superoxide . In African children and Asian adults with severe malaria , NO bioavailability decreases and plasma phenylalanine increases , together suggesting possible BH4 deficiency . The primary three biopterin metabolites ( BH4 , BH2 and B0 [biopterin] ) and their association with disease severity have not been assessed in falciparum malaria . We measured pterin metabolites in urine of adults with severe falciparum malaria ( SM; n=12 ) , moderately-severe malaria ( MSM , n=17 ) , severe sepsis ( SS; n=5 ) and healthy subjects ( HC; n=20 ) as controls . In SM , urinary BH4 was decreased ( median 0 . 16 ¼mol/mmol creatinine ) compared to MSM ( median 0 . 27 ) , SS ( median 0 . 54 ) , and HC ( median 0 . 34 ) ]; p<0 . 001 . Conversely , BH2 was increased in SM ( median 0 . 91 ¼mol/mmol creatinine ) , compared to MSM ( median 0 . 67 ) , SS ( median 0 . 39 ) , and HC ( median 0 . 52 ) ; p<0 . 001 , suggesting increased oxidative stress and insufficient recycling of BH2 back to BH4 in severe malaria . Overall , the median BH4/BH2 ratio was lowest in SM [0 . 18 ( IQR: 0 . 04-0 . 32 ) ] compared to MSM ( 0 . 45 , IQR 0 . 27-61 ) , SS ( 1 . 03; IQR 0 . 54-2 . 38 ) and controls ( 0 . 66; IQR 0 . 43-1 . 07 ) ; p<0 . 001 . In malaria , a lower BH4/BH2 ratio correlated with decreased microvascular reactivity ( r=0 . 41; p=0 . 03 ) and increased ICAM-1 ( r=-0 . 52; p=0 . 005 ) . Decreased BH4 and increased BH2 in severe malaria ( but not in severe sepsis ) uncouples NOS , leading to impaired NO bioavailability and potentially increased oxidative stress . Adjunctive therapy to regenerate BH4 may have a role in improving NO bioavailability and microvascular perfusion in severe falciparum malaria .
Malaria remains the most important parasitic infection in humans , causing an estimated 207 million cases and 627 , 000 deaths in 2010 [1 , 2] . Mortality from severe Plasmodium falciparum malaria has decreased with use of intravenous artesunate , but case fatality rates remain at 8% and 15% for African children and Asian adults [3 , 4] . Improved understanding of the pathogenesis of severe falciparum malaria may allow identification of new targets for adjunctive therapy . Decreased nitric oxide ( NO ) bioavailability is associated with increased disease severity in African children as well as Asian adults and children with falciparum malaria [5–7] , but the full reasons for this observation are not known . Mechanisms identified to date include low levels of L-arginine [the substrate for NO synthase ( NOS ) ] [6 , 8] , impaired mononuclear cell NOS2 expression [5] , inhibition of NOS by ADMA [9 , 10] , and quenching of NO by increased plasma cell-free hemoglobin [11] . In Asian adults with moderately severe falciparum malaria ( MSM ) , L-arginine infusion increased endothelial NO and pulmonary NO bioavailability [6] . However , a pilot trial of low-dose L-arginine infusion in adult severe falciparum malaria ( SM ) did not result in improvement in endothelial NO bioavailability or lactate clearance [12] . While greater L-arginine clearance in severe malaria suggest that higher doses may be more effective [12] , additional mechanisms beyond L-arginine deficiency are likely to be involved . Tetrahydrobiopterin ( BH4 ) is an obligate co-factor for NO synthesis by NOS [13 , 14] . BH4 stabilizes the homodimeric NOS enzyme and participates in L-arginine oxidation and heme-iron reduction for NO production . NOS lacking BH4 remains catalytically active , transferring electrons from NADPH to dioxygen to produce superoxide [14 , 15] . Conversion of NOS catalysis from NO synthesis to superoxide production under conditions of low or absent BH4 is termed “uncoupling , ” meaning that NADPH consumption and oxygen activation are no longer “coupled” to BH4-dependent L-arginine oxygenation [14 , 15] . In an oxidizing environment , NOS uncoupling may be related to the instability of BH4 because this reduced pterin spontaneously oxidizes to quininoid-BH2 , which rapidly rearranges to the stable metabolite 7 , 8-dihydrobiopterin ( BH2 ) that is inactive as a cofactor for NO synthesis . BH2 can be reduced back to BH4 via a tetrahydrofolate-dependent salvage pathway [16] . However if BH2 levels rise at the expense of BH4 oxidation , BH2 competes with BH4 at the NOS active site leading to NOS uncoupling and superoxide production . In cardiovascular disease , an increased BH4/BH2 ratio ( as opposed to the BH4 concentration alone ) has been found to be the best correlate for endothelial cell-dependent NO synthesis [14 , 15] . BH4 is also a co-factor for the enzyme phenylalanine hydroxylase that converts phenylalanine to tyrosine in the liver . We have found in both African children with cerebral malaria ( CM ) and Asian adults with SM that plasma phenylalanine levels are markedly increased [17] . We hypothesized that in SM the systemic level of BH4 , relative to the oxidized biopterin species ( BH2 + B0 ) , would be decreased . This could explain depression in both phenylalanine hydroxylase activity and NOS functionality in severe malaria . Biopterin oxidation states in plasma and urine ( which reflect systemic levels ) have not been measured in malaria . Therefore we undertook measurements of urinary BH4 , BH2 and B0 in Indonesian adults with SM and MSM and compared these to levels in healthy controls and a group presenting with severe sepsis . We hypothesized that ( a ) BH4 levels and BH4/BH2 ratios would be decreased , and BH2 increased in proportion to malaria disease severity , and ( b ) decreased BH4/BH2 ratios would be associated with increased endothelial activation and impaired NO-dependent microvascular reactivity .
The clinical features of these subjects have previously been described [18] . We measured urinary pterin metabolites in their various oxidized states [ ( including biopterin ( B0 ) , 7 , 8 dihydrobiopterin ( BH2 ) , tetrahydrobiopterin ( BH4 ) , dihydroneopterin ( NH2 ) and neopterin ( N0 ) ] levels in 12 adults with severe malaria ( SM ) and 17 with moderately severe malaria ( MSM ) , with 20 healthy adults ( HCs ) and 5 with severe sepsis ( SS ) as controls . In SM patients , 5 had single organ dysfunction ( 4 with cerebral malaria and 1 with acute renal failure ) , while the remaining 7 had two or more severity criteria . All SM and MSM patients received intravenous artesunate . In SS patients , two had pneumonia and gastroenteritis , and one each had pneumonia , gastroenteritis , and meningitis . There were 4 deaths in the SM group , and none in the MSM and SS patients . The baseline demographic details , clinical features , hematological and biochemical results of the patients are summarized in Table 1 . BH4 was decreased in patients with SM ( median 0 . 16 μmol/mmol creatinine; IQR 0 . 04–0 . 30 ) compared to those with MSM ( 0 . 27 , IQR 0 . 19–0 . 41 ) , SS ( 0 . 54; IQR 0 . 48–0 . 94 ) , and controls ( 0 . 34; IQR 0 . 27–0 . 46 ) ; Kruskal-Wallis p<0 . 001 ( Table 2 , Fig . 1A ) . In contrast , BH2 was increased in SM ( median 0 . 91 μmol/mmol creatinine; IQR 0 . 62–1 . 35 ) compared to MSM ( 0 . 67; IQR 0 . 52–0 . 76 ) , SS ( 0 . 39; IQR 0 . 38–0 . 88 ) and HCs ( 0 . 52: IQR 0 . 43–0 . 69 ) ; Kruskal-Wallis p<0 . 001 ( Table 2 , Fig . 1B ) . The BH4/BH2 ratio was also decreased in patients with SM ( median 0 . 17; IQR 0 . 04–0 . 32 ) compared to those with MSM ( 0 . 45 , IQR 0 . 27–61 ) , SS ( 1 . 03; IQR 0 . 54–2 . 38 ) and controls ( 0 . 66; IQR 0 . 43–1 . 07 ) ; Kruskal-Wallis p<0 . 001 ( Table 2 , Fig . 1C ) . Conversely NH2 and N0 levels were increased in SM compared to MSM , SS , and HCs ( p<0 . 001 ) ( Table 2 ) , but there were no significant differences in the total biopterin ( BH4+BH2+B0 ) levels among groups ( p = 0 . 1 ) ( Table 2 , Fig . 1D ) . The ratio of reduced:oxidized neopterin ( NH2:N0 ) was 4 . 4 in healthy controls compared to 2 . 0 in severe malaria ( p = 0 . 002 , Table 2 ) . In the 29 patients with malaria , an increased BH4/BH2 ratio was associated with severe disease ( p = 0 . 03 ) , however no significant associations were found for BH4 , BH2 , B0 , total biopterin , NH2 , N0 and total neopterin . The risk of death in malaria was not associated with levels of any of the pterin metabolites . There was no association between serum creatinine and urinary BH4 , BH2 , N0 and NH2 in patients with malaria and in the groups with severe or uncomplicated disease . On controlling for blood creatinine , there was still a significant difference in urinary BH2 ( p = 0 . 011 ) and BH4/BH2 ( p = 0 . 04 ) levels but not BH4 between the groups . Peripheral parasitemia was correlated with increasing BH2 ( r = 0 . 46 , p = 0 . 01 ) and N0 ( r = 0 . 50 , p = 0 . 006 ) levels , and parasite biomass ( estimated using plasma HRP2 ) was positively correlated with BH2 ( r = 0 . 44 , p = 0 . 02 ) , and inversely with the BH4/BH2 ratio ( r = -0 . 41 , p = 0 . 03 ) in all malaria patients but not after controlling for malarial disease severity . Increasing venous lactate was associated with higher BH2 levels ( r = 0 . 48 , p = 0 . 008 ) and a lower BH4/BH2 ratio ( r = -0 . 43 , p = 0 . 01 ) in all malaria patients but not after controlling for severity of disease . BH4 , BH2 , BH4/BH2 ratio , microvascular reactivity , and endothelial activation . Similar to our previous published results [18] , microvascular reactivity and endothelial function were reduced in SM compared to MSM and HCs ( Table 2 ) . In all malaria patients , higher microvascular reactivity was associated with an increased BH4/BH2 ratio ( r = 0 . 41 , p = 0 . 03 ) and lower BH2 levels ( r = -0 . 42 , p = 0 . 024 ) , with no association found for the other biopterin metabolites . The associations with the BH4/BH2 ratio and BH2 remained significant after controlling for disease severity ( partial correlation coefficient = 0 . 34 , p = 0 . 04 and partial correlation coefficient = -0 . 38 , p = 0 . 04 , respectively ) . Impaired endothelial function was also associated with increasing BH2 in all malaria patients ( r = -0 . 42 , p = 0 . 03 ) and those with severe malaria ( r = -0 . 48 , p = 0 . 04 ) but not in the MSM group alone . The association between endothelial function and BH2 remained significant after controlling for disease severity ( partial correlation coefficient = -0 . 37 , p = 0 . 04 ) . Evaluation of markers of endothelial activation showed that ICAM-1 levels were positively associated with BH2 ( r = 0 . 4 , p = 0 . 02 ) and inversely associated with BH4 ( r = -0 . 38 , p = 0 . 04 ) and the BH4/BH2 ratio ( r = -0 . 52 , p = 0 . 003 ) in all malaria patients , but only with BH4/BH2 ( r = -0 . 63 , p = 0 . 03 ) in the SM group . The association between ICAM-1 with BH4 ( partial correlation coefficient = -0 . 38 , p = 0 . 035 ) and the BH4/BH2 ratio ( partial correlation coefficient = -0 . 40 , p = 0 . 03 ) remained significant after adjustment for malaria severity . The level of angiopoietin-2 , another marker of malaria severity , was associated with increasing BH2 ( r = 0 . 44 , p = 0 . 02 ) , but was not significant after adjusting for disease severity . Plasma phenylalanine levels were significantly increased in SM ( median 176 μmol/L , IQR 85–250 ) compared to MSM ( 101μmol/L; IQR 84–110 ) , SS ( 114μmol/L; IQR 112–332 ) , and HCs ( 54μmol/L; IQR 51–58 ) ; Kruskal-Wallis p<0 . 001 ( Table 2 ) . Among all patients with malaria , plasma phenylalanine levels were inversely related to the BH4/BH2 ratio ( r = -0 . 44 , p = 0 . 04 , including after controlling for disease severity [partial correlation coefficient = -0 . 38 , p = 0 . 04] ) and positively related to BH2 levels ( r = 0 . 39 , p = 0 . 03 , including after controlling for disease severity [partial correlation coefficient = 0 . 48 , p = 0 . 02] ) , but not BH4 , B0 , NH2 , or N0 .
In adults with falciparum malaria , urinary tetrahydrobiopterin ( BH4 ) was decreased and 7 , 8-dihydrobiopterin ( BH2 ) increased in proportion to disease severity , and a decreased BH4/BH2 ratio was associated with an increased risk of severe disease . The BH4/BH2 ratio is a reliable correlate for endothelial cell-dependent NO synthesis in vascular diseases [14–16] . The finding of an association between decreased BH4/BH2 ratio and increased BH2 with impaired microvascular reactivity and increased endothelial activation is consistent with a mechanistic role for oxidative stress and vascular NOS dysfunction . The association of increased BH2 and low BH4/BH2 ratios with increased phenylalanine levels suggests that systemic deficiency of BH4 causes impaired phenylalanine hydroxylase function as well as NOS dysfunction in malaria . We have previously shown decreased systemic NO production in both African children and Indonesian adults , proportional to disease severity [5 , 6] . In adult falciparum malaria , there is also decreased endothelial and pulmonary NO bioavailability associated with low levels of the NOS substrate L-arginine [6] , increased levels of the endogenous NOS inhibitor asymmetric dimethylarginine ( ADMA ) [10] , NO quenching by cell-free hemoglobin [11] and L-arginine reversible endothelial dysfunction in moderately severe malaria [6] . The role of the key NOS cofactor , BH4 , has not hitherto been shown in human malaria . In a recent murine severe malaria model , uncoupling of NOS with increased production of superoxide and impaired microvascular perfusion has been observed , and this was partially reversed by administration of intravenous BH4 [19] . Our results suggest that uncoupling of NOS due to decreased BH4 bioavailability and increased BH2 , is also a key mechanism of impaired NO bioavailability in human severe falciparum malaria and in pathogenesis of severe disease . The physiological role of NOS is oxidation of L-arginine and oxygen reduction to produce NO and citrulline [14 , 15 , 20] . BH4 regulates the coupling of the heme-oxygen intermediate to oxidation of L-arginine in NOS , and deficiency of BH4 as a co-factor can result in the output changing from NO to superoxide [14 , 15 , 20] . Increased oxidative stress can convert BH4 to the oxidized form BH2 , with the decrease in BH4 increasing superoxide , resulting in a feed forward cycle with further oxidization of BH4 to BH2 [16] . Since BH2 can serve as a competitive inhibitor at the BH4 binding site in NOS , the BH4/BH2 ratio is likely to determine NOS coupling in malaria and determine the relative proportions of NO and superoxide production , as others have observed in vitro [16] . Systemic bioavailability of BH4 depends on three pathways of pterin metabolism . First is de novo synthesis from GTP . A second is regeneration of BH4 from quinonoid dihydrobiopterin by dihydropteridine reductase ( e . g in hepatocytes for phenylalanine hydroxylase activity ) and third is the salvage of 7 , 8 dihydrobiopterin ( BH2 ) back to BH4 via dihydrofolate reductase ( important for NOS activity in endothelial cells ) . We found no diminution of total biopterins excreted , suggesting that mechanisms controlling overall biopterin production are not impaired . Instead the decrease in BH4 associated with severe malaria appeared to result from its oxidation coupled with inadequate reduction of BH2 to BH4 . In vivo recycling of BH2 to BH4 is the main regulator of the BH4:BH2 ratio , which in turn controls NOS coupling [16] . Our urine collection procedure allowed for capture of pterins , both biopterins and neopterins , in their excreted oxidation states . Our liquid chromatography methods allowed quantification of both dihydroneopterin and neopterin , the reduced and oxidized metabolites found in humans . This was of interest because these measurements provided information , in addition to biopterins redox status , on the partitioning of oxidized and reduced neopterins . We expected high total neopterin values in malaria and in septic patients and indeed this was found ( Table 2 ) . Elevated total neopterin has been reported previously and is the result of interferon-gamma-induced macrophage/monocyte activation with transcriptional induction of GCH1 mRNA [21] . Mononuclear phagocytes have extremely low pyruvoyl tetrahydropterin synthase ( PTPS ) activity . Consequently the product of GTPCH catalysis , 7 , 8 dihydroneopterin triphosphate , accumulates , is dephosphorylated intracellularly , and diffuses to extracellular fluid and then plasma as NH2 . Neopterin in healthy controls is excreted primarily as reduced dihydroneopterin ( NH2:N0 = 4 . 4 ) . In patients with severe malaria , despite marked elevation in urinary levels of total neopterins , the portion excreted as NH2 fell significantly ( NH2:N0 = 2 . 0 ) . Importantly the oxidation of NH2 to N0 is non-enzymatic . This suggests a milieu of oxidative stress in SM . It provides additional support for the redox imbalance observed for the biopterins , that is a fall in the ratio of reduced to oxidized metabolites . An increase in oxidative stress has been observed in Bangladeshi adults with severe falciparum malaria [22] . This may explain the increased conversion of BH4 to BH2 as seen in this study , with the decreased BH4/BH2 ratio suggesting impaired recycling of BH2 to BH4 in severe malaria . Similar to certain cardiovascular diseases [16] , our results suggest it is the BH4/BH2 ratio and not BH4 or BH2 alone that reflects NOS coupling in malaria . A decreased BH4/BH2 ratio was associated with an increased risk of severe disease , while decreased BH4 or BH2 alone were not associated with risk of severe disease . The association of a decreased BH4/BH2 ratio with impaired microvascular reactivity and endothelial activation , both previously shown to be associated with increased mortality in malaria , suggests that NOS coupling has an important role in determining malaria severity . Our results also show that there is impaired microvascular reactivity and increased endothelial activation in severe sepsis , as we have shown previously [23 , 24] . However , it is notable that sepsis patients had high BH4 levels and high BH4/BH2 ratios compared to control subjects and malaria patients . The findings of increased BH4 levels in sepsis are similar to results from a previous study in which plasma biopterin levels were measured with high performance liquid chromatography [25] . The mechanism ( s ) of impaired vascular function in these sepsis patients is unclear , but does not appear to be related to impaired BH4 bioavailability . Furthermore , the high BH4/BH2 ratio in sepsis indicates that the low BH4/BH2 ratio in severe falciparum malaria is not simply a result of a nonspecific pathogen-wide systemic inflammatory response . Results from our study also suggest that , in addition to low plasma L-arginine concentrations , increased ADMA and impaired NOS2 expression in severe malaria [5 , 6 , 8 , 10] , decreased BH4 and increased BH2 can also affect NO bioavailability by altering NOS function in malaria . While increased L-arginine clearance in SM was seen in our pilot study of low dose L-arginine in severe malaria [12] , decreased BH4 and increased BH2 could result in low NO despite the presence of normal levels of L-arginine . Results of studies with higher doses of L-arginine in severe falciparum malaria ( ACTRN 12612000571875 ) are awaited , but future studies in severe malaria targeting hypoargininemia may need to consider simultaneously increasing both L-arginine and BH4 to increase NO production by NOS . Use of intravenous BH4 in patients with endothelial dysfunction associated with hypercholesterolemia and smoking results in acute improvement in endothelial NO production [26 , 27] . However , a randomized controlled trial of oral BH4 in patients with coronary artery diseases found that BH4 administration only resulted in increased conversion of BH4 to BH2 with no beneficial effects in clinical outcome [28] . Using anti-oxidants as adjunctive agents in severe malaria could also increase the BH4/BH2 ratio , but a recent trial using intravenous N-acetylcysteine ( without BH4 ) in adult severe malaria did not show a benefit in clinical outcomes [22] . BH4 also plays a role as a co-factor for the enzyme phenylalanine hydroxylase , which converts phenylalanine to tyrosine [17] . As previously shown [17] , both adults and children with clinical malaria are almost invariably hyperphenylalaninemic at presentation , which originally suggested a deficiency of BH4 in these patients . Blood levels of phenylalanine are normally tightly regulated between 30–80 μM by the BH4-dependent phenylalanine hydroxylase ( PAH ) in the liver [17] . The skewed BH4/BH2 ratio and high BH2 levels in these subjects correlated significantly with hyperphenylalaninemia . Hyperphenylalaninemia in SM is a transient acute abnormality , and it is relatively mild compared to the high levels observed chronically in untreated infants with phenylketonuria , a condition leading to severe brain damage caused by the direct toxicity of phenylalanine [29] . While it is not clear if the resulting hyperphenylalaninemia in malaria ( especially cerebral malaria ) is clinically relevant , it provides important supportive evidence for the functional significance of impaired BH4 bioavailability on BH4-dependent enzyme function in severe malaria . This study has several limitations . The relatively small number of patients in each group and the small number of deaths in the SM group do not allow us to examine the independent effect of the biopterin metabolites on mortality or adjust for confounding variables . The numbers were however sufficient to demonstrate significant differences between groups . Also , the use of urinary measures of pterin metabolites as a measure of systemic biopterin bioavailability may not fully reflect intracellular concentrations in specific organs , though urinary biopterin quantitation has been shown to reflect systemic biopterin bioavailability [30–33] . It is possible that urinary BH2 and BH4 quantitation may be affected by renal function , although there was no association between blood creatinine and urinary BH4 , BH2 , N0 and NH2 in patients with malaria . Furthermore , measurement of the urine BH4/BH2 ratio is independent of creatinine excretion and is therefore not confounded by renal impairment . Importantly , the specialized collection techniques and assays we have used to measure urinary biopterin metabolite levels allow us to exclude artefactual ex-vivo oxidation . In summary , the BH4/BH2 ratio is decreased in severe falciparum malaria but not in severe sepsis , and it is associated with an increased risk of severe disease , impaired microvascular function and endothelial activation , probably secondary to NOS uncoupling . The elevated levels of BH2 suggest that increased conversion of BH4 to BH2 due to increased oxidative stress and insufficient recycling of BH2 back to BH4 are the mechanisms of the low BH4/BH2 ratio in severe malaria . Our findings identify an additional mechanism of impaired NO bioavailability in severe falciparum malaria and pose an additional challenge to NOS-based adjunctive interventions to increase NO bioavailability in severe malaria .
Measurement of urine pterin concentrations , expressed as biopterins and neopterins in micromoles per millimole urine creatinine are used for diagnosis of gene mutations leading to BH4 synthesis , recycling and salvage deficiencies and reflect systemic pterin bioavailability [30–33] . BH4 is unstable and spontaneously oxidizes to its inactive metabolites , dihydrobiopterin ( BH2 ) and to a lesser extent fully oxidized biopterin ( B0 ) [38 , 39] . To prevent ex vivo spontaneous oxidation , urine was collected , via voluntary micturition or immediately after insertion of a Foley catheter , directly into vials containing the antioxidant pterin stabilizers 1 , 4-dithioerythritol ( DTE ) and diethylenetriaminepentaacetic acid ( DETAPAC ) [38 , 39] ( as described in S1 ) . Urine was then frozen at -70°C , and shipped in liquid nitrogen to Medical Neurogenetics Laboratories , LLC , Atlanta , GA United States . Concentrations of biopterin , 7 , 8-dihydrobiopterin , 5 , 6 , 7 , 8-tetrahydrobiopterin , and neopterin were quantified by high performance liquid chromatography using sequential electrochemical and fluorescence detection , as previously described [38 , 39] . Concentrations of pterin metabolites were normalized to creatinine concentrations in millimoles . Statistical analysis was performed using STATA 11 software . The sample size for the patients with severe malaria was calculated from our previous study comparing RH-PAT in adults with severe and uncomplicated malaria with controls [6] . Using the difference and standard deviations found in RH-PAT index between severe malaria and controls , we estimated that a sample size of 14 in each group would have 80% power to detect a 25% difference between these two groups . Intergroup differences among malaria ( MSM and SM ) and controls were compared by ANOVA or Kruskal-Wallis test , where appropriate , with Wilcoxon Rank-Sum test used for pairwise comparisons . Pearson’s or Spearman’s correlation coefficients were determined depending on normality of distributions . Partial correlation coefficients were calculated adjusting for malaria disease severity . Logistic regression was used to determine the association between binary outcomes and goodness-of-fit was assessed by the Hosmer-Lemeshow test . A two-sided value of p<0 . 05 was considered significant . The study was approved by ethics committees of the National Institute of Health Research and Development , Indonesia , and the Menzies School of Health Research , Australia . Written informed consent was obtained from patients or relatives if patients were comatose or too ill to give informed consent . Specific approval for this was obtained from both ethics committees .
|
Vascular nitric oxide ( NO ) bioavailability is decreased in severe falciparum malaria and associated with microvascular dysfunction and increased endothelial activation . Nitric oxide synthase ( NOS ) requires tetrahydrobiopterin ( BH4 ) as a co-factor to convert L-arginine to NO , but when BH4 is low , NOS is “uncoupled” and produces superoxide instead of NO . In conditions of increased oxidative stress , BH4 is converted to dihydrobiopterin ( BH2 ) and biopterin ( B0 ) : the resulting BH2 competes with remaining BH4 as a competitive inhibitor of NOS , further decreasing NO production . We measured BH4 and BH2 in the urine of adults with severe and uncomplicated falciparum malaria and compared results to those of controls or those with sepsis . There was a significant decrease in urinary BH4 and increase in BH2 in severe malaria compared to uncomplicated malaria , sepsis , and controls , suggesting increased oxidative stress and insufficient recycling of BH2 back to BH4 . The BH4/BH2 ratio was associated with increased risk of severe disease , endothelial activation and microvascular dysfunction , likely through impaired NOS function . This additional mechanism of decreased NO in severe malaria suggests that trials evaluating use of adjunctive L-arginine to increase NO in severe malaria may require concurrent therapy to regenerate BH4 .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[] |
2015
|
Impaired Systemic Tetrahydrobiopterin Bioavailability and Increased Dihydrobiopterin in Adult Falciparum Malaria: Association with Disease Severity, Impaired Microvascular Function and Increased Endothelial Activation
|
All three pathogenic Yersinia species share a conserved virulence plasmid that encodes a Type 3 Secretion System ( T3SS ) and its associated effector proteins . During mammalian infection , these effectors are injected into innate immune cells , where they block many bactericidal functions , including the production of reactive oxygen species ( ROS ) . However , Y . pseudotuberculosis ( Yptb ) lacking the T3SS retains the ability to colonize host organs , demonstrating that chromosome-encoded factors are sufficient for growth within mammalian tissue sites . Previously we uncovered more than 30 chromosomal factors that contribute to growth of T3SS-deficient Yptb in livers . Here , a deep sequencing-based approach was used to validate and characterize the phenotype of 18 of these genes during infection by both WT and plasmid-deficient Yptb . Additionally , the fitness of these mutants was evaluated in immunocompromised mice to determine whether any genes contributed to defense against phagocytic cell restriction . Mutants containing deletions of the dusB-fis operon , which encodes the nucleoid associated protein Fis , were markedly attenuated in immunocompetent mice , but were restored for growth in mice lacking neutrophils and inflammatory monocytes , two of the major cell types responsible for restricting Yersinia infection . We determined that Fis was dispensable for secretion of T3SS effectors , but was essential for resisting ROS and regulated the transcription of several ROS-responsive genes . Strikingly , this protection was critical for virulence , as growth of ΔdusB-fis was restored in mice unable to produce ROS . These data support a model in which ROS generated by neutrophils and inflammatory monocytes that have not been translocated with T3SS effectors enter bacterial cells during infection , where their bactericidal effects are resisted in a Fis-dependent manner . This is the first report of the requirement for Fis during Yersinia infection and also highlights a novel mechanism by which Yptb defends against ROS in mammalian tissues .
Bacterial pathogens utilize both “defensive” and “offensive” strategies to survive in mammalian tissue sites and withstand the host immune response [1] . “Defensive” strategies often consist of physiological adaptations to stresses encountered in tissues , such as changes in pH or temperature , nutrient restriction , or influxes of toxic gases or proteins released by immune cells [2 , 3] . Many of these stresses are also found in other environments pathogens inhabit , such as soil , fomites , or in aerosol particles . By contrast , “offensive” strategies include the secretion of toxins or effector proteins that kill or block the actions of responding immune cells [1] . One such example is the Type 3 Secretion System ( T3SS ) used by many bacterial pathogens , including Yersinia , Salmonella , Shigella , Pseudomonas , and Chlamydia [4] . These systems translocate effector proteins into mammalian cells from the bacterial cytosol and , often , promote bacterial growth by neutralizing the anti-bacterial actions of these cells . Additionally , T3SS effectors are used by some pathogens to rearrange host cell processes to enable intracellular growth [4] . In other cases , effector proteins act to kill mammalian cells by targeting essential proteins or triggering cell death pathways [4] . T3SS effector proteins play critical roles in the virulence of the pathogenic Yersinia species , which include the pneumonic and bubonic agent Yersinia pestis , as well as the gastrointestinal pathogens Yersinia enterocolitica and Yersinia pseudotuberculosis ( Yptb ) [5] . These three organisms target the translocation of their T3SS effectors , called Yops , into responding phagocytic cells , particularly into neutrophils , where they dismantle a number of bactericidal responses , including the ability to phagocytose bacteria , release reactive oxygen species ( ROS ) , and produce certain inflammatory cytokines [5–11] . The contributions of the T3SS and Yops to Yersinia pathogenesis have been extensively studied for more than two decades , and a number of reports have been published on Yop targets and their actions in mammalian cells [5 , 12] . However , during infection of mammalian tissue sites , not all immune cells are intoxicated with Yops [13 , 14] , indicating that Yersinia must also employ additional , T3SS-independent strategies for surviving within the host and resisting the immune response , as remaining , non-intoxicated immune cells are competent to execute bactericidal functions . For example , it is known that at least one of these bactericidal functions , release of nitric oxide , restricts bacterial growth from a distance by cells not directly intoxicated with Yops [15] . Furthermore , Yptb lacking the pIB1 virulence plasmid , which encodes the T3SS and Yops , is capable of infecting and replicating within mouse tissues , in some cases at levels equivalent to a WT strain [16–18] . Even though infection with this strain seldom leads to death , these findings indicate that T3SS-deficient Yptb can withstand host defenses for several days and thus , likely encode “defensive” factors on its chromosome that allow for survival in harsh environments . Indeed , some of these genes have been identified in high throughput screens in various Yersinia species [19–24] , but all of these screens were performed in the presence of the virulence plasmid , which may mask the functions of some chromosomal factors . To determine which chromosomal factors contribute to infection of Yptb in the absence of the T3SS , we previously screened a library of 20 , 000 transposon insertions in a plasmid-deficient ( pIB1- ) Yptb strain during systemic infection and identified more than 30 mutants that were attenuated in livers [17] . One of these mutants , ΔmrtAB , was attenuated for virulence in the absence of the T3SS in all tissues , but only attenuated in the mesenteric lymph nodes in the presence of the T3SS , indicating that this screen uncovered factors that are redundant with the T3SS and/or that pIB1+ and pIB1- Yptb encounter distinct environments in some tissue sites [17] . Here , we follow up on our original work by employing a high-throughput , sequencing-based assay to evaluate the contribution of 18 additional genes identified in the screen to infection by both a plasmid-deficient and WT Yptb . In contrast to our findings with ΔmrtAB , we found that most of the genes evaluated were important for systemic infection by both pIB1+ and pIB1- Yptb , indicating that these genes play essential roles in virulence , regardless of the T3SS . Additional testing of the mutants in immunocompromised mice demonstrated that 4 loci were critical for virulence when interfacing with phagocytic cells . One operon , dusB-fis , prevented growth restriction by phagocytes and killing by ROS both in vitro and in vivo , in the presence of the T3SS . This work highlights the importance of studying both “offensive” ( T3SS-dependent ) and “defensive” ( T3SS-independent ) mechanisms of survival during Yersinia infection models , as both strategies are essential for the establishment of virulent infection .
By evaluating the virulence of 20 , 000 transposon mutants in a plasmid-deficient Yptb strain , we identified 33 mutants that were significantly defective for colonization of and/or growth within the liver , but were otherwise capable of growing in rich media at physiological temperatures [17] . However , using large pools of transposon mutants in animal infection models can sometimes result in “false negatives , ” as libraries are subject to bottleneck constraints and transposon insertions can have polar effects on nearby loci [25] . Therefore , we devised and implemented a high-throughput , sequencing-based approach ( a “mini” TnSeq ) to simultaneously compare the survival of multiple in-frame deletion mutants in small infection pools to further evaluate the loci identified in our previous TnSeq screen ( S1 Fig ) . Eighteen gene or operon deletion mutants containing identical in-frame scar sequences ( S1A Fig ) were generated in both a plasmid-deficient ( pIB1- ) YPIII strain ( the parental strain of our original transposon library ) and a plasmid-containing ( pIB1+ ) IP2666 strain to determine whether the in vivo contributions of some of these genes may be influenced by the T3SS . We chose the IP2666 pIB1+ strain for further investigation because it encodes the known virulence factor , phoP , which is non-functional in YPIII due to a mutation [26] . The 18 operons and genes examined represent several broad functional classes , including biosynthesis of metabolic compounds , LPS synthesis and modification , and several other previously uncharacterized virulence factors ( Table 1 ) . In order to ensure that the bacterial pools used to infect mice contained an equal proportion of attenuated mutants and WT bacteria , we also constructed two deletions of “neutral genes” ( Table 1 ) , which were selected because transposon disruptions in these genes had no deleterious effects in the original TnSeq screen [17] . Mice were infected intravenously with 104 or 103 CFU of the pIB1- or the pIB1+ library , respectively . Following infection with these doses , equivalent bacterial loads were recovered from spleens at 3 days post-infection regardless of the presence of the T3SS ( S2A Fig ) ; however , livers infected with pIB1- bacteria contained lower bacterial loads than those infected with libraries generated in the WT background ( S2B Fig ) . In each pool , the two neutral strains each comprised 25% of the inoculum , while the remaining 18 mutants each comprised ~3% of the population . Following recovery of bacteria from infected livers and spleens at 3 days post-infection , genomic DNA was processed for Illumina sequencing and fitness values were calculated for each mutant ( Fig 1 ) . Strikingly , 14 of the 18 mutants generated in the pIB1- YPIII background had statistically significant virulence defects in infected liver tissues . Of those 14 genes , all but one were also critical for growth of pIB1+ IP2666 within the liver ( Fig 1A and 1B , Table 1 ) , indicating that more than 70% of the genes evaluated were important for infection , regardless of the presence of the T3SS . Mutants attenuated for growth within the liver included the auxotrophic strains ΔaroA and ΔaroE , which are unable to produce aromatic amino acids [27 , 28] , and ΔpurM , which lacks a component of the purine biosynthesis pathway [29] . With the exception of one strain ( ΔYPK_3185 ) , all of the strains with mutations in genes involved with LPS synthesis and modification were attenuated for virulence in at least one tissue site . Importantly , several factors that had not been previously characterized in Yersinia infection models , including YPK_2594 , which has no predicted function , YPK_1920 , which is predicted to encode a lipoprotein , YPK_3765 , which is predicted to encode a murein peptide ligase , and the dusB-fis operon , which encodes the nucleoid associated protein Fis , were critical for infection . Six pIB1+ mutants , ΔaroA , ΔYPK_3184 , ΔarnDT , ΔYPK_1920 , ΔYPK_2594 , and ΔpsaEFABC , were defective for growth in the liver ( Fig 1B ) , but not the spleen ( Fig 1D and Table 1 ) , indicating tissue specific functions of these genes . To evaluate whether mutants were attenuated when they were not a small minority of the input pool , traditional competition experiments were performed using bacterial mutants generated in the pIB1+ background . Mutants were mixed at a 1:1 ratio with a drug resistant WT strain , and C . I . values were obtained after intravenous infection ( Fig 2 ) . All of the mutants evaluated were attenuated in this assay . In conclusion , using our efficient and highly sensitive mini-TnSeq assay many bacterial mutants were attenuated in both WT and plasmid-deficient Yptb , indicating that most of these loci do not have redundant roles with the T3SS . Infection with Yptb produces a pronounced inflammatory response , where bacteria growing in tissue sites are surrounded by phagocytic cells [15–17] . Therefore , we hypothesized that some of the genes evaluated in the mini-TnSeq assay may encode proteins that directly interface with phagocytic cells , or are important for surviving in the face of anti-microbial responses generated by these cells . To test this , mice pre-treated with the RB6-8C5 antibody , which depletes Gr1pos cells ( Ly6Gpos neutrophils and Ly6Cpos inflammatory monocytes , dendritic cells , and lymphocytes ) or the 1A8 antibody , which depletes Ly6Gpos cells ( neutrophils only ) were infected with the pIB1+ mini-TnSeq library . Surprisingly , very few significant changes in fitness scores were detected following infection of immunocompromised mice with these mutants ( S3 Fig ) , suggesting that most of these genes are important for bacterial colonization and growth in animal tissues , regardless of the presence of these innate immune cells . However , four mutants displayed significantly altered fitness scores upon infection of immunocompromised mice ( Fig 3 ) . Growth of ΔYPK_3765 was restored to WT levels in the livers and spleens of both RB6-8C5- and 1A8-treated mice ( Fig 3A–3D ) , indicating that this gene is required for resisting growth restriction by phagocytic cells . Surprisingly , depletion with the RB6-8C5 and 1A8 antibodies resulted in decreased growth of the ΔpsaEFABC mutant in spleens ( Fig 3B and 3D ) , and depletion with 1A8 decreased the growth of ΔrfaH in livers ( Fig 3A and 3C ) . These results suggest that neutrophils may protect these mutants from further growth restriction by other cells or factors in these tissue sites . Interestingly , the growth changes observed with ΔYPK_3765 , ΔrfaH , and ΔpsaEFABC were specific to neutrophil depletion , as treatment with 1A8 was sufficient to alter the fitness of these mutants . By contrast , the fitness of ΔdusB-fis was restored in mice treated with the RB6-8C5 antibody ( Fig 3A and 3B ) , but not in mice treated with the 1A8 antibody ( Fig 3C and 3D ) . This result suggested that ΔdusB-fis is sensitive to all Gr1pos cells , to one or more Ly6Cpos cell types ( inflammatory monocytes , dendritic cells , and lymphocyte subsets ) , or to Ly6Gpos neutrophils and a subset of Ly6Cpos cells . To distinguish among these possibilities , we performed 1:1 co-infection experiments with ΔdusB-fis in mice treated with an antibody , MC-21 , which blocks the chemokine receptor CCR2 and prevents recruitment of Ly6Cpos inflammatory monocytes to tissue sites during microbial infection [30–33] . Additional cohorts of mice were treated with 1A8 , RB6-8C5 , or with a combination of the 1A8 and MC-21 antibodies prior to infection . Depletion of cell subsets was confirmed by flow cytometry using Gr1 and Cd11b markers to distinguish between neutrophil and inflammatory monocyte populations ( S4 Fig ) . Treatment with either 1A8 or MC-21 alone did not restore the virulence of the ΔdusB-fis mutant ( Fig 4 ) , indicating that the presence of either Ly6Gpos or Ly6Cpos cell type ( s ) at the site of infection was sufficient to restrict the growth of this mutant . By contrast , depletion with a combination of the MC-21 and 1A8 antibodies restored growth of ΔdusB-fis in livers and spleens , demonstrating that ΔdusB-fis is specifically sensitive to neutrophils and CCR2-recruited inflammatory monocytes during tissue infection . Importantly , growth of ΔdusB-fis was also restored when we complemented this mutant by re-introducing the dusB-fis genes into the deleted strain ( Fig 4 ) . To determine whether a dusB-fis mutant was more attenuated than fis , a deletion of fis was generated and evaluated in mice . The fis mutant was attenuated to the same extent as ΔdusB-fis ( Fig 4A and 4B ) , indicating that Fis is essential for the virulence of Yptb . In summary , these results demonstrate that Fis promotes Yptb resistance to or evasion of killing by both neutrophils and inflammatory monocytes during mouse infection . Because the virulence of the ΔdusB-fis mutant was restored in the absence of neutrophils and inflammatory monocytes , it is possible that these immune cells restricted survival of this mutant in the bloodstream after intravenous infection , thereby preventing high levels of tissue colonization . Alternatively , or in addition , neutrophils and inflammatory monocytes may restrict the growth of ΔdusB-fis in the systemic tissue sites once these cells surround the bacteria . To distinguish between these two possibilities , the growth kinetics of the ΔdusB-fis mutant were determined during systemic mouse infection at 4 , 24 , 48 , and 72-hour time-points . In both co-infections with a WT strain ( Fig 5A and 5B ) and in single-strain infections ( Fig 5C and 5D ) , the ΔdusB-fis mutant colonized tissue sites and grew for the first 24 hours post-infection with kinetics similar to WT Yptb . However , by 48 hours post-infection , the level of ΔdusB-fis failed to increase as rapidly as WT , indicating that the growth of this strain was not restricted until after initial seeding and expansion in tissue sites . Combined with our findings from the depletion experiments , these results suggest that ΔdusB-fis cannot adapt to a change in the tissue environment that likely occurs due to the influx and/or activities of neutrophils and inflammatory monocytes . Fis serves as a transcriptional regulator of virulence factors in several pathogens [34] . Therefore , we speculated that the virulence defect of ΔdusB-fis was due to an inability to mount a transcriptional response to protect against the bactericidal actions of neutrophils and inflammatory monocytes in systemic tissue sites . Neutrophils and inflammatory monocytes use a variety of mechanisms to restrict bacterial growth upon recruitment to tissue sites , including the phagocytosis of bacteria , release of toxic granules and diffusible reactive gases ( ROS and reactive nitrogen species ) , and chelation of metals [35 , 36] . Because T3SS effectors interfere with many of these processes [37] , and because Fis regulates expression of the SPI-1 and SPI-2 pathogenicity islands in Salmonella [38] , we first tested whether Fis positively regulated expression of the T3SS or its effectors . Under conditions that induce expression and secretion of T3SS effectors , ΔdusB-fis and Δfis mutants secreted effectors into culture supernatants at equivalent levels to WT Yptb ( Fig 6A ) . Additionally , engineered strains of WT and ΔdusB-fis Yptb containing the beta-lactamase , TEM , fused to the first 100 amino acids of the T3SS effectors YopE or YopH [13 , 39] exhibited no difference in cleavage of the beta-lactamase substrate nitrocefin by those effectors ( Fig 6B and 6C ) . Because Fis could regulate other factors , such as adhesins , which also contribute to efficient effector translocation into host cells [14] , the ability of ΔdusB-fis to translocate T3SS effectors into cultured epithelial cells was measured using the CCF4-FRET based translocation assay . The ΔdusB-fis mutant had no defect in translocating YopE-TEM or YopH-TEM into cultured cells ( Fig 6D and 6E ) , suggesting that Fis plays no role in regulating the expression of the Yptb T3SS machinery or in regulating the expression of other factors that promote efficient effector translocation through this system . Furthermore , a dusB-fis deletion generated in a strain lacking the T3SS needle was attenuated for virulence in the presence of Gr1pos cells ( Fig 6F and 6G ) , indicating that dusB-fis is critical for preventing growth restriction by phagocytes , even in the absence of a functional T3SS . To evaluate whether Fis promotes resistance to one or more of the bactericidal stresses imposed by neutrophils and inflammatory monocytes , the ΔdusB-fis mutant was exposed to several conditions that simulate the actions of these cells . These conditions included exposure to low pH ( Fig 7A ) , low concentrations of iron ( Fig 7B ) , nitric oxide ( Fig 7C ) , and ROS ( Fig 7D ) . While ΔdusB-fis was often delayed in entering into exponential growth compared to WT , its growth rate in broth with a low pH or titrated iron was not more impaired than a WT strain exposed to the same conditions ( Fig 7A and 7B ) . Additionally , exposure to the nitric oxide donor DETA NONOate did not affect survival of ΔdusB-fis , but did result in killing of a mutant lacking hmp , which is known to play a role in nitric oxide detoxification by Yptb [15] ( Fig 7C ) . By contrast , the survival of ΔdusB-fis and Δfis strains was significantly impaired after exposure to H2O2 ( Fig 7D ) , suggesting that Fis is required for resistance to ROS . To determine whether Fis protects against ROS by altering the transcription of one or more genes that are responsive to ROS in other organisms [40–43] , we performed qRT-PCR on transcripts isolated from WT and Δfis following exposure to 20μM H2O2 , a concentration that is sublethal to both strains ( S5 Fig ) . In WT Yptb , these conditions were sufficient to induce transcription of four genes , katG , ahpC , grxA , and recA ( Fig 7E ) . However , we observed significantly less transcriptional induction of these four genes in the Δfis mutant ( Fig 7E ) , suggesting that Fis promotes expression of these genes during oxidative stress . By contrast , there were no differences between WT and the Δfis mutant in the expression of a non-ROS inducible gene , rpoC . In order to determine whether overexpression of a single ROS detoxifying protein was sufficient to restore growth of the Δfis mutant after exposure to lethal concentrations of H2O2 , the coding regions of ahpC and katG , which encode an alkyl hydroperoxide reductase and a catalase , respectively , and have been shown to contribute to H2O2 detoxification in other organisms [44–46] , were each fused to a constitutive tetracycline promoter on the plasmid pACYC184 and introduced separately into WT and ΔdusB-fis strains . Notably , while expression of these genes was enhanced in the ΔdusB-fis mutant ( S6A and S6B Fig ) , the sensitivities of these strains to H2O2 were no different from isogenic strains expressing gfp downstream of the same promoter ( S6C Fig ) , indicating that expression of more than one Fis-regulated gene may be required to resist killing by H2O2 . Alternatively , other regulatory targets , such as the SOS-response regulator recA , may play an essential role in Fis-dependent protection against oxidative stress . Combined , these results indicate that Fis protects against killing by ROS by either directly or indirectly regulating the transcription of multiple genes required for resistance to oxidative stress . To test the possibility that Fis protects Yptb from ROS in vivo , gp91phox-/- mice , which cannot assemble a productive NADPH oxidase complex [47] , were infected with a mixture of WT and ΔdusB-fis . Strikingly , ΔdusB-fis was fully virulent in these mice ( Fig 8A and 8B ) . Interestingly , gp91phox-/- mice were only slightly more susceptible to Yptb infection , with total bacterial loads in the spleen and liver that were only ~3 . 5x higher than those in tissues recovered from C57Bl/6 mice ( Fig 8C and 8D ) . Furthermore , most of this increase was attributed to the relief in ΔdusB-fis growth restriction in the gp91phox-/- mice , as analysis of the CFU burden of each individual bacterial strain recovered from co-infected mice showed little difference in WT CFU levels between gp91phox-/- and C57Bl/6 mice ( Fig 8E and 8F ) . This result , coupled with our earlier observations , indicates that the primary role of dusB-fis during Yptb infection within deep tissue sites is to protect against ROS produced by neutrophils and inflammatory monocytes , likely by initiating a transcriptional response that enables Yptb to resist killing by ROS that have entered the bacterial cell .
As bacteria evolve to adapt and grow in different niches , they often acquire new traits through the acquisition of plasmids , pathogenicity islands , or integration of phages or other mobile genetic elements [48] . However , these organisms can also exploit or rely on other traits they previously possessed to survive in these new environments [48] . These previously held abilities likely reflect the environments that had been central to the survival of the organism in prior niches . Thus , the observation that pIB1- strains of Yptb display a remarkable ability to grow and persist within several tissue sites during mammalian infection suggests that an ancestor of this bacterium may have relied on a number of chromosome-encoded factors to grow within mammalian tissue sites and withstand restriction by the host immune response prior to acquisition of the T3SS-encoding virulence plasmid . One of these host responses is the release of ROS , which are produced following the oxidative burst of phagocytic cells in response to fungal and bacterial infections in several tissue sites , including the GI tract , lungs , and systemic organs [49–51] . In phagocytic cells , oxidative burst occurs via the activation and assembly of the NADPH oxidase complex , usually in response to bacterial contact or pattern recognition receptor activation [52] . Our results , in aggregate , support a model ( S7 Fig ) whereby the dusB-fis operon in Yptb controls the transcription of genes critical for resisting killing by ROS that are generated by the NADPH oxidase complex of neutrophils and inflammatory monocytes surrounding bacteria . Specifically , ( 1 ) dusB-fis was important for defense against both neutrophils and inflammatory monocytes , as the growth of this mutant was only restored when both immune cell populations were depleted ( Figs 3 and 4 ) ; ( 2 ) ΔdusB-fis initially colonized spleens and livers , but was unable to sustain growth in these tissue sites by 48 post-infection ( Fig 5 ) ; ( 3 ) Fis was required for protection against oxidative stress ( Fig 7D ) and regulated the transcription of at least 4 genes , katG , ahpC , grxA and recA , which are predicted to contribute to resistance to ROS in Yersinia ( Fig 7E ) ; and ( 4 ) growth of ΔdusB-fis was restored in gp91phox-/- mice , whose immune cells lack a functional NADPH oxidase complex and thus cannot undergo oxidative burst ( Fig 8 ) . Remarkably , these mice contained equal bacterial loads of WT and ΔdusB-fis ( Fig 8E and 8F ) , suggesting that the primary contribution of this operon to Yptb intravenous infection is to prevent against restriction by ROS . The findings that dusB-fis was necessary for resisting killing by ROS in vitro and that this operon was dispensable for growth within gp91phox91-/- mice were unanticipated for several reasons . First , previous studies of Yersinia gene expression in animal models have observed little transcriptional induction of ROS-detoxifying genes during infection of lymphoid tissue sites , suggesting that Yersinia may not experience oxidative stress during growth in these organs [15 , 53] . However , these studies were largely directed at analyzing the transcriptional induction of ROS-responsive genes compared to in vitro growth , where bacteria may also encounter some endogenous oxidative stress , and did not assess the survival of Yptb mutants lacking one or more of these genes during animal infection . In fact , a prior study analyzing the phenotype of a mutant lacking the superoxide dismutase sodA determined that this gene was critical for growth of Y . enterocolitica within livers and spleens , suggesting that Yersinia do encounter ROS during tissue infection [54] . Second , it has been well established that two T3SS effectors , YopE and YopH , prevent oxidative burst in Yop-intoxicated phagocytic cells [7 , 11] . However , YopE and YopH can only function within the cells into which they have been delivered . As only a small fraction of immune cells are intoxicated with Yops during infection of some tissue sites [13] , it is possible that in these tissues , Yptb encounters ROS produced by non-injected cells , and would therefore require mechanisms to resist killing by these species . Because Fis is dispensable for T3SS effector translocation , but is required for protection against ROS both in vivo and in vitro , our work suggests that Yptb does encounter ROS during infection of the spleen and liver , and that these species must be coming from neighboring immune cells not intoxicated with Yops . Furthermore , the observation that gp91phox-/- mice were no more sensitive to WT Yptb than C57Bl/6 mice ( Fig 8E and 8F ) suggests that WT Yptb is completely resistant to ROS produced by the immune response during infection . Therefore , we propose that Yptb utilizes both offensive and defensive measures to counteract ROS produced by phagocytic cells during mammalian infection by preventing oxidative burst in T3SS-intoxicated host cells , and also by initiating a Fis-dependent transcriptional response to protect against killing by ROS released by non-injected phagocytic cells ( S7 Fig ) . The dusB-fis operon is conserved in Enterobacteriaceae family members of the Gammaproteobacteria [55] and encodes the nucleoid-associated protein ( NAP ) Fis . While no published work has characterized a function for Fis in Yersinia , Fis and other NAPs have been well studied in E . coli and other organisms , where these small , histone-like proteins play important roles in modulating DNA architecture , as well as in directly and indirectly regulating transcription at a global level [34] . In E . coli , the two genes are co-regulated and transcribed from a single promoter upstream of dusB [56 , 57] , where the dusB mRNA transcript is believed to play a regulatory role in promoting translation of Fis [55] . Interestingly , Fis serves as a transcriptional regulator of virulence factors in several mammalian pathogens , including Vibrio cholerae , Shigella flexneri , Pasteurella multocida , Salmonella typhimurium , and pathogenic Escherichia coli [34 , 38 , 58–65] . In these organisms , it activates a diverse range of virulence functions , including quorum sensing , capsule production , adhesion , and Type 3 Secretion [58–60 , 65] . Notably , a study performed in E . coli also characterized a role for Fis in protection against oxidative stress [66] , suggesting that defense against ROS may be a conserved function of Fis across multiple bacterial species . However , the contribution of Fis to ROS resistance has not been examined in other pathogens . Our findings indicate that , following exposure of Yptb to oxidative stress , Fis promotes the transcriptional induction of several ROS-detoxifying genes , as well the SOS response regulator recA . This suggests that Fis may prevent ROS-mediated killing of Yptb both by stimulating detoxification as well as by promoting the repair of DNA damage . Expressing the detoxifying genes ahpC and katG under the control of a constitutive promoter did not restore resistance of ΔdusB-fis to oxidative stress; however , this is not surprising because it is likely that Fis promotes expression of multiple genes that contribute to survival under these conditions . Future studies aimed at identifying global regulatory targets of this protein will further inform our understanding of how Fis promotes survival under these conditions . Another surprise from our mini-TnSeq assay was the finding that the virulence defects of the ΔpsaEFABC and ΔrfaH mutants were exacerbated following neutrophil depletion during Yptb infections of the spleen and liver , respectively , indicating that these loci may promote survival in a non-inflammatory niche or in the presence of a host cell subset “unmasked” by neutrophil depletion . Consistent with this idea was the observation that the ΔpsaEFABC mutant was significantly attenuated in the pIB1- , but not the pIB1+ , background in the spleen , as WT Yptb recruits a more robust , neutrophil-rich inflammatory response than its plasmid-deficient derivative in lymphoid tissue sites [16 , 17] . The psaEFABC operon encodes the fimbrial-like adhesin pH 6 antigen , which has been shown to contribute to lung colonization by Y . pestis [67] . Notably , Y . pestis undergoes an early “quiet” stage during infection by the pneumonic route , in which neutrophils are not recruited to the lungs until at least 24 hours post-infection [68] . Y . pestis may therefore require pH 6 antigen to colonize and grow within the lungs at early time-points because neutrophils have not yet been recruited . RfaH has been characterized as a global regulator of LPS synthesis in several gram-negative organisms , including Y . enterocolitica , where deletion of this gene results in a “rough” phenotype , in which core inner core LPS is exposed [69] . In Y . pestis , exposed core LPS promotes interactions with and uptake by dendritic cell subsets [70 , 71] . While neutrophils are the primary cells contacting bacteria during Yptb infection [15 , 17] , upon neutrophil depletion , it is possible that bacteria encounter dendritic cell subsets . Thus , during this condition , the ΔrfaH mutant may come into contact with and be phagocytosed by certain dendritic cell subsets . In contrast to the ΔrfaH and ΔpsaEFABC mutants , the virulence of a mutant lacking YPK_3765 was restored in the absence of neutrophils , indicating that this gene is important for protection against clearance by these cells . YPK_3765 is predicted to encode a murein peptide ligase ( Mpl ) , a class of proteins important for peptidoglycan synthesis and recycling in other organisms [72] . Peptidoglycan is a known activator of pattern recognition receptors [73] , so a loss of this protein in Yptb may result in aberrant expression or release of peptidoglycan outside of the bacterial cell , which could further enhance killing by neutrophils during tissue infection . Our mini-TnSeq assay offers a number of advantages in evaluating smaller cohorts of mutants initially identified in a large transposon-based screen , where extreme bottlenecks can inhibit the ability of an otherwise competent mutant to colonize tissues . In addition , transposon disruptions of genes can often have polar effects on the expression of nearby loci . Finally , screening individual mutants in single-strain and 1:1 co-infections often requires large numbers of mice . To address these issues , our assay utilizes deep sequencing as a read-out , where in-frame mutants contain scar sequences that can be used as a primer template for PCR amplification of Illumina libraries . This allowed us to use small pools of bacterial mutants in mouse infections , thereby bypassing bottleneck issues and also minimizing animal usage . Unlike our initial study , in which the operon containing the most significantly attenuated transposon mutant , mrtAB , was not required for systemic infection by a pIB1+ strain [17] , the vast majority of mutants with defects in the absence of the virulence plasmid were also attenuated for infection of pIB1+ Yptb in the liver . In fact , only one mutant , ΔoppD , was attenuated for growth in livers only in the absence of pIB1 . Curiously , both oppD and mrtAB encode components of transporters , suggesting that they could carry out functions that are redundant with the T3SS , or that they are critical for survival in niches that are not predominantly inhabited by WT Yptb . Interestingly , six mutants , ΔaroA , ΔYPK_3184 , ΔarnDT , ΔYPK_1920 , ΔYPK_2594 , and ΔpsaEFABC , were defective for growth of the WT strain in the liver , but not the spleen , reflecting the fact that the original screen was performed in the liver and suggesting that different tissue sites can influence the repertoire of bacterial virulence factors required during infection . Indeed , it has been established that mammalian organs differ in their mechanisms of sensing and responding to microbial infections [74 , 75] and that , consequently , bacteria may utilize genes to survive in some tissues that are dispensable in others . For example , the bacterial pathogen Francisella tularensis specifically requires tryptophan biosynthesis genes during infection of the lung in order to counteract restriction of this amino acid by a host-encoded enzyme expressed in this organ [76] . Likewise , Yptb may require certain factors , such as aroA , to survive in the liver because their products are limiting in this organ . Additionally , certain mutants , such as ΔYPK_3184 and ΔarnDT , may be more readily detected by pattern recognition receptors in the liver and would therefore fail to colonize or sustain growth in this organ . Future work with these mutants may help to uncover host immune mechanisms specific to this tissue site . Altogether , our findings reinforce the argument that Yptb relies on a number of chromosome-encoded defense factors to grow within tissue sites and withstand restriction by immune cells . In particular , the small , histone-like protein Fis plays a critical role in protecting Yptb from ROS produced by phagocytic cells during tissue infection . Future work will be aimed at identifying the global network of Fis- regulated genes during conditions of oxidative stress to understand how this protein promotes bacterial adaptation to this condition .
This study was performed in accordance with the recommendations in the Guide for Care and Use of Laboratory Animals of the National Institutes of Health . The Institutional Animal Care and Use Committee ( IACUC ) of Tufts University approved all animal procedures . Our approved protocol numbers were B2012-54 and B2015-35 . All efforts were made to minimize suffering; animals were monitored following infection and were euthanized upon exhibiting substantial signs of morbidity by CO2 asphyxiation followed by cervical dislocation . Strains utilized in this study are listed in S1 Table and primers are listed in S2 Table . Yptb gene deletions were generated in pIB1- YPIII and pIB1+ IP2666 , as indicated in S1 Table . Deletions replacing genes of interest with in-frame scar sequences were created using allelic exchange as follows: primers were designed to amplify ~800bp regions directly up and downstream of each targeted gene ( S2 Table ) . These oligos also contained overlapping sequences necessary to create a ~60bp scar sequence after gene deletion . Overlapping products were combined using splicing by overlap extension ( SOE ) PCR and ligated into the sacB-based vector pCVD442 following restriction digestion . The resulting plasmids were introduced into E . coli DH5αλpir and integrated into the Yptb chromosome by mating in the presence of a third mating strain containing pRK600 . Deletions were confirmed by PCR utilizing primers located 800bp up and downstream of the deleted gene . For fis deletion , primers with overlapping sequences were designed to amplify ~800bp regions directly up and downstream of the gene . These products were combined by SOE PCR , ligated into pCVD442 , and the resulting plasmid was introduced into E . coli DH5αλpir and mated into Yptb as described above . Deletion of fis was confirmed by PCR . To complement dusB-fis , the entire operon and 800bp of both up and downstream sequences was amplified , and the product was cloned into pCVD442 by restriction digestion and ligation . The resulting plasmid was introduced into E . coli DH5αλpir and mated into Yptb ΔdusB-fis and successful restoration of the operon was confirmed by PCR . Strains containing YopE-TEM and YopH-TEM fusions were generated by mating Yptb strains with a SM10λpir strain containing the plasmid pSR47-YopETEM or pSR47-YopHTEM [13 , 39] . Following conjugation , bacteria were plated on kanamycin and irgasan to select for crossover of the chimeric YopE- or Yop-HTEM genes into the yopE and yopH loci . Successful crossover was confirmed by PCR . To generate strains constitutively expressing either ahpC or katG , the open reading frames of these genes were amplified by PCR and products were fused downstream of a constitutive tetracycline promoter on the plasmid pACYC184-gfp [17] , using PCR [77] to replace the open reading frame of gfp with each respective product . Successful integration of ahpC and katG open reading frames was confirmed by sequencing , and plasmids were introduced into WT and ΔdusB-fis strains by electroporation and selection with 20mg/mL chloramphenicol . All Yptb cultures were grown in L broth , with the exception of nitric oxide and H2O2 sensitivity assays ( described below ) . Following mouse infections , tissue homogenates were plated onto L agar containing 0 . 5 μg/mL irgasan or a combination of 0 . 5 μg/mL irgasan and 50 μg/mL kanamycin to select for marked bacterial strains . During strain construction , 50 μg/mL carbenicillin and 0 . 5 μg/mL irgasan were used to select for strains containing integrated plasmids following matings , and 10% sucrose was utilized to select for strains that had resolved the integrated plasmid . With the exception of the T3SS and translocation assays , all cultures were incubated at 26°C with aeration . For animal infections , strains were inoculated into L broth 48 hours prior to infection . Following overnight growth , these strains were diluted 1:40 and incubated for ~8 hours , after which they were diluted 1:100 and incubated overnight . All infections were performed by intravenous injection in 8–10 week C57Bl/6 or C67Bl/6 gp91phox-/- mice obtained from Jackson , NCI , and Taconic labs . For infections with strains constructed in pIB1- YPIII , mice were inoculated with 1 x 104 bacteria . For infections with strains constructed in pIB1+ IP2666 , mice were inoculated with 1 x 103 bacteria . Competition experiments were performed using a 1:1 mixture of an unmarked strain and a strain harboring an insertion of miniTn5 KanR in a neutral locus [78] . Following infections , spleens and livers were isolated , weighed , homogenized , and plated on L agar containing 0 . 5 μg/mL irgasan . The quantity of CFU/gram of organ was determined by dividing the number of recovered CFU by the weight of the tissue sample extracted , or in cases where the entire organ was extracted , CFU/organ values were determined . For competition experiments , tissue homogenates were plated onto non-selective media as well as onto media containing 50 μg/mL kanamycin . The CFU count for each strain was determined by subtracting the number of KanR colonies from the total number of colonies recovered on non-selective plates . The proportion of each strain in the inoculum was confirmed using the same methods . C . I values were determined by the following equation: C . I . = ( mutant/WT output ratio ) / ( mutant/WT input ratio ) . For Ly6G and Gr1 cell depletions , mice were intraperitoneally injected with 50 μg of 1A8 ( Fisher ) or RB6-8C5 ( eBioscience ) antibody 24 hours prior to and 24 hours post-infection . For inflammatory monocyte depletions , mice were intraperitoneally injected with 20 μg of MC-21 antibody [79] 1 day prior to infection and each day after until completion of the experiment . To confirm successful neutrophil and inflammatory monocyte depletion , spleen homogenates were stained with CD11b PE-Cy7 ( eBioscience ) and Gr1 PE Cy-5 ( eBioscience ) and analyzed by flow cytometry , as previously described [80] . Overnight cultures of individual strains were mixed so that each putatively attenuated mutant would represent ~3% of the inoculum , and the combined neutral mutants would represent ~50% of the inoculum . Libraries were intravenously injected into 10 untreated C57Bl/6 mice and 7–8 C57Bl/6 mice treated with either RB6-8C5 or with 1A8 . At 3 days post-infection , tissues were isolated , homogenized , and plated for CFUs on 150mm agar plates so that each plate would contain ~1x104 CFUs . Bacteria were scraped off plates , mixed , and genomic DNA was extracted from a volume equivalent to ~2x109 CFUs using the Qiagen DNeasy Blood and Tissue kit . DNA libraries were prepared for sequencing using the homopolymer tail-mediated ligation PCR technique as previously described [81] . Briefly , genomic DNA was sheared by sonication and treated with terminal deoxytransferase in order to generate a 3’ poly C-tail sequence . Two rounds of nested PCR were then employed to amplify regions immediately downstream of deleted genes . These products were multiplexed using 6bp indexing primers and sequenced on the Illumina Hi-Seq 2500 . Following sequencing , reads were mapped to the region immediately downstream of the deleted genes and the total number of reads for each mutant in a given organ or input pool was divided by the total number of reads obtained for that organ or input pool . Fitness values were obtained by dividing the abundance of a mutant in a given organ by its abundance in the input pool . Strains were grown overnight at 26°C with aeration , then diluted 1:40 into L broth containing 20mM sodium oxalate + 20mM MgCl2 . Cultures were grown for 2 hours at 26°C with aeration and then shifted to 37°C for 2 hours and grown with aeration . Following growth , the OD600 of each culture was measured and strains were diluted to achieve equivalent optical densities . Cultures were centrifuged and 10% trichloroacetic acid was added to culture supernatants to precipitate all secreted proteins . Precipitated proteins were pelleted by centrifugation , washed with acetone , and resolved by electrophoresis on a 12 . 5% SDS-polyacrylamide gel . Strains containing chimeric YopE-TEM and YopH-TEM fusions were grown overnight at 26°C with aeration , then diluted 1:40 into L broth containing 20mM sodium oxalate + 20mM MgCl2 . Cultures were grown for 2 hours at 26°C with aeration and then shifted to 37°C for 2 hours and grown with aeration . When performing T3SS assays , the OD600 of each culture was measured and strains were diluted to achieve equivalent optical densities . Cultures were centrifuged and 40 μL of the culture supernatant was applied to 10 μL of 500 μg/mL nitrocefin , for a final concentration of 100 μg/mL . After a 10-minute incubation , the A490 of samples was measured using a BioTek Synergy HT plate reader . When performing translocation assays , cultures were used to infect HEp-2 cells at the indicated multiplicities of infection . After 1 hour , cells were treated with gentamicin to stop the infection . Cells were lifted from plates using trypsin and then treated with 1 μg/ml CCF4 ( Invitrogen ) and 1 . 5 mM probenecid ( Sigma ) . Following a 20-minute incubation , cells were analyzed by flow cytometry to quantify fluorescence following excitation at a 388 nm and blue fluorescence ( 450nm ) and green fluorescence ( 530 ) were measured . Blue fluorescence indicated the presence of translocated effectors inside of the cell . The %blue cells were determined by dividing the number of blue cells by the total number of cells analyzed in a given sample . For low pH growth assays , WT and ΔdusB-fis Yptb were grown overnight at 26°C with aeration , then diluted 1:100 into either L broth or L broth at pH 5 . 5 . Cultures were grown at 26°C with aeration , and the OD600 of cultures was measured at 1-hour intervals for 12 hours . For low iron growth assays , cultures were grown overnight as described above and diluted 1:100 into a well of a 96-well plate containing L broth or L broth containing 250 μM 2 , 2’- Bipyridyl ( Sigma ) . Plates were incubated for 20 hours in a BioTek Synergy HT plate reader at 26°C with aeration , and OD600 measurements were recorded for each well at 15-minute intervals . Stationary phase cultures were diluted 1:40 into L broth and grown for 4 hours at 26°C with aeration . Cultures were then washed and diluted 1:50 into M9 glucose medium or into M9 glucose medium containing either 1 . 5mM H2O2 or 2 . 5mM of the nitric oxide donor DETA NONOate ( Cayman Chemical ) . Samples were incubated at 26°C with aeration for 1 hour , and dilutions were then plated onto L agar in order to quantify surviving bacteria . Stationary phase cultures were diluted 1:40 into L broth and grown for 4 hours at 26°C with aeration . Cultures were then washed and diluted 1:50 into M9 glucose medium or into M9 glucose medium containing 20 μM H2O2 , and were incubated with aeration for 10 minutes . For experiments with strains containing pACYC184-ptet::katG and pACYC184-ptet::ahpC , cultures were incubated with 1 . 5mM H2O2 for 60 minutes prior to RNA isolation . H2O2 -treated samples were pelleted and resuspended in buffer RLT ( Qiagen ) + ß-mercaptoethanol , and RNA was isolated using the Qiagen RNeasy kit . DNA contamination was eliminated using the DNA-free kit ( Ambion ) , and RNA was reverse transcribed into cDNA using M-MLV reverse transcriptase ( Invitrogen ) , in the presence of RNase-OUT ( Invitrogen ) . cDNA was utilized as a template in qPCR reactions with 0 . 5μM F and R primers ( S2 Table ) and SYBR Green ( Applied Biosystems ) , using the BioRad CFX Real-Time PCR detection system . Samples were normalized to an endogenous 16S RNA control and relative expression was determined using the ΔCT and ΔΔCT methods ( Applied Biosystems ) , when comparing treated to untreated samples . Accession numbers for the genes described in this study in NCBI are: aroA , YPK_2670; aroE , YPK_0321; purM , YPK_1253; rfaH , YPK_3937; wecC , YPK_4030; arnDT , YPK_1834-YPK_1835; dusB , YPK_0453; fis , YPK_0452; flgD , YPK_2423; psaEFABC , YPK_2761-YPK_2757; katG , YPK_3388; ahpC , YPK_3267; grxA , YPK_2733; recA , YPK_3375; rpoC , YPK_0341 .
|
The pathogenic members of the genus Yersinia share a conserved virulence plasmid that primarily serves to encode a Type 3 Secretion System and its associated effector proteins . During mammalian infection , these effectors are targeted toward phagocytic cells , where they neutralize a multitude of functions , including oxidative burst . However , it has previously been reported that strains of Yersinia pseudotuberculosis lacking the virulence plasmid retain the ability to grow in mammalian tissue sites , suggesting that the Yersinia chromosome encodes a number of poorly appreciated factors that enable survival in mammalian tissue sites , even in the absence of a functional T3SS . Here , we further characterize a number of these factors , including the operon dusB-fis . Using a variety of in vitro and vivo approaches , we determined that Fis regulates the transcription of several genes implicated in ROS resistance and that dusB-fis is essential for preventing growth restriction by ROS produced by the NADPH complex of phagocytes , even in a T3SS-expressing strain . Combined , these data suggest a model in which , during tissue infection , Yersinia evade killing by ROS through both T3SS-dependent and independent mechanisms .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
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"blood",
"cells",
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"laboratory",
"medicine",
"spleen",
"pathogens",
"immunology",
"microbiology",
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] |
2016
|
Fis Is Essential for Yersinia pseudotuberculosis Virulence and Protects against Reactive Oxygen Species Produced by Phagocytic Cells during Infection
|
Inhibition of the protein-protein interaction ( PPI ) mediated by breast-cancer-gene 1 C-terminal ( BRCT ) is an attractive strategy to sensitize breast and ovarian cancers to chemotherapeutic agents that induce DNA damage . Such inhibitors could also be used for studies to understand the role of this PPI in DNA damage response . However , design of BRCT inhibitors is challenging because of the inherent flexibility associated with this domain . Several studies identified short phosphopeptides as tight BRCT binders . Here we investigated the thermodynamic properties of 18 phosphopeptides or peptide with phosphate mimic and three compounds with phosphate groups binding to BRCT to understand promiscuous molecular recognition and guide inhibitor design . We performed molecular dynamics ( MD ) simulations to investigate the interactions between inhibitors and BRCT and their dynamic behavior in the free and bound states . MD simulations revealed the key role of loops in altering the shape and size of the binding site to fit various ligands . The mining minima ( M2 ) method was used for calculating binding free energy to explore the driving forces and the fine balance between configuration entropy loss and enthalpy gain . We designed a rigidified ligand , which showed unfavorable experimental binding affinity due to weakened enthalpy . This was because it lacked the ability to rearrange itself upon binding . Investigation of another phosphate group containing compound , C1 , suggested that the entropy loss can be reduced by preventing significant narrowing of the energy well and introducing multiple new compound conformations in the bound states . From our computations , we designed an analog of C1 that introduced new intermolecular interactions to strengthen attractions while maintaining small entropic penalty . This study shows that flexible compounds do not always encounter larger entropy penalty , compared with other more rigid binders , and highlights a new strategy for inhibitor design .
The tandem ~100-amino acid repeats of breast-cancer-gene 1 ( BRCA1 ) C-terminal ( BRCT ) are known to bind to phosphorylated proteins which are important for a number of tumor suppressor functions , which include , DNA repair , cell-cycle checkpoint , and transcription regulation [1–4] . The BRCT repeats recognize and bind phosphorylated protein partners such as CCDC98/Abraxas , BACH1 and CtIP in response to DNA damage [5–10] . Mutations in the BRCT domain of BRCA1 predispose women to breast and ovarian cancers [11] . A recent study showed that inhibitors of BRCT ( BRCA1 ) –phosphoprotein interface can be combined with DNA damaging agents as a viable therapeutic strategy for non-BRCA mutation carriers [12] . The same binding interface on BRCT ( BRCA1 ) promiscuously interacts with various phosphoproteins and short phosphopeptides containing the pSer-X-X-Phe sequence , where X denotes any residue [5–10] . Several modular domains , such as SH3 , SH2 , FHA , WW , Polo-box and PDZ , are also known to interact with multiple proteins through a consensus recognition sequence [13–18] . Here , we investigated the promiscuous recognition of the BRCT ( BRCA1 ) domain to better understand the mechanism that drives diverse ligands to bind to the same binding site . Our studies will provide insights into molecular detection , inhibitor discovery , and the search for binding partners . The BRCT ( BRCA1 ) domain is a tandem repeat; each N-terminal BRCT and C-terminal BRCT contain 90–100 residues with a central four-stranded β sheet ( β1-β4 and β1ʹ-β4ʹ ) and three α-helices ( α1-α3 and α1ʹ-α3ʹ ) . The BRCT–pSXXF interaction is anchored via a two-point binding mode: a hydrophilic contact made by the phosphoserine ( pS ) residue formed by N-terminal BRCT and a hydrophobic binding pocket from C-terminal BRCT for the phenylalanine ( F ) residue ( Fig 1 ) . The two-point binding scheme is also conserved for compounds with phosphate groups via a phosphate group and a hydrophobic ring group . Unlike most classical pharmaceutical targets such as enzymes with very defined binding cavity , the mostly solvent-exposed and plastic binding pockets such as the phosphoprotein binding interface of BRCT ( BRCA1 ) were considered un-druggable years ago [19–21] . The phosphopeptides are successful inhibitors of protein–protein interactions ( PPI ) [22–24] . Recently , many new PPI inhibitors have been developed for the BRCT domain , which include a number of short pSXXF tetra-phosphopeptides [12 , 24–26] and new phosphopeptide analogs with phosphate groups [27 , 28] . Although challenging to design , the demand for inhibitors of PPI has steadily increased [29 , 30] . Significant progress has been made in developing inhibitors targeting PPIs , and the development of effective therapeutics from PPI inhibitors will be improved by both experimental and computational approaches . Recent advances in computer modeling have provided powerful tools to study peptide-domains binding and protein dynamics . Molecular dynamics ( MD ) , Brownian dynamics simulations , and molecular docking have been used to investigate BRCT dynamics , interactions between inhibitors and BRCT , and the ligand association processes [25 , 27 , 31 , 32] . Bioinformatics tools were used to assess the functional impact and likelihood of pathogenicity of variants in the BRCT domain [33 , 34] . The promiscuous recognition of BRCT also makes it convenient to investigate the relationship between binding entropy and enthalpy changes . In addition to BRCT , other modular domains serve as good model systems for inspecting promiscuous recognition and the paradox associated with changes in entropy and enthalpy upon ligand binding that targets PPIs by computational methods [35–43] . This study aimed to further understand ligand–BRCT binding and provide strategies for designing inhibitors of PPIs . We selected several tetrapeptides and compounds with phosphate groups to computationally evaluate their driving forces to bind to BRCT ( BRCA1 ) . We performed MD simulations and detailed analysis of MD trajectories to examine the approaches BRCT uses to achieve promiscuous binding and the interaction energy of the ligand-BRCT . The MD simulations illustrated the molecular flexibility in the free and bound states for BRCT ( BRCA1 ) and ligands . We analyzed loop movements and the population of dihedral rotations of backbone and side-chains . Conformations from MD simulations were used as initial structures for thorough conformational search and free energy calculations with the M2 method , to reveal the contribution of configuration entropy and enthalpy to ligand binding affinities . We focused on how to optimize the balance between enthalpy gain and entropy loss . Using an accepted practice in ligand design , we synthesized a ligand that incorporates a benzene ring to possibly constrain its conformation . Upon ligand binding , changes of each energy term , conformations , rotameric state , and configurational entropy were evaluated by both MD and M2 tools; and the findings were used to suggest new inhibitors .
Table 1 lists 14 short peptides ( P1–P14 , among which P11 contains a phosphate mimic and others contain phosphorylated amino acids ) [24] , one compound ( C1 ) [27] , one new compound ( N1 ) , one designed compound ( D1 ) and 4 long phosphopeptides ( L1–L4 ) that bind to the BRCT domain: pS and pT is phosphorylated amino acid serine and threonine , respectively , and γcE is γ-carboxyglutamate , which is chosen to mimic pS interaction as a non-phosphorylated peptide binder . We ran MD simulations on BRCT-ligand complexes , free ligands and free protein , and the PDB IDs used as initial structures to perform MD simulations were listed in S1 Table . The initial bound conformation of all tetrapeptides was generated by superimposing the backbone atoms of -pSXXF- within phosphorylated BACH1 peptide ISRSTpSPTFNKQ in the C-terminal domain of the BRCA1 protein ( PDB code 1T29 ) [46] . Besides 1T29 , we included the other three BRCT domain structures in complex with long phosphopeptides from CtIP , ACC1 proteins and library screening , with PDB IDs 1Y98 ( PTRVSpSPVFGA ) , 3COJ ( PQpSPTFPEAG ) and 1T2V ( AAYDIpSQVFPFA ) , respectively , for promiscuous molecular recognition study [47–49] . The initial structure of the bound conformation of C1 , N1 and D1 , where no available crystal structures , were from docking with Autodock tools 1 . 5 . 6 [50 , 51] and then further checked manually by ensuring important interactions hold . Notably , Autodock was used for only the three ligands that did not have co-crystal structures with BRCT . The docking method used the Lamarckian genetic algorithm , which fixed the protein and allowed the ligand to move around in the docking box . The partial charges of ligands were calculated by using the Vcharge program [52] . The Autodock scoring function is a subset of the AMBER force field that treats molecules using the united atom model . Autogrid version 4 . 0 was used to create affinity grids with 0 . 375 Å spacing in 19 . 5 x 11 . 25 x 11 . 25 Å3 space at binding site . The final docking result was obtained by 10 runs of simulation with 2 . 5 million rounds of energy evaluation in each run . Ligand conformations with the lowest docked energies and reasonable conformation ( pSer forms hydrogen bonds with S1655 , G1656 and K1702 , and the P+3 Phe locates in the hydrophobic packet formed by M1775 , N1774 and F1704 ) were further analyzed . We selected two initial conformations with similar low energy computed by Autodock for ligands C1 and D1 , and N1 has one initial conformation ( S1 Fig ) . We performed MD simulations on an apo BRCT domain , 21 complexes , and 21 free ligands to study the dynamic nature of a given system . The standard simulation package , Amber14 [53] with the Amber 99SB force field [54–57] , was used . For pSer and pThr , we used the force field reported by Homeyer et al [58] . Amber atom types were manually assigned to non-standard amino acid and functional groups of the ligands C1 , N1 and D1 . Each system was set up as follows . First , we minimized the hydrogen , side-chain and whole system for 500 , 5 000 and 5 000 steps , respectively; then the systems were solvated in a rectangular box of a 12-Å explicit TIP3P water model by the tleap program in Amber14 . Each system contains about 50 000 atoms . Counter ions Na+ were added to keep the whole system neutral , and particle mesh Ewald was used to consider long-range electrostatic interactions [59] . Before equilibration , we ran energy minimization of 10 000 and 20 000 steps for the waters and system , respectively; next , we ran equilibrium of solvent molecules for 40 ps . Then the systems were gradually heated from 250 K for 20 ps , 275 K for 20 ps , to 300 K for 160 ps . We saved a frame every 1 ps with a time step of 2 fs in the isothermic−isobaric ( NPT ) ensemble . The Langevin thermostat with a damping constant of 2 ps−1 was used to maintain a temperature of 300 K , and the hybrid Noseé−Hoover Langevin piston method was used to control the pressure at 1 atm . We also used the SHAKE procedure to constrain hydrogen atoms during MD simulations [60] . Finally , all production runs were performed for 100 ns at 300 K . To ensure that all simulations reached stable energy fluctuations , we considered only trajectories during 20−100 ns for post-analysis . The second-generation mining minima method , M2 , calculates the standard free energy of binding by computing the free energy of the free BRCT ( G°BRCT ) , ligand ( G°ligand ) , and ligand-BRCT complex ( G°comp ) . M2 uses the classical formulation of the partition function for calculating free energy G° . Go≈−RTln ( 8π2co∑iZi ) ( 2 ) Zi=∫well ie−β ( U ( r ) +W ( r ) ) dr_int ( 3 ) where U is potential energy , W is the solvation free energy and Zi is the local configuration integral from distinct energy wells . The external degrees of freedom were integrated out and C° provides a correction to the standard state , and r_int indicates the variables of the internal bond-angle-torsion coordinates . Formally , the configuration integral must be determined over all spaces along the remaining internal degrees of freedom . M2 approximates this configuration integral by using the concept of considering local energy minima only [61 , 62] . Therefore , the M2 approach replaces the configurational integral over all spaces with a sum over separate local configurational integrals ( Zi ) associated with the low energy minima of the system . Determining Zi allows for the probability to be associated with each energy well , which in turn , allows for determining a Boltzmann averaged energy <U+W> , which is then subtracted from the total free energy to give the system configurational entropy , useful when analyzing and interpreting predicted binding affinities . Note that the configurational entropy S°config includes both a conformational part , which reflects the number of energy wells ( conformations ) , and a vibrational part , which reflects the average width of the energy wells . The solvent entropy is included in the solvation free energy , W . Therefore , the computed configurational entropy changes cannot be directly compared with experimentally measured entropy changes , which contain both configurational and solvent entropy . In brief , M2 contains two parts: 1 ) an aggressive conformational search for distinct low-energy wells , with repeats detected and removed; and 2 ) an enhanced harmonic approximation for computing the configuration integral Zi of each well i . Each distinct conformation is energy minimized , first by conjugate gradient method and then by Newton-Raphson method . Both parts involve the Hessian matrix with respect to bond-angle-torsion coordinates , and our harmonic approximation accounts for anharmonicity of eigenvectors of the Hessian matrix with eigenvalues < 2 kcal/mol/Å or 2 kcal/mol/rad . The correlation between different degrees of freedom ( e . g . , multiple dihedrals may rotate in concert or move with ligand translation/rotation ) is captured in the Hessian matrix . We used the VM2 package for the calculation [63–65] and performed three iterations for each ligand and 3 to 10 iterations for the free BRCT and the complexes until the cumulated free energy converged ( S2 Fig ) . To reduce the computational cost , only parts of BRCT were flexible , called the "live set" ( Fig 3 ) , which are residues within 7 Å of a long peptide ISRSTpSPTFNKQ in complex with BRCT ( PDB code 1T29 ) . The rigid set , called the "real set" , contained the residues within 5 Å of the live set . Other atoms not included in these two sets were not considered during the M2 calculations . All ligands were completely flexible and can freely translate and rotate within the binding site , and the same rigid and flexible parts of BRCT were applied to all systems . To compare the conformational changes of a molecular system between its free and bound states , we analyzed the selected ligand and BRCT dihedral angles during MD simulations and M2 calculations . Dihedral angles were measured by using T-analyst [66] , which can detect the angle population to find discontinuity in a dihedral distribution such as one energy well splitting into two wells near -180° and +180° . A shifted angle by adding or subtracting 360° is then applied to illustrate proper rotamer states . The population of each dihedral was then plotted by using Matlab with a histogram of 144 bins ranging from -360° to +360° to ensure coverage of all rotamer states after angle shifting . When analyzing the rotameric states , because the analysis does not need more than 1000 data points [66] , we used trajectories with a smaller file size that a frame was saved every 100 ps ( 1000 frames ) for each 100 ns MD run . We used the molecular mechanics/Poisson-Boltzmann surface area ( MM/PBSA ) -type post-processing method to compute ligand-BRCT intermolecular interactions during MD simulations [67–76] . The interaction energy , Δ ( U+W ) associated with BRCT and a ligand is computed by Δ ( U+W ) = <Ecomplex>—<Ebound BRCT>—<Ebound ligand> . The bracket <E> denotes the average energy computed from a given MD trajectory and the energy terms include a valence term ( bond , angle and dihedral ) , van der Waals ( UVDW ) , Coulombic ( UCoul ) , solvation free energy computed by the Possion-Boltzmann equation ( WPB ) and by cavity/surface area ( WNP ) . The dielectric constants of the interior and exterior protein were set to 1 and 80 , respectively . The valence term was canceled because of the single trajectory approach .
One unique feature of promiscuous protein systems such as BRCT is to bind to various ligands with significantly different size and shape by using the same binding interface . BRCT needs to provide adequate conformational isomers to recognize these ligands , which involves both side-chain rotation and additional plasticity provided by the backbone . As what shown in crystal structures of BRCT , the relatively rigid alpha helix and beta sheets hold the overall geometry . The variety of side-chains of residues in loops ( β3-α2 connection loop , β1'-α1' connection loop and linker between N-terminal and C-terminal ) creates a binding surface for ligand recognition except for the reserved binding region for the phosphate group [46] . The backbone nitrogen of G1656 and side-chain of S1655 of the β1 sheet and K1702 of the α2 helix form at least three stable hydrogen bonds with the phosphate group and also orient a ligand in the binding site ( Fig 1 ) . Notably , the pocket reserved to bind the phosphate group is located between a structurally rigid region constructed by a helix and a sheet . In contrast , the hydrophobic pocket for the P+3 phenylalanine is built by M1775 and N1774 of the β1'-α1' connection loop and F1704 of the α2 helix , with the β1'-α1' connection loop providing a certain flexibility for peptide binding [48] . To study the flexible regions in the binding pocket of BRCT , we measured the root mean square fluctuation ( RMSF ) of Cα and the standard deviation of phi and psi angles of residues in the BRCT backbone within 7 Å of 18 peptides ( P1–P14 , L1–L4 ) and compound C1 . The RMSF in S5 Fig shows that residues contacting with a ligand generally have smaller fluctuations and residues without contact with a ligand generally have larger fluctuations , Except for P13 , where the middle two proline residues of tetrapeptides do not form optimized contacts with BRCT . Although RMSF plot suggested that residues contacting with a ligand have small fluctuations in the Cartesian space , the standard deviation of phi and psi angles in Fig 4 ( A ) shows that the backbone dihedral angle can still rotate considerably . As illustrated in Fig 4 ( B ) , the most flexible region in the center part of the binding pocket , which directly contacts with the middle two residues of a pSer-X-X-Phe peptide and middle atoms of compound C1 . Utilizing the flexible loop region allows for the polar residues E1698 and R1699 of the β3-α2 connection loop to form a hydrogen bond with backbone atoms of the phosphopeptides and also accommodate ligands with different shapes . For example , the standard deviations for E1698 , R1699 and T1700 were especially large when BRCT bound to P13 and C1 , followed by concerted motions of N1742 and G1743 in the linker region . Although P13 still can fit into the binding cavity , the two proline residues limit the arrangement of both molecules to optimize the intermolecular interactions . In contrast , C1 was flexible and adopted multiple bound conformations to strengthen its binding affinity , as discussed in the following sections . For the long peptides , F1772 , T1773 from the β1'-α1' connection loop and D1692 , A1693 from the β3-α2 connection loop fluctuate to adjust the size of the binding cavity . The size change of binding site agrees with our previous molecular dynamics study , where the size of cavity can be characterized by two angles E1698-A1752-E1836 and S1655-A1752-N1774 , which can have difference of 10° upon binding of different peptides ( S6 Fig ) [25] . In summary , BRCT uses the power of loops to alter the shape and size of the binding site to fit various ligands , combined with a rigid region designed to form stable hydrogen bonds with the phosphate group . Because the BRCT domain has a highly adaptable binding pocket , we hypothesized that some ligands may feature diverse binding modes . We therefore examined the ligand binding modes and the rotamer of each rotatable bond for every ligand to discover their differences between the free and bound states . For all peptides P1–P14 , only one major bound conformation was observed: pSer forms hydrogen bonds with S1655 , G1656 and K1702 and the P+3 Phe locates in the hydrophobic packet ( Fig 1 ) . Interestingly , compound C1 can establish multiple bound conformations in the binding site by fitting either a benzene ring into the hydrophobic pocket and an indole ring into a cluster of residues G1656 , L1657 , T1658 of the β1-α1 connection loop and K1690 of the β3-α2 connection loop , and vice versa ( Fig 5 ( C ) and 5 ( B ) ) . C1 can also bind to BRCT with its folded form , whereby two rings form a T-shape stacking interaction ( Fig 5 ( A ) ) . Fig 6 illustrates the rotameric states of selected rotatable bonds of P4 and C1 in their free and bound states . All peptides show the same trend as in the histogram plots of P4 , with most rotatable bonds becoming more rigid and losing rotameric states in their bound state ( S7 Fig ) . However , compound C1 does not lose rotamers in the bound state , and a few dihedrals are even more flexible in the bound form . BRCT does not reduce the number of rotamers after binding to C1 either , which differs from the bound states with other peptides ( S8 Fig ) . MM/PBSA calculations suggested that the intermolecular interactions between all the peptides/ligands and BRCT are about the same , which agrees with experiments finding that ΔΔGexp is within 3 kcal/mol ( Table 2 ) . To understand why or why not a ligand loses the rotamers after binding , we clustered conformations of the free peptides and ligands and compared them with those in the bound complexes . For the peptides and C1 , they generally have two distinct conformations in the free state , folded and extended , which except for P13 ( Ac-pSPPF-NH2 ) , can switch back and forth in MD simulations of free ligands ( Fig 7 ) . However , the bound peptides are locked to only the extended form , which results in reduced rotamers in side-chains and also backbone φ and ψ angles ( Figs 6 and S7 ) . To test the robustness of MD simulation on rotameric states analysis , we ran and analyzed another MD run with different initial conformations for several ligands . The simulated rotameric states are nearly identical to the other MD , showing that multiple rotameric states in free states reduce to single rotameric state in bound state ( S9 Fig ) . For C1 , both folded and extended forms are observable in the bound states; free energy calculations with M2 further revealed that all these distinct ligand conformations are stable energy minima ( Fig 5 ) . To gain insights into the mechanism of binding , we needed thorough sampling and accurate ligand binding free energy calculations that included both enthalpic and configurational entropic contributions for molecular recognition . Although MM/PBSA calculations provide valuable information for intermolecular interactions , our calculations based on 100-ns MD simulations may have missed some important conformations , and contributions from changing configurational entropy and molecular conformations are neglected in Table 2 . In addition , because of different non-polar solvation models and use of a real set in M2 for energy calculations ( Fig 3 ) , the values of non-polar and polar interaction energies , ΔENP and ΔEPolar , from MM/PBSA and M2 cannot be compared directly . We therefore computed ligand-binding free energy with the M2 method , which involved an aggressive conformational search engine to locate local energy minima and a rigorous modified harmonic approximation approach to compute free energy for each minimum found . Table 3 and Fig 8 show that the computed related binding free energy , ΔΔGcalc , was in good agreement with experimental values , which validated the method as well . Because M2 uses accumulated energy which is different from dynamics-based method , it does not have fluctuated energy . Eq 2 shows that when a low energy minimum is found by M2 conformational search and added to the accumulated energy , the computed free energy G° drops . Search and computation continue until the accumulated free energy is converged . Here we calculated error interval for y-intercept of linear regression line in Fig 8 [83–85] . Part of the variance comes from experimental noise , which is typically about 0 . 3–0 . 5 kcal/mol for accurate binding free energy measurements [86] . If the binding free energies of two ligands are measured independently in experiments , then experimental relative binding free energies between the two ligands would have error around 0 . 4–0 . 7 kcal/mol . Therefore , the errors for free energy calculation method versus experimental data can only be larger than experimental noise of 0 . 4–0 . 7 kcal/mol , indicated by the range of y-intercept of linear regression line ( ~3 kcal/mol ) , and experimental noise is expected to be a at least 13% of the total observed error . With agreement of early studies on ligand–protein binding , the strong Coulombic attraction is largely compensated by the solvation free energy , and the vdW attraction is the major driving force for ligand binding [32 , 64] . Moreover , peptides with large non-polar residues at the P+2 position , such as P2 , P3 , P5 , P8 , P10 and P12 , generally have stronger vdW interaction ( Table 3 ) . Although M2 revealed more bound conformations for the complex from various combinations of side-chain rotations , the major binding mode of BRCT-pSXXF is the same as that obtained by MD sampling , whereby the phosphate group forms hydrogen bonds with S1655 , G1656 and K1702 , and P+3 Phe or Tyr locates in the hydrophobic pocket ( S10 Fig ) . M2 also revealed more conformations for free ligands , including the folded and extended forms , and their computed conformational free energies are similar . Therefore , the folded and extended conformations may have similar population in the free ligands . It is not surprising to observe the enthalpy Δ<U+W> and configuration entropy–TΔ<S> compensation for tight binders; however , the outlier C1 is particularly of interest ( Fig 9 ) . Although C1 forms a moderate enthalpy attraction with BRCT , ~ -38 kcal/mol , which is similar to that for peptides P4–P9 , with the remarkable ~2–4 kcal/mol small configuration entropy loss , C1 outperforms other peptides ( Table 3 ) . Compared with peptides , some rotamers of BRCT and C1 can gain new rotameric states rather than losing them , and the vibrational entropy loss is smaller than that for P4–P9 , as seen from the change in width of M2 histogram peaks that correspond to the width of energy wells ( Figs 6 and S7 ) . Upon ligand binding , M2 histogram peaks for P1–P14 become narrower , whereas C1 has the same or even wider peaks . In S2 Table , we list the number of complex , ligand and protein conformations from M2 calculations . For example , M2 calculations generated 482 distinct conformations of free P1 within 10 RT of the most stable free conformation . Even if free P1 were equally stable in all 482 energy wells with only one bound conformation , the maximum change in conformational entropy would only be reduced by RTln 482 = ~3 . 7 kcal/mol , which is significantly smaller than the -TΔS values in Table 3 . We may approximate vibrational entropy through -TΔSvib = -TΔSconfig + TΔSconf . S3 Table shows that C1 has much smaller vibrational entropy loss than peptides P1–P14 . In sum , both conformational entropy and vibrational entropy are attributed to the smaller configuration entropy loss of C1 . Interestingly , P7 , with a small residue alanine in the P+1 position , has the second smallest entropic penalty in M2 results but not P13 , which has two proline residues in the middle of the peptide . P13 managed to partially eliminate the folded conformations because of the geometric constraint proline residues; however , the entropy cost does not decrease substantially due to the big vibrational entropy loss ( S7 Fig and S3 Table ) . Moreover , the restraint by the two prolines resulted in the incorrect orientation of ligand-bound conformations , which significantly weakens the polar attractions ( S11 Fig ) . Two strategies are commonly used in ligand design for enhancing binding affinities: increasing intermolecular attractions and decreasing entropy loss upon binding . For example , new interactions between ligands and receptors , such as adding hydrogen bonds , can be introduced to increase enthalpic attractions [87–93] . The other way is via reducing the entropy cost by pre-rigidifying the ligand to its bound conformation [94 , 95] . This pre-organization of the ligand to its bound conformation lessens the decrease in number of rotameric states , and thus affinity is increased primarily because of optimizing the entropic term . Because the number of potential hydrogen bonds may already be maximized by the presence of the phosphate group , we used the latter strategy to pre-organize a ligand by introducing a benzene ring in the ligand backbone to limit its conformational flexibility . Having a benzene ring in the middle at a certain level prevents the ligand from bending and forming intra-molecular hydrogen bonds like other tetrapeptides do . A new ligand , N1 , was synthesized ( Fig 2 ) and its binding to BRCT was tested experimentally . Although the conformations were constrained to some degree to reduce conformational entropy penalty ( S7 Fig ) , the loss from the vibrational part was not reduced enough . The conformational constraints by the benzene ring restricted the ligand rearrangement to optimize the polar and non-polar contacts to the protein , thereby resulting in weak binding ( Table 3 and S12 Fig ) . N1 performed similar to P13 , so over-rigidifying a ligand is not advantageous , which suggests the challenge in retaining optimized intermolecular interactions in pre-rigidifying a peptidomimic compound . Previous work in design of potent Cbl ( TKB ) -binding peptides drew the same conclusion [96] . Therefore , because of conformational flexibility at the binding interface of a modular domain , flexible ligands may be favorable . Another strategy to lower entropy penalty , although less common , is by introducing a less rigid complex while the molecules bind . Because the strategies to further modify the short peptides to increase their bound conformations may be exhausted , compounds with phosphate groups are a better alternative . On the basis of our calculations and the structure of compound C1 , we further modified it to enhance intermolecular attractions by the formation of additional hydrogen bonds between the ligand and BRCT . In the meantime , we kept the template structure intact to maintain its flexibility . We added one hydroxyl group to the para site of the benzene ring of C1 , which can form hydrogen bonds with K1690 or N1774 with different bound conformations ( S13 Fig ) . Therefore , designed compound D1 shows improved binding affinity , by 2 kcal/mol , with more negative Δ ( U+W ) as compared with C1 ( Fig 9 and Table 3 ) . As compared with C1 , D1 has a stronger Coulombic interaction with BRCT because of the additional hydogen bonds ( Table 3 ) . Moreover , because D1 can also adopt mutiple distinct bound conformations , the entropy cost is minimal , as is found in C1 . The enthalpy-entropy compensation plot shown in Fig 9 clearly indicates that D1 outperforms other peptides by both increasing intermolecular attraction and reducing entropic penalty . In summary , designing a pre-rigidified ligand to reduce entropy cost can be tricky considering the potential loss of intermolecular attraction due to lack of proper rearrangement in the bound state . Fortunately , making a ligand more flexible and able to retain its plasticity in the bound conformation provides an effective strategy to reduce entropy cost , while the optimization of interactions between such a flexible ligand and a target protein can further improve binding affinity . Although for designing tight binders such as many drug-protein binding systems , pre-rigidified may still be the best strategy , our study points out a new direction for designing inhibitors targeting promiscuous modular domains and PPIs . Supplemental figures , data , results and detailed experimental method are provided in Supporting Information . All files , including the input and output files for MD simulations , post-analysis and M2 methods , MD trajectories , and results from various energy calculations are freely available upon request ( email: chiaenc@ucr . edu ) .
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Promiscuous proteins are commonly observed in biological systems , such as modular domains that recognize phosphopeptides during signal transduction . The use of phosphopeptides and compounds with phosphate groups as inhibitors to protein–protein interactions have attracted increasing interest for years . By using atomistic molecular dynamics simulations , we are able to perform detailed analyses of the dihedral space to explore protein fluctuation upon ligand binding to better understand promiscuous molecular recognition . Free energy calculation can further provide insights into the mechanism of binding , including both enthalpic and entropic contributions for molecular recognition , which assist in inhibitor design . Our calculation results show that pre-rigidifying a ligand is not always advantageous , suggesting the challenge in retaining optimized intermolecular interactions in pre-rigidified ligand . Instead , certain flexible ligands with multiple binding conformations can reduce entropic penalty , and therefore improves binding affinity . According to our computations , we can introduce new intermolecular interactions to flexible ligand to strengthen attractions while maintaining small entropic penalty by retaining its plasticity in the bound conformation . The study might cast light on a new general strategy for designing inhibitors targeting promiscuous modular domains and protein–protein interactions .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"and",
"Discussion"
] |
[
"chemical",
"bonding",
"classical",
"mechanics",
"chemical",
"compounds",
"phosphates",
"vibration",
"enthalpy",
"thermodynamics",
"hydrogen",
"bonding",
"physical",
"chemistry",
"entropy",
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"solvation",
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] |
2016
|
Characterization of Promiscuous Binding of Phosphor Ligands to Breast-Cancer-Gene 1 (BRCA1) C-Terminal (BRCT): Molecular Dynamics, Free Energy, Entropy and Inhibitor Design
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The human 1q21 . 1 deletion of ten genes is associated with increased risk of schizophrenia . This deletion involves the β-subunit of the AMP-activated protein kinase ( AMPK ) complex , a key energy sensor in the cell . Although neurons have a high demand for energy and low capacity to store nutrients , the role of AMPK in neuronal physiology is poorly defined . Here we show that AMPK is important in the nervous system for maintaining neuronal integrity and for stress survival and longevity in Drosophila . To understand the impact of this signaling system on behavior and its potential contribution to the 1q21 . 1 deletion syndrome , we focused on sleep , an important role of which is proposed to be the reestablishment of neuronal energy levels that are diminished during energy-demanding wakefulness . Sleep disturbances are one of the most common problems affecting individuals with psychiatric disorders . We show that AMPK is required for maintenance of proper sleep architecture and for sleep recovery following sleep deprivation . Neuronal AMPKβ loss specifically leads to sleep fragmentation and causes dysregulation of genes believed to play a role in sleep homeostasis . Our data also suggest that AMPKβ loss may contribute to the increased risk of developing mental disorders and sleep disturbances associated with the human 1q21 . 1 deletion .
Recent genome-wide association studies have identified copy-number variants ( CNVs ) associated with high risk of schizophrenia and other neuropsychiatric disorders [1–4] . Sleep disturbance is one of the most common problems observed in individuals with psychiatric disorders [5] . Although the 1q21 . 1 CNV deletion , spanning 10 genes over 1 . 35 million base pairs , has been demonstrated to genetically predispose carriers to sleep disturbances , intellectual disability , autism , and schizophrenia [4] , the impact of individual genes in this interval on the development of these disorders is unclear . PRKAB2 , encoding a β-subunit of the AMP-activated protein kinase ( AMPK ) complex , is located on the 1q21 . 1 chromosome arm and is a promising candidate gene that may contribute to sleep disturbances and some of the behavioral effects observed in 1q21 . 1 patients . The AMPK complex is the main cellular energy sensor , functioning as a metabolic master switch that is required for maintenance of energy homeostasis [6] . When activated as a response to increased energy demands , by detecting increased intracellular AMP:ATP and ADP:ATP ratios , AMPK upregulates catabolic processes that generate adenosine triphosphate ( ATP ) while downregulating anabolic processes to maintain energy balance [7] . Interestingly , energy metabolism dysfunction is among the most consistent features observed in psychiatric disorders and has been shown to include pathways for ATP production [8] . The evolutionarily conserved AMPK complex that regulates ATP comprises a catalytic ( serine/threonine kinase ) α-subunit , a scaffolding β-subunit , and a regulatory γ-subunit [9 , 10] . Unlike mammalian genomes , which encode multiple isoforms of each AMPK subunit ( α1 , α2; β1 , β2; γ1 , γ2 ) that are , at least in part , functionally redundant , the genome of the fruit fly Drosophila melanogaster contains only one gene encoding each subunit , encoded by AMPKα , alicorn ( alc ) , and SNF4Aγ , respectively , making this organism an ideal model in which to study AMPK function [11] . While the function of AMPK in whole-body metabolism and cellular energy sensing is well-known , its role in neuronal maintenance , and the manner in which nervous-system-specific effects of AMPK manifest in global phenotypes , are poorly defined . Neurons in particular may be vulnerable to disturbances in AMPK function , as these cells have high metabolic demands and are ineffective at storing energy [12]–the human nervous system accounts for at least 20% of the body’s energy consumption while only making up 2% of the body’s mass [13] . Studies in mice have shown that AMPK is activated in the brain upon introduction of stresses such as glucose deprivation , ischaemia , and hypoxia [14 , 15] . Furthermore , loss of the AMPKβ1 subunit in mice was shown to have strong effects on brain development and structure [16] . Studies using Drosophila have shown that the AMPK complex affects cellular homeostasis , longevity , and neurodegeneration [17–19] . In Drosophila , upregulation of AMPKα in the nervous system has been linked to a slowing of systemic aging and thus to prolonged lifespan [20] , which indicates that AMPK function in the nervous system has a broad , systemic impact . Furthermore , alc , encoding the single Drosophila AMPKβ homolog , has been shown to have a neuroprotective role in the retina [19] . However , a more generic role for the AMPK complex in the nervous system , and any downstream impacts on behavior , remain largely unknown . To investigate the potential contribution of PRKAB2 to the 1q21 . 1 CNV syndrome , and the role of AMPK signaling in neuronal maintenance and sleep regulation , we examined the function of its Drosophila ortholog , Alc/AMPKβ , in the nervous system . Here , we show that alc and AMPK signaling are required in the nervous system to maintain behaviors such as sleep and learning . Specifically , we observed reduced learning in animals with neuronal knockdown of alc . Furthermore , we find that neuronal knockdown of alc and loss of AMPK function cause severe sleep fragmentation , and animals with reduced nervous-system alc expression are vulnerable to stress induced by sleep deprivation and show no sleep rebound . We suggest that PRKAB2 is a promising candidate gene that may contribute to the behavioral disturbances seen in 1q21 . 1 patients such as sleep disturbances and cognitive deficits related to learning .
Although AMPK activity has been linked to lifespan , the effect on longevity of reducing AMPK signaling specifically in the brain has not been reported . To determine whether neuronal reduction of AMPK signaling impacts overall physiology and health in Drosophila , we used the pan-neuronal elav-GAL4 ( elav> ) driver to express either RNAi against alc ( alc-RNAi; two independent constructs , in conjunction with Dicer-2 ) or overexpression of a kinase-dead dominant-negative form of the AMPKα subunit ( AMPKα-DN ) [17] , and we monitored survival for 89 days . Both manipulations resulted in reduced survival compared to the driver ( elav>Dcr-2/+ ) and corresponding RNAi ( alc-RNAi/+ ) and AMPKα-DN/+ controls ( Fig 1A ) . To quantify lifespan , we fit the survival curves for individual experimental vials to a Weibull distribution to obtain a Weibull scale parameter that is analogous to the median survival but takes into account the increasing risk of mortality in aging systems ( S1 Fig ) . Using this measurement , we found a significant reduction of lifespan in animals with nervous-system-specific knockdown of alc or overexpression of AMPKα-DN , conditions that reduce neuronal AMPK signaling ( Fig 1B ) . This shows that effects of AMPK on lifespan can be attributed to its function in the nervous system . Several studies have reported an increased sensitivity to starvation in addition to decreased longevity in animals with reduced AMPK signaling [17 , 21] . This effect has been speculated to involve AMPK-regulated metabolic pathways in adipose tissue , muscle , and intestine [22] . To investigate whether this reduced survival under starvation stress can be attributed to effects of AMPK in the nervous system , we measured the survival of animals with reduced neuronal AMPK signaling ( knockdown of alc or overexpression of AMPKα-DN ) under starvation . As we did in normal-lifespan measurements , we observed reduced starvation survival with pan-neuronal alc-RNAi-mediated knockdown compared to controls ( Fig 1C and 1D ) . Although animals expressing AMPKα-DN in the nervous system exhibited reduced survival compared to the AMPKα-DN/+ control , we observed no difference compared to the elav>Dcr-2/+ driver control . Together our results show that AMPK-mediated neuronal homeostasis plays a significant role in promoting organismal longevity and resistance to starvation . For the subsequent experiments , we chose to use the UAS-alc-RNAi ( KK ) line , which reduces expression of alc by ~85% in heads , and more strongly than the alc-RNAi ( 8057R-2 ) ( S2A and S2B Fig ) . To further assess the specificity of this RNAi towards alc , and knockdown of the AMPK complex in general , we examined levels of phosphorylated AMPKα ( pAMPKα ) subunit , a readout of AMPK-complex activation . We found that neuronal knockdown of alc ( AMPKβ ) caused a ~60% reduction in pAMPKα levels in the head , confirming that alc knockdown using this RNAi line specifically reduces AMPK activity ( S2C Fig ) . The reduced lifespan observed in animals with neuronal loss of AMPK signaling suggested that lack of this activity may lead to impaired neuronal function and maintenance . Since changes in neuronal dendritic branching and morphology are a hallmark of neuropsychiatric disorders and have been suggested to alter the function of neuronal circuitry [23–26] , we chose to analyze the effect of alc manipulations on dendritic development and maintenance . In Drosophila , the class-IV dendritic arborization ( da ) neurons are peripheral sensory neurons that develop highly stereotyped dendritic processes , making them an ideal model for studying changes in neuronal morphology [27] . Disruption of alc has been shown to cause progressive and activity-dependent degeneration of photoreceptor neurons [19] , but the impact of these disruptions upon dendritic arborization has not been explored . To determine whether alc has a role in dendrite development or maintenance , we reduced expression of alc using the da-neuron-specific pickpocket ( ppk ) -GAL4 ( ppk> ) driver line while simultaneously expressing GFP to visualize neuronal structures . We imaged individual class-IV da neurons in both large feeding ( ~100 hours after egg lay ) and wandering ( ~120 hours after egg lay ) 3rd-instar larvae . RNAi-mediated knockdown of alc caused a dramatic reduction of dendritic arborization in feeding larvae , reducing the total dendrite length by 25% ( Fig 2A and 2B ) and branching order by 14 . 5% , limiting dendrite structure to mainly primary and secondary branches ( Fig 2C ) . Knockdown of alc also led to 35% fewer dendritic branches yet did not alter the total area spanned by individual neurons , compared to controls ( Fig 2D and 2E ) . The severity of this dendritic phenotype was progressive , appearing more severe in older ( wandering ) animals , with a 44% reduction in total dendrite length ( Fig 2B ) , 38% reduction in branch order ( Fig 2C ) , and 68% reduction in branch number ( Fig 2E ) . This observation suggests that AMPK is required for the maintenance of dendritic structures , rather than for their formation . Furthermore , we observed a “beaded” dendritic morphology as well as thinned and fragmented primary dendritic branches , which are hallmarks of dendritic degeneration . Our AMPKβ observations in da neurons are further supported by previous observations of similar dendrite-morphological abnormalities associated with loss of AMPKα and AMPKγ subunit activity in these cells [17] . Together these observations suggest that AMPK activity is necessary for the maintenance of dendritic arbors . Since proper dendritic structure is required for a functional nervous system , we then asked whether AMPK signaling , and specifically alc , might be required for proper expression of behaviors relevant to the development of neurological disorders . Intellectual disability and general learning disabilities are prevalent in patients carrying the 1q21 . 1 CNV [4 , 28] . We hypothesized that alc might also be important for behaviors such as learning . In Drosophila , one simple behavioral assay that tests learning is courtship conditioning [29] . In this experiment , naïve male flies are presented with an non-receptive mated female and soon associate her mating-associated olfactory and gustatory cues with the courtship-rejection behaviors she expresses , leading the males to suppress futile courtship behavior towards other mated females in the future . To determine whether alc is required for learning following courtship conditioning , we conducted a courtship-conditioning assay with adult ( 4–7 day old ) flies . Following a 1-hour training session in which naïve males were presented with a mated ( non-receptive ) female , individual naïve or trained males were introduced to a mated female . Animal pairs were video-recorded using a custom recording setup in custom chambers ( S3 Fig ) , and stereotypical courtship behaviors were manually scored . Courtship indices ( CIs ) were calculated as the fraction of time each male spent courting within the 10-minute recording period . Males from control genotypes ( elav>Dcr-2/+ and alc-RNAi/+ ) showed typical levels of courtship suppression following training , compared to naïve flies exposed to a sham training session ( Fig 3A ) . In sharp contrast , there was no significant difference in CI between naïve and trained elav>Dcr-2 , alc-RNAi flies with reduced expression of alc in the nervous system , indicating that pan-neuronal knockdown of alc results in an inability to learn to suppress courtship behavior . The percentage reduction in CI of naïve versus trained flies is scored as the learning index ( LI ) , which is essentially zero in animals with reduced neuronal expression of alc ( Fig 3B ) . Naïve elav>Dcr-2 , alc-RNAi males display normal levels of courtship compared to controls , but fail to suppress courtship towards mated ( non-receptive ) females following training . This indicates that loss of alc in the nervous system specifically impairs learning and is not reflecting a defect in courtship behavior . Thus , these data suggest that neuronal expression of alc is required for learning during courtship conditioning . Patients with the 1q21 . 1 microdeletion also suffer from non-intellectual behavioral deficits , one common manifestation being disrupted sleep patterning [30] . It has been suggested that sleep is essential for the brain to replenish energy sources ( ATP ) depleted during wakefulness [31] . Because one of the major roles of the AMPK complex is to upregulate processes that generate ATP , we asked whether knockdown of alc in the nervous system would impair sleep consolidation . We hypothesized that if sleep drive is associated with the depletion of energy stores during metabolically demanding wakefulness , thereby stimulating AMPK-dependent signaling , levels of activated AMPK would fall during sleep as ATP levels are restored . Under this scenario , a decrease in AMPK activity would be associated with decreased sleep drive , increased wakefulness , and reduced consolidation of sleep episodes . Sleep in Drosophila can be measured using the Drosophila Activity Monitor ( DAM ) system ( TriKinetics ) , an automated beam-crossing locomotion assay that is the widely accepted standard in Drosophila sleep studies . It is well established in Drosophila that periods of locomotor quiescence of longer than 5 minutes indicate a sleep-like state exhibiting the hallmarks of mammalian sleep [32 , 33] . To determine whether loss of alc impairs sleep , we expressed alc-RNAi in the nervous system and monitored sleep using the DAM system in a light/dark illumination-controlled incubator . Animals with reduced alc expression in the nervous system exhibited significantly reduced overall sleep compared to control genotypes , during both day and night phases . This is also reflected in an overall increase in activity compared to controls ( Fig 4A–4C ) . Thus reduced “sleep , ” defined as it is as long-term immobility , can be indicative of a hyperactivity phenotype . To clarify whether we were observing differences in “sleep” or “activity” , we examined the properties of activity bouts during sleep . Since Drosophila adults spend circadian light transition events in an elevated state of arousal during which foraging and courtship behaviors are dominant , we separated these locomotion periods from more “steady state” periods . These transitional periods were defined as consolidated periods of activity which include the ZT 0 and ZT 12 time-points . Activity during wakeful periods ( number of beam crosses per minute awake ) that were not associated with light transitions ( and thus reflective of sleep-time activity ) was significantly increased during the light phase , but not during the dark phase ( Fig 4D ) . Flies also demonstrated a significant increase in the duration of wakeful periods during light ( compared to both controls ) and dark ( compared to UAS-alc-RNAi/+ control only ) ( Fig 4E ) . Interestingly , when we analyzed the periods around light-transition events we did not observe increased level or duration of activity for elav>Dcr-2 , alc-RNAi flies compared to controls ( S4A and S4B Fig ) , which indicates that the loss of alc causes a specific sleep phenotype that is not a consequence of overall hyperactivity . For Drosophila sleep studies , sucrose-based medium is generally a standard diet . However , since AMPK is involved in intracellular energy sensing , we also tested the phenotype on regular cornmeal-based food to see whether the observed effects are related to this sucrose-based diet . Nervous-system-specific alc knockdown causes a loss-of-sleep phenotype on regular cornmeal-based food , similar to what is observed on the standard sucrose-based food used for sleep studies ( S4C Fig ) . Together these data suggest that animals with reduced expression of alc , and thus reduced AMPK activity , in the nervous system have an increased drive for wakefulness and reduced sleep drive . To further assess the sleep properties of animals with neuronal loss of alc function , we investigated sleep-bout duration and number . Animals with reduced alc expression in the nervous system showed an increase in sleep-bout number and a significant concomitant reduction in average sleep-bout length , indicating highly fragmented sleep ( Fig 4F and 4G ) . The average longest single sleep bout during both day and night was also greatly reduced compared to control genotypes ( Fig 4H ) . To more closely examine these sleep changes , we analyzed the distribution of sleep episodes by binning them by duration and calculating the time spent in each bin . Whereas control flies consolidated most of their daytime and nighttime sleep into long sleep episodes of 150–499 minutes , animals with neuronal loss of alc demonstrated a shifted sleep structure with significantly more time spent in short sleep bouts ( binned into 5–14 and 15–50 minutes ) ( Fig 4I and 4J ) . In the aggregate , these data indicate that alc is necessary for proper sleep architecture and consolidation in Drosophila . Recent studies suggest that overall metabolic state , in particular when regulated within the fat body ( the adipose and liver tissue of Drosophila ) , may be an important regulator of complex behaviors such as sleep , reflecting the need to modulate these behaviors according to internal energy stores and availability of nutrients [34] . Ubiquitous AMPK down-regulation has previously been shown to increase overall dietary intake , yet flies with reduced AMPK activity have reduced nutrient stores and display starvation-like lipid accumulation in the fat body which suggests that they are in a persistent state of starvation [17] . Furthermore starvation is known to suppress sleep in Drosophila , which indicates a higher demand for foraging behaviors [35] . Therefore , we sought to determine whether reducing alc in the fat body might have a distinct phenotype from that which we observe in the nervous system by expressing alc-RNAi using the fat body driver line CG-GAL4 ( CG> ) . Interestingly , we observed a reduction in sleep specifically during the day , presumably when foraging behaviors are prevalent ( S4E and S4F Fig ) . This reduction is characterized by more sleep episodes of shorter duration ( S4G and S4H Fig ) . However , the length of the maximum sleep-bout duration is not reduced when compared with driver control ( S4I Fig ) . This indicates that although these animals spend more time locomoting during the day , when they do enter a consolidated sleep episode , its duration is unaltered by reduced alc in the fat body . Furthermore , total sleep and length of sleep episodes during the night are not reduced when alc is reduced in the fat body . This indicates that the sleep fragmentation that we observe in this study is specific to AMPK signaling in the nervous system and is separate from potential metabolic effects that are mediated within the fat body . To confirm the reduced-sleep phenotype observed for pan-neuronal knockdown of alc , we used a second , independent alc-RNAi line ( 8057R-2 ) and determined average sleep-bout duration and number during both day- and night-time . We observed similar reduced sleep and sleep fragmentation characteristics with this alc-RNAi ( 8057R-2 ) line as with the alc-RNAi ( KK ) line ( Fig 5A and 5B and S5 Fig ) . Furthermore , we verified that the sleep phenotype observed with the alc-RNAi ( KK ) line is caused specifically by loss of alc function . To do this , we tested whether the phenotype might be rescued by expression of alc in the nervous system . As expected , neuronal expression of alc rescues the loss-of-alc-function sleep phenotype , both for the reduced total sleep as well as the sleep fragmentation ( S6 Fig ) . Together this confirms that the sleep phenotype caused by alc-RNAi in the nervous system can be attributed specifically to reduced alc expression . To further examine whether this phenotype was specifically due to altered AMPK signaling in the nervous system , we overexpressed AMPKα-DN to block AMPK-complex activity via inhibition of the alpha subunit . Surprisingly , these animals initially exhibited no significant reduction in average sleep-bout duration or number . However , over longer observation periods , sleep phenotypes arose that were similar to those displayed by alc-knockdown animals—average sleep-bout duration significantly decreased over time , while the number of sleep bouts significantly increased , during both day and night ( Fig 5C and 5D ) . This progressive deterioration in sleep consolidation over time was specific to reduced AMPK signaling , since no significant changes in sleep-bout architecture were observed with control genotypes aside from slightly increased daytime sleep-bout duration in one control line . Thus , the loss of AMPK signaling in the nervous system causes sleep fragmentation that worsens over time . This progressive deterioration is reminiscent of the morphological phenotypes observed in larval class-IV da neurons . It is possible that a similar morphological deterioration of sleep-regulatory neurons might underlie these defects . To further determine whether the fragmented-sleep phenotype of animals deficient in alc is due to hyperactive animals , we measured basal locomotion velocities . Behavior was recorded in 37-mm behavioral chambers , and spontaneous velocity was quantified using the C-trax software [36] . Example tracks for the wild-type Canton-S , control elav>Dcr-2/+ , and animals with reduced alc expression in the nervous system ( elav>Dcr-2 , alc-RNAi ) are shown in Fig 6A . Animals with reduced expression of alc in the nervous system ( elav>Dcr-2 , alc-RNAi ) did not show elevated velocities when compared to either control and spent less time locomoting , resulting in a shorter total distance traveled over the observed 10- or 20-minute period . ( Fig 6B–6D ) . This is consistent with the conclusion that the sleep phenotype of animals with reduced neuronal alc is due to the fragmentation of sleep episodes and not elevated activity . Since defective neuronal AMPK signaling due to tissue-specific alc knockdown resulted in fragmented and reduced sleep , we suspected that these animals might therefore exhibit altered expression of sleep-stress-response genes . To assess this possibility , we performed RNA sequencing on adult head samples of animals with neuronal knockdown of alc versus controls . Amylase has been identified as a biomarker for sleep drive in Drosophila , and another , previously uncharacterized gene , heimdall , has recently been linked to animals’ response to sleep deprivation and starvation [37 , 38] . Interestingly , both of these genes are significantly upregulated in the heads of alc-knockdown flies–heimdall exhibited the greatest transcriptional up-regulation of any gene , with a roughly 180-fold increase compared to control heads ( Table 1 and S1 Table ) . Thus , alc-knockdown animals show a genetic response consistent with disrupted sleep . A major hallmark of sleep is homeostatic regulation , which is required for the recovery of lost sleep after sleep deprivation ( SD ) . To investigate whether AMPK might be important for the homeostatic process of sleep regulation , we tested whether animals expressing neuronal alc RNAi were defective in “recovery sleep” following deprivation . To assess recovery sleep following SD in alc knockdown animals , we exposed elav>Dcr-2/+ control animals and elav>Dcr-2 , alc-RNAi animals to mechanical SD , using a vortex mounting plate ( Trikinetics ) that regularly jostled the animals . Sleep deprivation was initiated during the second day of recording , for the 6 hours of the latter half of the dark phase immediately preceding “lights-on” . This resulted in similar levels of sleep loss in alc knockdown and control animals ( Fig 7A and 7B , S7A Fig ) . However , whereas controls showed substantial subsequent sleep rebound following SD , elav>Dcr-2 , alc-RNAi animals did not show any such post-SD rebound ( Fig 7B and 7C , S7A and S7B Fig ) . Following relief from SD , control flies fell into extended sleep and entered their longest consolidated sleep episode faster ( Fig 7D ) , with an increase in average bout duration initiated during the first two hours following SD ( Fig 7E ) . In contrast , animals lacking alc in the nervous system took significantly longer to reach a maximally consolidated sleep bout and did not increase sleep bout duration following SD ( Fig 7D and 7E ) . This indicates that animals with impaired neuronal AMPK signaling not only fail to initiate rebound sleep , but also seem to be generally impaired in their sleep following SD . To further examine this deprivation-dependent shift in sleep architecture , we analyzed the distribution of sleep bouts during day and night following the deprivation period . Directly following sleep deprivation , control animals spent more time in prolonged sleep episodes ( 500–720 min ) compared to baseline periods , and returned to baseline sleep-length distribution in the following dark phase ( Fig 7F ) . In contrast , elav>Dcr-2 , alc-RNAi animals displayed more-fragmented sleep immediately following deprivation , indicative of an impaired sleep drive ( Fig 7G ) . Surprisingly , this fragmentation persisted into the following dark phase , indicating that SD of flies with altered AMPK signaling has longer-term effects on sleep architecture ( Fig 7G ) . The defect in recovery sleep following sleep deprivation can be the result of animals that become hyper-aroused following mechanical perturbation . To rule out this possibility , we measured sleep latency following deprivation . We observed that both the control ( elav>Dcr-2/+ ) and alc knockdown ( elav>Dcr-2 , alc-RNAi ) animals enter the first sleep episode faster following deprivation , indicating that they are not hyper-aroused ( S7C Fig ) . To further assess the sensitivity of animals with reduced alc expression , we measured the response of these animals to a mechanical vibration stimulus . While a pulse train of five consecutive ( 1 sec apart ) vibration stimuli induced elevated locomotion in all genotypes tested , animals with reduced neuronal alc showed similar response to the Canton-S wild-type and reduced response compared to the driver control ( elav>Dcr-2/+ ) and return to baseline velocity at a similar or increased rate compared to the two controls ( Fig 6E ) . These results further support the conclusion that the observed sleep-rebound phenotype is due to disruption of sleep architecture and not the result of hyper-sensitivity . To determine whether the apparent detrimental effect of sleep deprivation impacts survival , we monitored sleep-deprived animals for 24 hours following sleep deprivation . We observed increased mortality of elav>Dcr-2 , alc-RNAi animals following sleep deprivation compared to both un-deprived age-matched elav>Dcr-2 , alc-RNAi animals and SD and non-SD driver controls ( S8A Fig ) . To control for a possible elevated sensitivity to mechanical stress , we performed a mechanical-stress assay in which flies were exposed to prolonged repeated mechanical perturbation , and stress-induced mortality was assessed [39] . Knockdown of alc in the nervous system did not result in a higher incidence of lethality over this period ( S8B Fig ) , which confirms that the observed lethality following sleep deprivation and the corresponding deterioration of sleep architecture is the result of sleep loss rather than physical weakness of the animals . Together , these data indicate that alc knockdown animals are incapable of consolidating rebound sleep and are highly sensitive to sleep deprivation , with long-lasting detrimental effects following SD . Our findings indicate that AMPK activity is required for rebound sleep following deprivation , indicating that the AMPK complex is involved in the homeostatic regulation of sleep . According to this view , AMPK activity would increase over periods of wakefulness , when higher neuronal energy demands deplete cellular energy stores . Consistent with this , we observed upregulation of pAMPKα levels in heads of control animals after 6 hours of SD , whereas this signal did not increase in animals with reduced neuronal alc expression ( S8C Fig ) . Elevated pAMPKα levels were also observed in wild-type ( Canton S ) flies after SD , confirming AMPK-complex activation following periods of SD in wild-type animals .
AMPK signaling has been shown to play a critical role in aging and lifespan determination [40] . Upregulation of AMPK signaling in Drosophila has been shown to extend lifespan by mediating effects specifically in the intestine and the brain [20] . Furthermore , tissue-specific RNAi-mediated knockdown of AMPKα in the fat body and muscle was shown to reduce lifespan [41 , 42] . We find that AMPK signaling in the nervous system affects longevity and survival under starvation stress . This indicates that non-metabolic pathways may contribute to the impairment of survival upon loss of AMPK signaling . As non-cell-autonomous mechanisms have been implicated in the regulation of longevity upon enhanced AMPK signaling in the brain [20] , it is possible that similar mechanisms are involved in the lifespan reduction we observe . Interestingly , reduction of AMPK signaling in neuroendocrine cells releasing the energy-mobilizing Adipokinetic Hormone resulted in a significant extension of life span under starvation [43] . An intriguing possibility is that the starvation sensitivity that we observed with nervous-system AMPK loss is mediated through effects on neuromodulatory or neurohormonal systems . The cellular mechanisms by which altered metabolism brings about neuropathology are not clear . It is well established that neuronal morphogenesis defects may result from mitochondrial dysfunction in various cell types [44 , 45] . As mitochondria and AMPK are both involved in maintaining levels of ATP , defects in these systems may phenocopy each other . In fact , it has been shown that loss of AMPK activity enhances neurodegeneration in Drosophila models of mitochondrial abnormalities [46] . Normal neuronal activity and synaptic transmission involve molecular and cellular processes , such as maintenance of resting membrane potential and generation of action potentials , transporter activity , transmitter synthesis , and vesicle transport and dynamics , that are energy-intensive . Therefore , a disruption of restorative AMPK signaling that is required to replenish energy stores could result in a state of neuronal energy depletion , which could lead to progressive degeneration . Here we show that knockdown of alc results in progressive reduction of the dendritic arbors of class-IV da neurons , a phenotype consistent with reduced AMPK activity [17 , 47] . Reducing AMPK signaling by knockdown of alc/AMPKβ specifically affects terminal dendrite branching , not the distance ( reach ) from the soma . Our observations are consistent with previous reports showing that altered AMPK signaling results in neurodegeneration [48 , 49] . RNAi-mediated knockdown of the α- or γ-subunits of the AMPK complex in class-IV da neurons in Drosophila larvae causes aberrant dendrite morphology , indicative of faulty neuronal development , neuronal damage , and degeneration . In sum , this suggests that neuronal loss of AMPK activity is associated with progressive neurodegeneration , originating with insufficient energy to maintain neuronal structures . These results are also consistent with the emerging role of AMPK in neurodegenerative diseases such as Alzheimer’s , Parkinson’s and Huntington’s [50] . The role of AMPK in mediating the synaptic plasticity that underlies learning and memory consolidation is unclear . One possibility to explain our observations would be that the neuronal connections necessary for associative learning in the mushroom body of the insect brain [51] have degenerated , thus resulting in a system incapable of forming , retaining , or recalling memories . Another possibility is that loss of AMPK may impair cAMP second-messenger signaling , which underlies the neuronal plasticity necessary for learning and memory [52 , 53] . In mammalian adipocytes , AMPK has been shown to be activated by intracellular cAMP levels [54]; it is possible that a similar mechanism is present in neurons and is involved in mediating neuronal activity and plasticity . Interestingly , in mice , treatment with the AMPK agonist AICAR increases spatial memory in a Morris water maze [55 , 56] . Our results suggest that Alc/AMPKβ and thus the AMPK complex are required for learning , although further experiments need to be conducted to evaluate the role of reduced AMPK signaling in the formation and maintenance of memory . Interestingly , it has been shown that sleep deprivation has a negative effect on learning and memory in several animal models , which opens the possibility that the role of AMPK in memory is associated with its role in sleep [57–59] . A persistent state of sleep deprivation in animals deficient in AMPK signaling may explain their inability to learn to repress courtship upon rejection . Other learning paradigms need to be tested to determine if this is a general effect on the association center of the Drosophila brain and to rule out contribution from potential sensory defects that would lead to inability to sense olfactory mating cues . Sleep is a highly conserved animal behavior , and humans spend roughly one-third of their life sleeping [60 , 61] . Sleep and wakefulness are under the control of circadian and homeostatic processes . The circadian clock determines the timing and rhythmic nature of sleep onset , whereas homeostatic mechanisms are involved in sensing sleep drive and provide increasing sleep pressure as a function of time spent awake . Neurons have high demands for ATP , the major form of cellular energy , and low capacity to store nutrients [62] , which has led to the hypothesis that sleep is required to replenish neuronal energy that is depleted during wakefulness [63] . This theory suggests that energy levels are reflected in glycogen and adenosine changes accumulated during metabolically demanding wakefulness and that these molecules play key roles in homeostatic sleep regulation [64] . Consistent with this notion , glycogen levels are indeed affected by the rest and wake cycle and drop after short periods of rest deprivation in Drosophila [65] . Adenosine is a breakdown product reflecting the depletion of ATP , the primary energy currency used by brain cells [66] . Consistent with the idea that sleep is necessary to reestablish energy stores , ATP has been shown in rats to increase during the initial hours of sleep when neuronal activity is low [31] . If the homeostat senses sleep drive by measuring energy levels , the molecular mechanism of the homeostat must involve an energy sensor that is activated by low cellular energy levels and initiates processes that restore energy levels to relieve sleep pressure after a nap . As the major cellular energy sensor activated by low energy levels , AMPK promotes processes that replenish energy levels [67] . Furthermore , the AMPKβ subunit contains a glycogen-binding domain that likely enables the AMPK complex to sense energy levels in the form of both ATP and glycogen in the nervous system [68] . If the AMPK complex is involved in homeostatic sleep regulation , then rebound sleep following sleep deprivation would not occur without this complex . Our results indicate that neuronal loss of AMPK affects sleep regulation and leads to loss of rebound sleep following deprivation , providing evidence that AMPK is indeed involved in homeostatic sleep regulation . It will be of great interest to determine whether AMPK is part of the mechanism of sleep homeostasis and its role in psychiatric disorders characterized by sleep disturbances .
Drosophila larvae and adults of mixed sexes were raised on standard cornmeal medium ( Nutri-Fly “Bloomington” formulation ) under a 12:12 light/dark cycle at 25 °C with 60% humidity , unless otherwise stated . Fly lines Cg-GAL4 ( #7011 ) , elav-GAL4; UAS-Dicer-2 ( Dcr-2 ) ( #25750 ) , ppk-GAL4 ( #32078 ) , UAS-mCD8::GFP ( #5137 ) , UAS-AMPKα-K56R ( UAS-AMPKα-DN , #50760 ) , CG-GAL4 ( #7011 ) , and Canton S were obtained from Bloomington Drosophila Stock Center ( BDSC; Bloomington , IL ) . UAS-alc-RNAi ( KK ) ( #109325 ) and the w1118 ( #60000 ) genetic background line were procured from Vienna Drosophila Resource Center ( VDRC; Vienna , Austria ) . UAS-alc-RNAi ( 8057-R2 ) was obtained from the NIG-Fly stock center ( Mishima , Shizuoka , Japan ) . UAS-alc was a generous gift [19] . We used a cross to w1118 , the isogenic genetic background of the RNAi line and the genetic background for most fly lines , as controls . For starvation-survival experiments , thirty 3-5-day-old adult males were placed in vials containing 2% agar in water . Survival was assessed every 2–4 hours during the main course of the experiment for 9–10 replicate vials for each cross until all animals were dead . For each vial , the median survival was calculated in MatLab ( The MathWorks , Inc . , Natick , MA ) as the time-point when the cumulative survivor function using the Kaplan-Meier method fell below 50% . For lifespan experiments , thirty male flies were collected upon eclosion into vials containing normal diet . Ten replicates for each cross were used , and the animals were transferred to fresh vials every 2 to 3 days . During each transfer , the numbers of dead animals left behind and carried over were recorded . Escaped flies or accidental deaths during transfer were recorded as censored . Longevity was monitored for 89 days . Survival data was analyzed for each vial in MatLab using the Kaplan-Meier nonparametric method accounting for censored data . As some control vials did not reach 50% mortality , to quantify survival , the Weibull distribution function was fit to the data , right-censoring the animals still alive at the end of the experiment . This analysis extends exponential distributions of failure ( death ) probability to allow for the increasing hazard rates associated with aging systems [69] . The scale parameter was determined for each vial and used for comparisons . Feeding and wandering third-instar larvae were selected and anesthetized by exposure to chloroform for 1 minute in a sealed container . Larvae were mounted in 90% glycerol , and GFP fluorescence in live animals was imaged . One individual GFP-labelled neuron , located in segment A2 , was imaged per larva . Larvae were imaged at 20X using a Zeiss 780 LSM confocal microscope . Z-stacks were processed using FIJI ( NIH ) software [70] , and analysis was performed using the TREES toolbox in MatLab [71] . A courtship-conditioning assay was used to assay learning in adult flies [29] . Male flies were collected upon eclosion and housed individually until the start of the experiment . Virgin females were collected upon eclosion and housed in groups of 30 . Mated females were generated by housing with males for 24 hours prior to experimental start , after which the males were removed . Male flies were split into two groups , naïve flies and flies to be trained . For training , individual male flies were incubated with mated females for 1 hour prior to testing in custom 2-cm-diameter mating chambers with a food source . For imaging courtship behavior , custom chambers were laser cut from clear acrylic . Each chamber set consisted of an array of 20 , 1 . 5-cm individual chambers allowing for testing of all conditions in parallel ( S3 Fig ) . Channels were cut to allow for a thin separator to be inserted through all of the chambers to keep the loaded males and female separate until the start of recording . Single naïve or trained males were then transferred into a testing arena together with individual virgin females ( loaded through a separate loading hole and kept separate from males ) . Transfer was done using gentle aspiration to avoid disturbing the animals . Once all animals were loaded , the chamber was placed on a custom-built image-acquisition setup consisting of a 20-cm diffuse infrared LED backlight ( Falcon illumination FLFL-Si200-IR24 ) and a Basler acA2000-50gmNIR GigE camera fitted with an IR filter ( S3 Fig ) . Once the dividers separating males and females was removed , video was recorded for 10 minutes using LabView ( National Instruments , Inc . , Austin , TX ) . All experiments were done in a climate-controlled room at 25 °C and 70% humidity . Courtship behaviours were manually scored , with the scorer blinded to the genotype . The experiment was repeated 11 times , with three trials ( individual fly pairs ) per experiment . Courtship indices ( CIs ) were calculated as the percentage of time that a male fly spends courting during a 10-minute period . The learning index ( LI ) was calculated as the relative reduction of CI in trained male flies compared to naïve flies . For basal locomotion measurements , animals were video recorded at 15 Hz using the imaging setup described above ( S3 Fig ) . Animals were gently aspirated into 37 mm diameter behavioral chambers and their behavior recorded for 10 or 20 minutes . Spontaneous locomotion was quantified using the Ctrax MatLab package [36] . Running velocity was determined for periods where animals moved faster than 2 mm/sec . Mechanical stimulus was delivered using a 10 mm brushless vibration motor ( Precision Microdrives #910–101 ) driven by an Arduino Uno microcontroller interfaced with the custom imaging LabView program to deliver a train of five 500 msec stimuli at 1 Hz every 60 seconds over the 10 minute recording period . For analysis , velocities for 60 second periods were aligned by stimulus onset for the first 5 stimuli and baseline velocity ( average velocity during 5-minute period preceding stimulus ) was subtracted . Locomotion over a 24-hour period was measured using the Drosophila Activity Monitor ( DAM ) system ( TriKinetics , Inc . , Waltham , MA ) . Adult males were collected in groups of 30 upon eclosion and housed under standard conditions until experimental start . Four-to-seven-day-old males were used for experiments , housed in 65-mm glass tubes with a plug of 5% sucrose and 2% agar medium at one end . Experiments were performed under a 12-hour light/12-hour dark cycle , and activity measurements were binned in one-minute periods . Episodes of sleep were defined as at least 5 minutes of uninterrupted quiescence . Animals with less than 10 minutes of activity during either the light or dark phase were flagged as dead . All analyses of sleep- and motion-bout dynamics were done in MatLab . For sleep-deprivation experiments , flies were mounted in DAM monitors and were mechanically stimulated using a vortexer mounting plate ( TriKinetics ) for 2 seconds every minute , over a 6-hour period prior to lights-on . Recovery sleep from flies with >60% loss of sleep during the deprivation period was analyzed and compared to baseline conditions 24 hours prior to the commencement of sleep deprivation . Recovery sleep was defined to occur in the first three hours following the end of sleep deprivation . Fly heads were homogenized in Laemmli sample buffer ( #1610737 , Bio-Rad , Hercules , CA ) . For separation of polypeptides , samples were electrophoresed through precast polyacrylamide gels ( Bio-Rad , #4561094 ) for 1 hour in a Tris/glycine/SDS buffer . Separated proteins were transferred to a nitrocellulose membrane and were then blocked with Odyssey blocking buffer ( LI-COR Lincoln , NE , #927–40000 ) for 1 hour prior to incubation overnight with rabbit anti-pAMPKα ( 1:1000 , Cell Signaling technology , Danvers , MA , #2535 ) and mouse anti-α-Tubulin ( Sigma #T9026 , diluted 1:5000 ) antibody at 4 °C . After 3 x 15-minute washes in PBS at RT , samples were incubated with IRDye 680RD and 800CW secondary antibodies diluted 1:10 , 000 ( LI-COR ) for 30 minutes at RT . Western blots were imaged using an Odyssey Fc imaging system ( LI-COR ) and ImageJ was used to quantify probe signal intensity . For quantitative real-time PCR ( qPCR ) , total RNA was prepared from 10 adult male heads using the RNeasy Mini Kit ( Qiagen #74106 ) with DNase treatment ( Qiagen #79254 ) . cDNA was synthesized using the iScript Reverse Transcription Supermix for RT-PCR ( Bio-Rad #1708840 ) , and qPCR was performed using the QuantiTect SYBR Green PCR Kit ( Fisher Scientific #204145 ) on an Mx3005P qPCR system ( Agilent Technologies ) . Expression was normalized to RpL32 . Primers: alc; GGGCGACCATCAGTACAAGT and GCGTTCTCCACGCTTTTC; RpL32 , AGTATCTGATGCCCAACATCG and CAATCTCCTTGCGCTTCTTG . For RNA-sequencing transcriptomics ( RNA-seq ) , ten adult fly heads from 4-7-day-old males were harvested for each condition , and RNA libraries were prepared for Illumina HiSeq sequencing of paired-end 100-bp reads . Triplicates were sequenced for each genotype to determine differentially expressed genes . The mechanical stress test was conducted on 3-day-old male flies as described [39] . For each genotype , three vials containing 10 flies each were evaluated , with genotypes masked during test and evaluation . Each vial of flies was subjected to 5 seconds of mechanical stimulation ( vortexing ) , followed by 55 seconds of recovery . The number of upright flies was counted after this time and is represented as a percentage of total number of flies .
|
The human 1q21 . 1 chromosomal deletion is associated with increased risk of schizophrenia . Because this deletion affects only a small number of genes , it provides a unique opportunity to identify the specific disease-causing gene ( s ) using animal models . Here , we report the use of a Drosophila model to identify the potential contribution of one gene affected by the 1q21 . 1 deletion–PRKAB2 –to the pathology of the 1q21 . 1 deletion syndrome . PRKAB2 encodes a subunit of the AMP-activated protein kinase ( AMPK ) complex , the main cellular energy sensor . We show that AMPK deficiency reduces lifespan and causes structural abnormalities in neuronal dendritic structures , a phenotype which has been linked to schizophrenia . Furthermore , cognitive impairment and altered sleep patterning are some of the most common symptoms of schizophrenia . Therefore , to understand the potential contribution of PRKAB2 to the 1q21 . 1 syndrome , we tested whether AMPK alterations might cause defects in learning and sleep . Our studies show that lack of PRKAB2 and AMPK-complex activity in the nervous system leads to reduced learning and to dramatic sleep disturbances . Thus , our data links a single 1q21 . 1-related gene with phenotypes that resemble common symptoms of neuropsychiatric disorders , suggesting that this gene , PRKAB2 , may contribute to the risk of developing schizophrenia .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] |
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"learning",
"invertebrates",
"medicine",
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"sleep",
"deprivation",
"nervous",
"system",
"sleep",
"social",
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"neuroscience",
"animals",
"learning",
"and",
"memory",
"sleep",
"disorders",
"animal",
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"physiological",
"processes",
"drosophila",
"melanogaster",
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"psychology",
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"neuronal",
"dendrites",
"drosophila",
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"lipids",
"animal",
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"animal",
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"arthropoda",
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"cellular",
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] |
2018
|
AMPK signaling linked to the schizophrenia-associated 1q21.1 deletion is required for neuronal and sleep maintenance
|
Lipid droplets are ubiquitous triglyceride and sterol ester storage organelles required for energy storage homeostasis and biosynthesis . Although little is known about lipid droplet formation and regulation , it is clear that members of the PAT ( perilipin , adipocyte differentiation related protein , tail interacting protein of 47 kDa ) protein family coat the droplet surface and mediate interactions with lipases that remobilize the stored lipids . We identified key Drosophila candidate genes for lipid droplet regulation by RNA interference ( RNAi ) screening with an image segmentation-based optical read-out system , and show that these regulatory functions are conserved in the mouse . Those include the vesicle-mediated Coat Protein Complex I ( COPI ) transport complex , which is required for limiting lipid storage . We found that COPI components regulate the PAT protein composition at the lipid droplet surface , and promote the association of adipocyte triglyceride lipase ( ATGL ) with the lipid droplet surface to mediate lipolysis . Two compounds known to inhibit COPI function , Exo1 and Brefeldin A , phenocopy COPI knockdowns . Furthermore , RNAi inhibition of ATGL and simultaneous drug treatment indicate that COPI and ATGL function in the same pathway . These data indicate that the COPI complex is an evolutionarily conserved regulator of lipid homeostasis , and highlight an interaction between vesicle transport systems and lipid droplets .
Lipid homeostasis is critical in health and disease , but remains poorly understood ( for review see [1] ) . Non-esterified free fatty acid ( NEFA ) is used for energy generation in beta-oxidation , membrane phospholipid synthesis , signaling , and in regulation of transcription factors such as the peroxisome proliferator-activated receptors ( PPARs ) . Essentially all cells take up excess NEFA and convert it to energy-rich neutral lipids in the form of triglycerides ( TG ) . TG is packaged into specialized organelles called lipid droplets . NEFA is regenerated from lipid droplet stores to meet metabolic and energy needs , and lipid droplets protect cells against lipotoxicity by sequestering excess NEFA . Lipid droplets are the main energy storage organelles and are thus central to our understanding of energy homeostasis . Despite their importance , we know very little about the ontogeny and regulation of these organelles . Lipid droplets are believed to form in the ER membrane by incorporating a growing TG core between the leaflets of the bilayer , and ultimately are released surrounded by a phospholipid monolayer . Cytosolic lipid droplets possess a protein coat and grow by synthesis of TG at the lipid droplet surface [2] and by fusion with other lipid droplets [3] . Formation of nascent droplets and aggregation of existing droplets is likely to require a dynamic exchange of lipids and proteins from and to the droplet . Indeed , the range of proteins identified in lipid droplet proteomic studies suggests extensive trafficking between lipid droplets and other cellular compartments , including the endoplasmic reticulum ( ER ) [4–6] . Additionally , lipid droplet-associated proteins translocate between the cytosol and lipid droplets [7] . For example , tail interacting protein of 47 kDa ( TIP47 ) associates with small , putative nascent , lipid droplets [8–10] , but is not found on larger droplets , which are coated by other members of the perilipin , adipocyte differentiation related protein ( ADRP ) , TIP47 ( PAT ) protein family . Intriguingly , TIP47 mediates mannose 6-phosphate receptor trafficking between the lysosome and Golgi [11] , raising the possibility that trafficking is involved in lipid droplet ontogeny or fate . However , unlike the well-studied Golgi trafficking system , the routes to and from the lipid droplet are unknown . Once lipid droplets are formed , stored TG is mobilized in a regulated manner . Triglyceride , diglyceride ( DG ) , and monoglyceride lipases convert TG back into NEFA . Most of our knowledge concerning lipolysis is based on extensively studied adipocytes in which at least two lipolytic enzymes have been identified: adipocyte triglyceride lipase ( ATGL ) [12–14] and hormone sensitive lipase ( HSL ) [15] . Due to the hydrophobic properties of the lipid droplet TG core , lipases are likely to act at the surface of lipid droplets [16] , where members of the PAT protein family regulate lipase access to the TG core . Mammalian genomes encode at least five PAT-proteins . Whereas perilipin is the dominant PAT protein in adipocytes , ADRP is the dominant PAT protein in nonadipose tissues in which it is tightly associated with the lipid droplet surface [17] . PAT members appear to have a hierarchical affinity for the lipid droplet surface . In nonmammalian genomes , there are fewer PAT proteins . For example , two PAT proteins termed lipid storage droplet 1 and 2 ( LSD-1 and LSD-2 ) are found in Drosophila melanogaster [10] . The crucial role of PAT proteins is evolutionary conserved as the absence of perilipin in mice [18 , 19] , or LSD-2 in flies [20 , 21] results in lean animals . Overexpression of LSD-2 results in obese flies [20] . These data indicate the conserved PAT proteins at the lipid droplet surface are important regulators of energy storage . It seems likely that PAT proteins protect lipid from lipolysis , but the role of PAT proteins may not be limited to passive steric hindrance of lipase access to the TG core , as illustrated by perilipin . Unphosphorylated perilipin protects the lipid droplet from lipase activity . Following stimulation by protein kinase A ( PKA ) , however , phospho-perilipin acts as a docking site for HSL [22 , 23] , which translocates from the cytosol to the droplet surface [24] . Whereas phospho-perilipin promotes massive NEFA release from the droplet , this is not mediated exclusively by HSL , as mice lacking HSL function show a relatively mild phenotype marked by the accumulation of DG , thus demonstrating that HSL acts as a DG lipase in vivo [25] . The TG lipase functioning in HSL null mice is ATGL . In the current view of adipocyte lipolysis , ATGL is responsible for the first step in TG hydrolysis , liberating DG and NEFA , whereas HSL acts as a DG lipase . We know very little about how ATGL is targeted to the lipid droplet . In contrast to the lean phenotype in animals lacking perilipin ( mouse ) or LSD-2 ( fly ) , both mice and flies lacking ATGL are obese . In mice , the absence of ATGL results in excessive TG accumulation in liver and muscle [12 , 14] . Similarly , human patients suffering from neutral lipid storage disease carry mutations resulting in truncated ATGL isoforms [26] . ATGL function is evolutionary conserved , as flies lacking the Drosophila ATGL ortholog , Brummer , accumulate copious amounts of body fat [13] . The lipid droplet-associated protein Comparative Gene Identification-58 ( CGI-58 ) acts as an ATGL colipase [27] . Mutations in the CGI-58 gene result in ectopic fat accumulation in patients suffering from Chanarin Dorfman Syndrome ( CDS , [28] ) , supporting the idea that both ATGL and CGI-58 are required for mobilizing lipid stores in nonadipose tissue . Interestingly , CGI-58 physically interacts with perilipin as demonstrated by both coimmunoprecipitation and fluorescence resonance energy transfer ( FRET ) studies [22 , 29 , 30] . In addition , there are other lipases and probably many more cofactors encoded in the genome . Understanding which ones act at the lipid droplet surface and how their localization is regulated will be important . Drosophila is a powerful model for pathway discovery due to well-developed genetics . Additionally , greater than 60% of the genes associated with human disease have clear orthologs in Drosophila [31] . Drosophila is highly relevant to lipid droplet study , as lipid droplets in Drosophila and mammals are associated with many of the same proteins [4–6 , 32–35] . Finally , the emerging model of lipid storage and endocrine regulation are similar in humans and Drosophila [36] , suggesting that Drosophila will be a good genetic model for lipid storage and lipid storage diseases in humans . We therefore utilized genome-wide RNA interference ( RNAi ) screening in Drosophila tissue culture cells to identify and characterize novel regulators of lipid storage . We then tested for the function of these regulators in mouse lipid droplet regulation by directed RNAi studies . We identified 318 Drosophila genes required to limit lipid storage and 208 Drosophila genes required to promote lipid storage . These genes encode known regulators of lipid storage as well as genes not previously associated with lipid storage regulation . Because the protein composition of the lipid droplet surface is so critical for lipid droplet function , and because very little is known about how lipid droplet decoration is regulated , we focused on the exciting finding that the retrograde vesicle-trafficking machinery , utilizing the Coat Protein Complex I ( COPI ) and COPI regulators , was required to utilize lipid stores . COPI subunit knockdown by RNAi , as well as COPI inhibition with compounds , resulted in increased lipid storage both in Drosophila and mouse tissue culture cells , demonstrating evolutionary conservation of our findings . COPI and COPII vesicles are essential components of the trafficking machinery cycling between the ER and Golgi ( reviewed in , e . g . , [37] ) . COPI vesicles mediate cargo transport from the Golgi back to the ER , including escaped ER-resident proteins . The anterograde counterpart , COPII , mediates transport of proteins and lipids from the ER to the Golgi . Whereas interference with either COPI or COPII complexes disrupts Golgi function [38 , 39] , only COPI was required for lipid droplet utilization , clearly demonstrating that COPI and not general Golgi function is required for TG utilization . Although we certainly do not rule out communication between the Golgi and lipid droplet , we suggest that there is a novel ER/lipid droplet trafficking system using a subset of the ER/Golgi transport machinery . We found that the basis for lipid overstorage following COPI knockdown was a decreased lipolytic rate . Using our existing knowledge of the PAT family members and lipases in the regulation of lipolysis , we examined changes in protein composition at the lipid droplet surface . Interestingly , we found that interfering with the COPI pathway results in ectopic accumulation of TIP47 at the lipid droplet surface . Furthermore , ATGL at the lipid droplet surface was greatly reduced . Combining the effects of ATGL knockdown and compounds affecting COPI function did not elicit a stronger decrease in lipolysis , indicating that ATGL and COPI are both part of the same lipolytic pathway . Thus , our studies provide a functional link between COPI retrograde trafficking and the proteins at the lipid droplet surface . More generally , these results indicate that Drosophila RNAi screening is suited to detect uncharted pathways affecting NEFA regulation and to achieve a deeper understanding of cellular lipid droplet regulation .
Lipid droplets are well studied in mammalian cells , but Drosophila cells have not been extensively used in lipid droplet studies . Lipid droplets are ubiquitous organelles , and we found that Drosophila S2 and SL2 ( unpublished data ) , as well as S3 and Kc167 cells ( this study ) accumulated TG in lipid droplets in the presence of excess NEFA . Kc167 cells , for example , stored little lipid when grown on standard media ( Figure 1A ) , whereas in the presence of NEFA ( 400 μM oleic acid ) , they readily ( within 12 h ) accumulated TG packaged in droplets ( Figure 1B ) , which we visualized with the lipid-specific dye BODIPY493/503 [40] . Treatment of Drosophila cells with double-stranded RNA ( dsRNA ) decreases , or “knocks down , ” transcript levels for genes sharing the dsRNA sequence , a process known as RNAi [41] . To help determine whether Drosophila tissue culture is a good model for lipid droplet function , we used RNAi to target genes encoding known lipid droplet regulators . Flies or mice lacking ATGL store more TG than wild type ( “overstorage” ) [12–14] , whereas those lacking diacylglycerol acyl transferase1 ( Dgat1 ) , a key enzyme in TG synthesis [42 , 43] , store less lipid ( “understorage” ) . Knockdown of bmm , which encodes Drosophila ATGL , increased lipid storage as expected ( Figure 1C and 1D ) . Conversely , treating cells with dsRNA targeting midway ( mdy ) , which encodes Drosophila Dgat1 , decreased lipid storage ( Figure 1E and 1F ) . Thus , Drosophila cells can be used to analyze gene functions necessary to increase as well as decrease lipid storage . Although differences in lipid storage are often obvious , we were interested in generating a fully quantitative dataset to support future meta-analysis . To systematically identify and characterize the genes involved in lipid storage , we developed a microscopy-based quantification method based on image segmentation and measurement of nuclear to lipid droplet cross-sectional area ( see Figure 2A–2D and Materials and Methods ) . This technique allowed us to detect lipid storage differences caused by the different feeding conditions and control dsRNA treatments ( Figure 2E ) . We used this imaging method to perform a genome-wide RNAi screen with the well-characterized dsRNA library of the Harvard Drosophila RNAi Screening Center ( DRSC ) . This collection covered more than 95% of the predicted Drosophila genes [44] . dsRNAs against bmm and mdy were included in each screening plate as controls . We also included wells with no dsRNA and with or without oleic acid as controls . As a screening cell line , we used Kc167 cells , which showed the best balance of lipid droplet deposition , RNAi susceptibility characteristics , and adhesion during assay development ( unpublished data ) . Following dsRNA treatment of oleic acid-fed cells and image analysis , ratiometric data were normalized within plates and across the entire screening collection using linear models , B-score , Z-score/median absolute deviation ( MAD ) , and strictly standardized mean difference ( SSMD ) [45–48] , all of which gave similar results . B-score normalization [46] across the entire screen marginally out-performed other methods ( see Materials and Methods , Table S1 ) . B-score results were used for all analyses reported here . Rank-order analysis of the genome-wide screening results demonstrated that the majority of dsRNAs had no effect on lipid storage . However , two cohorts of dsRNAs resulted in lipid overstorage , as expected for genes required for promoting lipid utilization , or understorage , as expected for genes required for promoting lipid storage ( Figure 2F ) . Thresholds for determining whether a particular dsRNA resulted in a phenotype were selected to balance false negatives and false positives based on the results for bmm , mdy , and no oleic acid controls . At B-scores ≥ 2 . 0 and ≤ −1 . 7 , greater than 89% of wells treated with dsRNA targeting bmm or without oleic acid resulted in the correct overstorage or understorage call , respectively ( Figure 2G ) . Using these cutoffs , we identified 208 candidate genes required for increasing lipid storage ( understorage on knockdown , B-score ≤ −1 . 7 , Tables S2 and S9 ) and 318 required for reducing lipid storage or lipid utilization ( overstorage on knockdown , B-score ≥ 2 . 0 , Tables S3 and S9 ) . These data suggest that about 3% of the Drosophila genome is directly or indirectly involved in lipid storage . All data are available in the supplement ( Table S4 ) and at http://lipofly . mpibpc . mpg . de/ and http://flyrnai . org . The most critical test of screen performance is coherence as measured by the identification of multiple genes in a multisubunit complex or a known pathway [49] . Such coherent gene sets are also the best candidates for more detailed analysis . To categorize the dsRNA phenotypes according to molecular networks , we analyzed the identified genes using Gene Ontology ( GO ) [50] terms with the VLAD tool [51] . This analysis allows for the detection of statistically overrepresented GO terms among a set of genes and projects those enrichments onto the GO-term hierarchy . Genes with a possible function in lipid storage regulation as detected by the RNAi screen were tested against the complete Drosophila gene set for enrichment of GO terms associated with biological process , molecular function , and cellular component . Identified , enriched terms were structured in hierarchical networks ( Figures 3–5; the results are also tabulated in Table S5 ) . We also took advantage of data from a concurrent lipid storage screen using an independent dsRNA library and Drosophila S2 cells [52] . This allows us to develop a robust overview of lipid droplet storage . Duplication of extensive RNAi screens using different libraries on different cell types provides a cross-validating function that is extremely useful in the analysis of comprehensive datasets . The overlap ( 25% , 57 genes ) between the S2 cell screen ( 227 genes identified; Table S6 ) and our genome-wide study on Kc167 cells ( 526 genes identified ) was highly significant ( p < 1e−14 , Wilcox test ) . More importantly , the GO term networks were quite similar and suggest that key pathways have been identified ( Figure 3 ) . For example , both screens show that interfering with translation factors and ribosomes result in lipid storage defects ( GO:0022613 , GO:0006412 ) . Additionally , genes resulting in lipid storage defects are enriched for transcriptional regulators in both screens ( GO:0010467 ) and trafficking ( GO:0006911 , GO:0006890 ) . The only major differences between the screens were that genes involved in pre-mRNA processing were enriched in our Kc167 cell screen and genes involved in proteasome function were enriched in the S2 cell screen . However , five genes required for lipid storage in our study ( suppressor of deltex , ubiquitin conjugating enzyme 2 , ubiquitin activating enzyme 1 , Roc1a , and Roc1b ) are involved in ubiquitin-mediated proteolysis at the proteasome [53] . Thus , the screens are largely cross-validating . Gene knockdowns resulting in understorage have a candidate wild-type function in promoting lipid storage . Whereas we identified gene functions linked to neutral lipid synthesis ( Table S5 ) , the most striking enrichments were for regulatory functions within the nucleus ( Figure 4 ) . GO terms associated with the nuclear functions transcription or transcript processing were particularly prominent ( Figure 4 ) . These data suggest that lipid storage requires a complex regulatory network . In contrast , the candidate genes required for lipid utilization were enriched for cytoplasmic functions ( Figure 5 , Table S5 ) . We found that lipid storage increased after treatment with dsRNAs targeting genes encoding lipid droplet-associated proteins ( GO:0005811 ) . In addition to GO term analysis , we directly compared the identified candidate lipid storage-modulating genes functions with genes encoding proteins of the recently described , but functionally uncharacterized , lipid droplet-associated mammalian [5 , 6 , 32 , 35] and Drosophila [4 , 33] subproteomes , only some of which have lipid droplet GO terms . These genes were far more likely to result in a lipid overstorage phenotype when subjected to knockdown in Drosophila ( p > 1e−16 , Wilcox test ) than the reference genome-wide dsRNA targets . This suggests that many of the genes revealed by our RNAi experiments encode direct regulators of lipid storage . Gene functions involved in mitochondrial fatty acid beta-oxidation , which utilize NEFA as a substrate , as well as genes involved in protein synthesis , were also enriched . Indeed , knockdown of 12% of the Drosophila genes encoding translation-related functions ( GO:0006412 ) , including 32% of the genes encoding ribosomal subunits ( GO:0033279 ) , resulted in lipid overstorage ( Figure 5 , Table S5 ) . It is possible that decreased ATP demand for protein synthesis and decreased ATP generation in mitochondria simply decrease the need for energy in the cells , resulting in increased lipid storage . Mitochondrial uncoupling and beta-oxidation pathways are areas of therapeutic interest for diabetes and other metabolic disorders [54–57] . One of the most striking results was the prevalence of cellular transport functions in general ( GO:0006909 , GO:0006890; and GO:0000022 ) , and the COPI trafficking pathway mediating Golgi to ER transport in particular , among the genes resulting in a lipid overstorage phenotype on knockdown ( Figure 5 , Table S5 ) . Nascent lipid droplets are thought to form at the ER and then enlarge and fuse to form larger droplets [8–10] . Thus , our result is somewhat surprising , as we expected that wild-type ER functions might be involved in promoting lipid storage rather than lipid utilization . Similarly , it is known that lipid droplets are transported as cargo on microtubules in Drosophila embryos and that such transport is required for fusion of lipid droplets in muscle cells [3 , 58] . There was a strong enrichment for genes involved in spindle microtubule elongation ( Figure 5C ) among the genes showing overstorage on knockdown . Again , whereas microtubule involvement in lipid storage is predicted , interfering with microtubule cargo transport might be expected to decrease lipid storage . To validate a “gold set” of genes ready for extended follow-up , we selected genes for additional Drosophila treatments using original and secondary dsRNAs . At least two different nonoverlapping dsRNAs in our screen or in the Guo et al . screen [52] resulted in confirmed understorage or overstorage phenotypes for a subset of candidate genes ( Table S7 ) . Additionally , mouse orthologs of 127 Drosophila genes selected on the basis of lipid storage phenotypes in Kc167 cells ( including orthologs of 54 genes that failed to pass our cutoff ) were knocked down in two mouse cell lines using short interfering RNAs ( siRNAs ) . We used a mouse fibroblast cell line ( 3T3-L1 ) , in which lipid droplets have been extensively characterized , and a liver cell line , AML12 , which was previously used as a model of ectopic fat deposition [59] . Retesting in mouse cells is a particularly stringent validation of the Drosophila dsRNA data as it simultaneously provides information about evolutionary conservation as well as obviating concerns about spurious off-target effects [49 , 60 , 61] . The 33 genes resulting in lipid storage defects when knocked down in both Drosophila and in mouse cells validate the involvement of many of the biological processes implicated by the primary screen ( Table S7 ) . For example , knockdown of the Ubiquinol cytochrome c reductase complex III subunit VII gene ( Uqcrq; ortholog of the Drosophila CG7580 gene ) , which encodes a component of the mitochondrial respiration chain , results in greatly enlarged AML12 cells storing dramatically more lipid than control cells ( Figure 6A–6C ) . Similarly , knockdown of Smarca4 ( ortholog of the Drosophila brahma gene ) , which encodes a member of the SWI/SNF chromatin modifying complex [62] , results in lipid overstorage ( Figure 6D ) . Knockdowns of COPI complex members resulted in overstorage in Drosophila S2 and Kc167 cells , and in mouse 3T3-L1 and AML12 cells ( Table S7 ) . Although there is much to be gleaned from the screen , we focused our attention on the Golgi to ER trafficking COPI complex . Overrepresentation of genes encoding ER/Golgi vesicle-associated proteins among the genes showing a lipid overstorage phenotype on knockdown suggests that vesicle trafficking proteins participate in lipid utilization . Most strikingly , six out of the seven genes encoding COPI subunits ( Figure 7 ) that mediate retrograde transport from the Golgi to the ER , showed dramatically increased lipid storage following dsRNA treatment in the genome-wide RNAi screen ( B-score = 4 . 6 to 11 . 1 , false discovery rate [FDR]-corrected p = 1e−5 to 1e−34 ) . Enrichment for members of such multisubunit complexes in RNAi screens has outstanding predictive value [49] . Our observed enrichment for essentially all the COPI-associated factors among the knockdowns resulting in lipid overstorage , strongly suggests that COPI is required for limiting lipid storage ( FDR-corrected p < 1e−6 ) . In addition , dsRNAs targeting ADP ribosylation factor at 79F ( Arf79F ) had the same effect as COPI knockdown . Arf79F encodes a small G protein homologous to mammalian Arf1 , the key regulator of COPI vesicle formation at the Golgi [63] . Surprisingly , εCOP was the only COPI subunit repeatedly failing to produce a lipid storage phenotype following RNAi in both the S2 [52] and our Kc167 cell screens . Although this is a negative result , we suggest that this subunit is not involved in lipid storage regulation ( see Discussion ) . Interestingly , none of the seven COPII members required for anterograde transport from the ER to the Golgi [37 , 38] showed a lipid accumulation phenotype following RNAi ( Figure 7B; B-score = 0 . 0 to 1 . 4 , FDR-p = 0 . 99 to 0 . 78 ) , strongly suggesting that lipid overstorage due to COPI knockdown is not a general consequence of disrupted trafficking between the ER and Golgi . In organisms , cells are exposed to differing NEFA levels due to feeding and fasting . Therefore , to test for the function of the COPI complex in physiological conditions without elevated NEFA , we also performed new RNAi experiments with or without supplementing the media with oleic acid ( Figure 8A–8G; additional data not shown ) . Even in the absence of oleic acid , knockdowns of all the members of the COPI complex that promoted lipid droplet deposition under fed conditions also promoted accumulation without feeding ( Figure 8A–8G; additional data not shown ) . Thus , the lipid storage phenotype was also independent of the nutritional status of the cells . To further investigate whether the observed lipid storage phenotype after the loss of COPI-subunit function is due to a specific pathway or a more general effect of interference with Golgi and ER integrity , we also tested additional dsRNAs targeting transcripts encoding the COPII-associated proteins CG10882 , Sar1 , Sec23 , Sec31 , and PLD ( Figure 8G ) . Furthermore , the Drosophila genome encodes five Arf proteins [64] , which we also reinvestigated in additional RNAi experiments . Arf79F encodes ARF1 , which is required for COPI function , but Arf51F , Arf72A , Arf84F , and Arf102F are not known to be required for COPI-mediated transport [65] . Only Arf79F resulted in a mutant lipid droplet phenotype upon RNAi knockdown ( Figure 8G ) . These experiments demonstrated that the lipid overstorage phenotype is specific to COPI loss of function and raise the possibility that the lipid overstorage phenotype is Golgi independent . Although multiple dsRNAs verified the phenotypic effect of COPI knockdown , we sought to further validate those results with an independent technique , to rule out effects based on the RNAi treatment , or the prolonged incubation time ( 4 d ) due to the knockdown procedure . Therefore , we also tested pharmacologically for COPI involvement in lipid storage . We treated Drosophila S3 cells for 18 h with 24 different concentrations of Exo1 , a selective inhibitor of Arf1 activity [66] , and determined the dose response ( Figure 8H ) . Lipid droplets were stained with the same dye as for the RNAi experiments . As in the RNAi experiments , we used internal controls , including cells with no oleic acid feeding , cells treated with the compound solvent , and cells treated with Triacsin C , a known inhibitor of TG synthesis [67] . Dose-response curves for Exo1 were determined by enumerating cells that showed an increase in lipid staining ( or relative to the enumerated cells based on a cytosolic counterstain; inset in Figure 8H ) and expressing this as per cent activity relative to cells incubated in oleic acid and full inhibited by Triacsin C . Thus , increased activity indicates lipid overstorage . Treatment of Drosophila S3 cells with Exo1 resulted in a dose-dependent ( half maximal effective concentration [EC50] = 5 μM ) increase in lipid storage that was greater than 10-fold . Thus , multiple dsRNAs targeting COPI and Arf79F mRNAs as well as Exo1 , a compound targeting Arf79F ( Arf1 in mammals ) , resulted in the same phenotype . These data strongly indicate that COPI is required to limit lipid storage in droplets in Drosophila . To explore the function of COPI in lipid droplet cell biology in greater detail , we performed additional experiments in the mouse 3T3-L1 and AML12 cells . As positive and negative controls , we used irrelevant “ALLStars negative control” siRNAs , or siRNAs targeting transcripts encoding known lipid droplet regulators , and compared the resulting cellular phenotypes to the results of parallel siRNA treatments targeting transcripts encoding COPI components . As in the Drosophila experiments , we required that at least two siRNAs resulted in the same phenotype . Like AML12 cells , 3T3-L1 cells also stored little lipid in the absence of exogenous NEFA ( Figure 9A and 9G ) , whereas small , clustered lipid droplets appeared upon addition of oleic acid ( Figure 9B and 9H ) . Depletion of both ADRP and TIP47 by RNAi resulted in fewer and much larger lipid droplets ( Figure 9C and 9I [68] ) relative to wild type ( we used double knockdowns for these controls because single knockdowns resulted in a minimal phenotype [68] ) . Conversely , knockdown of Atgl ( bmm in Drosophila ) transcripts resulted in increased lipid storage ( Figure 9D and 9J ) , but no differences in the appearance of the lipid droplets . Targeting the genes encoding α , β , β′ , γ , δ , or ζ COPI subunits by siRNAs resulted in increased lipid storage ( Figure 9E , 9F , and 9K–9P ) . As in the Drosophila knockdown experiments , εCOP knockdown failed to increase lipid storage ( unpublished data ) . We also failed to observe a phenotype following knockdown of either of two genes , sec24 and Pld1 , encoding COPII components ( unpublished data ) . Thus , the Drosophila and mouse RNAi experiments unambiguously indicate that COPI subunits ( with the exception of εCOP ) have evolutionarily conserved lipid droplet functions . Both Arf1 and Gbf1 , an Arf guanine nucleotide exchange factor ( GEF ) , are required for COPI recruitment from the cytosol to Golgi [69] . We also asked whether Arf1 and any of three pharmacologically related GEFs were required for lipid utilization . The Gbf1 , Big1 , and Big2 proteins are GEFs inhibited by Brefeldin A ( BFA ) [70] . BFA treatment and knockdowns of either Arf1 or Gbf1 ( the latter confirmed at the protein level ) resulted in lipid overstorage ( Figure 9Q and 9R ) , whereas we observed no lipid overstorage following knockdown of Big1 or Big2 ( unpublished data ) . Thus the COPI complex and critical regulators of COPI translocation are required for lipid utilization . Lipid overstorage in the absence of COPI could be due to decreased release of NEFA from droplets , or increased synthesis of TG for storage , or both . In order to explore whether COPI is required for one or both of these general functions , we measured both NEFA release and esterification of NEFA into TG in AML12 cells ( Figure 10A ) . As expected , we observed increased release of NEFA from cells treated with control siRNAs targeting Adrp and Tip47 transcripts , which is mediated by increased amounts of lipid droplet-associated ATGL [68] . In contrast , NEFA release decreased when Atgl lipase transcripts were targeted as controls . Additionally , we observed increased incorporation of NEFA into TG following Atgl knockdown , suggesting that the tremendous increase in TG seen in those cells is due to decreased NEFA release and continued synthesis of TG despite the reduced efflux . The modest increase in incorporation of NEFA into TG following COPI knockdown was insignificant . However , we observed approximately 40% of wild-type NEFA release in cells treated with siRNAs targeting either γCOPI or ζCOPI transcripts—in the same range as after Atgl knockdown ( Figure 10A ) . In separate experiments , we also observed decreased NEFA release following Gbf1 knockdown , but not following Big1 or Big2 knockdown ( Table S8 ) . These data indicate that COPI is a novel regulator of lipolysis . We also asked whether short-term pharmacological inhibition of COPI trafficking phenocopies the COPI knockdown phenotype in mouse cells , as we noted in Drosophila cells . We used COPI inhibitors Exo1 and BFA [39 , 66] , both of which result in increased lipid storage . Both compounds reduced NEFA release to the same extent as the siRNAs targeting COPI subunit mRNAs ( Figure 10B ) . To dissect the role of COPI in lipolysis , we used a combination of siRNAs targeting different genes in the lipolytic pathway , and Exo1 or BFA treatment , to mimic genetic epistasis experiments ( a proven tool for dissecting functional relationships between members of the same or different pathways [71] ) . Combining siRNA-mediated knockdown of COPI members and BFA or Exo1 treatment did not enhance the decreased lipolysis phenotype ( Figure10B ) , indicating that the observed effects following drug treatment are only COPI mediated . Additionally , these data suggest that there are no serious compound-based side effects vis-a-vis lipid droplets , even for the broad-spectrum inhibitor BFA ( also note that other BFA-sensitive GEFs , Big1 and Big2 , did not result in a lipid storage phenotype on knockdown ) . Decreased lipolysis could be due to decreased lipase activity at the lipid droplet . To determine whether that lipase was ATGL , we combined siRNAs targeting ATGL transcripts and BFA or Exo1 drug treatment ( Figure 10B ) . If ATGL were responsible , then ATGL knockdown would have no effect on BFA- or Exo1-treated cells . Indeed , the lipolysis rate was not further decreased , suggesting that COPI-mediated lipolysis effects are mediated by ATGL . This conclusion is further supported by experiments in which we treated cells with siRNAs targeting ADRP and TIP47 transcripts in combination with either BFA or Exo1 . In the absence of ADRP and TIP47 , more ATGL is found at the lipid droplet surface [68] . We also found that Exo1 or BFA treatment rescues the effect of ADRP and TIP47 knockdown . This , along with the finding that COPI and ATGL are in the same pathway , suggests that COPI is an important positive regulator of ATGL . Wild-type COPI could mediate release of NEFA from lipid droplets by altering the heterogeneous and dynamic collection of lipid droplet-associated proteins found in different cell types and conditions [72] . To further explore what happens to lipid droplets following COPI knockdown , we examined the distribution of TIP47 and ADRP on the lipid droplet surface . These are the only PAT proteins expressed in AML12 cells [68] . In control cells incubated with oleic acid , and control siRNAs , ADRP was associated with the lipid droplet surface whereas TIP47 was mostly found in smaller punctate cytoplasmic inclusions and more ill-defined cytoplasmic locations ( [68] and Figure 11A ) . TIP47 and ADRP were not colocalized in untreated cells . Following siRNA treatments targeting α , β , β′ , γ , and ζ COPI subunit or Gbf1 transcripts , both ADRP and TIP47 were observed on the same lipid droplets ( Figure 11B–11H ) . Treating the cells with BFA had the same effect on TIP47 localization ( Figure 12A and 12B ) . These data indicate that COPI is required for a wild-type pattern of PAT localization to the lipid droplet . PAT proteins are tightly associated with the lipid droplet surface . In order to distinguish localization to the region of the lipid droplet from true localization to the lipid droplet surface , we treated cells with BFA after oleic acid feeding , and isolated lipid droplets by sucrose gradient ultracentrifugation . This treatment separates the lipid droplets from cytosol and other membrane fractions . To determine what proteins were on the lipid droplets , western blots were probed with antibodies detecting ADRP , TIP47 , and ATGL , as well as the ATGL cofactor CGI-58 . Whereas ADRP and CGI-58 remained quantitatively unchanged after BFA treatment , TIP47 protein levels in the lipid droplet fraction increased nearly 2-fold ( Figure 12C ) . There was no change in TIP47 in the cytosolic fraction ( unpublished ) . The cell-staining experiments showed a more dramatic increase in TIP47 at the ADRP-positive lipid droplets than we observed in the western blots , but importantly , both cell staining and western blotting show increased TIP47 on COPI inhibition . Strikingly , ATGL levels decreased to near or below the detection limit , suggesting that BFA treatment drives ATGL off the lipid droplet surface , or prevents ATGL association with the lipid droplet ( Figure 12C ) . Thus , both cell staining and analysis of isolated droplets indicate that wild-type COPI limits abundance of TIP47 at the lipid droplet surface and is required for ATGL localization to the droplet surface . Taken together with the epistasis results demonstrating that COPI and ATGL function in the same pathway , these results indicate that COPI-mediated targeting of ATGL to the lipid droplet is required for lipolysis .
Positive regulation of lipolysis by the COPI retrograde-vesicle trafficking pathway was the most striking and unexpected result of our screen . We have found that interference with COPI function , either by RNAi or compounds , in Drosophila Kc167 or S3 cells , or in mouse 3T3-L1 or AML12 cells , results in increased lipid storage . Furthermore , recent and parallel studies in yeast [73] and Drosophila S2 cells [52] also suggested a role of COPI function in lipid droplet regulation . Interestingly , only the ε-subunit of the COPI complex failed to result in a lipid droplet deposition phenotype on knockdown . Although we cannot rule out limited RNAi efficacy or increased protein stability , εCOP was the only canonical COP subunit not resulting in a lipid storage phenotype in a parallel study using different cells and reagents [52] , and we found that targeting of εCOP transcripts by RNAi in AML12 cells had a weak effect on lipid storage at best . Finally , εCOP is the only dispensable subunit in a recent study identifying COPI activity coupled with fatty acid biosynthesis as a host factor important for Drosophila C virus replication [74] . This is especially interesting , as certain enveloped viruses , including Hepatitis C virus , assemble on lipid droplets [75 , 76] . Taken together , these results indicate that six out of the seven wild-type COPI subunits mediate lipid storage by positively regulating lipolysis . COPI could have a direct or indirect effect on lipid storage . The indirect mechanism is poorly defined , but if the Golgi is a “sink” for phospholipids derived from TG stores , then decreased Golgi function could simply decrease demand for TG substrate . If NEFA ( from the media in fed cells , and from biosynthesis in unfed cells ) conversion to TG continues , then increased lipid droplet volume would occur . It is also possible that canonical COPI function transporting lipids and proteins from the Golgi to the ER is ultimately responsible for lipid droplet utilization and protein composition at the lipid droplet surface . For example , COPI might be required for the particular phospholipid composition in hemimembranes formed on nascent droplets , which secondarily alter TIP47 and ATGL localization in mature lipid droplets . However , evidence that Golgi function per se is not linked to lipid storage phenotypes , as well as direct association of COPI members and regulators with the lipid droplet or PAT proteins supports a more direct model . The COPI and COPII pathways have established roles as constitutive vesicle transport systems that cycle proteins as well as lipid from the Golgi to the ER ( COPI ) , or vice versa ( COPII ) [37] . Interference with either of the COP trafficking systems results in disturbed ER and Golgi function [38 , 39] . The lipid overstorage phenotype was only seen in the case of interference with COPI trafficking . This indicates that the lipid overstorage phenotype is not a simple consequence of ER and Golgi function . Finally , in an indirect model in which COPI shuttles only between the Golgi and the ER , COPI should not be lipid droplet associated . However , COPI subunits are directly associated with the lipid droplet surface as shown by proteomics [6] . Additionally , Arf1 binds to ADRP , which is exclusively associated with the lipid droplet surface [77] . Arf79F , the Drosophila homolog of mammalian Arf1 , also localizes to lipid droplets in Drosophila S2 cells [52] . We propose that COPI is likely to function directly at the lipid droplet surface and not indirectly through the Golgi ( Figure 13 ) . Perhaps COPI is a destination-specific transporter returning lipid droplet surface hemimembrane and Golgi membrane to the ER . The transport system that brings nascent lipid droplets from the ER to the lipid droplet has not been elucidated , but it is intriguing that the transport/PAT protein TIP47 is found preferentially on small lipid droplets . Small lipid droplets derived from the ER are thought to help build larger droplets by fusion . TIP47-coated droplets might form in the ER , and then COPI could return TIP47 to the ER after the lipid cargo is deposited . In this model , TIP47 becomes trapped at the lipid droplet surface in the absence of COPI . Although we observed increased TIP47 on ADRP-positive droplets by both western blot and cell staining , the cell staining result was more dramatic . Our model might also explain why . The punctate staining of TIP47 in untreated cells could be due to TIP47 on nascent droplets that might also cofractionate with the larger ADRP-positive droplets in the western blots , leading to a less dramatic enrichment for TIP47 relative to ADRP in that experiment . However , we cannot rule out other explanations , such as nonlinear detection of antigen concentration or epitope masking in the cell staining experiments . COPI perturbation increases stored TG by decreasing the lipolysis rate ( this study , [52] ) indicating that the wild-type COPI complex promotes lipolysis . We have shown that COPI directly or indirectly removes TIP47 from the lipid droplet surface and promotes ATGL localization to the droplet surface , where lipolysis occurs . ATGL has a key role in lipid droplet utilization , and ATGL association with the droplet is reduced by ADRP and Tip47 [68] . Our epistasis experiments combining siRNA-mediated ATGL knockdown and BFA or Exo1 compound treatment demonstrated that the decrease in lipolysis rate is due to loss of ATGL activity . COPI activity specifically alters lipid droplet surface composition by increasing the amount of TIP47 and reducing the amount of ATGL at ADRP-coated lipid droplets . We suggest that COPI negatively regulates localization of TIP47 . TIP47 in turn prevents ATGL localization . The rescue of the double-knockdown phenotype of TIP47 and ADRP by BFA or Exo1 suggests that COPI has an independent feed-forward effect on ATGL levels at the lipid droplet surface . Although we have focused our attention here on COPI , our systematic and genome-wide exploration of gene functions required for lipid storage in Drosophila significantly increases experimental access to the complex molecular processes regulating lipid storage and utilization . Further , the use of multiple screens using different cell types and different organisms greatly increases confidence in the genes in the intersection . Given widespread concerns about RNAi screening efficacy and off-target effects , as well as the time and effort required for downstream analysis , systematic use of multiple species and libraries to address a single biological question might be cost effective in addition to resulting in more durable datasets . Primary screens in Drosophila cells followed by secondary screens in mouse cells are much less expensive than a similar genome-wide screen in mammalian cells . Additionally , the availability of mutants in most Drosophila genes , along with demonstrated translation to mammalian systems , provides a valuable entry point for in-depth analyses in both fly and mouse; and eventually for the selection of therapeutic targets for emerging problems associated with obesity and other metabolic disorders .
We used the Harvard Drosophila RNAi Screening Center ( DRSC , http://www . flyrnai . org ) dsRNA collection , which covers more than 95% of the transcriptome ( Release 3 . 2 BDGP ) with a total of 17 , 076 dsRNAs [44] in duplicate . We seeded 1 . 5 × 104 Kc167 cells ( DRSC ) in 10 μl of serum-free Schneider's medium ( GIBCO ) in each well of microscopy-quality 384-well plates containing the pre-aliquoted dsRNAs ( approximately 250 ng of dsRNA/well ) . Plates were spun at 1 , 200 rpm for 1 min and incubated for 45 min at 25 °C . We then added 40 μl of complete Schneider's medium supplemented with 10% FCS ( JRH Biosciences ) , 50 units penicillin; and 50 μg of streptomycin/ml ( GIBCO ) and ±400 μM oleic acid ( Calbiochem ) complexed to 0 . 4% BSA ( Sigma ) . Plates were sealed and incubated in a humidified incubator at 25 °C for 4 d . The cells were subsequently fixed for 10 min in 4% formaldehyde in PBS followed by a 10-min permeabilization step in PBS including 0 . 1% Triton X-100 . For lipid droplet visualization and cell counting ( nuclei ) , we incubated for 1 h with PBS including 5 μg/ml BODIPY493/503 ( Molecular Probes ) and 5 μg/ml DAPI or 5 μg/ml Hoechst33342 ( Molecular Probes ) . After two washes with PBS including 0 . 01% Tween-20 , cells were kept in 40 μl of PBS and visualized with a 20× objective on a Discovery1 automated microscope system ( Molecular Devices ) . A subset of 276 genes of the primary screen library were targeted by 362 additional dsRNAs ( Table S10 ) generated from PCR products obtained from the Drosophila RNAi screening center of Harvard ( DRSC ) . PCR fragments were reamplified using a modified T7 oligonucleotide ( 5′-GTA ATA CGA CTC ACT ATA GG-3′ ) and a touchdown PCR protocol . PCR products were subsequently used for in vitro transcription reactions using T7 RNA polymerase ( Fermentas ) . Following DNAse-mediated digestion of the PCR template , dsRNAs were purified with Multiscreen PCR purification filter plates ( Millipore ) . RNAi treatment was performed either as described for the primary screen in optical-quality 96-well plates ( BD ) with adjusted dsRNA and cell numbers , in duplicate ( approximately 1 μg of dsRNA and 5 × 104 cells/well ) . Imaging was performed either with a BD Pathway 855 Bioimager automated microscope ( BD ) or with a Zeiss Axiovert200M ( Carl Zeiss ) and the OpenLab software ( Improvision ) . For the secondary mouse siRNA screen ( Table S10 ) , we used AML12 murine liver cells ( Steven Farmer , Boston University ) and 3T3-L1 fibroblast cells ( ATCC ) grown according to protocols of the American Type Culture Collection ( ATCC ) . Assays were done in 96-multiwell plates ( Fisher Scientific ) at a density of 0 . 25 × 104 cells/well on growth medium supplemented with 200 μM oleic acid , which was added 18 h prior to fixation of the cells . Cells were transfected with Hiperfect transfection reagent ( 0 . 75 μl/well ) ( Qiagen ) and experimental or ALLStars negative control siRNA oligonucleotides ( 10 nM ) , according to the manufacturer's instructions ( Qiagen ) . Four days after transfection , cells were fixed and stained as described above for the Drosophila cells and imaged with a BD Pathway 855 Bioimager automated microscope system ( BD ) . Images of Drosophila cells ( two sites/well in the primary screen; six sites/well for the secondary screen ) were processed with a custom image segmentation algorithm ( available from M . Beller upon request ) written for the ImageJ software package [78] . After a sharpening and a brightness/contrast adjustment ( for the BODIPY images; equal values for all images ) or a gamma correction ( for the DAPI images; same values for all images ) , a background subtraction followed by an Otsu thresholding step was run ( Figure 1A–1D ) . Watershed processing to identify solitary particles followed . Finally , lipid droplets or nuclei were identified with the generic “analyze particles” function of the ImageJ software with the following settings: ( 1 ) settings for the nuclei: size from 10 to 10 , 000 pixels , 256 bins , outlines as well as measurement results displayed , measurements on the edges excluded , clear results , flood , and summary of the results; and ( 2 ) settings for the lipid droplets: identical parameters except size ranging from one to 200 pixels and a circularity from zero to one . For each detected particle , the size and area were measured . For each image , the total numbers of particles ( “counts” ) or cumulative measured area for all particles ( “area” ) are reported . A custom Perl script concatenated the summarized measurements , and the obtained information was used to calculate the ratio of lipid droplets per cells as a measure of lipid storage ( “lipid droplet/nuclei ( area ) ” or “lipid droplet/nuclei ( counts ) ” ) . Mammalian cell image analysis ( four sites/well ) was performed as described above with some adjusted settings reflecting the larger mammalian cell size as well as differences in imaging equipment ( no brightness or contrast adjustments were applied ) . The generic “analyze particles” function of the ImageJ software was used with the following settings: ( 1 ) settings for the nuclei: size from 80 to 10 , 000 pixels , 256 bins , outlines as well as measurement results displayed , measurements on the edges excluded , clear results , flood , and summary of the results; and ( 2 ) settings for the lipid droplets: identical parameters , except the size ranging from one to 2 , 000 pixels and a circularity from zero to one . The general thrust of the analysis is given below and is followed by a detailed description . Screen data are available ( Table S4; http://lipofly . mpibpc . mpg . de/ ) . Results were robust to data handling method ( Table S1 ) . Genes passing thresholding conditions ( Tables S2 and S3 ) were used for the GO term analysis ( Table S5 ) . B-score p-values can be used to further restrict the gene lists shown in Tables S2 and S3 . Data analysis was performed with custom scripts written in the R language and packages provided by the Bioconductor project [79] . The lipid droplets and nuclei area measurements of the two images per well were used to calculate an averaged lipid area per nuclei area value per well . Additionally to the primary images , a number of wells required reimaging based on visual inspection ( size of the complete dataset: N = 48 , 241 wells ) . To identify and extract images with bad quality , the values for lipid droplet ( LD ) area and count measurements as well as for the corresponding nuclei measurements of the two images per well were plotted against each other to look for variation within wells . In addition , the corresponding “LD area per nuclei area” and “LD count per nuclei count” ratios were plotted against each other per well . These plots showed 95 prominent outliers ( segmentation artifacts/“bad” wells ) , which were removed ( resulting N = 48 , 146 wells ) . The data values of reimaged wells were averaged . The screen dataset was platewise normalized for within-plate and between-plate differences by four different algorithms . Because of the limited number of controls per plate , 98% of the wells per plate were used as a reference set in the normalization procedure as proposed in [47] in which the largest and smallest 1% values of the plate were removed to generate the reference set . Before data normalization , LD areas per nuclei area ratios were log-transformed . A classical robust Z-score normalization was performed first [zi = ( xi − medianj ) /madj , where zi is the Z-score of well i; xi is the raw value of well i; and medianj and madj are the median and median absolute deviation ( MAD ) of the plate j] in addition to the recently proposed strictly standardized mean difference normalization [SSMDi = ( xi − meanj ) /square root ( 2/nj − 2 . 5 × ( ( nj − 1 ) × SDj2 ) ) ] . Those related algorithms were supplemented with both a fitted linear model normalization using the Prada package [45] and by B-score normalization [46] . Benjamini and Hochberg FDR-corrected p-values for all dsRNAs were calculated with the complete screen data ( without the largest and smallest 1% ) as a reference set . Scoring was done both on a platewise and screenwise manner . For the platewise hit identification , positives were identified by a quartile-based thresholding algorithm [48] . For this purpose , the first quartile ( Q1 ) , the median ( Q2 ) , and the third quartile ( Q3 ) were calculated first . Afterwards , threshold T were calculated [Tupper = Q3 + c × ( Q3 − Q2 ) and Tlower = Q1 − c × ( Q2 − Q1 ) , where c is a variable depending on the targeted error rate] [48] . The same hit selection strategy was also chosen for the screen-wide hit identification among the linear model normalized dataset . For the other normalization algorithms , fixed thresholds were selected . In all cases , threshold levels ( as well as the c in the quartile-based thresholding ) were chosen based on the identification rates of the internal controls brummer dsRNA , midway dsRNA , and wells with no oleic acid , which were present on every screening plate . The highest possible threshold was chosen capable of balancing both false-positive and -negative rates . Identified Drosophila lipid regulating gene functions ( Tables S2 and S3 ) were subjected to in silico analysis for enriched GO terms . For this purpose , we used the standard settings of the VLAD tool ( Mouse Genome Informatics Web site [51] ) using the complete Drosophila genome as a reference set . Results of the enrichment analyses were visualized by pruned GO term networks ( pruning threshold = 4; collapsing threshold = 5 ) , and results ( pruning threshold = 3; collapsing threshold = 6 ) are additionally tabulated ( Table S5 ) . Detailed lists of the scoring genes were annotated with the following information ( Table S9 ) : GO terms from FlyBase [80]; orthologs from FlyMine [81]; human disease gene orthologs from Homophila ( http://superfly . ucsd . edu/homophila/ , used with a significance threshold of E < 1 × 10–50 , [31]; InParanoid [82] orthologs ( http://inparanoid . sbc . su . se/cgi-bin/index . cgi ) ; and Drosophila [4 , 33]; as well as mammalian [5 , 6 , 32 , 34] lipid droplet subproteome data . A subset of genes identified in the genome-wide screen with a potential function in cellular lipid storage regulation was assayed by at least one additional dsRNA . In total , 276 genes were tested by targeting with 362 dsRNA sequences ( Table S10 ) . Because we were interested in validating the full range of phenotypes observed and not just the positives , we sampled across a broad range of B-scores . We performed directed retesting on the genes encoding COPI members . To test for COPI specificity , we used secondary dsRNA sequences targeting Arf family members not involved in COPI function as well as COPII vesicle transport encoding transcripts as controls . dsRNAs targeting those genes did not result in a phenotype in the primary screen . For a “positive” identification , we required that two independent nonoverlapping dsRNAs or siRNAs give the same phenotype . In addition , we tested mouse AML12 hepatocytes and mouse 3T3-L1 fibroblasts for an evolutionary conservation of the identified lipid storage modulators . Assuming that off-target effects are random , this also minimizes misleading off-target effects , and is certainly more stringent than the current standard of two positive RNAi reagents with retesting in the same species and cell type [60] . In total , 127 mouse genes covered by 312 siRNAs were tested ( Table S10 ) . Genes across the screen that were validated using the image-based analysis with additional RNAi reagents are listed in Table S7 . Additional gene and COPI validation comes from small compound phenocopy , cell staining experiments , and measurements on lipid metabolism as outlined further below . Lipid droplet area and nuclei area measurements obtained from the image segmentation procedure , which was carried out as described for the primary screen results , was used to express the ratio of lipid per cell . For each screen , plate data were median normalized . In order to identify genes modulating lipid storage , a basic thresholding of median ± 2 × MAD was used . Since the datasets were enriched for modulators of lipid storage , the median as well as MAD was calculated on the basis of control wells incorporated in the assay plates . For the Drosophila , AML12 , and 3T3-L1 datasets , those wells contained no RNAi reagent , but were otherwise treated identical to the experimental wells . Screening plates also contained other control dsRNAs/siRNAs wells . The Drosophila secondary screen plates contained wells with dsRNAs targeting bmm or mdy as in the primary screen . In the case of the 3T3-L1 and AML12 cells , plates contained siRNAs targeting Atgl or a combination of two siRNAs targeting both Adrp and Tip47 transcripts [68] . Median ± thresholds were adjusted in order to fulfill the same prerequisites as in the primary screen , namely a maximum of identified controls with a minimum of false positives . False positives were scored based on the wells lacking RNAi reagent . Small-molecule compound experiments were performed with embryonic Drosophila S3 cells ( Bloomington Drosophila Stock Center [DGRC] ) , which showed excellent oleic acid feeding characteristics during RNAi assay development but inferior RNAi characteristics as compared to the Kc167 cells . S3 cells showed superior adherence during automated liquid handling in 1 , 536-well format . We dispensed 4 μl of cells at 1 . 25 × 106 cells/ml into LoBase Aurora COC 1 , 536-well plates ( black walled , clear bottom ) with a bottle-valve solenoid-based dispenser ( Aurora ) to obtain 5 , 000 cells/well . A total of 23 nl of compound solution of different concentrations were transferred to the assay plates using a Kalypsis pin tool equipped with a 1 , 536-pin array containing 10-nl slotted pins ( FP1S10 , 0 . 457-mm diameter , 50 . 8 mm long; V&P Scientific ) . One microliter of oleic acid ( 400 μM ) was added , and the plate was lidded with stainless steel rubber gasket-lined lids containing pinholes . After 18–24-h incubation at 24 °C and 95% humidity , BODIPY 493/503 ( Molecular Probes ) was added to the wells to stain lipid droplets , and the Cell Tracker Red CMTPC dye ( Molecular Probes ) was added to enumerate cell number . Fluorescence was detected by excitation of the fluorophores with a 488-nm laser on an Acumen Explorer ( TTP Lab Tech ) . The total intensity in channel 1 ( 500–530 nm ) reflected lipid droplet accumulation . Cells were detected using channel 3 ( 575–640 nm ) with 5-μm width and 100-μm depth filters . The ratio of the total intensity in PMT channel 1 over total intensity of channel 3 was also calculated . Percent activity was computed relative to an internal control ( 100% inhibited lipid droplet deposition due to the presence of 20 μM Triacsin C ) , which was added to 32 wells/plate . Measurements of NEFA released from lipid droplets or incorporated into the TG fraction were performed as previously described [23 , 68 , 83] . Briefly , AML12 cells treated with or without specific siRNAs ( 10 nM ) for 4 d were incubated overnight with growth medium supplemented with 400 μM oleic acid complexed to 0 . 4% bovine serum albumin to promote triacylglycerol deposition and [3H] oleic acid , at 1 × 106 dpm/well , was included as a tracer . In lipolysis experiments , re-esterification of fatty acids in AML12 cells was prevented by including 10 μM Triacsin C ( Biomol ) , an inhibitor of acyl coenzyme A synthetase [67] , in the medium . Quadruplicate wells were tested for each condition . Lipolysis was determined by measuring radioactivity released into the media in 1 h . For the lipid extraction and thin layer chromatography , the cell monolayer was washed with ice-cold PBS and scraped into 1 ml of PBS . Lipids were extracted by the Bligh-Dyer method [84] , and 10% of the total lipid was analyzed by thin layer chromatography [83 , 85] . AML12 cells treated with or without specific siRNAs were additionally incubated with either vehicle ( DMSO ) , 5 μM of Exo1 ( 12 . 5 mg/ml DMSO ) , or BFA ( 10 mg/ml DMSO ) during the time of radioactivity release into the media ( 2 h ) . NEFA incorporation into the TG fraction and NEFA release are calculated as nanomoles/milligram protein ( Table S8 ) . Protein measurements were performed using a commercial BCA assay kit ( Pierce Biotechnology ) according to the manufacturer's instructions . Statistical significance was tested by impaired Student t test ( GraphPad software ) . Rabbit anti-TIP47 and goat anti-ADRP were used as previously published [9] . Antibodies targeting mouse ATGL were purchased from Cell Signaling Technology . The CGI-58 antibody was a gift from Dr . Osumi [29] . Cells were plated in four-well Lab-Tek chamber slides ( Nunc ) and incubated overnight with 400 μM oleic acid . In compound experiments , wells received vehicle ( DMSO ) or 5 μM BFA ( 10 mg/ml DMSO ) treatment for 6 h . RNAi treatment prior to immunocytochemistry is outlined above . For ADRP and TIP47 staining , cells were fixed in 3% v/v paraformaldhyde/PBS for 15 min at room temperature . Staining was performed by published methods [9 , 86] . Cells were viewed with a confocal laser scanning microscope ( LSM510; Carl Zeiss MicroImaging ) using a 63× oil objective lens . Eight 100-mm dishes for each condition were treated with 400 μM oleic acid overnight and further treated with DMSO or BFA ( 5 μM ) for 6 h on the next day . Cells were washed three times with phosphate buffered saline ( PBS; pH 7 . 4 ) , scraped into PBS , and then pelleted by low-speed centrifugation . LD isolation was as reported [8] . The lipid fat cake was isolated and resuspended in 150 μl of PBS containing 5% SDS before 150 μl of 2× Laemmli sample buffer were added . For CGI-58 and ATGL western blots , those protein extracts were directly loaded . For ADRP and Tip47 , the samples were diluted 200-fold ( ADRP ) or 20-fold ( TIP47 ) , respectively . A total of 35 μl were loaded then on each lane . X-ray films were used to detect the western blots . Quantification was done with ImageJ [78] .
Drosophila RNAi screen hits: FBgn0000028 , FBgn0000042 , FBgn0000114 , FBgn0000339 , FBgn0000489 , FBgn0000547 , FBgn0000567 , FBgn0001186 , FBgn0001204 , FBgn0001301 , FBgn0002878 , FBgn0003048 , FBgn0003118 , FBgn0003339 , FBgn0003380 , FBgn0003392 , FBgn0003462 , FBgn0003557 , FBgn0003607 , FBgn0003691 , FBgn0004167 , FBgn0004187 , FBgn0004401 , FBgn0004587 , FBgn0004595 , FBgn0004611 , FBgn0004652 , FBgn0004797 , FBgn0004838 , FBgn0004856 , FBgn0004879 , FBgn0005411 , FBgn0005626 , FBgn0005630 , FBgn0010083 , FBgn0010215 , FBgn0010355 , FBgn0010638 , FBgn0010750 , FBgn0011571 , FBgn0011701 , FBgn0013746 , FBgn0014020 , FBgn0015320 , FBgn0015818 , FBgn0015919 , FBgn0016926 , FBgn0016940 , FBgn0019643 , FBgn0020611 , FBgn0020908 , FBgn0021768 , FBgn0022246 , FBgn0023143 , FBgn0024285 , FBgn0024308 , FBgn0024555 , FBgn0024754 , FBgn0025638 , FBgn0026206 , FBgn0026317 , FBgn0026620 , FBgn0026722 , FBgn0026878 , FBgn0027495 , FBgn0027589 , FBgn0027885 , FBgn0027951 , FBgn0028360 , FBgn0028420 , FBgn0028982 , FBgn0029123 , FBgn0029526 , FBgn0029661 , FBgn0029731 , FBgn0029766 , FBgn0029824 , FBgn0029850 , FBgn0029873 , FBgn0029935 , FBgn0030075 , FBgn0030077 , FBgn0030087 , FBgn0030093 , FBgn0030189 , FBgn0030244 , FBgn0030390 , FBgn0030434 , FBgn0030492 , FBgn0030608 , FBgn0030872 , FBgn0030904 , FBgn0031008 , FBgn0031030 , FBgn0031031 , FBgn0031074 , FBgn0031093 , FBgn0031232 , FBgn0031390 , FBgn0031518 , FBgn0031626 , FBgn0031673 , FBgn0031816 , FBgn0031836 , FBgn0031888 , FBgn0031894 , FBgn0032049 , FBgn0032340 , FBgn0032351 , FBgn0032360 , FBgn0032363 , FBgn0032388 , FBgn0032454 , FBgn0032622 , FBgn0032800 , FBgn0032868 , FBgn0032945 , FBgn0033155 , FBgn0033160 , FBgn0033541 , FBgn0034071 , FBgn0034402 , FBgn0034646 , FBgn0034709 , FBgn0034839 , FBgn0034946 , FBgn0034967 , FBgn0035085 , FBgn0035136 , FBgn0035294 , FBgn0035546 , FBgn0035569 , FBgn0035631 , FBgn0036274 , FBgn0036374 , FBgn0036470 , FBgn0036556 , FBgn0036734 , FBgn0036761 , FBgn0036811 , FBgn0037024 , FBgn0037149 , FBgn0037178 , FBgn0037250 , FBgn0037278 , FBgn0037304 , FBgn0037568 , FBgn0037920 , FBgn0037924 , FBgn0038168 , FBgn0038191 , FBgn0038343 , FBgn0038359 , FBgn0038391 , FBgn0038592 , FBgn0038633 , FBgn0038662 , FBgn0039054 , FBgn0039941 , FBgn0039959 , FBgn0039997 , FBgn0040279 , FBgn0040291 , FBgn0040369 , FBgn0040534 , FBgn0040651 , FBgn0040777 , FBgn0042693 , FBgn0050126 , FBgn0050470 , FBgn0051313 , FBgn0051374 , FBgn0051632 , FBgn0051814 , FBgn0052056 , FBgn0052062 , FBgn0052112 , FBgn0052121 , FBgn0052150 , FBgn0052202 , FBgn0052352 , FBgn0052397 , FBgn0052440 , FBgn0052635 , FBgn0052704 , FBgn0052710 , FBgn0052711 , FBgn0052970 , FBgn0053207 , FBgn0053500 , FBgn0053516 , FBgn0058413 , FBgn0061200 , FBgn0083976 , FBgn0083992 , FBgn0085381 , FBgn0086441 , FBgn0086674 , FBgn0086899 , FBgn0243486 , FBgn0259162 , FBgn0259169 , FBgn0259171 , FBgn0259217 , FBgn0259228 , FBgn0259240 , FBgn0259243 , FBgn0000008 , FBgn0000100 , FBgn0000116 , FBgn0000212 , FBgn0000409 , FBgn0000492 , FBgn0000636 , FBgn0000986 , FBgn0001133 , FBgn0001216 , FBgn0001217 , FBgn0001218 , FBgn0001942 , FBgn0002023 , FBgn0002590 , FBgn0002593 , FBgn0002607 , FBgn0002906 , FBgn0002921 , FBgn0003031 , FBgn0003060 , FBgn0003209 , FBgn0003277 , FBgn0003279 , FBgn0003360 , FBgn0003600 , FBgn0003687 , FBgn0003701 , FBgn0003941 , FBgn0003942 , FBgn0004110 , FBgn0004922 , FBgn0004926 , FBgn0005593 , FBgn0005614 , FBgn0005630 , FBgn0005648 , FBgn0008635 , FBgn0010078 , FBgn0010220 , FBgn0010348 , FBgn0010352 , FBgn0010391 , FBgn0010409 , FBgn0010410 , FBgn0010412 , FBgn0010431 , FBgn0010612 , FBgn0010808 , FBgn0011211 , FBgn0011272 , FBgn0011284 , FBgn0011701 , FBgn0011726 , FBgn0011745 , FBgn0011837 , FBgn0012034 , FBgn0013275 , FBgn0013276 , FBgn0013277 , FBgn0013278 , FBgn0013279 , FBgn0013325 , FBgn0013981 , FBgn0014020 , FBgn0014857 , FBgn0015024 , FBgn0015288 , FBgn0015393 , FBgn0015756 , FBgn0015774 , FBgn0015778 , FBgn0015834 , FBgn0016120 , FBgn0016694 , FBgn0016926 , FBgn0017397 , FBgn0017545 , FBgn0017566 , FBgn0017579 , FBgn0019624 , FBgn0019886 , FBgn0019936 , FBgn0020129 , FBgn0020386 , FBgn0020439 , FBgn0020910 , FBgn0022343 , FBgn0022935 , FBgn0023170 , FBgn0023171 , FBgn0023213 , FBgn0023531 , FBgn0024150 , FBgn0024330 , FBgn0024733 , FBgn0024939 , FBgn0025286 , FBgn0025582 , FBgn0025724 , FBgn0025725 , FBgn0026262 , FBgn0026666 , FBgn0026741 , FBgn0027321 , FBgn0027348 , FBgn0027615 , FBgn0028530 , FBgn0028867 , FBgn0028968 , FBgn0028969 , FBgn0029088 , FBgn0029161 , FBgn0029504 , FBgn0029761 , FBgn0029799 , FBgn0029822 , FBgn0029860 , FBgn0029897 , FBgn0030025 , FBgn0030088 , FBgn0030174 , FBgn0030259 , FBgn0030341 , FBgn0030384 , FBgn0030386 , FBgn0030606 , FBgn0030610 , FBgn0030669 , FBgn0030692 , FBgn0030696 , FBgn0030726 , FBgn0030915 , FBgn0030951 , FBgn0030990 , FBgn0031300 , FBgn0031392 , FBgn0031545 , FBgn0031696 , FBgn0031771 , FBgn0031842 , FBgn0031980 , FBgn0032053 , FBgn0032215 , FBgn0032261 , FBgn0032330 , FBgn0032400 , FBgn0032518 , FBgn0032587 , FBgn0032596 , FBgn0032619 , FBgn0032656 , FBgn0032675 , FBgn0032833 , FBgn0032987 , FBgn0033029 , FBgn0033081 , FBgn0033085 , FBgn0033282 , FBgn0033313 , FBgn0033341 , FBgn0033368 , FBgn0033379 , FBgn0033403 , FBgn0033591 , FBgn0033652 , FBgn0033699 , FBgn0033902 , FBgn0033912 , FBgn0034020 , FBgn0034258 , FBgn0034487 , FBgn0034488 , FBgn0034537 , FBgn0034579 , FBgn0034649 , FBgn0034751 , FBgn0034902 , FBgn0034948 , FBgn0034968 , FBgn0034987 , FBgn0035276 , FBgn0035315 , FBgn0035422 , FBgn0035562 , FBgn0035563 , FBgn0035638 , FBgn0035699 , FBgn0035753 , FBgn0035872 , FBgn0035976 , FBgn0036135 , FBgn0036213 , FBgn0036288 , FBgn0036343 , FBgn0036351 , FBgn0036360 , FBgn0036398 , FBgn0036449 , FBgn0036462 , FBgn0036492 , FBgn0036532 , FBgn0036534 , FBgn0036576 , FBgn0036613 , FBgn0036728 , FBgn0036820 , FBgn0036825 , FBgn0036895 , FBgn0036990 , FBgn0037010 , FBgn0037028 , FBgn0037093 , FBgn0037097 , FBgn0037098 , FBgn0037102 , FBgn0037207 , FBgn0037249 , FBgn0037270 , FBgn0037356 , FBgn0037415 , FBgn0037429 , FBgn0037529 , FBgn0037546 , FBgn0037559 , FBgn0037566 , FBgn0037610 , FBgn0037637 , FBgn0037752 , FBgn0037813 , FBgn0037912 , FBgn0037942 , FBgn0037955 , FBgn0038049 , FBgn0038074 , FBgn0038131 , FBgn0038281 , FBgn0038345 , FBgn0038538 , FBgn0038628 , FBgn0038629 , FBgn0038734 , FBgn0038760 , FBgn0038881 , FBgn0038996 , FBgn0039205 , FBgn0039214 , FBgn0039302 , FBgn0039359 , FBgn0039402 , FBgn0039404 , FBgn0039464 , FBgn0039520 , FBgn0039580 , FBgn0039857 , FBgn0040007 , FBgn0040010 , FBgn0040233 , FBgn0040512 , FBgn0040529 , FBgn0040634 , FBgn0040766 , FBgn0040793 , FBgn0043001 , FBgn0043904 , FBgn0050007 , FBgn0050290 , FBgn0050387 , FBgn0051158 , FBgn0051284 , FBgn0051291 , FBgn0051302 , FBgn0051354 , FBgn0051361 , FBgn0051450 , FBgn0051453 , FBgn0051554 , FBgn0051613 , FBgn0051754 , FBgn0051774 , FBgn0051847 , FBgn0052050 , FBgn0052105 , FBgn0052179 , FBgn0052193 , FBgn0052219 , FBgn0052311 , FBgn0052600 , FBgn0052633 , FBgn0052720 , FBgn0052733 , FBgn0052773 , FBgn0052778 , FBgn0052797 , FBgn0053128 , FBgn0053147 , FBgn0053256 , FBgn0053271 , FBgn0053300 , FBgn0053319 , FBgn0058337 , FBgn0062412 , FBgn0062413 , FBgn0083950 , FBgn0085392 , FBgn0085408 , FBgn0085424 , FBgn0085436 , FBgn0086710 , FBgn0086712 , FBgn0086758 , FBgn0086904 , FBgn0250791 , FBgn0250814 , FBgn0250834 , FBgn0250908 , FBgn0259113 , FBgn0259212 , FBgn0259232 , and FBgn0259246 . Mouse genes with a confirmed function in lipid storage regulation: MGI:107807 , MGI:107851 , MGI:1333825 , MGI:1334462 , MGI:1335073 , MGI:1351329 , MGI:1353495 , MGI:1354962 , MGI:1858696 , MGI:1861607 , MGI:1891824 , MGI:1891829 , MGI:1913585 , MGI:1914062 , MGI:1914103 , MGI:1914144 , MGI:1914234 , MGI:1914454 , MGI:1915822 , MGI:1916296 , MGI:1917599 , MGI:1929063 , MGI:2385261 , MGI:2385656 , MGI:2387591 , MGI:2388481 , MGI:2443241 , MGI:3041174 , MGI:3694697 , MGI:88192 , MGI:95301 , MGI:98342 , and MGI:99431 .
|
Fat cells , and cells in general , convert fatty acids into triglycerides that are stored in droplets for future use . Despite the enormous importance of lipid droplets in obesity and other disease processes , we know very little about how lipid reserves in droplets are formed and how those reserves are drawn down . We have used the model fruit fly Drosophila to identify candidate regulators of lipid storage and utilization , and have shown that many of these candidates have functions that are conserved in mammals . We focused our attention on a vesicle-trafficking pathway that we show is required for the modulation of the types of regulatory and enzymatic proteins found on the lipid droplet surface . Interfering with the function of this trafficking system with either RNA interference or small-molecule compounds alters lipid storage . The understanding of this new pathway , as well as the specific reagents we used , may ultimately lead to new therapeutics .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods",
"Accession",
"numbers"
] |
[
"cell",
"biology",
"physiology",
"diabetes",
"and",
"endocrinology",
"genetics",
"and",
"genomics"
] |
2008
|
COPI Complex Is a Regulator of Lipid Homeostasis
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Sequence changes in coding region and regulatory region of the gene itself ( cis ) determine most of gene expression divergence between closely related species . But gene expression divergence between yeast species is not correlated with evolution of primary nucleotide sequence . This indicates that other factors in cis direct gene expression divergence . Here , we studied the contribution of DNA three-dimensional structural evolution as cis to gene expression divergence . We found that the evolution of DNA structure in coding regions and gene expression divergence are correlated in yeast . Similar result was also observed between Drosophila species . DNA structure is associated with the binding of chromatin remodelers and histone modifiers to DNA sequences in coding regions , which influence RNA polymerase II occupancy that controls gene expression level . We also found that genes with similar DNA structures are involved in the same biological process and function . These results reveal the previously unappreciated roles of DNA structure as cis-effects in gene expression .
Proper control of gene expression is central for the unique phenotype of each organism . Phenotypic diversity can be generated through changes in gene expression . Divergence in gene expression of a specific gene between closely related species can result from sequence changes in its coding region and regulatory region ( cis ) , or from changes in sequences or expression of its direct or indirect upstream regulators ( trans ) . The binding of transcription factors ( TFs ) to sequence-specific sites in gene upstream regions plays a very important role in regulation of gene expression . Changes in TF-binding sequences and changes in abundance and binding domains of TFs can influence TF binding , which may cause variation in gene expression . The divergence of gene expression is also coupled to that of gene sequences in multicellular organisms [1]–[7] . In addition , as chromatin structure is critical for the regulation of gene expression , gene expression divergence between species correlates with divergence of nucleosomal organization [8] , [9] . Nucleosome positioning is determined by cis effects ( i . e . the intrinsic DNA sequence preference for nucleosome ) , and trans effects ( e . g . chromatin modifiers ) . The effects of cis and trans regulation on gene expression divergence can be measured by comparison of different strains of the same species [10] , [11] and by analysis of hybrid species [12] , [13] . Experiments on specific genes have revealed that the contribution of cis regulation to gene expression divergence between Drosophila species is much greater than that of trans regulation [14] . A genome-wide study on yeast species has also reproduced similar observation [15] . Cis-regulatory changes in gene expression are supposed to be driven by sequence mutations in TF binding sites or those in coding regions . However , most mutations in TF-binding sequences between yeast species have only little effect on gene expression divergence [16] , though it cannot rule out the possibility that backup mechanisms exist among TF binding . Moreover , evolution of gene sequence in coding regions and gene expression divergence are not correlated in yeast [17] . These results leave open the question of what drive gene expression divergence in cis . The three-dimensional structure of DNA , which reflects the physicochemical and conformational properties of DNA , is critical for the packaging of DNA in the cell [18] . The structure of DNA has been recognized to be important for protein-DNA recognition [19] , [20] . Specific proteins-DNA interactions are fundamental to many biological processes , such as transcription , recombination , and DNA replication . DNA bending plays a role in the regulation of prokaryotic transcription [21] . DNA structure can be used as discriminatory information to identify core-promoter regions [22] , [23] . Specific replication-related proteins show a preference to bind curved DNA sequences [24] . DNA curvature is also involved in the binding of recombination-related proteins [25] . A recent study has found that DNA structure in the human genome is more evolutionary constrained than the primary nucleotide sequence alone [26] . Moreover , the DNA structure-conserved regions correlate with non-coding regulatory elements , better than sequence-conserved regions identified solely on the basis of primary sequence [26] . These results indicate that DNA structure is important for regulation of gene expression . We presume that DNA structure is an ideal candidate for directing gene expression divergence in cis . We evaluated DNA structure in terms of various physicochemical and conformational properties . We found that high levels of cis-driven gene expression divergence between yeast species correspond to high evolution rates of DNA structure in coding regions . This result also holds true between Drosophila species . The relationships of various types of structural evolution with gene expression divergence are conserved between yeast and Drosophila . We next investigated whether DNA structure is associated with gene characteristics . Genes that differ in DNA structure are distinguished by chromatin remodeler occupancy and histone modification levels , indicating that DNA structure influences gene expression by regulating the binding of chromatin regulators to DNA . Genes with similar DNA structures tend to belong to the same biological process and function .
We examined the role of primary nucleotide sequence evolution in cis-driven gene expression divergence . Although a previous study has already found that gene expression divergence is not correlated with evolution of gene sequence in yeast [16] , this result is confounded by the trans-effects in gene expression divergence . A recent study has designed a microarray to experimentally measure the relative contribution of cis and trans effects to gene expression divergence by using the hybrid of Saccharomyces cerevisiae and Saccharomyces paradoxus [15] . These valuable data allow for a direct examination of the contribution of primary nucleotide sequence evolution to cis-driven gene expression divergence . First , we tested the relationship between sequence evolution in upstream regulatory regions and cis-effects to gene expression divergence . TF-binding sequences in promoter regions are the best-characterized elements that regulate gene expression . A previous study has analyzed the conservation of TF-binding sequences in promoters of closely related yeast species and identified the loss of TF-binding sites [27] . If mutation of TF-binding sequences influences gene expression divergence , genes with loss of TF-binding sites ( i . e . whose promoters contain divergent sequence motifs ) should show higher levels of cis-effects on gene expression divergence than genes without loss of TF-binding sites . However , genes with loss of TF-binding sites show relatively low levels of cis-effects on gene expression divergence ( , Mann-Whitney U-test; Figure S1A ) . Although changes of TF-binding sequences can significantly affect TF binding affinities which should be associated with changes in gene expression , backup mechanisms might compensate for the changes in TF-binding sequences which leads to the apparent little effect of loss of TF-binding sites on gene expression divergence . On the other hand , as yeast intergenic distances are relatively short , divergently oriented ( i . e . reversely-oriented ) gene pairs share a bi-directional cis-regulatory region in which TF-binding sequences might control the expression of both flanking genes [28] . If changes in TF-binding sequences have cis-effects on gene expression divergence , mutation of TF-binding sequences in a bi-directional cis-regulatory region might simultaneously influence gene expression divergence of both flanking genes . As a result , divergently oriented gene pairs should show higher similarity in cis-driven gene expression divergence levels than tandem or convergent gene pairs . However , we found that pair-wise differences in cis-effect levels for divergent gene pairs are comparable to those for tandem and convergent gene pairs ( Figure S1B ) . Second , we investigated into the contribution of sequence evolution in 3′ untranslated regions ( UTR ) to cis-driven gene expression divergence . Cis-regulatory elements in 3′ UTR are crucial for controlling RNA stability and expression . A previous study has calculated the evolutionary conservation of 3′ UTR cis-regulatory elements between closely related yeast species [29] . If mutation of 3′ UTR cis-regulatory elements influences gene expression divergence , genes with divergent 3′ UTR cis-regulatory sequence should show higher levels of cis-effects on gene expression divergence than genes with conserved 3′ UTR cis-regulatory sequences . However , the two classes of genes show comparable levels of cis-driven gene expression divergence ( Figure S2 ) . Third , we examined the relationship between gene sequence evolution and cis-effects on gene expression divergence . In the measurement of contribution of cis effects to gene expression divergence [15] , as both alleles of each gene are under the same nuclear environment ( the same trans effects ) in the hybrid of S . cerevisiae and S . paradoxus , differences in their expression reflect cis effects on gene expression divergence [15] . We defined the genes whose both alleles show significant difference in gene expression ( above 2-fold ) within the hybrid as genes with significant cis-effects to gene expression divergence . This is a stricter threshold compared to that ( 1 . 4-fold ) in the original literature [15] . Initially , we found that though genes with significant cis-effects to gene expression divergence show higher gene sequence evolutionary rates between S . cerevisiae and S . paradoxus than the other genes , the statistical significance is rather weak ( Mann-Whitney U-test; Figure S3; see Materials and Methods ) . This is consistent with the previous observation that evolution of gene sequence and gene expression divergence are not correlated in yeast [17] . Next , we examined whether cis-driven gene expression divergence is linked to codon bias . We found that genes with significant cis-effects to gene expression divergence and the other genes show similarity in codon bias divergence ( , Mann-Whitney U-test; see Materials and Methods ) . This result suggests that cis-driven gene expression divergence between S . cerevisiae and S . paradoxus is not mainly caused by codon bias divergence . We have shown that genes with significant cis-effects to gene expression divergence and the other genes have comparable evolution rates of primary nucleotide sequence , indicating that evolution of primary nucleotide sequence in coding regions has little cis-effect on gene expression divergence in yeast . Although primary nucleotide sequences determine three-dimensional structures of DNA , and thus evolution rate of primary nucleotide sequences should correlate with evolutionary rate of DNA structures , this correlation is not complete . As similar changes in DNA sequence can cause significantly different changes in DNA structure ( see Figure 1 for example ) , evolution of DNA structure might influence gene expression divergence . We thus asked whether genes with significant cis-effects to gene expression divergence show significant difference in evolution of DNA structure . To test this possibility , we used 35 types of di- or trinucleotide DNA structural scales ( Table S1 ) , which were mainly collected in two references [23] , [30] . The structural scales chosen in this study have been frequently used and have been extensively studied in previous literatures [31] , [32] . These structural scales provide important information on the structure of DNA and capture structural properties that might be of importance for transcription . Each scale contains complementary information and provides a unique insight into the DNA structure ( see Table S1 for more details about each of these structural scales ) . For the structural scales that have at least two different datasets , we used the most recently published dataset . The scales were classified into two types: conformational and thermodynamic [30] . The rationale for exploiting di- or trinucleotide properties is the widely accepted nearest neighbor model saying that DNA structure can be understood and caused largely by interactions between neighboring base pairs [33] , [34] . This model is typically in the form of dinucleotide or trinucleotide scales . Each possible di- or trinucleotide and its reverse complement are assigned with a parametric value for a single structural property ( Table S1 ) . The origins of the parametric values are either derived from experimentally determined structures , or from simulated structures of a DNA helix or a DNA–protein complex . In order to get insight into the different structural scales , we analyzed the structural data using principal component analysis ( PCA ) and clustering analysis . As most ( 32 out of 35 ) of the structural scales are based on dinucleotide , we performed the two analyses above on the dinucleotide structural scales . Considering that the dinucleotide and its reverse complement have the same parametric value for a single structural property , there are only 10 unique dinucleotides . We first performed a PCA calculating the 32 principal components for the 10 dinucleotides . Only the first 9 principal components ( PCs ) carry relevant information , roughly indicating that about this low number of scales is needed to represent all information of the complete set of 32 scales . As the first 5 PCs carry ∼88% of information ( 30% , 22% , 18% , 12% , and 6% ) , we next clustered the 32 scales into 5 classes using K-means clustering ( Figure 2 ) . Each scale was represented by a vector of length 10 which contains the parametric values of dinucleotides . We calculated pair-wise Pearson correlation coefficients for the 32 scales ( vectors ) , and used the absolute resulting values as the measure of the clustering . The absolute value of the correlation indicates whether two scales contain similar information . In Figure 2 , it can be seen that all thermodynamic scales contain similar information . This is likely due to the fact that these thermodynamic scales are associated with the stability of DNA structure . Interestingly , the thermodynamic scales also contain similar information with some conformational scales , such as DNA bending stiffness and propeller twist . The rest of conformational scales are separated into four clusters . The most uncorrelated clusters ( the lowest values in Figure 2 ) are the cluster containing all thermodynamic scales and the cluster containing twist ( free DNA ) . For each pair of orthologous genes between S . cerevisiae and S . paradoxus , we calculated DNA structural evolution rate for each of the 35 DNA structural scales ( see Materials and Methods ) . Although DNA structural evolution rates show positive correlation with primary nucleotide sequence evolution rates , the correlation is not complete: The correlation coefficients range from 0 . 21 to 0 . 57 ( Figure S4 ) . As defined above , genes with significant cis-effects to gene expression divergence are the genes whose both alleles show significant difference in gene expression ( above 2-fold ) within the hybrid . Genes with significant cis-effects to gene expression divergence show significantly higher DNA structural evolution rates than the other genes in each of the 35 scales ( , Mann-Whitney U-test , after Bonferroni correction for multiple testing , Figure 3A ) . In 5′ UTR and 3′ UTR , genes with significant cis-effects to gene expression divergence show comparable DNA structural evolution rates to those of the other genes in terms of each of the 35 scales ( , Mann-Whitney U-test ) . These results demonstrate that high levels of cis-driven gene expression divergence correspond to high evolution rates of DNA structure in coding regions . The above correspondence of high cis-driven gene expression divergence with high evolution rates of all the 35 structural scales seems likely to be caused by evolution of primary nucleotide sequence . However , we have shown that genes with significant cis-effects to gene expression divergence show comparable gene sequence evolutionary rates with the other genes . These apparent discrepancies can be reconciled if different genes with significant cis-effects to gene expression divergence show higher evolution rates in different structural scales . As a result , genes with significant cis-effects to gene expression divergence as a whole show significantly higher evolution rates in all the structural scales . To test this possibility , we calculated the number of structural scales in which each gene with significant cis-effects to gene expression divergence shows significantly high evolution rates ( , ) . Indeed , we found that the resulting numbers range from 0 to 3 ( Figure S5 ) . For each structural scale , we randomly shuffled the parametric values among the di- or trinucleotides . We generated 10 , 000 randomized profiles for each structural scale . We calculated DNA structural evolution rates in coding regions between orthologous genes as above based on these randomized profiles . If the correspondence between cis-driven gene expression divergence and DNA structural evolution observed above is not an artifact , the difference in DNA structural evolution rates between genes with significant cis-effects to gene expression divergence and the other genes should be more statistically significant than those based on the randomized structural profiles . For each structural scale , genes with significant cis-effects to gene expression divergence show higher DNA structural evolution rates in some of these shuffled profiles , but lower or comparable evolution rates in the other shuffled profiles . For each structural scale , most of the statistical significances ( regardless of higher or lower evolution rates that genes with significant cis-effects to gene expression divergence show ) in randomized experiments are weaker than that on the realistic profile ( , see Figure 3B for one example structural scale ) . We next quantitatively evaluated the contribution of DNA structural evolution to gene expression divergence compared with that of primary nucleotide sequence evolution in coding regions . We calculated the correlation of primary nucleotide sequence evolution rate with cis-driven gene expression divergence ( Pearson correlation coefficient , ) . For each DNA structural scale , we calculated the correlation of its structural evolution rate with cis-driven gene expression divergence . We used the resulting correlation coefficients to represent the contribution of DNA structural evolution or primary nucleotide sequence evolution to cis-driven gene expression divergence . The correlation coefficients for DNA structural evolution are significantly higher than that for evolution of primary nucleotide sequence ( Figure 3C ) . Moreover , when using partial correlation to control evolution of primary nucleotide sequence , DNA structural evolution is still significantly correlated with cis-driven gene expression divergence ( Figure S6; see Materials and Methods ) . We sought to evaluate the total contribution of DNA structural evolution to cis-driven gene expression divergence . Restricting analysis to genes with significant cis-effects to gene expression divergence , a multiple linear regression of cis-driven gene expression divergence against DNA evolution rates of 35 structural scales without considering any other factors gave an of 0 . 09 ( ) , implying that about 9% of the variation of cis-driven gene expression divergence is attributable to DNA structural evolution . We also performed a linear regression of cis-driven gene expression divergence against primary nucleotide sequence evolution rates which gave an of . These results collectively demonstrate the significant association of DNA structural evolution with gene expression divergence relative to that of primary nucleotide sequence evolution . It is very interesting to explore what other factors in cis contribute to the variation of cis-driven gene expression divergence . Although we have found that genes with loss of TF-binding sites and genes with divergent 3′ UTR cis-regulatory sequences do not show significantly high cis-driven gene expression divergence ( Figure S1 , S2 ) , it is very likely that divergence of unknown elements in promoters and 3′ UTR could be associated with cis-driven gene expression divergence . As gene expression divergence data we used above were measured in a microarray [15] , we examined whether the correspondence of cis-driven gene expression divergence to DNA structural evolution is an artifact of bias in microarray data . First , we examined the structural evolution of DNA sequences in the microarray probes . Changes in structural properties at the probe sequences might influence microarray hybridization and thus lead to apparent cis-driven gene expression divergence . We found that genes with significant cis-effects to gene expression divergence and the other genes show comparable DNA structural evolution rates in probe regions in terms of each of the 35 scales ( , Mann-Whitney U-test , Figure S7; see Materials and Methods ) . Moreover , when restricting analysis to genes whose probe sequences have low structural evolution rates , genes with significant cis-effects to gene expression divergence still show significantly higher DNA structural evolution rates in coding regions than the other genes in each of the 35 scales ( , Mann-Whitney U-test , after Bonferroni correction , Figure S8 ) . These results indicate that cis-driven expression divergence is not an artifact caused by DNA structural evolution in microarray probe regions . Second , we tested the relationship of cis-driven gene expression divergence with DNA structural evolution using gene expression divergence data between S . cerevisiae and S . bayanus measured in RNA-seq platform [35] . We found that genes with significant cis-effects to gene expression divergence show significantly higher DNA structural evolution rates in coding regions than the other genes in each of the 35 scales ( , Mann-Whitney U-test , Figure S9 ) . These results collectively indicate that the relationship of cis-driven gene expression divergence to DNA structural evolution is robust to the choice of experimental platforms . We examined the relationship of gene expression divergence to DNA structural evolution in other species . Previous studies have revealed a significant positive correlation between evolution rate of gene sequence and gene expression divergence in Drosophila species [2] , [4] . As different DNA sequences might have similar DNA structures [26] , high evolution rates of primary nucleotide sequence do not always correspond to high evolution rates of DNA structure . The relationship between evolution of DNA structure and gene expression divergence in Drosophila species remains to be elucidated . Using gene expression divergence data in Drosophila [36] , [37] and the 35 DNA structural scales above , we found that genes with significant cis effects on gene expression divergence also show significantly higher DNA structural evolution rates than the other genes ( , Mann-Whitney U-test , Figure S10 ) . When normalizing DNA structural evolution rates by gene sequence evolution rates , genes with significant cis effects on gene expression divergence still show higher normalized DNA structural evolution rates than the other genes ( , Mann-Whitney U-test , Figure S10 ) , albeit with weaker statistical significance . Taken together , these results demonstrate that the relationship between DNA structural evolution and gene expression divergence is conserved between Drosophila and yeast species . We further examined whether the relationships of 35 types of structural evolution with gene expression divergence are conserved . For each type of structural evolution , we used the above P-value from Mann-Whitney U-test , which was performed between genes with significant cis-effects to gene expression divergence and the other genes , to represent the degree of contribution of this type of structural evolution to gene expression divergence . The more significant the P-value is , the more the contribution is . We found that S . cerevisiae-S . paradoxus pair and D . melanogaster-D . simulans pair , S . cerevisiae-S . paradoxus pair and D . melanogaster-D . sechellia pair , D . melanogaster-D . sechellia pair and D . melanogaster-D . simulans pair show significant positive correlation in the contribution of structural evolution to gene expression divergence ( Table S2 ) . However , S . cerevisiae-S . bayanus pair shows no correlation with the other three pairs . We have shown that high levels of gene expression divergence correspond to high evolution rates of DNA structure , but whether the converse relationship holds true remains to be answered . In the following analysis , we focused on DNA structural evolution in coding regions between S . cerevisiae and S . paradoxus . We first identified cohort of genes for each DNA structural scale . Genes belong to the cohort of one DNA structural scale if they display significantly high evolution rates ( , ) of the corresponding DNA structural scale in coding regions . In this way , we obtained 35 sets of cohorts . 14 out of the 35 gene cohorts show significantly higher cis-driven gene expression divergence than the other genes ( , Mann-Whitney U-test , after Bonferroni correction; See Figure 4A for the list of the 14 structural scales ) . Considering only dinucleotide scales , we found that absolute values of pair-wise Pearson correlation coefficients among parametric values ( i . e . profiles ) of these significant dinucleotide scales are comparable to those among the other scales ( , Mann-Whitney U-test ) , ruling out their potential redundancy in DNA structure . For these 14 DNA structural scales , their high structural evolution rates can cause high gene expression divergence . Whereas for the other DNA structural scales , though high gene expression divergence can be explained by their high structural evolution rates , other factors might limit the contribution of their structural evolution to gene expression divergence , which leads to the observation that their high evolution rates do not correspond to high gene expression divergence . In the following analysis , we focused on these 14 significant DNA structural scales . We investigated into the roles of DNA structure in gene expression in a single species . We have shown that evolution of DNA structure in coding regions is correlated with gene expression divergence . If this correlation is biologically meaningful , DNA structural levels in coding regions should also be correlated with gene expression levels in a single species . For each of the 14 significant DNA structural scales above , we calculated the structural profile in each coding region from DNA sequences ( see Materials and Methods ) , and used the average value of the structural profile to represent the level of this structural scale in the coding region . We found that structural levels of 12 out of the 14 scales show significant correlation with gene expression levels ( Pearson correlation coefficient , , , Figure 4A ) . Similar results were reproduced on gene transcription rate data and RNA polymerase II occupancy in coding regions ( Figure S11 ) , implying that most of these correlations are caused at the transcriptional level . 6 scales show significant positive correlation , while 6 scales show significant negative correlation ( Figure 4A ) . 4 thermodynamic scales , including duplex disrupt energy , duplex free energy , enthalpy and entropy , show significant correlation with gene expression levels . As duplex disrupt energy is positively correlated with stability of DNA duplex and the other three scales is negatively correlated with stability of DNA duplex , these results indicate that stability of DNA duplex in coding regions is positively correlated with gene expression levels . It has been shown that RNA polymerase elongation tends to pause when the DNA duplex is unstable [38] , [39] . The high stability of DNA duplex in coding regions should facilitate transcription elongation and raise mRNA gene expression level . 2 nucleosome-related scales , including DNA bending stiffness and nucleosome position preference , show significant positive correlation with gene expression levels . High values of DNA bending stiffness correspond to dinucleotides that will bend more easily , which facilitates the packaging of DNA into nucleosome . This result is consistent with previous observation that nucleosome occupancy within coding regions positively correlates with transcription level [40] . 3 conformational scales , including rise ( DNA-protein complex ) , roll ( free DNA ) and slide ( DNA-protein complex ) , show significant positive correlation with gene expression levels . Following the definitions of the structural parameters in the EMBO workshop [41] , these three scales are positively correlated with the distance between two successive base pairs . Maybe the increase in the distance between two successive base pairs in coding regions facilitates transcription . Another 2 scales , including shift ( DNA-protein complex ) and major groove depth , show significant negative correlation with gene expression levels . Shift ( DNA-protein complex ) could increase major groove depth which might influence gene expression . We further investigated into how DNA structure influences gene expression . As chromatin remodeler occupancy and histone modification levels in coding regions influence gene expression , we examined the relationship of DNA structural levels with these two chromosomal features . First , we used genome-wide occupancy data for chromatin remodelers [42] . These data were measured with single-gene resolution based on microarray . We found that DNA structural levels show significant correlation with chromatin remodeler occupancy in coding regions ( , Figure 4B ) . Moreover , the directions of correlation are the same as those between structural levels and gene expression levels , indicating that these chromatin remodelers facilitate gene expression . Second , using available genome-wide histone modification data measured in microarray [43] , [44] , we found that DNA structural levels are also significantly correlated with histone modification levels ( Figure 4C , Table S3 ) . We also found that the bias of microarray probes on our observations is very limited ( see Materials and Methods ) . DNA structure is critical for protein-DNA recognition . Difference in DNA structure might change the binding of chromatin remodelers and histone modifiers to DNA , leading to the difference in gene expression levels . We next investigated into the relationship of DNA structural level with nucleosome occupancy . DNA sequence is an important determinant of nucleosome positioning which is critical for gene expression . A previous study has measured genome-wide in vitro nucleosome occupancy that is determined only by the intrinsic DNA sequence [45] . Sequences covered by high in vitro nucleosome occupancy have high sequence preference for nucleosome formation , while sequences covered by low in vitro nucleosome occupancy inhibit nucleosome formation . We found that DNA structural levels are significantly correlated with in vitro nucleosome occupancy in coding regions: some structural scales facilitate nucleosome formation , while others inhibit nucleosome formation ( Figure 4D ) . We also found that DNA structural levels are also significantly correlated with in vivo nucleosome occupancy , though the correlations become weak ( Figure 4D ) . We asked whether DNA structure is linked to biological process and function . We have shown that DNA structure is associated with gene expression and chromatin regulators . As genes with similar gene co-expression patterns or genes regulated by similar regulators are known to be involved in similar biological processes and functions , we asked whether genes with similar DNA structural levels are involved in similar biological processes and functions . We tested this possibility using the 14 significant DNA structural scales above whose high evolution rates correspond to high gene expression divergence . As stated above , for each of the 14 DNA structural scales , we calculated the structural profile in each coding region from DNA sequences ( see Materials and Methods ) , and used the average value of the structural profile to represent the level of this structural scale in the coding region . We sorted all yeast genes in ascending order based on the corresponding DNA structural levels for each DNA structural scale , and split them into five equal gene clusters . Genes in the same gene cluster have similar structural levels of the corresponding structural scale . We found that genes in the same gene cluster tend to belong to the same biological process or function as indicated by Gene Ontology [46] ( see Table S4 for the full results of all structural scales ) . We found that genes in the same gene cluster are involved in diverse biological processes and functions , including those are regulatory or housekeeping . There is no gene cluster that is characterized only by regulatory or housekeeping processes . Different clusters also have some processes and functions in common . We also binned genes into different numbers ( from 3 to 10 ) of equal groups based on their structural levels , respectively . Similar results that genes in the same gene cluster tend to belong to the same biological process or function could be reproduced , which indicates that our observation is robust to the choice of the numbers of gene clusters .
Cis-effects dominate gene expression divergence between yeast species . However , evolution of primary nucleotide sequences are not correlated with gene expression divergence , suggesting that other factors in cis drive gene expression divergence . Here , we used various physicochemical and conformational DNA properties to investigate into the relationship between evolution of DNA structure and gene expression divergence . We found that evolution of DNA structure in coding regions is coupled to gene expression divergence in yeast and in Drosophila . We also found that DNA structure in coding regions is associated with gene expression in a single species . DNA structure in coding regions is also associated with the binding of chromatin regulators to DNA that regulates gene expression , leading to the observed association between DNA structure and gene expression . These results highlight the important role of DNA structure as cis-effect in gene expression . The evolution of both DNA sequence and structure in non-coding regulatory regions are not correlated with gene expression divergence . But gene expression has been thought to be mainly regulated by the regulatory elements in non-coding regions . These apparent discrepancies can be reconciled if backup mechanism exists in gene regulatory programs . A previous study has revealed that most genes in yeast are not affected when any TF is knocked out [47] , indicative of redundant TFs which mask the TF knockout effect . As DNA binding sequences of TFs are usually short and degenerate , there might be multiple binding sequences for one specific TF in the regulatory region . This redundancy compensates for changes in TF-binding sequence , maybe leading to the apparent little effect of their changes on gene expression . Although we found that DNA structure is associated with gene expression , the mechanisms of this relationship remain to be elucidated . We found that DNA structure is associated with distinct gene features . These results collectively reveal how DNA structure influences gene expression . We found that DNA structure is correlated with chromatin remodeler occupancy , histone modification levels and nucleosome occupancy . These results suggest that DNA structure influences the binding of chromatin remodelers and histone modifiers to DNA , and nucleosome positioning along DNA in coding regions . Chromatin remodeling , histone modification and nucleosome positioning could influence elongation of RNA polymerase II which controls gene expression . However , further experimental work will be required to more fully understand how DNA structure modulates gene expression .
Yeast genome sequences and gene coordinate were downloaded from the Saccharomyces Genome Database ( http://www . yeastgenome . org/ ) . Yeast transcript coordinate data were taken from David et al . [48] . Orthologous genes between S . cerevisiae and S . paradoxus were taken from Wapinski et al . [49] . Orthologous genes and their sequences between D . melanogaster and D . simulans were taken from Heger et al . [50] . The relative contribution of cis and trans effects to gene expression divergence between S . cerevisiae and S . paradoxus were taken from Tirosh et al . [15] . As both alleles of each gene are under the same nuclear environment ( the same trans effects ) in the hybrid , differences in their expression reflect cis effects on expression divergence , whereas expression differences between the parental genes that disappear in the hybrid reflect trans effects . In the original literature , genes whose both alleles show >1 . 4-fold difference in gene expression within the hybrid were considered to have significant cis effects [15] . In this study , we set a stricter threshold and defined the genes whose both alleles show significant difference in gene expression ( above 2-fold ) within the hybrid as genes with significant cis-effects to gene expression divergence . Cis-driven gene expression divergence data between S . cerevisiae and S . bayanus were taken from Bullard et al . [35] . Genes with statistical significance in the original literature were defined as genes with significant cis-effects to gene expression divergence . Gene expression and transcription rate data in S . cerevisiae were taken from Holstege et al . [51] . Gene expression divergence data between adults of D . melanogaster and D . simulans were taken from Ranz et al . [36] . Genes with statistical significance in the original literature were defined as genes with high levels of gene expression divergence . Gene expression divergence data between D . melanogaster and D . sechellia were taken from McManus et al . [37] . We used the same definition of genes with significance cis effects on gene expression divergence as that in the original literature [37] . The conservation of sequence motifs in promoters of closely related yeast species was analyzed and the loss of TF-binding sites was predicted by Doniger et al . [27] . We identified genes with loss of TF-binding sites ( divergent ) or without loss of TF-binding sites ( conserved ) in their promoters . This results in two gene clusters . Some genes have multiple TF-binding sites in promoter regions . Some binding sites in one promoter region might be conserved , ant the other binding sites in this promoter region might be divergent . Some genes thus might belong to two gene clusters simultaneously . We excluded genes shared by the two gene clusters for analysis . The evolutionary conservation of 3′ UTR cis-regulatory elements between yeast species were taken from Shalgi et al . [29] . 3′ UTR cis-regulatory sequences with significant conserved P-value are considered to be conserved . As the method above , we identified genes with conserved 3′ UTR cis-regulatory elements and divergent 3′ UTR cis-regulatory elements , respectively . Genome-wide in vivo and in vitro nucleosome occupancy data in S . cerevisiae were taken from Kaplan et al . [45] . We calculated the average in vivo and in vitro nucleosome occupancy in coding region for each gene , respectively . Genome-wide RNA polymerase II occupancy ( RNA polymerase II subunit Rpo21 ) data in S . cerevisiae were taken from Venters et al . [42] . We calculated the average RNA polymerase II occupancy in coding region for each gene . Chromatin remodeler occupancy in coding regions was taken from Venters et al . [42] . Histone modification data were taken from ChromatinDB [43] , a database of genome-wide histone modification patterns for S . cerevisiae . We added the histone modification data from Pokholok et al . [44] , a total of 25 histone modifications . For each coding region , we calculated the average level for each histone modification . We performed the global alignment on gene sequences between orthologous genes . We used the rate of nonsynonymous substitutions ( Ka ) normalized by the rate of synonymous substitutions ( Ks ) as a measure of gene sequence evolutionary rate . We used the codon adaptation index ( CAI ) to indicate codon bias . We calculated CAI for each gene as a previous method [52] . For each pair of orthologous genes between S . cerevisiae and S . paradoxus , we calculated their absolute value of difference in CAI values , and defined the resulting value as its CAI divergence . We compared genes with significant cis-effects to gene expression divergence with the other genes in CAI divergence . We used 35 types of conformational and thermodynamic DNA di- or trinucleotide structural scales , which were mainly collected by two references [23] , [30] , as measures of DNA structure . We normalized each of the 32 dinucleotide structural scales ( their means are zero and standard deviations are one ) , and performed a PCA calculating the 32 principal components for the 10 dinucleotides . Each scale was represented by a vector of length 10 which contains the parametric values of dinucleotides . We calculated pair-wise Pearson correlation coefficients for the 32 scales ( vectors ) , and classified the 32 scales into 5 clusters using K-means clustering based on the measure . For a DNA region , the sequence is divided into overlapping di- or trinucleotide sequences . Structural profiles from DNA sequences are calculated for each structural scale ( except for hydroxyl radical cleavage pattern ) as follows: The corresponding parametric value for each di- or trinucleotide was assigned to the first nucleotide of the di- or trinucleotide . In this way , the nucleotide sequence is converted into a sequence of numbers ( i . e . , a numerical profile ) . For hydroxyl radical cleavage intensity data , structural profiles are calculated as the reference where the data was published [53] . The hydroxyl radical cleavage intensity data are assigned to each nucleotide in each trinucleotide sequence . Note that the three nucleotides in each trinucleotide sequence have different values of hydroxyl radical cleavage intensity . As each nucleotide ( except for the two terminal nucleotides at each end of the DNA region ) is covered by three overlapping trinucleotide sequences , it has three values of hydroxyl radical cleavage intensity ( one for each trinucleotide ) . The three values are averaged to produce hydroxyl radical cleavage intensity for each nucleotide . In this way , the nucleotide sequence is converted into a sequence of numbers ( i . e . , a numerical profile ) . For each pair of orthologous genes , we calculated the Euclidean distance of structural profiles after pairwise alignments on gene sequences between orthologous genes . We considered the resulting Euclidean distance normalized by the length of coding region as a measure of evolution rate of DNA structure . In this way , there were 35 measures of structural evolutionary rate for each pair of orthologous genes . We also calculated structural evolutionary rates for 5′ UTR and 3′ UTR for yeast species . Partial correlation can measure the degree of association between two variables with the effect of controlling variables removed . indicates the partial correlation between and when controlling . It is defined as:Where is the correlation between x and y . We calculated the partial correlation between DNA structural evolution rates and cis-driven gene expression divergence when controlling primary nucleotide sequence evolution rates . The DNA structural evolution rates in microarray probes which were used to measure gene expression divergence are calculated as follows . For each probe , we profiled the values of each specific structural scale versus its sequence positions , and called this graph its structural profile of this structural scale . For each pair of orthologous genes , we calculated the Euclidean distance between structural profiles of their two probes , and used the resulting values normalized by the length of the probe as a measure of evolution rate of DNA structure . For orthologous genes with more than one pair of probes , we calculated the Euclidean distance normalized by the length of the probe for each pair of probes , and used the average resulting distance value as a measure of DNA structural evolution rate . In this way , there were 35 measures of structural evolutionary rate in probe regions for each pair of orthologous genes . To evaluate the microarray probe bias on the measurement of chromatin remodeler occupancy , we calculated for each coding region the average structural value of each structural scale across its coding regions after excluding the sequences of its microarray probe . The resulting DNA structure values are still significantly correlated with chromatin remodeler occupancy ( data not shown ) . For each probe in microarray that were used to measure histone modification level , we calculated the average structural value of each structural scale across its sequence positions . We found that histone modification levels are weakly correlated with the DNA structures in probe regions ( Pearson correlation coefficients , ) , suggesting that the bias of probes in histone modification level is very limited .
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The unique phenotype of each organism is partly determined by gene expression . Changes in gene expression are an important source of phenotypic variation , and can be caused by changes in regulatory and coding sequences of the gene itself ( cis ) and changes in regulatory factors ( trans ) . The contribution of cis regulation to gene expression divergence between closely related species is much greater than that of trans regulation . However , evolution of primary nucleotide sequences is not correlated with gene expression divergence in yeast , suggesting that other factors in cis drive gene expression divergence . Here , we found that evolution of DNA structure in coding regions is coupled to gene expression divergence in yeast . We also found that DNA structure is associated with specific gene characteristics . Genes with similar DNA structures are involved in the same biological process and function . These results demonstrate the important roles of DNA structure in directing gene expression .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"genome",
"evolution",
"gene",
"expression",
"genetics",
"biology",
"genomics",
"genetics",
"and",
"genomics"
] |
2011
|
Gene Expression Divergence is Coupled to Evolution of DNA Structure in Coding Regions
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One of the most important roles of cells is performing their cellular tasks properly for survival . Cells usually achieve robust functionality , for example , cell-fate decision-making and signal transduction , through multiple layers of regulation involving many genes . Despite the combinatorial complexity of gene regulation , its quantitative behavior has been typically studied on the basis of experimentally verified core gene regulatory circuitry , composed of a small set of important elements . It is still unclear how such a core circuit operates in the presence of many other regulatory molecules and in a crowded and noisy cellular environment . Here we report a new computational method , named random circuit perturbation ( RACIPE ) , for interrogating the robust dynamical behavior of a gene regulatory circuit even without accurate measurements of circuit kinetic parameters . RACIPE generates an ensemble of random kinetic models corresponding to a fixed circuit topology , and utilizes statistical tools to identify generic properties of the circuit . By applying RACIPE to simple toggle-switch-like motifs , we observed that the stable states of all models converge to experimentally observed gene state clusters even when the parameters are strongly perturbed . RACIPE was further applied to a proposed 22-gene network of the Epithelial-to-Mesenchymal Transition ( EMT ) , from which we identified four experimentally observed gene states , including the states that are associated with two different types of hybrid Epithelial/Mesenchymal phenotypes . Our results suggest that dynamics of a gene circuit is mainly determined by its topology , not by detailed circuit parameters . Our work provides a theoretical foundation for circuit-based systems biology modeling . We anticipate RACIPE to be a powerful tool to predict and decode circuit design principles in an unbiased manner , and to quantitatively evaluate the robustness and heterogeneity of gene expression .
State-of-the-art molecular profiling techniques[1–4] have enabled the construction or inference of large gene regulatory networks underlying certain cellular functions , such as cell differentiation[5 , 6] and circadian rhythm[7 , 8] . However , it remains a challenge to understand the operating principles of these regulatory networks and how they can robustly perform their tasks , a prerequisite for cell survival . Mathematical and computational systems biology approaches are often applied to quantitatively model the dynamic behaviors of a network[9–20] . Yet , quantitative simulations of network dynamics are usually limited due to several reasons . First , a proposed network might contain inaccurate or missing regulatory genes or links , and modeling an incomplete network might produce inaccurate predictions . Second , kinetic parameters for each gene and regulatory interaction , which are usually required for quantitative analyses , are difficult to obtain altogether directly from in vivo experiments[21] . To deal with this problem , network parameters are either inferred from existing data [22 , 23] or educated guesses , an approach which could be time-consuming and error-prone . This approach is hard to extend to very large gene networks due to their complexity . Alternatively , a bottom-up strategy has been widely used to study the regulatory mechanisms of cellular functions . First , one performs a comprehensive analysis and integration of experimental evidence for the essential regulatory interactions in order to construct a core regulatory circuit , typically composed of only a small set of essential genes . The core gene circuit is then modeled either by deterministic or stochastic approaches with a particular set of parameters inferred from the literature . Due to the reduced size of the systems and the inclusion of data derived directly from the literature , the bottom-up approach suffers less from the above-mentioned issues . Examples of the bottom-up approach include the modeling of biological processes such as Epithelial-to-Mesenchymal Transition ( EMT ) [24–26] , cell cycles[27 , 28] , and circuit designs in synthetic biology , such as genetic toggle switch[29] and repressilator[30] . Due to the success of these and other circuit-based modeling studies , we hypothesize that a core circuit module should emerge from a complex network and dictate the decision-making process . It is reasonable to assume that a large gene network could be decomposed into a core gene circuit and a peripheral part with the residual genes . The core would then be the driving force for the network dynamics and should be robust against cell-to-cell variability and extrinsic fluctuations in stimuli arising from cell signaling . While the peripheral genes would either act to regulate the signaling status for the core circuit and probably also enhance the robustness of the core dynamics by introducing redundancy ( in-components–genes that regulate the core unit ) or simply have no regulatory effects on the core ( e . g . out-components–genes that are regulated by the core unit ) . This scale-separation picture is consistent with ideas such as the existence of master regulators and network modularity[31 , 32] . On the basis of this conceptual framework , we developed a new computational method , named random circuit perturbation ( RACIPE ) , for modeling possible dynamic behaviors that are defined by the topology of a core gene regulatory circuit . In RACIPE , we focus the modeling analysis on the core circuit and regard the effects of the peripheral genes and external signaling as random perturbations to the kinetic parameters . In contrast to traditional modeling methods[33] , RACIPE generates an ensemble of mathematical models , each of which has a different set of kinetic parameters representing variations of signaling states , epigenetic states , and genetic backgrounds ( including cells with genetic mutations leading to disease ) . Here we randomize the model parameters by one or two orders of magnitude and utilize a specially designed sampling scheme ( details in Methods ) to capture the key role of the circuit topology . This random field approach allows the inclusion of the contributions from the peripheral genes to the network dynamics and the evaluation of their roles in modulating the functions of the core circuit . From the in silico generated data , we apply statistical analysis to identify the most probable features within all of the models , a process which can uncover the most robust functions of the core circuit . It is worth-noting that RACIPE is unique in the way it utilizes perturbation and the integration of statistical tools , compared to the traditional parameter sensitivity analysis[34–38] and the previous studies on random circuit topology[39 , 40] . In the following , we will first describe in detail the RACIPE method , and then present the results of applying RACIPE to several simple standalone circuit motifs and also coupled toggle switch motifs . In addition , we will show the application of RACIPE to a 22-component network for the decision-making core of the Epithelial-to-Mesenchymal Transition ( EMT ) . We will see that RACIPE is capable of identifying accessible gene states via statistical analysis of the in silico generated data , from which we can further decode the design principles and evaluate the robustness of the core gene circuit . We therefore expect RACIPE to be a powerful tool to analyze the dynamic behavior of a gene network and to evaluate the robustness and accuracy of proposed network models .
We developed a new computational method , namely random circuit perturbation ( RACIPE ) , for modeling a gene network . The procedure of RACIPE is illustrated in Fig 1 . The input of RACIPE is the topology of the core circuit under study , which can be constructed according to either the literature , interaction databases ( e . g . Ingenuity pathway analysis ( IPA® , QIAGEN Redwood City , www . qiagen . com/ingenuity ) , KEGG[41] , GO[42] ) , or computational methods[43] . From the circuit topology , we establish a set of mathematical equations for the time evolution of the levels of all the genes . We then generate an ensemble of models where the parameters of the rate equations are sampled by a carefully designed randomization procedure ( see below for details ) so that these kinetic models can capture the behavior of the circuits under different conditions . Each model is subject to standard analysis to discover possible dynamics of the circuit ( Fig 1B ) . The dynamics could converge to a stable steady state , a stable oscillation , or chaotic behaviors . To find all possible behaviors of a gene network , we typically choose many different sets of initial conditions ( randomly sampled on a logarithmic scale ) and numerically solve the rate equations for each case . The procedure is repeated for many times to collect sufficient data for statistical analysis . In particular , for a multi-stable system , this ODE-based method is useful for identifying all the distinct stable states for a multi-stable system . Thus , the RACIPE method can generate a large amount of simulated gene expression data , which can be further analyzed by biostatistical tools ( Fig 1C ) . Potentially , RACIPE can be further extended to study oscillatory ( S1 Fig ) or adaptive dynamics , and is also compatible with other types of modeling methods such as stochastic analysis , but these are out of scope of this study . In the following , we will illustrate RACIPE in the context of a multi-stable gene circuit by deterministic analysis . As an example , we start with the deterministic rate equations for a toggle switch circuit ( Fig 2 ) with mutually inhibitory genes A and B . The kinetic model takes the form: 10 . 1371/journal . pcbi . 1005456 . g002 Fig 2 Randomization scheme to estimate the ranges of the threshold parameters . ( A ) Schematic of the procedure to estimate the ranges of the threshold parameters , so that the level of a regulator has 50% chance to be above or below the threshold level of each regulatory link ( “half-functional rule” ) . First , for a gene A without any regulator , the RACIPE models are generated by randomizing the maximum production rate and the degradation rate according to S1 Table . The distribution of A level is obtained from the stable steady state solutions of all the RACIPE models ( top left panel , yellow histogram ) . Second , for a gene A in a gene circuit , the distribution of A level is estimated only on the basis of the inward regulatory links ( i . e . the B to A activation and the C to A inhibition in the bottom left panel ) . The distributions of the levels of the inward regulators B and C are assumed to follow the same distributions as a gene without any regulator ( bottom left panel , blue and red distribution ) ; the threshold levels for these inward links are chosen randomly from ( 0 . 02M to 1 . 98M ) , where M is the median of their gene expression distributions . Finally , the distribution of A level is obtained by randomizing all the relevant parameters . That includes the levels of B and C , the strength of the inward regulatory links ( i . e . , the threshold level , the Hill coefficient and the fold change ) , the maximum production rate and the degradation rate of A , and the threshold for any regulatory link starting from A is chosen randomly from ( 0 . 02M to 1 . 98M ) , where M is the median level of the new distribution of A level ( orange in the bottom panel ) . The same procedure is followed for all other genes . ( B ) Tests on several simple toggle-switch-like circuit motifs and the Epithelial-to-Mesenchymal Transition ( EMT ) circuit show that the “half-functional rule” is approximately satisfied with this randomization scheme . For each RACIPE model , we computed the ratio ( x/x0 ) of the level of each gene X at each stable steady state ( x ) and the threshold ( x0 ) for each outward regulations from gene X . The yellow region shows the probability of x/x0 > 1 for all the RACIPE models , and the green region shows the probability of x/x0 < 1 . A˙=gAHS ( B , BA0 , nBA , λBA− ) −kAAB˙=gBHS ( A , AB0 , nAB , λAB− ) −kBB , ( 1 ) where A and B represent the expression levels of gene A and B respectively . gA and gB are the basal production rates ( the production rates of the genes without any regulator bound to the promoter ) . kA and kB are the innate degradation rates . Regulation of gene B expression by A is formulated as a non-linear shifted Hill function HS ( A , AB0 , nAB , λAB− ) , defined as λAB−+ ( 1−λAB− ) H− ( A , AB0 , nAB ) , where H−=1/ ( 1+ ( A/AB0 ) nAB ) is the inhibitory Hill function , AB0 is the threshold level for A , nAB is the Hill coefficient of the regulation , λAB− is the maximum fold change of the B level caused by the inhibitor A ( 0≤λAB−<1 ) . In the case of an activator , the fold change is represented by λAB+ ( λAB+>1 ) . The inhibitory regulation of gene A by gene B can be modeled in an analogous way . In RACIPE , randomization is performed on all five types of circuit parameters: two of them are associated with each gene , including the basal production rate ( g ) and the degradation rate ( k ) ; and three of them are associated with each regulatory link , including the maximum fold change of the gene expression level ( λ ) , the threshold level of the regulation ( X0 ) and the Hill coefficient ( n ) . Our parametric randomization procedure is designed to ensure that the models can represent all biologically relevant possibilities . In detail , the Hill coefficient n is an integer selected from 1 to 6 , and the degradation rate k ranges from 0 . 1 to 1 ( See S1 Table for the explanation of the units ) . Here each parameter is assigned by randomly picking values from either a uniform distribution or some other distributions , for example the Gaussian distribution . In this work , we mainly used uniform distribution for sampling parameters unless other distributions are explicitly mentioned . The fold change λ+ ranges from 1 to 100 if the regulatory link is excitatory , while λ− was varied from 0 . 01 to 1 if the regulatory link is inhibitory . Note that for the latter case , a probability distribution ( e . g . a uniform distribution ) is sampled for the inverse of λ− , i . e . 1/λ− , instead of λ− itself . By doing so , we make sure that the mean fold change is about 0 . 02 , instead of ~ 0 . 5 . The choice of such a wide range of λ values ensures the consideration of both strong and weak interactions . In addition , two assumptions are made in RACIPE to ensure that it generates a representative ensemble of models for a specific circuit topology . First , the maximum production rate of each gene should lie roughly within the same range ( from 1 to 100 in this study , see S1 Table ) , as the maximum rate is determined by how fastest the transcriptional machinery can work . For a gene regulated by only one activator , the maximum production rate ( G ) is achieved when the activator is abundant , and thus the basal production rate of the gene g = G/λ+ . For a gene regulated by only one inhibitor , the maximum rate ( G ) is achieved in the absence of the inhibitor , i . e . g = G . This criterion can be generalized to genes regulated by multiple regulators . Therefore , in practice , we directly randomize the maximum production rate ( G ) instead of the basal production rate ( g ) , and then calculate the value of g according to the above criterion . The ranges of these parameters are summarized in details in S1 Table . The RACIPE randomization procedure allows a gene to have a relative expression ratio of up to 1 , 000 for two sets of parameters , even when it is not regulated by other genes . Second , we also assume that , for all the members of the RACIPE model ensemble , each regulatory link in the circuit should have roughly equal chance of being functional or not functional , referred to as the half-functional rule . For example , in the case that gene A regulates gene B , all the threshold parameters are selected in such a way that , for the RACIPE ensemble , the level of A at the steady states has roughly 50% chance to be above and 50% chance to be below its threshold level . Otherwise , if the threshold level is too large or too small , the regulatory link is either not functional most of the time or constitutively active , thereby changing the effective circuit topology , and limiting the comprehensive understanding of circuit function ( S2 Fig ) . To achieve this , we estimate the range of the threshold levels by a mean-field approximation , and use this range to randomly sample the threshold parameters . For a regulatory link from gene A ( regulator ) to gene B ( target ) , the threshold level AB0 can be estimated as follows . We first estimate the range of expression of gene A without considering any of its regulators . The A level without regulation satisfies A˙=G−kA , ( 2 ) By randomizing both G and k by the aforementioned protocol ( S1 Table ) , we generate an ensemble of random models , from which we obtain the distribution of the steady state levels of gene A ( Fig 2A ) . To meet the half-functional rule , the median of the threshold level should be chosen to be the median of this distribution . When gene A is regulated by some other genes ( i . e . its upstream regulators ) , we estimate its median threshold level by taking A’s regulators into account , and assume that the levels of all these regulators ( e . g . gene B , C etc . ) follow the same distribution as an isolated gene ( top right panels in Fig 2A section 2 ) . We randomly sample the threshold of every inward regulation from the range of 0 . 02M to 1 . 98M , where M is the median of the distribution of an isolated gene . With all of the information , we can again generate a new ensemble of models , from which we calculate the distribution of gene A ( bottom panel in Fig 2A section 2 ) and its median . For every target gene regulated by the gene A , we randomly select the threshold levels of the regulations in the range from 0 . 02M to 1 . 98M , where M is the above obtained median level of gene A . The same approach is used to estimate the threshold levels of the other genes . It is worth-noting that this simple estimation strategy works quite well for the cases we have tested ( Fig 2B ) according to the half-functional rule . In the following , we will first demonstrate the application of RACIPE to some simple toggle-switch-like motifs , then to a set of motifs of coupled toggle-switch circuits , and eventually to a more complex gene regulatory network of EMT . For each case , we will illustrate how we can utilize an ensemble of RACIPE models to identify the dynamic behavior of a gene circuit .
We first tested RACIPE on several basic toggle-switch-like circuit motifs ( Fig 3A ) . These circuit motifs are considered to be some of the main building blocks of gene regulatory networks[44] . A genetic toggle switch ( TS ) , composed of two mutually inhibitory genes , is commonly considered to function as a bi-stable switch—it allows two stable gene states , each of which is characterized by the dominant expression of one gene . TS has been shown to be a central piece of decision-making modules for cell differentiation in several incidences[45–47] . Here we apply RACIPE to the TS motif . We created an ensemble of 10 , 000 models ( Fig 3A ) and we observed that about 20% of models allow two coexisting stable steady states ( bi-stability ) , while the others allow only one steady state ( mono-stability ) . The observation that only a small fraction of TS models works as a bi-stable system is consistent with a previous study[39] . Next , we tested RACIPE on a toggle switch with an extra excitatory auto-regulatory link acting on only one of the genes ( a toggle switch with one-sided self-activation , or TS1SA ) . The circuit motif now has ~ 50% chance of being bi-stable , much larger than the original TS motif . Interestingly , TS1SA also has ~1% chance of having three co-existing stable steady states ( tri-stability ) , so it can potentially act as a three-way switch[44] . Hence , the RACIPE analysis suggests that TS1SA is more robust than TS for functioning as a switch . Moreover , adding excitatory auto-regulatory links on both sides of the TS motif ( TS2SA ) further increases the likelihood of bi-stability to ~60% , and meanwhile dramatically increases the likelihood of tri-stability to ~13% . This suggests that TS2SA has more of an ability than these other motifs to function as a three-way switch . Indeed , TS2SA has been proposed to be the core decision-making motif for several cell differentiation processes , and many of these processes exhibit multi-stability[45 , 46] . Thus , the statistical analysis of the ensemble of random models generated by RACIPE can identify the most robust features of a circuit motif . Another way to utilize RACIPE is to evaluate the possible gene expression patterns of a circuit motif . We can construct a large set of in silico gene expression data , consisting of the gene expression levels of the circuit at every stable steady state for each RACIPE model . In the dataset , the columns correspond to the genes and the rows correspond to the stable steady states . For a RACIPE model with multiple stable steady states , we enter the data in multiple rows . The expression dataset takes a form similar to typical experimental microarray data , and so it can be analyzed using common bioinformatics tools . For each of the above two-gene cases , we visualized the expression data by a scatter plot of the levels of the two genes ( Fig 3B ) . Surprisingly , despite large variations in the circuit parameters across the RACIPE model ensemble , the expression data points converge quite well into several robust clusters . For example , the TS circuit data has two distinct clusters , where one has a high expression of gene A while a low expression of gene B and vice versa for the other cluster . The TS2SA circuit has not only the above two clusters but also an additional cluster with intermediate expression of both genes . These patterns have also been observed in previous experimental[29] and theoretical[44 , 45 , 48] studies of the same circuits . Interestingly , if we only include models with a fixed number of stable states ( e . g . restrict the ensemble to mono-stable models , or bi-stable models ) , a similar pattern of clusters can still be observed ( S3 Fig ) . These clusters represent distinct patterns of gene expression that the circuit can support , so we will refer to these clusters as “gene states” . These gene states are robust against large perturbations of circuit parameters because the circuit topology restricts possible gene expression patterns . RACIPE in a sense takes advantage of this feature to interrogate the circuit so that we can unbiasedly identify the robust gene states . Since these states may be associated with different cell phenotypes during cell differentiation or cellular decision-making processes , RACIPE can be especially helpful in understanding the regulatory roles of the circuit during transitions among different states . These simple cases demonstrate the effectiveness of RACIPE in revealing generic properties of circuit motifs . Recall that our basic hypothesis is that the dynamic behaviors of a circuit should be mainly determined by circuit topology , rather than a specific set of parameters . The rich amount of gene expression data generated by RACIPE allows the application of statistical learning methods for the discovery of these robust features . For example , as shown in Fig 3C , we applied unsupervised hierarchical clustering analysis ( HCA ) to the RACIPE gene expression data , and again we identified similar gene state clusters for each circuit . Notably , the predictions of these gene states by RACIPE should be robust against different sampling distributions and different ranges of kinetic parameters . To verify this , we tested on the TS circuit versions of RACIPE created with three different distributions ( uniform , Gaussian and exponential distributions ) and three different ranges of parameters ( Fig 4 ) . Even though the precise shape of gene states appears to be slightly different for the different cases , the number and the locations of these gene states are consistent ( Fig 4 ) . For the cases with exponential distribution , in order to reduce the range of the parameters , we decreased the mean of the distribution; therefore , the two gene states become closer ( Fig 4 ) . We also found that the changes of the parameter ranges still result in similar gene states ( S4 and S5 Figs ) . To evaluate the effectiveness of RACIPE on larger circuits , we further applied the method to circuits with two to five coupled toggle-switch ( CTS ) motifs ( Fig 5 ) . Different from the above simple circuit motifs , the gene expression data obtained by RACIPE for these CTS motifs are now high-dimensional; thus in the scatter plot analysis we projected these data onto the first two principal components by principal component analysis ( PCA ) . For each circuit , we observed distinct gene states from PCA for the RACIPE models ( Fig 5A ) . More interestingly , the number of gene states found via PCA increases by one each time one more toggle switch is added to the circuit . Moreover , we applied HCA to the gene expression data , from which we identified the same gene states as from PCA ( Fig 5B ) . At this stage , we can also assign high ( red circles ) , intermediate ( blue circles ) or low expression ( black circles ) to each gene for every gene state . Unlike in Boolean network models , the assignment in RACIPE is based on the distribution of expression pattern from all the models in the ensemble ( S6 and S7 Figs ) . We can easily understand the meaning of each gene state . In each case , the rightmost cluster in the scatter plot ( Fig 5A ) corresponds to the topmost cluster in the heatmap ( Fig 5B ) , a state where all the A genes have high expression and all the B genes have low expression . Similarly , the leftmost cluster in the scatter plot corresponds to the bottommost cluster in the heatmap . These two clusters are the most probable ones , and represent the two extreme states of the coupled toggle switch network . As also illustrated in the scatter plots , for circuits with additional toggle switches , these two states separate from each other and the circuit now allows intermediate states . By closely examining these intermediate states , we found that they ( from top to bottom ) correspond to a cascade of flips of the state of each consecutive toggle switch . This explains why we observe one additional gene state every time we include an additional toggle-switch motif . In addition , intermediate expression levels were frequently observed for genes lying in the middle toggle-switch motifs , instead of those at the edge . The tests on CTS circuits demonstrate again the power of RACIPE in identifying robust features of a complex circuit . The above examples were used for illustrative purposes and do not immediately reflect any actual biological process . In our last example , we apply RACIPE to a more realistic case , the decision-making circuit of EMT ( Fig 6 ) . EMT is crucial for embryonic development , wound healing , and metastasis[49] , the last being a major cause for 90% cancer-related deaths[50] . Cells can undergo either a complete EMT to acquire mesenchymal phenotype or partial EMT to attain hybrid E/M phenotype[51 , 52] , which maintains both E and M traits . Transitions among the Epithelial ( E ) , Mesenchymal ( M ) and hybrid epithelial/mesenchymal ( E/M ) phenotypes have been widely studied either experimentally or theoretically[52] . Here , we utilized data from the literature and Ingenuity Pathway Analysis ( see details in S1 Text ) to construct a core gene regulatory circuit model of EMT ( Fig 6A ) , which contains 13 transcriptional factors ( TFs ) , 9 microRNAs ( miRs ) and 82 regulatory links among them . Among the gene components , two biomarkers–CDH1 and VIM–are commonly used to distinguish different phenotypes during EMT . The circuit is a much-extended version of several previous EMT models[24 , 25] , which consist of only four gene families . It is similar in terms of scale to a recently proposed Boolean model of EMT[53] , but as stressed here our models allow for continuous expression levels . For simplicity , we modeled the EMT circuit with the same approach as above , i . e . all the genetic components were coupled with Hill functions , typical of transcriptional control . This may not be completely accurate for a circuit with different types of regulations , such as the translational regulation by microRNA ( miR ) , but we leave this complication for future study . Notably , although the genome of cancer cells during EMT does not change , the core EMT circuit is still regulated by peripheral genes , epigenetic modifications , and cell signaling , etc . All of these factors contribute to the random perturbations to the kinetic parameters of the 22-node EMT gene regulatory circuit . Even with this simplification , RACIPE can provide insightful information of the EMT regulation . Consistent with what we learned from the test cases , unsupervised HCA of the RACIPE gene expression data can reveal distinct gene states ( Fig 6B ) . Here there are four such states . We can map these gene states to different cell phenotypes possible during EMT–an E phenotype with high expression of the miRs , low expression of TFs , and CDH1HIVIMLO; a M phenotype with low expression of the miRs , high expressions of TFs , and CDH1LOVIMHI; and two hybrid E/M phenotypes with intermediate expression of both miRs , TFs and CDH1/VIM . The E/M I state lies closer to the E state , and the E/M II state lies closer to the M state . More intriguingly , we found SNAI1 and SNAI2 become highly expressed in the E/M I phenotype while ZEB1 and ZEB2 are not fully expressed until the E/M II or the M phenotype ( Fig 6C ) , which is a possibility supported by recent experimental results[25] . Moreover , RACIPE can help to find genes of similar function and filter out less important genes in the core circuit . As shown in Fig 6B , genes are grouped into two major clusters according to their expression levels throughout all the RACIPE models–miRs/CDH1 and TFs/VIM . The former genes are highly expressed mainly in E phenotypes while the latter are highly expressed in M phenotypes . We also found three microRNAs ( miR-30c , miR-205 , and miR-9 ) to be randomly expressed in the RACIPE models , indicating these three microRNAs are less important to these EMT phenotypes . From the topology of the circuit , we see that these three microRNAs lack feedback regulation and act solely as inputs . A typical approach taken in cell biology is to use two biomarkers to identify cells of different states in a mixed population by fluorescence-activated cell sorting ( FACS ) . To mimic the analysis , we projected the gene expression data of the RACIPE models onto the two axes of important genes , as shown in the scatter plots in Fig 6D–6F . In all of the three cases , the E and the M phenotypes can be distinguished . However , for the hybrid phenotypes , the E/M I and the E/M II states overlap in the CDH1-VIM plot ( Fig 6D ) . These two hybrid phenotypes can be separated more easily in the ZEB1-miR200b plot ( Fig 6E ) . In the SNAI1-miR34a plot ( Fig 6F ) , however , the two E/M states overlap with the M state . The theoretical prediction that the SNAI1-miR34a axis is less efficient in distinguishing the states is supported by transcriptomics data from the NCI-60 cell lines[54] ( Fig 6G–6I ) . We see here that either VIM-CDH1 or the ZEB1-miR200b axes are indeed better than the SNAI1-miR34a axes in separating different EMT phenotypes . Our results are also consistent with our previous theoretical finding that ZEB1 is more crucial than SNAI1 in the decision-making of EMT[25] .
Recently , the rapid development of genomic profiling tools has allowed the mapping of gene regulatory networks . Yet , it remains a challenge to understand the operating mechanisms and the design principles of these networks . Conventional computational modeling methods provide insightful information; however , their prediction power is usually limited by the incompleteness of the network structure and the absence of reliable kinetics . To deal with these issues , we have developed a new computational modeling method , called RACIPE , which allows unbiased predictions of the dynamic behaviors of a complex gene regulatory circuit . Compared to traditional methods , RACIPE uniquely generates an ensemble of models with distinct kinetic parameters . These models can faithfully represent the circuit topology and meanwhile capture the heterogeneity in the kinetics of the genetic regulation . By modeling the dynamics of every RACIPE model , we can utilize statistical analysis tools to identify the robust features of network dynamics . We have successfully tested RACIPE on several theoretical circuit motifs and a proposed core Epithelial-to-Mesenchymal Transition ( EMT ) gene regulatory circuit . In each circuit , RACIPE is capable of predicting the relevant gene states and providing insights into the regulatory mechanism of the decision-making among gene states . Unlike other methods that utilize randomization strategies to explore the parameter sensitivity for gene circuit[34–37] , RACIPE adopts a more carefully designed sampling strategy to randomize circuit parameters over a wide range , but meanwhile to satisfy the half-functional rule to gain a comprehensive understanding of circuit dynamics . Instead of looking for the sensitivity of the circuit function to parameter variations [34 , 37] and the parameters best fitting the experimental data[35 , 36] , we focused on uncovering conserved features from the ensemble of RACIPE models . This was carried out by standard statistical learning methods such as hierarchical clustering analysis . We showed the power of RACIPE to predict the robust gene states for a circuit with a given topology . Also , conceptually similar to the mixed-effects models used to describe a cell population for a very simple system [36] , i . e . a one-gene transcription without a regulator , RACIPE could be potentially applied to a very large gene circuit to describe the gene expression dynamics of a cell population with an ensemble of models—an aspect we will work on in our future study . Moreover , it is easy to implement gene modifications such as knockdown or overexpression treatments with the RACIPE method to learn the significance of each gene or link in the circuit . Therefore , RACIPE provides a new way to model a gene circuit without knowing the detailed circuit parameters . Another parameter-independent approach people often use for gene circuit modeling is Boolean network model[55] , which digitalizes the gene expression into on and off states and uses logic functions to describe the combinatorial effects of regulators to their targets . Compared with the Boolean network model , RACIPE is a continuous method , so it is not restricted to the on and off values . Instead , RACIPE enables us to find the intermediate levels of gene expressions beyond the on and off states , as we showed in Fig 5B and Fig 6C . From the ensemble of RACIPE models , we can predict the expression distribution of each gene , which can be directly compared with experimental expression data . The comparison will allow us to further refine the core circuit . In addition , in RACIPE , we not only obtain in silico gene expression data , but we also have the kinetic parameters for each model . From these parameter data , we can directly compare the parameter distributions for different gene states , from which we can learn the driving parameters that are responsible for the transitions among the states . To conclude , here we have introduced a new theoretical modeling method , RACIPE , to unbiasedly study the behavior of a core gene regulatory circuit under the presence of intrinsic or extrinsic fluctuations . These fluctuations could represent different signaling environments , epigenetic states , and/or genetic backgrounds of the core circuit and can cause cell-cell heterogeneity in a population . By approximating these fluctuations as variations of the model parameters , RACIPE provides a straightforward way to understand the heterogeneity and to explain further how gene circuits can perform robust functions under such conditions . Moreover , RACIPE uniquely generates a large set in silico expression data , which can be directly compared with experimental data using common bioinformatics tools . RACIPE enables the connection of traditional circuit-based bottom-up approach with profiling-based top-down approach . We expect RACIPE to be a powerful method to identify the role of network topology in determining network operating principles .
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Cells are able to robustly carry out their essential biological functions , possibly because of multiple layers of tight regulation via complex , yet well-designed , gene regulatory networks involving a substantial number of genes . State-of-the-art genomics technology has enabled the mapping of these large gene networks , yet it remains a tremendous challenge to elucidate their design principles and the regulatory mechanisms underlying their biological functions such as signal processing and decision-making . One of the key barriers is the absence of accurate kinetics for the regulatory interactions , especially from in vivo experiments . To this end , we have developed a new computational modeling method , Random Circuit Perturbation ( RACIPE ) , to explore the dynamic behaviors of gene regulatory circuits without the requirement of detailed kinetic parameters . RACIPE takes a network topology as the input , and generates an unbiased ensemble of models with varying kinetic parameters . Each model is subjected to simulation , followed by statistical analysis for the ensemble . We tested RACIPE on several gene circuits , and found that the predicted gene expression patterns from all of the models converge to experimentally observed gene state clusters . We expect RACIPE to be a powerful method to identify the role of network topology in determining network operating principles .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
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2017
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Interrogating the topological robustness of gene regulatory circuits by randomization
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Protein homeostasis is critical for cellular survival and its dysregulation has been implicated in Alzheimer's disease ( AD ) and other neurodegenerative disorders . Despite the growing appreciation of the pathogenic mechanisms involved in familial forms of AD , much less is known about the sporadic cases . Aggregates found in both familial and sporadic AD often include proteins other than those typically associated with the disease . One such protein is a mutant form of ubiquitin , UBB+1 , a frameshift product generated by molecular misreading of a wild-type ubiquitin gene . UBB+1 has been associated with multiple disorders . UBB+1 cannot function as a ubiquitin molecule , and it is itself a substrate for degradation by the ubiquitin/proteasome system ( UPS ) . Accumulation of UBB+1 impairs the proteasome system and enhances toxic protein aggregation , ultimately resulting in cell death . Here , we describe a novel model system to investigate how UBB+1 impairs UPS function and whether it plays a causal role in protein aggregation . We expressed a protein analogous to UBB+1 in yeast ( Ubext ) and demonstrated that it caused UPS impairment . Blocking ubiquitination of Ubext or weakening its interactions with other ubiquitin-processing proteins reduced the UPS impairment . Expression of Ubext altered the conjugation of wild-type ubiquitin to a UPS substrate . The expression of Ubext markedly enhanced cellular susceptibility to toxic protein aggregates but , surprisingly , did not induce or alter nontoxic protein aggregates in yeast . Taken together , these results suggest that Ubext interacts with more than one protein to elicit impairment of the UPS and affect protein aggregate toxicity . Furthermore , we suggest a model whereby chronic UPS impairment could inflict deleterious consequences on proper protein aggregate sequestration .
As technology and medicine further extend the human lifespan , age-related diseases will become more prevalent . Alzheimer's disease ( AD ) is a neurodegenerative disorder that affects 20 million people worldwide and is the most common form of late-onset dementia [1] . The study of genetic mutations that cause early onset AD has provided insight into some of the factors involved , but most cases of AD are sporadic and of unknown origin . Uncovering the risk factors involved in any multi-factorial disease is challenging but vital for disease treatment and prevention . Many fundamental pathways , including the ubiquitin proteasome system ( UPS ) , have been suggested to play a role in AD . Therefore , investigating the relationship between AD and the UPS could lead to new therapeutic targets . The UPS is an evolutionarily conserved pathway that selectively eliminates short-lived and damaged proteins . A number of cellular processes , including the cell cycle , stress response , and DNA repair , require the UPS [2] . Protein degradation by the UPS involves a series of enzymes that ultimately attach ubiquitin , a small well-conserved protein , to an internal lysine residue in the target protein [3]–[5] . Multiple ubiquitin proteins can be connected to form a polyubiquitin chain which serves as a degradation signal recognized by the 26S proteasome . A series of events involving E1 , E2 and E3 enzymes are required to attach ubiquitin via its C-terminal glycine residue to the target protein . The formation of polyubiquitin chains and the process of ubiquitin conjugation to protein targets displays exquisite specificity , in part by the multitude of E2 and E3 enzymes . Despite intensive study , the roles of many components of the UPS remain to be elucidated . The importance of the UPS in cellular homeostasis is apparent not only by the redundancy and conservation of the components , but also by its role in disease [5] , [6] . The complex interplay between protein aggregation and UPS function is easily appreciated , yet it is often difficult to determine the causal nature of the problem . UPS dysfunction can prevent the degradation of misfolded proteins , which can lead to aggregation . Conversely , protein aggregates can be challenging substrates for the UPS and can thus cause proteasomal impairment [7] . Protein aggregation is a hallmark of many neurodegenerative disorders [6] . In addition , mutations in ubiquitin processing enzymes , such as UCHL1 and Parkin , can lead to inherited forms of neurodegenerative diseases [8] , [9] . Furthermore , many protein aggregates associated with disease show ubiquitin deposition [10] , suggesting that dysfunctional UPS activity may contribute to pathogenesis . Understanding the interplay between protein aggregation and clearance is an active area of research , but most systems are complicated by cellular toxicity , which alone can have negative consequences on protein homeostasis . A mutant form of ubiquitin was found associated with AD and other diseases and was proposed to act as a natural proteasome inhibitor [11] . The generation of this mutant ubiquitin protein is unusual - the mutation is found in the messenger RNA , but not in the DNA sequence of the ubiquitin-B gene . The mutant ubiquitin results from a dinucleotide deletion near the 3′ end of the mRNA transcript which shifts the reading frame for translation . The mutant protein has been named UBB+1 [12] . The dinucleotide deletion event in the mRNA has been termed “molecular misreading” , though the mechanism by which the deletion occurs remains elusive [13] , [14] . Many human mRNA transcripts , including all copies of ubiquitin , contain potential sites for molecular misreading , since hotspots for these events are hypothesized to occur near simple repeat sequences ( e . g . GAGAG ) [15] . The best characterized +1 mutant ubiquitin protein has a short C-terminal extension , with the majority of the protein being identical to ubiquitin [12] . As such , the protein is presumably folded and recognized as ubiquitin , but the C-terminal glycine residue essential for conjugation to substrates is absent . The accumulation of the UBB+1 protein in the neurological hallmarks of AD is curious , since the mutant cannot be conjugated to target proteins [12] . The presence of UBB+1 has been proposed to represent an endogenous readout of proteasomal dysfunction [16] , [17] . Due to its association with protein aggregation , it was also suggested that UBB+1 could contribute to disease pathology [18] . UBB+1 protein accumulation has been documented in multiple disorders such as polyglutamine expansion diseases ( including Huntington's disease ) , Pick's disease and even non-neuronal tissue diseases [11] , [19] . However , the mechanism of UBB+1 action in these diseases remains unclear . To evaluate the role of UBB+1 in disease , the effects of ectopic UBB+1 expression have been investigated in cultured mammalian cells . Although UBB+1 cannot be conjugated to target substrates , it can be ubiquitinated by wild type ubiquitin and degraded by the proteasome [20] . However , high levels of UBB+1 expression cause proteasomal impairment [16] , [21] , [22] . As a natural inhibitor of the UPS , UBB+1 could be another example whereby proteasomal impairment induces protein aggregation . Therefore , UBB+1 might act as a disease modifier . Recently , a UBB+1 transgenic mouse has been characterized [23] . UBB+1 expression resulted in constant UPS impairment that caused a minor learning deficit and caused changes in transcription profiles that mirror those found in brains of humans with AD [23] . The expression of UBB+1 in mammalian cells enhances the toxicity and aggregation of an expanded polyglutamine protein [24] . However , measuring changes in protein aggregation in cells that are dying from toxic protein aggregates is challenging . Hence , it remains to be determined if UBB+1 affects protein aggregation per se , or if it affects the ability of the cells to cope with the aggregates . We developed a model system using Saccharomyces cerevisiae to evaluate the cellular effects of UBB+1 . We expressed a mutant ubiquitin protein ( Ubext ) analogous to UBB+1 and found that it caused UPS impairment in yeast . Furthermore , we found that Ubext changed the ubiquitination pattern on a UPS substrate . Taking advantage of non-toxic protein aggregates in yeast , we demonstrated that the expression of Ubext neither induced nor changed these aggregates . However , Ubext did make cells more susceptible to toxic protein aggregates . We propose that Ubext does not cause protein aggregation , but rather acts as a phenotypic enhancer of deleterious aggregation . We present a model based on our work and other recent advances in the field to explain how this might occur .
The mechanism by which +1 proteins , such as UBB+1 , are produced is currently unknown . To create a yeast model of UBB+1 , we generated an expression vector containing the sequence of the first ubiquitin-coding region of the yeast tandem ubiquitin gene , UBI4 , such that a dinucleotide deletion occurred near the carboxy terminus ( Figure 1A ) . The deletion caused a frameshift in the coding sequence of ubiquitin and extended the open reading frame to the next stop codon ( termed extended ubiquitin or Ubext ) . This construct mimics the generation of UBB+1 from the human tandem ubiquitin gene ( ubiquitin-B ) . Constitutive expression of Ubext in log-phase yeast did not cause a growth defect when assessed in either liquid medium ( data not shown ) or on solid medium ( Figure 1B ) . Wild type cells expressing Ubext did show a reduced growth rate after recovery from stationary phase ( data not shown ) . To evaluate the functionality of Ubext , we analyzed its ability to replace wild type ubiquitin . The stress-inducible UBI4 gene encodes a tandem array of five ubiquitin moieties that are separated post-translationally by deubiquitinating enzymes ( DUBs ) that cleave after the C-terminal glycine residue , G76 [25] . UBI4 is non-essential in vegetatively growing cells but is required for cells to recover from various stress conditions [26] , [27] . We utilized a strain lacking UBI4 to evaluate the functionality of Ubext . Δubi4 cells were transformed with expression plasmids that contain wild type ubiquitin , Ubext or empty vector . The transformants were grown for two weeks to allow them to reach stationary phase and then plated again to evaluate their ability to recover . Only cells expressing extra wild type ubiquitin were rescued from the loss of UBI4 and could grow after this stress ( Figure 1C ) . This demonstrates that Ubext is a non-functional ubiquitin , as expected due to the lack of the C-terminal glycine residue required for conjugation to target substrates . If Ubext affects UPS functionality in yeast as UBB+1 does in mammals , then we hypothesized that Ubext would display synthetic lethality with a proteasome mutant . We evaluated the cellular viability of a temperature-sensitive catalytic proteasome mutant strain ( pre1-1 pre2-2 ) [28] expressing Ubext . As predicted , Ubext-expressing pre1-1 pre2-2 cells were inviable at the restrictive temperature ( Figure 2A ) . Wild type cells expressing Ubext grown at the restrictive temperature did not show a growth defect ( Figure 2A ) . Next we evaluated another ubiquitination-dependent process to determine if Ubext effects are more widespread . We challenged Ubext-expressing cells to DNA damage induced by UV irradiation and found that they survived as well as the control cells ( data not shown ) . Ubext cannot be conjugated to target protein substrates , but can be recognized as a UPS substrate . Therefore , we assessed its ubiquitination . Protein lysate from Ubext-expressing cells and control cells were evaluated by SDS-PAGE and western blot . Cells expressing Ubext exhibited a unique band which represents the extended mutant ubiquitin protein ( Figure 2B , grey arrow ) which is larger than wild type ubiquitin ( Figure 2B , arrowhead ) . Cells expressing Ubext also displayed a distinctive laddering pattern which suggests that Ubext is conjugated by wild type ubiquitin moieties ( Figure 2B , black arrows ) . A similar laddering pattern was previously observed in cells expressing UbΔGG [29] , a mutant ubiquitin protein lacking only the two C-terminal glycine residues , and we observed the same pattern when we expressed UbΔGG in yeast ( data not shown ) . Additionally , a strain lacking the ubiquitin recycling DUB ( Δubp14 ) accumulates free ubiquitin chains [29] and we also observed that Δubp14 cells show the same ubiquitin laddering pattern as cells expressing Ubext ( data not shown ) . The expression of Ubext also caused an increase in the level of unconjugated wild type ubiquitin , which was evident by the accumulation of the mono-ubiquitin band in the Ubext lane in comparison to the empty vector control lane ( Figure 2B , black arrowhead ) . Further analysis by quantitative western blot showed approximately a 10-fold increase in wild type mono-ubiquitin in the presence of Ubext ( data not shown ) . Transcriptional activity from the UBI4 promoter using a UBI4promoter-LacZ reporter in Ubext-expressing cells demonstrated a modest two-fold increase ( data not shown ) , suggesting that UBI4-induced transcription may be one , but perhaps not the only source for the increased ubiquitin . Cells expressing Ubext also displayed an increase in the abundance of high molecular weight ubiquitin-conjugated proteins in comparison to the empty vector control ( Figure 2B , compare left lane WT to right lane Ubext ) . The fact that Ubext caused lethality in the proteasome mutant strain and Ubext-expressing cells accumulated ubiquitinated-protein conjugates , suggests that it is affecting protein degradation . An accumulation of high molecular weight ubiquitinated proteins also occurred with the over expression of wild type ubiquitin ( Figure 2B , middle lane ) . Most likely this occurs because of more ubiquitination of endogenous proteins due to an excess of functional ubiquitin provided by the over expression construct . We tested the functionality of the UPS in cells expressing Ubext using two different proteasome reporters constructs: an N-end rule substrate and a ubiquitin fusion degradation ( UFD ) substrate [30] . These substrates are processed by the UPS using distinct enzymes [3] , [31] , [32] . The N-end rule substrate is a Ub-R-LacZ fusion . The ubiquitin moiety is efficiently cleaved by endogenous DUBs to expose the N-terminal amino acid ( arginine ) of β-galactosidase ( βgal ) . According to the N-end rule , R-βgal is an unstable protein that is polyubiquitinated and rapidly degraded by the 26S proteasome [33] . The UFD reporter substrate is Ub-P-LacZ . In yeast , no DUB can cleave ubiquitin from βgal if the first amino acid after ubiquitin is proline . Because of the ubiquitin fusion , Ub-P-βgal is unstable and is rapidly degraded by the proteasome . These constructs , along with a stable LacZ control ( Ub-M-LacZ ) , were transformed into cells expressing Ubext to assess UPS function by βgal activity assays . Cells expressing Ubext and either of the unstable proteasome reporters displayed higher levels of specific βgal activity ( Figure 2C and 2D ) . Cells expressing extra wild type ubiquitin showed a slight increase in the stabilization of the reporter constructs . The expression of extra wild type ubiquitin also generated a large steady state population of ubiquitin-conjugated proteins ( Figure 2B , middle lane ) , which could be taxing the degradation capacity of the proteasome . To evaluate if LacZ fusion expression was affected by Ubext , stable M-βgal activity was measured and showed no difference ( data not shown ) . These results demonstrate that the expression of Ubext in yeast inhibits the degradation of two different UPS reporter substrates . Such stabilization of the proteasome reporter constructs could be due to a lack of ubiquitination of the reporter , since the expression of Ubext also causes accumulation of unconjugated wild type ubiquitin . The reporter substrates ( βgal protein ) were immunoprecipitated from cells with and without the co-expression of Ubext . Western blot with an anti-βgal antibody revealed that more β-gal protein was precipitated in Ubext-expressing cells ( Figure 2E , left ) . This result correlates with the higher levels of βgal activity measured in Ubext-expressing cells ( Figure 2C and D ) . Analysis with an anti-ubiquitin antibody showed ubiquitin-conjugated R-βgal and Ub-P-βgal in cells expressing Ubext ( Figure 2E , right ) . This data demonstrates that Ubext is not stabilizing these UPS substrates by blocking their ubiquitination . Another plausible explanation for the UPS inhibition could be that Ubext binds to the proteasome and this interaction precludes other proteasome substrates from being efficiently degraded . Alternatively , Ubext could interact with other component ( s ) of the UPS and inhibit their function . To examine whether Ubext is clogging the proteasome , we took advantage of a ubiquitin-independent proteasome substrate . Ornithine decarboxylase ( ODC ) is an enzyme involved in polyamine biosynthesis [34] , [35] and a short peptide from this protein serves as a ubiquitin-independent degradation signal ( i . e . degron ) [36] . Measuring the degradation of ODC reflects the functionality of the proteasome in a manner independent of the non-proteasomal components of the UPS cascade . A fusion of GFP with the degron of ODC ( GFP-ODC ) serves to target GFP to the proteasome where it is rapidly degraded [37] . A point mutation in the ODC degron ( C441A ) stabilizes the fusion protein by lowering its affinity for the proteasome [38] , [39] . GFP-ODC fusions were transformed into cells expressing Ubext and the steady state level of GFP-ODC was evaluated by western blot ( Figure 3A ) . Cells expressing Ubext were able to degrade the GFP-ODC protein while the stable GFP-ODCC441A protein accumulated ( Figure 3A ) . Even prolonged exposure showed that the steady state level of GFP-ODC was approximately equal with or without Ubext expression ( Figure 3B ) . Thus , Ubext permits the degradation of a ubiquitin-independent proteasome substrate , suggesting that the proteasomal degradation capacity is not significantly impaired in cells expressing Ubext . We sought to determine how Ubext exerts its negative effects on the UPS pathway . We asked whether Ubext was sequestrating wild type ubiquitin proteins . Ubiquitinated-Ubext could be refractory to DUBs , thereby tying up ubiquitin , as suggested for UBB+1 [20] . To test this hypothesis , we expressed extra ubiquitin in the presence of Ubext and found that the UPS test substrates were still stabilized ( data not shown ) . This result was not surprising since monomeric ubiquitin appears to be abundant in cells expressing Ubext ( Figure 2B , arrowhead ) . This suggests that a lack of wild type ubiquitin is not the cause of the UPS impairment elicited by Ubext . Ubext lacks the essential C-terminal glycine residues ( G75 and G76 ) required for ubiquitin conjugation and these glycine residues are vital for many proteins to interact with ubiquitin [40] . We tested whether adding back two glycine residues to the C-terminal extension of Ubext ( Ubext+GG ) could restore these interactions and alleviate the proteasomal impairment . Cells expressing Ubext+GG still displayed proteasomal impairment ( data not shown ) , indicating that the C-terminal extension plays a mechanistic role in the phenotype observed . UPS-mediated protein degradation is a selective process and polyubiquitination is the signal which targets proteins to the proteasome for degradation [41] , [42] . Therefore , we asked whether blocking the ubiquitination of Ubext would alleviate the associated UPS inhibition . Polyubiquitination can occur on multiple lysine residues of ubiquitin [43] . We mutated four of the lysine residues typically utilized for polyubiquitination by changing them to arginine ( referred to as UbextKxR ) . Ubiquitin conjugation of Ubext was visualized by a distinct laddering pattern on a western blot ( Figure 2B , black arrows ) . While none of the single point mutations prevented ubiquitination of Ubext , the double lysine mutant , UbextK29/48R , did prevent the conjugation ( Figure 4A , black arrows ) . We evaluated the degradation of the UPS substrates in the presence of the UbextKxR mutants . The expression of each single UbextKxR mutant stabilized the N-end rule substrate , R-βgal ( Figure 4B ) . However , the expression of the UbextK29/48R double mutant allowed for better degradation of the reporter protein , suggesting that the ubiquitination of Ubext is necessary to impair the degradation of the N-end rule substrate . The steady state levels of βgal protein were detected by western blot and corroborated the result of the βgal activity assay ( Figure 4B , lower ) . Next , we evaluated the degradation of the UFD substrate in the presence of the UbextKxR mutants . Each UbextKxR mutant , including the double mutant ( UbextK29/48R ) , impaired the degradation of the UFD reporter protein Ub-P-βgal ( Figure 4C ) . Since these data contradict the effects of UbextK29/48R on N-end rule substrate stability ( Figure 4B ) and previously published results with UBB+1 [22] , we evaluated another UFD substrate , a ubiquitin-GFP fusion ( UbG76V-GFP ) . Western blot analysis revealed that this UFD substrate was also stabilized by Ubext as well as each UbextKxR mutant , including the double mutant ( Figure 4D ) . Taken together , these data demonstrate that the conjugation of Ubext is necessary to cause impaired degradation of an N-end rule substrate , but mono-Ubext ( i . e . UbextK29/48R ) can still impair the degradation of UFD substrates . Based on these data , we suggest that ubiquitin conjugation to N-end rule substrates and UFD substrates is different . The degradation pathways utilized for these two reporters are distinct [3] , [31] , [32] , however they typically report on the same degradation competence of the proteasome , although differences have been cited under certain circumstances [29] , [44] , [45] . The observed differences here could be explained if different proteins interact with the substrates to perform the ubiquitin conjugation . Perhaps preformed ubiquitin chains are conjugated en masse to N-end rule substrates but ubiquitin is added sequentially to UFD substrates . Thus , in the presence of UbextK29/48R the substrates would be affected differently . Furthermore , this emphasizes that the mode of ubiquitin conjugation , which remains somewhat of a mystery [46] , may be an important factor in the differential ability of the cells to cope with one UPS substrate versus another . Our data suggest that Ubext might be interacting with multiple components of the ubiquitin processing pathway , sequestering proteins required for efficient degradation of proteasome target substrates . Ubiquitin contains a hydrophobic patch ( L8 , I44 and V70 ) that is critical for its interaction with many other proteins and the proteasome [47] , [48] . The ubiquitin mutation I44A disrupts the hydrophobic patch and this mutant fails to interact with some of its partner proteins [48] . We created a UbextI44A mutant and tested whether its expression caused UPS impairment . Cells expressing UbextI44A still stabilized the N-end rule substrate , R-βgal ( Figure 5A ) . However , expression of UbextI44A resulted in a modest , yet reproducible , increase in the degradation of UFD substrate Ub-P-βgal ( Figure 5B ) . This differential stabilization of the reporters did not occur with different type of mutant ubiquitin , UbΔGG I44A ( data not shown ) . These data suggest that the interaction of Ubext with other proteins is partially disrupted by mutating the hydrophobic patch and further supports that Ubext may have multiple interacting partners to impose the UPS impairment . The UPS is required for the removal of misfolded proteins . Failure to remove misfolded proteins can lead to aggregation and have detrimental phenotypic consequences . Since the expression of Ubext exacerbates UPS defects , we next analyzed whether the tolerance to misfolded proteins was decreased in cells expressing Ubext . Canavanine is an arginine analog which becomes incorporated into newly synthesized proteins and causes misfolding [49] . Serial dilutions of cells expressing Ubext were spotted onto solid medium containing canavanine . Ubext-expressing cells showed impaired growth on canavanine containing medium ( Figure 6 ) . This suggests that Ubext interferes with the ability of the UPS to degrade natural substrates and challenges cell viability when presented with misfolded proteins . We next asked whether misfolded proteins that aggregate would present an additional challenge to cells expressing Ubext . Using tools and properties uniquely available in the yeast system , we sought to determine if Ubext affects protein aggregation by evaluating both toxic and non-toxic protein aggregates . Since cell death associated with toxic protein aggregates makes it difficult to evaluate the potential contribution of UPS dysfunction , the use of non-toxic aggregates in yeast could provide additional insight as to the direct effects of Ubext . UBB+1 enhanced the aggregation and toxicity of a polyglutamine-expanded protein in cultured mammalian cells [24] . To perform similar experiments in our yeast model , we used a galactose-inducible expanded Huntingtin ( Htt ) polyglutamine construct , TOXIC-Q103 , which creates a toxic protein aggregate [50] , [51] . Cells expressing Ubext could only tolerate a very low amount of TOXIC-Q103 , and even with minimal induction , Ubext-expressing cells grew much worse in comparison to control cells ( Figure 7A ) . Interestingly , the expression of UbextI44A did not result in the same enhanced protein aggregate toxicity ( data not shown ) . Thus , partially alleviating the UPS impairment by altering Ubext protein interactions relieved the enhanced toxicity . To determine whether Ubext expression might affect the aggregates themselves , we imaged a non-toxic version of a polyglutamine-expanded Htt protein fused to GFP ( HttQ103-GFP ) [52] . Evaluation of these protein aggregates eliminates the complication of cell death associated with toxic aggregates . Previous studies have demonstrated that genetic manipulations , such as altering chaperone levels , can change the abundance and pattern of polyglutamine-GFP aggregates in cells [53] . Thus , we tested whether UPS dysfunction caused by the expression of Ubext would change the aggregate distribution . Neither the abundance nor the pattern of HttQ103-GFP aggregates was altered in cells expressing Ubext ( Figure 7B ) . Thus , although the expression of Ubext did enhance the cellular susceptibility to toxic aggregates , it did not grossly alter the formation or maintenance of non-toxic polyglutamine protein aggregates . One mechanism by which Ubext could be enhancing the toxicity of TOXIC-Q103 could involve stabilization of the protein , as the level of expression directly correlates to the amount of toxicity . The stability of TOXIC-Q103 protein was evaluated from cells expressing Ubext after protein translation was inhibited by cycloheximide . No drastic stabilization of TOXIC-Q103 protein was apparent in cells expressing Ubext ( Figure 7C ) . We next asked whether the TOXIC-Q103 aggregates themselves caused UPS impairment . The stability of the UPS reporter protein , Ub-P-βgal , was monitored in cells containing TOXIC-Q103 aggregates in comparison to a non-pathological polyQ25 protein . No stabilization of the reporter was observed in cells harboring the toxic aggregates ( Figure 7D ) . In addition , the UPS impairment caused by Ubext was not further increased by the presence of TOXIC-Q103 ( Figure 7D ) . Thus , the enhanced toxicity of TOXIC-Q103 caused by Ubext is not due to additive effects on UPS impairment . To evaluate the generality of the effects of Ubext on the phenotypic response to toxic protein aggregates , we used a yeast prion protein . Prion proteins in yeast form ordered aggregates that are not harmful to the cells [54]–[56] . Sup35p , an essential translation termination factor , is the protein determinant of the yeast prion [PSI+] [55] . The aggregated prion state of Sup35p , [PSI+] , causes read through of stop codons in translated mRNAs ( nonsense suppression ) . The percentage of read through is low and generally has no deleterious effects to cells grown in rich medium [54] . The presence of the [PSI+] prion can be monitored in a strain carrying an ade1-14 mutant allele with a premature stop codon [57] . In [psi−] cells , Sup35p is soluble and functional , and translation is terminated at the premature stop codon in ade1-14 . Thus , [psi−] ade1-14 cells cannot grow on medium lacking adenine and when grown on rich medium they appear red due to the accumulation of an intermediate in the adenine biosynthetic pathway . Conversely , aggregated Sup35p in [PSI+] cells limits the amount of functional Sup35p , thereby causing nonsense suppression of the ade1-14 premature stop codon and translation of full-length Ade1 protein . These cells are adenine prototrophs and appear white on rich medium . As such , one can evaluate the functional state of Sup35p as it relates to protein aggregation by monitoring the color of the yeast colony . Cells can be maintained stably as [psi−] , but they can be induced to become [PSI+] by over expressing the Sup35 protein . The [PSI+] prion state is not toxic , however , over expression of Sup35p in [PSI+] cells inhibits cell growth due to the lack of sufficient translation termination [58]–[60] . As one would expect , the over expression of Sup35p is not toxic to [psi−] cells . Thus , the toxicity results from too much aggregation of Sup35p in the prion state . These toxic aggregates provide a means to assess the effects of aggregation of a protein of known function in combination with UPS dysfunction . Since most toxic protein aggregates cause cell death by unknown mechanisms , analyzing the Sup35p aggregates in [PSI+] cells provides a unique opportunity to dissect the contributions of the toxic protein aggregates and UPS dysfunction . To evaluate the effects of UPS dysfunction on toxic protein aggregates , [PSI+] cells harboring a copper-inducible SUP35 were transformed with Ubext and assayed for cell viability ( Figure 8A ) . Ubext-expressing [PSI+] cells were more susceptible to the over expression of Sup35p ( Figure 8A , red box ) . The expression of Ubext did not increase basal levels of Sup35p , as determined by SDS-PAGE and western blot analysis ( data not shown ) . Intriguingly , the expression of a different mutant ubiquitin protein , which caused UPS impairment similar to Ubext ( data not shown ) , UbΔGG , did not enhance the toxicity of Sup35p over expression to the same extent ( Figure 8A , compare fourth row to sixth row ) . These results show that Ubext enhances the toxicity of protein aggregates by a mechanism that cannot be solely attributed to its effects on UPS impairment , since UbΔGG did not have the same effect . Furthermore , the hydrophobic domain mutant , UbextI44A , did not result in the same sensitivity to over expressed Sup35p in [PSI+] cells ( Figure 8A ) . This suggests that the mechanism by which Ubext enhances the toxicity of protein aggregates requires interactions with other proteins via the hydrophobic domain . We evaluated whether the aggregation of Sup35 is altered by the expression of Ubext . A previous study demonstrated that altering ubiquitin levels by either increasing the expression of ubiquitin or preventing its recycling caused an increase in the formation of the [PSI+] prion [61] . Furthermore , deletion of a ubiquitin conjugating enzyme also enhanced [PSI+] induction [62] . Thus , there is genetic precedence for perturbations of the UPS affecting prion protein aggregation . We asked whether the presence of Ubext would alter the spontaneous formation of aggregated Sup35p and change cells from [psi−] to [PSI+] . We did not observe a change in the spontaneous conversion rate ( data not shown ) , which we have measured to be ∼1 in 105 in our strain [63] . We next evaluated the induction of the [PSI+] prion state in the presence and absence of Ubext by over expressing Sup35p in [psi−] cells . Since Ubext perturbs the UPS , one might predict an effect on the induction of protein aggregation . To the contrary , the expression of Ubext did not enhance the induction of [PSI+] ( Figure 8B ) . The enhanced toxicity of protein aggregates caused by Ubext could be the result of a general stress response elicited in cells expressing Ubext . The expression of a heat shock element ( HSE ) -LacZ reporter fusion was evaluated in Ubext-expressing cells and no increase in transcription from the HSE promoter at 30°C or at a sub-lethal heat stress of 37°C was observed ( data not shown ) . We next asked whether the presence of Ubext increased the translation of a stress-inducible heat shock protein . Protein lysate from Ubext-expressing cells and control cells showed similar levels of Hsp104p ( Figure 8C ) , a stress-responsive chaperone . Finally , we tested the tolerance of the cells to oxidative stress . Cells challenged with hydrogen peroxide showed no change in survival in the presence of Ubext ( Figure 8D ) . These results suggest that Ubext expression in yeast neither induces a general stress response nor preconditions the yeast to exogenous insult . Therefore , the enhanced susceptibility of Ubext-expressing cells to toxic aggregates is not likely the result of Ubext inducing a general stress . Overcoming the enhanced protein aggregate toxicity induced by Ubext expression could shed light on the mechanism by which Ubext exerts its affects . In attempts to alleviate the Ubext-enhanced aggregate toxicity we conducted a genomic over expression screen using the toxicity caused by over expression of Sup35p in [PSI+] cells . We uncovered two rescuing factors , HSP104 and SUP45 . Both of these proteins alleviate the toxicity by affecting Sup35p aggregation and the associated phenotypic readout . Over expression of Hsp104p affects the Sup35p aggregates [64] and Sup45p can sequester Sup35p away from the aggregates [65] . To verify that the enhanced protein aggregate toxicity in the presence of Ubext can be overcome by altering nonsense suppression , we over expressed the C-terminal domain ( CTD ) of Sup35p , which is sufficient for translation termination but cannot aggregate and form or join the prion state [58] , [66] . We found that the expression of the CTD not only restored translation termination of [PSI+] cells ( Figure 8E , upper ) , but also alleviated the enhanced toxicity caused by the expression of Ubext ( Figure 8E , lower ) . These results demonstrate that alleviating the primary deficit in the cells ( i . e . the effects of [PSI+] ) is sufficient to overcome toxicity even in the presence of a modifier ( Ubext ) . We next asked whether Ubext affected the toxic Sup35p aggregates , since the enhanced cellular toxicity caused by Ubext and excess Sup35p is [PSI+]-dependent . We assayed Sup35p aggregates by semi-denaturing detergent agarose gel electrophoresis ( SDD-AGE ) [67] . This technique allows large protein aggregates to migrate into the gel and can resolve aggregates of different sizes , as demonstrated by a strain variant of [PSI+] ( weak [PSI+] ) , which harbors larger Sup35p aggregates than our [PSI+] starting strain ( Figure 8F ) . We observed no change in the size of Sup35p aggregates from cells over expressing Sup35p in combination with Ubext or UbΔGG . One possible explanation for the enhanced toxicity in the presence of Ubext could relate to a change in the degradation of misfolded Sup35p . As such , we asked whether Ubext was promoting the accumulation of ubiquitinated-Sup35p . We reprobed the SDD-AGE membrane with an anti-ubiquitin antibody but did not find any ubiquitinated Sup35p by this approach . In additional attempts to look for ubiquitination of Sup35p , we purified Sup35 aggregates [68] but again were unable to detect any ubiquitinated Sup35 protein ( data not shown ) . Other researchers have also noted an inability to identify ubiquitinated-Sup35p [61] , [62] . Thus , we conclude that although Ubext affects the ability of cells to tolerate toxic Sup35p over expression , it is unlikely a direct consequence of blocking the ubiquitination and degradation of Sup35p . We also evaluated whether the polyglutamine-expanded Htt proteins are ubiquitinated . We were unable to detect ubiquitinated polyglutamine protein in yeast by immunoprecipitation , SDD-AGE or immunofluorescence ( data not shown ) . The inability to find ubiquitinated polyglutamine protein has also been noted previously [52] , [69] , [70] . Therefore , as with toxic Sup35p , Ubext is affecting the tolerance to TOXIC-Q103 aggregates by an indirect means . How could Ubext be affecting the toxicity of protein aggregates if those proteins are not subject to ubiquitination and degradation by the UPS ? One possible explanation of the effects of Ubext on protein aggregate toxicity could be due to a change in the ability to efficiently sequester the toxic proteins into large aggregates ( Figure 9 ) . A toxic polyglutamine protein expressed in yeast was rendered non-toxic when sequestered into a single , large aggresome-like structure [70] . Furthermore , a non-toxic polyglutamine protein , which localizes to an aggresome-like structure , became dispersed in ubiquitination-deficient cells . We hypothesize that Ubext alters the localization of toxic proteins into the large aggregate structures due to its effects on UPS function . The enhanced toxicity could be the consequence of a reduced ability to sequester toxic soluble oligomer species ( Figure 9 ) . Based on our hypothesis , we predict that protein aggregate toxicity can be affected by perturbations in ubiquitination or by overwhelming the UPS in general . We took advantage of a temperature-sensitive ubiquitin activating enzyme ( E1 ) mutant ( uba1-204 ) [71] to evaluate the effect of an overall reduction in ubiquitination on the phenotypic response to TOXIC-Q103 aggregates . UBA1 is an essential gene responsible for the first step of the ubiquitination cascade . At the restrictive temperature , the uba1-204 mutant limits substrate ubiquitination . A recent study demonstrated that polyglutamine protein aggregate patterns were altered in cells expressing the uba1-204 mutant [70] . uba1-204 cells expressing TOXIC-Q103 or the control ( Q25 ) were grown in inducing conditions at the permissive ( 30°C ) and restrictive temperatures ( 32°C ) and colony survival was measured ( Figure 10A ) . Cells expressing TOXIC-Q103 showed approximately 50% survival in comparison to those expressing Q25 , and this survival was further decreased in conditions of limiting ubiquitination ( i . e . 32°C ) . To directly compare the affect of Ubext expression on the TOXIC-Q103 aggregates , we measured colony survival as performed above . Cells harboring TOXIC-Q103 aggregates in the presence of Ubext allowed for only a 7% survival in comparison to TOXIC-Q103 aggregates alone ( 56% survival ) . Thus , Ubext is a more potent modifier of toxic protein aggregates than perturbations in ubiquitination . Since decreased ubiquitination had an affect on the protein aggregate toxicity , we asked if protein aggregate toxicity could also be enhanced by increasing the burden on the UPS . We measured the viability of cells expressing TOXIC-Q103 or over expressing Sup35p in the presence of canavanine . Serial dilutions of cells expressing Q25 and TOXIC-Q103 were spotted onto inducing media containing canavanine . The effects of the glutamine expansion on cell viability can be seen on inducing plates and in the presence of a UPS burden ( canavanine ) the toxicity is enhanced ( Figure 10B ) . Over expressed Sup35p in [PSI+] cells also shows toxicity and in the presence of canavanine the toxicity is slightly enhanced ( Figure 10C ) . However , canavanine is less potent at enhancing the toxicity of over expressed Sup35p in comparison to the effect of Ubext ( Figure 8A ) . Nonetheless , perturbations to the UPS in general do appear to enhance protein aggregate toxicity . We propose that this is due to a change in efficient sequestration of toxic proteins into insoluble aggregates ( Figure 9 ) . Since Ubext enhanced the toxicity of TOXIC-Q103 , we tested whether Ubext-containing cells were compromised in their ability to sequester or retain TOXIC-Q103 in the insoluble aggregates . Protein lysates from Ubext and controls cells ( EV ) were subjected to high speed ultracentrifugation and analyzed to determine whether Ubext influences the amount of soluble TOXIC-Q103 . Serial dilutions of the total and resulting soluble fraction were applied to PVDF and visualized by western blot . The amount of soluble protein as normalized to total protein was determined by densitometry ( Figure 10D ) . The amount of soluble TOXIC-Q103 was higher in Ubext-expressing cells than wild type cells . Thus , the enhanced toxicity of TOXIC-Q103 in Ubext-expressing cells correlates to an increased pool of soluble protein and supports the model proposed in Figure 9 . Since altered ubiquitination affected the distribution of expanded polyglutamine proteins [70] and enhanced the cellular susceptibility to toxic polyglutamine aggregates ( Figure 10A ) , we asked whether Ubext has a direct effect on the ubiquitination of proteasome substrates . In light of the fact that the toxic protein aggregates are not ubiquitinated , we evaluated the ubiquitination pattern of the UPS reporters . To compare the ubiquitination of these constructs with and without the expression of Ubext , we utilized a temperature-sensitive proteasome mutant strain ( pre1-1 pre2-2 ) [28] . This strain is defective in proteolysis and when grown at the restrictive temperature , R-βgal and Ub-P-βgal accumulate ( Figure 11A ) . Striking substrate ubiquitination can be observed in pre1-1 pre2-2 cells expressing Ubext and control cells after IP . When we compared the R-βgal substrate ubiquitination in EV and Ubext-containing cells , we did not discern any difference in the ubiquitination pattern ( Figure 11A ) . However , a subtle yet reproducible ubiquitination pattern difference was seen with the Ub-P-βgal substrate ( Figure 11B ) . Three independent IP experiments are shown and two ubiquitinated-βgal bands appear in control cells ( EV ) which are absent or greatly reduced in Ubext-expressing cells . The altered ubiquitination pattern of some UPS substrates in the presence of Ubext could change the ability of these proteins to be processed by the proteasome . Furthermore , such changes could be an important modifier of the cellular effects of toxic protein aggregates .
We created a novel model of UBB+1 by constitutively expressing an analogous mutant ubiquitin protein in yeast to investigate the causal relationship between this proteasomal inhibitor and protein aggregation . We demonstrated that the Ubext mutant was not functional as ubiquitin and was not deleterious to the cells . Importantly , the expression of Ubext in yeast caused impairment of the UPS . Since proteasome dysfunction can lead to protein aggregation , we were intrigued that the presence of Ubext served to neither induce nor alter non-toxic protein aggregates in yeast . However , the expression of Ubext rendered the cells more susceptible to toxic protein aggregates , and this could not be attributed to an increase in general stress elicited by Ubext . We propose that the reduced UPS functionality and altered ubiquitination of UPS substrates in Ubext-expressing cells creates an environment in which toxic amyloidogenic proteins either cannot join or are not maintained as large insoluble aggregates . As a result , protein aggregate toxicity is enhanced due to an increase in soluble or oligomeric toxic protein . Thus , this yeast model system revealed that Ubext is a phenotypic modifier of toxic protein aggregates . This genetically tractable model provides a platform to further dissect how UBB+1 affects the cellular tolerance to toxic protein aggregates . The mechanism of UPS impairment caused by UBB+1 is not well understood . We asked whether Ubext causes a reduction in proteasome activity . Using an unstable ubiquitin-independent substrate ( GFP-ODC ) [37] , we observed no significant change in the activity of the proteasome in Ubext-expressing cells . Based on this result , we suggest that Ubext is not clogging the core of the proteasome and propose that Ubext is interacting with other components of the ubiquitin processing cascade or with the regulatory cap of the proteasome . We hypothesized that disrupting the interaction of Ubext with component ( s ) of the ubiquitin processing pathway would alleviate the proteasomal impairment . Mutational analysis revealed that ubiquitin conjugation and the hydrophobic patch affect the extent to which Ubext causes UPS impairment . Interestingly , the effects were distinct with different substrates . This supports the idea that Ubext is interacting with multiple components of the UPS; reduction of its interaction via the hydrophobic patch or elimination of its ubiquitination weakened some of the observed effects but not others . Previous studies have investigated the connection between UPS dysfunction and protein aggregation , especially in the context of protein conformational disorders [72] . It remains difficult , however , to discern the precise nature of the causal relationship between protein aggregation and proteasomal impairment . Evidence that UBB+1 and other disease-associated mutations in the UPS can cause proteasomal impairment and increase protein aggregation supports the idea that proteasome dysfunction plays a stimulatory role in protein aggregation . However , in some cases , such as that with mutant Parkin in familial Parkinson's Disease , decreased UPS function is not associated with protein aggregation [8] . Using non-toxic protein aggregates in yeast , we have demonstrated that a UBB+1-like protein , Ubext , neither induced nor changed protein aggregates . Our results provide evidence that a compromised UPS does not necessarily affect protein aggregation per se but can cause phenotypic effects by decreasing cellular tolerance to deleterious protein aggregates . We hypothesize that Ubext is altering the sequestration of aggregated proteins ( Figure 9 ) . Due to the altered substrate ubiquitination and the general UPS impairment caused by Ubext , misfolded proteins are not efficiently degraded and somehow perturb the sequestration of amyloidogenic proteins into the insoluble aggregates which may have a protective function . How the UPS functionality plays a role in the ability of the cell to efficiently sequester non-ubiquitinated proteins remains to be elucidated . One recent study suggests that different cellular compartments retain aggregates of ubiquitinated and non-ubiquitinated proteins and a reduction in UPS activity can cause a change in this localization [69] . If proper localization of aggregated proteins protects the cell from smaller toxic oligomeric species [73] , [74] , then the inability of toxic oligomers to be efficiently sequestered would be deleterious ( Figure 9 ) . Indeed , the expression of Ubext resulted in an increase in the relative amount of soluble TOXIC-Q103 protein ( Figure 10D ) and the combination of Ubext and TOXIC-Q103 was more deleterious to cell survival ( Figure 7A ) . Further evidence to support the idea that the redistribution of aggregates can lead to cell death comes from a recent report investigating the nature of the aggregates formed in response to the expression of expanded polyglutamine protein in yeast [70] . A single large aggregate , an aggresome-like structure , was formed by polyglutamine proteins that were not toxic to the cells . When the large aggregate was unable to form , multiple small aggregates were observed and the appearance of these correlated with toxicity . Thus , the single large aggregate appears to be protective against polyglutamine protein aggregate toxicity . Among the cellular factors found that could disrupt the formation of the single aggregate when mutated were two ubiquitin-associated proteins . Furthermore , limiting general cellular ubiquitination by the uba1-204 mutant also disrupted the formation of the large aggregate [70] . We show that uba1-204 enhanced the cellular toxicity of the toxic polyglutamine aggregates used in our study ( Figure 10A ) . Taken together , the data support the proposed model of the effect of Ubext on protein aggregate toxicity ( Figure 9 ) . Since Ubext causes UPS impairment and a change in ubiquitination of substrates , this could cause the mis-handling or redistribution of some ubiquitin-conjugated proteins and hinder toxic protein aggregates from being rapidly sequestered , resulting in enhanced cell death ( Figure 9 ) . Thus , even though the toxic protein aggregates may not be substrates of the UPS , perturbations in the processing of normal UPS substrates may affect cellular tolerance to toxic aggregates . Our data suggest that all perturbations in the UPS are not equally potent at altering the cellular tolerance to toxic aggregates . Therefore , we conclude that the magnitude of the enhanced protein aggregate toxicity in the presence of the extended mutant ubiquitin is exceptional . This is likely due to its interactions with other proteins and supports further that UBB+1 may be a potent disease modifier . Since protein conformational disorders result from a combination of cellular perturbations , often including the unknown affects of aging , then eliminating individual modifiers or enhancers may prove useful for disease therapy . Obviously , alleviating the primary causative agent , when known , could prove to be the most beneficial . For example , when we used the Sup35p toxic aggregate model we were able to rescue the Ubext-enhanced toxicity by restoring the loss of function caused by Sup35p sequestration into aggregates . However , in many protein conformational diseases , the function of the proteins found in the aggregates and cellular toxicity is not understood . Therefore , investigating ways to alleviate the effects of known modifiers represents an important therapeutic avenue for disease treatment and prevention . The insight gained by developing a yeast model of UBB+1 has provided a means to further investigate the role of protein aggregate compartmentalization in toxicity , which may underlie some of the effects observed in cells or tissues experiencing chronic UPS impairment . The identification of UBB+1-interacting proteins may allow for the elucidation of the mechanism whereby a natural modifier of UPS function affects cellular tolerance to toxic protein aggregates .
Yeast strains were grown and manipulated by standard techniques [75] . Unless otherwise indicated , all yeast strains used in this study were derivatives of 74-D694 ( MATa or MATα ade1-14 trp1-289 his3Δ-200 ura3-52 leu2-3 , 112 ) [64] . The Δubi4 strain was created by PCR amplification of the antibiotic resistance marker KanMX4 with primers A and B and subsequent transformation of the resulting product into 74-D694 . For all primer sequences , see Table 1 . The Δubp14 strain was created by PCR amplification of BY4741 Δubp14 genomic DNA with primers C and D and subsequent transformation of the resulting product into 74-D694 . The proteasome mutant strain , WCG4-11/22a ( MATa his3-11 , 15 leu2-3 , 112 ura3 pre1-1 pre2-2 ) and control strain , WCG4a ( MATa his3-11 , 15 leu2-3 , 112 ura3 ) were a kind gift of P . Coffino [37] . The 74-D694 [PSI+]-inducible prion strain [psi−] [RNQ+] and the weak [PSI+] strain variant were a kind gift from S . Liebman [76] . A 74-D694 [PSI+] [RNQ+] strain was used in the PQ toxicity study . The uba1-204 strain was a kind gift from R . Deshaies [71] . All plasmids were created using standard molecular biology protocols [77] and verified by DNA sequencing . For primer sequences , refer to Table 1 . Where appropriate , the enzyme used is listed parenthetically . To create p413TEFUbext , ubiquitin was PCR amplified from 74-D694 genomic DNA using primers E and F and cloned into p413TEF [78] at XbaI and BamHI . To create p413TEFUb , ubiquitin was PCR amplified from 74-D694 genomic DNA using primers G and H and cloned into p413TEF at BamHI and SalI . Ubext was subcloned from p413TEFUbext to p423TEF and p426TEF at SpeI and BamHI . Ubiquitin was subcloned from p413TEFUb to p423TEF and p426TEF at SalI and BamHI . All Ubext amino acid substitutions ( p423TEFUbextK11R , UbextK29R , UbextK48R , UbextK63R , UbextK29/48R , UbextI44A ) were created using either three-way ligation or bridge PCR into p423TEF using p423TEFUbext as a template ( except for the p423TEFUbextK29/48R mutant which utilized p423TEFUbextK29R ) and following standard molecular biology techniques [77] . p423TEFUbext+GG was created by PCR amplification of ubiquitin DNA with primers G and P and cloned into p423TEF at BamHI and SalI . p423TEFUbΔGG was created by PCR amplification of ubiquitin DNA with primers G and Q and cloned into p423TEF at BamHI and SalI . The 4xHSE-LacZ plasmid was a kind gift of S . Lindquist . In vivo UPS functionality was measured using Ub-X-LacZ reporters: pGal-Ub-M-LacZ , pGal-Ub-R-LacZ , and pGal-Ub-P-LacZ [30] . The ubiquitin-independent proteasome substrates , p416ADH1GFP-mODC and p416ADH1GFP-mODCC441A were a kind gift from P . Coffino [37] . The UBI4promoter-LacZ reporter was a kind gift from M . Altmann [79] . [PSI+] induction assays used the inducer plasmid pEMBL Sup2 ( referred to as pSup35 in this manuscript ) [58] . Non-toxic polyglutamine aggregation assays used p416GPD polyQ103-GFP [52] , referred to as HttQ103-GFP in this manuscript . Toxic polyglutamine aggregation assays employed p416Gal FLAG103Q-CFP ( referred to as TOXIC-Q103 ) and p416Gal FLAG25Q-CFP ( referred to as Q25 ) ( kind gift M . Duennwald ) [50] , [51] . For the toxicity assay in [PSI+] cells , Sup35p was over expressed from a copper inducible promoter . pRS315Cup-SUP35 was generated by cloning Cup1-SUP35 between XhoI and SacI . pRS316-TEF-CtermSup35 contains only the C-terminal domain ( amino acids 254–685 ) of Sup35 and was created by subcloning TEF-CtermSup35 from pRS306TEF-CtermSup35 [80] at HindIII and SacI . Protein lysates were analyzed by standard SDS-PAGE . Protein lysis followed the β-galactosidase assay ( see below ) . The following antibodies were used: Ubiquitin ( PD41 ) ( Santa Cruz sc-8017 ) , Hsp104 ( kind gift of S . Lindquist ) , GFP ( kind gift of M . Linder ) , β-galactosidase ( Promega Z378A ) , Pgk1 ( Molecular probes A6457 ) , and Sup35 ( kind gift of S . Lindquist ) [81] . Large Sup35 protein aggregates were separated by SDD-AGE as previously described [82] with modifications previously described [63] . Sup35p over expression was achieved by growing the cultures in 50 µM copper sulfate overnight . Immunoprecipitations were carried out as previously described [83] using 5 µl of mouse anti-β-galactosidase . TOXIC-Q103 protein stabilization was measured after a six hour induction ( 2% galactose and 1% raffinose containing media ) in the presence of 0 . 5 mg/ml cycloheximide in cultures with equal numbers of cells . The relative amount of TOXIC-Q103 soluble protein was determined by slot blot . Cells containing TOXIC-Q103 and either EV or Ubext were grown overnight in selective medium , washed in inducing medium containing 2% galactose/1% raffinose and induced for 14–16 hours . Cells were harvested and lysed with glass beads in PEB ( 250 mM Tris HCl pH 7 . 5 , 50 mM KCl , 10 mM MgCl2 , 1 mM EDTA , 10% glycerol , 10 mM PMSF , 5 µg/ml Aprotinin , Roche Protease cocktail inhibitor ( Roche ) ) . Equal protein ( 100 µg ) from EV and Ubext-containing cells was subjected to ultracentrifugation ( 80 , 000 rpm for 30 minutes at 4°C ) . Serial dilutions of the supernatant and total fractions ( diluted 1/10 ) were applied to activated PVDF and probed with an anti-GFP antibody . The supernatant fraction and corresponding total fractions were quantified using Image J software and graphed as normalized arbitrary units . UPS functionality was determined by the degradation of Ub-LacZ fusions [30] using Galacto-light™ ( Applied Biosystems ) . Cells containing pGal-Ub-M-LacZ , pGal-Ub-R-LacZ and pGal-Ub-P-LacZ were grown in selective medium for 24 hours . The cultures were washed three times in selective medium containing 2% galactose / 1% raffinose and grown overnight in the 2% galactose / 1% raffinose . The cultures were harvested and lysed in Galacto-light Lysis Solution using glass beads . Cell lysate was pre-cleared for 30 seconds at 6 , 000 rpm at 4°C . In a flat bottom , black-sided 96-well dish , 70 µl of Galacto Reaction Buffer was added to 10 µl of protein lysate and incubated for 60 minutes at room temperature . Luminescence was read immediately after the addition of 100 µl of Light Emission Accelerator . Luminescence values were normalized to protein concentration as determined by Bradford reagent ( BioRad ) . Error bars in all βgal activity assays represent the standard deviation from three independent cultures for each sample . The TOXIC-Q103 protein βgal activity assay was conducted as described above using a TRP1 version of pGal-Ub-P-LacZ ( subcloned into p424Gal vector ) with a 24 hour induction . All statistical analyses were conducted using Student's T-Test . Polyglutamine aggregation was monitored by GFP fluorescence in a 74-D694 [PSI+] [RNQ+] strain background . Three independent samples of mid-log phase cells containing p416GPD polyQ103-GFP [52] and p423TEF EV or p423TEF Ubext were visualized . Individual fluorescent cells were evaluated for a single aggregate , few aggregates ( 2–3 per cell ) or multiple aggregates ( greater than 3 aggregates per cell ) as previously described [53] . Approximately 200 cells were analyzed for each sample in triplicate . Error bars represent the standard deviation .
|
The accumulation of cytotoxic protein aggregates occurs in many neurodegenerative diseases . It is difficult to determine if the protein aggregates found in these diseases represent a cause or consequence of the disorder . Degradation pathways , such as the ubiquitin/proteasome system ( UPS ) , remove misfolded proteins that are prone to aggregate . The UPS involves many players that work in concert to target proteins for degradation by the proteasome . A mutant form of ubiquitin has been associated with many diseases , including Alzheimer's disease . We developed a yeast model of the mutant ubiquitin protein in order to investigate its effect on UPS function and protein aggregation . We demonstrate that this mutant ubiquitin causes impairment of the UPS and suggest that it does so by interacting with multiple components of the pathway . Using this model , we evaluated the effects of the mutant ubiquitin on nontoxic protein aggregates and found that they were unaltered by its presence . We demonstrate that the mutant ubiquitin acts as a modifier , which increases cellular susceptibility to the phenotypic effects of deleterious protein aggregates by altering UPS functionality and substrate ubiquitination . Furthermore , the system we developed can be utilized to further understand the complex interplay of proteasomal impairment and protein aggregate toxicity .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"genetics",
"and",
"genomics/disease",
"models",
"biochemistry/protein",
"folding",
"neurological",
"disorders",
"cell",
"biology/cellular",
"death",
"and",
"stress",
"responses"
] |
2009
|
Disease-Associated Mutant Ubiquitin Causes Proteasomal Impairment and Enhances the Toxicity of Protein Aggregates
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The main consequence of oxidative stress is the formation of DNA lesions , which can result in genomic instability and lead to cell death . Guanine is the base that is most susceptible to oxidation , due to its low redox potential , and 8-oxoguanine ( 8-oxoG ) is the most common lesion . These characteristics make 8-oxoG a good cellular biomarker to indicate the extent of oxidative stress . If not repaired , 8-oxoG can pair with adenine and cause a G:C to T:A transversion . When 8-oxoG is inserted during DNA replication , it could generate double-strand breaks , which makes this lesion particularly deleterious . Trypanosoma cruzi needs to address various oxidative stress situations , such as the mammalian intracellular environment and the triatomine insect gut where it replicates . We focused on the MutT enzyme , which is responsible for removing 8-oxoG from the nucleotide pool . To investigate the importance of 8-oxoG during parasite infection of mammalian cells , we characterized the MutT gene in T . cruzi ( TcMTH ) and generated T . cruzi parasites heterologously expressing Escherichia coli MutT or overexpressing the TcMTH enzyme . In the epimastigote form , the recombinant and wild-type parasites displayed similar growth in normal conditions , but the MutT-expressing cells were more resistant to hydrogen peroxide treatment . The recombinant parasite also displayed significantly increased growth after 48 hours of infection in fibroblasts and macrophages when compared to wild-type cells , as well as increased parasitemia in Swiss mice . In addition , we demonstrated , using western blotting experiments , that MutT heterologous expression can influence the parasite antioxidant enzyme protein levels . These results indicate the importance of the 8-oxoG repair system for cell viability .
Oxidative stress is often defined as a situation in which the balance between oxidants and antioxidants is disrupted . The main source of oxidative stress in living organisms is reactive oxygen species ( ROS ) , which are molecules , such as hydrogen peroxide , superoxide and hydroxyl radicals , that are derived from oxygen and are highly reactive toward biomolecules [1] . One of the most deleterious consequences of oxidative stress may be the formation of DNA lesions . Over 100 different types of oxidative DNA modifications have already been identified in the mammalian genome . However , due to its low redox potential , guanine ( G ) is the most vulnerable base [2] . The main product of G oxidation is 7 , 8-dihydro-8-oxoguanine ( 8-oxoG ) . Therefore , this product is the most common and best-characterized lesion created by ROS [3] . The strong relation between ROS production and 8-oxoG formation makes it a good and commonly used cellular biomarker of oxidative stress [4] . Its importance can be attributed to the fact that when 8-oxoG assumes its syn conformation , it is particularly mutagenic because of its strong ability to functionally mimic thymine . When 8-oxoG is inserted during DNA replication , it can generate double-strand breaks , which makes this lesion very deleterious [5] . The so-called GO-system is a three-component 8-oxoG repair pathway . In bacteria , MutT , MutY and MutM ( also called Fpg ) constitute this system [6] . The corresponding enzymes for humans are MTH1 , MUTYH and OGG1 , respectively [7] . MutT ( or MTH1 ) hydrolyses 8-oxo-dGTP in the nucleotide pool , returning it to the monophosphate form so that it cannot be incorporated into DNA by polymerases [8] , [9] . The enzymes MutM ( or OGG1 ) and MutY ( MUTYH ) are responsible for repairing 8-oxoG paired with cytosine in the DNA or removing the adenine in the 8-oxoG:A mispair [2] , [3] , [10] . The focus of our group is the Trypanosoma cruzi parasite and how oxidative stress and DNA repair mechanisms could affect its cell viability . This flagellate parasite is responsible for the development of a malady called Chagas disease [11] , a major public health problem in Latin America that affects over 10 million people , according to the World Health Organization . Currently , the disease is a world health concern due to globalization and population migration from endemic to non-endemic areas [12] . During its life cycle , T . cruzi infects two different hosts: a vertebrate mammalian host and an invertebrate insect vector from the Reduviidae family . This complex life cycle involves various different environments that the protozoan parasite has to address . The oxidative stress encountered in all these environments is one of the main threats to the trypanosome cell viability [12]–[16] . In the vertebrate host , cell infection depends initially on recruitment and fusion of lysosomes , which contribute to the formation of a stable parasitophorous vacuole [17] . Lysosomes are very acidic organelles with a high oxidative potential . Later , the parasite escapes from its vacuole into the cytosol , which may also represent a source of oxidative stress via the generation of ROS due to electron leakage from mitochondrial respiratory complexes [15] , [18] . In the triatomine , the parasite develops inside the gut , where it is confronted with several changes , such as temperature , osmolality , nutrient supply , acidic or alkaline pH , as well as the oxidative stress caused by ROS production through hemoglobin degradation and nitrogen intermediate production by host defense mechanisms [13] , [19] , [20] . To cope with these oxidant environments , T . cruzi uses its defense machinery against oxidative damage . Its antioxidant machinery compose an efficient and well-compartmentalized network that acts in the detoxification of the reactive oxygen and nitrogen species produced during parasite-host cell interactions [14] . In addition , there is growing evidence that this antioxidant network may play an important role in parasite virulence [21]–[24] . Despite all of these antioxidant defenses , the parasite macromolecules , especially DNA , can undergo oxidative damage that can be deleterious if not repaired . The T . cruzi genome project identified several DNA repair pathway elements in the parasite genome [25] . Among these proteins , many of them act in response to oxidative lesions . Since the publication of the parasite's genome , some of this DNA repair enzymes have been characterized , as reviewed by Passos-Silva et al . [26] . However , some important elements of the DNA repair machinery , such as a MutT homolog , have yet to be identified . This prompted our group to transform a T . cruzi CL Brener clone with the E . coli mutT gene to investigate the importance of 8-oxoG during parasite infection of mammalian cells . MutT-expressing cells demonstrated more resistance to the oxidative stress caused by hydrogen peroxide ( H2O2 ) treatment , as well as increased growth in in vitro and in vivo infection experiments . This difference could be due to a MutT enzyme product 8-oxo-dGMP , which can generate an oxidative stress signal , enabling the cells to overcome this stress . Furthermore , we demonstrated that E . coli MutT expression in T . cruzi reduces the amount of nuclear DNA lesions compared with control cells . In addition , we demonstrated that T . cruzi has a MutT homolog , termed here TcMTH , that is able to complement mutT-deficient bacteria and enhances parasite survival against oxidative treatment in the same manner that we observed for the bacteria gene .
This study was conducted in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the Brazilian National Council of Animal Experimentation ( http://www . cobea . org . br/ ) and the Federal Law 11 . 794 ( October 8 , 2008 ) . All animals were handled in strict accordance with good animal practice as defined by the Internal Ethics Committee in Animal Experimentation of the Centro de Pesquisas René Rachou/Fundação Oswaldo Cruz ( CPqRR/FIOCRUZ ) , Belo Horizonte ( BH ) , Minas Gerais ( MG ) , Brazil . The protocol number P-441-07 was approved by CEUA/FIOCRUZ with the license n° LW-61/12 . The pROCK_MutT_HYGRO expression vector was generated by polymerase chain reaction ( PCR ) amplification of the mutT gene from AB1157 E . coli ( GeneID: 5590913 ) and employed the following primers: 5′-TCTAGAATGAAAAAGCTGCAAATTGC-3′ ( forward ) and 5′-CTCGAGCTACAGACGCTTAAGCTTCGCA-3′ ( reverse ) . The pROCK_TcMTH_HYGRO expression vector was generated by PCR amplification of the tcmth gene from T . cruzi strain CL Brener genomic DNA ( GenBank: KC630985 ) and employed the following primers: 5′-TCTAGAATGGCCGCGATGACTGCGAC-3′ ( forward ) and 5′-CTCGAGTCAGCTGGAGTTTTCCTTGT-3′ ( reverse ) . The PCR products were cloned into the pGEM-T Easy ( Promega , Brazil ) cloning vector . The mutT or tcMTH gene fragment was digested using XbaI and XhoI and then inserted into the pROCK_HYGRO vector , previously digested with the same restriction enzymes , to produce pROCK_MutT_HYGRO or pROCK_TcMTH_HYGRO [27] . T . cruzi CL Brener strain epimastigote forms were grown in liver infusion tryptose ( LIT ) medium ( pH 7 . 3 ) supplemented with 10% fetal bovine serum ( FBS , GibcoBRL , Invitrogen , CA , USA ) , streptomycin sulfate ( 0 . 2 g l−1 ) and penicillin ( 200000 units l−1 ) at 28°C . The parasite transfection was performed using electroporation following a previously described protocol [27] . The transfected parasites were cultured for 6 weeks in the presence of hygromycin ( 200 mg/mL , Sigma , MO , USA ) for selection of parasites containing stably incorporated pROCK_MutT_HYGRO or pROCK_TcMTH_HYGRO . T . cruzi total RNA purification was performed from 107 epimastigotes using TRIzol ( Invitrogen , Life technologies , CA , USA ) reagent and treated with DNAse ( Invitrogen ) for DNA contaminant removal according to the manufacturer's instructions . The purified RNA was then used in a cDNA synthesis reaction with 500 ng oligo ( dT ) 12–18 , using the SuperScript III First-strand Synthesis System for RT-PCR ( Invitrogen ) . The subsequent mutT fragment specific amplification was performed using the following primers: 5′-GTAGGTATTATTCGCAACGAGA-3′ ( forward ) and 5′-TTTCACCCATTTCAATTTTACCG-3′ ( reverse ) . The negative control was processed in the same conditions as the other samples but without reverse transcriptase enzyme . Wild-type and transfected parasite growth curves were started at 5×106 cells/mL . The cells were counted for 6 days . To test the resistance to H2O2 , parasite cultures containing 1×107 cells/mL were treated with 0 , 150 , 200 or 250 µM H2O2 . The cells were counted after 72 h . The results are expressed as the percentage of growth compared with untreated cultures . In both experiments , the cell numbers were determined in a cytometry chamber using the erythrosine vital stain to differentiate living and dead cells . The experiments were performed in triplicate . To compare the amount of DNA lesions from parasites transfected either with pROCK empty vector or pROCK_MutT , a quantitative polymerase chain reaction ( QPCR ) protocol adapted from Santos et al . [28] was employed . Epimastigotes cultures were grown on LIT medium under normal conditions . Parasites were harvested at 1×107 cells/mL concentration by centrifugation at 3000 g for 10 min . Following that , high-molecular weight DNA extraction , quantification , QPCR amplification and result analyses were conducted as previously described [28] . This QPCR assay was performed by comparing the amplification of the DNA from the cells carrying the pROCK empty vector with the amplification of the cells expressing the MutT enzyme . Specific primers were used to amplify large and small fragments of the nuclear and mitochondrial DNA . The large nuclear fragment was amplified using the forward primer QPCRNuc2F ( 5′-GCACACGGCTGCGAGTGACCATTCAACTTT-3′ ) and the reverse primer QPCRNuc2R ( 5′-CCTCGCACATTTCTACCTTGTCCTTCAATGCCTGC-3′ ) . The small nuclear fragment was amplified employing the internal primer QPCRNuc2Int ( 5′-TCGAGCAAGCTGACACTCGATGCAACCAAAG-3′ ) and the reverse primer QPCRNuc2R . The large mitochondrial fragment was amplified using the forward primer QPCRMitF ( 5′-TTTTATTTGGGGGAGAACGGAGCG-3′ ) and the reverse primer QPCRMitR ( 5′-TTGAAACTGCTTTCCCCAAACGCC-3′ ) . The small mitochondrial fragment was amplified with the internal primer QPCRMitInt ( 5′-CGCTCTGCCCCCATAAAAAACCTT-3′ ) . The small fragment ( 250 bp ) amplification was used to normalize the amplification results obtained with the large fragments ( 10 kb ) , as the probability of introducing a lesion in a short DNA segment is very low , and this strategy eliminates the bias of changes in the proportion between nuclear and mitochondrial genomes . The normalized amplification of pROCK cells samples was then compared with MutT , and the relative amplification was calculated . These values were then used to estimate the average number of lesions per 10 kb of the genome , using a Poisson distribution . The final results are the mean of two sets of PCR for each target gene of at least 2 biological experiments . For the differentiation of T . cruzi epimastigotes parasites to metacyclic trypomastigotes , an in vitro metacyclogenesis protocol was employed , using the chemically defined TAU and TAU3AAG media as previously described [29] . Following differentiation , the parasites were centrifuged , resuspended in DMEM ( Gibco ) supplemented with 2% FBS , 1% penicillin-streptomycin and 2 mM glutamine and used to infect LLC-MK2 monolayers . Once the infection was successfully established , the parasites were maintained in LLC-MK2 monolayers and purified as described previously [30] . All in vitro infection experiments in non-professional phagocytic cells were performed using a mouse fibroblast cell lineage ( WTCl3 ) derived from mouse embryonic fibroblasts [31] . Prior to infection , the cells were plated at 2 . 5×104 cells/mL in medium containing 10% FBS on 24-well tissue culture plates containing 12-mm round coverslips and grown for 24 h at 37°C in a humidified atmosphere containing 5% CO2 . Infection of WTCl3 fibroblasts with purified tissue culture trypomastigotes ( TCTs ) was performed for 30 min at 37°C at a multiplicity of infection ( MOI ) of 50 or 100 ( for vacuole escape experiments ) . For hydrogen peroxide pre-treatment experiments , the parasites were treated with 50 µM H2O2 for 2 hours before incubation with cells . Immediately after cell infection , the cells were washed four times with phosphate-buffered saline ( PBS ) to remove extracellular parasites and reincubated with medium for 30 min ( 0 h time point ) or different times according to the experiment , before fixation with 4% ( wt/vol ) paraformaldehyde/PBS overnight at 4°C . After fixation , coverslips with attached cells were washed three times in PBS , incubated for 20 min with PBS containing 2% bovine serum albumin ( PBS/BSA ) and processed for an inside/outside immunofluorescence invasion assay as described previously [32] . Briefly , to distinguish extracellular parasites from intracellular ones , the cells were incubated for 50 min with a 1∶500 dilution of rabbit anti-T . cruzi polyclonal antibody in PBS/BSA , followed by 40 min incubation with Alexa-Fluor 546 goat anti-rabbit IgG ( Invitrogen ) diluted 1∶250 in PBS/BSA . For the vacuole escape kinetics experiments , after extracellular-parasite staining , the cells were permeabilized with PBS/BSA containing 0 . 5% saponin ( PBS/BSA/saponin ) for 20 min . Parasites associated with parasitophorous vacuole were labeled using a 50-min incubation with rat anti-LAMP1 antibody diluted 1∶50 in PBS/BSA/saponin , followed by 40-min incubation with a 1∶250 dilution of Alexa-Fluor 488 goat anti-rat IgG ( Invitrogen ) in PBS/BSA/Saponin . The cell and parasite DNA was stained with DAPI diluted 1∶1000 . Slides were mounted and then examined on a Zeiss Axioplan-2 microscope . At least 300 cells were analyzed per coverslip in triplicate . The polyclonal antibodies anti-TcCPx and anti-TcMPx were obtained as previously described [33] , [34] . Parasites ( 1 . 0–2 . 0×107 cells/mL ) were incubated or not ( control ) with H2O2 ( 50 µM ) for 30 minutes in LIT medium . The cells were harvested by centrifugation ( 2700 g , 10 min ) and resuspended in 80 µL of PBS/1 mM MgCl2 , and an equal volume of lysis buffer ( 50 mM Tris-HCl pH 7 . 4 , 1% Tween 20 , 150 mM NaCl , 1 mM EGTA , 1 mM Na3VO4 , 1 mM NaF , 0 . 1 mM PMSF , aprotinin 1 µg/mL; leupeptin 1 µg/mL ) was added . The suspension was sonicated ( Bandelin Sonoplus Homogenisatoren ) for 10 cycles of 1 sec , with an interval of 1 sec and 30% max amperage . The material was kept for 2 h on ice and subsequently centrifuged ( 13 , 000 g , 4°C , 15 min ) . An equal volume of loading buffer was added to the protein extract ( 100 mM Tris-HCl , pH 6 . 8 , 4% SDS , 0 . 02% bromophenol blue , 20% glycerol , 200 mM β-mercaptoethanol ) , and the samples were heated at 96°C for 4 min [35] . Protein concentration was determined by the Lowry technique in samples without loading buffer [36] . The protein extracts ( 30 µg ) were separated and electroblotted onto a nitrocellulose membrane using the Trans-Blot SD Semi-Dry Electrophoretic Transfer Cell ( Bio-Rad , CA , USA ) . The membranes were blocked by incubation with 5% instant nonfat dried milk in PBS 0 . 05% Tween 20 ( PBS-T ) for 1 h , washed and incubated in the presence of polyclonal antibodies raised against T . cruzi TcMPx ( 1∶2000 ) and TcCPx ( 1∶2500 ) for 2 h . After three 15 min washes with PBS-T , the membranes were incubated with HRP-linked anti-rabbit IgG ( Cell Signaling Technology , MA , USA , 1∶5000 dilution ) for 1 h at room temperature and washed three times with PBS [22] , [37] . Bands were revealed using the Super Signal Detection Kit ( Thermo Scientific , Pierce , IL , USA ) . Data were analyzed using the Scion Imaging Program and normalized using a loading control ( anti-tubulin ) . Three-week-old female Swiss mice bred and maintained in the animal breeding units at CPqRR/FIOCRUZ were used . TCTs were purified , counted and diluted in DMEM to inoculate each animal with 5000 parasites equally via the intraperitoneal route . The experiments consisted of two groups of 6 animals for the WT and MutT parasites . Parasitemia was assessed by counting the trypomastigotes in 5 µl of tail vein blood of the infected mice , on alternate days from the 3rd day p . i . until the time at which the parasites became undetectable . The number of parasites per mL was calculated as previously described [38] . DNA sequencing reactions were performed using the ABI3130 automated capillary DNA sequencer ( Applied Biosystems , Life Technologies , CA , USA ) and BigDye Terminator cycle sequencing kit v3 . 1 ( Invitrogen ) by Myleus Biotechnology ( www . myleus . com , MG , Brazil ) . The DNA Baser Sequence Assembler v3 . 2 . 5 ( Heracle Biosoft ) software was employed for contig assembly . The TcMTH sequences were deposited in GenBank and can be viewed under the accession number KC630985 . The GenBank accession numbers of the other MutT homologs sequences used in this work are as follows: Trypanosoma brucei ( TbMTH ) , accession number XP_822715 . 1; Escherichia coli ( EcMutT ) , accession number ZP_12082602 . 1; and Homo sapiens ( hMTH1 ) , accession number BAA04013 . 1 . Sequence alignments were performed using the Multalin [39] and Boxshade v3 . 21 ( www . ch . embnet . org ) interfaces . Motif and catalytic site identification were performed using the Conserved Domain Database of the NCBI [40] . The spontaneous mutation rate was determined using a rifampicin ( rif ) antibiotic resistance assay following a previously described protocol [41] . An E . coli BH600 ( mutT- ) bacteria strain was used for the heterologous complementation test , and the rif resistant mutants ( rifR ) frequency was determined . BH600 bacteria were transformed with the following pMAL-c2G ( NE BioLabs Inc . , MA , USA ) constructs: pMAL_mutT , pMAL_TcMTH and pMAL empty vector . After confirming the transformants , 105 cells were plated on 2×YT medium containing ampicillin ( 100 µg/mL ) , tetracycline ( 12 . 5 µg/mL ) and IPTG ( 0 . 1 mM ) to confirm the number of viable cells . The resistance assay was performed by plating the same cultures in 2×YT medium as described above supplemented with rif ( 100 µg/mL ) . At least 14 clones were plated in duplicates for each condition . The plates were grown for 16 hours at 37°C , and the number of rifR revertant colonies was counted . The mutation rate was calculated according to Lea & Coulson [42] . The macrophages used in this study were obtained from the peritoneal cavity as previously described [43] . Briefly , the cells were isolated from the peritoneal cavity of mice 3 days after injection of 2 mL of 3% thioglycollate medium ( Biobras S . A . , MG , Brazil ) into the peritoneal cavity . The cells were resuspended in DMEM ( Gibco ) , supplemented with 10% FBS , 1% penicillin-streptomycin and 2 mM glutamine . The macrophages were counted in a Neubauer chamber prior to seeding 5×105 cells into each well of a 24-well plate and incubated at 37°C , 5% CO2 for 1 hour . The TCTs were purified , counted and diluted in DMEM medium , and infection was performed for 2 hours at an MOI of 5 . Immediately after macrophage infection , the cells were washed four times with PBS to remove extracellular parasites and either fixed or reincubated with medium for 48 and 72 hours before fixation with methanol . Coverslips with attached macrophages were stained with Giemsa , and a minimum of 300 macrophages per coverslip were analyzed . The results were expressed as an infection index ( % infected macrophages × number of amastigotes/total number of macrophage ) . The experiments were performed in triplicate . The statistical analyses in this work were performed using the GraphPad Prism 5 . 0 program ( GraphPad Software Inc . , CA , USA ) . Data are presented as the mean ± standard deviation ( SD ) , and all experiments were repeated at least three times . Results were analyzed for significant differences using ANOVA or Student's t test . Statistical tests used are described at each figure legend . The level of significance was set at P<0 . 05 .
To investigate 8-oxoG lesion importance for the parasite , we generated a T . cruzi cell line stably expressing the MutT enzyme using the integrative vector pROCK_HYGRO . This vector integrates by homologous recombination at the β-tubulin locus , which contains several copies of the β-TUBULIN gene along with the α-TUBULIN gene [27] . The heterologous expression of E . coli MutT in the T . cruzi cell line transfected with the vector containing the mutT gene was confirmed through RT-PCR . The expected 112-bp fragment of the mutT gene was amplified from the cDNA of parasites transformed with the gene of interest and positive controls ( Fig . 1A ) . To facilitate reading , the cell lines used in this work will be referred to as WT for wild-type CL Brener , pROCK for CL Brener transformed with the pROCK empty vector , and MutT for the parasites that express the E . coli MutT enzyme . After MutT heterologous expression was confirmed , we went on to investigate the behavior of epimastigotes growth in vitro . The expression of MutT did not alter the parasite growth curve in normal conditions ( Figure 1B ) . To verify if MutT expression alters T . cruzi response to oxidative stress , the epimastigote cultures were treated with H2O2 , and cell viability was determined after 3 days . As shown in Figure 1C , MutT parasites displayed greater resistance to H2O2 toxicity than WT cells ( P<0 . 05 ) . In the presence of 250 µM H2O2 , approximately 95% of the transfected cells survived in contrast to only 22% of the wild-type cells . The QPCR technique was employed to compare the extent of DNA damage in the genome of the parasite populations used in this work . The normalized amplification of pROCK cells was compared to MutT ( Table S1 ) , and the relative amplification was calculated . These values were used to estimate the average number of lesions per 10 kb of the genome in relation to MutT parasites . As Figure 2 shows , pROCK cells presented 0 . 34 more DNA lesions per 10 kb in the nuclear genome compared with MutT cells ( P<0 . 0001 , unpaired t test ) . The difference in DNA lesions in the mitochondrial genome for the two parasite populations was not significant . The invasion and intracellular development of WT , pROCK and MutT parasites in mammalian cells were assayed by the in vitro infection of murine fibroblasts . These experiments allowed us to determine if MutT heterologous expression influenced any step of parasite infection of mammalian cells . To investigate the influence of MutT on T . cruzi invasion processes in host cells , murine fibroblasts were exposed to parasites for 30 min , washed to eliminate extracellular parasites , and fixed after a 30 min incubation in fresh medium . An analysis of the number of internalized parasites per 100 counted cells indicated that there was no difference in the invasion rates for the three parasite populations tested , as confirmed by a one-way ANOVA test , indicating that MutT heterologous expression does not affect the invasion process ( Figure 3A ) . We also performed immunostaining of LAMP-1 ( a lysosomal membrane protein found in the parasitophorous vacuole ) in infected cultures to evaluate parasite vacuole escape kinetics over the first 24 hours following parasite invasion . During the first 8 hours of infection , 100% of internalized parasites were associated to LAMP-1 , indicating they were still inside the parasitophorous vacuole ( Figure 3B ) . In the subsequent hours , a decrease in the number of LAMP-1-associated parasites was observed , indicating parasite escape from its vacuole , which occurred at the same rate for the three parasite populations ( Figure 3B ) . These results indicate that MutT heterologous expression also did not influence T . cruzi trypomastigote intracellular traffic . Following vacuole escape , T . cruzi differentiates into the amastigote replicative form and starts replication in the host cell cytoplasm . To investigate whether MutT heterologous expression would affect intracellular replication , fibroblast cultures infected with WT , pROCK or MutT parasites were followed for 96 hours post-infection . Intracellular development was determined by counting the number of intracellular parasites per infected cell at 24 , 48 , 72 and 96 hours post-infection . During the first 24 h of infection WT , pROCK and MutT cells behaved similarly with the same counts of intracellular parasites per infected cell . However , an increased number of intracellular parasites could be observed for MutT-infected cells at 72 h post-infection , compared with WT or pROCK parasites . This difference became even more evident at 96 h post-infection ( Figures 3C and 3D ) . In addition , the number of trypomastigote forms released by MutT-infected cultures after 96 hours was higher than controls ( data not shown ) . These data not only demonstrate that MutT parasites are able to complete the parasite intracellular life cycle but also demonstrate that the heterologous expression of mutT gene influences parasite intracellular development by increasing its replication rate . To evaluate if the increased intracellular replication rate of MutT parasites could be attributed to oxidative stress resistance , parasites were incubated with 50 µM H2O2 prior to infection . Oxidative stress inflicted on the parasites by H2O2 did not change the parasite invasion rate compared with non-treated parasites and , as expected , was the same among the different parasite populations ( Figure 4A ) . The cultures were followed during the 96 hours following parasite invasion . Intracellular growth curves of untreated parasites were very similar to the previous experiment in which the number of intracellular parasites per cell was higher for MutT-infected cultures 48 h post-infection ( Figure 4B ) . Pre-treatment of MutT parasites , however , further increased the number of intracellular parasites per infected cells when compared with MutT non-treated parasites ( Figure 4B ) . The intracellular replication rate kinetics of the WT and pROCK parasites were affected by previous treatment of parasites with hydrogen peroxide , and these cells were able to grow better after hydrogen peroxide treatment . These results might suggest that MutT cells present improved recovery from the oxidative damage inflicted to their DNA through hydrogen peroxide treatment , and pre-treatment prepared these cells to face the oxidative stress that occurs during cell infection . As pROCK and MutT parasite populations markedly diverge in their resistance to oxidative stress and growth , we decided to analyze their cytosolic and mitochondrial tryparedoxin peroxidases ( TcCPX and TcMPX ) protein levels through western blotting experiments ( Fig . 5A ) . Band densitometry revealed that MutT parasites presented higher levels of TcCPX ( Fig . 5C ) compared with the pROCK control ( P<0 . 001 ) . After submitting these parasites to a non-lethal dose of H2O2 ( 50 µM ) , pROCK cell TcCPX protein levels increased 29% , and MutT treated parasites presented a 2 . 5-fold increase compared with pROCK untreated controls ( P<0 . 001 ) . The TcMPX expression profile was slightly different , as neither untreated MutT nor treated pROCK parasites differed from the untreated pROCK control . However , MutT parasites treated with 50 µM H2O2 presented an approximately 70% increase in TcMPX expression level compared with pROCK controls ( P<0 . 001 ) . To investigate whether the increase in the intracellular growth rate observed for the MutT parasites would also affect development in vivo , Swiss mice were infected with 5000 TCTs of WT or MutT parasites , and parasitemia was evaluated from day 3 post-infection ( p . i . ) . The data obtained revealed that MutT-infected mice presented significantly higher parasitemia ( P<0 . 05 ) compared with animals infected with WT parasites ( Figure 6 ) . This difference was more prominent at 9 days p . i . , and animals infected with MutT parasites sustained higher parasitemia levels over the time course of infection compared with WT-infected mice . After a detailed search of the T . cruzi genome database using the Nudix motif as query , a highly conserved 23-residue sequence found in the Nudix hydrolase superfamily [44] , we identified a putative MutT homolog . This gene was successfully amplified from T . cruzi CL Brener strain genomic DNA , and the gene sequence was deposited in GenBank ( accession number KC630985 ) . Figure S1 shows the alignment of the TcMTH-deduced amino acid sequence and orthologs from other organisms . Sequence analysis revealed that TcMTH is a 306-amino acid protein that possess a perfect Nudix motif , a catalytic site and a divalent cation interaction residue , typical of an 8-oxo-dGTP pyrophosphohydrolase MutT enzyme . To investigate the activity of TcMTH in vivo , we examined its ability to complement the hypermutator phenotype of a mutT− bacterium strain ( BH600 ) . The TcMTH gene was cloned into an IPTG-inducible expression vector ( pMAL ) as well as the bacterium mutT gene . The mutT− strain was transformed with the constructs pMAL_TcMTH , pMAL_MutT and the pMAL empty vector . As seen in Table 1 , the mutation rate was higher in mutT-deficient cells transformed with pMAL empty vector when compared with BH600 cells transformed with either EcMutT or TcMTH genes . Reverting the mutT deletion by transforming cells with pMAL_MutT vector resulted in a 6-fold decrease in mutation rate , whereas cells expressing the T . cruzi MutT homolog presented a 7-fold decreased mutation rate . We generated a T . cruzi population overexpressing TcMTH using the integrative vector pROCK carrying the gene TcMTH and analyzed their response to hydrogen peroxide treatment . As shown in Figure 7 , TcMTH overexpression enhanced survival to H2O2 treatment , when compared to WT cells . Thus , MutT heterologous expression and TcMTH overexpression produced the same effects in T . cruzi epimastigotes , namely improving the parasite response to oxidative stress . Finally , we compared MutT-expressing parasites and TcMTH-overexpressing parasites with WT and pROCK control parasites in macrophage infection experiments . The results indicated that the modified parasites ( MutT and TcMTH ) presented enhanced replication inside murine inflammatory macrophages when compared with control parasites ( Figure 8 ) . The infection index obtained demonstrated that MutT parasites display the same advantage in growth exhibited in the fibroblast and mouse infection experiments , and TcMTH cells behave similarly to MutT-expressing parasites .
In the present work , we addressed the importance of 8-oxo-dGTPase activity in T . cruzi , which was previously supposed to lack a MutT homolog in its genome . Considering the different oxidative stress environments T . cruzi has to address through its entire life cycle [14] , [45] , [46] , one would expect that oxidized nucleotides might be generated in this organism . These oxidative stress conditions may cause serious damage to the parasite DNA that would represent a threat to T . cruzi cell viability if not properly repaired . In this study , we created a T . cruzi parasite population expressing E . coli MutT mRNA ( MutT ) . The MutT parasite population exhibited similar behavior to the wild-type ( WT ) parasites in terms of the epimastigote growth curve in LIT medium . This was not totally unexpected because , unlike the invertebrate host niche , the culture medium will not contain oxidative stress sources , such as free radicals from heme production , usually found in the triatomine gut . Therefore , in this condition , the parasite intrinsic DNA oxidative damage repair system might be sufficient to allow satisfactory growth . However , when we performed a hydrogen peroxide survival curve in LIT medium , the MutT population was more resistant to the oxidant treatment than WT parasites . In addition , the QPCR analysis of DNA lesions demonstrated that parasites expressing the bacteria MutT presented fewer lesions in the nuclear DNA compared with the pROCK controls . These results indicate that the expression of exogenous MutT allows an improved control of oxidized nucleotide incorporation to DNA , preventing lesions that can arise from it , and these results emphasize the importance of 8-oxo-dGTP hydrolysis . These results are in agreement with previous data that showed the importance of oxidized nucleotide clearance in bacteria [47] . It was previously shown that major classes of bactericidal antibiotics act using a common pathway that produces hydroxyl radicals and that after antibiotic stress , E . coli cells maintain a constant level of MutT [48] . Foti et al . [47] demonstrated that much of the cell death caused by bactericidal antibiotics is related to oxidation of guanine to 8-oxo-guanine in the nucleotide pool . Their findings suggest that nucleotide sanitizing enzyme up-regulation may improve cell fitness by decreasing double-strand breaks and thus lethal lesions . Another possibility is that the MutT-expressing parasites produce more 8-oxo-dGMP that could serve as a signal for oxidative stress , making the cells modify their metabolism to respond to this stress . Infectivity in murine fibroblasts of MutT-expressing parasites in comparison with control ( WT and pROCK ) populations was also tested . Our results indicate that during the first 24 hours of infection , while the parasites are still in the trypomastigote form , the three parasite populations tested presented similar invasion and parasitophorous vacuole escape behavior . These results can be explained by the fact that during this period , the parasites are in a non-replicative form , and the target for MutT , 8-oxo-dGTP , would not be incorporated in DNA until the next round of replication . Thus , the effect of MutT heterologous expression in T . cruzi did produce an apparent phenotype in the trypomastigote form . On the other hand , after parasites differentiated into the amastigote replicative form , MutT parasites exhibited faster replication rate than controls . This difference was evident after 48 hours of infection , when we could observe parasite intracellular multiplication . This result corroborates the findings of Gupta et al . [15] that showed an exponential increase in ROS production in T . cruzi-infected cells up to 48 hours post-infection . Thus , MutT-expressing parasites could be growing faster because they are better able to combat the oxidative stress present in the murine fibroblast cytoplasm . Moreover , treating parasites with hydrogen peroxide prior to infection augmented parasite fitness because at 96 hours p . i . , the treated parasites presented increased growth compared with untreated parasites , and the MutT-treated parasites displayed an even higher growth rate increase . To investigate these results , we analyzed T . cruzi antioxidant enzyme protein levels through western blotting experiments . The results indicate that E . coli MutT expression influenced peroxidases expression levels . The cytosolic peroxidase protein level increased for both pROCK and MutT parasites after H2O2 treatment . However , the MutT parasites presented a more pronounced increase , with a 2 . 5-fold change compared with the pROCK untreated controls . The mitochondrial peroxidase protein level displayed no variation in pROCK parasites after H2O2 treatment , but the MutT parasites presented a 70% increase after oxidative treatment . Overall , these data demonstrate that MutT parasites enhanced the antioxidant proteins levels after the oxidative treatment to much higher levels than pROCK parasites , indicating that MutT expression can influence antioxidant enzyme expression . Our hypothesis for MutT differential behavior is that the formation of 8-oxo-dGMP by MutT serves as a signal and stimulates cells to become more proficient in responding to oxidative stress , changing antioxidant proteins expression and/or other BER and DNA repair pathways enzymes . The oxidative stress inflicted by H2O2 treatment would lead to the formation of excess 8-oxo-dGTP , which could be hydrolyzed by the heterologous MutT . The product of this reaction , 8-oxo-dGMP , or another secondary metabolite from this process , could be acting as a second messenger to the cell , indicating the presence of oxidative stress and shifting the parasite in a way that makes it more apt to act in response to that . The participation of the 8-oxoG repair pathway in cellular signaling pathways was recently demonstrated by Boldogh et al . [49] . Their study demonstrated that the 8-oxoG excised from DNA by OGG1 binds back to the enzyme at a nonsubstrate site with high affinity . The OGG1-8-oxoG complex formed interacts with the Ras GTPase enzyme , acting as a guanine nucleotide exchange factor . This interaction increases the Ras-GTP bound forms , enabling this GTPase to activate signaling pathways , including those that may modulate the expression of enzymes involved in oxidative stress response . The results presented here suggest that pre-treatment may induce an adaptive response to the oxidative stress encountered in the host cell , preserving its genetic content integrity during the oxidative stress and allowing the parasite to replicate faster . Similar behavior has been documented previously for bacteria [50] , yeast [51] , mammalian cells [52] , and T . cruzi [23] . The latter study reported that there is an adaptation to oxidative stress when parasites are treated with low non-toxic concentrations of H2O2 and then submitted to higher , though sub-lethal , concentrations of H2O2 . Apparently , pre-treatment induces an increase in TcCPX levels from the parasite antioxidant network , preparing the parasite to address the fluctuating levels of ROS [23] . The recent findings of Nogueira et al . [53] confirm that low levels of ROS production induced by heme in T . cruzi epimastigotes favors parasite proliferation via a Ca2+ calmodulin kinase II ( CaMKII ) -like pathway . In addition , antioxidant activity ( urate and GSH ) inhibited heme-induced ROS and parasite proliferation [53] . In addition , Paiva et al . [54] demonstrated that maintenance of high parasite burden during T . cruzi infection might be dependent of oxidative stress generation . The in vivo infection experiment results confirmed our in vitro findings , demonstrating that MutT parasites replicate faster in animal models , as demonstrated by the higher parasitemia found in MutT infected mice . The in vitro experiments had shown that MutT expression favors amastigote pronounced growth inside host cell , which reflected on the number of released trypomastigote forms ( data not shown ) . In vivo , the enhanced intracellular growth could also promote substantial release of trypomastigotes in the bloodstream , which would explain the higher parasitemia levels . Altogether , these results suggest that E . coli MutT heterologous expression in T . cruzi allows parasites to cope with oxidized nucleotides more efficiently in both the replicative amastigote and epimastigote forms . Nevertheless , these results do not deny the possibility of a MutT-like activity in T . cruzi . Indeed , previous studies have shown that despite the action of various enzymes and metabolic pathways in the control of oxidative stress effects , cellular defenses may be insufficient in a situation of intense stress , as in the hydrogen peroxide treatment [55] . However , these defenses could be amplified by the expression of one or more of its elements in higher levels . Thus , the enhanced efficiency of oxidative stress response presented by the recombinant parasites could be a consequence of high levels of MutT heterologous expression in either cells that were devoid of 8-oxo-dGTPase activity or cells that had low , yet sufficient , levels of this activity in the absence of oxidative stress . Recently , our research group discovered a T . cruzi MutT homolog candidate ( termed here as TcMTH ) that was not annotated in the parasite genome project [25] . The DNA sequence and the predicted protein sequence for this candidate were characterized , and the elements for an enzyme from the Nudix superfamily were identified . This is a family of enzymes that displays great catalytic versatility [44] , which could indicate that the candidate is not necessarily a MutT pyrophosphohydrolase . However , we demonstrated in this study that this possible TcMTH is able to complement a mutT-deficient bacteria strain , diminishing its mutation rate , which strongly suggests a case of functional homology . Moreover , T . cruzi parasites overexpressing this gene demonstrated increased resistance to oxidative stress , as previously shown for parasites heterologously expressing the E . coli MutT . The recombinant parasite populations generated in this study were compared in a macrophage infection experiment . The infection index obtained for this experiment demonstrated that MutT as well as TcMTH parasites presented increased growth inside inflammatory macrophages when compared with the control parasites . This result might be additional evidence that TcMTH is indeed a MutT homolog in T . cruzi . Therefore , assuming that T . cruzi is endowed with an 8-oxo-dGTPase specific activity , we can speculate that its level might be low due to the wild-type sensitivity to oxidative stress treatment . The persistence of oxidized nucleotides in the nucleotide pool as a consequence of this low activity could increase the 8-oxoG frequency in DNA . This could be compensated for by modulating the activity of other enzymes associated with the 8-oxoG repair pathway , such as TcOGG1 , or alternative glycosylases . One example would be the Nei glycosylase that has been reported to act in oxidative lesions ( including 8-oxoG ) during transcription and/or replication [56] . The T . cruzi genome contains the sequence ( Tc00 . 1047053506357 . 80 ) of a hypothetical NEIL glycosylase that has not been characterized and could be compensating for MutT low activity . Alternatively , T . cruzi translesion synthesis processes could neutralize the effects of 8-oxoG incorporation in DNA . The nuclear DNA polymerase TcPolη and the mitochondrial DNA polymerase TcPolκ are capable of bypassing 8-oxoG in an error-free manner , and parasites that overexpress TcPolη or TcPolκ demonstrate increased resistance to H2O2 treatment [57] , [58] . In addition , transcription-coupled repair ( TCR ) [59] and mismatch repair ( MMR ) [60] , [61] elements have been reported to participate in 8-oxoG repair . Despite of all this repair machinery that could be involved in DNA oxidative damage repair , the results presented here indicate that the MutT 8-oxo-dGTPase activity can be crucial for guaranteeing parasite replication efficiency . In summary , we demonstrated that modifying the expression levels of an element from the 8-oxoG repair system can influence the parasite cell viability in both replicative phases , as previously observed for the parasite antioxidant machinery [62] . Considering that oxidative stress is important to T . cruzi proliferation in both replicative forms as demonstrated by Paiva et al . [54] and Nogueira et al . [53] , mechanism for counteracting DNA oxidative damage would be of great importance for this parasite . T . cruzi DNA repair machinery presents some particular features from typical eukaryotic DNA repair machinery . Studying this particularities might indicate a path for drug development against Chagas disease .
|
The parasite Trypanosoma cruzi is the causative agent of Chagas disease , a malady endemic throughout Latin America . Studying the DNA repair machinery of this parasite could provide us with good insights about T . cruzi biology and virulence . We focused on the 8-oxoguanine ( 8-oxoG ) DNA lesion and its repair system . This lesion is considered particularly deleterious because it can generate DNA double strand breaks if inserted during the DNA replication . Our approach to investigating the importance of the 8-oxoG repair system in T . cruzi was to generate a parasite population expressing the Escherichia coli MutT enzyme , which is responsible for removing 8-oxo-dGTP from the nucleotide pool . Different parameters such as growth curves , cell infection experiments , antioxidants , enzymes expression , and DNA lesion quantification were used to study this modified parasite in comparison with a control WT population . We also characterized a gene in T . cruzi that has functional homology with the E . coli MutT gene . The overexpression of this gene in T . cruzi caused the same phenotypes observed when we expressed the heterologous gene . Overall , the results indicate the importance of this DNA repair enzyme for T . cruzi resistance to oxidative stress and improving its proliferative ability in the vertebrate host .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[
"medicine",
"infectious",
"diseases",
"chagas",
"disease",
"nucleic",
"acids",
"neglected",
"tropical",
"diseases",
"dna",
"dna",
"repair",
"biology",
"molecular",
"cell",
"biology",
"parasitic",
"diseases"
] |
2013
|
Oxidative Stress and DNA Lesions: The Role of 8-Oxoguanine Lesions in Trypanosoma cruzi Cell Viability
|
Bacillus cereus biovar anthracis ( Bcbva ) is a member of the B . cereus group which carries both B . anthracis virulence plasmids , causes anthrax-like disease in various wildlife species and was described in several sub-Saharan African rainforests . Long-term monitoring of carcasses in Taï National Park , Côte d’Ivoire , revealed continuous wildlife mortality due to Bcbva in a broad range of mammalian species . While non-lethal anthrax infections in wildlife have been described for B . anthracis , nothing is known about the odds of survival following an anthrax infection caused by Bcbva . To address this gap , we present the results of a serological study of anthrax in five wildlife species known to succumb to Bcbva in this ecosystem . Specific antibodies were only detected in two out of 15 wild red colobus monkeys ( Procolobus badius ) and one out of 10 black-and-white colobus monkeys ( Colobus polykomos ) , but in none of 16 sooty mangabeys ( Cercocebus atys ) , 9 chimpanzees ( Pan troglodytes verus ) and 9 Maxwell’s duikers ( Cephalophus maxwellii ) . The combination of high mortality and low antibody detection rates indicates high virulence of this disease across these different mammalian species .
Anthrax is a zoonosis occurring worldwide , characterized by septicemia and sudden death , mainly in herbivores . The disease is regularly observed in arid and savanna ecosystems , where animals ingest bacterial spores from soil while grazing [1–3] . In the past anthrax was thought to be exclusively caused by bacteria of the clonal Bacillus anthracis clade within the Bacillus cereus group . Anthrax-like disease , caused by Bacillus cereus biovar anthracis ( Bcbva ) , was first reported in 2001 from Taï National Park ( TNP ) , Côte d’Ivoire , where it caused sudden death in chimpanzees [4] . While Bcbva carries the two B . anthracis virulence plasmids , pXO1 and pXO2 , it is more closely related to other members of the B . cereus group at the chromosomal level [5 , 6] . The fatalities in TNP represented the first observation of anthrax-like disease in wild non-human primates and in a rainforest ecosystem [4] . Subsequently , Bcbva was found to be widespread throughout tropical forests of sub-Saharan Africa , including Cameroon , the Central African Republic , the Democratic Republic of the Congo and Liberia [6–8] . In TNP , continuous carcass monitoring from 2001 to 2015 showed Bcbva to be a major driver of wildlife mortality; Bcbva was the cause of death for over 40% of carcasses found by researchers in this tropical ecosystem [8] . The high prevalence of anthrax-like disease observed at TNP is exceptional , even when compared to other African national parks where anthrax caused by B . anthracis is endemic and considered common [9 , 10] . Anthrax outbreaks in African savanna national parks , caused by B . anthracis , are usually wavelike and primarily affect a few ( ungulate ) species at a time [1 , 3 , 11 , 12] . This contrasts with the situation at TNP where a broad range of mammalian hosts succumb to the disease simultaneously [8] . To date , lethal Bcbva infections have been documented in chimpanzees ( Pan troglodytes verus ) , six species of monkeys ( Cercocebus atys , Cercopithecus campbelli , Cercopithecus diana , Cercopithecus petaurista , Procolobus badius and Colobus polykomos ) , duikers ( Cephalophus spp . ) , mongooses ( fam . Herpestidae ) and porcupines ( fam . Hystricidae ) . Fatalities were observed year-round and were distributed evenly across the area of research ( Fig 1 ) . Culturable Bcbva was detected in 5% of randomly caught carrion flies , which highlights the persistent nature of Bcbva in TNP and its broad distribution throughout the sampled region of the park [8] . Gross and histopathology of Bcbva cases in wildlife are comparable to that of anthrax cases caused by B . anthracis . In small animal models Bcbva exhibits a similar virulence to what has been observed for B . anthracis , previously [4 , 13] . Unfortunately nothing is known about the likelihood of survival following infection with Bcbva for rainforest fauna living in the wild . Monkeys and chimpanzees were observed to die within hours of the onset of disease symptoms by the primatologists of the Taï Chimpanzee Project . The rapid mortality following the onset of symptoms could potentially be a product of the generally suppressed expression of signs of weakness in wild animals [14]; we do not know the incubation time for Bcbva , though available evidence suggests Bcbva is highly virulent [13] . To date , anthrax surveillance in TNP was largely carried out by carcass monitoring [8] . However , carcass detection is heavily biased in its detection probability for larger–bodied species , especially in areas with dense vegetation , and it only provides information about infections with a lethal outcome [8 , 15 , 16] . Serological approaches offer a complementary means of understanding disease ecology; in combination with carcass monitoring data , insights can be made about the susceptibility of different species to a disease . Carcass monitoring and serological studies from savanna ecosystems suggest , that herbivorous ungulates are generally highly susceptible to anthrax , while carnivores appear to be more resistant [1] . This is supported by high levels of seropositivity observed in most carnivore and omnivore savanna species , combined with low observed fatality rates , which suggests regular non-fatal exposure [10 , 16 , 17] . On the other hand , the relatively low seroprevalence in combination with high mortality rates , which are observed in savanna herbivores , suggests these species are more susceptible to the disease [10 , 16] . Bagamian et al . [18] used such logic to call for the broad combination of postmortem data with serological surveillance , specifically in non-savanna ecosystems , to further assess the dynamics of anthrax disease [16] . Here we present a serological investigation in TNP in the context of existing mortality records . We focused on five species for which we previously detected Bcbva associated fatalities [8]: herbivorous red colobus monkeys ( 3/30 carcasses Bcbva positive ) and black-and-white colobus monkeys ( 1/5 carcasses Bcbva positive ) , omnivorous sooty mangabeys ( 11/23 carcasses Bcbva positive ) and chimpanzees ( 31/55 carcasses Bcbva positive ) and opportunistically scavenging omnivorous Maxwell’s duikers ( 26/40 carcasses Bcbva positive ) . We examined serum , plasma or whole blood samples and tested for antibodies against the anthrax protective antigen ( PA ) and lethal factor ( LF ) to characterize the Bcbva antibody detection rate .
TNP is an evergreen rainforest located in the south-west of Côte d’Ivoire ( 0°15’– 6°07’N , 7°25’– 7°54’W ) . The climate of TNP is sub-equatorial with two rainy seasons ( major: August-October , minor: March-June ) and a total average annual rainfall of 1800 mm . While the Upper Guinea Forest belt once stretched from Ghana to Sierra Leone , TNP is the largest remaining section today , covering an area of 3300 km2 , and is surrounded by a 200 km2 buffer zone . Almost 1000 species of vertebrates have been described in the TNP ecosystem , and the park was awarded UNESCO Natural World Heritage status in 1982 [19 , 20] . This work has been performed in the research area of the Taï Chimpanzee Project that has studied the local habituated chimpanzee groups since 1979 [19] . All wildlife samples were collected with permission of the research ministries of Côte d’Ivoire and ethical approval of the Ivorian Office of National Parks , which reviewed the study design ( permits Nr . 048/MESRS/DGRSIT/KCS/TM and 90/MESRS/DGRSIT/mo ) . Samples have been exported with the required CITES ( Convention on International Trade in Endangered Species of Wild Fauna and Flora ) permits . The study was approved by the Centre Suisse de Recherche Scientifique en Côte d’Ivoire and the Laboratoire National de la Pathologie Animale , Bingerville , Côte d’Ivoire . All chimpanzee samples originated from free-ranging chimpanzees . Samples were collected from chimpanzees that had died of natural reason in outbreaks of respiratory disease by our team of veterinarians routinely investigating wildlife mortality in TNP [23 , 24] . In one case samples were obtained from an individual on whom surgery had to be performed due to a life threatening infection [22] . No chimpanzee was anesthetized or touched for the sole purpose of sample collection . Human sera used as controls in this study were donated by the authors of the study themselves and were anonymized immediately after sample donation . Humans were not vaccinated against anthrax to serve as controls in this study , but had received anthrax vaccinations in the past due to their work in anthrax endemic areas . All human sera were donated by adults after giving written informed consent , and the use of human sera in this study was approved by the ethics committee of Charité-Universitätsmedizin Berlin . No standardized approaches are available to investigate anthrax seroprevalence in wildlife . Thus , all samples were tested for antibodies against the anthrax protective antigen ( PA , Quadratech Diagnostics , Surrey , UK ) using an in-house ELISA and an in-house Western Blot and for antibodies against anthrax lethal factor ( LF , Quadratech Diagnostics ) using an in-house Western Blot . Assays are described in detail below . Testing for PA and LF does not allow for a discrimination between classical B . anthracis and Bcbva , as both pathogens produce the typical anthrax toxins [25] . However , during our extensive carcass monitoring over the last 15 years , each of the 81 anthrax cases that were detected in TNP was caused by Bcbva . This was shown by qPCR screening for the Bcbva specific genomic island IV , isolation and subsequent whole-genome sequencing [8] . Therefore , in the TNP ecosystem antibodies generated against PA and LF likely originate exclusively from exposure to Bcbva . Human positive and negative controls were used for all monkey and chimpanzee assays as no species-specific controls were available . Negative controls were selected from a set of available human sera of unvaccinated donors , which were unreactive in Western Blot against PA and LF . Two of these sera that were representative of the range for PA-negative human sera in PA ELISA were chosen as negative controls and included on each ELISA test-plate under the same conditions as the samples for inter-plate comparison . A positive control serum from a hyper-immunized human donor was included as an 8-step log2 serial dilution curve ( starting concentration: 1 in 4000 ) with repetitious reactivity and accurate results on every test-plate . For duiker assays , a negative control was available from a red forest duiker ( Cephalophus natalensis , courtesy of Berlin zoo ) , which was unreactive in PA and LF Western Blot . A pool of goats vaccinated with the B . anthracis Sterne spore live vaccine [26] ( courtesy of Dr . W . Beyer ) was used as a positive control in the same fashion as stated for the positive human control ( starting concentration: 1 in 1000 ) . No specific conjugated antibodies were available for any of the species tested . For primate samples , polyvalent goat anti-human horseradish peroxidase ( HRP ) labeled conjugate ( Dianova , Hamburg , Germany ) was used , as described previously [27 , 28] . For duikers , we tested the reactivity of duiker serum with different commercially available conjugates from the Bovidae family ( sheep , cow , goat ) in a comparative dot blot approach with logarithmic duiker serum dilutions starting at 1:10 . We found that polyvalent rabbit anti-goat HRP labeled conjugate ( Dianova , Hamburg , Germany ) was the most suitable commercially available conjugate for duiker samples . PA ELISA was performed as described by Hahn et al . , with slight modifications [29 , 30] . Briefly , each well of high-binding microtiter plates ( Maxisorp Nunc , Sigma Aldrich , Munich , Germany ) was coated with 0 . 1 μg of recombinant PA in PBS at 4°C overnight . Wells were washed with phosphate-buffered saline containing 0 . 02% ( v/v ) Tween 20 ( PBS-Tween ) and blocked with 5% skimmed milk powder in PBS-Tween . Samples and negative controls were diluted 1:500 in blocking solution and incubated in duplicate together with the positive and negative controls for 2 hours at room temperature . Secondary antibodies were used in a concentration of 1:10000 and 1:4000 ( previously evaluated and adjusted ) for humanoid and duiker assays , respectively . Plates were developed in the dark with 100 μl of TMB SeramunBlau fast ( Seramun Diagnostics GmbH , Heidesee , Germany ) substrate per well for 10 min and stopped with 100 μl H2SO4 ( 2M ) . Absorbance was measured at 450nm ( reference wavelength 620nm ) using a Tecan Sunrise 96-well-reader ( Tecan Group Ltd . , Männedorf , Switzerland ) . The mean of the negative controls plus two times their standard deviation ( SD ) was set as the cut-off value for each plate ( S1 Table ) . For the PA and LF Western Blot assay , 380 ng of purified recombinant PA or LF diluted in 125 μl of phosphate-buffered saline ( PBS ) were blotted onto an Immobilion-P PVDF-Membrane ( Merck , Darmstadt , Germany ) after running in a 12% agarose gel . Then 3 mm stripes ( approx . 25 per gel ) were cut from the membrane and samples and controls were added in a dilution of 1:1000 in the dilution buffer containing tris-buffered saline with 0 . 05% Tween ( TBS-Tween ) and 3% powdered milk . Samples were incubated at room temperature for one hour . Goat anti-human HRP conjugate was added to primate samples and human controls in a 1:10000 dilution in the dilution buffer ( 1:8000 for LF Western Blot ) . For duiker samples and goat/duiker controls , rabbit anti-goat HRP conjugate was diluted 1:4000 in the dilution buffer ( 1:8000 for LF Western Blot ) . The conjugate was left to incubate for one hour . Reactions were detected with precipitating peroxidase substrate TMB SeramunBlau prec ( Seramun Diagnostics GmbH , Heidesee , Germany ) after 10 min of incubation .
A total of 59 serum , plasma or whole blood samples from five different TNP wildlife species were tested , mainly representing primates . All samples originated in an area where Bcbva is known to be endemic and where 40% of carcasses detected in the past were tested positive for Bcbva by qPCR , mostly confirmed by bacterial isolation , histology and whole genome sequencing [8] ( Fig 1 ) . Despite the continuous occurrence of the disease in the research area , we found that antibody detection rates in wildlife were low . For red colobus monkeys ( n = 15 ) and black-and-white colobus monkeys ( n = 10 ) , one sample for each species was clearly positive in PA ELISA and could be confirmed in PA and LF Western Blot . One more red colobus sample reacted in PA Western Blot , but in none of the other assays . For duikers ( n = 9 ) four samples were borderline positive in PA ELISA , but none of those were confirmed in PA or LF Western Blot . None of the sooty mangabey ( n = 16 ) or chimpanzee ( n = 9 ) samples reacted in PA ELISA , PA or LF Western Blot . Results are presented in detail in Table 1 and the supplementary S1 Table .
We found low seroprevalence for antibodies against anthrax , despite Bcbva being responsible for a substantial proportion of disease-induced mortality in each of the tested species . Specific antibodies against PA ( in Western Blot ) were only detected in three samples with two individuals ( a red colobus monkey and a black-and-white colobus monkey ) also showing OD values in ELISA in the range of the positive control and a positive result in LF Western Blot . While the sample set presented here may not appear extensive in comparison to datasets collected in savanna ecosystems , it represents the largest available collection of serum from TNP wildlife . The small sample sizes cause considerable uncertainty , when calculating species-specific seroprevalence ( Table 1 ) , but the data imply low overall seroprevalence in TNP wildlife . In many anthrax serological studies , samples are broadly screened with an ELISA alone and positive samples are then confirmed by Western Blot . However , serological anthrax surveillance of wildlife populations is often complicated by a lack of species-specific controls , conjugates , and validated assays . This makes definition of fixed ELISA cut-off values difficult , which complicates the interpretation of results . In our study , human conjugates and controls were used to investigate primate samples and goat conjugates and vaccinated goats as positive controls were used for duiker assays . For duikers , a negative control from a related species , a red forest duiker , was available from a zoo . However , using zoo animals as controls for wildlife can be potentially misleading , because these animals are often unexposed to many other pathogens and symbionts circulating in an ecosystem that may increase immunological background . Determining an ELISA cut-off with the same approach as used for primates ( mean of negative controls + 2*SD ) classified four of the duiker samples as positive , as the zoo negative control had an extremely low OD value . However , none of these samples that were reactive in ELISA were subsequently confirmed in Western Blot ( for neither PA nor LF ) . While a species-specific negative control was available in this case , the ELISA cut-off was likely artificially low due to the different immunological background in animals of diverging origin . In contrast , for red colobus monkeys a sample classified as negative in ELISA was found to be positive in the more sensitive PA Western Blot . Otherwise , results from ELISA and Western Blot approaches were largely congruent . The OD amplitudes of animals that tested positive in Western Blot showed higher ODs than the rest of their sampling group ( if not higher than the cut-off ) . ELISA testing of negative control animals from zoos or related species can provide a guideline for the interpretation of wildlife ELISA results , but our results highlight the problems associated with using strict ELISA cut-offs as a stand-alone criterion for non-validated wildlife assays and the importance of using both ELISA and Western Blot screening approaches . While the finding of only three seropositive samples could be interpreted as a consequence of low exposure to Bcbva , our previous studies have shown that Bcbva cases occur in TNP throughout the year [8] . The finding of culturable Bcbva in carrion flies randomly caught in the study area further indicated the occurrence of Bcbva cases even when no carcasses were detected . In fact , comparison of whole genome sequences of Bcbva isolated from carrion flies and simultaneously found carcasses revealed carcass monitoring data only reflects a fraction of the actual Bcbva mortality in this dense rainforest environment [8] . The low seroprevalence observed from live animals together with the previously described high mortality rates in these species ( 5 ) support the hypothesis that sublethal infections exist , but at very low rates . It has been shown previously that sublethal ( B . anthracis ) anthrax infections do not only occur in carnivores . Different degrees of anthrax seroprevalence have been reported for herbivores in African savannas [10 , 17 , 32] . The finding of anthrax antibodies in wildebeest ( Conochaetes taurinus mearnsi ) ( 19% ) and buffalo ( Syncerus caffer ) ( 46% ) in the Serengeti showed that not all herbivores succumb to infection and that susceptibility can vary widely between herbivore species [10] . Significant seropositivity was also observed in zebra ( Equus quagga ) in Etosha National Park [32] . Our study in TNP included arboreal species ( red colobus monkeys and black-and-white colobus monkeys ) and species that live and feed at least partly on the forest floor ( chimpanzees , sooty mangabeys and duikers ) . Two individuals of the arboreal monkey species tested showed high OD values in PA ELISA and were confirmed in PA and LF Western Blot . These strong immune reactions might suggest that these animals recently survived an infection . One potential scenario for infection of arboreal monkeys could be cutaneous infection by a vector , e . g . , biting flies , as observed previously in cattle and humans [33 , 34] or mechanical transmission onto canopy foods by carrion flies . Anthrax positive carrion flies have been found in the forest canopy at TNP lending some credence to the latter possibility [8] , though experimental studies are needed to confirm the plausibility of such infection routes . Unfortunately , serological testing for antibodies against PA and LF cannot differentiate infection with subsequent recovery from low dose exposure with seroconversion . Seroconversion through intestinal absorption of PA and other toxin components was suggested for scavengers by Turnbull et al . [35] and must also be considered when investigating a newly detected pathogen in an ecosystem where transmission pathways are largely unknown . It is striking that for the three species suffering the highest observed Bcbva related mortality rates in the park and that are likely regularly exposed to soil containing anthrax spores ( chimpanzees , mangabeys and duikers ) , not a single animal showed a measurable immune response against anthrax that would suggest exposure to Bcbva . Despite the ubiquitous presence of Bcbva in TNP causing high amounts of Bcbva related mortality , the majority of animals tested here show no antibodies against this disease . These results suggest that systemic infections with Bcbva are generally fatal in the five tested species and that this disease could potentially pose a serious threat to conservation efforts in the region .
|
Anthrax is a deadly zoonosis , predominantly known to affect wild and domestic herbivores . It has long been assumed that the disease is exclusively caused by B . anthracis , but recently another member of the B . cereus group , Bacillus cereus biovar anthracis ( Bcbva ) , was found to carry both B . anthracis virulence plasmids . Bcbva causes anthrax-like disease in wildlife throughout sub-Saharan Africa and was shown to be an important cause of wildlife mortality in Taï National Park , Cote d’Ivoire , affecting a broad range of mammalian species . While mortality data has routinely been collected in the area for decades , it remains unknown whether non-lethal Bcbva exposure occurs . We therefore conducted a serological study in four primate and one duiker species in which Bcbva-related fatalities were previously documented . Frequent non-lethal exposure should result in a high antibody seroprevalence within wildlife populations , while high lethality would result in low antibody seroprevalence . We found that antibody detection rates were low , suggesting that Bcbva infections in these species are likely often lethal .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"methods",
"Results",
"Discussion"
] |
[
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] |
2017
|
Low antibody prevalence against Bacillus cereus biovar anthracis in Taï National Park, Côte d'Ivoire, indicates high rate of lethal infections in wildlife
|
The effectiveness of systemic antimonial ( sodium stibogluconate , Pentostam , SSG ) treatment versus local heat therapy ( Thermomed ) for cutaneous leishmaniasis was studied previously and showed similar healing rates . We hypothesized that different curative immune responses might develop with systemic and local treatment modalities . We studied the peripheral blood immune cells in a cohort of 54 cutaneous Leishmania major subjects treated with SSG or TM . Multiparameter flow cytometry , lymphoproliferative assays and cytokine production were analyzed in order to investigate the differences in the immune responses of subjects before , on and after treatment . Healing cutaneous leishmaniasis lead to a significant decline in circulating T cells and NKT-like cells , accompanied by an expansion in NK cells , regardless of treatment modality . Functional changes involved decreased antigen specific CD4+ T cell proliferation ( hyporesponsiveness ) seen with CD8+ T cell depletion . Moreover , the healing ( or healed ) state was characterized by fewer circulating regulatory T cells , reduced IFN-γ production and an overall contraction in polyfunctional CD4+ T cells . Healing from cutaneous Leishmaniasis is a dynamic process that alters circulating lymphocyte populations and subsets of T , NK and NKT-like cells . Immunology of healing , through local or systemic treatments , culminated in similar changes in frequency , quality , and antigen specific responsiveness with immunomodulation possibly via a CD8+ T cell dependent mechanism . Understanding the evolving immunologic changes during healing of human leishmaniasis informs protective immune mechanisms .
Leishmaniasis , a vector-borne parasitic disease , remains a pressing global health concern with 12 million persons infected , 2 million new infections each year , limited therapeutic options and no effective vaccine [1] . Healing cutaneous leishmaniasis ( CL ) relies on the development of an effective and balanced protective immune response . The intracellular parasite needs to be contained , while the pathologic immune response needs to be controlled . The murine model for L . major substantially contributed to our understanding of protective immunity and helped establish the T helper 1 ( Th1 ) /Th2 paradigm that explained resistance and susceptibility to Leishmania infection [2 , 3] . This model demonstrated that T lymphocytes are key for the generation of this protective response through their IFN-γ production which activates macrophages to produce toxic nitrogen and oxygen metabolites to kill the intracellular amastigotes [4] . The Th1 cytokine profile , i . e . IFN-γ , TNF-α and IL-12 , is crucial to eliminate Leishmania [5] , while the development of a Th2 immune response with the production of IL-4 , TGF-β and IL-10 favors parasite multiplication and fails to control the infection [6] . The quality of a T cell response , defined by the pattern of cytokine production at the single cell level , underscores the importance of polyfunctional CD4+T cells specifically producing IFN-γ , TNF-α and IL-2 for protection [7 , 8] . Additionally , immunoregulatory mechanisms involving regulatory and memory T cells can significantly influence leishmaniasis outcome [9] . The precise role of human CD4+T cell subsets , their cytokine patterns and the immune response pathways engaged during and after effective leishmaniasis therapy are incompletely understood . While pentavalent antimonial drugs ( i . e . SSG , meglumine antimoniate ) have been used to treat CL for decades [10] , they are toxic , require extended duration of treatment , and drug resistant parasites have emerged as a problem [11 , 12] . The mechanism of action of SSG includes effects on both the host macrophage and parasite [13] . Thermotherapy is an alternative treatment for CL [14 , 15] , delivering localized radiofrequency waves into skin lesions to physically destroy the temperature sensitive parasites . Thermomed ( TM , Thermosurgery Technologies , Phoenix AZ ) , cleared by the Food and Drug Administration , is one of the most studied devices in use [15] . Clinical trials comparing local heat to systemic antimonial therapy showed similar CL cure rates [14 , 16–20] . We previously reported that subjects treated with the TM device showed similar healing by 2 and 12 months follow-up , with less associated systemic toxicity than those treated with intravenous SSG [21] . We hypothesized that an immunomodulatory systemic therapy would induce a different immune response compared to a locally applied physical treatment , though both methods were ultimately curative . This work comparatively evaluated the immune response profile over time in the participants treated with SSG or TM . We showed a modulation of immune response occurs during healing from cutaneous leishmaniasis independent of either treatment modality .
All participants provided written informed consent and study protocols were approved by Institutional Review Boards at both WRAMC and the Walter Reed Army Institute of Research . All participants were U . S . military personnel referred to the Walter Reed Army Medical Center ( WRAMC ) for treatment of parasitologically confirmed L . major infection ( Table 1 ) . Details of the clinical trial are published [21] . Seven healthy uninfected control subjects were recruited under a separate protocol . Whole blood subjects were drawn at time points designated “pre-treatment” ( PRE ) , “on-treatment” ( ON ) and “post-treatment” ( POST ) ( Days 0 , 9±1 and 219±68 following treatment initiation , respectively ) . For pre- and on-treatment subjects , blood was drawn at WRAMC and processed fresh . At POST , blood was drawn at alternate medical facilities and shipped via overnight carrier for processing . Peripheral blood mononuclear cells ( PBMC ) were isolated from whole blood as previously described [22] . The following fluorescence-conjugated antibodies were used for multiparameter flow cytometry: CD3 ( SK7 ) , CD4 ( SK3 ) , CD8 ( SK1 ) , CD14 ( M5E2 ) , CD19 ( HIB19 ) CD25 ( 2A3 ) , IL-10 ( JES3-19F1 ) , TNF-α ( Mab11 ) , IL-2 ( 5344 . 111 ) , γδ TCR ( B1 ) ( BD Biosciences , San Jose , CA ) ; CD4 ( SFCI12T4D11 ) ( Beckman Coulter , Fullerton , CA ) ; IL-17 ( eBio64DEC17 ) and αβ TCR ( IP26 ) ( BioLegend , San Diego , CA ) ; IFN-γ ( 4S . B3 ) ( eBioscience , San Diego , CA ) . All antibodies were titrated prior to use to determine optimal staining concentrations . Flow cytometry data was acquired either on a FACS Calibur or LSR-II flow cytometer ( BD Biosciences ) and data analyzed using FlowJo software ( TreeStar , Ashland OR ) . Prior to cryopreservation , a PBMC aliquot was stained for cell surface markers and analyzed by flow cytometry . Markers included the BD SimulTEST ( CD45 , CD14 ) and BD MultiTEST ( CD3 , CD16 , CD56 , CD45 , CD19 ) reagents . T cell populations were further analyzed by staining with CD3 , CD4 , CD8 , and CD25 . Following staining , cells were fixed in 2% paraformaldehyde , data collected with a FACS Calibur flow cytometer ( BD Biosciences ) and analyzed using FlowJo software ( TreeStar , Ashland OR ) . Cryopreserved PBMC were thawed in complete media . A portion of the PBMC was depleted of CD8+T cells ( CD8depl PBMC ) using the Dynal CD8 Positive Isolation Kit ( Invitrogen , Carlsbad CA ) . Total PBMC or CD8depl PBMC were plated in the presence of soluble Leishmania antigens from L . major parasites ( SLA , 2 . 5 μg/mL , generous gift of Dr . Frank Neva ) for 6 days at 37°C , 5% CO2 . Pokeweed mitogen ( PWM , 5 μg/mL , Sigma ) was used as a positive control . Cell-free supernatant was collected from each well , triplicate subjects pooled , and used to quantify cytokines using the Q-Plex Human Cytokine–IR Array ( Quansys Biosciences , Logan , UT ) according to manufacturer’s protocol [23] . For LPA , cells were pulsed as previously reported [24] . Cryopreserved PBMC were thawed and labeled with carboxyfluorescein diacetate succinimidyl ester ( CFDA-SE , Invitrogen , Carlsbad , CA ) according to the manufacturer’s instructions [25] . Cryopreserved PBMC were thawed and incubated overnight at 37°C , 5% CO2 . Cells were plated at 1 x 106 per well and stimulated with L . major whole lysate ( 1μg/mL , generous gift from David Sacks ) for 24 hours at 37°C , 5% CO2 . Brefeldin A ( 10μg/mL , Sigma ) was added to all wells at 18 hours . All cells were costimulated with 1 μg/mL CD28 and CD49d antibodies ( BD Biosciences ) . Following stimulation , cells were stained for population identification markers ( CD3 , CD4 , CD8 , CD14 and CD19 ) and intracellular cytokine expression ( TNF-α , IFN-γ , and IL-2 ) . T cell receptor ( TCR ) phenotyping antibodies were included for the αβ TCR and γδ TCR . All statistics were performed using GraphPad Prism 4 . 0 ( GraphPad Software , San Diego , CA ) .
Fifty-four U . S soldiers ( 98% male ) with CL were enrolled and randomized to either local heat therapy ( TM ) or 10 days of intravenous SSG ( Table 1 ) . To evaluate the immune response profiles in these subjects , PBMC were isolated from whole blood at three time points . Pre-treatment ( PRE ) cells were collected upon enrollment into the study ( n = 54 , 100% ) . The on-treatment cells were collected on 9±1 treatment day ( n = 54 , 100% ) , and post-treatment ( POST ) subjects collected at a mean of 7 months ( range 4 . 7–9 . 2 months ) , after treatment ( n = 39 , 72% ) . Because 39/54 participants provided cells at all time points , the majority of our analysis is restricted to this subcohort ( Table 1 ) . No significant differences were noted between treatment arms or subcohort and cohort regarding demographic characteristics , disease burden and therapy outcome . Freshly isolated cells were stained and analyzed by flow cytometry to characterize the circulating lymphocyte populations . Data from 30 subjects for which there were adequate numbers of cells for all time points is shown ( Fig 1 ) . The distribution of lymphocyte populations , including T cells , B cells , NKT-like and NK cells , was unchanged from pre-treatment through the first ten days of treatment ( Fig 1A ) . At POST we observed a significant decrease in circulating T cells ( pre , 73%; post , 63%; p< 0 . 0001 ) , and a concomitant increase in circulating NK cells ( pre , 8%; post , 12%; p = 0 . 0005 ) . The proportion of B cells was unchanged while NKT-like cells showed a modest yet significant decrease ( p = 0 . 036 ) . Results were not affected by removing the few treatment failures from each group ( S1 Fig ) . The observed changes did not correlate with the severity of disease in terms of lesion size ( S2 Fig ) . Analysis of NK subsets based on CD56 and CD16 markers showed a significant decrease in CD16+CD56+ cells at POST in the SSG group ( Fig 1B ) . The subjects were stratified and reanalyzed to determine if the observed changes in cell populations in POST correlated with treatment arm . Similar declines in circulating T cells were seen in both the SSG and TM subjects . Surprisingly , there was no difference when comparing the percentage of T cells in POST between treatment groups ( Fig 1C ) . Similar population changes for NK cells and NKT-like cells were observed in both treatment arms ( S3 Fig ) . We next investigated CD4+ and CD8+T cells subsets before and after treatment . There was a marked decrease in the median percentage of CD4+T cells ( pre , 62 . 3; post , 57 . 9; p = 0 . 0089 ) and a proportionate increase in CD8+T cells ( pre , 30 . 3; post , 34 . 8; p = 0 . 0128 ) post-treatment , with no changes in the CD4-CD8- ( double negative , DN ) population ( Fig 1D ) . We determined the TCR distribution in CL caused by L . major , using flow cytometry to profile the TCR repertoire of each of the four subsets of T cells ( based on CD4 and/or CD8 expression ) in our subjects and in healthy donors ( n = 7 ) . Here the αβ TCR was exclusively expressed on single-positive CD4+T cells and double-positive CD4+CD8+T cells , and predominantly on the single-positive CD8+T cells ( representative donor shown , S4 Fig ) . The DNT cells , on the other hand , were a mixture of αβ expressors , γδ expressors and a population that was negative for both of these TCR . Surprisingly , our results for the αβ and γδ TCR align in healthy and L . major infected subjects . A decrease in αβ expression ( p = 0 . 052 ) ( Fig 1E ) and trend in increase of γδ was observed in POST ( Fig 1F ) while the overall percentage of DNT cells remained unchanged during the course of the study . The lymphoproliferative response in 34 evaluable subjects was analyzed at different time points with concurrent cytokine production . Interestingly , a significant decrease in Leishmania antigen-specific T cell proliferation against SLA ( p = 0 . 0005 ) was seen in POST subjects of total PBMC ( Fig 2A ) . These differences persisted when analyzed without the few treatment failures in each group ( S5 Fig ) . However , when analyzed by treatment arm , this decrease in proliferation after therapy was only observed in the SSG but not TM treatment ( Fig 2B ) . Recent reports suggest that CD8+T cells play a regulatory role in immunity to leishmaniasis [26] . In testing the role of CD8+ cells in proliferation responses PRE and POST , we depleted CD8+ cells from the bulk PBMC prior to stimulation . The proliferation differences between PRE and POST responses were abrogated with CD8+T cell depletion pointing to a potential immunomodulatory or regulatory role for CD8+T cells ( Fig 2A ) . Cytokines were quantified to determine if the suppressive effect of the CD8+T cells involved soluble mediators . Interestingly , IFN-γ , IL-10 and TNF-α were produced at significantly lower levels in POST , whether the CD8+T cells were present or not ( Fig 2C , 2D and 2E ) which restricts the CD8+T cell effects to modulation of lymphocyte proliferation independent of cytokines tested here . We next used CFSE labeling to identify antigen-specific proliferating cell subsets in both bulk and CD8+T cell depleted PBMC . Aggregate data is shown in Fig 3Aand 3B and a representative gating example in S6 Fig . While the predominant proliferative fraction consisted of CD4+T cells ( 68% ) , there was a modest expansion of CD8+T cells ( 7% ) and CD4-CD8- DNT cells ( 15% ) ( Fig 3A ) . As expected , the vast majority ( >90% ) of responding cells were activated , as assessed by CD25 expression ( Fig 3B ) . Based on CD25 expression and the observed modulation of proliferative immune response , we investigated the role of T regulatory ( Treg ) cells in the healing process . PBMC were analyzed by flow cytometry to determine the levels of activated T cells , identified by CD25 expression . At POST , we observed a decrease in the percentages of circulating activated T cells in both the CD4 and CD8 compartments ( Fig 4A ) . We identified Treg as those cells within the CD4+T cell compartment that expressed the highest levels of CD25 ( CD25+ bright ) and FoxP3 ( S7 Fig ) . Aggregate data from n = 20 sets of subjects shows that while there was no effect on the Treg population during treatment , there was a marked reduction in circulating CD4+ Treg cells in POST ( pre , 3 . 1%; on , 3 . 3%; post , 2 . 3%; p-values = 0 . 0007 and 0 . 0036 ) ( Fig 4B ) . The degree of protection against various infections including leishmaniasis [7] is predicted by the frequency of polyfunctional CD4+ memory T cells that produce IFN-γ , TNF-α , and IL-2 . We assessed intracellular cytokine production by CD4+T cells PRE and POST using multiparameter flow cytometry . First , we were able to independently quantify production of IFN-γ , TNF-α and IL-2 by the CD4+ cells , and observed a significant decrease in production of IFN-γ at POST ( Fig 5A ) . Next , we used Boolean gating to analyze the polyfunctionality of these SLA-specific CD4+T cell responses and found a significant decrease in the frequency of triple positive CD4+ T cells expressing IFN-γ , IL-2 and TNF-α at POST also ( Fig 5B ) . For the subjects that failed to meet the validation criteria in the Boolean gating ( minimum 50 events ) , no values are reported which explains the fewer numbers of points in certain subsets .
Little is known about the cellular phenotypic profile and immune response of humans prior-to and following treatment with different leishmaniasis therapeutic regimens . In this study , we compared the immune response profile in a cohort of L . major infected subjects treated with intravenous SSG or locally applied heat therapy ( TM ) [21] . The mechanism of actions of these two treatment modalities and the nature , location and distribution of therapy are markedly different . Although both treatments resulted in clinical healing , we hypothesized that an immunomodulating systemic therapy might act through different immune mechanisms compared to a localized , physical , direct parasite-killing therapy . In this study , we report two important findings with functional immunologic underpinnings . First , downmodulation of Leishmania antigen-specific CD4+T cell proliferative responses possibly through a CD8+T-cell dependent mechanism was observed after therapy . Second , we report that Leishmania-specific polyfunctional CD4+T cells also decrease after therapy . Since clinical cure from leishmaniasis is classically and primarily dependent on T cell subtypes and relevant cytokine production profiles [27 , 28] , cells were phenotyped from subjects before and after treatment . After treatment and independent of the treatment modality , circulating T cells and NKT-like cells were decreased with a concomitant increase in circulating NK cells highlighting the relevance of the innate immune system for Leishmania control . NK and T cells seemed to have reciprocal effects; wherein NK cell-produced IFN-γ which resulted in T cell activation and the T cell derived IL-2 lead to NK triggering [29] . Similarly , an association between the increased frequency of NK cells and lesion healing is reported after immunotherapy with BCG/Leishmania antigens [30] . NKT-like cells share several characteristics with NK cells [31] and serve as frontline innate immune effectors and potential regulators of adaptive immune responses against microorganisms [32] . Although only a trend , the increase of NKT-like cells observed during treatment could be explained by their ability to serve as an early source of regulatory cytokines and their degranulation-related killing function . In our T cell subset analysis , we showed a high percentage of CD4+T cells in the early treatment phase , suggesting their association with disease progression [33]; while the percentage of CD8+T cells increased post treatment . This could reflect the down-modulation of the immune response , as a means to mitigate immunopathology , consistent with other studies linking CD8+T cell subset induction with the healing process [26] and lesion resolution during antimonial therapy [34] . Contraction of CD4+T cells and expansion of CD8+T cells during healing suggests CD4 modulation after cure [35] . CD8+T cells were also increased in healed Brazilian CL subjects suggesting potential modulation of the activity of CD4+ cells by direct cytolytic effect of infected macrophages , or by other regulatory effects [33] . Our results confirm that a balance between the proportion of CD4+ and CD8+T cells is important for leishmaniasis healing [33 , 36–38] . We also analyzed DN T cells , and in particular the αβ subpopulation , a highly activated T cell subset producing cytokines to activate monocytes and macrophages [39] . DN lymphocytes are the second most prevalent cell type producing IFN-γ in human CL [40] and contribute to a leishmanicidal immune environment [39] . DN T cells were recently described as important players in effective and protective primary and secondary anti L . major immunity in experimental cutaneous leishmaniasis [41] . Leishmania-reactive DN T cells express predominantly αβ TCR , are restricted by MHC class II molecules , lack immunoregulatory properties and display transcriptional profile distinct from conventional CD4+ T cells . Current dogma that DN T cells are CD4 and CD8 T cells that have lost their co-receptors is being challenged by the emerging theory that Fas-mediated apoptosis actively removes normally existing DN T cells from the periphery . Impaired Fas-mediated apoptosis may lead to accumulation of these cells rather than de novo generation of DN T cells from activated CD4 or CD8 T cells [42] . In our study , both αβ and γδ subpopulations were similarly represented in the L . major and uninfected control subjects and remained stable during the course of treatment . DN T cell population changes were previously described in human infection with L . ( V ) braziliensis . In that study , 75% of DN T cells from subjects expressed the αβ TCR compared to uninfected persons where 80% of DN T cells express the γδ TCR [39] . This discordance was not observed here and this may be attributed to different Leishmania species with differing disease patterns and/or genetic backgrounds of the individuals studied . Leishmania induced immunity is based upon the generation of memory T cells that recognize cognate Leishmania antigens and proliferate after exposure thus activating the effector cells [43] . In our study , responses to SLA were consistently diminished in the post treatment phase . Surprisingly , the proliferative responses were significantly decreased only for subjects receiving systemic treatment but not subjects receiving local treatment . This could in part be explained by the higher numbers of treatment failures at 6 months in TM ( 4/19 in TM group versus 2/20 in SSG group ) causing LPA due to parasite persistence . Similarly to our findings , others also report a decline of the lymphoproliferative response after therapy [28 , 36–38] . The CD8+T cell-dependent decrease in CD4+T cell proliferation suggested a post treatment , curative type counter-regulatory mechanism . In contrast , in a BALB/c mouse model , CD8 T cell depletion did not interfere with the proliferative ability of draining lymph node CD4 T cells and was associated with an increase in parasite load [44] . As demonstrated for CD4 T cells [45] , CD8 immunomodulation maybe due , for example , to up- regulation of Fas expression on CD4 to induce their apoptotic death . We know that CD8 T cells play a role in the healing process and resistance to reinfection in New World human CL . Conversely , other studies associate CD8+ to tissue injury [46] . Recently , it was hypothesized that changes in the frequency of effector CD8+ T cells , during and after antimonial therapy is a critical step to generate an efficient immune response either for by triggering or resolving the lesion [34] . In vivo experiments with human cells showed that CD8 T cells produce IFN-γ and drive Th1 differentiation [47] . However in our study , after treatment , all subjects showed decreased IFN-γ , IL-10 and TNF-α levels , with or without CD8+ depletion . This indicated that CD8+T cell mediated regulation of the CD4+T cell response was not attributable to the soluble mediators studied here . The high IFN-γ production observed pre-treatment suggests that the subjects have initiated an immune response to eliminate the parasite [48] . Additionally , during effective treatment , gradual parasite destruction by macrophages is expected with a diminishing parasite load . Overall , our results add evidence that local heat therapy of CL elicits a systemic cytokine response similar to that of systemic pentavalent antimony . In fact , a decrease in IFN-γ , IL-5 and TNF-α in both groups was seen at day 28 post treatment with meglumine antimoniate in a previous study [49] . These results indicate that proinflammatory responses were progressively downmodulated after therapy and that the cytokine profile produced after cure is shaped during the active phase of disease [50] . Our results were contrary to our hypothesis , as the subjects in the both treatment arms generally exhibited similar cellular immune response profiles . This may be explained , in part , by the tendency of CL to eventually self-heal so cure processes may have occurred despite therapy [51] . Another potential limitation of our study is that there were fewer subjects collected at the 6 month time point , however this was similar between treatment arms . A local immune analysis in the skin may have provided additional clues to immune response alterations induced by different treatments , as might an earlier post timepoint . Taken together , our findings highlight the existence of regulatory mechanisms that counterbalance early immune responses without altering the CL healing outcome . The magnitude of effector T cell responses can be controlled by regulatory T cells at the lesion site by suppressing lymphocyte proliferation [52] . These mechanisms are important to maintain the host tissue integrity against a subsequent or persistent inflammatory response . Induction of Tregs during chronic infections results from antigen presentation in a particular cytokine environment [53 , 54] . Interestingly , we found that the percentage of CD25hiCD4+Foxp3+ cells decreased after treatment suggesting that Tregs may be responsible for the suppression that was associated with healing and that their drop is not an artifact of CD4 decrease demonstrated earlier . Tregs have been shown to substantially contribute to tissue repair by providing regulation at sites of healing [55] . To gain a better understanding of the complex immunopathogenesis of CL , study of the quality of a Th1 response , not solely its magnitude , was recently adopted [7 , 8] . Our analysis evaluated polyfunctional CD4+T cells in response to treatment . Overall , we observed a contraction in polyfunctional CD4+T cells in the post-treatment group , both in terms of number of responding cells and production of multiple cytokines . In conclusion , healing of CL is a dynamic but consistent process . Similar changes in frequency , quality , and antigen specific responses were observed in both treatment arms and may represent a signature for curative responses .
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Globally , leishmaniasis treatment relies on the use of antimonial drugs ( i . e . SSG ) . In an earlier study we showed that skin lesions due to L . major treated by the ThermoMed ( TM ) device healed at a similar rate and with less associated systemic toxicity than lesions treated with intravenous SSG . The current study compared the immune responses of these two therapeutic groups before , during and after therapy which may be relevant to resistance to reinfection and also in consideration for the development of local ( versus systemic ) therapy . Antimonials have immune effects on both the host and parasite while heat treatment locally kills the parasite and induces inflammation from a secondary burn . We demonstrated that healing from cutaneous leishmaniasis is a dynamic process associated with a modulation of immune responses independent of treatment modalities .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[] |
2015
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The Immunology of a Healing Response in Cutaneous Leishmaniasis Treated with Localized Heat or Systemic Antimonial Therapy
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Chagas disease ( American trypanosomiasis ) is a zoonotic or anthropozoonotic disease caused by the parasite Trypanosoma cruzi . Predominantly affecting populations in poor areas of Latin America , medical care for this neglected disease is often lacking . Médecins Sans Frontières/Doctors Without Borders ( MSF ) has provided diagnostic and treatment services for Chagas disease since 1999 . This report describes 10 years of field experience in four MSF programs in Honduras , Guatemala , and Bolivia , focusing on feasibility protocols , safety of drug therapy , and treatment effectiveness . From 1999 to 2008 , MSF provided free diagnosis , etiological treatment , and follow-up care for patients <18 years of age seropositive for T . cruzi in Yoro , Honduras ( 1999–2002 ) ; Olopa , Guatemala ( 2003–2006 ) ; Entre Ríos , Bolivia ( 2002–2006 ) ; and Sucre , Bolivia ( 2005–2008 ) . Essential program components guaranteeing feasibility of implementation were information , education , and communication ( IEC ) at the community and family level; vector control; health staff training; screening and diagnosis; treatment and compliance , including family-based strategies for early detection of adverse events; and logistics . Chagas disease diagnosis was confirmed by testing blood samples using two different diagnostic tests . T . cruzi-positive patients were treated with benznidazole as first-line treatment , with appropriate counseling , consent , and active participation from parents or guardians for daily administration of the drug , early detection of adverse events , and treatment withdrawal , when necessary . Weekly follow-up was conducted , with adverse events recorded to assess drug safety . Evaluations of serological conversion were carried out to measure treatment effectiveness . Vector control , entomological surveillance , and health education activities were carried out in all projects with close interaction with national and regional programs . Total numbers of children and adolescents tested for T . cruzi in Yoro , Olopa , Entre Ríos , and Sucre were 24 , 471 , 8 , 927 , 7 , 613 , and 19 , 400 , respectively . Of these , 232 ( 0 . 9% ) , 124 ( 1 . 4% ) , 1 , 475 ( 19 . 4% ) , and 1 , 145 ( 5 . 9% ) patients , respectively , were diagnosed as seropositive . Patients were treated with benznidazole , and early findings of seroconversion varied widely between the Central and South American programs: 87 . 1% and 58 . 1% at 18 months post-treatment in Yoro and Olopa , respectively; 5 . 4% by up to 60 months in Entre Ríos; and 0% at an average of 18 months in Sucre . Benznidazole-related adverse events were observed in 50 . 2% and 50 . 8% of all patients treated in Yoro and Olopa , respectively , and 25 . 6% and 37 . 9% of patients in Entre Ríos and Sucre , respectively . Most adverse events were mild and manageable . No deaths occurred in the treatment population . These results demonstrate the feasibility of implementing Chagas disease diagnosis and treatment programs in resource-limited settings , including remote rural areas , while addressing the limitations associated with drug-related adverse events . The variability in apparent treatment effectiveness may reflect differences in patient and parasite populations , and illustrates the limitations of current treatments and measures of efficacy . New treatments with improved safety profiles , pediatric formulations of existing and new drugs , and a faster , reliable test of cure are all urgently needed .
Discovered 100 years ago in 1909 , Chagas disease ( American trypanosomiasis ) is an endemic disease of the Americas , caused by infection with the protozoan parasite Trypanosoma cruzi . According to varying estimates , there are about 10–15 million existing cases , 50 , 000 new annual infections , and 14 , 000 deaths per year [1]–[5] . Chagas disease primarily affects populations in low-income , resource-poor areas , where health care is often lacking or difficult to access . The first initiatives for controlling Chagas disease focused primarily on prevention through vector control and screening of blood donors , but with limited resources directed towards diagnosing , treating , and following up those already infected either during or after vector control activities . In 1999 , the international medical humanitarian organization Médecins Sans Frontières/Doctors Without Borders ( MSF ) started its first program for the diagnosis and treatment of Chagas disease for affected populations . Through its Spanish , French , and Belgian sections , MSF implemented six Chagas disease diagnosis and treatment programs in Honduras , Nicaragua , Bolivia , and Guatemala , from 1999 to 2008 , focusing on pediatric populations [6] . In addition to its field programs , MSF helped develop information , education , and communication ( IEC ) modules in Argentina , Colombia , and Ecuador , and together with the Pan American Health Organization ( PAHO ) produced a virtual medical training course for the diagnosis and treatment of Chagas disease [7] , [8] . Since 1999 , MSF has treated over 3 , 100 patients for Chagas disease . Here we describe four programs run by MSF Operational Centre Barcelona Athens ( OCBA ) in 1999–2008 in endemic areas of Honduras , Guatemala , and Bolivia . We discuss the feasibility of implementing such projects in resource-limited settings in remote rural areas , through the analyses and validation of shared programmatic components , drug safety , and treatment effectiveness .
In collaboration with national health ministries , MSF implemented Chagas disease diagnosis and treatment programs in three rural districts and one periurban setting from 1999 to 2008 . The rural programs were in Yoro , Honduras ( latitude 15 . 3 , longitude −87 . 1 ) from 1999 to 2002; Olopa , Guatemala ( 14 . 6 , −89 . 3 ) from 2003 to 2006; and Entre Ríos , O'Connor Province , Tarija , Bolivia ( −21 . 5 , −64 . 7 ) from 2002 to 2006 . The periurban program was in Sucre , Bolivia ( −19 . 0 , −65 . 2 ) from 2005 to 2008 . All four programs were in areas with relatively poor populations who had limited access to medical care , with the rural areas in remote , difficult-to-access locations . All four programs focused on pediatric and adolescent patients , but with an increase in age group treated over time: Yoro , <12 years old; Olopa and Entre Ríos , <15 years; and Sucre , <18 years . The increase in treatment age groups over time and projects reflected MSF's strategy to first diagnose and treat young children and then expand these services to older children and adolescents . These areas were selected for medical intervention based on available T . cruzi seroprevalence information from national Chagas disease programs and preliminary seroprevalence surveys by MSF , presence of active vector control programs ( indoor and peridomestic residual spraying and entomological surveillance ) , and limited health care access . Requirements for opening these programs included health care structures ( eg , clinics , laboratories , offices , etc ) , equipment , supplies , and human resources at the primary health care level for carrying out laboratory serodiagnosis and storage of serum samples , together with immediate operational capacity for adequate diagnoses , treatment , and follow-up . MSF helped provide equipment; contracted additional temporary healthcare , logistical , and administrative staff , when needed; and bought all necessary supplies . All the programs had close interaction with national , departmental , and municipal programs regarding vector control activities , entomological evaluation and surveillance , and health education . All the Chagas disease diagnosis and treatment projects shared six principal , essential features directly assuring program feasibility in remote , rural settings ( Table 1 ) : 1 . Information , education , and communication ( IEC ) : The four main target audiences were community authorities , health staff , key community figures ( eg , teachers , religious leaders , etc ) , and patient families . The first step was to learn about the local reality and situation through published and reported information and through direct contacts , and second step was to establish a dialogue with different leaders and actors to design the correct IEC approach taking into account socioeconomic and cultural contexts . In each program , meetings to spread IEC messages within the four target populations were held before screening activities . A more focused IEC session on diagnosis and treatment ( especially on follow-up and adverse events ) was given to families prior to patient detection , confirmation , and treatment . Informed consent forms signed by the patient's family were compulsory before inclusion in each program . After treatment , community meetings were held to obtain feedback on activities . 2 . Vector control: As a precondition for treatment in national programs for Chagas disease , MSF involvement in vector control activities was adapted to each country's situation and capacity . In Yoro , Honduras , MSF was directly involved in vector control activities; in the programs in Guatemala and Bolivia , vector control was directed by the national programs . Before starting diagnosis and treatment of patients , a prerequisite was that infestation rates of the given community had to be <3% following national protocols . If a child was found to be seropositive , the family's house was checked to be vector-free , with targeted spraying if needed . Entomological surveillance in the community ( Puesto de Informacion de Vinchucas [PIV] , or Vector Information Post ) was regularly performed . 3 . Training of health staff: Chagas disease-oriented training of health staff members was carried out for teaching specific diagnosis and treatment skills , and how to communicate and work with patient families to help ensure adherence and follow-up and for early adverse event detection and rapid intervention . 4 . Active screening and diagnosis: Active disease screening at the community level was implemented in all four projects . In each program , the whole population found in the municipality based on target patient age group was screened . Different diagnostic guidelines were established in the four projects depending on agreed-upon protocols , field availability of tests , feasibility of implementation , and expert technical advice . Screening and diagnosis were implemented at the primary health care level . 5 . Treatment and compliance , including family-based strategies: Inclusion criteria for etiological treatment in all programs were enrollment of children of different cut-off age groups , with age groups expanded in newer programs as programmatic experience and evidence were gained; patients in acute or recent chronic phase ( indeterminate form ) regardless of transmission route; populations within the catchment area of the project; and signed , informed consent by parents or guardians . Exclusion criteria included pregnant and lactating women; patients with renal or hepatic impairment or failure; any severe or generalized disease; and drug hypersensitivity . Some weekly follow-up sessions were handled by doctors , focusing on treatment initiation and addressing adverse reactions , while the remainder of follow-ups were handled by nurses focused on treatment compliance . When adverse events were unmanageable , a referral system including third-level hospitals was used , with referred patients followed up on a daily basis . Defaulters to follow-up visits were actively traced by health staff or community health workers . Serological follow-up was emphasized among patients at the time of initial result , with serum samples taken before treatment initiation . Reinforcement messages for treatment adherence were given with full course of treatment . All care was provided free of charge . Treatment and compliance included a family-based strategy in which parents and guardians of patients were co-responsible for daily drug administration , early detection of adverse events , and requesting medical help for patient treatment withdrawal , if needed . 6 . Logistics: Logistic activities focused on access to remote communities and close monitoring and evaluation of vector control measures . The supply chain for drugs and laboratory reagents was maintained , as was storage of frozen samples for serological testing . Community structures , such as schools , were used for relevant activities , including community meetings , IEC sessions , training , screening , and treatment follow-up . MSF worked in close collaboration with national Chagas disease programs in terms of logistics in all four projects . According to World Health Organization ( WHO ) recommendations , diagnosis of Chagas disease was confirmed using two different tests . In case of doubtful or discordant results , a third test was used . Following national and regional recommendations , each project used different tests , as follows . Diagnostic testing for T . cruzi was performed by ELISA ( conventional and recombinant ) , indirect hemagglutination ( HAI ) , and , for exceptional confirmation needs , indirect immunofluorescence ( IFI ) . The source of reagents for ELISA was Wiener or Biochile . In Yoro and Olopa , screening was conducted using conventional ELISA using filter paper . Confirmation of diagnosis was done with recombinant ELISA . Similarly , in Entre Ríos , conventional ELISA and HAI tests were conducted , with recombinant ELISA as the tiebreaker . When necessary , IFI was used instead of HAI . Later in the Entre Ríos program , Chagas Stat-Pak ( Chembio Diagnostic Systems , Inc , Medford , NY ) rapid diagnostic test ( RDT ) was introduced for screening , using whole blood samples , and all positive results were systematically confirmed by conventional ELISA and HAI , and recombinant ELISA used as a tiebreaker . In Sucre , screening was conducting using Chagas Stat-Pak on whole blood . As in Entre Ríos , positive results were confirmed using conventional ELISA and HAI , with tiebreakers assessed via recombinant ELISA . For conventional and recombinant ELISA , cut-off values were calculated according to manufacturer recommendations by taking the sum of the absorbance of all negative controls and adding this to a constant factor ( 0 . 200 for conventional , 0 . 300 for recombinant ) . Positive results were those samples with an optic deviation ( DO ) above cut-off+10% . Negative results were those with DO below cut-off−10% . Doubtful results were those with DO between ( cut-off−10% ) and ( cut-off+10% ) . For HAI , positive results were those samples with reactivity for dilution ≥1/16 titration . Positive reactions for dilutions at 1/2 , 1/4 , or 1/8 were considered cross-reactive and false-positive; protocol called for these samples to be treated with 2-mercaptoethanol 1% and HAI repeated . For Chagas Stat-Pak RDT , positive results were those samples giving two pink/purple lines , one in test area and one in control area , at reading at 15 minutes ( maximum 30 minutes ) . Tests with no line visible in the control area were considered invalid , and these samples were retested using a new device . Quality control ( QC ) measures were systematically performed in the programs . For RDT QC , for every 10th negative RDT result , venous blood was taken and sent to the laboratory for ELISA/HAI testing . For ELISA/HAI QC , internal QC was performed using the positive and negative controls present in the test kit ( and a performance checklist was also used for QC on the procedure itself ) . Overall , 10% of positive samples and 10% of negative samples were sent to the reference laboratory for external QC . T . cruzi-positive patients were treated with benznidazole 5–7 . 5 mg/kg/day , 2 or 3 times per day over 60 days ( maximum 300 mg/day; if necessary , the total dose was calculated and divided for more than 60 days ) . In the four programs , counseling for the parents/guardians of infected children as provided , informing them of how to give treatment , potential treatment benefits , risk factors , and adverse events , including how to proceed if adverse events occur . Treatment and follow-up ( at days 0 , 7 , 14 , etc ) were provided by health staff , while daily drug tablets were administered at home by the parenets/guardians . Treatment adherence sheets were filled out by parents/guardians or patients . In results analysis , a patient was considered as having completed treatment when >30 days of treatment were accomplished . Passive , and when necessary , active , weekly patient follow-up was performed in all projects by physicians or nurses . When necessary , more intensive and/or more frequent follow-up was performed . Clinical presentation and adverse events were recorded . The severity of adverse events was recorded at each follow-up visit . Adverse events were classified as mild , moderate , or severe . Mild adverse events were defined as those requiring no treatment interruption . Moderate adverse events were defined as those requiring temporary treatment interruption , with the patient returning to treatment within 14 days . Severe adverse events were defined as those requiring treatment stoppage . All adverse events were evaluated by a physician , and symptomatic treatment was given according to their type and severity . The types of adverse events observed were as follows: dermatological , gastrointestinal , and neurological . To assess seroconversion from positive to negative for T . cruzi infection , the first post-treatment serologic evaluation was generally conducted at 18 or 36 months post-treatment . Post- and pre-treatment blood samples were processed simultaneously using conventional ELISA . Negative results from conventional ELISA were confirmed with recombinant ELISA . All ELISA tests used serum or plasma samples . All pre-treatment samples ( serum/plasma ) were aliquoted and frozen ( without glycerin ) at −20°C , less than 24 hours after collection . All ELISA test results were obtained using an ELISA reader ( optical density visible in the reader screen ) . Based on WHO protocol , cure was defined as two non-reactive ELISA tests ( one conventional , one recombinant ) performed on the same sample on the same date . For patients with positive or indeterminate results in the first evaluation , a second serology evaluation was generally performed at 36 months post-treatment . Normalized differences in antibody titers were calculated in consecutive assessment comparisons to pre-treatment baseline values by using the following equation: ( final antibody titers−initial antibody titers ) /initial antibody titers ) ×100 . Likewise , differences in T . cruzi antibody titers between pre-treatment baseline and post-treatment control values were compared using Wilcoxon ranked sum test , and negative seroconversion rates between 18 and 36 months after treatment were compared using McNemar test . Mann-Whitney U-test and Kruskal-Wallis test were used to compare differences in T . cruzi antibody titers , while Chi-square or Fisher's exact test were used to analyze negative seroconversion and tendency to seroconversion rates according to age and gener . 95% confidence intervals for rate differences were calculated . Statistical significance was set at 5% . All tests of significance were two-tailed . Informed written consent was obtained before treatment from parents or guardians of patients who tested positive . If parents/guardians were illiterate , oral explanation was given , and consent was obtained by fingerprint . All data were collected routinely and managed confidentially . All the projects were discussed , reviewed , and approved by the national Ministry of Health ( MOH ) , with MOH permission granted before starting each program .
Drug safety was assessed by recording treatment-related adverse events in terms of severity and type . In all four programs , most adverse events were mild . No deaths due to treatment occurred in any of the programs . In the Central American programs in Yoro , Honduras and Olopa , Guatemala , 50 . 2% and 50 . 8% of patients , respectively , had adverse events related to treatment ( Table 3 ) . In Yoro , most of the adverse events were mild , with no moderate cases and 3 severe cases due to neurological adverse events ( neuromuscular disturbances of the lower limbs after 6 weeks of treatment ) . The most frequent adverse events were gastrointestinal disorders ( 26 . 8% , mainly epigastralgia and/or abdominal pain , and less frequently nausea and/or vomiting and anorexia ) , followed by dermatological conditions ( 13 . 0% , mainly pruritus and less frequently maculopapular exanthema ) and neurological problems ( 10 . 4% , mainly neuromuscular disturbances ) . In Olopa , 80 . 9% ( 51/63 ) of the adverse events were mild , 14 . 3% ( 9/63 ) moderate , and 4 . 8% ( 3/63 ) severe ( 2 neuromuscular and 1 cutaneous ) . Adverse events were 26% dermatological in nature , 25% gastrointestinal , 23% neuromuscular , and 26% other types . In both Yoro and Olopa , no differences were seen in the proportion of adverse events depending on age or sex ( Chi-square test ) . Lower rates of treatment-related adverse events were observed in the Bolivian programs . In Entre Ríos , adverse events were observed in 25 . 6% of treated children , with increasing risk in older age groups ( 12% in <5 years old; 25% 10–14 ) . In Sucre , 37 . 9% of patients had adverse events , also with increasing risk in older groups ( 13 . 4% in <5 years old; 50% 15–18 ) . In Entre Ríos , 56% of adverse events were dermatological , 25% digestive , and 18% neuromuscular , of which 11% were mixed . In Sucre , 68 . 5% of adverse events were dermatological . In both programs , the majority of side effects were mild , with risk increasing with age . Six and 41 severe adverse events were reported in Entre Ríos and Sucre , respectively . In these two programs , 1 case of Lyell syndrome ( toxic epidermic necrolysis ) and 1 case of Stevens Johnson syndrome were reported . Lyell syndrome occurred in a 13-year-old girl at day 34 of benznidazole treatment . In the weekly follow-up , the patient showed a generalized itchy rash with good general clinical status and was treated with oral antihistamine drugs . Two days later , a MSF physician was contacted and visited the child , who presented with high fever and general cutaneous rash with infected pustules . The patient was given intravenous fluids and ceftriaxone until admission to Tarija hospital . She was managed and discharged after 7 days with good clinical improvement . Treatment effectiveness was measured by rates of seroconversion in the patients . A marked difference was seen in the rates of seroconversion between patients treated in the two earlier Central American programs ( Yoro , Olopa ) compared with the two later programs in South America/Bolivia ( Entre Ríos , Sucre ) . In Yoro , Honduras , seroconversion rate for T . cruzi was 87 . 1% ( 202/232 ) at 18 months post-treatment , showing a high seroconversion rate achieved in a relatively short period of time ( Table 1 ) . At 36 months , seroconversion rate was 92 . 7% ( 215/232 ) . In Olopa , Guatemala , from available patient data ( 25 . 5% of the treatment cohort ) , seroconversion at 18 months post-treatment was 58 . 1% ( 18/31 ) . Seroconversion rates observed in Entre Ríos and Sucre in Bolivia were much lower . Preliminary results of overall seroconversion post-treatment was 5 . 4% ( 59/1 , 101 ) in Entre Ríos by up to 60 months post-treatment , with over 950 of the patients sampled having had follow-up later than 18 months post-treatment . Seroconversion rates were found to be lower in older age groups compared with younger ones in Entre Ríos: 24 . 2% ( 16/66 ) <5 years old; 4 . 6% ( 14/303 ) 5–9 years old; 1 . 9% ( 12/638 ) 10–14 years old , at 18–60 months follow-up . To date , of 276 patients followed up between 9 and 27 months post-treatment , no patient has been found to have seroconverted in Sucre .
The 10-year operational experience of MSF in these four programs in Honduras , Guatemala , and Bolivia demonstrates that diagnosis and treatment of Chagas disease are feasible , relatively safe , and potentially effective in low-income , resource-constrained settings . Through the lessons learned from earlier studies [10] , [11] and these MSF projects and their common , essential logistical components , we propose that this programmatic approach is feasible at the primary health care level and replicable in other Chagas-disease endemic countries and regions , even in periurban and remote rural areas . With proper coordination between different stakeholders focused on integrated health care services for Chagas disease , including national and regional programs , the diagnosis and treatment of the disease in early chronic phases ( mainly indeterminate form ) can be safely implemented and should be deemed necessary for affected populations [12] , [13] . Etiological treatment of Chagas disease can and should be integrated at the primary health care level because most patients are near primary health care services , and the majority of patients would be able to receive medical care at this level , taking into account the proportion of Chagas patients with the indeterminate form of the disease . In MSF's programs , this implementation was achieved in remote rural settings through the application of six central features and criteria: IEC , vector control , health staff training , logistics , screening/diagnosis , and treatment/compliance , with family-based support . IEC was a chief component of program strategies and is vital to ensure treatment compliance and early detection of adverse events , especially when providing care for populations with differing cultures , practices , and modes of communication , among others . IEC was crucial for raising awareness in the general population about the disease ( regarding transmission routes , clinical manifestations , and treatment and prevention possibilities ) and inform patients and patient families that diagnosis and treatment services were available . Vector control carried out by national programs was also an important program component and should be simultaneously implemented with patient access to diagnosis and treatment [14] , [15] . MSF involvement varied as projects progressed , depending on the need and capacity of national authorities and other partners . After treatment , vector control was continued through the national programs , but regular spraying every 6 months was not always carried out . Community entomological surveillance occurred regularly , but spraying for vector control was irregular at times . Eliminating the vector from the environment and households of patients and those at risk is critical . Health staff training and family IEC for family-based treatment monitoring were exceptional ways of both ensuring quality of diagnosis and treatment compliance , as well as engaging the family in the health care process . Diagnosis and treatment of Chagas disease in all the projects relied on well-trained health personnel to apply their medical skills to care for patients and to establish family commitment to treatment adherence and follow-up care . With minimum logistical capacity , especially support for outreach teams , our program experience may be replicable in other endemic areas . The fundamental program component of screening and diagnosis used differing diagnostic protocols adapted to the contexts of each country/region . For diagnosis in our programs , the two tests selected were the two with minimum acceptable sensitivity and specificity ( ideally 99–100% ) and which could be feasibly implemented at the primary health care level [6] , [16] . Filter paper blood samples were used in the earlier programs mainly for sensitivity and adapted ease of use ( ie , no need for centrifugation , relatively easy to supply/refill , portability ) in remote rural settings . We introduced the use of Chagas Stat-Pak RDT in the Bolivian programs and carried out a field evaluation using whole blood samples . Recent studies using this RDT have shown relatively low sensitivity ( 93–94% ) compared with conventional tests [9] , [17] , [18] , and this limited sensitivity must be considered in the use of this test . A whole-blood RDT with high sensitivity would be ideal for screening and diagnosis in resource-limited settings [19] . For treatment and compliance , a large number and proportion of patients started and finished treatment according to protocol in our programs , with over 90% of patients completing >55 days of treatment . Access to treatment , follow-up , and referral of complicated cases were successful elements of the protocol . Relatively low dropout before treatment and low default rates ( mostly migrations of patients and adverse reactions ) were observed . However , in one program , Sucre , about 9% of diagnosed patients did not start treatment . The main reasons for this were migration , reluctance to start treatment ( after counseling and informed consent ) , pregnant or lactating mothers , and treatment being offered by MOH national programs . Still , overall we found that Chagas treatment and follow-up can be achieved with adequately trained , sensitized , and motivated health staff and family members in both rural remote and periurban settings . The family-based approach for daily drug administration and compliance was key for Chagas disease because of the length of therapy and occurrence of adverse events . Drug treatment was safely administered in these four programs , with no deaths occurring due to adverse events . Despite this , nearly half of all patients had some type of adverse event , a few of which were severe , including 1 case of Lyell syndrome , and 1 case of Stevens Johnson syndrome . Although no previous studies of Chagas disease have reported either of these syndromes , these two cases must be viewed in the context of over 3 , 000 patients treated in the four programs , with no deaths in even the most severe cases . The majority of adverse event cases were treated with a reduced dosage of benznidazole ( to the minimum dose of 5 mg/kg/day ) or temporary suspension of treatment . The time of appearance , intensity , and clinical patterns of adverse events were not different than those observed in other experiences [20] , except that we did not see any hematological reactions ( ie , no clinical manifestations such as anemia , severe infection , or hemorrhage were observed to make us suspect detrimental effects on bone marrow ) . However , hematological reactions were only followed clinically , without routine laboratory testing , due to issues of practicality under field conditions . This therefore poses a limitation in that hematological adverse events cannot be completely excluded , especially since severe hematological reactions ( such as bone marrow suppression ) can be asymptomatic . Proximal neuromuscular adverse events presented later ( after 35 days of treatment ) compared with other adverse event types , demonstrating cumulative drug toxicity . Overall , the large number of children and adolescents treated and observed in the four programs ( >3 , 100 ) provides valuable insight into drug safety for current Chagas disease drug treatment . Previous studies have reported experiences from lower numbers of patients [10] , [21] . Of note , we observed sizeable variations in reported adverse events in the study locations , namely between the two programs in Central America ( Honduras/Guatemala ) and the two in South America ( Bolivia ) . In recording adverse events and their severity in our four programs , observer bias no doubt played a role . The identification and classification of an adverse event is often dependent on the observing medical staff , and misclassifications were possible in the programs . We attempted to address this by defining mild , moderate , and severe adverse events based on whether treatment was temporarily interrupted or fully stopped . Other biases in adverse event profiles may exist , such as differences in early detection of side effects and more or less intensive medication and management for adverse events . While a well-designed program should be able to minimize risks and ensure safe treatment , the lack of a non-toxic alternative drug remains a major obstacle to wider access to treatment for both adults and children . No pediatric formulation currently exists for benznidazole ( nor nifurtimox , the only other drug used for treating Chagas disease ) , increasing risks of under- or overdosing in children . For the youngest patients , cutting tablets and mixing with water or other liquids for oral administration is difficult and has important pharmacological implications in terms of absorption and bioavailability . The seroconversion rates detected in treated patients were relatively high in the Central American projects , Yoro , Honduras and Olopa , Guatemala , showing that therapy can clear T . cruzi infection . However , seroconversion was far lower in the South American Bolivian projects in Entre Ríos and Sucre . The findings in Bolivia are similar to those reported from earlier studies in Argentina and Brazil [22]–[25] . Also , seroconversion was detected earlier in the Central American programs compared with the Bolivian programs . Previous research has shown that in South America seroconversion is sometimes not detected until 5–7 years later [26] . Thus , the higher and earlier seroconversion we detected in Central America supports previously reported findings [27] and may have important public health implications . The differences in seroconversion rates may be explained by a number of reasons . One primary explanation may be based on the presence of different parasite lineages in different geographic regions , with T . cruzi type I predominating in Central America and T . cruzi type II in South America , with varying degrees of overlap [28] . Because of the potential differences in T . cruzi subtypes present in Honduras and Guatemala compared with Bolivia , drug treatment effectiveness may have differed . Another factor to consider is the time between vector control activity and drug treatment , since cases ( mostly asymptomatic ) closer to the acute phase of the disease can possibly account for more rapid seroconversion . Also , statistical limitations of our data analysis may exist due to the varying age groups and varying times of post-treatment follow-up in the four projects , as has been examined in other studies [29] . Finally , differences in immune response among populations may play a role . Whatever the reason , the lack of a better marker for indicating parasitological cure is a major impediment to advances in treatment and development of more effective drugs [4] . The observed differences between seroconversion rates in Central and South America highlight the need for further studies to confirm our findings and help improve etiological treatment protocols with dosages and duration adapted to the Chagas disease cycle in different geographic regions . Since the start of our first Chagas disease program in 1999 , which focused on young children , MSF has pushed to deliver diagnosis and treatment of this disease to wider and wider age groups . Over the past decade , treatment for Chagas disease has expanded from children <12 years old , to <15 , then <18 , and finally adults . This strategy has helped bring broader coverage of treatment delivery for Chagas disease . Bolivia is the most highly endemic country in the world for T . cruzi infection , with up to 1 . 8 million people believed to be infected [1] , [30]–[32] . MSF currently has two active programs in Cochabamba , where Chagas disease treatment is integrated into primary health care and offered to adults as well as children and adolescents . Because of high prevalence in Bolivia , Chagas disease diagnosis and treatment remain an operational priority there for MSF . MSF's programs , both past and present , highlight where and what the needs are for people affected by Chagas disease . In addition to increasing public awareness and patient access to existing diagnostics and drugs , the development of new , less toxic , more effective drugs; adapted pediatric formulations of treatments; and a reliable test of parasitological cure are all urgently required . Because Chagas disease and those afflicted with it are often neglected , medical care for this patient population should be implemented whenever and wherever possible , as MSF has demonstrated as feasible in these four programs , and research and development for the disease should be scaled up dramatically .
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Chagas disease was discovered 100 years ago by the Brazilian physician Carlos Chagas . Predominantly affecting poor populations throughout Latin America , recognition and treatment of this parasitic disease are often neglected . Since 1999 , the international medical humanitarian aid organization Médecins Sans Frontières ( Doctors Without Borders ) has offered diagnostic and therapeutic care for Chagas disease , and here we describe four of our programs in Honduras , Guatemala , and Bolivia , 1999–2008 . The earliest programs focused on treating young children and in subsequent programs expanded up to 18 years of age . We identified six program components essential for project viability: information , education , and communication; vector control; health staff training; screening and diagnosis; treatment and compliance; and logistics . The number of children and adolescents screened for Chagas disease ranged from over 7 , 500 to nearly 25 , 000 in each program . Early analysis of cure rates ranged widely: from 87% and 58% , respectively , in Honduras and Guatemala , to 0%–5% in Bolivia . No deaths occurred in any of the programs , though drug-related side effects were observed in a quarter to half of all patients . Through our findings and experience , we discuss the feasibility , safety , and effectiveness of treatment programs for Chagas disease in resource-limited settings .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"public",
"health",
"and",
"epidemiology/global",
"health"
] |
2009
|
Feasibility, Drug Safety, and Effectiveness of Etiological Treatment Programs for Chagas Disease in Honduras, Guatemala, and Bolivia: 10-Year Experience of Médecins Sans Frontières
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A number of studies published during the last 15 years showed the occurrence of insecticide resistance in Triatoma infestans populations . The different toxicological profiles and mechanisms of resistance to insecticides is due to a genetic base and environmental factors , being the insecticide selective pressure the best studied among the last factors . The studies on insecticide resistance on T . infestans did not consider the effect of environmental factors that may influence the distribution of resistance to pyrethroid insecticides . To fill this knowledge gap , the present study aims at studying the association between the spatial distribution of pyrethroid resistant populations of T . infestans and environmental variables . A total of 24 articles reporting on studies that evaluated the susceptibility to pyrethroids of 222 field-collected T . infestans populations were compiled . The relationship between resistance occurrence ( according to different criteria ) with environmental variables was studied using a generalized linear model . The lethal dose that kills 50% of the evaluated population ( LD50 ) showed a strong linear relationship with the corresponding resistance ratio ( RR50 ) . The statistical descriptive analysis of showed that the frequency distribution of the Log ( LD50 ) is bimodal , suggesting the existence of two statistical groups . A significant model including 5 environmental variables shows the geographic distribution of high and low LD50 groups with a particular concentration of the highest LD50 populations over the region identified as the putative center of dispersion of T . infestans . The occurrence of these two groups concentrated over a particular region that coincides with the area where populations of the intermediate cytogenetic group were found might reflect the spatial heterogeneity of the genetic variability of T . infestans , that seems to be the cause of the insecticide resistance in the area , even on sylvatic populations of T . infestans , never before exposed to pyrethroid insecticides , representing natural and wild toxicological phenotypes . The strong linear relationship found between LD50 and RR50 suggest RR50 might not be the best indicator of insecticide resistance in triatomines .
Chagas disease is the most important vector-borne infection in Latin America , affecting approximately 5–6 million individuals [1] . The disease is caused by the protozoa Trypanosoma cruzi ( Trypanosomatidae ) and the most frequent transmission mechanism is through the feces of infected blood-sucking insects belonging to the subfamily Triatominae ( Heteroptera: Reduviidae ) . The main vector of T . cruzi in the countries of the southern cone of South America is Triatoma infestans ( Klug ) . This species lives mainly in warm and dry rural areas and in close association with human dwellings , including domiciles and peridomiciliary structures [2 , 3] . During the last years , a number of wild foci of T . infestans have been described , mainly in the Inter-Andean Valleys of Bolivia , in the Gran Chaco of Argentina , Bolivia and Paraguay [4–8] and in a Metropolitan region from Chile [9] . By 1960 , the maximum geographic distribution of T . infestans occupied an estimated area of 6 . 28 million km2 [10] , including parts of Argentina , Bolivia , Brazil , Chile , Paraguay , Peru and Uruguay . This species was responsible for well over half of the 18 million people infected by T . cruzi , as estimated by WHO [11] for the 1980 decade . After the establishment of the Southern Cone Initiative ( INCOSUR ) in 1991 , wich had the main goal of interrupting the T . cruzi transmission using chemical insecticides to eliminate T . infestans populations and through blood transfusion , the vectorial transmission of T . cruzi was interrupted in Uruguay ( 1997 ) , Chile ( 1999 ) and Brazil ( 2006 ) , according to the certification of the Pan-American Health Organization ( PAHO ) [12] . In Argentina ( seven provinces ) and Paraguay ( eastern ) the transmission of T . cruzi was interrupted in several areas where the disease had been historically endemic [13] . The Departments of La Paz and Potosí in Bolivia recently certified the interruption of the vectorial transmission of T . cruzi [14] . As a consequence of the vector control interventions in the region , there was a significant reduction of the distribution area of T . infestans to less than 1 million km2 , leading to a strong reduction of the new infections by T . infestans [10 , 14–18] . Despite the constant efforts of vector control the success was not complete , T . infestans persists as domestic populations in several areas of the Gran Chaco region from Argentina , Bolivia and Paraguay and parts of the Inter-Andean Valleys of Bolivia , and southern Peru [19–21] . Persistent bug populations that survived the insecticide application at local spatial scales , were related with sources of peridomestic populations , operational failures , reduced residual effect of insecticide or development of resistance to pyrethroid insecticides that decrease the vector control efficacy [22–26] . Resistance to insecticides is a microevolutionary process , over which the dynamics , the structure of the population and the gene flow between groups of individuals would determine the maximum geographical spread of each process of resistance evolution [27 , 28] . In different geographic areas of Argentina and Bolivia , resistance of T . infestans to pyrethroids was detected by 2000 , [29–38] . The occurrence of insecticide resistance was relatively unexpected [39 , 40]for a long life cycled-insect duration ( compared with other pest species with insecticide resistance records ) , relatively low frequency of insecticide applications , unsustained-over-time , and low insecticide efficacy in the peridomestic structures that would leave many residual populations ( not necessarily resistant , but susceptible individuals that were not affected by the insecticide that degraded before contacting the insects ) . Studies showed different toxicological profiles and mechanisms of resistance [31 , 35 , 36 , 41] . High levels of insecticide resistance ( populations that need 1000 times the amount of active ingredient to kill the same fraction of a susceptible population ) leading to control failures were described in the biogeographic region of the Gran Chaco , coinciding with the area of persistent house reinfestation even after insecticide application . The accumulation of evidence over the last years suggests that the occurrence of insecticide resistance in T . infestans populations is associated with the high genetic variability detected in the historical dispersion site of the species towards the southern cone of South America [21 , 42 , 43] and the strong spatial structure of the populations derived by low population dispersal rates [24 , 44–45] . Why is it that control failure associated with insecticide resistance has only been recorded in this particular area and not anywhere else over the historical geographic distribution of T . infestans ? The cause of the appearance of pyrethroid resistance is still under discussion . The repeated application of pyrethroid insecticide does not seem to be the only cause of pyrethroid resistance appearance , as resistant populations occurred in areas that received less insecticide pressure than others where resistance did not occur , and because multiple independent resistance mechanisms were detected in several populations . The diversity of resistance mechanisms and the genetic variability around the putative dispersion center of the T . infestans encouraged the consideration of the influence of environmental variables as another potential cause of pyrethroid resistance occurrence [28] . As far as we know , there is no demonstration of a causal relation between the effect of environmental variables and insecticide resistance , partly because of the difficulty of identifying the individual contribution of the genetic background and the abiotic factors . However , a number of studies have shown direct or indirect effects of environmental variables over the appearance of insecticide resistance in several insect pest species . For example , according to Foster et al [46] the selection for resistance to insecticides in Myzus persicae is subject to counteracting selection by cold , wet and windy conditions; whereas [47] showed that adaptive responses and DNA regions that control their expression have been linked to evolutionary responses to pollution , global warming and other changes . Interestingly , a significantly higher diapause propensities in carriers of the resistance alleles ( 37 . 0–76 . 2% ) than in susceptible homozygotes ( 6 . 7% ) was shown [48] . Although no diapause was ever shown to exist in Triatominae , it was shown that the developmental delays in fifth instar nymphs of Triatominae could be due to an adaptive risk-spreading diapause strategy [49] , if survival throughout the diapause period and the probability of random occurrence of ‘‘bad” environmental conditions are sufficiently high . The influence of environmental variables on the geographic distribution of triatomine was studied for several species , showing significant correlations between a number of environmental variables ( particularly temperature ) and species occurrence e . g . [10 , 50] . As other phenotypic characters , the different toxicological profiles and mechanisms of resistance to insecticides is due to a combination of a genetic base and environmental factors [28] , with the selective insecticide pressure being the best studied among the last factors . So far , studies on insecticide resistance on T . infestans did not consider the effect of environmental factors , that may influence the distribution of resistance to pyrethroid insecticides in T . infestans populations . Guided by the question about the particular occurrence of vector control failures caused by pyrethroid resistance in this particular area , we explored in this study for the first time the geographic distribution of pyrethroid resistance of T . infestans populations and its association with environmental variables .
An exhaustive compilation of all available data on studies about susceptibility of T . infestans to pyrethroid insecticides was carried out . Repeated data were discarded . A database containing information on the field-collected specimens and methods used in the susceptibility studies based on topical application of insecticide was created . The database includes collection location coordinates , collection ecotope ( intradomestic/peridomestic/sylvatic ) , value of the lethal dose that kills 50% of the evaluated population ( LD50 ) , resistance ratio 50 ( RR50 ) ( calculated as LD50 of the evaluated population/ LD50 of the susceptible population ) and diagnostic dose ( DD ) ( defined as percent mortality produced by twice the minimum concentration of the insecticide that causes 99% of mortality in the susceptible laboratory strain ) . All tests were carried out using first instar nymphs between 3–5 days , topicated with a 0 . 2 uL droplet applied with a Hamilton microsyringe . Identifying a T . infestans population as resistant to pyrethroids is not easy , because no objective definition of resistance for triatomines exists . At least three criteria have been proposed to operationally define triatomines' resistance . Pan American Health Organization [51] defined as resistant all populations with RR50 > 5 ( PAHO criteria from now on ) . Zerba and Picollo [52] suggested that a population should be considered resistant when RR50 > 2 ( Z&P criteria from now on ) . WHO [53] proposed the use the DD and considered a population as resistant if mortality is < 80% , and susceptible if mortality > 98% ( WHO criteria from now on ) , although the latter criteria is used mainly to evaluate resistance in mosquitos . Using the three criteria mentioned above , T . infestans populations studied for pyrethroid resistance were classified as susceptible or resistant according to 7 different estimates of resistance-occurrence categories as follows . The first three categories derived directly from the three criteria mentioned above ( namely , PAHO , Z&P , WHO ) . A fourth category ( RR1 ) recorded as resistant any T . infestans sample that was classified as resistant by any of the three criteria . A fifth category ( RR2 ) recorded as resistant any T . infestans sample that was classified as resistant by at least two of the three criteria . A sixth category ( RR3 ) recorded as resistant a T . infestans sample that was classified as resistant by the three criteria . A seventh category ( LD50 ) ( strictly not a resistance category ) considered the value of the LD50 ( i . e . the amount of the active ingredient that produced 50% of mortality within the sample under study ) . It is worth remarking that RR1 , RR2 and RR3 are derived variables from PAHO , Z&P and WHO variables , not independent of each other , as they are defined as “both” or “either”of the other criteria . The analysis of the association between resistance occurrence and environmental variables was carried out using the WorldClim dataset ( http://www . worldclim . org ) [54] , that characterizes climatic conditions over the Earth surface between 1950–2000 in a grid format , with a pixel resolution of 1km . Variables included 19 bioclimatic statistics derived from monthly total precipitation , and monthly mean , minimum and maximum temperature ( Bio1 to Bio19 described in full at http://worldclim . org/bioclim ) . Altitude above sea level was added to the climatic variables . The distribution of T . infestans resistance to pyrethroid insecticides occurrence was carried out using the species distribution modelling approach [55] , with the geographic coordinates of resistance occurrence recorded as “presences” . We explored two different approaches on the consideration of “absences” . On one approach , we considered the coordinates of T . infestans populations defined as susceptible , and on the other approach ( usual within the context of species distribution modelling [56] ) , we considered a random selection of 1000 background ( pseudo-absence ) points taken over the Gran Chaco region and Inter-Andean valleys , the area where T . infestans populations still persists after the successful interventions of the Southern Cone Initiative [18] . For the study of the association between environmental variables and resistance occurrence we used a binary response variable , assigning 1 to cases recorded as a site with a T . infestans resistant population , according to each of the seven resistance criteria mentioned above , and 0 to the susceptible populations or the randomly selected background points . The case of the LD50 data was analyzed similarly as a binary variable based on a threshold value that divided the dataset in high LD50 ( assigned the value 1 ) and low LD50 ( assigned 0 ) ( see the appropriate section in Results for additional details ) . The analysis was based on a generalized linear model ( GLM ) with a logit link . Colinearity between bioclimatic variables was estimated using the variance inflation factor ( vif ) , of the R car package . Only variables with vif<10 were considered for the construction of the model . The evaluation of the model was estimated using the partial area under the receiver operation curve , calculated with the pAUC package of R . Cross-validation ( through the cv . glm function ) was used to measure the robustness of model estimation . Data analysis was carried out with R ( version 3 . 2 . 0 ) . In order to qualitatively explore the association between population genetics characteristics of T . infestans and the LD50 measured on the populations compiled in this study we used the geographic coordinates of the populations categorized by cytogenetics groups ( andean , non andean and intermediate ) , as reported by [21] .
A first set of analysis for the first six categories using the recorded resistant and susceptible populations to fit the generalized linear model ( GLM ) with the environmental variables as predictors showed a low ability to explain the variability of the resistant populations ( of any considered category ) occurrence . Among the fitted models , the best one explained 43% of the resistance occurrence distribution , based on the PAHO criteria . This model was fitted using 100 points of resistance occurrence and 41 of susceptibility occurrence and included the highest number of environmental variables ( Table 2 ) . These 41 susceptibility points is a probably biased sample of the susceptible populations occurrence , driven by the special interest in the region of the border between Argentina and Bolivia; the actual distribution of susceptible populations is probably more widely distributed . A similar result was found when the observed susceptible populations were replaced by the set of 1000 randomly selected points taken from the entire Gran Chaco region and Inter-Andean valleys , where T . infestans populations are still patchily present . The analysis showed that none of the environmental variables ( either considering the location of the susceptible population or taking background points ) were able to account for more than 50% of the resistance occurrence , defined by each of the 6 mentioned criteria . The descriptive analysis of LD50 values , showed that the frequency distribution of the Log ( LD50 ) is bimodal , suggesting the existence of two statistical subpopulations ( groups ) . The value 2 . 6 is the threshold value that best separates the two groups . Calculating the descriptive statistics separately for the two groups , the group with lower Log ( LD50 ) has an average = 0 . 17 and standard deviation = 1 . 47 , whereas the group with higher Log ( LD50 ) has values 3 . 82 and 0 . 74 , respectively ( Fig 2 ) . Driven by this identified pattern , we plotted the distribution of T . infestans populations classifying them in two groups , with low ( ≤ 2 . 6 ) and high ( > 2 . 6 ) Log ( LD50 ) . The geographic distribution of these groups show a particular concentration of populations with highest LD50 over the region identified as the putative center of dispersion of T . infestans . Thus , we pursued the analysis classifying the two LD50 groups assigning 0 to those showing Log ( LD50 ) ≤ 2 . 6 and 1 to those showing Log ( LD50 ) > 2 . 6 . The analysis of the geographic distribution of these two T . infestans populations based on the Log ( LD50 ) , with 2 . 6 as the threshold value , showed a significant fit of the GLM model with the environmental variables as predictors . The model was based on 48 population samples where the Log ( LD50 ) > 2 . 6 and 92 population samples where Log ( DL50 ) was < 2 . 6 . After variable selection to eliminate colinearity ( vif<10 ) , a model including 5 significant environmental variables was able to explain 55% of the variation in the distribution of the Log ( LD50 ) groups ( Table 2 ) . The environmental variables Mean Diurnal Range ( Mean of monthly ( max temp—min temp ) ) ( Bio2 ) , Mean Temperature of the Driest Quarter ( Bio9 ) , Precipitation Seasonality ( Coefficient of Variation ) ( Bio15 ) ; Precipitation of the Warmest Quarter ( Bio18 ) are positively correlated , whereas isothermality ( Bio2/Bio7 ) ( * 100 ) ( Bio3 ) is negatively correlated with the occurrence of high LD50 populations . Using the model describing the distribution of populations with low and high Log ( LD50 ) , a map with the potential distribution of populations with highest LD50 values was created ( Fig 3 ) . The area identified as the one where T . infestans populations could show highest LD50 includes the border between Bolivia and Argentina ( see S2 Fig ) , and southward to the east of Salta and north central Santiago del Estero provinces ( Argentina ) . The model predicts a disjunction area towards the border of La Rioja and San Juan provinces ( Argentina ) and towards the north of the Cochabamba Department ( Bolivia ) ( see S3 Fig ) . The model fails at describing the occurrence of one highly resistant population ( Log ( LD50 ) >2 . 6 ) in Chuquisaca ( -65 . 25 , -19 . 05 , a population studied by [28] ) and 5 populations ( out of 13 ) concentrated at the south of the Guemes Department ( Chaco Province , Argentina , see S4 Fig ) . The location of the other 40 populations is correct . The model showed a high goodness of fit , with an , AUC = 0 . 95 , pROC = 77 . 8 ( 61 . 9–90 . 9 ) and highly robust , with only 3 . 6% error estimated by the leave-one-out cross validation method . After identifying the significant model that described the distribution of high and low Log ( LD50 ) groups , separated by the 2 . 6 threshold , we calculated 3 additional models , using 2 , 2 . 2 and 2 . 4 as threshold values to separate low and high Log ( LD50 ) groups . All models were significant , included the same environmental variables and explained over 50% of the variation of the geographic distribution of the newly defined groups . From each model , a map showing the prediction of high Log ( LD50 ) occurrence was produced . Fig 3 shows the geographic distribution of the highest values of Log ( LD50 ) in the four models defined by different threshold values ( Log ( LD50 ) >2 . 0 to Log ( LD50 ) >2 . 6 with a step of 0 . 2 ) . A map of the distribution of the three T . infestans cytogenetic groups ( andean , non-andean and intermediate reported by [21] ) and the distribution of Log ( LD50 ) measured on T . infestans populations shows an almost perfect match between the highly resistant T . infestans populations ( Log ( LD50 ) > 2 . 6 ) and the intermediate cytogenetic group ( Fig 3 ) .
Pyrethroid insecticides were introduced into the pest control market by the end of 1970 , and were rapidly identified as a major tool for the control of agricultural pests and vectors of human diseases [56] . At present , pyrethroid insecticides have a 25% share of the insecticide market , and are used in different formulations in the public health sector because of their efficacy , toxicity profile , persistence and low impact over the environment [57–59] . Pyrethroids were incorporated as a tool for the control of domestic triatomines by mid 1980s . The elimination of T . infestans in wide areas of the Southern Cone Countries of South America and good results in other vector control initiatives showed the high susceptibility of triatomines to pyrethroids [21 , 39] . The reduction of house infestation by T . infestans is a success story over about 90% of its maximum geographic distribution area . This success is backed by a long history of vector control programs effort that started in the mid 1950s and made the strongest advances through the INCOSUR , coordinated by PAHO . The main tool for the elimination of house infestation by T . infestans was the application of residual insecticides ( particularly pyrethroids ) . However , other socio-demographic factors , such as rural-urban migration , improvement of house quality in rural areas , community education and/or land use changes had contributed to this trend [60] . Although high impact was obtained in the elimination of intradomestic populations of T . infestans in most parts of the southern cone of South America , houses of several rural communities in the Gran Chaco are still infested by T . infestans . A number of reasons have been mentioned to explain the persistence of T . infestans populations in the area , including low insecticide efficacy when applied to peridomestic structures , unsustainability of vector control interventions , and insecticide resistance . Low pyrethroid efficacy is caused by rapid degradation , as has been shown by field measurements of the residual activity of the insecticide sprayed over wood and adobe [61] and by a number of field studies that repeatedly recorded the persistence of frequent residual populations shortly after the insecticide spraying [62] . In addition to the low efficacy of pyrethroid insecticides , the unsustainability of vector control interventions allows the recovery of the even small residual populations of T . infestans [58 , 63] . Nevertheless , the majority of the sustained vector control failures in the area still occupied by this vector can be attributed to the factors mentioned ( low efficacy of pyrethroid insecticides and unsustainability of vector control interventions ) . Resistance in the identified hot spot is higher than in other places and it is apparently independent of the frequency of insecticide application in the area , that is not different to the frequency of insecticide application elsewhere . Therefore , we propose that the occurrence of pyrethroid resistant populations in the border between Argentina and Bolivia is not a primary result of the insecticide selection pressure , but a consequence of the existence of naturally tolerant populations of T . infestans , shown by the occurrence of resistant T . infestans population of sylvatic origin . The resistance remains high not because of an insecticide-based selection process , but as a natural selection process acting over a population having a naturally high frequency of resistant individuals . A similar interpretation was produced in the review of Mougabure-Cueto and Picollo [28] . The compilation of studies on pyrethroid resistance in T . infestans analyzed in this study , shows that the frequency distribution of the Log ( LD50 ) for pyrethroids is bimodal , with two well spatially separated statistical groups . This is the first time this resistance feature is shown . The significance of these two groups is not clear . It might reflect the spatial heterogeneity of the high genetic variability of T . infestans , that seems to be one possible cause of the insecticide resistance in the area , even on sylvatic populations of T . infestans , never before exposed to the pyrethroids , representing natural and wild toxicological phenotypes . The spatial heterogeneity of the LD50 is associated with a combination of 3 temperature- and 2 rainfall-derived environmental variables , as shown by the significant fit of the generalized linear model developed in this study . This is the first time the spatial heterogeneity of resistance is shown significantly associated with environmental variables . Panzera et al . [21] speculated that the intermediate cytogenetic group might have appeared recently , as before 1998 house infestation was very low ( ranging between 0 . 5 and 0 . 8% ) . These authors suggest that since 1998 , and despite continued vector control activities , there has been a gradual increase of insects in houses , reaching house infestation levels of 50 to 80% in 2004 . An alternative explanation is that this intermediate cytogenetic group was already in the area and was revealed only after continued vector control activities over a population with high frequency of resistant individuals selected the most resistant ones . If T . infestans showed widespread resistant populations , why is it that control failures have only been reported in a limited area of the T . infestans distribution , even though pyrethroid insecticides for the control of the species are in use for more than 30 years now ? The vector control failure within a limited area might suggest that the resistance in areas outside of the problematic area is not increasing , even though pyrethroid insecticides are in use , at the same frequency during the last 20 years , or even at higher frequency as it occurred during the last decade in some provinces in Argentina [64] . The occurrence of independent resistance mechanisms suggests that the process is widespread , but that it is not evolving rapidly , as expected by the demographic features of the species . Resistance to pyrethroids is widespread over the arid chaco and Andean valleys of Bolivia , although the high level of pyrethroid resistance ( and other active ingredients , such as fipronil ) occurs around the putative center of dispersion of the species , where the genetic variability is very high , and a particular combination of environmental variables exists . We do not have enough information about the process that lead to the occurrence of the highly resistant T . infestans populations in the hotspot , to produce a meaningful mechanistic model able to analyze the relation between the occurrence of resistant populations of T . infestans and environmental variables . This is a limitation of the study , that can not demonstrate a causal relation between pyrethroid resistance and environmental variables , because the model we based our study on is a statistic one . To be able to demonstrate a causal relationship , we would need a mechanistic model integrating population dynamics , population genetics and environmental variables . Regrettably , we were not able to find publications compiling a geographic database on population genetics characteristics , equivalent to our compilation on pyrethroid resistance , to carry out an equivalent study on the relationship between environmental variables and population genetics . The compiled published data shows that the highly resistant T . infestans populations are geographically limited ( except one location in central Chaco province ( Argentina ) and one north of Potosi ( Bolivia ) ) within an environmental variable space that does not occur towards the north of Bolivia , but does occur south , down to Santiago del Estero in central Argentina . As we can not claim a causal relation between insecticide resistance and environmental variables , we can not use the resulting model to predict the occurrence of highly resistant T . infestans populations . However , we can identify the area highlighted by the model as the one that possesses a similar combination of environmental variable values to the one where the highly resistant T . infestans populations occurs . If there is a causal relation between environment and pyrethroid resistance , then the area identified by the model should be carefully considered as an area of potential occurrence of highly resistant T . infestans populations . An important consideration should be given to the fact that if insecticide resistance existed in the area , without the need of selection by insecticide pressure , even if the use of pyrethroids is stopped , the frequency of resistant individuals will remain high . The analysis of the relation between RR50 and LD50 revealed the existence of two groups of populations in the compiled database . It is difficult to discern the cause of this discrepancy , as it could appear as a consequence of the use of different susceptible populations , or that the studied populations really have a different relation between RR50 and LD50 . Additional studies on this relation could determine wether these two population groups are artifacts or not . More importantly , if shown that there is only one linear relationship between RR50 and LD50 , the use of RR50 for resistance detection should be revised , as it would mean that LD50 multiplied by a constant ( the slope ) would give the RR50 value .
|
The elimination of T . infestans in wide areas of the Southern Cone countries of South America and good results in other vector control initiatives showed the high susceptibility of triatomines to pyrethroid insecticides . Despite the constant efforts of vector control , the success was not complete in several areas of the Gran Chaco region of Argentina , Bolivia and Paraguay and parts of the Inter-Andean Valleys of Bolivia , and southern Peru , where persistent populations of domestic and wild T . infestans still persist . Additionally , high levels of insecticide resistance leading to control failures were described in the biogeographic region of the Gran Chaco , within the area of persistent house reinfestation after insecticide application . The influence of environmental variables on the geographic distribution of triatomine was previously studied for several species , showing significant correlations between Triatominae species occurrence and a number of environmental variables . We investigated the association between the spatial distribution of pyrethroid resistant populations of T . infestans and environmental variables . This study shows that pyrethroid resistance in T . infestans causing control failures is a highly localized event , spatially associated with the putative dispersion origin of the species , the location of the intermediate cytogenetic group , and with a particular combination of environmental variables , near the border between Argentina and Bolivia . The strong linear relationship found between LD50 and RR50 suggest RR50 might not be the best indicator of insecticide resistance in triatomines .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
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"paraguay",
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"population",
"dynamics",
"geographical",
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"argentina",
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"infectious",
"disease",
"control",
"triatoma",
"agrochemicals",
"infectious",
"diseases",
"south",
"america",
"epidemiology",
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2016
|
Distribution of Pyrethroid Resistant Populations of Triatoma infestans in the Southern Cone of South America
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The oncoproteins of the small DNA tumor viruses interact with a plethora of cellular regulators to commandeer control of the infected cell . During infection , adenovirus E1A deregulates cAMP signalling and repurposes it for activation of viral gene expression . We show that E1A structurally and functionally mimics a cellular A-kinase anchoring protein ( AKAP ) . E1A interacts with and relocalizes protein kinase A ( PKA ) to the nucleus , likely to virus replication centres , via an interaction with the regulatory subunits of PKA . Binding to PKA requires the N-terminus of E1A , which bears striking similarity to the amphipathic α-helical domain present in cellular AKAPs . E1A also targets the same docking-dimerization domain of PKA normally bound by cellular AKAPs . In addition , the AKAP like motif within E1A could restore PKA interaction to a cellular AKAP in which its normal interaction motif was deleted . During infection , E1A successfully competes with endogenous cellular AKAPs for PKA interaction . E1A’s role as a viral AKAP contributes to viral transcription , protein expression and progeny production . These data establish HAdV E1A as the first known viral AKAP . This represents a unique example of viral subversion of a crucial cellular regulatory pathway via structural mimicry of the PKA interaction domain of cellular AKAPs .
As obligate intracellular parasites , all viruses are critically dependent upon the host cell . Intensive selective pressure , rapid replicative cycle times and severe restrictions on viral genome size combine to drive virus evolution . As a consequence , viral regulatory proteins have been relentlessly forged into exquisitely sophisticated instruments that functionally reprogram the infected cell [1] . Studies of human adenovirus ( HAdV ) , a small DNA tumor virus , illustrate the profound impact of viral proteins on multiple host functions to maximize viral propagation [2–7] . The multifunctional E1A proteins of HAdV are particularly adept at targeting key cellular regulators . Through these interactions , E1A creates a cellular milieu more conducive for replication . Indeed , E1A enhances cell cycle entry , subverts innate immunity and intensively reprograms the cellular gene expression program [5 , 6 , 8] . The modular E1A proteins are dense with short linear sequence motifs that bind to and alter the activity of dozens of critical cellular proteins [9 , 10] . Many of the interaction motifs in E1A are functional mimics of highly similar sequences present in cellular regulatory proteins . Thus , viral evolution has converged to generate specific high affinity protein interaction surfaces that perturb cell regulation by competing with endogenous targets . Cellular compartmentalisation of proteins is a widespread cellular mechanism that ensures the interaction of signalling molecules with a localized subset of appropriate effector proteins . As one well studied example , the activation of protein kinase A ( PKA ) signalling by the second messenger cyclic AMP ( cAMP ) is precisely restricted to discrete subcellular regions [11] . This is primarily achieved by a diverse set of cytoplasmic scaffolds collectively known as A-kinase anchoring proteins ( AKAPs ) . AKAPs bind to PKA regulatory subunits via a well characterized amphipathic α-helix , localizing them to distinct cellular loci near PKA’s substrates [12] . Compartmentalization of PKA allows its enzymatic activity to be directed in a spatially defined and temporally specified manner and disregulation of this compartmentalization has pathophysiological consequences [13] . Although the E1A proteins from multiple HAdVs can synergize with cAMP to alter viral and cellular gene expression [14–18] the exact mechanism remains unclear . Interestingly , HAdV-12 E1A binds directly to the regulatory subunits of PKA , resulting in the relocalization of one isoform from the cytoplasm to the nucleus [19 , 20] . These results suggest that E1A may function as a ‘viral AKAP’ by redirecting the subcellular localization of PKA to alter transcription . Here we show that HAdV E1A mimics cellular AKAPs in both appearance and function . We found that the PKA RIα and RIIα subunits are conserved targets of most HAdV E1A species . Structural modeling and a docking analysis predict a remarkable similarity between the binding of E1A and cellular AKAPs to PKA , which was confirmed experimentally . In addition , we observed E1A-mediated relocalization of PKA subunits and competition between E1A and cellular AKAPs during infection that contribute to HAdV gene expression and overall viral replication . Together , our studies identify E1A as the first known viral AKAP , and reveal a unique example of viral subversion of the PKA pathway via structural mimicry .
The E1A proteins from multiple HAdVs synergize with cAMP to alter viral and cellular gene expression . A direct interaction between HAdV-12 E1A and the type I and type II regulatory subunits of PKA ( RIα and RIIα ) was previously reported , but has not been investigated further [19] . It was also not known if this interaction was specific to HAdV-12 E1A . To further explore the E1A-PKA interaction , A549 lung adenocarcinoma cells were infected with wildtype ( WT ) HAdV-5 or a ΔE1A virus and co-immunoprecipitations were performed ( Fig 1A ) . Similarly to HAdV-12 E1A , HAdV-5 E1A interacted with endogenous PKA regulatory subunits RIα and RIIα . Interestingly , we also found a previously unknown interaction between HAdV-5 E1A and the endogenous PKA catalytic subunit Cα . siRNA-mediated downregulation of specific PKA subunits demonstrated that E1A’s association with Cα required expression of RIα and RIIα ( Fig 1B ) . This suggests that the interaction with the Cα subunit may be indirect and that E1A binds the entire PKA holoenzyme . To determine if the interactions between E1A and the PKA subunits are evolutionarily conserved across the different HAdV species , HT1080 fibrosarcoma cells were transfected with vectors expressing the PKA subunits and the largest E1A isoform from six different HAdV species . Co-immunoprecipitation analysis revealed that RIα , RIIα , and Cα all interacted with each of the E1A proteins tested , with the exception of HAdV-4 ( Fig 1C ) . The conservation of the E1A-PKA interaction across most HAdV species suggests that targeting of PKA is an important evolutionarily conserved function of E1A . E1A is comprised of a series of protein interaction modules that often can function independently [8] . To grossly define which portion of E1A is required for PKA interaction , lysates from HT1080 cells expressing the PKA subunits and the indicated large fragments of HAdV-5 E1A expressed as EGFP-fusions , were subjected to Co-IP . The N-terminal 82 residues of HAdV-5 E1A were sufficient for association with PKA ( Fig 1D ) . Interestingly , this region of E1A has been previously shown to be involved in alterations in cAMP signalling [21] . In addition , the interaction of HAdV-12 E1A with PKA similarly mapped to residues 1–79 in a yeast interaction assay [19] . As can be seen from the amino acid sequence alignment , there are several areas of high sequence similarity in this region in the E1A proteins from various HAdV species ( Fig 1E ) . To determine the minimal region of HAdV-5 E1A necessary and sufficient for PKA interaction , we carried out a detailed mutational analysis of the N-terminus of E1A . Cells were co-transfected with vectors expressing PKA subunits and the indicated E1A mutants , each expressed in the context of full-length HAdV-5 E1A and containing a small in-frame deletion in the N-terminus . As expected , deletion of residues 1–82 abrogated interaction with PKA , confirming that the E1A N-terminus as necessary and sufficient for binding PKA ( Fig 2A ) . Several smaller , overlapping deletions also had similar defects for PKA-binding , specifically Δ1–29 , Δ4–25 , Δ16–28 , and Δ26–35 . However , adjacent deletion mutants Δ1–14 and Δ30–49 , or more distant deletions retained interaction . This suggests that a region spanning residues 14–29 of HAdV-5 E1A is necessary for PKA binding . We next co-transfected cells with PKA and small E1A fragments expressed as EFGP fusions . Co-immunoprecipitation on lysates of these cells demonstrates that the 14–28 region of E1A was sufficient to confer an interaction with PKA ( Fig 2B ) . This region is similar in the E1A proteins from most HAdV species ( Fig 1E ) and also has noticeable sequence similarity to the PKA-binding regions of a number of cellular AKAPs ( Fig 2C ) . Interestingly , AKAPs bind PKA regulatory subunits via an amphipathic α-helix secondary structure motif [22 , 23] , and modeling of the N-terminus of HAdV-5 E1A predicts it also forms an amphipathic α-helix ( Fig 3 ) . Furthermore , the E1A proteins from all HAdV species are strongly predicted to form an α-helix in this region [8 , 21] , with the exception of HAdV-4 E1A , which is predicted to form a lower-confidence helix ( S1A Fig ) and does not bind PKA efficiently ( Fig 1C ) . Taken together , this suggests that E1A binds PKA by structurally mimicking the AKAPs’ amphipathic α-helix motif . We performed in silico molecular modeling to predict the docking of the N-terminus of E1A with PKA . Docking simulations performed using the crystal structure of a dual-specificity cellular AKAP in complex with the RIα homodimer of PKA suggest that the interaction of E1A with RIα is virtually equivalent to that of the cellular AKAP ( Fig 3A–3D and 3F ) . This model predicts a number of distinct interactions between E1A and RIα ( Fig 3E and 3G ) , which were experimentally tested ( Fig 3H and 3I ) . E1A mutants D21K , E26K , V27K and E26A/V27A , reduced the interaction with RIα as predicted , whereas substitution of E25 with K , which is not predicted to alter binding , had no effect ( Fig 3H ) . Similarly , RIα mutants Q28E , L31A K32A and I35A/V36A displayed a reduced ability to bind E1A as predicted by the model ( Fig 3I ) . These results indicate that the docking model can correctly predict key residues necessary for binding , which further suggests that E1A structurally mimics a cellular AKAP in order to bind PKA . Cellular AKAPs bind to the docking/dimerization ( D/D ) domain located at the N-terminus of the PKA regulatory subunits RIα and RIIα [22 , 24] . Given the sequence and predicted structural similarity between E1A and cellular AKAPs , we tested if the D/D domain was necessary for the interaction with E1A . Transfected HAdV-5 E1A was unable to co-immunoprecipitate RIα or RIIα lacking their D/D domain ( Δ1–63 and Δ1–45 , respectively , Fig 4A and 4B ) . In addition , when the D/D domains of RIα and RIIα were expressed as fusions to EGFP , they alone were sufficient to co-immunoprecipitate E1A ( Fig 4C ) . Thus , the N-terminus of E1A not only resembles an AKAP based on sequence , but also binds to the same site on the PKA regulatory subunits targeted by cellular AKAPs . We next determined if the structural similarity between E1A and cellular AKAPs extended to functional similarity . We tested whether E1A could compete with endogenous AKAPs for PKA-binding during infection . A549 cells were infected with WT HAdV-5 , a ΔE1A virus , or a virus expressing an E1A mutant unable to bind PKA ( Δ4–25 ) . Cell lysates were prepared 18 hours post-infection , subjected to immunoprecipitation with an anti-AKAP7 antibody and any co-precipitating PKA subunits were detected via western blot with specific antibodies for each target . AKAP7 is a dual-specificity AKAP [25] , which binds both RIα and RIIα directly , and indirectly binds Cα . Infection with HAdV-5 did not alter the expression of AKAP7 or the various PKA subunits . However , infection disrupted the endogenous interactions between AKAP7 and PKA . Disruption of the AKAP7-PKA interaction during infection required E1A and was dependent on the AKAP like domain in E1A ( Fig 5A ) . These data establish that the AKAP like region in E1A competes with endogenous AKAPs for PKA interaction during infection . These results also suggests that E1A can out-compete at least some cellular AKAPs for binding to PKA , which likely contributes to previously observed perturbation of cellular cAMP signalling by HAdV infection [14 , 16 , 21] . We next tested whether in silico-designed peptide inhibitors , which block AKAP-PKA interactions by binding PKA regulatory subunits with sub-nanomolar affinities , could affect E1A’s interaction with PKA . These well characterized inhibitors are short peptides expressed as EGFP-fusions which specifically block binding to RIα ( RIAD ) or RIIα ( sAKAPis ) [26 , 27] . HT1080 cells were co-transfected with vectors expressing the PKA subunits , WT E1A , and each of the inhibitors . Lysates were subjected to immunoprecipitation with an anti-E1A antibody and interacting PKA subunits were detected by western blot . As expected based on their high affinity , both RIAD and sAKAPis competitively reduced E1A’s interaction with PKA in a subunit-specific manner , reinforcing E1A’s role as a dual-specificity viral AKAP ( Fig 5B ) . Using an expression construct for a known cellular dual-specificity AKAP ( AKAP1 ) [27] , we next tested E1A’s ability to rescue the PKA-binding function of this AKAP when its PKA-binding domain was deleted . HT1080 cells were co-transfected with PKA subunits and EGFP-fusions of WT AKAP1 , an AKAP1 mutant lacking its PKA-binding domain ( AKAP1Δ ) , or an AKAP1 construct with E1A residues 14–28 cloned in lieu of the deletion ( AKAP1-E1A ) . Lysates were subjected to immunoprecipitation with an anti-EGFP antibody and co-precipitating PKA was detected via western blot . As expected , the AKAP1Δ mutant lost the ability to bind PKA . However , incorporation of the E1A AKAP-like sequence into this mutant rescued PKA-binding to WT levels ( Fig 5C ) . Together , these results strongly suggest that the AKAP-like motif in E1A is functionally indistinguishable from that found in an authentic cellular AKAP . Transfection of cells with HAdV-12 E1A induces a relocalization of the RIIα subunit of PKA from the cytoplasm to the nucleus [19] . We tested E1A’s ability to alter PKA’s subcellular localization in vivo during a HAdV-5 infection ( Figs 6 and S2A ) . A549 cells were infected with WT virus ( dl309 ) , a ΔE1A virus ( dl312 ) , or the Δ4–25 E1A deletion mutant virus ( dl1101 ) that does not bind PKA . At 18 hours post-infection , cells were subjected to immunofluorescence staining and biochemical fractionation to determine the subcellular localization of PKA . In WT-infected cells , endogenous RIα was rerouted from the cytoplasm into the nucleus . Additionally , in infected cells , RIα appeared to overlap with the HAdV-5 encoded DNA-binding protein ( DBP ) , suggesting possible co-localization with viral replication centres during infection ( S3 Fig ) . In contrast , the distribution of PKA subunits in cells infected with either the ΔE1A or Δ4–25 virus resembled uninfected cells . Thus , the relocalization of RIα is E1A-dependent and requires the AKAP motif . Subcellular localization of RIIα appeared to be unaffected by the presence of E1A and Cα retained its nuclear/cytoplasmic phenotype in both uninfected and infected cells , thereby rendering any conclusions regarding its relocalization difficult ( S2A Fig ) . Interestingly , RIα , but not RIIα , is similarly trafficked into the nucleus of HEK293 cells , which stably express HAdV-5 E1A . Knockdown of E1A in HEK293 cells reduces the amount of RIα in the nucleus , further suggesting that E1A is functioning as an AKAP in these cells to redistribute PKA ( S2B Fig ) . Additionally , A549 cells transiently transfected with HAdV-5 E1A conferred a similar result , whereas cells transfected with HAdV-4 E1A ( which does not bind PKA via Co-IP [Fig 1] ) did not affect PKA localization ( S4 Fig ) . These results demonstrate that the AKAP function of HAdV-5 E1A can alter the localization of PKA whereas E1A from a HAdV species that does not bind PKA lacks this biological function . Interestingly , HAdV-5 E1A appears to primarily affect type-I PKA , whereas the previously reported effect of HAdV-12 E1A was restricted to type-II PKA . Previous studies indicated that E1A and cAMP synergize to activate viral gene expression [14–16 , 18 , 21 , 28] . To determine if the E1A-PKA interaction contributes to HAdV early gene transcription , A549 cells were first treated with control siRNA or siRNA specific for each PKA subunit and then infected with WT ( dl309 ) , ΔE1A ( dl312 ) , or Δ4–25 ( dl1101 ) HAdV-5 . Cells were harvested 20 hours post-infection , cDNA was prepared and the expression of a panel of HAdV early genes known to be activated by E1A was determined by quantitative real-time PCR . Knockdown of RIα , RIIα , or Cα did not affect expression of the E1A ( Fig 7A ) or E1B ( Fig 7B ) transcription units for any of the tested viruses . However , mRNA levels were significantly reduced for both the E3 ( Fig 7D ) and E4 ( Fig 7E ) transcription units in WT virus infected cells treated with siRNA for each of the PKA subunits , demonstrating that PKA plays a role in the regulation of these transcription units . Importantly , cells infected with the Δ4–25 virus also showed decreased expression of E3 and E4 as compared to WT infection , and this was not further reduced by knockdown of any PKA subunit ( Fig 7D and 7E ) . This is fully consistent with the inability of this mutant E1A protein to bind PKA and relocalize it to the nucleus . Mechanistially , Chromatin immunoprecipitation ( ChIP ) experiments showed that PKA’s catalytic subunit ( Cα ) was recruited to the HAdV E3 and E4 promoter regions in an E1A-dependent manner ( S6 Fig ) . In contrast , E1A did not specifically recruit Cα to the E1B or GAPDH promoters , whose transcription was unaffected by the E1A-PKA interaction ( Fig 7 ) . These results strongly support a mechanism of early gene activation that relies on the AKAP function of E1A . Although knockdown of PKA regulatory subunits had no statistically significant effect on E2 transcripts , knockdown of the catalytic subunit reduced E2 expression for both WT and Δ4–25 virus ( Fig 7C ) . This suggests an independent effect for PKA on this transcription unit that does not rely on the AKAP motif . To extend the observations that PKA plays a role in HAdV gene expression , we further examined PKA’s role in HAdV-5 protein production ( S5 Fig ) . A549 cells were treated with control siRNA or siRNA specific for each PKA subunits and infected with WT HAdV-5 . Cell lysates were collected at 12 , 24 , and 36 hours post-infection . Viral protein production was assayed by western blot using antibodies against an array of HAdV-5 proteins representing both early and late transcription units . Compared to control-treated cells , knockdown of PKA subunits had no effect on the production of HAdV-5 E1A proteins . In contrast , knockdown of the individual PKA subunits caused a notable reduction in several early proteins . These included a reduction in E3-19K at each time point examined , a reduced level of E4orf6 expression at 24 hours post-infection and a delay in expression of the E2-encoded DBP . E1B-55K was also reduced , most notably in the RIα knockdown . Interestingly , many of the late proteins also exhibited lower expressions levels in PKA-knockdown cells , including hexon , penton , protein V , and protein VII . This confirms a role for PKA in regulating HAdV-5 gene expression . To establish the biological significance of E1A’s role as a viral AKAP , we also assessed the effect of the E1A-PKA interaction on viral replication ( Fig 8 ) . A549 cells were treated with either control siRNA or siRNA specific for each PKA subunit and infected with either WT or Δ4–25 HAdV-5 . Production of infectious virus progeny was assayed at various time points over 72 hours by plaque assay . The production of WT virus was reduced by knockdown of each PKA subunit when compared to control-treated cells . Although the production of the Δ4–25 virus was reduced as compared with WT infection , it was not further reduced by knockdown of either RIα or RIIα . This again suggests that the lack of PKA-binding by this E1A mutant is functionally equivalent to PKA knockdown . These results indicate that HAdV replication requires PKA activity and that E1A’s interaction with PKA’s regulatory subunits is required for WT-levels of replication . Interestingly , we observed a reduction in progeny production for both WT and Δ4–25 virus in cells treated with Cα-specific siRNA . However , the observed reduction compared to control-siRNA treated cells was more severe in the WT infection , suggesting an additional role for PKA in HAdV-5 infection that is E1A-independent and specific for PKA’s catalytic subunit . Altogether , these results confirm that the targeting of PKA by the AKAP motif in E1A is a critical aspect in the HAdV-5 replicative cycle .
Cellular AKAPs function as scaffolds that target PKA and other signaling enzymes to specified subcellular locations . These multivalent anchoring proteins serve as important focal points for the processing and integration of intracellular signalling [29 , 30] . We report here that the adenovirus E1A oncoproteins function as the first known viral AKAPs . Intriguingly , E1A interacts with the with both the RIα and RIIα subunits of PKA in a way that precisely mimics that of cellular dual-specificity AKAPs . Specifically , we found that E1A bound to the N-terminal D/D domain of the regulatory subunit dimer of PKA , which is the same exact domain targeted by cellular AKAPs [11 , 12] . We identified a short conserved sequence in HAdV-5 E1A spanning residues 14–28 that was necessary and sufficient for interaction with either RIα or RIIα . Like the PKA interaction domains of cellular AKAPs , this region of E1A is predicted to form an amphipathic α-helix . This apparent structural mimicry allows E1A to bind PKA with an affinity comparable to cellular AKAPs , such that E1A can successfully compete with endogenous cellular AKAPs for PKA interaction during infection ( Fig 9 ) . In support of our in vivo and in vitro results , molecular modeling based on a known structure of the AKAP/PKA interaction predicts that E1A binds the exact same surface of the PKA regulatory subunit in a fashion virtually identical to that determined for cellular AKAPs ( Fig 3 ) . Substitution of specific residues predicted by this model to make contacts reduced the interaction in vivo , supporting the validity of this structural model of molecular mimicry . Functionally , as observed for cellular AKAPs , E1A relocalizes PKA to target sites of action . In the case of E1A , the interaction with PKA induces a specific relocalization to the nucleus , which contributes to viral gene expression and efficient virus propagation during infection . Competition by E1A with cellular AKAPs for PKA interaction may also influence cellular gene expression , which may provide some insight into the previous observations that E1A influences cAMP signalling [14–16 , 21 , 28 , 31] . The E1A region mapped as necessary and sufficient for PKA-binding also overlaps with regions previously implicated in E1A’s ability to act as a transforming oncoprotein [32] . Whether PKA contributes to the transforming ability of E1A remains unknown , though PKA itself has been investigated in a variety of cancer-related functions [13 , 33 , 34] Our results also demonstrate that PKA is a conserved target of the E1A proteins from multiple HAdV species , suggesting that this interaction is functionally important for the virus . The E1A proteins from all HAdV types tested bound PKA strongly , with the exception of HAdV-4 E1A which also failed to relocalize PKA ( S4 Fig ) . Modeling of an interaction between HAdV-4 E1A and PKA predicts that key electrostatic and hydrophobic contacts are absent , which are necessary for the HAdV-5 E1A PKA interaction ( Figs S1 and 3H ) . Interestingly , HAdV-4 is unique as it is the sole member of species E HAdV and arose from an interspecies recombination event between chimpanzee and human adenovirus [35] . As mentioned above , during HAdV-5 infection , E1A was able to out-compete endogenous cellular AKAP7 for PKA interaction; however , there exist a plethora of other , diverse AKAPs with varying affinities for PKA . For example , the in silico-designed ‘super AKAPs’ RIAD and sAKAPis [26 , 27] blocked the binding of E1A to the PKA RIα and RIIα subunits , respectively . Thus , the affinity of the E1A/PKA interaction is not high enough to compete with synthetic AKAPs with sub-nanomolar affinities for PKA . Consequently , these inhibitors are potential tools for further study of E1A function in the context of its role as a viral AKAP . During HAdV-5 infection , a substantial fraction of the RIα subunit was trafficked from the cytoplasm into the nucleus in an E1A-dependent manner . We also observed signal overlap between RIα and HAdV DBP ( S3 Fig ) , suggesting co-localization with viral replication centres . Interestingly , the HAdV-5 E1A-mediated shift in RIα localization is the opposite finding reported for E1A from HAdV-12 , which relocalized RIIα only [19] . While both E1As bound to both type-I and–II PKA in Co-IP assays , our studies suggest that in biologically-relevant conditions they each may exhibit higher affinity or preference for one PKA flavour over another , a property shared by many cellular AKAPs [11 , 12 , 27] . The binding affinities and potential preferences of E1A proteins from the other HAdV species during infection remains to be fully explored . It also remains to be determined if type-I and type-II PKA are completely interchangeable , or if there are functional consequences driving the preference of each virus for each regulatory subunit type . Interestingly , nuclear localization of the PKA holoenzyme is considered relatively unusual , but has been studied in detail in HEK-293 cells [36] . We confirmed nuclear localization of RIα in these cells , which constitutively express HAdV-5 E1A [37] . Our results suggest that nuclear localization of PKA in HEK-293 cells is a likely consequence of the AKAP function of E1A . Furthermore , our data suggests that the results of studies of PKA function in these cells may be confounded by the impact of viral manipulation of this pathway . The targeting of PKA by E1A is required for maximal expression of the HAdV-5 E3 and E4 transcription units . It appears that E1A is using the regulatory subunits of PKA as a bridge to bind Cα , redistributing it to associate with other E1A binding partners at preferred sites within the nucleus , such as the HAdV early gene promoters ( S6 Fig ) . This could establish new localized connections with cAMP-regulated transcriptional machinery , such as CREB or ATF , at viral or cellular loci . This may help explain the previously-observed ability of E1A to cooperate with cAMP in transcriptional activation [14–16] . The importance of PKA during a productive infection is further underscored by our observation that siRNA-mediated downregulation of PKA subunits reduces progeny production by WT HAdV-5 . It is likely that the observed defect in the virus’ ability to express numerous crucial transcripts and proteins in the absence of PKA ( or the AKAP function of E1A ) contributes greatly to this . It is also possible that the E1A-PKA interaction affects cellular tasks that influence HAdV replication , given that PKA and cAMP have been previously shown to extensively modulate cellular transcription , protein expression , and cell signalling [38–41] . As expected , growth of a virus expressing an E1A mutant unable to bind PKA ( Δ4–25 ) was reduced relative to WT . Importantly , knockdown of regulatory subunits RIα and RIIα did not further reduce the overall replication of this mutant , confirming that the lack of the E1A-PKA interaction contributes to its growth defect . Interestingly , loss of Cα expression negatively affected overall viral replication for both WT and Δ4–25 viruses , suggesting an E1A-independent effect of Cα on the HAdV life cycle . This may be related to reports that PKA activity is involved in dynein-mediated transport of species C HAdV virions to the nucleus during the establishment of infection [42 , 43] . Although E1A is presently unique in its ability to function as a viral AKAP , the important role of PKA in cellular homeostasis makes it an attractive target for modulation during infection by other viruses . For example , the Herpes simplex virus-1 US3 kinase interacts with and activates PKA to block apoptosis [44] . Varicella-zoster virus also upregulates PKA expression and modulates phosphorylation of PKA substrates to improve replication [45] . More typically , PKA is recruited to phosphorylate viral proteins , altering their stability , folding or ability to interact with other targets [46–49] . As one well characterized example , the E6 oncoprotein from human papillomavirus ( HPV ) is phosphorylated by PKA during infection , allowing it to interact with numerous cellular proteins [50 , 51] . While E1A does not appear to be a substrate for PKA , its unique mechanism of commandeering this enzyme via mimicry highlights the diverse ways in which viruses can repurpose the same cellular factors . It is also interesting that rather than encoding an entire PKA ortholog or an entire viral protein to subvert PKA function , HAdV uses a short 15 amino acid fragment of the versatile E1A protein to retask PKA for the benefit of the virus . The fact that the AKAP mimic motif in E1A also overlaps regions required for targeting other cellular regulatory proteins [7 , 52 , 53] further demonstrates the incredible effect of selective pressure on maximizing the impact of the relatively limited coding capacity of HAdV . In summary , we conclusively identify E1A as the first known viral AKAP . We demonstrate that the N-terminus of E1A has evolved to mimic the appearance , structure and function of the PKA interaction domain of cellular AKAPs . Furthermore , we have established that the AKAP function of E1A plays a biologically significant role in redirecting PKA to the nucleus during infection , where it is repurposed to enhance HAdV early gene expression and viral progeny production .
Human A549 ( provided by Russ Wheeler , Molecular Pathology/Genetics London Health Sciences Centre ) , HT1080 ( purchased from the American Type Culture Collection ) , and HEK293 cells [37] were grown at 37°C with 5% CO2 in DMEM ( Multicell ) supplemented with 10% fetal bovine serum ( Gibco ) . Plasmids were transfected into A549 and HT1080 cells using XtremegeneHP ( Roche ) following the manufacturer’s recommendation . After 24 hours of incubation , transfected cells were used for downstream experiments . All viruses were derived from the HAdV-5 dl309 background and express the 289R and 243R E1A proteins [54 , 55] . A549 cells were infected with WT ( dl309 ) or HAdV containing the indicated E1A mutant: ΔE1A ( dl312 ) , Δ4–25 ( dl1101 ) . Cells were infected at a multiplicity of infection ( MOI ) of 5 pfu/cell . Cell cultures were infected at 50% confluence and subconfluent cells were collected at indicated time points for downstream experiments . Downregulation of PKA subunits RIα , RIIα , and Cα was performed using Silencer Select siRNA ( Thermo ) . Four hours after seeding , siRNA was delivered to A549 cells via transfection with Silentfect ( BioRad ) according to the manufacturer’s instructions . A scrambled siRNA was used as a negative control . Treated cells were used for experiments 48 hours post-transfection . Downregulation of E1A in HEK293 cells was performed using a cocktail of E1A-specific siRNAs generated by Thermo Fisher’s custom siRNA design platform . All siRNAs used can be found in S1 Table . All constructs were expressed in vectors under control of the CMV promoter . WT RIα , RIIα , and Cα were PCR amplified ( from Addgene 23741 , 23789 and 23495 ) and cloned into pcDNA4-HA and pCANmyc . RIα Δ1–63 and RIIα Δ1–45 were similarly derived and expressed in pCANmyc . D/D fragments of RIα and RIIα were both expressed as EGFP fusions from pEGFP-N1 . E1A fragments were expressed as fusions to EGFP and either described previously ( 1–82 , 93–139 , 139–204 , 187–289 ) [56] or derived via PCR and cloned into pEGFP-C2 ( 1–29 , 1–14 , 14–28 , 16–28 , 29–49 ) . WT HAdV-5 E1A and its associated deletion mutants were all expressed in pcDNA3 . These constructs were previously described ( Δ4–25 , Δ26–35 , Δ30–49 , Δ48–60 , Δ61–69 , Δ70–81 ) [56] or generated via PCR ( Δ1–82 , Δ1–14 , Δ1–29 , Δ16–28 ) . Point of mutants of E1A ( D21K , E6K , V27K , E26A V27A , E5K ) and RIα ( Q28E , L31A K32A , I35A V36A ) were generated by PCR and expressed in pcDNA3 and pcDNA-HA respectively . The largest E1A isoform from the six HAdV species were cloned as EGFP fusions . D-AKAP1 mutants were generated via PCR of a construct kindly provided by Thomas Kuntziger ( Oslo ) and expressed in pEGFP-C2 . RIAD-EGFP and sAKAPis-EGFP were provided by Alan Howe ( Vermont ) . Cells were lysed in NP40 lysis buffer ( 150mM NaCl , 50mM Tris-HCL pH 7 . 5 , 0 . 1% NP-40 ) with protease inhibitor cocktail . Protein concentrations were determined using BioRad protein assay reagent using BSA as a standard . Immunoprecipitations were carried out at 4°C for 4 hours , or overnight for endogenous interactions . 2% of sample was kept as input control . After washing with NP40 buffer , complexes were boiled in 25 µL of LDS sample buffer for 5 minutes . Samples were separated on NuPage 4–12% Bis-Tris gradient gels ( Life Technologies ) and transferred onto a PVDF membrane ( Amersham ) . Membranes were blocked in 5% skim milk constituted in TBS with 0 . 1% Tween-20 . All antibodies used can be found in S2 table . Horseradish peroxidise-conjugated secondary antibody was detected using Luminata Forte or Crescendo substrate ( Millipore ) . For biochemical fractionation of infected A549 cells , nuclear and cytoplasmic extracts were acquired using an NE-PER kit from Thermo-Fisher . Cells were fixed in 3 . 7% paraformaldehyde , permeabilized on ice using 0 . 2% Triton X-100 , and blocked using 3% BSA in phosphate-buffered saline ( PBS ) . Samples were incubated in the indicated primary antibody for 1 hour at room temperature or 4°C overnight and another hour at room temperature with secondary antibodies ( Alexa Fluor 594 α-rabbit , Alexa Fluor 488 α-mouse ) ( Life Technologies ) . Samples were mounted with Prolong Gold reagent containing DAPI ( Life Technologies ) . Confocal images were acquired using a Fluoview 1000 laser scanning confocal microscope ( Olympus Corp ) . Non-confocal images were acquired using an Eclipse Ti-U inverted laser microscope ( Nikon ) . Quantification of total cellular signal and nuclear signal was conducted using ImageJ . Cells were normalized for both cytoplasmic and nuclear size and %nuclear signal was determined as previously described [57] . Total RNA was prepared with Trizol extraction ( Life Technologies ) . A total of 1 μg of RNA was reverse transcribed into cDNA by random priming using the qScript cDNA supermix ( Quanta Biosciences ) following the manufacturer’s instructions . Quantification of cDNA was done using Power SYBR-Green mastermix ( Applied Biosystems ) with oligonucleotide sequences that specifically recognize the indicated target . GAPDH was used as a control for total CDNA along with a no-RT negative control . Results were normalized to the GAPDH and uninfected samples and calculated using the ΔΔCt method [58] . Primers used can be found in S3 Table . Approximately 107 cells per sample were cross-linked in 2mM ethylene glycol bis ( succinimidyl succinate ) ( EGS ) for 1 hour followed by 1% formaldehyde for 15 minutes at room temperature . Reactions were quenched with 0 . 125M glycine and washed twice with cold PBS . Cell pellets were processed in ChIP buffer 1 ( 10mM HEPES [pH 6 . 5] , 10mM EDTA , 0 . 5mM EGTA , 0 . 25% Triton X-100 ) , ChIP buffer 2 ( 10mM HEPES [pH 6 . 5] , 1mM EDTA , 0 . 5 mM EGTA , 200mM NaCl ) , and ChIP buffer 3 ( 50mM Tris-HCl [pH 8] , 10mM EDTA , 0 . 5% Triton X-100 , 1% SDS , and protease inhibitors ) . Lysates were sonicated in an ultrasonic bioruptor bath ( Diogenode ) to yield DNA fragments between 200–500 basepairs . 80 μg of chromatin supernatant was used for ChIP , 1% of this was kept for input controls . Samples were diluted 10-fold in ChIP dilution buffer ( 50mM Tric-HCl [pH 8] , 10mM EDTA , 150mM NaCl , 0 . 1% Triton X-100 , protease inhibitors ) and precleared with 30μL of Protein G Dynabeads ( Invitrogen ) for 1 hour at 4°C . Immunoprecipitations were performed overnight at 4°C using 5μg of the indicated antibody in S2 Table . The next morning , 30μL of Dynabeads were incubated with each sample for 2 hours . Beads were then washed with twice each with wash buffer 1 ( 20mM Tris-HCl [pH 8] , 2mM EDTA , 150mM NaCl , 1% Triton X-100 , 0 . 1% SDS ) , wash buffer 2 ( 20mM Tris-HCl [pH 8] , 2mM EDTA , 500mM NaCl , 1% Triton X-100 , 0 . 1% SDS ) , and wash buffer 3 ( 10mM Tris-HCl [pH 8] , 1mM EDTA ) . Immunocomplexes were extracted twice with 150μL of elution buffer ( 0 . 1M NaHCO3 , 1% SDS ) . 25μL of 2 . 5M NaCl was added to the 300μL pooled elutions and incubated overnight at 65°C to de-crosslink the complexes . DNA was purified using a PCR purification kit ( Thermo ) . qPCR using SYBR-Green was performed as described previously using 80nM oligos and 0 . 5μL of ChIP DNA per 15μL reaction . All experiments were carried out with three biological replicates performed in duplicate . Graphs represent mean and standard error of the mean ( S . E . M . ) of all biological replicates . For western blots a representative image was selected . Statistical significance of numerical differences was calculated using one-way ANOVA and Holm-Sidak post-hoc comparison between experimental conditions . To model the interaction between PKA and E1A , we first performed a structural prediction of the amino terminus of E1A by submitting the primary sequence to Phyre 2 [59] . The predicted structure of E1A was subsequently docked onto PKA ( PDB ID: 3IM4 ) using the standard settings profile of ClusPro2 . 0 [60] . Residues forming an E1A-PKA binding interface within 4 Angstroms were selected for further experimental analysis . All images were generated in the PyMOL Molecular Graphics System , Version 1 . 8 Schrödinger , LLC . Additional in silico comparisons of HAdV-5 and HAdV-4 E1A were conducted using Clustal Omega [61] and the UCL Department of Computer Science’s PSI-PRED protein sequence analysis workbench [62] .
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Studies of human adenovirus ( HAdV ) , a small DNA tumor virus , illustrate the profound impact of viral proteins on multiple host functions . The multifunctional E1A proteins of HAdV are particularly adept at targeting key cellular regulators . Mechanistically , E1A alters or inhibits the normal function of the cellular proteins that it targets , and also establishes new connections in the cellular protein interaction network . Through these interactions , E1A creates a cellular milieu more conducive for replication . Here we show that HAdV E1A mimics cellular A-kinase anchoring proteins ( AKAPs ) in both appearance and function . We found that the protein kinase A ( PKA ) regulatory subunits are conserved targets of most HAdV E1A species . Structural modeling and a docking analysis predict a remarkable similarity between the binding of E1A and cellular AKAPs to PKA , which was confirmed experimentally . In addition , we observed E1A-mediated relocalization of PKA subunits and competition between E1A and cellular AKAPs during infection that contribute to HAdV gene expression and overall viral replication . Together , our studies identify E1A as the first known viral AKAP , and reveal a unique example of viral subversion of the PKA pathway via structural mimicry .
|
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"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
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2016
|
Functional and Structural Mimicry of Cellular Protein Kinase A Anchoring Proteins by a Viral Oncoprotein
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Prediction of genetic merit using dense SNP genotypes can be used for estimation of breeding values for selection of livestock , crops , and forage species; for prediction of disease risk; and for forensics . The accuracy of these genomic predictions depends in part on the genetic architecture of the trait , in particular number of loci affecting the trait and distribution of their effects . Here we investigate the difference among three traits in distribution of effects and the consequences for the accuracy of genomic predictions . Proportion of black coat colour in Holstein cattle was used as one model complex trait . Three loci , KIT , MITF , and a locus on chromosome 8 , together explain 24% of the variation of proportion of black . However , a surprisingly large number of loci of small effect are necessary to capture the remaining variation . A second trait , fat concentration in milk , had one locus of large effect and a host of loci with very small effects . Both these distributions of effects were in contrast to that for a third trait , an index of scores for a number of aspects of cow confirmation ( “overall type” ) , which had only loci of small effect . The differences in distribution of effects among the three traits were quantified by estimating the distribution of variance explained by chromosome segments containing 50 SNPs . This approach was taken to account for the imperfect linkage disequilibrium between the SNPs and the QTL affecting the traits . We also show that the accuracy of predicting genetic values is higher for traits with a proportion of large effects ( proportion black and fat percentage ) than for a trait with no loci of large effect ( overall type ) , provided the method of analysis takes advantage of the distribution of loci effects .
Genomic prediction of future phenotypes or genetic merit using dense SNP genotypes can be used for prediction of disease risk , for forensics , and for estimation of breeding values for use in selection of livestock , crops and forage species [1]–[4] . In dairy cattle , estimated breeding values predicted from genomic information are now in wide spread use [3] , [5] . The accuracy of genomic predictions will depend on the number of phenotypes used to derive the prediction equation , the heritability of the trait , the effective population size , the size of the genome , the density of markers , and the genetic architecture of the trait , in particular number of loci affecting the trait and distribution of their effects [6]–[8] . In simulated data the distribution of loci effects affects the accuracy of predicting genetic values . However in real data it has been difficult to show that traits vary in this distribution . For instance , in many cases a statistical method ( Best linear unbiased prediction or BLUP ) designed for traits with many loci all of small effects performs as well as methods assuming other distributions of loci effects , such as a t-distribution [9]–[10] . If it is true that most complex traits are controlled by very many polymorphisms of very small effect ( a nearly infinitesimal model ) , this has important consequences for prediction of genetic merit or future phenotypes such as disease risk . Formulae for the accuracy of genomic prediction under this model suggest that sample sizes >100 , 000 individuals will be needed to achieve high accuracy , except for populations with a small effective population size [7] . Thus it is important to determine the distribution of effect sizes for a range of traits , use this information in genomic prediction and plan future experiments accordingly . Coat colour in mammals is usually regarded as trait controlled by a few loci of large effect . However , aspects of coat colour have been suggested as a model for investigating complex trait architecture , given the close relationship between genotype and phenotype [11] . White spotting of the coat is one such “quantitative” coat colour trait , as it can be recorded as the proportion of the coat which is white . White spotting occurs in many domesticated mammals , including cattle , horses , dogs and cats . In dogs , mutations causing white spotting have been mapped to the microphthalmia-associated transcription factor ( MITF ) [12] . In mice , at least ten genes have been demonstrated to affect white spotting [13] . In horses , an inversion on chromosome 3 in the region of the Hardy-Zuckerman 4 feline sarcoma viral oncogene homolog ( KIT ) gene is associated with tobiano white-spotting pattern [14] , and a seven other mutations at the KIT gene are associated with white coat colour phenotypes [15] . Further , mutations in KIT are also associated with roan coat [16] . In domestic pigs , a number of alleles of the KIT gene have been characterised and associations with dominant white colour demonstrated [17] . Recently , in black and white dairy cattle , variation in the degree of white spotting ( measured as proportion of the coat which is black ) has been mapped using linkage to large genomic intervals on chromosome 6 and chromosome 22 , which contain the KIT and MITF loci respectively [18] . However a genome wide association study ( GWAS ) has not been reported for this trait . Complex traits which have been studied by GWAS in dairy cattle include fat% and “type” , a complex conformation trait [19]–[20] . A single mutation in the DGAT1 gene accounts for 30% of the variation in fat% from Holstein Friesian cattle [21] . This is in contrast to “type” , a complex trait combining scores for a number of aspects of cow confirmation ( termed “overall type” ) , for which only modest effects have been reported . In this paper , we use proportion of black on the coat , fat% and overall type to show that differences in the distribution of loci effects are recognisable using a new method to estimate the distribution of variance explained by each QTL . We demonstrate that three loci , KIT , MITF and a locus on chromosome 8 together explain a considerable proportion of the variation in proportion of black , but a large number of loci of small effect are necessary to capture the remaining variation . We then contrast the accuracy of genomic prediction which can be achieved for this trait with the accuracy of genomic predictions for overall type and fat% in milk . The results demonstrate a clear effect of trait architecture on the accuracy of genomic predictions .
While GWAS results for fat% have been reported previously , no GWAS results have been reported for proportion of black [19]–[20] . In our population of 756 Holstein bulls , phenotypes for proportion of black varied from almost completely black to completely white , Figure 1 . A GWAS study using 43115 SNPs detected three genome regions containing SNPs with P values<10−4 in the discovery population . We tested these in an independent validation population and confirmed three SNPs at P<0 . 001 , Table 1 and Figure 2 . The most significant SNP was within the KIT locus on chromosome 6 ( 72 , 104 , 530 bp ) . There was another highly significant SNP at 32 , 459 , 763 bp on chromosome 22 which is in very close proximity to the MITF locus ( 32 , 353 , 746–32 , 397 , 952 bp ) . There was also a highly significant SNP on chromosome 8 at 64 , 164 , 842bp . This SNP is within the zinc finger CCHC domain containing 7 gene ( ZCCHC7 ) . However zinc finger CCHC domain containing genes have not been implicated in coat colour development in any species . Perhaps a more plausible candidate in this region is PAX5 ( 63 , 778 , 241–63 , 950 , 395bp ) . Other members of this family , PAX3 and PAX6 , have been demonstrated to interact with MITF [22] . Planque et al . [22] pointed out that the structure and docking of PAX5 should be nearly identical to PAX6 , because their C-terminal subdomains are 75% identical and all DNA-contacting residues are conserved [and 23] . However the interaction between PAX5 and MITF remains to be demonstrated . Together the three loci on chromosomes 6 , 8 and 22 accounted for 24% of the variation in proportion of black phenotypes in the validation population . There was no evidence of dominance for any of these significant SNPs , and no statistical support for an interaction between the significant SNPs in KIT , MITF and the locus on chromosome 8 when we fitted models evaluating these effects . Figure 2 also illustrates an interesting property of genome wide association studies in black and white dairy cattle , and other breeds and species with small recent effective population size . The SNP residing in the KIT gene has the largest F-value , exceeding the next largest SNP by 10 F units . However there are significant SNPs extending 10-15Mb either side of the most significant SNP . This is likely to be caused by the pattern of linkage disequilibrium in livestock: while at short distances levels of r2 between markers are similar to that observed in humans , low levels of LD ( r2≤0 . 1 ) extend for many Mb in Holstein-Friesian cattle , probably due to recent reduction in effective population size [24] . A GWAS for fat% has been conducted in the same data [20] . Briefly , 40 SNPs had validated associations ( P<0 . 01 ) for fat% , with the largest effects on chromosome 14 in close proximity to the DGAT1 gene , and other large effects on chromosomes 2 , 6 and 20 . For overall type , a small number of SNPs had validated associations , however the false discovery rate in the validation population was close to 100% ( Figure S1 ) . To overcome the tendency to find significant SNPs up to 15 Mb from a causal variant , we then used a different approach to conduct the genome wide association studies , where all SNPs were fitted simultaneously as random effects sampled from a t-distribution ( method BayesA of Meuwissen et al . [25] ) . The effects of the SNPs associated with KIT and MITF , and the SNP on chromosome 8 that was significant in the GWAS , had the largest absolute value , but there were other smaller effects on chromosome 4 , 7 and 17 , Figure 3 . Genome scans conducted in a similar way for fat concentration in milk ( fat% ) revealed large effects on chromosome 14 in close proximity to the DGAT1 gene ( 443 , 937 bp ) , on chromosome 5 ( position 101 , 015 , 511 bp ) and 20 ( 34 , 036 , 832 bp ) for fat% . However there were no effects greater than 5×10−5 phenotypic standard deviations for overall type . Although these analyses demonstrate the importance of a small number of loci , they do not describe the complete distribution of gene effects . Estimated SNP effects will reflect both the QTL effect and the LD between the QTL and the SNP . Although the level of LD between SNP and QTL is unknown , the average level of LD ( r2 ) between adjacent SNPs in our population was only 0 . 271 . Therefore we took the approach of using chromosome segments to derive the distribution of effects as chromosome segments with multiple SNPs are more likely to capture the complete effect of the QTL . A chromosome segment was defined as consisting of 50 adjacent SNP loci . The SNPs were approximately equally spaced , such that a 50 SNP segment was 3350kb long . This size of segment was chosen as a compromise between having too little SNP information to accurately estimate its contribution to the variance , and having sufficiently small segments to enable interpretation regarding the distribution of effects on the trait . Then a genomic relationship matrix among the animals for that chromosome segment was constructed ( as described in materials and methods below ) . To remove variance due to genes in the rest of the genome and due to population structure , a second genomic relationship matrix was constructed from all SNPs other than the 50 in the current chromosome segment . Then the proportion of variance explained by the 50 SNP chromosome segment was estimated , with both effects fitted simultaneously . However , estimates of proportion of variance explained derived in this way contain sampling error . For instance , even if a chromosome segment has no effect on the trait , the estimated variance explained can be positive ( it cannot be negative because maximum likelihood estimation is restricted to the parameter space and real variances cannot be negative ) . This was reflected in the fact that the sum of the variances across the segments without correction for sampling error was greater than the total genetic variance . We wish to estimate the distribution of the true effects of chromosome segments rather than the distribution of estimated effects . To do this we used permutation to derive the distribution of the proportion of variance explained due to the sampling error alone . Then we used maximum likelihood to estimate the distribution of true effects ( Figure 4 ) which , when combined with the distribution of sampling errors , would yield the observed distribution of the estimated variance explained by 50 SNP chromosome segments . When we did this , for all three traits many segments explain <0 . 1% of the genetic variance and for proportion black 96% of segments fall into this category . If the genetic variance contributed by the segments explaining less than 0 . 1% of the genetic variance is summed , such segments appear to explain half the variance for both overall type and proportion of black . However , there are tens of segments that explain 0 . 1–4 . 7% of the variance for all three traits . For proportion of black there are also a three segments explaining 4 . 7% to 18 . 8% of the variance and for fat% there are three segments explaining 4 . 7–37 . 5% . This concurs with the results of the GWAS for these traits . The total variance explained is greater then 100% because segments next to the segment containing DGAT1 , for instance , explain a significant amount of variance , so that the variance explained by DGAT1 is counted more than once ( the total summed variances were 204% , 107% , and 213% for fat% , overall type and proportion of black ) . The distribution of variances of chromosome segments can also be expressed as the cumulative proportion of the total variance explained when the segments are ranked from largest variance to smallest ( Figure 5 ) . The variances of the segments surrounding the segment containing KIT , MITF and the locus on chromosome 8 were set to zero so variance caused by these mutations was not double counted . The same procedure was used for the segments surrounding the DGAT1 gene and other large effects for fat% . For proportion of black and particularly fat% , a small proportion of segments are necessary to capture a significant proportion of the variance , while for overall type a greater number of segments are required . Note that the sum of the variance from the segments explaining the largest proportion of the variance is now 20% , compared with 24% estimated from the GWAS . This reflects the fact that the estimate of variance explained is regressed to account for estimation error . For proportion of black , as segments other than the three containing KIT , MITF and the significant SNP on chromosome 8 only explain a small proportion of the variance , many of them are required to explain even the majority of the variance . To investigate the effect of the distributions of loci effects on the accuracy of genomic estimated breeding values , we used SNP effects for each trait from the Bayesian approach described above to predict genomic estimated breeding values . This was done for the independent validation population of 400 bulls , as , where X is a matrix with a row for each animal and a column for each SNP and Xij is the number of “2” alleles where they alleles are designated 1 or 2 , is a vector containing the estimate of the size of the effect of marker ( the effect of inheriting on copy of allele 2 ) when the effect of the first allele is set to zero . The phenotypes of the animals in the validation population were not used to predict the SNP effects . To estimate the accuracy of the GEBV we used the correlation between it and the phenotype of each animal corrected for the correlation of the phenotype with the true genetic value . The accuracies of genomic estimated breeding value were 0 . 56 , 0 . 69 and 0 . 80 for overall type , proportion of black and fat% respectively , Table 2 . The accuracy of these GEBVs was compared to that obtained using a statistical analysis ( BLUP ) that assumed all SNP effects are sampled from a normal distribution and therefore no large effects exist . These accuracies of the GEBVs using the Bayes A method were higher than those using the BLUP method for fat% and proportion of black but lower for overall type , Table 2 . GEBV was also calculated using subsets of SNPs ranked in order of the size of their effect . For each subset , BayesA was re-run to predict SNP effects . For proportion of black , a very small number of SNPs were required to achieve close to 95% of the accuracy possible with the full set of SNPs , while at the other extreme for overall type 2000 SNPs were required to achieve greater than 90% of the accuracy possible with the full set of SNPs , Figure 6 . For traits with a few moderate effects , and many small effects , such as proportion of black , the accuracy of estimating the moderate effects will be much higher than the accuracy of estimating the very small effects . There is also a large effect of the number of records used to estimate the effects – for proportion of black there were only 327 records while for the other traits there were 756 records . When the estimated effects are used in a prediction equation for estimated breeding values , the moderate effects therefore contribute the overwhelming majority of the total accuracy of prediction . With a small number of phenotypic records , the estimates of segments with small effects can be so inaccurate that they contribute nothing to the accuracy of prediction . This explains the apparent discrepancy between Figure 5 , where many chromosome segments are needed to capture the total genetic variance of the trait , and Figure 6 , where close to the maximum accuracy of prediction achievable with all SNPs ( 0 . 59 , Table 2 ) is achieved with less than 10 SNPs . Our results demonstrate that large differences exist in the architecture of different complex traits . For both proportion of black and fat% there are segregating mutations of moderate effect so that the distribution of effects is leptokurtotic . This in contrast to overall type which has only loci of small effect , and the distribution of these effects could be assumed to be normal . Information on the degree of leptokurtosis of the distribution of effects can be used to guide the design of experiments that will subsequently enable genomic predictions . A deterministic method has been developed to predict the accuracy of genomic estimated breeding values [7] . The parameters of this formula were the number of phenotypic records in the reference population ( N ) , the heritability of the trait ( h2 ) , the length of the genome ( L ) , and the distribution of QTL effects . The distribution of effects could be either normal or leptokurtotic . When a normal distribution of effects is assumed , the accuracy of genomic breeding values can be predicted as where a = 1+2 λ/N , and λ = qk/h2 , with k = 1/log ( 2Ne ) , where Ne is the effective population size . The parameter q = number of independent chromosome segments in the population . The value of q used here was 2NeL , where L is the length of the genome in Morgans . Using the same number of phenotypic records as were used in our experiment , and the same heritabilities of the traits , the deterministic prediction of accuracies are given Table 2 . For leptokurtotic distributions , there is no closed form equation for the accuracy of breeding values , but these accuracies can be derived by numerical integration of the accuracy of predicting the effects given the assumed distribution and allele frequencies [7] . A t distribution with 4 . 012 degrees of freedom was used to model the distribution of effects , and a U shaped distribution of allele frequencies as expected under the neutral model was used [25] . As expected , the leptokurtotic distribution of effects gave higher predicted accuracies of genomic breeding value than a normal distribution of effects . The observed accuracy of GEBVs for overall type in our experiment , 0 . 35 , matches closely the prediction for accuracy of GEBV for a quantitative trait with the same heritability and a normal distribution of effects . Conversely , both fat% and proportion of black better match the predictions when a leptokurtotic distribution of effects was used . The maximum accuracy for GEBVs should be obtained when the assumed distribution of effects matches the true distribution [7] . In the absence of knowledge about the true distribution two extreme approaches have been used . In one all SNP effects are assumed to come from a single normal distribution ( the analysis called BLUP above ) . In the other only a small number of highly significant and validated SNPs from GWAS are used . For example , vanHoek et al . [26] used 9 validated genetic polymorphisms to predict disease risk for type 2 diabetes . In their study , the value of the SNP information was low , with only marginal improvement as a result of using the genetic polymorphisms beyond clinical characteristics . In this paper we have demonstrated that for some traits , such as overall type , a large number of SNPs will be required to predict the trait with any accuracy . An approach where all SNPs are fitted simultaneously to derive a prediction equation , ignoring significance levels , should lead to higher accuracies of prediction , than an approach which uses only associations detected in GWAS with stringent thresholds . The accuracies achievable with this approach can be predicted deterministically provided we have some knowledge of whether the distribution of QTL effects is normal or leptokurtotic . The deterministic results agree only reasonably well with those we observed for proportion of black , fat% and overall type , suggesting that further knowledge about the distribution of effect would be beneficial . However , even with current knowledge the deterministic approach can be used to design experiments to develop genomic predictions . It interesting to speculate on why large effects are segregating for fat% and proportion of black , but not overall type . For fat% , the fact that DGAT1 continues to segregate in the population may reflect the change in breeding goal for dairy cattle over time [21] . The mutant allele decreases milk fat yield but increases milk volume so artificial selection is likely to have favoured it at times but not consistently . This swept the allele to moderate frequencies in the population . Mutations causing white spotting must have been selected by breeders of black and white cattle since it is their defining feature . Thus in both cases , mutations which would have been unfavourable before domestication , were selected and still segregate at intermediate frequencies . Overall type has also been subject to artificial selection pressure since domestication . However , any mutations of large effect would have a detrimental effect on overall fitness ( natural and artificial ) and would likely have been quickly removed from the population . There is little evidence for alleles of large effect for most complex traits [27] . Thus most complex traits are like overall type in architecture . Fat% and proportion black may be examples of transient situations where a change in selection pressure has driven a mutation to intermediate frequency . Recently Eyre-Walker [28] argued that rare alleles of large effect should explain much of the variation in complex traits if there is natural selection for the trait . Our results suggest that if alleles of large effect do exist , they are at such low frequency that they individually explain a small proportion of the variance . For overall type and proportion of black at least we find that the majority of variance is contributed by a large number of chromosome segments , each explaining a small proportion of the total variance . The question is then do the segments explain a small proportion of the total variance because they harbour QTL of small effect at moderate frequency , or because they harbour QTL of large effect at very low frequency . While our experiment cannot answer this question directly , some evidence that the former explanation might be true comes from linkage experiments . Linkage experiments can estimate QTL effect sizes directly , rather than through SNP in LD with the QTL , as the association of the marker and QTL within families will be almost perfect , provided enough markers are used . Provided at least one sire in the experiment is heterozygous at the QTL , a QTL of large effect should be detected . However , despite quite large linkage mapping studies in dairy cattle with many sires and very large numbers of progeny , very few QTL of large effect were found for complex traits [29]–[30] . One exception was the DGAT1 region on chromosome 14 , which was highly significant in many linkage mapping experiments [eg 21] . Taken together , our results and the results of the linkage mapping studies suggest that , although mutations of moderate effect occur ( as demonstrated here for fat% and proportion black ) , they are very rare for complex traits compared to mutations of small effect . Our results have some implications for explaining the “missing heritability” in GWAS of human population data [27]; namely that some of the missing heritability is explained by mutations with very small effects on the trait ( undetectable by GWAS ) , but there are very many of them . Dairy cattle have some advantages for studying this question because large amounts of data are available through the breeding programme , because analyses of sires with large numbers of tested progeny produce traits with high effective heritabilities and because the LD structure may be relatively favourable for capturing genetic variance with 10-fold fewer markers than are used in humans . However it must be pointed out that conclusions results from cattle may not be relevant for other species: the larger LD blocks in cattle than other species will mean more variance per “effective” locus than in populations with larger effective population size . Further , the history of cattle domestication with at least two separate domestications followed by hybridisation events and strong artificial selection may produce unusual patterns of diversity and LD and the distribution of allele effects may owe more to recent population demographics and artificial selection than to the natural selection for fitness that will drive other populations including humans .
The data set consisted of 1200 Australian Holstein bulls . For fat% and overall type the ‘phenotype’ used for each bull was the mean phenotype of his daughters . To obtain this phenotype we de-regressed the Australian breeding values ( ABVs ) to remove the contribution from relatives other than daughters [3] while retaining the correction for non-genetic effects such as herd . All bulls with de-regressed estimated breeding values had at least 80 daughters . The traits measured in the bull's daughters were fat% in a sample of the milk on each test day , and overall type . Overall type is composite trait combining scores for a number of aspects of the cow's conformation , including frame-capacity , rump , feet and legs , fore udder , rear udder , mammary system and dairy character ( see http://www . adhis . com . au/ for more details ) . For portion of black , each bull himself was scored according to the proportion of black on the entire body , from 0% to 100% black . The values ranged from 5% black to 95% black . The scorer was the same for all the bulls . The bulls were genotyped for the Illumina Bovine50K array , which includes 54 , 001 Single Nucleotide Polymorphism ( SNP ) markers [31] . The following criteria and checks were applied to the bull's genotypes . Mendelian consistency checks revealed a small number of either sons who were discordant with their sires at many ( >1000 ) SNPs or sires with many discordant sons . These animals ( 17 ) were removed from the data set . We omitted bulls if they had more than 20% of missing genotypes . 1181 bulls passed these criteria . Criteria for selecting SNPs were; less than 5% pedigree discordants ( eg . cases where a sire was homozygous for one allele and progeny were homozygous for the other allele ) , 90% call rate , MAF>2% , Hardy Weinberg P<0 . 00001 . 40077 SNPs met all of these criteria . A small number of these were not assigned to any chromosome on Bovine Genome Build 4 . 0 , and were omitted from the final data set , as were SNPs on the X chromosome . Parentage checking was then performed again , and any genotypes incompatible with pedigree were set to missing . To impute missing genotypes , the SNPs were ordered by chromosome position . All SNPs which could not be mapped or were on the X chromosome were excluded from the final data set , leaving 39 , 048 SNPs . To impute missing genotypes , the genotype calls and missing genotype information was submitted to fastPHASE chromosome by chromosome [32] . The genotypes were taken as those filled in by fastPHASE . The accuracy of imputing genotypes was 98 . 6% [5] . The discovery dataset consisted of bulls progeny tested before 2004 ( n = 756 ) . For proportion of black portion 327 bulls in the reference set had phenotypes . The bulls in the validation dataset were progeny tested during or after 2004 ( n = 400 ) In the discovery set of bulls , a linear model was fitted to the bull's proportion of black phenotypes to determine if the SNPs accounted for any variation . The top–bottom called genotypes were re-coded as 0 for the homozygote of the first alphabetical allele , 1 for the heterozygote , and 2 for the homozygote of the second alphabetical allele . The effect of each SNP was estimated in turn using the model where y is a vector of proportion of black , μ is the mean , S is the ( random ) effect of the sire of each bull , x is a vector of genotypes , b is the effect of the SNP , and e is a vector of random residuals . The variance of the sire effects was Iσ2S where I is an identity matrix and σ2S is the sire variance . Fitting the sire effect should remove any spurious associations due to family structure . All data analyses were performed using mixed linear models with variance components estimated by residual maximum likelihood [33] . SNPs that were significant at P<0 . 0001 were fitted in the validation set using the same model as above . For each 50SNP segment of chromosome , we estimated the proportion of variance explained by building a genomic relationship matrix ( as described above ) based on the 50SNPs only ( G1 ) , and a second genomic relationship matrix ( G2 ) using all SNPs except those in the current 50 SNP segment . We the fitted the modelWhere y is a vector of phenotypes , μ is the mean , 1n is a vector of 1s , Z is a design matrix allocating records to animals , g1 is a vector of genetic effects for a 50 SNP segment , assumed to be normally distributed with mean 0 and co ( variance ) , g2 is a vector of breeding values based on all the other segments , assumed to be normally distributed with mean 0 and co ( variance ) and e is a vector of random normal deviates ∼ . Variance components were estimated with ASREML [33] , and the proportion of variance explained by each segment was calculated as . The estimate of the proportion of variance explained by a chromosome segment i ( ) is naturally subject to some sampling error . is analogous to the squared correlation between the effect of the segment and the phenotype so yi is analogous to the correlation . We modelled yi as where ti is the true correlation between segment i and phenotype and ei is a sampling error While it is not possible to estimate the sampling error for a specific segment , we can estimate the distribution of sampling errors . To do this the phenotypes were permuted across the genotypes 1000 times and the proportion of variance explained by each segment re-calculated . Under the null hypothesis that there is no real correlation between segments and phenotypes , the distribution of the estimated proportion variance explained should be a mixture of zero and a chi-square with 1 degree of freedom ( half the time the correlation would be estimated to be negative but maximum likelihood always reports an estimate within the parameter space and so half he reported estimates of variance are zero ) . Therefore the square roots of these estimates were assumed to be near-zero ( half the time ) and the positive half of a normal distribution the other half . The standard deviation of e , σ , was then taken as the square root of the average proportion of variance explained multiplied by 2 ( the multiplication by two was to account for the fact that negative estimates of the proportion of variances explained are reported as zero ) . We then used maximum likelihood to estimate the distribution of true chromosome segment variances ( ti2 ) given that we had a sample of estimated chromosome segment variances ( ) and with ei∼N ( 0 , σ ) . We estimate the distribution of t and then convert that to a distribution of t2 . We did not wish to assume any parametric form for the distribution of t so we approximate it by a discrete distribution in which the proportion explained can only take values j = 0 . 00 , 0 . 005 and so on to 1 ( eg 100 classes between 0 and 1 , but including 0 ) . We then estimate the frequency of these discrete values . The probability of observing given j and σ was taken as if and if where is the density function of the normal distribution and is the cumulative function of the normal distribution . ( If t+e is negative for a segment then y2 would be reported as zero since negative variances are not allowed ) . Then an expectation maximisation ( EM ) algorithm was used to estimate the proportion of chromosome segments in each class fj . The EM algorithm had three steps Steps 2 and 3 were repeated until the fj values did not change between iterations . The results ( Figure 4 ) are presented as a distribution of t2 where the frequencies all values of t between √0 . 01 and √0 . 03 are summed and presented as the frequency of 0 . 01<t2<0 . 03 etc .
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Prediction of future phenotypes or genetic merit using high-density SNP chips can be used for prediction of disease risk in humans , for forensics , and for selection of livestock , crops , and forage species . Key questions are how accurately these predictions can be made and on what parameters does the accuracy depend . In this paper , we use three dairy cow traits—proportion of black on coat , fat percentage in milk , and overall type , which measures cow confirmation—to demonstrate the large differences among genetic architectures of complex traits . For example 24% of the genetic variance in proportion of black is determined by three loci , KIT , MITF , and a locus on chromosome 8; however a surprisingly large number of additional loci , all of small effect , are required to capture the remaining variation . For overall type , a very large number of loci are necessary to capture the same level of variance . We also show that the accuracy of predicting genetic values is higher for traits with a proportion of large effects ( proportion black and fat percentage ) than for a trait with no loci of large effect ( overall type ) , provided the method of analysis takes advantage of the distribution of loci effects .
|
[
"Abstract",
"Introduction",
"Results/Discussion",
"Materials",
"and",
"Methods"
] |
[
"genetics",
"and",
"genomics/complex",
"traits",
"genetics",
"and",
"genomics/population",
"genetics"
] |
2010
|
Genetic Architecture of Complex Traits and Accuracy of Genomic Prediction: Coat Colour, Milk-Fat Percentage, and Type in Holstein Cattle as Contrasting Model Traits
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Enteropathogenic Escherichia coli ( EPEC ) , a common cause of infant diarrhea , is associated with high risk of mortality in developing countries . The primary niche of infecting EPEC is the apical surface of intestinal epithelial cells . EPEC employs a type three secretion system ( TTSS ) to inject the host cells with dozens of effector proteins , which facilitate attachment to these cells and successful colonization . Here we show that EPEC elicit strong NF-κB activation in infected host cells . Furthermore , the data indicate that active , pore-forming TTSS per se is necessary and sufficient for this NF-κB activation , regardless of any specific effector or protein translocation . Importantly , upon infection with wild type EPEC this NF-κB activation is antagonized by anti-NF-κB effectors , including NleB , NleC and NleE . Accordingly , this NF-κB activation is evident only in cells infected with EPEC mutants deleted of nleB , nleC , and nleE . The TTSS-dependent NF-κB activation involves a unique pathway , which is independent of TLRs and Nod1/2 and converges with other pathways at the level of TAK1 activation . Taken together , our results imply that epithelial cells have the capacity to sense the EPEC TTSS and activate NF-κB in response . Notably , EPEC antagonizes this capacity by delivering anti-NF-κB effectors into the infected cells .
Enteropathogenic and enterohemorrhagic E . coli ( EPEC and EHEC , respectively ) are important human pathogens that cause symptoms ranging from subclinical chronic colonization to acute , life threatening infections [1] . EPEC and EHEC form typical attaching and effacing ( AE ) lesions on intestinal epithelial cells . These lesions are characterized by intimate attachment to the epithelium and effacement of the brush border microvilli [2 , 3] . Throughout infection , these pathogens remain either in the intestinal lumen , or attached to the apical surface of the intestinal epithelia . From this extracellular location these pathogens manipulate epithelial cell functions to facilitate efficient host colonization [2 , 4] . Although the apical surface of the intestinal epithelium is constantly challenged with massive amounts of MAMPs ( Microbial Associated Molecular Patterns ) , the reaction to these MAMPs is tightly regulated and restrained to prevent chronic inflammation . MAMPs , such as LPS , flagellin and CpG DNA , derived from commensal or pathogenic E . coli , are identical . Yet , the epithelium cells must distinguish commensal from pathogenic bacteria in order to maintain tolerance towards the beneficial commensal bacteria , while unleashing a defense response against pathogens . How this is achieved is only partially understood . AE pathogens employ a type three secretion system ( TTSS ) to translocate dozens of effector proteins into the host cell . These effectors subvert host cell processes , promoting colonization and formation of AE lesions [2 , 4] . The genes encoding for the TTSS machinery and related proteins are clustered on a pathogenicity island , 35 kb in size , termed the Locus of Enterocyte Effacement ( LEE ) [5] . This locus encodes for intimin and for the translocated intimin receptor ( Tir ) , which together form the typical pathogen-cell adhesion pattern named “intimate attachment” , characteristic of AE pathogens . Additional effectors are encoded by other pathogenicity islands and prophages . These include NleC and NleD , both metalloproteases that specifically and efficiently cleave and inactivate NF-κB and MAPK , respectively [6–10] , NleH , which represses transcription of a subset of NF-κB target genes [11] , NleE , which inhibits activation of NF-κB signaling pathways through blocking TAK1 activation , and NleB , which catalyzes GlcNAcylation of a specific subset of Death Domains causing modest inhibition of TNF-mediated NF-κB activation , but robust inhibition of TNF-induced cell death [12–14] . In this report we show that , while infection with wild type EPEC leads to repression of NF-κB signaling , strong NF-κB activation in cells infected with an EPEC mutant deleted of the nleB , nleE , nleC and nleD effector genes was observed . Furthermore , we report that this NF-κB activation is TTSS-dependent . Investigation of the basis for this activation suggests that epithelial cells can sense the active TTSS apparatus per se and respond by triggering a novel signaling pathway , resulting in NF-κB activation .
We previously reported that HeLa cells infected with an EPEC ΔnleB ΔnleC ΔnleD ΔnleE mutant ( ΔnleBCDE ) secrete fourfold more IL-8 than cells infected with EPEC mutants lacking active TTSS ( i . e . escV::kan mutant ) [6] . These results imply that in wild type EPEC some combination of the activities of NleB , NleC , NleD and NleE masks the capacity of EPEC to activate NF-κB . To test this idea , HEK293 cells containing a luciferase reporter for NF-κB activity were infected with different EPEC strains and luciferase activity was determined ( Fig 1A ) . The results showed modest NF-κB activation upon infection with wild type EPEC , or EPEC lacking active TTSS ( ΔescV ) , whereas the ΔnleBCDE mutant or a mutant deleted of the pathogenicity islands containing these genes ( i . e . PP4 and IE6 ) triggered significantly increased NF-κB activation ( p<0 . 01 ) ( Fig 1A ) . As an additional readout for NF-κB activation , we tested translocation of the NF-κB subunit p65 upon infection from the cytoplasm to the nucleus . We found that p65 translocation was induced by EPEC ΔnleBCDE , or ΔnleB , ΔnleC , ΔnleE ( ΔnleBCE ) , or ΔPP4 , ΔIE6 mutants ( Fig 1B and 1C ) . Infection with a ΔnleBCDE mutant lacking also fliC ( encoding flagellin ) triggered similar NF-κB activation ( Fig 1B ) , indicating that flagellin is not required for this activation . In contrast , activation was not observed upon infection with the EPEC wild type , the escV::kan mutant , or the EPEC ΔnleBCE , escV::kan mutant ( Fig 1B ) . Taken together , these results show that deletion of three anti-inflammatory effectors , nleB , nleC , and nleE reveals NF-κB inducing activity by EPEC , which is dependent on expression of an active TTSS . The requirement for a functional TTSS for EPEC-induced NF-κB activation suggests that this activation might be mediated by an injected effector . To identify the involved putative effector , we constructed a panel of mutants using the EPEC ΔPP4 , ΔIE6 mutant as a parental strain . Each mutant in this panel was deleted of a different effector gene ( tir , map and espZ ) , a TTSS chaperone gene ( cesF , cesT ) , or an entire pathogenicity island , as indicated , or other genes including eae ( encoding intimin ) and fliC . We performed infection assays using the above mutants and tested for p65 translocation to the nucleus and for induction of NF-κB-dependent expression . Notably , our results show that all the tested triple mutants activated NF-κB similarly to the parental strain ( Fig 2A–2C ) , indicating that none of the genes located within the pathogenicity island or known effectors are essential in order to elicit the observed NF-κB activation . The above results prompted us to examine whether the TTSS apparatus itself is sufficient for triggering NF-κB activation . To test this possibility we generated a commensal laboratory E . coli K12 , strain W3110 , that harbors a plasmid encoding for the entire LEE region ( pTOK-O2 , [8] ) . This strain , W3110/pLEE , produces a functional TTSS apparatus , but lacks other EPEC-unique genes , including those encoding non-LEE effectors . We infected HeLa cells with W3110/pLEE and found that it induced formation of actin pedestals in the host cells , confirming that the TTSS is functional and injects the Tir effector into host cells ( Fig 3A ) . GrlA and PerC are known redundant positive regulators of TTSS expression [15–17] . In agreement with this , we found that expression of GrlA or PerC by W3110/pLEE strongly enhanced pedestal formation ( Fig 3A ) . Importantly , W3110/pLEE strains , but not wild type W3110 , induced p65 translocation to the nucleus ( p<0 . 01 ) , IκB degradation and NF-κB-dependent gene expression ( p<0 . 01 ) ( Fig 3B and 3C ) . Moreover , these phenotypes were all enhanced upon up-regulation of TTSS expression by GrlA or PerC . We conclude that expression of the LEE genes is sufficient to trigger NF-κB activation in the infected cells . To investigate whether a LEE-encoded effector is responsible for NF-κB activation , we deleted from the pLEE plasmid the genes encoding espF , espG , espZ effectors and the region spanning espH to cesT ( ΔespH-cesT ) . The latter region includes three effector genes ( espH , tir and map ) ( S1 Fig ) , and two chaperone genes , cesF and cesT ( S1 Fig ) , which are required for translocation of the LEE-encoded EspF and EspZ and multiple none-LEE effectors . As a negative control we also constructed pLEE deleted of escV and thus defective in TTSS biogenesis . We found that W3110 containing any of these pLEE mutants , except pLEE-ΔescV , induced NF-κB activation with a comparable efficiency to that of W3110 carrying wild type pLEE ( Fig 3D ) . Similar results were obtained upon infection of T84 cells ( S2 Fig ) , an epithelial cell line derived from human colon carcinoma , which better mimics intestinal epithelial cells . These results show that LEE effectors , including Tir and thus intimate attachment , are not required for the TTSS-dependent NF-κB activation . Furthermore , these results indicate that the TTSS apparatus is necessary and sufficient to induce NF-κB activation . To test if a TTSS component is sensed by a pattern recognition receptor ( PRR ) localized on the host cell surface , such as TLRs , we constructed two additional pLEE variants . One was deleted of the espH-cesT fragment as well as espG ( S1 Fig ) . This mutant cannot deliver any effector into the host cell , but should still connect with the host cell by the TTSS and EspA filament [18] . The other mutant was deleted of the espB gene ( S1 Fig ) . Notably , W3110/pLEE ΔespB still forms nearly intact TTSS , including EspA filaments , but is deficient in assembly of the TTSS pore in the host cell membrane and is thus incapable of protein translocation [19] . We found that W3110/pLEE containing ΔespH-cesT ΔespG deletions strongly activated NF-κB , whereas the W3110/pLEE ΔespB mutant failed to activate NF-κB ( Fig 4A–4C ) . These results suggest that a surface PRR is not involved in NF-κB stimulation . Instead , an active pore-forming TTSS is required . To further test this notion , we took advantage of the requirement of de-novo protein synthesis for the activity of the EPEC TTSS [20] . For these experiments we used a different infection protocol . First , we grew W3110/pLEE under conditions that induced formation of functional TTSS , but in the absence of host cells . We then infected cells for 30 minutes with these cultures in the presence or absence of a translation inhibitor ( gentamicin ) . This antibiotic is expected to inhibit translocation and likely reduce EPEC viability , but we assume that regardless of viability , within the 30 min infection period the molecules on the EPEC surface remain intact and capable of activating PRRs presented on the host cell surface . In the absence of gentamicin we observed efficient pedestal formation and strong NF-κB activation , while gentamicin strongly inhibited both ( Fig 4D ) , indicating that active TTSS is required for host cell stimulation . Taken together , these results indicate that the host cell detects a functional and active TTSS by a sensor , which is not surface exposed , possibly cytoplasmic . TTSS activity is associated with formation of translocon pores in the host cell membrane , mediated by EspD and EspB , and with translocation of effectors through these pores [21 , 22] . Systematic introduction of short , in-frame , inserts into the espB gene lead to identification of mutations that are specifically deficient in either pore formation or protein translocation , suggesting that pore formation and protein translocation are distinct , not necessarily linked , processes [21] . We took advantage of these mutants to ask if the TTSS-dependent NF-κB activation correlates with pore formation or with protein translocation . To this aim we constructed a set of EPEC strains where the wild type espB gene was replaced by mutated espB alleles , including espBK179 , espBE203 , espBT239 , espBL241 , and espBL282 [21] . EPEC ΔnleBCDE mutant was used as a parental strain for construction of these espB mutants . In addition , as a negative control , we constructed EPEC ΔnleBCDE deleted of espB ( ΔespB ) . These strains are listed in S1 Table . We next used these strains to infect HeLa cells and quantified protein translocation ( using pedestal formation , which is dependent on Tir translocation , as readout ) , pore formation ( using penetration of propidium iodide ( PI ) into the cells as described [22] ) , and NF-κB activation ( using p65 translocation to the nucleus as readout ) . We found that the pore forming activity and protein translocation by the different espB mutants were consistent with a previous report ( compare Table 1 and [21] ) . In addition , the analysis showed that mutants deficient in both pore formation and protein translocation fail to activate NF-κB . These mutants carry the ΔespB or espBL282 alleles , which are likely deficient in placing the EspB-EspD pore in the host cell membrane . Importantly , we could not detect direct correlation between NF-κB activation and either pore formation or protein translocation . For example , the espBT239 mutant showed somewhat reduced pore formation and very little protein translocation . Yet , it activated NF-κB to levels similar to those displayed by the parental strain , expressing wild type espB ( Table 1 ) . Furthermore , the espBE203 and espBT239 mutants induced similar levels of pore formation , yet exhibited significant differences in NF-κB activation ( Table 1 ) . Finally , the espBK179 mutant shows markedly reduced pore-formation , but efficient protein translocation , yet , it shows clear reduction in NF-κB activation ( Table 1 ) . Taken together , these results show that neither efficient protein translocation , nor membrane pore per se , is important for the TTSS-dependent NF-κB activation . Therefore , it appears that the host cells detect a cue specific to the TTSS pore , possibly some structural element of EspB . We next examined whether a single TTSS component present inside the host cell is capable of stimulating NF-κB activation . To this end , we transfected HeLa cells with plasmids expressing different TTSS components , EscF , EscI , EscP , EspA , EspB , EspD and EtgA , which might leak into the host cell as a result of TTSS activity . We then tested whether expression of these proteins results in NF-κB activation . Notably , none of the tested TTSS components activated NF-κB ( Fig 5A and 5B ) . These results suggest that , rather than a specific single TTSS component , the host likely senses other events associated with TTSS activity , such as the EspBD channale , membrane perturbation or leakage of non-proteinaceous components through the TTSS syringe . To gain better understanding of the TTSS-sensing mechanism , we searched for host factors that are involved in sensing the cue generated by TTSS activity . First we tested the requirement for MyD88 , which is central to multiple TLRs as well as IL-1 signaling pathways that lead to NF-κB activation . We infected HeLa cells , stably expressing anti-MyD88 shRNA and thus deficient in MyD88 production ( S3 Fig ) , with EPEC or W3110/pLEE . We found that both strains induced NF-κB ( Fig 6A and 6B ) . To further control for loss of MyD88-dependent signaling , we treated these cells with TNFα or IL-1β . As expected , TNFα , but not IL-1β , activated these cells ( Fig 6C ) [23] , confirming the functional knock-down of MyD88 . Similar results were obtained using primary fibroblasts cultured from MyD88-/- mice ( Fig 6D ) . Unlike HeLa and HEK293 cells lines , we noted that primary mouse fibroblasts display MyD88-dependent NF-κB activation even upon infection with the EPEC escV mutant . This probably reflects expression of some TLRs that are triggered by EPEC PAMPs such as LPS or flagellin ( e . g . TLR4 , TLR5 ) , but this signaling is inhibited by TTSS effectors upon infection with the wt EPEC , but not by the TTSS-deficient escV mutant . Nevertheless , in MyD88-/- primary fibroblasts , robust NF-κB activation was revealed upon infection with the EPEC nleB nleC nleE mutant , but not with the escV mutant ( Fig 6D ) . Taken together , these results show that MyD88 is not required for TTSS-dependent NF-κB activation . RIP2K and TRAF6 are essential components in various signaling pathways that lead to NF-κB activation [24 , 25] . We therefore investigated their involvement in TTSS-sensing . We generated RIP2-/- and TRAF6-/- HEK293 cells ( see Materials and Methods ) . To functionally confirm the RIP2-/- and TRAF6-/- knockouts , the cells were transfected with plasmids overexpressing Nod1 or MyD88 , respectively . As expected , Nod1 overexpression did not specifically activate NF-κB in the RIP2K-/- cells , while MyD88 overexpression did not specifically activate NF-κB in the TRAF6-/- cells ( S5A and S5B Fig ) . We infected these cell lines with either W3110 , W3110/pLEE or W3110/pLEE ΔescV , and found that W3110/pLEE , but not W3110 or W3110/pLEE ΔescV , induced NF-κB activation ( Fig 7A ) . These results indicate that TRAF6 and RIP2 are not required for TTSS-mediated NF-κB activation . We further infected TRAF6-/- mouse embryonic fibroblasts ( MEFs , a gift from Dr . Kate Fitzgerald , University of Massachusetts ) with EPEC strains and found that the EPEC ΔnleBCE mutant triggered NF-κB activation ( Fig 7B ) . Finally , we found that dominant negative RIP2 ( RIP2-DN ) failed to inhibit TTSS-mediated NF-κB activation ( Fig 7C ) . These results show that TRAF6 and RIP2 are not required for TTSS-dependent activation of NF-κB , reinforcing the premise that TTSS recognition is not mediated by TRAF6/MyD88-dependent signaling , or by the Nod1/2-RIP2 pathway , which is involved in peptidoglycan and ER stress sensing [26 , 27] . NF-κB activation frequently involves formation of K63-linked ubiquitin chains catalyzed by the E2 enzyme Ubc13 . These ubiquitin chains serve as binding sites for the TAB2/3 components of the TAK1 complex , leading to TAK1 activation and subsequent NF-κB activation [28] . The Shigella effector OspI and the EPEC effector NleE inhibit NF-κB activation through Ubc13 deamination , and TAB2/3 methylation , respectively [14 , 29] . We used these effectors as tools to examine whether Ubc13 or TAK1 are involved in the signaling that leads to TTSS-dependent NF-κB activation . HEK293 cells were transfected with plasmids expressing mCherry-OspI , mCherry-NleE , or a vector expressing mCherry and were then infected with EPEC or W3100 strains . We found that expression of OspI or NleE strongly inhibited TTSS-dependent NF-κB activation ( Fig 8A and 8B ) . These results suggest that OspI and NleE are each sufficient to block TTSS-dependent signaling that leads to NF-κB activation . Given that OspI inactivates Ubc13 [29] , we assumed that Ubc13 is required for TTSS-sensing by the host cell . To test this prediction , we generated Ubc13-/- HEK293 cells and infected them with W3110 , W3110/pLEE or W3110/pLEE ΔescV . Unexpectedly , we found that W3110/pLEE induced NF-κB activation in the absence of Ubc13 ( Fig 8C ) . In contrast , we showed that overexpression of MyD88 failed to specifically activate NF-κB in the Ubc13-/- cells , confirming the lack of Ubc13 functionality ( Fig 8D ) . Two alternative hypotheses may explain these results: i ) OspI acts on an additional putative target , possibly another E2 enzyme that is required for TTSS-dependent NF-κB activation , or ii ) the deaminated Ubc13 functions as a dominant negative form , which blocks TTSS-mediated NF-κB activation by stimulation of a deubiquitinating enzyme , as previously shown [30] . If the second possibility is correct , OspI should not inhibit TTSS-dependent NF-κB activation in the Ubc13-/- cells . To examine this point we transfected Ubc13-/- cells with a plasmid expressing OspI , followed by infection with W3110 , W3110/pLEE or W3110/pLEE ΔescV . The results show that in these Ubc13-/- cells OspI also inhibits TTSS-mediated NF-κB activation ( Fig 8E ) . These data exclude the possibility that deaminated Ubc13 functions as dominant negative and indicate that OspI may act on an alternative target to inhibit TTSS-dependent NF-κB activation .
Cells of the immune system employ an array of PRRs to detect minute amounts of MAMPs , resulting in a rapid and substantial inflammatory response , which includes activation of the NF-κB and MAPK pathways [31] . In contrast , the response of intestinal epithelial cells to the massive amounts of microbiota-derived MAMPs deposited on their apical surface must be restrained to allow colonization of the beneficial commensal microbiota and to avoid chronic inflammation [32] . This phenomenon was termed tolerance [33] . Epithelial tissues of other none-sterile mucosal source , such vaginal or nasal , should exhibit similar properties . In addition to tolerance , epithelial cells should detect small amounts of infiltrating pathogens within the enormous “noise” generated by the microbiota’s MAMPs . Rapid reaction against pathogens is advantageous for controlling the infection , but erroneous recognition of normal resident microbes as pathogens my lead to inflammatory disease [34] . The way the epithelium differentiates normal microbiota from pathogens is not fully understood and is likely to involve multiple mechanisms [32 , 35] . In this study we show that epithelial cells , including HeLa , HEK293 and T84 , sense a functional TTSS , a virulence factor common to many gram-negative pathogens . Our findings suggest that TTSS-recognition is one of the mechanisms by which epithelial cells differentiate commensal from pathogenic bacteria , even among strains of the same species , such as commensal and pathogenic E . coli strains . Activation of NF-κB upon TTSS recognition was previously tested for Salmonella and Yersinia , with opposite results . Salmonella expressing functional SPI-1 TTSS ( TTSSSPI-I ) , but lacking several of the known effectors , failed to induce NF-κB in infected epithelial cells [36] . Furthermore , the ability to induce NF-κB by this pathogen was dependent on injection of specific effectors by TTSSSPI-I [36 , 37] . Notably , more recent reports identified several TTSSSPI-I anti-NF-κB effectors in Salmonella [38–40] . It is possible , but has never been proven , that , like in the case of TTSSEPEC , these effectors mask an inherent capacity of the TTSSSPI-I to activate NF-κB . In contrast to Salmonella , a Yersinia mutant lacking the six known effector genes activated NF-κB in macrophages through the presence of the TTSSYersinia apparatus [41] . However , the presence of an additional effector in the Yersinia genome that might have NF-κB stimulatory activity cannot be excluded . Indeed , a more recent report suggests that translocation of unknown TTSS cargo leads to this activation [42] . Further studies are needed to determine if TTSS recognition is species-specific , or even specific to the TTSS-type , for pathogens that carry two or more TTSSs . Several lines of evidence suggest that surface PRRs , such as TLRs , are not involved in TTSS sensing by epithelial cells: i ) TTSS-mediated NF-κB activation was not dependent on MyD88 or TRAF6 , which are required for TLR-mediated signalling; ii ) EPEC , or TTSS-expressing E . coli K12 that carry espB deletion , fail to induce NF-κB , although these bacteria produce an almost complete TTSS apparatus; iii ) blocking the ability of intact TTSS to translocate effectors by treatment with a translation inhibitor resulted in strong inhibition of both pedestal formation ( a readout for TTSS protein translocation activity ) and NF-κB activation . Collectively , our results show that epithelial cells sense only active , pore forming TTSS . Previous work has shown that the inflammasome complex , another arm of the innate immune response expressed in myeloid cells , is activated upon binding of TTSS inner rod or needle proteins to NAIP ( NLR family , apoptosis inhibitory protein ) cytoplasmic sensors [43] . Our work suggests that epithelial cells might utilize a cytoplasmic or membrane-embedded sensor to detect an active TTSS and trigger the NF-κB pathway . The signalling cascade induced upon TTSS-recognition is inhibited by NleE and OspI , suggesting that the TTSS-mediated pathway converges with other signalling pathways that lead to NF-κB activation at the level of binding of the TAB2/3-TAK1 complex to K63-linked ubiquitin chains , resulting in TAK1 activation . NF-κB activating effectors were described for several pathogens , including Salmonella and rare EPEC isolates [36 , 37 , 44] . We therefore initially examined the hypothesis that EPEC activates NF-κB through injection of a specific pro-NF-κB effector . However , extensive analysis , using several complementing approaches , indicated that no known effector of EPEC is required for TTSS-mediated NF-κB activation . Therefore , our results suggest that NF-κB activation is mediated by a TTSS-related cue , but is independent of protein translocation or a specific effector . Furthermore , our data using different espB mutants show that the pore-forming per se is not sufficient to elicit NF-κB activation . Thus , the host specifically detects the active TTSS . The precise cue that is sensed by host cells is yet to be defined . It might be a structural element of the pore , possibly some EspB structure . Alternatively , a common metabolite , such as monosaccharide heptose-1 , 7-bisphosphate ( HBP ) [45] , or peptidoglycan ( PG ) products [46] , might leak into the host cell through the TTSS and be sensed by the host . HBP and PG are not likely to be related to the TTSS-dependent activation since they are dependent on TRAF6 and RIP2 , respectively . Nevertheless , metabolite analogues of HBP or PG might be involved . An alternative possibility is that the transient membrane damage typical to TTSS might provoke signalling that leads to NF-κB activation . AE pathogens reside mainly on the apical surface of the intestinal epithelium and inject into these cells several effectors ( NleC , NleD , NleE , NleH ) that repress NF-κB and MAPK signalling [6 , 8 , 9 , 11 , 14 , 47] . Our finding that NF-κB signalling is activated upon TTSS-recognition provides a plausible role for the anti-NF-κB effectors , i . e . , to neutralize the host pro-inflammatory response mediated by TTSS-recognition . In this context , it is worth mentioning that accumulating data suggest that EPEC can establish a long-term sub-clinical carrier state in humans [48 , 49] . Thus , preventing or attenuating the host inflammatory response elicited by these effectors might provide EPEC with a concealed niche allowing it to establish long-term colonization . Furthermore , dampening the host response by EPEC might be involved in the increased susceptibility to secondary challenges , as was recently reported [50] . In conclusion , our results show that the host epithelial cells can detect the active , pore forming , TTSS of EPEC to trigger a novel signalling pathway , leading to NF-κB activation . Notably , EPEC acquired effectors that dampen this NF-κB activation through horizontal gene transfer .
Plasmids , bacterial strains , and primers used in this study are listed in S1–S3 Tables , respectively . Mutants of EPEC and pLEE plasmid were constructed using the lambda red system and selective cassettes [51–54] . Genomic espB short-insertion mutants were constructed using as templates plasmids encoding the mutants [21] , lambda red system and tet-sacB cassette as described [51] . Plasmids were constructed using standard methods or isothermal assembly . Bacteria were grown in LB supplemented , when appropriate , with ampicillin ( 50 μg/ml ) , streptomycin ( 50 μg/ml ) , tetracycline ( 10 μg/ml ) , chloramphenicol ( 25 μg/ml ) or kanamycin ( 40 μg/ml ) . Infection was performed by diluting an overnight standing LB culture of bacteria ( 1:100 for EPEC , or 1:75 for W3110 strains ) with antibiotic-free DMEM . Bacteria were grown overnight in a static LB culture , at 37°C to OD600 ~0 . 8 , diluted 1:100 in antibiotic-free DMEM and applied on the cells ( MOI ~100 ) . Infections proceeded for 3 hours . Of note , initially the bacteria do not express TTSS . The transition to DMEM activates TTSS expression , which becomes fully functional after 2 hours . For luciferase assays , infection was stopped by adding gentamicin followed by additional 3-hour incubation to allow production of luciferase by the host cells . In other cases , infection was stopped by fixation , or by protein extraction . To pre-induce TTSS formation by W3110 as shown in Fig 4D , an overnight standing culture in LB was diluted 1:75 in DMEM and incubated at 37°C for 1 hour . IPTG ( 0 . 2 mM ) was then added to activate TTSS expression and incubation was continued for another 2 hours . Primary fibroblasts were produced from tails of wild type and MyD88 knock-out C57BL/6 mice by incubating the tissue in a trypsin-EDTA solution for 1 hour at 37°C . HeLa cells stably expressing MyD88-targeting shRNA or control shRNA were constructed using shRNA from Sigma Inc . MEF TRAF6-/- were a gift from K . Fitzgerald . Generation of HEK293 TRAF6-/- and RIP2-/- cell lines using the CRISPR/Cas9 method has been recently described [55] . The inactivation of the corresponding genes was verified by sequence analysis . The guide RNA ( gRNA ) target sequences used were CCACGCAGACTGGCGCGTCC ( for RIP2 KO ) and ATCTTTTGTTACAGCGCTAC ( for TRAF6 KO ) . Cell clones with the desired gene knockout were checked by sequencing of the PCR fragments . Cells were grown in DMEM supplemented with 10% fetal calf serum , penicillin and streptomycin . When appropriate , cells were treated with TNFα ( Peprotech , 300-01A ) , IL-1β ( Biotest , 201-LB-005 ) , or gentamicin ( 100 μg/ml ) . HeLa cells were grown overnight to 70% confluence in 24-well plates and infected for the indicated time . Cells were then washed , fixed and stained with anti-p65 ( SC-372 , Santa Cruz ) , phalloidin-rhodamine ( P1951 , Sigma ) and DAPI ( D9542 , Sigma ) , followed by washing and staining with anti-rabbit Alexa Fluor 488 conjugated antibody ( #4812 , Cell Signaling ) . Cells were visualized using fluorescence microscopy . All experiments were repeated at least three times and significance was tested using the student T-test ( un-paired , two-tailed ) . The assay was performed according to the instructions in the Dual-Luciferase Reporter kit ( Promega ) . Briefly , HEK293T cells were grown overnight in DMED media to 70% confluence on top of poly-lysine ( P8920 , Sigma ) -coated glass cover slips in 24-well plates , co-transfected with 0 . 2 μg and 0 . 025 μg of pNF-κB-luc and pRL-TK Renilla luciferase vector ( Promega ) DNA , respectively , using the TurboFect transfection reagent ( R0531 , Fermentas ) . After 24 hours , the medium was replaced with fresh DMEM and the cells were infected for 3 hours with bacteria from an overnight bacteria culture at a MOI of 1:100 . The medium was then supplemented with gentamicin ( 100 μg/μl ) in order to stop bacterial replication and avoid significant cell death . Cells were incubated for another 3 hours to allow accumulation of the reporter gene expression to detectable levels . Relative luminescence units ( RLU ) were normalized to those of uninfected cells . All experiments were repeated at least three times and significance was tested using the student T-test ( un-paired , two-tailed ) . HeLa cells were infected with bacterial strains for 3 hours and harvested by centrifugation . Proteins were extracted using the NE-PER kit ( Thermo Scientific ) , analyzed by Western blot and stained with anti-IκB ( #9242 , Cell Signaling ) and anti-tubulin ( T9026 , Sigma ) . Pore-forming activity was determined as described [22] , with slight modifications . Briefly , 2x105 HeLa cells per well were seeded in 24 well plates ( In Vitro Scientific , black plate , glass bottom ) . Upon reaching confluency of ~90% the cells were infected , washed with cold PBS , incubated for 2 min with 7 . 5 mM propidium iodide ( PI ) in PBS , washed twice , fixed with 2% paraformaldehyde and washed twice in PBS . The amount of PI in the cells was determined using a plate reader ( SPARK-10M TECAN , monochromators set at 533nm excitation , 620nm emission ) .
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The intestine harbors a dense community of commensal bacteria that play a vital role in host health and homeostasis , but it is also the port of entry for many pathogens . An important function of the intestinal epithelial cells is coordinating the immune response to microbial signals , ranging from tolerance towards beneficial species to a robust anti-pathogen immune response . The mechanisms underlying the ability of intestinal epithelial cells to specifically distinguish pathogens from commensal bacteria are only partially understood . Commensal and enteropathogenic E . coli strains are highly similar and both reside in the gut lumen . However , only the pathogen is equipped with a type III protein secretion system , which is employed to inject effector proteins into the host cells . Here we show that epithelial cells distinguish the pathogenic from the highly similar commensal E . coli strains through specific sensing of the TTSS activity and respond by triggering a novel defense signaling pathway , but the pathogen attempt to avoid its detection by injection effectors that block the defense signaling .
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"cultured",
"tumor",
"cells"
] |
2017
|
Epithelial cells detect functional type III secretion system of enteropathogenic Escherichia coli through a novel NF-κB signaling pathway
|
To determine a molecular basis for prognostic differences in glioblastoma multiforme ( GBM ) , we employed a combinatorial network analysis framework to exhaustively search for molecular patterns in protein-protein interaction ( PPI ) networks . We identified a dysregulated molecular signature distinguishing short-term ( survival<225 days ) from long-term ( survival>635 days ) survivors of GBM using whole genome expression data from The Cancer Genome Atlas ( TCGA ) . A 50-gene subnetwork signature achieved 80% prediction accuracy when tested against an independent gene expression dataset . Functional annotations for the subnetwork signature included “protein kinase cascade , ” “IκB kinase/NFκB cascade , ” and “regulation of programmed cell death” – all of which were not significant in signatures of existing subtypes . Finally , we used label-free proteomics to examine how our subnetwork signature predicted protein level expression differences in an independent GBM cohort of 16 patients . We found that the genes discovered using network biology had a higher probability of dysregulated protein expression than either genes exhibiting individual differential expression or genes derived from known GBM subtypes . In particular , the long-term survivor subtype was characterized by increased protein expression of DNM1 and MAPK1 and decreased expression of HSPA9 , PSMD3 , and CANX . Overall , we demonstrate that the combinatorial analysis of gene expression data constrained by PPIs outlines an approach for the discovery of robust and translatable molecular signatures in GBM .
Glioblastoma multiforme is the most common primary brain tumor in adults and , unfortunately , also the most fatal . While GBMs are categorized histologically , the nature of the disease leads to significant variability in both tumor classification and patient outcome . To more specifically define the disease and simultaneously reveal the etiology , an unbiased search for “molecular signatures” of GBM has been undertaken by several groups [1] , [2] , resulting in a variety of GBM markers which , unfortunately , have modest overlap . Given the large degree of molecular heterogeneity of GBMs , analysis of thousands of patient samples may be required to identify comprehensive gene sets by conventional statistical approaches [3] . However , suggestions that these myriad lists can be integrated via a systems-level analysis , e . g . using molecular networks to find consensus marker sets [4] , may help to simplify the observed heterogeneity . In such an approach , an individual gene can affect the algorithmic contribution of a neighboring gene when they coexist in pathways or networks that act to integrate molecular heterogeneity . While approaches measuring gene expression across a group can capture gene interaction effects , they often employ summary measures , e . g . averaging , that omit valuable information regarding inter- and intra- patient differences . In this work , we hypothesize that the considerable patient-to-patient variability of GBM can be simplified into molecular networks by identifying molecular “state functions” using the computational method , CRANE ( for Combinatorially dysRegulAted subNEtworks ) [5] . The use of molecular states – where the binary expression pattern of a gene set is considered as a whole – allows us to identify subsets of genes whose configuration ( i . e . the expression pattern rather than expression level alone ) distinguishes between the two phenotypes of interest . In this approach , we do not assign a single expression state to a phenotype , but , rather , we search for the set of all states matching a particular phenotype . These expression states are grounded in well-known sets of biological interaction data , as defined by curated protein-protein interaction ( PPI ) networks . We applied CRANE to the gene expression data collected by The Cancer Genome Atlas [6] for patients with primary ( de novo ) GBM . We identified novel subnetwork signatures of survival , which we then tested against an independent gene expression dataset . We also hypothesized that mRNA dysregulation analyzed in the context of PPI subnetworks more efficiently translates to detectible dysregulation at the protein level . To test this , we examined protein expression of selected targets using label-free proteomics in a retrospectively selected set of GBM tumor samples . The workflow presented here is a prototype for identifying manageable subsets of genomic and proteomic targets to ultimately drive the design of cost-effective clinical assays for predicting patient survival – a much desired endpoint for clinicians and patients alike .
We began by using GBM patient information and microarray data from The Cancer Genome Atlas [6] ( TCGA ) as compiled by Verhaak et al . [7] . CRANE , an established method for mining molecular networks [5] ( illustrated in Figure 1 ) , successfully identified several subnetworks that were informative in separating short-term ( STS ) from long-term survivors ( LTS ) using TCGA mRNA data . The expression patterns for individual genes comprising the top ten states within subnetwork 1 are shown in Figure 1 , illustrating how the varied configurations of an individual subnetwork drive the identification of specific subgroups of patients . As an example , note that subnetwork state 3 ( LHLHLHLLLL ) occurs in two short-term survivors , whereas subnetwork state 4 ( LHLHLLLLLL ) occurs in two long-term survivors; though state 3 and state 4 differ only in the switch of one gene from H to L , they predict opposite outcomes . Also , note that the top ten states using these 10 targets only capture 39% of the total patients , reflecting the significant heterogeneity at the patient level . The complete list of subnetwork signature genes can be found in Table S1 . To investigate the reproducibility of CRANE subnetworks in predicting survival , we tested the TCGA-discovered subnetworks' classification performance on an independent GBM dataset published by Lee et al . [8] . In this analysis , subnetwork discovery and training of the classifier was done on the TCGA data , and testing of the classifier was done on the Lee et al . data . In this test of the TCGA training set , the targets were fixed by the training data ( Table S1 ) , and classification accuracy on the Lee et al . data was incrementally calculated for each 10-gene subnetwork ( see Methods ) . We achieved a maximum classification accuracy of 80% when using the top 5 subnetworks generated by CRANE from TCGA data ( Figure 2 , further details in Table S2 ) ; we henceforth refer to this 50 gene set as the subnetwork signature . With only 1 subnetwork , or 10 genes , the positive predictive value ( PPV ) of short-term survival is slightly better than random chance ( 57% ) while the PPV for long-term survival is 74% . The PPV for short-term survival reaches 90% with 5 subnetworks while the maximum observed PPV for long-term survival was 85% with four subnetworks . The cumulative value of using multiple networks – each with a defined set of states – is illustrated in Figure 1 . For example , state 1 in sub-network 1 ( LLLLLLLLLL ) is seen in 21% of patients , while the next 9 states cover only an additional 18% of patients ( total of 39% for the top ten states ) . Thus , the heterogeneity of the patient population cannot be captured even with 10 binarized states from a single subnetwork; multiple subnetworks ( each with multiple states ) are needed to provide adequate patient coverage and clinically useful prediction accuracy . Known molecular subclasses of GBM exhibit differences in survival [9] , [10] , and we examined whether our subnetwork signature was acting as a surrogate for known subtypes . A well-accepted basis for the molecular subtyping of GBMs was recently established by Verhaak et al . using an 840-gene signature [7] . Only four of our top 50 CRANE targets – phospholipase C ( PLCG1 ) , paxillin ( PXN ) , transforming growth factor beta 3 ( TGFB3 ) , and topoisomerase ( TOP1 ) – overlap with this list , strongly suggesting that our subnetwork signature is not classifying patients by these existing subtypes . Since the CRANE targets may be acting as proxies for the 840 genes , we also checked for an association between our predefined survival groups and molecular subtype using the molecular subtype calls made by Verhaak et al . for the 173 “core” TCGA samples ( i . e . those samples most representative of a molecular subtype ) . When using the 50-gene subnetwork signature for classification , our LTS group consisted of 21% Classical , 35% Mesenchymal , 38% Proneural , and 5% Neural samples while our STS group consisted of 13% Classical , 37% Mesenchymal , 42% Proneural , and 8% Neural samples . Using a chi-square test of independence , we found that these molecular subtypes are not significantly associated ( p-value>0 . 05 ) with membership in our survivor groups in the TCGA data . To examine the extent to which our subnetworks were capturing true differences in survival , we investigated the concordance between the predictions of the network-based classifier and the survival times of the 166 patients in the Lee et al . dataset . As seen in Figure 3 , significant differences in survival are apparent between patient groups predicted by the 50-gene subnetwork signature ( p-value<1e-6 , logrank test ) , indicating the expected performance of the CRANE classifiers within the test dataset . We then compared CRANE's performance against the four subtypes proposed by Verhaak et al . Though the Verhaak signatures were not designed to segregate patients by survival , the Proneural subtype has slightly longer survival than the other subtypes ( Figure 3 ) . By the logrank test , there is no significant difference among the four Verhaak subtype survival curves; the four subtypes track the survival curve of the CRANE long-term survivors while the curve for the CRANE short-term survivors is quite distinct . Given that younger patients tend to have better prognosis [11] , we also tested for differences in the age distributions of the two CRANE predicted groups of patients . The age distributions of patients classified by the 50-gene subnetwork signature were similar ( Figure S3 ) , and a logrank test indicated that there is insufficient evidence to conclude that the age distributions differ ( p-value = 0 . 14 ) . Overall , the above tests show that our CRANE gene expression subtypes are distinct from the Verhaak subtypes and represent novel , age-independent subtype classifications for GBM . CRANE examines heterogeneity at the mRNA level to produce state-based classifiers , and we hypothesized that the identified subnetworks transduce this heterogeneity into protein-level differential expression . We tested this hypothesis by examining protein-level changes in an independent cohort of 16 patients from the Ohio Brain Tumor Study , 10 of which were STS and 6 were LTS based on the criteria outlined above . We employed a label-free proteomic approach using ultra-long chromatographic gradients , which permitted the accurate identification and quantification of 5019 peptides from 1491 proteins across the patient samples . Differential expression of proteomic targets was defined using a mixed model of peptides , and we report p-values for the differential expression of each protein . Using this model , 338 proteins were significantly up- or down-regulated at a p-value≤0 . 05 ( Table S3 ) . We did not make false-discovery rate corrections for these p-values as this is not an unbiased discovery experiment . Instead , we were interested in modeling how proteomic expression varied for pre-specified subsets of genes . Although proteomics has less dynamic range than gene expression analysis , the above method permitted the confident identification and quantification of over one-third of the CRANE subnetwork signature ( 17/50 targets ) . Of the 17 targets of interest that were identified and measured ( see Table 1 ) , five proteins were significantly down-regulated and two were significantly up-regulated in LTS . Interestingly , these 7 proteomic targets have modest classification potential at the level of individual gene expression , as illustrated by the irregularity of their gene expression patterns in the TCGA dataset ( Figure S2 ) . To explore the prognostic potential of the proteomic targets , we used classification and regression trees ( CART ) to identify patterns of proteins that would robustly classify STS from LTS using the significant proteomic targets; for classification , we used the 7 significantly differentially expressed proteomic targets , as well as YWHAQ , which was of borderline significance . This yielded a simple 2-gene protein-level classifier , illustrated in Figure S4 . Using only CANX and MAPK1 , the classifier is able to correctly identify 100% of long-term survivors in the group and 90% of short-term survivors . For example , when CANX has a normalized value greater than −1 . 05 and MAPK1 is less than 0 . 50 , we can identify 9 of our short-term survivors , though such high sensitivity and specificity are likely indicative of over-fitting . To explore our hypothesis that the use of network topology improves our ability to detect targets at the protein level , we compared the performance of CRANE-identified targets versus that of individual gene markers in identifying dysregulated proteins . The “individual gene markers” refers to a set of the most differentially expressed genes selected without respect to any underlying interaction structure . Specifically , we identified all genes with a fold-change ≥2 between the 86 LTS and STS survivors in the TCGA data and then ranked these genes according to their absolute t-statistic ( i . e . the difference in group means divided by the pooled standard deviation ) . Of the top 200 individual gene markers , only one – ACTG1 – overlapped with the 50-gene subnetwork signature . Thus , 49/50 genes identified using a network-based classifier could not be discovered based on conventional analysis of individual gene markers . As seen in Figure 4A , the use of an interaction network in an mRNA-based classifier markedly improves our ability to identify targets differentially expressed at the protein level compared to examination of individually dysregulated genes . Specifically , CRANE identified dysregulated subnetworks that were better represented in the proteomic data , and these subnetworks included more differentially expressed proteins when compared to dysregulated individual gene markers . When interrogating the proteomics data for the top 200 network-based genes ( i . e . the top 20 CRANE subnetworks ) , over 50 proteins were identified ( 25% ) and 18 of these subnetwork proteins showed differential expression ( 36% differentially expressed among those identified ) . In contrast , when using the top 200 differentially expressed individual genes , 21 were identified via proteomics ( 10% ) and only 3 showed significant changes ( 14% differentially expressed among those identified ) . Fitting a linear regression model to the data , we find that individual gene markers yield differentially expressed proteins at a rate of 1 . 5% ( relative to the number of genes used ) , whereas the network-based approach has a rate of return of 9 . 8% - a 6 . 5-fold improvement in the yield of our proteomics validation experiment . We also explored the proteomic yield of the four Verhaak et al . subtypes . As shown in Figure 4B , the 210-gene Neural subtype had the best yield in the proteomics experiment , with 41 targets identified via proteomics ( 20% of all targets identified ) and 20 showing significant changes ( 49% differentially expressed among those identified ) . However , the number of proteomic targets identified by the Proneural , Classical , and Mesenchymal subtypes was considerably lower . While the rate of return for these three subtypes ( ranging from 1%–2 . 7% ) was comparable to that of the individual gene markers , the rate of return for the Neural subtype was 9 . 9% .
In this work , we analyzed the mRNA-level heterogeneity of GBMs using protein interaction networks , arriving at a succinct list of 50 genes that predicts patient survival at 80% accuracy . Not only does the unique subnetwork signature show reproducible prediction of patient survival at the mRNA level , it also exhibits protein-level dysregulation that segregates short-term from long-term survivors of glioblastoma – a valuable characteristic in light of recent evidence suggesting that many mRNA-level signatures have questionable classification power and modest biological significance [12] . Additionally , the 50-gene subnetwork signature indentified here represents an experimentally tractable number of targets – measurable in a streamlined proteomics experiment – while previously discovered target lists are not likely to be translated into clinical assays due to their large size [7] . While past work on unsupervised classification of high-grade gliomas was complicated by the use of mixed WHO grade III and grade IV patient samples [1] , [13]–[16] , we herein develop a molecular signature based solely on primary , untreated grade IV tumors from the TCGA database . We note the caveat that the number of subnetworks included in the signature was selected based on the classification performance on the test ( Lee et al . ) data and , thus , requires further validation to be useful as a standalone classifier of gene expression data . In this work , we choose , instead , to explore how this 50-gene subnetwork signature behaves at the protein level . Building upon the success of gene pair classifiers [17] , the network analysis framework presented here identifies multigene subnetworks based on mRNA state functions – series of 1's and 0's – allowing us to account for patient-level heterogeneity in expression profiles . While binarization of continuous expression data certainly involves a loss of information , this concept lends itself to the design of therapeutic interventions , where targeted molecular therapies inhibit or activate key “switches” in the circuits of distinct patient subtypes . For instance , upregulation of insulin-like growth factor receptor ( IGF1R ) , seen in subnetwork 3 , has been identified in a wide variety of human cancers [18] , and in vitro evidence suggests that this upregulation contributes to resistance against EGFR inhibitors [19] . Our results suggest that IGF1R has variable expression – on , or 1 , in some tumors and off , or 0 , in others – in patients within the same GBM survival class , indicating that experimental IGF1R monotherapies [20] , [21] , while inappropriate as a population-level intervention , may be highly effective in precisely selected individuals . A binary model of expression-activation is an oversimplification in some instances , however , where protein activity does not necessarily correlate with expression levels , e . g . in the case of kinases . In contrast to proteomic approaches , several groups have worked on classifying the genomic alterations underlying GBM [22] , [23] . Of the 309 unique , validated mutations identified through sequencing of the TCGA GBM tumor samples , CTNNB1 , EP300 , STAT3 , and TOP1 also appear in the 50-gene subnetwork signature . These genomic alterations are likely to play causative roles in establishing the global state function of the subnetwork signature . β-catenin ( CTNNB1 ) , for instance , complexes with N-cadherin to coordinate tumor invasiveness [24] and shows some promise as a prognostic marker [25] . Additionally , TOP1 is targeted by topoisomerase inhibitors to treat a wide variety of cancers [26] , [27] . CRANE identifies these key genes not simply because they show consistent expression across a group , but , rather , because their expression levels form a distinct pattern when viewed in conjunction with the 46 other genes in the milieu . This is in line with the known patient-to-patient variability in the mutational landscape of cancer [28] . In this light , the presence or absence of common mutations in patient subgroups differentially disrupts network state functions , and a single chemotherapeutic agent is unlikely to be effective in every patient . We hypothesized that the underlying network structure would ultimately lead to differences in protein expression between survival groups . Using a mixed model accounting for inter-peptide dependencies within a protein , we identified 7 dysregulated proteins out of a total of 17 detected in the proteomics experiment from the 50-gene subnetwork signature . Though the stochastic nature of proteomics workflows may have discouraged their use as validation platforms , we demonstrate that ultra-long chromatographic gradients coupled with high-resolution mass spectrometers allow us to probe the signaling networks of interest in a high-throughput fashion , with chromatographic reproducibility ( Figure S1 ) sufficient for the development of targeted assays ( i . e . using pre-specified lists of M/Z values to measure daughter peptides of network targets ) . To gauge how the interaction network influenced our success in identifying dysregulated protein targets , we compared the proteomic performance of CRANE against that of a signature based on differentially expressed individual genes . We found a marked improvement in our ability to detect protein-level changes in identified markers when a network-guided combinatorial algorithm is used to detect mRNA-level dysregulation signatures ( see Figure 4 ) , and the improved representation of subnetwork targets in the proteomic data can be attributed , in part , to the use of the PPI network . Sources of experimental bias in the measurement of protein expression can be similar to those in the identification of PPIs ( i . e . more abundant proteins are more easily identified ) . However , when we consider the fraction of differentially expressed proteins among all proteins identified , the top 200 CRANE targets always deliver more than 30% precision in identifying differentially expressed proteins , reaching a maximum of 43% when 150 targets are evaluated . In contrast , when we consider the products of the top 200 individual gene markers ( i . e . those having significant mRNA differential expression ) , the fraction of differentially expressed proteins reaches a maximum of only 14% . Assuming the trend in discovery is linear , the network-based approach affords a nearly 7-fold improvement in the rate of discovery of differentially expressed proteins . As a testament to the combinatorial aspect of our analysis , our seven differentially expressed proteomic targets ( in Table 1 ) would not have been discovered if we had based our classifier on individually differentially expressed genes , for these proteins did not exhibit consistent mRNA expression across survival groups in the TCGA data ( Figure S2 ) . While it is well known that dysregulation at the level of individual gene expression does not necessarily correlate with protein expression ( the mRNA-to-protein correlation is 0 . 43 for humans [29] ) , our observations clearly suggest that combinatorial , network-based mRNA-signatures serve as better indicators of post-transcriptional dysregulation when compared to sets of differentially expressed single genes . This result speaks to the ability of network-based algorithms to reproducibly detect dysregulated proteins at the population level , as opposed to uncovering the relationship between mRNA expression and protein expression within a single sample . As an alternative explanation , the network-based targets may point to proteins that are more abundantly expressed and for which dysregulation can be more efficiently measured . Given that the Verhaak et al . subtypes were constructed through hierarchical clustering of gene expression data , we expected that their yield in a proteomics experiment would largely compare to the performance of individual gene markers ( which were constructed based on ranked differential expression ) . While this was the case for Proneural , Classical , and Mesenchymal subtypes , the Neural subtype performed relatively well in predicting differentially expressed proteins , yielding proteomic targets at a rate comparable to the CRANE signatures . This suggests that the Neural subtype contains hidden network structure that boosts the visibility of the group at the protein level and/or that both the CRANE signature and the Neural subtype contain classes of proteins ( e . g . structural and metabolic proteins ) that are more amenable to proteomic measurement . In support of the latter hypothesis , the top gene ontology ( GO ) term in the Neural subtype was nucleotide metabolic process ( GO:0009117 , p-value = 4 . 72e-5 ) [7] , and metabolic enzymes are typically well-represented in proteomic experiments [30] . We also examined gene ontology ( GO ) term enrichment of our CRANE signature using DAVID [31] , and we compared the results to the enrichment of the Verhaak et al . subtypes . Of the CRANE GO terms significant at the 0 . 01 level , only 6 overlapped and were significant ( p-value≤0 . 01 ) in the Verhaak et al . dataset , including terms such as “regulation of transcription , ” “regulation of cell proliferation , ” and “cytoskeletal organization” ( see Table S4 for the complete list of significant overlapping terms ) . The most significant and informative GO terms found in the CRANE signature included items such as “protein kinase cascade” ( GO:0007243 , p-value = 3 . 98e-8 ) , “I-kappaB kinase/NF-kappaB cascade” ( GO:0007249 , p-value = 6 . 56e-5 ) , and “regulation of programmed cell death” ( GO:0043067 , p-value = 8 . 08e-5 ) , all of which were absent or not significant in the Verhaak et al . subtypes ( see Table S5 for the complete list of terms significant in the CRANE signature ) . These results indicate that the CRANE subnetwork signature emphasizes kinase cascades and the NF-κB pathway . NF-κB expression has been shown to be positively correlated with astrocytoma grade and inversely correlated with patient survival [32] . Importantly , deletions of NF-κB inhibitor α ( NFKBIA ) and amplifications of EGFR have been shown to be mutually exclusive events in GBM [33] , suggestive of underlying genomic subtypes . Our work recapitulates the importance of understanding patient-to-patient variability in NFKB signaling to better direct therapeutic decisions . Seven subnetwork targets were validated using proteomics , and these proteins have interesting connections to both glioma and cancer . For example , HSPA9 is not only upregulated in a variety of cancers [34] , [35] , but its expression also correlates with glioma grade and the proliferative potential of cells [36] . In our data , HSPA9 is strongly ( fold change = 0 . 80 ) and significantly ( p-value = 1 . 34e-5 ) downregulated in the tumors of long-term survivors , suggesting that , even between tumors of the same grade , HSPA9 biology may differentially affect patient survival . Similarly , we found that calnexin ( CANX ) has 0 . 74-fold diminished protein expression in long-term survivors , and this result is in line with the observation that CANX expression is significantly correlated with the transition from angiogenesis-independent to angiogenesis-dependent ( i . e . more invasive ) tumor growth in xenografts [30] . In turn , PSMD3 , a subunit of the 26S proteasome , was also found to be downregulated in the tumors of long-term survivors , which is in line with the promising results of proteasome inhibitors in pre-clinical studies [37] , [38] . More recently , a novel role for PSMD3 was proposed by Okada et al . , who identified a SNP near the gene associated with the regulation of neutrophil count by both GWAS and eQTL analysis [39] . It has long been recognized that cancer and inflammation are synergistic processes [40] , and it appears that increased neutrophil activity is associated with highly infiltrative gliomas [41] , [42] . Given the potential role of PSMD3 in neutrophil recruitment in GBMs , our data are consistent with a hypothesis that downregulation of PSMD3 leads to less neutrophil-mediated inflammation and longer survival . In assessing patient outcomes of GBM , we argue that the most informative prediction is whether or not a patient has a poor prognosis , i . e . is a “short-term survivor , ” as this prognosis identifies patients who are poor candidates for the standard of care and for whom more aggressive therapies may be beneficial . To demonstrate the therapeutic potential of proteomic targets , we used CART to identify a decision tree useful in classifying our proteomic cohort . We found that two proteins could effectively classify our cohort of 16 patients with near perfect sensitivity and specificity , though this result may be due to overfitting in our cohort . Nonetheless , this result illustrates how gene expression targets may be translated into clinical proteomics biomarkers . We note that the many of the GBM patients with a poor prognosis in our proteomic validation cohort did not receive the full standard of care: surgery , radiation , and chemotherapy . Consequently , survival classification in our study is not a proxy for response to the standard of care . In future clinical work , efforts should be directed to identifying cancer survivors matched on treatment protocols to allow for the identification of molecular features that render them susceptible to various therapies . While our 50-gene network signature is currently useful for prognostication , analysis of a treatment-matched cohort would potentially allow for the identification of targets to guide therapeutic decision making .
The results published here are in part based upon data generated by The Cancer Genome Atlas ( TCGA ) pilot project established by the NCI and NHGRI . Information about TCGA and the investigators and institutions who constitute the TCGA research network can be found at http://cancergenome . nih . gov/ . Patient data was obtained from TCGA , where clinical data and corresponding microarray data were available for 200 glioblastoma patients [6] . Samples run on three different array platforms – the Affymetrix U133A GeneChip , the Affymetrix Human Exon GeneChip , and a custom-made Agilent array – were pooled into a composite dataset by Verhaak et al . [7] , and these data were used for further analysis . To select only de novo GBM , we removed those patients with a pretreatment history , a histologic classification of “treated primary GBM” , or a prior history of glioma . We also excluded patients whose final vital status ( living vs dead ) was unknown . The remaining patients were separated into two groups based on survival , taking the top 25% ( 43 patients , surviving>635 days , ages 11–83 ) as long-term survivors and the bottom 25% ( 43 patients , surviving<225 days , ages 39–85 ) as short-term survivors . CRANE [5] was employed to discover subnetworks of proteins coordinately dysregulated at the level of mRNA; the MATLAB code is available . The global human protein-protein interaction network was compiled from publicly available interactions in the Human Protein Reference Database [43] , and the CRANE search algorithm was constrained to subnetworks of consisting of at most proteins . We binarized gene expression data by setting the genes in the top quartile of expression intensity to H ( high expression ) and all others ( bottom 75% ) to L ( low expression ) . This threshold for high expression ( 25% ) was previously shown to be most effective in identifying discriminative subnetworks using a range of datasets [5] . After binarizing the data , we were interested in identifying subnetworks whose “state” – the binary sequence of H's and L's – was informative in regards to the phenotype ( STS vs LTS ) . This is formulated as an optimization problem , where the objective function to be maximized is the mutual information between phenotype and expression state , the J-value . Mutual information is a measure of the reduction in our uncertainty of a patient's phenotype , given observations of the subnetwork's expression state . More precisely , denoting the phenotype random variable with and letting denote the k-dimensional binary random variable representing the expression state of a subnetwork of size k , the mutual information between the expression state and phenotype is defined as . Here , denotes the entropy of the phenotype random variable , and denotes the entropy of the phenotype given the expression state of the subnetwork , . The entropy of a random variable X is defined as , where A denotes the set of all possible values of X and px denotes . We refer to a particular expression state of a particular subnetwork as a “state function . ” For a state function , the J-value is defined as the amount of information provided by that particular state on the phenotype , i . e . its contribution to the mutual information between phenotype and the state of the corresponding subnetwork . Namely , for a given state function f for a subnetwork composed of k proteins ( i . e . , f is an observation of random variable ) , the J-value is defined as . Here , denotes , and denotes . It can be shown that . In this analysis , we first identified high-scoring subnetworks according to their J-values and then sorted these high-scoring subnetworks according to their mutual information for survival . Additional parameters used to assess a network's prediction accuracy are the support ( the fraction of samples containing a particular subnetwork state , ) ; the confidence ( the fraction of long-term survivors possessing a particular subnetwork state , ) ; and the anti-confidence ( the fraction of short-term survivors possessing a particular subnetwork state , ) . A subnetwork and an associated state function have a high J-value if the state function provides high support , high confidence , and low anti-confidence ( or , symmetrically , high anti-confidence and low confidence ) . To test the network features discovered using TCGA , we explored their prediction accuracy using an independent GBM microarray dataset , GSE13041 , available via the Gene Expression Omnibus [8] . After removing patients known to have received prior radiotherapy , chemotherapy , and/or temozolomide treatment , a total of 166 patients remained; using the survival time cut-offs as before , the short-term survivor group consisted of 41 patients ( ages 34–86 ) , and the long-term survivor group consisted of 50 patients ( ages 22–78 ) . A neural network ( NN ) was trained on the TCGA data using the top k subnetworks ( ranked by mutual information , where k is a variable ) , and test performance was gauged using classification accuracy , calculated as , where S is the number of correctly predicted short-term survivors , L is the number of correctly predicted long-term survivors , and T is the total number of test samples in the test dataset . We calculated the cumulative classification accuracy for k ranging from 1 to 10 , i . e . examining accuracy of the best performing network alone , and then examining the performance of the best two networks , and then the best three networks , etc . Overall classification accuracy reached a maximum of 80% when using k = 5 subnetworks , each composed of size d = 10 genes ( Table S2 ) . For comparison , we assessed how the four GBM subtypes proposed by Verhaak et al . stratified patient survival in the testing dataset , GSE13041 . We first removed pretreated patients from the testing dataset , and the data was then log transformed , median centered , and normalized by each array's standard deviation; gene expression was inferred by averaging probe-level expression . For the 840 genes in the Verhaak et al . GBM subtype classifier , we calculated the Spearman correlation coefficient between the centroid expression profiles ( derived from the TCGA dataset ) and each sample in the testing dataset , assigning each sample to the subtype with maximum correlation . To identify statistically significant proteomic changes , missing values were imputed using the median intensity per peptide within each survival group , and the data was standard normalized for each peptide . We used a mixed model to compare the group-wise protein intensity differences of interest , with the survival group set as a fixed effect and the peptide set as a random effect , which allowed us to account for the within-protein correlation of the peptides inherent in mass spectrometry-based proteomic experiments [44] . In the results , we only compare differences between various prespecified protein sets observed in the data , namely the proteins coded by the following genes: the genes in the top-ranking subnetworks identified by CRANE ( 200 genes in total ) , the genes in the Verhaak molecular subtypes ( 840 genes in total ) , and the top 200 genes with the most significant individual differential expression . Using a likelihood ratio test , a p-value≤0 . 05 for the proteins of interest was considered significant and no correction for multiple hypothesis testing was performed . These statistical analyses were performed using R 2 . 13 . 2 and SAS version 9 . 2 ( SAS Institute Inc . , Cary , NC ) .
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Glioblastoma multiforme ( GBM ) is the most common and aggressive brain tumor in adults , and , while the median survival time for treated patients is approximately one year , subgroups of patients respond differently to the same treatments , with some patients showing little improvement and other patients living far longer than expected . These differences in treatment response indicate that the tumors may show molecular differences that we can harness to tailor cancer therapy . To this end , we sought to identify biomarkers of patient survival in GBM . To improve the applicability of our molecular markers to other patient groups , we constrained our markers using maps of protein-protein interactions , and we also employed a unique computational strategy that incorporates patient-to-patient molecular variability into the results . We identified a set of 50 genes comprising a subnetwork signature that successfully separated GBM patients by their survival times . Our approach to identifying this subnetwork signature also improved our ability to identify its protein products in an independent cohort of patients . In the ongoing search to improve cancer detection and treatment , our work represents a successful strategy for identifying reproducible biomarkers that can more efficiently lead to the discovery of druggable protein targets .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[] |
2013
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Network Signatures of Survival in Glioblastoma Multiforme
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α-Conotoxins potently inhibit isoforms of nicotinic acetylcholine receptors ( nAChRs ) , which are essential for neuronal and neuromuscular transmission . They are also used as neurochemical tools to study nAChR physiology and are being evaluated as drug leads to treat various neuronal disorders . A number of experimental studies have been performed to investigate the structure-activity relationships of conotoxin/nAChR complexes . However , the structural determinants of their binding interactions are still ambiguous in the absence of experimental structures of conotoxin-receptor complexes . In this study , the binding modes of α-conotoxin ImI to the α7-nAChR , currently the best-studied system experimentally , were investigated using comparative modeling and molecular dynamics simulations . The structures of more than 30 single point mutants of either the conotoxin or the receptor were modeled and analyzed . The models were used to explain qualitatively the change of affinities measured experimentally , including some nAChR positions located outside the binding site . Mutational energies were calculated using different methods that combine a conformational refinement procedure ( minimization with a distance dependent dielectric constant or explicit water , or molecular dynamics using five restraint strategies ) and a binding energy function ( MM-GB/SA or MM-PB/SA ) . The protocol using explicit water energy minimization and MM-GB/SA gave the best correlations with experimental binding affinities , with an R2 value of 0 . 74 . The van der Waals and non-polar desolvation components were found to be the main driving force for binding of the conotoxin to the nAChR . The electrostatic component was responsible for the selectivity of the various ImI mutants . Overall , this study provides novel insights into the binding mechanism of α-conotoxins to nAChRs and the methodological developments reported here open avenues for computational scanning studies of a rapidly expanding range of wild-type and chemically modified α-conotoxins .
Nicotinic acetylcholine receptors ( nAChRs ) are a large family of ligand-gated ion channels that mediate rapid synaptic transmission in the central and peripheral nervous system [1] , [2] . nAChRs are implicated in disorders such as Alzheimer's diseases , schizophrenia , depression , hyperactivity disorders and tobacco addiction [3]–[6] . All nAChRs are comprised of five homologous subunits , which are divided into a large N-terminal extracellular ligand-binding domain ( LBD ) , a transmembrane domain , and an intracellular domain [7] ( Figure 1 ) . The nAChR subtypes include hetero- or homo-pentamers of α1-10 , γ , β1-4 , δ and/or ε subunits . These subtypes differ in their pharmacological and kinetic properties , as well as their localization [8] , [9] . For example , the α7-nAChR is widely expressed in the brain , whereas the α3β2-nAChR is mostly expressed in the cerebellum and spinal cord [10] . Conotoxins are disulfide-rich toxins produced in the venom gland of marine cone snails [11] , [12] . Each of the >500 species in the Conus genus produces hundreds of different conotoxins [13]–[15] , which together form a large pool of many thousands of bioactive peptides . Conotoxins target a diverse range of membrane receptors and ion channels to rapidly and efficiently immobilize prey [13] . The α-conotoxin family specifically and potently inhibits nAChR subtypes and , consequently , these conotoxins are useful tools in neurophysiological studies . The ability to specifically target nAChRs has also attracted interest for the development of drugs , and several conotoxins or derivatives are currently in clinical trials for the treatment of pain [16] , [17] . The majority of known α-conotoxins display a similar topology , as shown in Figure 1 . This topology includes four cysteines arranged in a common sequence pattern -CCXmCXnC- , where X is any non-cysteine residue , and n and m are the numbers of inter-cysteine residues . Disulfide bonds connect cysteines I-III and II-IV [18] , [19] . ImI is one of the shortest α-conotoxins , with a loop spacing topology of m = 4 , n = 3 [20] and , initially , was reported to specifically interact with α7- and α9-nAChRs [21] . Later , the α3β2-nAChR was also found to be blocked by ImI [22] . ImI has been extensively studied: its structure has been determined using NMR [23]–[25] , and its interaction with the α7-nAChR has been probed by several mutational studies [26]–[31] . In the absence of a crystallographic structure of any nAChR , several early structural models of the binding of ImI to the LBD of α7-nAChR were generated [22] , [32] , but they are now superseded because better templates , additional experimental data and improved modeling methods are available [33]–[35] . In this study , an improved model of the interaction of α7-nAChR with wild-type ImI has been developed and the structural and energetic impact of more than 30 mutations of ImI and of selected positions of the receptor were investigated . We describe for the first time a model able to explain the majority of mutation studies . Optimal methods to predict relative mutational energies were investigated , and an approach that used energy minimization produced excellent correlations with experimental values , producing R2 values of 0 . 74 . Finally , an energy decomposition of the mutational energies was done and showed that different terms of the energy function played distinct roles . Although we focus here on conotoxin ImI , experimental mutational studies have been carried out on a range of other conotoxins , in a first step toward their development as drugs [30] , [31] , [36] . In silico mutational studies such as those described here could dramatically accelerate the development of conotoxin-based drugs and also help identify wild-type toxins with interesting pharmacological activity among the thousands of conotoxins that are predicted to exist .
Two series of 10 ns molecular dynamics simulations of the α7-nAChR , either in the apo state or bound to ImI , are summarized in Figure 3 . The α carbon root-mean-square deviation ( RMSD ) to the initial conformation became stable after 2000 ps for both simulations , indicating that they had reached equilibrium ( Figure 3A , B ) . Indeed , the largest fluctuation , which is displayed by the third subunit , is <1 Å over the last 8000 ps of the simulation . The α-carbon root-mean-square fluctuations ( RMSF ) indicate that the β-strand regions are conformationally stable , but that the C-loop and Cys-loop regions are flexible ( Figure 3C , D ) . The dynamic property of the C-loop is particularly interesting , as the change of conformation of this loop is thought to be vital for the physiological role of nAChRs [33] , [37]–[40] . It has been shown that the interaction of agonists with nAChRs causes the C-loop to adopt a closed conformation and this change of conformation has been hypothesized to trigger the opening of the channel [41] . According to this hypothesis , competitive antagonists stabilize the C-loop in an open conformation , potentially preventing the channel from opening . Interestingly , in our study , the C-loop in the apo model fluctuates significantly ( Figure 3E ) , whereas the C-loop of the α7-nAChR in complex with ImI is stabilized in an open conformation ( Figure 3F ) . It can therefore be concluded that ImI stabilizes the C-loop in an open conformation , which , according to previous studies , should inhibit channel activity . Molecular dynamics simulation significantly refined the conformation of the α7-nAChR/ImI model . Indeed , after 10 ns molecular dynamics , the conformation of the C-loop of the α7-nAChR/ImI model is stable and different from that of the two templates . As shown in Figure 4 , the C-loop of the α7-nAChR/ImI is more closed than the C-loop of AChBP in complex with ImI but more opened than that of α1-nAChR subunit in complex with α-bungarotoxin , which is a classical antagonist of nAChR . The positions of the β-sheets are conserved between the template AChBP crystal structure and the α7-nAChR/ImI model . The h1 α-helices occupy slightly different positions , with the α7-nAChR α-helices being closer from the center of the pore than the AChBP ones ( not shown ) . Our model of α7/ImI significantly differs from those [22] , [32] that were developed before the publication of the crystallographic structures of AChBP/ImI [33] , [34] . In the previous studies , models were built by homology with crystallographic structures of AChBP with the C-loop in a closed conformation , but several recent studies suggest that this C-loop conformation is incompatible with the nAChR inactive state [38] , [41] . Moreover , the previous studies tentatively tried to justify the binding mode of ImI using weak mutational energy couplings revealed by mutant cycle analyses , which were interpreted as pairwise interactions [28] . It proved to be impossible to reproduce all the pairwise interactions identified by this method [28] . Recently , Gleitsman et al . [42] measured similar weak mutational energy couplings occurring between residues located 60 Å from each other , one being in the C-loop of an nAChR and the other in the middle of the trans-membrane domain . That study demonstrated that weak couplings are not evidence of direct interaction . On the contrary , a strong coupling was observed between α7-Y195 and ImI-R7 [28] , and in our model , the side chains of these two residues are tightly packed together , as is apparent in Figure 5 . Recently Armishaw et al . [30] docked ImI into a structural model of the α7-nAChR derived by comparative modeling , using one of the AChBP/ImI crystallographic structures . Their strategy involved the mutation of α7-Y93 to Ala before performing the docking procedure , and finally the “back” mutation of position 93 into Tyr . Presumably , the docking strategy did not succeed to place the conotoxin without this mutation step . Indeed , docking molecules onto a structure derived by comparative modeling is a challenging task because the low accuracy of the receptor conformation either causes steric hindrance or does not allow side chains to be tightly packed around the docked ligand [43] . The model presented by Armishaw et al . [31] is very similar to the final conformation of our molecular dynamics , despite the use of different strategies . Their model was not compared to previous experimental mutation studies , but we here provide qualitative and qualitative explanations to those mutation studies . The binding of ImI to the α7-nAChR has been investigated experimentally and the impact of mutations of α7 and/or ImI on the affinity ( Kd ) or inhibition activity ( IC50 ) are known [26]–[29] . Here we investigate structural explanations for the influence of single point mutations on α7/ImI affinity through an analysis of models of the mutated complex . Mutations involving unnatural residues have not been considered here because their parameters are less refined than those for standard amino acids . The aim of our study is to compare different methods to predict the impact of single point mutations on binding affinities between conotoxin ImI and α7-nAChR; the use of unnatural residues would complicate the interpretation of those comparisons as the deviation between computed and experimental mutational energies could arise from inaccuracy in the parameters as well as from the methodological differences . The α7/ImI model will be referred to as the “wild-type model” , whereas the models of the complexes presenting mutations are referred to as “mutated models” . Three positions of ImI , i . e . , D5 , P6 , and R7 , have been found experimentally to be important for the interaction [26] . Four receptor positions , α7-N111 , α7-Q117 , α7-P120 and α7-153 , have some influence on the affinity of the complex but are not directly in contact with ImI in our model [28] . Mutational energies of single point mutants were computed using two energy functions: molecular mechanics generalized Born surface area ( MM-GB/SA ) and molecular mechanics Poisson-Boltzmann surface area ( MM-PB/SA ) energy functions . The mutated models were first refined using either the minimization based approach ( MBA ) or the molecular dynamics simulation based approach ( MDBA ) . For the MBA , mutations were introduced in 15 frames extracted from the wild-type 10 ns molecular dynamics simulation and the mutated models were minimized using either explicit water ( EWM ) or a distance-dependent dielectric constant ( DDDCM ) . For the MDBA , mutations were created on the model of the last frame of the wild-type 10 ns molecular dynamics simulation , and the mutated models were subjected to 500 ps molecular dynamics . Because the complex showed only small conformational variations during the last 8 ns of the simulation , the last frame can be chosen as representative of the wild-type structure . The energies were averaged on the minimized mutated models for MBA , and on 50 frames extracted from the last 100 ps of the 500 ps molecular dynamics for MDBA . To better understand the energetic components stabilizing the ImI/α7-nAChR complex , the free energies of the system and the mutational free energies of the mutated complexes , computed using EWM , were decomposed into entropic , electrostatic , van der Waals , and hydrophobic contributions . The solute entropic contribution to the binding energy has been neglected in our previous calculations , but it was estimated using normal-mode analysis in this section . As shown in Table 3 , the van der Waals interactions and the hydrophobic effects stabilize the complex , whereas the electrostatic contribution is destabilizing . The observation that the van der Waals interactions and hydrophobic effect are predominant over the electrostatic interactions correlates with a statistical analysis of interface features carried out over the 10 ns of the molecular dynamics simulation . During the simulation , the average buried surface area of the wild-type complex was of 1150 Å2 , which is twice as large as the average 500 Å2 of peptide/protein interfaces [50] , and can be associated with the important van der Waals and hydrophobic effect energies . ImI and α7-nAChR form , on average over the simulation , three hydrogen bonds and one salt-bridge , and this small number of electrostatic interactions is consistent with average values for α-helical peptides [50] . The decomposition of the mutational free energies are displayed in Table 4 and the correlation between different contributions and experimentally derived mutational energies are shown in Figure 9 . The electrostatic contribution has by far the best agreement with experimental energies ( R2 = 0 . 62 with MM-GB/SA ) and is therefore the major contributor to the specificity between mutants . The van der Waals interactions also participates , but to a lesser extent ( R2 = 0 . 35 ) . On the contrary , the hydrophobic effect does not allow differentiation of the mutants ( R2 = 0 . 01 ) . With a correlation coefficient of 0 . 23 , the solute entropy term has only a small influence on specificity . Furthermore , the correlation coefficient between the experimentally derived and predicted mutational energies does not change substantially after including the solute entropic component ( Figure 7 and 9 ) . This justifies the proposal that solute entropic contributions , which are computationally demanding , can be neglected when predicting the ranking of single point mutant binding affinities based on the computation of binding energies . In this study , an extensive computational analysis of a complex between an α-conotoxin , ImI , and an nAChR , α7-nAChR , was carried out . In the absence of the crystal structure of a complete nAChR extracellular domain , modeling the interaction of an inhibitor of an nAChR is difficult . Nevertheless , we successfully studied the binding mode between α-conotoxin ImI and α7-nAChR using a combination of comparative modeling and molecular dynamics simulation . Using this model , we have explained the effect of mutations described in previous experimental studies . The structures of 16 mutated ImI/α7-nAChR complexes were refined using MBA or MDBA , and the binding energies were predicted using MM-PB/SA and MM-GB/SA . To our knowledge , this study constitutes the first attempt to use these energy functions to study the binding of a range of α-conotoxin variants to an nAChR . The approach using a simple minimization to refine the model ( MBA ) led to the best agreement between predicted mutational energies and experimental values . Another important conclusion of our study is that affinity between ImI analogues and α7-nAChR was mainly governed by van der Waals and non-polar desolvation energies , whereas the electrostatic interactions were mainly important for the specificity . Interestingly , the entropy had little influence on the mutational energy of single point mutants . Because α-conotoxins share the same tightly packed structural fold , our observations on the energy decomposition are likely to help in the rational optimization of α-conotoxins pharmacological properties in general . In order to perform extensive computational scanning of α-conotoxins , a fast and accurate approach is necessary . In this respect , we have identified that the best method to achieve this goal is to refine the mutated models by minimization using explicit solvation and to compute mutational energies using MM-GB/SA .
In the absence of the crystal structure of α7-nAChR , the human α7-nAChR LBD was modeled using a comparative approach , following a strategy described previously [51]–[53] . The crystal structure of an isolated Mus musculus muscle type extracellular domain of the α1-nAChR subunit in complex with the inhibitor α-bungarotoxin was solved at 1 . 94 Å resolution ( PDB ID: 2qc1 ) [35] . The Mus musculus α1-nAChR subunit shares 38% sequence identity with the Homo sapiens α7 subunit and superimposes with , on average , 2 . 9 Å rmsd with the AChBP subunits . An electron microscopy structure of a complete muscle type nAChR of Torpedo marmota ( PDB ID: 2bg9 ) revealed a similar arrangement of subunits as the one presented by AChBP . As the electron microscopy structure is of low resolution ( 4 Å ) , the AChBP structures ( PDB ID: 2c9t ) were employed as structural templates in our comparative modeling strategy to orient the five α7 subunits in the pentamer . The orientations of the side chains were modeled according to the α1 template ( PDB ID: 2qc1 ) due to its overall higher sequence identity to the α7 subunit than AChBP . A sequence alignment between the two structural templates and α7-nAChR LBD is displayed in Figure 2 . The secondary structure elements and ligand-binding sites observed on the experimental structures and predicted for α7-nAChR are also shown in Figure 2 . The modeling of the nAChR C-loop required special attention as its change in conformation allows the binding site to accommodate ligands of different sizes [33] . In our model , the structure of AChBP in complex with ImI ( PDB ID: 2c9t ) was used to derive restraints in the C-loop region because AChBP has locally higher sequence identity than α1 , and because the C-loop conformation in the AChBP structure allows ImI to fit in the binding site . Conversely , the Cys-loop , the β1-β2 loop , the A-loop , and the B-loop were modeled using information from the α1 template because it displays higher sequence identity than AChBP . Multiple sequence alignment between Aplysia californica AChBP and the LBD of α1 , α2 , α3 , α4 , β1 , β2 , α6 , α7 , α9 and α10 was generated using MUSCLE with default parameters [54] . The multiple alignment between AChBP , α7 and α1 was then manually adjusted based on structural superimpositions of the crystal structures of AChBP ( PDB ID: 2c9t ) and α1 ( PDB ID: 2qc1 ) . The comparative modeling program Modeller [55] ( version 9v7 ) was then employed to generate 100 three-dimensional structural models of the α7-nAChR complex . The Cys-loop region was modeled using the α1 subunit template only , whereas the C-loop and B-loop were modeled based on AChBP . The model selected according to the DOPE score [56] was analyzed using MolProbity [57] and 94% residues were in the favorable region of the Ramachandran plot , which is acceptable for a comparative model [56] . A model of the structure of the complex ImI/α7-nAChR was obtained by comparative modeling . An X-ray diffraction structure of the complex between ImI and AChBP ( PDB ID: 2c9t ) was used to provide restraints between ImI and the nAChR and also structural restraints to ImI conformation . The structure of α7-nAChR was modeled using the same sequence alignment/structure described previously to model the apo state . The use of a comparative modeling approach is justified by the fact AChBP and α7-nAChR are likely to have very similar binding modes because they share a high level of sequence identity in their binding sites ( 52% identity according to alignment in Figure 2 ) . Molecular dynamics simulations ( MD ) were performed using Gromacs 3 . 3 . 1 package [55] and the 53a6 forcefield . The ImI/α7-nAChR model was solvated in a cubic box with an edge length of 11 . 4 nm solved by adding 40 , 773 SPC water molecules . 74 Na+ and 27 Cl− ions were added to simulate a physiological NaCl concentration of 0 . 1 M and to neutralize the system . The system was minimized using 1000 steps of steepest descent algorithm . The temperature was progressively raised from 0 K to 300 K over 100 ps of constant pressure and temperature ( NPT ) MD simulation with all the protein atoms restrained to their initial position . Ten nanosecond NPT MD was then performed on the whole system without restraints with Berendsen temperature bath coupling set at 300 K and an isotropic molecule based scaling setup at 1 atm [58] . The electrostatic interaction between non-covalent atoms was computed with particle-mesh Ewald method [59] with a distance cutoff of 10 Å . The LINCS algorithm [60] was used to constrain all bonds and the time step of the simulation was set to 2 fs . The simulation of the apo state α7-nAChR was prepared using the same procedure . Ten nanosecond MD simulations were performed twice for ImI/α7-nAChR and three times for the α7-nAChR in apo state systems . Stability and conformational variabilities of those five simulations are provided in Figure 3 and in supplementary material figure S1 . In the MBA , 15 frames were extracted every 500 ps in the interval between 3–10 ns of the 10 ns molecular dynamics simulation trajectory of the wild-type model . Each frame was minimized using AMBER10 [61] ( with the AMBER ff03 forcefield ) by 2000 steps of steepest descent algorithm followed by 2000 steps of conjugate gradient algorithm with the backbone of the complex restrained . Two thousand five hundred steps of steepest descent minimization and 2500 steps of conjugate gradient minimization were then performed without restraints . The side chains in the ligand were mutated using Modeller and all the residues ( including residues of the ligand ) were minimized using AMBER10 [61] . In the DDDCM approach , ε = 4r was used , whereas in the EWM approach , the protein was solvated in a water box with a minimum of 8 Å between the solute and the side of the box . In the MDBA , a water cap with a radius of 16 Å from the center of the binding pocket was added . MD was only performed on the mutants of the last frame obtained from MBA above . Before performing MD , the system was minimized using 2000 steps of steepest descent minimization followed by 2000 steps of conjugate gradient minimization . The water box was equilibrated by increasing the temperature from 0 to 300 K while maintaining the solute under constraints , and then further maintaining the simulation at 300 K for 40 ps . In the production phase , the restraints in the binding site were removed and 500 ps MD was performed with a 2 fs time step . The non-bonded cutoff was set to 12 Å and SHAKE was applied for all the bonds involving hydrogen atoms . For strategy ( i ) , water molecules and residues within 6 Å of the ligand were flexible; for strategy ( ii ) , water molecules and residues within 4 . 5 Å of the ligand were flexible; for strategy ( iii ) , water molecules and residues within 6 Å of the mutated residues were flexible; for strategy ( iv ) , all the atoms of the ligand and water molecules were flexible; and for strategy ( v ) , only the water molecules were flexible . For every strategy , 50 frames were extracted every 2 ps in the interval comprised between 400–500 ps of the 500 ps MD simulation . To provide qualitative explanations to the effect of the mutations of ImI-D5N , ImI-R7Q , α7-Q117A , α7-R186V , α7-N111S , α7-S113A , α7-P120A and α7-G153S , additional MD were performed for at least 500 ps . In those MD , a similar water cap was used , as described previously , and residues within 6 Å of the mutated side chain were flexible . The values of the binding free energy ( ΔG binding ) for each mutant were calculated based on the following equation: ( 1 ) The free energy can be decomposed into three components: ( 2 ) where G solute is the solute Gibbs free energy , G epol represents the polar contribution to the solvation energy and G SA represents non-polar contribution to the solvation energy . Polar contribution to the solvation energy is determined by solving the Poisson-Boltzmann Equation using the PB module implemented in AMBER10 [61] , or the GB approach implemented in AMBER10 [62] . The non-polar contribution to the solvation energy is calculated using: ( 3 ) where solvent-accessible surface area ( SASA ) was determined using the Molsurf [63] algorithm with a probe radius of 1 . 4 Å . The surface tension γ and constant parameter a in equation ( 3 ) were taken to their default values 0 . 0072 kcal/mol−1 Å2 and , 0 kcal/mol−1 Å2 respectively . The effect of residue mutation on the binding energy was computed using: ( 4 ) where ΔΔG binding was defined as mutational energy that is the binding energy difference between the wild-type ligand ( ΔG binding ( WT ) ) and its mutants ( ΔG binding ( mut ) ) . The entropy contribution was estimated using normal-mode analysis , which employed the atomic fluctuation matrix produced from a normal mode calculation [64] . Alternatively , for some calculations , we made the approximation that the wild-type and mutated complexes have similar entropies . Using this approximation: ( 5 ) where E MM is the molecular mechanical energies of the proteins as given by the molecular mechanics potential . This equation was used to compute the difference of internal Gibbs free energy for the complex , the ligand and the receptor . The binding free energy of the mutants was calculated by solving equations ( 4 ) and ( 5 ) using the MMPBSA . py script , which is part of the AMBER10 distribution . The Poisson-Boltzmann equations were solved using internal dielectric and external dielectric constants set to 2 . 0 and 80 . 0 , respectively , a probe radius of 1 . 4 Å , a grid spacing set to 0 . 5 Å and ionic strength set to 0 . 15 M/L . For the GB algorithm , the salt concentration was set to 0 . 15 M/L .
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Conotoxins are peptide toxins extracted from the venom of carnivorous marine cone snails . Members of the α-conotoxin subfamily potently block nicotinic acetylcholine receptors ( nAChRs ) , which are involved in signal transmission between two neurons or between neurons and muscle fibers . nAChRs are important pharmacological targets due to their involvement in the transmission of pain stimuli and also in numerous neurone diseases and disorders . Their potency and specificity have led to the development of α-conotoxins as drug leads , and also to their use in the investigation of the role of nAChRs in various physiological processes . The most studied conotoxin/nAChR system , ImI/α7 , was modeled in this study , and several computational methods were tested for their ability to explain the perturbations observed experimentally after introducing single point mutations into either ImI or the α7 receptor . The aim of this study was to establish a theoretical basis to rapidly identify new α-conotoxin mutants that might have improved specificity and affinity for a given receptor subtype . Furthermore , hundreds of thousands of conotoxins are predicted to exist , and computational methods are needed to help streamline the discovery of their molecular targets .
|
[
"Abstract",
"Introduction",
"Results/Discussion",
"Materials",
"and",
"Methods"
] |
[
"medicine",
"protein",
"interactions",
"molecular",
"dynamics",
"molecular",
"mechanics",
"neuroscience",
"toxicology",
"ion",
"channels",
"protein",
"structure",
"nicotinic",
"acetylcholine",
"receptors",
"proteins",
"chemistry",
"biology",
"proteomics",
"toxin",
"binding",
"macromolecular",
"complex",
"analysis",
"biochemistry",
"cellular",
"neuroscience",
"computational",
"chemistry",
"biophysic",
"al",
"simulations",
"computational",
"biology",
"macromolecular",
"structure",
"analysis"
] |
2011
|
Blockade of Neuronal α7-nAChR by α-Conotoxin ImI Explained by Computational Scanning and Energy Calculations
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Human immunodeficiency virus type 1 ( HIV-1 ) persists in a latent state within resting CD4+ T cells of infected persons treated with highly active antiretroviral therapy ( HAART ) . This reservoir must be eliminated for the clearance of infection . Using a cDNA library screen , we have identified methyl-CpG binding domain protein 2 ( MBD2 ) as a regulator of HIV-1 latency . Two CpG islands flank the HIV-1 transcription start site and are methylated in latently infected Jurkat cells and primary CD4+ T cells . MBD2 and histone deacetylase 2 ( HDAC2 ) are found at one of these CpG islands during latency . Inhibition of cytosine methylation with 5-aza-2′deoxycytidine ( aza-CdR ) abrogates recruitment of MBD2 and HDAC2 . Furthermore , aza-CdR potently synergizes with the NF-κB activators prostratin or TNF-α to reactivate latent HIV-1 . These observations confirm that cytosine methylation and MBD2 are epigenetic regulators of HIV-1 latency . Clearance of HIV-1 from infected persons may be enhanced by inclusion of DNA methylation inhibitors , such as aza-CdR , and NF-κB activators into current antiviral therapies .
In HIV-infected individuals , highly active anti-retroviral therapy ( HAART ) dramatically reduces HIV-1 plasma titers [1]–[3] and decreases morbidity and mortality [4] . However , a reservoir of latent virus persists within resting CD4+ T cells [5]–[8] and contributes to the reemergence of viremia upon discontinuation of HAART [9]–[11] . Reactivation of latent HIV-1 , thus rendering it susceptible to HAART , is a critical component of any strategy for HIV-1 clearance [12]–[14] . Transcriptional repression is an important component of HIV-1 latency , necessitating identification of cellular proteins that repress HIV-1 transcription and the testing of small molecules that inhibit these cellular proteins . In resting CD4+ T cells , HIV-1 is maintained in a latent state by multiple factors that inhibit virus gene expression after integration into cellular DNA . In particular , several studies have highlighted the critical role of chromatin structure at the site of provirus integration in repressing provirus transcription . Sequence-specific transcription factors can recruit histone deacetylases ( HDACs ) and other chromatin-modifying enzymes to the provirus promoter , resulting in transcriptional repression and virus latency [15]–[19] . Interestingly , the mechanism by which virus escapes silencing by these sequence-specific factors in a productive infection is unknown . Additionally , resting CD4+ T cells are deficient in transcription factors essential for HIV-1 transcription [20] , and latent virus can be reactivated by stimulation of T cell pathways that activate these factors [5]–[8] . The provirus integration site can also be a determinant of latency , either by making the provirus susceptible to transcriptional interference from cellular genes [21]–[24] or by suppressing virus transcription through the formation of heterochromatin [25] . Post-transcriptional mechanisms affecting the export [26] or translation [27] of HIV-1 mRNAs constitute other blocks to HIV-1 gene expression during latency . The resting state of CD4+ T cells and the activity of HDACs are two of the best-understood characteristics of latency , but stimulation of resting CD4+ T cells or inhibition of HDACs in HIV-infected patients do not appreciably decrease the latent reservoir when combined with HAART [28]–[32] . The study of latently infected cells is hampered by their rarity in HIV-infected individuals and the lack of a marker for latent infection . For these reasons , we developed the J-Lat cell lines as an in vitro model of HIV-1 latency [33] . Similar to latently infected CD4+ T cells , the J-Lat cells harbor a full-length HIV-1 genome that is transcriptionally competent , is integrated within actively transcribed cellular genes , and is inhibited at the transcriptional level . Additionally , the latent provirus integrated in the J-Lat cell lines encodes the GFP gene , providing a fluorescent marker of HIV-1 transcriptional activity . To identify novel mechanisms of HIV-1 latency , we have conducted a cDNA screen in J-Lat cells for genes that reactivate latent HIV-1 . This screen identified a portion of methyl-CpG binding domain protein 2 ( MBD2 ) , a transcriptional repressor that binds methylated DNA . We found that the HIV-1 promoter is hypermethylated in J-Lat cell lines and in primary CD4 T cells at two CpG islands surrounding the HIV-1 transcriptional start site . Most importantly , we found that a small molecule inhibitor of DNA methylation , 5-aza-2′deoxycytidine ( aza-CdR ) , synergizes with NF-κB activators to promote a dramatic increase in virus gene expression . Aza-CdR is approved for use in humans to treat myelodysplastic syndrome [34] and may promote the reactivation of latent HIV-1 and the clearance of latently-infected cells in combination with HAART in HIV-infected patients .
The J-Lat cells are clonal cell lines isolated after infection of Jurkat cells with a HIV-1 virus encoding GFP . Latently infected cells were selected that were GFP-negative at the basal state but became GFP-positive after treatment with TNF-α . Treatment of each cell line with TNF-α reactivated latent HIV-1 to a different extent , depending on the cell line ( Figure 1A ) . To identify cellular genes that control HIV-1 latency in this system , a complementary DNA ( cDNA ) library was made from the Jurkat T cell line and cloned into a plasmid encoding the pBMN-CSI-T retrovirus vector , which expresses tomato fluorescent protein as a marker ( Figure 1B ) . To confirm that this vector mediates expression of cloned cDNAs at a level sufficient for reactivation of latent HIV-1 , a positive control virus was produced that encodes NF-κB RelA , which reactivates latent HIV-1 in J-Lat cells [18] . Infection of J-Lat cell line 6 . 3 with the RelA-encoding virus caused a 3 . 5-fold increase in HIV-1 gene expression compared to a control virus that lacks an insert ( Figure 1C ) . The cDNA library was packaged into retroviral particles and introduced into the J-Lat 6 . 3 cell line via infection ( Table 1 ) . GFP-positive cells , indicative of reactivated latent HIV-1 , were isolated by fluorescence activated cell sorting ( FACS ) . cDNA library inserts were amplified from genomic DNA obtained from these cells by PCR with virus-specific primers ( Figure S1A ) and recloned into pBMN-CSI-T . One clone identified in this screen , MBD21345-1947 , corresponded to nucleotides 1345–1947 of the mRNA encoding the MBD2 transcriptional repressor ( Figure S2 ) . Importantly , the first ATG within this clone is in frame with the authentic MBD2 initiation codon , indicating a truncated protein corresponding to amino acids 388 to 411 of full-length MBD2 could be translated . MBD2 is a member of the methyl-CpG binding domain family of proteins , which possess methyl-CpG binding domains ( MBDs ) . Similar to other members of this family , MBD2 specifically binds methylated DNA and mediates transcriptional repression by recruitment of the nucleosome remodeling and histone deacetylation ( NuRD ) complex that includes chromatin remodeling and HDAC activities [35]–[37] . To confirm that MBD21345–1947 reactivates latent HIV-1 , J-Lat cells were transfected with an expression vector for this polypeptide . Transfection of J-Lat 6 . 3 with MBD21345–1947 induced a 5-fold greater reactivation of latent HIV-1 in comparison to an empty vector control ( Figure 2A ) . Since MBD2 inhibits transcription of methylated DNA [35] , the identification of a C-terminal fragment of MBD2 in our screen indicated that this fragment inhibits endogenous MBD2 function in a dominant-negative manner . Furthermore , identification of this fragment implicated full-length , endogenous MBD2 in the repression of HIV-1 transcription during latency . To establish the role of endogenous MBD2 in HIV-1 latency , J-Lat 6 . 3 was transfected with a pool of siRNAs corresponding to this factor . This resulted in an 80 percent reduction in the level of MBD2 mRNA compared to cells transfected with a non-targeting control siRNA pool ( Figure 2B , left panel ) . Depletion of MBD2 resulted in a 300 percent increase in HIV-1 mRNA compared to those transfected with the control siRNA pool ( Figure 2B , right panel ) . These data demonstrate that MBD2 participates in the repression of HIV-1 transcription during latency . Since MBD2 inhibits transcription of methylated DNA [35] , we believed the C-terminal MBD2 fragment identified in our screen might reactivate latent virus by inhibiting endogenous MBD2 function . To test MBD21345-1947 for this activity , we examined its effect on transcription of methylated DNA in a heterologous system . 293T cells were cotransfected with an expression vector for MBD21345–1947 and with another plasmid encoding GFP under the control of the CMV promoter ( pEGFP-N1 ) . This latter plasmid was either methylated in vitro ( meGFP ) or left unmethylated ( GFP ) . Plasmid methylation was confirmed by resistance to Hpa II cleavage ( Figure S1B ) and reduced GFP expression in transfected 293T cells ( Figure 2C ) . Importantly , cotransfection of the MBD21345–1947 plasmid with methylated pEGFP-N1 increased the proportion of GFP-positive cells from 58 to 72 percent ( Figure 2D , left panel ) . Furthermore , derepression by MBD21345–1947 was preferential for methylated DNA , and a similar effect was not observed with non-methylated pEGFP-N1 ( Figure 2D , right panel ) . These results implicate MBD2 and cytosine methylation in the regulation of HIV-1 latency in the J-Lat system . Cytosine methylation is an epigenetic modification that inhibits transcription when CpG islands , clusters of CpG dinucleotides proximal to a transcription start site , are hypermethylated [38] . To determine whether the HIV-1 genome encodes CpG islands , the methprimer program [39] was used search the HIV-1 provirus nucleotide sequence . Two CpG islands were identified flanking the transcription start site at positions -194 to -94 and 180 to 368 ( Figure 3A ) . These islands overlap with two regions that were previously shown to be nucleosome-free [40] and rich in transcription factor binding sites [41] , two features usually associated with bona fide CpG islands [38] . To determine whether HIV-1 CpG islands are methylated during latency , their methylation state was analyzed by bisulfite-mediated methylcytosine mapping . Figure S3 shows nucleotide sequence of the HIV-1 promoter , positions of CpG islands , and the particular CpGs subjected to methylation analysis . We found that both CpG islands were hypermethylated in four different J-Lat cell lines , with the majority of CpGs methylated more than 70 percent of the time ( Figure 3B and Figure S4A–D ) . In sodium bisulfite-treated DNA , cytosine was converted to thymine in greater than 99 percent of all CpN dinucleotides ( N = A , T , or C ) , confirming efficient bisulfite conversion of non-methylated cytosines ( Figure S5A ) . MBD2 mediates transcriptional repression by acting as a bridge between hypermethylated CpG islands and chromatin modifying enzymes , including HDACs [42] . To test whether MBD2 is recruited to the HIV-1 provirus in vivo , we performed chromatin immunoprecipitation ( ChIP ) assays . Chromatin from J-lat cells was incubated with MBD2 antisera and the immunoprecipitated material analyzed by quantitative PCR for presence of HIV-1 provirus . We observed recruitment of MBD2 to CpG island 2 of the HIV-1 genome , but observed no recruitment to CpG island 1 in comparison to a negative control ( Figure 4B , first panel ) . Treatment of J-Lat 6 . 3 with aza-CdR , an inhibitor of DNA methylation , caused up to a 50 percent decrease in methylation , depending on the CpG analyzed ( Figure 4A and Figure S4E ) , demonstrating that HIV-1 DNA methylation is reversible . It should be noted that the data for PBS-treated J-Lat 6 . 3 in Figures 3B and 4A are from the same experiment . Importantly , MBD2 recruitment to CpG island 2 was eliminated when cytosine methylation was inhibited by treatment of the cells with aza-CdR ( Figure 4B , second panel ) . Next , we tested for the presence of HDAC2 , an MBD2 cofactor , at CpG island 2 during latency . Comparable to MBD2 , HDAC2 was recruited to CpG island 2 during latency and was lost after treatment with aza-CdR ( Figure 4B , third panel ) . In contrast , inhibition of methylation by aza-CdR was associated with increased Sp1 recruitment to CpG island 2 ( Figure 4B , fourth panel ) . These data demonstrate that during latency , multiple components of the NuRD complex recognize the methylated HIV-1 CpG island 2 and that this recruitment may be pharmacologically reversed . The finding that methylation of CpG islands flanking the HIV-1 transcription start site can be reversed with aza-CdR suggests that aza-CdR could reactivate latent HIV-1 . Aza-CdR alone , however , showed little effect in terms of mean fluorescence intensity or the proportion of GFP-positive cells ( Figures 4C and 4D ) . As previously observed , treatment with TNF-α reactivated latent HIV-1 in only a fraction of the cell population ranging from 16 to 41 percent depending on the J-Lat cell line studied ( Figure 1A ) . In contrast , dual treatment of latently infected J-Lat clonal cell lines ( lines 6 . 3 , 8 . 4 , 9 . 2 and 15 . 4 ) with both aza-CdR and TNF-α induced a dramatic increase in HIV-1 gene expression ( Figures 4C and 4D ) . Powerful synergy was observed when aza-CdR was used at concentrations as low as 0 . 5 µM in combination with TNF-α or the NF-κB activator prostratin ( Figure 4E ) . The combination of aza-CdR and TNF-α increased HIV-1 expression 196- , 101- , 76- , and 47-fold over PBS-treated control cells ( Figure 4D , upper panel and Table S1 ) . Each of these increases was nearly 20-fold greater than the additive effect of the two reagents ( Table S1 ) . Synergistic activation of transcription was specific for the HIV-1 promoter . Analysis of J-Lat 6 . 3 by RT-PCR found that aza-CdR and TNF-α synergistically activated HIV-1 transcription ( Figure S6A , left panel ) but had only an additive effect on transcription of IκB-α , another NF-κB-regulated gene ( Figure S6A , right panel ) . The same result was observed for J-Lat 8 . 4 . Aza-CdR and TNF-α synergistically activated HIV-1 transcription ( Figure S6B , left panel ) but had only an additive effect on transcription of IκB-α ( Figure S6B , right panel ) . To determine if aza-CdR acts directly on HIV-1 transcription , J-Lat cells were treated with cycloheximide to inhibit expression of other factors . Cycloheximide activity was confirmed by the inhibition of GFP expression after treatment of J-Lat cells with TNF-α ( Figure S6C ) . Under these conditions , aza-CdR still induced HIV-1 transcription ( Figure S6D ) , indicating that aza-CdR acts directly upon the HIV-1 provirus . Synergistic reactivation of latent virus was not observed when aza-CdR was combined with the HDAC inhibitor valproic acid ( VPA ) . Weak synergistic reactivation was observed when aza-CdR was combined with the HDAC inhibitor suberoylanilide hydroxamic acid ( SAHA ) , with an effect about two-fold greater than the additive effect of the drugs ( Figure S7 ) . We show in four different J-Lat cell lines that near-complete reactivation of latent HIV-1 required treatment with both an NF-κB activator and an inhibitor of DNA methylation ( Figure 4D , lower panel ) . J-Lat A2 is another clone that harbors a latent HIV-derived vector encoding only the viral promoter and Tat . In contrast to the other cell lines analyzed here , latent virus in J-Lat A2 did not require aza-CdR for full reactivation . TNF-α alone reactivated the majority of latent virus in J-Lat A2 ( Figures 4D , lower panel , and 4F ) [33] . These data show that treatment with a methylation inhibitor is necessary for full reactivation of some , but not all , J-Lat cell lines . To confirm that cytosine methylation is regularly associated with HIV-1 latency , a polyclonal population of latently infected Jurkat T cells was generated by infection with virus produced from the R7/E−/GFP clone . All HIV-1 proteins are expressed from this full length HIV-1 molecular clone , except Nef , which is replaced with GFP , and Env , which is suppressed by a frameshift mutation . FACS was used to separate latently infected/uninfected GFP-negative cells from productively infected GFP-positive cells ( Figure 5A ) . To compare the infection rate of this population to that of the J-Lat cells , quantitative PCR for HIV R7/E−/GFP sequence was performed on genomic DNA from the polyclonal population 14 and 72 days post infection . The quantity of HIV-1 DNA was normalized to cellular DNA using PCR primers that anneal upstream of the β-actin gene . The level of HIV DNA in these cells ranged from 9- to 14-fold less than that detected in J-Lat cells , indicating a lower rate of infection ( Figure 5B ) . Bisulfite-mediated methylcytosine mapping of HIV-1 DNA from the productive population found hypomethylation , with no detectable methylation at most CpGs . In direct contrast , methylcytosine mapping of the latent population found hypermethylation , with the majority of CpGs methylated more than 68 percent of the time ( Figure 5D and Figure S4F ) . In sodium bisulfite-treated DNA , cytosine was converted to thymine in greater than 99 percent of all CpN dinucleotides ( N = A , T , or C ) , confirming efficient bisulfite conversion of non-methylated cytosines ( Figure S5B ) . Reactivation of latent HIV-1 was also examined in this population . After approximately two months , the proportion of cells with active HIV-1 remained stable at 0 . 65% ( Figure 5C ) . Cells were then treated with TNF-α , aza-CdR , or TNF-α plus aza-CdR . TNF-α reactivated latent HIV-1 , with a 1 . 5-fold greater proportion of cells with active virus ( Figure 5E ) . Importantly , latent HIV-1 was also reactivated by aza-CdR alone , with a two-fold greater proportion of cells with active virus ( Figure 5D ) . These observations indicate that , after infection of Jurkat cells in vitro , a subset of latently infected cells exists that can be reactivated solely by inhibition of DNA methylation . The similarities of J-Lat cells to latently infected CD4+ T cells have established the utility of this experimental system for identifying and characterizing mechanisms of HIV-1 latency . However , because J-Lat cells divide autonomously and possess other aberrations associated with cellular transformation , cytosine methylation was analyzed in a recently developed primary cell model of latency [43] . In this system , naïve CD4+ T cells are purified from uninfected donors and activated under conditions that drive them to become memory cells with either a Th1 , Th2 , or non-polarized ( NP ) phenotype [44] . These differentiated cells are then infected with HIV-1 and viral expression is monitored . The phenotype of NP cells generated ex vivo ( Figure S8 ) closely resembles that of central memory CD4+ T cells found in vivo , which persist for years in secondary lymphoid organs and can differentiate into effector memory CD4+ T cells [45] . A high rate of HIV-1 latency is observed in NP memory CD4+ T cells [43] . To determine if HIV-1 latency is associated with cytosine methylation in primary CD4+ T cells , bisulfite-mediated methylcytosine mapping was performed on CD4+ T cells activated under NP , Th1 , and Th2 polarizing conditions and infected with HIV-1 . Cells were infected with virus produced from the DHIV virus clone [46] , in which CpG island 2 is conserved . Five days post-infection , p24gag was detected in all three subsets ( Figure 6A ) . At this early time point , the HIV-1 CpG island in the NP and Th1 populations was hypomethylated , with most CpGs methylated only 0 or 10 percent of the time , respectively ( Figure 6D and Figure S4G ) . Significant methylation was detected in Th2 cells , with most CpGs methylated 33 percent of the time ( Figure 6D and Figure S4G ) . Two weeks post-infection , NP cells had returned to a quiescent state and HIV-1 gene expression , as measured by intracellular p24gag expression , was low ( Figure 6C , left panel ) . However , stimulation with antibodies against CD3 and CD28 dramatically increased HIV-1 gene expression , indicating a large population of latently infected cells ( Figure 6C , right panel ) . Importantly , CpG island methylation in latently infected NP cells was greater than in productively infected NP cells , with the majority of CpGs methylated 67 percent of the time ( Figure 6E , S5D , and S4G ) . In sodium bisulfite-treated DNA , cytosine was converted to thymine in greater than 98 percent of all CpN dinucleotides ( N = A , T , or C ) , confirming efficient bisulfite conversion of non-methylated cytosines ( Figure S5C ) . These data confirm that T cell quiescence is associated with methylation of HIV-1 CpG islands and latency in memory CD4+ T cells .
Here , we describe a novel , phenotype-based screen to identify cellular proteins that control HIV-1 latency . This screen identified the transcriptional repressor MBD2 and led to the discovery that the latent HIV-1 provirus is hypermethylated in an in vitro model for HIV-1 latency and in primary lymphocytes latently infected with HIV-1 . Based on these observations , we designed and tested a novel strategy for reactivation of latent HIV-1 using the synergistic activities of an inhibitor of cytosine methylation and activators of NF-κB signaling . HIV-1 latency is likely to be a multifactorial process and a number of different mechanisms have been proposed to account for the establishment and the maintenance of the latent phenotype [13] , [14] , [20] . NF-κB signaling reactivates latent HIV [47]–[50] , but data reported here and elsewhere [16] , [51] , [52] indicate that a significant proportion of latent HIV-1 remains silent when NF-κB is activated in the J-Lat clones or other cells . We show here that inhibiting provirus methylation leads to an almost complete reactivation of latent HIV-1 in the J-Lat cell lines when combined with activators of NF-κB . These data are consistent with the model that sequence-specific transcription factors and cytosine methylation cooperate to maintain HIV-1 latency . In the latent state , HDAC1 is recruited to the HIV-1 promoter by several sequence-specific factors including NF-κB p50 [18] , CBF-1 [19] , and Yin-Yang 1 [15] . Additionally , in microglial cells CTIP-2 has been shown to recruit HDAC1 to the HIV-1 promoter [53] . Our new observations demonstrate that MBD2 is also recruited to the latent HIV-1 promoter via the second CpG island ( Figure 7A ) . We propose that MBD2 silences transcription by recruitment of the NuRD complex or other factors . This is supported by our finding that another component of NuRD , HDAC2 , is also recruited to hypermethylated CpG island 2 during latency . NF-κB activation relieves one component of the transcriptional block , causing decreased CBF-1 [19] and NF-κB p50 homodimer recruitment to the HIV-1 promoter , as well as increased binding of the NF-κB RelA activator [18] ( Figure 7B ) . Inhibition of cytosine methylation relieves another component of the transcriptional block , causing decreased MBD2 and HDAC2 recruitment to HIV-1 CpG island 2 ( Figure 7C ) . The combination of NF-κB activation and methylation inhibitors eliminates both transcriptional blocks , causing a synergistic increase in HIV-1 transcription and reactivating virus in the majority of cells ( Figure 7D ) . In polyclonal Jurkat cells , the magnitude of HIV-1 reactivation appeared to be smaller than for the J-Lat clones . This was not , however , because TNF-α or aza-CdR were ineffective , but because of the small proportion of latently infected cells in this population compared to the J-Lat cells , each of which harbor a provirus . To ensure no more than one provirus per cell , they were infected at a low multiplicity that left approximately 90 percent of the cells uninfected . Quantitative PCR for HIV DNA demonstrated the small proportion of infected cells in this population compared to the J-Lat clones . Virus reactivation by TNF-α and aza-CdR is highly significant , but is somewhat obscured by the large background of GFP-negative uninfected cells . The role of epigenetic mechanisms in suppression of HIV-1 transcription during latency has not been fully addressed to date . Sequence-specific transcription factors contribute to latency by recruiting HDACs and other repressors to the virus promoter . These findings present a paradox , however , because latent virus can be of wild-type nucleotide sequence [33] , and yet transcription is suppressed . Here , we present evidence that HIV-1 latency is also maintained at the epigenetic level by the methylation of provirus DNA and recruitment of MBD2 . This protein brings transcriptional repressors to methylated DNA , and the MBD21345–1947 fragment isolated from the screen may reactivate latent HIV-1 by disrupting the interaction of MBD2 with an interaction partner . Importantly , after each round of DNA replication , cytosine methylation is faithfully reproduced in a process that is directed by previously methylated DNA [54] . Thus , identical DNA sequences can be either active or silenced depending on their methylation status . Our and previous findings suggest that both HDACs and cytosine methylation contribute to HIV-1 latency , in agreement with a growing body of evidence demonstrating cooperation between these two gene silencing mechanisms [55] , [56] . The rarity of latently infected cells and the lack of a marker for latent HIV-1 infection necessitate the use of in vitro model systems for detailed studies of this process . Transformed cells such as the Jurkat line may show aberrant DNA methylation patterns at specific loci [57] , possibly complicating analyses of cytosine methylation and HIV-1 latency . However , when Jurkat cells are infected with HIV-1 the proportion of cells that become latently infected vs . productively infected is small , suggesting that transformation does not result in the indiscriminate methylation and repression of HIV-1 . Furthermore , high-resolution analysis of cytosine methylation in primary and transformed cells has found less aberrant methylation of CpG island promoters in transformed cells than had been previously hypothesized based on candidate gene studies [58] . Importantly , we confirmed the association between HIV-1 latency and cytosine methylation in a primary cell model of HIV-1 latency . The findings reported here , based upon a near full-length HIV-1 with wild type LTR and Tat sequences , add to previous studies that have used mutated forms of the HIV-1 promoter to describe a role for methylation of HIV-1 DNA in latency [59]–[61] . One report , however , has described latent HIV-1-derived vectors , or “minigenomes , ” that lack all virus genes except for Tat . J-Lat clone A2 harbors exactly such a minigenome . Importantly , these latent minigenomes are not methylated [62] and are almost fully reactivated by TNF-α treatment , unlike the full-length genome [33] . Apparently , screens to isolate latently infected clones produced very different results when mini- instead of full-length genomes were used . For the minigenomes , removal of virus genes and repositioning of Tat out of its normal genomic context are likely to have altered transcriptional control . This alteration may have influenced the type of latently infected cell recovered from the screen . The screens that produced the J-Lat cells also selected for mechanisms that silence HIV-1 within several days after infection . Other screens for cells that silence HIV-1 at later time points have identified additional silencing mechanisms [19] . Our results indicate that cytosine methylation can be an important component of HIV-1 latency . In the case of the full-length J-Lat clones , a high degree of cytosine methylation is detected during latency . In the case of minigenomes such as J-Lat A2 or that characterized by Pion et al , the persistent lack of methylation may permit efficient reactivation by TNF-α alone . Pion et al also describe a lack of cytosine methylation in latently infected PBMCs , but the large proportion of productively infected cells in the analyzed population complicates this assay . Novel approaches are required to reactivate latent HIV-1 in infected persons . Therapies that interfere with cytosine methylation are attractive candidates to reactivate suppressed virus and purge the latent HIV-1 reservoir . In uninfected human subjects , aza-CdR causes decreased CpG island methylation and reactivation of a silenced gene [63] , [64] . In HIV-infected individuals , a similar decrease in methylation should be attainable and could reactivate latent virus . Furthermore , mechanisms by which aza-CdR induces hypomethylation are well understood [65] , [66] and this pharmaceutical is approved for use in humans . Aza-CdR acts directly upon the HIV-1 provirus , because it reactivates HIV-1 transcription in the presence of cycloheximide . HIV-1 was reactivated to a lesser extent in this experiment , and this could result from cellular toxicity or inhibition of an indirect component to reactivation . Any indirect component to HIV-1 reactivation would not , however , make aza-CdR any less effective a drug for reactivation of latent HIV-1 in humans . Aza-CdR synergizes with prostratin , a phorbol ester that triggers reactivation of latent HIV-1 in the absence of T cell activation and inhibits de novo virus infection [67] . Thus , the combination of aza-CdR and prostratin may reactivate latent HIV-1 while minimizing additional HIV-1 infection and side effects associated with T cell activation [29] . Therefore , the inclusion of cytosine methylation inhibitors in antiretroviral therapy could represent a significant step toward elimination of the latent HIV-1 reservoir and clearance of virus from infected patients .
Jurkat and J-Lat cells were cultured in RPMI ( Invitrogen ) with 5% FBS ( Gemini Bio-Products ) and 5% Fetalplex ( Gemini Bio-Products ) . For analysis of virus reactivation by flow cytometry , aza-CdR ( Sigma ) and TNF-α ( Biosource ) treatments were for 24 h , after which medium was replaced . Reactivation was assayed after an additional 48 h . For ChIP and bisulfite-mediated methylcytosine mapping , cells were treated for 30 h with aza-CdR . For cycloheximide experiments , cells were treated for 24 hours , either with or without 40 ng/ml cycloheximide . 20 µg of pEGFP-N1 ( Clontech ) was methylated at CpGs with M . Sss I ( New England Biolabs ) according to the manufacturer's protocol . DNA was purified and subjected to a second round of methylation . To generate pBMN-CSI-T , the multiple cloning site ( MCS ) and GFP gene from pBMN-I-GFP ( Addgene plasmid 1736 ) were replaced with the MCS from pDNR-LIB ( Clontech ) and the tomato fluorescent protein . Also , the human cytomegalovirus ( hCMV ) immediate early promoter was inserted upstream of the MCS . For production of RelA-expressing retrovirus , RelA was cloned from pCMV4 ( hind ) , kindly provided by W . Greene , into a version of pBMN-CSI-T lacking the hCMV promoter . MBD21345–1947 was cloned into pBMN-CSI-T as part of cDNA library generation . The cDNA library was generated using the Creator SMART cDNA Library Construction Kit ( Clontech ) with oligodT-purified ( Quickprep mRNA Purification Kit , Amersham ) RNA isolated ( TRIzol , Invitrogen ) from Jurkat T cells . Amplified cDNAs were cloned into pBMN-CSI-T and electroporated into E . coli strain DH5α . The library was amplified 240 , 000-fold by plating of bacteria on solid medium , and DNA was extracted from aliquots ( Plasmid Maxi Kit , Qiagen ) . J-Lat cells were transfected by electroporation using Kit R and program O-28 ( Amaxa Biosystems ) . HIV-1 reactivation was assayed by flow cytometry four days post-transfection . HIV-1 R7/E−/GFP pseudotyped with the vesicular stomatitus virus G ( VSV-G ) protein was produced by cotransfecting 293T cells with pEV1335 and a plasmid encoding VSV-G by the calcium phosphate method . Supernatant was harvested 48 h post-transfection and frozen at −80°C in aliquots . Aliquots were thawed , diluted 1∶160 , and used to infect Jurkat T cells overnight at a multiplicity of 0 . 1 infectious units per cell with 2 ml supernatant per 1 million cells . Three days post-infection , GFP-negative and -positive cell populations were isolated by FACS . Retrovirus pseudotyped with VSV-G was produced as described previously [68] by cotransfection of Phoenix-ampho cells with pBMN-CSI-T or plasmids derived thereof and a plasmid encoding VSV-G . Supernatant was harvested 48 h post-transfection and J-Lat cells were infected overnight at a ratio of 250 , 000 cells to 2 ml supernatant with centrifugation at 2500 rpm for the first 1 . 5 h . 293T cells were transfected by the calcium phosphate method . Cells were cotransfected with a plasmid encoding the tomato fluorescent protein and either methylated or unmethylated pEGFP-N1 , and the tomato-positive population was analyzed for GFP expression . For measurement of J-Lat activation , cells were infected with undiluted virus and analyzed by flow cytometry 2 days post-infection . For cDNA screening , cells were infected at a multiplicity of 0 . 15 infectious units per cell . GFP-positive cells were purified by FACS two days post-infection , cultured for two days , and genomic DNA was isolated ( DNeasy Tissue Kit , Qiagen ) . The cDNA inserts were amplified from genomic DNA by PCR using oSK57 ( 5′-AAATGGGCGGTAGGCGTGTACGGTG-3′ ) and oSK58 ( 5′-GCGGCTTCGGCCAGTAACGTTAGGG-3′ ) as primers , cloned into pBMN-CSI-T , and identified by determination of nucleotide sequence ( Molecular Cloning Laboratories ) . Cell fluorescence was measured with the FACSCalibur or LSRII ( BD Biosciences ) . Cell sorting was performed with the FACS Vantage DiVa ( BD Biosciences ) . To phenotype CD4+ T cells , they were stained with the following mAbs: phycoerythrin-conjugated ( PE ) –anti-CD4 , TC–anti-CD45RA , or PE–anti-CXCR4 ( Caltag ) . Flow cytometry and sorting data were analyzed with FlowJo software ( Treestar ) or Cellquest ( BD Biosciences ) , in the case of primary cells . Analysis was restricted to the live population , as defined by the forward versus side scatter profile . Flowjo transforms fluorescence plots to a linear scale at the origin , permitting intelligible display of cells with low fluorescence . To assess intracellular p24gag expression , cells were fixed and permeabilized with Citofix/Cytoperm ( BD Biosciences ) . Cells were washed with Perm/Wash buffer ( BD Biosciences ) and were stained with anti-p24 antibody ( AG3 . 0 ) . Cells were washed with Perm/Wash Buffer and incubated with Alexa Fluor 488 goat anti-mouse IgG ( H+L ) in 100 µl of Perm/Wash buffer . Cells were washed with Perm/Wash buffer and samples were analyzed by flow cytometry . HIV-1 p24gag -positive gates were set by comparison with uninfected cells treated in parallel . J-Lat cells were transfected with siRNAs corresponding to the MBD2 mRNA or non-targeting control siRNAs ( siGENOME SMARTpool or siCONTROL pool , Dharmacon ) by electroporation using Kit R and program O-28 ( Amaxa Biosystems ) . Two days after transfection of siRNAs , RNA was isolated from cells with TRIzol Reagent ( Invitrogen ) , treated with DNAse I ( Promega ) , and first strand cDNA was synthesized with reverse transcriptase ( Superscript II , Invitrogen ) using a dT16 primer . Quantitative PCR was performed with the 7900HT Sequence Detection System ( Applied Biosystems ) and the 2× Hot Sybr real time PCR kit ( Molecular Cloning Laboratories ) , with each PCR reaction receiving 1/20 of the reverse transcription . HIV R7/E−/GFP mRNA and DNA were assayed using oSK1 ( 5′-ATGGTGAGCAAGGGCGAGGAG-3′ ) and oSK5 ( 5′-GTGGTGCAGATGAACTTCAG-3′ ) , oligonucleotides , corresponding to the GFP gene , as primers . HIV R7/E−/GFP DNA was normalized to a DNA sequence upstream of the human β-actin gene using 5USBACT ( 5′-GCCAGCTGCAAGCCTTGG-3′ ) and 3USBACT [18] ( 5′-GCCACTGGGCCTCCATTC-3′ ) as primers . MBD2 mRNA was assayed using oSK61 ( 5′-CCCACAACGAATGAATGAACAGC-3′ ) and oSK62 ( 5′-TGAAGACCTTTGGGTAGTTCCA-3′ ) as primers . As an internal control , HIV and MBD2 mRNA levels were normalized to that of cyclophilin A . Cyclophilin A mRNA was assayed using oSK6 ( 5′-GTCTCCTTTGAGCTGTTTGC-3′ ) and oSK7 ( 5′-CCATAGATGGACTTGCCACC-3′ ) as primers . IκB-α mRNA was assayed using oSK135 ( 5′- CTCCGAGACTTTCGAGGAAATAC-3′ ) and oSK136 ( 5′- GCCATTGTAGTTGGTAGCCTTCA-3′ ) as primers . SDS 2 . 3 software ( Applied Biosystems ) was used to quantify each cDNA relative to cyclophilin A and to confirm the specificity of each PCR reaction by melting curve analysis . Sodium bisulfite treatment was performed according to the Pikaard protocol ( http://www . biology . wustl . edu/pikaard/PDFs%20and%20protocol%20files%20/Bisulfite%20Sequencing . pdf ) with minor modifications . Jurkat T cell DNA was digested overnight with Pst I and purified with the Qiaquick PCR Purification Kit ( Qiagen ) . Bisulfite-treated DNA was amplified in nested PCR reactions with the following reaction conditions: denature ( 95°C , 5 minutes ) , cycle 35 times ( 95°C , 30 seconds then 60°C , 60 seconds ) , and extend ( 60°C , 7 minutes ) . For J-Lat cells , the first reaction used oSK100 ( 5′-CGCCTCGAGTTTATTGATTTTTGGATGGTGTTAT-3′ ) and oSK101 ( 5′-CGCTCTAGACCATTTACCCCTAAATATTCTACAC-3′ ) as primers and the second reaction used oSK71 ( 5′-CGCCTCGAGATATTTTGTGAGTTTGTATGGGATG-3′ ) and oSK94 ( 5′-CGCTCTAGACCCAATATTTATCTACAA-3′ ) as primers . For primary cells infected with NL4-3-derived virus , the first reaction used oSK123 ( 5′-CGCCTCGAGTTTATTGATTTTTGGATGGTGTTTT-3′ ) and oSK124 ( 5′-CGCTCTAGACCATTTACCCCTAAAAATTCTACAC-3′ ) as primers and the second reaction used oSK122 ( 5′-CGCCTCGAGATATTTTATGAGTTAGTATGGGATG-3′ ) and oSK94 as primers . All PCR reactions were performed in triplicate and then pooled to reduce chances of clonality in recovered fragments . Products were cleaved with Xho I and Xba I and cloned into pBluescript ( Stratagene ) cleaved with the same enzymes . Nucleotide sequence was determined of at least nine cloned inserts using the universal M13 reverse primer . The efficiency of sodium bisulfite conversion was calculated using the Quantification Tool for Methylation Analysis ( QUMA ) software [69] . The nucleotide sequence of untreated DNA was also determined to ensure that readings do not result from virus mutations . The effect of MBD21345–1947 upon GFP expression ( Figure 2A ) was evaluated by a two-tailed , two sample Student's t-test with a null hypothesis of no effect . Reactivation of latent HIV-1 ( Figure 5D ) was evaluated with a one-tailed , two sample Student's t-test with a null hypothesis of no increase in GFP expression . In bisulfite-mediated methylcytosine mapping experiments , at least nine independent clones of sodium bisulfite-treated HIV-1 DNA were analyzed from each sample . For J-Lat cell lines in the latent state ( Figure 3B ) and CD4+ T cells ( Figures 6D and E ) , a one-tailed , single sample Student's t-test was performed for each CpG with a null hypothesis of no methylation . For J-Lat 6 . 3 treated with either aza-CdR or a PBS control ( Figure 4A ) , a one-tailed , two-sample Student's t-test was performed for each CpG with a null hypothesis of no decrease in methylation after aza-CdR treatment . For sorted populations of GFP-negative and -positive Jurkat T cells ( Figure 5B ) , a one-tailed , two sample Student's t-test was performed for each CpG with the null hypothesis that the GFP-positive population did not have less methylation . ChIP was performed as described previously [70] with modifications . J-Lat 6 . 3 cells Cells were diluted to 5×105 per ml , lysed , and sonicated ( Model 500 Ultrasonic Dismembranator , Fisher Scientific ) . Lysates were incubated overnight with 5 µg of antibody against MBD2 ( Upstate Cell Signaling Solutions cat . 07-198 ) , HDAC2 ( Santa Cruz Biotechnology cat . sc-7899 ) or Sp1 ( Santa Cruz Biotechnology cat . sc-59 ) . Immune complexes were recovered by incubation for 1 h with protein A agarose beads ( Invitrogen ) . Immunoprecipitated DNA was quantified by quantitative PCR using the 7900HT Sequence Detection System ( Applied Biosystems ) and 2× Hot Sybr real time PCR kit ( Molecular Cloning Laboratories ) . Negative control DNA was assayed using 5USBACT and 3USBACT as primers , CpG island 1 was assayed using oSK92 ( 5′-TCAGTTCAGATAATTTCAGTTGTCC-3′ ) and oSK93 ( 5′-CCCAGTACAGGCAAAAAGCA-3′ ) as primers , and CpG island 2 was assayed using oSK89 ( 5′-AAGCGAAAGGGAAACCAGAG-3′ ) and oSK90 ( 5′-TCTCCCCCGCTTAATACTGA-3′ ) as primers . SDS 2 . 3 software ( Applied Biosystems ) was used to analyze precipitated DNA relative to input and to confirm the specificity of each PCR reaction by melting curve analysis . PBMCs were obtained from leukopaks from unidentified , healthy donors . Naïve CD4+ T cells were isolated by MACS microbead negative sorting using the naïve T cell isolation kit ( Miltenyi Biotec ) . The purity of the population was always higher than 95% . Naïve T cells were primed with beads coated with anti-CD3 and anti-CD28 ( Dynal/Invitrogen ) as previously described [44] . Seven days after stimulation , cells were infected by spinoculation . Seven days after infection , cells were reactivated with beads coated with anti-CD3 and anti-CD28 for 72 h in the presence of IL-2 at a ratio of 1 bead per cell . The integrase inhibitor 118-D-24 did not have any effect on viral reactivation .
|
Current drug therapies inhibit replication of the human immunodeficiency virus ( HIV ) . In patients undergoing these therapies , the amount of HIV is reduced to an undetectable level and HIV-related disease subsides . However , stopping antiviral drug therapy results in the quick return of HIV and of disease . One reason for this is latently infected cells , in which virus replication is temporarily halted . When drug therapy is stopped , virus from these latently infected cells can resume infection and spread to other cells in the patient , resulting in the return of disease . Here , we demonstrate that one mechanism of latency is DNA methylation , in which chemical groups called methyl groups are added to HIV DNA . We also identify a host protein called methyl-CpG binding domain protein 2 ( MBD2 ) that binds methylated HIV DNA and is an important mediator of latency . Furthermore , we demonstrate that a drug that inhibits DNA methylation potently reactivates latent HIV . Novel strategies to eliminate or reduce the latent reservoir are necessary . Our findings may prove useful in the development of novel therapies to efficiently reactivate latent HIV-1 , thus making it susceptible to current drug therapies .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"immunology/cellular",
"microbiology",
"and",
"pathogenesis",
"virology/persistence",
"and",
"latency",
"molecular",
"biology/transcription",
"initiation",
"and",
"activation",
"molecular",
"biology/dna",
"methylation",
"microbiology/cellular",
"microbiology",
"and",
"pathogenesis"
] |
2009
|
Epigenetic Regulation of HIV-1 Latency by Cytosine Methylation
|
Leishmania mexicana ( Lm ) causes localized ( LCL ) and diffuse ( DCL ) cutaneous leishmaniasis . DCL patients have a poor cellular immune response leading to chronicity . It has been proposed that CD8 T lymphocytes ( CD8 ) play a crucial role in infection clearance , although the role of CD8 cytotoxicity in disease control has not been elucidated . Lesions of DCL patients have been shown to harbor low numbers of CD8 , as compared to patients with LCL , and leishmanicidal treatment restores CD8 numbers . The marked response of CD8 towards Leishmania parasites led us to analyze possible functional differences between CD8 from patients with LCL and DCL . We compared IFNγ production , antigen-specific proliferation , and cytotoxicity of CD8 purified from PBMC against autologous macrophages ( MO ) infected with Leishmania mexicana ( MOi ) . Additionally , we analyzed tissue biopsies from both groups of patients for evidence of cytotoxicity associated with apoptotic cells in the lesions . We found that CD8 cell of DCL patients exhibited low cytotoxicity , low antigen-specific proliferation and low IFNγ production when stimulated with MOi , as compared to LCL patients . Additionally , DCL patients had significantly less TUNEL+ cells in their lesions . These characteristics are similar to cellular “exhaustion” described in chronic infections . We intended to restore the functional capacity of CD8 cells of DCL patients by preincubating them with TLR2 agonists: Lm lipophosphoglycan ( LPG ) or Pam3Cys . Cytotoxicity against MOi , antigen-specific proliferation and IFNγ production were restored with both stimuli , whereas PD-1 ( a molecule associated with cellular exhaustion ) expression , was reduced . Our work suggests that CD8 response is associated with control of Lm infection in LCL patients and that chronic infection in DCL patients leads to a state of CD8 functional exhaustion , which could facilitate disease spread . This is the first report that shows the presence of functionally exhausted CD8 T lymphocytes in DCL patients and , additionally , that pre-stimulation with TLR2 ligands can restore the effector mechanisms of CD8 T lymphocytes from DCL patients against Leishmania mexicana-infected macrophages .
Leishmaniasis is a zoonotic disease that infects humans as well as a variety of mammalian species . Several Leishmania species , such as L . mexicana , L . amazonensis , L . braziliensis and L . aethiopica can cause two opposite clinical forms of cutaneous leishmaniasis: localized cutaneous leishmaniasis ( LCL ) and diffuse cutaneous leishmaniasis ( DCL ) [1] . While the former is relatively benign , consisting of a single ulcer that forms at the infection site , patients with DCL have a continuous uncontrolled spread of the parasite throughout the skin and , in advanced stages , these patients also show parasite invasion of the oro- and nasopharygeal mucosae . Although the prevalence of DCL patients in Mexico is low , they represent a public health problem for which no successful cure has been found . These patients lack an effective T cell immune response capable of activating MOi , and antimonial treatment only achieves transitory remission [2 , 3 and 4] . Murine models infected with L . major have shown that both the innate and acquired immune responses are necessary for parasite clearance . Cells such as MO , dendritic cells , NK cells , CD8 and CD4 T lymphocytes; cytokines such as interleukin ( IL ) -12 and interferon gamma ( IFNγ ) , pattern recognition receptors such as Toll like receptors ( TLRs ) [6] , [7] , [8] and effector molecules such as nitric oxide ( NO ) and superoxide anion ( O2- ) [9] have been reported to mediate protection , both in mouse models and in humans . It has also been proposed that CD8 T cells play a crucial role in infection clearance , although the role of CD8 cytotoxicity in disease control has not been elucidated . Elevated numbers of CD8 have been reported in blood and lesions of patients infected with L . major and L . mexicana and their protective role has been associated with IFNγ production [10] . Additionally , we have previously reported that the number of CD8 is importantly reduced in lesions of DCL patients infected with L . mexicana , as compared to LCL patients [11] . Thus , a comparative analysis of the overall immune effector functions of CD8 from LCL and DCL patients would permit a more precise definition of the role played by these cells in the disease outcome , both by their cytokine production as well as by their cytotoxicity . We here report a functional analysis of CD8 isolated from peripheral blood of LCL and DCL patients infected with Leishmania mexicana . Our results show significant differences between CD8 of both groups: while CD8 from LCL patients produce high levels of IFNγ and show cytotoxicity against autologous MOi , the CD8 from DCL patients show a diminished response both in cytokine production as well in Leishmania-specific cytotoxicity . Thereafter we analyzed if the differential cytotoxicity observed in vitro also correlated with the number of apoptotic cells in lesions of both groups of patients and found that DCL patients have significantly less TUNEL+ cells than LCL patients . The diminished CD8 response in DCL patients resembled the cellular “exhaustion” reported for CD8 in other chronic diseases [12] , [13] , [14] , [15] , where CD8-effector capacity could be restored by different mechanisms including TLR signaling [16] , [17] , [18] . Since TLR2 signaling in CD8 enhanced proliferation and survival in vitro [19] , we analyzed whether TLR2 stimulation of CD8 from DCL patients could also restore their functional capacity . We therefore stimulated CD8 from DCL patients with the TLR2-specific agonist Pam3Cys and with Leishmania mexicana LPG , which has been shown to be a TLR2 ligand capable of activating human peripheral blood mononuclear cells and NK cells to produce Th1-promoting cytokines [6] , [8] . We show that stimulation with TLR2-specific agonists Pam3CyS or with Leishmania LPG can restore the effector functions of CD8 from DCL patients , including IFNγ production , antigen specific cellular proliferation and cytotoxicity against MOi . In addition to restoring these functions , TLR2-stimulated CD8 cells showed a reduction in PD-1 expression , a molecule frequently present in cellular exhaustion . This phenomenon had previously been described with the TLR9 ligand CpG ODN in mice [18] .
Human experimentation guidelines of the Mexican Health authorities were strictly followed . The study was reviewed and approved by the Ethics Research Committee of the Medical Faculty of UNAM . Written informed consent was required for all patients . Ten individuals with LCL ( 4 females and 6 males , mean age = 34 . 6±13 . 9 ) and four with DCL ( 1 female and 3 males , mean age = 46±12 . 9 ) from La Chontalpa – Tabasco State ( except one DCL ) , an endemic area in southeastern Mexico , were analyzed . Patients were diagnosed by clinical criteria , parasite presence in lesions , and immunoreactivity to the Montenegro skin test . LCL patients showed skin ulcers containing few parasites and all were positive to the Montenegro test . In contrast , DCL patients had multiple non-ulcerative nodules , harboring an intense parasite load and all were negative in the Montenegro test . Peripheral blood for in vitro experiments was collected in tubes with EDTA ( BD Biosciences ) . Skin biopsy specimens were taken from the lesions with a 4 mm biopsy punch ( Stiefel Laboratories , Inc . , Coral Gables , FL ) after local anesthesia ( 2% xylocaine ) . The biopsy specimens were embedded in OCT compound ( Miles Scientific , Napperville , IL ) , snap frozen and stored in liquid nitrogen until examined . All patients received antimonial therapy after sample collection . Frozen sections were cut with a cryostat and air-dried overnight before the immunostaining procedure . Apoptotic cells in lesions of 7 LCL and 5 DCL patients were detected by the TUNEL method ( In Situ Cell Death Detection Kit , POD , Roche ) . Briefly , cryostat sections ( 4 µm ) were thawed onto coated slides , fixed in paraformaldehyde 4% in PBS pH 7 . 4 for 20 min , washed 30 min with PBS and incubated with permeabilization solution for 2 min on ice . Slides were rinsed twice with PBS and 50 µl TUNEL reaction mixture were added to the tissue sections and incubated for 60 min at 37°C in a humidified atmosphere in the dark . Slides were washed 3 times with PBS and incubated with 50 µl of Converter-POD ( Anti-fluorescein antibody conjugated with horse-radish peroxidase ) in a humidified chamber for 30 min at 37°C . Slides were washed 3 times with PBS and incubated with 50 µl of DAB substrate for 10 min at 25°C and washed 3 times with PBS . Then , samples were mounted under glass coverslips and analyzed under light microscope . Double staining with anti-CD8 antibodies was carried out in six samples of LCL patients as follows: after TUNEL staining , slides were washed with 100 mM Tris-HCl , pH 7 . 4 , 150 mM NaCl . The samples were blocked with Blotto ( a solution containing 5% skim milk powder and 0 . 1% Tween 20 in PBS , pH 7 . 4 ) for 30 min at RT . The samples were incubated with primary mouse anti-CD68 ( 1∶100; Dako: Dako Corp . , Santa Barbara , CA ) during 1 h in a humid chamber at RT . Five-minute washes with 10 mM Tris-HCl , pH 7 . 4 , 150 mM NaCl were followed by 1 h incubation with biotin-conjugated second-step antibody ( goat anti-mouse IgG ) at a dilution of 1∶50 and with the preformed streptavidin-biotin alkaline phosphatase complex ( Dako ) for 1 h at RT . The presence of alkaline phosphatase was evidenced by incubation in AP substrate solution containing 1 mM levamisole for 30 min . Then , slides were washed twice in distilled water . Counterstaining was performed with H&E ( Sigma Chemical ) . The apoptotic cells were identified in a light brown color , whereas MO showed red staining . Leishmania mexicana ( MHOM/MX/92/UADY/68 ) promastigotes were grown in RPMI-1640 medium ( Life Technologies Laboratories , Gaithersburg , MA , USA ) supplemented with 5% heat-inactivated FBS at 28°C . Parasites were sub-cultured every 4 to 5 days and grown to a density of 1×106/ml . Promastigotes were harvested from stationary-phase cultures , centrifuged at 3500 rpm for 10 min , washed three times in PBS , and finally counted after immobilization with glycerol . Peripheral blood mononuclear cells were separated by Ficoll-Hypaque gradient ( Sigma ) and CD8 were isolated by magnetic cell sorting system as described by manufacture' instructions ( Miltenyi Biotec , Bergisch Gladbach , Germany ) . Briefly , 1×107 PBMC was suspended in 40 µl PBS containing 10 µl of anti-CD8 microbeads and incubated for 30 min at 4°C . The cells were washed with PBS and centrifuged at 1500 rpm for 10 min . They were then passed through the LS separation columns ( Miltenyi ) placed in magnetic field . The positive fraction was cultured in RPMI/FBS 10% . The negative fraction was processed again in order to purify MO ( 1×107 cells in 40 µl of PBS with 10 µl of anti-CD14 microbeads ) . MO were cultured in RPMI/FBS 10% . Both CD8 and MO were maintained at 37°C in a humidified atmosphere containing 5% CO2 incubator for 12 h before stimulus in order to reach a resting condition . 1×106 MO were co-incubated with 1×107 L . mexicana promastigotes for 3 h at 28°C in 1 ml RPMI . Afterwards , cells were cultured for additional 18 h at 37°C . Non-ingested promastigotes were washed away with RPMI . The cytotoxicity of CD8 cells on MOi was analyzed by two methods: 51Cr-release and Flow cytometry . The optimal CD8/MOi ratio was established as 10/1 . The proliferation of CD8 in response to autologous MOi was analyzed as previously described [22] . Briefly , PBMC were suspended in RPMI with CFDA ( Sigma ) [5 µM] during 10 min at 37°C and washed twice in RPMI . Cells were cultured at 1×106 per ml and 2×105 MOi were added . MO or Con A [5 µg/ml] ( Sigma ) were used as controls . The cells were co- cultured for 7 days at 37°C . Cells were harvested and incubated in the presence of anti-CD3 CyChrome and anti-CD8 PE ( BD ) . Stained samples were fixed with 2% paraformaldehyde in PBS , and analyzed by multicolor flow cytometry immediately after the end of the incubation period . The percentage of CD3+CD8+CFDAlow was recorded . The effect that MOi had on the production of IFNγ by CD8 was analyzed as follows . 1×106 PBMC were co-incubated with 2×105 MOi or MO during 18 h after which 1 µl GolgiPlug ( BD ) was added and incubated for additional 5 h . Cells were harvested and incubated in the presence of anti-CD8 PE ( BD ) . They were washed twice with PBS . Cells were suspended in Citofix/Citoperm ( BD ) and washed with Perm/Wash . Then , cells were incubated with anti- IFNγ PE-Cy7 ( BD ) and washed twice on Perm/Wash . Stained samples were suspended in PBS and analyzed by multicolor flow cytometry . IFNγ production was also analyzed in the supernatants of the cytotoxicity experiments using standard ELISA assays . In brief , 96-well microtitre plates ( Costar , Corning , NY ) were coated with un-conjugated anti-IFNγ ( clone NIB42; 6 µg/ml; BD ) capture antibody in 100 mM Na2HPO4 , pH 9 . 0 during 12 h at 4°C , and blocked with PBS 0 . 1N NaOH , 0 . 5% casein , pH 7 . 4 . Supernatants and hIFNγ recombinant standard ( R&D Systems , PR ) were incubated in RPMI/FBS 10% during 2 h at RT . hIFNγ was detected using biotinylated mouse Ab anti-hIFNγ ( clone 4S . B3 , 0 . 5 µg/ml; BD ) in 1% BSA , 0 . 05% Tween 20 using streptavidine labeled with alkaline phosphatase ( Life Technologies ) and p-nitrophenil phosphate ( 4 mg/ml , Life Technologies ) as substrate . Absorbance was read at 405 nm and IFNγ concentration was evaluated in the hIFNγ recombinant standard curve . IFNγ concentration in every sample was calculated by lineal regression using mean absorbance ( average of three lectures by sample ) . Detection limit was of ∼15 pg/ml . Purified Leishmania mexicana LPG was obtained as previously described ( 6 ) . Commercial Pam3Cys-Ser- ( Lys ) 4 ( PAM ) was used as control ( EMC Microcollections GmbH , Tübingen , Germany ) . Purified CD8 ( 1×106 ) of DCL patients were stimulated with LPG 10 µg/ml or PAM 2 µM during 24 h . Cells were harvested , washed twice , and used in cytotoxic assays . For proliferation and IFNγ production assays , 1×105 pre-stimulated purified CD8 were co-incubated with autologous 9×105 PBMC and experiments were carried out as described above . Additionally , PD-1 expression was analyzed in non-stimulated and stimulated CD8 stained with anti-CD8 PE , anti-CD3 CyChrome and anti-PD-1 FITC . Data are expressed as mean ± SEM and were tested using Mann–Whitney U-test or Kruskall-Wallis test . A value of p<0 . 05 was considered statistically significant .
In our previous report about cellular infiltrate in lesions of DCL patients we described that the number of CD8 is importantly reduced as compared to LCL patients [11] . The reduction in the number of CD8 cells in lesions of DCL patients led us to analyze if their cytotoxic effector function was also altered . Thus , we analyzed the cytotoxic capacity of CD8 of LCL and DCL patients against autologous MOi . The cytotoxic capacity was measured by two methods: flow cytometry and 51Cr radiolabeling ( Figure 1 ) . Prior to the cytotoxic experiments , the optimal CD8: MOi ratio had been established as 10∶1 ( data not shown ) . The cytometry assay revealed that patients with LCL had a C . I . of 19 . 8 as compared to DCL patients , which showed a C . I . of 0 . 4 , as evidenced by double positive MOi dot blots ( Figure 1A and 1B ) . Additionally , 51Cr radiolabeling showed that the mean percentage of cytotoxicity in LCL patients was 18% as compared to 3% en DCL patients ( Figure 1C ) . Thus , both methods showed a significantly higher cytotoxicity of CD8 cells from LCL patients , as compared to DCL patients , where cytotoxicity was almost null ( p<0 . 01 ) . In order to rule out the possibility that CD8 death could be responsible for the low cytotoxicity in DCL patients , we analyzed CD8 expression of annexin V by flow cytometry assay and found that CD8 do not undergo apoptosis when they are co-incubated with MOi ( data not shown ) . CD8 T lymphocytes can exert cytotoxicity by two mechanisms: granule exocytosis and death ligands . Granzyme B and FasL are prominent executor molecules of these pathways [23] . To determine the mechanism by which CD8 kill MOi , we measured the expression of granzyme B and FasL , before and after cytotoxic challenge with autologous MOi from LCL patients , which were the only ones that had exhibited cytotoxic capacity . These assays however were carried out in a CD8:MOi ratio of 1∶1 since this permitted better FACS analysis of protein expression in CD8 T cells ( Figure 2 ) . Whereas FasL expression remained unchanged , granzyme B expression decreased significantly ( p<0 . 01 ) ( Figure 2B ) after the cytotoxic challenge , which suggests that cytotoxicity is mediated by cytotoxic granules . As CD8 from DCL patients had shown a different cytotoxic capacity with regard to LCL patients , we investigated other functions that could be altered in the cells of both groups of patients , such as specific proliferation of cells in contact with Leishmania antigens . CD8 proliferation was evaluated in PBMC cultures incubated with MOi . After 7 days of culture , a CD8+CFDAlow group was detected in LCL patients , formed by cells that proliferated as a response to the stimulus of MOi ( Figure 3A ) . This group was not found in DCL patients , however , a CD8-CFDAlow group mainly consisting of CD3+CD4+ cells was detected ( data not shown ) . The proliferation of CD8 was only evidenced when the cells were stimulated with MOi and the difference between LCL and DCL patients was statistically significant ( % CD8+CFDAlow MOi LCL = 18 . 37+/−7 . 1 vs DCL = 2 . 3+/−1 . 5 ) ( p<0 . 01 ) ( Figure 3B ) . Stimulation with Con A , as positive control , did not show differences in CD8 proliferation between LCL and DCL patients . This shows that the lack of response in CD8 of DCL patients is specific towards autologous MOi with Leishmania . Another reported function of CD8 T cells in leishmaniasis is IFNγ production [10] . To examine if there are differences in the cytokine secretion of between CD8 of LCL and DCL patients when they are stimulated with autologous MOi , a 24-h in vitro assay was performed after which lymphocytes were labeled for intracellular IFNγ . Only CD8 LCL patients showed response to MOi ( %CD8+IFNγ+ DCL = 1 . 8+/−2 . 5 vs LCL = 17 . 5+/−3 . 9 ) ( p<0 . 01 ) ( Figure 4A and 4B ) . This group of CD8+IFNγ+ cells was not detectable in DCL patients . None of the patients showed CD8+IL4+ cells ( data not shown ) . IFNγ was also measured in the supernatants of flow cytometric cytotoxic assays by ELISA . The results show that IFNγ was only detected in LCL patients ( IFNγ CD8+MOi DCL = 20 pg/ml +/−4 vs LCL = 241 . 8pg/ml +/−137 . 1 ) ( p<0 . 01 ) ( Figure 4C ) . The lack of IFNγ production by CD8 of DCL was specific towards Leishmania antigens since an appropriate response was found to the non-specific mitotic stimulus PMA/Ionomycin ( Figure 4A and 4B ) . As in vitro CD8 cytotoxic assays showed an impaired function of DCL cells as opposed to the robust response found in LCL patients ( Figure 1 ) , we examined if biopsies of skin lesions of both groups of patients showed any evidence of the different cytotoxicity between both groups of patients We analyzed apoptotic cells through TUNEL staining in skin biopsies of LCL and DCL patients ( Figure 5A and B ) . The stain revealed a high proportion of apoptotic cells in tissues of 7 LCL patients ( Figure 5A ) , which was significantly higher as compared to 5 DCL patients ( % TUNEL+ LCL = 63 . 4% +/−12 . 08 vs DCL = 20 . 9% +/−7 . 43 ) ( p<0 . 01 ) ( Figure 5B and 5D ) . Double staining in LCL patients showed that a large number of apoptotic cells were MO ( Figure 5C ) . As CD8 from DCL patients had shown deficiencies in Leishmania-specific cellular effector functions , we analyze if this apparent “exhaustion” could be reversed by TLR2 stimulation , as had been previously shown for other chronic diseases . We examined cytotoxicity , antigen-specific proliferation and IFNγ production of CD8 from DCL patients , which had previously been stimulated with LPG or Pam3Cys for 24 h . We observed a recovery of the CD8 effector functions , including cytotoxicity , IFNγ production and proliferation ( Figure 6A , B and C ) . The stimulus with Pam3Cys induced higher levels in IFNγ production as compared to LPG . The response was highest when CD8 were prestimulated with TLR2 ligands and challenged with MOi ( Figure 6B ) . Proliferation was also induced in CD8 cells although they were not the only ones that responded to the stimulus , since a population of CD8- lymphocytes also proliferated ( Figure 6C ) . Again , Pam3Cys plus MOi could induce the highest response . Since exhausted T cells have been shown to have increased expression of programmed death-1 ( PD-1 ) molecules , which could be modified by TLR-9 ligand , we analyzed if PD-1 expression in CD8 cells of DCL patients could be modified by TLR2 agonists . We found that PD-1 expression in CD8+ of DCL patients was significantly reduced after stimulation with both TLR2 agonists , LPG ( p = 0 . 02 ) and Pam3Cys ( p = 0 . 005 ) ( Figure 6D ) .
The protective immune response against Leishmania , amply studied in mice , is mediated by macrophage-activating cytokines , such as IFNγ and IL-12 [5] . MO activation involves iNOS expression and NO production , the latter being the most important leishmanicidal agent [24] . In humans , IFNγ production and iNOS expression have also been directly associated with the resolution of infection . Additionally , CD8 have been shown to play an important role during Leishmania infection , both in humans as well as in the mouse model . During the acute phase of the disease , large numbers of CD8 have been described in the lesions as well as in the peripheral blood of patients and they have also been observed during the healing process [10] . Yet thus far , the important role of CD8 in leishmaniasis has been related to their IFNγ production [25] , [26] and little is known of the protective response mediated by the cytotoxicity exerted by these cells [27] . It is additionally not clear , if these cytotoxic cells relate with disease progression in patients with diffuse cutaneous leishmaniasis . Previous studies of our group have shown that lesions of DCL patients infected with L . mexicana harbor low numbers of CD8 , as compared to patients with LCL . Interestingly , the leishmanicidal treatment of the DCL patients not only reduced the parasite load but also led to an increase in CD8 during the healing process of the skin lesions of these DCL patients [11] . To date , the mechanism responsible for the reduced presence of CD8 in lesions of DCL patients remains unclear . The marked response of CD8 towards Leishmania parasites led us to analyze possible functional differences between these cells from patients with LCL and DCL . We therefore compared in vitro antigen-specific proliferation , IFNγ production and cytotoxicity of CD8 purified from PBMC of both groups of patients against autologous macrophages infected with Leishmania mexicana . Additionally we analyzed tissue biopsies from both groups of patients for evidence of cytotoxicity associated with apoptotic cells in the lesions . Our data demonstrate that CD8 from DCL patients show a significant reduction in their effector response when coincubated in vitro with autologous macrophages infected with Leishmania mexicana , as compared to patients with LCL . This diminished cytotoxic response was also evidenced in lesions of both groups of patients , since DCL patients showed significantly less apoptotic cells as compared to LCL patients ( Figure 5 ) . Although not much is known of the overall effector mechanisms of CD8 T cells in human leishmaniasis , results obtained in the present work show that both IFNγ production , as well as cytotoxicity against Leishmania-infected macrophages are hampered in DCL cells . It is therefore feasible to speculate that CD8 cells play an important role in the protective response of LCL patients against the infection with Leishmania mexicana , based on our in vitro results , together with the large number of apoptotic cells in the tissue lesions of these patients . Although data on the expression of apoptosis in skin lesions are scarce , our work is in accordance with the literature , where apoptotic CD4 and CD8 T lymphocytes have been described in lesions of LCL patients infected with L . braziliensis , albeit cell death was attributed to hypersensitivity towards Leishmania antigens , leading to activation-induced cell death [28] . MO cell death in tissues could be due to Leishmania infection and/or be a consequence of CD8 cytotoxic activity against infected cells . The former mechanism is unlikely , since it has been demonstrated that Leishmania can inhibit apoptotic mechanisms in phagocytic cells [29] , [30] , [31] , [32] . The possible benefits derived from cytotoxic CD8-induced apoptosis of the host cells infected with Leishmania could be dual: the elimination of the parasite and the expansion of the specific immune response by providing novel parasite antigens [10] , as has described for M . tuberculosis [33] . Controversy remains regarding the route of activation of CD8 in leishmaniasis , since these cells require antigen presentation through MHC class I to become activated . It is not known how Leishmania antigens escape from the parasitophorous vacuole of phagocytic cells into the cytosol to be degraded and transported into the endoplasmic reticulum to be bound to the MHC I molecule . One of the possible mechanisms described in dendritic cells and macrophages is through cross-presentation and involves the chaperone protein Sec61 . Another mechanism of cross-presentation of exogenous antigens could be through phagocytosis of apoptotic bodies of infected cells by dendritic cells , which aids the immune response by providing novel parasite antigens [10] . Good prognosis in patients with cutaneous leishmaniasis , infected with L . major , has been associated with the expression of granzyme B in tissue lesions [34] . In the present work , granzyme B was also associated with the cytotoxic activity of CD8 of LCL patients against MOi ( Figure 2 ) . Since the presence of CD8 in the lesion has also been correlated with recovery of Leishmania-infected patients [11] , it is feasible that lack of cytotoxicity of CD8 against MOi ( Figure 1 ) can lead to uncontrolled parasite spread in DCL patients . However the functional efficacy of CD8 cells from patient tissues cannot be comparatively analyzed with those from peripheral blood due to the small size of lesions in LCL patients , which are often no larger than 2 cm of diameter and therefore limit the amount of lymphocytes available for functional experiments . Although we cannot rule out functional differences between CD8 T cells from lesions and those from peripheral blood , data in the literature have suggested that immunological responses in vitro of peripheral blood mononuclear cells closely mimic the immune response that occurs in the whole organism [35] , [36] . One of the possible mechanisms underlying the reduced effector capacity CD8 of DCL patients could be a functional “exhaustion” of these cells induced by a suppressive environment and antigen persistence , as has recently been described [13] . Despite the small number of DCL patients available for this study , due to the low incidence of this form of the disease in Mexico , we found that CD8 of these patients showed certain functional and phenotypic characteristics that resemble the exhaustion condition described in some chronic viral infections [12] . “CD8 cell exhaustion” was initially described in chronic LCMV infection in mice , in which virus-specific CD8 persist , but lack effector functions [37] . In this viral infection , CD8 clones are initially generated , but cytotoxicity and proliferation are lost at an early stage , while IFNγ persists for a longer period [38] . Similar types of dysfunctions have been described in human chronic infections and during cancer [12] , which suggests that chronic antigen stimulation alone suffices to drive CD8 into exhaustion [13] . Recently in a murine model of L . donovani infection , it has been describes that parasites initially induce CD8 to divide and produce IFNγ . However , CD8 rapidly lose their effector functions and die as the infection progresses [16] . Our present results suggest that the same phenomenon could be occurring in DCL patients , which have a chronic intracellular infection of more than 20 years evolution . These patients do not present delayed cellular hypersensitivity toward specific Leishmania antigens , as evidenced by their negative Montenegro skin test , nor do they present a cell-mediated immunity towards the parasite [1] ( Figures . 1 , 3 and 4 ) . It must be noted that the lack of lympho-proliferative response and the lack of IFNγ production of CD8 from DCL patients was specific against Leishmania antigens , since these cells preserved their capacity to respond to mitogens such as Con A and PMA/Ionomycin ( Figures . 3 and 4 ) . These results suggested that a functional exhaustion towards Leishmania mexicana could be present in CD8 of DCL patients . “Exhausted” cells have been shown to express higher levels of PD-1 , among other inhibitors , and essays of restoration of these exhausted T-lymphocytes have focused on the use of anti-PD-L1 and PD-L2 antibodies , which prevent binding of T-cell PD-1 to the antigen-presenting cell ( APC ) ligands PD-L1 and PD-L2 [16] , [17] , [38] . In these studies , TCR and CD28 signals sufficed to rescue CD8 in exhaustion , once PD-1 activity was blocked [17] . Recently , Wong and co-workers [18] demonstrated that peptide vaccination in the presence of CpG ODN ( TLR9 ligand ) reduced expression of PD-1 in mice . In addition to TLR9 , another innate receptor that has recently been described in CD8 is TLR2 , which triggers cellular activation similar to that of CD28 [39] . Therefore , we hypothesized that TLR2 stimulation could reduce PD-1 expression and restore CD8 functional activity in DCL patients against MOi . Since we had previously shown that LPG , the most abundant surface molecule expressed on the Leishmania , is a TLR2 ligand capable of inducing IFNγ production in human NK cells [6] , we therefore stimulated CD8 from DCL patients with LPG and also with Pam3Cys , a recognized specific TLR2 ligands ( Figure 6A , B and C ) . We found that this preactivation could increase CD8 cytotoxicity against MOi . Also , proliferation and IFNγ-producing CD8 could be detected when they were pre-stimulated with LPG or Pam3Cys . Due to a lack of knowledge of Leishmania-specific CD8 T cell epitopes , antigen-specific CD8 responses in CL have not been studied and therefore phenotypic characterization of possible CD8 exhaustion has not been feasible . Although CD8 exhaustion has not been reported in patients with leishmaniasis , our results on “functional exhaustion” is in accordance with the literature since Joshi and coworkers could demonstrate that L . donovani limits CD8 expansion and induces functional exhaustion in an experimental model [16] which was associated with increased PD-1 expression by Leishmania-specific CD8 . Our analysis also showed that PD-1 expression on CD8 of DCL seems to be associated with functional exhaustion . We were able to show that PD-1 expression on CD8 from DCL patients could be reduced by stimulation with TLR2 ligands and that the reduction correlated with functional restoration ( Figure 6D ) . These results suggest that stimulation by TLR2 could represent a pathway hierarchically higher than PD-1 . The TLR2 receptors are constitutively expressed in memory T lymphocytes , both in CD4 and CD8 , and their activation leads to a co-stimulating signal that induces cytokine production and proliferation [39] . Moreover , TLR2 - MyD88 signaling is a critical pathway in CD8 clonal expansion and memory formation in vivo [19] . Additionally , TLR signaling influences Treg function , in particular , Pam3Cys has been shown to transiently reduce the expression of FoxP3+ in Treg cells and to suppress their activity [40] . Thus , TLR2 stimulation is not only limited to innate immune responses , but also regulates the adaptive immune response . In conclusion , we here show that CD8 of DCL patients lack a cytotoxic activity against autologous macrophages infected with Leishmania mexicana and they present functional and phenotypic characteristics of exhausted cells , possibly as a consequence of chronic intracellular infection by Leishmania mexicana . We additionally present the first evidence in the literature that TLR2 stimulation can restore their effector functions . It remains to be determined if the exhausted condition of CD8 towards parasite antigens is a cause or a consequence of the progressive infection of DCL patients by Leishmania mexicana and if TLR2 induces downregulation of PD-1 pathway . This finding not only broadens our knowledge of the pathogenesis of the disease but will allow the design of activators of CD8 from DCL patients , based on TLR2 stimulation of the immune response .
|
Leishmania mexicana causes localized and diffuse cutaneous leishmaniasis . Whereas the former is a benign form the disease , diffuse cutaneous leishmaniasis is a chronic disfiguring disease , for which no cure is available , and the immune cells of these patients respond poorly to the parasite . It has been proposed that the elimination of Leishmania-infected cells by CD8 T cells is crucial for disease control . We compared the functional characteristics of CD8 T cells from patients with localized and diffuse cutaneous leishmaniasis . We found that CD8 T cells from patients with diffuse cutaneous leishmaniasis were functionally exhausted , as compared to patients with the benign form of the disease . We were able to restore functional capacity of these cells by culturing them with molecules that stimulate TLR2 . This is the first report showing that stimulation of the TLR2 can restore effector mechanisms in functionally exhausted CD8 cells from patients with diffuse cutaneous leishmaniasis . This finding will help design novel treatment schemes for patients infected with the parasite Leishmania mexicana who have the progressive , incurable form of diffuse cutaneous leishmaniasis .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[
"infectious",
"diseases/neglected",
"tropical",
"diseases",
"infectious",
"diseases/skin",
"infections",
"infectious",
"diseases/protozoal",
"infections",
"infectious",
"diseases/tropical",
"and",
"travel-associated",
"diseases",
"immunology/immunity",
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] |
2010
|
CD8 Cells of Patients with Diffuse Cutaneous Leishmaniasis Display Functional Exhaustion: The Latter Is Reversed, In Vitro, by TLR2 Agonists
|
DNA replication is initiated upon binding of “initiators” to origins of replication . In simian virus 40 ( SV40 ) , the core origin contains four pentanucleotide binding sites organized as pairs of inverted repeats . Here we describe the crystal structures of the origin binding domain ( obd ) of the SV40 large T-antigen ( T-ag ) both with and without a subfragment of origin-containing DNA . In the co-structure , two T-ag obds are oriented in a head-to-head fashion on the same face of the DNA , and each T-ag obd engages the major groove . Although the obds are very close to each other when bound to this DNA target , they do not contact one another . These data provide a high-resolution structural model that explains site-specific binding to the origin and suggests how these interactions help direct the oligomerization events that culminate in assembly of the helicase-active dodecameric complex of T-ag .
Viral DNA replication involves a sequence of carefully orchestrated steps including recognition of the origin by a protein ( the initiator ) or proteins , melting of the origin DNA , replication protein A ( RPA ) -dependent unwinding of the DNA , and recruitment of polymerase and other replication factors ( for reviews , see [1–3] ) . Study of this process in eukaryotes has been hampered by uncertainty regarding the eukaryotic origin sequences and by the complexity of the proteins involved in eukaryotic origin recognition . While origin sequences have been identified for Saccharomyces cerevisiae , they are not yet identified in the genomes of higher eukaryotes [4 , 5] . In contrast , replication of small DNA tumor viruses such as SV40 and papilloma virus involves well-defined origin sequences and requires far fewer proteins for formation of the preinitiation complex . In the case of SV40 , a single virally encoded initiator , large T-antigen ( T-ag ) , can bind the SV40 origin , assemble as a set of two hexameric rings , and cause local distortions ( ie , melting ) of the DNA [6] . In the presence of the single-stranded binding protein ( SSB ) human RPA [6] , SV40 T-ag also unwinds origin containing DNA . Once assembled on the origin , SV40 T-ag also recruits host machinery to replicate the viral DNA ( for reviews , see [1–3] ) . Prokaryotic and viral origins contain multiple initiator binding sites . For DNA viruses , these binding sites consist of short DNA sequences , often organized as pairs of inverted repeats . The SV40 core origin is a 64-bp sequence that contains four such binding sites , termed P1 through P4 ( collectively referred to as Site II ) . Each repeat has the sequence GAGGC . These pentameric sequences appear as a pair of inverted repeats , with a 1-bp spacer between each repeat ( Figure 1A ) . The four GAGGC sequences are flanked by an early palindrome region on one side and an AT-rich region on the other side . There are , however , significant variations among viral origins in the spacing , the orientation within the origin , and the sequence of the binding sites . In the case of the related DNA tumor virus , bovine papilloma virus ( BPV ) , the origin contains two pairs of imperfect repeats , and these are organized in a much more compact manner , such that the individual repeats overlap [7 , 8] . SV40 T-ag is a 708–amino acid protein containing at least three independent functional domains: an N-terminal J domain ( amino acids 1–130 ) , a central origin binding domain ( obd ) ( amino acids 131–260 ) , and a C-terminal helicase domain ( amino acids 266–625 ) . A flexible linker connects the obd to the helicase domain [9] . While there is no atomic resolution structure of the intact SV40 T-ag , structures of these individual domains are available . The crystal structure of the J domain has been solved in complex with retinoblastoma protein [10] . The crystal structure of the C-terminal helicase domain has been determined in the presence and absence of adenosine nucleotides [9 , 11] and with p53 [12] . Structural data of the T-ag obd in the absence of DNA include an NMR structure of a T-ag obd monomer [13] and a crystal structure of the T-ag obd in an open-ring form ( spiral ) having six subunits per turn [14] . Cryoelectron microscopy and biochemical studies of the full-length T-ag indicate that T-ag forms a “double donut” of hexameric rings in the presence of origin-like DNA and adenosine nucleotides [15 , 16] . In electron microscopy reconstructions , the J domains and the obds are near the center , and the helicase domains are at the distal ends of the intact dodecameric complex on DNA . The J domain is not required for replication in vitro ( see [17 , 18] and references therein ) , and several lines of evidence suggest that the head-to head interaction of the hexameric rings is mediated by the obds and nearby residues [15 , 19] . The routing of DNA through the double hexamers is unclear , and none of the high-resolution structures of T-ag to date have included DNA . However , the recent structure of the BPV initiator E1 helicase domain shows that E1 forms a hexameric ring which contains single-stranded DNA ( ssDNA ) within its central channel [20] . “Rabbit ear” protrusions emanating from the dodecameric T-ag complex have been observed on electron microscopy , and these protrusions have been attributed to ssDNA coated by RPA [21] . Electron microscopic studies have also demonstrated considerable flexibility in the central region of the double hexamers where the obds are located [22–24] . T-ag has multiple functions , and the ability of the T-ag obd to transit between multiple modes of DNA binding and oligomerization states fits with the differing requirements of recognition , melting , and unwinding of DNA that must occur during DNA replication . The T-ag obd recognizes the GAGGC-containing duplex DNA at the origin and also binds double-stranded DNA ( dsDNA ) and ssDNA in a non–sequence-specific manner ( reviewed in [1] ) . Previous biochemical experiments identified regions of the T-ag obd important in recognition of the GAGGC pentameric sequences , in particular , the A1 and B2 motifs [25] ( amino acids 147–159 and amino acids 203–207 , respectively ) . In addition , residues within these motifs also interact with ssDNA [26] . Moreover , regions of the T-ag obd ( specifically , amino acids 167 , 213 , 215 , and 220 ) participate in cooperative double-hexamer assembly in the context of the full-length T-ag [19] . Residues within the T-ag obd ( amino acids 152–156 , 181–182 , 199–204 , 255–258 ) also interact with other members of the replication machinery such as the C-terminal domain of human RPA32 [27] and RPA70AB [63] . Protein–DNA footprinting experiments have delineated the regions of the SV40 origin that are protected by T-ag . 1 , 10-Phenanthroline–copper footprinting data of DNA from the SV40 core origin complexed to either full-length T-ag or just the T-ag obd show similar protection patterns [28] . Such studies demonstrate that the DNA at P2 is protected by T-ag obd even when the P2 sequence is altered and that the DNA at P4 is less protected than sites P1 through P3 , despite having the identical pentamer sequence . As assembly of double hexamers of T-ag on DNA requires only P1 and P3 [28] , it appears that P2 , and perhaps P4 , is not essential for initial assembly in vitro . These data coupled with electron microscopic and mutagenesis data suggest that the obds bound to sites P1 and P3 could perhaps interact and guide subsequent assembly events . Despite this wealth of biochemical and structural knowledge surrounding T-ag , it is unclear how the T-ag obd site-specifically recognizes the origin , whether DNA distortions are induced by this interaction , or how the obd participates in assembly of the double hexamer . Our recent crystal structure of the T-ag obd “spiral hexamer” [14] detailed the obd–obd interactions that occur upon formation of a single hexamer as well as the interactions between obds on opposing hexameric rings that could occur in the context of a double hexamer; however , it provided no insights into the T-ag obd–DNA interactions required for site-specific binding to the origin . To address these issues , we have solved two crystal structures of T-ag obds oriented head-to-head; with and without a DNA target . The structures of four other DNA binding domains from viral initiator proteins have also been determined ( reviewed in [29] ) , and although they share no apparent sequence homology with T-ag , the obds from SV40 T-ag [13 , 14] , BPV E1 [30 , 31] and human papilloma virus E1 [32] , the Rep proteins from adeno-associated virus 5 [33] , and tomato yellow leaf curl virus [34] share a common fold . The SV40 T-ag is most closely related to BPV E1 , but whereas SV40 large T-ag can bind to its origin DNA on its own , the BPV initiator E1 requires a loader or “matchmaker” protein , E2 . Three crystal structures of the BPV E1 obd have been solved: the E1 obd dimer [30] , the E1 obd dimer on DNA , and the E1 obd “tetramer” ( two dimers ) on DNA [31] . T-ag and E1 both form hexameric and double-hexameric helicase complexes on DNA , and their structural conservation suggests similarities in their mechanism of origin binding and helicase activity . However , there are significant differences in the architecture of these two viral origins . Thus , our structures of the SV40 T-ag obd have allowed us to differentiate aspects of origin recognition and helicase assembly that are specific to the individual viruses from those which are general and may be applicable to eukaryotic systems . Herein , we present the structural determinants of SV-40 origin recognition and a model of the structural rearrangements that accompany the transition from origin recognition of duplex DNA to formation of the dodecameric helicase .
In this paper we describe two crystal structures of the SV40 large T-ag obd: one in complex with duplex DNA and one as a dimer in the absence of DNA . The DNA oligomer used in the first crystallographic study contains two pentameric sites , P1 and P3 , with P2 altered ( Figure 1B ) . The second crystal structure is that of a T-ag obd dimer containing an intermolecular disulfide bridge between two Cys216 residues . Though the disulfide we observe may well be an artifact of crystallization , both of the structures reported here contain two T-ag obds arranged in a head-to-head orientation reminiscent of that seen in the structures of papilloma virus E1 obd . Thus , the subunits we see would presumably belong to opposing hexamers upon subsequent formation of double hexamers of large T-ag . The crystal structure of the SV40 T-ag obd ( amino acids 131–260 ) with duplex DNA containing two high-affinity binding sites , P1 and P3 ( Figure 1 ) , was refined to 2 . 4-Å resolution ( Table 1 ) . Pentanucleotide binding site P2 has been altered to abrogate site-specific binding . Longer DNA fragments having the same mutated P2 site as in our crystals have previously been shown to support assembly of double hexamers of T-ag [28 , 35] . The asymmetric unit contains two T-ag obd subunits and a DNA duplex 21 nucleotides long . The T-ag obd construct used in this study is shown in Figure 1D with the secondary structural elements and protein–DNA and protein–protein contacts indicated . In the crystal , the DNA stacks along its helical axis and forms a pseudo-continuous helix . The DNA oligomer is pseudo-palindromic , and the P1 and P3 binding sites can be considered as inverted repeats with a 7-bp spacer . The two T-ag obds are oriented head-to-head on approximately the same face of the DNA and make almost identical DNA interactions with their respective GAGGC sequences ( Figures 2A and 3A ) . The obds are related by a pseudo 2-fold symmetry axis with a 171-degree rotation relating the two proteins . The DNA positions the obds such that the residues within the B3 loop ( amino acids 213–220 ) are facing each other in an antiparallel fashion , with Phe218 from one monomer and Thr217 from the other are close to one another but not quite contacting . The electron density of the side chain of Phe218 is not clear , suggesting this side chain is flexible , and it could contact the second obd molecule in certain orientations . Consistent with the observation that the nucleotides flanking the individual GAGGC sequences have little effect on binding affinity [36] , all sequence-specific interactions in this crystal structure occur within the GAGGC sequence . Also in keeping with previous biochemical studies [37 , 38] , each obd interacts with the DNA in the major groove primarily through the A1 ( amino acids 147–159 ) and B2 ( 203–207 ) loops ( Figures 2 and 3 ) . A subset of residues within the A1 loop ( amino acids 147–155 ) contacts both the phosphate backbone and the bases . Two residues within this motif , Asn153 and Arg154 , make most of the base-specific interactions , with the pentanucleotide binding sites ( P1 or P3 ) . Residues adjacent or within the B2 loop ( amino acids 202–204 ) interact primarily with the DNA phosphate backbone , with only Arg204 making sequence-specific interactions . For simplicity , we will continue to refer to the DNA binding loops as A1 ( amino acids 147–155 ) and B2 ( amino acids 202–204 ) , although the precise definition of the residues within these loops differs somewhat from that described in the original biochemical work [25] . The site-specific binding of the T-ag obd to DNA buries approximately 1 , 600 Å2 per GAGGC pentamer ( Figure 3C ) . This large buried surface area is consistent with the high affinities ( Kd of approximately 60 nm [36] ) of the T-ag obd for the GAGGC sequence . The nucleotides in the structure are numbered in Figure 3B , but we will refer to a given nucleotide within the GAGGC ( or its complement , GCCTC ) by decreasing the font of the other nucleotides . For example , gAggc refers to the adenosine in position 2 . The two residues from the A1 loop , Asn153 and Arg154 , are situated deep in the major groove with the side chain of Asn153 extending toward the 3′ end and the side chain of Arg154 pointed toward the 5′ end of the GAGGC pentamer . Remarkably , these two residues interact with four of the five GAGGC nucleotides ( GAGGc ) in a sequence-specific manner through backbone and side chain interactions . Ser152 also makes sequence-specific contacts with gAggc ( A27 or A4 ) . The B2 loop residue Arg204 contacts the nucleotide Gcctc ( G15 or G38 ) at both the base and the backbone . In terms of sequence specificity , both the N7 and O6 atoms ( hydrogen bond acceptors ) of the three guanines ( GaGGc ) participate in hydrogen bonds , explaining the importance of having a G at those positions . Indeed , two of these guanines have been shown to be essential ( gaGGc ) [39] . Conversely , only the N7 atom of the adenine ( gAggc ) accepts a hydrogen bond , suggesting that a guanine would also be tolerated at this position , as is the case in other polyomavirus origins [40] . Finally , both the N7 and O6 of the guanine on the complement strand ( which base paired with the cytosine gaggC ) participate in hydrogen bonds with Arg204 , again , explaining a preference for a C-G base pair at this position ( gaggC ) . There are no sequence-specific interactions between the obd and the altered P2 site . The majority of the protein–DNA interactions from the A1 loop occur on the DNA strand that contains the sequence GAGGC . The protein–DNA interactions are summarized in a schematic in Figure 3B . In addition to the nucleotide-specific interactions , there are approximately ten hydrogen bonds and salt-bridges between the obd and nonbridging phosphate oxygen atoms per GAGGC sequence ( Figure 3B ) . Most of these are from residues in the A1 loop ( Ser147 , His148 , Val150 , and Phe151 ) or the B2 loop ( His203 and Arg204 ) , but a few occur outside these loops ( Asn210 , Asn227 , and Lys228 ) . His203 has been previously shown to hydrogen bond with the phosphate backbone of GAGGC-containing dsDNA by NMR titration experiments [41] . Only one interaction is seen between a ribose oxygen O5′ , and that occurs between Arg202 and Gcctc ( G15 or G38 ) . A number of van der Waals ( ie , carbon–carbon ) interactions ( less than 4 Å ) between the obd and DNA help stabilize the complex . Interestingly , most of these interactions occur between residues in the A1 motif ( 149 , 151 , 152 , 153 , 154 , and 155 ) and with the base or the sugar carbons of the GAGGC-containing strand . van der Waals interactions occur outside of the GAGGC pentamer as well , at one nucleotide upstream of the pentamer Xgaggc ( C2 or A25 ) and one nucleotide upstream of the complement pentamer Xgcctc ( G14 or T37 ) . Five water-mediated protein–DNA interactions ( donor–acceptor distance less than 3 . 5 Å ) are observed in the co-structure ( Figure 3B ) . These interactions differ between the obds , and thus it is not clear that these are important specificity determinants . The interaction of the two obds on P1 and P3 induces a 17-degree bend in the DNA . This bend allows the two obds to be significantly closer to one another than would be possible if the DNA were straight . Only a minor alteration in the DNA or protein structure would be needed for the odbs to interact with one another , and perhaps nucleate subsequent double-hexamer formation . The most severe distortions from canonical B-DNA are the compression of the minor groove and the phosphorous–phosphorous distance between the pentameric sequences P1 and P3 is 9 . 4 Å ( versus 12 . 8 Å for standard B-form DNA ) . As changes from the natural sequence at site P2 could affect the DNA conformation , we cannot conclude that the native origin DNA is bent by the T-ag obd . We can , however , say with confidence that significant DNA deformation would be required for the T-ag obds to interact , a major departure from the picture presented in the structures of BPV E1 obd in complex with DNA derived from the BPV origin [31] . The BPV E1 origin also contains two inverted repeats ( Figure 1C ) , but unlike the SV40 origin , the repeats in the BPV origin are overlapping and imperfect . This results in a much closer arrangement of the obds on their respective binding sites . Nonetheless , these two systems are grossly similar in the way they bind DNA . Both interact in the major groove via the same two loops . Both exhibit significant shape complementarity at the DNA–protein interface , and both obds use two adjacent residues splayed out in opposite directions to make most of their contacts within the major groove of the DNA ( Asn153 and Arg154 in T-ag versus Lys186 and Thr187 in E1 ) . The SV40 T-ag obd , however , makes more base-specific interactions than its BPV counterpart ( wherein the only sequence-specific interactions are with the methyl group of thymine ) , and the SV-40–T-ag obd interactions are generally more electrostatic in nature . In addition , the T-ag obd engages both strands of the DNA to a greater degree than the E1 obds [31] , as seen in the exploded view of the interaction surface ( Figure 3C ) . T-ag and E1 obds also differ in their orientation within the major groove of the DNA , and when one superimposes the SV40 and BPV obds , the respective DNA molecules do not overlay ( Figure 4A ) . Conversely , superposition of the DNA molecules results in poorly superimposed obds . Differences also result because of the spacing of the binding sites . In the SV40 origin , the direct repeats ( P1 and P2 , or P3 and P4 ) are separated by one nucleotide and occur on opposite faces of the DNA , and the inverted repeats ( P3 and P1 or P4 and P2 ) are separated by seven nucleotides and occur on the same face of the DNA ( Figure 4B ) . In contrast , the analogous direct repeats in E1 overlap by three nucleotides , and the inverted repeats are separated by only three nucleotides ( Figure 4B ) . Thus , it is not surprising that the E1 obds interact with each other upon binding DNA , whereas the T-ag obds do not . This difference in origin architecture is noteworthy because E1 dimerization upon the BPV origin is thought to be an important event in nucleation of the E1 double hexamer [42] . As discussed below , we believe that in the case of SV40 , this dimerization event either occurs later in the assembly process , when the obds are no longer engaged with the GAGGC sequence , or is accompanied by significant DNA deformation . The dissociation constant of the T-ag obd for DNA containing both pentamers P1 and P3 is 60 nM , very similar to that for a single GAGGC sequence within a larger DNA oligomer ( Kd = 57 to 150 nM ) [36] . This is in contrast to the much weaker affinity of the BPV E1 obd for a single site ( Ki = 517 nM ) and a comparable affinity for two correctly spaced E1 sites ( 32 nM ) [43] . Consistent with its more numerous DNA contacts , the SV40–T-ag obd–DNA interaction buries a larger surface area ( approximately 1 , 600 Å2 per obd–GAGGC interaction , shown in Figure 3C ) than the analogous E1 obd–DNA interaction ( approximately 1 , 000 Å2 for E1/ATTGTT ) . This could help explain the higher affinity of T-ag obd for its DNA target site . In addition , T-ag obd binds approximately 10-fold more tightly to its specific binding site than to random DNA [36] , whereas E1 binds less than 2-fold more tightly [43] . These data may also explain why DNA binding by T-ag obd is more specific than that of the E1 obd and why E1 requires a helper protein ( E2 ) to load it onto the DNA and T-ag does not . The second crystal structure we report is that of a T-ag obd dimer in the absence of DNA . This structure has been refined to 2 . 6-Å resolution ( Table 1 ) . The asymmetric unit contains two T-ag obd molecules linked together by a disulfide bond . Although the presence of the disulfide bond is likely an artifact of crystallization , we include it here because it facilitates our description of structural changes associated with DNA binding . Perhaps coincidentally , the obds in this dimer are oriented in a head-to-head fashion and contact one another using the same loops which mediate the inter-obd contacts in the structures of BPV E1 ( Figure 1B ) . As shown in Figure 5A , the monomers are related by a pseudo 2-fold symmetry axis with a rotation of 178° between the molecules . The dimer interface contains a mixture of hydrophobic and hydrophilic interactions and buries a surface area of approximately 740 Å2 . For comparison , the E1 obd dimer interface , an interface which is seen in crystal structures with and without DNA , buries only approximately 500 Å2 . The T-ag obd–obd interface is nearly symmetric with almost identical residues ( 18 total ) from each monomer contributing atoms to the interaction surface . These residues are from helix αB ( Glu166 , Leu170 , Lys173 , and Lys174 ) , residues at the end of helix αC , and residues from the B3 loop ( amino acids 213–218 ) ( Figure 5B and 5C ) . Interestingly , T-ag mutants within the B3 loop ( Q213H , L215V , and F220Y ) are impaired in their ability to form double hexamers , and mutation of other residues nearby ( K167R and A168V ) is impaired in both double-hexamer formation and unwinding duplex DNA [19] . In addition , the cysteine residue bridging the two obds ( Cys216 ) is completely conserved across the Polyoma virus family , and the C216G mutation in T-ag has been shown to be defective in unwinding closed circular DNA [44] . In summary , although the existing literature clearly indicates that the residues at the protein–protein interface observed in the disulfide-linked dimer are important for T-ag assembly and helicase function , this similarity could be coincidental . Furthermore , while we believe that something like the dimeric structure we observe may well be important for stabilization of the T-ag double hexamer , the structure we present cannot be considered evidence of this . Our previously published crystal structure of T-ag obd in the absence of DNA showed an open-ring conformation having six obds per turn [14] . Together with the two crystal structures presented here , each with two copies of the obd in the asymmetric unit cell , we now have five crystallographically independent structures of T-ag obd monomers for comparison . Interestingly , the B2 loop , which makes the majority of the DNA contacts , is virtually identical with and without the DNA ( Figure 6A ) . Pairwise least-squares superpositions of these T-ag obd monomers reveal root-mean-square deviations in Cα positions of 0 . 4–1 Å . The superposition , shown in Figure 6A , reveals that the most dramatic difference in the structures occurs in the A1 loop and that the amino acid that varies most is Phe151 ( approximately 4-Å Ca–Cα distance , approximately 7-Å tip–tip distance ) . Although there are five crystallographically independent molecules , only two conformations are seen . The two obds from the DNA complex structure have the A1 loop in one orientation ( flipped “down” ) , while the three obds crystallized in the absence of DNA have the A1 loop in another conformation ( flipped “up” ) . There is no steric clash of the A1 loop that would force this change in conformation ( from “up” to “down” ) upon binding DNA . Rather , shape and charge complementarity appear to favor the “down” orientation in the presence of DNA . Phe151 comprises an integral portion of the protein–protein interface observed in the spiral structure and perhaps plays a role in the structural reorganization of the obds from origin recognition to oligomerization . Interestingly , in the portion of the A1 loop that provides sequence-specific interactions , namely Asn153 and Arg154 , the position of the Cαs hardly changes between the DNA-bound and DNA-unbound forms . This indicates that the sequence-specific determinants for DNA binding are preformed in the absence of DNA . The residues in loop B3 also exhibit some differences among the three structures , but the electron density in this region was poor in all structures except the disulfide-linked one . While the structures of the individual monomers of T-ag obd are very similar , there are significant differences in the relative orientation of the monomers in the two crystal structures reported here . Both the co-structure and the dimer structure are oriented in a head-to-head fashion with the B3 loops pointed toward one another , but when one superimposes one monomer of the disulfide-linked dimer onto a DNA-bound monomer , the second set of monomers differ in orientation by 104° ( Figure 6B ) . The molecular orientations in these two structures also differ significantly from that seen in the spiral ring of obd subunits , and from our model of the head-to-head interaction of these spirals . These differences reinforce our prediction that the T-ag obd spiral seen in the previous crystal structure of this domain cannot exist at the same time as the T-ag obd–DNA–specific complex . If the DNA travels down the center of the spiral structure , the A1 and B2 loops in the spiral are neither close enough nor oriented properly to engage the GAGGC sequences as seen in the co-structure ( Figure 7 , right ) . Significant structural rearrangement would be required , and the consequences of these rearrangements are considered below . In this paper we present crystal structures of the SV40 T-ag obd in the presence and absence of DNA . Together , with the previously solved high-resolution “spiral hexamer” of T-ag obd , these results provide a structural framework upon which to describe the molecular events require for initiation of SV40 DNA replication . Formation of the helicase-competent T-ag–DNA complex involves at least four molecular events: monomer recognition of the dsDNA at the origin , assembly of hexamers and double hexamers on DNA , DNA melting , and threading of the DNA through the T-ag complex . Although the sequence of these events remains unclear , and some steps may occur simultaneously , the extensive literature on T-ag and related systems allows us to propose a temporal context for the crystal structures presented here ( Figure 8 ) . In our model , the initial step in origin recognition involves formation of a complex very similar to that seen in our DNA co-structure . Although the helicase domain can bind DNA [45–47] , only the obds contain significant nucleotide sequence specificity , and it is thus reasonable to propose that binding of individual obds to individual GAGGC binding sites occurs first in the assembly process . As suggested by earlier studies involving T-ag ( reviewed in [1] ) and those involving papillomavirus E1 [31] , the T-ag obds occupying P1 and P2 would ultimately belong to one hexamer , while those occupying P3 and P4 would belong to the other hexamer ( Figure 7 ) . A single pentameric sequence is statistically likely to occur once every 512 base pairs [ ( 45 ) /2] and does not in itself provide much selectivity . Two correctly spaced pentamers should , however , occur only once every 500 , 000 base pairs . Consistent with this idea , an individual GAGGC sequence supports single-hexamer formation of T-ag [28] , but occupancy of at least two correctly spaced binding sites ( eg , the inverted repeats P1 and P3 ) is required for double-hexamer formation [28 , 35 , 48] . Within a single hexamer , the dominant T-ag–T-ag interaction likely occurs through the helicase domains ( an interaction that buries 4 , 344 Å2 in the presence of ATP [11] ) . Whereas these domains readily form hexamers in the absence of the obd [49] , isolated obds have little propensity to interact with one another in solution , and in the crystal structure of the obds arranged in a 6-fold symmetric spiral , the buried surface area between these domains is only 1 , 300 Å2 [14] . Nonetheless , mutation of residues within the obd at positions F183 and S185 disrupts formation of T-ag hexamers , suggesting that the obds are also important to the integrity of the hexameric complex [50] . Both of these residues occur at or near the T-ag obd–obd interface seen in the open-ring obd structure [14] and both are far from the DNA-binding interface . Mutation of residues in the B3 loop , another region far from the DNA , also impairs double-hexamer formation [19] . Thus , several lines of evidence suggest that interaction among obd subunits may be important for the integrity of the double-hexamer T-ag complex . As described above , we believe that the first step in origin recognition involves the binding of obd subunits to unmelted GAGGC pentamers , and in the co-structure presented here , obds do not interact with one another while bound to DNA . This is consistent with the observation that isolated obds exhibit no cooperativity in their DNA binding [36] . Thus , while interactions among the obds may be important later in the assembly process , interaction among these domains ( in either single- or double-hexamer formation ) does not seem likely during the very early stages of assembly . The model in which the obds bind to their respective GAGGC sites before other assembly events is attractive in a number of respects , most importantly , because it suggests an explanation for how the DNA is threaded through the T-ag double hexamer . In this model , double-hexamer assembly and DNA threading occur simultaneously . The obd of T-ag serves to anchor and orient the complex at a distinct location on the DNA , and strand selection occurs as a consequence of this location , the nature of the protein–DNA interactions , and the dynamics of the spontaneous ring formation of the helicase domains . Similar models have been presented ( reviewed in [51] ) ; however , given the structures presented here and recent developments in our understanding of helicase domain–ssDNA interactions [20 , 26 , 47] , we believe these models need some modification . As pointed out by Enmark et al . [20] , the diameter of the central channel of the T-ag helicase domains is too small to accommodate dsDNA , but it can accommodate ssDNA . Thus , we believe that DNA strand separation occurs as a consequence of the hexamerization of the helicase domains around a single DNA strand . Binding of a single strand is supported by the crystal structure of the BPV E1 helicase domain in complex with ssDNA [20] , and our model of SV40 assembly is also in line with the steric exclusion mechanism used by other hexameric helicases such as the Escherichia coli transcription termination factor Rho [52] . Once the proper strands have been selected and assembly of the double hexamer is under way , T-ag must release the double-stranded GAGGC binding sites to which it is attached . In our model , once the obds are no longer needed for origin recognition , they transition into a double-ring structure , and we believe this structure helps to hold together the T-ag double hexamer . This model positions the amino acids in helix αB ( K167 ) and in loop B3 ( Q213 , L215 , and F220 ) , which when mutated result in defects in double-hexamer formation [19] , opposite one another on two head-to-head rings composed of obds , and is consistent with electron microscopic images showing the obds at the hexamer–hexamer interface [15] . We envision that the DNA containing the pentamers is melted at this point , with opposing strands passing through each of the two helicase domain rings . The diameter of the inner channel ( approximately 30 Å ) of the T-ag obd spiral hexamer crystal structure is sufficiently large to accommodate either dsDNA or two single strands of DNA . In the obd spiral structure , the DNA-binding regions ( the A1 and B2 loops ) are rotated away from the DNA axis and thus can no longer engage the pentamers in a sequence-specific manner . This structural rearrangement explains how the same residues on the T-ag obd can be responsible for both base-specific DNA recognition of the duplex and nonspecific duplex and ssDNA binding [26] . It has been shown that assembly of the double hexamer of T-ag causes DNA strand separation of the early palindrome region ( reviewed in [6] ) and , presumably , melting of the AT flanking sequences would follow . If the assembly of the double hexamer of T-ag causes DNA strand separation on either side of Site II ( within the flanking sequences ) , the structural transition of the obds from origin recognition to formation of a hexameric ring could be promoted by melting of the DNA within the obd binding sites . The high local concentration of obds resulting from formation of this dodecameric complex also might be expected to shift the equilibrium in favor of ring formation of the obds , despite their weak propensity for self-association ( Figure 8 ) . Many of the same residues of T-ag obd that bind the DNA have also been shown to bind the ssDNA binding-protein human RPA [27] , an interaction that is likely to cause steric clashes in the spiral hexamer unless one or more of the obds rotate out from the central ring so as to more fully expose their A1 and B2 loops . While the spiral already provides a “gap” for the ssDNA strand to exit the ring , such a rotation of obds away from the DNA would allow both easier access for accessory proteins such as hRPA and easier egress for ssDNA . This model suggests that the region in the center of the T-ag double hexamer is dynamic and would lack the distinct , 6-fold symmetric symmetry present in the helicase domains , a picture consistent with the results of recent single particle analysis of T-ag on DNA [24] . In conclusion , the various structures of the SV40 T-ag obd on and off its DNA target have delineated the atomic determinants of DNA binding and have allowed us to propose a model of the rearrangements that the obd undergoes as T-ag progresses from origin recognition to formation of the dodecameric complex . Despite the gross similarities between SV40 T-ag and BPV E1 , there appear to be some significant differences in the modes of assembly between the two systems . First , the BPV E1–E1 dimer interface has been shown to be important for E1 to bind its DNA target [30] . Interaction between SV40 T-ag obds on DNA is not observed in our crystal structure , and such interactions cannot occur without significant DNA deformation . Second , the BPV E1 is thought to form head-to-head double trimers on DNA prior to forming double hexamers [42] . In BPV , the obd-binding sites analogous to P1 and P2 are separated by approximately 120° along the DNA helical axis , and it is easy to see how a trimer of obds on DNA might form . The architecture of the SV40 origin , however , places sites P1 and P2 roughly 180° apart . Thus , from a structural standpoint , a 3-fold symmetric intermediate of T-ag on DNA is hard to justify . Furthermore , biochemical studies suggest that T-ag forms only monomers , hexamers , and , in the presence of an appropriate DNA , double hexamers [49] . Recent progress has provided atomic-resolution pictures of a number of key interactions among T-ag domains and with DNA . Among these are the interaction between obd monomers that facilitate their assembly into open rings containing a 30-Å inner diameter [14] , the interactions between the helicase domain subunits that allow these domains to assemble into 6-fold symmetric machine that couples ATP hydrolysis to DNA translocation [9 , 11] , the interactions between the related BPV E1 helicase rings and ssDNA [20] , and the interactions between the obds and dsDNA that explain how origins are recognized . While some aspects , most notably , the determinants holding together the double hexamers , remain uncertain , this collection of high-resolution structures has allowed us to develop very specific predictions which can now be probed experimentally to test and refine our understanding of this complex system .
The SV40 T-ag obd ( amino acids 131–260 ) was overexpressed in E . coli as a GST-fusion and purified as previously described [14] . The purified protein was dialyzed into storage buffer ( 10 mM Tris [pH 7 . 5] , 50 mM NaCl , 10% glycerol , 1 mM DTT ) , concentrated by ultrafiltration using a VivaSpin 500 ( VivaScience , http://www . vivascience . com ) , aliquoted and flash-frozen in liquid nitrogen , and stored at −80 °C . Synthetic DNA oligonucleotides were synthesized leaving the trityl group on by the phosphoramidite method ( Keck Facility , Yale University , New Haven , Connecticut , United States ) . The oligomers were cleaved and deprotected while in the cartridge . The oligomers were detritylated and purified in a single step using a semipreparative DNAPure column ( Rainin Instruments , http://www . rainin . com ) . Oligomers were lyophilized to dryness and resuspended in 10 mM Tris ( pH 7 . 5 ) , 50 mM NaCl . Duplex DNA was formed by mixing a 1:1 ratio of complementary oligomers in annealing buffer ( 10 mM Tris [pH 8] , 50 mM NaCl ) based on the calculated extinction coefficient at 260 nm . The concentration of DNA was approximately 0 . 1 mM . The mixture was heated in a water bath to 94 °C and allowed to cool slowly over several hours to 4 °C . The duplex DNA was stored at −20 °C until ready for use . The T-ag obd–DNA complex was prepared in a 2:1 . 1 molar ratio by slowly adding duplex DNA ( approximately 0 . 1 mM ) to the T-ag obd ( approximately 5 to 8 mg/ml ) . The resultant mixture was further concentrated to a T-ag obd concentration approximately 20 mg/ml by ultrafiltration using a VivaSpin 500 ( VivaScience ) . The complex was flash frozen in liquid nitrogen and stored at −80 °C . Crystals of T-ag obd in complex with the 21-mer duplex DNA were grown at 4 °C under paraffin oil in sitting drops using a microbatch optimization strategy [53] . From 3 to 6 μl of crystallization solution ( 0 . 12 M sodium cacodylate [pH 6 . 5] , 0 . 24 M calcium acetate , 14% v/v PEG 8000 ) were mixed with 5 μl of the T-ag obd–DNA complex in a 150-μl PCR tube . This mixture was placed in a sitting drop tray under paraffin oil . Crystals grew in approximately 5 d as thin plates . Crystals of the T-ag obd dimer ( in the absence of DNA ) were grown by vapor diffusion using the hanging drop method at 20 °C . Then 1 μl of the T-ag obd ( 8 . 8 mg/ml ) in storage buffer was mixed with 1 μl of a reservoir solution consisting of 30% PEG 4000 , 0 . 1 M sodium citrate ( pH 5 . 6 ) , and 0 . 2 M ammonium acetate . The drop was equilibrated over a 0 . 4 ml reservoir solution . Crystals grew in approximately 1 wk . For the DNA complex , single crystals were harvested and slowly transferred to a final cryogenic solution ( 0 . 1 M sodium cacodylate [pH 6 . 5] , 0 . 1 M calcium acetate , 30% v/v PEG 8000 , 20% glycerol ) and flash-frozen in LN2 . Data to 2 . 4 Å were collected at Beamline X29 at the National Synchrotron Light Source ( Brookhaven , New York , United States ) at a wavelength of 1 . 1 Å , at 100K , and using a Quantum 315 detector . The data were processed with HKL2000 [54] and scaled with SCALA [55] . A molecular replacement search model based upon coordinates of a T-ag obd in complex with a 5-bp duplex GAGGC ( Alexey Bochkarev , personal communication ) was constructed . Molecular replacement was performed with the program PHASER [56] in all primitive orthorhombic space groups . PHASER identified the space group as P212121 and positioned two molecules of the T-ag obd in the asymmetric unit . The missing DNA was visible in the resulting electron density map and was built using the molecular graphics program COOT [57] . Although the protein in these crystals has a unique orientation , the DNA can be positioned in two different orientations without changing the R-factor . As the DNA sequence is pseudo-palindromic , this static disorder in our crystals had no deleterious effect on the quality of the electron density at the GAGGC repeats or at the DNA phosphate backbone . The density for the bases outside of the protein-binding sites , however , was equally consistent with either of the two possible DNA orientations . As attempts to model both DNA orientations simultaneously ( each with half occupancy ) did not significantly reduce either the working or the free R-factor , only one of the two orientations is present in our final model of the DNA–protein complex . In addition , no sequence-specific protein–DNA contacts occur outside the GAGGC sequences . Multiple rounds of building and simulated annealing were performed with the program CNS [58] or REFMAC [59] . A simulated annealing omit map is presented in Figure S1 . The final rounds of refinement included TLS refinement . The final model consists of two molecules of T-ag obd , one 21-mer duplex DNA , and 70 water molecules . The final R-factor and R-free are 20 . 5% and 29 . 0% ( from REFMAC ) . Refinement statistics for the final 2 . 4-Å model are summarized in Table I . For the T-ag obd dimer crystal , single crystals were harvested and slowly transferred to a final cryogenic solution ( 0 . 1 M sodium citrate [pH 5 . 6] , 0 . 2 M ammonium acetate , 30% PEG 4000 , 20% glycerol ) and flash-frozen in LN2 . Data to 2 . 6 Å were collected at Beamline X29 at the National Synchrotron Light Source at a wavelength of 0 . 9791 Å , at 100K , and using a Quantum 315 detector . The data were processed with HKL2000 and scaled with SCALA . The crystals were characterized as having the space group C2 with two molecules in the asymmetric unit . A molecular replacement search model from the x-ray coordinates of the T-ag obd was made . The structure was solved by molecular replacement using the program PHASER . The resulting electron density map showed clear density for a disulfide bridge between the two monomers . The model was built and refined using the molecular graphics program COOT . Several rounds of building and simulated annealing were performed with the program CNS or REFMAC . The final model consists of two molecules of T-ag obd and 51 waters . The final R factor and Rfree values are 20 . 82% and 29 . 58% ( from REFMAC ) . Refinement statistics are summarized in Table I . Figures were made using the molecular graphics program PyMOL [60] . The DNA structure was analyzed using the programs 3DNA [61] and MADBEND [62] .
Protein Data Bank ( http://www . rcsb . org/pdb ) accession numbers for the coordinates for the T-ag obd co-structure and disulfide-linked dimer are 2NTC and 2IF9 , respectively , and for the T-ag obd residues that comprise the protein–protein interface in the spiral hexamer , 2FUF . The information for E1 was obtained from the crystal structures of E1-obd with and without DNA ( Protein Data Bank accession numbers 1F08 , 1KSY , and 1KSX ) .
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How DNA replicates is a critical question for understanding life . DNA replication remains difficult to investigate in eukaryotes , where it involves a complex , multi-protein apparatus which initiates replication at multiple poorly-defined DNA sequences . This process is far easier to study in viral systems , where the DNA sequences at the origin of replication are well-defined and only one or two proteins are required to initiate replication . In simian virus 40 ( SV40 ) , the large T-antigen protein ( T-ag ) is responsible for recognizing DNA sequences required to start replication , called the origin of replication . SV40 T-ag can also cause DNA to melt or unwind . We report here the crystal structure of the DNA-binding domain of SV40 T-ag on a DNA fragment derived from the viral origin of replication . The structure shows that although T-ag and its functionally analogous protein , papilloma virus E1 , share no detectable sequence homology in this region , the two domains bind the DNA in similar ways . In both cases , DNA binding is thought to initiate assembly of a complex of the full-length proteins on DNA . Interestingly , SV40 T-ag DNA-binding domains do not interact with one another when bound to DNA . In addition to describing the molecular details of the DNA–protein interactions and the alterations in protein structure induced by DNA binding , we present a model describing the subsequent assembly events .
|
[
"Abstract",
"Introduction",
"Results/Discussion",
"Materials",
"and",
"Methods",
"Supporting",
"Information"
] |
[
"oncology",
"viruses",
"biochemistry",
"molecular",
"biology"
] |
2007
|
The Crystal Structure of the SV40 T-Antigen Origin Binding Domain in Complex with DNA
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A high particle to infectivity ratio is a feature common to many RNA viruses , with ~90–99% of particles unable to initiate a productive infection under low multiplicity conditions . A recent publication by Brooke et al . revealed that , for influenza A virus ( IAV ) , a proportion of these seemingly non-infectious particles are in fact semi-infectious . Semi-infectious ( SI ) particles deliver an incomplete set of viral genes to the cell , and therefore cannot support a full cycle of replication unless complemented through co-infection . In addition to SI particles , IAV populations often contain defective-interfering ( DI ) particles , which actively interfere with production of infectious progeny . With the aim of understanding the significance to viral evolution of these incomplete particles , we tested the hypothesis that SI and DI particles promote diversification through reassortment . Our approach combined computational simulations with experimental determination of infection , co-infection and reassortment levels following co-inoculation of cultured cells with two distinct influenza A/Panama/2007/99 ( H3N2 ) -based viruses . Computational results predicted enhanced reassortment at a given % infection or multiplicity of infection with increasing semi-infectious particle content . Comparison of experimental data to the model indicated that the likelihood that a given segment is missing varies among the segments and that most particles fail to deliver ≥1 segment . To verify the prediction that SI particles augment reassortment , we performed co-infections using viruses exposed to low dose UV . As expected , the introduction of semi-infectious particles with UV-induced lesions enhanced reassortment . In contrast to SI particles , inclusion of DI particles in modeled virus populations could not account for observed reassortment outcomes . DI particles were furthermore found experimentally to suppress detectable reassortment , relative to that seen with standard virus stocks , most likely by interfering with production of infectious progeny from co-infected cells . These data indicate that semi-infectious particles increase the rate of reassortment and may therefore accelerate adaptive evolution of IAV .
The influenza A virus ( IAV ) genome comprises eight segments of negative sense RNA , each of which encode at least one essential viral protein [1 , 2] . This genome structure supports the generation of viral diversity through two major mechanisms: genetic drift due to an error prone viral polymerase , and exchange of gene segments between viruses through reassortment [3] . While drift allows the accumulation of small changes over time , reassortment allows substantial genetic change to occur quickly . Reassortment is highly prevalent among avian and swine IAVs and has been implicated repeatedly in the emergence of epidemically significant human strains [reviewed in 4] . The 1957 , 1968 and 2009 pandemic strains arose through reassortment involving seasonal human strains and viruses adapted to avian and/or swine hosts [5–7] . In addition , reassortment among co-circulating human IAVs facilitated the spread worldwide of adamantane resistant H3N2 viruses and has brought about unusually severe seasonal epidemics including the Fujian-like outbreak in 2003/2004 [8–11] . The potential for reassortment to purge the viral genome of deleterious changes and bring together multiple beneficial mutations makes it a powerful catalyst of viral evolution [12] . The ratio of total particles to plaque forming units for influenza and other RNA viruses is on the order of 10:1 to 100:1 [13–17] . Thus , only ~1–10% of virions are thought to initiate productive infection of a cell under low multiplicity conditions of infection . The precise make up of the remaining virus particles is not clear but is likely a mixture of virions carrying qualitatively different defects [18 , 19] . Some may be non-infectious in that they fail to deliver viral RNA to the site of replication due to the lack of a genome , defects at the protein level , or a stochastic failure to initiate infection . Some may be classical defective-interfering ( DI ) particles , which carry one or more segments with a large internal deletion and act as parasites , hindering the production of fully infectious progeny [20–23] . Some will harbor a lethal point mutation in one or more segments [24 , 25] . Finally , some virions may be semi-infectious particles , which deliver fewer than eight segments to the nucleus [26 , 27] . Like DI particles and those with a lethal point mutation , SI particles cannot complete the viral life cycle . In contrast to these other particle types , however , SI virions do not carry a defective gene and are therefore not expected to interfere with the production of infectious progeny in the context of co-infection . Support for the existence of semi-infectious particles was recently gleaned through a careful analysis of viral protein expression in individual infected cells [27] . The majority of cells infected at low multiplicity failed to express one or more viral proteins , suggesting that the corresponding genes were disrupted or missing entirely from the viral genome . In that study , the probability of any segment being present and functional was estimated to be 0 . 781 , which suggests that semi-infectious particles outnumber fully infectious particles by 6:1 [27] . In terms of their potential biological significance , there is an important difference between non-infectious and DI or SI particles . Any genetic material packaged into non-infectious particles will not be replicated . In contrast , under high multiplicity conditions , the genomes of DI and SI particles can be propagated through complementation by a co-infecting virus . This phenomenon of complementation , termed multiplicity reactivation , yields a greater number of infected and co-infected cells than would be predicted based on infectious titers determined at limiting dilution [28 , 29] . In addition , fully infectious viral progeny emerging from cells co-infected with only DI and/or SI viruses would necessarily be reassortant . Due to the anticipated increase in co-infected cells and the requirement for reassortment to yield fully infectious progeny from two incomplete parents , we hypothesized that the presence of DI or SI particles in an influenza virus population would promote genetic diversification through reassortment . We tested this hypothesis using a combination of computational and experimental approaches . Our previously described system [30] for studying reassortment in the absence of fitness differences among parental and progeny strains was central to the experimental work and allowed the development of a relatively simple and robust model . Viral infection of cultured cells with two phenotypically identical viruses was simulated computationally at a range of multiplicities of infection . The model was then used to indicate expected relationships among infection , co-infection and reassortment in the absence and presence of increasing levels of semi-infectious particles . We tested the model by comparing co-infection with standard virus stocks to that with viruses that were UV irradiated to artificially increase SI particle content . By comparing experimental outcomes to the model , we were able to estimate semi-infectious particle content of non-irradiated virus stocks and obtained results in agreement with those of Brooke et al . indicating a high proportion of SI particles in IAV populations [27] . Our results furthermore suggest that the frequency with which each of the eight segments is missing from a virion varies among the segments . When the effect of DI segments was tested in the model , we found that their presence could promote or suppress reassortment relative to theoretical “perfect” virus stocks , depending on the potency with which a modeled DI segment interfered with infectious progeny production . To test the effect of DI particles experimentally , we used serial passage at high MOI to enrich for DI particles and studied co-infection with these virus stocks . The results indicated that DI segments reduce measured reassortment efficiency relative to standard virus stocks . Reassortment levels observed with DI-rich viruses were , however , higher than those predicted in the absence of any type of defective particle . In sum , we show herein that delivery of incomplete or defective genomes to target cells promotes reassortment by increasing the proportion of productively infected cells that are co-infected .
To determine the expected relationships between infection , co-infection and reassortment , we performed simulations where a 1:1 mixture of viruses of type A and B were randomly distributed over a computational set of cells ( see Methods for details ) . Multiplicity of infection ( MOI ) was varied . For each MOI , we evaluated all cells and determined which were infected ( defined as cells infected with A , B or A and B ) and which were co-infected ( with A and B ) . We calculated the % reassortment expected for each infected cell , taking into account the number of virions of each virus type present and allowing the segments to assort at random [30] . For example , cells that are infected with only a single virus type will produce 0% reassortant progeny , while cells infected with one of each type are expected to produce 99 . 22% reassortant progeny . The average % reassortment for all cells was then calculated to reflect the pool of progeny viruses released from all infected cells . The results , shown in Fig 1 , indicate that % co-infection and % reassortment depend in a non-linear , but monotonically increasing fashion , on the % infection . For low levels of infection , the likelihood of infections with multiple virus types is small , hence the % co-infection is low . To illustrate the interrelationship among % infection , % co-infection and % reassortment , we offer an example: at a low level of co-infection of 3 . 3% , where infection was 32 . 87% , the average expected % reassortment is calculated to be 9 . 899% . This result makes sense because ~10% of infected cells are co-infected and these co-infected cells are expected to produce nearly 100% reassortant viruses , while the infected cells that are not co-infected will produce only parental ( type A or B ) progeny . Because they deliver an incomplete genome to the site of replication , semi-infectious particles ( SI particles ) are expected to affect levels of reassortment . To evaluate how the relationships among reassortment , infection and co-infection are impacted by incomplete virions , we introduced SI particles into the simulated A and B virus populations and varied their prevalence using the parameter PP . The value assigned to PP indicates the probability that a given segment is present in a virus particle . ( Note that a segment that is not “present” could be physically missing from the particle or , alternatively , could fail to be delivered to the site of replication . ) We initially assigned the same PP value to all eight segments and explored a range of values between 0 . 3 ( where the probability that a virion has all eight segments is 0 . 38 = 6 . 5x10-5 ) and 1 ( where all virions contain eight segments ) . The presence of SI particles in a virus population gives rise to different types of infected cells: those that express HA and those that do not , and those that produce virus and those that do not . Thus , to allow meaningful discussion of the impact of SI particles on infection , co-infection and reassortment , we have generated the lexicon presented in Table 1 . Within a simulated co-infection we are able to monitor all infected cells; that is , all cells into which a virus enters . For the purposes of comparing results of the simulation to those of the experimental co-infections described below , however , it is useful to also monitor cells that are expected to express HA protein on their surface . In the model , we defined an HA positive cell as an infected cell that has at least one copy of PB2 , PB1 , PA , NP and HA gene segments [31 , 32] . Given that some semi-infectious particles may lack one or more of these segments , a cell can be infected but be HA negative . To count as dually HA positive ( i . e . expressing HAs of both type A and B viruses ) , a cell must have copies of both HA segments and at least one copy of PB2 , PB1 , PA and NP segments . Thus , a cell can be co-infected ( at least one virus A and one virus B entered the cell ) , but only express one ( or none ) of the HA types , depending on the segments present in the type A and B viruses . Fig 2A monitors the cells that were infected and co-infected , regardless of the presence of segments needed for HA expression , and shows that the results are insensitive to parameter PP . This outcome is as expected since the absence of segments does not alter infection status . However , when studying HA positive cells and dually HA positive cells , the results vary with PP ( Fig 2B ) . For a given % HA positive cells , the % dually HA positive cells increases as more viral genomes become incomplete ( lower PP ) . This observation can be explained as follows . To achieve a given percentage of HA positive cells with more incomplete genomes , the number of virions per cell must increase to allow sufficient complementation . If the number of virions per cell ( i . e . the MOI ) is increased , the percentage of cells that are dually HA positive will also rise . Similarly , missing segments will affect reassortment: a cell infected with one virion of type A and one virion of type B , both missing a different segment and thus complementing the other , will by definition produce 100% reassortant progeny . However , missing segments can also prevent progeny from being made , as at least one copy of each of the 8 segments is required to produce progeny in the model . In Fig 2C , reassortment levels expected under various conditions of PP were calculated by averaging the expected % reassortment across all cells that were able to produce progeny . The results show that average % reassortment readily increases as PP is lowered and more viral genomes are incomplete . This observation reflects the requirement for complementation , achieved via multiple infection , for infected cells to produce progeny viruses when viral populations are characterized by lower values of PP . We performed a series of co-infections in Madin Darby canine kidney ( MDCK ) cells at a range of MOIs and monitored reassortment outcomes . To avoid fitness differences among parental and reassortant progeny viruses that could complicate the interpretation of results , we used our previously described A/Panama/2007/99 ( H3N2 ) wild-type ( Pan/99wt ) and variant ( Pan/99var ) viruses . These viruses differ by silent mutations introduced into each gene segment of Pan/99var virus and by the insertion of a His epitope tag in the Pan/99wt virus vs . an HA tag in the Pan/99var virus [30] . Infections were performed in triplicate , synchronized by allowing virus attachment at 4°C and limited to a single cycle by the addition of ammonium chloride at 3 h post-infection [33] . At 12 h post-infection , supernatants were collected to genotype released virus and cells were processed for flow cytometry to enumerate cells with surface expression of the Pan/99wt and Pan/99var HA proteins ( using the His and HA epitope tags ) . The resultant data were analysed by examining the relationship between i ) % cells positive for any HA and % cells dually HA positive and ii ) % cells positive for any HA and % reassortment ( Fig 3 ) . The results show that both % dually HA positive cells and % reassortment increase monotonically with % HA positive cells , but with differing patterns . The % dually HA positive cells shows a nearly linear relationship with % HA positive cells . In contrast , % reassortment increases quickly at lower levels of % HA positive cells , but plateaus at higher levels of % HA positive cells ( Fig 3 ) . Initial comparisons of the experimental data with the model revealed a poor match for the relationship between % HA positive cells and % dually HA positive cells , regardless of the values assigned to PP ( Fig 4 ) . We hypothesized that the assumption that PP is constant among the eight segments might account for the discrepancy . We therefore modified the model to allow PP to differ among the segments . Specifically , PP was varied between 0 . 25 and 1 . 0 in increments of 0 . 25 and all possible combinations were tested , taking into account the redundancy of PB2 , PB1 , PA and NP as well as that of NA , M and NS in our readouts . A total of 2800 possible PP combinations were evaluated . We quantified the fit for each of the 2800 combinations of PP values as the sum of the distances between each experimental data point and the lines plotted using modeled results . This sum of errors was calculated for i ) % HA positive cells vs . % dually HA positive cells; ii ) % HA positive cells vs . % reassortment; and iii ) % dually HA positive cells vs . % reassortment . The modeled results for the top 1% of PP combinations are shown in Fig 5 together with the experimental data . These analyses highlighted that several PP combinations gave results that matched the experimental data well . Given the uncertainty in the experimental measurements , the lines plotted in Fig 5 cannot be said to be meaningfully different . Additionally , the error between the experimental data and computational model varied slightly as a result of small stochastic variations in the outcome of the computational model when the same settings were repeated , resulting in small changes and uncertainties of the rank order of the top runs . Finally , note again that there are redundancies among certain segments ( PB2 , PB1 , PA and NP are equivalent , as are NA , M and NS ) . Therefore , we do not consider the parameters giving the best fit with the data ( as follows for segments 1–8 , respectively: 0 . 25 , 0 . 5 , 0 . 75 , 0 . 75 , 1 , 1 , 1 , 1 ) , to be the final answer . Instead , we investigated which features the top 1% of PP combinations had in common: each included at least one segment among PB2 , PB1 , PA and NP with PP = 0 . 25; HA with PP = 0 . 75; and at least 3 and up to 8 segments with PP<1 . 0 . Taking the product of all eight PP values yields the proportion of virions with all eight segments present , which for the best fit was 7 . 0% . When this proportion was determined from all sets of eight PP values shown in Fig 5 , the range obtained was 2 . 2–9 . 4% . In sum , comparison of the experimental data obtained with standard virus stocks to the model revealed that , for the model to fit the data , PP must be less than 1 . 0 for multiple segments and PP of the eight segments cannot be equivalent . To test the validity of our model and more rigorously evaluate the hypothesis that semi-infectious particles augment reassortment , we generated Pan/99wt and Pan/99var virus populations with increased semi-infectious particle content . This increase was achieved by exposing each virus to a low dose of UV irradiation . Since polymerase read-through of pyrimidine dimers is not possible , viral segments carrying UV lesions will not be replicated or transcribed and will behave similarly to missing segments . A UV dose sufficient to decrease PFU titers by approximately 10-fold was used . Co-inoculation of MDCK cells with Pan/99wt and Pan/99var viruses that had been UV treated was then performed at a range of MOIs ( in parallel with co-infections using standard virus stocks , described above ) . Again , results were analysed by assessing the relationships among % HA positive cells , % dually HA positive cells and % reassortment . As predicted by the model , co-inoculation with UV treated viruses yielded similar levels of dually HA positive cells , but higher reassortment frequencies at intermediate levels of HA positivity , compared to co-inoculation with mock treated viruses ( Fig 6 ) . To evaluate whether UV treatment has a statistically significant impact on % reassortment , we performed a multiple linear regression analysis of % reassortment vs . log10 ( % HA positive ) , treating UV as a categorical variable . The results showed that , for every increase of 1 log10 ( % HA positive ) , the % reassortment goes up by 40% ( P = 3 . 9x10-16 ) . Having UV treatment further increases % reassortment by 16% , and is a significant categorical variable ( P = 6 . 6x10-6 ) . These data support the validity of the model and specifically verify the model’s prediction that increasing semi-infectious particles in a virus population enhances the production of reassortant progeny . We also assessed whether the increase in reassortment seen with UV treatment was quantitatively related to the observed difference in infectivity between UV treated and untreated virus stocks . Analysis of results with the UV treated virus stocks indicated that the levels of reassortment and co-infection observed best matched those predicted for a virus population that had suffered 2 . 0 hits per genome on average ( Fig 7 ) . Based on a Poisson distribution of UV hits per virus , this UV dose would be expected to reduce PFU titer by 7 . 4-fold . The observed knock-down in PFU titers with UV treatment was 11-fold . These results are comparable , particularly when one considers the typical range of error of a plaque assay ( approximately 2-fold ) [34] , and therefore further support the validity of the model . We hypothesized that SI particles could augment reassortment through one of three , non-mutually exclusive , mechanisms: i ) by simply increasing the number of particles entering each cell ( i . e . the MOI ) given a constant number of productively infected cells; ii ) by increasing the frequency of reassortant viruses emerging from productively co-infected cells at a given MOI; and/or iii ) by increasing the proportion of productively infected cells that are co-infected at a given MOI . To distinguish among these possibilities , we used the model to examine the impact of varying PP and MOI on levels of reassortment and co-infection . To simplify this theoretical analysis , we assigned the same PP value to all segments . When MOI was held constant and % reassortment , averaged across all productively infected cells , was plotted as a function of PP , the result clearly showed increasing reassortment with declining PP ( Fig 8a ) . This result indicates that SI particles do not act on reassortment solely by increasing MOI ( item i above ) . When MOI was held constant and average % reassortment for only productively co-infected cells was analysed as a function of PP , the model indicated that levels of reassortment were high across the full range of PP ( Fig 8b ) . This result reflects the high efficiency of IAV reassortment in co-infected cells and excludes item ii above as an important mechanism driving enhanced reassortment with increasing SI content . Lastly , when MOI was held constant and the ratio of productively co-infected to productively singly infected cells was plotted as a function of PP , the results indicated that decreasing PP leads to an increase in the proportion of cells that are potential vessels for reassortment ( Fig 8c; note the log scale on the Y-axis ) . This last result reveals that mechanism iii above is functional: addition of SI particles to a virus population increases the likelihood that productively infected cells will produce reassortant viruses by changing this population of cells to be more often co-infected , even when the number of virus particles is not changed . We undertook analysis of the impact of DI particles on influenza virus reassortment to confirm that the semi-infectious particles detected in our previous analyses were not , in fact , DI particles , and to determine the potential for these naturally occurring deletion mutants to contribute to viral evolution . Similar to SI particles , DI particles deliver an incomplete genome to the site of replication . DI particles differ from SI particles , however , in that they carry one or more segments with a large internal deletion [35–38] . Importantly , these internally deleted segments have been shown to interfere with the production of infectious progeny and to accumulate over multiple rounds of replication so that they quickly outnumber the corresponding standard genome segments [22 , 39–43] . Thus , DI particles are expected to affect levels of reassortment in two ways: by delivering an incomplete genome and by interfering with production of infectious progeny . To evaluate how the relationships among reassortment , infection and co-infection are impacted by DI virions , we introduced DI particles into the simulated A and B virus populations and varied their prevalence using the parameter PI . The value assigned to PI indicates the probability that a given segment in a virus particle is intact ( i . e . does not have an internal deletion or other lethal mutation ) . Since the polymerase segments of DI particles are more commonly found to be defective than the remaining five segments , we assigned PI values < 1 . 0 to PB2 , PB1 and PA , while maintaining PI = 1 . 0 for HA , NP , NA , M and NS segments . The interfering behavior of defective segments was controlled with the parameter DIX , the fold change in infectious progeny production attributed to each single DI segment in a productively infected cell . When DIX = 0 . 5 , a DI segment and the corresponding standard segment have equivalent likelihoods of being incorporated into progeny virions and thus half of the progeny produced will be non-infectious ( carrying the DI ) while half will be infectious ( carrying the standard segment ) . Since the total number of virus particles produced by a given cell is held constant , a DI with DIX = 0 . 5 reduces infectious progeny by half . To account for the experimental observation that DI particles accumulate over multiple passages [23 , 41–44] , we reasoned that DIX must be less than 0 . 5 . The true value of DIX for a given DI segment is not , however , clear from the literature , may be variable depending on the context , and may vary among differing DI segments . In our analyses , we therefore varied DIX over a range of 0 . 05 to 0 . 5 or evaluated three disparate settings of 0 . 01 , 0 . 1 and 0 . 45 . With the aim of evaluating whether DI segments ( rather than missing segments ) could account for the reassortment outcomes shown in Fig 3 , our initial analysis was performed with PP = 1 . 0 for all segments . The results of this computational analysis are shown in Fig 9 . The results obtained for PI values of 0 . 25–1 . 0 , independently varied among PB2 , PB1 and PA in increments of 0 . 25 , are displayed , with DIX set to 0 . 01 ( very potently interfering ) , 0 . 1 ( potently interfering ) and 0 . 45 ( mildly interfering ) in panels A , B and C , respectively . Note that stochastic effects occur in the simulations at low values of PI and/or PP due to a low “n” of computational cells producing the computational virus that is analyzed . These stochastic effects give rise to the noisy peaks seen in Fig 9 and later figures . The results with DIX of 0 . 01 reveal that very potently interfering DI segments are expected to suppress the production of infectious reassortant progeny viruses . When DIX was set to a more moderate value of 0 . 1 , predicted levels of reassortment fell either above or below those for virus stocks in which all particles are complete , depending on the PI values used . In contrast , regardless of PI , virus stocks carrying mildly interfering DI segments ( represented with DIX = 0 . 45 ) were predicted to result in higher reassortment levels than virus stocks with only complete genomes . To test whether the reassortment outcomes observed experimentally with Pan/99wt and Pan/99var standard virus stocks could be accounted for by the presence of mildly interfering DI segments , we overlaid the experimental data with the modeled predictions for DIX = 0 . 45 and the broad range of PI value combinations analyzed previously ( Fig 9D ) . All conditions tested yielded % reassortment values lower than those seen experimentally . This result indicates that DI particles do not underlie the relatively high levels of reassortment observed with our standard virus stocks , and furthermore provides a theoretical prediction that DI particles will suppress reassortment relative to standard virus populations . We also evaluated a potential role for defective segments that carry a lethal point mutation rather than a deletion . Such mutations can arise in all eight segments and do not confer a competitive advantage upon the segment carrying them [40] . We therefore varied PI for all eight segments in the model and assigned DIX = 0 . 5 . These parameters also did not allow a good match between modeled and experimental results ( S1 Fig ) . Thus , the presence of defective segments is not sufficient to explain the levels of reassortment as a function of % HA positive cells seen in Pan/99wt and Pan/99var virus co-infection . To test the model of IAV reassortment in the presence of DI particles , and differentiate among the outcomes predicted for differing values of DIX , we evaluated reassortment and HA positivity following co-infection with virus stocks that carried high levels of DI particles . Virus stocks rich in DI particles were generated by serial passage of Pan/99wt and Pan/99var viruses at high MOI in MDCK cells . As expected , an increase in the ratio of genome copy number to PFU , relative to the standard virus stocks , was observed with increasing passage number ( Fig 10 ) . We selected passage 3 ( P3 ) and P4 virus stocks for further experiments since both wt and var viruses at these passage numbers showed >10-fold increases in the ratio of genome copy number to PFU , while the very low titers of the P5 viruses precluded their use . To confirm the presence of DI segments , we used an RT qPCR assay in which RNA copy number ( relative to standard “P0” stocks ) detected with primers binding near the 3’ end of the vRNA was compared to that obtained with primers binding internally , in a region typically deleted within DI segments [37 , 38] . The proportion of viral gene segments that were intact ( PI ) , relative to P0 stocks , was calculated as described in the Methods and is reported in Table 2 . The results reveal low PI values for PB2 , PB1 and/or PA segments of the P3 and P4 viruses , confirming that high proportions of these segments carried internal deletions ( Table 2 ) . To evaluate the consequences of DI particles for co-infection and reassortment frequencies , we co-inoculated MDCK cells with standard Pan/99wt ( P0wt ) and Pan/99var ( P0var ) viruses , P3wt and P3var viruses , or P4wt and P4var viruses at a range of MOIs . To allow comparison among P0 , P3 and P4 viruses on a per particle level , multiplicities of infection were based on RNA copy number of the three shortest segments ( NS , M and NA ) rather than infectious titers . Infections were performed in triplicate and synchronized by allowing virus attachment at 4°C . At 12 h post-infection , supernatants were collected to genotype released virus and cells were processed for flow cytometry to enumerate Pan/99wt and Pan/99var infected cells . Trypsin was excluded but , in contrast to the infections described above , we did not add ammonium chloride at 3 h post-infection . Data were analyzed by examining the relationships between i ) % HA positive cells and % HA dually positive cells , ii ) % HA positive cells and % reassortment and iii ) the proportion of HA positive cells that were dually HA positive and % reassortment ( Fig 11 ) . The results show enhanced frequencies of dually HA positive cells at a given % HA positive for the P3 and P4 virus stocks relative to P0 stocks ( Fig 11A ) . This observation likely reflects the need for complementation to support the expression of an HA gene carried by a DI particle . Despite the occurrence of such complementation and detection of abundant dually HA positive cells , relatively few reassortant progeny viruses emerged from P3 and P4 virus co-infections ( Fig 11B ) . This result is very clear when % reassortment is plotted against the proportion of HA positive cells that were dually HA positive ( Fig 11C ) . Even when a high proportion of cells ( up to 0 . 9 ) were dually HA positive and therefore infected with both wt and var viruses , co-infection with the P3 or P4 viruses stocks yielded <25% reassortment . As suggested by the model , this reduction in reassortant progeny relative to that produced by the P0 viruses is likely due to the interfering effects of short , DI , segments [45] . In other words , the predominance of progeny viruses with a parental genotype suggests that cells within the P3 or P4 co-infected dishes that are singly infected with a fully infectious virus produce the majority of the infectious progeny , even when such cells represent a small proportion of HA positive cells ( Fig 11C ) . In sum , these data demonstrate that , as predicted by the model , the DI rich P3 and P4 virus stocks gave rise to fewer reassortant viruses compared to standard virus stocks . We next compared the experimental results obtained with the P3 and P4 virus stocks to the model directly by overlaying the observed data points with the modeled predictions for % HA positive cells vs . % reassortment and % HA positive cells vs . % dually HA positive cells . In each case , PI parameters measured for the P3 and P4 viruses stocks ( Table 2 ) were used and DIX was varied from 0 . 05 to 0 . 5 in increments of 0 . 05 . Since our results indicated that PP was less than 1 . 0 for the standard Pan/99wt and Pan/99var virus stocks , we set PP within the model to those values found above to yield the best fit between modeled and experimental data ( Fig 5 ) . The results , shown in Fig 12A-12D , indicated that , when combined with PP values of 0 . 25 , 0 . 5 , 0 . 75 , 0 . 75 , 1 . 0 , 1 . 0 , 1 . 0 , 1 . 0 for segments 1–8 , respectively , the presence of DI particles at levels seen in the P3 and P4 virus stocks is expected to yield very high % reassortment across nearly all levels of % HA positive cells . In other words , when parameterized in this way , the model did not match the data . We therefore evaluated the outcomes when PP was set to 1 . 0 for all segments and PI to those measured for P3 and P4 virus stocks ( Fig 12E-12H ) . Although the model gave a range of predictions depending on the value assigned to DIX , the results obtained with PP = 1 . 0 ( or PP = 0 . 9; S2 Fig ) for the DI-containing viruses were consistent with those observed following P3 and P4 virus co-infections . One explanation for this result is that short , DI , segments may be packaged ( or delivered to the site of infection ) more efficiently than standard segments . To further explore the inter-relationships among PP , PI and DIX , we tested a range of theoretical settings for each parameter within the model and show 12 representative results for % HA positive cells vs . % reassortment in S3 Fig . The theoretical outcomes displayed indicate that there is a complex interplay among PP , PI and DIX in determining reassortment levels . Importantly , the presence of mildly interfering DI segments ( i . e . those with DIX near to 0 . 5 ) in virus populations is predicted to enhance reassortment under all PP conditions tested . This result indicates that a combined effect of missing segments and mildly interfering DI segments could lead to reassortment levels comparable to those observed experimentally with our standard Pan/99wt and Pan/99var virus stocks . As shown in Fig 9D , however , inclusion of DI segments but not missing segments in the theoretical virus populations is insufficient to account for the experimental reassortment data .
The results of both simulated and experimental IAV co-infections indicate that the presence of incomplete particles in parental virus populations enhances the frequency with which reassortant progeny viruses emerge . Our results suggest that both non-interfering , semi-infectious particles and classical defective interfering particles can act to enhance reassortment above that expected for theoretical virus populations that carry only intact genomes . The extent to which DI particles can enhance reassortment is limited , however , by the interference with infectious progeny production imposed by DI segments . As a consequence of this interference , DI particles suppress reassortment relative to that seen with standard , biological , virus stocks . Higher average reassortment levels seen with SI particles are not due to increased efficiency of reassortment within individual co-infected cells , but rather are brought about by an increase in the proportion of productively infected cells that are co-infected when SI particles are present . This mechanism differentiates reassortment outcomes between virus populations containing and lacking SI particles even when the total number of particles entering each cell is unchanged . In addition , if instead of holding MOI constant one considers a constant number of infected cells , the particle number required to reach a given level of infection will be higher for parental viruses carrying SI particles . In this situation , the resultant increase in MOI will lead to more co-infection and more reassortment . Thus , SI particles could enhance reassortment by two mechanisms in an infected host . The former mechanism will be more important if the total number of virus particles that can be produced in an infected tissue is limiting , while the latter would be more important if the total number of cells that can be infected is limiting . The results of Brooke et al . indicate that IAV of diverse strain backgrounds and grown under a range of culture conditions ( including in vivo ) carry a high proportion of SI particles [18 , 27] . These results were based on the expression of an incomplete set of viral proteins in cells infected at low multiplicity . Herein , we confirm the presence of SI particles in an additional strain background ( Pan/99 ) and using a distinct methodology ( tracking reassortment ) to detect SI particles . Our analyses yielded a range of possible values for the probability of a segment being present and , while our results clearly show that PP is not equivalent among the segments , they do not allow more precise definition of PP . Nevertheless , our results are consistent with the average PP value for all eight segments estimated by Brooke et al . for an influenza A/Puerto Rico/8/34 ( H1N1 ) virus ( average PP of Brooke et al . = 0 . 781; averages among our 28 best PP settings ranged from 0 . 65 to 0 . 78 ) . Coupled with the finding reported herein that SI particles enhance reassortment efficiency , their detection in diverse strain backgrounds suggests that these incomplete virions play a significant role in the evolution of influenza viruses . Reassortment among variants within a viral population is expected to act like sexual reproduction of cellular organisms in that it allows the combination of multiple adaptive mutations within a single genome , as well as separation of lethal or fitness decreasing changes in one segment from adaptive changes in another segment . In these ways , reassortment is predicted to increase the rate of evolution of a diverse viral population under selection pressure [12 , 46] . Of course , in the context of a host co-infected with multiple influenza viruses of distinct lineages , reassortment also facilitates genetic exchange that gives rise to large shifts in viral genotype and phenotypes . These instances of genetic shift can very rapidly advance adaptation of an influenza virus to a new environment , including a new host species [4–6 , 8 , 12] . For these reasons , the potential for SI particles to increase reassortment efficiency suggests that these virions may accelerate the evolution of IAV . Defective interfering particles were found experimentally to suppress reassortment relative to that seen with standard virus stocks , but to yield higher levels of reassortment than those predicted for theoretical “perfect” virus stocks that carry only complete genomes . DI particles are similar to SI particles in that they require complementation for infectious progeny production . In addition , DI particles are well known to decrease the production of fully infectious progeny from co-infected cells [17 , 20 , 39 , 42 , 47] . Thus , DI segments are expected to enhance reassortment by increasing the proportion of productively infected cells that are co-infected , but suppress reassortment by reducing the number of infectious progeny emerging from those cells . Our experimental data obtained with Pan/99-based P3 and P4 viruses suggests that the latter , suppressive , effect of DIs may be most important from a biological standpoint: reassortment levels characteristic of standard virus stocks ( that have non-zero baseline levels of SI particles ) are lowered by the emergence of DI particles . Mathematical modeling allowed us to explore whether similar outcomes are expected when the prevalence and/or the potency of interference of the DI segments is varied . The interfering effect of a DI segment arises due to segment-specific competition between a DI and the corresponding standard segment: if a DI PB2 is packaged into a virion , for example , the full length PB2 will not be [45 , 48] . The accumulation of DI particles over multiple passages indicates that DI segments carrying internal deletions are furthermore more likely to win this competition than are the full-length segments . The mechanism that leads to favoring of influenza virus DI segments over standard segments is not fully resolved , but likely occurs at the level of genome replication and/or packaging and may be related to segment length [44 , 45 , 48–50] . Importantly , differing DI segments interfere more or less potently [44] . We found that defective segments that interfere mildly are expected to enhance reassortment relative to the presence of only complete genomes . In contrast , if a DI segment has a strong competitive advantage over the corresponding standard segment , leading to a 10–100 fold reduction in progeny , the presence of DI particles can suppress reassortment . Thus , whether DI particles have a positive or negative impact on reassortment is determined mainly by the potency with which DI segments interfere with infectious progeny production . Our experiments show that , in the context of biological virus populations , the overall impact of all types of DI particles is to suppress reassortment . Modeling also revealed an additional layer of complexity governing the behavior of DI particles: the presence of SI particles in a virus population can change the expected impact of DI particles on reassortment , presumably by increasing the requirement for complementation . Comparison of experimental data obtained with P3 and P4 viruses to the model suggests , however , that SI particles do not comprise a large fraction of these virus stocks . In this way , the data suggest that , compared to standard segments , DI segments are less likely to be missing . This finding fits well with reports indicating that DI segments are incorporated into virions more efficiently than their full-length counterparts [45 , 48 , 51] . Since the behavior of DI particles is complex , it is important to highlight that modeling of segments that compete equally to , or with an advantage over , full-length segments in computational virus populations cannot alone account for the levels of reassortment observed experimentally . Rather , the inclusion in the model of virions that fail to deliver one or more segments to the site of replication is needed to match the experimental data . We report the effects of SI and DI particles on reassortment in the context of a cell culture model where replication is limited to one cycle . One important difference between this experimental system and infection in an animal host , where IAV will undergo multiple rounds of replication , is in the multiplicity of infection . By tracking reassortment in co-infected guinea pigs , we have seen that MOI increases with viral load in vivo and , at the time of peak shedding , is sufficient to support the production of reassortant viruses at a frequency of about 70% [52 , 53] . Similar results were obtained whether mixed infection was achieved by intranasal inoculation or through dual transmission events . Thus , MOIs achieved in vivo are sufficiently high to allow complementation of SI or DI particles . The impact of these particles most likely varies with the time after infection , however , since MOI changes as infection spreads in the target tissue . A second important difference between our cell culture model and an animal host is the potential for multiple rounds of replication . DI particles may have a stronger positive effect on reassortment in vivo , since reassortant DI viruses would have the opportunity to be complemented through co-infection in subsequent rounds of replication . Another important consequence of multi-cycle replication in vivo is the potential for rare reassortant viruses to be amplified . If certain reassortant genotypes confer higher fitness than the parental genotypes in the host where they arise , even low overall levels of reassortment can lead to major biological changes . Our data , and those of Brooke et al . , suggest that SI particles outnumber fully infectious particles in a typical IAV population . We have furthermore attributed important biological activities to these particles [18 , 27] . Nevertheless , the precise nature of SI particles remains unclear . SI particles may lack one or more gene segments due to a failure to package all eight vRNAs during assembly . This possibility is substantiated by lower rates of detection of NA vRNA in a mutant virus population that was shown to have increased SI particle content relative to the wild type strain [54] . A failure to package some segments is not , however , supported by recent fluorescence in situ hybridization data that show a high percentage of virus particles contain eight different viral RNAs [55] . Although not quantitative , electron microscopic analyses of RNPs within IAV virions also show the presence of eight segments arranged in an ordered fashion [56 , 57] . The possibility that SI particles carry eight fully functional vRNA molecules but fail to deliver one or more to the nucleus is feasible but weakened by FISH analysis of IAV genomes in infected cells , which suggests that the segments remain associated prior to nuclear import [58] . Direct visualization of IAV ribonucleoproteins allowed identification of the polymerase complex bound to the 3’ and 5’ termini of the RNA , but also revealed some segments that did not appear to be associated with a polymerase [59] . Thus , SI particles might carry one or more segments that are not bound by a polymerase complex and are therefore not copied during primary transcription and may be more susceptible to exonuclease activity . While the data presented herein are informative regarding the potential biological implications of SI particles , they do not elucidate the physical nature of these particles , nor narrow down the possibilities listed above . In summary , our data show that the presence of semi-infectious particles in an influenza virus population increases the potential for genetic diversification through reassortment . This activity of semi-infectious particles is due to an increase in the proportion of productively infected cells that are co-infected , which in turn reflects the need for complementation in order for cells infected with SI particles to produce progeny . Similar to SI particles , the presence of DI particles increases the proportion of infected cells that are co-infected; however , since DI segments inhibit the production of infectious progeny viruses , their overall effect is to decrease rather than increase levels of reassortment relative to those seen with standard virus stocks . We conclude that IAV particles that are not fully infectious may have an important role in influenza virus biology through their effects on reassortment and , in turn , adaptive evolution of the virus .
The RNA:PFU ratios were higher for rPan/99wt-HIS virus than for rPan/99var-HA virus at each passage ( Fig 10 ) . Appropriate volumes of P0 rPan/99wt-HIS virus were therefore spiked into both P3wt and P4wt viruses to yield stocks with RNA:PFU ratios equivalent to their P3var and P4var counterparts . P3wt and P3var virus stocks were then mixed in a 1:1 ratio and P4wt and P4var virus stocks were similarly mixed in a 1:1 ratio . One-to-one mixtures of P0wt and P0var viruses were also prepared . Each virus mixture was diluted with PBS to the appropriate titer for inoculation at MOI 3 , 1 , 0 . 3 , 0 . 1 , 0 . 03 and 0 . 01 PFU/cell of each virus . MOI 3 was not carried out for the P4 viruses due to insufficient titers . PFU values refer to those of the P0 viruses . The amount of P3 and P4 viruses used in each infection was based on the RNA:PFU ratios . Thus , equivalent units of RNA were used for P0 , P3 and P4 infections and the amounts of RNA used corresponded to 3–0 . 1 PFU/cell of the P0 viruses . Inoculation of MDCK cells was performed as described above for untreated and UV treated viruses , except that medium was not changed at 3 h post-infection to introduce NH4Cl . At 16 hours post-infection , supernatant was collected and stored at -80°C for subsequent genotyping of released virus . MDCK-infected cells were harvested and prepared for flow cytometry ( see below ) .
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Since the genome of an influenza A virus has eight non-contiguous segments , two influenza A viruses can exchange genes readily when they infect the same cell . This process of reassortment is important to the evolution of the virus and is one reason why this pathogen is constantly changing . It has long been known that a large proportion of the virus particles that influenza and many other RNA viruses produce are not fully infectious , but the biological significance of these particles has remained unclear . Here we show that virus particles that deliver incomplete genomes to the cell enhance the rate of reassortment . Thus , despite their limited potential to produce progeny viruses , these incomplete particles may play an important role in viral evolution .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2015
|
Influenza Virus Reassortment Is Enhanced by Semi-infectious Particles but Can Be Suppressed by Defective Interfering Particles
|
The ability to identify directional interactions that occur among multiple neurons in the brain is crucial to an understanding of how groups of neurons cooperate in order to generate specific brain functions . However , an optimal method of assessing these interactions has not been established . Granger causality has proven to be an effective method for the analysis of the directional interactions between multiple sets of continuous-valued data , but cannot be applied to neural spike train recordings due to their discrete nature . This paper proposes a point process framework that enables Granger causality to be applied to point process data such as neural spike trains . The proposed framework uses the point process likelihood function to relate a neuron's spiking probability to possible covariates , such as its own spiking history and the concurrent activity of simultaneously recorded neurons . Granger causality is assessed based on the relative reduction of the point process likelihood of one neuron obtained excluding one of its covariates compared to the likelihood obtained using all of its covariates . The method was tested on simulated data , and then applied to neural activity recorded from the primary motor cortex ( MI ) of a Felis catus subject . The interactions present in the simulated data were predicted with a high degree of accuracy , and when applied to the real neural data , the proposed method identified causal relationships between many of the recorded neurons . This paper proposes a novel method that successfully applies Granger causality to point process data , and has the potential to provide unique physiological insights when applied to neural spike trains .
Neurons in the brain are known to exert measurable , directional influences on the firing activities of surrounding neurons , and a detailed analysis of these interactions improves our understanding of how the brain performs specific functions [1] . Attempts to identify associations between neurons , such as the cross-correlogram [2] , joint peri-stimulus time histogram [3] , smoothed ratio of spiking activity [4] , and gravitational clustering [5] , have been useful in the past . However , these methods provide little insight into the directional nature of the interactions that they detect , are less reliable in their detection of inhibitory interactions , and usually do not consider the point process nature of neural spike train data . Occasionally they may also give a misleading picture of the relationships between neurons if the detected associations are caused by common inputs or mediated by other neurons [6] . Granger causality has proven to be an effective method for the investigation of directional relationships between continuous-valued signals in many applications [7]–[11] . The basic idea of causality between signals was introduced by Wiener [12] but was too general to be implemented . Granger formalized this idea in order to enable practical implementation based on the multivariate autoregressive ( MVAR ) models [7]: if past values of contain information that helps predict above and beyond the information contained in past values of alone , then is said to Granger-cause ( or g-cause ) . Its mathematical formulation is based on the MVAR modeling of processes . However , it is difficult to apply this method directly to spike train data , since they can not be described by the MVAR model , and standard distance measures such as the mean squared error ( MSE ) are not designed for spike train data . Recently , several methods have been developed to apply Granger causality to spike train data [13]–[19] . Attempts at transforming neural spike trains into continuous-valued data by convolving spike trains with either a smooth kernel [13] or a lowpass filter [14] , [15] have been proposed , but they introduced unwanted distortion of the point process characteristics of spike train data . Granger causality analysis based on an MVAR-nonlinear-Poisson model has been proposed [16]; however , this approach lacks an explanation of the physical meaning of the model that is being applied . A method called transfer entropy using mutual information has also been proposed [17] , [18] , and it is sensitive to nonlinear signal properties , but unfortunately its application is restricted to bivariate cases . A nonparametric method based on spectral matrix factorization has been proposed [19]; however , it required the second-order stationarity of spike train data . To address these issues , this paper proposes a point process framework for assessing Granger causality between multiple neurons . The spiking activity of each neuron is simultaneously affected by multiple covariates such as its own spiking history and the concurrent ensemble activity of other neurons . The effect of these factors on a neuron's spiking activity is characterized by a statistical framework based on the point process likelihood function , which relates the neuron's spiking probability to the covariates [20] , [21] . Using the point process likelihood function , Granger causality between neurons is assessed based on the likelihood ratio statistic . That is , Granger causality from neuron to neuron is measured based on the relative reduction of the point process likelihood of neuron obtained by excluding the covariates corresponding to the effect of neuron compared to the likelihood obtained using all the covariates . If the likelihood ratio is less than one , we say that there is a causal influence from neuron to , and if the ratio is one , we say that there is no causal influence . In continuous-valued cases , the Granger causality measure based on the MVAR prediction error was shown to be the likelihood ratio test statistic if the prediction error is assumed to be Gaussian [22] . In addition , the point process likelihood ratio statistic enables us to perform statistical hypothesis testing to investigate the significant causal interactions between neurons , since it asymptotically follows a chi-squared distribution when the conditional intensity function ( CIF ) of the point process is modeled by the generalized linear model ( GLM ) [23] . When performing a set of statistical inferences simultaneously to detect statistically significant causal interactions among all possible interactions , multiple hypothesis testing problems where the null hypothesis is more likely to be incorrectly rejected should be considered . The present study uses the false discovery rate ( FDR ) correction to control the expected proportion of incorrectly rejected null hypotheses [24] . The proposed framework was used in an attempt to identify the causal relationships between simulated spike train data , and accurately estimated the underlying causal networks presented in the simulations . It was also applied to real neural data recorded from the cat primary motor cortex ( MI ) in order to assess the causal relationships that occur between multiple simultaneously recorded neurons during performance of a movement task .
The experiments that were performed for the collection of real neural spiking data were approved by the Animal Ethics Committee of the University of Western Australia , and the National Health and Medical Research Council of Australia ( NH&MRC ) guidelines for the use of animals in experiments were followed throughout . Statistical analysis of the potential causal relationships between neurons was performed based on a point process likelihood function . The likelihood function related a neuron's spiking probability to possible covariates , such as its own spiking history and the concurrent activity of all simultaneously recorded neurons . The causal relationships between associated neurons were assessed based on the point process likelihood ratio , which represents the extent to which the likelihood of one neuron is reduced by the exclusion of one of its covariates , compared with the likelihood if all of the available covariates are used . The Granger causality measure based on the point process likelihood ratio also enabled us to detect significant causal relationship through a hypothesis testing based on the likelihood ratio statistic . A point process is a time series of discrete events that occur in continuous time [25] . The discrete , all-or-nothing nature of a sequence of action potentials together with their stochastic structure suggests that neural spike trains may be regarded as point processes [20] , [26]–[28] . Given an observation interval , let be a set of spike times point process observations for recorded neurons . Let denote the sample path that counts the number of spikes of neuron in the time interval for . A point process model of a spike train for neuron can be completely characterized by its CIF , , defined as ( 1 ) where denotes the spiking history of all the neurons in the ensemble up to time for neuron [25] . In this work , is defined in the interval , which is divided into non-overlapping rectangular windows of duration ; We denote the spike count of neuron in a time window of length covering the time interval as for and . The CIF , , of ( 1 ) represents the firing rate of neuron at time , so it quantifies the probability that neuron fires a spike at time given its covariates . Each neuron has a different , since each has a history dependency of different length , . The probability that neuron fires a single spike in a small interval can be approximated as . To model the effect of its own and ensemble's spiking histories on the current spiking activity of a neuron , a GLM framework is often used to model the CIF . In the GLM framework , the logarithm of the CIF is modeled as a linear combination of the functions of the covariates that describe the neural activity dependencies [20] , [21] . Thus , the logarithm of the CIF is expressed as ( 2 ) where relates to a background level of activity of neuron , and represents the effect of ensemble spiking history on the firing probability of neuron . The parameter vector is given as ( 3 ) which represents the dependency of neuron on the spiking history of all neurons in the ensemble . Especially , the parameters represent the dependency of neuron on the spiking history of neuron for . The model for the CIF of ( 2 ) is not a fixed form , but can change depending on its covariates and its relationship to them . A point process likelihood function was used to fit the parametric CIF and analyze Granger causality between neurons since it is a primary tool used in constructing statistical models and has several optimality properties [29] . Here , we used a discrete time representation of the point process likelihood function in order to simplify ensuing calculations . To obtain this representation , we partitioned the observation interval into subintervals each of length where is a large integer . Usually , is chosen to make as 1 ms . We denote the continuous time variables defined above as the discrete time versions such as for , for , for and so forth . Since we chose a large value for , there is at most one spike per subinterval , that is , takes on the value 0 if there is no spike in or 1 if there is a spike . The parametric form of the CIF of ( 2 ) for neuron is represented as . Given the ensemble spiking activity in , the likelihood function of the spike train of neuron is given as in [20] using its CIF by ( 4 ) where the term relates the probability that neuron includes two or more spikes in any subinterval . Based on the likelihood function of ( 4 ) , a point process framework for assessing the causal relationships between neurons is proposed . A potential causal relationship from neuron to neuron is assessed by calculating the relative reduction in the likelihood of producing a particular set of spike trains of neuron if the spiking history of neuron is excluded , compared with the likelihood if all of the available covariates are used . The log-likelihood ratio , , is given by ( 5 ) where the likelihood is obtained using a new CIF , , which excludes the effect of neuron from , given as ( 6 ) The parameter vector is obtained by re-optimizing the parametric likelihood model after excluding from in order to remove the effect of neuron on neuron , and is obtained by leaving out from . Since the likelihood is always greater than or equal to the likelihood , the log-likelihood ratio is always less than or equal to 0 . If the spiking activity of neuron has a causal influence on that of neuron in the Granger sense , the likelihood that is calculated using all the covariates of neuron is greater than the likelihood that is calculated using the same covariates , save for the history of neuron , which is excluded . Excitatory and inhibitory influences of neuron on neuron can be distinguished by the sign of that represents an averaged influence of the spiking history of neuron on neuron . The equality holds when neuron has no influence on neuron . Thus , the Granger causality measure from neuron to neuron is proposed as ( 7 ) which provides an indication of the extent to which the spiking history of neuron affects the spike train data of neuron . A positive result is indicative of neuron having an excitatory effect upon neuron , a negative result indicates an inhibitory effect , and zero indicates that no interactions are detected . Finally , a Granger causality matrix can be produced , , whose th element is , and represents the extent to which neuron has either an excitatory or inhibitory influence on neuron for . The Granger causality matrix represents the relative strength of estimated causal interactions between neurons , but does not provide any insight into which of these interactions are statistically significant . To address this issue , a hypothesis testing based on the likelihood ratio test statistic is performed to evaluate the statistical significance of the estimated causal interactions of . For this , the goodness-of-fit ( GOF ) statistics are applied as follows [23] , [29] . Let us denote the deviance obtained using the model parameter as and the deviance obtained using the model parameter as ; The deviance is obtained by comparing the estimated model with a more general model that has a parameter for every observation so that the data fits exactly , which is called a full model [25] , [30] . Its expression is −2 times the log-likelihood ratio of the estimated model to the full model , which is mathematically expressed by ( 8 ) where and are the parameters for the estimated and the full models , respectively . In the GLM framework the deviance is used to compare two models , which are nested like the above case , since a model of is a special case of the more general model of . Consider the null hypothesis ( 9 ) which corresponds to the model of ( 6 ) . An alternative hypothesis is ( 10 ) which corresponds to the model of ( 2 ) . We can test against using the difference of the deviance statistic as the test statistic , which is given by ( 11 ) Thus , the deviance difference between two models is equivalent to −2 times log-likelihood ratio given by ( 5 ) . If both models describe the data well , then the deviance difference may be asymptotically described as where is equal to the difference in dimensionality of the two models [23] , [29] . If the value of is consistent with the distribution , the hypothesis is accepted since it is simpler . This result indicates that the past values of neuron contain no significant information that would assist in predicting the activity of neuron . Thus , neuron has no causal influence on neuron . On the contrary , if the value of is in the critical region , i . e . , greater than the upper tail of the distribution where determines false positive rates , then may be rejected in favor of since the model of ( 2 ) describes the data with significantly more accuracy . This indicates that past spike times of neuron contain information that improves the ability to predict the activity of neuron . Thus the activity of neuron g-causes the activity of neuron . In any attempt to identify the causal relationships between multiple neurons simultaneously , the total number of the possible causal interactions to be investigated is usually large . Thus , the use of common statistical thresholds cited above to assess the causal interactions would lead to an unacceptably large number of false causal interactions ( false positives ) where the null hypothesis is incorrectly rejected [31] . The multiple comparison problem could potentially be addressed by the use of stricter statistical thresholds , which would result in a reduction in the proportion of the falsely rejected null hypotheses . However , stricter thresholds would also reduce the probability that true causal interactions between neurons were identified . The present study uses a multiple-hypothesis testing error measure called the FDR to address the multiple comparisons problem . The FDR controls the expected proportion of false positive findings among all the rejected null hypotheses [24] . In situations where the number of hypothesis tests is large , other approaches that attempt to control the familywise error rate ( FWER ) , which is the probability of making one or more false discoveries among all the hypotheses , can be too strict and decrease the power . Thus , the FDR is a less conservative , but more powerful , quantity to control for multiple comparisons than the FWER at a cost of increasing the likelihood of obtaining false positive findings [32] . Combining the multiple hypothesis testing results with the sign of , we detect the inhibitory , excitatory , and non-causal interactions , which are denoted as the blue , red , and green colors , respectively . Thus , a causal connectivity matrix whose th element corresponds to one of three interactions is constructed . In this paper , the connectivity matrix was obtained by controlling the FDR as 0 . 05 .
In order to evaluate the proposed framework's ability to identify Granger causality for ensemble spiking activity , we analyzed synthetically generated spike train data . Simulated spike train data were synthetically generated based on the nine-neuron network of Figure 1 . The firing probability of each neuron was dependent on its own spiking history and the concurrent ensemble activity through the inhibitory and the excitatory interactions of Figure 1 . The inhibitory and the excitatory interactions were represented as black and white circles , respectively . Each neuron was influenced by other neurons through two inhibitory interactions including its own self-inhibition and through one or two excitatory interactions . The overall network of Figure 1A consisted of three sub-networks each with three neurons . The interactions between neurons within sub-networks were set to have relatively small duration , and the parameter vectors for the inhibitory and the excitatory interactions among neurons were set to = [−0 . 8 −0 . 6 −0 . 3] and = [1 2 2] , respectively . For interactions between different sub-networks , the parameter vectors for the inhibitory and excitatory interactions were set to have relatively long duration such as = [0 0 0 −0 . 8 −0 . 9 −0 . 5] and = [0 0 0 1 2 1] , respectively . The parameter vector for the self-inhibition was set to = [−0 . 6 −0 . 5 −0 . 4] . All neurons had the same spontaneous firing rate ( 18 Hz ) . Spike trains for neuron were generated using a commonly used procedure as follows [33]: A random number , uniformly distributed between 0 and 1 , is generated at every interval; if , a spike is presumed to have occurred in ; otherwise , no spike is generated . The time resolution was set to 1 ms . An absolute refractory period of 1 ms was enforced to prevent neurons from firing a spike in adjacent time steps . Based on the experimental settings cited above , we generated 100 , 000 samples for each neuron , and the total number of spikes for each neuron ranged from 2176 through 2911 . Examples of generated neural spike trains during the first 5 sec ( 5 , 000 samples ) are illustrated in Figure 2 . It can be seen that neurons generally fire less ( or more ) spikes after other neurons with inhibitory ( or excitatory ) influence on them fire spikes . However , it is hard to estimate the underlying causal network between neurons from this plot . In order to select a model for each neuron we fit several models with different history durations to each spike train data and then identified the best approximating model from among a set of candidates using Akaike's information criterion ( AIC ) [34] , [35] . Using this criterion , an optimum model order for each neuron was selected . The spike counting window length was set to 2 ms . For neurons 1 , 3 , 4 , 5 , 8 , and 9 , which were influenced by other neurons through relatively long interactions , the selected GLM spike order was 3 , which indicates a 6 ms history duration , and for neurons 2 , 6 , and 7 influenced by other neurons within same sub-network only through short interactions , the selected GLM order was 2 , which corresponds to a 4 ms history duration . Based on the estimated model , two kinds of causality maps were obtained using the proposed method . Firstly , the Granger causality map , which is illustrated in Figure 3A , represents the relative strength of the causal interaction between neurons . It represents the extent to which a trigger neuron has a causal impact on a target compared to other interconnections , but provides little insight into which causal impact is statistically significant . In order to make up for , the causal connectivity map was obtained through the hypothesis testing when we controlled the FDR as 0 . 05 . This is shown in Figure 3B . The red , blue , and green colors denote the presence of excitatory , inhibitory , or no interactions from trigger neuron to target , respectively . The estimated pattern of matches the actual network of Figure 1 exactly . This causality map does not show a connection between neurons that do not have direct interactions , even though they have indirect interactions . The FDR procedure was used as a solution for the multiple comparisons problem when considering a set of statistical inferences simultaneously . When controlling the FDR at a specific significance level , we expect that on average there will be false positives amongst detected significant interactions . In order to verify that the FDR is actually being controlled at the significance levels that we are claiming in the present study , the Monte-Carlo ( MC ) simulations were conducted by varying both the number of causal interactions between the virtual neurons , and the signal-to-noise ratio ( SNR ) of the simulated spikes . These MC simulations show how effectively the FDR is being controlled under different experimental conditions . Firstly , we conducted a series of the MC simulations by changing the number of causal interactions from 8 to 64 . Data was synthetically generated to resemble four different kinds of networks ( seen in Figure 4 ) , each having a different incidence of interaction between neurons . Fifty data sets were generated for each network condition , while all other experimental parameters remained the same . The dashed circle in Figure 4A and B represents a neuron whose firing activity does not depend on the spiking history , and thus follows a homogeneous Poisson process , i . e . , = 0 . Networks of Figure 4A to D consist of 8 , 16 , 32 , and 64 interactions ( including self-interactions ) , respectively . The observed FDR is calculated by averaging the ratio of the number of false positives to the number of detected significant interactions over 50 simulations , and it is illustrated in Figure 5A for significance levels of 0 . 01 , 0 . 05 and 0 . 1 . The FDR was generally controlled at the significance level that we were attempting to control except for the 8-interaction case with less false positives than the number expected at that significance level . We then performed another MC simulation by changing the SNR . Noisy neural spike trains were generated using the CIF of ( 2 ) in the following: We added a Gaussian noise to the logarithmic CIF , i . e . , the right-hand side of ( 2 ) , and then generated spike trains using the perturbed CIF . The noise changed the background level of firing rate over time . The SNR is defined as the ratio between the unperturbed logarithmic CIF and the perturbation itself . Fifty data sets of noisy spike trains were synthetically generated based on the nine-neuron network of Figure 1 with different levels of noise , which led to about 0 , 10 , 20 , 30 , and 40 dB SNRs , respectively . All other experimental conditions are same to the previous case . Figure 5B illustrates the simulation results obtained for significance levels of 0 . 01 , 0 . 05 , and 0 . 1 . When the SNR is approximately 0 dB , more false positive events were detected than what was expected at the specified significance level , but in most cases the observed FDR was no different from the theoretical FDR . In summary , unless the perturbation level is similar to or higher than the level of the logarithmic CIF of ( 2 ) that is modulated by the intrinsic dynamics of the neurons , the FDR is effectively controlled at the significance level that we are attempting to control . To illustrate the application of the proposed method to real spike train data , 15 neurons were simultaneously recorded from the cat MI shown in Figure 6 and analyzed . The experimental methodology that was implemented to collect the neural activity used for the following analysis was described in detail in Ghosh et al . [36] . Briefly , an adult cat was trained to perform a skilled reaching movement , using its preferred forelimb to retrieve food pellets placed between 2 upright Perspex barriers spaced 4 cm apart . After behavioral training was complete , PTFE coated Platinum-Iridium microwires were implanted into the cortex to a depth of about 1 . 5 mm into forelimb and hindlimb representations of MI ( identified using intracortical microsimulation ) . Neural recordings were made as the animal performed the reaching task , and only neurons that significantly modulated their firing rate during task performance were isolated for analysis in this study . Interspike interval , spike duration and spiking rate analyses were performed on neurons isolated from adjacent recording sites . This was done in order to rule out the possibility of the same neuron being counted more than once due to cross-talk between neighboring electrodes . Autocorrelogram , interspike interval and ‘burst surprise’ ( using a surprise value of 3 ) analysis were performed on all neurons in order to identify any potentially bursting neurons in the data set ( there were none ) [37]–[39] . The data set includes 150 , 000 samples ( 3 , 000 samples/trial50 trials ) for each channel , and the total number of spikes for each neuron across all trials ranged from 613 to 5716 . The sampling rate was 1 KHz . Using the AIC , an optimum model for each neuron is selected to minimize the criterion . The non-overlapping spike counting window was intuitively set to 3 ms to obtain a relatively small number of parameters while maintaining the temporal resolution . Figure 7 shows the selected GLM spike order of each neuron for , and for each neuron 1 parameter ( 3 ms ) through to 18 parameters ( 54 ms ) were used to model its interconnection . The GOF of the estimated model is assessed by using the Kolmogorov-Smirnov ( KS ) plots [40] . Prior to making inferences from an estimated statistical model , it is crucial to measure the agreement between a statistical model and the spike train data . For continuous-valued data , the GOF of the model can be quantitatively measured as standard distance such as MSE . However , this distance measure can not be applied to neural spike train data . To address this problem , we utilized the previously proposed time-rescaling theorem to transform point process measures such as neural spike train data to a continuous measure appropriate for a GOF assessment [40] . Once a CIF is estimated , rescaled times can be computed using the estimated CIF . These rescaled times will be uniformly distributed random variables on the interval if the estimated CIF is a good approximation to the true conditional intensity of the point process . To evaluate whether the rescaled times follow the uniform distribution , we order these rescaled times from the smallest to the largest , and then plot the quantiles of the cumulative distribution function of the uniform distribution on against the ordered rescaled times . This form of graphical representation is termed a KS plot . If the model is consistent with the data , then the points should lie on a 45-degree line . Approximate 95 confidence bounds for the degree of agreement between the model and the data may be constructed using the distribution of the KS statistic [41] . Figure 8 shows the best and the worst KS plots obtained using estimated GLMs across all the given spike train data . Most KS plots were almost within the confidence intervals , which indicates that most estimated GLMs fit the data well . The causal connectivity between the recorded neural spike train data was assessed using the proposed framework , and the results are illustrated in Figure 9 . Figure 9A and B show the causal connectivity maps , , estimated using the proposed framework without and with the FDR correction , respectively . As illustrated in Figure 9A when the multiple comparison problem was not considered , more causal connectivity was estimated; however , there was a high probability that the false rejection of the null hypotheses of the multiple comparison caused the extra causal relationships . In the present study , for the hypothesis testing was set to 0 . 05 . After the FDR correction for the multiple comparison problem , the incidence of interactions between the recorded neurons was sparser , and is shown in Figure 9B . In Figure 9B , neurons 2 , 3 , 4 , 5 , 6 , 9 , and 10 exchanged causal interactions with a handful of other neurons including themselves , neurons 7 , 12 , 14 , and 15 showed purely self-inhibitory interactions , and finally neurons 1 , 8 , 11 , and 13 did not receive any influence from other neurons , nor did they show signs of self-interaction . Interestingly , neurons 5 , 6 , 9 , and 10 appeared to display evidence of self-excitatory interactions , which is highly unusual behavior for a neuron . Interspike interval and autocorrelogram analysis were performed on these neurons in order to exclude the possibility that these interactions were occurring due to bursting behavior [37] , [38] . Further analysis of these neurons revealed that they also had the four highest history orders among neurons as shown in Figure 7 . Figure 9C shows the connectivity map obtained using the neural spike train data recorded during a period of postural maintenance from the same recording sites in MI following completion of a satisfactory number of task trials shown in Figure 6 . The data set includes 54 , 000 samples ( 3000 sampless/trial18 trials ) , and the total number of spikes for each neuron across all trials ranged from 55 to 1030 . As shown in the figure , during the state of postural maintenance , most neurons did not show any evidence of significant interactions . It could be argued that the decrease in the number of detected significant interactions that were seen during the state of postural maintenance was actually related to the decreased number of spikes that were observed during this behavioral period . In order to prove that this decrease is actually related to a physiological phenomenon rather than a decreased spike count , causality analysis was performed using the first 11 trials ( of a total of 50 ) of the ‘reaching’ data set , which decreased the averaged number of spikes in that set of data ( 522 spikes ) to a similar level as the ‘postural maintenance’ set ( 520 spikes ) . Figure 9D illustrates the obtained causal connectivity map , and more significant interactions were still seen between neurons during reaching movement than during postural maintenance . Note that the obtained causal connectivity maps do not necessarily represent interactions as a result of direct anatomical connection , but suggests that a functional causal connectivity exists between the recorded neurons . The estimated GLM parameters that correspond to the self-interactions of all neurons for are illustrated in Figure 10 . The red , blue , and green colors represent the excitatory , inhibitory , or no self-interactions , respectively . In all cases , the first parameter is always negative due to the absolute refractory period , and the remaining parameters generally have positive values for the self-excitatory interactions and negative values for the self-inhibitory interactions , respectively . In cases where no-interactions was occurring , only one negative parameter ( indicated with green asterisk ) existed . Neurons showing evidence of excitatory self-interactions have the four highest history orders , and those indicating inhibitory self-interactions have higher orders than those with no self-interactions , which have only one parameter .
We proposed a point process framework for identifying causal relationships between simultaneously recorded multiple neural spike train data . Granger causality has proven to be an effective method to test causality between signals when using the MVAR model , but to date it has been used for continuous-valued data [7]–[11] . The method described in this study represents a novel attempt to apply Granger causality to point process data . The high level of accuracy that our method displayed when predicting the nature of the interactions occurring in the simulated data set was an encouraging indication that the proposed method is sound . Furthermore , the marked disparity in incidence of interactions during movement and non-movement periods in the experimental data is in keeping with the findings of previous studies investigating interactions in MI [36] . Thus , the outcome of both our simulated and experimental data analysis provides compelling evidence that Granger causality can be successfully applied to point process data . This is an important finding , as there are currently very few techniques that assess interactions between multiple neurons as well as providing insight regarding the causal relationships that exist between them . The ability to infer causal relationships between interacting neurons provides us with important information about networks of neurons being studied with this method . A detailed understanding of the interactions occurring in ensemble activity recorded from MI may lead to improved accuracy in algorithms used to control devices such as brain-computer interfaces and neural prosthetics [20] , [42] , [43] . Other model-based methods for assessing the directional relationships between neurons have been recently developed [21] , [42] . These methods infer underlying interactions between neurons based on estimated model parameters , which contain the information on the dependencies between all of the recorded neurons . Thus , functional connectivity between neurons is inferred when the estimated model parameters achieve non-zero magnitude , that is , when their confidence intervals do not cross the zero-magnitude line . However , no quantitative criteria currently exists to guide users of these methods to accept or reject detected interactions when the suspected interaction is of low magnitude , or high magnitude but with wide confidence intervals for the estimated parameters . Thus , in more difficult cases where a model-based method produces uncertain results , the acceptance of a spurious interaction , or the rejection of a legitimate one , may compromise the reliability of experimental data analysis . The proposed point process framework addresses this issue by performing statistical significance tests that investigate the causal interactions based on the likelihood ratio statistic , eliminating this uncertainty . Thus , the proposed method may be of use to researchers who are having trouble quantifying some of the connections that they are detecting when using other model-based methods . Some of the neurons included in this analysis showed no evidence of either self-interaction , or interactions with other neurons . Although these neurons also had non-zero GLM parameters for self-interaction , as indicated with green asterisks in Figure 10 , their effects were tested to be statistically insignificant compared to those caused by other neurons or background firing activity . In these cases , we must consider that the hypothesis testing to evaluate the significant causal interactions depends on the FDR value that is chosen . Decreasing the FDR value means that a statistical threshold for the significance test is more strict , and would lead to a sparser causal connectivity map . Therefore , it should be noted that the inferred causal connectivity maps generated by this method are not absolute , and may change depending on the user's selection of the FDR value . The identification of excitatory self-interactions for some of the analyzed neurons was an unexpected and interesting finding . Analysis of the spiking features of these neurons verified that they were not engaged in any manner of bursting behavior that may explain the self-excitation result . Based on the high history orders that were also seen in those neurons as shown in Figure 10 , we infer that the self-excitation result may be caused by ‘hidden’ positive feedback networks , that is , networks involving neurons that were not recorded by our microwires . To support our inference , we have performed another simulation to investigate the effect of hidden feedback networks . We identified the causal interactions among ensemble spiking activity , which was synthetically generated based on the five-neuron network of Figure 11 . Compared to the nine-neuron network of Figure 1A , the five-neuron network of Figure 11A had hidden neurons 4 and 5 , which composed hidden positive feedback networks together with neuron 1 . So the firing activity of neuron 1 was not only dependent on the spiking activity of observed neurons 2 and 3 , but also on the spiking activity of hidden neurons 4 and 5; however , only neurons 1 , 2 , and 3 were observable . The parameter vector for the excitatory interaction of the hidden network was set to = [0 0 1 2 2 1] . The other experimental settings were all same to the previous case . We generated 100 , 000 samples for each neuron , and the total number of spikes for the observed neurons 1 , 2 , and 3 were 4247 , 2606 , and 2314 , respectively . Due to the hidden positive feedback , neuron 1 fired more spikes than other neurons . The model orders were selected using the AIC , and the selected orders for neurons 1 , 2 , and 3 were 5 ( 10 ms history duration ) , 2 ( 4 ms ) , and 2 ( 4 ms ) , respectively . Neuron 1 had a relatively longer history duration than other neurons due to the hidden feedback networks . Using the proposed method , we obtained both the Granger causality map and the causal connectivity map , which is illustrated in Figure 12 . The estimated causality map matches well the original network of Figure 11 except that neuron 1 was estimated to have a self-excitatory interaction , which was caused by the hidden positive causal interactions with neurons 4 and 5 . This hidden interaction also led to the relatively long history duration of neuron 1 compared to the other neurons . This simulation supported the idea that hidden positive feedback network leads to the relatively long history duration and can change inhibitory self-interaction to excitatory one , which we could also observe in this real data analysis case . Similarly , self-inhibitory interactions , which had a relatively long history duration as shown in Figure 10 , were also identified in this study , and may be the result of hidden negative feedback networks . Self-inhibitory interactions ( as they are defined using this method ) may be difficult to quantify in some cases , as a neuron with a very low firing rate may produce a self-inhibitory result that is similar in appearance to that which would occur due to hidden negative networks . However , the majority of the neurons in the present study that showed the evidence of self-inhibition had quite high firing rates . Thus , the inference of hidden negative feedback networks is a plausible explanation in these cases . The proposed framework creates an unprecedented opportunity to investigate interactions from hidden neural networks that have either excitatory or inhibitory causal influences on recorded neurons . Recently a method called partial Granger causality to identify the underlying causal interactions in the presence of exogenous inputs and latent variables for the continuous-valued case has been proposed [44] , [45] . It would be useful to extend this work to neural spike train data in order to deal with the effects of exogenous inputs or hidden neurons beyond the investigation of the hidden feedback network . The Matlab software and the data sets used to implement the methods presented here are available at the website ( http://www . neurostat . mit . edu/gcpp ) .
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Recent advances in multiple-electrode recording have made it possible to record the activities of multiple neurons simultaneously . This provides an opportunity to study how groups of neurons form functional ensembles as different brain areas perform their various functions . However , most of the methods that attempt to identify associations between neurons provide little insight into the directional nature of the interactions that they detect . Recently , Granger causality has proven to be an efficient method to infer causal relationships between sets of continuous-valued data , but cannot be directly applied to point process data such as neural spike trains . Here , we propose a novel and successful attempt to expand the application of Granger causality to point process data . The proposed method performed well with simulated data , and was then applied to real experimental data recorded from sets of simultaneously recorded neurons from the primary motor cortex . The results of the real data analysis suggest that the proposed method has the potential to provide unique neurophysiological insights about network properties in the cortex that have not been possible with other contemporary methods of functional interaction detection .
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[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"neuroscience/motor",
"systems",
"mathematics/statistics",
"neuroscience/theoretical",
"neuroscience"
] |
2011
|
A Granger Causality Measure for Point Process Models of Ensemble Neural Spiking Activity
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The Fragile X-Related 1 gene ( FXR1 ) is a paralog of the Fragile X Mental Retardation 1 gene ( FMR1 ) , whose absence causes the Fragile X syndrome , the most common form of inherited intellectual disability . FXR1P plays an important role in normal muscle development , and its absence causes muscular abnormalities in mice , frog , and zebrafish . Seven alternatively spliced FXR1 transcripts have been identified and two of them are skeletal muscle-specific . A reduction of these isoforms is found in myoblasts from Facio-Scapulo Humeral Dystrophy ( FSHD ) patients . FXR1P is an RNA–binding protein involved in translational control; however , so far , no mRNA target of FXR1P has been linked to the drastic muscular phenotypes caused by its absence . In this study , gene expression profiling of C2C12 myoblasts reveals that transcripts involved in cell cycle and muscular development pathways are modulated by Fxr1-depletion . We observed an increase of p21—a regulator of cell-cycle progression—in Fxr1-knocked-down mouse C2C12 and FSHD human myoblasts . Rescue of this molecular phenotype is possible by re-expressing human FXR1P in Fxr1-depleted C2C12 cells . FXR1P muscle-specific isoforms bind p21 mRNA via direct interaction with a conserved G-quadruplex located in its 3′ untranslated region . The FXR1P/G-quadruplex complex reduces the half-life of p21 mRNA . In the absence of FXR1P , the upregulation of p21 mRNA determines the elevated level of its protein product that affects cell-cycle progression inducing a premature cell-cycle exit and generating a pool of cells blocked at G0 . Our study describes a novel role of FXR1P that has crucial implications for the understanding of its role during myogenesis and muscle development , since we show here that in its absence a reduced number of myoblasts will be available for muscle formation/regeneration , shedding new light into the pathophysiology of FSHD .
The Fragile X-Related 1 ( FXR1 ) gene belongs to a small gene family that includes the Fragile X Mental Retardation 1 ( FMR1 ) and Fragile X-Related 2 ( FXR2 ) genes ( reviewed in [1] ) . Human FMR1 is located on chromosome Xq27 . 3 [2] and inactivation of FMR1 expression leads to the Fragile X syndrome in human , the first cause of inherited mental retardation [5] . FXR1 and FXR2 are autosomal genes , respectively mapping at 3q28 and 17p13 . 1 [3] , [4] . The FXR1 gene is highly expressed in muscle and its pre-mRNA is known to undergo extensive alternative splicing , which generates distinct FXR1 mRNA variants that produce FXR1P isoforms with divergent C-terminal regions [6] , [7] . Four isoforms ranging from 70 to 80 KDa ( Isoa , Isob , Isoc , Isod ) are ubiquitously expressed , including in murine [7] , [8] and human myoblasts [9] . Myoblasts also express long muscle-specific FXR1 mRNA variants , termed Isoe and Isof , which are massively induced upon muscular differentiation [7] , [8] , [9] , [10] . Importantly , these muscle-specific mRNA variants of FXR1 are the only expressed in adult muscle [6] , [7] , [8] , [9] , [11] . Defects in FXR1 gene muscular pattern of expression have been observed in patients affected by Facio-Scapulo Humeral Distrophy ( FSHD ) , the most prevalent muscular dystrophy affecting adults and children [9] . Similar defects were observed in a mouse model of myotonic dystrophy ( DM1 , [12] ) . As a result , the long isoforms FXR1P Isoe and Isof of 82–84 kDa are depleted in myopathic muscle . Consistent with these altered expression pattern of FXR1 in myopathic patients , Fxr1-knockout mouse die shortly after birth most likely due to an abnormal development of cardiac and respiratory muscles [13] . A mouse model with reduced levels of Fxr1 expression has also been generated , and displays reduced limb musculature and a shorter life span of about 18 weeks [13] . Moreover , during Xenopus embryogenesis , complete or partial inactivation of xFxr1 disrupts somitic myotomal cell rotation and segmentation , impeding normal myogenesis [14] . Finally , depletion of zFxr1p during early development of the zebrafish leads to cardiomyopathy and muscular distrophy [15] . All these data point out an evolutionarily conserved role for FXR1P in myogenesis . FXR1P contains two KH domains and one RGG box that are characteristic motifs in RNA-binding proteins [4] , [16] . In addition , FXR1P harbours nuclear localization and export signals ( NLS and NES ) enabling nucleocytoplasmic shuttling [4] , [17] . In most cell types and tissues studied , FXR1P isoforms are associated to messenger ribonucleoparticles ( mRNPs ) present on polyribosomes , suggesting a consensus role in translation regulation for FXR1P [18] . However , it was reported that , in undifferentiated myoblasts , FXR1P long isoforms Isoe and Isof are not detected on polyribosomes , suggesting a role other than translation regulation for these isoforms at this stage [7] , [8] . Very few specific target mRNAs for FXR1P have been identified so far , and even more scarcely in the context of myogenesis . First , two independent studies reported that the shortest isoform of FXR1P , Isoa , binds the AU-rich element ( ARE ) present in the 3′UTR of proinflammatory cytokine tumor necrosis factor ( TNFα ) mRNA [19] , [20] . In this context , FXR1P associates with AGO2 on TNFα−ARE to modulate its translation [20] . Second , we have previously shown the ability of FXR1P Isoe , its long muscle-specific isoform , to interact specifically and with high affinity with the G-quadruplex RNA structure in vitro [21] . However , no mRNA target of FXR1P bearing a G-quadruplex has been identified yet in vivo . Finally , one study reports the presence of Desmoplakin and Talin2 mRNAs in FXR1P-mRNP complexes and subsequent disturbance of the expression of the encoded proteins in Fxr1-KO heart extracts [22] . However , neither the binding motif/sequence recognized by FXR1P on these mRNAs nor the exact functional significance of these interactions have been explored . To gain further insights into the muscular roles of FXR1P and the pathways perturbed in its absence , we performed a large-scale microarray analysis of the C2C12 myoblastic cell line inactivated for Fxr1 . This analysis revealed that Fxr1-depletion lead to premature cell cycle exit of myoblasts . We link this to a robust increase in the levels of the cyclin-dependant inhibitor p21/Cdkn1a/Cip1/Waf1 , that is also observed in FSHD-derived myoblasts . In this study , we further explore the role played by the direct interaction of FXR1P with p21 mRNA in the post-transcriptional control of p21 levels .
To understand the functional role of FXR1P in myoblasts , we used as a cellular model the C2C12 myoblastic cell line . This murine cell line enables to reproduce myogenesis in vitro [23] and expresses all the myogenic factors as well as FXR1P [7] , [8] . In this model , we inactivated the expression of all FXR1P isoforms by transient transfection of siRNAs targeting exon 14 , a constitutive exon present in all Fxr1 mRNAs [6] . As shown in Figure 1A , quantitative RT-PCR performed on C2C12 cells transfected with siFxr1 siRNAs reveals a significant reduction in Fxr1 mRNA as compared to siControl-transfected cells ( 13 . 45%±3 . 4% residual expression , Figure 1A ) . Knockdown of all isoforms of FXR1P was obtained by siFxr1 transfection , as shown by western-blot analysis using the 3FX antibody ( Figure 1B , [8] ) . Note that the levels of FXR2P , the close homologue of FXR1P , also recognized by 3FX antibody , remain unaffected , confirming the specificity of the knockdown strategy ( asterisk , Figure 1B ) . In siFxr1-transfected myoblasts , the decrease in epifluorescence signal after FXR1P-immunolabeling as compared to siControl-transfected cells confirms the efficiency of the knockdown ( Figure 1C ) . The knockdown appears to homogenously affect all the cells since the signal is uniformly decreased . Note that in C2C12 cells , FXR1P immunoreactivity is mainly cytoplasmic , however , signal is also detected in the nucleus ( Figure 1C ) . Indeed , we confirmed the partial nuclear localization of FXR1P in myoblasts by confocal microscopy ( Figure 1D ) , as described previously for the long isoforms of FXR1P in C2C12 myoblasts [7] and in human myoblasts [9] . To determine the impact of the inactivation of Fxr1 on gene expression in myoblasts , total RNA was extracted from siControl and siFxr1-transfected C2C12 myoblasts and simultaneously analysed using whole genome mouse microarrays . Among the genes showing measurable differential levels of expression , a significant change was observed for 105 transcripts ( 32 down- and 73 up-regulated ) of which 79 were annotated in the RefSeq database ( Figure 1E and Table S1 ) . As expected , Fxr1 mRNA appears among the most significantly down-regulated in siFxr1-transfected cells ( Figure 1E and Table S1 ) . To confirm the observed dysregulation of a subset of mRNAs in Fxr1-knockdown C2C12 myoblasts , we performed quantitative RT-PCR analysis ( Figure 1F ) . Interestingly , in Fxr1-depleted myoblasts , we were able to confirm by quantitative RT-PCR a significant upregulation of mRNAs encoding: Semaphorin 7a ( Sema7a ) , the Ca2+-binding multiple C2 domains transmembrane protein 2 ( Mctp2 ) , asialoglycoprotein receptor 1 ( Asgr1 ) , the cyclin-dependant kinase inhibitor p21 ( p21/Cdkn1a/Waf1/Cip1 ) , Hepatocyte growth factor ( Hgf ) , Dual specific phosphatase ( Dusp6 ) and finally Limb-bud and heart protein ( Lbh , Figure 1E ) . Conversely , we confirmed a significant down-regulation of Cdk15 mRNA encoding the cyclin-dependent kinase 15 . Finally , the mRNAs encoding the myoregulatory factors MyoD and Myogenin for which no mRNA variations were detected by microarray analysis remained unaffected ( Figure 1F ) . These analyses were further repeated on C2C12 cells inactivated for Fxr1 by transfection of a different siRNA ( siFxr1#2 ) targeting Fxr1 exon 6 , another constitutive exon of Fxr1 present in all its variants [6] . This second siRNA leads to a 37% residual expression of Fxr1 mRNA ( Figure S1A ) and reduces all FXR1P isoforms ( Figure S1B ) as compared to siControl . In addition , siFxr1#2-mediated knockdown of Fxr1 efficiently modulated the previously studied subset of mRNAs to induce variations similar to the one observed with the first siRNA against Fxr1 ( Figure S1C ) . Importantly , this cross-analysis using two siRNAs targeting distinct regions of Fxr1 mRNA exclude the fact that the observed variations could derive from off-target effects of the siRNAs . To gain insights into the pathways perturbed by Fxr1 depletion , we performed an analysis of the biological functions or processes selectively enriched among the altered transcripts , using the Ingenuity Pathway Analysis ( IPA ) software ( Table S2 ) . Interestingly , Fxr1 knockdown in C2C12 myoblasts significantly affected the functional categories ‘cell cycle’ ( Table S2 ) , ‘skeletal and muscular system development and function’ and ‘skeletal and muscular disorders’ ( Table S2 ) . Importantly enough , a subset of mRNAs perturbed in siFxr1-knockdown myoblasts compared to control repeatedly appeared determinant for the definition of the affected functional categories: the cyclin-dependent kinase ( Cdk15 ) , the cyclin-dependent kinase inhibitor ( p21/Cdkn1a/Cip1/Waf1 ) and the Hepatocyte growth factor ( Hgf ) . One of the most recurrent terms in IPA analysis of dysregulated mRNA upon Fxr1 depletion were ‘cell cycle progression’ , ‘arrest in G0/G1’ , ‘proliferation’ and also ‘cell viability’ ( Table S2 ) . This prompted us to analyse myoblasts' viability and proliferation abilities upon Fxr1-depletion . Fluorescence-Activated Cell Sorting ( FACS ) analysis of the DNA intercalant Propidium Iodide ( PI ) incorporation on living cells allowed us to detect no changes in the overall viability of Fxr1-knockdown ( 92 . 5% viability ) compared to control ( 90 . 53% viability ) C2C12 cells ( Figure 2A ) . To assess the proliferation ability of Fxr1-depleted myoblasts , we conducted tetrazole MTT proliferation assays . Interestingly , after 48 hours in culture , siFxr1-transfected C2C12 cells exhibit a significant 15% decrease in MTT reductase activity as compared to control ( Figure 2B ) . This suggests that Fxr1 depletion may induce alterations of myoblasts cell cycle . We therefore further analysed the distribution in the various cell cycle phases of siFxr1- or siControl transfected myoblasts . The DNA content of the cells was assessed by FACS-measurement of the amount of PI incorporated in cells . Surprisingly , in a normal asynchronous cell population , we did not observe any significant change in the cell cycle phases distribution of the C2C12 cells transfected with siFxr1 or siControl , in normal growth conditions ( Figure 2C ) . To highlight specific defects in cell cycle , we synchronized siFxr1- and siControl-transfected myoblasts by treatment with the cell cycle blocker mimosine , that arrests cell cycle progression at the G1/S phase border [24] . Since the effects of this cell cycle blocker are fully reversible , we then allowed the synchronized cells to reenter cell cycle by incubating them in normal growth medium for 16 hrs before FACS analysis . In these conditions , we did observe a significant 27 . 6% increase in the number of cells in the G0/G1 phase in Fxr1-knockdown myoblasts , as compared to control . This increase in the G0/G1 population is accompanied by a 51 . 9% decrease in the number of cells in the G2/M phase . Importantly , no differences were observed in the proportion of cells in the Sub-G1 phase - corresponding to cellular debris with a lower DNA content liberated by apoptotic cells [25]- in asynchronous cells ( Figure 2A ) and after release from cell cycle blocker ( Figure 2D ) . These data indicate that FXR1P depletion in myoblasts does not lead to cell viability defects but rather causes a blockade and accumulation of cells in the G0/G1 phase to the detriment of mitosis . Thus , to determine whether the cells were blocked in G0 or G1 , we performed immunolabeling of C2C12 cells in normal growth conditions and quantified the number of DAPI-positive nuclei and the amount of cells positive for the proliferation marker Ki67 ( Figure 3 ) . We observed that the number of nuclei in cultures of siFxr1-transfected myoblasts is decreased by 26% , suggesting that Fxr1 depletion limits the proliferating abilities of myoblasts ( Figure 3B ) . Quantification of cells expressing Ki67 enabled us to detect that siRNA-meditated depletion in Fxr1 leads to a subtle , but significant 10% decrease in the number of Ki67-positive cells compared to control ( Figure 3C ) . Since Ki67 is expressed during all active phases of the cell cycle ( G1 , S , G2 , and mitosis ) , but absent from quiescent cells ( G0 ) [26] , the unlabeled cells most likely represent resting cells blocked in G0 . The premature cell cycle arrest we observed in Fxr1-depleted myoblasts prompted us to examine the subset of deregulated mRNAs identified by microarray analysis in order to identify candidates for regulation by FXR1P that could contribute to explain this phenotype . The most promising mRNA candidate appeared to encode the ubiquitous cyclin-dependent kinase inhibitor ( CDKI ) p21 –also known as Cdkn1a/Cip1/Waf1- that belongs to the Cip/Kip family of CDKI . In myoblasts , p21 is known to block cell cycle progression to trigger cell-cycle exit , a prerequisite to muscular differentiation [27] , [28] , [29] . In Fxr1-depleted myoblasts , we found that p21 mRNA level is significantly increased by microarray analysis ( Figure 1E , Table S1 ) and confirmed a 1 . 76-fold upregulation of the transcript by quantitative-RT PCR in these Fxr1 loss-of-function experiments ( cf Figure 1F ) . This upregulation of p21 mRNA level in Fxr1-depleted myobasts was further confirmed using a second siRNA targeting Fxr1 ( Figure S1 ) . We had previously shown that the muscle-specific long isoforms of FXR1P , notably Isoe , are depleted in myoblasts derived from Fascio-ScapuloHumeral Distrophy ( FSHD ) patients and had hypothesized that this could induce deregulation of mRNA targets specific to this isoform FXR1P Isoe [9] . To test this hypothesis on this new potential mRNA target of FXR1P , we assessed the status of human P21 in the same samples used in our previous study . Interestingly enough , P21 mRNA levels are significantly increased in FSHD patients by a 1 . 8 factor ( Figure 4A ) . We then sought to verify whether this increase in p21 at the mRNA level was translated at the protein level by western-blotting ( WB ) analysis . Quantification of WB of siFxr1-transfected C2C12 using the ImageJ software revealed a 1 . 92 fold increase in p21 protein levels ( Figure 4B ) . Concomitantly , we observed by western-blotting that the levels of P21 protein are increased in FSHD myoblasts compared to control by a 1 . 66 factor ( Figure 4C ) . These data indicate that depletion of FXR1P and particularly of its long muscle-specific isoforms increases p21 mRNA and correlatively increase the levels of p21 protein both in murine and human myoblasts . To assess the specificity and the direct nature of the effects we observed on p21 mRNA levels by FXR1P loss of function experiments , we first used a gain-of-function approach . For these experiments , we used FXR1P long isoform Isoe since its depletion in FSHD myoblasts recapitulates the effects on p21 mRNA levels of a knockdown of all FXR1P isoforms in C2C12 cells ( cf Figure 4 ) . Interestingly , in contrast to Fxr1 loss-of-function in C2C12 myoblasts , over expression of FXR1P Isoe lead to a 19 , 1% significant decrease in endogenous p21 mRNA levels as compared to transfection with empty vector ( Figure 5A ) . This ascertains the fact that the effects we observe on p21 mRNA levels are directly related to the levels of FXR1P present in the cell . Secondly , we performed rescue experiments using a pTL1 plasmid bearing FXR1 Isoe cDNA in which we generated by site-directed mutagenesis 4 mismatches to avoid recognition of the transgene by siFxr1 ( Figure 5B ) . This strategy enabled to efficiently re-express FXR1P Isoe in Fxr1-knocked down myoblasts ( Figure 5C ) . Rescue of the expression of FXR1P Isoe lead to a significant reduction in p21 mRNA levels as compared to unrescued myoblasts . The rescue with FXR1P Isoe is total since the levels of p21 mRNA in rescued cells are restored to control levels . Of notice , similar results were obtained using another mutant plasmid of pTL1 . Isoe ( data not shown ) , confirming the efficiency of the rescue strategy . These data confirm the specificity of our approach and suggests that p21 mRNA may be a target of FXR1P in C2C12 murine myoblasts and in human myoblasts , either directly by RNA-protein physical interaction , or indirectly by modulating a pathway involved in p21 levels controls . Murine p21 mRNA is 1910 nts long ( GenBank Accession number: GI 161760647 ) , with a very short 5′UTR of less than 100 nts , a 480 nts coding sequence and a 1329 nts long 3′UTR where lie most of the regulatory elements for the stability of this mRNA ( Figure 6A ) . Notably , the ARE located at position 86–103 nts on the 3′UTR is bound by the RNA-binding protein HuR to regulate the stability of the mRNA during muscle differentiation [30] . Given the ability of FXR1P Isoa to bind ARE sequences [19] , [20] , we hypothesized that the ARE present in p21 mRNA could be the binding site of FXR1P . To test the physical interaction between FXR1P and p21 mRNA and determine the portion of the mRNA involved in the interaction , we performed in vitro filter-binding assays [21] using recombinant FXR1P and radiolabeled fragments of p21 mRNA 3′UTR described in Figure 5A . We chose to use FXR1P Isoe , the longest muscle-specific isoform of FXR1P for binding experiments since i ) it was described to have RNA-binding properties [21] , ii ) its depletion in FSHD myoblasts recapitulates the effect on p21 mRNA levels of a complete knockdown of all FXR1P isoforms in C2C12 cells ( cf Figure 4 ) and iii ) Isoe is able to restore p21 mRNA levels to normal in Fxr1-knockdown myoblasts ( cf Figure 5 ) . As controls for interaction , we used the N19 fragment of FMR1 mRNA containing a G-quadruplex RNA structure [31] , known to be specifically bound by FXR1P Isoe , and its truncated version N19Δ35 unable to be bound by FXR1P [21] . As expected , FXR1P was able to recognize the G-quadruplex containing N19 fragment ( Figure 6B ) . Surprisingly , the binding activity of FXR1P towards p21 3′UTR-α fragment ( nts 1–345 ) that contains a well characterized ARE sequence was null , being equal to the binding activity of the negative control N19Δ35 . Also , p21 3′UTR-β fragment ( nts 324–868 ) was not recognized by FXR1P . Interestingly , the most distal portion of p21 3′UTR , termed γ fragment ( nts 851–1321 ) , was specifically bound by FXR1P . These data indicate that FXR1P Isoe does not recognize p21 mRNA via the ARE motif present in the proximal portion of the 3′UTR ( α fragment ) , but most likely via an uncharacterized motif or sequence present in the distal portion of its 3′UTR-γ fragment . Knowing that FXR1P interacted , at least in vitro , with p21 mRNA , we further sought to validate that this interaction occurs in vivo . To test this hypothesis , we isolated immunocomplexes containing FXR1P by performing UV-crosslinking and immunoprecipitation assays ( CLIP , [32] ) . Immunoprecipitation of FXR1P mRNA complexes was carried out using the polyclonal antibody #830 against exon 16 of FXR1P present in all isoforms except the short ones [7] , [8] on C2C12 cell extracts ( Figure 6C ) . Control CLIP was performed using non-immune rabbit IgGs . As expected , using the #3FX monoclonal antibody [7] against the constitutive exon 14 present in all isoforms of FXR1P , all the isoforms of FXR1P were detected in both inputs ( Figure 6C , lane 1 and 2 ) . Medium and long isoforms of FXR1P were selectively enriched in #830 immunoprecipitates ( Figure 6C , Lane 4 ) and concomitantly depleted in #830 post-immunoprecipitation supernatant ( Figure 6C , lane 6 ) . The low amount of FXR1P small isoforms detected in the #830 immunoprecipitates most likely corresponds to the fraction of small isoforms interacting with FXR1P medium and long isoforms , since FXR1P is known to homodimerize [4] . In contrast , FXR1P is not recovered in immunoprecipitates obtained with control rabbit IgGs ( Figure 6C , lane 3 ) and still present in the corresponding post-immunoprecipitation supernatant ( Figure 6C , lane 5 ) , confirming the specificity of the CLIP assay performed with #830 antibodies . RT-PCR analysis of mRNAs extracted from both inputs and immunoprecipitates was then carried out ( Figure 6D ) . The mRNA encoding p21 , β-tubulin and the myogenic factors Myogenin and MyoD are detected in the input fractions ( Figure 6D , lanes 1 and 2 ) . Interestingly , only p21 mRNA was found selectively enriched in #830 immunoprecipitates ( Figure 6D , lane 4 ) as compared to control immunoprecipitates ( Figure 6D , lane 3 ) , while Myogenin , MyoD and β-tubulin mRNAs were undetectable . This confirms the specificity of the approach and suggests that , in the C2C12 myoblastic cell line , endogenous p21 mRNA is present in mRNA complexes containing FXR1P . To elucidate the functional significance of FXR1P interaction with p21 3′UTR-γ fragment , we conducted luciferase assays on C2C12 cells expressing FXR1P normally ( siControl-transfected ) and inactivated for Fxr1 ( siFxr1-transfected ) . The various portions of p21 3′UTR used for binding assays were cloned in the 3′ of Renilla luciferase cDNA in a reporter system ( Figure 7A ) . The influence of the 3′ regulatory elements on Renilla mRNA and protein levels was then assessed , in the presence and in the absence of FXR1P , and compared to control vector without regulatory elements in the 3′UTR ( Figure 7B , 7C ) . In the presence of FXR1P or when FXR1P is knocked-down , no significant difference to control is observed in the Renilla mRNA levels , when its cDNA is fused either to the proximal α or central β fragments of p21 mRNA 3′UTR . However , the distal γfragment bound by FXR1P significantly increases Renilla mRNA levels in the presence of FXR1P ( 1 . 33-fold , Figure 7B ) . Intriguingly , removal of FXR1P by siRNA-mediated knockdown potentiated the mRNA stabilizing effect of the p21 3′UTR-γ fragment ( 1 . 76-fold; Figure 7B ) compared to control . To assess whether variations of Renilla mRNA correlated to protein variations , we performed classical luminescence luciferase assays ( Figure 7C ) . Interestingly , Fxr1-depletion lead to a significant increase in Renilla luciferase activity when its cDNA was either fused to the central β or distal γ fragment of p21 3′UTR ( Figure 7C ) . However , the amplitude of variation was , again , higher when considering the γ fragment in siControl conditions ( 2 . 2-fold ) or Fxr1 knockdown conditions ( 3 . 4-fold ) , compared to control empty vector . These data support the hypothesis that FXR1P normally destabilizes p21 mRNA via binding to a motif present in the distal γ portion of its 3′UTR . To test in vivo the hypothesis of FXR1P involvement in the control of endogenous p21 mRNA stability , we treated siControl- or siFxr1-transfected C2C12 cells with the transcription inhibitor actinomycin D ( ActD ) , and measured the decay rate of p21 mRNA by quantitative RT-PCR . Interestingly , p21 mRNA appears to cycle rapidly in control myoblasts . Linear regression on semi-log values of p21 mRNA decay rate in siControl-transfected cells , provides an estimated half-life of 2 . 57±0 . 14 hrs ( Figure 7D ) , with only 16% mRNA remaining after 8 hrs . Conversely , upon Fxr1-depletion , p21 mRNA decay rate is strongly affected and its half-life is significantly increased , reaching 5 . 98±0 . 42 hrs ( p-val<0 . 05 ) . As a consequence , even after 8 hrs of ActD treatment , 43% of p21 mRNA is still present ( Figure 7D ) . The slowing down of p21 mRNA decay rate following Fxr1-knockdown was further confirmed using 5 , 6-Dichlorobenzimidazole riboside ( DRB ) , an adenosine analogue inhibiting mRNA synthesis ( Figure S2 ) . These data suggest that Fxr1-depletion increases endogenous p21 mRNA stability . The previous data support a negative role for FXR1P in the control of p21 mRNA stability via binding to the 561 nts long p21 3′UTR-γ portion . The next step was to determine the RNA motif responsible for FXR1P recognition . So far , two mRNA motifs have been described to be recognized by FXR1P: the ARE motif of TNFα mRNA [20] and the G-quadruplex present in FMR1 mRNA [21] . Our in vitro data clearly indicate that the ARE present in the 3′UTR of p21 mRNA does not mediate the binding of FXR1P Isoe to p21 mRNA , we therefore looked for the presence of putative G-quadruplex motifs in the γ fragment of p21 3′UTR . For this purpose , we used the QGRS webtool [33] that indicated three putative G-quadruplexes spread along the sequence of the γ fragment ( Figure S3 ) , and notably a high-score central G-quadruplex motif ( nts 931–955 ) . It is worth noticing that this high-score putative G-quadruplex is located within a 51 nts G-rich region ( position 918–955 , 54% of G ) . To confirm the predicted G-quadruplex , we used the property of G-quadruplex forming regions to be detected by comparing reverse transcriptase elongation on RNA templates in the presence of either K+ , Li+ or Na+ [31] . Indeed , stabilization of G-quadruplex structures by K+ , but not by Li+ or Na+ , results in cation-dependent pauses detectable on a sequence gel . The experiments were performed on the full-length 3′UTR and on the γ fragment alone and allowed us to identify two strong ( position 939 and 940 ) and two weak G-quadruplex pauses ( position 955 and 969 ) in the 3′UTR of p21 mRNA ( Figure 8A ) . Both the full-length and the γ fragment exhibited the same pauses , indicating that the γ fragment retains the ability to form the G-quadruplex structure in a comparable manner to the full-length native 3′UTR ( Figure 8A ) . Alignment of sequences corresponding to G-rich regions of p21 distal 3′UTR in mouse and human indicate high evolutionary conservation of this portion of non-coding sequences ( Figure 8B ) and argues in favour of its functional importance . To explore the functional role of the G-quadruplex present in the 3′UTR of p21 mRNA , we constructed γ fragments mutants with partial ( γΔ9 ) or full ( γΔ38 ) deletion of the G-rich region containing the G-quadruplex ( Figure 8C ) that were cloned downstream of Renilla luciferase mRNA . Then , the levels of Renilla mRNA of the resulting constructs were assessed for each mutant in C2C12 cells . As previously shown in Figure 6B , the presence of the γ fragment did increase significantly the levels of Renilla mRNA , but partial or full deletion of the G-quadruplex potentiated the increase in the cognate mRNA levels ( Figure 8D ) , mimicking the effect of Fxr1 knockdown in C2C12 cells ( cf Figure 7B ) . These data argue in favour of a role of the G-quadruplex in mRNA stabilization that is potentiated by deletion of the binding site of FXR1P .
Microarray analysis of our myoblastic model inactivated for Fxr1 enabled to show that FXR1P depletion affects the expression of a wide range of mRNA species that control several cellular pathways . One of the most represented functional categories correspond to ‘skeletal and muscular system development’ and ‘skeletal and muscular disorders’ , in line with the evoked role of FXR1P in myogenesis and its altered pattern of expression in two human myopathies: FSHD [9] and DM1 [12] . Interestingly , the functional category ‘cell cycle’ appears also overrepresented in the affected functions , in particular , terms corresponding to ‘arrested in G0/G1 phase transition’ ( related to the genes p21/Cdkn1a , HGF , IGF , IL6 ) actually reflect what we observed at the physiological level for Fxr1 inactivated myoblasts which remain blocked in the G0 phase , without undergoing further differentiation . Apart from p21 , several mRNAs with altered levels in the absence of FXR1P seem to influence the functional categories affected and appear iteratively in our Ingenuity pathway analysis . These candidates for interaction with FXR1P in the context of myogenesis now deserve further investigation . Notably , Hepatocyte growth factor ( Hgf ) mRNA is significantly upregulated in the absence of FXR1P ( Table S1 , Figure 1E and 1F , Figure S1 ) and is known to play an essential role in the migration and proliferation of myogenic cells [34] . Similarly , the Insulin-like growth factor 1 ( Igf1 ) would be a relevant target of FXR1P in the muscle context , since Igf1 plays a key regulatory role in skeletal muscle development , as well as muscle fiber regeneration and hypertrophy [35] . Finally , Cyclin-dependent kinase 15 ( Cdk15 ) mRNA which , contrary to p21 mRNA , is downregulated in Fxr1-deficient myoblasts ( Table S1 , Figure 1E and 1F , Figure S1 ) would be an interesting candidate for regulation of cell-cycle progression by FXR1P . In this case , FXR1P would stabilize Cdk15 mRNA via recognition of a yet unknown specific motif . Murine and human Cdk15 mRNA are not annotated in the AREsite database [36] and therefore do not seem to bear a canonical AU-rich element sequence in their 3′UTR . However , analysis of the 3672 nts long human Cdk15 mRNA using QGRS G-quadruplex mapping webtool reveals the presence of 8 putative G-quadruplex sequences ( Table S3 ) , with 2 putative G-quadruplex in the 3′UTR that represent binding sites for FXR1P . To ascertain the importance of FXR1P in the regulation of its putative mRNA targets newly identified in this study , it would be worth investigating the presence of ARE sequences , G-quadruplexes RNA structures in their 3′ untranslated region . Adequate regulation of the balance between proliferation and cell cycle arrest of myoblasts is a crucial step during myogenesis . The decision to progress through a new division cycle appears primarily regulated before the G1 to S phase transition , with p21 upregulation playing an important role in this process by blocking the formation of proliferation-inducing Cyclin A/Cdk2-E2F complexes [37] . In this context , p21 gene undergoes extensive regulation , both at the transcriptional and posttranscriptional level . Our data do not support a transcriptional mechanism for the maintenance of elevated p21 mRNA levels in Fxr1-depleted muscle cells . Indeed , in myoblasts , p21 is under the sole transcriptional control of the myogenic transcription factor MyoD that activates its promoter [38] . Our microarray and quantitative RT-PCR analyses reveal that MyoD levels remain normal in Fxr1-deficient myoblasts ( Figure 1E ) . Finally , in luciferase assays , Ren mRNA levels are increased when p21 mRNA G-quadruplex region is fused to its 3′UTR , even though this mRNA does not contain the endogenous promoter of p21/Cdkn1a gene ( Figure 7B , Figure 8D ) . These evidences privilege an FXR1P-mediated posttranscriptional mechanism of regulation of p21 mRNA levels involving the binding of FXR1P . In myoblasts , FXR1P long isoforms Isoe and Isof are most likely not playing a role in translational regulation , since they are detected in the nucleus and faintly in the cytoplasm but do not associate to polyribosomes [7] , [8] , [17] , [39] . On the other hand , we cannot exclude a mechanism involving translational inhibition via binding of small or medium isoforms of FXR1P to p21 mRNA to another motif , which may be located in the central part of p21 mRNA 3′UTR ( β fragment ) that activates translation in the absence of FXR1P ( Figure 7A , 7B ) . This would be consistent with the previously described role of FXR1P small isoform Isoa in translational control [20] . However , our data strongly support the fact that the FXR1P-dependant translational control of p21 mRNA is mainly regulated by FXR1P long isoforms , notably Isoe , via binding to a 3′UTR-located G-quadruplex motif ( Figure 8 ) . To date , the G-quadruplex has been described to be a negative [31] , [40] or positive [41] regulator of translation , and a zip-code for dendritic transport and synaptic localization [42] depending on its location on the mRNA ( e . g . 5′UTR or 3′UTR ) ( for review see [43] ) . We report here an evolutionary conserved G-quadruplex motif as a novel RNA-binding motif present in a G-rich region of the distal portion of p21 mRNA 3′UTR . This motif , distinct from the classical ARE present in the proximal portion of the 3′UTR [30] , appears nevertheless to control the stability of p21 mRNA . Indeed , when fused to the 3′UTR of Renilla luciferase , the G-quadruplex induces an increase in Renilla mRNA levels , ( Figure 7B , Figure 8D ) and this effect is potentiated by deletion of the G-quadruplex ( Figure 8D ) . Collectively , these data argue that the G-quadruplex of p21 mRNA 3′UTR participates in the control of mRNA stability via a mechanism involving FXR1P . A few reports describe the involvement of 3′UTR-located G-rich stretches as downstream sequence elements ( DSE ) promoting polyadenylation and leading to increased stability of mRNA when located downstream the polyadenylation site [44] , [45] . However , in the context of p21 mRNA , the G-quadruplex ( position 918–955 nts ) located upstream of p21 mRNA polyadenylation site ( AAUAAA sequence in position 1309–1314 nts ) could act as an upstream sequence elements ( USE ) promoting polyadenylation , as described for a U-rich sequence in Prothrombin mRNA 3′UTR [46] . An alternate mechanism would involve that FXR1P long isoforms drive degradation of p21 mRNA via recruitment of microRNAs and the RISC complex . RNA interference is well described to occur in the cytoplasm , but it was recently shown that small non-coding RNAs can associate with complementary pre-mRNA target both in the nucleus and in the cytoplasm , by binding to Ago2 [47] . The lattest is a key component of the RNA-Induced Silencing Complex ( RISC ) [47] and a well-known interactor of FXR1P in human cells [20] , Xenopus oocytes [48] , and in Drosophila [49] , [50] . Interestingly , p21 mRNA 3′UTR contains an evolutionarily conserved binding site for miR-22 100 nts upstream of the G-quadruplex motif ( Figure S3 ) . This microRNA was recently shown to regulate p21 mRNA levels [47] and is bound in vivo by Ago2 [51] . In this context , Fxr1-depletion or p21 3′UTR G-quadruplex deletion could prevent recruitment of the RISC complex on p21 mRNA and contribute to increase its stability , ultimately leading to an accumulation of p21 mRNA and of the cognate protein . In myoblasts , FXR1P is not the sole RNA-binding protein playing a key role in the regulation of p21 mRNA . Several reports demonstrate the importance of the proximal ARE of p21 mRNA 3′UTR- present in the α fragment- to control the stability of this mRNA . In myoblasts , the ARE-mediated stabilization of p21 mRNA is mediated by cooperative binding of HuR and hnRNPC1 [30] , [52] , while its decay is controled by KSRP [53] . Members of the hnRNPE family of proteins , PCBP1 and 2 , control the central part of p21 3′UTR -the β fragment- [54] . Finally , another hnRNPE , PCBP4 , binds and stabilizes the γ fragment [55] , while we show in this study that binding of FXR1P to the G-quadruplex motif of p21 3′UTR-γ fragment destabilizes the mRNA . Here , we wish to propose a double system of regulation in which FXR1P and PCBP4 cooperate to regulate the levels of p21 using the distal 3′UTR while HuR , RNPC1 and KSRP use the ARE in the proximal part . These complex regulatory systems enable a fine-tuning of p21 mRNA levels , and our data indicate a prominent role for FXR1P as a modulator of p21 levels . We report that , when FXR1P is depleted in the C2C12 cell line and in FSHD myoblasts , p21 levels increase ( Figure 1 , Figure 4 ) . As a consequence , a subset of myoblasts becomes more permissive to cell cycle arrest , resulting in a reduced yield of myoblasts at each cycle of division ( Figure 2 , Figure 3 ) . We also observed that the Cyclin-dependent kinase 15 ( Cdk15 ) mRNA levels are decreased ( Table S1; Figure 1E and 1F; Figure S1 ) it would be worth investigating whether its decreased levels also have an impact in this premature cell-cycle exit we observe in Fxr1-depleted myoblasts . Our data are in line with other studies in which overexpression of p21 in myoblasts is sufficient to trigger cell cycle exit , even in mitogenic medium [28] , [56] , [57] . In our study , p21 upregulation upon Fxr1-depletion causes cell cycle exit without onset of differentiation . Indeed , the levels of the myogenic factors MyoD and Myogenin remain normal , as assessed by microarray ( Table S1 ) and quantitative RT-PCR ( Figure 1F ) . Moreover , we did not observe spontaneous myoblasts fusion into myotubes in Fxr1-knockdown cultures in normal growth conditions , which would be indicative of premature differentiation ( Davidovic & Bardoni , unpublished data ) . Nevertheless , it would be worth investigating in details the impact of Fxr1-knockdown on the differentiation of C2C12 myoblasts . Indeed , our data predict that premature cell cycle exit of myoblasts in the absence of FXR1P decreases the pool of myoblasts available for differentiation . This would directly contribute to explain the reduced musculature detected in Fxr1-KO mice [13] and in xfxr1-knockdown Xenopus [14] at early stages of embryogenesis and development . The fact that p21 mRNA is an mRNA target for FXR1P Isoe has also crucial implications for the understanding of the pathophysiology of myopathies . Indeed , splicing defects of the FXR1 gene in FSHD myoblasts leads to reduced expression of the long FXR1P Isoe , the one that specifically binds p21 3′UTR . We and others have shown that FSHD myoblasts exhibited higher levels of p21 than controls , under normal growth conditions ( this study and [58] , [59] ) . It is now tempting to speculate that depletion in FXR1P Isoe directly participates to the physiopathology of FSHD , by causing p21-mediated premature arrest of the cell cycle in FSHD myoblasts . Ultimately , this may limit the pool of myoblasts available for regeneration of muscle fibers , inducing progressive muscle wasting in FSHD patients . This hypothesis is supported by a study which demonstrates that p21 is essential for normal myogenic progenitor cell function in regenerating skeletal muscle [60] . A similar scenario may be envisioned in the case of the mouse model of DM1 in which reduced expression of FXR1P Isoe was determined [12] . In conclusion , our study highlights for the first time the direct involvement of an RNA-binding protein , FXR1P , in a new pathway that regulates p21 levels to control myoblasts cell cycle exit . Perturbations of this pathway will have a strong impact in muscle development and implicates a new signal dependant on a 3′-UTR located G-quadruplex-RNA structure . In the future it will be important to explore the implication of FXR1P in pathophysiology of muscle disorders and the pleiotropic functions of FXR1P during myogenesis . Furthermore , our study opens new perspectives on the role of the other Fragile X related proteins in the control of cell cycle . Noteworthy , FMRP is known to recognize G-quadruplex mRNA structures and it would be tempting to speculate that FMRP could control p21-dependant cell cycle exit of neuronal progenitors during neurogenesis .
The C2C12 cell line , a subclone of the C2C4 murine myoblastic cell line [61] , [62] , was cultivated under confluence state in the conditions described by ATCC . C2C12 cells were transfected with siRNA targeting exon 14 or exon 6 of Fxr1 mRNA ( see Table S1 ) and/or constructs using the Lipofectamine 2000 reagent ( Invitrogen ) , according to the manufacturer's protocole . Control experiments were performed using commercially available control random siRNA of matching GC content ( Invitrogen ) . Transfected cells were always analysed 48 hrs post transfection . mRNA decay experiments were performed by adding actinomycin D ( Act D , 5 µg/mL ) or 5 , 6-Dichlorobenzimidazole riboside ( DRB , 50 µM ) to culture medium for 0 to 8 hrs . Human myoblasts derived from muscle biopsies of n = 3 FSHD patients and n = 3 controls of matching age and gender were described in [9] . The procedures to generate myoblasts derived from human muscle biopsies were agreed by the French Health Authorities ( AFSSAPS ) . Myoblasts cultures were established as previously described [9] . Total RNA of C2C12 cells transfected with siFxr1 or siControl siRNAs was extracted using the RNeasy kit ( Qiagen , Hilden , Germany ) . Integrity of RNA was assessed by using an Agilent BioAnalyser 2100 ( Agilent Technologies ) ( RIN above 8 ) . RNA samples were then labeled with Cy3 dye using the low RNA input QuickAmp kit ( Agilent ) as recommended by the supplier . 825 ng of labeled cRNA probe were hybridized on 8×60K high density SurePrint G3 gene expression mouse Agilent microarrays . Two biological replicates were performed for each experimental condition . The experimental data are deposited in the NCBI Gene Expression Omnibus ( GEO ) ( http://www . ncbi . nlm . nih . gov/geo/ ) under the series record number GSE40577 . Normalization of microarray data was performed using the Limma package available from Bioconductor ( http://www . bioconductor . org ) . Inter slide normalization was performed using the quantile methods . Means of ratios from all comparisons were calculated and B test analysis was performed . Differentially expressed genes were selected based on a B-value above 0 . Data from expression microarrays were analyzed for enrichment in biological themes ( Gene Ontology molecular function and canonical pathways ) and build biological networks using Ingenuity Pathway Analysis software ( http://www . ingenuity . com/ ) and Mediante ( http://www . microarray . fr:8080/merge/index ) , an information system providing information about probes and data sets . Total RNA was extracted from myoblasts using the RNeasy kit ( Qiagen , Hilden , Germany ) and a reverse transcription ( RT ) reaction was performed using the Superscript II RT-PCR system ( Invitrogen , Carlsbad , California , USA ) according to the manufacturers' protocol . RT products were subjected to polymerase chain reaction ( PCR ) . All primers were designed using the Primer 3 software ( Table S4 ) . Standard RT-PCR was performed using the Promega PCR Master Kit ( Promega , Madison , Wisconsin , USA ) . Real-time PCR reactions were carried out using the Syber Green I qPCR core Kit ( Eurogentec , Liège , Belgium ) in a LightCycler system ( Roche , USA ) . The comparative threshold cycle ( Ct ) for the amplicons of each sample was determined by the LightCycler software and normalised to the corresponding Ct of TATA Box Binding Protein ( TBP ) mRNA for endogenous p21 mRNA levels , and to the Ct of Firefly luciferase in the case of Renilla luciferase mRNA assessment . Finally , the 2-ΔΔCt method [63] was used to analyse the relative changes in the various studied mRNAs between C2C12 myoblasts transfected with control siRNA ( Invitrogen ) or anti-Fxr1 siRNA ( Invitrogen ) , or between FSHD myoblasts and controls ( n = 3 ) . Data were expressed as means ±SEM . Each assay was performed in triplicate with n = 3–4 independent replicates . Cell extracts were analysed by western blotting as described previously [64] , [65] . Previously described primary antibodies against FXR1P were polyclonal rabbit antibody #830 ( 1∶5 , 000 ) and monoclonal 3FX ( 1∶500 ) , the latter also cross-reacting with FXR2P [7] . Anti-β-actin monoclonal antibody ( Sigma ) and anti-p21 polyclonal rabbit antibodies ( Santa Cruz ) were used respectively at 1∶10 , 000 and 1∶200 . Digital acquisition of chemiluminescent signal was performed using the Las-3000 Imager system ( Fujifilm ) . Quantitation of western-blot was performed using the ImageJ software and normalized to the β-actin signal . Immunofluorescence was performed as described [9] , using anti-FXR1P #830 polyclonal antibodies ( 1∶5 , 000; [8] ) and anti-Ki67 monoclonal antibody ( 1∶100; Millipore ) . Secondary Alexa 594-coupled antibodies ( Invitrogen , Carlsbad , California , USA ) were used at 1∶250 . After counterstaining with DAPI , coverslips were mounted on slides with anti-fading reagent and observed using a Zeiss Axioplan2 epifluorescence microscope equipped with a CoolSNAP HQ CCD cooled camera ( Roper Scientific ) or an Olympus FV10i confocal digital microscope . Micrographs were then analysed with ImageJ software . For viability assessment 48 hrs post transfection with anti-Fxr1 and control siRNAs , both attached cells and culture supernatant were collected and then incubated in the presence of propidium iodide ( PI , 50 µg/mL ) . The incorporation of PI in dead cells was then analysed with a FACScan instrument ( Becton , Dickinson ) . MTT proliferation assay was used to determine the proliferation ability of the cells as recommended by the manufacturer ( Sigma ) . For cell cycle distribution assessment , cells were fixed in 70% ethanol , treated with RNAseA ( 50 µg/mL ) , stained with PI ( 50 µg/mL ) and their DNA content was assessed using FACS analysis . For synchronisation experiments , cells were treated with 500 nM of the cell blocker mimosine for 8 hrs . Release from cell cycle blockade was performed for 16 hrs in growth medium before FACS analysis . Human FXR1P Isoe recombinant protein His-tagged in the C–terminus was produced in bacteria using the pET21a/FXR1 Isoe construct [21] , as described [64] . The control RNA fragments used in this study: N19 ( RNA sequence derived from FMR1 mRNA and containing a G-quadruplex forming structure ) and N19Δ35 ( N19 sequence in which the G-quadruplex is deleted ) were cloned in pTL1 plasmid [31] . The various fragments from p21 cDNA were amplified by RT-PCR of C2C12 cDNAs and cloned in the pGemTEasy system ( Promega ) using the primers described in Table S1 , as advised by the manufacturer . For filter binding assay , N19 or p21 constructs were in vitro transcribed using T7 RNA polymerase ( Promega ) , the RNA products being labeled by cotranscriptional incorporation of [γ−32 P]-ATP . Labeled RNAs were purified on a 1% low-melting agarose gel ( Ambion ) . Labeled RNAs ( 50 , 000 c . p . m . , 4 fmol ) were renatured for 10 min at 40°C in binding buffer ( 50 mM Tris–HCl ( pH 7 . 4 ) , 1 mM MgCl2 , 1 mM EDTA , 150 mM KCl , 1 mM DTT ) . In the presence of 2 U/mL of RNase inhibitors ( RNasin , Invitrogen ) , 0 , 1 mg/mL of Escherichia coli total tRNA and 0 . 01% BSA , radiolabeled RNA were incubated to increasing amounts of FXR1P protein . RNA–protein complexes were allowed to form for 10 min on ice , filtered through MF-membranes ( 0 . 45 HA , Millipore ) and washed with 2 mL binding buffer . Filters were air-dried and Cerenkov counting was used to assess the levels of remaining radioactivity on filters . Data were plotted as percentage of total RNA bound versus the protein concentration and one-site binding curve was drawn using the Prism 4 software . To isolate mRNAs associated with FXR1P in vivo , UV-crosslinking and immunoprecipitations ( CLIP ) were performed with extracts of C2C12 cells using a protocol adapted from [65] and the #830 polyclonal antibody directed against the C-terminus of FXR1P [8] . For each assay , 10 µg of polyclonal antiserum was used to immunoprecipitate 25×106 cells . An equivalent amount of unrelated rabbit IgGs ( Sigma ) were used as negative control . Approximately 1/20th of the homogenate and 1/4th of the immunoprecipitate were loaded on a 11% SDS–PAGE gel . Proteins transferred onto a 0 . 45 µm nitro-cellulose membrane were revealed using the 3FX antibody recognizing both FXR1P and FXR2P [8] . mRNAs were extracted from C2C12 input lysate and immunoprecipitates using Trizol reagent ( Invitrogen ) according to the manufacturer's protocole and subjected to reverse transcription ( RT ) using the SuperscriptScript III RT-PCR system ( Invitrogen ) . RT products were subjected to polymerase chain reaction ( PCR ) , using a PCR Master Kit ( Promega ) and primers detailed in Table S4 specific for p21 , Myogenin , MyoD and β-Tubulin mouse cDNAs . The PCR program consisted in 10 min . of initial denaturation at 95°C followed by 35 cycles −30 s . at 95°C , 30 s . at 58°C , 30 s . at 72°C- and a final elongation step of 10 min at 72°C . PCR products were visualised on a 2% TAE agarose gel and amplicon size was verified using the 1 Kb+ DNA ladder ( Invitrogen ) . Luciferase assays were performed using the pSiCheck2 system ( Promega ) according to the manufacturer's protocole . Briefly , the various fragments from p21-3′UTR cDNA ( α , β and γ ) were excised from the pGemTEasy vectors using the NotI site and inserted downstream of the Renilla luciferase cDNA using the NotI site of the pSiCheck2 vector . C2C12 cells were co-transfected in 96-well plates with the siRNA control or against Fxr1 and pSiCheck2 constructs . Luciferase assays were performed 48 hrs post transfection using the DualGlow Luciferase Kit ( Promega ) according to the manufacturer's protocole . pTL1/FXR1Isoe plasmid was cloned as described in [8] . The mutated version of this plasmid bearing 4 silent mutations in human FXR1 cDNA that impede recognition by siFxr1#1 was produced by site-directed mutagenesis using primers described in Table S4 and the QuickChange kit ( Stratagene ) . To compare numerical data , non-parametric Mann & Whitney test was used for small sample size ( n<30 ) and a Student T-test was used when n>30 . Wilcoxon non-parametric tests were used to assess significance of Renilla luciferase mRNA or activity levels variations between each fragment relative to the empty vector ( arbitrarily set to 1 ) . All statistical analysis and data graphs were performed with the Prism 4 software . Only significant differences are displayed on the graphs .
|
Muscle development is a complex process controlled by the timely expression of genes encoding crucial regulators of the muscle cell precursors called myoblasts . We know from previous studies that inactivation of the Fragile X related 1 ( FXR1 ) gene in various animal models ( mouse , frog , and zebrafish ) causes muscular and cardiac abnormalities . Also , FXR1P is reduced in a human myopathy called Fascio-Scapulo Humeral Dystrophy ( FSHD ) , suggesting its critical role in muscle that findings presented in this study contribute to elucidating . Cell-cycle arrest is a prerequisite to differentiation of myoblasts into mature myotubes , which will form the muscle . One key regulator is the p21/Cdkn1a/Cip1/Waf1 protein , which commands myoblasts to stop proliferating , and this action is particularly important during muscle regeneration . In this study , we have identified FXR1P as a novel regulator of p21 expression . We show that FXR1P absence in mouse myoblasts and FSHD-derived myopathic myoblasts increases abnormally the levels of p21 , causing a premature cell cycle exit of myoblasts . Our study predicts that FXR1P absence leads to a reduced number of myoblasts available for muscle formation and regeneration . This explains the drastic effects of FXR1 inactivation on muscle and brings a better understanding of the molecular/cellular bases of FSHD .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"rna",
"interference",
"rna",
"stability",
"gene",
"function",
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"molecular",
"genetics",
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2013
|
A Novel Role for the RNA–Binding Protein FXR1P in Myoblasts Cell-Cycle Progression by Modulating p21/Cdkn1a/Cip1/Waf1 mRNA Stability
|
Heme is a ligand for the human nuclear receptors ( NR ) REV-ERBα and REV-ERBβ , which are transcriptional repressors that play important roles in circadian rhythm , lipid and glucose metabolism , and diseases such as diabetes , atherosclerosis , inflammation , and cancer . Here we show that transcription repression mediated by heme-bound REV-ERBs is reversed by the addition of nitric oxide ( NO ) , and that the heme and NO effects are mediated by the C-terminal ligand-binding domain ( LBD ) . A 1 . 9 Å crystal structure of the REV-ERBβ LBD , in complex with the oxidized Fe ( III ) form of heme , shows that heme binds in a prototypical NR ligand-binding pocket , where the heme iron is coordinately bound by histidine 568 and cysteine 384 . Under reducing conditions , spectroscopic studies of the heme-REV-ERBβ complex reveal that the Fe ( II ) form of the LBD transitions between penta-coordinated and hexa-coordinated structural states , neither of which possess the Cys384 bond observed in the oxidized state . In addition , the Fe ( II ) LBD is also able to bind either NO or CO , revealing a total of at least six structural states of the protein . The binding of known co-repressors is shown to be highly dependent upon these various liganded states . REV-ERBs are thus highly dynamic receptors that are responsive not only to heme , but also to redox and gas . Taken together , these findings suggest new mechanisms for the systemic coordination of molecular clocks and metabolism . They also raise the possibility for gas-based therapies for the many disorders associated with REV-ERB biological functions .
The closely related REV-ERBα and REV-ERBβ proteins generally act as transcriptional repressors , either on their own , by recruiting co-repressor proteins [1–3] , or by competing with the Retinoid-related Orphan Receptors ( RORs ) α , β , or γ for the same DNA binding sites [4–6] . Physiologically , the REV-ERB proteins play a number of diverse and important roles ranging from the control of circadian biology to the homeostasis of lipids . REV-ERBα and β directly regulate circadian rhythm , both in the brain , and in peripheral tissues , by targeting the circadian clock genes Bmal1 and clock [6–11] . Regulation of lipid metabolism and stimulation of adipogenesis by the REV-ERBs is mediated in part through repression of the apolipoprotein A1 ( ApoA1 ) and apolipoprotein C3 ( ApoCIII ) gene promoters , which play major roles in cholesterol metabolism [12–14] . REV-ERBs also control inflammatory responses by inducing nuclear factor kappa-light-chain-enhancer of activated B cells ( NFκB ) , interleukin-6 ( IL6 ) , and cyclooxygenase2 ( COX2 ) expression , and by repressing IκBα expression [15 , 16] . In the liver , REV-ERBs also help regulate gluconeogenesis , consistent with an overall role in energy storage and conservation [17] . In Drosophila , the REV-ERB homologue , Ecdysone-induced protein 75 ( E75 ) is best known for its role in developmental timing , acting together with the ROR orthologue Drosophila Hormone Receptor 3 ( DHR3 ) to control ecdysone-induced molting , pupariation , and eclosion [18] . To perform these functions , E75 appears to require heme as a requisite ligand , bound presumably within its ligand-binding domain ( LBD ) ligand pocket . E75 is not stable in the absence of heme , nor does the bound heme dissociate readily , suggesting that heme is an obligate component of E75 . Interestingly , the presence of heme allows E75 to also bind the diatomic gases nitric oxide ( NO ) and carbon monoxide ( CO ) , which function to reverse E75-mediated transcription repression [19] . The REV-ERBs also bind heme but , unlike E75 , the heme can readily dissociate from the REV-ERBs , and this reversible binding regulates REV-ERB transcription activity [17 , 20] . The REV-ERB LBDs do not appear to share the ability to bind gases or to respond to redox [20] , raising the possibility that E75 and REV-ERBs have evolved two different ways to exploit heme-binding . The structural basis for heme binding in REV-ERB proteins , and heme and gas binding in E75 , is unknown , although mutagenesis and transcription studies have implicated conserved histidine and cysteine residues in heme binding . A crystal structure of the REV-ERBβ LBD in the absence of heme has revealed a classic nuclear receptor ( NR ) fold , but the mechanisms of heme binding could not be deduced from the structure , because the conserved histidine residue points away from the putative ligand-binding pocket and the conserved cysteine residue was not present in the construct that generated the structure . Moreover , the putative pocket is fully occupied by hydrophobic side chains [21] . The extensive structural similarities between the REV-ERB and E75 LBDs coupled with the apparent mechanistic differences prompted us to explore more deeply the basis for both heme binding and the potential for gas regulation . The potential involvement of gases in circadian rhythm has been noted in physiological studies for some time , but the molecular mechanisms only began to emerge with the finding that Neuronal PAS domain protein ( NPAS2 ) , a CLOCK protein analog , is a hemoprotein [22–24] . NPAS2 and CLOCK heterodimerize with Brain and Muscle Arnt-like protein-1 ( BMAL1 ) to activate transcription of various genes , including the molecular clock components Period , Cryptochrome , and Rev-erb [25–29] . The binding of CO to the NPAS2-heme complex , in vitro , inhibits its binding to BMAL1 and DNA binding [23] . Using transcription assays from native and model promoters in cells , we report that the REV-ERBs are also gas-binding components of the molecular clock and that gas and redox state modulate the structure and function of the REV-ERB LBDs . Using crystallography and spectroscopy , we determined the structure of the heme-bound REV-ERBβ LBD from which we are able to propose a model for heme and gas binding .
Overexpression of either of the REV-ERB LBDs in Escherichia coli produces apo forms of the repressors . The heme-bound form is produced only if the culture medium is supplemented with hemin ( Figure S1 ) . While REV-ERBs expressed with and without hemin supplementation appear to be equally abundant and soluble ( Figure S1 ) , they differ strikingly in color , with the heme-bound form intensely red , and the apo form colorless . Incubation of either purified REV-ERBα or β apo LBD with hemin in solution , results in full heme occupancy within seconds or less ( unpublished data ) , while washing the heme-bound LBDs with buffer lacking heme leads to a much slower release of the bound heme ( T1/2 ∼13–16 h; Figure S2 ) . Overall a Kd of approximately 6 μM was observed ( unpublished data ) . Thus , unlike E75 , where heme appears to act as a requisite structural component , in the REV-ERBs it can function potentially as a reversible ligand . Spectroscopic evidence has suggested that the coordination of heme in E75 involves a cysteine residue , and mutagenesis has further suggested that this thiolate bond is contributed by Cys396 ( JR , unpublished data ) [30] , which corresponds to Cys418 in REV-ERBα and Cys384 in REV-ERBβ . Mutagenesis of E75 has also suggested that the second protein-heme coordinate bond is provided by the side chain of His574 ( His602 in REV-ERBα; His568 in REV-ERBβ ) [19 , 30] . Consistent with this finding , the mutation of His602 of REV-ERBα ( His568 in REV-ERBβ ) to phenylalanine essentially eliminates heme binding [17 , 20] . Our mutagenesis of REV-ERBβ confirms that Cys384 and His568 are both key mediators of heme binding ( Figure 1 ) , although the His568 mutant of REV-ERBβ did maintain some heme-binding activity compared with the His602 mutant of REV-ERBα [17 , 20] . In our REV-ERBβ analysis , the levels of bound heme decrease in each of the purified Cys384 and His568 mutant proteins , but neither of the single mutations to alanine , nor the Cys384A/His568A double mutation , completely eliminate heme binding ( Figure 1 ) . These differences may be attributable to the different choices for amino acid substitution ( Ala versus Phe ) , or other differences between the two proteins . Thus , residues in addition to these particular Cys and His residues must also contribute to heme binding . The structures of other heme-containing transcription factors are modulated by redox state [23 , 31–33] . To investigate the effects of redox state on the interaction of REV-ERBs with heme , we subjected the heme-bound proteins to electronic absorption spectral analysis . The heme in coordinately bound proteins absorbs at characteristic wavelengths , producing what are referred to as α , β , and γ ( or Soret ) heme absorption peaks . The existence , positions , and sizes of these peaks provide insight into the oxidation and spin states of the iron center and the number and types of coordinate bonds formed . The oxidized Fe ( III ) REV-ERBβ LBD yielded an absorption spectrum ( Figure 2A ) that was almost identical to those produced by aerobically purified Drosophila E75 [19] and the bacterial thiolate-heme Fe ( III ) -containing transcription factor CooA [31] . All three proteins exhibit characteristic α ( ∼575 nm , shoulder ) , β ( 542 nm ) , and γ ( 419 nm ) absorption peaks as well as a prominent δ-band ( 359 nm ) ; altogether , these are indicative of a hexa-coordinated heme bound to at least one thiolate ( e . g . , Cys ) group [30] . Upon subjecting the REV-ERBβ LBD to the reducing agent sodium hydrosulphite ( dithionite ) , the resultant shifts in absorption peaks ( Figure 2B ) indicate reduction of the iron center from Fe ( III ) to Fe ( II ) , and loss of the thiolate coordinate bond ( diagrammed in Figure 3 ) ; this reduction is seen most readily by the loss of the δ-band ( 359 nm ) . We have also reported elsewhere [34] , using magnetic circular dichroism and resonance Raman spectroscopy , that reduction of the REV-ERBβ iron center appears to yield both a 5-coordinated system , with a single neutral residue coordinate bond ( e . g . , His or Pro ) , as well as a 6-coordinated system with two neutral residue bonds ( Figure 3 ) . Thus , the reduced REV-ERB-heme complex comprises at least two structural states in which the heme-coordinating amino acid side chains change . This form of redox-regulated coordinate bond switching is not unique to REV-ERBs . For example , similar side chain-switching states have been observed in CooA ( Cys75 to His77 ) , a bacterial CO-responsive heme thiolate protein transcription factor [35–37] , and in NPAS2 ( Cys170 to His171 ) , whose axial coordinate bonds are also different in the Fe ( III ) and Fe ( II ) states [23 , 33 , 38] . In these transcription factors , these redox-dependent structural changes also result in functional changes for their host proteins . Redox was also shown to modulate E75 coordinate bonding and function [19] , but it remains to be seen if the redox-dependent structural changes in the REV-ERBs also have analogous functional consequences . As pointed out earlier , many coordinately bound heme proteins , including proteins such as hemoglobin , cytochromes , and the transcription factors CooA and NPAS2 , have the added ability to bind gases [23 , 31 , 37] . This ability is also the case for the REV-ERB insect orthologue E75 [19] . As a first step in assessing the gas-binding potential of REV-ERB proteins , the reduced REV-ERBβ LBD was incubated with CO or NO gas and analyzed for characteristic changes in the electronic absorption spectra . The altered electronic absorption profiles confirm direct binding of both diatomic gases to the heme-bound forms of both REV-ERB LBDs . When incubated with either NO or CO , the reduced ( but not oxidized ) REV-ERBβ LBD exhibits classic shifts in the absorption peaks ( Figure 2C , 2D ) similar to those seen in other gas-bound hemoproteins [31 , 39 , 40] . More detailed studies of the heme in REV-ERB LBDs [34] have shown that the NO and CO gases bind opposite to a neutral side chain in a 6-coordinated state ( Figure 3 ) . Thus , the heme-binding data reported previously [17 , 20] and the spectroscopic data reported here and in Marvin et al . [34] reveal that REV-ERBs comprise modular ligand systems that can adopt a minimum of six different LBD structural states ( Figure 3 ) , each of which has the potential for distinct functional interactions , transcriptional outputs , and biological roles . Among the many genes targeted for repression by the REV-ERB proteins are their own genes and the clock gene Bmal1 . To test the effects of NO or CO on the activities of REV-ERBα and β in vivo we monitored transcription levels of the endogenous Bmal1 and Rev-erb genes in human embryonic kidney ( HEK ) 293T and hepatocellular carcinoma ( HepG2 ) cells in response to gas . We first confirmed that transcription from these genes is regulated by the REV-ERBs by measuring their transcription in the presence and absence of Rev-erb small interfering RNA ( siRNA ) . Figure 4A shows that each of the Rev-erb siRNAs specifically targets its corresponding Rev-erb gene . As expected , the knock-down of either Rev-erb gene results in an increase in Bmal1 expression ( 1 . 5–2-fold; Figure 4A ) , due presumably to derepression of ROR-mediated transcriptional activation . Effects were readily observed within 12 h of siRNA treatment , and peaked at 72 h post-treatment . We then asked whether NO , supplied by the chemical donor diethylenetriamine/NO ( Deta/NO ) , relieves REV-ERB-mediated repression of the endogenous Bmal1 , Rev-erbα , and Rev-erbβ genes . Addition of Deta/NO increased levels of Bmal1 , Rev-erbα , and Rev-erbβ mRNAs by 2–3-fold ( Figure 4B ) . The addition of Li2+ , which has been shown to cause REV-ERB degradation via inhibition of Glycogen Synthase Kinase-3 β ( GSK3β ) kinase-mediated phosphorylation [41] , led to a similar derepression of the endogenous Rev-erb and Bmal1 target genes . Importantly , combining NO and siRNA treatments did not have additive effects , suggesting that NO acts via derepression of the REV-ERBs and not via a parallel pathway ( Fig 4C ) . NO-dependent transcription was also observed in HepG2 cells , with the exception that NO-mediated upregulation of ROR/Rev-erb target gene expression was only ∼2-fold ( Figure S3 ) . This may reflect lower levels of available heme in this cell type , as heme levels vary significantly in different cell types and in other cell states [42 , 43] . Analogous studies conducted in the presence of 500 ppm CO , yielded only modest changes in REV-ERB target gene expression ( unpublished data ) . This minimal response may be due to differences in affinity or effectiveness between NO and CO in cells , or to differences in the effectiveness of the different gas delivery protocols used ( see Methods ) . To provide evidence that NO acts as a direct regulator of heme-bound REV-ERB proteins , the LBDs of either REV-ERBα or β were fused to the DNA-binding domain of yeast GAL4 , and their activities tested using a luciferase reporter regulated by a thymidine kinase promoter containing upstream activating sequences ( UAS ) GAL4 binding sites . As expected , co-transfection of the GAL4-REV-ERBα or GAL4-REV-ERBβ fusion proteins repressed transcription driven by the UAS-containing thymidine kinase promoter ( Figure 4D ) . This repression was reversed by greater than 3 . 5-fold by the addition of either of two NO donors , Deta/NO ( Figure 4D ) or S-nitroso-N-acetyl-l , l-penicillamine ( SNAP ) ( unpublished data ) , suggesting that the REV-ERBs are direct targets of NO . As earlier , similar studies with 750–2 , 000 ppm CO had a more modest effect ( ∼15% of NO effect; unpublished data ) . In summary , both REV-ERB proteins are transcriptional repressors whose activities can be reversed by NO binding . To determine if the heme and gas effects on REV-ERB activity might be attributable to the recruitment of co-repressor proteins , GAL4-REV-ERBα and GAL4-REV-ERBβ fusion proteins were co-transfected with full-length co-repressor expression constructs into 293T cells . As expected , addition of the known REV-ERB co-repressor Nuclear Receptor Co-repressor ( NCOR ) to GAL4-REV-ERB transfection assays increases repression by 2–3-fold . Similar results were obtained by coexpression with another co-repressor , Receptor Interaction Protein 140 ( RIP140 ) , which has not been previously tested for REV-ERB binding ( Figure 5A , 5B ) . This augmented NCOR or RIP140-mediated repression is reversed by the addition of Deta/NO ( Figure 5A , 5B ) . Similar reductions in Gal4-REV-ERB/co-repressor mediated repression were obtained by treating the transfected cells with valproic acid , which is an inhibitor of the histone deacetylases that are recruited by NCOR and RIP140 [44–46] . We conclude that NO signaling reduces REV-ERB repression activity , at least in part , by overcoming the recruitment or activities of these co-repressors . The effects of heme-binding on REV-ERB function are unclear . Previous studies have shown that the availability of heme negatively affects the ability of REV-ERB proteins to bind co-repressor peptides in vitro but is required for the REV-ERB proteins to interact functionally with co-repressors in vivo [17 , 20] . To explain this finding , it has been suggested that co-repressor interactions in vivo must be modulated by interactions or conditions that are not reflected in experiments with purified components . To test if the in vitro interactions could be influenced by gas , we used fluorescence polarization to follow the recruitment of peptides corresponding to the LXXI/HIXXXI/L interaction domain I ( IDI ) of NCOR ( Figure 6 ) and Silencing Mediator for Retinoid and Thyroid hormone receptor ( SMRT ) ( unpublished data ) in the absence and presence of heme and gas . As expected , based on the previous study , both peptides interact specifically with the REV-ERBα and β LBDs ( Figure 6 ) , and the addition of heme acts negatively on co-repressor peptide binding . As might also be expected , only the heme-bound form of the REV-ERB LBDs are responsive to the addition of NO gas ( Figure 6A–6C and unpublished data ) . As with heme binding though , this effect is the opposite of that which occurs in vivo , with NO acting to increase co-repressor peptide recruitment , rather than blocking it . We can only conclude , as did Yin et al . [17] , that interactions or conditions that exist in the cell , are not reflected in the in vitro system . To shed light on the structural basis of heme , gas , and redox regulation , we crystallized the REV-ERBβ LBD in the heme-bound state . The formation of well-ordered crystals required the addition of trypsin to the crystallization solution [47] . Two identical structures were obtained using constructs comprising either the complete LBD ( residues 212–579 ) or the LBD with an internal deletion ( residues 241–579 Δ 275–357 ) . Both 1 . 9 Å resolution structures include α-helices 3–11 , ( residues 381–576; REV-ERBβ381–576 ) , which is slightly larger than the fragment used to derive the unliganded LBD structure [21] . Both of the crystals were obtained under nonreducing conditions . The two REV-ERBβ381–576 Fe ( III ) heme structures verify that the heme-binding pocket is in fact present at the same position as ligand-binding pockets observed in other NR family members ( Figure 7B ) . As predicted by the mutagenesis and spectroscopic analyses for the oxidized state of REV-ERB , a single heme molecule is hexa-coordinated within the pocket by Cys384 and His568 side chains . As mentioned above , Cys384 was not included in the previously published unliganded receptor structure constructs [21] . The structural changes that facilitate heme binding are confined primarily to helices 3 , 7 , and 11 . In the absence of heme , helix 3 breaks at Pro411 allowing its N-terminal portion to move into the unliganded pocket ( Figure 7A ) [21] . A number of aromatic residues from this helix face into the pocket , contributing substantially to the hydrophobic core that stabilizes the unliganded structure . In the presence of heme , helix 3 straightens , swinging the end of its N-terminal half ( Cα atom Gly398 ) 16 . 4 Å away from its position in the unliganded structure ( Figure 7B ) . Although helix 7 shifts in a less dramatic manner , the 3 . 0-Å movement ( Cα atom Leu482 ) further increases the pocket volume . The movement of residues 480–483 , in particular , allows heme and its propionate side chains to assume their observed planar orientation ( Figure 7C ) . In the absence of heme , helix 11 shields the hydrophobic core by bridging helices 10 and 3 . To facilitate heme binding , it also undergoes a major conformational change , swinging its C terminus ( Cα atom Leu576 ) 15 Å away from the ligand-binding pocket , forming a gently curving , uninterrupted α-helix that covers the ligand pocket ( Figure 7A , 7B ) . The unprecedented formation of LBD-ligand coordinate bonds involves some equally novel and elegant structural changes . First , the imidazole side chain of His568 makes a ∼120° rotation around the axis of the helix to allow bonding with the Fe ( III ) heme center ( Figure 7D , 7E ) . Cys384 , the other coordinate bond-forming residue in this Fe ( III ) structure , derives from a flexible loop N-terminal to helix 3 , which does not appear in the apo structure ( Figure 7D , 7E ) . In addition to opening the pocket and correctly positioning the two heme-coordinating residues , the newly positioned helices and loop also help to shield the hydrophobic heme moiety from the solvent , with only 8% ( 66 Å2 ) of the ligand exposed . This value falls well within the expected normal range of 1%–28% for hemoproteins [48] . The majority of residues surrounding heme in the pocket stabilize heme binding via van der Waal interactions . Within 4 Å of the heme moiety are 25 residues ( Table S1 ) derived from five different regions of the REV-ERBβ secondary structure ( H3 , H5 , H7 , and H11 , and loop N-terminal to H3 ) . The majority of these residues form the core of the apo-structure [21] , and must swing out and away to facilitate heme binding ( Figure 8A ) . In other hemoproteins the residues forming hydrophobic heme contacts include Ile , Leu , Val , Phe , Trp , and Tyr [48] . The REV-ERBβ pocket is also enriched with these residues , along with six phenlyalanines ( Table S1 ) . With two exceptions , all of these residues are conserved in REV-ERBα and among all the vertebrate REV-ERBβ orthologues . Although these contacts occur all around the heme ligand , Trp402 , Phe405 , and Phe454 are striking examples of how van der Waal radii of the protein side chains and heme can interlock ( Figure 8A ) . Taken together , these precisely fitted hydrophobic contacts must contribute significantly to the strength and specificity of heme binding . Aside from Cys384 and His568 , the only other polar residues within 4 Å of heme are His381 and Glu571 , and while at this point their role is undetermined , their presence in an otherwise nonpolar environment , and their conservation in other REV-ERBβ homologues , suggests a functional role ( Figure 8A , Table S1 ) . Ligand -binding specificity in many NR ligand-binding pockets often involves hydrogen bonding between polar group ( s ) on the ligand and charged residue ( s ) of the LBD . The most common polar interaction in the NR pocket is with an arginine side chain that precisely orients ligands to ensure specificity [49] . While this Arg is conserved in the REV-ERB LBDs , neither it nor any other Arg residue faces the ligand-binding pocket in the apo or liganded forms [21] . Glu571 , however , is positioned 3 . 8 Å from the negatively charged propionate groups of heme ( Figure 8A ) . This is unusual because the carboxy termini of heme propionate groups usually interact with positively charged residues such as Arg or Lys [48] . It may be possible that the negatively charged propionate side chains are repelled by the acid group of Glu571 in a way that helps to properly center the heme group , or perhaps helps to facilitate exchange . Interestingly , in REV-ERBα and E75 the analogous residue is lysine , which would be predicted to attract the carboxy termini of the heme molecule , as observed in other heme-binding proteins . The other polar residue within close proximity to the heme group is His381 , which is close to the heme coordinating Cys384 residue , and is highly conserved throughout vertebrate REV-ERBβ homologues ( Figure 8A ) . Given the spectroscopic data , which suggest switching of coordinate bonds from a Cys to a neutral residue such as histidine upon heme reduction , His381 is a good candidate for this substituting residue . Indeed coordinate bond switching in other heme proteins tends to involve nearby residues [33 , 35] . Interestingly , within this loop there are three other His residues that may also be capable of coordinate bond formation . All three are also within HXXC motifs ( Figure S4 ) , which serve as metal binding sites in the unstructured loops of olfactory receptors [50] and other hemoproteins . Alternative switching between these Cys/His residues has the potential to ratchet the loop peptide along the plane of the heme molecule , and to reshape the external LBD surface into novel protein interaction sites . Also worth noting is that the residue next to the coordinately bound Cys384 is a proline ( Pro385 ) . This highly conserved Cys-Pro duo fits a consensus for “heme regulatory motifs , ” which also include flanking residues such as His , Leu , Val , Met , Lys , Arg , and Asp [51–53] . This heme regulatory motif in REV-ERBβ includes six of those seven residues ( Figure S4 ) . Such motifs have been shown to be capable of binding heme reversibly with low micromolar affinity . Mutational analyses of the corresponding prolines in other heme thiolate proteins suggest that these residues help to direct the Cys residue toward the heme moiety , as well as to contribute to the reversibility of Cys-heme binding [54–56] . The Drosophila E75 LBD is a notable exception to this reversibility , although this may be explained by the presence of a second heme binding cysteine ( Cys468 ) that is not flanked by a proline and has no counterpart in the REV-ERBs [19 , 30] . A final consideration based on this structure is how the NCOR and SMRT co-repressor peptide-binding site on the LBD surface changes upon the addition of heme . Heme binding appears to affect the previously characterized co-repressor binding site in two ways . First , the hydrophobic groove becomes broader . Second , helix 11 , at the base of the groove , swings away from the binding site . This ligand-dependent movement of H11 from the co-repressor binding site supports the notion that H11 serves as a proxy for the missing H12 , which in other NRs would serve as a platform for co-repressor binding [21] . Both of these heme-induced changes are predicted to impact negatively on co-repressor binding . It is interesting to note that the helices that show the greatest movement upon heme binding are those that border the co-repressor binding groove H3–5 , H10 , and H11 ( Figures 7 and 8 ) [57–60] . A number of specific REV-ERBβ residues are critical for co-repressor binding , and have been identified previously [60] . Examples include residues from H11 , which are in position to form a number of critical co-repressor contacts in the apo form ( L572 , F575 , K576 ) but that are shifted dramatically in position by movement of the helix , making them unlikely to maintain these interactions ( Figure 8B , 8C ) . Likewise in H3 , F409 , which has also been identified as essential [60] , shifts from presumably holding H11 in position for co-repressor interaction to becoming a hydrophobic contact for heme . K414 of H3 also appears to make a critical shift that leads to widening of the hydrophobic peptide-binding groove . At either end of the hydrophobic groove , there are also charged residues ( K421 , R427 , and E570 ) that have been predicted by modeling to play important roles in anchoring the NCOR peptide [21] . Two of these three residues , R427 and E570 , shift dramatically away from the co-repressor binding groove in the heme-bound form ( Figure 8B , 8C ) [21] . Notably , hydrophobic vinyl and methyl groups from the heme moiety also extend to the surface of the groove close to the region where H11 was positioned . While this does not appear to provide interference , it does indicate the possibility for heme to either interact or interfere with co-repressor binding under different conditions . These alterations in the co-repressor binding site are consistent with the effects of heme on peptide binding in vitro . Presumably , disruption of one of the coordinating heme ligands by NO would restore peptide binding by relieving the strain imposed on the LBD by the hexa-coordination of heme . Changes to the structure of the binding site cannot , however , explain why heme and the presence of NO have the opposite effects in vivo . The answer to this apparent paradox will most likely require structural analyses under different conditions , in the presence of other REV-ERB or co-repressor protein domains , or with other known or unknown cofactors .
Over 20 different protein folds can specifically bind b-type heme , which is the most abundant of the hemes and serves as the functional group for essential proteins such as hemoglobin , myoglobin , and cytochrome b5 . Under different evolutionary constraints and pressures , these various heme-binding folds have adopted additional functional properties , which include electron transfer , redox sensing , and the sensing or transport of various gases [48] . The REV-ERBβ381–576/heme structure adds a new and highly dynamic representative to the heme binding-fold family . The molecular volume of heme ( ∼520 Å3 ) is relatively large in comparison to most other NR ligands . Hence , the conformational changes that allow entry and occupancy of the apo LBD pocket are considerable . Such structural plasticity has been observed for an increasing number of NRs ( e . g . , Ecdysone Receptor [ECR] [61] , Liver X Receptor [LXR] [62] , and Estrogen Receptor [ER] [63] ) . This plasticity is an important point , as it indicates the potential for other “orphan” receptors , with seemingly inadequate ligand-binding pockets , and “constitutive” activities , to also be regulated by novel small molecule ligands within their various natural in vivo environments . As with many other heme-containing proteins , which include E75 [19] , both REV-ERB proteins are also able to monitor redox state and to bind gases . E75 and the REV-ERBs are unusual however , in that while discriminating against O2 , they are able to bind both NO and CO gases in vitro . Although the CO gas responses observed in vivo were much weaker than those observed for NO , this may be a consequence of the different methods of gas delivery used , or differences in the cellular functions and biochemistry of the two gases . The different kinetics of gas and heme binding to the REV-ERB LBDs , and the different rates at which these molecules are produced and metabolized within the body , suggest that these ligands may have different physiological roles in different tissues . Gas and redox exchange observed in vitro occurs within seconds , whereas heme exchange requires many hours . In the body , changes in redox and gas levels can be rapid [32 , 64] , whereas heme levels oscillate over hours or days [42 , 43] . It may also be of relevance that heme exchange does not appear to be possible for the fly orthologue E75 , such that the levels of E75 accumulation in the cell are dependent on the abundance of available heme [19] . Thus , while both E75 and REV-ERB proteins may function as heme sensors , REV-ERBs appear to have the added ability to function in the absence of heme . Although we also attempted to capture the structure of REV-ERBβ in reduced Fe ( II ) and gas-bound states , and were able to derive crystals , the latter diffracted poorly due possibly to the predicted multiplicity of Fe ( II ) coordinate bond isoforms ( Figure 3 ) . This heterogeneity would be consistent with our spectroscopic analyses , and those of Marvin et al . [34] , which suggest that the Cys384-heme coordinate bond is replaced in the Fe ( II ) population by one of several alternative neutral donors . It is tempting to speculate that His381 , which is conveniently positioned just N-terminal to Cys384 , may serve as one of these residues . In fact , the ∼133 residue loop between helices 1 and 3 ( Figure S4 ) contains at least 23 residues that could coordinate heme ( nine His residues , seven Met residues , and seven Cys residues ) . This abundance of His , Met , and Cys residues is around three times their general frequency in the human proteome . There are also three more histidine residues ( His395 , His399 , and His475 ) surrounding the ligand-binding pocket that could serve as alternate binding partners . If any of these residues do in fact form alternative coordinate bonds , this would lead to an additional and unprecedented number of LBD conformational and functional variants . In terms of how heme and gases affect REV-ERB LBD functions , our results suggest a major role for both ligands in co-repressor recruitment . The presence of heme leads to significant broadening of the co-repressor-binding groove and a highly unfavorable redistribution of interacting residues , consistent with the dramatic drop in co-repressor peptide binding observed in vitro . Addition of NO to the heme-bound LBD reverses the negative effect of heme on peptide binding , suggesting that NO acts by increasing the affinity for co-repressor binding . As with heme though , the effect of NO gas on REV-ERB activity in cultured cells appears opposite , with , the addition of gas leading to a drop in the ability of REV-ERB proteins to repress transcription . This apparent dichotomy in the effects of heme and gas on co-repressor function in vitro and in vivo cannot yet be explained by current structural findings . Structures for reduced and gas-bound forms of the LBD may help solve this apparent paradox . On the other hand , the answer may involve structures of additional parts of the REV-ERB or co-repressor proteins , or other interacting factors . While NCOR is an established co-repressor for the REV-ERBs [65] , this study is the first to implicate a role for RIP140 as a REV-ERB co-repressor . RIP140 contains ten known NR interaction motifs , which provide the functional capacity to interact with different classes of NR partners [66] . Consistent with a role in REV-ERB modulation , RIP140 has also been implicated in the regulation of lipid and glucose metabolism [67] . Furthermore , like the REV-ERBs , its expression is also induced by retinoic acid [68 , 69] , and it is an important regulator of skeletal muscle metabolism [70 , 71] . Hence , this interaction may have important implications for the study of cancer and metabolic diseases . The evolution of a central circadian clock has allowed higher eukaryotes to anticipate the daily light and dark cycles , and to coordinate these with appropriate changes in behavior and metabolism [72] . As components of the molecular clock , the REV-ERBs and RORs play important roles in this daily cycling by regulating clock gene expression in a ligand-dependent manner . They also play a central role in the regulation of glucose and lipid metabolism within metabolically intensive tissues [17 , 73 , 74] . We propose that the REV-ERBs coordinate these two different functions by monitoring the metabolic indicators/signals: heme , NO , and/or CO and redox . Heme has long been recognized as an important molecule in metabolism . It is required for oxygen and carbon dioxide transport , for cytochrome function in the mitochondria and for the neutralization of reactive oxygen species arising as a consequence of metabolism . It is also a required component of the cytochrome P450s that produce and break down most lipids , including those that serve as the ligands of most NRs [23 , 42 , 43 , 48 , 75–77] . More recently , heme has been shown to oscillate during the circadian cycle , to influence the circadian cycle , and to be a component of the circadian clock proteins Period 2 ( mammalian PER 2 [mPER2] ) , NPAS2 , and now the REV-ERBs [23 , 42 , 43 , 76 , 78] . Given that heme is so central to respiration and other central metabolic processes , and that its abundance appears to oscillate over time , we suggest that heme serves as a fundamental measure of the diurnal metabolic state and as such provides feedback through the REV-ERBs , and other clock proteins , to entrain the molecular clock . Further support for this central role of heme is the reciprocal nature of heme and CO production . Expression of Aminolevulinate synthase 1 ( ALAS1 ) , the rate-limiting enzyme in heme synthesis , is positively regulated by the clock complex mPER2/NPAS2/BMAL1 , making this expression circadian in nature . As heme abundance increases , so does the expression of the heme-regulated enzyme Heme Oxygenase and its product CO . In turn , the presence of CO leads to dissociation of the mPER2/NPAS2/BMAL1 complex and down-regulation of Alas1 transcription . Consequently , heme concentrations fall , and the cycle is reset [8 , 23 , 42 , 79] . As REV-ERBs also bind heme , their expression is heme dependent and they repress Bmal1 [6 , 11 , 17 , 20 , 76] , thereby forming a second reciprocal feed-back loop between heme synthesis and circadian rhythm . Heme is also an essential component of the NO and CO producing enzymes Nitric Oxide Synthetase and Heme Oxygenase . Not surprisingly , both NO and CO production have also been shown to oscillate diurnally . In the suprachiasmatic nucleus of the hypothalamus , where the central molecular clock is located , the activity and products of these enzymes peak during the night [77 , 80 , 81] . Given that the transcriptional activities of NPAS2 [23] , REV-ERBα and β have all now been shown to be gas responsive , these diatomic gases may provide a secondary layer of regulation to the heme-enriched molecular clock . The membrane permeability and short half lives of these gases make them ideal neurotransmitters [82 , 83] for communication between the different nuclear regions of the hypothalamus , where circadian and metabolic homeostasis are regulated . The cycling of redox state offers a third potential mechanism for entrainment of the molecular clock . Redox homeostasis can be affected by the generation of reactive oxidant species ( ROS ) , a large proportion of which arise not surprisingly from mitochondrial respiration . The redox state of a cell , or organelles , is dependent on the ratio of ROS generated by metabolic activity and the abundance of antioxidants , both of which cycle diurnally ( reviewed in [84–86] ) . Aside from the damage that ROS can cause , these molecules have become recognized as important signaling molecules . Interestingly , ROS signaling is commonly associated with stress response [87] , and the hypothalamus controls the body's response to stress [88 , 89] . Considering the redox-sensing abilities of mPER2 [76] , NPAS2 [90] , and the REV-ERBs , it seems likely that both central and peripheral molecular clocks are also entrained by redox signaling . As means of entraining circadian rhythm , redox cycling is not without precedent . It can be traced back to the primordial biological clock of cyanobacteria , where light and the redox state , as a measure of metabolism , synchronize the global transcriptional rhythm of the organism [91] . In summary , our findings indicate a complex reciprocal relationship between metabolism and the molecular clock in which the molecular clock serves to synchronize circadian metabolic activity [14 , 17 , 20 , 23 , 33 , 42 , 57 , 73 , 76 , 79] , and in turn heme , diatomic gases , and redox serve as local and systemic indicators of this activity , thereby helping to entrain clocks within different tissues . Thus , the circadian cycle is not only a means of metabolic regulation , but is in fact a metabolic cycle [85] . As a whole , the combined ligand set of heme , gases , and redox state , combined with the even greater number of induced structural changes in REV-ERB LBDs , provide the potential for many different protein interactions and physiological functions that are in line with the central role that the REV-ERB and E75 proteins serve in coordinating metabolic processes with circadian and developmentally timed events . Taken further , the rapid responses of REV-ERB proteins to gas signaling , and the importance of these gas- and REV-ERB-regulated physiological processes , illustrate the potential for novel , gas-based therapies for the treatment of related diseases such as mood and sleep-based disorders , depression , obesity , diabetes , atherosclerosis , and osteoarthritis .
For bacterial expression of the LBD of REV-ERBα ( GenBank [http://www . ncbi . nlm . nih . gov/Genbank] accession: CAB53540 ) , the construct comprised residues 274–614 with an internal deletion of residues 324–422 . The first REV-ERBβ ( GenBank accession: CAG33715 ) construct for bacterial expression comprised residues 212–579 , and the second included residues 241–579 , with an internal deletion of residues 275–357 . All constructs were subcloned into a modified pET28a vector ( Novagen ) ( GenBank accessions EF442785 and EF456735 ) . GAL4 fusion constructs for REV-ERBα and REV-ERBβ were generated by first cloning the Gal4 DNA-binding domain ( DBD ) ( amino acids 1–132 ) into pBluescript II ( Stratagene ) , containing an SV40 3′UTR . PmeI and NheI restriction sites were introduced as cloning sites for NR LBD introduction . The following primers were used to amplify and clone the Rev-erbα LBD ( aa 215–610; 5′-ATTAGCTAGCATGCTTGCTGAGATGCAGAGTGCC and 3′-ATTAGTTTAAACCTACTAGTCCACCCGGAAGGACAGCAGC ) , Rev-erbβ ( aa 223–577; 5′-ATTAGCTAGCGCCCAGGAACAGCTGCGACCCAAGCC and 3′-ATTAGTTTAAACCTACTAAACTTTAAAGGCCAAGAGCTCC ) into pBS Gal1 NheI-PmeI . The Gal4-NR cDNA was then subcloned into pcDNA 3 . 1 V5/His using HindIII-PmeI restriction sites . Hexahistidine-tagged proteins were expressed in E . Coli ( BL21-Gold[DE3] pLysS; Stratagene ) grown in 1 l of either Terrific Broth or selenomethionine medium [92] in the presence of 50 μg/ml kanamycin , 25 μg/ml chloramphenicol , and in the absence or presence of hemin ( 12 . 5 μM; Sigma . Cells were grown at 37 °C to an OD600 of 1 . 2 , and , following the addition of isopropyl-1-thio-D-galactopyranoside ( final concentration 1 mM ) to induce expression , the cells were incubated overnight with shaking at 25 °C . Following centrifugation , the cell paste was resuspended in 30 ml binding buffer ( 5 mM imidazole , 500 mM NaCl , 0 . 5 mM TCEP , 5% glycerol 50 mM Hepes [pH 7 . 5] ) and sonicated on ice ( 3-s intervals ) for 5 min . Protein was purified from clarified supernatant using Ni-NTA affinity chromatography ( column volume 3 ml ) . Once loaded , the column was washed with 300 ml of buffer containing 30 mM imidazole , 500 mM NaCl , 5% glycerol , 0 . 5 mM TCEP ( tris ( 2-carboxyethyl ) phosphine ) , and 50 mM Hepes ( pH 7 . 5 ) . Protein was eluted from the column using an equivalent buffer containing 250 mM imidazole , and dialysed overnight into a buffer containing 500 mM NaCl , 0 . 5 mM TCEP , and 50 mM Hepes ( pH 7 . 5 ) . Using hemin supplemented protein , in situ proteolysis of REV-ERBβ ( 241–579 Δ 275–357 , 17 mg/ml or 212–579 , 13 . 8 mg/ml ) was crystallized using the hanging drop vapor diffusion method at 22 °C by mixing 2 μl of the protein solution with 2 μl of the reservoir solution containing 1 . 6 M ammonium sulfate , 0 . 1 M Na Hepes ( pH 7 . 6 ) , 4% Jeffamine M-600 , and was performed in the crystallization drop by adding a 1:2 , 000 ratio ( w/w ) of trypsin ( 1 . 5 mg/ml; Sigma ) to the protein [47] . Heme-bound REV-ERBβ ( 17 μM ) was reduced to the Fe ( II ) state using 2 mM dithionite . A CO stock solution was prepared by saturating degassed storage buffer ( 10 mM Tris [pH 8 . 0] at 4 °C , 500 mM NaCl ) with CO ( Praxair ) . Similarly , NO stock solution was prepared using degassed storage buffer supplemented with 200 mM Tris-HCl saturated with NO gas ( Aldrich ) , then adjusted to ( pH 8 . 0 ) . The estimated concentration of CO and NO in the stock solutions was 1 mM and 1 . 9 mM , respectively , based on the mole fraction solubility of the gases in water [93] . The gases were then added to the protein samples at a final concentration of 100 μM , providing an approximate 5-fold molar excess of gas in solution . To test dissociation of heme , heme-bound forms of both REV-ERB LBDs ( 4 mg ) were bound to Ni-NTA agarose beads ( Qiagen ) through hexahistidine tags . Proteins were washed for 12 h at a rate of 1 ml/min using a buffer containing 150 mM NaCl , 10 mM Tris ( pH 8 . 3 ) , 5% glycerol , and 0 . 1% Triton X-100 . Proteins were eluted from Ni beads using the same buffer supplemented with 500 mM imidazole . Electronic absorption spectra were taken of washed and unwashed reference samples at equivalent protein concentration to compare heme content . Data from flash-cooled crystals of the Se-Met REV-ERBβ/heme complex were collected at 0 . 97 Å at 100 K at the APS at Argonne National Laboratory ( SER CAT , beamline 19ID ) , and the data integrated and scaled to 1 . 9 Å resolution by using DENZO/SCALEPACK [94] . The structure was solved by molecular replacement using the apo REV-ERBβ structure ( PDB IDs: 2V7C , 2V0V ) [21] as a search model using PHASER [95] . Structures were initially traced by ARP/WARP [96] and then manually rebuilt in COOT [97] . Final refinement was performed by using REFMAC [98] . Additional crystallographic statistics are given in Table S2 . The drawings were generated with PYMOL [99] . Solvent exposure of heme in the REV-ERBβ structure was calculated using Swiss PDB viewer [100] . Heme volume calculations were made using the Java molecular editor ( Peter Ertl , Novartis ) using the smiles descriptors: Heme , CC1 = C ( C2 = CC3 = C ( C ( = C ( [N]3 ) C = C4C ( = C ( C ( = N4 ) C = C5C ( = C ( C ( = N5 ) C = C1[N]2 ) C = C ) C ) C = C ) C ) C ) CCC ( = O ) O ) CCC ( = O ) O . [Fe+3] ( NCBI Pubchem ) . Modeling of the SMRT co-repressor motif with both REV-ERB structures was done using PYMOL . REV-ERB/SMRT models were generated by structural alignment of the REV-ERB structures ( PDB IDs: 2V0V , [21]; 3CQV ) with the structure of antagonist-bound PPARα in complex with SMRT peptide ( PDB: 1KKQ , [101] ) . The structure of the PPARα LBD and antagonist were then removed , leaving the model of each REV-ERB structure with the SMRT co-repressor motif . HEK 293T and HepG2 cells were grown in Dulbecco's modified Eagle's medium ( Wisent ) supplemented with 10% fetal bovine serum ( Sigma ) , 2 mM L-glutamine , 100 IU/ml penicillin , 0 . 1 mg/ml streptomycin , and nonessential amino acids ( Invitrogen ) . Cells were incubated at 37 °C in 5% CO2 and , upon harvest , washed with PBS ( Wisent ) . Gas treatment of cells was performed in the presence of 25 μM Hemin ( Sigma ) and used the NO donors Deta/NO ( 300 μM , Sigma ) freshly dissolved in 10 mM NaOH , or two doses of SNAP ( 200 μM , Sigma ) , dissolved in 5 mM EDTA and 10 mM PBS , separated by 5 h . CO treatment of cells was done by culturing cells in sealed chambers with 5% CO2 and 0 . 05% ( 500 ppm ) − 0 . 2% ( 2 , 000 ppm ) CO ( Praxair ) at 37 °C . Prior to transfer of cells to chambers , culture medium was spiked with a volume of CO-saturated buffer equivalent to their respective treatment concentration . All experiments were conducted a minimum of three times in triplicate , and the mean +/− standard deviation of a representative experiment is shown . Using antibiotic free medium , 293T cells were seeded at a density of 2 × 105 cells per well in 24-well plates 1 d prior to transfection . Transfection was carried out using Lipofectamine 2000 ( Invitrogen ) as described in the manufacture's instructions . Unless otherwise stated , transfections contained 400 ng of 2X-UAS luciferase reporter , 50 ng pSV40 β-gal , 50 ng of either Rev-erbα-Gal4 or Rev-erbβ-Gal4 and pSP empty vector to a total of 800 ng DNA per well . REV-ERBα-GAL4 and REV-ERBβ-GAL4 were expressed using the modified pcDNA 3 . 1 plasmid described above; NCOR , luciferase , and β-GAL constructs were expressed as previously described [102 , 103]; and the pI RIP140 was a kind gift from M . G . Parker . Compound treatments were applied 6 h post-transfection as described above and cells harvested 24 h later . Luciferase values were normalized to β-GAL activities as described previously and are presented as a ratio of the reporter alone under equivalent conditions [19] . In the case of Deta/NO treatments , luciferase values were also normalized to GAL4 only reporter controls . HEK 293T cells were transfected with siRNA using Lipofectamine 2000 ( Invitrogen ) according to the manufacturer's protocol . For each targeted gene , a pool of four siRNAs ( Dharmacon ) with the following sequences were used . Rev-erbα: GCAUGGACGCAGUGGGCGAUU , GGGCAUGUCUCGAGACGCUUU , CGGCAGGGCAACUCAAAGAUU , GGGCGAACGGUGCAGGAGAUU; Rev-erbβ: GAAGAAUGAUC GAAUAGAUUU , GAACAUGGAGCAAUAUAAUUU , GAGGAGCUCUUGGCCUUUAUU , and UAAAC AACAUGCACUCUGAUU . Experimental treatments were completed between 24 h and 96 h of siRNA transfection . Total RNA was isolated from 1 to 2 . 5 × 106 cells using the RNeasy mini kit ( Qiagen ) according to manufacturer's protocol . 2 μg RNA samples were individually treated with DNase I ( Fermentas ) and then reverse-transcribed to synthesize cDNA using pd ( N ) 6-random hexamer primers ( Promega ) and RevertAid H Minus M-MuLV Reverse Transcriptase ( Fermentas ) . Quantitative PCR was performed in triplicate for each sample with SYBR Green ( Sigma ) using the ABI Prism 7000 sequence detection system ( Applied Biosystems ) . Transcript levels were determined using the comparative Ct method with β-actin as reference . Primer sequences used were as follows: Rev-erbα , forward , ACTTCCCACCATCCCCCACT , reverse , GGAAGAAGGGGAGCCGTCAT [15]; Rev-erbβ , forward , TCTTGTCACAGTGAGGGTTCT , reverse , GCGAGATCACCATTCTTGGGA; Bmal1 , forward , GAAAAGCGGCGTCGGGATAA , reverse , GGACATTGCGTTGCATGTTGG [104]; and β-actin , forward , TGGACTTCGAGCAAGAGATGG , reverse , GGAAGGAAGGCTGGAAGAGTG [105] . Using the purified REV-ERB LDB constructs , peptide interaction was monitored using fluorescence polarization . N-terminally fluorescein-labeled peptides , corresponding to interaction domain I for NCOR ( 110 nM; DPASNLGLEDIIRKALMGSF ) and SMRT ( 110 nM; ASTNMGLEAIIRKALMGKYD ) , were combined with a dilution series of either LBD in a buffer of 100 mM Tris ( pH 8 . 2 ) and 150 mM NaCl . Bacterial expression of the LBDs was done in the presence or absence of supplemental heme , as described above , resulting in either apo- or holo-forms of the receptors . Gas treatment of solutions was done using the NO donor SNAP ( 1 , 200 μM ) . Detection of changes in polarization was measured using a Biotek Synergy 2 plate reader ( λexcitation = 485 nm , λemission = 528 nm ) in 384-well format using black PCR microplates ( Axygen ) . Fluorescence polarization , in millipolarization ( mP ) units , was calculated as mP = ( I − I⊥ ) / ( I + I⊥ ) × 1 , 000 where I = parallel emission intensity measurement and I⊥ = perpendicular emission intensity measurement . Increases in mP units reflect increased binding of the peptide to the REV-ERB LBDs . Measurements in graphs are in triplicate and represent a single time point during a 5-h incubation . Binding parameters were calculated by nonlinear curve fitting using the one site binding ( hyperbola ) formula Y = Bmax × X/ ( Kd + X ) ( GraphPad Prism version 4 . 0 for window , GraphPad Software ) .
|
Much of human biology , such as sleeping , eating , and even the prevalence of heart attacks , occurs in daily cycles . These cycles are orchestrated by a master “clock” located in the brain . The basic components of this clock are proteins that control the expression of important genes . In this study , we analyze one of these regulatory proteins , named REV-ERB , and show that it is regulated by the combination of heme and nitric oxide gas , both of which are important regulators of human physiology . By determining the 3-D structure of the REV-ERB protein , we were able to uncover clues as to how this regulation occurs . REV-ERB belongs to a protein family called nuclear hormone receptors , which are known to be excellent drug targets . Thus , this paper opens the door to possible gas-based therapies for diseases known to involve REV-ERB , such as diabetes , atherosclerosis , inflammation , and cancer .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"biochemistry",
"cell",
"biology",
"diabetes",
"and",
"endocrinology",
"biophysics",
"neuroscience"
] |
2009
|
The Structural Basis of Gas-Responsive Transcription by the Human Nuclear Hormone Receptor REV-ERBβ
|
Patients with mycetoma usually present late with advanced disease , which is attributed to lack of medical and health facilities in endemic areas , poor health education and low socio-economic status . With this background , an integrated patient management model at the village level was designed to address the various problems associated with mycetoma . The model was launched in an endemic village in the Sudan , between 2010 and 2013 . This model is described in a prospective , descriptive , community-based study , aimed to collect epidemiological , ecological , and clinical data and to assess knowledge , attitude and practice ( KAP ) in order to design effective and efficient management measures . In this study , the prevalence of mycetoma was 14 . 5 per 1 , 000 inhabitants . The patients were farmers , housewives and children of low socio-economic status , and no obvious risk group was detected . All had surgery performed in a mobile surgical unit in the village which encouraged patients to present early with small early lesion leading to a good clinical outcome . The close contact with the Acacia tree thorns , animals and animal dung , walking bare footed and practising poor hygiene may all have contributed to the development of mycetoma in the village . Knowledge of mycetoma was poor in 96 . 3% of the study population , 70% had appropriate attitudes and beliefs towards interaction with mycetoma patients and treatment methods , and 49% used satisfactory or good practices in the management of mycetoma . Knowledge and practices on mycetoma were found to be significantly associated with age . Based on the KAP and epidemiological data , several health education sessions were conducted in the village for different target groups . The integrated management approach adopted in this study is unique and appeared successful and seems suitable as an immediate intervention . While for the longer term , establishment of local health facilities with trained health staff remains a priority .
Mycetoma is a neglected tropical medical and health problem . It is a chronic , specific , granulomatous , progressive and disfiguring inflammatory disease caused by true fungi or by certain bacteria and hence it is usually classified into eumycetoma and actinomycetoma respectively [1] , [2] . Madurella mycetomatis is the commonest causative agent for eumycetoma , while Streptomyces somaliensis and Nocardia brasiliensis are the common causative organisms for actinomycetoma [3] , [4] . Mycetoma is reported worldwide but highly endemic in what is known as the “mycetoma belt” and the Sudan seems to be the mycetoma homeland [1] , [2] . The true incidence and prevalence of mycetoma world-wide is not precisely known [5] , [6] , [7] , [8] . It is interesting to note that most of the reported mycetoma data are from hospital-based studies and from patients with advanced disease while there are few field-based observations [9] , [10] , [11] . The triad of a painless subcutaneous mass , sinus formation and purulent or sero-purulent discharge that contains grains is pathognomonic of mycetoma [12] , [13] . It may spread to involve the skin and the deep structures , resulting in destruction , deformity and loss of function; occasionally it can be fatal . The foot and hand are the most frequently affected sites accounting for 82% of cases . In endemic areas other parts of the body may be involved such as the knee , arm , leg , head and neck , thigh and perineum [14] . No age is exempted in mycetoma; however , it occurs more frequently in young male patients in the age range 20–40 years [1] , [5] . Most of the affected patients are farmers and workers of low socio-economic status [3] , [4] , and almost 30% of reported patients are children [15] . Currently , diagnostic tools include various imaging techniques and methods to demonstrate the organism e . g . by molecular techniques such as PCR or by culture; there is no reliable serodiagnosis test . A histo-pathological specimen is useful but definite identification of the pathogen is not always possible [16] , [17] , [18] , [19] , [20] . It is important to note most of these techniques are not available in majority of mycetoma endemic regions . Patients tend to present late with massive lesions . This is attributed to the nature of mycetoma which is usually painless and slowly progressive , the lack of health facilities in endemic areas , the low socio-economic status of the affected patients and their poor health education [1]–[5] . For this reason , the current treatment of mycetoma is suboptimal , characterised by low cure rates and frequent recurrence often leading to amputation [21] . However , clinical experience shows that early and small mycetoma lesions are associated with good outcome and prognosis . Presently , there are no prevention and control measurements for mycetoma [22] , [23] . The Mycetoma Research Centre ( MRC ) was established in 1991 under the auspices of the University of Khartoum , based at Soba University hospital to provide high quality medical care , research , education and teaching in the various aspects of mycetoma and to provide integrated community development activities in endemic areas . Since its establishment , more than 6800 patients were seen and treated at the centre ( www . mycetoma . edu . sd ) . In an attempt aimed at improvement of case detection with early diagnosis and thus better outcome , the MRC has developed a new and innovative integrated management approach that addresses various problems associated with mycetoma at the village level . In this communication , we report on this integrated comprehensive management experience in a village in the endemic area for mycetoma in Sudan .
Data were collected on demographic and ecological characteristics as well as clinical aspects of patients with mycetoma; the KAP data were collected using a closed-ended questionnaire . A pilot study was conducted in the two weeks preceding the survey to validate the data collection forms and questionnaire in a similar village ( Al Firdoos ) , 30 km south of Al Andalous village . The surveyors had been trained in data collection before the study . The study was carried out by the Mycetoma Research Centre , University of Khartoum , in collaboration with the Mycetoma Control Programme of the Ministry of Health of Sudan and the University of Bakht-El-Ruda Medical School at Ed Dueim . The capital of White Nile state situated close to the endemic area . The studied village was divided into three clusters and every house in these clusters was visited by a team of three surveyors . The head of the household was interviewed and the details of household members were recorded . A household was defined “as people living under one roof preparing and eating together the same food” . The village's ecological and geographic characteristics were recorded including the type of soil , trees , plants , houses and animals and water supply . The meteorological information was obtained from the Sudan Meteorological Authority , 2010 . A house to house survey was carried out and all suspected patients were referred to the village health centre to be examined for the presence of mycetoma by a team consisting of two consultant surgeons , two surgical registrars and two senior house officers in surgery . During the study period , four three-day clinics were conducted . The patients' demographic characteristics were recorded and all suspected patients had a fine needle aspiration for cytology and grains culture . All patients underwent wide local surgical excisions under general or spinal anaesthesia in a mobile surgical unit based at the village health centre and all surgical biopsies were histo-pathologically examined . All patients were followed up during the study period by the surgical team and in between visits , by the medical officer from the health centre and a medical assistant . All diagnostic procedures and treatment was provided free of charge and any other illness detected among household members during the village survey were managed on a similar basis . Households were divided proportionately at a ratio of 1∶1 , male to female; the male head of the household or senior female in the same household . The selected member from each household was interviewed by a team of three surveyors . A direct interview technique was used . The survey included 402 households . The knowledge of mycetoma referred to the understanding of the concepts of mycetoma that related to mode of transmission , risk groups , symptoms , diagnosis , treatment and prevention . This section consisted of 22 statements that were scored 1 or 0 for a correct or incorrect answer respectively . Scores were summed for each respondent and levels of knowledge were categorized as poor [1–5] , mild [6–10] , satisfactory [11–15] and good [>15] . The attitudes to mycetoma referred to the degree of positive or negative agreements with statements concerning attitudes and beliefs in the interaction with mycetoma patients as well as appropriate treatment methods . There were 4 statements that were scored 1 or 0 for positive or negative attitude , respectively . The levels of attitude scores were summed for each respondent and grouped into five categories as totally negative [0] , mild [1] , satisfactory [2] , good [3] , totally positive [4] . The section on practice related to mycetoma consisted of 7 statements that refer to food consumption , shoe wearing as well as other habits . The statements were scored 1 or 0 for good or poor practice , respectively . The levels of practice scores were summed for each respondent and grouped into four categories as poor [0–1] , mild [2–3] , satisfactory [4–5] and good [6–7] . During the study period and following analysis of the KAP data , several health education sessions on mycetoma were conducted . The sessions were delivered by well-trained medical students and volunteers . In addition , health education materials were prepared and distributed during the sessions . All patients were followed up during the study period for evidence of recurrence by the surgical team and the health centre staff . Data were managed using the epidata software , incorporating appropriate skips and range checks . Stata statistical software version 12 was used for analysis . Written informed consents were obtained from the local health authority , village leaders and the individual study participants and an ethical clearance was obtained from Soba University Hospital Ethical Committee .
The village is in White Nile State , Sudan; a known mycetoma endemic area it is 250 kilometres south of the capital Khartoum with a population of 2835 inhabitants divided over 405 households . All the residents are from one ethnic group; the Hassania tribe . The villagers were mainly agriculturalists and pastoralists . They have farms just outside the village with irrigation from the White Nile and the main crops included sorghum , wheat , cotton and vegetables . In addition , they rear cattle , goats and sheep . Other animals in the village included chickens , donkeys and dogs . The village has two parts; an older part with poor hygiene , where houses are overcrowded and where often many animals are kept inside the compound or in a fenced area made of Acacia thorny branches that surround the houses . The animals were sheep , cattle , goats , chicken , donkeys , camels and dogs ( Figure 1 ) . As a consequence the ground is covered with a layer of animal dung . In the newer part of the village , the houses were separated from each other , less crowded and have better hygiene; in only some of the houses animals were kept in the same compound . The village has small health centre staffed by one doctor , one laboratory technician , one medical assistant and four nurses . There were different types of houses; while some were made of bricks and mud , others were from tree branches . In most houses , the ground was covered with straw , thorns and animal dung . Different types of trees were found in the village and its immediate surroundings and that included Acacia senegal , Acacia seyal , Acacia nilotica as well as Zizyphus spp . and Balanites aegyptiaca . People used branches of Acacia to demarcate areas for storage of hay or to keep animals ( Figure 2 ) . The village has a loamy soil similar to other mycetoma endemic Sudanese states such as Gezira and Sennar States . Water is provided by a central pump and distributed by donkey cart . The daily temperatures in the dry season range from 19–43°C with relative humidity of 21–38% . There is a short but heavy rainy season between June and September with the monthly rainfall reaching 136 mm; in this rainy season the daily temperature ranges from 34–41°C and humidity 60–70% ( Sudan Meteorological Authority , 2010 , unpublished data ) . In the studied village , malaria , schistosomiasis and mycetoma were the three most common health problems with mycetoma ranking third . The prevalence of mycetoma was 14 . 5/1000 inhabitants . Most patients were from the older part of the village with the prevalence of 8 . 3/1000 which is higher than recorded in the newer part of the village ( 6 . 2/1000 ) , but the difference did not reach statistical significance ( p = 0 . 07 ) . The study included 33 patients with confirmed mycetoma; 16 ( 49% ) were males . The age ranged between 11 and 70 years with a median of 23 years and mean 30 years ±0 . 25 SE ) . The majority [22 , ( 67% ) ] were <30 years , ( Table 1 ) . In this study , school children 11 ( 33% ) were affected most , followed by farmers 8 ( 24% ) . One patient ( 3% ) was unemployed because of the prolonged illness and disability . Housewives constituted 21% of the affected patient population . Most patients were born and lived in the village; those who lived outside the village were living in nearby villages in the same State . ( Table 1 ) The disease duration ranged between 0 . 17 and 50 years with a median of 2 years and mean 7 years ±0 . 3 SE ) . While the disease onset , course and progress were typical , it was remarkable that 12 patients had small lesions ( <5 cm ) of more than 5 years duration . Sixteen patients ( 48% ) had previous surgical excisions and recurrence . Seventeen patients ( 52% ) had family history of mycetoma and only one patient had diabetes as a concomitant medical problem ( Table 1 ) . The foot was most frequently affected seen in 28 ( 85% ) , followed by the hand in 4 ( 12% ) and gluteal region in one ( 3% ) . The lesions ranged between small ( <2 cm , n = 12 ) , moderate ( 5–10 cm , n = 9 ) and massive ( >10 cm , n = 12 ) . Six of the patients had lesions with active sinuses , 12 patients had healed sinuses while 15 patients did not have sinuses . ( Figures 3 , 4 , 5 ) In the village , there were another eight patients with past history of mycetoma and they had been cured by wide surgical excisions ( n = 6 ) and limb amputation ( n = 2 ) . All patients underwent wide local surgical excisions except one patient who had a massive lesion , who was referred to MRC for further management . In three patients , thorns were identified during surgical excisions . All patients had uneventful post-operative recovery . The histopathological examination suggested the diagnosis of eumycetoma due to M . mycetomatis in all of patients . All patients were followed up regularly with a mean duration of 1±0 . 5 year with no evidence of recurrence .
Currently there is no control or preventive programme in mycetoma . In order to achieve disease control , many questions need to be answered and these include identification of the organisms involved , estimation of the incidence rate of clinical and subclinical infection and ecological studies that focus on transmission risk and infection route . These were among the objectives of this long term longitudinal study and will be the subject of further surveys in the near future . Health education is clearly needed and the use of media such as the radio , television , newspapers , journals and brochures are important tools . However , in this study , the media was not a major information source due to the lack of objective and well-planned health educational programmes addressing mycetoma issues . Furthermore , the majority of the population were illiterate and of low socio-economic background and the television , newspapers and journals are a luxury and unaffordable . However , the use of mobile phones is being explored more and more in patient management and health education and perhaps this approach may be valuable for mycetoma as many individuals in the studied village had mobile phones . Many studies showed that mycetoma commonly affects school children [3] , [4] , [15] , therefore there is a critical need for targeting health messages through schools and teachers in order to reach this most susceptible group . This will empower the school children with the basic knowledge and skills which will ultimately protect them from acquiring mycetoma; this was achieved in this study . Medical and health professionals were the least information source in this study , despite the fact that primary health care represents the core of health service in this country; better training and supervision are important . In this study , medical students from the local Bakht el Ruda University have actively participated in conducting the survey and health education programme in the village . This is to be encouraged as it helps to raise the population awareness and provides health education; it obviously constitutes an opportunity to train medical students in the various community development activities and issues . In conclusion , the integrated management approach adopted in this study is unique and appeared successful; the various problems associated with mycetoma were addressed simultaneously: epidemiology , ecology , KAP in the community and immediate patient diagnosis and management , including follow-up . This was achieved in a joint effort by the specialist team and the local community . We propose this management model for immediate implementation in mycetoma endemic areas in the short term , while for the longer term , strengthening of the local health services which includes training of health workers and providing improved infrastructure with adequate diagnostic and treatment facilities should be a priority . The model may be applicable to other neglected tropical diseases .
|
The Mycetoma Research Centre ( MRC ) in Sudan adopted an integrated village management model in an attempt to encourage patients to present early for treatment . The model consisted of a house to house survey to detect mycetoma suspected patients and to refer them for further management , KAP , epidemiological and ecological studies . In this study , 33 new patients were detected , and no definite risk factor was detected . However , contact with thorns , animals and animal dung may have contributed to the development of mycetoma . All patients had medical treatment and wide local surgical excisions in a mobile surgical unit at the village , except one with massive lesion who was referred to the MRC for further management . As the study population's knowledge , attitude and practice to mycetoma were poor , several health education sessions were conducted . This integrated management approach proved to be practical and successful . The various problems associated with the late presentation of patients and poor treatment outcome were addressed simultaneously at village level . That had encouraged patients to present early with small lesions with good outcome . However , for the longer term management of mycetoma patients , establishment of local medical and health facilities with qualified health staff remains essential and urgent .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[
"infectious",
"diseases",
"medicine",
"and",
"health",
"sciences",
"mycetoma",
"neglected",
"tropical",
"diseases",
"tropical",
"diseases",
"fungal",
"diseases"
] |
2014
|
A New Model for Management of Mycetoma in the Sudan
|
Chagas disease ( CD ) profoundly affects the social and emotional dimensions of patients’ lives , and disproportionately impacts poor , marginalized populations in Latin America . Biomedical treatment for CD fails to reach up to 99% of the people affected , and in any case seldom addresses the emotional health or socioeconomic conditions of patients . This study examines patient strategies for coping with CD in the department of Santa Cruz , Bolivia . In this ethnographic study , semistructured interviews took place from March-June 2013 with 63 patients who had previously tested positive for CD . During the fieldwork period , participant observation was conducted and patient family members , providers , community members , and public health officials were consulted . Patients often experienced emotional distress when diagnosed with CD , yet were generally unable to find biomedical treatment . Respondents stressed the need to avoid powerful emotions which would worsen the impact of CD symptoms . To manage CD , patients embraced a calm state of mind , described in Spanish as tranquilidad , which partially empowered them to return to a normal existence . In the perceived absence of biomedical treatment options , patients seek their own means of coping with CD diagnosis . Rather than fatalism or resignation , patients’ emphasis on maintaining calm and not worrying about CD represents a pragmatic strategy for restoring a sense of normalcy and control to their lives . Programs focused on treatment of CD should remain mindful of the emotional and social impact of the disease on patients .
Bolivia has the highest prevalence of CD in the world , with at least 6% of the population affected[1] . More than 99% of people with CD in Bolivia and worldwide are undiagnosed and untreated[15–17] . It was previously thought that treatment of adults with chronic CD was ineffective , and that complications from CD were due to an overactive immune response[18] . However , Argentinian studies in the mid-2000s demonstrated lower mortality in adult patients with chronic CD who received antiparasitic treatment[19 , 20] . Shortly thereafter , Médecins sans Frontières/Doctors without Borders began treating adult patients in the department of Cochabamba , and the Pan American Health Organization’s 2010 Chagas strategy asserted that “care of infected adults—should be a guaranteed part of primary care”[21] . Nonetheless , many Bolivian doctors still operate under the assumption that CD should not be treated in adults[22] . In 2006 , newly elected President Evo Morales signed the National Chagas Law , which made elimination of the disease a “national priority”[23] . In 2008 , Bolivia’s National Chagas Program began developing pilot programs to treat adults in Cochabamba , Tarija and Potosí[24] . In 2011 , Santa Cruz’s departmental Chagas program began offering treatment to adults via three clinics and two hospitals in the departmental capital[25] . In an effort to promote accessibility , the program provides free medication and consultations to patients , although patients still have to pay for some laboratory analyses and an examination with a cardiologist . Moreover , this treatment is extremely difficult for patients in rural areas and smaller communities to access . These patients often perceive there is no viable treatment option , forcing them to simply accept the disease as an unalterable aspect of their lives . Meanwhile , they deal with the considerable emotional toll and uncertainty of living with a potentially fatal infection . This study examines how patients in an endemic area of Bolivia cope with the social and emotional impact of CD ,
The study received ethical approval from the University of South Florida Institutional Review Board ( Approval # Pro00010734 ) , as well as an ethical review board at the CMHP . Because the patient population is highly marginalized , has low access to education , and in some cases speaks Spanish as a second language , the IRB granted a waiver of written consent and approved the use of a verbal informed consent script . Recording of interviews only commenced after participants provided verbal informed consent , which was documented by the researcher . Respondents’ names have been changed to protect their anonymity .
Respondents were asked how they felt when they were diagnosed with the disease . Twenty ( 31 . 7% ) replied that they felt frightened , worried or emotionally devastated . “I felt bad , ” said Jacinta , “it affected me a lot to know I had that disease . I didn’t eat , I didn’t sleep . I felt bad , thinking that I was going to die . ” When Betty , a 46-year-old homemaker , found out she had CD , she cried and “became frantic . It was the saddest thing , to have Chagas , because they said it was dangerous , that you died from it . ” Constanza , a 62-year-old supervisor , described becoming upset , “because I have family with Chagas . And of course , when they said I have Chagas , and at any moment I could die , of course I became sad . I thought a lot about the disease . I worried a lot . ” Constanza’s sadness and worry stems from the fear that her life could end unexpectedly . People who receive a diagnosis of Chagas must learn to cope with the knowledge that at any moment their lives could end . This feeling is evident in the words of Daniel from Yapacaní: Daniel , an otherwise healthy 39-year-old father and agricultural worker , was forced to confront the possibility of death following his diagnosis of CD . In Bolivia’s public health system , providers are classified into three levels . Level I facilities provide basic care , often via a nurse or community health worker , and are more readily available in smaller communities . These constitute more than 90% of health facilities in the public system[35] . Level II facilities are similar to clinics or community hospitals in the United States , and are able to provide services such as surgery or internal medicine . Level III facilities are the largest hospitals , treat the full range of health issues , and are generally only located in urban areas . In the study area , only the city of Santa Cruz , 90 km distant from the CMHP , had level III facilities where patients with complications from advanced chronic Chagas disease could receive surgeries or other necessary interventions . Anti-parasitic treatment for CD was not available in the level I clinics in patients’ communities , whereas the level II hospital in Warnes only treated children under 15 . Only 4/63 patients ( 6 . 3% ) had received antitrypanosomal treatment for their CD , all via private or semi-private providers . No patients had received free medication through the Departmental Chagas Program; all but 2 lived in communities where this option was unavailable . None of the respondents were aware that free treatment for adults with CD existed . Patients , providers and public health officials described several barriers to diagnosis and treatment of CD . The main barrier to diagnosis was the lack of confirmatory testing . The ELISA required to confirm diagnosis of CD and initiate treatment was only available in larger hospitals in urban centers ( referred to as Level II and III centers ) . Level I facilities , which provide primary care in smaller communities , did not always have supplies available even to perform initial screening for CD . Obtaining the confirmatory diagnosis required sending a sample to an urban level II or III hospital with ELISA capability and waiting up to several weeks for results . Moreover , patients sometimes experienced confusion about the purpose of laboratory testing . While clinicians used testing simply to detect the presence of T . cruzi antibodies , many patients expected the results to indicate if their CD had progressed to an advanced phase . This sometimes caused patients who already had a positive diagnosis to request unnecessary repeat testing with rapid assays which were unable to provide information about the progression of the disease . The main barriers to treatment were distance , indirect costs such as laboratory analyses , transportation and missed work , and contradictory information from providers about the benefits of antiparasitic treatment for CD . Patients had to travel substantial distances to reach the few hospitals and clinics in the departmental capital which offered treatment for CD[36] , which entailed additional expenses and missed work . As the following quotes illustrate , patients were apprehensive about accessing biomedical care for fear of incurring heavy costs . All three of these patients perceived that , after considerable investments of time and money , they had been unable to make headway in their efforts to seek medical care for CD . They lacked the resources to continue to make efforts to seek care , and were not aware of the existence of free antiparasitic treatment through the departmental Chagas program . The lack of perceived treatment options compounded the emotional impact of diagnosis; patients in this study felt they were living with a deadly disease for which there was no medical recourse . Upon perceiving that treatment for their CD was unavailable or difficult/impossible to access , patients sought alternative means of maintaining their health and continuing with their normal lives . Many made use of ethnomedical treatments in an effort to cure their CD or manage its symptoms[37] . Most patients also conveyed a sense of acceptance and peace with their diagnosis , affirming that it did not worry or upset them . One of the most frequently coded themes in the interview transcripts , with 54 related quotations , was the concept of tranquilidad . Literally , this translates to calm , peacefulness , or contentment . Patients with CD consider tranquilidad a desirable state of consciousness . In part , this is because freeing the mind of worry allows them to continue with their daily lives . As the following quotes illustrate , patients feel keeping emotions calm and maintaining a state of tranquilidad is critical for preventing a worsening of CD symptoms: These examples underscore the need to be calm ( tranquilo ) not only for one’s emotional wellbeing , but to lessen the effects of CD . Even otherwise positive emotions might be destructive if they impact the tranquilidad of a person with CD . “I can’t get excited , ” says Pura . “I avoid anything like that . I can’t go to parties . Sometimes I get mad , I get really happy , and all that does damage . It does damage , so you have to be just normal . ” Ten of the twenty respondents who initially felt upset upon receiving a diagnosis of Chagas describe a shift where they take control of their feelings and become tranquilos . “I felt so bad , thinking that I was going to die , ” says Jacinta , a 61-year-old homemaker . “It affected me a lot . So then , I asked the Lord and the Virgin to take those thoughts away . And I got rid of them , and I forget that I have Chagas . ” According to Doris , a 60 year-old who ran a small restaurant out of her home , “When a person gets the disease , a lot of the times it’s psychological . Because when I first found out I wept . ‘How could I get sick with [Chagas] , and I’m not even 30 ? ’ Then I said , bah , and I lived my life and I forgot about it . ” In Betty’s case , talking to a friend helped her overcome the initial anguish over her diagnosis: “…my friend told me , ‘don’t worry because all of us have Chagas . I have Chagas and look at me . ’ From that I felt a little more tranquila . ” In each instance , the individual overcame worry about CD , while emphasizing that this helped them live their lives normally . In three other cases , respondents describe doctors reassuring them and calming them when giving the diagnosis . If their disease is latent and asymptomatic , doctors often tell them their CD is sleeping . “You have Chagas , but it’s no cause for alarm , ” the doctor told Elisa , 44 . “Don’t worry , it’s asleep , ” the doctor told Yoana , 64 . When Fernanda , a 57 year-old homemaker received her initial diagnosis , she felt “really scared . They told me , ‘No , no , no . It’s asleep . ’ So I calmed down . ” In these examples , practitioners are concerned with ensuring patients feel calm upon receiving their diagnosis . Thirty participants ( 47 . 6% ) affirmed that they felt calm–tranquilo–and did not think or worry about their Chagas disease . Alma likens the state of tranquilidad to not having Chagas at all . In part , the peace she feels is due to her lack of symptoms , but just as importantly , she does not permit herself to worry about the possibility of developing a more severe , symptomatic form of the disease . Alma perceives that such worrying would act as a trigger that aggravates her CD . In a similar example , Porfiria , 54 , indicates her diagnosis of CD “didn’t affect me… I didn’t say , ‘Oh no , it’s going to kill me , ’ or anything . Well , you’ve got Chagas . Tranquila , nothing more . As if it were nothing . ” Importantly , tranquilidad implies acceptance of the possibility of death which a diagnosis of CD entails . Hugo , a 62-year-old teacher , avows he feels , “Tranquilo . Because we have arrived in this world , and I am conscious that we have to depart as well . It’s not forever . Tranquilo . Tranquilo . ” Elsa , a 35-year-old storekeeper , also considers herself “tranquila . Anyway , you’re going to die of something . You just have to wait for it . ” Josefina , 53 , who works cleaning houses , asks , “Why are we going to get upset ? Tranquilo , you have to die from something . ” Five respondents ( 7 . 9% ) mentioned faith in God or the Virgin helps bring about acceptance of the disease and tranquilidad . Constanza “worried a lot” when she first found out she had Chagas , but later came to accept it , something she attributes to “God . Because God comes first , you see ? And that gave me more courage with this disease . ” In all these examples , faith helps individuals come to terms with having the disease , and in particular enables them to put an end to worrying . Two people mentioned they use work as a means of putting their minds at ease . “I felt tranquilo , even though I had Chagas , I didn’t give it a lot of importance , ” says Eduardo , a 54-year-old agricultural worker . “That was the good thing . ” When asked what helped him feel calm , Eduardo responded , “being able to work , do something , clean or straighten up , I feel happy . And well , you get used to it . ” “They told me it was better not to worry about the disease that I had , ” recalls Anita , a 47-year-old vendor of salteñas , a local delicacy similar to an empanada . “I know it can get worse very quickly . I spent my whole life working , and in that I keep things normal . It’s as if I didn’t have the disease . I work and work and work , and I don’t even know if I’m sick or anything . ” For these individuals , being able to continue functioning normally is the key to tranquilidad . At the same time , these patients perceive it is essential not to become too worried or upset , since this might set off the symptoms of Chagas disease . It would be incorrect to state that tranquilidad stems from avoidance of dealing with CD . In an illustrative example , Blanca , 62 , states that while she does not worry about her CD , she does pay attention to her diabetes: “No , I don’t think about [CD] . I think of it as nothing . What I think about is just my sugar , that it doesn’t go up on me . ” Blanca’s CD is asymptomatic , so in part this might reflect a pragmatic choice on her part about where to focus her time and attention . On the other hand , there are specific actions she feels she can take to combat her diabetes , such as watching her glucose and controlling her diet . She is able to do something about her diabetes . With CD , often the only viable action is to avoid stress and worry . Leticia , who is also both diabetic and Chagas-positive , states that “the only thing I do is try to stay tranquila . Yes , because I can’t take any type of medicine . Here in the countryside we use sweetened teas to calm down and I can’t because of my sugar ( diabetes ) . ” Leticia feels she is unable to avail herself of medicinal teas ( usually made from locally available plant sources such as mandarin tree leaves , lemongrass , and the roots of Alpinia speciosa , a flower related to ginger ) because they are typically prepared with sugar . Thus , trying to stay tranquila is the only option left to her . Importantly , her comment expresses the idea that medicinal teas themselves can help a person feel tranquilidad . Fig 1 describes the process patients go through following diagnosis of CD . Substantial barriers impede access to biomedical treatment , and patients opt instead to self-manage their CD . Maintaining a feeling of tranquilidad helps patients obtain freedom from anxiety and regain a sense of normality in their lives . A return to normalcy could also result from successful biomedical treatment , but the path is much more difficult .
Because of the difficulties in confirmatory testing , most patients had only had one test for CD , whereas the World Health Organization recommends both an initial and a confirmatory test . I did not collect reliable data on how many patients were suffering from advanced symptoms of CD . While most were asymptomatic , some had clinically evident CD-related complications , and several suffered from comorbidities including diabetes , age , high blood pressure , and obesity . This study describes a convenience sample of patients whose experiences may not be representative of other patients with CD . Because the author was simultaneously serving as a volunteer at the CMHP , patients may have perceived him as part of the clinic’s staff , which could have influenced some of their responses . The author explained to patients at the beginning of each interview that he was an independent investigator and not a doctor with the clinic , but his positionality may have been unclear to some , especially since most patients were recruited within the clinic . This is essentially a cross-sectional study , so the author was unable to document changes in patients’ coping strategies over time . This is important because the department’s program of free treatment for Chagas disease was in a process of expansion during the study period , so patients may have greater awareness of this program now than they did in 2013 when the investigation took place .
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An estimated 99% of people with Chagas disease do not receive treatment . I interviewed 63 patients with a diagnosis of Chagas disease at a rural clinic in an endemic area of Bolivia . Only 4 had obtained biomedical treatment . Although the departmental government provided free medication for Chagas disease , this was not typically available for adults in patients’ communities , and additional costs such as consultations and cardiologic examinations were prohibitively expensive for this marginalized population . Travel distances and contradictory messages from providers also made it difficult for patients to access treatment . Furthermore , patients believed strong emotions would worsen the symptoms of their Chagas disease , yet often felt anguished or worried when diagnosed . To deal with the emotional impact of the disease and restore normalcy to their lives , patients adopted a strategy of remaining calm and minimizing worries about Chagas disease . This is not fatalism or resignation , but a pragmatic strategy patients utilize to feel more in control of the disease . Treatment programs need to consider the emotional and social implications of Chagas disease in addition to addressing structural barriers which impede treatment .
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2017
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"I Cannot Be Worried": Living with Chagas Disease in Tropical Bolivia
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The human bacterial pathogen Listeria monocytogenes is emerging as a model organism to study RNA-mediated regulation in pathogenic bacteria . A class of non-coding RNAs called CRISPRs ( clustered regularly interspaced short palindromic repeats ) has been described to confer bacterial resistance against invading bacteriophages and conjugative plasmids . CRISPR function relies on the activity of CRISPR associated ( cas ) genes that encode a large family of proteins with nuclease or helicase activities and DNA and RNA binding domains . Here , we characterized a CRISPR element ( RliB ) that is expressed and processed in the L . monocytogenes strain EGD-e , which is completely devoid of cas genes . Structural probing revealed that RliB has an unexpected secondary structure comprising basepair interactions between the repeats and the adjacent spacers in place of canonical hairpins formed by the palindromic repeats . Moreover , in contrast to other CRISPR-Cas systems identified in Listeria , RliB-CRISPR is ubiquitously present among Listeria genomes at the same genomic locus and is never associated with the cas genes . We showed that RliB-CRISPR is a substrate for the endogenously encoded polynucleotide phosphorylase ( PNPase ) enzyme . The spacers of the different Listeria RliB-CRISPRs share many sequences with temperate and virulent phages . Furthermore , we show that a cas-less RliB-CRISPR lowers the acquisition frequency of a plasmid carrying the matching protospacer , provided that trans encoded cas genes of a second CRISPR-Cas system are present in the genome . Importantly , we show that PNPase is required for RliB-CRISPR mediated DNA interference . Altogether , our data reveal a yet undescribed CRISPR system whose both processing and activity depend on PNPase , highlighting a new and unexpected function for PNPase in “CRISPRology” .
Listeria monocytogenes is a gram-positive foodborne pathogenic bacterium that has evolved two distinct lifestyles: a saprophytic one , primarily in decaying vegetation and a parasitic one in the tissues of mammals and birds , causing a disease known as listeriosis . Infection in humans starts by the ingestion of contaminated food products that deliver the bacteria in the intestinal lumen . In the course of the infection of susceptible individuals e . g . elderly and pregnant women , Listeria can cross three barriers of the organism: the intestinal , blood-brain and feto-placental barriers , causing meningitis , encephalitis and abortion . The main and best studied regulator that orchestrates the Listeria infectious process is PrfA ( Positive regulatory factor A ) , a transcription factor that activates expression of the major known virulence genes [1] . In addition to protein determinants contributing to infection , Listeria possesses a virulence gene repertoire that expands to non-coding RNA ( ncRNAs ) molecules [2]–[4] . Bacterial ncRNAs are key regulatory molecules of metabolic , physiological and pathogenic processes and can be generally classified in four groups: a ) the RNA regulatory elements located in the 5′ untranslated regions ( 5′UTRs ) which regulate the expression of the corresponding mRNAs through the binding of various factors , like proteins ( e . g . CsrA ) and small metabolites ( riboswitches ) or by sensing environmental cues like temperature ( thermosensors ) ; b ) the trans-acting small RNAs ( sRNAs ) regulating one or several target mRNAs located elsewhere on the chromosome; c ) the sRNAs that sequester RNA-binding proteins; and d ) the antisense transcripts ( asRNAs ) , which overlap and are complementary to their target genes in the same genomic locus [5] . A novel class of non-coding RNAs , named CRISPRs ( clustered regularly interspaced short palindromic repeats ) has been shown to mediate bacterial adaptive immunity against invading bacteriophages and conjugative plasmids . A CRISPR is defined by the alternating array of identical 20–40 nucleotides ( nt ) long repeat sequences , interspaced by non-repetitive spacer sequences . In the proximity of the locus , are usually found gene clusters called CRISPR-associated ( cas ) genes . Cas genes form 23–45 different gene families ( depending on the classification ) , encoding diverse proteins with nuclease , helicase , integrase , polymerase or nucleotide-binding activities , which are involved in the different steps of CRISPR generation , maintenance , processing and the interference mechanism . Analysis of the various sets of cas genes has revealed that CRISPR-Cas system generally cluster into three basic types ( Type-I , Type-II and Type-III ) which are further divided into at least ten subtypes ( Types IA–F , Types IIA–C and Types IIIA–B ) [6] , [7] . The first clue about CRISPR function was brought about by the discovery that the different spacers were homologous to bacteriophage and plasmid sequences [8]–[10] . It was thus hypothesized that CRISPRs could play a role in immunity against invading genetic elements , which was later experimentally demonstrated in several elegant studies , e . g . in Streptococcus thermophilus [11] , Escherichia coli [12] and Staphylococcus epidermidis [13] . The mechanism of action underlying the whole process is still not entirely understood , but can be roughly divided in three major stages: i ) CRISPR adaptation that occurs when bacteria first encounter the foreign invader after transformation , conjugation or transduction . The CRISPR system recognizes the foreign element and incorporates parts of its DNA into what becomes a new spacer in the CRISPR locus; ii ) CRISPR expression that generates a long poly-spacer precursor RNA , which is then cleaved by Cas proteins producing smaller , mature RNAs ( crRNAs ) . Each crRNA generally contains part of the repeat and a single spacer that serves as a guide for the sequence specific recognition of the foreign invader; iii ) CRISPR interference mediated by mature target-specific crRNAs that , with the help of cas gene products , inactivate the foreign , bacteriophage or plasmid nucleic acid [14] , [15] . In Listeria , 14 plasmids [16] and 11 bacteriophages [17] have been sequenced so far . Bacteriophages infecting Listeria belong to the Siphoviridae and Myoviridae families in the Caudovirales order . They are either temperate integrating in the host genome by site-specific recombination or virulent actively replicating and forming virion particles that subsequently lyse the host cell . Comparative genomic analysis of Listeria bacteriophages revealed that their genomes are highly mosaic , characterized by interspecies homology as well as homology to bacteriophages infecting Bacillus , Enterococcus , Clostridium and Staphylococcus [17] , [18] . Prophages are considered as the major source of diversity within the Listeria genus [18] and can constitute up to 7% of the Listeria coding genes [19] , [20] . Recently , CRISPR-Cas systems have started to be analyzed in Listeria [18] , [21] . We previously described in L . monocytogenes strain EGD-e , a small CRISPR RNA ( RliB ) exhibiting five identical repeats interspaced by non-related spacer sequences of similar size . Strikingly , no cas genes were found either in the proximity of RliB or elsewhere in the L . monocytogenes EGD-e genome [22] . Despite the absence of Cas proteins , RliB is expressed and significantly upregulated in bacteria isolated from the intestinal lumen of gnotobiotic mice , in bacteria grown in the human blood , or bacteria exposed to hypoxia . More importantly , we showed that RliB is involved in L . monocytogenes virulence [4] . Here , we characterized the cas-less RliB-CRISPR by first determining its secondary structure and analyzing its processing . Furthermore , we undertook a search for RliB protein ligands , to address the molecular machinery underlying RliB processing in the absence of Cas proteins . By using two different protein affinity purification approaches , we showed that RliB binds and is a substrate for polynucleotide phosphorylase ( PNPase ) , a bi-functional enzyme harboring a 3′ to 5′ exoribonuclease and 3′ polymerase activities [23] . Furthermore , we performed a global analysis of CRISPR-Cas systems in all sequenced Listeria genomes , revealing a striking ubiquity of the RliB-CRISPRs in L . monocytogenes strains . Surprisingly , RliB-CRISPRs are never associated with cas gene clusters and we could demonstrate that even in Listeria strains harboring a complete set of cas genes , RliB-CRISPRs are processed by PNPase . Finally , we carried out a functional assay for RliB-CRISPR and demonstrated it requires presence of the cas genes of a second CRISPR system to lower the acquisition frequency of a plasmid carrying the matching protospacer . Moreover , we show that PNPase is required for this DNA interference activity . Together , our data highlight a novel type of CRISPR system that relies on the activity of PNPase , highlighting a new role for this enzyme in bacteria .
In L . monocytogenes EGD-e , RliB is located between the genes lmo0509 and lmo0510 that encode a protein similar to phosphoribosyl pyrophosphatase and a hypothetical protein , respectively . Its primary sequence resembles a typical CRISPR . It is composed of 5 identical 29 nt repeat sequences ( GTTTTAGTTACTTATTGTGAAATGTAAAT ) interspaced by four 35–37 nt long spacer sequences ( S1 , S2 , S3 and S4 in the Figure 1A ) . The spacer 3 ( S3 ) has identity with the Listeria temperate bacteriophage B054 sequence and spacer 4 ( S4 ) identity to Listeria virulent bacteriophage P70 sequence [17] , [24] . We analyzed the secondary structure of the full length RliB that is detectable in vivo , using RNase V1 ( specific for helical regions ) , RNase T2 ( specific for unpaired nucleotides with a preference for adenines ) and dimethylsulfate ( which methylates N1 of adenine and N3 of cytosine ) ( Figure S1 ) . The secondary structure of RliB , that explains most of the probing data , involves six stem-loop structures among which five contain a GUUUU motif within the loops , followed by a hairpin terminator at the 3′ end ( Figure 1B ) . In contrast to CRISPR systems where the repeat sequences form independent and stable palindromic structures [25] , the RliB hairpin structures are mostly formed by base pairings between the repeat sequences and the adjacent spacer sequence . These data suggest that RliB structure largely depends on the nature of the incoming spacer DNA . We had previously noticed that RliB in L . monocytogenes EGD-e is processed to a smaller fragment [22] . Here , we examined this processing by Northern blot analysis of total RNA isolated from the wild type ( WT ) L . monocytogenes EGD-e and bacteria expressing RliB from a constitutive promoter ( Phyper-RliB ) . We used probes complementary to the repeat ( R ) , to each unique spacer ( S1 , S2 , S3 and S4 ) and to the 3′ end of the molecule including the terminator region ( T ) ( Figure 1C ) . All the probes allowed detection of a 400 nt fragment , which corresponds to the full length RliB molecule . The probes for S1 , S2 , S3 and R regions detected an additional 280 nt long fragment . The probes for S3 and S4 regions showed a minor 100 nt long fragment and the probe for S3 region a fragment smaller than 50 nt . Altogether , our data show that the RliB-CRISPR has a secondary structure largely determined by the interactions between each repeat and the adjacent spacer and , despite the absence of Cas proteins , it is processed and exists under two major forms: i ) a 400 nt full length RliB molecule and ii ) a shorter form of RliB molecule , approximately 280 nt long , comprising spacers S1 , S2 and S3 . Considering the complete absence of cas genes in L . monocytogenes EGD-e strain , we hypothesized that the RliB-CRISPR processing is governed by another bacterial ribonuclease . To identify which enzymes are involved in this process , we first searched for proteins that interact with RliB using the affinity purification method with a tagged RliB molecule . Given that addition of a tag may perturb the folding of the bait-RNA molecule and/or change its accessibility , resulting in a loss of interaction with its binding partners , we used two strategies using two different tags added either at the 5′ or at the 3′ end of the bait-RNA . The first affinity purification was performed with the 3′-biotinylated full length RliB ( RliB-B ) and a control RNA , the quorum sensing induced RNAIII from Staphylococcus aureus ( Figure 2A ) . In the second approach , we used as a bait RliB tagged at the 5′ end with two hairpin structures constituting the “MS2 binding sequence” ( RliB-MS2 ) , i . e . the RNA binding sequence of bacteriophage MS2 coat protein ( MS2 ) ( Figure 2B ) . Structure probing using enzymes show that the MS2-tag did not change the structure of RliB ( Figure S1 ) . The RliB-B or RliB-MS2 RNAs were bound to streptavidin or MBP-MS2 coated beads , respectively and incubated with total Listeria cell extracts . After extensive washing of unspecific proteins , the bound fraction was eluted and loaded on SDS-polyacrylamide denaturing gel . To verify the integrity of the bait-RNA , we also analyzed the eluted tagged RNA using polyacrylamide-urea gel electrophoresis . For both experiments , we detected a single and major protein band of approximately 78 kDa , specific to RliB-bound elution fractions ( RliB-B and RliB-MS2 ) ( Figures 2A , B ) . The protein was identified by mass spectrometry to be the Listeria polynucleotide phosphorylase ( PNPase ) encoded by gene lmo1331 ( pnpA ) , a bi-functional enzyme that acts as 3′-5′ exoribonuclease and a 3′-terminal polymerase [23] , [26] . We then analyzed whether the interaction between RliB and PNPase is direct or requires another binding partner . The L . monocytogenes PNPase protein carrying 6 histidines at its C-terminal end was purified and binding experiments were carried out using gel retardation assays with in vitro transcribed P32-labeled full length RliB and increasing amount of the purified PNPase . Formation of a complex between RliB and PNPase was observed with 400 nM PNPase , showing that the interaction is direct and does not require another binding partner . To demonstrate the specificity of PNPase binding , competition experiments were done with various non-labelled RNAs . The addition of non-labelled RliB outcompeted the interaction between PNPase and P32-labeled RliB in contrast to the addition of a non-labelled control RNA from S . aureus ( RsaA ) that did not affect the complex formation ( Figure 2C ) . Altogether , our results show that PNPase specifically interacts with RliB . PNPase is a bifunctional enzyme , which in vivo acts primarily as a 3′ to 5′ exoribonuclease of single stranded target RNAs [26] . We investigated if RliB is a substrate of PNPase . We first verified the activity of the purified PNPase protein and performed an in vitro assay where a 37 nt P32-end labeled substrate RNA ( RNA37 ) was incubated alone or with 200 nM purified PNPase ( Figure 3A ) . The presence of PNPase resulted in the degradation of the 5′ end P32-labeled RNA37 while no cleavage reaction was observed using a P32-pCp 3′ end labeled RNA37 ( results not shown ) . These data demonstrate that the purified protein is active and able to degrade single-stranded RNA substrates . Three non-labeled competitor RNAs were then added to the reaction; i ) a non-labeled RliB; ii ) a non-labeled control RNA ( RsaA ) ; iii ) and the non-labeled RNA37 substrate . As expected , the addition of non-labeled RNA37 substrate decreased the cleavage reaction . Strikingly , the addition of 100 nM non-labeled RliB resulted in the loss of PNPase mediated RNA37 degradation , whereas addition of RsaA did not alter the degradation of RNA37 , indicating that RliB acts as a competitive inhibitor of PNPase . To investigate further the activity of PNPase on RliB , we incubated the full length 5′ end-labeled RliB with increasing concentrations of purified PNPase ( Figure 3B ) . We observed on the gel the appearance of a band migrating around 270 nt , an RliB processing product generated by the PNPase-mediated degradation up to the stem-loop IV . This cleavage reaction was inhibited by the addition of the full length non-labeled RliB . Altogether , these data suggest that in vitro , PNPase is processing RliB until its exoribonuclease activity is stalled in the S4 repeat region . To study the effect of PNPase on RliB in vivo , we constructed a pnpA deletion mutant ( ΔpnpA ) and compared by Northern blot the size of the RliB transcript in the ΔpnpA mutant and WT bacteria . In the absence of PNPase , two major bands migrating as 300 and 330 nt long RNAs , were observed . Upon complementation ( ΔpnpA-pnpA ) , the RliB processing was restored , identical to that observed in the WT strain ( Figure 3C ) . Our results thus strongly suggest that RliB is a substrate for PNPase in vivo . CRISPR arrays are thought to evolve rapidly in prokaryotic genomes [14] , [27] , [28] . Therefore , we investigated the presence of RliB in other Listeria strains . For this , we searched for CRISPRs in 29 complete and 17 draft Listeria genomes ( Table S1 ) . As mentioned in the introduction , the highly diverse CRISPR-Cas systems are classified into three main types ( I , II and III ) each including several subtypes [6] . In Listeria , we found two types of CRISPR-Cas systems: i ) CRISPR-Cas systems type-I ( subtype I-B ) with the cas operon composed of cas6-cas8a1-cas7-cas5-cas3-cas1 , including also in some cases cas4 , associated with the repeat sequence GTTTTAGTTACTTATTGTGAAATGTAAAT that is almost identical to the repeat of RliB-CRISPR; ii ) CRISPR-Cas systems type-II ( subtype II-A ) associated with csn2-cas2-cas1-cas9 operon and the repeat sequence GTTTTGTTAGCATTCAAAATAACATAGCTCTAAAAC ( Figure 4A ) . CRISPR-I is present at the locus between lmo0517 and lmo0510 in 7 complete L . monocytogenes genomes , 10 draft L . monocytogenes genomes , in Listeria seeligeri and Listeria ivanovii ( Figure 4B and Table S1 ) and it is always associated with a type-I cas operon located in close proximity . The CRISPR-II was detected between lmo2591 and lmo2596 in 6 complete L . monocytogenes genomes , 9 draft genomes and in Listeria innocua ( Figure 4B , Table S1 ) . The CRISPR-II is also found exclusively associated with type-II cas operon . The tight association of CRISPR-I and CRISPR-II with type-I and type-II cas genes , suggests that the function of those CRISPRs is dependent on the activity of the corresponding Cas protein machinery . In contrast to CRISPR-I and CRISPR-II that are found in about 30% of the complete Listeria genomes , the RliB-CRISPR is present at the same genomic locus in all analyzed complete and draft L . monocytogenes genomes as well as in other Listeria species ( Figure 4B , Table S1 ) . This suggests a stronger selective pressure on this element relative to the cas-associated CRISPRs . In silico structure prediction performed on three representative RliB-CRISPRs carrying different number of repeats revealed a putative secondary structure that is highly similar to that experimentally determined in L . monocytogenes EGD-e strain ( Figure S2 ) . Cas operons have not been detected in the close proximity to the RliB-CRISPRs . Furthermore , 14 complete Listeria genomes completely lack cas genes . The number of repeats in RliB-CRISPRs range from 1 to 11 and does not correlate with the presence or absence of cas genes elsewhere in the genome ( Table S3 ) . Together , the conservation of RliB-CRISPRs among Listeria strains suggests that they may have a function despite the absence of Listeria Cas proteins . Although RliB-CRISPR and CRISPR-I have different pattern of conservation the two systems share almost identical repeat sequences ( Figure 4A ) . To investigate the correlation between the two systems , we compared their putative leader and upstream sequences . Multiple alignments of the DNA fragment preceding the identified RliB-CRISPR and CRISPR-I systems revealed a striking homology ( Figure S3A ) . More interestingly , the putative leader sequences harbour a highly conserved sequence homologous to RpoD dependent promoter , that was previously reported upstream of RliB in the L . monocytogenes EGD-e strain [22] ( Figure S3B ) . High homology of the repeats and the leader sequences of RliB-CRISPR and CRISPR-I systems suggest a possible close relationship between the two systems . To investigate if the PNPase-mediated processing of RliB-CRISPR is specific to L . monocytogenes EGD-e strain or is more general , we examined if PNPase is also involved in RliB-CRISPR processing in Listeria strains containing a complete set of cas genes . We constructed a pnpA deletion mutant in the L . monocytogenes Finland strain ( ΔpnpA-Fin ) , which carries a complete CRISPR-Cas system type I and in the L . monocytogenes EGD strain ( ΔpnpA-EGD ) , which has a complete CRISPR-Cas system type II . We also constructed the deletion mutants for the RliB-CRISPR in the same strains ( ΔrliB-CRISPR-Fin and ΔrliB-CRISPR-EGD , respectively ) . The RliB-CRISPR processing was examined by northern blot in the corresponding strains ( Figure 5 ) . The RliB-CRISPR in EGD strain ( RliB-CRISPR-EGD ) is composed of 11 identical repeats and 10 spacer sequences among which spacers S2 , S6 , S7 and S8 share similarity to Listeria temperate bacteriophages B054 , B025 and A006 ( Figure 5A , 6 ) . In the WT EGD strain , RliB-CRISPR is expressed as a 750 nt long RNA that is processed into shorter fragments with the major processed form being 280 nt long . In the absence of PNPase , the total amount of full-length RliB-CRISPR-EGD increased and the transcript processing changed compared to the WT bacteria , i . e . we observed additional bands with a major one of 700 nt ( Figure 5B ) . The RliB-CRISPR in the Finland strain ( RliB-CRISPR-Fin ) is composed of 12 identical repeats and 11 spacer sequences among which 8 spacers are shared with RliB-CRISPR-EGD . Spacers S1 , S3 , S5 , S7 , S8 and S9 show high similarity to sequences in Listeria bacteriophages P70 , B025 , B054 and A006 ( Figure 5A , 6 ) . The full-length RliB-CRISPR-Fin is expressed in the WT bacteria , as a 780 nt long RNA that it is processed to several shorter fragments with the 280 nt being again the most abundant form . In the absence of PNPase , RliB-CRISPR-Fin processing changed as additional bands are observed compared to the WT bacteria ( Figure 5B ) . Together , our results suggest that PNPase contributes to the RliB-CRISPR processing in vivo , independently of the presence of either CRISPR-Cas system type I in the L . monocytogenes Finland strain , or the presence of CRISPR-Cas system type II in the L . monocytogenes EGD strain . We analyzed CRISPR spacers to compare the putative functions of cas-less RliB-CRISPR and cas-associated CRISPR-I and CRISPR-II . In total , we identified 978 spacers that correspond to 348 unique sequences ( Table S2 ) . These were used to search for the similarity with the sequences of all complete prokaryote , plasmid and virus genomes available in the Genbank as well as the sequences of integrated temperate bacteriophages ( prophages ) identified in complete Listeria genomes ( Figure 6 , Table S3 ) . We identified 142 ( 41% ) spacers that share identity to bacteriophages known to infect Listeria species ( Figure 6 ) . They match sequences detected in 6 temperate ( B054 , B052 , A118 , A500 , A006 , PSA ) , 4 virulent phages ( A115 , P35 , P70 , P100 ) as well as 35 distinct prophages found in complete Listeria genomes ( Figure S4 ) . Overall , we found matching protospacers for 33% RliB-CRISPR spacers , 41% CRISPR-I spacers and 42% CRISPR-II spacers ( Figure 6 , Table S3 ) . RliB-CRISPR and CRISPR-I systems share an identical protospacer adjacent motif ( PAM ) CCA at the 5′ of the protospacer , in contrast to CRISPR-II harboring NGG at the 3′ of the protospacer ( Figure S5 ) . Numerous spacers showed 100% identity with viral sequences ( 14% RliB-CRISPR spacers , 15% CRISPR-I spacers and 24% CRISPR-II spacers ) . None of the spacers matched bacterial ( excluding prophages ) or plasmid sequences . The high abundance of spacers perfectly matching bacteriophages in the RliB-CRISPRs and in the CRISPR-I and CRISPR-II , suggests that both cas-less and cas-associated CRISPR-Cas systems have a role in the immunity against bacteriophages . To investigate the nature of the phage nucleic acid potentially targeted by the RliB-CRISPR , CRISPR-I and CRISPR-II systems , we first examined the orientation of the protospacers in respect to the corresponding spacers and then the function of the phage genes where the protospacers are located . Protospacers targeted by CRISPR-I and CRISPR-II systems originate both from sense and antisense DNA strand and are equally distributed along the phage genome , which suggests that these systems target phage DNA ( Table S4 , S5 , Figure S6 ) . Among 13 protospacers targeted by RliB-CRISPR , 9 protospacers are in the antisense orientation , 3 are positioned in intergenic regions and 2 are in the sense orientation . Moreover , among 11 protospacers for which the function of the targeted gene is known , 10 protospacers are located in the late phage genes encoding DNA packaging and structural proteins ( Table S6 , Figure S6 ) . The occurrence of both sense and antisense oriented protospacers suggests RliB-CRISPR most probably targets DNA . However , higher occurrence of the antisense oriented protospacers and more interestingly , specificity for the function of the targeted bacteriophage gene suggests that RliB-CRISPR could potentially have a function in RNA interference . Furthermore , we identified genomes containing a number of spacers matching their own prophages . For example , L . monocytogenes strain EGD has prophage B025 ( our unpublished data ) and carries one RliB-CRISPR spacer ( S7 ) and three CRISPR-II spacers ( S21 , S22 , S23 ) that match the same prophage with up to 97% identity ( Figure S4A , B ) . We also identified two RliB-CRISPR spacers , three CRISPR-I spacers and two CRISPR-II spacers in 9 Listeria genomes that correspond to prophages in the same genome with 100% identity . In two cases , the strains ( L . monocytogenes 08-5578 and 08-5923 ) lack the cas genes and in one case the repeat flanking the self-targeting spacer carries a point mutation ( L . monocytogenes J0161 ) , suggesting that in three instances the spacers are presumably inactive ( Table S7 , Figure 6 ) . The remaining spacers are either in cas-associated CRISPRs or in the RliB-CRISPR . Furthermore , the PAMS corresponding to self-targeted protospacers do not significantly deviate from the consensus ( Table S7 ) . These results show that spacers matching the protospacer located in the same bacterial chromosome do not necessarily have strong negative fitness effects . To test if the cas-less RliB-CRISPR might provide Listeria with DNA interference activity , we designed an experiment using a conjugation system and two plasmids that differ in the presence or absence of protospacer: i ) a protospacer plasmid ( P ) and ii ) the control plasmid ( C ) , as previously done by Almendros et al . [29] ( Figure 7 ) . The plasmid P carries a protospacer matching spacer 3 ( S3 ) of the RliB-CRISPR in the L . monocytogenes EGD-e strain and spacer 5 ( S5 ) of the RliB-CRISPR in the Finland strain . The plasmid C is identical to the plasmid P , but the protospacer sequence is shuffled in silico ( C ) and does not correspond to any known sequence in the NCBI database . Listeria is not naturally competent and the plasmid transformation efficiency in this bacterium is very low in comparison to other bacteria such as Bacillus or Streptococcus . Moreover , the Δpnp genetic background has a severe effect on bacterial growth , and plasmid transformation is even more difficult than in the WT strain . Therefore , the plasmids P and C were conjugated simultaneously via Escherichia coli S17 strains to L . monocytogenes EGD-e and Finland WT strains and their isogenic mutants deleted for RliB ( ΔrliB ) and PNPase ( ΔpnpA ) . Quantitative PCR ( Q-PCR ) was used to determine the identity of the plasmids distributed among the transformants ( see materials and methods ) . We then calculated for each individual strain the ratio ( R ) of the number of colonies carrying plasmid P and the number of colonies carrying plasmid C ( R = nP/nC ) ( Figure 7B ) . The proportion of the transformants carrying the plasmid P for each experiment is an indication of the interference activity driven by the spacer , a lower proportion of transformants with the plasmid P ( R<1 ) suggesting interference activity . In the L . monocytogenes EGD-e in which no cas genes was identified , there is no significant difference in the R values between the strains carrying the RliB-CRISPR ( WT ) and the strains lacking either the RliB-CRISPR ( ΔrliB-EGDe ) or PNPase ( ΔpnpA-EGDe ) , demonstrating that both plasmids are equally acquired and that the system is not able to provide any detectable DNA interference in the tested experimental conditions ( Figure 7B ) . In contrast , in the L . monocytogenes Finland that bears an additional CRISPR-Cas Type-I system , the R ratio is significantly smaller than 1 in the WT strain whereas it reaches 1 in the strains lacking either the RliB-CRISPR ( ΔRliB-Fin ) or the PNPase ( ΔpnpA-Fin ) . Thus , RliB-CRISPR can lower the plasmid P acquisition in the strain carrying the CRISPR-I system , suggesting that the RliB-CRISPR is able to use the trans encoded Cas proteins encoded by the CRISPR-I and confer to Listeria a DNA interference activity . Interestingly , PNPase is required for this process .
In silico analysis of the secondary structures of CRISPR repeats across bacterial and archaeal CRISPR-Cas systems suggested that some CRISPR repeats can form stable stem-loops due to the palindromic nature of their repeats , but that other lack any detectable conserved structure [25] . RliB repeats are only weakly palindromic and unlikely form a stable stem-loop structure . Here , we experimentally determined the secondary structure of RliB and surprisingly , discovered that RliB contained 6 hairpin structures formed mostly by base-pair interactions between the spacer sequences and the adjacent repeats , and with GUUU-rich apical loops ( Figure 1B ) . The structure of RliB is thus dependent on the nature of the acquired spacer . In silico analysis of other representative RliB-CRISPRs showed their putative structures rely on the same principle , suggesting that base-pair interactions between the repeat and the spacer could be a common structural motif among RliB-CRISPRs ( Figure S2 ) . It is tempting to hypothesize that successful acquisition of a new spacer requires some degree of complementarity with the repeat . Spacer acquisition is the least understood step of CRISPR-Cas system function [31] and our data potentially highlight new aspects of the integration mechanism via homology with the repeat sequence . It is generally accepted that CRISPR arrays require Cas proteins for their processing and activity . A first example of CRISPR-Cas system that does not rely solely on the Cas proteins but requires also the activity of endogenously encoded enzymes has been recently reported in Streptococcus pyogenes . In this case , a CRISPR array type-II is processed by the widely conserved endoribonuclease III and a small trans-acting RNA tracrRNA [32] . In addition , a recent study of Zhang et al [33] revealed a CRISPR in Neisseria meningitidis , where crRNAs are transcribed from promoters that are present within each repeat and require RNase III and trans-encoded tracrRNA-mediated processing for their maturation . Surprisingly , the maturation processing is dispensable for the CRISPR interference [33] . Here , we characterized a CRISPR array that is processed in a bacterium completely devoid of cas genes . In contrast to other CRISPRs , RliB-CRISPR is present in all sequenced strains of L . monocytogenes , even in other members of the genus and never co-localizes with cas operons . We demonstrated that RliB binds to and is a substrate for endogenously encoded PNPase , both in cas-less Listeria strains and in those encoding a complete set of cas genes elsewhere in the genome . Generally , PNPase degrades single-stranded RNA in a processive manner along the substrate until it stops , stalled by a stable RNA structure [23] . For instance , a hairpin structure in a bacteriophage mRNA can block the processivity of PNPase to protect the RNA against degradation [34] . It remains to be understood at the molecular level how PNPase specifically recognizes the RliB-CRISPR and how the progression of the enzyme stops . In the three analyzed L . monocytogenes strains , RliB-CRISPRs is expressed as a full length molecule that is processed to several fragments out of which a 280 nt fragment is the most abundant form . The consistency of processing that is independent of the number of the repeat/spacer units suggests that the molecular mechanisms guiding the processing in the tested CRISPRs is conserved . Interestingly , in the bacteria deleted for pnpA ( Δpnp-EGDe , Δpnp -EGD , Δpnp -Fin ) , some processing still occurs , indicating there are other endogenously encoded ribonucleases contributing to this mechanism , particularly in the EGD-e strain that is devoid of cas genes ( Figures 3C and 5B ) . Listeria encodes at least 17 different putative RNases identified by homology with closely related Bacillus subtilis [35] . Future work will have to determine which enzymes might function together with PNPase and also contribute to the CRISPR processing . We showed that cas-less RliB-CRISPRs are rich in spacers matching virulent and temperate bacteriophages . In addition , a large fraction of those spacers have 100% matches with phages , strongly suggesting a function for RliB-CRISPR even in the absence of cas ( Figure 6 , Table S3 ) . Accordingly , we showed that cas-less RliB-CRISPR lowers the acquisition of a plasmid carrying the corresponding protospacer , provided a CRISPR-I system is present ( Figure 7 ) . The RliB-CRISPR and CRISPR-I share many similar features; almost identical repeat sequence ( Figure 4 ) , homologous putative leader sequences ( Figure S3 ) and identical PAM motifs ( Figure S5 ) , indicating that these two systems are closely related and are possibly functionally linked . It is thus not surprising that RliB-CRISPR can share the Cas machinery with the CRISPR-I to acquire the DNA interference activity , however future analysis will be required to establish the exact mechanism by which this crosstalk occurs . More interestingly , the DNA interference activity of the RliB-CRISPR is also dependent on the presence of PNPase ( Figure 7 ) , indicating that the processing by this enzyme is important for the activity of the RliB-CRISPR . PNPase is a highly complex enzyme with 3′ to 5′ exoribonuclase and RNA polymerase activities being the most studied up to date . However , it was recently shown that PNPase can degrade single stranded DNA ( ssDNA ) and also catalyze template independent polymerization of dNDPs into 3′ends of ssDNA , which established a molecular model for the role of PNPase in DNA repair [36] , [37] . In Escherichia coli , PNPase affects the stability of several regulatory sRNAs [38] , [39] . Here , we hypothesize that Listeria PNPase , potentially with other endogenously encoded enzymes , may contribute to the RliB-CRISPR maturation . Alternatively , PNPase may affect the RliB-CRISPR RNA stability and turnover , and hence , regulate the levels of its mature form . Finally , PNPase dependent processing of the RliB-CRISPR and the DNA interference might be uncoupled activities . Hence , this complex enzyme could use different enzymatic activities to contribute to different processes . It will be also important to determine if PNPase is involved in other CRISPR-Cas system activities , such as new spacer acquisition . Currently , our data do not provide evidence on which form of RliB molecule is active in the DNA interference . These and other mechanistic details , such as the role of PAMs are to be determined in the future . Noticeably , the RliB-CRISPR mediated DNA interference is not 100% effective . This might be the consequence of our experimental design or this CRISPR-Cas system did not evolve to eradicate the bacteriophage from a population but rather to fine-tune its copy number in the bacterial cytoplasm . Our functional assay showed that the RliB-CRISPR in the L . monocytogenes EGD-e strain that completely lacks cas genes , although processed by PNPase , is not able to provide DNA interference activity against a plasmid carrying a matching protospacer . This lack of activity is probably due to the absence of trans encoded CRISPR-I system required for RliB-CRISPR DNA interference activity , as shown in L . monocytogenes Finland strain . However , the conservation of the RliB-CRISPRs in Listeria is independent on the presence of CRISPR-I , strongly suggesting that it is a functional element with an important function even in the absence of Cas Type-I , as sequences lacking selection pressure for their maintenance are quickly lost in bacterial genomes [40] . Interestingly , RliB-CRISPRs in average possess a smaller number of repeats and the variability of their spacers is lower compared to the spacer content of the CRISPR-I and CRISPR-II . Have they evolved a new function ? It is to be kept in mind the remarkable finding that all RliB-CRISPRs accumulate as a 280 nt fragment , which might be the functional form . In support for a functional role of RliB , our recent RNA-seq analysis has shown that RliB-CRISPR is not only conserved but also expressed in the more distant L . innocua species that also lacks CRISPR-I [41] . Although RliB-CRISPRs share many similarities with cas-associated CRISPR-I system , the identified RliB-CRISPR protospacers are more often in the antisense orientation with respect to the corresponding spacer and in addition they are mostly located in the late phage genes encoding DNA packaging and envelope proteins . It is tempting to speculate that “the” RliB-CRISPRs cas-independent activity might be RNA interference . In this scenario RliB-CRISPR would not destroy the bacteriophage DNA but would rather control the bacteriophage late gene expression i . e . , it would prevent the formation of viral particles and lysis of the bacterial cell . RliB-CRISPR interference could be also based on transcription-dependent DNA targeting , as recently described in Sulfolobus islandicus REY15A [42] . Alternatively , RliB-CRISPR might have evolved a broader function relevant for Listeria physiology that is not related to the immunity . Such examples have been described in Pseudomonas aeruginosa , where a CRISPR appear to be involved in lysogeny dependent biofilm formation [43] , in myxobacteria where CRISPR has been implicated in swarming motility [44] and more recently in Francisella novicida where a tracrRNA was shown to regulate an endogenous transcript encoding a lipoprotein important for the bacterial infection [45] . The interaction between bacteriophages and bacteria is mostly seen as a parasitic interaction where the virus exploits the host resources for its own benefit . However , there are some viruses that have a beneficial effect on their host [46] . In case of pathogenic bacteria , bacteriophages often carry virulence factors required for successful infection [47] . More recently , a study by Rabinovich et al . ( 2012 ) showed that during Listeria intracellular infection , a temperate prophage is excised , which reconstitutes a function of the gene where the bacteriophage was integrated , and promotes bacterial escape from macrophage phagosomes . Remarkably , the excision event does not lead to propagation and release of the progeny virions neither to the subsequent lysis of the bacterial cell . Hence , the virion production is actively aborted [48] . This example highlights an important crosstalk between the phage and the pathogenic bacteria during the infection of the mammalian cell , and more importantly , it emphasizes the conditional advantage for a bacterium to maintain a bacteriophage and control its virulence . Our previous studies have shown that RliB expression is upregulated in the bacteria grown in human blood and in the intestine of gnotobiotic mice and is important for Listeria virulence [4] . Whether RliB-CRISPR expression and prophage excision followed by aborted virion production are linked processes , remains to be examined . Our study thus paves the way for new regulatory studies on the interactions between bacteriophages and bacteria during saprophytic life or during infection .
Strains used in this study are L . monocytogenes EGD-e ( BUG1600 ) and its isogenic mutants ΔrliB ( BUG2621 ) and ΔpnpA ( CMA751 ) , L . monocytogenes EGD ( BUG600 ) and its isogenic mutant , ΔpnpA-EGD ( BUG3415 ) and ΔrliB-EGD ( BUG3243 ) , L . monocytogenes Finland 1998 ( BUG3297 , CLIP2012/00396 , FE49845/IHD42536 ) and its isogenic mutants ΔpnpA-Fin ( BUG3465 ) and ΔrliB-Fin ( BUG3466 ) . Mutants were obtained by deletion of the corresponding ORF or non-coding RNA by PCR-ligation and amplicon cloning in the suicide vector pMAD as previously described [49] . Overexpression of RliB was obtained by cloning the rliB gene into the pAD vector carrying Phyper constitute promoter [50] , resulting in the strain Phyper-RliB ( BUG2987 ) . PNPase complementation was obtained by cloning the PnpA ORF into the pPl2 vector [51] resulting in the strain ΔpnpA+pnpA ( CMA752 ) . Bacteria were grown overnight in Brain heart infusion ( BHI ) medium ( Difco ) at 37°C with shaking at 200 rpm . Cultures were subsequently diluted 1/500 into 100 ml BHI and grown at 37°C until mid-exponential phase ( OD600 = 1 . 0 ) . When required , erythromycin and chloramphenicol were used at 5 µg/ml and 20 µg/ml , respectively as final concentration . For RNA extraction , bacteria were pelleted , by centrifugation at 10 , 000 X G for five minutes , flash frozen in liquid nitrogen and stored at −80°C . Bacterial pellets were resuspended in 400 µl solution A ( ½ volume Glucose 20%+½ volume Tris 25 mM pH 7 . 6+EDTA 10 mM ) to which an additional 60 µl of 0 . 5M EDTA was added . Bacteria were lysed in FastPrep homogenizer ( Bio101 ) and RNA was subsequently extracted using TRI reagent ( Invitrogen ) as described previously [4] . RNA integrity was verified using the Experion Automated Electrophoresis system ( Biorad ) . 10–20 µg of total RNA was mixed with two volumes of Formaldehyde Loading Buffer ( Ambion ) followed by denaturation at 65°C for 15 min . Samples were separated by electrophoresis on 5% TBE-Urea polyacrylamide gels ( Criterion-Biorad ) at 100 V for 2 hours in 1× TBE running buffer at RT , followed by an overnight transfer at 4°C/100 mA to Nytran membranes ( Sigma ) . Membranes were UV-crosslinked and probed with RNA probes or DNA oligo probes . Briefly , RNA probes were synthesized and α32P-UTP labelled using the Maxiscript T7RNA polymerase kit ( Ambion ) with PCR generated templates according to the manufacturer's instructions . Oligonucleotide DNA probes were 5′ labelled with γ32P-ATP using the T4 Polynucleotide Kinase according to the manufacturer's protocol ( New England Biolabs ) . Membranes were prehybridized for 60 min in Ultrahyb buffer ( Ambion ) and hybridizations were performed overnight at 64°C for RNA probes and at 37°C for oligonucleotide probes . Following hybridization , membranes were washed twice for 5 min with 2× SSC , 0 . 1% SDS at room temperature . When hybridized with RNA probes , membranes were additionally washed twice for 15 min in 0 . 1× SSC , 0 . 1% SDS at 60°C . The size marker was a 50-bp ladder ( Invitrogen ) , which was 5′ end labelled with γ32P-ATP . We first have optimized an affinity purification assay using 3′-biotinylated RliB and streptavidin sepharose modified as described in Jestin et al . [52] . As a negative control , we used the regulatory RNAIII from S . aureus . Total cell extract prepared from 500 ml of culture of L . monocytogenes ΔrliB mutant strain was first incubated with streptavidin sepharose beads to remove proteins unspecifically bound to the beads . The beads were first incubated with the 3′ biotinylated RNA and the pre-cleaned crude extract was passed through the column and washed with the binding buffer containing 50 mM Hepes-NaOH pH 7 , 5 , 5 mM MgCl2 , 1 mM DTT , and 150 mM KCl . The elution of the proteins was done with the same buffer containing 6 M urea , 2 M thiourea and 30 mM d-biotin . The fractions were then analyzed by 4–15% gradient SDS-PAGE , and the proteins were identified by mass spectrometry . A second approach was used to purify proteins associated with RliB carrying at its 5′ end two hairpin motifs recognized by the coat protein of the MS2 bacteriophage . As a control we used the untagged RliB . Both RNAs were transcribed in vitro using homemade T7 RNA polymerase . The experimental conditions were as previously described [53] . The MS2 coat protein fused to Maltose binding protein was expressed in E . coli and purified on an amylose column followed by a monoQ column . The MS2-MBP coat protein was first immobilized on the amylose resin , and the tagged-RNA was loaded on the column , which was washed with 2 ml of the Binding Buffer . Subsequently , the pre-cleared bacterial lysate was loaded onto the column , followed by three washes with 2 ml Binding Buffer , and the proteins were eluted with the binding buffer containing 10 mM maltose . The fractions were loaded on a SDS-PAGE and the proteins were identified by mass spectrometry . Enzymatic hydrolysis was performed with 1 pmol of RliB in 10 µl of a buffer containing 50 mM NaOH-Hepes pH 7 . 5 , 10 mM MgCl2 , 150 mM KCl , in the presence of 1 µg carrier tRNA at 20°C for 5 min: RNase T2 ( 0 . 01 units ) , RNase V1 ( 0 . 5 units ) . Chemical modifications were performed on 2 pmol of RliB at 20°C in 20 µl of the same buffer containing 2 µg of carrier tRNA . Methylation of C ( N3 ) and A ( N1 ) positions was done with 1 µl DMS ( diluted 1/8 and 1/16 in ethanol ) for 2 min at 20°C . Modification of U ( N3 ) and G ( N1 ) was performed with 2 , 5 µl and 5 µl of CMCT ( 40 mg/ml ) for 20 min at 20°C . The cleavage or modification sites of unlabeled RNAs were detected by primer extension . Details for hybridization conditions , primer extension , and analysis of the data have been previously described [54] . PNPase cleavage assays were done using a 5′ end-labelled RliB or RNA37 . Reaction was performed in 10 µl of TMK buffer containing 20 mM Tris-HCl pH 7 . 5 , 10 mM magnesium-acetate , 100 mM KCl , 1 mM DTT at 37°C for 15 min in the presence of PNPase 200 nM in the presence of 1 µg of carrier tRNA . Competition experiments were carried out in the presence of 200 nM , 400 nM of cold RliB and its derivatives ( RliB-3′ domain or RliB-5′ domain ) . Reactions were stopped by phenol extraction followed by RNA precipitation . The assays were loaded on a denaturing 12% polyacrylamide-urea gel electrophoresis . The PNPase cleavage sites were assigned by running in parallel RNase T1 ladder and an alkaline ladder on a denatured end-labelled RNA [54] . To perform gel retardation assays , 5′ end-labelled transcript ( 20000 cpm , <1 nM ) was incubated in the presence of increasing concentrations of PNPase ( 100 to 800 nM ) in TMK buffer containing 20 mM Tris-HCl pH 7 . 5 , 10 mM magnesium-acetate , 100 mM KCl at 37°C for 15 min . At the end of the binding reaction 6X loading dye ( 30% glycerol , 0 . 25% bromophenol blue and 0 . 25% xylene cyanol ) was added to the samples and they were analyzed on a 6% polyacrylamide gel under non-denaturing conditions . We analyzed 45 Listeria genomes taken from GenBank , available at the time of analysis . These include 28 complete genomes and 17 draft genomes with less than 800 contigs ( Table S1 ) . We also added the genome of L . monocytogenes EGD strain ( unpublished data ) . We used GenBank annotations , excluded genes with stops in phase and with lengths not multiple of three . We re-annotated the prophages in the genomes using a methodology described previously [55] .
|
CRISPR-Cas systems confer to bacteria and archaea an adaptive immunity that protects them against invading bacteriophages and plasmids . In this study , we characterize a CRISPR ( RliB-CRISPR ) that is present in all L . monocytogenes strains at the same genomic locus but is never associated with a cas operon . It is an unusual CRISPR that , as we demonstrate , has a secondary structure consisting of basepair interactions between the repeat sequence and the adjacent spacer . We show that the RliB-CRISPR is processed by the endogenously encoded polynucleotide phosphorylase enzyme ( PNPase ) . In addition , we show that the RliB-CRISPR system requires PNPase and presence of trans encoded cas genes of a second CRISPR-Cas system , to mediate DNA interference directed against a plasmid carrying a matching protospacer . Altogether , our data reveal a novel type of CRISPR system in bacteria that requires endogenously encoded PNPase enzyme for its processing and interference activity .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"bacteriology",
"bacterial",
"physiology",
"gene",
"identification",
"and",
"analysis",
"genetics",
"molecular",
"genetics",
"comparative",
"genomics",
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2014
|
A PNPase Dependent CRISPR System in Listeria
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Here , we describe the generation of a novel type of HIV entry inhibitor using the recently developed Designed Ankyrin Repeat Protein ( DARPin ) technology . DARPin proteins specific for human CD4 were selected from a DARPin DNA library using ribosome display . Selected pool members interacted specifically with CD4 and competed with gp120 for binding to CD4 . DARPin proteins derived in the initial selection series inhibited HIV in a dose-dependent manner , but showed a relatively high variability in their capacity to block replication of patient isolates on primary CD4 T cells . In consequence , a second series of CD4-specific DARPins with improved affinity for CD4 was generated . These 2nd series DARPins potently inhibit infection of genetically divergent ( subtype B and C ) HIV isolates in the low nanomolar range , independent of coreceptor usage . Importantly , the actions of the CD4 binding DARPins were highly specific: no effect on cell viability or activation , CD4 memory cell function , or interference with CD4-independent virus entry was observed . These novel CD4 targeting molecules described here combine the unique characteristics of DARPins—high physical stability , specificity and low production costs—with the capacity to potently block HIV entry , rendering them promising candidates for microbicide development .
The increasing need for a vaccine to control the HIV pandemic is undoubted , but recent failures of vaccine programs have made clear that it will be years to decades before a successful vaccination program can be installed [1] . In the meantime , drug based intervention strategies must be found to fill the gap and put the continuous spread of HIV at halt , particularly in resource poor settings where 90% of the estimated 33 million HIV infected individuals live [2] . HIV infection is predominantly acquired via heterosexual transmission across mucosal surfaces [3] . Strategies that prevent mucosal transmission are therefore considered to significantly impact on diminishing viral spread [4] . Microbicides , agents that by topical application on mucosal surfaces protect from HIV infection , are regarded as one of the most promising preventive intervention strategies in the absence of effective vaccination programs [2] , [4] , [5] . The sought for microbicides against HIV have to fulfill highly specific requirements: Besides promoting strong and reliable protection from HIV infection , these compounds have to be inexpensive , readily available , stable , well tolerated and easy to apply to allow a wide spread use . Recent efforts in microbicide research have mainly focused on chemical compounds of relatively simple composition that provide protection from HIV infection by largely nonspecific ( non HIV specific ) mechanisms as for instance charge-charge interactions [6] . Although in vivo efficacy of two such candidate microbicides , nonoxynol-9 [7] and cellulose sulfate [8] , could not be established [9]–[12] several other pan-reactive molecules are in development that show promise [4] , [6] , [13] . As for all drug interventions against HIV , combination therapy will likely also be necessary in microbicide application to reach potent and broad efficacy . Thus microbicides that target HIV specifically and potentially can be used in combination with pan-reactive molecules are urgently sought for . Prime targets for microbicide attack are the virus and cellular proteins involved in the early events in infection: the entry receptors CD4 , CCR5 and CXCR4 , the viral envelope proteins and compounds that interfere post entry with reverse transcription and integration of HIV into the host cell . Application of specific HIV inhibitors targeting these events as topical microbicides has proven effective in blocking mucosal HIV transmission in the SHIV macaque infection model underlining their potential in HIV prevention [4] , [14]–[18] . To date only few small molecules that inhibit HIV entry have been defined [4] . While protein-based inhibitors are commonly more expensive in production , they can have functional advantages . Most importantly , they provide outstanding target specificity since the contact area between agent and target protein is formed by comparatively large surface patches as for instance in antibody-antigen interactions . The aim of our study was to derive inhibitors of HIV entry that achieve the desired specificity and potency together with the high physical stability and low production costs required for the application as microbicide . To this end , we made use of the recently established Designed Ankyrin Repeat Protein ( DARPin ) technology which is based on the principle of naturally occurring ankyrin repeat proteins , a ubiquitously expressed family of proteins mediating specific protein-protein interactions across species [19] . DARPins were designed as an alternative to antibodies: they share the antibodies' ability to be selected and to bind any given target with high affinity and specificity but are clearly superior in terms of physical stability and production costs [20] , [21] . Highly diverse DARPin DNA libraries , comprising at least 1011 different sequences per reaction , have successfully been employed to identify enzyme inhibitors and specific binding proteins in diverse biological systems [22]–[29] . The specificity and high affinity achieved in DARPin-target interactions , paired with the fact that the 12 to 19 kDa DARPin proteins have a remarkable physical stability and are expressed in prokaryotic systems , allowing large scale production at relatively low costs , renders DARPins promising candidates for the selection of HIV inhibitors . Here , we report the successful selection and characterization of CD4-specific DARPins and their function as broadly active inhibitors of HIV entry , which underlines the potential of this novel type of inhibitor molecules in HIV infection .
An introduction into the DARPin technology and ribosome display is provided as Supporting Information ( Protocol S1 and Figures S1 and S2 ) . Detailed specifics on the use and generation of DARPin libraries has been described previously [20] . Here , N2C and N3C libraries encoding for DARPins consisting of an N- and a C-terminal capping repeat , and either two ( N2C ) or three ( N3C ) internal ankyrin repeat modules containing randomized amino acid residues , were used . The theoretical diversity of the N3C library is 3 . 8×1023 . Ligated library DNA used in the selection described here encoded for a minimum of 1011 individual members [20] . The diversity of the library is further increased by introducing errors through the polymerase used in subsequent PCR cycles . Library selections were performed against the tetrameric CD4 fusion protein CD4-IgG2 ( kindly provided by Bill Olson , Progenics Pharmaceuticals; [30] ) which was immobilized via a Fab-specific anti human IgG-antibody ( Sigma ) . For selections , PCR-amplified libraries were transcribed and three standard ribosome-display selection rounds were performed as described [23] , [31] , [32] . Two alternate approaches were probed in the fourth selection round to achieve highly specific binders: i ) a standard ribosome display selection round with more extensive washing ( 3 h in total ) and ii ) the use of purified gp120 of the R5-tropic virus JR-FL ( 1 µM; kindly provided by W . Olson Progenics Pharmaceuticals ) to elute binders that compete with viral glycoprotein for binding to CD4 . The RT-PCR products of the genes obtained after both fourth cycles were combined in a pool ( termed 1st series binders ) and then used for a single clone analysis as described below . In a separate line of experiments we aimed to select binders with improved affinities for CD4 . To this end , all round 3 and round 4 sublibraries were transcribed and translated in vitro as described [33] . Then the ternary complexes of ribosome , mRNA , and displayed proteins were equilibrated with 1 nM biotinylated CD4-IgG2 at 4°C for 1 h before 1 µM non-biotinylated CD4-IgG2 was added . The aliquots were incubated for 3 h at 4°C and the complexes were recovered by binding to streptavidin-coated magnetic beads ( Roche Applied Science ) for 30 min . The beads were washed five times , and the RNA was eluted and purified as described [33] . The pool of binders derived from this affinity selection was termed 2nd series binders and characterized as described below . CD4-IgG2 was immobilized via a Fab-specific anti-IgG capture antibody ( Sigma ) on Maxisorp 96-well plates ( Nunc ) . To screen for CD4 binders , 100 µl each of crude Escherichia coli extracts containing DARPins or purified DARPins were applied to wells containing immobilized CD4-IgG2 and to wells containing the capture antibody alone . Bound DARPins were detected upon incubation with anti-RGS-His antibody ( Qiagen ) , anti-mouse-IgG-alkaline phosphatase conjugate ( Sigma ) and p-nitrophenylphosphate ( Sigma ) as substrate . Wells without CD4-IgG2 were used as negative controls to verify the binding specificity of the tested DARPins . For the gp120 competition ELISA the same setup as described above was employed . CD4-IgG2 coated plates were incubated with JR-FL gp120 ( 0–800 nM; kindly provided by Progenics Pharmaceuticals ) for 1 h at 25°C before pure DARPins ( 200 nM ) were added . Detection and readout was carried out as described above . For the competition ELISA using CD4-directed monoclonal antibodies ( mAbs ) as competitors , soluble CD4 ( 20 nM , Progenics Pharmaceuticals ) was biotinylated using EZ-link sulfo-NHS-LC-biotin ( Pierce ) according to the manufacturer's instructions and immobilized via neutravidin ( Pierce , 66 nM ) on Maxisorp 96-well plates ( Nunc ) . mAbs L222 , Q4120 , 13B82 [34] , [35] and 5A8 [36] were kindly provided by Q . Sattentau . DARPin ( 20 nM ) plus different CD4-antibodies ( 66 nM ) were added and incubated at 25°C for 1 h . Bound DARPins were detected by ELISA using an anti-poly-His-alkaline phosphatase conjugate ( Sigma ) as described above . Wells without added antibody where included as control and defined as 0% competition . Competition was rated as follows: − , + , ++ , and +++ for signal decreases of 0–25% , 25–50% , 50–75% and 75–100% , respectively . DARPins were produced in soluble form in E . coli and purified using Ni-NTA affinity chromatography as described [37] . Endotoxins ( lipopolysaccharides ) were removed using 0 . 1% Triton X-114 as described [38] and the DARPins were further purified using EndoTrap red columns ( Profos ) according to the manufacturer's recommendations . The remaining endotoxin content was determined using the kinetic chromogenic limulus amebocyte lysate assay ( Endotell ) according to the manufacturer's instructions . All DARPin preparations used for investigation of cellular activation had endotoxin levels below 0 . 5 EU/mg . All SPR measurements were performed at 25°C using a Biacore 3000 instrument and a SA sensor chip ( Biacore ) . To immobilize CD4-IgG2 , the protein was first chemically biotinylated using EZ-Link sulfo-NHS-LC-biotin ( Pierce ) . The individual DARPins were applied in various concentrations ( 0 . 25–1'000 nM , depending on affinity ) to a flow-cell with immobilized CD4-IgG2 for 180 s at 50 µl/min , followed by washing with buffer . The signal of an uncoated reference cell was subtracted from the measurements . The kinetic data of the interactions were evaluated with a global fit using the BIAevaluation 3 . 0 software ( Biacore ) . A chimeric construct coding for human CD4 , where the human domain 1 sequence is replaced by its murine homologue sequence , was constructed as follows: in pEYFP-N1-hCD4 ( a kind gift from Jun-ichi Fujisawa [39] ) , an expression vector for human CD4 , a ScaI restriction site was introduced at position 10 in CD4-domain 1 by two conservative nucleotide exchanges via site directed mutagenesis ( QuikChange XL , Stratagene ) , resulting in plasmid pEYFP-N1-hcD4-Sca . The murine CD4-D1 domain was amplified by PCR from the plasmid pCMV-Sport6-mCD4 with primers mD1_fw: gtcactcaagggaagacgctagtactggggaaggaaggg and mD1_rev: ggtcaggctctgcccctgcagcaggtgggtacccggactgaagg . The PCR product and pEYFP-N1-hCD4-Sca , which harbour unique ScaI and AarI restriction sites , were digested with these two enzymes and the PCR-derived insert encoding the murine CD4-domain 1 was ligated into the human CD4 plasmid finally resulting in pEYFP-N1-hCD4mD1 . Cells ( 100'000/well ) were incubated with DARPins ( 200 nM ) for 20 min at 25°C . Bound DARPin was detected using anti-RGS-His antibody ( Qiagen ) and goat-anti-mouse phycoerythrin labeled antibody ( Caltag ) . Binding of DARPins to CD4+ A3 . 01 cells , CD4− A2 . 01 cells ( NIH AIDS Research & Reference Reagent Program , No . 2059 and 166 ) , CEM5 . 25luc . gfp ( CD4+; provided by N . Landau ) and TZM-bl cells ( CD4+; [40] ) was investigated . Cells were washed three times between all incubation steps using PBS containing 0 . 1% azide and 1% BSA . After the last step , cells were fixed ( in PBS , 0 . 1% azide , 1 . 25% formaldehyde ) and subjected to flow cytometry using a FACSCalibur flow cytometer ( BD Biosciences ) and Flowjo software ( Tree Star ) . To measure the effect of DARPin on cellular CD4 expression , untouched CD4+ T cells were isolated from CD8-depleted peripheral blood mononuclear cells ( PBMC ) of healthy donors using the CD4+ T cell isolation kit II ( Miltenyi Biotech ) according to the manufacturer's instructions . Purity of the isolated CD4+ T cells was routinely >97% . CD4+ T cells were cultured in the presence or absence of the indicated DARPins at 200 nM for 1 h , 3 h , or 18 h . Thereafter , CD4+ T cells were washed twice , stained with PE-labeled anti-CD4 ( Caltag ) and analyzed for CD4 expression by flow cytometry . To analyze overlapping binding patterns amongst the selected CD4 specific DARPins , competition of DARPins to bind to cellular CD4 was investigated . To this end , DARPins 29 . 2 and 57 . 2 were chemically modified with the HLX633 fluorescent dye ( Invitrogen ) according to the manufacturer's recommendations and purified by size exclusion using NAP5 columns ( GE Healthcare ) . CD4+ A3 . 01 cells were incubated with the fluorescently labeled DARPins at 20 nM ( 10′ , 25°C ) followed by addition of the unlabeled DARPins ( 1 µM , 20′ , 25°C ) . Cells were washed thereafter and analyzed by flow cytometry . To define the domain-specificity of selected DARPins , 293T cells were transiently transfected with plasmids pEYFP-N1-hCD4 ( see above ) , pCMV-Sport6-mCD4 ( obtained from RZPD ) or the newly created chimeric construct pEYFP-N1-hCD4mD1 ( see above ) with 25 kD polyethylenimine ( Polysciences ) as described [41] and stained 48 h post transfection with fluorescently labeled DARPins at a concentration of 5 to 50 nM and subsequently analyzed by flow cytomtery . CD8+ T-cell depleted ( Rosette Sep cocktail , StemCell Technologies Inc . ) PBMC were isolated by Ficoll-Hypaque centrifugation of buffy coats obtained from three healthy blood donors . Cells were adjusted to 4×106/ml in culture medium ( RPMI 1640 medium , 10% fetal calf serum , 10 U/ml interleukin-2 , glutamine , and antibiotics ) , divided into three parts , and stimulated with 5 µg/ml phytohemagglutinin ( Sigma ) , 0 . 5 µg/ml phytohemagglutinin , or anti-CD3 mAb OKT3 as previously described [42] . After 72 h , cells from all three stimulations were combined ( referred to as three-way-stimulated PBMC ) and used as a source of stimulated CD4+ T cells for infection and virus isolation experiments . Replication competent viruses were produced by infection of three-way stimulated PBMC . The 50% tissue culture infectious dose ( TCID50 ) was determined by end point dilution . Infections were detected by p24 ELISA . In sum 10 subtype B viruses , including 7 R5 users ( JR-FL , SF-162 , Pat 17 , Pat 020 , Pat 111 , Pat 114 , Pat 120 ) and 3 X4 users ( NL4-3 , 2044 and Pat 19 ) were probed . Pat 17 is a R5 tropic primary isolate derived from plasma of a chronically HIV infected individual as described [43] . The origin of the other viruses has been described previously [42] , [43] . Env-pseudotyped HIV was generated by transfection of 293T cells with plasmids encoding the reporter gene expressing virus backbone , pNLluc [44] ( kindly provided by A . Marozsan and J . P . Moore ) and the respective functional envelope clone using 25 kD polyethylenimine as described [41] . Viral supernatants were harvested 2 days post transfection and the TCID50 was determined by end point dilution . Infections were measured by firefly luciferase activity ( Bright-Glo Luciferase Assay System , Promega ) . Plasmids encoding envelopes of R5 using viruses of subtype B ( AC10 . 0 . 29 , PVO . 4 , QHO692 . 42 , REJO4541 . 67 , RHPA4259 . 7 , SC422661 . 8 , TRJO4551 . 58 , TRO . 11 , WITO4160 . 33 ) and subtype C ( DU123 . 4 , DU151 . 2 , DU156 . 2 , DU422 . 1 ) were kindly provided by D . Montefiori [45] , [46] . Plasmids encoding envelopes of JR-FL and NL4-3 were provided by N . Landau and the plasmid encoding the envelope of SF162 was provided by L . Stamatatos . Neutralization assays on TZM-bl cells using pseudotype viruses were performed as described [40] . Briefly , TZM-bl cells ( 10'000/well; 96well format ) were preincubated for 1 h at 37°C with serial dilutions of DARPins and were then infected with aliquots of the viruses ( 100 TCID50 ) together with DEAE dextran ( 10 µg/ml ) in a total infection volume of 200 µl . After three days , the cells were lysed using Glo lysis buffer ( Promega ) and luciferase activity determined upon addition of Glo substrate ( Promega ) on a Dynex Technologies Luminometer . The DARPin concentration causing 50 , 70 , 90% reduction in luciferase reporter gene production after 48 h was determined by regression analysis . Potential synergistic effects of combinations of the CD4-specific DARPin 25 . 2 with other entry inhibitors were investigated with JR-FL pseudotyped virus on the TZM-bl reporter cell line . Combination indices [CI] were calculated using the Loewe additivity formula [47]–[49]:DA ( I ) is the dose of drug A alone required to result in inhibition I and DA|AB ( I ) the dose of drug A in the combination of A+B required to give the inhibition I . CI of 1 indicates additivity , <1 synergy and >1 antagonism . Inhibition of replication-competent virus infection of primary human CD4 T cells was assessed essentially as described [50] . Briefly , stimulated CD8 depleted PBMC ( 100'000/well ) were preincubated for 1 h with DARPins at 37°C , followed by infection with the respective replication-competent virus ( 100 TCID50 ) . After incubation for 6 to 8 days , p24 antigen production was determined in cellular supernatant by ELISA as described [49] , [51] . The DARPin concentration causing 70% reduction in p24 antigen production was determined by regression analysis as described [42] . For macaque PBMC based neutralization assays , macaque PBMC were cultured with 5 µg/ml of PHA-P ( Sigma ) for 3 days , before being plated at 2×105 cells per well of a 96 well plate ( Becton Dickinson ) in medium with 50 U/ml IL-2 . Graded doses of the CD4-specific DARPin 25 . 2 or the control E3_5 DARPin were added to each well ( duplicates per dose ) and incubated for 1 h at 37°C . After the incubation , 1000 TCID50 of SIVmac239 was added to each well ( with 50 U/ml IL-2 ) . The cells were cultured for 7 days ( adding more IL-2 every other day ) , after which the cells were collected and lysed for PCR . SIV infection was measured using a Q-PCR assay for SIV gag DNA [52] , [53] . The DARPin concentration causing 90% reduction in SIV gag DNA was determined by regression analysis . Monocytes were isolated from PBMC by positive selection with CD14 microbeads ( Miltenyi ) . Purified monocytes were cultured for 4 days in RPMI-10% FCS containing 1000 U/ml GM-CSF and 1'000 U/ml IL-4 ( both from Immunotools ) . Monocyte-derived DC were then washed twice , seeded at 1×106/ml and treated with the purified DARPin preparations ( 375 nM ) for 24 h . E . coli lipopolysaccharide ( 2 . 5 EU/ml; Charles River Endosafe ) was used as control . Finally , to assess the activation status of the cells , DC were stained with PE-labeled anti-CD80 ( BD Biosciences ) and with propidium iodide ( BD PharMingen ) and CD80 expression levels were quantified by flow cytometry . Labeling of PBMC with CFSE ( carboxy-fluorescein succinimidyl ester ) was performed as described [54] . Briefly , CD8-depleted PBMC from a single donor were stained 8 min at room temperature with 3 µM CFSE ( Molecular Probes ) . Staining was stopped by addition of an equal amount of FCS and cells washed three times with PBS containing 1% FCS . CFSE-labeled cells were incubated with 500 nM endotoxin purified DARPin ( 1 h at 37°C ) and cultured for 4 days in RPMI 1640 containing 10% FCS , antibiotics , 100 U/ml interleukin-2 and anti-CD3 mAb OKT3 . The cells were analyzed by flow cytometry using anti-CD3-PE and propidium iodide for gating . Proliferation of cells was assessed on the basis of the shifts in the CFSE- labeling intensity using the FlowJo software as described [54] . To assess whether CD4-specific DARPins interfere with T helper memory cell functions , activation of antigen-specific T cells in presence and absence of DARPin 55 . 2 and the control E3_5 using a standard interferon-γ ELISpot assay was assessed [55] . Briefly , 96-well membrane plates ( MAIP S45 , Millipore ) were coated overnight with anti-human IFN-γ antibody ( 1-D1K , MAbtech ) . CD8-depleted PBMC were isolated one day prior to the experiment and cultured in RPMI 1640 containing 10% FCS and antibiotics overnight . The next day cells were preincubated with 200 nM ( streptokinase/streptodornase experiment ) or 250 nM ( cytomegalovirus experiment ) endotoxin free DARPins 55 . 2 and E3_5 for 1 h at 37°C . Cells ( 2×105 ) were then seeded into wells of the coated 96-well membrane plates and stimulated with either streptokinase/streptodornase ( 400 U/ml ) or cytomegalovirus ( CMV ) -lysate ( 10 µg/ml ) overnight at 37°C . Phytohaemagglutinin ( 10 µg/ml ) was used as positive control . IFN-γ production was detected by sequential addition of a detection antibody cocktail containing a biotinylated anti-human IFN-γ antibody ( 7-B6-1 , MAbtech ) , streptavidin alkaline phosphatase ( MAbtech ) , followed by washing . AP ( alkaline phosphatase ) conjugate substrate kit ( Biorad ) was used and the resulting colored spots were quantified using an ELISpot reader ( AID ) . Background reactivity observed in cultures without stimulation was subtracted and results are expressed as specific spot forming cells ( SFC ) per 106 CD8-depleted PBMC . To study if CD4 specific DARPins interfere with CD4:MHC class II interaction directly , we performed a cell based binding assay based on rosette formation between CD4 and MHC class II expressing cells [56] . Briefly COS-7 cells ( ATTC CRL-1651; cultivated in DMEM , 10% FCS ) were seeded at a density of 200'000 cells per 6-well , and one day later transfected with the CD4 encoding plasmid pEYFP-N1-hCD4 ( [39] ) using the Ca-phosphate transfection system ( Promega ) according to the manufacturer's instructions . Transfection medium was replaced 8 h later and two days post transfection cells were utilized in the rosette assay . To this end CD4 expressing and control COS-7 cells were treated with CD4 specific DARPins ( 23 . 2 , 25 . 2 , 27 . 2 , 29 . 2 , 55 . 2 , 57 . 2 ) , and a control DARPin ( E3_5 ) , buffer or the anti-CD4 antibody Q4120 specific for domain 1 ( Sigma; 100 nM ) , which is known to block CD4 binding to MHC II , for 30 min at 37°C at a concentration of 50 nM or 200 nM . Subsequently , medium was removed , and cells incubated with 1×107/well Raji B cells ( NIH AIDS Research & Reference Reagent Program , No . 9944 ) cultivated in RPMI1640 , 10% FCS ) containing identical concentrations of inhibitors . After 1 h incubation at 37°C non-adherent cells were removed by washing the wells gently seven times with medium . Cells were then fixed with 1 . 5% paraformaldehyde ( PFA ) and rosette formation assessed microscopically . Crossreactivity with rhesus CD4 was investigated using PBMC from adult male and female chinese rhesus macaques ( Macaca mulatta ) which were housed at the Tulane National Primate Research Center ( TNPRC; Covington , USA ) . Animals were anesthetized with ketamine-HCl ( 10 mg/kg ) prior to heparinized blood samples being taken ( no more than 10 ml/kg/month/animal ) . Protocols were reviewed and approved by the Institutional Animal Care and Use Committee of the TNPRC . Animal care procedures were in compliance with the regulations detailed in the Animal Welfare Act and in the “Guide for the Care and Use of Laboratory Animals” . PBMC were isolated using Ficoll-Hypaque density gradient centrifugation ( GE Healthcare ) . Cells were washed twice in 1× PBS and resuspended in FACS wash ( FW ) buffer ( 1× PBS supplemented with 1% human serum and 1 mM EDTA , both from Sigma ) . For DARPin staining , 4×105 macaque PBMC were resuspended in 50 µl FW buffer in a 96 well plate ( BD Biosciences ) . DARPins , 2 µl of each ( 5 µM ) , were added to the cells and incubated for 20 min at 4°C . Cells were washed twice in FW buffer and CD4 T cells were identified using a 1/25 dilution of FITC-conjugated anti-CD3 ( clone Sp34 , BD PharMingen ) and PE-conjugated anti-CD4 ( clone L200 , BD PharMingen ) . PE- and FITC-conjugated isotype Ig controls were included in all experiments and typically gave signals <1 log of fluorescence . To detect DARPin binding , cells were incubated with a 1/100 dilution of the anti-Penta-His Alexa Fluor 647 conjugate ( Qiagen ) . The DARPin negative control was no DARPin with anti-Penta-His Alexa Fluor 647 . Gates were set to include all mononuclear leukocytes based on the forward- and side-scatter characteristics ( excluding any contaminating neutrophils ) . The gates used to define the CD3/CD4 cells were determined based on the isotype controls . All samples were acquired on a FACSCalibur ( BD Biosciences ) and analyzed using FlowJo software ( Tree Star , Inc ) . Mean fluorescent intensities ( MFI ) of DARPin staining in the CD3/CD4 population were adjusted by subtracting the MFI of the negative DARPin control . Standard deviations represent n = 4 animals , processed and stained in parallel .
DARPins specific for human CD4 were selected using N2C and N3C DARPin libraries , which harbor two and three randomized ankyrin repeats , respectively . Specific DARPins were isolated from these libraries by performing ribosome-display selection rounds [31] , [32] against the tetrameric CD4-immunoglobulin molecule , CD4-IgG2 , expressing domains D1 ( encompassing the binding site for the HIV envelope protein gp120; [57] ) and D2 of human CD4 [30] . Although an enrichment of binders was observed already after the second ribosome-display selection round ( data not shown ) , four selection rounds were performed to increase specificity before the selected library members were further analyzed . This pool of DARPins obtained after four rounds ( referred to as 1st series pool ) was screened for CD4 specificity directly from crude bacterial lysates by ELISA ( Figure 1A ) . More than 50% of the examined candidate DARPins showed specific binding ( signal/background ≥2 ) , whereas unselected DARPins showed no interaction with immobilized CD4-IgG2 ( data not shown ) . Out of this pool of CD4-specific DARPins , six candidate proteins with the most favorable binding properties in the ELISA screen were chosen ( referred to as 1st series binders ) and purified to homogeneity for further investigations . The six selected proteins were purified and their capacity to bind to CD4 in presence and absence of gp120 assessed ( Figure 1B ) . Notably , all six selected DARPins interfered with gp120 binding to CD4 . We further analyzed the ability of DARPins to interact with the native CD4 receptor in a cellular context . All probed selected DARPins bound to CD4+ cell lines and to primary CD4+ T cells but not to CD4− cell lines , whereas the unselected control DARPin , E3_5 , did not interact with any of the tested cell lines ( Figure 1C and data not shown ) . As affinity and kinetics of the interaction with CD4 are anticipated to steer the efficacy of the DARPins as inhibitors of HIV entry , we investigated the interaction of one candidate from the 1st series pool , DARPin 3 . 1 , with CD4 by kinetic SPR measurements . Association and dissociation experiments at various concentrations of DARPin 3 . 1 with immobilized CD4-IgG2 yielded a dissociation constant ( KD value ) of 8 . 9 nM , which is in the range of high affinity antibodies ( Table 1 ) . To explore the effect of CD4-specific DARPins on HIV entry we evaluated the inhibitory activity of our panel of CD4-DARPins in vitro using a standardized assay system based on infection of TZM-bl reporter cells with envelope pseudotyped HIV particles [58] . All tested DARPins inhibited HIV entry of JR-FL , SF-162 and NL4-3 env-pseudotype viruses in a dose-dependent manner with IC50 values ranging from 67 . 8 nM to 820 nM ( Supporting Table S1 ) . Importantly , none of the CD4-selected DARPins had an effect on CD4-independent virus entry as demonstrated by their inability to block entry of murine leukemia virus ( data not shown ) . Equally , an unselected DARPin ( E3_5 ) had no effect on HIV entry ( Figure 2B and data not shown ) . When we further explored the effects of the DARPins against a panel of 10 replication-competent R5 or X4 virus isolates of subtype B on primary lymphocytes ( Figure 2A ) we confirmed that all selected DARPins inhibited replication of the tested virus isolates , even over multiple rounds of replication . Notably though , we observed a considerable variability in the sensitivity of different viruses with IC70 values ranging from <24 nM up to >1 µM , with a relatively high resistance of the three probed X4 isolates to the DARPin inhibitors . This relatively high variability in suppressing virus replication on primary CD4+ T cells suggested that DARPins with superior activity are needed to achieve potent and broad inhibition of genetically diverse isolates in vivo . We reasoned that increasing the affinity of the DARPins to CD4 is the most feasible strategy to boost their potency in inhibiting HIV entry . To enrich for DARPins with high affinity for CD4 we performed off-rate selections during ribosome display to specifically select for proteins with low dissociation rates [59] . To that end , we combined the DNA-sublibraries generated during the first selection rounds and performed a single round of off-rate selection where dissociation of DARPins with low affinity was induced by addition of excess CD4 in solution . From this pool of binders we chose a panel of six DARPins , D23 . 2 , D25 . 2 , D27 . 2 , D29 . 2 , D55 . 2 and D57 . 2 ( referred to as 2nd series binders ) , for further analysis . When we assessed this panel of 2nd series binders using kinetic SPR measurements we found that off-rate selection had indeed resulted in selection of binders with dissociation constants ( KD ) that were almost exclusively in the subnanomolar range ( Table 1 ) . When compared to DARPin 3 . 1 , the most potent inhibitor of the 1st series , this represents a 5 to 10-fold decrease in KD values . Importantly , this substantial increase in affinity was also reflected by a dramatic increase in HIV entry inhibition potency of the 2nd series over the 1st series binders ( Figure 2B ) . The IC50 values of the six affinity improved binders against the reference strains JR-FL , SF162 and NL4-3 in the TZM-bl based assay were in the range of 1 . 1 to 5 . 1 nM , 1 . 2 to 7 . 7 nM , and 2 . 7 to 10 . 5 nM , respectively ( Supporting Table S1 ) . In sum , this represents about a 70-fold reduction in inhibitory concentrations ( p<0 . 0001 , unpaired t test ) over the 1st series DARPin inhibitors and renders the 2nd series inhibitors equal in potency to the clinically approved entry inhibitor T-20 [60]–[62] , which was probed alongside and inhibited replication of JR-FL , SF-162 and NL4-3 pseudotyped viruses with IC50 values of 1 . 1 nM , 3 . 1 nM and 8 . 1 nM , respectively . While the 1st series DARPins displayed a relatively wide variability in their potency to inhibit infection of PBMC by replication-competent viruses ( Figure 2A , Table S1 ) , the 2nd series DARPins were significantly improved and blocked virus replication at IC70s in the very low nanomolar range ( 2 . 1 nM-30 . 9 nM; Figure 2B and Table S1 ) . The most potent inhibitors of this pool , DARPins 55 . 2 and 57 . 2 blocked HIV replication of the three probed viruses , JR-FL , SF-162 and NL4-3 , with IC70 values between 2 . 1 and 7 . 8 nM . To obtain more detailed information on potency and breadth of the CD4-specific DARPins we analyzed the activity of DARPin 3 . 1 , the most potent inhibitor of the 1st series pool , and DARPin 55 . 2 , as representative of the 2nd series , against a reference panel of nine subtype B and four subtype C env-pseudotyped R5 viruses ( Figure 3A ) . Notably , D3 . 1 only reached IC50 values between 20 . 2 and 144 . 8 nM ( median: 67 nM ) against clade B viruses and 11 . 3 to 52 . 5 nM ( median: 28 nM ) against clade C viruses while DARPin 55 . 2 inhibited both subtype B and C viruses very potently with IC50 values of 0 . 4–4 . 1 nM for subtype B ( median: 1 . 3 nM ) and 0 . 3–1 . 6 nM for subtype C viruses ( median: 0 . 7 nM ) . The latter confirmed the result of the initial screen and verified that the 2nd series DARPins have a markedly improved capacity to inhibit HIV , irrespective of the genetic background of the virus . As with all inhibitors against HIV , effective application of CD4-specific DARPins for prevention or therapy will require their use in combination with other types of inhibitors . To probe potential effects of CD4-DARPins in drug cocktails , DARPin 25 . 2 was tested for its efficacy in inhibiting HIV entry in combination with a series of entry inhibitors: the neutralizing mAbs IgG-b12 [63] , 2F5 [64] , 4E10 [65] and 2G12 [66] , the fusion inhibitor T-20 [60] , the anti-CCR5-mAb PRO140 [67] and CD4-IgG2 [30] . The results showed a clear pattern: DARPin 25 . 2 acted in synergy ( CI 70: 0 . 42–0 . 77 , CI 90: 0 . 25–0 . 54 ) with all anti-cell and anti-viral inhibitors with the exception of CD4-IgG2 for which - consistent with the CD4-specificity of the DARPins - antagonism was observed ( CI 70: 2 . 31 , CI 90: 2 . 05; Figure 3B and C ) . The precise mechanisms by which blocking of CD4 promotes synergistic effects in combination with anti-envelope targeting inhibitors remain to be determined . Synergistic effects could , for example , arise when thresholds of receptor levels required for successful entry are not met . In summary , our data underline the potential of CD4-specific DARPin inhibitors , as they promote higher inhibitory activity in conjunction with entry inhibitors directed to different targets . To derive further information on their target specificity , we studied binding of a selection of 2nd series DARPins to CD4 in competition with a panel of CD4-binding mAbs . In general , strong competition with the three D1 binding mAbs ( L222 , Q4120 , 13B82 [34] , [35] ) was observed , while less interference was found with 5A8 [36] , a D2 binding antibody ( Table 2 ) . Notably , this competition by mAb 5A8 was not observed with DARPin 23 . 2 , but with all other tested DARPins . In summary these experiments suggest that the selected DARPins have overlapping specificities mainly directed against D1 . We confirmed these experiments in competition experiments in which binding of fluorescently labeled DARPin 29 . 2 or 57 . 2 to CD4 expressing cells was probed in presence of unlabeled competitor DARPins ( Figure 4A ) . Both sets of experiments gave identical results: the labeled DARPin was competed off by all other CD4 specific but not the control DARPin E3_5 , indicating that the probed CD4-specific DARPins have closely overlapping epitopes . To more specifically define the binding domain of the DARPins we generated a chimeric CD4 molecule in which domain 1 of human CD4 was exchanged by the corresponding domain of mouse CD4 . The chimeric CD4 molecule expressed well upon transfection in 293-T cells , and had the required specificities , as antibody S3 . 5 , specific for human D1 , failed to bind , whereas mAb GK1 . 5 , specific for mouse D1 , bound the chimeric molecule but not wild type human CD4 ( data not shown ) . Likewise mAb OKT4 , specific for human CD4 D3 , bound equally well to both wildtype human CD4 and the chimeric molecule ( Figure 4B ) . Binding studies with the CD4 specific DARPins revealed that while all DARPins bound wildtype human CD4 , they failed to bind the chimeric mouse domain 1 molecule mirroring the binding pattern of mAb S3 . 5 and thus confirming their specificity for CD4 domain 1 ( Figure 4B ) . Since the action of CD4-specific DARPins is directed against the host cell , particular care has to be taken to assess their effect on cell function before these agents can be considered for further development as HIV inhibitors . In a first step , we investigated whether CD4-specific DARPins interfere with CD4 T cell proliferation , by probing the effect of a candidate CD4-specific DARPin ( D55 . 2 ) and a nonspecific control DARPin ( E3_5 ) on primary CD4+ T cell proliferation over a four day period . As Figure 5A shows , addition of the CD4-binding DARPin had no noticeable impact on cell proliferation compared to the untreated control . To explore the effects of CD4 engagement by DARPins on dendritic cells ( DC ) , we assessed whether treatment of immature monocyte-derived DC with DARPin 55 . 2 for 24 h induced activation and maturation of these cells , which is reflected by increased expression of the costimulatory molecule CD80 . Neither the CD4-specific DARPin 55 . 2 nor the control DARPin induced DC maturation ( Figure 5B ) , whereas E . coli lipopolysaccharide ( LPS ) , known to induce DC maturation via TLR-4 , gave rise to a pronounced shift in CD80 expression ( data not shown ) . Notably , the DARPins did not reveal any cytotoxic effects: prolonged incubation of primary cells with DARPin - CD4-specific or unselected - did not result in increased cell death as measured by uptake of propidium iodide: Both the CD3+ T cells ( incubated with DARPins , 500 nM , for 4 days ) and the dendritic cells ( incubated with DARPins , 375 nM , for 24 h ) remained unaffected ( Figure 5C ) . As our competition binding experiments with gp120 indicate ( Figure 1B ) , CD4-specific DARPins most likely act by blocking viral attachment to the receptor . Theoretically , binding of the DARPin to CD4 could also induce receptor internalization and DARPins thus may exhibit their antiviral activity through decreasing CD4 receptor density on the target cells . To probe this , we explored the effect of DARPin binding on surface CD4 receptor levels of primary CD4 T cells . Treatment of CD4 T cells from healthy donors with DARPin for 0 , 1 , 3 and 18 h at 37°C ( to allow receptor internalization ) or at 4°C ( to limit internalization ) yielded identical results: Neither treatment with the CD4 specific nor the unspecific DARPin resulted in down- or upregulation of CD4 ( Figure 5D ) . Recognition of CD4 by the CD4 mAb used in these FACS analyses was not impaired in the presence of CD4 specific DARPin . Most importantly , CD4 staining in presence of CD4 specific DARPin remained stable independently whether DARPin and mAb were added simultaneously or cells were pretreated with DARPin for extended time periods ( Figure 5D ) . In the absence of T cell receptor interaction the binding of CD4 to MHC class II is of extremely low affinity ( KD = 200 µM; [68] ) . Using a previously established assay that allows to study this weak interaction based on rosette formation between CD4 and MHC-II expressing cells [56] , we were able to show that all tested DARPins , 23 . 2 , 25 . 2 , 29 . 2 , 55 . 2 and 57 . 2 , as well as the CD4-D1 specific antibody Q4120 blocked rosette formation efficiently ( Figure 6A and data not shown ) . Hence , in the absence of cognate T cell receptor ( TCR ) and peptide , the CD4 specific DARPins interfered with CD4 binding to MHCII . To probe the effect on specific T cell functions , we assessed if the CD4-specific DARPin 55 . 2 affects activation of memory T helper cells specific for either streptokinase/streptodornase or cytomegalovirus antigens . When we quantified antigen specific IFN-γ producing cells that were stimulated in presence or absence of 200 nM of D55 . 2 or the non-binding control DARPin E3_5 we observed in both cases no inhibition of the T cell functions ( Figure 6B ) . This indicates that , at least for the CD4-specific DARPin probed , even at high dosing of the molecule specific memory T helper responses are activated . To evaluate the potential of using these binders directly in non-human models , crossreactivity of the DARPins with CD4 from rhesus macaques was investigated . The sequence identity between human and macaque CD4 is 91% on the amino acid level , as opposed to 54% between human and murine CD4 . Experiments using PBMC from macaques revealed that 4 out of 7 tested DARPins recognize also rhesus CD4 ( Figure 7A ) , while none of them interacts with murine CD4 ( data not shown and Figure 4B ) . This finding is intriguing as it opens the possibility to probe the potential of DARPins as candidate microbicides in the macaque infection model . To obtain an initial insight into the potential of these DARPins in inhibiting SIV infection , we probed the efficacy of DARPin 25 . 2 in blocking SIVmac239 infection of primary rhesus macaque cells . Results obtained in infection experiments with cells from three individual donors depicted in Figure 7B indicate that DARPin 25 . 2 potently inhibit SIV infection of these cells .
The nucleotide and the amino acid sequences of the 12 DARPins described here were deposited in the EMBL Nucleotide Sequence Data Base ( www . ebi . ac . uk/embl ) and are available under the accession numbers AM997259–AM997270 .
|
There is an increasing need to develop inhibitors of HIV entry into target cells for both application in therapy and prevention . The development of specific HIV inhibitors as microbicides , agents that by topical application prevent infection , is considered particularly important in limiting the spread of HIV in the absence of effective vaccines . To derive highly potent and specific inhibitors of HIV entry for potential use as microbicide , we employed the recently developed Designed Ankyrin Repeat Protein technology . Using this technique , Designed Ankyrin Repeat Proteins can be evolved that bind their target molecules as specifically and efficiently as antibodies . In the present study , we generated a panel of Designed Ankyrin Repeat Proteins that bind specifically to the cellular CD4 receptor , the main entry receptor of HIV . The obtained proteins are very potent and highly specific inhibitors of HIV entry and provide a broad reactivity against genetically different virus strains . Due to the high physical stability of Designed Ankyrin Repeat Proteins and their low cost production , these novel HIV entry inhibitors represent promising candidates for microbicide development .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"virology/host",
"invasion",
"and",
"cell",
"entry",
"virology/immunodeficiency",
"viruses",
"virology/new",
"therapies,",
"including",
"antivirals",
"and",
"immunotherapy",
"virology/antivirals,",
"including",
"modes",
"of",
"action",
"and",
"resistance"
] |
2008
|
CD4-Specific Designed Ankyrin Repeat Proteins Are Novel Potent HIV Entry Inhibitors with Unique Characteristics
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Here , we investigated the role of EhVps32 protein ( a member of the endosomal-sorting complex required for transport ) in endocytosis of Entamoeba histolytica , a professional phagocyte . Confocal microscopy , TEM and cell fractionation revealed EhVps32 in cytoplasmic vesicles and also located adjacent to the plasma membrane . Between 5 to 30 min of phagocytosis , EhVps32 was detected on some erythrocytes-containing phagosomes of acidic nature , and at 60 min it returned to cytoplasmic vesicles and also appeared adjacent to the plasma membrane . TEM images revealed it in membranous structures in the vicinity of ingested erythrocytes . EhVps32 , EhADH ( an ALIX family member ) , Gal/GalNac lectin and actin co-localized in the phagocytic cup and in some erythrocytes-containing phagosomes , but EhVps32 was scarcely detected in late phagosomes . During dextran uptake , EhVps32 , EhADH and Gal/GalNac lectin , but not actin , co-localized in pinosomes . EhVps32 recombinant protein formed oligomers composed by rings and filaments . Antibodies against EhVps32 monomers stained cytoplasmic vesicles but not erythrocytes-containing phagosomes , suggesting that in vivo oligomers are formed on phagosome membranes . The involvement of EhVps32 in phagocytosis was further study in pNeoEhvps32-HA-transfected trophozoites , which augmented almost twice their rate of erythrophagocytosis as well as the membranous concentric arrays built by filaments , spirals and tunnel-like structures . Some of these structures apparently connected phagosomes with the phagocytic cup . In concordance , the EhVps32-silenced G3 trophozoites ingested 80% less erythrocytes than the G3 strain . Our results suggest that EhVps32 participates in E . histolytica phagocytosis and pinocytosis . It forms oligomers on erythrocytes-containing phagosomes , probably as a part of the scission machinery involved in membrane invagination and intraluminal vesicles formation .
Entamoeba histolytica is the protozoan responsible for human amoebiasis , considered the third cause of death in the world due to parasitic diseases [1] . Phagocytosis is a key factor in the parasite virulence and several proteins involved in this event have been already unveiled [2–9] , among them the Gal/GalNac lectin [10] , EhC2PK , EhCaBP1 , EhAK1 [4 , 11 , 12] and the EhCPADH complex , formed by a protease ( EhCP112 ) and an adhesin ( EhADH ) [2] , which is a member of the ALIX ( apoptosis-linked gene 2-interacting protein X ) family [13] . In addition to the Bro1 domain located at its N-terminus , EhADH possesses an adherence epitope at the C-terminus which functions as a receptor during adherence to and phagocytosis of erythrocytes [2 , 13 , 14] . BRO1 was described as endosome associated protein that functions in the multivesicular bodies ( MVBs ) pathway in Saccharomyces cerevisiae [15] . EhADH interacts with EhVps32 [16] , a protein described in mammals as a member of the endosomal sorting complex required for transport ( ESCRT ) . ESCRT is a system composed by class E vacuolar protein sorting ( Vps ) factors and it is highly involved in endocytosis [17] . Additionally , ESCRT participates in a number of cellular events such as cell division and autophagy , among others [18–20] . In eukaryotes , nascent endosomes undergo a maturation process that is controlled by fusion and fission events [21] . Early endosomes mature to intermediate endosomes , which fuse to MVBs where cargo molecules and receptors are segregated to be digested or recycled . Then , late endosomes and endolysosomes are generated . During this process , endosomes acquire different pH , size , appearance and lipid and protein composition [22 , 23] . Hybrids with characteristics of both intermediate and late endosomes and lysosomes are also formed [24] . In general , assembly of the ESCRT machinery begins with recognition of monoubiquitinated cargo by ESCRT-0 ( Vps27 and Hse1 ) . Then , ESCRT-0 interacts with ESCRT-I ( Vps20 , Vps23 , Vps37 and Mvb12 ) that binds to endosomal membranes [25] . ESCRT-I activates ESCRT-II ( Vps22 , Vps25 and Vps36 ) , producing membrane invagination to form intraluminal vesicles ( ILVs ) . At this point , ESCRT-III subunits ( Vps2 , Vps20 , Vps24 and Vps32 ) are recruited , leading to the generation of oligomers that regulate formation and release of ILVs [26] and acting as scission machinery in preformed vesicle necks . Subsequently , Vps4 AAA ATPase catalyzes the dissociation of ESCRT-III components from the membrane to re-start the cycle [27 , 28] . In other cases , the Alix protein mediates the ubiquitin-independent , but ESCRT-III-dependent endocytosis [29] . ESCRT-III members have coiled-coil protein-protein interaction domains common to the Snf7 family [30] . Its main component , Vps32 ( Snf7 in S . cerevisiae [31] and CHMP4 in humans [32] ) , has a positively charged N-terminus that binds to negatively charged lipids . N-terminus also binds to the negatively charged C-terminus domain to generate the EhVps32 auto-inhibited form . Vps32 and Vps20 form the ESCRT-III sub-complex I , which is in direct contact with endosomes . Afterward , they recruit Vps2 and Vps24 that form sub-complex II [33] . E . histolytica possesses the genes encoding ESCRT proteins [34] and those encoding EhVps4 AAA ATPase and EhADH , both ESCRT associated proteins [13 , 35] . Here , we show the participation of EhVps32 in both receptor-mediated and non-specific phagocytosis as well as in pinocytosis; we also revealed its co-localization with EhADH , Gal/GalNac lectin and actin during erythrophagocytosis . Besides , we identified the presence of membranous helicoidally and tunnel-like structures in trophozoites constituted by EhVps32 and EhADH that seem to be involved in the dynamic membrane remodeling during phagocytosis . These events are crucial for target cells destruction during parasite invasion to host tissues .
As a tool to study the location and function of EhVps32 in E . histolytica trophozoites , we produced antibodies against the EhVps32 recombinant protein ( rEhVps32 ) [16] . By western blot assays , αrEhVps32 antibodies recognized a 32 kDa band in trophozoite lysates , in cytoplasm and in membrane fractions ( Fig 1A and 1B ) . However , after membrane fractionation by ultracentrifugation , EhVps32 was only detected in internal membranes ( Fig 1B ) . In the same nitrocellulose filter , the αGal/GalNac lectin antibodies identified a 170 kDa band [36] , in both plasma and internal membrane fractions . The Gal/GalNac lectin has been described as a membrane protein marker [10] . Through confocal microscopy and TEM , EhVps32 appeared in cytoplasmic vesicles of distinct size , some of them , close to the plasma membrane ( Fig 1C and 1D ) . In addition , Gal/GalNac lectin and EhVps32 co-localized close to the plasma membrane . Little signal was observed in cells treated with preimmune serum and in non-permeabilized cells ( Fig 1C and 1D ) , confirming that EhVps32 is not a plasma membrane protein , but it is located in cytoplasmic vesicles , some of them adjacent to the plasma membrane . The involvement of EhVps32 in erythrophagocytosis was studied by confocal microscopy through kinetics from 0 ( resting conditions ) to 60 min . In parallel , we investigated its co-localization with EhADH , Gal/GalNac lectin and actin , three proteins involved in endocytosis [2 , 10 , 37] . EhADH is a receptor for erythrocytes during target cell adherence and it has been found on phagosomes [2 , 14 , 38] . Additionally , in vitro , it binds to EhVps32 through its Bro1 domain [16] . At resting conditions EhVps32 co-localized with EhADH close to the plasma membrane ( Fig 2A ) , with a similar pattern to that observed with Gal/GalNac lectin ( Fig 1C ) . According to the results obtained with non permeabilized cells and membrane fractionation , EhVps32 might be adjacent to the plasma membrane ( Fig 1C ) , and , as it has been reported , EhADH could be facing the extracellular space [16] . After 2 min , EhVps32 also decorated phagocytic cups , where it co-localized with EhADH , but EhVps32 did not appear surrounding the erythrocytes , whereas EhADH decorated adhered erythrocytes ( Fig 2A and 2B ) . Between 5 to 30 min of phagocytosis , EhVps32 presented different patterns on erythrocyte-containing phagosomes: some erythrocytes remained without fluorescence , while others appeared completely covered by the αrEhVps32 antibodies ( Fig 2A ) . At this time , EhVps32 and EhADH co-localized on some phagosomes; although other phagosomes recognized by αEhADH antibodies were not stained by αrEhVps32 antibodies ( Fig 2A ) . At 60 min , when digestion had advanced , the majority of EhVps32 returned to its resting position and its presence on phagosomes diminished , whereas EhADH remained in them ( Fig 2A ) . Nevertheless , at this time , many trophozoites had ingested more than 20 erythrocytes per trophozoite distributed in phagosomes with a distinct number of erythrocytes inside . Pearson’s coefficient showed that the EhVps32 and EhADH co-localization was 0 . 47 at 0 min , at 30 min it reached 0 . 65 , while at 60 min it diminished to 0 . 3 ( Fig 2C ) . The proportion of erythrocytes inside phagosomes decorated by αEhADH or αrEhVps32 antibodies with relation to total ingested erythrocytes confirmed that , whereas the majority of phagosomes were stained by EhADH , EhVps32 was detected only in 20% of them at 2 min and in 58% at 30 min , dropping close to zero after this time ( Fig 2D ) . Immunoprecipitation assays using αEhADH antibodies confirmed the association of EhVps32 with EhADH in resting conditions and during phagocytosis ( Fig 2E ) . By these experiments we could not accurately distinguish differences in the amount of both interacting proteins at different phagocytosis times . Nevertheless , altogether our results suggest that during erythrophagocytosis EhADH recruits EhVps32 , probably after adherence to target cells and before their digestion . As the phagosome maturation process is fast , continuous and non-synchronous , it is difficult to observe EhVps32 in all phagosomes at a given time . Additionally , we cannot discard the participation of both proteins in other functions distinct to phagocytosis . Gal/GalNac lectin is another E . histolytica protein involved in adherence and phagocytosis . It is located in the plasma membrane and in the endosomes generated during the endocytic process [10] . Furthermore , in resting conditions it co-localized with EhVps32 ( Fig 1C ) , adjacent to the plasma membrane . Then , we investigated the location of EhVps32 and Gal/GalNac lectin during erythrophagocytosis . At 2 min , we found both proteins in the plasma membrane and in the phagocytic cups , close to adhered erythrocytes which would be ingested ( Fig 3A ) . In addition , both proteins co-localized with actin , detected by phalloidin ( Fig 3A ) . Interestingly , 20 min after incubation with erythrocytes , the three proteins appeared together at some points of the plasma membrane and around the ingested erythrocytes , confirming the participation of them in erythrophagocytosis ( Fig 3A ) . Pearson’s coefficient of EhVps32 and Gal/GalNac lectin co-localization at plasma membrane , at resting conditions it was 0 . 38 , after 2 min of phagocytosis it was 0 . 42 , and after 20 min it was 0 . 32; whereas in the entire cell , Pearson’s coefficients were 0 . 27 , 0 . 23 and 0 . 29 , respectively ( Fig 3B ) . These results suggest that in addition to EhADH , EhVps32 interacts with Gal/GalNac lectin and actin during erythrophagocytosis . Interestingly , in non-specific phagocytosis of latex microspheres , EhVps32 was detected in all phagosomes containing fluorescent microspheres with a Pearson’s coefficient at 5 min of 0 . 43; at 30 min , 0 . 63; and at 60 min , 0 . 68 ( S1 Fig ) . However , fluorescent microspheres and EhADH exhibited poor co-localization , with a Pearson’s coefficient lower than 0 . 3 at all times tested ( S1 Fig ) . In contrast to erythrophagocytosis findings , EhVps32 participates in the whole process of non-specific phagocytosis , whereas EhADH participation appeared to be minimal . In mammalian cells , intermediate endocytosis is characterized by formation of typical MVBs , that once formed , rapidly acidify reaching a pH of 5 . 5 [39] . In resting conditions , many small vesicles were positive for Lysotracker in the cytoplasm ( Fig 3C ) , indicating that trophozoites possess a significant amount of acidic vesicles , probably due to their basal endocytosis . Between 5 to 30 min , Lysotracker stained all erythrocytes-containing phagosomes that were positive for αEhVps32 antibodies . On the other hand , other phagosomes were stained only by Lysotracker ( Fig 3C and 3E ) . At 45 and 60 min , EhVps32 appeared in the cytoplasm and adjacent to the plasma membrane , whereas Lysotracker decorated almost all erythrocytes-containing phagosomes ( Fig 3E ) . Pearson’s coefficient showed that association between EhVps32 and Lysotracker augmented from 0 . 22 at 0 time to 0 . 65 at 30 min , and it diminished to 0 . 1 after this time ( Fig 3D ) . Between 10 and 30 min , gold-labeled antibodies detected through TEM the EhVps32 protein in phagosome membranes ( Fig 4B ) , in concentric membranous structures close to ingested erythrocytes ( Fig 4C , 4D and 4E ) and in erythrocyte fragments ( Fig 4D and 4E ) . Trophozoites treated only with gold labeled secondary antibodies gave no signal ( Fig 4A ) . At 60 min , TEM images exhibited erythrocytes inside phagosomes with putative ILVs that could correspond to MVBs or to other unidentified structures . At this time , EhVps32 scarcely appeared in late phagosomes , which exhibited erythrocyte fragments in advanced phases of digestion ( Fig 4F ) . These results corroborate that EhVps32 is in phagosomes of acidic nature , but it is poorly located in phagolysosomes . In other organisms , the role of Vps32 has been elucidated in pinocytosis , but not in phagocytosis [40] . To confirm the participation of EhVps32 in pinocytosis we studied the relationship of EhVps32 with EhADH and Gal/GalNac lectin during FITC-labeled dextran uptake . Confocal images showed that at 30 min incubation , EhVps32 , EhADH and Gal/GalNac lectin clearly co-localized in dextran-containing endosomes ( Fig 5A and 5B ) . This pinosomes appeared larger at 60 min ( Fig 5A ) . However , detection of actin co-localizing with dextran and EhVps32 was less evident ( Fig 5C ) . According to results presented above , EhVps32 seems to be involved in phagocytosis and pinocytosis . However , to carry out its function as scission factor and generate ILVs during erythrophagocytosis , EhVps32 needs to form oligomers on curved membranes of phagosomes , as it has been described for this protein in other systems [41] . First , we explored in vitro whether rEhVps32 was able to form oligomers , employing TEM negative staining assays of purified rEhVps32 . Images showed the presence of long filaments ( 10–75 nm width ) with ramifications and many small rings ( 0 . 1–0 . 15 μm ) that presumably could augment in size and complexity by continuous oligomerization of the protein ( Fig 6A and 6F ) . Fig 6A exhibits a long ring ( 0 . 7 x 0 . 65 μm ) , containing other smaller rings ( 0 . 1–0 . 15 μm ) and Fig 6B and 6C showed concentric structures . Antibodies against rEhVps32 recognized these structures ( Fig 6D and 6F ) . Size exclusion chromatography of the rEhVps32 purified protein followed by western blot analysis confirmed the presence of oligomers , with a migration rate ( Rf ) corresponding to EhVps32 multiples ( Fig 6G and 6I ) . However , little differences were found in the western blot patterns obtained from the fractions containing the larger oligomerized molecules and the one containing the monomer . We attributed this to the fast polymerization of rEhVps32 in the tube . To investigate the location of EhVps32 monomers and oligomers in trophozoites , we generated polyclonal antibodies ( pEhVps32 ) directed against an antigenic region formed by 18 amino acids located in the first alpha helix at the EhVps32 amino terminus ( Fig 6J ) . According to reports in other systems [41 , 42] , this peptide may be in contact with the phagosome membrane because it contains positively charged amino acids that bind to negatively charged membrane lipids [43] . Thus , it is predictable that αpEhVps32 antibodies would react with the exposed epitope in EhVps32 monomers , but not with oligomers , in which this region is hidden ( Fig 6J ) . In western blot assays , αpEhVps32 antibodies recognized the 32 kDa protein in trophozoites lysates and competed with the rEhVps32 purified protein and with the αrEhVps32 antibodies ( Fig 6K ) , evidencing their specificity . In confocal microscopy experiments , αpEhVps32 antibodies decorated cytoplasmic small vesicles but not phagosomes containing erythrocytes ( Fig 6L ) . These results suggest that , as in other systems [44] , in vivo EhVps32 forms oligomers on the phagosome membranes , which would be necessary to function as scission machinery during ILVs formation in endocytosis . We searched for further insights on EhVps32 function using pNeoEhvps32-HA transiently-transfected trophozoites . Transfected trophozoites were viable for 72 h , probably due to the excessive oligomerization of EhVps32 . However , until this time , they appeared healthy , with active movement , pleomorphic and able to divide . Twelve hours after transfection we confirmed by western blot experiments that pNeoEhvps32-HA transfected cells expressed 1 . 6 to 2 fold more EhVps32 protein than pNeo-transfected and non-transfected cells , respectively ( Fig 7A and 7B ) . The rate of erythrophagocytosis was evaluated 12 h after transfection in cultures with 95% of viability . pNeoEhvps32-HA-transfected trophozoites ingested between 56 and 105% more erythrocytes than pNeo transfected trophozoites ( Fig 7C and 7D ) . Number of erythrocytes inside trophozoites was counted by light microscopy in 3 , 3’ diaminobenzidine-stained preparations ( Fig 7C ) and by the amount of hemoglobin inside the trophozoites at different times of phagocytosis , with similar results ( Fig 7D ) . Confocal microscopy images of resting pNeoEhvps32-HA-transfected trophozoites revealed the presence of structures of distinct size protruding from the plasma membrane , which were recognized by αrEhVps32 and αEhADH antibodies ( Fig 7E ) . Trophozoites exhibited structures decorated in one end by αrEhVps32 antibodies and in the opposite end by αEhADH antibodies , whereas in the middle , both proteins merged . We do not know the significance of this protein distribution . However , these results reinforce the hypothesis that both proteins interact even in resting trophozoites , and that this interaction was more evident when EhVps32 is overexpressed . During phagocytosis in some trophozoites , αrEhVps32 antibodies decorated membranous concentric structures that were located inside cells and forming part of the phagocytic cup ( Fig 7F , 7G and 7H ) . They were visible in the cytoplasm forming filaments or tunnel-like structures extended to the ingested erythrocytes and contacting phagosomes ( Fig 7F , 7G and 7H ) . EhADH appeared around adhered erythrocytes , in erythrocytes in process of ingestion and in erythrocytes inside phagosomes ( Fig 7H ) . We investigated the appearance of these structures on the trophozoite surface by SEM . Images of pNeoEhvps32-HA-transfected trophozoites revealed the presence of a high amount of membrane rings of 0 . 9–1 . 6 μm diameters , with 0 . 3 to 0 . 7 μm holes ( Fig 8B ) , probably corresponding to the extreme of the tunnel-like and other structures observed in immunofluorescence experiments . Trophozoites exhibited membrane projections of 2 . 2 to 2 . 4 μm length , and 1 . 5 to 1 . 9 μm widths ( Fig 8C ) similar to those recognized by αrEhVps32 antibodies in confocal microscopy experiments ( Figs 7E and S2A–S2F ) . They also appeared , although in fewer 2amount and with less diversity , in the wild type strain ( S2K and S2L Fig ) and in pNeo transfected trophozoites ( S2G–S2J Fig ) . Thus , EhVps32 seems to form oligomers by the assembly of rings and filaments that protruded from the plasma membrane , which , in excess , might kill the cells . We explored by TEM the ultrastructure of the arrays detected by confocal microscopy and SEM . Thin sections of pNeoEhvps32-HA-transfected trophozoites , exhibited helicoidally structures up to 2 . 12 x 1 . 75 μm in diameters , formed by 5 to 7 concentric filaments of 75–100 nm width ( Fig 8D , 8E and 8F ) . Then , we prepared thin sections of trophozoites embedded in LR White resin , to better allow the access of αrEhVps32 antibodies . Antibodies recognized these structures , confirming the presence of EhVps32 in them ( Fig 8G–8L ) . Additionally , these structures were similar to those formed in vitro and detected by TEM in negative stain preparations of the rEhVps32 purified protein ( S3 Fig ) . To get more evidence on the role of EhVps32 in phagocytosis , we employed trophozoites of the G3 strain [45] to transcriptional silence the Ehvps32 gene . EhVps32-silenced G3 trophozoites , grown in 7 μg/ml of G418 , presented a growth rate similar to G3 strain and both were used to determine the level of expression of EhVps32 . Western blot assays showed 80% reduction in the amount of EhVps32 protein in EhVps32-silenced trophozoites compared with G3 strain ( Fig 9A and 9B ) . EhVps32-silenced trophozoites showed a poor capacity to ingest erythrocytes , presenting 80% less amount of ingested erythrocytes than the G3 strain ( Fig 9C and 9D ) ( a mean of 1 . 27 OD400 hemoglobin per G3 trophozoites vs 0 . 26 OD400 hemoglobin per EhVps32-silenced trophozoites , after 20 min phagocytosis ) . The 3 , 3’ diaminobenzidine-stained images clearly showed these differences ( Fig 9C ) . Confocal microscopy images also evidenced differences between G3 strain and EhVps32-silenced trophozoites . Differences of αrEhVps32 fluorescence intensity between G3 and EhVps32-silenced trophozoites were evident at 0 and 20 min phagocytosis . Whereas in G3 trophozoites , EhVps32 , EhADH and actin co-localized in the phagocytic cups and around phagosomes , in EhVps32-defficent trophozoites , EhADH presented a diffused pattern around phagosomes and actin was re-distributed in actin points beside the phagosomes , but not around them ( Fig 9E ) . By SEM we also observed changes in the EhVps32-silenced trophozoites surface . They contained less projections and roughness than the G3 trophozoites ( Fig 9F ) . Additionally , the doughnut structures showed a flat appearance with fewer edges . In summary , our results show the involvement of EhVps32 protein in phagocytosis , suggesting that the ESCRT machinery participates in this virulence event . Association of EhVps32 with EhADH , Gal/GalNac lectin and actin , all of them proteins involved in phagocytosis , gives further support to this assumption .
In this paper , we identified EhVps32 protein in E . histolytica trophozoites , which in eukaryotes acts as a scission machinery during the ILVs formation in endocytosis . We showed here the association of EhVps32 with EhADH , Gal/GalNac lectin and actin in trophozoites under resting condition and during erythrophagocytosis . Additionally , we discovered in this parasite membranous tunnel-like and helicoidally structures formed by EhVps32 . Filaments , rings , spirals , circular arrays and helicoidally structures resemble those described in COS7 cells and yeast , transfected with CHMP4 and Snf7 , respectively [46 , 47] . Vps32 has been widely studied during endocytosis in other eukaryotes , however , as far as we know , this is the first study showing its participation in phagocytosis . Furthermore , studies on ESCRT machinery have been performed in yeast and complex organisms that are not professional phagocytes . The unique membrane exchange and constitutive endocytosis of the unicellular protozoan E . histolytica provide an excellent model to investigate novel roles of ESCRT machinery and other molecules involved in phagocytosis . The molecular weight of EhVps32 appeared larger ( 32 kDa ) than the expected one from its amino acid sequence ( 24 kDa ) . Accordingly , Snf7 migrates at 35 kDa when its predicted molecular weight is 27 kDa [33] . This has been attributed to the electric charge of the protein [33] , and EhVps32 is also rich in charged residues . The presence of EhVps32 adjacent to the plasma membrane ( Fig 1 ) , as well as its polarization in phagocytic cups during phagocytosis , support the hypothesis that EhVps32 is activated since trophozoites initiate the cargo recognition ( Fig 2 ) . The direct participation of EhVps32 in erythrophagocytosis started to be visible at 5 min , when EhVps32 interacted with EhADH on erythrocytes-containing phagosomes ( Fig 2 ) . Its co-localization with Gal/GalNac lectin and actin in phagocytic cups , in phagosome membrane and in phagosomes ( Fig 3 ) , strengthened this hypothesis . EhVps32 is present in phagosomes until 30 min of phagocytosis , but during advanced lysis of erythrocytes , it was poorly detected in phagosomes stained with Lysotracker and αEhADH antibodies ( Figs 2 and 3 ) . These findings corroborated the role of EhVps32 in phagocytosis , when ILVs are formed and released . In yeast , MVBs formation is restricted to intermediate and late endocytosis; while in mammals , typical and uniform MVBs appear during intermediate endocytosis , although multivesicular vacuoles are found in all stages of the endocytic pathway [39] . E . histolytica trophozoites also present structures similar to MVBs ( Fig 4 ) [13 , 48] , but neither the time of their formation , nor molecules participating have been fully identified [16 , 35] . EhADH has been found in putative MVBs [16] and here we demonstrated its co-localization with EhVps32 in phagosomes between 5 to 30 min of phagocytosis , these structures may correspond to MVBs-like bodies . However , EhVps32 appeared poorly in late phagosomes and phagolysosomes that are formed after 30 min phagocytosis ( Fig 4 ) . MVBs possess an acidic pH ( ~5 . 5 ) , and in humans , they mediate microtubule-dependent transport toward late endosomes [28] . However , in E . histolytica , suitable endosomal markers are not available and the existence of microtubules has been demonstrated only in dividing nuclei of trophozoites [49] . According to previous reports [50] , MVBs-like structures could be also formed in trophozoites before erythrocytes digestion ( Fig 4 ) . Further studies are needed to deeply analyze putative MVBs in trophozoites and to precisely define the EhVps32 participation during formation of these structures . In mammalian cells , an alternative ubiquitin-independent MVBs formation pathway has been reported , in which Alix and ESCRT-III proteins are involved [29] . In human , PAR1-activated receptor directly binds to Alix; then , Alix recruits CHMP4B and the rest of the ESCRT-III subunits , in an ubiquitin-independent manner [29] . This also could be happening in E . histolytica , where EhADH acts as an erythrocyte receptor by its adherence domain located at the carboxyl terminus , whereas by its Bro1 domain in the amino terminus , it recruits EhVps32 [2 , 13] , as it has been experimentally proved by pull down experiments [16] . This interaction is also happening in resting conditions , probably it is due to constitutive endocytosis , as it has been seen in immunofluorescence and immunoprecipitation assays ( Fig 2 ) . EhVps32 also participates during pinocytosis , co-localizing with EhADH and Gal/GalNac lectin , but not with actin ( Fig 5 ) . This is in agreement with many reports describing Vps32 participation in endocytosis [40] and with other reports indicating that actin is involved only in phagocytosis of E . histolytica [51] . Interestingly , EhVps32 participates in phagosomes formation since the beginning of the non-specific phagocytosis pathway , whereas EhADH participation appeared diminished there ( S1 Fig ) . This gives further evidence of the presence of different mechanisms for cargo ingestion by trophozoites and distinct functions for both proteins in these pathways . Negatively stained preparations observed by TEM showed that rEhVps32 forms oligomers in vitro ( Fig 6 ) that are similar to those formed by Vps32 protein in other cell types [47] . Recognition of these structures by αrEhVps32 antibodies , confirmed the capacity of EhVps32 to form oligomers . Size exclusion chromatography corroborated that EhVps32 purified protein forms oligomers . Thus , it is logical to assume that fluorescence due to αrEhVps32 antibodies , detected on phagosomes during phagocytosis , may correspond to EhVps32 oligomers in vivo . This assumption was strengthened by the differential arrays observed in trophozoites by confocal microscopy using αpEhVps32 and αrEhVps32 antibodies ( Fig 6 ) , that might specifically detected monomers and oligomers , respectively . Confocal microscopy , TEM and SEM assays using pNeoEhvps32-HA transfected trophozoites evidenced the presence of tunnel-like and helicoidally arrays of different size ( Figs 7 and 8 ) . Some of these arrays are similar to those described for Vps32 and its homologues in other systems [46 , 47] . The filaments forming these structures ( 75–100 nm ) appeared wider than the filaments formed in vitro by oligomerization of the purified rEhVps32 ( 10–75 nm ) . This may be explained because , in vivo , in addition to EhVps32 , other proteins may be in these structures that were more abundant during phagocytosis , forming part of phagocytic cups and connecting phagosomes ( Fig 7 ) . Nakada-Tsukui , et al ( 2009 ) also visualized tunnel-like structures in E . histolytica trophozoites during phagocytosis using the PtdIns ( 3 ) P biomarker [3] . However , further studies are necessary to define the relationship of structures reported here with those reported by them . An excess of EhVps32 in the cell and its permanent expression due to the presence of pNeoEhvps32-HA plasmid leads to an increase of membrane rings and filaments , whose fine structure was revealed by ultrastructural studies , in which EhVps32 was present ( Fig 8 ) . Differences in size of these arrays could be due to distinct stages of oligomerization . Growth in size and an increase in number of these structures may eventually lead to cell lysis , explaining why the pNeoEhvps32-HA transfection produced healthy trophozoites only until 72 h transfection . In non-transfected trophozoites , EhVps32 oligomerization may be controlled by the EhVps4 AAA ATPase activity [35] , that in other eukaryotes catalyzes the dissociation of ESCRT-III components [27 , 28] . In pNeoEhvps32-HA transfected trophozoites , the excess of EhVps32 could alter the equilibrium between EhVps32 and EhVps3 AAA ATPase . Although we cannot discard the presence of class E vacuoles formed by alterations in the phagocytosis process due to EVps32 overexpression , the membranous concentric structures found here do not seem to correspond to class E vacuoles . This assumption is based on: i ) overexpression of EhVps32 promoted a higher rate of phagocytosis and did not abolish it , as it was the case for Bro1-truncated transfected trophozoites that resulted to be dominant negative mutants [16] . In these trophozoites , Bro-1 recruited important proteins for phagocytosis and leaded to the formation of class E vacuoles . ii ) We did not detect empty phagosomes ( without erythrocytes ) stained by αrEhVps32 or αEhADH antibodies . iii ) The novel structures described here appeared on erythrocytes-containing phagosomes of wild type and transfected trophozoites , although they are in smaller number in wild type trophozoites . v ) These structures are very similar to those reported in other systems as ESCRT III structures involved in endocytosis [46] . vi ) EhVps32-silenced G3 trophozoites exhibited a low rate of erythrophagocytosis . In addition to the low expression of EhVps32 , the EhADH and actin re-localization and the morphological alterations ( Fig 9 ) , may explain this erythrophagocytosis activity . A number of studies have identified at least 50 Vps genes and proteins in yeast and mammals . All they are involved in vesicle trafficking , forming complexes known as ESCRT , retromer , CORVET , HOPS , GARP and PI3K-III [52] . Except for a study where Nakada-Tsukui et al [53] have characterized a retromerlike complex formed by Vps26 , Vps29 and Vps35 , all them EhRab7A-binding proteins , little is known about the orthologues of Vps proteins in E . histolytica . Among proteins of ESCRT complex , only EhVps32 ( in this paper , [16] ) , EhADH and Vps4 AAAtpase [16 , 35] have been studied . Therefore , it is relevant to characterize the Vps proteins in a unicellular organism with a very active membrane fusion and fission . Knowledge of Vps’s will provide a basis for understanding these events in E . histolytica . Learning more on the vesicular trafficking across species , starting with an antique protozoan parasite will supply a basis for further addressing specific roles of Vps , not only in E . histolytica , but also in other organisms . In conclusion , EhVps32 is a vacuolar protein of the ESCRT-III complex that formed oligomers as it does its homologues in humans and yeast . Besides , this protein was involved in phagocytosis , interacted with EhADH in acidic vesicles , co-localized with Gal/GalNac lectin and actin and formed structures unveiled here . There are many reports on the role of Vps32 in endocytosis; however , this is the first study of the Vps32 role during phagocytosis in a unicellular eukaryotic organism , and its active participation in this event that is crucial for virulence expression of the parasite .
Trophozoites of E . histolytica ( strain HM1:IMSS ) clones A and G3 ( kindly provided by Dr . David Mirelman , from Weizmann Institute of Science , Israel ) were axenically cultured in TYI-S-33 medium at 37°C [45 , 54] and harvested in logarithmic growth phase . All experiments presented here were performed at least three times by duplicate . EhVps32-silenced G3 trophozoites were initially selected by adding 1 μg/ml of Neomycin ( G418 , Gibco ) to the medium and then cultured in 7 μg/ml G418 , before performing the experiments . Escherichia coli BL21 ( DE3 ) pLysS bacteria ( Invitrogen ) were transformed with the pGEX-5X-1-EhVps32 plasmid containing the full open reading frame of Ehvps32 gene to produce a GST-tagged EhVps32 recombinant protein ( rEhVps32 ) , which was purified as described [16] . rEhVps32 ( 50 or 150 μg for each animal , respectively ) emulsified in Titer-Max Classic adjuvant ( Sigma ) was subcutaneously and intramuscularly inoculated into Balb/cJ male mice and into New Zealand male rabbits . Two more doses of rEhVps32 ( 25 or 100 μg for each animal , respectively ) were injected at 20 days intervals and then , animals were bled to obtain αrEhVps32 antibodies; preimmune serum was obtained before immunization . Additionally , EEYDRKRMEMELEKAKEC polypeptide ( 27 to 44 residues in EhVps32 ) was synthesized together with KLH ( Keyhole Limpet Hemocyanin ) tag to increase the immunogenicity ( GenScript ) . Rabbits were immunized with 100 μg of this polypeptide and then , they received two more immunizations ( 50 μg each ) to generate αpEhVps32 antibodies . Trophozoites ( 108 ) were harvested , washed twice with 19 mM potassium phosphate buffer , pH 7 . 2 , and 0 . 27 M NaCl ( PD solution ) . Cellular pellet was resuspended to 2 × 107 cells/mL in PD solution containing 10 mM MgCl2 and mixed with an equal volume of 1 mg/mL concanavalin A . After 5 min , cells were spun at 50 g for 1 min . The supernatant was discarded and cellular pellet was resuspended in 12 mL of 10 mM Tris-HCl buffer , pH 7 . 5 , containing 2 mM phenylmethylsulfonyl fluoride ( PMSF ) and 1 mM MgCl2 . After 10 min swelling in hypotonic buffer , cells were homogenized by 20 strokes in a glass Dounce homogenizer with a tight-fitting pestle ( Wheaton Scientific Div . ) . Cellular lysis and membrane sheets formation were verified by phase-contrast microscopy . The homogenate was layered over a two-step gradient consisting of 8 mL of 0 . 5 M mannitol over 4 mL of 0 . 58 M sucrose , both in Tris buffer , and spun at 250 ×g for 30 min . Material remaining at the top of 0 . 5 M mannitol was centrifuged at 40 000 ×g for 1 h to separate soluble molecules ( cytoplasmic fraction ) from small membrane fragments and vesicles ( internal membranes ) . Large plasma membrane fragments and other heavy debris formed a tight pellet at the bottom of the gradient ( crude membrane fraction ) . This pellet was resuspended in 1 mL Tris buffer containing 1 M α-methyl mannoside and left on ice for 40 min with occasional mixing . Plasma membranes free of concanavalin A were diluted into three volumes of Tris buffer , homogenized by 80 strokes with a glass Dounce homogenizer , layered on a 20% sucrose Tris gradient and spun for 30 min at 250 g . Vesiculated plasma membranes floating above the initial sucrose layer were collected and then concentrated by centrifugation at 40 , 000 g for 1 h . The pellet , enriched in plasma membranes , was resuspended in Tris buffer . All steps were performed at 4°C [55] . Trophozoites lysates ( 30 μg ) or cytoplasmic or membrane fractions or internal membranes or plasmatic membranes obtained as described [2 , 55] were separated in 12% SDS-PAGE , transferred to nitrocellulose membranes and probed with mouse αrEhVps32 ( 1:500 ) , rabbit αpEhVps32 ( 1:3 000 ) , mouse αGal/GalNac lectin ( kindly given by Dr . William A . Petri Jr , University of Virginia , USA ) ( 1:100 ) or mouse αactin ( 1:2 000 ) antibodies . Then , membranes were incubated with the corresponding α-mouse or α-rabbit HRP-labeled secondary antibodies ( Zymed; 1:10 000 ) , respectively , and revealed with ECL Prime western blotting detection reagent ( GE-Healthcare ) . For some experiments , αpEhVps32 antibodies were pre-incubated overnight ( ON ) with 100 μg of rEhVps32 purified protein or membranes were pre-incubated with αrEhVps32 antibodies before incubation with αpEhVps32 antibodies . Trophozoites were grown on coverslips , fixed with 4% paraformaldehyde ( PFA ) at 37°C for 1 h , permeabilized with 0 . 2% Triton X-100 or non-permeabilized , and blocked with 10% fetal bovine serum ( FBS ) in PBS . Then , cells were incubated with mouse αrEhVps32 ( 1:100 ) or rabbit αpEhVps32 ( 1:100 ) antibodies , at 37°C for 1 h , followed by incubation for 1 h with α-mouse or α-rabbit FITC-labeled secondary antibodies ( Zymed; 1:100 ) , respectively . For co-localization experiments , samples were incubated first with mouse αrEhVps32 and rabbit αEhADH ( 1:100 ) or rabbit αrEhVps32 ( 1:100 ) and mouse αGal/GalNac lectin ( 1:25 ) , followed by the corresponding α-mouse FITC-labeled , α-rabbit FITC-labeled , α-mouse Pacific blue-labeled , α-rabbit TRITC-labeled , α-mouse TRITC-labeled and α-rabbit Cy5 ( Zymed , 1:100 ) secondary antibodies . For some experiments , Rhodamine-phalloidin ( Sigma , 1:100 ) was employed to detect actin . For co-localization with Lysotracker , live trophozoites were incubated ON with 2 μM Lysotracker red ( Molecular Probes ) and then , with mouse αrEhVps32 antibodies as described above . In some experiments , nuclei were counterstained with 2 . 5 μg/ml 4’ , 6-diamidino-2-phenylindole ( DAPI; Zymed ) for 5 min . All preparations were preserved using Vectashield antifade reagent ( Vector ) , examined through a Carl Zeiss LMS 700 confocal microscope and processed with ZEN 2009 Light Edition software ( Zeiss ) . To quantify co-localization , 1 μm z-stacks of entire cells or an area around plasma membrane were analyzed using the Just Another Co-localization Plugin ( JACoP ) [56] in the Image J 1 . 48i software [57] . Each point represented an average of 12–25 cells and values are given as means ± standard error . Trophozoites were incubated with human erythrocytes ( 1:25 ratio ) or with 2 μg/ml FITC-dextran ( 70 kDa , Sigma ) for 5 , 10 , 30 , 45 and 60 min at 37°C and then , processed for immunofluorescence assays as described above . For erythrophagocytosis assay , hemoglobin concentration was quantified by spectrophotometry at OD400 [50] . In parallel , samples of all interaction times were stained by 2 mg/ml 3 , 3’ diaminobenzidine ( Sigma ) [58] . For other experiments , the proportion of erythrocytes inside phagosomes ( decorated by αEhVps32 or αEhADH antibodies or Lysotracker or FITC-microspheres ) with relation to total ingested erythrocytes per trophozoite was evaluated in at least 20 confocal images . For non-specific phagocytosis assays , trophozoites were incubated with FITC-label latex microspheres ( 1 μm diameter; 1:100; Molecular Probes ) at 37°C for different times and then , exhaustively washed and processed for immunofluorescence . Trophozoites were lysed with 10 mM Tris-HCl , 50 mM NaCl and 100 mM protease inhibitors ( PHMB , IA , NEM and TLCK ) , followed by cycles of freeze-thawing in liquid nitrogen and vortexing . In parallel , 200 μl of recombinant protein G-agarose ( rProtein-G; Invitrogen ) were incubated with 100 μg of rabbit αEhADH antibodies or preimmune serum for 2 h at 4° C , with gentle stirring . Then , rProtein-G beads were washed with 0 . 5% BSA in PBS , followed by additional washes with PBS for 5 min , under gentle stirring and centrifuged at 11 , 600 g for 2 min . Trophozoites lysates ( 1 mg ) were pre-cleared with 200 μl of rProtein-G ( previously blocked with 2% BSA ) and incubated 2 h at 4°C under gentle stirring . Samples were centrifuged at 11 , 600 g to obtain the supernatant that was added to rProtein-G previously incubated with antibodies . Preparations were incubated ON at 4° C and then , beads were recovered by centrifugation . After washes with PBS , 60 μl of 4 x sample buffer ( 40% glycerol , 240 mM Tris-HCl pH 6 . 8 , 8% SDS , 0 . 04% bromophenol blue and 5% β-mercaptoethanol ) were added . Samples were boiled for 3 min and centrifuged again at 11 , 600 g for 2 min at 4° C . Supernatant ( 30 μl ) was loaded into 12% SDS–PAGE and subjected to western blot assays . Samples were fixed with 2 . 5% ( v/v ) glutaraldehyde in 0 . 1M sodium cacodylate buffer , pH 7 . 2 , for 60 min . Then , they were postfixed for 60 min with 1% ( w/v ) osmium tetroxide in the same buffer . After dehydration with increasing concentrations of ethanol and propylene oxide , samples were embedded in Polybed epoxy resins and polymerized at 60°C for 24 h . Thin sections ( 60 nm ) were contrasted with uranyl acetate and lead citrate before being examined in a Joel JEM-1011 transmission electron microscope . For gold immunolabeling experiments , trophozoites were fixed with 4% PFA and 0 . 5% glutaraldehyde in PBS for 1 h at room temperature ( RT ) . Samples were embedded in LR White resin ( London Resin Co ) and polymerized under UV at 4°C ON . Thin sections were incubated ON with mouse αrEhVps32 antibodies ( 1:20 ) and then , ON at RT with α-mouse IgGs antibodies conjugated to 20 nm gold particles ( Ted Pella Inc; 1:60 ) . rEhVps32 was purified as described [16] and the GST-tag was removed using Factor Xa protease ( GE-Healthcare ) , according to manufacturer’s instructions . rEhVps32 ( 2 . 5 μg in 5 μl ) was pipetted onto the surface of the formvar- coated copper grids . After 5 min , samples were blotted off with filter paper and stained with 2 . 5% uranyl acetate . Grids were then left to air dry and carbon coated . In some experiments , samples were treated with αrEhVps32 antibodies , followed by gold-labeled secondary antibodies ( 30 nm ) . Preparations were examined through a JEM-1011 transmission electron microscope . Glutaraldehyde fixed samples were dehydrated with increasing concentrations of ethanol and CO2 critically point dried in a Samdri apparatus . Then , they were gold coated in an ion sputtering device ( Jeol-JFC-1100 ) and examined with a Jeol JSM-7100F field emission scanning electron microscope . Purified rEhVps32 without GST-tag was subjected to size exclusion chromatography using a gel filtration column ( 2 . 5 cm x 30 cm ) , packed with 45 ml of Sephacryl-HR 100 ( GE Healthcare ) , previously equilibrated with buffer A ( 20 mM Tris-HCl pH 8 . 0 , 100 mM NaCl and 1 mM EDTA ) and resolved at a flow rate of 1 ml/min using a NGC Q10 chromatographic system ( Bio-Rad ) . The column was equilibrated with gel filtration standards containing thyroglobulin ( 670 kDa ) , γ-globulin ( 158 kDa ) , ovalbumin ( 44 kDa ) , myoglobin ( 17 kDa ) and vitamin B12 ( 1 . 35 kDa ) ( Bio-Rad ) . The elution volume ( Ve ) of the ovalbumin , myoglobin and vitamin B12 were used to obtain the calibration curve by plotting the Log MW vs Kva . The elution volume of the first peak ( thyroglobulin and γ-globulin ) was taken as the void volume ( Vo ) to estimate Kva . Eluted fractions were separated by 12% SDS-PAGE and submitted to western blot assays , using αrEhVps32 antibodies . PCR-amplified Ehvps32 full gene with the hemagglutinin ( HA ) tag in the 3’ end were cloned into pJET1 . 2/blunt plasmid ( Fermentas ) , accordingly to manufacturer’s instructions . Then , Ehvps32-HA gene was subcloned into BamHI and KpnI sites of pExEhNeo ( pNeo ) plasmid [59] , producing the pNeoEhvps32-HA construct . E . coli DH5α bacteria were transformed with pNeoEhvps32-HA or pNeo plasmids . Plasmids were purified using Qiagen Midi kit ( Qiagen ) and automatically sequenced . To perform transfection , 3 x 105 trophozoites were cultivated ON with 5% CO2 , then , washed with M199 medium ( Sigma ) and incubated with M199 medium supplemented with 15% FBS . Subsequently , transfection mix ( 20 μg of plasmid , 20 μl of Superfect [Qiagen] in 100 μl of M199 medium ) was added and incubated 10 min at RT . Trophozoites were cultivated for 3 h at 37°C and 5% CO2 . Finally , cells were cooled and transferred to a tube with 10 ml of TYI pre-warmed medium and cultivated for 12 h at 37°C . The first 431 bp from the 5' end of the Ehvps32 gene were PCR-amplified and cloned into pJET1 . 2/blunt plasmid and then , subcloned into pSAP2/Gunma plasmid , downstream of the 5' upstream segment of the EhAp-A gene [45] , using a 5' StuI site and a 3' SacI site with the following primers: forward , 5'-AGCTAGGCCTATGTCTTGGTTCAGAAGAAATACT-3'; reverse , 5'-GCATGAGCTCATGTCTTGTAAATCTTCACCTAAA-3' ( restriction sites are underlined ) . Trophozoites of G3 clone were transfected with pSAP2/GunmaEhVps32-431 plasmid , using the Superfect-based method as stated above . Statistically analyses were performed by t-Student test , using GraphPad Prism 5 . 0 software . The scores showing statistically significant differences are indicated with asterisks in the graphs . The corresponding p values are indicated in the figure legends . EhVps32 ( C4M1A5/EHI_169820 ) , EhVps2 ( C4LZV3/EHI_194400 ) , EhVps20 ( C4MAC7/EHI_066730 ) , EhVps22 ( C4LXI0/EHI_131120 ) , EhVps23 ( C4LUR9/EHI_135460 ) , EhVps24 ( C4M2Y2/EHI_048690 ) , EhVps26 ( Q53UB0/EHI_137860 ) , EhVps27 ( C4LYX5/EHI_117910 ) , EhVps29 ( Q9BI08/EHI_025270 ) EhVps32 ( C4M1A5/EHI_169820 ) , EhVps35 ( Q6Y0Y5/EHI_002990 ) , EhVps36 ( CALTE5/EHI_045320 ) , EhVps37A ( C4MAH4/EHI_077870 ) , EhVps37D ( C4M6J5/EHI_060400 ) , EhHse1 ( C4M9E1/EHI_091530 ) , EhVta1 ( C4M0R8/EHI_010040 ) , EhADH112 ( Q9U7F6/EHI_181220 ) , EhCP112 ( Q9U7F7/EHI_181230 ) , Gal/Gal lectin ( C4LTM0/EHI_012270 ) , EhC2PK ( C4M3C4/EHI_053060 ) , EhCaBP1 ( C4M7Q6/EHI_120900 ) , EhAK1 ( C4M9G9/EHI_105830 ) .
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Trophozoites of E . histolytica represent an excellent model to study endosomal-sorting complex required for transport components due to their high endocytic activity and vesicle trafficking . The key role of EhVps32 on phagocytosis is supported by: i ) its presence on phagosomes , ii ) its interaction with EhADH ( an erythrocytes receptor ) , Gal/GalNac lectin and actin , iii ) the higher rate of erythrophagocytosis showed by EhVps32 overexpressing trophozoites , iv ) the diminish rate of phagocytosis in EhVps32-silenced G3 trophozoites , and v ) its location in erythrocytes-containing acidic phagosomes . Here , we discovered the presence of membranous concentric helicoidally and tunnel-like structures constituted by EhVps32 and EhADH that may have a dynamic role in membrane remodeling and in the generation of intraluminal vesicles in the phagosomes . Elucidating molecular mechanisms of endocytosis-exocytosis pathways will help us to better understand the pathogenic process of E . histolytica and develop new drugs for diagnosis and vaccine methods .
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[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2015
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EhVps32 Is a Vacuole-Associated Protein Involved in Pinocytosis and Phagocytosis of Entamoeaba histolytica
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Neurons in the insect antennal lobe represent odors as spatiotemporal patterns of activity that unfold over multiple time scales . As these patterns unspool they decrease the overlap between odor representations and thereby increase the ability of the olfactory system to discriminate odors . Using a realistic model of the insect antennal lobe we examined two competing components of this process –lateral excitation from local excitatory interneurons , and slow inhibition from local inhibitory interneurons . We found that lateral excitation amplified differences between representations of similar odors by recruiting projection neurons that did not receive direct input from olfactory receptors . However , this increased sensitivity also amplified noisy variations in input and compromised the ability of the system to respond reliably to multiple presentations of the same odor . Slow inhibition curtailed the spread of projection neuron activity and increased response reliability . These competing influences must be finely balanced in order to decorrelate odor representations .
The olfactory system must accomplish two seemingly conflicting goals —generate distinct representations of different odors , yet maintain stable representations of a repeated odor despite variability introduced by noise . These conflicting ends , separability and reliability , are met as information about odors traverses multiple levels of the olfactory system . Odor detection begins when odorant molecules bind to olfactory receptor neurons ( ORNs ) and initiate cellular mechanisms leading to the opening of ion channels , the depolarization of the receptor neuron cell membrane , and the generation of action potentials [1] . Because each receptor type responds better to some odorants than others , the representation of an odor can be described as a spectrum of activation patterns across the receptor population [2] , [3] , [4] . Similar odors are presumably represented by similarly distributed patterns of activation [5] . Information about most odors , to a first approximation , is encoded in ORNs in a combinatorial manner [6] . In insects , these patterns of receptor activation are then conveyed to an olfactory structure called the antennal lobe ( AL ) , which contains far fewer neurons than there are ORNs ( in the locust , for example , ∼90 , 000 ORNs converge onto just 830 projection neurons ( PNs ) and 300 local inhibitory interneurons ( LNs ) [7] ) . Antennal lobe neurons respond to odor-elicited input with a rich variety of spatiotemporal patterns [8] , [9] , [10] , [11] . Many investigators , beginning with Adrian [12] , have suggested the temporal pattern of spiking in these second order neurons encodes information about odor quality . These spatiotemporal patterns of activation unfold along multiple spatial and temporal scales , transiently and successively recruiting different groups of neurons , contributing to the progressive decrease in the overlap between odor representations in the AL [11] , [13] , [14] . What network interactions shape spatio temporal patterning in the AL to accomplish essentially opposed information processing goals: that representations of different odors may be rapidly distinguished; yet the same odor presented under changing environmental circumstances is reliably identified ? To address this question we examined the contributions of two factors in a realistic model of the locust AL [15] , [16]: 1 ) lateral local excitation between PNs mediated by putative local excitatory interneurons [17] , [18] , [19]; and 2 ) slow inhibition from LNs to PNs [15] , [20] . We propose that these two complementary excitatory and inhibitory influences must be optimized to achieve both reliable and separable odor representations .
In the insect olfactory system input from ORNs converges into PNs and LNs of the AL . With a model of the AL network we sought to test the complementary effects of fast lateral excitation and slow inhibition , both of which have been observed in vivo . The model network was based on locust anatomy and consisted of reciprocally connected PNs and LNs . Lateral excitation was implemented by a class of excitatory interneurons ( eLNs ) that have been described in the AL of Drosophila [17] , [18] , [19] . Although experiments to directly label eLNs have not yet been performed in the locust , we infer they exist in this species from their prevalence in other insect species ( fly [21] , moth [22] ) , and from the broadly-tuned and complex responses of locust PNs consistent with widespread excitation . In the model eLNs receive input from both the PN and LNs and also provide lateral excitation to both these classes of neurons ( see Figure 1a for a schematic diagram of the network architecture ) . To test the network's responses to external input we simulated two classes of odor stimuli: odors represented by blue traces in Figure 1b are similar to each other ( they activate overlapping sets of neurons ) but are highly dissimilar to the odor represented by the red trace ( see Methods ) . Input was provided to PNs , LNs and eLNs . We reasoned that unrestricted lateral excitation within the AL could potentially recruit neurons explosively , and we hypothesized that slow inhibition ( mediated by GABAB receptors ) could provide both a suitable counterbalance to this , and an ability to generate broadly distributed , temporally structured responses in the PN ensemble . Indeed , our simulations showed that a balance of lateral excitation and slow inhibition prevented cascading excitation that could recruit all neurons in the network , and at the same time allowed some neurons that receive sub–threshold input directly from ORNs to become activated ( Figure 2a , compare top left and bottom right panels ) . Note that fast lateral inhibition mediated by GABAA receptors was present in all the simulations including those in which slow inhibition was removed ( Figure 2a , top left ) . Fast inhibition is responsible for the suppression of PN responses in Figure 2a , top left , despite the lack of GABAB mediated slow inhibition . To visualize these population-wide responses , we calculated the peri–stimulus time histogram ( PSTH ) for each PN and projected the collective dynamics of the model's three hundred PNs onto the first three principal components ( Figure 2b ) . Before stimulus onset the trajectories wandered near the baseline ( marked in Figure 2b ) . Upon odor stimulation the trajectory moved toward a state defined by increased population activity . The trajectory then returned to the baseline following odor termination . As the strength of lateral excitation increased , the resulting trajectories swept out increasingly wide loops ( Figure 2b , top panel ) , indicating stronger population responses . Increasing the strength of slow inhibition had the opposite effect ( Figure 2b , bottom panel ) , a general decrease in the activity of PNs . Further , we examined the time taken for a stimulus to push the system to its maximal distance from baseline . Because the response amplitude was determined by the number of PNs that were recruited during odor stimulation , active state properties varied with values of lateral excitation and slow inhibition . To compare the speed with which networks with different characteristics reached maximum response amplitude , we normalized the amplitude of the trajectories by the maximum amplitude and plotted the different traces as a function of time . We found that increasing lateral excitation increased the maximum value of the response amplitude ( data not shown ) . The time taken by the system to arrive at its maximum distance ( compare the traces in Figure 2b middle panel ) also increased with increasing excitation . Increasing slow inhibition , on the other hand , caused a less pronounced effect in the opposite direction . We also found that the baseline shifted as a function of lateral excitation since more PNs were active even in the absence of an odor stimulus ( Figure 2b right ) . A histogram of the responses of PNs ( Figure 2c ) showed that , in the absence of lateral excitation , very few neurons generated more than 10 spikes during an odor presentation ( green and black traces ) . The green trace shows that in the absence of lateral excitation , at the highest value of slow inhibition simulated here ( ) , most of the PNs remained silent except for those receiving supra–threshold input . When lateral excitation was increased in the model , the response distribution shifted toward higher density spiking ( blue trace ) . Introducing slow inhibition modulated the spread of activity caused by excitation ( red trace ) . Next , to characterize each PN's tuning properties we simulated a broad range of odors by successively displacing the Gaussian input ( Figure 1b ) by five unit steps . Figure 3a shows the response of a representative set of three PNs to an array of 21 odors; the input each PN received as a function of 21 different odorants is plotted as a blue trace in the top panels . We found the responses of PNs to this array of odors ( red traces ) depended on the amount of lateral excitation and slow inhibition . In the absence of both types of lateral input ( top left panels ) , responses of PNs were driven entirely by the input simulating the activity of ORNs , and each PN responded only when it received direct supra–threshold input . However , consistent with recent studies in the fly showing that PNs not receiving direct input from ORNs may be activated by indirect input from other PNs via lateral excitation [18] , our model showed that , as the value of lateral excitation was increased , responses of PNs became less selective ( the mean response over the duration of the odor presentation is shown by the red trace ) ( Figure 3a , top right panels ) . Beyond broadening the response to an array of odors , the addition of lateral excitation led to qualitative changes in the shape of the response curve ( for example , PN1 in the top right panels started to show strong response to odors 15–21 , Figure 3a ) . Thus , the output of PNs may interact with the input from ORNs in a nonlinear manner . Increasing slow inhibition caused opposite effects in PNs , narrowing the widths of their tuning curves ( Figure 3a , bottom left panels ) . Including both lateral excitation and slow inhibition in the model allowed PNs to respond reliably and specifically; however , it also allowed a nonlinear remapping of the output of ORNs to the responses of PNs . This nonlinear transformation of ORN activity is evident in Figure 3a ( compare top left and bottom right panels ) . The example PNs shown in Figure 3a were chosen to show the range of response patterns observed in PNs given different parameter values . The specific proportions of each type of response varied considerably across the range of parameter values . Further , we analyzed the roles lateral excitation and slow inhibition play to shape the complexity of spatiotemporal responses of PNs to an odor presentation . We first determined each PN's response to a panel of 21 odors ( similar to each panel in Figure 3a ) . To provide a measure of the complexity of the response pattern of each PN to an array of odors we then used the following procedure . For a given PN , the response to each odor of a set of 21 odors was binned ( 50 msec bins ) . The PN's response to the entire odor set was represented as a trajectory in a 21 dimensional space ( each dimension corresponding to one of the odors ) . If the PN's response remained static over the duration of the odor presentation , then the trajectory would appear as a single point in this space . The complexity of this trajectory reflected both the diversity of the PN's responses to the set of 21 odors and the variability of these responses over time . We computed the principal components of the [21 odors ×20 time bins] array and then determined the variance explained by each 21 dimensional principal component ( given by its eigen value ) . We then computed the number of principal components required to explain at least 80% of the variance observed in PN response patterns . This number provided a measure of the complexity of the response pattern of each PN to an array of odors . We calculated this number for each of 300 PNs and 10 presentations of each odor stimulus , with varying amounts of lateral excitation and slow inhibition . Thus , we obtained 3000 measurements to assess the complexity of the network response . Finally , we plotted the normalized distribution of these values for different amounts of lateral excitation and slow inhibition . By normalizing the histogram for each value of lateral excitation and slow inhibition , we were able to detect a trend in the peak location of each histogram despite changes in the height of the distribution . Figure 3b shows that , as the strength of lateral excitation grew , the peak of the distribution shifted to higher values , indicating an increase in the complexity in the responses of PNs . Again , slow inhibition had the opposite effect – it led to the recruitment of fewer neurons during odor stimulation , particularly in the absence of lateral excitation ( see Figure 3a , bottom left ) . Note , however , that for larger values of slow inhibition the distribution also became broader . This suggests that while the activity of some PNs was suppressed and became less complex , the activity of other PNs remained diverse across odors and variable over time even given the greatest strength of slow inhibition . Decorrelation of odor representations , a process that reduces the overlap between odors , occurs over the duration of the stimulus presentation . Network interactions between PNs and LNs likely play a crucial role in this process . In this study AL neurons received a stable pattern of input from the ORNs . If the AL neurons respond to this input by generating a spatially distributed but static pattern of activation , then the pattern should not decorrelate over time . Decorrelation over time is only possible if the odor representation is transformed either by network interactions or by temporally varying noise . To determine the degree to which noise can play a role in transforming the odor representation , we first calculated the correlation coefficient between the onset and subsequent epochs of the input vector provided to the PNs ( Figure 4a , blue line ) . We then compared this value with the correlation between the initial responses of 300 PNs to an odor and their responses at subsequent times ( Figure 4a , red line ) . We found that the correlation between the onset and subsequent epochs of the odor response by PNs decreased dramatically over the first 200 ms ( Figure 4a , red line ) whereas the correlation between the onset and subsequent epochs of the input vector decayed to a far lesser extent . This result demonstrates that network interactions within the AL play a crucial role in reducing the similarity between responses of PNs at the odor onset and at later points in time [14] , [23] and that the decorrelation is not driven entirely by noise . Next , we sought to characterize the ability of the population of PNs to differentiate among different odors . We presented a set of 21 odors and calculated the correlation between the responses of PNs to any two odors over time . Together , the correlation coefficients for each 50 ms time window formed a 21×21 matrix . To analyze the AL mechanism responsible for this decorrelation we then calculated the change over time in the correlation coefficient averaged for all similar and , separately , for all dissimilar odors , as a function of increasing amounts of lateral excitation and slow inhibition ( Figure 4b , c ) . We found that over the duration of the odor presentation , responses of the PN ensemble to similar odors became progressively different from one another , as evident in the decreasing correlation coefficients plotted in Figure 4b . In contrast , correlations between responses to dissimilar odors progressively increased ( also compare the left and right panels in Figure 4b over the range from ∼500 to 1500 ms ) . Note , that in the absence of odor input ( <500 ms in our simulations ) very few neurons generated spikes , resulting in activity vectors with many zero elements . The correlations between these vectors therefore tended to be high ( ∼1 . 0 ) . Overall , our findings are in a good agreement with results previously obtained in vivo from the zebrafish olfactory bulb [14] . The decrease in correlation can be attributed in large part to the dynamical behavior of the AL circuitry since a similar decrease is not observed in the input to PNs and LNs ( Figure 4a , top panel ) ; and noise in the input should not play a significant role in decorrelating the odor responses . For similar odors we found that increasing the amount of lateral excitation lead to a decrease in the correlation between odor responses at a given time ( Figure 4b , left panels ) . Increasing the strength of slow inhibition led to a decrease in the correlation between responses , but to a lesser extent than that seen when lateral excitation was increased ( Figure 4c ) . Surprisingly , we observed the opposite in the correlation between dissimilar odors ( Figure 4b , right panels ) . The increase in correlation over time for dissimilar odors may be attributed to the fact that the dissimilar odors were maximally decorrelated to begin with ( note that the correlation coefficient at the odor onset ( ∼500 ms is nearly 0 ) . Lateral excitation tended to recruit additional neurons that were not activated in the absence of excitation ( compare bottom left and bottom right panels of Figure 2a ) . Increasing lateral excitation would increase overlap in the population of neurons recruited by a given odor , thus increasing the correlation coefficient . Indeed , as the strength of lateral excitation increased from 0 . 0 µS to 0 . 001 µS and the degree of overlap presumably increased , the correlation between dissimilar odors also increased ( Figure 4b , right panel , compare lines of different color ) . In contrast , upon increasing the strength of slow inhibition , the overlap between the sets of neurons representing dissimilar odors decreased and led to a concomitant decrease in the correlation ( Figure 4c , right panels ) . Recordings made in vivo from mitral cells in zebrafish olfactory bulb also showed a similar trend [14] . The correlation coefficient ( see analysis in Figure 4 ) and the Euclidean distance between PN activity vectors offer two distinct measures of the separation between odor representations . The correlation coefficient , which is the cosine of the angle between the 300–dimensional PN activity vectors representing the odors during each time window , is based on the normalized vectors and does not change in response to changes in the amplitude ( the norm ) of the vector . The correlation coefficient , therefore , depends mainly on the relative changes of the firing rates of individual PNs . Complex and odor-specific spatiotemporal PN dynamics would lead to rapid decreases in the correlation between odor responses . The correlation coefficient , however , would not change if the firing rates of all PNs increased or decreased proportionally . The Euclidean distance , on the other hand , can change both as a function of the angle and the amplitude of the activity vectors . With these tools we could examine how a change in the strengths of lateral excitation and slow inhibition would modify the distance between representations of similar odors . We anticipated that increasing lateral excitation would recruit more PNs that in turn would generate activity vectors with larger amplitudes . The distance between odor representations would therefore increase with increasing lateral excitation . In order to measure the distance between representations of similar odors ( the Gaussian inputs to the ALs corresponding to each odor were separated by 5 units ) we first constructed a PSTH for each of 300 PNs using 50 ms time bins . For each of these time bins the odor was represented by a 300–dimensional vector of PN activity . We calculated the Euclidean distance between these vectors in each time bin . The distance averaged across all time bins over the duration of an odor presentation was a measure of the distance between odor representations for a given network configuration . We found that an increase in lateral excitation increased the distance between odor representations ( Figure 5a ) . As expected , an increase of slow inhibition led to the opposite trend , decreasing the amplitude of the response and , therefore , decreasing the distance between odors . A constant ratio of excitation to inhibition would correspond to the diagonal on this graph . To determine effect of the ratio of excitation to inhibition on the Euclidian distance and the correlation coefficient , we plotted the distance and correlation over time for different values of the excitation to inhibition ratio ( Figure 5b ) . We found that changing the ratio of excitation to inhibition had a significant impact on the distance . Each row in the matrix ( Figure 5b , middle panel ) shows the Euclidean distance between odor response patterns for a value of the E/I ratio determined by the black trace ( Figure 5b left panel ) . So , the top row corresponds to the minimal ratio ( no excitation ) and the bottom row illustrates distance for the maximal E/I ratio . Plotting all the time series in increasing order of the E/I ratio revealed that increasing the E/I ratio led to a very systematic increase in the distance . To determine whether the correlation coefficient reflected a similar trend , for each value of lateral excitation and slow inhibition we calculated the correlation coefficient between 300–dimensional PN activity vectors generated as the network responded to the two similar odors independently . The correlation coefficients were determined during 50 ms epochs of time and the resulting time series were then plotted in increasing order of the E/I ratio ( Figure 5b , right ) . We did not find any systematic changes in the rate of change of the correlation coefficient as a function of increasing E/I ratio . This suggests that changing E/I ratio leads to a systematic change in the mean amplitude of the PN responses ( e . g . , increase of the firing rates of all PNs ) ; however , not necessarily to a systematic change in the relative balance of the individual PN firing rates . The individual firing rates may increase or decrease depending on the specific values of excitation and inhibition . Animals are able to recognize an odor reliably each time it is presented despite the inevitable small variations in each presentation . Thus , our model of the olfactory system should be robust enough to avoid classifying each encounter with a given odor as unique . Correlations between the activity of PNs generated by one odor and that of another odor provide a measure of how well their representations may be distinguished by follower neurons . The olfactory system should therefore maximize correlations between multiple presentations of an odor while simultaneously minimizing correlations between representations of different odors . We found this could be achieved in our model with a balance of lateral excitation and slow inhibition . In Figure 6a the panels show changes in the correlation coefficient as a function of lateral excitation for two values of slow inhibition ( red and the blue arrows in the panels Figure 6b below . ) Increasing lateral excitation decreased the correlation between similar odors ( Figure 6a , right ) , effectively augmenting the ability of the system to distinguish between these odors . However , it also decreased the correlation between different trials of the same odor presented with noise ( Figure 6a , left ) , thus potentially sacrificing the reliability of responses . Increasing the amount of slow inhibition had different effects depending on the value of lateral excitation , further decreasing the correlation coefficient when lateral excitation was minimal but increasing the correlation coefficient when lateral excitation was stronger ( compare relative position of red and blue curves in Figure 6a ) . A similar trend emerged from a comparison of correlations between multiple presentations of the same odor , indicating that balanced amounts of lateral excitation and slow inhibition are required to decorrelate different odors while maintaining the similarity of responses to different trials of the same odor . We could readily achieve such a balance by maximizing the quantity ( Ctrials+ ( 1−Codors ) ) where Ctrials is the correlation between multiple presentations of the same odor and Codors is the correlation between the representations of different odors ( see Methods for a detailed description of how these quantities were calculated ) . Even for small differences between odors ( the peaks of the input to similar odors are shifted by only 5 units; the maximum possible shift between two odors is 150 units ) , Ctrials and Codors differed in magnitude . These differences implied that the term ( Ctrials+ ( 1−Codors ) ) was not uniform across the parameter space queried . Increasing the strength of both excitatory and slow inhibitory AL connections decreased correlations between trials ( Ctrials; Figure 6b , left ) ; however , at first , it led to an even faster decrease of correlation between similar odors , Codors . The latter corresponded to a rapid increase in the “anti-correlation” parameter ( 1- Codors; Figure 6b , middle ) , so the term ( Ctrials+ ( 1−Codors ) ) increased ( Figure 6b , right ) . When values of excitation and inhibition were larger , the opposite trend emerged – the correlation between trials decreased faster than the correlation between odors ( i . e . , Ctrials decreased faster than ( 1−Codors ) increased ) , so the term ( Ctrials+ ( 1−Codors ) ) decreased ( Figure 6b , right ) . Furthermore , when slow inhibition was increased while the lateral excitation was minimal , we observed an immediate decrease in correlations between multiple trials of the same odor ( Figure 6b , left ) . However , an increase in slow inhibition in the presence of stronger lateral excitation ( e . g . , within the range , Figure , 6b , left ) at first led to a small but reliable increase in correlations between trials of the same odor ( note that the maximum of Ctrials is found for positive values of when is positive ) ; only when slow inhibition was further increased did the correlations decay . Together , these results illustrate an optimal range of excitation and inhibition in which gains in reliability across trials could be balanced against gains in the separation of similar odors ( Figure 6b right ) . While the exact position of this maximum will vary with the choice of optimization function , these results indicate that both non-zero lateral excitation and inhibition are required to achieve a balance between the system's performance in the discrimination of similar odors and the reliable identification of the same odor across multiple trials with noise . The primary reason to compare the correlation between multiple presentations of the same odor versus correlations between similar odors was to understand the network mechanisms that enhance odor classification . In this section we examine how well a simple classification algorithm could differentiate similar odors despite realistic , noisy variations between multiple presentations of the same odor . The correlation between representations of the odor provides a useful metric of distance between representations . Figure 7a shows the average ( over time ) correlation between the responses of two similar odors ( the inputs were separated by five units ) . Each odor was presented 20 times . The resulting correlation matrix consists of two diagonal blocks with higher correlations . These correspond to the correlation between different trials of the same odor . The off–diagonal blocks show a lower mean correlation coefficient between different odors . We used this correlation matrix as a measure of the pair–wise distance between individual presentations of odors . Since lower correlation indicates greater distance , we defined the pair–wise distance , , between representations and as , where are elements of the correlation matrix . This distance was then used to cluster the representations into two groups using a hierarchical clustering algorithm ( see Figure 7b and the Methods section ) . Since we knew a priori which representations corresponded to a particular odor , we could track the individual representations that were misclassified . We conducted the same analysis with nine more pairs of similar odors and different values of the relevant parameters , lateral excitation and slow inhibition . The average proportion of errors for these nine odor pairs is shown as a function of and ( Figure 7c ) . Optimal odor classification implies that the errors in classification were minimized . The pattern of errors that resulted from this classification could be broadly inferred from the measure Ctrials+ ( 1−Codors ) used in the previous section ( see Figure 6b , right panel ) . Regions of minimum error in Figure 7c ( ) approximately coincided with the regions where Ctrials+ ( 1−Codors ) were high ( ) , while the errors were maximal when Ctrials+ ( 1−Codors ) was low . Consistent with the correlation analysis ( Figure 6b ) , when lateral excitation was present ( e . g , for >0 . 0002 µS ) , the lowest classification error was obtained for non-zero values of slow inhibitory conductance ( Figure 7c ) . A high error rate was obtained when lateral excitation was maximized and was set to zero ( see bottom right corner in Figure 7c ) . For these parameter values , most PNs were recruited and overlaps between representations of the same odor and also between different odors were large . The exact value of and when the errors were minimized did not coincide exactly with the maximum of Ctrials+ ( 1−Codors ) . However , a qualitative demarcation between regions of high and low error rates could be inferred from Ctrials+ ( 1−Codors ) .
In insects , tens of thousands of ORNs converge onto a few hundred excitatory PNs and local inhibitory neurons in the AL [7] . Interactions among AL neurons contribute to the generation of spatiotemporal activity patterns that unfold over multiple timescales . This process may contribute to a progressive decrease in the overlap between representations of similar odors , a phenomenon that was originally described in the olfactory bulb of zebrafish [14] . In this study , using a realistic model of the locust AL , we examined the potential contributions of lateral excitatory and inhibitory connections to this temporal decorrelation . Excitatory interneurons ( eLNs ) have recently been described in the Drosophila AL [17] , [18] , [21] and are likely to exist in locust as well , although no direct tests of their existence have yet been reported . In the Drosophila AL , a recently identified class of local cholinergic cells exhibits a widespread pattern of innervation that is not glomerulus–specific [19] . Electrophysiological recordings indicate that these cells tend to recruit PNs that receive zero or sub–threshold input from ORNs [18] , potentially boosting the transmission of signals generated in the AL to follower neurons in the mushroom body [17] . In contrast to lateral excitation , slow inhibition [24] decreases the average activity of the AL over time scales spanning hundreds of milliseconds . ( This gain modulation is distinct from the role of fast inhibition that , in concert with reciprocal excitation from PNs , is known to produce a fast oscillatory rhythm and synchronization of PN spikes over relatively fast time scales [15] , [16] , [24] . ) We tested the hypothesis that both lateral excitatory and slow inhibitory connections , in proper balance , are required to achieve two apparently opposing goals during the processing the olfactory stimuli: to separate different but chemically similar odors ( sensitivity , capacity ) and to identify repeated instances of the same odor in a noisy environment ( reliability ) . We found that lateral excitation improves the sensitivity of the olfactory system by recruiting additional PNs that do not receive direct input from ORNs , thereby amplifying differences between the representations of similar odors [17] , [18] , [19] , [25] . Increased sensitivity , however , could compromise the robustness of the AL's responses to multiple presentations of the same odor when noisy variations were included in the input . Slow inhibition could curtail the spread of PN activity and introduce reliable variations in spatiotemporal patterning over a time scale of hundreds of milliseconds . This effect depended on the level of lateral excitation . We found that increasing slow inhibition could lead to an increase in the correlation between trials of the same odor ( increase in reliability ) only when non-zero lateral excitation was implemented . Our study shows that both slow inhibitory connections and lateral excitatory connections mediated by local interneurons are required to enhance the decorrelation of similar odors while keeping the representations of odors robust across multiple encounters in the presence of noise . The decorrelation achieved by excitation and inhibition , in turn , enhances the ability of the olfactory system to classify odors; the error rate of classification was minimal in the presence of the balanced slow inhibitory and lateral excitatory connections . ORNs are preferentially sensitive to some odors . This preference is manifest in the non-uniform firing rate distribution of ORNs with a high peak at low frequencies and a long tail over high frequencies [17] . The optimal distribution of neuron firing rates for odor discrimination would be one without peaks [26] , [27] , [28] . Such a response distribution may be achieved by a nonlinear transformation function , implemented in the AL , with a high gain for low firing rates that saturates for high firing rates , thereby employing the dynamic range of the PNs more effectively [17] . Our study suggests that this transformation may be achieved by the coordinated efforts of lateral excitation and slow inhibition in the AL . Indeed , in our model , increasing the strength of lateral excitatory connections mediated by local excitatory interneurons increased the fraction of PNs responding to an odor and also increased firing rates in many responding PNs . However , we found this effect could be balanced by slow inhibitory connections that reduced the firing rates of the most active PNs while maintaining a broad response profile across the PN population ( Figure 2c ) . These results lead us to predict a strong link between odor decorrelation and the optimization of odor representations: maximal decorrelation is achieved in the AL network when firing rates of PNs are optimally distributed . In our simulations we focused on the role network interactions play in decorrelating odor representations . Another contributor to the temporal patterning in the AL driving decorrelation appears to be the response dynamics of olfactory receptor neurons . Recent studies have characterized the temporal responses of ORNs by their response latency , rise time and adaptation to a prolonged odor presentation . Variations in these temporal properties , while not causing decorrelation in the responses of the ORNs themselves [29] , may enable lateral inhibition in the AL to have this effect . Our model does not test the roles specific forms of lateral excitation and inhibition may play in the network , but rather argues more fundamentally that a balance of excitatory and inhibitory drive to PNs is required to enhance the ability of the system to decorrelate odor representations . Our study suggests local excitatory and inhibitory interneurons of the insect AL provide balanced , functional circuitry that significantly reformats and optimizes odor representations in the AL network . While the effects of excitation and inhibition would cancel each other if averaged across the entire population of AL neurons , heterogeneous interconnectivity among the lobe's neurons would allow a given receptor to trigger responses dominated by inhibition in some PNs and by excitation in others . The combined effect of excitation and inhibition may provide an improved representation of the identity of an odor by being both robust against noise and sensitive to relatively small variations in the identities of active ORNs .
Individual projection and local inhibitory interneurons were modeled by a single compartment that included voltage and Ca2+ dependent currents described by Hodgkin–Huxley kinetics . Consistent with locust physiology , isolated PNs displayed overshooting Na+ spikes at a fixed frequency throughout DC stimulation , and local inhibitory neurons , by contrast , fired low amplitude Ca2+ spikes and displayed spike frequency adaptation caused by Ca2+–dependent potassium currents . A separate population of excitatory local interneurons with properties identical to the PNs was also simulated . The model AL network consisted of 300 PNs , 100 local inhibitory interneurons ( LNs ) and 50 local excitatory interneurons ( eLNs ) ( Figure 1a ) . The ratio ( 3∶1 ) of the PNs to inhibitory LNs used in our model is based on known features of locust olfactory anatomy , which includes 830 cholinergic excitatory PNs and 300 inhibitory LNs [30] . Excitatory LNs have not been described ( or comprehensively searched for ) in locusts , but a number of indirect lines of evidence suggest some do exist . We included 50 eLNs in most of the simulations , but , by varying their numbers in a few control experiments , we found that the absolute number of eLNs did not significantly affect our results as long as we also compensated the strength of excitatory connections to ensure the same overall level of excitation per cell . Fast GABAergic ( LN–PN , LN–eLN , and LN–LN connections ) and nicotinic cholinergic synaptic currents ( PN–LN , PN–eLN , eLN–LN ) were modeled by first order activation schemes . Connection probabilities were as follows . P ( PN–eLN ) = 0 . 5 , P ( eLN–PN ) = 0 . 1 , P ( PN–LN ) = 0 . 5 , P ( LN–LN ) = 0 . 5 , P ( LN–PN ) = 0 . 5 , P ( eLN–LN ) = 0 . 5 , P ( LN–eLN ) = 0 . 5 . These probabilities are constrained by estimates made from locust AL circuits [20] , [30] , [31] . Each locust LN receives excitatory input from 50–75% of the PNs as well as fast GABAA type and slow GABAB type inhibitory lateral inputs from 25–50% of the remaining LNs [30] . No self-inhibition has been reported in the locust AL . Each PN receives fast GABAAtype and slow GABABtype inhibitory lateral inputs from 75% of the LNs ( G . Laurent , personal communication ) . Probabilities of eLNs connections are presently unknown , however , varying their connection probability in our model produced effects similar to varying the number of eLNs ( see above ) . The AL network was simulated for a range of values of lateral excitation and slow inhibition . The maximal conductance denoting the total lateral excitation received by a given cell was set to a value ranging from to in steps of . Similarly , the maximal conductance due to inhibitory GABAB type receptors was set to values ranging from to in steps of . The distribution of intensities provided to the PNs followed a Gaussian profile ( Figure 1b ) . The standard deviation of the distribution was fixed atwhere is the input to PNs . The variable x ranged from −1 to 1 . The index of PN is related to x as follows , . The identity of the odor is determined by the peak of the Gaussian input and is given by . The input was truncated to zero for values of the Gaussian below 0 . 1 and scaled by a current of amplitude 0 . 00001 nA . The truncated Gaussian profile of the odor is shown in Figure 1b . The time course of the stimulus was modeled as a current pulse with a rise time constant of 100 ms and a decay time constant of 200 ms . This was scaled by the factor for each neuron . The form of the input that each neuron received is given below , where , . is the maximum amplitude of the input and is the minimum . indicates the start of the stimulus with a rise time of and a decay time constant of . The stimulus decay began after ms . In addition to the stimulus pulse , we also added a low amplitude noise term ( ∼5–10% of the stimulus amplitude ) . A similar input was also provided to the LNs and eLNs . This input was scaled by the term IPN and was used to drive individual PNs . Different odors were generated by progressively shifting the Gaussian input profile by5 unit steps . Similar odors were defined as odors with input profiles shifted by 5 units; dissimilar odors were shifted by 40 units ( Figure 1b ) . For each pair ( ) we stimulated the network with a sequence of 21 odors , each presented 10 times . Each presentation , termed a trial , lasted 1000 ms and consisted of an initial onset at 500 ms followed by a fast rise and a more gradual decay beginning at 1500 ms . To calculate all measures of correlation we first generated a PSTH for individual neurons by determining the number of spikes produced by each neuron in consecutive 50 ms time bins that overlapped over 25 ms durations . The activity of the population of PNs ( n = 300 ) during each time interval could then be characterized as a 300–dimensional vector . Each 3000 ms ( 500 ms before onset +1000 ms stimulus +1500 ms from offset ) odor trial could then be represented by a 300×120 matrix with elements providing the number of spikes generated by the ith neuron during the jth time–bin for specified trial , odor and ( ) values . For a specified value of and we first calculated the correlation between all pairs of odors ( n = 21 ) to generate a 21×21 matrix for each time point . Each element of this matrix denoted the correlation between a 300–dimensional PN vector corresponding to the ith odor and the PN vector corresponding to the jth odor during a specific 50 ms time window . A similar set of matrices was also constructed to determine the correlation between the inputs to PNs ( [14] employed similar measures to analyze the response of mitral cells in the zebrafish olfactory bulb ) . Next , we picked all pairs of odors separated by 5 units ( similar odors are defined by |i-j| = 5 , where i and j are the matrix indices ) . The value of the correlation coefficient was averaged across all such pairs of similar odors for different values of and . We also calculated the correlation between dissimilar odors ( odor pairs with |i-j| = 40 ) . We then evaluated the correlation between multiple trials ( n = 10 ) of the same odor . For each set of ( ) values we generated a sequence of 10×10 matrices denoting the correlation between PN vectors corresponding to different trials of the same odor . We then averaged the value of the correlation coefficient across all trials . For each ( ) pair we determined the mean correlation coefficient between similar odors by averaging the correlation coefficients over the duration of the odor presentation . This provided us with a matrix of values Codors . Similarly , we calculated a matrix for the mean correlation between multiple trials of the same odor Ctrials . We then obtained the optimal value of and to minimize the correlation between similar odors while maximizing the correlation between multiple trials of the same odor by finding the location of the maximum value of the matrix , Ctrials+ ( 1−Codors ) . The use of correlation coefficient over the Euclidean distance is preferred for this analysis as the correlation coefficient is already normalized between −1 and 1 . We performed the clustering analysis using the Matlab Statistics Toolbox . To cluster odor representations we first defined a distance between the spatiotemporal patterns generated in response to two odor stimulations . The two responses ( i and j ) could be the outcome of either different odors or different trials of the same odor . The distance between i and j was given by dij = 1−cij , where cij is the mean ( averaged over the duration of the odor stimulus ) correlation coefficient between the two response i and j . The greater the correlation ( cij ) , the smaller the distance between representations ( dij ) . Once the distance between every pair was calculated we sought to cluster the responses into two groups for each value of lateral excitation and slow inhibition . Since we know the identity of the odor that generated a particular response we could easily determine whether the response was classified accurately . The hierarchical classification algorithm ( the ‘linkage’ function in Matlab ) links pairs of proximate objects ( based on the correlation distance defined above ) into binary clusters that are then grouped together recursively to generate larger clusters until a hierarchical tree can be constructed . We then divided the odor representations into two classes by cutting the hierarchical tree at a level such that exactly two clusters were generated . Each of these clusters ( say cluster 1 and cluster 2 ) was then assigned to one of the odors ( say odor A or odor B respectively ) . We then determined the number of odor representations that were misclassified ( Nerr1 ) . We then switched the assignment ( cluster 2 was assigned to odor A and cluster 1 was assigned to odor B ) . The number of errors ( Nerr2 ) was computed again . The actual number of errors was then defined as Nerr = min ( Nerr1 , Nerr2 ) . The error proportion Nerr/Ntotal , where Ntotal is the total number of odor representations is shown in Figure 7 . The chance level is 0 . 5 .
|
The antennal lobe of insects and the olfactory bulb of vertebrates represent the first centers of the olfactory system where information about odor properties can be reorganized and optimized for further processing . Complex excitatory and inhibitory synaptic interactions within the antennal lobe and the olfactory bulb alter the responses of the principal neurons throughout the duration of the odor stimulation . These dynamic changes progressively increase the difference between firing patterns evoked by structurally similar odors , potentially helping the animal distinguish one odor from another . However , this process , called odor decorrelation , appears to oppose another important goal of olfactory processing , to minimize the inevitable noisy variations in representations of the same odor encountered under different environmental conditions; such variations could potentially lead to misclassification . It remains an interesting mystery how olfactory circuitry can solve these two seemingly contradictory goals as they process olfactory stimuli: first , separating different but chemically similar odors ( sensitivity , capacity ) ; and second , identifying representations of the same odor in a noisy environment ( reliability ) . Our results suggest a balance between inhibitory and excitatory connections mediated by local antennal lobe interneurons enhances the decorrelation of similar odors while keeping the representation robust in the presence of noise .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"circuit",
"models",
"computational",
"neuroscience",
"olfactory",
"system",
"biology",
"sensory",
"systems",
"neuroscience"
] |
2012
|
Excitatory Local Interneurons Enhance Tuning of Sensory Information
|
HIV-1 infects CD4+ T cells and completes its replication cycle in approximately 24 hours . We employed repeated measurements in a standardized cell system and rigorous mathematical modeling to characterize the emergence of the viral replication intermediates and their impact on the cellular transcriptional response with high temporal resolution . We observed 7 , 991 ( 73% ) of the 10 , 958 expressed genes to be modulated in concordance with key steps of viral replication . Fifty-two percent of the overall variability in the host transcriptome was explained by linear regression on the viral life cycle . This profound perturbation of cellular physiology was investigated in the light of several regulatory mechanisms , including transcription factors , miRNAs , host-pathogen interaction , and proviral integration . Key features were validated in primary CD4+ T cells , and with viral constructs using alternative entry strategies . We propose a model of early massive cellular shutdown and progressive upregulation of the cellular machinery to complete the viral life cycle .
The life cycle of HIV-1 and its interaction with the host cell has been extensively studied [1] . However , previous analyses did not assess all relevant steps of viral replication in a longitudinal study in a single experimental system . Transcriptome ( and miRNA ) analyses have used microarray technology , usually in cross-sectional experiments , generally at the completion of the viral replication cycle ( 24–48 hours ) [2] . Analyses of viral integration first evaluated how the transcriptional status of genes contributes to preferential integration of proviruses [3]; however , there is limited data on how the viral integration contributes to host transcription at genome-wide level . Analyses have also been hampered by the heterogeneity of the infectious system , where the transcriptome profile reflects contribution by infected and uninfected cells . Recent studies have approached this problem by magnetic sorting of cells infected in vitro identified by a marker recombinant protein that is expressed during the late-phase of viral replication cycle [4] . Here , we jointly investigated , through repeated measurements in time , the dynamics of viral products and cellular responses in a model of universal cell infection ( Figure S1 in Text S1 ) . To this end , we applied high-throughput sequencing technologies for mRNA , small RNAs , and viral integration site profiling , as well as detailed quantification of viral replication intermediates . A highly permissive T cell line ( SupT1 ) was chosen , because it could be transduced at 100% efficacy with an HIV vector ( NL4-3Δenv::eGFP , VSV . G pseudotyped ) . This model system allowed effective synchronization through infection and avoided confounding of transcriptional profiles by uninfected bystander cells . Transcriptome changes were shown to be specific to the infectious process , and representative results were subsequently validated using different infection rates , primary cells and alternative viral constructs . The aim of this project was to create a first model of the productively infected cell by capturing the dynamics of all expressed host genes , concomitantly with viral replication steps . Integration of the cellular and viral data was achieved through rigorous mathematical approaches . The analyses underscored the features of the successful viral replication occurring despite a profound perturbation of the cell at the transcriptional level . Data are provided as a fully interactive web resource to allow reader-specific queries .
Progression of the viral life cycle was characterized through quantitative measurement of nine species of viral intermediates ( Figure 1A , and Figure S2 in Text S1 ) . To generate a high-resolution picture of the various steps of the viral life cycle , we developed a parametric viral progression model based on ordinary differential equations . We found that initiation of viral reverse transcription ( defined as reaching 1% of its total progression ) occurs as early as 3 hours after infection , with double-stranded viral cDNA appearing 2 hours later ( referred hereafter as reverse transcription phase ) . 2-LTR circles began accumulating as early as 7 hours post-infection , with integration beginning 1 . 5 hours later ( integration phase ) . All viral transcripts emerged by 15 hours and , at the peak of expression , 0 . 6% of all transcripts in the cell were of viral origin , consistent with previous estimates [5] . Transcription was tightly coupled with translation , and it was followed by the release of viral particles starting at 18 hours after infection ( late phase ) ( Figure 1B ) . The temporal patterns of these features of the viral life cycle were used to explain the host genome-wide expression dynamics in response to the invading virus ( Figure 2A ) . High-throughput RNA sequence analysis identified 10 , 559 genes and 399 miRNAs expressed in the experimental system . Fifty-two percent of the overall variability in the transcriptome was explained by linear regression on the three main phases of the viral life cycle as identified by the viral progression modeling , namely reverse transcription , integration , and late phase ( Figure 2 ) . 73% of all expressed genes ( n = 7 , 991 ) demonstrated significant correspondence of their temporal expression patterns with steps of the viral life cycle at the 5% false discovery rate . Using the regression weights as a measure of regulation of a gene in each of the three viral life phases defined above , we found 18 co-regulated gene clusters ( Figure 2 , and Figure S7 and S8 in Text S1 ) . Clusters were assessed for enrichment in gene ontology terms and pathways . Detailed inspection of clusters , individual querying of genes and of gene sets is provided at a dedicated web resource ( www . peachi . labtelenti . org [6] ) . Downregulation of cellular genes was generally early ( 4 hours post-infection ) , profound , and persistent throughout the experiment ( Figure 2 , and Figure S8C in Text S1 ) . The eight downregulated clusters , including 4 , 719 ( 43% ) genes , coherently exhibited enrichment in several functional gene sets . For example , downregulation concerned 70% ( 538/751 , p<10−13 ) of all expressed genes encoding nuclear proteins , 70% ( 338/484 , p<10−6 ) of genes involved in the Reactome expression machinery , such as those encoding RNA polymerase II components , splicing factors , ribosomal proteins , tRNA synthetases and translation initiation factors , and 75% ( 185/248 , p<10−7 ) of genes involved in protein metabolism . Despite the observed cellular response to infection , cell viability was similar in mock and infected cells ( 75% vs 72% at 24 hours , respectively ) . The observed pattern of cellular shutdown is more consistent with a cellular response to viral invasion than with experimental stress , given that two hours after infection , the transcriptome of HIV-infected cells is undistinguishable from that of mock samples . In contrast to the downregulated genes , upregulation occurred progressively and at later time points ( Figure 2 , and Figure S8A in Text S1 ) . Six clusters containing a total of 2 , 161 ( 20% ) genes were upregulated in response to infection . Overrepresented gene groups notably identified the Reactome generic transcription pathway ( 43% , 47/108 , p<10−4 ) that includes components of the mediator complex and zinc finger proteins . Individual upregulated clusters showed overrepresentation of several signaling and innate immune pathways , such as cytokine-cytokine receptor interaction ( p<10−3 ) , TLR signaling ( p = 0 . 0016 ) , and activation of NF-κB ( p<10−3 ) . Genes involved in antiviral defense and cell death signaling were also enriched in the four clusters , comprising 1 , 111 ( 10% ) of the genes , that exhibited mixed patterns of upregulation and downregulation ( Figure 2 , and Figure S8B in Text S1 ) . Thus , the early , large-scale , coordinated shutdown of the cell was followed by upregulation of immune response signals suggesting the triggering of defense mechanisms by the cell . However , detailed analysis of selected mechanisms of antiviral defense portrayed the extent to which the highly permissive cell line used in the current study may be poorly equipped to respond to the incoming virus . For example , of 331 interferon stimulated genes previously tested against HIV-1 [7] , less than half ( n = 144 , 43% ) were expressed in SupT1 cells , and only 61 ( 18% ) were upregulated in concordance with the viral life cycle . In particular , among the 6 most active anti-HIV interferon stimulated genes described before [7] , most were not expressed and only IRF1 was upregulated . Similarly , of the four prototypical lentiviral restriction factors , TRIM5α , APOBEC3G , BST2/Tetherin , and SAMHD1 , only TRIM5α was expressed and upregulated . The paucity of innate immunity gene expression may contribute to the high permissiveness of SupT1 T cells to infection , and thus , to their frequent use of in HIV-1 research . We further examined the pattern of expression of host genes reported to interact with HIV-1 proteins . We first analyzed the expression profile of 443 genes previously identified in a screen of physical interactions of all 18 HIV-1 proteins with human factors [8] . Of these , 382 were expressed in our experimental system , and 290 ( 76% ) showed modulation associated with viral progression features; specifically , 55% downregulation , 13% upregulation , and 8% mixed regulation . The enrichment of virus interaction partner genes was significantly higher in down-regulated clusters as compared to overall cellular transcripts ( 43% , 20% , and 10% , respectively; p<10−6 ) . Specific clusters were enriched with genes encoding interaction partners of the viral proteins Vif , gp41 , Vpr , and Tat . Additional databases of HIV-1 host factors [9] , [10] , [11] , [12] , [13] , as well as genes present in HIV-related pathways extracted from Reactome were inspected in a similar fashion [6] . Most of them were downregulated , emphasizing the importance of assessing interactions between viral and host genes in the context of the dynamics of the infection process and not as static events . Transcription factors and miRNAs are two key components of transcriptional regulation . Over two thirds of the 18 co-regulated gene clusters exhibited significant overrepresentation of the putative targets of one or more transcription factor or miRNA . Several major transcription factor genes were downregulated along with their corresponding targets . For example , 1 , 080 ( 23% ) of the downregulated genes were targets of the large-scale transcriptional regulators SP1 , MAZ , and ELK1 , that were found also to be downregulated ( Table S1 in Text S1 ) . In contrast , there was limited agreement between miRNA expression and that of genes sharing the experimentally verified miRNA targets ( Table S2 in Text S1 ) . Specifically to HIV-1 , only miRNAs that target the viral 3′LTR ( miRNA-125b , miRNA-150 , and miRNA28-3p ) and experimentally shown to inhibit HIV-1 [14] , were found to be upregulated during the infection [6] . These results underscore the difficulty in interpreting regulator-to-regulated gene activities in complex settings such as the infected cell . Many chromosomal regions were enriched in gene clusters , suggesting location-specific co-regulation . We investigated the possibility that such regional gene expression profiles are influenced by the spatial pattern of HIV-1 integration into the host genome . We identified 40 , 430 unique viral integration sites . Consistent with previous work [15] , integration favored genes that are transcriptionally active prior to infection , and this association remained at the time of integration , although many genes had , by then , undergone significant downregulation ( Figure 3 ) . At 24 hours there was a negative correlation of −0 . 26 ( p<10−64 ) between the frequency of integration in a given gene and the observed change in gene expression . However , given the low prevalence of integrations in the overall cell population ( Figure 3 ) , even genes with the highest number of integration events were unlikely to be downregulated by more than 0 . 008 log2 fold at the cell population level ( Figure S9 in Text S1 ) . Thus , while cellular gene expression levels influenced integration rates , proviruses did not contribute significantly to global cellular expression levels . This observation does , however , not preclude an impact of integration at the single cell level . One of the difficulties in trying to study HIV infection in cultured cells , as compared with what may happen in vivo , is the use of a large multiplicity of infection , and the exposure of the cells to large concentration of non-infectious particles . To assess the possibility that the profound transcriptome modifications were due to exposure to non-infectious viral particles , we compared the transcriptome of cells that were universally infected , cells exposed to heat-inactivated virus , cells exposed to a mixture of 1∶10 infectious/heat-inactivated virus , and non-infected ( mock ) cells . The transcriptome of mock cells and after exposure to heat-inactivated viruses clustered together across the top principal components ( Figure 4 ) . Infected cells spread away from the mock space as infection progressed . These data confirm that the transcriptome changes reflect the viral progression and is not a mere result of exposure to viral material . The experimental system consisted of a highly permissive T cell line ( SupT1 ) and a VSV . G pseudotyped HIV vector to achieve universal infection . To validate our results , we used primary cells and natural viral entry . Activated CD4+ T cells from two donors were transduced with HIV vectors pseudotyped with both VSV . G and CXCR4-tropic envelopes . As expected , the rate of infection of primary cells was inferior to that of the T cell line ( Figure S10 in Text S1 ) . We analyzed the expression of 14 genes representative of various clusters by RT-qPCR . First , we compared the gene expression findings based on RNA sequencing with RT-qPCR data . For example , at 24 hours after infection , the correlation between the two techniques was r2 = 0 . 77 ( p<10−4 ) even though the dynamic range is larger for RNA sequencing ( Figure S11 in Text S1 ) . Second , we re-assessed the role of exposure to non-infectious viral material in modifying expression of the marker genes ( Figure 5A ) . We also applied RT-qPCR to the analysis of gene expression patterns over the 24-hour viral life cycle in primary cells ( Figure 5B ) . Finally , we compared transduction of primary cells by HIV-1 carrying natural ( CXCR4 ) with vectors pseudotyped with VSV . G envelope ( Figure 5C ) . Overall , genes upregulated in SupT1 cells were generally confirmed as upregulated in primary cells , but the signal was weaker in primary cells due to dilution by RNA of non-infected bystander cells and possibly by cell-specific responses to HIV-1 . Downregulation was muted in primary cells despite equal experimental conditions , including biological stress , indicating that cellular shutdown is a response to successful infection . In support of this notion , a lower infection rate ( 1/10 inoculum , diluted in heat-inactivated virus preparation ) resulted in proportional modulation ( up- or down regulation ) of the signal; and cells exposed to 100% heat-inactivated virus were comparable in expression pattern to mock-treated cells . Research on the infected cell generally follows the paradigm of “single gene , single interaction” . However , this approach fails at fully capturing and quantifying the complexity of the system . In contrast , the non-reductionist study presented here reflects the intricate cellular response to infection where , at the transcription level , a large proportion of genes are modulated in concordance with key steps of viral replication . As such , this work provides a referential resource on the viral life cycle that can be contrasted across cellular systems and viral strains , and also across diverse pathogens . The approach should be extended to study the establishment of and reactivation from viral latency [16] . Ultimately , it can guide intervention of the viral life cycle at specific time points through the modulation of selected host genes and pathways . Progress in single-cell transcriptome analysis should allow in the future to investigate primary cells infected with replication-competent virus .
|
Viral pathogens , such as HIV-1 , are fully dependent of the cellular machinery to complete the replication cycle . The cell offers a permissive environment , and deploys a number of antiviral defense strategies . The present work follows the process of infection of the cell with simultaneous measurements of viral replication intermediates together with the concurrent assessment of the host transcriptional changes . The main observation is that the cell undergoes a profound modification of its physiology , with a marked early decrease in expression of several thousands of genes , followed by a more discrete increase in the expression of sets of genes that may contribute to the success of the viral replication program . The cell system used in this study has limited response of paradigmatic cellular defense genes . Key features of the experimental model were validated in primary cells and with different viral vectors . The data and model generated here constitute a resource that can be used for the assessment of single gene responses to HIV-1 infection , and as comparative reference for the understanding of other viral and cellular programs , such as those implicated in successful defense against viral infection or in latency .
|
[
"Abstract",
"Introduction",
"Results/Discussion"
] |
[
"genome",
"expression",
"analysis",
"statistics",
"microbiology",
"host-pathogen",
"interaction",
"immunodeficiency",
"viruses",
"mathematics",
"biostatistics",
"gene",
"expression",
"regulatory",
"networks",
"biology",
"nonlinear",
"dynamics",
"viral",
"replication",
"differential",
"equations",
"systems",
"biology",
"computer",
"science",
"virology",
"calculus",
"genetics",
"genomics",
"computational",
"biology",
"genetics",
"and",
"genomics"
] |
2013
|
24 Hours in the Life of HIV-1 in a T Cell Line
|
As information about the world traverses the brain , the signals exchanged between neurons are passed and modulated by synapses , or specialized contacts between neurons . While neurotransmitter-based synapses tend to exert either excitatory or inhibitory pulses of influence on the postsynaptic neuron , electrical synapses , composed of plaques of gap junction channels , continuously transmit signals that can either excite or inhibit a coupled neighbor . A growing body of evidence indicates that electrical synapses , similar to their chemical counterparts , are modified in strength during physiological neuronal activity . The synchronizing role of electrical synapses in neuronal oscillations has been well established , but their impact on transient signal processing in the brain is much less understood . Here we constructed computational models based on the canonical feedforward neuronal circuit and included electrical synapses between inhibitory interneurons . We provided discrete closely-timed inputs to the circuits , and characterize the influence of electrical synapse strength on both subthreshold summation and spike trains in the output neuron . Our simulations highlight the diverse and powerful roles that electrical synapses play even in simple circuits . Because these canonical circuits are represented widely throughout the brain , we expect that these are general principles for the influence of electrical synapses on transient signal processing across the brain .
Electrical synapses are prevalent across many brain regions , including thalamus , hypothalamus , cerebellum , and the neocortex [1–3] . In contrast to neurotransmitter-based synapses , electrical synapses are a mode of intracellular communication that transmits signals almost instantaneously , and without inactivating . Because signals cross two cell membranes , the net effect of an electrical synapse is that of a lowpass filter [3–5]: spikes are heavily attenuated , while longer or slower events , such as bursts , subthreshold rhythms , and the depolarizations that lead to spikes , are more readily shared between cells . Further , because the signal delivered is proportional to the signed difference between membrane potentials of coupled neurons , electrical synapses can exert either inhibitory or excitatory effects on a coupled neighbor , by increasing leak at rest or by transmitting activity such as post-spike hyperpolarizations , depolarizations or spikelets in either direction . A growing body of work has demonstrated ways in which electrical synapses can be modulated or modified by either synaptic [6–11] or spiking [12 , 13] forms of neuronal activity . The roles of electrical synapses in neuronal signal processing have mainly been explored in terms of their contributions to or regulation of synchrony of ongoing oscillations [14–20] . Studies focusing on the influence of electrical synapses on transient signals as they traverse the brain are fewer , but hint at specific and potentially powerful roles . For instance , propagation of spike afterhyperpolarizations through electrical synapses acts to reset and desynchronize regular firing in coupled cerebellar Golgi neurons [21] . Electrical synapses accelerate timing of spikes elicited near threshold in coupled thalamic reticular neighbors by tens of milliseconds [22 , 23] . In coupled cerebellar basket cells , electrical synapses enhance and accelerate recruitment for coincident or sequential inputs [24] . Axonal gap junctions between neurons in the fly visual stream aid efficient encoding of the axis of rotation [25] . Our previous work focused on the impact of electrical synapses on transient signals in the thalamacortical relay circuit , showing that electrical coupling between inhibitory neurons leads to increased separation of disparately-timed inputs while facilitating fusion of closely-timed inputs [26] . In order to generalize a role for electrical synapses and variations in their strength in neuronal information processing , here we considered the canonical microcircuit , wherein two principal neurons , connected by an excitatory synapse , are also connected by disynaptic feedforward inhibition ( Fig 1A1 ) [27] . This circuit motif reappears through the brain in areas ranging from the hippocampal CA1 pyramidal neurons [28] , somatosensory L4 cortical neurons receiving inputs from the ventrobasal complex [29] , and the cortical translaminar inhibitory circuits [30] ( Fig 1A2-4 ) . Starting with a canonical circuit , we progressively expanded models and analysis from a single circuit to a network composed of canonical circuits . We provided these models with closely timed inputs , in order to determine how the embedded electrical and inhibitory synaptic connections between interneurons influence subthreshold integration and spiking statistics at the output stage of the model . Our simulations demonstrate that electrical synapses enable a high degree of specificity and diversity of processing of transient signals for both subthreshold activity and network activity . Because electrical synapses are widespread throughout the mammalian brain , we expect that these are principles that apply widely to neuronal processing of newly incoming information as it passes through the brain .
We started our inquiry by creating a three-cell circuit composed of Izhikevich-type neurons ( see Methods ) to represent the canonical neuronal microcircuit: two excitatory neurons , with an interneuron providing feedforward inhibition ( the simple canonical circuit ( SCC ) , Fig 1A ) . Upon excitation of the source ( Src ) neuron , this model produces a compound postsynaptic potential ( PSP ) in the target ( Tgt ) neuron that is a sum of a purely excitatory PSP from the Src neuron and an inhibitory PSP arriving with a delay from the inhibitory interneuron ( Int ) . The features of the compound PSP ( Fig 1B ) –its peak amplitude , its net total excitation ( area under the positive component of the PSP curve , or AUC ) , and the duration of the integration window–together determine whether Tgt will generate a spike given sufficient input . The PSP depends predictably on the strength of the PSPs arriving from Int ( Fig 1C ) and Src ( Fig 1D ) ; generally , inhibition curtails the excitation , while the Tgt PSP peak is proportional to excitation from Src ( Fig 1E ) . More specifically , in feedforward circuits GGABA→Tgt does not limit the PSP peak ( Fig 1E ) , but increases in GGABA→Tgt do limit the integration window ( Fig 1F ) and the net total excitation ( Fig 1G ) . Thus , the interneuron limits the overall excitation and possibility of Src triggering an action potential in Tgt . To understand how Tgt might sum input from multiple sources , our next step towards building larger models was to couple two canonical circuits , using two Src neurons and two Int neurons leading to a common Tgt ( the coupled canonical circuit ( CCC ) , Fig 2A ) . Using the CCC , below we explore the effects of varied connection types between the Int neurons . To start , in the absence of connectivity between Int neurons ( Fig 2A ) , we provided both Src neurons with brief inputs sufficient to evoke single spikes in the Src neurons while varying the time delay between the inputs Δtinp . From these simulations , we observed that the inhibition from the Int neurons limited summation of the two Src signals in Tgt ( Fig 2B ) . First , we noted that as for the SCC ( Fig 1 ) , the peak of the Tgt PSP is preserved across large ranges of GGABA→Tgt ( Fig 2C ) , as it mainly depends on the delay Δtinp . The integration window and AUC depend on both the delay Δtinp and the strength of inhibition . Increases in GGABA→Tgt curtailed the integration window and AUC of integration in the Tgt PSP ( Fig 2D and 2E ) , diminishing these measures in a monotonic and straightforward manner . These results provide a baseline of expectations for the following simulations in which the Int neurons are connected by electrical and inhibitory synapses . Next , we included an electrical synapse between the two Int neurons of the CCC ( Fig 3A ) . We limited the range of strength of the electrical synapse to vary between 0 ( uncoupled ) and a coupling coefficient of ~0 . 3 , which represents common strengths found in the thalamus [13 , 31] and cortex [32–34] . We again provided this circuit with identical inputs , with varying time delay between the inputs Δtinp ( Fig 3B ) . As electrical synapse strength increased we noted increased delays in Int1 spiking due to increased leak from the electrical synapse , and we also noted accelerations in Int2 spiking due to the excitatory spikelet it received from Int1 ( Fig 3B , rasters and insets ) . Together , these changes in Int spike times result in a net synchronizing effect on summed Int inhibition for electrical coupling in this regime of input timing . As a result , within the CCC , electrical coupling enhanced Tgt input integration for closely timed inputs by allowing for increased PSP peaks ( Fig 3C ) and AUC ( Fig 3E ) , while narrowing the integration window ( Fig 3D ) for the PSP . In the same circuit , for more than ~4 ms of Δtinp , small values of electrical synapse strength only served to increase leak in Int2 well after the spikelet had finished , ultimately delaying its spike ( Fig 3B , lower right ) . Increases in electrical synapse strength , however , allowed for the spikelet from Int1 to directly elicit spiking in Int2 , which spiked earlier than it might have otherwise ( Fig 3B , lower right ) . The net effect in this range of larger input timing allows the PSP in Tgt to increase by small amounts in peak amplitude ( Fig 3C , Δtinp>4ms ) , but the shortened integration window ( Fig 3D , Δtinp>4ms ) , resulting from the earlier spike in Int2 , effectively prevents summation of the two Src inputs in Tgt . Thus , the varied effects of increased leak or excitatory spikelets between Int neurons resulting from an electrical synapse with varied strengths increases flexibility for responses to signals passing through this version of the CCC , as compared to the CCC with no connections between the Int neurons ( Fig 2 ) . While GABAergic coupling is rare between nearby electrically coupled inhibitory neurons of the thalamus [31 , 35] , it is sometimes observed between coupled pairs of inhibitory interneurons in cortex [32–34 , 36] . To test the additional effects of GABAergic connectivity between electrically coupled interneurons , we included symmetrical GABAergic synapses between Int neurons in the CCC model ( Fig 4A ) . From these simulations , we see that for transient inputs separated by Δtinp , the additional synapse further expanded the possibilities for subthreshold summation of inputs in Tgt . While the effect of GGABA→Int on the peak PSP in Tgt ( Fig 4B ) was not substantially different from the CCC without an inhibitory synapse , the integration window expanded with stronger inhibition ( Fig 4C ) , and the AUC of the PSP also increased for stronger GGABA→Int ( Fig 4D ) . Further , in the presence of stronger reciprocal inhibition , increased electrical coupling shifted the maxima in Tgt integration windows and AUCs rightwards , towards larger values of Δtinp ( Fig 4C and 4D , right columns ) . Thus , comparing Figs 3C–3E , 4C and 4D , the interaction between electrical and inhibitory synapses is nonmonotonic and complex . In particular , we note that strong inhibition competes with electrical synapses alone in terms of the impact on PSP integration window for closely timed inputs: increases in electrical synapse strength shorten the window in the context of weak inhibition , while stronger inhibition broadens the window , especially for weaker electrical synapses ( Fig 4C , left to right ) . However , for larger Δtinp , both types of interneuron coupling broadened the window . Thus , similar to our previous demonstration [26] , we note that electrical synapses act directly on inhibitory interneurons and indirectly through inhibitory synapses onto a target in diverse ways to control the processing of transient signals passing through a neuronal circuit . We also note that changes in electrical synapse strength can potentially halve or double the PSP ( Fig 3C1 ) , the integration window ( Figs 3D1 and 4C ) , or area under the curve ( Figs 3E1 and 4D ) . Thus , modulation [8–10] or activity-dependent modifications of electrical synapses [12 , 13] potentially exert powerful impacts on subthreshold summation of transient inputs in the Tgt cell , and on canonical neuronal circuits . To study responses of a population of Tgt neurons , we embedded 50 units of the canonical circuit into a network ( the coupled canonical network ( CCN ) , Fig 5A ) , and started our analysis with electrical coupling between the Int neurons . In order to study spiking rather than subthreshold activity in the Tgt population , we increased GAMPA from the Src to the Tgt ( GAMPA→Tgt ) and decreased GAMPA from Src to Int ( GAMPA→Int ) in each unit , along with modest increases to Tgt excitability ( see Methods ) in order to elicit spiking in the Tgt neurons within 5–6 ms of Src spiking , latencies that are consistent with latency to input in the regular spiking neurons in hippocampus [37] . To each Src neuron in the layer of 50 , we provided identically sized inputs drawn from Gaussian distributions of input times with a standard deviation of σinp ( Fig 5B ) . We then quantified the distribution of spike times in the Int and Tgt populations ( Fig 5B ) . From these results , we observed that increases in electrical synapse strength acted to narrow and delay the distributions of spike times in the Int layer ( Fig 5B , middle row ) , and markedly increased maximal spiking density for smaller σinp . In the Tgt population , the narrowed Int distributions that resulted from increased electrical coupling allowed some Tgt neurons to spike earlier , hence decreasing the latency of Tgt population from the input ( Fig 5B , bottom row and insets; Fig 5C ) . Increased electrical synapse strength also decreased total spiking in Tgt ( Fig 5D ) , in fact selectively reducing later spikes and thereby shifting mean Tgt spike times towards smaller latencies ( Fig 5E ) , as a result of changes in Int spiking . Finally , in addition to electrical coupling , we included GABAergic connectivity between neighboring Int pairs of the CCN ( Fig 6A and 6B ) . The effects of electrical synapses on this network were similar to the previous model ( Fig 5 ) : increases in electrical synapse strength decreased latency ( Fig 6C ) , decreased total spiking in Tgt ( Fig 6D ) , and selectively reduced later spikes , but here shifting its distributions towards later times overall ( Fig 6E ) . Increased reciprocal inhibition was most effective for small values of σinp ( Fig 6B , left column ) , where stronger inhibition between Int neurons allowed Tgt neurons to spike more often ( Fig 6D , solid lines ) and somewhat earlier ( Fig 6E , solid lines ) , thus effectively counteracting the effect of electrical synapses . We compared the behavior of the CCN with and without reciprocal inhibition by plotting the change in spiking properties due to electrical synapses relative to the uncoupled case ( ∑Gelec = 0 ) across input time distributions for the Int ( Fig 7A ) and Tgt ( Fig 7B ) populations . While the input was Gaussian , the Tgt distributions were often not Gaussian; therefore , we measured mean spike times , standard deviations of spike times , maximal density and total density of spike time distributions , along with the relative latency ( see Methods ) . We observed that most of the effects that electrical synapses exerted on the output Tgt distribution were strongest for small σinp , except for latency . Mean spike times both increased and decreased for different combinations of σinp with inhibitory and electrical synapse strengths , while the spread ( standard deviation ) of spike times consistently increased with electrical synapse strength . Maximum density ( corresponding to peak spiking ) and total density ( total spike count ) of spiking , as well as relative latencies , decreased with increase in electrical synapse strength . Further , inclusion of larger reciprocal inhibition between the Int neurons led to decreased spiking within the Int population , thereby allowing later-activated Tgt neurons to spike faster , especially for the electrically uncoupled cases ( Fig 6B , blue lines ) . Increased electrical coupling combined with reciprocal inhibition led to increased inhibition within the Int layer , leading to better -synchronized Int activity but decreased total responses of the Int population . As seen previously [26] , the effects of electrical and inhibitory synapses within the Int layer interacted in complex ways; for one , Tgt spiking decreased less for stronger inhibition . Together , these results show that electrical synapses embedded within a network composed of canonical circuits have powerful and heterogeneous effects on the spiking of the Tgt output population , by altering spike times and total responses properties , as inputs from Src propagate through the network . We quantified the mutual information between the spike time distributions of Src and Tgt , as well as the transmission efficiency from Src to Tgt ( Fig 8 ) . For the electrically uncoupled case with no reciprocal inhibition , each Src elicited a single spike within its Tgt unit with predictable latency , leading to Tgt spike time distributions that mirrored Src distributions and resulted in maximal mutual information and 100% transmission efficiency . Increases in electrical synapse strength acted to disperse Tgt spike times ( Fig 8A1-3 ) and increased the joint distribution entropy ( Fig 8A4 ) , and thus tended to diminish the information shared between Src and Tgt ( Fig 8B ) . Both mutual information and transmission efficiency were modulated by ∑Gelec for any given input distribution , but without inhibition , neither measure recovered its peak value of ∑Gelec = 0 ( Fig 8B and 8C , left ) . Transmission efficiency decreased with larger values of electrical coupling , with more notable decreases with smaller σinp ( Fig 8C ) . The largest decrease was roughly 35% . As interneuron reciprocal inhibition was added and spiking in the Int population decreased , some neurons within the Tgt distribution spiked much faster but with less uncertainty , creating narrower distributions and smaller entropy . As a result , both mutual information and transmission efficiency overall increased relative to the network with no inhibitory synapses ( Fig 8B and 8C ) . For all networks with nonzero inhibitory synapses , the maximal values of mutual information and transmission efficiency occurred for ∑Gelec > 0 .
Overall , our simulations together demonstrate that electrical synapses between interneurons in canonical networks regulate both subthreshold activity and network spiking activity , ultimately exerting powerful and complex effects on the output activity of the network as it processes and passes on its inputs . The general effect was that for closely-timed inputs , increases in electrical coupling strength often led to delay of spiking in the inhibitory interneurons , which enabled larger summations of source inputs in the target output and at earlier times . Yet simultaneously , increased coupling strength produced stronger synchronized inhibition from the interneuron population to the target population , at later times , which limited the output of the Tgt neurons . These complex interactions effects highlight the diverse roles that electrical synapses of dynamically varying strength might play in the circuits that contain them across the brain . In TRN , as we have previously shown [26] , electrical synapses ultimately act to either further fuse or to aid in discrimination of sensory inputs as they are passed to cortex by relay cells . Within for instance somatosensory cortex , the impacts of electrical synapses within the networks that embed canonical circuits may be similar–to sharpen the timing spread or modify gain in principal cell firing within a barrel , in response to whisker stimulation [38] . In hippocampus , electrical synapses may aid in place cell spatial precision [39] . Electrical synapses in a local network regulate subthreshold summation of inputs to a target neuron . Our simulations show that stronger electrical coupling allow the target neuron to integrate its source inputs with higher summed PSP peaks , yet limit time windows for further inputs to summate , thereby acting as a coincidence detector [40] . Furthermore , changes in electrical coupling in a local network of interneurons , as might result from activity-dependent electrical synapse plasticity , lead to more flexibility in regulating subthreshold summation . However , our results also show that reciprocal inhibition between the electrically coupled interneuron pair expanded the integration window and the area under the excited portion of the target PSP , especially for relatively large differences in input timings . Increases in reciprocal inhibition allow for widening integration windows of disparately-timed inputs , in that case acting more like an integrator . This suggests that the interactions between the electrical coupling and reciprocal inhibition within the local interneuron networks could regulate the ability for the target neuron to either be a coincidence detector or an integrator [40] for its inputs . At a network level , we find that electrical coupling of the interneuron population modulates the target population activity over different distributions of input timings . Similar to the subthreshold effect , increase in electrical synapse strength led to a more delayed , yet denser activity in the interneuron population , effectively synchronizing its activity . Hence , stronger electrical coupling allowed stronger and earlier responses of the target layer activity , but weaker later responses . However , because the activity of the interneurons was limited within a smaller temporal window due to their electrical coupling , inhibition towards the target population was limited in time . Hence the output activity was more sustained compared to uncoupled cases . As a result , electrical coupling allowed earlier yet sparser Tgt responses , and effectively reduced both the mutual information and the transmission efficiency between Src and Tgt . This effect was strongest for small input distribution sizes . One result of this interaction is that although the integrity of Src-Tgt coding was corrupted , electrical coupling between the interneurons increased temporal heterogeneity ( increased sparsity ) amongst the Tgt population as inputs coming from different Src neurons arrived . In contrast , reciprocal inhibition decreased interneuron activity and thereby enhanced the target response in presence of electrical coupling . However , for closely-timed input distributions in the presence of reciprocal inhibition , the target temporal code distribution narrowed in timing spread , especially for electrically uncoupled or weakly coupled cases , resulting in loss of mutual information and transmission efficiency . These results point toward yet another type of interaction between electrical coupling and reciprocal inhibition within the interneuron population that regulates the temporal code of the output distribution . Although both types of synapse disrupt the input-output temporal integrity of closely-timed input distributions , electrical synapses act to increase temporal heterogeneity in the output layer , while the inhibitory synapses decrease output temporal heterogeneity . As the issue of transient neuronal signal processing in models that include electrical coupling between inhibitory neurons has been understudied , even broader implications from this work remain to be determined . Interactions between electrical synapse-transmitted excitatory spikelets and inhibitory afterhyperpolarizations showed a temporarily reduced probability of spike generation [21] that foreshadowed the results here . Recent work shows that gap junctions couple PV interneurons across barrel boundaries [41] , suggesting that electrical coupling may connect broader and more complex circuits than the simple canonical circuits used here; in barrel cortex , both within and across different barrel columns . The result we have described above demonstrate urgent necessity for considering electrical synapses in simpler and more complex models of neuronal networks .
The canonical disynaptic feedforward-inhibition network can be simplified as a small 3-cell simple canonical circuit ( SCC ) ( Fig 1A1 ) , comprising of a Src ( source ) , an Int ( interneuron ) and a Tgt ( target ) neuron . For subthreshold investigations , we explored a small network ( the CCC ) composed of two canonical circuits . For activity explorations , we used a network model ( the CCN ) comprising subunits of canonical circuits . In this paper , we present results from N = 50 subunits and target neurons . For generalizability , we modelled Src and Tgt as regular spiking ( RS ) neurons and Int as a fast spiking ( FS ) neuron with Izhikevich formulism [42] . Briefly , Eqs 1 and 2 describe the dynamics of the membrane potential v and recovery current u respectively , with the spiking condition in Eq 3 . Additionally , implementation of FS neuron model also differs from RS as described in Eqs 4 and 5 [42] . We applied a holding current Ihold ( Eq 6 ) of 50 pA to Int to easily evoke spiking in response to input from Src . For subthreshold investigation ( SCC , CCC ) , we modelled Src and Tgt with the same set of parameters of an RS neuron ( Table 1 ) . To inspect network activity ( CCN ) , we tuned the parameters ( halved capacitance and lowered threshold potential ) and applied a 10 pA holding current for each Tgt neuron in order for its Src to easily evoke its spiking . In all cases , only Src received external input: a brief 20–30 ms of 200–300 pA DC input , sufficient to evoke a single action potential in in Src ( Eq 7 ) . For the SCC and CCC , we varied the arrival time differences between input to Src2 and input to Src1 as Δtinp from 0 to 20 ms . For the CCN , timings of Src inputs were drawn from a normal distribution with standard deviation as σinp , which we varied from 1 to 10 ms . For synaptic inputs , neurons excite each other via AMPA synapses , inhibit each other via GABA synapses or couple with each other via electrical synapses , as described in Eqs 8–11 . Src sends AMPA excitatory input to Tgt and Int separately sufficiently to drive Int to spike and for Tgt to receive a noticeable EPSP ( SCC , CCC ) or to spike ( CCN ) . Where indicated , Int also sends GABAergic inhibitory input to Tgt . Int neurons are also connected by an electrical synapse . Electrical synapses were implemented as symmetric linear resistance , as shown in Eq 9 . For two coupled Int neurons , we varied the electrical synapse conductance of from 0–8 nS ( unless otherwise noted ) , corresponding to coupling coefficients ( cc ) of roughly 0–0 . 33 . For the CCN , Int neurons are electrically coupled homogeneously in an all-to-all manner ( Figs 5A and 6A ) , with each coupling conductance scaled to the number of Int neurons as Gelec = ∑Gelec /NInt . Chemical synapses were implemented with a single exponential decay as described in Eqs 10 and 11 , and implemented following the example of [43] in Brian2 documentation . The synaptic reversal potentials and time constants were fixed: EAMPA = 0 mV , τAMPA = 2 ms and EGABA = -80 mV , τGABA = 10 ms . The conductance parameters were either fixed or varied as in Table 2 . For the CCN where inhibition is included , the Int population also reciprocally inhibits itself in an all-to-all manner . Each inhibitory conductance was also scaled to the number of Int’s as GGABA→Int = ∑GGABA→Int /NInt . Simulations were run in the Python-based open source simulator Brian2 [44] . Subthreshold simulations were run for 100 ms with dt = 0 . 01 ms ( SCC , CCC ) . For each parameter set in network activity investigations ( CCN ) , 50 random simulations were run as with external input timings to Src population drawn from a normal distribution with size σinp . Each simulation was 200 ms and dt = 0 . 05 ms because less accuracy was required and for speed . Analysis and visualization were mainly performed in MATLAB ( MathWorks R2018a ) and the open source graphics editor Inkscape 0 . 92 . 3 . For subthreshold investigations ( Figs 1–4 ) , we obtained the net postsynaptic potential ( PSP ) of the Tgt neuron and quantified the peak potential , duration ( or integration window ) and area under the curve ( AUC ) of the positive portion of the PSP ( Fig 1B ) . For each set of parameter θ , we obtained the raw distribution of spike times X ( θ , C ) = {Xk ( θ , ci ) } population C aggregated from all Xk ( θ , ci ) , which is the spike time array of neuron ci in simulation kth . The symbol C ( or c ) represents the population name , can either be any of the following {Src , Int , Tgt} . i = {1 , 2 … NC} with NC as the number of neurons in population C . k = {1 , 2 … Ns} with Ns as the number of random simulations . In this paper , we used NC = 50 with all C and Ns = 50 as described earlier . To easily compare between different initial input distributions , we generally normalized all quantifications to the Src population ( Figs 5–7 ) . More specifically , for each XC = X ( θ , C ) , we defined normalized mean spike time as the difference between the mean of XC and that of XSrc . The normalized standard deviation was the standard deviation of XC normalized over the standard deviation of XSrc . For each XC = X ( θ , C ) , we calculated the spike density from the smoothed histograms of spikes times . More specifically , each array of spike times XC was histogrammed with a bin width that equals to one-tenth of the σinp in order to avoid under-sampling with small σinp and over-sampling with large σinp; then it was smoothed by convoluting with a Hanning window of size 20 to obtain the un-normalized density dC ( t ) . For visualization , the spike times were translated relative to the mean Src spike time distributions , whereas the densities were scaled over the maximum density of the Src distribution to calculate the normalized density DC ( t ) . Note: neither DC ( t ) nor dC ( t ) represented estimated probability density function , because the smoothed histograms were not normalized by their number of samples . For quantification comparison , we defined normalized maximum density as the maximum density of dC ( t ) normalized over that of dSrc ( t ) . The normalized total response was calculated by normalizing the area under the curve of dC ( t ) over that of dSrc ( t ) ( note: neither DC ( t ) nor dC ( t ) represented estimated probability density function , hence AUC was not necessarily 1 ) . Lastly , relative latency was defined as the time point which dC ( t ) reached 10% of maximum density , relative to the same measure calculated for Src spike time distribution XSrc . Additionally , gain of a particular property Q of a spike time distribution due to a parameter set θ was defined as the difference between itself and the same property when the electrical coupling parameter in set θ equals to 0 , in other words Gain[Q ( θ ) ] = Q ( θ ) –Q ( θelectrically uncoupled ) . For network investigation , we also quantified the mutual information and transmission efficiency between the Src and Tgt population spike time distribution ( Fig 8 ) . Here we considered Src to be an input channel , and Tgt was an output channel . For each XC = X ( θ , C ) , we estimated the probability function p ( C ) by histogramming the spike time arrays XC with a fixed bin width of 0 . 01ms . The joint probability function p ( Src , Tgt ) of Src and Tgt was also estimated by histogramming all the spike time pairs of ( XSrc XTgt ) with similar bin widths without any smoothing . We consider any missing spike ( for example , cases when Srci failed to induce a spikes in Tgti due to certain network configurations or parameter set ) to take the value of max ( XC ) + 2σ ( XC ) to minimize distortions in the marginal distributions of both Src and Tgt . Removing those cases entirely led to misrepresentation of the marginal distribution and join distribution . For demonstration purposes , the value used for missing spikes was 1000 ms ( Fig 8A4 ) . We calculated the mutual information between Src and Tgt with Eq 12 in which H ( A ) is the entropy of the distribution p ( A ) ( Eq 13 ) and H ( A , B ) is the entropy of the joint distribution p ( A , B ) ( Eq 14 ) . We measured the transmission efficiency from the input channel ( Src ) to the output channel ( Tgt ) with Eq 15 [45] . This could be interpreted as % of the entropy of output that could be attributed to the input . All code is available at https://github . com/jhaaslab/elec_ffwd_inh_circuit .
|
The roles that electrical synapses play in neural oscillations , network synchronization and rhythmicity are well established , but their roles in neuronal processing of transient inputs are much less understood . Here , we used computational models of canonical feedforward circuits and networks to investigate how electrical synapses regulate the flow of transient signals passing through those circuits . We show that because the influence of electrical synapses on coupled neighbors can be either inhibitory or excitatory , their role in network information processing is heterogeneous , and powerful . Because electrical synapses between interneurons are widespread across the brain , and in addition to a growing body of evidence for their activity-dependent plasticity , we expect the effects we describe here to play a substantial role in how the brain processes incoming sensory inputs .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"cell",
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"membrane",
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2019
|
Electrical synapses regulate both subthreshold integration and population activity of principal cells in response to transient inputs within canonical feedforward circuits
|
Dengue Fever and Dengue Hemorrhagic Fever are diseases affecting approximately 100 million people/year and are a major concern in developing countries . In the present study , the phylogenetic relationship of six strains of the first autochthonous cases of DENV-4 infection occurred in Sao Paulo State , Parana State and Rio Grande do Sul State , Brazil , 2011 were studied . Nucleotide sequences of the envelope gene were determined and compared with sequences representative of the genotypes I , II , III and Sylvatic for DEN4 retrieved from GenBank . We employed a Bayesian phylogenetic approach to reconstruct the phylogenetic relationships of Brazilian DENV-4 and we estimated evolutionary rates and dates of divergence for DENV-4 found in Brazil in 2011 . All samples sequenced in this study were located in Genotype II . The studied strains are monophyletic and our data suggest that they have been evolving separately for at least 4 to 6 years . Our data suggest that the virus might have been present in the region for some time , without being noticed by Health Surveillance Services due to a low level of circulation and a higher prevalence of DENV-1 and DENV- 2 .
Dengue virus ( DENV ) is a single stranded RNA virus , with four immunologically related serotypes ( DENV-1 , DENV-2 , DENV-3 and DENV-4 ) associated with Dengue Fever ( DF ) and Dengue Hemorrhagic Fever ( DHF ) [1] . The virus is widespread in tropical and Sub-Tropical areas of Asia , Africa and Americas . The virus is transmitted by mosquito bites , and is primarily associated with Aedes aegypti as its main vector [2] . The disease affects , approximately , 100 million people/year , causing 250 , 000 cases of DHF with a case fatality rate up to 15% , and is a major concern for Public Health authorities around the globe , primarily in developing countries [2] . Historically , the State of Sao Paulo , Brazil , has been suffering dengue outbreaks since 1990 when DENV-1 was introduced in the area . Subsequent epidemics were detected in 1997 and 2002 , caused by DENV-2 and DENV-3 , respectively , with increasing casuistic and detection of severe cases of DHF or Shock Syndrome [3]–[5] . DENV-4 had a brief circulation in Brazil in 1982 in the Northwestern region of Brazilian Amazon in a focal epidemic . No further cases of infection had been registered in the country until 2008 , when the virus was detected in three patients , who had no international traveling history , in Manaus [6] . After this episode , the Brazilian Ministry of Health implemented the use of the NS1 ELISA test in 16 states in order to increase the percentage of viral isolates and the determination of the serotypes circulating in the country . Before the screening with the NS1 ELISA test , virus isolation was obtained in only 10% of samples submitted to isolation . With the screening of samples the percentage of detection of serotype rose to 82% [7] . The introduction of the NS1 ELISA assay as a tool for screening positive samples led to an important increase in the success of virus isolation . In São Paulo State , only 33 . 3% of the total of the samples inoculated in 2008 resulted in successful virus isolation , while in 2009 and 2010 , 85 . 7% succeeded . The number of São Paulo state counties that sent samples for isolation also increased from 0 . 9% in 2008 to 10 . 2% in 2009 ( Bisordi I , 2011 , unpublished data ) . DENV-4 reemerged in the country in 2010 in the municipalities of Boa Vista and Cantá in Roraima State [8] . The virus spread to different geographic regions of Brazil with cases of infection registered in the North ( Roraima , Amazonas , Pará ) , Northeast ( Bahia , Pernambuco , Piauí ) and Southeast ( Rio de Janeiro , Sao Paulo ) [9] . Despite the importance of the virus distribution , little is known about its rate , pattern of spreading and evolution . Each serotype represents a cluster of different genetic lineages constantly evolving and changing within the population [10] . In the present work , six strains of the first autochthonous cases of DENV 4 infection occurred in Sao Paulo State and Rio Grande do Sul State , Brazil , in 2011 were studied using a Bayesian Phylogenetic approach . Nucleotide sequences of the envelope gene were determined and compared with the corresponding sequences of representative strains of the known DENV-4 genotypes . The main objectives of the present study are the identification of the genotypes of the newly introduced strains , the examination of the phylogenetic relationships between strains and the estimation of emergence time of DENV-4 strains .
The specimens analysed in this study were retrieved from a collection formed from materials received for diagnostic purposes in the Instituto Adolfo Lutz . The samples were sent by reference hospitals and the patients names are confidentially anonymized , and only reference numbers were used during the diagnostic procedures and in the analysis that originated this study . All new DENV-4 strains characterized in this study were isolated directly from patient serum and detected by RT-PCR between February and March of 2011 . The origin of the strains are detailed in Table 1 . Twenty microliters of the patients blood or serum were inoculated in tubes seeded with cultured cells of Aedes albopictus , clone C6/36 . Indirect immunofluorescence assay ( IFA ) with polyclonal anti-flavivirus antibodies and anti-mouse immunoglobulin conjugated ( fluorescein isothiocyanate – Sigma ) were performed [11] . The positive samples were typed by IFA with monoclonal antibodies to DENV ( Biomanguinhos ) . Total RNA was extracted from the supernatant fluid of C6/36 infected cells using the commercial kit QIAamp® Viral RNA ( Qiagen Inc . , Ontario , CA ) , according to the manufacturer's instructions . One step RT-PCR was performed employing the protocol described by Lanciotti et al , [12] in the presence of a set of primers targeting the complete envelope gene sequence , described by Lanciotti et al [13] . RT-PCR products were purified and directly sequenced using the Big Dye v . 3 . 1 terminator chemistry . Sequences were determined using the Applied Biosystems 3130XL DNA sequencer . All nucleotide sequences of the envelope gene for DENV-4 serotype generated for this study are deposited in GenBank under accession numbers JN092553 and JN848496–JN848500 ( Table 1 ) . Sequences representative of the known genotypes I , II , III and Sylvatic for DEN4 were retrieved from GenBank and included in the phylogenetic analysis for comparison with the sequences generated in this study ( Table 1 ) . Sequence alignment was performed using the BioEdit software [14] . The Bayesian inference method available in the software BEAST v . 1 . 6 . 2 was used in order to analyze the phylogenetic relationship of the strains of this study [15] . The analysis of phylogenetic relationships and evolution , encompassed the entire Envelope gene , including six DENV-4 strains generated in this study and 107 sequences retrieved from GenBank ( Table 1 ) . Each sequence of the corresponding data set was dated and maximum clade credibility ( MCC ) tree was generated . The internal nodes were inferred using a Markov Chain Monte Carlo ( MCMC ) Bayesian approach under a GTR model with Gamma-distributed rate variation ( γ ) and a proportion of invariable sites ( I ) , using a relaxed ( uncorrelated lognormal ) molecular clock . Previously published data [10] , [16] suggest that dengue evolution generally approximates a molecular clock with occurrence of minor differences in rate . Four independent MCMC runs of four chains each were run for 10 millions generations . Convergence of parameters during MCMC runs were assessed by their Effective Sample Size ( ESS ) reaching values above 150 as calculated with Tracer V 1 . 5 [15] . We used a Bayesian skyline coalescent prior to estimating population dynamics through time and access an estimative of evolutionary rate and the time of the most recent common ancestor ( TMRCA ) in the Envelope gene analysis .
A fragment of 1487 nucleotides representing the entire sequences encoding the envelope gene was determined from 6 strains of DENV-4 and further aligned with other 107 envelope gene sequences retrieved from GenBank . The phylogenetic relationships among those strains were reconstructed by Bayesian analysis with a relaxed ( uncorrelated lognormal ) molecular clock model . The analysis generated a MCMC phylogenetic trees ( Fig . 1 ) . All samples sequenced in this study were located in Genotype II , and coupled with samples from the Caribbean region and northern South America ( Fig . 1 ) . In general , the group is strongly supported ( posterior probability of 0 . 98 ) with Internal relations within the clade showing a lower support , most likely due the higher homology of the samples , which hinders the separation , but the isolated strains are monophyletic in origin , supported by a high posterior probability ( 0 , 99 ) . The isolated strains in this study are monophyletic and our data suggest that they have been evolving separately for at least 4 to 6 years . Nonetheless , they are quite similar and relatively unchanged in relation to the DENV-4 introduced originally in the Caribbean region and northern South America . The relaxed molecular clock estimated after the analysis of the envelope gene encopassed a time of evolution for DENV-4 of 50–60 years and an average replacement rate of 2 . 0037×10−3 Subs/Site/Year , considering an Effective Sample Size of 334 . 79 calculated in Tracer 1 . 5 . The replacement rate of the branch of the isolated strains is of 1 . 238×10−3 Subs/Site/Year , and the branch originated within 4 to 6 years probably diverging from virus circulating in Venezuela as the closest sister branch reunited Venezuelan strains supported by a posterior probability of 0 , 99 .
All sequenced strains were encompassed in genotype II , with a high medium posterior probability ( 0 . 98 ) , slightly lower in the terminal clades due to the genetic similarity of samples which hinders the separation . The isolated strains formed a strongly supported monophyletic branch ( posterior probability of 0 , 99 ) . Not all Brazilian samples included in this study belonged to genotype II . The sequence AM 1619 , from Manaus , 2008 , retrieved from GenBank , grouped with genotype I . Our data also support the recent circulation of DENV-4 , genotype I , reported in Manaus County in 2008 [6] . The studied period of evolution of DENV-4 after the analysis of the Envelope gene was estimated between 50–60 years , with an average replacement rate of 2 . 0037×10−3 Subs/Site/Year , considering an Effective Sample Size of 334 . 79 . This estimative is supported by previously published data [10] , [17] . Our result strongly suggests that the introduction of genotype II in South America occurred between 30–35 years ago , most probably through the Caribbean region or the northern South America . These results corroborate previously published data , since the first cases associated with DENV-4 from the American Continent are dated around 1982 , in the Caribbean islands [10] , [13] , [18] . These data indicate that Dengue evolution approximates a molecular clock with minor rating variances . It is interesting to observe that raising ratings are mostly associated with increasing case occurrences or the emergence of the virus in a new region , meaning that the virus , when confronted with a susceptible population , undergoes an explosion of diversity . These phenomena were previously reported concerning Dengue and other Flaviviruses [1] , [19]–[21] . The clade directly associated with the studied strains showed a replacement rate of 1 . 238×10−3 Subs/Site/Year , slightly under the average rate . However , the rates observed within the clade formed by the isolated strains show higher replacement rates when compared with the sister branches ( Figure 2 ) . Such findings may indicate that the virus started to evolve more quickly , suggesting that it may have recently found a susceptible population and is spreading . The DENV-4 samples , sequenced in this study , represent a recent emergence of a viral strain circulating in South America around 20 to 25 years ago . Results suggest that a local evolution has been taking place for about 4 to 6 years . These data could indicate that the virus might have been present in the region for some time , without being noticed by Health Surveillance Services due to a low level of circulation and a higher prevalence of DENV-1 and DENV- 2 . It is possible that , since DENV-4 is associated with a milder disease [22] , [23] , the human cases may have been below the line of screening , going unnoticed . It is probable that the recent efforts to increase the success of virus isolation and serotyping allowed the study of a greater number of cases that otherwise would not have been serotyped , enabling the notification of less prevalent serotypes . However , the hypothesis of a recent introduction cannot be ruled out , but it would imply in multiple recent introductions of the virus , in a very short period of time , in relatively distinct areas , or a single introduction event in a significantly important area that facilitated the virus introduction in new areas . The simultaneously occurrence of DENV-4 in different Brazilian States , forming a strongly supported clade , in the beginning of 2011 , favors a recent emergence of the virus followed by a quickly introduction . However , such occurrence did not provide any clue to substantiate whether the virus was widespread but circulating in a low level , or circulating in a restricted area and subsequently taken to new localities with susceptible hosts . The isolated strains are monophyletic in origin and the molecular clock supports a local evolution , but by no means it indicates where that evolution occurred . It may have occurred in northern Brazil , and the virus quickly were introduced in Southern region due the constant human traffic . As the closest branch in our phylogenetic analysis is formed by Venezuelan strains of DENV-4 , a Venezuelan origin of Brazilian DENV-4 may be a plausible hypothesis . Either way , the virus may have evolved in an imperceptible manner in an undisclosed place , it was not reported and later emerged subtly and spread fast among a susceptible population . The recent DENV-4 cases reported elsewhere may represent a cryptic circulation that was only recently detected . The analysis of more sequences from a broader geographical perspective , encompassing other Brazilian regions , is crucial in order to understand how the virus evolved and how it got widespread . The reemergence of DENV-4 should be a concern for Health authorities since there are evidences that the replacement of a dominant circulating genotype is associated with the rising of a previously rare lineage . These phenomena were observed in Puerto Rico [24] and could be a plausible scenario in Brazil . The authors indicate the necessity to study the phylodynamics of Dengue virus and the dynamics of genotypes and serotypes circulation and substitution in the population . It is equally necessary to extend the efforts of virus isolation and sequencing towards the mosquito population . The mosquitoes are a reliable source of information on circulating virus , as mosquitoes do not depend on medical screening or the spontaneous search for medical services by the symptomatic patients . Our results indicate the recent circulation of DENV-4 in São Paulo .
|
Dengue virus infections are a major concern in developing countries , affecting approximately 100 million people/year . The virus has four immunologically related serotypes ( DENV-1 , DENV-2 , DENV-3 and DENV-4 ) associated with human disease . The virus is widespread in tropical and Sub-Tropical areas of Asia , Africa and Americas . The virus is transmitted by mosquito bites , and is primarily associated with Aedes aegypti as its main vector . To understand the reemergence of DENV-4 in Brazil in 2010–2011 we carried out a Bayesian phylogenetic analysis of the envelope gene sequences sampled in Brazil in 2011 . Our results indicate that the studied samples are close related to strains circulating since 1981 , when DENV-4 was first introduced in South America , but have gone trough recent evolution for at least 4 to 6 years . Our results also suggests that the virus may have penetrated Brazilian population earlier than 2010 , indicating that the virus could have been present but not detected due a higher prevalence of DENV-1 and DENV- 2 and the failure of the surveillance system to locate the milder disease commonly associated with DENV-4 .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"sequence",
"analysis",
"virology",
"emerging",
"viral",
"diseases",
"biology",
"computational",
"biology",
"microbiology",
"viral",
"evolution"
] |
2011
|
Dengue Virus Type 4 Phylogenetics in Brazil 2011: Looking beyond the Veil
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A protein at equilibrium is commonly thought of as a fully relaxed structure , with the intra-molecular interactions showing fluctuations around their energy minimum . In contrast , here we find direct evidence for a protein as a molecular tensegrity structure , comprising a balance of tensed and compressed interactions , a concept that has been put forward for macroscopic structures . We quantified the distribution of inter-residue prestress in ubiquitin and immunoglobulin from all-atom molecular dynamics simulations . The network of highly fluctuating yet significant inter-residue forces in proteins is a consequence of the intrinsic frustration of a protein when sampling its rugged energy landscape . In beta sheets , this balance of forces is found to compress the intra-strand hydrogen bonds . We estimate that the observed magnitude of this pre-compression is enough to induce significant changes in the hydrogen bond lifetimes; thus , prestress , which can be as high as a few 100 pN , can be considered a key factor in determining the unfolding kinetics and pathway of proteins under force . Strong pre-tension in certain salt bridges on the other hand is connected to the thermodynamic stability of ubiquitin . Effective force profiles between some side-chains reveal the signature of multiple , distinct conformational states , and such static disorder could be one factor explaining the growing body of experiments revealing non-exponential unfolding kinetics of proteins . The design of prestress distributions in engineering proteins promises to be a new tool for tailoring the mechanical properties of made-to-order nanomaterials .
The principle of ‘minimal frustration’ [1] , [2] underlies the thermodynamic picture of protein folding . According to this picture , proteins negotiate a rough , funnel-shaped energy landscape during the folding process , and eventually settle in a state that , as much as possible , satisfies the energetic constraints arising from the multitude of interatomic covalent , electrostatic and van der Waals interactions . Although frustration is minimised in the native state , it is not completely eradicated . Even in the simplest crystals , the equilibrium state is one that minimises the energy of the structure as a whole , not every atom-atom interaction individually; global constraints prevent every pairwise interactions from being perfectly satisfied . This is even more the case for proteins , in which the topological contraints of the backbone peptide bonds further restrict the freedom of individual atoms to individually satisfy every interaction . Such local frustration in a protein must give rise to residual mechanical forces – thus , proteins are in some sense prestressed materials . D . Ingber has proposed that proteins and other biological structures should be understood in light of the architectural concept of tensegrity [3] , [4] , popularised by Buckminster Fuller , describing structures the mechanical stability of which arises purely from a balance between pre-tensed and pre-compressed members . The concept of biomolecular tensegrity has come under focus very recently in the work of Shih , Ingber , and co-workers , who have designed and synthesised prestressed DNA structures [5]; it has also been invoked in a novel method for interpreting free energy profiles inferred from the forced unfolding of single biomolecules [6] . In contrast to this tensegrity picture , classic coarse-grained models of proteins , which have been used extensively to study protein folding and dynamics , typically neglect prestress . Both G-style models [7]–[9] and elastic network models [10] , [11] define the equilibrium separation of every residue pair to be precisely the separation measured in the native state , and thus every residue-residue interaction is individually relaxed; as such , the native state is defined to contain no residual force . Thus , especially in research areas that rely on these coarse-grained approaches , the consequences of prestress for folding and dynamics have not been well explored . It has been demonstrated recently [12] that , in graphene sheets , the prestress of bonds around defects at grain boundaries is the key determining factor for toughness of the sheets . This result highlights the fact that the existence of prestress can qualitatively change the mechanical properties of a structure , and raises the question of to what extent such effects are utilised by nature to tune the mechanical stability of proteins . Since concentrations of internal force in a molecule could be used to drive conformational changes if released , via thermal fluctuations or due to interactions with other molecules , the spatial distribution of prestress in a protein may also provide important clues for understanding the mechanisms for protein-protein interactions [13] , [14] , protein-DNA interactions [15] , and allostery; indeed , the existence of ‘tensed’ and ‘relaxed’ states in allosteric proteins has been a central concept in models of allosteric transitions since the classic early models of inter-domain allostery in hemoglobin [16]–[18] . Elastic stress also plays a central role in the more recent allosteric model of Savir and Tlusty [19] . Using short lengths of pre-tensed double-stranded DNA to stretch individual molecules , it is now possible to directly observe the role played by elastic stress in the allosteric control of protein enzymes [20] , [21] and ribozyme [22] . But how is such a global elastic stress built up within a protein scaffold ? We used all-atom Molecular Dynamics ( MD ) simulations to quantify the importance of prestress in the native state of ubiquitin . To this end , we adapted an earlier technique for measuring force distributions in mechanically perturbed proteins [23] , [24] to allow the calculation of effective pairwise residue-residue force profiles . This procedure is a direct force measurement , unlike other methods based on inferring pairwise forces from fluctuations [25] , [26] , and does not require any a priori assumptions about the form of the force profile . From the effective force profiles we extracted average forces for each residue pair , thereby constructing a prestress network for the protein . We found that high residual forces exist throughout the protein , and are particularly associated with hydrogen bonds and salt bridges . The magnitude of these forces is shown to be enough to significantly influence the protein's mechanical properties , most notably its unfolding pathway . We also discover that , for some side-chains , prestress is dynamic – inter-residue mechancial coupling switches between a number of distinct regimes depending on side-chain conformations .
From 100 ns of MD trajectories , we calcuated effective force profiles for every pair of amino acid residues in ubiquitin ( Fig . 1a ) , as described in the Methods section . The average forces inferred from these profiles are plotted in Fig . 1b superimposed on the 3D structure of the protein . For clarity , the same force network is also represented in Fig . 1c as a circular graph , with each vertex corresponding to a residue . Covalently bonded residue pairs are neglected [see the Supplementary Material ( Text S1 and Figs . S6 and S7 ) for details on covalent bond forces] . Red ( blue ) edges represent attractive ( repulsive ) forces , and edge thicknesses correspond to the magnitude of the forces , which range between −490 pN ( attractive ) and +407 pN ( repulsive ) . In the context of cell biology these are high forces – for comparison , the forces generated by the kinesin walk have been measured to be on the order of 2 pN [27] . An animation showing the projection of this network on the three-dimensional structure of the protein is provided in the Supplementary Material ( Video S1 ) . The most obvious large-scale structures in the network are the relatively ordered bands of both tensile and compressive forces that connect neighboring beta strands: specifically , the two parallel pairs of beta strands 1/2 and 3/5 , and the anti-parallel beta strand pair 1/5 . In contrast , isolated cases of strongly tensed ( red ) residue pairs are also observed , such as Lys27-Asp52 and Lys11-Glu34 , which do not correlate with neighboring residues . These high tensile forces occur only between residues with charged side-chains; as discussed in more detail below , they correspond to tensed salt bridges . To get a clearer picture of the prestress pattern associated with the main-chain interactions in beta sheets , the force network accounting for only inter–main-chain interactions is shown in Fig . 2a and Video S2 [here ‘main-chain’ refers to the N , C , C , O , and H atoms making up the backbone] . Inter–main-chain interactions are found to be predominantly attractive , with a few strongly repulsive pairs . To better understand this phenomenon we examine in more detail the residue-wise force distribution in beta strands 1 and 5 . These strands are of special relevance to the mechanical stability of ubiquitin , since they form a ‘force clamp’ that provides the primary resistance against rupturing of the protein by stretching from the N and C termini [28] . In Fig . 2b the residue-wise average main-chain forces within the beta force clamp are illustrated . Forces between neighboring , covalently-bonded residues are not shown , and will be discussed separately . There are five hydrogen bonds between these beta strands , formed by residues Gln2 and Glu64 , Phe4 and Ser65 , Phe4 and Leu67 , Lys6 and Leu67 , and Lys6 and Leu69; and it is evident that these pairs are precisely those for which the average pairwise force is repulsive ( blue ) . Apart from the hydrogen-bonded pairs , every other residue pair in beta strand pair 1/5 experiences an average attractive force ( red lines in Fig . 2a ) ; they are all pre-tensed . This gives the beta sheet an overall appearance reminiscent of a tensegrity structure , the mechanical stability of which is determined by a balance between tensed and compressed structural members [3] . The origin of the pre-compression of the hydrogen bonds can be understood via this tensegrity analogy: the ‘tensed’ attractive interactions between the two beta strands act to pull the strands closer together than they would otherwise like , compressing the hydrogen bonds until the tensile and compressive forces balance . The same pattern , of hydrogen bonds compressed by other attractive cross-strand interactions , also holds for the other beta strand pairs in the protein , both parallel and anti-parallel; see Fig . S1 for the force distributions in the anti-parallel beta strand pairs 1/2 and 3/5 . The underlying atomic forces that give rise to the attractive and repulsive residue-residue forces are illustrated in Fig . S2 . To investigate how the combination of atomic forces gives rise to an effective force profile for each residue pair , we plot the distribution of residue-wise force versus separation of the C atoms . Fig . 2c shows the result of this procedure for the hydrogen-bonded residue pair Gln2-Glu64 . Each point in the figure corresponds to a single frame of the trajectory . The scatter of the data points is large , due to fluctuations in the conformations and relative orientations of the two residues . The average fit ( blue curve ) represents an ‘effective’ pairwise force profile averaging over these fluctuations [29] . Around the mean separation , the effective force profile is approximately linear , and thus has the character of a compressed Hookean spring . But the curve is clearly non-linear at larger separations , approaching the rupture distance of the bond . The overall shape is reminiscent of a Morse-type potential traditionally used to approximate chemical bonds . Similar profiles are obtained for the other hydrogen bonds in the sheet . Fig . 2d shows the effective force profile for one of the ‘tensed’ non-hydrogen-bonded pairs ( Ile3-Ser65 ) . The magnitude of the attractive force is found to reduce with separation . Such behaviour cannot be approximated by a physical Hookean spring , since the local effective spring constant is negative; it is instead more like a Morse-type potential where the interacting pair only samples the tail of the potential , never even approaching the equilibrium separation . Thus the analogy with macroscopic tensegrity structures is only superficial: it is not accurate to think of the tensed residue pairs as prestressed cables , which would exhibit Hookean behavior . Due to the partially non-Hookean springs in the network of ubiquitin , the prestress can be expected to have an impact on both the elastic behavior of the protein ( if any ) as well as the inelastic behavior including rupture . Although the alpha helix does not play a direct role in determining the mechanical stability of the protein , it is interesting to look at the pattern of prestress in the helix and see whether pre-compression of hydrogen bonds is a general phenomenon or one restricted to beta sheets . Fig . S3 shows the main-chain-only residue-wise forces within the helix . Similar to the beta sheets , the helix exhibits a tensegrity-like pattern of balancing compressive and tensile forces . However , in this case the hydrogen bonded residue pairs are under tension , in contrast to the compressed beta-sheet hydrogen bonds . We conclude that pre-compression of hydrogen bonds is not a property intrinsic to all hydrogen bonds , but rather a context-dependent phenomenon: prestress in a given bond is determined by the interactions between other residues in its immediate neighborhood , and the local molecular geometry . This points to the fascinating possibility that the distribution of prestress in a protein can be engineered by intelligent modifications to the amino acid sequence , providing a new tool for designing proteins with made-to-order mechanical properties [30] . Any applied external force must work against the inherent compression imposed by the protein onto the rupturing bonds . We propose that the hydrogen bond compression influences the unfolding force and pathway of the force clamp between beta strands 1 and 5 . Fig . 3 is a plot of the average force for each of the five bonds in this clamp . Of the two bonds at the edge of the sheet , pair Gln2-Glu64 ( ) is significantly more compressed than Lys6-Leu69 ( ) . Arguing from Bell's theory of the rupture of individual bonds under force [31] , it can be shown that pre-compression of a bond should increase its average lifetime . Based on the kinetic theory of thermally-activated rupture in metals [32] , Bell wrote down the following expression for the lifetime of a single bond subjected to an external force : ( 1 ) where is the inverse of the atomic oscillation frequency ( s ) , is the height of the energy barrier separating the bound and unbound states , and is a measure of the distance between the bound and transition states . If the bond is also subjected to a compressive ‘prestress’ force , we then have ( 2 ) Eq . 2 can be used to estimate the contribution of pre-compression to the lifetimes of the two end hydrogen bonds . For Gln2-Glu64 , we have at room temperature , assuming Å; the characteristic lifetime of the Gln2-Glu64 bond is enhanced by a factor of 400 , with respect to a non-compressed hydrogen bond . The analogous calculation for the Lys6-Leu69 bond gives , suggesting that hydrogen bond compression extends the lifetime of ubiquitin under a stretching force significantly , by approximately two orders of magnitude . We note that the elastic energy stored in such a prestressed hydrogen bond can be expected to be minor , as a force of 100 pN approximately corresponds to an energy of only approximately 1 J/mol . The magnitude of compression of the hydrogen bonds is not uniform along the beta strand pair 1/5 . We propose that differences in hydrogen bond compression influence the unfolding pathway for the beta force clamp . It is known from earlier MD simulation work [28] , [33] that the Lys6-Leu69 hydrogen bond always ruptures first when the protein is unfolded by stretching the N- and C-termini . The stronger pre-compression of pair Gln2-Glu64 relative to pair Lys6-Leu69 should be a contributing factor in determining this unfolding pathway . The ratio of the lifetimes for the two edge hydrogen bonds is . Thus , differences in pre-compression of hydrogen bonds of the magnitude we observe here are enough to more than double the relative lifetime of the more-compressed bond , all else being equal . This calculation is made under the assumption that the magnitude of pre-compression does not change as the protein is stretched , which is unlikely to be the case in reality; how the network of pre-tensile and pre-compressed forces evolves under an applied stretching force will be a topic for future study . Despite this simplification , our rough calculation serves to demonstrate that prestress is an important factor in determining a protein's mechanical stability , and should be taken into account along with other factors such as the orientation of the bonds relative to the pulling direction and the shielding from water by hydrophobic side-chains [33] . Apart from intra-main-chain interactions , we found that side-chain–side-chain interactions also exhibit prestress . For clarity , the inter-side-chain forces are separated into those for side-chains comprising the hydrophobic core of the protein ( Fig . 4a ) and for side-chains facing outwards into the solvent ( Fig . 4b ) . The two are also shown together , projected on the protein structure , in Video S3 . The inward-facing hydrophobic side-chains , with few exceptions , repel each other . None of their atoms are highly charged , and thus the inter-residue forces are dominated by steric repulsion . This is consistent with the hydrophobic core being compressed by tension in the ‘skin’ of the protein comprising the main-chain and outer side-chains , as well as by entropic forces related to the hydrophobic effect . In contrast to the core side-chains , the forces between outward-facing side-chains are found to exhibit a mix of both compressive and tensile prestress . The strongest attractive forces ( red in the figure ) all correspond to salt bridges between charged side-chains ( lysine and arginine are positively charged , aspartic acid and glutamic acid negatively charged ) . The pair with the highest tensile prestress is Lys11-Glu34 , which comprises a salt bridge connecting the C-end of the alpha helix with the N-end of beta strand 2 . This particular salt bridge has been shown experimentally to contribute significantly to the thermodynamic stability of ubiquitin [34] . Because of the relatively large distance between the two residues , their side-chains are forced to fully extend to satisfy the electrostatic attraction , giving rise to the observed prestress of the residue-residue force: the electrostatic attraction is counterbalanced by entropic stretching of the side-chains . It is generally true that the residue pairs with the strongest tensile prestress ( eg . Asp21-Lys29 , Lys27-Asp52 , and Asp39-Arg74 ) are salt bridges between spatially separated residues . Conversely , salt bridges between nearby residues , such as Glu51-Arg54 , can be satisfied without stretching the side-chains and accordingly the inter-residue forces show no significant prestress . We find evidence that some of the pre-tensed salt bridges generate significant torsion in the backbone , and this torque can be removed by mutating one of the salt bridge partners to ‘break’ the salt bridge ( see Supplementary Text S1 for more details ) . Thus , side-chain prestress should be an important factor in stabilising the protein's native conformation . Unlike the main-chain-only prestress network , for which each residue has significant interactions with at most two others , some nodes in the side-chain network are connected to as many as four or five others , widely separated in sequence-space . In the context of network theory , these residues may be thought of as ‘hubs’ of the network; perturbing these residues may be expected to lead to a wide-spread redistribution of force in the prestress network . In fact , simulations in which two of the most obvious hub residues , Asp52 and Arg72 , were separately mutated to glycines exhibited no statistically significant changes to the prestress network beyond the local neighborhood of the mutated residue . This suggests that , at least with respect to perturbations of these specific residues , redundancy in the mechanical network imbues the pre-stress distribution with a certain amount of rigidity , and that intentional engineering of a protein's prestress network may require a more sophisticated mutation strategy beyond simply perturbing individual network hubs . The connections between the hub residues Lys27 , Asp52 and Arg72 form a clear triangle in Fig . 4b , most notably featuring a strong tensile prestress between the salt-bridged residues Lys27 and Asp52 . A clue to how the high connectivity of these hubs arises comes from the effective force profile for Asp52 and Arg72 ( Fig . 5a ) . Unlike the main-chain hydrogen bond profiles , this distribution seems to show at least three separate overlapping force profiles . This suggests that the side-chains involved are visiting a number of distinct conformational states over the course of the simulation . We indeed find evidence of very complex dynamics for Arg72 and its neighbors , which alternately involves hydrogen bonds to Asp52 and other competing residues , involving their sidechains , backbone , or both ( Fig . 5a , right ) . It is now possible to detect the dynamics of arginine side-chains from NMR [35] , so it should be feasible to directly validate our predictions of Arg72's propensities for binding to its neighbors . Such switching between discrete states is also observed for hydrophobic residues . Fig . 5b shows the effective force profile for the residue pair Leu8-Val70 . These two residues are functionally important , since they comprise a hydrophobic binding patch that is crucial for the binding of Lys48-C-linked polyubiquitin to the proteasome [36] . The force-distance distribution seems to show two distinct force curves , one with an equilibrium separation around 5 . 5 Å , and another around 6 . 5 Å . The existence of two states is confirmed from examining representative states of the trajectories ( Fig . 5b , right ) , as well as by analysing the distribution of the angle between the two side-chains as a function of residue-residue separation over the length of the simulation ( Fig . S4 ) . As for the Asp52-Arg72 pair discussed above , the overlapping effective force curves here reveal that these different orientational states for the side-chains correspond to different inter-residue mechanical coupling regimes . It is conceivable that the switching between these states has an influence on the local balance of tension and compression , and thus on the protein's mechanical stability . This degeneracy in mechanical stability may contribute to the signature of static disorder detected in ubiquitin's rupture kinetics as measured by recent AFM experiments [37] , [38] . To what extent the local sidechain disorder influences the mechanical response might depend in nature on the type of polyubiquitin linkage , which is a topic for future research .
We have shown that forces in the native ensemble of ubiquitin , measured from all-atom MD simulations , generate a tensegrity-like pattern of prestress at the residue-residue level . This includes pre-compression of the hydrogen bonds connecting beta strands , and conversely pre-tensing of alpha helix hydrogen bonds . The differences between the pre-compression of individual beta strand hydrogen bonds are sufficient to significantly modify the kinetics of hydrogen bond breakage under force , and thus should be an important factor in determining the protein kinetic stability and unfolding pathway under mechanical perturbation . Salt bridges known to be important for ubiquitin's thermodynamic stability are found to be strongly pre-tensed , and the effective force profiles for side-chain–side-chain interactions reveal a connection between side-chain dynamics and inter-residue mechanical coupling . We propose that the observed dynamic equilibrium of multiple side-chain states contributes to the complex rupture kinetics observed in AFM experiments , since each discrete side-chain state corresponds to a different well in the rough global energy landscape . A correlation is found between tensed salt bridges and twisted peptide bonds in the protein backbone , which suggests that tension in stretched side-chains , transmitted as torque to the backbone , might play a role in determining the conformation of the protein's native state . Finally , we find the tensegrity network remarkably robust with regard to mutations at network hubs . It remains to be shown whether the observations reported here apply generally to all proteins , or are specific to ubiquitin . A preliminary study of the titin immunoglobulin [I27] domain ( PDB code 1WAA [23] ) also found compression of hydrogen bonds in beta sheets , and tension in salt bridges , suggesting that these are general properties ( Fig . S5 ) . An early atomic force microscope study of the mechanical stability of I27 mutants [39] showed that the point mutations Val11Pro , Val13Pro and Val15Pro reduced the protein's rupture force , as expected due to proline's inability to form inter-strand hydrogen bonds; conversely , and unexpectedly , the mutant Tyr9Pro was found to be more stable than the wild type . Our I27 prestress network ( Fig . S5 ) gives an intriguing clue as to the origin of this effect . Tyr9 is seen to be involved in a number of repulsive force pairs , with sequentially distant partners - not the case for Val11 , Val13 and Val15 . It may be that the mutation Tyr9Pro , by removing these frustrating repulsive forces , allows neighboring residues do adopt a more favorable conformation and thereby stabilise the protein . A detailed study of how such mechanically important point mutations involve changes to the prestress network will be a focus of future work , as will a survey of a wide range of protein structural types , necessary to better appreciate to what extent prestress is a ubiquitous aspect of protein structure . Futhermore , we have found that the prestress network is dynamic , due to the influence of side-chain dynamics on residue-residue forces , but more work needs to be done to quantify the relationship between applied force , side-chain states and protein function . Another question is whether the effective force profiles measured here can be used as a basis for prestressed coarse-grained protein models , and in what ways the predictions of such a model would differ from traditional elastic network models , which by definition lack any prestress . Our study opens the road to re-engineer molecular tensegrity structures , to eventually allow the rational tuning of mechanical or allosteric response .
We used the Gromacs 4 . 0 . 5 package [40] to perform equilibrium all-atom simulations of ubiquitin , starting from the x-ray structure with PDB accession code 1UBI [41] . This structure is illustrated in Fig . 1a . The protein was solvated with TIP4P water [42] in a periodic cubic box of 6 . 5 nm per side . 16 pairs of sodium and calcium ions were added to give an effective salt concentration of 0 . 15 M . The OPLS all-atom forcefield [43] was chosen to describe interatomic energies . The system was subjected to a steepest-descent energy minimization , followed by a 1 ns solvent equilibration with position restraints on the heavy atoms of the protein . Then a further 1 ns equilibration run was performed with no position restraints . From the second half of the resulting trajectory , five snapshots were chosen to be the starting conformations for five independent production runs , each of which was carried out for 20 ns , giving a total of 100 ns of simulation time . All runs were performed in the NpT ensemble , with a Nosé-Hoover thermostat [44] , [45] set to 300 K and Parrinello-Rahman barostat [46] at 1 atm , using a time-step of 2 fs . Electrostatic interactions were calculated using the particle mesh Ewald algorithm [47] . The same procedure was also carried out for the single-residue mutants Asp52Gly and Arg72Gly , initial structures of which were generated using PyMOL [48] . For each of the production runs , all pairwise atomic forces within the protein were output with a frequency of 1 ps using the modified FDA version of Gromacs 4 . 0 . 5 [23] . These pairwise atomic forces were then converted to residue-wise forces by summing in a vector-wise fashion , for each frame of the trajectory , all atomic forces between each pair of residues , and then projecting this total force on the vector connecting the C atoms of the two residues at that instant of the simulation . We note that due to the projection , any forces orthogonal to this connecting vectors , i . e . torques , are neglected . Their contribution to a protein's pre-stress will be subject of future investigations . The magnitudes of the residue-residue forces were then averaged for each residue pair over the full 100 ns of the simulation to give the average prestress distribution of the protein . Note that this procedure differs from earlier applications of FDA , in which residue-wise forces were calculated simply by summing the scalar magnitudes of the mean atomic pairwise forces . The protein-water and protein-ion forces were neglected . Effective force profiles for each pair of residues were obtained by selecting 10000 evenly-spaced frames from the total trajectory , and plotting the residue-wise force for each frame against the separation of the residues' C atoms . For studying the specific atomic contributions to inter-residue forces in more detail , the average atom-atom force distribution was also calculated , simply by averaging the total force between each pair of atoms in the protein over the 100 ns of simulation time . We refer to the network of forces in the protein also as ‘prestress’ , in aid of establishing an analogy to previous work on the link between prestress and protein function and allostery , even though a normalization of forces by area has not been carried out , and ‘preforce’ would be the more accurate terminology . The standard error of the mean for time-averaged forces from the five independent trajectories was typically in the range of 10 pN , which is less than 10% of typical forces in hydrogen bonds , suggesting sufficient convergence . Protein visualisations were carried out with VMD [49] and PyMOL [48] .
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A tensegrity structure is one composed of members that are permanently under either tension or compression , and the balance of these tensile and compressive forces provides the structure with its mechanical stability . Macroscale tensegrity structures , which include Buckminster Fuller's geodesic domes , achieve exceptional structural integrity with a minimal use of resources . The question we address in this work is whether nature makes use of molecular-scale tensegrity in the design of proteins . Using Molecular Dynamics simulations of the protein ubiquitin , we measure the network of pairwise forces connecting the amino acid residues and show that this network does indeed have the character of a tensegrity structure . Furthermore , we find that the arrangement of tensile and compressive forces is such that hydrogen bonds in the protein's beta sheet , which are crucial for bearing mechanical loads , are compressed . This pre-compression is enough to significantly lengthen the lifetime of a bond under a given force , and thus should be an important factor in determining the protein's mechanical strength . The rational design of molecular prestress networks promises to be a new avenue for the engineering of proteins with made-to-order mechanical properties , for applications in medicine , materials and nanotechnology .
|
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2012
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Dynamic Prestress in a Globular Protein
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This study investigated the efficacy of two collars for the treatment and prevention of flea infestations . Additionally the effect of these collars on the incidence of Leishmania infantum infection as compared with a group of vaccinated dogs was evaluated . A total of 224 young dogs from private animal shelters were enrolled in April/May into four groups: G1 , 55 dogs treated with 10% imidacloprid + 4 . 5% flumethrin collar ( Seresto , Bayer Animal Health ) ; G2 , 60 dogs treated with 4% deltamethrin collar ( Scalibor protector band , MSD Animal Health ) ; G3 , 54 dogs vaccinated with CaniLeish ( Virbac Animal Health ) ; and G4 , 55 dogs left non-treated as controls . Dogs were followed up at days 120 ( September ) , 210 ( December ) , and 360 ( April-May ) . At those time points , clinical assessments , ectoparasite counts and blood , bone marrow and skin samples , to detect the presence of L . infantum , were performed . The efficacy of Seresto in protecting dogs from flea infestation was 100% ( P < 0 . 01 ) on day 120 and 210 , while animals treated with Scalibor showed a prevalence of the infestation ranging from 23 . 3% to 33 . 3% on day 120 and 210 , respectively . At the end of the study , the incidence of L . infantum infection in collared dogs—based on animals being positive in any of the tests—was 5 . 5% in Seresto-treated dogs and 20% in Scalibor-treated dogs , resulting in overall efficacy of prevention of 88 . 3% for Seresto and 61 . 8% for Scalibor . No statistical difference was detected in L . infantum positive dogs for bone marrow PCR and/or cytology at day 360 between the CaniLeish ( 15 . 4% ) and non-treated control dogs ( 10 . 0% ) . Both collars proved to be effective ( P < 0 . 01 ) in preventing L . infantum infection throughout one transmission season , whereas no significant difference was recorded in the frequency of active infections between dogs vaccinated with CaniLeish and control dogs , emphasizing the importance of using repellent/insecticide actives as a priority measure for protection against canine leishmaniosis .
The veterinary importance of ectoparasites ( e . g . ticks and fleas ) is characterized by their impact on the health of companion animals [1] . Ectoparasites interact intensively with their animal hosts through blood feeding , and have the capacity to transmit pathogens of both medical and veterinary significance , causing the so-called vector-borne diseases ( VBD ) , which are among the principal causes of morbidity and mortality in companion animals [2] . In Mediterranean countries , such as Italy , ticks and fleas represent a year-round hazard especially in sheltered animals [3 , 4] . The control of ectoparasites in dogs , by means of ectoparasiticide products , has proved to be successful under different environmental and housing conditions [5] and efficient to reduce the risk of transmission of several VBD [6] . Visceral leishmaniosis caused by Leishmania infantum is a vector-borne parasitic disease affecting mainly dogs and humans [7] , being endemic in southern Europe , Middle East , Central Asia and South America [8] . Dogs represent the principal reservoirs of the infection and thus play an important role in the epidemiology of the disease [7] . Canine leishmaniosis ( CanL ) may evolve through a plethora of clinical presentations spanning from subclinical infections to fatal illness [9] . The main method for preventing L . infantum infections in animals and humans is to avoid the bites of phlebotomine sand fly vectors by means of repellents [10 , 11] . Indeed , pyrethroids , either applied as spot-on formulations or as collars , have been proven effective in preventing phlebotomine sand fly bites under laboratory conditions or L . infantum infection in dogs under field conditions [11] . For example , a collar containing 4% deltamethrin ( Scalibor protector band , MSD Animal Health ) showed to be useful in controlling the infection by L . infantum in endemic areas with a range of efficacy from 50% to 84% after one transmission season [12 , 13] . A polymer matrix collar containing a combination of 10% imidacloprid and 4 . 5% flumethrin ( Seresto , Bayer Animal Health ) , recently licensed for the control of ticks and fleas in dogs and cats up to eight months [14] , though not registered against phlebotomine sand flies , was effective ( i . e . efficacy from 93 . 4 to 100% ) in protecting sheltered dogs living in CanL endemic areas [15 , 16] . In addition , considerable efforts have been put into the development of a vaccine against CanL by selecting several vaccine candidates and adjuvants , which lead to the launching of three vaccines in the past 10 years [11] . For example , a vaccine based on excretory-secretory antigens of L . infantum with Quillaja saponaria ( LiESP-QA-21 ) as adjuvant , has been licensed in Europe ( CaniLeish , Virbac Animal Health ) . Following a primary course consisting of three injections at 21-days intervals , this vaccine induces a one-year Th1-dominated cell-mediated immune response against L . infantum , protecting dogs from developing clinical signs after L . infantum infection [17 , 18] . When tested in the field in naïve dogs ( n = 41 ) , this vaccine showed an efficacy in preventing active infection of 68 . 4% and a protection against the development of clinical signs of 92 . 7% [19] . As none of the currently available vaccines are capable to protect against infection [20] , their use must be considered as part of an integrate control program for CanL and cannot replace anti-vectorial measures . In this study , we investigated the efficacy of two collars for the treatment and prevention of ectoparasite infestations as compared with an untreated control group . Additionally , we assessed the effect of these collars on the incidence of CanL as compared with a group of CaniLeish-vaccinated dogs .
This was a negative controlled , multicentre study conducted according to the principles of Good Clinical Practices ( VICH GL9 GCP ) [21] , and the Guideline on Statistical Principles for Clinical Trials for Veterinary Medicinal Products ( CVMP EMA/CVMP/EWP/81976/2010 ) [22] . The study was performed under the framework of a large research project for monitoring and controlling vector-borne diseases and ectoparasites ( including phlebotomine sand flies ) in sheltered dogs . The project and activities were defined in a master agreement between the Department of Veterinary Sciences of the University of Messina and the four shelters where the study was carried out . The study protocol was approved by the Ethical Committee of the Department of Veterinary Sciences of the University of Messina ( no . 002/2016 , prot . 18894 , March 23rd 2016 ) . Animals were housed in four private animal shelters one in Catania province ( S1 ) and three in Syracuse province ( S2-S4 ) , Sicily ( southern Italy ) . Study sites had a history of ectoparasite infections on dogs and were located in a L . infantum hyper-endemic area in which a mean annual incidence of L . infantum infection of 39 . 4% has been estimated in unprotected sheltered dogs [16] and where competent phlebotomine sand fly vectors—i . e . Phlebotomus neglectus , Phlebotomus perniciosus and Phlebotomus perfiliewi–occurred from late spring to autumn , i . e . May to November [16 , 23] . Animals at the study sites ( i . e . n = 380 , n = 450 , n = 400 and n = 470 dogs in S1 , S2 , S3 and S4 , respectively ) were housed in open enclosures according to the time of admission into the facility , their attitude and behaviour . Dogs had a covered resting area with concrete floor with beds and an external uncovered area with concrete ( S4 ) or fine gravel floor ( S1 , S2 and S3 ) . Covered areas were separated by walls or aluminium composite panels . In April-May 2013 , a total of 247 dogs ( i . e . S1 = 60 , S2 = 65 , S3 = 60 , S4 = 62 ) were examined and sampled for the study enrolment ( Day 0 ) . In order to minimise the risk to include L . infantum infected dogs , only dogs with a maximum age of 18 months were selected for the study . Dogs were physically examined and weighed , and blood , skin and bone marrow samples were collected ( see below ) . Animals were enrolled in the study if they fulfilled the following criteria: normal general health , ≥ 7 weeks to 18 months of age , not treated with ectoparasiticides within the time of activity reported for the used product and not treated with immunosuppressive drugs within 14 days prior to study start . Only dogs that tested negative for L . infantum in serology ( IFAT ) and PCR in skin and bone marrow at the time of inclusion were maintained in the study . Dogs included were identified using microchips and assigned to one of the four groups using a random treatment allocation plan . Dogs in group 1 were treated with Seresto , those in group 2 with Scalibor and animals in group 3 were vaccinated with three doses ( at 21-days intervals ) of CaniLeish , after being tested negative with Speed Leish K ( Virbac ) . Also , according to the requirements for CaniLeish vaccination , only dogs older than 6 months were included in that specific group . Group 4-dogs were kept as non-treated controls . Within the study sites dogs included were kept in pens in smaller groups with an average size of 6 ( 1 to15 ) dogs per pen . Randomization was conducted pen-wise in order to avoid animals from different groups being in direct physical contact and pens containing study animals were patchily disseminated within the study site . Collared dogs ( groups 1 and 2 ) were kept under label-conform medication for approximately seven months , according to the length of L . infantum transmission season in the study area . Dogs were followed up on days 120 ±10 ( September ) , 210 ±10 ( December ) and 360 ±15 ( April-May ) after inclusion . At each follow-up , dogs were physically examined for ectoparasite ( flea and tick ) presence and CanL related signs were recorded ( e . g . loss of weight , dry exfoliative dermatitis , muscular atrophy , periocular alopecia , pale mucous membranes , onychogryphosis , lymphadenopathy , splenomegaly and conjunctivitis ) . During those follow up visits skin and blood samples were also collected . Briefly , blood samples of approximately 5 ml were collected in serum separator gel tubes ( Vacumed ) from the brachial or jugular veins , being immediately refrigerated ( +4°C ) . Skin tissue samples ( about 0 . 5 cm² ) were collected from the inter-scapular region and stored in individual micro-tubes containing 1 ml of phosphate buffered saline ( PBS ) solution . Additionally , bone marrow samples were collected at the enrolment and on days 210 ±10 and 360 ±15 . Bone marrow samples were aspirated from the iliac crest using Rosenthal needles ( 16 or 18 gauge ) , then a few drops were smeared on slides for cytological examination and the remaining part was stored in individual micro-tubes with 1 ml of PBS solution . Dogs included in the collar treated groups were wearing collars up to day 210 of the study . Seresto collars were replaced only if they were lost or if the animal’s weight increased above the threshold of 8 kg for the small collar size , whereas Scalibor protector-bands were replaced in case of losses and substituted on day 120 according to the recommendations given in the product leaflet . All dogs included in the study were observed daily for any changes in their health and abnormal health conditions were recorded . The use of other ectoparasiticides on dogs or in the environment was not allowed throughout the study period . However for all groups , individual treatments with fipronil in spot-on formulation were eventually authorized when heavy tick or flea infestations occurred . Personnel performing laboratory tests was blinded . In the laboratory , blood samples were centrifuged ( 1 , 500 g for 10 minutes ) and the serum was split into two aliquots . Serum , skin and bone marrow samples were stored at –20°C . Serum samples were tested for circulating anti-L . infantum antibodies by IFAT using a cut-off of 1:80 as described elsewhere [24] . Positive sera were also titrated using serial dilutions until negative . DNA extraction and PCR amplification of Leishmania kinetoplast DNA was performed on bone marrow and skin samples as described elsewhere [16] . Bone marrow smears were stained with MGG Quick Stain ( Bio Optica , Italy ) and microscopically examined for L . infantum amastigotes . Each smear was examined for about 10 minutes under light microscopy ( 100 microscopic fields ) using a 100X oil immersion objective . Dogs in the two collar treated groups and non-treated control dogs were defined as infected by L . infantum when positive in at least one of the diagnostic methods ( i . e . IFAT , PCR on skin and bone marrow , and cytology ) during the course of the study . Since the presence of anti-L . infantum antibodies in CaniLeish-vaccinated dogs could be due to the immune response induced by the vaccine , the detection of the parasite in bone marrow samples by PCR and/or cytology at day 210 and 360 was considered as indicative of a failure in controlling the infection . At the last visit , infections were further classified as active infections when IFAT positive results ( ≥ 1:160 ) were associated with bone marrow PCR and cytology positive findings; dogs with active infections were ranked into sick or clinically healthy according to the presence of clinical signs [9] . At each site , light traps were used to collect phlebotomine sand flies . Starting from May 2013 , two traps were placed biweekly in each shelter at 50 cm above the ground before sunset and left in situ for at least 12 hours ( i . e . from 6:00 p . m . to 6:00 a . m . ) . Monitoring activity was suspended in November 2013 after three consecutive negative trapping sessions . Phlebotomine sand flies captured were separated from other insects , differentiated by sex with the aid of a stereomicroscope and stored into single vials containing 70% ethanol according to site and sampling date . Each sand fly specimen was prepared for microscopic observation as described elsewhere [23] and identified at species level using appropriate morphological keys [25] . The minimum sample size of 48 dogs per group was determined considering an expected L . infantum incidence of 4% in vector protected ( collared ) dogs ( Seresto and Scalibor groups ) and of 16% in animals exposed to vector bites ( CaniLeish and control groups ) with a power of 85% and 95% confidence level [26] . The homogeneity for dog variables such as sex , age , coat length and body weight of the four groups was calculated at the inclusion ( Day 0 ) using χ2 test or Fisher’s Exact test for qualitative data ( sex , coat length ) and using analysis of variance ( ANOVA ) . Efficacy ( % ) in preventing flea infestation was calculated using the following formula: Efficacy = ( % of infested animals in control group—% of infested animals in treatment group ) / ( % of infested animals in control group ) x 100 . Leishmania infantum incidence for each group was calculated as year-crude incidence ( YCI ) considering only results of the final sampling ( day 360 ) regardless of what happened in between as follows: Year crude incidence = number of L . infantum newly infected animals/ ( number of negative animals initially enrolled − number of animals lost to follow up ) × 100 . In addition , in order to overcome the problem of dogs lost to follow-up during the study , the incidence of L . infantum infection was studied using the incidence density rate ( IDR ) [27] , adapted on a monthly basis using a standard 30 days/month . IDRs were calculated at each follow-up as the number of positive dogs , either serologically or molecularly , divided by the number of dog-months of follow-up ( i . e . the number of months between the previous and the current assessment for each dog at risk for L . infantum infection ) . IDRs were expressed per year . Dogs tested once ( e . g . lost , dead ) did not contribute at any time to the incidence calculation . The efficacy ( % ) in preventing L . infantum infection was calculated per each collar treated group using the same formula adopted to calculate the efficacy against flea infestation . The significance of the efficacies was tested using χ2 test . Differences in the frequency of bone marrow PCR and cytology results as well as in the number of active infections between vaccinated and untreated dogs were analysed using χ2 test or Fischer’s test , as appropriate . The level of significance was set at 0 . 05 .
The four groups were homogenous ( P > 0 . 05 ) for variables at the time of inclusion with the exception of age and weight of animals in CaniLeish group because the label of this vaccine requires a minimum age of 6 months . Of the 247 dogs initially screened , 23 were excluded because they were either positive at IFAT or at IFAT and cytology ( n = 2 ) , exceeded the maximum age defined in the inclusion criteria ( n = 4 ) or died before the first follow-up ( n = 16 ) . Additionally one dog was adopted . During the study , collars were replaced on 13 dogs in the Seresto treated group to readjust the dose on the basis of the changes in weight and/or were replaced one or more times for loss or damages in 21 and 36 dogs of Seresto and Scalibor groups , respectively . The efficacy of the Seresto against flea infestation was 100% ( P < 0 . 01 ) on days 120 and 210 , whereas animals treated with Scalibor showed a prevalence of flea infestation of 23 . 3% and 33 . 3% on days 120 and 210 , respectively ( Table 1 ) . Additionally , between days 120 and 210 , 24 dogs ( 11 in the Scalibor group , 8 in the CaniLeish group and 5 in the control group ) were found heavily infested by fleas and received an individual rescue treatment with a spot-on product containing fipronil . Tick infestations were only very sporadically observed in dogs throughout the study ( Table 1 ) , and this did not allow any meaningful statistical evaluation of the efficacy of the two collars against ticks . The number of dogs positive for L . infantum at any test and at any time point varied from four in the Seresto group to 35 in the CaniLeish group ( Table 2 ) . At the last visit , three dogs in the Seresto group and 12 dogs in Scalibor group tested positive for L . infantum in at least one of the diagnostic tests , with the IFAT being the test with the highest number of positive animals ( Table 2 ) . The YCI calculated on the total amount of dogs remained in the study until the last visit was 5 . 5% ( 3/55 ) , 20% ( 12/60 ) and 38% ( 19/50 ) in the Seresto , Scalibor and control groups , respectively , with a statistical significant difference between Seresto vs . controls ( P < 0 . 001 ) and Scalibor vs . controls ( P < 0 . 05 ) . Accordingly , the mean IDR ranged from 7 . 8% ( Seresto ) to 66 . 9% ( Controls ) ( Table 3 ) , resulting in an overall efficacy of the two collars in preventing L . infantum infection of 88 . 3% in the Seresto group and of 61 . 8% in the Scalibor group ( P < 0 . 01 ) . An equal number of three dogs scored positive at bone marrow PCR in the CaniLeish group ( 5 . 7% ) and control group ( 5 . 9% ) at day 210 , whereas no statistical difference ( P = 0 . 417 ) was detected in animals positive at bone marrow PCR and/or cytology at day 360 in the CaniLeish group ( 15 . 4%; 8/52 ) and the control group ( 10%; 5/50 ) . The majority of these positive animals was also positive to IFAT , with titres ranging from 1:180 to 1:2 , 560 in the CaniLeish group , and from 1:80 to 1:1 , 280 in the control group ( Table 2 ) . Active symptomatic infections , characterized by high IFAT titres ( i . e . 1:320 and 1:2 , 560 ) , positive PCR and cytological results associated to lymph node enlargement , were diagnosed in two dogs of the CaniLeish group ( Table 2 ) , but no differences in the frequencies of such events were found between the CaniLeish vaccinated and the control group ( P = 0 . 495 ) . Phlebotomine sand flies ( n = 2 , 008 ) , belonging to six species , were trapped from the end of May ( S1 ) up to October ( at all sites ) . The largest number of phlebotomine sand flies was captured in S3 ( n = 910 ) followed by S1 ( n = 733 ) , S4 ( n = 256 ) and S2 ( n = 109 ) . The largest variability of species ( n = 6 ) was found in S1 where the most prevalent species were P . perniciosus ( n = 521 ) and P . perfiliewi ( n = 124 ) . Sergentomyia minuta and P . perniciosus were the most common species in all the sites with frequencies ranging from 11 . 3% ( S1 ) to 95 . 1% ( S3 ) and from 4 . 9% ( S3 ) to 71% ( S1 ) , respectively . Phlebotomus perniciosus was systematically captured in all the trapping sessions , but not at the end of July in S2 . Phlebotomus sergenti ( one male and one female ) and P . papatasi ( one male ) were captured in site S1 only in June-July and July , respectively , while P . neglectus was only captured in S4 in June . No abnormalities were observed in collared dogs in the seven months of application of the collars or as a consequence of the vaccination , except three dogs in the Scalibor treated group showing neck cutaneous lesions , appearing two to five months after collar application , likely due to the mechanical scrubbing of the area with the collar . All lesions recovered from 3 to 25 days after further slackening of the Scalibor without any treatment ( 1 ) or following topical treatment with antibiotic cream or antibacteric foam ( 2 ) . Five dogs ( one from the Seresto group and four from the control group ) were affected by demodectic mange and one dog from the control group was affected by sarcoptic mange . They were treated topically with 25 ml/5l amitraz once every 4 days for 20 days .
The Seresto collar proved to be effective in protecting dogs against flea infestation , while no difference in the rate of infestation was observed between animals treated with Scalibor and non-treated dogs . Both collars were efficacious in preventing L . infantum infection , with efficacies ranging from 61 . 8% for Scalibor to 88 . 3% for Seresto after one transmission season . The frequency of active infections in dogs vaccinated with CaniLeish was similar to that of a previous field trial [19] , and no significant differences in L . infantum infection rates were recorded between vaccinated and controls animals after one year . All the products proved to be safe and their use should be considered when control strategies against CanL are planned . However , because of its inefficacy in the prevention of L . infantum infection and according to the company prescriptions , the vaccine is always recommended in combination with repellents/insecticides and cannot replace their use in CanL endemic areas .
|
Dogs are exposed to ectoparasites ( e . g . ticks and fleas ) and associated vector-borne infections . Among others , Leishmania infantum is a widespread protozoan of public health concern transmitted by phlebotomine sand flies . The prevention of canine leishmaniosis has become a priority in many endemic areas and it includes the adoption of control strategies by preventing the infection ( avoiding the vector bites ) or by preventing disease through vaccination . We investigated the efficacy of two collars for the treatment and prevention of flea infestations . Also , the effect of these collars on the incidence of L . infantum infection as compared with a group of vaccinated dogs was evaluated . At the end of the study , after one transmission season , both collars proved to be effective in preventing L . infantum infection , though to different levels of efficacy . However , no significant difference was recorded in the frequency of active infections between vaccinated and control dogs . Results emphasize the importance of using repellents/insecticides as a priority measure for protection against canine leishmaniosis , while vaccination can be considered as part of an integrate control program and cannot replace anti-vectorial measures .
|
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2016
|
Field Evaluation of Two Different Treatment Approaches and Their Ability to Control Fleas and Prevent Canine Leishmaniosis in a Highly Endemic Area
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The stringent response is characterized by ( p ) ppGpp synthesis resulting in repression of translation and reprogramming of the transcriptome . In Staphylococcus aureus , ( p ) ppGpp is synthesized by the long RSH ( RelA/SpoT homolog ) enzyme , RelSau or by one of the two short synthetases ( RelP , RelQ ) . RSH enzymes are characterized by an N-terminal enzymatic domain bearing distinct motifs for ( p ) ppGpp synthetase or hydrolase activity and a C-terminal regulatory domain ( CTD ) containing conserved motifs ( TGS , DC and ACT ) . The intramolecular switch between synthetase and hydrolase activity of RelSau is crucial for the adaption of S . aureus to stress ( stringent ) or non-stress ( relaxed ) conditions . We elucidated the role of the CTD in the enzymatic activities of RelSau . Growth pattern , transcriptional analyses and in vitro assays yielded the following results: i ) in vivo , under relaxed conditions , as well as in vitro , the CTD inhibits synthetase activity but is not required for hydrolase activity; ii ) under stringent conditions , the CTD is essential for ( p ) ppGpp synthesis; iii ) RelSau lacking the CTD exhibits net hydrolase activity when expressed in S . aureus but net ( p ) ppGpp synthetase activity when expressed in E . coli; iv ) the TGS and DC motifs within the CTD are required for correct stringent response , whereas the ACT motif is dispensable , v ) Co-immunoprecipitation indicated that the CTD interacts with the ribosome , which is largely dependent on the TGS motif . In conclusion , RelSau primarily exists in a synthetase-OFF/hydrolase-ON state , the TGS motif within the CTD is required to activate ( p ) ppGpp synthesis under stringent conditions .
Bacteria react to nutrient limitation via a stress response that is characterized by the synthesis of pyrophosphorylated GTP ( pppGpp ) or GDP ( ppGpp ) ( previously reviewed in [1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11] ) . Synthesis of ( p ) ppGpp , induced under these stress conditions ( stringent conditions ) , results in many physiological changes , including inhibition of rRNA synthesis , replication and translation but also activation or repression of various genes . In many pathogenic bacteria , ( p ) ppGpp influences virulence , persistence and host interaction ( see reviews [9 , 10] ) . ( p ) ppGpp is synthesized by cytoplasmic enzymes that contain a conserved synthetase domain . RelA of Escherichia coli was the first such enzyme described and has been shown to synthesize ( p ) ppGpp under conditions of amino acid limitation [12] . E . coli and many other gram-negative bacteria possess an additional enzyme , SpoT , that possesses ( p ) ppGpp synthetase and hydrolase activities . The ( p ) ppGpp synthetase activity of SpoT is stimulated by various conditions , e . g . fatty acid deprivation [13 , 14] . In Firmicutes , homologous enzymes ( Rel ) constitute a distinct class of ( p ) ppGpp synthetases [11 , 15 , 16] . RelA , SpoT and Rel enzymes all belong to RSH ( for RelA/SpoT homolog ) superfamily [15 , 17] . Similar to SpoT , the Rel enzymes from Firmicutes are bifunctional proteins with ( p ) ppGpp synthetase and hydrolase activities; however , similar to RelA , the synthetase activity of these enzymes is stimulated upon amino acid starvation [18 , 19] . RSH enzymes share a multi-domain architecture with a C-terminal regulatory domain ( CTD ) and an N-terminal enzymatic domain ( NTD ) containing synthetase and hydrolase motifs . The only available crystal structure of an RSH enzyme is that of the NTD of Rel from Streptococcus equisimilis [20] . The structure indicates two conformations of the enzyme , corresponding to the reciprocal active states of the enzyme: ( p ) ppGpp-synthetase-ON/hydrolase-OFF ( stringent ) and synthetase-OFF/hydrolase-ON ( relaxed ) . It has been proposed that the CTD is involved in reciprocal regulation of the enzymatic states . The current model suggests that under non-stringent ( relaxed ) conditions , the interaction of the CTD with the NTD maintains the enzyme in the synthetase-OFF/hydrolase-ON conformation [21 , 22 , 23]The CTD of RelA stimulates ( p ) ppGpp synthesis in a ribosome-dependent manner when uncharged tRNA , as a consequence of amino acid limitation , is located in the ribosomal A-site [24 , 25] . Interestingly , Rel from S . equisimilis is responsive to amino acid starvation only within its native genetic background and not when expressed in E . coli [13 , 21] . Bioinformatic analyses have revealed the presence of three conserved motifs within the CTDs of RSHs: TGS , ACT and DC . The TGS motif ( named after the presence in ThrRS , GTPases , and SpoT ) was shown to be responsible for the interaction of SpoT with the acyl-carrier protein . The ACT motif ( named after three of the allosterically regulated enzymes in which this domain is found: aspartate kinase , chorismate mutase and TyrA ) was proposed to be a conserved regulatory ligand-binding fold [26 , 27] . Recently , major insights into the ribosome-RelA structure were provided by cryo-EM analyses [28 , 29 , 30] . The structures revealed that RelA adopts an open conformation in which the CTD is intertwined around an A-site tRNA within the intersubunit cavity of the ribosome , and the NTD extends into the solvent . The structures support a model in which association of monomeric RelA with the ribosome relieves the autoinhibitory effect of the CTD on the NTD . It was hypothesized that autoinhibition in the unbound state is mediated by oligomerization of RelA . Oligomerization was previously demonstrated to occur via a conserved aspartate-cysteine motif ( DC ) in the CTD [31 , 32 , 33] . Interaction of monomeric RelA with the ribosome and putative RelA oligomerization in the unbound state indicate that the switching of enzymatic activities occurs via a complex mechanism that has not yet been elucidated . In the human pathogen Staphylococcus aureus , the stringent response plays an important role in virulence [18] , phagosomal escape [34] and antibiotic tolerance [35] . In S . aureus , in addition to RelSau , two enzymes with ( p ) ppGpp synthetase activity ( RelP and RelQ ) are present . These enzymes form homotetramers that lack the CTD and the hydrolase domain [36 , 37 , 38] and are transcriptionally induced under conditions of cell-wall stress [35] . The basal ( p ) ppGpp level produced by these enzymes is controlled by the hydrolase activity of RelSau [35] . The phenotypic consequences of ( p ) ppGpp accumulation vary among species and can be mediated by different mechanisms . In S . aureus , as in other Firmicutes , ( p ) ppGpp regulates transcription by an indirect mechanism that strongly relies on the lowering of intracellular GTP levels [39 , 40 , 41] . Low GTP levels lead to de-repression of the CodY regulon . CodY , when loaded with GTP and branched-chain amino acids , acts as a repressor of a variety of genes , e . g . , genes involved in amino acid synthesis and virulence [41] A decrease in GTP levels could also lead to the repression of sensitive GTP-initiating promoters ( e . g . , those of stable RNA genes ) [42 , 43] . All these studies illustrate the complex role of ( p ) ppGpp during the bacterial life cycle . The cellular concentration of ( p ) ppGpp has to be tightly regulated not only to support survival under stressed conditions but also to avoid toxicity under relaxed conditions . The molecular switch between the synthetase and hydrolase activities of RelSau is crucial for the maintenance of this balance . Here , we aim to elucidate the role of the CTD in controlling the activity of Rel of the major human pathogen S . aureus ( RelSau ) in vivo . We show that the ( p ) ppGpp synthetase activity is restricted in S . aureus and that the synthetase is activated only upon interaction of the CTD with ribosomal partners under stringent conditions . The TGS and DC motifs within the CTD are essential for the enzymatic switch to the synthetase-ON state and play a major role in the interaction between RelSau and the translational apparatus .
We aimed to analyze the role of the CTD of RelSau in the stringent response in S . aureus . In RelSau the canonical domains and motifs could be identified through alignment with RelA and SpoT from E . coli ( Fig 1A ) . We first established a readout system for ( p ) ppGpp activity . To this end , we analyzed strain HG001 ( wild type ) as well as an isogenic mutant of this strain that carries mutations in all three ( p ) ppGpp enzymes ( full deletion of rel , synthetase mutation in relP and relQ ) and thus is unable to synthesize pppGpp ( designated ( p ) ppGpp0 ) [35] . The mutant exhibits no phenotypic difference compared to the wild type when grown in rich medium ( Fig 1B ) . The stringent response can be evoked by mupirocin , an inhibitor of isoleucyl-tRNA synthetase [18 , 34] . ( p ) ppGpp synthesis results in higher tolerance towards mupirocin . The ( p ) ppGpp0 strain exhibited a typical decline in OD600 when treated with mupirocin ( Fig 1C ) . Furthermore , synthesis of ( p ) ppGpp results in repression of genes coding for ribosomal proteins ( e . g . , rpsL ) and de-repression of the CodY target genes ( e . g . , SAOUHSC_02923 , a putative amino acid transporter ) ( Fig 1D ) . Therefore , we used the enhanced mupirocin tolerance and typical transcription pattern ( rpsL down , SAOUHSC_02923 up ) as a readout for ( p ) ppGpp synthesis in S . aureus . As a first approach , we deleted the CTD of the wild-type RelSau ( SAOUHSC_01742 ) to generate strain HG001-531 . In contrast to a full-length rel deletion ( Geiger et al . , 2014a ) , truncation of the CTD had only a slight effect on growth ( Fig 1C ) . It has been previously shown that RelSau is essential due to its hydrolase function [35] . Thus , the CTD does not seem to impede the hydrolase activity in vivo . Northern blot analysis revealed that the CTD mutant was unable to elicit a mupirocin-induced stringent response ( Fig 1B ) . The transcriptional pattern of the marker genes rpsL and SAOUHSC_02923 as well as the mupirocin tolerance ( Fig 1D ) of the CTD mutant were indistinguishable from those of the ( p ) ppGpp0 strain . This finding is consistent with the general assumption that the CTD is required for sensing amino acid deprivation . However , hydrolase activity seems to be hardly effected by the CTD . Next , we complemented the ( p ) ppGpp0 strain with anhydrotetracycline ( ATc ) -inducible full-length and truncated rel constructs ( Fig 2A ) and analyzed the effects under relaxed growth conditions ( exponential growth phase in nutrient-rich medium ) . As a positive control , we induced relQ expression . RelQ is a small synthetase without a regulatory CTD and thus can activate the stringent response in a ( p ) ppGpp0 mutant by transcriptional induction alone . This activation was demonstrated by downregulation of rpsL and upregulation of SAOUHSC_02923 ( Fig 2B ) and by the immediate growth arrest after relQ induction ( Fig 2C ) . In contrast to relQ , transcriptional induction of full-length rel showed no effect on the transcription of marker genes or on growth ( Fig 2B and 2D ) . This finding confirms that additional post-transcriptional activation is required to activate the synthetase activity . Induction of a construct lacking the CTD also failed to induce the stringent response phenotype ( Fig 2B and 2D ) . At first glance , these results may indicate that under relaxed conditions , the enzymatic domain of RelSau , with or without the CTD , is tightly held in a synthetase-OFF conformation . Alternatively , the hydrolase might be hyperactive , so any ( p ) ppGpp synthesized would be immediately degraded . To test this hypothesis , we mutated the hydrolase domain in full-length and CTD-deleted rel constructs . Indeed , both full-length and truncated rel lacking the hydrolase domain elicited a stringent response pattern similar to that of the wild type , as indicated by transcriptional and growth analyses ( Fig 2B and 2D ) . Thus , we presume that there might be some synthetase activity under relaxed growth conditions . However , due to hydrolase activity , any ( p ) ppGpp present is efficiently degraded under these conditions . To analyze the hydrolase activity of RelSau in vivo , we used a conditional rel mutant strain ( HG001-55 ) [18] in which genomic rel was placed under an IPTG-inducible promoter complemented with different rel constructs ( Fig 3A ) . Without IPTG , the rel mutant is unable to grow ( Fig 3B ) because it cannot degrade the ( p ) ppGpp synthesized by RelP and RelQ [35] . We introduced ATc-inducible full-length or truncated rel into the conditional rel mutant and monitored growth after ATc induction . As expected , constructs with mutated hydrolase could not rescue the growth defect of HG001-55 ( Fig 3C ) . However , full-length and CTD-truncated rel , with intact hydrolase , fully complemented the growth defect of the conditional rel mutant . These results show that the hydrolase was constitutively active , independent of the presence of the CTD . In summary , the data indicate that under relaxed conditions , the wild-type RelSau enzyme , with or without sensory domain , is tightly held in the hydrolase-ON state . To confirm the data from the in vivo experiments under relaxed conditions , full-length or CTD-truncated RelSau proteins ( with or without hydrolase domains ) were purified and tested in vitro for enzymatic activities ( Fig 4A and 4B ) . In the synthetase reaction , pyrophosphate is transferred from ATP to GTP , yielding AMP and pppGpp . The presence of both products was measured by HPLC-MS . AMP production was detectable with all constructs ( Fig 4C ) ; however , the AMP levels were significantly higher for the constructs that lacked the CTD ( Fig 4C ) , indicating that the CTD negatively interferes with synthetase activity . Interestingly , pppGpp production was not detected for constructs with intact hydrolase ( Fig 4D ) . However , proteins with mutated hydrolases synthesized detectable amounts of ( p ) ppGpp . The enzyme lacking the CTD showed slightly higher pppGpp synthetase activity than the full-length RelSau supporting the inhibitory effect of the CTD on the synthetase domain . Thus , RelSau exhibits strong hydrolase activity , which prevents pppGpp accumulation . This finding was confirmed by the rapid degradation of pppGpp ( Fig 4E ) and ppGpp ( Fig 4F ) by full-length and CTD deleted RelSau . Notably , RelSau preferentially degraded ppGpp over pppGpp . The CTD apparently has a minor impact on hydrolase activity . Our analysis of RelSau in its native background seemed to be inconsistent with the results of previous studies , in which different CTD-deleted enzymes from other organisms were expressed in E . coli [20 , 21 , 44 , 45] . These studies indicated that RSH enzymes that lack CTDs are in a synthetase-ON/hydrolase-OFF state . Thus , based on these studies , we also expressed full-length and CTD-deleted rel in E . coli using an arabinose-inducible promoter . We tested the capacity of different rel constructs ( Fig 5A ) to complement the defective phenotype of MG1655 , a relA/spoT mutant , under stringent conditions ( Fig 5B and 5C ) . Full-length and CTD-deleted RelSau were able to complement the relA/spoT mutation . The complementation could be attributed to ( p ) ppGpp synthetase activity: mutation within the synthetase domain abolished complementation , whereas mutation within the hydrolase domain did not affect the complementation assay . Thus , in E . coli , CTD-deleted RelSau , similar to other RSH enzymes , is predominantly in a synthetase-ON/hydrolase-OFF state , whereas in S . aureus , this enzyme is primarily in a hydrolase-ON state . Within the CTDs of RSHs , several conserved motifs can be identified . The conserved TGS , DC and ACT motifs of RelSau were predicted based on sequence alignments , and the critical residues of these motifs were mutated ( Fig 1A ) . Wild-type and CTD-mutated rel were cloned to be under the control of the native rel promoter and introduced into the ( p ) ppGpp0 strain ( Fig 6A ) . The stringent response upon mupirocin treatment was analyzed by Northern blotting and growth analysis ( Fig 6B and 6C ) . A ( p ) ppGpp0 strain containing the empty vector showed the typical decrease in OD600 after mupirocin treatment . Induction of full-length rel in the ( p ) ppGpp0 mutant fully complemented the mutant phenotype , whereas the CTD-deleted rel was unable to do so . Mutation of the ACT motif resulted in slightly impaired complementation . However , mutation of the TGS or DC motif resulted in complete inactivation of the stringent response . Expression of these mutated rel genes resulted in a phenotype that was not distinguishable from the phenotype of the ( p ) ppGpp0 strain in terms of growth and gene expression pattern . Thus , the TGS and DC motifs are required for stringent response , while the ACT motif plays only a minor role . We aimed to analyze the role of the conserved motifs within the CTD for interaction with cytosolic proteins . Therefore , we performed Co-IP experiments using whole-cell lysates of ( p ) ppGpp0 mutants expressing wild-type or mutated ( ACT , DC and TGS see Fig 1A ) versions of RelSau . For each pull-down experiment , the wild-type or mutant RelSau was the most abundant protein detected , with no significant difference observed between wild type and mutant proteins ( Data S1 Dataset ) and the expression of all proteins was similar as shown by Western blot analysis ( Blot in S1 Fig ) . Mainly ribosomal proteins were co-immuno-precipitated with native RelSau . When RelSau with mutated TGS motif was used as bait significant less proteins were enriched ( Fig 7 first column ) . Most of these putative TGS interacting proteins were also found to be effected when RelSau harboring mutations in ACT or DC motifs were used , although to a lesser extent . Immuno-precipitated proteins that were strongly influenced by the TGS mutation are ribosomal proteins , proteins associated with RNA degradation and proteins involved in DNA-related pathways . In summary , the results indicate that all three motifs within the CTD work together to dock RelSau onto the translational apparatus . The strongest interaction is mediated by TGS , whereas the ACT motif seems to have a low impact . The TGS motif seems to mediate also interaction with non-ribosomal proteins .
RSH enzymes are major players in the synthesis and hydrolysis of the second messenger ( p ) ppGpp . There is still limited information about the molecular switch that regulates the two activities , both present in long RSH enzymes . Here , we analyzed how the CTD of RelSau influences the enzymatic activities in vivo . We showed that RelSau exists primarily in a synthetase-OFF/hydrolase-ON conformation . Only under stringent growth conditions was the switching to the synthetase-ON conformation detectable , and this switching occurred only when the CTD possessed intact TGS and DC motifs . In S . aureus and probably in other Firmicutes , Rel combines the functions of the two prototypic RSH enzymes , RelA and SpoT , from Proteobacteria . The synthetase activity is needed to elicit a stringent response phenotype , presumably via interaction with ribosomes and uncharged tRNA , as previously shown for RelA [24 , 25] . However , similar to SpoT , RelSau also possesses strong hydrolase activity , which is necessary to counteract the ( p ) ppGpp production by the small synthetases RelP and RelQ present in Firmicutes . The equilibrium between these two activities needs to be tightly regulated in order to attain an appropriate level of ( p ) ppGpp based on the growth conditions . So far , potential differences between RelA , SpoT and Rel associated with the molecular switch could not be inferred from the sequence or in vitro analyses . In vivo activities of different RSH enzymes were mainly analyzed by heterologous expression in E . coli . These analyses indicated that without CTDs , RSH enzymes possess strong synthetase activity [21 , 44 , 45] . Similarly , RelSau , with or without the CTD , can complement an E . coli relA/spoT mutant , also indicating that ( p ) ppGpp synthesis can occur with or without the CTD . However , analysis of the same construct in the native background clearly showed that RelSau lacking the CTD is tightly held in the hydrolase-ON state , and synthetase activity is detectable only in constructs that lack the hydrolase . Thus , RelSau , with or without the CTD , exhibits net ( p ) ppGpp synthetase activity when expressed in E . coli but net hydrolase activity when expressed in S . aureus . It would be interesting to see whether enzymes from other organisms show a similar discrepancy between E . coli and native backgrounds . Our results indicated that the enzymatic activity of RelSau is influenced by species-specific interactions of the enzymatic NTD with unknown factors . To date , there is no evidence that the NTD alone interacts with the ribosome . Thus , other interaction partners or intracellular properties of the NTD should be elucidated in the future . An alternative possibility is that less ( p ) ppGpp is needed to complement the phenotype of a pppGpp mutant in E . coli allowing growth even if RelSau has a weak synthetase . However , this is not supported by our in vitro results , showing that synthetase activity is only detectable when the hydrolase is mutated in constructs with or without CTD . Analysis of full-length or truncated RelSau in vitro largely confirmed the results obtained with the in vivo data obtained in S . aureus . The presence of an intact hydrolase abrogates the synthetase activity . Synthesis of pppGpp was detectable only in hydrolase-deficient constructs . Moreover , we show that the CTD has an inhibitory effect on synthetase activity since truncated versions of RelSau showed higher accumulation of the reaction products AMP and pppGpp compared to the full-length enzyme . However , the CTD had only a minor impact on the strong hydrolase activity of the purified enzymes . Interestingly , RelSau preferentially hydrolyzes ppGpp over pppGpp . In vivo , it was shown that RelP and RelQ mainly produce ppGpp [35] , which is toxic at high concentrations and requires efficient hydrolysis . This observation could explain the preference of RelSau for ppGpp hydrolysis . The Co-IP results indicate that RelSau interacts with the translation machinery and that the TGS strongly influence this interaction . This is largely consistent with previous data obtained for RelA [24 , 25 , 28 , 29 , 30] . Among the 10 detected interacting ribosomal proteins , L16 , S13 , and S12 are homologous to E . coli proteins that have been previously identified to interact with RelA [28 , 29 , 30] . Of note , the TGS motif also seems to hamper the putative interaction of RelSau with other proteins of the RNA and DNA pathways . Whether such interactions are specific and involved in the molecular function of RelSau remains to be investigated . The in vivo analyses combined with the Co-IP results provided some clues regarding the roles of the different motifs of the CTD of RelSau . Of the three motifs , the TGS motif showed the strongest effect , and the ACT motif showed the weakest effect , on ( p ) ppGpp activation and ribosomal interaction . Thus , the role of the ACT motif remains to be elucidated but seems to be minor . The TGS motif is clearly required for synthetase activation , most likely interacts with the ribosome to sense whether or not the tRNA in the A-site is aminoacetylated as shown for RelA [28 , 29 , 30 , 46] . Similar to the TGS motif , the DC motif was also found to be required for synthetase activity and to influence interactions with ribosomal proteins . DC has also been reported to interact with 23S rRNA ASF [28] which is critical to RelA activation in E . coli [46] , presumably through stabilizing ribosome interaction . This finding contradicts the simple model in which the DC motif causes oligomerization and thereby autoinhibition [31 , 32 , 33] . This would imply that DC mutation alleviates autoinhibition leading to increased synthase activity . In contrast , our data showed that the DC-mutated RelSau is held in a synthetase-OFF state . Thus , our data support a model in which the DC motif participates in specific activation upon ribosomal contact , and that this interaction is involved in the intramolecular switch .
Strains and plasmids are listed in the table in S1 Table . For strains carrying resistance genes , antibiotics ( 10 μg/ml erythromycin , 5 μg/ml tetracycline , 10 μg/ml chloramphenicol , and 100 μg/ml ampicillin ) were used only in precultures . For the conditional mutant HG001-55 , IPTG ( final concentration of 0 . 5 mM ) was added only in the preculture . S . aureus strains were grown in CYPG ( 10 g/l casamino acids , 10 g/l yeast extract , 5 g/l NaCl , 0 . 5% glucose and 0 . 06 M phosphoglycerate ) medium [47] . Bacteria from an overnight culture were diluted to an initial optical density ( OD600 ) of 0 . 05 in fresh medium and grown with shaking ( 220 rpm ) at 37°C to the desired growth phase . Expression of cloned proteins was induced in exponential phase ( OD600 = 0 . 3 ) with 0 . 1 μg/ml anhydrotetracycline ( ATc ) and in stringent conditions by addition of 0 . 3 μg/ml mupirocin . E . coli strains were grown in an overnight preculture in LB medium . Stringent conditions were applied by growing cells in modified M9 medium ( 33 . 7 mM NaHPO4 , 22 mM KH2PO4 , 8 . 55 mM NaCl , 9 . 35 mM NH4Cl , 1 mM MgSO4 , 0 . 3 mM CaCl2 , 1 μg/ml thiamine hydrochloride , 0 . 4% glycerol , 1 mM serine , 1 mM methionine and 1 mM glycine ) [48] . For growth on solid media , single colonies grown on LB agar were streaked on M9 agar plates . For growth curve analyses , bacteria were inoculated to the desired OD600 ( S . aureus initial OD600 = 0 . 05; E . coli initial OD600 = 0 . 1 ) in a 96-well plate , and growth was monitored in an Infinite M200 Pro microplate reader ( Tecan ) . All oligonucleotides are listed in S2 Table . ATc-inducible plasmids , derived from pCG248 , were generated with a restriction enzyme cloning strategy . Amplicons and vector were digested with EcoRI restriction enzyme . Substitution of the hydrolase domain and the ACT , TGS and DC mutations were achieved by overlapping PCR . For expression of the rel constructs under the native promoter , the shuttle vector pCG443 was designed based on pJL77 [49] . pJL77 was digested with AscI and SphI to remove the previous insert , including the promoter . The rel promoter was amplified from genomic DNA , digested using the same restriction enzymes , and ligated to generate pCG443 . Full-length , truncated and mutated versions of rel were subcloned from the pCG248 plasmids into AscI-digested pCG443 by Gibson assembly [50] . All inserts were verified by sequencing ( 4base lab AG advanced molecular analysis ) , electroporated into the restriction-deficient S . aureus strain RN4220 , and then transduced into the final S . aureus strains . All S . aureus strains were tested by PCR for the presence of the correct plasmid . For expression in E . coli , different derivatives of rel were cloned into the EcoRI site of pBAD30 via Gibson assembly using the oligonucleotides listed in Table S2 . Resulting vectors were verified by sequencing and moved to MG1655 E . coli strains ( wild type and relA/spoT mutant ) [51 , 52] . For protein purification , different rel derivatives were subcloned from the pCG248-based plasmids into BamHI-digested pET15b using Gibson assembly . The markerless rel CTD-deletion mutant was obtained using the ATc-inducible suicide vector pBASE6 [34] . Deletion was introduced by overlapping PCR with the primers listed in S2 Table , and the amplicon was cloned into BglII- and SalI-digested pBASE6 by Gibson assembly . The resulting plasmid was verified by sequencing and electroporated into RN4220 , from which the plasmid was transduced into HG001 . Mutagenesis was performed as described previously [34] . Mutation was verified by PCR . RNA isolation and Northern blot analysis were performed as described previously [53] . Briefly , 5 ml of bacteria were collected at the desired time point ( 30 minutes after induction ) and centrifuged . The pellet was resuspended in 1 ml of TRIzol reagent ( Thermo Fisher Scientific ) with 0 . 5 ml of zirconia/silica beads ( 0 . 1-mm diameter ) and lysed using a high-speed homogenizer ( Thermo Fisher Scientific ) . RNA was isolated following the instructions provided by the TRIzol manufacturer . For the detection of specific transcripts on the Northern blot , digoxigenin-labeled probes were generated using the DIG-labeling PCR Kit as described by the manufacturer ( Roche Life Science ) . E . coli BL21 ( DE3 ) ( New England Biolabs ) cells that were freshly transformed with plasmids carrying full-length rel constructs were grown for 16 hours at room temperature under constant shaking ( 150 rpm ) in LB medium supplemented with D ( + ) -lactose-monohydrate ( 12 . 5 g/l ) and ampicillin ( 100 μg/ml ) . Cells were harvested ( 20 minutes , 3000 x g , 4°C ) and resuspended in ice-cold high-KCl buffer A ( 20 mM HEPES ( pH 7 . 4 ) , 20 mM NaCl , 20 mM MgCl2 , 1 M KCl , 30% ( v/v ) glycerol , and 40 mM imidazole ) supplemented with 10 μg/ml DNAse and cOmplete protease inhibitor cocktail ( Roche ) . Cells were lysed by a French press at 1000 psi . The lysate was centrifuged ( 50 , 000 x g , 45 minutes , 4°C ) , and the clear supernatant was filtered ( 0 . 22-μm pore size ) before being loaded onto a 1-ml HisTrap HP column ( GE Healthcare Life Sciences ) equilibrated with high-KCl buffer A . Purification was performed with an ÄKTA purification system ( GE Healthcare Life Sciences ) , and elution was carried out with an imidazole gradient to a final concentration of 500 mM . Fractions were analyzed by SDS-PAGE , and the fractions containing the protein of interest were collected and concentrated to 5 ml with an Amicon Ultracel-50K ultracentrifugal device , with a cut-off of 50 kDa ( Merck Millipore ) . Protein was further purified by size-exclusion chromatography ( HiLoad 16/600 Superdex 200 pg , GE Healthcare Life Sciences ) . The size-exclusion column was previously equilibrated with ice-cold high-KCl SEC buffer ( 20 mM HEPES ( pH 7 . 0 ) , 20 mM NaCl , 20 mM MgCl2 , 1 M KCl , and 30% ( v/v ) glycerol ) . Protein-containing fractions were pooled , concentrated by ultra-filtration with a 50-kDa cut-off , aliquoted and stored at -80°C . For purification of CTD-truncated constructs , the same procedure was followed using different buffers: low-KCl buffer A ( 20 mM HEPES ( pH 7 . 4 ) , 200 mM NaCl , 20 mM MgCl2 , 20 mM KCl , 30% ( v/v ) glycerol , and 40 mM imidazole ) for affinity purification and low-KCl SEC buffer ( 20 mM HEPES ( pH 7 . 0 ) , 200 mM NaCl , 20 mM MgCl2 , 20 mM KCl , and 30% ( v/v ) glycerol ) for size exclusion . For concentration of truncated RelSau , Amicon Ultracel-30K ( Merck Millipore ) was used . Synthetase assays were performed in reaction buffer ( 20 mM HEPES ( pH 7 . 0 ) , 200 mM NaCl , 20 mM MgCl2 , and 20 mM KCl ) with 1 mM ATP , 1 mM GTP and 2 μM purified enzyme . Hydrolase assays were performed in the same reaction buffer with 1 mM ppGpp or 1 mM pppGpp ( both from Jena Biosciences ) and 0 . 1 μM purified enzyme . Assays were performed at 37°C; aliquots were taken at the indicated times; and the enzyme reactions were stopped by addition of an equal volume of chloroform . The mixtures were briefly vortexed and centrifuged ( 3 minutes , 11 , 000 × g ) . The aqueous phase containing the nucleotides was collected and stored at -20°C prior to analysis . Nucleotide analysis was performed using an ESI-TOF mass spectrometer ( micrO-TOF II , Bruker ) operated in negative-ion mode and connected to an UltiMate 3000 high-performance liquid chromatography ( HPLC ) system ( Dionex ) . 5 μl of each sample at 10°C was injected onto the SeQuant ZIC-pHILIC column ( Merck , PEEK 150 × 2 . 1 mm , 5 μm ) , and the system was run at 30°C as previously described [43] . The following 40-minute gradient program was used at a flow rate of 0 . 2 ml/min: 5 minutes of 82% buffer A ( CH3CN ) and 18% buffer B ( 100 mM ( NH4 ) 2CO3 , pH 9 . 2 ) ; 25 minutes of a linear gradient to 42% buffer A; and finally , 10 minutes of 82% buffer A . The DataAnalysis program ( Bruker ) was used to present the nucleotide masses as extracted-ion chromatograms , and the peak areas were calculated and quantified with Prism 5 ( GraphPad ) . Dilution series of commercially available nucleotides ppGpp ( m/z , 601 . 95 ) , pppGpp ( m/z , 681 . 92 ) and AMP ( m/z , 346 . 06 ) were used for calibration to quantify the amounts of nucleotides in the reactions . To generate RelSau-specific antibodies , 0 . 5 mg of purified full-length protein was sent to Davids Biotechnologie GmbH to generate antiserum and affinity-purified IgG . The specificity of the IgG was verified by Western blot analysis ( Blot in S1 Fig ) . For Co-IP , bacteria were grown in 100 ml of CYPG medium to an OD600 of 1 and centrifuged ( 5 , 000 μ g , 5 minutes ) . The pellet was washed 2 times with PBS and resuspended in 500 μl of cold Co-IP buffer ( 20 mM HEPES ( pH 7 . 0 ) , 200 mM NaCl , 20 mM MgCl , 20 mM KCl , 0 . 5 mM DTT , 0 . 2% ( v/v ) Tween 20 and cOmplete protease inhibitor cocktail ) . The resuspended pellet was lysed with 0 . 5 ml of zirconia-silica beads ( 0 . 1 mm diameter ) using a high-speed homogenizer ( two times , 6 , 500 rpm , 20 s ) . Lysed cells were centrifuged for 1 hour at 14 , 000 x g at 4°C , and the supernatant was aliquoted ( 100 μl ) and frozen at -80°C . Co-IP was performed with Dynabeads ( Thermo Fisher Scientific ) following the manufacturer’s instructions with some minor modifications . Briefly , 50 μl of Dynabeads slurry was used for each sample . The storage solution was removed , and the beads were incubated with 30 μg of Anti-RelSau IgG resuspended in PBS ( pH 7 . 4 ) with 0 . 02% Tween 20 for 30 minutes at room temperature under constant rotation . Coated beads were pelleted using a magnetic rack; the supernatant was removed; and 100 μl of the cell lysates were added and incubated for 30 minutes at room temperature under constant rotation . After incubation , the beads were gently washed 3 times with Co-IP buffer using a magnetic rack . Washing solution was removed , and the beads were resuspended in SDS sample buffer , boiled at 95°C for 5 minutes , and run approximately 1 cm into an SDS-PAGE gel . The gel slice was subsequently analyzed by mass spectrometry . Three biological replicates of ( p ) ppGpp0 complemented with WT and mutant RelSau were analyzed . Gel slices were digested as described previously [54] . Peptide mixtures were then separated on an EasyLC nano-HPLC ( Proxeon Biosystems ) coupled to an LTQ Orbitrap Elite mass spectrometer ( Thermo Fisher Scientific ) as described elsewhere [55] with the following modifications: peptides were eluted with an 87-min segmented gradient of 5–33–90% HPLC solvent B ( 80% acetonitrile in 0 . 5% acetic acid ) . Each sample was run in triplicate . The acquired MS spectra were processed with the MaxQuant software package , version 1 . 5 . 2 . 8 [56] with the integrated Andromeda search engine [57] as described previously [55] . Database searches were performed against a target-decoy S . aureus all-strains database obtained from UniProt , containing 126 , 225 protein entries and 248 commonly observed contaminants . The label-free algorithm was enabled , as was the “match between runs” option [58] . Label-free quantification ( LFQ ) protein intensities from the MaxQuant data output were used for relative protein quantification . Downstream bioinformatic analysis ( ANOVA and two-sample t-tests ) was performed using the Perseus software package , version 1 . 5 . 0 . 15 . P < 0 . 05 was considered to be statistically significant . For the heatmap , among the 4 different proteins , those that showed significant differences according to ANOVA were selected ( Data in S1 Dataset ) . For these selected candidates , the t-test differences , indicating changes in the amount , were calculated between the protein immunoprecipitated with WT or mutant RelSau and plotted on the heatmap . The results for the growth and in vitro analyses represent the mean ± SD of at least three biological replicates . Significance was calculated using Prism 5 by one-way ANOVA with Bonferroni correction .
|
The stringent response is a general stress response , which allows bacteria to survive nutrient limited conditions and to better tolerate antibiotic treatment . In the human pathogen , Staphylococcus aureus , the stringent response plays an important role for virulence , phagosomal escape and antibiotic tolerance . The response is initiated by the synthesis of the nucleotide derivative ( p ) ppGpp which in turn leads to growth arrest and reprogramming of gene expression . However , a rapid and controlled inactivation of these growth inhibitory molecules is equally important for the organism . ( p ) ppGpp synthesis as well as hydrolysis is accomplished by a bi-functional RelA/SpoT homolog , RelSau bearing distinct synthetase , hydrolase and sensory domains . We elucidated how the C-terminal sensory domain of RelSau controls the intermolecular switch between hydrolase and synthetase activities in S . aureus . The switch is crucial for the appropriate response of S . aureus to adapt to changing environment encountered during infection .
|
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2018
|
Regulation of the opposing (p)ppGpp synthetase and hydrolase activities in a bifunctional RelA/SpoT homologue from Staphylococcus aureus
|
Alanine-glyoxylate aminotransferase is a peroxisomal enzyme , of which various missense mutations lead to irreversible kidney damage via primary hyperoxaluria type 1 , in part caused by improper peroxisomal targeting . To unravel the molecular mechanism of its recognition by the peroxisomal receptor Pex5p , we have determined the crystal structure of the respective cargo–receptor complex . It shows an extensive protein/protein interface , with contributions from residues of the peroxisomal targeting signal 1 and additional loops of the C-terminal domain of the cargo . Sequence segments that are crucial for receptor recognition and hydrophobic core interactions within alanine-glyoxylate aminotransferase are overlapping , explaining why receptor recognition highly depends on a properly folded protein . We subsequently characterized several enzyme variants in vitro and in vivo and show that even minor protein fold perturbations are sufficient to impair Pex5p receptor recognition . We discuss how the knowledge of the molecular parameters for alanine-glyoxylate aminotransferase required for peroxisomal translocation could become useful for improved hyperoxaluria type 1 treatment .
Primary hyperoxaluria type 1 ( PH1 ) is an autosomal recessive disorder that generally becomes symptomatic during childhood or adolescence and ultimately leads to renal failure , usually between the ages of 25 and 45 [1] . Although several therapeutic options have been established , the only curative treatment to date is by liver-kidney transplantation [2] . At the molecular level , PH1 is caused by functional deficiencies in the liver-specific , pyridoxal-dependent enzyme alanine-glyoxylate aminotransferase ( AGT , EC 2 . 6 . 1 . 44 ) [3] . AGT catalyzes the transamination of the peroxisomal intermediary metabolite glyoxylate to glycin . Human AGT consists of a 86 kDa homodimer and bears an atypical Lys-Lys-Leu ( KKL ) peroxisomal targeting signal 1 ( PTS1 ) motif at its C-terminus , which is required for translocation of the enzyme into peroxisomes . The absence of AGT in hepatic peroxisomes , owing to either dysfunction or mistargeting of AGT , causes glyoxylate to escape into the cytosol where it is further metabolized to oxalate and glycolate . The accumulation of oxalate—a compound that cannot be further metabolized in humans—leads to the progressive formation of insoluble calcium oxalate in the kidney and urinary tract , resulting in urolithiasis and/or nephrocalcinosis as the principal clinical manifestations . To date , around 150 polymorphic variants of the human AGXT gene have been described [4] . These mutations are scattered over virtually the entire encoded AGT sequence and the associated three-dimensional structure of the enzyme ( Figure S1 ) . In 2%–20% of human populations in geographically distinct regions , a minor allele haplotype ( AGXT-Mi ) is found , which encodes an AGT variant with two missense mutations ( P11L , I340M ) . AGT-Mi has around one-third of the catalytic activity of the wild-type enzyme and reduced stability , yet by itself does not lead to a serious clinical phenotype . However , the presence of AGXT-Mi in combination with further mutations causes almost 50% of the reported PH1 , demonstrating synergistic disease effects [4] . Only some of the characterized PH1-causing AGXT variants can be directly correlated with AGT enzymatic activity , suggesting that other molecular parameters such as its correct compartmental localization have important implications for AGT function as well . Therefore , it is not surprising that there is no uniform response by PH1 patients to pyridoxine intake , which is thought to stabilize the AGT active site but does not directly affect the localization of the enzyme [5] , [6] . On the basis of biochemical and structural data , the molecular mechanism of AGT catalytic activity is well established [7] , [8] , but the mechanism of peroxisomal AGT targeting is poorly understood . The non-canonical PTS1 Lys-Lys-Leu sequence in human AGT has been described as non-optimal , based on in vitro interaction studies of chimeric proteins formed by fusing the motif with non-human AGTs and other peroxisomal target proteins [7] , [9] . Truncation studies of human AGT led to the prediction of an additional binding site within the small C-terminal domain of AGT , proximal to the established PTS1 C-terminus [9] . Another non-overlapping AGT-Pex5p recognition segment was proposed to be located close to the AGT N-terminus [10] . However , in the absence of residue-specific interaction data , it is not known whether additional interactions with the Pex5p receptor are direct or mediated by putative adaptors , or even whether allosteric effects are involved [3] , [11] . Moreover , a generalization of the interpretation of available data is virtually impossible , as neither the PTS1 sequence nor a consistent pattern for peroxisomal localization are taxonomically conserved among AGTs from different species [12] . Indeed , depending on the organism , AGTs have been found , partly in parallel , in mitochondria , the cytosol , and peroxisomes [13] . Alternative transcription and translation sites in several AGTs lead to elongated isoforms with an additional N-terminal mitochondrial targeting signal sequence , which overrides the PTS1 required for peroxisomal translocation [3] , [14] . Even in the absence of an additional mitochondrial-targeting signal , residual mitochondrial localization has been observed for AGT mutants that tend to aggregate and misfold [4] , [14] . The aim of this work has been to unravel the role of non-PTS1 PH1 mutations in AGT mistargeting , to ultimately provide a molecular model for genetically imprinted PH treatment . To identify the complete Pex5p receptor-interaction site , we have first determined the atomic structure of the AGT–Pex5p receptor complex , which forms an elongated Pex5p- ( AGT ) 2-Pex5p assembly . In addition to the established PTS1-binding site , the structure reveals extensive but rather non-specific contributions from sequence segments of the C-terminal AGT domain . To test how perturbations in the AGT structure could result in effects on AGT–Pex5p receptor binding , we mutated several residues of the AGT C-terminal domain near the Pex5p interface and investigated the properties of the resulting mutants by biophysical and functional in vitro and in vivo assays , as well as their ability to bind Pex5p . The interactions observed are highly sensitive to any minor changes in the AGT structure caused by single-residue mutations—including those that have been identified in PH1 patients—demonstrating that non-PTS1 interactions are essential in Pex5p receptor recognition .
To determine the molecular basis of the recognition of AGT by the peroxisomal import receptor Pex5p and its implications in PH1 , we purified human AGT and the C-terminal cargo-binding segment of human Pex5p ( residues 315–639 ) , referred to as Pex5p ( C ) [15] . The AGT–Pex5p ( C ) complex forms with an apparent ( 1∶1 ) stoichiometry and has a moderate dissociation constant of 3 . 5 µM ( Table 1 ) . AGT , alone or in complex with Pex5p ( C ) , has a catalytic activity of close to 2 , 000 µM mg−1 h−1 , which is in agreement with previously reported AGT data [16] , [17] and suggests that binding to Pex5p does not compromise AGT activity . We then determined the crystal structure of the AGT–Pex5p ( C ) complex at 2 . 4 Å resolution ( Figures 1 and S2; Table 2; Text S1 ) . The structure comprises an elongated Pex5p ( C ) - ( AGT ) 2-Pex5p ( C ) assembly with overall dimensions of around 140 Å×50 Å×50 Å . The 1∶2∶1 stoichiometry of the complex is in agreement with our isothermal titration microcalorimetry ( ITC ) , gel filtration , and static light scattering data , indicating equal stoichiometric contributions of both protein components ( Table S1 and Figure S3 ) . Each of the two complete AGT polypeptide chains is visible in the final electron density , except for N-terminal residues 1–3 and 1–5 , respectively . The overall conformation of the two AGT molecules is identical ( Table S2 ) and the structure shows that they both contain the cofactor pyridoxal-5′-phosphate ( PLP ) covalently bound to Lys209 ( Figure S4 ) . We confirmed the AGT PLP-adduct to be present by spectroscopic analysis of the protein material submitted for crystallization ( Figure S4C ) . The Pex5p ( C ) -bound AGT dimer superimposes well onto that of the enzyme in the absence of the receptor ( PDB entry 1HOC ) [8] , with a root-mean-squares deviation of 0 . 41 Å ( Table S2 ) . This confirms that AGT dimeric assembly and overall conformation , a prerequisite for AGT catalytic activity [3] , is not affected by Pex5p receptor binding . Well interpretable electron density is visible for most of the two Pex5p ( C ) receptor molecules ( residues 315–639 ) , with the exception of the N-termini ( residues 315–323/324 ) , part of the distorted tetratricopeptide repeat ( TPR ) 4 segment ( residues 441–464 , 444–460 ) and the so-called 7C-loop ( residues 591–592 , 590–596 ) that connects the 7-fold array of TPR segments with the C-terminal bundle of Pex5p ( C ) [15] . These regions were either invisible or mobile in previous structures of the same receptor [15] , [18] , indicating that these sequence segments are generally flexible . Overall , increased flexibility of Pex5p ( C ) , which we attribute to these regions and to the loose arrangement of neighboring TPR domain modules , is reflected in higher root-mean-squares deviations of around 1 Å when Pex5p ( C ) polypeptide chains of the Pex5p ( C ) -AGT complex are either superimposed on each other or onto the coordinates of the same receptor from the previously determined Pex5 ( C ) -SCP2 cargo complex ( Table S2 ) [15] . By contrast , there are significant deviations in the overall structure of Pex5p ( C ) bound to AGT when it is superimposed onto the apo conformation of the same receptor ( PDB entry 2C0M ) . The matching part of the respective structures is limited to residues of the 7-fold TPR array , excluding the C-terminal bundle domain . Hence , the structure of the Pex5p ( C ) -AGT complex supports the conformational changes of the receptor that have been observed previously on cargo binding [18] , [19] . The structure of the AGT–Pex5p complex reveals that the C-terminal AGT domain ( residues 283–392 ) is the exclusive and direct binding module of the Pex5p receptor ( Figures 1 and 2 ) . This domain comprises a bundle of the three helices α11 ( residues 284–305 ) , α12 ( residues 332–343 ) , and α13 ( residues 370–387 ) , in which the two longest helices ( α11 , α13 ) are in a parallel orientation to each other and the third ( α12 ) crosses helix α13 . The three helices are connected by a small two-stranded β-sheet ( β8 , residues 321–325; β9 , residues 358–362 ) that forms an interface with the N-terminal catalytic AGT transaminase domain . The C-terminal sequence Pro-Lys-Lys-Lys-Leu ( residues 388–392 ) , corresponding to the PTS1 , immediately follows helix α13 . The overall AGT–Pex5p interface consists of three distinct surface patches ( Figures 1 and 2 ) : the first involves the AGT-PTS1 ( residues 389–392 ) that binds , as expected , into the central tunnel-like cavity of the ring-forming array of seven TPR segments of Pex5p ( C ) , generating an interface of 550–600 Å2 ( Interfaces Ia and Ib in Figure 1; Figure S5 , left panel ) . The second includes the C-terminal part of the AGT helix α13 that immediately precedes the PTS1 ( residues 381–388 ) and the loop connecting β9-α12 ( residues 327–330 ) that interacts with this part of α13 ( Interfaces IIa and IIb in Figure 1; Figure S5 , central panel ) . We refer to this site in AGT as the “extended PTS1” interface , as it is directly upstream of the PTS1 . Pex5p interactions from this interface overlap with hydrophobic core contacts by residues from α13 with other parts of the C-terminal AGT domain . Ala383 , the most C-terminal AGT residue that is entirely buried within the AGT fold , is preceded by Arg381 , which marks the most proximal residue in α13 that contributes to the extended PTS1–Pex5p interface . The third interface is topologically separate from the PTS1 and involves the loop that connects AGT helix α11 and strand β8 ( residues 303–307 ) ( Interfaces IIIa and IIIb in Figure 1; Figure S5 , right panel ) . These two additional surface patches , when combined with the PTS1 binding site , increase the overall AGT–Pex5p ( C ) interface area by almost 2-fold , to around 1 , 000 Å2 ( Table 3 ) . A detailed structural description of all the three interface patches is provided in Text S1 . The three binding sites are topologically preserved in the two AGT–Pex5p ( C ) modules . However , direct comparison reveals that when using the structure of Pex5p ( C ) as the basis of superposition , the orientation of the two bound AGT molecules deviate substantially ( Figures S5 and S6 ) . If the two protein components are assumed to be rigid bodies , the tilt and twist angles defining their relative orientation [20] change by 27 and 11 degrees , respectively . The difference originates from a limited conformational flexibility with a pivot point at the C-terminus of the AGT helix α13 , preceding the PTS1 motif . Owing to the rigidity of the remaining AGT structure , the spatial differences in the superimposed complexes increase to around 20 Å in those parts of each AGT protomer that are most distal to the Pex5p ( C ) receptor-binding site ( Figure S6 ) . Because of these conformational differences , there is little conservation in the specific AGT–Pex5p ( C ) interactions . With the exception of a few conserved hydrogen bonds formed between three asparagines of Pex5p ( Asn415 , Asn526 , Asn561 ) and the C-terminal main-chain carboxylate group of Leu392 , along with the preceding peptide bond connecting Lys391 and Leu392 , the remaining side chains of the AGT PTS1 sequence Lys389-Lys390-Lys391 are either not involved in further specific interactions or , if observed , these interactions are not conserved within the complete Pex5p- ( AGT ) 2-Pex5p complex ( Figure 2 and Figure S5 ) . These findings are in agreement with an overall endothermic assembly process under the experimental in vitro conditions , indicating that AGT–Pex5p ( C ) complex formation is an entropy-driven process ( Tables 1 and S1 ) rather than being dominated by specific enthalpic interactions . A key finding from our structural data is that binding of the AGT PTS1 motif to the Pex5p receptor is not autonomous from the additional cargo–receptor binding sites , both in terms of sequence connectivity and surface topology . These data could explain why many pathological AGT disease mutations that lead to AGT mistargeting are remote from the Pex5p-binding site . On the basis of our structural data , we argue that even minor folding defects or conformational alterations in AGT could compromise the binding of the AGT composite Pex5p interface , formed by the AGT C-terminal domain and PTS1 . To address this assumption , we mutated several residues in the AGT C-terminal domain close to the Pex5p-binding interface , which we expected to lead to conformational changes in this domain without compromising AGT activity ( Figures 2 and S1 ) . The first set of mutations involved two residues from the β9–α12 loop ( Ala328 , Tyr330 ) that interact with residues from the C-terminal helix α13 ( Leu384 and Lys389 ) . We introduced either more bulky side chains ( A328W , Y330W ) or removed side chain-specific intramolecular interactions ( Y330A ) . For the second set of AGT variants , we aimed to affect the hydrophobic interactions of the C-terminal helix α13 with other parts of the AGT C-terminal domain . For this purpose , we mutated two residues from this helix ( Val376 , Leu380 ) that are completely buried into either an aspartate or proline . Additionally , to provide a structural rationale for established AGT disease mutations , we selected two AGT single residue polymorphisms ( G170R , V336D ) and the corresponding AGT double mutant G170R/V336D , which have been found in combination with the minor allele haplotype ( AGXT-Mi ) in PH1 patients . The AGT double mutant G170R/V336D results in a serious pathogenic effect and is non-responsive to pyridoxine treatment [2] , [4] . However , the disease-causing mechanism of this AGT polymorphism , like various other mutations , has remained enigmatic . More specifically , the aggravating effect of the V336D mutation from the C-terminal domain in conjunction with the widespread G170R mutation seemed to be inexplicable , as the latter ( G170R ) is coupled with unwanted mitochondrial import in the AGXT-Mi isoform [21] , again by an unknown mechanism of action . A structure of the AGT G170R mutant revealed only minor local conformational changes [22] . First , we attempted to purify all the AGT mutants to test their ability to bind the Pex5p receptor in vitro and to measure their catalytic activities ( Table 1 ) . However , the AGT variants with mutations in residues of the C-terminal helix α13 ( Val376 , Leu380 ) were insoluble when overexpressed in Escherichia coli , demonstrating that the hydrophobic core interactions of helix α13 are essential for proper folding of the enzyme under the chosen experimental conditions . The same problem of aggregation arose for the pathogenic AGT double mutant G170R/V336D , whereas each of the two single residue variants ( G170R , V336D ) could be expressed in significant quantities as soluble proteins . Although the aggregated AGT mutants could not be further characterized in vitro , they were used in functional assays to assess their tendency for aggregation in vivo and to investigate the level of peroxisomal targeting from AGT versions with suspected folding defects ( see below ) . All remaining mutants were purified by affinity chromatography and gel filtration ( Figure S3 ) . Proper folding of each protein was confirmed by far-UV circular dichroism spectroscopy ( Figure S3 ) . These AGT mutants had catalytic activities similar to the wild-type enzyme irrespective of Pex5p binding with the exception of the G170R mutant , which showed a decrease in activity of around 25% , in qualitative agreement with previous data [17] . Whereas the two pathogenic AGT single-residue mutants ( G170R , V336D ) did not show a significant change in Pex5p receptor binding , the AGT variants with mutations in the β9–α12 loop showed 2- to 6-fold decreased binding affinities for the Pex5p receptor when compared with the wild-type enzyme ( Table 1 ) . The weakest interaction , with a Kd of 19 . 4±8 . 3 , was found for the Y330A AGT variant , indicating an important contribution of the side chain of Tyr330 to keep the β9-α12 loop in a conformation that is competent for Pex5p receptor binding . As for wild-type AGT , Pex5p binding by all of the AGT mutants is endothermic under the in vitro experimental conditions ( Table S1 ) . To test the functional properties of all selected AGT variants in vivo , we employed a protein import assay in human fibroblasts , using enhanced green fluorescent protein ( EGFP ) -tagged AGT . When expressing EGFP-AGT without further modification in fibroblasts , we observed that more than 90% of the cells exhibited a punctuated pattern of peroxisomal localization ( Figure 3 ) . By contrast , a control version of the enzyme without the PTS1 ( ΔPTS1 ) was evenly distributed in the cytosol without any visible sign of peroxisomal import ( Figure S7 ) , confirming that the presence of a PTS1 in AGT is crucial for recognition by the Pex5p receptor . None of the AGT helix α13 variants showed significant measurable peroxisomal translocation , suggesting that fold defects in AGT lead to an almost complete loss of Pex5p import ( Table 1; Figure 3 ) . Aggregation of these AGT versions under in vivo experimental conditions is reflected by the formation of large fluorescent plaques in the cytosol , which are abundant in 28%–100% of transfected cells . This indicates substantial variability depending on the AGT mutant investigated . Whereas AGT ( L380P ) , for instance , aggregates completely ( Figure 3B ) , other AGT mutants ( V376D , L380D ) reveal a predominantly cytosolic background , suggesting a soluble cellular state with no peroxisomal association ( Figure S7 ) . These observations indicate that both misfolding and local conformational changes in the C-terminal domain have a synergistic effect , leading to a loss of AGT targeting to peroxisomes . The data also suggest that indirect effects , arising from altered structural properties of the AGT cargo , rather than direct and specific receptor interactions , are sufficient to abolish proper cargo recognition by the Pex5p receptor for peroxisomal targeting . A slightly milder effect was observed with the pathogenic double mutant G170R/V336D , with 28% of the protein-forming plaques in the cytosol , and another 28% being properly translocated into peroxisomes ( Figure 3 ) . The overall level of non-peroxisomal localization of this AGT mutant is 72% . All remaining AGT variants , including those from the β9–α12 loop and the two pathogenic single-residue mutants ( G170R , V336D ) < displayed 59%–76% peroxisomal localization , which is in agreement with our in vitro binding data and indicates a weakening but not an abolishment of Pex5p binding . Two AGT mutants from this category ( G170R , A328W ) showed around 5% aggregation , whereas no significant level of aggregation was measured for the remaining mutants . Taken together , the data show that even minor structural perturbations in AGT have a measurable and significant effect on AGT translocation .
The AGT–Pex5p structure is the second cargo protein–Pex5p receptor complex determined to date , the first being sterol carrier protein 2 ( SCP2 ) –Pex5p [15] . Our data indicate that the dimeric and cofactor-bound arrangement of AGT is preserved and that the enzyme remains functional prior to and upon binding of the Pex5p receptor ( Table 1 ) . This observation is in agreement with the unique ability of peroxisomes to import even large and oligomeric cargos as functional protein assemblies [23]–[25] . As our studies have been carried out in the absence of any additional protein components , a potential requirement of adaptor proteins as previously suggested [9] , [11] is unlikely . Our data confirm the involvement of segments from the C-terminal AGT domain—previously described as the “PTS1A” binding site [9]—in Pex5p receptor binding , but do not support earlier suggestions that an N-terminal AGT sequence segment contributes to receptor recognition [10] . Comparison of the complexes of Pex5p with SCP2 and AGT allows for the first time the identification of common and diverging principles in target protein recognition ( Table 3 ) , beyond the well-established C-terminal PTS1 motif that is shared by most Pex5p cargos [26] . Notably , the measured AGT–Pex5p interaction is about 30-fold weaker than that observed for SCP2 . This argues in favor of AGT being highly sensitive to perturbations that affect Pex5p recognition ( Table 1; Figure 3 ) and may mirror the large number of known disease-causing AGT mutations that have been associated with protein mistargeting rather than with catalytic activity effects [4] . The two protein cargo–receptor complex structures reveal that there are almost no specific , conserved side-chain interactions between polar residues from each PTS1 motif with Pex5p , with the notable exception of the very C-terminal leucine residue ( Figure 4 ) . This observation is supported by previous findings on AGT that indicate side-chain tolerance at PTS1 position −3 and , albeit more limited , at position −1 [27] . By contrast , our data only partly agree with observations from Pex5p–PTS1 peptide complexes , in which a more extensive hydrogen bond network over several PTS1 residues was observed [25] , [28] , [29] . Comparison with the available Pex5p–cargo protein complex structures indicates that the adaptability of possible PTS1 conformations to optimize specific interactions with the Pex5p receptor is restricted owing to the additional non-PTS1 protein interfaces that are formed between the C-terminal bundle domain of the receptor and cargo , as previously shown for SCP2 [15] , [30] and for AGT in this contribution ( Figure 2 ) . Collectively , however , the additional non-PTS1 interactions ( marked as IIa , b and IIIa , b in Figure 1 ) only slightly enlarge the overall Pex5p–AGT interface , in comparison to that observed in the Pex5p-SCP2 complex , in one of the two Pex5p-AGT complexes ( Table 3 ) . The specific Pex5p binding abilities of the PTS1 cargo peptides , corresponding to AGT and SCP2 sequences , are weak , with dissociation constants in the low to sub-µM range [15] , [26] . The gain in binding affinity for AGT when the complete protein is used is around 4-fold—3 . 5 µM instead of 13 . 5 µM ( Table 3 ) . Similarly , a gain in binding affinity of around 6-fold has been found for SCP2–Pex5p assembly when the protein complex is compared with the corresponding PTS1 peptide complex [15] . However , a recent analysis of additional non-PTS1 interactions confirmed that their contribution is only of minor importance , in turn suggesting that SCP2 recognition by the Pex5p receptor is principally driven by autonomous recognition of its PTS1 motif [30] . By contrast , our structural and functional data on AGT–Pex5p show that complex formation is both dependent on the presence of the AGT PTS1 motif and the correct Pex5p binding-competent conformation of the AGT C-terminal domain . Based on these findings , we argue that previously reported problems in establishing in vitro binding with purified protein components and by transfection experiments have failed for several PTS1 protein cargos in vivo owing to contextual defects in protein folding and possibly oligomerization [31] , [32] . Moderate binding of the cargo in vivo may facilitate subsequent release of the cargo into the peroxisomal lumen , a process that at present is still less well understood than the mechanism of cargo binding [18] , [33] , [34] . Further investigation of our structural data of the AGT–Pex5p complex reveals that the sequence segments in AGT that constitute the PTS1 and the hydrophobic core of AGT topologically overlap , whereas in SCP2 the corresponding sequence segments are well separated ( Figure 5 ) . Specifically , the PTS1 interactions observed extend to Arg381 ( PTS1 position −11 , when considering the C-terminal Leu392 as position 0 ) , and the side chains of three residues within the extended PTS1 segment ( Ala383 , Leu384 , Cys387 ) are also involved in hydrophobic core interactions of the C-terminal AGT domain . The overlapping interactions thus generate a seven-residue segment ( 381–387 ) from the C-terminus of helix α13 ( Figure 2A ) [8] that is involved in both the overall AGT fold and Pex5p receptor recognition . These structural observations indicate that , in contrast to our previous findings on the SCP2–Pex5p complex [15] , Pex5p receptor recognition of the PTS1 in AGT is structurally non-autonomous with respect to the remaining fold of the enzyme . Our structural data also explain previous observations on the translocation of AGT molecules that contain mutations in the extended PTS1 motif . These studies showed that diminished binding is caused by folding defects rather than by loss of cargo–receptor interactions that were predicted prior to available structural data [27] , indicating that AGT PTS1 binding depends on properly folded AGT and thus is also functionally non-autonomous . This is well illustrated by the strong translocation defects of several extended PTS1 mutations in AGT ( L380P; V376P ) [27] , which are involved in AGT hydrophobic core interactions rather than specific AGT–Pex5p interactions ( Figure 5 ) . In AGT , the additional Pex5p non-PTS1 interactions observed are not as specific as one may expect ( Figures 2A and S5 ) and perhaps explain the moderate overall binding affinity . These findings are further supported by the observation that Pex5p–AGT binding in vitro is an entropy-driven process , suggesting that binding is dominated by order/disorder processes rather than by enthalpy-driven specific interactions . AGT is an enzyme with a well-established genotype/phenotype database , including about 150 different missense mutations , many of which lead to serious forms of PH . Our structural and functional characterization of the molecular parameters for AGT to be recognized by the Pex5p receptor and its subsequent translocation into peroxisomes offers an opportunity to rationally address functional implications of pathogenic PH-causing missense mutations . We assume that those PH mutations that lead to irreversible AGT aggregation , irrespective of the presence of the Pex5p receptor , will be difficult or even impossible to treat by chemical intervention as these AGT variants are expected to lose both their enzymatic activity and their ability to be recognized by the peroxisomal Pex5p receptor as a consequence of misfolding . Based on our mapping of known AGT missense mutations on the three-dimensional structure of the AGT–Pex5p complex ( Figure S1 ) , we estimate that around half of these lead to fold defects , as they reside in regions that are completely buried within the AGT fold . The fraction of misfolded AGT mutants is probably even higher when associated with the widespread AGXT-Mi gene [3] , which leads to additional destabilization of the enzyme . Partial rescue of some of these mutations , by adding chaperones or osmolytes for instance [35] , may be possible but remains challenging , as most of these additives tend to be non-specific . On the basis of our data , we further expect that the loss of function of many of the remaining patient mutations ( Figure S1 ) that result in AGT , which does not aggregate or is only partially prone to aggregation , could be potentially restored by proper chemical intervention . As the topology of the AGT active site is well characterized by PLP binding ( Figure S4 ) and the presence of several highly conserved residues , mutations that directly affect AGT enzymatic activity are predictable and their effect can be verified by AGT activity tests [4] , [5] . For mutants of this category , it has been shown that pyridoxine treatment may lead to additional active-site stabilization , resulting in a reduction of clinical symptoms such as calcium oxalate crystallization and an increasing preservation of renal function [36] . However , prior to this work , a rational basis for predicting mutations involved in the loss of peroxisomal targeting has been largely missing . A paradigm pathogenic mutation within this category is the G170R/V336D variant located on the AGXT-Mi allele [36] , which creates a serious disease phenotype . For this type of mutation , which predominantly affects peroxisomal targeting , it is desirable to identify compounds that would lead to a gain in AGT binding to the Pex5p receptor , by targeting identified AGT–Pex5p interface areas such as the PTS1 site , the extended PTS1 site , and relevant Pex5p-binding surfaces from the AGT C-terminal domain ( Figures 1–2 and S5 ) . The knowledge of designed AGT variants compromised in Pex5p recognition , such as AGT ( Y330A ) , may be useful for targeting the restoration of AGT–Pex5p recognition to wild-type levels . The observed limited flexibility in the non-PTS1 binding areas and the lack of optimized interactions within the PTS1 binding site of AGT ( Figures S5 and S6 ) may provide a knowledge-based system by which Pex5p receptor binding can be maximized by compounds that have the potential to improve protein-protein interactions .
Human AGT ( major allele haplotype ) and human Pex5p ( C ) ( residues 315–639 ) were expressed from a modified pET24d vector ( G . Stier , EMBL Heidelberg ) in Escherichia coli BL21 ( DE3 ) RIL . The two genes were amplified by polymerase chain reaction ( PCR ) using primers containing NcoI and KpnI restriction sites , respectively ( Table S4 ) . Following the digestion of the PCR products and the vector , the two constructs were created by ligation ( Rapid Ligation Kit , Fermentas ) . Cultures were grown in Lysogeny Broth medium containing 50 mM Tris pH 7 . 5 and 1% ( w/v ) glucose , and induced mid-log phase with 0 . 5 mM isopropyl-β-D-thiogalactopyranosid overnight at 21°C . Both proteins contained an N-terminal hexahistidine–glutathione S-transferase fusion , which is cleavable with tobacco etch virus ( TEV ) protease . The cleared lysate was loaded onto a nickel-nitrilotriacetic acid column and the purified proteins were eluted with 50 mM Tris pH 8 . 0 , 150 mM NaCl , 2 mM ß-mercaptoethanol , and 500 mM imidazole . Fusion proteins were cleaved with tobacco etch virus protease overnight at 4°C , along with dialysis into 50 mM Tris pH 8 . 0 , 150 mM NaCl , 2 mM ß-mercaptoethanol , and 20 mM imidazole . The samples were then applied to a nickel-nitrilotriacetic acid column and the flow-through was collected . As a final purification step , gel filtration was performed using a Superdex 75 ( 16/60 ) column ( GE Healthcare ) . In vivo analysis of EGFP-AGT was carried out with the expression vector pEGFP-AGT , which was derived from subcloning a PCR amplification product of AGT into the pEGFP-C1 plasmid ( Clontech ) . Point mutations were introduced into pEGFP-AGT by using the Quickchange XL Site Directed Mutagenesis Kit ( Stratagene ) . All primers are listed in Table S3 . AGT point mutants that were tested in vitro were subcloned into a pET151 D-TOPO vector . Expression and purification of these proteins was performed as described above . The Pex5p ( C ) –AGT complex was formed by mixing purified Pex5p ( C ) and AGT and confirmed by analytical gel filtration and static light scattering , using a MiniDAWN instrument ( Wyatt ) . Specific activity measurements of AGT in the presence and absence of Pex5p were performed as described previously [37] , [38] , using the following concentrations: 100 mM potassium phosphate pH 8 . 0 , 0 . 15 mM PLP , 10 mM glyoxylate , and 150 mM alanine . To confirm specific binding of the cofactor to the recombinant enzyme , we recorded absorption spectra between 300 and 600 nm . All measurements were performed on an Infinity 1000 spectrophotometer ( Tecan ) . Pex5p ( C ) and AGT were mixed in a 3∶2 molar ratio and concentrated to 5 mg/ml . Crystals were obtained by submitting a mix of 1 µl protein and 1 µl reservoir solution , comprising 0 . 1 M Bis-Tris ( pH 5 . 3 ) , 0 . 15 M LiSO4 , 17% [w/w] PEG3350 , to hanging drop vapor diffusion at 20°C . Streak seeding of a drop with 2 . 5 mg/ml protein concentration was used to obtain single large crystals . X-ray data were collected at BM14 . 1 at ESRF , Grenoble . Data were processed with MOSFLM [39] and scaled with SCALA [40] . Five percent of the reflections were randomly selected for cross-validation . The structure of the Pex5p ( C ) –AGT complex was solved by molecular replacement using the coordinates of apo-AGT ( PDB code: 1H0C ) and the Pex5p–SCP2 complex ( PDB code: 2C0L ) as search models with the program PHASER [41] . REFMAC [42] was used to refine the structure , applying translation/libration/screw parameterization [43] . Manual building and structure analysis were carried out in COOT [44] . The structure quality was assessed with MOLPROBITY [45] . Programs of the CCP4 package [46] were used for structure manipulation , analysis , and validation . The coordinates of the structure have been deposited in the Protein Data Bank ( code: 3R9A ) . Tilt and twist angles were calculated using MOD22 [20] . All proteins were dialyzed against 100 mM HEPES ( pH 7 . 5 ) , 150 mM NaCl , and 2 mM ß-mercaptoethanol . ITC measurements were conducted on a MicroCal VP-ITC using 25–46 µM AGT as a sample and 250–460 µM Pex5p ( C ) as a titration ligand . Experiments were performed at 25°C . Pex5p ( C ) was injected in volumes of 10 µl in a total of 27 steps , resulting in a 2-fold excess of AGT at the end of each titration experiment . Ligand heating effects by dilution were subtracted , and data were fitted using MicroCal Origin 5 . 0 . Circular dichroism experiments were performed on a J-810 spectropolarimeter ( Jasco ) . Proteins were dialyzed into 10 mM potassium phosphate ( pH 8 . 0 ) and 1 mM dithiothreitol . Far-UV spectra were recorded between 190 and 260 nm , using a 1 mm cuvette and a concentration of 0 . 15–0 . 22 mg/ml protein , as determined by specific absorbance at 280 nm . The machine settings were 1 nm bandwidth , 1 s response , 1 nm data pitch , and 100 nm/min scan speed . Secondary structure content was calculated with the Diochroweb server [47] , using the analysis program CDSSTR and reference set 4 . All circular dichroism data presented are the averages of three separate experiments . Human fibroblast cells ( strain GM5756T ) were cultured as described previously [15] and transfected with pEGFP-AGT variants , using FuGENE 6 Transfection Reagent ( Roche Diagnostics ) . At 24 h after transfection , cells were fixed with 3% paraformaldehyde , solubilized with 1% Triton X-100 , and subjected to immunofluorescence microscopy . Polyclonal rabbit antibodies against Pex14p were used to label peroxisomes [48] . Secondary antibodies were conjugated with Alexa Fluor 594 ( Invitrogen , Germany ) . All micrographs were recorded on an Axioplan 2 microscope ( Zeiss ) with a Plan-Apochromat 63×/1 . 4 oil objective and an Axiocam MR digital camera and were processed with AxioVision 4 . 6 software ( Zeiss ) . Statistical analysis was carried out from at least three independent transfections of each AGT expression plasmid . Based on the appearance of the AGT fluorescence pattern , around 100 cells of each experiment were visually categorized into three classes: ( i ) predominant peroxisomal localization , ( ii ) mostly cytosolic , or ( iii ) forming aggregates , as indicated by fluorescent plaques over cytosolic background .
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Peroxisomes are cell organelles contain proteins involved in various aspects of metabolism . Peroxisome proteins translocate from their site of synthesis in the cytoplasm across the organelle membrane in a fully folded and functional form . One such protein is the enzyme alanine–glyoxylate aminotransferase ( AGT ) . It contains a targeting signal in its C-terminus that is recognized by a receptor protein , Pex5p , in the cytoplasm , which allows its subsequent translocation into the peroxisome . Mutations in AGT cause a disease known as primary hyperoxaluria type 1 , in which patients suffer irreversible kidney damage; this disease results , in many cases , from improper targeting of AGT into peroxisomes . To understand better the mechanism of AGT import into peroxisomes and the molecular basis of this disease , we have determined the crystal structure of the complex between AGT and its receptor Pex5p . The structure reveals how overlapping segments of the protein sequence are crucial for both receptor recognition and maintaining the folded structure of the enzyme . Subsequently , we created and studied several mutants of the enzyme , including mutants that are known to cause disease , and found that even minor folding defects in the enzyme prevent its recognition by Pexp5 and its import into peroxisomes . Our data thus provide novel insights into the consequences of mutations in AGT on the catalytic activity of the enzyme , as well as into the mechanisms that cause primary hyperoxaluria type 1 .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"biomacromolecule-ligand",
"interactions",
"medicine",
"genetic",
"mutation",
"enzymes",
"macromolecular",
"assemblies",
"gene",
"function",
"biocatalysis",
"protein",
"folding",
"mitochondrial",
"diseases",
"chromosomal",
"disorders",
"membranes",
"and",
"sorting",
"biology",
"biophysics",
"metabolism",
"clinical",
"genetics",
"biochemistry",
"autosomal",
"recessive",
"cell",
"biology",
"genetics",
"human",
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"molecular",
"cell",
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"genetics",
"and",
"genomics"
] |
2012
|
Molecular Requirements for Peroxisomal Targeting of Alanine-Glyoxylate Aminotransferase as an Essential Determinant in Primary Hyperoxaluria Type 1
|
Mechanisms that enable injury responses to prompt regenerative outgrowth are not well understood . Planarians can regenerate essentially any tissue removed by wounding , even after decapitation , due to robust regulation of adult pluripotent stem cells of the neoblast population . Formation of pole signaling centers involving Wnt inhibitors or Wnt ligands promotes head or tail regeneration , respectively , and this process requires the use of neoblasts early after injury . We used expression profiling of purified neoblasts to identify factors needed for anterior pole formation . Using this approach , we identified zic-1 , a Zic-family transcription factor , as transcriptionally activated in a subpopulation of neoblasts near wound sites early in head regeneration . As head regeneration proceeds , the Wnt inhibitor notum becomes expressed in the newly forming anterior pole in zic-1-expressing cells descended from neoblasts . Inhibition of zic-1 by RNAi resulted in a failure to express notum at the anterior pole and to regenerate a head , but did not affect tail regeneration . Both injury and canonical Wnt signaling inhibition are required for zic-1 expression , and double-RNAi experiments suggest zic-1 inhibits Wnt signaling to allow head regeneration . Analysis of neoblast fate determinants revealed that zic-1 controls specification of notum-expressing cells from foxD-expressing neoblasts to form the anterior pole , which organizes subsequent outgrowth . Specialized differentiation programs may in general underlie injury-dependent formation of tissue organizing centers used for regenerative outgrowth .
Regeneration is widespread in animals but still poorly understood . In animals with this ability , injury responses and positional cues likely interact to signal the production of missing structures . Across vertebrates and invertebrates , regeneration can involve either stem cells of high potency or cells with more limited potential [1] . Therefore , rather than the presence or absence of a single regenerative cell type , patterning systems that directly or indirectly control such cells may instead be the critical determinate for regenerative abilities . Injuries can alter tissue variously , so regenerating animals must have robust cell signaling mechanisms that enable appropriate restoration of structures damaged or lost by wounding . An important unresolved issue in understanding regeneration mechanisms is whether stem cells function only in production of differentiated cell types that comprise fully regenerated structures , or whether they could have additional functions in directing outgrowth by controlling tissue patterning . Planarians can regenerate nearly any tissue damaged by injury [2] , and the processes of head or tail regeneration serve as simple models for study of mechanisms that relate wounding to stem cell activation and growth signaling . Planarians rely on adult pluripotent stem cells of the neoblast population for producing all differentiated cell types needed for whole-body regeneration and for viability through ongoing tissue homeostasis [3] . Neoblasts express the PIWI homolog smedwi-1 [4] and are the only known proliferating cells in planarians [5] , so FACS isolation of G2/S/M cells labeled with DNA-binding vital dyes can purify this cell population [4] , [6] . Neoblasts respond to injury through changes in proliferation [7] , localization [8] and gene expression [9] . RNAi and small molecule treatments have implicated several signaling pathways and processes in head and tail regeneration that therefore directly or indirectly control the function of neoblasts or their differentiating progeny , including Wnt [10]–[18] , BMP [19]–[23] , Activin [24] , [25] , Hedgehog [15] , [26] , FGF , and calcium signaling [27] , as well as communication through gap-junctions [28] , [29] and bioelectric signaling [30] , [31] . Neoblast-dependent processes likely provide input into the control of canonical Wnt signaling that directs head and tail regeneration and polarized responses on the anteroposterior ( A/P ) axis [10]–[12] . wnt1 is required for tail regeneration and is activated early by 6 hours after injury [13] , [32] in body-wall muscle cells [33] in a neoblast-independent manner . Subsequently , by 48–72 hours , in fragments regenerating a new tail , wnt1 becomes expressed in a focus of cells at the posterior pole in a neoblast-dependent manner . By contrast , notum encodes a secreted hydrolase that can inhibit Wnt signaling in planarians [14] , Drosophila [34] , [35] , and zebrafish [36] , and is required for head regeneration in planarians [14] . Injury activates notum expression preferentially at anterior-facing injury sites within body-wall muscle cells [33] with kinetics similar to wound-induced wnt1 expression . Subsequently , by 48–72 hours , in fragments regenerating a new head , notum is expressed in a focus of cells at the newly forming anterior pole . Temporal RNAi experiments found that wnt1 and notum are required during tail and head regeneration [13] , [14] , indicating the importance of these expression behaviors for their growth promoting functions . The involvement of additional genes in head and tail regeneration also points to the importance of neoblast-dependent processes in pole formation . pbx is a TALE class homeodomain transcription factor expressed broadly , including within neoblasts , and is required for head and tail regeneration as well as pole expression , but not wound-induced expression , of wnt1 and notum [37] , [38] . Tail regeneration and expression of wnt1 in the tail requires the transcription factor pitx [39] , [40] . Additionally , the forkhead transcription factor foxD is activated early by 6 hours in body-wall muscle cells at injury sites that disrupt the midline , including anterior and posterior amputation sites , is subsequently expressed in neoblasts and the anterior pole , and is needed for notum anterior pole expression and head regeneration [41] . Therefore , these studies indicate that pole formation is either a consequence of neoblast-dependent growth or alternatively might require specialized differentiation activities of neoblasts . We used transcriptional profiling to identify a Zic transcription factor , Smed-zic-1 , as a likely candidate for control of neoblasts to produce an anterior pole in head regeneration . Zinc fingers of the cerebellum ( Zic ) proteins , homologous to Drosophila odd-paired , are conserved in animals , can act as transcription factors , and participate in axis formation , neurogenesis , and mesoderm formation [42]–[49] . However , pleiotropy and redundancy have complicated the identification of specific shared functions for vertebrate Zic genes , and loss-of-function studies have not yet pointed to their common use in only a single signaling pathway [50] . Additionally , functions for Zic proteins in injury responses have not been described . The injury-dependent program of adult organ formation in planarians provides a simple system to identify and clarify highly conserved , and therefore central , zic gene functions used in stem cell control , tissue patterning and growth . Our studies indicate that Zic proteins can participate in organ regeneration through stem cell-dependent production of signaling centers that direct subsequent tissue outgrowth .
To identify genes involved in putative stem cell-dependent patterning processes , we first examined neoblast requirements for injury-induced expression of notum , a Wnt inhibitor required for head regeneration [14] . In head regeneration , notum expression is activated in a number of cells near the anterior-facing amputation site by 18 hours and subsequently by 48–72 hours its expression occurs prominently at the regenerating anterior pole . Animals treated with lethal doses of gamma irradiation were depleted of smedwi-1-positive neoblasts , as described previously [4] , and succeeded in expressing notum at 18 hours but not at the anterior pole by 72 hours ( Fig . 1A , Fig . S1 ) . These results confirm previous observations that anterior pole formation is neoblast-dependent [41] . We reasoned that genes important for a neoblast-dependent step in anterior pole formation might be expressed in neoblasts specifically during head regeneration . To identify such genes , we used Hoechst staining and FACS to isolate X1 neoblasts ( G2/S/M cells ) from tissues near anterior- and posterior-facing wound sites over three days of regeneration and transcriptionally profiled these cells using custom microarrays ( Agilent ) performed in biological triplicates ( Fig . 1B , Fig . S2A ) . 66 genes had expression changes predicted with a false discovery rate of 10% ( Benjamini-Hochberg method for correction of multiple hypothesis testing , see Methods ) , of which 24 were detected as upregulated in neoblasts during regeneration with various kinetics and specificities ( Fig . 1C , Fig . S2B , Table S1 ) . We used qPCR to validate the expression behavior of these genes . Expression upregulation was confirmed for 21 of these genes ( Fig . 1D ) , and with broadly similar kinetics as measured by microarray . Hierarchical clustering classified genes as having induction broadly polarized to anterior regeneration ( distalless , zic-1 , ap2 , hmx-1 , otp , znf840 ) , posterior regeneration ( wntP-3 ) , or that occurred in both anterior and posterior regeneration in either analysis . Nine of the 21 confirmed injury-induced genes encoded predicted transcription factors with six previously identified as expressed in neoblast subpopulations ( runt-1 , hmx-1 , ap-2 , pax-6 , dlx-3 , and sp6-9 ) [9] , [51] , [52] and three not yet identified as neoblast-expressed ( zic-1 , znf840 , and otp ) . Of these nine genes , expression profiles from the microarray indicated four were induced in early head regeneration ( 24 hours ) much greater than in tail regeneration: zic-1 , distalless ( dlx ) , ap-2 , and hmx-1 . dlx , ap2 , and hmx-1 have reported expression patterns and functions related to regeneration of eye cells and specific neuronal subpopulations rather than pole regionalized expression or functions in head formation [9] , [51] , [52] . Therefore , we focused subsequent analysis on zic-1 . A previous phylogenetic study identified this gene as one of two planarian Zic family members ( previously designated as zicA and zicB ) , with representatives in all animals [45] . We renamed the zicA gene Smed-zic-1 ( hereafter referred to as zic-1 ) in keeping with the Schmidtea mediterranea gene nomenclature guidelines [53] . We first used in situ hybridizations to confirm zic-1's injury-induced expression behavior . In uninjured animals , zic-1 was expressed in the head region and at the anterior pole ( Fig . 2A ) . In animal fragments fixed during regeneration of a head and/or a tail , zic-1 was expressed near anterior-facing injury sites by 18 hours ( Fig . 2B ) . zic-1 was expressed preferentially at anterior- versus posterior-facing amputation sites made at similar axial positions , although it was initially expressed more strongly at injury sites derived from the anterior ( prepharyngeal ) versus the posterior ( postpharyngeal ) of the amputated animal . By 42 hours , zic-1 expression in posterior-facing wound sites was reduced but expression at anterior-facing sites persisted , and by 72 hours zic-1 was expressed strongly in the anterior pole and in surrounding regions . Injury induced zic-1 expression was not exclusive to the anterior , as expression at posterior-facing wound site of head fragments was observed by 18 hours and later near but not coincident with the posterior pole of regenerating head fragments by 72 hours . However , expression at anterior-facing injury sites was prominent and persistent . We conclude that zic-1 expression is activated early following injury , marks the anterior pole , and occurs differentially in head versus tail regeneration . We next performed experiments to verify expression of zic-1 in neoblasts . We used double fluorescence in situ hybridization ( FISH ) to detect injury-induced zic-1 expression in smedwi-1-positive neoblasts ( Fig . 2C ) . In freshly amputated animals , a small number of zic-1-positive cells were identifiable , and some co-expressed smedwi-1 ( 13/29 cells , n = 3 animals ) . By 24 hours of anterior regeneration , the number of zic-1/smedwi-1 double-positive cells near the injury site greatly increased ( 506/530 cells , n = 4 animals ) . Additionally , there were a large number of smedwi-1+ cells that lacked zic-1 expression , suggesting that zic-1 expression marks a neoblast subpopulation near injury sites . The entire neoblast population increases in number due to amputation by 24 hours [5] , [7] but does so significantly less than the ∼10-fold increase in zic-1+/smedwi-1+ cells we observed . Anterior pole cells expressing zic-1 at 72 hours lacked expression of smedwi-1 ( 10/10 cells , n = 3 animals ) . All non-neoblast cells are descended from neoblasts [3] , so these results indicated that injury activates expression of zic-1 in a subpopulation of neoblasts at anterior-facing injury sites and subsequently in neoblast descendants located at the regenerating anterior pole . We performed two additional tests to confirm expression of zic-1 in the neoblast population due to injury . First , we quantified zic-1 expression in X1 cells by realtime PCR , and observed a 14-fold induction of zic-1 expression during head regeneration by 24 hours that was specific to anterior-facing injury sites ( Fig . 2D ) . X2/Xins cells representing G1 cells of the animal also increased their expression of zic-1 due to amputation but to a lesser extent than did X1 cells . Second , we examined zic-1 expression in animals exposed to lethal doses of gamma irradiation the day of head and tail amputation ( Fig . 2E ) . This dose was sufficient to eliminate neoblasts , and abolished both early wound-induced and anterior pole expression of zic-1 ( 8/8 animals ) . These experiments support the conclusion that zic-1 expression is induced by injury in neoblasts . Planarian genes induced transcriptionally by injury vary in their responsiveness to wound site orientation , extent and location [9] , [13]–[15] , [25] , [32] . To examine requirements for injury-induced zic-1 expression , we made symmetric lateral amputations or asymmetric wedges that removed part of the brain without pole removal or injuring through the animal midline ( Fig . 2F ) . In both cases , zic-1 was expressed in cells near the injury sites by 18 hours after wounding ( lateral amputation , 12/12 worms had expression; wedge incision , 6/7 worms had expression ) in irradiation-sensitive cells ( lateral amputation , 0/8 worms had expression; wedge , 0/5 had expression ) . Additionally , wound-site proximal zic-1 expression was preferentially anterior and not posterior in laterally amputated animals at 18 hours ( 11/12 animals assayed ) . These results indicate that zic-1 activation in neoblasts occurs with anterior bias and independently of midline disruption , pole removal and head removal . We examined the relationship between injury-induced neoblast expression and anterior pole expression of zic-1 . First , we tested whether zic-1 co-expressed with other genes that together mark cells the anterior pole: notum , follistatin ( fst ) , and foxD . Double FISH experiments performed on uninjured animals detected co-expression of zic-1 and notum , zic-1 and foxD , and zic-1 and follistatin at the anterior pole ( Fig . 3A ) . Additionally , we performed similar experiments to analyze the relationship between these four genes during both early ( 18 hours ) and late ( 48–72 hours ) regeneration phases ( Fig . 3B ) . At 18 hours , notum is expressed primarily in the body wall musculature [33] whereas zic-1 is expressed primarily in neoblasts ( Fig . 2C ) . Consistent with these observations , the majority of notum-expressing cells did not express zic-1 at 24 hours of regeneration ( 142/151 notum+ cells were zic-1− , n = 3 animals ) , only a small fraction of zic-1+/notum+ cells were identifiable ( 9/151 notum+ cells were zic-1+ , n = 3 animals ) . Subsequently , at 72 hours , notum and zic-1 were co-expressed in many cells at the regenerating anterior pole ( 34/39 notum+ cells were zic-1+ , n = 7 animals ) . Similarly , at early times in regeneration ( 6–18 hours ) , foxD and follistatin are primarily expressed in collagen+ cells of the body wall musculature near injury sites that disrupt the midline , and at generic injury types , respectively [25] , [33] , [41] . By 72 hours , however , both genes were expressed in cells of the anterior pole that co-express zic-1 ( 32/36 zic-1+ cells were foxD+ , n = 5 animals; 36/69 zic-1+ cells were fst+ , n = 6 animals ) . Therefore , zic-1 and notum , foxD , and follistatin are not co-expressed early after head amputation , but later at the regenerating anterior pole . We further used multiplex histology and labeling experiments to characterize the cell composition of the regenerating anterior pole using notum as a marker . Some 48-hour anterior pole cells expressing notum also expressed SMEDWI-1 protein ( 86 . 5±8 . 3% notum+ cells co-expressed SMEDWI-1 , n = 5 animals ) ( Fig . S3 ) , suggesting some pole cells are recently differentiated from smedwi-1+ neoblasts [54] . notum+ anterior pole cells did not express prog-1 , which marks a postmitotic population of neoblast descendants [55] . A fraction of notum+ cells at the anterior pole of 72-hour regenerating animals expressed collagen ( 17 . 3±16 . 8% notum+ cells co-expressed collagen , n = 5 animals ) , which marks body-wall muscle cells that express secreted regulators of regeneration [33] . Together , these results suggest that anterior pole cells include cells of the body-wall musculature formed by neoblast differentiation during regeneration . To further examine the hypothesis that cells of the regenerating anterior pole are descended recently from neoblasts , we performed bromodeoxyuridine ( BrdU ) labeling experiments . As neoblasts are believed to be the only proliferating cell population in planarians , BrdU first marks neoblasts and subsequently their differentiating progeny and differentiated cell types [52] , [55] . We administered a pulse of BrdU to label neoblasts two days prior to head amputation , fixed animals in a time series and detected zic-1 mRNA expression by FISH and BrdU incorporation by immunostaining ( Fig . 3C ) . BrdU+zic-1+ cells were observed at 24 hours after amputation in the parenchyma near the injury site ( 35 . 2±16 . 6% of zic-1+ cells were BrdU+ , n = 6 worms ) , and at 48 hours near the anterior pole ( 80 . 4±20% of zic-1+ cells were BrdU+ , n = 5 worms ) . Furthermore , we detected BrdU+notum+ cells at the anterior pole of 72-hour regenerating fragments . Therefore , these results indicate that the anterior pole contains cells recently differentiated from neoblasts and are consistent with the hypothesis that some zic-1+ neoblasts form zic-1+ anterior pole cells . We next used RNAi to investigate functions for zic-1 in regeneration . Animals treated with zic-1 double-stranded RNA ( dsRNA ) were amputated to remove heads and tails and scored for phenotypes 8 days after surgeries . Inhibition of zic-1 caused overt defects in head but not tail regeneration , including cyclopia ( 25% of 74 animals assayed ) , absence of eyes ( 12% of 74 animals assayed ) and head regeneration failure ( 63% of 90 animals assayed ) ( Fig . 4A ) . Two dsRNAs targeting non-overlapping regions of the zic-1 gene individually caused identical defects in anterior regeneration , suggesting these effects are likely due to zic-1 inhibition and not an off-target effect of RNAi ( 5′ dsRNA: 7/14 animals were headless; 3′ dsRNA: 3/6 animals were headless ) . Similar anterior regeneration defects have been observed due to inhibition of notum [14] , foxD [41] , follistatin [24] , [25] , patched [15] , prep [56] , pbx [37] , [38] , and H+ , K+-ATPase [30] . We used in situ hybridizations to analyze the tissue content and regional expression status of headless zic-1 ( RNAi ) animals . Headless zic-1 ( RNAi ) animals lacked a brain ( gpas , chat ) and also lacked expression of markers of the head tip ( sFRP-1 ) , head region ( prep ) , and anterior pole ( notum , follistatin and foxD ) ( Fig . 4B–C ) . Headless ( Fig . 4B ) and eyeless ( Fig . S4A ) zic-1 ( RNAi ) animals had normal expression of posterior markers fzd-4 and wnt1 , suggesting that posterior regeneration occurred normally and that such animals did not undergo a polarity transformation to form a tail at the anterior-facing wound sites . To further test whether zic-1 has functions in midline formation , we analyzed expression of slit , a marker of the midline [57] . zic-1 ( RNAi ) animals had laterally expanded anterior put not posterior expression of slit ( Fig . 4D ) . zic-1 ( RNAi ) animals maintained broad expression of pbx ( Fig . S4B ) , indicating zic-1 likely does not function in head regeneration by regulating pbx expression . Together , these results indicate zic-1 participates in head formation rather than pole identity determination . zic-1 inhibition resembled some defects due to inhibition of notum , so we determined the stage ( s ) of notum expression affected by zic-1 RNAi . zic-1 inhibition prevented anterior pole expression of notum by 72 hours , but not its early wound-induced expression ( Fig . 4E ) , similar to the impact of irradiation on notum expression ( Fig . 1A ) . Together , these experiments indicate zic-1 is required for head regeneration , midline patterning , and anterior identity . zic-1 inhibition produced multiple regeneration phenotypes , so we sought to understand their relationships by performing RNAi attenuation experiments and additional histological analysis . Dilution of zic-1 dsRNA with an equal amount of control dsRNA increased the penetrance of cyclopia and decreased the penetrance of headlessness ( Fig . S4C ) . In addition , cycloptic zic-1 ( RNAi ) animals had a low level of notum and sFRP-1 expression at the anterior pole ( Fig . S4D ) . We conclude that cyclopia is likely a hypomorphic phenotype due to zic-1 RNAi and we did not investigate it further . An additional Zic-family gene in the S . mediterranea genome , formerly referred to as zicB , we renamed Smed-zic-2 ( here after referred to as zic-2 ) in keeping with the S . mediterranea guidelines for gene names [53] . zic-2 was expressed in the anterior of uninjured animals ( Fig . S5A ) , and we speculated that it could have redundant functions with zic-1 . Double fluorescence in situ hybridizations revealed that zic-2 was expressed in neoblasts at 24-hours and in cells of the anterior region at 72-hours in an irradiation sensitive manner ( Fig . S5B ) . Inhibition of zic-2 by RNAi resulted in weakly penetrant cyclopia after amputation and regeneration ( Fig . S5C ) . However , simultaneous inhibition of zic-1 and zic-2 increased the frequency of headless animals versus inhibition of zic-1 or zic-2 alone ( p-value<0 . 001 , Fisher's exact test ) . zic-2 RNAi reduced expression of injury-induced zic-1 ( p = 0 . 07 ) , as measured by manual cell scoring and realtime PCR ( Fig . S5D–E ) , providing a possible explanation of these effects given that dsRNA treatments in general likely reduce rather than eliminate gene function . By contrast , zic-1 RNAi caused a reduction in zic-2 expression ( p = 0 . 07 ) ( Fig . S5E ) . Taken together , these results suggest zic-1 and zic-2 function together in head regeneration . Because head regeneration strongly required zic-1 , we focused subsequent analysis on that gene . Given the prominent injury-induced behavior of zic-1 expression , we hypothesized that it may have functions specific to regeneration as opposed to those tissue maintenance in the absence of injury , similar to follistatin [25] , runt-1 [9] , and foxD [41] . To test whether zic-1 functions only in regeneration , we fed animals bacteria expressing dsRNA for 10 weeks . No overt abnormalities became apparent in these animals ( Fig . S6A ) , and sFRP-1 expression was normal ( Fig . S6B ) . However , prolonged zic-1 RNAi without injury caused a reduction in the number of notum+ cells near the anterior pole ( 11±1 notum+ cells in control animals versus 5 . 8±0 . 5 cells in zic-1 ( RNAi ) animals , p<0 . 005 by t-test ) ( Fig . S6B ) . This defect was specific to expression of notum at the anterior pole , as notum expression at the brain commissure was normal ( 10 . 7±2 . 3 notum+ cells in control animals versus 13±1 . 6 cells in zic-1 ( RNAi ) animals , p>0 . 05 by t-test ) . By contrast , amputation of animals undergoing RNAi in parallel treatments for only three weeks resulted in head regeneration failure ( 40% , n = 20 anterior-facing wounds ) . These results point to the importance of injury-induced zic-1 expression for regeneration and indicate injury-independent functions in anterior pole maintenance . We next examined determinants of zic-1 expression and function . Expression of some Wnt signaling ligands and secreted inhibitors occurs independently of neoblasts and also with A/P polarization at injury sites [13] , [32] . Therefore , we tested canonical Wnt signaling as a candidate pathway controlling early zic-1 activation ( Fig . 5A–B ) . Inhibition of beta-catenin-1 by RNAi resulted in ectopic zic-1 expression at posterior-facing wounds in trunk fragments by 24 hours after injury . Conversely , inhibition of APC , an intracellular inhibitor of beta-catenin-1 , reduced zic-1 expression at anterior-facing wound sites in trunk fragments . Because zic-1 expression behavior depends on A/P axis position ( Fig . 2A ) , we further investigated the impact of beta-catenin-1 or APC inhibition in anterior-facing wound sites from anterior- and posterior-facing injury amputation sites at different A/P axial positions ( see cartoons to show surgeries in Fig . 5A–B , S7A–B ) . APC inhibition decreased zic-1 expression at anterior-facing injury sites in both the anterior and posterior of the body , and also in posterior-facing injury sites from the anterior of the body ( Fig . 5A–B , S7A–B ) . By contrast , beta-catenin-1 inhibition increased zic-1 expression at anterior- and posterior-facing amputation sites located in posterior regions of the animal but not in the anterior of the animal ( Fig . 5A–B , S7A–B ) . Taken together , these results indicate that perturbation of beta-catenin or APC function has opposite effects on zic-1 expression that are independent of polarity transformations caused by these treatments . Animal-wide gradients of beta-catenin activity have been proposed based on phenotypic evidence and expression domains of Wnt ligands [58] . We speculate that anterior regions may normally have sufficiently low beta-catenin-1 activity that beta-catenin-1 RNAi does not further increase zic-1 expression after injury . Furthermore , in uninjured animals , beta-catenin-1 or APC RNAi did not simply result in constitutive activation or inhibition of zic-1 in the regions assessed in the regeneration assay ( 4/4 animals scored , Fig . S7C ) , indicating these effects were specific to an injury context . wnt1 and notum are expressed prior to zic-1 activation in response to injury , so we tested their requirements for zic-1 expression . wnt1 RNAi resulted in ectopic zic-1 expression at posterior-facing amputation sites , and notum RNAi reduced expression at anterior-facing amputation sites . We confirmed the effects of Wnt signaling perturbation on zic-1 expression using manual cell counting ( Fig . 5B ) and qPCR ( Fig . 5C ) . Therefore , taken together with previous observations , these results indicate that Wnt signaling inhibition by early injury-induced notum is necessary and sufficient to activate early zic-1 expression by 24 hours . We examined additional genes involved in head and tail regeneration for their requirements for 24-hour zic-1 expression . Inhibition of hedgehog and patched had no apparent effect on zic-1 expression as measured by qPCR and did not alter numbers of zic-1+ cells as measured by manual counting ( Fig . S8A ) . hedgehog and patched oppositely regulate wnt-1 expression , suggesting that transcriptional activation of zic-1 could have additional inputs other than Wnt signaling . However , patched ( RNAi ) animals of the same cohort regenerated with anterior defects , including anterior tail formation ( 3/7 animals ) and headlessness ( 2/7 ) , suggesting that Wnt signaling inhibition rather than axis polarization or regionalization is a driver of zic-1 expression due to injury . Additionally , pbx inhibition reduced but did not eliminate zic-1 expression 24 hours after amputation , as measured by reduced numbers of zic-1+ cells and reduced zic-1 mRNA levels ( Fig . S8B–C ) . follistatin inhibition reduced zic-1+ cell numbers at 24 hours after amputation and reduced zic-1 mRNA levels , although not significantly as determined by a t-test , consistent with follistatin participating in early signaling due to any injury that removes tissue [25] . pitx inhibition reduced the numbers of zic-1+ levels , although not significantly as determined by a t-test , and reduced zic-1 mRNA expression . foxD inhibition did not visibly alter zic-1 expression or numbers of zic-1+ cells at 24 hours , though reduced mRNA expression of zic-1 but not significantly ( as determined by t-test ) . Additionally , prep RNAi did not alter numbers of zic-1+ cells nor mRNA levels due to injury expression . Finally , pitx RNAi reduced the numbers of zic-1+ cells and reduced zic-1 mRNA levels . We conclude that pbx , follistatin are likely required for maximal levels of zic-1 expression , and that zic-1+ cells are still abundant after inhibition of hedgehog , patched , foxD , pitx and prep . Given the central function of Wnt signaling in head regeneration [10] , [11] , [59] , we next tested candidate functional interactions with zic-1 . Whereas zic-1 promotes head regeneration ( Fig . 4A ) , beta-catenin-1 suppresses it [10] , [11] , [59] , so we examined the outcome of simultaneous inhibition of both genes . zic-1 ( RNAi ) ;beta-catenin-1 ( RNAi ) animals all regenerated heads at anterior-facing wounds and posterior-facing wounds , identical to beta-catenin-1 ( RNAi ) animals ( Fig . 6A–B ) . qPCR performed on RNA from animals of the same cohort harvested 24 hours after amputation in biological triplicate verified zic-1 transcript reduction by zic-1 dsRNA treatments ( Fig . 6C ) . These experiments indicate the suppressive effect of beta-catenin-1 dsRNA was not simply due to reduction in zic-1 RNAi efficiency . Additionally , we used in situ hybridizations to measure notum expression in animals from this experiment fixed at 72 hours and 14 days of regeneration ( Fig . S9A ) . beta-catenin-1 inhibition reduced anterior pole expression of notum at 72 hours , consistent with notum's described property as a feedback inhibitor of Wnt signaling . Such animals regenerate an anterior head with reduced numbers of notum+ cells at the anterior pole by 14 days . zic-1 inhibition eliminated notum expression at the pole by 72-hours . Simultaneous inhibition of beta-catenin-1 and zic-1 resulted in head regeneration but in the absence of notum expression at 72 hours . These results indicate that experimental inhibition of beta-catenin-1 can promote head regeneration in the absence of zic-1 and notum , likely by fulfilling the normal function of anterior pole-expressed notum as a Wnt inhibitor . In support of the first model , wntP-2/wnt11-5 , a gene whose expression is activated by Wnt signaling in the posterior [13] , is inappropriately expressed in the anterior of zic-1 ( RNAi ) animals by 48/72 hours and even 7 days after decapitation ( Fig . 6D ) , suggesting zic-1 inhibits beta-catenin-1 signaling . notum is required for anterior polarity and head regeneration whereas zic-1 is required only for head regeneration , and this distinction may account for the difference in extent of ectopic anterior wntP-2 expression observed after inhibition of the two genes [14] . We additionally found that zic-1 RNAi had no effect on beta-catenin-1 mRNA levels ( Fig . S9B ) . beta-catenin-1 shares a similar epistatic relationship to zic-1 and notum , and zic-1 is required for notum expression , so we propose that injury-induced zic-1 promotes notum expression at the anterior pole which in turn represses Wnt signaling to allow head formation . We suggest that early , zic-1-independent , wound-induced notum expression is required for anterior polarity , whereas later zic-1-dependent notum expression at the pole orchestrates head patterning and outgrowth . zic-1's expression in neoblasts suggested it might function to control neoblast behaviors relevant for pole formation and/or specification of anterior cell types . Notably , however , tail regeneration and wnt1 expression at the posterior pole , both neoblast-dependent processes , occurred normally in zic-1 ( RNAi ) animals ( Fig . 4A–B ) , indicating zic-1 is not required for generic neoblast function . Additionally , zic-1 ( RNAi ) animals had abundant smedwi-1 expression in both the anterior and posterior ( Fig . S10A ) , indicating this gene is not required for regional maintenance of neoblasts in the anterior . We next tested for possible involvement of zic-1 in neoblast specification programs . We purified total RNA from X1 neoblasts isolated by FACS from animals treated with zic-1 or control dsRNA in a time series and analyzed expression of 15 transcription factors and signaling molecules described to be activated in neoblast subpopulations due to regeneration ( heat map shown in Fig . 7A with fold-changes and p-values shown in Table S2 ) . zic-1 RNAi caused a significant ( t-test p-value<0 . 05 ) reduction greater than 2-fold in expression of ovo ( 0 , 24 and 48 hours ) , zic-1 ( 0 , 24 , 48 hours ) , notum ( 48 hours ) and distalless ( dlx ) ( 24 hours ) . zic-1 RNAi reduced hmx-1 expression although not significantly ( p = 0 . 11 at 24 hours ) , and significantly elevated expression of ap2 by 1 . 7-fold ( 48 hours ) . Several genes did not have significant fold-changes greater than 1 . 7-fold ( p>0 . 05 , t-test ) : hmx-1 , sp6/9 , OtxA , pax6A , soxB , runt-1 , six1-2 , six3-1 , sim , and coe . We confirmed the observations of this expression analysis for selected genes using in situ hybridizations . zic-1 inhibition reduced ovo expression by 48 hours ( Fig . S10B ) and had minimal effects on runt-1 by 24 hours ( Fig . S10C ) . Because runt-1 is also activated in neoblasts early ( by 3 hours ) in response to injury , we examined candidate reciprocal interactions with zic-1 . However , runt-1 RNAi did not alter expression of wound-induced zic-1 ( Fig . S10D ) . Therefore , zic-1 and runt-1 likely do not regulate each other transcriptionally . Finally , ap2 was induced by injury and expressed highly in the anterior of zic-1 RNAi animals ( Fig . S10E ) , indicating that zic-1 RNAi does not simply result in a generic failure of neoblast functions in the anterior . These results indicate zic-1 is needed for expression of notum , ovo and distalless within the neoblast population and highlight the specificity of these functions . To identify neoblast responses regulated by zic-1 and specific for anterior pole formation , we turned our attention to notum and foxD , which are both needed for head formation and expressed in subpopulations of neoblasts during regeneration . notum expression was reduced by 48 hours in the analysis of X1 cells from zic-1 ( RNAi ) animals ( Fig . 7A ) , suggesting that zic-1 may control anterior pole formation by regulating specification of notum in neoblasts . To confirm this prediction , we analyzed zic-1 ( RNAi ) versus control animals at 48 hours of regeneration using double FISH and counting of notum+/smedwi-1+ neoblasts . zic-1 ( RNAi ) animals had reduced numbers of notum+/smedwi-1+ cells , indicating zic-1 is required for specification of notum+smedwi-1+ cells in addition to expression of notum in smedwi-1− cells of the anterior region ( Fig . 7B , Fig . 4E ) . We next examined the relationship between zic-1 and foxD . foxD expression is irradiation sensitive by 24 hours and co-expressed in a neoblast subpopulation believed to participate in pole formation [41] . zic-1 inhibition did not alter the abundance of foxD+/smedwi-1+ neoblasts at anterior-facing wound sites by 24 hours ( Fig . 7C ) . In addition , foxD is expressed in a subpopulation of zic-1+ cells at 24 hours , with 66% of foxD+ cells co-expressing zic-1 ( 14/21 cells , n = 5 worms ) , and 3% of zic-1+ cells co-expressing foxD ( 14/420 cells , n = 5 ) ( Fig . 7D ) . We did not observe a general requirement of foxD for 24-hour zic-1 expression ( Fig . S8B–C ) , but we cannot rule out the possibility that foxD is required for zic-1 expression in this small minority of zic-1+ cells that co-express foxD . Taken together , these results suggest zic-1 is not likely needed for specification of foxD+ neoblasts but rather for their utilization or further specification to form notum+ neoblasts that regenerate the anterior pole . We further noted differences in the abundance of these progenitor cells at anterior- and posterior-facing wounds important for considering their relationships . foxD+ neoblasts were present at both anterior- and posterior-facing wounds in similar numbers ( p-value>0 . 05 , t-test ) ( Fig . 7C ) . By contrast , zic-1 expression in neoblasts was polarized for anterior-facing injury sites at 24 hours ( Fig . 1C–D ) , and notum+ neoblasts were enriched near anterior-facing wounds versus posterior-facing wound sites ( Fig . 7B ) . These results suggest that zic-1 may act as a polarizing cue to promote foxD progenitors to undergo anterior pole cell specification only at anterior-facing injury sites .
Tissue organizers orchestrate growth and patterning in embryonic development , but it is not clear whether or how such signaling centers could contribute to coordinated growth in adulthood . In adult regeneration , stem cells or other progenitor cell types provide a supply of new differentiating cells necessary to replace missing structures , but such cells likely require instructive information prompted by injury in order to construct complex tissues . Here we describe the function of a Zic family transcription factor that couples injury signaling and polarized Wnt signaling cues [10] , [11] , [13] , [14] , [32] , [59] for activation of stem cells to produce an anterior signaling center needed for head regeneration ( Fig . 8 ) . The functional and expression data support a model for head regeneration in which an initial anterior polarity decision , controlled by injury-induced notum , activates zic-1 expression in neoblasts , which enables head regeneration by production of notum+ cells of the anterior pole . Before 24 hours at anterior-facing amputation sites , notum is activated in cells of the body-wide musculature where it inhibits injury-induced wnt1 to create a low Wnt signaling environment , anterior identity , and subsequently zic-1 expression in neoblasts . By contrast , posterior-facing injury sites do not abundantly express notum , allowing injury-induced wnt1 signaling to predominate and enforce high levels of beta-catenin-1 signaling that ultimately suppress zic-1 expression . Anterior- or posterior-facing injury sites have different zic-1 expression responses according to their position along the A-P axis . We suggest that the initial expression of zic-1 at the posterior-facing wound sites of head fragments reflects a low Wnt signaling environment present at the time of injury , but in such fragments zic-1 expression is ultimately suppressed as the region takes on posterior polarity and identity through wnt1 signaling . foxD is expressed in neoblast subpopulations at both anterior- and posterior-facing amputation sites . zic-1 activity at anterior-facing wound sites triggers further specification or utilization of foxD-expressing neoblasts for expression of notum and subsequent anterior pole formation by 48 hours . zic-1 is neoblast-expressed early and co-expresses with foxD in pole progenitors at the anterior pole , so the most likely scenario is that it acts autonomously to control this specification process , but we cannot rule out possible indirect functions for zic-1 in head regeneration or patterning . The regenerating anterior pole produces NOTUM protein , which likely sustains a low Wnt signaling environment , possibly through positive feedback by specification of additional zic-1+ neoblasts that continue to contribute new cells to the anterior pole to promote head regeneration . Injuries can alter tissue content in numerous ways , so regenerating animals must have mechanisms that robustly allow production of appropriate missing structures . It is difficult to envision a complex contingency system that continuously probes each tissue type for its presence or absence to address this need . Alternatively , a relatively simple system that utilizes information about the position , orientation , and extent of a wound site could be sufficient for achieving this goal . zic-1 expression activation after wounding depends both on the injury's A/P axial location ( Fig . 2F ) as well as its A/P orientation ( Fig . 2B ) , suggesting the existence of such systems . It is also possible that zic-1 expression is activated at other sites or tissues requiring maintenance of low Wnt signaling , as we observed expression of zic-1 in some non-anterior regions during regeneration , similar to notum [14] . However , sustained and abundant zic-1 expression is enriched at anterior-facing amputation sites ( Fig . 2B ) . Wound-induced signaling through 6–24 hour expression of wnt1 and notum may have a particular importance in regeneration scenarios that require significant alteration of A/P tissue identity . Examining the relationship between pre-existing regional cues and wound-activated signaling will be important for understanding how regenerative outgrowth is achieved . Planarian transcription factors have been identified that function in either neoblast subpopulations for specification of specific differentiated cell types or function broadly in the process of axis formation . Several lineage-specific transcription factors are expressed within subpopulations of neoblasts for forming cells of the eye , protonephridia , intestine , brain [3] , [51] , [52] , [60]–[62] and additional post-mitotic progeny [55] , [63]–[65] . For example , runt-1 is expressed within neoblasts [9] and is required for eye formation [66] . By contrast , transcriptional regulators have also been implicated in axis formation . Tail regeneration and expression of wnt1 in the tail requires islet1 , a transcription factor expressed in the posterior [67] , as well as pitx , expressed at the anterior and posterior poles [39] , [40] . The TALE homeobox gene prep is needed for head formation [56] , and pbx , another TALE transcription factor , is needed for both head and tail formation [37] , [38] . Our results identify zic-1 as an injury-induced neoblast-expressed gene with possible related functions in both cell specification and axis formation . The participation of vertebrate Zic proteins in multiple developmental processes and the existence of several Zic family members have complicated efforts to identify their common use in only a single signaling pathway , and interactions with Hedgehog , Wnt , Activin , and retinoic acid signaling have been reported [50] , [68] . Early wound-induced wnt1 expression that occurs by 18 hours after injury is dependent on hedgehog signaling , and still occurs in irradiated animals [13] , [32] that lack zic-1 expression ( Fig . 2E ) . Therefore , neoblast-expressed zic-1 is unlikely to be a general regulator of Hedgehog signaling . Our results indicate repression of Wnt signaling , possibly through expression of signaling inhibitors , could be an ancient and conserved function for Zic proteins . Additionally , Activin signaling repression by follistatin is needed for both head and tail regeneration in planarians [25] , suggesting that zic-1 is not a general regulator of that pathway in planarians . Vertebrate Zic2 and Zic3 regulate organizer function by inhibition of Wnt signaling [42] , [69] . Additionally , mutations in human zic2 lead to holoprosencephaly [70] , a midline defect of the anterior due to perturbed organizer function [43] . Our studies of planarian zic-1 suggest Zic proteins may have had ancient functions and conserved functions in Wnt signaling inhibition involved in injury responses or control of tissue organizing regions . In principle , the positional information necessary for regeneration could either be dependent or independent of stem cell function . Planarians depleted of neoblasts maintain the ability to undergo some generic injury-induced signaling and also signaling polarized along the body axes [9] , [13] , [32] . By contrast , our analysis of zic-1 suggests that a critical step in blastema formation is the production of new differentiated cells to perform signaling functions that subsequently direct the activity of additional cells for tissue outgrowth . Programs that use stem cells or specialized progenitors to produce signaling centers after injury may be utilized in general for normal regenerative growth . The use of stem cells to construct tissue organizers post-embryonically could facilitate the synthesis or repair of non-regenerative organs .
Animals of the asexual strain of the planarian Schmidtea mediterranea were maintained in planarian water ( 1× Montjuic salts ) at ∼19°C as previously described [14] . Planarians were fed a liver paste and starved for at least seven days before experiments . Where indicated , animals were gamma-irradiated with a lethal dose of 6 , 000 Rads using a Cesium source irradiator 48 hours before surgeries ( Fig . 1A , S1 , S2A and S5B ) or the day of amputation ( Fig . 2E ) . For gene expression profiling by microarray ( Fig . 1C ) and realtime PCR ( Fig . 1D ) , neoblasts were isolated from tissue near wound site by FACS in a time series ( 0 , 24 , 48 and 72 hours post amputation ) . For each biological replicate , tissue from anterior- or posterior-facing injury sites from 8 animals was collected , macerated , labeled with Hoechst 33342 and propidium iodide and sorted as previously described [64] . For each biological replicate , 40 , 000–100 , 000 cells were collected into Trizol-LS , and RNA was extracted using RNeasy Plus Mini Kit ( Qiagen , 74134 ) . Libraries were prepared using a WTA2 amplification kit ( Sigma ) , and arrays were hybridized and scanned according to the manufacturer's instructions at the Washington University Genome Technology Access Center . Custom oligonucleotide arrays from Agilent had 140 , 000 probes intended to represent the majority of S . mediterranea genes identified through previous EST sequencing , RNA-seq and transcriptome assembly , and gene predictions [53] , [64] , [66] , [71] , [72] . The limma Bioconductor software package ( Linear Models for Microarray Data ) implemented in R was used for analysis of single-channel microarray data [73] . Briefly , array intensities were background subtracted ( method = “normexp” , offset = 16 ) , quantile-normalized between arrays , and differential gene expression was evaluated using eBayes which produces for each gene a t-statistic of the ratio of the log2-fold change to the standard error moderated across all genes in the experimental series to obtain a p-value that was used to compute a false-discovery rate by correcting for multiple hypothesis testing using the Benjamini-Hochberg method . Genes with at least one time point having a with a false-discovery rate of <0 . 10 as compared to the 0 hour expression for either anterior- or posterior-facing wound sites , as appropriate , were selected for further investigation . GeneCluster3 . 0 and Java TreeView were used for hierarchical clustering and visualization . Microarray data have been deposited at NCBI with accession number GSE56178 . For analysis of progenitor genes in zic-1 RNAi worms ( Fig . 7A ) , neoblasts were isolated in similar way from animal fragments generated by head and tail removal ( “trunks” ) by FACS in a time series using a BD FacsAria SORP 5-Laser . RNA was extracted using RNeasy Mini Kit ( Qiagen , 74104 ) and gene expression analyzed by RT-qPCR . Array oligonucleotide sequences matching clone BPKG17485 [74] were blasted to a planarian transcriptome database ( PlanMine , http://planmine . mpi-cbg . de ) identifying exact matches for dd_Smed_v4_22585_0_1 and uc_Smed_v1_Contig48469 . The predicted ORF encoded a protein of 529 amino acids with 87% identity to Dugesia japonica zic-A and identical to Schmidtea mediterranea zicA as named previously in a phylogenetic study [45] , and we renamed this gene zic-1 . zic-2 was identified through a transcriptome search of Zic-family orthologs as dd_Smed_v4_15531_0_1 ( PlanMine ) . The predicted ORF encoded a protein of 478 amino acids with 86% identity to Dugesia japonica zicB and identical to Schmidtea mediterranea zicB in a previous phylogenetic study [45] . Riboprobes for zic-1 were made from a PCR product cloned by RT-PCR into pGEM vector using the primers 5′-CACTGCATGTATCAACACCAAG-3′ and 5′-AAGCAATTCTCCCACCGTTA-3′ . Double-stranded RNA ( dsRNA ) was generated by in vitro transcription or expression from a bacterial dsRNA expression vector ( pPR244 ) as previously described [75] . Unless otherwise noted , all zic-1 RNAi experiments used dsRNA derived from a 1611-bp zic-1 fragment cloned using the primers 5′-CACTGCATGTATCAACACCAAG-3′ and 5′-AAGCAATTCTCCCACCGTTA-3′ . Additionally , non-overlapping zic-1 fragments were cloned to create a ∼700 bp 5′ fragment ( using primers 5′-AAGCAATTCTCCCACCGTTA-3′ and 5′-AACCATTTCAATGCCCTTTC-3′ ) and a ∼900 bp 3′ fragment ( using primers 5′-CGCGGACAATCTTTCCAATA-3′ and 5′-CACTGCATGTATCAACACCAAG-3′ ) . zic-2 was cloned using the primers 5′-TCACGGAATCTGAATGTGGA-3′ and 5′-TGAAACCGAGAGGTTTTCGT-3′ . Other riboprobes and/or dsRNAs ( smedwi-1 , notum , beta-catenin-1 , APC , wnt1 , wntP-2 , gpas , chat , sFRP-1 , fzd-4 , prep , pbx , pitx , follistatin , foxD , ovo , ap2 , runt-1 , slit , collagen , prog-1 , ap2 ) were as previously described [9] , [14] , [37]–[39] , [41] , [51] , [56] . Animals were fixed and stained as previously described [76] . In brief , animals were killed in 5% N-acetyl-cysteine in 1×PBS for 5 minutes and then fixed in formaldehyde for 17 minutes at room temperature . Subsequently , animals were bleached overnight ( ∼16 hours ) in 6% hydrogen peroxide in methanol on a light box . Riboprobes were synthesized as previously described as digoxigenin- or fluorescein-labeled [76] . Colorimetric ( NBT/BCIP ) or fluorescence in situ hybridizations were performed as previously described [76] . For FISH , blocking solution was modified to MABT with 10% horse serum and 10% western blot blocking reagent ( Roche ) [77] . Anti-Dig-HRP and anti-FL-HRP antibodies were used at a 1∶2000 dilution , and anti-Dig-AP was used at a 1∶4000 dilution . Hoechst 33342 ( Invitrogen ) was used 1∶500 as counterstain . Images of colorimetric staining were acquired using a Leica M210F scope with a Leica DFC295 camera and adjusted for brightness and contrast . Fluorescence imaging was performed on either a Leica DM5500B compound microscope with Optigrid structured illumination system for optical sectioning or a Leica laser scanning SP5 confocal microscope at 40× or 63× . Images are maximum projections of a z-series with adjustments to brightness and contrast using ImageJ and Photoshop . In FISH staining , cells expressing one or two genes of interest were manually counted from z-stacks using a ∼300 micron region near injury sites , each cell was labeled with color-coded marks and double checked by comparing with neighbor planes using ImageJ and Metamorph . For enumeration of WISH staining , NBT-BCIP precipitated material with the size and shape of cells were counted as the sum of ventral and dorsal views using a ∼300 micron region near injury sites using a dissecting microscope at 12× and additional magnification with a 1 . 7× turret dorsal and a manual counter . As a measure of the reproducibility of this assay , the signal to noise ratio ( average/standard deviation ) from 5 technical replicates scored on 3 specimens each was determined to be 9 . 8+/−2 . 3% . Uninjured worms were injected a solution of 5 mg/ml BrdU ( Sigma-Aldrich/Fluka 16880 ) dissolved in water , two days prior to amputation of heads . Animals were fixed using NAC/FA solutions and rehydrated in a methanol series before FISH staining . Post-fixation in 4% formaldehyde and acid hydrolyzation in 2N HCl were performed after FISH detection , followed by antibody labeling of incorporated BrdU . Animals were blocked in PBSTB ( PBSTx+0 . 25% BSA ) for 4–6 hours at room temperature and incubated overnight in rat anti-BrdU ( Oxford Biotechnology OBT0030S ) antibody diluted 1∶1000 in PBSTB . Animals were rinsed in PBSTB and incubated overnight in goat anti-rat-HRP ( AbCam ab7097 ) antibody diluted 1∶1000 in PBSTB blocking solution . Tyramide development was performed at room temperature for 1 hour in 1∶150 red tyramide ( Alexa Fluor 568 T20914 ) in amplification buffer . BrdU imaging was performed on a Leica SP5 Confocal microscope . Polyclonal rabbit anti-SMEDWI-1 antibody ( a kind gift of P . Reddien ) was diluted 1∶2000 in PBSTB and detected by tyramide amplification as above [7] . RNAi treatment was performed either by injection or by feeding . For RNAi by injection , dsRNA was synthesized from in vitro transcription reactions ( Promega ) using PCR templates with flanking T7 sequences ( Denville ) , purified by phenol extraction and ethanol precipitation , resuspended in RNase-free water and annealed at 65°C for 10 minutes , 37°C for 20–30 minutes , then on ice for at least 10 minutes . dsRNA corresponding to C . elegans unc-22 , a gene not present in the planarian genome , served as a negative control . RNAi by injection was performed similarly to that described previously [37] and was performed as follows . Unless otherwise noted , animal fragments generated by head and tail removal ( “trunks” ) were injected 2–3 times with 30 nL of 3 µg/µl dsRNA and injected again the following two days , the animals were amputated anteriorly and posteriorly to remove ∼300 microns of tissue near the prior injury site , allowed to regenerate 6–8 days prior to another dsRNA injection and amputation of heads and tails for subsequent assaying of regeneration phenotypes and histological analysis . For double-RNAi experiments , a similar injection strategy was used that involved three consecutive days of dsRNA injection , amputation and regeneration for 8 days , another three consecutive days of injections , then amputation of heads and tails and scoring of regeneration phenotypes 8 days later . dsRNA concentrations were normalized using control dsRNA so that every animal received an equivalent dose of zic-1 and/or beta-catenin-1or zic-2 dsRNA . For inhibition of beta-catenin-1 , animals were given control dsRNA in the first set of dsRNA injections and beta-catenin-1 dsRNA during the second set of injections . To generate zic-1 ( RNAi ) ;beta-catenin-1 ( RNAi ) animals , worms were given zic-1 dsRNA during the first set of injections and a 1∶1 mixture of beta-catenin-1 and zic-1 dsRNA during the second set of injections . For inhibition of zic-2 , animals were given a 1∶1 mixture of zic-2 and control dsRNA in both sets of dsRNA injections . To generate zic-1 ( RNAi ) ;zic-2 ( RNAi ) animals , worms were given a 1∶1 mixture of both zic-1 and zic-2 dsRNA . For experiments involving RNAi by bacterial feeding , animals were fed a mixture of liver paste and E . coli expressing dsRNA , as described previously [14] , two times a week ( every 3–4 days ) for either 10 weeks without injury ( Fig . S6 ) , or 2 weeks ( Fig . 5 and S7 ) prior to amputation of heads and tails . mRNA of zic-1 was detected by realtime PCR using SYBR Green PCR Master Mix ( Applied Biosystems ) . Total RNA was extracted by mechanical homogenization in Trizol ( Invitrogen ) from three regenerating fragments in three ( Fig . 6C , S5E , and S9B ) or four ( Fig . 5C , S5E , and S8 ) biological replicates . RNA samples were DNase-treated ( TURBO DNase , Ambion ) and cDNA was synthesized using SuperScript III reverse transcriptase ( Invitrogen ) . zic-1 mRNA was detected using the following primers: 5′-TGGAAATAGAAATCTTGGTGGATT-3′ and 5′-AATCGGTTGTAATAGATTCGATGG-3′ , zic-2 mRNA was detected using 5′-CCTATGGTTGGATAAACACATGAA-3′ and 5′-CGACATGATCAAGTGTTAAGTGGT-3′ , and gapdh mRNA was detected using previously described primers [14] . Primer sequences used in Fig . 1D and Fig . 7A are described in Table S3 . Relative mRNA abundance was calculated using the delta-Ct method after verification of primer amplification efficiency . p-values below 0 . 05 were considered as significant differences .
|
Some animals are capable of regenerating organs damaged or removed by injury , and this ability likely requires precise control of secreted proteins that promote growth . Planarians are flatworms that can regenerate any missing tissues by regulating the activity of adult stem cells that can produce any specialized cell type . We identify the zic-1 gene as activated in planarian stem cells by injury and needed for head regeneration after decapitation . This gene's product likely acts as a transcription factor to produce cells that secrete a growth-promoting protein , NOTUM , at the tip of the regenerating tissue outgrowth to organize and enable head regeneration . These results suggest that regeneration requires specialized uses of stem cell descendants to orchestrate new tissue production following injury .
|
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2014
|
zic-1 Expression in Planarian Neoblasts after Injury Controls Anterior Pole Regeneration
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Adenoviral replication depends on viral as well as cellular proteins . However , little is known about cellular proteins promoting adenoviral replication . In our screens to identify such proteins , we discovered a cellular component of the ubiquitin proteasome pathway interacting with the central regulator of adenoviral replication . Our binding assays mapped a specific interaction between the N-terminal domains of both viral E1B-55K and USP7 , a deubiquitinating enzyme . RNA interference-mediated downregulation of USP7 severely reduced E1B-55K protein levels , but more importantly negatively affected adenoviral replication . We also succeeded in resynthesizing an inhibitor of USP7 , which like the knockdown background reduced adenoviral replication . Further assays revealed that not only adenoviral growth , but also adenoviral oncogene-driven cellular transformation relies on the functions of USP7 . Our data provide insights into an intricate mechanistic pathway usurped by an adenovirus to promote its replication and oncogenic functions , and at the same time open up possibilities for new antiviral strategies .
Human adenoviruses constitute a group of more than 60 adenovirus types . In general , adenoviruses cause self-limiting infections of the eye , or gastrointestinal and respiratory tract , which can lead to epidemic keratoconjunctivitis , diarreah , and severe acute respiratory diseases [1]–[9] . However , with increasing prevalence of transplantations with concomittant downregulation of the immune system ( such as in bone marrow transplations ) , the frequency of disseminated adenoviral infections is also rising in immuno-compromised patients , resulting in high mortality rates [10] , [11] . Unfortunately , no specified antiviral treatments or wide-spread vaccination strategies are currently available to counteract adenoviral outbreaks in an efficient manner [12] , [13] . For successful infection , adenoviruses , like other viruses , must circumvent certain antiviral defense mechanisms . In this regard , the ubiquitin proteasome system ( UPS ) adopts a central position in aiding viral infections . For example , HSV-1 , HPV-16/18 and EBV have been shown to use strategies which involve targeting cellular proteins with antiviral functions , such as p53 , for proteasomal degradation using viral encoded or components of cellular E3 ubiquitin ligases [14]–[17] . Adenoviruses use two viral regulatory proteins , E4orf6 and E1B-55K , to exploit cellular factors to form an SCF-like E3 ubiquitin ligase complex promoting p53 , Mre11 , Bloom helicase ( BLM ) , DNA ligase IV , integrin alpha 3 and Tip60 polyubiquitination followed by subsequent proteasomal degradation [18]–[23] . In contrast to all the functions involving adding ubiquitin moieties to target substrates , viral exploitation of the reverse mechanism in host cells has become increasingly important over the past few years . Deubiquitination is mediated by deubiquitinating enzymes ( DUBs ) , and the replication of several viruses has been shown to either benefit from , or be inhibited by certain DUBs . Liao and colleagues demonstrated that Usp11 specifically inhibits influenza virus infection [24] , whereas Perry and coworkers have shown that Usp14 is necessary for efficient viral replication of a panel of viruses , including norovirus , encephalomyocarditis virus , Sindbis virus , and La Crosse virus [25] . Among those DUBs , USP7 ( herpesviral associated ubiquitin-specific protease [HAUSP] ) was the first to be associated with viral infection , through interacting with herpesviral ICP0 [26] . Since then , more herpesviral regulatory proteins have been found to use the functions of USP7 for their own benefit . For example , EBV EBNA1 utilizes USP7's properties to stimulate its DNA-binding activity , to initiate disruption of PML proteins , to reduce p53 steady-state levels or to enhance the deubiquitination of histone H2B resulting in EBV oriP transcriptional activation . Furthermore , KSHV LANA probably interacts with USP7 in order to regulate latent viral genome replication [27]–[30] . Since cellular DUBs obviously represent an important family of proteins used by viral proteins , studies are underway to develop specific inhibitors of these enzymes . Like herpesviruses , adenoviruses also encode several proteins that bind to and manipulate key cell growth regulatory proteins to promote viral replication . The adenoviral protein E1B-55K is a multifunctional phospho-protein performing central roles during productive infection , including viral mRNA transport and degradation of cellular components ( e . g . p53 and Mre11 ) , using the ubiquitin proteasome system ( UPS ) [21] , [22] . Moreover , E1B-55K is able to induce cellular transformation of primary cells in cooperation with the adenoviral protein E1A [31] , [32] . Although adenoviruses are known to be closely involved in manipulating proteins of the UPS especially through E1B-55K , to date the activity of cellular DUBs during adenoviral infection remains enigmatic and has not been studied so far . Here , we demonstrate that the adenoviral protein E1B-55K interacts with the cellular DUB USP7 . We found that USP7 is relocalized in a time-dependent manner during adenoviral infection even though independent of E1B-55K . To our interest , USP7 knockout/knockdown and inhibitor assays demonstrate that expression and/or stability of E1B-55K is strongly dependent on the presence and functions of USP7 . In addition , it became evident that USP7 promotes viral growth by regulating expression and/or stability of additional adenoviral proteins . We also illustrate that adenovirus oncogene-induced transformation relies on the presence and function of USP7 . Therefore , we demonstrate for the first time that general HAdV5 functions strongly depend on the availability and functions of the cellular protein USP7 .
E1B-55K plays key regulatory roles during adenovirus infection . This is mainly achieved through interactions with several binding partners directly or indirectly involved in p53 regulation or DNA damage response , a common strategy employed by almost all known viruses to promote viral replication and hinder antiviral defense mechanisms [33] . To discover more about the functions of E1B-55K , we profiled cellular interaction partners of E1B-55K using a yeast two-hybrid system . With the N-terminal region of HAdV5 E1B-55K protein as a bait , we identified several positive clones encoding USP7 ( two positive “hit”-sequences are displayed , Figure S1 ) . Next , we wanted to verify the results from the yeast two-hybrid screen in vitro . Therefore , as summarized in Figure 1A , we generated GST fusion proteins including full-length , and a series of truncated or alternative splicing variants of E1B-55K protein and evaluated their interaction with cellular USP7 by GST pull-down experiments . GST purification of intact full-length E1B-55K protein was inefficient since it proved unstable when expressed in bacteria ( Figure 1B , lower panel , lane 3 ) . However , the combination of full-length protein and its bacterial degradation products could precipitate USP7 ( Figure 1B , upper panel , lane 3 ) . Besides the full-length protein , all the fusion products containing the first 79 residues of E1B-55K precipitated USP7 regardless of their C-terminal extensions ( Figure 1B , upper panel , lanes 4 , 5 , 7 and 8 ) . Taken together , these data demonstrate that the N-terminal 79 amino acid region of E1B-55K is necessary and sufficient for binding to USP7 in vitro . Previous investigations had revealed that the USP7 protein can be roughly divided into four domains [34]–[36]: the N-terminal TRAF-like domain ( TD; residues 53–208 ) , central catalytic domain ( CD; residues 208–560 ) , and two C-terminal structural domains ( C1 and C2; residues 600–870 and 885–1061 , respectively ) ( Figure 1C ) . To identify which of these domains interacts with E1B-55K , we generated GST fusions corresponding to these regions and carried out GST pull-down experiments in wt ( H5pg4100 ) -infected H1299 cell extracts ( Figure 1D ) . Neither the central CD , nor the C-terminal domains C1 and C2 interacted with E1B-55K; however , the N-terminal segment of USP7 ( residues 1–215 ) was found to strongly and specifically precipitate E1B-55K ( Figure 1D , lane 3 ) . To further investigate the USP7 interaction , endogenous USP7 was immunoprecipitated from p53-negative H1299 cells transfected with a plasmid encoding wt pE1B-55K and stained for coprecipitated E1B-55K ( Figure 1E , lanes 1 and 2 ) . Additionally , H1299 ( p53-negative ) and A549 ( p53-positive ) cells were mock-infected and infected with E1B minus ( H5pm4149 ) or wt ( H5pg4100 ) virus . Subsequent USP7 immunoprecipitation experiments confirmed USP7-E1B-55K interactions in both cell lines ( Figure 1B , lanes 5 and 8 ) whereas control immunoprecipitation experiments with an unspecific IgG2a antibody proved to be negative for E1B-55K as well as USP7 precipitation ( Figure 1B ) . To verify this interaction in living cells , flow cytometry-based FRET ( Foerster's Resonance Energy Transfer ) analyses were employed as described by Banning and colleagues [37] , with USP7-CFP serving as a donor chromophore and YFP-E1B-55K as an acceptor chromophore ( scheme , Figure 1F ) . When both proteins interact , excitation of the CFP chromophore results in secondary excitation of the YFP chromophore leading to FRET signal emission displayed in the corresponding FRET gate ( Figure 1G , panel b or d ) . False-positive signals are excluded by using different controls , as indicated , together with an appropriate gating strategy ( Figure 1G , panel a and c ) . This assay revealed ca . 17% FRET-positive cells ( FRET+; Figure 1G , graph + panel d ) , indicating that USP7 and E1B-55K also interact in living cells . Although FRET+ cell levels were relatively low compared to the positive control ( fusion of CFP and YFP resulting in “constant” FRET emission; CFP-YFP ) , possibly explained by CFP/YFP-tag interference and/or competition between endogenous and exogenous USP7 , nevertheless , FRET+ cells scored significantly more than the negative controls ( CFP cotransfected with YFP; compare Figure 1G , panels a and d ) . Taken together our results establish E1B-55K as a new specific interaction partner of USP7 which could also be confirmed in living cells . To determine if adenoviral infection affects USP7 subcellular localization in a time-dependent manner , we performed extensive time course immunofluorescence studies . A549 cells were mock-infected or infected at an MOI of 20 FFU per cell with wt HAdV5 virus ( H5pg4100 ) and then methanol-fixed at indicated hours post infection ( h p . i . ; Figure 2A to D ) . USP7 and E1B-55K ( E1B ) were detected with specific monoclonal antibodies , and visualized using double-label immunofluororescence microscopy . In uninfected cells ( mock ) , USP7 localized diffusely in the nucleus with a few prominent dot-like structures ( Figure 2A ) . However , upon wt adenoviral infection ( H5pg4100 ) , USP7 localization changed dramatically . Several different relocalization patterns of USP7 were observed , which were categorized for each investigated time point with respect to E1B-positive cells ( percentages of each category are denoted in Figure 2B to D ) . In general , USP7 increasingly accumulated into dense , ring-like structures over time during adenovirus infection . Interestingly , as infection proceeds USP7 colocalization with E1B-55K increasingly correlates with these ring-like structures ( merge ) . This is especially displayed in cells with USP7 relocalization of categories 5 and 6 . However , the initial step of USP7 redistribution is probably independent of E1B-55K , since USP7 relocalization could be observed before detection of E1B-55K , and in the absence of colocalization ( Figure 2C , panels E–H and I–L ) and during infection with a virus lacking all E1B functions ( Figure S2 , panels D–F ) . Nevertheless , USP7 redistribution in the nucleus forming ring-like structures as seen in categories 5 , 6 and 7 was observed in the majority of patterns analyzed up to 24 hours post infection ( Figure 2C , panels M–T; 2D panels M–X ) . Strikingly , the relocalization pattern of USP7 during wt adenoviral infection ( H5pg4100 ) strongly resembled staining patterns of the adenoviral E2A protein ( also called DBP , Figure 2E and F ) [38]–[41] . E2A is a single-stranded DNA ( ssDNA ) binding protein involved in adenoviral genome replication and can be found not only colocalized with sites of viral ssDNA , but also surrounding sphere-shaped sites of double-stranded DNA , and is thus a marker for sites of both transcription and replication [42] , [43] . To test whether USP7 is relocalized to sites of viral DNA replication and transcription , in situ costainings of E2A and USP7 were prepared to detect significant colocalization of both proteins in a time-dependent manner ( from 8–48 h p . i . data not shown ) . In a similar approach as above , A549 cells were infected at an MOI of 20 FFU/cell and methanol-fixed at different time points . Upon analyzing the staining patterns of USP7 in E2A-positive cells , it was evident that nearly all the cells displayed USP7 staining patterns strongly correlating with the E2A-stained structures which is exemplified in Figure 2E or 2F ( 16 h p . i . or 24 h p . i . ) . Altogether , these observations demonstrate that time-dependent USP7 relocalization is strictly related to the formation of viral replication centers , where interaction with E1B-55K probably occurs . Moreover , this points to a functional exploitation of the cellular DUB like it has been shown for a number of other cellular proteins relocalized to adenoviral replication centers ( e . g . BLM , RPA32 , Mre11 , ATR , ATRIP , E1B-AP5 [44]–[46] ) . To examine the role of USP7 in adenoviral infection , we analyzed the effects of reducing USP7 steady-state levels and inhibiting USP7 on E1B-55K protein levels and adenoviral replication . We generated USP7 knockdown and corresponding control cell lines ( using H1299 and A549 as parental cell lines ) and synthesized a small-molecule compound [47] ( here called “HBX”; Figure 3A ) which was previously shown to inhibit USP7 [48] . Moreover , we utilized a second USP7 inhibitor ( HBX41108 ) , which is a derivative of HBX , to support specificity of our assays [47] , [48] . To assess optimal conditions for the inhibitor assays , growth behavior and viability of the cells were tested under mock infection conditions plus inhibitor treatment . In a first attempt to characterize HBX , MTS-based proliferation assays were carried out on cell lines used in our experiments . As shown in Figure S3 , sigmoidal dose response curves were generated for three inhibitor treatment durations ( 24 , 48 and 72 h ) with several dilution rows performed at least in triplicate . The summarized GI50 values are represented in Figure 3B and reveal that HBX administration in the micromolar range results in growth inhibition . Previous reports demonstrated that loss of USP7 through knockout leads to decreased proliferation of the respective cell line [49] . Therefore , since USP7 plays a critical role in cell proliferation , it was necessary to determine inhibitor treatment conditions that did not significantly inhibit cell growth . Otherwise it would be difficult to distinguish between cell growth defects or specific compound-mediated effects negatively influencing virus yield . First , time-of-addition experiments after mock infection were performed to determine tolerable inhibitor concentrations leading to insignificant cell growth inhibition . In effect , it turned out that 15 hours of inhibitor treatment prior to cell harvest worked best for all investigated cell lines . For example , A549 cells exhibited no statistically significant decrease in cell number compared to untreated cells 24 hours post mock infection ( h p . m . i . ) at both HBX concentrations ( Figure 3C ) . However , a significant reduction in cell numbers was observed 48 h p . m . i . after HBX application at both concentrations ( ∼25% reduction ) . Nevertheless , trypan blue exclusion to determine the number of viable cells displayed no significant cytotoxic effect on A549 cells either 24 or 48 h p . m . i . , meaning that cell cytotoxic effects could be excluded in subsequent experiments ( Figure 3D ) . Similarly , H1299 cells experienced no cell growth defect after HBX treatment 24 h p . m . i . , but underwent ca . 25% reduction 48 h p . m . i . ( Figure 3E ) . However , again the number of dead cells did not increase after HBX incubation compared to untreated cells ( Figure 3F ) . Previous reports have shown that inhibitors of USP7 such as HBX41108 ( Figure 4A ) induce , among others , p53 protein accumulation and a decrease in Mdm2 protein levels [48] , [50]–[52] . This is because , upon USP7 inhibition , Mdm2 deubiquitination/stabilization is heavily decreased and auto-ubiquitination of Mdm2 increased with subsequent lower p53 turnover and p53 accumulation [35] . In order to assess whether HBX exerts similar effects on Mdm2 and p53 , the two cell lines mainly used in this study were treated with HBX and HBX41108 for 24 or 15 hours ( assay conditions ) . As expected , both compounds induce an increase in the steady-state protein levels of p53 in A549 cells ( Figure 4B , lanes 2 and 3; Figure 4D , lanes 3 , 4 , 7 and 8 ) . However , a decrease of Mdm2 protein levels could not be detected in this cell line either owing to deregulation of the USP7-p53-Mdm2 pathway or low/too short inhibitor treatment duration . Nevertheless , a decrease in Mdm2 protein levels could be detected in H1299 cells ( Figure 4C , lanes 2 and 3; Figure 5C , lanes 3 and 4 ) . USP7 knockdown ( kd ) cell lines were generated as an additional tool for this study . The A549-derived USP7 knockdown cell line APU6 ( Figure 5A , lane 3 ) displayed slower growth rates than the parental cell line ( data not shown ) . However , another cell line also transfected with the shRNA plasmid construct against USP7 and derived from A549 cells , APU5 , with slow growth comparable to APU6 cells presents normal USP7 levels ( Figure 5A , lane 1 ) . To exclude the possible influence of slower growth , this APU5 cell line was used as a control ( Figure 4E ) . Similarly , the USP7 knockdown cell line HU5 derived from H1299 cells ( Figure 5A , lane 6 ) possessed almost identical growth rates compared to the control cell line HC2 ( Figure 4F ) and the H1299 parental cell line ( data not shown ) . In summary , it was possible to establish two USP7 knockdown cell lines with corresponding control cells , and find suitable conditions for HBX treatment in infection experiments where cell growth and viability were not significantly affected . Nonetheless , cell growth defects after USP7 inhibition were observed at later stages of mock infection , which corresponds to previously described effects of USP7 on cell proliferation . Therefore , these effects were expected and taken into consideration in subsequent experiments ( by normalizing the virus yield per cell and subsequently normalizing yield without inhibitor treatment ) . Additionally , as expected , both compounds HBX and HBX41108 were able to induce an increase in p53 protein levels but also a decrease in Mdm2 protein levels indicating specific inhibitory effects exerted upon USP7 . When we transfected E1B-55K expression constructs along with an expression construct for EYFP ( YFP ) into the USP7 knockdown and corresponding control cell lines , we detected severely reduced E1B-55K steady-state levels in the USP7 knockdown background ( HU5 ) without affecting expression levels of the control plasmid encoding YFP ( Figure 5A , lane 6 ) . However , a slight reduction of YFP was detected in APU6 cells ( Figure 5A , lane 3 ) . Therefore same samples were reanalyzed by Western blot ( Figure 5A , lower panel ) with double amounts of APU6 ( lane 3 ) in comparison to APU5 lysates ( lanes 1 and 2 ) . Here , in APU6 USP7 knockdown cells ( lane 3 ) E1B-55K protein levels still displayed strong reduction in comparison to APU5 cells with normal USP7 protein levels ( lanes 1 and 2 ) . To support the knockdown data , we treated A549 and H1299 parental cell lines with the USP7 inhibitors HBX ( Figure 5B; Figure 5C; Figure 5D ) or HBX41108 ( Figure 5C , lane 4 ) . Neither DMSO nor HBX/HBX41108 affected steady-state protein levels of USP7 , but in HBX/HBX41108-treated cells protein levels of transfected E1B-55K were severely reduced , comparable to the knockdown experiments ( Figure 5B , lanes 6 and 9 ) . Moreover , coimmunoprecipitation of E1B-55K by USP7 was reduced after treatment with HBX ( Figure 5B , lane 9 ) and HBX did not display reduction of cotransfected GFP ( Figure 5D ) . Additionally , transfecting an E1B-55K expression construct into HCT116 USP7 knockout cells ( USP7 KO ) also resulted in strong reduction of E1B-55K protein levels ( Figure 5E , upper panel ) . An identical result was obtained when HCT116 lysates ( Figure 5E , lower panel , lanes 1 and 2 ) were compared to double amounts of USP7 KO lysates ( Figure 5E , lower panel , lane 3 ) by immunoblotting . The same effect was obtained after infection of the respective cell lines ( Figure 5F ) . Corresponding p53 staining showed significantly increased p53-levels despite infection with wt ( H5pg4100 ) virus at an MOI of 50 FFU per cell indicating insufficient adenoviral E3 ligase activity due to low E1B-55K protein levels ( Figure 5F ) . The stabilizing effect of USP7 upon E1B-55K was further supported by cycloheximide chase assays demonstrating a reduced half-life of E1B-55K in the USP7 knockdown background ( Figure S4A ) and after HBX or HBX41108 treatment ( Figure S4B and C ) . Taken together , USP7 knockdown/knockout or inhibition led to greatly reduced E1B-55K protein levels , indicating a stabilizing role of USP7 for E1B-55K . Herpesviruses , like HSV-1 and KSHV rely on the functions of USP7 to efficiently promote virus growth or genome replication [29] , [53] . In contrast , the role of cellular DUBs in adenovirus replication has not been investigated . In a first step to evaluate the influence of USP7 on adenovirus infection , the generated USP7 kd cells APU6 and HU5 ( 79 . 5% and 86 . 6% knockdown efficiency respectively; Figure 6A and S5A ) were infected with wt virus ( H5pg4100 ) , and the synthesis of early and late viral proteins , as well as the production of progeny virions were compared to those of the control cell lines at different time points ( Figure 6 and S5 ) . First , the effect of USP7 depletion on the synthesis of early viral proteins E1A , E1B-55K and E2A was assayed by immunoblotting . Surprisingly , being the first gene products expressed , E1A proteins showed a defect in accumulating protein levels in APU6 and HU5 cells compared to the USP7+ counterparts . Similarly , E2A levels were also detected to be slightly lower in these cells than in the control USP7+ APU5 and HC2 cells . Consequently , when the expression pattern of E1B-55K was investigated , a significant defect was observed not only in the expression time , but also in the amounts of this protein ( Figure 6A and S5A ) . The expression of late structural proteins was also investigated in the knockdown cells . As expected , the observed inefficient synthesis of the early viral proteins resulted in delayed accumulation of late structural proteins in both APU6 and HU5 cells compared to the USP7+ counterparts during wt infection ( H5pg4100 ) . Late structural protein synthesis was either delayed in USP7 kd lines ( e . g . pIII in HU5 , or minor capsid proteins in APU6 ) , or these proteins did not accumulate to the parental cell line levels ( e . g . pVI in HU5 or pII in both knockdown lines ) ( Figure 6A and S5A ) . Moreover , in order to investigate whether USP7 inhibition leads to effects similar to USP7 knockdown , infected H1299 and A549 cells were subsequently treated with inhibitor HBX . USP7 protein steady-state levels were not affected after inhibitor treatment , either in infected or mock-infected cells in both cell lines ( Figure 6B and D; Figure S5C and D ) . Similar to the knockdown experiments , a reduction of E1B-55K and structural capsid proteins could be detected after HBX treatment in both cell lines ( Figure 6B and S5C , each lane 3 ) . Less E1B-55K was further confirmed by quantifying E1B-positive cells after HBX treatment of infected cells using immunofluorescence microscopy ( Figure S6 ) . However , decreased E1B-55K could only be observed 24 h p . i . , but not 48 h p . i . , consistent with the immunofluorescence quantification data ( compare Figure 6B and S5C with Figure S6B and D ) . This may suggest that functional inhibition of USP7 cannot overcome the likely high transcription-translation activity at this stage of infection ( at least for the early protein E1B-55K ) . Interestingly , E1A levels seemed to increase whereas E2A levels only show a slight decrease in A549 cells , and L4-100K protein levels displayed a modest decrease after HBX incubation ( Figure 6B and S5C , each lane 3 ) . It is probable that differences between both approaches ( knockdown vs . inhibition ) may reflect variable efficiencies of functional inhibition . However , overall , knockdown or inhibition of USP7 led to reduced steady-state protein levels of various adenoviral proteins . Virus yield experiments performed in both USP7 kd and their respective control cell lines , demonstrated 76 . 3% or 72 . 5% reduced viral progeny numbers 24 h p . i . in APU6 or HU5 ( Figure 6C and S5B ) . At 48 h p . i . the virus yield was still reduced by 40 . 4% ( APU6 ) or 26% ( HU5 ) , implying that USP7 is biologically significant for efficient adenovirus infection , even at the late stage of infection . More importantly , USP7 KO cells , devoid of any USP7 function , were infected with wt virus ( H5pg4100 ) along with the respective control cell line HCT116 . The USP7 KO cells were kept in 15%FBS containing medium to compensate for the growth defect this cell line exhibits in comparison to HCT116 cells [49] , [54] . Nearly identical numbers of wt-infected HCT116 and USP7 KO cells were harvested at 24 h p . i . ( Figure 7A ) and virus yield was determined ( Figure 7B ) . As expected , the number of infectious virus progeny particles was severely diminished up to 95 . 9% ( Figure 7B ) even though a relatively high MOI of 50 FFU per cell was used . These results strongly support the findings that USP7 is required for efficient adenovirus infection . As with the knockdown experiments or in the USP7 KO background , inhibitor treatment ( 15 h before assaying ) strongly impaired virus growth in A549 and H1299 cells 24 h p . i . ( A549 = 80 . 4% and H1299 = 91 . 4% , Figure 6E and S5E ) . Even at a later time point ( start 33 h p . i . with harvest 48 h p . i . ) structural capsid proteins ( Figure 6B and S5C , each lane 6 ) and virus progeny numbers ( Figure 6E and S5E ) were significantly reduced ( 27 . 5% A549 and 44 . 1% H1299 ) . This clearly supports the notion that USP7 may exert its effects not only during early , but also at late times of infection . Moreover , the degree of virus growth inhibition was comparable to that after USP7 knockdown ( compare Figure 6E and C ) . To exclude off-target effects and to demonstrate specificity towards USP7 virus yield was determined in cell lines expressing an shRNA against GFP ( A549shGFP and H1299shGFP ) and compared to the control cell lines APU5 and HC2 ( Figure 6F and G ) . No significant reduction in virus progeny production was observed in A549shGFP and H1299shGFP cells , respectively ( Figure 6F and G ) . Furthermore , another USP7 inhibitor , HBX41108 , demonstrated similar efficacy in reducing adenoviral progeny numbers as HBX ( Figure 6F , HBX 58 . 8% reduction , HBX41108 66% reduction; Figure 6G , HBX 98 . 7% reduction , HBX41108 80 . 3% reduction ) . Next , our knockdown cell lines were treated with both USP7 inhibitors . In APU6 cells neither HBX nor HBX41108 could further significantly reduce virus yield ( Figure 6F ) . This indicates that the effects observed in our hands are specific to USP7 inhibition . Interestingly , in HU5 cells further reduction of progeny virus numbers could be achieved , but this reduction is comparable to that after HBX treatment in the HC2 control cell line ( Figure 6G ) indicating that remaining USP7 activity might be better exploited by HAdV5 in the H1299 background of HU5 ( Figure 6G , compare HC2 + HBX with HU5 + HBX/HBX41108 and HU5 + DMSO ) . In conclusion , USP7 probably exerts global positive effects upon adenoviral protein steady-state levels , which become visible when USP7 functions are artificially compromized . As expected , those general decreases in viral protein steady-state levels led to severely reduced progeny virion production , meaning that USP7 plays a pivotal role in adenoviral infection . Together with adenoviral E1A , E1B-55K possesses the ability to transform primary rodent cells [31] . To clarify the potential role of USP7 in cell transformation mediated by adenovirus E1A and especially E1B-55K proteins , we used USP7 specific RNAi ( shUSP7 ) and the USP7 inhibitors HBX and HBX41108 in transformation assays . Primary baby rat kidney ( Brk ) cells were transfected with plasmids encoding E1A in combination with E1B-55K and shUSP7 ( Figure 8A ) . Consistent with previous results [32] , [55] , E1A alone had more restricted focus forming activity , but cotransfecting the cells with E1B-55K expression plasmids increased the number of foci three to four-fold . As expected , in the presence of shUSP7 , E1B-55K had little effect in cooperative focus formation , suggesting a strong requirement for USP7 in E1A/E1B-55K-mediated cell transformation . Additionally , shUSP7 had no significant effect on sole E1A-induced focus formation ruling out off-target effects ( Figure 8A ) . To investigate in detail how USP7 shRNAs might inactivate cell transformation by adenovirus oncogenes , a panel of transformed monoclonal Brk cell lines was established from E1A/E1B-55K ( AB ) , and E1A/E1B/shUSP7 ( ABshU ) transformed foci . An shUSP7 cotransformed cell line ( ABshU729 , ca . 25% USP7 kd efficiency; Figure 8B , lane 5 ) was characterized by immunoblot in comparison to the Brk1 cells ( a spontaneously transformed rat cell line derived from primary Brk cells ) and reference cell lines transformed with E1A/E1B-55K plus empty vector constructs for shRNAs ( AB718–720; Figure 8B , lanes 2–4 ) . In accordance with the transfection data in the USP7 knockdown cell lines , E1B-55K expression was detected in ABshU729 cells , although reduced in comparison to reference AB cells . In all of the established cell lines E1A protein was found to be presented in similar amounts . Thus , it can be concluded that the influence of shUSP7 on the transformation process mainly affects the functions of E1B-55K . The USP7 inhibitor HBX was applied in similar transformation assays to substantiate the role of USP7 in adenoviral oncogene-mediated cell transformation processes ( Figure 8C ) . Plasmid-based transformation of primary rodent cells with E1A and E1B encoding plasmids was visualized by crystal violet staining of cell foci ( representative plates in Figure 8D ) . Quantification of several experiments revealed a marked reduction in cell foci number upon HBX treatment ( Figure 8C ) similar to the shRNA experiments ( Figure 8A ) . Interestingly , applying the inhibitor reduced foci formation to that of E1A-induced transformation alone , again suggesting that the effect of HBX treatment was specifically exerted upon E1B-55K . DMSO control-treated cells showed no significant change in foci formation compared to untreated cells ( Figure 8C ) . To test the ability of another compound against USP7 functions , HBX was applied in parallel with HBX41108 . In effect , both compounds exerted almost identical efficacy in reducing the focus forming activity of E1A-E1B indicating similar specificity upon USP7 ( Figure 8E ) . More importantly , application both compounds displayed no further reduction in foci formation activity after sole E1A transfection . This should rule out mere detrimental effects upon cell growth being responsible for reduced foci formation after HBX or HBX41108 treatment ( Figure 8F ) . It is notable that different to the previous transformation assays with inhibitor application ( Figure 8C ) , in Figure 8E and F inhibitors were applied only after foci were already visible to reduce the overall time of inhibitor incubation . This might explain the lower efficiency in reducing E1A–E1B focus formation activity compared to the assay in Figure 8C . Since interaction between USP7 and E1B-55K was only shown in transformed human cells , it was necessary to demonstrate that this binding also occurs in transformed rat cells . Indeed , it was possible to coprecipitate E1B-55K from E1B-plasmid transfected Brk1 cells , indirectly implying that this interaction also plays an important role in this setting ( Figure 8G , lane 2 ) . These results clearly demonstrate the important role of USP7 in adenoviral oncogene-mediated transformation processes and show that shRNA or small-molecule inhibitor treatment can efficiently reduce the number of transformed cells in the experimental set-ups .
Many efforts have been invested to find new drugs against DUBs or other proteins related to the ubiquitin-proteasome system ( UPS ) . Aberrantly regulated DUBs are described to be involved in specific human diseases such as cancer and neurodegenerative disorders [57] . Since USP7 is as yet the only DUB discovered to be directly connected to both cancer and infectious diseases , it is very enticing to find suitable inhibitors that can be used efficiently and specifically against USP7 . In a patent from 2006 , Hybrigenics described several cyano-indenopyrazine substances that exerted functional inhibition of USP7 [47] . One of these Hybrigenics substances was resynthesized ( due to the lack of commercially available inhibitors; here called HBX ) and used in this study to perform inhibitor assays on adenovirus-infected cells in order to investigate the functional consequences of the USP7-E1B-55K interaction and prove that USP7 inhibition , like USP7 knockdown or knockout , can efficiently reduce virus replication . In the course of our studies , a derivative of HBX , HBX41108 , was released and this compound was also implemented in our assays to compare efficacy of both compounds supporting specificity upon USP7 in diverse assays of this study . Both USP7 knockdown and inhibitor application severely reduced E1B-55K protein levels , and in the knockdown setting as well as after USP7 inhibitor application the half-life of E1B-55K was significantly shorter ( Figure S4A and S4B ) . In trying to reveal the precise mechanism underlying USP7-mediated E1B-55K stabilization , we invested much effort in demonstrating first , ubiquitination of E1B-55K and second , subsequent deubiquitination by USP7 . Unfortunately , our efforts were not successful . As for now , there is no report that has investigated possible ubiquitination of E1B-55K which also involves identification of the respective E3 ligase . So , only after clarifying these two questions deubiquitination by USP7 can be tackled . Moreover , it is far from clear that stabilization of E1B-55K is mediated through deubiquitination and degradation by ubiquitination , although some indications may lead to that assumption and our study might support this theory . However , considering known functions of USP7 in gene expression control through regulation of histone proteins and the known relationship between adenoviral gene expression activity and the chromatinization of the adenoviral genome inside the nucleus of infected cells , it is also likely that USP7-mediated E1B-55K stability might be exerted through a mechanism other than direct deubiquitination of E1B-55K [58]–[60] . In keeping with the functions exerted by E1B-55K in the adenoviral life cycle , many defects in virus replication , such as reduced late protein production , can be explained by decreased functionality of this protein and the complexes it forms during productive replication . For example , late adenoviral mRNA transport is carried out by a complex comprising E1B-55K and E4orf6 [61]–[66] . As a result of reduced complex formation , it is very likely that these mRNA species do not accumulate sufficiently , which is eventually reflected by lower adenoviral capsid protein production ( Figure 6 and S5 ) . However , during the course of this study , it became clear that USP7 not only specifically targets E1B-55K , but also exerts positive effects on other early proteins ( and late proteins ) . To our surprise E1A and E2A steady-state protein levels were also negatively affected by USP7 knockdown and inhibition . However , certain differences between knockdown and inhibition were observed . For instance , E1A protein levels are clearly lower in knockdown versus the control cell line ( Figure 6A and S5A ) , but showed no differences or even increased after inhibitor treatment depending on the cell type treated ( Figure 6B and S5C ) . While it remains enigmatic why increased E1A levels do not lead to higher protein levels of those genes regulated by early viral promoters ( e . g . E2A , Figure S5C ) , it is much easier to understand that lower E1A protein levels lead to reduced activation of viral early promoters , with a subsequent delay in protein expression of the respective genes . Interestingly , Fessler and Young demonstrated that lowered expression from the major late promoter ( MLP ) leads to increases in the expression of early genes among them E1A and E1B ( the mechanism of this phenomenon has not been clarified in detail yet ) especially when MLP expression is hampered at late times of infection [67] . This might explain the contrast between USP7 knockdown and inhibition in relation to the E1A and E1B-55K reduction ( compare Figure 6A and S5A with Figure 6B and S5C ) . Knockdown is a permanent condition in our assays leading to the assumption that USP7 is needed for proper E1A expression in the initial phase of the adenoviral replication cycle . However , inhibitor treatment in our assays starts earliest 9 h p . i . and compromising late gene expression/late protein stability at this time point through USP7 inhibition rather increases than decreases E1A levels and might also explain why E1B-55K reduction is attenuated in contrast to the single transfection assays . But how can USP7 affect late gene expression ? Taking into account that USP7 not only affects E1B-55K but also , for example , E2A levels it is probable that E2A functions in promoting DNA replication might be hampered which in turn lead to the observed negative effect on late gene expression [67] , [68] . Another aspect to consider , while knockdown affects protein levels in toto , inhibitor treatment might affect only one function of a protein without affecting other functions at all . Due to USP7's multi-domain structure , its functions are not only carried out by the enzymatic domain [36] , [69] . However , the specific effects of both approaches ( knockdown and inhibition ) upon other viral proteins such as E1B-55K or structural proteins clearly suggest that USP7 functions are necessary during the whole course of infection . In this context , USP7 , having enzymatic activity , represents a potent target for small-molecule inhibitors . Our results clearly indicate that adenoviral progeny virions can be reduced in a significant manner ( up to over 90% ) even after an established infection using an inhibitor of USP7 , results that could , at least qualitatively , also be confirmed with RNAi experiments . Strikingly , both approaches to disrupting USP7 functions also diminished the ability of adenoviral oncogenes to induce cellular transformation . It is not sure but possible that shUSP7 induced low levels of E1B-55K which might explain the fewer counted cell foci in this set-up ( Figure 8B ) . Similarly , addition of the USP7 inhibitors HBX ( Figure 8C and E ) and HBX41108 ( Figure 8E ) also lowered the number of transformed cell foci . Considering that USP7 is already known to be involved in tumor pathways , the observed phenotypes may be explained by two possible scenarios: First , p53 is activated , accumulates and promotes antiproliferative activities due to the instability of its negative regulator Mdm2 . It has been shown that Mdm2 is the primary target of USP7-mediated stabilization . Thus , inhibition of USP7 in this setting might lead to reduced Mdm2 levels . Second , increased Daxx proapoptotic activity supports cell death . As in the case of p53 , Daxx levels increase due to missing negative Mdm2 regulation after USP7 inhibition . Additionally , in transformation settings , Daxx functions are antagonized by E1B-55K , and as shown in Figure 5 E1B-55K levels definitely depend on functional USP7 . Indeed , it was possible to demonstrate an E1B-55K-USP7 interaction in rat ( equivalent to human ) cells ( Figure 8G ) , indirectly supporting a direct relationship between USP7 and E1A-E1B-55K-mediated cellular transformation . Therefore , similar USP7-dependent mechanisms may play an important role during adenoviral lytic infection and adenoviral-oncogene-mediated transformation processes , emphasizing the extraordinary relationship between USP7 and E1B-55K . In summary , for the first time , one cellular protein can be linked to efficiently reducing adenovirus yield and virus-mediated cellular transformation . Therefore blocking the activity of USP7 could potentially be used to treat adenovirus infections . Particularly , pediatric patients undergoing allogenic stem cell transplantation are vulnerable to disseminated adenovirus infections , leading to a high mortality rate [11] . Hence , there is a need for potent antiviral therapeutics against adenoviruses that allow suppression of the virus at different stages in the replication cycle [12] . USP7 represents a striking drug target .
A549 and H1299 cells were cultivated as described [70] . HCT116 and HCT116 USP7 double-knockout cells ( USP7 −/− [KO]; kind gifts of Dr . Bert Vogelstein ) were grown in McCoy's 5a Medium ( GIBCO ) supplemented with 10% or 15% ( USP7 KO ) fetal bovine serum ( FBS , PAA ) , 100 U of penicillin , and 100 µg of streptomycin per ml . Primary Brk ( baby rat kidney ) cells and the Brk-derived cell line Brk1 [31] were grown in Dulbecco's modified Eagle medium ( DMEM , PAA ) supplemented with 5 to 10% FBS , 100 U of penicillin , and 100 µg of streptomycin per ml in a 5% CO2 atmosphere at 37°C . APU5 and APU6 ( USP7 knockdown , kd ) cells were grown under the same conditions as Brk cells , and HC2 and HU5 ( USP7 kd ) cells were grown under the same conditions as H1299 cells . Additionally , all the knockdown and control cell lines were also grown under constant puromycin challenge ( 2 µg/ml , Calbiochem ) . Infections with wt ( H5pg4100 ) and E1B minus ( H5pm4149 ) adenoviruses and subsequent virus yield experiments were carried out as described earlier [70] . Infection with E1B minus2 ( dl1520 ) was carried out like described earlier [39] . Virus yield was calculated as described earlier [70] in virus particles per cell ( FFU/cell ) , these results were normalized and presented as a function of untreated or control cells . This calculation allowed negative cell growth effects exerted by HBX or HBX41108 to be neglected . In indicated experiments HBX , HBX41108 and DMSO was added 15 hours before cell harvest at denoted concentrations or incubated for 24 hours . Transfections with plasmid DNAs used PEI as a transfecting agent and were carried out as described earlier [70] . Other experiments included cycloheximide addition ( SIGMA ) and harvest at indicated time points ( end concentration 10 µM ) . The USP7 inhibitor HBX ( example 1 ) was synthesized as described earlier [47] . Indirect immunofluorescence staining and image capturing was carried out as described earlier [70] . Processing and layout of images were accomplished using Adobe Photoshop and Illustrator CS4 software tools . Statistical analyses were all performed with Microsoft Excel 2007 and GraphPad Prism 5 . Western blot band intensities were analyzed with ImageJ 1 . 45s . Coimmunoprecipitation ( Co-IP ) assays were performed with the anti-USP7 ( 3D8 or 6E6 ) and , as a control , a non-specific IgG2a monoclonal rat antibody ( 1–2 µg/sample ) was used . Protein analyses , Western blots and antibody usage in general were also carried out as described earlier [70] . The USP7 antibodies 3D8 and 6E6 were used for Western blots as a 1∶10 dilution in phosphate-buffered saline ( PBS ) containing 0 . 05% Tween 20 ( AppliChem ) and 1% nonfat dry milk . Following steps as referenced above . 2 . 5×105-1 . 0×106 H1299 cells were seeded into 6-well plate wells or 10 cm dishes ( Sarstedt ) transfected with pECFP-C1 , pEYFP-C1 , pEYFP-ECFP ( pEYFP-ECFP is a fusion protein; the first three plasmid constructs were kind gifts from Dr . Carina Banning ) , YFP-E1B-55K ( YFP-55K ) and USP7-CFP in different combinations as indicated . 24–48 hours post transfection cells were harvested and assayed . Following steps were performed as previously described [37] , [71] . The GST fusion proteins E1B-55K , E1B 156R , E1B 93R , E1B 83–188 , E1B 93R 1–82 , E1B 1–162 , USP7 TD , USP7 CD , USP7 C1 and USP7 C2 were expressed and purified as described earlier [70] . For the GST pull-down assays equal amounts of fusion proteins were incubated with a defined quantity of cell lysate . This mixture was then incubated for 2 h at 4°C on a turning rotor . The proteins bound to the Glutathione Sepharose ( GE Healthcare ) were subsequently precipitated by centrifugation ( 6500 rpm , 5 min , 4°C ) , six times washed with PBS or lysis buffer , centrifuged and boiled in 25 µl of SDS sample buffer . The protein samples were then analyzed by SDS-PAGE and Western blotting . Input of recombinant proteins was analyzed by Coomassie brilliant blue staining ( CB ) . 1 . 5×103 cells were seeded per 96-well plate well ( Falcon ) . 12–20 hours later , culture medium with different compound concentrations ( concentration series in triplicates ) was added to cells , replacing the old medium . As controls , untreated and compound solvent ( DMSO ) treated cells were used . For all compound and solvent treated cells , the final concentration of DMSO ( usually 0 . 05% ) was equal . Cells were incubated for different time points with compound , usually 24 , 48 and 72 h and then cell proliferation was measured with the Promega CellTiter 96 Aqueous One Solution Cell Prolifertation Assay ( MTS = ( 3- ( 4 , 5-dimethylthiazol-2-yl ) -5- ( 3-carboxymethoxyphenyl ) -2- ( 4-sulfophenyl ) -2H-tetrazolium ) ) according to manufacturer's instructions . The resulting color reaction was measured with a plate reader at 490 nm ( BioTek SynergyMx ) . Transformation assays were carried out as described earlier [72] , [73] . In addition , the USP7 inhibitor HBX and DMSO were also included in the growth medium as indicated . Transformation assay in Figure 8C was carried out with inhibitor addition 4–6 days after transfection . Transformation assay in Figure 8E and F were carried out with inhibitor addition after first foci were visible . To establish stable rat cell lines , foci were isolated using a glass cloning cylinder ( 5 mm diameter ) circling single colonies . The cells within the cylinders were trypsinized with 100 µl of trypsin/EDTA solution . When the cells were detached from the dish , they were transferred into the wells of a 24-well plate ( Falcon ) containing DMEM with 10% FBS . These cells were grown for several weeks and expanded to obtain monoclonal cell lines . To establish stable monoclonal USP7 knockdown cell lines from A549 and H1299 , these cells were seeded onto 6-well dishes and transfected with pSuper-shUSP7 ( shUSP7 ) or empty vector using PEI . One day after transfection , fresh media containing 2–3 µg/ml of puromycin ( Sigma ) was added to the transfected cells . Three days later , the cells were split in a ratio of 1∶30 , and seeded onto two 150 mm-diameter tissue culture dishes ( Falcon ) . Fresh media containing puromycin were added to the cells every 3–4 days to select the stably transfected ones . Three weeks after splitting , foci were chosen and isolated as above to establish monoclonal cell lines and expanded . Puromycin was always included in the growth medium of these cells . HC2 contains the pSuper . retro . puro empty vector and HU5 is stably transfected with shUSP7 . APU5 and APU6 are both stably transfected with shUSP7 whereas USP7 knockdown is only detected in APU6 . HAdV5 E1B-55K protein: AP_000199 . 1 . Human USP7 ( HAUSP ) protein: NP_003461 . 2 .
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Adenoviral infections can result in severe outcomes leading to mortality especially in children undergoing immunosuppressive therapies . Unfortunately , no specific anti-adenoviral treatments are available to treat disseminated adenoviral infections . We have set out to identify host factors promoting adenoviral growth and could identify the cellular protein Ubiquitin-specific protease 7 ( USP7 ) being central to adenoviral infection . Here we show that USP7 interacts with the viral protein E1B-55K , a central regulator of adenoviral replication and adenoviral oncogene-mediated cellular transformation . We demonstrate that USP7 ensures stability and/or proper expression levels of adenoviral proteins at early and late time points of infection . Consistent with this , small-molecule inhibitors of USP7 showed efficient reduction of capsid protein levels and viral progeny numbers . Thus , USP7 inhibition might be a useful treatment option in the context of disseminated adenoviral infections . Moreover , we were also able to show that adenoviral oncogene-mediated cellular transformation can be hampered by USP7 disruption . In summary , this study shows that two different adenoviral disease mechanisms can be inhibited by targeting one host cellular factor .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"biology",
"molecular",
"cell",
"biology"
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2013
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A Ubiquitin-specific Protease Possesses a Decisive Role for Adenovirus Replication and Oncogene-mediated Transformation
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Many enteropathogenic bacteria target the mammalian gut . The mechanisms protecting the host from infection are poorly understood . We have studied the protective functions of secretory antibodies ( sIgA ) and the microbiota , using a mouse model for S . typhimurium diarrhea . This pathogen is a common cause of diarrhea in humans world-wide . S . typhimurium ( S . tmatt , sseD ) causes a self-limiting gut infection in streptomycin-treated mice . After 40 days , all animals had overcome the disease , developed a sIgA response , and most had cleared the pathogen from the gut lumen . sIgA limited pathogen access to the mucosal surface and protected from gut inflammation in challenge infections . This protection was O-antigen specific , as demonstrated with pathogens lacking the S . typhimurium O-antigen ( wbaP , S . enteritidis ) and sIgA-deficient mice ( TCRβ−/−δ−/− , JH−/− , IgA−/− , pIgR−/− ) . Surprisingly , sIgA-deficiency did not affect the kinetics of pathogen clearance from the gut lumen . Instead , this was mediated by the microbiota . This was confirmed using ‘L-mice’ which harbor a low complexity gut flora , lack colonization resistance and develop a normal sIgA response , but fail to clear S . tmatt from the gut lumen . In these mice , pathogen clearance was achieved by transferring a normal complex microbiota . Thus , besides colonization resistance ( = pathogen blockage by an intact microbiota ) , the microbiota mediates a second , novel protective function , i . e . pathogen clearance . Here , the normal microbiota re-grows from a state of depletion and disturbed composition and gradually clears even very high pathogen loads from the gut lumen , a site inaccessible to most “classical” immune effector mechanisms . In conclusion , sIgA and microbiota serve complementary protective functions . The microbiota confers colonization resistance and mediates pathogen clearance in primary infections , while sIgA protects from disease if the host re-encounters the same pathogen . This has implications for curing S . typhimurium diarrhea and for preventing transmission .
Bacterial diarrhea is a global cause of morbidity and mortality . In most cases , the acute disease symptoms cease after a few days and the pathogen is eliminated from the gut . However , the mechanisms eliminating enteropathogenic bacteria from the gut lumen are poorly understood . Most “classical” effector mechanisms of the immune system are ineffective in the gut lumen ( i . e . complement-mediated killing , opsonophagocytosis , T-cell mediated toxicity ) . In the gut , innate and adaptive immune responses such as antimicrobial peptides , natural and pathogen-specific mucosal secretory IgA ( sIgA ) antibodies are considered to be cardinal defense mechanisms . In addition to the host's immune system , the highly dense and diverse bacterial community in the gut ( the microbiota; >500 different species [1] , [2] ) plays a key role by inhibiting pathogen growth in the gut lumen right from the beginning . This phenomenon is referred to as ‘colonization resistance’ and efficiently blocks infections by Clostridium difficile , Salmonella spp . and many other pathogenic bacteria [3] . Colonization resistance might be based on nutrient limitation , release of inhibitory metabolites , production of bactericidal compounds , the competition for binding sites and other , unidentified features of the dense microbial community [4] , [5] . Much less is known about the mechanisms clearing enteropathogenic bacteria from the gut lumen once they have established an infection in this niche . ‘Pathogen clearance’ differs significantly from colonization resistance as both , the mucosa [6] and the microbiota , must recover from pathogen-inflicted disturbance while eliminating the pathogen [7] . Here , we have studied the mechanisms of pathogen clearance from the gut lumen using the example of non-typhoidal Salmonella ( NTS ) diarrhea . NTS infections , including S . enterica spp . I serovar Typhimurium ( S . tm ) , account for a significant share of food-borne diarrhea in Europe and Northern America . In sub-Saharan Africa , NTS are also an important cause of invasive disease with high mortality , particularly in HIV infected individuals [8] . In humans , colonization resistance confers partial protection , but antibiotic treatment increases the risk of Salmonella diarrhea [9] , [10] . In the typical cases of NTS diarrhea , the pathogen begins to grow in the gut and disease symptoms manifest eight to 24h after consumption of contaminated food or water . Usually , the pathogen remains limited to the gastrointestinal tract and diarrhea subsides within several days . After cessation of symptoms , Salmonella remains detectable in the stool for weeks , several months or sometimes even longer [11] , [12] . Pathogen clearance seems to fail in these long-term ‘asymptomatic excretors’ . This is problematic , as ‘asymptomatic excretors’ pose a significant risk of transmission , in particular when food workers in restaurants or the food industry are affected [13] . So far , we can only speculate about mechanisms mediating pathogen clearance from the gut lumen . Antimicrobial peptides might be involved in some infections , but should not affect S . tm clearance , as this pathogen is particularly resistant against this type of compound [14] , [15] . Antibody responses , i . e . pathogen-specific secretory IgA ( sIgA ) , might also clear pathogens from the gut lumen . S . tm elicits profound antibody responses against LPS and protein antigens [16] . In systemic infection models antibody responses can confer some degree of protection [17] , [18] . Previous work on the role of sIgA in intestinal S . tm infection yielded conflicting results . sIgA protected cultured epithelial cells from S . tm infection , but did not reduce intestinal pathogen densities [18] . Similar findings were made for the enteropathogenic bacterium Citrobacter rodentium [19] . However , the role of sIgA in pathogen clearance in models of acute Salmonella enterocolitis with high intestinal pathogen loads has not been addressed so far . Finally , we reasoned that the microbiota itself might contribute to pathogen clearance . It remained to be shown which mechanisms contribute to pathogen clearance . We have used a Salmonella diarrhea mouse model to analyze the relative importance of sIgA and the intestinal microbiota in S . tm clearance after infection . In mice , the intestinal microbiota confers colonization resistance . Normally , <10% of mice permit pathogen growth and get mucosal inflammation upon oral S . tm infection [20] . Oral antibiotic-treatment alleviates colonization resistance and wild type S . tm grows up to very high densities in the intestinal lumen and induces mucosal inflammation ( colitis ) in 100% of the animals [6] . The gut inflammation allows S . tm to out-compete the microbiota thus promoting pathogen overgrowth [21] . Here , we have extended this mouse model to study pathogen clearance at later phases of the primary infection when acute mucosal inflammation has ceased . We analyzed the levels of pathogen shedding , sIgA responses and the role of the microbiota . This revealed that the microbiota plays an essential role in pathogen clearance . The implications for curing asymptomatic excretors and preventing S . tm diarrhea are discussed .
In sm-treated mice , infection with an attenuated S . typhimurium strain ( S . typhimurium SL1344 sseD; termed S . tmatt; Table S1 ) is known to recapitulate key aspects of the early stages of human NTS diarrhea , i . e . gut inflammation 8h after orogastric exposure with infection confined to the gastrointestinal tract [22] . Symptoms of the acute gut inflammation usually decline by 5–7 days after infection [23] . In order to assess , if this model may be useful to dissect the role of pathogen-specific sIgA and the intestinal microbiota in pathogen clearance at the final stage of a primary infection we analyzed the outcome of long-term S . tmatt infections [6] , [24] . We monitored S . tmatt shedding for up to 60 days after infection . S . tmatt shedding in stool began to decrease after a few days , varied extensively between different animals and lasted for 2 to 8 weeks ( Fig . 1A ) . At 60 day p . i . , S . tmatt shedding was reduced below 105 cfu/g ( p<0 . 05 day 1 vs . day 60 p . i . ) . At all stages , the infection remained largely confined to the gastrointestinal tract and draining mesenteric lymph nodes ( MLN ) and gut inflammation subsided after 7–44 days ( Fig . 1B , C ) . Interestingly , we observed a high incidence of ‘asymptomatic excretors’ around day 44 post infection ( p . i . ) . These mice were characterized by a low pathological score ( ≤3 ) and high cecum pathogen loads ( ≥105 cfu/g stool; Fig . 1C , right panel , green symbols ) . This may indicate that pathogen clearance from the gut lumen is not necessary in order to resolve gut inflammation . In fact , both might be independent from each other . We concluded that this model could be useful to analyze the mechanism of pathogen clearance from the gut lumen after infection . Next , we wanted to address if mice that had experienced a primary S . tmatt infection in our model developed an adaptive immune response that would protect against gut inflammation upon re-infection with the same pathogen . This would be a pre-requisite for functional analysis of antibody responses in pathogen clearance . Therefore , we extended the infection model as depicted in Fig . 2A ( ‘immunization-challenge’ protocol ) . Sm-treated mice were infected with S . tmatt for 39 days as in the standard protocol ( = experimental group; mock-immunization = negative control ) . This allowed sufficient time for recovering from acute inflammation and the generation of a S . tm-specific adaptive immune response ( Fig . 1C; see also Fig . 2C , D ) . At day 39 , the mice were treated with ampicillin to transiently suppress the microbiota and eliminate any S . tmatt which may have persisted in the gut . We then challenged the animals with wild type S . typhimurium ( wt; ampicillin resistant; 200 cfu by gavage ) . In the mock-immunized mice , wt S . typhimurium efficiently colonized the gut and elicited acute intestinal inflammation within two days after challenge ( Fig . 2B ) . In contrast , the S . tmatt-immunized mice were generally protected against wt S . typhimurium-inflicted disease ( 8/10 mice with cecal pathology score ≤3; p = 0 . 0099; Fig . 2B ) . Bacterial surface structures and secreted proteins are dominant targets of adaptive immune responses [18] , [25] , [26] , [27] , [28] , [29] . Therefore , we analyzed whether surface-protein or O-antigen specific immune responses might explain the protection of S . tmatt-immunized mice . No protection was observed against challenge with the NTS serotype S . enteritidis ( S . enwt ) , harboring a different LPS-O-antigen , or an O-antigen deficient isogenic S . typhimurium mutant ( S . tmΔO; ΔwbaP; Table S1; p>0 . 05 vs . colitis in mock immunized controls ) . Thus , S . tmatt-immunized mice mounted an adaptive immune response which protected from mucosal disease on re-infection with the pathogen in an O-antigen-dependent way . The exquisite O-antigen specificity of protection from a second round of inflammation suggested that adaptive immunity and particularly sIgA may be the crucial mechanism not only for preventing inflammation on re-infection , but also for clearing pathogens from the gut . Therefore , we determined the kinetics of the Salmonella-specific humoral immune response by measuring specific Ig via surface staining of live , intact bacteria by flow cytometric analysis ( Fig . 2C ) . This assay accurately differentiates S . tm specific antibodies from antibodies directed against closely related species , such as E . coli [30] ( Fig . S1A–C ) . S . tm-specific IgM , IgG and IgA were detectable in the serum as early as 7 days post immunization . By day 14 , all mice secreted S . tm -surface-specific sIgA into the gut lumen . Mucosal sIgA responses were confirmed by immunohistochemistry ( Fig . S2 ) . Salmonella antigens targeted by this strong , specific humoral immune response were analyzed by Western blotting . The antibody response was indeed pathogen-specific , as Lactobacillus reuteri RR and Enterococcus faecalis , two commensals isolated from our mouse colony , were not recognized ( Fig . 2D; Fig . S3 ) . In analogy to the human infection ( Fig . S4 ) , the antibody response included sIgA recognizing the O-antigen of S . tm ( protease resistant ladder-like bands in the Western blot; Fig . 2D ) , a highly repetitive sugar structure of the lipopolysaccharide ( LPS ) , coating the surface of the pathogen . In contrast , the O-antigens from S . enteritidis and E . coli , which have a different sugar structure or LPS from the O-antigen deficient mutant S . tmΔO were not recognized . In addition , antibodies to several prominent protein antigens were detected . Most of these protein antigens were conserved in different Salmonella and E . coli strains , but not in L . reuteri RR or E . faecalis . It should be noted that acute mucosal inflammation seems necessary to elicit immune responses protecting from enterocolitis . It was also shown previously , that invasive Salmonella strains triggered more potent adaptive immune responses [31] . Mice not pretreated with sm before immunization ( low antigen loads , no gut inflammation ) , sm-treated mice immunized with S . tmavir ( high antigen loads , no gut inflammation ) and parenterally immunized mice ( S . tmatt i . v . ; systemic antigen loads , no gut inflammation ) did not mount detectable levels of O-antigen-specific sIgA . None of the mice were protected against wild type S . tm ( S . tmwt ) mediated enteropathogenesis ( Fig . S5 ) . Overall , these data demonstrated that the LPS O-antigen was the dominant protective antigen and that mice mount a robust pathogen-specific sIgA response during the first round of infection . This is in line with earlier data from studies in the mouse typhoid fever model , in chicken and data from human patients [18] , [25] , [26] , [27] ( Fig . S4 ) . However , from these first sets of experiments we could not conclude whether pathogen-specific sIgA was sufficient for S . tm clearance from the gut . In order to address sIgA functions in pathogen clearance , we analyzed the outcome of S . tmatt infection in different KO-mice lacking key mediators of functional adaptive immune responses . We determined whether T-cell dependent or -independent mucosal sIgA immune responses [30] , [32] , [33] were critical for termination of inflammation , pathogen clearance and protection from inflammation on re-infection . ‘Immunization-challenge’ experiments were performed on mice lacking the T-cell receptor ( TCRβ−/−δ−/−; T-cell deficient ) , B-cells ( JH−/− ) , IgA ( IgA−/− ) or sIgA and sIgM-transport into the gut lumen ( pIgR−/−; Table S2 ) . Two days after initial infection with S . tmatt , all knockout mice displayed pronounced gut inflammation ( data not shown ) and gut inflammation subsided by day 40 ( Table 1 ) . This demonstrated that the acute mucosal inflammation can be efficiently terminated in the absence of T-cells , B-cells , antibodies or sIgA . Furthermore , several IgA−/− ( 3/4 ) and pIgR−/− ( 2/5 ) animals managed to clear S . tmatt from the gut lumen by day 40 p . i . This indicated that pathogens can ( at least in some cases ) be cleared from the gut lumen , in the absence of pathogen-specific sIgA ( and sIgM ) in the gut lumen . In order to exclude differences attributable to alterations in microbiota composition between different mouse lines , we have compared the S . tmatt clearance kinetics between IgA−/− and wild type littermates ( IgA+/− , IgA+/− , IgA+/+; Fig . 3 ) . This verified that kinetics of pathogen clearance was not affected by presence or absence of sIgA . Strikingly , none of the S . tmatt -immunized knockout mice developed O-antigen specific antibodies and none were protected from intestinal inflammation upon challenge with S . tmwt ( pathological score ≫3; Table 2 and Fig . S6 ) . Thus , a T-cell dependent , adaptive mucosal sIgA response is essential for protection from secondary disease , but is dispensable for resolving the initial inflammatory response to S . tmatt and for clearing the pathogen from the intestinal lumen . Though dispensable , our findings did not exclude that sIgA exerts an effector function [34] which contributes in some way to pathogen clearance . To identify such mechanisms , we analyzed the effects of sIgA on pathogen growth and its interaction with the host's intestinal mucosa in greater detail . First , we applied a modified ‘immunization-challenge’ protocol . Sm-treated mice were infected with S . tmatt , an equivalent S . enteritidis strain ( S . enatt; S . enteritidis 125109 sseD; [35] ) or mock . Antibody responses and S . tmatt/ S . enatt loads in the stool were monitored ( Fig . S7 and data not shown ) . After 39 days , immunized mice were treated with ampicillin ( elimination of microbiota and remaining S . tmatt or S . enatt ) and challenged with a 1∶1 mixture of S . tmavir and S . enavir ( ampicillin resistant; sseDinvG mutants; 200 cfu each by gavage; Table S1 ) . These latter mutants can colonize the gut lumen of naïve mice for up to four days , remain confined to the gastrointestinal tract and they do not elicit enteropathogenesis , thus mimicking the situation in the intestines of ‘asymptomatic excretors’ [21] , [24] . We decided not to use S . tmΔO for this type of competition experiments as it displays a pronounced competitive growth defect in mice when co-infections are performed with an isogenic wild type strain [36] . In the gut lumen of S . tmatt immunized mice , S . enavir out-competed S . tmavir ( Fig . 4A; black symbols ) . In S . enatt immunized mice , S . tmavir out-competed S . enavir ( red symbols ) , and in mock-immunized mice , both strains colonized with equal efficiency . Therefore , O-antigen specific sIgA may help controlling pathogen growth or survival in the gut lumen . Furthermore , S . tmavir ( but not S . enavir ) was aggregated in the gut lumen and occluded from the mucosal surface of S . tmatt immunized mice ( Fig . 4A , right panels ) . Pathogen occlusion was confirmed by assessing pathogen loads in the gut tissue of challenged mice . In S . tmatt -immunized animals , S . tmwt tissue loads were 100-fold lower than in mock-immunized controls ( Fig . 4B ) . In contrast , S . tmatt immunization did not prevent the invasion of S . enteritidis . Furthermore , S . tmatt immunized pIgR−/− mice , which cannot transport sIgA across the gut epithelium , failed to prevent gut tissue invasion by wt S . typhimurium into the mucosal tissue ( Fig . 4B ) . Thus , the O-antigen-specific sIgA response conferred protection by restricting pathogen growth in the gut lumen and preventing the interaction of the pathogen with the intestinal mucosa . To some extent , this may also contribute to pathogen clearance from the gut lumen . While O-antigen-specific sIgA was indispensable to prevent disease , it did not seem to be a major determinant in pathogen clearance from the gut lumen . The onset of adaptive sIgA responses and cessation of symptoms seemed to occur well ahead of S . tmatt elimination from the intestines . Moreover , IgA deficiency did not affect pathogen clearance kinetics ( Table 1; Fig . 3 ) . This was different from most well studied paradigms of acute systemic infection where the onset of protective immunity coincides with declining pathogen loads . This strongly suggested that sIgA-independent mechanisms may underlie pathogen clearance from the gut . Thus , we hypothesized that the microbiota might play a crucial role in pathogen clearance . The microbiota is a dense bacterial community composed of approx . 500–1000 different species [9] , [37] . It confers numerous beneficial effects to the host [38] including ‘colonization resistance’ , i . e . a generalized interference with the growth of many pathogens in the gut of a naïve host [3] . Antibiotic treatment disrupts the normal microbiota , alleviates colonization resistance and constitutes a known risk factor for Salmonella infections in humans and mice [7] , [9] , [10] , [21] , [39] . Furthermore , the species composition of the microbiota - and by inference the degree of colonization resistance - can vary significantly between different individuals [40] . Therefore , the microbiota composition might explain why Salmonella shedding by ‘asymptomatic excretors’ can last for months or years . In sm-treated mice , the microbiota is transiently reduced , but rapidly returns to pretreatment community composition , re-establishes ‘colonization resistance’ and ‘asymptomatic excretion’ occurs just transiently ( Fig . 1C; [21] , [41] ) . For this reason , our original infection model was not optimally suited for dissecting the differential role of the microbiota and sIgA in pathogen clearance . To overcome this problem we used ‘L-mice’ which harbor a well defined , low complexity microbiota ( L = ‘LCM mice’; [20] ) . L -mice are ex-germ free mice that are stably associated with the ‘Altered Schaedler Flora’ [42] comprising <20 species . The representatives with the highest abundance are ASF500 ( Firmicutes; Clostridia; Clostridiales; Lachnospiraceae; unclassified_Lachnospiraceae ) and ASF519 ( Bacteroidetes; Bacteroidia; Bacteroidales; Porphyromonadaceae; Parabacteroides ) . Thus , the L microbiota resembles the conventional ( C ) microbiota of mice and men at broad lineages levels [43] . However , in spite of an equally high bacterial density as the C microbiota , the L microbiota does not confer colonization resistance [20] . Accordingly , S . tmatt efficiently colonized the gut lumen of L-mice at high levels ( ≥108cfu/g ) and elicited pronounced enteropathogenesis by day 2 p . i . even without previous antibiotic treatment ( Fig . 5A ) . After 40 days , all immunized L-mice had resolved acute inflammation , but kept on shedding S . tmatt at high levels for at least 83 days ( Fig . 5A; see also below ) . This was not due to a defective O-antigen-specific sIgA response: sIgA responses in L-mice were as pronounced as in C-mice as indicated by the increased numbers of IgA+ cells in the cecal mucosa ( Fig . 5B , C and Fig . S2 ) and by Western Blot analysis ( Fig . S8 and Fig 2D ) . The strong adaptive mucosal immune response was also confirmed by gene expression profiling of the cecal mucosa ( Fig . 5D , Fig . S9 , Table S3 ) . Furthermore , challenge experiments confirmed the O-antigen-specific protection from enteropathogenesis ( Fig . 6 ) . However , despite this O-antigen-specific sIgA response , high-level pathogen shedding persisted in all analyzed animals ( Fig . 5A; see also below ) . Therefore , O-antigen-specific sIgA was insufficient for luminal S . tmatt clearance . This was in line with our hypothesis that elements of the normal , complex microbiota ( which is lacking in L-mice ) may play a key role in terminating fecal S . tmatt shedding . To formally define the importance of the commensal microbiota in pathogen clearance , two groups of L-mice were infected with S . tmatt for 83 days . The first group was kept under strict hygiene isolation and shed high loads of S . tmatt until the end of the experiment ( S . tmatt→L; Fig . 7A; open symbols ) . The second group was exposed to C microbiota at day 40 by placing C donor mice into the same cage ( S . tmatt→L/C; 6 independent cages ) . Both groups of mice developed the typical pathogen-specific , adaptive sIgA response by day 83 p . i . ( Fig . 7B , C ) . Upon introduction of the C donor mice , fecal shedding decreased gradually and ceased in most of the S . tmatt→L/C mice by day 83 ( <105cfu/g; Fig . 7A; black symbols ) but not in the S . tmatt→L group . This suggested that pathogen clearance was mediated in some way by the complex microbiota . In order to verify microbiota-transfer , we assessed microbiota composition using high-throughput 16S rRNA gene sequence analysis ( Materials and Methods ) . S . tmatt→L/C mice displayed significantly higher diversity than S . tmatt→L mice as well as LCM mice at day 2 and 40 after S . tmatt immunization ( Fig . 7D ) . The rarefaction curves indicated that S . tmatt→L/C mice had acquired a microbiota of the similar complexity as the C donor mice . This was confirmed by assessing the richness ( actual diversity ) of the samples by calculating the Shannon index ( H ) and species evenness ( E ) as well as the Chao1 diversity estimate ( Table S4 ) . Accordingly , the taxonomy assignment confirmed that the number of bacterial taxa in the stool increased significantly in S . tmatt→L/C mice . All S . tmatt→L mice carried high loads of Enterobacteriaceae ( i . e . Salmonella spp . , E . coli spp . ; red colors ) in their stools . In contrast , no Enterobacteriaceae were detected in the stools of 3 ( out of 6 ) S . tmatt→L/C mice and the remaining 3 animals carried low levels of this family ( yellow colors , Fig . 7E ) . In addition , the microbiota composition was similar between all S . tmatt→L/C animals as demonstrated by hierarchical cluster analysis of eubacterial family profiles ( Fig . 7E ) . This indicated that pathogen displacement occurs in a reproducible , stereotypic fashion and may not result from random transfer of only few members of the conventional microbiota . Most importantly , these data demonstrate that members of the conventional microbiota can upon transfer lead to the termination of sustained pathogen shedding in L-mice . It remained unclear whether pathogen clearance was mediated directly by the microbiota or by microbiota-induced mucosal responses . Recently , it has been shown that parts of the microbiota ( i . e . segmented filamentous bacteria ) induce mucosal TH-17 cell responses that can protect from pathogen infection [2] . However , we did not observe differences in IL-17A or IFN gamma-producing CD4 T-cells in the MLN of S . tmatt→L and S . tmatt→L/C animals by day 83 ( Fig . 8 ) . Furthermore , we tested if total MLN cells obtained from S . tmatt→L/C animals would , upon transfer into S . tmatt→L ( d . 40 ) induce clearance of intestinal S . tmatt . However , this was not the case ( Fig . 8 ) . This was in line with the notion that the microbiota may directly mediate pathogen clearance .
In this study , we have defined the contributions of sIgA and the microbiota in protecting the host from NTS infection . During the first encounter with the pathogen , the microbiota mediates at least two different protective functions , colonization resistance and pathogen clearance . The former is well established and prohibits the growth of diverse incoming pathogens , thus preventing colonization right on [3] , [5] , [20] , [44] . Here , we identified pathogen clearance as a second protective function attributable to the microbiota . Pathogen clearance eliminates the pathogen from the gut lumen after an episode of acute infection , i . e . after Salmonella diarrhea . This differs from colonization resistance as the pathogen starts out at high density and the normal microbiota must re-grow from a state of depletion and disturbed composition ( i . e . caused by the pathogen and the inflammatory response ) . Compared to the microbiota , an adaptive sIgA response mounted during the later stages of an acute infection contributes little to clearing the pathogen from the gut lumen . However , pathogen-specific sIgA protects from mucosal inflammation if the same pathogen is encountered for a second time . Thus , the microbiota and sIgA have complementary functions which jointly protect against enteropathogenic bacteria during the initial infection and subsequent exposure . How does the microbiota mediate pathogen clearance ? The lack of suitable assay systems has hampered addressing this question in the past . Clearly , S . tm clearance starts out in a situation where the pathogen has grown up in the gut lumen , inflicted disease and thereby slashed microbiota density , composition and/or function [45] . This situation is gradually reversed , involving the decrease of luminal pathogen loads as well as microbiota re-growth . Finally , normal microbiota composition , density and function are restored . Conceivably , some of the mechanisms conferring colonization resistance , i . e . bacteriocin production , inhibitory metabolites , oxygen depletion , receptor blocking , stimulating mucin- or antimicrobial peptide release , stabilization of the mucosal barrier , improvement of gut motility and/or nutrient limitation [5] , might also contribute to different phases of pathogen clearance . Also , microbiota-mediated stimulation of the mucosal cellular immune system may be involved [2] , [46] even though TH-17 mediated responses do not seem to contribute significantly to S . tm clearance , as indicated by adoptive transfer experiments ( Fig . 8 ) . The mechanisms mediating clearance and the relative importance of the microbiota , sIgA and other mucosal immune responses may differ between different pathogens or even between different strains of a given pathogen . Identifying the commensal species ( or consortia ) involved and the molecular mechanisms mediating pathogen clearance will be an important task for future research . Does sIgA contribute to pathogen clearance ? Pathogen specific sIgA is produced by wild type animals during the phase of pathogen clearance . O-antigen-specific sIgA led to aggregation of luminal pathogens , prevented access to the enterocyte surface and reduced net pathogen growth as indicated by a reduced competitive index . Surprisingly , this had little effect on S . tm clearance . Wild type mice and IgA−/− littermates displayed equivalent rates of pathogen clearance . Moreover , sIgA did not reduce pathogen loads in the stool , at least in the L-mice in the absence of a complex gut flora . Thus , for pathogen clearance at the end of a primary enteric S . tm infection , a pathogen-specific sIgA response is neither necessary nor sufficient . Instead , sIgA protected from mucosal inflammation upon re-infection with the same pathogen . The LPS O-antigen was the key protective antigen of this adaptive immune response . Protection was attributable to pathogen-aggregation in the gut lumen , reduced net pathogen growth and pathogen-exclusion from the epithelial surface , thus inhibiting pathogen invasion into the gut tissue . This required sIgA transport into the gut lumen , as immunized pIgR−/− mice , which fail to transport sIgA across the intestinal epithelium , have equivalent pathogen loads in the gut mucosa as non-immunized littermates or wild type animals . Thus , pathogen specific antibodies do not seem to contribute much to protection , once S . tm has breached the mucosal barrier . This is in line with earlier work on the roles of antibody responses in systemic S . tm infection [17] , [47] . In conclusion , our experiments show that O-antigen-specific sIgA responses protect against Salmonella-mediated gut inflammation upon re-infection . It is interesting to consider the protective function of sIgA and the microbiota from an evolutionary perspective . The intestines of most animals are colonized by bacterial communities [43] . It seems safe to assume that microbiota have an evolutionary ancient function in protecting from infection . We speculate that this pertains to colonization resistance and to pathogen clearance and that both have evolved to provide protection against a broad range of pathogens . The elaborate adaptive immune system of modern mammals , including sIgA responses , evolved much later . It evolved in the presence of the protective functions provided by the microbiota , i . e . colonization resistance and pathogen clearance . The high efficiency of this microbiota-mediated protection may explain why sIgA responses have not evolved to affect the first round of infection with a given pathogen . This was simply not necessary . In contrast , evolving the sIgA response to protect in the case of repeated exposure to the same pathogen may have represented a strong benefit which cannot be accomplished by the microbiota . This evolutionary history may explain why the sIgA response contributes little during the primary infection . Anyhow , in modern mammals the microbiota and sIgA have quite different protective functions which complement each other during the initial- and subsequent encounters with a given pathogen . An ‘unfavorable’ microbiota composition , e . g . in L-mice , can result in long term shedding . Asymptomatic NTS excretion is also observed in humans recovering from acute diarrhea . This period of asymptomatic excretion normally lasts for two to eight weeks , but may last for more than a year in a few patients . This poses a risk of transmission . In analogy to the long term shedding by L-mice , we propose that these individuals might lack some unidentified component of the normal microbiota . In L-mice , the pathogen is cleared upon transferring microbiota from a healthy donor . This may have implications for managing human long term asymptomatic excretors . Traditionally , patients are advised to adhere to strict personal hygiene and might even be isolated in order to reduce the risk of transmission . However , at the same time this deprives the patients from exposure to conventional microbiota from healthy individuals which might enhance pathogen clearance . So far , we do not know the species of the microbiota , the cellular interactions , and molecular mechanisms explaining pathogen clearance . However , the experimental systems presented in our study may provide the tools to address these important issues . Our findings provide a basis for future research on optimal management of ‘asymptomatic excretors’ , NTS vaccine development and microbiota-directed therapy for acute diarrheal NTS infections .
Specified pathogen-free ( SPF ) wild type C57BL/6 mice , JH−/− [48] and IgA−/− [49] , pIgR−/− [50] and TCRβ−/−δ−/− mice [51] ( 7–10 weeks old; all C57BL/6 background ) were bred at the Rodent center HCI ( RCHCI ) under barrier conditions in individually ventilated cages ( Ehret ) . IgA−/− , IgA+/− and IgA+/+ littermates were generated by crossing IgA−/− with C57BL/6 mice and breeding IgA+/−×IgA+/− animals . L-mice were generated by colonizing germfree C57BL/6 mice with the Altered Schaedler flora ( ASF ) . Mice , housed in a bubble isolator , were inoculated at eight weeks of age by intra-gastric and intra-rectal administration of 107–108 cfu of ASF bacteria on consecutive days ( www . taconic . com/library ) . Later , L-mice ( C57BL/6 background ) were maintained under barrier conditions in IVCs with autoclaved chow and autoclaved , acidified water . Mice with complex microbiota were never housed together with these in the same room to prevent contamination with additional commensal bacteria . All animal experiments were approved ( license 201/2004 and 201/2007 Kantonales Veterinäramt Zürich ) and performed according to local guidelines ( TschV , Zurich ) and the Swiss animal protection law ( TschG ) . Salmonella infections were performed in individually ventilated cages at the RCHCI , Zurich , as previously described [52] . In brief , wild type C57BL/6 mice , JH−/− , IgA−/− , pIgR−/− and TCRβ−/−δ−/− mice were pretreated with 20mg of streptomycin ( sm ) by gavage . 24h later , the mice were inoculated with 5×107 CFU of S . tmatt , PBS ( mock ) or as indicated . ‘Challenge infections’ were performed 40 days later ( or as indicated ) . Mice were treated with ampicillin ( 20mg; by gavage ) and 24h later infected with a dose of 200 CFU of the respective ampicillin-resistant ( pM973 ) challenge strain . Samples of cecal tissue were cryo-embedded , and inflammation was quantified on cryosections ( 5 µm , cross-sectional ) stained with hematoxylin and eosin ( H&E ) . Pathogen-colonization was assessed as described , below . H&E-stained cecum cryosections were scored as described , evaluating submucosal edema , PMN infiltration , goblet cells and epithelial damage yielding a total score of 0–13 points [53] . Mesenteric lymph nodes ( MLN ) , spleen and liver were removed aseptically and homogenized in cold PBS ( 0 . 5% tergitol , 0 . 5% BSA ) . The cecum content was suspended in 500ìl cold PBS and bacterial loads were determined by plating on MacConkey agar plates ( 50 ìg ml−1 streptomycin ) as described [21] . Colonization levels of the challenge strain ( carrying pM973 with an antibiotic marker ) and immunization strain ( S . tmatt; kmR ) were determined by selective plating ( 100ìg ml−1 ampicillin or 30 ìg ml−1 kanamycin; levels of challenge strain: ampR-kmR Salmonella ) . For co-infection experiments shown in Fig . 2C , competitive indices were calculated according to the formula CI = ratio S . tm:S . encecal content/ratio S . tm:S . eninoculum . MLN were harvested from S . tmatt→L ( total of 5 mice ) and S . tmatt→L/C ( total of 5 mice ) at day 80 post S . tmatt infection . Single cell suspensions were prepared using 100µm cell strainers and 40µg/ml DNAse ( Roche ) . S . tmatt→L mice ( at day 40 post S . tmatt infection ) were injected intravenously with 3×107 cells ( pooled for each of the two groups ) in 200µl PBS . Fecal S . tmatt shedding was monitored for another 40 days . Single-MLN-cell suspensions were prepared as described above . For intracellular staining of IFN-γ and IL-17A , 1×107 nucleated MLN cells were cultured for 3 h in 1 ml of RPMI 1680 supplemented with 10% heat-inactivated FCS and stimulated with PMA ( 5pg/ml ) /ionomycin ( 500pg/ml ) . After adding 20 µg/ml brefeldin A , the cells were incubated for another 3h at 37°C . Cells were harvested and washed in ice-cold FACS buffer ( PBS , 2% heat-inactivated FCS , 5 mM EDTA , and 0 . 02% sodium azide ) . Cells were resuspended in FACS buffer and stained on the surface with fluorescently-labeled antibodies for 30 min on ice . For intracellular staining of IL-17A and IFN-γ , cells were washed once and fixed/permeabilized for 10 min at room temperature using 500 µl of FIX/perm solution ( FACSLyse; BD Biosciences; diluted to 2× concentration in distilled water and 0 . 05% Tween 20 ) . Cells were washed once and stained with directly conjugated Abs against IFN-γ-APC ( BD ) and IL-17A-PE ( Biolegend ) . Cells were then washed again and resuspended in PBS . Data were collected on a LSRII flow cytometer ( BD Biosciences ) and analyzed using FlowJo software ( Tree Star ) . The intestine was flushed with 2 ml of a washing buffer containing PBS , 0 . 05M EDTA pH8 . 0 and 66µM PMSF . Intestinal wash was briefly vortexed and centrifuged at 4°C , 30 min , 40 . 000 rpm ( Eppendorf centrifuge ) . Aliquots of supernatants were stored at −80°C . Bacteria harboring pM973 ( GFP expression after tissue entry ) in the lamina propria and epithelium were enumerated by fluorescence microscopy as described [22] using cryo-sections of PFA-fixed cecal tissue stained with Armenian hamster anti-CD54 ( clone 3E2; stains lamina propria ) antibody ( Becton Dickinson ) , Cy3-conjugated goat anti–Armenian hamster Ig ( Jackson ImmunoResearch Laboratories ) , DAPI ( stains DNA; Sigma-Aldrich ) , and Alexa647-conjugated phalloidin ( stains polymerized actin; Fluoprobes ) . We evaluated three 20 µm thick sections of the cecum per mouse and plotted for each mouse the average of the three values . For detecting S . tm and S . en pM979 in the gut lumen in situ , cecal tissues were recovered and treated as described recently [54] . Briefly , the tissues were fixed in paraformaldehyde ( 4% in PBS , pH 7 . 4 over night , 4°C ) , washed with PBS , equilibrated in PBS ( 20% sucrose , 0 . 1% NaN3 over night , 4°C ) , embedded in O . C . T . ( Sakura , Torrance , CA ) , snap-frozen in liquid nitrogen and stored at −80°C . Cryosections ( 7ìm ) were air-dried for 2 h at room temperature , fixed in 4% paraformaldehyde ( 5 min ) , washed and blocked in 10% ( w/v ) normal goat serum in PBS for 1h . S . tm was detected by staining for 1h with a polyclonal rabbit á-Salmonella-O-antigen group B serum ( factors 1 , 4 , 5 and 12 , Difco; 1∶500 in PBS , 10% ( w/v ) goat serum ) and Cy3-conjugated secondary goat-α-rabbit antibody . S . en pM979 expresses gfp under the control of a constitutive promoter and bacteria were detected in the green channel . F-Actin ( epithelial brush border ) was visualized by staining with Alexa-647-conjugated phalloidin , as indicated ( Molecular Probes ) . Sections were mounted with Vectashield hard set ( Vector laboratories ) and sealed with nail polish . Images were recorded with a microscope ( Axiovert 200; Carl Zeiss , Inc . ) , an Ultraview confocal head ( PerkinElmer ) , and a krypton argon laser ( 643-RYB-A01; Melles Griot ) . Infrared , red , and green fluorescence was recorded confocally , and blue fluorescence was recorded by epifluorescence microscopy . Frozen consecutive sections of spleen , liver , cecum , colon and small intestine ( 7ìm thick ) were briefly fixed ( 10 min ) in acetone and blocked for 30 min with phosphate buffered saline ( PBS ) containing 0 . 5% bovine serum albumin ( BSA ) . Sections were then incubated with the primary antibody for 1 h at room temperature . Primary antibodies included: B220/CD45R ( Pharmingen 553084; 1∶200 ) , CD4 ( clone YTS191; 1∶200 ) and CD8 ( clone YTS169; 1∶200 ) for T-cells ( kindly provided by Rolf Zinkernagel; 1∶50 ) , Ly-6G ( Gr-1 ) for neutrophils ( clone RB6-8C5; 1∶600 ) , F4/80 for macrophages ( Serotec MCAP 497; 1∶50 ) , CD11c for dendritic cells ( BD Biosciences 553800; 1∶100 ) and IgA ( rat-anti-mouse IgA; Pharmingen 556969; clone C10-3; 1∶4000 ) . Secondary antibodies and detection chromagens were applied and visualized using standard methods ( see also [55] ) . Statistical analysis was performed using the exact Mann-Whitney U test ( Prism 4 . 0c ) . A P value of <0 . 05 ( two tailed ) was considered to be statistically significant . In mouse experiments , values were set to the minimal detectable value ( 10 cfu for cecum; 10 CFU for MLNs; 20 CFU for the spleen ) for samples harboring “no bacteria . ” Two figures ( Fig . 1A and 4F ) were generated using the statistical software package R . To assess the distribution of Salmonella loads in mice during the 60 day infection experiments , median and quantiles ( corresponding to 0 . 05 , 0 . 25 , 0 . 75 and 0 . 95 probabilities ) were plotted for each day or group of days . We performed a linear regression on medians and both 0 . 05 and 0 . 95 quantiles , weighted by the number of data points sampled for each day . The OTUs abundance heatmap represents the mouse normalized OTU abundances ( log2 ) clustered by average linkage clustering on Euclidean distances . This was generated using the function ‘heatmap . 2’ from the ‘gplots’ R library . The equivalent of 1 OD600 units/ml ( where OD600 is the optical density at 600nm ) of liquid o . n . cultures of S . tmΔO , Lactobacillus reuteri RR , Enterococcus faecalis , S . enwt , E . coli , S . tmwt , S . tmwt proteinase K treated ( Gibco/Life Technologies; 0 . 4 mg/ml; 1h 57°C ) or S . tm M933 was pelleted by centrifugation at 14 , 000×g for 2 min , and the supernatant was discarded . Cells were resuspended in Laemmli sample buffer ( 0 . 065 M Tris-HCl [pH 6 . 8] , 2% [wt/vol] sodium dodecyl sulfate [SDS] , 5% [vol/vol] β-mercaptoethanol , 10% [vol/vol] glycerol , 0 . 05% [wt/vol] bromophenol blue ) and lysed for 5 min at 95°C . Equal amounts of the different strains and purified S . tm flagellin FliC were loaded on a 12% SDS-polyacrylamide gel and proteins wer separated by electrophoresis . Immunoblots were stained with mouse serum ( diluted 1∶200 in PBS ) or intestinal lavages ( diluted 1∶20 in PBS ) from naïve or immunized mice , goat-α-mouse-IgA HRP ( Southern Biotech ) , goat-anti-mouse-IgG HRP ( Bethyl Laboratories ) and developed using an ECL kit ( Amersham ) . The same protocol was used for the analysis of the human patient serum ( dilution 1∶20 in PBS ) , where goat-anti-human-IgA-HRP ( 2050-05 , NEB ) and goat-anti-human-IgG-HRP ( 2040-05 , NEB ) were used as secondary antibodies . Analysis was performed as described in [30] . 3ml LB cultures were inoculated from single colonies of plated bacteria and cultured overnight at 37°C without shaking . 1ml of culture was gently pelleted for 4min at 7 , 000 rpm in an Eppendorf minifuge and washed 3× with sterile-filtered PBS ( 1% BSA , 0 . 05% sodium azide ) before resuspending to yield a final density of 107 bacteria per ml . Mouse serum was diluted 1∶20 in PBS ( 1% BSA , 0 . 05% sodium azide ) and heat-inactivated at 60°C for 30min . The serum solution was then spun at 13 , 000 rpm in an Eppendorf minifuge for 10min to remove any bacteria-sized contaminants and the supernatant was used to perform serial dilutions ( 1∶20 , 1∶60 , 1∶180 ) . 25µl serum solution and 25µl bacterial suspension were mixed and incubated at 4°C for 1h . Bacteria were washed twice before resuspending in monoclonal FITC-anti-mouse IgA ( 559354; BD Pharmingen ) , PE-anti-mouse total IgG ( 715-116-151; Jackson Immunoresearch Europe ) and APC-anti-mouse IgM ( 550676; BD Pharmingen ) . After a further hour of incubation bacteria were washed once with PBS ( 1% BSA , 0 . 05% sodium azide ) and then resuspended in PBS ( 2% PFA ) for acquisition on a FACSCalibur using FSC and SSC parameters in logarithmic mode . Data were analysed using FlowJo software ( Treestar , USA ) . Analysis of specific IgA in intestinal lavages was achieved using an identical protocol , using a dilution of 1∶2 , 1∶6 and 1∶18 of gut wash . The cecum tissue was excised ( 3 biological replicates per group ) , washed in cold PBS , placed in 300µl RLT-buffer ( RNeasy Mini Kit , Qiagen; 1% β-Mercaptoethanol ) and snap-frozen in liquid nitrogen . Total RNA was extracted with the Nucleospin RNA II kit ( Macherey Nagel , Germany ) and prepared for hybridization as recommended by the manufacturer ( Applied biosystems , USA ) . Briefly , 2µg of total RNA and a T7-oligo ( dT ) primer were used for reverse transcription . The double-stranded cDNA was purified and converted to DIG labeled-cRNA by in vitro transcription using DIG-UTP ( Roche , Germany ) . The cRNA was purified , fragmented and hybridized on ABI Mouse Genome Survey v2 . 0 microarrays for 16h . The microarray was washed and incubated with anti-DIG antibodies conjugated to alkaline phosphatase and a chemiluminescent substrate . The microarrays were scanned with the Applied Biosystems 1700 chemiluminescent microarray analyzer [56] . Normalization was achieved using the NeONORM method [57] . Significance of log2 fold changes ( log2Q ) were determined based on a double-log normal distribution hypothesis of signal intensities using mixture ANOVA methodology [56] . A change in the gene expression profiles was considered as significant if p<0 . 001 . Heat maps were created according to standard methods [56] . Gene Ontology ( GO ) annotations were analyzed using the Panther Protein Classification System ( http://www . pantherdb . org ) . Microarray data were deposited in the publicly available database: http://mace . ihes . fr with accession number: 2947924142 . Total DNA was extracted from cecal contents using a QIAmp DNA stool mini kit ( Qiagen ) . Bacterial lysis was enhanced using 0 . 1mm glass beads in buffer ASF and a Tissuelyzer device ( 5 minutes , 30Hz; Qiagen ) . V5-V6 regions of bacterial 16S rRNA were amplified using primers B-V5 ( 5′ GCCTTGCCAGCCCGCTCAG ATT AGA TAC CCY GGT AGT CC 3′ ) and A-V6-TAGC ( 5′GCCTCCCTCGCGCCATCAG [TAGC] ACGAGCTGACGACARCCATG 3′ ) . The brackets contain one of the 20 different 4-mer tag identifiers [TAGC , TCGA , TCGC , TAGA , TGCA , ATCG , AGCT , AGCG , ATCT , ACGT , GATC , GCTA , GCTC , GATA , GTCA , CAGT , CTGA , CAGA , CTGT , CGTA] . Cycling condition were as follows: 95°C , 10min; 22 cycles of ( 94°C , 30s; 57°C , 30s; 72°C , 30s ) ; 72°C , 8min; 4°C , ∞; Reaction conditions ( 50µl ) were as follows: 50ng template DNA; 50 mM KCl , 10 mM Tris-HCl pH 8 . 3 , 1 . 5 mM Mg2+ , 0 . 2mM dNTPs; 40pmol of each primer , 5U of Taq DNA polymerase ( Mastertaq; Eppendorf ) . PCR products of different reactions were pooled , ethanol-precipitated and fragments of ∼300bp were purified by gel electrophoresis , excised and recovered using a gel-extraction kit ( Machery-Nagel ) . Amplicon sequencing of the PCR products was performed using a 454 FLX instrument ( 70×70 Picotitre plate ) according to the protocol recommended by the supplier ( www . 454 . com ) . PCR to detect ASF bacteria in the feces was done as described in [42] . We applied quality control to 454 reads in order to avoid artificial inflation of ecosystem diversity estimates [58] . Reads containing the consensus sequence ( ‘ACGAGCTGACGACA[AG]CCATG’ ) of the V6 reverse primer were filtered with respect to their length ( 200nt≤length≤300nt ) . Quality filtering was then applied to include only sequences containing one of the exact 4nt tag sequences and displaying at maximum one ambiguous nucleotide ‘N’ . The latter criterion has been reported as a good indicator of sequence quality for a single read [41] . We identified 6 , 754 reads with an incorrect primer sequence , 1 , 155 reads shorter than 200nt , 8 reads longer than 300nt and 119 reads containing more than one ‘N’ . After filtering , 140 , 237 reads remained ( out of an initial total of 149 , 786 raw reads ) and were processed as described below for OTU definition and chimera filtering . To estimate the reliability of sample discrimination using our primer-tagging approach , we assessed the number of reads observed to have an illegitimate 4-mer tag ( i . e . , different from our set of 20 tags ) . The sequencing plate produced a total of 141 , 784 quality-filtered reads from which 1 , 547 contained an incorrect tag ( 1 . 09% ) . Given that 256 distinct 4-mer tags are possible and that we used only 20 of these , the majority of sequencing or primer errors in this region are detectable . Correcting for the small fraction of undetectable errors ( 20/256 ) and division by four yields an error rate of 0 . 296% per single nucleotide - at the position of the tag in the primer ( this includes errors during primer synthesis as well as sequencing ) . Since most errors are actually visible as errors , the rate of unintentional ‘miscall’ of sample identity is 0 . 092% . To reduce computational time and complexity , we built OTUs using the complete filtered dataset covering all non-redundant reads from the 20 samples . Exactly identical sequences were represented by one representative only; after OTU computation , redundant sequences were taken into account for OTU abundance analysis . For subsequent taxonomy classification , we included additional quality-filtered 16S rRNA reference sequences , selected from the Greengenes database ( http://greengenes . lbl . gov/Download/Sequence_Data/Greengenes_format/greengenes16SrRNAgenes . txt . gz , release 01-28-2009 [59] ) . This reference database is based on full-length non-chimerical sequences with a minimum length of 1100nt ( in order to fully cover the V6 region of all entries ) . No archaeal sequences were included in the analysis . The alignment of non-redundant reads from all mice with the reference database was performed using the secondary-structure aware Infernal aligner ( http://infernal . janelia . org/ , release 1 . 0 , [60] ) and based on the 16S rRNA bacterial covariance model of the RDP database ( http://rdp . cme . msu . edu/; [61] ) . Before defining OTUs , we first removed reference sequences for which the alignment was not successful ( Infernal bit-score<0 ) . The alignment was then processed to include an equivalent amount of information from every read . To do so , we identified the consensus reverse primer sequence of the V6 region within the aligned sequence of Escherichia coli K12 , as a reference . The full alignment was then trimmed from the start position ( defined by the E . coli V6 reverse primer ) and ended after 200nt . This also ensured a further limitation of the effect of pyrosequencing errors by trimming the 3′ end of each read , a region which is more sequencing-error prone ( the trimmed and aligned reads length ranged from 152 to 231nt ) [58] . Using this alignment , OTUs were built by hierarchical cluster analysis at various distances ( 0 . 01 , 0 . 03 , 0 . 05 and 0 . 10 ) using the ‘complete linkage clustering’ tool of the RDP pyrosequencing pipeline http://pyro . cme . msu . edu/; [61] ) . In a first step , taxonomy was inferred for all reads using the stand-alone version of the RDP classifier ( http://sourceforge . net/projects/rdp-classifier , revision 2 . 0 , [62] ) . Taxon-level predictions were considered reliable when supported by a minimum bootstrap value of 80% . In order to predict taxonomy for each OTU , we either used any reference sequences present within a cluster , or the taxonomy of the reads present in the cluster , as predicted by the RDP classifier . To increase the resolution of the prediction , we privileged any reference sequences over the reads . For each OTU , taxonomy was inferred by a simple majority vote: if more than half of the reference sequences ( or reads ) present within a cluster agreed on a taxon , the OTU was annotated according to this taxon . In case of conflicts , we assigned a consensus taxon to a higher phylogenetic level for which the majority vote condition was met . Deep pyrosequencing on the 454 platform has revealed extensive microbial diversity that was previously undetected with culture-dependent methods [63] . Nevertheless , sequencing data generated from pools of PCR products have to be interpreted carefully; limitations and biases of the PCR technique have to be taken into account . This can lead to over-estimations of microbial diversity as has been recently reported [58] , [64] . Moreover , during amplification , chimerical sequences can be generated . On such short sequences , recombination points ( recombination can occur from an incompletely extended primer or by template-switching; [65] ) are extremely difficult to detect . Recently , a new tool to filter noise and remove chimera in 454 pyrosequencing data has been published [64] . There , the authors suggest that because of sequencing errors , diversity estimates may be at least an order of magnitude too high . To our best knowledge , at the time of our analysis , there were no available tools to detect chimera within libraries of short 454 reads . Therefore , in order to detect chimeras we decided to compare taxonomies assigned to N-terminal and C-terminal read fragments using BLASTn . In order to ensure a reasonable alignment length and a relatively high identity to the matching reference sequences , we only analyzed reads for which both fragments had a minimum identity of 95% and a minimum bit-score of 150 ( these cutoffs were selected heuristically ) . A given read was deemed chimeric when the taxonomies of the best hits of each half were clearly not congruent ( i . e . , differing at the phylum level ) . Our simple chimera detection method resulted in a slightly higher rate of detected chimera compared to the method of Quince et al . , 2009 ( ∼4 . 5% compare to ∼3% in their example ) , suggesting that our approach is at least of comparable stringency [64] .
|
Numerous pathogens infect the gut . Protection against these infections is mediated by mucosal immune defenses including secreted IgA as well as by the competing intestinal microbiota . However , so far the relative importance of these two different defense mechanisms remains unclear . We addressed this question using the example of non-typhoidal Salmonella ( NTS ) gut infections which can be spread in stool of infected patients over long periods of time . We used a mouse model to reveal that the intestinal microbiota and the adaptive immune system hold different but complementary functions in fighting NTS infections . A primary Salmonella infection disrupts the normal microbiota and elicits Salmonella-specific sIgA . sIgA prevents disease when the animal is infected with NTS for a second time . However , sIgA was dispensable for pathogen clearance from the gut . Instead , this was mediated by the microbiota . By re-establishing its normal density and composition , the microbiota was necessary and sufficient for terminating long-term fecal Salmonella excretion . This establishes a novel paradigm: The microbiota clears the pathogen from the gut lumen , while sIgA protects from disease upon re-infection with the same pathogen . This has implications for the evolutionary role of sIgA responses as well as for developing microbiota-based therapies for curing infected patients .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"microbiology/immunity",
"to",
"infections",
"immunology/immune",
"response",
"infectious",
"diseases/bacterial",
"infections",
"immunology/immunity",
"to",
"infections",
"infectious",
"diseases/gastrointestinal",
"infections"
] |
2010
|
The Microbiota Mediates Pathogen Clearance from the Gut Lumen after Non-Typhoidal Salmonella Diarrhea
|
Visceral leishmaniasis ( VL ) is a severe vector-born disease of humans and dogs caused by Leishmania donovani complex parasites . Approximately 0 . 2 to 0 . 4 million new human VL cases occur annually worldwide . In the new world , these alarming numbers are primarily due to the impracticality of current control methods based on vector reduction and dog euthanasia . Thus , a prophylactic vaccine appears to be essential for VL control . The current efforts to develop an efficacious vaccine include the use of animal models that are as close to human VL . We have previously reported a L . infantum-macaque infection model that is reliable to determine which vaccine candidates are most worthy for further development . Among the few amastigote antigens tested so far , one of specific interest is the recombinant A2 ( rA2 ) protein that protects against experimental L . infantum infections in mice and dogs . Primates were vaccinated using three rA2-based prime-boost immunization regimes: three doses of rA2 plus recombinant human interleukin-12 ( rhIL-12 ) adsorbed in alum ( rA2/rhIL-12/alum ) ; two doses of non-replicative adenovirus recombinant vector encoding A2 ( Ad5-A2 ) followed by two boosts with rA2/rhIL-12/alum ( Ad5-A2+rA2/rhIL12/alum ) ; and plasmid DNA encoding A2 gene ( DNA-A2 ) boosted with two doses of Ad5-A2 ( DNA-A2+Ad5-A2 ) . Primates received a subsequent infectious challenge with L . infantum . Vaccines , apart from being safe , were immunogenic as animals responded with increased pre-challenge production of anti-A2-specific IgG antibodies , though with some variability in the response , depending on the vaccine formulation/protocol . The relative parasite load in the liver was significantly lower in immunized macaques as compared to controls . Protection correlated with hepatic granuloma resolution , and reduction of clinical symptoms , particularly when primates were vaccinated with the Ad5-A2+rA2/rhIL12/alum protocol . The remarkable clinical protection induced by A2 in an animal model that is evolutionary close to humans qualifies this antigen as a suitable vaccine candidate against human VL .
Human VL is a severe systemic disease caused by protozoan parasites of the Leishmania donovani complex [1] . It remains one of the major infectious diseases primarily affecting some of the poorest regions of the world , with an estimated occurrence of approximately 0 . 2 to 0 . 4 million new cases of clinical VL annually worldwide , in addition to an estimated 20 , 000 to 40 , 000 VL deaths per year . At present , VL occurs in at least 83 countries or territories , but more than 90% of the global human cases were recorded in India , Bangladesh , Sudan , South Sudan , Ethiopia and Brazil . Although recognition of the geographic distribution of VL and its prevalence has increased during recent years , the disease is still grossly underreported [2] . Furthermore , most infections with the visceralizing Leishmania spp . remain asymptomatic or sub-clinical [3]–[5] . Frank disease ( also known as kala-azar ) is characterized by prolonged fever , diarrhea , hepatosplenomegaly , weight loss , and even death , if left untreated [6] . In addition to be partially influenced by the genetic background [7] , [8] , other risk factors such as young age , malnutrition , and immunosuppression [9]–[11] are important determinants of host susceptibility to VL . Chemotherapy is toxic and expensive , and a limited number of anti-Leishmania agents are available , to which drug resistance is documented [12] , [13] . In addition , no proven successful vaccine for controlling human VL is in routine use [14] . The epidemiology of this disease is complex and can be altered by changes at any point in the transmission cycle that is formed by humans , the reservoir hosts and the phlebotomine sand fly vectors . In some parts of both the Old and New World , transmission occurs mainly in the peridomestic setting , where domestic dogs serve as primary reservoir host of L . infantum ( syn . L . chagasi ) . Hence , measures employed to control zoonotic VL include mass elimination of seropositive dogs , but the impact of euthanasia programs on human and canine VL incidence is doubtful in theoretical and practical grounds [12] , [15] . In other cases , the parasite is transmitted from human to human via infectious sand fly bites , as for L . donovani VL in India and Bangladesh and during epidemic spread in the East African region [2] . Thus , strategies employed to control anthroponotic VL have focused on active case detection and treatment and use of insecticide-impregnated materials [13] . However , a sustainable prevention of the disease using these control measures is costly and usually fails in developing countries [12] , [13] . Nevertheless , most experts believe that prophylactic or possibly post-exposure vaccination will be essential for ultimate control of the disease [14] , [16] . Several Phase III clinical trials testing crude vaccine approaches have given conflicting results [17] . Overall , the results vary from 0 to 75% efficacy against CL and little ( < 6% ) or no protection against VL [16] . Although host genetics can have dramatic effects on T-cell responses to existing vaccines [18] , technical problems ( including changes in the quality , stability and potency of the antigens ) may provide explanation for some of the variation in efficacy observed in those human vaccine studies . To circumvent these obstacles , many recombinant vaccines using either subunit proteins in adjuvants , naked DNA and live vectors encoding genes for specific antigens have been tested for immunogenicity and protective efficacy in animal models of leishmaniasis [16] . In addition to crude parasite extracts , partially purified fractions containing secreted proteins of Leishmania or the Fucose Manose Ligant ( FML ) were shown efficacious and are currently used as commercial vaccines for canine VL [19] , [20] . In addition , recombinant antigens such as A2 , LACK , Cysteine Proteases A and B , or multicomponent vaccines including KMP-II , TRYP and GP63 or LeIF , LmSTI1 and TSA antigens have shown some level of protection in pre-clinical trials . A comprehensive list of the antigens along with immune responses and protection of respective trials are described in detail elsewhere [21] . Among the recombinant antigens selected as candidates for a prophylactic vaccine against VL , one of specific interest is the amastigote specific antigen A2 from L . donovani [22] , [23] . The recombinant A2 ( rA2 ) conferred protection in mice challenged with L . donovani , L . infantum or L . amazonensis when administered as recombinant protein [22] , [24] , DNA [25] , viral vector [26] , or transfected parasites ( L . tarentolae ) [27] . In the form of a currently licensed veterinary product ( designated Leish-Tec ) , this rA2-saponin vaccine induced partial protection in the high dose L . infantum-beagle dog model [28] . Whether prophylactic immunization using A2-based vaccines can achieve similar levels of immunity against VL in genetically diverse human subjects has yet to be determined . Although the predictive value for any animal model in vaccine development ultimately depends on validating data from human trials , the primate M . mulatta , which diverged from humans approximately 25 million years ago , has been accepted as a system that more closely mirrors human immunity for vaccine-development studies against infectious diseases [29] , [30] . In this communication , we provide evidence that rA2 , as a single antigen , confers marked clinical protection in outbred macaques against L . infantum challenge , and may by itself constitute a promising vaccine candidate against human VL .
The experimental protocols involving monkeys and all the conditions of animal maintenance and handling were reviewed and approved by the Institutional Animal Care and Use Committee ( CEUA-FIOCRUZ , resolution # P0048-00 and P . 0215/04 ) . All the invasive procedures were performed in accordance with the national guidelines for animal biosafety . Rhesus monkeys ( Macaca mulatta ) were obtained from a breeding colony from FIOCRUZ Primate Research Centre in Manguinhos ( Rio de Janeiro , Brazil ) and housed individually for experiments , in stainless-steel squeeze-back cages and fed daily with a commercially available primate diet supplemented with fresh fruits and vegetables . Water was provided ad libitum . The welfare of the primates was closely monitored by a veterinarian , under the supervision of nonhuman primate care specialists . All the procedures involving non-human primates were carried out according to the Brazilian guide for care and use of laboratory animals ( Projeto de lei 3 . 964/97-www . planalto . gov . br ) , which is conformed to the recommendations of the Weatherall report for the use of non-human primates in research ( http://www . acmedsci . ac . uk/images/project/nhpdownl . pdf ) . To minimize suffering before interventions , such as infectious challenge , sampling or clinical procedures , animals were anaesthetized with ketamine hydrochloride 10 mg . kg−1 ( Cetamin , Synthec Vet , São Paulo , Brazil ) , and midazolam 0 . 10 mg . kg−1 ( Dormonid , Farma-Roche , São Paulo , Brazil ) , both injected intramuscularly . Animals were submitted to euthanasia with a lethal overdose of thiopental sodium ( Euthasol , Virbac Animal Health , Fort Worth , TX ) administered intravenously . The rA2 protein from L . donovani containing a tag of six histidine residues ( A2-HIS ) used for vaccination and for detecting A2-specific antibodies was purified from E . coli BL-21 containing pET16bA2 plasmid as reported elsewhere [31] . The pCIneo-A2 plasmid ( DNA-A2 ) was constructed following the procedure described by Ramiro and co-workers [32] . The adenovirus recombinant vectors encoding either the L . donovani A2 or the Trypanosoma cruzi Amastigote Specific Surface Protein 2 ( ASP2 ) genes were obtained as previously described elsewhere [26] , [33] . The 17 males and 16 females outbred macaque , aged between five and seven years old , weighing around 6 kg , were acclimatized to the laboratory conditions for at least two weeks before the experimental procedures began . As indicated in Table 1 and Figure 1 , different homologous and heterologous prime-boost vaccination regimens were used in this study . All vaccine and control formulations were prepared to give a final volume of 1 ml/dose . Briefly , primates were randomized by sex and assigned to seven groups . Group 1 contained three animals that received phosphate saline buffer ( PBS ) . All other groups contained five animals each . The animals vaccinated with rA2 ( rA2/rhIL-12/alum ) or adenovirus and rA2 ( Ad5-A2+rA2/rhIL-12/Alum ) received , respectively , three and four subcutaneous doses with 21 days intervals . The animals vaccinated with DNA and adenovirus ( DNA-A2+Ad5-A2 ) received four intramuscular injections in the left deltoid muscle region with 21 days interval . Forty days after the last boost , each macaque was inoculated intravenously with a single dose of 2×107 amastigotes/kg of body weight of a virulent L . infantum strain ( MHOM/BR/2001/HP-EMO ) . Amastigotes were harvested from heavily infected hamster spleens , prepared as previously described [34] , and typed by multilocus enzyme electrophoresis before use to challenge control and vaccinated primates . Clinical follow-up was performed by accurate inspection of monkeys for the presence of typical signs of human VL ( fever , diarrhea , body weight loss , hepatomegaly and splenomegaly ) . Additionally , blood collected into BD vacutainer tubes containing EDTA as an anticoagulant was used for assessment of hematological and blood chemistry parameters . The following blood components were measured with a computer-directed analyzer , using commercially available kits ( CELM Cia Equipadora de Laboratórios Modernos , Barueri , SP , Brazil ) : cholesterol , urea nitrogen , total protein and albumin , alanine aminotransferase ( ALT ) , aspartate aminotransferase ( AST ) activities . Total erythrocyte , leukocyte and haemoglobin counts were carried out with the cellular counter 530/550 ( CELM Cia Equipadora de Laboratórios Modernos , Barueri , SP , Brazil ) . Commercial assays were conducted in accordance with the manufacturer's instructions . Animals were scored for clinical and laboratory signs on a semi-quantitative scale from 0 ( absent ) to 3 ( severe ) , and the scores added up to give an overall clinical score for each animal . Monkeys with a total score of 0 to 3 were arbitrarily classified as being affected by sub-patent ( low tissue parasitism ) or asymptomatic patent infection ( steady detection of parasite-positive specimens ) ; those with a score of 4 to 18 were classified as suffering from symptomatic patent infection . To ascertain the immunogenicity of rA2 antigen , the antibody response was evaluated by ELISA and serum samples from all experimental animals obtained at different time of the experiment . Animals were also assessed by Soluble Leishmania Antigen ( SLA ) -based ELISA to measure seropositivity for infection . The test procedure was that as described previously [35] . Briefly , ELISA plates ( Corning , Tewksbury , MA ) were coated with either 5 µg/ml of rA2 or 10 µg/ml of SLA , blocked with PBS 1% BSA , and then incubated with 100 µl of macaque serum diluted 1∶80 . After washing three times , 100 µl/well of a peroxidase conjugate rabbit anti-rhesus monkey immunoglobulin G ( Accurate Chemical & Sci Co , Westbury , NY , USA ) diluted at 1∶20 , 000 was added , and incubated with the substrate OPD ( Zymed , CA , USA ) . Absorbance at 490 nm was measured with a microplate reader ( Model 680 , Biorad Laboratories , Hercules , CA ) . A group of sera with previously known titers as control values , as well as naïve rhesus controls , were included in each test . SLA was prepared from stationary-phase promastigotes of L . infantum ( MHOM/BR/2001/HP-EMO ) as reported elsewhere [34] . The specificity of circulating anti-A2 antibodies in sera from vaccinated macaques was also assessed by Western blot analysis . Briefly , 2 µg samples of rA2 were loaded , run in a 10% SDS polyacrylamide electrophoresis gel ( SDS PAGE ) ( Biorad Laboratories ) and transferred to nitrocellulose sheet ( Biorad Laboratories ) , as previously described by Towbin et al [36] . Nitrocellulose strips corresponding to different SDS PAGE lanes were incubated with serum samples diluted at 1∶200 and rA2-antibody specific binding revealed after incubation with a rabbit antibody anti-rhesus monkey IgG conjugated with horseradish peroxidase ( Accurate Chemical & Sci Co ) . Anti-A2 monoclonal antibody was kindly provided by Dr . Greg Matlashewski ( McGill University , Montreal , Canada . ) and used as positive control . For assessment of parasites , biopsy specimens were removed from liver at distinct stages of infection and processed for culture and histological examination or DNA isolation . Biopsy samples were cultured using NNN blood agar medium ( Difco , Franklin Lakes , NJ ) overlaid with complete Schneider’s Drosophila insect medium ( Sigma-Aldrich Corporation , St . Louis , MO ) prepared as reported elsewhere [34] . Relative parasite load quantification in terms of DNA amplification was carried out according to the procedure reported by Vitale and co-workers [37] . Briefly , DNA was extracted from the tissue samples using the Illustra tissue and cells genomic Prep Mini Spin Kit ( GE Healthcare , Cleveland , OH ) , according to the manufacturer’s instructions . All samples were submitted to real time PCR with oligonucleotides synthesized by Life Technologies ( Carlsbad , CA ) for the macaque endogenous β-actin gene ( 5’- CTTCTACAACGAGCTGCGCG -3’ and 5’ TCATGAGGTAGTCGGTCAGG-3’ ) to normalize results . The Leishmania kDNA was amplified using oligonucleotides ( 5’-GGCGTTCTGCGAAAATCG-3’ and 5’- AAAATGGCATTTTCGGGC-3’ ) designed to amplify the conserved region of the minicircle . Standard curves were obtained from 500 ng to 1 pg ( detection limit ) of DNA for both targets . The threshold cycle was determined for each point . All real time PCR reactions were also submitted in parallel to gel electrophoresis and melting curves . Results were converted into ng of DNA based on the standard curve; kDNA amplification was then converted into number of parasites , assuming that 200 fentograms of DNA correspond to one parasite ( 10−6 ng = 1 fg; 2×10−4 ng – 1 parasite ) . The experiment was terminated at week 24 post-challenge . Gross and light microscopic examinations of the liver and spleen were performed at necropsy . Paraffin sections from biopsy and necropsy tissues ( fixed in 10% neutral buffered formalin ) were stained with haematoxylin-eosin ( Sigma-Aldrich Corporation ) . Student’s t-test was used in comparative analysis of quantitative data and means were defined as significantly different when p-value < 0 . 05 .
Upon immunization with different prime-boost regimens , apart from a rise in body temperature by 1–2°C recorded after the last boost , no other systemic adverse reaction in the monkeys was observed throughout the whole period of experiment . Post-vaccination local effect was observed only in two macaques that received a mixture of rhIL-12 and alum as adjuvants . A small transient nodule developed at the site of injection and self-resolved in approximately 10 days ( Figure S1 ) As shown in Figure 2 , all animals vaccinated with either rA2/rhIL-12/alum or rAd5-A2+rA2/rhIL-12/alum protocols , but not with DNA-A2+Ad5-A2 , showed higher A2-specific antibody response after the last boost and before infectious challenge . Interesting , the levels of circulating anti-A2 IgG antibodies in animals from rA2/rhIL-12/alum and rAd5-A2+rA2/rhIL-12/alum were decreased after challenge with L . infantum . As can be seen in Figure 3A , the specificity inherent of circulating A2-specific antibodies from immunized macaques was confirmed by immunoblot . The reactivity of a mAb anti-rA2 in lysates of cells infected with Ad5-A2 is also shown in Figure 3B . Following the infectious challenge , there was an initial increase of anti-SLA IgG antibodies in all the groups of monkeys at week 14 post-infection ( day 193 ) ( Figure 2 ) . The specific disease course was quite variable among macaques , ranging from mild to severe VL . This appears to result from the outbred genetics of macaques used in this study . Nevertheless , whereas 80% ( 12/15 ) of vaccinated monkeys had asymptomatic patent infections 6 weeks after the infectious challenge , at this time point 72% ( 13/18 ) of animals in the control groups were found symptomatic . Moreover , 61% ( 11/18 ) primates of the control groups were still considered symptomatic at week 24 post-infection , while only three symptomatic cases of the groups rA2/rhIL-12/alum and DNA-A2+Ad5-A2 clinically recovered from infections ( Table 2 ) . None of the macaques vaccinated with Ad5-A2+rA2/rhIL-12/alum were symptomatic at 24 weeks post infection . Figure 4 shows the overall clinical score estimated for each monkey challenge-infected animal . According to their clinical condition , 6 macaques ( with scores of 7–8 ) and twelve others ( with scores of 4–6 ) in the control groups were classed as poly-symptomatic and oligo-symptomatic , respectively . Conversely , 9 ( with scores of 1-3 ) out of 15 vaccinated monkeys were classed as asymptomatic cases . The most consistent clinical parameters observed in affected monkeys were an intermittent rise in body temperature by 1–3°C , diarrhea , decrease in body weight ( 12–30% change ) , anemia and increases in Alanine Aminotransferase ( ALT ) and Aspartate Aminotransferase ( AST ) ( Table S1 ) . These changes were evident by week 6 post-infection and became more pronounced in those with progressing disease ( Table 2 ) . The impact of vaccination on establishment of infection was assessed through time by parasitological examination or real-time PCR of the liver at 6 and 24 weeks post-infection . As indicated in Table 2 , monkeys in all groups had sustained course of infection , ranging from sub-patent ( low parasitism and asymptomatic ) to asymptomatic ( patent parasitism ) or symptomatic ( patent parasitism and symptomatic ) . Nevertheless , steady detection by histopathology analysis occurred only in primates that remained with patent infection , i . e . , amastigote-containing macrophages were found in post-mortem specimens removed from liver or lymphoid organs . In contrast , most of the cases clinically recovered from infection following vaccination displayed low or undetectable tissue parasitism . Accordingly , the relative DNA quantities of the parasite were significantly lower in immunized macaques than in PBS treated animals ( Figure 5 ) . Of note , a more marked reduction on parasite load was found in animals vaccinated with rAd5-A2+rA2/rhIL-12/alum , thus indicating that these animals more efficiently controlled parasite growth . The main histopathological findings in the liver and spleen of challenged macaques are illustrated in Figures 6 . Images shown in Figures 6A to 6G show liver images of immature ( poorly differentiated ) granuloma , immune ( tuberculoid-type ) granuloma , immune granuloma composed of epithelioid cells and Langhans-type multinucleated giant cells , immature granuloma containing parasitized macrophages , intrasinusoidal lymphocytosis , mononuclear infiltrate in a portal space , and reactions of Kupffer cells , respectively . All animals at 6 weeks post-challenge developed poorly differentiated hepatic granulomas ( Figure 7 ) , typical of the initial stage of infection , thus confirming the establishment of L . infantum parasitism . These granulomas consisted of an aggregation of activated macrophages containing amastigotes , surrounded by lymphocytes and occasional plasma cells ( Figures 6 and 7 ) . Although not remarkable as the later stage of infection ( Figure 8 ) differences were already seen when comparing vaccinated and control groups . In particular , immature granulomas were less frequent and contained less marked parasitised macrophages in macaques vaccinated with the Ad5-A2+rA2/rhIL-12/alum protocol . At the chronic stage of infection ( 24 weeks post challenge ) , older hepatic granulomas composed of concentric layers of macrophages , epithelioid cells , Langhans-type multinucleated giant cells and lymphocytes ( Figures 6 and 8 ) were documented only in groups of control macaques ( i . e . , PBS , rhIL-12/alum , Ad5-ASP-2+rhIL-12/alum and DNA-wt/Ad5-ASP-2 ) , thus revealing that parasite persisted until the end of the experiment . At this time point , primates vaccinated with Ad5-A2+rA2/rhIL-12/alum exhibited almost complete granuloma resolution . Into a less extent , monkeys of the groups immunized with rA2/rhIL-12/alum or DNA-A2 + Ad5-A2 also displayed a regression of the hepatic lesions , as compared to those from control groups . The quantitative analysis of histological findings ( Figure 9 ) are consistent with tissue liver parasitism ( Figure 5 ) and clinical scores ( Figure 4 and Table 2 ) , all analyzed at 24 weeks post challenge , reflecting that protective immunity to L . infantum infection can be induced in heterogeneous macaque population by an A2-based vaccination . In addition , we examined the lymphoid structure in vaccinated and non-vaccinated controls . Sections from the spleen revealed a high frequency of well organized lymphoid follicle ( Figure 6H ) in most of the vaccinated macaques , whereas non-vaccinated animals showed more often extensively disorganized lymphoid tissue with follicles decreased in number and size ( Figure 6I ) , as well as sinusoidal congestion at the cortical zone ( Figure 6J ) . Additional histological findings in controls ( not vaccinated ) included amastigote-containing macrophages in the subcapsular area and/or in the red pulp ( data not shown ) .
On the base of compelling evidence that both CD4+ ( including multifunctional Th1 cells and central memory CD4+ T-cells ) and CD8+ T-cells are key players in the immune response to leishmaniasis , researchers have focused considerable efforts on the development of prophylactic vaccines that elicit T-cell responses [14] , [16] , [23] with the premise that such interventions will confer protective effects . Ample evidence supports the notion that heterologous prime-boost vaccination regimens can elicit greater immune responses than single immunization modalities . In this regard , combining DNA priming with a live vectored boost [32] , [38] , [39] or two different live vectors to prime and boost a response [40] , [41] have been explored as a means of raising protective T-cell responses . Of note , sustained immunity elicited by these vaccines correspond to , in addition to the emergence of an specific Th1 response , CD8+ T-cells response [32] , [39] that may also provide additional beneficial cytokines and/or their cytotoxic potential may allow release of amastigotes to facilitate killing by activated macrophages [14] . A variety of non-human primate models for both cutaneous leishmaniasis and VL have been used to assess the safety , immunogenicity , and protective efficacy of different vaccine protocols [30] . In most studies of this nature , it is difficult to accurately assess partial host immunity since clinical outcome , a highly variable parameter , is commonly used as a correlate of protection . Although Leishmania-specific T-cell responses can be induced safely in primates by vaccination , it depends on the particular protocol and may ranges from non-existing to full protection after the infectious challenge . However , it has become evident that the current parameters of cell-mediated immunity , i . e . , delayed-type hypersensitivity skin tests , or in vitro recall T lymphocyte responses , do not always correlate with clinical recovery and resistance to infectious challenge [30] . Neither study in the L . amazonensis [42] or L . major [43] macaque models , nor those in the L . major-vervet monkey model [44] have resulted in a clear definition of what T-cell responses are required for vaccine-induced protection . Therefore , the only way to determine acquired resistance afforded by a candidate vaccine is to challenge the vaccinated animals with virulent leishmania parasites . In the present study , we compared the potential efficacy of various A2 vaccination assays , using either recombinant protein , viral and DNA vectors . Our work showed that the vaccine preparations at the dose employed , apart from being safe and well tolerated , also stimulated specific antibody response to the rA2 . The transient local adverse reaction recorded in two macaques that had received the recombinant antigen formulated in a mixture of rhIL-12 and alum is in agreement with the results obtained in our previous study [43] , but differs from the findings reported by Kenney and co-workers [42] . The duration of these skin nodules was in general longer in their studies . These data are apparently accounted for the different antigen preparations ( particulate antigens versus subunit proteins ) and the amount of antigen used in the vaccine formulation . Here , the vaccination protocols including rA2/rhIL-12/alum and Ad5-A2+rA2/rhIL-12/alum were highly immunogenic in that animals developed marked pre-challenge A2-specific antibody response . The lower number of A2 reactors in macaques vaccinated with DNA-A2+Ad5-A2 indicates that response to antigen in the monkey model is quite variable depending on the mode of immunization . For instance , it is well known that alum favor the induction of humoral responses , whereas Ad5 or DNA vaccination are known to induce a stronger T cell mediated immunity , and in particular CD8+ T cell responses . It is noteworthy that the anti-A2 antibody response was downregulated by infection . Likewise , differences in whether infection boosted ( or not ) the specific antibody responses to the recombinant leishmanial proteins Leish-110f , HI and HASPBI were obtained in a vaccine trail against experimental canine VL [45] . Although B lymphocytes can play an important role in shaping host defense against a number of intracellular pathogens through a variety of interactions with the cellular immune response [46] , the precise value of high titers vaccine-induced parasite-specific antibodies in VL has yet to be fully defined [14] . Not surprisingly , macaques vaccinated with the L . donovani A2 antigen in different formulations and application regimens showed varying degrees of parasitological and clinical protection following infectious challenge . Overall , attempts to detect parasite-positive specimens through time by conventional diagnostic procedures ( either by culture or direct microscopic examination ) were less successful in vaccinated animals as compared to controls . Accordingly , the findings from the real time PCR-based quantification of L . infantum loads in liver samples revealed that most of the vaccinated animals had significantly lower parasitism following the time course of infection . This lower level of parasite burden correlated with reduction of L . infantum-induced granuloma formation in the liver and improvement of clinical conditions , particularly in macaques vaccinated with Ad5-A2+rA2/rhIL-12/alum . The efficiency of this specific regimen may be explained by the combined ability to induce antigen specific CD8+ T cells , and CD4+ Th1 cells , by Ad5-A2 and rA2 combined with rhIL-12/alum , respectively . All thought to be important immunological components in mediating protective immunity against Leishmania parasites . The vaccine-induced clinical resistance was more evident at week 24 post-infection . At that time point , while only 15% ( 2/13 ) of the non-vaccinated macaques had recovered from symptomatic to asymptomatic patent infection , among the vaccinated groups 67% ( 10/15 ) animals had sub-patent infection with absence of clinical signs and lower serum levels of Leishmania-specific antibodies or reversion from a positive to negative serology for infection . It is well known that after clinical healing , immune responses likely maintain a state of persistent infection for the life of the host [14] , thus suggesting that the protective immune response can control , but not fully eliminate , the sub-patent infection . Our comparative analysis of the L . infantum-induced hepatic damage in groups of control and vaccinated macaques at week 24 post-infection indicates that all macaques in the control groups developed longstanding immune granulomas with structural properties remarkably similar to those seen in humans infected with this pathogen . Conversely , most of the vaccinated monkeys exhibited either almost complete resolution ( Ad5-A2+rA2/rhIL-12/alum regimen ) or marked regression ( rA2/rhIL-12/alum and DNA-A2+Ad5-A2 regimens ) of the poorly differentiated granulomas . The immunologically active granulomas are thought to restrain the infection , kill the microbial target , and repair any accompanying tissue injuries . However , the overall antimicrobial efficacy of the granulomatous response to Leishmania appears to be variable , and ultimately depends on host determinants and pathogen virulence [47] . In L . donovani-infected mice , the development of effective ( parasite-free ) hepatic granulomas requires early IL-12-dependent IFN-γ production by T cells for the activation of monocytes/macrophages [48] . On the other hand , Foxp3−CD4+ T subset appears to be the dominant source of IL-10-mediated immune suppression in chronic forms of leishmanial disease in mice [49] and humans [50] . Despite these findings , the way in which IL-10 functions in uncontrolled growth of Leishmania-induced granulomas in infected non-human primates remains unclear [51] . Finally , the atrophy of lymphoid tissue and the disorganization of splenic microenvironments have been observed during canine VL [52] , [53] . The mechanisms responsible for splenic protection against systemic infection are based on the clearly defined structural organization of the spleen into compartments [54] . In this study , whilst inflammation and structural changes of the splenic white pulp occurred in control animals , immunized monkeys exhibited well-organized lymphoid follicles , thus suggesting vaccine-induce protective immunity . In conclusion , the results from this macaque vaccine trial testing different modalities and formulations by using the L . donovani A2 as amastigote specific antigen showed varying degree of protective immunity with respect to parasite load , hepatic granuloma resolution and clinical outcome . Combinations of priming with DNA or Ad5-A2 followed by a boosts with alum formulated subunit A2 protein plus rhIL-12 cytokine were safe , and showed promising protective effects . Giving the genetic variability of human T-cell responses across HLA haplotypes , monomeric vaccines can elicit variable protective immunity [18] . Therefore , a successful DNA and viral vectors as well as subunit protein-based vaccines will likely require a cocktail of proven immunogens . Accordingly , we are currently identifying novel amastigote specific immunogenic proteins that could be aggregated to A2 to further improve the level of vaccine-induced cell-mediated immunity and protection against VL [30] .
|
Human visceral leishmaniasis causes significant morbidity and mortality , constituting an important global health problem . Absence of safe and cost effective anti-leishmanial drugs , together with emergence of drug resistance and HIV co-infection have posed a serious challenge to the disease containment . Given the urgent need to prevent approximately 0 . 2 to 0 . 4 million new VL cases annually worldwide , all reasonable efforts to achieve a safe and effective Leishmania vaccine should be made . We have previously reported the protective properties of the rA2 protein against experimental L . infantum infections both in mice and canines . To further evaluate the efficacy of A2 in a more relevant animal model to human disease , we used the primate Macaca mulatta . Primates vaccinated with different rA2-based prime-boost regimes displayed varying degrees of protective immunity , as indicated by a marked reduction of symptoms and parasite burden in the liver . In particular the vaccination approach with non-replicative adenovirus vector expressing A2 ( rAd5-A2 ) and boosted with the rA2 protein resulted in a more efficient control of parasites as well as resolution of hepatic immune granulomas at 24 weeks post-infection . The clinical efficacy provided by A2 in an animal model that is evolutionary close to humans qualifies this antigen as a promising candidate vaccine against human VL .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"immunity",
"immunity",
"to",
"infections",
"biology",
"and",
"life",
"sciences",
"immunology",
"vaccination",
"and",
"immunization"
] |
2014
|
Clinical and Parasitological Protection in a Leishmania infantum-Macaque Model Vaccinated with Adenovirus and the Recombinant A2 Antigen
|
The genetics underlying the autism spectrum disorders ( ASDs ) is complex and remains poorly understood . Previous work has demonstrated an important role for structural variation in a subset of cases , but has lacked the resolution necessary to move beyond detection of large regions of potential interest to identification of individual genes . To pinpoint genes likely to contribute to ASD etiology , we performed high density genotyping in 912 multiplex families from the Autism Genetics Resource Exchange ( AGRE ) collection and contrasted results to those obtained for 1 , 488 healthy controls . Through prioritization of exonic deletions ( eDels ) , exonic duplications ( eDups ) , and whole gene duplication events ( gDups ) , we identified more than 150 loci harboring rare variants in multiple unrelated probands , but no controls . Importantly , 27 of these were confirmed on examination of an independent replication cohort comprised of 859 cases and an additional 1 , 051 controls . Rare variants at known loci , including exonic deletions at NRXN1 and whole gene duplications encompassing UBE3A and several other genes in the 15q11–q13 region , were observed in the course of these analyses . Strong support was likewise observed for previously unreported genes such as BZRAP1 , an adaptor molecule known to regulate synaptic transmission , with eDels or eDups observed in twelve unrelated cases but no controls ( p = 2 . 3×10−5 ) . Less is known about MDGA2 , likewise observed to be case-specific ( p = 1 . 3×10−4 ) . But , it is notable that the encoded protein shows an unexpectedly high similarity to Contactin 4 ( BLAST E-value = 3×10−39 ) , which has also been linked to disease . That hundreds of distinct rare variants were each seen only once further highlights complexity in the ASDs and points to the continued need for larger cohorts .
The Autism spectrum disorders ( ASDs , MIM: 209850 ) are a heterogeneous group of childhood diseases characterized by abnormalities in social behavior and communication , as well as patterns of restricted and repetitive behaviors [1] . Twin studies have demonstrated much higher concordance rates of ASD in monozygotic twins ( 92% ) than dizygotic twins ( 10% ) [2] , [3] , indicating a strong genetic basis for autism susceptibility . Although previous work has implicated numerous genomic regions of interest [4]–[8] , the identification of specific genetic variants that contribute to ASD risk remains challenging . Substantial progress towards the identification of genetic risk variants has come from recent characterization of structural variation ( i . e . , copy number variation or CNV ) . For example , an initial report involving patients with syndromic autism characterized genomic variation using array comparative genomic hybridization ( CGH ) and identified large de novo CNVs in 28% of cases [9] . Similarly , subsequent work demonstrated that the frequency of de novo CNVs is higher in cases versus controls [7] , [8] . CNV analyses have proven useful in the identification of regions that are potentially disease-related [8] , [10]–[13] and have begun to be employed to advance the candidacy of individual genes , including NRXN1 , CNTNAP2 , and NHE9 [6] , [14]–[16] . Recent work characterizing structural variation in cases and ethnically matched controls associating ubiquitin-pathway genes with autism with replicating this finding in the AGRE dataset is likewise notable [17] , although family data was not reported here . Using the AGRE dataset as a discovery cohort , along with family information available for AGRE samples , we describe distinct and complementary analyses , prioritizing exonic events over CNVs in introns and intergenic intervals , which provide important new insights into the genetic architecture of the ASDs . Towards the identification of additional genes and regions that may modulate disease risk , we have assembled a resource characterizing genome-wide structural variation from over nine hundred multiplex ASD families . Presented below are results from analyses contrasting events observed in cases and healthy ethnically matched controls , focusing on three classes of genic events: exonic deletions ( eDels ) , exonic duplications ( eDups ) , and whole gene duplication ( gDups ) . Recovery of known ASD loci – together with the identification of novel regions harboring variants in multiple cases but no controls – supports the utility of this dataset . Consistent with enormous inter-individual variation , we further document a large number of events observed in only individual cases ( Table S4 ) . Importantly , all of these data have been made available to the scientific community pre-publication ( www . agre . org ) , greatly enhancing the utility of existing publicly accessible biomaterials and phenotype data . These data further highlight the extent of structural variation in both human and the ASDs and offer an important resource for hypothesis-generation and interrogation of individual loci .
To characterize structural variation in ASD multiplex families and unrelated controls , we typed individuals at 561 , 466 SNP markers using Illumina HumanHap550 version 3 arrays . After excluding samples that failed to meet QC thresholds ( see Table S1 ) , we obtained array data on 3832 individuals from 912 multiplex families enrolled in the Autism Genetic Resource Exchange ( AGRE ) [18] , 1070 disease-free children from the Children's Hospital of Philadelphia ( CHOP ) , and 418 neurologically normal adults and seniors from the National Institute of Neurological Disorders and Stroke ( NINDS ) control collection [19] . Using the PennCNV software [20] , we detected CNVs with a mean size of 59 . 9 Kb and mean frequency of 24 . 3 events per individual ( see Table S2 ) . Sensitivity compares favorably with previous BAC array-based [9] , [21] and SNP-based methods [8] , in which mean resolution was observed to be in the range of Mbs and hundreds of Kbs , respectively . As a first step towards validation of genotyping accuracy we examined the inheritance of CNVs in the AGRE cohort . Consistent with high quality , 96 . 2% of CNV calls made in children were also detected in a parent . To explore the issue of genotyping accuracy further , we generated CNV calls for an independently generated data set in which an overlapping set of 2 , 518 AGRE samples were genotyped using the Affymetrix 5 . 0 platform [11] . For CNVs ( >500 kb ) in known ASD regions ( e . g . 15q11–13 , 16p11 . 2 , and 22q11 . 21; Table 1 ) [8] , [11] , [21] , [22] , we observed 100% correspondence between the two platforms for individuals genotyped on both platforms . For further confirmation of CNV calls , we compared de novo variants identified here to those highlighted in previous analyses of AGRE families . We identified all five de novo CNVs reported by Sebat et al [7] , three of the five de novo CNVs reported by Szatmari et al [6] , one de novo CNV within A2BP1 reported by Martin et al [23] , and all five 16p11 . 2 de novo deletions reported by Weiss et al [11] and Kumar et al [10] . Of the two of thirteen de novo CNVs reported by Szatmari et al not detected as de novo in our study , one was very small ( 2 SNPs , 180 bp on 8p23 . 2 ) , and the second clearly appears to be inherited ( 469 SNPs , 1 . 4 Mb on 17p12 ) . Thus , our data are concordant with several other studies , and provide a more comprehensive picture of de novo CNVs in multiplex autism families . To further evaluate the quality of these data on another independent platform , we used Taqman to determine relative copy number at 12 previously unreported de novo CNVs identified in AGRE probands , confirming 11/12 loci ( Figure 1 and Table S3 ) . Together these results suggest that the CNVs calls we report are consistent and reliable . We therefore undertook additional analyses to identify specific loci in which structural variants were enriched in cases versus controls . Because the majority of such variants were intronic or intergenic , we sought to prioritize CNVs most likely to interfere with the molecular function of specific genes . We first filtered CNV calls to include only exonic deletions ( eDels ) observed to overlap with a RefSeq gene . Overall , such eDels were observed at similar frequencies in AGRE cases , 1st degree relatives of AGRE cases , and unrelated controls ( CHOP and NINDS cohorts ) , with an average of ∼2 such variants per person ( Table S2 ) . To identify events related to the ASDs we then looked for genes harboring eDels in at least one case but no unrelated controls . Among the 284 genes that met this criteria ( Table S4 ) we observed several known ASD or mental retardation genes including: ASPM [24] , DPP10 [8] , CNTNAP2 [25] , [26] , PCDH9 [16] , and NRXN1 [6] . To enrich for genes most likely to contribute to ASD risk , we used family-based calling to evaluate which of these genes carried eDels in three or more cases from at least two unrelated families ( Table S5 ) . This stringent filtering resulted in 72 genes at 55 loci , including NRXN1 . This is notable , given that eleven distinct disease-linked NRXN1 variants have been identified [6] , [8] , [15] , [27] , [28] . Neurexin family members are known to interact functionally with ASD-related neuroligins [29]–[32] , and likewise play an important role in synaptic specification and specialization [33] , [34] . eDels in more recently identified candidates , including DPP10 and PCDH9 , were likewise retained . Similarly , recovery of RNF133 and RNF148 within intron 2 of CADPS2 [7] , [35] highlights additional complexity at this locus . Although CNV breakpoints cannot be mapped precisely using SNP data alone , it is possible to determine overlap with protein coding exons and use these data to predict impact on gene function . Consistent with perturbation of function , distinct alleles at the loci highlighted here are predicted to eliminate or truncated the corresponding protein products ( Figure 2 ) . Importantly , CNVs at a majority of these eDel loci show unique breakpoints in different families and/or result in the loss of distinct exons , demonstrating that they are independent . Moreover , because it is well established that CNVs at a subset of loci show identical breakpoints in unrelated individuals [10] , this result is likely to underestimate the extent to which variants described here arose independently . Results from multi-dimensional scaling are likewise consistent with the interpretation that variants we highlight arose independently ( Figure S1 ) . Given the large number of variants identified , it was critically important to confirm in an independent case-control analysis , how many of these eDels were truly overrepresented in cases , as opposed to being potentially attributable to Type I error . To address this concern , we sought to determine eDel frequency in these same genes in a replication dataset comprising 859 independently ascertained ASD cases and 1051 unrelated control subjects from the Autism Case Control cohort ( ACC , see Description in Methods ) . One third of the loci identified in the discovery phase were observed in one or more ACC controls ( 18/55; 32 . 7% ) , suggesting that while rare , eDels at these loci are not limited to ASD cases and family members . In contrast , and providing evidence for formal replication , 14 separate loci encompassing 22 genes were observed to carry eDels in both AGRE and ACC cases , but none of 2539 controls ( Table S2 ) . Our replication data lend strong support to the involvement of specific loci in the ASDs ( Table 2 ) . However , to ensure that these results were not observed by chance alone , we performed 10 , 000 permutation trials on data from the replication cohort by permuting case/control status across individuals . In each permuted dataset , we maintained the same numbers of cases and controls as in the original data , and calculated the number of genes harboring CNVs exclusively in cases . None of the 10 , 000 permutation trials gave results comparable to experimental observations for replicated case-specific loci ( n = 14; p<0 . 0001; Figure 3 ) . In contrast , findings comparable to those for non-replicated loci ( highlighted as case-specific in the discovery phase but subsequently seen in replication controls ) were seen in controls in 246/10 , 000 trials ( n = 18; p = 0 . 02; Figure S2 ) . Although additional experimental work in independent cohorts will be required to determine if variation in any of the genes highlighted here do in fact impact ASD risk , no more than 5 replicated loci would be predicted to be observed by chance alone . Despite the challenges associated with obtaining statistical support for individually rare events [7] , [36] we next sought to assign P values for replicated eDel loci . We were able to obtain support for each of the following loci: BZRAP1 at 17q22 ( p = 8 . 0×10−4 ) , NRXN1 at 2p16 . 3 ( p = 3 . 3×10−4 ) , MDGA2 at 14q21 . 3 ( p = 1 . 3×10−4 ) , MADCAM1 at 19q13 ( p = 5 . 5×10−5 ) , and a three gene locus at 15q11 ( p = 1 . 3×10−11 ) . CNV calls at each of 15q11 and 19p13 are highly-error prone , suggesting that results here be interpreted with caution ( see footnotes C and F in Table 2 ) . Recovery of NRXN1 , however , provides confidence for involvement of additional loci that were likewise replicated . Benzodiazapine receptor ( peripheral ) associated protein 1 ( BZRAP1 , alternatively referred to as RIMBP1 ) , is an adaptor molecule thought to regulate synaptic transmission by linking vesicular release machinery to voltage gated Ca2+ channels [37] . Identification of this synaptic component here , in a hypothesis-free manner , is particularly satisfying and also provides additional support for synaptic dysfunction in the ASDs [29] , [38] . Less is known about MDGA2 [39] , although comparison of the predicted protein to all others within GenBank by BLASTP indicated an unexpectedly high similarity to Contactin 4 ( 24% identity over more than 500 amino acids; Expect = 3×10−39 ) . Given previous reports of hemizygous loss of CNTN4 in individuals with mental retardation [40] and autism [17] , [41] . similarity between MDGA2 and CNTN4 , surpassed only by resemblance to MDGA1 , is notable . Likewise intriguing in light of the suggestion that common variation in cell adhesion molecules may contribute to autism risk [42] is the structural likeness of MDGA2 to members of this family of molecules . Although some published analyses emphasize the greater contribution of gene deletion events in autism pathogenesis [7] , there are also clear examples of duplications that strongly modulate ASD risk [43] , [44] . We therefore conducted a parallel analysis of duplications , distinguishing between events involving entire genes ( gDups ) which might increase dosage and those restricted to internal exons ( eDups ) which could give rise to a frameshift or map to a chromosomal region distinct from the reference gene . For gDups , we identified 449 genes that were duplicated in at least one AGRE case but no CHOP/NINDS controls ( Table S4 ) . Of those , 200 genes at an estimated 63 loci , including genes at 15q11 . 2 [43] , met the more stringent criteria of being present in three or more cases from at least two independent families ( Table S5 ) . Of these , 11 . 5% ( 23/200 ) were also seen in ACC controls , whereas 24 . 5% ( 49/200 ) were case-specific in the replication cohort . Strong statistical support was obtained for established loci ( e . g . p = 9 . 3×10−6 for UBE3A and other genes in the PWS/AS region at 15q11–q13 ) , and nominal evidence was observed for the following novel loci: CD8A at 2p11 . 2 ( p = 0 . 069 ) , LOC285498 at 4p16 . 3 ( p = 0 . 028 ) , and CARD9/LOC728489 at 9q34 . 3 ( p = 0 . 005 ) . For eDups , we reasoned that duplication of one or more internal exons could serve to disrupt the corresponding open reading frame and be predicted to impair gene function as a result . Despite the caveat that observed copy number gains need not map to the wild-type locus , known ASD genes including TSC2 [45] and RAI1 [44] , [46] within the Potocki-Lupski Syndrome critical interval were amongst the 159 loci observed in at least one AGRE case , but no CHOP/NINDS controls ( Table S4 ) . Such events were also seen in one family at the NLGN1 locus , which is of interest given previous support for NLGN3 and NLGN4 [29] . Filtering of these results , using the more stringent criteria employed above in consideration of eDels , limited this set of events to 76 loci observed in at least three cases from two separate families ( Table S5 ) . Interestingly , BZRAP1 , reported above to harbor eDels at significantly higher frequencies in AGRE and ACC cases versus controls ( p = 8 . 0×10−4 ) , was amongst these , with eDups observed here in four unrelated AGRE cases ( screening p = 0 . 021 ) . Eight other genes , including the voltage gated potassium channel subunit KCNAB2 ( p = 4 . 7×10−3 ) remained absent from ACC controls and were also replicated in the independent case cohort . Although eDups at BZRAP1 were not detected in ACC cases , eDels at this locus were replicated , underscoring the importance of variation here . When considering eDels and eDups at the BZRAP1 locus together , the likelihood of such an observation occurring by chance alone is small ( p = 2 . 3×10−5 ) . Although none of the variants we highlight were observed in any of 2539 unrelated controls , key events , including eDels at NRXN1 , BZRAP1 , and MDGA2 were observed in both cases and non-autistic family members ( Figure 4 ) . This is in keeping with previous work which suggests that haploinsufficiency at NRXN1 may contribute to the ASDs [15] , but is insufficient to cause disease . Such data are also consistent with the well established finding of the “broader autism phenotype” , such as subclinical language and social impairment in first degree relatives of cases with an ASD , which supports a multi-locus model [47] , [48] . We were also surprised to see that key variants at these loci appear to be transmitted to only a subset of affected individuals in some families ( Figure 4 ) . These observations parallel findings at other major effect loci including 16p11 . 2 [11] and DISC1 [49] , [50] and are consistent with a model in which multiple variants , common and rare , act in concert to shape clinical presentation [51]–[53] . Results are also consistent with the idea that true risk loci are likely to show incomplete penetrance and imperfect segregation with disease [13] , a reality that will complicate gene finding efforts . Related to this is that substantial effort will be required to determine whether rare alleles of moderate effect act independently on distinct aspects of disease ( endophenotype model ) or together to undermine key processes in brain development ( threshold model ) . How distinct alleles may interact to shape presentation is yet another question that will require larger cohorts along with multigenerational families to resolve [54] . By limiting CNV calls to include only exonic deletions ( eDels ) and duplications ( eDups and gDups ) , we have attempted to enrich for variants most likely to impact gene function and in doing so improve the signal to noise ratio similar to work in other complex diseases [55] . At the same time , like other gene-based strategies , we preserve our ability to consider eDels involving the same transcriptional unit as separate but equivalent . Given that such events appear rare , this is an important consideration . Pathway analysis by DAVID [56] found support for overrepresentation of cell adhesion molecules amongst recurrent eDel genes ( uncorrected p = 0 . 002; CDH17 , PCDH9 , LAMA2 , MADCAM1 , NRXN1 , POSTN , SPON2 ) , although it should be noted that this analysis does not adjust for gene size and may favor larger genes . Nevertheless , aside from SPON2 no eDels in these genes were observed in any of the controls interrogated . In contrast , no evidence for such overrepresentation was observed for genes in the ubiquitin degradation pathway and neither term was highlighted as overrepresented amongst eDups or gDups . Given that this study focused only on events encompassing RefSeq exons , differences from Glessner and colleagues [17] are to be expected . Despite the large cohorts interrogated at each phase of our investigations , only a minority of loci ( established or novel ) were replicated between AGRE and ACC cases . For example , variants at each of the following previously reported loci were observed multiple times in AGRE cases but not once amongst ACC probands: PCDH10 and DPP10 ( eDels ) , RAI and TSC2 ( eDups ) , and DIDO1 ( gDups ) . This suggests that even with current numbers , the present experiments are underpowered to obtain replication for a subset of recurrent variants . Because events seen only in single cases collectively account for a substantial fraction of observed variation even larger cohorts still will be required for a thorough understanding of the genetic basis of complex disorders like the ASDs . In summary , we have performed a high resolution genome-wide analysis to characterize the genomic landscape of copy number variation in ASDs . Through comparison of structural variation in 1 , 771 ASD cases and 2 , 539 controls and prioritization of events encompassing exons we identified more than 150 loci harboring rare variants in multiple probands but no control individuals . For each class of structural variant interrogated , the recovery of known loci serves to validate the methods employed and results obtained . Greatest confidence should be placed in loci harboring variants in multiple unrelated cases but no controls and also recovered in both screening and replication cohorts . Amongst novel genes , best support was obtained for BZRAP1 and MDGA2 , intriguing candidate genes for which additional study is warranted .
For initial screening we assembled three sample collections: 1 ) 943 ASD families ( 4 , 444 unique subjects ) from the Autism Genetic Resource Exchange ( AGRE ) collection; 2 ) 1 , 070 de-identified and unrelated children of European ancestry from the Children's Hospital of Philadelphia ( CHOP ) , with no evidence of neurological disorders; 3 ) 542 unrelated neurologically normal adults and seniors of European ancestry from the National Institute of Neurological Disorders and Stroke ( NINDS ) control collection . The AGRE families include 917 multiplex families , 24 simplex families and 2 families without an ASD diagnosis . For all analyses , AGRE cases annotated with “Autism” ( n = 1 , 463 ) , “Broad Spectrum” ( n = 149 ) or “Not Quite Autism” ( n = 71 ) were treated equally and as affected . Samples from AGRE and NINDS were genotyped using DNA extracted from Epstein-Barr Virus ( EBV ) -transformed lymphoblastoid cell lines , while the CHOP controls were genotyped using DNA extracted from whole blood . All AGRE and control samples included in these analyses were genotyped on the Illumina HumanHap550 version 3 arrays , and 281 samples genotyped on version 1 arrays were excluded from the present analysis . Since the NINDS controls were genotyped at a different location and time , they were used to assess the frequency of specific CNVs in an independent cohort and to address concerns of cell line artifacts . This study was approved by the Institutional Review Board of Children's Hospital of Philadelphia . All subjects provided written informed consent for the collection of samples and subsequent analysis . The Autism Case-Control ( ACC ) cohort included 859 cases from multiple sites within the United States , all of whom were of European ancestry affected with ASD . Of those , 703 were male and 156 were female; 828 met diagnostic criteria for autism , and 31 met criteria for other ASDs . Subjects ranged from 2–21 years of age when the Autism Diagnostic Interview ( ADI ) was given . Of the case subjects , 54% were from simplex families with the balance coming from multiplex families . The control group used for replication included 1051 children of self-reported Caucasian ancestry who had no history of ASDs . These controls were recruited by CHOP nursing and medical assistant staff under the direction of CHOP clinicians within the CHOP Health Care Network , including four primary care clinics and several group practices and outpatient practices that included well child visits . For each data set , we applied identical and stringent quality control criteria to remove samples with low signal quality . CNV calls were generated using PennCNV [20] , an algorithm which employs multiple sources of information , including total signal intensity , allelic intensity ratios , SNP allele frequencies , distance between neighboring SNPs , and family information to generate calls . We excluded samples meeting any of the following criteria: a ) standard deviation for autosomal log R ratio values ( LRR_SD ) higher than 0 . 28 , b ) median B Allele Frequency ( BAF_median ) higher than 0 . 55 or lower than 0 . 45 , c ) fraction of markers with BAF values between 0 . 2 and 0 . 25 or 0 . 75 and 0 . 8 ( BAF_drift ) exceeded 0 . 002 . We also excluded from our analysis CNVs within IGLC1 ( 22q11 . 22 ) , IGHG1 ( 14q32 . 33 ) and IGKC ( 2p11 . 2 ) , and the T cell receptor constant chain locus ( 14q11 . 2 ) , as well as CNVs in chromosomes showing evidence of heterosomic aberrations ( chromosome rearrangements in sub-populations of cells ) in BeadStudio . CNV calls were mapped onto genes by identifying overlap with RefSeq exons , the coordinates of which we obtained from the UCSC table browser . Deletion events overlapping with exons retrieved in this way were listed as eDels . eDups were defined as gains overlapping one or more coding exons and seen to be internal to the beginning and end of the corresponding transcript . Gains observed to encompass all exons for a given gene were annotated as gDups . P values for relative CNV burden in cases and controls were calculated at each locus by Fisher's exact test . P values presented in Table S2 , S4 , S5 have not been subjected to correction for multiple testing . To compare our CNV calls with other publications that have used AGRE families [10] , [11] , [21] , [22] , we examined published calls on the same individuals with the same AGRE identifiers . The CNV calls were retrieved from the Supplementary Materials of each corresponding publication . TaqMan primer/probe sets were designed to query random CNVs using FileBuilder 3 . 0 on the repeat-masked human genome ( NCBI_36; March 2006 release; http://genome . ucsc . edu/ ) . For each assay , 10 ng of genomic DNA was assayed in quadruplicate in 10-µL reactions containing 1× final concentration TaqMan Universal Master Mix ( ABI part number 4304437 ) , and 200 nM of each primer and probe . Cycling was performed under default conditions in 384-well optical PCR plates on an ABI 7900 machine . Copy number was defined as 2−ΔΔCT , where ΔCT is the difference in threshold cycles for the sample in question normalized against an endogenous reference ( RNAseP ) and expressed relative to the average values obtained by three arbitrary control DNAs . A list of TaqMan probes against the 12 CNVs tested is included in Table S3 .
|
Autism spectrum disorders ( ASDs ) are common neurodevelopmental syndromes with a strong genetic component . ASDs are characterized by disturbances in social behavior , impaired verbal and nonverbal communication , as well as repetitive behaviors and/or a restricted range of interests . To identify genes likely to contribute to ASD etiology , we performed high density genotyping in 912 multiplex families from the Autism Genetics Resource Exchange ( AGRE ) collection and contrasted results to those obtained for 1 , 488 healthy controls . To enrich for variants most likely to interfere with gene function , we restricted our analyses to deletions and gains encompassing exons . Of the many genomic regions highlighted , 27 were seen to harbor rare variants in cases and not controls , both in the first phase of our analysis , and also in an independent replication cohort comprised of 859 cases and 1 , 051 controls . More work in a larger number of individuals will be required to determine which of the rare alleles highlighted here are indeed related to the ASDs and how they act to shape risk .
|
[
"Abstract",
"Introduction",
"Results/Discussion",
"Methods"
] |
[
"genetics",
"and",
"genomics/genetics",
"of",
"disease"
] |
2009
|
Genome-Wide Analyses of Exonic Copy Number Variants in a Family-Based Study Point to Novel Autism Susceptibility Genes
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The objective of this study is to conduct a systematic review of studies reporting the frequency of neurocysticercosis ( NCC ) worldwide . PubMed , Commonwealth Agricultural Bureau ( CAB ) abstracts and 23 international databases were systematically searched for articles published from January 1 , 1990 to June 1 , 2008 . Articles were evaluated for inclusion by at least two researchers focusing on study design and methods . Data were extracted independently using standardized forms . A random-effects binomial model was used to estimate the proportion of NCC among people with epilepsy ( PWE ) . Overall , 565 articles were retrieved and 290 ( 51% ) selected for further analysis . After a second analytic phase , only 4 . 5% of articles , all of which used neuroimaging for the diagnosis of NCC , were reviewed . Only two studies , both from the US , estimated an incidence rate of NCC using hospital discharge data . The prevalence of NCC in a random sample of village residents was reported from one study where 9 . 1% of the population harboured brain lesions of NCC . The proportion of NCC among different study populations varied widely . However , the proportion of NCC in PWE was a lot more consistent . The pooled estimate for this population was 29 . 0% ( 95%CI: 22 . 9%–35 . 5% ) . These results were not sensitive to the inclusion or exclusion of any particular study . Only one study has estimated the prevalence of NCC in a random sample of all residents . Hence , the prevalence of NCC worldwide remains unknown . However , the pooled estimate for the proportion of NCC among PWE was very robust and could be used , in conjunction with estimates of the prevalence and incidence of epilepsy , to estimate this component of the burden of NCC in endemic areas . The previously recommended guidelines for the diagnostic process and for declaring NCC an international reportable disease would improve the knowledge on the global frequency of NCC .
Scientific evidence is instrumental to improving global public health , as health policies should be based on accurate and meaningful data . In early 1990 , the first Global Burden of Diseases ( GBD ) study , commissioned by the World Bank , was launched to develop a method to estimate and compare the burden of 107 diseases and injuries around the world . A standardized indicator , the “Disability Adjusted Life Years” ( DALY ) method , was developed for this purpose [1]–[2] . Unfortunately , only a few neglected tropical diseases ( NTD ) and no neglected tropical zoonoses were taken into account in the original GBD study . Neglected tropical diseases are a public health issue worldwide and especially in developing countries , where risk factors for their transmission are common [3] . These conditions tend to affect the poorest of the poor , which has led to limited research interest and investments for these infections . The few research initiatives that have been undertaken to estimate the burden of NTDs have been criticized for grossly underestimating their global impact [4]–[6] . In addition , the burden of several zoonotic NTDs , such as Taenia solium cysticercosis , has never been estimated . The lifecycle of T . solium is illustrated in Figure 1 . Humans acquire cysticercosis when ingesting food that has been contaminated with infected feces or through auto-infection . Neurocysticercosis ( NCC ) , which occurs when the larvae of T . solium migrate to the brain , has been reported as the most frequent helminthic infection of the central nervous system ( CNS ) [5] , [7] . Yet , there have been very few studies conducted to estimate the prevalence of NCC . This is mostly due to the fact that NCC can only be diagnosed with certainty through neuro-imaging or autopsy . Hence , the frequency of sequelae following infection with the larval stages of T . solium remains largely unknown [8] . Exact data on the worldwide frequency of CNS infections with cysticercosis is lacking [9] . The primary objective of this study was to conduct a systematic review of the literature to gather data on estimates of NCC frequency between 1990 and 2008 by age group and region . A secondary aim was to estimate the proportion of people living with epilepsy ( PWE ) who have NCC . This study was commissioned by the World Health Organization's Foodborne Disease Burden Epidemiology Reference Group ( FERG ) . The FERG is the World Health Organization's advisory body to estimate the global burden of foodborne diseases .
The search strategy was conducted in three phases . In phase I , PubMed , Commonwealth Agricultural Bureau ( CAB ) Abstracts , and 23 international databases ( Table 1 ) were screened for articles published from January 1 , 1990 to June 1 , 2008 . Searches were restricted to languages that at least two of the members of the team could read and understand , namely English , French , Italian , Romanian , German , Chinese , Spanish and Portuguese . In PubMed , our search strategy included the Medical Search Heading ( MeSH ) terms: “Neurocysticercosis/epidemiology” . Because CAB Abstracts and the international search engines did not allow for searches using MeSH terms , they were queried using the following keywords: “Taenia solium” , “taeniasis” , “cysticercosis” , and “neurocysticercosis” . Only one copy of duplicated documents was kept for analysis . Studies were selected that included original epidemiological data on NCC frequency . Books and conference abstracts were excluded because they were unlikely to present original data or to have sufficient details on methods to judge the validity of the study . Dissertations , theses , and memoirs were included . Moreover , due to the under-representation of publications from Sub-Saharan Africa , three unpublished studies ( at that time ) were reviewed in addition to one paper published online in November 2008 [10] . One unpublished study on the incidence rate of NCC from Oklahoma was also included since very few publications reported incidence rates of NCC ( Thompson J , unpublished data ) . Another component of this project was to assess the proportion of sequelae associated with NCC ( details reported elsewhere ) . This search led to the finding of four additional studies which described the proportion of NCC among people with epilepsy and seizures [11]–[12] and among children with partial seizures [13]–[14] . The inclusion and exclusion criteria were defined a priori . In phase I , all documents retrieved were screened based on title and abstract . The exclusion criteria for phase I were: 1 ) wrong agent ( for example , T . saginata ) ; 2 ) animal data only; 3 ) no original data on the frequency of NCC; 4 ) case series with less than 20 participants; 5 ) review article without original data; and 6 ) editorials or letters to the editors without original data . Documents without abstracts were included in the next phase . All eligible documents after phase I were obtained in full . Each full document was read and reviewed by at least two investigators and subjected to the two subsequent phases of review . Phase II and III corresponded to a qualitative and quantitative appraisal of the information , respectively . The exclusion criteria for phase II included all criteria used in phase I in addition to: 1 ) no neuro-imaging ( CT-scans or MRI ) or autopsies used for the diagnosis of NCC; 2 ) high potential for selection bias ( study of volunteers , study population obviously more at risk of NCC than the target population ) ; or 3 ) all available data from before 1990 or after 2008 ( except for sub-Saharan Africa where two studies published in 2009 were included ) . The data from documents included after phase II were extracted in phase III . During phase I , the full reference of each article , the country where the study was conducted , the decision on inclusion for phase II , the reason for exclusion ( if applicable ) and the language of the document were entered into an Excel spreadsheet ( Microsoft Corp . , Redmond , WA ) . Data were extracted independently by two authors and a third author checked a random sample of 10% of all the entries . Any differences were resolved by discussion until agreement was reached . The quality assessment ( phase II ) and data extraction ( phase III ) for each document were carried out by two reviewers ( except for Chinese articles ) one of whom was a senior researcher ( HC or CB ) . All documents published in French , Portuguese or Spanish were reviewed by those who could read those languages ( PN and HC ) . All documents published in Chinese were reviewed by a Chinese collaborator ( Y-JQ ) . No articles in German , Romanian or Italian were identified . In addition , a random sample of 10% of all English documents was reviewed by all reviewers . Disagreements were resolved through discussion in a meeting with all the reviewers . Data were entered into standardized electronic forms of the data extraction tool which was developed in Access ( Microsoft Corp . , Redmond , WA ) specifically for this review ( available from the authors on request ) . Data were collected on the study characteristics ( design , geographic location , period and duration of the study ) , participant selection , case definitions , and ascertainment of outcome . For documents that could potentially be included in the review but had incomplete or missing data , the authors were contacted at least twice for clarification and/or additional information . The number of documents included in each phase of the systematic review was first plotted geographically using ArcView GIS software ( ESRI; Redlands , CA ) . Results from studies reporting separately the overall prevalence or incidence rate of NCC in the population are reported for each study . Measures of frequency: We report the proportion of patients with NCC in studies where a specific group of patients seeking care in a hospital or clinic were included . This is obtained by dividing the number of people with lesions of NCC by the number of people included in the study ( all with neuro-imaging ) . We report the proportion of NCC among PWE using the same approach . The term prevalence is applied to the proportion of people with lesions of NCC at the CT-scan in studies where the general population or community residents without epilepsy were randomly sampled . The 95% Confidence Intervals ( 95% CI ) of those proportions was estimating using the Clopper-Pearson exact interval provided in the Stata ( StataCorp , College Station , TX ) software . The annual incidence rate of hospitalized cases of NCC was calculated by dividing the number of cases discharged with an International Classification of Disease ( ICD ) code for cysticercosis by the person-years living in the U . S . state where the study was conducted ( based on census data ) . The prevalence of NCC-associated epilepsy was estimated by multiplying the prevalence of epilepsy by the proportion of NCC conditional on having epilepsy in community-based studies where both these estimates were available . This prevalence was estimated using WinBugs 1 . 4 . 3© and represents the proportion of people in a community estimated to have both epilepsy and NCC . A random-effects model was used to summarize the data on the proportion of NCC among PWE using R META package ( Version 0 . 8–2; by Guido Schwarzer in the R-META metagen function ) and the METFOR package ( Version 1 . 3-0; by Wolfgang Viechtbauer ) from R statistical software ( R Development Core Team , www . R-project . org ) . We used the inverse variance method [15] to pool the proportion estimates in the random-effect model and calculate the appropriate 95% Confidence Interval ( 95% CI ) [16] . Tests of homogeneity were used to determine whether it was appropriate to combine different proportions across studies , and the I2 index was used to summarize the total variability in proportion estimates due to between-study variation [17] . In order to determine the influence of potential outlying effect-size estimates , a sensitivity analysis was done by estimating the pooled prevalence proportion after omitting one study at a time . A mixed-effects regression model was used to determine whether the study setting ( community-based or clinical-based ) significantly influenced the estimated percentage of NCC among PWE value .
The literature search identified 565 documents that could potentially have original data on the frequency of NCC . The flow diagram in Figure 2 shows the number of papers identified in each database and the review process from phase I to phase III . Figure 2 also includes the number and reason for exclusion of documents at each phase of the review . After the first screening ( phase I ) , 290 publications , including 9 additional studies not originally identified , were read and critically reviewed . Of the 264 articles excluded during phase II , the two most common reasons for exclusion were the lack of frequency data and the lack of neuro-imaging . Phase III included 26 documents ( 4 . 5% ) containing estimates of NCC prevalence proportion or incidence rate ( Table 2 ) . As shown in Figures 3a–b , most of the articles identified in the search were from China , India , Brazil and the United States of America . The 26 documents that were retained for the quantitative appraisal ( phase III ) were from studies conducted in the WHO regions of Latin America ( 15 ) , North America ( 3 ) , Africa ( 3 ) and Asia ( 5 ) . Figure 3c illustrates the geographic distribution of the papers that were retained for the quantitative synthesis . Study design , the target populations and the quantity measured varied greatly across articles ( Table 2 ) . Only two studies did not sample from a target population of people with a specific symptom or disease [18]–[19] . Most studies reported the proportion of people with NCC among symptomatic target populations . Two studies from the United States of America reported the incidence rate of NCC based on hospital discharge data ( [20] , Thompson J , unpublished data ) , and four studies reported the proportion of NCC among people who were autopsied after death for any reason [7] , [21]–[23] . In seven community-based studies , NCC was assessed among PWE or people with seizure disorders ( [8] , [24]–[28] , Carabin et al , unpublished data ) . In another five studies , NCC was assessed among PWE attending a health clinic [10]–[12] , [29]–[30] . In addition , in three studies , NCC was specifically measured among children with partial seizures [13]–[14] , [31] . Only one study reported the prevalence of NCC in a random sample of the general population ( Table 3 ) . In this Mexican study , 154 residents were sampled at random to receive a CT-scan of the brain [19] . The prevalence of NCC was estimated to be 9 . 1% ( 95% CI: 5 . 1%–14 . 8% ) . None of the sampled subjects had clinically apparent manifestations of NCC . The prevalence of NCC was considerably higher among children ( aged 0–19 years old ) with an estimated prevalence of 13 . 2% ( 95% CI: 7 . 0%–21 . 9% ) than among adults ( 20–54 years old ) with an estimated prevalence of 3 . 2% ( 95% CI: 0 . 4%–11 . 0% ) . Two studies were conducted among patients without epilepsy and epileptic seizures , both part of a larger door-to-door survey to identify people with epilepsy conducted in Ecuador . In the first study , lesions suggestive of NCC at CT were found in 17 out of 118 randomly selected people without epilepsy [8] , for a percentage of 14 . 4% ( 95%CI: 8 . 5%–21 . 2% ) . In the second study , NCC lesions were identified among a matched age-gender sample of 19 people without epilepsy ( matched to those with epilepsy ) , for a percentage of 5 . 2% ( 95%CI: 0 . 1%–26 . 0% ) [24] . No details on the age distribution or types of lesions found were provided in those articles . The proportion of NCC in seropositive and seronegative community residents was estimated in two studies [18] , [27] . In Honduras , 480 people aged 2 years and older from Salama county , provided a blood sample to estimate the seroprevalence of cysticercosis using a Western Blot ( EITB ) [18] . A total of 80 people tested positive to the EITB , of whom 74 accepted to receive a CT-scan of the brain . An age-gender-village matched sample of 74 sero-negative people also received a CT-scan of the brain . In the second study , 825 out of 913 residents of seven villages of the district of Matapalo in Peru provided a blood sample for EITB testing . A random sample of 53 of 60 people testing positive and 58 of 60 people testing negative to EITB without epilepsy accepted to have a CT-scan of the brain [27] . The percentage of NCC was 23 . 0% ( 95% CI: 14 . 0%–34 . 2% ) and 34 . 0% ( 95% CI: 21 . 5%–48 . 3% ) among seropositive participants , and 18 . 9% ( 95% CI: 10 . 7%–29 . 7% ) and 13 . 8% ( 95% CI: 6 . 1%–25 . 4% ) among seronegative patients in Honduras and Peru , respectively . Age-stratified prevalence of NCC results was not reported . It is important to note that none of those groups represents the general population of those villages . Two studies , both using data from discharge diagnosis of patients hospitalized in the United States of America , reported estimates of the incidence rate of hospitalized NCC per 100 , 000 person-years ( Table 2 ) . In Oregon and Oklahoma , the incidence rates were estimated at 1 . 50 per 100 , 000 person-years and 0 . 29 per 100 , 000 person-years , respectively ( [20] , Thompson , unpublished data ) . As expected , the proportion of NCC was extremely variable among studies with different source and target populations ( Table 3 ) . In Peru , the percentage of NCC in children with partial seizures was 52 . 0% ( 95% CI: 38 . 5%–65 . 2% ) [31] . This estimate was considerably lower in two studies conducted among children with partial seizures in India ( Table 2 ) [13]–[14] . The latter two studies did not consider solitary calcified cysts as NCC lesions . In a study conducted among a group of patients attending two imaging diagnostic centers in Brazil , the percentage of NCC was estimated at 0 . 20% ( 95% CI: 0 . 15%–0 . 24% ) [32] . We found only one study from a developed country ( United States ) reporting the percentage of NCC among people with seizures attending emergency rooms [33] . In that study , the overall prevalence of NCC was 2 . 1% ( 95% CI: 1 . 5%–2 . 9% ) , but was 9 . 1% ( 95% CI: 6 . 2%–12 . 8% ) among Hispanics . Four studies reported the proportion of NCC among people who were autopsied [7] , [21]–[23] . The percentages were similar across the four studies , varying from 1 . 5% to 2 . 6% , with three of the four studies conducted in the same area of Brazil ( Table 3 ) . The estimated prevalence of people with NCC-associated epilepsy in community-based studies ranged from 0 . 1% in India [28] to 1 . 3% in a study of three rural communities in Burkina Faso ( Carabin , unpublished data ) ( Table 4 ) . Such estimates could not be combined because the proportion of NCC was never obtained from all PWE in the population . The proportion of NCC among PWE was remarkably homogeneous across studies conducted in children and adults ( Figure 4 ) . The lowest estimated percentage was from a study conducted in several urban clinics in Colombia , with an estimated 13 . 9% NCC among PWE ( 95% CI: 11 . 1%–17 . 1% ) [29] . Interestingly , all patients with single calcifications in that study were considered as negative for NCC . The pooled estimate across 12 studies from the random-effects model for the percentage of NCC among PWE of all ages was 29 . 0% ( 95% CI: 22 . 9%–35 . 5% ) . The I2 statistic indicated that 92 . 5% ( 95% CI: 88 . 1%–94 . 6% ) of the total variability in the percentage values was due to between-study variation . No study had a significant impact on the result and the between-study variability was not explained by a single study . We ran the model excluding the study from Peru where some single seizures cases were included [27] and only including patients with epilepsy or recurring acute symptomatic seizures from one of the India studies [12] . The resulting pooled estimate is 27 . 6% ( 95%CI: 22 . 8%–32 . 6% ) , which is very close to the previous estimate . We also ran random-effect models stratified by the target population ( clinical vs community ) . The estimates were 31 . 7% ( 95% CI: 25 . 6%–38 . 2% ) for community-based and 25 . 4% ( 95%CI: 16 . 3%–35 . 7% ) for clinical-based studies . A mixed-effects regression model was used to determine whether the study setting ( community-based or clinical-based ) significantly influenced the percentage value . The estimated percentage among clinic-based studies is expected to be 5 . 9% lower ( absolute difference ) than that for community-based studies ( 95% CI: 17 . 2% lower to 5 . 5% higher ) , which is not statistically significant ( p = 0 . 31 ) . Only five of the 12 studies had sufficient information to obtain estimates stratified by two broad and consistent age groups . In patients less than 20 years of age , the percentage of NCC among PWE ranged from 11 . 1% ( 95% CI: 2 . 4%–29 . 2% ) in a community-based study in Burkina Faso ( Carabin , unpublished data ) to 45 . 2% ( 95% CI: 27 . 3%–64 . 0% ) in the Eastern Cape Province of South Africa [30] , with an overall estimate of 24 . 8% ( 95% CI: 18 . 2%–32 . 2% ) ( Figure 5a ) . The I2 statistic suggested that 43 . 1% ( 95%: CI 0%–76 . 1% ) of the total variability in the values was due to between-study variation . In the analysis of adults aged 20 to 54 years , the estimate of the percentage of NCC among PWE was more variable , ranging from 14 . 2% ( 95% CI: 8 . 6%–21 . 5% ) in an outpatient clinic in Tanzania [10] to 50 . 0% ( 95% CI: 28 . 2%–71 . 8% ) in a community in Peru [27] , with an overall estimate of 28 . 3% ( 95% CI: 19 . 9%–37 . 5% ) ( Figure 5b ) . I2 , the percentage of total variation across studies due to between-study heterogeneity , was 74 . 8% ( 95% CI: 46 . 6%–88 . 2% ) .
After a systematic review , only studies that are likely to be valid and are similar enough in their methods and definitions should be reported [34] . Only 4 . 5% of all publications identified were considered valid enough to be included in the systematic review . However , only studies reporting on the proportion of NCC among PWE were similar enough to be combined in a pooled estimate . The second most common reason for exclusion of documents , after an absence of measurement of NCC frequency , was the lack of neuro-imaging for the diagnosis of NCC . Underdeveloped countries where sanitation and proper pig management methods are lacking are often endemic for T . solium infections [8] , [35] . These same countries are those where imaging facilities are scarce [28]; this especially applies to Sub-Saharan Africa [36] . The absence of appropriate diagnostic technologies leads to an unequal distribution of studies included in this systematic review . Indeed , there are few articles from Africa , the Western Pacific , the Eastern part of Europe and Asia , with the exception of India and China . The small number of studies from the Middle East and parts of Africa is not surprising since pig rearing and pork consumption are rare in those areas . . Yet , a small number of studies have reported NCC cases in Islamic or Jewish communities [37] and it would be commendable to conduct more studies of NCC in those communities . Infection usually occurs when individuals from endemic areas are taeniasis carriers [38] . Restriction to studies using neuroimaging was based on the internationally accepted descriptions of lesions of NCC which requires the use of imaging for the diagnosis of this disease [39] . The third most common reason for exclusion of a study was the use of flawed methodology . Studies were excluded that did not mention when and where they were conducted as were primary studies with a high potential of selection bias , since the strength of a systematic review depends on the quality of the primary studies that are included [40] . Methodologically poor studies may bias the conclusions and produce incorrect overall estimates when quantitative methods are used [41] . Target and study populations were very different across the documents we analyzed . For example , studies were conducted on patients with Japanese encephalitis ( JE ) [42] , a simple random sample of people living in a community in Mexico [19] , patients attending imaging diagnostic centers [11] , [32] and children with partial seizures [13]–[14] , [31] . The frequency of NCC across these populations varied widely and the heterogeneity between studies hindered the calculation of an overall estimate . Only studies conducted among PWE were homogeneous enough to warrant the use of a meta-analysis . The only cross-sectional study among a random sample of the population found a prevalence of NCC lesions , all calcified , of 9 . 1% [19] . This prevalence is similar to what was found in one study conducted in Ecuador where people without epilepsy and epileptic seizures were selected at random [8] . None of those studies reported on the presence of other possible past or present neurological manifestations of NCC . Therefore , even though it is possible that those participants were truly asymptomatic , it is impossible to know with certainly in the absence of a full neurological examination and anamnesis . In another study , people who were sero-positive to the EITB and an age-sex-village matched sample of sero-negative people underwent a CT-scan of the brain [18] . Even though this study is interesting in showing that the prevalence of NCC was very similar in the two groups , suggesting the poor performance of the EITB to detect NCC in community-based studies conducted in endemic areas , it cannot be used to estimate the prevalence of NCC . This is because people who are sero-positive ( and their match controls ) may represent people who are more exposed to the larval stages of T . solium in their community . Given that the incubation of NCC is unknown , those participants testing negative to the EITB may have been exposed a long time ago but have seen their immunity wane with time . Those cases may also have never developed antibodies to the brain infection . Such studies are unlikely to be repeated in the future . Due to potential adverse effects of the contrast materials used for CT or MRI , it is usually considered unethical to perform neuro-imaging in apparently healthy individuals . This limits our ability to truly measure the burden of NCC as some people may be asymptomatic for extended periods of time [43] . The study among participants seropositive and seronegative to EITB from Peru is even more difficult to interpret since it was conducted only among those without epilepsy [27] . We identified four studies conducted in autopsied patients from large hospitals in Brazil and Mexico [7] , [21]–[23] . These results were very similar , but three of the four studies were conducted in the same province of Brazil , thus representing the same population . In addition , an extrapolation of those results to the general population is impossible since people who are autopsied are likely to systematically differ from the general population . The prevalence of people with NCC-associated epilepsy in community-based studies varied considerably . This prevalence is the product of the prevalence of PWE in the community and the proportion of NCC among PWE . Since the prevalence of NCC among PWE tends to be similar across studies ( about one-third ) , the prevalence of epilepsy in communities is the parameter that contributes the most to the observed variability across communities . There are diverse , competing etiologies for epilepsy across countries and in addition to NCC include malaria , paragonomiasis , toxocariasis , and other parasites of the brain [5] , a plethora of metabolic disorders , traumatic brain injuries as well as febrile seizures during childhood [44] . The inconsistency in prevalence estimates can also be explained by the fact that the definition of epilepsy and of active epilepsy varied from study to study . Some authors used a cutoff of one year of unprovoked seizures whereas others used three or five years to define active epilepsy . The results from our meta-analysis show that epilepsy is consistently associated with NCC in over one quarter of patients residing in endemic regions . This result was very robust , regardless of the type of epilepsy , if single epileptic seizures were included or not , and where and among whom the study was conducted . In an older study of 100 consecutive patients with epilepsy , Medina et al . found a prevalence of 50% for NCC [45] . Another study in South Africa conducted on 578 PWE , calculated a proportion with NCC of 28% [46] , which is very close to the average in the articles reviewed in this meta-analysis . These estimates confirm the importance of NCC infection in the etiology of epilepsy in developing countries [45] and suggest that NCC may be associated with a very large burden in cysticercosis endemic areas where epilepsy is prevalent . It is difficult to determine if our finding of the proportion of NCC lesions among PWE is an over or underestimate of the truth . First , epidemiological studies are generally conducted in areas where the infection is expected to be common . Second , as describe in the Mexican study [19] , some proportion of the population have NCC lesions in the brain that are not manifesting ( at least at the time of the study ) . These two factors would support an overestimation of the proportion of epilepsy that could be attributable to NCC in endemic communities . However , in a pilot study conducted in three communities in Burkina Faso , one of the communities selected had very few pigs and most of the residents were Muslim . There were no NCC cases among PWE in that community , which was located only about 10 km from another community where about 45% of PWE had lesions of NCC ( Carabin , unpublished results ) . The combined proportion of NCC among PWE was 29% . This suggests that our estimate may be accurate if the selected study communities represented rural areas of a country . However , if communities with clusters of NCC were specifically selected , then our results would be an overestimation of the country-wide reality . In three of the studies , single calcifications and/or single enhancing lesions were not considered as lesions compatible with NCC . This goes against the lesions described in Del Brutto et al . [39] which consider single , calcified lesions as a minor criterion , and does not consider the fact that some single calcified lesions may very well be NCC if combined with a positive result to EITB [47] . This could lead to an underestimate of the true prevalence . Even though not all single calcified lesions of the brain will be NCC , we assessed what impact the inclusion of all of those lesions as NCC would have had on the results . In the two India studies , the percentage of NCC among children with partial seizures would have increased from 10 . 1% to 38 . 1% in one study [14] and from 2 . 0% to 12 . 0% in the other study [13] . In the study by Palacio et al . in Colombia [29] , the estimate of NCC among PWE would have increased from 13 . 9% to 22 . 7% . We conducted a sensitivity analysis assuming that those solitary cysts were NCC in the Palacio study . This analysis yielded a pooled estimate of NCC among PWE of 30 . 3% ( 95% CI: 25 . 3%–35 . 5% ) , which is very similar to the previous estimate . It is generally believed that NCC is a cause of late-onset epilepsy . Our meta-analysis contradicts this belief by obtaining very similar pooled estimates of the proportion of NCC among PWE in adults ( aged 20 years old or more ) and children ( aged less than 20 years old ) . The proportion of NCC among children with partial seizures varied considerably . However , it does support the fact that children are affected by NCC . Our study has some limitations with regards to missing data , potential biases , and misclassification . Although a very broad search in seven different languages was conducted , relevant papers may have been overlooked . This situation may have the consequence of introducing a bias in the synthesis we were aiming to produce . Another potential bias may be publication bias , as we mainly considered published papers [48] . Apart from the specific region of Sub-Saharan Africa , we were unable to locate unpublished studies from other areas . To our knowledge , there have not been any published studies of NCC using neuro-imaging conducted in Viet-Nam , Cambodia , Laos or the Philippines ( Willingham , personal communication , May 2009 ) . Another important limitation is that the ascertainment of NCC cases remains a problem . Although CT and MRI are considered the best tools to diagnose NCC , they can miss early stages of the larvae infestation in the brain [49] . The definitive diagnosis of NCC has to be made by a set of methods including neuro-imaging procedures , histological techniques and immunological investigations , because the use of any single method may provide flawed diagnoses [50] . As mentioned earlier , many neuro-imaging lesions are not pathognomonic of NCC [51] . Unequivocal diagnosis can only be achieved by absolute recognition of a scolex on neuro-imaging , or by biopsy or autopsy [39] , [49] . However , invasive procedures are rarely routinely performed for diagnostic purposes [49] . Hence , our findings , which rely on a CT-scan diagnosis , may over- or underestimate the actual frequency of NCC . Whether it is an over-estimate due to counting lesions that are not NCC depends on how much of NCC does manifest as epileptic seizures . Indeed , since all our studies are cross-sectional in nature , it is impossible to determine if the NCC lesion is indeed the cause of the epileptic seizures . This problem could be exacerbated by the fact that radiologists may have had a wide variance in CT interpretation , especially for small , calcified lesions [51] . A further limitation of this systematic review is that most published studies have been based on small sample sizes . Gender and age-specific data were often not available and , when reported , the age groups were not consistent . Most of the authors we contacted for additional information did not answer our correspondence . Hence , we could only report the prevalence estimates from a sub-sample of the studies and in two very broad age groups . Interestingly , we noticed that the prevalence of NCC among PWE in children aged less than 20 was much higher in South Africa . This supports prior reports suggesting that NCC may be more common in children in South Africa than elsewhere [52] . Gender-specific estimates could not be calculated , and we could not verify whether females are more affected by NCC , as has been previously hypothesized [50] . Finally , all the available literature is based on cross-sectional studies in communities or clinics that selected PWE and offered them a CT-scan of the brain . It is impossible to determine the temporality of the link between NCC and epilepsy in such study design . Unfortunately , a cohort study of people developing brain lesions of NCC which follows them to see if they develop epilepsy is not ethically feasible . Among the literature reviewed , one was a prevalence case-control study and reported a prevalence odds ratio of 6 . 9 between NCC and epilepsy [8] . This systematic review has shown several challenges for the assessment of NCC's global burden . One way to improve the assessment of the global burden of NCC would be to encourage and enforce the use of a standard diagnosis for NCC , such as that developed by Del Brutto et al . [39] . This may require the provision of adequate diagnostic tools and expertise to all endemic countries . A second step would be to follow the proposal of some authors to declare NCC an international reportable disease [53] . This proposal was reviewed and rejected by the World Health Assembly in 2003 because it was felt that only diseases which can lead to large-scale international outbreaks should be included in the list of internationally notifiable diseases [54] . However , countries were encouraged to add this disease to their national list of notifiable diseases . Compulsory notification would have the benefit of providing accurate quantification of NCC prevalence in endemic areas . In 1992 , the municipality of Ribeirão Preto in Brazil , decided to make NCC a reportable disease in that region [7] . With the standardization of NCC diagnostic criteria and compulsory notification , the global burden of NCC would be easier to establish . A future systematic review of published and unpublished documents , extended to all relevant documents reporting NCC cases , will help capture more complete data . In order to standardize how NCC is reported in articles , we also propose to share the Access™ data extraction tools that were developed for this review . Data collected in the same way will be easier to combine . Collaborative data would improve the focus and decision-making regarding preventive measures for a disease that has severe complications . Despite these challenges , this study found that approximately one-third of people with epilepsy living in regions endemic for T . solium were associated with NCC . While this estimate may be biased due to measurement error , its robustness across populations and studies suggest that it is likely to be accurate . The number of DALYs lost due to epilepsy worldwide was estimated to be 6 , 223 , 000 , with slightly higher values for males ( 3 , 301 , 000 ) than for females ( 2 , 922 , 000 ) [55] . Many risk factors for epilepsy are linked with a lower level of economic development; thus , the burden is highest in South Asia , followed by Sub-Saharan Africa [54] . In India , the DALY for epilepsy estimated in 2002 was 1539 [56] . Given the very robust estimate of the proportion of people with NCC lesions among PWE and , in some cases people with epileptic seizures , it may be possible to use the range of the percentage of NCC among PWE to estimate the number of NCC-associated epilepsy cases in endemic areas . To achieve this goal , we will need to obtain information on the prevalence of epilepsy in areas that are endemic for cysticercosis , that is , those countries were sanitation is poor , pork consumption occurs , and pigs have access to human feces . In the literature that we have reviewed , we have not come across publications suggesting that NCC is not endemic in areas where these three conditions are met . By multiplying the range of prevalence of epilepsy by the range of the proportion of NCC among PWE in each endemic area , we would obtain a range of values for the prevalence of NCC-associated epilepsy . Since epilepsy has been reported as the most common manifestation of NCC , such estimates will probably capture the majority of NCC-associated burden . This information will ultimately lead to the estimation of prevalence DALYs associated with NCC .
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Neurocysticercosis ( NCC ) is a parasitic infection of the brain caused by the tapeworm Taenia solium , which infects humans and pigs . There have been increasing case reports and epidemiological studies on this disease , but its global frequency has never been determined , partly due to the fact that blood tests are not very good for the diagnosis of NCC . We present here a systematic review of the literature on the frequency of NCC diagnosed with neuroimaging worldwide . Overall , 565 articles were retrieved and 290 ( 51% ) selected for further review . Of those , only 26 had information valid enough to estimate the frequency of NCC in various populations . Only one study estimated the prevalence of NCC in the general population . The most striking finding was that the proportion of NCC among persons with epilepsy was very consistent and estimated at 29 . 6% ( 95%CI: 23 . 5%–36 . 1% ) from 12 studies conducted in Latin America , Sub-Saharan Africa and Southeast Asia . A reinforcement of the suggested universal guidelines for the diagnostic process , declaring NCC an international reportable disease and standardizing procedures for data collection could improve our understanding of the frequency of NCC worldwide and hence its global burden .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"infectious",
"diseases/infectious",
"diseases",
"of",
"the",
"nervous",
"system",
"infectious",
"diseases/neglected",
"tropical",
"diseases",
"neurological",
"disorders/infectious",
"diseases",
"of",
"the",
"nervous",
"system",
"public",
"health",
"and",
"epidemiology/epidemiology",
"infectious",
"diseases/helminth",
"infections",
"public",
"health",
"and",
"epidemiology/global",
"health",
"public",
"health",
"and",
"epidemiology/infectious",
"diseases",
"neurological",
"disorders/epilepsy"
] |
2010
|
A Systematic Review of the Frequency of Neurocyticercosis with a Focus on People with Epilepsy
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The teratogenic mechanisms triggered by ZIKV are still obscure due to the lack of a suitable animal model . Here we present a mouse model of developmental disruption induced by ZIKV hematogenic infection . The model utilizes immunocompetent animals from wild-type FVB/NJ and C57BL/6J strains , providing a better analogy to the human condition than approaches involving immunodeficient , genetically modified animals , or direct ZIKV injection into the brain . When injected via the jugular vein into the blood of pregnant females harboring conceptuses from early gastrulation to organogenesis stages , akin to the human second and fifth week of pregnancy , ZIKV infects maternal tissues , placentas and embryos/fetuses . Early exposure to ZIKV at developmental day 5 ( second week in humans ) produced complex manifestations of anterior and posterior dysraphia and hydrocephalus , as well as severe malformations and delayed development in 10 . 5 days post-coitum ( dpc ) embryos . Exposure to the virus at 7 . 5–9 . 5 dpc induces intra-amniotic hemorrhage , widespread edema , and vascular rarefaction , often prominent in the cephalic region . At these stages , most affected embryos/fetuses displayed gross malformations and/or intrauterine growth restriction ( IUGR ) , rather than isolated microcephaly . Disrupted conceptuses failed to achieve normal developmental landmarks and died in utero . Importantly , this is the only model so far to display dysraphia and hydrocephalus , the harbinger of microcephaly in humans , as well as arthrogryposis , a set of abnormal joint postures observed in the human setting . Late exposure to ZIKV at 12 . 5 dpc failed to produce noticeable malformations . We have thus characterized a developmental window of opportunity for ZIKV-induced teratogenesis encompassing early gastrulation , neurulation and early organogenesis stages . This should not , however , be interpreted as evidence for any safe developmental windows for ZIKV exposure . Late developmental abnormalities correlated with damage to the placenta , particularly to the labyrinthine layer , suggesting that circulatory changes are integral to the altered phenotypes .
Zika virus ( ZIKV ) is a mosquito-borne flavivirus that was initially thought to produce a benign disease characterized by mild fever , muscle and joint pain , rash and conjunctivitis [1] . However , the recent ZIKV epidemic in Brazil has been associated with severe transient , as well as irreversible neurological manifestations , such as ascending paralysis ( Guillain-Barré syndrome ) and microcephaly , respectively [2–6] . Furthermore , it is becoming increasingly clear that ZIKV pathogenicity is not restricted to the aforementioned conditions [7] . In fact , ZIKV shows many similarities to ‘TORCH’ pathogens ( Toxoplasma gondii , other , rubella virus , cytomegalovirus and herpes simplex virus ) , especially in the way it accesses embryos and fetuses [8] , a realization that increases our public health concerns . In order to face the challenges posed by an infectious agent with such potential , the scientific community worldwide directed its attention to the biology of ZIKV and its pathogenicity . However , at this junction , there are few clues on the mechanisms triggered by viral infection to damage the embryonic and/or fetal human . This is because we are still lacking a suitable animal model of intrauterine injury after exposure to ZIKV . Large strides have been made towards a relevant animal model for ZIKV teratogeny [9–15] . However , the currently available models are still somewhat limited . This is because these models rely on unnatural prerequisites such as genetically modified immunodeficient animals , exaggerated viral loads and facilitated access of the virus to target tissues via surgical procedures ( e . g . direct brain injection ) [10 , 12 , 16] . For instance , although the model of Miner et al . [9] utilizes viral loads and infection routes eminently compatible with the epidemiological setting , it depends largely on the failure of the host to mount an interferon-based immunological response . Exposure to ZIKV in this setting compromises the conceptus , but also induces maternal encephalitis , which has not been shown to be a necessary component , or frequent association with ZIKV teratogeny in humans [1 , 17] . Although the use of IFN1 antibodies can eliminate encephalitis from their model [9] , the associated costs may be prohibitive for most laboratories . Despite its elevated viral load , the model described in Cugola et al . [11] is not capable of infecting immunocompetent wild-type C57 mice , which is an important issue to be clarified , as contradictory results have been published [13] . More recently , Yockey and colleagues [13] described an intriguing model of ZIKV infection through intravaginal exposure that greatly expands our understanding of ZIKV pathogeny . However , even as Yockey et al . [13] report brain infection in conceptuses from immunocompetent dams , their model is not associated with overt morphological damage to the embryonic/fetal brain in wild-type animals . Therefore , our assessment is that we need models of ZIKV teratogeny that are better related to the human disease . Here we present a mouse model of developmental damage induced by direct maternal injection of ZIKV into the external jugular vein . Our results established that immunocompetent animals from widely available wild-type FVB/N and C57BL/6J strains are indeed affected by ZIKV , which produces a host of congenital abnormalities including dysraphia , hydrocephalus , arthrogryposis , gross malformations and disturbances such as intrauterine growth restriction ( IUGR ) , which is often correlated with damage to the placenta .
The ZIKV HS-2015-BA-01 was isolated from a serum sample of a ZIKA infected patient during a 2015 outbreak in Bahia State , Brazil . The complete cds ( polyprotein gene ) sequence of the isolate is available under GenBank accession KX520666 . 1 . ZIKV HS-2015-BA-01 was isolated by passage in C6/36 mosquito cells , up to passage 3 . Aliquots from cell culture contents were repeatedly processed for virus detection through reverse transcription polymerase chain reaction ( RT-PCR ) . Subsequently , the virus was propagated and titrated in the green monkey kidney epithelial cell line Vero using the method described by Medina et al . [18] . Vero cells were cultivated in DMEM ( Sigma Aldrich ) supplemented with 10% fetal bovine serum , 100 units/ mL of Penicillin and 100 μg/mL of Streptomycin , at 37°C , 5% CO2 . ZIKV infected Vero cells were fixed with paraformaldehyde 4% ( w/v ) for 30 min at room temperature , treated with 0 . 25% ( v/v ) Triton-X for 30 min and incubated with the primary monoclonal antibody 4G2 from mouse ascites ( raised against the flaviviral envelope protein ) in Phosphate-Buffered Saline ( PBS ) 1X containing 2 . 5% FBS at 37°C for 2 h [19] . After two washes with PBS 1X , the cells were incubated with AlexaFluor594 conjugated goat anti-mouse IgG ( Thermo Scientific ) and 5 μg/mL of DAPI ( 4' , 6 diamidino-2-phenylindole ) ( Sigma Aldrich ) in Dulbecco’s PBS at room temperature for 1 h and then washed again twice with PBS 1X . After the final washing cycle , digital images were acquired at 20X magnification using a high-throughput confocal fluorescence imaging system ( Operetta Perkin Elmer ) . Adult FVB/NJ ( JAX#1800 ) and C57BL/6J ( JAX#664 ) mice were housed in the pathogen-free animal facility at the Laboratory of Genome Modification ( LNBio ) . Animals were maintained on a photoperiod of 12:12 light/dark cycle at 21–24°C . Landmarks typical of relevant developmental days were compiled from Kauffman [20] . Six to eight-week-old pregnant mouse females ( 5 . 5 , 7 . 5 , or 9 . 5 dpc ) weighing 22–28 g were anaesthetized with intraperitoneal injections of ketamine/xylazine ( 100/10 mg . kg-1 ) . Since more traditional infections routes , such as lateral tail vein injection ( n = 3 at 5 . 5 dpc ) and intraperitoneal injection ( n = 8 at 5 . 5 dpc ) had no impact in embryos , a jugular venous access was established as previously described [21] . Briefly , after positioning the animal on its back , we performed midscapular and diagonal incisions , isolated the right external jugular vein and inserted a polyethylene cannula ( PE-10 ) into it . A viral stock solution of 109 plaque-forming units per ml was serially diluted with PBS to produce 100 μl aliquots containing viral loads of 108 , 107 , 106 , 105 , 104 , 103 plaque-forming units . These viral loads were administered using a 1 ml plastic syringe with a polished 20-gauge needle connected to the PE-10 cannula . Control pregnant females were injected with 100 μl of PBS . After jugular infection , the PE-10 cannula was withdrawn , the jugular vein closed , and the animals sutured and allowed to recover under heating from an infrared lamp . ZIKV-injected pregnant females and PBS-injected controls were monitored daily after infection . Individual embryos , fetuses and placentas from ZIKV-infected pregnant females , as well as their corresponding PBS-injected controls and reference animals from our mouse colony were collected from 9 . 5 dpc to 18 . 5 dpc ( S1 Table ) . Optical images were captured before and after amnion dissection , using a Nikon stereomicroscope . Immediately after dissection , embryos , fetuses and their respective placentas were weighed and their crown to rump lengths were measured using a digital caliper and a previously calibrated ImageJ software ( https://imagej . nih . gov/ij/ ) . Brain size was evaluated by measuring both biparietal and occipital-frontal diameters using ImageJ software . Linear regression and One-way ANOVA followed by Tukey’s multiple comparisons test were performed using GraphPad Prism version 7 . 0a for Mac OS X ( GraphPad Software , La Jolla California USA , www . graphpad . com ) . The ontogenetic periods associated with each conceptus were determined according to the staging scheme of Theiler [22] , complemented by Downs and Davies [23] , as well as by Kauffman [20] . To determine the staging at the presumed time of death , we utilized the status of both fore and hindlimbs , as well as additional characters such as: 1- extent of covering of the external acoustic meatus by the pinna of the ear; 2- degree of eyelid closure; presence , or absence of skin wrinkles in the neck , trunk and limbs; 3- status of the vibrissae ( arrangement of rows and eruption ) ; 4-presence , or absence of the sinus hair follicle; 5- status of the umbilical hernia . A state of delayed development at the time of death was inferred whenever a given conceptus failed to present the complete set of morphological characters typical of its estimated stage at the time of death . Embryos , fetuses and their respective placentas , as well as the spleens from ZIKV-infected pregnant females were collected at 10 . 5 , 12 . 5 , 16 . 5 and 18 . 5 dpc . After fixing overnight in 4% paraformaldehyde , the tissues were embedded in paraffin blocks and cut into 6 μm-thick sagittal sections using a Leica microtome . Embryos were divided in two sagittal halves and both fetal and maternal sides of placentas were included . Embryos at 18 . 5 dpc were decalcified before embedding and stained for Hematoxylin & Eosin according to established protocols . For immunoreactions , dewaxed and hydrated placental sections were sequentially incubated in: 1- three washes with 2% H2O2 in Tris-buffered saline ( TBS ) for 5 min to quench endogenous peroxidase activity; 2- blocking solution containing bovine serum albumin 2% ( v/v ) in TBS and primary antibodies for 1 h at room temperature; 3- peroxidase-conjugated goat anti-rabbit IgG secondary antibody ( 1:200 in TBS , Abcam #6721 ) at 37°C for 1 h , followed by development with DAB ( Sigma-Aldrich ) and counterstained with Mayer's hematoxylin . Primary antibodies were rabbit polyclonal anti-CD31 ( Abcam#28364 ) , anti-SCL16A3 ( Abcam #1904987 ) and anti-EpCAM ( Abcam #71916 ) respectively diluted 1:50 , 1:150 and 1:100 in TBS ( v/v ) . For ZIKV ( flaviviral ) staining , a blocking solution with goat IgG polyclonal isotype control ( 1:200 in TBS , Abcam #37373 ) was utilized for 30 min at room temperature . This was followed by unconjugated rabbit F ( ab ) fragment anti-mouse IgG ( 1:200 in TBS , Sigma #3700999 ) for 1 h at room temperature and then by mouse flavivirus-specific monoclonal IgG 4G2 ( hybridoma D1-4G2-4-15 , ATCC HB-112 ) ( 1:200 in TBS ) for 2 h at 37°C . Peroxidase-conjugated goat anti-mouse IgG ( 1:200 in TBS , Abcam , #6789 ) was used as secondary antibody at 37°C for 1 h , followed by development with DAB and counterstaining with Mayer's hematoxylin . Negative controls were carried out by omitting step ( 4 ) of the immunohistochemical reaction . A 339 bp ZIKV amplicon ( 1791–2130 ) was amplified by PCR with the following set of primers: forward 5’- GATAAACTTAGATTGAAGGGCGTG -3’; reverse 5’- TCCAATGGTGCTGCCACTC -3’ , using a cDNA template obtained from ZIKV-infected Vero cells . The amplicon was cloned into pGEM-T vector ( PROMEGA ) following manufacturer's instructions . The purified pGEM-ZIKV plasmid was sequenced to verify ZIKV viral identity and used to derive standard curves in real time PCR assays . Quantitative real time PCR ( qRT-PCR ) was utilized to confirm maternal ZIKV infection . Samples were homogenized in Trizol Reagent ( Invitrogen ) , and total RNA was isolated . The isolated RNA ( 2 μg ) was used for cDNA synthesis with the Superscript pre amplification system , following manufacturer's instructions . The qRT-PCR was performed using a MX3000P system ( Stratagene , La Jolla , CA ) and SYBR green reagent ( Applied Biosystem–USA ) . We designed qRT-PCR primers to amplify a 148bp amplicon ( 1808–1955 ) . Primer sequences are forward 5’-AGGGCGTGTCATACTCCTTG-3’ and reverse 5’-TGCATGTCCACCGCCATCT-3’ . Each qRT-PCR contained 30 ng of reverse-transcribed RNA , each primer at 400 nM , and 6 μl of SYBR Green PCR Master Mix ( Invitrogen ) in a final volume of 12 μl . Each sample was analyzed in triplicate . PCR conditions were: 50°C for 2 minutes ( 1 cycle ) ; 95°C for 5 minutes ( 1 cycle ) ; 95°C for 30 seconds , 59°C for 45 seconds and 72°C for 45 seconds ( 35 cycles ) . Since pGEM-ZIKV also harbors this amplicon , this plasmid was used to generate the standard curve , which allowed determining virus copy number in the tissue samples . Samples were run in triplicate . The virus copy number was interpolated from the cycle thresholds of SYBR green qRT-PCR assay , using standard linear curve ( R2 values of 0 . 99 ) generated from known amounts of control pGEM-ZIKV plasmid ( range of 1 x 107 to 1 x 101 copies/reaction ) , as described previously [24 , 25] . Blood and organ samples from ZIKV-infected and PBS-injected pregnant mice were stored at -80°C until viral assessment . To measure ZIKV viremia , 50–100 μl of blood were collected through the lateral tail vein at indicated time points after infection . Samples were thawed and submitted to a tissue culture infectious dose ( TCID50 ) assay in Vero cells . Briefly , whole blood , or minced suspensions of organs were serially diluted and placed in cell culture plates containing confluent Vero cell monolayers for one hour . Samples were removed and fresh complete DMEM was added to the cell plates . Cell culture plates were maintained in the incubator for 5 days , following fixation with 10% w/v formaldehyde and staining with methylene blue at 1% w/v . TCID50 was calculated by direct observation of cell culture plates . Individual placentas and maternal tissues ( brain , kidney , spleen and liver ) from control , or ZIKV-infected pregnant females were harvested at 12 . 5 dpc , minced and lysed in ice-cold RIPA buffer ( 50 mM Tris-HCl pH 8 . 0 , 150 mM NaCl , 0 . 5% sodium deoxycholate , 0 . 1% SDS , 1% Nonidet P40 ) supplemented with Complete Protease Inhibitors ( Roche ) , DNAse I ( Promega ) and RNAse A ( Sigma ) . Subsequently , the lysate was cleared at 14 . 000×g at 4°C for 30 min and protein concentration was determined using the BCA Protein Assay Reagent Kit ( Thermo Scientific ) . Fifty micrograms ( 50 μg ) of proteins from the extract were mixed to Laemmli buffer containing beta-mercaptoethanol , loaded into a gel and submitted to 10% SDS-PAGE electrophoresis and subsequently transferred to nitrocellulose membrane . After blocking with 5% skim milk in Tris-buffered saline ( 25 mM Tris , pH 7 . 4 , 137 mM sodium chloride , 2 . 7 mM potassium chloride ) containing 0 . 05% Tween 20 ( TBST ) for 1 h , membranes were hybridized with the flavivirus-specific monoclonal antibody 4G2 ( 1:1000 in TBST , hybridoma D1-4G2-4-15 , ATCC HB-112 ) for 1 h . After washing with TBST , membranes were incubated with the goat anti-mouse horseradish peroxidase-conjugated secondary antibody for 1 h and washed with TBST . Detection was performed using Pierce™ ECL Western Blotting Substrate ( Thermo Scientific ) according to the manufacturer's recommendations . This study was carried out in strict accordance with the recommendations set forth in the Guide for the Care and Use of Laboratory Animals of the Brazilian National Council of Animal Experimentation ( http://www . cobea . org . br/ ) and the Federal Law 11 . 794 ( October 8 , 2008 ) . The Institutional Committee for Animal Ethics of the Brazilian Center for Research in Energy and Materials ( CEUA-CNPEM , License 29-B ) approved all the procedures used in this study .
To characterize the ZIKV strain used in our experiments , we infected Vero cell cultures with aliquots of our working ZIKV stocks . Monolayers of Vero cells were stained with an anti-flavivirus E protein antibody ( 4G2 ) , which is commonly used in in vitro assays of flavivirus infection . As shown in Fig 1A , ZIKV HS-2015-BA-01 efficiently infected Vero cell monolayers , which were almost completely positive for the 4G2 antigen ( red ) . Uninfected cell cultures were negative for flavivirus E-protein staining . As the 4G2 antibody detects a flaviviral E protein epitope , we further confirmed the identity of ZIKV by sequencing an amplicon of the ZIKV E-protein gene , which established it as a bona fide fragment of the ZIKV genome . These results were complemented by the complete cds ( polyprotein gene ) sequencing of the ZIKV HS-2015-BA-01 strain ( Fig 1B ) , which is available in GenBank under the accession code KX520666 . 1 . The rationale for our model is underpinned by the conjecture that adult immunocompetent wild-type mice are resistant to ZIKV infection , after cutaneous exposure due to defenses that are at play in the skin , subcutaneous tissues and lymph nodes , as shown for the Dengue virus [26] . Thus , we hypothesized that ZIKV inoculation directly to the circulation would allow ZIKV infection of maternal target tissues and placenta . Accordingly , we devised a scheme in which we infected pregnant FVB/N and C57BL/6J females with ZIKV via an external jugular access ( Fig 2A ) . The establishment of comprehensive relationships between inoculum size and developmental damage is outside of the scope of this initial model characterization . However , we established that viral loads of 103 to 104 plaque-forming units ( pfu ) either produced no phenotypes , or were associated with very low frequencies of developmental abnormalities , which precluded their practical use . On the other hand , viral loads of 106 , 107 and 108 were too aggressive , producing too many early embryonic resorptions ( i . e . dead embryonic sacs compatible with 5 . 5 to 7 . 5 dpc stages ) , which made them impractical to model the effects of ZIKV on late embryonic morphogenesis , or fetal maturation . Accordingly , in pilot experiments , we obtained the best results when pregnant FVB/NJ or C57BL/6J females were injected with 105 pfu in PBS ( see methods ) . The proportion of conceptuses undergoing early resorptions associated with the ZIKV 105 pfu inoculum was 8 . 45% ( 06/71 ) , which is not significantly different from 10 . 26% ( 4/39 ) of resorptions in PBS controls ( p = 0 . 7408 , Chi-square test ) . This is consistent with our interpretation that the chosen viral load of 105 pfu is ideal to investigate the late embryonic and fetal effects of ZIKV and , thereafter , we refer only to this viral titer . Pregnant females injected intravascularly with ZIKV recovered quickly from anesthesia and did not demonstrate signs of encephalopathy , or overall distress . Tables 1 and 2 provide a complete quantitative description of experimental and control animals utilized , viral titers , developmental periods , affected embryos and fetuses as well as other relevant parameters . The S1 Table lists all embryos and fetuses obtained from healthy , non-disturbed , pregnant females at our animal facility ( reference embryos and fetuses ) . To establish whether intravascular ZIKV injections were effective to cause infection , females were injected at multiple stages of pregnancy , and tissue samples were collected for quantitative real time PCR assays ( qRT-PCR ) ( Fig 2 ) . As shown in Fig 2C–2E , we confirmed the presence of ZIKV in the spleen , liver and kidneys of 12 . 5 dpc pregnant females . In contrast , we did not detect copies of the ZIKV genome in any of the brain tissues examined . To look for the presence of ZIKV protein in tissues , we analyzed brains , kidneys , spleens , and livers from control and infected females by Western blot . Using the anti-flavivirus 4G2 antibody , we observed specific bands in infected samples ( Fig 3A and 3B ) , as previously described in reducing conditions [27 , 28] , confirming ZIKV infection . The lower band ( arrow in Fig 3A and 3B ) corresponds to the mature viral protein E , which has a theoretical MW of 54 . 4 kDa , but migrates as a 50 kDa band , as described [29] . Higher MW bands are likely due to immature polyprotein precursors such as prM/E ( precursor membrane/E protein ) ( arrowheads in Fig 3A and 3B ) . There was no evidence of ZIKV proteins in brain samples , in agreement with qRT-PCR findings ( Fig 2E ) . Taken together , these data indicate that wild-type dams tolerate well intravascular ZIKV injection . When infected , pregnant females will harbor the virus in spleen , kidneys and , occasionally , in liver , but not in the brain , which is consistent with the absence of any neurological signs in these animals . To outline a window of susceptibility for ZIKV-induced embryonic/fetal disruption , we set up ZIKV injections at 5 . 5; 6 . 5; 7 . 5; 9 . 5 , as well as 12 . 5 days post coitum ( dpc ) , and harvested embryos and/or fetuses at 9 . 5; 10 . 5; 11 . 5; 12 . 5; 13 . 5; 14 . 5; 15 . 5; 16 . 5 and 18 . 5 dpc ( Fig 2A , Table 1 ) . These experiments indicated that intravascular maternal injection of ZIKV is capable of producing embryonic , or fetal abnormalities from 5 . 5 to 9 . 5 dpc , but failed to produce noticeable malformations at day 12 . 5 dpc . These results suggest that exposure to ZIKV is comparatively far less likely to produce overt morphogenetic consequences at 12 . 5 dpc , than at earlier stages . Thus , there seems to be a developmental window of opportunity for ZIKV-induced teratogenesis that encompasses early gastrulation , neurulation and early organogenesis stages . It is important , however , to stress that our data should not be interpreted as evidence that there are any safe developmental stages when the conceptus is protected from ZIKV . We next set out to determine the relationship between ZIKV exposure , maternal , placental , as well as conceptus infection and the presence , or absence , of malformations . To check whether ZIKV can reach the placenta and eventually the conceptus after maternal exposure and infection , we established a group of pregnant females that were injected at day 5 . 5 dpc with ZIKV ( n = 4 ) , or with PBS ( control ) ( n = 1 ) . In Fig 2B , we show that only one among the four ZIKV-injected females was infected at the day of harvest ( 12 . 5 dpc ) . Among the four pairs of embryos/placentas harvested from this 12 . 5 dpc ZIKV-infected pregnant female , three were found to display ZIKV infection , indicating vertical transmission , while the remaining embryo and its placenta were negative for the ZIKV genome . These data are in agreement with the documented human experience of vertical transmission from infected mothers and with the observation that not necessarily all conceptuses are infected by the virus following maternal exposure in twin pregnancies [17] ( Fig 2B ) . The three other pregnant females exposed at 5 . 5 dpc and harvested at 13 . 5 , or 15 . 5 dpc , tested negative for ZIKV , as did the PBS-injected control dam . Thus , at least at the times investigated ( i . e . 13 . 5 and 15 . 5 dpc ) , our results are consistent with epidemiologic data in the human setting , in that not all exposed females are infected with the virus . Interestingly , but also disquietingly , we demonstrate that one fetus/placenta pair harbored copies of the ZIKV genome , in spite of the fact that the maternal organism tested negative for the viral genome at the day of harvest ( 15 . 5 dpc ) . This suggests that ZIKV may have reached this particular conceptus , but not its siblings , before the exposed pregnant female was able to mount a successful immunological response . None of the ZIKV-positive conceptuses in this group displayed overt morphological abnormalities . Because ZIKV-infected conceptuses represented in Fig 2B did not display evident morphological changes , we repeated the injections at 5 . 5 dpc , but this time examined the embryos earlier , at 10 . 5 dpc , in the hope to find malformed embryos . In this dataset of thirteen living 10 . 5 dpc embryos , we found five malformed individuals ( Fig 2B' ) . As represented in Fig 2B' , we demonstrated the presence of ZIKV genome in two out of five malformed embryos . Unfortunately , we could not recover ZIKV RNA from the remaining three embryos , probably because of their very small sizes . As depicted in Fig 2B , we confirmed that infected dams could transmit ZIKV vertically , but that the virus does not necessarily invade all embryo/placenta pairs following maternal infection . Interestingly , the experiments in Fig 2B suggest that there may be some level of discrepancy between placental and embryonic infection , in that three embryos tested positive for the ZIKV genome , while their respective placentas were negative . Conversely , we also detected one occurrence of ZIKV-positive placenta dissociated from embryonic infection . In the first case , embryonic infection without placental involvement is possible because ZIKV injection at 5 . 5 dpc preceded placental morphogenesis , so that embryos may have become infected very early through non-placental routes [13 , 30] . Indeed , TORCH viruses may reach the embryo before and during implantation through multiple ways [30 , 31] such as: direct infection of early trophoblast progenitors ( e . g . see [32] ) , or through intercellular routes; intrauterine viral infections via ascending infection from the vagina through intact fetal membranes , or after loss of membrane integrity [13 , 30] . Alternatively , ZIKV RNA levels were simply beyond detection . In the second ( single ) case , placental infection without embryonic involvement is also conceivable , since the placentas , especially the more mature ones , may well be capable of protecting the conceptus from ZIKV invasion for at least some period of time after maternal infection . To test the concept that increased placental maturity may constitute a significant physiological barrier to the spread of viable ZIKV particles from the pregnant female to the conceptus , we set out to determine viable viral contents using a TCID50 assay . On a subset of the animals represented in Fig 4 ( n = 4 ) , we injected ZIKV , or PBS , into 12 . 5 dpc females and collected maternal blood at 1 , 12 and 24 hours post-injection . In the day of harvest ( 16 . 5 dpc ) we removed a further sample of maternal blood , along with maternal organs , conceptuses and placentas . Consistent with previous experience with intravascular injections of flaviviruses [13] , all blood samples investigated were negative for viable ZIKV particles . This indicates that ZIKV is cleared from the circulation in less than one hour after direct intravascular injection , and that immunocompetent mouse females may not display viable ZIKV particles in their blood throughout the rest of their pregnancies . In contrast , we detected viable ZIKV particles in spleens and livers , while kidneys and brains were negative . This confirms that despite the absence of viremia , ZIKV may reside in a viable form in the spleens and livers of some , but not all immunocompetent females . Importantly , although we detected the highest viral levels in all the placentas , we identified the presence of viable ZIKV particles in only half of the embryos . This indicates that 12 . 5 dpc placentas are capable of defending the conceptuses from ZIKV in approximately 50% of the cases , consistent with their well-established protective functions against viral threats . We did not find morphological abnormalities in conceptuses from this dataset . As expected , all organs , placentas and conceptuses from control ( PBS-injected ) females were negative for ZIKV . A substantial number of embryos/fetuses exposed to ZIKV at 5 . 5 , 7 . 5 or 9 . 5 dpc and harvested at 12 . 5 , 16 . 5 or 18 . 5 dpc ( Fig 6A and 6D and in Fig 7A–7H; labelled with an “m” in Fig 8B ) displayed small head dimensions as indicated by the occipito-frontal diameter ( OFD ) . These malformed embryos/fetuses ( 9 out of 10 ) failed to display the complete set of developmental landmarks associated with their estimated ontogenetic stages at the time of death ( S2–S5 Tables ) [20 , 22] and were typically small in relation to their phenotypically normal littermates , to embryos from PBS-injected dams , or to non-injected reference conceptuses from FVB animals of our mouse facility . Even if these ZIKV-exposed conceptuses display small OFDs , in the most rigorous classification the diagnosis of microcephaly is only established when cephalic parameters are three standard deviations ( SD ) below correctly age and stage-matched controls [37] . Thus , when we compared normalized OFDs from all embryos/fetuses associated with ZIKV-injected dams with the interval defined by three SDs , only two fetuses displayed evidence of microcephaly ( Fig 8C , arrows ) . Interestingly , none of these two fetuses showed obvious morphological abnormalities . Importantly , when we staged embryos/fetuses according to established ontogenetic landmarks , rather than by the nominal day of litter development , we determined that cephalic proportions were within the interval defined by three SDs at each specific stage in all but two malformed conceptuses ( Fig 9A–9C ) . One fetus , depicted in Fig 6D constituted the only objective evidence for microcephaly in our study . Interestingly , the other anomalous , grossly malformed fetus ( Fig 7H ) displayed a disproportionally increased OFD , secondary to collapse of the cephalic region in the cranio-caudal axis ( arrowhead Fig 9C ) . It is important to note that PBS-injected females did not show any consistent reduction in litter size in relation to our control , uninjected , reference litters . This indicates that all injected females tolerated well the experimental stresses associated with anesthesia and surgical procedures without detrimental effects on intrauterine growth . It has been reported that ZIKV infection in humans associates with fetal damage to only one fraternal twin in a sib pair [17] . This anecdotal finding suggests that there are significant physiological and/or immunological checks to embryonic/fetal infection , even after ZIKV gains the circulation . Consistent with this , five out of thirteen ( 5/13 ) littermates from FVB/NJ dams injected with ZIKV at 9 . 5 dpc were outwardly normal ( Fig 7 , Table 1 ) . Likewise , six out of eight conceptuses ( 6/8 ) from dams injected with ZIKV at 5 . 5 dpc , or eight out of nine fetuses ( 8/9 ) from C57BL/6J pregnant females injected with ZIKV at 7 . 5 dpc were apparently unaffected on purely morphological grounds . One of the likely physiological checks to ZIKV infection in a conceptus is the placenta . In a successful pregnancy , the placenta plays a crucial role in enabling vital exchanges between mother and conceptuses , as well as in protecting them from microorganisms [38] . Therefore , we hypothesized that the placenta may play a significant role in the pathophysiology of ZIKV-induced embryonic/fetal disruption . In Fig 10 , we provide evidence that ZIKV-induced abnormalities correlate with a severe pattern of placental injury in malformed embryos harvested from pregnant females exposed to the virus at 5 . 5–9 . 5 dpc . In contrast , exposure to ZIKV outside of the window of susceptibility to malformations ( e . g . at 12 . 5 dpc ) was associated with infected placentas , but overtly normal fetuses . This suggests that increased placental maturity can afford some level of protection against ZIKV-induced teratogenesis . To establish whether placental alterations are directly associated with ZIKV , we performed immunohistochemistry with the anti-flavivirus 4G2 antibody . Fig 10A indicates that placentas from outwardly normal ZIKV exposed fetuses stain positive for the flaviviral antigen , which is consistent with our qRT-PCR and Western blot data ( Fig 2E , Fig 3A amd 3B ) . The staining is concentrated in the labyrinthine area , which is the site where metabolic exchanges between maternal and fetal organisms take place [38] . Trophoblast channel walls are often compromised ( Fig 10A , negative control shown in Fig 10B ) , while the labyrinths from control placentas do not show any ZIKV staining . At closer inspection , 16 . 5 dpc placentas from ZIKV disrupted embryos/fetuses showed fibrosis , resorption and hemorrhagic areas of varying dimensions , as compared to placentas from outwardly normal ZIKV exposed fetuses , or to placentas from reference conceptuses ( Fig 10C –10F ) . As before , the most relevant alterations were recorded in the labyrinthine layer ( Fig 10G–10H ) . Placentas from outwardly normal littermates were histologically similar to control placentas , but exhibited persistence of nucleated red blood cells in the labyrinthine fetal capillaries ( Fig 10I ) . This suggests a delay in erythroid differentiation and/or a physiological response to fetal hypoxia . Placentas collected at 12 . 5 dpc of pregnant females infected with ZIKV , or injected with PBS on 5 . 5 dpc showed all the patterned placental layers: chorionic plaque , labyrinth and junctional zone . However , when compared to control placentas , placentas of infected mothers exhibited interhemal membranes separated by large areas filled with cellular clusters . Immunoreactivity to CD31 , which localizes endothelial cells , underscored this abnormal feature ( Fig 11A and 11B ) . The cell clusters that abounded in the labyrinth of these placentas were stained by EpCAM ( Fig 11C and 11D ) , a marker of multipotent labyrinth trophoblast progenitor cells [39] . It is likely that functional alterations are associated to these morphologic changes . Indeed , the expression of the monocarboxylate transporter SCL16A3 , essential for the transport of lactate , ketone bodies and other monocarboxylates [40] , also showed a weaker and less extensive staining throughout the placental barrier in infected , rather than in control placentas ( Fig 11E and 11F ) . Collectively , these data suggest a critical developmental change in the ZIKV-challenged mouse placenta , hinting at a disequilibrium between endothelial function and precursor expansion , which may compromise both the exchange and barrier functions of the placenta .
In our model , ZIKV affects development at initial phases such as neurulation and beyond . In general , early exposure is associated with important morphological defects , while late exposure is often associated with IUGR , rather than with overt anatomical consequences . When embryos are exposed to ZIKV within the 5 . 5-to-9 . 5 dpc window of susceptibility ( akin to the second and third weeks in humans ) , the resulting phenotypes are varied . Nonetheless , it is already possible to outline a clear and logic succession of morphologic consequences according to the developmental day of exposure and of harvest . An example of the increased morphogenetic severity associated with early harvests can be found , for example , in a comparison between two datasets of embryos exposed at 5 . 5 dpc , but harvested at 10 . 5 dpc , or at 12 . 5 dpc . It is possible to conclude that the most dramatic , devastating , phenotypes are observed at 10 . 5 dpc ( Fig 5 ) , which include dysraphia of the anterior ( telencephalon/rhomboencephalon ) and posterior neural tube ( spinal cord ) , hydrocephalus , posterior truncation , as well as stunted development of eyes and ears . In contrast , at 12 . 5 dpc , we detected posterior , mostly caudal , hypotrophy , as well as hypotrophic eyes ( apparently arrested at an early optic pit stage ) and otic placodes ( Fig 6A ) . Although most of the 10 . 5 dpc embryos exposed to ZIKV at 5 . 5 dpc were alive ( judging from their beating hearts ) , the defects associated with them were so overwhelming that one would not expect to find living embryos at 12 . 5 dpc . In fact , the affected embryo exposed at 5 . 5 dpc and harvested at 12 . 5 dpc was dead . Consistent with this interpretation , the number of dead embryos undergoing reabsorption does increase according to the day of harvest in embryos exposed at 5 . 5 dpc , going from zero at 9 . 5 dpc , one at 10 . 5 dpc , four at 11 . 5 dpc and five at 13 . 5 to 15 . 5 dpc ( Table 1 ) . The importance of the embryonic day of exposure to the gravity of ZIKV-induced phenotypes is almost self-evident when we compare the morphological outcomes of embryos exposed at 5 . 5 dpc to those exposed at 9 . 5 dpc ( Fig 5 and Fig 7 , respectively ) . The morphologies of the former group reflect the dire consequences of interference with early , basic embryonic morphogenesis . In contrast , in the latter group the defects ( e . g . amniotic hemorrhage , generalized edema , blood pooling , posterior hyperemia , anterior pallor and vascular rarefaction ) , while lethal , are clearly associated with interference with maturation , rather than with morphogenetic processes , which further underscores the stepwise reduction in severity with developmental progression . In summary , we believe that the paradigm of more frequent early morphogenetic consequences and more pronounced late disturbances in maturation suggested by our results will be a useful guide to understand the complex pathogeny of ZIKV-induced damage to the human conceptus . Even from the standpoint of our limited series of neural tube defects associated with embryonic exposure to ZIKV , it is apparent that there are two major components within the brain and spinal cord phenotypes that we described , namely: dysraphia and hydrocephalus . As we show in Fig 5B , 5D , 5D’ , 5E and 5E’ , dysraphia and hydrocephalus coexist in affected embryos . Complete description of neural phenotypes and establishment of cause and effect relationships between dysraphia and hydrocephalus are out of the scope of this initial characterization . However , established clinical and experimental knowledge suggest that these two manifestations are related , and that dysraphia is , perhaps , the more general condition , while hydrocephalus is a consequence [44] . These two phenotypes are reminiscent of complex and varied conditions , such as pre-natal Arnold-Chiari and Dandy-Walker syndromes , or , conceivably , are related to the incipient embryonic stages of these syndromes . These features are rarely reported in human embryos [45] , perhaps due to practical problems in the access to very early conceptuses . Consistent with this interpretation , Arnold-Chiari syndrome type two includes hydrocephalus of the fourth ventricle and is associated with myelomeningocele , which is a dysraphic disorder ( see Fig 5B ) . Moreover , the Dandy-Walker syndrome is defined by hypoplasia of the cerebellar vermis and by cystic dilatation of the fourth ventricle , and is sometimes associated with hydrocephaly of the fourth ventricle and rostral ventricles as well as occipital encephalocele , the latter being another form of dysraphia [46] . Many of these anatomic abnormalities have been described in ZIKV exposed and infected human conceptuses [5 , 36] , as well as in TORCH phenotypes [47] . These findings suggest that , in spite of specific characteristics of each disease , there are some stereotypic pathways of aggression and morphogenetic responses and anatomical consequences in humans and in mice . Ultimately , these common features may hold the clues to understand the sequence of events that leads to the drastic neural phenotypes associated with ZIKV infection in the conceptus . Recently , relevant articles described mouse models of ZIKV infection . Four studies reported alternative models of mouse teratogeny by ZIKV [9–11 , 13] , and another work described a model of ZIKV vertical transmission [12] . The models described have their pros and cons . Cugola et al . [11] provide high quality data on brain organoid infection by ZIKV . However , the results from Cugola et al . [11] contain few specific morphological consequences in fetuses from pregnant females injected with ZIKV . Perhaps this can be attributed to the late timing of injection ( 13 . 5 dpc ) and/or to the quality of intravascular delivery via caudal vein , which is technically demanding and often leads to animal stress and to subcutaneous extravasation of injected contents [48] . The SJL mutant strain utilized by Cugola et al . [11] is known for displaying high levels of circulating T cells and for its propensity to develop experimental autoimmune encephalomyelitis [49–52] . It is currently unclear how increased levels of circulating T-cells in SJL mice would lead to an increased propensity to develop ZIKV-induced damage to maternal and embryonic/fetal tissues as compared to wild-type C57BL/6 mice . Wu et al . [12] , Yockey et al . [13] and ourselves ( Fig 1 , Fig 2 , Fig 3 ) demonstrated that ZIKV can infect maternal , placental and embryonic/fetal tissues in wild-type C57BL/6 mice . Thus , it is unlikely that there is any fundamental need for increases in T-cell levels for ZIKV teratogeny . In the best scenario , even if there are marginal differences in ZIKV susceptibility among mouse strains due to T lymphocyte function , it is difficult to assume that they represent an important component of the variation in the human setting . The work of Miner et al . [9] is both comprehensive and convincing . However , because of its reliance on a precondition of immunodeficiency ( i . e . Ifnar1 knockout mice ) , more work is necessary to show whether it will accurately reproduce the human condition . Notwithstanding , the work of Miner et al . [9] represents a highly credible demonstration of the complex interplay of factors associated with ZIKV transmission and damage to the conceptus . It is more difficult to judge the applicability of the results produced by direct brain microinjection of Li et al . [10] , Wu et al . [12] and Shao et al . [16] as models for the human disease . This is because the strategy chosen by the authors is more reminiscent of the protocols utilized to grow and passage the virus [43] , than to model the relevant pathophysiological steps and checks involved in the expression of the human condition . In our opinion , a better and more realistic balance is provided by approaches such as intraperitoneal , or intravascular injections in wild-type animals , as described by Wu et al . [12] and by the present study . Nonetheless , the approach described by Li et al . [10] and Wu et al . [12] and colleagues may be especially interesting in defining the different susceptibilities among neurons and neural progenitors , once ZIKV eventually finds its way into the central nervous system of the conceptus . Another aspect that is worth discussing is the ZIKV load administered to pregnant mice . The number of ZIKV plaque-forming units given to mice in Cugola et al . [11] reached as far as 2 . 0 x 1011 and , as such , was several orders of magnitude higher than in Miner et al . [9] , in Wu et al . [12] , or in the present work , ( 105 pfu ) . It is unclear whether the viral challenge utilized by Cugola et al . [11] will find any correspondence with the human setting . Nonetheless , although their viral numbers seem elevated , close inspection of the results obtained by Miner et al . [9] suggests that the actual viral load administered may be less important than the immune state of the animal . Even if the 103 pfu load administered subcutaneously in Ifnar1 knockout females in Miner et al . [9] compares favorably with the 105 pfu that we utilized intravascularly , our mice did not develop any signs or symptoms of brain infection displayed by the animals in Miner et al . [9] . These data suggest that the key parameter involved in the expression of damage to the conceptuses is the interplay between viral load and susceptibility to the virus . The developmental delay associated with late ZIKV exposure constitutes a vexing problem when the objective is to make direct comparisons between exposed and control conceptuses , because it demands careful assessment of developmental stages before meaningful comparisons can be made . Concerns with the heterogeneity of development are not restricted to embryos/fetuses , but are of paramount importance when comparing the effects of ZIKV in brain organoids . In contrast to the approaches in organoids , our in vivo model with bona fide embryos and fetuses display a host of morphological characteristics that can be accessed to establish developmental stages . We believe these characteristics make the in vivo model a better approximation to the human setting , while brain organoids may be more adequate for high content approaches . Contrary to what has been reported , here we demonstrate that ZIKV produces severe developmental phenotypes in immunocompetent , wild-type , embryonic/fetal mice . This indicates that , although strain-specific differences in sensitivity to ZIKV may exist , it is less clear whether the levels of type I/II interferon in C57BL/6J constitute an absolute deterrent to maternal-fetal transmission of ZIKV through the placenta [11] . Our objective with this contribution was to describe the critical periods and windows of opportunity associated with the complex effects of ZIKV on embryonic and fetal development , rather than to develop a platform for mass studies on therapeutics . For that we reasoned that the best and most original approach was to establish an immunocompetent model , rather than an artificially enhanced method with immunodeficient animals produced through loss of function paradigms , which increase penetrance , but run the risk of biasing results . Notwithstanding the close similarity of our frequencies with the reported low prevalence of congenital defects associated with ZIKV in the human setting ( in the report of Schuler-Faccini et al . [36] , arthrogryposis is present in only 11% of the individuals ) the defects we report are admittedly relatively infrequent ( e . g . arthrogryposis ) . However , these frequencies are also the result of the need to rapid survey multiple developmental stages of exposure and of analysis within an ethical number of experimental animals at each stage . For example , in our data we detected only two instances of arthrogryposis in two separate litters from females exposed and harvested , respectively at days 7 . 5/18 . 5 ( Fig 6D ) and 9 . 5/16 . 5 ( Fig 7E and 7O ) . This is in part because a substantial number of embryos was exposed too late for these morphologic defects , or were analysed at stages before morphogenesis of fore and hindlimbs , when arthrogryposis could not be scored . Moreover , the true prevalence of arthrogryposis in our models must be higher than we report due to ZIKV-induced growth restriction , which reduces the number of embryos at suitable stages for diagnosis . At this point our model represents the first demonstration that dysraphia , hydrocephalus and arthrogryposis associated with ZIKV can be modelled and studied in mice . Future use of our model in therapeutic projects will require focus on the appropriate stages of exposure and analysis , as well as in other relevant parameters . In addition to recent studies , our results suggest the important role played by the placenta in ZIKV embryonic or fetal infection . ZIKV displays multiple characteristics in common with TORCH agents [8 , 53] , notably in that the maternal organism is often asymptomatic , or mildly affected , while the conceptus can be severely compromised [54] . The specific mechanisms that underlie the developmental effects of ZIKV in humans remain to be established . However , we hope that our demonstration that ZIKV produces severe developmental phenotypes such as dysraphia , hydrocephalus , arthrogryposis , placental damage and IUGR in immunocompetent wild-type mice will be useful as a new experimental paradigm to advance research on ways to counter the ZIKV threat .
|
Previously thought to produce a harmless bout of fever associated with skin rash and muscle , or joint pain , the Zika virus ( ZIKV ) is an important cause of morbidity/mortality to human embryos/fetuses . Different from other models , here we report data from wild-type immunocompetent mice , rather than transgenic animals with suppressed immune responses . We found that intravascular ZIKV produced infection of maternal tissues , placentas and conceptuses , but that embryos/fetuses were comparatively much more affected than pregnant females , which seemed to tolerate well the viral challenge with no signs of encephalopathy . Importantly , 10 . 5 days post-coitum ( dpc ) embryos exposed to ZIKV at embryonic day 5 ( second week in humans ) displayed dysraphia , which is a regional failure of neural tube closure , and hydrocephalus , which is a symptom recently shown to precede microcephaly in humans . Characteristic phenotypes in more developed embryos/fetuses included abnormal articular postures analogous to arthrogryposis , which are typical human congenital contractures , gross and generalized malformations and intra uterine growth restriction ( IUGR ) , rather than isolated microcephaly . Some developmental abnormalities and IUGR correlated with placental damage , suggesting that loss of placental function may play an important role in the disease . We believe our model is an asset in the search for useful concepts and prospective therapies for ZIKV because it better reproduces the human condition .
|
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2017
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Hydrocephalus and arthrogryposis in an immunocompetent mouse model of ZIKA teratogeny: A developmental study
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Acute pyelonephritis ( APN ) , which is mainly caused by uropathogenic Escherichia coli ( UPEC ) , is the most common bacterial complication in renal transplant recipients receiving immunosuppressive treatment . However , it remains unclear how immunosuppressive drugs , such as the calcineurin inhibitor cyclosporine A ( CsA ) , decrease renal resistance to UPEC . Here , we investigated the effects of CsA in host defense against UPEC in an experimental model of APN . We show that CsA-treated mice exhibit impaired production of the chemoattractant chemokines CXCL2 and CXCL1 , decreased intrarenal recruitment of neutrophils , and greater susceptibility to UPEC than vehicle-treated mice . Strikingly , renal expression of Toll-like receptor 4 ( Tlr4 ) and nucleotide-binding oligomerization domain 1 ( Nod1 ) , neutrophil migration capacity , and phagocytic killing of E . coli were significantly reduced in CsA-treated mice . CsA inhibited lipopolysaccharide ( LPS ) -induced , Tlr4-mediated production of CXCL2 by epithelial collecting duct cells . In addition , CsA markedly inhibited Nod1 expression in neutrophils , macrophages , and renal dendritic cells . CsA , acting through inhibition of the nuclear factor of activated T-cells ( NFATs ) , also markedly downregulated Nod1 in neutrophils and macrophages . Silencing the NFATc1 isoform mRNA , similar to CsA , downregulated Nod1 expression in macrophages , and administration of the 11R-VIVIT peptide inhibitor of NFATs to mice also reduced neutrophil bacterial phagocytosis and renal resistance to UPEC . Conversely , synthetic Nod1 stimulating agonists given to CsA-treated mice significantly increased renal resistance to UPEC . Renal transplant recipients receiving CsA exhibited similar decrease in NOD1 expression and neutrophil phagocytosis of E . coli . The findings suggest that such mechanism of NFATc1-dependent inhibition of Nod1-mediated innate immune response together with the decrease in Tlr4-mediated production of chemoattractant chemokines caused by CsA may contribute to sensitizing kidney grafts to APN .
Urinary tract infection ( UTI ) often complicated by acute pyelonephritis ( APN ) , which is mainly caused by uropathogenic Escherichia coli ( UPEC ) , is the most frequent infectious complication following renal transplantation [1] , [2] . Despite improvement of the surgical procedures and the use of post-operative antibiotic prophylaxis , the rates of post-graft APN still remain higher than in the normal population [2] , and late UTI occurring after more than 6 months after transplantation are associated with increased risk of death , and post-graft APN may also compromise long-term graft outcome [3] , [4] . Although many factors including age , sex , and co-morbidity conditions play a role in the susceptibility to infection , long-term immunosuppressive therapy used to prevent episodes of acute graft rejection obviously increases the risk of bacterial , viral or fungal infections in the context of transplantation [5] , [6] . Calcineurin inhibitors , such as Cyclosporine A ( CsA ) , are almost incontrovertible drugs widely used to prevent renal graft rejection . Their main function is to inhibit the phosphatase activity of calcineurin , which regulates the nuclear translocation of the nuclear factor of activated T-cells ( NFATs ) transcription factor [7] . Impaired activation of NFATs then prevents the transcription of cytokine genes , including IL-2 , in activated T cells [8] . However , the mechanism ( s ) by which CsA could alter the innate immune system , and thereby decrease renal host defenses against ascending UPEC remain largely unknown . Early recognition of bacterial motifs by a number of pattern recognition receptors , including Toll-like receptors ( TLRs ) and ( Nod ) -like receptors ( NLRs ) , is essential for the removal of bacterial pathogens [9] . UPEC colonizing the urinary tract are recognized by several TLRs , including TLR2 , 4 , 5 , and 11 [10] . Studies using experimental murine models of ascending UTI have demonstrated that Tlr4 , which senses lipopolysaccharide ( LPS ) from Gram-negative bacteria [10] , and also Tlr11 , that is expressed in murine bladder epithelial cells and RTECs [11] , regulate susceptibility to UTIs in mice . TLRs play key roles in activating the transcription factor NF-κB and the mitogen-activated protein kinases ( MAPKs ) signaling pathways leading to the production of chemoattractant cytokines and subsequent recruitment of neutrophils and monocytes/macrophages for efficient clearance of the bacteria . Nod1 and Nod2 also promote the activation of NF-κB and MAPKs through the recruitment of the kinase RIP-2 ( also known as RIP2K or RICK ) , which is a member of the caspase activation and recruitment domain ( CARD ) protein family [12] , [13] . Nod1 recognizes muramyl tripeptide ( M-TriDAP ) , a degradation product of peptidoglycan ( PGN ) containing DAP which is present in most Gram-negative bacteria and some Gram-positive bacteria [14] , [15] , while Nod2 recognizes muramyl dipeptide ( MDP ) , a motif common to PGNs from all classes of bacteria [15] . Nod2 is mainly expressed in monocytes and macrophages , and mutations of NOD2 are associated with Crohn's disease , an inflammatory bowel disease mainly driven by T cells [16] , [17] . The functions of Nod1 , which is more ubiquitously expressed , differ somewhat from those of Nod2 . Recent studies have demonstrated that Nod1 plays a key role in the migration of neutrophils into the intestine and liver [18] , and in activating phagocytic mechanisms of bacterial killing [19] , [20] . The fact that altered leukocyte functions and decreased capacity for bacterial phagocytosis are the most common abnormalities in the immune status of renal transplant recipients [21] , [22] , led us to investigate the possibility that CsA alters the Nod1-mediated neutrophil functions and bacterial phagocytic killing of UPEC . In the present study , we used an experimental mouse model of ascending UTI and show that the administration of CsA to wild-type ( WT ) mice decreases renal resistance to UPEC infection . CsA impaired Tlr4-mediated activation and subsequent production of chemoattractant chemokines in the epithelial collecting duct cells , to which UPEC bind preferentially during their retrograde ascent along the urinary tract system [23] . In addition , CsA , through its inhibitory action on NFATs , also markedly inhibited the functional expression of Nod1 in phagocytic cells , including neutrophil migration capacity and phagocytic killing of UPEC . Similar to CsA-treated WT mice , Nod1−/− mice exhibited greater susceptibility to UPEC than their WT counterparts . Using 11R-VIVIT , a synthetic peptide inhibitor of NFATs [24] , we also demonstrate in vitro and in vivo the relevance of the regulatory role of the NFATc1 isoform in controlling the Nod1-mediated renal susceptibility to UPEC . We also report the functional downexpression of human NOD1 and decreased phagocytic capacity of E . coli in leukocytes from renal transplant recipients treated with CsA . The combined inhibitory effects of CsA on Tlr4-mediated chemokine production and Nod1-mediated migration of neutrophils and bacterial phagocytic capacities , which contribute to decrease renal antibacterial defenses in mice , may explain , at least in part , the susceptibility of CsA-treated renal transplant recipients to bacterial infections .
WT mice treated with CsA ( 15 mg/kg ) or its vehicle for 5 days were then infected by transurethral inoculation with the UPEC HT7 strain isolated from the urine of a woman with acute pyelonephritis [4] , [25] , to test whether CsA affected renal antibacterial defenses . 24 h after the inoculation of live UPEC , the bacterial burden and E . coli positive immunostaining were greater in CsA-treated mice than vehicle-treated mice ( Figure 1A and B ) . As a control , we checked that CsA did not modify the growth rate of UPEC ( not shown ) , thus excluding any direct effect of the calcineurin inhibitor on the bacteria . CsA also increased renal bacterial loads in kidneys from Rag2−/− mice , which lack mature lymphocytes , to almost the same extent as in WT kidneys ( Figure 1A ) , suggesting that CsA promotes UPEC infection independently of its inhibitory effect on the adaptive immune system . The amount of secreted chemokines MIP-2/CXCL2 and KC/CXCL1 , which play key roles in the chemoattraction of neutrophils during experimental UTI [23] , [26]–[28] , was also significantly lower in kidneys from CsA-treated mice than vehicle-treated mice ( Figure 1C ) . Fewer Ly-6G+ neutrophils were detected in the kidney medulla and in the urinary space in the CsA-treated mice than in untreated mice ( Figure 1D ) . Quantification of neutrophils assessed by measuring myeloperoxidase ( MPO ) activity also revealed significant lower MPO activity in the 24 h post-infected kidney homogenates from CsA-treated mice than vehicle-treated mice ( Figure 1E ) . Flow cytometry ( FACS ) analysis revealed that the CD45+ leukocyte population detected in the 24 h post-infected kidneys was essentially composed of F4/80+ CD11bLO Gr1−/LO MHC-II+ CD11c+ renal dendritic cells ( DCs ) , which have been shown to form a contiguous network in the renal tubulointerstitium [29] , F4/80− CD11b+ Gr1HI MHC-II− CD11c− neutrophils , and to a lesser extent F4/80+ CD11b+ Gr1INT MHC-II− CD11c− inflammatory monocytes/macrophages . FACS analysis revealed a significant decrease in the proportion of neutrophils over the total CD45+ renal cell population detected in the 24 h post-infected kidneys from CsA-treated mice compared to vehicle-treated mice kidneys ( Figure S1 and Figure 2A and B ) . In contrast , CsA only slightly , and non-significantly reduced the number of monocytes/macrophages or DCs present in the 24 h post-infected kidneys ( Figure S1 ) . This suggests that CsA preferentially impairs the migration capacity of neutrophils in the UPEC-infected kidneys . In vitro experiments using the Boyden chamber method also revealed that the migration capacity of neutrophils isolated from the blood of the 5-day CsA-treated mice and stimulated by the neutrophil activating agent N-formyl-methionyl leucyl-phenylalanine ( fMLP ) or by CXCL2 was significantly decreased compared to that of neutrophils from vehicle-treated WT mice ( Figure S2A and B ) . CsA also altered the bacterial phagocytic killing capacity by neutrophils . In contrast to neutrophil-enriched peritoneal cells ( NPCs ) isolated from vehicle-treated mice , NPCs collected from CsA-treated mice exhibited significantly lower ex vivo capacity to internalize Texas red-coupled E . coli and kill serum-opsonized E . coli ( Figure 2C and D ) . Nod1−/− neutrophils were shown to exhibit deficient capacity of bacterial phagocytic killing and lower migration capacity than WT neutrophils [20] , [30] . Given that neutrophil migration and their phagocytic killing capacities were markedly reduced in CsA-treated mice , we tested whether CsA directly alters intrarenal expression of Nod1 . Quantitative real-time PCR revealed that the levels of Nod1 mRNA expression and , to a lesser extent those of Tlr4 , but not of Nod2 , were markedly decreased in the 24 h post-infected CsA-treated mice kidneys compared to those of the infected vehicle-treated mice ( Figure 2E ) . In accordance with these findings , the amount of the immunodetected Nod2 protein remained equivalent in the infected kidneys from CsA-and vehicle-treated mice , whereas the amounts of Nod1 and Tlr4 proteins were ∼50% and ∼30% , respectively , lower in the infected kidneys from CsA-treated mice than in those from their vehicle-treated counterparts ( Figure 2F ) . Despite the decrease in Nod1 expression , the level of phosphorylated over total RIP-2 , which is involved in the control of Nod-mediated NF-κB activation [12] , was similar in the 24 h post-infected kidneys from CsA-treated mice and vehicle-treated mice ( not shown ) . Collectively , these findings suggest that , in addition to an inhibitory effect on Tlr4 mRNA and protein expression , CsA impairs the recruitment and functions of neutrophils in the inflamed kidneys by a mechanism possibly linked to downregulation of Nod1 expression . We next investigated the consequence of Nod1 deficiency in host renal bacterial defenses . The renal bacterial burden and the number of immunodetected UPEC were significantly greater in the kidneys of Nod1−/− mice than in those of WT , 24 h after the inoculation of UPEC ( Figure 3A and B ) . Less Ly-6G+ neutrophils were detected in the urinary space from post-infected Nod1−/− mice than from post-infected WT or Nod2−/− mice ( Figure 3C ) , and , like in UPEC-infected CsA-treated mice , the MPO activity also remained significantly lower in the post-infected Nod1−/− than in post-infected WT or Nod2−/− kidneys ( Figure 3D ) . FACS also showed that the proportion of polymorphonuclear neutrophils infiltrating the 24 h post-infected Nod1−/− mice kidneys was lower than in the WT kidneys ( Figure 3E and F ) . In accordance with the findings of Dharancy et al . [30] , in vitro experiments using a Boyden chamber revealed that the migration capacity of neutrophils isolated from naive Nod1−/− mice and activated by fMLP ( or with CXCL2 , not shown ) was significantly lower than that of WT neutrophils ( Figure S2C and D ) . These findings also suggested that Nod1 is implicated in the migration capacity of neutrophils . Consistently with a role for Nod1 in the bacterial phagocytic killing capacity of neutrophils [20] NPCs collected from Nod1−/− mice , but not those isolated from WT or Nod2−/− mice , were unable to internalize Texas red-coupled E . coli ( Figure S3A and B ) . A significant reduction in the killing of serum-opsonized UPEC was also observed in Nod1−/− neutrophils compared to WT or Nod2−/− neutrophils ( Figure S3C ) . We then analyzed the effects of CsA on the renal susceptibility of Nod1−/− mice to UPEC . Administration of CsA slightly , but not significantly , increased the renal bacterial loads in kidneys from Nod1−/− mice compared to those from untreated Nod1−/− mice ( Figure 3G ) , further suggesting that CsA impairs Nod1-mediated antibacterial defenses to UPEC . Previous studies had demonstrated that bladder epithelial cells and renal epithelial tubule cells are actively involved , together with bone marrow-derived cells , in the chemoattraction of neutrophils to the site of inflammation in experimental models of ascending UTI [31] , [32] . We also showed that UPEC preferentially binds to the apical side of epithelial cells constituting the terminal collecting duct ( Figure 4A ) [33] . Activation of TLR4 signaling in the urinary tract system infected by UPEC plays a key role in this process [23] , [34] . Because renal tubule cells also express the Nod1 and Nod2 receptors , that can be activated in inflamed kidneys [35] , [36] , experiments were carried out to analyze the effects of CsA on both Tlr4 , and Nod1 or Nod2 signaling in renal tubule cells and bone marrow-derived cells activated by LPS , Nod ligands or UPEC . We checked that the renal medullary collecting duct ( MCD ) cells did express Tlr4 , Nod1 , and Nod2 mRNAs ( Figure 4B ) . LPS , and to a much lesser extent the Nod1 agonist FK156 and the Nod2 agonist MDP , stimulate the production of CXCL2 in cultured WT MCD cells ( Figure 4C ) . CsA inhibited in a dose-dependent manner the Tlr4 mRNA and protein expressions without altering Nod1 and Nod2 expressions ( Figure 4D and E ) . We then tested whether 100 nM ( corresponding to 120 ng/ml ) CsA alters the TLR4- and/or Nod-mediated cellular response in MCD cells . The CXCL2 production caused by UPEC significantly decreased in untreated Tlr4−/− MCD cells compared to WT , Nod1−/− , or Nod2−/− MCD cells , and also decreased to almost the same extent in CsA-treated WT , Nod1−/− , or Nod2−/− MCD cells than in CsA-treated Tlr4−/− MCD cells ( Figure 4F , upper panel ) . As controls , similar profiles of CXCL2 production were obtained by incubating WT , Nod1−/− , and Nod2−/− MCD cells with LPS , and no significant production of CXCL2 was detected in Tlr4−/− MCD cells ( Figure 4F , lower panel ) . These findings thus suggest that CsA mainly affects the predominant TLR4-mediated production of CXCL2 and has only a minor effect on epithelial Nod1- and Nod2-mediated renal tubule cell activation caused by UPEC . CsA also significantly reduced the ability of LPS-activated confluent WT MCD cells , which developed high electrical transepithelial resistance ( ∼4500 Ω . cm2 ) , to stimulate the in vitro migration capacity of neutrophils as compared to untreated WT MCD cells incubated with LPS ( Figure S4A to C ) . As a control , Tlr4−/− MCD cells challenged with LPS did not stimulate the migration of neutrophils ( Figure S4B and C ) . We next analyzed the effects of CsA on Tlr4 and Nod mRNAs expression in neutrophils , macrophages , and renal DCs . Incubating primary bone marrow neutrophils with CsA for 8 h or bone marrow macrophages ( BMMs ) with CsA for longer times ( 48 h ) significantly decreased the relative levels of Nod1 mRNA and protein expressions , and to a lesser extent reduced the expression of Nod2 mRNA , but not that of Tlr4 mRNA ( Figure 5A , B , D and E ) . Renal DCs expressing Nod1 and Nod2 [37] , were shown to produce substantial amounts of CXCL2 , which is involved in the recruitment of neutrophils in the kidneys following UPEC challenge [26] , [27] , [38] . Incubating highly-enriched CD11c+ cells isolated from WT kidneys by gradient centrifugation and magnetic beads separation [39] with 100 nM CsA for 48 h significantly reduced Nod1 mRNA expression without affecting the expression of Tlr4 or Nod2 ( Figure 5G ) . However , the small number of purified renal CD11c+ cells obtained did not permit reliable Western blot analysis of the Nod1 protein . Consistent with an inhibitory action of CsA on Nod1 , the production of CCL5 , which has been shown to be highly sensitive to Nod agonist stimulation [40] , when stimulated by the Nod1 agonist FK156 was significantly lower in CsA-treated than in untreated neutrophils and macrophages ( Figure 5C and F ) . CsA only slightly , and non-significantly , reduced the FK156-stimulated production of CCL5 in renal DCs ( Figure 5H ) . Given that CXCL2 plays an essential role in the recruitment of neutrophils , we went on to test the effects of CsA on CXCL2 production in neutrophils , macrophages , and renal DCs . Unlike renal MCD cells , CsA only slightly reduced the CXCL2 production stimulated by LPS in neutrophils , macrophages , and renal DCs ( Figure 5C , F and H ) . Taken as a whole , these findings indicate that CsA globally impairs the functional expression of Nod1 in neutrophils , macrophages , and renal DCs . Since Nod1 senses a number of invasive Gram-negative bacteria , we tested whether UPEC can directly activate Nod1 mRNA expression in macrophages and whether CsA impairs the UPEC-induced activation of Nod1 . Incubating WT BMMs with UPEC for 3 h had almost no stimulatory effect on Tlr4 mRNA expression , but in contrast induced a significant increase in Nod1 and Nod2 mRNAs expression ( Figure S5 ) . Pre-incubating BMMs with CsA impairs the increase in Nod1 mRNA expression , and to a much lesser extent that of Nod2 , caused by the subsequent incubation with UPEC for additional 3 h ( Figure S5 ) . These findings further suggest that CsA preferentially alters the activation of Nod1 induced by UPEC in phagocytic cells . CsA inhibits the nuclear translocation of NFATs , which in turn inhibit the transcription of T cell effector cytokines [8] . Because CsA preferentially alters Nod1 expression in phagocytic cells , experiments were carried out to test whether the downregulation of the NFATc1 isoform , which is highly expressed in both murine and human neutrophils and macrophages [41] , [42] , impairs mRNA expression of Nods . Because neutrophils have a limited life-span , experiments were carried out on mouse BMMs . Knockdown of NFATc1 mRNA expression using a multiple set of NFATc1 siRNAs ( referred to as NFATc1a–d siRNA ) in WT BMMs resulted in the almost complete inhibition of the expression of NFATc1 mRNA when compared to non-transfected WT BMMs or cells transfected with a control siRNA ( Figure 6A ) . Silencing NFATc1 by the set of NFATc1 siRNAs markedly inhibited the relative level of Nod1 mRNA expression , but had almost no effect on Nod2 mRNA expression ( Figure 6B ) . To further assess the inhibitory action of CsA on Nod mRNAs expression , experiments were performed on WT BMMs incubated with 11R-VIVIT , a cell-permeable peptide that specifically inhibits the calcineurin-NFATs interaction without affecting calcineurin phosphatase activity [43] . Incubating WT BMMs with 1 µM 11R-VIVIT for 48 h also markedly inhibited the relative levels of Nod1 mRNA expression , and , to a lesser extent , that of Nod2 mRNA ( Figure 6C ) . In contrast , knock-down of NFATc1 mRNA expression or incubation of BMMs with the 11R-VIVIT had no effect on Tlr4 mRNA expression ( Figure S6A and B ) . Given that inhibition of NFATc1 can affect the transcriptional expression of many proteins , the possibility that a contrario in vitro activation of NFATs could specifically stimulate the expression of Nod1 was investigated . NFATs are activated by increased intracellular calcium concentration during T-cell activation [7] . Calcium mobilization induces the dephosphorylation of cytosolic NFATs which translocate into the nucleus [44] . Ionomycin ( 2 µM , 60 min ) induced the translocation of NFATc1 from the cytosol into nuclei from WT BMMs , whereas the pre-incubation of WT BMMs with CsA or 11R-VIVIT totally or almost totally impaired the nuclear translocation of NFATc1 caused by subsequent addition of ionomycin ( Figure 6D ) . Ionomycin also significantly stimulated Nod1 , but failed to stimulate Nod2 and Tlr4 mRNAs expression in WT BMMs compared to untreated cells , or to cells pre-treated with CsA or 11R-VIVIT ( Figure 6E and Figure S6C ) . Collectively , these data strongly suggest a role for NFATc1 as a transcriptional activator of Nod1 . Given that the 11R-VIVIT inhibited Nod1 mRNA expression , 11R-VIVIT should also impair the Nod1-mediated bacterial phagocytic function and decrease the renal defense against UPEC . NPCs isolated from WT mice given daily intraperitoneal injections of 10 µg/kg 11R-VIVIT for 48 h exhibited a significant lower ex vivo capacity to internalize E . coli and lower phagocytic killing of serum-opsonized UPEC than untreated NPCs ( Figure 7A and B ) . 24 h after the inoculation of UPEC , 11R-VIVIT-treated mice exhibited reduced intrarenal MPO activity and reduced amount of immunodetected Nod1 protein , and significant greater renal bacterial burden than in non-treated WT mice ( Figure 7C to E ) . The fact that the stimulation of Nod1 can enhance systemic innate immunity [20] and that the administration of Nod1 peptide agonists to mice confer resistance against several pathogens [45] , led us to test whether the stimulation of Nod1 by synthetic Nod1-stimulating agonists could reinforce renal defense against UPEC . The cell-permeable Nod1 activating agonist C12-iEDAP ( 50 µg/ml for 24 h ) induced a significant increase in Nod1 mRNA expression , which overcome the inhibition of Nod1 mRNA expression caused by CsA alone ( not shown ) . We then tested whether the in vivo administration of synthetic Nod1 agonists can reactivate Nod1-mediated phagocytic function and reinforce renal resistance of CsA-treated mice to UPEC . The capacity of neutrophils to internalize Texas red-coupled E . coli was greater in neutrophils from CsA-treated mice that had been treated with C12-iEDAP than in those collected from CsA-treated mice which had not received C12-iEDAP ( Figure 8A ) . Furthermore , intra-peritoneal injection of C12-iEDAP or FK156 ( 80 µg/mouse ) one day before the transurethral inoculation of UPEC to CsA-treated WT mice , induced significant reduction in the renal bacterial burden when compared to CsA-treated mice which had not received C12-iEDAP or FK156 , or CsA-treated mice which had received the Nod2 agonist MDP ( Figure 8B ) . The observed decrease in renal bacterial burden was associated with greater MPO activity in the infected kidneys from CsA-treated mice pre-treated with the Nod1 agonists ( Figure 8C ) . Figure 8D illustrates the greater amount of Nod1 protein in the infected kidneys from CsA-treated mice which had been pre-treated with FK156 . The amounts of CXCL2 and CXCL1 secreted were not significantly different in the 24-h , post-infected kidneys from CsA-treated mice and the vehicle-treated mice ( Figure 8E ) , suggesting that the administration of Nod1 agonists does not induce any major renal inflammatory response . Collectively , these findings are consistent with the restimulation of Nod1-mediated host protective functions in CsA-treated mice . Investigations were performed on blood samples from random renal transplant recipients treated with CsA ( n = 25 ) to test whether human renal transplant recipients exhibit similar decrease in NOD1 expression and defective NOD1-mediated bacterial phagocytosis capacity . The demographic characteristics of renal transplant recipients are summarized in Table S1 . Transplant recipients all received CsA and additional immunosuppressive drugs , including prednisolone and mycophenolate mofetil , which is a selective inhibitor of the de novo synthesis of guanosine nucleotides in T and B lymphocytes [46] . For comparison , investigations were also performed on healthy volunteers ( n = 10 ) used as controls . The production of IL-8 was first measured in whole blood samples incubated with various TLR and NOD agonists . The levels of IL-8 triggered by all TLR agonists tested did not significantly differ in blood samples from transplant recipients and normal healthy controls ( Figure 9A ) . In contrast , the levels of IL-8 production stimulated by the human NOD1 synthetic agonist M-TriDAP was significantly less in blood samples from the transplant recipients treated with CsA than in normal controls ( Figure 9A and B ) . The levels of NOD1 mRNA , but not those of NOD2 , TLR2 , or TLR4 mRNAs extracted from whole blood samples , were also significantly lower in transplant recipients than in normal controls ( Figure 9C ) . Flow cytometry analysis of phagocytosis of Texas red-coupled E . coli and quantification of NOD1 in human neutrophils also revealed that the low levels of NOD1 in the neutrophils from CsA-treated renal transplant recipients were closely correlated with their capacity to phagocyte E . coli , which was significantly lower than in the neutrophils from normal healthy controls ( Figure 9D ) . Moreover , three of the transplant recipients analyzed who exhibited low NOD1 expression and low capacity of E . coli phagocytosis by neutrophils had a previous history of UTI/APN . These findings suggest that the NFATs-dependent inhibitory mechanism of Nod1-mediated innate immune response identified in the mouse also occurs in human transplant recipients treated with CsA .
In the present work , we show that in mice , CsA reduces renal resistance to the retrograde inoculation of uropathogenic E . coli . CsA induces a significant decrease in the production of the chemoattractant chemokines and impairs the recruitment of neutrophils in kidneys from mice infected by UPEC . The primary source ( i . e . the epithelial tubule cells or circulating immune cells ) of pro-inflammatory mediators produced in experimental models of UTI remains discussed . In accordance with a number of previous studies , medullary collecting duct epithelial cells , which are the first renal tubule cells to come into contact with UPEC during their retrograde ascent , produce substantial amounts of TLR4-mediated CXCL2 , which play a key role in the recruitment of neutrophils in the infected kidneys [23] , [27] , [47] . The fact that CsA impairs LPS-induced production of CXCL2 in renal MCD cells and LPS-induced recruitment of neutrophils , further suggests that the Tlr4-mediated activation of renal epithelial cells contributes to the recruitment of neutrophils in the infected kidneys , at least during the initial phase of infection . Nor can we exclude the possibility that the decrease in Tlr4 mRNA expression detected in the infected kidneys and in the Tlr4-mediated cell activation detected in murine MCD cells , are due , at least in part , to the cytotoxic action of calcineurin inhibitors [48] . In contrast , CsA had no in vitro inhibitory effect on Tlr4 expression in neutrophils , macrophages , or renal DCs . After being bound to renal collecting duct cells , UPEC induces the rapid recruitment of neutrophils ( during the first 6 h ) , followed by the recruitment of monocytes/macrophages over the next 12–24 h . Although LPS stimulates the in vitro production of CXCL2 by neutrophils and inflammatory monocytes/macrophages , these cells do not seem to play major roles in the renal production of chemoattractant chemokines during UPEC infection [38] . Recently , resident renal DCs have been shown to be major source of CXCL2 production , compared to neutrophils and monocytes/macrophages , 20 h following the retrograde inoculation of UPEC [38] . Furthermore , the migration capacity of neutrophils has been reported to be significantly lower in UPEC-infected CD11c deficient mice than in their WT counterparts [38] , indicating that CXCL2 production by renal DCs certainly plays some role in the chemoattraction of neutrophils . Here we show that CsA altered Nod1 mRNA expression in renal DCs , without impairing Tlr4 mRNA expression and the number of renal DCs in the infected kidneys . Although in vitro incubation of renal DCs with CsA did not have much effect on the in vitro LPS-induced CXCL2 production , we cannot exclude any in vivo participation of renal DCs in the defective migration capacity of neutrophils within infected kidneys from CsA-treated mice . The present study demonstrated an unexpected effect of CsA on Nod1-mediated neutrophils migration capacity and bacterial phagocytosis . We show both in vivo and in vitro that CsA impairs Nod1 expression in neutrophils and macrophages . The stimulation of Nod1 by Nod1 stimulating agonist or bacteria was shown to play a role in the recruitment of neutrophils in the intestine [18] , [49] , and that the number of infiltrating neutrophils was shown to be significantly reduced in injured livers from Nod1−/− mice challenged with carbon tetrachloride [30] . We also detected defective recruitment of neutrophils in kidneys from Nod1−/− mice infected by UPEC , and in infected kidneys of WT mice treated with CsA . Given that CsA , which affects TLR4-mediated CXCL2 production in MCD cells and alters the expression of Nod1 in neutrophils , macrophages , and also renal DCs , these findings suggest that , in addition to impairing the epithelial TLR4-mediated production of chemoattractant chemokines , CsA may also alter the Nod1-mediated capacity of neutrophils to migrate in kidneys infected with UPEC . Recent studies have highlighted the role of NFAT/calcineurin signaling pathways in controlling innate immunity and in regulating homeostasis of immune cells . Calcineurin/NFATs signaling was shown to negatively regulate myeloid lineage development [50] . The susceptibility to fungal infection caused by CsA was also shown to be the consequence of NFAT-dependent inhibition of an immune innate pathway regulating antifungal resistance in neutrophils . Indeed , Greenblatt et al . [42] reported that the neutrophils of both calcineurin-deficient mice and CsA-treated mice exhibited a defective ability to kill Candida albicans without any noticeable changes in the classical fungicidal activity of neutrophils . These authors showed that calcineurin regulates the ability of neutrophils to kill C . albicans via another anti-microbial pathway , which involves the C-type lectin-like receptor dectin-1 and IL-10 production . Given that Nod1 and Nod2 are not directly involved in the recognition of C . albicans [51] , these findings suggest that CsA may affect NFATs-dependent cellular signaling activated by Gram-negative bacteria or fungi in different ways . The NFATc3/c4 isoforms were also shown to be required for TLR-induced innate inflammatory response in monocytes/macrophages [52] . Our results strongly suggest that NFATc1 controls Nod1 at the transcriptional level . In silico analysis ( Genomatix Software GmbH ) has identified putative NFAT binding sites in human and murine Nod1 and Nod2 promoter regions . Downexpression of NFATc1 inhibited Nod1 more efficiently than Nod2 . However , the in silico analysis did not permit us to predict differences in the number of putative binding sites for NFATc1 on the promoter regions of Nod1 and/or Nod2 . We have no direct explanation for the preferential inhibitory effect of silencing NFATc1 on Nod1 expression . The fact that much less Nod1 than Nod2 is present in immune cells may account , at least in part , for the greater reduction in Nod1 mRNA expression induced by NFATc1 silencing . Nevertheless our findings strongly suggest that CsA , through NFATc1 inhibition , alters Nod1-mediated phagocytic functions . In agreement with this , inhibition of the calcineurin phosphatase activity has been reported to decrease phagocytosis in macrophages [53] , further suggesting that NFATc1 is essential for proper activation of the phagocytosis process . Collectively , these findings indicate that NFATs , which play key roles in adaptive T cell functions , are critical cellular mediators of the innate immune responses . Although the present findings indicate that CsA directly alters Nod1 expression , it may also affect other immune receptors and downstream signaling pathways in various different ways . Calcineurin serine threonine phosphatase downregulates TLR-mediated signaling pathways in macrophages , whereas CsA and its newer counterpart Tacrolimus have been shown to activate NF-κB and induce cytokine expression in inactivated macrophages [54] . It has been also reported that the activation of DCs and macrophages by Tacrolimus can induce a state of reduced responsiveness to LPS [55] , similar to the LPS-induced transient state of tolerance observed following a subsequent LPS challenge [56] . During bacterial infection , it seems likely that various TLRs and NLRs are activated in response to the invading pathogen or to microbial components , such as LPS or PGN , released into the bloodstream and in the infected tissues [57] . Moreover , the interplay between Nods and TLRs may be critical for the induction of protective immune responses [14] , [58] , [59] . Therefore , it is conceivable that altered epithelial TLR4-mediated chemokine production caused by CsA , may potentiate the deleterious inhibitory effects of CsA on Nod1-mediated neutrophil functions , leading to a more pronounced decrease in host resistance to bacterial infection . Long-term use of immunosuppressive drugs , used to prevent graft rejection , increases the susceptibility of transplant recipients towards bacterial infection [2] , [5] , [6] . Until recently , the impact of immunosuppressive therapy was considered to be largely non-specific . However , several groups of researchers have reported changes in the numbers and/or functions of circulating leukocytes , including polymorphonuclear neutrophils from transplant patients acquiring infections [21] , [22] . Moreover , it has already been suggested that abnormalities in neutrophil functions , including impaired migration capacity following fMLP stimulation , are indicators of sepsis in solid organ transplant recipients [60] . Neutrophils from renal transplant recipients have been reported to exhibit diminished phagocytic activity and reduce bactericidal activity against Klebsiella pneumoniae , compared to the activities seen with neutrophils from healthy subjects [61] . In vitro studies have also shown that CsA reduces both neutrophil phagocytosis capacity and ROS production [62] , [63] . Analysis of a panel of blood leukocyte phenotypes and functions also revealed that transplant recipients ( renal and renal/pancreas ) most of whom were receiving CsA and subjected to infection exhibited a reduction in ROS production [21] . In the present study , the investigations performed on renal transplant recipients have revealed downregulated expression of NOD1 in circulating leukocytes , similar to that found in CsA-treated mice . The low capacity for E . coli phagocytosis appears to be closely correlated with a low expression of NOD1 in the neutrophils of renal transplant recipients . Since Nod1−/− mice are more susceptible than WT to early Streptococcus pneumoniae sepsis , and conversely , that PGN recognition by Nod1 enhances killing of S . pneumoniae and Staphylococcus aureus by neutrophils [19] , it is conceivable that the observed downregulation of NOD1 caused by CsA may also impair the capacity of neutrophils from renal transplant recipients to kill Gram-positive bacteria . However , we cannot exclude the possibility that the results from investigations performed in human renal transplant recipients could have been flawed by several confounding factors , such as the concomitant administration of several immunosuppressive drugs and some degree of renal impairment . The consistency with which CsA downregulated NOD1 strongly suggests that the impairment of NOD1-mediated bacterial phagocytic capacity caused by CsA may therefore represent an additional risk factor for the occurrence of UTI/APN in human transplant recipients . Despite antibiotic prophylaxis , the frequency of post-graft UTI/APN still remains relatively high , and increasing the resistance of bacteria to antibiotics may also increase the risk of recurrent episodes of UTI/APN . This raises the question of whether alternative therapeutic strategies could help to reduce the frequency of post-graft UTI/APN . A number of studies have provided convincing evidences that pre-treatment of mice with Nod agonists enhances host protection against sepsis , bacterial infection , viruses , or even parasites [64] . Here we also show that administration of Nod1 agonists can restore efficient renal clearance of UPEC in CsA-treated mice . However , further studies will be required to find out whether the administration of synthetic Nod1 agonists alone or in combination with antibiotics could potentially help to reduce the occurrence of UTI/APN in renal grafts . In summary , we have identified a hitherto-unknown mechanism of the NFATc1-dependent inhibitory action of the Nod1-mediated innate immune response , which may affect host renal antibacterial defenses against invasive uropathogens , and possibly favor the emergence of bacterial infection in renal transplant recipients receiving long-term CsA treatment .
All animal experiments were approved by and conducted in accordance with guidelines of the French Agricultural Office and in compliance with the French and European regulations on Animal Welfare ( Service de la protection et Santé animale; Approval Number 75–687 , revised 2008 ) and with Public Health Service recommendations . All the efforts were made to minimize suffering of mice . Blood samples were obtained from transplant recipients and healthy volunteers after being informed and given oral consent , according to French law for non interventional studies using a leftover or a small additional blood sample ( Public Health Code , article L1121-1 , revised in May 2009 ) . All samples were anonymized . Human and animal studies were approved by the Institutional Ethics Committee ( Comité de Protection des Personnes ( CCP #5 ) affiliated to the Tenon Hospital ( AP-HP ) -University Paris 6 ( Approval CCP-0612/2011 ) . All experiments were conducted in accordance with the principles expressed in the Declaration of Helsinski . Adult female ( 8–10 week old ) WT mice ( supplied by the Centre d'Elevage Janvier , Le Genest-Saint-Isle , France ) , Rag2−/− mice , and Nod1−/− and Nod2−/− mice from the same C57BL/6 genetic background were used . Mice were infected with the uropathogenic E . coli strain HT7 ( 108 bacteria in 50 µl sterile PBS ) introduced via the transurethral route into the bladder as described [4] , [25] . 100 µl CsA ( Neoral , Novartis International Pharmaceutical Ltd , 15 mg/kg ) , or its vehicle ( castor oil ) were administered sub-cutaneously to mice for 5 days before the inoculation of UPEC . Bacterial loads ( CFU ) in kidneys were determined 24 h after infection by plating . Kidney sections were stained using anti-E . coli antibody ( Interchim ) , anti-Ly6-G antibody ( BD Biosciences ) , or aquaporin-2 ( AQP-2 ) as described [23] . Primary cultures of medullary collecting duct ( MCD ) isolated from WT , Nod1−/− , Nod2−/− , and Tlr4−/− mice kidneys were grown as described [23] . Experiments were carried out on confluent cells two weeks after seeding . Bone marrow neutrophils and circulating blood neutrophils were isolated by gradient density centrifugation using Ficoll-Paque PREMIUM ( GE Healthcare ) as described elsewhere [20] . Bone marrow-derived macrophages ( BMMs ) were isolated and grown as described [40] . Indirect immunofluorescence studies were performed on WT BMMs using a mouse anti-NFATc1 monoclonal antibody ( Thermo Scientific Pierce Antibodies ) and Sytox green nucleic acid stain ( Invitrogen ) . Renal dendritic CD11c+ cells were isolated as previously described with slight modifications [39] , [65] . For each cell preparation both kidneys from 5 naïve WT mice were used . Briefly , the kidneys of each mouse were minced and then digested for 45 min at 37°C with 1 mg/ml collagenase ( Roche Diagnostics , Meylan , France ) and 10 µg/ml DNAse I in RPMI 1640-Glutamax medium ( Life Technologies ) supplemented with 10% heat-inactivated fetal calf serum , 10 mM HEPES , 100 U penicillin , and 0 . 1 mg/ml streptomycin . Kidney homogenates from each mouse were then filtered through 70 µm nylon mesh , washed with PBS , centrifuged ( 250 g , 5 min ) , resuspended in 3 ml of 0 . 01 M ethylenediaminetetracetic acid ( EDTA ) in FCS and layered on top of 3 ml Histopaque-1077 ( Sigma ) . Density centrifugation ( 400 g , 30 min ) was performed at room temperature . The interphase cells were then harvested , washed , and resuspended in 600 µl MACS buffer ( Miltenyi Biotec . ) . CD11c+ cells were then isolated using microbead-labeled specific monoclonal antibody ( clone N418 , Miltenyi Biotec . ) , and separated using magnetic beads according to the manufacturer's instructions . The enriched- CD11c+ cell suspension obtained from 5 mice were then pooled and used for the cytokine assay . The migration capacity of neutrophils isolated from untreated or CsA-treated WT mice or Nod1−/− mice was analyzed using the Boyden chamber technique as previously described [30] . After the lysis of red blood cells , blood samples from vehicle- and CsA-treated WT were laid on the top of a Ficoll-Paque PREMIUM ( GE Healthcare , Uppsala , Sweden ) density gradient , then centrifuged ( 400 g , 30 min at 4°C ) , and the bottom layer containing the neutrophil-enriched fraction was collected . 106 neutrophils were then resuspended in 200 µl Hank's buffered salt solution ( HBSS ) containing 0 . 5% bovine serum albumin and added to the upper compartment of a Transwell Clear membrane insert ( 3 µm pore size , Corning Inc . , Lowel , MA ) . The lower compartment ( 600 µl ) of the chamber contained either HBSS alone or supplemented with fMLP ( 10−7 M ) or CXCL2 ( 200 ng/ml ) . Incubations were performed at 37°C for 40 min in a 5% CO2/95% air atmosphere . A neutrophil migration assay was also carried out using isolated WT and Tlr4−/− MCDs seeded and grown to confluence in defined DMEM/Hams'F12 culture medium [23] on the apical side of the filters . Confluent WT MCD cells were then incubated with or without 100 ng/ml CsA for 48 h , then with or without ( 10 ng/ml ) LPS , which was added when required to the upper compartment of the chamber for 4 h in a 5% CO2/95% air atmosphere . The lower compartment contained 106 WT neutrophils resuspended in 600 µl defined culture medium . In all cases , the filters were rinsed , then fixed in methanol and stained using the RAL 555 Kit ( Réactifs RAL , Martillac , France ) . The neutrophils ( stained deep purple ) detected in the filters were counted by microscopic observation . MCD cells ( stained pale red ) were also stained using the RAL kit containing eosin . In parallel , the transepithelial electrical resistance ( RT ) was measured using dual silver/silver chloride ( Ag/AgCl ) electrodes connected to a Millicel-ERS voltohmmeter ( Millipore , Billerica , MA ) . Enriched-neutrophil peritoneal cells collected by peritoneal lavages 3 h after a single intraperitoneal injection of 1 . 5 ml of thioglycollate ( Bio-Rad Laboratories ) were incubated with Texas red-coupled E . coli ( 104 bacteria/107 cells ) for 30 min at 37°C , and then stained with CD11b-FITC or Wheat Germ Agglutinin ( WGA ) -Alexa Fluor 647 ( Invitrogen ) to delineate cell peripheries . The internalization of E . coli was determined by measuring the intracellular red fluorescence intensity using confocal microscopy analysis as described elsewhere [66] . For the ex vivo bacterial killing assay , E . coli were mixed without or with peritoneal neutrophils ( 103 bacteria/106 neutrophils ) following the same procedure as described elsewhere [20] . Blood samples from 25 renal transplant recipients with a functioning graft during the first three years after surgery and exposed to CsA ( Table S1 ) were randomly taken during the regular routine consultations at Tenon hospital ( Assistance Publique-Hôpitaux de Paris , France ) . In all cases , the blood samples were taken at least 6 months after surgery . In addition , ten healthy volunteers served as normal controls . Total RNA from mouse kidneys , neutrophils , or macrophages was purified with RNAble ( Eurobio laboratories ) and reverse transcribed using Moloney Murine Leukemia Virus reverse transcriptase ( Invitrogen ) . cDNA was subjected to quantitative real-time PCR using a Chromo IV sequence detector ( MJ Research ) . The mouse Tlr2 , Tlr4 , Nod1 , Nod2 and ß-actin primers used and the corresponding Taqman probes are listed in Table S2 . PCR data were reported as the relative increase in mRNA transcripts versus that found in uninfected kidneys or vehicle-treated neutrophils or macrophages cells and corrected using the respective levels of ß-actin mRNA . Quantitative real-time PCR was also performed on RNA extracted from blood samples of renal transplant recipients using human TLR2 , TLR4 , NOD1 , NOD2 , and ß-ACTIN primers and corresponding Taqman probes ( listed in Table S2 ) . PCR data were reported as the relative increase in mRNA transcripts versus that found in a pool of RNA of untreated leukocytes from healthy volunteers . For reverse transcription PCR , cDNA and non-reverse transcribed RNA ( 250 ng ) from cultured mouse MCD cells or BMMs were amplified for 35 cycles in 35 µl of Platinum Blue PCR SuperMix ( Invitrogen ) containing 10 pmol of mouse NFATc1 , Tlr4 , Nod1 , Nod2 , or GAPDH primers ( described in Table S2 ) . Amplification products were run on a 2% agarose gel containing SYBR Safe DNA gel stain ( Invitrogen ) and photographed . Experiments were performed using different predesigned HP GenomeWide ( Qiagen , Courtaboeuf , France ) siRNAs ( referred to as NFATc1a , b , c , and d ) for the murine NFATc1 gene target DNA sequence . NFATc1a DNA sequence: 5′-TCGGATCGAGGTGCAGCCCAA-3′; sense: 5′-GGAUCGAGGUGCAGCCCAATT-3′; antisense: 5′-UUGGGCUGCACCUCGAUCCGA-3′; NFATc1b DNA sequence: 5′-CACGGTTACTTGGAGAATGAA-3′; sense: 5′-CGGUUACUUGGAGAAUGAATT-3′; antisense: 5′-UUCAUUCUCCAAGUAACCGTG-3′; NFATc1c DNA sequence: 5′-CCCGTCCAAGTCAGTTTCTAT-3′; sense: 5′-CGUCCAAGUCAGUUUCUAUTT-3′; antisense: 5′-AUAGAAACUGACUUGGACGGG-3′; NFATc1d DNA sequence: 5′-CCGGGACCTGTGCAAGCCAAA-3′; sense: 5′-GGGACCUGUGCAAGCCAAATT-3′; antisense: 5′-UUUGGCUUGCACAGGUCCCGG-3′ . A universal negative control siRNA ( target DNA sequence: 5′-AATTCTCCGAACGTGTCACGT-3′; sense: 5′-UUCUCCGAACGUGUCACGUdTdT-3′; antisense: 5′ ACGUGACACGUUCGGAGAAdTdT-3′ ) was also used ( Qiagen ) . Single strand sense and antisense RNA nucleotides were annealed to generate a RNA duplex according to the Manufacturer's instructions . WT BMMs were seeded in 6-well plates and incubated with 10 nM of each siRNA tested and 2 µl of Lipofectamine RNAiMAX Reagent ( Invitrogen ) for 48 h at 37°C before use . As a control , we checked that each of the NFATc1 ( a to d ) siRNAs inhibited NFATc1 mRNA expression in macrophages using reverse-transcription PCR ( not shown ) . Mouse kidney homogenates and BMMs were lysed and processed for Western blotting using mouse anti-TLR4 [25] ) , anti-Nod1 ( Cell Signaling ) or anti-Nod2 ( eBiosciences ) antibodies , and phospho-RIP-2 ( Ser 176 ) , and total RIP-2 ( Ozyme ) antibodies . Protein bands were revealed using horse raddish peroxidase-conjugated goat anti-rabbit IgG ( Jackson Immunoresearch ) , and analyzed by Western Blotting . Cytokine production was measured in mouse kidney homogenates , or cell supernatants using DuoSet mouse ELISA kits ( R&D Systems , Minneapolis , MN ) . Neutrophils , macrophages , or cultured MCD cells were incubated either with LPS ( Escherichia coli 0111:B4 LPS Ultra-Pure , InvivoGen , Toulouse , France ) , 50 µg/ml C12-iEDAP ( InvivoGen ) , 1 µM FK156 ( provided by Nami Kawano , Astellas Pharma Inc . , Osaka , Japan ) , or 1 µM MDP ( InvivoGen , Toulouse , France ) for 8 to 18 h at 37°C . For FK156 and MDP stimulations , mouse macrophages were pre-treated with 1 µM cytochalasin D ( Calbiochem , Darmstadt , Germany ) for 30 min to allow efficient internalization of the synthetic Nod activating agonists as described elsewhere [40] . Human blood samples ( 10 µl ) were incubated in 500 µl RPMI culture medium ( Invitrogen ) at 37°C alone or with 1 ng/ml Pam3CSK4 ( InvivoGen ) or LPS , 1 µg/ml flagellin ( InvivoGen ) ; 50 µg/ml unmethylated CpG-DNA ( HyCult Biotechnology , Uden , The Netherlands ) , 50 nM MDP or various concentrations ( 0 . 05–2 µM ) of M-TriDAP ( InvivoGen ) for 18 h . IL-8 production was measured using a DuoSet human ELISA kit ( R&D Systems , Lille , France ) . All the reagents used were tested negative for endotoxin contamination using the Limulus amoebocyte assay according to the Manufacturer's recommendations ( QCL-1000 , Biowhittaker , Buckinghamshire , UK ) . MPO activity was measured using HyCult Biotechnology ELISA kit . The cell populations infiltrating the infected mouse kidneys were analyzed by flow cytometry . 24 h after UPEC infection , kidneys were carefully rinsed with PBS to remove the remaining circulating blood cells . The kidneys were then minced and digested for 45 min at 37°C with 1 mg/ml collagenase ( Roche Diagnostics ) and 10 µg/ml DNAse I in the same RPMI 1640-Glutamax medium ( Life Technologies ) as described above for the isolation of renal DCs . After rinsing , kidney homogenates were then passed through a 70 µm pore sized nylon Cell Strainer ( BD Biosciences ) with 15 ml PBS . The resulting cell suspension was centrifuged ( 1600 rpm , 10 min ) again and then resuspended ( 10×106 total cells/ml ) in FACS buffer containing 2% BSA and 0 . 05% sodium azide . Non-specific binding of antibody to Fc receptors was blocked by incubating the cell suspension with the anti-mouse CD16/32 ( 2 . 4G2 ) antibody ( 10 µg/ml ) and ChromePure rat IgG ( 100 µg/ml , Jackson Immunoresearch ) for 30 min at 4°C . Cells were then incubated in pre-determined optimal concentrations of fluorochrome-conjugated antibodies to cell surface antigens or matching isotype control antibodies for 30 min at 4°C . APC anti-mouse Ly-6G/Ly-6C ( Gr1; RB6-8C5 ) , Pe anti-mouse F4/80 ( BM8 ) , PerCP/Cy5 . 5 anti-mouse CD11b ( M1/70 ) , Pacific Blue anti-mouse ( MHC-II/IA/IE , M5/114 . 15 . 2 ) and PeCy7 anti-mouse CD45 ( RA3-6B2 ) , and matching fluorophores-conjugated antibodies isotypes were purchased from Biolegend . PeCy7 anti-mouse CD11c ( HL3 ) and V500 anti mouse CD45 . 2 ( 104 ) were purchased from BD Pharmingen . Fluorescent measurements were conducted with identical settings on at least 100 , 000 CD45+ cells per kidney per experiment using a BD FACSCanto II cytometer operating BD FACSDiva software v6 . 1 . 3 ( BD Biosciences , Erembodegem , Belgium ) , and FlowJo v7 . 6 . 5 ( Tree Star . Inc . , Ashland , OR ) . Analyses of NOD1 expression and bacterial phagocytosis capacity by human neutrophils were also analyzed by flow cytometry . Human whole blood samples ( 1 ml ) were incubated with Texas red-coupled E . coli ( 107 bacteria ) ( Molecular Probes ) with gentle stirring for 30 min at 37°C , while negative control samples were kept on ice before analysis . Red blood cells were then lyzed by adding 10 ml NH4Cl 0 . 8% wt/vol for 15 min . Trypan blue ( 0 . 05 mg/ml ) was added to the samples to reduce the quenching of surface-bound fluorescence . Samples were centrifuged ( 400 g , 10 min ) at 4°C to remove cell debris , and pelleted leukocytes were then rinsed in PBS . In parallel , aliquots of NH4Cl-treated blood samples were permeabilized with methanol , and then incubated with an anti-human NOD1 antibody ( Imgenex Corp . ) . All fluorescence measurements were conducted with identical settings and forward and side-scatter parameters to identify the neutrophil population and to gate out other cells and debris [67] . Statistical analysis was performed using the GraphPad Prism program . The unpaired t test , ( two-tailed p values ) was used to compare two groups . The distribution of three or more groups was analyzed by One-Way ANOVA and the Kruskal-Wallis test . The Mann-Whitney test was used to compare the group with one another . A p value<0 . 05 was considered significant . The mouse gene accession numbers ( GenBank ) are as follows: ß-actin , NM_007393 . 3; GAPDH , AK144690; NFATc1/NFAT2 , NM_016791 . 4; Nod1 , NM_172729; Nod2 , NM_145857; RipK2 , NM_138952 . 3; Tlr2 , NM_011905 . 3; Tlr4 , NM_021297 , Tlr5 , AF186107 . 1; Tlr9 , AF314224 . The human gene accession numbers ( GenBank ) are as follows: GAPDH , NM_002046 . 3; NOD1 , NM_006092 . 2; NOD2 , NM_022162 . 1; TLR2 , NM_003264 . 3; TLR4 , NM_138554 . 3 . The mouse protein accession number ( UniProtKB/Swiss-Prot ) are as follows: ß-actin , P60710; CCL5 , P30882; CD11b/integrin alpha-M/beta-2; P05555; CXCL1; P12850; CXCL2 , P10889; NFATc1/NFAT2 , 088942; Nod1 , Q8BHB0; Nod2 , Q8K3Z0; RipK2 , P58801; Tlr2 , Q9QUN7; Tlr4 , Q9QUK6 , Tlr5 , Q9JLF7; Tlr9 , Q9EQU3 . The human protein accession numbers ( UniProtKB/Swiss-Prot ) are as follows: IL-8 , P10145; NOD1 , Q9Y239 .
|
Patients who have received a kidney graft are treated with immunosuppressive drugs , such as the cyclosporine A ( CsA ) . Transplanted patients under CsA are prone to bacterial infections . In this study , we used an experimental mouse model of kidney infection with Escherichia coli ( E . coli ) bacteria to study the effect of CsA . We show that CsA treatment of mice reduced their renal defense against E . coli . We found that CsA , in addition to its inhibitory action on the TLR4-mediated production of chemoattractant chemokines , also inhibited the expression of nucleotide-binding oligomerization domain 1 ( Nod1 ) , an intracellular receptor involved in the innate immune response against bacteria , in phagocytic cells . CsA acts by inhibiting the functions of the transcription factor NFAT . We show that NFAT is required for the proper expression of Nod1 . Since Nod1 has already been reported to be involved in the phagocytic functions of polymorphonuclear neutrophils , we looked for and found a severe defect in neutrophil bacterial killing associated with reduced expression of Nod1 in both mice and patients treated by CsA . Interestingly , when mice treated with CsA are given synthetic molecules known to bind Nod1 , this permitted the restoration of the Nod1 expression and renal defenses . This paper describes a novel mechanism which may explain , at least in part , why transplant patients receiving CsA have increased susceptibility to bacterial infection , and also provides a potential therapeutic strategy to restore renal antibacterial defenses .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"medicine",
"immunology",
"microbiology",
"bacterial",
"diseases",
"renal",
"transplantation",
"immune",
"defense",
"bacterial",
"pathogens",
"infectious",
"diseases",
"biology",
"gram",
"negative",
"clinical",
"immunology",
"immunity",
"innate",
"immunity",
"nephrology"
] |
2013
|
Cyclosporine A Impairs Nucleotide Binding Oligomerization Domain (Nod1)-Mediated Innate Antibacterial Renal Defenses in Mice and Human Transplant Recipients
|
Few drugs are available for soil-transmitted helminthiasis ( STH ) ; the benzimidazoles albendazole and mebendazole are the only drugs being used for preventive chemotherapy as they can be given in one single dose with no weight adjustment . While generally safe and effective in reducing intensity of infection , they are contra-indicated in first-trimester pregnancy and have suboptimal efficacy against Trichuris trichiura . In addition , drug resistance is a threat . It is therefore important to find alternatives . We searched the literature and the animal health marketed products and pipeline for potential drug development candidates . Recently registered veterinary products offer advantages in that they have undergone extensive and rigorous animal testing , thus reducing the risk , cost and time to approval for human trials . For selected compounds , we retrieved and summarised publicly available information ( through US Freedom of Information ( FoI ) statements , European Public Assessment Reports ( EPAR ) and published literature ) . Concomitantly , we developed a target product profile ( TPP ) against which the products were compared . The paper summarizes the general findings including various classes of compounds , and more specific information on two veterinary anthelmintics ( monepantel , emodepside ) and nitazoxanide , an antiprotozoal drug , compiled from the EMA EPAR and FDA registration files . Few of the compounds already approved for use in human or animal medicine qualify for development track decision . Fast-tracking to approval for human studies may be possible for veterinary compounds like emodepside and monepantel , but additional information remains to be acquired before an informed decision can be made .
Soil-transmitted helminthiasis ( STH ) is caused primarily by four species of nematode worms , Ancylostoma duodenale and Necator americanus ( hookworms ) , Ascaris lumbricoides ( roundworm ) , and Trichuris trichiura ( whipworm ) , that parasitize the human gastrointestinal tract [1] . Some 2–3 billion people are thought to have active infections and billions more are at risk of infection [2]–[4] . STH is estimated to be responsible for the loss of 39 million disability adjusted life years ( DALYs ) annually [2] but the burden of disease is currently being re-evaluated [5] . There are only four drugs recommended by the World Health Organization ( WHO ) for STH , and they have been in use for several decades: the two benzimidazole carbamates ( BZs ) albendazole and mebendazole , levamisole , and pyrantel pamoate [6] , [7] . While STH related morbidity can be controlled through chemotherapy , various problems need to be faced as anthelmintics are increasingly deployed in mass drug administration ( MDA ) programs [6] . For practical reasons MDA requires a single drug administration to all subjects without prior diagnosis or checking for contra-indications . For this reason , the BZs are preferred over levamisole and pyrantel ( which require weight-based dosing and are also intrinsically less potent ) . However , the BZs are not perfect drugs either: first , the efficacy against some of the STHs ( especially T . trichiura ) is suboptimal when delivered as a single dose [8]; second the BZs are contra-indicated in early pregnancy , which may go unnoticed or unreported in the first trimester; and finally , the wide-spread coverage with a single class of compounds exposes parasites to selective pressure potentially leading to resistance , which has already occurred widely in veterinary practice [9] , [10] . Therefore , there is a pressing need for concerted efforts to discover and develop the next generation of anthelmintic drugs and for drug combinations . The main drivers are risk of emerging BZ resistance , the limited spectrum of activity and the contraindications of the current drugs . Discovering and developing new drugs ( Research & Development , R&D ) is a complicated , expensive , risky and time-consuming endeavour . For a new drug to be granted marketing authorization in humans , it must be developed following strict regulatory requirements [11] . Clinical development is also the most expensive part of R&D [11] . Therefore , the decision to move a compound from the discovery into the development stage must be carefully considered and based on sound science and cost considerations . Our goal is to identify and evaluate potential development candidates for STHs to assess whether all data required to inform the decision to initiate development are available or what additional data are needed . Compounds registered for veterinary medical use by the US Food and Drug Administration ( FDA ) , European Medicines Agency ( EMA ) and other regulatory bodies have extensive safety , pharmacokinetics ( in some cases ) and efficacy data derived in animals . Therefore , these should have the potential for accelerated transition into human use . Almost all products currently available for human helminth diseases have been transitioned from veterinary/animal health companies since the 1950's [7] , [12] . The average transition time was 3 years , but for older veterinary drugs , this was longer because of the need to conduct additional studies ( e . g . safety , pharmacology ) to satisfy modern requirements . Modern rules for veterinary medicine licensure means that data available now are essentially equivalent to those required for human medicines; therefore transitioning can be achieved earlier . However , criteria for veterinary medicine anthelmintic efficacy are much more stringent than current human requirements , e . g . generally require an efficacy defined as 90% or greater clearance of the target organism [12] , [13] . The underlying approach for this analysis was first to assess candidates based on publicly available information ( through US Freedom of Information ( FoI ) statements , European Public Assessment Reports ( EPAR ) , scientific meetings , publications , patents , etc ) that could be summarized and shared . For the compounds that emerge as promising from this initial assessment , further information will be sought by directly contacting the relevant data owner for additional confidential data as appropriate . This will allow identification and analysis of the missing elements to permit an informed decision to be made on taking the compound forward and for planning the additional experiments that are required for human registration . This paper summarizes the general findings and more specific information on two veterinary anthelmintics ( monepantel , emodepside ) and on nitazoxanide , a licensed antiprotozoal drug with evidence for anthelminthic activity , compiled from an analysis of publicly available information in the EMA EPAR and FDA registration files . Complete reviews with these data are provided as supplementary data ( see below ) . None of these has been assessed for STH from a human drug development perspective . These summaries are made available to help define and stimulate decisions by potential researchers and drug developers , identify additional investigations that may be needed for an informed decision , as well as creating development partnerships .
Two searches were performed in parallel: The candidate compounds for the searches were assessed against a product profile that had been previously generated following discussions at several meetings of experts at WHO and elsewhere ( see proposed target product profile ( TPP ) which is provided in detail in Appendix S1 ) . The assessment criteria used for the analysis contain the key points of the TPP and required the drug to be potentially: To identify compounds amenable to rapid development , we aimed for compounds that are already in human or veterinary use or that had gone through extensive animal testing in their development as veterinary drugs .
The electronic search on PubMed yielded 299 hits . After discarding duplicate publications and studies outside our scope ( e . g . molecular papers ) and veterinary drugs ( as these were already identified in our parallel search as described below ) , 25 potential drug candidates remained . The majority of these were natural product compounds and with the exception of tribendimidine , none of these had undergone extensive animal testing , and hence were not considered further . Through expert consultation an additional compound was identified ( nitazoxanide ) . In addition , we identified several primary anthelmintics used in veterinary medicine today . These include various representatives from the macrocyclic lactones ( MLs , avermectins and milbemycins , including some experimental compounds that did not reach the market ) , BZs , depsipeptides , paraherquamides , hexahydropyrazines , tetrahydropyrimidines , imidathiazoles , amino-acetonitriles , salicylanilides , phenylsulfonamides , biphenylsulfides and miscellaneous compounds . Although there are some compounds ( e . g . phenothiazines ) that have been used in veterinary medicine , these are very old drugs and were not thought to be worth including here . A representative of each class is summarized in Table 1; details are provided in Appendix S2 . Additional details on approved animal health compounds identified , including compound class , generic name , chemical structure , current supplier , patent approval , U . S . approval , mode of action , more specifics on parasite claims and efficacy/resistance , dose rates , more on safety and toxicity issues and an overall assessment of current use in veterinary medicine . We did not further consider the avermectin class ( which comprise a large number of animal health registered compounds , such as doramectin , eprinomectin , ivermectin and selamectin and agrochemical-registered compounds such as abamectin and emamectin ) to be potentially interesting drug development candidates , as these drugs would likely be cross resistant to ivermectin-resistant parasites . In addition , ivermectin is characterized by low efficacy against hookworms [14] , [15] . The aforementioned compounds are all very similar structurally and act by the same mode of action , and therefore are unlikely to offer a clear advantage over ivermectin in terms of efficacy or resistance . No further search was undertaken for moxidectin , a milbemycin macrocyclic lactone , as this drug is under development for systemic helminths ( onchocerciasis ) in humans . The physico-chemical characteristics of this molecule result in pharmacokinetic advantages over ivermectin ( longer residence time , larger volume of distribution ) . Though milbemycin oxime , another compound in this class , was effective in the treatment of ascarids and hookworms in naturally infected cats [16] and in dogs ( Interceptor® product label ) and is approved for Trichuris vulpis in dogs ( Interceptor® product label ) , only moderate egg reduction rates were observed in baboons infected with T . trichiura [17] . Further , this compound is only registered for use in companion animals so much of the data generated for a food producing animal that would accelerate any human health program would not be readily available . The BZs represent a wide variety of molecules developed by several animal health companies and launched mostly in the 1960's and early 1970's for livestock , horses and companion animals . As mentioned above , albendazole and mebendazole are the most widely used drugs against STH today . The BZs act as inhibitors of tubulin formation , affecting cell synthesis and function [18] , [19] . The main disadvantage is that , as a class , the BZs are teratogens in animals and are contraindicated for use in the first trimester of pregnancy . Additionally , there is concern that their widespread use in public health programmes in highly endemic countries will result in helminth resistance , just as seen in the veterinary field a few years after their introduction . Before selecting any of the other BZs identified ( fenbendazole , flubendazole , oxfendazole , oxibendazole , thiabendazole and netobimin ) for development for STHs , any advantage over albendazole or mebendazole in terms of potential cross-resistance , improved efficacy profile or contraindications , would have to carefully considered . For example , fenbendazole given at doses of 30–50 mg/kg only achieved a cure rate of 28 . 6% in 28 Korean patients with T . trichiura respectively [20] . Finally , oxfendazole and flubendazole are currently being investigated for treatment of systemic helminth infections in humans by the NIH and BMGF , and no publicly available information in humans exists for oxibendazole , which had been in clinical development for STH by SmithKlineBeecham/GlaxoSmithKline until about 2003 . Paraherquamide A is a natural product produced by Penicillium paraherquei which was discovered in 1981 [21] . It was evaluated by Merck in the late 1980's and a small chemistry effort was conducted to produce analogs [22] . Paraherquamide A was found to have outstanding broad spectrum nematocidal activity against various sheep gastro-intestinal nematodes [23] . It is a nicotinic antagonist that blocks depolarization in muscles and induces a rapid paralysis of the mid-body of the parasite [24] . However , it was severely toxic in mice and dogs , which prevented its development [25] , as these species are the standard models for safety studies . In addition , poor activity was observed against T . vulpis in dogs [25] . UpJohn , later Pfizer , conducted semi-synthetic medicinal chemistry on Paraherquamide A [26] and eventually identified derquantel as a safer but still effective compound against sheep gastrointestinal parasites . Derquantel was noted , however , to cause lethality in horses [27] and was not pursued for this species . This product is being developed as a sheep product in Australia and New Zealand in combination with abamectin [28] . It remains to be confirmed whether derquantel offers improved efficacy against Trichuris spp . In addition , a thorough evaluation of potential toxicity of derquantel or any metabolites will have to be done prior to any administration to humans , acknowledging the history of this compound class . This issue lowered the priority for this compound in our evaluation . No new compounds were identified within the hexahydropyrazine and imidazothiazole classes . Many of the hexahydropyrazines ( DEC , piperazine , praziquantel and epsiprantel ) and the imidazothiazole levamisole have been used for many years in human health . Similarly , the tetrahydropyrimidine class of neuromuscular blocking agents , such as pyrantel , has been used for decades in human health [8] , [29] , and the related molecule morantel would not offer any advantage over pyrantel . Amidantel ( BAY d 8815 ) , a precursor of tribendimidine , was evaluated by Bayer in late 1970's . It showed efficacy against hookworms and ascarids in dogs with a single oral dose of 25 mg/kg [30] , including Toxacara canis , which was completely eliminated by a single 10 mg/kg oral treatment . Early studies showed the compound acted as an acetylcholine agonist [31] . The compound was not marketed as a veterinary product as the drug had to be given twice on two consecutive days , which was a great disadvantage in the face of other existing anthelmintics for companion animals [32] . As tribendimidine , a symmetrical diamidine derivative of amidantel , is marketed in China for STH [33] and being pursued for human use , amidantel was not considered as a candidate from our analysis . The salicylanilides ( closantel , niclosamide , oxyclozanide , rafoxanide ) , the phenylsulfonamide clorsulon , the biphenylsulfides bithionol and febantel , and nitroscanate and nitroxynil are structurally similar with all containing one or more phenyl groups with halide or phenolic hydroxyls and/or nitro group substitutions . They are among the older anthelmintics developed for veterinary medicine and are still used , although safer and broader spectrum parasiticides take precedence except where price is more of a priority . They generally act as uncouplers of oxidative phosphorylation and so would not be prime candidates for human development without a thorough toxicology evaluation . In addition , several of these drugs ( e . g . bithionol , clorsulon , rafoxanide ) are only active against the trematode Fasciola hepatica [34] , [35] and do not possess nematocidal activity . Hence , from our initial assessment we selected 4 compounds , which fulfilled further progression criteria ( no cross resistance to already available drugs , excellent activity and toxicity profile ) . These four drugs are already marketed for either human ( nitazoxanide , tribendimidine ) or veterinary ( the depsipeptide , emodepside and the aminoacetonitrile , monepantel ) use . We did not compile a dossier on tribendimidine , which is registered for human use in China and is being developed for regulatory approval by a consortium composed of XPC China , Swiss Tropical and Public Health Institute ( Swiss TPH ) , and Institute for One World Health ( iOWH . ) The structures of emodepside , monepantel and nitazoxanide are depicted in Figure 1 . Information was available through documentation in the EMA and FDA registration files ( EPAR and FOI summaries , respectively ) and from available Material Safety Data Sheets as well as scientific publications . Complete dossiers with these data are provided as supplementary data and a brief summary of each of the drugs is provided below . The physico-chemical characteristics of these compounds are summarized in Appendix S3 . Emodepside is a semi-synthetic derivative of the cyclooctadepsipeptide PF1022A , a natural product compound produced by fermentation of the fungus Mycelia sterilia [36] . Anthelmintic activities of emodepside have been demonstrated in several in vitro and in vivo studies against various nematodes [37] , [38] . Bayer Animal Health developed emodepside for use in cats and registered a topically administered product in combination with praziquantel to treat hookworms and ascarids ( emodepside ) and tapeworms ( praziquantel ) in Europe in 2005 and in the U . S . in 2007 . The compound has a unique dual mechanism of action at the neuromuscular junction that involves on the one hand binding to a presynaptic latrophilin-like receptor and on the other hand pre- and post-synaptic interactions with a Ca2+-activated K+ ion channel ( SLO-1 ) . Binding of emodepside to the latrophilin receptor and the SLO-1 ion channel in the parasite leads to inhibition of pharyngeal pumping , paralysis and death [39] , [40] . Emodepside appears to be of low general toxicity and exhibits no genotoxic properties . Although some adverse effects were noted in embryotoxicity/teratogenicity studies in rats and rabbits , the use of the compound in pregnant cats has not been associated with any teratogenic findings . This compound represents a new class of anthelmintic , which could potentially be useful to treat helminthiasis in humans . The primary issue will be cost and to our knowledge , no studies have been published to date on the efficacy of the drug against specific soil-transmitted helminths ( For detailed information see Appendix 4 ) . In addition , some safety aspects ( notably safety pharmacology , reproductive toxicity and neurotoxicity ) remain to be elucidated . Of note , PF1022A , the emodepside precursor should also be considered , since the drug has a broad spectrum of activity [38] , [41] and might have lower production costs . The amino-acetonitrile derivatives ( AAD ) represent a novel class of anthelmintic drugs developed by Novartis Animal Health for use in sheep and potentially in cattle [42] . Monepantel , a member of this family , was registered in New Zealand as Zolvix® in 2009 for sheep abomasal parasites ( Haemonchus contortus , Trichostrongylus colubriformis , and Teladorsagia circumcincta ) and certain intestinal parasites ( Oesphagostomum spp . , Nematodirus spp . , and Chabertia spp . but not T . ovis ) . As of January 2011 the product has been registered in a total of 32 countries in Australasia , Europe and Latin America . It is an agonist of a helminth-specific subfamily ( DEG-3 ) of the nicotinic acetylcholine receptor , specifically attacking its subunit Hco-MPTL-1 . Activation of the receptor through this agonist action causes hyper-contraction of the parasite body and spasmodic contraction of the pharynx [43] . It has been reported to be effective against veterinary parasites resistant to known anthelmintics including macrocylic lactones , benzimidazoles and levamisole [43] , a characteristic which could potentially give it an advantage as a human health drug . As the product was just recently launched there has been no field resistance reported as yet . It has recently been demonstrated that monepantel is not active against Strongyloides ratti in vitro , which lacks such a MPTL-1 homolog [44] . A complete program of safety pharmacology and toxicology studies has been conducted . The compound appears to be of adequate safety with only adaptive effects noted in general toxicity studies and with no adverse effects in reproductive toxicity ( paternal and embryo-foetal ) observed in different animal species [45] , [46] . Monepantel is also without mutagenic activity [47] . Hence , in conclusion , monepantel ( i ) has a clean safety profile and is reportedly not contraindicated in pregnancy; ( ii ) is not cross-resistant with the BZ family; and ( iii ) has a contemporary state-of-the-art regulatory dossier for veterinary use . The efficacy of the drug against some of the soil-transmitted helminths is not yet known . Specifically , there is no information on its activity on Necator americanus and Trichuris trichiura . Since the genomes of these two species are not published yet , and since predicting sensitivity on the basis of genomic information ( such as whether the receptors conferring sensitivity to monepantel are present in Trichuris ) might be inaccurate or insufficient , in vitro and in vivo experiments will be needed to complete its efficacy profile . Hence , we have started in vitro and in vivo studies with monepantel against T . muris and hookworms in our laboratories . For detailed information on monepantel see Appendix S5 . Nitazoxanide is an antiprotozoal drug used for the treatment of infections with Cryptosporidium parvum and Giardia intestinalis . The drug ( trade name: Alinia® ) is commonly given in six divided doses ( 500 mg bid for 3 days for adults and 200 mg bid for 3 days for children aged 4–11 years ) . The safety and tolerability of nitazoxanide in humans has been documented by >10 years of commercial use , during which more than 20 million people have been treated with this drug ( Romark Laboratories , pers . commun . ) , most of them for relatively short durations ranging from 3 to 10 days . Three studies carried out in Mexico have shown that nitazoxanide achieved high cure rates against T . trichiura and A . lumbricoides [48]–[50] . For example , in Mexico cure rates of 78 and 56% were achieved against light and moderate infections with T . trichiura [48] . However , studies against hookworms and using single doses remain to be done . Its mode of action involves inhibition of enzymes relevant for the survival of the parasites in an anaerobic environment , such as pyruvate:ferredoxin/flavodoxin oxidoreductases , nitroreductases and/or protein disulphide isomerases . Nitazoxanide appears to be a drug with no major safety issues emerging from non-clinical safety pharmacology and toxicology studies . Specifically , reproductive toxicity was not significantly affected due to the low absorption from the gastrointestinal tract , thus allowing its use in pregnancy . Although the haematotoxicity observed in rats and dogs might warrant special consideration for the use of nitazoxanide in G6PD-deficient patients , there was no evidence from the post-marketing experience for any major safety problems associated with the human use of nitazoxanide at recommended doses . For detailed information see Appendix S6 .
The main objective of this work was to identify potential drug candidates that would be eligible for rapid transitioning into development for human STH infections . We have not considered possible drug combinations in the present work , as this strategy has already been discussed in recent reviews [7] . Several elements have to be taken into consideration when deciding whether a compound deserves further investigation and before investment is made to provide sufficient data for an informed development track decision . Examples are: cost of goods , suitability of formulation for human use , additional non-clinical pharmacology data such as efficacy against the target human helminths , safety , including potential drug∶drug interactions , and pharmacology . Many of these issues have yet to be addressed in detail and may result in further reduction in the already sparse list of candidates . It is clear from these searches that the majority of compounds that could be developed still come from animal health . While this analysis has focussed on single drug candidates , it is important to recognise that there may not be one simple solution to the problem , especially since humans , like their animal counterparts , may be infected with several species of helminth at one time . Thus drugs may be identified that , taken together in combination , may also enhance efficacy and also reduce the risk of generating resistance . This strategy is followed in the chemotherapy of HIV , tuberculosis and malaria , and there is no reason to suppose that it would not also be effective for STHs . Indeed , for example a recent study has shown that a combination of mebendazole and ivermectin has enhanced efficacy against trichuriasis , while protecting from the poor efficacy of ivermectin against hookworm [51] . Although drugs registered for animal health could be rapidly transitioned into humans , it is essential to conduct discussions with the relevant regulatory authorities to ensure that all the necessary pre-clinical studies have been or can be conducted to permit human studies . The examination of data compiled in analyses such as presented here , together with expertise on regulatory process , should enable to identify the most suitable candidates , requiring the lowest progression investment . While this can accelerate the transition into humans , it should also be recognised that the most expensive phase of development is yet to be faced , and not all potential candidates will make it through human efficacy testing . Finally , it is one thing to register a drug ( difficult though it may be ) , but getting it into use is a challenge of a different level of complexity , especially in the case of STH or other helminthic diseases . Here , the product is not chosen by the individual customer or prescriber , but is rather selected by control programmes or even internationally for procurement and distribution through MDA . With all their limitations , displacing the current BZs will be complex even for a very good drug . After all , BZs are capable of reducing infection intensity ( and thus morbidity ) , are generally safe , are given in a single dose and the same dose for all , and are donated to a large extent . Cost-effectiveness will be an issue and will include consideration of the cost of changing policy as well , against the prospective advantages of the new drug . We believe it was important both to conduct this analysis and to share and make the results publicly available . STH and helminthiasis in general are among the most neglected diseases in terms of drug R&D , even compared to other tropical diseases , such as those caused by kinetoplastids ( leishmaniasis , African trypanosomiasis , Chagas disease ) or malaria [52] . Today there are few dedicated funds for anthelmintic , particularly STH , R&D for human use; there are some potential but scattered initiatives and little or no cohesive approach thus far . With scarce resources , and the high costs and long development times for new drugs , developing the wrong candidate or a “me-too” drug ( drugs that will offer no significant public health advantage over existing interventions ) is not an option . Making the results of this search publicly available will hopefully assist decision making for R&D in the community of developers and funders . However , this will only be the beginning , as more needs to be done . Based on our assessment , we will endeavour to access proprietary information through confidentiality agreements with the respective companies and to generate the data that we feel will be required to make a development track decision . Recently , TDR , BIO Ventures for Global Health ( BVGH ) and the Sabin Vaccine Institute initiated discussions with a number of public and not-for-profit organizations potentially interested in drug development for helminths including developers , researchers and funding agencies . The objective is to favour an enabling environment for anthelmintic R&D , consistency and openness ( with sharing of information ) . Hopefully a degree of cohesion can be reached . In the case of STH ( and helminths at large ) R&D , the situation is such ( little resources , few candidate drugs , few development partners ) that it must be approached considering the global R&D pipeline rather than individual initiatives by single organizations . This will hopefully provide consistency and consolidate development efforts . And this is the spirit underlying this paper .
|
There are few drugs - none ideal - for the treatment and control of gastrointestinal helminths ( soil-transmitted nematodes ) which , as chronic infections jeopardize children's growth , learning and ultimately individual , community and country development . Drugs for helminths are not attractive in human medicine , but are lucrative in animal health . Traditionally , investment in veterinary medicines has benefited humans for these diseases . With modern regulations an approved veterinary medicine can be tested in humans with little adaptation , reducing time and cost of development . We searched for products that could easily be transitioned into humans , having the necessary characteristics for use in communities exposed to these infections . A limited number of candidates met the main criteria for selection . We provide here a detailed analysis of two veterinary products , emodepside and monepantel , and nitazoxanide , which is approved for human use . In addition we include a less detailed analysis of all products examined , and the criteria on which the analysis was based . It is clear that the pipeline of easily obtainable human anthelminthics remains extremely limited , and further efforts are needed to find replacements for the inadequate number of products available today .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[
"medicine",
"infectious",
"diseases",
"soil-transmitted",
"helminths",
"neglected",
"tropical",
"diseases"
] |
2011
|
Potential Drug Development Candidates for Human Soil-Transmitted Helminthiases
|
Anthrax poses a community health risk due to accidental or intentional aerosol release . Reliable quantitative dose-response analyses are required to estimate the magnitude and timeline of potential consequences and the effect of public health intervention strategies under specific scenarios . Analyses of available data from exposures and infections of humans and non-human primates are often contradictory . We review existing quantitative inhalational anthrax dose-response models in light of criteria we propose for a model to be useful and defensible . To satisfy these criteria , we extend an existing mechanistic competing-risks model to create a novel Exposure–Infection–Symptomatic illness–Death ( EISD ) model and use experimental non-human primate data and human epidemiological data to optimize parameter values . The best fit to these data leads to estimates of a dose leading to infection in 50% of susceptible humans ( ID50 ) of 11 , 000 spores ( 95% confidence interval 7 , 200–17 , 000 ) , ID10 of 1 , 700 ( 1 , 100–2 , 600 ) , and ID1 of 160 ( 100–250 ) . These estimates suggest that use of a threshold to human infection of 600 spores ( as suggested in the literature ) underestimates the infectivity of low doses , while an existing estimate of a 1% infection rate for a single spore overestimates low dose infectivity . We estimate the median time from exposure to onset of symptoms ( incubation period ) among untreated cases to be 9 . 9 days ( 7 . 7–13 . 1 ) for exposure to ID50 , 11 . 8 days ( 9 . 5–15 . 0 ) for ID10 , and 12 . 1 days ( 9 . 9–15 . 3 ) for ID1 . Our model is the first to provide incubation period estimates that are independently consistent with data from the largest known human outbreak . This model refines previous estimates of the distribution of early onset cases after a release and provides support for the recommended 60-day course of prophylactic antibiotic treatment for individuals exposed to low doses .
The causative microorganism of anthrax , Bacillus anthracis ( B . anthracis ) , is classified by the US Centers for Disease Control and Prevention ( CDC ) as a Category A ( highest priority ) bioterrorism pathogen , with the potential for causing a large number of infections and deaths after an effective aerosol release in a community [1] , [2] . Reports of natural infections [3]–[6] and large scale accidental or intentional releases causing infections [7] , [8] provide limited insight into the risk . To evaluate the threat posed by potential release scenarios , risk assessors , public health analysts , biodefense modelers , and other researchers require robust quantitative dose-response analyses to estimate the magnitude and timeline of potential consequences and the effect of public health intervention strategies [9]–[11] , such as the administration of prophylactic antibiotic regimens to potentially exposed cases [12] , and to interpret the significance of sampling results for detecting B . anthracis spores in indoor environments [13] , [14] . For these analyses , it is particularly important to estimate the probability of infection after low dose exposures , which could cause the majority of cases after a large-scale release [15] , [16] . Due to the deadly nature of the disease , there are no experimental data on exposure and response of humans to aerosolized B . anthracis . Analyses of quantitative information from natural and accidental exposures and infections of humans [15] , [17] and experimental infections of non-human primates [18] , [19] are scattered in the literature , poorly understood , and often contradictory . Mathematical dose-response modeling is useful when experimental data on the effects of low dose inhalational exposures are scarce or non-existent . These models utilize information about the height and shape of a dose-response curve at higher doses where data or estimates are available and use an assumed functional form to extend the curve to lower doses where data are not available , but where risk estimates are required . Different model forms can lead to very different extrapolated estimates from the same set of data . This creates substantial uncertainty regarding the minimum dose required to cause infection in humans [20] and the dose-dependent time from exposure to appearance of illness ( incubation period ) , key parameters required for sound risk assessment by public health and emergency preparedness authorities [12] , [21] . In this study , we critically evaluate the available published literature and identify candidate raw data sets to develop refined quantitative dose-response models for B . anthracis infection in humans with an emphasis on the low-dose effect . We use the resulting models to estimate the incubation period as a function of the exposure and the relationships between duration of antimicrobial treatment after exposure and the probability of infection .
Three outbreaks of inhalational anthrax in humans having information to estimate dose-response are the 2001 letter attacks through the U . S . Postal Service , industrial workers handling contaminated animal products in the early-mid 1900's , and an accidental airborne release of spores from a facility in Sverdlovsk , Russia in 1979 . The doses to which victims of the 2001 letters were exposed are not known , and it is a challenge to estimate exposure amounts without knowing the means by which spores were released from the envelopes , aerosolized , and inhaled . Therefore , despite modeling efforts [16] , [22] , these incidents shed limited information on quantitative dose-response . Some quantitative data exist for exposure of non-vaccinated industrial workers handling animal products contaminated with B . anthracis . This evidence suggests that the infection rate for humans exposed in this setting is very low , especially for inhalational anthrax , as most of the infections that did occur were cutaneous [3] . Workers in one mill were thought to have been inhaling hundreds of spores on a daily basis with not a single infection documented [23] . A recent analysis of this case concluded that 600 spores or fewer would not be expected to cause disease in healthy humans and advocated the use of 600 spores as a threshold to use in risk assessments [17] . However , it is possible that the industrial workers were immune to clinical infection from repeated low-level exposure , that there were undiagnosed cases , or that infections would result from low-dose exposures of individuals with unusual susceptibility [24] . B . anthracis spores were accidentally released from a facility in Sverdlovsk ( Russia ) in the former Soviet Union in 1979 , causing infections in both humans and animals downwind of the facility [7] . Doses inhaled by the infected individuals are not known , nor is it known how many spores were released from the facility . However , human dose-response information has been inferred using atmospheric data on the day the release likely occurred , the likely locations of the infected individuals when they were exposed , and the epidemiology of the tabulated cases . Meselson et al . [7] calculated that the attack rate at a ceramics factory 2 . 8 km downwind of the Sverdlovsk release was approximately 1–2% ( 18 out of about 1500 employees were infected , including 10 out of 450 employees working in a single unpartitioned building ) . Wilkening [15] analyzed the Sverdlovsk case data and applied a series of theoretical dose-response models , finding that both the spatial ( distance from release ) and temporal ( incubation period , assumed to vary with dose ) distribution of cases are consistent with dose-response curves that predict a slow decrease in the probability of infection as the dose decreases . They conclude that these data do not support a distinct exposure threshold below which no one is infected and above which everyone is infected . In the absence of other human data , experimental studies involving non-human primates provide the best available data from which to gain insights into potentially appropriate dose-response relationships for humans . We summarize three candidate data sets and dose-response models that have been applied to them . Note that , while these studies generally use death as an endpoint and express their results in terms of lethal dose ( LD ) , we make the assumption that LD and ID are equivalent , i . e . , that infection with inhalational anthrax invariably leads to death in the absence of treatment . Two of the following three studies do not make note of infected animals that survived . The third study found evidence of infection in two surviving animals sacrificed at the termination of an experiment , but noted that “these animals were undoubtedly in the early stage of disease and presumably would have developed systemic disease and died , had the experiment not been terminated” [24] . There is also evidence that humans with inhalational anthrax infection have a fatality rate approaching 100% in the absence of treatment . Holty et al . , in reviewing 82 of the best-documented human inhalational anthrax cases [25] , found only one instance of an infected and untreated person ( an at-risk veterinarian thought to have some prior immunity ) who did not progress to the fulminant stage of disease . They found only two cases ( 3% ) of humans surviving the fulminant stage of disease under any circumstance , and both of those cases received treatment . Glassman [26] reports on data from unpublished work performed by Jemski in which 1 , 236 cynomolgus monkeys ( Macaca fascicularis ) were exposed to aerosols of B . anthracis . While the raw data are not published , the paper reports that a log-probit model was fit to the data , resulting in a dose that is lethal to 50% of animals exposed ( LD50 ) of 4 , 130 spores ( 95% confidence interval 1 , 980 to 8 , 630 ) and a probit slope of 0 . 669 probits per base-ten log dose ( 95% confidence interval 0 . 520 to 0 . 818 ) . Under our definition of the log-probit model ( see Materials and Methods ) , the best fit parameters are ID50 = 4 , 130 and m = 0 . 291 ( Table 1 , model J ) . Extrapolation using these values results in ID10 of 50 spores and ID1 of 1 spore . Without raw data , it cannot be determined whether any of the monkeys in the Jemski experiments were exposed to low doses and , if so , whether any of those doses proved fatal . Furthermore , without the full data set it is not possible to evaluate whether alternative dose-response models would have fit the data better than the log-probit model , which has been outperformed by other models in fitting other data sets [18] . Two studies [11] , [15] applied a log-probit model based on the Jemski data to analyses of human exposure scenarios , although they applied ID50 = 8 , 600 ( the upper limit of the 95% confidence interval reported by Glassman ) . Two studies contain raw data from a substantial number of monkeys exposed to a range of dose amounts . Druett et al . [27] exposed rhesus monkeys ( Macaca mulatta ) to aerosols of B . anthracis spores resulting in a range of inhaled doses estimated between about 35 , 000 to 200 , 000 spores . We summarize the data from these experiments in Table S1 . The authors also fit a log-probit model to their data ( Table 1 , model D1 ) resulting in optimal parameters equivalent to ID50 = 53 , 000 spores ( 95% confidence interval 30 , 000 to 52 , 000 ) and m = 1 . 39 . Haas [18] reported a fit of the exponential model ( model D2 ) to this data set and also stated that the best fit log-probit and beta Poisson models did not provide a statistically significantly improved fit compared to the exponential model . The second study containing raw data , Brachman et al . [24] , exposed cynomolgus monkeys to B . anthracis-contaminated air from a goat hair mill . The data consist of estimated dosage and the number of deaths from anthrax , sacrifice , or other cause on each day across three model runs and are shown graphically in [24] . We visually estimated the daily exposure data from their figures and manually adjusted those estimates until they were consistent with the cumulative dose numbers reported in the source text . Our estimates of these numerical data are shown in Tables S2 , S3 , S4 . The authors did not fit a dose response model to their data , but two more recent studies have done so . Haas [18] used an averaging technique [28] to fit a time-independent exponential model ( Table 1 , model B1 ) to the data , and Mayer et al . fit a time-dependent exponential model and an extended exponential model ( Table 1 , models B2 and B3 ) . The published literature also includes quantitative human inhalational anthrax dose-response estimates based on the opinion or judgment of experts . For example , biodefense experts from the US Army Institute of Infectious Diseases ( USAMRIID , Fort Detrick , MD ) state the infective dose ( presumably ID50 ) of inhalational anthrax for humans is 8 , 000–50 , 000 spores [29] , [30] . An expert elicitation of seven anthrax subject matter experts [31] indicated an ID10 of 1 , 000–2 , 000 spores , an ID50 of 8 , 000–10 , 000 spores , and an ID90 of 50 , 000–100 , 000 spores . Webb and Blaser [16] extended those expert-derived estimates to age-specific values for the ID10 and ID50 , but without providing quantitative evidence or reasoning used to derive these estimates . Several dose response models have been proposed and applied based entirely or in part on the values from these expert elicitations ( Table 1 , models E1–E5 ) . We evaluate the previously published models against the criteria listed in Materials and Methods in Table 1 . Versions of six of the models in Table 1 ( J and E1–E5 ) have been applied in recent mathematical modeling or simulation studies of human exposure to anthrax [9]–[11] , [15] , [16] , [32] . Models J , D1 , D2 and B1–B3 were fit to one of three non-human primate dose-response data sets and , therefore , satisfy criterion 1 ( although model J is based on a data set by Jemski for which the raw data are not published , which limits transparency ) . Models E1–E5 do not have a clear basis in quantitative dose-response data , but are instead based entirely or partly on assumptions , recommendations , or expert opinions for which the reasoning has not been made clear in published accounts . All models except for three of the log-probit models with steeper slopes ( E1 , E2 , and D1 ) produce dose-response curves with shapes that either were shown to be consistent with the Sverdlovsk data in Wilkening [15] or produce similar estimates to the models tested in that study and , therefore , satisfy criterion 2 . The models taking the exponential form ( E5 , D2 , and B1–B3 ) are based on simple assumptions about the fate of individual spores inhaled in the lung , satisfying criterion 3 , while the other models make use of statistical distributions with no clear basis in assumed mechanisms of infection . Model E5 produces incubation period estimates as an extension of the assumptions that form the basis of the model and , therefore , satisfy criterion 4 . Models B2 and B3 produce estimates for the time course of infection but not for the incubation period . I . e . , they specify time to infection take-off ( initial germination of inhaled spores ) and to death , but not to onset of symptoms . The other previously existing models do not contain time components for disease progression among those infected . Although an incubation period distribution can be added to any dose-response model exogenously ( as was done by Wilkening [15] to a version of model J and model E2 ) , our preference under criterion 4 is for models in which the incubation period estimates are derived ab initio in conjunction with a dose-response model . Of the five models with a quantitative basis in expert opinion , model E5 has the most ( three ) of the desired characteristics of an anthrax dose-response model . However , while some of the time-based parameters of this model have been estimated from non-human primate data and human data from Sverdlovsk [32] , the full dose-response model is incomplete without assuming a fixed point on the dose-response curve ( e . g . , the ID50 ) which does not have a firm basis in those data . Non-human primate data sets can be used to fill that need . Model J based on the Jemski data does not satisfy criteria 3 and 4 , and the raw data are not available to attempt further modeling with improved characteristics . Therefore , we focus on models fit the Druett et al . and Brachman et al . data sets in the following sections . We checked the results for the optimal parameters of the log-probit model D1 and the exponential model D2 when fit to the Druett et al . data . Our best fit parameters for the log-probit model confirm the results of model D1 . For the exponential model , our best fit parameter is r = 1 . 43×10−5 ( 95% confidence interval 0 . 92×10−5 to 2 . 19×10−5 ) , which is twice the estimate of model D2 . We have listed our novel result as model D3 , and we explain the source of the difference from model D2 below . We also fit the beta Poisson model to the data set , and the result produced a nearly identical curve to model D3 , so we did not list it in Table 1 . The exponential model contains one fewer parameter than the beta Poisson model and is , therefore , more parsimonious , so the beta Poisson model need not be considered further , as it does not improve the fit to the data . Models D1 and D3 have a statistical deviance ( defined in Materials and Methods ) of and 10 . 3 and 11 . 3 , respectively , which are less than the corresponding 95th percentile chi-squared statistics ( 14 . 1 and 15 . 5 ) with degrees of freedom equal to the number of dose points ( 9 ) minus the number of parameters in each model ( 2 and 1 ) . Under this criterion , both models provide an adequate fit to the data [33] . The deviance under model D1 is lower than under D3 , which suggests a better fit , but the difference is less than the difference in the chi-squared statistics , so that the exponential model would be chosen as the best combination of fit and parsimony [33] . The ID estimates shown for models D1 and D3 in Table 1 illustrate the sensitivity of extrapolated estimates to model choice . The ID50 estimates , which are within the range of the doses actually supplied to the animals , agree closely , whereas the estimates for doses farther below the lowest dose from the data set ( ≈35 , 000 spores ) differ substantially . While the extrapolations from the exponential model are better supported according to the statistical criteria described above , even a small amount of additional data at lower doses could have shifted support to the estimates of the log-probit model . Dose-response models fit to the Druett et al . data have not been applied to mathematical models or simulations of human anthrax exposure , to our knowledge . While both the exponential and log-probit models provide adequate fits to the data and , therefore , satisfy our first criterion , the exponential model better satisfies our other criteria: it is derived from testable , mechanistic assumptions , while the log-probit model is not [18] , and it produces a less steep dose-response curve that is more consistent with the Sverdlovsk data [15] . However , neither model can satisfy our criterion of providing a time-to-infection component without making additional unsupported assumptions , as the time of death was not reported in the Druett et al . data . Therefore , we turn to the Brachman et al . data , which have the ability to support a model that satisfies all four of our criteria . We fit a novel Exposure–Infection–Symptomatic illness–Death ( EISD ) model to the Brachman et al . data set [24] , resulting in Model B4 ( Table 1 ) . The overall model , summarized here and described in detail in Materials and Methods , contains five parameters . The exponential dose-response model parameter r , the probability of one spore germinating before being cleared , governs the probability that infection will eventually occur after exposure to a given dose . Among those infected , the time from exposure to infection , defined as the time of the first successful spore germination leading to a sustained population of vegetative cells in the host , is governed by the parameters r and θ , the rate of clearance of spores from the lung . The time from infection to the onset of symptomatic illness is represented by the fixed parameter T , and the time from the onset of symptoms to death is governed by the parameters a and b , which are shape and scale parameters of a gamma distribution . Estimates for three of these five model parameters are available from independent data of B . anthracis infections in humans and in non-human primates . Brookmeyer et al . [32] calculated the probability-per-time for clearance of spores from the lung , θ , to be 0 . 07 per day , based on data from examination of the lungs of non-human primates at varying times after inhalation [34] . Data are also available for the time between the onset of symptoms and death in humans . Holty et al . [25] assembled data from 82 human inhalational anthrax cases , occurring between 1900 and 2001 , that met their inclusion criteria concerning sufficient documentation of anthrax infection , symptoms , and treatment . Their data set includes , for 75 of the cases , the time from the onset of symptoms to death , if it occurred , and/or to appropriate antibiotic therapy , if it occurred , which may have prevented or delayed death . We used a maximum likelihood procedure , designed to account for time censoring ( see Materials and Methods ) , to fit a gamma distribution for the time between onset of symptoms and death to their compiled data set . We determined the shape parameter a = 5 . 43 and scale parameter b = 0 . 864 , which results in an average time of 4 . 7 days , with a standard deviation of 2 . 0 days . By fixing those values of the three parameters θ ( rate of clearance of spores from the lung ) , a ( shape parameter ) , and b ( scale parameter ) , we estimated the values for the remaining parameters r ( probability of one spore germinating before being cleared ) and T ( delay between initial spore germination and onset of symptoms ) from the Brachman data . The best fit model estimates r = 6 . 4×10−5 ( 95% confidence interval of 4 . 0×10−5–9 . 5×10−5 ) and T = 2 . 3 days ( 0–5 . 4 ) . The optimal deviance of 129 is less than the corresponding 95th percentile chi-squared statistic ( 170 ) with 142 degrees of freedom ( the number of daily dose points minus the number of optimized parameters in the model ) , suggesting that the model provides an adequate fit to the data . The optimal value of r leads to an ID50 of 11 , 000 spores ( 7 , 200–17 , 000 ) , ID10 of 1 , 700 spores ( 1 , 100–2 , 600 ) and ID1 of 160 spores ( 100–250 ) . The optimal value of T , when combined with the dose-dependent delay from exposure to infection , produces dose-dependent incubation periods ( exposure to symptom onset ) . For an ID50 dose , the median incubation period is estimated to be 9 . 9 days ( 7 . 7–13 . 1 ) . For ID10 , the estimate is 11 . 8 days ( 9 . 5–15 . 0 ) and for a low dose of ID1 , the estimate is 12 . 1 days ( 9 . 9–15 . 3 ) . Our best fit model to the Brachman data satisfies all four criteria that we propose for a defensible anthrax dose-response model that is useful for quantitative risk assessment . All parameter values are transparently derived from human and non-human primate data , the model is derived from biological assumptions about the establishment of infection and progression of disease , the model provides estimates for dose-dependent infection probability and distribution of incubation period , and the shape of the dose-response curve is consistent with what was observed in the Sverdlovsk data . We compare the results from this model to others in the literature in the following sections . We compare the uncertainty range of the dose-response curve ( probability of infection at any time after exposure to a given dose ) produced by model B4 to the curves from selected models shown in Table 1 , focusing on low doses ( Figure 1 ) . Models E3 , E4 , and E5 from Table 1 are in agreement with model B4 , as those curves fall entirely within the shaded region representing the 95% range . Models E1 and E2 are in agreement for doses above 200 and 400 spores , respectively , but they produce a significantly lower probability of infection for lower doses . Model J produces a significantly higher infection probability at doses less than 5 , 000 spores . Models D1 and D3 produce lower infection probabilities at all doses . Our optimal estimate for the exponential model parameter r fit to the Druett data set ( model D3 ) is two times higher than the value calculated by Haas [18] ( model D2 ) for the same model fit to the same data set . The lower infectivity produced by model D2 results from an estimated respiration rate of 2 . 4 L/min rather than the value of 1 . 2 L/min reported and used for calculations in the original paper [27] . See Table S1 for our calculation of the doses inhaled by each group of non-human primates in the original study . For the Brachman data , our model B4 produces an estimated range for the exponential model parameter r , and thus for infectivity at a given dose , that is somewhat higher than the values calculated by Haas [18] ( model B1 ) and Mayer et al . [35] ( model B2 ) for exponential model fits to the same data set ( Figure 2 ) . There were several assumptions made by the three studies that contributed to the differing infectivity results among these three models . In theory , the averaging technique used by model B1 should have produced the same value as the other two models for the exponential parameter r [28] . An important reason why the model B1 result is lower is that its calculation resulted from an incorrect assumption of a higher total cumulative dose for runs three and four of the Brachman experiments than was reported in the original paper ( see Table S5 ) . We recalculated r using the technique of model B1 with the correct dose values and found r = 3 . 8×10−5 , which is very close to the result of model B2 . Also , model B1 included animals that died of non-anthrax causes during the Brachman experiments in the group of survivors; if those cases had been excluded entirely , their estimate for r would have increased slightly . The main reason why our novel result for the parameter r ( model B4 ) is less than both model B2 and corrected model B1 , is that models B1 and B2 both assumed that all animals sacrificed and not found to be infected at the end of each run would not have become infected had the experiment continued . Our modeling process allows for the possibility that animals dying of other causes or sacrificed could have become infected with B . anthracis at later dates had they lived . Model B4 estimates that there was approximately a 7% , 4% , and 4% chance of infection after the day the animals were sacrificed in Brachman runs 3 , 4 , and 5 , respectively , assuming no further exposure . If those probabilities are accurate , then there likely would have been a few more deaths from anthrax across the three runs had the animals lived longer . Our model B4 also differs from model B2 in its assumptions and results for the time course from exposure to death in anthrax cases . In their procedure for model B2 , Mayer et al . [35] independently assumed that the delay between infection take-off and death was 1 , 2 , 3 , or 4 days with equal probability ( an assumption not based clearly on data ) . They then optimized their equivalent to our parameter θ to account for the remaining portion ( exposure to infection take-off ) of the overall delay between exposure and death , finding an optimal value of θ = 0 . 11 . We chose different assumptions that rely more directly on quantitative data , fixing θ = 0 . 07 based on data of spore clearance rates in non-human primates and expressing the symptoms onset to death delay with a gamma distribution fit to rigorously reviewed human anthrax case data , leaving the infection to symptoms onset delay T to be optimized ( resulting in T = 2 . 3 days ) . Model B2 does not provide estimates of the incubation period that can be compared to our estimates from model B4 , because model B2 does not specify the time of symptoms onset in its formulation . However , both models do provide estimates for the time from exposure to death ( the endpoint of the Brachman experiments ) . We find that our model B4 produces significantly longer estimates than model B2 for this time interval . For example , after a single ID10 exposure , our model B4 estimates a median time from exposure to death , among those infected and untreated , to be 16 . 6 days ( 95% confidence range , 14 . 4 to 19 . 8 days ) , while model B2 estimates 8 . 6 days . It is unclear why these time progression estimates differed so widely , given that the two models were fit to the same data set . We evaluated model B2 against our optimization criterion ( the minimized deviance Y , defined in Materials and Methods ) and found that it provided a poorer fit to the Brachman data by our measure ( Y = 158 compared to Y = 129 for our model B4 ) . Our model B4 also outperforms model B2 in describing the distribution of human exposure-to-death time estimates from the Sverdlovsk release reported by Abramova et al . [36] ( Figure 3 ) . To further explore the implications of our assumptions in constructing model B4 compared to model B2 , we tested the sensitivity of our results to our choice of θ ( Figure S1 ) . We reran our optimization procedure fixing θ = 0 . 11 , which results in optimized values of r = 5 . 6×10−5 and T = 3 . 4 days . The new r value is a small decrease in the infectivity estimate compared to our model B4 result , causing an increase in the ID50 estimate from 11 , 000 to 12 , 000 spores . The new value of θ caused the optimized time from initial germination to symptom onset to increase by about 1 . 1 days; however , the new value of θ causes the median time from exposure to initial germination to decrease by about 3 . 6 days at low doses . Therefore , applying θ = 0 . 11 instead of 0 . 07 would have decreased our median incubation time and time-to-death estimates at low doses by about 2 . 5 days , not enough to fully account for the 8-day difference described above between models B4 and B2 . A final difference between model B2 and B4 is that the model B2 parameters were only fit to runs 3 and 4 of the Brachman experiments , whereas we made use of runs 3 , 4 , and 5 ( see Tables S2 , S3 , S4 ) in producing model B4 . We found that deleting the data from Brachman run 5 had a negligible effect on our infectivity results ( the optimal r value was unchanged to two significant digits ) , so the additional data we incorporated did not contribute to the differing infectivity results of the two models . Next , we compare the incubation period distribution produced by our model B4 to three other estimates of the incubation period for human inhalational anthrax found in the literature [12] , [32] , [37] ( Figure 4 ) . Our model is unique in that , while the shape of the dose-response curve being consistent with the Sverdlovsk data was a criterion for model choice , we did not actually use incubation period data or time-to-death data from Sverdlovsk to determine parameter values . Therefore , we also check our model's estimates against the Sverdlovsk data ( Figure 4 ) as a validation for the utility of applying model B4 to a human outbreak . The Institute of Medicine ( IOM ) performed a detailed review [12] of data from analyses of Sverdlovsk patients: Abramova et al . [36] reported on 41 autopsy-confirmed cases , among which 30 cases had known dates of symptom onset; Meselson et al . [7] compiled data from 77 cases , 60 with known symptoms timing , but no additional confirmed cases beyond those that were reported by Abramova et al . ; Brookmeyer et al . [38] analyzed 70 cases with known symptoms onset dates , but again , no additional cases beyond the Abramova data that were confirmed by autopsy or microbiological testing . The IOM committee reviewing these data wrote “in its analysis of previous anthrax incidents , the committee required either microbiologic or histopathologic confirmation of infection with B . anthracis when determining the minimum incubation period of patients with inhalational anthrax” [36] . We chose to follow the lead of this committee and used only the autopsy-confirmed Abramova et al . data in Figure 4 to test the performance of our model . These data , when choosing April 2 , 1979 as the assumed date of release and exposure ( an assumption supported by compelling evidence [12] ) , consist of 30 estimated incubation periods ranging from 5 to 40 days , with median 13 days and mean of 16 . 0 days . Of the data from the other two studies excluded from this set , the IOM cast doubt in particular on unconfirmed reports of shorter incubation periods , as low as 2 days , which are not well supported [12] . We compared the incubation period distribution provided by our model B4 under the assumption of exposure to the ID1 ( consistent with an approximate 1% attack rate observed at given locations downwind of the Sverdlovsk release [7] ) to the distribution of the estimated incubation periods of confirmed Sverdlovsk cases ( Figure 4 ) . Our model appears to provide a good match to these data , as most points fall within our 95% confidence range , despite the fact that these data were not used in fitting parameter values for our model . We find that model B4's consistency with these Sverdlovsk incubation time data is robust to assuming that infected cases were exposed to a much higher dose ( ID50 ) and to the alternate assumption of θ = 0 . 11 , discussed above ( Figure S1 ) . We also compare our model to dose-dependent incubation period distributions provided by Brookmeyer et al . [32] and by Wilkening [37] ( Figure 4 ) and to a dose-independent incubation period model provided by the IOM for use by risk assessors in comparing intervention strategies over the first 8 days after exposure ( Figure 4 inset ) . Descriptions of the three models can be found in Materials and Methods . These models fall within the 95% confidence bounds of model B4 during the first 7 days after exposure , but they estimate that cases after the first week would appear more rapidly than does our model . We note that the IOM model was not designed to be accurate after the first eight days . The other two models made use of the larger Sverdlovsk data sets , including incubation time estimate of cases not confirmed to the standards of the IOM review , which shows that those unconfirmed data were skewed towards earlier dates of onset . Our incubation period estimates assume that prophylactic treatment is not administered to the population . In the case that prophylaxis is made available , the model also can estimate the effects of various durations of antibiotic use on reducing the probability of infections . Brookmeyer et al . [39] provided an equation for the probability that an individual exposed at a given level and adhering to an effective prophylactic regimen would becomes infected after ending use of antibiotics a given number of days after exposure . We have reproduced their equation using our parameter definitions in Materials and Methods . Given that the Brookmeyer paper applied the same spore clearance rate ( θ = 0 . 07 ) as our model B4 , their results are applicable to our model . For example , they calculated that , to reduce the risk of infection below 0 . 01% ( one in ten thousand chance ) , someone exposed to the ID0 . 5 , ID1 , ID10 , and ID50 would have to remain on antibiotic prophylaxis for at least 56 , 66 , 99 , and 126 days after exposure , respectively [39] . Our contribution to this result is that , using the Model B4 result , we can express the exposures in terms of the number of spores in the dose in addition to the ID level ( see , e . g . , ID1 , ID10 , and ID50 shown in Table 1 for model B4 ) . In Figure 5 , we show the relationship between duration of prophylaxis ( days , post-exposure ) and the estimated chance under model B4 of infection in humans after antibiotics are no longer taken , at exposures of 100 , 1 , 000 , and 10 , 000 anthrax spores . A 60-day course of antibiotics for those potentially exposed has been recommended by the CDC [40] . Brookmeyer et al . suggested that this course should be adequate at doses lower than the ID1 . Using model B4 , we estimate that ending a course of antibiotics 60 days after exposure would reduce the probability of infection below 0 . 1% for those exposed to doses of 1 , 000 spores ( ID6 ) or less , and below 0 . 01% for those exposed to doses of 100 spores ( ID0 . 7 ) or less .
As with most biothreat pathogens , the dose-response relationship of aerosolized B . anthracis in humans , especially in the low-dose range , remains highly uncertain . In the absence of human experimental data , risk assessments have relied on dose-response models that extrapolate from information on higher doses in animals . Despite an impressive body of published literature on this topic , these models have produced contradictory results and are based on assumptions that are poorly understood . In order to make informed decisions regarding preparations for and response to accidental or malevolent release of B . anthracis spores , the scientific and public health community need to have access to plausible and defensible models . These models ideally should be based on available measured dose-response data from non-human primates , be derived from mechanistic assumptions , provide estimates of incubation periods , and produce plausible results when applied to human exposure scenarios . Using our focused evaluation of the published literature on significant accidental and intentional exposures to humans and on non-human primate studies , we identify candidate dose-response models that satisfy our objective criteria and fit them to non-human primate dose-response data . We use these refined models to estimate incubation periods and evaluate the duration of antimicrobial treatment required to achieve a low probability of infection after exposure to aerosolized anthrax spores . We propose Model B4 ( Table 1 ) for use in quantitative analyses that require dose-response assessment for human inhalational anthrax , because it satisfies all four of our proposed criteria and improves on existing models fit to the same data set . The ID50 ( 7 , 200–17 , 000 ) and ID10 ( 1 , 100–2 , 600 ) confidence ranges produced by model B4 are remarkably consistent with the corresponding ranges produced by an expert panel surveyed in 1998 [31] , ( 8 , 000–10 , 000 ) and ( 1 , 000–2 , 000 ) , respectively . While four of the seven subject-matter experts questioned in that study reported having experience with animal testing , it is not known if or how their ID estimates were based on nonhuman primate data . Models E3 , E4 , and E5 ( Table 1 ) , which were fit to these expert estimates , produce low-dose extrapolations that are consistent with those produced by our model B4 . At a dose of 600 spores , our model B4 estimates that infection would occur sometime after exposure in about 2–6% of untreated cases , with the incubation time distribution of those cases being close to what is shown in Figure 4 . This estimate would appear to run counter to the conclusion by Cohen et al . [17] that 600 spores can be used as threshold in risk analyses . For example , a risk analysis estimating a 2–6% infection rate for visitors to a contaminated building likely would not conclude that building is safe for the general public . However , these authors recommend the 600 spore threshold only for healthy individuals . A widespread release likely would include individuals who are unusually predisposed or immune-compromised , for whom exposure to 600 spores or less could result in infection . Although the exponential model we develop here does not explicitly include heterogeneous susceptibility in the host population , the estimated average susceptibility should be conservative enough to apply the model to an exposed population that includes a larger proportion of susceptible individuals . Furthermore , the bacterial strains present at the factories on which the Cohen et al . estimate is based may have been less virulent to humans than other strains that could be released . It has not been proven that a single dose less than the 600 spore threshold recommended by Cohen et al . has ever infected a human or a non-human primate . To our knowledge , the lowest dose shown to cause infection in non-human primates occurred in the first part of run 5 in the Brachman experiments , in which two animals ( 8 . 3% ) died of anthrax after inhaling an estimated cumulative dose of approximately 950 spores over three days . If it is true that infections never occur in humans at doses in the hundreds of spores , the log-probit model fit to the Druett et al . data set ( Model D1 , Table 1 ) might be a viable alternative . With this model , the estimated probability of infection at 600 spores is less than one in one billion . However , a dose-response curve with a slope as steep as Model D1 is not consistent with the spatial distribution of human cases observed at Sverdlovsk [15] . Also , given that the exponential model D3 provides an equally good fit to the Druett data , we find the choice of the log-probit model D1 to be unjustified in the absence of a coherent biological theory that can explain steepness of the dose-response curve . A model applying one such biological theory is provided by Mayer et al . [35] , who extended the exponential model to investigate potential effects of immune system dynamics [41]–[43] , using an assumption that the immune system is more likely to be overwhelmed when receiving a large dose all at once as compared to receiving the same total exposure in a series of smaller doses over an extended period . I . e . , the per-spore infection probability would be higher after a higher single dose , thus producing a dose-response curve that is steeper than the exponential model , which assumes that the size of a single dose does not affect the per-spore infection probability . However , when they fit their model to the Brachman data ( model B3 ) , the resulting dose-response curve was only slightly different from the best fit curve under the more parsimonious exponential model ( model B2 ) . We also tested their model against the Druett data and found that , similar to the log-probit model D1 , the improvement in fit over the exponential model did not justify the decrease in parsimony under the criterion we used for model comparison [33] . These results provide some justification for recommending the simpler exponential model for use in modeling and simulation studies until the role of the immune system in preventing infection at various levels and time courses of exposure is better understood at a quantitative level . While the log-probit model D1 discussed above produces a steep dose-response curve , the log-probit model based on the Jemski data set , model J , has the most gradual slope of all models found in the literature . It produces very high infectivity estimates at low doses , significantly higher than those produced by our recommended model B4 . Wilkening [15] was unable to rule out the possibility that the shape of the model J dose-response curve was consistent with the spatial distribution of Sverdlovsk cases . Heterogeneity in host susceptibility could provide a biological explanation for a dose-response curve with a more gradual slope than the exponential model . That is , some individuals in a population might be significantly more susceptible to lower doses , while others may be able to tolerate high doses with unusually high probability . The beta Poisson dose-response model ( see Materials and Methods ) can quantify this kind of heterogeneity in a transparent manner that encompasses the mechanistic assumptions of the exponential model . However , because the raw Jemski data are not published , it is not possible to test whether alternate models would have provided a good fit , and it is possible that the low dose estimates of model J are highly extrapolated from the data points . Given this possibility , the fact that the goodness of fit for the log-probit model was not reported , and that the log-probit model does not have a defensible theoretical derivation , we feel that model B4 is better supported for use in quantitative analyses . Our models D3 and B4 also differ in key ways from previously published models fit to the same data sets . Our optimal estimate for the exponential model parameter r fit to the Druett data set ( model D3 ) is two times higher than the value calculated by Haas [18] for the same model fit to the same data set . The lower infectivity estimated in that paper results from an estimated respiration rate of 2 . 4 L/min rather than the value of 1 . 2 L/min reported and used for calculations in the original paper [27] . Our value of r for model B4 is also higher than published estimates by both Haas [18] and Mayer et al . [35] for models to the same data set ( Figure 2 ) . Our refinement demonstrates that it can be important to consider the possibility that apparently healthy animals sacrificed after being exposed might have become infected had they lived , especially if they were exposed to a pathogen like B . anthracis for which substantial incubation periods can occur . Our model B4 provides estimates for the distribution of the incubation period , that is , the time between exposure and the onset of symptoms . The estimate of 12 days ( 95% range 10–15 days ) for the median incubation period for those infected by low doses ( ID10 or less ) is consistent with the 13-day median observed among autopsy-confirmed cases after the Sverdlovsk release , for which a less than 2% attack rate was estimated . The full distribution of incubation periods is important for risk planning under a large scale release scenario , as it indicates how soon after a release cases would begin appearing , the period during which the bulk of cases would appear , and how long new cases might continue to appear toward the end of the outbreak . For example , model B4 estimates a minimum incubation period of 2 . 3 days ( 95% range 0 to 5 . 4 days ) , suggesting that no symptomatic cases would appear until at least that amount of time after an exposure event . While this estimate is primarily derived from non-human primate data , it appears to be consistent with observations of human cases . The IOM found no examples of well-documented human incubation periods less than 4 days , but there are unconfirmed reports of incubation periods as low as 2 days among Sverdlovsk cases [12] . Under a scenario similar to Sverdlovsk in which a large population is exposed to the ID1 , model B4 estimates that , in the absence of prophylactic treatment , the first 10% of cases would appear between 2 and 4 days after exposure , the middle half ( interquartile range ) of cases would appear between 6 and 22 days after exposure , and the last 10% of cases would appear over 35 days after the minimum incubation period . These estimates and their associated confidence intervals are largely consistent with the distribution of autopsy-confirmed cases after Sverdlovsk ( Figure 4 ) . The incubation period distribution produced by our model B4 is unique among others in the literature [12] , [32] , [37] in that Sverdlovsk data were not used to derive its parameter values . Nevertheless , its estimates compare quite favorably with those of the other models in capturing the distribution of autopsy-confirmed Sverdlovsk cases ( Figure 4 ) under the assumption of ID1 exposure . The other models generally predict shorter incubation periods than our model , although the curves fall within our 95% confidence region over approximately the first week after exposure . The difference might be explained by the fact that the other models optimized parameter values using larger Sverdlovsk data sets that include unconfirmed cases of unusually short incubation periods , which were questioned in a recent IOM review [12] . Our analyses also provide a framework for modeling the effects of inhaling multiple doses at different times as a natural extension to the mechanistically based competing risks model [32] . This allowed us to make use of the Brachman data consisting of irregular exposures over several weeks ( similar to Mayer et al . [35] ) , which had previously been modeled only using averaging techniques [18] in which the temporal information in the data were lost . The model is potentially useful for any pathogen in which chronic low-dose exposure is important . In model B4 , we have provided a framework for modeling the time between four key moments of disease progression: exposure , infection ( initial spore germination ) , onset of symptoms , and death ( Figure 6 ) . We designed the mathematical representation of this process both to make use of the best available data in a transparent manner and to create a parsimonious model that relies on as few free parameters as possible for adequate fitting to data . Our choice of the spore clearance rate parameter θ = 0 . 07 , which characterizes the exposure-to-infection portion of the disease progression timeline , was based on calculations from direct observation of the lungs of exposed non-human primates . Other estimates of this parameter [32] , [35] were derived from model fitting procedures that relied in part on ad-hoc assumptions of other portions of disease stage timing process . The gamma distribution we applied to quantify the time from symptom onset to death was fit to data from the best documented human cases [25]; shorter estimates derived from Sverdlovsk data may suffer from inaccurate or incomplete information from those cases [37] . Our choice of a two-parameter gamma distribution is , for our purposes , more parsimonious than the four-parameter model used by Holty et al . [25] . Their more complicated model has the benefit of separating the symptomatic period into distributions for prodromal and fulminant stages , although the individual-level data for the timing of transitions between these sub-stages are not provided , which limits reproducibility . Finally , we modeled the remaining portion , the germination-to-symptoms delay , using a single-parameter fixed delay , for simplicity . Wilkening [37] used a more complicated model for this delay incorporating dose-dependency . A large dose could cause a shorter expected delay if multiple spores germinate in a short time period , thus contributing more initial vegetative spores that undergo exponential growth towards the symptoms threshold . For lower doses , the primary focus of our paper , the probability of even one spore germinating on a given day is small , and the probability of additional spores germinating in a time frame short enough to contribute substantially to the expected delay is assumed to be negligible . As in the results of Brookmeyer et al . [39] , our estimates of the probability of infection at 60 days post-exposure based on various inhaled doses of spores provide a defensible rationale and support for the current recommendation of a 60 day duration of prophylaxis using appropriate antimicrobials after low dose exposure scenarios . For doses close to the ID1 ( 100–250 spores , by our model B4 ) , which was approximately the attack rate after the Sverdlovsk release [7] and the 2001 incident at two postal facilities and a media company [39] , an antibiotic course completed 60 days post exposure reduces the probability of infection to 0 . 015% ( about one in 7 , 000 chance ) . As illustrated in Figure 5 , applying our parameter values to the Brookmeyer equation ( stated in Materials and Methods ) can shed light on the implications of higher dose exposures for the issue of prophylactic duration , as well as the implications of shortened courses due to non-adherence to recommendations , which has been an important issue historically [44] and in public health planning for potential release events [45] . Development of extended mathematical models that incorporate variable effectiveness of antibiotics , the effects of irregular adherence patterns , and balancing decreased infection probability against adverse effects of long term antibiotic exposure [46] could be an important direction for future work . Our analysis has some limitations . In reviewing the literature , there are experimental studies of B . anthracis dose-response using mice , rabbits , and guinea pigs [47]–[49] . We have restricted our studies to non-human primate data . Components of our analyses and discussions based on data from and prior analyses of the Sverdlovsk release and other human data are subject to potential limitation of those data and analyses . Namely , epidemiologic data collected in retrospect may contain errors , and simplifying assumptions regarding the airborne transport of released spores at Sverdlovsk may have caused inaccurate representations of the exposure profile across the affected population . Finally , our quantitative estimates are largely based on data from non-human primates , which may have important differences from humans with regard to susceptibility and disease progression . However , the consistency of our incubation time model with the Sverdlovsk data offers compelling evidence for the plausibility of the model under human exposure scenarios . In conclusion , we have synthesized and improved existing inhalational anthrax dose-response models to derive defensible and plausible estimates with respect to infectious doses , incubation periods , and duration of antibiotic prophylaxis needed in the event of human exposure .
This study was reviewed by the Institutional Review Board ( IRB ) of the University of Utah and determined to be exempt from IRB oversight as the project does not meet the definitions of Human Subjects Research according to Federal regulations . We used R version 3 . 0 . 0 [50] for calculations , optimization of parameter values for fitting models to data ( standard functions optim and optimize ) , and generation of figures to display results ( standard plotting functions and the gridBase package ) , all freely available . We use or compare the following models in this paper . In the equations , I ( d ) is the probability of infection after inhaling dose d . We developed objective criteria to evaluate candidate dose-response models for human inhalational anthrax . The criteria were informed by a review of the literature , a critical evaluation of existing dose-response models for strengths/weaknesses , and discussion with members of our research team consisting of professional risk assessors , mathematical modelers , microbiologists , veterinarians , and infectious disease physicians and epidemiologists . Precedence for developing such criteria exist in the field of biodefense , specifically with regard to applying mathematical and simulation modeling to inform public health action and policy [56] . The four criteria are: For fitting to the Druett at al . [27] data , shown in Table S1 , we use three different mathematical forms for the function I ( d ) , the probability of infection if dose d is received: the log-probit model , the exponential model , and the beta Poisson model . We obtain the parameter values of these models using maximum likelihood estimation employing the binomial distribution [33] . Under this method , the optimal parameter values minimize the deviance , Y , defined as In this formula , k is the number of the experimental group ( there were nine different groups that each received different levels of exposure ) , nk is the number of animals exposed in group k , pk is the number of positive responses ( anthrax deaths ) in group k , and dk is the dose received by the animals in group k . The function B ( n , p , q ) is the probability mass function for the binomial distribution , which represents the probability of p successes in n trials when the probability of success in each individual trial is q . In essence , the parameters embedded in the I ( d ) function are being optimized so that the formula matches as closely as possible the infection rates , pk/nk , observed in the exposure groups . The data derived from the three experimental runs of Brachman et al . [24] , shown in Tables S2 , S3 , S4 , describe the doses received on each day , the days of death due to anthrax or other causes , and the number of animals found to be infected upon sacrifice . To fit these data using a time-dependent model , a conceptual framework is required for the time course of anthrax infection from exposure to death . We develop such a framework , which differs from the framework developed in [35] , as follows . The model represents three main stages of the anthrax infection timeline in humans after inhalational exposure ( Figure 6 ) , as follows , in preparation for fitting to the Brachman et al . data set . The best fit model to the Brachman data produces an estimate for the time-independent dose-response curve , which is simply the regular exponential model: I ( d ) = 1−e−rd . We use our result for the optimized parameter r and associated confidence interval for comparing our result to other dose-response curves used in the literature . We also display our model's estimates of the incubation period ( time between exposure to symptom onset among those infected ) distribution S* ( d , t ) , which is the probability of symptoms appearing by time t after a single dose d , S ( d , t ) , divided by the probability that infection occurs at any time , I ( d ) . I . e . , or We apply our best fit model parameters to the equation developed in [39] for calculating the effect of various durations of antibiotic use on reducing the probability of infection , if prophylactic medications were administered after exposure . If an individual is exposed to a dose d and adheres to an effective prophylactic regimen for τ days after exposure , the probability Q ( τ ) that infection occurs after the prophylactic regimen ends is given by This equation assumes that i ) spores cannot germinate successfully during the antibiotic course , ii ) antibiotics do not affect the clearance rate of spores that have not germinated , and iii ) spores germinating after the antibiotic course is finished would cause infection . The views expressed in this paper are those of the authors and do not necessarily represent the views of the Department of Veterans Affairs or the United States Government .
|
Anthrax poses a potential community health risk due to accidental or intentional aerosol release . We address the need for a transparent and defensible quantitative dose-response model for inhalational anthrax that is useful for risk assessors in estimating the magnitude and timeline of potential public health consequences should a release occur . Our synthesis of relevant data and previous modeling efforts identifies areas of improvement among many commonly cited dose-response models and estimates . To address those deficiencies , we provide a new model that is based on clear , transparent assumptions and published data from human and non-human primate exposures . Our resulting estimates provide important insight into the infectivity to humans of low inhaled doses of anthrax spores and the timeline of infections after an exposure event . These insights are critical to assessment of the impacts of delays in responding to a large scale aerosol release , as well as the recommended course of antibiotic administration to those potentially exposed .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"medicine",
"bacterial",
"diseases",
"infectious",
"diseases",
"anthrax",
"population",
"modeling",
"infectious",
"disease",
"modeling",
"biology",
"computational",
"biology"
] |
2013
|
Quantitative Models of the Dose-Response and Time Course of Inhalational Anthrax in Humans
|
When neurons fire action potentials , dissipation of free energy is usually not directly considered , because the change in free energy is often negligible compared to the immense reservoir stored in neural transmembrane ion gradients and the long–term energy requirements are met through chemical energy , i . e . , metabolism . However , these gradients can temporarily nearly vanish in neurological diseases , such as migraine and stroke , and in traumatic brain injury from concussions to severe injuries . We study biophysical neuron models based on the Hodgkin–Huxley ( HH ) formalism extended to include time–dependent ion concentrations inside and outside the cell and metabolic energy–driven pumps . We reveal the basic mechanism of a state of free energy–starvation ( FES ) with bifurcation analyses showing that ion dynamics is for a large range of pump rates bistable without contact to an ion bath . This is interpreted as a threshold reduction of a new fundamental mechanism of ionic excitability that causes a long–lasting but transient FES as observed in pathological states . We can in particular conclude that a coupling of extracellular ion concentrations to a large glial–vascular bath can take a role as an inhibitory mechanism crucial in ion homeostasis , while the pumps alone are insufficient to recover from FES . Our results provide the missing link between the HH formalism and activator–inhibitor models that have been successfully used for modeling migraine phenotypes , and therefore will allow us to validate the hypothesis that migraine symptoms are explained by disturbed function in ion channel subunits , pumps , and other proteins that regulate ion homeostasis .
The Hodgkin–Huxley ( HH ) model is one of the most successful models in mathematical biology [1] . This formalism , i . e . , a HH–type model , describes voltage changes across cell membranes that result in excitability . Not only neurons are excitable cells , also myocytes , pancreatic –cells , and even a plant cell ( Chara corallina ) exhibit excitable dynamics [2]–[4] . The dynamic range of phenomena includes single action potentials ( spikes ) , periodic spiking , and bursting ( slow modulation of spiking ) . For example , in pancreatic –cells bursting is induced by a calcium current [4] , [5] . A more complete treatment of this phenomenon , however , also requires the inclusion of pumps [6] . The dynamics of ion pumps and ion concentrations is also crucial for cardiac alternans ( periodic beat–to–beat variations ) and higher–order rhythms in the ischemic ventricular muscle [7]–[9] . In the literature such augmented HH–type models are also called second–generation HH models [10] . In the context of certain pathologies of the brain , whose fundamental dynamic structure we study here , we prefer the simpler name ‘ion–based’ models . This indicates that ion concentrations are major dynamical , that is , time–dependent variables . Their dynamical role in neuron models goes beyond merely modulating spiking activity . Ion dynamics can lead to a completely new type of ionic excitability and bistability , that is , the phenomena of so–called ‘spreading depolarizations’ and ‘anoxic depolarization’ , respectively . ( Spreading depolarizations are also called ‘spreading depression’ and we will use both names interchangeably in this paper . ) These depolarized states of neurons are related to migraine , stroke , brain injury , and brain death , that is , to pathologies of the brain in which a transient or permanent break–down of the transmembrane potential occurs [11] , [12] . Another even more characteristic property of this ‘twilight state close to death’ [13] are the nearly completely flat transmembrane ion gradients . The almost complete break–down of both membrane potential and—due to reduced ion gradients—Nernst potentials together cause a nearly complete release of the Gibbs free energy , that is , the thermodynamic potential that measures the energy available to the neurons for normal functioning . We hence refer to this state as a state of free energy–starvation ( FES ) . We want to stress that such phenomena require the broader thermodynamical perspective , because it goes beyond the HH description in terms equivalent electrical circuits in membrane physiology ( see discussion ) . The object of this study is to clarify quantitatively the detailed ion–based mechanisms , in particular the time–dependent potentials , leading to this condition . In fact , early ion–based models have been introduced in a different context to describe excitable myocytes and pancreatic –cells with variable ion concentrations [14]–[16] . Neuronal ion–based models have been used to study spreading depolarizations ( SD ) [17]–[22] and anoxic depolarizations [21] . In these phenomenological studies the types of ion dynamics related to the pathologies have been reproduced , but not investigated in a bifurcation analysis . Hence the fundamental phase space structure of these high–dimensional models that underlies the ionic excitability characterisitic of SD remains poorly understood . Furthermore , neuronal ion–based models have been used to study seizure activity [23] , [24] and spontaneous spiking patterns in myelinated axons with injury–like membrane damaging conditions ( e . g . , caused by concussions ) [25] , [26] . In these models , the phase space structure was investigated , however , only with respect to the modulating effect of ion concentrations on the fast spiking dynamics ( seizure activity , injuries ) , and with respect to spiking node–to–node transmission fidelity ( myelinated axons ) . In this paper we present bifurcation analyses of several minimal biophysical ion–based models that reveal bistability of extremely different ion configurations—physiological conditions vs . free energy–starvation—for a large range of pump rates . In related models certain bistabilities have been explored before . For example , Fröhlich et al . [27]–[29] found coexistence of quiescence and bursting for certain fixed extracellular potassium concentrations and also bistability of a physiological and a strongly depolarized membrane state in a slow–fast analysis of calcium gated channels . Bistability of similar fixed points has also been found for the variation of extracellular potassium [30] or , similarly , the potassium Nernst potential [31] . Also the effect of pump strength variation has been explored under fixed FES conditions [32] . In this paper , however , we do not treat slow variables as parameters and show bistability of fast dynamics , but instead we address the stability of ion concentrations themselves , which are subject to extremely slow dynamics . This allows us to find bistability of extremely different ion distributions , a feature that distinguishes these two states from the polarized and depolarized states studied in the afore mentioned work . A study that also had significantly different ion distributions was done by Cressman et al [33] , however , the seizure-like phenomena discussed in their work are quite different—though clinically related—from those presented in this paper . Because of the occurrence of ion state bistability we conjecture that our model describes a threshold reduction of a mechanism that leads to ionic excitability in form of spreading depolarizations . In other words , we conclude that an important inhibitory mechanism to describe ion homeostasis such as glial buffering or diffusive regulation of extracellular ion concentrations plays a crucial role in ion homeostasis and the pumps alone are insufficient to recover from free energy–starved states . We show that when the extracellular concentration is regulated by linearly coupling it to an infinite bath , the bistable system changes to an excitable system , which we call ionic excitability . The effect of turning off glial buffering and diffusion has been discussed in more detailed ion–based models [27] , [29] before , but has not been related to the fundamental phase space structure of the system . Our conclusions have been validated by demonstrating the robustness of the results in a large variety of minimal ion–based models , which all consistently show this insufficiency of pumps , and also in a very detailed membrane model that has been used intensively for computational studies of spreading depolarizations and seizure–like activity [17] , [34] .
A simple ion–based neuron model can be obtained as a natural extension of the Hodgkin–Huxley ( HH ) model [1] . We list the basic equations of HH that we used for the sake of completeness , and also comment on two often used model reductions of which one must be modified for our study . Furthermore leak currents are specified , which is necessary for the extension towards ion–based modeling . In the HH model , single neuron dynamics is described in terms of an electrically active membrane carrying an electric potential , and the three gating variables , and that render the system excitable . Ion species included are sodium , potassium , and an unspecified ion carrying a leak current , which can be attributed to chloride in our extended model . The rate equations read [1]: ( 1 ) ( 2 ) ( 3 ) ( 4 ) The top equation is simply Kirchhoff's current law for a membrane with capacitance and membrane potential . is an externally applied current that may , for example , initiate voltage spikes . The gating variables , , and are the potassium activator , sodium inactivator , and sodium activator , respectively . Their dynamics is defined by their voltage–dependent asymptotic values and relaxation times ( , , ) . These are given byHere is a common timescale parameter , and the Hodgkin–Huxley exponential functions are ( 5 ) ( 6 ) ( 7 ) ( 8 ) ( 9 ) ( 10 ) The individual ion currents read ( 11 ) ( 12 ) ( 13 ) with denoting leak and gated conductances . In fact , Hodgkin and Huxley set up their model with an unspecified leak current and non–leaking sodium and potassium channels . As long as ion dynamics is not considered this is mathematically equivalent to specifying the leak current as being partially sodium , potassium and chloride , but it is physically inconsistent because the reversal potentials for the ions differ . In an ion–based approach , however , the main task of the ion pumps under physiological conditions is to compensate for sodium and potassium leak currents ( see next section ) while gated currents are extremely small in the equilibrium . So at this point leak currents for all ion species are important . The Nernst potentials are given in terms of the ion concentrations in the intracellular space ( ICS ) and the extracellular space ( ECS ) denoted by subscripts and , respectively: ( 14 ) for , , and and is the ion valence . All model parameters are listed in Table 1 . The units chosen are those typically used and appropriate for the order of magnitude of the respective quantities . Time is measured in , potentials in , and ion concentrations in . The units for conductance densities imply that ionic and pump current densities are in . For better readability we omit the square brackets on the ion concentrations and simply write , , and . For this model is monostable with an equilibrium at . Note that and imply that under equilibrium conditions neither nor vanish , but only their sum does . Sufficiently strong current pulses can—depending on their duration—initiate single voltage spikes or spike trains . Constant applied currents can drive the system to a regime of stationary oscillations . The minimal current required for this is usually called rheobase current . The HH model can be reduced to two dynamical variables in a way that preserves these dynamical features . One common simplification [35] is to eliminate the fastest gating variable adiabatically and set ( 15 ) Second , there is an approximate functional relation between and that is usually realized as a linear fit [36] . The ion–based model presented in this article , however , contains a stable fixed point with large , and a linear best fit would then lead to a negative . Therefore we will use the following sigmoidal fit to make sure is non–negative: ( 16 ) After this reduction the remaining dynamical variables are and . While in the original HH model ion concentrations are model parameters , in ion–based modeling intra– and extracellular ion concentrations become dynamical variables , which causes the Nernst potentials to be dynamic . The model defined by the rate eqs . ( 1 ) , ( 2 ) and contraint eqs . ( 15 ) , ( 16 ) can straightforwardly be extended to make ion concentrations dynamic since currents induce ion fluxes . However , under those equilibrium conditions found in HH neither nor . Hence we need to include ion pumps [15] to make sure that the rate of change in ion concentration inside the cell ( ) and extracellular ( ) can vanish in the resting state ( ) . The rate equations for ion concentrations in the intracellular space ( ICS ) are then ( 17 ) ( 18 ) ( 19 ) The factor converts currents to ion fluxes and depends on the membrane surface and Faraday's constant : ( 20 ) Dividing the ion fluxes by the ICS volume gives the change rates for the ICS ion concentrations . The pump current represents the ATP–driven exchange of ICS sodium with potassium from the extracellular space ( ECS ) at a –ratio . It increases with the ICS sodium and the ECS potassium concentration . Chloride is not pumped . We are using the pump model from [23] , [24]: ( 21 ) where is the maximum pump current . As a consequence of mass conservation ion concentrations in the ECS can be computed from those in the ICS [33]: ( 22 ) with the ECS volume . Superscript zero indicates initial values . Since all types of transmembrane currents , i . e . , also the pumps , must be included in eq . ( 1 ) for the membrane potential , we have to add the net pump current : ( 23 ) The rate equations for the ion–based model are thus given by eqs . ( 2 ) , ( 17 ) – ( 19 ) , ( 23 ) . These rate equations are complemented by the gating constraints eqs . ( 15 ) , ( 16 ) and the mass conservation constraints ( 24 ) ( 25 ) ( 26 ) Dynamic ion concentration imply that the Nernst potentials in eqs . ( 11 ) – ( 13 ) are now dynamic ( see eq . ( 14 ) ) . The additional parameters of the ion–based model are listed in Table 2 . The morphological parameters and are taken from [17] . In cortical ion–based models , the extracellular volume fraction ranges from in [17] to in [21] . In experimental studies , is about , a value that can increase , for example , in focal cortical dysplasias type II , a frequent cause of intractable epilepsy , to [37] or during sleep to ( the latter only , if we transfer the increase observed in mouse data to human ) [38] . It is important to note that in experimental studies , the extracellular volume fraction refers to the fraction with respect to the whole tissue , which includes also the glial syncytium . Assuming equally sized neuronal and glial volume fractions of 40% each , an experimentally measured value of would in our model , which does not directly include the volume of the glial syncytium , correspond to or 33% . We choose an intermediate value of for , but address the influence of the volume ratio in Sec . Results . We prefer to give these morphological parameters in the commonly used units which are appropriate to their order of magnitude rather than unifying all parameters , e . g . the cell volume is given in instead of which ion concentrations are related to . Consequently from Table 2 must be multiplied by a factor of to correctly convert currents to change rates for ion concentration in the given units . Because of the extremely small value of the membrane dynamics , i . e . , the dynamics of and , is five orders of magnitude faster than the ion dynamics . A consequence of this large timescale separation is that the system will attain a Donnan equilibrium when the pumps break down . The Donnan equilibrium is a thermodynamic equilibrium state ( not to be confused with merely a fixed point , though it is one ) that is reached for ion exchange across a semipermeable membrane . Since we have not explicitly included large impermeable anions inside the cell , this is at first surprising . For no applied currents and , the ion rate equations imply that an equilibrium requires all ion currents to vanish . Since conductances are strictly positive it follows that all Nernst potentials and the membrane potential must be equal . Ion concentrations will then adjust accordingly . However , eqs . ( 17 ) – ( 19 ) and ( 23 ) imply the following constraint on the ICS charge concentration : ( 27 ) where denotes the difference between the initial and final value of a variable . Since is very small , changes in ion concentrations must practically satisfy electroneutrality . This condition together with the equality of all Nernst potentials defines the Donnan equilibrium , so we see that it is contained in our model as the limit case with no pumps and no applied currents . It should be noted that this observation provides a necessary condition for the correctness of biophysical models . In this extension of the HH model the ion dynamics makes Nernst potentials time–dependent . The simultaneous effect of a diffusive and an electrical force acting on a solution of ions is described more accurately by the Goldman–Hodgkin–Katz ( GHK ) equation though . Nevertheless we prefer Nernst currents , because this formulation allows us to use well–established conductance parameters so that the model is completely defined by empirically estimated parameters . In Sec . Results we will see how GHK currents can be modelled and that the qualitative dynamical behaviour of the system is not affected .
In the ion—based model introduced above current pulses can still initiate voltage spikes ( not shown ) . However , extremely strong pulses , in fact comparable to those used in [17] to trigger spreading depolarizations , can drive the system away from the physiological equilibrium to a second stable fixed point that is strongly depolarized ( see Fig . 1 ( a ) ) . This is a new dynamical feature . The depolarized state can also be reached when the ion pumps are temporarily switched off ( see Fig . 1 ( b ) ) . Apart from the depolarization this state is characterized by almost vanishing ion gradients . This free energy–starvation ( FES ) is reminiscent of the Donnan equilibrium . Extracellular potassium is increased from to more than while the extracellular sodium concentration is reduced from to less than . The gated ion channels are mostly open ( potassium activation is % ) , and it is no longer possible to initiate voltage spikes . In this section we will present a phase space analysis of the model and derive conditions for the observed bistability between a physiological equilibrium and a state of FES . Note that the transition from the physiological state to FES happens via ion accumulation due to spiking , and we will see in Sec . Results that indeed the membrane ability to spike is a necessary condition for the bistability . Similar processes of ion accumulation were regarded as unphysiological in modelling of cardiac cells [8] , but are familiar in cortical neurons where ion accumulation is central to seizure–like activity [24] , [33] and spreading depression [17] ( SD ) . In fact , we will briefly demonstrate how the bistability relates to local SD dynamics . The ion–based model we have analysed so far has been motivated as a natural extension of the Hodgkin–Huxley membrane model . However , there are different variants of ion–based models [17]–[24] , [32] that use different pump and current models , ion content , and ion channels . We will hence address the question how general our results are in this respect . Furthermore we vary the geometry–dependent parameters ( membrane surface and extracellular volume fraction ) continuously to test their effect on the phase space , too .
Computational neuroscience complements experimental and clinical neuroscience . Simulations help to interpret data and guide a principal understanding of the nervous systems in both health and disease . The HH–formulation of excitability was “so spectacularly successful that , paradoxically , it created an unrealistic expectation for its rapid application elsewhere” as Noble remarked [9] . While his statement refers to modeling of cardiac cells it certainly holds true also for neurological diseases and brain injury [11] , [13] . In both fields , the incorporation of the Na+/K+ pump in the original excitability paradigm formulated by Hodgkin and Huxley is of major importance . The fundamental structure of such models has to our knowledge not been exploited in neuroscience beyond merely modulating spiking in epileptiform activity [23] , [24] or in models that have energy–starved states [17]–[22] , [32] yet without investigating the fundamental bifurcation structure . As we stressed in the introduction , this extension of the original HH model enforces a physical or rather thermodynamical perspective , which was , of course , the starting point of Hodgkin and Huxley , too . For instance , we also considered the Goldman–Hodgkin–Katz ( GHK ) current equation which is derived from the constant field assumption applied to the Nernst–Planck equation of electrodiffusion . Electroneutrality is important to consider , as can be seen by the indirect insertion of impermeable counter anions only reflected by observing a thermodynamic Donnan equilibrium . Furthermore , a thermodynamic description of osmotic pressure ( which would require a direct insertion of a concentration of a counter anion with valence ) and corresponding changes in cell volume can be included . There are further physical mechanisms that may alter the dynamics in biophysical ion–based models . At the same time , we have to avoid “an excruciating abundance of detail in some aspects , whilst other important facets […] can only be guessed” [41] , like using various new currents but guessing the correct value of the valence of an impermeable counter anion . For this reason , we decided to use the original ion currents from the HH model . The comparison of our results to a physilogically more realistic and much more detailed membrane model in Fig . 7 support the assumption that the basic structure will not be changed by just adding or modifying gating . This question has also been addressed experimentally and in simulations by showing that only the simultaneous blockade of all known major cation inward currents did prevent hypoxia–induced depolarization with FES [17] , [42] . In the model [17] , five different currents were investigated . Of course , to apply our model to a particular pathological condition , like migraine which is a channelopathy [43] , [44] ( disease caused by modified gating ) , these details will become important . This can easily be incorporated in future investigations . Moreover , note that changes in cell volume , which are very important in brain injuries , are in this study only treated by varying it as a parameter . Our bifurcation analysis shows that a whole class of minimal ion–based models is bistable for a large range of pump rates ( ) . Bistable dynamics was suggested by Hodgkin to explain spreading depression [11] , [12] , and a corresponding model has been investigated mathematically by Huxley but never been published ( cf . Ref . [45] ) . Dahlem and Müller suggested to extend this ad hoc approach , i . e . , a single so–called activator variable with a bistable cubic rate function , by including an inhibitory mechanism in form of an inhibitor species with a linear rate function coupled to the activator [45] . This , of course , leads to the well known FitzHugh–Nagumo paradigm of excitability type II [46] , [47] , that is , excitability caused by a Hopf bifurcation [48] , but should not be mistaken as a modification of conductance–based excitability in form of HH–type model in the ‘first generation’ and the interpretation as an equivalent electrical circuit . FitzHugh used his equation in this way , he investigated a long plateau as seen in cardiac action potentials . Dahlem and Müller suggested to use the same mathematical structure of an activator–inhibitor type model [45] to describe a fundamental new physiological mechanism of ionic excitability that originates from bistable ion dynamics . Our current results provide the missing link between this ad hoc activator–inhibitor approach , which has been widely used in migraine and stroke pathophysiology [45] , [49]–[58] , and biophysical plausible models . The major result from this link is the new interpretation of the physiological origin of the proposed inhibitory variable [45] . We wrongly interpreted it as being related to the pump rate [49] , [51] , [53] , [57] . As our ion–based model shows bistable dynamics , we see it as essentially capturing the activator dynamics of an excitable system and briefly show in Figs . 2 and 3 that it can be transformed into such a system by the introduction of a inhibitory process . Vice versa , excitable systems can be reduced to bistable dynamics by singular perturbation methods . Such reductions are referred to as a threshold reduction . From this perspective our model can be interpreted as the threshold reduction of an excitable system , and we conclude that without contact to an ion bath , physically realistic ion–based models miss an important inhibitory mechanism . Our analysis shows that unlike what we thought before [49] , [51] , [53] , [57] , ion pumps alone are insufficient . If the pump rate is temporarily decreased to less than the minimal physiological rate , the neuron depolarizes , and normal pump activity does not suffice to recover the physiological state . Depending on the particular model the required recovery pump rates range from three times up to more than 30 times the original value . These high values suggest that also more detailed pump models that , for example , include the coupling of the maximal pump rate to oxygen or glucose [22] will not resolve this bistability . It can , however , also be seen that a regulation term for the extracellular ion concentrations that mimics glial buffering and coupling to the vasculature will allow only monostability . An additional diffusive coupling to a bath value in the extracellular rate equations forces all such buffered extracellular species to assume the respective bath concentrations . There are no two points on the solution branch that share the same extracellular potassium concentrations ( see Fig . 4 ) . Hence one fixed point is selected , the other state becomes unstable . We consequently suspect that coupling to some bath ( glia/vasculature ) plays a crucial role in maintaining ion homeostasis and our results from Figs . 3 and 4 confirm that an ion–based model including such coupling will recover from superthreshold perturbations by a large excursion in phase space that is characterized by long transient free energy–starvation .
|
Theoretical neuroscience complements experimental and clinical neuroscience . Simulations and analytical insights help to interpret data and guide our principal understanding of the nervous systems in both health and disease . The Hodgkin—Huxley–formulation of action potentials is certainly one of the most successful models in mathematical biology . It describes an essential part of cell–to–cell communication in the brain . This model was in various ways extended to also describe when the brain's normal performance fails , such as in migraine hallucinations and acute stroke . However , the fundamental mechanism of these extensions remained poorly understood . We study the structure of biophysical neuron models that starve from their ‘free’ energy , that is , the energy that can directly be converted to do work . Although neurons still have access to chemical energy , which needs to be converted by the metabolism to obtain free energy , their free energy–starvation can be more stable than expected , explaining pathological conditions in migraine and stroke .
|
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"Abstract",
"Introduction",
"Model",
"Results",
"Discussion"
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[
"neurology",
"biology",
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"life",
"sciences",
"medicine",
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2014
|
Bistable Dynamics Underlying Excitability of Ion Homeostasis in Neuron Models
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Late-onset Alzheimer's disease ( LOAD ) is the most common form of dementia in the elderly . The National Institute of Aging-Late Onset Alzheimer's Disease Family Study and the National Cell Repository for Alzheimer's Disease conducted a joint genome-wide association study ( GWAS ) of multiplex LOAD families ( 3 , 839 affected and unaffected individuals from 992 families plus additional unrelated neurologically evaluated normal subjects ) using the 610 IlluminaQuad panel . This cohort represents the largest family-based GWAS of LOAD to date , with analyses limited here to the European-American subjects . SNPs near APOE gave highly significant results ( e . g . , rs2075650 , p = 3 . 2×10−81 ) , but no other genome-wide significant evidence for association was obtained in the full sample . Analyses that stratified on APOE genotypes identified SNPs on chromosome 10p14 in CUGBP2 with genome-wide significant evidence for association within APOE ε4 homozygotes ( e . g . , rs201119 , p = 1 . 5×10−8 ) . Association in this gene was replicated in an independent sample consisting of three cohorts . There was evidence of association for recently-reported LOAD risk loci , including BIN1 ( rs7561528 , p = 0 . 009 with , and p = 0 . 03 without , APOE adjustment ) and CLU ( rs11136000 , p = 0 . 023 with , and p = 0 . 008 without , APOE adjustment ) , with weaker support for CR1 . However , our results provide strong evidence that association with PICALM ( rs3851179 , p = 0 . 69 with , and p = 0 . 039 without , APOE adjustment ) and EXOC3L2 is affected by correlation with APOE , and thus may represent spurious association . Our results indicate that genetic structure coupled with ascertainment bias resulting from the strong APOE association affect genome-wide results and interpretation of some recently reported associations . We show that a locus such as APOE , with large effects and strong association with disease , can lead to samples that require appropriate adjustment for this locus to avoid both false positive and false negative evidence of association . We suggest that similar adjustments may also be needed for many other large multi-site studies .
Alzheimer's disease ( AD , MIM 104300 ) is by far the most common form of dementia in the elderly . Late onset Alzheimer's disease ( LOAD ) , defined by the onset of symptoms after age 60 years , has annual incidence rates increasing from 1% at 65–70 years to 6–8% at 85 years and older [1] . By age 85 years and up , prevalence is 10–30% or more [2] . While the underlying causes of LOAD are still unknown , there is ample evidence for genetic factors affecting risk , including high estimated heritability of LOAD ( 58–79% ) [3] , and evidence from both twin [4] , [5] and family studies [6]–[9] . A small number of genes have been identified in which variation contributes to Alzheimer's disease risk . Multiplex early-onset Alzheimer's disease ( EOAD ) pedigrees [10]–[12] facilitated the identification of mutations in three genes: the amyloid precursor protein ( APP ) [13] , presenilin 1 ( PSEN1 ) [14] and presenilin 2 ( PSEN2 ) [15] . In contrast to the success in familial EOAD , only one gene , APOE , is an unequivocally established “susceptibility” gene for LOAD [16] , with the ε4 allele associated with increased risk in a dose-dependent manner and the ε2 allele with decreased risk [17] . There is incomplete lifetime penetrance even in the highest-risk APOE genotypes [18] , and the fraction of genetic variance for LOAD risk attributed to APOE is estimated as only 10–20% [19] , [20] . This , coupled with results of oligogenic segregation analyses supporting the presence of at least 4–6 additional major genes [21] , [22] , suggests that additional risk loci remain to be discovered . Multiple approaches have been used to identify additional loci contributing to LOAD . Several regions have been implicated as a result of multiple linkage-based genome scans [23]–[32] . With the exception of the APOE gene region , there is only modest overlap among the chromosomal regions identified by different analyses [33] , and it has been difficult to identify causal variants . Multiple genome-wide association studies ( GWAS ) of unrelated subjects have now also been carried out [34]–[44] . With the exception of single nucleotide polymorphisms ( SNPs ) near APOE , all associated SNPs in these studies have had small estimated effect sizes , with odds ratios reported in the range of 1 . 1 to 1 . 5 , and also with little overlap among studies . Such estimated odds ratios are likely to be highly inflated , and the true effects much smaller [45] , complicating replication and identification of causal variants . However , among recent GWAS studies , a small number of loci have shown some evidence for replication across samples including clusterin ( CLU ) , phosphatidylinositol binding clathrin assembly protein ( PICALM ) and complement component ( 3b/4b ) receptor 1 ( CR1 ) [42]–[44] . The use of densely affected families with LOAD , which are expected to carry higher frequencies of risk alleles , is an excellent alternative method of identifying additional genes contributing to LOAD . For example , the APOE-ε4 frequency is higher in LOAD cases with a positive family history than in sporadic LOAD cases [46] , [47] . Compared to the more typical use of unrelated subjects , often without a family history of LOAD , family-based designs may enrich for variants with higher penetrance and consequent increase in odds ratios , and thus increase the power for their detection [48] . Such families can be used in both linkage and association-based designs , with appropriate correction for inclusion of related individuals [38] . Here we present results from a GWAS in multiplex LOAD families . Unlike many other studies , unaffected relatives were also evaluated and are included to increase the amount of genetic information and to provide additional phenotypes that can be used in subsequent analyses . A supplementary control group consisting of unrelated individuals was also recruited and underwent the same phenotypic evaluation . Thus , this cohort represents the largest family-based GWAS of LOAD to date , allowing us to explore issues related to stratification as well as providing a powerful approach for detailed modeling of the effects of APOE in the search for other novel risk loci in LOAD . The genotypic and phenotypic data generated in this study are part of the NIA-LOAD/NCRAD family study ( http://ncrad . iu . edu ) and are available to the research community through dbGaP ( http://www . ncbi . nlm . nih . gov/gap ) . Biological samples from these well-characterized individuals and families are also available through NCRAD . Our results implicate a new region on chromosome 10p in individuals with the APOE ε4/ε4 genotype , and provide support for some of the recently implicated loci . They also suggest that sample structure and ascertainment bias related to the strong APOE association with AD risk are important confounders . This affects the interpretation of some of the recently implicated loci as well as other GWAS studies of LOAD .
We performed bioinformatics analysis to identify genes that are located near the top SNP signals from our own GWAS , or genes that are biologically related to the genes at the SNP locations . For this purpose , we used SNAP and GRAIL ( http://www . broadinstitute . org/mpg/grail/grail . php ) to identify candidate genes . SNAP ( http://www . broadinstitute . org/mpg/snap/ldsearch . php ) identifies genes , extending in both directions until r2<0 . 5 , while GRAIL searches for genes that are in the SNP region and that are biologically related to each other based on the published literature . As an independent replication of the CUGBP2 association identified in the main analysis in the presence of APOE ε4 homozygotes , we examined a combined sample consisting of a Caribbean Hispanic cohort , and subjects from the combined Washington University case-control dataset and the Alzheimer's Disease Neuroimaging Initiative ( WU-ADNI ) [77] . These datasets were chosen because all had been genotyped on one of the Illumina platforms and shared multiple SNPs . The Caribbean Hispanic sample comprised 549 cases and 544 controls from two studies , including the Washington Heights-Inwood Columbia Aging Project ( WHICAP ) study [78] and the Caribbean Hispanic family study of familial AD [79] . The WU-ADNI data set comprised 788 EA cases and 643 EA controls . For the replication analysis , we used a conservative sample of 231 cases and 187 controls from the Caribbean Hispanic sample and 386 cases and 386 controls from the WU-ADNI sample , restricting subjects to homozygotes for each of the APOE ε4 and ε3 alleles , respectively , to avoid the heterogeneity caused by pooling different APOE genotypes that was identified in our primary analysis . While the Caribbean Hispanic sample is ethnically different that the European-American NIA-LOAD sample , this is advantageous since it reduces the probability of inflated evidence for association due simply to the shared ancestry of repeated samples from the same population [80] . SNP genotypes were from the Illumina HumanHap 650Y panel ( Caribbean Hispanics ) and several different Illumina platforms ( WU-ADNI ) . We evaluated a total of all 24 SNPs in CUGBP2 that were genotyped in all of these samples as well as in the NIA LOAD sample . APOE genotyping was based on the same method as that for the NIA-LOAD cohort , or as described elsewhere [57] . We tested possible association of SNPs in CUGBP2 on LOAD risk in joint analyses across cohorts . For our primary analysis , we analyzed only rs201119 in the independent replication cohorts because this SNP gave the strongest genome-wide-significant evidence for association in the NIA-LOAD/NCRAD sample in the APOE ε4/ε4 stratum . For this analysis we did not apply a multiple testing correction because it was the single primary SNP tested for replication . In a second analysis we carried out analysis of all 24 SNPs that were available in both the replication and original cohorts , using a Bonferroni correction for multiple testing . In a final analysis , we combined the CCun component of the original NIA LOAD/NCRAD cohort with the replication sample , and carried out the same joint analysis using all of the cohorts . The analysis model used was stimulated by the APOE genotype-specific association identified in the main sample , which suggested an interaction between APOE ε4/ε4 and rs201119: we used logistic regression with an additive model for cohort , number of SNP alleles , APOE genotype ( ε3/ε3 vs . ε4/ε4 ) , and an interaction between the SNP and APOE , testing for both a SNP main effect and an interaction with APOE genotype . The component of the analysis of interest here was the interaction coefficient , given the original results that suggested such an interaction .
The final genotyped NIA-LOAD/NCRAD cohort consisted of 5 , 220 subjects . The complete sample was ethnically diverse , with 4 , 232 who were self-declared European-Americans , and the remainder 180 self-declared African American subjects , 309 Hispanic subjects , 300 subjects with other backgrounds , and 199 subjects with no self-reported race and ethnic information . Some individuals clustered with a group other than their self-reported group , leaving 3839 individuals ( Table 1 ) that clustered as European-Americans based on a principal components analysis of all unrelated subjects ( Figure 1 ) and were used in the CCall sample . Of the 3 , 839 European-American subjects , 993 cases and 884 controls were used in the CCun sample . As expected in a geographically distributed sample from North America , the fraction of subjects from any one self-reported ethnic group varied across collection site . The European-American-specific principal components ( PCs ) revealed substructure within the sample . Although apparent with the first two principal components ( PC1 and PC2 ) , three subgroups were most clearly defined by the first and fourth principal components ( Figure 2 ) . Estimated fractions of each subpopulation varied across sites ( Table 2 ) , with the NW group the largest ( 90 . 2% ) sample ( Table 2 ) . A few subjects fell between the main clusters , and were excluded in subsequent subgroup analyses ( Figure 2 ) . Subgroup assignments were strongly supported by likelihood computations based on European subgroup-specific AIMs , and by comparison of allele frequencies in the three groups with those of the AIMs . Large between-group allele frequency differences between the NW and other groups near lactase on chromosome 2 and HLA on chromosome 6 [81] further supported these subgroup assignments: e . g . , allele frequency differences >0 . 55 for SNPs near lactase , as do overall comparison of allele frequency differences between pairs of populations . Although the median allele frequency difference was relatively low ( <0 . 04 ) for all three pairs of populations ( Figure 3A ) , 7% , 9% and 12% of the markers had a substantial allele frequency difference of >0 . 1 in the NW-SE , AJ-SE , and NW-AJ comparison , respectively . These larger allele frequency differences coupled with varying fractions of cases from the different contributing sites ( Table 2 ) predispose to confounding . APOE allele frequencies also differed among the three sub-groups , along with a higher fraction of cases relative to the subgroup sample size drawn from the AJ and SE sub-groups than the NW sub-group ( Table 3 ) . The allele frequencies in the unrelated controls varied in a manner that is consistent with a known north-south ε4 allele frequency gradient , with higher ε4 allele frequencies in northern than southern European populations [82]–[84] , and with lower ε4 frequencies reported in Jewish populations [85] , [86] . In these unrelated controls , the ε4 allele frequency was higher in subjects of NW ancestry ( 0 . 139 ) than in subjects of SW ( 0 . 109 ) or AJ ( 0 . 092 ) ancestry , with the same allele frequency patterns also apparent in the unrelated ( control ) family members , and in the affected individuals ( cases ) . The cumulative distribution of European-American PC4 values in the whole European-American sample differed among APOE genotypes in a manner that was also consistent with existence of sub-structure ( Figure 3B ) , with similar results observed in the NW group alone ( not shown ) . The distribution of p-values obtained from the unadjusted genome scans deviated from a uniform distribution , suggesting the presence of uncorrected confounding . This effect was mild near the median test value ( λ = 0 . 97 for CCun , λ = 1 . 02 for CCall ) but more apparent in the tails of the distribution , providing evidence for potential confounding in analysis of both samples ( Figure 6A , 6B; magenta points; Figure S2 ) . Some of the deviation from the null distribution is likely to be attributable to the greater sensitivity to HWD for the allele-based tests than for logistic regression in CCun ( Figures S2 , S3 , CCun results ) . However , deviation from the null distribution in the direction of an increased type I error over the nominal level was especially marked in the upper 0 . 1% of the tail of the distribution for the unadjusted analysis of the CCun sample even under analysis with logistic regression ( Figure S4 ) , and in the upper 1% for the CCall sample ( Figure 6A , 6B , magenta points ) . The excess fraction of small p-values was not explained by SNPs in the APOE region ( Figure 6C ) , some of which had , as expected , much more extreme p-values . This deviation from the null distribution was not explained by inadequate correction for relationships in the CCall sample since the same excess pattern of extreme p-values occurred in the analysis of both the CCun and CCall samples , and over a wider range of p-values when the CCun sample was analyzed with a chi-square test instead of with logistic regression ( Figure S2 ) . Control for test statistic inflation was also not achieved by incorporation of the first four principal components as covariates [64] ( Figure 6A , grey points; Figure S3 ) , or by restricting analysis to the more uniform NW group ( Figure 6B , grey points ) . Two sources of evidence suggested that an important source of potential confounding was APOE genotype . The first was the effect of adjustment for APOE genotype , which had a notable effect on the distribution of resulting genome-wide p-values . Simple adjustment of APOE through binary ε4-status yielded a distribution of p-values that was closer to a uniform distribution than was obtained from unadjusted analysis . However , deviation from the expected null distribution was still evident ( Figure 6A , 6B , cyan points ) , and there was still evidence for association with SNP rs2075650 near APOE ( Figure 6C ) in both the unrelated and related samples ( p = 1 . 5×10−9 for CCun , and p = 1 . 2×10−7 for CCall ) . The full APOE adjustment achieved the best control of the null distribution of p-values ( Figure 6A , 6B , black points ) , and produced close to the expected uniform distribution of p-values under the null distribution ( Figure S3 ) . Addition of the PCs as covariates alone did not produce the desired distribution of p-values ( Figure S4 , Table S7 ) and in addition to the full APOE adjustment in the CCun sample did not provide further improvement to the distribution of p-values over the APOE adjustment ( Table S8 versus Table S3 ) . This analysis also eliminated all statistically significant association with SNPs in the APOE region ( Figure 6C ) , and evidence for adequate genomic control within each APOE stratum was reasonable ( λ = 0 . 997 , 1 . 02 , 1 . 009 , 1 . 003 for the ε4/ε4 , ε4/ε3 , ε3/ε3 and ε3/ε2+ε2/ε2 strata , respectively ) . A second source of evidence for confounding or population stratification was obtained from the results from the case-only analysis: the genome-wide distribution of p-values from the allele frequency comparison in ε4 carriers vs . non-carriers in the case-only sample also showed an overall deviation from the expected null distribution in the direction of an excess of small p-values ( Figure 7 ) . This indicates that there are many markers that are correlation with APOE in the highly-ascertained case sample .
Analysis of the NIA-LOAD/NCRAD sample indicates that unraveling susceptibility to LOAD is complex even when individuals from genetically-loaded multiplex families are included . As with other studies , support for the association between LOAD and SNPs near APOE was strong . By taking advantage of this association , we were able to identify a potential novel locus , CUGBP2 , on chromosome 10p14 with genome-wide significant evidence of association within the highest-risk APOE ε4/ε4 stratum , with replication in an independent sample . We also found support for association with recently-reported SNPs in CLU and BIN1 , and to a lesser extent with CR1 . However , we found that the strong APOE association also introduced a source of structure into the sample that had effects that were detectable through standard evaluation of analysis results . Our results provide strong evidence that this correlation with APOE explains the association in this sample with some , but not all , previously-noted SNPs , including PICALM and the recently-proposed association near EXOC3L2 , both of which have significantly different allele frequencies in AD cases who are carriers vs . non-carriers of the APOE ε4 allele . Detection of true risk loci in a GWAS of LOAD requires careful attention to potential sampling biases [87] . Large samples such as ours are necessary for detecting modest associations , but such samples usually involve multiple collection sites , introducing the potential for confounding or other complications . Consistent with this , across our participating sites we found variability in the numbers of cases and controls , the fraction of underlying identifiable ethnic subgroups , differences among subgroups in terms of APOE genotype frequencies , and differences in APOE genotype distributions as a function of an indicator of genetic differentiation . None of this is surprising , given the history of US colonization and immigration coupled with differentiation among European populations [81] , [88] . Other large samples in Europe and other locations are likely to have similar issues , as suggested by genome-wide inflation factors reported by recent studies [42] , [43] that were higher than those in our study . Appropriate accommodation for confounding or structure when it is present can provide both protection against false positive associations , as well as increased power to detect associations that are confined to a subset of the sample , as we have demonstrated as part of our investigations surrounding the influence of APOE on our results . We also found that common methods failed to provide the necessary correction for APOE-induced associations , including use of principal components adjustment [64] and genomic control [74] . Together these observations have important implications for interpretation of results from other large combined samples . Accommodation for APOE genotype was key for obtaining appropriate genomic control in our sample . Incorporation of individual APOE genotypes , as opposed to the more typical use of presence or absence of ε4 , resulted in the closest approximation to a uniform distribution of p-values over a wide range of the test results . This likely resulted in a reduction in false positive association results since such control must be achieved before accepting evidence of association . Not only were our genome-wide results impacted by adjustment for APOE genotypes , but the support for some SNP associations from previous studies was similarly affected . For the SNPs that were most sensitive to APOE-adjustment , the allele frequencies differed among cases as a function of APOE genotype , suggesting a relatively simple diagnostic for which SNPs require adjustment for APOE as part of the analysis: for such SNPs , a full adjustment for APOE genotype may be critical for genomic control in part because of allele frequency differences among populations [82] , [89] . These differences could lead to structure in the ascertained sample through variability in disease risk or survival in underlying subpopulations , as seen across the subpopulations identified in this sample . It thus may represent a corollary to confounding through ascertainment of cases , possibly related to the effects discussed by Voight and Pritchard [90] . Alternatively , it may represent statistical interaction resulting from population stratification , which can create mild linkage-disequilibrium between many markers that are on different chromosomes , with the strongest such LD occurring between loci with the largest frequency differences across populations . Such genome-wide effects of population stratification have recently been demonstrated both in simulated data , and in breast cancer , where there is association , detectable in cases , between SNPs in LCT and genome-wide SNPs , with a similar genomewide shift in the distribution of p-values [91] . Such adjustments for loci with strong effects may also be important in other diseases with such strong risk loci . Stratification on APOE genotype did facilitate the identification of a novel region with genome-wide significant evidence for association on chromosome 10p14 , which replicated in a second sample consisting of three additional cohorts . This region was identified only in the APOE ε4/ε4 stratum or in a logistic analysis that contrasted ε4 and ε3 homozygotes in a model with an interaction term with APOE . The relative infrequency of ε4 homozygotes means that these results will need to be further investigated in other large data sets to determine its importance . Data sets that consist of high-risk families , such as our sample and the NIMH AD sample [92] , may be preferable in such analyses , since such sample ascertainment may have contributed to the detection of this locus through the resulting presence of a relatively high fraction of APOE ε4 homozygotes . It is also worth noting that an earlier linkage analysis of a subset of the families used here , based on the Illumina 6K mapping panel , obtained lod scores for rs1537626 of 2 . 35 in the whole sample and 1 . 6 in an analysis that retained only APOE ε4-positive cases . This SNP is within 10 cM of rs201119 [93] . This SNP was not on the marker panel used here , nor was rs201119 on the earlier 6K marker panel , preventing further comparison of results . It is also possible that analysis within the high-risk APOE ε4/ε4 genotype improved detection of this region in the current study by increasing the within-genotype penetrance , possibly by affecting age-at onset . If so , this would be similar to the strategy of identifying risk- or age-at-onset modifier loci on a background of a single , early-onset AD mutation [94]–[96] . The implicated region on chromosome 10p14 contains the genes CUGBP2 and PITRM1 . CUGBP2 has one isoform that is expressed predominantly in neurons , with experimental evidence suggesting involvement in apoptosis in the hippocampus [97] , with both these observations consistent with a role in pathogenesis of Alzheimer's disease . PITRM1 can degrade amyloid β4 APP protein when it is accumulated in mitochondria [98] . Our results both support and refute recently proposed association with SNPs in several genes [42]–[44] . Evidence for association with SNPs previously reported in each of BIN1 , CLU , and CR1 was relatively robust to APOE adjustment within this European-American sample , with evidence for BIN1 and CR1 also obtained across an analysis that conditioned on ethnic background . Recent reports by others that include portions of the sample we used here also report evidence for association with PICALM [99] , [100] , but did not report the results of quality control analyses that allow evaluation of adequacy of correction for confounding . In our analyses , with correction for sources of confounding , evidence for association with SNPs in PICALM and EXOC3L2 was much less convincing than for these other three loci because of the exquisite sensitivity to APOE adjustment . One interpretation of sensitivity of these associations to APOE adjustment is that this statistical interaction is indicative of biological interaction in an analysis that includes a subset of the current sample [99] . However , the differences in SNP allele frequencies across APOE strata within cases that we showed here coupled with information demonstrating the existence of population stratification raise concerns that the original associations for these latter SNPs may represent confounding or other aspects of sample or population structure . This could include linkage disequilibrium with APOE , even for unlinked markers . Further investigation in genetically more diverse populations will still be necessary to clarify even the role of SNPs with positive evidence for association , because shared history can lead to spurious replication in samples drawn from the same population [80] . The results presented here and in other GWAS reports of LOAD underscore the view that such studies do not necessarily identify the specific genetic alterations contributing to disease risk . Rather , they are useful in identifying genes or gene pathways involved in disease pathogenesis or risk . In that sense , GWAS represents a method of screening the genome for genes that may also contain rare variants . While the large number of subjects in current GWAS provides a benefit in terms of perceived statistical power , it comes at a price . For example , despite the very low p-values representing genome-wide statistical significance , the effect sizes in most recent GWAS involving LOAD are small . It has also been suggested that different significance thresholds as a function of sample size are needed in order to balance power against the false-discovery rate [101] , with very large studies requiring more stringent thresholds . This means that subtle differences in the genetic architecture of either the cases or the controls become more important with increasing sample sizes . In this situation some of the “significant” differences in allele frequency may also represents differences in ancestral origins rather than disease phenotype-genotype associations , and would likely not lead to further biological insights . As we have shown here , genetic variability within European-American groups exists and can affect analyses of association . Moving forward , GWAS in LOAD should consider more detailed care to control for population stratification or APOE genotypes prior to drawing firm conclusions about associations . In this sense bigger studies of LOAD or of other diseases with similar influential risk loci may not always be better , if the increases in sample size result in added data structure or confounding .
|
Genetic factors are well-established to play a role in risk of Alzheimer's disease ( AD ) . However , it has been difficult to find genes that are involved in AD susceptibility , other than a small number of genes that play a role in early-onset , high-penetrant disease risk , and the APOE ε4 allele , which increases risk of late-onset disease . Here we use a European-American family-based sample to examine the role of common genetic variants on late-onset AD . We show that variants in CUGBP2 on chromosome 10p , along with nearby variants , are associated with AD in those highest-risk APOE ε4 homozygotes . We have replicated this interaction in an independent sample . CUGBP2 has one isoform that is expressed predominantly in neurons , and identification of such a new risk locus is important because of the severity of AD . We also provide support for recently proposed associated variants ( BIN1 , CLU , and partly CR1 ) and show that there are markers throughout the genome that are correlated with APOE . This emphasizes the need to adjust for APOE for such markers to avoid false associations and suggests that there may be confounding for other diseases with similar strong risk loci .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"neurological",
"disorders/alzheimer",
"disease",
"neurological",
"disorders/cognitive",
"neurology",
"and",
"dementia",
"genetics",
"and",
"genomics/genome",
"projects",
"genetics",
"and",
"genomics/genetics",
"of",
"disease",
"genetics",
"and",
"genomics/population",
"genetics"
] |
2011
|
Genome-Wide Association of Familial Late-Onset Alzheimer's Disease Replicates BIN1 and CLU and Nominates CUGBP2 in Interaction with APOE
|
Iron has an integral role in numerous cellular reactions and is required by virtually all organisms . In physiological conditions , iron is abundant in a largely insoluble ferric state . Ferric reductases are an essential component of iron uptake by cells , reducing iron to the soluble ferrous form . Cytochromes b561 ( cyts-b561 ) are a family of ascorbate reducing transmembrane proteins found in most eukaryotic cells . The identification of the ferric reductase duodenal cytochrome b ( dcytb ) and recent observations that other cyts-b561 may be involved in iron metabolism have opened novel perspectives for elucidating their physiological function . Here we have identified a new member of the cytochrome b561 ( Sjcytb561 ) family in the pathogenic blood fluke Schistosoma japonicum that localises to the outer surface of this parasitic trematode . Heterologous expression of recombinant Sjcyt-b561 in a Saccharomyces cerevisiae mutant strain that lacks plasma membrane ferrireductase activity demonstrated that the molecule could rescue ferric reductase activity in the yeast . This finding of a new member of the cytochrome b561 family further supports the notion that a ferric reductase function is likely for other members of this protein family . Additionally , the localisation of Sjcytb561 in the surface epithelium of these blood-dwelling schistosomes contributes further to our knowledge concerning nutrient acquisition in these parasites and may provide novel targets for therapeutic intervention .
Iron is an essential co-factor of many biological processes in nearly all organisms , and serves as a major strengthening and stabilizing metal in invertebrates [1] . Although iron is the second most abundant element on Earth and the fourth most abundant element in the crust , it exists in inorganic form , most often as insoluble trivalent ferric hydroxide or ferric oxide salts , forms that are not readily bio-available to organisms [2] . In mammalian tissues , iron is predominantly stored or transported by an array of molecules , including organic chelates , the serum transporter transferrin , or in the cytoplasmic storage complex ferritin , in its ferric form [3] , [4] . However , it is in the divalent , or ferrous , state that iron participates as a co-factor in biological processes . Accordingly , it is necessary for cells to be able to reduce and solubilise iron in order to use it for a variety of cellular functions . Early studies showed that eukaryotes acquire iron from their environment more readily as a ferrous ion . In yeast , chelators of ferric iron do not inhibit transmembrane iron transport and uptake , whereas ferrous chelators do [2] , [5] . This biological characteristic has led to the identification and functional characterization of many ferric reductases , molecules able to convert ferric to ferrous iron , from a wide range of organisms , most notably the well studied FRE family of metalloreductases of yeast and the ferric reductase oxidase ( FRO ) proteins of plants [6] , [7] , [8] , [9] , [10] , [11] , [12] , [13] . In mammals , the two major ferric reductase families that have been characterised include the cytochrome b561 homologues , among which the duodenal cytochrome b ( Dcytb ) is known most prominently , and the Steap family of metalloreductases [4] , [10] , [14] , [15] . Schistosomes , platyhelminth parasites of humans , and other mammals , are a major source of human morbidity in many developing countries in tropical areas [16] . Adult schistosomes live within the vasculature of their human hosts and feed predominantly on erythrocytes , which are ingested , lysed and digested in a primitive gut , the gastrodermis . Females , in particular , have high metabolic requirements for iron , which is stored in abundance in vitelline , or egg shell precursor cells , to be used subsequently for embryogenesis , where it is thought to assist in stabilization of protein cross-links in the tanned eggshells [17] , [18] . In addition to this choriogenic requirement , schistosomes also depend on iron for early establishment and growth in the human host [19] . In view of the importance of iron in the development of schistosomes , there has been growing interest in understanding how they acquire iron in their parasitic environment as a means to discovering new drug and vaccine targets for their control [20] . The surface of schistosomes consists of a syncytial anucleate layer , known as the tegument . This cytoplasmic layer serves as a nutritive and protective surface , and acts in immune evasion and , possibly , signalling for parasite development [21] , [22] . A surface-associated pathway for iron absorption has been postulated for schistosomes [19] , [20] . This hypothesis is supported by recent functional analysis of the membrane spanning divalent metal transporter ( DMT1 ) family of proteins in Schistosoma mansoni [23] . The schistosome DMT1s , which show a tegumentary distribution by immunofluorescence microscopy , have been demonstrated to transport ferrous iron when expressed heterologously in yeast [23] . The presence of a membrane-spanning protein in schistosomes that transports ferrous iron into cells thus suggests that these blood-dwelling parasites must , of necessity , express molecules with ferric reductase activity [20] . To date , however , no functional ferric reductase has been characterised in schistosomes , or any other helminth . One family of proteins recently identified as having ferric reductase capability are the cytochromes b561 . These are intrinsic membrane proteins , containing two heme molecules , and are considered to be reducible by ascorbate [24] . Initially thought as only electron transporters [25] , b561 family members were shown to have ferric reductase capacity following functional characterization of a mammalian duodenal cytb561 ( dcytb ) [26] , [27] , [28] , [29] . Expressed sequence tags of putative cytb561 family members have been shown to be present in the genomes of S . japonicum [15] , [30] , [31] and S . mansoni [32] , [33] . Here we describe the identification and molecular characterisation of a protein of the cytb561 family ( Sjcytb561 ) from S . japonicum and demonstrate that it has functional ferric reductase activity and occurs in the tegument of these blood-dwelling parasites .
Oncomelania hupensis hupensis snails , infected with a Chinese mainland strain of S . japonicum ( Anhui Province population ) , were kindly provided by the National Institute of Parasitic Diseases-CDC , Shanghai , China . Adult worms were perfused from BALB/c mice 4 or 7 weeks after cutaneous infection with 30 S . japonicum cercariae . Eggs were collected following their extraction from the livers of infected mice by digestion of liver matrix by collagenase B and differential centrifugation using Percoll [34] . Miracidia were hatched from eggs by exposure to freshwater . The human-invasive cercariae were harvested from infected O . h . hupensis snails that were shed by exposure to direct light for approximately 2 h . Lung schistosomula were isolated from infected mice using a previously published procedure [35] . Approximately 1 , 000 cercariae were pooled from several infected O . h . hupensis snails , and used to challenge female BALB/c mice . Three days later the lungs were removed , minced with a razor blade and incubated in RPMI at 37°C for 3 h on a shaker . The lung tissue solution was sieved and schistosomula removed using a fine tipped glass pipette . Total RNA was obtained from the five different life stages by homogenizing parasite tissue in Trizol ( Invitrogen ) according to the manufacturer's instructions . Expressed sequence tags ( EST ) encoding schistosome cytb561 were first identified by BLAST searches of the S . mansoni and S . japonicum EST databases . The GeneRacer kit ( Invitrogen ) was used for full length Rapid Amplification of 5′ and 3′ ends ( RACE ) . Total RNA from adult worms was used and cDNA synthesis was carried out following the GeneRacer protocol . Numerous primers were designed based on ESTs and used in conjunction with proprietary GeneRacer 5′ and 3′ universal primers to amplify the full-length Sjcytb561 sequence . This cDNA was amplified using Platinum taq DNA polymerase High Fidelity ( Invitrogen ) , with the following cycling parameters: initiation at 96°C for 5 min , and 35 cycles of denaturation ( 94°C for 1 min ) , annealing ( 50°C for 1 min ) and extension ( 68°C for 2 min ) . Transcriptional patterns for the different S . japonicum life- stages were evaluated using real time PCR . Primers were designed to amplify sequences for Sjcytb561 ( forward: 5′-TGGACCAATGCAAACACAGT-3′; reverse: 5′-TGATTCCCAGGACACCAAAT-3′ ) and a region of S . japonicum NADH ubiquinone reductase ( forward: 5′-CGAGGACCTAACAGCAGAGG-3′; reverse: 5′-TCCGAACGAACTTTGAATCC-3′ ) , used as a constitutively expressed control as previously described [35] , [36] . All cDNA samples synthesised from total RNA were adjusted to 5 ng/µL , and quantified using a NanoDrop ND-1000 spectrophotometer ( Thermo Scientific ) . Five µL aliquots of cDNA were then combined with 10 µL of SYBER Green ( Applied Biosystems ) , 3 µL of water and 2 µL ( 5 pmol ) of each forward and reverse primer in a Gene-Disc 100 ring ( Qiagen ) using a CAS-1200 automated PCR setup robot ( Qiagen ) . Cycling conditions were: 10 minutes at 93°C followed by 40 cycles of 93°C for 20 seconds , 60°C for 15 seconds and 72°C for 15 seconds . All reactions were performed in a Rotor-Gene ( 3000 ) thermal cycler ( Qiagen ) and the data analysed using Rotor Gene 6 software ( Qiagen ) . A standard curve was created with a mixed template ( contained 50 µL from each separate template ) and the mean copies per reaction values were calculated from the mean of 3 normalised CT ( cycle threshold ) values . All PCR experiments were conducted in triplicate . The full-length cDNA of Sjcytb561 was cloned into the pCR4-TOPO vector ( Invitrogen ) and chemically transformed in E . coli TOP10 cells ( Invitrogen ) according to the manufacturer's guidelines . Colonies were screened by colony PCR using a combination of vector and insert primers . Plasmid mini-preps were prepared ( Qiagen ) and the sequence verified from plasmids using M13F and M13R vector primers and Big Dye sequencing chemistry ( ABI ) on an ABI automated DNA sequencer . The consensus sequence was compared with those already deposited in GenBank and the dbEST databases using the BLAST algorithm on the NCBI server . Topological data were obtained using the TMPRED server ( http://www . ch . embnet . org/software/TMPRED_form . html ) and the SignalP server ( http://www . cbs . dtu . dk/services/SignalP/ ) was used to predict the presence of signal peptide cleavage sites . Multiple sequences were aligned using the Clustal W 1 . 8 algorithm on the EBI server ( http://www . ebi . ac . uk/Tools/clustalw2/index . html ) . A phylogenetic tree was constructed using MEGAv3 . 0 software for Molecular Evolutionary Genetics Anaylsis [37] to compare S . japonicum and S . mansoni DMT1 sequences with homologous protein sequences identified from the public databases by BLAST searches . A minimum evolution phylogenetic tree was constructed using the JTT substitution model , with uniform rates among sites being assumed . The dataset was bootstrapped 1000 times , with the resulting values shown on branches of the midpoint-rooted tree . The Sjcytb561 cDNA sequence was used to interrogate the recently published genomic dataset for S . japonicum [31] to gain insight into genomic organization of the molecule . Alignment between the cDNA and genomic sequences was performed using SIM 4 [38] , ( http://pbil . univ-lyon1 . fr/ ) , to identify exons and introns in the gene sequence . Rabbit antiserum was raised against a synthetic peptide ( CISGITEKNFFSKNY ) corresponding to a region of the third extracellular loop of Sjcytb561 . The region was chosen due to its extracellular location , its predicted immunogenicity ( Invitrogen PeptideSelect; http://peptideselect . invitrogen . com/peptide/ ) and its lack of homology to mammalian b561 homologues , thereby reducing cross-reactivity with host tissues in the immunocytochemistry studies . The peptide was commercially synthesised and conjugated to Keyhole Limpet Hemocyanin ( KLH ) as carrier protein ( Sigma ) . The Institute of Medical and Veterinary Science ( IMVS ) , Adelaide , South Australia prepared the antiserum in a New Zealand White rabbit against the cytb561-KLH peptide using a protocol of 4 immunisations of 2 mg at weekly intervals . Freund's Complete Adjuvant ( FCA ) was used for the first immunisation and Freund's Incomplete Adjuvant ( FIA ) for the subsequent three immunisations . Serum was collected 2 weeks after the fourth immunisation and control , pre-immune serum was collected prior to commencement of the immunisation schedule . Cyanogen bromide-activated Sepharose 4B ( GE Healthcare ) , coupled with KLH , was used to deplete the anti-b561-KLH peptide antiserum of KLH immunoreactive antibodies . Serum was diluted at 1∶1 with PBS pH 7 . 4 , added to the Sepharose 4B and rotated end over end , at 4°C over night , to allow efficient binding of KLH-specific antibodies . The flow through was collected to obtain purified anti-cytb561 peptide antibodies . Antibodies showing specific immunoreactivity against KLH were eluted from the column using 100 mM glycine , pH 2 . 5 , then equilibrated by adding 1/10 volume of Tris-HCl , pH 8 . 0 . Specificity of the anti-cytb561 and KLH reactive antibodies was confirmed by dot and Western blot analysis of a detergent-solubilized membrane extracts of S . japonicum ( see below ) and KLH solution . A protein extract from the tegument was prepared following methods of van Balkom and colleagues [39] . Five µg of adult parasite extracts ( either tegument or ‘stripped’ ) and commercial KLH ( Sigma ) were separated by gel electrophoresis on a 12% ( w/v ) SDS-PAGE gel at 200 V for 1 hour at room temperature ( RT ) and then transferred to nitrocellulose membrane at 200 mA for 1 hour at RT . The membrane was blocked for 1 hour at RT in 6% ( v/v ) skim milk powder diluted in PBS-T . All washes were carried out in PBS-T . The membrane was incubated with primary SjCytb561 antiserum a diluted at 1∶100 , 000 . A pre-immune serum was used at equivalent dilutions for the negative control . In addition anti-KLH specific antibodies were used as a control . After incubation with the primary serum , the membrane was washed for 15 minutes in PBS-T and incubated with horseradish peroxidase-conjugated goat anti-rabbit IgG ( 1∶2000 , BioRad ) for 1 hour at RT . The membrane was washed a further 3 times ( 5 minutes each ) in PBS-T and then developed with chemiluminescence ( ECL Plus , Amersham ) . Freshly perfused , unfixed adult S . japonicum worms were embedded in Tissue-Tek Optimal Cutting Temperature ( OCT ) compound ( ProSciTech ) and snap-frozen on dry ice . The block was then cryostat-sectioned at a thickness of 7 . 0 µm and mounted onto Superfrost slides . Sections were labeled using an indirect immunofluorescence labelling protocol , whereby sections were initially blocked for 1 hour at room temperature ( RT ) in 2% ( w/v ) skim milk powder in PBS-Tween 20 ( 0 . 5% ) ( PBS-T ) , washed 3 times ( 5 minutes each ) with PBS-T , and incubated with Sjcytb561 antisera at a 1∶1000 dilution . Sections were also incubated with pre-immune serum acting as a negative control . After washing , sections were incubated with goat anti-rabbit IgG secondary antibody conjugated to Cy3 fluorophore ( Jackson ImmunoResearch Laboratories ) at a 1∶500 dilution . After 3 further washes with PBS-T , slides were air-dried briefly and sections were mounted in Vectorshield mounting medium ( Vector Labs ) that contained a 4′ , 6-diamidino-2-phenylindole ( DAPI ) counterstain . Sections were examined and photographed using a Leica DM IRB fluorescence microscope equipped with a Leica DC 500 digital camera . Freshly perfused adult worms were fixed in 4% ( v/v ) paraformaldehyde and 0 . 1% glutaraldehyde in 0 . 1 M phosphate buffer for 1 hour . Worms were then dehydrated in ethanol and infiltrated and embedded in LR White resin ( ProSciTech ) blocks . The blocks were sectioned on an ultramicrotome ( Leica EM UC6 ) . Sections were washed for 3×5 minutes in PBS , then blocked for 15 minutes in a blocking buffer consisting of 200 µL 10% ( w/v ) bovine serum albumin ( BSA ) , 200 µL 10% ( w/v ) fibroblast surface glycoprotein ( FSG ) , 1 mL 200 mM glycine , 2 mL phosphate buffered saline ( PBS ) and made up to 10 mL with ultra high quality ( UHQ ) water . Sections were then incubated in primary antibody , either anti-Sjcytb561 or anti-KLH , for 30 minutes . Primary antibodies were diluted 1∶75 in blocking buffer and the negative control was blocking buffer only . Grids were washed in blocking buffer for 4×5 min and incubated in Protein A conjugated to 10 nm colloidal gold particles ( Aurion Immuno Gold Reagents & Accessories ) for 30 minutes at a 1∶100 dilution . Subsequently , sections were rinsed with 4×5 minute washes in PBS and 4×2 minutes washes in UHQ water . Sections were contrasted using uranyl acetate ( 1 minute ) and lead citrate ( 30 seconds ) and viewed using a JEM 1011 transmission electron microscope operating at 80 kv and equipped with digital camera system . Yeast strains S288C wildtype ( MATα ura3-52 leu2-1 ) and mutant S288CΔfre1Δfre2 ( MATα fre1:URA3 fre2:HISG leu2-1 ) used in the assays were a gift from Professor Jerry Kaplan ( University of Utah , USA ) . Full-length Sjcytb561 cDNA was amplified by PCR from adult worms and cloned into the pESC-Leu vector ( Stratagene ) . Yeast genomic DNA was obtained from the S288C wildtype strain using Prepman Ultra Sample Preparation Reagent ( Applied Biosystems ) according to the manufacturer's instructions . The resulting template was used to amplify the Saccharomyces cerevisiae fre1 gene ( GenBank accession no . M86908 ) , which was cloned into pESC-Leu as a positive control . The clones were transformed into the S288CΔfre1Δfre2 cells according to the manufacturer's ( Stratagene ) protocol . The transformed yeast cells were grown in SD dropout medium lacking Leucine ( SD-Leu ) . Expression was induced by transferring to dropout medium containing galactose rather than dextrose ( SG-Leu ) . Ferrireductase assays were carried out as described [28] . Briefly , for the plate assay , low iron plates were made ( SG-Leu minimal medium , 2% ( w/v ) agarose , 50 µM Fe3+- ethylenediaminetetraacetic acid ( EDTA ) . Fifty µM bathophenanthroline disulphonate , ( BPS , Sigma ) , a ferrous iron chelator , was added to remove any residual ferrous iron from the plates . Transformed yeast were grown overnight in liquid culture medium . Cells were grown until the culture reached an OD600 of 0 . 8 . Cells were then centrifuged , washed twice with double distilled water . Five µL of cells were plated at different concentrations and grown for 3 days at 30°C . For the cell-surface ferrireductase assays , cells were grown , collected and washed as per the plate assays , but were resuspended in assay buffer ( 5% ( w/v ) glucose , 0 . 05 M sodium citrate , pH 6 . 5 ) . For the ferricyanide ( FeCN ) reductase assays , cells were added to assay buffer , with FeCN to a final concentration of 0 . 356 mM . The absorbance change was monitored for 5 min at 420 nm and the ferricyanide reduction rates were calculated using the established extinction coefficient of 1 . 02 mM−1cm−1 [28] . For Fe3+-EDTA reductase assays , cells were added to assay buffer containing 1 mM ferrozine and 250 µM Fe3+-EDTA as substrate . Cells were incubated with shaking at 30°C . Over a time course , 1 mL of cells was removed , centrifuged for 20 seconds and the absorbance of the supernatant measured at 562 nm . Blanks containing assay buffer and ferrozine , but no cells , were incubated at the same time , and the corresponding readings were subtracted from each sample time point . The previously established extinction co-efficient for the formation of ferrozine-ferrous iron complex of 27 . 9 mM−1cm−1 was used in the activity calculations [28] .
Schistosoma japonicum datasets were searched by BLAST to identify homologues of known ferric reductase proteins from other organisms . This search revealed an EST with homology to the cytochrome b561 family of proteins ( Genbank , AY816000 ) . Using RACE PCR , the full-length cDNA was obtained , sequenced and revealed a 1645 bp coding region . The translated sequence encoded a 243 amino acid protein ( Fig . 1A–B ) , with a predicted molecular mass of 26 . 9 kDa . The recent release of the S . japonicum genome [31] allowed us to compare the sequence we confirmed for Sjcytb561 with the putative in silico predicted sequence from the genome project . Using BLAST function against the sequences in the S . japonicum genome project ( GeneDB_Sjaponicum_Gene . v4 . 0 ) , we identified the region of the genome in which the gene is located . BLAST analysis of the genomic contigs ( sjr2_contig . fas ) identified a region designated as: >gnl|lsbi|CNUS0000126016 . 1 ( Schistosoma japonicum isolate Anhui ( wildtype ) SJC_C017791 , Length = 24880 , Bit Score = 402 E value = e-110 , Identities = 203/203 ( 100% ) ) as the genomic site of Sjcytb561 . Using the program Sim 4 , we aligned cDNA sequence to the contiguous genomic sequence previously identified by BLAST . This resulted in the identification of 5 exons ( Fig . 1C ) . Analysis of the translated nucleotide sequence revealed the presence of a signal peptide . The predicted topology of the putative protein contained six transmembrane spanning regions , four of which formed the ‘core domain’ identifiable in the cytochrome b561 family of proteins [15] . In addition to these six transmembrane helices , the predicted peptide of S . japonicum contains four completely conserved histidine residues ( thought to co-ordinate two heme centers ) ( Fig . 2 ) and predicted substrate binding sites ( Fig . 2 ) . Despite these completely conserved structural features , the cytb561 sequences exhibit relatively low homology both within and among species ( Fig . 2 ) . The S . japonicum sequence shared between 30 and 45% homology with other cytb561 family members ( identity with human Dcytb , NP_079119 was 38%; with that of Arabidopsis [GenBank accession no . CAA18169] , 31%; and with Xenopus cytb561 [AAA65644] , 44% ) . A homologous sequence ( Smp 136400 ) was also identified in the S . mansoni GeneDB , which is a first pass annotation of the S . mansoni genome assembly , with 85% identity to the S . japonicum sequence ( http://www . genedb . org/genedb/smansoni/ ) . Minimum evolution phylogenetic analysis ( Fig . 1D ) showed the schistosome sequence grouped most closely with that of the freeliving turbellarian , Dugesia japonica , another member of the Phylum Platyhelminthes . However , this grouping was not supported , having a low bootstrap value . There was a distinct clustering of mammalian duodenal cytochromes ( Human , NP_079119 and Mouse AAK50909 ) relative to other mammalian cytb561 proteins . There are also distinct branches representing the type 1 and type 2 mammalian cytochromes b561 . Type 1 cytb561 members include neuroendocrine expressed proteins , such as chromaffin granule cytochromes , which are present in the adrenal medulla ( Human , P49447 , Mouse , Q60720 ) . Type 2 mammalian cytsb561 , found in humans and the mouse , are termed ‘ubiquitious’ cytochromes as they are more widely expressed ( Human , NP_705839 , Mouse , Q6P1H1 ) [24] . Individual functions of the proteins contained in this tree have not all been experimentally assessed and confirmed , particularly those from invertebrates . Quantitative analyses of expression of SjCytb561 in different stages of the life cycle ( Fig . 3 ) , showed highest mRNA expression in adult ( 7-week-old ) males , with less expression in developing ( 4-week-old ) males and females . Expression in the 7-week-old females appeared to diminish compared with 4-week-old females . Lower expression levels were evident in the schistosomula and cercariae . Western blot analysis ( Fig . 4 ) of adult schistosome membrane extracts using KLH-depleted anti-Sjcytb561 sera showed a tight band at approximately 30 kDa ( Fig . 4 , lane 2 ) , which is in accordance with the predicted molecular mass of 26 . 8 kDa . No band was observed with pre-immune sera probed against the tegument exacts ( Fig . 4 , lane 4 ) . Anti-SjCytb561 sera probed against commercial KLH did not elicit a reaction , suggesting KLH-cross-reactivity was not a problem ( Fig . 4 , lane 3 ) . Further , anti-KLH antibodies did not show reactivity against the schistosome tegument extracts ( Fig . 4 , lane 6 ) . Sections of adult worms probed with pre-immune rabbit serum and secondary serum conjugated to Cy3 did not show any significant fluorescence ( Fig . 5A ) , nor did the secondary antibody only sections ( not shown ) . Sections probed with anti-Sjcytb561 antibodies showed strong labelling on the distal cytoplasm of the outer tegument ( Fig . 5B and 5C ) and more diffuse reactivity over the underlying parenchyma . These results suggest a tegumental role for cytochrome b561 in S . japonicum . Electron micrographs of adult tegument probed with anti-SjCytb561 serum ( Fig . 6A and 6B ) showed the presence of gold probes in the apical membrane complex . Labelling with anti-KLH serum and with the negative controls did not show labelling of gold probes in this region . In light of the discovery that some cytochromes b651s exhibit ferric reductase capability [26] , [27] , [28] , [29] , [40] , the S . japonicum cytochrome b561 was expressed in a yeast model deficient in ferric reductase activity . Fre1 and Fre2 are yeast ferrireductases that are responsible for almost all ferric reductase activity at the plasma membrane [6] , [41] , [42] . The fre1Δfre2Δ mutant yeast grows poorly in iron-restricted conditions , but can be rescued by complementation with the Fre1 gene . The Sjcytb561 protein was expressed in this mutant model to determine whether it exhibits ferric reductase activity . Empty vector was used as the negative control in these experiments . Growth of the transformed yeast on solid phase media showed that the presence of the Sjcytb561 protein restored growth equal to that of the Fre1 positive control , indicating the molecule has ferric-reductase activity ( Fig . 7A ) . To further investigate the kinetics of ferric reductase activity occurring in the rescued cells , assays were performed on intact yeast cells in liquid culture ( Fig . 7B ) . Ascorbate is the predicted and commonly accepted electron donor for cyts-b561 under physiological conditions [43] , [44] , so cultures were also tested in the presence or absence of ascorbate to determine its contribution to activity . The cells transformed with empty vector did not exhibit significant ferric reductase activity . The Fre1-transformed cells had a high level of ferric reductase activity that was independent of the presence or absence of ascorbate . The Sjcytb561 transformed cells also exhibited a high level of ferric reductase activity , which was greatly enhanced by the addition of ascorbate . There was also a difference seen in the form of iron substrates used in the experiments , with FeCN a more efficient substrate .
All organisms that require iron have at least one ferrous iron transport system , and often many more than one . The limited availability of ferrous iron in many environments has necessitated the evolution of a range of ferric reductases to allow ferrous iron uptake to occur [2] , [8] . Cytochrome b561 has been identified in a large number of phylogenetically distant taxa and many species contain more than one cytb561 [24] . This family of proteins has long been known to play a role in ascorbate regeneration and electron transfer , but its role as a reducer of ferric iron has only been recognised recently [26] , [27] , [28] , [29] , largely through studies of mammalian duodenal cytochrome b [10] , [27] , [40] , [45] , [46] . Sjcytb561 is the first of this family of proteins identified from any parasitic platyhelminth or nematode , which is surprising as the protein is found ubiquitously in the plant and animal kingdoms [24] . The S . japonicum cytb561 has all the structural hallmarks of the family , including the six transmembrane spanning regions , four completely conserved histidine residues and the conserved substrate binding sites ( Fig . 2 ) . However , unlike other organisms which possess multiple Cytsb561 , S . japonicum and S . mansoni appear to have only one Cytb561 transcript in their respective genomes . A study by Tsubaki and colleagues [15] aimed to predict the function of all known members of the cytochrome b561 family based on protein sequence motif analyses in the conserved binding regions . This study predicted that only a subset of the family would have ferric reductase function . Notably , the Sjcytb561 protein identified here was predicted to belong to the group with ferric reductase potential [15] . The yeast rescue assay presented here provides the first evidence of ferric reductase activity in a non-mammalian metazoan cytochrome b561 . Yeast is one of the best characterised model eukaryotes for iron metabolism and iron transport . Fre1 and Fre2 are responsible for almost all of the ferric reductase activity in yeast cells that if knocked out result in a significant defect in growth [3] , [6] , [41] , [42] , [47] , [48] , [49] . Functional assays in the yeast Δfre1Δfre2 mutant demonstrated the ability of Sjcytb561 to reduce ferrous iron and , thereby , restore iron uptake and normal growth ( Fig . 7 ) . This mechanism for the S . japonicum Cytb561 appears to be ascorbate-dependent , as there was a significant increase in reductase activity with the addition of ascorbate . Fre1 transformed yeast are not affected in the same manner by the addition of ascorbate , as Fre1 is a NADPH-dependent reductase [41] . Yeast are unable to synthesise ascorbate but they do synthesise a homologue called erythroascorbate . The low ferric reductase activity that was observed without the addition of ascorbate may suggest that erythroascorbate , or some other reductant , in the yeast cells may be able to function as the electron donor in this reaction [43] , [44] . The ability to synthesise ascorbate has been lost a number of times in evolution , most notably in humans , and it is unknown if schistosomes are able to synthesise their own ascorbate or , if indeed , it too must be scavenged from the host [50] , [51] . The localisation of Sjcytb561 to the tegument ( Fig . 5 and 6 ) is consistent with that of schistosome DMT1 , a known ferrous iron transporter [23] . It is possible that these two proteins act together to take up iron from the host , although the presence of other ferric reductases at this site cannot be excluded . Indeed , the lack of phenotype observed in mammalian Dcytb knockout studies makes it likely that there are other ferric reductases able to perform this role [54] . Neither the schistosome DMT1 nor this newly identified cytochrome b561 have been identified in proteomic studies of the schistosome tegument [39] , [52] , [53] . Iron homeostasis proteins of eukaryotes show transcriptional changes in response to cellular iron levels , whereby expression of proteins involved in acquisition and utilization of iron are increased in response to iron deficiency [54] . Mechanisms also exist to change the sub-cellular localisation of iron transport proteins , a process that is considered the ‘first line’ response of cells after changes in iron levels . It is often necessary to induce iron-starvation conditions to achieve significant up-regulation for the analysis of these proteins [54] . Given that the previous proteomic studies [39] , [52] , [53] were conducted on parasites obtained from iron-replete hosts it is possible that the schistosome iron transport molecules are , in fact , present at very low levels and combined with changes in sub-cellular localisation were not identified in these analyses . Future proteomic studies could be conducted on parasites cultured under iron-deficient conditions to confirm this . Mammalian duodenal cytochrome b has been demonstrated to reduce copper in vitro and it is possible that cytochromes b561 may play a more general role in metal iron uptake [55] . The molecular mass of iron is similar to metals such as copper , manganese , zinc and cobalt and most other ferrous iron transport systems can also take up these and other transition metals [2] . Notably , a transporter for copper has been identified in proteomic studies of schistosome tegument extracts [52] . The high expression levels for Sjcytb561 in adult males complemented the tegument localisation data . In vivo , the male worm surrounds the female schistosome , holding her in his gynaecophoral canal . The tegument of the male is in constant contact with host serum molecules , and has a larger surface area than that of the female [56] . Hence , Sjcytb561 would be expected to be highly expressed at this site in adult males . The reduction of transcript expression from the 4-week females to the 7-week females is logical since at 7 weeks more genes would be upregulated for egg production , rather than purely for growth and nutrition needs . The question of iron utilisation in egg production by females , be it for egg shell formation or for use by the developing miracidia within the egg [17] , [18] , remains undefined . However , the relatively low SjCytb561 ferric reductase expression levels in the miracidia and eggs of S . japonicum is noteworthy in that it suggests that if iron is being liberated from vitelline cells by the developing embryo , it is unlikely to involve cytochrome b561 reductase-mediated release of iron . It has been postulated that ferric reductases , by decreasing the affinity of biological carriers for iron , may play a role in the removal of iron from chelators [57] . In the case of schistosomes , this may mean aiding in the release of iron from the abundant host serum iron glycoprotein , transferrin . It has also been hypothesised that using ferric reductase coupled with iron uptake mechanisms may allow parasites and other pathogens to survive in a more diverse range of hosts and tissues , than organisms restricted to specific receptor-mediated iron uptake [13] [58] . In the absence of any clear research regarding haem uptake and usage in schistosomes it can be postulated that this surface mediated iron uptake mechanism is at least used to supplement the adult worms iron needs . S . japonicum cytb561 is the first ferric reductase identified in any parasitic helminth and emphasises the importance of iron and other divalent cations in this group of important parasites . Further understanding of the mechanisms of metal uptake and utilization in schistosomes may uncover novel drug and vaccine targets for controlling schistosomiasis .
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Parasites acquire their food from their hosts , either by feeding directly on tissues of the host , or by competing for ingested food . Adult schistosomes live within the vasculature of humans and rely on the blood cells and plasma they ingest and dissolved solutes they derive across their body surface , the tegument , for their nutrition . Schistosomes require host trace elements , notably iron , which is used as a co-factor in many biological reactions . Iron is especially important for schistosomes , for it has a significant role in egg formation and embryogenesis . In human tissues , iron predominates in the trivalent ( ferric ) form; however , it is the divalent ( ferrous ) form that is used as an essential co-factor for multiple biomolecules and enzymes . In order to be acquired from the host environment , the valency of iron must be modified to render it suitable for transport across the parasite membrane . This paper describes the molecular characterisation of a schistosome molecule that is crucial for bringing about this change in iron . Schistosoma japonicum Cytb561 is the first ferric reductase characterised in any parasitic helminth and emphasises the importance of iron , and other divalent cations , in these organisms .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[
"microbiology/parasitology"
] |
2010
|
A Cytochrome b561 with Ferric Reductase Activity from the Parasitic Blood Fluke, Schistosoma japonicum
|
Elevated IL-10 has been shown to be associated with severe dengue infection ( DI ) . We proceeded to investigate the role of IL-10 in the pathogenesis of acute DI . Ex vivo and cultured IFNγ ELISpot assays for dengue virus ( DENV ) NS3 protein and non dengue viral proteins were carried out in 26 patients with acute DI ( 16 with dengue haemorrhagic fever ) and 12 healthy dengue seropositive individuals from Sri Lanka . DENV serotype specific ( SS ) responses were determined by using a panel of SS peptides . Serum IL-10 level were significantly higher ( p = 0 . 02 ) in those who did not have in vitro responses to DENV-SS peptides ( mean 144 . 2 pg/ml ) when compared to those who responded ( mean 75 . 7 pg/ml ) . DENV-NS3 specific ex vivo IFNγ ELISpot responses were also significantly lower ( p = 0 . 0001 ) in those who did not respond to DENV-SS peptides ( mean 42 SFU/million PBMCs ) when compared to those who responded to DENV-SS peptides ( mean 1024 SFU/million PBMCs ) . Serum IL-10 levels correlated significantly ( p = 0 . 03 ) and inversely ( Spearmans R = −0 . 45 ) with ex vivo DENV-NS3 specific responses but not with ex vivo non DENV specific responses ( Spearmans R = −014 , p = 0 . 52 ) . Blockage of IL-10 in vitro significantly increased ( p = 0 . 04 ) the ex vivo IFNγ ELISpot DENV-NS3 specific responses but had no effect on responses to non DENV proteins . IL-10 appears to contribute to the pathogenesis of acute dengue infections by inhibiting DENV-specific T cell responses , which can be restored by blocking IL-10 .
Dengue viral infections have become one of the most important mosquito borne viral infections in the world and are one of the major emerging infectious diseases . Dengue is a major public health problem in over 100 countries in the tropical and sub-tropical regions . It has been predicted that 390 million dengue infections occur per year resulting in approximately 96 million clinically apparent infections [1] . Although the case fatalities due to dengue viral infection are around 1–5% depending on the country and epidemic [2] , infection is thought to be fatal in approximately 2 . 5% individuals with dengue haemarhagic fever ( DHF ) [3] . Currently there are no effective antivirals to treat this infection and there is no effective vaccine . Infection with the dengue virus ( DENV ) , may manifest as asymptomatic or a mild febrile illness , DF , DHF or dengue shock syndrome ( DSS ) , which can be fatal [3] , [4] . Dengue may occur due to infection with any one of the four DENV serotypes . Initial infection with a particular serotype is known as a primary infection , which is usually asymptomatic or results in mild disease manifestations [5] . Primary infection can occasionally associate with severe disease , but it is subsequent infection with other serotypes ( secondary dengue infections ) that more commonly leads to severe disease [5] . Severe clinical disease manifestations such as DHF/DSS are thought to result from a complex interplay between the virus , host genetic background and host immune factors . However , many questions regarding factors that lead to severe disease and the pathophysiology of dengue viral infection itself remain unanswered . Both cross reactive antibodies and T cells to the previous infecting DENV serotype are thought to contribute to disease pathogenesis [5] , [6] . Highly cross reactive T cells have been shown to occur in patients with acute dengue viral infection [7]–[9] , and are thought to contribute to disease pathogenesis as they are believed to be suboptimal in clearing the virus [10] . Some have expressed different opinions regarding the possibility of cross reactive T cells contributing to disease pathogenesis [11] . In fact a more recent study showed that a large proportion of individuals living in dengue endemic areas had a high magnitude of polyfunctional responses to multiple CD8+ T cell epitopes of the DENV , which were associated with protection [12] . Therefore , it is possible that rather than causing immunopathology , DENV-specific T cells responses may actually be protective in acute dengue infection . Massive apoptosis of T cells has been shown to occur in acute dengue viral infection [13] , [14] . Furthermore , it has been shown that patients with more severe forms of disease have higher viral loads and prolonged viraemia [15] , [16] . Therefore , it is possible that an impaired T cell response in acute dengue is associated with delayed viral clearance leading to clinical disease severity . We previously found that serum IL-10 levels were associated with T cell apoptosis although in subsequent in vitro experiments we found that IL-10 itself did not cause apotosis of T cells in PBMCs in healthy volunteers [13] . In these experiments , PBMCs of healthy individuals were incubated with varying concentrations of human recombinant IL-10 for 24 hours and we found that in these in vitro experiments higher IL-10 levels were not associated with apoptosis of T cells in these healthy individuals . However , these experiments do not rule out IL-10 causing T cell apoptosis in patients with acute dengue infection as we did find that serum IL-10 levels correlated well with T cell apoptosis in patients with acute dengue . Patients with more severe clinical disease have been shown to have higher serum IL-10 levels [17] , [18] and IL-10 has also been shown to be associated with poorer disease outcome in other viral infections [19] , [20] . Therefore , in this study , we proceeded to investigate the role of IL-10 in the pathogenesis of dengue viral infections and its effect on DENV-specific T cells . We found that both DENV- serotype-specific ( SS ) specific T cell responses and DENV-NS3 specific responses were impaired in patients with higher serum IL-10 levels . Serum IL-10 levels did not appear to have any effect on non dengue viral protein specific responses . IL-10 blockade significantly increased IFNγ production , and other antiviral responses such as CD107a expression and TNFα production in response to DENV-NS3 peptides but not to non dengue viral proteins in acute dengue infection .
26 adult patients ( mean age 26 . 3 , SD±7 . 2 ) with clinical features suggestive of acute dengue viral infection and confirmed to be antibody positive ( see below ) were admitted to a general medical ward in a tertiary care hospital in Colombo and were enrolled in the study following informed written consent . Blood samples were collected during day 4–5 of illness ( day 1 was considered as the first day of fever ) in all patients and during the convalescent phase in 12 patients ( 10–14 days from collection of first blood sample and approximately one week after the patients had left the hospital ) . Blood was also collected from 12 healthy dengue seropositive individuals . Serial recordings of their clinical features and laboratory investigations ( platelet counts , haematocrits , white cell counts ) were made until they were discharged from the hospital in order to determine the severity of dengue infection . The severity of acute dengue infection was classified according to the 2011 WHO guidelines [3] . Patients with DHF with a pulse pressure of ≤20 mmHg were classified as having shock [3] . Of the 26 patients , 16 patients had DHF based on the 2011 WHO criteria and 10 patients had DF . Of the 16 patients with DHF , 6 patients developed DSS as their pulse pressure dropped to 20 mmHg during the course of the illness . The study was approved by the Ethical Review Committee of the University of Sri Jayawardanapura . Informed written consent was obtained from all subjects who participated in the study . A panel of 17 peptides which were previously described and found to be serotype specific ( SS ) originating from highly conserved regions of the four DENVs was used [21] . These were 20mer peptides which were synthesized in house in an automated synthesizer using F-MOC chemistry . The purity of the peptides was determined to be greater than 90% by high-pressure liquid chromatography analysis and mass spectrometry . There were four peptides specific to DEN-1 , five specific to DEN-2 , four specific for DEN-3 and four specific for DEN-4 . The DENV- NS3 peptides were 20 mer peptides overlapping by 10 amino acids , which spanned the whole length of the DENV-3 NS3 protein . The synthetic NS3 20mer peptides were pooled together to represent the whole NS3 protein . The FEC peptides that were used contained a panel of 23 , 8–11 amino acid CD8+ T cell epitopes of Epstein Barr virus ( EBV ) , Flu and CMV viruses and have been used as quality control in ELISpot assays [22] . Ex vivo Elispot assays were performed as previously described [23] , [24] in 26 patients with acute dengue infection and the healthy volunteers . ELISpot plates ( Millipore Corp . , Bedford , Massachusetts , USA ) were coated with anti-human IFNγ antibody overnight ( Mabtech AB , Nacka , Sweden ) . For ex vivo ELISpot assays , 0 . 1×106 PBMC were added to a final volume of 200 µl . DENV-NS3 overlapping peptides and FEC peptides were added at a final concentration of 10 µM as previously described [10] , [25] . All peptides were tested in duplicate . PHA was always included as a positive control and media alone with the PBMCs was included as a negative control . The plates were incubated overnight at 37°C and 5% CO2 . The cells were removed and the plates developed with a second biotinylated Ab to human IFNγ and washed a further six times . The plates were developed with streptavidin-alkaline phosphatase ( Mabtech AB ) and colorimetric substrate , and the spots enumerated using an automated ELISpot reader . Background ( cells plus media ) was subtracted and data expressed as number of spot-forming units ( SFU ) per 106 PBMC . Ex vivo ELISpot assays were used to determine T cell responses to DENV-SS peptides in a previous cohort of patients with acute dengue infection ( n = 20 ) . We found that the majority of patients did not have ex vivo responses to SS peptides during acute symptomatic infection and in the few who had responses , the frequency of responses were very poor ( unpublished data ) . However , after acute symptoms had resolved and the overall lymphopaenia recovered ex vivo SS responses became detectable . In contrast , cultured T cells specific to SS peptides were detectable in patients with acute dengue infection . Cultured ELISpot assays were performed on all the acute samples , on 12 convalescent samples and on the healthy volunteers to detect responses to DENV-SS peptides . PBMC from each donor were incubated with a pool of peptides consisting of all the 17 SS peptides . Briefly , 5 . 0×106 PBMCs were incubated for 10 days with 200 µl of 40 µM peptide pool in a 24 well plate . IL-2 was added on day 3 and 7 at a concentration of 100units/ml . All cell lines were routinely maintained in RPMI 1640 supplemented with 2 mM L-glutamine , 100 IU/ml penicillin and 100 µg/ml plus 10% human serum at 37°C , in 5% CO2 . T cell lines were tested individually after 10 days culture for responses to the 17 SS peptides . To determine IL-10 production , ex vivo- PBMCs were stimulated at 1×106 to 2×106/ml in RPMI 1640 plus 10% FCS with DENV-NS3 overlapping peptides and PMA and ionomycin for 16 hours according to manufacturer's instructions in the presence of Brefeldin A ( Biolegend , USA ) . For TNFα detection and CD107a expression , cells were washed and stained with anti CD3 APC ( Biolegend , USA ) , anti CD4 PerCP ( Biolegend , USA ) and anti CD8 PE ( Biolegend , USA ) . Cells were then permeabilized and fixed with Cytofix/Cytoperm ( BD Biosciences ) and then stained for intracellular TNFα FITC or CD107a FITC ( Biolegend , USA ) . Propidium Iodide was used in the CD107a assays to gate out dead cells . For detection of IL-10 , cells were stained with CD3 APC , CD14 ( FITC , Biolegend USA ) , CD19 PerCP ( Biolegend , USA ) and then fixed and stained for intracellular IL-10 PE ( Biolegend , USA ) . Cells were acquired on a Partec Cyflow Cube 6 and analyzed with De novo FCS Express version 4 . IL-10 quantitative cytokine assays were done in duplicate on serum according to manufacturer's instructions . Serum IL-10 levels ( Mabtech , Sweden ) , MIF levels ( Biolegend , USA ) and IL-21 levels ( Biolegend , USA ) were done in all serum samples and were also done in all the serum samples of patients with acute infection and healthy volunteers and also in serum samples obtained during the convalescent phase . Both IL-10 and TGFβ levels ( Mabtech , Sweden ) were done in ex vivo ELISpot supernatants of the unstimulated wells ( containing PBMCs and media alone ) , DENV-NS3 stimulated wells and FEC peptide stimulated wells . All reactions were carried out in duplicate . To determine the effect of IL-10 blockage on PBMCs using ex vivo IFNγ ELISpots , the ELISpots were coated and the PBMCs added as usual , and before the peptides were added , the PBMCs were incubated with anti IL-10 ( 5 µg/ml , Biolegend , USA ) and anti IL-10R ( 10 µg/ml , Biolegend , USA ) for 1 hour . After blockage for 1 hour , ELISpots were carried out as usual . To determine the effect of IL-10 blockage on TNFα production and CD107a expression , the PBMCs were incubated with anti IL-10 antibody and anti IL-10R antibody for 1 hour before peptides were added . After blockage for 1 hour , the ICS was done as usual . Dengue virus RNA was extracted from serum using QIAmp viral RNA mini kit ( Qiagen ) . RNA was reverse transcribed and the PCR was performed by using primer and conditions as previously described [26] . When determining the serotype of the infecting DENV , positive controls for DENV-1 , DENV-2 , DENV-3 and DENV-4 were used in all experiments . DENV infection was confirmed by testing the serum samples which were collected after day 6 of illness with a commercial capture-IgM and IgG enzyme-linked immunosorbent assay ( ELISA ) ( Panbio , Brisbane , Australia ) . The ELISA was performed and the results were interpreted according to the manufacturer's instructions . Patients who only had dengue virus specific IgM were classified as having a PD infection while those who had a positive result for both IgM and IgG were classified as having a SD infection [2] . Statistical analysis was carried out using PRISM version 6 . As the data was not normally distributed , differences in means were compared using the Mann-Whitney t test ( two tailed ) . To determine the effect of IL-10 blockade on DEN-V NS3 specific T cells , Wilcoxon matched-pairs sign ranked test ( two tailed ) was used . To determine positive and negative associations , the Spearman's correlation test was used ( two tailed ) .
T cell responses to DENV SS peptides were determined by in vitro cultured ELISpot assays where PBMCs were cultured with a panel of previously defined SS peptides for each DENV serotype [21] . 10/26 patients ( 3/16 patients with DHF and 7/10 patients with DF ) with acute dengue infections responded to at least one SS peptide of a DENV serotype , while all 12 healthy dengue seropositive individuals responded to at least one SS peptide . 4/12 healthy volunteers responded to peptides of only one serotype , while the rest ( 8/12 ) responded to peptides of two DENV-serotypes . Although , all the patients with acute infection had a secondary dengue infection , 16/26 patients did not have responses to any DENV SS specific peptides of any serotype suggesting altered DENV-specific T cell responses during acute infection . 6/10 patients who had DENV SS responses , responded to at least 1 SS peptide of DENV-1 , 4/10 patients responded to at least 1 SS peptide of DENV-3 , 5/10 responded to at least 1 SS peptide of DENV-2 and two patients responded to at least one SS peptide of DENV-4 ( Table 2 ) . 4/10 patients responded to SS peptides of only 1 serotype and 6/10 responded to SS peptides of 2 DENV serotypes ( Table 2 ) . Two of the patients who responded to SS peptides of 2 DENV serotypes , gave a history of a past symptomatic secondary dengue infection requiring hospitalization , which suggested that the current infection was third or fourth dengue infection . These two patients , who previously had a documented secondary dengue infection in the past , responded to SS peptide of DENV-3 and DENV-1 ( CS10 ) and DENV-2/DENV-1 ( CS31 ) . The infecting DENV serotype could only be identified in 4/26 patients using PCR , and it was found to be DENV-1 which was compatible with the known dominant circulating dengue type in Sri Lanka [27] . Only one of these patients ( CS29 ) responded to one of the SS peptides of the DENV-1 during acute infection . This patient ( CS29 ) had DF . Four of the patients who did not respond to any SS peptide in the acute phase of the illness , responded to SS peptides in the convalescent phase of the illness . This further supported the possibility that dengue-specific immune responses were impaired during acute infection . Although there was no statistically significant difference in the serum IL-10 levels in patients with DF and DHF ( p = 0 . 28 ) , the serum IL-10 levels were higher in patients with DHF ( mean 140 . 2 pg/ml ) when compared to patients with DF ( mean 91 . 9 pg/ml ) . However , the serum IL-10 level were significantly higher ( p = 0 . 02 ) in those who did not have DENV-SS specific responses ( mean 144 . 2 pg/ml ) when compared to those who responded ( mean 75 . 7 pg/ml ) ( Fig . 1A ) . There were no differences in serum MIF levels or serum IL-21 levels in patients who responded to SS peptides and those who did not respond ( data not shown ) . This raised the possibility that the impaired T cell responses could be related to IL-10 and was explored further below . DENV-NS3 specific IFNγ ELISpot responses were also significantly lower ( p = 0 . 0001 ) in those who did not respond to DENV-SS peptides ( mean 42 SFU/million PBMCs ) when compared to those who responded SS peptides ( mean 1024 SFU/million PBMCs ) ( Fig . 1B ) . However , there was no difference ( p = 0 . 71 ) in IFNγ ELISpot responses to non-dengue FEC peptides in those with SS responses ( mean 397 . 5 SFU/million PBMCs ) when compared to those who did not have SS responses ( mean 239 . 3 SFU/million PBMCs ) ( Fig . 1B ) . In addition , DENV-NS3 specific responses were significantly higher ( p = 0 . 04 ) in patients with DF ( mean 592 . 2 SFU/1 million PBMCs ) , than in patients with DHF ( mean 249 . 3 SFU/1 million PBMCs ) . Serum IL-10 levels significantly ( p = 0 . 03 ) and inversely ( Spearmans R = −0 . 45 ) correlated with DENV-NS3 specific IFNγ ELISpot responses , but not with FEC responses ( p = 0 . 52 , Spearmans R = −0 . 14 ) ( Fig . 1C ) . - We determined IL-10 and TGFβ levels in ELISpot culture supernatants in the unstimulated wells ( media alone ) , NS3 stimulated and FEC stimulated wells . As described in our previous work [13] , we again found that the IL-10 and the TGFβ levels in the unstimulated wells were higher than in the DENV-NS3 peptide stimulated wells and FEC stimulated wells . The difference in IL-10 levels in the DENV-NS3 peptide stimulated wells and the unstimulated wells were calculated by deducting the IL-10 concentrations in the unstimulated wells from the IL-10 concentrations in the NS3 stimulated wells . The difference of the IL-10 concentrations in the unstimulated and the NS3 stimulated wells ( IL-10 values in unstimulated minus IL-10 values in NS3 stimulated wells ) significantly ( p<0 . 0001 ) and positively correlated ( Spearmans R = 0 . 81 ) , with ex vivo IFNγ ELISpot responses ( Fig . 2A ) . No such association was seen in the difference between FEC stimulated wells and unstimulated wells ( p = 0 . 09 , Spearmans R = 0 . 36 ) . This shows that the overall higher IFNγ ELISpot response correlates with lower secretion of IL-10 in stimulated wells . Although TGFβ levels were also higher in the unstimulated wells when compared to FEC and NS3 stimulated well , no associations were found with NS3 or FEC responses . Since IL-10 appeared to have a significant effect on DENV-specific T cell responses and also since PBMCs appeared to be producing significant amount of IL-10 , we set out to identify the cells that were responsible for the production of IL-10 in the peripheral blood . For instance the mean IL-10 levels in unstimulated wells in the ELISpot plate , which only contained PBMC and media was 94 . 3 pg/ml . Using intracellular cytokine assays ( ICS ) , we found that unstimulated PBMCs of patients with acute dengue infection produced a significant amount of IL-10 and the major source of IL-10 in the PBMC population was monocytes ( Fig . 2B ) . Interestingly , during ICS , when PBMCs were stimulated with PMA and Ionomycin , the IL-10 production by monocytes drastically reduced ( Fig . 2B ) as observed in our ELISpot assays . ICS assays in B cells and T cells showed that insignificant amounts of IL-10 was produced from either T or B cells ( <0 . 02% of T and B cells ) . In order to determine if IL-10 could be suppressing DENV-specific T cell responses , we carried out ex vivo IFNγ ELISpots for NS3 and FEC in 10 of the patients with acute dengue and 4 healthy controls following blockade with anti-IL-10 and anti IL-10R antibodies . We found that IFNγ DENV-NS3 responses were significantly increased ( p = 0 . 04 ) with IL-10 blockade ( mean 925 . 4 SFU ) when compared to no blockade ( mean 463 . 2 SFU ) ( Fig . 3A and 3B ) . However , IL-10 blockade had no effect on FEC responses in patients with acute dengue infection ( p = 0 . 11 ) . IL-10 blockade also did not have any effect on DENV-NS3 specific responses in healthy dengue seropositive volunteers . We then went on to determine if IL-10 had any effect on additional T cell effector functions such as TNFα production and degranulation ( CD107a expression ) . Using ICS , with IL-10 blockade prior to adding the peptides , we found that although statistically not significant ( p = 0 . 12 ) IL-10 blockade increased CD107a expression by DENV-NS3 specific T cells in 4/6 ( Fig . 3C ) . The 2 patients did not have any CD107a expression when stimulated with DENV-NS3 and there was no response even after IL-10 blockade . Again although not statistically significant ( p = 0 . 57 ) TNFα production by DENV-NS3 specific CD8+ T cells were also higher with IL-10 blockade ( median 0 . 18 SEM±0 . 06 ) when compared to non blockade ( median 0 . 025 ) ( Fig . 3D ) . As we used PI as a marker of dead cells in the ICS assays , we found that with IL-10 blockade , the cells appeared to be more viable in the forward and side scatter . Therefore , we went on to determine the effect of IL-10 blockade on lymphoid cells , CD3+ T cells and CD8+ T cells . We found that IL-10 blockade significantly reduced PI expression on CD3+T cells ( p = 0 . 03 ) . The PI mean expression on CD3+ T cells with IL-10 blockade was 36 . 6% whereas the mean expression on CD3+ T cells in the absence of IL-10 blockade was 42 . 7 . PI expression was also reduced in CD8+ T cells with IL-10 blockade ( mean 54 . 6% ) , when compared to those without IL-10 blockade ( mean 58 . 9% ) although this was not statistically significant ( p = 0 . 12 ) .
Patients with more severe clinical disease have been shown to have higher serum IL-10 levels [17] , [18] . In our previous studies , which were done in a large cohort of patients with dengue infection , we found that higher serum levels of IL-10 were associated with higher T cell apoptosis [13] . Therefore , in this study we investigated the role of IL-10 in patients with acute dengue infection and its effect on DENV specific T cells . We found that serum IL-10 levels were significantly higher ( P = 0 . 02 ) in patients who did not have SS peptide responses . For detection of SS peptides , we used a panel of previously published SS peptides originating from highly conserved regions of the DENV , which included peptides specific for all for serotypes of the DENV [21] . All the healthy dengue seropositive volunteers who did not have a documented dengue viral infection in the past responded to the SS peptides of at least one serotype . All the patients with acute dengue suffered from a secondary dengue infection and they should have responded to the SS peptides of the previous infecting serotype . However , only 10/26 patients did so . Although only 3/16 patients with DHF responded to the SS peptides of at least one DENV serotype , 7/10 patients with DF responded , suggesting that patients with milder form of clinical disease are more likely to have responses to SS peptides during acute infection . Interestingly , 2 patients with previously documented DHF due to a secondary dengue infection responded to SS peptides , of two DENV serotypes . We found that patients who did not respond to any of the SS peptides also had a lower frequency of DENV-NS3 specific T cells , but no difference in responses to non dengue viral peptides ( FEC ) . Therefore , patients who did not respond to SS peptides of any of the DENV-serotypes also appear to have a lower frequency of other DENV-specific T cell responses but not T cell responses to other viral proteins . In addition , patients with more severe forms of disease ( DHF ) also had significantly lower DENV- NS3 specific T cell responses than patients with DF . Interestingly , as seen for DENV-SS responses , patients with higher serum IL-10 levels had a lower frequency of DENV-NS3 overlapping peptide specific T cell responses . However , there was no association between serum IL-10 levels and responses to non dengue viral proteins ( FEC peptides ) . Therefore , it appears that IL-10 only had an effect on DENV specific immune responses in acute dengue viral infection and did not appear to have any effect on existing memory T cell responses to non dengue viral peptides . This was further confirmed by the fact that IL-10 blockade increased a number of assays of antiviral responses such as IFNγ production , CD107a expression and TNFα production to DENV-NS3 overlapping pool of peptides but not to the FEC pool of peptides . The mechanism underlying this selectivity is unclear . We found that in experiments where PBMCs from patients with acute dengue infection were incubated with anti-IL-10 antibodies and anti-IL-10 receptor blocking antibodies in vitro , IL-10 blockade significantly reduced T cell death in the PBMCs of these patients . In our previous studies , we had found that serum IL-10 levels significantly correlated with T cell apoptosis , while inversely correlating with T cell numbers [13] . In this study we found that serum IL-10 levels inversely correlated with DENV-NS3 specific T cell responses but not with T cell responses to non dengue viral proteins . In addition , patients with lower IL-10 levels also were more likely to have DENV-SS T cell responses . Therefore , it is possible that IL-10 preferentially causes apoptosis of DENV-specific T cells or causes apoptosis of activated T cells . Therefore , IL-10 blockade would thus lead to recovery of DENV-NS3 specific responses and also DENV-SS T cell responses . High serum IL-10 levels have been shown to be associated with a worse outcome in many viral infections including influenza and hepatitis B [19] , [20] , [28] , [29] , while in some viral infections such as in Japanese Encephalitis virus infection high IL-10 levels were shown to be associated with a favorable outcome [30] . In a murine model of West Nile virus , blockade of IL-10 signalling has been shown to increase production of antiviral cytokines and improve the disease outcome [29] . Il-10 has shown to have many immunomodulatory properties and has shown to inhibit antiviral T cell responses in other viral infections such as hepatitis B viral infection and HIV in vitro models [31] , [32] . In chronic viral infections such as hepatitis B , elevation of IL-10 was found to coincide with disease flares and in vitro blockade of IL-10 was found to enhance polyfunctional antiviral responses in CD8+ T cells [32] . We too found that in acute dengue viral infection , the frequency of DENV-specific T cell responses were lower in patients with higher serum IL-10 levels and antiviral responses such as IFNγ production , CD107a expression and TNFα production were increased with in vitro IL-10 blockade . Therefore , it appears that IL-10 contributes to disease pathogenesis by inhibiting DENV-specific effector T cell responses . Although , with our data it is evident that IL-10 does appear to reduce DENV-specific T cell responses , it is not clear if this is associated with a worse disease outcome or not . We found that both DENV-SS peptide responses and DENV- NS3 specific T cell responses were lower in patients with DHF when compared to patients with DF . However , our results are contradictory to a previous study which has shown that patients with severe clinical disease were more likely to have a higher frequency of DENV-NS3 specific T cells [33] . However , the timing of determining NS3 responses in this cohort of patients is not clear and it is possible that higher DENV-NS3 specific responses were observed by Duangchinda et al due to a later time point of sample collection [33] . Many studies have shown that T cells are highly cross reactive in acute dengue infection and it is speculated that such cross reactive T cells are likely to contribute to disease pathogenesis by production of inflammatory cytokines and suboptimal control of virus infection [7] , [10] , [34] . However , some have questioned the possible role of cross reactive T cells in pathogenesis of dengue , suggesting that cross reactive T cells are unlikely to be involved causing severe disease [11] . Another study carried out in a large cohort of children with acute dengue , which investigated the timing of the appearance of DENV-NS3 epitope specific T cells , fluid leakage and thrombocytopenia showed that DENV-NS3 specific T cells appeared only after the occurrence of fluid leakage and thrombocytopenia suggesting that they probably did not contribute much to disease pathogenesis . A more recent study by Weiskopf et al shows that healthy individuals with past dengue infection , living in a dengue endemic areas had a high magnitude and polyfunctional T cell responses to a large number of DENV specific CD8+ T cell epitopes . In fact the frequency of T cell responses restricted by HLA- alleles associated with more severe disease was less in this population and the responses were skewed towards more conserved epitopes [12] . Therefore , the data further strengthen the likelihood that DENV-specific T cell responses may actually be associated with reduced severity of dengue . Patients with more severe forms of disease have been shown to have higher viral loads and prolonged viraemia [16] . Studies that were done to determine the kinetics of plasma viraemia have shown that primary infection with certain serotypes were associated with a higher viral load , which took longer to resolve [35] . Duyen et al also showed that those who had the highest plasma viraemia at day 3 were more likely to have lower platelet counts and more severe vascular leak [36] . Collectively , these studies suggest that a suboptimal immune response or an impaired T cell response may lead to severe disease possibly by the inability to eliminate the virus . Therefore , it appears that IL-10 contributes to disease pathogenesis by impairing T cell responses to the DENV . We found that the main source of IL-10 in acute dengue viral infection in the blood is from monocytes . In our earlier studies we found that IL-10 levels in the unstimulated ELISpot culture supernatants were higher in patients with severe dengue when compared to those with non severe dengue [13] . Therefore , spontaneous IL-10 production by monocytes appears to be higher in patients with severe clinical disease . The DENV is known to preferentially infect monocytes directly and through antibody dependant enhancement [37] , [38] . Although studies have not been carried out to determine if higher infection rates of monocytes in acute dengue viral infection , is associated with a higher production of IL-10 , many in vitro studies have shown that higher infection rates in monocytes are associated with production of higher levels of proinflammatory cytokines [37] . Therefore , it is possible that patients with higher infection rates in monocytes induced higher production of IL-10 , which in turn contributes to disease pathogenesis . In summary , we have found that both DENV- SS specific T cell responses and DENV-NS3 specific responses were impaired in patients with higher serum IL-10 levels . Serum IL-10 levels did not appear to have any effect on non dengue viral proteins such as FEC . IL-10 blockade significantly increased IFNγ production , CD107a expression and TNFα production in response to DENV-NS3 overlapping pool of peptides but not to non dengue viral proteins . Therefore , our results suggest that IL-10 could be contributing to disease pathogenesis by inhibiting DENV-specific T cell responses .
|
Dengue viral infections have become one of the most important mosquito borne viral infections in the world and are one of the major emerging infectious diseases . The occurrence of dengue haemorrhagic fever is thought to result from a complex interplay between the virus , host genetics and host immune factors . Patients with more severe clinical disease have been shown to have higher serum IL-10 levels and IL-10 has also been shown to be associated with poorer disease outcome in other viral infections . Therefore , in this study , we proceeded to investigate the role of IL-10 in the pathogenesis of dengue infections and its effect on DENV-specific T cells . We found that both DENV- serotype-specific ( SS ) specific T cell responses and DENV-NS3 specific responses were impaired in patients with higher serum IL-10 levels . Serum IL-10 levels did not appear to have any effect on non dengue viral protein specific responses . IL-10 blockade significantly increased IFNγ production , in response to DENV-NS3 peptides but not to non dengue viral proteins in acute dengue infection . Therefore , our results suggest that IL-10 could be contributing to disease pathogenesis by inhibiting DENV-specific T cell responses .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[
"medicine",
"infectious",
"diseases",
"dengue",
"fever",
"neglected",
"tropical",
"diseases",
"immunology",
"dengue",
"biology",
"viral",
"diseases"
] |
2013
|
Suppression of Virus Specific Immune Responses by IL-10 in Acute Dengue Infection
|
Position-dependent cell fate determination and pattern formation are unique aspects of the development of plant structures . The establishment of single-celled leaf hairs ( trichomes ) from pluripotent epidermal ( protodermal ) cells in Arabidopsis provides a powerful system to determine the gene regulatory networks involved in cell fate determination . To obtain a holistic view of the regulatory events associated with the differentiation of Arabidopsis epidermal cells into trichomes , we combined expression and genome-wide location analyses ( ChIP-chip ) on the trichome developmental selectors GLABRA3 ( GL3 ) and GLABRA1 ( GL1 ) , encoding basic helix-loop-helix ( bHLH ) and MYB transcription factors , respectively . Meta-analysis was used to integrate genome-wide expression results contrasting wild type and gl3 or gl1 mutants with changes in gene expression over time using inducible versions of GL3 and GL1 . This resulted in the identification of a minimal set of genes associated with the differentiation of epidermal cells into trichomes . ChIP-chip experiments , complemented by the targeted examination of factors known to participate in trichome initiation or patterning , identified about 20 novel GL3/GL1 direct targets . In addition to genes involved in the control of gene expression , such as the transcription factors SCL8 and MYC1 , we identified SIM ( SIAMESE ) , encoding a cyclin-dependent kinase inhibitor , and RBR1 ( RETINOBLASTOMA RELATED1 ) , corresponding to a negative regulator of the cell cycle transcription factor E2F , as GL3/GL1 immediate targets , directly implicating these trichome regulators in the control of the endocycle . The expression of many of the identified GL3/GL1 direct targets was specific to very early stages of trichome initiation , suggesting that they participate in some of the earliest known processes associated with protodermal cell differentiation . By combining this knowledge with the analysis of genes associated with trichome formation , our results reveal the architecture of the top tiers of the hierarchical structure of the regulatory network involved in epidermal cell differentiation and trichome formation .
Position-dependent cell fate determination and pattern formation are unique aspects of the development of plant structures . The establishment of single-celled leaf hairs ( trichomes ) from pluripotent epidermal ( protodermal ) cells provides a powerful system to determine the genetic networks and positional cues involved in cell fate determination [1]–[4] . In the Arabidopsis leaf , trichomes constitute the first differentiated cell type . While the number is variable between different leaves and ecotypes , trichomes represent 1–2% of the roughly 1 . 2×104 cells that constitute the Arabidopsis leaf adaxial epidermis . In a developing Arabidopsis leaf , mature trichomes first appear at the tip of the young leaf resulting in a progression of younger trichomes towards the base of the leaf . Mature trichomes are characterized by the presence of a stalk with 2–4 branches and an average DNA content of 32 C [5] . Because of the ease to score mutants , ∼70 genes involved in various aspects of trichome initiation , spacing , size and morphology have been identified [3] ( Table S1 ) . Trichome initiation is regulated by the combinatorial action of the R2R3-MYB GLABRA1 ( GL1 ) together with the bHLH GLABRA3 ( GL3 ) or ENHANCER OF GLABRA3 ( EGL3 ) transcription factors [2] , [6]–[11] . While gl1 mutants are mostly glabrous , mutations in gl3 have a modest effect , primarily affecting branching , DNA endoreduplication and trichoblast size [5] , [8] . In contrast , egl3 plants have no obvious trichome defect , but gl3 egl3 double mutants are glabrous [7] . Thus , GL3 and EGL3 have partially redundant functions , yet they display distinct expression patterns during leaf development . Maximum GL3 and EGL3 expression is observed in leaf primorida . In mature leaves , GL3 expression persists in trichomes , while EGL3 expressed at low levels in both pavement cells and trichomes [12] . Highlighting the central role of GL3 in the selection of protodermal cells to the trichome pathway , four hours of induction of a post-translationally regulated version of GL3 ( GL3-GR , where GR corresponds to the ligand-binding domain of the glucocorticoid receptor ) are sufficient to trigger , in a discrete region of young leaves , the initiation of the trichome differentiation pathway [13] . Within this timeframe , GL3 , in cooperation with GL1 , binds to and activates expression of GLABRA2 ( GL2 ) [13] , encoding a homeo-domain Zip ( HD-Zip ) transcription factor [14] , as well as TRANSPARENT TESTA GLABRA2 ( TTG2 ) [12] , which encodes a WRKY regulator [15] . Within four hours , GL3 also activates the expression of a subset of partially redundant single-repeat R3 MYB proteins , including CAPRICE ( CPC ) [16] and ENHANCER OF TRY and CPC1 ( ETC1 ) . Similarly as TRIPTYCHON ( TRY ) [5] and ENHANCER OF TRY and CPC2 ( ETC2 ) [17] , CPC and ETC1 play central roles in lateral inhibition , by targeting specific components of the MYB/bHLH/TTG1 regulatory complex , making it non-functional [18] . However , only CPC has so far been shown to move to adjacent cells in the leaf epidermis [12] . TRY is also a GL3 direct target , but in contrast to CPC and ETC1 , GL3 binds to the TRY promoter at later stages during trichome development and independently of GL1 [13] . Recent studies have also established a role for the TTG1-GL3 interaction in establishing the regular patterns of trichomes on a leaf; by sequestering TTG1 in the nucleus of trichome cells , GL3 creates a field of cells with lower TTG1 levels not competent to entering the trichome pathway [19] . Based on the studies described above , GL3 and GL1 meet many of the characteristics of selector genes [20] , [21] that govern the fate of a discrete niche of leaf epidermal cells ( protodermal cells ) . So far , however , only five immediate direct targets of GL3 have been identified ( Figure 1A ) . Three of these , TRY , CPC and ETC1 , are predicted to participate in lateral inhibition , resulting in the normal trichome spacing pattern . In contrast , GL2 and TTG2 are the only two GL1/GL3 direct targets known to play a positive role in trichome formation ( Figure 1A ) . Mutants in gl2 and ttg2 , however , form either small trichome primordia that fail to progress [14] or unbranched trichomes [22] , respectively , suggesting that GL3 and GL1 must directly control other proteins that function in the initial stages of epidermal cell differentiation . Here , we describe the identification of a set of genes directly regulated by GL3 and GL1 . These GL3/GL1 direct targets , identified by a combination of chromatin immunoprecipitation ( ChIP ) methods coupled with the hybridization of whole-genome Arabidopsis tiling arrays ( ChIP-chip ) and ChIP analyses on factors known to be involved in trichome formation , include genes involved in the regulation of gene expression , in the control of endoreduplication , in metabolic functions as well as several genes with previously unknown functions . Gene expression in plants expressing the dexamethasone- ( DEX ) inducible GL1-GR or GL3-GR fusions indicated that many of these genes peak very rapidly ( few minutes to a few hours ) after GL3/GL1 induction , suggesting that they play important roles in early events associated with the differentiation of protodermal cells into trichomes . We compared changes in gene expression between wild type and gl3 or gl1 mutant plants , and using a meta-analysis statistical approach , we combined this data with temporal alterations in gene expression in plants expressing the GL1-GR or GL3-GR fusions . These analyses resulted in the identification of a minimal set of 513 genes associated with trichome formation . This information was combined with the GL3/GL1 direct target identification to start establishing the architecture of the trichome regulatory network .
To identify the genomic regions bound in vivo by the GL1 and GL3 transcription factors , we took advantage of the ability of the pGL3::GL3-YFP and pGL1::GL1-YFP-MYC transgenes to complement the trichome defect of the gl3 egl3 and gl1 trichome mutants ( Figure 1B ) . We adapted chromatin immunoprecipitation ( ChIP ) methods coupled with the hybridization of whole-genome Arabidopsis tiling arrays ( ChIP-chip ) using antibodies against GFP ( αGFP ) to immunoprecipitate the chromatin fragments associated with the GL3-YFP and GL1-YFP-MYC regulators obtained from formaldehyde cross-linked green tissues of three-week old Arabidopsis plants . As negative controls , we utilized similar tissues from wild type Arabidopsis plants ( i . e . , not expressing pGL3::GL3-YFP or pGL1::GL1-YFP-MYC ) , and we performed ChIP-chip experiments with IgG on chromatin obtained from gl3 egl3 pGL3::GL3-YFP-MYC plants . For each antibody , two independent biological replicas were performed . To identify genomic regions with a significant signal enrichment for both GL3-YFP and GL1-YFP-MYC , we utilized MAT [23] , which provides a robust tool for the analysis of ChIP-chip experiments on Affymetrix tiling arrays [24] . Applying a cut-off P-value of 0 . 001 , a total of 5 , 328 and 5 , 085 probes ( identified by a sliding window approach using MAT , hence the value of the probes do not correspond to the raw signal values from single probes in the array , but rather to a combination of ten probes integrated through the sliding window ) showed significant scores for GL1 and GL3 , respectively ( Table 1 and Figure S1 ) . To identify the specific regions enriched in GL3 and GL1 , we used the Integrated Genome Browser ( IGB ) , by defining a peak as one or several probes with a significant score separated by less than 100 bp . Applying this criterion , the 5 , 328 probes identified as preferentially enriched in the GL1-YFP-MYC ChIP could be clustered into 680 peaks , and the 5 , 085 probes from GL3 into 873 peaks ( Table 1 ) . The regions recognized by GL3 and GL1 were significantly enriched ( P = 5 . 5×10−6 for GL3 and P = 2 . 2×10−16 for GL1; χ2 test ) in intergenic regions , compared with the overall distribution of the probes in the array . In contrast , both the GL3 and GL1 bound regions were significantly under-represented in coding sequences ( P = 1 . 6×10−6 for GL3 and P = 2 . 2×10−16 for GL1 ) ( Figure S2A ) . Enriched signals from both GL1 and GL3 were clearly located in the proximal region with respect to the transcription start site ( TSS ) . Strikingly , the maximum enriched locations for GL1 were significantly closer to the TSS than the ones for GL3 ( Figure S2B ) . To identify the genes most likely corresponding to these peaks , we scanned the genome for ∼3 kb downstream of where the significant signals were located . The negative controls ( ChIP carried out on wild type plants with αGFP or on pGL3::GL3-YFP/PGL1::GL1-YFP-MYC with IgG ) were analyzed in a similar way , and used for subtracting the signals from the GL3 and GL1 experiments . A total of 537 and 708 genes were identified as located proximal and downstream to the GL1 and GL3 binding regions , respectively ( Table 1 ) . To validate the results from the ChIP-chip experiments in an unbiased fashion , we randomly selected 20 and 15 genomic regions identified by MAT as enriched for GL3 and GL1 , respectively . A total of 14 out of 20 regions provided robust and reproducible signals in ChIP experiments using pGL3::GL3-YFP plants ( Figure S3B ) , and 12 out of 15 for GL1 ( Figure S3A ) , suggesting experimentally validated ( rather than predicted ) false positive discovery rates of 0 . 3 and 0 . 2 for GL3 and GL1 , respectively . Among the genes previously shown to be direct targets of GL3 or GL1 [12] , [13] , the ChIP-chip experiments identified TTG2 , CPC and ETC1 as targets for GL3 , and TRY for GL1 ( Table 2 ) . In addition , ChIP-chip identified At5g04470 ( SIM ) , At3g12280 ( RBR1 ) , At2g26250 ( FDH ) and At4g01060 ( CPL3 ) as GL3 direct targets and At1g63910 ( AtMYB103 ) as a target of GL1 ( Table S1 and Figure S5 ) . SIM , RBR1 , FDH , CPL3 and AtMYB103 are among approximately 70 genes that have been identified as participating in various aspects of trichome initiation , branching , morphology and distribution ( Table S1 ) . Taken together , these results suggest that the ChIP-chip experiments have been successful in identifying putative targets for the trichome regulators , GL3 and GL1 . We predicted that at least some of the genes involved in trichome initiation should be targets of both GL3 and GL1 , similar as we demonstrated previously for GL2 and TTG2 [12] , [13] . A total of 20 genomic regions ( corresponding to 21 genes ) showed a significant enrichment for both GL3 and GL1 ( Figure 2 ) . One of these regions corresponded to a tandem repeat of 14 genes corresponding to At4g20530 - At4g20670 on which a lower signal was also detected in the ChIP-chip negative control ( Figure 2 , see At4g20530 - At4g20670 ) . Thus , for the subsequent studies , we will not focus on genes within this tandem arrangement since we are not confident on whether the signal observed with GL3 and GL1 is real or not . It is not uncommon , however , for simple tandem repeats to be associated with false positives in ChIP-chip experiments [24] . We individually analyzed by regular ChIP the seven remaining genes identified as putatively bound by both GL3 and GL1 . Six of the seven genes ( At1g77670 , At3g50790/At3g50800 , At4g20960 , At5g28350 and At5g52510 ) were confirmed as recognized by both GL3 and GL1 , and one ( At3g10113 ) showed no binding by either regulator in the promoter region tested ( Table 2 and Figure 3A ) . At5g52510 corresponds to SCL8 , a divergent member of the GRAS family of regulatory proteins [25] . At3g50800 and At5g28350 are annotated as unknown ‘expressed proteins’ in TAIR . However , the protein structure threading program , PHYRE ( http://www . sbg . bio . ic . ac . uk/phyre/html/index . html ) predicted them as TGS-like domain and WD-repeat proteins , respectively ( Figure S4A ) . While WD-repeats are often associated with protein-protein interaction [26] , the function of the TGS domains , named after ThrRS , GTPase , and SpoT , is less well know , but was proposed to bind nucleotides [27] . PHYRE also predicted At5g28350 to contain a motif conserved in the yeast RIC1 protein ( the RIC1 domain , Figure S4A ) , perhaps involved in the transport of endosome-derived vesicles to the Golgi network [28] . At3g50790 encodes a putative hydrolase , which belongs to the late embryogenesis abundant ( LEA ) proteins [29] , and which is broadly expressed in green tissues at most developmental stages ( Figure S4B , C ) . At4g20960 is annotated in TAIR as diaminohydroxyphosphoribosyl aminopyrimidine deaminase ( EC 3 . 5 . 4 . 26 ) , which catalyzes the second step in riboflavin biosynthesis . At1g77670 is predicted to encode pyridoxal phosphate dependent transferase involved in the biosynthesis of amino acids and amino acid-derived metabolites [30] . Previously , we described three mechanisms by which GL3 could bind and presumably control , target gene expression . The first mechanism requires the presence of a functional GL1 protein to tether GL3 to the GL2 , CPC and ETC1 gene promoters [13] . Working by the second mechanism , GL3 can bind the TRY promoter independently of GL1 , although both GL3 and GL1 are necessary for TRY activation . By the third mechanism , GL3 binds and regulates transcription independently of GL1 , as we showed for the negative auto-regulation of GL3 [13] . Thus , we investigated which of these mechanisms might be at play in the control of the six newly identified GL3 and GL1 direct targets . Towards this goal , we expressed the pGL3::GL3-YFP transgene in the gl1 mutant , as previously described [13] , and performed ChIP experiments ( with αGFP ) . For At3g50790/At3g50800 , At4g20960 , At5g28350 and At5g52510 , the binding of GL3 required the presence of GL1 , suggesting that the regulation of these genes occurs by the first mechanism , as is the case for GL2 , TTG2 , CPC and ETC1 . Only in the case of At1g77670 , the binding by GL3 was independent of GL1 ( Figure 3A , compare gl3 egl3 pGL3::GL3-YFP and gl1 pGL3::GL3-YFP ) , despite the fact that GL1 also bound this promoter ( Figure 3A , gl1 pGL1::GL1-YFP-MYC ) . Together , these results identify a set of new direct targets for both GL3 and GL1 , corresponding to genes of known and unknown functions likely involved in early stages of trichome initiation . Despite being an outstanding tool for the identification of direct targets for transcription factor , ChIP-chip has a notorious false negative rate ( i . e . , real positives that fail to be identified ) [24] . Thus , we took a complementary approach to identify additional putative direct targets of GL3 and GL1 . Based on the effect of mutations , ∼70 genes have been identified as participating in various aspects of trichome initiation , pattern formation , endoreduplication and morphology ( Table S1 ) . Only six of these genes ( GL2 , TTG2 , GL3 , TRY , ETC1 and CPC ) had been previously identified as GL3-GL1 direct targets [12] , [13] , and our ChIP-chip experiments identified five more ( SIM , RBR1 , FDH , CPL3 and AtMYB103 ) as targets of GL3 , GL1 or both ( Figure 3B and Table 2 ) . Thus , we asked whether genes described as involved in trichome morphogenesis might be direct targets for GL3 and/or GL1 , by testing the presence of a region spanning a 500 bp upstream of the TSS for each of these candidate genes in ChIP experiments performed on gl3 egl3 pGL3::GL3-YFP or gl1 pGL1::GL1-YFP-MYC transgenic plants . Representative examples of the results of these experiments are shown in Figure S5 and the data is summarized as part of Table 2 and Table S1 . Interestingly and highlighting the false negative discovery rate of ChIP-chip experiments , SIM , RBR1 , CPL3 and FDH , which were only found in the ChIP-chip experiments with GL3-YFP , showed reproducible tethering of both GL3 and GL1 to the corresponding promoters in ChIP assays , suggesting that they should be added to the list of shared direct targets of GL3 and GL1 ( Table 2 ) . MYC1 , which did not come up in the ChIP-chip experiments as either a target of GL3 nor of GL1 , showed robust binding by both regulators in conventional ChIP assays ( Figure 3B ) . In contrast , MYB103 , a gene involved in endoreduplication [31] and identified as a GL1 target by ChIP-chip , could so far not be validated by ChIP as a GL1 target , thus MYB103 will not be further considered in this study . Taken together , these results expand to 19 the set of genes directly regulated by both GL3 and GL1 . GL3 and GL1 participate in complexes with other R2R3-MYB and bHLH proteins , respectively . For example , GL1 interacts with EGL3 and MYC1 , and GL3 interacts with MYB23 [32] . Thus , genes regulated just by GL3 or GL1 could be very interesting in understanding how different MYB/bHLH complexes target distinct sets of target genes . To investigate the role of GL3 and GL1 on the expression of trichome genes , we took two complementary strategies . In the first approach , we performed genome-wide gene expression analyses using Affymetrix ATH1 arrays with RNA extracted from green tissues obtained from 14 days-old wild-type , gl1 or gl3 egl3 seedling . Statistical analysis performed on biological triplicates revealed that 3 , 341 genes were differentially expressed in gl1 plants , compared to wild type , and 731 genes were differentially expressed in gl3 egl3 plants , compared to wild type ( Figure 4A ) . Out of the 3 , 341 genes , 41 genes were identified in the ChIP-chip experiments as GL1 direct targets , and out of the 731 genes , 20 genes were found to be direct targets of GL3 ( Figure 4B ) . Since trichome formation progresses in parallel with leaf development , the plants used for these expression analyses contain leaf hairs at all possible stages , making it difficult to determine at what stage of trichome formation the GL3/GL1 targets may function . As a first approximation to identify GL3/GL1 targets participating in early stages of trichome initiation ( likely under-represented in the previous analyses ) , the second approach took advantage of plants expressing translational fusions of GL3 or GL1 with GR , driven by the corresponding promoters ( pGL3::GL3-GR and pGL1::GL1-GR ) . As previously described , gl3 egl3 pGL3::GL3-GR and gl1 pGL1::GL1-GR plants accumulate trichomes only in the presence of dexamethasone ( DEX ) [13] . Genome-wide expression analyses were performed on gl3 egl3 pGL3::GL3-GR and gl1 pGL1::GL1-GR plants at 4 hrs and 24 hrs after DEX induction , and compared with Mock-treated plants . Statistical analyses resulted in the identification of 255 and 56 genes affected by GL1-GR induction at 4 and 24 hrs , respectively . Similar analyses performed on pGL3::GL3-GR plants resulted in the identification of 118 and 221 genes affected at 4 and 24 hrs , respectively ( Figure 4A ) . Interestingly , the identity of the genes affected by GL3 and GL1 after 4 or 24 hrs of induction were strikingly different ( Figure S6 ) , suggesting a clear distinction in the gene functions necessary for earlier and later stages of trichome formation . The lower number of genes affected by GL1-GR at 24 hrs , compared with GL3-GR at 24 hrs , is in agreement with models suggesting that the function of GL1 is primarily limited to earlier stages of trichome development , while the effects of GL3 extend into later stages , including branch formation [33] . To establish a minimal set of genes uniquely associated with the formation of trichomes controlled by GL1 and/or GL3 , statistical meta-analyses were performed . Briefly , using the P value statistics obtained from the six microarray experiments ( gl1 versus wild type , gl3 egl3 versus wild type , gl1 pGL1::GL1-GR 4 and 24 hrs DEX induction , and gl3 egl3 pGL3::GL3-GR 4 and 24 hrs DEX induction; all experiments done in biological duplicates or triplicates , see Materials & Methods ) , q-values were calculated as described [34] ( see Materials & Methods ) . This analysis resulted in the identification of a minimal set of 513 genes ( q<0 . 05 ) associated with the GL1/GL3 induction of trichomes . Based on Gene Ontology ( GO ) analyses , this group of genes showed a significant enrichment in ( 1 ) metabolism , ( 2 ) energy , ( 3 ) protein fate , ( 4 ) cellular communication and signal transduction mechanism , ( 5 ) cell rescue , defense and virulence , ( 6 ) interaction with the environment , ( 7 ) systemic interaction with the environment , ( 8 ) development and ( 9 ) subcellular localization ( Figure S7 ) . These findings define a minimal set of 513 genes associated with trichomes , a set that is hierarchically positioned downstream of GL3 , GL1 or both . Only 4 and 20 genes were found to overlap between the meta-analysis and GL1 or GL3 ChIP-chip experiments , respectively ( Figure 4C ) . This may reflect GL1 and GL3 bind many promoters without a significant effect on their expression , as has been found to be the case for some transcription factors in animals [35] , and that many of the meta-analysis identified genes correspond to indirect targets of GL3/GL1 . To further delineate the specific stages during trichome formation at which the immediate direct targets of the GL3/GL1 complex function , we explored their expression by quantitative real-time RT-PCR ( qRT-PCR ) in gl3 egl3 pGL3::GL3-GR and gl1 pGL1::GL1-GR plants at various times after DEX induction ( Figure 5 ) . While in some cases , biological variation between the triplicates used in these experiments interfered with statistical significance tests , specific trends in the response of the target genes to GL3 and GL1 induction become evident when looking at overall patterns . Consistent with the gl2 and ttg2 mutant phenotypes , suggesting functions after trichome initiation , perhaps during the growth and maturation of a trichome primorida , GL2 and TTG2 expression peak 24–48 hrs following GL3 or GL1 induction . In contrast , the peak in CPC mRNA accumulation occurs 1–4 hrs after GL3 or GL1 induction . Similar to CPC and suggesting functions needed very early in trichome initiation , At3g50790 , At5g52510 , At5g28350 , MYC1 and FDH show mRNA accumulation peaks within 10 hrs of GL3 or GL1 induction . The increase in At5g52510 mRNA accumulation controlled by GL3 is slightly delayed , compared with GL1 , and the steady-state mRNA levels of At5g28350 are primarily affected by GL1 , with a lesser effect by GL3 despite it being recruited to the At5g28350 promoter ( Figure 3A ) . RBR1 and SIM show very similar mRNA accumulation patterns , with an early induction peak within 15 minutes , and a later peak after 24 hrs of GL3 and GL1 induction . This later peak is also observed for At3g50800 and At4g20960 . ChIP-chip and ChIP analyses did not permit us to determine whether the GL3/GL1 recruitment to the intergenic region of At3g50790/At3g50800 ( Figures 2 and 3 ) regulated the expression of one gene , the other or neither . It is evident from the qRT-PCR experiments that GL3 and GL1 modulate the mRNA accumulation of both At3g50790 and At3g50800 in different ways . While At3g50790 mRNA peaks at around 4 hours after GL3 and GL1 induction , the expression of At3g50800 peaks at around 24 hrs ( Figure 5 ) . The TSSs for these genes are separated by just 320 bp , and the genes are oriented in a head-to-head organization ( Figure S8 ) . Taken together , these results show that GL3 and GL1 direct targets peak early during trichome formation , with a clear distinction between very early genes ( <10 hrs ) or later genes ( >24 hrs ) , suggesting that the corresponding gene products are similarly required within those particular developmental windows .
Previous studies had identified just six direct targets for the GL3/GL1 trichome regulators , from which GL2 and TTG2 were the only known positive regulators ( Table 2 ) . Yet , the phenotype of gl2 and ttg2 mutations ( trichomes arrested as small protuberances ) indicated that , while the GL2 and TTG2 gene products are important for the maturation of trichome initials , they probably did not function during early trichome initiation steps . Thus , we combined two approaches towards the identification of novel GL3/GL1 direct targets: ChIP-Chip experiments using GFP-tagged proteins and candidate gene approaches , taking advantage of the rich collection of trichome mutants available ( Table S1 ) . We attempted to identify GL3 and GL1 direct targets using plants harboring the corresponding GR fusions [12] , [13] , by comparing genes affected by DEX in the presence and absence of the protein synthesis inhibitor cycloheximide ( CHX ) , but CHX often masked the effects of DEX , making the approach , at least for this particular set of regulators , impractical . Table 2 lists all the so far known GL3/GL1 direct targets and the evidence supporting it . Among the new GL3/GL1 direct targets , our studies identified SIM ( SIAMESE ) , RBR1 ( RETINOBLASTOMA RELATED1 ) , FDH ( FIDDLEHEAD ) , MYC1 , MYBL2 and CPL3 ( CAPRICE-LKE MYB3 ) ( Table 2 ) . FDH encodes a β-ketoacyl-CoA synthase related protein , which has been implicated in modifying the properties of the cuticule , preventing epidermal fusions [36]–[38] . Consistent with a role in trichome formation , fdh mutants show a significant reduction in the number of trichomes [37] . Suggesting a participation of FDH and cuticule functions early in trichome formation , FDH mRNA levels peak at around 4 hrs after GL3/GL1 induction ( Figure 5 ) . MYC1 encodes a bHLH factor [39] closely related to GL3 and EGL3 [40] . QTL analyses implicated MYC1 in controlling trichome numbers [41] , [42] . Both GL3 and GL1 bind the MYC1 promoter , and the tethering of GL3 requires the presence of GL1 ( Figure 3B ) , suggesting similar mechanisms for MYC1 regulation by GL3/GL1 as for GL2 , CPC and TTG2 . MYC1 mRNA accumulation follows a pattern different from other regulators: It drops 1 hr after GL3 and GL1 induction to then increase back , earlier for GL3 than for GL1 ( Figure 5 ) . Thus , our results suggest the existence of a regulatory motif in which MYC1 mRNA accumulation is partially controlled by GL3/GL1 ( Figure 6 ) . Since MYC1 was shown to interact with GL1 and other related R2R3-MYB factors [32] , it is possible that the regulation of MYC1 by GL1/GL3 represents a feedforward network motifs , perhaps participating in amplifying signals for trichome initiation , or maybe involved in switching the targets from a GL1/GL3 complex to a GL1/MYC1 ( or MYB23/MYC1 ) complex . CPL3 ( CAPRICE-LKE MYB3 ) and MYBL2 , similar to CPC , ETC1 and TRY , encode single repeat MYB proteins [43]–[45] . As CPC but distinct from TRY [13] , the in vivo recruitment of GL3 to promoter sequences in CPL3 and MYBL2 requires GL1 , which is also tethered to DNA ( Figure 3B ) . The expression of CPL3 , however , is controlled with different kinetics by GL3 and GL1 . In the gl3 egl3 pGL3::GL3-GR plants , CPL3 peaks within 15 min of DEX treatment , whereas GL1 induces its expression around 12 hrs ( Figure 5 ) . MYBL2 has been primarily implicated as a negative regulator of anthocyanin biosynthesis [43] , [44] , yet MYBL2 over-expression suppressed Arabidopsis trichome formation [46] . Our results , exposing MYBL2 as a GL3/GL1 direct target , further highlight its function in trichome formation . The results presented here show that GL3 and GL1 directly activate the expression of most of the known single MYB repeat inhibitors of trichome formation , including CPC , ETC1 , CPL3 , MYBL2 and TRY . These single repeat MYB proteins are conserved in sequence and have been predicted to have similar functions , thus it remains to be determined why several of them need to be directly controlled by GL1 and GL3 . SCL8 encodes a GRAS family transcription factor and SCL8 mRNA levels peak sharply within the first few hours of GL3 or GL1 induction , to then level off at quantities similar as found in the absence of the regulators ( Figure 5 ) , suggesting a need for SCL8 function at early stages during trichome initiation . The function of SCL8 remains unknown . However , similar to the formation of trichomes , the initiation of axillary meristems is controlled by the action of bHLH and MYB transcription factors , leading to the speculation that similar regulatory motifs might participate in the control of these two processes [47] . Axillary meristem formation involves the LAS ( LATERAL SUPRESSOR ) GRAS family member [48] . Thus , the identification of SCL8 as a GRAS family member involved in trichome formation further expands similarities between the regulation of these two processes . A recent study reported the analysis of genes differentially expressed in trichome by exploring the transcriptome of dissected trichomes [49] . In agreement with our results , SCL8 is among the genes described in this study as displaying increased expression in trichomes . In addition , our studies identified several GL3/GL1 direct target genes with unknown functions ( Table 2 ) . Through the utilization of structure-prediction programs , some specific domains were identified in the encoded proteins , which will facilitate their functional characterization and participation in trichome formation . We also found many genes that are direct targets of either GL1 or GL3 , but not of both together ( Figure S5 and Table 2 ) . It is possible that the regulation of those genes involves other MYB-bHLH complexes , such as GL1-EGL3 or MYB23-GL3 . Indeed , based on our results and the observation that MYB23 participates in later stages of trichome morphogenesis [11] , we speculate that GL3 direct targets such as BRK1 and DIS1 , which function in trichome morphogenesis , and CDKA;1 and CYCA2;3 , likely involved in maintaining endoreduplication , are targeted by the GL3-MYB23 complex ( Table S1 and Figure S6 ) . MYB23 was not identified as a target of GL3 or GL1 in ChIP experiments , nor was MYB23 expression significantly affected by these regulators in either the DEX induction experiments , or the genome-wide expression analyses comparing wild-type and gl1 or gl3 egl3 mutant plants ( not shown ) . Cell differentiation is often associated with a change from mitotic cell division to endoreduplication [50] . This is also the case for trichomes , which show an average DNA content of 32 C [5] . During the initial stages of differentiation of protodermal cells into trichomes , the first phenotypic change that anticipates trichome appearance is an enlargement of the nucleus , corresponding to the initiation of the endocycle [5] . GL3 was previously implicated in the control of endoreduplication [5] , yet the mechanisms associated with the early cellular reprogramming associated with the switch from mitosis to the endocycle remain unknown . The identification of SIM and RBR1 as immediate direct targets of GL3/GL1 provides clues on the earliest molecular mechanisms associated with this switch . SIM encodes a small protein with a region of similarity to cell cycle inhibitor proteins and which interacts with D-type cyclins and cyclin-dependent kinases ( CDKs ) , such as CDKA;1 [51] . In sim mutants , multicellular trichomes and trichome clusters form [52] . Consistent with SIM being a direct target of GL3 , the levels of SIM expression in gl3 egl3 plants were found to be very significantly reduced [51] . Our results , highlighting a role of GL1 in the regulation of endoreduplication early during trichome initiation , is consistent with the recent identification of sim mutant allele as a modifier of the trichome phenotype of an allele of GL3 ( gl3-sst ) impaired in its ability to interact with GL1 [9] , [53] . RBR1 , through its interaction with members of the E2F family of transcription factors , regulates the balance between cell division and the endocycle , and the conditional inactivation of RBR1 results in trichomes with altered morphologies , which include more branches [54] . Thus , while SIM participates in activating the endocycle and repressing cytokinesis during trichome formation , RBR1 is likely to restrict the number of endocycles associated with normal trichome morphogenesis . Interestingly , SIM and RBR1 show very similar mRNA accumulation patterns after induction of GL3/GL1 function ( Figure 5 ) . The mRNA for both genes peaks very early ( 15 minutes ) after the treatment with DEX , followed by a decrease that suggests rapid mRNA turnover , to increase again 10–24 hrs after GL3/GL1 induction . Our findings provide the first evidence directly implicating GL3/GL1 in the control of endoreduplication very early after the trichome initiation program has been triggered . In addition , they suggest that the initial steps in the switch from mitosis to the endocycle involve a two-pronged strategy: the inhibition of CDKs by SIM triggering the endocycle , and the inhibition of E2F by RBR1 , restricting the number of endocycle rounds . One of the questions that this study intended to answer is whether the function of GL3 and GL1 is solely channeled through TTG2 and GL2 , the only two positive regulators previously known to be regulated by the trichome regulators . Our analyses indicate , however , that this is not the case , and that at least two additional transcription factor genes , SCL8 and MYC1 ( Table 2 ) , are direct targets of the GL3/GL1 complex . In addition , genome-wide expression analyses , comparing genes differentially expressed between wild type and gl3 egl3 or gl1-1 plants , as well as those affected by DEX in gl3 pGL3::GL3-GR and gl1 pGL1::GL1-GR plants , implicated a minimum set of 513 genes ( “trichome genes” ) as directly or indirectly controlled by GL3 or GL1 . Thus , starting from the assumption that the trichome regulatory module has a hierarchical layout with the GL3/GL1 regulators at the top ( first tier regulators ) , we investigated the relationship between the “trichome genes” and the corresponding regulators . GL2 , TTG2 , MYC1 and SCL8 all correspond to regulatory factors directly controlled by GL3/GL1 , hence constitute second tier regulators ( Figure 7 ) . The other identified GL3/GL1 direct targets ( Table 2 ) are either not predicted to correspond to transcription factors ( Figure 7 ) , or modulate the activity of the GL3/GL1 complex , as is the case of the single MYB repeat proteins , and thus feed-back control tier 1 regulators . Thus , the 513 “trichome genes” , if they are not direct targets of GL3/GL1 , they must be downstream of one or several of the second tier regulators . To determine the relationship of the “trichome genes” with each one of the second tier regulators , we investigated which genes were co-expressed more tightly with each of the regulators , using Pearson's Correlation Coefficient ( PCC ) obtained from ATTED-II ( http://www . atted . bio . titech . ac . jp/ ) , and surveying the expression data generated by AtGenExpress [55] . Interestingly , the distribution of PCC scores of “trichome genes” with GL2 and TTG2 was almost identical , as evidenced by heat-maps of PCC values after hierarchical clustering ( Figure 6A , B ) . In contrast , there was no significant overlap in the “trichome genes” co-expressed with SCL8 and those co-expressed with GL2 or TTG2 ( Figure 6A , C ) , although a weak negative correlation between the genes co-regulated with SCL8 and those co-regulated with MYC1 was observed ( Figure 6A , D ) . These results suggest that SCL8 controls a very different set of trichome genes than GL2 , TTG2 and MYC1 . Based on the hierarchical clustering of coexpression values with the GL2 , TTG2 , SCL8 and MYC1 regulators , the “trichome genes” were classified into 5 arbitrary groups ( Figure 6A , I–V ) . For each group , the major GO class represented was identified ( Table S3 ) . For example , Groups I , II and IV correspond to genes whose expression strongly correlates with the expression of TTG2 and GL2 , and which are enriched in the categories of metabolism , protein synthesis , development and interaction with the environment . In contrast , the expression of SCL8 strongly correlates with Group V , which is primarily enriched in genes involved in cellular transport . These analyses permit us to start narrowing down the specific sets of “trichome genes” that are likely downstream of the second tier regulators ( Figure 7 ) , providing a set of candidate genes to continue expanding the regulatory network . The expression of GL2 and TTG2 follow a very similar pattern after GL3 and GL1 induction ( Figure 5 ) . Moreover , the co-expression analyses suggest that the functions of GL2 and TTG2 largely overlap ( Figure 6A , B ) , which is consistent with the similar arrest at the trichome initial stage observed in both mutants [15] . Although GL2 mRNA levels are not affected in the ttg2-3 mutant , the expression of a dominant negative version of TTG2 ( TTG2-SRDX ) almost completely abolished GL2 expression , suggesting that GL3/GL1 , TTG2 and GL2 may form a feed forward loop ( Figure S9A and Figure 7 ) , by which TTG2 would control , at least in part , GL2 expression [22] . To determine whether TTG2 directly controls GL2 , we performed ChIP experiments on p35S::TTG2-GFP plants , using αGFP . Although we detected in vivo binding of TTG2 to its own promoter ( Figure S9B ) , consistent with the proposed positive feedback regulation of TTG2 [22] , we failed to detect in vivo binding of TTG2 to GL2 ( Figure S9C ) . From these results , we conclude that , while the regulatory function of TTG2 and GL2 largely overlap , it is unlikely that TTG2 is directly controlling GL2 expression . They could be functioning together to modulate the expression of down-stream genes , or TTG2 might indirectly control GL2 expression . Here , we describe the first steps towards establishing the regulatory network involved in the differentiation of epidermal cells into trichomes in Arabidopsis . By combining ChIP-chip and genome-wide expression analyses , we have identified direct targets shared by the first tier trichome selectors , GL3 and GL1 , in addition to a number of genes putatively controlled by one or the other regulator , most likely as part of regulatory complexes with other characterized R2R3-MYB or bHLH factors , respectively . Among the GL3/GL1 direct targets , at least four transcription factors constitute the second tier regulators of the network hierarchical structure . Co-expression analyses of genes specifically associated with trichome induction were utilized to identify candidate genes downstream of each of the four second-tier regulators , further delineating lower tiers in the network architecture . These studies identified some of the earliest steps involved in trichome initiation , while providing a number of candidate genes that may participate in trichome formation .
The Arabidopsis thaliana gl3 egl3 pGL3::GL3-YFP , gl1 pGL3::GL3-YFP , gl1 pGL1::GL1-YFP-MYC , gl3 egl3 pGL3::GL3-GR , gl1 pGL1::GL1-GR seed stocks have been previously described [12] , [13] . Plants were grown on soil containing 100 µM BASTA ( Liberty™ , AgrEvo ) ( gl3 egl3 pGL3::GL3-GR and gl1 pGL1::GL1-GR ) or MS media supplemented with 50 µM kanamycin ( gl3 egl3 pGL3::GL3-YFP , gl1 pGL1::GL1-YFP-MYC , gl1 pGL3::GL3-YFP , p35S::TTG2-GFP ) at 22°C , under a photoperiod of 16 hours of light and 8 hours dark , unless otherwise indicated . For DEX treatments experiments , 15 days-old seedlings were transferred from plain MS media to MS media containing 30 µM DEX or 2% ethanol ( Mock ) . DEX was kept as a 3 mM solution in ethanol at −20°C . Green tissues or whole seedlings were collected 4 and 24 hours after DEX treatment and frozen immediately in N2 ( l ) . Approximately 30 to 40 seedlings were used for each RNA extraction . Plant materials were ground in liquid nitrogen and homogenized in 7 . 5 ml Trizol reagent . After incubation at room temperature for 5 min , the insoluble material from the homogenate was removed by centrifugation at 12 , 000×g for 10 min at 4°C , supernatant transferred to a fresh tube and 1 . 5 ml chloroform was added and mixed by vortexing for 30 sec . Samples were incubated at room temperature for 3 min followed by centrifugation at 10 , 000×g for 15 min at 4°C . RNA was precipitated from the aqueous phase by mixing the aqueous phase with 3 . 75 ml isopropyl alcohol . Following incubation at room temperature for 20 min , the samples were centrifuged at 10 , 000×g for 10 min at 4°C . The RNA precipitates were washed with 10 ml of 70% ethanol and centrifuged again . RNA pellets were dried for 10 min at room temperature and then dissolved in 150 µL nuclease free water by incubating at 60°C for 10 min . RNA samples were further concentrated through Qiagen RNesay® mini columns following the RNeasy mini protocol for RNA cleanup protocol from the manufacturer . Approximately 100 mg of green tissues were used for each RNA extraction by using Qiagen RNesay mini columns following the manufacture's instruction . After DNase treatment using RQ1 RNase-free DNase ( Promega ) , reverse transcription ( RT ) reactions were performed using Superscript II reverse transcriptase ( Invitrogen ) on approximately 100 ng of total RNA from each sample after DNase treatment ( Promega ) for 30 min at room temperature . Real-time RT-PCR ( qRT-PCR ) was performed using iQ SYBR Green Supermix ( BIO-RAD ) on an iCycler equipment ( BioRad ) . Primers for qRT-PCR were designed to generate 80 bp to 100 bp fragments ( See Table S4 ) . We used At1g13320 , which has been reported to be an appropriate reference gene for qRT-PCR [56] , as an internal reference to normalize expression ratios . The qRT-PCR analyses of the test and reference genes were performed simultaneously , following normalization by calculating the fold ratios between test samples and reference gene . Ct values of test samples obtained from qRT-PCR , Ctsample were subtracted by Ct values of reference , Ctref , then the ratios of DEX and Mock were calculated using normalized values using the following equation: Where and are Ct values of sample and reference genes in DEX treated plants , respectively , and and are Ct values of sample and reference genes in Mock treated plants , respectively . Three biological independent materials were used . Two to four biological independent materials were used for RNA preparation . The integrity and concentration of the RNA was verified by capillary electrophoresis using a Bioanalyzer 2100 ( Agilent ) . Sample preparation for hybridization and detection were according to Affymetrix protocols . Raw data ( . CEL files ) were obtained from the hybridization of Arabidopsis Affymetrix ATH1 Arrays with the samples described in Table S2 . Whole tissues of gl1 and wild type , and green tissues of gl3 egl3 and GL1-GR and GL3-GR plants were used for RNA extraction . Microarray data analyses were performed using the R software with AffylmGUI of the Bioconductor package [57] . The data was normalized by GCRMA prior to further analysis . For the calculation of DEX induced ratios , values from DEX-treated samples were divided by ones from Mock-treated samples , resulting in the DEX/Mock ratios . The ratios of wild type versus mutant ( Wild type/mutants ratios ) were calculated by wild type expression values divided . Ratios were subjected to Student's t-test statistical analyses with a cut-off value of P<0 . 01 . Meta analysis was performed as described [34] . Briefly , P values of a gene of each microarray experiment was integrated using Fisher's inverse method:where is the P value for gene g in the experiment i . P values were integrated from six experiments consisting of 4 and 24 hours DEX inductions of plants carrying pGL1::GL1-GR or pGL3::GL3-GR genes , and wild type and gl1 or gl3 egl3 mutants . corresponds to the chi-square distribution with 12 degrees of freedom . Then , the P value for gene g based on the integral analysis of all the datasets was calculated using the distribution . For controlling the False Discovery Rate ( FDR ) , q-values were calculated by the R module , QVALUE [58] and genes that showed q value less than 0 . 05 were considered for further analyses . The q-value of this test measures the minimum FDR that is incurred when calling that test significant , whereas the P value of a test measures the minimum false positive rate that is incurred when calling that test significant . Using q-values , it is possible to assign a measure of significance to each one of many tests . Whole seedlings from three-week-old plants grown on soil were subjected to ChIP experiments , which were performed as described [13] , [59] . For ChIP-chip experiments , precipitated and input DNA were amplified using the GenomePlex Whole Genome Amplification Kit ( Sigma ) , following the method modified for ChIP-chip [60] . DNA fragmentation , labeling , hybridization , washes and detection were performed following the Affymetrix 100K protocol ( http://www . affymetrix . com/products/arrays/specific/100k . affx ) . CEL files were further analyzed by MAT ( Model-based Analysis of Tiling array; http://chip . dfci . harvard . edu/̃wli/MAT/ ) [23] using the following parameters: BandWith = 300 , MaxGap = 300 , MinProbe = 10 and Pvalue = 0 . 001 . Peaks consisting of continuous probes with significant MAT scores were evaluated using IGB ( Integrated Genome Browser , Affymetrix ) with the additional criteria that the minimum gap should be less than 100 bp . We defined target genes as those for which 3 kbp upstream regions contained at least one peak showing significant MAT scores . To investigate the distribution of binding sites , relative MAT score were calculated . Each MAT score for GL1 and GL3 was divided by the average MAT scores of the corresponding negative controls obtained from ChIP-chip experiments with IgG on the pGL3::GL3-YFP or pGL1::GL1-YFP-MYC plants , or on wild type plants , which do not carry GFP , with αGFP . We first aligned the transcription start site ( TSS ) of all Arabidopsis genes and divided the genomic regions into 50 bins ( 60 bp each ) in the [−3 , 000; +3 , 000] interval , followed by plotting means of relative MAT scores based on the bins . Heatmaps of expression profiles were drawn with TM4 ( TIGR , http://www . tm4 . org/ ) [61] . Hierarchical clustering with metrics of Euclidean distances and average linkage clustering was utilized for making heatmaps . We used custom-made Perl scripts ( available at http://grassius . org/help . html ) . PCC of GL2 , TTG2 , SCL8 and MYC1 with each of approximately 500 genes affected by GL1 and/or GL3 were obtained from ATTED-II ( http://www . atted . bio . titech . ac . jp/ ) [62] . Five clusters were chosen manually after hierarchical clustering of PCC distribution of GL2 , TTG2 , SCL8 and MYC1 with genes affected by GL1 and GL3 . Main GO distributions for each class of genes were determined using the FunCat application ( [63]; http://mips . gsf . de/proj/funcatDB/search_main_frame . html ) from data in MIPS . All the microarray data generated as part of this study has been deposited in the Gene Expression Omnibus ( http://www . ncbi . nlm . nih . gov/geo/ ) with accession numbers GSE12551 , GSE12522 and GSE13090 .
|
The establishment of single-celled leaf hairs ( trichomes ) from pluripotent epidermal ( protodermal ) cells provides a powerful system to determine the gene regulatory networks involved in plant cell fate determination . Two transcription factors—GL1 and GL3—have been associated with the initiation of trichome formation; yet only a handful of GL1-GL3–regulated genes have previously been characterized . In this study , we combined expression analyses performed in a number of different genotypes to identify a minimal set of about 500 genes associated with trichome formation . We also used ChIP-chip to identify a set of about 20 genes that are immediate targets of GL3 and GL1 . Many more genes are targeted by GL1 or by GL3 , likely in cooperation with other bHLH of MYB partners , but not by both GL1 and GL3 . As predicted for genes involved in the initiation of epidermal cell fate determination , several of the GL3/GL1 direct targets are expressed early during trichome formation , including the transcription factors MYC1 ( bHLH ) , SCL8 ( GRAS ) , and genes involved in the regulation of the endocycle ( SIM and RBR1 ) . Co-expression analyses permitted us to identify sets of target genes likely downstream of the GL3/GL1 regulated transcription factors , providing the first steps towards building the regulatory network associated with the differentiation of protodermal cells into trichomes .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Material",
"and",
"Methods"
] |
[
"computational",
"biology/systems",
"biology",
"genetics",
"and",
"genomics/gene",
"expression",
"computational",
"biology/transcriptional",
"regulation",
"developmental",
"biology/plant",
"growth",
"and",
"development",
"plant",
"biology/plant",
"growth",
"and",
"development",
"plant",
"biology/plant",
"genetics",
"and",
"gene",
"expression",
"genetics",
"and",
"genomics/plant",
"genetics",
"and",
"gene",
"expression"
] |
2009
|
A Systems Approach Reveals Regulatory Circuitry for Arabidopsis Trichome Initiation by the GL3 and GL1 Selectors
|
Correlation patterns in multiple sequence alignments of homologous proteins can be exploited to infer information on the three-dimensional structure of their members . The typical pipeline to address this task , which we in this paper refer to as the three dimensions of contact prediction , is to ( i ) filter and align the raw sequence data representing the evolutionarily related proteins; ( ii ) choose a predictive model to describe a sequence alignment; ( iii ) infer the model parameters and interpret them in terms of structural properties , such as an accurate contact map . We show here that all three dimensions are important for overall prediction success . In particular , we show that it is possible to improve significantly along the second dimension by going beyond the pair-wise Potts models from statistical physics , which have hitherto been the focus of the field . These ( simple ) extensions are motivated by multiple sequence alignments often containing long stretches of gaps which , as a data feature , would be rather untypical for independent samples drawn from a Potts model . Using a large test set of proteins we show that the combined improvements along the three dimensions are as large as any reported to date .
The large majority of cellular mechanisms are executed and controlled by the coordinated action of thousands of proteins , whose biological function is strongly connected to their three-dimensional ( 3D ) arrangement . As shown by Anfinsen almost 40 years ago [1] , the native three-dimensional structure and function of any given protein is unambiguously encoded by its amino acid sequence . Despite many years of intensive work in the field , and many partial successes , the problem of predicting structural properties of a protein from sequence information alone is still to be considered as an open problem . Recent years have seen a staggering increase in the amount of available protein sequence data , which can be attributed to the developments in the sequencing technologies . Currently , sequences of more than 80 million proteins are known , which is a figure that continues growing by over 50% yearly [2] . This , coupled with advances in sequence homology detection methods [3]–[5] , allows for construction of accurate multiple sequence alignments ( MSA ) , capable of capturing the evolutionary history of proteins of interest . As a result of the trade-off between the evolutionary drift and the constraint imposed by biological function , proteins comprising such a multiple sequence alignment are generally characterized by: ( i ) a considerable sequence variation , ( ii ) a striking similarity between their 3D structures . In particular , the evolutionary pressure to conserve structure suggests that residues in spatial proximity should exhibit patterns of correlated amino acid substitutions in these multiple sequence alignments . The approach of using co-evolutionary information encoded in the MSA of homologous proteins to predict structural features of its members was proposed long ago [6]–[11] ( see also [12] , [13] for recent reviews on the subject ) . The last five years have witnessed a renewed interest in the problem: after a first wave of works inspired by statistical physics based on Bayesian methods [14] , [15] , or on different mean-field approximations to a maximum-entropy model [16] , [17] , a burst of scientific activity produced new and increasingly accurate global inference methods [18]–[24] . Apart from inferring structural properties for single protein domains , co-evolutionary methods provide reliable predictions for: ( i ) inter-chain structural organization [17] , ( ii ) specificity and partner identification in protein-protein interaction in bacterial signal transduction system [15] , [25] , ( iii ) essential residue-residue contacts to determine native 3D structures [26]–[28] . The basis of all these computational methods is the idea of global statistical inference . The global approach has the advantage that it is able to disentangle direct from indirect couplings between residues . By modeling the whole data set at once , and not only pairs of residues independently , it is , for example , possible to identify a case in which high correlation between two residues is the indirect consequence of both being directly correlated to a third variable . Methods that address this problem are collected under the umbrella term of Direct Coupling Analysis ( DCA ) . Some methods used so far are ( i ) the message passing based DCA ( mpDCA ) [16] and the mean-field DCA ( mfDCA ) [17] , ( ii ) sparse inverse covariance methods ( PSICOV ) [20] , ( iii ) pseudo-likelihood based optimization [18] , [22] , [23] . The techniques proposed in ( iii ) , and in particular the plmDCA algorithm [22] , [24] , seem to achieve the most accurate predictions so far , when validated against experimentally determined protein structures . Nonetheless , plmDCA shows systematic errors that can be traced back to certain intrinsic characteristics of MSAs , such as the existence of repeated gap stretches in specific parts of the alignment . This phenomenon reflects the tendency of homologous proteins to include large-scale modular gene rearrangements in their phylogenetic evolution , as well as point insertions/deletions . As an empirical way to describe such complex rearrangements , sequence alignment methods typically use a form of substitution matrix to assign scores to amino acid matches and a gap penalty for matching an amino acid in one sequence and a gap in the other . In either case , the most widely utilized gap-penalty schemes assign a large cost to open a gap and a smaller one to extend a gap , so that the overall penalty Q of creating a stretch of gaps of length l is Q ( l ) = a+b ( l−1 ) , where typically a∼−10 and b∼−2 [29] . This introduces an intrinsic asymmetry between gaps and amino acids , where subsequences consisting only of the gap variable are much more likely to occur in an MSA than subsequences of one and the same amino acid . In this work we highlight that contact prediction can be improved in three different ways , or dimensions , all important for overall success and accuracy . The first dimension is Data; it matters which MSA one uses as input to a DCA scheme . Continuing recent work of one of us [30] we show that in a large test data set MSAs built on HHblits alignments give more useful information than MSAs derived from the Pfam protein families database . This conclusion is perhaps not surprising , as the Pfam database was not constructed with potential applications to DCA in mind , but is practically important if DCA is to reach its full potential . The second dimension is Model; it matters which global model one tries to learn from an MSA , and it is possible to systematically improve upon the pairwise interaction models , or Potts models , which have hitherto been the focus of the field . This we show starting from the empirical observation that several DCA methods typically produce high-ranking false positives in parts of an alignment rich in gaps , and the simple fact that any subsequence of one of the same variable has low sequence entropy , and is thus unlikely to occur in random samples drawn from a Potts model , unless its model parameters take special values , i . e . unless at least some of them are quite large . We therefore enhance the Potts model by including terms depending on gaps of any length , much in the spirit of a simplified model for protein folding proposed long ago [31] . In this way we are able to effectively reduce the false positive rate in gap-rich regions of the MSA over a large test data set of diverse proteins . The third dimension is Method . It is well known that DCA by learning a Potts model describing an MSA by exactly maximizing a likelihood function is computationally unfeasible for realistic protein sizes . Most DCA methods can therefore be seen as circumventing this fact , either by approximating the likelihood function , or by using a different ( weaker ) learning criterion . Here , we show that pseudo-likelihood based optimization methods , which have demonstrated the best performance among standalone methods , have the additional advantage of being flexible and easily adaptable to learning other models . This we show by including terms depending on gaps of any length in the score function optimized in the recently developed asymmetric version of the plmDCA algorithm [22] , [24] resulting in a method we denote gplmDCA . We show as well , that improvement achieved by introduction of gap terms can be attained also by a modification to the scoring of inferred matrices ( plmDCA20 ) . Important recent developments , not touched upon in the present work , are combining two or more DCA methods and/or incorporating supplementary information in a prediction process , as done in [30] and [23] . One motivation is that it is theoretically interesting by itself to see how much useful information can be learned by simply starting from the data , proposing a model , and then learning the model more or less well from the data; a second motivation is computational speed , as a stand-alone method is ( typically ) much faster than meta-predictors . A pragmatic motivation for this choice is that any meta-predictor is based on combining stand-alone methods . Hence , improving stand-alone methods gives scope for further improvements of the meta-predictors . Indeed , we believe that the method developed here should allow for further improvements to the methods of [30] and [23]; this we leave however for future work .
The new method gap-enhanced pseudo maximum-likelihood direct contact analysis ( gplmDCA ) uses as underlying inference engine the recent asymmetric pseudo maximum-likelihood [24] augmented by gap parameters , as described in Methods . The added gap parameters have the same status as the other parameters of the model , and the inference task posed by gplmDCA is therefore formally the same as in plmDCA . The number of additional parameters is less than , with N being the length of a alignment , a small fraction of the number of parameters in Potts model based DCA . We have found that the computing time our new method gplmDCA is almost indistinguishable from the asymmetric version of plmDCA [24] . This introduction of gap parameters significantly alleviates a well-known negative trait of plmDCA – the presence of gap-induced artifacts in many contact maps . The reduction of strong , but spurious couplings in the inference process allows for the detection of other couplings , improving prediction qualitatively . Figure 1 shows two examples where conspicuous incorrect predictions at the N-terminus and the C-terminus are removed . Using a large test set , the main data set as described in Methods , we have found that adding gap parameters increases positive predictive value ( PPV ) for a large majority of all proteins in the data set . This increase holds for our main criterion ( Cβ criterion ) for both absolute PPV and PPV relative to protein length , see Figure 2 . The average relative improvement of gplmDCA over plmDCA , as measured by mean absolute PPV , is 10 . 4% ( 8 . 6% to 12 . 2% within a 95% confidence interval ) . In this paper our focus is on the possibility of learning models which lead to better contact prediction , and not of learning a given model more or less well . To set a scale of the improvement we include however in the comparisons in Figures 2 and 4 also PSICOV [20] , another leading approach to the DCA , which can be understood to learn the same model as plmDCA , but by a different inference method . Supporting Information S1 contains results of the analysis conducted in this paper based on our former criterion ( 8 . 5 Å heavy atom criterion ) for the sake of immediate backwards comparability with previous work [22] , [24] . A regression analysis of prediction accuracy , as measured by absolute PPV , reveals clear systematic differences between plmDCA and gplmDCA . As shown in Figure 3 the overall advantage of gplmDCA primarily arises from proteins where PPV is relatively high , i . e . where prediction by plmDCA itself is accurate . Quantitative statistics of this effect are summarized in Table 1 . Including all 729 proteins in the main test set we find that in 82% of the cases gplmDCA does at least as well as plmDCA , but if we include only the 608 instances where the PPV from both plmDCA and gplmDCA are larger than a relatively low cut-off of 0 . 1 this fraction rises to 86% , eventually reaching 91% . It is evident that the expected utility of DCA-like contact prediction is heavily dependent on the information content in the input alignment . The information content is closely correlated to the number of unique protein sequences in the alignment . Until recently , it has been a rule of thumb that one needs at least 10 times as many sufficiently diverse proteins in the alignment as there are amino acids in the protein in question . That meant that contact prediction with alignments of fewer than 1000 sequences was considered unfeasible . As shown in Figure 4 the improvement in prediction performance by using gplmDCA depends on how many sequences there are in an alignment . When considering the top ranked contacts per protein , where L is protein length , the improvement is centered in an interesting intermediate range of approximately 90–2500 sequences with at most 90% sequence similarity , while gplmDCA and plmDCA are similar in performance when the number of sequences is less than 90 ( where it is poor ) or more than 2500 ( where it has saturated at a PPV around 65% ) . Even with as few as 300 unique sequences in alignment , gplmDCA is able to achieve 40% positive prediction rate for these highest ranked contacts . As more contacts are considered , the range where gplmDCA holds an advantage moves successively to proteins with more sequences . A proposed explanation of these observations is that the less information ( sequences ) are available , the more prominent the confounding factor of the gaps becomes for plmDCA . Introducing gap parameters alleviates this phenomenon , increasing the prediction precision for top ranked contacts for information-poor alignments and improving the amount of correct contacts predicted for the information-rich alignments . An alternative method of accounting for gap stretches in the inference is to not include the inferred couplings involving gap variable in the final scoring of coupling matrices J . This approach we subsequently denote as plmDCA20 . While ignoring gap observations in their entirety , leads to diminished prediction precision [24] , discarding the contributions from the gap state in computing the average product corrected Frobenius norm , does indeed improve the prediction precision on a level exceeding the improvements achieved by gplmDCA . The average relative improvement of plmDCA20 over plmDCA , as measured by mean absolute PPV , is 13 . 1% ( 11 . 5% to 14 . 7% within a 95% confidence interval ) . On the data set used in this paper , plmDCA20 is notably more precise than gplmDCA , with the relative improvement of 3 . 9% ( 95% confidence interval 2 . 5% to 5 . 1% ) . It is important to note , that inferred couplings involving gaps are discarded only after gauge fixing , which means that gap observations are included in the inference process and consequently contribute to scoring , although in an indirect way .
The extensive benchmark performed for the purposes of the paper has validated our previous claim that proper input alignment matters for accurate contact prediction [30] . To compare HHblits and Pfam alignments we have from our main data set constructed a reduced data set of 384 proteins . As shown in Figure 5 and Table 2 , gplmDCA and plmDCA20 have a larger advantage over plmDCA on HHblits alignments than on Pfam alignments . Note that plmDCA on HHblits alignments has comparable prediction performance to either gplmDCA or plmDCA20 on Pfam alignments , confirming again the importance of the data dimension in contact prediction . On the level of single proteins , both with Pfam alignments and HHblits alignments , gplmDCA has a clear advantage over plmDCA in terms of the prediction precision , see top row of Figure 6 . The difference is more pronounced for HHblits alignments , which can be quantified by the slope of OLS regression line , that is 1 . 034±0 . 005 in case of HHblits alignments , but only 1 . 023±0 . 003 for Pfam alignments . In the other dimension of the same test , gplmDCA gains more from use of HHblits over Pfam than plmDCA ( bottom row of Figure 6 ) , with the regression line slopes of 1 . 047±0 . 13 for gplmDCA and 1 . 033±0 . 013 for plmDCA . For plmDCA20 , the same effect is also observable ( see middle row of Figure 6 ) , with a comparable slopes of regression lines , that is 1 . 053±0 . 004 for HHblits alignments and 1 . 025±0 . 003 for Pfam alignments in the dimension of the alignment . In the dimension of the inference method , plmDCA20 benefits from HHblits alignments slightly more than gplmDCA , with a slope of OLS regression line equal to 1 . 072±0 . 013 ( bottom row of Figure 6 ) . Contact prediction in DCA has hitherto been considered in terms of a pairwise interaction model , typically motivated by maxentropy arguments cf [27] . In a context where one tries to learn from all of the data and not from a reduced set of observables such as e . g . pair-wise correlation functions , maxentropy arguments do not apply , and there is a vast array of possible models that could describe the biological reality more accurately . We have shown here that the addition of what is arguably the simplest and most obvious non-pairwise term , the gap term , does make a significant difference to the quality of resulting contact predictions , although the beneficial effect is not always consistent and similar improvement may be achieved by correcting the scoring method . Therefore we posit that the pairwise interaction term is not the end of the story , but rather a prelude , and that there remains a lot that can still be done in respect to constructing data models that more accurately reflect the evolutionary relationships in proteins . As previously shown by some of us [22] , [24] , [30] , pseudo-likelihood maximization tends to outperform mean-field DCA ( mfDCA ) [17] and sparse inverse covariance methods ( PSICOV ) [20] in terms of the prediction precision . Recently , a decimation strategy for improving the inference of the topology of an Ising model has been proposed in the context of pseudo-likelihood inference [32] . The idea is to run the inference several times , setting a fraction of the weakest couplings to zero after each run and constraining them to remain zero in consecutive runs . In order to test whether this additional step improves protein contact prediction , we adapted the method for the asymmetric inference of the Potts Model used in the present work . The implementation details can be found in the Supporting Information S2 . We have benchmarked our implementation of gplmDCA with decimation ( decgplmDCA ) basing on the reduced test set used for comparison between Pfam and HHblits . According to our results , inference with decimation does not produce on average significantly different results in comparison to inference without decimation , when run on Pfam alignments . For HHblits alignments , decimation-aided inference performs roughly equally well to the regular one , until roughly 50% of couplings are set to 0 . From this point on , the average prediction performance starts decreasing , as can be seen in Supporting Information S2 . Since the matrix of coupling strengths resultant from the inference should be sparse , as there are significantly more non-contacting amino acid pairs than contacting ones , decimation is expected to be beneficial in a general case . We believe , that the fact that we observed no such effect indicates that more work is needed on designing the decimation-aided inference method in unison with the data model and data itself . The improvement in terms of the average PPV over the whole protein set , as well as the fraction of proteins for which gplmDCA and plmDCA20 produce more accurate predictions , cannot be be underestimated , but is not the only distinguishing feature of these methods . Eliminating strong couplings induced by gaps in the alignments allows for detection of relatively weaker ones , which may be important for the future applications of the method , such as contact-assisted protein folding . One example of such contacts being predicted , shown in Figure 7 , is the contacts between N-terminal helices ( marked in blue ) and the β-sheet of the sensor domain of histidine kinase DcuS ( deposited in PDB as 3BY8:A ) . This structure is classified in CATH [33] as a two-layer α/β sandwich and while plmDCA is able to position strands of the β-sheet in a correct order , it fails at predicting contacts between the α-helices of the sandwich and the β-sheet . As can be seen in central panel Figure 7 , gplmDCA in addition to the already predicted contact between residues 34 and 113 ( green dot next to the blue region ) predicts also contacts between residues 34 and 121 , as well as 21 and 126 . This in theory should allow for proper positioning of helices in case of structure prediction . For this protein plmDCA20 also predicts these additional contacts and while plmDCA20 predictions are not identical to gplmDCA ones , both methods achieve the same prediction precision . The addition of a gap term , while beneficial for vast fraction of proteins , occasionally results in lower prediction accuracy in comparison to the inference performed on a model without gap term ( plmDCA ) . One of the most striking examples ( see Figure 8 ) is protein S , a member of the beta gamma-crystallin superfamily , from Myxococcus xanthus ( deposited in PDB as 1NPS:A ) , which is one of the most prominent outliers in Figure 3 . For this protein plmDCA predicts contacts allowing theoretically for proper assembly of protein , with most of the false positives concentrating in the areas immediately close to diagonal ( with sequence separation ≤10 ) . On the the hand gplmDCA predicts here significantly fewer such false contacts , but at the same time neglects to predict nearly all close range contacts . Another example depicted in panel ( B ) of the same figure is transcription elongation factor Spt4 from Pyroccocus furiosus ( deposited in PDB as 3P8B:A ) . In this case , all the contacts predicted by gplmDCA concentrate in rectangular regions between residues 24–49 , 53–56 , 59–75 , which we believe could be due to the high percentage of sequences with identical gap distribution in the alignment , either ( case 1 ) 1–23 , 50–52 , 56–59 , 77–81 ( 31 . 7% of sequences ) or ( case 2 ) 1–23 , 50–52 , 56–59 , 64–65 , 74–81 ( 28 . 4% of sequences ) . We believe that the sub-par prediction accuracy for these and most of the other outliers is due to the way input multiple sequence alignment has been constructed . HHblits ( the method used for constructing input multiple sequence alignments ) tends to result in multiple sequences in the alignment containing identical distributions of gaps , which causes gplmDCA to assign lower coupling strengths to the gap-rich regions . Alignments of similar size produced by different methods ( i . e . jackhmmer , data not shown ) , do not seem to exhibit such a behavior . Despite this shortcoming , we have found that HHblits alignments are highly suitable for contact inference ( cf . the data section ) . In contrast to gplmDCA , we did not find any proteins for which plmDCA20 performs significantly inferior to the original plmDCA ( as demonstrated by Figure 3 ) . In particular , for proteins discussed above plmDCA20 provides predictions on par or better than plmDCA . With an exception of approximately 5% proteins , prediction performance of plmDCA20 and gplmDCA is comparable for our test set . Elimination of artifacts in predicted contact maps , as well as increased sensitivity ( predicting correct contacts between more secondary elements ) in comparison to plmDCA , coupled with increased prediction precision , strongly suggest that gplmDCA and plmDCA20 should provide valuable input for the future ab-initio protein structure prediction attempts . The previous incarnation of pseudo-likelihood maximization for direct coupling analysis ( plmDCA ) has been successfully used for protein structure prediction endeavors ( c . f . [12] ) as it objectively provides higher prediction accuracy than other methods ( as demonstrated , for example in [30] ) . As both methods presented in this paper are at the same time faster and more accurate than the version used in reported structure prediction work , we strongly recommend them for future use .
Contact prediction has advanced greatly in the last five years , reaching a level of accuracy which was previously believed to be unattainable . We have shown here that the three dimensions of data , model and method are all important for overall prediction success , and we have shown that one can can significantly improve prediction along the second dimension by going beyond pairwise maxentropy models mainly used in the field up to now . Finally , we have shown that the gap correction behavior can be achieved by alternative method of scoring the resultant coupling matrices . We believe that these are only the first steps in a rational approach to incrementally improve contact prediction , and that with the ongoing explosion in the number of available protein sequences much further progress should be possible on these issues .
In a substantial fraction of the contributions to the development of DCA contact predictions have been based on MSAs obtained from the Pfam protein families database: [3] , [39] . However , as recently shown by one of us in [30] , and as also shown here ( see Discussion ) , these alignments are not the optimal input for DCA and DCA-like methods . Instead of PfamA alignments , we use a state-of-art homology detection method HHblits [40] , based on iterative comparison of Hidden Markov models ( HMMs ) . This approach is able to arrive at very accurate multiple sequence alignments , tailored to the protein of interest , while still including remotely homologous proteins . We have constructed a heterogeneous set of 729 protein chains of known structure , sampled from Protein Data Bank which we refer to as main test set . This set is an amalgam of four smaller data sets as follows: • 150 proteins reported in PSICOV paper [20] . • ∼120 proteins with known structures , with relatively few detectable homologous proteins of known sequence . • ∼180 proteins of the most common Structural Classification of Proteins ( SCOP ) folds [41] . • ∼280 proteins sampled at random from PDB . We excluded from the main test set proteins that were significantly too long for a reasonable contact prediction ( the mean and median lengths of a protein in the considered set are 168 . 4 and 150 amino acids correspondingly , with maximum of 494 amino acids ) , or not compact enough ( not having enough long-range contacts ) , probably stabilized by interaction with their environment . We did not exclude multimeric proteins , or filter out multidomain proteins , though . The alignments in the main test set have been constructed using HHblits , as contained in HHsuite 2 . 0 . 16 with a bundled uniprot20_2013_03 database . We have run five iterations of search , with a E-value cutoff of 1 , allowing for inclusion of distantly homologous protein in the alignment . The search was conducted without filtering the result MSA ( -all parameter ) , without limiting the amount of sequences allowed to pass the second prefilter and allowing for realigning all the hits , hence obtaining the most information-rich and accurate alignment at cost of increased running time . To compare Pfam and HHblits-based predictions we have from the main test set also constructed a reduced test set by the following procedure . For each of the proteins in the main test set we searched for its PDB identifier against an official Pfam-PDB mapping , to identify the longest Pfam family corresponding to this protein ( in case of potential multiple Pfam hits per PDB identifier ) . This resulted in alignments for 481 proteins , reflecting inter alia the fact that not all proteins in the main test set have an official Pfam-PDB mapping . Then we identified the sequence in the appropriate Pfam alignment which is closest to the sequence of protein in question by Smith-Waterman algorithm using BLOSUM100 matrix . From this set we reject alignments where we the number of residues in both sequences aligned to gaps is more than 50% of length shorter of sequences plus length difference between sequences , and subsequently we trim the Pfam alignment to only the columns aligned to protein in question . Finally , the reduced test set contains 384 proteins with both Pfam and HHblits MSAs which form the input for plmDCA , plmDCA20 and gplmDCA in the comparisons presented in Discussion and Figures 5 and 6 . The comparison is there done by filtering down the predictions to include only the columns present in the Pfam alignments . Protein sequences present in sequence database ( and hence used for alignments in this work ) are biased towards sequences from genomes of organisms that are of special interest to humans . Many such sequences are closely similar , and following [16] sequences that are more similar than some threshold are reweighted before being used in a DCA . We here use the reweighting recently described in [24] , with threshold 0 . 1 , that is , by reweighting sequences that are more than 90% identical . A multiple sequence alignment can be considered as samples from an unknown probability distribution . Each row , corresponding to one protein in the alignment , is then one of the qN possible realizations of a random variable which at each of the N positions along the row can take q = 21 different values ( the amino acid or the gap symbol at that position ) . The ( unknown ) probability distribution is , in principle , the result of the complete evolutionary history of all forms of life , and is therefore a very complicated object . However , it is not necessary to know the probability distribution exactly to extract useful information . The Direct-Coupling Analysis ( DCA ) , as introduced in [34] and [16] , assumes that the probability distribution is the Potts Model of statistical physics [42]: ( 1 ) The use of the Potts model in the DCA has often been motivated by maxentropy arguments cf [27] . As we base our approach an inference method which uses all the data ( see below ) , we cannot refer to maxentropy principles . Instead , one may observe that it has been found in many branches of science and engineering , that probability distributions over a collection of a large number of similar objects often obey a large deviation principle [43] . The full distribution P can then be written as P ( a ) ≈exp ( −L ( a ) ) , where the function L in the exponent is “simple” , a classical example being the Gibbs-Boltzmann distribution of equilibrium statistical mechanics . An unknown probability distribution can then be expanded in a series ( 2 ) where the first order contribution S1 ( linear ) contains terms only depending on one component of a , the second order contribution S2 ( bi-linear ) contains terms depending on two components of a , and so on . If L in fact is simple , then a low order truncation should give a useful approximation to P , and the Potts model of ( 1 ) is nothing but the truncation of ( 2 ) after the second order terms . We note that hierarchies of exponential probability distributions have non-obvious properties , and may for instance be taken as a basis of an invariant decomposition of the entropy [44] . Any multiple sequence alignment procedure typically produces stretches of gaps , a fact which is obvious by visual inspection . It is therefore an immediate observation that a real MSA data cannot be a set of independent realizations of the rather simple model in ( 1 ) , since such stretches of one and the same variable ( the gap variable ) are very unlikely to occur in a random variable drawn from the distribution ( 1 ) . In a DCA based on ( 1 ) we manifestly learn from data a model which does not generate the same data . We therefore hypothesized that by learning a model which describes the data better , we might also better predict amino acid contacts . To investigate this we introduced additional gap parameters and try to learn ( 3 ) where the are new parameters describing the propensity of a site i to be the beginning of a gap of length l , is an indicator function which takes the value 1 if there is a gap of length l beginning at site i , and otherwise zero , and L is a meta-parameter , the largest gap length included in the gap parameters . We set L to the largest gap length found in a given alignment . The number of additional parameters to be learned is thus not larger than NL , to be compared to the number of parameters already used in ( 1 ) , which is about . The benchmark of learning a model from data is maximum likelihood where one chooses the probability distribution in a class which minimizes a negative-log-likelihood function L . The main problem in learning ( 1 ) from data by maximum likelihood is that the normalizing constant ( ) cannot be evaluated exactly and efficiently in large systems , and that therefore maximum likelihood learning can only be done approximately e . g . by variational methods [45] . Therefore , we instead use the weaker learning criterion of pseudo-likelihood maximization [46] , first applied in the DCA setting by one of us in [22] . A further issue is that the number of parameters in a Potts model based DCA is ( typically ) larger than the number of observations ( number of sequences in an MSA ) , and regularization is therefore necessary . We here base our work on the recently developed asymmetric pseudo-likelihood maximization [24] , which is considerably faster than the version presented in [22] while showing essential identical performance as a predictor of amino acid contacts . Learning the new model including ( 3 ) is especially convenient using the pseudo-likelihood maximization approach . We have developed a new code gplmDCA based on the asymmetric version of plmDCA of [24] . The outcome of learning a model of the Potts type is a set of pairwise interaction coefficients Jij ( ai , aj ) . For each pair ( i , j ) ( each pair of positions ) this is a matrix in two other variables ( ai and aj ) and how an inferred interaction is scored depends on which matrix norm one uses . We here use the Frobenius norm augmented by the Average Product Correction ( APC ) , as introduced in the context of DCA by one of us in [22] , and order the pairs ( i , j ) , for each multiple sequence alignment , by the value of this score . An alternative method of handling the gaps in the alignment ( plmDCA20 ) is to change the scoring function , such that the Frobenius norm is computed only on the 20×20 sub-matrix which does not involve the gap variables . The procedure is to ignore the gap couplings after computing the coupling matrix J , which is manifestly not the same as ignoring data on the gap variables altogether . Since L2 penalty in plmDCA enforces the Ising gauge for the couplings , the gap observations are used in the inference and consequently contribute to the result , although in a non-trivial way . In our experience ( Aurell & Hartonen , unpublished results ) , ignoring the data on gap variables in the inference does not result in any improvement in the prediction precision . To benchmark the predictions of the DCA one compares against known crystal structures . In this work we use as the main benchmark criterion , that two amino acids are in contact , if their Cβ atoms are at most 8 Å apart in the crystal structure . This we denote as Cβ criterion and use predominantly throughout this article . In order to facilitate comparison to previously published work on the DCA we present also an alternate metric that considers the amino acids to be in contact if any of their heavy ( non-hydrogen ) atoms are at most 8 . 5 Å apart . This metric is denoted as 8 . 5 Å heavy atom criterion We strongly believe that this metric tends to label unduly high fraction of short-range contacts ( i . e . contact separated by less than 8 positions in sequence space ) as positive . At the same time original plmDCA predicts significantly more short-range contacts in comparison to the background distribution in native protein structures . Both observations in conjunction cause the improvements to the prediction precision to be less perceptible . We demonstrate this effect in Supporting Information S1 . In this article we use the terms precision and PPV ( positive predictive value ) interchangeably , with metric denoting the ratio of true positives to all predictions ( within a certain count threshold ) . In line with previously published work on contact prediction , we consider only the contacts with sequence separation greater or equal to 5 amino acids ( we do not consider very short range contacts , that is contacts between amino acids i and j when ) . By the term weighted moving average with window w , authors understand a weighted arithmetic mean of a value at a given position and w values on either side of the center position , thus resulting in 2w+1 values to be averaged . The central position is scaled with weight w , whereas the weights decrease in arithmetic progression while moving away from the center ( i . e positions −1 and +1 are scaled with weight w−1 , whereas positions −2 and 2 with weight w−2 etc . ) . The code of gplmDCA is freely available at http://gplmdca . aurell . org . This website contains also a link to all the data the benchmark is based on , that is: multiple sequence alignments , predicted couplings ( both plmDCA and gplmDCA ) , protein structures and contacts derived from them .
|
Proteins are large molecules that living cells make by stringing together building blocks called amino acids or peptides , following their blue-prints in the DNA . Freshly made proteins are typically long , structure-less chains of peptides , but shortly afterwards most of them fold into characteristic structures . Proteins execute many functions in the cell , for which they need to have the right structure , which is therefore very important in determining what the proteins can do . The structure of a protein can be determined by X-ray diffraction and other experimental approaches which are all , to this day , somewhat labor-intensive and difficult . On the other hand , the order of the peptides in a protein can be read off from the DNA blue-print , and such protein sequences are today routinely produced in large numbers . In this paper we show that many similar protein sequences can be used to find information about the structure . The basic approach is to construct a probabilistic model for sequence variability , and then to use the parameters of that model to predict structure in three-dimensional space . The main technical novelty compared to previous contributions in the same general direction is that we use models more directly matched to the data .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Conclusions",
"Methods"
] |
[
"biochemistry",
"mathematics",
"statistics",
"(mathematics)",
"protein",
"structure",
"prediction",
"proteins",
"mathematical",
"and",
"statistical",
"techniques",
"protein",
"structure",
"statistical",
"methods",
"biology",
"and",
"life",
"sciences",
"physical",
"sciences",
"molecular",
"biology",
"research",
"and",
"analysis",
"methods",
"macromolecular",
"structure",
"analysis",
"statistical",
"inference"
] |
2014
|
Improving Contact Prediction along Three Dimensions
|
In endemic areas , leptospirosis is more common and more severe in adults compared with children . Reasons to explain this discrepancy remain unclear and limited data focusing on adolescents are available . The objective of the study was to describe disease spectrum and outcome differences in children and adolescents admitted for leptospirosis in a large at-risk population . Clinical and laboratory data were obtained on hospitalized cases in New Caledonia from 2006 to 2012 . Data of 60 patients <18 years of age ( 25 children under 14 and 35 adolescents aged 14 to 17 ) with confirmed leptospirosis were analyzed . Compared with children , adolescents presented more often with classic features of Weil disease ( p = 0 . 02 ) , combining hepatic and renal involvement with or without pulmonary participation . Jarisch-Herxheimer reactions were observed more often among adolescents ( p<0 . 01 ) . The overall case fatality rate was low ( 1 adolescent versus 0 children ) . Severe leptospirosis in adolescents may be more likely to show adults' characteristics compared with children . Further studies are required to explore age-dependant host factors , including puberty-related physiological changes .
Leptospirosis is an important zoonosis of worldwide distribution caused by pathogenic spirochaetes of the genus Leptospira . Humans usually become infected through contact with water or soil contaminated by the urine of mammalian reservoirs such as rodents , dogs , cattle and pigs [1] . Infection most commonly results in asymptomatic or self-resolving illness both in adults and children [2] . In severe cases requiring hospitalization , the disease is however potentially fatal classically presenting with jaundice and renal dysfunction ( Weil disease ) with or without pulmonary hemorrhagic manifestations . According to estimates from the World Health Organization , more than 500 , 000 severe cases occur every year worldwide , mostly in tropical and sub-tropical regions . Along with experienced clinicians' beliefs , several studies suggest leptospirosis to produce more severe presentation in adults compared with children [3] , [4] , [5] , [6] , [7] . Pathogen as well as host-related factors are believed to play a role in the development of severe leptospirosis in adults [1] , [8] . However , factors responsible for the milder presentation among children remain unclear . There is limited information available about symptomatic leptospirosis in the under 18 age group in the Pacific region . New Caledonia is an overseas French-administered territory located in the South Pacific and located 1500 kilometres East of Australia . According to the 2009 census , the population is 245 580 inhabitants with a mean increase of 1 . 7% per year since 1996 [9] . The population has good access to the health care system of European standards . Climate in New Caledonia is marked by a cool and dry season ( from June to September ) and a warm and wet one ( from December to March ) . Leptospirosis is endemic to New Caledonia , and is a leading cause of hospital admission during the rainy season [10] , [11] . From 2006 to 2009 , the average annual incidence was 45 cases per 100 , 000 inhabitants but reached 150 per 100 , 000 inhabitants during the rainiest months . The objective of the study was to describe disease spectrum and outcome differences in children and adolescents admitted for laboratory-confirmed leptospirosis in a large at-risk population . We hypothesized that adolescents were more likely to present with all the classic features of Weil disease compared with younger children .
We carried out an observational retrospective study among patients aged under 18 years old with a biologically confirmed leptospirosis admitted between January 2006 and December 2012 in either of two public hospitals ( Centre Hospitalier Territorial , Noumea and Centre Hospitalier du Nord , Koumac ) . Demographic , epidemiologic , clinical , and laboratory information were recorded . Outpatients testing results for leptospirosis or adults over 18 were not included in the analysis . Leptospirosis was defined by a compatible clinical syndrome ( any combination of fever , chills , myalgia , jaundice , conjunctival suffusion , renal failure , hemorrhage , or pulmonary failure ) and laboratory confirmation with one or more of the following features: 1 ) positive results for a microscopic agglutination test ( using a panel of 11 serovars ) and one acute-phase serum sample titer >1∶800 , 2 ) seroconversion between times of testing acute-phase and convalescent phase serum samples , 3 ) a four-fold increase in titers between two examinations , or 4 ) a positive PCR . Biological tests performed at the Institut Pasteur in New Caledonia ( IPNC ) have been described in a previous study [8] . Patients were stratified by age group: 0–13 years of age ( children ) and >13 years ( adolescents ) . Oliguria was defined as urine output <0 . 5 mL/kg/hour and pulmonary involvement was defined as dyspnea , rales , and/or chest radiographic abnormalities . Laboratory results refer to samples collected at the time of admission . Reference values were serum creatinine = 30–88 µg/dL , total bilirubin = 0 . 8–1 . 2 mg/dL , and platelet counts = 150 000–400 000/mm3 . The time ( in days ) between onset of symptoms and hospitalization was compared between groups . Occurrence of Jarisch-Herxheimer reaction ( JHR ) was also noted when reported . JHR was defined as the combination of sudden onset of shivering or rigors , with rise in temperature , with or without a fall or a rise in blood pressure , increase of respiratory rate occurring after administration of the first dose of antibiotics . For each study participant , a standardized form was retrospectively completed . Clinical manifestations and medical history were collected as mentioned in medical records . Demographic data and laboratory results were extracted from electronic records . Both methods ( MAT and PCR product sequence polymorphism ) were used to identify the serogroup or the putative serogroup of the infecting strain . The study was approved by the Institutional Review Board of Centre Hospitalier Territorial . Informed consents were not obtained from the patients as this was a retrospective study . All data were anonymized . STATA ( Stat Corp . , College Station , TX ) was used for analysis . Quantitative and qualitative variables were compared by using paired Student's t-tests and chi-square tests , respectively . When the frequency of events was <5 or values did not follow normal distributions , Fisher's Exact and Mann-Whitney tests were used .
A total of 128 patients with leptospirosis were diagnosed during the study period . Sixty eight of these cases were excluded from further analysis because they were not hospitalized in one of the two participating centres ( n = 50 ) or records could not be traced ( n = 16 ) . Of the 60 patients included in the study , 25 ( 42% ) were children and 35 ( 58% ) were adolescents . The majority of study subjects were boys ( 79% in children vs 77% in adolescents , p = 0 . 6 ) with a mean age of 9 [IQR 6–11] years for children and a mean age of 16 [IQR 14–17] for adolescents . Subjects were mostly Melanesian ( 83% ) living in tribes in rural areas ( 85% ) ( Table 1 ) . The frequency of symptoms and complications are presented in Table 2 and biological parameters are presented in Table 3 . Fever and jaundice were more frequent among adolescents whereas the incidence of conjunctival suffusion , myalgia , abdominal pain , headache and oliguria were not significantly different between groups . Mean total serum bilirubin and serum creatinine levels were higher in adolescent than paediatric groups ( 41 versus 7; p<0 . 001 and 108 versus 59; p<0 . 001 , respectively ) . Platelet count was lower in adolescents ( 148 000 [95%CI 124 000–173 000] ) than in children ( 202 000 [95%CI 157 000–248 000] ) ( p = 0 . 02 ) . Compared with children , adolescents presented more often with classic features of Weil disease ( p = 0 . 02 ) , combining hepatic and renal involvement with or without pulmonary participation . The groups showed no differences with respect to pulmonary involvement . Exposition factors for leptospirosis transmission did not differ between the groups; most patients in both groups had self-reported direct contact with water ( swimming in rivers or canals , wading through water ) ( 87% in children and 81% in adolescents ) , and direct or indirect contact with mammalian carriers ( 25% in children and 28% in adolescents ) . The median time between onset of symptoms and initiation of antibiotics was 2 days for both groups . Antibiotics were administered intravenously for 5–7 days and included ampicillin ( 100 mg/kg of body weight/day ) in 43 patients ( 18 children and 25 adolescents ) and cefotaxime ( 100 mg/kg of body weight/day ) in 17 patients ( 7 children and 10 adolescents ) . Jarisch-Herxheimer reactions were observed more often among adolescents ( p<0 . 01 ) ( Table 4 ) . The overall case fatality rate was 1 . 6% ( 1 adolescent versus 0 children ) . The single patient with fatal outcome required dialysis for acute renal failure , mechanical ventilation for alveolar hemorrhage and vasoactive drug for shock . Four other adolescents required vasoactive drugs only . No other patients required dialysis or mechanical ventilation . Among the 26 cases for whom the serogroup was identified , 15 ( 58% ) were Icterohaemorrhagiae ( 7/11 in children , 8/15 in adolescents ) . Other detected serogroups included Australis ( n = 4 ) , Pyrogenes ( n = 4 ) , Canicola ( n = 2 ) , and Panama ( n = 1 ) ( Table 3 ) . For the 34 remaining cases , identification of the serogroup was not possible . At the time of admission , 8 patients had acute co-infections ( 5 children and 3 adolescents ) with viral diseases ( rotavirus n = 2; viral respiratory syncitial n = 1 ) , or bacterial diseases ( bacteraemia with Serratia n = 1; urinary tract infection n = 2 , pyodermititis n = 1 , tuberculosis n = 1 ) . All patients diagnosed with coinfections were leptospirosis confirmed cases by PCR .
This retrospective study allowed us to identify an age-dependant association with severity of leptospirosis . Similar observations in children have been reported in other settings [3] , [12] . The frequency of several classic severe disease manifestations were significantly lower among small children in this study compared with adolescents . Our study found that a substantial proportion of hospitalized children with leptospirosis had fewer of the classic features of Weil disease than adolescents . Although severe disease caused by leptospirosis may occur in the paediatric age group , clinical and biological presentation in adolescents overlaps with the spectrum seen in adults . Since most studies performed in children did not discriminate age groups , our findings are uneasy to compare with other series . However , some characteristics of small children in our study were similar to what has been previously reported in Thailand [13] and Brazil [7] . In contrast , our results differ from a previous report of 43 children , 4–14 years of age [14] showing more severe manifestations of leptospirosis than the present study , including renal failure and thrombocytopenia . Several factors may explain these differences: first , different leptospiral serogoups were identified in New Caledonia; second , time to refer was potentially higher in the Brazilian study; finally , host factors and higher organism loads may lead to more severe manifestations . Conversely , presentation of severe illness in adolescents was similar to clinical and biological profile reported in adults in Brazil [3] and New Caledonia [8] . Consistent with some previous studies [5] , [14] , our results showed that overall case-fatality rates in children are lower than in adults . Recently identified factors associated with severe disease in adults in the same setting included tobacco use , leptospiral serogoup and delay between onset of symptoms and initiation of antibacterial therapy [8] . The discrepancy between children and adolescents does not appear related to differences in seroreactivity to leptospiral serogroups , which was similar in both groups . Similarly , onset of symptoms to administration of antibiotics was similar in both groups suggesting identical time to refer for children and adolescents . Host factors may contribute to this association and could include higher organism loads with increasing age . However , since risk factors for exposure were identical in children and adolescents , it is unlikely that initial bacterial inoculum was different in both groups . Age-dependant changes in innate and adaptative immune responses to leptospiral infection are plausible explanations for differences between children and adolescents . Several hypotheses regarding leptospirosis severity are based on host genetic susceptibility factors [15] , [16] and/or on bacterial virulence [17] , although the virulence mechanisms are poorly understood and are probably multifactorial [18] . The male predominance reported in our study both in children and adolescents is very similar to previous studies of symptomatic leptospirosis [3] , [14] . Gender-specific activities may explain such differences . However , it is common in New Caledonia to see boys and girls swimming in water and assisting their parents with cultivating fields . The number of symptomatic cases admitted in paediatrics is low compared with adults over the same period of time [8] . This result is surprising since clinicians have a lower threshold to admit children with suspected or confirmed leptospirosis to the hospital . Age specific activities may explain this finding . Another major finding of our study showed that JHRs were more likely to occur in adolescents . Although this adverse event is scarcely described in leptospirosis [19] , our results support the presentation and outcome overlap between adolescents and adults . The incidence of JHR in children included in our study is in line with the few studies reporting JHR in paediatric cases [13] . In contrast , the incidence of JHR in adolescents was elevated when compared with other studies . JHR unobserved or unreported by clinicians are potential reasons for this reduced frequency . The delay before antibacterial therapy had a major impact on outcome in adults [8] , [20] . The need for early initiation of antimicrobial therapy to reduce disease severity remains to be proven in children . However , presumptive treatment based on clinical and epidemiological evidence appears justified while waiting for the laboratory results . Modalities of antibiotics administration to prevent JHR to occur remain to be explored . Our study suffers from several limitations . First , due to the retrospective design of the study , a significant proportion of patients with untraceable records were secondary excluded of the analysis . Second , leptospiral serogroups were identified in a limited number of cases only . Third , important clinical data were missing , including presence of puberty . Finally , indications for hospitalization may be different in younger children compared to adolescents and mild disease in adolescents may not have been seen because they were not hospitalized . In New Caledonia , leptospirosis is responsible for a smaller number of hospitalizations in paediatrics due to milder symptomatic forms of the disease . However , leptospirosis remains a public health threat , including in the younger age group which can present with atypical signs and symptoms . Puberty may impact on the severity reported in the older age groups . Physiologic and immunologic factors associated with improved paediatric outcomes require further investigation to provide insights into the pathogenesis of severe leptospirosis .
|
Leptospirosis is endemic in tropical areas and seems to affect adults more often and more severely than children . Factors responsible for such differences have not been clearly established . However , host-related factors are believed to play a role in the development of severe leptospirosis . The study aimed to describe disease spectrum and outcome differences in confirmed cases in children and adolescents in New Caledonia . One major finding is the milder presentation of children compared with adolescents . Clinical and biological characteristics in adolescents are similar to adults , including occurrence of Jarisch-Herxheimer reactions . Further studies are required to explore age-dependant host factors , including puberty-related physiological changes .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[] |
2013
|
Association between Age and Severity to Leptospirosis in Children
|
Human African trypanosomiasis ( HAT ) manifests as an acute form caused by Trypanosoma brucei rhodesiense ( Tbr ) and a chronic form caused by Trypanosoma brucei gambiense ( Tbg ) . Previous studies have suggested a host genetic role in infection outcomes , particularly for APOL1 . We have undertaken candidate gene association studies ( CGAS ) in a Ugandan Tbr and a Tbg HAT endemic area , to determine whether polymorphisms in IL10 , IL8 , IL4 , HLAG , TNFA , TNX4LB , IL6 , IFNG , MIF , APOL1 , HLAA , IL1B , IL4R , IL12B , IL12R , HP , HPR , and CFH have a role in HAT . We included 238 and 202 participants from the Busoga Tbr and Northwest Uganda Tbg endemic areas respectively . Single Nucleotide Polymorphism ( SNP ) genotype data were analysed in the CGAS . The study was powered to find odds ratios > 2 but association testing of the SNPs with HAT yielded no positive associations i . e . none significant after correction for multiple testing . However there was strong evidence for no association with Tbr HAT and APOL1 G2 of the size previously reported in the Kabermaido district of Uganda . A recent study in the Soroti and Kaberamaido focus in Central Uganda found that the APOL1 G2 allele was strongly associated with protection against Tbr HAT ( odds ratio = 0 . 2 , 95% CI: 0 . 07 to 0 . 48 , p = 0 . 0001 ) . However , in our study no effect of G2 on Tbr HAT was found , despite being well powered to find a similar sized effect ( OR = 0 . 9281 , 95% CI: 0 . 482 to 1 . 788 , p = 0 . 8035 ) . It is possible that the G2 allele is protective from Tbr in the Soroti/Kabermaido focus but not in the Iganga district of Busoga , which differ in ethnicity and infection history . Mechanisms underlying HAT infection outcome and virulence are complex and might differ between populations , and likely involve several host , parasite or even environmental factors .
The tsetse transmitted African trypanosomes are flagellated protozoa , a range of which cause disease in animals ( known as Nagana ) and humans ( Human African Trypanosomiasis , HAT , also known as sleeping sickness ) . These diseases are responsible for significant morbidity and mortality [1–3] and therefore directly impact on public health and animal productivity . Current reports indicate that annual HAT incidence is on the decline , although under reporting is typical , especially in areas where conflicts and civil unrest interrupt control efforts and regular epidemiological surveys [4–6] . HAT is caused by two microscopically indistinguishable sub-species: Trypanosoma brucei rhodesiense that causes an acute form of the diseases that develops within a few weeks or months of infection , and Trypanosoma brucei gambiense that causes a chronic form of the disease that can take years to become patent . The acute form of the disease is prevalent in Eastern and Southern Africa while the chronic form of the disease is prevalent in West and Central Africa [4] . Uganda is the only country with active foci for both forms of the disease , though in geographically distinct regions . Studies in the Democratic Republic of Congo ( DRC ) , Cameroon , Cote D’Ivoire , Guinea and Uganda have found evidence for polymorphisms in HP , IL6 and APOL1 associated with outcome of infection [7–12] . In the present study , we investigated the possible association of selected gene polymorphisms with HAT by undertaking a candidate gene association study ( CGAS ) using case-control samples from the Tbr and Tbg HAT endemic areas of Uganda . The IL10 , IL8 , IL4 , HLAG , TNFA , TNX4LB , IL6 , IFNG , MIF , APOL1 , HLAA , IL1B , IL4R , IL12B , IL12R , HP , HPR , and CFH genes that were selected have protein products that are involved in the HAT immune response . The CGAS approach was used to compare the frequencies of genetic polymorphisms between cases and controls in order to identify risk variants for HAT in the two Ugandan populations .
This study was approved by the Uganda National Council of Science ( UNCST; assigned code HS 1344 ) following review by the IRB of the Ministry of Health . Participants were identified through community engagement and active field surveys; they gave written informed consent administered in their local language by trained local health workers . In instances where participants were below 18 years of age , consent was sought from a parent or primary guardian . Any individuals for whom it was not possible to obtain consent or blood samples were excluded from the study . The Tbr HAT endemic area samples were from the traditional Tbr HAT foci in the South East of Uganda [13] . Samples were collected mainly from Iganga district and included individuals from the predominantly Basoga ethnic group , with a few Baganda , Banyole , Balamogi , Basiginyi , Itesot , and Japadhola ethnicities . The Tbg HAT endemic area samples were from the traditional Tbg HAT foci in the Northwest of Uganda [13] . Samples were collected from Adjumani , Arua , Koboko , Maracha , and Moyo districts and comprised of individuals from the Kakwa , Lubgbara and Madi ethnicities . In both areas , only individuals who were born and lived in these traditional foci were selected , as they were most likely exposed to HAT for most of their lives . HAT cases were defined as individuals in whom trypanosomes have been detected by microscopy in at least one of the body tissues including , blood , lymph node aspirates or cerebral spinal fluids . Controls were defined as individuals from the endemic area with no history or any signs/symptoms suggestive of HAT . Controls from the Tbg HAT endemic area were required to have no serological reaction to the CATT or Trypanolysis tests . Blood was drawn by venipuncture and collected in EDTA/heparin vacutainer tubes ( BD ) . Buffy coats were prepared from the whole blood in field laboratories using centrifugation , aliquoted , and then stored in liquid nitrogen in preparation for DNA extraction that was carried out at the Molecular Biology Laboratory , COVAB , Makerere University . The DNA was quantified using Qubit ( Life Technologies ) . This study was one of five studies of populations of HAT endemic areas in Cameroon , Cote d’Ivoire , Guinea , Malawi and Uganda . The studies were designed to have 80% power to detect odds ratios ( OR ) >2 for loci with disease allele frequencies of 0 . 15–0 . 65 and 100 cases and 100 controls with the 96 SNPs genotyped . The study design included an overall total of 462 samples , 239 samples from Tbr HAT endemic regions ( 120 cases , 119 controls ) and 223 samples from Tbg HAT endemic regions ( 110 cases and 113 controls ) . Power calculations were undertaken using the pbsize routine in Genetics Analysis Package gap version 1 . 1–16 in R [14] . The selection of the genes depended on prior knowledge of the genes and their association with the HAT . The following genes IL10 [9] , IL8 [7] , IL4 [15] , HLAG [16] , TNFA [7] , TNX4LB [17] , IL6 [7] , IFNG [18] , MIF [19] , APOL1 [8] , HLAA [20] , IL1B [21] , IL4R [21] , IL12B [21] , IL12R [21] , HP [22] , HPR [22 , 23] , and CFH [24] were selected . 96 SNPs were selected for genotyping using two strategies: 1 ) SNPs that had been previously reported to be associated with HAT or 2 ) in the cases of IL4 , IL8 , IL6 , HLAG and IFNG by using sets of SNPs in LD ( r2>0 . 5 ) with each other , such that all bases in the gene were in LD with at least one SNP . The SNPs in this second group of genes were selected using a merged SNP dataset obtained from 10X coverage whole genome sequence data generated from 230 residents living in regions ( DRC , Guinea Conakry , Ivory Coast and Uganda ) where trypanosomiasis is endemic ( TrypanoGEN consortium , sequences at European Nucleotide Archive Study: EGAS00001002602 ) and 1000 Genomes Project data from African populations . Linkage ( r2 ) between loci was estimated using the PLINK v1 . 9 package ( https://www . cog-genomics . org/plink/1 . 9/ ) [25] and sets of SNPs that covered the gene were identified . Some SNP loci were excluded during assay development or failed to genotype and were not replaced . Approximately 1μg of gDNA per sample were submitted to INRA ( Plateforme Genome Transcriptome de Bordeaux , France ) for genotyping . A multiplex analysis ( two sets of 80 SNPs each ) was designed using Assay Design Suite v2 . 0 ( Agena Biosciences ) . SNP genotyping was achieved with the iPLEX Gold genotyping kit ( Agena Biosciences ) for the MassArray iPLEX genotyping assay , following the manufacturer’s instructions . Products were detected on a MassArray mass spectrophotometer and the data acquired in real time with MassArray RT software ( Agena Biosciences ) . SNP clustering and validation was carried out with Typer 4 . 0 software ( Agena Biosciences ) . SNPs that failed genotyping at INRA and some additional SNPs were genotyped at LGC Genomics , Hoddesden , UK where SNPs were genotyped using the PCR based KASP assay [26] . A summary of the candidate genes and SNPs is shown in S1 Table . The raw genotypic data were converted to PLINK format and quality control ( QC ) procedures implemented using the PLINK v1 . 9 package [25] . PLINK was used to determine the level of individual and genotype missingness , Hardy-Weinberg Equilibrium ( HWE ) , estimate allele frequencies , and linkage disequilibrium ( LD ) . Testing for population stratification and admixture was carried out using Admixture 1 . 3 [27] and the plot was visualized using StructurePlot2 [28] . Testing for the association of SNPs with HAT was done using a Fisher’s exact test [29] implemented in PLINK and producing a 95% confidence interval for the odds ratios . Controlling for multiple testing was implemented using a Bonferroni correction ( α* = α/n , where α is the level of significance , n is the number of independent SNP association tests and α* is the adjusted threshold of significance ) [30] . The Bonferroni correction assumes that each of the statistical tests are independent; however , this was not always true since there was some linkage disequilibrium between the SNPs in IL4 , IL8 , IL6 , HLAG and IFNG which were subject to complete linkage scans . Where the assumption of independence is not true , the correction is too strict potentially leading to false negatives . Thus , an alternative correction for multiple testing was also employed . The Benjamini-Hochberg false discovery rate ( FDR ) estimates the proportion of significant results ( p < 0 . 05 ) that are false positives [30 , 31] .
Uganda is the only country where both acute and chronic HAT are endemic [32] . The two forms of the disease however occur in geographically isolated regions [32] . The two samples represented two distinct forms of the disease and regions inhabited by distinct ethnic groups ( Nilo-Saharan language speakers in the Tbg region and Bantu language speakers in the Tbr region ) . The cohorts were analyzed separately including initial quality control . Ninety-six ( 96 ) SNPs in 15 genes were genotyped from each of the Tbr and Tbg HAT endemic area samples as shown in S1 Table ( the Plink MAP and PED files are available in S1 and S2 Data ) . Before association testing , histograms of the distribution of missing data both by individual and by locus ( Supplementary Figures S1 Fig–S4 Fig ) were inspected in order to identify appropriate cut-offs to apply in each population . Individuals with missing data or loci with missing data above the cut-off threshold were removed as were loci that were not in HWE , or those that were poorly genotyped [33 , 34] . Individuals with more than 20% or 15% missing data were excluded from the Tbr and the Tbg HAT endemic datasets , respectively , resulting in a final dataset of 238 ( 119 cases and 119 controls , 1:2 male to female sex ratio ) individuals from the Tbr HAT endemic sample and 202 ( 99 cases and 103 controls , 1:1 male to female sex ratio ) individuals from the Tbg HAT endemic sample ( Supplementary Figures S1 Fig and S2 Fig ) . Similarly , loci that were missing more than 30% or 40% data were excluded from the Tbr and the Tbg HAT endemic area samples ( Supplementary Figures S3 Fig and S4 Fig ) . We used a HWE p-value cut-off of 1 x 10−8 and further selection of loci below the HWE cut off was done basing on their genotype scatter plots to see which loci were to be excluded . In order to get a high LD between marker and causal SNPs , loci that were in a five SNP window after a single step with a variance inflation factor ( VIF ) [VIF = 1/ ( 1-R2 ) ] beyond 0 . 2 were excluded from both sample datasets . This was done because a high LD between marker SNPs increases redundancy and reduces power . After quality pruning , 79 loci from Tbr and 85 loci from the Tbg HAT endemic samples were included in the association testing . Admixture was used to test for population structure that might confound the association study . Eight values of K ancestral populations from 1–8 were tested to identify which had the lowest coefficient of variations ( CV ) error . CV error was at a minimum for K = 4 , but the CV error was very similar for all values of K ( 0 . 42–0 . 46 ) providing no persuasive evidence for any particular number of ancestral populations . The Admixture plot showed no clear evidence for any gross population structure and therefore no correction for population structure was applied in the analysis . Six SNPs in the Tbr HAT endemic area and four in the Tbg endemic had raw p < 0 . 05 but none of these remained significant after Bonferroni correction ( Table 1 ) . Surprisingly , there was no evidence for association with any SNP in APOL1 .
In this case-control CGAS , we found no evidence for variants associated with Tbr or Tbg HAT in two Ugandan populations . We tested for association between candidate genes and the disease caused by Tbg and Tbr separately as they present two distinct forms of the disease . Tbr and Tbg parasite resistance to human serum is mediated by different mechanisms which place distinct selective pressures on the host genes [35] . Furthermore , the two populations were from different broad ethnolinguistic groups , and were geographically isolated from each other [13] . Admixture analysis found no evidence of population structure with these SNP which might have reduced the power of the study ( S5 Fig ) . We found no SNP associated with HAT after multiple testing corrections . Our power calculations indicated that we had power to detect odds ratios > 2 , however 7 of the 10 SNPs with P <0 . 05 had odds ratios < 2 . 0 , which the study was not powered to detect . Larger populations would be required to confirm these findings and the data presented could be used to estimate the necessary sample size . The most striking feature of the data was the absence of any association at APOL1 . The APOL1 G2 ( deleted allele for indel rs71785313 ) allele has been shown to be lytic to T . b . rhodesiense in vitro [36] and a recent study in the Soroti and Kaberamaido focus in Eastern Uganda found an association with APOL1 G2 and protection from Tbr HAT with an odds ratio of 0 . 2 [8] . The present study in the Busoga focus was well powered to discover such a strong effect , but the frequencies of APOL1 G2 in cases and controls were almost equal ( 8 . 1% and 8 . 6% , 95% odds ratio confidence interval: 0 . 37–2 . 34 ) which indicates that an odds ratio as large as seen in Kaberamaido ( OR = 0 . 2 , 95% odds ratio confidence interval: 0 . 07–0 . 48 ) is very unlikely to be seen in Busoga ( Supplementary data S2 Table ) . The frequency of the G2 allele in the control population in Kabermaido ( 14 . 4% ) [8] was higher than in Busoga ( 8 . 6% ) . Although this difference in G2 allele frequency is not significant with the sample sizes that were used ( Chisq Test p = 0 . 12 ) , it may be indicative of real differences between these populations in selection pressure on this allele . Despite their geographical proximity ( 240km ) these populations have divergent ethnic backgrounds; with the Kaberamaido population being Luo speakers which is a Nilotic family language originating in Sudan and Ethiopia and the Busoga population being Niger-Congo-B ( Bantu ) language speakers with origins in West Africa . These linguistic groups are believed to have diverged over 5 , 000 years ago giving plenty of time for allele frequencies to diverge . Therefore , despite the well-established function of APOL1 in response to trypanosome infection and the evidence for protection associated with G2 in Kaberamaido [8] , the role of APOL1 G2 in response to T . b . rhodesiense infection more generally remains to be clarified . Despite the suggestively significant associations found at nine SNP loci , none of them passed Bonferroni correction for multiple testing [30] . The FDR_BH shows the rate of type 1 errors or false positives , eg for rs9380142 in HLA-G there is an 18% chance that this is a false positive and conversely a 82% chance that it is a true positive . There was a greater than 38% probability for each of these nine SNPs being associated with HAT [30 , 31] . The finding of suggestive associations in multiple populations would increase the probability that these are genuine associations with disease [37] . For example , our findings suggest that HLA-G variants may be important in both forms of the disease . These observations should be followed up in future studies .
|
Human African Trypanosomiasis ( HAT ) occurs in two distinct disease forms; the acute form and the chronic form which are caused by microscopically indistinguishable hemo-parasites , Trypanosoma brucei rhodesiense and Trypanosoma brucei gambiense respectively . Uganda is the only country where both forms of the disease are found , though in geographically distinct areas . Recent studies have shown that host genetic factors play a role in HAT resistance and/or susceptibility , particularly by genes involved in the immune response . In this study , we identified single nucleotide polymorphisms in selected genes involved in immune responses and carried out a case-control candidate gene association study in Ugandan participants from the two endemic areas . We were unable to detect any polymorphisms that were robustly associated with either Tbr or Tbg HAT . However , our findings differ from recent studies carried out in the Tbr HAT another endemic area of Uganda that showed the APOL1 ( Apolipoprotein 1 ) G2 allele to be protective against the disease which merits further investigation . Larger studies such as genome wide association studies ( GWAS ) by the TrypanoGEN network that has >3000 cases and controls covering seven countries ( Cameroon , Cote d’Ivoire , DRC , Malawi , Uganda , Zambia ) using the H3Africa customized chip reflective of African genetic diversity will present novel association targets ( https://commonfund . nih . gov/globalhealth/h3aresources ) .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"methods",
"Results",
"Discussion"
] |
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] |
2018
|
No evidence for association between APOL1 kidney disease risk alleles and Human African Trypanosomiasis in two Ugandan populations
|
RbgA is an essential GTPase that participates in the assembly of the large ribosomal subunit in Bacillus subtilis and its homologs are implicated in mitochondrial and eukaryotic large subunit assembly . How RbgA functions in this process is still poorly understood . To gain insight into the function of RbgA we isolated suppressor mutations that partially restored the growth of an RbgA mutation ( RbgA-F6A ) that caused a severe growth defect . Analysis of these suppressors identified mutations in rplF , encoding ribosomal protein L6 . The suppressor strains all accumulated a novel ribosome intermediate that migrates at 44S in sucrose gradients . All of the mutations cluster in a region of L6 that is in close contact with helix 97 of the 23S rRNA . In vitro maturation assays indicate that the L6 substitutions allow the defective RbgA-F6A protein to function more effectively in ribosome maturation . Our results suggest that RbgA functions to properly position L6 on the ribosome , prior to the incorporation of L16 and other late assembly proteins .
The assembly of the 30S and 50S ribosomal subunits is a complex and tightly coordinated series of events that consists of the synthesis , processing and modification of 5S , 16S and 23S rRNA and the addition of more than 50 ribosomal proteins ( r-proteins ) [1] , [2] , [3] . The in vitro reconstitution of a mature 50S subunit has been extensively studied in Escherichia coli and the formation of a mature 50S subunit from its constituent r-proteins and rRNA is a multi-step process that requires non-physiological conditions such as high ionic concentration , high temperatures and long incubation times [4] , [5] , [6] , [7] . Relatively fewer studies focused on ribosome assembly in other bacterial species , such as Geobacillus stearothermophilus , and these demonstrated that the intermediates formed in this system are different than those in E . coli , however similar non-physiological steps are required for formation of a functional ribosomal subunit [5] , [8] . Moreover , recent studies have utilized biophysical techniques to study ribosome assembly in vivo and demonstrated that assembly of the ribosome subunits is a multistage process that appears to follow multiple parallel pathways in which the accumulation of assembly intermediates identified in vitro do not accumulate in vivo [9] , [10] , [11] . The slow kinetics and attenuated efficiency of in vitro assembly strongly suggest that assembly factors are involved in vivo and indeed , several classes of assembly factors such as GTPases , RNA helicases , RNA modification enzymes and chaperone proteins have been implicated in in vivo ribosome assembly in bacterial and eukaryotic cells [2] , [12] , [13] , [14] , [15] . However , while studies show that these factors are functionally significant and play a critical role in ribosome assembly , the molecular functions of these factors remain elusive . RbgA ( ribosome biogenesis GTPaseA ) is an essential GTPase that is required for a late step in assembly of the 50S subunit in Bacillus subtilis [16] , [17] . RbgA is a widely conserved protein and its eukaryotic homologs such as Mtg1 , Lsg1 , Nug1 and Nog2 have also been implicated in assembly of the large ribosomal subunit [18] , [19] , [20] , [21] . RbgA depleted cells do not form mature 50S subunits but instead accumulate a 45S complex . Quantitative mass spectrometry analysis of this particle shows that the 45S completely lacks ribosomal proteins L16 , L28 , and L36 and contains severely reduced amounts of L27 , L33 , and L35 [16] , [22] . Proteins L16 and L27 are crucial components of the peptidyltransferase center in 50S subunit and directly contact the A-site and the P-site respectively [23] , [24] . Functional studies have shown that both proteins play a role in stabilization of the peptide bond formation , the positioning of tRNA on their respective sites and are required for optimal functioning of the ribosome [25] , [26] , [27] . While there have been no reports of deletion of L16 , the deletion of L27 in E . coli causes a severe growth defect [28] . However , studies in B . subtilis indicate that both proteins are essential and deletion mutants could not be obtained for either protein [29] . In vitro assembly experiments have demonstrated that incorporation of L16 into the growing complex occurs at a late stage in the assembly process and is accompanied by a large conformational change [30] . In yeast , the RbgA homolog Lsg1 has been proposed to play a role in the incorporation of the L16 homolog Rpl10 into the large ribosomal subunit , suggesting that RbgA and its homologs regulate an evolutionarily conserved step during biogenesis [31] , [32] . RbgA has been shown to interact directly with both the 45S complex and the 50S subunits and the GTPase activity of RbgA is enhanced ∼60 fold in the presence of the mature 50S subunit [33] . Mutational analysis of RbgA has shown that a stretch of 15 amino acids in the N-terminal domain , which is largely conserved among all bacterial RbgA homologs as well as eukaryotic homologs , plays a crucial role in this GTPase activity [34] . Mutations that affect GTP hydrolysis result in the accumulation of the 45S complex similar to RbgA depleted cells indicating that GTP hydrolysis plays a key role in maturation of the 50S subunit [34] . To further investigate the role of RbgA in the assembly of the 50S subunit we constructed a B . subtilis strain that expressed a mutated RbgA protein that results in a severe growth defect and screened for suppressors that alleviated this growth defect . We isolated and characterized eight independent suppressor strains and found they contained six distinct mutations in the rplF gene , which encodes for ribosomal protein L6 . Analysis of ribosome assembly in these strains led to discovery of a novel ribosomal intermediate that differs from the 45S complex observed in the parental strain and also in RbgA-depleted cells . We discuss the implications of these results and present a model for the role of RbgA in assembly of the 50S subunit .
To generate a strain that displayed a strong growth defect that would be amenable to suppressor analysis , we analyzed the phenotypes of over 40 site-directed mutations in the rbgA gene [34] . We were interested in identifying substitutions in RbgA that displayed reduced GTPase activity upon association with the ribosome and were still able to bind to the ribosome . One such mutation , rbgA-F6A , was identified as meeting both of these criteria . Our results showed that GTPase activity of RbgA-F6A was reduced ∼12 fold , however the mutation did not prevent stable association with the 45S complex and the 50S subunit [34] . Therefore we constructed a strain in which rbgA-F6A was the only functional copy of rbgA in the cell expecting that cells harboring rbgA-F6A would be viable but display reduced growth . To achieve this we constructed strain RB1043 by cloning the rbgA gene ( containing a mutation that results in a F6A substitution ) fused to its native promoter into the plasmid pAS24 and inserted this construct at the amyE locus ( Table 1 ) . A control strain ( RB1006 ) that contains a wild-type copy of the rbgA gene at the amyE locus was constructed in similar manner as a control . The native rbgA gene was inactivated in both strains by the insertion of a MLS cassette by marker replacement , which led to the complete removal of the native rbgA gene . Comparison of the two strains showed that the strain expressing RbgA-F6A ( RB1043 ) was severely growth compromised and exhibited a growth rate ∼7 fold slower than the RB1006 strain ( Figure 1A ) . This severe growth defect was utilized to isolate suppressor mutations that allowed this strain to grow more rapidly . To isolate independent , spontaneous suppressor mutations we inoculated a single colony of the RB1043 ( rbgA-F6A ) strain per flask into a total of 50 flasks and isolated suppressors that exhibited faster growth at 37°C ( only one per flask ) . We identified eight independent suppressor strains that partially alleviated the growth defect of RB1043 ( Figure 1 and Table 2 ) . Individual suppressors were grown in liquid medium and their growth rates were compared to the parental RB1043 strain and the control strain RB1006 . The wild-type control strain RB1006 and the parental RB1043 strains exhibited a doubling time of 23 minutes and 173 minutes , respectively , whereas the growth rate of the suppressor strains ranged from 46 to 77 minutes ( Table 2 ) . Next , we sequenced the rbgA-F6A gene to check for reversion mutations and found that all eight strains lacked intragenic suppressor mutations . We then proceeded to backcross each suppressor strain with the wild-type RB247 strain and inactivated the native rbgA gene . The reappearance of RB1043 phenotype ( ∼7-fold increase in doubling time ) in each backcrossed strain indicated that the suppressor mutation was unlinked to the rbgA-F6A mutation . To identify the genetic changes responsible for the partial suppression of the growth defect we obtained the whole genome sequence of all eight suppressor strains , RB247 ( wild-type background ) and the parental RB1043 . The sequence reads from the parental RB1043 ( rbgA-F6A ) strain were compared with each suppressor strain sequentially . After accounting for mutations that have arisen in our genetic background or were sequencing errors in the original B . subtilis sequencing project [35] , we found that each suppressor strain bore a single point mutation in the ribosomal protein L6 encoding gene rplF gene . Three suppressor strains had the same mutation ( Table 2 ) and thus we obtained six unique suppressor mutations that caused single amino acid substitutions in L6; R3C , G5C , G5S , H66L ( 3 isolates ) , T68R and R70P . Alignment of L6 proteins from phylogenetically diverse bacteria indicates that these residues are conserved in bacterial L6 proteins , with T68 demonstrating the most conservation when compared to L6 homologs from archaea and eukaryotes ( Figure S1 ) . We constructed a homology model of the B . subtilis L6 protein based on the structure of the L6 protein from Geobacillus stearothermophilus and mapped the suppressor mutations onto the modeled structure of the protein . Our analysis shows that all of the six suppressor substitutions reside in close vicinity in the protein structure ( Figure 2 ) and are contained within the N-terminal structural domain . To assess the status of ribosome assembly in the suppressor strains , we analyzed the ribosome profiles using 10–25% sucrose density gradients . Our results showed that all of the suppressor strains accumulated a novel ribosomal intermediate that migrated at ∼44S and was distinct from the 45S complex that accumulates in RbgA-depleted cells and RB1043 strain expressing RbgA-F6A ( Figure 3 ) . In addition , each suppressor strain exhibited an increased 70S ribosome peak compared to RB1043 , corresponding to the increased growth rate of the suppressor strains . Given the changes in these suppressor strains' intermediate particle migration patterns , we set out to identify compositional differences between the 44S and 45S intermediates by isolating the particles on a sucrose gradient and measuring their r-protein composition using quantitative mass spectrometry ( qMS ) . As described in materials and methods , our SILAC-like approach resulted in multiple independent peptide measurements for the ribosomal proteins . Additionally , standard curves measured with our technique exhibited a linear dose-response between 0 . 1 and 1 . 6 r-protein equivalents ( Figure S2 ) , providing confidence in the precision of the approach . Whereas most proteins were present at stoichiometric levels in the intermediates , we found that these particles were severely lacking in proteins L16 , L28 , L35 , and L36 ( occupancy<0 . 4 ) , and were significantly depleted of proteins L27 and L33 ( occupancy<0 . 8 ) ( Figure 4A ) . These latter depletion effects were more pronounced in the parental RB1043 strain than in any of the suppressor strains , suggesting that these proteins are more efficiently incorporated as a result of the suppressor mutations . Protein L6 showed the greatest variability in protein occupancy across the strains with the parental RB1043 exhibiting full protein incorporation whereas the suppressors RB1051 and RB1068 largely lacked L6 ( occupancy<0 . 3 ) . Interestingly , the intermediates from suppressor strains RB1055 and RB1057 showed more mild L6 depletion effects ( occupancy ∼0 . 8 and 0 . 6 , respectively ) despite migrating similarly to the other suppressor strain intermediates . This result suggested that the difference in migration between the 44S and the 45S intermediates arouse from conformational changes in the intermediates and was not a direct result of the extent of L6 incorporation . Finally , we measured protein occupancy in these intermediates from three independent biological replicates and consistently identified that the aforementioned proteins were depleted from the intermediate particles ( Figure 4B ) , confirming the significance of the observed effects . We then determined the protein composition of each 70S particle from these strains to test whether the protein depletion effects we observed in the intermediate particles persisted into the 70S fractions . The depletion effects observed in the intermediates were largely absent from the mature particles , with only strain RB1057 exhibiting relatively mild occupancy defects ( occupancy>0 . 7 ) in proteins L6 , L16 , L27 , L28 , L35 , and L36 ( Figure 4A , B ) . Whether these subtle effects result from instability of the RB1057 70S particles during purification or from a subpopulation of particles that lack these r-proteins in vivo remains to be investigated . In all other strains tested , each r-protein was present at equal stoichiometry with the exception of the rapidly exchanging protein L7/L12 [36] , which likely dissociates during particle purification . Taken together , these data affirmed that the mutant L6 proteins , along with the full complement of other large subunit proteins , are integrated during the late assembly stages of the 70S particles . To test if the L6 levels in the complex influenced the GTPase activity of RbgA , we incubated RbgA with each 44S intermediate and measured the rate of GTP hydrolysis . Our results show that GTPase activity of RbgA is stimulated ∼4–6 fold in the presence of the each 44S intermediate , with no correlation between the hydrolysis rate and L6 occupancy ( Figure S3 ) . This increase in GTPase activity is similar to the fold change observed in the presence of the 45S complex and highly reduced compared with the ∼60 fold stimulation observed in the presence of the mature 50S subunit [33] . We were interested in studying the effects of the alterations in ribosomal protein L6 on cell growth and ribosome assembly in an otherwise wild-type background . To do this we created strains in which the rplF mutations were linked to an antibiotic resistance marker and moved into the wild-type background RB247 . Once each mutation was transferred into a wild-type background , the antibiotic resistance marker was easily removed by passage on media without selection , resulting in strains that only contained mutations in rplF ( see materials and methods for details ) . We successfully constructed strains in which mutations in rplF resulting in the R3C ( RB1125 ) , G5S ( RB1131 ) , T68R ( RB1133 ) , and R70P ( RB1123 ) substitutions were the only alterations in the chromosome ( Table 1 ) . Each L6 mutant strain's growth rate was indistinguishable from the congenic wild-type RB247 strain , demonstrating that the partial suppression of the RbgA-F6A growth phenotype was not due to an impairment of growth due to defects in L6 . Although the rplF mutations did not have an effect on cell growth , we were interested to identify if they had any impact on ribosome maturation . Ribosome profiling of strains RB1123 , RB1125 , RB1131 and RB1133 through 10–25% sucrose gradients was performed and in each case L6 substitutions resulted in abnormal ribosome profiles ( Figure 5 , S4 ) . The mutants had increased levels of individual ribosomal subunits when compared to wild-type cells , indicating that the L6 substitutions impacted subunit joining or maintenance of 70S ribosome stability . We further analyzed the 50S subunits that accumulated in these strains and found that both ribosomal proteins L6 and L16 were present in levels similar to wild-type 50S subunits indicating that these substitutions in RplF ( R3C , G5S , T68R and R70P ) do not impact the association of L6 or L16 with the 50S subunit in the context of wild-type RbgA . One possible mechanism for how L6 substitutions may suppress the RbgA-F6A defect is that 44S particles may be more easily matured into 50S subunits than 45S particles . To address this possibility we concentrated cellular lysates from RB1043 ( rbgA-F6A ) and RB1055 ( rbgA-F6A , rplF-R3C ) and incubated them for 1 hour at either 37°C or , as a negative control , at 0°C . After incubation , these lysates were centrifuged over 18–43% sucrose gradients in the presence of 20 mM Mg2+ ( to facilitate mature subunit joining since L6 mutants show subunit association defects in 10 mM Mg2+ , see Figure 5 ) [37] . After incubation of the RB1055 lysate at 37°C , we found that many of the 44S particles in the RB1055 lysate were converted into 50S subunits that subsequently partnered with 30S subunits to form 70S ribosomes ( Figure 6A ) . Indeed , 70S ribosomes showed a more than 100% increase during 37°C incubation with a concomitant decrease in 44S and 30S subunits . The RB1043 lysate yielded a much lower level of 70S formation , with only a 10% increase in 70S ribosomes when incubated at 37°C and similar small reductions in 45S and 30S subunits ( Figure 6B ) . These data were consistent with the hypothesis that 44S particles mature into 50S subunits more quickly than 45S particles in vitro . Previously , we found that cells depleted of RbgA had very small precursor pools for the r-proteins L16 , L27 , L28 , and L35 [22] . This result suggested that upon RbgA depletion the cell down-regulated synthesis of these proteins through an as-yet uncharacterized mechanism , resulting in very low cytosolic levels of free ( unbound ) copies of these proteins . To determine if these suppressor strains also lacked free equivalents of these proteins , we directly measured their whole-cell stoichiometry using qMS , specifically , an isotope-label based selective reaction monitoring protocol ( SRM ) focused on ribosomal peptides ( see materials and methods ) . As predicted by our precursor pool measurements , we found depressed whole-cell protein levels for L16 , L27 , L28 , and L35 in strain RB301 starved for RbgA ( Figure 7 , S5; RB301:6 µM ) . In contrast , cells grown with near wild-type levels of this factor exhibited significantly greater levels of each of these proteins ( Figure 7 , S5; RB301:1 mM IPTG ) . Assays with strain RB1043 , and the L6 suppressors also revealed significant cellular depletion of this set of proteins , indicating that this same regulatory mechanism is activated the RbgA F6A strain and the suppressor mutants . In contrast , protein L36 , which is also depleted from the intermediate particles , was abundant in the whole cell lysate . This result is consistent with unregulated synthesis or degradation that results in significant free ( unbound ) quantities of protein L36 . To determine if the suppressor mutations affected translation or degradation of protein L6 , we next inspected the abundance of this protein in each lysate . Interestingly , its level varied greatly between the different suppressor strains with RB1055 lysates bearing ∼2 . 5 more L6 than those of RB1051 . Indeed , the low cellular L6 abundance in RB1051 may explain the low protein occupancy observed in its 44S intermediate particle . Notably , however , with the exception of strain RB1051 , L6 abundance in the whole cell lysates correlated poorly with occupancy in the intermediate particles ( Pearson's r = 0 . 37 ) . This result argues that the variable L6 occupancy observed in the 44S particles is not strictly a result of altered translation or degradation of the mutant proteins but , rather , results at least in part from effects of the mutations on protein incorporation or complex stability . Given the depressed levels of proteins L16 , L27 , L28 , and L35 in the suppressor strain lysates , we were curious if these proteins were in fact incorporated into the 70S particles during our in vitro maturation assays . Using qMS , we measured the protein composition of both the precursor 44/45S and product 70S particles from RB1043 and the suppressor strain RB1055 at 0 or 37°C . Whereas the precursor particles were depleted of L16 , L27 , L28 , L33 , L35 , and L36 ( Figure 8; light circles ) , we found that the 70S particles contained nearly stoichiometric quantities of each of these proteins ( Figure 8; dark circles ) . Although the extent of maturation in strain RB1055 showed a strong temperature-dependence ( Figure 6A ) , the protein occupancy patterns were effectively temperature-independent indicating that the 70S particles formed during our 37°C in vitro maturation assay were indistinguishable from those formed in vivo and maintained during the 0°C incubation ( Figure 8; dark orange , dark red ) .
We provide evidence that mutations causing substitutions in the N-terminal domain of L6 can suppress ribosome assembly defects associated with a mutation that impairs the function of RbgA . Formally , these L6 suppressor mutations could be acting to facilitate assembly either by allowing the defective RbgA-F6A protein to function more effectively in assembly or by allowing ribosomes to assemble in an RbgA-independent pathway . In testing for an RbgA-independent assembly pathway , we repeatedly attempted to generate an rbgA null mutation in the background of several of the rplF suppressor mutations but were unsuccessful . If the L6 substitutions were able to completely bypass the need for RbgA during maturation then we should have easily isolated null mutations in rbgA in the rplF mutant backgrounds . Additionally , if significant flux where flowing through an RbgA-independent assembly pathway in these mutant L6 strains , we would expect to find 44S particles in strains bearing wild-type RbgA and L6 mutations . Instead , we only identified 50S particles . Critically , these 44S particles , which require RbgA-F6A , can be matured into 70S ribosomes in vitro . Taken together , we propose that the partial suppression of growth and ribosome assembly defects observed are not due to the L6 substitutions completely bypassing the requirement for RbgA in the cell , but rather , that RbgA function is still required for maturation . L6 is a two-domain protein that is located on the L7/L12 side of the 50S subunit and forms an L-like structure that appears to bridge between the front and the back of the subunit ( Figure 9 ) [38] , [39] . The N-terminus of the protein interacts with helix 97 ( h97 ) of the 23S rRNA , while the C-terminus of L6 interacts with the sarcin/ricin loop ( SRL ) [38] , [40] , [41] . All of the L6 substitutions that suppress the RbgA-F6A defect map to a small region in the N-terminus of the protein and , in some cases , appear to disrupt direct interactions between L6 and h97 ( Figure 9 ) . Although some of the suppressor mutations cause L6 to unstably associate with the 44S intermediate ( T68R , R70P ) , the ability to suppress the RbgA-F6A defect does not seem to correlate with L6 binding as 44S particles isolated from the other suppressors contain near wild-type levels of L6 ( Figure 4B ) . The consequence of the L6 substitutions in a wild-type background appears to be at the level of 70S stability . Individual 50S subunits that contain mutant L6 proteins appear to have normal amounts of both L6 and L16 , indicating that once matured , these proteins are stably incorporated . However , clearly there is some disruption of 50S subunit structure that causes decreased stability of 70S ribosomes . This is possibly due to improper positioning of the intersubunit bridge helix 89 , which is located between and makes direct contacts with L16 and L6 . What effect might mutations in L6 have in suppressing ribosome assembly defects associated with reduced function of RbgA ? L6 binds prior to L16 and has been implicated in setting up the binding site for L16 [4] , [42] . In E . coli , the expression of ribosomes that are deleted for the SRL , which interacts with the C terminus of L6 , are dominant-lethal and result in the accumulation of 50S subunits that lack L16 [43] . Lancaster et al . propose that L6 binds to the assembling subunit via initial interactions between the N-terminus of L6 and h97 , which then results in the subsequent assembly of the functional core of the 50S subunit [43] . This includes the formation of several key interactions between h97 , h42 , h89 , h91 , and h95 , which are predicted to be initiated by the binding of L6 with h97 . When the SRL is deleted these interactions are disrupted and the L16 binding site , along with other functional regions of the large subunit , are improperly assembled and non-functional . While the precise role that RbgA plays during ribosome assembly is still unknown , the identification of the second-site suppressors in L6 supports a model in which RbgA participates in facilitating the correct association of L6 with the ribosome to allow the subsequent maturation events to take place ( Figure 10 ) . Recent studies have postulated that ribosomal subunits can be formed via multiple parallel pathways [44] . We suspect that the large subunit pathways converge on a late assembly intermediate ( LAI50-1 ) and GTPases , such as RbgA , act on LAI50-1 to complete maturation . We envision two scenarios in which RbgA could act on LAI50-1 to facilitate maturation . In scenario 1 , RbgA binds to an undefined late assembly intermediate ( LAI50-2 ) , and promotes the rearrangement or movement of helix 97 to facilitate the correct incorporation of L6 . In scenario 2 , L6 binds to the ribosome prior to RbgA ( resulting LAI50-3 , equivalent to the 45S complex ) in an unproductive interaction and the role of RbgA binding is to promote the correct interaction of L6 with the helix 97 [43] . We suspect in RbgA mutants the interaction of L6 with LAI50-2 is reversible and the suppressor mutations in L6 enhance this reversible step by weakening the interaction with the ribosome . Recently , we have shown that the 45S particle is not a dead end particle and can be fully matured into a 50S particle in vivo . The fact that L6 is not fully visible in the cryo-EM structures of the 45S complex provides support that L6 is not in its proper conformation . In both scenarios , correct positioning of L6 and h97 allows for proteins L16 , L27 , L28 , L33 , L35 , and L36 to be stably incorporated into the large subunit . Once RbgA senses that incorporation of these proteins has taken place GTP hydrolysis occurs , a final maturation event takes place , and RbgA leaves the subunit . Because we have not been able to isolate RbgA mutants that are deficient in GTPase activity that form 50S subunits , we predict that the GTP hydrolysis plays a dual role in both promoting conformational changes in the ribosome while also resulting in RbgA dissociation . Support for this latter step stems from the fact that 50S subunits lacking only ribosomal proteins L16 and L28 do not stimulate the GTPase activity to levels observed with wild-type 50S subunits [22] . Although we do not know the order of binding of L6 and RbgA , in both scenarios the proposed role of RbgA is to properly position L6 and helix 97 to facilitate assembly . This interaction between L6 and h97 is evolutionarily conserved ( see Figure S6 ) and , given that RbgA homologs are present in archaea and eukaryotes , the role of RbgA proteins in ribosome assembly is likely to be conserved as well . Thus it appears that in small subunit and large subunit ribosome biogenesis , one function of assembly factors is to prevent binding of late binding ribosomal proteins until the subunit is ready to receive them [22] , [45] , [46] . Whether or not these potential checkpoints are related to quality control mechanisms that insure only functional ribosomes enter into translation remains to be seen [22] , [47] . Interestingly , E . coli and many other proteobacteria lack RbgA , a function that was present in the last common ancestor and subsequently lost in this lineage of bacteria . We are currently using a comparative genomics approach to identify differences between E . coli and B . subtilis ribosomes in an attempt to further localize the precise site and mechanism of RbgA function .
All strains were grown at 37°C in LB medium and cultures were shaken at 250 rpm . Antibiotics were added at the following concentrations when required: chloramphenicol ( 5 µg/ml ) , erythromycin ( 5 µg/ml ) , lincomycin ( 12 . 5 µg/ml ) , spectinomycin ( 100 µg/ml ) and ampicillin ( 100 µgml ) . IPTG was added to a final concentration of 1 mM when required for strain growth . Plasmid pMA1 was derived from pSWEET , an amyE insertion vector with a chloramphenicol resistance cassette , by placing the rbgA gene under the control of a xylose inducible promoter . Plasmid pAS24 , an amyE insertion vector with a spectinomycin resistance , was used to construct pMG28 by inserting a wild-type copy of rbgA under the control of its native promoter . Plasmid pMG29 bearing a F6A mutation in the rbgA gene ( accomplished by a TTC to GCC codon change ) was constructed from pMG28 using the QuikChange II XL kit ( Stratagene ) by following the manufacturer's instructions . Plasmid pJCL87 was derived from pDR111 and contains a chloramphenicol resistance cassette and the IPTG inducible Phyperspank promoter . Plasmid pMG30 was constructed from pJCL87 by cloning the first 330 bp of the map gene under the control of the Phyperspank promoter . All strains used in this study are derived from the wild type strain JH642 ( RB247 ) and listed in Table 1 . The construction of strain RB301 and RB418 has been described previously [16] . RB395 was constructed by transforming RB247 with pMA1 and knocking out the native rbgA gene by using a MLS cassette . Strain RB1006 was constructed by transforming RB247 with plasmid pMG28 at the amyE locus and knocking out the native rbgA gene by using a MLS cassette . The strains were checked for interruption of amyE by growth on starch plates . Strain RB1043 was constructed by transforming RB247 with plasmid pMG29 and knocking out the native rbgA gene by using chromosomal DNA from RB395 . Independently , strain RB1044 was constructed in a manner identical to RB1043 to serve as a biological duplicate . All strains discussed in this study were confirmed for desired change using PCR to amplify the region of interest followed by sequencing . Strains RB1043 and RB1044 were used for suppressor analysis . A single colony from each of these strains was inoculated per flask ( 25 colonies per strain , total of 50 colonies ) and grown at 37°C for 16 hours . The undiluted culture from each flask as well as two serial dilutions ( 10- , and 100-fold ) were plated on LB plates and incubated overnight at 37°C . The parental strains RB1043 and RB1044 were also plated along with RB1006 carrying wild-type RbgA to serve as controls . Isolated colonies from eight strains-RB1051 , RB1055 , RB1057 , RB1059 ( from RB1043 ) and RB1061 , RB1063 , RB1065 , and RB1068 ( from RB1044 ) that grew faster than parental strains were identified and characterized further . Genomic DNA from RB247 , RB1043 , RB1051 , RB1055 , RB1057 , RB1059 , RB1061 , RB1063 , RB1065 and RB1068 was isolated using the Wizard genomic DNA isolation kit ( Promega ) . The genomic DNA was analyzed on a 0 . 8% agarose gel to ensure that the quality was suitable for sequencing . Whole genome sequencing was performed on a Genome Analyzer II instrument equipped with a paired end module ( Illumina ) at the MSU Research Technology Support Facility . The sequencing reads obtained were quality tested using FASTQC and trimmed if needed . Next we aligned sequence reads from RB247 and RB1043 against the reference B . subtilis strain 168 genome using R2R software . We identified the insertion of pMG29 in RB1043 when compared with RB247 reads and the insertion of the MLS cassette in RB1043 at the native rbgA locus . The sequence of suppressor strains RB1051 , RB1055 , RB1057 , RB1059 , RB1063 , RB1065 and RB1068 was then compared to RB1043 ( the parental strain ) . In addition to the expected insertions found in RB1043 and each suppressor strain ( corresponding to pMG28 at the amyE locus and the MLS cassette at the native rbgA locus ) we identified only a single change in each suppressor strain in the rplF gene . The suppressor mutations that were identified utilizing the R2R platform were confirmed by PCR amplification of the rplF gene and sequencing the amplified product . Homology model of L6 from B . subtilis was obtained by using Modeller 9 . 12 [48] , utilizing the crystal structure of L6 ( PDB code: 1RL6 ) from G . stearothermophilus as a template . Out of 20 models constructed , the model with lowest energy ( molpdf ) was chosen for further analysis . All structural analysis for figure 9 were carried out in Chimera using the 50S structure ( PDB: 2AW4 ) [48] , [49] . Strain RB1095 was constructed by transforming RB247 with pMG30 such that that the expression of the map gene ( at the end of the operon that contains the rplF gene ) was placed under the control of the IPTG inducible Phyperspank promoter . RB1102 was constructed by transforming suppressor strain RB1051 with chromosomal DNA from RB1095 and selecting cells on IPTG , chloramphenicol and MLS ( lincomycin and erythromycin ) such that the rbgA-F6A gene at amyE locus was selected and the mutated rplF gene operon was linked to the chloramphenicol marker . RB1103 , RB1106 and RB1107 were constructed similarly by using RB1055 , RB1065 and RB1068 as the parental strains , respectively . RB1117 was constructed by transforming RB247 with chromosomal DNA from RB1102 and selecting cells on IPTG and chloramphenicol , thus ensuring that this strain had a wild type rbgA gene at the native locus and the mutated rplF gene ( operon was tagged with the chloramphenicol marker ) . RB1118 , RB1121 and RB1122 were constructed similarly by utilizing chromosomal DNA from RB1103 , RB1106 and RB1107 respectively . RB1123 was constructed by growing RB1117 on LB plates without chloramphenicol and IPTG such that the plasmid pMG30 was excised out leaving the mutated rplF gene in a wild type background . RB1125 , RB1131and RB1133 were constructed similarly from RB1118 , RB1121 and RB1122 respectively . Ribosomal subunits were prepared by sucrose density centrifugation . 50S and 45S complexes were isolated from lysates of RB418 and RB301 cells , respectively as previously described [34] . RB1051 , RB1055 , RB1057 , RB1063 , RB1065 and RB1068 were grown to OD600 of 0 . 5 at 37°C in LB medium . Chloramphenicol ( Sigma ) was added to a final concentration of 100 µg ml−1 5 minutes prior to harvest . Cells were harvested by centrifugation at 5000 g for 10 min and resuspended in lysis buffer [10 mMTris-HCl ( pH 7 . 5 ) , 60 mMKCl , 10 mM MgCl2 , 0 . 5% Tween 20 , 1 mM DTT , 1× Complete EDTA-free protease inhibitors ( Roche ) and 10 U ml−1RNase-free DNase ( Roche ) ] . Cells were lysed by three consecutive passes through a French press set at 1400 to 1600 psi and clarified by centrifugation at 16000×g for 20 minutes . Clarified cell lysates were loaded on top of 10–25% sucrose density gradients equilibrated in buffer B ( 10 mMTris-HCl , pH 7 . 6 , 10 mM MgCl2 , 50 mM NH4Cl ) and centrifuged using a SureSpin 630 rotor ( Sorvall ) for 4 . 5 hours at 30 , 000 rpm . Gradients were then fractionated on a BioLogic LP chromatography system ( BioRad ) by monitoring UV absorbance at 254 nm . Fractions corresponding to ribosomal subunits of interest were pooled , concentrated using 100 kDa cutoff filters ( Millipore ) and stored in buffer A ( 10 mM Tris-HCl , pH 7 . 6 , 10 mM MgCl2 , 60 mM KCl and 1 mM DTT ) at −80°C . For qMS , we followed the protocol as described above except that we used 18–43% sucrose gradient that was centrifuged at 21000 rpm for 14 hours . Cell lysates from RB1043 and RB1055 were obtained as described above . Lysates were concentrated using 4 mL Amicon ultra-4 centrifugal filters with 10 kDa cutoff ( Millipore ) . An equal volume of lysate was incubated at 37°C or 0°C for 1 hour , then loaded onto 18–43% sucrose gradient made in buffer C ( 10 mM Tris-HCl , pH 7 . 6 , 20 mM MgCl2 , 50 mM NH4Cl ) followed by centrifugation at ∼82000 g for 14 hours at 4°C in SureSpin 630 rotor ( Sorvall ) . Gradients were fractionated on BioLogic LP system ( BioRad ) monitoring absorbance at 254 nm . The assay was performed as described [34] . Briefly , for measuring GTPase activity in the presence of ribosomal subunits/intermediates 100 nM RbgA protein was incubated with 100 nM 50S subunit or 45S subunit or 44S subunit and 200 µM GTP at 37°C for 30 minutes and for measuring intrinsic GTPase activity 2 µM RbgA protein was incubated with 200 µM GTP at 37°C for 30 minutes . We predetermined that under these conditions the values were in the linear range of the assay . The phosphate released was measured using the Malachite Green Phosphate Assay Kit ( BioAssaySystems ) . Experimental samples were prepared for quantitative mass spectrometry as described previously [22] , with the following noteworthy modifications . First , each 14N-labeled sample to be analyzed ( 20 pmol ) was mixed with a “double spike” internal reference standard mixture of 14N ( 10 pmol ) and 15N ( 30 pmol ) labeled 70S particles . The addition of the 14N standard still allowed for independent quantitation of the 15N isotope distribution for each peptide , but simultaneously ensured that each 15N peak bore a corresponding 14N peak pair irrespective of the concentration in the experimental sample . After precipitation , reduction , alkylation and tryptic digestion , peptides were analyzed on an Agilent G1969A ESI-TOF mass spectrometer . 14N:15N peak pairs were identified in the raw data , assigned to unique ribosomal peptides using a theoretical digest and the quantities of 14N and 15N species were calculated by fitting each isotope envelope using a Least Square Fourier Transform Convolution algorithm [50] . The contribution of the 14N material in the reference spike was eliminated from each experimental measurement by first analyzing the double spike mixture in isolation ( Figure S2A; red ) . To account for variations in sample preparation and ionization efficiency between experiments , the 14N fitted amplitude of each peptide in each sample was normalized using the 15N internal standard amplitude ( derived from a fixed concentration of 15N 70S ribosomes in each sample ) . Once normalized , the contribution of the double spike to the measured 14N amplitude could be eliminated from each experimental sample by simple subtraction of the 14N amplitude of the double spike sample measured in isolation ( Figure S2C ) . To test the efficacy of the approach and to establish a detection limit , we first measured a standard curve using 0 , 2 , 4 , 8 , 16 , or 32 pmol of 14N-labeled 70S ribosomes mixed with the double spike ( 10 pmol 14N , 30 pmol 15N 70S ) . As our analysis pipeline depends on the identification of peak pairs , this double spike approach greatly increased the number of peptides detected for low-abundance proteins ( J . H . Davis unpublished observation ) . Indeed , we consistently identified multiple peptides for each ribosomal protein , even at the low end of the standard curve ( Figure S2D ) . By comparing the measured 14N/15N ratio to the known ratio added we found the approach to be linear over the range of this standard curve ( Figure S2E ) . Moreover , this experiment established a quantitation limit of 2 pmol of the experimental sample , corresponding to occupancy = 0 . 1 when 20 pmol of the 44/45S or 70S particles were analyzed . For each experimental sample , relative protein levels were calculated as the 14N corrected isotope distribution amplitude divided by the 15N isotope distribution amplitude . Isotope distributions and local chromatographic contour maps were examined and peptides with low signal-to-noise were excluded . Finally , to account for differences in the total amount of sample added , each relative protein level was normalized to that of the primary binding protein L20 , which did not vary in occupancy between samples . To improve our quantitation accuracy in more complex samples such as the cell lysates , we developed a selective reaction monitoring ( SRM ) protocol focused on ribosomal proteins . First , 14N-labeled tryptic peptides were generated from 70S particles as described above . These peptides were eluted from an analytical C18 nano-column across a 90 min concave 5–50% acetonitrile gradient at 300 nL/min . Mass spectrometry was performed on an AB/Sciex 5600 Triple-TOF instrument with an information dependent acquisition method utilizing 250 ms MS1 scans followed by 20 successive MS2 scans , each lasting 50 ms ( cycle time of 1 . 3 sec , 4150 cycles/run ) . Each precursor was excluded from the MS2 target list for 12 seconds after observation . Using the fragmentation data and a theoretical digest of the B . subtilis proteome , precursor peptides were assigned using Mascot ( Matrix Science ) . After generating a spectral library from the Mascot identifications , 8 SRM methods each targeting ∼110 ribosomal precursor ions each were generated in Skyline [51] . An equal mixture of 14N and 15N labeled peptides derived from 70S ribosomes were analyzed using these methods on the Triple-TOF and transitions with low signal-to-noise were eliminated . Using the measured retention times bracketed by a 7 . 5 min window , Skyline was used to generate a single scheduled MRM method targeting 310 precursors and ∼10 product ions per precursor . MS1 and MS2 scans lasted 200 ms and 30 ms , respectively . To measure ribosomal protein levels in cellular lysates , 0 . 5 OD*mL of each culture was mixed with 20 pmol 15N-labeled 70S ribosomes and prepared for qMS as described above . Each sample was then analyzed using the scheduled MRM method . Transition chromatograms were extracted from the raw data using Skyline and 14N/15N peak areas were calculated , filtered to exclude those with low signal-to-noise , and plotted using a series of Python scripts . The Pearson correlation coefficient , r , was calculated between the whole cell protein level and immature particle datasets for strains RB1043 , RB1055 , RB1057 , and RB1068 using the SciPy library .
|
Ribosomes are complex macromolecular machines that carry out the essential function of protein synthesis in the cell . The assembly of ribosomal subunits is a multistep process that involves the accurate and timely assembly of 3 rRNA molecules and>50 ribosomal-proteins . In recent years many ribosome assembly factors have been identified in bacterial and eukaryotic cells; however , their precise functions in ribosome biogenesis are poorly understood . We have previously shown that the GTPase RbgA , a protein conserved from bacteria to humans , is essential for ribosome assembly in Bacillus subtilis . Here , we show that growth defect caused by a mutation in RbgA is partially suppressed by mutations in ribosomal protein L6 . The suppressor strains accumulate novel ribosomal intermediates that appear to suppress the RbgA defect by weakening the interaction of L6 for the ribosome and facilitating RbgA dependent assembly . Our work provides evidence for a functional interaction between ribosome assembly factor RbgA and ribosomal protein L6 during assembly , a function that is likely important for mitochondrial , chloroplast , and eukaryotic ribosome assembly as well .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"genetics",
"biochemistry",
"biology",
"and",
"life",
"sciences",
"microbiology"
] |
2014
|
Functional Interaction between Ribosomal Protein L6 and RbgA during Ribosome Assembly
|
DNA methylation is an ancient molecular modification found in most eukaryotes . In plants , DNA methylation is not only critical for transcriptionally silencing transposons , but can also affect phenotype by altering expression of protein coding genes . The extent of its contribution to phenotypic diversity over evolutionary time is , however , unclear , because of limited stability of epialleles that are not linked to DNA mutations . To dissect the relative contribution of DNA methylation to transposon surveillance and host gene regulation , we leveraged information from three species in the Brassicaceae that vary in genome architecture , Capsella rubella , Arabidopsis lyrata , and Arabidopsis thaliana . We found that the lineage-specific expansion and contraction of transposon and repeat sequences is the main driver of interspecific differences in DNA methylation . The most heavily methylated portions of the genome are thus not conserved at the sequence level . Outside of repeat-associated methylation , there is a surprising degree of conservation in methylation at single nucleotides located in gene bodies . Finally , dynamic DNA methylation is affected more by tissue type than by environmental differences in all species , but these responses are not conserved . The majority of DNA methylation variation between species resides in hypervariable genomic regions , and thus , in the context of macroevolution , is of limited phenotypic consequence .
Cytosine methylation is a heritable epigenetic modification found in the genomes of organisms spanning the eukaryotic phylogeny [1] , [2] , [3] , [4] . It occurs in three nucleotide contexts , CG , CHG , or CHH ( where H is any nucleotide except G ) [5] , and is enriched in the repeat rich heterochromatic regions of genomes , in nucleosome linkers , and at CG sites in the exon sequences of genes ( gene body methylation ) [4] , [6] , [7] , . Repeat-localized DNA methylation plays a role in transposon silencing [12] , [13] , but the direct relationship between transcription of protein coding genes and DNA methylation remains unclear . In contrast to repeat methylation , gene body methylation is associated with moderately transcribed sequences [6] , [7] , [14] , [15] , [16] , and has been proposed to stabilize gene expression levels by excluding H2A . Z [17] . Nevertheless , DNA methylation can vary between tissues and environments [18] , [19] , [20] , and in a handful of cases changes in methylation state contribute to heritable phenotypic variation , although the majority have been linked to structural differences near the affected genes [21] , [22] , [23] , [24] , [25] , [26] , [27] . These observations suggest that DNA methylation may regulate developmental processes and that it could potentiate phenotypic variation during evolution . Unlike mutational processes acting on DNA sequences , our understanding of the factors contributing to meiotically stable variation in DNA methylation is in its infancy [28] . The different molecular mechanisms governing DNA methylation constitute one factor impacting stability and subsequent inheritance at symmetric and asymmetric sites . In the plant Arabidopsis thaliana , initiation and maintenance of methylation at CG and CHG sites is divided primarily between DNA METHYLTRANSFERASE 1 ( MET1 ) and CHROMOMETHYLASE3 ( CMT3 ) [29] , [30] , [31] . During DNA replication these two enzymes copy symmetrically methylated cytosines onto the newly synthesized DNA strand using the parental strand as a template [32] , [33] . Unlike symmetric cytosine methylation , CHH methylation cannot be replicated from the template strand [34] . Instead , methylation at newly synthesized CHH sites is established after cell division by the RdDM RNA-directed DNA methylation pathway through the concerted action of small RNAs ( sRNAs ) produced from the methylated locus and the de novo DNA methyltransferases DRM1/DRM2 ( DOMAINS REARRANGED METHYLTRANSFERASE1/2 ) [34] , [35] , [36] , [37] . In addition , RdDM-independent asymmetric DNA methylation relies on DDM1 ( DECREASE IN DNA METHYLATION1 ) and CMT2 [38] . The extent to which DNA methylation varies at individual sites across generations , or the epimutation rate , has only recently been characterized in isogenic plant lines [39] , [40] . Repeat-associated methylation was remarkably stable over 30 generations , but some variability arose outside of repeats in euchromatic sequence [39] , [40] . Changes in DNA methylation accumulated non-linearly , indicating that a subset of methylated sites is particularly prone to spontaneous changes in methylation and , as a result , the absolute DNA methylation differences quickly reach saturation [39] , [40] . Variation of methylation across generations has been linked to the transgenerational cycling of transposon and repeats between methylated and unmethylated states in the germline [41] . Armed with the knowledge of within-species epimutation rate , the degree of epigenome stability over short evolutionary periods , within a single species , for example , can be addressed [18] . Using A . thaliana , intraspecific variation in methylation was surveyed in 140 geographically diverse accessions [18] . Most single site and RdDM-derived regional epimutations were rare , occurring in only a few of the 140 accessions [18] . The lack of intermediate frequency epimutations in these categories is consistent with the view that the vast majority of new methylation variants within a species may only exist for brief periods during evolution . Not too surprisingly , a significant subset of both rare and intermediate frequency RdDM-derived regional epimutations were associated with previously unknown structural variants [18] . Expansion and contraction of repeat-associated sequences leads to intraspecific structural variation; therefore , as a result of RdDM silencing , such structural variants should be linked to methylation variation . Over longer evolutionary periods , broad similarities in DNA methylation are observed across a variety of genomic features . Large-scale patterns of methylation are shared across flowering plants , including extensive methylation of heterochromatic transposon and repeat-associated sequences [6] , [7] , [8] , [9] , [10] , [11] likely due to conservation of the RdDM machinery in plants . Over shorter divergence times , similar levels of gene body methylation have been observed at orthologous genes within the grasses [11] , [42] . Similarly , in vertebrates , where most of the CG sites in the genome are methylated , absence of methylation at so-called CpG islands is usually found in all species examined [43] . Regardless of organism , the degree of DNA methylation conservation depends on both the evolutionary time scale under consideration and on the genomic feature of interest . Here we compare at single base resolution DNA methylation in three closely related Brassicaceae - Capsella rubella , Arabidopsis lyrata , and Arabidopsis thaliana . These three species , which diverged about 10 to 20 million years ago [44] , vary in genome size and architecture [45] , [46] , [47] . Both C . rubella and A . lyrata have a Brassicaceae typical set of eight chromosomes , while A . thaliana has only five chromosomes [48] , [49] . Both the A . lyrata and C . rubella genomes are about 50% larger than that of A . thaliana , but for very different reasons . Expansion of centromeric , heterochromatic regions has enlarged the C . rubella genome , but predominantly euchromatic regions have expanded in A . lyrata , driven by insertions of transposable elements ( TEs ) adjacent to genic sequences [46] , [47] . Reflecting these differences in genome architecture , the reference genome assemblies represent about 85% of the entire genome in A . lyrata , about 75% in A . thaliana , and about 60% in C . rubella ( Table S1 ) [46] , [47] , [50] , [51] , [52] , [53] , [54] . We show that the difference in genome structure is a major factor influencing the evolution of DNA methylation in these species . Furthermore , while overall DNA methylation is similar between species at many sites , dynamic DNA methylation responses between environments and tissues are rarely conserved . Using a comparative framework we were able to disentangle the contribution of genomic , environmental , and developmental factors to DNA methylation variation between species .
Using a factorial design , we subjected seedlings of the inbred reference strains , A . thaliana Col-0 , A . lyrata MN47 , and C . rubella MTE , to either a control or 23-hour cold treatment and separately harvested root and shoot tissues . This design provides the opportunity to determine conservation of DNA methylation as well as dynamic changes between and within species . In addition to extracting DNA for bisulfite-sequencing in duplicate , we also extracted RNA in triplicate for RNA-seq . Bisulfite-treated samples were sequenced to an average of 20× strand-specific coverage ( Table S2 ) . With this coverage , over 97 . 5% of the cytosines in the non-repetitive portion of the reference genome of each species could be interrogated ( 99 . 5% for C . rubella , 97 . 5% for A . lyrata , and 98 . 7% for A . thaliana ) . With a minimum coverage of three , we confidently estimated methylation rates at two thirds to three quarter of cytosines ( 62% for C . rubella , 65% for A . lyrata , and 75% for A . thaliana ) . Sites with significant methylation levels were identified using a binomial test [39] . False positive rates , determined from incomplete conversion of exogenous unmethylated phage lambda DNA , were very low ( Table S3 ) . Global patterns of DNA methylation in A . lyrata and C . rubella are similar to those reported before for A . thaliana , with highest levels in regions near the centromeres , which are populated by TEs and repeats , but contain few genes [6] , [14] , [15] ( Fig . 1 ) . There is little correlation between DNA methylation density and gene expression at the 500 kb scale ( Fig . 1 ) . Centromeric regions are plagued with TEs , and as expected , methylation is found preferentially at sites annotated as residing in TEs ( Fig . 2A ) . Methylation at CHG and CHH sites , which account for over half of methylated sites in all three species , occurs almost exclusively in TEs ( Fig . 2A ) . Methylation patterns in the three species reflect their genome architecture . While we mapped a similar number of methylated cytosines in A . thaliana and C . rubella , consistent with the almost equal size of euchromatic sequences in both species , we identified almost three times as many methylated cytosines in A . lyrata , even though its reference genome assembly is only 50 to 75% longer than that of the other two species . The larger number of methylated cytosines in A . lyrata has led to an elevation in the methylation rate at a number of genomic features ( Fig . 2B ) . This increase has only occurred at CHG and CHH sites , hallmarks of RdDM at TEs , and is especially evident in introns , correlating with the invasion of introns by TEs in this species ( Fig . 2B , C ) . Almost one third of intronic bases in A . lyrata overlap with a TE or repeat , compared to fewer than 10% in the other two species ( Fig . 2C ) , with the expansion found for all TE classes ( Fig . 2D ) . Intron-inserted TEs are frequently found in non-expressed genes ( Fig . S1 ) and are associated with increased methylation in flanking intronic and exonic sequences ( Fig . S2 ) , potentially due to pseudogenization or incomplete annotation of repeats . However , when a TE is inserted into the intron of an expressed gene , elevation of CHG and CHH methylation of exon sequences is not evident ( Fig . S2 , S3 ) . Despite TE expansion in A . lyrata , the level of A . lyrata gene body methylation is comparable to that of C . rubella , which has few TEs in its introns ( Fig . 2E ) . However , species-specific differences in methylation patterns are evident in flanking UTR and intergenic sequence ( Fig . 2E ) . In these regions A . lyrata is the most highly methylated in all contexts ( Fig . 2E ) . Depending on context , C . rubella displays methylation levels either similar to A . thaliana or intermediate between the two other species ( Fig . 2E ) . Arabidopsis thaliana lost three centromeres relative to A . lyrata and C . rubella , and this loss has been estimated to account for about 10% of the genome size reduction in A . thaliana [46] . Using orthologous genes , it is possible to reconstruct the gene , repeat , and methylation density using the ancestral chromosome positions ( Fig . 3 ) . As expected , repeat density and cytosine methylation next to these degraded centromeres is reduced in A . thaliana , while gene density is higher ( Fig . 3 ) . Particularly notable is the decrease in CG gene body methylation ( Fig . 3 ) . Although gene body methylation is positively correlated with gene expression in several species [6] , [7] , [14] , [15] , [16] , gene expression is not noticeably different in these regions between the three species ( Fig . 3 ) . Thus , the elimination of centromeres has had a measurable impact on repeat and methylation distribution in A . thaliana , but did not strongly affect the expression of ancestrally pericentromeric genes . Methylation of plant genomes is driven to a large extent by TEs , which are silenced via either the sRNA-mediated RdDM pathway [36] or the RdDM-independent pathway which relies on DDM1 [38] . Using a Hidden Markov Model algorithm , we identified methylated regions ( MR ) in each genome , which have a median length of 300 to 530 bp and cover between 26 and 73 Mb ( Table S4 ) . MRs are preferentially found in heterochromatic sequence next to centromeres , as they are enriched for TEs ( Fig . S4 , Fig . 4A ) . Since TEs are rapidly turned over , we expected MRs to be only poorly conserved . To test this assumption , we identified nearly 60 Mb of sequences with a 1∶1∶1 relationship in whole-genome alignments ( Table S5 ) [47] . Less than 1% of the MR space is contained in the alignable portion of the genomes ( Fig . 4B ) . In the rare cases where an MR spans alignable sequences , such sequences are almost always methylated in only one of the three species ( Fig . 4C ) . We conclude that DNA methylation targets primarily the variable portion of the genome , which is subject to species-specific expansion and contraction of TEs . To determine whether specific orthologs tend to be associated with methylation in all species , even in the absence of MR sequence conservation , we analyzed orthologs that contained a MR overlapping or within 1 kb of their coding region . Again , we found that the presence of MRs is rarely conserved ( Fig . 4D , Table S6 ) , although MR sharing is seen more often than expected by chance ( Fig . 4D , Tables S7 , S8 ) . This could , however , be simply due to genes near centromeres being more often associated with MRs because they are in an MR-rich genome environment . In contrast to RdDM of TEs and other repeats , the function of CG gene body methylation is still enigmatic , although it correlates positively with gene expression and negatively with mean normalized expression variance , or the coefficient of variation , across tissues and treatments ( Fig . S5 ) [6] , [7] , [14] , [15] , [16] , [17] . CG gene body methylation is found in the majority of genes ( Table S9 ) , and its rate is highly correlated between orthologs , while CG methylation up- and downstream of genes is much less correlated ( Fig . 5 ) . CHG and CHH methylation in gene bodies is often indicative of transcriptionally inactive pseudogenes , paralogs , or transposons wrongly annotated as protein coding genes [14] , [15] , [55] . Between 10 and 20% of genes exhibit CHG or CHH methylation , most of which were not expressed in our samples ( Table S9 ) . Genes with CHG or CHH methylation are underrepresented in the orthologous gene set , where their fraction drops to less than half of their fraction among all genes , supporting the assertion that CHG and CHH methylation point to a tendency toward pseudogenization ( Table S9 ) . Moreover , CHG and CHH methylation are generally not conserved , suggesting that these marks arise in a lineage-specific fashion . We used the cross-species alignments to identify 15 . 1 million conserved CG , CHG and CHH sites , which are located particularly in exons ( Fig . 6A , Table S5 ) . Although only a small portion , 2% , had significant methylation , most were shared between at least two species , with A . thaliana having the fewest methylated sites , reflecting the general decrease in global DNA methylation in this species ( Fig . 6B–D , Table S10 ) . Sites methylated in multiple species are further enriched in exons , with very few of these conserved sites being CHG or CHH sites ( Fig . 6B , C , Fig . S6 ) . Sites that differ in methylation between species can be used to study gain and loss of methylation . We consider sites that are methylated only in a single species as lineage-specific gains , and absence of methylation in only one species as lineage-specific losses . We found that the number of gains and losses reflect the differences in genome architecture between the three species ( Fig . 6 B , D ) . The many methylation losses in A . thaliana appear to be the result of genome shrinkage , and this species has also the fewest gains . In contrast , A . lyrata has the most gains , likely reflecting recent TE expansion ( Fig . 6 B , D ) . The density of variable sites across the genome ( in 10 kb windows ) illustrates that gains and losses are not randomly distributed ( Fig . 6D ) . Species-specific gains , which occur in all three sequence contexts , are concentrated in a subset of windows that are strongly enriched for TEs ( Fig . 6D , E ) , but are also frequently found in exons ( Fig . S6 ) . That methylation gains are particularly likely in first and last exons suggests that methylation spreading from nearby TEs makes an important contribution to newly methylated sites , regardless of TE class ( Fig . 6F , S7 ) [56] , [57] , [58] . Lineage-specific losses are more evenly distributed , without any signature of TE association . In addition , sites that are conserved in not only two , but all three species occur across a similar spectrum of genomic features ( Fig . S6 ) . Together these results indicate that unlike gains , losses occur in a random fashion , with the proviso that there is an overall global loss of methylation in A . thaliana ( Fig . 6D ) . Though centromere elimination contributes to the different methylation pattern in A . thaliana , this explains only a minority of these losses ( Fig . S8 ) . It appears more likely that they are caused by the global reduction in TE content . We also attempted to understand what factors might contribute to conservation of DNA methylation over time . Sites found in more than one species are enriched in exons of conserved length and are more frequent in the center of exons ( Fig . S9 , S10 ) . Because several studies have shown that DNA methylation can change between tissues and in response to external stimuli [19] , [20] , we wanted to address whether these responses are conserved . Principal component analysis on the four types of samples , control shoots , cold-treated shoots , control roots and cold-treated roots , for all three species according to global RNA-seq measurements revealed that tissue is the most important factor , with over 7 , 000 genes being differentially expressed between roots and shoots ( Fig . 7A , S11 ) . Tissue-specific differences in gene expression are the largest source of expression variance in this data set ( Fig . 7A ) . In contrast , species is the most important factor for differences in DNA methylation and explains 80% of the variance in our data ( Fig . 7B , Fig . S12 ) . Moreover , PC2 places A . lyrata closest to C . rubella instead of its congener A . thaliana , reflecting the methylation losses in A . thaliana ( Fig . 7B ) . To evaluate the degree to which within-species DNA methylation changes are conserved , we first estimated significant differential methylation at site and region levels . Four biologically appropriate comparisons were performed for each species to minimize multiple testing problems . Two tests identified differentially methylated positions ( DMPs ) between roots and shoots , and two tests identified DMPs between cold and control conditions regardless of tissue type . In each species , ten times as many DMPs were found between tissues than between treatments ( Figure 8A , Table S11 ) . Similar to DMPs , 20 to 50 times as many differentially methylated regions ( DMRs ) were detected between tissues than between treatments ( Fig . 8B , Table S12 ) . Importantly , DMPs and DMRs do not necessarily coincide ( Fig . S4 , S13 ) . DMPs in all contexts are rarely found within DMRs , indicating that significant regional changes in methylation are not just the extension of single base differences ( Fig . 8C ) . CHG and CHH DMPs reside mainly within MRs ( Fig . 8C ) ; since these are almost exclusively found in the non-alignable portions of the genome , including TEs ( Fig . 4A , Fig . 8D ) , the positions of DMPs and DMRs are typically not conserved between species ( Fig . 8E ) . In the rare case that DMPs or DMRs can be found in the portion of a species' genome that can be aligned with the genomes of the other two species ( Fig . 8E ) , they are only variable in a single species ( Fig . 8F ) . Methylation variation at both the site and region level is therefore not conserved across species . In the absence of sequence conservation at DMRs , we looked for conservation of their presence at orthologous genes . When only considering orthologs , fewer than 700 genes coincide with a DMR ( 405 in C . rubella , 652 in A . lyrata , and 221 in A . thaliana ) ( Table S13 ) . Orthologs only rarely shared the presence of an overlapping or adjacent DMR , similar to what we see for MRs . Despite the rarity of such cases , they occur more often than expected by chance for a subset of genomic features and species comparisons ( Fig . S14 , Table S14 , Table S15 ) . Lack of sequence conservation together with minimal overlap of DMR presence at orthologs supports the transitory nature of methylation variation during genome evolution . We also asked whether differential methylation in or near coding sequences is correlated with changes in gene expression . DMP and DMR overlap with genes was analyzed separately for those that overlapped with exons , introns , 5′ UTRs , 3′ UTRs and 1 kb upstream regions ( Table S13 , S16 ) . DMPs occur in many genes in all three species , and most of them are expressed in our samples ( 9 , 631 in C . rubella , 12 , 216 in A . lyrata , and 6 , 345 in A . thaliana ) , but there is no evidence for correlation between DMPs and gene expression . This holds true for tissue as well as treatment DMPs ( average Spearman rank correlation coefficient tissue = −0 . 04 , treatment = 0 . 02 , Table S17 ) . Only a small number of DMRs overlap with expressed genes ( 529 in C . rubella , 801 in A . lyrata , and 284 in A . thaliana ) . Again , there is no correlation with gene expression ( average Spearman rank correlation coefficient for CG DMRs = −0 . 16 , CHG DMRs = −0 . 06 , CHH DMRs = 0 . 00 , Table S18 ) . Although DMPs and DMRs are not conserved across species , there is consistently more variability between root and shoot samples at a number of genomic features . Importantly , the methylation profile across transposons is quite different between tissues . Transposons are consistently more highly methylated in all sequence contexts in shoots ( Fig . 9A ) . A similar trend is apparent for CHG and CHH sites in intergenic regions in A . lyrata , reflecting that TEs are closer to genes in this species ( Fig . 9B ) [46] .
As a molecular phenotype , many characteristics of DNA methylation are conserved between the species we examined . DNA methylation is generally associated with the repeat-dense sequences found in the centromeres , with CG methylation being in addition present at high levels in exonic sequences [6] , [7] , [8] , [9] , [10] , [11] , [14] , [15] , [16] . Furthermore , gene body methylation levels are conserved in orthologous genes indicating that DNA methylation rate may be subject to purifying selection , a finding consistent with previous wider evolutionary comparisons [42] . The close relationship of the species used in our experiments allows us to make inferences at base pair resolution . Given the substantial rate of epimutation in non-repetitive sequences [39] , [40] , we were surprised to discover that a large fraction of sites is methylated in more than one species . These sites were predominantly found in gene bodies , providing additional evidence for selective constraint . While gene body methylation is poorly understood , there is some evidence that it is correlated with nucleosome positioning in exons [14] , [59] . If nucleosome position is conserved , it could potentially explain long-term conservation of DNA methylation at some sites . An additional proposed feature of DNA methylation as a molecular phenotype is the ability to respond to external stimuli or internal developmental cues . In theory , such variation could control changes in gene expression . We found evidence for DNA methylation variation in all three species across both tissue type and environment . The changes in DNA methylation were in all three species much greater between tissues , and consistently resulted in lower methylation levels in the root [19] . Differences between the root and shoot tissues also explain a majority of the expression variation in the transcriptional data , but these changes are not directional . We found no evidence that changes in DNA methylation across tissues is associated with changes in gene expression . In fact , a large proportion of methylation changes were found in repetitive sequences . This pattern may result from the increased stringency of transposon silencing in the shoot , which includes the plant germline [60] . While transcriptional responses are highly conserved across all three species , we found no evidence for conservation of DNA methylation response at the sequence level . MRs and DMRs are predominantly found in the rapidly evolving repeat-rich regions of the genome and rarely reside in or near the same orthologous gene in more than one species . In many of the classical epimutants , epigenetic regulation of nearby transposon insertions can impact neighboring genes and cause phenotypic variation [21] , [24] , [25] , [26] . This additional regulation is in some cases beneficial; for example , for genes specifically expressed in the pollen [41] , [61] . The data presented here demonstrates that these events are both rare and likely lineage-specific . It is possible that the reported cases of differential methylation as a regulator of transcription are short-term innovations that are eventually replaced by genetically encoded regulation . The mode of inheritance of symmetrically methylated cytosines motivates the interpretation of DNA methylation as a molecular modification that increases the complexity of the genetic code . While mutational processes affecting DNA sequence are well described , epimutational processes are poorly understood . DNA mutations rarely revert and occur in a largely random fashion throughout the genome [62] . In contrast , recent studies have shown that the transgenerational stability of DNA methylation is very context dependent [39] , [40] . Over short evolutionary times , epimutations are more likely to occur in euchromatic sequences and are biased away from heavily methylated repetitive sequences [39] , [40] . Over the longer evolutionary times examined here , we find that changes in genome content and structure are the major contributors to DNA methylation variation . While the majority of single site and regional methylation is found in repetitive sequences that are unlikely under evolutionary constraint , the remaining observed patterns in euchromatic sequence reflect lineage-specific evolution of transposons . This is particularly obvious in A . lyrata , which has experienced a recent invasion of transposable elements into euchromatic sequences [46] and subsequent elevation in the methylation rate of euchromatic features , particularly introns . Large-scale structural changes that have perturbed the genome-wide DNA methylation landscape have also occurred in A . thaliana [48] , [49] . Loss of three repeat-rich centromeres in A . thaliana caused a decrease in DNA methylation in sequences flanking the ancestral centromeres . The impact of lineage-specific transposon evolution and subsequent methylation is similarly evident in genic sequences . Approximately 40% of methylation in conserved exon sequence is species-specific . These sites are non-uniformly distributed near the 5′ or 3′ edges of genes , likely due to spreading from adjacent transposons [56] , [57] , [58] . These observations support the hypothesis that surveillance of transposons is the primary contributor to the genomic distribution of DNA methylation in plants . Since transposon content and genome structure vary extensively even over short evolutionary time periods , DNA methylation appears to be similarly variable . This is supported by the poor resolution of species relationships in a principal component analysis of DNA methylation and a nearly ten-fold increase in divergence between A . lyrata and A . thaliana when comparing DNA methylation as opposed to nucleotide sequence [46] . Together , these results indicate that DNA methylation as a non-canonical nucleotide is very rarely conserved over intermediate evolutionary times scales . Despite the fact that we can estimate the epimutation rate of methylated cytosines and other parameters related to nucleotide mutations , it is misleading to equate DNA methylation changes to nucleotide substitutions . Our results indicate that the rapid evolution of repeat sequences is the major contributor to the equally rapid changes in the genomic distribution of DNA methylation . In this respect , it is more reasonable to regard DNA methylation primarily as a molecular phenotype resulting from the underlying genetic sequences . Although a few “pure” epialleles have been identified in nature , the majority of natural epimutations are linked to nearby transposon insertions or other genetic changes [21] , [24] , [25] , [26] . Fast evolution of repeat-sequences can , however , provide opportunities for lineage-specific cooption of DNA methylation for regulation of endogenous genes in response to various stimuli .
Seeds from the reference strain for each species ( A . thaliana Col-0 , A . lyrata MN47 , C . rubella MTE ) were sterilized with a 15 minute treatment of 30% bleach and 0 . 1% Triton X-100 . Sterilized seeds were plated onto 0 . 5× MS 0 . 7% agar plates with 1% sucrose . Each plate represented a single replicate consisting of 20 seedlings . In total , 7 replicates were sown and randomized into a 3×2×2 factorial design . The three factors in this experiment were species , tissue , and cold treatment . After sowing , plates were stratified in the dark at 4°C for 8 days , before being shifted to 23°C short-day conditions ( 8 hr light∶16 hr dark ) . Plates were oriented vertically . After 6 days in 23°C , half of the plates were exposed to 4°C short-day conditions for 23 hours . At the end of the cold treatment , both control ( 23°C ) and treated ( 4°C ) samples were harvested . Root and shoot tissues were harvested independently . Plants were cut just above and below the root-shoot junction to separate the tissues and avoid cross contamination of tissue types . To minimize daily collection times , replicates were blocked by day . Total RNA was isolated from three replicates of each factor combination using the Qiagen RNAeasy Plant Mini Kit ( catalog # 74904 ) . An on-column DNase digestion was included ( catalog # 79254 ) . Total RNA integrity was confirmed on the Agilent BioAnalyzer . Illumina TruSeq RNA libraries were constructed using 3 µg of total RNA . Samples were randomized before library construction . The manufacturer's protocol was followed with one exception - 12 PCR cycles were used instead of the recommended 15 . Libraries were quantified on an Agilent BioAnalyzer ( DNA 1000 chip ) . Samples were normalized to 10 nM library molecules and then pooled for sequencing . Three pools were constructed , each consisting of 12 random samples . Each pool was sequenced across three lanes of an Illumina GAII flowcell . DNA was extracted from two replicates of each factor combination using the Qiagen DNeasy Plant Mini Kit ( catalog # 69104 ) . DNA was quantified using the Qubit BR assay ( Life Technologies , catalog # Q32853 ) . Bisulfite libraries were confstructed using modifications to the Illumina TruSeq DNA kit and published bisulfite library protocols [15] , [39] . Depending on the sample , starting material ranged from 200 ng to 1 µg . Changes to the manufacturer's protocol will be noted here . After shearing of genomic DNA with a Covaris S220 instrument , sheared lambda DNA was spiked into each sample ( 1∶0 . 001 sample∶lambda ratio ) as a control . , for accurate estimation of failure to bisulfite convert non-methylated cytosines . Samples were randomized before library construction . During the ligation step , the amount of adapter was adjusted based on the amount of starting material in each sample . For 1 µg of input DNA , 2 . 5 µl of adapter were used . Adapter input was scaled linearly for samples with less starting DNA . For the second AMPure bead clean up after the ligation step , the ratio of sample to beads was adjusted to 1∶0 . 74 . A final elution volume of 42 . 5 µl was used for this step . After ligation , 40 µl of eluate was transferred to a new tube for subsequent bisulfite treatment . The Qiagen Epitect Plus Kit ( catalog # 59124 ) was used for bisulfite treatment . The manufacturer's protocol for ‘low concentrated and fragmented samples’ was followed , using 85 µl of bisulfite mix for conversion . Clean up of the bisulfite reaction included ethanol as a final wash step . The sample was eluted in 17 µl . After bisulfite treatment samples were amplified using Pfu Cx HotStart Polymerase from Agilent ( catalog # 600410 ) instead of the supplied PCR mix . Reaction conditions are all follows: 32 . 9 µl of water , 5 µl of 10× Pfu Cx Buffer , 5 µl of 2 mM dNTP , 1 . 6 µl of Illumina PCR Primer Cocktail , 0 . 5 µl of Cx Polymerase ( 2 . 5 U/µl ) , 5 µl of bisulfite-treated DNA eluate . Three PCR reactions were pooled for each bisulfite-treated sample . The following cycling conditions were used: 98°C - 30 seconds; 18 cycles of 98°C - 10 seconds , 65°C - 30 seconds , 72°C - 30 seconds; 72°C - 5 minutes . An AMPure bead clean up was used to purify the final PCR product ( 1∶1 sample to bead ratio ) . Samples were eluted in 32 . 5 µl of Illumina supplied Resuspension buffer . 30 µl of the final eluate was transferred to a new plate for subsequent quantification and sequencing . Libraries were quantified using the Agilent BioAnalyzer ( DNA 1000 chip ) . Libraries were diluted to 10 nM and then pooled . Samples were pooled based on genome size - and each pool consists of 2 random samples from each species . Four pools were constructed and each was sequenced across three lanes of the Illumina HiSeq 2000 . We sequenced bisulfite-converted libraries with 2×101 base pair paired-end reads on an Illumina HiSeq 2000 instrument with conventional A . thaliana DNA genomic libraries in control lanes . Each sample contained 0 . 1% lambda DNA as an unmethylated control . We pooled six different samples in each lane . The Illumina RTA software ( version 1 . 13 . 48 ) performed image analysis and base calling . Reads were filtered and trimmed as previously described [39] . Subsequently , trimmed reads were mapped against the corresponding reference genomes ( Crubella_183 , Alyrata_107 , Athaliana_167 ( TAIR9 ) [46] , [47] , [50] , [51] . The lambda genome sequence was appended to each species genome sequence in order to estimate the false methylation rates of each sample . All reads were aligned using the mapping tool bismark v0 . 7 . 3 [63] . Applying the ‘scoring matrix approach’ of SHORE as previously described [39] , we retrieved unique and non-duplicated read counts per position . Read and alignment statistics can be found in Table S2 . All command line arguments are listed in Text S1 . Raw reads are deposited at the European Nucleotide Archive under accession number PRJEB6701 . We used published methods [39] , with a few exceptions . Here we retrieved incomplete bisulfite conversion rates , or false methylation rates ( FMRs ) , from the alignments against the lambda genome rather than the chloroplast sequence . False methylation rates are found in Table S3 . In addition , we combined the read counts of replicate samples after removing sites that were differentially methylated between replicates . The methylation rates for combined replicates were used for all subsequent analyses . The number of DMPs detected between replicates can be found in Table S19 . In each species we required a methylation rate of at least 20% in one of the four tissue-treatment combinations in order for a site to be considered significantly methylated . To identify DMPs we followed published methods [39] , but we required positions to have a methylation rate of at least 20% in one of the treatment combinations before performing Fisher's exact test . This increased statistical power by reducing the number of multiple testing corrections . Pairwise tests were not performed between all treatment combinations , instead only relevant comparisons were performed within each species ( Root-23°C vs Shoot-23°C , Root-4°C vs Shoot-4°C , Root-23°C vs Root-4°C , Shoot-23°C vs Shoot-4°C ) . To detect contiguously methylated parts of the genome we modified a Hidden Markov Model ( HMM ) implementation [64] . Briefly , each cytosine can be in either an unmethylated or methylated state . The model trains methylation rate distributions for each state and sequence context ( CG , CHG , CHH ) independently using genome-wide data . In addition , transition probabilities between the states are trained . To make the original HMM implementation applicable to plant data , three different ( beta binomial ) distributions were estimated for each state ( methylated and unmethylated ) instead of just the single distribution used in mammals , which have almost only CG methylation [64] . To prevent identification of regions over uncovered bases , the genome was split at locations that lacked a covered cytosine position for 50 adjacent base pairs . On each of these segments , the most probable path through the methylation states was estimated after genome-wide parameter training . Transitions between states demarcated the methylated regions ( MR ) . Replicates of each treatment combination were combined for this analysis . The combined read counts at cytosines were used to calculate methylation rates , train the HMM , and identify methylated regions . As a result , there is a single segmentation of the genome per treatment combination . Methylated regions were trimmed on both 5′ and 3′ ends by removing positions with a methylation rate below 10% . Further details will be described in a manuscript by Hagmann , Becker et al . [65] . Based on the MRs identified for each sample using the HMM algorithm described above , we selected regions of variable methylation state between samples to test for differential methylation . Due to the very large number of MRs , it was critical to reduce the number of tests performed to identify DMRs . By filtering MRs using the criteria outlined in a forthcoming manuscript by Hagmann , Becker et al . [65] , we reduced the number of MRs four fold in each species . For each identified region , pairwise statistical tests were performed for the relevant comparisons listed above . The statistical test approximates the context-specific beta binomial distribution for the region of interest . Individual and joint distributions are approximated for two samples being compared . The statistical test compares the individual sample distributions to the joint distribution using a log-odds ratio . This ratio is compared against a chi-squared distribution to obtain confidence values . For each identified region , samples were assigned to groups by separating the samples with statistically significant methylation . To confirm groupings , we first combined read counts from treatment combinations in the same group . With the combined data , the same statistical test as described above was performed to test for differential methylation . Groups were confirmed in this way to identify and filter potentially erroneous DMRs . After false discovery rate ( FDR ) correction using Storey's method [66] , regions with an FDR below 0 . 01 were defined as differentially methylated regions ( DMRs ) . To resolve overlapping DMRs , we retained the non-overlapping regions containing the maximum number of samples with statistically significant differential methylation . Apart from the criterion used to resolve overlapping DMRs , the methods follow those that will be described in detail in a manuscript by Hagmann , Becker et al . [65] . We identified conserved sites using a published three-way whole genome alignment [47] . For CG sites , identical context was required while substitutions at the H positions were allowed in degenerate contexts as long as they did not mutate to G . Sites that transitioned contexts were not considered . Methylation rates for significantly methylated sites were then extracted from each species , tissue , and treatment combination for subsequent analysis . Three-way orthologs were identified using the reciprocal-best blastp hit approach as implemented in the multiParanoid pipline ( inParanoid v . 4 . 1 , blast v . 2 . 2 . 26 ) [67] . We sequenced each RNAseq library with 101 base pair single-end reads on the Illumina GAII instrument . We pooled twelve different samples in each lane . Each pool was sequenced over three lanes . The Illumina RTA software ( version 1 . 13 . 48 ) performed image analysis and base calling . Reads were trimmed using the shore import function in SHORE version 0 . 9 . 0 [68] . Command line arguments can be found in Text S1 . This function simultaneously trims reads and separates samples by barcode . Since all samples were sequenced over three lanes , after lanes are de-multiplexed sample reads were combined . Due to variable annotation qualities between species , only sequences annotated as CDS annotations were used to map RNA-seq reads . The following representative gene model annotation versions were used for each species: Crubella_183 , Alyrata_107 , Athaliana_167 ( TAIR10 ) [46] , [47] , [50] , [51] . Reads were aligned with one allowed mismatch to the appropriate annotation using bwa version 0 . 6 . 1 [69] . Read counts were obtained for each gene using a custom perl script . In summary , the script identified uniquely aligned read with a mapping quality score above 30 and stored the total read count for each target sequence . Read and alignment statistics can be found in Table S20 . Raw reads are deposited at the European Nucleotide Archive under accession number PRJEB6701 . Differentially expressed genes were identified using the R package edgeR ( 3 . 4 . 2 ) with minor modifications [70] . Using edgeR , we estimated the dispersion parameter for each gene using estimateGLMTagwiseDisp ( ) . Next , we fit a negative binomial generalized linear model ( GLM ) using glmFit ( ) . Significance testing for differential expression was performed using a custom GLM . Significance testing in edgeR was done via term-dropping of each factor level ( likelihood ratio test ) , and as a result performed more statistical tests than necessary . To minimize multiple testing problems , we implemented a negative binomial GLM that tested for differential expression significance using an ANOVA [71] . Dispersion estimates from edgeR were provided to the modified GLM . Using this model , differential expression analysis was performed in two ways . First , expression analysis was performed within species . There were 12 samples consisting of three replicates and four unique treatment combinations . All representative gene models were considered . The following custom GLM model was used: expression∼tissue*treatment . This included the main effects of tissue and treatment as well as their interaction . Secondly , we performed differential expression analysis between all species simultaneously . In this case , there are a total of 36 samples consisting of three replicates of each species , tissue , and treatment combination . Only 1∶1∶1 orthologous gene pairs were considered ( 14 , 395 in total ) . The following custom GLM model was used: expression∼species*tissue*treatment . This includes the main effects of species , tissue , and treatment as well as all two and three-way interactions . Corrections for gene length were performed , but this did not impact the results and was subsequently ignored . Transposon and repeat annotations for all three species were derived from the Capsella rubella genome paper [47] , [72] , [73] .
|
DNA methylation is an epigenetic mark that has received a great deal of attention in plants because it can be stably transmitted across generations . However , the rate of DNA methylation change , or epimutation , is greater than that of DNA mutation . In addition , different from DNA sequence , DNA methylation can vary within an individual in response to developmental or environmental cues . Whether altered characters can be passed on to the next generation via directed modifications in DNA methylation is a question of great interest . We have compared how DNA methylation changes between species , tissues , and environments using three closely related crucifers as examples . We found that DNA methylation is different between roots and shoots and changes with temperatures , but that such changes are not conserved across species . Moreover , most of the methylated sites are not conserved between species . This suggests that DNA methylation may respond to immediate fluctuations in the environment , but this response is not retained over long evolutionary periods . Thus , in contrast to transcriptional responses , conserved epigenetic responses at the level of DNA methylation are not widespread . Instead , the patterns of DNA methylation are largely determined by the evolution of genome structure , and responsive loci are likely short-lived accidents of this process .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
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"biotechnology",
"biochemistry",
"genome",
"evolution",
"plant",
"genomics",
"genetics",
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"life",
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2014
|
Evolution of DNA Methylation Patterns in the Brassicaceae is Driven by Differences in Genome Organization
|
Human disease is heterogeneous , with similar disease phenotypes resulting from distinct combinations of genetic and environmental factors . Small-molecule profiling can address disease heterogeneity by evaluating the underlying biologic state of individuals through non-invasive interrogation of plasma metabolite levels . We analyzed metabolite profiles from an oral glucose tolerance test ( OGTT ) in 50 individuals , 25 with normal ( NGT ) and 25 with impaired glucose tolerance ( IGT ) . Our focus was to elucidate underlying biologic processes . Although we initially found little overlap between changed metabolites and preconceived definitions of metabolic pathways , the use of unbiased network approaches identified significant concerted changes . Specifically , we derived a metabolic network with edges drawn between reactant and product nodes in individual reactions and between all substrates of individual enzymes and transporters . We searched for “active modules”—regions of the metabolic network enriched for changes in metabolite levels . Active modules identified relationships among changed metabolites and highlighted the importance of specific solute carriers in metabolite profiles . Furthermore , hierarchical clustering and principal component analysis demonstrated that changed metabolites in OGTT naturally grouped according to the activities of the System A and L amino acid transporters , the osmolyte carrier SLC6A12 , and the mitochondrial aspartate-glutamate transporter SLC25A13 . Comparison between NGT and IGT groups supported blunted glucose- and/or insulin-stimulated activities in the IGT group . Using unbiased pathway models , we offer evidence supporting the important role of solute carriers in the physiologic response to glucose challenge and conclude that carrier activities are reflected in individual metabolite profiles of perturbation experiments . Given the involvement of transporters in human disease , metabolite profiling may contribute to improved disease classification via the interrogation of specific transporter activities .
Disease heterogeneity has challenged the practice of medicine . Individuals with the same apparent disease at our current diagnostic resolution often show remarkable variation in prognosis and treatment responsiveness , presumably because a superficially similar disease state can arise from diverse combinations of genetic and environmental factors [1] . Efforts to resolve the heterogeneity have focused on collecting increasing amounts of quantitative patient information , including genotypic [2] and mRNA [3] and protein expression data [4] with the hope of establishing better clinical classifiers based on aberrant activities of specific , targetable biological pathways . Using tumor biopsy samples , oncologists are now exploring the incorporation of genomewide expression profiling into therapy [5] , [6] . However , for complex human diseases that span multiple organ systems , metabolomics—the analysis of a broad array of metabolite levels from biologic fluid samples such as blood or urine—represents a minimally-invasive way to obtain quantitative biologic information from patients to uncover disease pathophysiology and aid diagnostic and prognostic classification [7] . Metabolomics data analysis may be facilitated by techniques applied to other high-throughput ‘omic data types . For microarray data , the integration of network information from protein-protein interaction data or predefined biologic pathways has greatly assisted elucidation of underlying processes and led to the development of increasingly robust and accurate gene-based classifiers for disease [8] , [9] . We hypothesize that the characterization of human disease by metabolomic profiling should similarly benefit from interpreting metabolite changes in the context of known metabolic reactions . We use data derived from oral glucose tolerance tests ( OGTT ) in 25 individuals with normal ( NGT ) and 25 with impaired ( IGT ) glucose tolerance [10] . We first sought significant overlaps between observed metabolite changes and preconceived definitions of metabolic pathways . Next we applied an unbiased pathway analysis by mapping the metabolite changes to a recent reconstruction of the human metabolic network [11] and use a recently developed variant [12] of previous approaches [13] derived for mRNA expression analysis to find active metabolic modules—connected subnetworks of highly changed metabolites . While the biased approach yielded little , the resulting unbiased pathway models highlight the interconnectedness between changed metabolites and propose a role for solute carriers in OGTT metabolite profiles . Hierarchical clustering and principal component analysis confirmed the importance of specific transporters by demonstrating that metabolites cluster naturally according to activities of the System A and L amino acid and SLC6A12 osmolyte transporters . Furthermore , they suggest an important role for the SLC25A13 mitochondrial aspartate-glutamate transporter in interindividual metabolite profile variability . Comparison of NGT and IGT active modules suggest blunted glucose- and/or insulin-stimulated enzyme and transporter activities in the IGT group . Given that transporters are implicated in multiple human diseases , the interrogation of transporter activities by perturbation-based metabolic profiling may ultimately contribute to improved disease classification and resolution of disease heterogeneity .
We examined metabolite profiles from a previously descibed oral glucose tolerance experiment ( OGTT ) [10] , which involved the use of metabolite profiling to monitor physiologic responses to oral glucose challenges in individuals with normal ( NGT ) and impaired glucose tolerance ( IGT ) . Multiple metabolites were changed significantly in response to glucose in two separate NGT populations . Furthermore , interpreting the list of changed metabolites in terms of known mechanisms of insulin action allowed the authors to assign the observed results to established biochemical pathways , including glycolysis , lipolysis and ketogenesis , and led to the proposal of new downstream pathways of insulin action , such as bile acid metabolism [10] . Many of the changed metabolites were not , however , mapped to established pathways . We were thus interested in further elucidating the underlying biologic processes leading to the observed pattern of changes . Analyzing the OGTT metabolite profiles of the 25 NGT and 25 IGT Framingham Heart Study participants ( see Methods ) , we identified 57 and 31 metabolites , respectively , changed at an FDR of 0 . 05 ( see Table S1 ) . We first revisited whether the pattern of changed metabolites was consistent with predefined metabolic pathways using the FuncAssociate program [14] . FuncAssociate uses a hypergeometric test and correction for multiple hypothesis testing to formally evaluate statistical significance for pathway enrichment ( see Methods ) . Although originally designed to identify enriched “gene sets” among a list of genes , FuncAssociate can be adapted for “metabolite sets” . We used a recent reconstruction of the human metabolome , “Recon 1” , as a source of pathway information [11] . The significantly changed metabolites were ranked by magnitude of change and FuncAssociate was used to identify significant enrichment of any of the 99 separate metabolic pathways in Recon 1 . We evaluated NGT and IGT individually ( comparing metabolite abundance before and after oral glucose load ) and found enrichment solely in NGT for Bile Acid Biosynthesis at an adjusted p-value <0 . 001 . The low yield of pathway enrichment could arise in part from the sparseness of our metabolome coverage or from the fact that most metabolites are implicated in multiple pathways . Furthermore , even if a pathway has uniformly increased flux , this will not generally lead to uniform increases in metabolite abundance . The relationship between enzymatic activity and metabolite concentration can be understood in terms of the relative contribution of “metabolic regulation” and “hierarchical regulation” . Metabolic regulation involves control of reaction flux through the interaction of enzymes with the rest of the metabolic network , such as changing substrate , product or modifier concentrations [15] . On the other hand , hierarchical regulation achieves control through changes in maximal enzyme activity , typically by altered gene expression . In the extreme case where there is simultaneous and proportional modulation of the activity of all enzymes in the pathway , one would see no changes in metabolite concentrations in a pathway despite changes in metabolic flux . A final explanation for the low yield of enriched predefined pathways may be that the physiologic perturbation only affects a subnetwork of metabolites that may not correspond to any of the preconceived pathway definitions . In light of these possibilities , we investigated the application of additional , emerging bioinformatics approaches , which emphasize unbiased pathway models . We based our analysis on the fact that metabolites are linked via chemical reactions . We hypothesized that OGTT is a physiologic stimulus that alters flux through specific metabolic reactions . Since products from one reaction may serve as reactants for and drive other reactions , we sought groups of metabolites that are connected through metabolic reactions and collectively show a high degree of change . Furthermore we hypothesized that a perturbation such as OGTT would increase the activity of enzymes and transporters , many of which have multiple substrates . Thus , we were also interested in groups of changed metabolites linked by virtue of being substrates of a common enzyme or transporter . We framed the search for functionally-linked , highly changed metabolites in OGTT in terms of the discovery of active modules ( or subnetworks ) . Active module approaches have previously been applied in bioinformatics analysis to elucidate underlying biologic processes in gene expression data . In such analyses , the investigators typically overlaid gene scores based on differential expression in microarray experiments onto protein-protein interaction [16] , [17] and/or transcription-regulatory [13] networks and looked for highly-connected , differentially expressed genes . We undertook a similar approach , combining OGTT metabolite profiles with metabolic reaction information . We first built a Metabolic Reaction Network ( MRN ) using the 3338 metabolic reactions in Recon 1 . Although Recon 1 includes most known transport reactions , the specific transporters were not always explicitly mentioned . Thus we expanded this list with 737 additional reactions explicitly modeling transport processes for the metabolites measured in this experiment ( see Methods , Table S2 ) , highlighting the relevant transporter for each reaction . We treated all reactants and product metabolites as nodes . Cellular locations were assigned to each metabolite as specified in Recon 1 , and metabolites were split into multiple nodes ( each corresponding to a different location ) . For example , five nodes in the MRN were assigned to D-Glucose , corresponding to glucose in the cytoplasmic , lysosomal , Golgi , endoplasmic reticulum and extracellular compartments . Edges were drawn between reactants and products in chemical reactions ( see Methods and Figure 1 ) and between all substrates for each of the known enzymes or transporters catalyzing metabolic reactions ( Table S2 ) . In effect , we proceeded from a bipartite undirected graph [18] , where both metabolites and proteins ( enzymes/transporters ) are represented as nodes , and interactions between metabolites and proteins represented as edges , to a unipartite metabolite interaction graph , where metabolites that are common substrates of enzymes or transporters were connected by edges . For those reactions where enzymes/transporters are unknown or unneeded , the corresponding reactant and product metabolites were directly connected . We converted experimental measures of significance of change ( p-values ) for metabolites to scores ( see Methods ) . Since Recon 1 includes cellular locations for reactions , if a metabolite had multiple cellular locations , different active modules could emerge , depending on the location to which a score was assigned . We hypothesized that the plasma metabolite profiles of perturbation experiments reflect altered flux in both intracellular reactions and in metabolite transport between the cell and plasma . We focused on each of these processes separately , building two scored MRNs . In the first , the Scored Extracellular MRN ( EMRN ) , we assigned the score to the extracellular metabolite , modeling extracellular levels as being reflected in plasma . We also built a separate Scored Cytoplasmic MRN ( CMRN ) , assigning the same scores to the cytoplasmic metabolite to better capture interconnections via intracellular reaction processes ( we thus make the assumption that extracellular transport is not limiting , and that plasma metabolite abundance reflect intracellular abundances ) . We then looked for active modules , which represent connected subnetworks with high aggregate activity , using a recently published algorithm , developed for mRNA expression analysis [12] , to provide an exact solution . We pursued active module searches for NGT and IGT , for both the EMRN and CMRN . Since not all metabolites in the MRNs were measured in the metabolomics experiment , we randomly sampled scores for the remaining metabolites and computed 100 active module solutions for each of the scored MRNs ( see Methods , Figure 1 ) . Distributions of active module scores were evaluated for statistical significance relative to those obtained from random networks , where metabolite scores were permuted randomly amongst measured nodes . At an FDR threshold of 0 . 01 , all of the solutions were highly significant ( p = 8 . 5×10−8 for NGT-EMRN , 7 . 4×10−9 for NGT-CMRN , p = 7 . 0×10−15 for IGT-EMRN , p = 0 . 025 for IGT-CMRN , respectively , Mann-Whitney-Wilcoxon test ) indicating that the clustering of metabolite changes in the network is highly non-random . We selected all metabolites that appeared with sufficient frequency ( see Methods ) across the active module solutions . This resulted in an Active Module Group ( AMG ) for each of NGT-EMRN , NGT-CMRN , IGT-EMRN , and IGT-CMRN . Metabolite frequencies for searches are shown in Table S3 . As the AMGs represent unbiased pathway models for the OGTT experiment , we sought to characterize their relationship to predefined biological processes . We first repeated the FuncAssociate analysis and found marginal enrichment for Glycerophospholipid Metabolism ( padj = 0 . 029 ) for the NGT-EMRN AMG and more convincing enrichment for Glycine , Serine , and Threonine Metabolism ( padj = 0 . 008 ) within the NGT-CMRN AMG . However , these enriched predefined pathways encompass very few of the AMG metabolites . We next sought to characterize whether the AMG metabolites are active in any particular human tissue . To do so , we exploited recent predictions of which metabolic reactions in the Recon 1 network were likely to be active in ten specific human tissues , using constraint-based flux modeling [19] . We tested whether AMGs correspond to predicted metabolic activities in any of these tissues ( see Methods ) . AMGs all showed enrichment ( padj<0 . 05 ) for metabolites predicted to be active in kidney and/or liver , suggest that OGTT responses primarily involve metabolites produced in and/or consumed in these organs . Both tissues are established targets of insulin action , with liver demonstrating increased glycogen storage and fatty acid production and decreased gluconeogenesis [20] and kidney showing changes in electrolye clearance [21] in response to insulin . An inspection of the AMGs for NGT samples ( Figure 2 ) revealed a central cluster of highly interconnected standard ( 15 ) and nonstandard amino acids ( 5 ) , 19 of which decrease in plasma in response to glucose challenge . ( The AMGs for IGT samples ( Figure S1 ) consist entirely of standard ( 11 ) and non-standard amino acids ( 2 ) and are discussed further below . ) Standard amino acids represent building blocks for proteins , whereas nonstandard amino acids such as citrulline , ornithine , dimethylglycine and homoserine are implicated in other biologic processes ( see below ) . Within the EMRN , all edges between amino acids correspond to shared transporters , while within the CMRN , in addition to shared transporter activity edges , some edges constitute reactant-product pairs and/or shared enzyme substrates . The interconnectedness of both standard and nonstandard changed amino acids in the AMG supports the hypothesis that metabolite profiles reflect both increased protein synthesis and altered amino acid transporter activity . In fact , it is known that certain amino acid transporter activities are activated in response to insulin ( see below ) . Amino acid transport activities have historically been grouped into “Systems” that describe the chemical properties of the transported molecules ( e . g . cationic or small/neutral ) and the response to specific inhibitors [22] . Transporters can also be classified by whether their transport activity is primarily effected via facilitated diffusion or exchange reactions and also by the co-transported ions ( e . g . sodium , protons , potassium , or chloride ) . Individual transporters , once cloned , have been mapped to these Systems . We used FuncAssociate to identify which enzymatic activities or transporters are overrepresented in the AMGs . Table 1 indicates enzymes or transporters with substrate profiles demonstrating significant overlap with AMG metabolites in the experiment . Transporters with broad amino acid specificity such as the SLC6 and SLC7 families are favored in the FuncAssociate enrichment analysis . However , given that many of these transporters have tissue-specific activities , there is probable involvement of other transporter families in glucose-stimulated amino acid influx , including the SLC38 ( SNAT ) family of small polar amino acid transporters , and the SLC1 family of anionic amino acid transporters . The SLC38A2 transporter , a weakly-accumulating neutral amino acid transporter , is in fact known to be post-translationally regulated by insulin in murine adipocyte [23] and rat skeletal muscle cell lines [24] . Furthermore , insulin has been shown to increase System A ( small , neutral amino acid ) and System L ( large hydrophobic ) amino acid transport activities in cultured trophoblasts [25] . A complete list of all possible transporters and enzymes corresponding to the edges in the AMGs is provided in Table S4 . In addition to the core cluster of amino acids , the AMGs include additional changed metabolites on their periphery . These peripheral metabolites are connected to the amino acid core via unmeasured metabolites , which represent potential functional links . For example , in the NGT-CMRN AMG ( Figure 2B ) , glutamate is linked to nicotinamide and ribose-5-phosphate via the unmeasured metabolite phosphoribosyldiphosphate ( PRPP ) . Glutamate and PRPP are a reactant-product pair and common substrates of the enzyme glutamate-PRPP amidotransferase . In the corresponding reaction , involved in purine biosynthesis , glutamine transfers an amine group to PRPP to form glutamate and ribosylamine-5-phosphate . Ribosylamine-5-phosphate , in turn , is a building block for de novo purine biosynthesis . Interestingly , many of the other peripheral changed metabolites linked to PRPP in the NGT-CMRN AMG are also involved in nucleotide biosynthesis . Ribose-5-phosphate , which is interconverted with ribose-1-phosphate ( R1P; the two cannot be distinguished on our mass spectrometry platform ) , is phosphorylated to form PRPP . R1P combines with xanthine ( and hypoxanthine ) as part of the purine salvage pathway of nucleic acid biosynthesis , in a reaction catalyzed by purine nucleotide phosphorylase ( PNP ) . PNP also catalyzes the reaction of nicotinamide with R1P to ultimately form nicotinamide adenine dinucleotide ( NAD ) . Thus the peripheral metabolite cluster shown in NGT-CMRN captures the interrelationship of the various metabolites involved in insulin-stimulated purine nucleotide biosynthesis [26] . The other peripheral metabolite clusters in the NGT-CMRN and NGT-EMRN AMGs capture other insulin-stimulated activities including glycolysis , triglyceride biosynthesis , and an increase in bile salt plasma levels ( by unknown mechanisms ) . Although these were commented upon previously [10] , we note that the bile salts are linked by edges corresponding to common transporters ( see Figure 2 , Table 1 ) –thus one mechanism not noted previously by which glucose/insulin could increase plasma bile salt levels is via increased transporter activity , with diffusion outwards along a concentration gradient . For the NGT group , there is a significant drop in L-Proline and N , N-dimethylglycine levels and an increase in glycine betaine levels . All three amino acids appear in the NGT-EMRN and NGT-CMRN AMGs with the edges between them representing shared transport by the SLC6A12 carrier [27] ( Figure 2 ) . SLC6A12 is an ancient , highly-conserved osmoregulator , which controls cellular volume by regulating extrusion of the osmolytes GABA and glycine betaine when placed in solutions of varying osmolarity [28] . SLC6A12 can also transport proline , diaminobuytric acid , and beta-alanine , and to a lesser extent glycine , putrescine , dimethylglycine and choline [29] . Interestingly , from the CMRN we see that glycine betaine and dimethyl glycine are also reactants and products in a metabolic reaction ( catalyzed by betaine-homocysteine methyltransferase ) , which involves a reversible transfer of a methyl group from betaine to homocysteine , resulting in methionine and dimethylglycine ( Figure 3 ) . Insulin-triggered amino acid influx could , in fact , be coupled to glycine betaine extrusion since methionine influx would tend to drive the betaine-homocysteine reaction in reverse , leading to depletion of cellular dimethylglycine and increased glycine betaine . The latter two metabolites could then follow their respective concentration gradients resulting in betaine export and dimethylglycine import , explaining the respective increase and decrease in plasma levels . Presumably , the purpose of this coupling is to maintain cell osmolarity in face of the amino acid/glucose influx brought about by insulin . The IGT-EMRN and IGT-CMRN ( Figure S1 ) consist exclusively of a group of amino acids with decreased plasma level upon glucose load . Neither includes the SLC6A12 substrates , bile salts , and citric acid cycle metabolites , glycerol or glycerol-3-phosphate , or the purine nucleotide metabolism substrates . Thus , the glucose- and/or insulin-stimulated changes in the corresponding enzyme or transporter activities appear to have been blunted in the IGT group . Although the AMGs convincingly illustrate that changed metabolites are common substrates of small molecule transporters , they cannot establish coordinated activity of these cotransported substrates . To explore whether metabolite substrates of individual transporters are in fact coregulated , we performed hierarchical clustering across the 25 individuals from the NGT and IGT groups , looking to identify metabolites that show a similar absolute percentage of change across individuals . Heatmaps of the results of hierarchical clustering ( Figure 4 , 5 ) demonstrate that amino acids naturally group by transporter activity . For example , Clusters III and IV in NGT and IGT correspond to the activities of the System A and System L amino acid transporters , respectively . The System L transport activity is responsible for transporting large hydrophobic and aromatic amino acids with a particular preference for Phe , Leu/Ile ( indistinguishable on our platform ) , Met , and Val . The corresponding peak intensities of these 4 ( +1 ) amino acids cluster tightly together across individuals ( Spearman correlation coefficient 0 . 35–0 . 85 for NGT; 0 . 39–0 . 67 for IGT ) . Although Tyr and Trp show weaker correlation with the remaining system L amino acids , the clustering algorithm also groups them together in cluster IV . Likewise , 7 of the 10 ( IGT ) and 7 of the 12 ( NGT ) possible system A transport substrates , which are primarily small , neutral amino acids , are grouped into cluster III . The basic amino acids , which can be transported by System y+L ( via exchange for the large hydrophobic amino acids ) , System A or System ATB , 0+ carriers , show the most variability in terms of cluster membership . Cluster II in NGT includes all 3 of the measured SLC6A12 substrates ( proline , glycine betaine and dimethylglycine ) , which demonstrate absolute pairwise correlation coefficients ranging from 0 . 23 ( for dimethylglycine and glycine betaine ) to 0 . 57 ( for glycine betaine and proline ) . Proline and glycine betaine also are strongly correlated and co-cluster in IGT ( Spearman correlation coefficient = 0 . 60 ) . The proline-betaine correlation likely reflects the fact that these metabolites are cotransported by at least three carriers ( SLC6A12 , SLC6A20 and SLC36A2 ) . By contrast , these two metabolites are not known to participate in any common metabolic pathways , supporting the hypothesis that coordination of measured plasma levels of proline and glycine betaine is via regulation of their common transporters . In Cluster I , bile salts are found with citrulline in both NGT and IGT . In IGT , malate also clusters closely with citrulline . We searched PubMed ( http://www . ncbi . nlm . nih . gov/pubmed/ ) to find a connection between these metabolites and , interestingly , found that deficiency in the hepatic splice variant of the SLC25A13 protein ( citrin ) , a component of the mitochondrial aspartate-malate shuttle involved in liver NAD+/NADH shuttling , leads to a buildup of both hepatic bile salts and citrulline [30] . SLC25A13 deficiency , caused by a large number of possible mutations , underlies two recessive Mendelian metabolic diseases: neonatal intrahepatic cholestasis ( NICCD ) characterized by liver bile salt accumulation and elevated citrulline plasma levels in infants and Type II Citrullinemia ( CTLN2 ) , which is characterized by elevated citrulline plasma levels in adults [31] . The widespread metabolic defects in the two diseases arise from a lack of cytoplasmic aspartate in the liver , an organ inherently limited in its ability to take up aspartate from plasma . Hepatic aspartate deficiency in turn leads to abnormalities in gluconeogenesis , ureagenesis , glycolysis , nucleotide biosynthesis , and triglyceride biosynthesis either because of the need for aspartate as a reactant or due to the resulting imbalance in the cytoplasmic NAD+/NADH ratio . Interestingly , an examination of Cluster I in NGT and IGT reveals that a large majority of changed metabolites correspond to pathways known to be regulated by liver aspartate levels and/or show abnormalities in SLC25A13 deficiency [32] , [33] . These include pyrimidine biosynthesis ( OMP , ribose-1-phosphate ) , purine biosynthesis ( ribose-1-phosphate , xanthine , hypoxanthine , xanthosine ) , triglyceride biosynthesis ( glycerol , glycerol-3-phosphate ) , urea cycle ( citrulline , ornithine ) , bile salt accumulation ( taurochenodeoxycholate , glycocholate , glycochenodeoxycholate ) , glycolysis ( lactate , pyruvate ) , malate shuttling ( malate , alpha-ketoglutarate ) , and aspartate biosynthesis ( asparagine ) . The levels of several of these metabolites are known to be abnormal in affected humans and/or in mouse models of SLC25A13 deficiency [33] . Furthermore , in CTLN2 , the abnormalities in these pathways are exacerbated by glucose intake [34] , consistent with the observed OGTT-induced changes in metabolite levels . To further examine the relationship between distinct transport activities in OGTT metabolite profiling , we analyzed change in plasma levels of metabolites for NGT and IGT using principal component analysis ( PCA ) . This analytic technique attempts to find linear combination of metabolites that best explain the interindividual variation seen in metabolite profiles . PCA revealed that the top two eigenvectors for NGT coincided with SLC25A13 and amino acid transport activities , respectively , explaining a total of 39% of interindividual variance in metabolite changes ( see Figure 6 ) . The discovery of orthogonal axes of variation corresponding to these known transport activities supports the importance of metabolite transport in OGTT profiles . The demarcation between the two types of transport was not as well seen for the top two IGT eigenvectors , which may reflect the significant heterogeneity in insulin resistance across the IGT group . Given that metabolite profiling of perturbation experiments can interrogate specific underlying transporter activities , we investigated to what extent transporters are involved in human disease . We consulted the OMIM database of Mendelian diseases ( http://www . ncbi . nlm . nih . gov/omim ) , and found 179 human disease phenotypes associated with transporter mutations . These include some of the transporters whose activity is reflected in OGTT metabolite profiles , such as SLC25A13 , described above . In addition , the SLC6A14 amino acid/acyl-carnitine transporter , which primarily carries large hydrophobic and cationic amino acids , was both identified in our analysis as relevant to OGTT and previously been found to be associated with metabolic disease . Mutations in SLC6A14 have been shown to be associated with obesity in three independent populations [35] , [36] and multiple SLC6A14 SNPs are suggestively associated ( nominal p-value∼10−4–10−5 ) with waist circumference and weight in type 2 diabetes patients studied in the Diabetes Genomics Initiative genome-wide association study [37] , [38] . Interestingly , a recent analysis of fasting metabolic profiles in obese , insulin-resistant patients revealed that a metabolic signature consisting of acyl-carnitine and branched chain and aromatic amino acids was highly correlated with obesity and insulin-resistance [39] . Although the authors attributed the relationship to dietary branch chain amino acid intake , we note the overlap with our glucose-stimulated System L transport cluster and with NGT principal component #2 . Our results highlighting the importance of transporters in plasma metabolite profiles suggest that this signature may in part reflect basal insulin-responsive amino acid transporter activity . Given that we have measured plasma levels for only a small fraction of the human metabolome , the pathway models that we have discovered may be smaller than the actual enriched pathway . Conversely , for those AMGs that include unmeasured metabolites , measurement of additional metabolites may show that other pathways more convincingly explain the observed physiological changes . A further limitation is that our scoring method considered all significant changes in plasma levels equivalently , without considering direction of change . Additionally , we expect there are alterations in metabolic reaction flux within the cell in response to glucose challenge that may be difficult to decipher from plasma metabolite levels . Finally , metabolites that are significantly changed but which are not closely linked to other metabolites via chemical reactions or shared enzymes/transporters are unlikely to appear in AMGs , but may still reflect important altered tissue activities during OGTT .
We have directly integrated metabolic reaction connectivity and a collection of shared transporter relationships , extended here by manual literature curation , into metabolite profile interpretation to identify biologic processes relevant to a physiological perturbation experiment . Our approach makes use of a deterministic approach to identify active modules and directly integrates plasma measurements of metabolites with a unipartite graph capturing interrelationship between metabolic substrates . Through this method , we have uncovered a potentially important contribution for transporter activities in plasma metabolite profiles , which is ignored by using more traditional analysis of metabolic pathways . A prior application of active modules to metabolism relied on integrating microarray-derived gene expression information for enzymes with enzyme connectivity in metabolic graphs to identify clusters of functionally connected enzymes that collectively show a high degree of change in a perturbation experiment [18] . In the same study , “reporter metabolites” were identified by connection to enzymes that show a high degree of change at a transcriptional level . Our method allows deriving potentially unexpected underlying pathways from direct metabolite measurements , which should prove increasingly valuable given the emergence of metabolic profiling in both biological and clinical arenas [40] . One motivation for this approach is to redefine human disease in terms of aberrant metabolic activities . Given groups of affected and control individuals , active module analysis can achieve this in a number of ways . Baseline differences in each metabolite's abundance can be scored between affected and unaffected individuals [39] and interpreted in the context of a metabolic reaction network . As opposed to the lists of significantly changed biomarkers commonly generated in association analyses , the data integration involved in active module analysis allows hypotheses generation in terms of more meaningful biological activities . As an alternative to baseline comparisons , perturbation experiments such as OGTT ( or medication or exercise ) can identify physiologic pathways associated with the perturbation by identifying active modules within the control group . In this way , analysis of differences between affected and control individuals can be more tightly focused on metabolites relevant to the normal physiological process . Our study used this approach to suggest differences in particular transporter and enzyme activities between the NGT and IGT groups in response to glucose challenge . Using a perturbation experiment , differences in the induced change in metabolite abundance between affected and unaffected patients can also be scored directly and mapped onto a metabolic reaction graph to identify differentially active modules . Such modules may capture distinct facets of a heterogeneous disease . Finally , longitudinal data for a group of affected individuals permits subclassification of disease via active module-based metabolite scores that measure the ability to predict some adverse outcome ( for example heart attacks in individuals with stable coronary artery disease or diabetes development in individuals with impaired glucose tolerance ) . A similar approach used gene expression profiles [8] in the context of a protein-protein interaction network to predict the likelihood of cancer metastasis within individuals with breast cancer . As the relationship of metabolite abundance with disease incidence and outcomes is better understood , we may ultimately be able to use integrated analyses of metabolic profiles to subclassify disease on the basis of distinct enzymatic/transporter activities , thus allowing a more individualized approach to clinical medicine .
Metabolite abundance measurements from an oral glucose tolerance test have been described [10] . Briefly , 50 individuals from the Framingham Offspring Study , 25 with normal fasting glucose and normal glucose tolerance ( OGTT-NGT ) and 25 with impaired glucose tolerance ( OGTT-IGT ) were selected for metabolic profiling . The Framingham Heart Study is a longitudinal community-based investigation that was initiated in 1948 to prospectively identify cardiovascular disease risk factors [41] . The children and their spouses of the original cohort were recruited in 1971 and constitute the Framingham Offspring Study Cohort . All subjects were white and of European descent . OGTT was administered routinely at the baseline exam . After a 12-hour overnight fast participants were given 75 g glucose in solution orally . Blood samples were drawn fasting and 120 minutes after glucose ingestion and after HPLC purification , metabolite abundances were determined using a triple quadrupole mass spectrometer as previously described [10] . Metabolite peak intensities were determined as described previously [10] . We eliminated metabolites from subsequent analysis that: 1 ) were not included in the Recon 1 network; 2 ) were confounded by a potential contaminant; or 3 ) were measured in <50% of individuals In cases where two or more metabolites could not be separated on the basis of chromatographic elution patterns and parent and daughter mass-to-charge ion spectra , peak intensities for that group of metabolites were randomly assigned to one of the possible metabolites during each of the 100 simulations ( see below ) . This ambiguity was only present for 3 groups of metabolites ( leucine/isoleucine; ribose-5-phosphate/ribose-1-phosphate/ribulose-5-phosphate , citrate/isocitrate ) , with members of each group showing chemical similarity and participating in many common reactions in Recon 1 . By these criteria , the initial list of 171 peaks measured on the platform was thus narrowed to abundances for 88 and 84 metabolites , respectively , for the NGT and IGT groups . A one-sample Wilcoxon signed rank test was used to determine the statistical significance for change for the metabolite peak intensities for both groups ( percent difference in peak magnitude after glucose challenge relative to baseline ) . In order to identify significantly changed clusters of functionally connected metabolites , we converted experimental p-values from the Wilcoxon test to scores . Although a variety of methods can be selected for this purpose , we used the beta-uniform distribution method [12] , [42] , which allows control of false-positive and false-negative rates in the analysis . For NGT and IGT separately , the distribution of p-values was modeled as a mixture of a beta distribution for the signal and a uniform distribution for the noise as shown below: ( 1 ) where x is a given p-value , f ( x ) is the probability density at x , π represents the mixture parameter , and a represents a shape parameter for the beta distribution . The values of π and a were fit using the optim function in R . The parameter τ was chosen to achieve an FDR of 0 . 01 . p-values were converted into scores as described in [12] , [42] , where the score for each metabolite at the chosen FDR , SFDR ( x ) , is computed as follows: ( 2 ) Using this statistic , nodes for which the p-value>τ , will have a negative score . A Metabolic Reaction Network ( MRN ) was constructed based on the 3338 metabolic reactions in Recon 1 . We treated all 1500 reactant and product metabolites in Recon 1 as nodes . Cellular locations were assigned to each metabolite as specified in Recon 1 , and metabolites were split into multiple nodes ( each corresponding to a different location ) . This process resulted in 2779 total nodes . For each metabolic reaction , edges in the MRN were drawn between each reactant and product nodes and between all common metabolites substrates ( reactants and products ) of enzymes and transporters . Furthermore , since transporter annotation was not complete , for each of the measured metabolites we manually searched the literature to identify transporter-substrate relationships and identified all substrates for any transporter found . Finally , we expanded the list of reactions so that each transporter-reaction relationship was reflected in an independent entry . This approach resulted in an additional 737 reactions ( see Table S2 ) . To improve the specificity of the active module discovery process , nodes and edges involving the following 28 ‘promiscuous’ and/or buffered metabolites were also eliminated: UMP , UDP , UTP , FAD , FADH2 , Na+ , K+ , SO4 , NH4 , CO2 , Phosphate , O2 , Pyrophosphate , H2O , H+ , OH− , ATP , ADP , AMP , CTP , CDP , CMP , NAD , NADH , NADP , NADPH , H2O2 , and HCO3− . As many of these metabolites serve as cofactors , their inclusion would contribute to non-specific bridges between metabolites in active modules and would thus be of limited use for biological processes . We did however include nodes and edges for reactions where nucleotides serve as primary reactants and products , such as those involved in nucleotide biosynthesis or catabolism; NAD metabolism; and Riboflavin metabolism . Because abundance measurements were only available for a small fraction of the metabolic network , we limited the MRN to the union of measured metabolites and all nodes found on paths ( up to path-length three ) between two measured nodes . This filtering , used to reduce computing cost , did not alter downstream results because to be included in an active module , a metabolite must either be measured or lie on a path between measured metabolites . Scores generated above were assigned to measured nodes in the MRN . We built both a Scored Extracellular MRN ( EMRN ) and a separate Scored Cytoplasmic MRN ( CMRN ) . For the EMRN , if a metabolite had two cellular locations , the metabolite score was assigned to the extracellular metabolite , modeling extracellular levels; for the CMRN , scores , in such a situation , were assigned to the cytoplasmic metabolite . The EMRN and CMRN networks had 297 nodes and 5515 edges and 344 nodes and 6089 edges , respectively , after applying the above steps . To identify active modules in our MRN , we used a recently developed method [12] that provides an exact solution to the problem of finding a group of connected nodes with the highest combined score by transforming the problem to that of the Prize-Collecting Steiner Tree . The method requires that each metabolite in the network have a score ( see above for details on the required properties of that score ) . Because we had scores assigned for only a fraction of the network nodes , p-values for unmeasured nodes were sampled randomly from the uniform distribution expected of unchanged metabolites , thus sampling the joint distribution of unmeasured metabolite abundance under the null hypothesis . Such an approach is common practice in Bayesian data analysis to estimate posterior probability distributions and accounts for our uncertainty about unmeasured metabolites more accurately than would asserting that we are sure that the metabolite has not changed . It also permits an unmeasured metabolite to be included in a cluster if there is sufficient support , in that the unmeasured metabolite bridges multiple high-scoring measured metabolites . Scores were then determined for the unmeasured nodes as described in Methods , with π , a , and τ determined using the distribution of measured p-values . We repeated the random sampling 100 times for each scored network and identified one optimal solution per network . We explored searching also for suboptimal solutions but found that these primarily consisted of a subset of the metabolites of the optimal solution rather than other distinct areas of the graph . For the purpose of evaluating statistical significance of observed active modules , we generated random solutions by repeating the active module discovery process for 100 random scored MRNs . Although the topology was preserved , the scores for each random MRN were randomly permuted among measured nodes and p-values for unmeasured nodes were sampled uniformly at random . The distribution of scores for the original and permuted data were compared by a one-sided Mann-Whitney-Wilcoxon test using the R package ( www . Rproject . org ) . For each scored MRN , the frequency of appearance in the corresponding 100 solutions was measured for all nodes . Nodes with ≥0 . 20 relative frequency were grouped together to form an Active Module Group ( AMG ) , which was examined for significant overlap with metabolite sets corresponding to predefined pathways . To identify predefined pathway enrichment in the AMGs , we used a modified version of the FuncAssociate program [14] . FuncAssociate takes as input a query list of metabolites and a mapping of pathways to metabolites . For each pathway , it tests for enrichment of that pathway in the query list relative to the “tested universe” of metabolites by using the cumulative hypergeometric ( Fisher's Exact ) test . Adjustment for multiple hypothesis testing is achieved by resampling [14] . Briefly , the null distribution of p-values is generated by repeating the test for 1000 randomly generated query lists of the same size from the same universe of metabolites . For each random query list , the minimum p-value observed for any pathway is retained . The adjusted p-value ( padj ) is then the fraction of random query lists that yield a minimum p-value equal to or lower than the minimum p-value observed . We generated pathway and enzyme/transporter-to-metabolite mappings for Recon 1 , limiting our analysis to pathways , enzymes or transporters that included at least three metabolites in the metabolite universe . We were interested in enrichment for analysis for both AMGs and for our ranked list of changed metabolite . To look for pathway enrichment in the ranked list of changed metabolites , we used the “ordered” setting in FuncAssociate ( http://llama . med . harvard . edu/cgi/func1/ funcassociate_advanced ) , which tests a ranked list of metabolites and finds the rank cutoff that optimizes significance ( using resampling to adjust for multiple testing ) . For this analysis , the metabolite list was ranked in both increasing and decreasing order of magnitude of change . For assessing pathway and enzyme/transporter enrichment in our AMGs , we used as our universe of metabolites all nodes in the reduced networks . To address the fact that the AMGs may be biased in composition towards measured nodes , we modified FuncAssociate so that the null distribution of p-values was obtained using randomly generated metabolite lists that matched the query list in composition of measured and unmeasured nodes . We repeated the same process for enzymes and transporters , generating mappings for all Recon 1 reactions for which an enzyme or transporter could be identified . In a recent manuscript [19] , tissue-specific metabolic fluxes were predicted for the Recon 1 network . The authors solved a constraint-based modeling optimization problem by finding a metabolic flux distribution that satisfied constraints imposed by stoichiometric and thermodynamic conditions of the network and maximized agreement between flux and enzyme mRNA and protein expression for ten human tissues . We used the authors' predictions for metabolite activity in each of the ten tissues and looked for tissue enrichment for our measured metabolites and active modules clusters . For each metabolite and cellular location within each tissue , the authors provided an activity score that ranged from −2 to +2 , where positive scores indicated activity , negative scored indicated inactivity and the magnitude of the score indicated confidence in the prediction . A score of 0 indicated an ambiguous activity level . For each AMG , we computed an activity score for each tissue by summing the individual scores for each metabolite . The null distribution of scores was obtained as above , where 1000 random sets of metabolites were selected from the node universe , matching the AMG in composition of measured and unmeasured nodes , and the highest tissue score taken for each set . Hierarchical clustering and PCA was performed on the subset of metabolites that changed significantly in either NGT or IGT at an FDR<0 . 05 , determined using the qvalue package in R ( www . Rproject . org ) . L-Alanine and L-Cysteine , which were the only measured standard amino acids that failed to change significantly in either group , were also included . Input data corresponded to fractional change in metabolite abundance across individuals . For clustering , the absolute value of the Spearman coefficient was used to compute the dissimilarity matrix . Heatmaps and principal component analyses were performed using the heatmap and prcomp functions , respectively , in the R package . In our analysis , three parameters determined the balance between measured and unmeasured metabolites in our active module solutions: 1 ) the false-discovery rate ( FDR ) threshold used in the determination of scores for measured metabolites; 2 ) the path-length threshold between measured metabolites used in filtering unmeasured metabolites from the MRN; and 3 ) the frequency threshold used in selecting active module solution metabolites for inclusion into active module groups . Although unmeasured metabolites are useful for hypothesis generation in terms of identifying potentially novel markers of insulin function , such hypotheses should not be so numerous as to dominate the analysis . At the stringent FDR of 0 . 01 selected , the active module discovery process was relatively insensitive to changes in the other two parameters , with little difference in the observed results at higher or lower path lengths . In fact , all unmeasured metabolites in active module groups were directly connected to one or more measured metabolites . For the frequency cutoff for active module group metabolites , our primary conclusions were robust to all thresholds from 0 . 10 to 0 . 50 .
|
Human disease is complex , arising from the interaction of many genetic and environmental factors . Efforts to personalize treatment have been thwarted by “phenotypic heterogeneity” , the apparent similarity of disease states with diverse underlying causes . One approach to resolve this heterogeneity is to redefine diseases on the basis of abnormal physiologic activities , which should allow grouping patients into categories with similar treatment response and prognosis . Physiologic activities can be identified and assessed through quantitative measurements of biomolecules—proteins , mRNAs , metabolites—in individual patient samples . The field of metabolomics involves the analysis of a broad array of metabolite levels from clinical fluid samples such as blood or urine and can be used to evaluate disease states . Because metabolic profiles are complex , we have taken an integrative network-based approach to understand them in terms of abnormal activities of enzymes and small molecule transporters . We have focused on the oral glucose tolerance test , used to diagnose diabetes , and have found that multiple transporters play an important role in the normal response to ingesting sugar . Many of these transporter activities are abnormal in individuals with impaired glucose tolerance and differing activities among them may reflect the diverse underlying causes and variable clinical courses of such patients .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"physiology/endocrinology",
"computational",
"biology/metabolic",
"networks",
"diabetes",
"and",
"endocrinology/type",
"2",
"diabetes"
] |
2010
|
Interpreting Metabolomic Profiles using Unbiased Pathway Models
|
Conserved moieties are groups of atoms that remain intact in all reactions of a metabolic network . Identification of conserved moieties gives insight into the structure and function of metabolic networks and facilitates metabolic modelling . All moiety conservation relations can be represented as nonnegative integer vectors in the left null space of the stoichiometric matrix corresponding to a biochemical network . Algorithms exist to compute such vectors based only on reaction stoichiometry but their computational complexity has limited their application to relatively small metabolic networks . Moreover , the vectors returned by existing algorithms do not , in general , represent conservation of a specific moiety with a defined atomic structure . Here , we show that identification of conserved moieties requires data on reaction atom mappings in addition to stoichiometry . We present a novel method to identify conserved moieties in metabolic networks by graph theoretical analysis of their underlying atom transition networks . Our method returns the exact group of atoms belonging to each conserved moiety as well as the corresponding vector in the left null space of the stoichiometric matrix . It can be implemented as a pipeline of polynomial time algorithms . Our implementation completes in under five minutes on a metabolic network with more than 4 , 000 mass balanced reactions . The scalability of the method enables extension of existing applications for moiety conservation relations to genome-scale metabolic networks . We also give examples of new applications made possible by elucidating the atomic structure of conserved moieties .
Conserved moieties give rise to pools of metabolites with constant total concentration and dependent individual concentrations . These constant metabolite pools often consist of highly connected cofactors that are distributed throughout a metabolic network . Representative examples from energy metabolism include the AMP and NAD moieties [1 , 2] . Changes in concentration ratios within these cofactor pools affect thermodynamic and mass action kinetic driving forces for all reactions they participate in . Moiety conservation therefore imposes a purely physicochemical form of regulation on metabolism that is mediated through changes in concentration ratios within constant metabolite pools . Reich and Sel’kov likened conserved moieties to turning wheels that are “geared into a clockwork” [2] . They described the thermodynamic state of energy metabolism as “open flow through a system closed by moiety conservation” . Identification of conserved moieties in metabolic networks has helped elucidate complex metabolic phenomena including synchronisation of glycolytic oscillations in yeast cell populations [3] and the function of glycosomes in the African sleeping sickness parasite Trypanosoma brucei [4] . It has also been shown to be relevant for drug development [4 , 5] . Identification of conserved moieties has been of interest to the metabolic modelling community for several decades [6 , 7] . It is particularly important for dynamic modelling [8] and metabolic control analysis [9] where metabolite concentrations are explicitly modelled . Moiety conservation relations provide a sparse , physically meaningful description of concentration dependencies in a metabolic network . They can be used to eliminate redundant metabolite concentrations as the latter can be derived from the set of independently varying metabolite concentrations . Doing so facilitates simulation of metabolic networks and is in fact required for many computational modelling methods [6 , 7] . Mathematically , moiety conservation gives rise to a stoichiometric matrix with linearly dependent rows . The left null space of the stoichiometric matrix therefore has nonzero dimension ( see Theoretical Framework , Section Moiety vectors ) . Vectors in the left null space , hereafter referred to as conservation vectors , can be divided into several interrelated sets based on their numerical properties and biochemical meaning ( Fig 1 ) . Moiety vectors constitute a subset of conservation vectors with a distinct biochemical interpretation . Each moiety vector represents conservation of a particular metabolic moiety . Elements of a moiety vector correspond to the number of instances of a conserved moiety in metabolites of a metabolic network . As moieties are discrete quantities , moiety vectors are necessarily nonnegative integer vectors . Methods exist to compute conservation vectors based only on the stoichiometric matrix of a metabolic network . These methods compute different types of bases for the left null space of the stoichiometric matrix ( see S1 Appendix for mathematical definitions ) . Each method draws basis vectors from a particular set of conservation vectors ( Fig 1 ) . There is a tradeoff between the computational complexity of these methods and the biochemical interpretability of the basis vectors they return . At the low end of the computational complexity spectrum are linear algebraic methods such as singular value decomposition . Other methods , such as Householder QR factorisation [7] or sparse LU factorisation [10] are more efficient for large stoichiometric matrices . These methods construct a linear basis for the left null space from real-valued conservation vectors . Though readily computed , these vectors are also the most difficult to interpret as they generally contain negative and noninteger elements . Schuster and Höfer [11] introduced the use of vertex enumeration algorithms to compute the extreme rays of the positive orthant of the left null space . They referred to these extreme rays as “extreme semipositive conservation relations” . Famili and Palsson [12] later referred to them as “metabolic pools” and the set of all extreme rays as “a convex basis for the left null space” . Like moiety vectors , extreme rays are nonnegative integer vectors . They are therefore readily interpreted in terms of constant metabolite pools . However , extreme rays can currently only be computed for relatively small metabolic networks due to the computational complexity of vertex enumeration algorithms [13] . Moreover , the set of extreme rays is not identical to the set of moiety vectors ( Fig 1 ) . Schuster and Hilgetag [14] presented examples of extreme rays that did not represent moiety conservation relations , as well as moiety vectors that were not extreme rays . Moiety vectors are a property of a metabolic network while extreme rays are a property of its stoichiometric matrix . Multiple metabolic networks could in theory have the same stoichiometric matrix , despite consisting of different sets of metabolites and reactions . These networks would all have the same set of extreme rays , but could have different sets of moiety vectors . Schuster and Hilgetag [14] published an extension to the vertex enumeration algorithm in [11] to compute the set of all nondecomposable nonnegative integer vectors in the left null space of a stoichiometric matrix . This set is guaranteed to contain all nondecomposable moiety vectors for a particular metabolic network as subset ( Fig 1 ) . However , it is impossible to identify the subset of moiety vectors without information about the atomic structure of metabolites . Alternatives to vertex enumeration have been proposed to speed up computation of biochemically meaningful conservation vectors , e . g . , [15–17] . Most recently , De Martino et al . [17] published a novel method to compute a nonnegative integer basis for the left null space of a stoichiometric matrix . This method [17] relies on stochastic algorithms , without guaranteed convergence , but that were empirically shown to perform well even on large networks . Like extreme rays , the nonnegative integer vectors computed with this method are not necessarily moiety vectors ( Fig 1 ) . In general , methods to analyse stoichiometric matrices are not suited to specifically compute moiety vectors . Computation of moiety vectors requires information about the atomic composition of metabolites . To our knowledge , only one method has previously been published to specifically compute moiety vectors for metabolic networks [18] . This method was based on nonnegative integer factorisation of the elemental matrix; a numerical representation of metabolite formulas . Nonnegative integer factorisation of a matrix is at least NP-hard [19] and no polynomial time algorithm is known to exist for this problem . Moreover , only the chemical formula but not the atomic identities of the conserved moieties can be derived from this approach . Identifying the atoms that belong to each moiety requires additional information about the fate of atoms in metabolic reactions . This information is not contained in a stoichiometric matrix . Here , we propose a novel method to identify conserved moieties in metabolic networks . Our method is based on the premise that atoms within the same conserved moiety follow identical paths through a metabolic network . Given data on which substrate atoms map to which product atoms in each metabolic reaction , the paths of individual atoms through a metabolic network can be encoded in an atom transition network . Until recently , the necessary data were difficult to obtain but relatively efficient algorithms have now become available to predict atom mappings in metabolic reactions [20–22] . These algorithms have made it possible to construct atom transition networks for large metabolic networks . Unlike metabolic networks , atom transition networks are amenable to analysis with efficient graph theory algorithms . Here , we take advantage of this fact to identify conserved moieties in metabolic networks in polynomial time . Furthermore , starting from atom transition networks allows us to associate each conserved moiety with a specific group of atoms in a subset of metabolites in a metabolic network . This work combines elements of biochemistry , linear algebra and graph theory . We have made an effort to accommodate readers from all fields . The main text consists of informal descriptions of our methods and results , accompanied by illustrative examples and a limited number of mathematical equations . Formal definitions of italicised terms are given in supporting file S1 Appendix . We precede our results with a section on the theoretical framework for this work , where we introduce key concepts and notation used in the remainder of the text .
A metabolic network consists of a set of metabolites that interconvert via a set of metabolic reactions . Metabolic networks in living beings are open systems that exchange mass and energy with their environment . For modelling purposes , the boundary between system and environment can be defined by introducing a set of metabolite sources and sinks collectively known as exchange reactions . Unlike internal reactions , exchange reactions are artificial constructs that do not conserve mass or charge . The topology of a metabolic network can be represented in several ways . Here , we use metabolic maps and stoichiometric matrices . A metabolic map for a small example metabolic network is shown in Fig 2 . This example will be used throughout this section to demonstrate key concepts relevant to this work . A stoichiometric matrix for an open metabolic network with m metabolites and n reactions is denoted by S ∈ R m × n . Each row of S represents a metabolite and each column a reaction such that element Sij is the stoichiometric coefficient of metabolite i in reaction j . Coefficients are negative for substrates and positive for products . Substrates and products in reversible reactions are defined by designating one direction as forward . The stoichiometric matrix can be written as S = N , B , ( 1 ) where N ∈ Z m × u consists of columns representing internal ( mass balanced ) reactions and B ∈ R m × ( n - u ) consists of columns representing exchange reactions ( mass imbalanced ) . Note that N represents a metabolic network that is closed to the environment . In what follows we will refer to N as the internal stoichiometric matrix , B as the exchange stoichiometric matrix , and S as the total stoichiometric matrix . The total stoichiometric matrix for the example metabolic network in Fig 2 is given in Table 1 . Stoichiometric matrices are incidence matrices for generalised graphs known as hypergraphs [24] . Hypergraphs contain hyperedges that can connect more than two nodes . The metabolic map in Fig 2 is a planar visualisation of a hypergraph with one hyperedge , connecting four metabolites . A graph edge that only connects two nodes is a special instance of a hyperedge . Apart from the occasional isomerisation reaction , metabolic reactions involve more than two metabolites . As a result , they cannot be represented as graph edges without loss of information . Metabolic networks are therefore represented as hypergraphs where nodes represent metabolites and hyperedges represent reactions . Since reactions have a designated forward direction , they are directed hypergraphs . Representing metabolic networks as hypergraphs has the advantage of conserving basic structure and functional relationships . The disadvantage is that many graph theoretical algorithms are not applicable to hypergraphs [24] . An internal stoichiometric matrix N ∈ Z m × u for a closed metabolic network is always row-rank deficient , i . e . , rank ( N ) < m [11] . The left null space of N , denoted by N ( N T ) , therefore has finite dimension given by dim ( N ( N T ) ) = m - rank ( N ) . The left null space holds all conservation vectors for a stoichiometric matrix [8] . The number of linearly independent conservation vectors for a closed metabolic network is dim ( N ( N T ) ) . The total stoichiometric matrix S for an open metabolic network has a greater rank than the internal stoichiometric matrix N for the corresponding closed metabolic network ( e . g . , Table 1 ) , i . e . , rank ( N ) < rank ( S ) . Consequently , dim ( N ( S T ) ) < dim ( N ( N T ) ) , meaning that there are fewer linearly independent conservation vectors for an open metabolic network than the corresponding closed network . This is consistent with physical reality , since mass can flow into and out of open networks but is conserved within closed networks . All quantities that are conserved in an open metabolic network are also conserved in the corresponding closed network . That is , if z is a conservation vector for an open metabolic network S , such that ST z = 0 , then z is also a conservation vector for the corresponding closed network N , and NT z = 0 , since S = [N , B] . The set of conservation relations for an open network is therefore a subset of all conservation relations for the corresponding closed network , i . e . , N ( S T ) ⊆ N ( N T ) . In what follows we will mainly be concerned with the larger set of conservation relations for a closed metabolic network . Schuster and Hilgetag [14] defined a moiety vector l1 as a nonnegative integer vector in the left null space of a stoichiometric matrix , i . e . , N T l 1 = 0 , ( 2 ) l 1 ∈ N 0 m . ( 3 ) In addition , they defined l1 to be a maximal moiety vector if it cannot be decomposed into two other vectors l2 and l3 that satisfy Eqs 2 and 3 , i . e . , if l 1 ≠ α 2 l 2 + α 3 l 3 , ( 4 ) where α 2 , α 3 ∈ N + . We propose a more specific definition . The properties above define increasingly small sets of conservation vectors ( Fig 1 ) . Eq 2 defines the set of all conservation vectors . Addition of Eq 3 defines the set of nonnegative integer conservation vectors and addition of Eq 4 defines the set of nonnegative integer conservation vectors that are nondecomposable . Although this set includes all nondecomposable moiety vectors as subset it is not equivalent ( Fig 1 ) . To define the set of moiety vectors we require a fourth property . We define l1 to be a moiety vector if it satisfies Eqs 2 and 3 and represents conservation of a specific metabolic moiety , i . e . , an identifiable group of atoms in network metabolites . Element l1 , i should correspond to the number of instances of the conserved moiety in metabolite i . We define l1 to be a nondecomposable moiety vector if it satisfies condition 4 and a composite moiety vector if it does not . Nondecomposable moiety vectors for the DOPA decarboxylase reaction from the example metabolic network in Fig 2 are given in Table 2a . For comparison , conservation vectors computed with existing methods for conservation analysis of metabolic networks are given in Table 2b–2d . In general , these vectors do not represent moiety conservation . Metabolic reactions conserve mass and chemical elements . Therefore , there must exist a mapping from each atom in a reactant metabolite to a single atom of the same element in a product metabolite . An atom transition is a single mapping from a substrate to a product atom . An atom transition network contains information about all atom transitions in a metabolic network . It is a mathematical structure that enables one to trace the paths of each individual atom through a metabolic network . An atom transition network can be generated automatically from a stoichiometric matrix for a metabolic network and atom mappings for internal reactions . The atom transition network for the DOPA decarboxylase reaction from the example metabolic network in Fig 2 is shown in Fig 3 . Unlike metabolic networks , atom transition networks are graphs since every atom transition ( edge ) connects exactly two atoms ( nodes ) . They are directed graphs since every atom transition has a designated direction that is determined by the directionality of the parent metabolic reaction , i . e . , the designation of substrates and products . Because atom transition networks are graphs , they are amenable to analysis with efficient graph algorithms that are not generally applicable to metabolic networks due to the presence of hyperedges [24] .
We will demonstrate our method by identifying conserved moieties in the simple dopamine synthesis network DAS in Fig 4 . This network consists of 11 metabolites , four internal reactions and seven exchange reactions . The total stoichiometric matrix S = [N , B] is given in Table 3 . The internal stoichiometric matrix N is row rank deficient with rank ( N ) = 4 . The dimension of the left null space is therefore dim ( N ( N T ) ) = 7 , meaning that there are seven linearly independent conservation vectors for the closed metabolic network . Our analysis of an atom transition network for DAS will conclude with the computation of seven linearly independent moiety vectors that span N ( N T ) . To compute these vectors we require the internal stoichiometric matrix in Table 3 and atom mappings for the four internal reactions . Here , we used algorithmically predicted atom mappings [20] . These data are required to generate an atom transition network for DAS ( see Methods , Section Generation of atom transition networks ) . By graph theoretical analysis of this atom transition network we derive the first of two alternative representations of moiety conservation relations which we term moiety graphs . Nodes in a moiety graph represent separate instances of a conserved moiety . Each node is associated with a specific set of atoms in a particular metabolite . The second representation of moiety conservation relations are the moiety vectors which can be derived from moiety graphs in a straightforward manner . Moiety vectors computed with our method are therefore associated with specific atoms via moiety graphs . To identify all conserved moieties in DAS we require an atom transition network for all atoms regardless of element but for demonstration purposes we will initially focus only on carbon atoms . A carbon atom transition network for DAS is shown in Fig 5a . Our working definition of a conserved moiety is a group of atoms that follow identical paths through a metabolic network . To identify conserved moieties , we therefore need to trace the paths of individual atoms and determine which paths are identical . The paths of individual atoms through the carbon atom transition network for DAS can be traced by visual inspection of Fig 5a . For example , we can trace a path from C1 in L-phenylalanine to C7 in dopamine via C3 in L-tyrosine and C8 in levodopa . This path is made up of atom transitions in reactions R1 , R2 , and R3 from Fig 4 . In graph theory terms , these four carbon atoms and the atom transitions that connect them constitute a connected component [25] or , simply , a component of the directed graph representing the carbon atom transition network for DAS . A directed graph is said to be connected if a path exists between any pair of nodes when edge directions are ignored . A component of a directed graph is a maximal connected subgraph . In total , the carbon atom transition network for DAS in Fig 5a consists of 18 components . The paths of the first eight carbon atoms ( C1–C8 ) in L-phenylalanine are identical in the sense that they include the same number of atoms in each metabolite and the same number of atom transitions in each reaction . In graph theory terms , the components containing C1–C8 in L-phenylalanine are isomorphic . An isomorphism between two graphs is a structure preserving vertex bijection [25] . The definition of isomorphism varies for different types of graphs as they have different structural elements that need to be preserved . An isomorphism between two simple graphs is a vertex bijection that preserves the adjacency and nonadjacency of every node , i . e . , its connectivity . An isomorphism between two simple directed graphs must also preserve edge directions . We define an isomorphism between two components of an atom transition network as a vertex bijection that preserves the metabolic identity of every node . The nature of chemical reactions ensures that all other structural elements are preserved along with metabolic identities , including the connectivity of atoms and the number , directions and reaction identities of atom transitions . The 18 components of the carbon atom transition network for DAS in Fig 5a can be divided into three sets , where every pair of components within each set is isomorphic . An isomorphism between two components of an atom transition network is a one-to-one mapping between atoms in the two components . For example , the isomorphism between the two left-most components in Fig 5a maps between C1 and C2 in L-phenylalanine , C3 and C2 in L-tyrosine , C8 and C7 in L-DOPA , and C7 and C8 in dopamine . We say that two atoms are equivalent if an isomorphism maps between them . We note that our definition of isomorphism only allows mappings between atoms with the same metabolic identity . Two atoms can therefore only be equivalent if they are in the same metabolite . Equivalent atoms follow identical paths through a metabolic network and therefore belong to the same conserved moiety . In general , we define a conserved moiety to be a maximal set of equivalent atoms in an atom transition network . To identify conserved moieties , we must therefore determine isomorphisms between components of an atom transition network to identify maximal sets of equivalent atoms . The first eight carbon atoms ( C1–C8 ) in L-phenylalanine are equivalent . They are therefore part of the same conserved moiety , which we denote λ1 . The last eight carbon atoms ( C2–C9 ) in L-tyrosine are likewise part of the same conserved moiety . They make up another instance of the λ1 moiety . The λ1 moiety is conserved between L-phenylalanine and L-tyrosine in reaction R1 , between L-tyrosine and levodopa in reaction R2 , and between levodopa and dopamine in reaction R3 . Each of the four metabolites contains one instance of the λ1 moiety . The path of this moiety through DAS defines its conservation relation . This brings us to our first representation of moiety conservation relations , which we term moiety graphs . Moiety graphs are obtained from atom transition networks by merging a set of isomorphic components into a single graph . Moiety graphs for the three carbon atom moieties in DAS are shown in Fig 5b . Four additional moieties were identified by analysis of an atom transition network for DAS that included all atoms regardless of element . All seven moiety graphs are shown in Fig 6 . Atoms belonging to each node in the moiety graphs are highlighted in Fig 4 . The second way to represent moiety conservation relations is as moiety vectors . Above we defined a moiety vector as a conservation vector lk where element lk , i corresponds to the number of instances of moiety k in metabolite i of a metabolic network ( see Section Moiety vectors in Theoretical Framework ) . We can now make this definition exact by relating moiety vectors to moiety graphs . Each instance of a conserved moiety is represented as a node in its moiety graph . Element lk , i of a moiety vector therefore corresponds to the number of nodes in moiety graph λk that represent moieties in metabolite i . Moiety vectors are readily derived from moiety graphs by counting the number of nodes in each metabolite . Moiety vectors for DAS were derived from the moiety graphs in Fig 6 . The seven moiety vectors are given as columns of the moiety matrix L ∈ Z 11 × 7 in Table 4 . These seven vectors are linearly independent and therefore span all seven dimensions of N ( N T ) . The moiety matrix L is therefore a moiety basis for the left null space . Atom transition networks are generated from atom mappings for internal reactions of metabolic networks . However , atom mappings for metabolic reactions are not necessarily unique . Computationally predicted atom mappings , as used here , are always associated with some uncertainty . In addition , there can be biochemical variability in atom mappings , in particular for metabolites containing symmetric atoms . All reactions of the O2 molecule , for example , have at least two biochemically equivalent atom mappings since the two symmetric oxygen atoms map with equal probability to connected atoms . Different atom mappings give rise to different atom transition networks that may contain different moiety conservation relations . For the most part , we found that varying the set of input atom mappings did not affect the number of computed moiety conservation relations , only their atomic structure . An important exception was when atom mappings between the same pair of metabolites varied between reactions in the same metabolic network . The same pair of metabolites often exchange atoms in multiple reactions throughout the same metabolic network . Common cofactors such as ATP and ADP , for example , exchange atoms in hundreds of reactions in large metabolic networks [26] . In the dopamine synthesis network , DAS in Fig 4 , O2 and H2O exchange an oxygen atom in two reactions , R1 and R2 . Since the two oxygen atoms of O2 are symmetric , there are four possible combinations of oxygen atom mappings for these two reactions . Each combination gives rise to a different oxygen transition network as shown in Fig 7 . Two of these oxygen transition networks , shown in Fig 7a and 7b , contain two moiety conservation relations each , λ6 and λ7 , which are shown in Fig 7c . The other two oxygen transition networks , shown in Fig 7d and 7e , contain only one moiety conservation relation each , λ8 , which is shown in Fig 7f . The DAS atom transition network considered in the previous section was generated with the oxygen atom mappings in Fig 7a and thus contained the two moiety conservation relations λ6 and λ7 ( see Fig 6 ) . An atom transition network generated with the atom mappings in Fig 7d or 7e would contain the single moiety conservation relation λ8 instead of these two . What distinguishes the oxygen transition networks in Fig 7d and 7e is that the oxygen atom in O2 that maps to H2O varies between the two reactions R1 and R2 . The atom transition network for DAS therefore contains one less moiety conservation relation if the atom mapping between this recurring metabolite pair varies between reactions . The moiety matrix for these alternative atom transition networks , L = l 1 , l 2 , l 3 , l 4 , l 5 , l 8 , ( 5 ) only contains six linearly independent columns and is therefore not a basis for the seven dimensional left null space of N . The vector representation of moiety graph λ8 is l 8 T = 0 1 2 2 0 0 0 0 2 1 0 . ( 6 ) We note that l8 = l6 + l7 where l 6 T = 0 1 2 2 0 0 0 0 1 0 0 , ( 7 ) l 7 T = 0 0 0 0 0 0 0 0 1 1 0 , ( 8 ) from Table 4 . The moiety vector l8 therefore represents a composite moiety . It does not meet the definition of a nondecomposable moiety vector in Eq 4 . This example shows that variable atom mappings between recurring metabolite pairs may cause multiple nondecomposable moiety conservation relations to be joined together into a single composite moiety conservation relation . We formulated an optimisation problem , described in Methods , Section Decomposition of moiety vectors , to decompose composite moiety vectors . Solving this problem for the composite moiety vector l8 yields the two nondecomposable components l6 and l7 . We applied our method to identify conserved moieties in three metabolic networks of increasing size . The networks , listed from smallest to largest , were the dopamine synthesis network , DAS in Fig 4 , the E . coli core metabolic network , iCore [27] , and an atom mapped subset of the generic human metabolic reconstruction , Recon 2 [26] which we refer to here as subRecon . The dimensions of the three networks are given in Table 5a . Further descriptions are provided in Methods , Section Metabolic networks . There are seven linearly independent conservation relations for the closed DAS network , 11 for iCore , and 351 for subRecon . Atom transition networks were generated using algorithmically predicted atom mappings [20] as described in Methods , Section Generation of atom transition networks . Seven , ten and 345 moiety conservation relations were identified in the predicted atom transition network for DAS , iCore and subRecon , respectively ( Table 5b ) . Characterisation of identified moieties revealed some trends ( Fig 8 ) . We found a roughly inverse relationship between the frequency of a moiety , defined as the number of instances , and the size of that moiety , defined as the number of atoms per instance . We also found a relationship between moiety size , frequency and classification . Internal moieties tended to be large and infrequent , occurring in a small number of closely related secondary metabolites , e . g . , the 35 atom AMP moiety found in the three iCore metabolites AMP , ADP and ATP . Integrative moieties were usually small and frequent while transitive moieties were intermediate in both size and frequency . The smallest moieties consisted of single atoms . These were often highly frequent , occurring in up to 62/72 iCore metabolites and 2 , 472/2 , 970 subRecon metabolites . These results indicate a remarkable interconnectivity between metabolites at the atomic level . Due to their frequency , single atom moieties accounted for a large portion of atoms in each metabolic network . Single atom moieties accounted for nearly half ( 791/1 , 697 ) of all atoms in iCore , and approximately two thirds ( 104 , 268/153 , 298 ) of all atoms in subRecon . Moiety matrices derived from the predicted atom transition networks for iCore and subRecon did not span the left null spaces of their respective stoichiometric matrices , indicating that they might contain composite moiety vectors . Using the method described in Methods , Section Decomposition of moiety vectors , we found two composite moiety vectors in the moiety matrix for iCore , and 10 in the one for subRecon . Decomposition of these vectors yielded three new nondecomposable moiety vectors for iCore and 18 for subRecon ( Table 5b ) . The 11 nondecomposable moiety vectors for iCore were linearly independent . They therefore comprised a basis for the 11 dimensional left null space of N for iCore . The 353 nondecomposable moiety vectors for subRecon , on the other hand , were not linearly independent and only spanned 347 out of 351 dimensions in the left null space of S for subRecon . This indicated that there existed conservation relations for subRecon that were independent of atom conservation . Schuster and Höfer , citing earlier work by Aris [28] and Corio [29] , noted the importance of considering electron conservation in addition to atom conservation [11] . Unfortunately , it is not as straightforward to map electrons as atoms and no formalism currently exists for electron mappings . As a result , electron conservation relations cannot be computed with the current version of our algorithm . We therefore computed electron conservation relations for subRecon by decomposing the electron vector with the method described in Methods , Section Decomposition of moiety vectors . An electron vector for a metabolic network with m metabolites is a vector e ∈ N m where ei is the total number of electrons in metabolite i . Decomposition of e for subRecon yielded 11 new conservation vectors . When combined , the 11 electron vectors and the 353 fully decomposed moiety vectors for subRecon ( Table 5b ) spanned the left null space of the subRecon stoichiometric matrix . Internal moieties define pools of metabolites with constant total concentration and dependent individual concentrations . In the small dopamine synthesis network DAS in Fig 4 , the biopterin moiety ( l3 ) is classified as internal . This moiety is conserved between the metabolites BH2 and BH4 . The total concentration of BH2 and BH4 is therefore fixed at a constant value in DAS . If the concentration of BH2 increases , the concentration of BH4 must decrease by the same amount and vice versa . The concentration dependency between BH2 and BH4 couples all reactions that interconvert the two metabolites . Assume that DAS is initially at a steady state when there is a sudden increase in flux through reactions R1 , R2 , R3 and associated exchanges such that the concentrations of all primary metabolites remain constant . This would lead to net consumption of BH4 accompanied by net production of BH2 . The increased BH2/BH4 concentration ratio would increase thermodynamic and mass action kinetic driving forces through R4 , while simultaneously decreasing driving forces through R1 and R2 . The system would eventually settle back to the initial steady state or a new one depending on reaction kinetic parameters and substrate availability . Conservation of the biopterin moiety therefore imposes a purely physicochemical form of regulation on dopamine synthesis that is mediated through mass action kinetics and thermodynamics . This statement can be generalised to all internal moieties , as Reich and Sel’kov did in their 1981 monograph on energy metabolism [2] . Reich and Sel‘kov’s gearwheel analogy [2] is appropriate for the five internal moieties we identified in iCore . These five moieties define five well known cofactor pools ( Table 6 ) . Each pool is coupled to a set of reactions that interconvert metabolites within that pool . The five pools are also coupled to each other through shared reactions , forming a gearwheel-like mechanism ( Fig 9 ) . A change in concentration ratios within any pool will affect the driving forces that turn the wheels . The central wheel in iCore is the NAD moiety ( l6 ) . A change in concentration ratios within one pool will therefore be propagated to other pools via the NAD/NADH concentration ratio ( Fig 9 ) . This example shows how local changes in the state of a metabolic network can be propagated throughout the network via coupled cofactor pools defined by internal moieties . The majority of moieties identified in subRecon were classified as internal ( 237/345 ) . Most of these internal moieties were artefacts of the way the subset of reactions from Recon 2 were selected , i . e . , based on the availability of atom mapping data ( see Methods , Section Metabolic networks ) . Many reactions in subRecon were disconnected from the rest of the network and therefore could not carry any flux . To identify reactions capable of carrying flux , we computed the flux consistent part of subRecon [30] , which consisted of 3 , 225 reactions and 1 , 746 metabolites . We identified 118 moiety conservation relations for this part of subRecon , 33 of which were classified as internal . The metabolite pools defined by these moieties consisted of between 2 and 9 metabolites and were distributed across five cell compartments; the cytosol , mitochondria , nucleus , endoplasmic reticulum , and peroxisomes . Some moieties were compartment specific while others were distributed amongst metabolites in two different compartments . As in iCore , the internal moiety pools were not independent of each other but were coupled by shared reactions . Atoms in the same instance of a conserved moiety all follow the same path through a metabolic network . In an atom transition network these atoms are represented as separate nodes and their atom transitions as separate edges . A moiety graph encodes the paths of all atoms in an atom transition network in a reduced number of nodes and edges . In effect , they are reduced representations of atom transition networks that can be used in many of the same applications . Atom transition networks arise most frequently in the context of stable isotope assisted metabolic flux analysis where they underpin the ability to model the flow of isotopically labelled atoms through metabolic networks [31] . Stable isotope assisted metabolic flux analysis ( MFA ) deals with estimation of internal reaction fluxes in a metabolic network based on data from isotope labelling experiments [31] . Internal fluxes are estimated by fitting a mathematical model to measured exchange fluxes and isotopomer distributions . A basic MFA model consists of nonlinear flux balance equations formulated around isotopomers of metabolites in the metabolic network of interest [32] . A metabolite with n carbon atoms has 2n carbon atom isotopomers . Therefore , the number of isotopomer balance equations grows exponentially with the number of metabolites in the metabolic network . More sophisticated MFA modelling frameworks have been developed to reduce the complexity of the problem , notably the cumomer [33] and elementary metabolite unit ( EMU ) [34] frameworks . Cumomer models consist of flux balance equations formulated around transformed variables called cumomers . They are the same size as isotopomer models but have a simpler structure that makes them easier to solve . EMU models have a similar structure as cumomer models but are significantly smaller . They consist of flux balance equations formulated around transformed variables known as EMU species . The number of EMU species for a given metabolic network is much smaller than the number of isotopomers and cumomers . MFA models can be derived from moiety graphs instead of atom transition networks without loss of predictive capacity . We say that a moiety is labelled if any of its atoms are labelled and define moiety isotopomers as different labelling states of a metabolite’s moieties . The eight carbon containing metabolites in DAS ( Fig 4 ) have 2 , 820 possible carbon atom isotopomers . Their 55 carbon atoms can be grouped into 11 carbon moieties ( Fig 5b ) with only 22 possible carbon moiety isotopomers . The reduction in number of isotopomers is even more pronounced for the two larger metabolic networks ( Table 5c ) , reaching 12 orders of magnitude for iCore . It was less for subRecon where a greater proportion of moieties consist of a single atom ( Fig 8 ) . However , it was still substantial . Deriving MFA models from moiety graphs can therefore reduce the number of model equations by several orders of magnitude . Isotopomer and cumomer models , in particular , can be simplified with this approach . The algorithm to generate EMU species from atom transition networks ensures that atoms in the same instance of a conserved moiety are always part of the same EMU species . EMU models derived from moiety graphs will therefore be identical to those derived from atom transition networks ( see supporting file S1 Fig ) . Regardless of the MFA modelling framework , moiety graphs can be used to simplify design of isotope labelling experiments , by reducing the number of options for labelled substrates . Moiety vectors can be used to decompose a metabolic network into simpler moiety subnetworks [35] . An open metabolic network with total stoichiometric matrix S can be decomposed into t moiety subnetworks where t is the number of moiety conservation relations for the corresponding closed network N . Each moiety vector l k ∈ N ( N ) defines a stoichiometric matrix for one moiety subnetwork as S k = d i a g l k S . ( 9 ) Stoichiometric matrices for moiety subnetworks ( S ( k ) ) are generally more sparse than the stoichiometric matrix for the full metabolic network ( S ) . Each moiety subnetwork only includes the subparts of metabolites and reactions that involve a particular moiety . Moiety subnetworks of DAS are shown in Fig 10a . In addition to being more sparse than the full metabolic network ( Fig 4 ) , these subnetworks have simpler topologies . Of the seven moiety subnetworks of DAS only one ( S ( 6 ) ) was a hypergraph . All other DAS subnetworks were graphs . Four of 11 iCore subnetworks and 342 of 365 subRecon subnetworks were also graphs . We note that , although metabolic networks could in theory be decomposed with other types of conservation vectors , only moiety vectors are guaranteed to result in mass balanced subnetworks ( see Fig 10b ) . The results above were for moieties identified for metabolic network reconstructions where we assume each reaction is active . These moieties will only be relevant if all reactions in those reconstructions are actually active in practice , i . e . , carrying nonzero flux . In general , not all reactions in a metabolic network are active simultaneously , e . g . , oxidative phosphorylation reactions in iCore are only active in the presence of oxygen . The set of instantaneous conserved moieties , their conservation relations , and their classification depend on which reactions are active at any point in time . All steady state flux distributions v ∈ R n are in the right null space N ( S ) of the total stoichiometric matrix S for a metabolic network [36] . A convex basis for N ( S ) gives all extreme pathways of a metabolic network [37] . Extreme pathways are analogous to extreme semipositive conservation relations in the left null space N ( S T ) ( see Section Introduction ) . They are a maximal set of conically independent steady state flux distributions . Any steady state flux distribution can be written as a conical combination of extreme pathways . To see how instantaneous conserved moieties vary depending on what reactions are active we computed the extreme pathways of iCore with the vertex enumeration algorithm from [13] . Computation of the extreme pathways of subRecon with the same algorithm was not tractable . The algorithm returned 1 , 421 extreme pathways for iCore . The number of instantaneous moiety conservation relations for these pathways ranged from 4 to 11 and the total number of moieties ( i . e . , instances ) ranged from 18 to 520 . Fig 11 shows an example of instantaneous moieties in an extreme pathway that corresponds to glycolysis . We found that moieties classified as transitive or integrative in the entire iCore network , were often classified as internal in individual extreme pathways . In particular , the inorganic phosphate moiety ( Pi ) was classified as internal in all except one extreme pathway . The constant metabolite pool defined by the Pi moiety varied between pathways , consisting of Pi , ATP , AMP and 9 to 17 phosphorylated intermediates of glycolysis and the pentose phosphate pathway . The ammonia moiety ( NH4+ ) was also classified as internal in many extreme pathways ( 266/1 , 421 ) where it defined a constant metabolite pool consisting of NH4+ , glutamine and glutamate . The computational complexity of the method presented here is largely determined by the following two steps: 1 ) finding connected components of an atom transition network , and 2 ) determining isomorphisms between components . We used an implementation of Tarjan’s Algorithm [38] to find connected components of atom transition networks ( see Methods , Section Identification of conserved moieties ) . The worst case time complexity of this algorithm is O ( p + q ) where p is the number of atoms ( nodes ) and q is the number of atom transitions ( edges ) in the input atom transition network . We apply Tarjan’s algorithm to the simple graph underlying the input atom transition network , which generally contains significantly fewer edges . Algorithms to determine isomorphisms between two general graphs are an active research area . Atom transition networks are specialised graphs where every node is associated with a metabolite and every edge is associated with a reaction in the parent metabolic network . These additional structural elements of atom transition networks make it possible to determine isomorphisms between their components by pairwise comparisons ( see Section Identification of conserved moieties in Methods ) . Since every atom must be connected to at least one other atom , the number of components is bounded from above by p/2 . The number of components in the atom transition networks treated here was much lower . There were 57 components in the atom transition for DAS , 391 in the one for iCore , and 16 , 348 in the one for subRecon . If no component is isomorphic to any other component , we need to compare the first component to all other components , the second component to all others except the first , etc . The maximum number of comparisons is therefore ( p 2 − 1 ) + ( p 2 − 2 ) + ⋯ + ( p 2 − p 2 ) = p 2 4 − ∑ g = 1 p / 2 g = p 2 4 − 1 2 ( p 2 4 + p 2 ) = 1 4 ( p 2 2 − p ) . ( 10 ) The overall worst case time complexity of our method is therefore O ( p 2 + q ) . In practice , however , computation time scales much better ( Table 5d ) . Identification of conserved moieties in subRecon took under five minutes with our method . We compared this performance with an implementation of a vertex enumeration algorithm [13] to compute the extreme rays of the left null space of a stoichiometric matrix ( Table 5d ) . The two algorithms performed similarly on the two smaller networks but computation of extreme rays proved intractable for subRecon . The vertex enumeration algorithm did not complete after running for a week , at which point we terminated the process . It may be of interest to know how our method scales with the size of metabolic networks , instead of the size of atom transition networks . The number of atoms per metabolite varies greatly but is bounded from above . So is the number of atom transitions per reaction . The largest metabolite in the three metabolic networks treated here was the subRecon metabolite neurotensin ( Recon 2 ID C01836 ) , with 241 atoms . The largest reaction was the subRecon reaction peroxisomal thiolase 2 ( Recon 2 ID SCP2x ) , with 1 , 791 atom transitions . This is a composite reaction with large stoichiometric coefficients . Such large reactions are anomalous . The average number of atom transitions per metabolic reaction was much lower . The average ( ±standard deviation ) was 44 ( ±16 ) for DAS , 81 ( ±72 ) for iCore , and 105 ( ±90 ) for subRecon . The number of atoms and atom transitions scales approximately linearly with the number of metabolites and internal reactions , respectively ( Table 5d ) . We can therefore approximate the worst case time complexity of our method as O ( m 2 + u ) .
Moiety conservation relations are a subset of nonnegative integer conservation relations for a metabolic network . In principle , the latter can be computed using only a stoichiometric matrix , but the computational complexity of existing algorithms [11 , 12 , 14 , 15 , 17] has prohibited their application to large networks . Computation of moiety conservation relations requires information about the paths of atoms through metabolic networks in addition to reaction stoichiometry ( see , Section Theoretical Framework , Section Moiety vectors ) . Here , we incorporated this information in the form of atom transition networks . Doing so allowed us to formulate the problem of computing moiety conservation relations as a graph theory problem that is solvable in polynomial time . We related atom paths to connected components of atom transition networks and conserved moieties to equivalent nodes of isomorphic components . We provided a novel definition of isomorphism that is specific to the structure of atom transition networks . This definition enabled us to determine isomorphisms and identify conserved moieties in a fast and reliable way . The relationship between conservation relations and metabolite substructures has long been known [1 , 2 , 18] . A relationship between conservation relations and graph theoretical properties of atom transition networks has not , to our knowledge , been demonstrated prior to this work . This is also , to our knowledge , the first polynomial time method to compute nonnegative integer conservation relations for metabolic networks . Our method requires data on reaction stoichiometry and atom mappings for internal reactions of a metabolic network . Reliable data on reaction stoichiometry are readily available from high quality , manually curated metabolic network reconstructions that have been published for hundreds of organisms over the past couple of decades or so . These reconstructions are accessible in a standardised format [39] , e . g . , through the BioModels database [40] . Atom mapping data are increasingly becoming accessible through biochemical databases but are still largely algorithmically generated [21 , 22] . KEGG [41 , 42] and BioPath ( Molecular Networks GmbH , Erlangen , Germany ) provide manually curated atom mappings but the data are not freely accessible . No database currently provides mappings for hydrogen atoms or electrons which are required to compute all conserved moieties in a metabolic network . Data formats vary between databases as there is currently no agreed standard . However , the availability and quality of atom mapping data are rapidly increasing and we expect these issues will be remedied in the near future . We chose to use the DREAM algorithm [20] to predict atom mappings for this work . Advantages of DREAM include ease of use , the ability to map hydrogen atoms , and use of the information-rich rxnfile format . A disadvantage of DREAM is that it uses mixed integer linear programming ( MILP ) which has exponential worst case time complexity . Kumar and Maranas recently published the first polynomial time atom mapping algorithm , called canonical labelling for clique approximation ( CLCA ) [22] . An implementation of this algorithm has not yet been released but should further speed up the process of obtaining atom mapping predictions . CLCA predictions for 27 , 000 reactions are already accessible through the MetRxn database [22] . These predictions were not yet suitable for this work , however , as they do not include hydrogen atoms . Conserved moieties identified with our method depend on input atom mappings ( see Results , Section Effects of variable atom mappings between recurring metabolite pairs ) . We showed how variable atom mappings between recurring metabolite pairs could give rise to a non-maximal set of composite moiety vectors . Note that composite moieties are a biochemical reality , not just an artefact of the atom mapping algorithm used . Many metabolite pairs do have multiple biochemically equivalent atom mappings , each of which is realised in a living organism . For modelling purposes , however , it is desirable to identify a maximal number of linearly independent moiety conservation relations . We therefore formulated an MILP algorithm for decomposition of composite moiety vectors ( Methods , Section Decomposition of moiety vectors ) . It would be preferable to construct the atom transition network with minimal variability in atom mappings between recurring metabolite pairs to avoid composite moieties altogether . Doing so would be relatively straightforward if input data included all alternative atom mappings for reactions . Prediction of alternative atom mappings with the DREAM algorithm is possible but time consuming , both due to the longer running times required , and because DREAM outputs each alternative atom mapping in a separate rxnfile . Some effort is therefore required to integrate alternative predictions . The CLCA algorithm outputs alternative atom mapping predictions in a single file by default and should therefore facilitate identification of nondecomposable moiety conservation relations . Ultimately , however , predicted atom mappings need to be manually curated for alternatives . To span the left null space of Recon 2 we needed to decompose the electron vector e ∈ N 0 m ( Results , Section General properties of identified moieties ) with the MILP algorithm described in Methods , Section Decomposition of moiety vectors . We note that this MILP algorithm can also be used to decompose the elemental matrix for a metabolic network . This is in fact a method for nonnegative integer factorisation of the elemental matrix , similar to the algorithm presented in [18] . However , this method has exponential worst case time complexity . Also , while MILP decomposition of the elemental matrix returns the chemical composition of moieties it cannot be used to pinpoint the exact group of atoms in a metabolite that belong to each moiety . Empirically , we found that MILP decomposition of the elemental matrices for the three metabolic networks treated here completed in a reasonable amount of time although it scaled much worse than analysis of atom transition networks ( 3 . 4 × 10−1 s for DAS , 1 . 8 × 100 s for iCore , 4 . 7 × 103 s for subRecon , compare to Table 5d ) . In the absence of atom mapping data , MILP decomposition of the elemental matrix provides an alternative way to compute moiety conservation relations for metabolic networks . For the most part , decomposition of elemental matrices gave the same set of vectors as analysis of atom transition networks . The only exception was that decomposition of the elemental matrix for DAS returned the vector l 9 T = [ 0 1 2 0 2 2 0 0 1 0 0 ] , ( 11 ) in place of the oxygen moiety vector l6 in Table 4 . We note that l9 = l6 + 2 ( l2 − l1 ) does not correspond to a conserved moiety in DAS . Here , we highlighted three potential applications of our method; to identify constant metabolite pools ( Results Section The gearwheels of metabolism ) , to model isotope labelling experiments for metabolic flux analysis ( Results Section Application of moiety graphs to stable isotope assisted metabolic flux analysis ) , and to decompose metabolic networks ( Results Section Application of moiety vectors to decomposition of metabolic networks ) . These applications take advantage of our method’s unique ability to identify the exact group of atoms that correspond to each conserved moiety . As we alluded to in the introduction , another clear application area is metabolic modelling . A nonnegative integer basis for the left null space can be used to simplify metabolic models and to compute a full rank Jacobian which is required for many computational modelling methods [6 , 7] . Other applications would include minimisation of intermediate metabolite concentrations [43] , and computation of minimal cut sets [44] . We also believe our method may be of value to theoretical biologists . For example , the ability to decompose metabolic networks into simpler subnetworks may facilitate research on physical and mathematical properties that are otherwise obscured by topological complexity .
We tested our method on three metabolic networks of increasing sizes ( see Table 5a ) , two human and one E . coli network . The E . coli network consisted of core metabolic pathways including glycolysis , the pentose phosphate shunt , the TCA cycle , oxidative phosphorylation and fermentation [27] . We refer to this network as iCore for abbreviation . The two human networks were derived from the generic human metabolic reconstruction Recon 2 [26] . The smaller of the two consisted of four internal reactions from the dopamine synthesis pathway and seven metabolite exchange reactions . We refer to this network as DAS , and its four internal reactions as R1 , R2 , R3 , and R4 . R1 corresponds to Recon 2 reaction r0399 , R2 is a composite of reactions TYR3MO2 and THBPT4ACAMDASE , R3 corresponds to reaction 3HLYTCL , and R4 is a composite of reactions DHPR and FDH . The larger human network , which we refer to as subRecon , included approximately two thirds ( 4 , 261/6 , 691 ) of internal reactions in Recon 2 . This was the largest subset of Recon 2 reactions for which atom mappings could be predicted at the time of our analysis . For most of the remaining reactions ( 2 , 380/2 , 430 ) , we were unable to generate rxnfiles for input to the DREAM server [20] . For other reactions ( 50/2 , 430 ) , the DREAM algorithm timed out or failed to parse input rxnfiles . Rxnfiles could not be generated for 1 , 871/2 , 380 due to lack of information about metabolite structures , and for 509/2 , 380 reactions because they were not mass or charge balanced . Atom transition networks were generated based on atom mappings for metabolic reactions . Atom mapping predictions were obtained through the web interface to the mixed integer linear programming method DREAM [20] . The objective was set to minimise the number of bonds broken and formed in each reaction . Reactions were input to DREAM in rxnfile format ( Accelrys , San Diego , CA ) . Rxnfiles were written from data on reaction stoichiometry and metabolite structures in molfile format ( Accelrys , San Diego , CA ) . All hydrogen atoms were explicitly represented to obtain mappings for hydrogen atoms in addition to other elements . Care was taken to ensure that hydrogen and charge balancing of reactions was the same in rxnfiles as in the parent stoichiometric matrix . This was essential to ensure that computed moiety vectors were in the left null space of the stoichiometric matrix . We denote the internal stoichiometric matrix of a metabolic network by N ∈ Z m × u . Conserved moieties in the metabolic network were identified by analysis of an atom transition network that was generated as described in Generation of atom transition networks . We denote the incidence matrix of the input atom transition network by A ∈ {−1 , 0 , 1}p×q where p is the number of atoms and q the number of atom transitions . The first step in our analysis is to find connected components of A . To this end , we used an implementation of Tarjan’s algorithm [38] ( see Section Implementation ) . We denote the incidence matrix of component h of A by C ( h ) ∈ {−1 , 0 , 1}x×y . Each atom in a component belongs to a particular metabolite in the metabolic network . We define a mapping matrix M ( h ) ∈ {0 , 1}m×x that maps atoms to metabolites . It is defined such that M i , g ( h ) = 1 if the atom represented by row g in C ( h ) belongs to the metabolite represented by row i in N . Otherwise , M i , g ( h ) = 0 . The component C ( h ) represents conservation of a single atom throughout the metabolic network . We define its atom conservation vector as a h = M h 1 , ( 12 ) i . e . , it is the column sum of M ( h ) . Element ah , i is therefore the number of atoms in metabolite i that are in component C ( h ) . We define two components C ( h ) and C ( d ) to be isomorphic if they include the same number of atoms from each metabolite . It follows that the two components are isomorphic , with C ( h ) = C ( d ) , if ah = ad . A set of isomorphic components is denoted by K = {h , d ∣ ad = ah} . A moiety graph λk is obtained by merging a set K of isomorphic components into a single graph . The incidence matrix of λk is given by G k = 1 K ∑ h ∈ K C h . ( 13 ) We note that G ( k ) = C ( h ) ∀h ∈ K except that the rows of G ( k ) represent separate instances of a conserved moiety instead of atoms . A moiety vector lk is derived from the incidence matrix G ( k ) of a moiety graph in the same way that the atom conservation vector ah was derived from the incidence matrix C ( h ) of a component in Eq 12 . This is equivalent to setting lk = ah∀h ∈ K . We classified moieties according to the schema presented in [12] . Briefly , moieties were grouped into three categories termed transitive , integrative and internal . These categories were referred to as Type A , Type B , and Type C , respectively , in [12] . A moiety with conservation vector lk was classified as internal if it was conserved in the open metabolic network represented by the total stoichiometric matrix S , i . e . , if ST lk = 0 . Metabolites containing internal moieties were defined as secondary metabolites , while all other metabolites were defined as primary metabolites . Moieties that were only found in primary metabolites were classified as transitive moieties , while those that were found in both primary and secondary metabolites were classified as integrative moieties . Our method for analysing atom transition networks returns r moiety vectors { l k ∈ N 0 m ∣ k ∈ [ 1 , r ] } as the columns of the moiety matrix L ∈ N 0 m × r . As described in Results , Section Effects of variable atom mappings between recurring metabolite pairs , our method may return composite moiety vectors if the input atom transition network was generated from variable atom mappings between recurring metabolite pairs . Any composite moiety vector can be written as lk = xk + yk , where xk and yk are nonzero moiety vectors . To decompose a composite moiety vector lk , we solved the mixed integer linear programming ( MILP ) problem m i n 1 T x k , ( 14 ) s . t . l k = x k + y k , ( 15 ) N T x k = 0 , ( 16 ) x k ∈ ℕ 0 m × 1 , ( 17 ) 0 < 1 T x k < 1 T l k . ( 18 ) We denote this problem by Pk . The constraint in Eq 15 defines the solution vectors xk and yk as components of lk . The constraints in Eqs 16 and 17 correspond to Eqs 2 and 3 defining nonnegative integer conservation vectors ( see Theoretical Framework , Section Moiety vectors ) . These constraints are implicit for yk due to Eq 15 . The constraint in Eq 18 , when combined with Eq 15 , ensures that xk and yk are both greater than zero . We chose to minimise the sum of elements in xk but other objectives would also work . Problem Pk is infeasible for nondecomposable lk . We note that the solution vectors xk and yk might themselves be composite moiety vectors . To fully decompose the moiety matrix L we must therefore solve Pk iteratively until it is infeasible for all input moiety vectors . This process can be described with the algorithm , 1 . Input L ∈ ℕ 0 m × r . Initialise L′ = L and D = [ ] , where [ ] denotes an empty matrix . 2 . Set r ′ = dim ( L 1 , : ′ ) and L′′ = [ ] , where L 1 , : ′ denotes the first row of L′ . If r′ ≥ 1 , then go to Step 3 , else , go to Step 5 . 3 . For k = 1: r′ , denote lk = L: , k , solve Pk . If Pk is infeasible , set D = [D , lk] , else , denote the solution of Pk by xk and yk and set L′′ = [L′′ , xk , yk] . Go to Step 4 . 4 . Set L′ = L′′ and go back to Step 2 . 5 . Output the fully decomposed moiety matrix D ∈ ℕ 0 m × t . The same algorithm can be used for nonnegative integer matrix factorisation of an elemental matrix and electron vector for a metabolic network . We implemented the method presented here as an algorithmic pipeline in MATLAB ( MathWorks , Natick , MA ) . This implementation is freely available as part of the COBRA toolbox [45] at https://github . com/opencobra/cobratoolbox ( directory topology/conservedMoieties ) . Required inputs are an atom transition network and a stoichiometric matrix for a metabolic network . The method outputs moiety conservation relations both as moiety graphs and moiety vectors . All graphs are represented as incidence matrices . Support functions to generate atom transition networks ( Section Generation of atom transition networks ) , classify moieties ( Section Classification of moieties ) and decompose moiety vectors ( Section Decomposition of moiety vectors ) are included with the core code . A tutorial on identification of conserved moieties in the dopamine synthesis network DAS is available at https://github . com/opencobra/cobratoolbox ( directory topology/conservedMoieties/example ) , along with necessary data and MATLAB scripts that run through the example . To compute the connected components of atom transition networks we used and implementation of Tarjan’s algorithm available as part of the Bioinformatics Toolbox for MATLAB ( MathWorks , Natick , MA ) . This toolbox is not included with a standard installation of MATLAB . Users who do not have the Bioinformatics Toolbox can still run the pipeline with a free alternative to Tarjan’s algorithm to compute components of atom transition networks . If the Bioinformatics Toolbox is not installed in the MATLAB path , the pipeline calls a k-Nearest Neighbour algorithm in the MATLAB Network Routines toolbox by Bounova and Weck [46] . This toolbox is freely available with the COBRA toolbox . The k-Nearest Neighbour algorithm is considerably slower than Tarjan’s algorithm . All code in the COBRA toolbox is distributed under a GNU General Public Licence and we encourage implementations of our method for other platforms than MATLAB . We have taken care to document and comment our code to facilitate such efforts .
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Conserved moieties are transferred between metabolites in internal reactions of a metabolic network but are not synthesised , degraded or exchanged with the environment . The total amount of a conserved moiety in the metabolic network is therefore constant over time . Metabolites that share a conserved moiety have interdependent concentrations because their total amount is constant . Identification of conserved moieties results in a concise description of all concentration dependencies in a metabolic network . The problem of identifying conserved moieties has previously been formulated in terms of the stoichiometry of metabolic reactions . Methods based on this formulation are computationally intractable for large networks . We show that reaction stoichiometry alone gives insufficient information to identify conserved moieties . By first incorporating additional data on the fate of atoms in metabolic reactions , we developed and implemented a computationally tractable algorithm to identify conserved moieties and their atomic structure .
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2016
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Identification of Conserved Moieties in Metabolic Networks by Graph Theoretical Analysis of Atom Transition Networks
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Schistosomes are dioecious parasitic flatworms , which live in the vasculature of their mammalian definitive hosts . They are the causative agent of schistosomiasis , a disease of considerable medical and veterinary importance in tropical and subtropical regions . Schistosomes undergo a sexual reproductive stage within their mammalian host enabling interactions between different species , which may result in hybridization if the species involved are phylogenetically close . In Senegal , three closely related species in the Schistosoma haematobium group are endemic: S . haematobium , which causes urogenital schistosomiasis in humans , and S . bovis and S . curassoni , which cause intestinal schistosomiasis in cows , sheep and goats . Large-scale multi-loci molecular analysis of parasite samples collected from children and domestic livestock across Senegal revealed that interactions and hybridization were taking place between all three species . Evidence of hybridization between S . haematobium/S . curassoni and S . haematobium/S . bovis was commonly found in children from across Senegal , with 88% of the children surveyed in areas of suspected species overlap excreting hybrid miracidia . No S . haematobium worms or hybrids thereof were found in ruminants , although S . bovis and S . curassoni hybrid worms were found in cows . Complementary experimental mixed species infections in laboratory rodents confirmed that males and females of each species readily pair and produce viable hybrid offspring . These data provide indisputable evidence for: the high occurrence of bidirectional hybridization between these Schistosoma species; the first conclusive evidence for the natural hybridisation between S . haematobium and S . curassoni; and demonstrate that the transmission of the different species and their hybrids appears focal . Hybridization between schistosomes has been known to influence the disease epidemiology and enhance phenotypic characteristics affecting transmission , morbidity and drug sensitivity . Therefore , understanding and monitoring such inter-species interactions will be essential for optimizing and evaluating control strategies across such potential hybrid zones .
In recent years , developments in molecular tools and their use in epidemiological studies have revealed several cases of hybridization and introgression in plants and animals [1]–[8] , although there are very few examples from metazoan parasites [7]–[8] . Hybridization can have a major impact on species diversification and adaptive radiation [9]–[12] . With regard to parasites and pathogens , this may have a crucial impact on disease epidemiology and evolution , affecting factors such as virulence , drug efficacy and response to control , and host range , potentially ultimately leading , in certain cases , to the evolution of new species of pathogens [13]–[14] . Schistosomiasis is a parasitic disease of considerable medical and veterinary importance throughout tropical and subtropical regions , caused by dioecious digeneans of the genus Schistosoma . Schistosomes infect more than 207 million people worldwide ( the majority of whom are in sub-Saharan Africa ) and cause chronic diseases that can lead to severe liver , intestinal and bladder pathology and death [15] . Schistosomiasis is also a disease affecting domestic livestock such as cattle , sheep and goats throughout Africa , the Middle East , Asia , and some countries bordering the Mediterranean Sea . Indeed it has been estimated that over 165 million cattle are infected worldwide with chronic infections resulting in hemorrhagic enteritis , anemia , emaciation and death [16] . Schistosomes have a two-host life cycle with an asexual stage occurring in an intermediate freshwater snail and a sexual stage within the definitive mammalian host from which eggs are voided in the urine or faeces of the infected individual , depending on the schistosome species involved . The sexual stage of these dioecious parasites enables interactions between male and female worms within their definitive hosts , while the asexual stage within the aquatic intermediate snail host gives rise to clonal larvae facilitating exposure and potential infection of any mammal in contact with the water . Most Schistosoma species are host specific and geographically separated , which helps to maintain species barriers however , given the opportunity , heterospecific crosses between species can occur and have been demonstrated in controlled laboratory experiments [17]–[20] . Heterospecific crosses can lead to either parthenogenesis or hybridization depending on the phylogenetic distance of the species involved . Hybridization , with the production of generations of viable offspring , will result from heterospecific crosses between closely related species and viable hybrids have been observed experimentally to exhibit several enhanced phenotypic characteristics such as higher fecundity , faster maturation time , higher infectivity , increased pathology and the ability to infect both intermediate snail hosts of the parental species , thereby widening their intermediate host spectrum [19]–[20] . Whilst in nature host specificity and distribution may have restricted the potential for hybridization between schistosomes , natural and anthropogenic environmental changes , accompanied by host migration , introduction and/or radiation , can all serve to alter the distribution of schistosome species . This can result in novel interactions between schistosomes and host and also between different schistosome species infecting the same host . For example , dam construction and irrigation may be expected to give rise to changes in agricultural practice and create new habitats for intermediate snail hosts , bringing humans into even greater contact with their livestock , and hence the parasites of these animals , certain species of which can successfully hybridize with human schistosome species . Due to the inaccessibility of schistosome adult worms within their hosts , detecting natural interactions and hybridization between schistosome species , especially in humans , can be highly problematic . Past studies have speculated on such events using morphological characteristics such as egg shape , infection site and snail compatibility , although this can be misleading [19]–[26] . Recent developments in the ability to store and genetically analyze individual schistosome larval stages directly from their natural hosts have , nevertheless , proved revolutionary in detecting natural hybridization events [27]–[33] . Accordingly , studies incorporating such molecular analysis of the parasites have proved that interactions and hybridization between schistosome species does occur in nature with varying epidemiological outcomes [19] , [34]–[37] . In particular , a preliminary study reported on the bidirectional hybridization between a cattle schistosome , S . bovis , and a human schistosome , S . haematobium , in several villages along the Senegal River Basin in Northern Senegal [32] . Hybrid larval stages were found in human urine and stool samples and also transmitted through both the intermediate snail hosts ( Bulinus truncatus and B . globosus respectively ) of the parental species . This hybridization was hypothesized to have been able to occur due to the ecological and climatic changes that have taken place in these areas over the last 30 years facilitating the creation of areas of sympatry between these schistosome species and their hosts . Whilst this study provided the first conclusive evidence for the natural hybridization between these two species , it also inspired a number of further related questions such as: what is the occurrence , host use and distribution of these hybrids in Senegal and elsewhere . Additionally , in Senegal , there exists another schistosome species within the S . haematobium group , S . curassoni , which infects sheep , goats and cattle and is very closely related to both S . bovis and S . haematobium , potentially enabling all three species to interact if given the opportunity [38]–[41] . The definitive host range of S . curassoni and S . bovis does overlap , so it may be predicted that these two species also natural hybridize in areas of sympatry . Indeed an earlier report by Rollinson and colleagues [41] , provided initial field and experimental evidence , based on isoenzyme data , for the hybridization between these two species in Senegal and Mali . There has also been some speculation about the potential involvement of S . curassoni infecting or interacting with S . haematobium in humans in Niger [42] , although there was no conclusive evidence to support this as more sensitive molecular markers were needed to discriminate between S . haematobium and S . curassoni [41]–[44] . In the current study we use molecular sequencing tools and a multi-locus approach to provide novel data on the occurrence , interactions and host use of all three schistosome species and their natural hybrids at four foci across Senegal . In particular we aimed to confirm the presence of S . curassoni or hybrids thereof in children living in areas where S . curassoni occurs in ruminants and where transmission is likely to occur . We also aimed to elucidate the role of domestic livestock in the transmission and possible hybridization of the human schistosome S . haematobium and to provide data on the frequency and viability of the hybridization between , and also the transmission of , S . haematobium , S . bovis and S . curassoni at the specific field study sites . These data are discussed in relation to the implications for the transmission and control of schistosomiasis in Senegal and other neigbouring West African countries , where all three schistosome species may be occurring and interacting .
Parasitological surveys of domestic livestock and children were carried out in March 2009 and 2010 in four areas across Senegal; the Senegal River Basin ( SRB ) in the North , Vallée du Ferlo which is central , Tambacounda in the South East and Kolda in the South . ( Figure 1 ) . These sites were specifically selected as they were known foci for human and livestock schistosomiasis ( personal communication ) . As these were mainly pilot surveys aimed to positively identify the different Schistosoma species and any hybrids infecting livestock and children in each area only human urine samples and animal intestines were sampled . gDNA from individual miracidia was extracted as described in Webster [33] . Individual worm pairs were washed in TE buffer to remove any residual ethanol and allowed time to relax in the TE buffer so that they could be separated . The male and female worm from each pair was recorded . gDNA from individual worms was extracted using the DNeasy blood and tissue kit ( Qiagen ) according the manufacture's protocol and DNA was eluted in a total of 100 µl . To enable high throughput processing of the samples the majority of the gDNA extractions were carried out using the DNeasy blood and tissue kit 96 well plate spin protocol ( Qiagen ) according the manufacturer's protocol and DNA was eluted in a total of 100 µl . Hybrid detection of schistosomes requires a multi-locus approach , analyzing both mitochondrial and nuclear DNA simultaneously from individual specimens [32] . A partial region of the mitochondrial cox1 gene and the complete ITS1+2 rDNA were analyzed from each individual as described below . The cox1 and ITS reference sequences of S . haematobium , S . bovis and S . curassoni from Huyse and colleagues [32] , were used to identify and compare the sequences from the samples in this study . During the collection of the parasite material at the sampling sites , eggs were also collected to establish laboratory isolates of S . haematobium , S . bovis and S . curassoni . Ethical approval for these studies was obtained from the Imperial College Research Ethics Committee ( ICREC ) , Imperial College London and the Ministry of Health Dakar , Senegal . Before conducting the study , the MoH-approved plan of action had been presented and adopted by regional and local administrative and health authorities . Meetings were held in each village to inform the village leader , heads of the families , local health authority , teachers , parents and children about the study , its purpose and to invite them to participate voluntarily . According to common practice and with approval from the Imperial College Research Ethics Committee ( ICREC ) , due to low levels of literacy all village leaders , teachers , parents and study participants gave oral consent for the studies to take place . Informed consent for the urine examinations was obtained from each study participant and their parents or guardians . Oral consent for each participant was documented by inscription at school committees comprising of parents , teachers and community leaders . All the data were analysed anonymously and all schistosomiasis positive participants were treated with PZQ ( 40 mg/kg ) . In schools or classes where the percentages of infections were more than 50% , mass treatment of all children was carried out at the end of the study . Laboratory animal use was within a designated facility regulated under the terms of the UK Animals ( Scientific Procedures ) Act , 1986 , complying with all requirements therein , including an internal ethical review process at the NHM and regular independent Home Office inspection . Work was carried out under the Home Office project license number 70/6834 .
The prevalence of human urogenital schistosomiasis was high in all areas sampled , ranging from 57–100% , and visible haematuria was obvious in urines samples from all study areas . A total of 823 individual miracidia were collected and genetically analysed from 52 urine samples . 79% of the miracidia were molecularly identified as pure S . haematobium , however , 21% presented a mixed mt cox1 + nuclear ITS genotype suggesting that these miracidia had a hybrid origin . Hybrid miracidia were found in 88% of the urine samples analysed and in all areas surveyed . The numbers and type of hybrids varied between urine samples and areas ( Table S1 ) . Hybrids between S . bovis and S . haematobium were found in children from all areas except Barkedji in the Vallée du Ferlo and hybrids between S . curassoni and S . haematobium were only isolated in Tambacounda and the Vallée du Ferlo . In total , 1004 schistosome worms ( 502 pairs ) were dissected and genetically analysed from the mesenteric vessels of 33 animal intestines and 109 miracidia were hatched and analysed from three infected liver samples ( Table S2 ) . Only S . bovis was found in animals from Kolda and Richard Toll but S . bovis and S . curassoni were found in animals from Tambacounda and the Vallée du Ferlo . The number of worm pairs found in each individual animal varied considerably from 1 to over 100 . S . bovis/S . curassoni hybrid worms with different genetic profiles were found in small numbers in the intestines of five animals from Tambacounda and all these hybrids were paired with either S . bovis or S . curassoni worms . No worms with pure or hybrid S . haematobium genetic profiles were found in any domestic animal . All three species were successfully isolated from the field samples and maintained in laboratory Bulinus wrighti snails . The molecular ITS and cox1 identification of the cercariae resulting from the three groups of laboratory snail infections showed that each group was infected with one species ( Group 1 S . curassoni , Group 2 S . bovis and Group 3 S . haematobium ) . No hybrid profiles were observed in these isolates . The mixed species infections in all the laboratory rodents ( animal crosses 1–3 , Table 1 ) produced heterospecific pairs between male and female worms of all three species ( Table 1 ) and also homospecific pairs . The numbers of pairs varied between animals with the most abundant heterospecific parings occurring between S . bovis and S . curassoni worms . Miracidia were hatched from the infected livers of animals with mixed infections . Molecular typing of these miracidia detected hybrid offspring ( Table 1 ) and confirmed viable heterospecific pairings between all three species . Homospecific miracidia of each species were also identified . The numbers of each type of miracidia correlated to the number of homospecific or heterospecific worm pairs present .
Children were found excreting S . haematobium/S . curassoni hybrids in Tambacounda and the Vallée du Ferlo and S . haematobium/S . bovis hybrids in Tambacounda , Kolda and the SRB . A low number of S . bovis and S . curassoni hybrid worms were found in cows slaughtered in the Tambacounda abattoir but no S . haematobium worms or hybrids thereof were found in ruminants , however this could be due to the fact that only the intestines of the animals were sampled . . These data do suggest that at some point host switching has been able to take place between these three sister species enabling the two species involved to interact , hybridize and produce viable offspring . The laboratory mixed infection experiments further indicate that males and females of each species readily pair and produce viable hybrid offspring and in these experimental crosses there does not appear to be any competition , exclusion or mating preference between the species and each cross produced viable hybrid offspring . In each of the three types of hybrids two hybrid lines were observed , resulting from bidirectional introgressive hybridization with mtDNA from both the parental species introgressing into the other species involved in the hybridization ( see Table S1+S2 ) . Regarding the nuclear DNA profiles , initial hybrid generations usually display both parental nuclear rDNA ITS copies , resulting in double chromatogram peaks at the species-specific mutation sites [32] , [47]–[49] . In subsequent hybrid generations or backcrossing of hybrids with parental species , biased homogenisation towards one of the parental species may result in nuclear rDNA ITS sequences that can appear as just one species or the other [49] . As the dynamics of this homogenisation and silencing of the genetic signal from one species over the other is unknown it is impossible to decipher which generation the natural hybrids are from by looking at their genetic profiles . Nevertheless , the observation of both pure and mixed nuclear rDNA ITS sequences within our hybrid populations strongly suggest that there are different generations of hybrids and/or hybrid backcrosses persisting in nature . The hybridization of S . bovis and S . curassoni with S . haematobium is of particular interest , as for this to occur , host switching must have taken place of either , S . bovis and S . curassoni into humans or S . haematobium into domestic livestock . The oviposition site of a schistosome pair is generally assumed to primarily be dependent on the species of the male worm [37] , [43] , with S . bovis and S . curassoni males carrying their females to the intestinal tract and S . haematobium males carrying their females to the urinary tract . It is unknown how hybridization will affect this phenotypic/behavioural characteristic , however it would be expected that in the initial parental cross the worm pair would migrate to the oviposition site determined by the species of the male worm . Hybrid miracidia analysed from the human urine samples that present S . curassoni cox1 and mixed ITS sequences or S . bovis cox1 and mixed ITS sequences could be first generation hybrids resulting from pure parental crosses between S . haematobium males and S . curassoni or S . bovis females , however , it is also possible that second generation hybrids or hybrid backcrosses could also present these genetic profiles . It would be expected that first generation hybrids of the reciprocal cross ( S . haematobium females paired with males of S . bovis or S . curassoni ) would be excreted in stool samples , therefore miracidia from any eggs present in stool samples also need to be analysed to identify further hybrids and also any possible homospecific infections of S . bovis and or S . curassoni in humans . No S . haematobium/S . bovis or S . haematobium/S . curassoni hybrids or S . haematobium worms were found in the domestic livestock , possibly suggesting that S . haematobium and indeed S . haematobium hybrids may lack the ability to penetrate and or develop in ruminants . It is also necessary to consider whether major differences in the vasculature between ruminants and humans would restrict the migration of S . haematobium to the blood vessels of the bladder of ruminants . However , as only the intestinal tracts of the slaughtered animals were routinely available for inspection at the abattoirs , and as S . haematobium is a parasite of the urinary tract , the presence of S . haematobium or the hybrids may have been missed . A much earlier study in Zambia did report the possible finding of S . haematobium/S . mattheei ( another closely related ruminant schistosome ) hybrids in the mesenteric veins of cattle [50] however these findings remain unconfirmed . It is clear that more extensive and detailed dissections off and egg collections from the ruminants is warranted . The infectivity of a schistosome to a mammalian host depends on several physical and immunological factors , however , the variety of host use by different species of the Schistosoma genus suggests that host switching has occurred at several time points in the evolution on this genus [38] . It is possible that the close phylogenetic ancestral position of S . haematobium to its sister species S . curassoni and S . bovis , together with physical and anatomical host differences may enable the latter two species to retain an ability to infect humans . Another possibility is that the initial pairing between these human and domestic livestock parasites occurred first in another susceptible host , such as a rodent . Rodents have proved extremely efficient for passaging a variety of schistosome species in the laboratory [17] and are utilised as reservoir hosts by other species of schistosome [51]–[54] . The hybridization between S . curassoni/S . bovis is not that surprising given their neighbouring phylogenetic position and their overlap in definitive host associations [38] and the molecular data presented here conclusively confirm that these parasites are able to hybridize . Observations from the different survey sites clearly demonstrate the focality of the transmission of the different species and their hybrids . Hybridization between the human and domestic livestock schistosomes appears common with hybrid genetic profiles recovered from human urine samples from several areas where the different species are transmitted sympatrically . Also , S . haematobium/S . bovis hybrids have previously been sampled from humans from several widely dispersed villages along the SRB [32] , and the possibility of the hybridization between S . haematobium/S . bovis or S . curassoni was reported by [42] in Niger . Due to the wide distribution of the intermediate snail hosts B . globosus and B . truncatus , S . haematobium and S . bovis are common infections across much of Senegal , providing the opportunity for these species to interact , however the distribution of S . curassoni , transmitted through B . umbilicatus , is more restricted . The data presented here confirm an intense S . curassoni focus in the Vallée du Ferlo , increasing the known distribution of this species and the hybrids thereof . S . haematobium/S . curassoni hybrids were found in the urines of children sampled from the village of Barkedji , where only S . curassoni was found in the slaughtered sheep . This , together with the data from Tambacounda , provides the first confirmation of this species or hybrids thereof infecting children in Senegal . Only small numbers of S . curassoni/S . bovis hybrids were found in cattle at Tambacounda abattoir , with most worms being identified as S . curassoni in agreement with an earlier report [41] . There are no obvious isolating barriers other than intermediate snail host preferences that prevent S . curassoni and S . bovis from interacting and hybridizing as both species readily infect ruminants . However , transmission of these two species does appear to be localised , with the majority of the animals sampled in one particular place being infected with either S . bovis or S . curassoni . The origins of the animals sampled at the small abattoirs in this study are usually unknown and would be almost impossible to trace due to changes of ownership during an animal's lifetime . The worm burden reflects past exposure: S . bovis transmission being associated with B . truncatus habitats while S . curassoni is associated with B . umbilicatus [54] . With regard to the role of intermediate snail hosts , hybridization between Schistosoma species potentially enables the schistosomes to increase their host range with the hybrids being able to utilize both the intermediate snail hosts of the parental species , which will have important implications for schistosomiasis epidemiology by increasing transmission and distribution [19]–[20] , [27] , [32] . Snail surveys were not conducted during this study , however , the study of Huyse and colleagues [32] did provide evidence for the transmission of S . haematobium/S . bovis hybrids through both B . globosus and B . truncatus the intermediate snail hosts of S . haematobium and S . bovis respectively . Snail surveys and molecular screening of cercariae from the hybrid zones are needed to clarify what role each snail species plays in the transmission of the parental species and their hybrids . Some degree of sympatric transmission enabling interbreeding between these species may have always occurred but remained undetected due to the limitations of previously available sampling and analysis tools . However , hybridisation between species can be further facilitated by the loss of ecological barriers existing between species due to natural and or man-made changes . [35]–[37] . In Northern Senegal , it was speculated that the hybridization between S . haematobium and S . bovis was facilitated by the creation of water bodies for agriculture , through dam construction , which led to an increased prevalence and distribution of the intermediate snail hosts and movements of humans and livestock to these resources , creating sympatric transmission of these species , resulting in hybridization . In the additional hybrid zones confirmed in this study , while no such ecological change can be attributed to enabling hybridization to have taken place , the natural progression in farming , population ( both human and livestock ) movements and expansion will result in areas of increased close associations between humans and their domestic livestock , increasing the chances of interspecific interactions between the schistosomes they carry . Other behavioural factors could also have an important impact , for instance during the sampling of cattle in the abattoir in Richard Toll in the SRB where S . bovis was highly prevalent , it became apparent that due to the lack of running water the intestines of slaughtered animals were routinely washed in the local river , which was also frequented by the local people for their everyday activities , thus creating a potential sympatric transmission site for S . haematobium and S . bovis . Introgressive hybridization may lead to phenotypic changes that can dramatically influence disease dynamics and evolution of the parasites . Although treatment with praziquantel , the drug routinely used to control human schistosomiasis across Africa , is successful against S . haematobium , S . bovis and S . curassoni [55]–[56] , hybridization between different Schistosoma species have been reported to affect the success of drug treatment in cattle [57] , cause severe disease outbreaks and competitive exclusion of one species by the other [35]–[37] and laboratory hybrids have been observed to acquire enhanced characteristics such as infectivity , fecundity and growth rates [19] , [20] , [54] . S . haematobium has long been recognised as a parasite with few , if any , reservoir hosts [38] . However , it seems that in Senegal and possibly in other areas of West Africa , new genotypes may emerge that may pass from people to domestic livestock and vice versa . Furthermore , if the hybridization events reported here result in phenotypic characteristics that influence drug sensitivity , pathology and transmission , it will be highly important to re-evaluate control strategies in these hybrid zones . The increased host range of the hybrid parasites and changes in host distribution may have a direct impact on transmission of these schistosomes . Human and veterinary schistosomiasis in Senegal and neighbouring countries needs to be further monitored to clarify further the epidemiology and dynamic interactions of these closely related schistosome species .
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Schistosome blood flukes are transmitted through water contact and cause a severe debilitating disease in humans and their livestock . Understanding the biology and epidemiology of these parasites is essential to enable the development of better control strategies in endemic areas . Several species of schistosome exist and species barriers are normally maintained by differences in ecology , host specificity , and evolutionary history . However , hybridization between closely related species can occur if parasites infect the same definitive host . Here we report on the introgression between a human ( S . haematobium ) and two ruminant schistosomes ( S . bovis and S . curassoni ) , the prevalence and distribution of their hybrids and the novel evidence for the presence of S . haematobium/S . curassoni hybrids in Senegalese children . Modern sampling and genotyping techniques have brought to light the extent of these hybridization events which could have been facilitated by the natural progression in farming , population ( both human and livestock ) movements and expansion , as well as changes in snail distribution , creating areas of sympatric transmission . Hybridization can lead to phenotypic characteristics that can influence disease epidemiology and control success , highlighting the importance of monitoring these evolving hybrid zones .
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[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[
"biology",
"zoology",
"parasitology"
] |
2013
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Introgressive Hybridization of Schistosoma haematobium Group Species in Senegal: Species Barrier Break Down between Ruminant and Human Schistosomes
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Host defence against infection requires a range of innate and adaptive immune responses that may lead to tissue damage . Such immune-mediated pathologies can be controlled with appropriate T regulatory ( Treg ) activity . The aim of the present study was to determine the influence of gut microbiota composition on Treg cellular activity and NF-κB activation associated with infection . Mice consumed the commensal microbe Bifidobacterium infantis 35624 followed by infection with Salmonella typhimurium or injection with LPS . In vivo NF-κB activation was quantified using biophotonic imaging . CD4+CD25+Foxp3+ T cell phenotypes and cytokine levels were assessed using flow cytometry while CD4+ T cells were isolated using magnetic beads for adoptive transfer to naïve animals . In vivo imaging revealed profound inhibition of infection and LPS induced NF-κB activity that preceded a reduction in S . typhimurium numbers and murine sickness behaviour scores in B . infantis–fed mice . In addition , pro-inflammatory cytokine secretion , T cell proliferation , and dendritic cell co-stimulatory molecule expression were significantly reduced . In contrast , CD4+CD25+Foxp3+ T cell numbers were significantly increased in the mucosa and spleen of mice fed B . infantis . Adoptive transfer of CD4+CD25+ T cells transferred the NF-κB inhibitory activity . Consumption of a single commensal micro-organism drives the generation and function of Treg cells which control excessive NF-κB activation in vivo . These cellular interactions provide the basis for a more complete understanding of the commensal-host-pathogen trilogue that contribute to host homeostatic mechanisms underpinning protection against aberrant activation of the innate immune system in response to a translocating pathogen or systemic LPS .
Mechanisms of host protection by commensal organisms against infection vary depending on the nature of the pathogenic challenge . They range from mutual competition for nutrients and for microbial niches , to the production of specific anti-microbial peptides [1] , [2] . In addition , there is emerging evidence for engagement of host immune responses by commensal organisms [3] , [4] . The host response to infection is characterised by innate and acquired cellular and humoral immune reactions , designed to limit spread of the offending organism and to restore organ homeostasis . However , to limit the aggressiveness of collateral damage to host tissues , a range of regulatory constraints may be activated . Regulatory T cells ( Treg ) serve one such mechanism [5] . These are derived from the thymus but may also be induced in peripheral organs , including the gut mucosa [6] , [7] . CD103+ dendritic cells within the mucosa are largely responsible for the conversion of Tregs which is a TGF-β and retinoic acid dependent process [8] , [9] . The gastrointestinal specific environmental factors that contribute to dendritic cell conversion of Tregs is likely due in part to the presence of large numbers of commensal microbes . For example , encounter with specific experimental microbes within the murine gut , has been shown to drive the development of mucosal Tregs which is associated with attenuation of inflammation in a murine model of colitis [10] . However , it is unclear whether enhancement of Treg activity by commensal organisms contributes to protection of the host from mucosal damage and systemic inflammation associated with infection by invasive pathogens . Innate pro-inflammatory signalling in response to microbial exposure is mediated by activation of transcription factors , such as NF-κB , resulting in the transcription of a battery of effector molecules contributing to host defence and inflammation [11] . A number of bacterial products have been identified which directly block activation of the NF-κB pathway in epithelial cells via a range of novel mechanisms including the blockade of Iκ-B poly-ubiquination by non-pathogenic Salmonella strains [12] or the enhancement of NF-κB export from the nucleus by Bacteroides thetaiotaomicron [13] . However , these molecular events are likely to be restricted to the gastrointestinal mucosa where direct interactions with these bacterial organisms takes place . The cellular mechanisms permitting prokaryotic modulation of NF-κB activation following infection in cells outside the gastrointestinal tract is less clear . The purpose of the present study was: ( a ) to determine if bacterial signalling from the lumen of the murine gut after feeding with a well characterised commensal/probiotic could impact in vivo activation of the pro-inflammatory transcription factor NF-κB following infection thereby limiting infection-associated inflammatory injury; ( b ) examine the mechanism of such protective regulation by assessing Treg cellular activity in probiotic-fed mice and by adoptive transfer of T cells into naïve non-fed animals . The results show that the putative probiotic/commensal organism drives Treg activity which limits the pro-inflammatory response to invasive salmonella infection via down-regulation of NF-κB activation .
Mice consuming B . infantis were protected from the effects of infection with S . typhimurium . Biophotonic imaging of in vivo NF-κB activation illustrated a significant difference just four hours following infection ( Figure 1A ) . B . infantis-fed animals exhibited a less pronounced activation of the in vivo NF-κB response compared to the NF-κB response to infection in placebo-fed controls . This suppressed pro-inflammatory response persisted for the remainder of the study period ( Figure 1B ) . Murine clinical scores were not significantly different until 7 days after the initial infection at which stage B . infantis-fed animals were significantly better that placebo-fed controls ( Figure 2A ) Similarly , systemic pathogen numbers were not different between the groups until 6 days after infection ( Figure 2B ) . Therefore , B . infantis attenuation of NF-κB activation in vivo precedes a reduction in S . typhimurium disease severity and systemic translocation . In agreement with the results above suggesting a systemic anti-inflammatory effect of B . infantis consumption on S . typhimurium-induced inflammatory damage , in vivo imaging of the pro-inflammatory transcription factor NF-κB in response to LPS alone demonstrated significantly less activity in mice-fed B . infantis four hours after LPS injection ( Figure 3A ) . In order to identify the sites at which NF-κB activity was being suppressed , we removed the small intestine , large intestine , liver and spleen from each animal and quantified NF-kB activity for each organ individually ( Figure 3B ) . NF-kB activation within the small intestine , liver and spleen ( but not the colon ) was significantly reduced in B . infantis-fed animals ( Figure 3C ) . In order to further characterise the impact of NF-κB suppression on the host inflammatory response , cytokine production by isolated Peyer's patch and spleen-derived cells was assessed , in vitro , following anti-CD3/CD28 stimulation ( lymphocyte response ) or LPS stimulation ( innate TLR-4 response ) immediately prior to S . typhimurium infection ( Day 0 ) or four days ( Day 4 ) after infection . CD3/CD28 stimulation of Peyer's patch cells resulted in secretion of IFN-γ , TNF-α and IL-10 with no differences noted between placebo-fed controls or B . infantis-fed test animals prior to Salmonella infection ( Figure 4A ) . However , four days of S . typhimurium infection was associated with enhanced release of IFN-γ , TNF-α and IL-10 by anti-CD3/CD28 stimulated Peyer's patch cells in placebo-fed animals but not in animals consuming B . infantis . In vitro stimulation of Peyer's patch cells with LPS yielded cytokine responses below the assay detection limit and were not examined further ( results not shown ) . CD3/CD28 stimulation of splenocytes resulted in secretion of IFN-γ , TNF-α and IL-10 with low levels of IL-6 and MCP-1 ( Figure 4B ) . In agreement with the Peyer's patch results , no differences in the cytokine secretion patterns were noted for in vitro stimulated splenocytes from placebo or B . infantis treated mice prior to infection . However , the anti-CD3/CD28 stimulated IFN-γ and IL-10 response was significantly attenuated following Salmonella infection in the B . infantis-fed animals . LPS stimulated splenocytes released comparable amounts of cytokine from the two groups of animals prior to infection but four days following Salmonella translocation , LPS stimulated TNF-α , IL-6 and MCP-1 release was significantly less in the B . infantis-fed animals ( Figure 4C ) . Presentation of antigen by dendritic cells to naïve T cells requires the expression of co-stimulatory molecules such as CD80 for effective maturation and clonal expansion of the interacting T cells . We examined the percentage of mature dendritic cells ( CD11c+MHCII+ ) expressing CD80 in both the Peyer's patch and spleen of placebo and B . infantis-fed animals . Surprisingly , fewer Peyer's patch dendritic cells co-express CD80 when isolated from B . infantis-fed animals ( Figure 5A ) . Dendritic cell CD80 expression increases in the Peyer's patches of all Salmonella infected animals but remains significantly less in the B . infantis-fed mice . Splenocyte dendritic cell CD80 expression is similar in both groups of un-infected mice but is significantly up-regulated with Salmonella infection only in the placebo group ( Figure 5B ) . The percentage of CD4+ T cells expressing the α chain of the IL-2 receptor , CD25 , was examined in both Peyer's patch and spleen of un-infected and Salmonella infected animals . We noted a significant increase in the percentage of CD4+ cells co-expressing CD25 in the Peyer's patch of animals fed B . infantis , particularly following Salmonella infection ( Figure 6A ) . A statistical significant inverse correlation was noted between the percentage of CD80+ activated dendritic cells and CD4+CD25+ T cells within the Peyer's patch suggesting a link between dendritic cell co-stimulatory molecule expression and T regulatory cell induction ( r2 = 0 . 419 , p = 0 . 01 ) . This does not prove a causal relationship but simply implies that these cellular events may both be associated with bifidobacterial consumption . There are also significantly more CD4+CD25+ cells in the spleen of animals fed B . infantis ( Figure 6B ) . The majority ( >75% ) of the spleen-derived CD4+CD25+ T cells also stained positive for the Treg transcription factor Foxp3 ( Figure 6C & 6D ) . Finally , when these T cells were isolated and examined in vitro , the CD4+CD25+ T cells suppressed proliferation of naïve CFSE labelled CD4 cells while depletion of the CD25+ subset cells removed the suppressive effect ( Figure 6E ) . In order to examine the hypothesis that NF-κB suppression is mediated by bifidobacterial induction of Tregs , we isolated CD4+ T cells from placebo or B . infantis-fed animals and adoptively transferred these to naïve animals . Following LPS injection , NF-κB activation was significantly less in animals that received CD4+ T cells from B . infantis-fed mice compared to animals that received CD4+ T cells from placebo-fed mice ( Figure 7A & 7B ) . Depletion of the CD25+ subset from the T cell population removed the suppressive effect while adoptive transfer of CD25+ cells alone was sufficient to reduce NF-κB activation in situ and release of TNF-α from cultured splenocytes ( Figure 7C & 7D ) .
This report illustrates at a cellular and molecular level the impact of the commensal microbiota on host immune defence and immune homeostasis . The deliberate consumption of one commensal organism , Bifidobacterium infantis 35624 , resulted in the induction of Treg cells which protected the host from excessive inflammation during the course of infection as evidenced by reduced pro-inflammatory cytokine production , reduced T cell proliferation , reduced dendritic cell co-stimulatory molecule expression and attenuation of NF-κB activation . The role of Treg cells in this biological process was conclusively demonstrated by the adoptive transfer of CD4+CD25+ T cells into naïve mice which was sufficient to suppress NF-κB activation in response to LPS injection . Treg cells have been well described as suppressors of auto-reactive T cells [14] , [15] and suppress inflammatory disease in a wide range of murine models including experimental autoimmune encephalomyelitis [16] , inflammatory bowel disease [17] , bacterial-induced colitis [18] , collagen-induced arthritis [19] , type I diabetes [20] , airway eosinophilic inflammation [21] , graft-vs-host disease [22] and organ transplantation [23] . Similarly , supplementation of the microbiota with certain commensal micro-organisms has also shown anti-inflammatory effects in a number of these models [24]–[27] . It is tempting to speculate that induction of Treg cells by the commensal microbiota could be partly responsible for the observed anti-inflammatory activity in these model systems . However , the role of commensal bacteria in controlling the pro-inflammatory response associated with infection is less clear . Indeed , the activity of Treg cells in the models above is primarily mediated by suppression of antigen specific immune responses while our studies demonstrate an effect on innate immune activation , in particular activation through the TLR-4 pathway . It has been recently suggested that CD4+CD25+ T cells induce an alternative activation pathway in monocytes associated with a diminished capacity to respond to LPS which supports the findings of this study [28] , [29] . The host cellular mechanisms underpinning induction of Treg cells by the commensal microbiota was not examined in this study but is hypothesised to be mediated in part by dendritic cells . Following encounter with pathogenic or commensal microbes , dendritic cells provide instructive signals that influence T cell differentiation into Th1 , Th2 or regulatory phenotypes [30] , [31] . Multiple reports implicate dendritic cells in the induction of tolerance and regulatory cells to the commensal microbiota [32] . In vitro co-incubation of human mesenteric lymph node derived dendritic cells with B . infantis resulted in the secretion of IL-10 and TGF-β , but not TNF-α or IL-12 [33] . Both IL-10 and TGF-β are important cytokines in directing naïve T cell maturation down a regulatory pathway . In addition , in vitro co-incubation of monocyte derived dendritic cells with certain commensal lactobacilli strains have been shown to drive the in vitro generation of IL-10 producing T cells which inhibit bystander T cell proliferation [34] . Not all commensal micro-organisms may be equally effective in driving T regulatory cell activity . Co-incubation of a panel of commensal bacteria with peripheral blood mononuclear cells ( PBMCs ) in vitro resulted in varying amounts of IL-10 production suggesting that strain specific characteristics , as yet largely undefined , drive regulatory cytokine production [35] . Commensal-induced protection from infection may also involve additional strain-specific mechanisms of action including the production of selective anti-microbial compounds [2] . The relative importance of microbe-microbe and microbe-host interactions in protecting the host from infection is likely to also depend on the nature of the infectious organism . However , the mechanism underpinning the suppressed pro-inflammatory response to a translocating microbe described in this study strongly implicate Tregs as modulation of the host pro-inflammatory response precedes the difference in systemic salmonella recovery . In addition , LPS injection models systemic infection with a gram negative pathogen and suppression of the host response to LPS alone can not be explained by other mechanisms such as competition within the intestine or activation of pathogen-specific effector T cells . This report clearly demonstrates that activation of the host innate pro-inflammatory pathways to a translocating infectious agent , or systemic LPS , can be influenced by the commensal microbiota via the induction of Treg cells . NF-κB is a key transcription factor that is central to the observed anti-inflammatory effect and improved regulation of NF-κB is an important therapeutic target in a wide range of pro-inflammatory states , including sepsis [36] . This report supports the clinical evaluation of appropriately selected probiotic/commensal micro-organisms for the promotion of CD4+CD25+Foxp3+ T cells in vivo in order to control the innate inflammatory cascade to translocating microbes .
Balb/c mice were obtained from Charles River Laboratories ( Bicester , UK ) and bred in-house for bifidobacterial feeding and salmonella infection studies . NF-kBlux transgenic mice on a C57BL/6J-CBA/J background were obtained from Charles River Laboratories ( Wilmington , USA ) and bred in-house . Mice were housed under barrier maintained conditions within the biological services unit , University College Cork ( UCC ) . All animal experiments were approved by the UCC animal ethics committee and experimental procedures were conducted under appropriate license from the Irish government . Bifidobacterium infantis 35624 ( B . infantis ) was isolated from healthy human gastrointestinal tissue and its use as a probiotic organism has been previously reported [37] , [38] . B . infantis was routinely cultured anaerobically for 48 hours in deMann , Rogosa and Sharpe medium , MRS , ( Oxoid , Basingstoke , UK ) supplemented with 0 . 05% cysteine ( Sigma , Dublin , Ireland ) . Salmonella typhimurium UK1 ( S . typhimurium ) was provided by Roy Curtis ( Washington University , US ) and was routinely cultured aerobically at 37°C for 24 hours in tryptic soya broth ( Oxoid ) . B . infantis was administered to all animals as a freeze-dried powder reconstituted in water at approximately 1×109 colony forming units/day/animal . Mice consumed the commensal micro-organism in their drinking water ad libitum for at least 3 weeks prior to salmonella infection . Challenge with S . typhimurium was performed as described by Wilems-Riesenberg et al [39] , [40] . Briefly , S . typhimurium was grown overnight , pelleted and resuspended in buffered saline gelatin ( 0 . 85% NaCl , 0 . 01% gelatin and 2 . 2 mM KH2PO4 ) . Inoculation with Salmonella was performed by injecting 20 µl of a single inoculum of the Salmonella suspension behind the murine incisors with a micropipette tip in a flexible film isolator . A dose of 1×106 S . typhimurium viable cells was chosen as the optimal dose for these studies as higher doses did not result in increased recovery of viable cells from the murine liver and spleen while lower doses did not yield reproducible or consistent bacterial counts from systemic sites [41] . Animals were monitored for disease progression using a clinical scoring scale . This scale scored animals from 0 ( no disease ) to 7 ( severe disease ) and is illustrated in Table 1 . Salmonella titres were calculated in liver and spleen using quantitative real-time Polymerase Chain Reaction ( qPCR ) . Briefly , liver and spleen samples were transferred to sterile stomacher bags and mixed with 1X PBS ( Gibco BRL , UK ) and homogenised using mechanical means . Samples were stored at-20°C until analysis . DNA extractions were performed using the Qiagen DNAeasy Tissue Kit ( Qiagen , West Sussex , UK ) according to manufacturers instructions . Isolated DNA concentrations were ascertained using the Nanodrop ( Thermo Scientific ) and standardised to 20 ng DNA per reaction . LightCycler Real-time PCR was used to quantify Salmonella genomic DNA concentrations using the following primers INVA2_R: 5′-TGT CCT CCG CTC TGT CTA CTT -3′ INVA2_L: 5′-ATC AAC AAT GCG GGG ATC T -3′ ( 76 bp product ) , and Probe library #9 ( Roche Diagnostics ) . All primers were synthesized by MWG Biotech , ( Ebersberg Germany ) . PCR amplifications were carried out in glass capillaries ( Roche Diagnostics ) in a total final volume of 20 µl using a LightCycler ( Roche Diagnostics ) . A standard curve was generated by adding DNA from 10 fold dilution series of purified Salmonella genomic DNA . This was used to calculate Salmonella concentration . The LightCycler software identified the Ct ( threshold cycle number ) values and the concentrations of Salmonella were calculated by comparing Ct values to the crossing point values of the linear regression line of the standard curve . In vivo imaging of NF-kBlux transgenic mice were performed by firstly anaesthetizing the mice with isoflurane ( Inhalation Anaesthetic , Abbot Laboratories Ltd . , Kent , UK ) . D-luciferin ( 120 mg/kg; Biothema AB , Handen , Sweden ) dissolved in 200 ul PBS , pH 7 . 8 , was injected i . p . Immediately afterwards the mice were placed in a ventral recumbent position in a light-sealed chamber in the In Vivo Imaging System ( IVIS ) chamber ( Xenogen , Alameda , USA ) and imaged continuously for 5 minutes with a medium sensitivity setting starting 2 minutes after the injection of D-luciferin . A reference black and white image of the animal was taken in low light conditions then a sensitive cooled charge-coupled device camera collected the photons emitted . Photons were quantified using Living Image software ( Xenogen ) and the luciferase activity quantified as the amount of light emitted per second per cm2 from the animal/organs . The pseudo-colored images represent light intensity ( red is the strongest and violet is the weakest ) . Individual organs to be imaged were excised from the mice 5 minutes following D-luciferin administration . Organs were placed in a culture dish and immediately imaged , again with an acquisition integration time of 5 minutes . Organs examined were the spleen , liver , small intestine and colon . Spleens were aseptically removed from all animals and a single cell suspension generated using mechanical means . In addition , the small intestine of mice were removed and the lymphoid follicles of the Peyer's patches were carefully dissected from the intestinal serosal side with curved scissors and collected into 5 ml of PBS containing 1 mM EDTA and collagenase ( Sigma ) . Following incubation in a shaking oven at 37°C for 20 mins , the collected patches were placed between two sterile glass slides and crushed . This cell suspension was centrifuged ( 100 g×10 min ) and the pellet was resuspended and diluted in Dulbecco's modified eagle medium ( DMEM ) . Single cell suspensions were seeded , in duplicate , in 24 well tissue culture plates ( Sarstedt , Newton , USA ) at 1×106 cells per well . Single cell suspensions were stimulated for 72 hours with anti-CD3 and anti-CD28 antibodies ( BD Biosciences , Oxford , UK ) , LPS ( Sigma ) or remained non-stimulated to assess background cytokine secretion . Following a 72-hour incubation period ( @37°C and 5% CO2 humidified atmosphere ) all supernatants were harvested for cytokine analysis . These were aliquoted and stored at −70°C for analysis of cytokine production in batches . IL-6 , IL-10 , IL-12 , MCP-1 , TNF-α and IFN-γ levels were quantified using cytometric bead arrays ( BD ) . Cytokine levels were measured using a BD FacsCaliber flow cytometer and analysis was carried out using the BD CellQuest software and BD CBA Software . CD4 T cell proliferation was measured using the CellTrace™ CFSE Cell Proliferation Kit ( Invitrogen ) . Briefly , CD4+ T Cells were positively selected ( >98% purity ) from spleens using CD4 MicroBeads ( Miltenyi Biotech , Bergisch Gladbach , Germany ) and an autoMACS separator . These cells were labeled with CFSE and incubated in vitro for three days with anti-CD3 and anti-CD28 stimulation ( BD ) . Concurrently , CD4+ T cells were isolated from B . infantis-fed mice as outlined above and subdivided into CD4+CD25+ and CD4+CD25− populations using microbeads ( Miltenyi ) . These cells were not CFSE labeled and were co-incubated with the labeled cells for the three day stimulation period . Lymphocyte proliferation was measured by flow cytometry . Single cell suspensions from the Peyer's patch and spleens of mice were generated as outlined above . Monoclonal antibodies to CD3 , CD4 , CD11c , CD25 , CD80 and MHC II ( BD ) were used to label cells for T cell subset analysis ( CD3 , CD4 and CD25 ) and dendritic cell co-stimulatory molecule expression ( CD11c , MHC II and CD80 ) . Antibodies to the transcription factor Foxp3 ( eBioscience , San Diego , USA ) were used to label permeabilised cells in representative experiments . Cellular phenotypes were measured using a BD FacsCaliber flow cytometer and analysis was carried out using the BD CellQuest software . In vivo assessment of NF-κB activation was determined following feeding of wildtype ( NFkB−/− ) mice with either B . infantis or placebo for three weeks . The spleens were removed and CD4+ T Cells were positively selected ( >98% purity ) using the method outlined above . Adoptive transfer of 1×106 CD4+ T cells/ml into NF-kBlux+/+ transgenic mice ( n = 6/grp ) was performed by i . p . injection . 8 days later 3 mg/kg LPS ( Sigma ) was injected i . p . and NF-κB measurements taken after 4 hours . In vivo images were captured by the IVIS in the same manner as described above . In order to determine the CD4+ T cell subset responsible for mediating this effect , CD4 T cells from B . infantis-fed animals were further isolated into CD4+/CD25+ and CD4+/CD25− subpopulations as per manufactures protocol ( Miltenyi Biotech , Bergisch Gladbach , Germany ) . Adoptive transfer of 1 . 5×105 CD4+/CD25+ T cells/ml or 8 . 5×105 CD4+/CD25− T cells/ml into naïve mice ( n = 5/grp ) was performed by i . p . injection . 8 days later 3 mg/kg LPS ( Sigma ) was injected i . p . and animals were sacrificed after 4 hours and tissues harvested . Intestinal NF-κB p65 activation was measured in nuclear extracts using an NF-κB p65 ELISA-based transcription factor assay kit ( TransAM Assay , Active Motif , Germany ) according to the manufacturer's protocol . TNF-α release from LPS stimulated splenocytes was measured using flow cytometry as outlined above . GraphPad Prism software utilising 2Way-ANOVA with Bonferroni's Post-test was used to determine statistical significance . LightCycler data was analyzed using Pearson's correlation coefficient and students T-tests were used to determine differences between groups .
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The normal response to infection is rapid and effective clearance of pathogenic microbes . However , this immune response may occasionally cause collateral inflammatory damage to host tissue and in severe cases , such as systemic sepsis , results in organ failure . Various cellular mechanisms , including regulatory T cells , protect against aggressive immune responses . However , environmental agents which promote regulatory T cells are not well understood . We and others have previously shown that non-pathogenic or commensal micro-organisms can protect the host from aberrant pro-inflammatory activity within the gut , but the influence of these microbes on regulatory T cells in the context of systemic infection has not been examined . In this study , we demonstrate that consumption of a single commensal bacterium induces regulatory T cells in vivo which protect the host from pathogen-induced inflammatory responses by limiting activation of the pro-inflammatory transcription factor NF-κB via the toll-like receptor 4 ( TLR-4 ) pathway . This report conclusively demonstrates a cellular and molecular basis for the commensal-host-pathogen trilogue resulting in enhanced protection from systemic infection whilst limiting pro-inflammatory damage mediated by activation of the innate immune system .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"cell",
"biology/leukocyte",
"signaling",
"and",
"gene",
"expression",
"microbiology/immunity",
"to",
"infections",
"immunology/immunomodulation",
"immunology/innate",
"immunity",
"infectious",
"diseases/bacterial",
"infections",
"infectious",
"diseases/gastrointestinal",
"infections",
"immunology/leukocyte",
"activation"
] |
2008
|
Commensal-Induced Regulatory T Cells Mediate Protection against Pathogen-Stimulated NF-κB Activation
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Severe fever with thrombocytopenia syndrome ( SFTS ) is an emerging viral hemorrhagic fever with a high fatality rate and high frequency of person-to-person transmission and is caused by SFTSV , a tick-borne Phlebovirus . Because SFTS has similar clinical manifestations and epidemic characters ( such as spatial and temporal distributions ) with hemorrhagic fever with renal syndrome ( HFRS ) in China , we reason that SFTS patients might be misdiagnosed as HFRS . Acute-phase sera of 128 clinically diagnosed HFRS patients were retrospectively analyzed for Hantavirus IgM antibodies with ELISA . Hantavirus-negative patients’ sera were further analyzed for SFTSV IgM antibodies with ELISA . ELISA showed that 73 of 128 ( 57 . 0% ) of clinically diagnosed HFRS patients were IgM antibody positive to Hantaviruses . Among the 55 Hantavirus-IgM negative patients , four ( 7 . 3% ) were IgM antibody positive to SFTSV . The results indicated that the four SFTS patients were misdiagnosed as HFRS . The misdiagnosed SFTS patients had clinical manifestations common to HFRS and were unable to be differentiated from HFRS clinically . Our study showed that SFTS patients could be clinically misdiagnosed as HFRS . The misdiagnosis of SFTS as HFRS causes particular concern because it may increase the risk of death of SFTS patients and person-to-person transmission of SFTSV without proper care for and isolation of SFTS patients .
Severe fever with thrombocytopenia syndrome ( SFTS ) is an emerging hemorrhagic fever and its pathogen SFTS virus ( SFTSV ) was first discovered in China in 2010 [1] . SFTSV is transmitted by tick bite and frequently spread from person-to-person [2–4]SFTSV is a negative-strand RNA virus belonging to the Phlebovirus genus of the family Bunyaviridae [1] . SFTS is a viral hemorrhagic fever with higher frequency of hemorrhagic signs than Ebola virus disease or dengue [5 , 6] . SFTS caused attention and fear because of high case fatality and its potential to cause family cluster infection in China . The case fatality rate of SFTS is very high , ranging from 12 to 30% in China and 22 to 30% in South Korea and Japan [1 , 5 , 6] . SFTS was first recognized as a family cluster of infection in central China and family clusters SFTS has been reported in China and South Korea even after discovery of SFTSV [2 , 3 , 7–16] . Since discovery of SFTSV in 2010 , more than 2000 confirmed SFTS patients were reported annually in China [17] . Hemorrhagic fever with renal syndrome ( HFRS ) is caused by Hantavirus , which are RNA viruses belonging to the genus Hantavirus of the family Bunyaviridae . Hantaviruses are carried and transmitted by rodents . Humans acquire infection after direct contact with infected rodents or their excreta , which most likely occurs by inhaling virus-contaminated aerosols [18] . HFRS occurs worldwide with about 90% of HFRS cases having been reported in China [19] . In the past decades , more than 10 , 000 of HFRS cases were reported annually , and the fatality rate was about 1% in China [20] . HFRS has a high incidence rate ( average 1 . 96 cases/100 , 000 persons ) in Zibo City , Shandong Province [21 , 22] . We have also demonstrated SFTSV infection is present in patients and in domesticated and wild animals in Zibo City , Shandong Province [23 , 24] . These studies indicated Zibo City was hard hit by Hantavirus and SFTSV . Because SFTS and HFRS have similar clinical manifestations and epidemic features , we reasoned that SFTS might be clinically misdiagnosed as HFRS . The aim of this study is to determine whether SFTS was clinically misdiagnosed as HFRS by testing IgM antibodies against Hantavirus and SFTSV in acute sera of those clinically diagnosed HFRS patients .
The study was reviewed and approved by the ethics committees of Wuhan University and Zibo Center for Disease Control and Prevention . Acute serum samples of clinically diagnosed HFRS patients from 2013 to 2014 were retrospectively obtained from a local center for disease control and prevention in Zibo City , Shandong Province , China . Zibo City is a prefecture-level city which consists of 6 districts and 3 counties distributed over 5 , 938 km2 of land . The total population during the 2010 census was 4 . 53 million , of whom 900 , 000 persons were farmers . The city is about 42% mid-sized mountains , 30% hills , and 28% plains . Acute serum samples of clinically diagnosed HFRS patients were collected through physicians in hospitals which were under the jurisdiction of Zibo City . When a patient was diagnosed as HFRS according to the case definition , serum sample would be collected at once . Epidemiological and clinical data was also obtained including demographic information ( age , sex , and occupation ) , clinical characteristics ( time of disease onset and clinical features ) and epidemiological exposure to rodents and ticks . Clinical cases included in this study were diagnosed according to the criteria of the Chinese Center for Disease Control and Prevention for HFRS surveillance program ( http://www . chinacdc . cn/jkzt/crb/lxxcxr/cxrjc/200508/t20050810_24189 . htm ) . In the diagnosis criteria , a suspected HFRS patient meets the following conditions: initial symptoms such as a sudden onset of fever ( >38 °C ) and body ache plus orbital problems ( such as orbital pain ) or skin flushing ( redness of the face , neck or upper chest ) or bleeding disorders ( such as conjunctival injection , petechial , axillary hemorrhage ) . A suspected case with symptoms plus abnormal laboratory tests ( such as thrombocytopenia and proteinuria ) or clinical phases ( such as hypotensive , oliguric and polyuric phases ) is defined as a clinically diagnosed case . A confirmed HFRS case is a clinically diagnosed patient plus one of the following conditions: serum IgM antibody positive to Hantavirus , IgG antibody seroconversion or 4-fold increases , or RT-PCR positive to Hantavirus RNA . IgM antibody test was used in this study to have a confirmed diagnosis . The acute-phase of patients’ sera was tested for anti-hantavirus IgM and anti-SFTSV IgM antibodies with ELISA . Anti-hantavirus IgM was detected by using a HFRS-IgM ELISA Kit ( Wantai Biological Pharmacy , Beijing , China ) which coated with anti-Human IgM ( μ-chain specific ) antibodies to capture IgM antibodies in the serum and detected with HRP-conjugated Hantavirus antigen . The hantavirus-negative patients’ sera were further analyzed for anti-SFTSV IgM antibodies by using a SFTSV-IgM ELISA Kit ( Xinlianxin Biotech CO . , LTD , Wuxi , Jiangsu , China ) according to the manufacturer’s instructions . Briefly , 96-well plates were coated with anti-Human IgM antibodies and undiluted human serum was used for detection of serum IgM , with HRP-conjugated recombinant SFTSV-proteins antigens used to visualize the results . The optical density ( OD ) value of each ELISA was read using an ELISA reader at 450 nm . The proportions were used for descriptive statistics of characteristics and clinical features of patients and the rates for the detection of HFRS and SFTS . Statistical analysis was performed with R software , and pie charts and histograms were plotted by the R Graphics package .
The acute sera of 128 clinically diagnosed HFRS patients were first tested by ELISA for Hantavirus IgM antibodies . ELISA results showed that 57 . 0% ( 73/128 ) of patients were seropositive to hantavirus; that among the 55 sera which were IgM negative to hantavirus IgM antibodies , 4 ( 7 . 3% ) were positive to SFTSV IgM antibodies . The average days of HFRS between diseases onset and sampling was 10 . 2 days , ranged from 4 to 20 days; Four SFTS cases were 7 , 7 , 10 and 12 days , respectively . Among the confirmed HFRS patients 71 . 2% ( 52/73 ) were men , 72 . 6% ( 53/73 ) were farmers ( The percentages of men and farmers in general population in Zibo City were 50 . 1% and 19 . 7% , respectively ) ; 60 . 3% ( 44/74 ) were between 40 and 60 years of age . Temporal distribution by month of disease onset was calculated and no seasonal pattern was observed for the confirmed HFRS patients ( Fig 1 ) and the four SFTS patients occurred in May and June three of them were male and one was female . They all were farmers aged between 47 and 69 years . At the time of enrollment , all patients were interviewed about their previous exposure history to rodents . Among the confirmed HFRS patients , 24 . 7% ( 18/73 ) reported a history of contact with rodents or their excreta in the last two months before disease onset . Compared with the low percentage of direct contact , a total of 65 . 8% ( 48/73 ) patients had observed rodents or rodent’s excreta within the patients’ home or workplace . The data indicated that HFRS patients lived in an environment with high exposure potential to rodents and their excreta . Among all clinical diagnosed 128 HFRS patients , only 3 patients reported a history of tick bites and none of the four confirmed SFTS patients had histories of tick bites . Considering SFTS patients found in clinically diagnosed HFRS cases , we compared the characteristics of ELISA-confirmed HFRS and SFTS cases ( Table 1 ) . The clinical manifestation and laboratory test results of the 4 misdiagnosed SFTS patients were hard to differentiate from the confirmed HFRS patients ( Table 1 ) . Both diseases had fever , fatigue , and headache and the most common laboratory test result of the two diseases were thrombocytopenia and proteinuria . Skin flushing ( including facial flushing , reddening of neck and upper chest ) , oliguria , arthralgia , periorbital pain and edema , and hypotension appeared in a relatively low rate in HFRS ( 15–49% ) , but not in SFTS . Less than 10% of HFRS patients had icterus , and constipation , which were also not observed in SFTS patients ( Table 1 ) .
In this study we showed that SFTS was clinically misdiagnosed as HFRS in Shandong Province , China . Both SFTS and HFRS had a broad set of similar clinical manifestations . Both diseases have a sudden onset of fever , headaches , myalgia , hemorrhagic manifestations , gastro-intestinal problems , thrombocytopenia , proteinuria , and hematuria [1 , 25] , which make clinical differentiation between the two diseases difficult . Previous studies indicated that SFTS patients did not have back pain and leukocytosis [26 , 27] . However , our study showed that SFTS patients could have back pain and leukocytosis . Knowledge of salient features associated with HFRS may help health care providers in diagnosis . The sample size of SFTS was 4 and we did not expect the 4 cases would be statistically significant to compare the differences between HFRS and SFTS . To better differentiate SFTS from HFRS , we further estimated pooled proportions of the clinical manifestations and laboratory findings of SFTS and HFRS from previous studies by Meta-analyses as supporting information [1 , 5 , 23 , 24] ( S1 Table ) . The Meta-analyses included 3200 cases of HFRS and 5046 cases of SFTS respectively which could make up for the small size of comparison . We found that SFTS are hardly to be differentiated from HFRS because they had common clinical manifestations and laboratory tests; they had no clear exposure history; and they had overlapped spatial and temporal distribution in China . SFTS mainly occurred in the summer season , while HFRS occurred year-round with two peaks in the spring and fall [17 , 21 , 24] . Only the following symptoms and sings may be used to differentiate SFTS and HFRS clinically , but unfortunately these symptoms and signs may not always present in SFTS or HFRS patients , respectively . Skin flushing ( facial flushing , neck flushing , and chest flushing ) , oliguria , periorbital pain and edema , and hypotension may present in HFRS patients , but not in SFTS patients , while swollen lymph-nodes and expectoration may present in SFTS patients , but not in HFRS patients ( S1 Table ) [1 , 27] . Haemaphysalis longicornis ticks have been demonstrated could serve as a vector and reservoir of SFTSV [4] . SFTSV spill over to human by tick bite . Hantaviruses which can cause HFRS are carried and transmitted by rodents , for example Apodemus agrarius and Rattus norvegicus in China . Humans acquire infection after direct contact with infected rodents or their excreta , which most likely occurs by inhaling virus-contaminated aerosols [18] . Patients for both SFTS and HFRS are farmers that reside in hilly areas infested with rodents and ticks . Our study showed that only a quarter of HFRS cases reported they had direct contact with rodents or their excreta . A previous study showed also only a quarter of SFTS cases reported a history of tick-bites [28] . Majority of HFRS and SFTS cases had no clear exposure history reported , and as a result , physicians could not rely heavily on the exposure history to differentiate the two diseases . Although most cases had no clear report of direct contact , 65 . 8% of patients had observed rodents or their excreta within their home or workplace , and 72 . 6% of cases were farmers , which indicated these patients could have significant chances to be exposed to rodents in their living environment . It has been shown that Hantavirus could be transmissible by inhalation of respirable droplets of saliva or urine . It would be hard to report the history of exposure when infection was caused by inhalation of pathogens . These factors would make exposure information insufficient for clinical differential diagnosis . In this study we demonstrated that SFTS is difficult to differentiate from HFRS . Both SFTS and HFRS patients are treated based on the clinical diagnosis . Laboratory confirmation of both diseases was not performed in clinical hospitals and the patients’ blood was usually submitted to a local or provincial center for disease control and prevention . In most cases the confirmation diagnosis is to provide retrospective information rather than to guide clinical management . Therefore , physicians need to carefully differentiate SFTS and HFRS patients because the fatality of SFTS is much higher than HFRS and because SFTS is easily spread from person to person through contact with infected blood or even through aerosol [2 , 3 , 29] . IgM tests have proven useful and are commonly performed in diagnosis of acute infection [30] . Detection of IgM antibodies is a standard method for diagnosis of HFRS and SFTS in China as recommend by the Ministry of Health of China ( http://www . chinacdc . cn/jkzt/crb/lxxcxr/cxrjc/200508/t20050810_24189 . htm . ) . The sensitivity of HFRS-IgM kit was 98 . 19% and specificity was 99 . 28% according to the kit brochure . The specificity and sensitivity of SFTSV-IgM ELISA Kit were similar to those of the microneutralization assay and anti-SFTSV IgM exhibited no cross-reactivity with these antibodies to other closely related viruses such as Hantavirus [8] . In this cited article , SFTSV-IgM positive serum samples were diluted in 2-fold increments for detection of IgM antibodies with the same kit . Antibodies with titer as low as 1:32 still could be detected by this kit which indicated it performed well . The aim of the study was to determine whether SFTS was clinically misdiagnosed as HFRS . So , the specificity of SFTSV-IgM ELISA Kit was important to avoid false-positive results . We have tested 90 negative sera stored in our lab collected from healthy persons to evaluate the specificity of SFTSV-IgM ELISA Kit . None of them was positive which indicated the specificity could be as high as to 100% . We used ELISA to detect IgM antibodies to confirm hantavirus and SFTSV infection rather than viral RNA detection in this study because the sera have been frozen and thawed several times , which may diminish or destroy the viral RNA and we do not have sufficient quantities of samples to do both ELISA and nucleic acid detection . Our study was a retrospective analysis . It has been a few years since these samples were collected from patients between 2013 and 2014 . There was not any sequential sampling of the patients , and not obtained any other sequentially information during the hospitalization . In addition , serological diagnosis might include IgG-conversion detection . However , we do not have convalescent sera to confirm whether the remaining Hantavirus- and SFTSV-IgM negative patients were seroconverted to hantaviruses or SFTSV . Though some HFRS patients might not be confirmed , it was a reality that SFTS had been found in patients managed and treated as HFRS . In conclusion , our study showed that SFTS patients could be clinically misdiagnosed as HFRS . Both SFTS and HFRS have similar clinical manifestations , but the following manifestations including skin flushing ( facial flushing , neck flushing , and chest flushing ) , oliguria , periorbital pain , periorbital edema , hypotesion , and/or icterus only presented in HFRS and swollen lymph nodes and expectoration are mainly present in SFTS . The misdiagnosis of SFTS as HFRS causes particular concern because it may increase the risk of death of SFTS patients and person to person transmission of SFTSV without proper care for SFTS patients . To avoid misdiagnosis , rapidly and reliable diagnostic approaches like ELISA should be widely applied in the clinical settings .
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SFTS were clinically misdiagnosed as HFRS . It could cause particular concern in China . Physicians could not rely heavily on the exposure history . Both SFTS and HFRS patients are treated based on the clinical diagnosis in China . Laboratory confirmation of both diseases is not performed in clinical hospitals and the patients’ blood was usually submitted to a local or provincial center for disease control and prevention . In most cases the confirmation diagnosis is to provide retrospective information rather than to guide clinical therapy . Therefore , physicians need to carefully differentiate SFTS and HFRS patients because the fatality of SFTS is much higher than HFRS and SFTS is easily spread from person to person by contacting infected blood or even through aerosol .
|
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2019
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Severe fever with thrombocytopenia syndrome can masquerade as hemorrhagic fever with renal syndrome
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Investigations of human parasitic diseases depend on the availability of appropriate in vivo animal models and ex vivo experimental systems , and are particularly difficult for pathogens whose exclusive natural hosts are humans , such as Entamoeba histolytica , the protozoan parasite responsible for amoebiasis . This common infectious human disease affects the intestine and liver . In the liver sinusoids E . histolytica crosses the endothelium and penetrates into the parenchyma , with the concomitant initiation of inflammatory foci and subsequent abscess formation . Studying factors responsible for human liver infection is hampered by the complexity of the hepatic environment and by the restrictions inherent to the use of human samples . Therefore , we built a human 3D-liver in vitro model composed of cultured liver sinusoidal endothelial cells and hepatocytes in a 3D collagen-I matrix sandwich . We determined the presence of important hepatic markers and demonstrated that the cell layers function as a biological barrier . E . histolytica invasion was assessed using wild-type strains and amoebae with altered virulence or different adhesive properties . We showed for the first time the dependence of endothelium crossing upon amoebic Gal/GalNAc lectin . The 3D-liver model enabled the molecular analysis of human cell responses , suggesting for the first time a crucial role of human galectins in parasite adhesion to the endothelial cells , which was confirmed by siRNA knockdown of galectin-1 . Levels of several pro-inflammatory cytokines , including galectin-1 and -3 , were highly increased upon contact of E . histolytica with the 3D-liver model . The presence of galectin-1 and -3 in the extracellular medium stimulated pro-inflammatory cytokine release , suggesting a further role for human galectins in the onset of the hepatic inflammatory response . These new findings are relevant for a better understanding of human liver infection by E . histolytica .
The protozoan parasite Entamoeba histolytica is the etiological agent of human amoebiasis . The parasite has a simple life cycle alternating the contaminating cyst and the vegetative trophozoite form . Infection occurs upon uptake of cysts with contaminated water or food and their differentiation into trophozoites , which colonize the intestine . Amoebae may breach the intestinal barrier and disseminate through the portal vein system , mainly to the liver ( approximately 1% of the carriers ) . Hepatic amoebiasis is characterized by the induction of an inflammatory host response , the invasion of the liver parenchyma and the subsequent formation of abscesses , and leads to 70 , 000 deaths per year [1] . To study hepatic amoebiasis experimentally , we previously used hamsters ( Mesocricetus auratus ) since these are highly susceptible to E . histolytica infection and develop amoebic liver abscesses in a few days [2] . With this animal model we have shown that invasion of the liver parenchyma depends on amoebic adhesion to host cells through the activity of the galactose- or N-acetyl-galactosamine-inhibitable ( Gal/GalNAc ) lectin , the main amoebic adhesion [3] . For instance , trophozoites deficient in signalling through the Gal/GalNAc lectin ( HGL-2 strain ) form small foci close to the endothelium which do not progress to liver abscesses [4] . We also observed endothelial cell apoptosis in the vicinity of wild-type trophozoites as shortly as 1 h after infection , whereas with HGL-2 trophozoites cell death was delayed by almost 24 h [5] . However , the step in which HGL-2 trophozoites are blocked is currently not known . Liver sinusoidal endothelial cells ( LSEC ) and hepatocytes are the major cellular components of the liver , accounting for around 80% of the liver mass [6] , and LSEC constitute the first hepatic barrier during liver invasion . Little is known about the transmigration of E . histolytica through the hepatic endothelium , and the molecules required for interaction with LSEC . We have previously shown that cells of a human LSEC line respond in a localized manner to the presence of E . histolytica with changes in the integrin-mediated adhesion signalling , retraction , loss of their cytoskeleton organization and focal adhesion complexes [7] . These alterations may be relevant for the disease , by facilitating the endothelial barrier crossing or altering the immuno-modulatory function of LSEC . Further studies aiming to determine the mechanisms of amoebic adhesion to LSEC and endothelium crossing have been limited by the simplicity of two-dimensional ( 2D ) cell culture systems and the difficulty to molecularly handle the high complexity of animal models . Moreover , animal data should be extrapolated with caution to the human disease since humans being the exclusive natural hosts for E . histolytica and rodents do not reproduce human liver physiology and immunology , in particular regarding human inflammatory diseases [8] . The development of a human 3D-liver model , consisting of cells grown in 3D-scaffolds and bridging the gap between cell cultures and tissues , would provide a new alternative for the study of human hepatic amoebiasis . While in the cell biology field the utility and advantages of in vitro tissue-like models are recognized , for infectious disease studies they have been used only rarely [9] [10] [11] . Tissue-like models allow the use of primary or immortalized human cells , the control of the non-cellular components of the microenvironment and analysis by advanced imaging techniques [12] [13] . Major advantages of this approach are the reduction of the complexity to a controlled but still physiologically relevant level and the possibility to define the roles of individual components at the molecular level . The major objective of the present work was to analyse the early stages of hepatic sinusoid invasion by E . histolytica , in a human and physiologically relevant system . We intended to examine the mechanisms of E . histolytica adhesion to and crossing of the endothelial hepatic barrier , migration to and interaction with hepatocytes , and the induction of the immune response . To accomplish this objective we elaborated a human 3D-liver model mimicking basic features of the hepatic sinusoid environment . In vivo , LSEC form monolayers devoid of a basement membrane that are separated from hepatocytes by the Disse's space containing loosely organized extracellular matrix ( ECM ) components . Our 3D-liver model is thus composed of an LSEC layer co-cultivated on top of a hepatocyte layer embedded in a 3D collagen-I ( COL-I ) matrix . We chose to use cells from established cell lines , since primary cell cultures rapidly lose their hepatic phenotype [14] [15] and show higher phenotypic variability . The LSEC line used was established from non-tumour liver endothelial cell primary cultures , and cells maintain the expression of typical markers [16] . Huh-7 cells , though hepatoma-derived , are widely used for their phenotypic resemblance to differentiated hepatocytes [17] [18] . By analysing several physiological aspects such as sinusoid architecture , matrix porosity and barrier permeability , the presence of cell-cell and cell-ECM adhesion molecules , the expression of hepatic markers , as well as the secretion of soluble molecules , we demonstrated the value of the 3D-liver model for the study of hepatic invasion by E . histolytica . Two-photon microscopy was used to monitor interactions of E . histolytica with the 3D-liver model components . We investigated the organization of the LSEC and hepatocyte layers , as well as trophozoite cytoadhesion and matrix crossing , migration and cytotoxic effects . The data showed that LSEC efficiently function as an endothelial barrier for the parasite . Trophozoites impaired in Gal/GalNAc lectin ( the major adhesin ) function showed reduced ability to cross the LSEC barrier , indicating an important role for this protein in the early steps of liver invasion . The 3D-liver model made possible long-term incubations with E . histolytica under serum-free conditions permitting to determine the secretome ( proteome of soluble factors ) composition and to identify new factors released upon amoebic interaction with the human cells . The analysis identified for the first time galectin family members participating in amoebic infection . Galectin has been reported to play a role during the innate immune response to several microbial infections [19] [20] . ELISA confirmed the presence of galectins and further indicated that the hepatic cells respond to amoebic interactions by a temporally and spatially organized pro-inflammatory reaction ( IL-1β , IL-6 , IL-8 , TGF-β1 ) . Cell adhesion and binding assays revealed the participation of galectin-1 and -3 in amoebic adhesion to cells . The role of galectins was confirmed by the striking reduction of amoeba adhesion to siRNA-treated LSEC exhibiting reduced galectin-1 levels . The data reveal a dual role of human galectin-1 and -3 and describe , for the first time , the participation of human galectins in hepatic infection with E . histolytica . Taken together , the 3D-liver model we established we conclude on the combined action of amoebic Gal/GalNAc lectin and human galectins during the early steps of hepatic amoebiasis .
The human 3D-liver model we established is composed of a monolayer of Huh-7 hepatocytic cells embedded in a 3D COL-I matrix and an LSEC monolayer plated on top of the matrix facing the medium ( Figure 1 ) . The 3D-liver model architecture organization was evaluated by two-photon microscopy and SHG visualization of the hepatic cells layers and matrix structure ( Figure 1 ) . The matrix structure in the absence and in the presence of the hepatic cells was compared demonstrating that without cells ( Figure 1A–B ) the COL-I fibres were homogeneously distributed throughout the matrix , which was at least 900 µm high . In the presence of the cells ( Figure 1D–E ) , the same matrix layer had a mean height of only around 500 µm and its structure changed to a heterogeneous distribution of the COL-I fibres . The SHG signals of the fibres were more intense and the matrix porosity ( Figure 1C and F ) was significantly smaller in the vicinity of LSEC and hepatocytes , demonstrating that the hepatic cells remodel the matrix architecture of their 3D environment . To quantify the ability of E . histolytica to cross the LSEC layer and to determine the rate of migration in the 3D matrix towards the hepatocytes we used a 100 µm mean distance between LSEC and hepatocytes enabling the distinction of the compartments ( Figure 1 ) . This matrix height takes into account the size of trophozoites ranging from 25 to 50 µm and their high motility ( 10 µm/sec ) . The distance between LSEC and hepatocytes diminished progressively over culture time and the most reproducible 100 µm mean distance was obtained upon three days of culture . Furthermore , to decipher the individual contributions of LSEC and hepatocytes to the response to amoebae we compared the 3D-liver model with setups lacking either the LSEC or the hepatocyte layer ( schematically represented in Figure 2A ) . Several characteristics of the 3D-liver model were analysed to determine its physiological relevance . Cell morphology , growth and the presence of cell-cell and cell-ECM adhesion molecules were monitored over time . By immunofluorescence microscopy , LSEC and hepatocytes were positive for ICAM-1 and integrin-β1 , and hepatocytes showed a strong E-cadherin surface labelling ( Figure S1 ) . To evaluate LSEC barrier function , monolayer permeability was determined through the ability of 1 µm diameter fluorescent microspheres to penetrate into the compartments of the 3D-liver model ( Figure S2 ) . After 3 h of incubation , only 0 . 14% ( mean number ) of the beads added on top of the LSEC layer were found in the matrix beneath . In the absence of LSEC , the proportion of beads inside the matrix was comparable to the matrix without cells ( around 80% ) and the beads stopped at the position of the hepatocyte layer . Data show that both the LSEC and hepatocyte layer of the 3D-liver model are efficient barriers for 1 µm particles . Albumin secretion and the expression of transcripts encoding several hepatocyte-specific functions were compared between the 3D-liver model ( with or without LSEC ) and Huh-7 standard 2D monocultures ( Figure 2 ) . Albumin release was significantly higher in the 3D-liver model than in 2D cultures in all time points analysed ( Figure 2B ) . The hepatic markers we chose are either involved in drug metabolism as cytochromes P-450 ( CYP2C19 , CYP3A4 ) and UDP glucuronosyltransferase-1A6 ( UGT1A6 ) or belong to the solute carrier transporter family ( SLC2A1 , SLC2A2 ) . In addition , we tested expression of hepatocyte nuclear factor-α ( HNF4A ) , a key transcription factor for many hepatic genes . The expression of most of those genes was maintained or increased between the 3- to 14d-period tested ( Figure 2C and S3 ) . Moreover , transcript levels for SLS2A1 , SLC2A2 , CYP2C19 and UGT1A6 were higher in the 3D-liver model than in the 2D culture at the 3d time-point ( Figure 2D and S3 ) . Together the results demonstrate that the 3D-liver model retains hepatic barrier performance and hepatocyte functionality in a more physiological environment than standard 2D cell cultures . For the experiments described below we used the 3D-liver model 3d after having started its preparation . The 3D-liver model offers the possibility to characterize the molecules released by the cells into the culture medium on its top , since in this model LSEC have lost the strong serum dependence observed in conventional cell culture conditions , i . e . they are viable , morphologically normal and express adhesion markers ICAM-1 and integrin-β1 for at least 12 h without serum ( data not shown ) . The compounds released were identified after 3 h in fresh serum-free medium , using liquid chromatography–mass spectrometry ( LC-MS/MS ) analysis ( Table S1 ) . From the 64 human-specific proteins identified ( Table S2 ) , the 45 proteins known for being released were grouped according to their main functions ( Table 1 ) . Several components and regulators of the blood coagulation ( 9 proteins ) , the complement cascade and the innate immune response ( 7 proteins ) were detected . Within the group of 11 plasma transporters , all exclusively or mainly synthesized in hepatic cells , well-known hepatic markers [14] were present , such as serum albumin , α-fetoprotein , apolipoproteins A-I , A-II , B-100 , and E . In addition to COL-I , the ECM components fibronectin , nidogen-1 , and fibrinogens were found , as well as several adhesion molecules . These results reveal that a variety of hepatic functions are expressed and that the hepatic cells are capable to enrich their micro-environment in the 3D-liver model . The 3D-liver model was used to study initial steps of liver invasion by E . histolytica . Virulent ( i . e . inducing amoebic liver abscesses in the hamster ) trophozoites were added to the medium on top of the 3D-liver model ( Figure 3A ) . The majority of the amoebae adhered to the LSEC layer . For at least 6 h , all trophozoites localized inside the 3D-liver model were migrating and their mobility indicates that cells were likely alive . Approximately 20% to 30% of the amoebae have crossed the LSEC barrier ( Figure 3B ) after incubation for 1 . 5 h or 3 h , respectively . In the absence of hepatocytes , amoebic invasion was significantly lower ( Figure 3C ) , suggesting the existence of attractant molecules secreted by hepatocytes or a difference in a potentially mechanical effect of the remodelled matrix . In the absence of the LSEC layer ( Figure 3A ) more than 60% of the amoebae crossed the matrix after 3 h ( Figure 3C ) and their migration towards the hepatocytes was significantly increased resulting in a different invasion rate profile ( Figure 3F ) . Thus , the LSEC monolayer behaves as an efficient barrier for trophozoite invasion . Virulence-attenuated trophozoites ( unable to produce liver abscesses in the hamster ) adhered to LSEC in the 3D-liver model as efficiently as virulent trophozoites . However , their capacity to invade is significantly diminished , with less than 5% of the trophozoites crossing the LSEC barrier after 1 . 5 h ( Figure 3D ) and no significant increase after 3 h of interaction ( data not shown ) , showing their reduced ability to efficiently leap over the first barrier of liver infection , i . e . LSEC crossing . To characterize the morphological changes in the human cell monolayers upon interaction with E . histolytica , two-photon microscopy and SHG were used . During amoebic interaction with the LSEC , local detachment of individual cells from the COL-I matrix was frequently observed at sites of amoebae crossing the layer ( Figure 3G and H ) but areas of detachment did not obviously extend over time , suggesting the existence of a replacement or repair mechanism . Trophozoites containing vesicles labelled with the cell tracker used for the hepatic cells were frequently and specifically observed in the vicinity of the LSEC layer ( Figure 3I ) . Amoebic engulfment of LSEC portions could either originate from the detachment and the subsequent uptake of portions of live LSEC by trogocytosis , [21] , or from phagocytosis of apoptotic bodies or dead cell debris . Trophozoites rapidly migrated through the matrix in the direction of the hepatocytes ( 150 µm in 1 . 5 h ) . The percentage of amoebae crossing the hepatocyte layer was low and phagocytic-like structures were not frequent in trophozoites having reached the hepatocytes . No detachment or formation of gaps was observed during amoebic interaction with the hepatocyte monolayer , even after 6 h of interaction . However , immunofluorescence experiments with antibodies against E-cadherin , a marker for epithelial tight junctions , revealed a clear reduction of the signal in the presence of E . histolytica ( Figure S4 ) . This reduction could reveal changes in cell-cell contacts that may ultimately increase the epithelial barrier permeability and facilitate the crossing of the hepatocyte barrier after longer incubation periods . Adhesion to mammalian cells is a prerequisite for the cytotoxic effects of E . histolytica and depends upon the amoebic Gal/GalNAc lectin [3] . To examine its role in the penetration of the 3D-liver model , the effect of galactose on trophozoite invasion was first analysed . Galactose almost completely abolished the ability of virulent trophozoites to cross the LSEC layer ( Figure 3D ) , the proportion of amoebae being about five times lower than for the glucose control ( CTL ) . Amoebic crossing of the COL-I matrix ( tested in the setup without LSEC ) was also significantly reduced in the presence of galactose ( Figure 3E ) . To further investigate the participation of Gal/GalNAc lectin , amoebic transfectants ( HGL-2 strain ) were used expressing a dominant-negative form of the lectin [4] . Compared to control transfectants ( CTL ) , HGL-2 trophozoites presented a significant reduction in the LSEC ( Figure 3D ) and matrix crossing activity ( Figure 3E ) . The less pronounced effects found with HGL-2 transfectants compared with galactose could be due to the fact that in HGL-2 trophozoites only the intracellular signalling of Gal/GalNAc lectin is blocked , while the extracellular function remains unchanged [5] . Secretome analysis was performed to identify human proteins released during amoebic hepatic invasion ( Table S1 ) . Products released into the medium were determined after 3 h interaction with virulent E . histolytica . Within the 139 human-specific proteins found exclusively in response to amoebae ( Table S2 ) many human cytoskeletal proteins were present that likely originate from dying cells having lost their plasma membrane integrity . Note that the induction of host cell death is a main feature of E . histolytica infection . We found 24 known released or surface-associated proteins ( Table 2 ) , comprising further components of the complement cascade ( 3 proteins ) and the blood coagulation system ( 4 proteins ) , and 7 proteins involved in cell/cell or cell/ECM interactions . Among the 8 proteins with functions in antigen presentation and immune responses , macrophage migration inhibitory factor ( pro-inflammatory cytokine MIF ) , galectin-1 , and galectin-3 binding protein were present . Galectin-1 is a regulator of a variety of immune responses and inflammation in host–pathogen interactions . Interestingly , galectin-1 has not been described before in the context of amoebic liver infection . The identification of the immune-regulatory proteins MIF and galectin-1 in the secreted fraction suggests that cytokines may be released upon E . histolytica interaction with the 3D-liver model , which may not have been discovered in the secretome analysis due to their low abundance and/or degradation during the procedure . To increase the sensitivity of detection , we next performed ELISA for a selection of cytokines ( IFNγ , IL-1β , IL-6 , IL-8 , TNFα , TGF-β1 ) , growth factors ( acidic FGF , HGF and VEGF ) and in addition , galectin-1 and galectin-3 . Analysis was carried out for the 3D-model without and with E . histolytica , and the setup without LSEC as control . Samples were prepared from 3 distinct compartments of the 3D-liver model ( Figure 4A ) . The first corresponded to the medium on top ( outside ) of the 3D-liver model ( supernatant S1 , as used for the proteome approach ) , the second to the medium inside the 3D-liver model ( supernatant S2 ) , and the third to the matrix- and cell-associated molecules liberated after collagenase treatment of the non-soluble fraction ( S3 ) . FGF , HGF , IFNγ and TNFα were not found in any fraction tested . VEGF ( Figure 4B ) and TGF-β1 ( Figure 4C ) were readily detected in the three compartments of all samples , with different quantities . Without amoebae , IL-8 ( Figure 4D ) was , as expected , easily detected in all fractions , IL-6 ( Figure 5A ) only in low amounts in S1 and S2 of the 3D-liver model , whereas IL-1β ( Figure 5B ) was not found . Low galectin-1 ( Figure 5C ) and galectin-3 ( Figure 5D ) levels were present in the soluble fractions of the 3D-liver model . In the presence of amoebae , the VEGF release was unchanged ( Figure 4B ) , but many significant modifications in the cytokine profiles occurred , concerning different compartments ( Figure 5E–F ) and showing distinct kinetics ( Figure 6 ) . The presence of amoebae significantly increased the amounts of TGF-β1 ( Figure 4C ) in the soluble fractions , IL-8 ( Figure 4D ) augmented in S1 and S3 , and IL-6 ( Figure 5A ) and galectin-1 ( Figure 5C ) in all fractions of the 3D-liver model . Galectin-3 ( Figure 5D ) was only increased in S1 . Interestingly , IL1-β ( Figure 5B ) , undetectable in the absence of amoebae , was revealed inside the 3D-liver model ( Figure S2 and S3 ) after 3 h and 6 h ( Figure 6D ) . Without the LSEC layer , the inflammatory reaction was less pronounced ( Figure 5E–F ) , indicating the substantial participation of LSEC in the establishment of liver immune responses [22] . Moreover , the significant differences observed suggest that in the 3D-liver model , LSEC contribute to cytokine amounts either by a directional release into the underlying matrix or by modulating release from hepatocytes ( Figure 5E–F for scheme ) . The data demonstrate that the hepatic cells in the 3D-liver model create a pro-inflammatory environment in response to the presence of E . histolytica ( Figure 5E–F ) and thus initiate an innate immune response regulated in time and space . Galectin-1 and -3 exist in the extracellular milieu and in association with the surface of different cell types . Galectin-1 is characteristic of endothelial cells and galectin-3 is mainly present in epithelial cells [20] . The drastic reduction of amoebic invasion by the presence of galactose ( Figure 3 ) prompted us to investigate the potential role of galectin-1 and -3 in E . histolytica adhesion to the human cells . The ability of E . histolytica to bind human galectin-1 and -3 was examined using bacterially expressed human recombinant proteins . Their binding to trophozoite surfaces after 25 min of incubation was observed in immunofluorescence experiments using anti-galectin antibodies . The signal intensity was variable in the amoebic population and the labelling was frequently unevenly distributed ( Figure 7A and B ) , suggesting protein clustering . Binding was quantified using an ELISA-like assay ( Figure 7C and D ) . Moreover , trophozoites adhere to surfaces covered with galectin-1 or -3 ( Figure 7E ) . Immunofluorescence experiments with the 3D-liver model revealed the presence of galectin-1 at the LSEC and of galectin-3 at the Huh-7 surface ( Figure 8A , C ) . To test E . histolytica binding to the cell surface-associated galectins of the hepatic cells , Huh-7 ( Figure 8B ) and LSEC ( Figure 8D ) monolayers were incubated with the trophozoites in the presence or the absence of either the recombinant galectins , or galactose and lactose , both sugars known to inhibit human galectins ( lactose stronger than galactose ) and amoebic lectins as Gal/GalNac ( galactose stronger than lactose ) , and the number of amoebae adhered to the human cells was determined . Trophozoite adhesion was drastically diminished by the sugars or the recombinant proteins ( Figure 8B and D ) , indicating for the first time the ability of E . histolytica to bind and adhere to human galectin-1 and -3 . To further analyse the dependence of amoebic adhesion upon human surface galectin , we focussed on LSEC as the first target of amoeba interaction and performed siRNA knock-down experiments for galectin-1 . Galectin-1 specific siRNA strongly reduced the level of surface galectin-1 in transfected LSEC ( Figure 8E ) and trophozoite adhesion was decreased by around 40% ( Figure 8F ) , demonstrating the participation of surface-associated galectin-1 in E . histolytica adhesion to LSEC . The potential immuno-modulatory role of galectin-1 and -3 in the 3D-liver model was examined by testing the ability of the recombinant proteins to modify cytokine release ( Figure 9A–D ) . Bacterially expressed galectin-1 and -3 recombinant proteins ( 1 µg/ml ) were added for 6 h to the medium on top of the 3D-liver model in the absence of amoebae and cytokine amounts quantified by ELISA ( Figure 9A–D ) . Galectin-3 promoted the release of IL-1β and significantly increased IL-6 and IL-8 levels . Galectin-1 promoted the IL-6 and TGF-β1 release . In addition , lactose competition in the presence of E . histolytica ( Figure 9 ) completely abolished the enhanced cytokine release observed . To exclude that the stimulation was induced by bacterial endotoxin contaminations , we performed a control using galectin-1 purified from human cell lines ( note that galectin-3 was not available ) and obtained a similar capacity to induce the cytokine release . Overall the data suggest an important immuno-regulatory role for galectin-1 and -3 through the stimulation of the release of pro-inflammatory cytokines , which may be relevant for the induction of the host response during liver invasion by E . histolytica .
Liver abscesses are a fatal feature of infection with E . histolytica . Host and parasite factors leading to liver infection remain largely unknown . An important question is how this parasite adheres to and crosses the liver endothelium . Based on our previous data , we hypothesized in this work that amoebic Gal/GalNAc lectin and surface-bound ( or secreted ) human factors are involved in this key step of liver invasion . However , the existing experimental systems ( standard cell culture and animals ) did not allow the molecular analyses necessary to test this hypothesis . We thus established a new human 3D-liver model , reproducing main characteristics of hepatic sinusoids , designed for the study of E . histolytica infection . It is the first 3D-liver model using cells of human cell lines co-cultured in a 3D COL-I scaffold in the sandwich approach . The latter was chosen to obtain a hepatic sinusoid-like organization of the cells [12] [23] in a 3D architecture , which is more physiologically relevant than strategies like 2D systems [9] or 3D cellular spheroids [11] . Though our 3D-liver model was built with only a single collagen type , the hepatic cells remodelled the matrix and released further ECM components and adhesive proteins , suggesting that the cells are able to diversify the initially homogenous COL-I matrix . We showed that hepatocytes maintain several physiological functions beyond the time-point used for our analyses and LSEC express the surface receptors ICAM-1 and integrin-β1 , and exhibit barrier function . Major advantages of this 3D-liver model are: it is human-relevant ( uses human cells ) ; preserves a physiological context ( mimics the hepatic sinusoid architecture ) , presents a controlled , reproducible in vitro environment . One appealing perspective is the use of the 3D-liver model for the study of other important parasitic or viral hepatic infections , but specific adaptations to each of the pathogens under investigation will be required . Notably , the inclusion of other liver-resident ( stellate and Kupffer cells ) and immune ( monocytes , macrophages , NKT cells ) cell types , blood components related to the innate immune response , variations in the oxygen concentration or the application of flow to mimic mechanical forces of the blood stream can be envisaged . The 3D-liver model here described was used to analyse the initial events occurring upon E . histolytica interactions with hepatic host cells . We examined human cell responses and parasite abilities to cross the endothelial barrier . We identified new key elements of amoeba-cell interactions triggering a pro-inflammatory response ( the data are summarized in Figure 10 ) . For the first time , we have discovered the role of amoebic Gal/GalNAc lectin in the amoebic crossing of the endothelial barrier and a function of human galectins in amoebic adhesion . Also for the first time , we were able to characterize the spatio-temporal distribution of the components of the pro-inflammatory response against E . histolytica in a hepatic human-relevant system . The better characterization of the E . histolytica liver inflammatory process in the human host is an important issue for the understanding of the disease . In fact , neutrophils , macrophages and T cells have been related to the local host immune responses in human amoebic abscesses [24] , but the human host inflammatory response during E . histolytica liver invasion is poorly known . Though it appears relatively controlled over time ( i . e . restricted to areas surrounding amoebae-containing foci , mainly single abscess of restricted size in humans ) , it likely contributes to amoebic progression , diminished liver function , and clinical complications [25] . Here we showed that during amoebic invasion of the 3D-liver model several cytokines ( IL-1β , IL-6 , IL-8 as well as galectin-1 ) accumulated in the matrix-associated fraction . Although cytokine accumulation in ECM has been documented and their retention proposed as an essential mechanism for the establishment of gradients and compartments [26] , little is known on the functions of these ECM-associated molecules . Nonetheless , ECM-associated galectin-1 is able to trigger T cell death at lower concentrations than the soluble form [27] . In the context of liver infection , it will be interesting to examine the hypothesis that ECM-associated galectin-1 is able to trigger cell death . We also demonstrated the binding of soluble galectin-1 and -3 to amoebae and the participation of native ( i . e . cell surface-associated ) galectin-1 and -3 in amoebic adhesion to LSEC and hepatocytes . Moreover , incubation of the 3D-liver model with recombinant galectin-1 or -3 induced an inflammatory response , similar to the response to the presence of amoebae , though less complete . From all these data we can suggest a dual role of galectin-1 and -3 during amoebic hepatic infection . First , cell surface-linked galectin-1 and -3 promote amoebic adhesion to human liver cells , and second , released galectin-1 and -3 induce a pro-inflammatory hepatic response . It is possible that parasite binding to human cells directly facilitates the galectin release , but more data will be necessary to conclude on this . Amoebic liver abscess formation based on carbohydrate-sensitive adherence mechanisms is here for the first time suggested at the molecular level , by the discovery of the simultaneous participation of both parasite and human lectins in the invasion of the 3D-liver model . We do not know if amoebic Gal/GalNAc and human galectins interact in a direct way through sugar residues . In fact , galectin-1 and -3 are released into the extracellular milieu by a non-classical secretion pathway and thus seem not to be glycosylated in the ER-Golgi trafficking pathway [28] . Here we found that bacterially expressed galectins ( i . e . not glycosylated ) bind to E . histolytica and that binding was blocked by carbohydrates ( lactose ) , but we did not formally demonstrate that this binding depends on the lectin function of human galectins . Galectin-1 and -3 are the most ubiquitously expressed and extensively studied members of the galectin family . Several immuno-regulatory functions have been discovered for galectin-1 and -3 in acute and chronic inflammation [29] . For instance and of relevance for our findings , up-regulated galectin-3 expression during Toxoplasma gondii hepatic infection seams to exert an important role in innate immunity , including a pro-inflammatory and a dendritic cell regulatory effect [19] . We conclude that both , the adhesive and the immuno-regulatory role of galectin-1 and -3 we detected are relevant for the induction of the host response during liver invasion by E . histolytica .
A 3D COL-I matrix was polymerized in 35 mm diameter cell culture dishes ( ibidi 81156 ) by adding 650 µl of a bovine COL-I ( Nutagen , Advanced Biomatrix ) solution ( 1 . 0 mg/ml ) in DMEM ( 31966-047 Invitrogen ) and incubating for up to 1 h at 37°C in a humidified incubator at 5% CO2 . A suspension of hepatocytes ( 4×105 ) from the human Huh-7 cell line ( JCRB0403 , JCRB cell bank ) was added on top of the polymerized matrix and incubated overnight ( 37°C , 5% CO2 ) in complete DMEM medium ( i . e . with 10% foetal bovine serum ) . The medium was replaced by 150 µl of the same COL-I solution . After polymerization ( 40 min , 37°C ) of the new COL-I matrix layer , cells ( 3×105 ) from the LSEC line ( Salmon et al . , 2000 ) were plated on the top and the co-cultures incubated for 48 h ( 37°C , 5% CO2 ) in complete DMEM with daily medium change . The setups consisting of hepatocytes ( 4×105 ) embedded in the 2 COL-I matrix layers without LSEC or of an LSEC layer ( 4×105 ) on the top of the COL-I sandwich without hepatocytes were prepared identically . Note that the cell densities seeded were chosen to obtain monolayers for both cell types . Two-photon microscopy ( multiphoton microscope LSM710_NLO upright ) was used for visualization and acquisition of 3D images . Hepatic cells were labelled with 2 . 5 µM red cell tracker ( CMTPX , Invitrogen C34552; 30 min pre-incubation ) and the COL-I matrix fibres were detected by the second harmonic generation ( SHG ) signal ( Chameleon laser , λ = 800 nm ) . To decrease background fluorescence the samples were incubated 12 h prior to analysis with complete DMEM medium without phenol red ( Invitrogen 31053 ) with NEAA ( GIBCO 1140-035 ) , 4 mM glutamine ( GIBCO 25030-024 ) and 1 mM sodium pyruvate ( GIBCO 11360-039 ) added and images acquired in the same medium without serum . COL-I matrix fibres were visualized by two-photon microscopy using SHG . 3D images were subjected to thresholding to convert matrix pores to white and collagen fibrils to black objects . The porosity of each focal plane ( 1 µm ) of each image was evaluated using the “analyze particle function” of ImagePro software with the black fibres being defined as the particles . A total of 3 images of 3 independent experiments were analysed . 3D-liver models , the setup without LSEC , and Huh-7 cell standard 2D cultures , cultivated for 3–14d , were independently transferred to Eppendorf tubes and centrifuged for 5 min at 16000 g . The pellets ( containing cells and the COL-I matrix ) were lysed with lysis buffer from the RNAeasy Plus Mini kit ( Qiagen ) and total RNA was prepared with the kit . Reverse transcription was performed with Superscript II enzyme ( Invitrogen-18064-022 ) and 3 µg RNA . Reactions without reverse transcriptase added were used as controls . Real-time quantitative PCR amplification was carried out using Power SYBR Green PCR Master Mix ( Perkin Elmer Applied Biosystems ) and 2 µM of the primers and a Quantstudio 7 Flex QPCR system ( Applied biosystems , life technologies ) . Human genes and primers used are listed in Text S1 . Results were normalized to human GAPDH transcript levels . Changes in expression levels over time of culture were expressed relative to the levels determined for the corresponding 3d culture . The differences between the culture conditions at a given timepoint were expressed relative to the levels observed with 2D Huh-7 cultures . Permeability of LSEC monolayers or the COL-I matrix was determined through the ability of 1 µm diameter fluorescent carboxylate microspheres ( Polysciences , 15702 ) to pass through the cell layer or the matrix border . After 3 h of incubation , the beads added on top of the samples , the COL-I matrix and the cells were visualized by two-photon microscopy . For each microscopic field ( 0 . 28 mm2 ) the x- , y- and z-position of each fluorescent bead and the z-position of the cell layers or the upper matrix border were determined using ICY software ( http://icy . bioimageanalysis . org ) . A LabMat script was created to represents the z-stack position of individual beads , with the matrix border or the LSEC layer position set to 0 µm . 3D-liver models were fixed with 4% formaldehyde for 30 min . Blocking and incubation with the first antibody was ( overnight in PBS containing 1% BSA ) was followed by 2 h of incubation with secondary antibodies ( Cell Signalling ) in the same buffer . Immunostaining was performed with antibodies specific for human integrin-β1 ( Millipore , 3199Z ) , ICAM-1 ( R&D systems , 11C81 ) and E-cadherin ( Cell Signaling , 24E10 ) . Images were acquired with a multiphoton LSM710_NLO upright microscope , using the multi-photon laser and SHG to visualize the COL-I matrix , or the confocal laser the reflection mode to visualize the COL-I matrix and cell topography . To determine the intensity of E-cadherin labelling , 1 µm diameter fluorescent carboxylate microspheres ( Polysciences , 15702 ) were used as reference . Fluorescence intensities of E-cadherin signals and the beads was measured using Zen software from Zeiss . The intensity of the E-cadherin label was normalized by the intensity of the beads ( added prior to microscopy ) present in the same field and focal plane ( set as 100% ) . Trophozoites of the E . histolytica strain HM1:IMSS were cultivated in TY-S-33 medium as described [30] . The virulence-attenuated HM1:IMSS strain is a long-term in vitro cultivated ( i . e . more than 10 years in axenic culture ) derivative , having lost the ability to induce liver abscesses in the hamster , while maintaining adhesive and cytotoxic activities when tested with standard 2D mammalian cell cultures [31] . Strains modified for the Gal/GalNAc lectin activities CTL and HGL-2 were previously published [4] . For invasion experiments , amoebae were collected from exponentially growing cultures and labelled with 2 . 5 µM green cell tracker ( CMFDA , Invitrogen C2925 ) for 1 h . Labelled trophozoites ( 6×104 ) were added in 2 ml serum-free DMEM without phenol red to the top of the model , the setup without LSEC or Huh-7 or the COL-I matrix without cells . After 1 . 5 h or 3 h of incubation , invasion was evaluated by two-photon microscopy and SHG . For each microscopic field ( 0 . 28 mm2 ) the x- , y- and z-position of each amoeba and the z-position of the cell layers or the upper matrix border were determined using ICY software . The rate by which amoebae cross the LSEC layer or the matrix was calculated by the quantification of the number of amoebae having a z-position bigger than the z-position of the LSEC or the matrix border and the normalization with the total number of amoebae per field . Data were represented as the percentage of amoebae able to cross . The invasion rate ( i . e . migration towards hepatocytes ) was obtained by considering the amoeba position relative to the position of the LSEC and the hepatocyte layer set as 0% and 100% , respectively . For each condition , 10 fields were examined , in 5 independent experiments . Samples were prepared from 3 compartments of the 3D-liver model: S1 ( medium on top of , i . e . outside ) , S2 ( medium inside ) and S3 ( extracted from the non-soluble fraction containing cells , matrix and matrix-associated molecules ) . After collecting the S1 fraction , the remaining material was transferred to an Eppendorf tube and centrifuged for 5 min at 16000 g . The supernatant corresponds to the S2 fraction . The pellet containing the non-soluble components was resuspended in DMEM and incubated with collagenase ( 2 . 5 mg/ml ) for 15 min at 37°C to cleave the COL-I fibres . After centrifugation , the supernatant was used as fraction S3 . Cytokines and growth factors were quantified by ELISA assays ( R&D systems ) for human IL-1β , IL-6 , IL-8 , TGF-β , TNF-α , IFN-γ , HGF , acidic FGF , VEGF , galectin-1 and -3 . To identify compounds released by the hepatic cells into the culture medium on top of the 3D-liver model , fresh serum-free DMEM was added and collected after 3 h . A total of 15 samples ( Table S1 with complete raw data ) was collected: 2 biological replicates of the control COL-I matrix without hepatic cells ( 1A and B ) ; 4 biological replicates of the 3D-liver model in the absence ( 2A–D ) and 9 biological replicates in the presence of E . histolytica ( 3A–I ) . Proteins contained in the collected medium were precipitated with methanol-chloroform [32] redissolved in 8M urea in 25 mM NH4HCO3 , reduced with 5 mM TCEP ( 45 min , 37°C ) , alkylated with 50 mM iodoacetamide ( 60 min , 37°C ) in the dark , diluted to 1 M urea and digested for 16 h at 37°C with trypsin ( 1 µg , sequencing grade gold , Promega ) . Peptide mixtures were acidified to pH 2 . 8 with formic acid , desalted with minispin C18 columns ( Nest Group ) , vacuum-dried and solubilized in 0 . 1% formic acid and 2% acetonitrile for mass spectrometry . Liquid chromatography–mass spectrometry ( LC-MS/MS ) analysis , proteome data processing and analysis methods are described in Text S1 . Trophozoites ( 2×105 ) were incubated for 25 min at 4°C in serum-free DMEM containing 5 µg/ml of bacterially expressed recombinant human galectin-1 or -3 ( Prepotech; guaranteed for endotoxin levels less than 0 . 1 ng/µg ) , human galectin-1 expressed in human cell lines ( Creative BioMat LGALS1 ) or BSA as a control . Amoebae were washed twice with serum-free medium , fixed with 4% formaldehyde , harvested , seeded ( 6×104 per well ) into 96-well Immunoplates ( Nunc ) and prepared for immunofluorescence staining . Immunofluorescence labelling was performed with anti-galectin-1 or -3 antibodies ( Cell Signalling , D6008T and abcam , 31707 ) and secondary antibodies coupled to AlexaFluor 555 ( Cell Signaling ) . Images were acquired with a fluorescence microscope ( Olympus IX81 ) . For quantitative ELISA-like assays , trophozoites were harvested , allowed to adhere to wells of a 96 well plate ( 6×104/well ) , incubated for 25 min with recombinant galectin-1 , galectin-3 or BSA , gently washed and then incubated with biotinylated anti-galectin-1 or -3 antibodies ( R&D systems ) . Colorimetric detection of antibody binding was obtained with streptavidin-HRP ( R&D systems ) and peroxidase substrate ( SureBlue , KPL ) . Galectin-1 or -3 amounts in the samples were estimated using a standard curve established with the recombinant proteins . Wells of 96-well ImmunePlates ( Nunc ) were covered with recombinant galectin-1 , -3 or BSA ( 10 µg/ml ) ( overnight at room temperature ) . Trophozoites labelled with green cell tracker and resuspended in 200 µl of DMEM without phenol red were added to the wells ( 6×104 per well ) and incubated for 30 min at 37°C in an anaerobic bag ( Genbag Biomerieux , 45534 ) . Wells were washed 3 times with serum-free DMEM . The number of adherent amoebae was quantified using fluorimetric detection of the cell tracker and normalized with a standard curve of known amoeba numbers . LSEC or Huh-7 monolayers grown for 72 h on COL-I coated ( 50 µg per ml ) coverslips ( i . e . conventional 2D culture conditions ) were incubated for 30 min with the cell tracker-labelled trophozoites in the presence or absence of galactose ( 100 mM ) , lactose ( 100 mM ) or recombinant galectin-1 or -3 proteins ( 10 µg/ml ) . Samples were washed 3 times and fixed with 4% formaldehyde . Fluorescent amoebae remaining adhered to the human cells were visualized by confocal microscopy and quantified using ICY software . LSEC monolayers grown overnight on COL-I coated ibidi dishes were transfected with 10 nM siRNA specific for galectin-1 ( Santa Cruz , sc-35441 ) or negative control siRNA ( Santa Cruz sc-37007 and Invitrogen , 4390843 ) using Lipofectamine RNAiMAX ( Invitrogen , 13778 ) in Opti-MEM reduced serum medium ( Invitrogen , 31985 ) . After overnight incubation with the transfection solution , medium was changed and the dishes incubated for further 48 h . LSEC presented a significant reduction of the surface galectin-1 labelling at 72 h after transfection ( evaluated by immunofluorescence intensity measurements , as described above ) . Amoebic adhesion was thus determined ( as detailed above ) at the 72 h time-point . Fluorescein-conjugated control siRNA ( Santa Cruz sc-44239 ) was used to evaluate the transfection efficiency by fluorescence microscopy . After overnight incubation with the transfection solution , most of the LSEC were fluorescein-positive .
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The study of liver infection is based on animal models , but the animal physiology does not always reflect the reality of the human host . This is particularly true for pathogens whose exclusive natural hosts are humans , such as Entamoeba histolytica , the protozoan parasite responsible for amoebiasis . Here , we constructed an experimental human 3D-liver model able to reproduce the first steps of amoebic hepatic infection ( barrier crossing , tissue migration and pro-inflammatory reaction ) . Using this 3D-liver model we were able to decipher the first stages of hepatic invasion by E . histolytica and to unravel the role played by galectin-1 and galectin-3 during amoebic hepatic adhesion and pro-inflammatory reaction . Moreover , the model enables analysis usually not possible with in vivo samples , such as the quantification of pro-inflammatory cytokines released inside the tissue microenvironment . Our 3D-liver model has the potential to bridge the gap between animal models and the reality of the human host for the study of amoebic infection and other infectious diseases of the liver .
|
[
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"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
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2014
|
A New Human 3D-Liver Model Unravels the Role of Galectins in Liver Infection by the Parasite Entamoeba histolytica
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Recognition of conserved bacterial components provides immediate and efficient immune responses and plays a critical role in triggering antigen-specific adaptive immunity . To date , most microbial components that are detected by host innate immune system are non-proteinaceous structural components . In order to identify novel bacterial immunostimulatory proteins , we developed a new high-throughput approach called “EPSIA” , Expressed Protein Screen for Immune Activators . Out of 3 , 882 Vibrio cholerae proteins , we identified phosphatidylserine decarboxylase ( PSD ) as a conserved bacterial protein capable of activating host innate immunity . PSD in concentrations as low as 100 ng/ml stimulated RAW264 . 7 murine macrophage cells and primary peritoneal macrophage cells to secrete TNFα and IL-6 , respectively . PSD-induced proinflammatory response was dependent on the presence of MyD88 , a known adaptor molecule for innate immune response . An enzymatically inactive PSD mutant and heat-inactivated PSD induced ∼40% and ∼15% of IL-6 production compared to that by native PSD , respectively . This suggests that PSD induces the production of IL-6 , in part , via its enzymatic activity . Subsequent receptor screening determined TLR4 as a receptor mediating the PSD-induced proinflammatory response . Moreover , no detectable IL-6 was produced in TLR4-deficient mouse macrophages by PSD . PSD also exhibited a strong adjuvant activity against a co-administered antigen , BSA . Anti-BSA response was decreased in TLR4-deficient mice immunized with BSA in combination with PSD , further proving the role of TLR4 in PSD signaling in vivo . Taken together , these results provide evidence for the identification of V . cholerae PSD as a novel TLR4 agonist and further demonstrate the potential application of PSD as a vaccine adjuvant .
Innate and adaptive immunity are two arms of the immune system that help defend against invading microbes [1] , [2] . Innate immunity provides immediate defense against infection in a non-specific manner and also influences the antigen-specific adaptive immune response [3] , [4] . The innate response to microbes involves the recognition of conserved microbial products , collectively termed pathogen-associated molecular patterns ( PAMPs ) by specific host receptors [2] . Germ-line encoded Toll-Like Receptors ( TLRs ) are the best characterized of these receptors [2] , [5] . To date , 13 TLRs have been identified in mice , and a few of their cognate ligands include lipopolysaccharide ( LPS ) [6] , peptidoglycan [7] , diacyl- or triacyl-lipopeptide [8] , dsRNA [9] , unmethylated CpG DNA motifs [10] , and flagellin , a subunit of bacterial flagella [11] , [12] . When TLRs that are either surface-exposed or located in the endosomal membrane bind PAMPs , signal transduction events are activated leading to proinflammatory cytokine production [1] . The proinflammatory response induced by TLR activation can lead to active clearance of pathogens and an enhancement of the adaptive immune response . Genome-wide approaches [13] have defined bacterial factors that cause , for example , cytotoxicity after expression in yeast [14] , or the host factors that modulate bacterial intracellular replication [15] , [16] . However , a comprehensive screen of the predicted proteome of a bacterial organism for immune activating proteins has not been previously reported [13] . Comprehensive genome-wide screening for bacterial proteins that elicit innate immune responses has been limited by two factors . Firstly , bacterial genomes encode for thousands of proteins and this necessitated the development of a resource that allows high-throughput purification or expression of individual proteins before screening can take place . Secondly , because evolutionary pressure can readily select for changes in amino acid sequence , proteins have been generally thought to have lost conserved motifs that might be recognized by the innate immune system . To date , only flagellin and membrane-anchored lipoproteins were reported to contain conserved motifs that are recognized by host TLRs [11] , [17] , [18] , [19] , [20] . Interestingly , bacterial flagellins were also determined to have robust adjuvant activity enhancing specific T-cell responses against co-administered antigens in vivo [21] , [22] . Therefore , identification of new bacterial proteins that induce the host innate immunity will broaden our understanding of host responses to bacterial infection and provide an opportunity to develop new vaccine adjuvants . Recently , we reported the production of a complete full-length open reading frame ( ORF ) expression library for the pathogenic bacterium V . cholerae [23] . In the same work , we showed that flagellins ( FlaD and FlaC ) synthesized from each of their ORF expression clones by in vitro transcription/translation are capable of eliciting NF-κB activation in A549 human lung epithelial cells [23] . This suggests that proteins produced by this system can be used for high-throughput screening for other protein activators of host innate immunity . In this study , we developed EPSIA technology as a high-throughput proteome-wide screen for stimulators of host innate immune system . Using V . cholerae as a test organism , we identified numerous new proinflammatory proteins and characterized the proinflammatory signaling mechanism induced by one of these protein hits , phosphatidylserine decarboxylase ( PSD ) that stimulated murine macrophage cells with the greatest potency . PSD activated mouse macrophages to secrete proinflammatory cytokines in a TLR4-dependent , MyD88-dependent manner , and full induction required a processed , enzymatically active PSD . Moreover , PSD was shown to act as a robust adjuvant when co-administered with an inert protein antigen , bovine serum albumin ( BSA ) . Our results reveal the versatility of the EPSIA approach for uncovering new microbial activators of host innate immune responses .
The comprehensive genome analysis of V . cholerae 7th pandemic El Tor strain N16961 indicated that it possesses 3 , 887 protein coding genes out of total 4 , 009 genes in two chromosomes ( http://cmr . tigr . org/tigr-scripts/CMR/GenomePage . cgi ? org=gvc ) . In this work , 3 , 882 V . cholerae proteins were successfully synthesized using an in vitro expression system and screened for their ability to produce TNFα , a proinflammatory cytokine , in RAW264 . 7 murine macrophage cells . The RAW264 . 7 cell line was chosen because ( i ) they , as macrophage cells , express a broad spectrum of immune-related receptors and ( ii ) they grow with established culture stability . Schematic screening procedures are depicted in Fig . 1 . Positive protein pools were confirmed using a cut-off that was 1 . 8 fold above the TNFα levels obtained from the reticulocyte lysate only control treatment . Then , proteins in the corresponding pools were individually synthesized and used to stimulate cultures of RAW264 . 7 cells . In the final screen using individually synthesized proteins , TNFα production greater than 2 . 1 fold of the negative control value was considered positive for induction . Out of 3 , 882 V . cholerae proteins , 8 proteins were reproducibly identified to stimulate TNFα production and are listed in Table 1 . The positive proteins include a protein of unknown function ( protein 3 ) , protein-modifying enzymes ( proteins 4 , 6 , 7 , and 8 ) , a lipid-modifying enzyme ( protein 5 ) and two membrane-associated proteins ( proteins 1 and 2 ) . Although the proteins in Table 1 were identified using appropriate screening controls , the rabbit reticulocyte lysate that was used to drive the in vitro protein synthesis reaction contains many unknown materials that may be contaminating in the assay . To further verify the screening result , RAW264 . 7 cells were treated with purified proteins expressed in E . coli BL21 ( DE3 ) . Among the eight proteins shown in Table 1 , five proteins were successfully purified as His6 tagged-recombinant proteins ( Fig . 2A ) . The other three proteins were either not expressed or expressed as insoluble proteins ( data not shown ) . Since RAW264 . 7 cells are highly sensitive to LPS , LPS was removed from purified proteins down to <0 . 05 EU/ml as described in Materials and Methods ( data not shown ) . As shown in Fig . 2B , varying doses of the purified recombinant proteins were used to stimulate cultures of RAW264 . 7 cells . Proteins 1 , 4 , 5 , and 8 induced RAW264 . 7 cells to produce TNFα , whereas purified VC1893 ( Protein No . 3 ) failed to elicit TNFα production . Interestingly , phosphatidylserine decarboxylase ( Protein No . 5 , VC0339 , herein called PSD ) stimulated TNFα production even at the lowest concentration tested ( 100 ng/ml ) suggesting that PSD stimulates macrophages with the greatest potency . To further prove that PSD-induced TNFα production is not due to LPS contamination , we compared the level of TNFα produced in response to the purified PSD and E . coli LPS . As shown in Fig . 2C , in response to 2 EU/ml E . coli LPS , a level that is >40-fold higher than that detected in the purified PSD , RAW264 . 7 cells produced only ∼0 . 7% of the level of TNFα produced by PSD . It has been reported that LPS response , especially at lower concentration of LPS , is enhanced by the presence of LPS-binding protein ( LBP ) , which facilitates the efficient delivery of LPS to CD14 [24] , [25] . As expected , the addition of LBP dramatically enhances the production of TNFα ( Fig . 2D ) and IL-6 ( Fig . 2E ) in response to LPS . In contrast , pro-inflammatory cytokine production was robust regardless of the presence or absence of LBP in RAW264 . 7 murine macrophage cells responding to PSD ( Fig . 2D and E , right bars ) . Collectively , these results strongly support that PSD-induced proinflammatory response from stimulated macrophages is not caused by LPS contamination in the purified PSD protein . PSD is an important enzyme involved in the synthesis of phospholipid bilayer in both eukaryotic and prokaryotic organisms [26] , [27] , [28] . V . cholerae PSD is 285 amino acids long and is produced as an immature proenzyme , which then undergoes autocatalytic cleavage by α , β-elimination . Biologically active mature enzyme thus produced consists of two subunits , a 27 . 9 kDa β-subunit and a 3 . 6 kDa α-subunit ( black arrow in Fig . 2A ) [29] . To gain an insight into the signaling mechanism ( s ) by which PSD activates mouse macrophages , PSD was used to stimulate the induction of the proinflammatory cytokine , IL-6 , in MyD88−/− ( Myeloid Differentiation Factor-88 ) and MyD88+/− macrophages . MyD88 is one of most commonly used adaptor molecules that mediates signal transduction in mammalian innate immune activation [30] , [31] . Upon ligand binding , TLRs recruit many downstream signaling molecules via MyD88 to activate NF-κB , which then transcribes genes involved in the production of proinflammatory cytokines [1] . LPS is a known potent stimulator of MyD88-dependent inflammatory pathway and stimulates a high level of IL-6 secretion from MyD88 positive macrophages compared to MyD88 negative macrophages [32] . When treated with PSD or LPS , MyD88 knockout macrophage cells secreted significantly less IL-6 compared to the level of IL-6 detected in MyD88 positive macrophages ( Fig . 3A and C ) indicating that PSD triggers through a predominantly MyD88-dependent inflammatory signaling cascade similar to that observed with LPS . To determine the relative strength of PSD as an immunostimulant , adhered primary macrophages were also treated with various doses of other known TLR agonists ( Fig . 3A and C ) . FlaD ( VC2143 ) from flagella and CpG DNA are known TLR agonists that act through a MyD88-dependent pathway [11] , [33] . They both induce IL-6 secretion from MyD88 positive macrophages but not from MyD88 negative macrophages . In both cases , the level of secretion is not as great as that observed with either LPS or PSD stimulation ( Fig . 3A and C ) . To further rule out the possibility that the IL-6 production is due to any contaminant that might have been incorporated in PSD sample during purification , another V . cholerae protein ( VC0222 , pantetheine-phosphate adenylyltransferase ) , which was purified in parallel with PSD and FlaD , was also used as a negative control in this assay . After LPS was removed , the purity of these three proteins was shown by SDS-PAGE ( Fig . 3B ) . We observed that PSD was more potent at eliciting IL-6 production than FlaD , and IL-6 production was not detectable in cells treated with VC0222 ( Fig . 3A ) . Again , this suggests that IL-6 production by PSD is caused by the specific interaction of PSD with macrophage cells and not by any other unknown contaminant in the protein sample such as LPS . Poly I:C ( dsRNA mimic ) is a TLR agonist that stimulates innate immune activation through a MyD88-independent pathway [34] . When poly I:C was used to stimulate peritoneal macrophages from MyD88 knockout and positive mice , we observed a higher level of IL-6 ( Fig . 3C ) and IFN-β ( Fig . S1 ) in MyD88−/− macrophages than in MyD88 intact cells . This suggests that the decrease in IL-6 production from MyD88 knockout macrophages induced by PSD or LPS is not due to a non-specific decrease in ligand-responding capacity of the MyD88 knockout cells . Interestingly , we detected a decreased level of IL-6 production in MyD88+/− mouse peritoneal macrophages responding to the higher dose of PSD ( Fig . 3A , black and gray bars ) . To determine whether this result is due to PSD-induced cytotoxicity , we measured lactate dehydrogenase ( LDH ) activity in the cultures of peritoneal macrophage cells . We observed that PSD exerted a cytotoxic effect on MyD88+/−macrophage cells at 15 µg/ml concentration ( Fig . 3D , left side ) as well as in MyD88−/− macrophage cells ( Fig . 3D , right side ) , suggesting that the PSD-triggered cytotoxicity occurs irrespective of the presence of MyD88 . In contrast , LPS-stimulated macrophage cells did not exhibit LDH release in culture supernatants of either MyD88−/− or MyD88+/− cells suggesting that LPS induction of proinflammatory responses is not cytotoxic in nature ( Fig . 3D ) . To determine whether the proinflammatory response elicited by PSD is mediated by its enzymatic activity , an enzymatically inactive mutant of PSD was constructed and expressed . Biologically active enzyme consists of two subunits as shown in SDS-PAGE gels ( Fig . 2A and 3B ) . Fig . 4A shows primary sequence of V . cholerae wild-type PSD and the mutant PSD , in which the wild-type LGST motif ( underlined ) identified to be the processing site for autocatalytic cleavage [29] , [35] has been changed to LAAT in the mutant protein . As expected , the AA-PSD mutant was purified as a single polypeptide ( Fig . 4B ) , and no enzymatic activity was observed in the mutant ( Fig . 4C ) . When murine peritoneal macrophages were treated with either wild-type or mutant forms of PSD , a reduced level of IL-6 ( ∼40% ) was detected in culture supernatants of cells treated with AA-PSD compared to cells incubated with WT-PSD ( Fig . 4C ) . Furthermore , heat-inactivated WT-PSD , when compared to untreated WT-PSD , elicited significantly reduced IL-6 production in macrophage cells ( Fig . 4C ) . Because the stimulatory activity of LPS is not affected by heat inactivation ( data not shown ) , this results further supports our conclusion that the stimulatory activity of PSD is not due to LPS contamination . IL-6 was not detected in supernatants of cells incubated with the control protein , VC0222 ( Fig . 4C ) or PBS confirming that our purification protocols effectively removed residual LPS from these recombinant proteins . However , IL-6 production was not completely abrogated by treating macrophages with either the heat-inactivated WT-PSD or AA-PSD suggesting that a linear , nonconformational epitope of PSD is likely recognized by macrophages in this assay . We then asked if AA-PSD causes the cytotoxic effect in peritoneal macrophages similar to that observed by stimulating cells with WT-PSD ( Fig . 3D ) . As shown in Fig . 4D , LDH release was not detected in cells treated with AA-PSD at the two different concentrations tested suggesting that the cytotoxic effect is most likely due to the biological activity associated with the WT-PSD . Our results shown in Fig . 3A indicated that V . cholerae PSD elicits proinflammatory responses in a MyD88-dependent manner . Since most TLRs use MyD88 as a key adaptor protein to recruit downstream signaling molecules [1] , [31] , we hypothesized that PSD may signal through a TLR to activate innate immune responses and the production of proinflammatory cytokines . To test this hypothesis , HEK293 ( human embryonic kidney ) cells , which do not express endogenous TLR [36] , were transfected with each of the individual TLRs ( TLR2 , 3 , 4 , 5 , 7 , and 9 ) and assayed for activation by PSD . To ensure full responsiveness to LPS , the plasmid expressing tlr4 co-transcribes genes encoding CD14 and MD2 , which are involved in LPS responses [37] . HEK293 cells were also transfected with a reporter construct in which the expression of secreted alkaline phosphatase ( SEAP ) is driven from an NF-κB inducible promoter . Appropriate positive controls ( Fig . S2 ) were tested to compare their NF-κB signaling activity with that of PSD . As shown in Fig . 5A , HEK293 cells expressing each TLR responded to its corresponding ligand ( blue bars ) . No cross reactivity was detected in HEK293 cells responding to other control ligands ( data not shown ) . We observed that PSD-induced NF-κB activation was most efficiently detected in HEK293 cells expressing TLR4/MD2/CD14 in comparison to the other TLRs , indicating that PSD likely signals through TLR4 ( Fig . 5A ) . To further prove the role of TLR4 in PSD signaling , we next compared the IL-6 production from peritoneal macrophages freshly isolated from TLR4 WT ( C3H/HeOuJ ) and TLR4 hyporesponsive mice ( C3H/HeJ ) in response to PSD stimulation . Fig . 5B shows the time course of IL-6 production from cells in response to S . typhimurium LPS , PSD , VC0222 and PBS . In the presence of LPS , decreased levels of IL-6 production were detected at all time points in TLR4 deficient macrophages compared to levels detected in TLR4 intact cells ( Fig . 5B ) . However , IL-6 production was only reduced by approximately 50% in the TLR4 deficient macrophages after stimulation with LPS suggesting that alternative LPS responding pathways exist in these cells . Indeed , LPS-induced IL-6 production from TLR4-deficient macrophages may also be mediated by TLR2 as previously reported [38] , [39] . In contrast , IL-6 production was completely abrogated in PSD-treated TLR4-deficient macrophage cells , while a high level of IL-6 was produced in TLR4 intact cells ( Fig . 5B ) . When we treated the same TLR4 positive or deficient macrophage cells with phosphatidylserine ( PS ) or phosphatidylethanolamine ( PE ) , that is the substrate or product of PSD , respectively , no proinflammatory response was detected ( data not shown ) . This further suggests that the PSD-mediated IL-6 induction is not due to the lipid substrate or product of PSD . Collectively , these results show that PSD-induced proinflammatory signal transduction is mediated by TLR4 and further supports our conclusion that PSD's TLR4 agonist activity is not the result of LPS contamination . Using this high throughput screen , we have identified PSD as a TLR4 agonist that signals through a MyD88-dependant pathway . One of the hallmarks of TLR agonists is that they induce innate immune responses that can influence and shape adaptive immunity . Because of this property , many TLR agonists are used as adjuvants enhancing the adaptive immune response against co-administered antigens [40] . To determine if PSD can also exert an adjuvant effect , groups of mice were immunized with the inert antigen BSA alone or in the presence of either PSD or CpG as adjuvants . Mice that received BSA in conjunction with PSD showed enhanced anti-BSA antibody responses compared to mice that were immunized with BSA alone or naïve animals ( Fig . 6A ) . This enhanced effect on the anti-BSA immune response was similar to that observed when mice were immunized with a known TLR agonist , CpG [41] . This adjuvant effect exerted by PSD was due to TLR4 activation since TLR4-deficient mice immunized with PSD in conjunction with BSA demonstrated a significant decrease in the anti-BSA immune response compared to when TLR4 intact mice were immunized ( Fig . 6A ) . These data suggest that PSD is a bacterial protein that acts as an adjuvant against a co-administered antigen , BSA and further proves the role of TLR4 in PSD-induced signaling pathway in vivo . Monitoring antibody isotypes generated following immunization with a particular adjuvant can reflect the balance of Th1 or Th2 responses induced . Th1 responses favor the production of IgG isotypes such as IgG2a and Th2 responses favor the production of isotypes such as IgG1 [42] . Isotype characterization of serum from mice immunized with BSA in the presence of both PSD and CpG demonstrated elevated levels of both BSA-specific IgG1 and IgG2a compared to serum from mice that received BSA alone ( Fig . 6B and C ) . In contrast , immunization with BSA alone induced specific IgG1 but not IgG2a responses . These results suggest that PSD acts as an adjuvant that not only enhances the default Type 2 ( IgG1 ) humoral immune response , but also induces IgG2a , a hallmark of Type 1 immunity , against a co-administered antigen . Consistent with results in Fig . 6A , significantly decreased levels of both anti-BSA IgG1 and IgG2a were detected in TLR4-deficient mice . To determine if cellular responses mirrored the humoral responses , we determined cytokine profiles from antigen restimulated splenocytes . We isolated single cell suspensions of splenocytes from immunized and naïve animals and plated them into 24 well plates in the presence or absence of the BSA antigen or PSD adjuvant . In this assay , the splenocytes from immunized animals will recognize the antigen and start proliferating and secreting cytokines in response . Consistent with isotyping results ( Fig . 6B and C ) , BSA- and PSD-restimulated splenocytes from TLR4-intact mice immunized with PSD+BSA secreted IFN-gamma , a Type 1 cytokine ( Fig . 6D ) and IL-10 , a Type 2 cytokine ( Fig . 6E ) . Antigen-restimulated splenocytes from TLR4-deficient mice immunized with PSD+BSA and naive animals failed to secrete detectable Type 1 or 2 cytokines . Collectively these data show that PSD serves as an adjuvant which enhances both humoral and cell-mediated arms of the immune response against a co-administered antigen .
In this study , we show that V . cholerae phosphatidylserine decarboxylase is capable of stimulating innate immune effector cells ( macrophages ) to secrete proinflammatory cytokines , a hallmark of the innate immune response . PSD was identified in a high-throughout Expressed Protein Screen for Immune Activators ( EPSIA ) . EPSIA provides an approach to screening the entire protein repertoire of an infectious organism for agonists of immunological responses that can be assayed using appropriate eukaryotic reporter cell lines . Our successful application of EPSIA to the discovery of a novel TLR agonist can be attributed to following contributions; ( i ) the use of a well-established murine macrophage cell line RAW264 . 7 provided cytokine induction reproducibility and thus , minimized batch to batch variations , ( ii ) efficient in vitro protein synthesis was achieved using the rabbit reticulocyte lysate ( RRL ) expression system , ( iii ) LPS , to which RAW274 . 7 cells are highly sensitive , was successfully removed from plasmid DNA and purified recombinant proteins by ion-exchange column purification and detergent extraction method , respectively and finally , ( iv ) the control treatment containing RRL mixture , but no plasmid DNA , elicited only a basal level of proinflammatory response from RAW264 . 7 cells . The most convincing evidence that V . cholerae PSD may play a role as an immunostimulatory protein stems from the results demonstrated in Fig . 2 . Purified PSD elicited the strongest TNFα production at concentration as low as 100 ng/ml , while the other three proteins stimulated RAW264 . 7 cells only at the highest working concentration ( 10 µg/ml ) . The experiments using MyD88−/− peritoneal macrophages led to the observation that the PSD-induced proinflammatory response may be mediated by an innate immune signaling pathway . Like LPS , PSD stimulated a MyD88-dependent signaling mechanism to produce IL-6 ( Fig . 3A ) . IL-6 production in the freshly isolated peritoneal macrophages also indicates the in vivo relevance of the PSD-induced host proinflammatory responses . Bioinformatic analysis indicates that almost all bacterial species in the public database possess a gene encoding for PSD . In addition , the gene encoding PSD cannot be inactivated in V . cholerae [43] . This suggests that PSD is likely an essential enzyme for bacterial viability and thus , could be a conserved target for detection by the innate immune system . Interestingly , PSD is also present in mammalian cells and sequence alignment between eukaryotic and prokaryotic PSD suggests that eukaryotic PSD are also processed by autocatalytic cleavage using the same LGST motif [44] . Mouse PSD displays 31 . 7% sequence identity with V . cholerae PSD while the latter displays 60% sequence identity with E . coli PSD . It is not known whether the divergence in sequence between eukaryotic and prokaryotic PSD is sufficient to block its TLR agonist activity . Importantly , PSD in mammalian cells resides predominantly in mitochondria [45] , [46] . This may provide some degree of compartmentalization , where innate immune cells may not detect circulating levels of endogenous PSD unless host cells lyse and release mitochondrial contents . It is interesting that disruption of mitochondria is a hallmark of processes such as apoptosis [47] and thus , mitochondrial PSD may play a role in amplification of inflammatory responses occurring as a result of bacterial or viral replication within and lysis of host cells . We were also intrigued by the observations that ( i ) PSD at its highest concentration ( 15 µg/ml ) was cytotoxic to both MyD88+/− and MyD88−/− macrophages ( Fig . 3D ) , and ( ii ) this cytotoxicity was not detected when the non-functional alanine-substituted PSD mutant ( AA-PSD ) was used to stimulate cells ( Fig . 4D ) . This suggests that PSD triggers a MyD88-dependent proinflammatory signaling up to a certain threshold , after which PSD induces a non-specific cytotoxicity to host macrophage cells likely due to its enzymatic activity . Further study is necessary to precisely determine whether cytotoxic effect imposed on macrophages by PSD is mediated by apoptosis or random necrosis . TLR screening of transfected HEK293 cells and antigen stimulation of TLR4-deficient macrophages clearly identified TLR4 as a mediator of the PSD-induced proinflammatory signaling pathway . The gene encoding TLR4 in C3H/HeJ has a point mutation , which results in an amino acid substitution from proline to histidine in the intracellular domain [48] . This amino acid residue change was found to be crucial for the inhibition of binding of MyD88 to downstream signaling molecules [48] . Our results in Fig . 4 demonstrated that the enzymatic activity of PSD appears to be involved , at least in part , in the proinflammatory signaling . However , more detailed experiments are necessary to decipher the precise molecular mechanisms of the bacterial PSD acting on host cells . Recent evidences indicate that upon ligand binding , TLR4 is recruited to a specific domain in the plasma membrane , called the lipid raft [49] , [50] . Because PSD is an enzyme that possesses the ability to modify the phospholipid bilayer , we postulate that ( i ) PSD may directly interact with TLR4 to transduce the proinflammatory signaling or ( ii ) PSD acting on host cell membrane may affect the interaction between TLR4 and lipid rafts . Our data also suggest that the activation of macrophages by PSD may be due not only to the enzymatic activity of PSD , but also a binding determinant of PSD that interacts directly with the macrophage , presumably through TLR4 . There may be a difference in the accessibility of a binding determinant on PSD , where the binding determinant is more accessible in the active cleaved enzyme than the mutant PSD which effectively remains in the inactive pro-enzyme conformation . TLR agonists activate innate immune cells to secrete cytokines and more efficiently process and present antigens , which in turn , stimulates robust and effective adaptive immune response [21] , [51] . Similarly , we observed that V . cholerae PSD exhibited adjuvant activity against a co-administered inert antigen BSA ( Fig . 6 ) . PSD stimulated enhanced antibody responses against BSA similar to that observed when a known TLR agonist , CpG was used as an adjuvant compared to when mice were immunized with BSA alone . Isotype characterization of the humoral response and the assaying the cytokine profiles from antigen-restimulated splenocytes following immunization with PSD showed that PSD elicits a mixed Type 1 cell-mediated response in addition to the default Type 2 humoral response . Moreover , both of these responses are enhanced relative to immunization with BSA in the absence of PSD as an adjuvant . This study identifies V . cholerae PSD as a novel bacterial protein that stimulates the host innate immune system , but it still remains unclear ( i ) whether PSD , as an inner membrane protein , plays a direct role in modulating host innate immune system during a dynamic in vivo infection , and ( ii ) if so , how much and when PSD is released into the environment from invading bacterial cells . Bacterial infection is a complex process , during which bacterial cells are stressed by a number of factors including encountering harsh host immune responses and nutrient deficiency . Thus , it is expected that a subpopulation of invading bacterial cells are lysed during infection , releasing their intracellular components . Interestingly , spontaneous cell death was observed in bacterial biofilms , presumed to be the major mode of bacterial growth in host [52] , [53] . Therefore , it is likely that our innate immune system may have been evolved to target intracellular components , such as PSD and previously identified bacterial CpG DNA [10] . In summary , we utilized a proteome-wide screening technique called EPSIA to identify a novel bacterial immunostimulatory protein . Phosphatidylserine decarboxylase ( PSD ) was shown to activate the host macrophage cells and results provided in this work represent a previously undescribed interaction between host immune system and a bacterially conserved protein . The activity of PSD was further shown to have utility in stimulating immune responses to bystander antigens that were co-administered to animals with PSD as an adjuvant . Thus , EPSIA is a new tool for identifying microbial proteins which are recognized by the innate immune system and may therefore provides an exciting novel approach to identifying antigens and more effective vaccine adjuvants .
The methods for animal experimentations were approved by the Harvard Institutional Animal Care and Use Committee ( IACUC ) . V . cholerae proteome library was prepared as described in our recent publication [23] . In vitro protein synthesis was performed using the TnT® coupled reticulocyte lysate system kit ( Promega Cor . , Madison , WI ) following the manufacturer's instructions . In the primary screening , proteins were synthesized as a pool in each well of 96-well plate . For the secondary screening , proteins in each pool that triggered the TNFα production in RAW264 . 7 cells were individually synthesized and screened for their activity to produce TNFα . Supernatants were collected after 6 hr incubation and assayed for secreted TNFα by cytokine ELISA ( BD Pharmingen ) . RAW264 . 7 cells were seeded at 2×106 cells/ml in a 100 µl volume and grown for overnight before being treated with proteins . Cells were grown in DMEM containing 10% FBS at 37°C in a humidified 5% CO2 incubator . For recombinant protein production , the encoding genes were PCR-amplified and positionally cloned into pET21b ( Novagen ) . The resulting plasmid was then transformed into E . coli BL21 ( DE3 ) . 1 mM IPTG was used to induce overexpression and recombinant His-tagged proteins were purified using a Ni-NTA agarose ( Qiagen , Valencia , CA ) . The purity of purified protein was assessed by SDS-PAGE . QuickChange® site-directed mutagenesis kit ( Stratagene , Inc . , La Jolla , CA ) was used to introduce point mutation ( AA replacement ) . Enzyme activity assay was carried out following the previously published protocols [54] , [55] . LPS removal and LPS detection assay was performed using EndoClean™ Endotoxin Removal Kit ( Biovintage , Inc . , San Diego , CA ) and Endotoxin detection kit ( Cambrex Corp . , East Rutherford , NJ ) , respectively . For cytotoxicity assay , lactate dehydrogenase ( LDH ) was measured using a spectrometric assay kit ( Biovision Inc . , Mountain View , CA ) following manufacturer's instructions . RAW cells were seeded at 2×106 cells/ml in 96-well tissue culture plates overnight in DMEM containing 10% FBS . The next day , cells were washed 3 times with PBS and incubated for 2 hours with serum-free media . PSD and LPS at the indicated concentrations were added to cells in fresh serum-free media alone or to serum-free media containing 100 ng/ml LPS-binding protein ( R&D Systems ) . Cells were then incubated at 37 C , 5% CO2 for 6 hours . Supernatants were collected and assayed for TNFa and IL-6 by capture ELISA . TLR stimulation was tested by assessing NF-κB activation in HEK293 cells expressing a given TLR . The activity of sample is tested on six different mouse TLRs: TLR2 , 3 , 4 , 5 , 7 and 9 ( Invivogen , San Diego , CA ) . HEK293 cells were also transfected with a reporter construct , in which the secreted alkaline phosphatase ( SEAP ) is expressed by a NF-κB inducible promoter . TLR stimulation in the screening is tested by assessing NF-κB activation in the HEK293 cells expressing a given TLR . After a 16–20 hr incubation , SEAP activity was monitored by measuring the OD650 on a Beckman Coulter AD 340 C Absorbance Detector . Five to seven week old female BALB/c mice were purchased from Charles River . Five to seven week old C3H/HeJ and C3H/HeOuJ mice were purchased from Jackson Labs . MyD88−/− and MyD88+/− mice were from an in-house colony at Harvard Medical School . All mice were housed using sterile set-up and were allowed a one week acclimatization period before initiation of experiments . Mice were euthanized and injected with 10 ml of cold PBS into the peritoneum . Resident cells were then lavaged out of the peritoneum and pooled . Red blood cells were lysed using a hypotonic buffer and the cells were resuspended in complete RPMI-10 media . These cells were used to seed 96-well plates at 2×106 cells/ml in a 100 µl volume and allowed to adhere for 2–3 hours at 37 °C , 5% CO2 . Unbound cells were removed by washing the plate 3 times with PBS . Adhered cells were incubated with varying doses of stimulants as indicated in 100 µl volume of serum-free media for 15 hrs . Supernatants were collected at the times indicated and assayed for secreted IL-6 by cytokine ELISA ( BD Pharmingen ) . Groups of four to five BALB/c or C3H/HeJ mice were immunized three times at biweekly intervals intraperitoneally ( IP ) with 10 µg BSA in the presence or absence of 5 µg of PSD . Other groups of BALB/c mice were similarly immunized with 10 µg BSA alone or in conjunction with 10 µg CpG DNA . Two weeks after the last immunization , all animals were sacrificed . Blood was collected from each mouse by cardiac puncture , and serum was obtained for the determination of BSA-specific responses by ELISA . Spleens were collected from each mouse to perform antigen restimulation assays . Spleens from immunized or naïve animals were homogenized through a sterile cell dissociation sieve , pelleted and resuspended in RPMI 1640 ( 1% FBS , 2% Ab/Am ) . Splenocytes were isolated using density centrifugation with Histopaque-1119 , washed , and resuspended in complete RPMI 1640 ( 10% FBS , 2% Ab/Am , 2 mM L-Glutamine , 50 µM β-mercaptoethanol , 1 mM sodium pyruvate , and 1 mM MEM non-essential amino acids ) containing 0 . 4 ng/ml of IL-2 . Purified mononuclear cells were counted and 2 . 5×106 cells were added to the wells of a 24-well tissue culture plate . Cells were restimulated with 1 µg BSA or left unstimulated . Supernatants were collected at 3 , 4 , and 5 days and kept at −20°C until assayed for Type 1 ( IFN-gamma ) and Type 2 cytokines ( IL-10 ) by cytokine ELISA ( BD Pharmingen ) . Serum samples from groups of immunized mice were analyzed for antigen-specific IgG by ELISA . Immune sera was diluted in PBS-0 . 05% Tween 20 and added to microtiter plates precoated overnight with 1 µg BSA per well . Rabbit anti-mouse IgG , IgG1 , or IgG2a conjugated to alkaline phosphatase was used to determine serum anti-BSA levels . Plates were visualized by the addition of p-nitrophenol substrate to each well . Reactions were stopped with 2 N NaOH and the absorbance at 405 nm was determined on a spectrophotometer . Data are reported as reciprocal endpoint titer , with the cutoff calculated as two standard deviations above the mean of the negative control . Data are expressed as mean±standard error of the mean . An unpaired Student's t test was used to analyze the data . A P value of <0 . 05 was considered statistically significant .
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Innate immune responses are the first line of defense and involve the early recognition of pathogenic microorganisms . Furthermore , these early innate responses can help shape and influence the development of more specific adaptive immune responses . One way that innate immunity is triggered is by activation of TLRs , or Toll-like Receptors . TLRs recognize a wide spectrum of microbes by binding to pathogen-associated molecular patterns ( PAMPs ) , which are conserved microbial products . Here , we have used a high-throughput method to understand more about how a pathogen can trigger early innate immune responses and also how these early responses to infection can influence the adaptive , more specific , immune response . This technique can also be utilized for adjuvant discovery which is important in vaccine development since different adjuvants can induce or enhance different kinds of immune responses to a particular antigen . Using this method , we identified a novel bacterial protein that activates a TLR and further characterized its role as an adjuvant . Identifying the TLRs , their ligands , and the signal transduction events that they initiate has provided insight into our understanding of how the immune response to infection begins , and how these factors also collectively influence the adaptive immune response .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"microbiology/medical",
"microbiology",
"microbiology/innate",
"immunity",
"microbiology/immunity",
"to",
"infections"
] |
2009
|
Vibrio cholerae Proteome-Wide Screen for Immunostimulatory Proteins Identifies Phosphatidylserine Decarboxylase as a Novel Toll-Like Receptor 4 Agonist
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Sensory experience elicits complex activity patterns throughout the neocortex . Projections from the neocortex converge onto the medial temporal lobe ( MTL ) , in which distributed neocortical firing patterns are distilled into sparse representations . The precise nature of these neuronal representations is still unknown . Here , we show that population activity patterns in the MTL are governed by high levels of semantic abstraction . We recorded human single-unit activity in the MTL ( 4 , 917 units , 25 patients ) while subjects viewed 100 images grouped into 10 semantic categories of 10 exemplars each . High levels of semantic abstraction were indicated by representational similarity analyses ( RSAs ) of patterns elicited by individual stimuli . Moreover , pattern classifiers trained to decode semantic categories generalised successfully to unseen exemplars , and classifiers trained to decode exemplar identity more often confused exemplars of the same versus different categories . Semantic abstraction and generalisation may thus be key to efficiently distill the essence of an experience into sparse representations in the human MTL . Although semantic abstraction is efficient and may facilitate generalisation of knowledge to novel situations , it comes at the cost of a loss of detail and may be central to the generation of false memories .
Cognitive faculties enabling flexible adaption of behaviour are at the heart of the human species’ evolutionary success . Cognition operates on abstract representations of knowledge derived from prior experience [1] . Abstraction can have two separate but related meanings [2] . First , formation of a concept in semantic memory requires abstraction in the sense of generalisation across episodes . For example , the concept ‘dog’ , a furry animal that barks , is learned by extracting regularities among multiple encounters with various exemplars of dogs . Second , abstraction can also refer to the extraction of meaning from sensory input in a single instance of perception . Abstraction in the latter sense ranges from lower , more concrete levels ( e . g . , labelling a percept as ‘terrier’ ) to intermediate levels ( ‘dog’ ) and high , superordinate levels ( ‘animal’ ) . Abstraction both as a cross-episode generalisation and as an extraction of supramodal semantic information from sensory input are in constant interplay and shape episodic and semantic memory representations [3 , 4] . Our knowledge about semantic representations in the human brain is for the most part restricted to the cortex . Putative functional roles of involved neocortical regions correspond to sensory and/or motor features of an encoded concept [1 , 5] . Here , abstract categories such as , for example , living and nonliving things differ with respect to which portions of the neocortex are recruited for their encoding . Due to such macroscopic , topographical organisation of semantic representations in the neocortex , these representations can be investigated with rather coarse imaging techniques such as functional magnetic resonance imaging [5] . Large strides have also been made in elucidating the neuronal code of object and face recognition along the ventral processing pathway of nonhuman primates leading up to highly abstract representation in monkey inferotemporal cortex and the amygdala [6 , 7] . Next to categorical codes , influential approaches also entail mapping semantic concepts onto a multidimensional , semantic space along dimensions such as living–nonliving or abstract–concrete [2 , 8 , 9] . Investigating object recognition and semantic representations at the final stages of the ventral processing pathway in the human medial temporal lobe ( MTL ) , including the amygdala , has been notoriously difficult . Investigation of neuronal representations in the human MTL at the relevant level of detail seems impossible with noninvasive imaging techniques because—unlike the neocortex—most MTL areas lack semantic topographical organisation [10 , 11] . Studies conducted in the setting of invasive epilepsy monitoring using additional microelectrodes to record action potentials of single units have been instrumental for this purpose [10–15] . A seminal finding of these studies is that some MTL units responded in a selective and invariant manner to various images of a familiar person and even to their written and spoken name , suggesting that they encode the identity of that person and thus the contents of a concrete semantic concept in an all-or-none fashion [13 , 14] . However , further studies emphasised that MTL neurons can also respond to a wider range of stimuli in graded fashions in which sometimes more abstract semantic relations between stimuli can be identified such as , for example , membership to a broad category [9 , 14 , 15 , 16] . Thus , rather than all-or-nothing responses to specific concepts , it could be that neurons in the human MTL encode semantic features along continuous dimensions , resulting in ‘semantic tuning curves’ . Or as Kornblith and Tsao [6] put it in the context of face-patches in primate IT , they are ‘[…] measuring faces , they are not yet explicitly classifying them’ . Previous human single unit studies often preselected units based on rather conservative response criteria , which may have resulted in a potential overestimation of all-or-none responses to individual semantic concepts . In the current study , in contrast , we analyse representations at the level of the entire population of units we record from . By doing so , we investigate how and at what level of abstraction semantic information conveyed by visual input is encoded in activity of single units in the human MTL . In contrast to previous studies , we consistently used the same set of images across sessions and patients , and the images could be grouped at multiple levels of abstraction . This procedure , in combination with a large sample of epileptic patients , allowed us to record neuronal responses for each image in a population of neurons unprecedented in size . Using this procedure , we could characterise and compare the nature of representations and their level of abstraction at a population level for different regions of the MTL .
Subjects ( N = 25; 59 sessions ) were bilaterally implanted with depth electrodes for seizure monitoring in the amygdala , hippocampus , entorhinal cortex , and parahippocampal cortex . Subjects were presented with visual stimuli depicting objects from 10 semantic categories consisting of 10 exemplars each ( 100 images , 10 trials each ) . The subjects’ task was to indicate by button press whether a man-made or natural object was depicted . As expected , this task was very easy as reflected by high accuracy ( median = 97 . 62% , IQR = 2 . 25% ) and short reaction times ( median = 669 ms , IQR = 146 ms ) . We first analysed our data by classifying units into responsive and nonresponsive , according to an established criterion ( see Neuronal response test section in Materials and methods ) as in previous studies [12 , 13] ( Figs 1 and 2 ) . Our analyses confirm that some units in the MTL respond to only a few stimuli in the set ( Fig 1 ) . We recorded from a total of 4 , 917 units , 2 , 009 of which were classified as single units ( 41% ) . In the amygdala , we found 1 , 392 units ( 656 single units [47%] ) , in the hippocampus 1 , 863 units ( 706 single units [38%] ) , in the entorhinal cortex 828 units ( 328 single units [40%] ) , and 831 units ( 319 single units [38%] ) in the parahippocampal cortex ( Fig 2B ) . A subset of 785 units responded with increased firing rates to at least one of the 100 stimuli ( see Neuronal response test section in Materials in methods; Fig 2B ) . Selectivity as determined by the number of response-eliciting stimuli for a given neuron was similar in the entorhinal cortex , amygdala , and hippocampus but was markedly lower in the parahippocampal cortex [12] ( Fig 2C ) . Some units responded very selectively , sometimes to only one of the stimuli in the set ( Fig 1D–1F ) . In the amygdala , this was the case in 43% of the responsive units , in the hippocampus 57% , and in the entorhinal cortex 54% . This number was markedly lower in the parahippocampal cortex , namely , 35% . When units responded to multiple stimuli , the response-eliciting stimuli were often from the same semantic category ( Fig 1A–1C and 1G–1I ) . We also calculated the probabilities with which images from a given category elicited a neuronal response , separate for each anatomical region in the MTL . To this aim , we computed the number of significant responses across all units and divided this number by the total number of stimuli and the number of units . Observed response probabilities ranged between approximately 0 . 25% and 2% across anatomical regions and stimulus categories ( Fig 1D ) . Neurons responded more frequently to food stimuli than to stimuli of other categories , which was especially prominent in the amygdala and , to a lesser degree , also in the hippocampus and entorhinal cortex ( Fig 2A ) . Going beyond analyses of responsive versus nonresponding units , we next looked at responses of the whole population of units we recorded from . With these analyses , we find that population activity is determined by abstract , semantic features of the stimuli . We investigated population activity by representational similarity analyses ( RSAs ) [9 , 17 , 18] . To this aim , we quantified each neuronal response to a stimulus using a single Z score that expressed average firing across all trials of a stimulus in the 1 , 000 ms after stimulus onset , normalised using the distribution of baseline firing rates ( −500 to 0 ms relative to stimulus onset ) across all trials . The population response to a stimulus thus corresponded to a population vector of Z scores from all units in a given region . Representational dissimilarity ( i . e . , distance ) between two stimuli was then quantified as 1 − Pearson’s correlation coefficient of their population vectors . Representational dissimilarities are displayed as matrices of colour-coded distance between all pairs of stimuli ( Fig 3A–3D ) . Representational dissimilarity analyses showed that population firing patterns evoked by stimuli of the same category were more similar than those evoked by stimuli from different categories in all anatomical regions ( Fig 3A–3D; all p < 10−5; random permutation test , Inference statistics on representational dissimilarity and confusion matrices section in Materials and methods ) . To elucidate potential principles on higher levels of abstraction , we applied multidimensional scaling ( Fig 3E–3H ) and automated hierarchical clustering ( Fig 3I–3M , S3 Fig ) to these dissimilarity matrices . Remarkably , inspection of dendrograms obtained from hierarchical clustering revealed that the preconceived assignment of stimuli to superordinate categories was almost perfectly reflected in representational dissimilarity of the recorded population activity in the amygdala and hippocampus ( Fig 3I and 3K ) . That preconceived categories matched information present in neuronal representations is evidenced by the sorting on the x-axis of the dendrograms . Perfect correspondence between neuronal similarity and category membership is indicated in that all exemplars of a category line up next to one another on the x-axis after sorting according to similarity . This is the case for all but two categories in the amygdala , in which only one exemplar of the ‘computer’ category ends up closer to other exemplars from the ‘musical instruments’ category . A similar pattern of exemplar sorting is evident in the hippocampus , whereas this was not the case in the entorhinal and parahippocampal cortex ( Fig 3L and 3M ) . RSAs for units that did not show a response according to any of the stimuli in our set ( according to the statistical response criterion used in this and previous studies ) showed similar patterns of similarity ( S1 Fig ) . Consequently , representational similarities of nonresponding units alone are statistically significantly higher for within- versus between-category pairs ( all p < 10−5; see ‘Inference statistics on representational dissimilarity and confusion matrices’ section in Materials and methods ) , suggesting that even small variations in firing rate of MTL units contain considerable amounts of information at an abstract , categorical level . Representations clustered beyond our preconceived categories in a highly abstract but meaningful way . Abstract semantic clusters of representational similarity emerging from neuronal representations are visualised by the dendrograms resulting from hierarchical clustering ( Fig 3I–3K ) and by projections of multidimensional scaling onto a two-dimensional space ( Fig 3E–3H ) . In the amygdala , we saw a food cluster that consisted of all exemplars of man-made food and fruit categories . This food cluster becomes evident in that exemplars from the preconceived categories of ‘man-made food’ and ‘fruit’ are close together in the 2-dimensional projection generated by multidimensional scaling ( Fig 3E ) . An animal cluster entailed exemplars of wild animals , birds , and insects . The categories of all man-made objects together constituted a further cluster . In the hippocampus , we additionally observed a clear separation between man-made and natural objects . This separation becomes evident when one draws a diagonal from top left to bottom right in Fig 3F that almost perfectly separates manmade from natural exemplars . Such clearly semantic principles governing representational similarity at a high level of abstraction were less evident in the entorhinal and parahippocampal cortex . To assess whether low-level physical image similarity could have been responsible for these findings , we calculated four widely used statistics to compare physical properties of two images , namely , the Euclidean distance , the mean squared error , the peak signal-to-noise value , and the structural similarity index . We then performed analyses analog to the ones shown in Fig 3 using these image similarity measures ( S2 Fig ) . These analyses showed no emergence of higher-order grouping of images according to abstract semantics as was the case for the neural data ( Fig 3 ) . Therefore , we conclude that low-level physical similarity cannot account for the findings of representation similarity in our neuronal response patterns . Abstraction comes at a trade-off between generalisation of knowledge to new situations and confusion between similar exemplars . We used the population responses described above to train pattern classifiers ( multiclass support vector machine models; see Decoding of stimulus identity and category section in Methods and materials ) . A classifier was trained on the population responses of half the stimuli per category to predict the category label and was then tested out of sample on population responses of the other half of stimuli . This procedure was repeated 100 times with random divisions of the data into training and test sets . Successful generalisation to untrained stimuli was indicated by highly accurate out-of-sample classification of category labels from population responses ( Fig 4A; for separate analyses for each subject , collapsing across anatomical regions , see S4 Fig ) . Generalisation was best using population responses from amygdala units , intermediate using hippocampal and entorhinal units , and lowest using parahippocampal units . Nevertheless , generalisation exceeded chance performance in all MTL regions by far . To assess performance in classifying individual stimuli , we calculated Z scored population responses of unit firing for each trial in the same manner as described above . Pattern classification algorithms were then trained on population responses of half of the trials for each stimulus and tested out of sample on the other half . Again , out-of-sample performance was assessed in 100 random divisions of the data into training and test set . Classification performance exceeded chance level in all regions of the MTL ( Fig 4F ) . Interestingly , we found a systematic pattern of misclassifications when inspecting confusion matrices ( Fig 4G–4K ) . Confusion matrices cross-tabulate the number of classifier outcomes by predicted stimulus label in columns and true stimulus labels in rows . These analyses show that pattern classification algorithms trained to decode individual stimulus identity more often confused stimuli from the same versus different superordinate categories ( Fig 4F–4K; all regions p < 10−5 , permutation test; see ‘Inference statistics on representational dissimilarity and confusion matrices’ section in Materials and methods; for analogous analyses separately for each subject but collapsing across anatomical regions , see S5 Fig ) .
Taken together , our results provide a novel perspective on how information is encoded in the human MTL . We demonstrate that despite selective tuning of individual neurons to only a few stimuli in the set , activity at the population level is determined by information with a high degree of semantic abstraction . We find that population activity is similar in response to exemplars of the same category and that response pattern similarity extends to highly abstract semantic categories . Pattern classification results show high levels of semantic abstraction , which , on one hand , can be useful for successful generalisation of knowledge to novel situations . On the other hand , semantic abstraction comes at the cost of confusion between semantically similar stimuli . With respect to neuronal representations in the MTL , we demonstrate a semantic code that spans multiple layers of abstraction emerging at the population level . This perspective may aid to reconcile disparate findings from previous studies investigating response properties of individual units [11 , 13 , 16] . Some have concluded that unit activity encodes concrete concepts such as , for example , a person’s identity [13 , 14] . Others postulate superordinate category membership as a decisive feature driving unit activity [16 , 19] . Our study may reconcile these views as population-level analyses show that encoded information spans across multiple levels of abstraction ranging from the concrete exemplar level to the level of preconceived semantic categories and beyond . Pattern classification analyses demonstrate that information on the exemplar and superordinate categorical level can both be decoded from population activity , whereas categorical information seems predominant . These aspects may not become apparent when looking at response profiles of individual units and underscore the importance of analyses at the population level . Furthermore , our data refine the view on sparseness of coding in the human MTL . Hallmark human single unit studies suggest that very few concepts drive activity in one single neuron [13 , 14 , 20] . In fact , considerably more than 50% of responsive units were found previously to respond to only one out of approximately 100 stimuli [12] . This is true in the amygdala , hippocampus , and entorhinal cortex , whereas selectivity is lower in the parahippocampal cortex [12] . These findings led to the conclusion that the MTL uses a very sparse , almost ‘grandmother cell’-like code [21] . Although some units in our data set indeed only fired in response to one stimulus in the set , the overall selectivity in our study was lower ( see Fig 1F ) than reported earlier [12 , 20] . Previous studies used stimulus sets that were tailored to the patients’ interests , depicting relatives , preferred celebrities , and job- and hobby-related objects [12 , 13] . The aim in these studies was to screen for response-eliciting stimuli using a wide range of different concepts , likely resulting in rather low semantic feature overlap between stimuli . Our current stimulus material had a systematic semantic structure because images were grouped into categories of semantically related exemplars . Assuming that unit activity is determined by a rather narrow ‘semantic tuning curve’ , we would indeed expect that neurons fire less selectively when ‘semantic distance’ between stimuli is sufficiently low . Thus , semantic relatedness between stimuli in a set seems likely to influence estimates of sparseness of unit responses in the MTL . Two previous studies have applied RSAs to single units in the human MTL . First , in 2011 , Mormann and colleagues [17] used RSA in combination with images that could be grouped into 3 categories , namely , persons , animals , and landmarks . This study found that the amygdala is preferentially activated by animal stimuli but did not investigate the semantic nature and level of abstraction in amygdala unit activity . Furthermore , a 2015 paper again by Mormann and colleagues [18] used RSA to show that units in the amygdala encode face identity rather than gaze direction . Again , analyses focused on the amygdala , and semantic abstraction could not be assessed because stimuli consisted of pictures of faces with gazes pointed in different directions . Furthermore , the notion of an all-or-nothing response behaviour as implied in earlier studies ( for example , [13 , 20] ) should be critically reevaluated . Obviously , response behaviour strongly depends on the exact definition of the statistical response criterion employed . Previous studies have used a rather conservative response criterion and tended to regard any activity not meeting this criterion as background noise [12 , 13 , 20] . Our analyses demonstrate that even after excluding all neurons that showed statistical responses to any of the presented stimuli , semantic category information is still present in the population activity of the ‘nonresponsive’ neurons . Thus , such subthreshold responses according to this criterion are likely to carry relevant information about the presented stimulus . For example , looking at Fig 1 A and 1C , we see such subthreshold responses . Here , the units clearly prefer stimuli from one category ( for example , clothing items in case of 1A ) . Within this category , however , some images drive spiking activity more strongly than others . The jean jacket in Fig 1A is the fifth-most response-eliciting stimulus for that unit but falls short of being classified as a response by the criterion we use , as indicated by the absence of a grey box around the respective raster plot . In view of the other response-eliciting stimuli , we would probably conclude that this might be a true but subthreshold response . Arguably , there are some units in the data set for which we find only such subthreshold responses because the near-optimal stimuli for these units were not in our set . It thus seems that these subthreshold units carry a significant amount of categorical information at the population level . Together , these results suggest that neurons do not encode the identity of a concept in an all-or-none fashion but rather that firing patterns may be best described as graded with the assumption of an underlying ‘semantic tuning curve’ . The high levels of abstraction in population activity observed in this study could also suggest a single-unit mechanism in the MTL for the generation of false memories . Classically , false memories are studied by presenting semantically related words for study , for example , ‘giraffe’ , ‘lion’ , ‘elephant’ , or ‘tiger’ , followed by a recognition memory test requiring old–new judgments of old words ( for example , ‘lion’ ) , as well as new words that were either semantically related ( ‘leopard’ ) or unrelated ( ‘keyboard’ ) to the studied words [22] . False memories manifest in more frequent old judgments to new words with high versus low semantic relatedness [22 , 23] . Overlap of recruited neocortical regions corresponds to semantic feature overlap between studied and new words , which , in turn , is correlated with false-memory likelihood [24] . However , it seems likely that overlap in recruitment of neocortical regions is in fact the consequence of ‘false’ reinstatement initiated by the hippocampus rather than the cause of false memories [24 , 25] . The hippocampus has been shown to be equally active during false and true memories in humans [26] , and optogenetic activation of neurons in the rodent hippocampus has been shown to trigger reinstatement of ‘false’ contextual fear memories [25] . Our data suggest that confusion between semantically similar stimuli is facilitated by the abstract semantic code utilised by neurons in the hippocampus , and thereby provides a link between human behavioural and functional magnetic resonance imaging versus rodent optogenetic studies of false-memory generation [22 , 24–26] . The combination of RSA and pattern classification applied to our single neuron data reveals novel insights about the neuronal code for semantics in the MTL . Although we think that the decoding of semantic generalisation ( top row of Fig 4 ) and the RSA analyses ( Fig 3 ) convey similar aspects of the data , the decoding results are by no means a trivial consequence of the RSA analyses . First , the decoding analyses allow for a comparison of decoding accuracy for exemplar versus category decision . Second , the fact that confusions within category are more frequent than those across category offers a mechanistic explanation for the generation of false memories . Both of these points do not become apparent from the RSA results alone . These RSA results , in turn , show higher-order organising principles of semantic information in populations of single neurons in the MTL . Our study also contributes to the understanding of neuronal representations in the amygdala . We found a preference of amygdala units for stimuli depicting food items , which dovetails with findings of a potential role of the amygdala in modulating food consumption recently reported in rodents [27] and with views of the role of the amygdala in processing positive and negative value as well as relevance of stimuli [28 , 29] . However , human amygdala units have also been shown to preferably respond to animals [17] , to be involved in processing of faces and parts of faces [30 , 31] , and to encode the intensity of emotion in facial expressions [32] . More generally , the amygdala has been hypothesised to be involved in social cognition [31] . It is noteworthy that we do not see a preference for stimuli depicting animals in the amygdala as reported by Mormann and colleagues ( 2011 ) [21] . Response probabilities of animal stimuli in our study are comparable to this study ( approximately 1% ) . Mormann and colleagues ( 2011 ) , however , compared animal stimuli to pictures of persons , landmarks , and objects , which all had significantly lower response probabilities ( approximately 0 . 2% ) . Thus , we may not see a preference for animals because the categories to which we compare them ( for example , food , plants , musical instruments , etc . ) are different . It may help to reconcile this broad range of findings to consider that the amygdala is a complex and heterogeneous structure consisting of multiple nuclei involved in a wide range of different functions [33] and that the exact location of microwires with respect to these nuclei cannot be determined with sufficient accuracy in human subjects . Finally , our data connect to notions of hierarchical processing within the MTL . Strong tuning to highly abstract semantics has been found in the hippocampus and the amygdala . Both regions receive highly processed , supramodal input [12 , 33 , 34] . The use of a highly abstract semantic code appears plausible to aid in attributing value and relevance of stimuli , a function hypothesised to occur in the amygdala [28] . In the hippocampus , high levels of abstraction may facilitate efficient and sparse representations of large amounts of information encoded in neocortical firing patterns for subsequent encoding of episodic memories [35–37] . In contrast , abstract semantic representations were less pronounced in parahippocampal and entorhinal neurons . This finding connects with views that these structures are situated at a lower stage of the processing hierarchy within the MTL [12 , 34 , 38] . Here , the parahippocampal cortex acts as an input region for higher MTL regions . Parahippocampal neurons fire earlier , less selectively than in other MTL regions [12] , and display a preference for images with spatial layout of visual input [10] . Similarly , the entorhinal cortex relays reciprocal connections between hippocampus and neocortex [34] and has also been found to be involved in spatial processing in humans [39 , 40] .
A total of 25 epileptic patients ( 9 female ) aged 19 to 62 y ( M = 38 , SD = 13 ) were implanted with depth electrodes for chronic seizure monitoring . Their average stay on the monitoring ward was 7 to 10 d . The study was approved by the Medical Institutional Review Board of the University of Bonn ( accession number 095/10 for single-unit recordings in humans in general and 245/11 for the current paradigm in particular ) and adhered to the guidelines of the Declaration of Helsinki . Each patient gave informed written consent . One hundred images from 5 man-made and 5 natural categories of 10 exemplars each were selected as stimuli . The experiment was subdivided into 10 runs . One run entailed sequential presentation of all 100 images in the set in pseudorandom order . A trial entailed the presentation of a blank screen for a variable duration ( 200–400 ms ) and a fixation dot for 300 ms , followed by the image that stayed on screen until the subject responded with a button press . Subjects were instructed to press the left or right arrow key if the image on the screen depicted a man-made or natural object , respectively . Nine microwires ( 8 high-impedance recording electrodes , 1 low-impedance reference; AdTech , Racine , WI ) protruding from the shaft of the depth electrodes were used to record signals from MTL neurons . Signals were amplified and recorded using a Neuralynx ATLAS system ( Bozeman , MT ) . The sampling rate was 32 kHz , and signals were referenced against one of the low-impedance reference electrodes . Spike sorting was performed using wave_clus [41] in 33 sessions and using Combinato ( https://github . com/jniediek/combinato ) [42] in 26 sessions . Different spike-sorting routines were used as the reported paradigm also served as a procedure to screen for response-eliciting stimuli in the morning of a day of testing . Therefore , manual optimisation of spike sorting was performed immediately after recording . The lab as a whole switched to using Combinato for reasons unrelated to the reported research . A total of 5 , 033 units resulted from spike sorting , 4 , 917 of which were recorded in one of the anatomical regions considered ( amygdala , hippocampus , entorhinal cortex , and parahippocampal cortex ) . The number of microwires per patient was on average 71 . 60 ( SD = 21 . 32 ) and ranged from 32 to 96 . On average , we recorded 1 . 38 units per microwire ( SD = 0 . 44 ) . These values ranged from 0 . 41 to 2 . 24 across all 59 sessions . To determine whether a unit responded with increased spiking activity to one of the stimuli in the set , we calculated a binwise rank-sum test described earlier [12] . We obtained spike counts in 19 overlapping 100 ms bins ( [0:100:1 , 000] and [50:100:950] ms after stimulus onset ) for each trial in which a given image was presented . We computed 19 rank-sum tests , each of which compared the distribution of spike counts of one of the 19 bins against the distribution of spike counts in a baseline interval ( −500 to 0 ms ) of all trials in a session . The resulting 19 p-values were corrected for multiple comparisons using the Simes procedure . A stimulus was classified as eliciting a neuronal response in a unit when one or more of these 19 p-values was lower than α = 0 . 001 . Furthermore , we considered only increases in firing rates . Also , neuronal responses were only considered as such if at least one spike in the response period was recorded in more than 5 out of the 10 trials per image and if the average firing rate during the response window ( 0 to 1 , 000 ms ) was above 2 Hz . We counted the neuronal responses across all sessions , separate for superordinate category and anatomical location . To make these values comparable across anatomical regions and with previous work [17] , we calculated response probabilities by normalising these counts to the number of units in an anatomical region and the total number of stimuli presented ( 100 ) . Response probabilities were calculated for each of the four anatomical regions of interest . They thus represent the empirical probability that a unit in a given anatomical region will respond to a stimulus from a given semantic category . We obtained measures of dispersion of these response probabilities by using a subsampling procedure . We drew 2 , 000 random subsamples of 700 units without replacement from each region and derived 95% confidence intervals from the resulting distributions of response probabilities for each category of stimuli . A Fisher’s exact test on the response probabilities was conducted for each category and each anatomical region . To this aim , data were arranged in a 2 × 2 contingency table of the frequencies of significant and nonsignificant neuronal responses in a superordinate category of interest , and the frequency of significant and nonsignificant neuronal responses in all other superordinate categories . To assess the dissimilarity between neuronal representations of stimulus categories , firing rates during the response period ( 0 to 1 , 000 ms after stimulus onset ) of each stimulus were expressed as Z scores using the mean and standard deviation of firing rates in a base line interval ranging from −500 ms to stimulus onset ( 0 ) across all trials . These Z scores were arranged in a matrix of NS × NU , where NU is the number of units recorded and NS the number of stimuli in the set ( 100 ) . Representational dissimilarity between a pair of stimuli was calculated using 1 –Pearson’s correlation coefficient ( 1 − R ) of the vectors of Z scores corresponding to the population activity evoked by the two stimuli in a pair [9 , 17] . To assess representational dissimilarity on the level of individual trials , we computed Z scores for each trial in the experiment . These Z scores were arranged in a matrix of NT × NU , where NU is the number of units recorded and NT the number of trials during the paradigm ( 1 , 000 ) . Hierarchical clustering for dendrograms in Fig 3 was performed using unweighted average distance method on correlation distances . We used the matrices of Z scores described above ( NT × NU ) to assess pattern classification performance . We used the function fitcecoc . m from MATLAB’s ( MathWorks; www . mathworks . com ) statistics and machine-learning toolbox . This function was used to train a multiclass , error-correcting output codes model of linear support vector machines for binary choices . Binary support vector machines were specified according to a ‘one versus all’ coding scheme in which for each binary classifier , one class is positive and the rest are negative . The classifier was trained to predict the label of stimulus identity from individual trials ( NT × NU ) . Out-of-sample performance was assessed for 100 pseudorandom divisions of the data into training and test set ( 50% holdout for test ) . To test for semantic generalisation to ‘unseen’ members of category , further classifiers were trained on the mean responses ( NS × NU ) of half of the stimuli to learn category labels and tested on the other half of stimuli . Again , out-of-sample performance was assessed for 100 pseudorandom divisions of the data into training and test set . Classification performance was quantified by Cohen′sκ=PO−PC1−PC , where PO is the observed agreement and PC is chance agreement . S4 Fig and S5 Fig show these same analyses repeated separately for each subject but collapsing across regions . To assess whether dissimilarity ( 1 − R ) was significantly different within versus across exemplars of superordinate categories , we implemented a label-shuffling procedure . To this aim , we arranged dissimilarity between all pairs of stimuli in matrices of the format NS × NU . Next , we selected a set of indices to the elements in these matrices that correspond to within-category dissimilarity . Another set of indices was selected corresponding to between-category dissimilarity . We then computed a Mann-Whitney U test with the hypothesis that within-category dissimilarity is lower than between-category dissimilarity . From this test we obtained a test statistic ( rank-sum ) of the original assignments of the labels ( within- versus between-category dissimilarity ) to the data . We repeated this test 105 times with randomly shuffled assignments of labels to the data , that is , indices to the matrix corresponding to within- versus between-category pairs were randomised and hence mostly false . Of these 105 tests with random labels , we saved the distribution of resulting test statistics ( rank-sums ) . The reported p-values reflect the percentile of the test statistic that got the correct assignments of labels to the data within the distribution of test statistics derived with randomly relabelled data . The same procedure was carried out for the confusion matrices derived from pattern classification . Note that dissimilarity matrices were symmetric , whereas confusion matrices were not . We therefore computed statistics for dissimilarity on the triangular matrices only . We used MATLAB and its statistics and machine-learning toolbox in combination with custom code for analyses of the data . Spike sorting of 33 sessions was done using wave_clus ( https://github . com/csn-le/wave_clus ) [41] . The remaining 26 sessions were sorted using Combinato [42] requiring Python ( www . python . org ) . We used the psychtoolbox3 ( www . psythoolbox . org ) and octave ( www . gnu . org/octave ) running on a Debian 8 operating system ( www . debian . org ) on a standard laptop computer for stimulus delivery . All relevant data and custom code are available on https://github . com/rebrowski/abstractRepresentationsInMTL . git .
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What is the neuronal code for sensory experience in the human medial temporal lobe ( MTL ) ? Single-cell electrophysiology in the awake human brain during chronic , invasive epilepsy monitoring has previously revealed the existence of so-called concept cells . These cells have been found to increase their firing rate in response to , for example , the famous tennis player ‘Roger Federer’ , whether his name is spoken by a computer voice or a picture of him is presented on a computer screen . These neurons thus seem to encode the semantic content of a stimulus , regardless of the sensory modality through which it is delivered . Previous work has predominantly focused on individual neurons that were selected based on their strong response to a particular stimulus using rather conservative statistical criteria . Those studies stressed that concept cells encode a single , concrete concept in an all-or-nothing fashion . Here , we analysed the neuronal code on the level of the entire population of neurons without any preselection . We conducted representational similarity analyses ( RSAs ) and pattern classification analyses of firing patterns evoked by visual stimuli ( for example , a picture of an apple ) that could be grouped into semantic categories on multiple levels of abstraction ( ‘fruit’ , ‘food’ , ‘natural things’ ) . We found that neuronal activation patterns contain information on higher levels of categorical abstraction rather than just the level of individual exemplars . On the one hand , the neuronal code in the human MTL thus seems well suited to generalise semantic knowledge to new situations; on the other hand , it could also be responsible for the generation of false memories .
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2019
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Representation of abstract semantic knowledge in populations of human single neurons in the medial temporal lobe
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Mycobacterium tuberculosis ( Mtb ) remains a grave threat to world health with emerging drug resistant strains . One prominent feature of Mtb infection is the extensive reprogramming of host tissue at the site of infection . Here we report that inhibition of matrix metalloproteinase ( MMP ) activity by a panel of small molecule inhibitors enhances the in vivo potency of the frontline TB drugs isoniazid ( INH ) and rifampicin ( RIF ) . Inhibition of MMP activity leads to an increase in pericyte-covered blood vessel numbers and appears to stabilize the integrity of the infected lung tissue . In treated mice , we observe an increased delivery and/or retention of frontline TB drugs in the infected lungs , resulting in enhanced drug efficacy . These findings indicate that targeting Mtb-induced host tissue remodeling can increase therapeutic efficacy and could enhance the effectiveness of current drug regimens .
Mycobacterium tuberculosis ( Mtb ) continues to pose a threat to global health . In 2015 , 10 . 4 million people were estimated to have become infected with Mtb and 1 . 8 million people died because of TB ( 0 . 4 million deaths within from TB/HIV co-infection ) , making Mtb the leading cause of death worldwide from a single infectious agent , ranking above HIV/AIDS[1–3] . TB/HIV co-infection is responsible for about one fourth of all TB deaths and one third of all HIV/AIDS deaths[1 , 4] . Furthermore , the incidence of drug resistant TB increased significantly in 2015 compared to previous years[1–3] . Development of new or re-purposed drugs for TB treatment is needed to accomplish the Sustainable Development Goals , which aims to reduce 90% of TB incidence rate by 2030 [1 , 5] . Mtb’s success as a pathogen depends upon its ability to reprogram its host environment at both the cellular and tissue levels [6 , 7] . The tuberculosis granuloma is characterized by extensive tissue remodeling , extracellular matrix ( ECM ) deposition and angiogenesis , and ultimately tissue destruction in those granulomas progressing to active disease[8] . The matrix metalloproteinase ( MMP ) enzymes are major contributors to this remodeling process due to their ability to degrade ECM such as collagen and proteoglycans[9–11] . Among the MMP family , MMP-2 and MMP-9 are known to degrade type IV collagen , fibronectin and elastin in the lung[10 , 12 , 13] , and are markedly up-regulated in expression in human tuberculosis granulomas[14 , 15] . Other MMPs have been studied in human tuberculosis tissue and the expression of MMP-1[16–18] , MMP-8[19] and MMP-14[20] are significantly up-regulated . Many studies suggested that this up-regulation of MMPs is induced by Mtb infection , and eventually leads to collagen destruction and granuloma necrosis[16–25] . Studies using MMP inhibitors in Mtb infected animal models have generated conflicting data . Hernandez-Pando et al . observed a type-2 cytokine response profile and a delayed granuloma formation in murine pulmonary tuberculosis after treatment with MMP inhibitors[26] . In contrast , Izzo et al . observed increased collagen deposition in early granuloma formation after MMP inhibition , as well as a reduced bacterial burden in the lung at early phase[27] . However , a subsequent study from the same group did not observe a reduced bacterial burden in the lung following MMP inhibition[10] . These studies argue that there is value in further analysis of the impact of MMP inhibition on disease progression and on granuloma architecture . Most current TB regimens involve a combination of the four drugs ( isoniazid , rifampicin , ethambutol , pyrazinamide ) as the first-line of treatment . However , the duration of treatment required to generate an enduring cure is usually 6–9 months . Not surprisingly , issues of non-compliance and failure occur frequently , and lead to the ongoing emergence of drug-resistant strains . Selection for drug resistant Mtb happens independently at multiple different geographic locations and is a widespread problem . Therefore , effective strategies to shorten the treatment duration and reduce the incidence of drug resistance are critically important . In this study , we examined existing human TB granuloma datasets in combination with infectious and non-infectious granuloma models to probe the increased expression of MMP-2 and MMP-9 in Mtb granulomas . Treatment of Mtb-infected mice with a panel of small molecule MMP inhibitors alone had no effect on bacterial burden , but markedly enhanced bacterial killing by the frontline TB drugs INH and RIF approximately 10-fold . We verified the in vivo activity of these inhibitors through demonstrating that they block MMP-mediated cleavage of collagen and the mannose binding lectin ( MBL ) . Treatment with these inhibitors also impacted granuloma morphology and appeared to stabilize the blood vessels that irrigated the infection site . Consistent with the improved blood vessel health , we found that MMP inhibition enhanced drug penetrance/retention in the infected tissue , which explains the enhanced efficacy of the anti-TB compounds used in combination with MMP inhibitors .
Previously we had performed transcriptional analysis on material acquired from cryosections from human pulmonary tuberculosis granulomas[14] . We re-analyzed the datasets from 5 caseous human pulmonary TB granuloma and 2 normal lung parenchyma ( GSE20050 ) [14] . Differential analysis was performed using GEO2R to investigate the differential transcriptomic signature in TB granuloma tissue compared to the uninvolved tissue ( Fig 1A ) . Among the differentially-expressed genes , transcripts for both MMP-9 and MMP-2 were significantly more abundant , over 190 fold ( log2 ( FC ) = 7 . 6 , p value , adjust p value < 0 . 001 ) , and over 40 fold ( log2 ( FC ) = 5 . 35 , p value = 0 . 001 , adjusted p value = 0 . 016 ) , respectively ( Fig 1B ) . This is consistent with previous measurements of MMP expression in human tuberculosis granulomas[11 , 16–21 , 23 , 24] . To examine the induction and expression of MMP-2 and MMP-9 in experimental infectious and non-infectious murine TB granuloma models we used both a subcutaneous granuloma model[14 , 28 , 29] for in vivo imaging in parallel with the more conventional , intranasal Mtb challenge . Western blot analysis of equivalent amounts of tissue from Mtb-infected and uninfected mouse lungs confirmed the increased expression of MMP-2 and MMP-9 in the Mtb-infected tissue ( Fig 1C ) . In order to visualize the up-regulation in transcription of MMP-2 and MMP-9 genes we used reporter RAW cell lines that expressed luciferase under regulation of the MMP-2 and MMP-9 promoter regions . These cells were mixed with a Matrigel suspension containing polystyrene beads coated with the mycobacterial lipid TDM . TDM , or cord factor , is known to have granuloma-inducing properties and has been used previously in non-infectious granuloma models[28 , 30–32] . The suspension was inoculated subcutaneously into the mouse scruff , and the tissue response to the challenge was imaged at days 3 and 7 post-inoculation . Quantification of the level of luciferase expression ( Fig 1D and 1E ) demonstrated the up-regulation of MMP promoter activity in the TDM-bead containing granulomas in comparison to those containing uncoated beads . These data confirm previous reports of up-regulated expression on MMPs in both human TB granulomas and the murine experimental granuloma models used in the current study[16–25] . Marimastat is a broad spectrum MMP inhibitor that was developed as an anti-neoplastic drug candidate[33–35] . Treatment of mice with the drug induced morphological alterations in both subcutaneous TB granuloma models , including the TDM-bead Matrigel model and the Mtb-Matrigel subcutaneous challenge model ( S1 Fig ) . The treatment appeared to result in higher cellular consolidation within the matrix . We probed the biological significance of this morphological change to disease progression through determination of both bacterial burden and histological change in mice challenged intranasally with Mtb . The design of the experiment is shown in Fig 2A . Mice were infected with Mtb then treated with PBS ( control ) or Marimastat from day 7 onwards . Mice were also treated with the frontline TB drug INH ( ± Marimastat ) to explore the potential impact of MMP inhibition on drug efficacy . Mice were sacrificed at day 28 and process for both histology and bacterial counts . There was no statistically significant difference in bacterial load between the Marimastat and PBS groups at Day 14 , Day 28 or Day 42 ( Figs 2B and S2 ) , consistent with previous reports that inhibition of MMP activity did not impact bacterial burden[10 , 26] . However , when INH was added into the drinking water , a synergistic effect was observed in the INH and Marimastat co-treated group ( Fig 2B ) . INH alone reduced the bacteria load approximately ten-fold compared to the PBS control , while Marimastat and INH in combination reduced the bacterial load by another log over the INH only group ( Fig 2B ) . This synergistic effect was significantly reduced but still observable by Day 42 ( S2 Fig ) . The combination treatment also reduced size of the regions of cellular consolidation within the infected lung tissue , as shown by H&E staining ( Fig 2C ) . This reduction is likely due to both MMP inhibition and the reduced inflammatory stimulation caused by the lower number of bacteria . The percentages of consolidated region within the whole tissue were measured and the percentages of consolidated region in PBS and Marimastat groups were comparable ( Fig 2D ) . This percentage was decreased in the INH only group , and further reduced in the Marimastat and INH group ( Fig 2D ) . This reduction correlates most closely with the CFU measurements . These data suggest that targeting MMP activities enhances the efficacy of INH against Mtb . To determine if this was due to activity at the level of the host cell or the host tissue environment we treated Mtb-infected BMDM with Marimastat in the presence and absence of INH . This tissue culture infection model did not recapitulate the synergistic effect of the two drugs combination ( S3 Fig ) , indicating that the synergy observed is dependent on the in vivo tissue environment . Next , we investigated whether this synergetic effect could be observed with a different TB drug , RIF and/or with other MMP inhibitors . The combination of RIF and Marimastat decreased bacterial burden in comparison with RIF alone ( Fig 3A ) , demonstrating that the ability of Marimastat to enhance anti-TB drug activity was not restricted to INH , and therefore unlikely to be linked to the specific mode of action of one anti-TB drug . We then tested a panel of MMP inhibitors in combination with INH to determine if the synergy was Marimastat-specific . The panel of MMP inhibitors included ( 1 ) Batimastat , which shares a similar structure to Marimastat , but has lower water solubility , ( 2 ) Prinomastat , a structurally-unrelated broad spectrum MMP inhibitor , ( 3 ) Sb-3ct , a specific inhibitor of MMP-9 and MMP-2 and ( 4 ) MMP-9 inhibitor I , an inhibitor that exhibits much more specific activity against MMP-9[33] . In isolation , all these MMP inhibitors had minimal effect on bacterial burden . However , with the exception of Prinomastat , all the MMP inhibitors enhanced bacterial killing by INH ( Fig 3B ) . Therefore , the synergistic effect between MMP inhibition and antibiotics is not limited to Marimastat and INH , but applied to other MMP inhibitors and other frontline TB drugs . Furthermore , this synergistic effect is likely due to inhibition of MMP-9/MMP-2 . MMPs within the tissue have a wide range of substrates[12 , 27 , 34–37] . The most obvious target of these proteases is the extracellular matrix , including collagen . To verify the biological activity of Marimastat within the infection site we measured the levels of substrate within the tissue . The level of hydroxyproline , a modified amino acid specifically released from collagen degradation , was measured from infected mouse lung tissue . In the groups with Marimastat we observed a higher level of hydroxyproline , indicating higher collagen concentration ( Fig 4A ) . Similar data were also generated in the TDM-Matrigel and the Mtb-Matrigel granuloma models ( S4 Fig ) . However , interpretation of these data is complex because the fibrotic response itself is biologically-active[28 , 29] and the outcome , greater collagen deposition , could be generated by other mechanisms in addition to reduced degradation of collagen by MMPs . Other known MMP substrates include the mannose-binding lectin ( MBL ) , which has a collagen-like domain that is cleaved by MMP-2 , MMP-9 , and MMP-14[38] . MBL is an interesting immune modulator because it can recognize mycobacterial surface lipidoglycans[39–41] , act as an opsonin , and activate the complement cascade[41 , 42] . We examined the levels of MBL in the lung of Marimastat and INH treated , infected mice . Marimastat and INH combined treatment resulted in the highest MBL level ( Figs 4B and S5A ) . The MBL level was relatively low in the Marimastat group compared to the Marimastat and INH group , which was due to higher MMP-2 and MMP-9 protein levels in the Marimastat group ( S5B Fig ) . This may be a consequence of the larger number of bacteria present in the Marimastat group ( Fig 2B ) , which could lead to MMP overproduction to compensate for the inhibition of Marimastat . There is mounting evidence that MMPs have a strong effect on angiogenesis beyond ECM remodeling—MMPs can release ECM-bound angiogenic factors , detach pericytes from blood vessels , and degrade endothelial cell-cell adhesions[37 , 43] . To test whether MMP inhibition impacts the vasculature at the infection site , we stained lung tissue of 4-week infected mice treated with PBS or Marimastat with CD31 for endothelial cells[44 , 45] . We observed positive CD31 staining in sections from both PBS and Marimastat groups ( Fig 5A , upper panel ) . Blinded histological analysis indicated that there was no significant difference in the number of CD31 positive blood vessels upon Marimastat treatment ( Fig 5B ) . We also stained tissue with alpha smooth muscle actin ( α-SMA ) . α-SMA stains for pericytes which wrap around the endothelial layer to supply nutrients[44 , 46] , as well as for alpha smooth muscle cells around the bronchus , which can be easily distinguished from blood vessels by morphology the bronchus has a single layer of columnar epithelial cells ) . Both the PBS group and Marimastat group had positive staining of α-SMA ( Fig 5A , lower panel ) . Blinded histological analysis indicated that the percentage of positive α-SMA area was significantly higher in Marimastat group than that in PBS group ( Fig 5C ) . Based on the positive staining of α-SMA , the blood vessel numbers from both groups were counted . The Marimastat group had significantly more α-SMA positive blood vessels than the PBS group ( Fig 5D ) . This suggested that treatment with Marimastat increased the number of blood vessels covered by pericytes , while the total blood vessel number remained unchanged . Moreover , we counted the α-SMA positive blood vessels in the consolidated area ( granuloma like structure ) and the normal surrounding area separately . We found that the increase of pericyte coverage occurred in the surrounding area instead of in the consolidated area ( Fig 5E and 5F ) . The numbers of blood vessels with positive α-SMA staining were not significantly different between the PBS and Marimastat treated groups at 2 weeks post infection ( S6 Fig ) . As infection progressed to 4 weeks , the number of α-SMA positive blood vessels remained similar in the PBS control group , but was significantly increased by Marimastat treatment ( S6 Fig ) , indicating MMP inhibition increases pericyte-covered blood vessels over time . In summary , MMP inhibition reduced blood vessel abnormality by increasing pericyte coverage around the blood vessels . This increase of pericyte-covered blood vessel number and the improvement of blood vessel health could potentially enhance drug ( such as INH ) delivery or retention in the infected tissue environment . Inflammation is known to increase vascular permeability[47–49] and we hypothesized that the increased pericyte coverage induced by Marimastat treatment might reflect a reversal of this permeability . To test the impact of Marimastat treatment on vascular permeability , we injected fluorescent dextran with different molecular weights to Mtb infected mice . Under normal , homeostatic conditions one would expect 10kDa dextran to passively diffuse out of the vasculature , while 70kDa dextran would be retained for longer . However , under inflammatory conditions , one would expect to see increased leakage of the 70kDa dextran[47] . Lung tissues from treated mice , infected or uninfected with Mtb , were fixed and stained for CD31 to mark the blood vessels , and imaged by a confocal microscope . Both PBS group and Marimastat group from infected mice showed positive staining of CD31 and fluorescent signal from the two dextran dyes ( Fig 6A ) . We scored the 10kDa and 70kDa dextran signal that either co-localized with the CD31-positive blood vessels , or was present in the tissue outside the CD31-positive regions ( Fig 6B and 6C ) . Compared to uninfected samples , Mtb-infected tissue had more 10kDa dextran outside blood vessels , indicating Mtb infection promotes neo-vascularization ( Fig 6B ) . These new blood vessels appeared to be leaky , as the levels of 70kDa dextran outside blood vessels were much higher in infected mice ( treated with PBS ) relative to the uninfected group ( Fig 6C ) . Marimastat treatment reduced 70kDa dextran to a level similar to that of uninfected animals ( Fig 6C ) , while further increased the 10kDa dextran level ( Fig 6B ) . This suggests that Marimastat reduced blood vessel leakage and increased small molecule delivery in the lung , consistent with a “normalization” of the vasculature . The normalization of the vasculature in Mtb granulomas has been shown to improve small molecular delivery[44] . To determine whether or not Marimastat treatment could have a similar impact , we injected Evans blue dye intravenously into infected mice treated with or without Marimastat , and measured the dye retention in the lung tissue . We observed that there was an increase in Evans blue dye in the lungs of mice treated with Marimastat ( Fig 7A ) , indicating that inhibition of MMP activity enhanced small molecule delivery and/or retention in the infected tissues . To further demonstrate this enhanced delivery and/or retention by MMP inhibition also applies to frontline TB drugs , we injected RIF and INH intravenously into infected mice treated with or without Marimastat . Intravenous injection of drugs minimizes inter-animal variation in absorption , which is a frequent confounding factor , particularly for RIF . Drug concentrations were measured in the harvested lung tissues at different time points by high-pressure liquid chromatography coupled to tandem mass spectrometry ( HPLC-MS/MS ) analysis , and normalized to the drug concentrations in the plasma [50] . 4h post drug injection , the Marimastat-treated group had significantly higher RIF and INH lung/plasma ratios than the PBS control group ( Fig 7B and 7C ) , suggesting an enhanced drug delivery/retention by MMP inhibition . Although the drug concentrations decreased in the lung and plasma over time ( S7 Fig ) , MMP inhibition maintained a relatively high INH concentration in the lung ( S7C Fig ) . RIF concentration was not increased in the lung but decreased in the plasma upon MMP inhibition , suggesting a faster turn-over rate of RIF . Taken together , these data indicated that inhibition of MMP activity enhanced frontline TB drug delivery and/or retention in the infected tissues through improving blood vessel integrity .
Current therapeutic regimens for tuberculosis are cumbersome because of the need for multiple drugs ( 3–4 ) that have to be taken for 6–9 months . This places considerable strain on many healthcare systems , particularly those in under-resourced settings , and leads to ongoing problems of non-compliance and the emergence of drug-resistant Mtb strains . Given these challenges any strategy to increase the potency of our current anti-TB drugs could have tremendous benefit on tuberculosis control programs across the world . Here we show that small molecule MMP inhibitors increase the killing activity of the frontline anti-TB drugs INH and RIF . This enhanced killing was observed for several different MMP inhibitors suggesting that it is their anti-matrix metalloproteinase activity that confers their synergistic activity . We found that MMP inhibition improved blood vessel health , leading to increase of drug delivery and/or retention in the lung , which resulted in increased drug efficacy . The majority of Mtb animal studies use the mouse as host . Different mouse strains and different infection methods created a number of Mtb murine models [51 , 52] . However , mice infected with Mtb fail to form the well-defined and highly stratified granuloma structure , which is commonly seen in human TB patients [6 , 7] . The granuloma structure developed in our murine model resembles an early stage human granuloma , where activated macrophages surround the infected cells with a layer of lymphocytes at the peripheral [51 , 52] . Murine granulomas rarely progress to necrosis , which characterize late stage or disseminated human TB granulomas [6 , 7] . Moreover , mice do not express MMP-1 , which , along with other MMPs , is associated with tissue destruction and transmission in disseminated human granuloma [23] . However , while there are limitations to murine models of Mtb pathogenesis , one cannot ignore the convenience of such a tractable experimental system if used appropriately . Different murine models are widely used to study essential bacterial genes or host immune response , because of their availability and well-characterized genetic variations . Recently , studies using resistant mouse strain infected with M . marinum [53] , or susceptible mouse strain infected with Mtb [51 , 52] observed granuloma-like structures with necrotic center in the lung , resembling the human granuloma . Moreover , murine models provide an in vivo platform to screen novel TB drugs for efficacy or synergy with frontline TB drugs , before advancing to costly non-human primate experiments or human clinical trials . We therefore believe that the mouse represents a valuable tool for discovery prior to downstream validation of ones finding in more restrictive platforms . Treatment of Mtb-infected mice with Marimastat reduces extracellular matrix turnover and breakdown of the mannose-binding protein MBL . Histological analysis and vasculature-permeability experiments both indicate that the blood vessels in the TB granulomas were stabilized by Marimastat treatment . Healthy blood vessels , instead of leaky vessels , can improve the amount of drug , that is given orally , delivered to the lung . As a result , there is an improved tissue retention of small molecules and anti-TB drugs . Moreover , healthy blood vessels can potentially enhance drug accessibility to the bacteria at the center of a confined structure . Improvement of drug delivery through the normalization of blood vessels is widely accepted as a means of enhancing the efficacy of anti-cancer drugs[54 , 55] . Several phase III clinical studies have shown that combination of conventional chemotherapeutic drugs with FDA approved anti-angiogenesis drugs can significantly improve survival of patients with non-small lung cancer[56] , breast cancer[57] and metastatic colorectal cancer[58 , 59] . Moreover , the anti-VEGF drug , Bevacizumab , which is approved by FDA to treat metastatic colorectal cancer[60] , also enhances small molecule delivery to tuberculosis granulomas in rabbits[44] . Additionally , Oehlers et al . showed that VEGF inhibitors can synergize with RIF to reduce bacterial burden of M . marinum in Zebrafish [61] . Based on our findings , it is reasonable to add doxycycline , an anti-mycobacteria antibiotic [62] and the only FDA-approved MMP inhibitor , to the current anti-TB drug regimens as an adjunctive drug . Specific MMP inhibitors like Marimastat were well-tolerated in animals and have been tested in clinical trials to target cancer metastasis [33 , 63] . Respiratory dysfunction , a possible adverse effect considering increased fibrosis by MMP inhibition in the lung , was not identified in these clinical trials . Moreover , these side effects may be prevented by avoiding high-dose treatment [33 , 63] . These data underline the importance of exploiting strategies that improve the efficacy of existing drugs as a readily tractable means of increasing the effectiveness of our anti-TB therapy . The possible addition of cheap , well-tolerated drugs such as MMP inhibitors to current multi-drug regimens is a practical and attractive means of increasing potency .
All animal experiments were performed in strict accordance with the National Institutes of Health “Guide for the Care and Use of Laboratory Animals” , and approved by Cornell University Institutional Animal Care and Use Committee under protocols 2006–0019 , 2011–0086 , 2010–0100 , 2013–0003 . All animal experiments performed inside Biosafety Level 3 facility were approved under protocol 2011–0086 . All efforts were made to minimize suffering . C57BL/6J mice , MBL knockout mice ( B6 . 129S4-Mbl1tm1Kata Mbl2tm1Kata/J ) were obtained from the Jackson Laboratory and housed under pathogen-free conditions . Bone marrow-derived macrophages ( BMDMs ) were isolated from bone marrow of C57BL/6J wild type mice , and maintained in DMEM ( Corning Cellgro ) containing 10% FBS ( Thermo Scientific ) , 10% L929-cell conditioned media , 2mM L-glutamine , 1mM sodium pyruvate and antibiotics ( penicillin/streptomycin ) ( Corning cellgro ) , at 37°C in a 5% CO2 incubator[64 , 65] . To validate the up-regulation of MMP-2 and MMP-9 during Mtb infection , we constructed reporter cell lines that have promoters of Mmp-2 and Mmp-9 upstream of GFP and luciferase encoding cassettes . The luminescent signal was detected and quantified by the IVIS imaging instrument . The primers sequences for Mmp-2 and Mmp-9 promoter’s region were designed using PrimerPremier5 ( Table 1 ) . Mouse genome DNA was used as template to amplify the sequences , which were inserted into plasmid pGreen-Fire . Vectors with genes of interest were transferred into RAW 264 . 7 macrophage cell line ( ATCC TIB-71 ) using a lentivirus infection system . Single colonies were picked and validated for expression levels of GFP and luciferase . We used a non-infectious Mtb trehalose dimycolate ( TDM ) granuloma model to test whether these stable cell lines can express GFP and luciferase [29 , 31] . TDM coated beads were suspended in Matrigel , mixed with the reporter RAW cell lines , and inoculated subcutaneously in the scruff of a mouse . The mice were anesthetized at certain time points , injected with luciferase substrate and imaged with a IVIS machine ( Caliper Lifescience ) . TDM matrigel injection method has been described previously[14 , 28 , 29] . Briefly , 1mg TDM ( Enzo life science ) was dissolved in 100μl chloroform/methanol mixture ( 2:1 ) . 4μl of the dissolved TDM ( 40μg ) was transferred to a glass tube and dried under nitrogen gas . 150μl polystyrene microspheres ( approximately 104 particles 79 . 4±0 . 5μm Duke Scientific ) were washed with 1ml PBS and added to the tube . The tube was sonicated in 55°C water bath for 1h , in order to coat the TDM onto the beads . 5×106 BMDMs or reporter RAW cells were harvested and mixed with TDM-coated beads in 400μl cold Matrigel ( Corning ) . 27G syringe was used to inject the mixture subcutaneously in the mouse scruff . After 7 days , mice were sacrificed and matrigel tissue was extracted for histology or collagen content measurement . For Mtb/Matrigel granulomas , Mtb Erdman from frozen titered stocks was passaged through 25G syringe 8 times to dissociate clumps . The stock was diluted 1000 times in PBS containing 0 . 05% Tween 80 and 103 bacteria in 25μl were mixed with 400μl matrigel containing 5×106 BMDMs , and injected subcutaneously into the mouse scruff . After 28 days , mice were sacrificed and matrigel tissue was extracted for histology or collagen content measurement . In order to investigate drug effects on pulmonary Mtb infection , we infected mice intranasally as described previously[66 , 67] . Mice were anesthetized with isoflurane and then 25μl of PBS+0 . 5% Tween 80 containing 103 bacteria was delivered into the nares of the mice . Erdman WT and the fluorescent reporter strains Erdman ( smyc’::mCherry ) were used for infection experiments as described previously[66] . After mice were euthanized , the left lobe and the accessory lobe were used for CFU plating , while the right lobes were either fixed in 4% paraformaldehyde for confocal microscopy or histological analysis , or used for protein extraction and collagen content measurement . Marimastat and other MMP inhibitors were delivered via intra-peritoneal injection every other day starting 7 days post infection . INH ( 12 . 5mg/kg/day ) and RIF ( 5mg/kg/day ) were added to the drinking water starting 14 days post infection and replenished every week . Method is adapted from C . Kliment et al[68] . Briefly , tissue samples were weighed and put into glass tubes , which were placed in 100°C heat blocks inside a fume hood until the tissue were completely dry[68] . 2ml of 6M HCl was added to each tube , which was sealed under inert gas and incubated on the heat block for 24h . 2ml of PBS was added to each tube to reconstitute the sample and incubated at 60°C for 1h . The samples were centrifuged at 14 , 000rpm to remove the undissolved substance and analyzed for hydroxyproline content . 400μl sample was incubated with 200μl chloramine-T solution ( 50mM chloramine-T , 30% v/v 2-methoxyethnol and 50% v/v hydroxyproline buffer ) for 20 min at room temperature . Then 200μl perchloric acid ( BioVision ) was added and tubes were incubated 5min at room temperature . Finally , 200μl p-dimethylamino-benzaldehyde solution ( 1 . 34M p- dimethylamino-benzaldehyde dissolved in 2-methoxyethnol ) was added to each tube and tubes were incubated at 60°C for 20min . Absorbance measurements were read in a 96-well plate at 557nm on Envision plate reader ( PerkinElmer ) . Confocal analysis was conducted as detailed previously[66] . Briefly , lung tissue was sectioned in to 1 mm thick slices with a razor blade . Then tissue was blocked and permeabilized in PBS + 3% BSA + 0 . 1% Triton X-100 at room temperature for 1h in the dark . Samples were incubated with primary antibody ( CD31 , 1:100 , BD; α-SMA , 1:200 , Abcam ) overnight at 4°C and corresponding secondary antibodies , in the presence of DAPI ( 1:500 ) and Alexa fluor 647 conjugated Phalloidin ( 1:50 ) at room temperature for 2h in the dark . Samples were washed 3 times with PBS and mounted with mounting medium ( Vectorshield ) . Imaging was conducted using a Leica SP5 confocal microscope and signal was quantified by Volocity software[66] . Infected lung tissues were fixed and sectioned by the Histology lab of Animal Health Diagnostic Center in Cornell University . Briefly , unstained slides were hydrated and stained with primary antibodies ( CD31 , 1:1000 , BD; α-SMA , 1:500 , Abcam ) overnight at 4°C . Slides were washed and stained with secondary antibodies ( 1:200 , biotinylated goat anti-rabbit IgG antibody , Vector lab ) and ABC kit at room temperature for 2h . Sections were developed with DAB and mounted for digital scanning ( Scanscope , Aperio Technologies ) . Blind quantitative analysis of IHC images were performed with the Imagescope ( Aperio Technologies ) software . The middle lobe of the right lung from mice infected with Erdman strain Mtb was homogenized within cold RIPA buffer supplemented with protease inhibitor ( Roche ) to extract protein . Proteins were run on 10% SDS-PAGE gel and later transferred to PVDF membrane ( Millipore ) . The membrane was incubated with primary antibody ( MBL 1:100 Hycult; MMP-2 and MMP-9 , 1:2000 Abgent ) overnight at 4°C and with secondary antibodies at room temperature for 2h . The membranes were incubated with Super Pico Chemiluminescent Solution kit for 5min and developed with Amersham Hyperfilm ECL films ( GE Healcare ) . Mice infected with Erdman Mtb were treated with Marimastat as described above . At 4 weeks post infection , mice received a retro-orbital injection of 25mg/kg Evans blue dye and sacrificed 30min after injection . The left lobe and accessary lobe were used for CFU plating and the right lobes were homogenized and incubated in formamide at 55°C for 20h to extract the dye . The homogenate was centrifuged and the supernatant was read for absorbance at 620nm and 740nm . The following formula was used to correct for contamination with heme pigment[69–71]: E620corrected=E620raw− ( 1 . 426×E740raw+0 . 030 ) A standard curve was used to calculate the amount of dye in the lung , which was further normalized to tissue weight . Methods were adapted from previous studies measuring ethambutol in pulmonary TB lesion [50] . Briefly , mice were infected with Erdman Mtb and treated with Marimastat as described above . At 4 weeks , infected animals received a single retro-orbital injection of RIF 2mg/kg and INH 5mg/kg , and sacrificed 1h , 2h and 4h after injection . Lung tissue was collected and homogenized by adding 3 parts of PBS buffer . Samples were shaken using a SPEX Sample Prep Geno/Grinder 2010 for 5 minutes at 1500 rpm with steel beads . Blood samples were collected in tubes coated with K2EDTA and centrifuged at 4000 rpm for 10 min to collect plasma . Standards and QCs were created by adding 10 μL of spiking stock ( neat 1 mg/mL DMSO stocks of RIF , INH , and Acetyl-INH diluted in 50/50 ( Acetonitrile/water ) ) to 90 μL of drug free plasma ( Bioreclamation ) or control lung tissue homogenate . 20 μL of control , standards , QCs , or study samples were added to 200 μL of Acetonitrile/Methanol 50/50 protein precipitation solvent containing 20 ng/mL RIF-d8 , INH-d4 , and Ac-INH-d4 ( Toronto Research Chemicals ) . Extracts were vortexed for 5 minutes and centrifuged at 4000 rpm for 5 minutes . 100 μL of supernatant was combined with 100μL of 2% cinnamaldehyde in methanol to derivatize INH . Mixture was vortexed for 30 minutes to complete reaction . 100 μL of mixture was combined with 100 μL of Milli-Q water prior to HPLC-MS/MS analysis . High-pressure liquid chromatography ( HPLC ) coupled to tandem mass spectrometry ( LC/MS/MS ) analysis was performed on a Sciex Applied Biosystems Qtrap 4000 triple-quadrupole mass spectrometer coupled to an Agilent 1260 HPLC system to quantify the biological samples . Chromatography for RIF , INH , and Acetyl-INH was performed on an Thermo Hypersil Betasil C8 ( 2 . 1x50 mm; particle size , 5 μm ) using a reverse phase gradient elution . The gradient used 0 . 1% formic acid and 0 . 01% Heptafluorobutyric Acid ( HFBA ) in Milli-Q deionized water for the aqueous mobile phase and 0 . 1% formic acid 0 . 01% HFBA in acetonitrile for the organic mobile phase . RIF-d8 , INH-d4 , and Acetyl-INH-d4 were used as internal standards ( IS ) . The compounds were ionized using ESI positive mode ionization and monitored using masses RIF ( 823 . 50/791 . 60 ) , RIF-d8 ( 831 . 50/799 . 60 ) , INH ( 252 . 20/80 . 30 ) , INH-d4 ( 256 . 20/84 . 30 ) , Ac-INH ( 180 . 40/121 . 00 ) , and Ac-INH-d4 ( 184 . 40/142 . 10 ) . Two-tailed Unpaired Student t test with Welch-correction , 1-way and 2-way ANOVA with Šidák multiple comparison tests were conducted in Prism ( GraphPad ) . All experiments were repeated at least twice . Number of mice used in each experiment is indicated in Figure legends .
|
Mycobacterium tuberculosis ( Mtb ) continues to be the leading cause of death from a single infectious agent worldwide , leading to 1 . 8 million deaths in 2015 . The long treatment required ( 6–9 months ) , with all of its incumbent problems , can promote the emergence of multidrug-resistant ( MDR ) TB strains , so strategies to shorten the treatment duration are in dire need . Mtb’s success as a pathogen hinges on its ability to remodel the host tissue , characterized by extracellular matrix ( ECM ) deposition and leaky vascularization . Here we report that inhibition of matrix metalloproteinases ( MMPs ) significantly enhances the potency of frontline TB antibiotics . These MMP inhibitors increase the relative proportion of healthy blood vessels versus leaky dysfunctional vessels at the infection site , and enhance drug delivery and/or retention . Our study highlights the potential of targeting Mtb-induced host tissue remodeling to enhance the efficacy of current frontline antibiotics . It also suggests an alternative therapeutic strategy to repair the leaky blood vessels in TB granulomas to enhance drug delivery . Repurposing of MMP inhibitors may hold the key to shortening TB treatments and combating the emergence of MDR strains .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
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] |
2018
|
Matrix metalloproteinase inhibitors enhance the efficacy of frontline drugs against Mycobacterium tuberculosis
|
Community mass treatment with 30mg/kg azithromycin is central to the new WHO strategy for eradicating yaws . Both yaws and trachoma— which is earmarked for elimination by 2020 using a strategy that includes mass treatment with 20mg/kg azithromycin—are endemic in the Pacific , raising the possibility of an integrated approach to disease control . Community mass treatment with azithromycin for trachoma elimination was conducted in the Solomon Islands in 2014 . We conducted a study to assess the impact of mass treatment with 20mg/kg azithromycin on yaws . We examined children aged 5-14 years and took blood and lesion samples for yaws diagnosis . We recruited 897 children , 6 months after mass treatment . There were no cases of active yaws . Serological evidence of current infection was found in 3 . 6% ( 95% CI= 2 . 5-5 . 0% ) . This differed significantly between individuals who had and had not received azithromycin ( 2 . 8% vs 6 . 5% , p=0 . 015 ) ; the prevalence of positive serology in 5-14 year-olds had been 21 . 7% ( 95% CI=14 . 6%-30 . 9% ) 6 months prior to mass treatment . Not receiving azithromycin was associated with an odds of 3 . 9 for infection ( p=0 . 001 ) . National figures showed a 57% reduction in reported cases of yaws following mass treatment . Following a single round of treatment we did not identify any cases of active yaws in a previously endemic population . We found a significant reduction in latent infection . Our data support expansion of the WHO eradication strategy and suggest an integrated approach to the control of yaws and trachoma in the Pacific may be viable .
Yaws , caused by Treponema pallidum subsp pertenue , is a non-venereal infection closely related to syphilis that predominantly affects children living in remote , rural communities in tropical countries[1] . Infection manifests as lesions of the skin , bone and cartilage and , untreated , may progress to destructive tertiary lesions[2] . Yaws was once widespread throughout the tropics . Previous yaws control efforts in the middle of the twentieth century were based on treatment with injectable long-acting penicillin[3] , and resulted in significant reductions in the burden of disease worldwide[4] . Despite these initial successes , the disease subsequently rebounded in a number of countries and it is currently thought to be endemic in at least 12 countries across West Africa , South East Asia and the Pacific[1] . In 2012 , treatment with azithromycin was shown to be highly effective for yaws[5] , and community mass treatment became the foundation of the new WHO Morges yaws eradication strategy[6] . Azithromycin has a number of advantageous characteristics as a mass treatment agent , including oral route of administration , long tissue half-life , and an acceptable side-effect profile . Community mass treatment with azithromycin is also central to the control of trachoma[7] , but the recommended dose used in trachoma control ( 20mg/kg , max 1g ) is lower than that recommended for yaws ( 30mg/kg , max 2g ) . The International Task Force for Disease Eradication highlighted the need to investigate the effect of lower dose azithromycin for the treatment of yaws , and the possibility of synergies with trachoma control programmes in countries where the two diseases are co-endemic . In some areas of Ghana in which azithromycin mass drug administration was previously used for trachoma control , yaws is currently undetectable[8] , supporting the hypothesis that lower dose azithromycin may be effective . Unexpectedly , several recent studies have demonstrated that Haemophilus ducreyi[9 , 10] is a common cause of non-genital ulcerative skin lesions in children in yaws endemic communities . This is a finding which can present difficulties for clinical case identification . Community perceptions of the value of mass treatment campaigns may be affected by the impact of azithromycin on other common skin infections . Genital strains of H . ducreyi are responsive to azithromycin[11] , so it is possible that mass treatment with azithromycin may have a synergistic benefit on non-yaws ulcerative skin lesions in these communities . Both yaws and trachoma are endemic in the Solomon Islands[12] , which routinely reports the third highest number of cases of yaws among all countries worldwide[13] . In 2014 , the Solomon Islands Ministry of Health and Medical Services ( MHMS ) undertook community mass treatment with azithromycin as part of the SAFE strategy for trachoma elimination . We performed a prospective study in the Western Province of the Solomon Islands to assess the impact of azithromycin used against trachoma on the prevalence of active and latent yaws .
Serum samples were tested at LSHTM with the Treponema pallidum particle agglutination test ( TPPA , Mast Diagnostics , Merseyside UK ) . On samples that were TPPA-positive , a quantitative plasma reagin test ( RPR , Deben Diagnostics , Ipswich , UK ) was performed . Lesion swabs were tested at the CDC using a multiplex real-time ( RT ) PCR for the identification of Treponema pallidum sub-species DNA[16] . If the T pallidum PCR was positive , we intended to use a second multiplex RT PCR to detect mutations in the 23S rRNA gene associated with azithromycin resistance . Regardless of the result of the T . pallidum PCR , we performed an additional duplex RT PCR for the detection of Haemophilus ducreyi and Mycobacterium ulcerans DNA[9] . All laboratory testing was performed by individuals masked to the clinical findings . Suspected cases of yaws are reported via the MHMS Health Information System . We extracted data on the number of cases of yaws seen , per month , across all clinics in the Western Province of the Solomon Islands during the period 2011 to 2014 to allow an assessment of the impact of community mass treatment on the incidence of disease presentation . A positive TPPA was taken as evidence of previous or current infection . Individuals with clinical signs of yaws , a positive TPPA and an RPR titre of ≥1:4 ( dual-seropositivity ) were considered to have active yaws . Individuals without clinical signs of yaws and with a positive TPPA and an RPR titre of ≥1:4 were considered to have latent yaws . An RPR titre of ≥1:16 was considered to be a high-titre positive . We classified household size as ≤5 or >5 residents , 5 householders being the national average according to the most recent census[17] . Household treatment with azithromycin was categorized as complete , incomplete ( at least 1 individual not treated ) or none . The prevalence of active and latent yaws was compared between individuals who had and had not received treatment with azithromycin . Multivariable logistic regression was used to estimate unadjusted and adjusted odds ratios ( ORs ) for factors associated with both TPPA- and dual-seropositivity . Robust standard errors were used to calculate all confidence intervals ( CIs ) and P values , to account for village-level clustering[18] . The impact of mass treatment on cases reported to the MHMS was analysed by fitting a linear regression model to the time series on incident yaws cases , controlling for known seasonal variations and trend in yaws incidence . To account for autocorrelation , the error in the model was assumed to follow an autoregressive process , with a lag of one . All analyses were performed using Stata 13 . 1 ( Statacorp , Texas ) . Our pre-mass drug administration ( MDA ) survey had shown that the prevalence of dual-seropositivity in these communities was approximately 20%[12] . Assuming that treatment with azithromycin is 90% effective , the prevalence in people who receive treatment would be anticipated to be approximately 2% post treatment . The prevalence of yaws in untreated individuals was also predicted to fall due to reduced community transmission , although there were no data to guide the likely magnitude of this effect . Assuming , conservatively , that prevalence amongst untreated individuals would fall by 25% , 72 individuals receiving azithromycin and 72 individuals who did not receive azithromycin would have 90% power to detect a difference in the prevalence of yaws . Given anticipated community coverage of 90% , a total survey sample of 720 individuals would therefore be required . Written informed consent was obtained from each participating child’s parent or guardian by a member of staff fluent in the local dialect . Assent was obtained from all children . Ethical approval for the study was granted by the ethics committees of the Solomon Islands MHMS and the LSHTM ( 6358 ) .
We enrolled 897 children from 441 households in 11 communities . The median age of children was 9 years , and 466 ( 52% ) were male . 717 children ( 80% ) reported having been treated with azithromycin as part of the trachoma control programme ( Table 1 ) ( S1 File ) . Two hundred and thirty seven children ( 26% ) had a clinically apparent skin lesion . Twenty-eight children ( 3 . 1% ) had a skin lesion clinically consistent with yaws . Lesions were more common in individuals who had not received MDA , but this difference was not statistically significant . ( 4 . 9% vs 2 . 6% , p = 0 . 101 ) . No individual with a skin lesion consistent with active yaws had dual-positive serology . Bone swelling consistent with secondary yaws was rare , occurring in only 4 subjects ( 0 . 5% ) . Sixty children ( 6 . 7% ) had skin lesions consistent with healed yaws . Other skin lesions including ringworm and bacterial infections were common ( 158 children , 17 . 6% ) . Two hundred and twenty eight children ( 25% , 95% CI 23–28% ) had a positive TPPA . The prevalence did not differ significantly between individuals who had and had not received treatment with azithromycin ( 24% vs 26% , p = 0 . 598 ) . Thirty two children ( 3 . 5% , 95% CI 2 . 5–4 . 9% ) had a positive TPPA and an RPR titre of ≥ 1:4; the prevalence of this differed significantly between individuals who had and had not received treatment with azithromycin ( 2 . 8% vs 6 . 6% , p = 0 . 015 ) . 11 children ( 1 . 2% , 95% CI 0 . 06–2 . 2% ) had a high titre positive RPR and this also differed significantly between individuals who had and had not received azithromycin ( 0 . 8% vs 2 . 7% , p = 0 . 046 ) ( Fig 1 ) . We collected lesion swabs from twenty individuals . Swabs could not be collected from eight ulcerative lesions as they were dry . No sample tested positive for T . p subsp . pertenue , but 7 swabs ( 35% ) were positive for H . ducreyi . Given the small number of individuals with dual sero-positivity these subjects were combined into a single group for the purpose of further analysis . People who had not taken azithromycin had higher odds of dual sero-positivity than those who had ( OR = 2 . 49 , 95% CI 1 . 2–5 . 2 , p = 0 . 015 ) , and after adjusting for confounding due to age , gender , and community of residence , the odds ratio was 3 . 8 ( 95% CI 1 . 8–8 . 5 , p = 0 . 001 ) ( Tables 2 and 3 ) . Increasing age was associated with TPPA positivity , but no other variable was associated with dual sero-positivity . In the pre-mass treatment period ( n = 36 months ) the mean monthly number of cases of yaws reported by clinicians in the Western Province was 184 . In the interrupted time series analysis , the number of cases was 183 in the dry season and 158 in the wet season ( p = 0 . 440 ) , and mass treatment was followed by a reduction in the mean number of cases reported per month of 101 case ( relative reduction 57% , p = 0 . 044 ) ( Fig 2 ) .
In this study , a single round of community mass treatment with 20mg/kg azithromycin , given for trachoma elimination , resulted in a significant reduction in the prevalence of both active and latent yaws , from 1 . 5% and 20 . 2% pre-treatment[12] , to 0 . 0% and 3 . 6% post-treatment ( p = 0 . 002 and <0 . 001 , respectively ) . The prevalence of infection declined both in individuals who had received treatment and in those who had not , suggesting that a single round of treatment may have reduced transmission , resulting in a population level benefit that extended to individuals who were not themselves treated . Consistent with this , the impact of azithromycin appeared particularly marked in reducing the prevalence of high-titre positive individuals , who are thought to drive transmission at community level . Our results are mirrored in the routine reporting data for incident yaws cases , which showed a profound drop following mass treatment with azithromycin . There was also a reduction in the prevalence of any ulcerative skin lesion since our previous survey ( 6 . 0% vs 3 . 1% , p = 0 . 004 ) . Taken together , these data suggest that a single round of 20mg/kg azithromycin mass treatment given for trachoma may have interrupted yaws transmission , resulting in a reduction of both prevalent and incident yaws cases , and reducing the prevalence of skin lesions due to other bacteria . The results of this study are concordant with recently published data from Papua New Guinea , which also demonstrated that a single round of azithromycin mass treatment , albeit at a higher dose of 30mg/kg , significantly reduced the prevalence of active and latent yaws[19] . In our study , effectiveness was demonstrated with a lower dose of azithromycin ( 20mg/kg ) , evidence that facilitates integration of yaws control into national trachoma elimination plans . The absence of any lesions which were positive by PCR for T . p subsp . pertenue is consistent with the marked effect seen on serological markers of infection . Our failure to detect treponemal DNA is somewhat reassuring in the context of the theoretical potential for lower dose azithromycin to select for macrolide resistance[20] . Integrated , synergistic control efforts are likely to result in increased efficiencies and decreased costs for programmes and ministries of health , which will be vital in helping countries achieve elimination targets by 2020 . In this population , individual level coverage with azithromycin was about 80% , which is in line with that commonly achieved by trachoma elimination programmes . Our findings suggest that an initial mass treatment round with high coverage can significantly reduce the burden of infection . Whether subsequent treatment would be best delivered through community mass treatment or the detection of cases and contacts remains unclear and should be studied further using both observational and modeling approaches . In view of the extremely low positive predictive value of clinical signs for the diagnosis of yaws seen here , the call for point of care serological tests to be made available within the health care system[21] must be redoubled , in order to strengthen surveillance and guide post-mass treatment case detection and treatment . In trachoma control programmes in sub-Saharan Africa the use of height-based dosing algorithms commonly results in children receiving doses of azithromycin closer to 30mg/kg than 20mg/kg[22] which might make it difficult to detect meaningful differences in outcomes between the two dosing strategies . As there were limited anthropometric data to guide height-based dosing in the Pacific , weight-based dosing was used in the Solomon Islands , and children therefore received a dose as close as possible to 20mg/kg body weight , to a maximum of 1g . This study therefore provides the first prospective data supporting the effectiveness of lower dose azithromycin against yaws . This information is of particular value for countries where yaws and trachoma are co-endemic and which may therefore benefit from existing trachoma elimination activities . The most notable limitation of this study is its observational nature . Whilst a randomized design may have been desirable , this would have be unethical , given the need to implement international guidelines for trachoma elimination[23] , which mandate treatment of the whole population . A stepped-wedge design could have been considered[24] but may have been unethical , for the same reason . Follow-up in this study was limited to 6 months , and it is possible that longer observation would have revealed a more marked difference between the two groups . We relied on reported receipt of azithromycin , which may have introduced an element of recall bias . However it is likely that this would , in fact , have reduced any difference seen between individuals who did and did not receive azithromycin , and therefore would not affect the overall finding of our study . RPR titres normally fall rapidly in individuals successfully treated for yaws and , in the original randomized control trial of azithromycin conducted in Papua New Guinea[5] , combined clinical and serological cure was 96% at 6 months . It seems likely therefore that we have observed the greater part of the effect that might be expected to be derived from azithromycin mass treatment . Our findings support the roll out of mass treatment with azithromycin as an effective intervention for the simultaneous elimination of trachoma and yaws in co-endemic areas , and provide further observational data to recommend the WHO Morges strategy where yaws alone is endemic . The reduction in the prevalence of latent yaws following community mass treatment is a particularly important result , as a failure to adequately treat these individuals is thought to have contributed to the failure of previous yaws eradication efforts . Community mobilization , ongoing surveillance and lasting political support will be necessary to translate these findings into the ambitious goal of yaws eradication .
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Yaws is a bacterial infection closely related to syphilis . The WHO has launched a worldwide campaign to eradicate yaws by 2020 . This strategy relies on mass treatment of the whole community with the antibiotic azithromycin . Mass treatment with the same antibiotic is also recommended by WHO to treat the eye condition trachoma but the dose used for this is lower . In this study we assessed the impact of a trachoma control programme in the Solomon Islands on yaws . Following a single round of mass treatment the number of yaws cases fell significantly compared to before treatment . We also saw fewer new cases of yaws being reported to the Ministry of Health . Our findings suggest that mass treatment with the lower dose of azithromycin is also effective as a treatment for yaws . In countries where both yaws and trachoma are found it may be possible to develop an integrated strategy for both conditions .
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[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[] |
2015
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Impact of Community Mass Treatment with Azithromycin for Trachoma Elimination on the Prevalence of Yaws
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The Human T lymphotropic virus type-1 ( HTLV-1 ) infects predominantly T cells , inducing proliferation and lymphocyte activation . Additionally , HTLV-1 infected subjects are more susceptible to other infections caused by other intracellular agents . Monocytes/macrophages are important cells in the defense against intracellular pathogens . Our aims were to determine the frequency of monocytes subsets , expression of co-stimulatory molecules in these cells and to evaluate microbicidal ability and cytokine and chemokine production by macrophages from HTLV-1 infected subjects . Participants were 23 HTLV-1 carriers ( HC ) , 22 HAM/TSP patients and 22 healthy subjects ( HS ) not infected with HTLV-1 . The frequencies of monocyte subsets and expression of co-stimulatory molecules were determined by flow cytometry . Macrophages were infected with L . braziliensis or stimulated with LPS . Microbicidal activity of macrophages was determined by optic microscopy . Cytokines/chemokines from macrophage supernatants were measured by ELISA . HAM/TSP patients showed an increase frequency of intermediate monocytes , but expression of co-stimulatory molecules was similar between the groups . Macrophages from HTLV-1 infected individuals were infected with L . braziliensis at the same ratio than macrophages from HS , and all the groups had the same ability to kill Leishmania parasites . However , macrophages from HTLV-1 infected subjects produced more CXCL9 and CCL5 , and less IL-10 than cells from HS . While there was no correlation between IFN-γ and cytokine/chemokine production by macrophages , there was a correlation between proviral load and TNF and CXCL10 . These data showed a dissociation between the inflammatory response and microbicidal ability of macrophages from HTLV-1 infected subjects . While macrophages ability to kill an intracellular pathogen did not differ among HTLV-1 infected subjects , these cells secreted high amount of chemokines even in unstimulated cultures . Moreover the increasing inflammatory activity of macrophages was similar in HAM/TSP patients and HC and it was related to HTLV-1 proviral load rather than the high IFN-γ production observed in these subjects .
Human T lymphotropic virus type 1 ( HTLV-1 ) infects about 15 to 20 million people worldwide , with endemic foci in virtually all continents [1] , [2] . A large proportion of individuals remain asymptomatic until the end of life , but a subgroup of infected individuals will develop a malignant lymphoproliferative disease called adult T cell leukemia/lymphoma ( ATLL ) [3] , [4] or a chronic neurodegenerative inflammatory disease called HTLV-1 associated myelopathy/tropical spastic paraparesis ( HAM/TSP ) [5] . Additionally , more than 40% of infected individuals will present clinical manifestations , such as infectious dermatitis [6] , polymyositis [7] , sicca syndrome [8] , [9] , overactive bladder and/or erectile dysfunction [10] , [11] , chronic periodontitis [12] and HTLV-1 associated arthropathy among other diseases [13] , [14] , [15] . The pathogenesis of diseases associated to HTLV-1 is related predominantly to the proviral load and the exaggerated inflammatory response in HTLV-1 infection [16] , [17] . HTLV-1 infects predominantly CD4+ T cells , but CD8+ T cells [18] , monocytes/macrophages [19] , [20] and dendritic cells [21] can also be infected by the virus . The infection is characterized by a high spontaneous proliferation and activation of T cells , leading to high production and secretion of inflammatory mediators , such as TNF , IFN-γ , CXCL9 and CXCL10 [16] , [22] . Previous immunological studies have directed attention to the role of T cells in HTLV-1 infection , and seek to correlate the dysfunctions of the adaptive immune system with the development of diseases or clinical manifestations associated with the virus . Very few studies have evaluated the role of the innate immune response in HTLV-1 infection . It is well known that HTLV-1 infection increases susceptibility and severity to other infectious diseases [23] , [24] , [25] . The mechanism involved in the increased susceptibility of HTLV-1 infected subjects to other infectious agents is only partially known [23] , [26] . Regarding intracellular pathogens , despite the high IFN-γ and TNF production there is an increased susceptibility to Mycobacterium tuberculosis [27] , [28] , [29] and fungal infections [30] . It is known that cells of the innate immunity response , such as neutrophils and macrophages , are important effectors cells against infectious agents . However very few studies have evaluated monocytes , macrophages or neutrophils functions in HTLV-1 infection . It is known that HTLV-1 infection results in spontaneous activation of neutrophils , as indicated by increasing in the number of positive cells in the nitroblue tetrazolium test ( NBT ) ( indicating high burst oxidative activity ) , and by the decreasing in the number of neutrophils expressing CD62L and higher expression of CD66b [31] , [32] . Regarding dendritic cells ( DCs ) , some studies showed an increased expression of molecules involved in virus internalization process and T cells adhesion ( DC-SIGN ) [33] , [34] , and a decrease in CD14 and CD1a , molecules related with derived-monocytes DCs maturation have been described [35] . Moreover , DCs from HTLV-1 infected patients show an impaired expression of CD83 , CD86 and HLA-DR after stimulation with TNF and reduced ability to stimulate T cells not infected with the virus [35] . A recent study developed in Jamaica , with a cohort of HTLV-1 infected subjects , documented a decreased frequency of plasmocytoid DCs ( pDCs ) and expression of HLA-DR in ATL and HAM/TSP patients compared to ACs and HC . Myeloid DCs ( mDCs ) also showed a lower expression of HLA-DR in HAM/TSP patients . However , the expression of CD86 in both plasmocytoid and mDCs was higher in HAM/TSP patients compared to the other groups . They also demonstrated that the programmed death ligand 1 ( PD-L1 ) is high-expressed in DCs from HAM/TSP compared to ACs [36] . These and others dysfunctions in the myeloid cell lineage may modify the immune response of HTLV-1 infected subjects to antigens . However , studies regarding the inflammatory response and microbicidal activity of monocytes and macrophages in HTLV-1 infected subjects have not been performed . The aims of the present study were to evaluate monocytes and macrophages functions in HTLV-1 infected subjects , by comparing the frequency of monocyte subsets in HTLV-1 infected subjects and the ability of monocyte-derived macrophages from HTLV-1 infected subjects to produce cytokines and chemokines and to kill the intracellular pathogen Leishmania braziliensis . Moreover we evaluate if there are correlations between the frequency of monocytes subsets and cytokines/chemokines produced by macrophages with IFN-γ and proviral load in these individuals .
All HTLV-1 subjects have been followed at the HTLV-1 clinic of the Complexo Hospitalar Universitário Professor Edgard Santos ( COM-HUPES ) , Federal University of Bahia , Brazil . The study was approved by the Ethics Committee from the Federal University of Bahia and all patients signed a document of informed consent . This is a cross-sectional study with the purpose of evaluating the role of myeloid lineage cells ( monocytes and macrophages ) from HTLV-1 infected subjects . Participants included 45 HTLV-1 infected subjects , being 23 HTLV-1 carriers ( HC ) , 22 patients diagnosed with HAM/TSP and 22 individuals not infected with HTLV-1 constituted the healthy subjects group ( HS ) . Pregnant woman and individuals in use of immunosupressing drugs were excluded . The diagnosis of HTLV-1 infection was established by antibody detection by ELISA ( Murex HTLV-I+II , Abbot , Dartford , UK ) and confirmed by Western blot ( HTLV blot 2 . 4 , Genelabs . Singapore ) . Motor dysfunction and neurological involvement were determined by Osame's motor disability score ( OMDS ) [37] and Expanded disability status scale ( EDSS ) [38] . Individuals with an OMDS and EDSS equal to 0 were considered HC . Patients with OMDS ≥1 and presence of specific antibodies against HTLV-1 in the cerebrospinal fluid were diagnosed with HAM/TSP . Peripheral blood mononuclear cells ( PBMCs ) were obtained from heparinized blood of HTLV-1 infected subjects and healthy controls , and separated by density gradient with Ficoll-Hypaque ( GE Healthcare Bio – Sciences , Uppsala , Sweden ) . PBMCs from the interface were aspirated and washed with saline . After that , these cells were resuspended in RPMI 1640 culture medium with L-glutamine and 25 mM HEPES ( Gibco BRL , Grand Island , New York , USA ) supplemented with 10% fetal bovine serum ( FBS ) and 0 . 5% gentamicin at 10 mg/mL ( Gibco BRL , Grand Island , New York , USA ) . PBMCs were then directed to three separate experiments: a ) staining with specific antibodies for analyses of monocyte by flow cytometry; b ) culture for determination of spontaneous production of IFN-γ by PBMCs . 3×106 cells/mL were incubated without stimulus or stimulated with PHA ( 5 µg/mL ) at 37°C in 5% CO2 for 72 hours and then the supernatant was frozen for later determination of IFN-γ; or c ) use for the differentiation of cultured monocytes into macrophages , 5×106 cells/mL . These cells were added to 4-well plates ( Lab-Tek Permanox Chamber Slide , Electron Microscopy Sciences , Hatfield , PA ) and incubated for 2 hours at 37°C and 5% CO2 . Cells that did not adhere to the slides were removed by washing . The adherent cells ( monocytes ) were differentiated into macrophages after 6 days of culture at 37°C in 5% CO2 , with absence of stimulus , in the presence of LPS or L . braziliensis . The ex vivo frequency of monocyte subsets and expression of HLA-DR , CD80 and CD86 was determined using PBMCs from HC , HAM/TSP patients and HS . Cells were stained with monoclonal antibodies ( anti-CD14-FITC , anti-CD16-PE-Cy5 , anti-HLA-DR-PE , anti-CD80-PE e anti-CD86-PE , from eBioscience , San Diego , CA or R&D Systems , Minneapolis , MN ) for 20 minutes at 4°C . PBMCs were washed with PBS and then fixed with 2% paraformaldehyde . Cells were then analyzed on the flow cytometer ( II FacsCanto , BD Biosciences , San Jose , CA ) . Analysis was performed using FlowJo software version 7 . 6 ( TreeStar , Ashland , OR ) . The monocyte population was selected based on size and cell granularity and then subdivided into classical ( CD14++CD16- ) , intermediate ( CD14+CD16+ ) and non-classical monocytes ( CD14+CD16++ ) . A strain of L . braziliensis isolated from a patient with cutaneous leishmaniasis from the endemic area of Corte de Pedra , Salvador , Bahia , is maintained , cryopreserved , by the Immunology Service . Parasites were initially cultivated in tubes with biphasic medium ( NNN ) supplemented with 10% fetal bovine serum and maintained in culture in Schneider medium ( LGC Biotechnology , São Paulo , Brazil ) supplemented with 10% FBS and 1% penicillin streptomycin and glutamine ( Gibco BRL , Grand Island , New York , USA ) for expansion and proliferation of protozoa . L . braziliensis promastigotes were maintained in Schneider medium until reaching stationary ( infectious ) growth phase . Parasites were than centrifuged and resuspended in RPMI 1640 medium and used to infect macrophages cultured from both HTLV-1 infected subjects and HS . Unstimulated or macrophages stimulated with lipopolysaccharide ( LPS ) from Escherichia coli ( 100 ng/mL ) were used as controls . Infection with L . braziliensis was performed at a ratio of 5 parasites to 1 cell for 2 hours at 37°C in 5% CO2 . Following the incubation period , extracellular parasites were removed by washing , and then the cells were incubated at 37°C and 5% CO2 . The percentage of macrophages infected with L . braziliensis and the number of amastigotes per 100 macrophages were evaluated by optical microscopy after 2 , 48 and 72 hours of infection and staining with Giemsa . Counts were performed by two independent observers who were unaware if the slides were from an HTLV-1 infected subject or from a healthy control . The results expressed are the average of the results from both observers . Culture supernatants from unstimulated PBMCs or from macrophages were collected after 72 and 48 hours of incubation , respectively , and frozen at −20°C until used for determination of cytokines and chemokines . The IFN-γ levels ( supernatants of PBMCs ) and TNF , IL-10 , CXCL9 , CXCL10 and CCL5 ( macrophages supernatants ) were determined by ELISA , using commercial kits and following the manufacturer's instructions ( DuoSet R&D Systems , Minneapolis , MN , USA and BD Pharmingen , San Diego , CA , USA ) . Due to the limited amount of cells some experiments did not included all patients . Data about the production of cytokines and chemokines produced by macrophages after infection by L . braziliensis were not represented in the figures , because this pathogen did not induced the production of these molecules . DNA was extracted from 106 cells using proteinase K and salting-out method . The HTLV-1 proviral load was quantified using a real-time TaqMan PCR method [39] . Albumin DNA was used as an endogenous reference . Amplification and data acquisition were carried out using the ABI Prism 7700 Sequence detector system ( Applied Biosystems ) . Standard curves were generated using a 10-fold serial dilution of a double-stranded plasmid ( pcHTLV-ALB ) . All standard dilutions and control and individual samples were run in duplicate for both HTLV-1 and albumin DNA quantification . The normalized value of the HTLV-1 proviral load was calculated as the ratio of ( HTLV-1 DNA average copy number/albumin DNA average copy number ) ×2×106 and expressed as the number of HTLV-1 copies/106 cells . Mann-Whitney test was used to compare IFN-γ production by PBMCs and proviral load between ACs and HAM/TSP patients . Kruskal-Wallis followed by Dunn's post test was used to assess differences between the three groups studied under the same conditions . Wilcoxon test was used to evaluate the influence of stimuli ( LPS and L . braziliensis ) compared to condition without stimulation . Spearman correlation test was used in the correlations results . Data were expressed as median and range ( minimum and maximum values ) . GraphPad Prism 5 ( San Diego , CA ) was used to carry out the statistical evaluation and a P<0 . 05 was considered to indicate a significant difference .
The frequencies of monocyte subsets ( classical , intermediate and non-classical monocytes ) in HC , HAM/TSP patients and HS were determined by flow cytometry and are shown in Fig . 1 . The HC group showed a similar frequency of monocyte subsets as observed in the HS group . However , patients with HAM/TSP exhibit lower frequency of classical monocytes and higher frequency of intermediate monocytes than HC and HS . 89 . 4% of monocytes from HS were classical , while 6 . 4% were intermediate and 5 . 1% were non-classical . 83 . 9% of monocytes from HC were classical , 6 . 8% were intermediate and 7 . 5% were non-classical monocytes . HAM/TSP patients showed 75 . 1% of classical monocytes ( P = 0 . 0005 compared to HC and HS ) , 23 . 9% of intermediate monocytes ( P<0 . 0001 also compared to HC and HS ) and 5% of non-classical monocytes ( Fig . 1 ) . There was no difference in the expression of HLA-DR , CD80 and CD86 by monocytes between HC , HAM/TSP patients and HS ( P>0 . 05 ) . The increasing in frequency of intermediate monocytes in HAM/TSP patients was not associated with IFN-γ production by PBMCs ( Fig . 2 ) . To evaluate the susceptibility of macrophages from HTLV-1 infected subjects to be infected by an intracellular pathogen and the ability of these cells to kill it , macrophages were infected by L . braziliensis at a 5∶1 ratio . The percentage of infected macrophages and the number of amastigotes/100 macrophages were evaluated by optic microscopy after 2 , 48 and 72 hours of infection , as shown in Fig . 3 . There was no difference in the microbicidal activity between macrophages from the groups studied at any of the three time points following infection by L . braziliensis . Macrophages from HTLV-1 infected subjects ( both HC and HAM/TSP patients ) were initially infected by the parasite at the same proportion as macrophages from HS ( Fig . 3A ) , and there were similar amounts of L . braziliensis amastigotes inside the cells after 2 , 48 and 72 hours of infection in the groups ( Fig . 3B ) . Levels of TNF and IL-10 were evaluated in the supernatants from HTLV-1 infected subjects ( HC and HAM/TSP ) and HS macrophages cultured after 48 hours of incubation with or without LPS . These assays were performed by ELISA and data are shown in Fig . 4 . Neither macrophages from HS , HC nor from HAM/TSP patients produced spontaneously significant detectable levels of TNF ( 0 pg/mL , 0 pg/mL , 16 pg/mL , respectively ) . When macrophages from three groups were stimulated with LPS , high levels of TNF were detected ( P<0 . 0002 ) . Macrophages from HS produced 2 , 145 pg/mL while macrophages from HC produced 2 , 102 pg/mL , and macrophages from HAM/TSP produced 1 , 965 pg/mL , with no statistically significant differences between those three values ( Fig . 4A ) . HC , HAM/TSP patients and HS macrophages did not produced IL-10 in significant levels either spontaneously ( 0 pg/mL , 32 pg/mL and 0 pg/mL , respectively ) . Upon LPS stimulation , all groups showed increased IL-10 production , but macrophages from HS produced more of this cytokine ( 265 pg/mL ) than macrophages from HC and HAM/TSP patients ( 46 pg/mL and 31 pg/mL , respectively , P = 0 . 003 ) ( Fig . 4B ) . Levels of CXCL9 , CXCL10 and CCL5 were evaluated in the supernatants from macrophage cultures of HTLV-1 infected subjects ( HC and HAM/TSP patients ) and HC after 48 hours of incubation , with or without LPS , as shown in Fig . 5 . Macrophages from HC and HAM/TSP patient spontaneously produced more CXCL9 than macrophages from HS ( 32 , 115 pg/mL and 25 , 558 pg/mL vs 6 , 992 pg/mL , P = 0 . 003 ) . Macrophages from HC and HAM/TSP patients also produced more CXCL9 than HS's macrophages after LPS stimulus ( 31 , 080 pg/mL and 26 , 834 pg/mL vs 9 , 648 pg/mL , P = 0 . 001 ) . Furthermore , stimulation with LPS did not induce the production of CXCL9 in macrophages from HS and HTLV-1 infected subjects ( Fig . 5A ) . Macrophages from HC and HAM/TSP patients produced spontaneously similar levels of CXCL10 ( 2 , 458 pg/mL and 2 , 288 pg/mL ) than macrophages from HS ( 255 pg/mL ) , After stimulus with LPS , we also did not observed differences between the production of this cytokine by macrophages from HS ( 2 , 785 pg/mL ) , HC ( 3 , 418 pg/mL ) and HAM/TSP patients ( 3 , 201 pg/mL ) . However , while LPS increased the production of CXCL10 by macrophages from HS compared to the unstimulated condition ( P<0 . 03 ) , this stimuli did not increased CXCL10 production by macrophages from HC and HAM/TSP patients ( Fig . 5B ) . Macrophages from HAM/TSP patients produced more CCL5 than macrophages from HS ( 1 , 050 pg/mL vs 254 pg/mL , P<0 . 0001 ) . LPS was responsible to increase the production of CCL5 in all group studied ( P<0 . 003 ) , but we did not observe statistically significant differences between those groups ( Fig . 5C ) . To evaluate if the spontaneous production of IFN-γ by PBMCs was associated with proviral load , correlations were performed using Spearman correlation and r significance test . We observed a direct correlation between IFN-γ and proviral load in HTLV-1 infected subjects , r = 0 . 43 and P = 0 . 004 ( analyzing HC and HAM/TSP patients together ) ( Fig . 6 ) . To evaluate if the spontaneous production of IFN-γ by PBMCs was associated with cytokines/chemokines produced by macrophages from HTLV-1 infected subjects , correlations were performed using Spearman correlation and r significance test . We found no correlation between spontaneous production of IFN-γ and production of TNF ( r = 0 . 36 and P = 0 . 08 ) , IL-10 ( r = 0 . 09 and P = 0 . 74 ) , CXCL9 ( r = −0 . 02 and P = 0 . 90 ) , CXCL10 ( r = 0 . 41 and P = 0 . 09 ) and CCL5 ( r = 0 . 20 and P = 0 . 44 ) in HTLV-1 infected patients . Correlations between proviral load and cytokines/chemokines produced by macrophages were performed using Spearman correlation and r significance test . We observed a positive correlation , although weak , between proviral load and TNF ( r = 0 . 51 and P = 0 . 01 ) and CXCL10 ( r = 0 . 63 e P = 0 . 05 ) ( Fig . 7 ) . There was no correlation between proviral load and the IL-10 ( r = −0 . 34 and P = 0 . 26 ) , CXCL9 ( r = 0 . 26 and P = 0 . 24 ) and CCL5 ( r = 0 . 35 and P = 0 . 21 ) production by macrophages from HTLV-1 infected individuals .
The activity and phenotype of T cells in HTLV-1 infection have been well studied . These cells are characterized by the increased expression of proinflammatory cytokines , such as TNF and IFN-γ , and increased production of IL-2 , which helps maintain CD4+ and CD8+ T cell proliferation [16] , [22] . In contrast very little is known about innate immunity during HTLV-1 infection . We observed that HAM/TSP patients exhibit a higher frequency of intermediate ( inflammatory ) monocytes than HC and HS , but it was not associated with IFN-γ levels . While the microbicidal ability from HTLV-1 infected subject's macrophages was preserved , macrophages from HTLV-1 infected individuals produced more CXCL9 and CCL5 , and less IL-10 than macrophages from HS . Indeed while there was no correlation between IFN-γ and cytokine levels , there was a correlation between proviral loads and TNF and CXCL10 production . It is known that monocytes are a heterogeneous population of cells and can be classified , based on the expression of CD14 and CD16 , as classical , intermediate or inflammatory and non-classical . Here we documented that HAM/TSP patients have a higher frequency of intermediate monocytes than HC and HS . It is known that intermediate monocytes are the main source of TNF among the three subpopulations [40] . As PBMCs from HTLV-1 infected subjects , especially from HAM/TSP patients , produce more proinflammatory mediators such as CXCL9 , CXCL10 and TNF than PBMCs from HS [16] , [41] , we hypothesized that monocytes may play an important role in the inflammatory response and in the pathogenesis of HAM/TSP . Agreeing with this hypothesis , we observed that there was no correlation between IFN-γ production and increasing intermediate monocytes . Macrophages are capable of killing infectious agents but may also serve as habitat for intracellular pathogens . As HTLV-1 infection increases the susceptibility to infections caused by intracellular agents such as M . tuberculosis , we sought to evaluate macrophage microbicidal function in HTLV-1 infection . To evaluate macrophage killing we used L . braziliensis , an intracellular pathogen knowing to interact with TLR2 , TLR4 and TLR9 [42] , [43] and with the ability to multiply in macrophages . As the number of intracellular parasites inside the macrophages after 2 hours of infection was similar to that observed in HS , it was concluded that penetration and/or phagocytosis of L . braziliensis was equal in the three groups . Moreover the leishmania killing , evaluated at 48 hours and 72 hours by quantifying the number of intracellular amastigotes in macrophages , was similar . This data extend our previous observations that the ability of neutrophils from HTLV-1 infected subjects to kill leishmania parasites is preserved [32] . As IFN-γ is the main cytokine known to activate macrophages and high IFN-γ production is observed in HTLV-1 infected subjects , one could expect that macrophages from HAM/TSP patients had greater ability to kill an intracellular pathogen , but we did not find that the marked Th1 environment observed in these individuals modified the killing ability of myeloid cells . Macrophages activation has been used to indicate both , increasing ability of killing and secretion of molecules such as chemokines and cytokines . Macrophages are also a heterogeneous cells population and macrophage's subsets have been defined as classical macrophages that are associated with a type 1 immune response , and alternative macrophages that secret IL-4 and IL-10 [44] . Here we showed that the killing ability and secretion of cytokines by macrophages are not necessarily associated . While we did not observe an increase in the killing ability of macrophages from HTLV-1 infected subjects , manly in unstimulated cells or after LPS stimulation , macrophages produced higher levels of CXCL9 and CCL5 than HS's macrophages . This indicates that at the level of innate immunity , there was an enhancement of chemokines related with both Th1 and Th2 immune responses . This is an agreement with the observation that atopic diseases may occur in HTLV-1 infection [30] and that PBMC from HC produce higher amount of Th2 cytokines than cells from HS [16] . In this study we did not observed a higher production of CXCL10 by macrophages from HC and HAM/TSP patients compared to HS , but while the cells from HS were stimulated by LPS to produce this chemokine , it was not observed in macrophages from HC and HAM/TSP patients . A reasonable explanation for this observation is that cells from HTLV-1 infected subjects could have already reached the limit of production of these chemokine even before the addition of LPS in the cultures . It's known the ability of LPS to induce strong TNF production which is followed by IL-10 synthesis . The observation that HS's macrophages stimulated with LPS produced more IL-10 than cells from HTLV-1 infected subjects , both HC and HAM/TSP patients , suggests an impairment on macrophages of these individuals to secret this regulatory cytokine . Although the high production of proinflammatory mediators documented in HTLV-1 infection , especially in HAM/TSP patients , such as IFN-γ , IL-1 , IL-6 , could contribute to the increased production of chemokines and TNF by macrophages , we did not find a correlation between the IFN-γ production by PBMCs and TNF , IL-10 , CXCL9 , CXCL10 and CCL5 produced by macrophages . In contrast with the absence of correlation between IFN-γ and cytokines/chemokines levels , there was a direct correlation between proviral load and TNF and CXCL10 levels . This indicates that the HTLV-1 by itself or by inducing soluble mediators play a key role in the increased ability of macrophages to produce cytokine during HTLV-1 infection . Therefore it is important that future studies evaluate not only the role of viral proteins and other viral factors in activate innate immunity cells but also in disease expression associated to HTLV-1 . It is already known that PBMCs from HAM/TSP patients produced more IFN-γ and have a higher proviral load compared to HC [16] , [17] , [45] . In agreement with previous observations , we also documented a positive correlation between proviral load and IFN-γ production by PBMCs from HTLV-1 infected subjects . However our data clearly show while the IFN-γ production did not correlate with the increased cytokine/chemokine production by macrophages in HTLV-1 infected subjects , there was a correlation between proviral load and TNF and CXCL10 . This is the first study to evaluate the monocyte subsets and activation , as well as the inflammatory and microbicidal activity from macrophages in HTLV-1 infection . Our data indicate that patients with HAM/TSP have an increase frequency of intermediate monocytes . We also observed that macrophages from HTLV-1 infected subjects have the same ability to phagocytize and kill an intracellular pathogen as healthy subject's macrophages , but that proinflammatory activity was enhanced in HTLV-1 infected subjects . The dissociation between microbicidal activity and production of proinflammatory cytokines and chemokines is a relevant subject and show that inflammation and killing may be independent functions . As IFN-γ is the main cytokine that activate macrophages we expected at some extension a relationship between this cytokine and the inflammatory profile observed in monocytes and macrophages . However the absence of correlation between IFN-γ and cytokine/chemokine production and a direct correlation between proviral load and TNF and CXCL10 produced by macrophages suggests innate immune cells triggered by viral factors may play an important role in the inflammatory response and in the pathogenesis of HAM/TSP .
|
HTLV-1 predominantly infects T cells , inducing cell proliferation and activation . While there is a larger amount of studies regarding T cells functions in HTLV-1 infected subjects , little is known about innate immunity . We evaluated monocyte and macrophage functions in HTLV-1 infected subjects . We observed that HAM/TSP patients have an increased frequency of intermediate monocytes , but expression of co-stimulatory molecules in these cells was similar between HTLV-1 infected subjects and healthy subjects ( HS ) . Additionally , the microbicidal ability of macrophages from HTLV-1 infected subjects to kill Leishmania braziliensis is preserved , and these cells showed inflammatory profile , producing more CXCL9 and CCL5 , and less IL-10 than macrophages from HS . It was important to determine if the exacerbated ability of macrophages to secrete cytokine was due to IFN-γ production . While there was no correlation between IFN-γ levels by PBMCs and cytokine/chemokine production by macrophages , there was a direct correlation between proviral load and TNF and CXCL10 levels . Our data indicate that despite the high production of proinflammatory mediators , macrophages from HTLV-1 infected subjects kill an intracellular pathogen in similar levels than cells from HS and pointed out for the role of viral factors inducing the inflammatory response in these cells .
|
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"Abstract",
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"Results",
"Discussion"
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2014
|
Functional Activity of Monocytes and Macrophages in HTLV-1 Infected Subjects
|
A full understanding of gene regulation requires an understanding of the contributions that the various regulatory regions have on gene expression . Although it is well established that sequences downstream of the main promoter can affect expression , our understanding of the scale of this effect and how it is encoded in the DNA is limited . Here , to measure the effect of native S . cerevisiae 3′ end sequences on expression , we constructed a library of 85 fluorescent reporter strains that differ only in their 3′ end region . Notably , despite being driven by the same strong promoter , our library spans a continuous twelve-fold range of expression values . These measurements correlate with endogenous mRNA levels , suggesting that the 3′ end contributes to constitutive differences in mRNA levels . We used deep sequencing to map the 3′UTR ends of our strains and show that determination of polyadenylation sites is intrinsic to the local 3′ end sequence . Polyadenylation mapping was followed by sequence analysis , we found that increased A/T content upstream of the main polyadenylation site correlates with higher expression , both in the library and genome-wide , suggesting that native genes differ by the encoded efficiency of 3′ end processing . Finally , we use single cells fluorescence measurements , in different promoter activation levels , to show that 3′ end sequences modulate protein expression dynamics differently than promoters , by predominantly affecting the size of protein production bursts as opposed to the frequency at which these bursts occur . Altogether , our results lead to a more complete understanding of gene regulation by demonstrating that 3′ end regions have a unique and sequence dependent effect on gene expression .
Studies aimed at understanding the determinants of gene expression have traditionally been focused on promoter and enhancer sequences . However , regulatory information is also encoded in other genomic regions such as the 5′ and 3′ untranslated regions ( UTRs ) and may even be embedded within the coding regions themselves [1]–[3] . Since measurements of endogenous expression levels of mRNAs [4]–[9] and proteins [10]–[12] represent the net effect of all regulatory regions and regulatory layers ( e . g . , transcription , translation and mRNA/protein degradation ) , it is difficult to use such data to dissect the relative contribution of any single genomic region to the overall measured levels . Thus , if the expression level of one gene is higher than another , we cannot tell which regulatory region or combination thereof causes this behavior . The situation becomes even more complicated when considering the recent observations that suggest that the different regulatory layers often affect each other [13]–[22] . In the context of transcription initiation , the challenge of deciphering the regulatory code that maps sequence into expression levels was addressed by separately fusing the promoter of different genes to a fluorescent protein reporter , integrating the resulting constructs into the same genomic location , and then comparing the levels of the reporters for different promoters [23]–[31] . Since strains for different genes in such synthetic libraries differ only in the promoter sequence that is fused to the reporter , this approach allows a direct measurement of the effect of each promoter sequence on gene expression providing important insights into cis-regulatory mechanisms and principles of promoter activation . Here we adopted this approach to study the independent effect of 3′ end regions on gene expression . Sequences downstream to the promoter are well known to affect expression , yet our knowledge of this effect is usually based on studies that examined single regulatory interactions in the 3′ UTR [32]–[35] . A genome wide view of the interaction network between RNA binding proteins ( RBP ) and their target mRNA was done in yeast [36] , [37] revealing a rich and multidimensional network of interactions . While these results suggest extensive regulation , very few of these interactions were actually shown to affect protein levels . A systematic comparison of the effect of native 3′ end regions on protein expression , independent of genomic context , in similar ways with which promoter sequences were studied , has not been performed . Thus , basic questions such as what is the range of expression differences due to native 3′ end regions , and what fraction of genes have a 3′ end region that causes a significant effect on expression , are largely open . And our understanding of the sequence determinants , that affect protein expression in 3′ end regions , is limited . In addition , given that protein expression is known to occur in bursts [38] , it is interesting to test whether different regulatory layers will affect the dynamics of such protein production bursts differentially . To study the effect of 3′ end sequences on protein expression we constructed a library of yeast strains that differ only in the 3′ end sequence integrated immediately downstream to a reporter gene ( YFP ) with a constant promoter . The yeast S . cerevisiae lacks RNAi activity [39] and thus serves as a good model system to study more basic mechanisms by which 3′ end sequences modulate protein levels . We measured the effect on expression of 85 different 3′ end constructs , taken from metabolic and ribosomal protein yeast genes . Measuring florescence of the various strains in the library , both in batch and in single cells , we found a continuous and wide span of expression values displaying distinct dynamics . We found that nucleotide composition upstream to the polyadenylation site correlates with expression , highlighting the importance of this genomic region to protein levels and suggesting that the efficiency of 3′ end formation may be partly responsible for the observed sequence-specific difference in expression in our library . Further characterization of these sequence features will be needed to identify the exact mechanisms through which 3′ end sequences affect expression levels to pave the way to a more complete understanding of gene regulation that also incorporates the effect of these regions .
To measure the effect of 3′ end sequences on expression levels , we adapted an approach that we previously developed for measuring the transcriptional effect of promoter regions [29] , [31] ( Fig . 1 ) . To this end , we engineered a single master strain to have a genomically integrated YFP reporter downstream to the Gal1/10 inducible promoter [40]–[42] , and at the same genomic location , we also integrated an additional fluorescent reporter gene ( mCherry ) driven by the promoter of TEF2 , a transcriptional elongation factor , to be used as a control . A truncated URA3 selection marker , lacking a promoter and start codon , was integrated downstream to the YFP gene to increase specificity of the subsequent transformations . Next , we used PCR to amplify 85 3′ end regions from 47 ribosomal protein genes and 38 galactose metabolism genes from the yeast genome . We attached each selected region to the promoter and start codon of the URA3 selection marker using extension PCR , and separately integrated each of the resulting constructs into the master strain downstream to the YFP reporter using a robotically automated system to increase throughput . Ideally , our constructs should contain only native 3′ UTR sequence , yet the boundaries of the 3′ UTR are not easily defined and most genes show large heterogeneity in the 3′UTR end [6] , [43] . In addition , it is not clear how much sequence downstream to the polyadenylation site is required for proper 3′ end formation and transcription termination [44] . For these reasons , we took for each construct the genomic region from the stop codon of the gene of interest to the next open reading frame ( up to 1 kb , 12 constructs ) , while validating that all of the known polyadenylation sites for the gene reside within this region [43] . While it is reasonable to assume that most of the observed effect of each construct is due to the transcribed 3′ UTR sequence , we cannot exclude the possibility that some of the effect is due to sequence elements residing downstream to the 3′ UTR cleavage site , such as interference from a downstream promoter and antisense transcription . To investigate the possibility that the downstream promoter is a major determinant of 3′ end regulated expression , we compared the expression levels of strains containing convergent and tandem intragenic regions ( Fig . S1A ) . We did not find any significant difference between the two groups , suggesting that this is not a major contributor to the observed effect . We also checked the effect of the size of the intergenic region and the expression of the downstream gene in both YPD and galactose and found no significant correlation ( Fig . S1B–D ) . We validated the resulting clones in multiple ways . First , since the same constant promoter drives the mCherry reporter across all strains , mCherry expression should be highly similar across strains , and we thus used it to filter out clones with global transcriptional and growth deficiencies . Second , we used colony PCR and sequencing to validate the integrity of the integrated constructs . Finally , for each construct , we measured the expression of three different clones , and only took clones for which at least two independent strain constructions yielded highly similar YFP measurements ( Fig . S2 ) . Thus , differences in YFP levels across strains are solely attributable to differences in the 3′ end integrated construct , since the YFP transcripts generated from all library strains are otherwise identical and are all driven by the same Gal1/10 promoter . To test whether our experimental system can indeed be used to measure post-transcriptional effects , we cloned the 3′ end of COX17 gene , as its transcript is known to be destabilized by the Puf3p RNA binding protein through interaction with the COX17 3′UTR [45]–[47] . As a reference , we also cloned the 3′ ends of MFA2 and RPS6 . We found that the COX17 construct indeed resulted in significantly lower YFP levels compared to clones of MFA2 and RPS6 . Further , the YFP accumulation profiles of several different clones for each of these three distinct 3′ UTRs were highly similar to one another ( Fig . 2A ) . We first examined the overall effect of the integrated 3′ end sequences on YFP expression . Following inoculation in an inducing media containing galactose we measured the growth ( measured as optical density ( OD ) ) , mCherry and YFP levels over time using a robotically automated plate fluorometer . As a single value per induction curve , we took the amount of YFP fluorescence produced during the exponential growth phase divided by the integral of the OD curve during the same time period . This results in a measure of the average rate of YFP production per cell per second during exponential phase [29] . As expected , the profiles of both OD and mCherry were highly similar across the library strains , such that any measured differences in YFP expression should indeed be attributable to the different 3′ end integrated constructs ( Fig . 2B , C ) . Notably , in contrast to the highly similar OD and mCherry measurements across strains , YFP expression levels spanned a wide continuous range of expression values ( Fig . 2D ) across several different Galactose concentrations ( Fig . 3A ) . For example , at the highest galactose concentration and thus Gal1/10 promoter induction , we found a 12-fold difference between the YFP levels between the highest and lowest library strains with the lowest expressing strains displaying very low expression levels despite being controlled by a strong Gal1/10 promoter at a highly inducing galactose concentration . To determine if the expression differences between 3′ end regions depends on the specific growth condition or is constitutive and will be observed in other growth conditions we measured the library in a few other conditions and found similar ranking and dynamic range ( Fig . S3 ) . Our measured differences in florescence between the 3′ end strains may reflect differences in mRNA levels , translation rates , or both . To distinguish between these possibilities , we selected 11 strains from the library that span the whole range of YFP expression values and determined their YFP and mCherry mRNA levels by quantitative PCR ( qPCR ) ( Fig . S4 ) . We found very high correspondence between YFP protein and mRNA levels , indicating that most of the observed effect is due to differences in mRNA levels . A basic open question in gene expression is the quantitative contribution of each regulatory region to the endogenous differences in expression level between genes in their native context . To address this question , we first examined the regulatory potential of the 3′ end sequences by comparing the span of expression values between the 3′ end strains to the span of expression values by a library in which the promoters of the same set of genes were integrated upstream of the YFP reporter [29] ( and unpublished data ) with a constant 3′ UTR . The 3′ end strains exhibit approximately half of the dynamic range as the promoter strains ( interquartile range of 0 . 84 and 1 . 5 for 3′ end and promoters , respectively ) ( Fig . 3B ) . This range is observed at the highest galactose induction level and thus represents the maximal regulatory potential of these sequences and not their endogenous effect under lower promoter expression levels , as the range of effect of the 3′end sequences scales with promoter expression . Next , to see how much of our measurements explain endogenous variability in mRNA levels , we compared the YFP expression of each 3′ end library strain to mRNA levels of the respective genes in the native genomic context measured by RNA-seq [6] ( Fig . 3C ) . We found the correlation between these two measurements to be relatively high ( R = 0 . 46 ) , especially considering that we measure the effect of only the 3′ end separated from its native genomic region and promoter . As a reference , we also compared the same RNA-seq mRNA level values to measurements of the promoter strains and found , as expected , a higher correlation ( Fig . 3D , R = 0 . 81 ) . Combining these two measures in a regression analysis against endogenous mRNA levels [6] modestly increased the amount of explained variance ( R2 ) to 71% compared to 66% with only promoters ( Fig . S5 ) . To test the significance of this increase , considering the additional free parameter in the model containing the 3′ end measurements , we computed the F-statistic for nested regression models and obtained a p-value of 0 . 002 . Because differences in the YFP expression levels of strains in our library are attributable to differences in the integrated 3′ end sequences , we next sought to identify sequence features that cause the measured differences in YFP levels . Unlike promoter sequences , where many regulatory elements such as transcription factor binding sites and TATA box are known , relatively little is known about regulatory elements or general sequence features in 3′ end , and so far a few putative regulatory motifs have been predicted [1] , [47] . Accurate sequence analysis depends on our knowledge of the location of polyadenylation site ( 3′ UTR end ) . To determine if our cloned sequences create the same 3′ UTRs as in the native context , we used 3′ RACE followed by deep sequencing ( see materials and methods ) to map the distribution of polyadenylation sites for each of our strains . We created a high resolution map of polyadenylation sites for the 64 3′ UTRs with a sufficient number of reads ( >1000 ) ( Fig . S6 ) . We found multiple polyadenylation sites for a large fraction of genes , as previously observed [6] , [43] , and also much heterogeneity around each site . We compared the main polyadenylation site between our data and a published dataset that mapped genome wide polyadenylation sites [43] and found high correspondence between the two maps ( Fig . S7 ) . This shows that the formation of 3′ UTRs is intrinsic to the local 3′ end sequence and independent of the higher genomic context . We used the polyadenylation site with the highest number of reads for each gene as the 3′ UTR end for subsequent analysis and the published polyadenylation site mapping [43] for 3′ UTRs for which we did not have sufficient data . We first searched for sequence features ( G/C content of the 3′ UTR , enriched k-mers , minimal free energy of the predicted secondary structure of the 3′UTR and 3′UTR length ) computed over the whole 3′ UTR , that might explain the measured expression variability , but found no significant correlation between any of these measures and measured YFP levels . We next looked for positional information by aligning our cloned sequences by either the beginning ( right after the stop codon ) or the end of the 3′ UTR defined as the location of the most common polyadenylation site according to mapped 3′ end locations . We then computed the same features in sliding windows of various size and locations and correlated them with YFP levels to find sequence features that can explain the differential effect on expression of our constructs . Alignment of sequences by the 3′ UTR end revealed that higher A/T content upstream to the 3′ UTR end correlates with higher YFP expression values ( Fig . 4A ) . Although higher A/T content may suggest an effect on the stability of the 3′ UTR secondary structure , we found no significant correlation between predicted folding energies and YFP expression . Another possibility , given the proximity of this region in the 3′ UTR to the known location of polyadenylation signals [44] , is that increased A/T content results in higher 3′ end formation efficiency which increases protein levels , as has been recently suggested [48] . To determine if the A/T content signal is due to known 3′end processing , we mapped the two most well-known 3′end processing sequence motifs , namely the efficiency and positioning element [44] , [49] , [50] , and found no significant correlation ( Fig . S9 ) between the location of these elements and expression . As the sequence structure of 3′ processing signals is highly variable [51] , and given the relatively upstream location of our signal relative to the polyadenylation signal , we hypothesize that increased A/T content at that position creates a more efficient upstream efficiency element ( UAS ) that enhances 3′ end processing . To test whether A/T content upstream to 3′ UTR end may indeed be predictive of the effect of 3′ UTRs over all yeast genes , and not just a result of the relatively small number of strains that we used in our analysis , we performed a similar analysis but using all yeast genes and correlating the A/T content of their 3′ UTRs to genome-wide RNA-seq measurements of mRNA abundance [6] . Since endogenous mRNA abundance levels represent the combined effect of all regulatory layers and not just that of 3′ UTRs as in our library , we do not expect a high correlation even if 3′ UTRs contain true regulatory elements that affect mRNA levels . Despite this caveat , we indeed found a low yet highly significant correlation between the A/T content upstream of the 3′ UTR end and mRNA abundance at the genome-wide scale . In contrast to the library this correlation is observed only when A/T content is calculated in larger windows of 70–90 bp ( Fig . 4B , C ) . Together , our results suggest significant association between high expression and high A/T content , in the upstream vicinity of the polyadenylation site , and specifically pinpoints the importance of this genomic region to the determination of mRNA and protein levels . We next asked whether we could gain insights into the way in which 3′ end sequences affect YFP expression . Protein expression occurs in bursts which are characterized by frequency and size , i . e . number of bursts in unit time and number of proteins produced per burst [38] , [52]–[54] . We reasoned that an increase in YFP expression can arise from more frequent bursts or from a larger number of proteins produced from each burst , or from a combination thereof . For example , increasing the concentration of a transcription factor is expected to increase more the burst frequency than the size of the burst in the activated promoter state . In contrast , since we expect the effect of 3′ ends on YFP expression to be mediated by changes to YFP 3′ end formation , degradation rates , or by changes to YFP translation rates , we hypothesized that 3′ ends should mainly affect the average size of protein bursts . Under certain assumptions , burst frequency and burst size can be extracted from single cell protein expression measurements of an isogenic population [54] , [55] . We used this approach to measure the effect of 3′ ends on burst frequency and burst size . For comparison , we also modulated transcription activation by varying galactose levels and measured changes in burst size and frequency for each gene across various activation levels . To this end , we used flow cytometry to obtain single cell YFP fluorescence measurements of all of our 85 strains under increasing galactose concentrations , resulting in increasing Gal4 activity [56] . Thus , since all of our library strains are driven by the same target promoter ( Gal1/10 ) , comparing the same strain across increasing galactose concentrations allows us to examine the effect of increasing expression by increasing transcription factor activity , while comparing the different strains to each other at a fixed galactose concentration allows us to examine the effect of the various 3′ end regions . We devised an automated pipeline to extract the mean and variance of a cell population from flow cytometry data , while controlling for variance in physiological cell parameters ( see materials and methods ) . From these measurements , we could then compute the burst frequency of each strain at every galactose concentration as the inverse of the transcriptional noise ( variance divided by mean squared ) , and the burst size as the noise strength ( variance divided by mean ) [54] . Consistent with our hypothesis , we found that increasing expression by increasing galactose concentration and thus transcription factor concentration results in increased burst frequency , while at any fixed galactose and thus fixed factor concentration , the expression of strains with different 3′ end sequences is correlated with burst size while burst frequency remains constant ( Fig . 5 ) . We note that while it is appealing to conclude that the expression changes mediated by the different 3′ end sequences have no effect on burst frequency , small changes in such frequencies may not be detected . The differences in burst frequency at different galactose concentrations are mostly driven by fluctuation in the trans activation environment and are probably very large [57] , and we can thus only conclude that the effect of our different constructs is very small in comparison . Despite this reservation and even if the assumptions [54] , [55] under which our calculations correspond to burst size and frequency do not hold , it is clear that the two different strategies that we examined here for changing expression , namely changing transcription factor activity or changing 3′ end sequences , have vastly different effects on the shape of the distribution of gene expression within an isogenic population .
To assess the quantitative effect of different genomic regions it is essential to establish experimental systems that separate these regions from their native genomic context and measure their direct effect . While it is well established that regulatory features other than the promoter can affect gene expression , to our knowledge our work provides the first systematic measurements of the independent effect of regulatory regions other than promoters in yeast . We show that native 3′ end sequences span a broad and continuous range of expression values of greater than 10-fold . Our library represents a limited number of 85 sequences , chosen without any prior knowledge on their expected effect on expression , and is composed of two unrelated functional groups from the yeast genome . Thus , it is likely that the effect of 3′ end sequences in the genome is larger than the effect we observe due to the small sample size of our library . These genes were chosen as they represent two different regulatory strategies , with ribosomal genes being house-keeping genes expressed constitutively in all growth conditions , and the other group being condition specific genes expressed in the growth condition in which we conduct our measurements . Notably , we did not find any major differences in the 3′end mediated regulation . We quantify the independent effect and explained variance of 3′ end and promoter sequences by comparing our 3′ end library to a promoter library and correlating both to endogenous mRNA levels . The results show that constitutive expression levels are determined by a combination of both regulatory regions . Interestingly , despite the large regulatory potential ( dynamic range ) of isolated 3′ end constructs on YFP expression , their contribution to the explained variance of endogenous mRNA levels is relatively small . One possible explanation is that the effect of the two regions is not independent; it would thus be interesting to test different 3′ end sequences in different promoter contexts . Although we cannot say whether the A/T content itself causes higher expression or whether it is a proxy for a more specific signal , our results highlight the 3′UTR end as a genomic region that may have a significant effect on mRNA levels . This sequence signal depends on aligning the sequences by the polyadenylation site . We thus speculate that increased A/T content may result in more efficient 3′ end formation that gives rise to elevated protein expression . It has been previously shown that A/T content is required for efficient 3′ end processing as part of the upstream efficiency element ( UAS ) [49] , [50][51] , [58] . More efficient 3′end processing can result in efficient release of RNA polymerase after polyadenylation and recycling of transcription initiation machinery , given that polyadenylation and transcription termination were shown to be mechanistically coupled [59] , [60][61]–[64] . Additional potential means by which efficient polyadenylation could give rise to higher protein expression comes from a recent work in mammalian cells [48] , which suggested that with more efficient 3′ end processing , more transcripts escape from nuclear surveillance , resulting in more mature mRNA molecules exported into the cytoplasm . Notably , all of these mechanisms would result in changes in the size of expression bursts . Although it was shown that by deliberately mutating polyadenylation signals , mRNA and protein levels decrease [65]–[67] , we suggest that the efficiency of this process varies between native genes and is partly responsible for the observed variability in protein and mRNA expression in the genome . Our study demonstrates the strength of a synthetic approach in establishing a causal link between sequence features and their outputs . Observing correlations in the genome , e . g . between sequence features in the 3′ UTR and expression levels could always be explained by indirect non-causal effects . For example , one could argue that the genes with certain UTR features may also have strong promoters . Observing such connections in a setup such as the current library in which the effect of 3′ UTR sequences is measured in isolation partly removes those potential confounders . Finally , we showed that the observed span of YFP values in our library , mediated by the different 3′ end constructs , affect population noise in a very distinct way compared to expression changes that are mediated by differential promoter activations . Our results thus put 3′ end sequences as appealing candidates for the design of specific circuits in which changes in the mean expression level of a population are needed with little effects on noise . They also demonstrate how the different layers of gene expression regulation affect protein expression with distinct dynamics and propose that such analysis can be used to gain insights into the different layers of regulation involved in an observed change in protein levels .
The first step in the construction of the 3′ end synthetic library was to build a master strain into which the different 3′ end sequences would be integrated . We built a plasmid containing TEF2pr-mCherry-ADH1term , and a non-terminated YFP gene under the control on the Gal1/10 promoter . Downstream of the YFP gene we integrated a truncated URA3 gene lacking the promoter and start codon ( Fig . S8 ) . The whole construct was then lifted from the plasmid by PCR and integrated into yeast strain Y8205 ( courtesy of Charlie Boone ) at the his3 locus ( chromosome 15 , location 721987–722506 bp ) . Desired downstream intergenic regions , containing the tested 3′ ends were then lifted from the genome of BY4741 yeast strain . Forward primers had a 3′ end matching the sequence starting immediately downstream to the ORF related to the tested 3′UTR and a 5′ end matching the end of the YFP gene . We planned the library such that the integrated sequences will start from the stop codon till the next ORF ( but up to 1 kb if the intergenic region was longer , exact cloned sequences are given in table S1 ) . The reverse primers had a 3′ end matching this location and a 5′ end matching the URA3 promoter lifted from a plasmid with the start codon and a few base pairs into the URA3 promoter to match the sequence in the master strain ( Fig . 1 ) . Following PCR amplification of the library from the genome the URA3 promoter and start codon were attached to the PCR products using extension PCR and the intact sequences were then integrated into the master strain using homologous recombination to create the library strains . All steps were automated and preformed in a 96 well plate except for the final plating and selection of the final clones which was done in 6 well agar ( SCD-URA ) plates . From each transformation 3 clones where manually picked and grown on selective media ( SC-URA ) . Clones for the final library were chosen under the following criteria: ( 1 ) At least two clones gave the exact same expression values ( Fig . S2 ) . ( 2 ) integrated sequence length was validated by colony PCR . ( 3 ) OD and mCherry measurements were used to ensure no growth of general transcription deficiencies . ( 4 ) Few selected inserts were validated by sequencing . Final library strains were grown in 96 well plates containing YPD as a growth medium for two days into stationary phase and frozen by adding glycerol to a final concentration of 25% . Cells were inoculated from stocks of −80°C into SC+2% raffinose ( 180ul , 96 well plate ) and left to grow at 30°C for 48 hours , reaching stationary phase . Next , 5ul were passed into a fresh medium ( 175ul SC+2% raffinose ) supplemented with the varying amounts of Galactose . Measurements were carried out every ∼20 minutes using a robotic system ( Tecan Freedom EVO ) with a plate reader ( Tecan Infinite F500 ) . Each measurement included optical density ( filter wavelengths 600 nm , bandwidth 10 nm ) , YFP fluorescence ( excitation 500 nm , emission 540 nm , bandwidths 25/25 nm accordingly ) and mCherry fluorescence ( excitation 570 nm , emission 630 nm , bandwidths 25/35 nm accordingly ) . Measurements were replicated three times revealing high correlation between independent measurements . We also compared the measurements of the final library plate to the initial measurements of the different clones , representing same strain measured on a different geographical location within the 96-well plate , showing a minimal geographic effect on YFP measures . We used the same processing pipeline used by Zeevi at al . [29] to quantify promoter activity values from the exact same florescence and optical density measurements . Briefly , background levels are subtracted from OD , mCherry and YFP curves using media measurements , strain with only YFP reporter and strain with only mCherry reporter respectively . Next outlier removal was done on the measurement points which compose each individual curve , removing points which deviate considerably from their neighboring points . We validated by eye that all strains have same OD and mCherry curves ( Fig . 2 ) . To calculate one value per induction curve an automated procedure divides the OD curve into four growth phases: lag phase , exponential phase , linear phase and stationary phase . Then YFP accumulation was calculated by dividing the total amount of YFP produced during exponential phase by the integral of the OD curve during this time . Because both YFP and mCherry are very stable proteins , this measure represents the amount of YFP or mCherry produced per cell over this time course ( table S1 ) . We used YFP measurements not normalized to mCherry , but taking normalized values achieves qualitatively similar results ( data not shown ) . mCherry expression was used for two main purposes: ( 1 ) In the clone selection process it allows us to discard strains with growth and global transcription deficiencies . ( 2 ) In the noise measurements , gating on mCherry expression was used to minimize the effect of extrinsic factors on YFP expression . Eleven strains spanning the whole range of expression values were randomly selected for quantification of mRNA levels of both YFP and mCherry . Strains were grown in a 96 well plate with 6 replicate wells for each strain in rich media until stationary phase . 5ul of stationary cells were then inoculated into fresh synthetic media ( 175ul ) with 2% galactose to induce expression . Cells were collected after 4 . 5 hours from mid log phase centrifuged and pellet was immediately frozen in liquid nitrogen . RNA was then extracted using Yeast MasterPure kit ( Epicenter Biotechnologies ) with a long ( 1 hour ) DNAse treatment to avoid contaminations of genomic DNA . cDNA was prepared using M-MLV reverse transcriptase and random hexamers primers . Quantitative PCR analysis was performed by RT-PCR ( StepOnePlus , Applied Biosystems ) using ready-mix kit ( KAPA , KK4605 ) with primers spanning the ORF of either YFP ( Fw-CCAGAAGGTTATGTTCAA , Rv- CGATTCTATTAACTAAGGTATC ) or mCherry ( Fw-TGTGGGAGGTGATGTCCAACTTGA , Rv- AGATCAAGCAGAGGCTGAAGCTGA ) mRNA molecules in 20 ul volume with triplicate wells for each reaction . Standard curves were prepared by mixing all samples and preparing 4 serial dilutions of 1∶5 . The whole library was grown in a 96 well plate containing rich media to stationary phase . 5ul of stationary cells were then inoculated into a second 96 well plate with fresh synthetic media containing 2% of galactose to induce expression . 50ul of cells in mid-log phase were collected after 6 hours from each well into one tube . Cells were then thoroughly mixed , separated to two replicates , centrifuged and pellet was immediately frozen in liquid nitrogen . RNA was then extracted using Yeast MasterPure kit ( Epicenter Biotechnologies ) with a long ( 1 hour ) DNAse treatment to avoid contaminations of genomic DNA . YFP specific cDNA was prepared for Illumina sequencing using nested 3′RACE [68] . First-strand cDNA was generated from total RNA using M-MLV and a poly ( T ) primer ( GCTCAAGCCACGACGCTCTTCCGATCTNNNNNNNNNNNNTTTTTTTTTTTTTTTTTTVN ) . YFP cDNA enrichment was performed using a primer ( CTCACAATGTTTACATCACTGCTG ) complementary to YFP 440 bp downstream of the start codon and a primer ( GCTCAAGCCACGACGC ) complementary to the priming sequence on the polyT primer . Second round YFP cDNA amplification was performed using a primer ( CACGACGCTCTTCCGATCT ) complementary to the poly ( T ) primer and a primer ( TGACTGGAGTTCAGACGTGTGCTCTTCCGATCACCCATGGTATTGATG ) complementary to 579 bp downstream of the YFP start codon that contains part of the TruSeq Adapter Index11 primer . The final Illumina sequencing library was prepared by PCR using the TruSeq Universal Adapter primer and the TruSeq Adapter Index11 primer . Raw reads were then processed to extract only the relevant part of the read that would be mapped to the genomic sequence ( without the poly ( T ) ) and mapped to a reference genome containing only the cloned sequences using NovoAlign software ( http://www . novocraft . com/ ) . The junction between the poly ( T ) and the mapped genomic sequence was taken as the cleavage site . Our mapping resolution is thus limited to the first non-A nucleotide upstream to the real cleavage site . Similar to the bulk measurements , cells were inoculated from stocks of −80°C into SC+2% raffinose+0 . 1% of galactose ( to induce Gal4p ) ( 180ul , 96 well plate ) and left to grow at 30°C for 48 hours , reaching stationary phase . Next , 5ul were passed into a fresh medium ( 175ul SC+2% raffinose ) supplemented with the desired amount of galactose and grown with shaking at 30°C . Six hours following dilution into a fresh medium containing galactose , plates were subjected to flow cytometry measurements using an LSRII flow cytometry machine supplements with an High Throughput Sampler ( HTS ) to measure 96 well plates . To control for extrinsic variation we select a sub-population of cells with similar size and physiological status using an automatic gating procedure , which removes cells with spores or with outlier forward and side scatter parameters . Following gating , mean and standard deviation of YFP values was calculated for each strain in each galactose concentration and were used to calculate burst size and frequency following Friedman et al . [54] The general trends presented in this paper were highly robust to changing the gating parameters . To correct for differences in mean expression between plates measured at different days we used a strain containing a YFP gene driven by the RPL3 promoter which is insensitive to galactose levels . Specifically , for a group of plates the mean expression of the RPL3 promoter across plates was calculated . Then the mean expression of each gene in each plate was corrected to be when is the mean expression of the RPL3 strain in the specific plate . The noise and noise strength were corrected using the theoretical result that noise2 scales with 1/mean such that the noise is corrected to be and the noise strength to be the multiplication of the corrected noise and mean . We have verified that this correction does not affect our qualitative results that 3′ end sequences effect mainly noise strength and not noise .
|
A basic question in gene expression is the relative contribution of different regulatory layers and genomic regions to the differences in protein levels . In this work we concentrated on the effect of 3′ end sequences . For this , we constructed a library of yeast strains that differ only by a native 3′ end region integrated downstream to a reported gene driven by a constant inducible promoter . Thus we could attribute all differences in reporter expression between the strains to the different 3′ end sequences . Interestingly , we found that despite being driven by the same strong , inducible promoter , our library spanned a wide and continuous range of expression levels of more than twelve-fold . As these measurements represent the sole effect of the 3′ end region , we quantify the contribution of these sequences to the variance in mRNA levels by comparing our measurements to endogenous mRNA levels . We follow by sequence analysis to find a simple sequence signature that correlates with expression . In addition , single cell analysis reveals distinct noise dynamics of 3′ end mediated differences in expression compared to different levels of promoter activation leading to a more complete understanding of gene expression which also incorporates the effect of these regions .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"systems",
"biology",
"genomics",
"synthetic",
"biology",
"biology",
"computational",
"biology",
"molecular",
"cell",
"biology",
"molecular",
"biology",
"genetics",
"and",
"genomics"
] |
2013
|
Measurements of the Impact of 3′ End Sequences on Gene Expression Reveal Wide Range and Sequence Dependent Effects
|
Cameroon achieved the elimination target of leprosy in 2000 , and has maintained this status ever since . However , a number of health districts in the country continue to report significant numbers of leprosy cases . The aim of this study was to assess the burden of leprosy in Cameroon from 2000 to 2014 . We obtained and analysed using the new leprosy burden concept of analysis , leprosy surveillance data collected between 2000 and 2014 from the National Leprosy Control Programme . Cameroon achieved leprosy elimination in 2000 , registering a prevalence rate of 0 . 94/10 , 000 population . The prevalence rate dropped further to reach 0 . 20/10 , 000 population ( 78% reduction ) in 2014 . Similarly , the new case detection rate dropped from 4 . 88/100 , 000 population in 2000 to 1 . 46/100 , 000 population ( 85 . 3% reduction ) in 2014 . All 10 regions of the country achieved leprosy elimination between 2000 and 2014; however , 10 health districts were still to do so by 2014 . The number of high-leprosy-burden regions decreased from 8 in 2000 to 1 in 2014 . Seven and two regions were respectively medium and low-burdened at the end of 2014 . At the health districts level , 18 remained at the high-leprosy-burdened level in 2014 . The leprosy prevalence and detection rates as well as the overall leprosy burden in Cameroon have dropped significantly between 2000 and 2014 . However , a good number of health districts remain high-leprosy-burdened . The National Leprosy Control Programme should focus efforts on these health districts in the next coming years in order to further reduce the burden of leprosy in the country .
Leprosy is the oldest disease known to humanity , as recent genomic studies have traced Mycobacterium leprae , its causative agent , along human dispersal in the past 100 , 000 years [1] . It affects peripheral nerves , the skin and mucosa of the upper respiratory pathways [2] . Transmission is believed to be through nasal droplets or prolonged skin contact with an untreated patient , however , the exact mode of transmission is still unclear [3 , 4] . Untreated patients or those diagnosed late would develop irreversible disabilities and disfiguring complications . These physical complications associated with socio-cultural construction of leprosy are responsible for stigma and social exclusion of victims [5 , 6] . Effective control of leprosy only began in the 1940s with the discovery of dapson [6 , 7] . The dapson mono-therapy was replaced by multi-drug therapy ( MDT ) in the early 1980s [8 , 7] . Furthermore , a simplified classification as well as treatment regimens for each class was established , so that patients with 1–5 lesions were classified as paucibacillary ( PB ) leprosy and were treated for six months . Those with 6 or more lesions were classified as multibacillary ( MB ) and treated for 12–24 months , and subsequently for 12 months [9 , 10 , 11] . The results of MDT implementation were very encouraging , with a relapse rate of <1% and a remarkable drop in global leprosy prevalence from 5 . 37 million registered cases in 1985 to 3 . 74 million in 1990 [12] . These developments led the World Health Assembly ( WHA ) to adopt a resolution ( WHA 44 . 9 ) in 1991 , to eliminate leprosy as a public health problem by the year 2000 , defining elimination as a leprosy prevalence rate of <1 case per 10 , 000 population [13] . At the end of 2000 , leprosy elimination was achieved globally and in107 countries ( including Cameroon ) out 122 countries that were considered endemic in 1985 [14 , 15] . After achievement of leprosy elimination in Cameroon in 2000 , the objectives of the National Leprosy Control Programme ( NLCP ) were focused on consolidating the status at the national level and to further eliminate the disease at sub-national levels . However , some health regions and health districts ( HD ) have continued to report significant numbers of cases [16] . Furthermore , the declaration of elimination has led to significant reduction in resource allocation for leprosy control activities in the country . The objective of this study was to assess the leprosy burden in Cameroon in the post-elimination era from 2000–2014 , using data from routine reporting available at the NLCP , and to make recommendations for acceleration of its elimination at sub-national levels .
This study was instructed by the Cameroon Ministry of Public Health Decision N° 0486/D/MINSANTE/CAB and was approved the National Ethics Committee of Cameroon through the authorization No 172/CNE/SE/2011 . All data were anonymized and confidentiality was strictly respected in the data handling and analysis . There were missing data in some HDs for some of the years under review . A number of report files were damaged and some reports were incompletely filled . However , the proportion of missing data was not significant ( about 3% ) and did not influence the quality of the review .
Data was available for all the years from 2000 to 2014 except for information on females among new cases , which was available only from 2005 . We confirmed that Cameroon achieved leprosy elimination at the end of 2000 , recording a point prevalence rate of 0 . 94/10 , 000 population . The point prevalence rate declined from 0 . 94/10 , 000 in 2000 to 0 . 20/10 , 000 population in 2014 ( P<0 . 001 ) ( Table 2 and Fig 1 ) . This decline accounted for 78% reduction in the prevalence rate , with the largest reduction , 64 . 9% , occurring between 2000 and 2005 . From 2006 to 2014 , the annual leprosy prevalence rate was rather stagnant . A similar pattern was followed by the leprosy NCDR , with a decline from 4 . 88/100 , 000 population in 2000 to 1 . 46/100 , 000 population in 2014 ( P = 0 . 018 ) ( Table 2 and Fig 1 ) , accounting for an 85 . 3% reduction . The largest reduction occurred between 2002 and 2007 , followed by a relative stagnation in the NCDR from 2008 to 2014 . Two peaks in annual NCDR were however noticed in 2002 and 2006 with annual NCDR of 9 . 96/100 , 000 and 4 . 29/100 , 000 population respectively . Among the new cases of leprosy detected , the proportion of MB cases was relatively high throughout the 15-year period investigated , with an increasing trend from 62% in 2000 to 87% in 2014 ( P = 0 . 035 ) . The proportion of child cases generally remained low between 10% and 20% except for the year 2011 when a peak of 25% was observed ( P = 0 . 054 ) . The proportion of new cases with G2D was stable at an average of 6% ( P = 0 . 156 ) . The female proportion was fluctuating , with an overall increasing trend from 26% in 2005 to 43% in 2014 ( P = 0 . 244 ) ( Table 2 and Fig 2 ) . Between 2000 and 2014 , six regions witnessed more than 50% reduction in registered prevalence with the Far-north and the North-west leading with 96% and 86% reduction respectively ( Table 3 ) . From 2000 to 2005 , six regions namely the Adamawa , East , Far-north , North , North-west and South-west accounted for over 70% of new leprosy case detection in the country . After 2005 , four of these regions excluding the Far-north and North-west reported over 70% of new case detection and also registered some of the highest proportions of child and G2D cases in the country ( Table 3 ) . Out of the 10 regions in Cameroon , the number of leprosy endemic regions , with point prevalence rates of 1 or more per 10 , 000 population , decreased from 5 in 2000 to 0 in 2014 ( Fig 3A ) meanwhile the number of endemic HDs decreased from 53 to 10 over the same period ( Fig 3B ) . Table 4 lists the remaining 10 leprosy endemic HDs in Cameroon . At the national level the trend in G2D rate decreased slightly from 0 . 133/100 . 000 population in 2010 to 0 . 105/100 . 000 population in 2014 ( P = 0 . 747 ) ( Fig 4 ) , constituting a drop of 21% . A 5-year-interval trend analysis of leprosy burden by region ( Fig 5 ) , revealed that in the year 2000 , eight regions were high-leprosy-burdened and one medium-burdened . By 2005 the number of high-burdened regions decreased to 6 and then to 5 in 2010 , and further to 1 in 2014 . At the HD level , ( Fig 6 ) the number of high-leprosy-burdened districts stagnated at 68 and 69 between 2000 and 2005 , and then dropped to 49 in 2010 and further to 18 in 2014 . During the same period , the number of medium-burdened districts also witnessed a drop from 31 in 2000 , to 20 in 2014 . The decrease in the number of both high and medium-burdened districts was gained by low-leprosy-burdened districts that rose from 82 in 2000 to 143 in 2014 .
In Cameroon , the leprosy prevalence and detection rates have dropped significantly since 2000 but have been stagnating in the last years . Furthermore , the new concept of determining the leprosy burden by using the leprosy burden score , has unmasked problem areas that could not be determine by the prevalence rates alone and revealed alarming disparities of the total leprosy burden at sub-national levels . Thus , eighteen HDs of Cameroon have remained with a high leprosy burden in 2014 despite the long acquired elimination status by the country . The NLCP should focus efforts on these HDs while monitoring the 20 medium burdened HDs as well . With improved government funding and more partner support , the NLCP objectives and the WHO targets can be met in all health districts of Cameroon by 2020 .
|
Cameroon achieved the elimination of leprosy in 2000 , however , a number of areas in the country continue to report high numbers of cases . We conducted this study to assess the burden of leprosy in Cameroon from 2000 to 2014 . We obtained and analysed leprosy data for this period from the National Control Programme . After elimination in 2000 , the leprosy prevalence rate continued dropping , to reach 0 . 20/10 , 000 population ( 78% reduction ) in 2014 . Similarly , the new case detection rate dropped to 1 . 46/100 , 000 population ( 85 . 3% reduction ) in 2014 . All 10 regions of Cameroon achieved leprosy elimination by 2014; however , 10 health districts were still to do so . Using the new leprosy burden concept of analysis , the number of high-leprosy-burden regions decreased from 8 in 2000 to 1 in 2014 , meanwhile 18 health districts remained high-leprosy-burdened at the end of 2014 . In conclusion , leprosy prevalence and detection rates as well as the overall leprosy burden in Cameroon have dropped significantly between 2000 and 2014 . However , a good number of health districts remain high-leprosy-burdened . The National Leprosy Control Programme should focus efforts on these health districts in the next coming years in order to further reduce the disease burden in the country .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[
"medicine",
"and",
"health",
"sciences",
"disabilities",
"pathology",
"and",
"laboratory",
"medicine",
"nervous",
"system",
"tropical",
"diseases",
"geographical",
"locations",
"pediatrics",
"bacterial",
"diseases",
"signs",
"and",
"symptoms",
"global",
"health",
"neglected",
"tropical",
"diseases",
"africa",
"public",
"and",
"occupational",
"health",
"infectious",
"diseases",
"cameroon",
"lesions",
"child",
"health",
"people",
"and",
"places",
"diagnostic",
"medicine",
"anatomy",
"nerves",
"leprosy",
"biology",
"and",
"life",
"sciences"
] |
2016
|
The Burden of Leprosy in Cameroon: Fifteen Years into the Post-elimination Era
|
The effective size of populations ( Ne ) determines whether selection or genetic drift is the predominant force shaping their genetic structure and evolution . Populations having high Ne adapt faster , as selection acts more intensely , than populations having low Ne , where random effects of genetic drift dominate . Estimating Ne for various steps of plant virus life cycle has been the focus of several studies in the last decade , but no estimates are available for the vertical transmission of plant viruses , although virus seed transmission is economically significant in at least 18% of plant viruses in at least one plant species . Here we study the co-dynamics of two variants of Pea seedborne mosaic virus ( PSbMV ) colonizing leaves of pea plants ( Pisum sativum L . ) during the whole flowering period , and their subsequent transmission to plant progeny through seeds . Whereas classical estimators of Ne could be used for leaf infection at the systemic level , as virus variants were equally competitive , dedicated stochastic models were needed to estimate Ne during vertical transmission . Very little genetic drift was observed during the infection of apical leaves , with Ne values ranging from 59 to 216 . In contrast , a very drastic genetic drift was observed during vertical transmission , with an average number of infectious virus particles contributing to the infection of a seedling from an infected mother plant close to one . A simple model of vertical transmission , assuming a cumulative action of virus infectious particles and a virus density threshold required for vertical transmission to occur fitted the experimental data very satisfactorily . This study reveals that vertically-transmitted viruses endure bottlenecks as narrow as those imposed by horizontal transmission . These bottlenecks are likely to slow down virus adaptation and could decrease virus fitness and virulence .
Evolution of virus populations depends on several forces including mutation , recombination , genetic drift , selection and migration , acting concomitantly but exerting pressures that vary widely in direction and intensity . It makes therefore difficult to predict viral emergences or the durability of control strategies . The relative intensity of these forces will determine whether evolution follows predominantly stochastic or deterministic patterns . The concept of effective size of populations , Ne , plays a core role since it determines the rate of random fluctuations of the frequency of virus variants caused by genetic drift across generations in a model population . Ne estimates the number of individuals that pass on their genes through generations . It is usually much smaller than the total size of populations: although the total size of virus populations in their host plants can be tremendous and reach 107 to 109 virus particles [1] , [2] , estimates of Ne are below 500 and most of them are actually close to one [3] , [4] . Importantly , for populations affected by periodic size changes like bottlenecks or founder effects , Ne is given by the harmonic mean of population sizes over generations [5] . As a consequence , even short periods of small population size during the life cycle or history of populations can have disproportionately strong influences on Ne . Ne helps to predict the loss and distribution of neutral genetic variation [6] , the fixation probabilities of beneficial or deleterious alleles [7] , and the fitness and survival of small populations [8] . Therefore , knowledge of Ne is of major interest for modeling disease emergence and can be an important issue in agriculture as illustrated by the breakdown of plant resistance genes by adapted virus variants [9] , [10] . It has been shown recently that plant virus populations undergo transient and recurrent bottlenecks at different steps of their life cycle , like during horizontal transmission , i . e . plant inoculation by vectors [9] , [11] , by contact with an infected plant [12] or by artificial inoculation [13] , or during the colonization of plant cells [14] , [15] , [16] and tissues [4] , [13] , [15] , [17] . By contrast , no estimates of bottleneck sizes during vertical transmission of plant viruses , i . e . infection of plant progenies by the parental plant ( s ) , are available yet . There are three major ways of vertical transmission of plant viruses via the contamination of true seeds . In only a few examples , particularly stable viruses such as tobamoviruses can be retained in the seed coat and then transmitted to the seedling after germination [18] . In that case , there is no contamination of the embryo and the process of seedling infection resembles horizontal transmission through contact with an infected plant . The two other ways of contamination correspond to invasion of the embryo by the virus , either from infected maternal tissues or , more rarely , via infected pollen . Although seed embryos are usually protected against invasion by viruses that affect the mother plant , many viruses have the capacity to circumvent this barrier . Even low rates of seed transmission can be epidemiologically important because secondary spread of viruses can begin as soon as the germination stage [19] and virus seed transmission can be economically significant for at least 18% of plant viruses [20] . The goal of this work was to compare the size of bottlenecks affecting populations of Pea seedborne mosaic virus ( PSbMV ) ( genus Potyvirus , family Potyviridae ) in pea plants during vertical seed transmission and during the colonization of leaves .
The PSbMV isolate DPD1 and the variant DPD1-R only differ at codon position 116 in the VPg ( Virus protein genome-linked ) -coding region were used . Codon 116 is GTG ( valine ) and CGA ( arginine ) in DPD1 and DPD1-R , respectively [21] , and these three adjacent nucleotide differences allowed identification and quantification of the two PSbMV variants in mixed-infected plants ( see below ) . The pea ( Pisum sativum L . ) cultivar ‘Vedette’ that transmits PSbMV through seeds at high frequencies [22] was used for all experiments . No pollen transmission of PSbMV was observed in this genotype [23] . Plants were grown under greenhouse conditions from November 2011 to April 2012 . DPD1 and DPD1-R isolates were multiplied separately in Vedette plants and mixed at two different ratios , corresponding to 1∶1 and 1∶4 weights of infected leaf material , to create inocula 1 and 2 , respectively . For each inoculum , 25 Vedette plants were inoculated 28 days after sowing ( 7 to 8 expanded leaf stage ) on the two upper expanded leaves ( Fig . 1A ) . All plants were mechanically inoculated . The Vedette plants were then split into three sets corresponding to three different leaf and seed sampling designs , and randomized . For inoculum 2 , one plant died before leaf sampling . For plants numbered 1 to 19 , leaves were collected at two different dates ( Fig . 1B ) . At 22 days post inoculation ( dpi ) , corresponding to the anthesis of the first flower in the plant population , the three leaves immediately above the inoculated ones were collected separately ( leaves L1 to L3 , Fig . 1A ) and at 61 dpi ( end of flowering ) , the three leaves immediately above leaf L3 were collected separately ( leaves L4 to L6 , Fig . 1A ) . For plants 20 to 39 , only leaf L5 was collected ( at 61 dpi ) . Finally , no leaves at all were collected on plants 40 to 49 . For inoculation , RNA extraction and enzyme-linked immunosorbent assay ( ELISA ) , leaf tissue was homogenized in four volumes ( wt/vol ) of 0 . 03 M phosphate buffer ( pH 7 . 0 ) supplemented with 2% ( wt/vol ) diethyldithiocarbamate . For RT-PCR , total RNA was extracted from a 150 µL aliquot using the Tri Reagent kit ( Molecular Research Center Inc . , Cincinnati , OH , USA ) . To amplify the VPg coding region that contained the polymorphic codon between DPD1 and DPD1-R , reverse transcription ( RT ) was performed on 2 µL of each RNA extract using Avian myeloblastosis virus reverse transcriptase ( Promega Corp . , Madison , WI , USA ) followed by polymerase chain reaction ( PCR ) with Thermus aquaticus DNA polymerase ( Promega Corp . ) . Primer DPD1-VPGR ( 5′-AAACTGACCAAATCCGATGCC complementary to nucleotides 6690 to 6710 of DPD1 genome , accession number D10930 ) was used for RT and primers DPD1-VPGR and DPD1-VPGF ( 5′-AAAACACTGCAGCTTAAGGG corresponding to nucleotides 5868 to 5887 ) were used for PCR . The PCR program started with 3 min at 95°C followed by 35 cycles ( 45 s at 95°C , 30 s at 55°C , 50 s at 72°C ) and a final extension at 72°C for 10 min . Amplification products were sequenced directly with primer DPD1-VPGF by Genoscreen ( Lille , France ) . We estimated the relative proportions of the two PSbMV variants in inocula and leaves from the height of peaks corresponding to the three polymorphic codon positions in the chromatograms . The reliability of this quantification method was evaluated with artificial mixtures of known quantities of the two PSbMV variants obtained after virus purification . As illustrated in Fig . S1 , a linear regression allowed a very accurate prediction of the percentage of each variant in mixed-infected leaves ( slope = 1 . 01 , R2 = 0 . 99 ) . This chromatogram-based quantification method was also compared to another method based on the cloning of RT-PCR products obtained with primers DPD1-VPGR and DPD1-VPGF into an Escherichia coli plasmid vector . For this , 5 pea leaves with contrasted frequencies of variant DPD1-R ( from 24% to 69% based on the “chromatogram” method ) were chosen and , for each of them , the RT-PCR products were cloned into the pGEM-T Easy vector ( Promega Corp . , Madison , WI , USA ) and the number of clones corresponding to DPD1 and DPD1-R among a total of 40 clones per leaf was determined using the specific primers DPD1-VPgF-116V and DPD1-VPgF-116R described below . Again , the “chromatogram” and “cloning” methods provided highly similar frequency estimates ( slope = 1 . 07 , R2 = 0 . 96 ) , hence further validating the “chromatogram” quantification method . Pods produced by the main stem ( Fig . 1 ) of plants 1 to 19 and 40 to 49 were harvested at desiccation time . Harvested seeds were then sown and all leaves from each seedling were collected 22 days later . Seedling extracts were tested for PSbMV infection by antigen coated plate-ELISA ( ACP-ELISA ) using an antiserum specific for the PSbMV coat protein . To detect the presence of either the DPD1 or the DPD1-R PSbMV variants , total RNA was extracted from seedlings of mother plants with a minimum of nine ELISA-positive seedlings . The generic DPD1-VPGR primer was used for RT and for PCR in combination with either the primer DPD1-VPgF-116V ( 5′-CTCGATAAACAATTGTTTGTG ) or the primer DPD1-VPgF-116R ( 5′-CTCGATAAACAATTGTTTCGA ) corresponding to nucleotides 6336–6356 of DPD1 andDPD1-R , respectively . The PCR programs started with 3 min at 95°C followed by 40 cycles ( 45 s at 95°C , 30 s at 63°C , 30 s at 72°C ) and a final extension at 72°C for 10 min . Artificial mixtures of known proportions of RNAs of the two PSbMV variants obtained after virus purification [9] were used to evaluate the sensitivity of the RT-PCR method . In these artificial mixtures , each variant could be detected up to a 0 . 1% relative concentration . To estimate Ne during PSbMV colonization of upper uninoculated leaves ( L1 to L6 in Fig . 1A ) , we used the “variance method” based on the differences in the variance of the viral genotype frequencies between the two sampling dates at 22 and 61 dpi , and the “FST method” based on the difference between these 2 dates of Wright's FST statistics [24] calculated on within- and between-plant viral genetic diversities [25] . These methods are based on the assumption that the PSbMV variants within the viral population under consideration are equally competitive . According to the variance method , Ne = E ( P ) × ( 1−E ( P ) ) /[Var ( P′ ) −Var ( P ) ] , where P and P′ are the random variables of the frequencies of the viral marker for each plant at the first and second sampling dates , respectively , E ( P ) is the expected value of P in the plant population and Var ( P ) its variance . In practice , E ( P ) and Var ( P ) were estimated by the sample mean and variance of the frequencies of the viral marker measured on a set of plants ( Table 1 ) . Because Var ( P ) was negligible compared to E ( P ) in our datasets , the Ne estimates provided by this equation were almost identical to those obtained with equation ( 14 ) of [26]: Ne = [E ( P ) × ( 1−E ( P ) ) −Var ( P ) ]/[Var ( P′ ) −Var ( P ) ] . According to the FST method , Ne = ( 1−FST ) / ( FST′−FST ) , where FST and FST′ are values of the FST statistics of the viral populations at the first and second sampling dates , respectively ( see [25] for details ) . For both methods , Ne confidence intervals were obtained by bootstrapping 10 , 000 times among plants . With the nested sampling design used ( several leaves being analyzed for each plant ) and with the different plants sets available ( plants 1 to 19 , analyzed at 22 and 61 dpi and plants 20 to 39 analyzed at 61 dpi only , Fig . 1B ) , several datasets can be used to estimate Ne ( Table 1 ) . All leaves can be considered to estimate the variant frequencies at date 2 and Ne reflects the overall genetic drift process in the whole plant ( dataset 1 ) or a single leaf per plant can be considered at date 2 ( as in [25] ) and , in that case , Ne can be viewed as the number of founding virus particles contributing to the colonization of an individual leaf ( datasets 2 and 3 ) . In addition , different sets of plants can be considered for each date ( dataset 3 ) to test the influence of sampling leaves at date 1 on Ne estimates ( by comparing dataset 2 and dataset 3 ) . In order to estimate the size of bottlenecks undergone by PSbMV populations during seed transmission and to explore the mechanisms underlying seed transmission , we developed dedicated models . These models describe the two sequential processes leading to seedling infection: ( 1 ) virus entry into the seed ( or more precisely into seed embryos , see the Discussion section ) and ( 2 ) seedling infection from the contaminated seed . Concerning the first step , we assumed that the two virus variants act independently and , for a given variant , virus particles also act independently ( i . e . there is no variant-variant nor virus-virus interactions ) . Concerning the second step , both types of interactions were considered ( Table 2 ) . For the first step ( virus entry into the seed ) , we assumed that the proportions of PSbMV variants DPD1-R ( variant 1 ) and DPD1 ( variant 2 ) in coinfected plants can fluctuate in time during the period of seed infection ( i . e . from 22 to 61 dpi ) and within the plant because of spatial heterogeneity of distribution of virus variants . We considered that the relative frequencies f1 of variant 1 and f2 = ( 1−f1 ) of variant 2 in the vicinity of a given seed at infection time were realization of random variables that followed Beta distributions of parameters ( α , β ) and ( β , α ) , respectively , α and β varying from plant to plant . We assumed that the numbers of viral particles of each variant entering a given seed , N1 and N2 , were described by independent Poisson processes of parameters λ1×f1 and λ2× ( 1−f1 ) , respectively , where λ1 and λ2 are the efficiencies of seed infection by variants 1 and 2 . This hypothesis implies that all virus particles of a given variant have the same probability of entering a seed , and that they enter into the seeds independently of each other ( i . e . there is no virus-virus interactions ) . Moreover , assuming that these Poisson processes are independent implies that there is no interaction between DPD1 and DPD1-R variants for entering a seed ( however they can enter with different efficiencies ) . For the second step of PSbMV seed transmission ( seedling infection ) , we hypothesized that vertical transmission occurs if a minimal number Nc+1 of viral particles entered into a seed . Nc was chosen randomly and independently for each seed ( and plant ) from a Poisson distribution of parameter λc . Four alternative models were considered to describe the mechanism of seedling infection ( Table 2 , Fig . 2 ) . Models M1 , M2 and M3 assume virus-virus interactions , seedling infection being a virus density-dependent process . In models M1 and M2 , variant-variant interactions occur , as seedlings become infected if the total number of particles of virus variants 1 and 2 entering into a seed ( i . e . N1+N2 ) strictly exceeds Nc . In model M1 , a variant is transmitted vertically if at least one particle of this variant has entered into the seed , meaning that the contribution to seedling infection of a virus particle of one variant does not depend on the density of the other variant: virus particles are interchangeable , whatever their type . In contrast , in model M2 , a variant is transmitted vertically if its density is higher than Nc or higher than the density of the other variant ( when N1 = N2 the seedling becomes infected by both variants ) . Here , the contribution of a virus particle of one variant to seedling infection depends on the density of the other variant: virus particles are not interchangeable and model M2 assumed some inhibition between variants when one variant outnumbers the other . In model M3 , there is no variant-variant interaction: the virus variants initiate seedling infection independently . A variant is transmitted vertically if the number of particles of this variant entering into the seed strictly exceeds Nc . Finally , in model M4 there is no virus-virus , nor variant-variant interaction . Nc is indeed set to zero: a virus variant is transmitted vertically if at least one particle of this virus variant has entered the seed . The R plants of the experimental design , indexed by r , were assumed to be independent . For a given plant , the variables describing the infection status of the seedlings , indexed by s ( 1≤s≤Sr ) , were supposed to be independent and identically distributed , but potentially with different distributions , for distinct plants . The variable with , describes the infection status of seedling s issued from mother plant r . This seedling is either not infected ( ) , infected only by variant 1 ( ) , infected only by variant 2 ( ) , or infected by both variants ( ) . defines a categorical ( or 1-trial multinomial ) variable . Let for model M1 , M2 and M3 or for model M4 and be the probability density function ( pdf ) of Poisson distribution , and let be the beta pdf of the random variable standing for the proportion of variant 1 circulating into the phloem . For seedling s of plant r , if the proportion of variant 1 present in the circulating viral population is known , the conditional probabilities of the different seedling infection statuses are denoted . For model M1 , we have:For model M2 , we have:The formula for given for model M1 is the same for model M2 . For model M3 , we have:Finally , for model M4 , we have:Since only the variables are observed in the experiments but neither nor , the likelihood of observing is obtained by integrating over the values of variable Φ . In our case , the plant specific parameters ( αr , βr ) were considered as known parameters and have been estimated for a given plant using the proportions of the two virus variants in leaves L1 , L2 and L3 at 22 dpi and in leaves L4 , L5 and L6 at 61 dpi ( Fig . 1 ) . Since the realized frequencies f were not observed , the probability for a seedling of a given plant r to be in the infectious status ij is obtained by integrating over all possible realizations of , that is . The likelihood of a given model is obtained as the product of R multinomial distributions aswhere with since the Xrs are independent for the different plants . After checking that the four models were practically identifiable in our experimental conditions ( Text S1 ) , model parameter inferences were performed by minimizing the log of the likelihood function for each model Mj using the “bbmle” package with the “nlminb” optimization routines of the R software environment ( http://cran . r-project . org/ ) . 95% confidence intervals for model parameters were estimated using the function “profile” of the “bbmle” package .
To estimate the relative competitiveness of PSbMV variants DPD1 and DPD1-R for infection of leaves at the systemic level , we compared their relative frequencies in apical leaves sampled at 22 and 61 dpi ( Fig . 1 ) . Analysis a posteriori based on the sequence chromatograms ( Fig . S1 ) indicated that the DPD1-R variant represented 37 . 8% and 65 . 9% of inocula 1 and 2 , respectively ( the method used to estimate the relative frequency of the two PSbMV variants in inocula and mixed-infected leaves is described in details in Fig . S1 ) . Sequence chromatograms of the VPg coding region showed also clearly that both PSbMV variants were present in each of the 134 leaves examined at 22 and 61 dpi ( for plants 1 to 19 ) or at 61 dpi only ( for plants 20 to 39 ) ( Fig . 1B ) . Indeed , at the sequence region polymorphic between DPD1 and DPD1-R , the lowest of the six peaks ( two different nucleotides for each of the three polymorphic codon positions ) was 3 . 0 to 9 . 5 times ( 5 . 0 times on average ) higher than the highest peak of background noise . The minimum and maximum percentages of DPD1-R among the 134 leaves were 21 . 3 and 70 . 6% , respectively . At 22 dpi , the mean proportion of DPD1-R observed in three sampled leaves was 32 . 3% for inoculum 1 and 55 . 7% for inoculum 2 ( Table 3 ) . In the same plants at 61 dpi , these average proportions were 31 . 0% and 51 . 7% , respectively , indicating almost no change in average frequency between the two dates and equal competitiveness of the two viral variants during leaf colonization . Confirming this , the difference of variant proportions in the plants between the two dates was 2 . 5% on average ( with a 5 . 4% standard deviation ) . It was lower than 5% for 16 of the 19 analyzed plants . Twelve plants showed a decrease and seven an increase of DPD1-R frequency , which is not significantly different from random fluctuations ( P = 0 . 25; Wilcoxon matched pairs signed ranks test ) . In addition , the sampling at 22 dpi had no influence on the average composition of the viral populations . Indeed , the average proportions of DPD1-R frequency in plants sampled only at 61 dpi ( plants 20 to 39 ) were 31 . 4% and 58 . 2% for inocula 1 and 2 , respectively , which is not significantly different from the DPD1-R frequencies at 22 or 61 dpi in plants that were sampled twice ( plants 1 to 19 ) ( P>0 . 20; Mann-Whitney tests ) ( Table 3 ) . Consequently , the two PSbMV variants DPD1 and DPD1-R can be considered as equally competitive with regard to the colonization of leaves at the systemic level between 22 and 61 dpi . To estimate the relative competitiveness of PSbMV variants DPD1 and DPD1-R for seed transmission , we compared their relative frequencies in seedlings derived from inoculated mother plants and in leaves of these mother plants sampled at 22 and 61 dpi . The number of harvested pea seeds in the different PSbMV-infected plants varied from seven to 95 , with an average of 54 . All harvested seeds germinated and the infection status of each seedling was analyzed by ELISA . From a total of 1022 seedlings derived from mother plants 1 to 19 , the average seedling infection rate was 33 . 4% ( 33 . 1% and 33 . 7% for inocula 1 and 2 respectively ) . The average seedling infection rate was also similar to the infection rates observed in the seedlings of control plants inoculated by DPD1-R only ( 36% for a total of n = 206 seedlings ) or DPD1 only ( 31%; n = 195 ) , ( P = 0 . 27; Khi2 tests ) . Accordingly , the fact that similar percentages of seed transmission were observed in single-infected or mixed-infected plants suggests independence between PSbMV variants for seed infection and justifies the Poissonian assumptions made for modeling virus entry into seeds . Finally , the seedling infection rates were similar to those observed for mixed-infected plants for which no leaves were sampled ( plants 40 to 49 ) ( 34%; n = 584; P>0 . 30 for both inocula; Khi2 tests ) . All these values are in the range of seed transmission rates obtained independently with PSbMV DPD1 and Vedette pea plants ( 25 to 53% seed transmission [27] ) . For mother plants having nine or more infected seedlings , the proportion of seedlings corresponding to categories ( ii ) seedling infected only by variant DPD1 , ( iii ) seedling infected only by variant DPD1-R and ( iv ) seedling infected by both PSbMV variants was determined ( Table 3 ) . Accordingly , among plants 1 to 19 , the seedlings obtained from 12 plants ( six plants initially inoculated with 38% of variant DPD1-R ( inoculum 1 ) and six plants initially inoculated with 66% of variant DPD1-R ( inoculum 2 ) ) were analyzed . In contrast to plant leaves , the two PSbMV variants were detected simultaneously in a minority of infected seedlings , i . e . 28 . 5% of seedlings for inoculum 1 and 30 . 9% of seedlings for inoculum 2 . The DPD1-R variant was observed in 39 . 8% and 70 . 9% of seedlings infected by a single virus variant ( considering only seedling categories ( ii ) and ( iii ) ) for inocula 1 and 2 , respectively . Compared to the PSbMV variant frequencies in the leaves of the mother plants , DPD1-R seemed to be somewhat better seed-transmitted than DPD1 , a difference which is significant only for inoculum 2 ( P = 0 . 01; Khi2 test ) . Examining seed transmission results for each mother plant individually did not reveal any significant difference between the distributions of variants among the seedlings and the average proportion of PSbMV variants in leaves . The percentages of seedlings infected simultaneously by the two PSbMV variants were similar for mother plants which leaves were sampled twice ( numbers 1 to 19 ) and for mother plants for which no leaves were sampled ( plants 40 to 49 ) ( P>0 . 2; Khi2 tests ) ( Table 3 ) . The distributions of the two PSbMV variants among the seedlings were also similar for these two sets of mother plants ( P>0 . 2; Khi2 tests ) ( Table 3 ) . Consequently , the sampling procedure did not affect the seed transmission of the PSbMV variants and will not bias the estimates of bottleneck sizes during PSbMV seed transmission . Since the two inoculated PSbMV variants DPD1 and DPD1-R were equally competitive during the colonization of plant leaves from 22 to 61 dpi , we used the methods described in [25] to estimate Ne . These methods are based on the differences in variance of the viral variant frequencies ( “variance method” ) or on the difference of Wright's FST statistics ( “FST method” ) between two sampling dates . For these methods , an underlying assumption is that the variance of the viral variant frequencies ( or the FST statistics ) increases with time . Indeed , considering that all variants are equally fit in the population , variant frequency fluctuations are due only to genetic drift , which affects both the amount and distribution of neutral genetic diversity over time ( i . e . across generations ) and space ( i . e . between subpopulations at a given time ) . Whatever the datasets used , we observed very small differences of variance of virus variant frequencies or FST statistics for the PSbMV populations at 22 and 61 dpi , suggesting very limited effect , if any , of genetic drift on viral populations during the systemic invasion of apical leaves ( Table 1 ) . Accordingly , Ne estimates ranged from 59 to 216 , with a mean of all Ne estimates of 111 and 119 for the variance and FST methods , respectively , and of 172 and 77 for inocula 1 and 2 , respectively . In some cases , no Ne estimates could be obtained because the variance of viral frequencies and FST statistics decreased between 22 and 61 dpi ( no drift was observed ) . Overall , little difference was observed between the “variance” and “FST” methods and between the different datasets used to estimate viral frequencies at the two dates of observations ( Table 1 ) . Notably , leaf sampling at date 1 did not affect significantly the results: Ne estimates were comprised between 74 and 143 for dataset 3 ( independent sets of plants were sampled at each date ) and between 59 and 216 for dataset 2 ( the same set of plants was sampled at both dates ) ( Table 1 ) . Dataset 3 provided the most homogeneous Ne estimates and smallest confidence intervals . Bootstrapping among plants allowed obtaining confidence intervals for Ne estimates . The 95% confidence intervals were large because of the small number of plants and because the small differences in virus frequency variances or population FST between dates 1 and 2 had large impacts on Ne estimates ( Table 1 ) . All these results demonstrated the lack of narrow population bottlenecks during the leaf colonization at the systemic level , and provided Ne estimates similar to those obtained for CaMV [25] . The “variance” and “FST” methods provide unbiased estimates of Ne only if the variants analyzed are equally competitive . This is not the case for our vertical transmission dataset , as variant DPD1-R was somewhat better transmitted to seedlings than DPD1 . Thus , we developed stochastic models to estimate the size of bottlenecks undergone by PSbMV populations during seed transmission that ( i ) take into account the difference in seed transmissibility between variants and ( ii ) that allow to disentangle different seedling infection processes ( see the Materials , methods and models section ) . These models showed that the mean number of PSbMV particles contributing to the infection of an individual seedling was close to one . We first checked whether our experimental design ( number and nature of the data ) was sufficiently informative to estimate accurately the model parameters using practical identifiability tests ( Text S1 ) . Numerical simulations indicated clearly that all four models had a very good practical identifiability . Indeed , whatever the parameters considered , the coefficient of correlation between their true and estimated values were ≥0 . 94 ( Table 4 ) . Moreover , the four models of virus seed transmission could be very efficiently discriminated using Akaike Information Criterion ( AIC ) [28] . When the data were simulated under model 1 , the AIC selected model 1 ( respectively models 2 , 3 and 4 ) in 92% ( respectively 2% , 6% and 0% ) of simulations . Similarly , when the data were generated under models 2 , 3 or 4 , the AIC identified the correct model in 94% , 100% and 88% of the simulations . The model selection procedure applied to the experimental data set ( Table S1 ) indicates that the AIC values of models M1 to M4 were 195 , 196 , 203 and 207 , respectively . The corresponding Akaike weights , which provide the relative support for each model , were 0 . 59 , 0 . 40 , 0 . 01 and nearly zero ( 10−17 ) . Thus , although model M1 is supported best by the data , model M2 has also a substantial support [29] . Assuming that λ1 = λ2 , the AIC of the models increased to 207 , 210 , 219 and 279 for M1 , M2 , M3 and M4 , respectively , indicating that the mean number of viruses contributing to the infection of a seedling was significantly different for virus variants 1 and 2 . Under model M1 , parameter inference indicated that the mean number of DPD1-R variant infectious particles contributing to the infection of a pea seedling was 1 . 08 ( with a 95% confidence interval , CI95% , ranging from 0 . 9 to 1 . 29 ) and 0 . 74 for virus variant DPD1 ( with a CI95% ranging from 0 . 61 to 0 . 88 ) , while the mean number of virus particles required to infect a pea seedling was 0 . 84 ( CI95% = [0 . 63 , 1 . 05] ) . Parameter inferences under model M2 ( which is almost as likely as model M1 with an Akaike weight of 0 . 4 ) were close to those obtained with M1 , although always slightly higher: 1 . 52 for λ1 with a CI95% ranging from 1 . 08 to 2 . 74 , 1 . 06 for λ2 with a CI95% ranging from 0 . 75 to 1 . 95 and 1 . 36 for λc with a CI95% ranging from 0 . 85 to 2 . 63 . Importantly , models M1 and M2 fitted very satisfactorily the experimental data . First , the observed and predicted mean numbers of seedlings corresponding to the four categories of seedling infection ( i . e . ( i ) healthy , ( ii ) infected only by variant 2 ( DPD1 ) , ( iii ) infected only by variant 1 ( DPD1-R ) and ( iv ) infected by both PSbMV variants ) were highly correlated ( R2 = 0 . 88 ) for both models . Second , between-plant variability was very well represented , as an 80% ( resp . 90% ) confidence interval predicted by model M1 contained 78% ( resp . 83% ) of the observed data . For model M2 , an 80% ( resp . 90% ) predicted confidence interval contained 80% ( resp . 89% ) of the observed data .
We used the PSbMV-pea pathosystem to estimate the size of bottlenecks affecting a plant virus population during vertical transmission through seed embryo . We observed a very drastic genetic drift during vertical transmission , with an average number of infectious virus particles contributing to the infection of a seedling from an infected mother plant close to one . On the opposite , almost no genetic drift was observed during the infection of apical leaves of the mother plants during the same time-frame corresponding to the flowering period . Estimation of Ne during the infection cycle of plant virus populations is quite complicated because of ( i ) the lack of estimates of generation times for viruses [30] , which is due to the difficulties inherent to the definition of a viral generation ( different lengths of time may be required for the production of the different components of progeny virus particles , like structural proteins and genome components ) , to the overlap between replication of virus entities within populations , and to the complex kinetics of virus replication [31] and ( ii ) the succession of different steps in the virus infection cycle that potentially follow different growth dynamics ( intracellular accumulation , cell-to-cell movement , systemic translocation and plant-to-plant transmission ) . In spite of these limitations , several estimates of Ne or of the bottleneck size corresponding to particular steps of the virus life cycle have been obtained . Concerning the colonization of plant leaves by viruses , estimates obtained for Ne are quite contrasted [3] , [4] . The low genetic drift ( large Ne ) observed during the systemic colonization of pea plants by PSbMV corroborates previous results obtained with Cauliflower mosaic virus ( genus Caulimovirus ) [3] , [25] or Tobacco etch virus ( genus Potyvirus ) [4] , where the composition of virus populations were compared between inoculated and apical leaves [4] or between apical leaves sampled at two different dates [25] . In contrast , small Ne values were obtained by comparing the virus populations between the inoculum and apical leaves [13] , [17] . These observations were reconciled by showing that most genetic drift occurs at the inoculation step whereas little genetic drift is subsequently observed during the systemic colonization of plants [4] . However , genetic drift during the systemic colonization of plants by viruses is not necessarily low . For example , 13 years after inoculation , each leaf of a peach tree was colonized by a single viral variant of Plum pox virus ( PPV , genus Potyvirus ) whereas a total of 33 viral variants were observed in the whole set of leaves analyzed , indicating that narrow bottlenecks acted on PPV populations during the infection of individual leaves [32] . Clearly , additional studies are needed to unravel the plant , virus and environmental factors which determine the patterns and intensity of genetic drift during plant colonization by viruses . Recently , the number of virus colonizing leaves was shown to increase with the concentration of viruses circulating within the plant sap [33] . This suggests that the low level of genetic drift observed during the systemic colonization of pea plants by PSbMV during the flowering period could result from high concentrations of virus circulating into the plant vasculature . On the opposite , during the same time-frame , we showed that a single infectious PSbMV particle contributed on average to the infection of an individual seedling derived from an inoculated mother plant . To our knowledge , this is the first estimate of the bottleneck size imposed by vertical transmission to a virus population . Strong bottlenecks were also observed during vertical mother-to-child transmission of Human immunodeficiency virus-1 ( HIV-1 ) [34] , [35] , [36] . For the majority of in utero or intrapartum transmission cases examined in these three studies ( 65%; 22/49 ) the infants harbored a single viral variant , which suggested the occurrence of narrow population bottlenecks at transmission . Note , that this percentage is very close to our own estimates for PSbMV ( we observed from 66% to 82% single-infected pea seedlings among the infected ones , depending on inocula and plant sets; Table 3 ) . A recent study conducted on seed transmission of ZYMV ( Zucchini yellow mosaic virus , genus Potyvirus ) in Cucurbita pepo showed that 16 of 24 ZYMV variants present in the mother plant were also present in vertically-transmitted virus populations , either of the first or second plant generation [37] . These figures suggest that bottleneck sizes during vertical transmission could be larger in that case . However , in none of these studies was the transmissibility of the different virus variants or their abundance in the mother's plasma ( or in the mother plant ) taken into account , which hampers the derivation of unbiased estimates for the bottleneck size . Vertical transmission of PSbMV occurs through the infection of the pea seed embryos [38] . Usually , viruses are excluded from plant reproductive tissues . Because pathogens must cross several barriers intended to protect the developing embryo , the occurrence of narrow population bottlenecks during pathogen vertical transmission could be a quite general rule . The capacity of viruses to invade plant embryos and withstand seed maturation and desiccation depends both on virus and host genotypes , as demonstrated for PSbMV [23] , [27] , [39] . Seed transmission of PSbMV in pea occurs exclusively by direct invasion of immature embryos from virus-infected maternal tissues . It occurs only during a precise temporal window and from virus accumulated at a precise location in the developing seed . Such conditions are therefore favorable to the occurrence of strong virus population bottlenecks . Early infection of the mother plant is necessary for PSbMV vertical transmission to occur [23] . PSbMV invasion of pea embryos occurs from virus infection spreading from the maternal cells in the micropylar region of the embryo to the endosperm cytoplasm , then to the embryonic suspensor and finally to the embryo . Since the embryonic suspensor undergoes a programmed cell death , it acts for the virus as a “transient conduit” for embryo invasion [39] . The ability of the virus to invade the micropylar region before the suspensor programmed cell death therefore explains why early PSbMV infection of the mother plant is required for seed transmission , and could also explain why some pea cultivars are resistant to PSbMV seed transmission and why some PSbMV isolates are not seed transmitted in pea . In addition , no PSbMV replication could be detected in the endosperm cytoplasm [38] , suggesting that only a small amount of virus is able to accumulate into the endosperm cytoplasm and further enter the suspensor . Based on these observations , Roberts et al [38] suggested that seed transmission of PSbMV was largely based on the chance of the virus to be in the right place at the right time . In these conditions , even a small degree of heterogeneity in the distribution of virus variants in the cells of infected plants , as observed for some potyviruses [16] , [31] , [40] , could contribute to the genetic drift that occurs during PSbMV seed transmission . In agreement with these observations , the models that we used to estimate the bottleneck size during seed transmission considered that the virus variant frequency could fluctuate randomly at the time and place of virus entry into seed embryos . Consequently , the biological processes involved in PSbMV seed transmission are in accordance with , and provide plausible mechanisms for the narrow bottlenecks endured by virus populations during vertical seed transmission . To go further , it would be worth investigating whether the virus load in plants is linked to the intensity of genetic drift during the colonization of leaves , as evidenced by [33] , and whether it affects also genetic drift during vertical transmission , at least at some critical time points during embryo infection . From a methodological point of view , the mathematical framework introduced here allowed disentangling the relative importance of selection and genetic drift in shaping the genetic composition of viral populations during seed transmission . It could be of broad interest to estimate Ne when the effect of deterministic evolutionary forces , typically selection , cannot be excluded . Indeed , the temporal methods classically used to estimate Ne assume that the observed changes in allele frequency are due to genetic drift only and thus require the use of neutral genetic markers for the population of interest [6] . Such markers could be difficult to identify or to generate , especially for viruses , which typically possess highly constrained genomes and are impacted by strongly negative average mutational effects on fitness [41] . From a biological point of view , model selection analysis indicated that seedling infection was a virus density-dependent process , where particles of the two virus variants sum up their action to exceed an infection threshold , rather than a process where each variant acts independently ( models M1 and M2 were preferred to model M3 ) . These results echo the study of Lafforgue et al . [42] , who showed that the delay of systemic infection of a plant was determined by the cumulative effect of independently-acting foci of primary infection . Results also showed that one or a small number of viral particle ( s ) is ( are ) enough for virus seed transmission ( as λc was low ) , indicating that each virus particle has a quite high probability of causing efficient seed transmission . However , rejection of model M4 indicates that one viral particle is not always sufficient to initiate efficient seedling infection . Model M1 being only slightly preferable to model M2 , it remained unclear whether virus particles belonging to the two variants are interchangeable or not in the cumulative infection process , interchangeability meaning that the contribution to seedling infection of a virus particle of one variant does not depend on the density of virus particles of the other variant . Consequently , more data should be gathered to clearly distinguish whether frequency-dependent selection of PSbMV variants occurred ( as in model M2 ) or not ( as in model M1 ) during seed transmission . The small Ne values observed for PSbMV vertical transmission are expected to impact more deeply virus evolution than bottlenecks of the same size that would be experienced during horizontal transmission [9] , [11] , at least for large host populations . This is suggested by theoretical work on the evolution of parasites virulence ( defined as the harm that they inflict to their host ) according to their mode of transmission . The classical mechanism to explain why vertically-transmitted parasites evolve reduced virulence is through an indirect selection to improve host survival and/or reproduction [43] . Our study suggests that such reduced virulence could also be the consequence of narrow bottlenecks during vertical transmission . Indeed , using a model that assumed a tight association between parasites fitness and virulence , Bergstrom et al . [44] suggested that a direct effect of narrow bottlenecks is to select much lower levels of virulence in vertically-transmitted than in horizontally-transmitted pathogens . This was mainly due to the decrease of intra-host competition between virus variants in case of vertical transmission . Said another way , the strength of selection is reduced in case of vertical transmission as virus particles are separated into many distinct evolutionary host lineages . In their study , this difference between vertical and horizontal transmission was particularly strong when only one or two virus particles initiate the infection of a new host . In agreement with these theoretical results , repeated vertical transmission events were shown to affect drastically the evolution of PSbMV populations . As soon as the second generation of pea plants contaminated by PSbMV through seed transmission , PSbMV populations derived from four different isolates were shown to differ largely from the initial inocula: in contrast to the initially inoculated plants ( generation 0 ) , or plants of the first generation issued from contaminated seeds , the infected plants of the second generation did not express any symptom and PSbMV was not detectable in their vegetative parts [45] . Such a rapid evolution could be , at least in part , a consequence of the severe bottlenecks experienced by PSbMV populations during vertical transmission . Similar declines in virulence [46] or symptom induction [37] , [47] , [48] have been observed for other seed-transmitted plant viruses . Exploring to which extent such decrease in virulence or symptomatology ( two life history traits that are not necessarily correlated in plant viruses ) can be explained by bottleneck sizes is an important issue in parasite evolution . From an applied perspective , many vertically-transmitted plant viruses are also transmitted horizontally by vectors . For example , PSbMV is transmitted by a large number of aphid species . In the field , ecological ( e . g . host density , aphid population dynamics ) and agronomic factors ( e . g use of virus-free seeds ) determine which mode of transmission is prevailing . Undoubtedly , a deeper understanding of the balance between the relative importance of these transmission modes during the course of epidemics , coupled with a deeper knowledge of the bottleneck sizes associated with these transmission modes is needed to better understand the evolution of important pathogen life history traits such as virulence , symptom severity and yield losses . Ultimately , this research could help designing more efficient strategies of plant protection relying on the knowledge and manipulation of evolutionary changes in parasites populations .
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Short generation times and high mutation rates are the hallmarks of virus . They favor their fast adaptation as illustrated by their ability to overcome natural as well as man-made barriers such as host resistance or drug treatments . However , such a fast adaptation could be slowed down when genetic drift , which introduces random sampling effects in the evolution of virus populations , is important . Whether genetic drift or selection dominates depends on the effective size of populations ( Ne ) . Ne has been estimated for several steps of plant virus infectious cycle , such as horizontal transmission by insects and the colonization of plant cells and tissues . However , although economically important , no estimate of Ne during vertical transmission of viruses , i . e . the infection of plant progenies from parental plants , is available . Here , we report that Pea seedborne mosaic virus ( PSbMV ) , a seed transmitted virus infecting pea crops , undergoes very drastic genetic drift during vertical transmission , with an average number of infectious virus particles contributing to the infection of a seedling from an infected mother plant close to one . Such bottlenecks , as narrow as those imposed by horizontal transmission , could slow down virus adaptation and should be taken into account to improve plant protection strategies .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[] |
2014
|
Narrow Bottlenecks Affect Pea Seedborne Mosaic Virus Populations during Vertical Seed Transmission but not during Leaf Colonization
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While genome-wide association studies ( GWAS ) have primarily examined populations of European ancestry , more recent studies often involve additional populations , including admixed populations such as African Americans and Latinos . In admixed populations , linkage disequilibrium ( LD ) exists both at a fine scale in ancestral populations and at a coarse scale ( admixture-LD ) due to chromosomal segments of distinct ancestry . Disease association statistics in admixed populations have previously considered SNP association ( LD mapping ) or admixture association ( mapping by admixture-LD ) , but not both . Here , we introduce a new statistical framework for combining SNP and admixture association in case-control studies , as well as methods for local ancestry-aware imputation . We illustrate the gain in statistical power achieved by these methods by analyzing data of 6 , 209 unrelated African Americans from the CARe project genotyped on the Affymetrix 6 . 0 chip , in conjunction with both simulated and real phenotypes , as well as by analyzing the FGFR2 locus using breast cancer GWAS data from 5 , 761 African-American women . We show that , at typed SNPs , our method yields an 8% increase in statistical power for finding disease risk loci compared to the power achieved by standard methods in case-control studies . At imputed SNPs , we observe an 11% increase in statistical power for mapping disease loci when our local ancestry-aware imputation framework and the new scoring statistic are jointly employed . Finally , we show that our method increases statistical power in regions harboring the causal SNP in the case when the causal SNP is untyped and cannot be imputed . Our methods and our publicly available software are broadly applicable to GWAS in admixed populations .
Genome-wide association studies ( GWAS ) are the currently prevailing approach for identifying genetic variants with a modest effect on the risk of common disease , and have identified hundreds of common risk variants for a wide range of diseases and phenotypes [1] , [2] . Although GWAS have initially focused on populations of European ancestry , studies of other populations will capture additional genetic diversity that may be absent or present only at low frequency in Europeans . GWAS in non-Europeans will often involve admixed populations , such as African Americans and Latinos , with recent ancestry from two or more ancestral populations [3] , [4] . GWAS disease mapping in homogeneous populations relies on linkage disequilibrium ( LD ) between nearby markers to identify SNP association [5] . Admixed populations exhibit another form of LD at a coarse scale ( admixture-LD ) due to chromosomal segments of distinct ancestry [6] . This enables admixture mapping ( mapping by admixture-LD ) to be an effective approach for identifying disease genes in admixed populations [7]–[14] . As genotyping costs have decreased , however , GWAS have become an increasingly appealing alternative . Although GWAS and admixture mapping have historically been viewed as distinct approaches , GWAS in admixed populations can in theory capture both SNP and admixture association signals , which have been shown to contain independent information [15] . To date , GWAS in such populations have either considered SNP association only [3] , [16] , [17] , or SNP and admixture association separately [4] . We show below that combining these signals leads to increased statistical power because case-only admixture association statistics contain information independent from case-control SNP association statistics . It is important to complement theoretical methods development with empirical evaluation on large real data sets . To this end , we have evaluated our methods using 6 , 209 unrelated African Americans from the CARe cardiovascular consortium as well as 5761 unrelated African-American women from a GWAS for breast cancer . We ran comprehensive simulations based on real genotypes and phenotypes simulated under a variety of assumptions . Our main focus was on case-control phenotypes , in which case-only admixture association is particularly valuable . Our analysis of simulated and real ( coronary heart disease , type 2 diabetes and breast cancer ) case-control phenotypes shows that our combined SNP and admixture association approach attains significantly greater statistical power than can be achieved by applying either approach separately . Although our main focus is on case-control phenotypes , we also provide a detailed evaluation of association statistics for quantitative phenotypes , using simulated and real ( LDL and HDL cholesterol ) phenotypes . Since the general assumption in GWAS is that the causal SNP is not directly typed in the study , it is important to assess how the newly introduced scores perform in the context of genotype imputation . First , we show that imputation accuracy is marginally improved when local ancestry is taken into account in the imputation procedure . Second , our analysis in African Americans shows that for case-control studies our methods for combining SNP and admixture association outperform other approaches even in the presence of imputation . Finally , we show that when the causal SNP is not typed and cannot be reliably imputed our methods yield higher statistical power at finding the region harboring the causal variant when compared to previous approaches . Based on these findings we provide recommendations for the use of our combined approach in GWAS of admixed populations .
We analyzed data from 6 , 209 unrelated African Americans from the CARe consortium who were genotyped on the Affymetrix 6 . 0 chip , and merged in genotype data from the HapMap3 project ( see Methods ) [18] . We ran principal components analysis ( PCA ) on the merged data using the EIGENSOFT software , using only the CEU , YRI and CHB populations from HapMap3 to compute principal components [19] , [20] . The CARe samples generally occupy intermediate positions between CEU and YRI , consistent with previous work ( Figure S1 ) [21] , [22] . We ran the HAPMIX program for inferring local ancestry ( 0 , 1 or 2 European chromosomes ) at each location in the genome on the CARe samples , using phased CEU and YRI haplotypes from HapMap3 as reference [23] . HAPMIX was run in a mode that assigns European or African ancestry to each allele , thus resolving the local ancestry of each allele when both genotype and local ancestry were heterozygous ( see Methods ) . We defined genome-wide ancestry for each sample as the average of local ancestry estimates across the genome ( scaled to 0 . 0 , 0 . 5 or 1 . 0 ) . Genome-wide European ancestry estimates had a mean of 19 . 2% and standard deviation of 12 . 0% across samples ( consistent with previous work [21] , [22] ) , and were >99% correlated with the top eigenvector from PCA analysis . We defined average local ancestry at each location in the genome as the average of local ancestry values across samples . A plot of average local ancestry shows no unusual peaks in average local ancestry ( Figure S2 ) , consistent with the fact that the full set of CARe samples were not ascertained for a specific disease phenotype and thus would not be expected to produce an admixture peak , and confirming that HAPMIX does not produce artifactual deviations in average local ancestry . Importantly , we note that local ancestry can be estimated using any of the local ancestry inference methods that have been proposed ( e . g . [7] , [23] , [24] ) , as long as they are accurate and do not produce artifactual deviations in average local ancestry . We mention in passing that very strong selection since admixture for an allele differing in frequency between Europeans and West Africans could in theory produce a true local ancestry deviation , and our data could be used to provide an upper bound on the size of any such effect . We do not pursue this here . We used the Armitage trend test with correction for genome-wide ancestry as a baseline for the evaluation of other approaches , as this approach was used in previous association analyses using CARe data [25] ( see Methods ) . Next , we considered a SNP association score conditioned on local ancestry , as well as a case-only admixture score which evaluates the causal hypothesis that , restricting to disease cases , the proportion of European ancestry at the candidate locus differs from the genome-wide proportion [7] ( see Methods ) . Historically , an advantage of admixture association was that disease mapping could be performed using a coarse set of markers , due to the large size of ancestry segments and the resulting admixture linkage disequilibrium [22] . However , even when GWAS data are available , admixture scores that compare disease cases to the same disease cases elsewhere in the genome contain different information than SNP association scores that compare cases to controls; the additional information is particularly valuable when the causal SNP has very different allele frequencies in the ancestral populations . One possibility is to add the SNP association score conditioned on local ancestry to the admixture score to produce a χ2 ( 2dof ) score , but as we show below , the higher degrees of freedom leads to a reduction in statistical power . We instead propose a mixed χ2 ( 1dof ) score that jointly evaluates both SNP and admixture association using a single SNP odds ratio , by using the implied ancestry odds ratio ( see Methods ) . An important question is whether the odds ratio conditioned on African local ancestry differs from the odds ratio conditioned on European local ancestry , as this has implications for fine-mapping the causal SNP . This can be addressed by comparing the χ2 ( 1dof ) SNP association score conditioned on local ancestry to a χ2 ( 2dof ) SNP association score which allows different odds ratios for African versus European local ancestry ( see Methods ) . A final question , important in the context of localizing the causal SNP , is whether the ancestry odds ratio is fully explained by the SNP odds ratio . This can be addressed by comparing the χ2 ( 1dof ) MIX score that accounts for both admixture and case-control signal using a single SNP odds ratio and the χ2 ( 2dof ) SUM score that allows for independent SNP and ancestry odds ratios . We also explored whether it is necessary to assign African or European ancestry to each allele for a sample and SNP in which both local ancestry and genotype are heterozygous . Although the HAPMIX algorithm supports this functionality , it represents a significant complexity , particularly if representing local ancestry inference in terms of real-valued probabilities . We focus below on scores based on diploid local ancestry ( AA , AE or EE ) that do not require this extra information , and show that these scores perform nearly as well as scores that are based on haploid local ancestry ( A or E ) for each of two chromosomes with local ancestry inference and phasing performed jointly . We randomly selected 100 , 000 autosomal SNPs and , for each SNP , assigned simulated phenotypes based on either a null model or causal model for that SNP . Under the null model , we chose 1 , 000 cases and 1 , 000 controls at random . Under the causal model , we chose 1 , 000 cases and 1 , 000 controls corresponding to odds ratios R = 1 . 2 , 1 . 5 or 2 . 0 ( see Methods ) . Thus , our simulations use real genotypes , with simulated phenotypes that are different for each SNP being tested ( and different for each value of R ) . This framework automatically leads to admixture association signals as would exist with real phenotypes: for example , a causal SNP in which the risk allele has higher frequency in Europeans than in Africans will lead to the selection of 1 , 000 cases with higher than average European ancestry at the disease locus . We compared 5 scores: Armitage trend test with correction for genome-wide ancestry ( ATT ) , SNP association conditioned on local ancestry ( SNP1 ) , admixture association using cases only ( ADM ) , sum of SNP1 and ADM ( SUM ) , and our new mixed score ( MIX ) . All of these are χ2 ( 1dof ) scores , except for SUM which is χ2 ( 2dof ) . We note that the strength of the induced admixture signal at highly differentiated SNPs ( as measured by the ancestry odds ratio ) in the simulated data fits the model assumed in the MIX score . In Table 1 ( Typed Genotypes ) we display results obtained by all scores averaged across all SNPs , and averaged across SNPs with CEU versus YRI allele frequency difference of at least 0 . 4 , roughly the top decile of differentiation . We used a p-value cutoff of 5e-08 for all scores except ADM for which a threshold of 1e-05 was employed . The different ADM threshold is motivated by the smaller number of independent hypotheses tested across the genome in an admixture scan ( an effect of the large size of the ancestry segments ) [6] , [7] . The MIX score attains 8% higher power than the ATT score for random SNPs ( 24% higher power for SNPs in the top decile ) at R = 1 . 5 . The SNP1 score , which is conditioned on local ancestry , is analogous to disease mapping in Europeans or Africans ( see Text S1 ) . Thus , disease mapping in African Americans using the MIX achieves an increase in statistical power of 13% for random SNPs and of 67% for SNPs in the top decile of population differentiation over disease mapping in Europeans or Africans . This advantage is obtained both because MIX is a more powerful score than ATT , and because of the inherent advantage of disease mapping in admixed populations , which contain more polymorphic variation . As expected , the advantage of the MIX score is greatest for SNPs with large allele frequency differences between Africans and Europeans , for which admixture association produces a strong signal ( Table 1 ( Typed Genotypes ) and Figure 1 ) . We obtained similar results for a variant of the MIX score based on haploid local ancestry with joint local ancestry inference and phasing ( Text S1 ) . Thus , fully powered association statistics in admixed populations do not require joint local ancestry inference and phasing . We finally note that the heterogeneity score that tests for differences in effect size for African versus European local ancestry ( HET ) attained average values between 0 . 99–1 . 01 ( data not shown ) , exactly as expected since simulated phenotypes did not involve heterogeneity in effect size . We also assessed all scores at null simulated data ( R = 1 ) using the standard genomic control [26] statistic λGC which attained a value of 1 . 001 for MIX , 0 . 986 for SNP1 and 0 . 999 for the ATT score , respectively . We observed a λGC of 1 . 101 for the ADM score , which is suggestive of inflation , although we note that , for 1000 cases and a thousand independent genomic regions ( as expected in the ADM score ) , a λGC of 1 . 101 can arise by chance . However , since multiple factors ( e . g . deviations from random mating , correlation in errors of local ancestry estimates ) could potentially lead to inflation of the ADM statistic , we have also devised an admixture statistic , ADMGC that incorporates the empirical variance of the average local ancestry ( see Methods ) . It can be shown that ADMGC is equivalent to dividing the ADM statistics by λGC . Furthermore , we show how to incorporate ADMGC within the MIX framework to obtain a new version of our score ( MIXGC ) that incorporates the new admixture component . As expected , both ADMGC and MIXGC attain λGC of 1 . 000 ( data not shown ) in simulated null data . We note that MIXGC should be used when there is significant indication of inflation . As this was not the case here , we chose to use MIX for all results below . We also assessed the performance of our scores when the disease model assumptions are not met . We simulated causal SNPs under various disease models such as dominant and recessive or when two causal independent SNPs are present within an admixture block . To simulate two causal independent SNPs within same admixture block , we restricted to SNPs less than 5Mb apart and with LD less than . 1 ( as measured by r2 ) . Results in Table S3 confirm that for most scenarios studied the MIX score outperforms the standard ATT score with correction for genome-wide ancestry . Interestingly , when restricting to 2 causal SNP scenario in which one of the causal is in the top decile of differentiation ( which induces a strong admixture signal ) we observe that the SUM score outperforms all other scores in terms of power , showing the potential utility of this score at loci with multiple causal variants . We also looked at heterogeneous effects across Europeans and Africans by simulating 100 , 000 causal SNPs with R = 1 . 5 ( under no heterogeneity ) and assessing the scores at SNPs with different levels of LD with the simulated causal in the two populations . Different LD across populations will induce heterogeneous effects as a function of the allele frequencies and the population specific LD pattern . Results in Figure S4 show that under small heterogeneous effects ( difference in observed odds ratios <0 . 25 ) , the MIX score outperforms the other scores in terms of power while in the presence of larger heterogeneity all scores are underpowered in this simulation . Due to the limited number of markers present on the genotyping platforms , it is often the case that the causal SNP is not directly typed within the GWAS . However , genotypes typed in a study can be used as predictors , in conjunction with haplotypes over denser sets of SNPs from external repositories of human variation such as the HapMap [27] , to impute genotypes at SNPs untyped in the current study . Genotype imputation has been widely used as a method for boosting statistical power in association and fine-mapping studies as well as in meta-analysis that combines information across studies as a tool for increasing the number of markers interrogated for association with the phenotype [28]–[30] . Multiple methods [31] , [32] have been proposed for solving the imputation problem and have been shown to be very accurate when the haplotypes used as a reference panel provide a good match to the study population [28] , [30] , [33] . In admixed populations various imputation approaches have been proposed ranging from assigning global weights to the reference panels based on empirical estimates of ancestry [30] , to assigning coalescent-based weights to each of the reference haplotypes in every sample and every locus in the genome [34] . A standard approach for imputation in African Americans is to use a reference panel composed of European and African chromosomes [18] , [25] . Recent work has shown that imputation conditional on local ancestry estimates can boost the overall accuracy when compared to imputation based cosmopolitan reference panels that contain haplotypes from all the ancestral populations [24] , [35] . Here , through the use of real CARe genotypes , we show that imputation conditional on local ancestry yields a small improvement in imputation accuracy in African Americans . Our local ancestry aware imputation framework uses , at every locus in the genome , a reference a panel of haplotypes that is specified by the local ancestry ( see Methods ) . Following a standard masking approach , we masked 100 , 000 SNPs at random from the CARe data , imputed them and assessed imputation accuracy using a standard accuracy measure , the squared correlation between imputed and true ‘masked’ genotypes . We observe an average imputation r2 of 0 . 858 when our local ancestry aware framework is used , as opposed to 0 . 855 under the standard cosmopolitan approach , confirming that there is a small gain in accuracy by conditioning imputation on local ancestry . We observe a smaller improvement in imputation performance than the one reported in [24] , [35] which can be an effect of different imputation methods as well as of difference in size of reference HapMap panels used . We employed a much larger set of reference haplotypes ( HapMap phase 3 versus phase 2 ) in imputation that could potentially reduce the effect of incorporating local ancestry . Importantly , we note that the gain in accuracy is observed across all SNPs and leads to a small gain in statistical power for association ( see Figure 2 and Table S1 ) . We also point out that a large percentage of the imputed SNPs show a large difference in imputation performances between the European and African segments ( see Figure S3 ) . Roughly 40% of the imputed SNPs show accuracies differing by at least 0 . 1 in terms of squared correlation in European versus African segments with 26% being more accurately imputed in European segments versus 14% in African segments . A straightforward approach for extending association statistics at imputed SNPs is to use the maximum likelihood estimates for unobserved genotypes . Although this procedure does not fully account for the uncertainty in the imputed genotypes , it has been previously shown to perform well when there is considerable confidence in the imputed genotype calls . Throughout this paper we compute statistics over the maximum likelihood genotype calls . Although our novel scores could potentially be improved by fully incorporating the imputation uncertainty in the likelihood framework we note that the MIX score outperforms the standard ATT score , even when the ATT score accounts for the imputation uncertainty through the use of dosages instead of maximum likelihood genotype calls ( see Table 1 ( Imputed Genotypes ) ) . An important aspect of applying the case-control statistics to imputed data in African Americans is to properly account for the difference in imputation quality between African and European segments . We accomplish this by adjusting the observed allelic odds ratio as a function of imputation quality in the MIX and SNP1 score ( see Text S1 ) . We masked the 100 , 000 SNPs that were used for simulation of phenotypes and imputed genotypes at these SNPs using our local ancestry aware imputation framework ( see Methods ) . We computed the scores over the imputed genotype calls with the results displayed in Table 1 ( Imputed Genotypes ) . As expected , scores over imputed data show a reduction in statistical power because of the noise introduced by imputation errors . Importantly , we note that , similarly to typed data , the MIX score outperforms the other scores in terms of power , attaining 11% higher statistical power than the ATT score for random imputed SNPs ( 97% higher power for imputed SNPs in the top decile of allele frequency differentiation ) at R = 1 . 5 . Even when the ATT score allows for imputation uncertainty in the form of dosages , there is still a gain in statistical power of 6% at random SNPS ( R = 1 . 5 ) of MIX over ATT . We also note that adjusting the MIX score for different imputation qualities leads to a small improvement in statistical power at imputed SNPs ( see Table S1 ) . An important aspect in disease scoring statistics is to assess their performance when the causal SNP is untyped and , due to various reasons ( e . g . not present in the reference panel ) , cannot be imputed . To address this scenario we randomly picked 100 , 000 autosomal SNPs and simulated case-control phenotypes for R = 1 . 5 using the methodology described above . For all the SNPs we evaluated the statistics at 40 SNPs in the neighborhood of the simulated SNP and , for each score , computed the maximum statistic in this region by either masking the simulated causal SNP or by including it in the computation of the maximum . Results in Table 2 show that , both when the causal SNP is present in the data and when it is absent from the data , the MIX score outperforms all the other scores in terms of power . We again used the Armitage trend test with correction for genome-wide ancestry as the baseline for our analyses . We also considered a SNP association score conditioned on local ancestry , as well as an admixture score that associates the local ancestry to the continuous phenotype with genome-wide ancestry as a covariate . ( There is no analogue to a case-only admixture score for quantitative traits ) . As in the dichotomous case , we summed the SNP association score conditioned on local ancestry with the admixture score to produce a χ2 ( 2dof ) score , but show below that the higher degrees of freedom lead to a reduction in statistical power . Finally , we considered a χ2 ( 1dof ) heterogeneity score that tests for a difference in effect size conditional on African or European ancestry , by comparing a model that allows different effect sizes to a model with a uniform effect size ( see Methods ) . Analogous to simulations of dichotomous phenotypes , for 100 , 000 randomly chosen SNPs we used CARe genotypes and simulated phenotypes for 2 , 000 samples based on a null model or a causal model with effect sizes ε = 0 . 05 , 0 . 10 , 0 . 20 ( see Methods ) . We compared 4 scores: Armitage trend test with correction for genome-wide ancestry ( QATT ) , SNP association conditioned on local ancestry ( QSNP1 ) , local ancestry admixture association ( QADM ) , and sum of QSNP1 and QADM ( QSUM ) . All of these are χ2 ( 1dof ) scores , except for QSUM which is χ2 ( 2dof ) . Results are displayed in Table 5 ( Typed Genotypes ) . We display results averaged across all SNPs , and averaged across SNPs with CEU versus YRI allele frequency difference of at least 0 . 4 , roughly the top decile of differentiation . We see that the Armitage trend test ( QATT ) outperforms the other scores . Here , there is no advantage to incorporating admixture scores , since no case-only score is available and since summing SNP and admixture association scores ( QSUM ) loses statistical power due to increased degrees of freedom . We finally note that the heterogeneity score that tests for differences in effect size for African versus European local ancestry ( QHET ) attained average values between 0 . 99–1 . 01 ( data not shown ) , exactly as expected since simulated phenotypes did not involve heterogeneity in effect size . As in the case of the dichotomous phenotypes , we masked the 100 , 000 SNPs followed by imputation and we applied the above scores on the imputed genotypes ( see Table 5 ( Imputed Genotypes ) ) . Although the overall statistical power decreases for all scores because of imputation errors , we note that as before , QATT outperforms the other scores in terms of statistical power . We evaluated the above scores using data from two quantitative phenotypes from CARe , LDL and HDL cholesterol , for which associations at several loci have previously been reported . Results for genotyped and imputed SNPs in the region are displayed in Table S4 . As in our simulations , the QATT score yields the best performance the majority of the time . However , one aspect of the results is of particular interest . Multiple LDL and HDL SNPs on chromosome 2 produce strong admixture association ( QADM ) scores , with the result that the χ2 ( 2 dof ) QSUM score outperforms the χ2 ( 1 dof ) ATT score . We point out that the presence of multiple causal variants , or alternatively an untyped/unimputed variant with large allele frequency differentiation , may invalidate the assumptions made by the QATT score and lead to poor performance . This suggests that the QSUM score can be of value in a minority of instances where strong admixture associations exist . We caution that in such cases an additional multiple hypothesis testing correction may be needed and that the QSNP1 score conditioned on local ancestry will be needed for localization [38] .
Incorporating admixture association signals into GWAS of admixed populations is likely to be particularly informative for diseases for which risk differs depending on ancestry . Cardiovascular disease ( CVD ) is a prime example , as African ancestry is associated to higher CVD mortality and to CVD risk factors such as hypertension , serum lipid levels and left ventricular hypertrophy [39]–[41] . Other diseases for which African ancestry is a risk factor include prostate cancer , diabetic retinopathy , lupus and uterine fibroids [42]–[45] . Although we have focused here on African Americans , our methods are broadly applicable to other admixed populations . By analyzing real and simulated case-control phenotypes , we have shown that the MIX score , which incorporates both SNP and admixture association signals , yields a significant increase in statistical power over commonly used scores such as the Armitage trend test with correction for global ancestry . For randomly ascertained quantitative traits , in contrast to case-control phenotypes there is no case-only admixture score and thus no benefit from joint modeling of admixture and SNP association . Thus , for quantitative phenotypes , in general , the QATT score yields higher statistical power than other compared scores . Therefore , we recommend the use of MIX and QATT scores for dichotomous and quantitative traits , respectively , in future GWAS in admixed populations . However , we note that in various scenarios ( e . g . , multiple causal variants , heterogeneous effects , absence of the causal variant from the typed or imputed markers ) assumptions made by the MIX and QATT may be invalid and using them can lead to poor performance . To this extent , we recommend that special consideration be given to regions with high signals of admixture association , in which the SUM and QSUM scores may produce higher association signals than MIX and QATT . As a future direction , we note that an improved score for non-randomly ascertained quantitative traits could potentially be developed , which would generalize both the MIX score for dichotomous traits and the QATT score for randomly ascertained quantitative traits . As GWAS in European populations have demonstrated , association statistics need not be limited to SNPs that have been genotyped , because imputation algorithms that we and others have developed can be used to infer the genotypes of untyped SNPs by making use of haplotype information from HapMap . Our methods also perform well in the setting of imputation , when the causal SNP is not genotyped . As future work we consider the extension of our likelihood based scores to fully account for imputation uncertainty , where a promising direction is to define the likelihood as a full integration over the missing data given the observed data and the parameters of the model [46] , [47] . Our results using simulated phenotypes show that , although benefiting from a reduced multiple-hypothesis testing burden , the admixture scoring yields lower power for finding associations when compared to SNP association scoring . An explanation is the limited number of SNPs that show high allelic differentiation among the ancestral populations ( e . g . , in our simulations only 7 . 6% of the SNPs have an allelic differentiation greater than 0 . 4 between Europeans and Africans ) . However , we note that the question of whether there exists a combined SNP and admixture score that benefits from reduced multiple hypothesis testing for the admixture component of the score is an important open question that requires further consideration . While this paper focuses on frequentist approaches for disease scoring in admixed populations , we mention that joint modeling of admixture and SNP association signals could be developed in a Bayesian framework [48] . For example , SNPs that lie in regions of high admixture signals could be given a higher prior of association with phenotype . We expect this type of approach to provide added value especially in regions with multiple independent causal variants in which region-based scores could yield increased signal over marginal SNP scores . Although in this work we have focused on African Americans , in theory our approaches can be extended to other admixed populations such as Latino populations , which inherit ancestry from up to three continental ancestral ( European , Native American and African ) populations . The approaches presented in this work can be extended to three-way admixed populations either by considering one ancestry versus the rest strategy or by jointly modeling the three ancestry odds ratios so that a single SNP odds ratio would lead to implied ancestry odds ratios for each ancestry . However , we caution that in the context of Latino populations , more work is needed to assess the performance and possible biases of the local ancestry estimates and its potential effects on methods that incorporate admixture and case-control signals into disease scoring statistics . A final consideration is in fine-mapping causal loci . Here the availability of samples—or chromosomal segments—of distinct ancestry is valuable [38] for localization of the causal variant . We note that the HET score could be used in localizing the causal variant under the hypothesis of no heterogeneity across populations; recent studies have provided empirical support for this hypothesis [49] . Importantly , by comparing MIX and SUM score the question whether the admixture signal is fully explained by the SNP odds ratio can be assessed . An important open question and future research direction is designing optimal algorithms for cross-population fine mapping that leverage the different LD patterns among the chromosomal segments of distinct ancestry .
The CARe project has been approved by the Committee on the Use of Humans as Experimental Subjects ( COUHES ) of the Massachusetts Institute of Technology , and by the Institutional Review Boards of each of the nine parent cohorts . Affymetrix 6 . 0 genotyping and QC filtering of African-American samples from the CARe cardiovascular consortium was performed as described previously [25] . After QC filtering for each of ARIC , CARDIA , CFS , JHS and MESA cohorts and subsequent merging , 8 , 367 samples and 770 , 390 SNPs remained . To limit relatedness among samples we restricted all analyses to a subset of 6 , 209 samples in which all pairs have genome-wide relatedness of 0 . 10 or less ( inferred using the smartrel program in EIGENSOFT 3 . 0; see Web Resources ) . We merged CARe genotype data with genotype data from the HapMap3 project [18] . HapMap3 samples had been genotyped on both Affymetrix 6 . 0 and Illumina 1M chips . We excluded SNPs that did not pass QC in HapMap3 , as well as A/T and C/G SNPs to avoid allele complementarity issues , leaving 556 , 698 SNPs for further analysis . ( We note that HAPMIX accuracy is insensitive to the number of SNPs , if at least 250 , 000 SNPs are used [23] . ) When run in default mode , HAPMIX outputs local ancestry estimates as the expected probability of 0 , 1 or 2 copies of European ancestry at each SNP ( see ref . [23] and Web Resources ) . However , HAPMIX can also be run in a mode that outputs the inferred joint distribution of local ancestry and allele value , so as to resolve the “het-het” case ( both genotype and local ancestry heterozygous ) . In order to obtain integer estimates of local ancestry , one approach is to simply round the probabilities , which however can lead to biased estimates in regions with limited SNP coverage . We chose an alternative approach that does not produce these types of biases: sampling from the probabilities for 0 , 1 or 2 European chromosomes at each position . Results in this mode are highly concordant with the default mode , producing correlations of 100% in genome-wide ancestry and 98 . 8% in local ancestry . We selected a random subset of 100 , 000 autosomal SNPs . For each SNP , we simulated phenotypes for R = 1 . 0 ( null model ) and R = 1 . 2 , 1 . 5 , 2 . 0 ( causal models ) . For the null model , we chose random subsets of 1 , 000 cases and 1 , 000 controls . For causal models , we chose a random subset of 1 , 000 controls , and then chose 1 , 000 cases from the remaining samples so that samples with 0:1:2 reference alleles have relative probabilities 1:R:R2 of being chosen . We incorporate the observed variance of the average local ancestry across the genome assuming that the average local ancestry at each SNP is normally distributed with mean and standard deviation , where is the ancestry odds ratio . We estimate empirically and set , where is the empirical mean across the genome of the per SNP average local ancestry estimates . Then , the admixture likelihood becomes . We can then compute a χ2 ( 1dof ) statistic , ADMGC , that incorporates the empirical variance and in the ADM score as:In a similar manner we can replace with in the admixture component of the MIX likelihood to compute a new χ2 ( 1dof ) statistic MIXGC , that incorporates the empirical variance of the average local ancestry: Many of the likelihoods defined above require a multidimensional optimization . The number of parameters optimized in the likelihoods is 3 for the SNP1 score , 1 for the ADM score , 3 for the MIX score and 4 for the HET score . ( The HET score can be reduced to two independent 2-parameter optimizations by considering cases and controls separately . ) For the ADM score , Newton’s method was used . For the SNP1 , MIX and HET scores , Brent’s algorithm was used ( GSL software library implementation; see Web Resources ) . The maximization is performed in one dimension over each parameter in turn , repeating for each parameter until the algorithm converges . In rare instances , extreme variation in the slope of the log likelihood as a function of odds ratio can cause the algorithm to not converge; in this situations a simple binary search is used . We employed the widely used MACH [51] imputation method to infer genotypes at untyped SNPs in the CARe African American samples . As reference haplotypes we used either the cosmopolitan approach of providing all the CEU and YRI haplotypes from HapMap Phase 3 data [18] , or a local ancestry aware approach in which , for every locus in every sample , we provided either YRI , CEU+YRI , CEU reference haplotypes to MACH according to the number of copies of YRI ( 2/1/0 ) inferred by HAPMIX . We note that the local ancestry aware approach has been previously shown to boost imputation accuracy in admixed populations [24 . 35] . For both strategies we ran MACH in two steps , first by training the model parameters on a random sample of 200 individuals with the rounds parameter set to 50 followed by imputation of all the samples using the trained model from step 1 . Importantly , we note that the local ancestry aware approach can be applied as an add-on to any imputation method . Even when the true odds ratio is the same across populations , different imputation quality across the segments with different ancestries can lead to different estimates for the allelic odds ratios in European versus African segments . We account for this by adjusting the observed allelic odds ratios in the SNP1 and the MIX scores as follows . Following a derivation similar to [52] ( see Text S1 ) we show that the expected observed odds ratio at an imputed causal SNP with true odds ratio R , is a function of R , the imputation accuracy ( as measured by the correlation between true and imputed SNP ) , and the allele frequency: Unfortunately we do not know the true genotypes , and thus cannot compute the correlation between the true and imputed genotypes . However , reliable estimates for this correlation have been proposed; here we chose to use MACH estimates shown to produce robust estimates of imputation quality [53] . To estimate ancestry-specific imputation error rates , we restrict the computation to segments containing both alleles from that ancestry . Given that imputation accuracies are estimated directly from the data , depend on the term R and the allele frequencies . Then , the likelihood term from the MIX admixture association score becomes . As in the previous version of the score , the optimization is done over the three free terms and . SNP1 score is updated in a similar fashion . We randomly selected 100 , 000 autosomal SNPs and simulated phenotypes as described above using R = 1 . 5 . For all the compared scores , we computed the maximum statistic over all SNP across a region centered on the SNP of interest ( taking the 20 SNPs upstream and 20 SNPs downstream ) . We computed the maximum of the statistics either over 41 SNPs by including the simulated causal SNP or over 40 SNPs by ignoring the statistics at the simulated causal SNP . Case-control phenotypes for coronary heart disease ( CHD ) and type 2 diabetes ( T2D ) were ascertained as described previously [25] . In each case , phenotypes were available for only a subset of the five CARe cohorts . Restricting to 6 , 209 unrelated individuals as defined above , we analyzed 929 cases and 4 , 150 controls for T2D , and 179 cases and 3 , 328 controls for CHD . For every analyzed SNP we performed imputation within a region of 10Mb centered on the SNP of interest using the MACH imputation method under the local ancestry aware framework . We assessed the scoring statistics at all SNPs within 100Kb of the SNPs of interest . The FGFR2 locus has been associated with breast cancer in women of European and Asian descent [36] , and further fine mapping in African-American women has identified SNP rs2981578 as showing the highest signal of association [36] , [37] . We analyzed data from a GWAS including 5 , 761 unrelated African-American women from 11 epidemiological studies: The Multiethnic Cohort Study ( MEC ) [54] , The Los Angeles component of The Women’s Contraceptive and Reproductive Experiences ( CARE ) cohort [55] , The Women’s Circle of Health Study ( WCHS ) [56] , The San Francisco Bay Area Breast Cancer Study ( SFBC ) [57] , The Northern California Breast Cancer Family Registry ( NC-BCFR ) [58] , [59] , The Carolina Breast Cancer Study ( CBCS ) [60] , The Prostate , Lung , Colorectal , and Ovarian Cancer Screening Trial ( PLCO ) [61] , The Nashville Breast Health Study ( NBHS ) [62] , The Wake Forest University Breast Cancer Study ( WFBC ) [63] . Informed consent was obtained from all subjects . Detailed information about the design and organization of each study will be provided elsewhere ( C . Haiman and colleagues , unpublished data ) . Genotyping was conducted using the Illumina Human1M-Duo BeadChip . A total of 1 , 043 , 036 SNPs were kept after QC filtering . Imputation was performed using the MACH software , providing as reference all the haplotypes of CEU and YRI HapMap Phase 2 panels ) . We focused our analysis on all the typed or imputed SNPs , 251 in total , located 100Kb upstream and downstream of SNP rs2981578 . For each of 100 , 000 autosomal SNPs , we simulated phenotypes for ε = 0 ( null model ) and ε = 0 . 05 , 0 . 10 , 0 . 20 ( causal model ) , using a random subset of 2 , 000 samples . For the null model , phenotypes were sampled from a normal distribution with mean 0 and variance 1 . For the causal model , the mean was shifted to 0:ε:2ε for 0:1:2 reference alleles . In each case , we subtracted out the overall phenotypic mean . LDL and HDL cholesterol phenotypes in CARe samples were ascertained as described previously . We analyzed 5 , 801 samples for LDL and 5 , 946 samples for HDL for which phenotypic data were available , restricting to 6 , 209 unrelated individuals as defined above . For every analyzed SNP we performed imputation within a region of 10Mb centered on the SNP of interest using the MACH imputation method under the local ancestry aware framework . We assessed the scoring statistics at all SNPs within 100Kb of the SNPs of interest . http://www . hsph . harvard . edu/faculty/alkes-price/software/ ( MIXSCORE software ) http://www . hsph . harvard . edu/faculty/alkes-price/software/ ( EIGENSOFT software ) http://www . stats . ox . ac . uk/~myers/software . html ( HAPMIX software )
|
This paper presents improved methodologies for the analysis of genome-wide association studies in admixed populations , which are populations that came about by the mixing of two or more distant continental populations over a few hundred years ( e . g . , African Americans or Latinos ) . Studies of admixed populations offer the promise of capturing additional genetic diversity compared to studies over homogeneous populations such as Europeans . In admixed populations , correlation between genetic variants exists both at a fine scale in the ancestral populations and at a coarse scale due to chromosomal segments of distinct ancestry . Disease association statistics in admixed populations have previously considered either one or the other type of correlation , but not both . In this work we develop novel statistical methods that account for both types of genetic correlation , and we show that the combined approach attains greater statistical power than that achieved by applying either approach separately . We provide analysis of simulated and real data from major studies performed in African-American men and women to show the improvement obtained by our methods over the standard methods for analyzing association studies in admixed populations .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"genetics",
"and",
"genomics/gene",
"discovery",
"genetics",
"and",
"genomics/complex",
"traits",
"genetics",
"and",
"genomics/disease",
"models",
"genetics",
"and",
"genomics/genetics",
"of",
"disease",
"mathematics/statistics",
"genetics",
"and",
"genomics/medical",
"genetics",
"genetics",
"and",
"genomics/population",
"genetics"
] |
2011
|
Enhanced Statistical Tests for GWAS in Admixed Populations: Assessment using African Americans from CARe and a Breast Cancer Consortium
|
Our knowledge and control of the pathogenesis induced by the filariae remain limited due to experimental obstacles presented by parasitic nematode biology and the lack of selective prophylactic or curative drugs . Here we thought to investigate the role of neutrophils in the host innate immune response to the infection caused by the Litomosoides sigmodontis murine model of human filariasis using mice harboring a gain-of-function mutation of the chemokine receptor CXCR4 and characterized by a profound blood neutropenia ( Cxcr4+/1013 ) . We provided manifold evidence emphasizing the major role of neutrophils in the control of the early stages of infection occurring in the skin . Firstly , we uncovered that the filarial parasitic success was dramatically decreased in Cxcr4+/1013 mice upon subcutaneous delivery of the infective stages of filariae ( infective larvae , L3 ) . This protection was linked to a larger number of neutrophils constitutively present in the skin of the mutant mice herein characterized as compared to wild type ( wt ) mice . Indeed , the parasitic success in Cxcr4+/1013 mice was normalized either upon depleting neutrophils , including the pool in the skin , or bypassing the skin via the intravenous infection of L3 . Second , extending these observations to wt mice we found that subcutaneous delivery of L3 elicited an increase of neutrophils in the skin . Finally , living L3 larvae were able to promote in both wt and mutant mice , an oxidative burst response and the release of neutrophil extracellular traps ( NET ) . This response of neutrophils , which is adapted to the large size of the L3 infective stages , likely directly contributes to the anti-parasitic strategies implemented by the host . Collectively , our results are demonstrating the contribution of neutrophils in early anti-filarial host responses through their capacity to undertake different anti-filarial strategies such as oxidative burst , degranulation and NETosis .
Filarial nematodes constitute a large group of human pathogens ( i . e . Onchocerca volvulus , Brugia malayi , Brugia timori , Wuchereria bancrofti , Loa loa , Mansonella spp . ) infecting around 150 million people throughout the tropics with more than 1 . 5 billion at risk of infection . Filariases remain a health issue , as no selective and efficient treatments are able to prevent and eliminate filarial infections in the exposed and infected populations [1] . Early stages of infection are common to filarial nematodes with the infective stages of filariae ( L3 ) being delivered in the skin of the host by blood feeding arthropods [2] . However later in their life cycle , depending on the filarial species , adult stages settle in their preferred tissues for maturation/reproduction/release of microfilariae , strongly suggesting different migration paths for the larvae . Consequently , adult filariae reside in connective tissues ( i . e . dermis , subcutaneous tissue , aponeuroses , tendons ) , blood and lymphatic vessels , or coelomic cavities such as the pleural cavity in the case of the rodent filarial L . sigmodontis [3] , which is the focus of our study . L . sigmodontis is a widely used experimental model in which infective larvae migrate from the skin through the lymphatic system before ending up in the pleural cavity [4] . Almost 70–80% of the skin-inoculated L3 will not reach their maturation niches [4 , 5] , supportive of early defense mechanisms implemented in the skin by the mice . The innate immune response to nematodes can involve host cell populations such as eosinophils , neutrophils , mast cells and activated macrophages [6–9] . A clear consensus has emerged from the L . sigmodontis model regarding the role of eosinophils in the early protective immunity against L3 , induced within two days in infected mice immunized with irradiated L3 [10–13] . Several lines of evidence from the various filarial models including L . sigmodontis also indicate that neutrophils might contribute to the host immunity , but at late stages of the infection notably through the control of the adult worms and blood-circulating microfilariae [14–16] . Although it has not been reported that an innate immune response dominated by neutrophils might kill incoming L3 , neutrophils are recruited to sites of parasitic nematode entry in Nippostrongylus brasiliensis , Heligmosomoides polygyrus , Brugia pahangi [17–20] , and L . sigmodontis infections [12] . Additionally , neutrophils were reported to contribute to macrophage-dependent resistance mechanisms against Strongyloides stercoralis and macrophages-dependent resolution of tissue damage induced by Nippostrongylus brasiliensis [21–23] . Moreover , along with their essential role in responses to microbial pathogens , neutrophil activation and recruitment could be attributed to the endobacteria Wolbachia harbored by some filarial parasites [24–26] . Interestingly , the resistance mechanisms of the host in early defense against L3 also target these symbionts [26 , 27] . Neutrophil homeostasis , which is maintained by a balance between granulopoiesis in the bone marrow ( BM ) and migration between blood and tissues , is finely tuned by the chemokine system . The essential CXCL12/CXCR4 chemokine-chemokine receptor pair [28–30] , which induces typical activation of the Gαi protein- and β-arrestin-dependent pathways [31–33] regulates with other chemokine receptors , hematopoiesis and the lymphoid and peripheral trafficking of neutrophil and lymphocyte subsets [33] . The CXCL12/CXCR4 axis is notably critical for the release of neutrophils from the BM [34] and lungs [35] . CXCR4 engagement also promotes the migration of neutrophils from inflamed skin into draining lymph nodes , a process thought to participate in the control of pathogens through initiation of immune responses or conversely in the spreading of infection [36–38] . We have previously shown that a CXCL12-dependent cell response is associated with the “resistant phenotype” of C57BL/6 mice to L . sigmodontis infection [39] raising the possibility that this chemokine participates in the host immune-mediated resistance mechanisms . Here we thought to examine this possibility in C57BL/6 mutant mice , which harbor an inherited heterozygous Cxcr4 mutation engendering a gain-of-CXCR4 function [Cxcr4+/1013] , an anomaly linked to the rare immunodeficiency WHIM disorder [40 , 41] . As a consequence , [Cxcr4+/1013] mice exhibit a peripheral leukopenia affecting blood lymphocytes and neutrophils as do patients . This leukopenia is transiently reversed in mice [41] and patients [42] upon inhibition of CXCR4 with the selective antagonist AMD3100 thus demonstrating the causal role of the gain-of-CXCR4 function . Interestingly , neutropenia of the mutant Cxcr4+/1013 mice occurs in the context of normal maturation of the granulocyte lineage [41] in support of the fact that transient normalization of blood counts upon CXCR4 blockade or patient’s infections [43] likely arises from disturbed neutrophil trafficking rather than from a production defect [41] . Our findings reveal a dramatic blockade of L . sigmodontis infection in mutant mice , which manifests early in the skin of mice inoculated with infective L3 and is related to a higher number of neutrophils in the skin of mutant mice as compared to their wt counterpart . The identified host-resistance mechanisms such as the increase of neutrophils infiltrating the skin , the oxidative burst response , and the release of NET promoted by L3 were extended to wt mice demonstrating that neutrophils are important contributors of the early host resistance mechanisms against the nematode .
Infective larvae were subcutaneously injected into wild type ( wt ) and Cxcr4+/1013 C57BL/6 mice and were recovered in the pleural cavity 20 days post-inoculation ( p . i . ) , when the filarial load is still high and before it decreases , as observed in the resistant background of the mice ( S1 Fig ) . The mean number of worms recovered from the wt mice ( 9 . 72 ± 0 . 65 SEM , Fig 1 ) was similar to the one previously reported for resistant C57BL/6 mice at this stage of the infection [44–47] . As compared , the mean filarial load dramatically dropped in the mutant mice to minute levels ( 3 ± 0 . 26 SEM ) corresponding to a 70% decrease in the filarial parasites ( Fig 1 ) . An enumeration performed earlier at day 8 p . i . , after arrival of most of the L3 larvae in wt mice pleural cavity ( 11 ± 0 . 5 SEM ) , indicated a similarly small number of worms recovered from the mutant mice ( 4 . 4 ± 0 . 7 SEM ) ( S2 Fig ) . These results excluded the possibility that a killing of L3 larvae in the pleural cavity of mutant mice may account for the decreased filarial recovery in these mice . Upon arrival of the L3 in the pleural cavity , mice mount a cellular response directed against the worms , which reaches a peak 30 days p . i . when larvae are molting into young adults [39 , 46 , 48] . In this context , resistant C57BL/6 mice are characterized by having a higher increase of pleural exudate cells ( PleCs ) as compared to susceptible mice [39] . PleCs counts were increased upon infection in both type of mice but neither the levels nor the composition was significantly different between control wt and Cxcr4+/1013 mice ( S3 Fig ) at steady state and upon infection . These results are suggesting that the strongest resistance of mutant mice to filarial infection was not associated with any change in PleCs . We then investigated whether the leukocyte populations in the blood circulation were differentially affected in the mutant mice upon infection . Lymphocytes counts of wt and mutant animals were not significantly modified by 20 days post infection and the mutant mice remained lymphopenic ( Fig 2A , left panel ) throughout the infection up to 70 days p . i . In contrast , both eosinophil and neutrophils blood counts , also constitutively diminished in mutant mice , were almost normalized 20 days p . i . ( Fig 2A , middle and right panels ) . Time course analysis of the circulating neutrophils counts in Cxcr4+/1013 mice throughout subcutaneous ( SC ) infection of L3 revealed an increase , which tended to normalize the circulating neutrophil counts from 20 days followed by a progressive decay back to initial levels at day 70 p . i . ( Fig 2B ) . While a slight increase of blood neutrophil counts was observed in the first five days following injection ( days 1–5 ) , the levels remained significantly different between both wt and mutant mice ( see S1 Table ) . We further investigated further whether this late and sustained neutrophilia was related to infection as an indirect consequence of the SC delivery of L3 and/or directly via filarial immunomodulatory molecules present in whole body crude extracts ( WBE ) . To do so we counted blood neutrophils in Cxcr4+/1013 mice throughout 20 days p . i . in four different experimental settings: inoculation of L3 ( L3 ) versus L3 crude extracts ( WBE ) and SC inoculation compared to intravenous ( IV ) one . Neutrophils counts in Cxcr4+/1013 mice shown in Fig 2C were expressed as a ratio to wt mice counts , in order to illustrate the trend toward normalization ( raw data are presented in S2 Table ) . Inoculation of L3 in Cxcr4+/1013 mice via the IV route ( IV L3 ) was associated with an increase in circulating neutrophils , the levels of which tended toward a transient normalization between days 1 and 3 p . i . A similar response was observed upon IV inoculation of L3 crude extracts ( IV WBE ) indicating that this increase is independent upon the mobility of the larvae . As previously shown , SC injection with infective larvae ( SC L3 ) promoted a long-lasting normalization of neutrophils counts at 20 days p . i . , which persisted for at least 40 days ( Fig 2B and 2C ) . SC inoculation of crude extracts ( SC WBE ) only promoted a transient normalization , which occurred much earlier at 5 days p . i . ( Fig 2C ) . Altogether these results indicated that L3 either through immunomodulatory molecules ( WBE ) and/or their motility have , independently of the inoculation site , the potency to induce a transient neutrophilia in the mutant mice reaching neutrophils counts comparable to those in wt mice . However long-term normalization of blood neutrophil counts in the mutant mice over is strictly dependent upon the inoculation of live larvae into the skin . We then compared the parasitic success upon IV and SC inoculation of Cxcr4+/1013 mice at 20 days p . i . Interestingly , we unraveled that upon IV inoculation filarial larvae also ended up in the pleural cavity . Strikingly , IV inoculation reversed the resistant phenotype of Cxcr4+/1013 mice observed upon SC inoculation ( Fig 1 ) with a recovery of larvae in the pleural cavity reaching the levels obtained in the wt mice ( Fig 3 ) . Moreover this increase of the parasitic success was extended to wt mice , which displayed twice more filariae recovered in the pleural cavity upon IV inoculation as compared to SC inoculation ( Fig 3 ) . Collectively , these data support on one hand the concept that L3 migration from the skin to the pleural cavity would stand a blood stage . On the other hand , they strongly suggest that the mechanisms underlying the enhanced resistance of the Cxcr4+/1013 mice to infection are acting before larvae reach the bloodstream , and more generally bring evidence in support of the skin’s major role in the host defense against filariae . We thought to compare the steady state amounts of eosinophils , macrophages , mast cells and neutrophils in the skin of wt or mutant mice to those reached in both mice upon 6 hours p . i . considering the potential role of these cells in parasite clearance at early stages of the infection . The constitutive levels of eosinophils were similar in the skin of control wt and Cxcr4+/1013 mice whatever the layer either the dermis/hypodermis or the subcutaneous loose connective tissue ( SLCT ) . Additionally , upon infection , eosinophil levels were similarly increased ( 5-fold ) in both layers of the mutant and wt mice ( S4A Fig ) . The numbers of macrophages and mast cells , degranulated or not , were also found to be in the same range in Cxcr4+/1013 and wt mice , but not significantly increased upon infection in any of the two models ( S4B and S4C Fig ) . In contrast , the number of resident neutrophils constitutively present in the dermis-hypodermis of Cxcr4+/1013 mice was significantly higher than in wt mice ( 12 . 4 ± 1 SEM vs 4 . 79 ± 0 . 6 SEM neutrophils per mm2 of skin respectively , p < 0 . 001 ) ( Fig 4 ) . While an increasing trend was also observed in SLC layer , it was not significant . Filarial infection promoted a significant recruitment of neutrophils in the dermis/hypodermis of both mice , resulting in comparable levels of neutrophils at 6 hours p . i . in Cxcr4+/1013 and wt mice ( 28 ± 3 . 7 SEM and 23 . 4 ± 2 . 7 SEM neutrophils per mm2 of skin respectively ) . Cxcl12 , which is expressed in the dermal stroma [49 , 50] , was found at higher levels in mutant mice dermis as compared to their wt counterparts ( S5 Fig ) . Additionally , Cxcl12 levels were markedly increased in the dermis of both Cxcr4+/1013 and wt mice 6 hours p . i . ( S5 Fig ) . Thus the steady state levels of the chemokine and their increase after infection are mirroring the variation in dermal neutrophil numbers herein reported in mice strengthening the possible interplay between both processes . Significantly , this increase in skin neutrophil recruitment observed in the 6 hours following filarial infection was concomitant with an increase of blood neutrophil numbers in both wt and Cxcr4+/1013 mice , of 2 . 9 and 2 . 7 fold respectively ( S6 Fig ) . This phenomenon observed both in mutant and wt mice is highly suggestive of a neutrophil release in the bloodstream triggered upon immune sensing of the filariae by skin-resident cells . The possibility that the high number of neutrophils present in the skin of mutant mice contributed to the resistant phenotype of these mice was further investigated by depleting neutrophils prior to infection . To do so , mice neutrophils were selectively depleted by a classical method based on a single intraperitoneal injection of anti-Ly6G antibodies 6 hours before SC inoculation of filarial larvae . Either the anti-Ly6G 1A8 clone targeting more specifically neutrophils ( i . e . ly6G+ cells ) or the NIMP-R14 clone that can also deplete monocytes ( i . e . ly6C+ cells ) [51–53] . Depletion of circulating neutrophils was already effective at the time of infection ( Fig 5A , D0 ) . Then at 10 days p . i . , blood neutrophil levels in wt or Cxcr4+/1013 mice recovered values in the same range than those of control mice ( i . e . infected mice pre-injected with PBS ) . Importantly , depletion was also dramatic on skin-resident neutrophils as early as 6 hours after anti-Ly6G antibody injection leading to a 5 to 10 fold decrease compared with constitutive levels in wt and Cxcr4+/1013 mice , respectively ( Fig 5B ) . During the time-span of the experiment , lymphocyte numbers remained unchanged between control and neutrophils-depleted wt or mutant mice . We then compared the number of worms recovered in the pleural cavity 20 days p . i . in control and neutrophils-depleted wt or mutant mice . Strikingly , neutrophil depletion with anti-Ly6G antibodies whatever the antibody used ( 1A8 or NIMP-R14 clones ) , dramatically increased the number of filariae recovered in the pleural cavity of Cxcr4+/1013 mice to levels similar to those reached in the wt mice ( Fig 5C ) . In contrast , neutrophil depletion had no significant effect on the worm burden in wt mice although some increasing trend was observed upon treatment with the NIMP-R14 clone , which also affects ly6C+ monocytes ( Fig 5C , right panel ) . These results indicated that the sole depletion of neutrophils , also affecting skin-resident ones , reverted the mutant mice to a wt phenotype , supporting a critical role for this population in the resistance of Cxcr4+/1013 mice to filarial infection . Collectively , these data identified a critical role for skin neutrophils in the host protective mechanism against primary L3 infection . The resistant phenotype displayed by Cxcr4+/1013 mice might underlie , either that neutrophils must be in elevated numbers at the point of infection , and/or that mutant mice neutrophils have a heightened activation state or modified functions . From previous analyses of neutrophils derived from patients suffering from the WHIM syndrome [54] it is anticipated that Cxcr4+/1013neutrophils would be also prone to an enhanced Cxcl12-dependent chemotaxis [41] . Chemotaxis assays performed on BM-isolated neutrophils indeed confirmed that neutrophils derived from Cxcr4+/1013 mice were more sensitive to Cxcl12 than their wt counterparts ( S7 Fig ) . This increased Cxcl12-induced chemotaxis was similarly displayed by neutrophils isolated from infected mutant mice 20 days p . i ( S7 Fig ) . Moreover , neutrophils derived from wt and mutant mice , infected or not , were found to express equivalent levels of Cxcr4 and Cxcr2 receptors and displayed comparable chemotactic response to the Cxcr2 agonist Cxcl1 ( S7 Fig ) . These results thus indicated that Cxcr4+/1013 mice derived neutrophils display in vitro a selective enhanced responsiveness to Cxcl12-induced chemotaxis as a consequence of the gain-of-CXCR4-function they harbor . We then investigated in vitro other neutrophil functions such as the capacity of these cells to produce reactive oxygen species ( ROS ) and to undergo NETosis . ROS production was assessed with a nitroblue tetrazolium ( NBT ) assay . Results indicated that both wt and mutant mice-derived neutrophils were able to reduce the colorless NBT to black deposits within the cells indicating that the production of the superoxide anion ( O2- ) was not altered in neutrophils derived from the Cxcr4+/1013 mice ( Fig 6A ) . In contrast , quantification of the oxidative burst revealed that ROS production by neutrophils from both wt and Cxcr4+/1013 mice was increased upon exposure to L3 and significantly heightened in neutrophils derived from Cxcr4+/1013 mice as compared with wt mice ones ( Fig 6A , right panel ) . This oxidative burst in response to L3 was associated with a significant decrease in the intracellular content of myeloperoxidase ( MPO ) in neutrophils from both wt and Cxcr4+/1013 mice ( Fig 6B , left panel ) , which was mirrored by an increase of the neutrophil-released MPO ( Fig 6B , right panel ) . Interestingly , neutrophils from Cxcr4+/1013 mice released higher levels of MPO to the medium , indicating a stronger susceptibility to the degranulation process induced by exposure to L3 ( Fig 6B , right panel ) . Extracellular release of MPO , neutrophil elastase , histones and chromatin decorated with numerous active proteins are the signature of NETs formation . We therefore investigated the potential induction of NETosis upon neutrophils exposure to L3 larvae by quantifying both cell viability and extracellular DNA release using the cell-impermeable SYTOX dye ( Fig 6C , left panel ) . Quantification indicated that upon 4 hours exposure with L3 , neutrophils purified from Cxcr4+/1013 mice were significantly more engaged into a cell-death program than those from wt mice ( Fig 6C , right panel ) . The presence of extracellular DNA in culture supernatants increased with the exposure time to L3 larvae ( from 4 to 36 hours ) and was more marked in cultures with Cxcr4+/1013 BM-derived neutrophils suggesting that these cells are more prone to release NETs ( Fig 6D ) . Examination of immunofluorescence slides of skin from infected and control mice indeed revealed granular structures co-staining with MPO , neutrophil elastase and DAPI , which hallmark NETs structures [55] , strongly supporting the existence of neutrophils undergoing NETosis in the skin of mutant mice infected with L3 larvae ( S8 Fig ) .
We provide compelling evidence revealing the potential for skin neutrophils to contribute in the early host defense against primary L . sigmodontis infection by using the CXCR4-gain-of-function Cxcr4+/1013 neutropenic mice . These mice found to be highly resistant to primary infection allowed us to demonstrate that ( i ) mice harbor an elevated numbers of dermal neutrophils in steady state the depletion of which , prior to infection , ablates the resistant phenotype of the mutant mice , and that ( ii ) mutant mice-derived neutrophils produce increased amount of ROS mediators and NETs upon L3 larvae stimulation , as compared with wt mice-derived neutrophils . Further , an original setting of intravenous delivery resulted in a strong enhancement of the parasitic burden , which strikingly reached similar levels in both wt and mutant mice . This demonstrates that most of the incoming L3 larvae including in wt mice are destroyed in the skin upon typical SC infection thus strengthening evidence in favor of the essential role of the skin in the protective mechanism against primary L3 infection . Of note , the rate of larvae recovery in both mice upon IV delivery did not reach 100% success . One could argue that some L3 within the inoculums may be unviable , but it mainly suggests that the control of the filarial load does not only take place in the skin . Previous studies indeed revealed that among the 70% of larvae that are leaving the skin within the first day , only one third are reaching the pleural cavity [56] with some L3 being found in pulmonary alveoli and arteries [56] suggesting that the lung could act as a clearance organ [57] . Of importance and in line with this , the IV infection setting experimentally supported for the first time the Wenk’s early hypothesis that the infective larvae might pass through the cardiopulmonary blood system to reach the pleural cavity [4] . We found that mutant mice display a heightened steady state number of neutrophils in the skin . Although the mechanism of this increase remains to be determined , the enhanced expression of Cxcl12 in the dermis of mutant mice combined with the increased chemotaxis toward the chemokine displayed by the mutant mice-derived neutrophils likely contribute to this process . Numerous evidences support the hypothesis of a major contribution of this neutrophil resident pool in the protective mechanism of the mutant mice against primary L3 infection . First , the selective and transient depletion of neutrophils prior infection , which also affected neutrophils in the skin , resulted in normalization of the rate of larvae recovery in the pleural cavity of mutant mice; second , recruitment of neutrophils in the skin of wt and mutant mice 6 hours p . i . led to similar levels in both mice and was mirrored by a neutrophilia in both animals; third , infection via IV route bypassed the protective mechanism of the mutant mice and; fourth , mutant mice display normal steady state numbers of eosinophils , mast cells and macrophages in the skin , suggesting that these innate immune cells do not participate , at least quantitatively , in the resistant phenotype of the mutant mice . These results however do not rule out the potential qualitative contribution of these cells in the control of filarial infections notably in the setting up of adaptive immune response . The protective role of eosinophils was indeed described in mice vaccinated with irradiated L3 larvae [12 , 13] and that of mast cells in filarial-infected CCL17 deficient mice [7] . The blood leukopenia affecting the mutant mice , which constitutes a finely tuned sensor of the leukocytosis induced by the infection , affected neutrophils and eosinophils that were found to transiently reach normal levels upon infection . Although this process is not related to the early protective mechanism of mutant mice it supports the general concept that leukocyte trafficking promoted by the infection might impact the adaptive immune response against filarial infection . Moreover it indicates the potency of filarial-antigens to induce leukocytosis as demonstrated upon injection of filarial crude extract . Such early neutrophilia ( 2 hours p . i ) may be related to excretory-secretory ( ES ) proteins released by filarial nematodes and which originate from the oesophageal glands , the anterior sensory glands ( amphids ) , the posterior sensory glands ( phasmids ) , the secretory pore , the hypodermis through transcuticular secretion but also from exosome release [58] . Notably , we have recently reported that the excretory-secretory proteins from L3 differ quantitatively and qualitatively from the other stages of L . sigmodontis [59] . We investigated the possibility that the early protective mechanism of mutant mice might involve a higher activation of skin resident neutrophils as a consequence of the Cxcr4-gain of function . We indeed found an abnormally enhanced migration of the mutant-derived neutrophils toward CXCL12 , thus extending our previous observation made in this mouse model [41] . Moreover , with regard to the large length of the L3 larvae ( i . e . about 750 μm ) [58 , 60] we sought to investigate the potential of neutrophils in inducing NETs that can be released by in response to microbe size-sensing [61] . The NETs were reported to capture Gram-positive and Gram-negative bacteria , fungi , and viruses [62–64] as well as Apicomplexa parasites , Leishmania , Eimeria , Plasmodium , and Toxoplasma [65] and the Strongyloides stercoralis nematode [66] . NET formation or NETosis is a gradual process notably involving ROS generation , transport of MPO and the extracellular release of chromatin [67–69] . Both wt and mutant mice-derived neutrophils were able to generate ROS mediators upon L3 stimulation likely causing NETosis . Importantly , mutant mice-derived neutrophils display heightened levels of ROS content and increased release of MPO and extracellular DNA when cultured with L3 and were more prompt to death as compared to their wt counterpart . Whether the filarial excretory-secretory proteins and/or the bacterial content ( i . e . Wolbachia ) also contribute to activation of neutrophils , including triggering of NETosis in response to live L3 cannot be excluded . Further work is needed to investigate the mechanisms by which NETs contribute in L3 larval entrapment and killing , which can be indirect as suggested by recent work on the Strongyloides stercoralis nematode model [66] . In summary , our findings suggest that the higher responsiveness exhibited by Cxcr4+/1013 neutrophils may be critical in the mutant mice-protective antifilarial response thus accounting for the fact that neutrophil depletion in control wt mice does not affect parasitic burden . Hence , they emphasize the potential of skin resident neutrophils to contribute in the early host defense against filarial infections when they are sufficiently activated and present in significant numbers prior infection . Such increase of the proportion of more functionally active neutrophils maybe envisioned in a wild type host environment during inflammatory processes and the subsequent abnormal increase of aged neutrophils that represent an overly active subset with notably enhanced propensities to form NETs [70 , 71] or in the context of co-infections . Indeed , the immune responses evoked by bacterial or viral infections are associated with changes in the local cytokine environment and increases in the numbers and the activation state of neutrophils that could have implications for the outcome of filarial infections in light of recent findings highlighting interplay between host immune responses against parasites and virus in the course of co-infection ( reviewed in [72] ) . Finally , our results identifying an immuno-modulatory role for the CXCL12/CXCR4 pathway could have implications for the development of therapies that should be further studied in filarial nematodes-infected individuals .
All experimental procedures were carried out in strict accordance with the EU Directive 2010/63/UE and the relevant national legislation , namely the French “Décret no 2013–118 , 1er février 2013 , Ministère de l’Agriculture , de l’Agroalimentaire et de la Forêt” . National license number 75–1415 approved animal experiments: protocols were approved by the ethical committee of the Museum National d’Histoire Naturelle ( Comité Cuvier , License: 68–002 ) and by the “Direction départementale de la cohésion sociale et de la protection des populations” ( DDCSPP ) ( No . C75-05-15 ) . The filaria L . sigmodontis were maintained in our laboratory and infective third-stage larvae ( L3 ) were recovered by dissection of the mite vector Ornithonyssus bacoti [73] as previously described [74 , 75] . Wt and Cxcr4+/1013 mice were bred in our animal facilities on a 12-hours light/dark cycle . All data were obtained from 8–12-week-old mice . Mice were genotyped by PCR on genomic DNA as previously described [41] using specific oligonucleotide primers to distinguish the mutant and the endogenous Cxcr4 allele . Mouse infections were carried out by SC inoculation of 40 infective L3 in 200 μL of RPMI 1640 ( Eurobio , France ) into the left lumbar area of mice . Only when indicated , were mouse infections carried out by an intravenous inoculation of 40 infective L3 in 50 μL of RPMI 1640 into the caudal vein . In some experiments where indicated , mice were injected with crude extracts of L . sigmodontis worms which were obtained from the homogenization and sonication of L3 recovered from infected mites; the L3 derived crude extract concentration was determined by a Bradford assay ( Pierce ) following the manufacturer’s instructions . After centrifugation , the supernatant was collected and the protein content was determined by the modified Bradford method ( BCA Protein Assay kit , Pierce ) . Mice then received 10 μg of crude extract either subcutaneously in 200 μL RPMI 1640 or intravenously in 50 μL of RPMI 1640 as above . Mice were sacrificed at 6 hours , 20 and 80 days post-inoculation ( p . i . ) as indicated below . Blood smears and blood cell counts were performed before and throughout the challenge at different time points . Blood smears were obtained from the tail vein , stained with May-Grünwald-Giemsa ( VWR , France ) and the percentages of the different leukocyte populations were determined for 200 cells . Total blood cell counts were determined from tail blood mixed with 1% acetic solution ( 1:5 vol ) using a hemocytometer ( KOVA Glasstic Slide ) . At the indicated time p . i . , mice were anaesthetized and sacrificed by terminal bleeding . Blood was allowed to clot for 30 min at room temperature then centrifuged and sera were collected and kept at -20°C until further use . The pleural cavity was washed with 10 mL of cold phosphate-buffered saline ( PBS , EUROBIO , France ) , as previously described [12] . The infiltrating cells and the worms were collected from the pleural wash for further analysis . Pleural washes were then frozen at -20°C until further use . Worms were fixed in toto with 4% formaldehyde in cold PBS to avoid body shrinkage and the gender and development stages of the worms were analyzed by light microscopy . PleCs were centrifuged at 250 g for 8 min at 4°C , resuspended in 2 ml RPMI supplemented with 2% foetal calf serum ( FCS , EUROBIO , France ) and then counted in PBS/ 0 . 04% trypan blue ( Sigma-Aldrich ) using a haemocytometer ( KOVA Glasstic Slide ) . Proportions of the different leukocyte populations were determined by flow cytometry using the following rat anti-mouse antibodies: anti-F4/80-APC ( clone BM8 ) , anti-SiglecF-PE ( clone E50-2440 ) , anti-Ly6G-FITC ( clone RB6-8C5 ) , anti-CD3-PE ( clone 145-2C11 ) , and anti B220-FITC ( clone RA3-6B2 ) . All antibodies were purchased from eBioscience except the anti-SiglecF-PE ( BD Pharmingen ) and used at a 1/40 dilution . Flow cytometry analysis was performed using a FACSVerse flow cytometer running the FACSuite software ( BD Biosciences ) . Acquisition and analyses were performed as described in S2 Fig . Two days prior to the challenge , mice were anesthetised and depilated by means of Veet hair removal cream on an area of flank skin in the left inguinal region over the inguinal lymph node . For the challenge , wt and Cxcr4+/1013 mice were then inoculated either with 40 infective larvae or RPMI as a control and sacrificed 6 hours p . i . Skin sections of 1 cm2 were taken from the inoculation site , fixed overnight in 4% paraformaldehyde ( PFA , VWR , France ) and embedded in paraffin . Paraffin sections ( 5 μm ) were stained with hematoxylin-eosin ( H&E , VWR , France ) or toluidine blue ( VWR , France ) for the detection of eosinophils or mast cells respectively . A modified Hematoxylin Eosin ( HE ) staining , including an alkaline eosin solution-staining step ( pH 8 . 4 ) for 20 seconds has been selectively chosen to minimize background tissue eosin staining . For immunohistochemistry analyses , i . e . macrophages and neutrophils visualization , deparaffinized slides were incubated with the anti-F4/80 ( clone CI:A3-1 , Abdbiotech ) or anti-NIMP-R14 ( clone NIMP-R14 , Abcam ) antibodies respectively in 3% PBS-BSA overnight at 4°C . A peroxidase-based system was used for detection . For immunofluorescence staining , i . e . NETs , deparaffinized slides were incubated with anti-MPO ( 3 . 3 μg/mL , AF3667 R&D , Lille , France ) or anti-Elastase ( ELA ) ( 5 μg/mL , Ab68672 , Abcam , Paris , France ) antibodies followed by staining with goat anti-rabbit Alexa Fluor 488 or anti-rat Alexa Fluor 594 ( 20 μg/mL , A-11034 and A-11007 , respectively , Life Technologies , Saint-Aubin , France ) . DNA was visualized upon DAPI counterstaining . Images were acquired using the digital slide scanner HPF-NanoZoomer RS2 . 0 ( Hamamatsu ) coupled to a high definition 3-CCD digital camera . In some cases , images were acquired using an Olympus DP72 camera coupled to an Olympus BX63 motorized microscope running the cellSens Dimension ( v 1 . 9 ) software . Neutrophils were depleted by a single intra-peritoneal injection of either 0 . 25 mg of anti-Ly6G clone NIMP-R14 ( AdipoGen ) or 0 . 5 mg of anti-Ly6G clone 1A8 ( BioXCell ) 6 hours prior to infection ( performed as described above in the Material and Methods Section 2 ) . The two antibodies have been used extensively to deplete neutrophils in mice [51–53] with a potentially better selectivity for neutrophils of the 1A8 clone with regard to other Gr-1+ cells [51] . The effect of the treatment was assessed on blood and skin-resident neutrophils . Circulating neutrophil depletion was confirmed by blood smears and differential blood cell counts from 6 hours to 20 days post-injection of the depleting antibody . The effect of the depletion on skin-resident neutrophils was assessed by immunohistological analyses ( see above , Material and Methods section 5 ) using the anti-NIMP-R14 antibody on paraffin-embedded skin sections of control or injected mice 6 and 12 hours post-injection of the depleting antibody . Chemotaxis assays were performed on BM leukocytes using a Transwell assay as previously described [34] . Leukocytes were isolated from the BM of wt and Cxcr4+/1013 control or infected ( at 20 days p . i . ) mice and resuspended in assay buffer ( HBSS medium supplemented with 20mM HEPES and 0 . 5% bovine serum albumin ) . Leukocytes ( 3 x 106 cells ) were then added to the upper chamber of transwell filters ( Millipore , 3μm pore diameter ) that were placed in 24-well cell culture plates containing 300μl assay buffer with or without the indicated chemokines . In some experiments cells were pre-incubated at 37°C with Cxcl12 10 μM and AMD3100 200 μM for 30 min before being placed in the upper transwell chamber to confirm the specificity of the Cxcl12-dependent migration upon Cxcr4 . Chambers were then incubated for 60 min at 37°C with 5% CO2 and the cells that migrated to the bottom chamber were recovered and stained with anti-ly6G-FITC antibody for flow cytometry analysis . The number of neutrophils that migrated into the bottom chamber was determined by a flow cytometer ( BD Biosciences ) with relative cell counts obtained by acquiring events for a set time period of 30s . Chemotactic indexes were then calculated by dividing the number of neutrophils that were counted in the chemokine stimulated well by the number of neutrophil that were obtained in the non-stimulated well . Cell surface expression of Cxcr2 and Cxcr4 was also assessed on BM leukocytes from naïve and infected mice at 20 d . p . i . Neutrophils were identified with an anti-ly6G-FITC antibody and stained with either anti-Cxcr2-APC ( clone 242216 , R&D ) and anti-Cxcr4-PE ( clone 247506 , R&D ) antibodies or their respective control isotypes . Samples were processed by a FACSVerse flow cytometer ( BD Biosciences ) and analyzed using FACS Suite software . BM leukocytes from naive wt and Cxcr4+/1013 control mice were subjected to a discontinuous 72–64% Percoll density gradient centrifugation in 15 mL Falcon tubes for 30 minutes at 4°C . Mature neutrophils were collected at the 72–64% interface ( purity > 93% ) , washed three times in cold PBS then resuspended in PBS at the working concentration of 106 cells/mL . The following assays were performed: The production of superoxide anions ( O2- ) was investigated using a microscopic NBT assay . In brief , 105 mature neutrophils per mouse were incubated in an 8-well Lab-Tek Chamber Slide for 1 hour at 37°C allowing the cells to attach to the plate . The supernatant was then discarded and 100 μL of NBT ( 1 mg/mL ) was added to each well . Cells were incubated for 1 hour at 37°C and slides were analyzed by optical microscopy ( Olympus BX63 microscope ) . The slide was counterstained 3 min with a 10% Giemsa solution then water washed . Black NBT deposits within the cells revealed the production of O2- . ROS content was measured via a dichloro-dihydro-fluorescin diacetate ( DCFH-DA ) assay ( Sigma-Aldrich ) . Briefly , 105 mature neutrophils were incubated with 125 μM DCFH-DA in a 96-well cell culture plate for 15 minutes at 37°C to allow its entry into cells where it get converted in DCFH . Cells were then left unstimulated or stimulated with 20 L3 for 15 minutes at 37°C . Neutrophil oxidative response changes DCFH to green fluorescent DCF . The reaction was stopped by placing cells at 4°C . L3 were removed from the wells and cells were stained with anti-Ly6G-PECy7 antibody ( eBioscience , clone RB6-8C5 , dilution 1/40 ) . Samples were processed by a flow cytometer ( BD Biosciences ) and analyzed using FACS Suite software . ROS content was expressed as an activation ratio by dividing the FITC mean fluorescence intensity ( MFI ) of neutrophils from L3 stimulated conditions by that from unstimulated cells . MPO content was measured in wt or Cxcr4+/1013 mature mouse neutrophils ( 105 cells ) either unstimulated or stimulated with 20 L3 in a 96-well cell culture plate at 37°C for 1 hour . Cells were then permeabilized with Saponin-PBS ( 0 . 2% BSA + 0 . 05% saponin in PBS ) and stained with anti-Ly6G-PECy7 ( eBioscience , clone RB6-8C5 , dilution 1/40 ) and anti-MPO-FITC ( Hycult Biotech , clone 8F4 , dilution 1/40 ) . Samples were processed by a flow cytometer and analyzed using FACS Suite software . MPO content was expressed as MFI . Neutrophil necrosis and NETosis were quantified by immunofluorescence light microscopy in wt or Cxcr4+/1013 mature mouse neutrophils ( 105 cells ) either left unstimulated or stimulated with 20 L3 in 24-well culture plates at 37°C for 4 hour . SYTOX-Green ( Thermo Fisher Scientific ) , which does not enter into live cells , was then added ( dilution 1:15000 ) for the detection of either extracellular DNA indicative of NETs release or intracellular DNA of necrotic neutrophils . Viable cells appear as non-fluorescent cells whereas both necrotic and NETs releasing neutrophils become fluorescent . Necrotic neutrophils are characterized by compromised membrane integrity and pluri-lobed nuclei whereas NETs releasing neutrophils display a halo ( DNA area > 500 μm2 ) corresponding to chromatin decondensation and NETs release [60 , 76] . Images were acquired using an Olympus DP72 camera coupled to an Olympus BX63 motorized microscope running the cellSens Dimension ( v 1 . 9 ) software . Cells were counted and the numbers of necrotic , NETs releasing and living cells were determined . Extracellular DNA was also quantified: mouse neutrophils ( 1x105 ) were cultured with 10 infective larvae or 100 nM phorbolmyristate acetate ( PMA ) for 4 , 24 and 36 h in vitro . The culture supernatants were collected and the extracellular DNA was quantified using the Quant-iT PicoGreen dsDNA Assay kit ( Life Technologies ) , following manufacturer instructions . Samples were cultured with the PicoGreen reagent ( 1:1 dilution ) for 5 min . The samples were measured with a spectrofluorometer at 480 nm excitation and 520 nm emission . A DNA standard curve was used to determine the concentration of free DNA from samples Statistical analysis was carried out using GraphPad Prism v5 . Sample size , normality ( Shapiro-Wilk test ) and homoscedasticity ( Bartlett’s test ) were tested prior to further analysis . Data from separate experiments were pooled where possible . Most of the time one-way ANOVA analyses followed by a Bonferroni post-hoc test were performed . T-tests were used when comparing filarial load and lymphocytes , eosinophils and neutrophils cell counts at 20 days p . i . between wt and Cxcr4+/1013 infected mice . One-way ANOVAs with repeated measures were used to compare neutrophils cell counts in the blood of infected mice during the course of the infection . Significance was defined as *: p < 0 . 05; **: p < 0 . 01 and ***: p < 0 . 001 .
|
Filariases are chronic debilitating diseases caused by parasitic nematodes affecting more than 150 million people worldwide . None of the current drugs are selective , neither able to eliminate the parasites nor to prevent new infections once the drug pressure has waned . Therefore , blocking the entry and the migration of the infective larvae ( L3 ) could be an efficient way to control the infection . In the present study we investigated the early interaction between the host and the L . sigmodontis murine filariasis with a focus on the neutrophils in the innate host responses . We uncovered a key role of neutrophils in the control of infection provided by the CXCR4-gain-of-function mice ( Cxcr4+/1013 ) that display a blood neutropenia as well as an accumulation of skin-infiltrating neutrophils . Overall , we reveal that in the early phase of filariasis , i . e . after L3 are delivered into the skin and before they reach their site for reproduction , neutrophils are critical elements of the host innate protective response arsenal . A better understanding of their indirect and/or effector role ( s ) may provide mechanistic clues to host factors implicated in parasitic nematode entry and potentially lead to the identification of new drug targets .
|
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2016
|
Neutropenic Mice Provide Insight into the Role of Skin-Infiltrating Neutrophils in the Host Protective Immunity against Filarial Infective Larvae
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The impact of exposure to multiple pathogens concurrently or consecutively on immune function is unclear . Here , immune responses induced by combinations of the bacterium Salmonella Typhimurium ( STm ) and the helminth Nippostrongylus brasiliensis ( Nb ) , which causes a murine hookworm infection and an experimental porin protein vaccine against STm , were examined . Mice infected with both STm and Nb induced similar numbers of Th1 and Th2 lymphocytes compared with singly infected mice , as determined by flow cytometry , although lower levels of secreted Th2 , but not Th1 cytokines were detected by ELISA after re-stimulation of splenocytes . Furthermore , the density of FoxP3+ T cells in the T zone of co-infected mice was lower compared to mice that only received Nb , but was greater than those that received STm . This reflected the intermediate levels of IL-10 detected from splenocytes . Co-infection compromised clearance of both pathogens , with worms still detectable in mice weeks after they were cleared in the control group . Despite altered control of bacterial and helminth colonization in co-infected mice , robust extrafollicular Th1 and Th2-reflecting immunoglobulin-switching profiles were detected , with IgG2a , IgG1 and IgE plasma cells all detected in parallel . Whilst extrafollicular antibody responses were maintained in the first weeks after co-infection , the GC response was less than that in mice infected with Nb only . Nb infection resulted in some abrogation of the longer-term development of anti-STm IgG responses . This suggested that prior Nb infection may modulate the induction of protective antibody responses to vaccination . To assess this we immunized mice with porins , which confer protection in an antibody-dependent manner , before challenging with STm . Mice that had resolved a Nb infection prior to immunization induced less anti-porin IgG and had compromised protection against infection . These findings demonstrate that co-infection can radically alter the development of protective immunity during natural infection and in response to immunization .
Models examining immunity to experimental infections primarily focus on responses to a single pathogen or vaccine in an immunologically naïve host . Such studies have shaped our understanding of how infections develop and are controlled . However , in reality individuals are exposed to multiple pathogens , often concurrently during their life-time [1]–[4] . Whether infectious history may influence the type of immune response mounted by the host to a new vaccine pathogen has not been extensively explored . Of particular significance is that regions endemic for non-typhoidal Salmonella ( NTS ) serovars , such as Salmonella Typhimurium ( STm ) [5] are also endemic for parasitic nematode infections , such as hookworm [6] . This provides opportunities for concomitant STm and helminth infections to develop . In distinct forms both infections can be modelled in a murine system . Nippostrongylus brasiliensis ( Nb ) , a natural parasite of rats is used as a model infection in mice of human hookworm disease . Nb induces Th2 features such as interleukin 4 ( IL-4 ) , IL-13 , IgG1 and IgE [7]–[14] . Infection with Nb in mice is self-limiting , with worms cleared from BALB/c mice in a narrow period of 9–11 days post-infection when mice are infected with the common dose of 500–750 L3 larvae [7] , [11] . Having these defined kinetics for clearance enables identification of factors that interfere with immunity . Exposure to an additional agent after resolution of Nb infection enables any lasting influence of helminth infection on host immunity to the second antigen to be identified . Clearance of STm infections require Th1-mediated immunity , characterized by the induction of Interferon ( IFN ) γ and IgG2a in mice [15]–[18] . A mutation in the Slc11a1/Nramp gene renders mouse strains , such as BALB/c , hyper-susceptible to virulent strains of STm whilst attenuated strains are cleared gradually . For the latter strains , such as the AroA-deficient STm strain SL3261 , clearance is achieved 1–2 months after infection with a typical dose of 5×105 bacteria administered systemically [19]–[21] . A striking component of host immunity to attenuated STm is a rapid and extensive extrafollicular ( EF ) antibody response with switching to IgG2a and IgG2b , which occurs without parallel germinal centre ( GC ) induction [19] . While B cells and antibody are wholly dispensable for controlling primary STm murine infections [20] , [22] , [23] , the presence of antibody to STm prior to infection can be protective [19] , [22] , [24] , [25] . Indeed , we have found that immunization with purified porins induced antibody sufficient to protect against subsequent STm infection [26] , with IgG augmenting the protection afforded by IgM . Thus , factors that influence IgG responses are likely to affect protection and immunization with porin proteins . Helminth infections may modulate responses to other pathogens [27]–[29] and to vaccination [30]–[33] , although the nature of these influences have not been fully elucidated . Furthermore , such studies have often not addressed the impact of co-infection on the immunological response to each infection . In this study , we investigated the development and efficacy of immune responses after immunization with combinations of Nb , STm and porins . Our data shows that co-infection with Nb and STm impairs clearance of both pathogens . Whilst some changes in cytokine patterns were observed , the pathogen-associated pattern of isotype-switching was conserved so that specific IgG1 , IgE and IgG2a responses all developed in parallel . Furthermore , prior Nb infection impaired the protective efficacy of porin immunization indicating a longer-term impact of helminth infection , suggesting these effects were not necessarily dependent upon an active infection . These data not only further our understanding of the relationship between host and pathogen and the mechanisms used to regulate immune function , but also identify the need to consider the impact of infectious history on the host's capacity to implement protective immunity .
Specific pathogen-free 6–8 week BALB/c mice were obtained from the animal facility at the University of Cape Town , South Africa . All animal procedures were carried out under Protocol 012-006 which was approved by the Animal Research Ethics Committee at the University of Cape Town . All procedures were also conducted in strict accordance with the South African code of practice for laboratory animal procedures . STm SL3261 is an attenuated strain of STm SL1344 [34] . Nb was maintained through passage in rats . Outer membrane preparations from STm were generated by 2% ( vol/vol ) Triton X-100 extraction [19] . Purified porins from STm ( strain ATCC 14028 ) were generated as described previously [26] , [35] using SDS and FPLC and suspended in PBS in 0 . 1% ( wt/vol ) SDS . Nb total antigen preparations were generated by snap-freezing L3 stage larvae and homogenizing by sonication . Antigen was stored until use at −80°C . Mice were infected intraperitoneally ( i . p . ) with 5×105 STm SL3261 . Tissue bacterial burdens were determined by direct culturing . Mice were infected subcutaneously ( s . c . ) with 500 Nb L3 larvae . Adult worm burdens were determined by counting in the gut lumen under a dissecting microscope as previously described [9] . Where stated , mice were immunized i . p . with 20 µg porins in PBS . Opsonizing bacteria with antisera was performed as described previously [19] , [26] . A single serum was used per mouse and sera were heat-inactivated at 56°C for 0 . 5 h to inactivate complement . Bacteria ( 2 . 5×106/mL ) and sera ( 1∶100 ) were mixed for 1 h before infection . Bacterial viability and lack of agglutination were confirmed by plating . Splenic single cell suspensions were prepared and red cells were lysed with ACK lysing buffer ( Gibco Life Technologies ) . Cells were initially blocked prior to staining with anti-CD16/32 antibody before surface staining for 20 min at 4°C with CD3-FITC ( Clone 145-2C11 ) , CD4-PerCP Cy 5 . 5 ( Clone RM4-5 ) . Intracellular cytokine staining was performed by ex-vivo re-stimulation as described previously [15] . Briefly , 5×106 splenocytes were plated with 1 µg/ml anti-CD28 ( clone 37 . 51 ) and re-stimulated in a pre-coated well with anti-CD3 ( 10 µg/ml ) ( clone 145-2C11 ) . Cells were incubated for 2 . 5 h followed by 2 . 5 h with GolgiStop ( BD Biosciences ) . Cells were then surface stained , fixed and permeabilised with Cytofix/Cytoperm Plus for 20 min at 4°C before intracellular cytokine staining using IL-13 PE ( Clone JES10-5A2 ) or IFNγ-APC ( Clone XMG1 . 2 ) or isotype controls ( all BD Biosciences ) . Cells were acquired using a FACSCalibur ( BD Biosciences ) and analysed using FlowJo Software . Immunohistology was performed on frozen sections as described previously [19] , [36] with tissues frozen in liquid nitrogen . CD3 , IgG2a , IgG1 ( Clone LO-MG1-2 ) , IgE ( Clone LO-ME-2 ) and FoxP3 cells were detected using rat anti-mouse antibodies in conjunction with biotinylated rabbit anti-rat immunoglobulins . Signal was developed using streptavidin ABComplex alkaline phosphatase ( DakoCytomation ) with naphthol AS-MX phosphate with Fast Blue salt and levamisole . Sheep anti-mouse IgD binding was detected using horseradish peroxidase ( HRP ) -conjugated donkey anti-sheep immunoglobulins with Diaminobenzidine ( Sigma Aldrich ) . Hamster anti-mouse CD3 binding was detected using goat anti-hamster IgG followed by HRP-conjugated donkey anti-sheep immunoglobulins with Diaminobenzidine . The area of the spleen occupied by germinal centres and cells per square millimeter were calculated using a point-counting technique as described previously [37] . Enzyme-linked immunosorbent assay ( ELISA ) was performed as described previously [19] . NUNC Maxisorp plates were coated overnight with antigen at 5 µg/ml in coating buffer . Plates were then blocked with 1% BSA before serum was added in PBS-0 . 05% Tween-20 and diluted stepwise . Bound antibodies were detected using alkaline-phosphatase conjugated , goat anti-mouse secondary antibodies ( Southern Biotech ) and Sigma-Fast p-nitrophenylphosphate ( Sigma Aldrich ) . The absorbance at ODλ405 nm was determined using an Emax microplate spectrophotometer ( Molecular Devices , Germany ) . Relative reciprocal titres were calculated by measuring the dilution at which the serum reached a defined ODλ405 nm . Splenocytes ( 2×105 ) were plated for 48–72 h with 1 µg/ml anti-CD28 ( Clone 37 . 51 ) and re-stimulated with either 10 µg/ml anti-CD3 ( Clone 145-2C11 ) which was pre-coated overnight or 10 µg/ml heat-killed STm . Heat-killed STm was prepared by heat inactivation at 72°C for 1 hour . Control wells were stimulated with anti-CD28 and PBS . Cytokines secreted into the supernatants were then measured using the appropriate ELISA Ready-Set-GO kit ( eBiosciences ) as per manufacturers' instructions . Briefly , plates were coated overnight with capture antibody , blocked for 1 h at room temperature with 2% fat-free milk in PBS , after which samples and standards were added overnight at 4°C . Biotinylated secondary antibodies were then added and signal detected using streptavidin-HRP and 3 , 3′ , 5 , 5′-tetramethylbenzidine solution before stopping with 1 M H3PO4 . The absorbance at ODλ450 nm ( background at ODλ540 nm ) was determined using a Versamax tunable microplate spectrophotometer ( Molecular Devices , Germany ) . The data is expressed as the mean plus one standard deviation . Significant differences were determined using the Mann-Whitney non-parametric two-tailed test using GraphPad Prism Version 5 . P≤0 . 05 was accepted as significant .
To assess whether synchronous administration of STm and Nb altered the kinetics of clearance , we infected WT mice with either 5×105 attenuated STm i . p . , 500 L3 Nb larvae s . c . , or both pathogens for 5 , 10 , 18 or 32 days ( Figure 1A ) . While equivalent bacterial numbers were found in the spleens and livers of STm and co-infected mice at day 5 post-infection , after this time bacterial numbers were consistently higher in co-infected mice ( Figure 1B ) . As expected intestinal worm burdens in Nb-only infected mice were largely cleared by day 10 . However , co-infected mice demonstrated persisting Nb infection up to 32 days . Thus , co-infection with STm and Nb impairs immunity to both pathogens . The impact of co-infection on pathogen clearance suggested perturbed type-specific immunity to each pathogen . The proportion and numbers of T cells from co-infected mice that produced IFNγ or IL-13 after anti-CD3 re-stimulation were largely similar to that seen after single STm or Nb infection respectively , at all time-points ( Figure 2A ) . As the capacity to induce pathogen-associated Th1 and Th2 cytokines was maintained , levels of secreted cytokines from splenocyte cultures were examined ( Figure 3A ) . After re-stimulation with anti-CD3 in the presence of anti-CD28 secreted levels of IFNγ were similar between STm-only and co-infected mice at all time-points examined , reflecting the intracellular cytokine staining . In contrast , levels of IL-4 and IL-13 were greatly reduced at times after co-infection compared with Nb-only infected mice ( Figure 3A ) . To examine if this reflected cytokine responses induced after re-stimulation with STm , splenocytes from day 10 infected mice were re-stimulated with heat-killed STm instead of anti-CD3 ( Figure 3B ) . Levels of IFNγ were similar in both STm-infected groups but there was an increase in IL-4 and IL-13 in the co-immunized group . Therefore , co-infection has little impact on the development of Th1 and Th2 cytokine-producing T cells but can modulate the levels of cytokines secreted . Cytokines from non-T cells can influence the functional response of T cells [38] . Therefore IFNγ or IL-13 expression in CD3−ve cells was assessed by flow cytometry ( Figure 2B ) . This showed that at all times after infection <1% of cells were positive for IFNγ or IL-13 . Furthermore , co-infection did not dramatically alter the cytokine pattern seen after single infection . In addition , we examined cytokine secretion by splenocytes from mice infected for 10 days which were cultured without stimulation ( Figure 3C ) . Cytokines were detected at lower levels than after anti-CD3 stimulation . IFNγ levels were similar in all groups except for the group that only received Nb , where they were lower . IL-4 , but not IL-13 , levels were reduced in co-infected mice compared to Nb only infected mice . Helminth infections are associated with the induction of T regulatory ( Treg ) cells [39]–[41] . Therefore , it is possible that co-infection could either augment or diminish Treg responses and the levels of associated IL-10 observed compared to each pathogen alone . Initially , levels of secreted IL-10 after re-stimulation with anti-CD3 were assessed by cytokine ELISA ( Figure 3A ) . This revealed that IL-10 was readily detected after Nb infection , but after STm infection the levels were similar to those of non-infected cultures . When IL-10 was examined after co-infection it was found to be intermediate between the STm-only and Nb-only infected mice on days 10 and 18 ( Figure 3A ) . Thus , the presence of STm was associated with a moderation in the levels of IL-10 detected in Nb-infected mice . Since Treg are significant sources of T cell-derived IL-10 , the impact of co-infection on Tregs was assessed . To do this we used immunohistochemistry to examine the frequency of FoxP3+ T cells in the T zones of infected mice on day 5 after infection , when pathogen burdens were similar in mice that received one or both pathogens ( Figure 1A ) . Reflecting the IL-10 results , the density of FoxP3+ cells in the T zones of co-infected mice was significantly lower relative to mice that were only infected with Nb , yet significantly higher than mice only infected with STm ( Figure 4 ) . STm and Nb induces immunoglobulin-switching to the Th1-reflecting IgG2a isotype or the Th2-reflecting isotypes IgG1 and IgE respectively . Furthermore , in this model of STm infection GC are absent early in the response , only becoming detectable later , when the infection has largely cleared [19] . As the direction of immunoglobulin-switching in mice can be influenced , in part by the cytokine milieu , it was possible that the altered cytokine environment during co-infection could alter the immunoglobulin-switching profile . In mice infected only with Nb , robust IgG1 and IgE EF plasma cell responses were detected , with IgG2a barely detectable by day 10 post-infection . This response was further characterised by an extensive GC response ( Figure 5A ) . Mice infected with STm alone developed a robust IgG2a response with few IgG1 and no IgE cells detected . This response developed in the near total absence of GC , which only developed late in the response ( Figure 5A ) . Surprisingly , in co-infected mice at days 10 and 32 post-infection a mixed switching-pattern was observed with IgG1 , IgG2a and IgE plasma cells all readily detectable in EF foci . Interestingly , in co-infected mice development of the robust Nb-associated GCs was abrogated , only becoming detectable at day 32 post-infection ( Figure 5A ) . Thus , the direction of B cell switching is maintained during co-infection , with the features of the response to each individual pathogen conserved . Antibody responses are dispensable for the control of primary STm infection in mice , although they play a central role in protecting against secondary infection and in vaccine responses [16] , . Thus , we examined the impact of co-infection on serum antibody responses to both pathogens to identify if long-term protective immunity may be compromised by co-infection with Nb . Serum antibody responses against outer membrane antigens of STm at days 10 , 18 and 32 post-infection were measured . Reflecting the early conserved EF plasma cell responses , IgM and IgG antibody titres were similar at days 10 and 18 post-infection in both groups that received STm ( Figure 5B ) . In contrast , at day 32 post-infection when GC are detected , IgM , IgG and IgG2a titres were lower in co-infected mice relative to STm-only infected mice , despite the GC response being comparable between the two groups ( Figure 5A ) . Measurement of specific antibody responses in Nb-only and co-infected mice revealed that serum anti-Nb IgM , IgG and IgG1 titres were reduced in co-infected mice relative to Nb-only infected animals at day 10 post-infection , when GC responses were diminished . However , by day 18 IgG responses were similar between the two groups as the GC start to become more established in co-infected animals ( Figure 5A ) . Thus co-infection can impact serum antibody titres to each pathogen but does not necessarily alter the switching profile . Since simultaneous infection with Nb and STm could impair host control towards each pathogen the influence of sequential exposure was assessed . WT mice were infected with Nb and at day 16 ( 6–7 days post worm-expulsion ) mice were challenged with STm for 5 or 25 days ( Figure 6 ) . While early control of STm was comparable between non-Nb primed and Nb-primed mice , prior Nb infection impaired control of STm at day 25 , reflecting our earlier observations ( Figure 1B ) . The impact of prior Nb infection on serum antibody responses to STm was then examined . This showed that antecedent Nb infection had no influence on anti-STm IgM titres but impaired anti-STm IgG titres at day 25 post-STm infection , with both IgG2a and IgG2b titres lower in mice previously infected with Nb ( Figure 6 ) . Thus prior Nb-infection can impair antibody switching to STm and the late control of subsequent STm infection . Antibody induced during infection can protect against secondary STm infection [16] , [19] , [20] , [22] , [23] . Previously , we demonstrated that immunization with the porin proteins OmpC , D and F ( collectively called porins ) was sufficient to protect mice from STm infection via an antibody-dependent mechanism [26] . This offered an opportunity to examine the impact of prior Nb infection on antibody-mediated control of STm infection . To do this , groups of mice either received no intervention before STm infection , or combinations of Nb and porins before STm challenge ( Figure 7A ) . After 5 days of infection splenic bacterial burdens were assessed . This showed that both porin-immunized groups had significantly lower bacterial numbers relative to non-immunized mice . Nevertheless , porin-immunized mice that had first been infected with Nb had a greater bacterial load than mice that had only received porins before infection ( Figure 7A ) , indicating that Nb-infection can impair the protection conferred by porin-immunization . Prior Nb infection may impact upon the success of immunization through at least two routes . Firstly , it may alter the activity of antibody , possibly through altering macrophage populations and their opsono-phagocytic capacity . Secondly , reduced benefit from immunization may reflect lower levels of antibody induction . To test the former , bacteria were opsonized with complement-inactivated sera from mice that had either been infected with STm or immunized with porins ( Figure 7B ) . Opsonized bacteria were then given to mice i . p . that had either received PBS or Nb 18 days previously and bacterial burdens were enumerated 5 days later . In each case bacterial numbers recovered from mice infected with opsonized bacteria were similar irrespective of whether they had previously been infected with Nb ( Figure 7B ) . This suggests there was no intrinsic defect in antibody-mediated control of STm infection in Nb-infected mice . Since ≥95% of the protection provided by anti-porin antibody is through the induction of IgG [26] , anti-porin antibody titres in mice immunized with porins after Nb infection were assessed . After immunization , porin-immunized Nb-infected mice had lower total anti-porin IgG serum titres than non-Nb infected counterparts ( Figure 7C ) . Analysis of the distinct IgG isotypes induced showed there was diminution in IgG1 titres , whereas there was a negligible effect on IgG2a ( Figure 7C ) . Therefore , prior Nb infection influences the titre of anti-porin IgG induced , but does not necessarily affect the efficacy of killing bacteria pre-opsonized with antibody . Finally , we looked to see if boosting with porins in Nb-infected mice could restore anti-porin antibody titres ( Figure 7D ) . WT mice given PBS or Nb were immunized 18 days later with porins and 18 days after this some mice received a second porin-immunization . Antibody responses were then assessed after 7 days . Anti-porin IgG titres were similar in both boosted groups , irrespective of whether they were previously infected with Nb . This suggests that the reduced antibody titres observed after porin immunization can be restored through engagement of B cell memory .
This work identifies the mutual impairment in immune regulation when infection with Nb and STm occurs concurrently , as marked by the delayed clearance of STm and expulsion of Nb . This impaired host control was not limited to synchronous challenge with both pathogens as prior infection with Nb also impacted on the host response to STm and impaired vaccine-mediated protection , despite adult worms having been cleared . This indicates that the persistence of viable adult worms is not necessary for this effect , as described previously [42] . This is important as it supports the concept that the impact of infectious history or co-infection may not always require direct physical association between the pathogens , as shown with bacterial microflora and Trichuris muris [43] . The delay in STm clearance after infection with Nb was only apparent at times when adaptive immunity controls infection . Nevertheless , an impairment in the induction of Th1 cells or secretion of IFNγ after anti-CD3 stimulation was not obvious , nor was there a change in the levels of IFNγ after culture of splenocytes without stimulation . This suggests the underlying reason for defective immunity is not one of a failure to mount an appropriate immune response but may relate to other factors , such as the inefficient migration of T cells or inappropriate interactions between T cells and macrophages . Otherwise , the elevated IL-10 production observed in co-infected mice may alter the kinetics of STm clearance . Relevant to this perhaps is the increase in FoxP3 cells detected in the T zone after co-infection compared to STm alone . This may alter the functionality of T cells and limit their ability to promote bacterial clearance . Furthermore , during co-infection diminished , but not absent , Th2 cytokine secretion was observed and IL-4 and IL-13 were detectable after stimulation of splenocytes with killed STm . Although Th1 and Th2-associated responses can co-develop [44] , in vivo and in vitro Th1 and Th2 cytokines have been shown to have opposing and suppressing activities [45] , [46] . In the context of this study , only lower Th2 cytokine production was observed and this was partial , suggesting some potential Th1 dominance here , possibly because STm directly colonizes the spleen . Furthermore , IL-4 and IL-13 were both detectable in the day 32 STm-only group , probably reflecting the function of these molecules in GC development [47] . Nevertheless , it may be the balance between Th1 and Th2-associated cytokines , rather than the absolute amounts of each cytokine considered in isolation , which is the important factor . Such a consideration is relevant in other systems such as experimental Leishmania major infection [48] . Alternatively , this may simply reflect this specific combination of pathogens . Other reasons may help account for the delayed control of STm infection . Levels of IL-4 and IL-13 were higher in non-stimulated splenocyte cultures from co-infected mice relative to mice only infected with STm . This may indicate other non-T cells contribute or impair clearance of STm through collaboration with T cells . Obvious candidates are innate lymphoid cells . Group 2 innate lymphoid cells ( ILC2s ) have been shown to release IL-13 in response to helminth infection [49] and recently the importance of ILC2s for the efficient development of Th2 cell responses during a Nb infection was demonstrated [38] . Therefore , in the same way that ILC2s can contribute positively to clearance of helminth infection they may impede the functioning of Th1 immunity . Many of the factors identified that potentially explain the failure to properly control STm infection in co-infected animals may also explain the delayed clearance of Nb . The cytokine most associated with efficient clearance of helminth infection is IL-13 . Therefore , the diminished IL-13 cytokine production detected , in combination with the elevated levels of IFNγ , may inhibit the rate of worm expulsion . Other reasons that could help account for the delayed clearance of Nb include reduced levels of IL-4 production or a reduced expression of the respective receptors for IL-4 and IL-13 on cells such as smooth muscle cells [11] , [50] or B cells [14] . The intermediate levels of FoxP3 T cells observed during co-infection may paradoxically have a negative effect on Nb clearance through enhancing Th1 inflammation and thus restricting the limited Th2 response induced from functioning . Furthermore , in responses to other helminths loss of MyD88 in mice can enhance protection [51] . Therefore it may be that strong engagement of this molecule , for instance through the multiple TLRs triggered by STm , inhibits immunity . These factors could collaborate to limit the efficacy of the Th2 response induced and diminish the efficiency of worm clearance . One possibility to consider is if the addition of exogenous Th2 cytokines would recapitulate the protective immunity to Nb seen in the absence of STm co-infection . We would expect not for two reasons . First , the presence of Nb during STm infection has virtually no impact on IFNγ production , suggesting that the pro-inflammatory cytokine profile and possibly its anti-Th2 activities would be retained . Second , relates to the technical complexity of delivering IL-4 or IL-13 sufficient within the host to overcome this inhibition . This can be achieved by delivering these cytokines through a pump or as a complex with antibodies [52] , although being able to provide this continuously and throughout infection would be challenging and prohibitive . Antibody plays an important role in preventing re-infection with STm and the appearance of antibody to the pathogen correlates with reduced risk of bacteraemia in infants , but in the mouse it is not required for the control of primary infection [53] . Furthermore , the Vi capsular polysaccharide vaccine against typhoid works via the induction of antibody [54] and provides equivalent protection in the first few years after administration as the live , attenuated vaccine . Thus understanding how optimal levels of antibody to STm are induced is important to understand the mechanisms of control to this pathogen . STm alone failed to induce GC in the first weeks of infection , whereas Nb-infection induced pronounced GC responses and co-infection resulted in the abrogation of this response to Nb . Therefore , whilst the direction of EF switching in the spleen is largely independent of the presence of a second pathogen , the development of GC responses is not . In vitro and in vivo IL-4 is essential for directing B cell switching to IgE [55] , but is dispensable for IgG1 switching [56] . Unexpectedly , EF IgG1 and IgE switching in the spleen was detectable at similar levels in both co-infected and Nb-only infected mice , despite reduced levels of IL-4 after co-infection . This implies that whilst IL-4 is essential for IgE switching , it may only be required at low levels . Furthermore , the augmented levels of Th2 cytokines during co-infection did not moderate the induction of IgG2a to STm . Therefore , both Th1 and Th2 cell priming and the characteristic class-switching profile is conserved and co-developed in the same responding secondary lymphoid tissue during co-infection . This is compatible with our earlier observations immunizing with soluble flagellin and flagellated bacteria where the direction of antibody-switching was conserved relative to the direction of T cell differentiation [15] , [57] . This is important as it indicates that only selective elements of immunity are influenced by the presence of infecting organisms . Despite EF switched plasma cell numbers being similar between co-infected mice and mice challenged with either STm or Nb there were some effects of co-infection on antibody titres . The anti-STm antibody response was similar between both STm-infected groups at day 18 , yet at day 32 , a time when antibody would largely originate from the GC , there was a clear reduction in IgM and switched antibody titres despite no difference in the splenic area occupied by GC . One possibility is that although the total number of GC may be similar between STm-only and co-infected mice at day 32 , some of the GC in co-infected mice are Nb-specific and others STm-specific . Alternatively , it may relate to the higher bacterial burdens seen on day 32 in co-infected mice , which can alter the kinetics of GC induction [19] or other factors may be involved . Such influences may also explain why there was a lasting influence of Nb infection on anti-STm IgG antibody titres when Nb infection preceded STm infection . This impact on antibody titres was not restricted to live STm as the antibody response to STm porins was also lower when administered after Nb infection . Lower IgG titres were associated with diminished protection from infection , whilst the capacity of Nb-infected mice to control infection with antibody-opsonized STm was similar to non-Nb infected controls . This suggests that the capacity of cells to phagocytose and kill STm is not influenced by Nb-infection since antibody does not kill STm via cell-free complement-mediated mechanisms in mice [58] . Anti-NTS IgG strongly correlates with lower risk of invasive NTS infection in humans [53] , and our study implies that the level of anti-porin IgG titres may influence protection . Whether co-infection with STm and helminths in humans is associated with altered IgG titres to STm and risk of infection needs to be addressed . Helminth infections in humans are associated with lower vaccine efficacy to subunit and live vaccines [30]–[33] , [59] . For instance , helminth infections are associated with diminished IgG and IgA antibody responses to cholera toxin B subunit [60] and to a live-attenuated oral cholera vaccine strain [61] . Interestingly , while treatment for helminth infection prior to vaccination can improve vaccine responses [61] our results indicate that prior infection could continue to have a detrimental effect on efficacy , although this that can be circumvented by antigen boosting . In summary , helminth infections can influence antibody responses to STm and subunit vaccines and this should be considered when translating findings generated in animal models into humans , particularly in regions endemic for helminths . Understanding how helminths influence antibody induction will help us identify how best to employ vital life-saving vaccines . As antibody titres to porins post-Nb infection reached normal levels after boosting it would suggest that exploiting memory B cell responses would be important for the efficacy of subunit vaccines in helminth-endemic regions .
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Vaccination studies in animal models have focused on understanding responses in young , previously naïve mice . In reality , humans are vaccinated or respond to infection in the context of a life-time of accumulated exposure to multiple , systemic infections and other vaccines , some of which are themselves attenuated live organisms . This is even more pronounced in areas that are endemic for infectious diseases . We wished to examine the impact infectious history can have on the immune response against infection and the efficacy of vaccination . To do this , we used two classes of pathogens that model clinically important invasive infections . One pathogen is the bacterium , Salmonella Typhimurium against which we have also developed an experimental porin vaccine , and the second is an invasive helminth , Nippostrongylus brasiliensis , that models aspects of hookworm infections . Our studies indicate that exposure to a second , unrelated pathogen can reduce the efficiency of immunity generated during natural infection and immunity generated after vaccination . These results are important as they help to identify potential strategies for improving immune-mediated control of infection and the success of vaccination in infection-endemic regions .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"biology",
"and",
"life",
"sciences",
"immunology",
"vaccination",
"and",
"immunization"
] |
2014
|
Natural and Vaccine-Mediated Immunity to Salmonella Typhimurium is Impaired by the Helminth Nippostrongylus brasiliensis
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The aim of this study was to investigate the influence of killer cell immunoglobulin-like receptor ( KIR ) genes and their human leucocyte antigen ( HLA ) ligands in the susceptibility of chronic Chagas disease . This case-control study enrolled 131 serologically-diagnosed Chagas disease patients ( 59 men and 72 women , mean age of 60 . 4 ± 9 . 8 years ) treated at the University Hospital of Londrina and the Chagas Disease Laboratory of the State University of Maringa . A control group was formed of 165 healthy individuals - spouses of patients or blood donors from the Regional Blood Bank in Maringa ( 84 men and 81 women , with a mean age of 59 . 0 ± 11 . 4 years ) . Genotyping of HLA and KIR was performed by PCR-SSOP . KIR2DS2-C1 in the absence of KIR2DL2 ( KIR2DS2+/2DL2-/C1+ ) was more frequent in Chagas patients ( P = 0 . 020; Pc = 0 . 040; OR = 2 . 14 ) and , in particular , those who manifested chronic chagasic cardiopathy—CCC ( P = 0 . 0002; Pc = 0 . 0004; OR = 6 . 64; 95% CI = 2 . 30–18 . 60 ) when compared to the control group , and when CCC group was compared to the patients without heart involvement ( P = 0 . 010; Pc = 0 . 020; OR = 3 . 97 ) . The combination pair KIR2DS2+/2DL2-/KIR2DL3+/C1+ was also positively associated with chronic chagasic cardiopathy . KIR2DL2 and KIR2DS2 were related to immunopathogenesis in Chagas disease . The combination of KIR2DS2 activating receptor with C1 ligand , in the absence of KIR2DL2 , may be related to a risk factor in the chronic Chagas disease and chronic chagasic cardiopathy .
Chagas disease , caused by the flagellate parasite Trypanosoma cruzi , currently affects around 6 million to 7 million people worldwide and about 25 million have the potential risk of becoming infected [1] . The disease is endemic and its development occurs in acute and chronic phases . The latter may present in different clinical forms , indeterminate , cardiac and/or digestive , with the chronic cardiac form being the most serious [2] . This variation in pathological manifestation has been reported to be related to the complexity of parasite and to differences in host immune response , such as the ability to control parasitaemia , the strength of inflammatory reactions , and the induction of autoimmune-like responses [3–7] . Tissue damage resulting from inflammatory infiltrates and persistence of T . cruzi in myocardial tissue and changes in microcirculation and commitment of the autonomic nervous system are involved in the pathogenesis of cardiomyopathy . However , the precise pathogenic mechanism of Chagas' heart disease is not completely elucidated [7 , 8] . The inflammatory process in the chronic phase of Chagas disease shows signs of cellular activity with CD4+ T and CD8+ T lymphocytes in heart tissue; though fewer numbers of natural killer ( NK ) cells , macrophages and B cells are also present [9 , 10] . In asymptomatic or indeterminate chronic Chagas disease , the presence of circulating NK cells ( CD3-CD16+CD56+ and CD3-CD16+CD56dim ) coupled with the presence of immunoregulatory ( Treg-CD4+CD25high and NKT-CD3+CD16-CD56+ ) or macrophage-like cells ( CD14+CD16+ ) are responsible for the control the inflammatory mechanisms . However , failure in immunoregulatory mechanisms , with basal levels of NK , NKT and CD4+CD25high cells , associated with an increased expression of activated CD8+ T cells , are associated with heart disease [11 , 12 , 13] . The effective function of NK cells is regulated by a balance of activating and inhibitory signals mediated by a diverse set of receptors expressed on their surface , including killer immunoglobulin-like receptors ( KIR ) , which recognize and bind in HLA class I molecules present on the surface of the target cells [14] . KIR is a family of 15 closely linked genes and highly polymorphic , on chromosome 19q13 . 4 , that encodes both inhibitory and activating receptors . The receptors molecules may have two or three immunoglobulin-like domains , whereas those with long cytoplasmic tail ( 2DL and 3DL ) are inhibitory due to the presence of ITIMs ( tyrosine-based inhibitory motifs ) , responsible for signal transduction in order to inhibit NK functions . Molecules with short cytoplasmic tail ( 2DS and 3DS ) have an amino acid transmembrane region which allows the association with a particular protein ( DAP12 ) , which releases activating signals through ITAMs ( tyrosine-based activation motifs ) [15] . KIR receptors of NK cells may contribute to the occurrence of different immunological and clinical responses to the same disease in a specific population [16] . Several studies have described the participation of KIR ( and their ligands ) in infectious diseases , such as AIDS [17 , 18] , hepatitis C [19 , 20] , tuberculosis [21 , 22] , leprosy [23 , 24] and malaria [25 , 26 , 27] . KIR are also involved in autoimmune and inflammatory diseases such as pemphigus foliaceus , psoriasis , scleroderma , rheumatoid vasculitis and Crohn’s disease [28 , 29 , 30 , 31 , 32] , as well as in many types of cancer [33–36] and the survival of transplant patients [37] . The relationship between KIR and their HLA ligands in the immunopathogenesis of chronic chagasic disease remains unknown . Therefore , the aim of this study was to investigate the influence of the KIR genes and their HLA ligands in resistance or susceptibility to Chagas disease .
This case-control study enrolled 131 unrelated patients ( CCD group ) ( 59 men and 72 women , with a mean age of 60 . 4 ± 9 . 8 years ) with serologically-diagnosed chronic Chagas disease , living in different municipalities in the north/northwest region of the State of Parana ( located in the southern region of Brazil , between 22°29'30"-26°42'59"S and 48°02'24"-54°37'38"W ) and treated at the University Hospital of Londrina and the Chagas Disease Laboratory of the State University of Maringa . A control group was formed of 165 healthy , unrelated individuals , who were spouses of the patients or blood donors of the Regional Blood Bank of Maringa ( 84 men and 81 women , with a mean age of 59 . 0 ± 11 . 4 years ) , living in the same geographical area as the patients and whose serological examination for antibodies against Chagas disease was negative . All individuals ( patients and controls ) that had participated in this study were monitored and evaluated for clinical symptoms and epidemiological data . The inclusion criteria of the patient group were: positive laboratory diagnosis of Chagas disease and being in the chronic phase of the disease at the time of the study . The inclusion criteria for the control group were: negative laboratory diagnosis for Chagas disease and living in the same geographical region as the patients . The characteristics of the patients and controls are shown in Table 1 . Due to the great miscegenation in the Brazil population , and after considering the population composition of the state of Parana according to Probst et al . [38] , patients and controls were classified as a mixed ethnicity population . The risk of population stratification bias , due to differences in ethnic background between patients and controls and variations of allele frequencies according to ethnic background , was minimized by matching patients with controls individuals of the same ethnic background . Mean age , gender rates and residence in the same geographical areas were carefully matching to select the groups . Laboratory diagnosis of Chagas disease in patients and controls individuals was carried out by the Regional Blood Bank of Maringa with an ELISA ( Enzyme-Linked ImmunoSorbent Assay ) test of serum or plasma , using "Chagas III" reagents ( GrupoBios , Santiago , Chile ) following the manufacturer's instructions . The microplates were read using semi-automatic equipment ( ASYS Expert Plus , Cambridge , UK ) . ELISA cut-off was defined by the formula: cut-off value = ( average absorbance of the positive controls + average absorbance of the negative controls ) x 0 . 35; and absorbance equal to or greater than the cut-off value was considered reactive . The indeterminate zone was defined by the absorbance values observed between the cut-off ± 10% . The samples were tested in duplicate and positive and negative controls were included . When the absorbance value was in the indeterminate zone , the ELISA test was repeated in duplicate and the indirect immunofluorescence ( IFI ) test was performed , according WHO recommendation . The Clinical Immunology Laboratory of the State University of Maringa performed an IFI test with the IMUNOCRUZI® antigen ( Biolab , Rio de Janeiro , Brazil ) and human anti-immunoglobulin G conjugated to fluorescein ( Laborclin , Pinhais , Brazil ) . Samples were considered positive with titers ≥ 40 . The patients with chronic Chagas disease ( CCD group ) were divided into two distinct groups according to the changes observed in the standard electrocardiography examination at rest . Of the all Chagas disease patients , 44 of them ( 36 . 6% ) , 25 men and 19 women , mean age of 63 . 3 ± 10 . 5 years , were considered to have chronic chagasic cardiopathy ( CCC group ) as they had cardiac signs characteristic of Chagas disease such as right bundle branch block , left anterior hemi-block , unspecific ventricular repolarisation disorders and ventricular and supraventricular premature beats . The remaining 87 ( 66 . 4% ) patients with chronic Chagas disease , 34 men and 53 women , with a mean age of 59 . 0 ± 9 . 0 years , were considered as not having heart disease ( NC group ) ( Table 1 ) . DNA from all samples was extracted by the salting-out method adapted by Cardozo et al . [39] . The concentration and purity of DNA were verified using NanoDrop 2000 equipment ( Thermo Scientific , Wilmington , USA ) . KIR and HLA-A , B and C were genotyped according manufacturer’s instructions by Polymerase Chain Reaction-Sequence Specific Oligonucleotide Probes protocols with Luminex technology ( One Lambda Inc . , Canoga Park , CA , USA ) . First , target DNA was PCR-amplified using group specific primers sets . Each PCR product was biotinylated , which allowed later detection using R-Phycoerythrin-conjugated Strepavidin ( SAPE ) . Each PCR product was denatured and allowed to hybridise to complementary DNA probes conjugated to fluorescently coded microspheres . After washing the beads , bound amplified DNA from the test samples was tagged with SAPE . A flow analyser , the LABScan 100 , identified the fluorescent intensity of PE ( phycoerithrin ) on each microsphere . The fluorescent intensity varied based on reaction outcome , and was expected to be 1000 or above for control positive probes . The data were interpreted using a computer program ( HLA Fusion 2 . 0 Research , One Lambda ) . Some HLA-KIR ligands specificities belonging to the group C1 , C2 and Bw4 were considered according to Kulkarni et al . [40] and Thananchai et al . [41] . HLA molecules from the C1 group include the specificities from HLA-C*01 , *03 , *07 , *08 , *12 , *14 *16 and are ligands of KIR2DL2 , KIR2DL3 and KIR2DS2 . HLA molecules from C2 group that include HLA-C*02 , *04 , *05 , *06 , *07 , *15 , *17 , and *18 specificities interact with KIR2DL1 and KIR2DS1 . HLA-Bw4 epitopes ( HLA-A*23 , *24 , *32; HLA-B*13 , *27 , *44 , *51 , *52 , *53 , *57 , *58 ) are recognized by KIR3DL1 and KIR3DS1 . Specificities from HLA-A*03 and/or—A*11 are KIR3DL2 ligands . Based on the content of the genes , two types of KIR genotypes have been described and are designated AA and BX ( BB and AB ) . The main distinction between them is the number of genes encoding activating receptors . Individual genotypes were determined to be AA when the genes KIR2DL1 , KIR2DL3 , KIR2DL4 , KIR2DS4 , KIR3DL1 , KIR3DL2 , KIR3DL3 , KIR2DP1 and KIR3DP1 were present . The presence of one or more of the following genes: KIR2DL5 , KIR2DS1 , KIR2DS2 , KIR2DS3 , KIR2DS5 and KIR3DS1 characterised the genotype BX ( defined according http://www . allelefrequencies . net ) . KIR , HLA and KIR-HLA frequencies were obtained by direct counting . Comparisons of the frequencies of KIR ligands , KIR genes , KIR AA and BX genotypes and KIR with or without ligands between patients and controls were performed using the Chi-square test with Yates’ correction or Fisher's Exact Test . The associations of genetic trait between chronic Chagas disease and controls were measured by OR ( Odds Ratio ) and the 95% confidence interval ( 95% CI ) . Statistical analyses were performed using the Open Epi program: Open Source Epidemiologic Statistics for Public Health , version 2 . 3 . 1 ( http://www . openepi . com/Menu/OE_Menu . htm ) . P-values ≤ 0 . 05 were considered statistically significant . A correction for multiple testing was done by multiplying the P-values by the number of the tests ( Bonferroni correction ) . A Hardy-Weinberg equilibrium fit was performed by calculating expected genotype frequencies and comparing with the observed values using Arlequin software ( version 3 . 1 ) .
HLA and KIR genotypes frequency distribution in the studied populations was in Hardy-Weinberg equilibrium . The frequencies of KIR genes in the control group were similar to those found by Rudnick et al . [42] in individuals of the north/northwest region of the state of Paraná . Distribution of KIR gene frequencies among controls , chronic Chagas disease patients ( CCD ) , patients without heart involvement ( NC ) , and chronic chagasic cardiopathy patients ( CCC ) is shown in Table 2 . CCC presented a lower frequency of KIR2DL2 when compared to the control group ( 31 . 8% vs . 53 . 3%; P = 0 . 017; OR = 0 . 41; 95% CI = 0 . 20–0 . 83 ) ; however , significance was lost after Bonferroni correction ( Pc = 0 . 27 ) . No significant differences were found in the distribution of other KIR genes between all of the analysed groups . According to expected the KIR framework genes , KIR2DL4 , KIR3DL2 , KIR3DL3 and KIR3DP1 were present in all samples , which were important internal controls . The frequencies of the HLA class I ligands of the KIR ( A3 and/or A11 , Bw4 , C1 and C2 , in homozygosity and heterozygosity ) were analysed and were similar between groups . An exception was found for the specificities of the HLA-A*03 and/or—A*11 , ligands of KIR3DL2 , which were lower in chronic Chagas disease patients ( CCD ) ( 19 . 1% vs . 30 . 3%; P = 0 . 036; Pc = 0 . 144; OR = 0 . 54; 95% CI = 0 . 31–0 . 94 ) , but the significance was lost after Bonferroni correction . The distribution of the frequencies of the KIR and their HLA ligands ( KIR2DL2-C1; KIR3DL3-C1; KIR2DS2-C1; KIR2DL1-C2; KIR2DS1-C2; KIR3DL1-Bw4 , KIR3DS1-Bw4 ) are listed in Table 3 . The KIR2DL2-C1 ( 16 . 8% vs . 32 . 1%; P = 0 . 036; OR = 0 . 43; 95% CI = 0 . 24–0 . 75 ) , the KIR3DL2-A3/11 pair ( 19 . 1% vs . 30 . 3%; P = 0 . 037; OR = 0 . 54; 95% CI = 0 . 31–0 . 94 ) and KIR2DL2 in the presence of the ligands in the homozygous state ( KIR2DL2-C1/C1 ) ( 4 . 5% vs . 19 . 5% P = 0 . 031; OR = 0 . 23; 95% CI = 0 . 04–0 . 89 ) had lower frequency in the patients with chronic Chagas disease , but the significance was lost after Bonferroni correction . The correlation between the distribution of activating and inhibitory KIR and their respective HLA ligands is shown in Table 4 . An increased risk or susceptibility of developing chronic Chagas disease ( 12 . 2% vs . 4 . 2%; P = 0 . 020; Pc = 0 . 040; OR = 2 . 14; 95% CI = 1 . 25–7 . 88 ) and chronic chagasic cardiopathy ( 22 . 7% vs . 4 . 2%; P = 0 . 0002; Pc = 0 . 0004; OR = 6 . 64; 95% CI = 2 . 30–18 . 60 ) was observed for the KIR2DS2+/2DL2-/C1+ combination ( KIR2DS2 and the C1 ligand in the absence of KIR2DL2 ) . This correlation was also observed when CCC was compared to the NC ( 22 . 7% vs . 6 . 9%; P = 0 . 010; Pc = 0 . 020; OR = 3 . 97; 95% CI = 1 . 34–11 . 79 ) . Susceptibility was also observed when KIR2DL3 was present , KIR2DS2+/2DL2-/KIR2DL3+/C1+ combination , for CCD ( 10 . 7% vs . 4 . 2%; P = 0 . 050; Pc = 0 . 10; OR = 1 . 06; 95% CI = 1 . 1–6 . 9 ) and when CCC was compared to NC ( 18 . 2% vs . 5 . 7%; P = 0 . 041; Pc = 0 . 08; OR = 3 . 64; 95% CI = 1 . 12–11 . 91 ) , although it was lost after Bonferroni correction . However , the susceptibility of developing disease remained for CCC ( 18 . 2% vs . 4 . 2%; P = 0 . 004; Pc = 0 . 004; OR = 5 . 02; 95% CI = 1 . 71–14 . 73 ) when compared to controls . No significant difference was observed in the AA and BX genotype frequencies between all groups . Also , there was no significant difference in the frequencies of AA genotypes when the complete forms of the KIR2DS4 gene or its variants ( deleted form ) were present .
The combination of KIR2DS2 activating receptor with C1 ligand , in the absence of KIR2DL2 , may be related to a risk factor in the chronic Chagas disease and chronic chagasic cardiopathy .
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Chagas disease is an infection caused by the haemoflagellate protozoan Trypanosoma cruzi . It is one of the most important public health problems in Latin America , and was first described by Carlos Justiniano Ribeiro das Chagas , a Brazilian physician and scientist , in 1909 . It is mostly vector-borne transmitted to humans by contact with faeces of triatomine bugs . The World Health Organization estimates that about 6 to 7 million people are currently infected with T . cruzi worldwide . The disease is characterised by acute and chronic phases . The immune response during disease development is crucial for protection because immunological imbalances can lead to heart and digestive tract lesions in chagasic patients . In this work we analysed the role of receptors of immune cells known as Natural Killer cells ( killer cell immunoglobulin-like receptor—KIR ) and their ligands ( Human leukocyte antigens—HLA ) in chagasic patients compared to healthy individuals . The uncontrolled activation of NK cells can lead to tissue damage , which , in turn , leads to the development of serious chronic illness . We found that KIR-HLA complex may be related to a risk factor in the chronic Chagas disease and chronic chagasic cardiopathy .
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[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[] |
2015
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Killer Cell Immunoglobulin-like Receptors and Their HLA Ligands are Related with the Immunopathology of Chagas Disease
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Cell differentiation in multicellular organisms has the obvious function during development of creating new cell types . However , in long-lived organisms with extensive cell turnover , cell differentiation often continues after new cell types are no longer needed or produced . Here , we address the question of why this is true . It is believed that multicellular organisms could not have arisen or been evolutionarily stable without possessing mechanisms to suppress somatic selection among cells within organisms , which would otherwise disrupt organismal integrity . Here , we propose that one such mechanism is a specific pattern of ongoing cell differentiation commonly found in metazoans with cell turnover , which we call “serial differentiation . ” This pattern involves a sequence of differentiation stages , starting with self-renewing somatic stem cells and proceeding through several ( non–self-renewing ) transient amplifying cell stages before ending with terminally differentiated cells . To test the hypothesis that serial differentiation can suppress somatic evolution , we used an agent-based computer simulation of cell population dynamics and evolution within tissues . The results indicate that , relative to other , simpler patterns , tissues organized into serial differentiation experience lower rates of detrimental cell-level evolution . Self-renewing cell populations are susceptible to somatic evolution , while those that are not self-renewing are not . We find that a mutation disrupting differentiation can create a new self-renewing cell population that is vulnerable to somatic evolution . These results are relevant not only to understanding the evolutionary origins of multicellularity , but also the causes of pathologies such as cancer and senescence in extant metazoans , including humans .
Mature tissues of long-lived metazoans exhibit ongoing cell differentiation , with tissue-specific somatic stem cells dividing to renew populations of more differentiated cells that are not self-renewing . Although ongoing cell replacement is clearly necessary for long-lived organisms , it is not obvious why tissue renewal should involve ongoing differentiation from somatic stem cells . In principle , tissues could be maintained by the self-duplication of fully functional cell types , using the same kind of “cell memory , ” or direct epigenetic inheritance of cell state , that is typical of unicellular organisms [1] . Indeed , some metazoan tissues do seem to replace lost cells through such self-duplication of differentiated cells [2] . Such a simple system is both evolutionarily conserved and metabolically efficient . Instead , however , most adult tissues replace lost cells through a much more elaborate system that we call “serial differentiation . ” In this system , new fully differentiated cells are not produced by the division of fully differentiated cells ( which are incapable of division ) , but by the division of “transit” cells , or “transient amplifying cells” ( TACs ) . One tissue may include a series of several TAC stages , each of which results from the division of the preceding stage . These cells are transient in the sense that they only proliferate for a limited amount of time before they become terminally differentiated and are eventually shed from the tissue . They amplify the proliferative potential of the somatic stem cells , because each TAC stage doubles the number of descendent cells that ultimately result from the division of a somatic stem cell [3] . Serial differentiation has been described in a variety of self-renewing tissues [4–10] . Relative to direct epigenetic inheritance of cell state by fully differentiated cells , the more elaborate system of serial differentiation presumably requires greater genetic complexity and also entails a metabolic cost in supporting the additional cells . Therefore , it would seem unlikely to have evolved unless it provided some important advantage to the organism . Here , we propose that this advantage lies in the suppression of somatic selection and thus somatic evolution . A multicellular organism can be viewed as a population of cooperating cells . This population is subject to the same evolutionary processes as any other population undergoing reproduction , death , mutation , and competition for limiting resources . Selection within a metazoan will inevitably favor those cells that are better at reproductive competition and survival [3] . Yet , the characteristics that help cells compete effectively within the organism are generally detrimental to organismal integrity and fitness [11] . Thus , there is a fundamental conflict between selection among cells within organisms ( “somatic selection” ) and selection among organisms [3 , 11–14] . Multicellular organisms could not emerge as functional entities before organism-level selection had led to the evolution of mechanisms to suppress cell-level selection [15–18] . Cell differentiation has previously been recognized as a mechanism to control somatic evolution and its potential for carcinogenesis during development [10 , 19 , 20] . However , even in mature tissues , the combination of cell turnover and somatic mutation creates the conditions for somatic evolution . The conflict between the cellular and organismal levels of selection is exacerbated in long-lived organisms with extensive cell turnover . Humans are estimated to contain approximately 1014 cells with extensive cell turnover [21] . For example , each day , the small intestine and the hematopoietic system shed 1010 and 1011 cells , respectively [22 , 23] . Furthermore , many of the genes in a metazoan genome may function to constrain cellular competition and coordinate cellular cooperation [15] . This implies that many loss-of-function mutations may provide a competitive advantage for the mutant cell [24] . Accumulation of such somatic mutations through cell-level selection could lead to two general classes of pathology . First , diversion of cell resources into cell survival and replication and away from organismal function could impair a wide range of organismal functions , leading to general senescence [25] . Second , the shedding of restrictions on cell division and survival , if unchecked , ultimately leads to uncontrolled cell proliferation and cancer [3 , 20] . Somatic evolution is inevitable given the cumulative Darwinian selection that occurs in any self-renewing population of proliferating cells , together with a supply of variation in cell fitness from somatic mutation . In multicellular organisms with substantial cell turnover , both self-renewal and cell proliferation are necessary , and the complete suppression of all mutation may not be achievable . However , it may be quite feasible through differentiation patterns to almost entirely suppress somatic selection , without which the appearance of the occasional somatic mutation is harmless to the organism . We hypothesize that this is achieved in animals by compartmentalizing self-renewing tissues such that one cell population ( stem cells ) undergoes self-renewal , while another ( TACs ) undergoes active proliferation . If no cell population combines both these necessary elements of somatic evolution , somatic evolution is thereby suppressed . If this separation is imperfect , some somatic selection may occur , but it will be greatly weakened and slowed . Consequently , we would expect to see the pathologies associated with somatic evolution to persist at some level , but primarily as ailments of old age . We hypothesize that stem compartments are subject to little somatic evolution because stem cell populations are small and quiescent , with little proliferative activity . Populations of TACs are subject to little somatic evolution because they are not self-renewing , so that somatic Darwinian selection is not in effect: mutations conferring increased cell survival and replication do not increase in frequency within this cell population . Here , we test our hypothesis using a simplified computational model of cell population dynamics and differentiation in a tissue or proliferative unit ( e . g . , an intestinal crypt ) . Our hypothesis requires the representation of stem cells , TACs , symmetric and asymmetric cell divisions ( which may be self-renewing or differentiating ) , and the ability to track the fate of mutations that increase the fitness of a cell lineage . In addition to the effects of self-renewing cell divisions , we also study factors influencing the rate of somatic evolution under serial differentiation , including symmetric versus asymmetric differentiation , the number of differentiation stages , and loss-of-function mutations in differentiation pathways . The model is not designed to faithfully replicate the details of any one tissue , but rather to capture the essential dynamics relevant to our hypothesis .
Our first set of experiments was set in the context of serial cell differentiation , under the assumption that cell differentiation was symmetric , with both daughter cells sharing the same fate ( Figure 1 ) . We allowed somatic mutations that either increased the cell's intrinsic division rate , or reduced its intrinsic probability of death ( e . g . , through apoptosis ) , and observed the outcome of somatic selection . For some cells , differentiation may be asymmetric , with one daughter cell remaining in the parental cell stage and the other further differentiating [27] . Because such asymmetric division represents a form of self-renewal by the parental stage , we hypothesized that allowing it in TACs would increase the rate of somatic evolution . To test this hypothesis , in Experiment 2 we repeated Experiment 1A , but included one treatment with asymmetric instead of symmetric differentiation in all TAC stages . Some published models of cell differentiation assume that non-stem cell populations are partly self-renewing ( e . g . , [5 , 28] ) . To study this scenario , we assumed TACs were like stem cells in that both of their daughter cells could either remain in the parental differentiation stage or proceed to the subsequent stage , depending on homeostatic signaling mechanisms that maintain an equilibrium number of cells in each stage . According to our hypothesis , such self-renewing cell populations would be more vulnerable to somatic evolution than would tissues following strict serial differentiation ( Figure 1 ) . The foregoing experiments show that serial differentiation can effectively block the spread of selfish-cell mutations that increase cell survival and replication . However , loss-of-function mutations can also affect cell differentiation itself . In this section , we investigated whether differentiation knockout mutations were positively selected , and how they affected the dynamics of cellular evolution and proliferation . We found that differentiation knockout mutations were positively selected within the cell compartment they arose in because they caused both daughter cells to remain in the parental stage . They also caused a striking increase in cell proliferation .
We have shown that , in principle , ongoing serial cell differentiation in mature tissues can suppress cell level selection and somatic evolution . We suggest that this pattern of cell differentiation was a critical step in the evolution of large and long-lived metazoans with extensive cell turnover . Serial cell differentiation makes it possible to segregate proliferative activity and population self-renewal into different cell compartments so that no compartment possesses all the attributes necessary for somatic evolution . Our simulation experiments confirmed our a priori prediction that self-renewal in TACs would allow rapid somatic evolution because a mutant clone can sweep to fixation within a population of such cells . In addition , they revealed that asymmetric cell divisions are one of the forms of population self-renewal that would increase the rate of somatic evolution . Symmetric cell division without self-renewal suppresses somatic evolution by causing the constant removal of non-stem mutations from the proliferating cell population . All such mutations are trapped in cell lineages that are destined to rapidly die out through terminal differentiation . Any form of self-renewal , including asymmetric cell division , disrupts this purging dynamic , allowing the sequential accumulation of multiple selfish-cell mutations . The evolution of multicellularity from preexisting unicellular life is one example of repeated events during evolution in which new kinds of biological “individuals” have emerged from collections of previously existing entities . In each of these “transitions in individuality , ” selection at the level of the newly emergent individual is thought to have created mechanisms to suppress internal selection among its subunits , which would otherwise disrupt its integrity and lower its fitness [15–17] . Several such mechanisms have been proposed for the evolutionary transition to multicellular life . Buss argued for the central role of germ line segregation [15] , which protects the germ line from being affected by somatic evolution within the soma . In contrast , Queller [29] emphasized the importance of a single-celled stage in the life cycle for limiting the genetic variation passed on to offspring . Both of these mechanisms may be important to mitigating long-term somatic evolution across many organismal generations . Here , we propose another mechanism that has long been known to exist , but the functional significance of which has received little attention ( but see [30] ) . By suppressing somatic evolution within tissues , serial differentiation may not only suppress somatic evolution across generations of multicellular organisms . In addition , it may also suppress the short-term somatic evolution that can have significant deleterious consequences within the lifespan of a single organism that is large and long-lived [31] . Cell differentiation depends upon epigenetic inheritance . Thus , our results support the suggestion of Jablonka [1 , 32] that epigenetic inheritance played a central role in the transition from unicellular to multicellular life by helping to control selection among the cells of the newly emergent multicellular individual . Because the epigenetic state of the genome is heritable across cell generations , somatic evolution almost certainly occurs in the epigenome as well as in the genome . Epigenetic alterations are commonly detected in cancers and are thought to often be early events in carcinogenesis [33–37] . Thus , changes labeled as somatic “mutations” in our model could also be realized by epigenetic alterations . The main purpose of this paper has been to test a hypothesis for the evolutionary origin and function of an already-familiar pattern of cell differentiation in large metazoans . Given that the existence of this pattern is not in doubt , what testable predictions follow from our hypothesis that could be used to reject or support it ? Several predictions concerning the architecture of normal , healthy tissues in long-lived metazoans can be tested experimentally or even by careful observation . One such prediction is that large and proliferating ( e . g . , non-stem ) cell populations are not expected to be self-renewing . Another prediction is that when non-stem cells divide , both daughter cells must be committed to further differentiation . Thus , cell division should be intimately tied to differentiation . Dividing without committing to differentiation is the definition of self-renewal , and we have shown that this is a risk for somatic evolution and associated pathologies . Opportunities for testing our hypothesis arise where our predictions appear to conflict with the prevailing view of cell differentiation in some tissues . For example , it has been suggested that mouse pancreas β cells self-renew without contribution of non–insulin-producing stem cells [2] . If confirmed , this would raise questions about our results , or about how these pancreatic cell populations avoid rampant somatic evolution and accompanying pathologies . Other interpretations of the evidence are possible , however . One hypothesis proposed by Dor et al . that is consistent with our results is that not only terminally differentiated β cells , but also unipotent β stem cells and TACs , produce insulin [2] . If this is true , then serial differentiation in a cryptic form may be present even in pancreas β cells , as our hypothesis would predict . As another empirical test , the hematopoietic system is commonly assumed to involve self-renewing TACs [5 , 38] . We predict that closer study will reveal these morphologically indistinguishable cells to be functionally stratified into a series of non–self-renewing TAC stages . Our prediction is that when such a TAC divides , its daughter cells are one division closer to terminal differentiation than the parental cell . This must involve some form of “counter” for mitoses such that cells that are more generations removed from their ancestral somatic stem cell in the tissue are closer to terminal differentiation than are cells that are fewer generations removed from their ancestral stem cell . This prediction is supported by the observation that , although hematopoietic cells may appear to be self-renewing stem cells based on cell-surface markers , some have limited self-renewal capacity , as is typical of TACs [39] . In solid tissues such as an intestinal crypt , the mitosis “counter” might be implemented by position in the tissue , as long as proliferating cells move up the gradient of differentiation when they divide [40] . However , for the principles we have elucidated to apply , the functional sequence of differentiation stages need not correspond directly to physical location . Perhaps the most important empirical counterexample to our hypothesis for the suppression of somatic selection is the adaptive immune system , which contains large cell populations that are both self-renewing and actively proliferating throughout life . The apparent reason for this exception to the general rule of serial differentiation is that the adaptive immune system depends on somatic evolution though clonal selection for its effectiveness ( [41] , p . 15 ) . According to our hypothesis , serial differentiation greatly slows somatic evolution in most tissues , forestalling its pathogenic consequences until old age . Because this is not true of the adaptive immune system , we would expect pathologies arising through somatic evolution to manifest at much earlier ages in these cell populations than in other tissues . Indeed , leukemia and lymphoma are unusual cancers in that they are relatively common at younger ages . Whether the adaptive immune system is also vulnerable to accelerated senescence relative to the rest of the body has not been closely investigated to our knowledge , but there are intriguing clues . The thymus is an important site of somatic selection in the adaptive immune system ( [42] , p . 223 ) , and it is known to atrophy beginning at puberty ( [42] p . 44 ) . Thymectomized adult mice that received a transplanted thymus enjoyed improved immune function . However , the improvement was significantly greater when the donor was newborn versus 33 mo old ( [42] p . 45 ) . This functional decline , even before the age of 3 y , could be due either to deleterious somatic evolution or to other causes . In humans , the decline in immune competence that occurs with aging is a serious clinical concern . Aging is associated not only with declining competence but also with dysregulation of immunity , including increasing autoimmune disorders [43] . This is consistent with a possible failure to suppress inappropriate cell proliferation in the immune system , as might result from prolonged somatic evolution . Other empirical predictions of our hypothesis can be tested with phylogenetic data . In organisms with high cell turnover , and thus great potential for somatic evolution , suppression of somatic selection through serial differentiation is especially critical . It is in such organisms that organismal selection should most strongly favor the effective but costly mechanism of DNA methylation as a means of maintaining the differentiated state in somatic cell lineages . This prediction has been met by analysis of taxonomic patterns in DNA methylation [32] . Another empirical prediction concerns body size . Whenever evolution has scaled organisms up from small and short-lived to larger and longer-lived , the potential for somatic evolution has increased [44] . Our model suggests that because somatic stem cells normally form a self-renewing cell compartment , they pose the highest carcinogenesis risk on a per-cell basis of any cells in a tissue . In our model , it is possible to increase the number of cells and the amount of cell turnover per organism without increasing the number or proliferative activity of somatic stem cells , simply by increasing the number of non-stem stages ( n ) as per Equation 1 above . This is what we would expect to see in comparisons between species with different body size . This prediction is consistent with previous theory [17 , 44 , 45] and also with data showing a lower ratio of stem to fully differentiated cells in the feline hematopoietic system relative to that of the mouse [46] . If we are correct in our hypothesis for the control of somatic evolution through serial differentiation , it may have important medical implications . Both diseases involving uncontrolled cell proliferation ( cancers ) , and those involving generalized loss of normal tissue function , are good candidates for conditions arising through the expression of unrestrained somatic selection . For this reason , research into the etiology of such diseases should include a focus on postembryonic patterns of cell differentiation . If our hypothesis is correct , it may help in understanding why senescence and general loss of tissue function is a typical part of aging . Somatic evolution has been proposed as a fundamental source of senescence [25] . We have shown that serial differentiation can reduce somatic evolution , but not completely eliminate it . Some proliferation by stem cells is necessary , and self-renewal of TACs can arise by sporadic somatic mutations disrupting normal differentiation . Be slowing somatic evolution , serial differentiation may not entirely eliminate senescence but delay it until old age . This would suggest that conditions of accelerated senescence , or progeria syndromes , may result from a failure to suppress somatic evolution . In this regard , it is significant that patients with Hutchinson-Gilford progeria syndrome appear normal at birth , while the disease is usually diagnosed near the end of the second year of life after failure to thrive commences . This pattern suggests that in patients with this condition , fetal , but not postnatal , development is normal [47] . The conventional wisdom is that cancer begins with genetic or epigenetic lesions causing excessive cell proliferation . The results presented here suggest a more nuanced picture of the dynamics of carcinogenesis . We have shown that in the context of normal serial differentiation , a genetic lesion causing excessive division by its host non-stem cell will not result in uncontrolled cell proliferation . On the other hand , a genetic lesion that has no direct effect on division rate , but that disrupts normal cell differentiation , may quickly lead to abnormal cell proliferation . For purposes of prevention and early detection , it is critical to understand the earliest stages in carcinogenesis . Our results may be useful in this regard . It is clear that any cell population that is both actively proliferating and self-renewing is at high risk of somatic evolution and thus of carcinogenesis . It is also clear , however , that there are two distinct ways this situation may arise . One is that a ( normal ) self-renewing population of stem cells may acquire mutations that increase proliferation or reduce apoptosis . Such “selfish-cell” mutations will immediately be favored by selection among stem cells , and may rapidly go to fixation within the stem cell compartment if not lost by genetic drift . This route is facilitated by any factors that increase the rate of stem cell replication , including factors that are normal in themselves , such as wound healing and cyclic growth of breast and reproductive tissues [48 , 49] . The second basic route to tumorigenesis begins with non-stem cells such as TACs . These compartments are normally large and proliferative but not self-renewing . Here , somatic selection among cells will not favor increased replication or survival unless preceded by an initiating mutation that blocks normal differentiation and thereby converts the resulting clone into a self-renewing population . After this initiating step , all further selfish-cell mutations will spread and accumulate through somatic selection . In contrast to the stem cell pathway , neoplasia originating in TACs is not facilitated simply by higher rates of cell turnover and proliferation . Instead , it is highly dependent on a specific class of mutations , and thus may be more stochastic and unpredictable , but also more dependent on mutagens that tend to target genes involved in cell differentiation . It seems likely that distinguishing between these two distinctive pathways in early carcinogenesis may reconcile what could otherwise appear to be conflicting evidence about the earliest steps of tumorigenesis . Moreover , neoplasms resulting from these two different early pathways may retain persistent differences that are relevant to medical strategies for their detection and treatment . The potential importance of stem cells in tumorigenesis has received considerable attention recently , partly due to the recognition of cancer stem cells [50] . Some research models have therefore focused on the role of somatic stem cells and their differentiation patterns [27] . Our results , however , emphasize that proliferating cells with a stem-like capacity for unlimited self-renewal can potentially arise from mutations in either stem cells or TACs . The role in tumorigenesis of normal mechanisms for tissue homeostasis has received little attention , but our results suggest this might be a fruitful avenue of research . One intriguing result of our model is that the structure of the feedback loops that maintain tissue homeostasis can have a dramatic impact on the probability that the tissue will develop cancer . Without any redundant checks on cell proliferation in this simplified model , a differentiation knockout mutation will generate uncontrolled growth if the mutant cell is not quickly cleared by stochastic background mortality . This single mutation generates uncontrolled proliferation , though an interaction with the negative feedback controlling the production of terminally differentiated cells . When a differentiation knockout mutant arises , that cell's progeny will no longer contribute to the terminally differentiated compartment . Thus , when the terminally differentiated compartment drops below equilibrium levels , stem cells and TACs are stimulated to proliferate , including the mutant . The mutant clone will grow , taking over more of its compartment and thereby further reducing the tissue's capacity to replenish the terminally differentiated cells . A vicious cycle ensues in which the more the mutant clone grows , the more the terminally differentiated compartment signals the need for more proliferation . The result is an exponential expansion of the mutant clone . These results are similar to those of a computational model of skin in which differentiation was based on distance from the basal membrane mediated through mechanical and adhesive forces in the tissue [51] . Rashbass et al . found that disruption of the differentiation responses of the cells could lead to exponential cell growth . Of course , in a real tissue , the growth of the mutant clone would be limited by additional proliferative repression and by nutrient availability until further mutations could generate stimuli for neo-angiogenesis . Our model highlights the importance of the relatively understudied mechanisms of tissue homeostasis . Based on our results , we predict that genetic lesions disrupting differentiation are often critical to tumor initiation . Because of the small size and low activity of somatic stem compartments , it seems unlikely that any tissue with serial differentiation would accumulate the mutations necessary for cancer unless differentiation was disrupted early in the process . Considerable evidence supports this view . For example , blocked differentiation is a frequent theme in the development of hematopoietic malignancies ( [52] , p . 470 ) . Similarly , lesions in APC , a gene involved in differentiation in crypts of the intestine [53] , are considered “gatekeeper” lesions that initiate colonic adenomatous polyps and are necessary for the future development of colorectal cancer [54] . In another tissue , recent genome-wide analyses have shown that most alterations in acute lymphoblastic leukemia target the B cell differentiation pathways [55] . It is worth noting that many of the cell characteristics considered to be hallmarks of cancer are examples of “selfish-cell” traits that are favored by somatic selection when it is operating . These include traits that reduce intrinsic mortality rate , such as evasion of apoptosis , as well as traits that increase intrinsic division rate , such as self-sufficiency in growth signals and insensitivity to antigrowth signals [56] . When we understand that somatic selection is the underlying process driving carcinogenesis , it is clearly no coincidence that “most if not all cancers have acquired the same set of functional capabilities during their development , albeit though various mechanistic strategies” ( [56] p . 59 ) . Furthermore , when we understand the central role that disruption of normal differentiation plays in allowing somatic selection , this suggests that early loss of differentiation may eventually be recognized as one of the most fundamental , and earliest to appear , of the hallmarks of cancer . If true , this could point toward important directions in using genetic tests to screen for cancer , or even sporadic cancer risk , before the first directly observable symptoms appear . One active area of cancer research involves the use of chemotherapeutic agents that act by promoting cell differentiation [57] . The feedback loops that maintain tissue homeostasis are likely to modulate the efficacy of these differentiation agents . It may be important to interrupt those feedback loops so as to prevent the cancer cells from increasing their proliferation rate to compensate for cells lost to differentiation [10 , 20 , 28 , 38 , 55 , 58–61] . Some of the ideas we have explored in this study have been raised previously in the specific context of carcinogenesis . Cairns proposed that the elaborate system of somatic stem cells , TACs , and terminally differentiated cells in a gastrointestinal crypt is an adaptation to suppress cancer [20] . Mutations that occur in the TACs of a crypt are destined to be sloughed off in a matter of days [6] . Only the self-renewing stem cell population , or mutant cells that no longer differentiate properly , are vulnerable to mutations that may establish an expanding clone and become locally fixed . Somatic stem cells are typically quiescent and few in number [6 , 62] . These traits may be organismal adaptations that both reduce the frequency of somatic mutations and limit the role of somatic selection relative to genetic drift in stem cell populations [30 , 63] . We suggest that the structure of serial differentiation may be a general principle for the suppression of somatic evolution and thus neoplasia not just in gastrointestinal crypts but in all tissues with extensive cell proliferation . Even in the less physically structured hematopoietic system , serial differentiation may serve to limit somatic evolution . Charlton [64] proposed that somatic evolution underlies the pathogies of both cancer and senescence , but did not address how somatic evolution might have been avoided in people without these pathologies . The idea of somatic evolution has also been explored in some detail in the cancer biology literature: previous agent-based models of carcinogenesis have been used to explore theories of the clonal evolution that drives neoplastic progression [65–69] . Mathematical models have also been used ( e . g . , [27 , 70] ) . While these previous studies have all highlighted the detrimental effects of somatic evolution , they have not focused on the question of what normally suppresses somatic evolution , and thus have not completely explained what key turning points cause healthy tissue to become neoplastic . Kirkland has developed a model of differentiation in the hematopoietic system that is conceptually similar to ours . In Kirkland's model , cell stages are not discrete , but rather , “stemness” is represented as a continuous variable [71] . A probability density function determines the stemness of daughter cells . Although “stemness” was represented as a continuous variable in Kirkland's model , and as a discrete variable in our model , the same principles apply in both , and both reached similar conclusions: if daughter cells can be as undifferentiated as the parent cell , then self-renewal occurs and the tissue is vulnerable to somatic evolution . Only tissues in which the daughter cells are more differentiated than the parental cell are protected from somatic evolution . The same concept also applies to theories of stem cell niches where extrinsic properties of the microenvironment determine the differentiation state of cells [72] . In this case , differentiation is determined by migration , and we would predict that non-stem cells should migrate out of but not into the stem cell niche . Tomlinson and Bodmer also developed a similar model , with self-renewing compartments of stem cells , TACs , and differentiated cells [28] , which was extended to include homeostatic feedback mechanisms [70 , 73] . In this model , failures of apoptosis or differentiation led either to clonal expansion to higher equilibrium cell numbers ( benign tumors ) or to extended exponential growth of the cell population ( neoplasm ) . Nowak et al . showed that a linear model of a crypt , with a single stem cell and asymmetric division at all cell stages , limits somatic evolution and slows progression to cancer [30] . Frank et al . also analyzed a model of a crypt in which stem cells and TACs could have different mutation and division rates [73] . They found that differences in mutation rates between the cell compartments changed the optimal proportion of cell divisions between the compartments to minimize somatic evolution [73] . Several previous authors have proposed that the failure of cell differentiation plays an important role in tumorigenesis [10 , 20 , 28 , 58–60] . We have expanded on this idea by showing how cell differentiation prevents the onset of cell proliferation through controlling cells' selective environment and thereby suppressing somatic evolution . Similarly , several previous authors have recognized that somatic evolution occurs and is probably central to neoplastic progression [3 , 38 , 74–76] , and that tradeoffs in evolution and the selective pressure of cancer may have shaped multicellular genomes and bodies [11–14 , 31] . Here , we have shown how somatic evolution is normally controlled , and how that control can break down during the events preceding tumorigenesis . Finally , we have shown that the loss of differentiation interacts dramatically with the feedback loops that maintain tissue homeostasis and may lead to clonal expansion and carcinogenesis .
To investigate the role of cell differentiation in somatic evolution , we developed a simplified model of the evolutionary dynamics of cells within a tissue of an adult organism . This model consists of a set of assumptions about the behavior and population dynamics of cells within tissues , which we embodied in an agent-based computer simulation . The source code for the computational model is freely available from the authors upon request . A detailed description of the model's assumptions and algorithms follows . Each cell was represented by three heritable characteristics: intrinsic replication rate , intrinsic mortality rate , and whether or not it was capable of differentiation upon division . A fourth cell characteristic was its current differentiation stage . When a cell underwent mitosis , it ceased to exist , and was replaced by two daughter cells . Each daughter cell inherited the first three of the intrinsic characteristics listed above . Their current differentiation stage was typically incremented from that of the parent cell . ( Instead , the current differentiation stage was directly inherited without being incremented in various scenarios of self-renewing compartments described below , including stem cells , self-renewing TACs , and mutant TACs with differentiation knockouts . ) Intrinsic growth and mortality rates were modeled as quantitative traits . Capacity to differentiate was a binary value representing either the functionality of differentiation pathways in the cell , or their disruption by mutation . Current differentiation stage was an integer ( i ) ranging from 0 ( for a stem cell ) to n ( a terminally differentiated cell ) . A model parameter determined the total number of non-stem cell stages ( n ) . The control of cell division and differentiation is further described below ( Tissue homeostasis ) . At the start of a simulation run , all cells had the same intrinsic growth rate ( r ) and mortality rate ( d ) , set by parameters ( Table 1 ) . All initial cells were assumed to be capable of differentiation until a somatic mutation disrupted that capacity . During each timestep , every cell had the opportunity to divide or to die , with the stochastic probability of each determined by its intrinsic values of r and d , respectively . If a cell was capable of differentiation , then immediately upon dividing , its daughter cells had the opportunity to advance to the next differentiation stage . The control of cell division and differentiation is further described below ( Tissue homeostasis ) . The model represented a population of cells constituting a tissue or proliferative unit . This population included cells in a series of differentiation stages , indexed by i , ranging from stem cells ( i = 0 ) , continuing through 0 or more transient amplifying stages ( 0 < i < n ) , and ending with terminally differentiated cells ( i = n ) . The initial number of cells in each differentiation stage increased from one stage to the next by a factor of t ( for tapering ratio ) , where t = Ki+1 / Ki . Thus , the initial number of cells in each differentiation stage i ( Ki ) was determined by a combination of the parameters for the initial number of terminally differentiated cells ( Kn ) and the tapering ratio ( t ) such that: Thus , the total number of cells of all stages in the modeled cell population was: For comparison to tissues organized by serial differentiation , we also modeled hypothetical nondifferentiating tissues in which all cells were self-duplicating with unlimited replicative potential . These cells were also capable of performing the work of the organ for the benefit of the organism . For valid comparisons , it was important to use the same number of functional cells for differentiating versus nondifferentiating tissues . We assumed that all cells were functional in the hypothetical nondifferentiating tissues , but that under serial differentiation , only the terminally differentiated cells were functional . We therefore compared a nondifferentiating tissue containing a given initial total number of cells ( K ) against a serial differentiation series ending with that same number of terminally differentiated cells ( Kn = K ) , but containing a greater number of cells in total ( Ktot from Equation 3 above ) . Presumably , homeostatic mechanisms maintain the appropriate size of cell populations in the various tissues of metazoans . Cell proliferation must be stimulated when needed , and suppressed when not needed . Little is known about the homeostatic mechanisms in most tissues [6] . In our model , we assumed that cell division was regulated by extrinsic microenvironmental signals such as competition for limited growth factors [9 , 77] or end product inhibition , by end products generated by the terminally differentiated cells ( as has been shown in the hematopoietic system [78] ) , so that cell division is responsive to the number of terminally differentiated cells . In our model , the probability of non-stem cell division ( pi for 0 < i ≤ n ) followed a logistic function: where r was the cell-specific intrinsic growth rate and Kn and Nn were the initial and current number of terminally differentiated cells , respectively . The probability of division was truncated at the limits of 0 and 1 . The effect was to maintain the number of terminally differentiated cells close to the initial number . These feedback loops are a simplified representation of the roles of stromal cells , cytokines , morphostats [10] , and cell-to-cell contact in regulating cell proliferation to maintain tissue homeostasis . The result of implementing these rules in our model was that initial cell numbers were maintained as an equilibrium between cell production and cell loss to terminal differentiation and death ( Figure 12 ) . For any TAC stage i , ( 0 < i < n ) cells entered the stage through division and differentiation from stage i − 1 , and exited the stage through cell mortality as well as division and differentiation to stage i + 1 . In addition to non-stem cells , the number of stem cells must also be regulated [6] in order to replenish stem cell losses due to apoptosis and cytotoxic exposures and thereby preserve the integrity of the entire proliferative unit . We modeled the probability of stem cell division ( p0 ) as the sum of stimulation from both the stem and the terminally differentiated compartments: where K0 was the initial number of stem cells and N0 was the current number of stem cells . The use of the maximum function here prevented suppression of cell division due to an overabundance of one cell type from interfering with the replenishment of the other cell type . When a stem cell divided , each daughter cell differentiated into the next stage ( TAC stage 1 ) if and only if the stem cell population was at or above its initial population size ( N0 ≥ K0 ) . In our simulation , cells differentiated only immediately subsequent to mitosis ( in the same timestep ) , though this rule could represent differentiation at any time between mitosis and the next cell cycle . When a stem cell divided , both daughter cells remained stem cells if the stem cells were below their initial number ( N0 < K0 ) . Otherwise , one daughter cell differentiated into the first transient amplifying stage ( stage 1 ) , while the other became a stem cell ( stage 0 ) [79–81] . The differentiation of TACs was modeled in two different ways for comparison . Under symmetric differentiation ( Experiments 1A and 1B ) , when TACs divided , both daughter cells differentiated into the next stage in the series . Under asymmetric differentiation in TAC stages ( Experiment 1C ) , one daughter cell remained in the same cell stage as the parent and the other daughter cell progressed to the next cell stage . To model self-renewal by TACs ( Effect of Asymmetric Differentiation ) , we had them behave like stem cells in that daughter cells from each stage differentiated into the next stage if and only if their own stage was at or above its initial population size ( Ni ≥ Ki ) . Unless otherwise noted , parameter values for all simulation runs approximated values from the gastrointestinal crypt literature ( Table 1 ) [6] . In our experiments , we introduced somatic mutations of the three heritable cell characters: intrinsic replication rate , intrinsic mortality rate , and capacity for differentiation upon division . The evolutionary outcomes we measured were the average values of quantitative traits ( intrinsic replication or mortality rate ) , the frequencies of discrete mutant alleles for differentiation ability , or changes in total cell population size . To study the effects of mutations affecting rates of replication and mortality , we carried out two types of experiments , using either a controlled mutation of a single cell at a time , or stochastic mutation of all dividing cells . Controlled mutations . In controlled mutation experiments , we turned off stochastic mutation , let the model equilibrate for 500 timesteps , and then introduced a single mutant cell with an altered rate of intrinsic replication or mortality , or ( in Mutations that Disrupt Differentiation ) , with heritable loss of normal differentiation ability . We ran the model for 10 , 000 timesteps , or stopped it sooner if and when the mutant clone either went extinct or to fixation . To reduce run time and reduce the extreme stochasticity of time to complete fixation with drift , we used as a proxy for fixation a mutant allele reaching a frequency of >90% of the cell population . Because our model did not include any frequency-dependent fitness effects , it was safe to assume that any mutation that increased from an initial low frequency to >90% would eventually have gone to fixation given sufficient time . We introduced mutations of varying magnitudes into different differentiation stages , and each case was tested at least 100 times with different random number seeds . Stochastic mutations . In experiments with stochastic mutation , we let the growth or mortality rates mutate as follows: upon each cell division , the growth or mortality rates of the daughter cells were changed to represent the quantitative effects of mutations caused by DNA replication errors during cell division . At cell division , each daughter cell inherited the parental cell's quantitative trait multiplied by a normally distributed random variable with mean 1 and standard deviation 0 . 05 . Thus , stochastic mutations could either increase or decrease these traits , with equal probability . In experiments with stochastic mutation , we stopped each simulation run when either the average intrinsic growth rate doubled or the mortality rate was halved from the initial rates , or after 10 , 000 timesteps , whichever came first . We varied the number of cell stages in the model from just one stage ( nondifferentiating tissue ) to seven cell stages ( stem cells , terminally differentiated cells , and five intervening TAC stages ) . We also used mutation experiments to study the effects of differentiation knockout mutations ( see Mutations that Disrupt Differentiation ) . In Experiment 4 , we allowed stochastic mutations to disrupt differentiation pathways at a rate of 10−4 mutations per cell division . This mutation was heritable upon cell division , so that it prevented further differentiation in the entire resulting clone . We stopped the simulation run when either the clock reached 10 , 000 timesteps or the total cell number reached ten times the initial level , which we interpreted as the initiation of a neoplasm . Each experiment was replicated at least 100 times .
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Darwinian natural selection and evolution is usually studied in populations of organisms . However , it is possible , in principle , in any population of cells , including the population of cells that constitutes a multicellular animal . Such “somatic” evolution among cells within an organism tends to reduce their cooperation , and thus to threaten the integrity of the organism . It is believed that this problem must have been solved somehow to allow the evolutionary emergence of multicellular animals . However , it has also been suggested that some pervasive pathologies reflect the persistence of some level of somatic evolution Here , we propose that a well-known pattern of ongoing cell differentiation in the mature tissues of animals functions to suppress somatic evolution . We test his hypothesis using a computer simulation of cell population dynamics and evolution . The results are consistent with our hypothesis , and suggest that cancer and senescent decline with aging may be attributable to a failure of this mechanism to completely suppress cellular evolution .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"evolutionary",
"biology",
"non-clinical",
"medicine",
"animals",
"developmental",
"biology"
] |
2007
|
Animal Cell Differentiation Patterns Suppress Somatic Evolution
|
Avoiding collisions is one of the most basic needs of any mobile agent , both biological and technical , when searching around or aiming toward a goal . We propose a model of collision avoidance inspired by behavioral experiments on insects and by properties of optic flow on a spherical eye experienced during translation , and test the interaction of this model with goal-driven behavior . Insects , such as flies and bees , actively separate the rotational and translational optic flow components via behavior , i . e . by employing a saccadic strategy of flight and gaze control . Optic flow experienced during translation , i . e . during intersaccadic phases , contains information on the depth-structure of the environment , but this information is entangled with that on self-motion . Here , we propose a simple model to extract the depth structure from translational optic flow by using local properties of a spherical eye . On this basis , a motion direction of the agent is computed that ensures collision avoidance . Flying insects are thought to measure optic flow by correlation-type elementary motion detectors . Their responses depend , in addition to velocity , on the texture and contrast of objects and , thus , do not measure the velocity of objects veridically . Therefore , we initially used geometrically determined optic flow as input to a collision avoidance algorithm to show that depth information inferred from optic flow is sufficient to account for collision avoidance under closed-loop conditions . Then , the collision avoidance algorithm was tested with bio-inspired correlation-type elementary motion detectors in its input . Even then , the algorithm led successfully to collision avoidance and , in addition , replicated the characteristics of collision avoidance behavior of insects . Finally , the collision avoidance algorithm was combined with a goal direction and tested in cluttered environments . The simulated agent then showed goal-directed behavior reminiscent of components of the navigation behavior of insects .
Anyone who has tried to catch flying flies will be familiar with their amazing performance . Within a fraction of a second , flies perform high-speed turns to avoid a predator or a collision with an obstacle . The collision avoidance decisions are produced in a fly’s brain with very limited neural resources [1 , 2] and are transformed into an evasive turn within only a few milliseconds , a rather short time compared to human reaction times [3] . As such , flying insects have become an important model system for understanding the minimal computation requirements for spatial vision tasks , such as collision avoidance [4] . Engineers are also looking for fast and cheap collision avoidance algorithms , without the use of expensive devices , e . g . 3D laser rangefinders [5] , or extensive computations , e . g . Lucas-Kanade optic-flow computation [6] . Any motion of an agent , such as an insect or a robot , induces apparent movement of the retinal image of the surroundings , i . e . optic flow . The optic flow experienced during translations in a static environment depends on the agent’s speed , its nearness to objects and its motion direction . When the agent moves fast or close to objects , the optic flow amplitude will be high . By contrast , the rotational optic flow depends only on the ego-motion of the agent and , thus , is independent of the spatial layout of the environment . Information on the nearness of objects is relevant for determining a collision avoidance direction . Therefore , the translational optic flow can be exploited for collision avoidance . Flies , and also other insects and some birds , show an active gaze strategy , which separates the self-motion into saccades ( i . e . mainly rotation ) and intersaccades ( i . e . mainly translation ) [7–15] . The saccade amplitude of an insect or a bird is thought to be driven , at least in the vicinity of potential obstacles , by the optic flow gathered during the translation preceding the saccade . Insects estimate the optic flow with correlation-type elementary motion detectors ( EMDs ) , a concept first introduced by Reichardt and Hassenstein in the 1950s [16] . A characteristic property of the EMD is that its output does not exclusively depend on velocity , but also on the pattern properties of the stimulus , such as its contrast and spatial frequency content . Therefore , the nearness , extracted from optic flow estimated by insects , is expected to be entangled with properties of the textures of the environment . Visual-oriented tasks based on optic flow , such as collision avoidance , might , therefore , be a challenge . Several mechanisms of collision avoidance have been proposed based on behavioral experiments on various insect species [14 , 17–23] . However , these models have not yet been shown to be functional under a wide range of conditions , or do not use optic flow measured by correlation-type elementary motion detectors . In the present paper , we propose a model of collision avoidance based on EMDs which will be shown to be successful in various environments . The model of collision avoidance can be subdivided into three processing steps: ( 1 ) extraction of nearness from optic flow , ( 2 ) determination of a collision avoidance direction from the map of nearness estimations , i . e . where to go , and ( 3 ) determining a collision avoidance necessity , i . e . whether it is dangerous not to follow the collision avoidance direction . The nearness measurements will be shown to be proportional to a pseudo-norm of the optic flow , independent of the direction of motion , as long as the agent moves in a plane and has a spherical eye . The collision avoidance direction and necessity will be computed via spatial integration of the nearness . The collision avoidance algorithm will , firstly , be tested with geometrical optic flow , i . e . a measure of optic flow independent of object texture , to build a benchmark and show that optic flow information is sufficient to solve the problem . Then , EMDs will be used and the algorithm will be challenged in different environments . Finally , we will show that the collision avoidance algorithm based on EMDs can be coupled with a navigation direction in order to reach a given location without colliding with obstacles along the trajectory .
When a distant object is approached at a high speed , the situation might be as dangerous as when a close object is approached at a slower speed . The relative nearness , i . e . the nearness times the agent’s speed , can be seen as a measure of how soon the agent will collide with the object when the agent moves in the direction towards where the measurement was performed . This information is highly relevant for collision avoidance . Since the relative nearness is linked to the optic flow , the first step of the collision avoidance algorithm is to transform the optic flow into relative nearness . The translational optic flow , i . e . the optic flow experienced during the brief translatory phases of self-motion modeled after the intersaccadic intervals of insect flight , is determined jointly by the agent’s self-motion and the three-dimensional structure of the environment . The independent extraction of these two parameters entangled in the optic flow field is challenging [24] . We will show that the three-dimensional structure of the environment can be extracted from translational optic flow if the translation is confined to a plane and the eye of the agent is spherical . The optic flow field is a two-dimensional vector field , where each vector is the apparent velocity of the objects on the eyes of the agent . The optic-flow field experienced during translation results from the product of the relative nearness of objects in the environment and a factor depending on the angle between the direction of self-motion and the direction in which these objects are seen ( “viewing angle” ) . A transformation removing the factor depending on the viewing angle is required to extract relative nearness from optic flow . The dependence of this factor on the viewing angle can be understood best when the relative nearness is constant for the entire visual field , i . e . when the agent is placed in the center of a sphere and moves in the equatorial plane . The optic-flow field for a spherical eye can be expressed for each point in the visual field in terms of the vertical flow component , i . e . the flow along the elevation , and the horizontal flow component , i . e . the flow along the azimuth . The horizontal flow component , experienced during a translation in the equatorial plane in the center of a sphere , increases from the front to the side ( i . e . 90° away from the motion direction ) and then decreases again towards the back ( S1B Fig ) . The horizontal flow is independent of the elevation ( see S1 Text , Eq . S5 ) . Respectively , the vertical optic-flow component decreases from the front to the side , and then , increases again towards the back . By contrast , the vertical flow is not symmetric by rotation around the direction of motion and , therefore , depends on the elevation ( see S1 Text , Eq . S5 ) . It increases from the equator to the poles ( S1A Fig ) . Therefore , the horizontal flow and the vertical flow have an antagonistic variation from the front to the back . Due to the assumption that the movement of the agent is constrained to the equatorial plane , the variation of the vertical flow with elevation does not depend on the direction of motion . This variation can , therefore , be corrected ( see S1 Text , and S1C Fig ) . Interestingly , the sum of the horizontal flow squared and the corrected vertical flow squared can be shown to be independent of the viewing angle ( see S1 Text , Eq . S8 ) . The transformation will be called a retinotopically modified norm of the optic-flow field . When the agent does not move within a sphere , the result of this transformation will not be constant for every viewing angle , but equal to the product of speed ( v ) and nearness ( μ ) , i . e . the relative nearness . The optic flow has two singular points , the focus of expansion ( FOE ) and the focus of contraction ( FOC ) . At these two points , the result of the retinotopically modified norm of the optic flow will be null , independent of the nearness of objects . The relative nearness can be extracted from the optic flow independent of the viewing angle , except for the FOE and the FOC . This problem can be solved by combining translational flow-fields arising from different directions of translational movement and , thus , with different FOCs and FOEs . However , an agent cannot easily obtain several translational flow fields centered at a given point in the world . On the other hand , the nearness to objects does not strongly differ in realistic environments for two sufficiently close points in space . Therefore , let us consider an agent performing a translation composed of sub-translations in different motion directions , i . e . a combination of different forward and sideways motion components ( Fig 1 part 1 ) . Each sub-translation leads to an optic-flow field which has the retinotopically modified norm properties . The average optic-flow component obtained from the series of sub-translations also has the retinotopically modified norm properties , but does not have singular points ( Fig 1 part 3 ) . The agent can then compute the relative nearness to objects within its entire visual field by using the retinotopically modified norm of the averaged squared optic flow ( Fig 1 part 4 ) . When the object nearness for a viewing direction changes during the translation , the relative nearness map will be blurred . The longer the spatial lengths of the sub-translations are , the more the relative nearness map is blurred . This effect does not necessarily cause problems for collision avoidance , because the blurred relative nearness map still represents the overall depth-structure of the environment , though on a slightly coarser scale ( Fig 2 ) . Fig 2 shows the nearness map computed from the geometrical optic flow in an environment containing two objects . At a higher speed , the nearness map is blurred due to the integration of the geometrical optic flow over time . Once the relative nearness map is known , in which direction the agent should move to avoid a collision ( collision avoidance direction , CAD ) and how important it will be to follow this direction ( collision avoidance necessity , CAN ) need to be determined . Based on this information , the amplitude of the necessary saccade-like turn was determined . In order to establish a benchmark for the performance of this algorithm , it was developed , firstly , on the basis of the geometrical optic flow . Only the shape of the environment along the azimuth is required to perform collision avoidance for movements in a plane . Therefore , averaging the relative nearness map along the elevation does not lead to a loss in spatial resolution along the azimuth ( Fig 1 part 5 ) and , thus , should not affect the collision avoidance performance . As will be shown below , this averaging was especially relevant when the relative nearness map was estimated on the basis of EMD responses ( see CAD and CAN from EMD ) . The averaged relative nearness can be represented by vectors in polar coordinates with the argument of the vectors being the azimuth and their length the relative nearness averaged along the elevation ( Fig 1 part 6 ) . The vector sum of all averaged relative nearness vectors will be termed the Center-Of-Mass Average Nearness Vector ( COMANV ) . It points towards the average direction of close objects in the environment ( Fig 3 ) . It may , therefore , be a plausible strategy to turn in the opposite direction to the COMANV , i . e . the CAD , to avoid obstacles . This zero-order approach is , to some extent , similar to the collision avoidance algorithm used by 3D range finder robots [25] . It may lead to suboptimal trajectories . A more optimal strategy would be to pick a direction without obstructions [26] . However , this strategy would require a reliable relative nearness map provided by local self-localization and mapping [27] . The argument of the COMANV provides the agent with a direction to avoid a collision by pushing it away from obstacles . Fig 4A shows a closed-loop simulation of collision avoidance in a box . The agent trajectories converge at the center of the box . Indeed , if an agent is pushed away from obstacles at every location in an environment , it ends at a point in the environment equilibrating object distances . However , collision avoidance behavior is not necessary if the obstacles are sufficiently far from the agent . To allow the agent to assess when it has to avoid an object , a measurement of CAN is required . The argument of the COMANV , the CAD , has been used , so far , to compute the saccade amplitude and , thus , to determine the agent’s new direction of motion . However , the norm of the COMANV also has interesting properties: It has the same unit as the relative nearness , i . e . the inverse of a time . Hence , the norm of the COMANV can be regarded as a measurement of the CAN: the larger the norm of COMANV , the larger is the necessity for the agent to make an evasive behavioral response . To assess the relationship between the distance to objects and the norm of the COMANV , the relative nearness map has been extracted at different distances to the wall . Since the apparent size of the objects might also affect the norm of the COMANV , different wall heights were used ( Fig 3 ) . The norm of COMANV increases with both the apparent size of an object and the nearness to it . The apparent size of the object has a smaller effect on the norm than the nearness . Thus , the norm can be used as a measurement of CAN . Depending on the amplitude of the CAN , the agent may have one of two behavioral options . It should turn via a saccade toward the CAD calculated if the CAN is sufficiently high . Alternatively , it should continue moving straight if the CAN is smaller than a critical value . However , the CAN does not necessarily affect the behavior of the agent in an all-or-nothing fashion , i . e . making a turn according to CAD or making no turn at all . Rather , a kind of compromise may also be possible . Since the CAN is a continuous variable , the agent may turn , via a saccade , towards a direction which is a compromise between CAD and the previous direction of motion . The compromise can be modeled as a weight given by a sigmoid function of the CAN ( see Materials and Methods ) . The saccade amplitude is then the product of this weight and CAD . The sigmoid function of CAN is parameterized by a threshold and a gain . The gain controls how much the saccade amplitude corresponds to CAD . A high gain will approximate a behavior with two distinct states: “turn , by a saccade , toward CAD” or “continue moving straight” ( Fig 4D ) . A small gain will , however , generate a smooth transition between the two behaviors , modeling a decreasing saccade amplitude with decreasing CAN ( Fig 4C ) . The threshold determines the border between the zone in the environment where saccade amplitudes are mainly driven by CAD , i . e . collision avoidance is necessary , and the zone where the saccade amplitudes are mainly driven by the previous direction of motion , i . e . collision avoidance is not necessary ( S3 Fig ) . The effect of the threshold can be seen by comparison Fig 4B and 4C . In summary , the collision avoidance algorithm uses the COMANV to determine the collision avoidance direction CAD and to change the behavior of the agent . The algorithm can be subdivided into five steps . The collision avoidance algorithm has been designed on the basis of geometrical optic flow and operates successfully on this basis . However , the properties of the optic flow , as measured by EMDs , differ considerably from the geometrical optic flow . Several model variants of EMDs have been developed ( e . g . [28–30] ) . We used a rather simple EMD model version ( similar to [31 , 32] ) in this study , because we wanted to test whether collision avoidance can already be accomplished by the basic correlation-type motion detection mechanisms with as few model parameters as possible . The drawback with EMDs , at least from the perspective of velocity estimation , is that their responses do not only depend on the velocity of the retinal images , but also on their contrast and other textural properties [33 , 34] . Therefore , it is not clear in advance whether the collision avoidance algorithm , as described above and being successful based on geometrical optic flow , will also work with optic-flow estimates based on EMDs . The collision avoidance algorithm based on EMDs will be tested in two steps . In this section , we will assess to what extent the COMAMV derived from EMD measurements matches the COMAMV based on geometrical optic flow . In the next section , we will test the collision avoidance algorithm equipped with EMDs under closed-loop conditions . As the first essential step , the relative nearness map is extracted from EMD responses . The texture dependence of the EMD measurements is somewhat reduced by spatial averaging along the elevation of the visual field ( see above ) . The consequences of this averaging are shown in Fig 5 for an exemplary simulation ( see also S7 Fig ) . The agent performed a translation inside a box covered with natural images of grass . The relative nearness map obtained from EMDs does not only depend on the geometrical nearness , but also on the texture of the wall ( Fig 5 ) . Although integration along elevation reduces the pattern dependence to some extent , the integrated relative nearness map still contains “fake holes” ( e . g . those that result from extended vertical contrast borders; Fig 5 ) . These “fake holes” may mislead the agent when looking for relative nearness lower than a certain threshold . As the second step of the collision avoidance algorithm , the COMANV and the CAD have to be computed from the EMD-based relative nearness map . Ideally , the CAD based on EMDs should coincide with the one determined from the geometrical optic flow . The CADs determined in both ways are the same for the example shown in Fig 5 . To assess whether this finding also generalizes to other environments , the simulations were extended to cubic boxes with the agent translating parallel to one wall of the box from different starting positions . The angle between the CAD based on EMDs and the one based on geometrical optic flow were computed for every starting position . Fig 6 shows that the CADs based on geometrical optic flow are similar to the CADs based on EMDs if the agent is not too close to the wall and not too close to the center of the box ( S4 Fig ) . Moreover , the higher the walls of the box are , the more CADs determined in the two ways coincide ( Fig 6 ) . The collision avoidance algorithm requires an increasing length of the COMANV with an increasing relative nearness to objects , in order to provide a good estimate of the CAN . The variation of the length of the COMANV with the distance to the wall has been studied with geometrical optic flow in a cubic box . The same environment has been tested with EMDs , but with several different patterns . Similar to the simulation of CAD , the length of the COMANV has been computed for several points in the corridor . Fig 6 shows the dependence of the CAN on the nearness to the wall . As expected , the wall texture changes the CAN , as does the nearness to the wall . However , the CAN is still an increasing function of the nearness to the wall as long as the agent is not too close to the wall ( S4 Fig and S6 Fig ) . Therefore , the norm can be used as a reasonable estimate of CAN in this range . As shown above , information about the three-dimensional shape of the environment around the agent derived from EMD responses leads to appropriate CADs and a reliable estimate of CAN , as long as the agent is not too close to an obstacle , such as the wall of a flight arena . These results were obtained in open-loop simulations . Since EMDs use temporal filters , their responses also depend to some extent on the signal history . The time constant of the low-pass filter in one of the EMD branches is 35ms , i . e . in roughly the same range as the time between subsequent saccades of insects ( 20 to 100ms in flight arenas [8 , 14] and 50 ms in our simulation ) . Thus , the EMD response during a given intersaccade also depends on the signals generated during the previous saccade , resulting in a somehow disturbed nearness map . Taking all this into account , open-loop simulations do not allow the collision avoidance performance under closed-loop conditions to be predicted . A relatively simple and commonly used environment for experiments on collision avoidance behavior of insects are cubic or cylindrical flight arenas [8 , 35–37] . In such an environment , the agent has to avoid only the wall . Thus , the task is easier to accomplish than if objects are also present . Fig 7 shows closed-loop simulations in boxes covered with six different wall patterns ( see also S8 Fig ) . The agent is able to avoid collisions for all wall pattern conditions except the random pattern with relatively large ( 35mm ) pixels . However , the area covered by the flight trajectories varies tremendously with the pattern . The saccade amplitude depends on the gain and the threshold , which parameterize the sigmoid function of the CAN . These parameters have been kept constant for the different pattern conditions . By adjusting the threshold and the gain individually for each pattern condition , collision avoidance may be successfully performed by the agent , as long as the CAN increases with the nearness to objects and the CAD points away from obstacles ( S4 Fig and S5 Fig ) . Until now , we have used a rather simple environment compared to those experienced by an agent under more natural conditions . Objects were added to the flight box to increase the complexity of the collision avoidance task . The objects had the same sizes , shapes and textures . They were camouflaged , i . e . covered with similar patterns to the background , to increase the difficulty for the collision avoidance algorithm . Such situations occur frequently in nature , e . g . when a particular leaf is located in front of similar leaves . To discriminate such an object and to avoid a collision with it , relative motion on the eyes induced by self-motion of the animal and , thus , the relative nearness to the object as obtained from optic flow is the only cue available . Up to four objects were inserted into the box and covered with the same pattern as the walls . The agent was able to avoid collisions successfully , even in the box with four objects ( Fig 8 , S9 Fig and S10 Fig ) . However , collisions were observed in boxes containing two and four objects each covered with a 4mm random checkerboard pattern ( S10 Fig ) . Agents in natural environments may have to face even more complex situations than those tested so far , such as avoiding collisions in a cluttered environment with many objects . A forest is an example which contains many trees , i . e . many objects to avoid . Two different artificial environments with 35 randomly placed objects have been used to test the collision avoidance performance in cluttered environments . Again the objects were camouflaged with the same texture that covered the floor and the confinement of the environment . The agent tended to stay in a relatively small area of the environment where the walls were sufficiently distant ( Fig 9 ) . Hence , an agent equipped with only a collision avoidance algorithm did not travel through the artificial forest . This task was only accomplished if the collision avoidance algorithm was slightly modified to support a goal direction . The saccade amplitude , so far , was the result of a compromise , based on the CAN , between the CAD and the tendency to keep the previous direction of motion . If this direction was replaced by the direction toward a goal , the saccade amplitude became a compromise between the CAD and the goal direction , depending on the CAN . When the CAN was below the threshold , as parameterized by the sigmoid function of the CAN , the saccade amplitude is mainly driven by the goal direction . By contrast , when the CAN is higher than this threshold , saccades would be mainly driven by the CAD . The significance of the CAN could clearly be seen for trajectories close to objects . Far from the object , the agent moved toward the goal , but when it came close to the object , saccade amplitudes tended to be driven by the collision avoidance algorithm , pushing the agent in the opposite direction ( Fig 10 ) . When the goal was located at the other end of the corridor , the agent was efficiently , i . e . without making many detours , and reliably , i . e . with a low rate of collisions , able to reach the goal ( Fig 10 , S2 Text , S11 Fig ) . The number of different trajectories close to the goal location in cluttered environments is much lower than the number of starting conditions . Therefore , agents , starting from different locations , but heading towards the same goal location , have trajectories converging to similar routes . This behavior is not only a consequence of the walls confining the cluttered environment . Indeed , a similar behavior is observed in a cluttered environment without confining outer walls ( S11 Fig ) . In order to classify the similarity of the different trajectories , each trajectory was first simplified into a sequence based on the position of the agent relative to the objects in the environment . Trajectories sharing the same sequence formed one class , i . e . a route [see material and methods] . In the first ( resp . second ) cluttered environment , 8 ( resp . 11 ) and 4 ( resp . 3 ) distinct routes were found for objects and walls covered with 1mm and 4mm random checkerboard patterns , respectively ( S12 Fig , S13 Fig , S14 Fig , S15 Fig ) The routes followed by the agent may be determined by its starting position , i . e . neighboring starting positions may lead to the same route . Indeed , when an agent approaches an object from the right ( resp . left ) , it tends to avoid it by a left ( resp . right ) turn . This “decision” will be taken for every obstacle along the trajectory taken by the agent , but each “decision” depends sensibly on the position of the agent relative to the object and the goal location . Therefore , the route followed by an agent may be sensitive to the starting position . Fig 11 shows that neighboring starting locations may lead to different routes . The number of different routes close to the goal location is lower than the number of possible routes in a given environment . This indicates that , on the one hand , routes starting at different locations tend to converge into common routes and , on the other hand , different routes may share similar parts , i . e . sub-routes . As a measure of similarity between routes , the number of single sequence elements differing between two routes was used . Routes may be very similar to each other with less than five different single sequence elements ( e . g . compare route #8 and route #10 in S14 Fig ) . Different routes , therefore , share similar sub-routes . This indicates that a rather small number of locations exist where the agent “decides” to take a particular sub-route , e . g . avoid an object towards the left or the right , respectively . The collision avoidance algorithm is affected by the pattern covering the walls and the objects in the environment . This dependency may lead to different routes . Therefore , routes obtained in an environment with given object locations have been compared after changing the texture of the environment to pinpoint texture-dependent effects . Interestingly , certain classes of routes are indeed the same for the different patterns , e . g . the second route for a 1mm random checkerboard texture matches the third route for a 4mm random checkerboard texture ( Fig 12 ) . Three routes ( resp . one ) out of the four ( resp . three ) routes for the 4mm random checkerboard textures are indeed found also for the 1mm random checkerboard textures covering the first ( resp . second ) environment . This finding indicates that , despite pronounced pattern effects resulting from the properties of EMDs , the performance of the collision avoidance algorithm is , on the whole , quite stable and , to a large extent , depends on the spatial structure of the environment .
The assumptions underlying our algorithm to extract a nearness map from optic flow are: ( i ) a spherical eye , ( ii ) a translation phase combining several directions of motion ( i . e . a mixture of different forward and sideways motions ) , and ( iii ) all movements of the agent take place in the null elevation plane . The second assumption is required in order to average out the characteristic singularities in translational optic-flow fields , i . e . the FOE and the FOC , by integrating the optic-flow amplitudes obtained during translations in slightly different directions ( i . e . a mixture of forward and sideways motions ) . Indeed , the direction of translational movements between saccades of flying insects is not always constant with respect to the orientation of the body long axis . This means that the relationship between the forward and sideways motion components may change systematically even between two consecutive saccades . Extreme examples in this regard are shown in Fig . 3 of [8] . However , more moderate continual changes in the ratio between forward and sideways translational components occur , as a consequence of inertia , after virtually all saccades , with the strength of these changes depending on saccade amplitude [8 , 14] . Therefore , flying insects could average the optic flow generated on the eyes during these continual intersaccadic changes in flight direction and then extract the relative nearness to determine the direction and amplitude of the next saccade . However , as a consequence of the inevitable time constants of the motion detection system , the EMD responses following a saccade might also be affected by the rotational optic flow of the previous saccade . Although the rotational part of the optic flow could , in principle , be removed if the angular velocity of the agent was known , this transformation would not be straightforward on the basis of motion measurements based on EMDs [38] , given their dependency on the texture of the environment . The integration of the optic-flow amplitudes along intersaccades is also important to decrease the dependency on the texture in the environment characteristic of EMD responses . For simplicity , the intersaccade duration has been kept constant in our simulations , although in free-flying flies , it was found to vary from 20 to 100ms [8 , 14] . By increasing the duration of an intersaccade , the dependency on the texture in the environment can be further decreased if the integration time is increased accordingly . An increase in integration time has similar effects to increasing the extent of spatial integration along the direction of motion [39] . However , the longer the duration of the intersaccadic translation is , the more blurred the relative points of nearness are , as shown in Fig 2 . On the other hand , the intersaccade duration might be linked to the collision avoidance necessity . Indeed , collision avoidance may be unnecessary when no obstacles are encountered , as shown by the closed-loop simulations of goal-oriented behavior ( Fig 9 and Fig 10 ) . If the collision avoidance necessity is low , long intersaccades are possible . However , if the collision avoidance necessity is high , short intersaccades followed by an evasive turn are required . The third assumption of the algorithm of nearness calculation , i . e . that the agent only moves in the null elevation plane , is certainly not exactly satisfied in free-flying insects . However , during most flight manoeuvres , changes in height occur to a much smaller extent than changes in the horizontal plane . Nonetheless , if an agent moves in another direction than in the null elevation plane and estimates the nearness with our algorithm , its estimation will have an error proportional to the upward component ( S1 Text ) . As long as this component is small , the estimated nearness map will not be strongly affected . We are currently investigating how our algorithm for nearness estimation can be extended to arbitrary movements in three dimensions . We have shown that relative nearness can be extracted from geometrical optic flow . However , if the nearness algorithm receives its input from correlation-type EMDs , complications arise from the dependence of the EMD responses on ( i ) the contrast of the stimulus pattern , ( ii ) its spatial frequency content , and ( iii ) the fact that it is not related to velocity in a linear way , but first increases , reaches an optimum value and then decreases again [40] . Indeed , these dependencies are somehow reflected in the extracted relative nearness ( see , for example , Fig 5D ) . The EMD has a quadratic response dependence on contrast if no additional nonlinearities are inserted in its input lines [16] . Indeed , it has been recently shown that the norm of the EMD response is correlated to the nearness times the local contrast or , in other words , EMDs have been shown to respond best to the contrast contours of nearby objects ( see also , Fig 5 ) [41] . The dependency on contrast can , in principle , be reduced by applying a nonlinearity before the multiplication stage of an EMD [28 , 29 , 33] . Nonlinearities tend , however , to complicate the mathematical analysis of the EMD response . Therefore , we have chosen to use a simple EMD version . Moreover , we wanted to test how well the collision avoidance algorithm performs on the basis of the basic correlation-type motion detection mechanism with as few model parameters as possible . The average output of an EMD depends on the temporal frequency of a motion stimulus ( i . e . the ratio of angular velocity and wavelength of its spatial Fourier components ) rather than its real velocity [33 , 34] . To extract the real angular velocity from EMD responses , the dependency on temporal frequency needs to be reduced . Plett et al . suggested using the spatial power spectrum of a panorama to extract the angular velocity from EMD responses during rotation [38] . However this transformation is not suitable for translation , because the spatial frequency content of the panorama is not related unambiguously to the temporal frequency observed during translation . Therefore , it is not easily possible to compensate for these dependencies . Nearness extracted from the nonlinear , but monotonic response range of the EMDs may lead to a distorted representation of the depth-structure of the environment . Nevertheless , larger EMD responses still correspond to greater nearness . Ambiguous nearness estimates will arise for the nonlinear and non-monotonic , i . e . the ambiguous , response range of the EMD . Therefore , close objects leading to large retinal velocities might be mistaken for far objects . In the context of nearness extraction during translation , this problem will arise for objects on the lateral side of the agent . To avoid this problem , an agent has two possibilities: reduce its translational speed or extract nearness information only in the more frontal parts of the visual field . The agent in our simulations was moving at a relatively slow speed . Therefore , this ambiguity was not observed . Moreover , flying insects have been concluded to use the frontal part of their visual field to compute saccade amplitudes ( flies , [14] ) and to reduce their flight speed when the clearance to objects in the environment gets small ( e . g . bumblebees [42]; flies: [14] ) . It is , however , unclear how flying insects compute the CAD only taking into account motion measurements in the frontal part of the visual field . A robot does not need to estimate optic flow with EMDs . The estimation can be carried out with image-based methods , e . g . the Lucas Kanade algorithm [6] , or event-based methods [43] . Image-based methods are , however , time-consuming and , therefore , are not really suitable for applications in real-time . However , event-based flow-field detectors are fast and reliable . Our algorithm to extract nearness from optic flow is only local , i . e . it only uses the optic-flow vector in a given viewing direction to compute the relative nearness in this direction . Therefore , our collision avoidance algorithm could be easily coupled with EMDs to determine the nearness around the robot in real-time as a cheap alternative to a 3D laser rangefinder . Both biological and technical agents often need to reach a goal , e . g . their nest in the case of many insects or a charging station in the case of a robot , without colliding with the objects along their trajectory . This goal direction in a real world could be provided by path integration and visual navigation [44] or , in the case of a technical agent , by GPS . Therefore , when the agent is in cluttered environments , it needs to somehow integrate the goal direction and the collision avoidance direction . Our collision avoidance algorithm has been shown to support a goal direction by using CAN , leading to a behavior that represents a kind of compromise between collision avoidance and reaching the goal . Interestingly , the trajectories of the agent , even in complex cluttered environments , tend to converge on a limited number of distinct routes largely independent of the starting position ( see Fig 10 ) when coupled to a goal direction . The appearance of routes is not a unique property of our collision avoidance algorithm . Similar trajectories are also followed by an agent with different control strategies and different collision avoidance algorithms ( e . g . see Fig . 6 . in [19] , and Fig . 4 . in [23] ) . Ants perform in a similar way and also follow similar trajectories in cluttered environments when returning to their nest ( e . g . [45–48] ) . We could show that this type of behavior can be explained in a relatively simple way by combining our local collision avoidance algorithm only with an overall goal direction . By contrast , the similarities of trajectories of ants have often been interpreted within the conceptual framework of a route-following paradigm . According to this paradigm , the agent is assumed to store local information along the trajectory during an outbound run ( e . g . leaving the nest ) , which will be used to determine the direction to follow during an inbound run ( e . g . returning to the nest ) [49 , 50] . In our simulations , we observed that trajectories which may be interpreted as resulting from route-following could , alternatively , arise just from a collision avoidance algorithm coupled to a goal direction . Therefore , part of the route-following behavior observed in insects could be a consequence of a collision avoidance algorithm . Hence , the route-following direction does not need to be determined at every point along the trajectory , but its determination may be sparsely spaced . The routes to the goal followed by our agent depend on the starting location , i . e . neighboring starting locations may lead to different routes ( Fig 11 ) . The same behavior has been observed in ants [48] . However , the different routes are not equivalent in term of efficiency . Indeed , in Fig 11A and 11B , the route #2 is dead-end , and route #3 reaches the goal . An agent may need to use the most efficient route , i . e . add waypoints in the environment indicating which route to follow . The routes #2 and #3 ( resp . #1 and #2 ) shown in Fig 11 , for example , could be merged by adding only one waypoint just where the two routes emerge . Therefore , insects may place a small number of well chosen waypoints in the environment to prevent the dead-end problem observed in our simulations 10 and possibly to select the most efficient routes ( S2 Fig ) without requiring a large memory .
The environments used in our simulations have been inspired by previous behavioral experiments on flies and bees . Schilstra and van Hateren used a cubic box with an edge length of 40cm [8 , 9] . This box was covered with natural images on its side walls and with a black/gray and a white/gray irregular pattern on the floor and the ceiling , respectively . The side walls of the box were covered alternatively by 1mm , 8mm or 35mm black and white random checkerboard patterns to investigate the effect of the texture in another set of simulations . Simulations were also done in boxes with one , two or four obstacles covered with 1mm black and white random checkerboard patterns to make the collision avoidance task more difficult . The obstacles had the same height as the box and a square cross-section with an edge length of 30mm . The wall of the box had the same texture as the objects . The obstacles were placed at positions as shown in Fig 8; they were vertical bars of 40cm height and a quadratic base with a side length of 3cm . Cluttered environments with 35 obstacles of different sizes were used in another set of simulations . Every wall in the environment ( object sides and corridor walls ) was covered by either a 1 mm or a 4 mm random checkerboard pattern . The obstacles had a square base and a height five times their side length , and were randomly positioned in the inner part of a 2000 × 1000 × 400mm box . Two environments were selected based on the homogeneity of the obstacle positions . The first environment was composed of five objects with an edge length of 80mm , five with an edge length of 72mm , ten with an edge length of 64mm , five with an edge length of 56mm , five with an edge length of 48mm , and five with an edge length of 40mm . The second environment was composed of the same number of objects , except that ten objects with an edge length of 72mm and five objects with an edge length of 64mm were used . Once the environment had been created , a panoramic view from any position within the environment could be generated and the distance to objects determined . The set of vertices X , Y , Z that define an object , as well as the floor and the ceiling , were translated to the current position of the agent . An environment map was rendered using OpenGL . The input image was sampled by Gaussian-shaped spatial low-pass filters ( σ = 2° ) . The output of these filters formed the input to the photoreceptors that were equally spaced at 2° along the elevation and azimuth of the eye . The array of photoreceptors formed a rectangular grid in the cylindrical projection with 91rows and 181columns . The temporal properties of the peripheral visual system was modeled as a temporal filter with a kernel that was derived from an electrophysiological analysis of the responses of second-order visual interneurons in the fly visual system to white-noise brightness fluctuations [51 , 52] . The filter kernel is a kind of temporal band-pass filter with a DC component ( for a formal description , see [31] ) . The outputs were , furthermore , filtered with a first-order temporal high-pass filter ( time constant 20ms ) to remove the DC component . The filtered outputs of neighboring elements were fed into elementary motion detectors of the correlation type with a first-order temporal low-pass filter ( time constant 35ms ) in one of its branches . Each local movement detector consisted of two mirror-symmetrical subunits . In each subunit , the low-pass filtered signal of one input channel was multiplied with the high-pass filtered signal of the neighboring input channel . Elementary motion detector signals depend on the scenery ( e . g . [33] ) . Therefore , a benchmark of optic-flow measurement independent of scenery was necessary . The optic flow can be computed when the self-motion of the agent and the nearness to objects is known . In a virtual environment , both pieces of information are accessible . The set of vertices X , Y , Z , that defines the object were translated to the current position of the agent . Then , the nearest point on the retina for each viewing direction was extracted . The geometrical optic flow was then computed from the nearness and the self-motion [24] , giving a motion measurement independent of the texture of the scenery . The optic flow experienced during translation is linked to the nearness of the agent to objects in the environment and its self-motion . Assuming that the agent moves in the null elevation plane and uses a spherical eye , it can be shown that the relative nearness ( vμ ) is linked to a retinopically modified norm of the optic flow: ( vμ ( ϵ , ϕ ) ) 2=OF ( ϵ , ϕ ) ϕ^2+OF ( ϵ , ϕ ) ϵ^2sin2 ( ϵ ) ( 1 ) where v is the speed , μ is the nearness to the object in the viewing direction ( ϵ , ϕ ) , ϵ the elevation , ϕ the azimuth , and OF the optic-flow vector . This function holds as long as the elevation is not zero , but a similar equation can be used for null elevation ( S1 Text ) . The relative nearness cannot be computed at the FOE or the FOC due to the singularity , i . e . absence of apparent motion , in the flow field . To remove these singularities , the flow fields resulting in two different motion directions at the same location in the environment might be averaged . However , insects are unlikely to fly twice at the same position in the environment . However , they can fly subsequently in two different directions at two nearby points in space . The nearness at those points will be almost equal as long as the distance between these points is relatively small . Therefore , the agent performed a translation , composed of 50 segments with different motion directions . The motion direction of each segment followed a normal distribution centered at zero with a standard deviation of 18° ( Fig 1 part 1 ) . The translation is thought to correspond to an intersaccade of insect flight . A stack of optic-flow fields along the time , i . e . during the intersaccade , was gathered ( Fig 1 part 2 ) . Although it is possible to compute the nearness for each optic-flow field and then integrate the nearness over time , we used an alternative , but equivalent approach . The optic-flow fields were squared and integrated over time ( Fig 1 part 3 ) . Then , the integrated squared optic flow was used to compute the nearness map of the environment ( Fig 1 part 4 ) . When the optic-flow field is estimated by EMDs , the estimations also depend on the motion history due to the temporal filters in the EMD . The saccade preceding the translation , therefore , interferes with the optic-flow measurements during the intersaccadic interval . This effect decreases over time . Therefore , the optic-flow field was not integrated during the entire intersaccadic phase , but only for the last five segments ( i . e . last 5 ms ) . Once the nearness map is known , it is an obvious strategy to avoid collisions by moving away from the maximum nearness value . However , the nearness map derived from the EMDs also depends on pattern properties . Thus , the nearness map was averaged along the elevation , giving the average nearness for a given azimuth and , thus , reducing the texture dependence ( Fig 1 part 5 ) . Each of these averaged nearness values could be represented by a vector in polar coordinates , where the norm of the vector is the averaged nearness and its argument the azimuth . The sum of these vectors points towards the average direction of close objects in the environment when the effect of the pattern on the EMD responses is sufficiently averaged out . Thus , the opposite of this vector will point away from the closest object and , thus , is selected as the motion direction of the agent in order to avoid a collision ( Fig 1 part 6 ) . Moreover , the length of the vector increases with the nearness to objects and the apparent size of the object . Thus , its length can be used as a measure of the collision avoidance necessity . This measure drives the state of the animal between “collision avoidance” and “move in the previous direction” according to the following equation: γ = W ∥ C O M A N V ∥ arg C O M A N V + ( 1 - W ) ( α + σ ) W ∥ C O M A N V ∥ = 1 1 + ∥ C O M A N V ∥ n 0 - g ( 2 ) where , COMANV is the vectorial sum of the vertically integrated nearness values , W is the weighting function based on the norm of the COMANV , α the goal direction and σ is a goal direction noise . The weighting function used in the simulation is a sigmoid function , which is driven by a gain g and a threshold n0 . The goal direction has been fixed to zero for the simulation in boxes . The agent , thus , continues moving straight , i . e . a saccade with null amplitude , when the CAN is zero . In other simulations , the goal direction is different from zero . The agent then performs a saccade amplitude driven by the goal direction when the CAN is zero . Even when the starting position of different runs of the agent differ , the trajectories that are taken by the agent in a given cluttered environment tend to converge to similar routes . When approaching an obstacle , an agent may avoid it by a left or right turn , leading to either of two different routes . Therefore , the obstacles are the main factors affecting the overall structure of the trajectories and , thus , their similarity . In order to cluster trajectories into routes , a triangular meshing ( Delaunay triangulation [53 , p . 513-529] ) of the environment was calculated with the nodes of the meshing corresponding to the center of mass of the obstacles . The meshing is , thus , composed of triangular cells formed by three neighboring obstacles . The cells in the meshing do not overlap . A trajectory of the agent crosses a succession of triangular cells and can , therefore , be associated to a sequence . Here , each element in the sequence represents a given cell in the meshing , i . e . a given region of the environment . The agent may visit a region more than once , by making detours or oscillating between two neighboring cells . The sequence was simplified in order to remove multiple visit by suppressing subsequences between identical sequence elements . Once each trajectory has been associated to a sequence of cell occupancy , the trajectories sharing exactly the same sequence were attributed to a cluster , i . e . a route . Each route corresponds , therefore , to a unique sequence . Different routes may be similar . To quantify the similarity between routes , the number of single sequence elements , i . e . a cell , not shared by two routes was used . This measure of route similarity is similar to the Hamming distance [54] between route sequences .
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The number of robots in our surroundings is increasing continually . They are used to rescue humans , inspect hazardous terrain or clean our homes . Over the past few decades , they have become more autonomous , safer and cheaper to build . Every autonomous robot needs to navigate in sometimes complex environments without colliding with obstacles along its route . Nowadays , they mostly use active sensors , which induce relatively high energetic costs , to solve this task . Flying insects , however , are able to solve this task by mainly relying on vision . Any agent , both biological and technical , experiences an apparent motion of the environment on the retina , when moving through the environment . The apparent motion contains entangled information of self-motion and of the distance of the agent to objects in the environment . The later is essential for collision avoidance . Extracting the relative distance to objects from geometrical apparent motion is a relatively simple task . However , trying to accomplish this with biological movement detectors , i . e . movement detectors found in the animal kingdom , is tricky , because they do not provide unambiguous velocity information , but are much affected also by the textural properties of the environment . Inspired by the abilities of insects , we developed a parsimonious algorithm to avoid collisions in challenging environments solely based on elementary motion detectors . We coupled our algorithm to a goal direction and then tested it in cluttered environments . The trajectories resulting from this algorithm show interesting goal-directed behavior , such as the formation of a small number of routes , also observed in navigating insects .
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[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2015
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A Bio-inspired Collision Avoidance Model Based on Spatial Information Derived from Motion Detectors Leads to Common Routes
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Epistasis is a key concept in the theory of adaptation . Indicators of epistasis are of interest for large systems where systematic fitness measurements may not be possible . Some recent approaches depend on information theory . We show that considering shared entropy for pairs of loci can be misleading . The reason is that shared entropy does not imply epistasis for the pair . This observation holds true also in the absence of higher order epistasis . We discuss a method for reducing the number of false positives . However , our main conclusion is that entropy-based approaches have serious limitations in this context .
Methods for inferring gene epistasis , or gene interactions , without fitness measurements are valuable for many reasons . It may be difficult or costly , if even possible , to accurately measure fitness for populations in nature . One approach depends on information theory . Briefly , entropy can be used for finding nonrandom associations for individuals in a population [1] . For instance , if two mutations tend to co-occur , then they will have nonzero shared entropy . Gupta and Adami [2] consider entropy for HIV drug resistance mutations and interpret shared entropy for a pair of mutations as evidence for pairwise epistasis . However , the authors’ conclusion is not valid under realistic assumptions . One cannot deduce pairwise epistasis from shared entropy . Shared entropy may have other causes . For instance , it is well established that a particular mutation ( or substitution ) may serve as a door opener for new mutations [3 , 4] . The new mutations are selected for only if the first mutation has occurred . Such constraints are known from antimicrobial drug resistance , and they may explain many cases of parallel evolution . In concrete terms , suppose that A and B are drug resistance mutations , but that B is selected for only if A has occurred . This would be the case if the beneficial effect of B depends on the presence of A . In a list of clinically found drug resistance mutants , B would not appear unless A is present ( such patterns are far from rare for resistance mutations ) . The connection to entropy should be clear . Suppose that another mutation , C , depends on A as well . In that case it is quite plausible that B and C tend to co-occur in the population , and an analysis would reveal nonzero shared entropy for B and C . However , the fitness effects of B and C may be completely independent; i . e . , there is no epistasis for B and C . It is easy to see how misleading the shared entropy condition for pairwise epistasis can be . Suppose that ten mutations are mutually independent ( the presence of the others is neither an advantage nor a disadvantage ) but that they all depend on A . Then one would identify shared entropy for 55 pairs although there is epistasis for 10 pairs only . The shared entropy condition is misleading also for the most simple systems . Example 1 in S1 Text concerns a system with no higher order epistasis , i . e , no epistasis beyond pairwise interactions . Two mutations in the system have shared entropy , although their fitness effects are independent . Indeed , 2-way epistasis was excluded both according the geometric classification of gene interactions [5] and according to an approach that depends on Walsh coefficients [6] . Moreover , for a slightly more involved case where the starting point is a heterogeneous population ( see Example 3 in S1 Text and the subsequent discussion ) the shared entropy is as high as could be for two mutations , although there is no epistasis for the pair . However , the starting point in Gupta and Adami [2] is sound . If there is no epistasis at all in a system , then one would measure little or no shared entropy for mutations under ideal circumstances . The question is if one can learn anything more specific about epistasis from shared entropy . Our analysis of the simple rule that shared entropy for two loci implies epistasis for the pair revealed problems . The rule gives false positives . A method that filters out some false positives is discussed in S1 Text . The method depends on considering the entire network of pairs with shared entropy , so as to distinguish between direct and indirect causes for shared entropy . However , the method will not provide a complete solution to the problem of relating epistasis and entropy . Gene interactions are difficult to analyze from frequency data . Nevertheless , it is possible that some approach of the type proposed in Gupta and Adami [2] could work as a rule of thumb . However , that would require a statistical argument . As it currently stands , there is no foundation for the shared entropy condition for identifying pairwise epistasis . It should be remarked that the criticism expressed here does not apply to entropy-based methods in human genetics [e . g . , 7–9] . Applications of information theory to human genetics depends on the ability to compare genetic information to health conditions . No similar information that directly relates genotype and phenotype is available for the HIV data analyzed in Gupta and Adami [2] . In that sense , the authors considered a more difficult problem . For a more general perspective , detecting and quantifying epistasis for multilocus systems is a challenging problem , and various new methods have been proposed in recent years . For instance , one line of research provides tools for detecting gene interactions from qualitative data , such as rank orders of genotypes according to fitness [e . g . , 10–12] . There is no question that entropy-based approaches , as well as many other recent methods have potential . However , while conducting research in the field we noticed that not all of the methods have been justified by solid theoretical arguments . Moreover , experimentalists have reported seemingly contradicting results from different methods applied to the same protein data ( personal communication ) . Some caution is recommended , and it is probably fair to say that the field is “heroic” rather than “mature” at this point in time .
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Some recent approaches for identifying epistasis from sequence data depend on information theory . We show that considering shared entropy for pairs of loci can be misleading . The reason is that shared entropy does not imply epistasis for the pair . This observation holds true also in the absence of higher order epistasis . We discuss a method for reducing the number of false positives in the proposed method . However , our main conclusion is that shared entropy for pairs of loci is difficult to interpret . Gene frequencies reflect interactions in the entire system , and there is no natural way to decompose frequency data .
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2016
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Epistasis and Entropy
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Two-component systems constitute phosphotransfer signaling pathways and enable adaptation to environmental changes , an essential feature for bacterial survival . The general stress response ( GSR ) in the plant-protecting alphaproteobacterium Sphingomonas melonis Fr1 involves a two-component system consisting of multiple stress-sensing histidine kinases ( Paks ) and the response regulator PhyR; PhyR in turn regulates the alternative sigma factor EcfG , which controls expression of the GSR regulon . While Paks had been shown to phosphorylate PhyR in vitro , it remained unclear if and under which conditions direct phosphorylation happens in the cell , as Paks also phosphorylate the single domain response regulator SdrG , an essential yet enigmatic component of the GSR signaling pathway . Here , we analyze the role of SdrG and investigate an alternative function of the membrane-bound PhyP ( here re-designated PhyT ) , previously assumed to act as a PhyR phosphatase . In vitro assays show that PhyT transfers a phosphoryl group from SdrG to PhyR via phosphoryl transfer on a conserved His residue . This finding , as well as complementary GSR reporter assays , indicate the participation of SdrG and PhyT in a Pak-SdrG-PhyT-PhyR phosphorelay . Furthermore , we demonstrate complex formation between PhyT and PhyR . This finding is substantiated by PhyT-dependent membrane association of PhyR in unstressed cells , while the response regulator is released from the membrane upon stress induction . Our data support a model in which PhyT sequesters PhyR , thereby favoring Pak-dependent phosphorylation of SdrG . In addition , PhyT assumes the role of the SdrG-phosphotransferase to activate PhyR . Our results place SdrG into the GSR signaling cascade and uncover a dual role of PhyT in the GSR .
Two-component regulatory pathways enable bacteria to react to changing environmental conditions . Classical two-component systems consist of a sensor histidine kinase and a response regulator; the histidine kinase autophosphorylates upon sensing appropriate environmental signals and subsequently transfers the phosphoryl group to the response regulator , which in turn triggers an adaptation response [1–4] . Multicomponent phosphorelays represent more complex two-component systems involving either a so-called hybrid histidine kinase or , alternatively , a single domain response regulator ( SDRR ) which can be phosphorylated by multiple histidine kinases . Either way , a phosphotransferase subsequently transfers the phosphoryl group to an output response regulator . Therefore , by increasing the number of checkpoints in a phosphorylation pathway , phosphorelays allow for more precise regulation , e . g . [1–8] . The general stress response ( GSR ) is pivotal for alphaproteobacteria for environmental adaption and host microbe interactions [9 , 10] . Notably , it connects a two-component system to alternative sigma-factor regulation [11 , 12] . The GSR can be induced by a variety of different stresses and results in multiple stress resistance [9 , 10] . Studied systems involve for example plant-associated bacteria [13] such as Sphingomonas melonis Fr1 [14] , Methylobacterium extorquens [15] , Bradyrhizobium diazoefficiens [16] , Sinorhizobium meliloti [17] , intracellular pathogens like Brucella abortus [11] , or free-living species like Caulobacter crescentus [18] . The anti-sigma factor antagonist PhyR is phosphorylated under stressful conditions [14 , 19] and acts via a mechanism termed "sigma factor mimicry" [20 , 21] . A phosphorylation-induced conformational change of PhyR results in the release of its sigma-factor like domain that subsequently binds to the anti-sigma factor NepR . Thereby , NepR liberates the alternative sigma-factor EcfG , which can then bind to RNA polymerase and re-direct transcription towards the GSR regulon [20 , 21] . In the plant-protecting S . melonis Fr1 [22 , 23] , seven cytoplasmic histidine kinases ( Paks ) are involved in the GSR [19] . They belong to the HWE/HisKA_2 family [19 , 24] , encoding the HRxxN motif in their DHp domain . Paks integrate multiple stress signals [19] . Individual stresses are sensed by one or more Paks , and some of the Paks are able to sense more than one stress . In vitro , the Paks do not only phosphorylate PhyR ( in the presence of NepR ) , but also the SDRR SdrG [19] . The dual specificity of the Paks for PhyR and SdrG is supported at the structural level; in fact , the receiver domain of PhyR is the best structural homolog of SdrG [25] . Like Sma0144 from S . meliloti [26] , SdrG belongs to the FAT GUY family of response regulators [25] . The SDRR is a key regulator of the GSR in S . melonis Fr1 [19] and M . extorquens [27]; however , its function can be bypassed by overexpression of either PhyR or the Paks [19] . The exact role of SdrG in GSR remains enigmatic . Potential mechanisms of SdrG activity might involve protein-protein interaction , comparable e . g . to CheY from Escherichia coli , which is involved in chemotaxis regulation [28 , 29] or to DivK from C . crescentus , in which the regulator is involved in cell cycle control [30] . Alternatively , it is conceivable that SdrG participates in phosphotransfer , e . g . as the SDRR Spo0F , which is involved in sporulation initiation in Bacillus subtilis [5]; however , no phosphotransferase corresponding to Spo0B of B . subtilis has been identified for SdrG . While in S . melonis Fr1 the genes encoding SdrG and the Paks are scattered throughout the genome , PhyP , an additional regulator of the GSR , is encoded at the phyR locus . The gene encodes a predicted membrane-anchored histidine kinase with a putative periplasmic sensor domain [14] . Closer inspection revealed that PhyP contains the HRxxN motif in its DHp domain , but harbors a degenerated ATPase domain [9 , 14] . Due to its observed ability to disrupt the PhyR/NepR complex in vitro in a phosphorylatable His-341-dependent fashion , it has been proposed that PhyP acts as a PhyR phosphatase [14] . A phyP knockout was only obtained in GSR impaired strains , that is , in phyR , ecfG , or multiple pak knockout mutants , confirming PhyP as an essential negative regulator of the GSR in S . melonis Fr1 [14 , 19] . The identification of PhyP-type proteins in other alphaproteobacteria , e . g . Bab1_1673 in B . abortus [11] , RpaI_4707 in Rhodopseudomonas palustris TIE-1 [31] , RsiC in S . meliloti [17] , and LovK in C . crescentus [18] suggests a conserved key role of the protein in the GSR . Here , we investigate the role of PhyP ( re-designated PhyT in this study , see below ) in S . melonis Fr1 and identify a direct molecular link to SdrG . Our results support a GSR regulatory model in which PhyT acts as a phosphotransferase mediating phosphotransfer between Pak-phosphorylated SdrG and PhyR . Furthermore , our results suggest that PhyT simultaneously prevents lethal over activation of the GSR by inhibiting direct PhyR phosphorylation by Paks via complex formation . The dual function of PhyT explains its essentiality and assigns a key function to SdrG .
In S . melonis Fr1 , the phyR locus encodes the membrane-anchored protein PhyP , previously proposed to be a PhyR phosphatase , which is essential in strains with a functional GSR [14] . Here , we studied the function of this central regulator further . First , we set out to test in vitro PhyR dephosphorylation by PhyP using PhyR 32P-labeled at the Asp-194 residue [20 , 21] , which was generated in presence of NepR using one of the PhyR-activating kinases ( here PakF ) [19] . For dephosphorylation , we used E . coli membrane particles containing heterologously produced PhyP or the PhyP ( H341A ) derivative as a control [14] . However , no phosphatase activity was detected ( S1 Fig ) . We tested an alternative hypothesis regarding the function of PhyP that integrates the central , but not yet understood , role of the positive GSR regulator SdrG [19] . We speculated that PhyP could participate in the GSR-activating phosphotransfer and would assume the role of a PhyR-activating phosphotransferase rather than that of a PhyR phosphatase . According to our working model , Paks act as the primary phosphoryl group sources for SdrG [19] . The phosphoryl group could then be shuttled to PhyP and subsequently transferred to PhyR . To test such a putative phosphorelay , we performed time course in vitro phosphotransfer assays . To guarantee SdrG~P as the sole phosphodonor in the reaction mixture , we used an on-column phosphorylation approach , in which SdrG was phosphorylated by Ni-NTA-bound 32P-labeled PakF . The addition of SdrG~P to E . coli membrane particles harboring heterologously produced PhyP resulted in increasing PhyP phosphorylation and a concomitant decrease of SdrG~P ( Fig 1 ) . When SdrG~P was added to a mixture of PhyR and PhyP , SdrG~P dephosphorylation could be observed over time , while the level of PhyR~P increased . This transfer was enhanced in presence of the anti-sigma factor NepR , which is required for efficient direct phosphotransfer from Paks to PhyR in vitro [19] . No phosphotransfer from SdrG to PhyR was detectable in the absence of PhyP . Moreover , no decrease in SdrG~P and thus no phosphotransfer was observed for the inactive PhyP ( H341A ) derivative ( Fig 1 ) . Our in vitro data thus indicate that PhyP acts as a phosphotransferase that shuttles phosphoryl groups from SdrG~P to PhyR . Therefore , we have chosen to rename PhyP as PhyT and refer to it accordingly from now on . Next , we aimed to confirm that SdrG takes part in PhyR phosphorylation in presence of PhyT in vivo by testing whether the phosphorylatable Asp residue of SdrG is required to fulfill its role as a positive GSR regulator . Therefore , we performed EcfG-dependent β-galactosidase assays and compared sdrG knockout strains producing either wild-type SdrG or the SdrG ( D56E ) derivative encoding a Glu substitution of the phosphorylatable Asp-56 [25] , a substitution that renders many response regulators active by mimicking the phosphorylated state [32–34] . Only wild-type SdrG rescued the impaired GSR observed for the sdrG knockout mutant . The SdrG ( D56A ) derivative was used as a negative control ( Fig 2 ) . This observation confirms previous work that demonstrated the inability of the SdrG ( D56E ) derivative to complement the salt-sensitive phenotype of an sdrG knockout strain [19] and suggests that SdrG phosphorylation is essential for its positive regulatory function in GSR . Taken together , our data indicate that PhyT acts as a phosphotransferase in the Pak-SdrG-PhyT-PhyR phosphorelay of the GSR activating pathway in S . melonis Fr1 . The PhyT- and SdrG-dependent PhyR phosphorylation shown above implies that the Paks' main function in vivo is to phosphorylate SdrG rather than PhyR , although Paks phosphorylate PhyR in vitro in the presence of NepR [19] . To further investigate PhyT- and SdrG-dependent PhyR phosphorylation in stress-induced GSR activation in vivo , we determined EcfG activity using β-galactosidase reporter assays . We analyzed a pakB-G deletion mutant ( thus leaving pakA intact ) , with and without an additional sdrG knockout pre- and post-induction with a chemical stress mixture . Although we observed residual GSR in the absence of SdrG , no increased GSR activation could be observed upon addition of the chemical stress mixture ( Fig 3 ) . Next , we tested GSR induction upon overexpression of pakA in an sdrG knockout background and a pakA-G deletion mutant with and without an additional sdrG knockout . We observed that overexpression of pakA bypasses the sdrG knockout ( Fig 3 ) , which is congruent with previous work [19] . This implies that PhyT- and SdrG-independent PhyR phosphorylation by the Paks is possible in vivo , but plays only a minor role under physiological expression levels of the paks . Additionally , considering that an sdrG knockout may lead to an artificial increase of direct PhyR phosphorylation by Paks , the observed residual GSR activation is likely to be even lower in wild-type cells . We also tested stress-dependent GSR induction in a pakA-G deletion mutant . In this strain , the GSR was still inducible upon stress application ( Fig 3 ) , which points to an additional kinase present in S . melonis Fr1 leading to PhyR phosphorylation . In the light of this finding , we performed EcfG-dependent β-galactosidase assays emphasizing the dependency of GSR induction on both SdrG and PhyT . In a pakA-G deletion mutant , an additional phyT knockout abolished GSR induction under stress conditions , indicating that the so far unidentified kinase strictly depends on the phosphorelay involving PhyT and SdrG ( S3A Fig ) . GSR inducibility was rescued upon overexpression of phyT in this background . However , overexpression of phyT did not enable GSR induction under stress conditions in a pakA-G deletion mutant when an additional sdrG knockout was introduced ( S3A Fig ) . Importantly , overexpression of sdrG in a pakA-G deletion mutant with an additional phyT knockout did not increase GSR ( S3B Fig ) , which is in-line with the dependency of SdrG and PhyT on each other and their positive regulatory role in GSR activation . Because PhyT functions as a phosphotransferase ( Fig 1 ) , one would predict that it plays a role as a positive regulator of the GSR , which is puzzling in light of its described function as a negative regulator [14] . The latter function was deduced from the ability of PhyT to disrupt the NepR/PhyR complex in vitro—interpreted as phosphatase activity—and on the finding that its knockout is only possible in a GSR-impaired background , implying a lethal over activation of GSR in the absence of PhyT [14] . Based on these findings , we aimed to further characterize the negative regulatory role of PhyT . First , we performed EcfG-dependent β-galactosidase assays using a pakB-G deletion mutant with only pakA present . The knockout of phyT in this background resulted in increased GSR pre- and post-induction with a chemical stress mixture ( Fig 4 ) , which was caused by direct PakA-dependent PhyR phosphorylation . Overexpression of phyT complemented the observed phenotype ( Fig 4 ) . This result confirms that PhyT performs an additional negative GSR regulatory function in vivo . We speculated that PhyT controls the amount of phosphorylated PhyR by binding the response regulator and thereby preventing its direct phosphorylation by Paks . To test this working model , we first performed a bacterial two-hybrid ( BACTH ) assay . We used wild-type PhyR and a derivative in which the phosphorylatable Asp-194 residue of the receiver domain was mutated . In addition , we also examined a PhyR Glu-235 mutant , which contains a mutation located at the interface of the sigma factor-like and the receiver domain and results in a constitutively active PhyR [20] . Binding of the response regulator derivatives to the anti-sigma factor NepR was validated as controls . We showed that , indeed , PhyR and PhyT interact in the BACTH assay and that PhyT forms a dimer using plate assays ( Fig 5A and S4A Fig ) as well as β-galactosidase assays ( Fig 5B and S4B Fig ) . Interestingly , the D194A mutation in PhyR seems to weaken its interaction with PhyT , while no effect on PhyT interaction could be observed for the E235A derivative ( Fig 5 ) . However , we did not observe interactions between either PhyR and any of the Paks or between SdrG and PhyT ( S4 Fig ) . The protein-protein interaction of PhyR and PhyT predicts that PhyR is bound to the membrane under unstressed conditions . To test this , we studied the interaction between PhyR and PhyT by observing the association of sfGFP-tagged PhyR to the cell membrane depending on the stress condition and the presence/absence of PhyT using fluorescence microscopy . In a first set of experiments , we found that PhyR localized to the membrane under unstressed conditions , which supports binding of PhyR to the membrane-anchored PhyT ( Fig 6A ) , while a homogeneous distribution was observed for sfGFP alone ( S5 Fig ) . PhyR dissociated from the membrane under stress conditions , presumably due to PhyR phosphorylation and subsequent binding to NepR ( Fig 6A ) . To confirm that PhyT is required for PhyR membrane localization , the cellular distribution of sfGFP-tagged PhyR was examined in a pakA-G deletion mutant with and without an additional phyT knockout . Supporting our previous results , membrane association of PhyR was abolished in the absence of PhyT ( Fig 6B ) . Our data imply that PhyT binds to unphosphorylated PhyR , which is in agreement with the BACTH assay ( Fig 5 ) . In addition , we tested the relevance of the phosphorylatable His-341 of PhyT for the interaction with PhyR ( Fig 6C ) . We observed membrane localization of sfGFP-PhyR in a pakA-G deletion mutant with an additional phyT knockout mutant overproducing wild-type PhyT , while overproduction of the PhyT ( H341A ) derivative in the same strain background led to a homogeneous distribution of sfGFP-PhyR in the cells ( Fig 6C ) . Taken together , our results support complex formation between PhyT and PhyR . Furthermore , we showed that PhyR is released upon stress induction , indicating that only unphosphorylated PhyR is bound to PhyT , which provides a means by which PhyR is sequestered from direct phosphorylation by the Paks .
In this study , we uncovered essential functions in the regulation of the GSR in the alphaproteobacterium S . melonis Fr1 , which resulted in an extended model of the core pathway involved in the activation of the crucial regulon ( Fig 7 ) . Based on our data , we defined the function of membrane-anchored PhyT , formerly described as the PhyR phosphatase PhyP , [14] and placed the SDRR SdrG in the core mechanism of GSR . We present in vivo and in vitro evidence that the negative regulatory function of PhyT , which prevents the lethal over activation of the GSR [14] , relies on complex formation with unphosphorylated PhyR ( Figs 5 and 6 ) . Our results also identify PhyT as a phosphotransferase participating in a GSR-activating phosphorelay ( Fig 1 ) . The importance of the Pak-SdrG-PhyT-PhyR phosphorelay became evident when we discovered that both SdrG and PhyT are needed for appropriate GSR activation in stressful conditions in vivo ( Figs 2 and 3 , S3 Fig ) . Altogether , we suggest a model for GSR activation according to which Paks phosphorylate SdrG upon stress induction . When the concentration of SdrG~P reaches a threshold level , it induces the phosphotransferase activity of PhyT in a competitive way , thereby temporarily replacing PhyR to transfer its phosphoryl group to PhyT . EcfG-bound NepR could then eventually prime PhyR [35] via direct interaction for facilitated phosphorylation by PhyT . Overall , our data suggest a mechanism by which a precise interplay between the bifunctional regulator PhyT , SdrG , PhyR and the seven stress-sensing Paks is essential to ensure appropriate GSR induction via "sigma factor mimicry" and thus via NepR and the general stress sigma factor EcfG . Our in vitro data indicate that the phosphotransferase PhyT irreversibly transfers the phosphoryl group from SdrG~P to PhyR ( Fig 1 and S1 Fig ) . This is in contrast to other described bacterial phosphotransferases such as Spo0B of B . subtilits that catalyze reversible phosphotransfer reactions [36]; however , note that unidirectional phosphotransfer has been observed for the histidine phosphotransferase YPD-1 in Saccharomyces cerevisae to the response regulator SSK1-R2 [37] . Currently , we cannot rule out that PhyT switches its activity to PhyR dephosphorylation in vivo either by turning into a phosphatase or by catalyzing the reverse phosphotransfer reaction in response to external signals e . g . via its so far uncharacterized periplasmic domain or via protein-protein interaction with so far unknown interaction partners . It thus remains to be investigated how the GSR is shut down and whether an additional phosphatase exists . Additionally , it is unknown whether the system relies on the instability of the phosphoryl-Asp of PhyR , basal PhyR protein turnover or if stress-dependent PhyR degradation plays a role , as it has been observed in B . abortus [38] . S . melonis Fr1 inhabits the phyllosphere , a stressful environment characterized by constantly changing conditions [13] . Therefore , it is likely that a strict regulatory system such as the GSR is under constant pressure to evolve in terms of sensitivity and specificity . PhyT and SdrG might have co-evolved in S . melonis Fr1 in order to ensure appropriate GSR induction while simultaneously counteracting the danger of lethal over activation . We suspect that PhyT evolved from a histidine kinase , e . g . via degeneration of its catalytic domain [9 , 14] . Precedence exists in other two-component systems that involve degenerated histidine kinases . These include the dimeric histidine phosphotransferases Spo0B from B . subtilis involved in the initiation of sporulation [5] and ChpT from C . crescentus , which is involved in cell cycle progression [6 , 39 , 40] . Notably , the evolution of PhyT-type regulators is not limited to the model strain S . melonis Fr1 , but rather conserved among alphaproteobacteria [9] . Various studies have been conducted on PhyT-type negative GSR regulators [11 , 17 , 18 , 31] , albeit the mechanism by which they act at the molecular level remains to be elucidated . It is conceivable that this protein functions as a phosphotransferase in other alphaproteobacteria as well . Nonetheless , features and regulatory strategies of the regulators mediating the GSR seem to have diversified to different extents in the phylogenetic class , likely as a result of gene duplication events and divergent evolutionary pressures in the various linages of alphaproteobacteria . Regardless , GSR regulation must be precisely coordinated with the requirements of the corresponding bacterium , i . e . depending on the properties of the present stress-sensing kinases in terms of specificity and efficiency regarding PhyR phosphorylation . Thereby , an increasing number of partially redundant kinases as in S . melonis Fr1 [19] and M . extorquens [27] might bring the need to build in tunable negative regulators and a phosphorelay . Altogether , the characterization of PhyT together with SdrG as essential players in GSR regulation in S . melonis Fr1 shades light on an evolutionary pattern of histidine kinase derived regulators , which is conserved within and beyond the boundaries of the GSR .
S . melonis Fr1 in-frame deletion mutants were constructed with the plasmid pAK405 via double homologous recombination [41] . All plasmids constructed during this study are listed in S1 Table . The primers used for plasmid construction are listed in S2 Table . For heterologous expression of NepR , PhyR , and SdrG in E . coli , overnight cultures of BL21 ( DE3 ) Gold pET26bII-nepR-His6 , pET26bII-phyR-His6 and pET28b-sdrG-Strep ( S1 Table ) were grown in 5 mL LB-Lennox medium supplemented with kanamycin ( 50 μg/mL ) . Stationary cultures were diluted and incubated further at 37°C to an OD600 of 0 . 8 . After induction with 1 mM IPTG , cells were grown for 3 . 5 h at 37°C prior to harvest . The pellets were washed once with 1x cold PBS before being stored at -80°C . For expression of PakF , a BL21-Gold ( DE3 ) pDEST-His6-MBP-pakF pre-culture was inoculated in the morning in LB-Lennox medium supplemented with carbenicillin ( 50 μg/mL ) and grown until turbidity could be observed . The main culture was subsequently inoculated and incubated at 37°C . As soon as an OD600 of 0 . 8 was reached , the flask was placed on ice for 30 min . Afterwards , expression was induced with 50 μM IPTG and further incubation overnight was conducted at 16°C . Cells were washed once with 1x PBS prior to harvest and stored at -80°C . NepR- , PhyR- , and PakF-containing cell pellets were thawed and resuspended in lysis buffer ( 20 mM HEPES , 0 . 5 M NaCl , 10% glycerol , 1 tablet protease inhibitor ( EDTA-free , Roche ) , 2 mM beta-mercaptoethanol , 0 . 1 mg/mL DNase and 20 mM imidazole; pH 8 . 0 ) . Cells were passed 3x through the French press for cell lysis , followed by centrifugation ( 12 . 000g , 30 min , 4°C ) to remove cell debris . The supernatant was incubated for 1 h at 4°C with 500 μL Ni-NTA beads ( Macherey-Nagel ) ( 1 mL slurry , washed 2x with wash buffer ( 20 mM HEPES , 0 . 5 M NaCl , 10% glycerol and 20 mM imidazole; pH 8 . 0 ) ) . Afterwards , the beads were loaded on a polypropylene column . Proteins were eluted from the Ni-NTA resin with 2 . 6 mL elution buffer ( wash buffer with 200 mM imidazole ) after washing with 40 mL wash buffer by gravity flow . The SdrG-containing cell pellet was thawed and also resuspended in imidazole-free lysis buffer and passed 3x through the French press . After centrifugation , SdrG-containing supernatant was added to a polypropylene column loaded with 1 mL Strep-tactin beads ( IBA Lifesciences ) ( 2 mL slurry ) , which were equilibrated with 2 mL imidazole-free wash buffer prior to use . The beads were washed with 10 mL imidazole-free wash buffer by gravity flow , before SdrG-Strep was eluted with 3 mL wash buffer supplemented with 2 . 5 mM desthiobiotin . All cleaned up proteins were subjected to PD10 desalting columns for exchange to kinase buffer ( 10 mM HEPES , 50 mM KCl , 10% glycerol; pH 8 . 0 ) . The purified proteins were concentrated with 3 kDa cutoff amicon tubes ( Millipore ) . Protein concentrations were determined with a BCA protein assay ( Life Technologies Europe B . V . ) . 50 μL aliquots were stored at -20°C . Heterologous overexpression of PhyT and the PhyT ( H341A ) derivative was carried out as described for NepR and PhyR using IPTG-inducible pET26bII expression plasmids ( S1 Table ) . Cell pellets were thawed and resuspended in imidazole-free lysis buffer . Cells were passed 3x through the French press . The supernatant of the first centrifugation step ( 12 . 000g , 30 min , 4°C ) was subjected to ultracentrifugation ( 180 . 000g , 1 h , 4°C ) . The membrane pellets were resuspended in kinase buffer to a concentration of 100 mg membrane fraction/mL and 100 μL aliquots were stored at -80°C . Comparable amounts of PhyT and the PhyT ( H341A ) derivative in the prepared E . coli membrane particles were confirmed with standard SDS-PAGE followed by Western blot analysis using a mouse Tetra·His antibody ( 1:2 . 000 ) ( 34570 Qiagen AG ) and a goat α-mouse HPR-coupled antibody ( 1:3 . 000 ) ( BioRad ) ( S2A and S2B Fig ) . Protein expression and purification as well as preparation of E . coli membrane particles are described above . Purified proteins were thawed on ice . First , PakF was diluted to a concentration of 10 μM in kinase buffer supplemented with 10 mM MgCl2 and 1 mM DTT . 200 μL Ni-NTA beads ( 400 μL slurry ) were washed 2x with kinase buffer supplemented with 10 mM MgCl2 and 1 mM DTT before 320 μL PakF ( 10 μM ) were added and incubated at room temperature ( RT ) for 30 min . Afterwards , autophosphorylation of PakF was initiated with 3 . 2 μL [γ-32P]ATP ( 5 . 000 Ci/mmol; Hartmann Analytic GmbH ) . After 10 min of autophosphorylation , PakF bound to Ni-NTA resin was loaded on polypropylene columns and washed with kinase buffer until radioactivity of the wash fraction decreased significantly . S . melonis Fr1 strains carrying the reporter plasmid pAK501-nhaA2p-lacZ and an inducible vanillate or cumate expression plasmid ( S1 Table ) were streaked out from a cryo-stock on TYE agar plates ( 1% tryptone , 0 . 5% yeast extract , 2 . 4 mM Na2HPO4 , 37 . 6 mM KH2PO4 ) containing tetracycline ( 10 μg/mL ) and chloramphenicol ( 34 μg/mL ) and incubated overnight at 28°C . The plates were sealed with parafilm and protected from light , to reduce stress exposure . Pre-cultures were inoculated from a small loop in 20 mL TYE medium supplemented with tetracycline ( 10 μg/mL ) and chloramphenicol ( 34 μg/mL ) in 100 mL baffled flasks and incubated at 28°C , 160 rpm during the day . To test protein-protein interactions , C- and N-terminal translational fusions of the T18 and T25 domains of the adenylate cyclase CyaA of Bordetella pertussis [42] were used for PhyT , NepR , PhyR wild-type , PhyR derivatives and PakA-G ( S1 Table ) . Fusions were tested in pairwise combinations in E . coli BTH101 cya- ( Euromedex ) . For the interaction analysis the optimal pair for each protein combination is shown . For each transformation mixture , 5 μl were spotted onto LB agar plates containing 0 . 5 mM isopropyl 1-thio-β-D-galactopyranoside and 40 μg/ml 5-bromo-4- chloro-3-indolyl β-D-galactopyranoside ( X-Gal ) with selection for carbenicillin ( 50 μg/mL ) and kanamycin resistance ( 50 μg/mL ) . Plates were incubated at 30°C for 24 h . Formation of blue colonies was scored as a positive interaction . In addition , a volume of 20 μl of each transformation mixture was plated on LB agar plates containing carbenicillin ( 50 μg/mL ) and kanamycin ( 50 μg/mL ) for selection . Plates were incubated at 28°C overnight . For quantitative analysis , 5 mL cultures of LB supplemented with carbenicillin ( 50 μg/mL ) and kanamycin ( 50 μg/mL ) for selection and 0 . 5 mM IPTG were inoculated from single colonies . Overnight cultures were grown at 30°C and used to measure β-galactosidase activity ( Miller 1972 ) . 500 μl of bacterial culture were spun down and pellets were resuspended in 1x Lämmli buffer so that a final OD600 of 10 was reached . The samples were subjected to standard SDS-PAGE followed by Western blot analysis . For detection of T18 fusions the mouse α-CyaA ( 3D1 ) monoclonal antibody ( Santa Cruz Biotechnology ) ( 1:2 . 000 ) and a goat α-mouse HPR-coupled antibody ( 1:3 . 000 ) were used ( BioRad ) . S . melonis Fr1 strains carrying either pQY-sfGFP or pQYD-sfGFP-phyR ( S1 Table ) were streaked out from cryo-stock on TYE agar plates supplemented with tetracycline ( 10 μg/mL ) and were incubated overnight at 28°C . The plates were sealed with parafilm and protected from light to avoid stress induction . Pre-cultures were inoculated in 20 mL TYE medium supplemented with tetracycline ( 10 μg/mL ) in 100 mL baffled flasks and incubated during the day at 180 rpm and 28°C . Overnight cultures were inoculated at an OD600 appropriate to reach exponential growth phase at the time of the experiment . Incubation conditions were the same as for the pre-cultures . PhyR-sfGFP or sfGFP expression in the S . melonis Fr1 mutants was induced in mid-exponential phase via the addition of 25 μM cumate ( 100 mM stock in 100% ethanol ) for 12 min . In order to analyze the importance of His-341 of PhyT for binding of PhyR and therefore membrane localization of sfGFP-PhyR , the appropriate strains carrying pVH-sfGFP-phyR and either pAK200-phyT or pAK200-phyT ( H341A ) ( S1 Table ) were streaked out from cryo-stock on TYE agar plates supplemented with tetracycline ( 10 μg/mL ) and kanamycin ( 50 μg/mL ) . Following overnight incubation at 28°C , pre-cultures were inoculated in 20 mL TYE medium supplemented with tetracycline ( 10 μg/mL ) and kanamycin ( 50 μg/mL ) in 100 mL baffled flasks and cultivated as described above . In addition to both antibiotics , cumate ( 25 μM ) was added to the 20 mL overnight cultures . Production of sfGFP-PhyR was induced on the following day with 250 μM vanillate ( 250 mM stock in 100% ethanol ) for 12 min in mid-exponential phase . The bacteria were subsequently washed with Tris-buffered saline ( 50 mM Tris-HCl , 150 mM NaCl; pH 7 . 55 ) and mounted on a cover slip for imaging . The live-cell imaging was performed on an inverse spinning disc microscopy system ( Visitron software , Yokogawa CSU-X1 spinning-disk confocal unit ) equipped with a solid state Laser Unit ( Toptica ) at 488 nm excitation wavelength , a 100x Oil Plan-Neofluar Objective ( Zeiss , NA: 1 . 3 ) , and an Evolve 512 EMCCD camera ( Photometrics ) . The images were deconvolved using Huygens Professional version 17 . 04 ( Scientific Volume Imaging , The Netherlands , http://www . svi . nl/HuygensSoftware ) .
|
The general stress response ( GSR ) in alphaproteobacteria represents an essential feature for survival in stressful , constantly changing habitats . A variety of stresses are sensed by GSR-activating histidine kinases and lead to multiple stress resistance via the response regulator PhyR . Here , we describe the essential bifunctional regulator PhyT , which tunes GSR activation in the plant-protecting strain Sphingomonas melonis . It prevents lethal over activation of the GSR by impeding inappropriate phosphorylation of the response regulator PhyR via protein-protein interaction . In addition , PhyT acts as a phosphotransferase in a GSR activating phosphorelay . Our data suggest a model according to which histidine kinases are induced by environmental cues , which results in phosphorylation of the single domain response regulator SdrG . The phosphoryl group of SdrG is then transferred by the phosphotransferase PhyT to the master regulator of the general stress response PhyR . Histidine kinase derived PhyT-type regulators are found also in other alphaproteobacteria , implying that the identified regulatory strategy might be conserved .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"phosphorylation",
"chemical",
"compounds",
"enzymes",
"enzymology",
"organic",
"compounds",
"plasmid",
"construction",
"phosphatases",
"regulator",
"genes",
"basic",
"amino",
"acids",
"amino",
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"dna",
"construction",
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"biology",
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"gene",
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"analysis",
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"proteins",
"hyperexpression",
"techniques",
"chemistry",
"ethanol",
"molecular",
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"molecular",
"biology",
"assays",
"and",
"analysis",
"techniques",
"gene",
"expression",
"and",
"vector",
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"biochemistry",
"histidine",
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"chemistry",
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"modification",
"genetics",
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] |
2018
|
Phosphorelay through the bifunctional phosphotransferase PhyT controls the general stress response in an alphaproteobacterium
|
Microbial communities in animal guts are composed of diverse , specialized bacterial species , but little is known about how gut bacteria diversify to produce genetically and ecologically distinct entities . The gut microbiota of the honey bee , Apis mellifera , presents a useful model , because it consists of a small number of characteristic bacterial species , each showing signs of diversification . Here , we used single-cell genomics to study the variation within two species of the bee gut microbiota: Gilliamella apicola and Snodgrassella alvi . For both species , our analyses revealed extensive variation in intraspecific divergence of protein-coding genes but uniformly high levels of 16S rRNA similarity . In both species , the divergence of 16S rRNA loci appears to have been curtailed by frequent recombination within populations , while other genomic regions have continuously diverged . Furthermore , gene repertoires differ markedly among strains in both species , implying distinct metabolic capabilities . Our results show that , despite minimal divergence at 16S rRNA genes , in situ diversification occurs within gut communities and generates bacterial lineages with distinct ecological niches . Therefore , important dimensions of microbial diversity are not evident from analyses of 16S rRNA , and single cell genomics has potential to elucidate processes of bacterial diversification .
Animals contain complex bacterial communities in their guts that can impact health status [1]–[3] . In mammals , the phylogenetic architecture of gut communities has been described as fan-like , with few deep- and intermediate-branching lineages , but with many shallow branches [3] . Most of these bacteria live exclusively in the gut environment , suggesting that phylogenetic clusters have evolved in situ through diversification of a few founder species . Diversity of gut communities is typically assessed using deep-sequencing of 16S rRNA PCR amplicons [4] , [5] , often with the aim of illuminating community differences between closely related hosts or between hosts with different environments or diets [6]–[9] . To this end , 16S rRNA sequences are clustered into operational taxonomic units ( OTUs ) , and a cutoff of 97–98% identity is applied to discriminate these clusters . However , 16S rRNA studies have limited use for predicting functional differences or for understanding micro-evolutionary changes in gut communities , as bacteria with almost identical 16S rRNA sequences can exhibit high levels of sequence divergence at other loci and very different gene repertoires [10]–[12] . Consequently , little is known about diversification of bacterial lineages in the gut . Insect gut communities are relatively small and simple , and thus can serve as model systems to explore diversification in gut bacteria [13] . In particular , honey bees ( genus Apis ) and related bumble bees ( genus Bombus ) harbor characteristic gut communities dominated by <10 bacterial species in three phyla: Proteobacteria , Firmicutes , and Actinobacteria . Most of the bacterial taxa in honey bees are not found in other environments or in solitary bee species but are consistently present and abundant in the guts of adult Apis and Bombus [14]–[23] . Thus , these bacteria are adapted to live in the guts of social bees and likely possess specific symbiotic mechanisms affecting health of the host . Apis and Bombus are important pollinators and have suffered from severe population declines [24]–[26] . Therefore , studies on the characteristic bee gut microbiota are of interest not only for basic understanding of microbial communities , but also for potential applications in agriculture and biotechnology . Metagenomic analyses provided initial insights into the functional gene content of the gut symbionts of the honey bee , Apis mellifera [22] , and revealed polymorphism within two of the core species , Gilliamella apicola ( Gammaproteobacteria ) and Snodgrassella alvi ( Betaproteobacteria ) , which were each classified as single species on the basis of previous 16S rRNA analyses [20] , [21] , [27] . Reference genomes of Apis and Bombus isolates of G . apicola and S . alvi are now available [28] facilitating comparative analyses of the genomic variation across strains from conspecific and heterospecific hosts . Here , we used single-cell genomics to investigate the genomic variation in S . alvi and G . apicola sampled from a single colony of A . mellifera . By sorting individual bacterial cells from specific gut regions of adult worker bees , we were able to sequence genomic DNA of four single cells of S . alvi and of three single cells of G . apicola and compare their genomes against the completely sequenced reference strains . These analyses revealed surprising levels of genomic divergence between strains with near-identical 16S rRNA and illustrate the applicability of single-cell genomics for population genomic studies of host-associated bacteria .
We sorted 315 single bacterial cells from homogenate of the midgut and ileum gut regions of ten A . mellifera workers collected on the same day from a single colony in West Haven , CT , USA ( Figure S1 ) . Following bacterial lysis and multidisplacement amplification ( MDA ) , we obtained detectable DNA enrichment for 300 of the 315 cells ( 95% ) ( Figure S2 ) . Of the 315 sorted cells , 216 were confirmed to contain bacterial DNA using PCR of a partial fragment of the 16S rRNA gene ( Figure S2 ) . We sequenced 16S rRNA amplicons for a random selection of 126 of these 216 single amplified genomes ( SAGs ) and found that all 126 belonged to core members of the gut microbiota of A . mellifera ( Table S1 ) . As expected , most were S . alvi and G . apicola ( Figure 1 ) . However , a few cells of other core members were also identified , i . e . five cells of Frischella perrara ( Gammaproteobacteria ) [29] , one Firmicute , and one Alphaproteobacterium . Phylogenetic trees based on partial 16S rRNA sequences showed that SAGs of S . alvi and G . apicola both formed monophyletic clades together with their corresponding type strains , S . alvi wkB2 and G . apicola wkB1 , both previously isolated from A . mellifera [27] ( Figure 2 ) . Sequences originating from other bee species clustered outside the A . mellifera-specific clades , including the Bombus isolates for which genome sequences are available ( S . alvi wkB12 and wkB29 and G . apicola wkB11 and wkB30 ) . However , due to the high similarity between 16S rRNA sequences , most nodes within the species-clusters were not supported by bootstrap analyses . The average pairwise sequence divergence ( π ) between all analyzed SAGs was 0 . 27% and 0 . 56% for S . alvi and G . apicola , respectively . The average π for representative sequences originating from strains of different bee species was higher , at 0 . 67% and 2 . 47% for S . alvi and G . apicola , respectively . To analyze genome-wide diversity , we shotgun-sequenced four SAGs of S . alvi ( J21 , O02 , O11 , P14 ) and three SAGs of G . apicola ( B02 , I20 , P17 ) in a single multiplexed Illumina lane ( Table 1 ) . SAGs were selected based on distinct positions in the 16S rRNA gene trees ( Figure 2 ) and their low Cp ( critical point ) values , i . e . the time required to produce half of the maximal fluorescence during the MDA reaction ( Figure S2 ) . Low Cp values are indicative for ample template DNA , which should result in a less biased DNA amplification and better coverage of the genome ( R . Stepanauskas , unpublished data ) . For each SAG , we generated first-pass assemblies with SPAdes [30] and detected some low-coverage contigs originating from misassigned reads , i . e . , reads assigned to the wrong dataset due to dense clustering on the Illumina flow cell [31] ( see Materials and Methods for details ) . Following removal of misidentified reads , the curated datasets consisting of 25–32 million reads were re-assembled . Resulting contigs were included in subsequent analyses only if they met our quality criteria which were based on read coverage , contig length , redundancy , and homology ( see Materials and Methods for details ) . The final assemblies consist of 259–544 contigs and range in total size from 1 . 31 Mb to 2 . 33 Mb ( Table 1 ) . Based on the coverage of a minimal , essential gene set defined for the fully sequenced reference genomes of S . alvi wkB2 and G . apicola wkB1 , genome completeness of the sequenced SAGs was estimated to range from 47% to 93% ( Table 1 ) . We determined orthologous gene sets present in SAGs and reference genomes , mapped these onto the reference genome , and found that large genomic regions were missing from SAG assemblies ( Figure S3 ) . Many of these missing regions likely reflect incomplete genome recovery from the single cells . Missing regions are erratically distributed ( Figure S3 ) , suggesting that they correspond to random processes ( e . g . , incomplete single cell lysis and the stochastic nature of single cell MDA ) rather than compositional variation among genomic regions , consistent with prior studies [32] , [33] . Despite missing regions , we determined a shared set of 239 genes for S . alvi and 400 genes for G . apicola ( Figure S4 ) . Analysis of these orthologs revealed extended intraspecific variation in divergence at synonymous sites ( dS , estimated rate of synonymous substitutions per site ) . This was unexpected , because the analyzed strains show similar divergence at 16S rRNA gene loci ( Table 1 ) . Genome-wide dS values between SAGs and reference genome range from 0 . 064 to 2 . 087 for S . alvi and from 0 . 245 to 1 . 883 for G . apicola ( Table 1 ) . dS values of individual genes show similarly extreme variation in divergence across strains ( Figures 3A and 3B ) . With many orthologs exhibiting dS values near or at saturation ( i . e . dS values ≥3 ) , O02 of S . alvi and I20 and P17 of G . apicola are the most divergent SAGs compared to their respective reference genome . These SAGs seem to be almost as divergent from other honey bee isolates as they are from strains isolated from Bombus species ( Figures 3A and 3B ) . For both S . alvi and G . apicola , phylogenetic trees inferred from concatenated protein-encoding genes differ from 16S rRNA gene trees ( Figures 3C and 3D ) . Strains from A . mellifera do not form a single monophyletic clade exclusive of Bombus strains . For S . alvi , three of the four SAGs ( J21 , O11 , P14 ) and the reference strain from A . mellifera ( wkB2 ) form a tight clade , but the most divergent SAG , O02 , occupies a basal branch within the S . alvi cluster . For G . apicola , two strains from A . mellifera ( B02 and wkB1 ) form a clade that is sister to the clade of Bombus strains ( wkB11 and wkB30 ) ; the other G . apicola strains from A . mellifera ( P17 and I20 ) form a more basally branching clade . Thus , trees for both G . apicola and S . alvi support divergence among A . mellifera strains that started before and continued after the divergence from Bombus strains . Most relevant branches are supported by high bootstrap values ( ≥80 ) and by topology concordance of the majority of the single gene trees ( ≥50% of the analyzed genes ) ( Figures 3C and 3D ) . For both S . alvi and G . apicola , 16S rRNA sequences are highly similar across closely related and highly divergent strains . This could reflect the occurrence of frequent homologous recombination at 16S rRNA loci resulting in sequence homogenization [34] . Using the four-gamete test under the infinite sites assumption ( i . e . repeat mutations have zero probability ) , we found that at least 1 and at least 8 recombination events must have occurred between the 16S rRNA sequences of the ancestors of SAGs of S . alvi and G . apicola , respectively . This finding is concordant with a previous study showing homologous recombination of 16S rRNA genes in both S . alvi and G . apicola , with higher rates in G . apicola [20] . To test whether other genes of S . alvi and G . apicola show signs of recombination , we ( i ) examined single gene trees for topology discordance with the concatenated gene tree , ( ii ) analyzed patterns of sequence divergence at synonymous sites , ( iii ) determined the frequency at which substitutions occurred by mutation or recombination , and ( iv ) detected intragenic recombination events between orthologous genes . The O02 lineage of S . alvi appears to have low recombination rates with the other analyzed strains , as most single gene trees ( 75% ) support its basal position ( Figure 3C ) , and most trees with incongruent topologies have weak support for the position of O02 ( Figure S6 ) . In contrast , for the more closely related strains ( J21 , P14 , O11 , and wkB2 ) , many single gene trees show discordant topologies ( Figure S6 ) , indicating either recombination and/or insufficient phylogenetic signal . In the absence of recombination , dS values for a pair of genomes will reflect their divergence time and will show a consistent pattern across genes , but recombination will cause some genes to have anomalous dS values . We plotted dS values in ternary plots , in which the sum of all values for three pairwise comparisons equals 1 . For the ternary plot of O02 , P14 , and J21 , most genes exhibit similar divergence patterns , with dS values ∼40× lower for J21-P14 than for J21-002 or P14-O02 ( Figure 4A ) , indicating deeper branching of 002 and little subsequent recombination . Of the 13 genes dispersed over the plot area . 12 fell within three regions in the wkB1 reference genome , one encoding genes for urease and two encoding genes for efflux permeases ( Table S2 ) . A sliding window analysis over the aligned sequences of the urease-encoding gene cluster showed that sequence divergence between O02 and both J21 and P14 drops from high to very low ( Figure 5A ) , suggesting that the recombination breakpoint is located in the middle of the locus . Ternary plot analysis of the three closely related SAGs , J21 , O11 , and P14 , showed much more dispersal of genes ( Figure 4A ) , mostly reflecting low dS values that do not differ significantly . However , a considerable number of the dispersed genes show dS values >0 . 1 ( Figure 4A ) , suggesting that intraspecific recombination contributed to this variation in sequence divergence . To determine the relative importance of recombination versus mutation in sequences of S . alvi strains , we estimated the ratio ( r/m ) at which substitutions are generated via recombination ( r ) or mutation ( m ) across the 239 shared genes . Most r/m values were <1 for the closely related S . alvi strains , suggesting that mutations contribute more to their evolution . For the distant strain , O02 , no reliable estimates could be obtained due to saturation of substitutions at most sites ( Table S3 ) . Small fragments of genes exchanged by recombination might be missed by our phylogenetic or divergence analyses based on whole genes . Therefore , we tested for the occurrence of intragenic recombination within shared genes and found that 7–19% of genes carried signs of past recombination events ( Figure S7 ) . In agreement with its divergent phylogenetic position , O02 had the fewest genes with intragenic recombination ( 7% ) , while all other strains had at least 15% affected genes . Average recombination fragment length generally decreased with increasing phylogenetic distance of the analyzed strains ( Figure S7 ) . Most genes display congruent topologies for the splits between the three SAGs and the reference strain of G . apicola , providing no evidence for frequent recombination among their ancestors ( Figure 3D ) . Consistent with this , the ternary plot analysis does not detect much variation in relative dS among the shared genes , as indicated by low dispersal over the plot area ( Figure 4B ) . However , dS is near saturation for many genes , possibly obscuring evolutionary rate differences . Nevertheless , a slight dispersal of dS values along the axis plotting the comparison of I20 and P17 is evident . Concordantly , dS values for these two strains vary markedly among orthologs , in contrast to the other pairwise comparisons , for which most orthologs exhibit uniform dS values ( Figure 4B , Figure S8 ) . I20 and P17 form one of the two A . mellifera-associated clades of G . apicola , and the variation in dS suggests a high frequency of recombination in this particular sub-lineage . This was confirmed by estimates of r/m , revealing very high rates of recombination ( 5 . 1–23 . 5 ) for I20 and P17 . In contrast , r/m ratios for B02 and the reference genome wkB1 are much lower ( 0 . 4–0 . 8 ) ( Table S3 ) . The differences in recombination frequencies among G . apicola strains are further corroborated by the analysis for intragenic recombination . Recombination is evident in all pairwise comparisons , but is highest for I20 versus P17 ( Figure S7B ) for which 15% of shared genes ( 60 of 400 genes ) show evidence of at least one recombination event . In comparison , only 2–4% of the 400 shared genes show signs of recombination between any other pair of G . apicola genomes . A sliding window analysis over a genomic region of G . apicola illustrates these differences in recombination frequency between P17 and I20 , and B02 and wkB2 ( Figure 5B ) . Despite the clear evidence for recombination in both S . alvi and G . apicola , strains from the same bee colony can be highly divergent , potentially reflecting adaptation to distinct ecological niches in the bee gut . To test for differences in functional gene content between strains , we determined the accessory gene pool of SAGs , which we defined as the genes present in SAGs but absent from the completely sequenced reference genomes . Based on this criterion , we found 755 and 851 accessory genes for S . alvi and G . apicola , respectively ( Figure S4 ) . For S . alvi , the accessory gene pool is dominated by categories covering a broad range of functions ( Figure 6A ) . Among others , we found a considerable number of genes associated with mobile elements such as phages , plasmids , or transposons , and restriction-modification systems . In agreement with its distant phylogenetic position , strain O02 has the largest accessory gene pool , with 258 unique genes ( Figure S4 ) , suggesting that O02 differs substantially from other sampled strains in its functional capabilities . However , many of these genes are annotated as hypotheticals , preventing prediction of their functional roles . For G . apicola , 20% of the accessory genes encode carbohydrate-related functions , including many transporters of the phosphotransferase system and major facilitator families , and another 7% corresponds to amino acid transport and metabolism ( Figure 6B ) . These marked differences in gene content linked to metabolic functions suggest distinct ecological roles and effects in the host .
We sorted single cells directly from their environment to obtain an unbiased picture of genomic variation within populations . While isolates of G . apicola and S . alvi have been grown in the laboratory [27] , culturing often introduces sampling biases [35]–[38] , as certain strains possess metabolically costly genes or lytic phages which hinder growth in culture [39] . The A . mellifera gut microbiota is particularly suitable , because its low species richness facilitates high-frequency retrieval of single cells of the same species ( i . e . , cells with near-identical 16S rRNA sequences ) . By only targeting the bee gut ileum and midgut , we could increase the sorting frequency of G . apicola and S . alvi , which dominate in these regions [40] . Single-cell enrichment of specific bacteria from more complex communities , such as those in mammalian guts , may require a higher sorting throughput or specific labeling with fluorescent probes . An obvious limitation of single-cell genomics for population genetic analysis is the incomplete recovery of genomes from single cells [41] , [42] . We obtained 239 and 400 shared genes with an average genome completeness of 66% and 63% for four S . alvi SAGs and three G . apicola SAGs , respectively . These estimated genome recoveries were in the upper range of previous single-cell studies [35] , [38] , [43]–[47] and provided abundant genomic information for our analyses . Nevertheless , it is important to note that the number of shared genes rapidly decreases as SAGs are added to the analysis , due to the higher likelihood of a given gene being absent from one of the samples . Population genetic studies of larger SAG datasets would therefore require higher average genome coverage and new computational tools to take full advantage of partial genomes . Recent studies have shown that partial genomes can also be obtained from metagenomic datasets [48]–[51] . While metagenomic approaches might be cost-effective , reconstruction of closely related genomes is difficult , hindering evolutionary analysis of bacterial populations . There is no generally accepted species concept for bacteria , and microbiologists use different criteria to delineate species [52]–[54] . A pragmatic and commonly applied convention uses 16S rRNA sequence similarity to define OTUs , with an arbitrary cut-off of 97% for species delineation [55] . However , this criterion is not an indicator of biologically meaningful boundaries between ecologically and genetically distinct populations , and bacterial strains with near-identical 16S rRNA may be adapted to different ecological niches or harbor distinct functional capabilities [11] , [12] , [35] , [56] , [57] . Most G . apicola and S . alvi strains investigated in this study share 99–100% sequence identity in 16S rRNA with their respective type strain ( Table S1 ) , but often show much higher divergence at other loci as well as very different gene repertoires ( Figure 3 and Figure S4 ) . This appears to reflect the slow evolution of rRNA genes . But compared to other pairwise analyses of bacteria [58] , the extent of divergence of protein-encoding genes relative to 16S rRNA divergence is exceptionally high for the two honey bee gut symbionts analyzed in this study . Concerted evolution or ongoing homologous recombination at 16S rRNA loci ( even when other genomic regions continuously diverge ) could be two possible explanations for this phenomenon . It is not apparent why gut bacteria would have stronger purifying selection on rRNA genes than any other bacteria resulting in concerted evolution of these loci . Further , our analyses provide evidence that sequence homogenization likely originates from recombination . First , we found incongruence between tree topologies for 16S rRNA and those for protein-encoding genes ( Figure 2 and Figure 3 ) . Second , we detected recombination breakpoints in 16S rRNA sequences , which was consistent with a previously published analysis [20] . Consequently , 16S rRNA sequences fail to portray the extensive genetic diversity present in S . alvi and G . apicola populations , and other genomic regions must be considered to demarcate divergent intraspecific lineages . For example , O02 of S . alvi appears to have irreversibly separated from other strains . While frequent recombination and genome cohesion was evident among other S . alvi strains , O02 has undergone recombination in only a few genomic regions ( Table S2 ) . These few genes could correspond to adaptive functions important for survival in the shared habitat . The urease gene cluster , for example , might be responsible for tolerance to acidic conditions in the A . mellifera gut , based on the role of this enzyme in other host-associated bacteria [59] . Notably , divergent strains within S . alvi and G . apicola co-exist in an individual bee [20] , suggesting that they may occupy different niches and constitute distinct ecotypes [60] . In G . apicola , a large proportion of the accessory gene pool consists of carbohydrate-related functions ( Figure 6 ) , which might play a role in divergent adaptation to different metabolic niches . This corroborates previous results showing that strains of G . apicola differ in ability to breakdown pectin , a major component of the cell wall of pollen [22] , the major source of dietary protein of honey bees . Furthermore , the fully sequenced honey bee-associated strain wkB1 has a larger genome ( 3 . 14 Mb ) than the two Bombus-associated strains ( 2 . 26 Mb , 2 . 32 Mb ) , largely due to an expanded set of genes involved in carbohydrate metabolism [28] . These results suggest the possibility that G . apicola strains affect the use of diverse carbohydrates present in the diets of different honey bee colonies . Social bees are key pollinator species in almost all terrestrial ecosystems , including agricultural systems . In recent years , A . mellifera has undergone colony losses [25] , and Bombus species have also suffered from population declines and extinctions [24] , potentially influenced by pesticide usage and interactions with parasites . No consistent changes in microbiota are apparent in failing A . mellifera colonies [21] , [61] , but studies have been based on 16S rRNA , which lacks resolution to reveal differences in strain composition . Strain composition in the gut could affect nutrient availability or resistance to parasites . Preliminary support for such effects comes from experiments showing differences in G . apicola strains for pectin catabolism [22] and from experiments on Bombus showing that particular sources of gut symbionts vary in levels of protection against protozoan parasites [62] . Moreover , strains might vary in overall effects on hosts , from beneficial to neutral or even detrimental . All sampled A . mellifera workers harbor G . apicola and S . alvi , but differ in strain composition [20] , and these differences potentially impact health of bee colonies . Diversification of S . alvi and G . apicola likely occurred within the bee gut environment , as both species have been detected exclusively in the guts of Bombus and Apis [14] , [16] . It is possible that they descend from ancestors that colonized an Apis-Bombus ancestor living ∼85 million years ago [63] . Divergence of strains in different Apis and Bombus species reflects host evolutionary relationships , at least in part [64] . When transmission is largely intraspecific , divergence of strains confined to different host species is expected , and is likely reinforced by symbiont-host coevolution , resulting in barriers to colonization of non-native hosts . Parallel cases are Xenorhabdus species specialized to particular species of Steinernema nematodes [65] and Lactobacillus reuteri strains adapted to different vertebrate hosts [66] , [67] . Our study focuses on strain variation that appears to have arisen due to diversification within a single host species , A . mellifera; this situation likely parallels that of the human gut microbiota [3] , [68] . Diversification is likely driven by divergent selection reflecting ecologically distinct niches in the gut , but such diversifying selection might be countered by recombination with homologous DNA from other strains . Temporary isolation of host populations and colonies , followed by recontact and exchange of symbionts , might generate ecological and genomic diversity among symbiont strains . In A . mellifera , symbiont exchange among colonies likely occurs occasionally via robbing behaviors or foraging at the same flowers . The mode of colony founding , by a swarm containing hundreds of workers and a queen , also favors maintenance of strain diversity , because it does not impose a bottleneck in numbers of gut bacteria . In contrast , Bombus colonies are initiated annually from a single queen bee , potentially imposing a bottleneck that reduces diversity of gut bacteria . However , whether strain diversity of S . alvi and G . apicola is lower in Bombus hosts is unknown . The bacterial diversity in A . mellifera gut ecosystems consists of many closely related taxa , but relatively few deep-branching lineages , a pattern similar to that in mammalian gut microbiota [3] , [5] , [69] . Another parallel with mammalian systems is that strains categorized as single species on the basis of 16S rRNA can have extensive differences in genome content: as for S . alvi and G . apicola , over 25% of each genome may be unalignable across strains of human gut bacterial species [70] . In the human gut , strains are persistent within individual hosts and tend to be shared among relatives living together [70] , [71] . Colony-specific strain composition also appears to occur in A . mellifera [20] . Although 16S rRNA sequences are typical markers for assessing diversity in bacterial communities , we found that they correlate poorly with genomic content and divergence at protein-coding loci . Because most studies of genome-wide patterns of variation are based on metagenomic samples which do not reveal linkage of 16S rRNA and protein-coding genes ( e . g . , [71] ) , it is unclear how often this discrepancy occurs . We propose the following model for how this might evolve . If populations are isolated , for example in different bee colonies , then their genomes will begin to diverge . However , protein-coding genes , particularly synonymous sites , will diverge faster than rRNA genes , in which contiguous regions are conserved due to strong purifying selection to maintain function . If recontact of populations occurs following an appropriate time interval , regions of the rRNA may retain sufficient similarity to recombine through homologous recombination pathways , which require near-identity for a region of >50 base pairs [72] , while many or all protein-coding regions may exceed this divergence threshold . In this sense , the rRNA genes have not yet “speciated” , while protein-coding regions have . Ongoing coexistence could result in extensive recombination and homogenization at rRNA loci and continued divergence of protein-coding loci , increasing the discrepancy between their divergence levels . The continued coexistence of strains also suggests ecological specialization maintaining strain variation , as proposed for other communities ( e . g . , [73] , [74] ) . Such specialization would reinforce the highly distinct gene repertoires of strains , such as those we observed . Experiments on metabolism and host-relationships of isolates will illuminate this possibility and reveal the extent to which strain divergence and ecological differentiation correlate .
10 worker bees were collected from inside a single hive in West Haven , CT , USA . The midgut and the ileum ( anterior part of the hindgut ) were dissected with sterile forceps and homogenized with a pestle in 6% betaine in 1× PBS . The homogenate was pipetted into a fresh tube avoiding gut tissue debris and frozen at −80°C . Aliquots were shipped on dry ice to the Single Cell Genomics Center at the Bigelow laboratory , Maine , USA , for fluorescence-activated cell sorting ( FACS ) , single-cell lysis , and multiple displacement amplification ( MDA ) following procedures described previously [75] . An initial qPCR screen for the 16S rRNA gene was performed with primers 27F ( AGR GTT YGA TYM TGG CTC AG ) and 907R ( CCG TCA ATT CMT TTR AGT TT ) on each single-cell sample in the 384-well plate . None of the negative control samples gave a positive PCR signal ( Figure S2 ) . Initially , amplicons from 94 SAGs were selected for Sanger sequencing with primer 907R . For phylogenetic analyses , we generated longer 16S rRNA gene PCR amplicons with primers 27F and 1507R ( TAC CTT GTT ACG ACT TCA CCC CAG ) . Amplicons were Sanger-sequenced and assembled into near-full length 16S rRNA gene sequences . A total of 126 SAGs were genotyped by using the partial 16S rRNA sequences as queries in BLASTN against the NCBI non-redundant database and against the 16S rRNA gene sequences of F . perrara PEB0191 [29] , S . alvi wkB2 , and G . apicola wkB1 [27] ( Table S1 ) . Selected MDA products were sequenced on an Illumina HiSeq 2000 machine at the Yale Center for Genome Analysis . Illumina paired-end libraries with approximate insert sizes of 400 bp were constructed following Illumina standard protocols for genome sequencing using four PCR amplification cycles with the Bio HiFi polymerase ( Kapa Biosystems , Woburn , MA , USA ) . These libraries were sequenced as part of a larger multiplexed pool in a single 2×76 bp lane . Sequencing reads were corrected with BayesHammer and first-pass assemblies generated with SPAdes using standard parameters [30] , [76] . Illumina's multiplexing technology has a relatively high error rate ( 0 . 3% ) for assigning reads to the correct library adapter sequence [31] . The higher the read coverage for a given region , the more reads of this region are being misassigned . Sequencing data obtained from single-cell derived MDA products typically reveal large variation in read coverage [76] , [77] , with some regions being covered by >10 , 000× . We determined that a substantial number of reads were misassigned and assembled into contigs of low coverage ( mostly <10× ) , if the read coverage of a particular region in the original dataset exceeded 5 , 000× to 10 , 000× . To identify and remove such misassigned reads , we mapped every Illumina read dataset against assembled regions of other datasets exceeding a read coverage of 5000× . Reads were mapped with SOAP2 v2 . 21 [78] allowing for two mismatches per read . Reads that mapped with an average read coverage of ≤20× over the length of the read were removed from the dataset . Reads that mapped with an average read coverage of >20× were searched with BLASTN against the other datasets to avoid removing correctly assigned reads from highly conserved regions . Cleaned read datasets were again corrected with BayesHammer and assembled with SPAdes [30] , [76] . The resulting assemblies were annotated with the IMG/MER system ( Integrated Microbial Genomes and Metagenome Expert Review system ) using the standard metagenome pipeline [79] . To remove sequences originating from potential DNA contamination during cell sorting or MDA reaction , or from spontaneous DNA synthesis , we excluded contigs fulfilling any of the following three criteria: ( i ) contig length <250 bp , ( ii ) contig length <500 bp , and read coverage <5× or no BLASTX hit to the reference genomes wkB1 and wkB2 ( E-value cutoff of 10−5 ) , ( iii ) contigs with no BLASTX hit to any bacterial sequence in the non-redundant database . We also removed contigs identical to larger contigs in the same assembly , because these redundant contigs typically present assembly artifacts due to the high read coverage of certain regions . Orthologous gene sets were determined with OrthoMCL [80] , for S . alvi SAGs J21 , O02 , O11 , P14 and the reference genomes wkB2 ( CP007446 ) , wkB12 ( JFZW00000000 ) , wkB29 ( JFZV00000000 ) , and for G . apicola SAGs B02 , I20 , P17 and reference genomes wkB1 ( CP007445 ) , wkB11 ( JFON00000000 ) , wkB30 ( JFZX00000000 ) . To this end , separate all-against-all BLASTP searches with the protein sequences of S . alvi genomes and G . apicola genomes were performed . We only considered BLASTP hits with ≥50% protein identity , covering >50% of both query and hit protein length . Based on these BLASTP hits , CDSs were clustered into sets of homologs using the MCL algorithm [80] . Ortholog clusters of SAGs and reference genomes from A . mellifera were extracted and visualized as Venn diagrams ( Figure S4 ) . Paralogs were identified within these clusters and the paralog copy with the highest similarity to the other sequences was retained in the cluster . CDSs not belonging to any homolog cluster were classified as remnants , if they had a partial BLASTP hit in any other genome of the same species ( alignment length <50% over the length of the hit ) . They were classified as genome-specific genes , if they had no BLASTP hit in the other genomes of the same species ( E-value cutoff of 10−5 ) . 16S rRNA sequences were aligned with ClustalW [81] and overhanging ends removed . Phylogenies were inferred with PhyML [82] as implemented in Geneious R6 ( http://www . geneious . com/ ) using the GTR model with substitution rate categories set to four and all other parameters being estimated . Phylogenetic analyses of protein-encoding genes of S . alvi and G . apicola strains were conducted for genes having an ortholog in all outgroup species . These orthologs were identified with OrthoMCL comparing the reference genome of wkB2 ( S . alvi ) and wkB1 ( G . apicola ) and the complete genomes of six betaproteobacterial species and seven gammaproteobacterial species , respectively ( see Figure 3A and 3B ) . We applied the same BLASTP hit cutoffs as before . A total of 114 and 211 genes for S . alvi and G . apicola , respectively , were found to have an ortholog in all ingroup and outgroup genomes . These genes were aligned on protein sequence level with MUSCLE [83] and back-translated into aligned DNA sequences with a custom-made Perl script . Single gene trees were inferred with Garli 2 . 0 [84] using the model of evolution predicted by jModelTest 2 for each gene [85] . To infer the multilocus sequence phylogenies , DNA alignments were concatenated and maximum likelihood phylogenies inferred with Garli 2 . 0 . 100 non-parametric bootstrap trees were calculated for the concatenated alignments and the resulting supports for each split mapped with SumTrees [86] onto the maximum likelihood trees . To summarize the number of single gene trees supporting each split of the multilocus sequence phylogenies , we used the commands ‘Constraints’ and ‘Filter’ in PAUP 4 . 0 [87] . Nucleotide diversity ( π ) of 16S rRNA sequences within S . alvi and G . apicola was calculated with DNAsp v5 [88] . Pairwise sequence identity between 16S rRNA sequences of SAGs and reference genomes were obtained with ClustalW as implemented in Geneious R6 . To estimate the average pairwise sequence divergence at synonymous sites between orthologs of sequenced SAGs and reference genomes , orthologs were aligned as described before . Pairwise sequence divergence was based on maximum likelihood estimation of the synonymous substitution frequency per site ( dS ) using the program codeml implemented in PAML 4 . 7 ( runmode = −2 , CodonFreq = 2 ) [89] . We obtained mean pairwise dS and dN/dS values between SAGs and reference genomes by running codeml on the concatenated alignments of all shared genes ( 226 genes for S . alvi and 348 genes for G . apicola , including genomes of Bombus strains ) . Ternary plot analyses were conducted on genes shared between all A . mellifera strains ( 239 genes for S . alvi and 400 genes for G . apicola ) , following previously published methods [90]–[92] . In short , relative levels of dS values between ortholog triplets of SAGs were calculated and plotted with R using the ‘triangle . plot’ function [93] . The spread of the data points was calculated by averaging the distances between normalized dS values of each ortholog to the mean normalized dS value . The minimum number of recombination events in 16S rRNA gene alignments was calculated using the four-gamete test implemented in DNAsp v5 [88] . Sliding window analyses of nucleotide divergence over genomic regions were calculated with DNAsp v5 using the function ‘Polymorphism and Divergence’ with the Jukes-Cantor correction . For this analysis , genomic regions were aligned with ClustalW as implemented in Geneious R6 and stripped from all alignment gaps . To calculate the r/m ratios , two independent runs with the program ClonalFrame [94] were performed on orthologs shared between SAGs and reference genomes from A . mellifera . Each run consisted of 100 , 000 iterations , with a burn-in of 50 , 000 iterations . Parameters were recorded every 100th iteration . The r/m values were calculated from the output data of the two separate runs using two different methods . The first method considered all positions in the data , independent of the probability of a substitution at each site [95] . The second method only considered positions where the probability of a substitution by either mutation or recombination was ≥0 . 95 [90] . The program Geneconv was used to detect intragenic recombination events [96] . Different mismatch penalties ( gscale = 0 , 1 , or 2 ) were used to identify recombination events of different ages . We only considered global inner ( GI ) fragments , i . e . sequences that result from recombination of other sequences in the alignment . We applied a Karlin-Altschul p-value cutoff of 0 . 05 . The average fragment length for each pairwise comparison was calculated from all significant GI fragments . The sequences of SAGs B02 , J21 , I20 , O02 , O11 , P14 , and P17 are deposited in Genbank under accession numbers JAIM00000000 , AVQL00000000 , JAIN00000000 , JAIL00000000 , JAIK00000000 , JACG00000000 , and JAIO00000000 .
|
Gut microbial communities are often complex , consisting of bacteria from divergent phyla as well as multiple strains of each of the constituent species . But because the composition of these communities is typically assessed using 16S rRNA analyses , little is known about genomic changes associated with in situ diversification of bacterial lineages in animal guts . We undertook a single-cell genomic approach to investigate the diversification within two species of the gut microbiota of honey bees . Each species exhibited a surprisingly high level of genomic variation , despite uniformity in the 16S rRNA sequences . Our data indicate that genetically and ecologically distinct lineages can evolve in the gut of the same host species in the presence of frequent recombination at 16S rRNA genes . These findings parallel observations from mammals , suggesting that in situ diversification of a few bacterial lineages is a common pattern in the evolution of gut communities .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"taxonomy",
"genome",
"evolution",
"ecology",
"genome",
"analysis",
"genetics",
"biology",
"and",
"life",
"sciences",
"comparative",
"genomics",
"molecular",
"evolution",
"computational",
"biology",
"evolutionary",
"biology",
"population",
"genetics",
"microbiology",
"population",
"biology",
"microbial",
"ecology"
] |
2014
|
Hidden Diversity in Honey Bee Gut Symbionts Detected by Single-Cell Genomics
|
Productive infection of human parvovirus B19 ( B19V ) exhibits high tropism for burst forming unit erythroid ( BFU-E ) and colony forming unit erythroid ( CFU-E ) progenitor cells in human bone marrow and fetal liver . This exclusive restriction of the virus replication to human erythroid progenitor cells is partly due to the intracellular factors that are essential for viral DNA replication , including erythropoietin signaling . Efficient B19V replication also requires hypoxic conditions , which upregulate the signal transducer and activator of transcription 5 ( STAT5 ) pathway , and phosphorylated STAT5 is essential for virus replication . In this study , our results revealed direct involvement of STAT5 in B19V DNA replication . Consensus STAT5-binding elements were identified adjacent to the NS1-binding element within the minimal origins of viral DNA replication in the B19V genome . Phosphorylated STAT5 specifically interacted with viral DNA replication origins both in vivo and in vitro , and was actively recruited within the viral DNA replication centers . Notably , STAT5 interacted with minichromosome maintenance ( MCM ) complex , suggesting that STAT5 directly facilitates viral DNA replication by recruiting the helicase complex of the cellular DNA replication machinery to viral DNA replication centers . The FDA-approved drug pimozide dephosphorylates STAT5 , and it inhibited B19V replication in ex vivo expanded human erythroid progenitors . Our results demonstrated that pimozide could be a promising antiviral drug for treatment of B19V-related diseases .
Human parvovirus B19 ( B19V ) is a small , non-enveloped parvovirus with a single-stranded ( ss ) DNA genome of 5 . 6 kb . It belongs to the genus Erythroparvovirus of the Parvoviridae family [1] . The B19V genome is flanked by identical inverted terminal repeats ( ITRs ) at both ends [2] . B19V is pathogenic to humans and causes a myriad of pathologies , including fifth disease in children , transient aplastic crisis , persistent anemia in immune-compromised patients , hydrops fetalis in pregnant women , and arthropathy [3–7] . B19V infects human erythroid progenitor cells ( EPCs ) through initial attachment to its primary receptor ( P-antigen ) [8] and interaction with co-receptors , resulting in virus internalization [9 , 10] . Virus replication and assembly take place in the nuclei of infected cells . The B19V double-stranded ( ds ) DNA replicative form ( RF ) genome expresses the large non-structural NS1 protein , two small non-structural proteins ( the 11-kDa and 7 . 5-kDa proteins ) , and two capsid proteins ( VP1 and VP2 ) [11–13] . B19V infects human EPCs during the late stages of maturation , particularly burst forming unit-erythroid ( BFU-E ) cells and colony forming unit-erythroid ( CFU-E ) cells [14–17] . B19V also infects non-erythroid tissues [18–20] , but the infection of these tissues is non-productive , as virus replication is not fully supported [19 , 21 , 22] . Erythropoietin ( EPO ) , a hormone secreted by renal tissue in response to hypoxia , is essential for survival , differentiation , and development of EPCs during the late maturation stages [23] . In addition to the role in survivability of EPCs , EPO/EPO receptor ( EPO-R ) signaling is essential to B19V replication [24] . EPO binding to EPO-R activates Janus kinase 2 ( JAK2 ) -signal transducer and activator of transcription 5 ( STAT5 ) , phosphoinositide 3-kinase ( PI3K ) , and extracellular signal-regulated kinase ( ERK ) pathways . The JAK2-STAT5 pathway positively regulates B19V replication , the ERK pathway negatively regulates B19V replication , and the PI3K pathway is dispensable to B19V replication [25] . Expression of STAT5A is upregulated during hypoxia , and replication of B19V in human EPCs is facilitated by hypoxic conditions [25] . JAK2 predominantly phosphorylates STAT5A in the cells of erythroid lineage [26] , and thus STAT5A is largely involved in facilitating B19V replication of EPCs under hypoxic conditions [25] . The disease outcomes of hematological disorders caused by B19V infections result from the death of infected human EPCs . B19V infection inhibits erythropoiesis by inducing cell-cycle arrest [27–29] , and eventually results in apoptosis [30–33] . The results of this study confirmed that phosphorylation of STAT5 is essential for B19V DNA replication . Mechanistically , the B19V RF DNA genome harbors STAT5-binding element ( STAT5BE ) within the minimal origins of DNA replication ( Ori ) , located to the ITRs at each end of the viral genome . The binding site specifically binds phosphorylated STAT5 ( pSTAT5 ) . Moreover , our experiments revealed a novel interaction between STAT5 and minichromosome maintenance ( MCM ) complex; B19V exploits this interaction to recruit MCM complex to the viral replication centers for initiation of B19V DNA replication .
Our results previously demonstrated that pSTAT5A has a critical role in B19V infection of human EPCs cultured under hypoxic conditions [25] , leading us to consider in this study whether specific inhibition of STAT5 phosphorylation affects B19V replication . This possibility was tested by treating cells with a specific inhibitor of STAT5 phosphorylation , pimozide [34] . At a final concentration of 15 μM , pimozide abolished >90% of the STAT5 phosphorylation in CD36+ EPCs , without altering the total expression of STAT5 ( Fig 1A , lane 4 ) . CD36+ EPCs were incubated with pimozide 6 h prior to infection , and , at 48 h post-infection , numbers of B19V-infected ( capsid-expressing ) cells were reduced by 4 . 7-fold and 18 . 5-fold at 15 μM and 25 μM pimozide , respectively , compared with DMSO-treated cells ( Fig 1B ) . Pimozide abolished viral DNA replication at both concentrations ( Fig 1C ) . STAT5 dephosphorylation was confirmed in pimozide-applied infected cells ( Fig 1D ) . These results suggested that inhibition of STAT5 phosphorylation abolishes viral DNA replication in B19V-infected CD36+ EPCs . Notably , treatment with pimozide at 15 μM did not significantly inhibit cell proliferation , as assessed by the BrdU incorporation assay ( Fig 1E & 1F ) . Pimozide treatment , at a concentration as low as 10 μM , also abolished DNA replication of the B19V RF genome M20 in transfected UT7/Epo-S1 cells ( S1A Fig , lane 3 ) , and inhibited STAT5 phosphorylation ( S1B Fig , lane 3 ) . As controls , at 10 or 20 μM pimozide , cell proliferation was not significantly affected ( S1C & S1D Fig ) . Taken together , our results suggested that phosphorylation of STAT5 is essential for viral DNA replication . The requirement of pSTAT5 for B19V DNA replication suggested that there might be a direct involvement of pSTAT5 in viral DNA replication . In silico analysis of the B19V genome demonstrated the presence of several consensus STAT5-binding elements ( STAT5BEs ) throughout the genome . STAT transcription factor binds a GAS or GAS-like motif with a consensus sequence of TTCN3GAA , TTCN3TAA , or TTAN3GAA [35] . TTCN3TAA binds STAT5 [36] and is one of the top ten STAT5BEs identified in a genome wide analysis by ChIP-seq [37] . A consensus STAT5BE is located within the previously identified 67-nt Ori in the B19V genome ( Fig 2A ) [38] . Binding of pSTAT5 from nuclear lysates of UT7/Epo-S1 cells to the STAT5BE in the Ori was confirmed by EMSA . A shifted band , indicating binding of protein to the probe , was observed in the presence of wild-type ( wt ) Ori-derived probe wt-Ori-39 , but not the mut-Ori-39 that has the STAT5BE mutated ( Fig 2B and 2C , lanes 2 vs 3 ) . On incubation with an anti-pSTAT5 antibody , the level of shifted band was dramatically decreased ( Fig 2D , lane 3 ) . Because the EMSA was performed in the presence of excess amounts of non-specific competitor poly dI-dC , these results indicated specific binding of pSTAT5 to the B19V Ori . pSTAT5 was purified from UT7/Epo-S1 cells by the use of beads conjugated with high affinity STAT5-binding DNA oligonucleotides ( Fig 2E ) . EMSA was repeated with the purified pSTAT5 , which shifted the labeled wt-Ori-39 , but not the mut-Ori-39 ( Fig 2F , lanes 2 vs 3 ) . Shifting of wt-Ori-39 was abolished by addition of the STAT5-SH2 inhibitor , STAT5-SH2i , in a dose-dependent manner ( Fig 2G , lanes 4–6 ) . These binding assays confirm that pSTAT5 specifically binds to the STAT5BE of B19V Ori in vitro . The association of STAT5 with B19V NS1 and the viral capsid was demonstrated by immunofluorescence assays ( Fig 3A & 3B ) . STAT5 colocalized with NS1 and the viral capsid in the nucleus of B19V-infected CD36+ EPCs . The association of STAT5 with viral capsid was confirmed by the observation of fluorescent foci in B19V-infected cells in a proximity ligation assay ( Fig 3C ) , which produces an amplified signal when two labeled molecules are within 20 nm of one another [41] . Proximity ligation assay ( Fig 3D ) and confocal microscopy ( Fig 3F ) both demonstrated that STAT5 colocalized with replicating viral DNA that was pulse-labelled with BrdU in B19V-infected CD36+ EPCs , which are parvovirus replication centers [42 , 43] as shown by proximity ligation assay using anti-BrdU and anti-capsid antibodies ( Fig 3E ) . Interaction of pSTAT5 with the viral genome in cells was confirmed by ChIP assays in B19V-infected CD36+ EPCs and M20-transfected UT7/Epo-S1 cells . The pSTAT5-DNA complexes were pulled down with anti-pSTAT5 ( Y694 ) antibody , and bound viral Ori was detected by PCR . In the ChIP assay , cellular DNA was sheared to < 500 bp by sonication ( Fig 4A ) . A specific PCR band was amplified in samples from B19V-infected or M20-transfected cells pulled down by anti-pSTAT5 ( Y694 ) ( Fig 4B , lane 4 ) . Moreover , in UT7/Epo-S1 cells transfected with the B19V RF genome ( M20 ) , we observed that application of pimozide significantly decreased the amount of the Ori-containing fragments of the M20 , as assessed by the quantitative ChIP assay targeting Ori ( S4C Fig , Pimozide ) . Thus , these results confirmed the association of pSTAT5 with B19V Ori in B19V-infected CD36+ EPCs and M20-transfected UT7/Epo-S1 cells . A small molecule STAT5-SH2 inhibitor ( STAT5-SH2i , CAS no . 285986-31-4 ) specifically targets the SH2 domain of STAT5 and inhibits STAT5 binding to DNA [44] . EMSA was performed to determine whether STAT5-SH2i disrupts the interaction between the STAT5 and B19V Ori . Incubation of either UT7/Epo-S1 nuclear lysates or purified pSTAT5 with increasing concentrations of the inhibitor showed that the STAT5-SH2i prevented formation of the STAT5-DNA formation in a dose-dependent manner ( Fig 5A and Fig 2G ) . To examine the effect of the inhibitor on virus replication , CD36+ EPCs were pretreated with STAT5-SH2i 6 h prior to infection with B19V . The results showed that , at a final concentration of 500 μM , the inhibitor significantly decreased the virus-infected cell population by 10 . 7-fold ( Fig 5B ) , and the level of viral RF DNA by ~10-fold ( Fig 5C ) , but not the expression level of pSTAT5 ( Fig 5D ) , compared with the cells with DMSO treatment . Cell proliferation was not significantly affected by this level of inhibitor in mock-infected CD36+ EPCs ( Fig 5E & 5F ) . The inhibition of viral DNA replication by STAT5-SH2i was also demonstrated in M20-transfected UT7/Epo-S1 cells ( S2 Fig ) , and STAT5-SH2i significantly disrupted the interaction of pSTAT5 with the Ori of the B19V RF genome ( M20 ) in vivo as shown by a ChIP assay ( S4C Fig , STAT5-SH2i ) . The effect of mutation of the STAT5BE of the viral Ori on replication of the B19V RF genome was determined . The viral genome has an Ori sequence adjacent to each ITR , and the STAT5BE was mutated in either the left ITR ( N8mOriL ) or right ITR ( N8mOriR ) or both ITRs ( N8mOri ) of the N8 replicating RF DNA that has half ITRs at both ends , as shown in Fig 6A . The replication capability of these mutated RF genomes was examined in UT7/Epo-S1 cells . Although the N8 RF DNA replicated well , much less replication occurred with N8mOriL and N8mOriR , and no replication was observed with N8mOri ( Fig 6B ) . The mutations in the STAT5BEs were then introduced into both ITRs of M20 RF genome , to make the M20mOri mutant . No viral DNA replication was observed in M20mOri-transfected cells ( Fig 6C , lane 2 ) . Although both M20 and M20mOri RF genomes expressed NS1 , viral capsid ( a hallmark of B19V DNA replication [45] ) was present only in M20-transfected cells ( Fig 6D ) . During initiation of cellular DNA replication , the origin recognition complex ( ORC ) binds to autonomously replicating sequence sites and recruits cell division control protein ( CDC6 ) and DNA replication factor CDT1 to replication origins [46] . CDT1 recruits the MCM complex and primes replication initiation [47] . Although viral DNA replicates independently of ORC/CDC6/CDT1 , DNA viruses may require the MCM complex to initiate viral DNA replication [48] . In the parvovirus adeno-associated virus ( AAV ) , MCM complex is required for in vitro reconstitution of viral DNA replication [49] . In the case of B19V , we previously found that MCM complex is associated with the viral DNA replication centers and has a role in B19V replication [50] . Initially , to determine whether the viral NS1 protein has a role in recruitment of the MCM complex to the viral replication origin , we performed pull-down assays using lysates from NS1-expressing UT7/Epo-S1 cells . With pull-down of NS1 , MCM and pSTAT5 were not detected ( Fig 7A , lane 3 ) , but the positive control transcription factor E2F5 ( which interacts with B19V NS1 [27] ) was detected , which suggested that NS1 has no role in recruitment of the MCM complex . By contrast , co-immunoprecipitation ( Co-IP ) with an anti-pSTAT5 antibody pulled down MCM5 protein of the MCM complex from lysates of UT7/Epo-S1 cells ( Fig 7B ) . Similarly , Co-IP with an anti-MCM5 antibody pulled down pSTAT5 , in addition to MCM2 ( Fig 7C ) . The interaction between pSTAT5 and the MCM complex was DNA-independent , as DNase treatment of the lysate did not disrupt the interaction ( Fig 7D , lane 4 ) . Also , we show that MCM2 , MCM3 , MCM5 and MCM7 were associated with viral Ori in M20-transfected UT7/Epo-S1 cells , as confirmed by ChIP analyses ( S4A Fig ) . STAT5 and the MCM complex colocalized in CD36+ EPCs , irrespective of whether the cells were infected ( Fig 7E ) . An association of the MCM complex with STAT5 was confirmed in both B19V- and mock-infected cells by the proximity ligation assay ( Fig 7F ) . This association was blocked by treatment of pimozide in CD36+ EPCs ( Fig 7G ) . Immunofluorescence detection of the viral capsid demonstrated that , following B19V infection , most of the cells were infected ( Fig 7H ) . Our demonstration that pSTAT5 interacts with viral Ori as well as the MCM complex suggested that B19V might exploit these interactions to initiate viral DNA replication . To test this hypothesis , we infected CD36+ EPCs with B19V , and at 36 h post-infection ( when B19V DNA replication was at its peak ) , we treated the cells with STAT5-SH2i ( Fig 8A ) . At 6 h post-treatment , the cells were collected for ChIP assay with anti-MCM2 antibody , which showed that MCM abundance on viral Ori decreased significantly in the presence of STAT5-SH2i , compared with untreated control cells ( Fig 8B ) . Results with three-color confocal imaging demonstrated that MCM2 and STAT5 colocalized in mock-infected cells ( in the absence of viral NS1 ) ( Fig 8C , Mock ) . In infected cells , viral NS1 ( which binds viral Ori ) colocalized with both STAT5 and MCM , indicating that they were localized at viral DNA replication centers ( Fig 8C , B19V ) . These results suggested that B19V utilizes viral Ori-STAT5 and STAT5-MCM interactions to recruit the MCM complex to viral DNA replication origins , to initiate viral DNA replication . To confirm the efficacy of pimozide as a drug , we treated primary CD36+ EPCs with pimozide at various concentrations , and infected them with B19V . The cells were collected 48 h post-infection for quantification of viral DNA replication ( RF DNA ) by Southern blot analysis , which demonstrated that the IC50 of pimozide for inhibition of viral DNA replication ( the concentration at which 50% of viral DNA replication was inhibited ) was 2 . 7 ± 0 . 69 μM ( mean ± standard error ) ( Fig 9A ) . To examine the effect of pimozide on colony formation in the absence of virus infection , CD36+ EPCs were incubated with pimozide at increasing concentrations on Day 7 for 2 days , and then cultured in methyl cellulose-based medium for colony formation . After 10 days , numbers of colonies were counted ( Fig 9B ) . Pimozide only moderately reduced the numbers of colonies at higher concentrations ( 20–25 μM ) , but it did not affect the size or morphology of the colonies formed ( Fig 9C ) .
STAT5 is phosphorylated at a single conserved tyrosine residue ( Tyr694 in STAT5A and Tyr699 in STAT5B ) , and these phosphotyrosine motifs , upon intermolecular interaction , enable formation of either homodimers or heterodimers of STAT5A/B [52 , 53] . These dimers accumulate in the nucleus and bind DNA , to transactivate target genes [52] . EPO-activated JAK2 phosphorylates STAT5 in human EPCs [54] . We examined the relative expression of STAT5A and STAT5B in UT7/Epo-S1 and CD36+ EPC lysates with a STAT5A/B pan-specific antibody , and found that STAT5A was predominantly expressed in both cell types ( S3A Fig ) . This result agrees with the observations that JAK2 kinase predominantly phosphorylates STAT5A in cells of erythroid lineage [26] , and a constitutively phosphorylated STAT5A ( 1*6 ) variant enhances virus replication , whereas knockdown of STAT5A inhibits virus replication in B19V-infected EPCs [25] . STAT5B promotes viral DNA replication , but , during replication of human papillomavirus 16 ( HPV16 ) , STAT5B enhances viral DNA replication indirectly via regulation of TopBP1 expression , leading to the activation of ATR kinase [55] . In a proof-of-concept experiment , fusion of STAT5BEs to the DNA replication origin of polyoma virus replicon DNA improved replication efficiency in transfected mouse lymphoid BA/F3 cells , corroborating the direct role of STAT5 in viral DNA replication [56] . CD36+ EPCs have to be cultured in the presence of EPO for proliferation and differentiation [24] , which dominantly leads activation of STAT5A ( S3A Fig ) through the EPO-JAK2-STAT5 pathway [25]; however , a DDR or activation of ATR is not observed in normal ( uninfected ) CD36+ EPCs [29 , 57] ( S5A Fig ) . Furthermore , in hydroxyurea-treated CD36+ EPCs , both ATR and ATM were activated; however , application of pimozide did not change the level of phosphorylated ATR or ATM ( S5A Fig ) . As ATR activation enhances B19V replication [57] , these lines of evidence suggest that pSTAT5 does not utilize the STAT5-ATR pathway to facilitate B19V replication in CD36+ EPCs . Moreover , B19V infection per se did not affect STAT5 phosphorylation ( S5B Fig ) . Of note , the binding of pSTAT5 to the Ori , which locates in front of the B19V P6 promoter , did not obviously transactivate the P6 promoter ( S6 Fig ) . Thus , our results provide the first evidence that an authentic virus , B19V , depends on direct binding of pSTAT5 to its replication origin ( Ori ) for viral DNA replication . B19V infection induces late S-phase arrest in human EPCs , and S-phase factors are fully utilized by the virus to replicate its genome [50] . During cellular DNA replication , ORC-CDC6-CDT1 binding to the replication origin is a priming event that takes place in G1-phase [51] . Furthermore , CDT1 recruits the MCM complex and subsequently the whole replisome via formation of the MCM-CDC45 complex [51] . Notably , no such priming takes place during S-phase , so that chromosomes are not replicated multiple times [46] . However , viruses have evolved different mechanisms to initiate viral DNA replication . For examples , SV40 has the large T antigen that binds SV40 DNA replication origin and has helicase activity , and also recruits the replication machinery by interacting with DNA replication factors , such as replication factor A , DNA polymerase α and topoisomerase I [58] . Parvoviruses use the large non-structural protein NS1 , which binds directly to the viral origin and has helicase and nickase activities that facilitate viral DNA replication [59] . In parvovirus AAV , the MCM complex is essential to AAV2 DNA replication in vitro [49] , and is probably recruited by interaction with Rep78 , the large viral non-structural protein [60] . In the case of B19V , the MCM complex is localized to the viral DNA replication centers and is required for viral DNA replication [50] . However , we did not observe any interaction between the B19V NS1 protein and the MCM complex , suggesting that the complex is recruited to the viral DNA replication centers by an alternative mechanism . Here , our results provided evidence that STAT5 interacts with the MCM complex in human EPCs , without involvement of viral or cellular DNA . These cells express STAT5A more abundantly than STAT5B ( S3A Fig ) , but both STAT5A and STAT5B proteins interact with the MCM complex ( S3C Fig ) . During B19V infection , STAT5 is recruited to the viral DNA replication origin by direct interaction with STAT5BEs in the Ori sequences of the viral genome , thereby bringing the MCM complex to the viral Ori . Outside of the Ori , there are additional 6 putative STAT5BEs , and we tested that two of them in the capsid proteins-coding region also bound pSTAT5 ( S4D Fig ) . Since there is no putative terminal resolution site ( trs ) and NS1-binding sites outside of the Ori , we speculate that these STAT5BEs outside of the Ori do not contribute to B19V DNA replication . We hypothesize that MCM complex recruited by pSTAT5 at Ori may contribute to virus replication through its helicase activity or the recruitment of other DNA replication factors to the viral origin [51] . Notably , PIF ( parvovirus initiation factor ) , a member of the KDWK family of transcription factors , has been shown to bind two adjacent “ACGT” motifs in front of the NS1 binding site of left-hand replication origin ( OriLTC ) of the Protoparvoviurs minute virus of mice ( MVM ) [61 , 62] . PIF stabilizes the binding of NS1 to the Ori , which is critical for the activation of NS1 nickase [63] . In B19V , at least in an in vitro nicking assay , B19V NS1 is sufficient to cleave the Ori [40] . However , whether the binding of STAT5 to B19V Ori or the recruited MCM complex also involves in NS1 nickase activity of the Ori at trs ( Fig 2A ) warrants further investigation . To date , no specific treatment ( either anti-viral or vaccine-based ) exists for B19V infection . We have now demonstrated that pimozide , an FDA-approved anti-psychotic drug that is used in the treatment of a wide range of diseases [64] and could be potentially used to treat chronic myeloid leukemia , in which it specifically targets cancer cells , without affecting CD34+ hematopoietic stem cells [34] . Pimozide specifically inhibits STAT5 phosphorylation without affecting JAK2 activation or JAK2-derived signaling pathways; however , the underlying mechanism is unknown yet [65] . The pSTAT5 is presumably required for recruitment of the MCM complex to the viral Ori , and facilitates B19V replication in human EPCs . Pimozide is a potent inhibitor of B19V replication , with an IC50 of ~2 . 7 μM . At 15 μM , pimozide does not have a significant effect on proliferation of human EPCs expanded ex vivo , and has only moderate effect ( ~15% reduction ) on colony formation of EPCs . As STAT5A phosphorylation plays a key in B19V replication in human EPCs under hypoxic conditions [25] , these lines of evidence suggest that the inhibition of B19V replication in CD36+ EPCs is not a side-effect of the pimozide . Antivirals such as cidofovir and ribavirin are used in the treatment of adenovirus infection , and have IC50 values of 15 μM for cidofovir and 25 μM for ribavirin [66] . Importantly , when we applied both pimozide and STAT5-SH2i ( at 15 and 250 μM , respectively ) , a significant synergistic inhibition of B19V infection was observed ( S7 Fig ) . Therefore , we expect that a clinical trial should be conducted to examine pimozide as a treatment for B19V infection of patients with sickle-cell disease and immunocompromised patients and as anti-viral prophylaxis of transplant recipients .
We purchased CD34+ hematopoietic stem cells , which were isolated from bone marrow of a healthy human donor , from AllCells LLC ( Alameda , CA ) without any identification information on the cells , and , therefore , an institutional review board ( IRB ) review was waived . Primary human CD36+ EPCs were expanded ex vivo from CD34+ hematopoietic stem cells as previously described [24 , 25 , 67] . Briefly , hematopoietic CD34+ stem cells , purchased from AllCells , LLC ( Alameda , CA ) , were grown in Wong medium under normoxia up to Day 4 and frozen in liquid nitrogen [25] . In each experiment , Day 4 cells were thawed and grown under normoxia in an atmosphere containing 5% CO2 and 21% O2 at 37°C for 2–3 days , prior to incubation under hypoxia at 5% CO2 and 1% O2 . The megakaryoblastoid cell line , UT7/Epo-S1 , was cultured in Dulbecco's modified Eagle’s medium with 10% fetal bovine serum and 2 U/ml of EPO ( Amgen , Thousand Oaks , CA ) in 5% CO2 and 21% O2 at 37°C [38 , 68] . A UT7/Epo-S1 cell line expressing B19V NS1 protein ( NS1-S1 ) was cultured under the same conditions , except that 5 μg/ml doxycycline was used to induce NS1 expression when needed [29] . Plasma samples containing B19V at ~1 × 1012 viral genomic copies per ml ( vgc/ml ) were obtained from ViraCor Eurofins Laboratories ( Lee’s Summit , MO ) . After 2 days of hypoxia , CD36+ EPCs were infected with B19V at a multiplicity of infection ( MOI ) of ~1 , 000 vgc per cell . At 48 h post-infection , the infected cells were analyzed . STAT5-SH2 Inhibitor ( STAT5-SH2i , CAS 285986-31-4; catalog number ( cat# ) 573108 ) , a cell-permeable compound that selectively targets the SH2 domain of STAT5 [44] , and STAT5 Inhibitor III , pimozide ( CAS 2062-78-4 , cat# 573110 ) , which dephosphorylates STAT5 [34] , were purchased from EMD Millipore ( Billerica , MA ) . Both chemicals were dissolved in DMSO to produce stock solutions ( at 100mM ) that were kept at -80°C . Duo link In-Situ Red Mouse/Rabbit kit ( cat# DUO92101 ) was purchased from MilliporeSigma ( St Louis , MO ) . Proximity ligation assay was performed following the manufacturer’s instructions , as described previously [69] . Immunofluorescence assay was carried out as described previously [25 , 50] . Briefly , infected EPCs were deposited on slides by cytospinning , fixed with 3 . 7% paraformaldehyde for 30 min , and permeabilized with phosphate-buffered saline ( PBS , pH7 . 2 ) containing 0 . 5% Triton X-100 ( PBS-T ) for 5 min at room temperature . Non-specific interactions were blocked with 3% bovine serum albumin ( BSA ) before subsequent incubation with primary and fluorescence-labelled secondary antibodies . The slides were visualized with a Nikon confocal microscope , and images were taken at 100 × magnification . pM20 contains the full-length B19V replicative from ( RF ) genome ( nt 1–5596 ) , and pN8 contains a half-ITR deleted B19 RF genome ( nt 199–5410 ) [38 , 70] . They are diagramed in Fig 2A . pN8mOriL and pN8mOriR were constructed by mutating the STAT5BE of the Ori in the left and right half ITRs of the pN8 , respectively . Both STAT5BE were mutated in pM20 and pN8 resulted in pM20mOri and pN8mOri , respectively , which are diagramed with mOri shown , and the sequence of mutated Ori in the half right ITR is depicted ( Fig 6A ) . UT7/Epo-S1 cells were electroporated in V solution using Amaxa Nucleofector ( Lonza , Basel , Switzerland ) , as described previously [25] . Briefly , B19V infectious clone pM20 or mutants were enzymatically digested with Sal I . The linearized DNA was gel-purified . 2 μg of DNA was used for electroporation of 2 × 106 cells . After transfection , UT7/Epo-S1 cells were cultured under hypoxia of 1% O2 . B19V-infected CD36+ EPCs were examined for virus infection by flow cytometry analysis with an anti-B19V capsid antibody , as described previously [25 , 50] . For cell-cycle analysis , a bromodeoxyuridine ( BrdU ) incorporation assay was used , as described previously [50] . Lower molecular DNA ( Hirt DNA ) was extracted from either B19V-infected CD36+ EPCs or transfected UT7/Epo-S1 cells by a Hirt extraction method , as described previously [45] . Hirt DNA extracted from UT7/Epo-S1 cells was further digested with Dpn I to remove non-replicated plasmid DNA input . Southern blot analysis was performed as reported previously [28 , 45] . B19V RF DNA M20 excised from pM20 with Sal I was used as a probe . A biotinylated dsDNA probe [71] , 5’-Bio-GAT ACT AGT TTC GTG GAA TCG TGG CAC TAT GAA CCA-3’ , containing a STAT5BE ( underlined ) , was synthesized by IDT ( Coralville , IA ) and used to purify pSTAT5 , following a published protocol [72] with some modifications . Briefly , UT7/Epo-S1 cells grown in 14 dishes of 145 mm diameter were collected , washed with PBS , and resuspended in Lysis Buffer-1 ( 10 mM HEPES , pH 7 . 6 , 0 . 1 mM EDTA , 1 mM DTT , 0 . 5% NP-40 , 10 mM KCl , 0 . 5 mM PMSF , and protease inhibitor cocktail ( PIC , MillopreSigma ) for 5 min on ice . After vortexing , the lysate was centrifuged at 500 × g for 5 min at 4°C , and the nuclear pellet was washed with Lysis Buffer-1 without NP-40 . The pellet was resuspended again in Lysis Buffer-2 ( 50 mM Tris , pH 7 . 6 , 150 mM NaCl , 1 mM EDTA , 1% Triton X-100 , 1 mM DTT , and 1 mM PMSF , and PIC ) , vortexed , and kept on ice for 30 min . The nuclear lysate was sonicated , centrifuged at 12 , 000 × g for 20 min , and then passed through a 0 . 45 μm filter before being mixed with streptavidin beads pre-bound with the biotin-dsDNA probe and incubated for several hours . The beads were then washed in Wash Buffer ( 50 mM Tris-HCl , pH 7 . 6 , 150 mM NaCl , 3–4 μg/ml poly dI-dC ( MilliporeSigma ) , 1 mM PMSF , and PIC ) . Bound proteins were eluted in Wash Buffer with increasing salt concentrations ( 0 . 3–1 M NaCl ) . The fractions containing pSTAT5 were identified by Western blotting . Electrophoretic mobility shift assay ( EMSA ) was performed as previously reported [73] . Complementary forward and reverse oligonucleotides ( synthesized at IDT , Coralville , IA ) were annealed to form dsDNA probes , wt-Ori-39 and mut-Ori-39 that had the STAT5BE mutated [71] ( Fig 2B ) . The probes were 5’ end labeled with 32P using [γ-32P] ATP . Each 20 μl binding reaction contained 3 μg/ml of poly dI-dC ( MilliporeSigma ) . Chromatin immunoprecipitation ( ChIP ) assay was performed essentially as described previously [74 , 75] with modifications . Cells were fixed in 1% formaldehyde for 10 min at room temperature and then quenched in 125 mM glycine . Fixed cells were washed with PBS , and then lysed in 400 μl of Lysis Buffer ( 10 mM Tris-HCl , pH 8 . 0 , 10 mM NaCl , 0 . 2% NP-40 , 1 mM PMSF , and PIC ) and incubated for 10 min on ice . After centrifugation at 2 , 500 rpm for 5 min at 4°C , the nuclear pellet was resuspended in 100 μl of Nuclear Lysis Buffer ( 50 mM Tris-HCl , pH 8 . 1 , 10 mM EDTA , 1% SDS , and PIC ) for 10 min on ice . One ml IP Dilution Buffer ( 20 mM Tris-HCl , pH 8 . 1 , 2 mM EDTA , 150 mM NaCl , 1% Triton X-100 , and 0 . 01% SDS ) was added , and chromatin was sheared by sonication at 80% power for 10 cycles of 15 s pulse and 1 min rest . Sonicated samples were centrifuged to remove debris , and the supernatant was split aliquots . Antibody ( 2 . 5 μg ) was added to each aliquot , and the mixtures were incubated overnight at 4°C . For each sample , 10 μg of yeast tRNA was added to 40 μl of cold PBS-prewashed Protein A/G beads ( Gold BioTechnology , Inc . , St Louis , MO ) , and this mixture was added to the sample containing antibody and incubated with rocking for 6 h . Beads were collected by centrifugation and washed with IP Wash-1 ( 20 mM Tris , pH 8 . 1 , 2 mM EDTA , 50 mM/500mM NaCl , 1% Triton X-100 , 0 . 1% SDS ) three times ( first at low salt of 50 mM and then twice at 500 mM ) for 10 min each at 4°C , followed by one wash with IP Wash-2 ( 10 mM Tris , pH 8 . 1 , 1 mM EDTA , 0 . 25 M LiCl , 1% NP-40 , and 1% deoxycholic acid ) for 10 min at 4°C . The beads were then washed with cold TE , and protein-DNA complexes were eluted twice using 200 μl of Elution Buffer ( 100 mM sodium bicarbonate and 1% SDS ) for 10 min at room temperature . Crosslinking was reversed by addition of 16 μl of 5 M NaCl and incubation at 65°C . DNA was purified with a Qiagen PCR purification kit ( Qiagen , Hilden , Germany ) , and ChIP product was recovered in 50 μl of H2O , and used for PCR or quantitative PCR ( qPCR ) analysis . Immunoprecipitated viral DNA from ChIP assay was subjected to PCR analysis using either F1 and R1 or F1 and R2 primers spanning the viral origin region: F1 ( nt 5036–5053 ) , 5’-CCT GCC CCC TCC TAT ACC-3’ , R1 ( nt 5308–5285 ) , 5’-CAG GAA ATG ACG TAA TTG TCC GCC-3’ , and R2 ( nt 5393–5376 ) , 5’-ACG TCA ACC CCA AGC GCT-3’ . q-PCR analysis was done as described previously [24] , using the following primers: F ( nt 353–378 ) , 5’-GCA TCT GAT TTG GTG TCT TCT TTT AA-3’ , R ( 421–403 ) , 5’-TGG CTG CCC ATT TGC ATA A-3 , and probe ( nt 386–401 ) , 5’ FAM-CGG GCT TTT TTC CCG C/IABkFQ-3’ . The colony formation assay was performed with methyl cellulose-based medium ( R&D Systems , Minneapolis , MN ) according to the manufacturer’s instructions , with modifications . Briefly , CD36+ EPCs were cultured in Wong expansion medium and were treated with pimozide at various concentrations on Day 7 . After 48 hours , ≥3 × 104 cells from each well were cultured in semi-solid methyl cellulose-based medium for 10–12 days , at which time colony counts were assessed by someone who was blinded to the experimental conditions . Co-immunoprecipitation ( Co-IP ) assay was performed as previously described [73 , 76] . Briefly , UT7/Epo-S1 cells were collected , washed with PBS , and lysed in radioimmunoprecipitation assay ( RIPA ) buffer . After centrifugation at 12 , 000 rpm for 20 min at 4°C , supernatant was taken and split into aliquots . Each aliquot was incubated with 3 μg of an antibody of interest overnight at 4°C , and then 40 μl of Protein A/G beads ( washed with ice-cold PBS three times beforehand ) was added , followed by incubation for 6 h . The beads were collected by centrifugation and washed three to five times with 1 × PBS , and then resuspended in 1 × Laemmli sample buffer . Samples were boiled for 10 min and run on 10% SDS-polyacrylamide gels for Western blot analysis , which was performed as described previously [25 , 50 , 77] . Pull-down assay was performed similarly to Co-IP , except that anti-Flag-conjugated beads or control beads were used . The following primary antibodies were purchased: mouse anti-STAT5 ( cat# sc-74442 ) , rabbit anti-STAT5 ( cat# sc-835 ) , anti-STAT5A ( cat# sc-271542 ) and anti-STAT5B ( cat# sc-1656 ) , anti-BrdU ( IIB5 ) ( cat# sc-32323 ) were from Santa Cruz ( Dallas , TX ) ; anti-MCM2 ( cat# 12079 ) , and anti-pSTAT5 ( Y694 ) ( cat# 4322 ) were from Cell Signaling ( Danvers , MA ) ; anti-STAT5a/b pan-specific antibody ( cat # AF2168 ) and normal IgG rabbit ( cat# AB-105-C ) were from R&D Systems Inc ( Minneapolis , MN ) ; anti-MCM5 antibody ( cat# 2380–1 ) was from Epitomics ( Burlingame , CA ) ; anti-B19V capsid ( cat# Mab8293 ) was from Millipore ( Billerica , MA ) ; anti-BrdU ( clone B44 ) was from BD ( Franklin Lakes , NJ ) ; and anti-β-actin ( cat# A5441 ) was from Sigma; anti-MCM3 ( cat# A300-124A ) , anti-MCM5 ( cat# A300-195A; for ChIP ) , and MCM7 ( cat#A300-128A ) were from Bethyl Laboratories ( Montgomery , TX ) ; anti-ATM ( pS1981 ) ( cat#ab81292 ) were from Abcam ( Cambridge , MA ) ; and anti-ATR ( pT1989 ) ( cat#GTX128145 ) from GeneTex ( Irvine , CA ) . Rat anti-NS1 polyclonal antibody was prepared in our lab as previously reported [25] . Horseradish peroxidase ( HRP ) -conjugated anti-mouse and anti-rabbit secondary antibodies were purchased from Sigma , and fluorescein isothiocyanate ( FITC ) - , Texas Red- , and Dylight405-conjugated anti-mouse , anti-rat , and anti-rabbit secondary antibodies were all purchased from Jackson ImmunoResearch ( West Grove , PA ) . Statistical analysis was performed using GraphPad Prism Version 7 . 0 . Statistical significance was determined by using 1-way ANOVA analysis , followed by Tukey-Kramer post-test for comparison of three or more groups and unpaired ( Student ) t-test for comparison of two groups . Error bars show mean and standard deviation ( Mean ± SD ) unless otherwise specified .
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Human parvovirus B19 ( B19V ) infection can cause severe hematological disorders , a direct consequence of the death of infected human erythroid progenitor cells ( EPCs ) of the bone marrow and fetal liver . B19V replicates autonomously in human EPCs , and the erythropoietin ( EPO ) and EPO-receptor ( EPO-R ) signaling is required for productive B19V replication . The Janus kinase 2 ( JAK2 ) -signal transducer and activator of transcription 5 ( STAT5 ) signaling plays a key role in B19V replication . Here , we identify that phosphorylated STAT5 directly interacts with B19V replication origins and with minichromosome maintenance ( MCM ) complex in human EPCs , and that it functions as a scaffold protein to bring MCM to the viral replication origins and thus plays a key role in B19V DNA replication . Importantly , pimozide , a STAT5 phosphorylation-specific inhibitor and an FDA-approved drug , abolishes B19V replication in ex vivo expanded human EPCs; therefore , pimozide has the potential to be used as an antiviral drug for treatment of B19V-caused hematological disorders .
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2017
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Phosphorylated STAT5 directly facilitates parvovirus B19 DNA replication in human erythroid progenitors through interaction with the MCM complex
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Nuclear hormone receptors respond to small molecules such as retinoids or steroids and regulate development . Signaling in the conserved p38/PMK-1 MAP kinase pathway regulates innate immunity . In this study , we show that the Caenorhabditis elegans nuclear receptor DAF-12 negatively regulates the defense against pathogens via the downstream let-7 family of microRNAs , which directly target SKN-1 , a gene downstream of PMK-1 . These findings identify nuclear hormone receptors as components of innate immunity that crosstalk with the p38/PMK-1 MAP kinase pathway .
Innate immunity is an evolutionarily conserved response to pathogens and forms the first line of defense for most organisms . When infected by pathogens , the nematode Caenorhabditis elegans mounts a rapid innate immune response and produces an array of anti-microbial genes , similar to other organisms throughout the animal kingdom [1] , [2] . Several conserved signaling pathways that function in the perception of and defense against bacterial pathogens have been identified in C . elegans . These pathways include the NSY-1/PMK-1 MAP kinase signaling pathway , the DAF-2/DAF-16 insulin/insulin-like growth factor ( IGF ) -1 like signaling pathway , the DBL-1/transforming growth factor-β ( TGF-β ) signaling pathway and the BAR-1 β-catenin signaling pathway [1]–[4] . Although many conserved innate immune components have been identified in C . elegans using genetic and biochemical approaches , extensive characterization of the signaling networks that regulate the host response and outcome of infections is warranted . Nuclear hormone receptors ( NRs ) are a class of transcription factors that are regulated by small lipophilic hormones . In all , 284 NRs have been identified in C . elegans , and approximately 20 of them have been genetically analyzed [5] . The dauer formation abnormal gene daf-12 , a well-characterized nuclear hormone receptor , and the orphan receptors nhr-8 and nhr-48 are the conserved homologs of the mammalian vitamin D receptor and liver-X receptor [6] , [7] . DAF-12 regulates developmental progression and arrest in response to environmental cues [6] , [8] . In favorable conditions , the activation of TGF-β and insulin/IGF-1 signaling cascades results in the production of the DAF-12 steroidal ligands , dafachronic acids ( DAs ) . DAs are synthesized from cholesterol via a multi-step pathway involving the daf-36 Rieske-like oxygenase and the daf-9 cytochrome P450 enzyme , which promote a rapid progression through four larval stages ( L1 to L4 ) to reproductive adults [9]–[14] . In unfavorable environments , DAs expression is suppressed , and DAF-12 , without its ligand , binds to the co-repressor DIN-1 , resulting in an arrest at a stress-resistant , long-lived alternative third larval stage , called the dauer diapauses ( L3d ) [15] . In addition , DAF-12 regulates the normal lifespan of worms and the longevity of germline-ablated animals [16]–[21] . However , the role of DAF-12 in the immune regulation of C . elegans remains unknown . MicroRNAs ( miRNAs ) are small non-coding RNA molecules that repress target gene expression by base-pairing with partially complementary sequences in the 3′-untranslated regions ( 3′-UTR ) of target mRNAs [22] , [23] . MiRNAs influence molecular signaling pathways and regulate many biological processes , including immune function [24] . Originally discovered in C . elegans , lethal-7 ( let-7 ) miRNA is conserved across species in both sequence and temporal expression [25] , [26] . In C . elegans , the let-7 miRNA homologs mir-48 , mir-84 and mir-241 ( together referred to as let-7s ) regulate developmental timing and promote cellular differentiation pathways [27] , [28] . The human let-7-related miRNAs also have anti-proliferative functions , and the downregulation of let-7 levels is associated with a variety of cancers , such as lung , breast and colon cancer [27] , [28] . DAF-12 and its steroidal ligands activate the expression of let-7s , which downregulate the heterochronic gene hbl-1 , thus integrating environmental signals and developmental progression [29] , [30] . However , the functional role of let-7 family of miRNAs in the innate immune responses to pathogens is largely unknown . Hence , we sought to investigate whether DAF-12 and the let-7 family of miRNAs play a role in the regulation of the innate immune responses to bacterial infection in C . elegans .
We used an RNAi feeding method to search for the host components that influence the response of C . elegans to infection with Pseudomonas aeruginosa strain PA14 , which is a human opportunistic pathogen that can also infect and kill C . elegans . Using 399 RNAi clones targeting transcription factors , we identified 17 transcriptional factors that affect the survival of worms on the P . aeruginosa lawns ( Table S1 ) . Among these candidates , treatment with daf-12 RNAi improved either the resistance of C . elegans to P . aeruginosa infection or its survival on an avirulent E . coli lawn ( Fig . 1A , Fig . S1A ) . Transgenic daf-12 ( dhls26 ) worms containing daf-12::GFP were more susceptible to P . aeruginosa ( Fig . S1B ) . DAF-12 , along with NHR-8 and NHR-48 , is a conserved homolog of the mammalian vitamin D/liver X receptor ( LXR ) in C . elegans [6] , [7] . However , inhibition of nhr-8 and nhr-48 increased pathogenic susceptibility to P . aeruginosa infection ( Fig . S2A ) , suggesting that nhr-8 and nhr-48 have roles opposite to that of daf-12 in innate immune regulation . To further investigate the role of DAF-12 in the immune response to bacterial infection , we examined the survival rate and lifespan of daf-12 alleles that have been previously identified on the basis of development and aging [6] , [8] . A daf-12 null mutant daf-12 ( rh61rh411 ) that contained two nonsense mutations affecting both DNA binding domain ( DBD ) and ligand binding domain ( LBD ) [6] and was more resistant to P . aeruginosa , had a shortened lifespan compared to wild-type N2 animals ( Fig . 1B and 1C ) . The daf-12 ( sa156 ) mutant containing a C121Y mutation in the zinc finger of DBD [6] , which may interrupt the DNA binding activity of DAF-12 , displayed a normal lifespan but increased resistance to P . aeruginosa infection ( Fig . 1B and 1C ) . In contrast , the two other two mutants , daf-12 ( m20 ) , which has a nonsense mutation affecting DBD [6] , and daf-12 ( m25 ) , containing a M562I mutation in LBD [6] , exhibited extended lifespans and normal pathogenic resistance to P . aeruginosa infection ( Fig . S3A and 3B ) . These results not only identify DAF-12 as a negative regulator of innate immune responses to the infection of P . aeruginosa but also suggest a cross-talk between developmental progression and host defense . We have also examined whether DAF-12 is involved in C . elegans host defense to other different pathogens , and found that inhibition of daf-12 greatly increased the resistance of daf-12 ( sa156 ) mutants to Staphyloccocus aureus infection ( Fig . S2B ) . We next performed transmission electron microscopy analysis to examine gut cells of wild-type worms or daf-12 ( sa156 ) worms fed P . aeruginosa or E . coli . When fed E . coli , both of the wild-type N2 and daf-12 ( sa156 ) worms display normal intestinal ultrastructure , whereas when infected by P . aeruginosa , the daf-12 ( sa156 ) worms exhibited less severely damaged gut cells and more intact microvilli than in the wild-type worms ( Fig . 1D ) . To determine the cellular localization of DAF-12 , we utilized previously generated transgenic daf-12 ( dhls26 ) worms containing daf-12::GFP [7] and showed a significant accumulation of DAF-12 in the nuclei of neurons and intestinal cells when worms were fed E . coli . However , when infected with P . aeruginosa , DAF-12 expression was not affected ( Fig . S4C ) , but the associated GFP signal was diffusely distributed throughout both neuronal and intestinal cells ( Fig . S4A and S4B ) , suggesting that the P . aeruginosa infection suppresses nuclear localization of DAF-12 and promotes its translocation to the cytoplasm . We then examined the effect of P . aeruginosa infection on the expression of eight selected anti-microbial genes that are regulated by the NSY-1/PMK-1 pathway or the insulin/IGF-1-like pathway [31] . We found that in the daf-12 ( rh61rh411 ) and daf-12 RNAi-treated worms infected with P . aeruginosa , expression levels of five of the eight anti-microbial genes were significantly higher compared to the wild-type control ( Fig . 2A ) . To further confirm the quantitative RT-PCR results , we treated the dod-22::gfp or F55G11 . 7::gfp transgenic worms with daf-12 RNAi , fed them E . coli or P . aeruginosa , and then subjected them to confocal image analysis . Treatment with daf-12 RNAi greatly increased the expression of dod-22::GFP and F55G11 . 7::GFP at both basal E . coli levels and in P . aeruginosa-induced levels ( Fig . 2B , Fig . S5A and S5B ) . DAF-12 is known to control C . elegans response to its environment . Under favorable conditions , the stimulation of the insulin/IGF-1 andTGF-β pathways leads to the production of sterol-derived dafachronic acids ( DAs ) . Δ4-DA and Δ7-DA bind to DAF-12 , leading to developmental progression [10] , [11] . The substitution of dietary cholesterol with Δ7-DAs reduced the resistance of wild-type worms , but not daf-12 ( sa156 ) worms , to P . aeruginosa infection ( Fig . 3A ) . An increased dose of Δ7-DA did not lead to further increases in pathogenic susceptibility of the wild-type N2 worm to P . aeruginosa infection ( Fig . S6 ) . DAs are derivatives of dietary cholesterols that are synthesized via several pathways involving the cytochrome P-450 DAF-9 and the SAM-dependent methyltransferase STRM-1 [12] , [13] , [32] . Inhibition of DAF-9 expression by RNAi feeding increased the resistance of the worm to P . aeruginosa infection ( Fig . 3B ) . In unfavorable environments , the downregulation of the insulin/IGF-1 and TGF-β pathways suppresses DA production , and without its ligand , DAF-12 associates with the co-repressor DIN-1 to promote dauer programs [15] . Thus , we next examined the role of DIN-1 , a co-repressor of DAF-12 , in the immune response of C . elegans . The inhibition of din-1 did not affect the survival of wild-type worms on a P . aeruginosa lawn , but moderately attenuated the extended pathogenic resistance of daf-12 RNAi worms ( Fig . S7A ) , suggesting that DAF-12 regulating the immune response of C . elegans may be partially dependent on din-1 . Several conserved signaling pathways , including the NSY-1/PMK-1 pathway and the insulin/IGF-1-like pathway , are involved in the pathogenic defense of C . elegans [2] . The loss of function of the insulin receptor DAF-2 activates the downstream target DAF-16 , which triggers the expression of anti-microbial genes in response to pathogenic infection [3] , [33] . However , daf-16 RNAi had no effect on the prolonged survival of daf-12 ( sa156 ) worms infected with P . aeruginosa ( Fig . S7B ) . We then tested whether the NSY-1/PMK-1 pathway is involved in the enhanced resistance of daf-12 mutants to P . aeruginosa . Either inhibition of nsy-1 by RNAi or mutation of pmk-1 attenuated the enhanced pathogenic resistance of daf-12 ( sa156 ) worms or daf-12 RNAi-treated worms , respectively ( Fig . 4A and 4B ) , suggesting that DAF-12 may target the PMK-1 pathway to regulate the C . elegans immune response against P . aeruginosa infection . However , daf-12 RNAi did not markedly change the P . aeruginosa-stimulated phosphorylation of PMK-1 ( Fig . 4C ) , suggesting that DAF-12 might act upstream or parallel to PMK-1 to suppress the PMK-1/p38 MAPK pathway . MicroRNAs are approximately 20- to 22-nucleotide-long RNA molecules that bind to the 3′ untranslated region ( 3′UTR ) of target messenger RNAs ( mRNAs ) and that decrease their expression [34] , [35] . DAF-12 activates the expression of the let-7 miRNA homologs mir-84 and mir-241 ( referred to as let-7s ) to control developmental progression [29] , [30] . To test whether mir-84 or mir-241 play a role in pathogenic defense , we infected the strains mir-84 ( n4037 ) and mir-241 ( n4316 ) with P . aeruginosa . Both mir-84 ( n4037 ) and mir-241 ( n4316 ) worms were more resistant to P . aeruginosa infection than the wild type ( Fig . 5A ) . Likewise , both mir-84 ( n4037 ) and mir-241 ( n4316 ) worms had slightly longer lifespans than wild-type animals ( Fig . 5B ) . We then employed the quantitative real-time PCR method to detect miRNA expression and found that P . aeruginosa infection of wild-type worms induced higher levels of mir-84 and mir-241 compared to E . coli . However , the daf-12 mutation markedly reduced the expression of both mir-84 and mir-241 ( Fig . 5C ) . Confocal microscopic imaging of mir-84p::gfp also indicated that the mir-84 expression was highly upregulated in P . aeruginosa-infected wild-type worms , but not in the daf-12 ( rh61rh411 ) worms ( Fig . 5D , Fig . S5C ) . To further determine the role of let-7s miRNAs in C . elegans innate immunity , we tested the function of mir-48 , another let-7 relative , in P . aeruginosa infection and found that the mir-48 ( n4097 ) mutant exhibited decreased resistance to P . aeruginosa ( Fig . S8A ) , suggesting that the let-7s miRNAs may target different regulators of C . elegans innate immunity . Quantitative real-time PCR results showed that the expression of daf-12-tergeted antimicrobial genes was also upregulated in let-7s miRNAs mutants ( Fig . S9A ) , suggesting that these genes are also targeted by let-7s miRNAs . We then fed the daf-12 and let-7s mutants a GFP-tagged P . aeruginosa PA-14 strain and examined the bacterial burdens in worm intestines by confocal microscopy . We found there were significantly fewer accumulated bacteria in daf-12 ( sa156 ) and mir-241 ( n4316 ) worm intestines ( Fig . S10A and S10B ) , suggesting that the inhibition of daf-12 and mir-241 may suppress bacterial accumulation through antimicrobial gene expression . The nsy-1 RNAi also counteracted the pathogenic defense of mir-84 mutant worms , and mutations of mir-84 or mir-241 did not affect the phosphorylation of PMK-1 , suggesting that the let-7s miRNAs function downstream of DAF-12 to suppress the PMK-1/p38 MAPK signaling pathway ( Fig . 5E , Fig . S8B ) . The finger protein hbl-1 is one target of miRNAs let-7s , and the expression of hbl-1 is regulated by daf-12 and let-7s [29] . The inhibition of hbl-1 by RNAi reduced the pathogenic resistance , but not the lifespan of C . elegans ( Fig . S11A and S11B ) . To identify other target genes of let-7 family of miRNAs , we performed a bioinformatics analysis , determining that skn-1 is a potential target ( Fig . 6A ) . To determine whether let-7s miRNAs could bind to the 3′-UTR of the skn-1 mRNA and suppress it , we fused the 3′-UTR region of the C . elegans skn-1 mRNA to the 3′-end of a luciferase reporter gene and co-transfected it with synthesized dsRNAs mimicking let-7s miRNAs ( let-7s mimics ) into HEK293T cells . In contrast to the luciferase activity in the 3′-UTR seed region mutants ( skn-1 3′-UTR ( mut ) ) , which could not bind with and respond to let-7s miRNAs , the luciferase activity of the skn-1 3′-UTR decreased by approximately 30% in response to mir-48 mimics or mir-84 mimics and by approximately 10% in response to mir-241 mimics ( Fig . 6B ) . Western blot results also showed that the SKN-1 protein expression could be upregulated by inhibition of daf-12 , mir-84 and mir-241 ( Fig . 6C , Fig . S11C ) . These results suggested that skn-1 is a target of mir-84 and mir-241 . SKN-1 is a kinase substrate of PMK-1 and regulates C . elegans resistance to oxidative stress [36] , [37] . The inhibition of skn-1 by RNAi markedly attenuated the pathogenic resistance of C . elegans ( Fig . S12A ) [38] but did not affect the pathogenic resistance of pmk-1 ( km25 ) mutants ( Fig . S12B ) . In worms infected with P . aeruginosa but not E . coli , SKN-1 accumulated in the nuclei of intestinal cells ( Fig . S12C and S12D ) [38] , [39] . However , nsy-1 RNAi attenuated the nuclear accumulation of SKN-1 ( Fig . S12E ) , suggesting that skn-1 may also act downstream of NSY-1/PMK-1 to regulate the immune response . Conversely , skn-1 RNAi markedly reversed the enhanced pathogenic resistance of the daf-12 ( sa156 ) mutant as well as that of the mir-84 or mir-241 mutants ( Fig . 7A , 7B and 7C ) . Quantitative real-time RT-PCR results showed that the inhibition of daf-12 and of let-7s miRNAs significantly increase the expression of gcs-1 , a SKN-1 downstream gene ( Fig . S13A ) , suggesting that the DAF-12 and let-7s miRNAs may suppress SKN-1 activity . We treated the skn-1::gfp transgenic worms [37] with daf-12 RNAi and confocal imaging analysis revealed that daf-12 RNAi treatment dramatically increased both the expression and nuclear accumulation of SKN-1 ( Fig . S13B and S13C ) . These findings suggest that DAF-12-let-7s may target SKN-1 , thus counteracting the activation of SKN-1 by the NSY-1/PMK-1 pathway .
We have identified DAF-12 as a novel negative regulator of innate immune signaling pathways in C . elegans . DAF-12 , along with NHR-8 and NHR-48 , is a conserved homolog of the mammalian vitamin D/liver X receptor ( LXR ) in C . elegans [6] , [7] . The functions of vitamin D and LXR in mammalian innate immunity have been extensively investigated [40] , [41] . In a variety of human innate immune cell types ( i . e . , macrophages and monocytes ) , vitamin D stimulates antibacterial activity by increasing the expression of antimicrobial genes and by promoting autophagic mechanisms . Furthermore , vitamin D insufficiency , which is a global health issue , may increase the risk of many infectious diseases [40] . Whereas daf-12 negatively regulates the innate immunity of C . elegans , a mutation of either of nhr-8 or nhr-48 impairs the C . elegans host defense ( Fig . S2A ) , suggesting that the regulatory role of nuclear hormone receptors may depend on their ligands and their target genes . Hormone binding to nuclear hormone receptors regulates the C . elegans reproductive life cycle and entry into dauer diapauses [42] . We have shown that the sterol-derived dafachronic acids ( DAs ) , the ligand of DAF-12 , negatively regulate the pathogenic resistance of C . elegans in a DAF-12-dependent manner . In response to pathogenic infection , DAF-12 translocated from the nucleus into the cytoplasm . Although the regulation of DAF-12 translocation is not fully understood , the binding of the DAs and their co-factors is hypothesized to lead to the translocation of DAF-12 . DAF-9 , which synthesizes DAs , has also been shown to regulate C . elegans antibacterial activity , suggesting that the sterol hormones might be the key sensors of pathogenic infection and important regulators of pathogenic defense . DAF-12 is known to activate let-7s miRNAs and thus regulate the developmental progression through downstream target hlb-1 [29] , [30] . In this study , we showed that a P . aeruginosa infection induces the intestinal expression of mir-84 and mir-241 , which is consistent with Kudlow et al . 's earlier findings that multiple miRNAs accumulate in the intestinal miRISCs upon infection [43] and that DAF-12-mediated immunity is dependent on the activation of its downstream miRNA let-7s . Although our understanding of the role of miRNAs in the molecular signaling pathways of the immune response is rapidly expanding [24] , to the best of our knowledge , this is the first evidence of the involvement of miRNAs in the innate immune regulation in C . elegans . We have also observed that more DAF-12 accumulate in the nuclei of neurons or intestinal cells when worms were fed E . coli but in a diffuse distribution in P . aeruginosa-infected worms . However , there may be still enough DAF-12 present in the nuclei of the intestine cell , which would be responsible for the induction of let-7s miRNAs . Furthermore , we have demonstrated that SKN-1 is a direct target of let-7s miRNAs . SKN-1 accumulate in the nuclei of intestinal cells of worms infected with P . aeruginosa , but not E . coli , which is consistent with Papp et al . and Haeven et al . 's reports that exposure to P . aeruginosa leads to SKN-1 accumulation in intestinal nuclei [38] , [39] . Their data have also shown that PA14 infection triggers the transcriptional activation of gcs-1 and gst-4 , two downstream target gene of SKN-1 . However , data in our experiments suggested that infection of P . aeruginosa may induce more let-7s miRNAs and thus downregulate the production of SKN-1 . One possible explanation is that even if the SKN-1 production is downregulated during the infection , the activity of SKN-1 is more dependent on its protein modification rather than its quantity . Regulation of SKN-1 at both the level of its activity and quantity precisely modulate the innate immune response to microbial infection . . Furthermore , we found that the inhibition of skn-1 by RNAi markedly reduced DAF-12/let-7s-mediated pathogenic defense . These findings provide evidence that nuclear hormone receptors control let-7s miRNAs regulation of the C . elegans innate immunity , suggesting that DAF-12 may couple developmental progression and the response to pathogenic infection in order to coordinate appropriate immune responses . The oxidative stress response is an evolutionally conserved response to reactive oxygen species ( ROS ) , which are produced by mitochondrial respiration , toxins and pathogen virulence factors [44] . SKN-1 is required for the proper response of C . elegans to oxidative stress , which is mediated by the NSY-1/PMK-1 and DAF-2 insulin-like signaling pathways [36] , [37] . Our present findings demonstrate an essential role of SKN-1 in the pathogenic resistance of C . elegans , an observation that is consistent with two other independent studies [38] , [39] . Thus , SKN-1 appears to integrate longevity , stress resistance and pathogenic resistance . Although the molecular pathway by which SKN-1 regulates the innate response to pathogens remains unclear , an SKN-1-mediated oxidative stress response could potentially protect the worms from the peroxidation damage caused by ROS during pathogenic infection . Further investigation of the common downstream target of various SKN-1 actions is required to elucidate the role of SKN-1 in the pathogenic resistance of C . elegans . In summary , our data demonstrate that DAF-12 and its steroidal ligands , DAs , negatively regulate the innate immune responses of C . elegans to pathogenic infection . DAF-12 appears to activate let-7s miRNAs to directly target SKN-1 , a component of the NSY-1/PMK-1 immune signaling pathway , thus regulating the pathogenic resistance of C . elegans ( Fig . S14 ) . These findings not only reveal a novel signaling pathway in the C . elegans defense against pathogens but also provide a link between endocrine signaling and innate immune responses , thus integrating developmental progression and pathogenic resistance .
( 25S ) - Δ4- and Δ7-DAs were produced in the Knölker laboratory [45] . All C . elegans strains were obtained from Caenorhabditis Genetics Center ( CGC ) unless otherwise noted . The C . elegans strains used in this study are listed in Table S2 . All of the strains were maintained at 20°C using standard methods unless otherwise noted . Lifespan and P . aeruginosa killing assays were conducted at least three times , as previously described [46] . A P value less than or equal to 0 . 05 was considered statistically significant . Statistical analysis of lifespan and P . aeruginosa killing assay is shown in Table S3 , S4 , S6 and S7 . RNAi of candidate transcription factors in N2 worms was carried out using standard bacterial feeding methods . For all feeding assays , worms were exposed to RNAi bacteria from the time of hatching . Synchronized young adult animals were transferred to P . aeruginosa lawns supplemented with 50 µg/ml 5-fluorodeoxyuridine ( FUDR , Sigma ) . P . aeruginosa killing assays were performed as described above . Worms were washed from their plates with M9 , anaesthetized with M9 containing 0 . 1% NaN3 , fixed in the 2% soft agar and subjected to confocal imaging assay . Images were captured using Leica TCS SP5 . Wild-type N2 and daf-12 ( sa156 ) young adults were fed P . aeruginosa or E . coli for 48 hours . Worms were rinsed from plates with M9 buffer , and anaesthetized in 8% alcohol in M9 . Fixation and Sectioning was performed with a conventional two-steps method as described in Worm Method . Photographs were captured using HITACHI H-7650 . Wild-type N2 and daf-12 ( sa156 ) young adults were removed from plates and bathed with Δ4-DAs , Δ7-DAs or cholesterol ( 400 nM ) in M9 8 hours before killing assay . The killing assay was performed on P . aeruginosa plates supplemented with Δ4-DAs , Δ7-DAs or cholesterol ( 400 nM ) . A 0 . 5-kb region of the skn-1 3′ UTR containing the predicted miRNA let-7s binding sites was cloned into the psi-CHECK2 to obtain the skn-1 3′ UTR-luc construct . The skn-1 3′ UTR ( mut ) -luc construct was obtained from skn-1 3′UTR construct by mutating the complementary sequence of let-7s' seed region ( TACCTCA to TAGGTGA ) . Constructs were co-transfected with synthesized dsRNAs mimicking the let-7s miRNAs to HEK293T cells and the luciferase assay was performed using the dual-luciferase reporter assay system ( Promega ) . Synchronized C . elegans animals were treated essentially as described above for the killing assays except for the omission of FUDR . Infected samples were compared to control samples fed on the same medium with E . coli OP50-1 . Total RNA was extracted as described [31] and reverse transcribed using the ReverTra Ace Q-PCR RT kit ( Toyobo ) . cDNA was subjected to qRT-PCR analysis as described [31] . The primer sequences are listed in Table S5 . All values were normalized to act-1 . One-tailed t-tests were performed with GraphPad Prism4 . A P value less than or equal to 0 . 05 was considered significant . Synchronized worms were collected in TRIzol ( Invitrogen ) and treated as described [47] . The miRNeasy Mini kit ( QIAGEN ) and TaqMan MicroRNA Reverse Transcription kit ( Applied Biosystems ) were used for total RNA and cDNA preparation , respectively . qRT-PCR was performed with Power SYBR Green master mix ( Applied Biosystems ) on a 7900HT Fast Real-Time PCR System ( Applied Biosystems ) . Sno-RNA U18 was used as an internal control . The primer sequences were gifted from Prof . Adam Antebi from the Max Planck Institute for Biology of Ageing . One-tailed t-tests were performed with GraphPad Prism4 . A P value less than or equal to 0 . 05 was considered significant . Synchronized L4 populations of wild-type N2 , daf-12 ( RNAi ) , daf-12 ( rh61rh411 ) , mir-84 ( n4037 ) and mir-241 ( n4316 ) animals were infected with P . aeruginosa as described [1] . Western blot analyses of activated p38 MAPK were performed as described [46] . Western blot analyses of SKN-1 expression were performed using anti-SKN-1 ( Santa Cruz ) .
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When infected by the Pseudomonas aeruginosa , the nematode Caenorhabditis elegans invokes an innate immune response that protects the worm from pathogenic attack . The appropriate level of immune response in C . elegans requires the accurate regulation of multiple signal pathways , especially signals of repression , which attenuate the expression of pathogen-responsive genes . In the current study , we identified the nuclear hormone receptor DAF-12 and its downstream let-7 family of microRNAs , mir-84 and mir-241 , are required for the regulation of C . elegans innate immunity against P . aeruginosa infection . Dafachronic acids , as DAF-12 ligands , can dramatically suppress the resistance of C . elegans to P . aeruginosa infection . Inhibition of the conserved PMK-1/p38 MAP kinase pathway can markedly attenuate the promoted resistance of daf-12 and let-7 family of microRNAs mutants to P . aureginosa infection . However , neither daf-12 nor let-7 family of microRNAs affect the activation of PMK-1/p38 . Moreover , our data also reveals the role of SKN-1 in integrating the signals from the PMK-1/p38 MAPK and DAF-12-let-7s pathways to mediate the C . elegans innate immune response .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"medicine",
"biology"
] |
2013
|
Nuclear Hormone Receptor Regulation of MicroRNAs Controls Innate Immune Responses in C. elegans
|
Understanding the functional relevance of DNA variants is essential for all exome and genome sequencing projects . However , current mutagenesis cloning protocols require Sanger sequencing , and thus are prohibitively costly and labor-intensive . We describe a massively-parallel site-directed mutagenesis approach , “Clone-seq” , leveraging next-generation sequencing to rapidly and cost-effectively generate a large number of mutant alleles . Using Clone-seq , we further develop a comparative interactome-scanning pipeline integrating high-throughput GFP , yeast two-hybrid ( Y2H ) , and mass spectrometry assays to systematically evaluate the functional impact of mutations on protein stability and interactions . We use this pipeline to show that disease mutations on protein-protein interaction interfaces are significantly more likely than those away from interfaces to disrupt corresponding interactions . We also find that mutation pairs with similar molecular phenotypes in terms of both protein stability and interactions are significantly more likely to cause the same disease than those with different molecular phenotypes , validating the in vivo biological relevance of our high-throughput GFP and Y2H assays , and indicating that both assays can be used to determine candidate disease mutations in the future . The general scheme of our experimental pipeline can be readily expanded to other types of interactome-mapping methods to comprehensively evaluate the functional relevance of all DNA variants , including those in non-coding regions .
Owing to rapid advances in next-generation sequencing technologies , tens of thousands of disease-associated mutations [1] and millions of single nucleotide polymorphisms ( SNPs ) [2] , [3] have been identified in the human population . With the large number of ongoing whole-exome and whole-genome sequencing projects [2] , [3] , hundreds of thousands of new SNPs are now being discovered every month . Hence , there is an urgent need to develop high-throughput methods to sift through this deluge of sequence data and rapidly determine the functional relevance of each variant . Here , we focus on coding variants , firstly because trait- and disease-associated SNPs are significantly over-represented in nonsynonymous sites [4] , and secondly because the vast majority of disease-associated mutations identified to date reside within coding regions [1] . We evaluate the functional impact of coding variants by examining their effects on corresponding protein-protein interactions , because most proteins carry out their functions by interacting with other proteins [5] . Recent studies have begun to use large-scale protein interaction networks to understand human diseases and their associated mutations [5] , [6] . By integrating structural details with high-quality protein networks , we created a 3D interactome network where the interface for each interaction has been structurally resolved [7] . Using this 3D network , we demonstrated that in-frame disease mutations ( missense mutations and in-frame insertions/deletions ) are significantly enriched at the interaction interfaces of the corresponding proteins [7] . Our results indicate that alteration of specific interactions is very important for the pathogenesis of many disease genes , highlighting the importance of 3D structural models of protein interactions in understanding the functional relevance of coding variants . However , many important questions still remain unanswered – for example , what fraction of protein-protein interactions is altered by disease mutations to cause the corresponding disorders ? Furthermore , do structural details of the interacting proteins , especially the position of the mutation relative to the interaction interface , affect the ability of a given disease mutation to alter a specific interaction ? To address these questions , we decided to focus on proteins with known disease mutations that participate in interactions with available co-crystal structures in the Protein Data Bank ( PDB ) [8] . To detect the alteration of the interactions by disease mutations , it is necessary to first detect the interactions of the wild-type proteins using an assay of choice . This turns out to be a major bottleneck because all high-throughput interaction-detection assays have very limited sensitivity [9] , [10] . Our assay of choice is Y2H because there are over 16 , 000 human protein interactions detected by our version of Y2H that can serve as the reference interactome for comparison [11] , [12] , [13] , [14] , the largest for any assay performed to date ( Figure S1 ) . In total , there are 217 interactions detected by our version of Y2H with available co-crystal structures; 51 of these also have known missense disease mutations on corresponding proteins in the Human Gene Mutation Database ( HGMD ) [1] and the corresponding interactions for the wild-type proteins can be detected in our experiments with strong Y2H-positive phenotypes ( Figure S2; Materials and Methods ) . Here , we focused on missense mutations because they are intrinsically more likely to generate interaction-specific disruptions [6] . We established a high-throughput comparative interactome-scanning pipeline to clone disease mutations and examine their molecular phenotypes ( Fig . 1 ) . The methodologies established here can be readily applied to any non-synonymous variant in the coding region , including nonsense mutations .
The first step of our pipeline is a massively parallel approach , termed Clone-seq , designed to leverage the power of next-generation sequencing to generate a large number of mutant alleles using site-directed mutagenesis in a rapid and cost-effective manner . Current protocols for site-directed mutagenesis require picking individual colonies and sequencing each colony using Sanger sequencing to identify the correct clone [15] . This standard approach is both labor-intensive and expensive; therefore , it does not scale up to genome-wide surveys . In Clone-seq , we put one colony of each mutagenesis attempt into one pool ( Fig . 1a; in other words , each pool contains one and only one colony for each desired mutation ) and combine multiple pools through multiplexing for one Illumina sequencing run [16] . Colonies for generating different mutations of the same gene can be put into the same pool , which can be easily distinguished computationally when processing the sequencing results . This is true even for mutations occurring at the same site ( Fig . 2a , Text S1 ) . For the 51 selected interactions , we chose 27 disease-associated mutations of residues at the interface ( “interface residue” ) , 100 mutations in the rest of the interface domain ( “interface domain” ) and 77 mutations away from the interface ( “away from the interface”; Fig . 3a , b ) . These interfaces were determined using solvent accessible surface area calculations as previously described [17] , [18] on 7 , 340 co-crystal structures ( Materials and Methods ) . To set up our Clone-seq pipeline , we first started with 39 mutations from these 204 and picked 4 colonies for each mutation . As a reference , we also pooled together all the wild-type alleles in our human ORFeome library to be sequenced together with the 4 pools of the mutagenesis colonies . In total , there were 40 . 1 million Illumina HiSeq 1×100 bp reads for our Clone-seq samples ( Text S1 ) for an average of >2 , 500× coverage on all desired mutation sites . Therefore , our Clone-seq pipeline has the capacity to generate >3 , 000 mutations in one full lane of a HiSeq run with 1×100 bp reads , drastically improving the throughput and decreasing overall sequencing costs by at least 10-fold ( Text S1 ) . Fig . 2a presents a schematic of the criteria we use to determine which clones contain the desired mutation and can be used for subsequent steps . For example , in pool 1 , all reads ( ignoring sequencing errors ) confirm that genes I and II each contain the desired mutation – T116A and G298T , respectively . For gene III , we want to generate two separate clones with two separate mutations – IIIA41T and IIIC194T . Since half the reads contain T41 ( instead of A41 ) and the other half contain T194 ( instead of C194 ) , and we normalize DNA concentrations across all samples , we can infer that both mutant clones were generated successfully . In contrast , for gene IV , we see that while half the reads contain A511 ( instead of G511 ) , all the reads are wild-type at C74 . Thus , we infer that while the IVG511A clone is successfully generated , the IVC74T clone is not . For gene V , although both mutant clones are successfully generated , half the reads contain an additional mutation , C436G . Since it is impossible to know which of the two clones for V contains this unwanted mutation , neither clone is usable . Similarly , we can determine mutant clones IT116A , IIIA41T , IIIC194T , IVC74T , IVG511A , VT53G , and VG272A as usable clones in pool n . Based on these criteria , we developed the S score calculation and used it to determine successful mutagenesis attempts ( Materials and Methods ) . Out of 156 colonies for 39 mutations , 125 of them contain the desired mutations ( S>0 . 8 ) , an overall 80% PCR-mutagenesis success rate . In fact , we were able to pick correct clones for all 39 mutant alleles using only the first two pools in Clone-seq . All 78 clones from the first two pools , from which the correct ones were selected for use in subsequent steps , were also Sanger sequenced for verification . 55 Clone-seq positive results with S>0 . 8 were all confirmed and there is a clear separation in the S scores between the successful and failed mutagenesis attempts ( Fig . 2b ) . One major advantage of our Clone-seq pipeline is that it allows us to carefully examine whether other unwanted mutations have been inadvertently introduced during PCR-mutagenesis in comparison with the corresponding wild-type alleles , since we obtain reads spanning the entire gene . We found that there are on average 4–5 unwanted mutations introduced in each pool of 39 colonies . This corresponds to a 0 . 013% PCR error rate ( Materials and Methods ) , in agreement with previous studies [19] . The detection of unwanted mutations , especially those distant from the mutation of interest , is achieved in traditional site-directed mutagenesis pipelines by Sanger sequencing through the gene of interest . This is costly and labor-intensive , especially because multiple sequencing runs are needed for one long gene . However , since Clone-seq yields reads spanning the entire gene , we were able to determine which of the generated clones definitely do not have unwanted mutations in the full length of their sequences as illustrated in Fig . 2a ( Materials and Methods ) , and we pick only these clones for subsequent assays . To further test our Clone-seq pipeline , we applied it to generate clones for 113 SNPs on 66 genes from the recently published Exome Sequencing Project dataset [3] . Using the same approach as described above , we sequenced 4 colonies each for the 113 alleles of interest using one third of a 1×100 bp MiSeq run . We obtained 4 . 7 million reads for these 113 alleles . With a threshold of S>0 . 8 , we were able to determine that 370 out of the 452 colonies ( 82% ) contain the desired mutation , in perfect agreement with the PCR-mutagenesis success rate obtained earlier . We were able to choose colonies that contain only the desired mutation for all 113 alleles . Because the whole MiSeq run produced 17 . 7 million reads and we only used 4 . 7 million for generating the 113 mutant clones , the capacity of our Clone-seq pipeline using one full lane of a 1×100 bp HiSeq run is estimated to be >3 , 000 , exactly the same as our previous assessment ( Text S1 ) . Finally , we generated the remaining 165 disease mutations ( of the 204 ) and 717 other coding variants from the Exome Sequencing Project and the Catalog of Somatic Mutations in Cancer [20] using a full 1×100 bp HiSeq run , including 40 mutations on a single gene – MLH1 . Using 111 . 2 million reads for these 882 alleles , we found that 2 , 958 of the 3 , 528 colonies ( 84% ) contain the desired mutation , again in excellent agreement with our previously obtained PCR-mutagenesis success rate . There was at least one colony with only the desired mutation for all 882 alleles , including all 40 MLH1 mutations ( Table S1 ) . Therefore , our Clone-seq pipeline can generate a large number of mutations ( >40 ) even for a single gene . In fact , to generate even more mutations for one gene , we can implement a two-round barcoding approach: generate groups of 40 mutations and barcode them differently for one HiSeq run ( Figure S3 ) . Ten such groups will enable us to generate ∼400 mutations for a single gene ( Text S1 ) . Since the average coverage of these 882 alleles is >300× , the capacity of our Clone-seq pipeline using one full lane of a 1×100 bp HiSeq run is estimated to be >3 , 000 , again in agreement with our previous two estimates ( Text S1 ) . Overall , our pipeline has been significantly optimized to make it very efficient . We established a web tool ( http://www . yulab . org/Supp/MutPrimer ) to design mutagenesis primers both individually and in batch . MutPrimer can design ∼1 , 000 primers for ∼500 mutations in one batch in less than one second . All of the 2 , 068 primers for the 1 , 034 mutations in this study were generated by MutPrimer . All mutagenesis PCRs are performed in batch using automatic 96-well procedures . Since single colony picking after bacterial transformation of mutagenesis PCR product is a rate-limiting step , we rigorously optimized this step and found that adding 10 µL mutagenesis PCR products to 100 µL competent cells and plating 50 µL transformed cells give the best transformation yield and well-separated single colonies . Furthermore , rather than individually streaking transformed cells onto agar plates one sample at a time , we were able to significantly increase throughput by spreading colonies using glass beads onto four sector agar plates which are partitioned into four non-contacting quadrants ( Materials and Methods ) . In this manner , a 96-well plate of transformed bacteria can be plated out onto 24 four-sector agar plates in ∼15 minutes . Traditional site-directed mutagenesis pipelines require miniprepping each of the selected colonies and sequencing them separately by Sanger sequencing . To drastically improve the throughput of our Clone-seq pipeline , we pooled together the bacteria stock of a single colony for each mutagenesis attempt to perform one single maxiprep , which makes the library construction step much more efficient and amenable to high-throughput ( Text S1 ) . Furthermore , existing variant calling pipelines [21] cannot be applied to our Clone-seq results because the expected allelic ratios built into these pipelines are a function of the ploidy of the organism . However , in our Clone-seq pipeline there is no concept of ploidy . We pool together many mutations for one gene in the same pool ( e . g . , 40 mutations for MLH1 ) and different genes often have different numbers of mutations . Our S score calculation and unwanted mutation detection pipeline was designed according to our pooling strategy ( Materials and Methods ) . In total , we have used the novel Clone-seq pipeline successfully to generate 1 , 034 ( 39+113+882 ) mutant clones without any additional unwanted mutations , confirming the scalability , accuracy , and throughput of our Clone-seq pipeline . For the 204 mutations on proteins with co-crystal structures , we first examined whether the mutant proteins can be stably expressed in human cells . To do this , we tagged every wild-type and mutant protein with GFP at the C-terminus using high-throughput Gateway cloning ( Fig . 1b ) . The GFP constructs were transfected into HEK293T cells and fluorescence intensities were measured by a plate reader ( Fig . 3c; Materials and Methods ) . All fluorescence intensity readings were also confirmed manually under a microscope . Compared with the corresponding wild-type proteins , the expression levels of 3 of the 27 “interface residue” mutants , 8 of the 99 “interface domain” mutants and 6 of the 77 “away from the interface” mutants are significantly diminished ( Fig . 3c; Materials and Methods; S2 Table ) . To validate these findings , we also performed Western blotting for 8 random mutants that are stably expressed and 8 random mutants with significantly diminished expression levels ( Fig . 4a ) . Western blotting results confirm our GFP intensity readings . Next , we investigated whether these mutations could affect protein-protein interactions using Y2H ( Fig . 1c; Materials and Methods ) . We found that 21 of the 27 ( 78% ) “interface residue” mutations , 57 of the 100 ( 57% ) “interface domain” mutations , and only 22 of the 77 ( 29% ) “away from the interface” mutations disrupt the corresponding interactions , thereby demonstrating a clear difference ( Fig . 4b; P = 3×10−6 between “interface residue” and “interface domain” and P = 8×10−10 between “interface domain” and “away from the interface” ) in terms of ability to interfere with protein-protein interactions between mutations at different structural loci within the same protein . Furthermore , comparing with the GFP results , we found that all destabilizing mutations were shown to disrupt the corresponding interactions in our Y2H experiments . By considering only the mutations that do not affect protein expression based on the GFP experiments , we found the same difference: 13 out of 18 ( 72% ) “interface residue” stable mutations , 42 out of 83 ( 51% ) “interface domain” stable mutations , and only 9 out of 52 ( 17% ) “away from the interface” stable mutations disrupt the corresponding interactions ( Fig . 4b; P = 2×10−5 between “interface residue” and “interface domain” and P = 9×10−13 between “interface domain” and “away from the interface”; Table S2 ) . Since these interfaces are obtained from actual co-crystal structures , our results suggest that accurate structural information can help determine the functional impact of mutations on protein-protein interactions . Wild-type proteins corresponding to 113 of the 153 stably expressed mutant proteins also interact with other proteins as determined by our Y2H experiments ( 114 interactions in total , termed “other interactions” ) ; however , for these interactions , there are currently no co-crystal structures available in the PDB . Using these other interactions , we calculated the likelihood of a given mutation disrupting a specific interaction without any structural information to be 32% ( Fig . 4b ) . We then analyzed whether the molecular phenotypes measured by our high-throughput GFP and Y2H assays are correlated with corresponding disease phenotypes . We first examined how mutation pairs on the same gene affect protein stability and its relationship to their corresponding diseases . We find that pairs of mutations that are either both stable or both unstable cause the same disease in 68% and 70% of cases , respectively . However , pairs comprising one stable and one unstable mutation cause the same disease in only 30% of cases ( P = 6×10−9 and 8×10−10 , respectively , Fig . 5a ) . For example , we find that the mutations R727C and L844F on the spindle checkpoint kinase Bub1b both cause the protein to become unstable and lose all its interactors . These mutations are both associated with the same disease , mosaic variegated aneuploidy , an autosomal recessive disorder that causes predominantly trisomies and monosomies of different chromosomes [22] , [23] . Since our GFP assay shows that these two mutations cause loss of protein product , our results are consistent with Matusuura et al . 's finding that a more than 50% decrease in Bub1b activity leads to abnormal mitotic spindle checkpoint function and mosaic variegated aneuploidy [24] . We then examined whether mutation pairs on the same gene disrupt the same set or different sets of interactions ( i . e . , their interaction disruption profiles ) and investigated whether their disruption profiles correlates with disease phenotypes . We found that mutation pairs with the exact same disruption profile are significantly more likely to cause the same disease than those with different profiles ( 70% and 61% respectively , P = 3×10−5 , Fig . 5b ) . For example , we found that two mutations on Smad4 , R361C and Y353S , disrupt its interactions with Smad3 and Smad9 while leaving the interactions with Lmo4 and Rassf5 unaltered ( Fig . 5c ) . These two mutations both cause juvenile polyposis coli [25] , [26] , a disease is known to be caused by disruption of the core Smad/Bmp signaling pathways [27] . Our Y2H results clearly demonstrate that the R361C and Y353S mutations disrupt the Smad4-Smad3 and Smad4-Smad9 interactions ( Fig . 5c ) leading to disruption of core Smad signaling pathways . However , the mutation N13S on Smad4 does not disrupt any of these interactions ( Fig . 5c ) and is associated with a different disease , pulmonary arterial hypertension . Our results agree with Nasim et al . 's finding that the N13S mutation does not alter downstream Smad signaling [28] . Our findings provide support for the hypothesis that the N13S mutation either impacts pathways outside the core Smad signaling network or are pathogenic only when combined with other environmental and genetic factors [29] . Overall , these results show that mutation pairs with similar molecular phenotypes in terms of both protein stability and interactions are significantly more likely to cause the same disease than those with different molecular phenotypes . This confirms that the molecular phenotypes measured by our high-throughput GFP and Y2H assays are biologically relevant in vivo . Furthermore , by comparing the molecular phenotypes , in particular the protein interaction disruption profiles , of mutations/variants to those of known disease mutations , potential candidate mutations for a variety of diseases can be identified . While we use only those interactions that are supported by co-crystal structures to estimate the fraction of interactions that are disrupted by mutations at different structural loci , the described procedures can also be applied to interactions with predicted interfaces and structural models [30] , [31] , [32] , [33] . This is of particular importance because over 90% of known interactions do not currently have corresponding co-crystal structures [33] , [34] . For example , Mlh1 is known to interact with Pms2 , both of which are well-studied DNA mismatch repair genes frequently mutated in hereditary nonpolyposis colorectal cancer [35] . Although the structural basis of the Mlh1-Pms2 interaction still remains unknown , both our previous 3D reconstruction of the human interactome network [7] , [32] and the newly-established Interactome3D [33] database suggest that the HATPase_c domain is part of the interface for Mlh1's interaction with Pms2 . Previous work has shown that a point mutation ( I107R ) on the HATPase_c domain of Mlh1 is associated with colorectal cancer and disrupts the Mlh1-Pms2 interaction [7] , [35] , [36] . First , using Y2H , we were able to confirm the disruption ( Figure S4 ) . Next , we developed a high-throughput-amenable mass spectrometry pipeline using Stable Isotope Labeling by Amino acids in Cell culture ( SILAC ) [37] , [38] , which was designed to reveal both lost/weakened and gained/enhanced interactions of the target proteins ( Fig . 1d ) [39] . We added an HA-tag to the N-terminus of both wild-type and mutant Mlh1 , as well as to GFP as a control , and performed four SILAC experiments: wild-type Mlh1 ( heavy ) vs . GFP control ( light ) , mutant Mlh1 ( heavy ) vs . GFP control ( light ) , wild-type ( heavy ) vs . mutant ( light ) Mlh1 , and mutant ( heavy ) vs . wild-type ( light ) Mlh1 ( Fig . 6a; Materials and Methods ) . Interactors of wild-type/mutant Mlh1 are defined as those that bind wild-type/mutant Mlh1 more than 2× stronger than GFP control ( Materials and Methods ) . For a lost/weakened interaction , we required that the interaction be more than 2× stronger with wild-type Mlh1 than with mutant Mlh1 as confirmed both in wild-type ( heavy ) vs . mutant ( light ) and in mutant ( heavy ) vs . wild-type ( light ) experiments; we further required that the interaction be detected in the wild-type vs . control experiment ( Fig . 6a; Materials and Methods ) . For a gained/enhanced interaction , we required that the interaction be more than 2× stronger with mutant Mlh1 than with wild-type Mlh1 as confirmed both in wild-type ( heavy ) vs . mutant ( light ) and in mutant ( heavy ) vs . wild-type ( light ) experiments; we further required that the interaction be detected in the mutant vs . control experiment ( Fig . 6a; Materials and Methods ) . We were able to detect Pms2 as the only specifically weakened interactor caused by the mutation ( Figs . 6b , c; E = −1 . 77; P = 3×10−4 ) , in agreement with our Y2H results and previous studies [7] , [36] . Additionally , we were able to detect Hspa8 as the only specifically enhanced interactor of the mutant protein ( Figs . 6b , c; E = 2 . 71; P = 7×10−8 ) . Two other known interactors of Mlh1 , Pms1 ( Figs . 6b , c; E = −0 . 32; P = 0 . 21 ) [40] and Brip1 ( Fig . 6b , c; E = 0 . 18; P = 0 . 32 ) [41] , were also detected , although their interactions with Mlh1 are not affected by this particular mutation ( Materials and Methods ) . Hspa8 was not previously known to interact with Mlh1 and the impact of the Mlh1 I107R mutation on its interactions with Pms1 and Brip1 has not been reported in the literature . To verify our SILAC results , we performed in vivo co-immunoprecipitation using HA-tagged wild-type and mutant Mlh1 and tagged Hspa8 and Brip1 with V5 ( Materials and Methods ) . Our co-immunoprecipitation results confirm that Hspa8 only weakly interacts with wild-type Mlh1 , but the interaction is dramatically enhanced by a single amino acid substitution ( I107R ) ( Fig . 6d , lanes 3 and 4 ) , whereas the interaction between Mlh1 and Brip1 is not affected by this mutation ( Fig . 6d , lanes 6 and 7; Materials and Methods ) . Hspa8 is a constitutively expressed member of the heat shock protein 70 family [42] . It functions as a chaperone to facilitate protein folding [42] and also functions as an ATPase in the disassembly of clathrin-coated vesicles during membrane trafficking [43] . A recent study reported that Hspa8 is specifically recruited to reovirus viral factories , independent of its chaperone function [44] . Our Western blotting results demonstrate that the expression level of Mlh1 is not affected by the I107R mutation ( Figure S5 ) . Therefore , our SILAC results suggest that Hspa8 may play an important role in colorectal cancer and that its function could be independent of its role as a chaperone .
We have successfully developed the first massively parallel site-directed mutagenesis pipeline , Clone-seq , using next-generation sequencing . Our Clone-seq pipeline is entirely different from previously described random mutagenesis approaches [45] , [46] , [47] , [48] . Clone-seq is used to generate a large number of specific mutant clones with desired mutations; each individual mutant clone has a separate stock and different clones can therefore be used separately for completely different downstream assays . In random mutagenesis , a pool of sequences containing different mutations for one gene is generated using error-prone PCR or error-prone DNA synthesis . Therefore , it is not possible to separate one mutant sequence from another and the whole pool can only be used for the same assay ( s ) together . Furthermore , it is not possible to control which or how many mutations are generated on each DNA sequence . In fact , to improve coverage , most random mutagenesis pipelines generate on average two or more mutations on each DNA sequence [45] , which makes it impossible to distinguish the functional impact of each individual mutation on the same sequence . Site-directed mutagenesis and random mutagenesis are designed for different goals: if one wants to generate all possible mutations for a certain protein without the need to separate different clones , it would be more favorable to use random mutagenesis; whereas if one needs to have separate clones for each mutation , site-directed mutagenesis is required . As a result , the two approaches are complementary and not comparable . While there are highly efficient methods for random mutagenesis [45] , [46] , [47] , [48] , current protocols for site-directed mutagenesis are low-throughput and become prohibitively expensive if a large number of clones needs to be generated . Clone-seq directly addresses the necessity for a high-throughput site-directed mutagenesis pipeline . It is a robust , cost-effective and efficient method that can be used to generate a total of ∼3 , 000 distinct mutant clones in one full lane of a 1×100 bp HiSeq run . Clone-seq is suitable both for generating mutations across many genes as well as a large number of mutations on a few genes . The former situation is applicable when one wants to generate many mutations/variants from large-scale studies ( e . g . , whole-genome or whole-exome sequencing ) since they typically identify mutations/variants on a large number of genes [49] , [50] . The latter situation usually arises in a study focused on a single pathway with a few genes of interest ( e . g . , an alanine-scanning mutagenesis to determine functional sites on a gene of interest [51] ) . Integrating with Clone-seq , we also established a comprehensive comparative interactome-scanning pipeline , including high-throughput GFP , Y2H , and mass spectrometry assays , to systematically evaluate the impact of human disease mutations on protein stability and interactions . We examine each mutation individually , rather than looking at their combinatorial effects because these inherited germline disease mutations are extremely rare . Therefore , the probability of having even two of these in the same individual becomes infinitesimally small . Our results reveal that the overall likelihood of a given disease mutation disrupting a specific interaction is 32% . Accurate structural information of these interactions obtained from co-crystal structures greatly improves our understanding of the impact of disease mutations: 13 out of 18 ( 72% ) “interface residue” stable mutations , 42 out of 83 ( 51% ) “interface domain” stable mutations , and only 9 out of 52 ( 17% ) “away from the interface” stable mutations disrupt the corresponding interactions , unveiling a clear dependence of the molecular phenotypes of disease mutations on their structural loci . These estimates are not affected by the false negative rate of our Y2H assay as we only use those interactions for which we can detect the wild-type interaction with strong Y2H phenotypes . Thus , any observed disruption is due to the mutation of interest and not an assay false negative . Furthermore , our Y2H pipeline has been shown to be of high quality and has an experimentally measured false positive rate of ∼5% or lower in different organisms [9] , [12] , [52] , [53] . In addition , the interactions used to understand the relationship between molecular phenotypes and structural loci of disease mutations are all supported by co-crystal structures , therefore these interactions are not assay false positives . We also find that the molecular phenotypes detected by our GFP and Y2H assays correlate with known disease phenotypes , confirming the in vivo biological significance of our measurements . Moreover , as shown by the Mlh1 example ( Fig . 6 ) , our comparative interactome-scanning pipeline can also be used with predicted structural models [30] , [31] , [32] , [33] . The consequent experimental results will clearly be affected by the quality of these predictions , which is not part of our pipeline . In fact , our experimental interactome-scanning pipeline can be applied to evaluate or improve these predicted models by testing mutations at different loci of a protein of interest and examining how these mutations disrupt different interactions of this protein . Our comparative interactome-scanning pipeline described and validated here can be applied to experimentally determine in a high-throughput fashion the impact on protein stability and protein-protein interactions for thousands of DNA coding variants and disease mutations , which can directly lead to hypotheses of concrete molecular mechanisms for follow-up studies . Furthermore , the elucidation of molecular phenotypes of disease mutations is also vital for selecting actionable drug targets and ultimately for making therapeutic decisions . Finally , the general scheme of our pipeline can be readily expanded to other interactome-mapping methods , particularly other protein-protein [10] , protein-DNA [54] , [55] , protein-RNA [56] , and protein-metabolite interaction assays [57] , to comprehensively evaluate the functional relevance of all DNA variants , including those in non-coding regions .
To calculate atomic-resolution interaction interfaces , we systematically examined a comprehensive list of 7 , 340 PDB co-crystal structures . To define the interface , we used a water molecule of diameter 1 . 4 Å as a probe and calculated the relative solvent accessible surface areas of the interacting pair as well as the individual proteins involved in the interaction . Residues whose relative accessibilities change by more than 1 Å2 are considered as potential interface residues , because amino acids at the interface reside on the surfaces of the corresponding proteins , but will tend to become buried in the co-crystal structure as the two proteins bind to each other [58] . So , for these residues , there should be a significant decrease in accessible surface area when we compare the bound and unbound states of the protein chains . To identify interface domains , we required at least one of the following criteria to hold: We then identified the subset of these interactions that contain at least one disease mutation and are amenable to our version of Y2H [11] , [12] , [13] , [14] . Subsequently , we performed a pairwise retest of all these interactions and selected the ones that yield strong Y2H phenotypes , because subsequent steps involve detecting a significant decrease in these phenotypes . Primers for site-directed mutagenesis were selected based on a customized version of the protocol accompanying the Stratagene QuikChange Site-Directed Mutagenesis Kit ( 200518 ) . The following criteria are used: For cases where no primer satisfies all three criteria simultaneously , we relaxed criterion 2 to GC content ≥30% . We established a supplementary web tool ( http://www . yulab . org/Supp/MutPrimer ) to design mutagenesis primers individually or in bulk . All wild-type clones were obtained from the human ORFeome v8 . 1 collection [61] . To generate mutant alleles , sequence-verified single-colony wild-type clones and their corresponding mutagenic primers were aliquoted into individual wells of 96-well PCR plates . Mutagenesis PCR was then performed as specified by the New England Biolabs ( NEB ) PCR protocol for Phusion polymerase ( M0530L ) , noting that PCR was limited to 18 cycles . The samples were then digested by DpnI ( NEB R0176L ) according to the manufacturer's manual . After digestion , samples were transformed into competent E . coli . Since single colony picking after bacterial transformation of mutagenesis PCR product is a rate-limiting step , we rigorously optimized this step . First , we tried different volumes of competent cells for transformation and found that single colony yields peak when ∼100 µL of competent cells are used . It is also necessary to use ∼10 µL of mutagenesis PCR product: any lower volume of PCR product results in significantly reduced colony yields , while higher volumes of PCR product do not increase yield . Finally , colony picking was done using four-sector agar plates ( VWR 25384-308 ) that are partitioned into four non-contacting quadrants with glass beads poured onto each plate quadrant . Each bead-filled quadrant was inoculated with ∼50 µL of transformed bacteria . This was then spread by lightly shaking the four-sector agar plate . Our optimized transformation protocol results in a large number of well-separated single colonies that can be easily picked the next day . Upon recovery , single colonies from each quadrant were then picked and arrayed into 96-deepwell plates filled with 300 µL of antibiotic media . Four colonies per allele were picked for next-generation sequencing . DNA library preparation was performed using NEBNext DNA Library Prep Master Mix Set for Illumina ( NEB E6040S ) according to the manufacturer's manual . Briefly , 5 µg of pooled plasmid DNA ( ∼100 µL , all samples were normalized to the same concentration ) was sonicated to ∼200 bp fragments . The fragmented DNA was first mixed with NEBNext End Repair Enzyme for 30 mins at 20°C . Blunt-ended DNA was then incubated with Klenow Fragment for 30 mins at 37°C for dA-Tailing . Subsequently , NEBNext Adaptor was added to dA-Tailed DNA . Adaptor-ligated DNA ( ∼300 bp ) was size-selected on a 2% agarose gel . Size-selected DNA was then mixed with one of the NEBNext Multiplex Oligos ( NEB E7335S ) and Universal PCR primers for PCR enrichment . At each step , DNA was purified using a QIAquick PCR purification kit ( Qiagen 28104 ) . Multiplexed DNA samples were combined and analyzed in one lane of a 1×100 bp run by Illumina HiSeq 2500 . The mutant colonies were barcoded and pooled as shown in Fig . 1a . The multiplexed colonies were then run on an Illumina sequencer ( 2 HiSeq runs and 1 MiSeq run ) to give 1×100 bp reads . These reads were then de-multiplexed and mapped to the genes of interest using the BWA “aln” algorithm [62] . For each allele , we identified all reads that mapped to the position of the mutation of interest ( Rall ) and those that actually contained the desired mutation ( Rmut ) . We then calculated a normalized score ( S ) that quantifies the fraction of reads containing the desired mutation:where k is the number of different mutations for the same gene . For 39 mutations , we Sanger sequenced two mutant colonies per mutagenesis attempt to quantify the correlation between S and observation of the desired mutation . We found that all clones with S>0 . 44 are confirmed to be correct via Sanger sequencing with a clear separation between those that are correct and those that are not ( Fig . 2b ) . However , to further ensure that the clones we picked were correct , we require S>0 . 8 for a colony to be scored as containing the desired mutation . One major advantage of our Clone-seq pipeline over traditional site-directed mutagenesis protocols using Sanger sequencing [15] is that we can now carefully examine whether there are other unwanted mutations inadvertently introduced during the PCR process , in comparison with the corresponding wild-type alleles . It is essential to use clones with no unwanted mutations for downstream experiments , as the presence of these will make it impossible to determine whether the observed disruption is due to the desired or other undesirable mutation ( s ) . We use samtools “mpileup” [63] to obtain read counts for different alleles at each nucleotide for all the clones . We calculate the background sequencing error rate by calculating the average fraction of non-reference alleles across all nucleotides where we did not attempt to introduce a mutation . Any site that has a significantly higher fraction of non-reference alleles ( using a P value cutoff of 0 . 2 from a cumulative binomial test ) is considered to have an unwanted mutation . A lenient P value cutoff ( 0 . 2 as opposed to the more traditionally used 0 . 05 or 0 . 01 ) implies more stringent filtering in this case because we want to eliminate type II errors i . e . , we want to identify all unwanted mutations at the cost of discarding a few clones that actually do not have any unwanted mutations . We identified an average of 4–5 unwanted point mutations per pool . The overall per-base point mutation rate of Phusion polymerase was calculated to be ∼10−4 . NEB's advertised error rate for Phusion polymerase varies from 4 . 4–9 . 5×10−7 per PCR cycle . Since we perform 18 PCR cycles , the expected overall error rate is ∼10−5 . Our calculated mutation is within an order of magnitude of this advertised error rate . It is slightly higher than the advertised rate as we use stringent filtering criteria as described above . All wild-type and mutant clones were moved into the pcDNA-DEST47 vector with a C-terminal GFP tag using automated Gateway LR reactions in a 96-well format . After bacterial transformation , minipreps were prepared on a Tecan Freedom Evo 200 , and DNA concentrations were determined by OD 260/280 with a Tecan Infinite M1000 plate reader in 96-well format . A 100 ng aliquot of each expression clone plasmid was used for transfection into HEK293T cells in 96-well plates using Lipofectamine 2000 ( Invitrogen 11668019 ) according to the manufacturer's instructions . At approximately 48 hrs post-transfection , cells were processed with Tecan M1000 . Fluorescence intensities were measured at 395 nm for excitation and 507 nm for emission , according to Invitrogen's manual . As negative controls , the fluorescence intensities corresponding to cells transfected with the empty vector were measured . The normalized fluorescence intensity was calculated as:where I corresponds to the measured intensity and Ibackground corresponds to the average intensity of the empty vector controls for each plate . All Inorm values greater than K are considered to correspond to stable protein expression . K corresponds to the range ( maximum – minimum ) of background fluorescence intensities of the empty vector controls for each plate . For this study , all fluorescence intensity readings were also confirmed manually under a microscope . All transfection and GFP experiments were repeated 3 times . Y2H was performed as previously described [7] . All wild-type/mutant clones were transferred by Gateway LR reactions into our Y2H pDEST-AD and pDEST-DB vectors . All DB-X and AD-Y plasmids were transformed individually into the Y2H strains MATα Y8930 and MATa Y8800 , respectively . Each of the DB-X MATα transformants ( wild-type and mutants ) were then mated against corresponding AD-Y MATa transformants ( wild-type and mutants ) individually using automated 96-well procedures , including inoculation of AD-Y and DB-X yeast cultures , mating on YEPD media ( incubated overnight at 30°C ) , and replica-plating onto selective Synthetic Complete media lacking leucine , tryptophan , and histidine , and supplemented with 1 mM of 3-amino-1 , 2 , 4-triazole ( SC-Leu-Trp-His+3AT ) , SC-Leu-His+3AT plates containing 1 mg/l cycloheximide ( SC-Leu-His+3AT+CHX ) , SC-Leu-Trp-Adenine ( Ade ) plates , and SC-Leu-Ade+CHX plates to test for CHX-sensitive expression of the LYS2::GAL1-HIS3 and GAL2-ADE2 reporter genes . The plates containing cycloheximide select for cells that do not have the AD plasmid due to plasmid shuffling . Growth on these control plates thus identifies spontaneous auto-activators [64] . The plates were incubated overnight at 30°C and “replica-cleaned” the following day . Plates were then incubated for another three days , after which positive colonies were scored as those that grow on SC-Leu-Trp-His+3AT and/or on SC-Leu-Trp-Ade , but not on SC-Leu-His+3AT+CHX or on SC-Leu-Ade+CHX . Disruption of an interaction by a mutation was defined as at least 50% reduction of growth consistently across both reporter genes , when compared to Y2H phenotypes of the corresponding wild-type allele as benchmarked by 2-fold serial dilution experiments . All Y2H experiments were repeated 3 times . Wild-type MLH1 , HSPA8 , and BRIP1 entry clones are from the human ORFeome v8 . 1 collection [61] . Using Gateway LR reactions , wild-type MLH1 , mutant MLH1 ( I107R ) , and GFP were transferred into the pMSCV-N-FLAG-HA-PURO vector [65]; HSPA8 and BRIP1 were transferred into the pcDNA-DEST40 vector that contains a C-terminal V5 tag ( Invitrogen 12274-015 ) . HEK293T cells were grown in SILAC media comprising SILAC DMEM ( Thermo Scientific ) and 10% dialyzed FBS ( JR Scientific ) supplemented with either 0 . 1 mg/ml L-lysine and L-arginine ( light media ) or 0 . 1 mg/ml L-lysine 13C6 , 15N2 and L-arginine 13C6 , 15N4 ( heavy media ) . Heavy- or light-media cultured HEK293T cells were transfected using Lipofectamine 2000 ( Invitrogen ) in three 10 cm plates . 48 hrs after transfection , cells were washed three times in cold PBS and then resuspended in 5 ml RIPA buffer [1% NP-40 , 50 mM Tris-HCl pH 7 . 5 , 150 mM NaCl , 5 mM EDTA , 1× EDTA-free Complete Protease Inhibitor tablet ( Roche ) ] . Cells were lysed for 30 mins on ice before centrifuging at 13 , 000 rpm for 10 mins . Cell lysates were incubated with 60 µL EZview Red Anti-HA Affinity Gel ( Sigma-Aldrich ) for 3 hrs . After 3 washes with RIPA buffer , bound proteins were eluted with 3 resin volumes elution buffer ( 100 mM Tris-HCl pH 8 . 0 , 1% SDS ) . Eluted proteins from light and heavy media were mixed together , reduced with 5 mM DTT , alkylated with 15 mM of iodoacetamide , and then precipitated with 3 volumes PPT solution ( 50% acetone , 49 . 9% ethanol , 0 . 1% acetic acid ) . Proteins from pull-down experiments were solubilized with 50 µL Urea/Tris solution ( 8 M Urea , 50 mM Tris-HCl pH 8 . 0 ) and 150 µL NaCl/Tris ( 50 mM Tris-HCl pH 8 . 0 , 150 mM NaCl ) followed by the addition of 1 µg Trypsin Gold ( Promega ) . Protein digestion was performed overnight at 37°C after which trifluoroacetic acid and formic acid were added to a final concentration of 0 . 2% . Peptides were de-salted with Sep-Pak C18 columns ( Waters Corporation ) , dried in a speed-vac , and reconstituted in 85 µL of a solution containing 80% acetonitrile and 1% formic acid . Samples were fractionated by Hydrophilic Interaction LIquid Chromatography ( HILIC ) using a TSK gel Amide-80 column ( Tosoh Bioscience ) . HILIC fractions were dried in a speed-vac , reconstituted in 0 . 1% trifluoroacetic acid , and analyzed by LC-MS/MS using a 125 µM ID capillary column packed in-house with 3 µm C18 particles ( Michrom Bioresources ) and a Q-Exactive mass spectrometer ( Thermo Fisher Scientific ) coupled with a Nano LC-Ultra system ( Eksigent ) . Xcalibur 2 . 2 software ( Thermo Fischer Scientific ) was used for the data acquisition and Q-Exactive was operated in the data-dependent mode . Survey scans were acquired in the Orbitrap mass analyzer over the range of 380 to 2000 m/z with a mass resolution of 70 . 000 ( at m/z 200 ) . Up to the top 10 most abundant ions with a charge state higher than 1 and less than 5 were selected within an isolation window of 2 . 0 m/z . Selected ions were fragmented by Higher-energy Collisional Dissociation ( HCD ) and the tandem mass spectra were acquired in the Orbitrap mass analyzer with a mass resolution of 17 . 500 ( at m/z 200 ) . The fragmentation spectra were searched by using the SEQUEST software on a SORCERER system ( Sage-N Research ) and a human database downloaded from the International Protein Index ( version 3 . 80 ) . In all database searches , trypsin was designated as the protease , allowing for one non-tryptic end and two missed-cleavages . The following parameters were used in the database search: a mass accuracy of 15 ppm for the precursor ions , differential modification of 8 . 0142 Daltons for lysine and 10 . 00827 Daltons for arginine . Results were filtered based on probability score to achieve a 1% false positive rate . The Xpress software , part of the Trans-Proteomic Pipeline ( Seattle Proteome Center ) , was used to process the raw data and quantify the light/heavy peptide isotope ratios . Results were also manually inspected . We performed four SILAC experiments using both wild-type and mutant Mlh1 , as well as GFP as a control: wild-type ( heavy ) vs . control ( light ) [WT_Control]; mutant ( heavy ) vs . control ( light ) [Mutant_Control]; wild-type ( heavy ) vs . mutant ( light ) [WT_Mutant]; and mutant ( heavy ) vs . wild-type ( light ) [Mutant_WT] . We use the following variables and define four ratios for all subsequent calculations . In the WT_Control experiment , the relative abundance of protein p pulled down by wild-type Mlh1 to protein p pulled down by GFP ( WTp ) is quantified by the inverse of the geometric mean of rwc reads with Xpress values Xi . In the Mutant_Control experiment , the relative abundance of protein p pulled down by mutant Mlh1 ( I107R ) to protein p pulled down by GFP ( Mutp ) is quantified by the inverse of the geometric mean of rmc reads with Xpress values Yi . In the WT_Mutant experiment , the relative abundance of protein p pulled down with mutant Mlh1 ( I107R ) to protein p pulled down by wild-type Mlh1 is quantified by the geometric mean of rwm reads with Xpress values Pi . The amount of mutant Mlh1 ( I107R ) to wild-type Mlh1 is quantified by the geometric mean of twm reads with Xpress values Cj . In the Mutant_WT experiment , the relative abundance of protein p pulled down with mutant Mlh1 ( I107R ) to protein p pulled down by wild-type Mlh1 is quantified by the inverse of the geometric mean of rmw reads with Xpress values Qj . The amount of mutant Mlh1 ( I107R ) to wild-type Mlh1 is quantified by the inverse of the geometric mean of tmw reads with Xpress values Di . where both FCwm and FCmw denote the fold change in protein abundance as the normalized ratio of the amount of protein pulled down with mutant Mlh1 to that with wild-type Mlh1 . To identify interactors that are lost/weakened due to the I107R mutation , we required the following criteria to hold simultaneously: The first criterion ensures that the protein identified is a true interactor of wild-type Mlh1 . The second criterion ensures that the loss of interaction is significant and reliably observed across both WT_Mutant and Mutant_WT experiments . Similarly , to identify interactors that are gained/enhanced due to the I107R mutation , we required the following criteria to hold simultaneously: The first criterion ensures that the protein identified is a true interactor of the I107R mutant of Mlh1 . The second criterion ensures that the gain of interaction is significant and reliably observed across both WT_Mutant and Mutant_WT experiments . We also identify interactors of Mlh1 that are unaffected by the I107R mutation using the following criteria: Integrating both WT_Mutant and Mutant_WT experiments , we calculated a weighted average of the individual fold changes:P values are calculated using a two-sided Kolmogorov-Smirnov test ( with bootstrapping ) . HEK293T cells were maintained in complete DMEM medium supplemented with 10% FBS . Cells were transfected with Lipofectamine 2000 ( Invitrogen ) at a 6∶1 ( µL/µg ) ratio with DNA in 6-well plates and were harvested 24 hrs after transfection . Cells were gently washed three times in PBS and then resuspended using 200 µL 1% NP-40 lysis buffer [1% Nonidet P-40 , 50 mM Tris-HCl pH 7 . 5 , 150 mM NaCl , 1× EDTA-free Complete Protease Inhibitor tablet ( Roche ) ] and kept on ice for 20 mins . Extracts were cleared by centrifugation for 10 mins at 13 , 000 rpm at 4°C . 15 µL EZview Red Anti-HA Affinity Gel ( Sigma-Aldrich ) and 100 µL protein lysate were used for each co-immunoprecipitation reaction . The samples were rotated gently at 4°C for 2 hrs . HA beads were then washed three times with protein lysis buffer , treated with 6× protein sample buffer , and subjected to SDS-PAGE . Proteins were then transferred from the gel onto PVDF ( Amersham ) membranes . Anti-HA ( Sigma H9658 ) , anti-V5 ( Invitrogen 46-0705 ) , anti-β-tubulin ( Promega G7121 ) , and anti-GFP ( Santa Cruz sc-9996 ) antibodies were used at 1∶3 , 000 dilutions for immunoblotting analysis .
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With rapid advances in sequencing technologies , tens of millions of DNA variants have now been discovered in the human population . However , there are currently no experimental methods available for examining the impact of DNA variants in a high-throughput fashion . As a result , we have no functional data on the vast majority of these variants , which is a major roadblock to generating novel biological insights and developing new disease prevention therapeutic strategies . To address this issue , we have successfully developed the first massively-parallel site-directed mutagenesis approach , Clone-seq , to leverage the power of next-generation sequencing to generate a large number of mutant alleles in a fast and cost-effective manner . In conjunction with Clone-seq , we established a high-throughput comparative interactome-scanning pipeline to experimentally elucidate the effect of variants on protein stability and interactions . Additionally , Clone-seq can be used to generate clones for all DNA variants , including those in non-coding regions .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"mutagenesis",
"functional",
"genomics",
"mutagens",
"genetics",
"biology",
"and",
"life",
"sciences",
"genomics"
] |
2014
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A Massively Parallel Pipeline to Clone DNA Variants and Examine Molecular Phenotypes of Human Disease Mutations
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The causative mutation responsible for limb girdle muscular dystrophy 1F ( LGMD1F ) is one heterozygous single nucleotide deletion in the stop codon of the nuclear import factor Transportin 3 gene ( TNPO3 ) . This mutation causes a carboxy-terminal extension of 15 amino acids , producing a protein of unknown function ( TNPO3_mut ) that is co-expressed with wild-type TNPO3 ( TNPO3_wt ) . TNPO3 has been involved in the nuclear transport of serine/arginine-rich proteins such as splicing factors and also in HIV-1 infection through interaction with the viral integrase and capsid . We analyzed the effect of TNPO3_mut on HIV-1 infection using PBMCs from patients with LGMD1F infected ex vivo . HIV-1 infection was drastically impaired in these cells and viral integration was reduced 16-fold . No significant effects on viral reverse transcription and episomal 2-LTR circles were observed suggesting that the integration of HIV-1 genome was restricted . This is the second genetic defect described after CCR5Δ32 that shows strong resistance against HIV-1 infection .
Productive HIV-1 infection requires the interaction with cellular co-factors at virtually all the steps of the viral replication cycle [1] . Viral entry depends on fusion of viral and cellular membranes through successive interactions with CD4 receptor combined with CXC chemokine receptor type 4 ( CXCR4 ) or CC chemokine receptor type 5 ( CCR5 ) [2] . Once the core is released into the cytosol , the reverse transcriptase converts the viral RNA genome into a double-stranded copy DNA ( cDNA ) and the capsid ( CA ) uncoating process is initiated . HIV-1 cDNA gains access to the nucleus through the cellular nuclear transport machinery located at the nuclear pore , in the form of a pre-integration complex ( PIC ) . These PICs consist of viral cDNA and other HIV-1 components like integrase ( IN ) , matrix , nucleocapsid , CA and viral protein R ( Vpr ) , as well as various host proteins , such as the high mobility group protein B1 ( HMGB1 ) , barrier to autointegration factor 1 ( BAF1 ) , lamina-associated polypeptide 2α ( LAP2α ) and lens-epithelium derived growth factor ( LEDGF/p75 ) [3–7] . Several cellular import factors , including importin-7 , importin-α3 and Transportin 3 ( TNPO3 , also called TRN-SR2 ) have also been involved in HIV-1 nuclear import [8] . Apart from its implication in nuclear import of the viral PIC , it has been confirmed that N-terminal end of TNPO3 protein act as a direct binding partner of HIV-1 IN [9] . Interaction with the viral CA has also been documented [10 , 11] and nearly 30 CA-mutants able to modify HIV-1 dependence on TNPO3 have been described [12] . TNPO3 is a member of the karyopherin β superfamily of proteins [13 , 14] that imports into the nucleus mostly serine/arginine ( SR ) -rich proteins . Within these proteins are essential pre-mRNA splicing factors such as serine/arginine-rich splicing factor 1 ( SRSF1 ) , SR-rich splicing factor 2 ( SRSF2 , also known as SC35 ) and cleavage and polyadenylation-specific factor 6 ( CPSF6 ) [15 , 16] . The interaction between HIV-1 CA and CPSF6 impedes interferon ( IFN ) -mediated innate responses , allowing HIV-1 to escape from immune sensing and favouring infection . In fact , HIV-1 virions carrying CA mutation N74D that cannot interact with CPSF6 trigger innate sensors that induce an antiviral state against HIV-1 infection in macrophages [17] . Moreover , as HIV-1 is highly dependent on the cellular splicing machinery [18] , modifications in TNPO3-mediated nuclear import may indirectly affect HIV-1 replication through changes in the post-transcriptional mRNA maturation [19–22] . TNPO3 has been identified as a HIV-1 co-factor in two independent genome-wide siRNA screens [23 , 24] and as a specific binding partner of HIV-1 IN in a yeast two-hybrid screen [25] . These results support the idea that TNPO3 may be essential for HIV-1 life cycle along with other fundamental proteins such as CPSF6 . However , its precise role during HIV-1 nuclear import and viral integration is not fully understood [26] . LGMDs comprise a group of genetically heterogeneous disorders characterized by a progressive and predominantly proximal muscle weakness with histological signs of muscle degeneration and regeneration [27] . In 2001 , a novel form of LGMD classified as LGMD1F was reported , affecting 32 individuals in one large Spanish kindred spanning six generations [28] . The genetic defect of this autosomal recessive disease was identified as a single adenosine nucleotide deletion in TAG stop codon of one allele of TNPO3 gene , common to both protein isoforms encoded by this gene . As a result , the cells from these patients may synthesize both TNPO3_wt and TNPO3_mut proteins forms , being TNO3P-mut an extended form of TNPO3 with fifteen additional amino acids in the C-terminal end . Because the cargo-binding domain of TNPO3 resides in this part of the molecule [29] , this function might be altered in the mutated protein [30 , 31] . Being TNPO3 a co-factor of HIV-1 replication [23–25] , the susceptibility to HIV-1 infection of peripheral blood mononuclear cells ( PBMCs ) isolated from LGMD1F patients was analyzed . Our data revealed that the mutation of TNPO3 present in patients with LGMD1F protected PBMCs from HIV-1 infection . Therefore , this is the second genetic defect described so far after CCR5-Δ32 deletion [32 , 33] that is able to confer resistance to HIV-1 infection .
Twenty-three patients with LGMD1F were recruited for this study . All these patients belong to a Spanish/Italian family that shares a common old ancestor born in south-eastern Spain [28] , specifically to generations III , IV and V in the family tree ( Fig 1A ) . These patients have been closely followed up at the University Hospital La Fe ( Valencia , Spain ) and show a wide variety of clinical features ( Table 1 ) . Most patients included in this study presented onset symptoms such as difficulties in climbing stairs , rising from sitting , running or fatigue . Thirteen patients showed scapula-humeral and pelvic-femoral weakness and eight of them also presented hand and leg weakness and/or atrophy . The rest of patients showed pelvic-femoral weakness , hand atrophy and leg weakness . Two patients remained asymptomatic when this study was performed . Only three patients presented with grades > 6 in the Vignos score , which is given to individuals who need a long leg brace for walking or standing . Eight patients were graded as 3–4 in the Brooke score and were unable to elevate their shoulders [34 , 35] . Average levels of creatine kinase ( CK ) were 3 . 2-fold higher than the normal range . This human disease is caused by a deletion in the long arm of chromosome 7 ( 7q32 . 1 ) which compromises the TNPO3 gene . LGMD1F patients show a heterozygous single nucleotide deletion ( c . 2771del ) in exon 23 that generates a 15 amino acid extension of the C-terminus of the protein ( Fig 1B ) . The expression pattern of wt and mut variants of TNPO3 was analysed by RT-qPCR in PBMCs isolated from all LGMD1F patients and compared to twenty-seven healthy donors ( labelled as CT ) . All patients revealed co-dominant expression of each allele ( Fig 2A and 2B ) , in comparison with dominant expression of TNPO3_wt gene in healthy individuals . In order to know whether the longer protein encoded by TNPO3_mut allele was co-expressed with TNPO3_wt allele , protein extracts from four patients and two healthy controls were analyzed by immunoblotting using an antibody against TNPO3 that recognized both forms of the protein . Similar levels of TNPO3_wt and TNPO3_mut isoforms were observed in LGMD1F patients , whereas only one band corresponding to TNPO3_wt was observed in healthy controls ( Fig 2C ) . The expression levels of the HIV-1 receptor CD4 and the co-receptors CCR5 and CXCR4 were analyzed by flow cytometry in order to exclude an expression defect in cells from LGMD1F patients . No significant difference with healthy controls was found ( S1A Fig ) . PBMCs from LGMD1F patients and controls were then activated with antiCD3 , CD28 and IL2 for 48 hours . The expression of activation markers CD25 and HLA-DR was also analyzed but no significant difference was observed ( S1B Fig ) , thus excluding an activation defect influencing cellular susceptibility to HIV-1 infection . TNPO3 is involved in nuclear import of splicing factors such as ASF/SF2 , SC35 and CPSF6 [29] . Because it has been shown that TNPO3 knockdown induces the accumulation of CPSF6 –a predominantly nuclear protein- in the cytoplasm [12] we analyzed if the mutant form of TNPO3 from LGMD1F patients resulted in relocalization of CPSF6 , a predominantly nuclear protein , to the cytosol . We observed relatively equal levels of CPSF6 protein expression in the nucleus , but PBMCs from LGMD1F patients showed higher level of cytoplasmatic CPSF6 ( S2 Fig ) . In order to evaluate the susceptibility to HIV-1 infection , we analyzed the kinetics of viral replication in activated PBMCs isolated from seven LGMD1F patients and seven healthy individuals . PBMCs were infected ex vivo by spinoculation with NL4 . 3-Renilla and NL4 . 3_N74D-Renilla , a CA mutant in which the nuclear import is independent of TNPO3 [17] . The production of Renilla was measured in cell lysates several days post-infection as relative light units ( RLUs ) . The values were normalized with the total protein taking into account the cell viability during infection . There were no significant differences in cell survival after infection with either virus ( S3A and S3B Fig , respectively ) . Low HIV-1 replication was observed in PBMCs from LGMD1F patients 3–7 days after infection with NL4 . 3-Renilla ( Fig 3A ) but no significant difference in the replication of NL4 . 3_N74D-Renilla virus was observed ( Fig 3B ) . To confirm these results , activated PBMCs isolated from twenty-two LGMD1F patients and twenty-seven healthy individuals were infected ex vivo with NL4 . 3-Renilla strain by spinoculation . The production of Renilla was measured in cell lysates 5 days post-infection and results were normalized with total protein and viability . Average virus replication in all LGMD1F patients was reduced 18-fold compared with controls ( **** p<0 . 0001 ) ( Fig 3C ) . In order to determine whether the impairment in HIV-1 infection was due to the presence of TNPO3_mut in LGMD1F patients and not to other potential restrictive activity , PBMCs isolated from seven LGMD1F patients and seven healthy donors were activated and infected in vitro with NL4 . 3_N74D-Renilla ( Fig 3D ) . In contrast to the results obtained using a wt HIV-1 clone , reduction in infectivity was observed between LGMD1F and control patients . On the contrary , an increase in HIV-1 replication was observed in six out of seven patients in comparison with controls ( p<0 . 001 ) . PBMCs isolated from twenty patients and twenty-six healthy controls were activated with antiCD3/CD28 and IL-2 for 48 hours and then infected with NL4 . 3-Renilla by spinoculation . At 5 hours post-infection , DNA was extracted and synthesis of HIV-1 strong stop ( R/U5 ) and full-length ( R/ATG-gag ) reverse transcriptase products , which represent early and late reverse transcriptase transcripts , respectively , were quantified by qPCR ( Fig 4A ) . No significant difference in the efficiency of reverse transcription in PBMCs from LGMD1F patients and healthy controls was detected . In order to monitor the nuclear import of viral DNA , the accumulation of circular DNA intermediates was determined by measuring episomal 2-LTRs by ultrasensitive digital PCR 24 hours after infection . No significant difference was detected in the number of episomal 2-LTR circles between PBMCs from LGMD1F patients and healthy controls ( Fig 4B ) . To assess viral integration , infected cells were incubated for 5 days , DNA was extracted and proviral copies were quantified by qPCR . Proviral integration was on average 16-fold lower in PBMCs from LGMD1F patients than in controls ( **** p<0 . 0001 ) ( Fig 4C ) . These data indicate an integration defect in cells carrying TNPO3 mutation . CD4+ T cells isolated from patients with LGMD1F and healthy controls were infected with fluorescently labeled particles ( HIV-IN-eGFP ) [7 , 36] . The presence of IN-eGFP in PICs allows quantitative analysis of the number of PICs and their intracellular location by confocal microscopy . When HIV-1 infected CD4+ T cells were fixed 10 hours post-infection , no significant differences were detected between both groups , neither in the number of uninfected cells ( Fig 5A ) , nor in the number of cytoplasmic and nuclear PICs per cell ( Fig 5B and 5C , respectively ) . However , after 24 hours of infection , the number of cells without PICs was 1 . 7-fold higher in CD4+ T cells from patients with LGMD1F than in healthy controls ( p<0 . 001 ) ( Fig 5A ) . The number of cytosolic PICs was 3 . 1-fold reduced in CD4+ T cells from patients with LGMD1F compared to the healthy controls ( Fig 5B ) ( p<0 . 01 ) . This was not due to higher PIC translocation to the nucleus , as there was no significant difference in the number of nuclear PICs ( Fig 5C and 5D ) . HIV-1 infectivity was examined in HeLaP4 cell lines stably expressing TNPO3_wt or TNPO3_mut form . These cell lines were validated by western blot , immunocytochemistry and RT-PCR ( S4 Fig ) . Next , we compared the luciferase signal of HIV-fLucVSV-G in a cell line containing TNPO3_wt which was transduced with an empty vector ( control shRNA + empty vector ) . As described before , depletion of TNPO3 ( TNPO3 shRNA + empty vector ) , resulted in a three-fold drop in luciferase activity , reflecting a decreased viral infectivity ( Fig 6 ) ( p<0 . 01 ) [7 , 25] . Back-complementation with TNPO3_wt in stable knock-down cells ( TNPO3shRNA + TNPO3_wt ) restored the luciferase activity level to that of cell lines containing endogenous TNPO3 . Back-complementation with the mutant form of the protein ( TNPO3 shRNA+TNPO3_mut ) resulted in lower recovery of HIV-1 infection . These experiments supported that TNPO3_mut was not able to rescue HIV-1 replication in these cell lines thus validating its role as a defective host factor impairing HIV infection .
HIV-1 infection remains incurable despite efficient antiretroviral treatments that tackle HIV-1 enzymes and proteins . One potential strategy to develop new therapeutic targets can be based on the study of the interaction between viral proteins and their cellular cofactors . In this regard , TNPO3 and other importins have been previously described as essential cellular proteins for HIV-1 infection [23–25 , 37] . However the exact mechanism of action of TNPO3 still remains a matter of controversy [38] . Some studies suggest that TNPO3 participates in the nuclear import of PICs [9 , 25 , 39 , 40] whereas other authors propose that TNPO3 promotes HIV-1 infection though the interaction with HIV-1 CA [41–43] or indirectly through the interaction of CPSF6 with HIV-1 CA [12] . Besides an indirect role for TNPO3 in viral integration through its interaction with CA and/or the IN and their respective cellular partners such as CPSF6 or LEDGF/p75 [38] has been proposed . These cellular host factors may affect the nuclear landscape of HIV-1 infection [44] by targeting the viral genome to silent or actively transcribed chromatin [45] opening a new perspective in the mechanisms of HIV-1 latency and reactivation . Of note , nuclear transport has not been widely studied as a potential target to HIV-1 infection and only recently new hits impeding TNPO3-IN interactions blocking HIV-1 nuclear transport have been described [8] . Besides this role in HIV-1 infection , TNPO3 is also linked to a rare muscular dystrophy termed LGMD1F . The genetic cause for LGMD1F was found to be an adenosine deletion in the stop codon of TNPO3 gene , which leads to the addition of 15 amino acids to the C-terminal end [30 , 31] . The effect of this elongation for the functionality of TNPO3 protein is still unknown but previous data indicate that TNPO3_mut shows a perinuclear distribution whereas TNPO3_wt predominantly localizes inside the nucleus . This suggests that TNPO3 mutation could affect the subcellular localization of the protein [31] . Because the cargo-binding domain of TNPO3 resides in the C-terminal end , the additional 15 amino acids may also alter its cargo binding properties and subsequently influence alternative splicing [30 , 31] . Apart from these preliminary results , the mechanism by which TNPO3 mutation affects its function and causes LGMD1F remains undetermined . Interestingly , other rare muscular diseases known as laminopathies are related to changes in the nuclear lamin architecture , a fibrous structure located below the nuclear membrane which functions include among others the regulation of the nuclear transport [46 , 47] . Besides , other muscle diseases like myotonic dystrophies are due to mutations in splicing factors . For these reasons the term “splicepathies” has been proposed to design these genetic diseases [48 , 49] . In summary , the biomedical interest of TNPO3 is first , as an essential cellular protein in the HIV-1 cycle and second through a specific mutation , as the genetic cause of the ultra-rare disease LGMD1F . In this context , we propose that the understanding of HIV-1 biology in lymphocytes from LGMD1F patients represents a tool to get a better insight into the mechanisms of nuclear transport and to understand the pathogenic mechanisms leading to muscle disease such as muscular dystrophies that share common physiopathology pathways . Our results demonstrate that resistance to HIV-1 infection observed in CD4 lymphocytes from patients with LGMD1F is directly related with the mutation in TNPO3 gene because the infectivity was not affected when we infected with an HIV-1 clone carrying a CA-mutation ( N74D ) , in which the nuclear transport is independent of TNPO3 . As previously described [17] , the efficacy of infection is lower with N74D clone as compared to a wt virus , which explains lower luciferase levels . Interestingly , infection with N74D virus was not equal in the PBMCs of all LGMD1F patients as in some of them the infection with the mutant virus was higher than in controls , suggesting that PBMCs from some patients with LGMD1F can develop compensatory mechanism to overcome the deficiency in TNPO3-mediated transport . By unknown reasons , age of onset and severity of clinical symptoms are highly variable in LGMD1F patients , despite all of them carry the same mutation , pointing to the presence of alternative mechanisms that compensate the deficit in TNPO3 function . Due to the central role of TNPO3 in HIV-1 nuclear import , we hypothesized that HIV-1 infection might be impaired in PBMCs from LGMD1F patients . LGMD1F is an autosomal recessive disease and accordingly , both TNPO3_wt and TNPO3_mut form are co-expressed in similar quantity . The TNPO3_mut allele was expressed and translated as a 15 amino acids longer protein than TNPO3_wt [31] . However despite the presence of 50% of the normal protein , high resistance to HIV-1 infection ex vivo was observed in these cells , with a reduction of more than 18-fold on average in the production of viral proteins . These results suggest that in patients with LGMD1F TNPO3_mut was interfering with the normal function of TNPO3_wt . It has been proposed that TNPO3 multimerizes in order to carry out its nuclear import functions [29 , 40] . If TNPO3_mut cannot multimerize or interferes with regular multimerization of TNPO3_wt , this could explain the severe restriction of viral infection observed in LGMD1F cells despite the co-dominant expression of both TNPO3_wt and TNPO3_mut . The cytosolic location of CPSF6 in lymphocytes from patients with LGMD1F confirms the functional impact of the mutated protein “in vivo” for the first time and correlates with previous work showing that TNPO3 knockdown causes CPSF6 to accumulate in the cytoplasm [12] . Relocalization of CPSF6 to the cytosol could decrease its availability to bind to HIV-1 CA and contribute to the increased resistance to infection observed in LGMD1F patients . To confirm these data we generated HeLaP4 cell lines with stable knock-down of endogenous TNPO3 that were trans-complemented with either TNPO3_wt or TNPO3_mut form associated to LGMD1F . As described before , depletion of endogenous TNPO3 resulted in decreased viral infectivity that was recovered with the back-complementation of TNPO3_wt . However , expression of the TNPO3_mut form of the protein resulted in lower recovery of HIV-1 infection , which supports that the TNPO3_mut form found in LGMD1F patients restricts HIV-1 infection . In order to define the step at which TNPO3_mut impaired HIV-1 replication , the viral cell cycle was analyzed in PBMCs from LGMD1F patients in comparison with healthy controls . There were no significant differences between the cells of patients and controls in the formation of early and late reverse transcripts 5 hours after the infection . It has been previously described that TNPO3 depletion leads to a reduction in nuclear 2-LTR circles during HIV-1 infection [7 , 25 , 50] , although other studies did not corroborate these results [10 , 11 , 51] . This discrepancy has been attributed to the different primer sets used to detect the intranuclear viral DNA products [12] . In the present study we could not find differences between controls and LGMD1F patients at the level of the formation of episomal 2-LTR circles 24 hours post-infection ( Fig 4B ) . Accordingly , a direct role for TNPO3_mut on the entry of viral DNA into the nucleus cannot be concluded . However , integration was decreased more than 16-fold in PBMCs from LGMD1F patients in comparison to controls 5 days after infection suggesting an impairment of HIV-1 integration due to the TNPO3 defect leading to deep impact on viral replication . When using fluorescently labeled viruses ( HIV-IN-eGFP ) , no differences were detected in the cellular distribution at 10 hours after infection . However at 24 hours post-infection the number of infected cells , as defined by IN-eGFP detection , was significantly decreased in patients with LGMD1F . Unexpectedly , only cytoplasmic complexes were reduced , suggesting a decreased stability of the complexes that was not a consequence of higher translocation to the nucleus , as there was no increase in the number of nuclear PICs . The change in subcellular distribution of CPSF6 could not explain this observation since an increase in CA stability due to cytosolic CPSF6 location after silencing TNPO3 has been proposed [12] . One potential hypothesis explaining this sharp decrease in cytosolic PIC numbers could be related to the induction of innate immune responses leading to CA degradation . It has been described that in macrophages HIV-1 evades innate immune recognition in macrophages through specific recruitment of cellular factors to the CA , including TNPO3 and CPSF6 to the CA [16] . A deficient binding of TNPO3 and CPSF6 could increase CA sensing and induction of IFN-mediated mechanisms in lymphocytes of LGMD1F patients . Finally , because integration targeting of the HIV-1 genome to silent or actively transcribed chromatin is linked to nuclear import mechanisms and cellular factors as LEGDF/p75 and CPSF6 we cannot rule out that entry through TNPO3-independent mechanisms in patients with LGMD1F leads to different selection of chromosomal integration sites [52] . Our data confirm for the first time “in vitro” that adequate TNPO3 function is essential for HIV-1 replication infection , but they also provide insights into the role of TNPO3 in LGMD1F . It should be noted that although LGMD1F patients show progressive muscle weakness as the disease advances , they do not show any type of immunodeficiency or higher susceptibility to infectious diseases . Therefore , the presence of TNPO3_mut is not affecting all types of cells in a similar way , being the muscle cells clearly more affected . Moreover , PBMCs from all patients showed similar resistance to HIV-1 infection , while there is a wide variability in the muscular clinical symptoms that affects these patients , although all of them share the same TNPO3 mutation . One possibility that merits further investigation is that TNPO3_mut could be affecting the nuclear import of essential factors for alternative splicing such as SC35 and CPSF6 [15 , 16] . It has been described that skeletal muscle is the tissue with the highest number of differentially expressed alternative exons [53 , 54] , including isoforms of myogenic transcription factors , metabolic enzymes and myofibrillar proteins [55] . LGMD covers a group of genetically determined disorders that varies depending on factors such as age of onset , rate of disease progression , distribution of muscle weakness and genetic causes . Such array of factors implies different steps of muscle development and neural and hormonal influences that can affect differently the highly complex pattern of muscle-specific transcripts processed by alternative splicing [56] . Therefore , the analysis of muscle cells from LGMD1F patients could determine whether this disease might be caused by defects in TNPO3-mediated import of splicing factors involved in the alternative splicing of essential proteins for muscular contraction . In conclusion , TNPO3_mut protein expressed in LGMD1F cells is the second genetic defect leading to strong HIV-1 restriction in humans . Importantly , the first genetic defect shown to produce HIV-1 restriction , the CCR5 delta32 deletion , blocks entry of R5-tropic but not X4- tropic strains . TNPO3 mutation described here acts at a post-entry step in the virus life cycle , and may therefore be independent of viral tropism . These findings increase our understanding of the role of TNPO3 in HIV-1 infection , and support further characterization of LGMD1F as a splicing disease .
Twenty three patients with diagnosed LGMD1F and twenty-seven healthy donors were recruited for this study . Muscle strength was graded using the Modified Medical Research Council ( MMRC ) scale . The upper and lower extremity functions were assessed using Brooke and Vignos scores , respectively [34 , 35] . LGMD1F patients were recruited at the Hospital de La Fe ( Valencia , Spain ) and healthy donors were recruited at the Centro Regional de Transfusión from the Complejo Hospitalario de Toledo ( Toledo , Spain ) . All individuals gave informed written consent and this study was approved by the Institutional Ethical Committee Board of Hospital de La Fe ( 2016/0388 ) and Instituto de Salud Carlos III ( Madrid , Spain; CEI PI 22_2017-v3 ) . Total genomic DNA and RNA were isolated from PBMCs using DNA/RNA Mini Kit ( Qiagen ) . SNP genotyping assay was designed for detecting simultaneously TNPO3_wt and TNPO3_mut forms using the following primers and probes: forward primer ( 5´-GCGAGACTTCACCAGGTTGTT-3´ ) ; reverse primer ( 5´-CTGGGTGACAGGCACAGT-3´ ) ; TNPO3_wt probe: ( TNPO3 deletion-V , VIC , 5´-CAGGAGTGTGAGCTATCGA-3´ ) ; and TNPO3_mut probe ( TNPO3 deletion-M , FAM , 5´-AGGAGTGTGAGCATCGA-3´ ) . cDNA was synthesized from 200 ng of RNA by using GoScript Reverse Transcription System ( Promega ) , following manufacturers’ instructions . RT cycling conditions were as follows: 5 min at 25°C; 1h at 45°C; and 15 min at 70°C . SNP genotyping was also performed using 50 ng of genomic DNA and TaqMan Universal Mix ( Applied Biosystems ) . Analyses were performed in triplicate per sample using StepOne Real-Time PCR system ( Applied Byosistems ) with standard cycling conditions . Results for the allelic discrimination were represented as ΔRn , being Rn the ratio between the fluorescent emission intensity of the reporter dye and the passive dye . Whole protein extracts were obtained as described previously [57] and protein concentration was determined by Bradford method [58] . Ten micrograms of protein extracts were fractionated by SDS-PAGE and transferred onto Hybond-ECL nitrocellulose paper ( GE Healthcare ) . Subsequently , the membranes were blocked and incubated with an anti-TNPO3 antibody ( Abcam ) . Analysis was performed by chemiluminescence using a BioRad Geldoc 2000 ( Bio-Rad Laboratories , Madrid , Spain ) . Infectious supernatants were obtained from calcium phosphate transfection of HEK293T cells ( provided by the existing collection of the Instituto de Salud Carlos III , Madrid , Spain ) with plasmid pNL4 . 3-Renilla , which contains the HIV-1 proviral clone pNL4 . 3 with the nef gene replaced by renilla luciferase gene . The pNL4 . 3_N74D-Renilla clone was generated introducing the N74D mutation in the nucleotide position 1405 of the previously described plasmid pNL4 . 3-Renilla [59] . PBMCs were isolated from blood samples by centrifugation using a Ficoll-Hypaque gradient ( GE Healthcare ) and then activated for 3 days with purified anti-human CD3 ( clone OKT3 ) , CD28 ( clone CD28 . 2 ) ( Biosciences , San Diego , CA ) and 300 U/ml interleukin-2 ( IL-2 ) ( Chiron , Emeryville , CA ) . Activated cells were infected with 1 ng p24 of NL4 . 3-Renilla or NL4 . 3_N74D-Renilla per million of cells by spinoculation . Briefly , after 30 minutes of gently rotation at room temperature , cells were centrifuged at 600xg for 30min at 25°C and extensively washed with PBS1X . Infected cells were cultured for 5 days with IL-2 . Renilla activity ( RLU ) was quantified at different time points in the cell lysates with Renilla Luciferase Assay System ( Promega ) and measured with a Sirius luminometer ( Berthold Detection Systems , Oak Ridge , TN ) . Data were normalized for protein concentration measured with the Bradford method [58] and cell viability was measured with the CellTiter-Glo Luminescent Cell Viability assay ( Promega ) . At 5 hours after infection , DNA was extracted using QIAamp DNA Blood Mini Kit ( Qiagen ) and quantified with Nanodrop 2000C ( Thermo Scientific ) . Strong stop DNA was quantified using primer pairs specific for R and U5 regions of the HIV-1 long terminal repeat ( LTR ) , as described [60] . Serial dilutions of genomic DNA from 8E5 cell line , which contains a single integrated copy of HIV-1 [61] , were used as a standard curve . The ccr5 gene was used as endogenous control . qPCR was performed in triplicate in StepOne Real-Time PCR system ( Applied Biosystems ) using standard cycling conditions . Genomic DNA was extracted from PBMCs 24 hours after infection with NL4 . 3-Renilla , using DNeasy Blood and Tissue kit ( Qiagen ) , and quantified using the Nanodrop-1000 spectrophotometer ( Nanodrop ) . The samples were measured by the QuantStudio 3D Digital PCR System ( Life Technologies ) . The reaction mixture for Digital PCR ( dPCR ) is as follows: cDNA , QuantStudio 3D Digital PCR Master Mix v2 , 300nM of C1_2LTR primer , 300nM C4R_71 primer , 250nM 2nr4nr_FAM probe , 0 , 5x CCR5_VIC probe and H2O for a final volume of 14 . 5 μl . The digital PCR reaction mix was loaded onto the QuantStudio 3D digital PCR chips , according to the manufacturer instructions . The thermal cycling and amplifications were performed in a ProFlex 2xFlat PCR system , according to the manufacturer protocol: 96°C for 10 min , followed 39 cycles of 55 °C for 2 min and 98°C for 30 sec , 55 °C for 2 min and a stabilization phase at 22°C . The chips were transferred to a QuantStudio 3D digital PCR instrument for imaging and the final analysis of the files generated was carried out using the cloud-based . Total DNA from PBMCs infected for 5 days with NL4 . 3-Renilla was extracted using DNeasy Blood and Tissue kit ( Qiagen ) as described above and the integrated HIV-DNA was measured by nested Alu–HIV-LTR PCR [62–64] with modifications [65] . Briefly , 50 ng DNA were used for the first conventional PCR ( C1000 Thermal Cycler , Bio-Rad ) with 10x TaqMan Buffer , dNTPs , Alu-1 primer , Alu-2 primer , L-M667 primer and Platinum Taq DNA polymerase ( Roche ) ; 2x TaqMan Universal PCR Master ( Promega ) , AA55M primer , Lambda T primer and MH603 probe for the second quantitative PCR ( StepOne , Applied Biosystems ) . A standard curve of integrated HIV-DNA from 8E5 cell line using serial dilutions was prepared as reference and CCR5 was used as housekeeping gene . HIV-1 particles containing fluorescently labeled IN ( HIV-IN-eGFP ) were generated by Vpr-mediated trans-incorporation as described before [7 , 36 , 66] . For infection with HIV-IN-eGFP , PBMCs were previously incubated with CD3:CD28 bispecific monoclonal antibody ( NIH AIDS Reagent Program , Division of AIDS , NIH from Drs . Johnson Wong and Galit Alter ) during 5 days for CD4+ T cell enrichment . CD4+ T cells were infected with HIV-IN-eGFP by spinoculation . At 2 hours post-infection cells were washed and further incubated for 5 or 19 hours . Next , the cells were plated in poly-D-lysine chambers and allowed to adhere , to reach a total infection time of 10 h and 24 h , respectively . Cells were fixed for 15 min with 4% ( v/v ) paraformaldehyde and permeabilized during 5 min with 0 . 1% ( v/v ) Triton-X100 . Nuclei were immunostained with lamin A/C antibody ( Santa Cruz Biotechnology ) and secondary anti-Mouse IgG ( H+L ) Alexa Fluor 555 conjugate ( ThermoFisher Scientific ) diluted in blocking buffer ( 1% ( w/v ) BSA and 0 . 1% ( v/v ) Tween-20 in PBS ) . Imaging of the cells was performed using a laser scanning microscope ( Fluoview FV1000 , Olympus , Tokyo , Japan ) . An in-house MatLab routine ( MatWorks ) was used to determine the localization and number of IN-eGFP complexes [7] . In short , IN-eGFP complexes and the nuclear lamin were determined automatically using an intensity threshold based on the triangle algorithm . Based on the nuclear lamin staining , IN-eGFP complexes were divided into cytoplasmic or nuclear compartments and the percentage of nuclear IN-eGFP complexes was calculated . Typically , data were collected from 90 cells . Vesicular stomatitis virus glycoprotein ( VSV-G ) pseudotyped lentiviral vectors for the stable expression of TNPO3 were produced as described before [7 , 25] . In short , 6 . 5x106 HEK-293T cells were transfected with 10 μg of the packaging construct pCMVδR8 . 91 [67] , 20 μg transfer construct ( pCHMWS-3xFLAG-TNPO3-IRES-hygro with TNPO3_wt or TNPO3_mut ) and 5 μg pVSV-G using branched polyethylenimine ( bPEI , 10 μM , Sigma-Aldrich ) . Supernatant was collected 48 h and 72 h post-transfection , filtered through a 0 . 45 μm pore-size filter , and concentrated by ultrafiltration ( Amicon Ultra-15 Centrifugal Filter Unit , 50 kDa , Merck ) . HeLaP4 cells , a kind gift from Dr . P . Charneau ( Institut Pasteur , France ) , and HeLaP4 cells stably depleted of TNPO3 [7 , 25] were back-complemented with TNPO3_wt or TNPO3_mut through stable transduction with lentiviral vectors . Briefly , 3x104 HeLaP4 cells were plated in a 96-well plate the day before transduction . The next day , cells were transduced with a dilution series of vector containing cassettes coding for TNPO3_wt or TNPO3_mut . As a control , cells were transduced with an empty cassette vector ( empty vector ) . Transduced cells were selected with 160 μg/ml hygromycin . To determine the effect of the TNPO3_mut on the viral infectivity in cell lines , 1 . 5x104 HeLaP4 cells were seeded per well in a 96-well plate . The next day , the cells were infected with a three-fold dilution of a single-round HIV-fLucVSV-G [68 , 69] . At 72 h post-infection , cells were lysed in buffer ( 50 mM Tris , 200 mM NaCl , 0 . 2% ( v/v ) NP40 and 5% ( v/v ) glycerol ) and analysed for firefly luciferase activity ( ONE-Glo Promega GMBH , Mannheim , Germany ) . Chemiluminescence was measured with a Glomax luminometer ( Promega ) . Readouts were normalised for protein content as determined by a BCA protein assay . Data are represented as relative infectivity compared to a cell line expressing endogenous TNPO3_wt ( control shRNA ) and are means of at least two independent experiments . A Kruskal-Wallis test was used to evaluate statistical significance . Error bars represent the standard deviation . Statistical analysis was performed using GraphPad Prism 5 . 0 Software ( GraphPad ) . Comparisons between LGMD1F patients and healthy individuals were made with one-way analysis of variance ( ANOVA ) using Tukey´s Multiple Comparison Test to describe statistical differences among groups . The number of nuclear and cytoplasmic PIC was plotted in a scatter plot and a Mann-Whitney test was used to determine statistical significance . Differences were considered statistically significant when ** p < 0 . 01 , *** p < 0 . 001 and **** p < 0 . 0001 .
|
TNPO3 has been described as a key factor in the infection by the human immunodeficiency virus ( HIV-1 ) , the causative agent of AIDS . In 2013 , a relationship between a genetic defect in TNPO3 gene and a rare muscle disease named Limb Girdle Muscular Dystrophy 1F ( LGMD1F ) , with an autosomal dominant transmission , was discovered . LGMD1F patients show a heterozygous single nucleotide deletion in the TNPO3 gene that generates a TNPO3_mut protein . Our results demonstrate that cells from patients with this mutation in TNPO3 are resistant to HIV-1 infection in vitro . We are faced with an in vivo situation in which the genetic defect that causes this rare disease confers resistance to HIV infection . Therefore , TNPO3 mutation represents a natural model to understand the pathogenesis of both diseases . Cells from LGMD1F patients can be used to understand the mechanisms of action of TNPO3 in HIV infection and to design new therapeutic strategies for the treatment of both diseases . The use of HIV-1 as a methodological tool will permit a better understanding of the physiopathological mechanisms derived from the mutation in TNPO3 that causes the muscle disease .
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2019
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The mutation of Transportin 3 gene that causes limb girdle muscular dystrophy 1F induces protection against HIV-1 infection
|
Although Plasmodium vivax is a leading cause of malaria around the world , only a handful of vivax antigens are being studied for vaccine development . Here , we investigated genetic signatures of selection and geospatial genetic diversity of two leading vivax vaccine antigens – Plasmodium vivax merozoite surface protein 1 ( pvmsp-1 ) and Plasmodium vivax circumsporozoite protein ( pvcsp ) . Using scalable next-generation sequencing , we deep-sequenced amplicons of the 42 kDa region of pvmsp-1 ( n = 44 ) and the complete gene of pvcsp ( n = 47 ) from Cambodian isolates . These sequences were then compared with global parasite populations obtained from GenBank . Using a combination of statistical and phylogenetic methods to assess for selection and population structure , we found strong evidence of balancing selection in the 42 kDa region of pvmsp-1 , which varied significantly over the length of the gene , consistent with immune-mediated selection . In pvcsp , the highly variable central repeat region also showed patterns consistent with immune selection , which were lacking outside the repeat . The patterns of selection seen in both genes differed from their P . falciparum orthologs . In addition , we found that , similar to merozoite antigens from P . falciparum malaria , genetic diversity of pvmsp-1 sequences showed no geographic clustering , while the non-merozoite antigen , pvcsp , showed strong geographic clustering . These findings suggest that while immune selection may act on both vivax vaccine candidate antigens , the geographic distribution of genetic variability differs greatly between these two genes . The selective forces driving this diversification could lead to antigen escape and vaccine failure . Better understanding the geographic distribution of genetic variability in vaccine candidate antigens will be key to designing and implementing efficacious vaccines .
Plasmodium vivax causes 80 to 300 million infections per year and over 2 . 5 billion people remain at risk of infection despite malaria elimination efforts [1] . Now , concern over P . vivax is growing due to reports of increasingly severe disease [2] , emerging chloroquine resistance [3] , and multi-drug resistance [4] . Ultimately , an effective vaccine will be important for controlling P . vivax malaria [5] . The fact that humans naturally develop partial immunity to P . vivax and P . falciparum lends hope for effective vaccines against these parasites; however , because the majority of global malaria research funding targets P . falciparum [6] , [7] , only a handful of P . vivax antigens are currently being considered for vaccine development [8] . Among these are P . vivax merozoite surface protein 1 ( pvmsp-1 ) and circumsporozoite protein ( pvcsp ) . PvMSP-1 , an erythrocytic vaccine candidate , plays an important role in reticulocyte invasion [9] . Its C-terminus contains a 42 kDa region , which is processed into 33 and 19 kDa fragments ( Figure 1A ) . The 33 kDa fragment contains two high-affinity reticulocyte binding clusters ( HARBs ) ( 20 kDa and 14 kDa ) , and antibodies against the HARBs confer protection in monkeys [10] . In humans , antibodies to the 42 kDa region have also been associated with clinical protection , making this region an attractive vaccine candidate [11]–[14] . Another vivax protein , PvCSP , is a pre-erythrocytic vaccine candidate and is critical in sporozoite motility and hepatocyte invasion [15] . P . vivax circumsporozoite protein has an immunogenic central repeat , consisting of two major types of nonapeptide repeats ( VK210 and VK247 – there is also a rarer repeat type termed vivax-like ) flanked by highly conserved 5′ and 3′ regions ( Figure 1B ) . The P . falciparum ortholog of pvcsp , as formulated in RTS , S , is the most advanced P . falciparum vaccine candidate to date , showing modest efficacy at one year interim analysis in a Phase III trial [16] . Despite this knowledge of PvMSP-1 and PvCSP , little is known about the geospatial genetic diversity of these antigens . Variation in these antigens may become a mechanism of vaccine resistance if strain-specific immunity is important in protection , as has been seen in some P . falciparum vaccine candidates [17] . Vaccine trials of P . falciparum AMA1 and MSP2 as well as genetic crosses using P . chabaudi underscore the importance of strain-specific immunity as a determinant of outcome [18]–[21] . Additionally , despite initial evidence that strain-specific immunity may not impact RTS , S efficacy [22]–[25] , the incomplete protection afforded by the RTS , S vaccine in Phase II and III trials [16] , [26] , [27] has prompted a careful examination of strain-specific responses to this vaccine . Thus , as momentum grows for field trials of P . vivax vaccine antigens , carefully designed population genetic studies of P . vivax vaccine candidates will be key to assess the need for multivalent vaccine formulations . To better understand the selective forces on , and geospatial genetic diversity associated with pvmsp-1 and pvcsp , we used the Illumina sequencing platform to determine haplotypes for 42 kDa region of pvmsp-1 ( n = 44 ) and we used the PacBio and Illumina platforms to sequence the complete pvcsp gene ( n = 47 ) from Cambodian isolates [28] . To dissect the immune selection acting on these regions , we studied these sequences using population genetic tests of selection and models of tandem repeat evolution . To evaluate the global genetic diversity of pvmsp-1 and pvcsp , we extracted worldwide pvmsp-1 and pvcsp sequence data available in GenBank ( n = 238 for pvmsp-1 and n = 412 for pvcsp ) ( Figure S1 ) , and studied our sequence data alongside the sequences from GenBank msp-1 . Finally , we compare the performance of Illumina and PacBio sequencing to traditional Sanger sequencing , and discuss the potential and challenges of next-generation sequencing for population genetic studies of malaria parasite antigens .
Clinical samples from a previous study were used for this study [29] . Written informed consent was acquired from each individual and the study was approved by the IRB at University of North Carolina , the IRB of the Naval Medical Research Unit #2 , Jakarta , Indonesia , and the Cambodian National Ethical Committee for Health Research . Briefly , blood spots were collected from 109 patients with uncomplicated vivax malaria , presenting to a clinic in Chumkiri , Cambodia during 2006–07 . We selected 48 subjects with a multiplicity of infection ( MOI ) of one ( n = 20 ) or two ( n = 28 ) for sequencing . MOI was determined by heteroduplex tracking assay ( HTA ) [28] , [30] . Briefly , in an HTA , radiolabeled DNA probes are annealed to genomic DNA and drawn through a non-denaturing gel matrix . The number of bands observed represents the number of conformation differences present among heteroduplexes , and is a proxy for the number of infection clones ( MOI ) . Details of the method have been published elsewhere [31] . The pvmsp-1 42 kDa region ( nucleotides 3973–5239 of Sal1 PVX_099980 , www . PlasmoDB . org ) was amplified using primers F: 5′-CAG GAC TAC GCC GAG GAC TA-3′ and R: 5′-GGA GGA AAA GCA ACA TGA GC-3′ and an Eppendorf Mastercycler ( Eppendorf , Hauppauge , NY ) in 50 µL reactions containing 5 µL 10× Qiagen Hotstar Master Mix ( Qiagen , Valencia , CA ) , 0 . 25 µL Qiagen Hotstar Taq , 300 nM forward primer , 300 nM reverse primer , 1 µL 10 mM dNTPs , and 5 µL 5–10 mM template . Cycling conditions were: 95°C×15 m; 35 cycles of 95°C×45 s , 55°C×45 s , 72°C×3 m; and 72°C×10 m . The pvcsp gene ( PVX_119355 ) was performed by nested PCR . The outer step used primers F: 5′-GGC AAA CTC ACA AAC ATC CA-3′ and R: 5′-TGC GTA AGC GCA TAA TGT GT-3′ . Reactions were as above except for 600 nM forward primer , 600 nM reverse primer , 1 µL 10 mM dNTPs , 5 µL 5–10 mM template , 6 µL of 25 mM MgCl2 , and 28 . 75 µL H2O . Cycling conditions were: 95°C×15 m; 25 cycles of 95°C×45 s , 45°C×45 s , 72°C×3 m; and 72°C×10 m . The inner step used 600 nM of each of the primers F: 5′-AAA CAG CCA AAG GCC TAC AA-3′ and R: 5′-GAC GCC GAA AAT ATT GGA TG-3′ using 5–10 µL of the initial amplification . The cycling conditions were: 95°C×15 m; 25 cycles of 95°C×45 s , 54°C×45 s , 72°C×3 m; and 72°C×10 m . pvmsp-1 and pvcsp amplicons were fragmented by acoustic shearing ( Covaris , Woburn , MA ) using the following settings: 10% duty cycle , 5 . 0 intensity , 200 cycles per burst , and frequency sweeping mode . Forty-eight barcoded libraries were prepared using the NEXTflex multiplex library kit ( Bioo Scientific , Austin , Texas ) , each containing the pooled pvmsp-1 and pvcsp amplicons from one patient . Libraries were sequenced on the Illumina HiSeq2000 , using the paired-end 100 base pair chemistry ( Illumina , San Diego , CA ) . We used Lasergene SeqMan NGen v . 3 . 1 . 1 ( DNASTAR , Madison , WI ) to assemble pvmsp-1 short reads de novo and to determine SNP frequency within each assembly . For purposes of comparison and confirmation , we re-sequenced 9 pvmsp-1 amplicons with differing MAFs: 3 samples with all MAFs>90%; 3 samples with all MAFs between 60% and 90%; 3 samples with MAF<60% for at least one SNP . Sanger-sequence haplotypes were compared to predicted Illumina haplotypes . Based on these comparisons , only predicted pvmsp-1 haplotypes with MAF>60% at all polymorphic sites were used in our analysis . In addition to Illumina sequencing , pvcsp amplicons were sequenced using PacBio Circular Consensus Sequencing ( Pacific Biosciences , Menlo Park , CA ) . One PacBio SMRT cell produced a total of 12103 reads with a minimum of 3× circular consensus coverage , which were used for this study . These were further filtered , removing truncated reads or reads with errors in the barcode . This left 8430 reads ( 3979 forward and 4451 reverse ) . Clustering attempted to minimize false positive haplotypes due to erroneous base calls and PCR slippage within the tandem repeat region . For each sample , haplotypes were created by clustering reads , allowing reads differing only by indels of 1 and 2 bases and low quality mismatches to collapse . Low quality was defined as either a mismatching base Q<30 or any Q<25 within an 11 basepair region centered on the mismatch , as has been applied previously to rigorous SNP discovery from shotgun data [32] . To overcome artifacts of PCR infidelity due to slippage events leading to shortened repeats and false haplotypes , we set a high threshold requiring that co-occurring haplotypes of the same repeat type be at high frequency in order to exclude the low frequency variation/stuttering in the repeat region . Haplotype repeat type was then determined by translation and the most frequent haplotype of each major repeat type ( VK210 and VK247 ) present was kept >0 . 5% . Additional haplotypes of major repeat types were kept if they were common ( >20% ) and thus unlikely to be due simply to low frequency slippage events . In total across all samples 4081 of the 8430 reads clustered contributed to utilized haplotypes . The long-read haplotypes determined through consensus clustering were used as templates for short-read alignment using Bowtie2 v 2 . 1 . 0 [33] , with very-sensitive alignment parameters and stringent filtering for Mapping Quality Score and Alignment Score . Final sequence predictions were used for the analyses in this paper and were deposited in GenBank under accession numbers JX461243-JX461285 and KJ173797- KJ173802 for pvcsp , and JX461286-JX461333 for pvmsp-1 . Rarefaction curves of haplotypes were calculated using EstimateS v9 . 0 . Individual-based curves using sampling without replacement were estimated [34] and extrapolated to 2× the actual sample number [35] . Rarefaction plots were visualized in the R base package ( http://cran . us . r-project . org/ ) . GenBank was queried for population sets published prior to August 1 , 2013 , which included sequence data for the 42 kDa region of pvmsp-1 and the whole-gene of pvcsp . Sequences from a recent publication [36] were excluded because the isolates were collected over the course of a 12 year period . The authors provide evidence that the haplotype distribution of this population changed substantially over time , making this population inappropriate for our analysis of selection . Population datasets with >25 sequences that were collected over a span of ≤4 years were included for analysis of selection . We used DnaSP v5 . 1 to perform tests of selection [37] . We calculated polymorphism and Tajima's D across pvmsp-1 and the pvcsp constant regions using a 50 bp sliding window with a 25 bp step size . We also performed 1000 coalescent simulations with recombination to determine a 95% confidence interval and centile for each Tajima's D estimate [38] . To test for long-term selection , we used the McDonald-Kreitman ( MK ) test [39] . Skew was calculated using Fisher's exact test ( two tailed ) . For the pvmsp-1 42 kDa region amplicons reported here and by others , 15 Plasmodium knowlesi pkmsp-1 isolates from Thailand [40] ( Accession Nos . JF837339-JF837353 ) were used as the interspecies outgroup . Three insertions and deletions occurred in the 42 kDa region of pvmsp-1 relative to pkmsp-1 , and were not considered . We could not obtain MK estimates for pvcsp sequences due to numerous insertions and deletions relative to pkcsp . For analysis of pvcsp repeats , we performed pairwise comparisons of untranslated repeat units within individual pvcsp sequences [41] . We calculated skewness and mean nucleotide differences between repeat units , as previously reported [42] . Similar to the methods of Dias et al . , 2013 , we also calculated dN/dS on the first 1–459 bases of all 32 VK210 repeat regions and the first 1–540 bases of all 15 VK247 repeat regions . This analysis was performed in MEGA5 , using the Nei-Gojobori method [43] . Interpopulation heterogeneity was first assessed using Wright's fixation index ( FST ) . Pairwise fixation values between pvmsp-1 populations were calculated in DnaSP . Site-specific fixation values for pairwise comparisons among Cambodia , NW Thailand , S Thailand , India , and Turkey were generated using the analysis of molecular variance ( AMOVA ) function within Arlequin v3 . 11 [44] . Neighbor-joining trees for pvmsp-1 , pvcsp VK210 , and pvcsp VK247 were drawn using the APE package for R [45] . To generate trees based off pvmsp-1 , distance calculations between haplotypes were performed in MEGA5 using the maximum composite likelihood method to construct a neighbor-joining tree file . For the pvcsp CR , we used MS_Align ( v . 2 . 0 ) [46] , [47] to create genetic distance matrices separately comparing both the VK210 and VK247 repeat arrays . MS_Align generates an event-based genetic distance using a model of tandem repeat evolution ( expansion , deletion , substitution ) . Cost parameters for MS_Align were set to 0 . 1 for amplification or contraction and 5 for repeat insertion or deletion . A pairwise cost table of repeat-to-repeat mutations was created in MEGA5 using the maximum composite likelihood method and used as input for MS_Align [41] , [48] . MS_Align output matrices were used by FastME [49] , [50] to construct neighbor-joining trees with balanced branch-length estimation . To cluster geographic groups , we calculated Hudson's nearest-neighbor statistic ( SNN ) [51] . Input was in the form of a pairwise distance matrix between all haplotypes for each phylogeny . For this statistic , highly distant populations have values approaching 1 while panmictic populations have values near 0 . 5 . To test the reproducibility of the geographic clustering predicted by SNN , 1000 jackknife samplings were constructed for both pvmsp-1 and pvcsp VK210 and VK247 populations using Fast UniFrac [52] . For each jackknife replicate , 5 individuals , based on the size of the smallest population , were randomly selected from each population and used to redraw trees . Observed splits between geographic populations were quantified and used to assign confidence to predicted geographic clusters . To evaluate potential mutational paths connecting all pvmsp-1 haplotypes , we constructed a median-joining network using NETWORK v4 . 6 ( Fluxus Engineering , Suffolk , England ) [53] . This method expresses multiple plausible evolutionary paths in the form of cycles . A similar analysis was not completed for pvcsp due to the variable length of CR haplotypes .
We Illumina sequenced pvmsp-1 42 kDa-fragments ( Figure 1A ) from 48 patients , and compared these to Sanger sequencing data for selected samples . Illumina haplotypes with a major allele frequency of >60% agreed with Sanger haplotypes in every case tested ( n = 6 ) . Illumina haplotypes with a major allele frequency of <60% did not consistently agree with Sanger haplotypes ( n = 3 ) . Thus , we were able to build 44 complete pvmsp-1 42 kDa haplotypes ( 26 unique haplotypes ) with a major allele frequency of >60% at all polymorphic sites ( Table 1 ) . The average coverage depth for all isolates was >800 reads per base , with all bases having ≥100 reads of coverage . Haplotype accumulation ( rarefaction ) curves were estimated , and then further extrapolated to show that our sample captured fewer than half the total pvmsp-1 haplotypes in this region of Cambodia ( Figure 2 ) . In addition to these isolates , we identified 238 submissions in GenBank [54]–[58] ( Table S1 ) containing either the whole-gene or 42 kDa-region sequence information . The interaction between human host and the parasite has had a profound impact on the parasite genome , leaving behind characteristic “signatures” of natural selection [59] , which are detectable using population genetics approaches to examine sequence diversity . We first assessed nucleotide diversity ( Figure 3A ) , and observed a spike of polymorphism in the region between the two HARBs ( positions 4348–4731 in the Sal1 reference ) . We termed this the “intervening region” . To test whether the diversity in the intervening region is due to long-term selection , we used the McDonald-Kreitman ( MK ) test [39] to compare the ratio of non-synonymous to synonymous nucleotide polymorphisms between the Cambodian P . vivax population and a Thai P . knowlesi population [40] . We observed a highly elevated MK ratio ( p = 0 . 00427 ) in the intervening region but not in the HARBs ( data not shown ) or the entire 42 kDa region ( p = 0 . 681 ) , suggesting that the intervening region is under long-term selective pressure ( Table 2 ) . To determine whether the long-term selective pressure shaping the intervening region is potentially due to human immunity , we assessed balancing selection in this region , as balancing selection within a malaria antigen suggests that the antigen is a target of the human immune system [59] . We applied Tajima's D test of neutrality [60] to five geographically distinct P . vivax populations ( all populations with n>25 , accounting for 190 of 238 available sequences ) ( Table 1 , Figure 3B ) . In panmictic populations with an uncomplicated demographic history [59] , the Tajima's D statistic can indicate whether a nucleotide sequence is under directional ( D<0 ) or balancing selection ( D>0 ) . Populations not subjected to recent bottlenecks ( i . e . Cambodia , India , and NW Thailand , [54] , [58] ) demonstrated a significant signature of balancing selection in the pvmsp-1 42 kDa region ( Table 1 ) . This signature occurred specifically in the intervening region ( Figure 3B ) , and is consistent with the conclusion that human immunity targets the intervening region . The three regions of the pvmsp-1 fragment that are considered vaccine candidates were each assessed for diversity in the Cambodian population [9] , [61] . In contrast to the intervening region , the 20 kDa HARB ( Sal1 positions 4021–4347 ) and 14 kDa HARB ( Sal1 positions 4732–4941 ) showed no coding polymorphisms and no evidence of balancing selection , similar to recent reports [61] . The 19 kDa fragment ( Sal1 nucleotide positions 4918–5239 ) also showed limited diversity , with only a K1709E substitution , and no evidence of balancing selection . Although the pvmsp-1 42 kDa region contains potential vaccine candidates [9] , [61] , the 42 kD region's global genetic diversity has not been carefully evaluated . To study pvmsp-1 42 kDa diversity , we calculated Wright's Fixation index ( FST ) [62] for each pairwise comparison between five diverse populations ( Table 3 ) . FST values between naturally evolving parasite populations ( Cambodia , NW Thailand , and India ) approached zero , showing a high degree of genetic similarity , while comparisons with populations that have undergone a recent bottleneck ( S Thailand and Turkey ) showed a high degree of genetic distance due to their limited number of haplotypes . Similarly , FST values calculated for each variable site demonstrate a high degree of homogeneity in pairwise comparisons between the Cambodia , NW Thailand , and India populations across all sites , and substantial heterogeneity between S Thailand and Turkey across all sites ( Figure S2 ) . This is evidence that balancing selection maintains a similar range of alleles in the pvmsp-1 42 kDa region of multiple geographically diverse naturally evolving P . vivax populations . To visualize whether 42 kDa sequences cluster according to geography , we compared all unique haplotypes in a single neighbor-joining tree , which revealed little clustering according to geographic origin ( Figure 4 ) . We quantified the extent of this clustering using Hudson's nearest-neighbor statistic ( SNN ) , which assesses how frequently a variant's nearest neighbor is from the same population [51] . In both global and pairwise comparisons , pvmsp-1 42 kDa sequences from naturally evolving populations in Cambodia , India , and NW Thailand showed no evidence of strong geographic clustering ( Table 4 ) . To further confirm this finding , a neighbor-joining consensus tree was created and underwent 1000 jackknifed replicates ( Figure 5A ) . Results showed that the predicted splits between most populations occurred only less than 50% of the time , providing strong evidence that there is minimal geographic clustering of pvmsp-1 42 kDa sequences . To better understand the evolutionary relationships between pvmsp-1 haplotypes from around the world , we employed a median-joining network to describe the set of potential mutational paths between all available global pvmsp-1 42 kDa sequences [53] . The network shows extensive admixture of parasite populations from diverse locales , with numerous mutational paths connecting haplotypes ( Figure 6 ) . With the exception of populations from S Thailand and Turkey , which have undergone recent bottlenecks , these data provide further evidence that there is no clustering by geography . We sequenced the complete pvcsp gene from 43 isolates using the PacBio and Illumina platforms . de novo assembly of the Illumina paired-end short reads was not possible , due to over-collapse in the central repeat ( CR ) region , resulting in inappropriately short CRs . In contrast , PacBio long reads allowed the gene to be sequenced in its entirety and , after clustering , predicted 47 pvcsp haplotypes within the 43 samples . Reported error rates for PacBio sequencing have been high , especially for indels [63]; however , the use of Circular Consensus Sequencing allows single DNA fragments to be read multiple times , decreasing the error rate of the final predicted sequence . To check the accuracy of PacBio pvcsp haplotypes , individual haplotypes were used as a template for alignment of Illumina reads from the same clinical isolate . The addition of Illumina reads corrected only a single 1-bp deletion in a single haplotype . Therefore , after clustering , PacBio-predicted haplotypes have an error rate of 1/ ( ∼1200 basepairs/sequence ×47 sequences ) , or approximately 0 . 002% . Considering the entire gene , there were 24 unique haplotypes at the nucleotide level , and most genetic diversity was within the CR ( Figure 1 ) . Both nonapeptide repeat array types – VK210 ( total n = 32 , range 17–21 repeat units ) and VK247 ( total n = 15 , range 20–21 repeat units ) – were represented in our Cambodian population , with no VK210–VK247 hybrids ( reviewed in [64] ) . The average Illumina short-read depth for each isolate was >1000 , with all bases having ≥5 reads of coverage . In addition to our isolates , we identified one cohort of nearly complete pvcsp sequences ( n = 27 ) , and 12 cohorts of CR sequences ( n = 385 ) [65]–[70] ( Table 1 ) . An extrapolated rarefaction curve showed that we sampled more than two thirds of the pvcsp CR haplotypes in this part of Cambodia , and that there are significantly fewer pvcsp CR variants in this region of Cambodia than pvmsp-1 42 kDa variants ( Figure 2 ) . In contrast to pvmsp-1 , the 5′ and 3′ non-repeat regions of pvcsp had no significant signatures of selection either by the MK test ( data not shown ) or Tajima's D test ( Table 1 ) . The 5′ non-repeat region in the Cambodian cohort showed a non-significant signature of balancing selection ( Table 1 and Figure 3D ) , which was due to a G38N amino acid polymorphism . This polymorphism also was observed in 6/16 parasites from the Latin Pacific region ( JQ511263-JQ511276 , JQ511279 , JQ511286 ) and 2/27 parasites from Colombia ( GU339072 and GU339085 ) . The 3′ non-repeat region had little evidence of balancing selection , with Tajima's D values ∼0 ( Table 1 and Figure 3D ) . Within pvcsp , an 18 amino-acid C-terminal motif known as Region II ( amino acid residues 311–328 in Sal1 ) is important for parasite invasion of hepatocytes [71] and purportedly contains both B and T-cell epitopes [72] , [73] . Among all Cambodia and Colombia parasite isolates , this motif is completely conserved at the nucleotide and protein level , with an amino-acid sequence of EWTPCSVTCGVGVRVRRR , similar to previous reports [61] . To better understand the selective forces acting upon the pvcsp CR , we assessed the dN/dS ratio for Cambodian VK210 and VK247 [66] . Strikingly , synonymous substitutions were strongly favored in both VK210 ( dN/dS = 0 . 267; Z test p<0 . 001 ) and VK247 ( dN/dS = 0 . 166; Z test p<0 . 001 ) repeats . This is consistent with the finding that VK210 and VK247 isolates from around the world consistently demonstrate a depressed dN/dS ratio , suggesting that the VK210 and VK247 repeat regions are both under strong purifying selection [66] . The CR of P . falciparum csp is thought to evolve by slipped-strand mispairing [42] . To understand if a similar mechanism works in the pvcsp repeats , we studied the mismatch distribution of pairwise genetic distances between untranslated repeat units within each VK210 and VK247 repeat array type in Cambodia . Consistent with another study [68] , we observed a strong right skew in the proportion of genetic differences between pairwise VK210 repeat comparisons , and between pairwise VK247 repeat comparisons , evidence that pvcsp repeats have a high proportion of identical or nearly identical repeats ( data not shown ) . This finding is consistent with a continuous and rapid expansion and contraction of repeats by slipped-strand mispairing , which may be a mechanism to evade host immunity [42] . A recent study assessed global genetic diversity in the pvcsp CR , but did not define the correlates of differentiation between populations [66] . Moreover , this report investigated CR diversity by using a subset of the repeat region that was invariant in length . This approach may not reflect true population structure as it only assesses repeats early in the CR . Indeed , we have found that certain repeat types do cluster in locations within the repeat arrays ( data not shown ) . To more rigorously study the global diversity of the pvcsp CR , we modeled CR repeat expansion , contraction , and substitution using MS_Align , which calculates an event-based genetic distance between CR haplotypes [46] . From these data , we constructed neighbor-joining trees for global VK210 and VK247 repeat arrays isolates ( Figures 7–8 ) . In contrast to pvmsp-1 , the VK210 and VK247 trees revealed striking geographic clustering by country and continent . We quantified clustering using Hudson's SNN , and observed strong genetic differentiation between most geographically diverse parasite populations , in contrast to pvmsp-1 ( Table 4 ) . To confirm this finding , neighbor-joining consensus trees for both VK210 and VK247 were subjected to 1000 jackknife replicates and the reproducibility of predicted splits between populations was tested demonstrating a strong correlation between genetic distance and geography ( Figure 5B–C ) . We were able to define the peptide sequence basis of the clustering observed among pvcsp CR repeats . For VK210 repeats , almost all ( 81/84 ) Latin American repeat arrays contained either a 5′ ( GDRADGQPA ) 4 or an internal ( GDRADGQPA ) 3–4 , while very few ( 11/278 ) of the Asian sequences contained one or both of these features . Similarly , for VK247 repeat arrays , all ( 34/34 ) Latin American sequences began with a single EDGAGDQPG repeat , while only one ( 1/44 ) Asian sequence began with this repeat . These sequence features may represent a reliable method to assign sequences to a geographic region .
We found compelling genetic evidence that the pvmsp-1 42 kDa intervening region is under strong immune pressure in multiple panmictic populations . Results from the MK test suggested that this region is under sustained selective pressure ( Table 2 ) ; however , because a positive MK test can signify balancing selection or weak negative selection [74] , [75] , we tested the hypothesis that this region is under balancing selection using Tajima's D test of neutrality . Since multiple populations showed strong evidence of balancing selection by Tajima's D ( Table 1 , Figure 3B ) , we conclude that the intervening region is undergoing continual diversifying , balancing selection . An alternative hypothesis is that the positive Tajima's D values are an artifact of recent population contractions . Because ( 1 ) a positive Tajima's D was observed in multiple populations , and ( 2 ) other regions of pvmsp-1 contained negative Tajima's D values , we conclude that the 42 kDa intervening region of pvmsp-1 undergoes frequency-dependent ( and likely immune-mediated ) balancing selection . Because PvMSP-1 is a merozoite surface antigen , it is highly accessible to antibodies and complement . The predicted structure of the 42 kDa region shows that the 33 kDa fragment covers the 19 kDa fragment [11] , [76] , limiting its exposure to the human immune system relative to the 33 kDa fragment . This observation could explain the extensive balancing selection present in the 33 kDa fragment ( specifically , the intervening region ) but not in the 19 kDa fragment . Additionally , this finding suggests that the sliding window approach for evaluating polymorphism and balancing selection may help generate hypotheses about functionally important ( 19 kDa fragment , for example ) or immunologically dominant ( the intervening region , for example ) regions of P . vivax proteins . For pvmsp-1 , Tajima's D and FST were inversely correlated . Populations with strong evidence of high Tajima's D in the pvmsp-1 intervening region showed a low genetic differentiation by FST . This suggests that in naturally evolving populations , diversification of this region is extensive and maintains a similar range of genetic diversity despite geographic distance . Populations that have undergone a recent bottleneck show a low Tajima's D with relatively few variants and strong genetic differentiation from more diverse populations . This suggests that if strain-specific immune responses are important in vaccine efficacy , vaccines may work more effectively if other interventions can be used to bottleneck the population , thus decreasing its genetic diversity [54] . The central repeat region ( CR ) is a primary immunodominant region of PvCSP . Though alignment-based methods to assess for selection ( Tajima's D , for example ) cannot be employed in a tandem repeat region , there is wide-ranging evidence that selective pressures shape the genetic composition of the pvcsp CR [77]–[81] , including new evidence hinting that hosts develop strain-specific immunity to P . falciparum NANP repeats of varying lengths [82] . Indeed , the presence of two distinct repeat types ( VK210 and VK247 ) may itself be evidence of selection as suggested in a study of the P . cynomolgi csp CR [80] . Our analysis of the two CR array types , VK210 and VK247 , also suggests that selection is occurring in this region . In pairwise comparisons of nucleotide and amino acid differences we observed a positive skew showing decreased differences among repeat units . This finding is consistent with Patil et al . 's study of pvcsp isolates from Brazil [68] , and provides further evidence that both VK210 and VK247 repeat arrays may continuously evolve via slipped-strand mispairing [42] . Furthermore , consistent with a recent study of selection in worldwide pvcsp isolates [66] , we found that Cambodian pvcsp VK210 and VK247 isolates have a strong bias toward synonymous substitutions . This signature of purifying selection is consistent with reports from pfcsp [83]–[85] and suggests that there are a limited number of amino acid polymorphisms allowable within this repeat region . Taken together , these findings suggest that expansion , contraction , and rearrangement of repeat units , rather than generation of novel repeat units through mutation , maintain genetic diversity at the pvcsp locus in both VK210 and VK247 variants . This phenomenon may be responsible for immune evasion [68] , [86] . Although these two vivax genes are orthologs of well-characterized vaccine candidate antigens from P . falciparum malaria , substantial differences are seen in the effects of immune selection between these genes and their orthologs . Previous reports have shown that the functionally similar pfmsp-1 42 kDa fragment has relatively low nucleotide diversity and lacks evidence of balancing selection by Tajima's D [87] . pfcsp , on the other hand , shows a high level of nucleotide diversity [88]–[90] and modest Tajima's D elevations in the C-terminal T cell epitopes [88] , [91] . These patterns are in stark contrast to our observations in P . vivax , and this highlights the need for P . vivax-specific studies to determine appropriate candidate vaccine antigens . Finally , our analysis of the pvmsp-1 42 kDa region underscores the importance of selecting an appropriate parasite population for population-genetic studies . We did not observe signatures of balancing selection in pvmsp-1 populations from S Thailand or Turkey . This is likely due to bottlenecks secondary to robust malaria control measures employed in S Thailand [54] and limited human migration in Turkey [58] . Thus , appropriate selection of panmictic populations for these studies is critical . Using both tree-based and statistical methods [92] , we found that pvcsp , but not pvmsp-1 , showed strong clustering by geography ( Tables 3–4 and Figures 4–8 ) . For pvmsp-1 , we observed little geographic clustering among naturally evolving parasite populations , suggesting that immune selection maintains similar pvmsp-1 alleles around the globe . Notably similar findings have been described in Duffy Binding Protein and Thrombospondin-related anonymous protein in vivax malaria [61] , while a recent global survey of diversity in the Apical Membrane Antigen 1 found evidence of geographically restricted haplotypes [93] . In contrast to pvmsp-1 , we found that pvcsp variants demonstrate strong evidence of geographic clustering . This juxtaposition between pvmsp-1 and pvcsp sequences is similar to what has previously been described for merozoite and sporozoite antigens in P . falciparum [94] . The population sets included in this survey were collected in different years . While it is known that novel P . vivax surface antigen types can appear in the course of a decade [57] , it is difficult to assess the magnitude of this effect on our analyses . As more pvmsp-1 and pvcsp population sets are collected , this will become clearer . It is interesting that the CR of pvcsp shows evidence of multiple forms of selection: ( 1 ) the depressed number of non-synonymous mutations suggests purifying selection , ( 2 ) the differences in CR genotypes between geographic locations suggests directional selection , and ( 3 ) the genetic composition of the repeats suggests rapid expansion and contraction , possibly due to immune selection . It is unclear what drives the first two signatures of selection . We hypothesize a model in which purifying selection within a population limits the amino acid composition of repeats due to functional constraints of the protein , while directional selection between populations is driven by environmental factors . One environmental factor that may explain both the purifying and directional selection of parasite pvcsp CR sequences is the mosquito vector . The circumsporozoite protein is expressed in the mosquito during oocyst development [95] and in the salivary glands [96] , [97] . It is also critical in sporozoite motility [15] . We found no overlap in the distribution of Anopheline species between the countries from Asia and Latin America included in this study ( data not shown ) [98]–[100] . Furthermore , there is substantial evidence that different Anopheline species and strains show differential ability to be infected by malaria [101]–[104] . Regardless of the cause of the differing patterns of geospatial genetic diversity we observed in pvmsp-1 and pvcsp , the observation itself has significance for vaccine design . The malaria vaccine field is just beginning to unravel how antigenic diversity within a single parasite population can reduce vaccine efficacy [105] . Our findings highlight an additional level of complexity that will hinder the implementation of a vivax vaccine – antigenic variability . While the effects of immune cross-reactivity against different antigenic variants aren't fully known , the extensive intrapopulation variability seen in pvmsp-1 may necessitate a highly multivalent pvmsp-1 vaccine , while the dramatic interpopulation variability seen in pvcsp suggests that a PvCSP-based vaccine that is effective in one part of the globe may not be effective in other regions . Thus , a thorough understanding of the geospatial genetic diversity of candidate vaccine antigens must inform antigen selection for vaccine design . pvcsp sequences: JX461243-JX461285 and KJ173797- KJ173802 pvmsp-1 sequences: JX461286-JX461333
|
Plasmodium vivax causes tens of millions of malaria cases each year . Although some vaccines against P . vivax are being developed , little is known about the geospatial genetic diversity and selective constraints of the parasite surface antigens that these vaccines target . In order to create vaccines that are both efficacious and useful in diverse regions of the world , the strain diversity of these potential vaccine targets must be well understood . Specifically , we must understand whether and how the human immune system develops immunity against these antigens as well as understanding whether these antigens are similar in geographically diverse parasite populations . Here , using next-generation sequencing and population-genetic analyses , we found evidence of likely immune selection in specific regions of two leading vivax vaccine candidate antigens , PvMSP-1 and PvCSP . At the pvmsp-1 locus , we also found more genetic variability within populations than between populations , with some DNA sequences from geographically diverse populations being highly similar . In contrast , pvcsp sequences from geographically diverse populations are very distinct from one another , with specific sequence patterns occurring in certain geographic regions . Our findings provide new insights into the geographic genetic diversity of these two antigens and can help inform the development of effective P . vivax vaccines .
|
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2014
|
Differing Patterns of Selection and Geospatial Genetic Diversity within Two Leading Plasmodium vivax Candidate Vaccine Antigens
|
Hepatitis C virus ( HCV ) is a positive-strand RNA virus replicating in a membranous replication organelle composed primarily of double-membrane vesicles ( DMVs ) having morphological resemblance to autophagosomes . To define the mechanism of DMV formation and the possible link to autophagy , we conducted a yeast two-hybrid screening revealing 32 cellular proteins potentially interacting with HCV proteins . Among these was the Receptor for Activated Protein C Kinase 1 ( RACK1 ) , a scaffolding protein involved in many cellular processes , including autophagy . Depletion of RACK1 strongly inhibits HCV RNA replication without affecting HCV internal ribosome entry site ( IRES ) activity . RACK1 is required for the rewiring of subcellular membranous structures and for the induction of autophagy . RACK1 binds to HCV nonstructural protein 5A ( NS5A ) , which induces DMV formation . NS5A interacts with ATG14L in a RACK1 dependent manner , and with the ATG14L-Beclin1-Vps34-Vps15 complex that is required for autophagosome formation . Both RACK1 and ATG14L are required for HCV DMV formation and viral RNA replication . These results indicate that NS5A participates in the formation of the HCV replication organelle through interactions with RACK1 and ATG14L .
Liver diseases caused by hepatitis C virus ( HCV ) constitute a significant health burden worldwide . Around 71 million people are chronically infected with HCV [1 , 2] , and more than 350 , 000 annual deaths are due to HCV infection , in most cases as a result of HCV-related liver cirrhosis and hepatocellular carcinoma ( HCC ) . Although antiviral drugs allow HCV elimination in the vast majority of infected individuals , it is becoming increasingly clear that global eradication will require a vaccine that , however , is not yet available . HCV is a Hepacivirus genus member , which belongs to the Flaviviridae family [3] . The HCV genome is a 9 . 6 kb long single-stranded RNA of positive sense encoding 10 or more viral proteins . The viral proteins are synthesized as a polyprotein via an internal ribosome entry site ( IRES ) residing in the 5′ untranslated region ( 5′UTR ) . The polyprotein is proteolytically cleaved into functional proteins by cellular and virus-encoded proteinases [4] . All known positive-strand RNA viruses to date replicate their genome in distinct membrane-associated compartments called replication organelles . This compartmentalization of replication machinery allows the enrichment and coordination of cellular and viral factors required for RNA replication , and the evasion from innate host defense systems recognizing non-self RNAs such as 5′-triphosphorylated RNAs and double-stranded RNAs [5 , 6] . Two distinct morphotypes of replication organelles have been reported , corresponding to the ‘invaginated vesicles/spherule type’ and the ‘double membrane vesicle ( DMV ) type’ [7] . DMVs are the major component of the HCV replication organelle [8] . Among the HCV proteins , nonstructural protein 5A ( NS5A ) , which is essential for HCV replication , was shown to induce DMV formation [9] . Consistent with this function , antiviral drugs targeting NS5A inhibit DMV formation [10] . Further investigation into the NS5A-mediated DMV formation revealed that NS5A domain 1 is required for the DMV formation [8] . Other viral protein ( s ) such as nonstructural protein 4B ( NS4B ) augments DMV formation activity of NS5A [7] . NS5A of HCV is a multi-functional protein that participates in viral RNA replication , virus assembly , and virion particle release , influencing several cellular processes such as apoptosis , stress-responses , immune responses , and the cell cycle [11] . NS5A is composed of an amino-terminal amphipathic α-helix tethering the protein to intracellular membranes , a highly structured domain 1 ( D1 ) , and two intrinsically unfolded domains ( D2 and D3 ) , separated from each other and D1 via low-complexity sequences ( LCSs ) [4] . Several lines of evidence , including the presence of active replicase complexes on DMV membranes , argue that DMVs are the HCV RNA replication site [9 , 12] . However , the molecular basis of the NS5A-mediated DMV formation and HCV RNA replication remains to be elucidated . Receptor for Activated C Kinase 1 ( RACK1; also designated GNB2L1 ) is a member of the tryptophan-aspartate repeat ( WD-repeat ) , family and shares significant homology to the β subunit of G-proteins . RACK1 plays essential roles in intracellular trafficking , anchoring proteins at particular locations , and stabilizing protein activity [13] . RACK1 is also a constituent of the eukaryotic ribosome . A cryo-electron microscopic study revealed that RACK1 is localized at the head region of the 40S ribosomal subunit in the vicinity of the mRNA exit channel [14] . Recently , RACK1 was reported to promote autophagy by enhancing the formation of ATG14L-Beclin1-Vps34-Vps15 complex under starvation conditions [15] . This complex herein referred to as “vesicle nucleation complex , ” is a critical component of autophagy induction [16–18] . Genome-wide small interfering ( si ) RNA screening experiments identified RACK1 as host dependency factor for HCV RNA replication , but not RNA translation [19] , but the underlying mechanism has not been addressed . Autophagy is an evolutionary conserved catabolic mechanism for the degradation of long-lived organelles and cytoplasmic materials and is crucial for cell homeostasis . Considering the morphological similarities between HCV-induced DMV and autophagosome , it has been proposed that autophagy plays a role in the biogenesis of viral replication compartments [5] . Increasing evidence has shown that the activation of autophagy by HCV is required for HCV replication owing to its contribution to the establishment of replication organelles and modulation of innate immunity [20–25] . The increase of autophagy flux is triggered by either HCV NS4B or NS5A [26–30] . In contrast , DMV formation is induced by NS5A , although the induction efficiency is greatly improved by other HCV proteins [9] . Here we sought to identify cellular proteins participating in HCV replication by using the yeast two-hybrid screening method that is often employed to identify the protein ( s ) directly interacting with a protein of interest [31 , 32] . We used HCV NS3 , NS5A , and Core as baits and identified , amongst others , RACK1 . RACK1 directly interacted with the NS5A domain 1 and showed the most potent effect on HCV replication . In order to understand how RACK1 participates in HCV RNA replication , we investigated the potential role of RACK1 in the autophagy induction by NS5A . We revealed that RACK is required for the NS5A-mediated autophagy induction . Both NS5A and RACK1 associate with the ATG14L-Beclin1-Vps34-Vps15 complex that is a crucial complex for autophagy induction . Interestingly , the interaction between NS5A and ATG14L was affected by RACK1 . Depletion of either RACK1 or ATG14L decreases the number of DMVs generated by HCV non-structural proteins and dampens HCV RNA replication . Our results indicate that NS5A facilitates HCV RNA replication by triggering autophagy to construct RNA replication organelles through the interactions with RACK1 and the components of the vesicle nucleation complex .
To investigate host factors involved in HCV life cycle , we performed yeast two-hybrid screening [32] using HCV core , NS3 and NS5A proteins as baits and human thymus and liver cDNA libraries as preys . Thirty-two host proteins were identified as potential human proteins interacting with HCV proteins ( S1 Table ) . Based on their subcellular localizations and functions , 7 proteins were selected as potential candidates participating in HCV proliferation ( Table 1 ) . To validate the relevance of these host factors for HCV replication , we performed knockdown experiments with siRNAs targeting each of the 7 proteins . HCVcc and HCV replicon systems were used to monitor the participation of the proteins in HCV proliferation ( Fig 1A ) . Among the host factors tested , knockdown of RACK1 showed the most potent inhibition of HCV proliferation in both experimental systems . However , RACK1 depletion did not affect cell proliferation ( Fig 1B and 1C ) . Therefore , we conducted subsequent studies on RACK1 and its role in the HCV life cycle . We confirmed the role of RACK1 in HCV life cycle by knockdown experiments with two different siRNAs against RACK1 . In both cases , NS5A protein levels were reduced in Huh7 . 5 . 1 cells infected with HCVcc ( JC1 ) 3 days post-infection ( Fig 2A ) . Similarly , HCV RNA levels were reduced in the RACK1-depleted cells , and best detectable 3 days post-infection ( Fig 2B ) . Consistent with the effects of siRNAs against RACK1 on HCVcc , RACK1 deletion dampened the replication of an HCV subgenomic replicon ( genotype 2a ) monitored by NS5A protein levels ( Fig 2C ) . We further validated the role of RACK1 in HCV replication through depletion and reconstitution experiments using Huh7 cell lines stably expressing either control GFP or GFP-fused RACK1 ( GFP-RACK1 ) proteins ( S1C and S1D Fig ) . The GFP and GFP-RACK1 genes do not encode the 3′ untranslated region ( 3′UTR ) of endogenous RACK1 gene , wich contain the target sites for siRACK1-3 and siRNACK1-4 . All siRNAs targeting the coding regions ( siRACK1-1 and siRACK1-2 ) and the 3′UTR ( siRACK1-3 and siRACK1-4 ) of endogenous RACK1 mRNAs reduced the levels of endogenous RACK1 protein ( S1A Fig ) and HCV RNA replication ( S1B Fig ) . In rescue experiments , siRACK1-3 and siRACK1-4 reduced HCV replication in control cells expressing GFP , but not in cells expressing GFP-RACK1 of HCV ( S1C and S1D Fig ) . This clearly demonstrates that RACK1 is required for HCV replication . To determine whether HCV RNA translation is affected , we investigated the role of RACK1 in HCV IRES-dependent translation using mono- and di-cistronic mRNA reporter systems ( Fig 2D ) . RACK1 knockdown did not affect HCV IRES-dependent translation ( Fig 2E and 2F ) . Our results are consistent with those reported in a paper suggesting that HCV IRES-dependent translation is not affected by RACK1 [19] . We further investigated whether RACK1 affects the IRES-dependent translation in the presence of other viral proteins since HCV IRES activity was reported to be modulated by NS5A [33–35] . For this purpose , we used a HCV replicon RNA containing firefly luciferase gene and replication defective mutations within the active site of NS5B ( ΔGDD ) . This replicon RNA was co-transfected with a capped reporter RNA containing Renilla luciferase gene as a reference mRNA . No significant change in translation of replicon RNA was detected in RACK1-depleted cells compared with RACK1-undepleted cells even at 4 and 6 h after RNA transfections when viral proteins were synthesized in the cells ( S2 Fig ) . The results indicate that RACK1 does not affect HCV IRES-dependent translation even in the presence of viral proteins . The region in HCV NS5A required for the interaction with RACK1 was determined by using the yeast two-hybrid system ( Fig 3A ) . Various prey vectors encoding different parts of NS5A and two bait vectors expressing different parts of RACK1 were generated , and two-hybrid analyses were performed ( Fig 3A and S3 Fig ) . The prey vectors expressing the domain 1 of NS5A ( aa 31–213 ) and the bait vectors expressing the C-terminal part of RACK1 ( aa 120–318 ) showed positive signals in the two-hybrid system ( Fig 3A , right panel ) . The results indicate that domain 1 of NS5A and aa 120–318 of RACK1 participate in the protein-protein interaction between NS5A and RACK1 . We confirmed the role of NS5A domain 1 in the interaction with RACK1 by co-immunoprecipitation experiments using mammalian cells ( Fig 3B and 3C ) . RACK1 protein was co-precipitated with the wild type ( WT ) ( Fig 3B , lane 6 ) and the domain 1 of NS5A protein ( Fig 3C , lanes 8 ) . In contrast , RACK1 protein was not co-precipitated with an NS5A with a deletion of domain 1 ( NS5A-ΔD1-Flag; lane 7 in Fig 3C ) even though the expression level of this polypeptide was higher than other derivatives of NS5A ( Fig 3B , compare lane 3 with lanes 2 and 4 ) . The results indicate that domain 1 of NS5A is necessary and sufficient for the interaction with RACK1 . In order to decipher the molecular basis of how NS5A and RACK1 contribute to HCV RNA replication , we focused on the autophagy pathway since autophagy-related proteins are required for HCV replication [21 , 24] . It is also known that DMVs induced by HCV are structurally very similar to autophagosomes and contain the viral replicase complex [5] . Moreover , NS5A was shown to induce autophagy [27 , 28 , 30] , as was the case for NS4B [26 , 29] . In the initial set of experiments , we monitored biochemical changes occurring during autophagy process such as LC3 conversion and degradation of p62 by using Western blotting after ectopic expression of either NS5A or NS4B ( Fig 4A ) . The ratio of LC3-II to LC3-I , which is an indication of autophagy , increased upon ectopic expression of NS5A-WT , NS5A-D1 , or NS4B proteins , but with NS5A-ΔD1 . Moreover , the level of p62 decreased dramatically when NS5A , NS5A-domain 1 , or NS4B protein were ectopically expressed , but only weakly when NS5A-ΔD1 protein was expressed ( Fig 4A ) . To further confirm the autophagy induction by these proteins , we used GFP-LC3 cells in which LC3 puncta are generated when autophagy is induced [36] . The number of LC3 puncta in the cell was increased when NS5A-WT , NS5A-domain 1 , or NS4B protein was expressed ectopically , but with NS5A-ΔD1 protein ( Fig 4B and S4 Fig ) . These results indicate that both NS4B and domain 1 of NS5A induce autophagy . We investigated the requirement of RACK1 in the NS5A-mediated autophagy induction by a biochemical method ( Fig 4C ) and a microscopy approach ( Fig 4D and S3 Fig ) . As expected , the level of p62 decreased when NS5A and NS5A-domain 1 proteins were expressed ectopically in control siRNA-treated cells ( compare lanes 2 and 3 with 1 in Fig 4C ) . Similarly , the ratio of LC3-II to LC3-I increased when NS5A-WT and NS5A-domain 1 proteins were expressed ectopically in control siRNA-treated cells ( compare lanes 2 and 3 with 1 in Fig 4C ) . In contrast , the changes in the level of p62 and the ratio of LC3-II to LC3-I mediated by the expressions of NS5A-WT and NS5A-domain 1 proteins were abolished by the RACK1 knockdown ( compare lanes 5 and 6 with 4 in Fig 4C ) . Consistent with these results , the knockdown of RACK1 abolished LC3 puncta formation induced by NS5A-WT and NS5A-domain 1 proteins ( Fig 4D and S5 Fig ) . These results suggest that RACK1 participates in the autophagy induced by the NS5A domain 1 . Furthermore , we investigated whether RACK1 is necessary for the induction of autophagy triggered by HCV infection , using a biochemical method ( Fig 4E ) and a microscopy approach ( Fig 4F and S6 Fig ) . HCV infection induced autophagy in control siRNA treated cells as shown by the reduction of p62 and the increased ratio of LC3-II to LC3-I ( Fig 4E , compare lane 2 with 1 ) . In contrast , RACK1 knockdown abolished both the reduction of p62 and the increase of LC3-II to LC3-I ratio induced by HCVcc ( JC1 ) infection ( compare lane 4 with 3 in Fig 4E ) . Consistently , the number of LC3 puncta significantly increased in the HCV-infected cells when a control siRNA was treated , but that remained the same in the HCV-infected cells when RACK1 was depleted ( Fig 4F and S6 Fig ) . These results indicate that RACK1 is required for HCV-mediated autophagy induction . Since RACK1 was shown to be a key mediator required for the autophagy induction by the vesicle nucleation complex [15 , 37] , we performed co-immunoprecipitation experiments to decipher the molecular basis of autophagy induction by NS5A and RACK1 . HEK293FT cells were transfected with various NS5A constructs and tagged Beclin1 or Vsp34 or ATG14L or Vps15 constructs and captured protein complexes were analyzed by Western blotting . We found that NS5A-WT was co-precipitated with Beclin1 , Vps34 , and ATG14L ( Fig 5A–5C ) , but not with Vps15 ( S7A Fig ) . This result indicates that distinct components of the vesicle nucleation complex interact with HCV NS5A directly or indirectly . Next , we investigated the effects of RACK1 knockdown on the interaction between NS5A and proteins of vesicle nucleation complex . The interaction between NS5A-WT and ATG14L was dramatically reduced after partial depletion of RACK1 ( Fig 5D and 5E ) . In contrast , the interactions of NS5A-WT with Beclin1 and with Vps34 were not affected by the RACK1 knockdown ( S7B and S7C Fig ) . By using the same co-immunoprecipitation method , we found that the NS5A domain 1 , which binds to RACK1 , was required and sufficient for the interaction with ATG14L ( Fig 5F , compare lane 7 with 8 ) . Importantly , RACK1 was required for the interaction between domain 1 of NS5A and ATG14L ( Fig 5G , compare lane 6 with 8 ) . Note that the residual co-precipitation of ATG14L with NS5A observed in RACK1 knockdown cells ( lane 8 in Fig 5D and 5G ) is most likely attributable to only the partial knockdown of RACK1 . In summary , these results suggest that NS5A interacts with ATG14L in the vesicle nucleation complex in a RACK1 and NS5A-D1 dependent manner . We examined the effects of ATG14L knockdown on HCV RNA replication by using three different siRNAs targeting ATG14L and the subgenomic HCV replicon sgJFH-Fluc [8 , 38] . Replication of this RNA was dampened by ATG14L knockdown with each of the siRNAs at all time points after transfection of the replicon RNA ( Fig 6A–6C ) arguing that ATG14L plays a vital role in HCV RNA replication . We confirmed the role of ATG14L in HCV RNA replication using an ATG14L-knockout Huh7-Lunet/T7 single cell line generated by a CRISPR-Cas9 system ( Fig 6D ) . We also investigated the effect of ATG14L ectopic expression in the ATG14L-knockout cells ( rescue experiments ) on HCV RNA replication . ATG14L knockout reduced the replication of HCV especially 24 h post-transfection of the replicon RNA ( Fig 6E , columns ATG14L KO ) . In contrast , the ectopic expression of ATG14L nullified the ATG14L-knockout effect on HCV RNA replication ( Fig 6E , columns ATG14L KO + ATG14L ) . Taken together , we concluded that ATG14L is required for efficient HCV RNA replication . As described above , autophagy induction and DMV formation triggered by NS5A are required for HCV RNA replication . Having found that RACK1 is required for autophagy induction ( Fig 4 ) , NS5A binding to the vesicle nucleation complex ( Fig 5 ) and HCV RNA replication ( Fig 2 ) we investigated whether RACK1 is required for HCV-mediated DMV formation . For this purpose , we used a cytoplasmic expression system in which the expression of the HCV NS3-5B polyprotein induces the DMV formation [9] . To this end , Huh7-Lunet/T7 cells were transfected with control or RACK1 siRNAs and transfected with the pTM NS3-3’UTR expression plasmid ( Fig 7A ) . DMV formation in the cells was monitored by transmission electron microscope ( TEM ) ( Fig 7B ) . In RACK1 knockdown cells , the number of DMVs was profoundly reduced as compared with control cells ( Fig 7C ) , whereas DMV size was not affected ( Fig 7D ) . We also investigated the role of ATG14L in DMV formation by using ATG14L KO cell and Huh7-Lunet/T7 as parental cell line ( Fig 8A ) . The plasmid pTM NS3-3’UTR containing GFP in the NS5A region was transfected into ATG14L KO cells or ATG14L KO cells transfected with pWPI-ATG14L ( ATG14L KO + pWPI-ATG14L ) . DMV formation was monitored by correlative light and electron microscopy ( CLEM ) 24 h after DNA transfection ( Fig 8A and 8B ) . In the ATG14L KO cells , the number of DMVs was profoundly reduced as compared with control cells ( Fig 8B and 8C ) whereas DMV size was not affected ( Fig 8D ) . The reduction of DMV number in ATG14L KO cells were completely abolished by ectopic expression of ATG14L in ATG14L KO cells ( Fig 8B and 8C , ATG14L KO + pWPI-ATG14L ) . These results indicate that both RACK1 and ATG14L play critical roles in the robust HCV-mediated establishment of the HCV replication organelle .
Although previous studies have reported that the autophagy process and DMV formation are required for HCV RNA replication [39] , the molecular mechanisms underlying membrane rearrangement induced by HCV infection remained mostly unknown . To gain insights into the molecular mechanism of HCV replication , we sought to identify cellular proteins facilitating the HCV proliferation by the yeast two-hybrid system using the core , NS3 and NS5A genes of HCV as baits . Through this screening , 32 cellular proteins were revealed as potential candidates interacting with HCV proteins . The demand for RACK1 in HCV replication was the highest one among cellular proteins tested by knockdown experiments ( Fig 1 ) . RACK1 was reported to interact with the components of vesicle nucleation complex composed of Beclin1 , ATG14L , Vps34 , and Vps15 during autophagy process [15] . RACK1 directly interacts with Beclin1 , ATG14L and Vps15 and indirectly with Vps34 when AMP-activated protein kinase ( AMPK ) phosphorylates the Thr50 of RACK1 under starvation conditions [15] . RACK1 supports the vesicle nucleation complex assembly [i . e . , the linkage between the ATG14L-Beclin complex with Vps15-Vps34 complex [16 , 37]] that activates phosphoinositide 3-kinase ( PI3K ) activity of Vps34 resulting in autophagy induction [15] . Here we report that HCV NS5A induces autophagy and DMV formation with the help of RACK1 ( Figs 4 and 7 ) . We also show that NS5A directly or indirectly interacts with Vps34 and Beclin1 and associates with ATG14L in the presence of RACK1 ( Fig 5 ) . Considering that RACK1 facilitates the formation of vesicle nucleation complex under certain physiological conditions such as starvation , we propose that an active vesicle nucleation complex is formed by NS5A with the help of RACK1 . The interaction of Beclin1 and Vps34 with NS5A may play a key role in linking the Beclin1-ATG14L complex with the Vps34-Vps15 complex for induction of vesicle nucleation . The NS5A-RACK1 interaction and the interactions of RACK1 with Beclin1 , ATG14L and Vps15 seem to augment the formation of the vesicle nucleation complex and/or to facilitate proper positioning of the components of the complex ( Fig 9 ) . The knockdown experiments with three different siRNAs targeting ATG14L mRNAs ( Fig 6C ) as well as the knockout and rescue experiments of ATG14L gene ( Figs 6E and 8 ) demonstrated that ATG14L plays a vital role in the construction of HCV replication organelles . Nevertheless , we observed that HCV RNA replication occurs in the absence of ATG14L , despite an absolute delay in the replication kinetics under this condition ( Fig 6E ) . The result suggests that ATG14L is required for efficient HCV replication , but an alternative pathway of autophagy induction , bypassing the requirement of ATG14L , might have been activated during the establishment of ATG14L-knockout cells , which results in the construction of HCV replication organelles albeit inefficiently in the ATG14L-knockout cells . In this respect , it is worth to note that HCV RNA replication was not recovered even at 72 h after transfection of replicon RNA in ATG14L-knockdown cells ( Fig 6C ) . Recently , it was reported that HCV infection induces ATG5- and ATG14-dependent selective autophagy . Interestingly , the HCV-mediated autophagy is independent of ATG13 [40] , arguing that HCV-induced DMV formation bypasses the autophagy induction complex composed of ATG13 , ATG101 , ULK1 and FIP200 [16] . Considering our study and previous reports , we propose that HCV NS5A induces DMV formation by hijacking cellular proteins participating in the early ( vesicle nucleation ) stage of autophagy . A recent study reported that components of the pre-autophagosomal structure ( PAS; ATG5 , ATG12 , and ATG16L1 ) , which induces vesicle expansion [16 , 17 , 41] , co-precipitated with HCV NS4B that was associated with DMVs isolated from HCV replicon-containing cells [39] . In contrast , LC3-I or LC3-II was not associated with NS4B-containing DMVs . Moreover , the knockdown of either ATG7 or ATG12 inhibited DMV formation in cells continuously expressing HCV nonstructural proteins , whereas LC3 knockdown did not affect DMV formation [39] . The results suggest that the vesicle expansion step of autophagy , but not the vesicle completion step demanding LC3-II , is required for the DMV formation . Interestingly , NS4B , which also induces autophagy in Huh7 cells ( Fig 4; also see [29] ) , seems to participate in DMV formation at the vesicle expansion step plausibly by interacting with PAS proteins . However , it is not clear whether other HCV proteins such as NS5A and NS5B also participate in the vesicle expansion step since these viral proteins are also present in the NS4B-containing DMVs [39] . It is noteworthy that other HCV proteins do not induce DMV formation in the absence of the NS5A domain 1 [8] and that NS5A protein alone , albeit weaker , can induce DMV formation [9] . It remains elusive how the autophagy components for vesicle expansion communicate with HCV proteins for DMV formation . It was shown that autophagosome formation occurs during HCV infection and in NS4B and NS5A expressing cells [26–29] . A resent study showed that NS5A expression leads to an increase in autophagy flux [30] . Autophagosome formation is thought to reduce innate immune responses by degrading innate immune signaling proteins , such as MAVS and ISG56 [22 , 24 , 25] . It remains elusive how the DMV and autophagosome formations are controlled . Although RACK1 is a constituent of the eukaryotic ribosome , at least under our experimental conditions , it did not affect HCV IRES-dependent translation ( Fig 2D–2F , S2 Fig ) . This result is consistent with the ones obtained by genome-wide screening indicating that RACK1 is required for HCV replication but not for HCV IRES-dependent RNA translation [19] . However , these results are inconsistent with a report by Majzoub and colleagues suggesting that RACK1 is required for the activity of the HCV IRES [42] . This variance might be due to the use of different experimental conditions , but further analyses are required to clarify this discrepancy . It is noteworthy that the endogenous RACK1 was not detected in the co-immunoprecipitation experiments using NS5A variants shown in Fig 5D and 5G . The result is likely due to the multifunctional activities of RACK1 . In fact , RACK1 has more than 100 known interaction partners with the majority of RACK1 proteins residing in the cytosol as a component of the 40S ribosome [13 , 14] . Therefore , only a minor portion of RACK1 proteins are likely to participate in autophagy induction . We confirmed the interaction between NS5A and endogenous RACK1 in NS5A immune-complexes isolated from NS5A-expressing cells ( S8A Fig ) or RACK1 complexes isolated from Huh7 cells containing a HCV replicon ( S8B Fig ) . Consistent with a previous report [43] , YWHAE ( also called 14-3-3 ) was identified as a Core-binding protein by using the two-hybrid screening ( S1 Table ) . This indicates that our screening is valid . Another potential partner of interest is NSUN5 that is likely to interact with Core ( S1 Table ) . NSUN5 is an m5C-RNA methyltransferase . According to a recent article , post-transcriptional modification ( PTM ) of cytosine ( s ) in HCV RNA to m5Cm is required for proliferation of HCV even though the enzyme responsible for this modification is not known [44] . Considering the function of NSUN5 and its potential partner Core , investigations into the role of NSUN5 in HCV proliferation in conjunction with Core protein may help to reveal the molecular basis of PTM functions in HCV proliferation . In conclusion , we report that the components of vesicle nucleation complex , acting at the early stage of autophagy , participate in DMV formation through interactions with NS5A and/or RACK1 . The interaction between RACK1 and NS5A domain 1 augments the DMV formation , which is the main component of the HCV replication organelle . These results contribute to our understanding of the molecular mechanisms underlying the formation of replication organelles of HCV and other positive-strand RNA viruses .
NS5A antibodies were kindly provided by Dr . Charles Rice ( Clone 9E10 , mouse monoclonal antibody ) , and Dr . Byung Yoon Ahn ( rabbit polyclonal antibody ) . NS4B antibody was generated from rabbit [12] . RACK1 antibodies were purchased from Abcam ( ab62735 ) , BD sciences ( 610177 ) , and Santa Cruz ( sc-17754 ) . The antibodies against NS5B ( sc-17532 ) , HA ( sc-7392 , sc-805 ) , GFP ( sc-9996 ) , p62 ( sc-28359 ) , and eIF3B ( sc-16377 ) were purchased from Santa Cruz . Flag antibodies ( F3165 , F7425 ) , and Tubulin antibody ( T6074 ) were purchased from Sigma Aldrich . The antibodies against LC3 ( #2775 ) , and PI4KA ( #4902 ) were bought from Cell Signaling . ATG14L antibody ( PD026 ) , Actin ( 0869100 ) , GAPDH antibody ( 4G5 ) were purchased from MBL , MP Biomedicals , and AbD Serotec . For Western blotting , sheep HRP-anti-mouse IgG ( NA931V; GE Healthcare ) , donkey HRP-anti-rabbit IgG ( NA934; GE Healthcare ) , and rabbit HRP-anti-goat IgG ( 81–1620; Invitrogen ) antibodies were used as secondary antibodies . For immunocytochemical experiments , goat cyanine 5 anti-mouse IgG ( A10524; Invitrogen ) , and goat Alexa Fluor 555 anti-rabbit IgG ( Z25305; Invitrogen ) were used as secondary antibodies . Hoechst33258 ( H3569; Invitrogen ) was used for nucleus staining . FugeneHD ( Promega ) , Lipofectamin3000 ( Invitrogen ) , Oligofectamin ( Invitrogen ) , Lipofectamine RNAiMAX ( Thermo Fisher ) and TransIT-LT1 ( Mirus Bio ) reagents were used for DNA transfection . Anti-Flag M2 Magnetic beads ( M8823; Sigma Aldrich ) were used for immunoprecipitation . Luciferase assay system ( E1960; Promega ) was used for luciferase assay experiments . ToxiLightTM bioassay kit ( Lonza ) , CellTiter-Glo Luminescent Assay kit ( Promega ) and MTT ( Methylthiazolyldiphenyl-tetrazolium bromide; Sigma Aldrich ) were used for measuring cell cytotoxicity and viability according to the manufacturer’s instructions . Huh7 , Huh7 . 5 . 1 , and Huh7-Lunet/T7 cells are human hepatocyte-derived carcinoma cell lines [8 , 45] . HEK293FT cell is originated from human embryonic kidney cells [46] . Huh7 , Huh7 . 5 . 1 , Huh7-Lunet/T7 , and HEK293FT cells were grown in Dulbecco’s modified Eagle’s medium ( DMEM; Gibco ) supplemented with 10% fetal bovine serum ( FBS; Hyclone ) , penicillin ( 100 U/ml; Sigma Aldrich ) , and streptomycin ( 100 μg /ml; Sigma Aldrich ) . Cells were maintained at 37°C with 5% CO2 environment . Huh7 cells containing HCV replicons were grown under the same conditions , with the additional inclusion of the antibiotic G418 ( 500 μg/ml; AG Scientific ) [47] . Huh7 cells expressing GFP-LC3 were cultured under the same conditions , with the additional antibiotic Blasticidin S ( 3 μg/ml; Sigma Aldrich ) . GFP-control and GFP-RACK1 cell lines were originated from Huh7 cells . These cells were selected by the antibiotic G418 ( 500 μg/ml ) after transfection of cells with DNAs containing the corresponding genes . In vitro transcription and RNA electroporation were performed as previously described [48] . Approximately 3 × 106 Huh7 . 5 . 1 cells were electroporated with in vitro-transcribed RNAs derived from JC1 or JFH1-ad34-5A-Rluc containing the cell culture adapted mutations in the E2 and p7 proteins [45] . The culture supernatants were collected at 3–5 days after electroporation , and naïve Huh7 . 5 . 1 cells were infected with the generated viruses . The cell culture media of infected cells were collected at 3–5 days after infection . The HCV-containing supernatants were then filtered through with a 0 . 45 μm filter , and the filtrate was concentrated with a Vivaspin ( 100-kD cut-off; Millipore ) . The concentrated HCV culture medium was loaded onto a 20% sucrose cushion , and HCV particles were collected by ultracentrifugation at 4°C for 4 h at 36 , 000 rpm ( SW41 rotor; Beckman ) . The viral pellets were resuspended with complete medium . Regarding the transient replication assay , in vitro transcripts of HCV subgenomic replicons were generated , purified , and electroporated into Huh7-Lunet/T7 cells . Firefly luciferase activities in cell lysates were measured as previously described [49] . Lentivirus system was used for ectopic expression of proteins in GFP-LC3 puncta assays . GFP-LC3 lentivirus was produced by co-transfection of pWPI-GFP-LC3 , Gag-pol , and pMD . G into HEK293FT cells [36] . Two days post-transfection , culture supernatants were collected and centrifuged at 2000 rpm for 5 min along with 0 . 45 μm filtration to remove cell debris . Virus stocks were kept at -70°C with Opti-prep ( Sigma Aldrich ) solutions . Huh7 and Huh7 . 5 . 1 cells were used to make GFP-LC3 cell lines . GFP-LC3 cells inoculated with lentivirus were treated with 4 μg/ml of polybrene overnight ( Sigma Aldrich ) . The cell culture media was changed with a selection media containing 3 μg /ml of Blasticidin S , and then cells were cultivated for 5 days . The GFP-LC3 expression was monitored by Western blotting and immunocytochemistry . GFP-LC3 expressing cells were sorted by FACS in order to enrich GFP-LC3 expressing cells . The top 5–10% of the cells were collected . DNA fragments corresponding to NS5A-WT-Flag , NS5A-ΔD1-Flag , NS5A-domain 1-Flag , NS4B-Flag , and GST-Flag were amplified by PCR using pcDNA-based constructs as templates . The DNA fragments were inserted into a pWPI-puro vector and used for lentivirus production . To construct the ATG14L sgRNA-resistant mutant , four mutations , which do not change amino acids , were introduced to the target sequence of ATG14L sgRNA . Annealed oligonucleotides were inserted into a lentiCRIPSRv2 plasmid ( Addgene ) . Single guide RNAs are transcribed from this vector through an U6 promoter . Lentiviruses were prepared as described previously [49] . In order to generate knockout ( KO ) cell lines , Huh7-Lunet/T7 cells were infected with respective lentiviruses . The cells were selected in medium containing 3 μg/ml puromycin ( Sigma Aldrich ) for 1 week . To isolate single KO clones , cell were seeded onto 96 well plates at 0 . 5 cells per well without antibiotics . After 4–6 weeks , the ATG14L protein expression was analyzed by Western blotting . Single KO clone cells were infected with lentivirus to express non-tagged ATG14L and selected in medium containing 3 μg/ml blasticidin ( Sigma Aldrich ) for one week . The sgRNA sequence against ATG14L is ‘5´-TCTACTTCGACGGCCGCGAC-3´’ . The DNA sequences encoding HCV genes ( Core , NS3 , and NS5A ) were amplified from JFH1 HCV genotype 2a cDNA clone [48] . RACK1 , Beclin1 , ATG14L , Vps34 , and Vps15 genes were obtained by PCR using cDNA libraries from human liver tissue or HeLa cell . The amplified genes were cloned into various plasmids ( pcDNA3 . 1 , pEGFP-C1 , pGBKT7 , and pGADT7 ) depending on the experimental purposes . Plasmids pcDNA3 . 1-Flag and pcDNA3 . 1-HA were constructed as previously described [46] . For the construction of NS5A-expressing plasmid , the DNA segment encoding 1–213 aa of HCV genotype 2a was amplified by PCR and inserted into the pcDNA3 . 1 vector . For the construction of NS5A-°CD1-expressing plasmid , the DNA segments NS5A 1–30 aa and 214–466 aa were amplified separately by PCR , and then the amplified DNAs were ligated together . The ligated DNA was re-amplified by PCR and inserted into the pcDNA3 . 1 vector . The sequences of all DNAs amplified by PCR were confirmed by sequencing . All NS5A constructs contain a Flag-tag at the C-terminal ends . pTM NS3-5B ( NS5A-GFP ) was used for CLEM [9] . pFK_i389LucNS3-3′JFH1_δg ( genotype 2a ) ( °CGDD ) and pRL-CMV ( promega ) were used for short-term translation assay [50] . Cells were lysed in IP buffer ( 50 mM Tris HCl ( pH 7 . 5 ) , 150 mM NaCl , 1% NP40 , 2 mM Sodium orthovanadate , 10 mM NaF , 10 mM β-glycerophosphate , 1 mM PMSF ) [15] . Proteins in lysates were resolved by sodium dodecyl sulfate-polyacrylamide gel electrophoresis ( SDS-PAGE ) followed by transfer to PVDF membrane ( Millipore ) . Horseradish peroxidase ( HRP ) -conjugated donkey anti-rabbit IgG , sheep anti-mouse IgG , and donkey anti-goat IgG were used as secondary antibodies . Chemiluminescence detection was performed using WEST-ZOL plus ( iNtRON ) and West Femto Substrate ( Thermofisher ) . X-ray films ( Agfa ) and LAS ( Image Quant; LAS4000 ) were used to visualize the chemiluminescence signal . 0 . 1% TBS-T was used for washing and blocking solution preparation , and 0 . 1% PBS-T was used for LC3B blotting [51 , 52] . Huh7 or Huh7 . 5 . 1 cells seeded on 12 well plates ( 5 × 104 cells/well ) . Cells were transfected with siRNAs ( 200 nM final ) or DNAs by using oligofectamine reagent or FugeneHD , respectively , according to the manufacturer’s instructions . Cell culture media were changed with fresh media containing FBS 4 h after the treatments . The levels of proteins were monitored by Western blotting 48 or 72 h post-transfection . For ATG14L knockdown , Huh7-Lunet/T7 cells seeded on 6 cm dish were transfected twice with 50 pmole siRNAs using Lipofectamine RNAiMAX Reagent in 24 h intervals according to the manufacturer’s instructions . Control siRNA ( 180831 ) was purchased from Bioneer . Two siRNAs targeting RACK1 are ‘5`-GCCUCUCGAGAUAAGACCAUCAUCAdTdT-3`’ , ‘5`-GGAACCUGGCUAACUGCAAGCUGAAdTdT-3`’ [53] , ‘5`- UGGCAGAGCUUU ACAAAUAdTdT-3`’ , and ‘5`-GGCAGAGCUUUACAAAUAAdTdT-3`’ . PI4KA siRNA sequence is ‘5`-GAGCAUCUCUCCCUACCUAUUdTdT-3`’ [54] . ANPEP siRNA sequence is ‘5`-AACGAUCUCUUCAGCACAUCAdTdT-3`’ [55] . AOX1 siRNA sequence is ‘5`-GCCAAUGCCUGUCUGAUUCdTdT-3`’ [56] . BAAT siRNA sequence is ‘5`-CUAUAAGGACAGGUACUAUdTdT-3`’ which was purchased from Bioneer ( 1011124 ) . CTSB siRNA sequence is ‘5`-GGAUCACUGCGGAAUCGAAdTdT-3`’ [57] . CYP2C8 siRNA sequence is ‘5`-UAAAGAACCUCAAUACUACdTdT-3`’ [58] . ISOC2 siRNA sequence is ‘5`-CUCCCGGAAAUGCAAAUGAdTdT-3`’ which is purchased from Bionner ( 1075591 ) . ATG14L siRNAs were ordered from Thermo Fisher Scientific: silencer select negative control no . 2 siRNA ( 4390846 ) , ATG14L-1 ( s22527; 5`- GGGAGAGGUUUAUCGACAAdTdT-3` ) , ATG14L-2 ( s22527; 5`- GGGAGAGGUUUAUCGACAAdTdT-3` ) , ATG14L-3 ( s22526 , 5`-GCUUUACAGUCGAGCACAAdTdT-3` ) . GFP-control and GFP-RACK1 cell lines were seeded on 12 well plates ( 5 × 104 cells/well ) . 1 day after cultivation of cells , siControl , siRACK1-3 , or siRACK1-4 was transfected into cells with oligofectamine , and the media were changed 4 h after transfection . The cells were further cultivated for 1 day and then inoculated with JFH1-ad34-5A-Rluc virus . Luciferase assays and Western blotting were performed 48 h after virus infection . HEK293FT cells , cultivated on 100 mm plates coated by poly-L-Lysine ( Sigma Aldrich ) for 24 h , were transfected with DNAs by Lipofectamine 3000 according to manufacturer’s protocol . Cells were harvested 48 h post-transfection , lysed in IP buffer , and then sonicated . Cell lysates were centrifuged at 12000 rpm for 30 min , and the supernatants were collected . Flag-resin was used to pull-down Flag-tagged proteins . Beads were washed 4 times by IP buffer and boiled with protein sample buffer to elute proteins . In testing the effects of siRNAs against RACK1 on protein-protein interactions , Huh7 cells were treated with siRNAs for 24 h and then transfected with DNAs . The cells were further cultivated for 48 h and then analyzed by Western blotting after immunoprecipitation . In immunoprecipitation experiments using a RACK1 antibody , Huh7 cells with or without replicon RNAs were lysed by IP buffer and then sonicated . Cell lysates were mixed with protein G agarose beads ( Roche ) for 1 h . Non-specific beads-binding proteins were removed by centrifugation at 12000 rpm for 15 min , and the supernatants were collected . 1 μg of RACK1 antibody ( sc-17754 ) and control mouse IgG antibody ( sc-2025 ) were used for immunoprecipitation . Protein G agarose beads were washed 3 times with IP buffer and incubated with antibodies for 4 h . The antibody conjugated beads were washed 3 times by IP buffer , and mixed with cell lysates for 2 h . Beads were washed 4 times with IP buffer and boiled with protein sample buffer to elute proteins . An HCVcc ( JFH1-ad34-5A-Rluc ) , genotype 1b replicon ( NK/R2AN ) , and genotype 2a replicon ( JFH1-Gluc ) containing cells were used to investigate the effects of host factors on HCV replication [48 , 51] . For the HCVcc infection , Huh7 . 5 . 1 cells were treated with different siRNAs for 24 h and then infected with JFH1-ad34-5A-Rluc virus . Cell lysates were obtained 48 h after infection , and Renilla luciferase ( Promega ) activities in the lysates were measured . In the case of replicon , NK/R2AN and JFH1-Gluc cells were transfected with different siRNAs for 72 h , and then luciferase assays and Western blotting were performed . HCV IRES activities were measured by RNA reporter to avoid cryptic promoter issue . Either co-transfection of 2 mono-cistronic mRNAs or transfection of 1 di-cistronic mRNA ( Fig 2C ) was performed . RACK1 was depleted by treating a siRNA against RACK1 to Huh7 cells for 72 h , and then 500 ng of reporter RNAs were transfected . Cell lysates were collected 4 h after transfection , and dual luciferase assay was performed . Data represent relative ratios of firefly luciferase activities directed by HCV IRES-dependent translation to Renilla luciferase activities directed by cap-dependent translation . Short-term RNA translation assay was performed as described previously with minor modifications [50] . To prepare RNA reporters , 5 μg of pRL-CMV plasmid ( linearized with BamHI ( NEB ) ) in 100 μl reaction mixture containing 20 μl of 5 × rabbit reticulocyte lysate buffer [400 mM Hepes ( pH 7 . 5 ) , 60 mM MgCl2 , 10 mM spermidin , 200 mM DTT] , 12 . 5 μl of 25 mM NTP solution containing 12 . 5 mM GTP , 2 . 5 μl of RNasin ( 40 U/μl ) , 0 . 1 U pyrophosphatase ( Sigma Aldrich ) , 12 . 5 mM m7G cap analog ( NEB ) , and 6 μl of T7 polymerase . The reactions were performed over night at 37°C . Subsequently , the input DNA was removed using RQ1 RNase-free DNase ( Promega ) . The RNA was purified by acid phenol chloroform extraction and isopropanol precipitation . The pellet was washed with 70% ethanol and resuspended in RNase free water . Huh7 . 5 cells were transfected with siRNA twice in 24 h interval . 3 days post-transfection , 3 x 106 cells were suspended in 200 μl ( total volume ) of Cytomix and electroporated ( 0 . 166 kV , 950 μF , 0 . 2 cm cuvettes ) with 5 μg of reporter replicon RNA ( ΔGDD ) and 5 μg of a capped Renilla transcript . Replication deficient ΔGDD mutant was used in this assay to exclude potential effects of RNA replication . Electroporated cells ( 5 x 105 ) were seeded onto 6-well plates and incubated at 37°C for 1 , 2 , 4 , or 6 h . The cells were lysed with 350 μl of luciferase lysis buffer per well . The firefly and Renilla luciferase activities were measured separately in a Lumat LB 9507 single tube reader ( Berthold ) . Renilla counts were used as an internal control for transfection efficiency . Cells infected by HCV were collected by using TRIzol reagent ( Ambion ) to purify RNA according to the manufacturer’s protocol . Purified RNAs were quantified by UV absorbance through Nanodrop ( Thermo Fisher Scientific ) , and 500 ng of RNA was used in quantitative RT-PCR . Panbionet performed yeast two-hybrid screening ( http://panbionet . com ) [59 , 60] . NS5A ( aa 31–466 , genotype 2a ) , Core ( genotype 1a ) , and NS3 ( genotype 2a ) genes were amplified by PCR and inserted into a pGBK-T7 bait vector which contains the DNA-binding domain ( BD ) of GAL4 . Screening was performed by using pGBK-T7 containing viral proteins as baits and human liver and thymus cDNA libraries fused with the activation domain ( AD ) of GAL4 as preys . Yeast strain PBN204 containing two different reporter genes ( URA3 and ADE2 ) was used in colony selection and screening . Transformed yeast cells were applied onto agarose plates with selection media lacking leucine and tryptophan ( SD-LW ) to select co-transformants of bait and prey vectors . Specific interactions between bait and prey proteins were monitored by yeast cell growth on a selective medium lacking leucine , tryptophan , and adenine ( SD-LWA ) or on a selective medium lacking leucine , tryptophan , and uracil ( SD-LWU ) . Polypyrimidine tract binding protein ( PTB ) gene fused with the GAL4 DNA-binding domain ( BD-PTB ) and PTB gene fused with the GAL4 activation domain ( AD-PTB ) were used as positive controls of bait and prey vectors , respectively . Sample preparation was performed as described previously [38 , 49] . Cells were fixed with 2 . 5% glutaraldehyde ( GA ) , 2% sucrose in 50 mM sodium cacodylate buffer ( CaCo ) , supplemented with 50 mM KCl , 2 . 6 mM MgCl2 and 2 . 6 mM CaCl2 for at least 30 min at room temperature . After five washes with 50 mM CaCo , samples were incubated with 2% osmium tetroxide in 25 mM CaCo for 40 min on ice , washed three times with EM-grade water , and incubated in 0 . 5% uranyl acetate in water overnight at 4°C . Samples were rinsed three times with water , dehydrated in a graded ethanol series ( from 40% to 100% ) at room temperature , embedded in Epon 812 ( Electron Microscopy Sciences ) , and polymerized for at least 48 h at 60°C . After polymerization , ultrathin sections of 70 nm were obtained by sectioning with an ultramicrotome Leica EM UC6 ( Leica Microsystems ) and mounted on a slot grid . Sections were counterstained using 3% uranyl acetate in 70% methanol for 5 min , lead citrate ( Reynold’s ) for 2 min , and imaged by using a JEOL JEM-1400 ( JEOL ) operating at 80 kV and equipped with a 4K TemCam F416 ( Tietz Video and Image Processing Systems GmBH ) . Correlative light and electron microscopy ( CLEM ) was performed as described previously [61] . In brief , Huh7-Lunet/T7 cells ( 5 x 104 cells ) [62] were seeded onto glass-bottom culture dishes containing gridded coverslips ( MatTek Corporation ) and then incubated overnight . Cells were transfected with pTM NS3-5B plasmid containing a GFP insertion in NS5A . 24 h after cultivation of transfected cells , differential interference contrast ( DIC ) and GFP signals were acquired by confocal microscopy with a 10 x objective lens . Cells were fixed with 2 . 5% GA , 2% sucrose in 50 mM CaCo buffer , supplemented with 50 mM KCl , 2 . 6 mM MgCl2 and 2 . 6 mM CaCl2 for at least 30 min at room temperature . After five washes with CaCo buffer , cells were treated in the same way as for EM sample preparation described above . GFP-LC3 lentivirus was used to generate Huh7 cells stably expressing GFP-LC3 by using Blasticidin S selection method [36] . GFP-LC3 Huh7 cells were inoculated with JC1 or lentiviruses expressing various viral proteins . Two days after infection , the cells were lysed for Western blotting or fixed for immunocytochemistry . The fixed cells were blocked by 1% BSA solution in PBS , and NS5A ( Rabbit ) or Flag ( mouse ) antibodies were treated to visualize the protein of interest . Cy5 labeled anti-mouse antibody was used as a secondary antibody , and Hoechst-33258 was used for nucleus staining . Microscopy images were obtained by Custom TCS SP5 II MP confocal microscope ( Leica ) . The images were analyzed by Las AF program , and GFP-LC3 puncta were counted per each cell . The protein bands of Western blots were quantified by using Image J ( V1 . 52a ) . Statistical analyses were performed using Prism ( GraphPad Software , V8 . 02 ) . The mean and standard error values in scatterplots are depicted as horizontal bars in the middle and on the top plus bottom , respectively . Statistical significances are depicted by asterisks in figures: ( * ) for p < 0 . 05 , ( ** ) for p <0 . 01 , ( *** ) for p < 0 . 001 , and ( **** ) for p < 0 . 0001 .
|
All positive-strand RNA viruses replicate their genomes in distinct membrane-associated compartments designated replication organelles . The compartmentalization of viral replication machinery allows the enrichment and coordination of cellular and viral factors required for RNA replication and the evasion from innate host defense systems . Hepatitis C virus ( HCV ) , a prototype member of the Flaviviridae family , rearranges intracellular membranes to construct replication organelles composed primarily of double-membrane vesicles ( DMVs ) which are morphologically similar to autophagosomes . Nonstructural protein 5A ( NS5A ) , which is essential for HCV replication , induces DMV formation . Here , we report that NS5A triggers DMV formation through interactions with RACK1 and components of the vesicle nucleation complex acting at the early stage of autophagy . These results illustrate how a virus skews cellular machineries to utilize them for its replication by hijacking cellular proteins through protein-protein interactions . This research sheds light on the molecular basis of replication organelle formation by HCV and possibly other viruses employing organelles with DMV morphology .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] |
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] |
2019
|
RACK1 mediates rewiring of intracellular networks induced by hepatitis C virus infection
|
Plants utilize proteins containing nucleotide binding site ( NB ) and leucine-rich repeat ( LRR ) domains as intracellular innate immune receptors to recognize pathogens and initiate defense responses . Since mis-activation of defense responses can lead to tissue damage and even developmental arrest , proper regulation of NB–LRR protein signaling is critical . RAR1 , SGT1 , and HSP90 act as regulatory chaperones of pre-activation NB–LRR steady-state proteins . We extended our analysis of mutants derived from a rar1 suppressor screen and present two allelic rar1 suppressor ( rsp ) mutations of Arabidopsis COI1 . Like all other coi1 mutations , coi1rsp missense mutations impair Jasmonic Acid ( JA ) signaling resulting in JA–insensitivity . However , unlike previously identified coi1 alleles , both coi1rsp alleles lack a male sterile phenotype . The coi1rsp mutants express two sets of disease resistance phenotypes . The first , also observed in coi1-1 null allele , includes enhanced basal defense against the virulent bacterial pathogen Pto DC3000 and enhanced effector-triggered immunity ( ETI ) mediated by the NB–LRR RPM1 protein in both rar1 and wild-type backgrounds . These enhanced disease resistance phenotypes depend on the JA signaling function of COI1 . Additionally , the coi1rsp mutants showed a unique inability to properly regulate RPM1 accumulation and HR , exhibited increased RPM1 levels in rar1 , and weakened RPM1-mediated HR in RAR1 . Importantly , there was no change in the steady-state levels or HR function of RPM1 in coi1-1 . These results suggest that the coi1rsp proteins regulate NB–LRR protein accumulation independent of JA signaling . Based on the phenotypic similarities and genetic interactions among coi1rsp , sgt1b , and hsp90 . 2rsp mutants , our data suggest that COI1 affects NB–LRR accumulation via two NB–LRR co-chaperones , SGT1b and HSP90 . Together , our data demonstrate a role for COI1 in disease resistance independent of JA signaling and provide a molecular link between the JA and NB–LRR signaling pathways .
During their life cycle , plants have to fend off microbial pathogens including fungi , bacteria , viruses , and nematodes . To protect themselves , plants rely on the innate immune system of each plant cell to detect pathogen attack and subsequently activate disease resistance responses . The plant immune system relies on two inter-related branches . The first branch utilizes pattern recognition receptors ( PRRs ) to identify conserved pathogen associated molecular patterns ( PAMPs ) . This recognition then initiates PAMP-triggered immunity ( PTI ) [1]–[3] . Although PTI can restrict further colonization in some cases , successful pathogens are still able to evade or suppress PTI with their effectors [4] . These proteins contribute to pathogen virulence by interfering with various plant defense-related cellular processes . However , effectors can also be recognized by the intracellular NB–LRR receptor proteins of the plant innate immune system [5] . Recognition of effectors results in effector-triggered immunity ( ETI ) and is the second branch of the plant immune system [1]–[3] . NB–LRR proteins contain a centrally located nucleotide binding site ( NB ) domain and a C-terminal leucine-rich repeat ( LRR ) domain . Mammalian NB–LRR containing ( NLR ) proteins mediate analogous processes in mammalian innate immunity [6] . NB–LRR-mediated ETI is typically associated with a form of programmed cell death at the infection site termed the hypersensitive response ( HR ) [1]–[3] . If not controlled , this strong response can lead to unnecessary tissue damage . Proper regulation of HR and therefore appropriate regulation of pre-activation , resting state NB–LRR proteins is critical [7]–[9] . Genetic analyses uncovered three genes , RAR1 , SGT1 and HSP90 , as key regulators of NB–LRR stability and activity [10]–[18] . RAR1 , SGT1 and HSP90 proteins can interact independently with one another [13] , [14] , [16] , and can cooperate as a molecular chaperone complex to regulate NB–LRR stability and function . HSP90 is usually thought to be the central subunit of the complex [19] , [20] . RAR1 affects the conformational dynamics of HSP90 , and modulates the “lid-open” conformation required for loading client NB–LRR proteins [21] , [22] . However , the functional mechanism by which the RAR1-SGT1-HSP90 complex maintains NB–LRR levels remains poorly understood . As highly conserved proteins , SGT1 and HSP90 also interact with each other in mammalian cells , and play essential roles in mammalian immune responses mediated by NLR proteins . By co-immunoprecipitation experiments , both SGT1 and HSP90 were found to associate with many NLR proteins including NOD1 ( Nucleotide-binding Oligomerization Domain 1 ) , NOD2 ( Nucleotide-binding Oligomerization Domain 2 ) , and NALP3 ( NACHT , LRR and PYD domains-containing Protein 3 ) [23] , [24] . In mammalian cells , treatment with geldanamycin ( GDA ) , a chemical inhibitor of HSP90 , impaired NOD2-induced NF-κB activity and NALP3-mediated inflammatory responses [24] . Knockdown of HSP90 by RNAi or GDA treatment also reduced the accumulation levels of NOD1 and NOD2 [23] . These results demonstrated that mammalian HSP90 is required for both NLR stability and function . In contrast , mammalian SGT1 is only required for NLR functions such as NOD1-mediated cytokine production , NOD1-mediated cell death , and NALP3-mediated inflammatory responses , but not for NLR stability [23] , [24] . Plant SGT1 , however , functions in both NB–LRR activity and stability [25] . Moreover , mammalian SGT1 knockdown reduced the association between HSP90 and the NALP3 LRR domain , indicating that mammalian SGT1 functions as a co-chaperone of mammalian HSP90 to regulate client NLR protein [24] . Unlike plant RAR1 , CHP1 ( CHORD-containing Protein 1 ) , a homolog of RAR1 in mammals , is not involved in regulating NLR protein accumulation or function [24] . Taken together , the SGT1-HSP90 chaperone complex has functions for mammalian NLR protein stability and activity , analogous to its functions for plant NB–LRR biology [19] , [20] . During infection , both host plants and pathogens regulate phytohormone signaling to enhance their defense and virulence respectively . Jasmonic Acid ( JA ) controls a well characterized example of phytohormone signaling required for both disease resistance and effector-induced susceptibility that is an outcome of the suppression of PTI [26] , [27] . The JA receptor , COI1 , is the key regulator of JA signaling [28]–[31] . Mutations in COI1 cause defects in JA responses and reproductive development [32] , [33] . Of note , mutations in COI1 also affect , negatively or positively , disease resistance against various plant pathogens [29] , [33]–[41] . COI1 encodes an F-box protein that is a component of the SCFCOI1 ( Skp1/Cullin/F-boxCOI1 ) E3 ubiquitin ligase complex [31] , [32] , [42] . The function of COI1 is to specifically bind target proteins to promote ubiquitination and degradation by the 26S proteasome [31] . It is therefore assumed that COI1 regulates JA signaling and disease resistance via degradation of specific proteins . The connection between JA signaling and SCFCOI1-mediated protein degradation has been confirmed . The JASMONATE ZIM DOMAIN ( JAZ ) family proteins act as repressors of MYC2 , a key transcriptional activator of JA responses , by directly interacting with MYC2 . JA-Ile , a bioactive JA conjugate , induces the degradation of JAZ proteins by enhancing the protein interaction between JAZs and COI1 , and thus de-represses JA-related transcription activation [28] , [29] , [31] , [43] . The JAZ and MYC proteins also play a role in disease resistance . Overexpression of JAZ1Δ3A , a C-terminal deletion form of JAZ1 , led to enhanced disease resistance against Pto DC3000 in Arabidopsis [29] . The triple mutant for transcription factor genes MYC2 , MYC3 , and MYC4 , which are all repressed by JAZ proteins , was as resistant against Pto DC3000 as the coi1 mutant [44] . In this study , we extend our previously described suppressor screen for new mutants that recover impaired RPS5 function in rar1 [21] . We introduce two novel missense alleles of COI1 that suppress the disease resistance phenotypes associated with rar1 mutation . Surprisingly , these two coi1 rar1 suppressor ( rsp ) alleles are completely fertility , in contrast to the male sterility associated with all other coi1 mutant alleles [32] , [43] , [45] . Like sgt1b and the hsp90 . 2rsp alleles [21] , these two coi1rsp alleles interact with rar1 to restore the disease resistance responses mediated by some NB–LRRs and the accumulation of at least RPM1 . Moreover , we demonstrate that overexpression of SGT1b can partially inhibit the coi1rsp-enhanced accumulation of RPM1 and RPM1-mediated disease resistance in rar1 . We also observe non-allelic non-complementation , a rare genetic interaction , between coi1rsp mutants and hsp90 . 2-7rsp mutant . These results support the hypothesis that coi1rsp proteins regulate NB–LRR levels via SGT1b and HSP90 .
To identify new genes that act with RAR1 to regulate NB–LRR accumulation and activation , we performed a suppressor screen for new mutants which can suppress the disease susceptibility observed in rar1-21 ( a stop mutation in Q52 ) [21] . Five rar1 suppressor ( rsp ) mutants were identified from approximately 200 , 000 M2 plants from 50 M2 pools that recover resistance responses to both Pto DC3000 ( avrPphB ) and Pto DC3000 ( avrRpm1 ) [21] . Based on map-based cloning and subsequent allele sequencing , two of the five mutants were found to have mutations in COI1 ( At2g39940 ) . To follow accepted nomenclature conventions , we designated these two mutant alleles , coi1-21rsp and coi1-22rsp , respectively ( Figure 1 ) . Based on disease symptoms after inoculation of Pto DC3000 ( avrRpm1 ) on backcross F1 and F2 populations , both of the coi1rsp mutants were completely recessive ( Table S1 ) . This conclusion was also confirmed by growth assays of Pto DC3000 ( avrRpm1 ) in backcross F1 plants ( Figure 2 ) . The coi1-21rsp mutation is a G/A transition which leads to a G330E missense change in the COI1 protein . The coi1-22rsp mutation is a G/A transition resulting in a G434E missense change in the protein . Both mutations are within conserved LRR domains ( Figure 1 ) . Using the crystal structure of the Arabidopsis COI1 protein , we observed that neither coi1rsp mutation is localized in the interfaces of COI1 that make up the ASK1-binding region and the ligand-binding pocket [31] . In addition , another rar1 suppressor ( rsp ) mutant called rsp3 was isolated from this screen . rsp3 suppressed all known rar1 phenotypes , and was localized in a 7 Mbp region on chromosome I ( Figure S1 ) . A single allele , dominant mutation was identified in rsp3; its detailed characterization is beyond the scope of this work . The disease resistance restoration phenotypes of hsp90 . 2-7rsp and either coi1rsp alleles in rar1 are fully recessive with respect to their respective wild type phenotypes ( [21] , Figure 2 ) . We monitored in planta growth of Pto DC3000 ( avrRpm1 ) to measure RPM1-mediated disease resistance in F1 plants of hsp90 . 2-7rsp×coi1rsp crosses ( Figure 2 ) . The resulting F1 plants were as resistant to Pto DC3000 ( avrRpm1 ) as their parental coi1rsp plants . We also tested F1 plants of crosses between hsp90 . 2-7rsp and either coi1rsp allele for disease symptoms after inoculation of Pto DC3000 ( avrRpm1 ) . The F1 plants displayed resistance against Pto DC3000 ( avrRpm1 ) ( Table S2 ) . In addition , we observed that a part of the F2 progenies from each F1 were susceptible to Pto DC3000 ( avrRpm1 ) ( Table S2 ) . These results clearly demonstrate non-allelic non-complementation between hsp90 . 2-7rsp and coi1rsp mutants , suggesting that the two proteins function in the same process , and likely do so in physical proximity [14] , [46] , [47] . hsp90 . 2rsp alleles isolated from our rar1 suppressor screen recover all known defective NB–LRR functions in a rar1 mutant background [21] . However , a previously published rar1 suppressor mutant , sgt1b , only affected a limited number of NB–LRR protein functions [25] . We therefore tested both coi1rsp alleles to determine whether they have any NB–LRR specificity in their suppression of rar1 . The coi1rsp alleles partially suppress rar1 for RPS5 and RPM1 functions , and fully suppress rar1 for RPS2 function ( Figure 2 , Figure S2A and S2B , Figure 3A ) . rar1 exhibits enhanced disease susceptibility to the virulent bacterial strain Pto DC3000 ( EV ) [18] , [21] , [25] . This phenotype might be due to a RAR1 function in basal defense , for example an additive effect of globally lowered accumulation of multiple NB–LRR proteins [1]–[3] . As measured by inhibition of bacterial growth , both coi1rsp alleles completely suppressed the enhanced disease susceptibility phenotype in rar1 ( Figure 3B ) . NB–LRR activation can trigger the hypersensitive response ( HR ) as well as disease resistance responses . RAR1 is required for HR mediated by many NB–LRR proteins . sgt1b is able to suppress the loss of RPS5-mediated disease resistance in a rar1 mutant , but not the loss of RPS5-mediated HR [25] . To test if NB–LRR-dependent HR is also recovered in coi1rsp rar1 double mutants , we measured ion leakage as a proxy for HR to quantify RPM1-mediated HR in plants . Notably , the coi1rsp alleles did not suppress rar1 for impaired RPM1-triggered HR ( Figure S2C ) . However , the coi1rsp rar1 plants did recover RPM1-mediated disease resistance , measured via pathogen growth restriction ( Figure 3A ) . RPS5 , RPM1 and RPS2 all belong to the CC-NB–LRR subclass . The functions of some TIR-NB–LRR proteins also require RAR1 . The effect of coi1rsp on TIR-NB–LRR function was tested using the pathogenic oomycete Hyaloperonospora arabidopsidis ( Hpa ) isolate Emwa1 to trigger RAR1-dependent RPP4-mediated disease resistance [48] . Neither of the two coi1rsp rar1 double mutants inhibited the growth of Emwa1 ( Figure S2D ) . This indicates that RPP4 function is not recovered in rar1 in the presence of either coi1rsp allele . Thus , the coi1rsp alleles possibly suppress rar1 only for CC-NB–LRR functions . The accumulation of all tested NB–LRR proteins is reduced in rar1 plants , implying that the biochemical function of RAR1 is to maintain the stability of NB–LRR proteins [7] , [18] , [21] , [25] , [49] . We wondered whether coi1rsp alleles could suppress the decrease of NB–LRR protein accumulation in rar1 . We introduced our transgenic , myc-tagged RPM1 [50] into the coi1rsp rar1 mutants by crossing and marker-assisted selection . The coi1rsp alleles suppressed the lowered RPM1-myc accumulation in rar1 ( Figure 3C ) . Hence , the coi1rsp alleles suppress the biochemical phenotype of rar1 . The coi1rsp alleles are phenotypically different from two reference alleles , coi1-1 ( a protein null ( encoding W467STOP [32]; Figure 1 ) and coi1-16 ( encoding L245F [45]; Figure 1 ) , which are also completely or conditionally male sterile . We therefore tested whether either coi1-1 or coi1-16 could suppress rar1 . Similar to the coi1rsp alleles , coi1-1 and coi1-16 enhanced disease resistance responses against both Pto DC3000 ( avrRpm1 ) and Pto DC3000 ( EV ) in a rar1 background ( Figure 3A , 3B ) . The increase in disease resistance against Pto DC3000 ( EV ) was even higher than that caused by the coi1rsp alleles ( Figure 3B ) . To our surprise , coi1-16 resulted in the recovery of RPM1-myc accumulation in rar1 , but coi1-1 did not ( Figure 3C ) . However , coi1-16 and coi1-1 express equivalent enhanced disease resistance in rar1 . Thus , the “restoration” of disease resistance responses against Pto DC3000 ( avrRpm1 ) that we observed in coi1-1 rar1 is not due to restoration of NB–LRR protein levels , but rather to bypass suppression of rar1 disease susceptibility . This is likely caused by enhanced basal defense possibly related to the antagonistic relationship between JA- and SA-dependent signaling ( Figure 3 ) . The growth of Pto DC3000 ( avrRpm1 ) and Pto DC3000 ( EV ) at 3 dpi was about the same in coi1-16 rar1 plants ( Figure 3 ) . Thus , the restored disease resistance in coi1-16 rar1 is likely due to enhanced basal defense , not RPM1 function , although there is a restoration of RPM1-myc accumulation in coi1-16 rar1 . Since the coi1-1 null allele cannot suppress rar1 , we suggest that the coi1rsp alleles and coi1-16 are recessive gain-of-function alleles for the rar1 suppression phenotypes . They are also loss-of-function alleles for the JA response phenotypes as detailed below . We introduced the coi1rsp alleles into an isogenic RAR1 background using marker-assisted breeding ( see Methods ) . To further study the role of COI1 in regulating RPM1 function , we inoculated both coi1rsp alleles , coi1-1 and coi1-16 plants with Pto DC3000 ( avrRpm1 ) and measured bacterial growth ( Figure 4B ) . The coi1rsp and coi1-16 mutants were as resistant as wild type . The coi1-1 mutant displayed slightly enhanced resistance compared with wild type . We also measured RPM1-mediated HR in these coi1 single mutants using the ion leakage assay ( Figure 4C ) . Surprisingly , both coi1rsp alleles weakly suppressed RPM1-mediated HR . We crossed RPM1-myc into these coi1rsp , coi1-1 and coi1-16 single mutants and measured RPM1-myc protein levels ( Figure 3C ) . We observed no obvious changes in RPM1-myc levels in any of the single coi1 mutant . We conclude from these data that coi1rsp mutations differentially regulate RPM1 function in rar1 or RAR1 backgrounds . Loss of COI1 leads to elevated levels of salicylic acid ( SA ) in plants [37] , and elevated SA levels can induce the expression of some NB–LRR-encoding genes [51]–[53] . NB–LRR expression is not changed in rar1 ( Figure S4 , [49] ) . We measured RPM1 mRNA levels in the coi1rsp , coi1-1 , and coi1-16 mutant plants in the context of wild-type RAR1 by RT-qPCR in order to determine whether the increased RPM1-myc protein levels noted in coi1rsp and coi1-16 were due to enhanced transcription . Wild type and rar1 plants were used as controls . We detected no enhancement of RPM1 mRNA levels among the tested coi1 mutants ( Figure S4 ) , indicating that the coi1rsp and coi1-16 alleles restore RPM1 protein levels by a post-transcriptional mechanism in rar1 . COI1 has an essential role in JA signaling; all previously isolated COI1 mutations caused insensitivity to JA-mediated inhibition of seedling growth [32] , [43] , [45] . We compared JA-insensitivity phenotypes of the coi1rsp alleles to coi1-1 using a growth inhibition assay where plants were grown in the presence of MeJA , a functional JA derivative ( Figure 5 ) . Like coi1-1 , the MeJA-treated coi1rsp seedlings grew on MeJA-containing media , while the growth of wild type seedlings was severely inhibited ( Figure 5A ) . MeJA treated coi1rsp seedlings were clearly smaller than the untreated seedlings , suggesting that the coi1rsp alleles are not as insensitive to JA as coi1-1 . We quantified these phenotypes with a root elongation assay ( Figure 5B ) . The null allele coi1-1 displayed root growth inhibition of only about 14% in the presence of 50 µM MeJA . Compared with coi1-1 , coi1-16 and both coi1rsp alleles displayed intermediate insensitivity to MeJA treatment . Their root growth was inhibited about 27% , 30% and 42% respectively , while the root growth inhibition was more than 60% in wild type seedlings . Thus , the coi1rsp alleles are JA-insensitive . JA signaling is important in disease resistance responses . coi1 and other JA insensitive mutants exhibit enhanced resistance to the virulent bacterial strain Pto DC3000 ( EV ) [29] , [37] , [38] . We measured the growth of Pto DC3000 ( EV ) in our coi1rsp alleles , coi1-1 , and coi1-16 ( Figure 4A ) . The coi1rsp alleles also displayed enhanced resistance to Pto DC3000 ( EV ) , although the increase in the coi1rsp alleles was slightly lower than in the reference alleles coi1-1 and coi1-16 . The coi1rsp alleles are quantitatively different than the coi1-1 null allele with respect to JA responses ( Figure 5B ) and enhanced resistance to Pto DC3000 ( EV ) ( Figure 4A ) . We noted decreased COI1 protein accumulation levels in coi1-21rsp , coi1-22rsp and coi1-16 plants compared to wild type and rar1 plants ( Figure 3C ) . As expected , no detectable amount of COI1 protein was observed in coi1-1 . The residual accumulations of COI1 protein confirmed that the coi1rsp alleles and coi1-16 are not COI1 null alleles . To determine whether other NB–LRR regulators function in regulating JA responses , we tested the JA response in the mutants of three NB–LRR co-chaperones , RAR1 , SGT1b and HSP90 . 2 by the root elongation assay ( Figure S3 ) . All rar1 and hsp90 . 2 mutants were as sensitive to MeJA treatment as wild type , suggesting that neither RAR1 nor HSP90 . 2 , plays a role in JA responses . As expected , the sgt1b mutant displayed an obvious insensitivity to MeJA [54] . We also noted MeJA insensitivity in the rar1 sgt1b double mutant ( Figure S3 ) . These results suggest that SGT1b is the only member of RAR1-SGT1-HSP90 NB–LRR co-chaperone complex required for JA signaling . coi1 mutations restored the disease resistance responses mediated by three NB–LRR proteins in rar1 ( Figure 3A , Figure S2A and S2B ) and thus possibly suppressed rar1 via effects upon NB–LRR regulators that control the accumulation , and hence the function , of multiple NB–LRR proteins . To examine this possibility , we determined the accumulation levels of three NB–LRR regulators , RAR1 ( Figure S5A ) , SGT1b ( Figure S5B ) , and HSP90 ( Figure S5C ) , in the coi1rsp , coi1-1 and coi1-16 mutants in either RAR1 or rar1 backgrounds . These coi1 mutants did not exhibit any dramatic change of RAR1 , SGT1b or HSP90 protein levels . Therefore , the coi1rsp and coi1-16 alleles do not suppress rar1 influencing by regulating the steady state levels of RAR1 , SGT1b and/or HSP90 . The coi1rsp mutants displayed opposite phenotypes: increased NB–LRR accumulation and function in rar1 and decreased NB–LRR HR function in RAR1 . A similar combination of phenotypes was previously observed in sgt1b as an rar1 suppressor [25] . The sgt1b mutation enhanced RPS5 accumulation and consequent restoration of RPS5-mediated disease resistance in rar1 , but did not restore RPS5-triggered HR in RAR1 [25] . This similarity implies that coi1rsp mutants might regulate NB–LRR proteins by inhibiting the function of SGT1b and hence mimic sgt1b phenotypes . Based on this hypothesis , we expected that a high dose of SGT1b would attenuate the rar1 suppression phenotypes of the coi1rsp mutants . To test this , we introduced a 35S:SGT1b-HA construct into coi1-21rsp rar1 plants containing RPM1-myc . Compared with parental coi1-21rsp rar1 plants , four independent T3 lines that expressed relatively high levels of SGT1b::HA exhibited both reduced RPM1-myc levels ( Figure 6A ) and RPM1-mediated disease resistance ( Figure 6B ) . However , the RPM1 accumulation and RPM1-mediated disease resistance observed in these T3 plants were still much higher than rar1 plants ( Figure 6A , 6B ) . These results demonstrated that modest over-expression of SGT1b can partially inhibit the rar1 suppression phenotypes of coi1rsp alleles . As a control , we measured the growth of Pto DC3000 ( EV ) in the plants used in the Pto DC3000 ( avrRpm1 ) growth assay . No enhanced growth of Pto DC3000 ( EV ) was observed in these T3 lines ( Figure 6C ) , demonstrating that the reduction of RPM1-mediated disease resistance in 35S:SGT1b-HA transgenic plants are not due to a decrease in basal defense . In addition , we measured the HSP90 protein levels and RPM1-myc mRNA levels in the transgenic plants used in the western blot analysis . No obvious decrease of HSP90 protein level ( Figure S6A ) or RPM1-myc mRNA level was detected ( Figure S6B ) , indicating that the reductions of RPM1-myc accumulation in 35S:SGT1b-HA transgenic plants are not due to the decrease of HSP90 accumulation or the silencing of RPM1-myc gene .
Mutations in COI1 affect , negatively or positively , disease resistance against various plant pathogens [29] , [33]–[41] . It is widely accepted that the defense phenotypes of coi1 depend on signaling antagonism between SA and JA signaling pathways [55] . COI1 mutations disable JA-signaling and consequently enhance SA signaling and SA-induced defense responses by an as yet unknown mechanism . In Arabidopsis , resistance against the virulent hemi-biotrophic pathogen Pto DC3000 is a measure of basal defense [56] . In our study , all four tested coi1 alleles , coi1-21rsp , coi1-22rsp , coi1-1 , and coi1-16 displayed enhanced disease resistance against Pto DC3000 ( EV ) in both rar1 and RAR1 backgrounds ( Figure 3B , Figure 4A ) . These results correspond to previously published data [29] , [37] , [38] , and confirm that COI1 represses basal defense , likely via JA-SA antagonism . Besides enhanced basal defense , the coi1 alleles also displayed enhanced ETI against Pto DC3000 ( avrRpm1 ) ( Figure 3A , Figure 4B ) . Hence , COI1 also inhibits ETI . Since the enhancement of ETI was found in rar1 mutant plants , RAR1 , which is necessary for NB–LRR-mediated ETI in this and many other cases , is not required by COI1 to repress ETI . Although all four coi1 alleles we analyzed restored resistance against Pto DC3000 ( avrRpm1 ) in rar1 ( Figure 3A ) , we could classify them into three classes based on how they influence RPM1 accumulation and RPM1-mediated immune response ( Figure 3C , Figure 4C ) . Class I , represented by the null allele coi1-1 , does not alter RPM1 levels . Class II , represented by coi1-16 , enhances RPM1 levels in rar1 and has no effect on RPM1-mediated HR in RAR1 . Class III , represented by coi1-21rsp and coi1-22rsp , enhance RPM1 levels in rar1 , but reduce RPM1-mediated HR in RAR1 . Since the null coi1-1 does not exhibit any detectable effect on RPM1 accumulation , the enhancement of RPM1 levels in rar1 is a gain-of-function phenotype conferred by the COI1 mutant proteins accumulating in coi1-16 and the two coi1rsp alleles . However , these alleles are all recessive for JA response phenotypes . The coexistence of these distinct genetic characteristics demonstrates that coi1-16 and coi1rsp alleles are recessive gain-of-function alleles which have lost the JA signaling function of COI1 , but gained new function , likely via interfering with the activity of other protein ( s ) . RPM1 is associated with , and activated at , the plasma membrane; there is no current evidence suggesting that it shuttles into the nucleus [50] , [57] . COI1 is expected to be localized in the nucleus , because it binds to the nucleus-localized JAZ proteins [58] . A biochemical mechanism to explain our genetic results would require a reconciliation of these findings . There may be sufficient coi1rsp protein at the plasma membrane to mediate the effects on RPM1 that we describe . Further , our inference that COI1 has a wild type function in mediating NB–LRR protein accumulation is consistent with suggestions that nucleo-cytoplasmic shuttling is required for the function of at least a subset of NB–LRR proteins [3] . Some publications suggest that the “target” protein with which recessive gain-of-function alleles interfere can share functional redundancy with it [14] , [59]–[62] . We found that mutants of two NB–LRR co-chaperones , SGT1b and HSP90 , have phenotypic similarities with coi1rsp alleles [14] , [21] , [25] . These include ( Table S3 ) : 1 ) enhanced NB–LRR accumulation in rar1: RPM1 in hsp90 . 2rsp rar1 [21] , RPS5 in sgt1b rar1 [25] , and RPM1 in coi1rsp rar1 ( this work ) ; 2 ) impaired NB–LRR-mediated HR in RAR1: RPM1-mediated HR in hsp90lra [14] , RPS5-mediated HR in sgt1b [25] , and RPM1-mediated HR in coi1rsp ( this work ) . COI1 is an F-box protein which is a component of an SCF complex . Both SGT1b and HSP90 have been reported to associate and function with various SCF complexes in plants [13] , [54] , [63] , [64] . These findings collectively imply that SGT1b and/or HSP90 are candidate target proteins of coi1rsp proteins in suppressing rar1 . Since the coi1rsp alleles did not affect steady state SGT1b levels ( Figure S5B ) , coi1rsp alleles might inhibit SGT1b activity to suppress the rar1 phenotype of reduced NB–LRR accumulation . To test this hypothesis , we overexpressed SGT1b in a coi1-21rsp rar1 background . The rar1 suppression phenotypes of coi1-21rsp , restored RPM1-myc accumulation and RPM1-mediated disease resistance , were partially complemented by SGT1b overexpression ( Figure 6A , 6B ) . This result supports our hypothesis , and suggests that SGT1 functions with COI1 to regulate NB–LRR accumulation . On the other hand , the incomplete complementation could mean that we need higher levels of SGT1b over-expression , or that coi1rsp proteins also down-regulate the activity of other targets , such as HSP90 . Our speculation is supported by the non-allelic non-complementation observed between coi1rsp mutants and hsp90 . 2-7rsp mutant ( Figure 2 , Table S2 ) . This specific genetic relationship suggests that COI1 and HSP90 physically interact with each other or belong to the same protein complex . The RAR1-SGT1-HSP90 chaperone complex has been related to the SCF complex by two sorts of evidence: 1 ) SGT1b and HSP90 associate and function with various SCF complexes [13] , [54] , [63] , [64] . RAR1 associates with the COP9 signalosome ( CSN ) which can inactivate the SCF complex [13] , [64] , [65]; 2 ) The SCFCPR1 complex negatively regulates the pre-activation steady state stability of two NB–LRR proteins , SNC1 and RPS2 , via the F-box protein CPR1 [66] . The SCF component SKP1 is required for NB–LRR N protein-mediated resistance response against tobacco mosaic virus ( TMV ) [64] . This relationship suggests that RAR1-SGT1-HSP90 chaperone complexes function with an SCF-mediated protein degradation pathway to control the accumulation levels of NB–LRR protein and thus avoid inappropriate NB–LRR activation [19] . The phenotypes observed in our recessive gain-of-function coi1rsp mutants support this hypothesis . The coi1rsp mutants suppressed the rar1 mutant for reduced NB–LRR RPM1 accumulation , and showed non-allelic non-complementation with hsp90 . 2 . Moreover , overexpression of SGT1b partially inhibited the phenotypes of the coi1rsp mutants . Similar to sgt1b and hsp90 . 2lra mutants , coi1rsp mutants caused impaired HR function when moved to a wild type background . The sum of these results is consistent the idea that the F-box protein COI1 functions with RAR1-SGT1-HSP90 chaperone complex and consequently affects NB–LRR protein accumulation and function .
We used coi1-1 [32] and coi1-16 [45] as reference alleles . For the pathology analyses and root elongation analyses , mutant lines used ( all in Col-0 background ) were rar1-21 [18] , rpm1-1 [67] , rps5-2 [68] , rps2-101c [69] , sgt1bedm1-1 [17] , rar1-21 sgt1bedm1-1 [25] , hsp90 . 2-2 [14] , hsp90 . 2-5KO [14] , hsp90 . 2-7 [21] and hsp90 . 2-8 [21] . Ecotype Ws was used as an rpp4 control [48] . We constructed coi1-1 rar1-21 and coi1-16 rar-21 double mutants by identifying F2s with PCR-based dCAP markers . The F2s with appropriate genotypes were selfed , and F3 individuals were further selected with PCR-based dCAP markers . To make the 35S:SGT1b-HA construct , the coding sequence of SGT1b without its stop-codon was amplified by PCR , and then moved into pGWB14 vector [70] . The final destination vector , pGWB14/35S:SGT1b-HA was electropolated into the Agrobacterium strain GV3101 for transformation of appropriate genotypes . Transformed plants were selected on MS medium plate ( PhytoTechnology Laboratories , KS , U . S . ) containing Hygromycin B ( SIGMA , St . Louis , MO , U . S . ) . Pto DC3000 derivatives containing pVSP61 ( EV ) , avrRpm1 , avrPphB , and avrRpt2 were maintained as described [71] . Plant inoculations and bacterial growth assays were performed as described ( spray-inoculation [21]; dip-inoculation [72]; hand-inoculation [25] ) . The HR test and ion leakage assays were carried out as described [21] . Hyaloperonospora arabidopsidis ( Hpa ) isolate Emwa1 was used to inoculated ten-day-old cotyledons of plants as described [21] . Asexual sporangiophores were counted 7 days post-inoculation on at least 30 cotyledons for each genotype . The rar1 suppressor screen was previously described [21] . Standard genetic analyses and map-based cloning were performed as described [21] . We used 892 disease resistant F2 individuals to define a 60 Kb interval on the chromosome II containing COI1 . By sequencing COI1 in the originally isolated rar1 suppressor mutant , a G/A transition at position 1849 ( nucleotide positions relative to the translation start site of the published sequence of COI1; AT2G39940 ) was identified in coi1-21rsp . The other mutant , coi1-22rsp , also contains a G/A mutation at position 2161 in COI1 . To obtain coi1-21rsp and coi1-22rsp single mutants , we backcrossed the coi1rsp alleles into an isogenic RAR1 background . PCR-based dCAP markers were designed for selecting these two coi1rsp mutations . For growth inhibition assays , seedlings were grown on MS medium with different concentrations of Methyl Jasmonate ( MeJA ) ( SIGMA ) at 22°C under 16 h light/8 h dark photoperiod . 10-day-old seedlings were taken picture to show the inhibition effects . For root elongation assays , seedlings were horizontally grown on MS medium at 22°C under 24 h light for 4 d . Then seedlings were transferred to new MS medium with or without 50 µM MeJA , and grown for additional 4 d . Root elongations during these four days were measured . For detection of RPM1-myc in the genotypes mentioned in this study , we introduced by crossing and segregation the mutants into plants expressing RPM1-myc from the native RPM1 promoter as described [14] . The protein extraction and western blot were performed as described [14] . For detection of SGT1b-HA in plants , the protein extraction and western blot were carried out based on the protocol that was previously used for RPS5-HA [25] . The anti-COI1 antiserum was kindly provided by Daoxin Xie ( Tsinghua University , Beijing , China ) . The protein extraction and western blot were performed as described [42] . anti-SGT1 and anti-RAR1 polyclonal antibodies against the full length SGT1b and full length RAR1 with C-terminus GST tag were generated in rabbits ( custom products of Cocalico Biologicals , Inc . ) . anti-HSP90-2 was the product of Agrisera company ( Swedish ) . The detailed protocols for detection of SGT1a , SGT1b , RAR1 , and HSP90 proteins are provided as Text S1 . Plant RNA was extracted with RNeasy Plant Mini Kit ( Qiagen ) . To eliminate DNA contamination , RNA was purified by Turbo DNA Free Kit ( Ambion ) and RNeasy Mini Kit ( Qiagen ) . 2 µg RNA was reverse transcribed with Random Decamers and RETROscript kit ( Ambion ) . RT-qPCR was performed in a total volume of 25 µl ( 12 . 5 µl SYBR Green PCR Master Mix ( Applied Biosystems ) , 0 . 5 µl cDNA , 1 µl Primer 1 ( 10 µM ) , 1 µl Primer 2 ( 10 µM ) and 10 µl H2O ) with MJ White 96-well plate and a DNA Engine OPTICON 2 system ( MJ Research ) . The reaction was run at 95°C for 5 min , followed by 40 cycles at 95°C for 15 sec , 55°C for 30 sec and 72°C for 30 sec . Dissociation analysis was performed after each reaction to confirm the specificity . The relative expression of RPM1/RPM1-myc gene in different genotypes was calculated by ΔΔCt method ( User Bulletin #2 , Manual of Applied Biosystems ) . The primers were newly designed or obtained from previous publication [73] , and are provided as Text S1 .
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To detect pathogen attack and subsequently trigger defense responses , plants utilize immune receptors composed of a nucleotide binding site ( NB ) domain and a C-terminal leucine-rich repeat ( LRR ) domain that function inside the cell . To identify regulators of NB–LRR protein accumulation and activity , we performed a genetic screen in the model plant Arabidopsis thaliana to isolate mutants that affect NB–LRR protein accumulation levels and NB–LRR triggered disease resistance . Here , we introduce two mutant alleles of COI1 , a gene which encodes a well-characterized receptor for the phytohormone Jasmonic Acid ( JA ) . It is widely accepted that COI1 is involved in JA signaling-dependent disease resistance . However , our new coi1 mutants affected NB–LRR accumulation in a manner independent of the JA signaling pathway . This indicated that not all disease resistance effects of COI1 require JA signaling . We also observed a link between COI1 and the RAR1-SGT1b-HSP90 co-chaperone complex , which plays a critical role in regulation of NB–LRR protein accumulations .
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[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"plant",
"science",
"plant",
"biology",
"biology"
] |
2012
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Specific Missense Alleles of the Arabidopsis Jasmonic Acid Co-Receptor COI1 Regulate Innate Immune Receptor Accumulation and Function
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In the Americas , leishmaniasis is endemic in 18 countries , and from 2001 through 2015 , 17 countries reported 843 , 931 cases of cutaneous and mucocutaneous leishmaniasis , and 12 countries reported 52 , 176 cases of visceral leishmaniasis . A Regional Information System ( SisLeish ) was created in order to provide knowledge of the distribution and tendency of this disease to analyze and monitor the leishmaniasis status . This article analyses the performance and progress of SisLeish from 2012–2015 . The performance of SisLeish was evaluated by country adhesion , data completeness and delay in entering the data , and also by the SWOT technique . Furthermore , we outlined the structure and modus operandi of the system and indicators utilized . In 2012 , only 18% of the countries entered the data in SisLeish before the deadline , where 66 . 7% and 50% of the countries with autochthonous CL/ML and VL reported their cases to the system , respectively . Whereas in 2015 , 59% of the countries reached the deadline , where 94 . 4% and 58 . 3% of the countries reported their CL/ML and VL data , respectively . Regarding data completeness , there was great progress for different variables since its launch , such as gender , which had an approximately 100% improvement from 2012 to 2015 . The SWOT analysis of SisLeish showed 12 strengths , 11 opportunities , seven weaknesses and six threats . From 2012–2015 there has been an improvement in the adhesion , quality and data completeness , showing the effort of the majority of the countries to enhance their national database . The SWOT analysis demonstrated that strengths and opportunities exceed weaknesses and threats; however , it highlighted the system frailties and challenges that need to be addressed . Furthermore , it has stimulated several National Programs to advance their surveillance system . Therefore , SisLeish has become an essential tool to prioritize areas , assist in decision-making processes , and to guide surveillance and control actions .
Leishmaniasis is an infectious disease caused by protozoan parasites of the genus Leishmania from the Trypanosomatidae family [1] . It is a vector borne disease transmitted by sandflies from the Psychodidae family , and the genus Lutzomyia is the most important vector in the Americas [2] . Leishmaniasis is among the most neglected diseases and are of great importance to public health [2 , 3] , and due to its epidemiological and clinical diversity , the burden of this disease remains a challenge for surveillance and control programs . Despite having global estimates available , the accuracy of these measures depend directly on the reliability of data collection , which is a result from the strengthening of national surveillance systems [1 , 4–7] . Since mid-2000 , the World Health Organization ( WHO ) has worked intensely to integrate leishmaniasis into the political agenda . In 2007 , during the World Health Assembly , Member States adopted the Resolution WHA 60 . 13 , assuming the commitment to strengthen the surveillance and control of leishmaniasis , which was reaffirmed by the approval of the Pan-American Health Organization ( PAHO ) /WHO Resolution , CD 49–19 . During a meeting between the PAHO/WHO and representatives from endemic countries of the Americas , in 2008 , the participants suggested the development of a regional tool to collect epidemiological data of leishmaniasis , which would standardize , update and release the information to all countries . In 2011 , the individual Regional Program for Leishmaniasis ( RPL ) was instituted by the PAHO/WHO , with the main goals of improving the surveillance and control actions in the Americas , and supporting endemic countries through cooperation , technical consultations and mechanisms to strengthen these actions . The RPL began defining the epidemiological variables and operation indicators to monitor this disease in the region , in 2012 , along with advisors of the PAHO’s communicable diseases , representatives of Leishmaniasis National Programs , and Surveillance Services from endemic countries . In sequence , the RPL developed the Leishmaniasis Regional Information System ( SisLeish ) , which was validated and refined at different stages by the National Programs . The official system was presented during the Leishmaniasis Regional Meeting , held in Panama City on October , 2013 , embodying efforts from all endemic countries of the Americas . SisLeish is a simple tool for analysing and monitoring leishmaniasis in the Americas . The system is based on epidemiological and operational indicators , allowing knowledge of distribution and tendency of this disease in the region . In the Americas , leishmaniasis is endemic in 18 countries , and from 2001 through 2015 , 17 countries reported 843 , 931 cases of cutaneous ( CL ) and mucocutaneous leishmaniasis ( MCL ) , and 12 countries reported 52 , 176 cases of visceral leishmaniasis ( VL ) [8] . In these endemic countries , these diseases are of compulsory notification , whether individual or collective . In some countries , data on the occurrence of leishmaniasis is collected as part of the National Disease Surveillance System , while in others countries there are specific tools for data collection . This information is usually available up to the second sub-national administrative level; however , in some countries this data is only available at the first sub-national administrative level . Health indicators are the synthesis of measures , which contain information about distinct attributes and dimensions , as well the performance of a health system , and they must reflect the sanitary status of a population and be suitable for surveillance of health conditions [9] . Therefore , the availability of indicators provides inputs for analysis , monitoring of health objectives , promotes professional analytical capacity and the development of information systems . Thus , indicators for disease occurrence are used by governments and international organizations as a tool for decision making , to manage resources and implement strategies , among others [10 , 11] . Raw single indicators are widely used in traditional epidemiology to compare situations across different countries and regions , as these are often readily available in most countries and are easy to interpret and to compare [11 , 12] . On the other hand , many disciplines prefer composite indicators [13] , as they may have a greater contribution to the decision making than the use of single indicators . A previous study of CL showed the limitations of the use of single indicators for epidemiological analyses and defined the high-risk areas to prioritize and plan actions accordingly [14] . The development of an indicator is a process where the complexity may vary from a simple case count of a given disease to more complex methods , such as proportions , reasons , rates or index . The objective of this article is to analyse the performance and progress of SisLeish from 2012 to 2015 . Additionally , this article provides a deep overview of the structure and modus operandi of the system and the indicators utilized . This study is intended as a reference to inform current or potential stakeholders involved with leishmaniasis to promote awareness and use of this initiative .
SisLeish is an on-line health information system , available in Spanish , developed with the purpose of being a simple tool and user-friendly system for inclusion and consolidation of data , and analysis of leishmaniasis in the Americas . The same provides knowledge and allows monitoring through information from epidemiological and operational indicators of distribution and tendency in the region . For the SisLeish analyses , we evaluated the system performance in aspects such as country adhesion and data completeness . Furthermore , we used the strengths-weaknesses-opportunities-threats ( SWOT ) technique , which is a simple tool to analyze different types of scenarios and support planning , management and improvement of projects , systems and companies [16] . This study utilized data available in the system from 2012 to 2015 . Additionally , these analyses also describe the mayor ongoing improvements carried out since SisLeish was launched .
By 2015 , all endemic countries have been granted access to SisLeish ( Argentina , Bolivia , Brazil , Colombia , Costa Rica , Ecuador , El Salvador , Guatemala , Guyana , Honduras , Mexico , Nicaragua , Panama , Paraguay , Peru , Surinam , Uruguay and Venezuela ) . In 2012 , six of the 12 countries with autochthonous VL reported data to SisLeish ( 2 , 892 cases ) , and 12 of the 18 countries with CL/ML transmission reported their cases ( 54 , 508 ) . As for 2015 , seven countries reported VL cases ( 3 , 456 ) and 17 reported CL/MCL cases ( 46 , 082 ) . Further details of the progress on country adhesion can be observed in Table 3 and Fig 2 . Regarding the established deadline to enter the annual data ( until April 30th of each year ) , in 2012 only three ( 18% ) countries did meet the deadline , increasing to 10 ( 59% ) countries in 2015 . The results are shown in Fig 3 . Concerning data completeness , there has been great progress for different variables present in the system since its implementation , such as gender , which had an approximately 100% improvement from 2012 to 2015 . The progress of the system is shown in Table 4 . The results of the SWOT ( Strengths , Weaknesses , Opportunities and Threats ) analysis are summarized in Fig 4 and it outlines the most important , critical and relevant points for each internal and external factors . The number of identified strengths ( twelve ) and opportunities ( eleven ) was greater than the weaknesses ( seven ) and threats ( six ) , reflecting the importance of SisLeish for the leishmaniasis surveillance in the Americas , and also , highlighting the challenges that need to be addressed .
This study describes SisLeish , which is an information system that consolidates and analyses the leishmaniasis data , and allows monitoring and knowledge of the epidemiological status of 18 countries in the Americas . One of the main objectives behind the development of this system was to provide information to the countries and Regional Programs in order to guide surveillance and control actions , against CL/ML and VL , within the countries and to assist the definition of regional technical cooperation priorities . In the Americas , leishmaniasis produces different clinical forms involving distinct species of Leishmania [2] . The confirmed cases of leishmaniasis reported to SisLeish were harmonized for the region , taking into account the definition of suspected cases for each country , in order to reduce biases and divergence between countries , this way permitting a uniform analysis of confirmed cases of leishmaniasis in the Americas . There has been a considerable improvement in the adhesion , quality and data completeness . We observed a decrease in the delay to meet the established deadline to enter data into the system by the reduction of the median of days after the due date from 2012 ( 71 days after deadline ) to 2015 ( -1days after deadline ) . The results of SisLeish show the effort of the majority of the countries to improve their national database in terms of completeness and availability , mainly for CL/ML data , which is of compulsory notification in all of the endemic countries . In fact , SisLeish has stimulated several country programs to advance their surveillance system , and has been of particular incentive for countries that currently do not have a national information system available . For some countries , nevertheless , the notification of cases is aggregated at the second sub-national administrative level , which means a limitation in the quality of information provided by these countries . Thus , quantity and quality of information remain the main limiting factors of SisLeish , such as the well-known underreporting of leishmaniasis cases [7 , 17 , 18] , and the high percentage of absence of clinical progression ( recovery or death ) data . Since this is , in general , a benign clinical form , many patients do not return to the health services for a follow-up and evaluation once the treatment is completed . This information system has become an essential tool to improve knowledge and prioritize areas where the occurrence of leishmaniasis requires additional attention . The areas are identified utilizing the information provided by three single indicators , on the occurrence of leishmaniasis , and the results of the CICL . The solely use of single indicators ( e . g . incidence , density or the total cases ) could lead , however , to an incomplete , or even misleading , interpretation of the occurrence . For instance , the relevance of an area with high number of cases could be “diluted” when accounting for the population and/or surface area , potentially deflecting the real problem . In contrast , the CICL allows combining the different indicators of occurrence , presenting their overall standardized additive value . Additionally , the Jenks natural breaks classification method allowed SisLeish to provide meaningful results on how to split the different categories of the CICL , in “natural clusters” . Thus , for example , SisLeish is able to identify areas with particularly very high results in the CICL , compared with the other categories , which are denoted as “very intense transmission” . These groups are clearly differentiated areas , since the Jenks method seeks to reduce the variance within classes while maximizing the variance between them [19] . Other widely used methods for splitting categories , such “quartiles” or “equal intervals” , would not provide similar useful results , as it can be argued that these methods split the data rather arbitrarily , regardless of the data distribution . Therefore , the CICL categorization contributes better to the identification of an actual need to prioritize actions and resources for the surveillance and control of this disease . When choosing health indicators it is essential to understand the nature of the data and the purpose behind the development of an indicator , otherwise the indicators could actually confuse or misguide decision-makers [20] . In SisLeish , the development process of the CICL was accompanied by experts and representatives of the Leishmaniasis Programs from endemic countries . The outputs were assessed to determine its performance , as well as the extent of usefulness , and the new CICL demonstrated to be sensitive to highlight areas where the incidence , density or “total number of cases” were important . Despite knowing the limitations of the use of single indicators for decision-making processes , the composite indicator for VL is still unavailable in the system . Brazil represents 96% of the VL cases in the Americas , and since 2003 the Brazilian MoH has been stratifying the areas of transmission , based exclusively on the average of human cases from the previous three years , to direct the surveillance and control actions . In addition to the limitation of the “average of cases” indicator , there is also a need to include other relevant epidemiological data , such as vector , infected dogs , among others; however , these additional data are not fully available and do not hold the required quality to be incorporated in an epidemiological analyses . In this context , due to a request from the Brazilian MoH , the RPL is currently working to combine the data available in the system into another composite indicator for VL . The visualization of the categorized data and indicators in maps allows a better understanding of the leishmaniasis epidemiological status . The database geocoding of SisLeish , at the first and second sub-national administrative levels , allows systematic mapping , which enables geographical monitoring of the occurrence . Hence , the data should be available at the most disaggregated administrative level to facilitate local analyses that contribute to the guidance and effectiveness of surveillance and control actions [21 , 22] . The information from SisLeish has shown an increase in the availability of disaggregated data of the endemic countries , at the second sub-national administrative level , from 64 . 7% in 2012 to 94% in 2015 . However , given that the SALB cartographic database incorporated into SisLeish was last updated in 2012 , administrative divisions modified or created after this event might be misrepresented in the output maps , demanding the need for a more up-to-date cartographic database . Through the use of geographical information systems tools [23 , 24 , 25 , 26] , SisLeish contributes to the knowledge of the geographical distribution of the disease in different scales . Additionally , the system can assist the development of other analyses in the future , by incorporating socioeconomic , distribution of the human population and environmental data , which could lead to the better understanding of the determinants of this disease . Hence , it would be possible to analyze particular determinants and circumstances capable of inducing the development of leishmaniasis in areas of epidemiological importance , allowing more adequate planning of actions aimed at prevention , surveillance and control of this disease . Acceptance and implementation of an information system is one of the greatest challenges for epidemiological surveillance programs , which require continuous monitoring to reinforce the countries commitment to share information . Furthermore , SisLeish is providing support to sub-regional projects for cross-border surveillance , facilitating the exchange of information on the occurrence of leishmaniasis in the country borders . Additionally , SisLeish has developed a pilot alert system for VL in the borders of the MERCOSUR countries , which allows data sharing on phlebotomine presence , human and canine VL cases . Likewise , a surveillance , control and assistance module was created in 2015 to monitor and follow up with National Programs; this module was designed to become compatible with the database from the WHO , in order to reduce efforts and to avoid database duplicity . At first , this module had a low adhesion , only incorporating the data from two countries; nonetheless , there was a rise to nine countries in 2015 . Despite being a simple methodology , the SWOT analysis has been increasingly used by managers and professionals to identify the strengths , weaknesses , opportunities and threats of programs , services or systems , to define and select appropriate strategies to improve actions and goals [27 , 28] . Fig 4 summarizes the SWOT analysis of SisLeish , showing that the strengths and opportunities exceed the initial expectative of the internal and external factors of the origin analysis , which are helpful to the objectives of the system . Nevertheless , the identified weakness and threats represent the frailties of the system , showing its limitations and calling attention to the needs of the system and what must be improved . Currently , SisLeish is undergoing a restructuring to enhance its functionality , which implies an improvement of the graphic and geographic interfaces , with the addition of interactive reports and maps , and providing public access to its information . Likewise , the system is being translated into the three main regional languages ( i . e . Spanish , English and Portuguese ) ; given that nowadays it is only available in Spanish . Since the implementation of SisLeish , the adhesion is continually growing , and presently has the data from all endemic countries available in the system . Therefore , it is imperative to continue encouraging countries to report their data , through improvements and an ongoing evolution of the system , so it can remain as an important surveillance tool in order to guide and implement actions .
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Leishmaniasis is among the most neglected diseases and remains a challenge for surveillance and control programs . In response to this challenge , the Regional Program for Leishmaniasis ( PAHO/WHO ) , with the support of advisors and representatives from endemic countries , have created a Regional Information System ( SisLeish ) , based on epidemiological and operational indicators , to provide knowledge of the distribution and tendency of this disease to analyze and monitor the leishmaniasis status in the Americas . Since the implementation of SisLeish , there has been an improvement in the adhesion , quality and data completeness and the results show the effort of the majority of the countries to improve their national database in terms of completeness and availability . Furthermore , it has stimulated several programs to advance their surveillance system and program , and has been an alternative for countries that currently do not have a national information system available . SisLeish has become an essential tool to prioritize areas , where the occurrence of leishmaniasis requires additional attention , assist in the decision-making process , and to guide surveillance and control actions . This article analyses the performance and progress of SisLeish from 2012–2015 .
|
[
"Abstract",
"Introduction",
"Material",
"and",
"methods",
"Results",
"Discussion"
] |
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"kala-azar",
"medicine",
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"health",
"sciences",
"decision",
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"tropical",
"diseases",
"social",
"sciences",
"geographic",
"information",
"systems",
"parasitic",
"diseases",
"neuroscience",
"geoinformatics",
"cognitive",
"psychology",
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"neglected",
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"infectious",
"disease",
"control",
"infectious",
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"information",
"sciences",
"geography",
"zoonoses",
"epidemiology",
"protozoan",
"infections",
"infectious",
"disease",
"surveillance",
"psychology",
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"leishmaniasis",
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"science"
] |
2017
|
SisLeish: A multi-country standardized information system to monitor the status of Leishmaniasis in the Americas
|
Gene expression is orchestrated by distinct regulatory regions to ensure a wide variety of cell types and functions . A challenge is to identify which regulatory regions are active , what are their associated features and how they work together in each cell type . Several approaches have tackled this problem by modeling gene expression based on epigenetic marks , with the ultimate goal of identifying driving regions and associated genomic variations that are clinically relevant in particular in precision medicine . However , these models rely on experimental data , which are limited to specific samples ( even often to cell lines ) and cannot be generated for all regulators and all patients . In addition , we show here that , although these approaches are accurate in predicting gene expression , inference of TF combinations from this type of models is not straightforward . Furthermore these methods are not designed to capture regulation instructions present at the sequence level , before the binding of regulators or the opening of the chromatin . Here , we probe sequence-level instructions for gene expression and develop a method to explain mRNA levels based solely on nucleotide features . Our method positions nucleotide composition as a critical component of gene expression . Moreover , our approach , able to rank regulatory regions according to their contribution , unveils a strong influence of the gene body sequence , in particular introns . We further provide evidence that the contribution of nucleotide content can be linked to co-regulations associated with genome 3D architecture and to associations of genes within topologically associated domains .
The diversity of cell types and cellular functions is defined by specific patterns of gene expression . The regulation of gene expression involves a plethora of DNA/RNA-binding proteins that bind specific motifs present in various DNA/RNA regulatory regions . At the DNA level , transcription factors ( TFs ) typically bind 6-8bp-long motifs present in promoter regions , which are close to transcription start site ( TSS ) . TFs can also bind enhancer regions , which are distal to TSSs and often interspersed along considerable physical distance through the genome [1] . The current view is that DNA looping mediated by specific proteins and RNAs places enhancers in close proximity with target gene promoters ( for review [2–5] ) . High-resolution chromatin conformation capture ( Hi-C ) technology identified contiguous genomic regions with high contact frequencies , referred to as topologically associated domains ( TADs ) [6] . Within a TAD , enhancers can work with many promoters and , on the other hand , promoters can contact more than one enhancer [5 , 7] . Several large-scale data derived from high-throughput experiments ( such as ChIP-seq [8] , SELEX-seq [9] , RNAcompete [10] ) can be used to highlight TF/RBP binding preferences and build Position Weight Matrixes ( PWMs ) [11] . The human genome is thought to encode ∼2 , 000 TFs [12] and >1 , 500 RBPs [13] . It follows that gene regulation is achieved primarily by allowing the proper combination to occur i . e . enabling cell- and/or function-specific regulators ( TFs or RBPs ) to bind the proper sequences in the appropriate regulatory regions . In that context , epigenetics clearly plays a central role as it influences the binding of the regulators and ultimately gene expression [14] . Provided the variety of regulatory mechanisms , deciphering their combination requires mathematical/computational methods able to consider all possible combinations [15] . Several methods have recently been proposed to tackle this problem [16–19] . Although these models appear very efficient in predicting gene expression and identifying key regulators , they mostly rely on experimental data ( ChIP-seq , methylation , DNase hypersensitivity ) , which are limited to specific samples ( often to cell lines ) and which cannot be generated for all TFs/RBPs and all cell types . These technological features impede from using this type of approaches in a clinical context in particular in precision medicine . In addition , we show here that , although these approaches are accurate , their biological interpretation can be misleading . Finally these methods are not designed to capture regulation instructions that may lie at the sequence-level before the binding of regulators or the opening of the chromatin . There is indeed a growing body of evidence suggesting that the DNA sequence per se contains information able to shape the epigenome and explain gene expression [20–25] . Several studies have shown that sequence variations affect histone modifications [21–23] . Specific DNA motifs can be associated with specific epigenetic marks and the presence of these motifs can predict the epigenome in a given cell type [24] . Quante and Bird proposed that proteins able to “read” domains of relatively uniform DNA base composition may modulate the epigenome and ultimately gene expression [20] . In that view , modeling gene expression using only DNA sequences and a set of predefined DNA/RNA features ( without considering experimental data others than expression data ) would be feasible . In line with this proposal , Raghava and Han developed a Support Vector Machine ( SVM ) -based method to predict gene expression from amino acid and dipeptide composition in Saccharomyces cerevisiae [26] . Here , we built a global regression model per sample to explain the expression of the different genes using their nucleotide compositions as predictive variables . The idea beyond our approach is that the selected variables ( defining the model ) are specific to each sample . Hence the expression of a given gene may be predicted by different variables in different samples . This approach was tested on several independent datasets: 2 , 053 samples from The Cancer Genome Atlas ( 1 , 512 RNA-sequencing data and 582 microarrays ) and 3 ENCODE cell lines ( RNA sequencing ) . When restricted to DNA features of promoter regions our model showed accuracy similar to that of two independent methods based on experimental data [17 , 19] . We confirmed the importance of nucleotide composition in predicting gene expression . Moreover the performance of our approach increases by combining the contribution of different types of regulatory regions . We thus showed that the gene body ( introns , CDS and UTRs ) , as opposed to sequences located upstream ( promoter ) or downstream , had the most significant contribution in our model . We further provided evidence that the contribution of nucleotide composition in predicting gene expression is linked to co-regulations associated with genome architecture and TADs .
RNA-seq V2 level 3 processed data were downloaded from the TCGA Data Portal . Our training data set contained 241 samples randomly chosen from 12 different cancers ( 20 cancerous samples for each cancer except 21 for LAML ) . Our model was further evaluated on an additional set of 1 , 270 tumors from 14 cancer types . We also tested our model on 582 TCGA microarray data . The TCGA barcodes of the samples used in our study have been made available at http://www . univ-montp3 . fr/miap/~lebre/IBCRegulatoryGenomics . Isoform expression data ( . rsem . isoforms . normalized_results files ) were downloaded from the Broad TCGA GDAC ( http://gdac . broadinstitute . org ) using firehose_get . We collected data for 73599 isoforms in 225 samples of the 241 initially considered . All the genes and isoforms not detected ( no read ) in any of the considered samples were removed from the analyses . Expression data were log transformed . All sequences were mapped to the hg38 human genome and the UCSC liftover tool was used when necessary . Gene TSS positions were extracted from GENCODEv24 . UTR and CDS coordinates were extracted from ENSEMBL Biomart . To assign only one 5UTR sequence to one gene , we merged all annotated 5UTRs associated with the gene of interest using Bedtools merge [27] and further concatenated all sequences . The same procedure was used for 3UTRs and CDSs . Intron sequences are GENCODEv24 genes to which 5UTR , 3UTR and CDS sequences described above were substracted using Bedtools substract [27] . These sequences therefore corresponded to constitutive introns . The intron sequences were concatenated per gene . The downstream flanking region ( DFR ) was defined as the region spanning 1kb after GENCODE v24 gene end . Fasta files were generated using UCSC Table Browser or Bedtools getfasta [27] . TCGA isoform TSSs were retrieved from https://webshare . bioinf . unc . edu/public/mRNAseq_TCGA/unc_hg19 . bed and converted into hg38 coordinates with UCSC liftover . For other regulatory regions associated to transcript isoforms ( UTRs , CDS , introns and DFR ) , we used GENCODE v24 annotations . The nucleotide ( n = 4 ) and dinucleotide ( n = 16 ) percentages were computed from the different regulatory sequences where: p e r c e n t a g e ( N , s ) = ♯ N l is the percentage of nucleotide N in the regulatory sequence s , with N in {A , C , G , T} and l the length of sequence s , and p e r c e n t a g e ( N p M , s ) = ♯ N p M l - 1 is the NpM dinucleotide percentage in the regulatory sequence s , with N and M in {A , C , G , T} and l the length of sequence s . Motif scores in core promoters were computed using the method explained in [11] and Position Weight Matrix ( PWM ) available in JASPAR CORE 2016 database [28] . Let w be a motif and s a nucleic acid sequence . For all nucleotide N in {A , C , G , T} , we denoted by P ( N|wj ) the probability of nucleotide N in position j of motif w obtained from the PWM , and by P ( N ) the prior probability of nucleotide N in all sequences . The score of motif w at position i of sequence s is computed as follows: s c o r e ( w , s , i ) = ∑ j = 0 | w | - 1 log P ( s i + j | w j ) P ( s i + j ) with |w| the length of motif w , si+j the nucleotide at position i + j in sequence s , The score of motif w for sequence s is computed as the maximal score that can be achieved at any position of s , i . e . : s c o r e ( w , s ) = max i = 0 l - | w | s c o r e ( w , s , i ) , with l the length of sequence s . Models were also built on sum scores as: s c o r e S u m ( w , s ) = ∑ i = 0 l - | w | s c o r e ( w , s , i ) , and further compared to models built on mean scores ( S1 Fig ) . Taking mean or sum scores per region yielded similar results ( Wilcoxon test p-value = 0 . 68 ) . DNA shape scores were computed using DNAshapeR [29] . Briefly , provided nucleotide sequences , DNAshapeR uses a sliding pentamer window to derive the structural features corresponding to minor groove width ( MGW ) , helix twist ( HelT ) , propeller twist ( ProT ) and Roll from all-atom Monte Carlo simulations [29] . Thus , for each DNA shape , a score is given to each base of each sequence considered ( DU , CORE and DD—see Fig 1 ) . We then computed the mean of these scores for each sequence providing 12 additional variables per gene . The coordinates of the enhancers mapped by FANTOM on the hg19 assembly [7] were converted into hg38 using UCSC liftover and further intersected with the different regulatory regions . We computed the density of enhancers per regulatory region ( R ) by dividing the sum , for all genes , of the intersection length of enhancers with gene i ( L e n h i ) by the sum of the lengths of this regulatory region for all genes: e n h D e n s i t y ( R ) = ∑ i ( L e n h i in R i ) ∑ i l e n g t h ( R i ) Processed data were downloaded from the firehose Broad GDAC ( https://gdac . broadinstitute . org/ ) . We used the genome-wide SNP array data and the segment mean scores . In order to assign a CNV score to each gene , the coordinates ( hg19 ) of the probes were intersected with that of GENCODE v19 genes using Bedtools intersect [27] and an overlap of 85% of the gene total length . The corresponding segment mean value was then assigned to the intersecting genes . In case no intersection was detected , the gene was assigned a score of 0 . We next computed Spearman correlations between genes absolute error ( lasso model ) and genes absolute segment mean score for each of the 241 samples of the training set . The v6p GTex cis-eQTLs were downloaded from the GTex Portal ( http://www . gtexportal . org/home/ ) . The hg19 cis-eQTL coordinates were converted into hg38 using UCSC liftover and further intersected with the different regulatory regions . We restricted our analyses to cis-eQTLs impacting their own host gene . We computed the density of cis-eQTL per regulatory region ( R ) by dividing the sum , for all genes , of the number of cis-eQTLs of gene i ( eQTLsi ) located in the considered region for gene i ( Ri ) by the sum of the lengths of this regulatory region for all genes: e Q T L d e n s i t y ( R ) = ∑ i # ( e Q T L s i in R i ) ∑ i l e n g t h ( R i ) Likewise we computed the density of SNPs in core promoters and introns by intersecting coordinates of these two regions ( liftovered to hg19 ) with that of SNPs detected on chromosomes 1 , 2 and 19 ( ftp://ftp . ncbi . nih . gov/snp/organisms/human_9606_b150_GRCh37p13/BED/ ) : S N P d e n s i t y ( R ) = ∑ i # ( S N P i in R i ) ∑ i l e n g t h ( R i ) Illumina Infinium Human DNA Methylation 450 level 3 data were downloaded from the Broad TCGA GDAC ( http://gdac . broadinstitute . org ) using firehose_get . The coordinates of the methylation sites ( hg18 ) were converted into hg38 using the UCSC liftover and further intersected with that of the core promoters ( hg38 ) . For each gene , we computed the median of the beta values of the methylation sites present in the core promoter and further calculated the median of these values in 21 LAML and 17 READ samples with both RNA-seq and methylation data . We compared the overall methylation status of the core promoters in LAML and READ using a wilcoxon test . We used 8 , 556 GTEx RNA-seq libraries ( https://www . gtexportal . org/home/datasets ) to compute the Gini coefficient for 16 , 134 genes on the 16 , 294 considered in our model . Gini coefficient measures statistical dispersion and can be used to measure gene ubiquity: value 0 represents genes expressed in all sam- ples while value 1 represents genes expressed in only one sample . To compute Gini coefficient we used R package ineq . We then computed , for the 241 samples , Spearman correlation between Gini coefficients and model gene absolute errors . Similar analyses were performed with 1 , 897 FANTOM 5 CAGE libraries to compute the Gini coefficients for 15 , 904 genes . Gene functional enrichments were computed using the database for annotation , visualization and integrated discovery ( DAVID ) [30] . We performed estimation of the linear regression model ( 1 ) via the lasso [31] . Given a linear regression with standardized predictors and centered response values , the lasso solves the ℓ1-penalized regression problem of finding the vector coefficient β = {βi} in order to minimize M i n ( | | y c ( g ) - ∑ i β i x i , g s | | 2 + λ ∑ i | β i | ) , where yc ( g ) is the centered gene expression for all gene g , x i , g s is the standardized DNA feature i for gene g and ∑i |βi| is the ℓ1-norm of the vector coefficient β . Parameter λ is the tuning parameter chosen by 10 fold cross validation . The higher the value of λ , the fewer the variables . This is equivalent to minimizing the sum of squares with a constraint of the form ∑i |βi| ≤ s . Gene expression predictions are computed using coefficient β estimated with the value of λ that minimizes the mean square error . Lasso inference was performed using the function cv . glmnet from the R package glmnet [32] . The LASSO model was compared to two non parametric approaches: Regression trees ( CART ) [33] and Random forest [34] . S1 Table summarizes accuracy and computing time of each approach . Regression trees achieved significantly lower accuracy than the two other approaches ( Wilcox test p-values < 2e−16 ) , while linear model and random forest yielded similar results ( p-value 0 . 18 ) . Moreover , computing time for linear model was much lower than that of random forest . These results emphasize the merits of linear model such as LASSO in their interpretability and efficiency . We used the stability selection method developed by Meinshausen et al . [35] , which is a classical selection method combined with lasso penalization . Consistently selected variables were identified as follows for each sample . First , the lasso inference is repeated 500 times where , for each iteration , ( i ) only 50% of the genes is used ( uniformly sampled ) and ( ii ) a random weight ( uniformly sampled in [0 . 5;1] ) is attributed to each predictive variable . Second , a variable is considered as stable if selected in more than 70% of the iterations , using the method proposed in [36] to set the value of lasso penalty λ . One of the advantage of this method is that the variable selection frequency is computed globally for all the variables by attributing a random weight to each variable at each iteration , thus taking into account the dependencies between the variables . This variable stability selection procedure was implemented using functions stabpath and stabsel from the R package C060 for glmnet models [36] . Regression trees were implemented with the rpart package in R [32] . In order to avoid over-fitting , trees were pruned based on a criterion chosen by cross validation to minimize mean square error . The minimum number of genes was set to 100 genes per leaf . We considered TADs mapped in IMR90 cells [6] containing more than 10 genes ( 373 out of 2243 TADs with average number of genes = 14 ) . The largest TAD had 76 associated genes . First , for each TAD and for each region considered , the percentage of each nucleotide and dinucleotide associated to the embedded genes were compared to that of all other genes using a Kolmogorov-Smirnov ( KS ) test . For a given dinucleotide ( for example CpG ) , we applied KS tests to assess whether the CpG frequency distribution in genes in one specific TAD differs from the distribution in genes in other TADs . Correction for multiple tests was applied using the False Discovery Rate ( FDR ) < 0 . 05 [37] and the R function p . adjust [32] . Second , for each of the 967 groups of genes ( identified by the regression trees , with mean error < mean error of the 1st quartile ) , the over-representation of each TAD within each group was tested using the R hypergeometric test function phyper [32] . Correction for multiple tests was applied using FDR< 0 . 05 [37] . The matrices of predicted variables ( log transformed RNA seq data ) and predictive variables ( nucleotide and dinucleotide percentages , motifs and DNA shape scores computed for all genes as described above ) as well as the TCGA barcodes of the 241 samples used in our study have been made available at http://www . univ-montp3 . fr/miap/~lebre/IBCRegulatoryGenomics .
We built a global linear regression model to explain the expression of genes using DNA/RNA features associated with their regulatory regions ( e . g . nucleotide composition , TF motifs , DNA shapes ) : y ( g ) = a + ∑ i b i x i , g + e ( g ) ( 1 ) where y ( g ) is the expression of gene g , xi , g is feature i for gene g , e ( g ) is the residual error associated with gene g , a is the intercept and bi is the regression coefficient associated with feature i . The advantage of this approach is that it allows to unveil , into a single model , the most important regulatory features responsible for the observed gene expression . The relative contribution of each feature can thus be easily assessed . It is important to note that the model is specific to each sample . Hence the expression of a given gene may be predicted by different variables depending on the sample . Our computational approach was based on two steps . First , a linear regression model ( 1 ) was trained with a lasso penalty [31] to select sequence features relevant for predicting gene expression . Second , the performances of our model was evaluated by computing the mean square of the residual errors , and the correlation between the predicted and the observed expression for all genes . This was done in a 10 fold cross-validation procedure . Namely , in all experiments hereafter , the set of genes was randomly split in ten parts . Each part was alternatively used for the test ( i . e . for comparing observed and predicted values ) while the remaining genes were used to train the model . This ensures that the model used to predict the expression of a gene has not been trained with any information relative to this gene . Our approach was applied to a set of RNA sequencing data from TCGA . We randomly selected 241 gene expression data from 12 cancer types ( see http://www . univ-montp3 . fr/miap/~lebre/IBCRegulatoryGenomics for the barcode list ) . For each dataset ( i . e sample ) , a regression model was learned and evaluated . See Materials and methods for a complete description of the data , the construction of the predictor variables and the inference procedure . We further evaluated our model on 3 independent ENCODE RNA-seq , 1 , 270 TCGA RNA-seq and 582 microarrays datasets ( see below ) . We first evaluated the contribution of promoters , which are one of the most important regulatory sequences implicated in gene regulation [38] . We extracted DNA sequences encompassing ±2000 bases around all GENCODE v24 TSSs and looked at the percentage of dinucleotides along the sequences ( S2 Fig ) . Based on these distributions , we segmented the promoter into three distinct regions: -2000/-500 ( referred here to as distal upstream promoter , DU ) , -500/+500 ( thereafter called core promoter though longer than the core promoter traditionally considered ) and +500/+2000 ( distal downstream promoter , DD ) ( Fig 1 ) . We computed the nucleotide ( n = 4 ) and dinucleotide ( n = 16 ) relative frequencies in the three distinct regions of each gene . For each sample , we trained one model using the 20 nucleotide/dinucleotide relative frequencies from each promoter segment separately , and from each combination of promoter segments . We observed that the core promoter had the strongest contribution compared to DU and DD ( Fig 2A ) . Considering promoter as one unique sequence spanning -2000/+2000 around TSS achieved lower model accuracy than combining different promoter segments ( Fig 2A ) . The highest accuracy was obtained combining all three promoter segments ( Fig 2A ) . Promoters are often centered around the 5’ most upstream TSS ( i . e . gene start ) . However genes can have multiple transcriptional start sites . The median number of alternative TSSs for the 19 , 393 genes listed in the TCGA RNA-seq V2 data is 5 and only 2 , 753 genes harbor a single TSS ( S3 Fig ) . We therefore evaluated the performance of our model comparing different promoters centered around the first , second , third and last TSS ( Fig 2B ) . In the absence of second TSS , we used the first TSS and likewise the second TSS in the absence of a third TSS . The last TSS represents the most downstream TSS in all cases . We found that our model achieved higher predictive accuracy with the promoters centered around the second TSS ( Fig 2B ) , in agreement with [16] . As postulated by Cheng et al . [16] in the case of TFs , the nucleotide composition around the first TSS may be linked to the recruitment of chromatin remodelers and thereby prime the second TSS for gene expression . Dedicated experiments would be required to assess this point . We noticed that incorporating the number of TSSs associated with each gene drastically increased the performance of our model ( S4 Fig ) . Multiplying TSSs may represent a genuine mechanism to control gene expression level . On the other hand this effect may merely be due to the fact that the more a gene is expressed , the more its different isoforms will be detected ( and hence more TSSs will be annotated ) . Because the number of known TSSs results from annotations deduced from experiments , we decided not to include this variable into our final model . Provided the importance of CpGs in promoter activity [38] , we first compared our model with a model built only on promoter CpG content . We confirmed that CpG content had an important contribution in predicting gene expression ( median R = 0 . 417 , Fig 2C ) . However considering other dinucleotides achieved better model performances , indicating that dinucleotides other than CpG contribute to gene regulation . This is in agreement with results obtained by Nguyen et al . , who showed that CpG content is insufficient to encode promoter activity and that other features might be involved [39] . We integrated TF motifs considering Position Weight Matrix scores computed in the core promoter and observed a slight but significant increase of the regression performance ( median r = 0 . 543 with motif scores vs . r = 0 . 502 without motif scores , Fig 2D ) . As DNA sequence is intrinsically linked to three-dimensional local structure of the DNA ( DNA shape ) , we also computed , for each promoter segment ( DU , CORE and DD ) , the mean scores of the four DNA shape features provided by DNAshapeR [29] ( helix twist , minor groove width , propeller twist , and Roll ) , adding 12 variables to the model . Although the difference between models with and without DNA shapes is also significant , the increase in performance is more modest than when including TF motif scores ( Fig 2D ) . Our model suggested that nucleotide composition had a greater contribution in predicting gene expression compared to TF motifs and DNA shapes . This is in agreement with the findings revealing the influence of the nucleotide environment in TFBS recognition [40] . Note however that nucleotide composition , TF motifs and DNA shapes may be redundant variables . Besides , a linear model may not be optimal to efficiently capture the contributions of TF motifs and/or DNA shapes . The highest performance was achieved by combining nucleotide composition with TF motifs ( Fig 2D ) . In the following analyses , the model was built on both dinucleotide composition and core promoter TF motifs . The wealth of TF ChIP-seq , epigenetic and expression data has allowed the development of methods aimed at predicting gene expression based on differential binding of TFs and epigenetic marks [16–19] . We sought to compare our approach , which does not necessitate such cell-specific experimental data , to these methods . We first compared our results to that of Li et al . who used a regression approach called RACER to predict gene expression on the basis of experimental data , in particular TF ChIP-seq data and DNA methylation [17] . Note that , with this model , the contribution of TF regulation in predicting gene expression is higher than that of DNA methylation [17] . We computed the Spearman correlations between expressions observed in the subsets of LAMLs studied in [17] and expressions predicted by our model or by RACER ( Fig 3A ) . For the sake of comparison , we used the RACER model built solely on ChIP-seq data , hereafter referred to as “ChIP-based model” . RACER performance was assessed using the same cross- validation procedure we used for our method . Overall our model was as accurate as ChIP-based model ( median correlation r = 0 . 529 with our model vs . median r = 0 . 527 with ChIP-based model ( Fig 3A ) ) . We then controlled the biological information retrieved by the two approaches by randomly permuting , for each gene , the values of the predictive variables ( dinucleotide counts/motif scores in our model and ChIP-seq signals in the ChIP-based model ) . This creates a situation where the links between the combination of predictive variables and expression is broken , while preserving the score distribution of the variables associated with each gene . For example , genes associated with numerous ChIP-seq peaks will also have numerous ChIP-seq peaks in random data . In such situation , a regression model is expected to poorly perform . Surprisingly , the accuracy of ChIP-based model was not affected by the randomization process ( median r = 0 . 517 , Fig 3A ) while that of our model was severely impaired ( median r = 0 . 076 , Fig 3A ) . We built another control model using a single predictive variable per gene corresponding to the maximum value of all predictive variables initially considered . Here again the ChIP-based model was not affected by this process ( median r = 0 . 520 , Fig 3A ) while our model failed to accurately predict gene expression with this type of control variable ( median r = -0 . 016 , Fig 3A ) . ChIP-seq data are probably the best way to measure the activity of a TF because binding of DNA reflects the output of RNA/protein expression as well as any appropriate post-translational modifications and subcellular localizations . However this type of data also reflects chromatin accessibility ( i . e . most TFs bind accessible genomic regions ) and TFs tend to form clusters on regulatory regions [41] . The binding of one TF in the promoter region is therefore likely accompanied by the binding of others . Hence , rather than inferring the TF combination responsible for gene expression , linear models based of ChIP-seq data predominantly captures the quantity of TFs ( i . e . the opening of the chromatin ) in the promoter region of each gene , which explains their good accuracy on randomized or maximized variables . We indeed observed a similar bias in the results obtained by TEPIC [19] , a regression method that predicts gene expression from PWM scores and open-chromatin data . Specifically , TEPIC computes a TF-affinity score for each gene and each PWM by summing up the TF affinities in all open-chromatin peaks ( DNaseI-seq ) within a close ( 3 , 000 bp ) or large ( 50 , 000 bp ) window around TSSs . This scoring takes into account the scores of PWMs in the open-chromatin peaks but is also influenced by the number of open-chromatin peaks in the analyzed sequences and the abundance of open-chromatin peaks ( “scaled” version ) . As a result , genes with many open-chromatin peaks tend to get higher TF-affinity scores than genes with low number of open-chromatin peaks . We trained linear models on three cell-lines using either the four TEPIC affinity-scores or our variables and compared the results ( Fig 3B ) . As for the ChIP-based models , we observed that our model was approximately as accurate as TEPIC score model , validating our approach with an independent dataset . Applying the random permutations on the TEPIC scores did not significantly impact the accuracy of the approach in most cases , especially for the scaled versions ( Fig 3B ) . Hence , as for the ChIP-based model , the TEPIC score model seems to mainly capture the level of chromatin opening rather than the TF combinations responsible for gene expression . Conversely , our model solely built on DNA sequence features is not influenced by the chromatin accessibility and thus can yield relevant combinations of explanatory features ( see the randomized control in Fig 3A and 3B ) . Note that the non-scaled version of TEPIC did show a loss of accuracy for cell-line H1-hESC ( as well as a moderate loss for K562 , but none for GM12878 ) when randomizing or maximizing the variables ( Fig 3B ) . This result indicates that , although taking the abundance of open-chromatin peaks in the analyzed sequences does increase expression prediction accuracy , it might generate more irrelevant combinations of explanatory features than non-scaled versions . Additional genomic regions were integrated into our model . We first thought to consider enhancer sequences implicated in transcriptional regulation . We used the enhancer mapping made by the FANTOM5 project , which identified 38 , 554 human enhancers across 808 samples [7] . This mapping uses the CAGE technology , which captures the level of activity for both promoters and enhancers in the same samples . It is then possible to predict the potential target genes of the enhancers by correlating the activity levels of these regulatory regions over hundreds of human samples [7] . However FANTOM5 enhancers are only assigned to 11 , 359 genes from the TCGA data , which correspond to the most expressed genes across different cancers ( S5 Fig ) . Provided that the detection of enhancers relies on their activity , it is expected that enhancers are better characterized for the most frequently expressed genes . Because considering only the genes with annotated enhancers would considerably reduce the number of genes and including enhancers features only when available would introduce a strong bias in the performance of our model , we decided not to include these regulatory regions . Second we analyzed the contribution of regions defined at the RNA level , namely 5’UTR , CDS , 3’UTR and introns , which can be responsible for post-transcriptional regulations [13 , 17 , 26 , 42–50] ( Fig 1 ) . For all genes , we extracted all annotated 5’UTRs , 3’UTRs and CDSs , which were further merged and concatenated to a single 5’UTR , a single CDS , and a single 3’UTR per gene . Introns were defined as the remaining sequence ( Fig 1 ) . We also tested the potential contribution of the 1kb region located downstream the gene end , called thereafter Downstream Flanking Region ( DFR , Fig 1 ) . Our rationale was based on reports showing the presence of transient RNA downstream of polyadenylation sites [51] , the potential presence of enhancers [7] and the existence of 5’ to 3’ gene looping [52] . We used a forward selection procedure by adding one region at a time: ( i ) all regions were tested separately and the region leading to the highest Spearman correlation between observed and predicted expression was selected as the ‘first’ seed region , ( ii ) each region not already in the model was added separately and the region yielding the best correlation was selected ( ‘second region’ ) , ( iii ) the procedure was repeated till all regions were included in the model . The correlations computed in a cross-validation procedure at each steps are indicated in S2 Table . As shown in Fig 4 , the nucleotide composition of intronic sequences had the strongest contribution in the accuracy of our model , followed by UTRs ( 5’ then 3’ ) and CDS ( Fig 4 ) . The nucleotide composition of core promoter moderately increased the prediction accuracy . In contrast the composition of regions flanking core promoter ( DU and DD , Fig 1 ) as well as regions located downstream the end of gene ( DFR , Fig 1 ) did not significantly improve the predictions of our model . Note that combining all regions improved the performance of our model compared to promoter alone ( compare Figs 2B and 4 ) . We compared models built on ssDNA and dsDNA , and ssDNA-based models yielded better accuracy S6 Fig . We also compared models built on percentages of nucleotides ( n = 4 ) , dinucleotides ( n = 16 ) and nucleotides+dinucleotides ( n = 20 ) . As shown S7A Fig , dinucleotides provided stronger prediction accuracy than nucleotides and the best accuracy was obtained combining both nucleotides and dinucleotides . We also built a model on trinucleotide percentage ( n = 64 ) ( S7A Fig ) . This model did yield better results than model built on nucleotide+dinucleotide . However , the correlation increase was not as important as that observed when adding dinucleotides to nucleotides . Besides , the model built on trinucleotides involves more variables and is computationally demanding . We compared models built on nucleotides+dinucleotides adding individually trinucleotide percentages of each region ( i . e . 8 models built on nucleotides+dinucleotides in all regions + trinucleotides in one specific region ) ( S7B Fig ) . This analysis revealed that the correlation increase observed when incorporating trinucleotides was mostly due to the contribution of trinucleotides computed in introns , reinforcing our conclusions regarding the importance of sequence-level instructions located in this region . Because RNA-associated regions ( introns , UTRs , CDSs ) had greater contribution to the prediction accuracy compared to DNA regions ( promoters , DFR ) , we compared the accuracy of our model in predicting gene vs . transcript expression . We retrieved the normalized results for gene expression ( RNAseqV2 rsem . genes . normalized_results ) and the matched normalized expression signal of individual isoforms ( RNAseqV2 rsem . isoforms . normalized_results ) for 225 TCGA samples . Accordingly , we generated a set a predictive variables specific to each isoform ( see Material and methods ) . We found that models built on isoforms are less accurate than models built on genes ( median r = 0 . 35 , S8 Fig and ( S3 Table ) ) . Focusing on the broad nucleotide composition may not be optimal to model isoform expression and to differentiate expression of one isoform from another . Yet another simple explanation could be that reconstructing and quantifying full-length mRNA transcripts is a difficult task , and no satisfying solution exists for now [53] . Consequently isoform as opposed to gene expression is more difficult to measure and thus to predict . In the above sections , our complete model , built on 160 variables corresponding to 4 nucleotide and 16 dinucleotide rates in 8 distinct regions ( Fig 1 ) , was trained with a data set containing 241 RNA-seq samples randomly chosen from 12 different cancers , and on 3 independent ENCODE RNA-seq datasets ( see TEPIC comparison ) . We further evaluated our approach using two independent additional datasets: ( a ) a set of 1 , 270 RNA-seq samples collected from 14 cancer types and ( b ) a set of 582 microarray data . Overall , the RNA-seq and the microarray samples were collected from respectively 109 and 41 source sites and sequenced in 3 analysis centers . Similar accuracy was observed in all datasets ( S9 and S10 Figs ) . Note that the correlations computed with microarray data were lower than that computed with RNA-seq data but involved lower number of genes ( 9 , 791 genes in microarrays vs . 16 , 294 in RNA-seq ) . For sake of comparison , we restricted RNA-seq data to the 9 , 791 microarray genes and we observed similar correlation ( S10 Fig ) . Because our model was built on human reference genome , we also have computed the Spearman correlations between absolute values of CNV segment mean scores and model prediction errors calculated for each gene in 241 samples corresponding to 12 cancer types . The median correlation was -0 . 014 , arguing against the model performance being related to CNV-density ( S11 Fig ) . We sought the main DNA features related to gene expression . The complete model built on all 8 regions ( 160 variables ) selected ∼ 129 predictive variables per sample . We used the stability selection algorithm developed by Meinshausen et al . [35] to identify the variables that are consistently selected after data subsampling ( see Materials and methods for a complete description of the procedure ) . This procedure selected a median of ∼ 16 variables per sample . The barplot in Fig 5A shows , for each variable , the proportion of samples in which the variable is selected with high consistency ( > 70% of the subsets ) . We next determined whether stable variables exert a positive ( activating ) or a negative ( inhibiting ) effect on gene expression . For each sample , we fitted a linear regression model predicting gene expression using only the standardized variables that are stable for this sample . The activating/inhibiting effect of a variable is then indicated by the sign of its regression coefficient: < 0 for a negative effect and > 0 for a positive effect . The outcome of these analyses for all variables and all samples is shown Fig 5B . With the noticeable exception of CpG in the core promoter , all stable variables had an invariable positive ( e . g . GpT in introns ) or negative ( e . g . CpA in DD and in 5UTR ) contribution in gene expression prediction in all samples . In contrast , CpG in the core promoter had an alternating effect being positive in LAML and LGG for instance while negative in READ . It is also the only variable with a regression coefficient close to 0 ( absolute value of median = 0 . 1 , see S12 Fig ) , providing a partial explanation for the observed changes . As CpG methylation inhibits gene expression [38] , we also investigated potential differences in core promoter methylation in LAML ( positive contribution of CpG_CORE ) and READ ( negative contribution of CpG_CORE ) . We used the Illumina Infinium Human DNA Methylation 450 made available by TCGA and focused on the estimated methylation level ( beta values ) of the sites intersecting with the core promoter . We noticed that core promoters in LAML were overall more methylated ( median = 0 . 85 ) than in READ ( median = 0 . 69 , wilcoxon test p-value < 2 . 2e-16 ) , opposite to the sign of CpG coefficient in LAML ( positive contribution of CpG_CORE ) and READ ( negative contribution of CpG_CORE ) . This argued against a contribution of methylation in the alternating effect of CpG_CORE . We observed that the accuracy of our model varied between cancer types ( S9 Fig ) . In order to characterize well predicted genes in each sample , we used a regression tree [54] to classify genes according to the prediction accuracy of our model ( i . e . absolute error ) . The nucleotide and dinucleotide compositions of the various considered regions were used as classifiers . This approach identified groups of genes with similar ( di ) nucleotide composition in the regulatory regions considered and for which our model showed similar accuracy ( S13 Fig ) . Implicitly , it identified the variables associated with a better or a poorer prediction . We applied this approach to the 241 linear models . The number of groups built by a regression tree differs from one sample to another ( average number = 14 ) . The resulting 3 , 680 groups can be visualized in the heatmap depicted in Fig 6 , wherein each column represents a sample and each line corresponds to a group of genes identified by a regression tree . This analysis showed that our model is not equally accurate in predicting the expression of all genes but mainly fits certain classes of genes ( bottom rows of the heatmap , Fig 6 ) with specific genomic features ( S13 Fig ) . Note that the groups well predicted in all cancers presumably correspond to highly and ubiquitously expressed housekeeping genes: groups with low prediction error in all samples and cancer types ( see S13 Fig for an example group of 996 genes identified by a regression tree learned in one PRAD sample ) are functionally enriched for general and widespread biological processes ( S4 Table ) . In contrast , groups well predicted in only certain cancers were associated to specific biological function . For instance , a regression tree learned on one PAAD sample identified a group of 1 , 531 genes , which has low prediction error in LGG and PAAD samples but high error in LAML , LIHC and DLBC samples ( Fig 6 and S13 Fig ) . Functional annotation of this group showed that , in contrast to the group described above ( S13 Fig and S4 Table ) , this group is also linked to specific biological processes ( S5 Table ) . We further computed Gini coefficient for 16 , 134 genes using 8 , 556 GTEx libraries [55] . Gini coefficient measures statistical dispersion which can be used to measure gene expression ubiquity: value 0 represents genes expressed in all samples , while value 1 represents genes expressed in only one sample . We observed that the correlations obtained between Gini coefficient and model errors in each TCGA sample ranged from 0 . 22 to 0 . 36 . We also compared model errors associated to first and last quartiles of the Gini coefficient distribution using a Wilcoxon test for each of the 241 samples . The test was invariably significant with maximum p-value = 2 . 881e−7 . Likewise analyses were performed with 1 , 897 FANTOM CAGE libraries [56] considering 15 , 904 genes . In that case , correlation between models errors and Gini coefficients ranged from 0 . 25 to 0 . 4 . Overall these analyses suggested that our model better predicts expression of highly and ubiquitously expressed genes . We do not exclude that , when predicting tissue-specific genes , ChIP-seq data collected from the same tissue may add explanatory power to the sequence model . Note , however , that the model performances vary between cancer and cell types implying that part of cell-specific genes are also well predicted by the model ( S9 Fig ) . We probed the regulatory activities of the selected regions . We first determined whether introns contained specific regulatory sequence code by assessing the presence of cis expression quantitative trait loci ( cis-eQTLs ) . Zhou et al . indeed showed that the effect of eQTL SNPs can be predicted from a regulatory sequence code learned from genomic sequences [25] . These findings also implied that cis-eQTLs preferentially affect DNA sequences at precise locations ( e . g . TF binding sites ) rather than global nucleotide composition ( i . e . nucleotide/dinucleotide percentages used as variables in our model ) . We used the v6p GTEx release to compute the average frequencies of cis-eQTLs present in the considered genomic regions and directly linked to their host genes ( S6 Table ) . We noticed that introns contained the smallest density of cis-eQTLs ( 10 times less than any other regions ) , while containing comparable amount of SNPs ( S7 Table ) . This result argued against the presence of a regulatory sequence code similar to that observed in promoters for instance [25] , despite the presence of enhancers ( S8 Table . These results rather unveiled the existence of another layer of intron-mediated regulation , which involves global nucleotide compositions of larger DNA regions . We then asked whether the groups of genes identified by the regression trees ( Fig 6 ) correspond to specific TADs . Genes within the same TAD tend to be coordinately expressed [57 , 58] . TADs with similar chromatin states tend to associate to form two genomic compartments called A and B: A contains transcriptionally active regions while B corresponds to transcriptionally inactive regions [59] . The driving forces behind this compartmentalization and the transitions between compartments observed in different cell types are not fully understood , but chromatin composition and transcription are supposed to play key roles [5] . Jabbari and Bernardi showed that nucleotide composition along the genome ( notably isochores ) can help define TADs [60] . As intronic sequences represent ∼ 50% of the human genome ( 1 , 512 , 685 , 844 bp out of 3 , 137 , 161 , 264 according to ENSEMBL merged intron coordinates ) , the nucleotide composition of introns likely resemble that of neighbor genes and more globally that of the corresponding TAD . We used the 373 TADs containing more than 10 genes mapped in IMR90 cells [6] . For each TAD and each ( di ) nucleotide , we used a Kolmogorov-Smirnov test to compare the ( di ) nucleotide distribution of the embedded genes with that of all other genes . We used a Benjamini-Hochberg multiple testing correction to control the False Discovery Rate ( FDR ) , which was fixed at 0 . 05 ( see Materials and methods section ) . We found that 324 TADs out of 373 ( ∼87% ) are characterized by at least one specific nucleotide signature ( Fig 7A ) . In addition , our results clearly showed the existence of distinct classes of TADs related to GC content ( GC-rich , GC-poor and intermediate GC content ) ( Fig 7A ) , in agreement with [60] . We next considered the 967 groups of genes defined in Fig 6 whose expression is accurately predicted by our model ( i . e . groups with mean error < mean error of the 1st quartile ) . We thus focused our analyses on genes for which we did learn some regulatory features . We evaluated the enrichment for specific TADs in each group ( considering only TADs containing more than 10 genes ) using an hypergeometric test ( Fig 7B ) . We found that 60% of these groups were enriched for at least one TAD ( p-value < 0 . 05 ) . Hence , several groups of genes identified by the regression trees ( Fig 6 ) do correspond to specific TADs ( Fig 7B ) . We concluded that our model , primarily based on intronic sequences , select gene nucleotide compositions that better distinguish active TADs .
In this study , we corroborate the hypothesis that DNA sequence contains information able to explain gene expression [20–25] . We built a global regression model to predict , in any given sample , the expression of the different genes using only nucleotide compositions as predictive variables . Overall our model provided a framework to study gene regulation , in particular the influence of regulatory regions and their associated nucleotide composition . A surprising result of our study is that sequence-level information is highly predictive of gene expression and in some occasions comparable to reference ChIP-seq data alone [17 , 19] . The similar accuracy of models built on real and randomly permuted experimental data indicated that , though the experimental data are biologically relevant , their interpretation through a linear model , in particular inference of TF combinations , is not straightforward as randomization of experimental data did not show the expected loss of accuracy ( Fig 3 ) . An interesting perspective would be to devise a strategy to infer TF combinations from experimental data without being influenced by the opening of the chromatin . The accuracy of our model confirmed that DNA sequence per se and basic information like dinucleotide frequencies have very high predictive power . It remains to determine the exact nature of these sequence-level instructions . Interestingly , nucleotide environment contributes to prediction of TF binding sites and motifs bound by a TF have a unique sequence environment that resembles the motif itself [40] . Hence , the potential of the nucleotide content to predict gene expression may be related to the presence of regulatory motifs and TFBSs . However , we showed that the gene body ( introns , CDS and UTRs ) , as opposed to sequences located upstream ( promoter ) or downstream ( DFR ) , had the most significant contribution in our model . Moreover , cis-eQTL frequencies argue against the presence of a regulatory sequence code in introns similar to that observed in promoters , suggesting the existence of another layer of regulation implicating the nucleotide composition of large DNA regions . Gene nucleotide compositions vary across the genome and can even help define TAD boundaries [60] . In line with [60] , we showed that genes located within the same TAD share similar nucleotide compositions , which provides a nucleotide signature for their TADs ( Fig 7A ) . Our model aimed at predicting gene expression , and therefore intimately linked to TAD compartmentalization , appeared to capture these signatures . Several studies have already demonstrated the existence of sequence-level instructions able to determine genomic interactions . Using an SVM-based approach , Nikumbh et al demonstrated that sequence features can determine long-range chromosomal interactions [61] . Similar results were obtained by Singh et al . using deep learning-based models [62] . Using biophysical approaches , Kornyshev et al . showed that sequence homology influences physical attractive forces between DNA fragments [63] . It would be interesting to determine whether the nucleotide signatures identified by our model are directly implicated in DNA folding and 3D genome architecture . Finally , although sequence-level instructions are—almost—identical in all cells of an individual , their usage must be cell-type specific to allow proper A/B compartimentalization of TADs , gene expression and ultimately diversity of cell functions . At this stage , the mechanisms driving this cell-type specific selection of nucleotide compositions remain to be characterized .
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Identifying a maximum of DNA determinants implicated in gene regulation will accelerate genetic analyses and precision medicine approaches by identifying key gene features . In that context decoding the sequence-level instructions for gene regulation is of prime importance . Among global efforts to achieve this objective , we propose a novel approach able to explain gene expression in each patient sample using only DNA features . Our approach , which is as accurate as methods based on epigenetics data , reveals a strong influence of the nucleotide content of gene body sequences , in particular introns . In contrast to canonical regulations mediated by specific DNA motifs , our model unveils a contribution of global nucleotide content notably in co-regulations associated with genome 3D architecture and to associations of genes within topologically associated domains . Overall our study confirms and takes advantage of the existence of sequence-level instructions for gene expression , which lie in genomic regions largely underestimated in regulatory genomics but which appear to be linked to chromatin architecture .
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2018
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Probing instructions for expression regulation in gene nucleotide compositions
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Human cancer is caused by the interplay of mutations in oncogenes and tumor suppressor genes and inherited variations in cancer susceptibility genes . While many of the tumor initiating mutations are well characterized , the effect of genetic background variation on disease onset and progression is less understood . We have used C . elegans genetics to identify genetic modifiers of the oncogenic RAS/MAPK signaling pathway . Quantitative trait locus analysis of two highly diverged C . elegans isolates combined with allele swapping experiments identified the polymorphic monoamine oxidase A ( MAOA ) gene amx-2 as a negative regulator of RAS/MAPK signaling . We further show that the serotonin metabolite 5-hydroxyindoleacetic acid ( 5-HIAA ) , which is a product of MAOA catalysis , systemically inhibits RAS/MAPK signaling in different organs of C . elegans . Thus , MAOA activity sets a global threshold for MAPK activation by controlling 5-HIAA levels . To our knowledge , 5-HIAA is the first endogenous small molecule that acts as a systemic inhibitor of RAS/MAPK signaling .
Human cancer is a complex polygenic disease caused by somatic mutations in oncogenes and tumor suppressor genes together with inherited polymorphisms in cancer susceptibility genes . Many of the oncogenes and tumor suppressor genes that are mutated in different cancer types have been investigated in detail . However , relatively little is known about the effect of the genetic background on disease onset and progression . It thus remains a challenge to identify functional links between oncogenic traits and associated natural variants [1 , 2] . The components of the RAS/MAPK signaling pathway are mutated in a large fraction of human tumors . In particular , activating ( “gain-of-function” ) mutations in HRAS and KRAS are among the most prevalent tumor initiating mutations found in human cancer cells [3] . Thanks to the strong conservation of this pathway in metazoans , genetic studies in model organisms , such as the nematode Caenorhabditis elegans , have provided important insights into various factors modulating RAS/MAPK signaling [4] . Moreover , C . elegans has become a platform species for quantitative genetic analyses of various phenotypes and pathways in order to identify and characterize polymorphic genes [5 , 6] In this study , we have used quantitative genetics to explore how the genetic background affects the phenotypes caused by the activating G13E ( n1046 ) mutation in the C . elegans ras gene let-60 [7] . The n1046 mutation is homologous to the HRAS and KRAS mutations that are frequently found in human cancer cells [3] . For the purpose of this study , we compared RAS/MAPK signaling in two highly diverse genetic backgrounds , C . elegans varieties Bristol ( N2 ) and Hawaii ( CB4856 ) [8] . Compared to the reference strain N2 , the Hawaiian CB4856 strain on average contains one polymorphism every 412 bp with around 75% of all genes carrying at least one coding polymorphism [9] . To measure the activity of the RAS/MAPK pathway in different genetic backgrounds , vulval induction can be used as a quantifiable and reproducible readout . During vulval development , the anchor cell in the somatic gonad secretes the EGF-like ligand that activates via an EGFR family receptor tyrosine kinase the RAS/MAPK signaling pathway in the adjacent vulval precursor cells ( VPCs ) [10] . In combination with a lateral NOTCH signal , RAS/MAPK signaling induces three of the six VPCs to adopt a 2°-1°-2° pattern of vulval cell fates ( Fig 1A ) . Mutations that hyperactivate RAS/MAPK signaling , such as the n1046 allele , cause the differentiation of more than three and up to six VPCs and a Multivulva phenotype , while mutations that reduce RAS/MAPK signaling result in the induction of fewer than three VPCs and a Vulvaless phenotype . Hence , the average number of induced VPCs per animal , the vulval induction ( VI ) , is a quantitative measure of RAS/MAPK signaling output in the VPCs [10 , 11] . Besides the vulva , RAS/MAPK signaling is activated in a variety of other tissues in C . elegans at different developmental stages , such as the meiotic germ cells in the hermaphrodite gonads , the excretory duct cell precursor in the embryo or the chemosensory neurons during olfaction in adults [4] . Using a quantitative genetics approach , we aimed at identifying globally acting as well tissue-specific modifiers of RAS/MAPK signaling . Here , we describe the identification of the polymorphic monoamine oxidase amx-2 gene as a global negative regulator of the RAS/MAPK pathway . amx-2 encodes a mitochondrial monoamine oxidase type A ( MAOA ) that catalyzes the oxidative deamination of biogenic amines such as dopamine ( DA ) and serotonin ( 5-HT ) [12] . We further show that AMX-2 activity in intestinal cells controls the levels of the serotonin metabolite 5-hydroxyindoleacetic acid ( 5-HIAA ) , which acts as systemic inhibitor of MAPK phosphorylation .
To identify polymorphic modifiers of the RAS/MAPK pathway , we generated a set of 228 “mutation included recombinant inbred lines” ( miRILs ) between the Bristol strain MT2124 that carries the activating let-60 ras ( n1046gf ) mutation [7] and the Hawaiian CB4856 strain ( Fig 1B ) . Since small genetic variations are efficiently buffered in a wild-type genome [13 , 14] , the inclusion of the let-60 ( gf ) allele created a sensitized genetic background , allowing us to identify genetic modifiers that increase or decrease RAS/MAPK signaling . After 10 generations of inbreeding and genotyping using fragment length polymorphisms ( FLPs ) [15] , 173 of the miRILs homozygous for the n1046 allele were used for further analysis ( Fig 1C , top ) ( see Materials and Methods for details on genotyping and the selection of informative miRILs ) . In addition , we quantified RAS/MAPK signaling output in each of these miRILs by measuring the VI of at least 20 animals . While the let-60 ( gf ) allele in the Bristol background exhibits a VI of 3 . 7±0 . 06 ( n = 100 ) , the VIs of the miRILs varied between 3 . 0 and 5 . 7 ( Fig 1C , bottom ) . Quantitative trait loci ( QTL ) mapping [14] identified at least three loci on chromosomes I ( QTL1 ) , II ( QTL 2 ) and V ( QTL 3 ) above the threshold LOD score of 3 that are associated with variation in the VI ( Fig 1D ) . For QTL1 , the Bristol genotype is associated with a decreased VI , while for QTL2 and QTL3 the Bristol genotype is associated with an increased VI ( S1 Fig ) . To estimate the effect size of each QTL and explore how the QTLs affect the VI when combined , we used two mapping models , one where the QTLs have additive effects and another one where they show an interaction ( S1 Table ) . This analysis did not detect any significant interactions between the QTLs . Since the let-60 ( gf ) mutation maps to chromosome IV , our approach did not permit us to identify QTLs on this chromosome . Moreover , the genetic incompatibility between the Bristol and Hawaii genomes caused by the zeel-1 and peel-1 loci on the left arm of chromosome I may have prevented the detection of QTLs in this region [16] . To confirm and refine the mapping of the detected QTLs , introgression lines ( ILs ) carrying defined segments of the Hawaii genome in the QTL regions of interest were crossed to the let-60 ( gf ) Bristol strain [17] . Lines homozygous for the introgressions and the let-60 ( gf ) mutation were compared to sibling lines without introgressions to identify those introgressions that cause significant differences in the VI ( see Materials and Methods ) . The results for the fine mapping of QTL1 are shown in Fig 2A and for all QTLs in S2 Fig IL mapping revealed that QTL1 is composed of two adjacent QTLs , termed 1a and 1b , and that QTL1b maps to an interval of 1 . 43 Mbp containing 142 polymorphic genes ( Fig 2A ) . Through this approach , we have identified several regions in the C . elegans genome that contain modifiers of the RAS/MAPK pathway . Notably , for QTL1a and QTL1b the Bristol genotype caused reduced RAS/MAPK activity , while for QTL2 and QTL3 the Bristol background increased RAS/MAPK activity . Since the QTL1b region does not contain any known regulators of RAS/MAPK signaling , we performed RNAi knockdown of 107 of the 142 genes in this region in let-60 ( gf ) single mutants as well as in let-60 ( gf ) mutants carrying the ewIR17 introgression , which spans QTL1b . We envisioned two possible scenarios that are not mutually exclusive: ( 1 ) The QTL1b region in the Bristol strain may contain a negative regulator of RAS/MAPK signaling that is inactive or weakly active in the Hawaii background . ( 2 ) The Hawaii background may contain a positive regulator of RAS/MAPK signaling that is inactive or weakly active in the Bristol background . We thus screened for candidates exhibiting allele-specific RNAi effects ( S2 Table ) . Note that when grown on the E . coli strain HT115 that is commonly used in RNAi feeding experiments [18] , the let-60 ( n1046 ) allele exhibits an increased VI compared to animals grown on standard OP50 bacteria [19] . Knockdown of five genes significantly increased the VI in the let-60 ( gf ) but not in the ewIR17; let-60 ( gf ) background , defining potential negative regulators of RAS/MAPK signaling that are active in the Bristol background ( highlighted in green in S2 Table ) , whereas knockdown of ten genes reduced the VI in the ewIR17; let-60 ( gf ) but not in the let-60 ( gf ) background , defining potential positive regulators active in the Hawaii background ( highlighted in blue in S2 Table ) . These data suggested that the QTL1b region is oligogenic , containing several polymorphic modifiers of RAS/MAPK signaling . Of particular interest was the amx-2 gene because it fulfilled the criteria of a polymorphic negative regulator of RAS/MAPK signaling acting in the Bristol strain , but being inactive in the Hawaii strain . amx-2 RNAi had no significant effect on the ewIR17; let-60 ( gf ) background , but amx-2 RNAi caused a robust increase in the VI of let-60 ( gf ) mutants ( Fig 2B ) . Furthermore , the amx-2 ( ok1235 ) deletion mutant , which most likely represents a null allele ( www . wormbase . org ) , increased the VI of let-60 ( gf ) mutants in the Bristol background ( Fig 2C ) . To individually assess the activities of the Bristol and Hawaii amx-2 variants , we generated single-copy insertions on chromosome II [20] of a 7 . 8 kb genomic fragment spanning the amx-2 locus that was isolated either from the Bristol or the Hawaii genome . These single-copy transgenes were then introduced ( homozygously ) into the amx-2 ( lf ) ; let-60 ( gf ) background . Insertion of the Bristol but not the Hawaii amx-2 variant reduced the VI of amx-2 ( lf ) ; let-60 ( gf ) double mutants to the value observed in let-60 ( gf ) single mutants ( Fig 2C ) . These results confirmed the different physiological activities of the two amx-2 variants . In addition , amx-2 ( lf ) partially suppressed the Vulvaless phenotype caused by reduction-of-function mutations in let-60 ras [7] or the EGFR homolog let-23 [10] and enhanced the Multivulva phenotype of the let-23 gain-of-function mutation sa62 [21] ( Fig 2D ) . We thus conclude that the Bristol variant of the amx-2 gene inhibits RAS/MAPK signaling in the VPCs . To determine the site of amx-2 action , we generated transcriptional Pamx-2::gfp reporters . amx-2 was expressed in head neurons , the intestine and in a subset of cells of the rectum and in the adult vulva ( Fig 2E–2H ) . However , we did not observe any amx-2 expression in the VPCs during vulval induction , though amx-2 reporter levels could be below the detection limit . Since neurons have a low sensitivity to RNAi [22] , yet amx-2i efficiently phenocopied the amx-2 ( lf ) phenotype , we suspected that amx-2 might act in intestinal cells , where we detected strongest expression . Intestine-specific amx-2 RNAi using an rde-1 ( lf ) ; let-60 ( gf ) ; Pelt-2::rde-1 ( + ) strain [23] increased the VI to a similar degree as systemic RNAi , while vulva-specific RNAi using the Plin-31::rde-1 ( + ) transgene [24] had no detectable effect , which is consistent with lack of detectable amx-2 reporter expression in the VPCs ( Fig 2I , note that the overall lower VI in the vulva-specific RNAi strain is due to the genetic background [24] ) . Taken together , AMX-2 most likely acts in the intestinal cells to negatively regulate RAS/MAPK signaling in the VPCs . To investigate a possible redundancy between the MAOA amx-2 and the MAOB gene amx-1 , we measured the transcript levels of amx-2 and its paralog amx-1 by quantitative real-time PCR . The abundance of amx-2 and amx-1 transcripts was not significantly different between the Bristol and Hawaii backgrounds ( Fig 2J ) . However , amx-2 transcript levels were around 60% decreased and amx-1 levels around 40% increased in amx-2 ( lf ) mutants . Possibly , the elevated amx-1 expression can partially compensate for a loss of amx-2 expression . amx-2 encodes a member of the mitochondrial monoamine oxidase ( MAO ) family [25] . Sequence alignments of the catalytic domains of different MAOs indicated that AMX-2 is most closely related to the ancestor of the mammalian MAOA , MAOB and L-amino oxidases ( S3 Fig ) . The Hawaii AMX-2 variant possesses two coding polymorphisms in the catalytic domain ( V410I and N461S ) and another four in the C-terminal region ( R521G , T532S , N535S and L617P ) ( S4 Fig ) . MAOs are key enzymes in the degradation of the neurotransmitters 5-HT and DA ( Fig 3A ) [12] . The products of the DA and 5-HT deamination reactions , 3 , 4-dihydroxyphenylacetaldehyde and 5-hydroxyindole-acetaldehyde respectively , are further oxidized by aldehyde dehydrogenases into 3 , 4-dihydroxyphenylacetic acid and 5-hydroxyindoleacetic acid ( 5-HIAA ) , which in humans are secreted through the kidneys ( Fig 3A ) [26] . Consistent with the predicted function of AMX-2 in degrading 5-HT , total extracts of amx-2 ( lf ) worms contained elevated levels of 5-HT when compared to wild-type extracts ( Fig 3B ) . We thus investigated if AMX-2 inhibits RAS/MAPK signaling by controlling the levels of DA , 5-HT or their metabolites . The addition of 10mM DA to the growth medium had no significant effect on the VI of let-60 ( gf ) single or amx-2 ( lf ) ; let-60 ( gf ) double mutants ( Fig 3C ) . However , 1mM 5-HT as well as 1mM of the 5-HT metabolite 5-HIAA caused a strong reduction of the VI in both backgrounds and a suppression of the Multivulva phenotype ( Fig 3C and 3D ) . Addition of 0 . 6mM melatonin ( MT ) , another 5-HT metabolite ( Fig 3A ) , had a slightly weaker yet significant effect on the VI ( Fig 3C ) . We conclude that the 5-HT metabolites , in particular 5-HIAA , inhibit RAS/MAPK signaling . To test the sensitivity of the RAS/MAPK pathway to 5-HT and 5-HIAA , we performed dose-response experiments in the presence and absence of amx-2 . For both compounds , the maximum reduction of the VI was observed at concentrations above 1mM ( Fig 3E and 3F ) . However , let-60 ( gf ) single mutants displayed a higher sensitivity to low concentrations ( 1μM ) of 5-HT than amx-2 ( lf ) ; let-60 ( gf ) double mutants , while the effects of 5-HIAA were independent of the amx-2 genotype . Overall , 5-HT exerted a slightly stronger effect than 5-HIAA , suggesting that additional 5-HT metabolites besides 5-HIAA may inhibit RAS/MAPK signaling . To determine at which step 5-HIAA regulates the RAS/MAPK pathway , we examined a strain expressing an activated form of the MAPK MPK-1 along with the MAPKK MEK-2 [27] ( mpk-1 ( gf ) ) . Application of 4mM 5-HIAA did not alter the VI of mpk-1 ( gf ) mutants ( Fig 3G ) . Also , 5-HIAA did not affect a lf mutation in lin-1 , which encodes an ETS family transcription factor that represses vulval induction downstream of MPK-1 [28] ( Fig 3G ) . Taken together , these results indicate that 5-HIAA inhibits RAS/MAPK signaling upstream of MPK-1 . We further characterized the inhibitory effect of 5-HIAA on the RAS/MAPK pathway by testing mutants in the 5-HT pathway for their response to 5-HIAA treatment . A mutation in the tryptophan hydroxylase gene tph-1 , which is essential for 5-HT biosynthesis [29] , slightly reduced the VI in let-60 ( n1046gf ) animals in the absence of 5-HIAA ( Fig 3H ) . However , treatment of tph-1 ( lf ) ; let-60 ( n1046gf ) double mutants with 4mM 5-HIAA further reduced the VI , indicating that 5-HIAA acts in the absence of endogenous 5-HT and hence does not compete with 5-HT . By contrast , the VI of let-60 ( n1046gf ) animals carrying a mutation in the 5-HT receptor gene ser-1 [30] was not reduced by 5-HIAA treatment . Surprisingly , the VI of let-60 ( gf ) ; ser-1 ( lf ) double mutants was even increased after 5-HIAA treatment . Moreover , a gain-of-function mutation in egl-30 , which encodes a Gqα protein acting in the 5-HT pathway [31] , rendered let-60 ( n1046gf ) mutants resistant to 5-HIAA and caused a slight increase of the VI in untreated animals ( Fig 3H ) . Since the SER-1/EGL-30 pathway plays an essential role in 5-HT stimulated egg laying [30] , we tested the effects of 5-HIAA on the egg laying rate with and without 5-HT stimulation . While 5-HIAA treatment alone caused a slight reduction in the egg laying rate , 5-HIAA did not significantly compete with the 5-HT stimulated increase in egg laying ( S5 Fig ) . We conclude that 5-HIAA acts via the SER-1 receptor and the downstream EGL-30 Gqα signaling pathway to repress RAS/MAPK activity . However , the inhibitory effect of 5-HIAA is independent of 5-HT activity . Besides the VPCs , RAS/MAPK signaling is required in several other organs of C . elegans [4] . Hence , let-60 ( gf ) mutants exhibit multiple defects besides a Muv phenotype . For example , the temperature-sensitive let-60 ( ga89gf ) allele causes accelerated exit of meiotic germ cells from the pachytene stage , resulting in the accumulation of many immature oocytes in the proximal gonad arm at the restrictive temperature [32 , 33] ( Fig 4A ) . Moreover , let-60 ( n1046gf ) mutants frequently contain two duct cells expressing the lin-48::gfp marker [34] ( Fig 4B ) . Treatment with 4mM 5-HIAA partially suppressed the let-60 ( gf ) phenotypes both in the germ line and the duct cell ( Fig 4A and 4B ) . To measure the global effect of 5-HT and 5-HIAA treatment on MAPK activation , we quantified the levels of activated , phosphorylated MPK-1 in total extracts of L4 larvae [11] . Treatment with 5-HT and 5-HIAA caused a similar reduction in phospho-MPK-1 levels in let-60 ( gf ) mutants . However , in the amx-2 ( lf ) ; let-60 ( gf ) background 5-HIAA exerted a stronger effect than 5-HT ( Fig 4C ) . Thus , 5-HIAA supplemented into the culture medium exerts a systemic effect to inhibit RAS/MAPK signaling in different organs of C . elegans .
We have identified several genetic modifiers of the oncogenic RAS/MAPK signaling pathway by comparing miRILs derived from the backgrounds of two highly diverged C . elegans isolates . The two parental strains used in this study display a level of sequence divergence that is comparable to the genetic variation observed in the human population [35] . The genetic modifiers of RAS/MAPK signaling we identified through this quantitative approach could not have been found in conventional forward genetic screens , as each locus alone only exerts a minor effect . Interestingly , both genetic backgrounds analyzed contain QTLs that enhance ( i . e . QTLs 2 and 3 for Bristol ) as well as QTLs that reduce ( i . e . QTL 1 for Bristol ) the relative strength of RAS/MAPK signaling . Thus , each isogenic background may represent a balanced state exhibiting intermediate RAS/MAPK pathway activity thanks to the opposing effects of the different modifiers . The interplay of these modifiers may be necessary to keep the activity of the RAS/MAPK pathway within a certain range and avoid the detrimental effects caused by increased or reduced RAS/MAPK signaling . The molecular characterization of one particular region ( QTL 1b ) identified the monoamine oxidase gene amx-2 as a negative regulator of RAS/MAPK signaling in multiple organs of C . elegans . Though , the RNAi analysis of the QTL1b region indicated that this region contains possibly up to ten additional polymorphic modifiers of RAS/MAPK signaling besides amx-2 . Single-copy gene insertion experiments [20] demonstrated that the Bristol variant can fully rescue an amx-2 deletion allele , while insertion of the Hawaii locus had no significant effect in this assay , indicating that amx-2 activity in the Hawaii background is severely reduced or even absent . The identification of a monoamine oxidase as a negative regulator of RAS/MAPK signaling was initially a surprising result , since MAOA is primarily known for its role in degrading neurotransmitters in the nervous system [12] . However , we observed strong AMX-2 expression in non-neuronal tissues , especially in the intestinal cells . The 5-HT metabolites such as 5-HIAA that result from AMX-2 catalysis are likely to be released into the body cavity in order to modulate RAS/MAPK signaling in distant organs . Such a globally acting regulatory mechanism may be useful to rapidly adjust RAS/MAPK signaling in response to changing environmental conditions , after food intake and to adapt the speed of reproduction [36] . Epistasis analysis by applying exogenous 5-HIAA points at a step downstream of RAS and upstream of MAPK that is repressed by 5-HIAA . Hence , 5-HIAA may simultaneously repress the RAS/MAPK pathway activated by various receptor tyrosine kinases in different tissues [4] . The observation that 5-HT exerts an inhibitory effect even in amx-2 ( 0 ) mutants may be explained by the presence of additional redundant MAOs , notably AMX-1 , and by spontaneous oxidation of 5-HT . Our epistasis analysis further indicates that 5-HIAA acts via the SER-1 receptor , which activates the EGL-30 Gqα signaling pathway [31] . One possible scenario is that 5-HIAA and 5-HT exert opposing effects on SER-1 , such that the balance between 5-HT and 5-HIAA levels determines the strength of EGL-30 activation , which in turn promotes RAS/MAPK signaling . In line with this model , Moghal et al . [37] have previously shown that egl-30 signaling in neuronal cells positively regulates vulval induction under different environmental conditions . The role of 5-HT as a neurotransmitter in the mammalian nervous system is well documented [12] . However , over 90% of the 5-HT in the human body is found outside of the nervous system , especially in enterochromaffin cells of the intestine [38] . Remarkably , Rybaczyk et al . [39] reported that the expression of the human 5-HT degrading enzyme MAOA , the closest AMX-2 homolog , is consistently down-regulated across many human tumor types . The functional implications and mechanisms of reduced MAOA expression in cancer cells are unclear . Our findings that systemic application of the 5-HT metabolite 5-HIAA globally inhibits RAS/MAPK signaling may explain the physiological consequences of MAOA down-regulation . Tumors expressing low levels of MAOA may generate less oncostatic 5-HIAA and at the same time contain higher levels of 5-HT , which can promote tumor growth and survival via cross-talk to the RAS/MAPK pathway [40 , 41] . Thus , MAOA levels may set a global threshold for the activation of the RAS/MAPK cascade by different extracellular signals . To our knowledge , 5-HIAA is the first endogenous small molecule that acts as a systemic inhibitor of the RAS/MAPK pathway .
Strains were maintained on NGM agar seeded with OP50 bacteria at 20°C [42] , unless otherwise stated . C . elegans Bristol refers to the wild-type N2 strain and Hawaii to CB4856 [8] . Transgenic lines were generated as described in [32 , 43] . LG I: amx-2 ( ok1235 ) , egl-30 ( tg26 ) [31]; LG II: let-23 ( sa62 ) [21] , let-23 ( sy1 ) [44] , tph-1 ( n4622 ) [29]; LG IV: let-60 ( ga89 ) [32] , let-60 ( n1046 ) [7] , let-60 ( n2021 ) [7] , lin-1 ( n304 ) [45]; LG V: rde-1 ( ne219 ) [46] LG X: ser-1 ( ok345 ) [47] . Transgenic strains: rde-1 ( ne219 ) ; duIs[Pelt-2::rde-1 ( + ) ; pRF4] [23] , let-60 ( n1046 ) ; rde-1 ( ne209 ) ; zhEx418[Plin-31::rde-1 ( + ) ; myo-2::mCherry] [24] , gaIS37[HS-mpk-1 , dmek] [27] , let-60 ( n1046 ) ; saIS14[lin-48p::gfp] [34] , zhEx533[Pamx-2::gfp , Pmyo-2::mcherry] , amx-2 ( ok1235 ) ; zhSi73[amx-2 Bristol]; let-60 ( n1046gf ) ; zhSi74[amx-2 Hawaii]; let-60 ( n1046gf ) ( all this study ) . miRILs were generated by crossing CB4856 males with MT2124 ( let-60 ( n1046 ) ) hermaphrodites . In the F2 generation , lines homozygous for the n1046 allele were singled out and allowed to self-fertilize for 10 more generations to reach homozygosity by random cloning of individuals . At generation F12 , lines were regarded as isogenic and frozen for long-term storage . All 228 miRIL lines were genotyped with the following 72 FLP markers as described in [15]: zh1-17; zh1-10a; zh1-07; zh1-18a; zh1-03; zh1-27; zh1-34; zh1-01;zh1-23; zh1-15; zh1-08; zh1-06; zh2-04a; zh2-16; zh2-07; zh2-13; zh2-19; zh2-02; zh2-20; zh2-25; zh2-27; zh2-09; zh2-10; zh2-12; zh3-17a; zh3-07; zh3-06; zh3-08; zh3-28; zh3-15; zh3-04; zh3-02; zh3-05a; zh3-35; zh3-10a; zh3-11; zh3-13; zh4-04a; zh4-5; zh4-06; zh4-16; zh4-08; zh4-17; zh4-18; zh4-19; zh4-20; zh4-21; zh4-12; zh5-13; zh5-03a; zh5-14; zh5-05; zh5-16; zh5-17; zh5-18; zh5-11; zh5-12; zh5-08; zh5-21/22; zh5-09zhX-17; zhX-08; zhX-13; zhX-15; zhX-10; zhX-24; zhX-07; zhX-12; zhX-11; zhX-21a; zhX-06; zhX-23 . miRILs that contained a 100% Bristol genotype and miRILs lacking the n1046 allele were excluded from further analysis , and miRIls with identical genotypes were combined . These criteria reduced the 228 initial miRILs to 173 informative lines . To generate ILs in the n1046 background , the ewIR ILs from [14 , 17] were crossed with the MT2124 ( let-60 ( n1046 ) ) mutant . For the exact breakpoints of the ILs used , see [17] . FLP mapping with 7 to 8 markers in the respective regions was used to identify and verify lines homozygous for the introgressions and exclude the presence of additional recombination events . Control siblings without an introgression were isolated in parallel , and the multivulva phenotype was used to identify homozygous let-60 ( n1046 ) lines . To quantify the VI , at least three independent introgression lines were compared to three sibling lines containing the let-60 ( n1046 ) allele but no introgression . To measure the VI , vulval induction was scored in L4 larvae using Nomarski optics as described [48] , and the average number of induced VPCs per animal was calculated . The duct cell duplication phenotype was scored using the lin-48::gfp marker to visualize the duct cells using fluorescence microscopy [34] . The oocyte maturation phenotype was scored in 2 day old adults under Nomarski optics microscopy . QTL mapping was performed using a single marker model on the per miRIL averages . Significance threshold was estimated using 1000 permutations [14] . All QTL data , phenotypes , QTL profiles and genotypes are stored in www . WormQTL . org [49] . Gene knock-down was carried out using RNAi feeding according to [18] . For intestine-specific RNAi , OLB11 ( rde-1 ( ne219 ) ; duIs[Pelt-2::rde-1 ( + ) ; pRF4] ) [23] was crossed with the MT2124 ( let-60 ( n1046 ) ) strain . For vulva-specific RNAi , the strain AH2927 ( rde-1 ( ne219lf ) ; let-60 ( n1046 ) ; zhEx418[Plin-31::rde-1; Pmyo-2::mcherry] ) described in [24] was used . A 7 . 8 kb genomic fragment spanning the entire amx-2 locus was amplified with the primers OTS123 ( GATTTTGGAGAAGAAACGAGGG ) and OTS124 ( ACTTCACTATGTTCCTCTACCG ) using either Bristol or Hawaii genomic DNA as template and subcloned into the XhoI restriction site of pCFJ151 [20] . Single-copy insertions of the amx-2 Bristol and amx-2 Hawaii containing plasmids into the ttTi5605 region on chromosome II were generated using the protocol by [20] to yield zhSi73 and zhSi74 , respectively . The insertions were verified by PCR amplification using primers flanking the insertion site before crossing them into the amx-2 ( lf ) ; let-60 ( gf ) background . For each genotype , at least three independent lines were scored . Primers OTS219 ( AAA AGG ATC CTT AGG TTT ATT GCT GGA AAA AT ) and OTS220 ( AAA AGG ATC CCC TTA ACC AAA TTT CAT ACC C ) were used to amplify 4kb of upstream promoter region . The PCR fragment was further cloned into the BamHI restriction site of pPD95 . 67 to generate a the Pamx-2::gfp transcriptional reporter plasmid that was co-injected at 50ng/μl with 2 . 5ng/μl of the pharyngeal Pmyo-2::mcherry marker . Animals were grown in 100ml liquid cultures and harvested by flotation on 50% sucrose . Worm pellets were resuspended in 2ml PBS buffer and lysed using a swing-mill homogenizer followed by high-speed centrifugation to remove insoluble debris . Total protein concentrations were measured in each sample using the amidoblack staining assay [50] . 5-HT levels were determined with an ELISA kit according to the manufacturer’s instructions ( BA E-5900 , Labor Diagnostika Nord ) and normalized to the total protein concentrations in the extracts . The average 5-HT concentrations for each genotype were determined with two separate measurements , each done in triplicate using extracts obtained from two independently grown cultures . Standard NGM plates were supplemented with the indicated concentrations of serotonin ( 5-HT ) ( H9523 , Sigma ) , 5-Hydroxyindoleacetic acid ( 5-HIAA ) ( H8876 , Sigma ) , dopamine ( DA ) ( H8502 , Sigma ) or Melatonin ( MT ) ( M5250 Sigma ) and kept in dark at 4°C prior to use . Phospho MPK-1 levels in total extracts of C . elegans L4 larvae were determined by Western blotting as described in [11] . As loading controls , total MPK-1 levels were quantified on parallel blots loaded with the same amounts of protein ( 20μg ) from the identical samples . Protein bands were quantified using the integrated density function in ImageJ . The ratios of phospho-MPK-1 to total MPK-1 levels were calculated for each extract and normalized to the ratios in untreated controls . Antibodies used: anti-MAP Kinase ( Sigma-Aldrich , M5670 ) , anti-phosphoMAP Kinase , Activated ( Diphosphorylated ERK-1&2 , Sigma-Aldrich , M8159 ) .
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Mutations that activate a RAS oncogene are found in a large proportion of human cancers . In this study , we have used the roundworm Caenorhabditis elegans ( C . elegans ) as a model to investigate how the genetic composition of the animal affects the outcome of oncogenic RAS mutations that activate the MAPK pathway . By comparing the effects of activated RAS/MAPK signaling in two genetically different C . elegans strains , we have identified the monoamine oxidase A ( MAOA ) gene amx-2 as a negative regulator of RAS/MAPK signaling . MAOA enzymes are primarily known to catalyze the degradation of the neurotransmitters dopamine and serotonin . Here , we show that a specific serotonin degradation product that is produced by MAOA ( 5-HIAA ) inhibits RAS signaling in different organs of C . elegans . Thus , by producing the inhibitory serotonin metabolite 5-HIAA the MAOA enzyme systemically controls the activation of the RAS/MAPK pathway .
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[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2015
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Systemic Regulation of RAS/MAPK Signaling by the Serotonin Metabolite 5-HIAA
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Control of human African trypanosomiasis ( sleeping sickness ) in the Democratic Republic of Congo is based on mass population active screening by mobile teams . Although generally considered a successful strategy , the community participation rates in these screening activities and ensuing treatment remain low in the Kasai-Oriental province . A better understanding of the reasons behind this observation is necessary to improve regional control activities . Thirteen focus group discussions were held in five health zones of the Kasai-Oriental province to gain insights in the regional perceptions regarding sleeping sickness and the national control programme's activities . Sleeping sickness is well known among the population and is considered a serious and life-threatening disease . The disease is acknowledged to have severe implications for the individual ( e . g . , persistence of manic periods and trembling hands , even after treatment ) , at the family level ( e . g . , income loss , conflicts , separations ) and for communities ( e . g . , disruption of community life and activities ) . Several important barriers to screening and treatment were identified . Fear of drug toxicity , lack of confidentiality during screening procedures , financial barriers and a lack of communication between the mobile teams and local communities were described . Additionally , a number of regionally accepted prohibitions related to sleeping sickness treatment were described that were found to be a strong impediment to disease screening and treatment . These prohibitions , which do not seem to have a rational basis , have far-reaching socio-economic repercussions and severely restrict the participation in day-to-day life . A mobile screening calendar more adapted to the local conditions with more respect for privacy , the use of less toxic drugs , and a better understanding of the origin as well as better communication about the prohibitions related to treatment would facilitate higher participation rates among the Kasai-Oriental population in sleeping sickness screening and treatment activities organized by the national HAT control programme .
Human African Trypanosomiasis ( HAT ) or African sleeping sickness is a parasitic disease unique to sub-Saharan Africa . It is caused by protozoa of the Trypanosoma Genus of which the species T . brucei gambiense and T . brucei rhodesiense cause disease in humans . According to WHO figures [1] , [2] the disease is present in 36 sub-Saharan countries , where 60 million people are at risk of which less than 4 million are under surveillance . In 2006 the annual number of new cases was estimated to be between 50 , 000 and 70 , 000 , but only a fraction of that number of cases is reported . Between 1998 and 2009 the number of annually reported HAT cases has dropped from 37 , 991 to 9 , 878 [2] , [3] . T . brucei gambiense causes the chronic form of the disease which is endemic in central and western Africa , while the acute form caused by T . brucei rhodesiense is found in East-Africa [4] . HAT control strategies are based on early case detection and treatment and vector control [1] , [5]–[7] . Active screening strategies conducted by mobile teams which travel from village to village have substantially lowered the case load in several African countries , most notably in Uganda and Sudan [8]–[10] . In the Democratic Republic of Congo ( DRC ) , where T . brucei gambiense is endemic , this control strategy has led to a considerable decrease in case numbers throughout the whole country , albeit with a large variability among the endemic provinces . In the North Equator and Kinshasa provinces a significant decrease in prevalence was observed . However , the number of cases detected in two other provinces , Bandundu and Kasai-Oriental , has remained unabated notwithstanding an increased screening effort [11] , [12] . Many factors could explain this intervariability in HAT prevalence among provinces which otherwise are all subject to the same HAT control strategy . Amongst others , such factors could be related to a low coverage of the population at risk , low community participation rates in active screening and treatment activities , or an inefficient management and coordination of the mobile teams . Treatment failure could also play an important role , as studies have shown significant treatment failure rates across the country's provinces , from 5 to 10% in Bandundu , to 25% in North Equator , and possibly as high as 50% in Kasai-Oriental [13] , [14] . In 2007 adherence to treatment in DRC was at 91 . 6% [15] , with very little variation between endemic provinces . A recent analysis of the operational effectiveness of HAT screening and treatment activities in Kasai-Oriental ( stagnation of infection rates ) and North Equator ( decrease of infection rates ) in 1998 , 2001 and 2005 by Lumbala ( unpublished , manuscript in preparation ) shows that low community participation rates in active screening activities and coverage of the population at risk could well explain the differences between these two provinces . While issues related to the coverage of the population at risk lie with the management and coordination of the national control programme's activities ( availability of resources , control strategy ) , community participation rates reflect the level of health service utilization which can be influenced by various social , economical and cultural factors . It is thought that the low participation rate of the rural population in the active screening activities is one of the main reasons for the continued HAT transmission in Bandundu and Kasai-Oriental . In a study on the effectiveness of active population screening and treatment for sleeping sickness in DRC , Robays et al [12] observed that the overall mean participation rate in the provinces of Equateur , Bandundu and Kasai-Oriental was 74% . However , important variability of the attendance rates between villages , between mobile teams and between provinces were observed . In Bandundu and Kasai-Oriental , participation rates as low as 64% and 50% were respectively found . The reasons for this low rate of community participation in screening activities in these two provinces has been little investigated , although a better understanding of relevant cultural and socio-economic barriers could significantly improve the effectiveness of HAT control programmes . Robays et al [16] showed for example that in DRC's Bandundu province the fear of drug toxicity , financial barriers and the lack of confidentiality during screening were the most important obstacles for participation in the HAT campaign in that province . As a result of those findings , the DRC's HAT control program abolished the nominal user fee for screening . Although this fee was minimal , it still presented a hurdle for large families with no or little income . These findings might be considered indicative for other provinces of DRC , but care should be taken not to generalise as socio-economic and cultural contexts are very heterogeneous across the country . Therefore , this study aims to document in a qualitative manner the economic and socio-cultural factors which may influence community participation in active HAT screening and treatment activities in the Kasai-Oriental province , where the disease puts not only rural communities at risk but also a large group of workers who are engaged in diamond mining activities . Additionally , as is illustrated by the difference in local languages ( Tshiluba in Kasai-Oriental , Kikongo in Bandundu ) , the Kasai-Oriental and Bandundu provinces differ distinctly on a cultural level , which may influence local disease perceptions and health seeking behaviour habits .
We performed a transversal descriptive qualitative study using the focus group discussion ( FGD ) technique in five health zones in the Kasai-Oriental province of DRC: Miabi , Tshilenge , Tshitengue , Kasansa and Mukumbi . Figure 1 shows the geographic locations of these health zones . These health zones were selected on the basis of their location in a historic and currently active focus of HAT near the Kasai-Oriental capital of Bakwanga ( Kalelu- Lubilashi ) which represents almost 70% of the total number of reported cases in the province [17] . Table 1 shows the HAT prevalence data for all five health zones in 2005 and 2007 . Along with the region's linguistic and cultural characteristics , the socio-economic setting in Kasai-Oriental is very diverse due to the presence of a diamond industry which employs many workers who live in encampments surrounding the diamond mines . In Kasai-Oriental the main populations at risk are diggers working in the diamond mines and farmers , who represent the most active groups in the population . The data collection took place in January 2008 . Thirteen FGDs were conducted . The focus groups were divided along three categories: gender , geographic characteristics ( i . e . worker camp or village ) and health zone . Table 2 shows the characteristics of each of the FGDs performed in this study . The number of FGDs conducted in the different health zones reflects the “richness” of the information found in those health zones . We continued to hold focus group discussions in a particular health zone until data saturation was reached and no new information was coming out of them . The FGDs were stratified by gender to ensure the homogeneity of the groups and to promote openness during the discussions , as women in general do not speak freely in front of men in Kasai-Oriental . One of the focus groups in Mukumbi was mixed because we were not able to identify a minimum of six women willing to participate in an FGD in that specific location . The question guide used in the FGDs was pre-tested and fine tuned in two focus group discussions performed in a health zone not included in the study area . The quality of these discussions was evaluated with the research team before finalizing the question guide . The following topics were covered: general knowledge of the disease; community practices regarding the disease; the community's attitude towards the mobile teams; the community's participation in screening and treatment activities; and community's expectations regarding HAT control services . The FGDs numbered six to nine participants who were invited by the principal investigator ( A . M . ) . They were selected at random based on their availability and whether they gave consent for their participation . Two exclusion criteria were used: ( i ) participants had to be resident of the local community for at least 2 years ( a definition also used by the national HAT control programme in their population surveillance data ) ; ( ii ) community leaders such as teachers , village chiefs and priests were excluded in order to avoid them dominating the discussion dynamics . The FGDs took place in a hut assigned by the community chief for this purpose . The average duration of the discussions was 45 minutes . They were held in the local language , Tshiluba . A local doctor and nurse , both native Tshiluba speakers , were trained to moderate and observe the FGDs . They switched roles for each discussion . Both had previously never been involved in the screening activities of the national HAT control programme . Their training , which was conducted by A . M . , consisted of a two-day course during which they were briefed on the study objectives and taught how to conduct FGDs . Each discussion was evaluated and discussed post-hoc by A . M . and the two assistants . A . M . attended all the FGDs to supervise the process . All discussions were fully recorded on a digital audio recorder . The FGDs were transcribed and translated into French by the two research assistants , who took turns in both tasks . A secretary prepared the transcripts in Microsoft Word . A . M . revised all the transcripts prior to analysis . QSR Nvivo8 was used to support the data analysis and identify trends in the data . This software allows researchers to organize and analyze complex and unstructured datasets by fragmenting and categorizing data whilst keeping a link with the source documents ( transcripts of the FGDs in this case ) . The analysis itself is an inductive process which allows themes to emerge from the data . These themes are coded into categories which are continuously refined throughout the analysis . Finally , relationships between categories are created and inferences are made . The analysis and its process were discussed with the co-investigators . The FGDs were coded , each element of the analysis representing an intervention by a focus group discussion participant . The codes were developed progressively and in an inductive manner , allowing relevant themes to emerge from the data . The coding book was discussed and refined with the co-investigators to assure the significance of the analysis . The study protocol was approved by the thematic HAT institutional review board ( IRB ) in DR Congo and the IRB of the Institute of Tropical Medicine , Antwerp , Belgium . Local community authorities were asked for permission to perform the study in their villages . Focus group discussion participants were informed about the voluntary nature of their participation . Permission for tape recording the conversations was requested prior to starting each of the focus group discussions . Anonymity of the participants was guaranteed and no personal details were recorded . Oral consent was obtained and audio recorded before the start of each focus group discussion . Oral consent was preferred since regional literacy levels are low . This procedure for consent was approved by the IRB .
In general , sleeping sickness is well known in the region and is considered an affliction which has been around for many generations . In the local tongue it is referred to as ‘disama dya tulu’ , which literally translates to ‘sleeping sickness’ . “Sleeping sickness has been around for ages . We have heard people talking about it since our childhood . It is an ancient disease . ” [FG10] Nevertheless , whilst many referred to specific cases from their direct environment during the discussions -“I know sick people . My mother has the disease and so does my sister . ” [FG4]- , not all participants had personally been confronted with the disease in the past . “Well , I can't really say as I have never seen the disease . A typical example of a case ? I haven't seen one yet . ” [FG1] A number of people said they did not have any ‘scientific knowledge’ of the disease and were not completely at ease with the medical rational approach adopted by the health workers of the national control programme . “I know sleeping sickness exists , but the knowledge about how to avoid it is given by doctors who ask people to avoid this or that disease in this or that way . ” [FG10] When discussing the symptoms which they relate to the disease , behavioural problems , sleep , tiredness , fever and headaches were all commonly referred to in the FGDs . Behavioural problems and the linked personality changes on the one hand , and irregular sleeping patterns on the other , were considered the most tell-tale signs of sleeping sickness . Women seemed to be more knowledgeable about the symptoms and their relation to the disease than men . This could be explained by the women's traditional role as caregiver . When a family member becomes sick , it is generally the mother who accompanies them to the health services and who is briefed by the health staff about the disease and how to take care of the patient . Women are also more likely to participate in the disease screening activities and thus to interact with health workers , a trend which was also observed in other studies [18] , [19] . The symptoms which are typical for the early stage of the disease , such as fever , headaches and tiredness , seem to be perceived less in the worker camps surrounding the diamond mines than in the villages . When the disease is in its late neurological stage the affected person is considered to be unaware of his state . Rather , it is his entourage which identifies him as abnormal and unable to take care of himself . “When a person has this disease , she is not able to reason for herself . Only those who are at her side can do it in her place . ” [FG5] Sleeping sickness is perceived as a severe illness since many people are affected by it and many patients die . “Many people have the disease , because we have seen with our own eyes how many people have died of it . We can't give an exact number , but many people have the sleeping sickness . ” [FG4] The consequences of sleeping sickness are numerous and very visible within the communities , adding to the perceived severity . The disease has serious repercussions on the patient , the family and the community . The high case fatality rate and the iatrogenic deaths induced by the toxic drug melarsoprol are very well known . Moreover , a large number of participants in our FGDs pointed towards the neuropsychiatric sequelae in those surviving , such as lunacy and trembling hands , which do not always regress after treatment . “This disease turns people into idiots . In order to control him , he needs to be restrained by force , even if he doesn't agree with that . He doesn't want a child to come near him . If it does happen , he might kill the child . It's the same with tall people . Afterwards , when he has been treated , his spirit never returns to normal . He remains confused and doesn't know how to do things . ” [FG13] It is considered shameful to be affected by the disease and stigmatisation is common . This does however not imply a rejection by the community , but rather signifies a shift of the patient's place in the community . In other words , the social role of the patient in the community changes , together with the expectations , rights and obligations which go along with that role . At the level of the family the repercussions are for one part socio-economic since when a family breadwinner becomes ill he no longer is able to work and provide for the family's needs . “The concern is that the sick person could be working to support the family . But now with him being sick , all those who depended on him share in his misfortune . ” [FG5] The disease puts many additional strains on family ties and marriages . When the man is sick for example , the woman is often obliged to leave the house in order to avoid sexual relations with her husband , which is locally considered to be strictly forbidden for actual or recovering HAT patients . If it is the woman who is sick , the man in general goes in search of another partner . This can have drastic consequences for the family unit , as illustrated in the following quote: “Women can also get the disease , in which case the man doesn't wait . He takes another woman into his house and puts his partner out on the street together with the children , as they might be sorcerers , so they are banished . We see them every day . We call them ‘the children of the market’” [FG6] . In some instances a case of HAT can also lead to family conflicts caused by the search for a potential sorcerer considered to be at the origin of the disease . “Sometimes they say that the sick person's mother , paternal aunt or uncle is at the source of the disease . They seem to forget that the tsé-tsé fly is where the disease comes from . ” [FG13] On the community level , an increase of HAT cases can have an impact on the general development of the village . “One can say that many people mainly work in the fields . When someone becomes sick , she no longer has the strength to cultivate the fields . That has a negative impact on the village because she no longer produces food for the village . ” [FG5] In extreme cases this can even lead to an implosion of the local community . “When many people , or everybody , becomes sick , no work is done anymore . Many have become idiots , others behave like madmen . The village is dead . ” [FG4] Such social and economic consequences can be very far reaching and eventually lead to forced migration to other villages . In general , the vector of African sleeping sickness and its role in disease transmission is well known in the communities . The tsetse fly was elaborated upon in all FGDs and is locally known under various names: dibudu , bibuiba and kabwibwibwa . “The fly bites , she leaves behind this disease . She can be found in the villages , in the forests and near water . This insect that we call ‘dibubu’” [FG2] However , several other causes or modes of transmission were also stated . For example , traditional beliefs and sorcery were sometimes referred to when the spread of African sleeping sickness was discussed . “When the adults of the village address solemn words against this disease , it diminishes . So it is provoked by the realm of darkness . ” [FG6] Some women talked about the role of the amaranth—a green plant cultivated in the region—and pigs as a source of the diseases . “When we arrived here , they told us that the amaranth gives people the sleeping sickness . When a person eats the amaranth , she will get the sleeping sickness . ” [FG8] “I have already heard that the disease comes from the pigs here in the village . They say that bad pigs carry this disease . ” [FG7] Such beliefs are likely grounded in the indirect role amaranths and pigs might play in the diseases , transmission , since tsetse flies seem to be more abundant in and around crop fields and animal pens . Other discussants thought that the members of the mobile teams played an important role in spreading the disease , transmitting the disease while they perform the screening . “Observing how those who have the disease suffer , the population thinks that the nurses of the team carry the disease and transmit it to you the moment you stand in front of them . ” [FG5] Finally , although rarely mentioned in the focus group discussions , contagion was also elaborated as a means by which the disease spreads . “Because it is a contagious disease . If we eat together with him , we might get contaminated . ” [FG1] In general , the communities talk about vector control activities as the most important way of prevention . This further reflects their general understanding of the tsetse fly's role in the transmission of the disease . “We take our machetes to cut down the trees , palms and others . Bibuibua will be scared and take flight . ” [FG3] “How the disease can be avoided ? By pouring medicine in the rivers and placing traps for the bibuibua . ” [FG3] “That person should be educated and told to wear white clothes when she goes into the jungle . ” [FG6] On the other hand , there is a feeling that prevention is in essence impossible because villagers cannot abandon their livelihood activities , which take place in locations , such as the fields , where the tsetse fly is found . “There is no way to avoid contact since when we go to the fields the flies bite us . ” [FG6] The importance of raising awareness in communities is also referred to as an efficient way of preventing infections . The mobile teams are seen as an important channel in this respect . When they arrive in a village and before they start the screening activities , the nurses of the team give a lesson on how to avoid the disease . “They explain us how we can avoid getting the disease , how the disease enters the village and why we should avoid shabbiness around our houses . ” [FG3] The low attendance rates at the screening activities organised by the national HAT control programme pose a significant problem . When discussing the reasons for this observation during the FGDs , six main barriers were identified: giving priority to occupational activities; the toxicity of the drugs used in treatment; distrust towards the nurses of the mobile teams; fear of lumbar punctures; fear of unsolicited HIV/AIDS tests; and the lack of confidentiality during the screening procedure itself . The population wakes early in the morning to leave for the diamond mines or crop fields . The mobile teams generally arrive in the villages later in the morning , with little or no prior notice , after most of the working populace have already left . Furthermore , people tend to avoid screening as long as they consider themselves to be healthy . Why risk being diagnosed and having to give up your livelihood activities for a prolonged period of time for treatment if you do not feel sick ? “The people are afraid . Everyone reasons that if they catch me with sleeping sickness , I will no longer be able to do all my work . My activities won't be able to take place anymore . So it is better not to be tested as long as I don't feel sick . Once I do , then I go to the doctors . They will take care of me . ” [FG12] This quote not only refers to the inability to work during treatment , but also for a significant period after treatment . The root of this logic lies in the regionally accepted notion that one must adhere to a number of prohibitions for six months after having been treated for HAT . Labouring is such a prohibition and is therefore considered to be strictly forbidden during the six-month rest period . These prohibitions are further elaborated below in the section on barriers to treatment . Drug toxicity is generally considered an important barrier to participation in the active screening activities . Also in this context people do not feel compelled to participate in the screening process as long as they feel healthy and consider the risks of screening to outweigh the benefits . “Many people have died , even the one who has only been injected once . You see him die , and he wasn't even sick . People are frightened and think: ‘If I have myself tested , I might be giving up my activities for nothing and I might even die . ’” [FG3] As was previously indicated when discussing the perceived aetiology of HAT , there seems to be a degree of distrust from the community towards the nurses of the mobile teams . In several FGDs the latter were suspected of injecting the disease during the screening procedure . This idea arises from the perception that even people considered to be in good health are regularly diagnosed with the disease by the mobile teams . “The people refuse to have themselves tested because the nurses are going to inject them with the disease . They leave the insect of sleep behind through their injections , making us sick . ” [FG7] Another example of this distrust which was voiced in several FGDs is the belief that an HIV test is part of the HAT screening procedure . There is a fear that one's potentially positive HIV status could be disclosed to the community , a risk people are not willing to take given the consequences . If someone is identified as HIV-positive , he or she becomes the focus of mockery and runs the risk of being rejected by the community . “They refuse to present themselves to the doctors because it is possible they are caught with AIDS . It is not unlikely as AIDS is present here . ” [FG9] The screening procedure itself was also criticized in the discussions . Especially the lack of confidentiality during the screening activities was considered to be an important issue . The procedure mostly takes place on a village square in plain sight of all those queuing up for the screening . It is considered embarrassing to be tested in public . Furthermore , people are afraid their disease status would become public knowledge . “Me , I feel shame about the possibility of being caught with this disease in public because I would be mocked . Therefore , I don't present myself for these tests . ” [FG5] The lumbar puncture in open air , which is part of the screening procedure , is also considered as a significant barrier . “Others are afraid of the syringe as it hurts in the spine . We are afraid of it . ” [FG5] Cases confirmed by the mobile teams are referred to HAT treatment centres . However , also here several important barriers can be identified . The toxicity of the drugs , the financial inaccessibility , the prohibitions related to the treatment , the lacking geographic accessibility of the treatment centres , the sense of feeling healthy notwithstanding a positive diagnosis , and the fear of the regular lumbar punctures which are performed during the follow-up of the treatment are all elements which influence one's decision to seek treatment after positive diagnosis by the mobile teams . Treatment of sleeping sickness in the region is free , but presents significant indirect costs to the patient and his escorts , mainly related to the travel to treatment centres on one hand , and nourishment on the other . “When I was caught with the disease , I left the village and left to Gandajika . I didn't have any family there , I was accompanied by my wife . When we ran out of food , we had to go back to our village for food before returning to the treatment center . This was a huge problem . That is why offering treatment in centres far away from the village brings along many difficulties to the sick . How will they get to the centre ? How do they feed themselves ? How can they be monitored by their relatives ? ” [FG13] This opinion , especially regarding the financial repercussions , was shared by many , though financial factors seemed to be less of a barrier for those living in the encampments surrounding the diamond mines . One's perceived health state is not only a factor in deciding whether to participate in the screening activities , but also for the next step , when a confirmed HAT case is referred to a treatment centre . Some people diagnosed with sleeping sickness simply do not feel sick and do not see the need to go through the long and painful process of treatment . “They had diagnosed her with this disease . Up to today she has decided not to receive treatment and she still is as she always has been , in good health . So the people make mistakes with their tests , and therefore we can't have them done . ” [FG5] Of special interest are a number of generally accepted prohibitions linked to the treatment of sleeping sickness . Patients are expected to adhere to these prohibitions during a six month resting period after treatment . The following prohibitions , illustrated by the quotes in Table 3 , were elaborated upon during the FGDs: no walking in the sun; no warm meals and hot spices; no alcohol consumption; no smoking; no heavy labour; and no sexual relations . The social and economic implications of these prohibitions form important barriers to HAT screening and treatment activities . The patient's chance of survival and the probability of making a full recovery are perceived as being directly linked to the degree to which the patients stick to the prohibitions . Treatment failure and other complications are blamed on the individual , reasoning they brought it upon themselves by not adhering to the prohibitions . Because of their importance , there is a strong element of social control involved , as the patient's entourage is mobilised to help him stick to the prohibitions . The communities are very much aware of these prohibitions . They were a recurring theme in most FGDs . Although the cited six month rest period is an existing guideline from the national HAT control programme [20] , the specific prohibitions mentioned in the FGDs are not .
African sleeping sickness is well known and recognised as a serious disease in the communities of Kasai-Oriental province . In general the symptoms , vector , and treatment procedures for sleeping sickness are well known amongst the population . Fear of drug toxicity , lack of confidentiality during screening procedures and financial barriers were all elaborated upon in the FGDs as primary reasons for non-participation in the active screening activities organised by the national control programme . These findings are in line with a similar study conducted in the province of Bandundu [16] , although two additional important barriers came forward in our focus group study . The first is the apparent incompatibility between the itineraries of the mobile screening teams and the population's livelihood activities . By the time the mobile screening teams arrive in the village , many workers have already set out to the diamond mines or crop fields . Part of this problem could be due to a lack of communication between the mobile teams and the communities . Improved planning of the screening activities to assure compatibility with the local population's habits would also be important in this respect . Factors such as daily routines and seasonal variations in the communities' activities should therefore be taken into account . A second barrier which seems to be of particularly high importance in Kasai-Oriental is the generally accepted belief that a HAT patient must adhere to a number of prohibitions for a period of six months after receiving treatment . These hold important social and economic implications . For example , it is forbidden to perform heavy labour for the first six months after treatment . This prohibition makes it nearly impossible to make a living during this period and signifies an important loss of income . Additionally , by implying significant restrictions on participation in everyday activities , the prohibitions also lead to a degree of social exclusion and put a considerable amount of pressure on family relationships . Strong social control regarding the prohibitions is in place and victim blaming is common . When a person becomes sick , suffers a relapse or dies during the rest period , this is considered to be the result of non-adherence to the prohibitions . Given the profound impact of the six month rest period and the accompanying prohibitions , their observed role as a barrier to active HAT screening and treatment activities in Kasai-Oriental is not surprising . Even when a person is diagnosed with HAT they will refuse to go to treatment centers up until the time they become severely ill . For the national HAT control programme , a good treatment is one that is administered within 10 days after diagnosis . The problem is however that part of the population refuses to even participate in active screening activities , as they prefer not to know with certitude whether they have sleeping sickness , thus ensuring they are able to avoid treatment and all the prohibitions linked therewith . Although similar prohibitions were reported in the Bandundu study by Robays et al [16] , in our study they seem to be a more profound element in the motivations of the community's actions as they constituted a recurring theme in almost all focus group discussions . Notwithstanding the important role the prohibitions seem to take up in the communities' perception regarding HAT , their precise origin remains unclear . However , the fact that the prohibitions do not seem to have a medical basis does not make them any less significant as a barrier to HAT screening and treatment . On the contrary , a better understanding of their origin would be a first step in a process which could lead to a significantly higher participation rate in the national programme's screening activities in Kasai-Oriental . Further in-depth research is therefore necessary to document them . The focus of such research should not only be on the communities , but should also investigate the possible roles of the health workers in HAT treatment centers and the mobile teams . The prohibitions related to nutrition may lie in traditional naturalistic or holistic beliefs about illness and health . Maintaining a form of natural balance is a central concept in such theories . Many Hispanic , African , Arab and Asian cultures incorporate elements of this basic idea in their understanding of disease . The Yin/Yang theory used in Asian cultures is a well-know example of such an approach . Another relatively common holistic framework used in traditional understanding of health and illness is based on a hot/cold dichotomy [21]–[23] . Some diseases are considered hot , whilst others are considered cold . The same dichotomy is applied to beverages , foods , herbs and medicines . Classification in either category is not necessarily based on physical characteristics of the item , but is more often according to their perceived effects on the body or their association with natural elements . There is a strong element of flexibility and subjectivity at play in the process of hot/cold classification . Regional and cultural variations in classification are therefore common . However , what is considered to be universal in this mode of thought is the practice of perceiving disease as a hot-cold imbalance which must be restored in order for the sick person to be cured . A similar logic might be at work behind the prohibitions which forbid warm meals , hot spices and possibly alcohol . If sleeping sickness is regarded as a hot disease , hot foods and beverages would be considered to upset the patient's hot/cold balance after treatment rather than allowing it to be fully restored . This imbalance , if not resolved , may then be perceived to lead to complications , relapse or even death . It appears from our FGDs that patients often remain with neurological and psychiatric sequelae . Sleeping sickness is a chronic disease that evolves in two stages . Stage 1 , or the hemolymphatic stage , is characterized by nonspecific symptoms and can even remain asymptomatic [24] . The treatment for early stage HAT is of low toxicity and recovery is complete . Unfortunately at this stage the patient often does not seek care as symptoms are mild or absent . When the patient does seek care , the non-specific symptoms are often confounded with malaria—which is also endemic in Kasai-Oriental—leading to misdiagnosis and a delay in receiving correct treatment . Stage 2 HAT is characterized by an infection of the nervous system which leads to neurological and psychiatric disorders . It is often only at this stage that a patient seeks care . The first-line treatment for second- stage HAT in DRC at the time of this study was Melarsoprol , a toxic organic compound of arsenic . Treatment with Melarsoprol can cause severe side-effects such as arsenical encephalopathy in 5 to 10% of cases , which can lead to neurological damage and death [25] , [26] . In Kasai-Oriental an important population at risk consists of diamond diggers , an active portion of the population . These workers are very concerned about their professional activities to the point of ignoring their own health-related problems . As long as they have the strength to continue working in the diamond mines , they will not seek medical care , even if they are ill . Only when they become severely sick and are no longer able to work do they go to health centers . They therefore often present with well-advanced HAT infections . Treatment at such a late stage is problematic and mostly does not improve the neurological and psychiatric sequelae caused by the disease , even after cure . Melarsoprol treatment failure is high in Kasai-Oriental and—as was shown in this study—is often accredited to non-compliance to the 6 month resting period and the many prohibitions linked therewith . Patients are blamed and are themselves held responsible if they do not make a full recovery after treatment . Ultimately , the fear of possibly failing to comply with these prohibitions becomes a barrier to active HAT screening and treatment in itself . The toxicity of the drugs used to treat HAT remains an important barrier in a person's decision to participate in the national programme's active screening activities . The adverse effects of Melarsoprol are well documented [26] , and the communities are well aware of the risks associated to its use in treatment . In November 2009 the WHO started rolling out a new nifurtimox-eflornithine combination therapy ( NECT ) in DRC . NECT has shown very high cure rates and low adverse effect rates . Furthermore , it is relatively easy to administer , requiring only fourteen infusions over the course of ten days . Although many challenges remain [27] , NECT has the potential to hugely improve the level of HAT care which is delivered to communities . However , it would be prudent to consider that it might take some time before the implementation of NECT positively influences the participation rates in the national programme's active screening activities . After all , perceptions and beliefs such as the fear of drug toxicity are deep-rooted and do not change with the wind . The legacy of melarsoprol will therefore most likely remain a barrier for some time to come . If NECT is to live up to its full potential , it is clear that communication and sustained community participatory approaches have important roles to play in the activities of national HAT control programmes , not just in the Democratic Republic of Congo , but also in all other HAT endemic countries . Additional socio-anthropological studies similar to the one reported in this paper could offer valuable insights in this respect .
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Active screening strategies are common disease control interventions in the context of poor and remote rural communities with no direct access to healthcare facilities . For such activities to be as effective as possible , it is necessary that they are well adapted to local socio-economic and cultural settings . Our aim was to gain insight into the barriers communities in the Kasai-Oriental province of the Democratic Republic of Congo experience in relation to their participation in active screening activities for African sleeping sickness . Participation rates seem to be especially low in this province compared to other endemic regions in the country . We found several important factors to be in play , a number of which could be addressed by adapting the operational procedures of the mobile teams that perform the active screening activities ( e . g . , improved confidentiality during the screening procedure ) . However , more profound considerations were found in the form of regional beliefs related to the treatment of the disease . Although not based on rational grounds , these prohibitions seem to pose a significant barrier in a person's decision to seek diagnosis and treatment . A better understanding of these prohibitions and their origin could lead to improved participation rates for sleeping sickness screening in Kasai-Oriental .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"medicine",
"infectious",
"diseases",
"public",
"health",
"and",
"epidemiology",
"social",
"and",
"behavioral",
"sciences",
"neglected",
"tropical",
"diseases",
"anthropology",
"public",
"health"
] |
2012
|
Should I Get Screened for Sleeping Sickness? A Qualitative Study in Kasai Province, Democratic Republic of Congo
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