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In 1996 Bomgioanni confirmed previous results and showed a significantly decreased percentage of CD4 + CD45RA+ cells in peripheral blood during a relapse in RR-MS. Decrease in CD4+ CD45RA+ subset was also detectable one month before clinical relapse. Up regulation of naive CD45RA+ T- lymphocytes and parallel down regulation of memory CD45RO+ cells seemed to be one of the main mechanisms by which Linomide inhibited the MS activity. Also in this study, the higher levels of naive CD4+ cells seem to be a positive prognostic marker.
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The above-mentioned studies prove the significance of monitoring naive CD4+ cell in the follow-up of our patients and our results are in agreement with them. Higher levels of naive CD4+ cells were shown to be a positive prognostic factor in our study, specifically more than 52.3% of naive cells in CD4+ cells and 64.6% of all naive lymphocytes. A reduction of naive helper cells by one third (exactly 30.5% in relationship to baseline) and only a slight reduction of total CD45RA+ lymphocytes (4.1% in relationship to baseline, statistically significant in the first, third and fourth year of follow-up) was associated with clinical worsening (CDP). According to these results, we could use monitoring of naïve CD4 + changes as an important predictive factor, but further studies will be required to examine the optimal frequency of (monitoring) measurement.
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The contribution of B cells to the mechanisms of conversion to MS is shown not only by CSF B-cell associated biomarkers, but also by peripheral B cells. Kreutzfelder in 1992 showed a reduction of CD19+ peripheral blood in MS patients. This observation combined with the reduced percentage of CD19+ circulating cells in patients with a stable RR form of MS Bomgioanni suggested that such a decrease is due to the sequestration of cells within the CNS. Lee-Chang supported this theory by finding an up-regulation of α4 integrin on peripheral B cells that may enhance B-cell accumulation within the CSF at the time of CIS.
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Our results confirm reduction of B cells in PB as a negative predictive factor. The cut off level of 9.5% CD19+ cells of lymphocytes was calculated and a decrease below it increased the relapse probability. Overall augmentation of B cells during our study corresponded with the finding that circulating B cells became distinctly reallocated by long-term treatment with IFN-beta, which is compatible with the protective effects attributed to this drug . Studies of the RR MS patients treated with IFN-1b confirmed that its in vivo therapeutic effects include: the inhibition of B cell stimulatory capacity and the B cells cytokine secretion changes, which may selectively inhibit Th17-mediated autoimmune response in RR MS. [33, 34] Given this, we could also conclude that lower levels of B cells in patients converting to CDMS were associated with lower responsiveness to interferon beta treatment and B cells analysis could predict need of treatment escalation so that relapse would be prevented.
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Completely different results were found in one CD5+ B lymphocyte subpopulations. Earlier, this subpopulation was thought to be a potential source of autoantibodies, but its role in autoimmunity is still being investigated. Elevated CD5+ expression on peripheral B lymphocytes correlated with MS disease activity expressed by a number of gadolinium-enhancing lesions on MRI and inversely correlated with disease duration and a recent study performed in blood of MS patients found an increase of CD5+ memory B cells in remitting stage of the disease . Villar concluded that increased percentages of blood CD5+ B cells were associated with further elevated risk of conversion to MS and relapse rate in these patients independently of cerebrospinal fluid oligoclonal bands presence and MRI. She calculated a cut off value of 3.5% of this subset for relapse risk. This value was based on mean +/− 2 SD of the percentage of blood CD5+ B cells of the control group. The association of blood CD5+ B cells with the conversion to MS suggests that this lymphocyte subset plays an important role in early phases of the disorder. We planned to use the same method for a cut off calculation, but, as we were looking for an individual predicted outcome, we used a different one.
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Number of CD5 + CD19+ lymphocytes was generally lower in our study (Table 1b) and we were not able to confirm Vilar’s results neither by presented statistical calculation nor by former one . We did not find significant changes in this population in relationship to the second relapse or CDP.
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A recent phenotyping study that performed cytometric staining for multiple cell surface markers revealed lower frequencies of circulating CD8lowCD56 + CD3-CD4- cells in untreated patients with relapsing–remitting multiple sclerosis or clinically isolated demyelinating syndrome than in healthy controls . The MS relapses and new brain lesions detected by magnetic resonance imaging are often preceded by a reduction in PB NK cell functional activity , but similar percentages of PB NK cells were detected in treated MS patients when compared to non-MS patients despite decreased frequency of CSF NK cells .
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There was a slight gradual decrease of the NK cells in our study during the whole 4 year follow-up and the value of 10.8% could be considered as a critical point. Contrary to this, an increase at year one (and three) should predict CDP. This inconsistent result could be related to the changes in NK subpopulation: CD56bright NK cells. During a follow-up we observed a decrease in the NK cells minor subpopulation, which lead to a lower control of T lymphocytes. Inter-annual increase of the NK cells preceding CDP could be caused by a transient increase of major CD56low population, which is found in chronic inflammatory process.
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Nevertheless, in our study, we only assessed the total population of CD3-CD16 + 56+ NK cells as the representation of CD56bright NK cells was low in our patients, and we therefore did not consider this population suitable for a long-term assessment and statistical analysis. However, in recent years, some publications described the necessity of examination of this subpopulation.
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Daclizumab-induced increased frequencies of circulating CD56bright NK cells were related to the therapeutic benefit of the drug . Likewise, during therapy with IFNb, the total number of circulating natural killer cell declined slightly or did not change but there was a marked increase in the proportion of CD56high NK cells in both 12 month follow up studies. One of the possible mechanisms of action of IFN-β in MS is the enhancement of NK activity, especially CD56bright NK cells . Given this finding, the analysis of NK cells seems to be a good predictive factor, in particular in combination with CD56bright subset analysis.
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In conclusion, our results confirm the potential role of monitoring early changes in immunophenotyping of peripheral blood lymphocytes for the prediction of a relapse and disability progression in patients after a first demyelinating event suggestive of MS. According to our results we can conclude that some changes in selected subpopulations should be considered as a signal for more frequent clinical and laboratory monitoring of our patients. More specifically, a decrease in B cells and NK cells populations (or a significant increase) and a marked reduction of naive CD4+ helper cells were the best predictors of a relapse or disability progression. Their predictive role has to be investigated and confirmed in further prospective studies. This study also shows difficulties of statistical assessment, and points to the fact that examination of inter-individual comparison of the measured parameters may be necessary instead of the comparison of whole groups of patients.
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Malaria parasites (Plasmodium spp.) remain one of the most severe and common causes of human disease (White, Pukrittayakamee, et al., 2014). Although interventions against malaria parasites have seen significant successes over the last 30 years (WHO 2015a), resistance has evolved to every antimalarial drug in widespread use (Hyde, 2005; White, 2004; WHO 2015a). In many cases, this resistance has been attributed to “classical” resistance mechanisms (sensu Schneider et al., 2012), including target site mutations or detoxification mechanisms (Hyde, 2002, 2005). However, changes in parasite behaviour, metabolism or life history, that is, “nonclassical” resistance mechanisms (Schneider et al., 2012), offer additional threats to drug efficacy.
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One potential mechanism for nonclassical resistance is evolving traits that give rise to higher within‐host parasite densities; this may offer protection against drugs by increasing the likelihood that some (genetically identical) parasites survive treatment (White, 1998). Experimental rodent malaria infections confirm that more virulent parasite strains, with faster within‐host replication, survive better in drug‐treated hosts (Schneider, Chan, Reece, & Read, 2008; Schneider et al., 2012). But within‐host densities are at least in part governed by a resource allocation trade‐off in malaria and other sexually reproducing parasites: achieving higher within‐host densities comes at the cost of producing fewer specialized sexual stages (gametocytes) that are required for transmission (Carter et al., 2013; Pollitt et al., 2011), as a parasite in a given infected host cell can follow only one of the two developmental routes. Transmission investment—by convention referred to as the conversion rate—varies plastically within artificial culture, increasing as conditions become more crowded (Bruce, Alano, Duthie, & Carter, 1990). While conversion rate can change plastically in response to changing environmental conditions, data suggest that there is parasite genetic variation for patterns of conversion (Pollitt et al., 2011; Birget, Repton, O’Donnel, Schneider, & Reece, 2017) and that this variation can be selected upon (reviewed in Bousema & Drakeley, 2011). It is well known, for example, that serial passage and culture experiments, which by their nature select for faster within‐host replication, result in reduced transmission investment (Dearsly, Sinden, & Self, 1990; Sinha et al., 2014; reviewed in Carter et al., 2013). Similarly, artificial selection for attenuation in a related parasite, Eimeria, resulted in indirect selection for earlier investment in transmission, which translated into a substantial reduction in total transmission potential (McDonald & Shirley, 2009). Therefore, conversion rates represent an evolvable parasite trait essential to transmission, and the challenge is to explore if and how drug treatment might alter parasite strategies.
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Malaria parasites appear to vary transmission investment in ways thought to be adaptive (Carter et al., 2013), and theory is an essential check on intuition regarding the fitness consequences of different strategies (Greischar, Reece, & Mideo, 2016). Models have shown that reducing transmission investment—though it might appear maladaptive (Taylor & Read, 1997)—can dramatically enhance parasite fitness by increasing the parasite numbers available to produce gametocytes later on and by improving persistence in the face of immunity and competing strains (Greischar, Mideo, Read, & Bjornstad, 2016a; Greischar, Read, & Bjørnstad, 2016; Koella & Antia, 1995; McKenzie & Bossert, 1998; Mideo & Day, 2008). It remains challenging to show experimentally that these predicted patterns are adaptive, actually improving parasite fitness in the face of environmental change, as techniques for forcing parasites to make alternative life history decisions are currently not available. However, the development of improved statistical methods now allows more accurate estimates of conversion rates in vivo (Greischar, Mideo, Read, & Bjornstad, 2016b), and theory is urgently needed to form clear expectations to compare with natural patterns. In contrast, conversion rates are comparatively easy to integrate into mathematical models by simply varying allocation to asexual growth and gametocyte production. Mathematical models demonstrate that changing allocation patterns can have significant impacts on parasite fitness (i.e., transmission potential) and can predict the optimal pattern in different environments (Greischar et al., 2014; Greischar et al., 2016a; Koella & Antia, 1995; McKenzie & Bossert, 1998; Mideo & Day, 2008). Understanding how selection imposed by drugs may alter transmission investment is critical, as any changes will have both clinical and epidemiological consequences.
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| 99.7 |
Here, we predict the resource allocation patterns of malaria parasites that maximize fitness in drug‐treated hosts. We extend a previously published mechanistic model of within‐host malaria infection (Greischar et al., 2014; Greischar et al., 2016a) and use numerical optimization techniques to determine optimal conversion rates, that is, the proportion of infected host cells that produce transmission stages. Into this framework, we incorporate a simple model of drug action that was parameterized for the treatment of experimental rodent malaria infections with the antimalarial drug pyrimethamine (Huijben et al., 2013). By holding constant the duration and timing of drug treatment, but varying dose, this heuristic model allows us to explore the predicted impact of treatment of variable efficacy—from small to large reductions in parasite load—on parasite life history evolution. We explore optimal investment in transmission stages, first, by assuming parasites are constrained to a constant conversion rate throughout infections and, second, by permitting parasites to employ time‐varying conversion rates. Finally, we quantify the extent to which altering life history according to these optimal patterns can buffer against the effects of drugs and we evaluate the consequences for host health and onward transmission.
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Following Greischar et al. (2014, 2016a), we use delay‐differential equations to model the within‐host dynamics of a malaria infection, which tracks uninfected red blood cells (R), infected red blood cells (I), extracellular malaria parasites (merozoites, M) and gametocytes (G). The change in density of uninfected red blood cells (RBCs) over time, t, is given by(1)dRdt=λ1−R(t)K−μR(t)−pR(t)M(t).
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The first term represents production of new RBCs by the host. Erythropoiesis is assumed to be a logistic function of current RBC density, where λ is the maximum realized rate of replenishing depleted RBCs and K determines the homeostatic equilibrium density. We assume that only uninfected RBCs count towards the homeostatic equilibrium because malaria parasites consume large amounts of haemoglobin during their development (e.g., Lew, 2003) and compromise the ability of infected RBCs to carry oxygen (Schmidt, Correa, Boning, Ehrich, & Kruger, 1994). We have found that including infected RBCs in this term makes little qualitative difference. In the absence of infection, RBC production balances natural death (which occurs at a rate, μ), so K=λR∗λ−μR∗, where R* represents the RBC density at homeostatic equilibrium. The final term represents a mass action infection process, and p is the rate at which merozoites invade RBCs upon contact.
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The dynamics of infected RBCs are given by(2)dIdt=pR(t)M(t)−μI−pRt−αMt−αSwhere S indicates the proportion of infected RBCs surviving development, equal to e−μα when t>α and in the absence of drugs. An infected cell is generated when a merozoite invades an uninfected RBC and can be lost via two different routes. First, infected RBCs can die at a background rate μ. Second, infected RBCs burst to release merozoites after a period of α days (i.e., 1 day for the rodent malaria parasite, P. chabaudi). For simplicity, we omit immune responses that remove infected RBCs, although simulations of this model including a saturating immune response have delivered similar optimal conversion rate profiles (results not shown).
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The dynamics of merozoites and gametocytes are described as(3)dMdt=1−c(t)βpRt−αMt−αS−pR(t)M(t)−μMM(t) (4)dGdt=c(t)pRt−αMt−αS−μGG(t)where c(t) is the proportion of parasites in a given cohort of infected RBCs that become gametocytes after successful development (i.e., the conversion rate). We allow the conversion rate to vary over the course of infection, as has been observed in experimental data (Greischar et al., 2016b; Pollitt et al., 2011; Reece, Duncan, West, & Read, 2005). The burst size, β, is the number of merozoites released from each infected RBC surviving the developmental period. Merozoites die at a rate μM, and gametocytes die at a rate μG.
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We incorporate the model of drug action presented in Huijben et al. (2013), which was parameterized to describe the consequences of pyrimethamine for Plasmodium chabaudi parasites (Landau, 1965) in infections of female C57BL6 mice (Schneider et al., 2012). According to this model, as long as the drug is present at a sufficiently high concentration in the host, it kills a fixed proportion (94%) of parasites each day. The underlying within‐host model assumed in Huijben et al. (2013) was in discrete‐time and cohorts of infected cells burst synchronously. To approximate this drug action in our model, we apply an additional death rate, μd, to infected cells. By setting μd=−ln(1−0.94)=2.81, we ensure that ∼94% of infected cells die within the 1 day parasite developmental cycle. Different drug doses, d, modify the length of drug action, L, beyond the days the drug was administered (see Figure A.1 in Appendix A, for how L varies with dose):(9)L=3.557−2.5861+e−8.821+d.
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Therefore, parasites are subject to a drug‐induced mortality rate for each day that the drugs are administered, plus an additional L days afterwards. To explore the consequences of different strengths of drug treatment on optimal patterns of conversion rates, we simulate several treatment regimes: drug doses of 0–15 mg/kg, each administered for two consecutive days (days 11 and 12 postinfection). Determining the survival of infected RBCs (S) requires integrating these mortality rates over the delay α. For the case of drug‐treated infections, that survival term is given by(10)S=exp(−μt),t<αexp−∫t−α11μdω+∫11tμ+μddω,11≤t<α+11,exp−∫t−αtμ+μddω,α+11≤t<L+12,exp−∫t−α12μ+μddω+∫12tμdω,L+12≤t<L+12+α,exp(−μα),otherwise.
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Given our other model parameters, these treatment regimes encompass outcomes from a small, transient reductions in parasite loads, to a strong reduction in parasite load that would prevent further transmission on the timescale of our simulation. A schematic of the model of drug action is presented in Figure A.2 in Appendix A.
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To find optimal patterns of transmission investment, we use the optim function in R version 3.0.2 and define the cumulative transmission potential as our measure of fitness. This metric translates daily estimates of gametocyte density into the probability of that density resulting in an infected mosquito, assuming mosquitoes are abundant and biting hosts on a regular basis. The relationship between gametocyte densities and transmission probability is assumed to be sigmoidal, as has been experimentally derived for P. chabaudi by Bell et al. (2012). Using their parametrization, our fitness function is calculated as(11)f(η)=∫0ηe−12.69+3.6log10G(t)1+e−12.69+3.6log10G(t)dt,where G(t) is the gametocyte density at time point t, and η is the day postinfection at which our simulated infection ends. A sigmoidal relationship between gametocyte density and transmission success has also been reported for P. falciparum (Huijben et al., 2010) and gives similar results if used instead of the fitness function described here (see Figure A.3 in Appendix A). Our model describes early infection dynamics, before major adaptive immune responses develop. We therefore simulate a 20‐day infection over which we calculate the cumulative transmission potential, as has been done previously (Greischar et al., 2016a).
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In a first set of optimizations, we define transmission investment to be a constant (c(t)=x, for all t) and determine the optimal time‐invariant conversion rate. Second, following Greischar et al. (2016a), we use cubic splines for the optimization of time‐varying conversion strategies, implemented in R with the “splines” package. Cubic splines require only four parameters to specify but allow considerable flexibility in the pattern of conversion over a 20‐day infection, and more complicated splines yield minimal fitness gains (Greischar et al., 2016a). Conversion rates must be constrained to vary between zero and one, so we take the complimentary log‐log of the value specified by the spline, that is, c(t)=exp(−exp(splinevalueattimet)). The starting values of the variables and the assumed value for each of the model parameters are given in Table 1, and each optimization is initiated by setting all spline parameters to an arbitrary starting guess of 0.5. Although no numerical optimization routine can guarantee finding a globally optimal solution, we sought to substantiate our findings by testing, for a given environment (i.e., drug dose), whether the putative optimal strategy for that environment outperformed the putative optimal strategies from other environments.
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Following previous work (Greischar et al., 2016a), we first constrained conversion rate in our within‐host model to be constant, and determined which single rate, maintained throughout the whole infection, produced the highest estimate of our parasite fitness proxy (i.e., cumulative transmission potential). In the absence of drugs, we find a similar optimal level of transmission investment as predicted previously (Greischar et al., 2016a). Drug treatment reduces the optimal level of transmission investment, with the lowest conversion rate predicted for the highest drug dose simulated (Figure 1a). We found little variation in the optimal transmission investment over low and moderate drug doses, as would be expected given our assumption that the drug dose changes the number of days of drug action rather than the killing rate (Huijben et al., 2013). For doses below 6 mg/kg, this formulation predicts little difference in the duration of drug action (see Figure A.1 in appendix A) or consequences for parasite fitness, as can be seen in Figure 1b. We therefore focus on 5 mg/kg, 8 mg/kg and 15 mg/kg as representative low, medium and high drug doses, respectively, for the remainder of our analyses. The step‐wise decrease in predicted conversion rates observed from a dose of 0 to 2 mg/kg and from a dose of 8 to 10 mg/kg closely follows the fitness effects that these increasing doses would have on parasites employing a non‐drug‐adapted conversion rate (Figure 1b, grey bars). Interestingly, we do not see a similar decrease in the predicted optimal conversion rate when the drug dose increases from 6 to 8 mg/kg, despite a substantial decrease in expected fitness for a non‐drug‐adapted strategy. An explanation for this may be found in the fact that a constant conversion rate represents a compromise, balancing the need to sustain a high enough asexual source population for conversion in the face of drug killing and having a sufficiently high conversion rate to successfully translate that asexual source population into onward transmission. Up to a dose of 8 mg/kg, slight increases in conversion rates can counteract lost fitness due to slight reductions in the asexual source population from higher doses. With a dose of 10 mg/kg or more, the asexual source population –and gametocytes– are reduced to such an extent that no more transmission is possible after the action of drugs. Therefore, the best option for a parasite is to restrain and increase the asexual source population that will be converted before the end of drug action.
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Lower conversion rates can buffer the effects of drugs. (a) Optimal constant conversion rates in the face of drug treatment (labelled as doses in mg/kg) are lower than in the absence of drugs. (b) As expected, drug treatment reduces parasite fitness (i.e., cumulative transmission potential). Grey bars indicate fitness when parasites are constrained to the drug‐free optimal conversion rate (~0.42). Black bars show the fitness gains achieved by adopting the dose‐specific optimal conversion rate (from A). With lower conversion rates, parasites are able to recoup some of the fitness that is lost due to drugs
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We assume that all parasites within an infection are genetically identical; consequently, our fitness proxy is the cumulative probability of transmission over the course of infection. As our simulated infections run for 20 days, 20 represents the maximum cumulative transmission potential that would be achieved by a parasite genotype that sustained a sufficiently high gametocyte density to transmit to mosquitoes with 100% efficacy every day. Even in the absence of drugs, parasites cannot achieve 100% transmission efficacy at every point in the simulation, especially at the beginning of the infection when parasite numbers are low; hence, the maximum cumulative transmission potential is approximately 11 for the optimal level of fixed transmission investment of 0.42 in the absence of drugs (Figure 1b). The grey bars demonstrate the fitness achieved by parasites employing this same conversion rate (0.42) in the face of drug treatment. As expected, parasite fitness is lost as drug treatment reduces numbers. Some fitness can be recouped by adopting lower conversion rates (the drug dose‐specific optima, black bars). Indeed, with low drug doses, reduced conversion rates allow parasites to maintain roughly 90% of the fitness achieved in the absence of drugs.
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Next, we allowed the conversion rate to vary over the course of the infection and determined what pattern of transmission investment would maximize cumulative transmission potential (Equation (7)). The work of Greischar et al. (2016a) suggests that, in the absence of drug treatment, optimal patterns of conversion rate comprise roughly four distinguishable phases: (i) an “initial replication” phase where parasites delay gametocyte production to increase their numbers; (ii) a “peak conversion” phase where parasites dramatically increase transmission investment to capitalize on their large numbers; (iii) a “trough” where parasites reduce transmission investment to compensate for declining numbers in the face of resource limitation; and finally, (iv) “terminal investment, ” where parasites invest heavily into gametocyte production before the infection ends. We find qualitatively similar strategies (with the same four phases) in drug‐treated infections (Figure 2). The corresponding dynamics of infected red blood cells and gametocytes are shown in Figure 3. A key difference in the predicted optimal patterns of conversion in drug‐treated compared to untreated infections is an earlier and faster reduction in conversion rates (i.e., greater reproductive restraint) following the initial peak conversion (compare black to coloured lines in Figure 2). Comparing low and medium dose treatment regimes, we find that increasing dose is accompanied by greater reproductive restraint following treatment. The best response to a high drug dose is early terminal investment, which ultimately ends the infection (see infection dynamics in Figure 3c).
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The optimal pattern of conversion over the course of infections. The black line shows the predicted best response in an untreated infection. When infections are treated (coloured lines), regardless of dose, parasites do better by reducing conversion (purple: low dose, 5 mg/kg; blue: medium dose, 8 mg/kg; red: high dose, 15 mg/kg). Drugs are administered on the days denoted by the grey bar. If drug treatment reduces the infection to a degree where parasites cannot expect any future transmission, then the best response for parasites is to terminally invest (as suggested by the red line). Note that the patterns diverge before drug treatment due to the constraints of our fitting regime; however, early differences in investment patterns contribute little to fitness differences (see text)
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| 99.94 |
The within‐host dynamics of infected red blood cells (i.e., asexual parasites; top row) and gametocytes (bottom row). Coloured lines show dynamics when parasites are using the optimal conversion profiles for a given drug treatment (a: low dose, purple; b: medium dose, blue; c: high dose, red). The black lines show dynamics in the absence of treatment, for parasites using the optimal drug‐free pattern of conversion, while the dashed grey lines show how the different drug treatment regimes impact these dynamics if parasite life history patterns are unchanged from the drug‐free optimum. Grey bars denote the days of drug treatment and the horizontal lines in the bottom row indicate the gametocyte density at which there is a 10% probability of transmitting to a mosquito, according to Bell et al. (2012)
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To identify the fitness consequences of these different strategies, we plot cumulative transmission potential over the course of infections. In Appendix A, we confirm that the putative optimal strategy against a given dose outperforms the putative optimal strategies from other doses (see Figure A.4). The optimal strategies—and the corresponding cumulative transmission potential—are similar prior to drug treatment (Figures 2 and 4, respectively). After drug treatment, the transmission investment strategies diverge, and there are clear costs to parasites that employ the incorrect strategy for the drug dose they encounter within the host (compare coloured to dashed grey curves in Figure 4). Specifically, in the absence of drug treatment, the optimal drug‐free strategy accrues fitness at nearly the maximal rate, corresponding to an almost 100% chance of transmitting to mosquitoes each day (black lines, Figure 4). But, this strategy performs successively worse in the face of increasing drug doses (dashed grey lines Figure 4; see also Figure 3 for corresponding infection dynamics). The optimal strategies for low, medium and high drug doses allow parasites to recoup a substantial portion of these fitness losses (coloured lines in Figure 4), attributable to greater reproductive restraint immediately after drug treatment (Figure 2). Notice that in the face of a high drug dose, the drug‐free strategy accrues no fitness following treatment (Figure 4c, dashed grey line), despite the fact that gametocytes are still circulating for days in those infections (Figure 3c, dashed grey line). This is because the densities are too low to achieve more than a negligible probability of transmission. In untreated infections, parasites that use reproductive restraint pay only a small fitness cost, whereas parasites employing strategies against high drug doses pay a more substantial fitness cost due to premature terminal investment (Figure 5a).
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Cumulative transmission potential (fitness) over the course of infections. Given our fitness function, a parasite can maximally transmit with a probability of 1 each day, reaching a cumulative transmission potential of 20 at the end of the simulated infection. Black lines show the fitness obtained by a parasite adopting the drug‐free optimal pattern of conversion over the course of an untreated infection. Dashed grey lines show the consequences of drug treatment on parasites using that same strategy in the face of drug treatment: (a) low dose, 5 mg/kg; (b) medium dose, 8 mg/kg; (c) high dose, 15 mg/kg. Coloured lines show the fitness obtained by parasites using the drug dose‐specific optimal patterns of conversion (from Figure 2) in the face of drug treatment and indicate that parasites can recover some of the fitness lost due to drug treatment by altering patterns of conversion. Grey bars denote the days of drug treatment
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Consequences of parasite adaptation to drug‐treated infections. (a) The cumulative transmission potential in untreated infections where parasites employ different conversion rate strategies. Reproductive restraint in untreated infections produces only small transmission costs (purple and blue line) compared to strategies for untreated infections (black line), whereas terminating an infection early has bigger fitness consequences (red line). (b) The dynamics of uninfected red blood cells in those infections. Simulations assume optimal strategies for untreated infections (black), infections treated with a low dose (purple), medium dose (blue) and high dose (red). The reproductive restraint predicted for drug‐adapted strategies leads to earlier declines in RBCs and lower minimum values (i.e., greater anaemia) when infections are not drug‐treated
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While reproductive restraint in response to treatment can, to some extent, buffer against the effects of drugs, our models predict that treatment still leads to reductions in parasite fitness and, importantly, reductions in transmission potential. As reproductive restraint necessarily means prioritization of asexual replication and it is these parasite stages that are most responsible for the virulence (harm) of a malaria infection, there may be consequences of shifting patterns of conversion at the host (or clinical) level. Drug treatment reduces infected RBC densities, even if parasites alter their conversion rates (Figure 3), but what if parasites employ drug‐adapted strategies in an infection that remains untreated? Figure 5b shows that, in an untreated host, infections composed of parasites using a drug‐adapted strategy (coloured lines) are predicted to result in much more rapid declines in uninfected RBC densities, and greater anaemia as measured by minimum RBC counts, compared to parasites using the best strategy in the absence of drugs (black line).
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Of course, the likelihood of a drug‐adapted strategy becoming fixed in the parasite population depends on the frequency that parasites encounter drug‐treated hosts, the benefits of altered patterns of conversion in a drug‐treated host, as well as the costs of that strategy in an untreated host. Using the fitness estimates for the different strategies in different environments (Table B.1 in Appendix B), we calculate the expected fitness for the drug‐adapted and non‐drug‐adapted strategies in a host population where some proportion of hosts are treated (Figure B.1). If b is the increase in fitness achieved by the drug‐adapted strain in the presence of drugs (i.e., the benefit), c is the reduced fitness of the drug‐adapted strain in an untreated host (i.e., the cost), and f is the proportion of infected hosts that are drug‐treated, then it is trivial to show (see Appendix B) that the drug‐adapted strategy has a higher fitness than the non‐drug‐adapted strategy when(12)f>cc+b.
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Given our estimated fitnesses for the different strategies in different host environments, the drug‐adapted strategy will be favoured over the non‐drug‐adapted strategy when at least ~40% of infections are treated with a low or medium dose, or at least 86% of infections receive a high dose treatment. The early terminal investment strategy predicted to be optimal in the face of a high drug dose gains only a small fitness advantage in a treated host, while it suffers a large fitness cost in an untreated host (see also Table B.1), explaining why drug treatment would have to be very common to generate a sufficient selection pressure to favour that strategy.
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The evolution of drug‐resistant parasites is a serious obstacle to the control of malaria (Dondorp et al., 2009; White, 2004). In addition to classical resistance mechanisms, we have shown that drug treatment can select for altered life history of malaria parasites and, specifically, changing patterns of allocation to transmission versus asexual parasite stages. Our work predicts that reproductive restraint is adaptive in drug‐treated infections, allowing parasites to partially compensate for the reductions in asexual densities caused by the drug. We also show that parasite adaptation to drug treatment could lead to worse outcomes for hosts that remain untreated, although as would be expected this outcome depends on the frequency with which parasites find themselves in treated hosts as well as the precise costs and benefits associated with different investment patterns in different environments.
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| 99.94 |
Experimental evidence suggests that malaria parasites do alter their investment in transmission in response to drugs. Reece, Ali, Schneider, and Babiker (2010), for example, found a decrease in conversion in human malaria parasites exposed to low doses of drugs in vitro, as our model predicts, unless they were known to be “classically” drug‐resistant parasites, which showed no change in investment (a result that highlights the multiple routes available for mitigating the effects of drugs). A similar study found no effect of drug dose on conversion rates (Peatey et al., 2009), and an in vivo rodent malaria experiment suggested that subcurative drug doses lead to increased conversion (Buckling, Taylor, Carlton, & Read, 1997). In contrast to the results of Reece et al. (2010), these latter two examples show parasite responses that appear maladaptive in the light of our model results, raising at least two further questions. First, have parasite strategies been accurately measured? Inferring conversion rates is fraught with difficulties that have only recently been resolved (Greischar et al., 2016b), and reanalysis of past data sets could reconcile the discrepancy between theoretical predictions and empirical estimates of transmission investment. Second, are parasites capable of evolving adaptive transmission strategies to the novel selection pressure of drug treatment? Addressing this question means evaluating whether the parasites in these experiments would have achieved greater fitness than ones with different responses, which necessitates tools for manipulating parasite strategies. Advances in understanding the molecular pathways associated with commitment to gametocytogenesis (e.g., Brancucci, Goldowitz, Buchholz, Werling, & Marti, 2015) may bring such tools for experimental manipulation into reach.
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| 70.9 |
Recent work has focused on dormancy as another nonclassical resistance mechanism thought to be employed by malaria parasites (e.g., Codd, Teuscher, Kyle, Cheng, & Gatton, 2011; Hott et al., 2015; Paloque, Ramadani, Mercereau‐Puijalon, Augereau, & Benoit‐Vical, 2016; Teuscher et al., 2010). This delayed development confers protection against the effects of fast‐acting drugs that decay rapidly within a host, but whether such a strategy would be beneficial against drugs with longer half‐lives is unclear. Parasites can stall their intra‐erythrocytic development for many days, but only a small fraction—less than two per cent—appear to successfully recover and resume development even at low drug doses (Teuscher et al., 2010). It is not clear that such a low percentage of parasites entering dormancy can explain malaria dynamics in patients (Saralamba et al., 2011). Further, the fitness consequences of dormancy are not intuitive: surviving the effects of drugs is clearly good from the parasite's perspective, but stalling development means stalling production of transmission stages and missing out on any transmission opportunities during the dormant phase. In contrast, parasites can recover substantially more than two per cent of their numbers by modifying transmission investment under some treatment regimes. Indeed, Figure 3 suggests that parasite densities can actually increase by an order of magnitude or more within less than 4 days and this modified life history translates to fitness gains (Figure 4). It is interesting to consider how these two mechanisms of nonclassical resistance would affect host health. At least in the short term, dormancy should reduce pathology associated with parasite replication as well as immunopathology, while reduced investment in transmission is likely to do the opposite.
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| 99.0 |
We have shown that, in principle, altered life history can protect against the effects of drugs, and while we have used a model of drug action that was parameterized for a particular drug (pyrimethamine; Huijben et al., 2013), the phenomenological description we employ should capture the effects of many different drugs. Although there will be differences among individual hosts in drug metabolism that would affect, for example, the duration of drug action, our exploration of a range of drug doses should capture much of this variation. One exception to this generality is drugs that directly target gametocytes (e.g., primaquine, White, Ashley, et al., 2014). The relative susceptibility of asexuals and gametocytes to the drug will alter the costs and benefits of producing each stage, so different drugs may be expected to have different effects on optimal patterns of transmission investment. For example, a drug with a strong gametocidal effect may generate an advantage to reproductive restraint when drugs are present but promote the production of surplus gametocytes to compensate for those killed by drugs when drugs have cleared or may promote earlier production of gametocytes to compensate for lost transmission opportunities during drug treatment. Predicting evolutionary trajectories in response to such drugs will require precise calibration of the relative susceptibility of different parasite stages.
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| 99.94 |
We have also ignored within‐host competition and thus evolution operating at the within‐host scale, but where malaria is endemic, multigenotype infections are the rule rather than the exception (e.g. Baruah, Lourembam, Sawian, Baruah, & Goswami, 2009; Juliano et al., 2010). Previous theoretical and experimental work shows that competition favours reproductive restraint (Greischar et al., 2016a; Greischar, Reece, et al., 2016; McKenzie & Bossert, 1998; Mideo & Day, 2008; Pollitt et al., 2011), so it is possible that our prediction of that same response in the face of drug treatment would remain unchanged. However, just as there is genetic variation for competitive ability (Bell, De Roode, Sim, Read, & Koella, 2006; de Roode, Helinski, Anwar, & Read, 2005; de Roode et al., 2005), there is likely to be genetic variation in sensitivity to drugs (and in P. falciparum, differentially sensitive genotypes may often share a host; e.g., Mideo et al. (2016)). If variation in drug sensitivity is unrelated to transmission investment, then it would alter the costs and benefits to different parasite genotypes of altering that investment. Modelling the dynamic consequences of competition and the interplay between different sources of resistance on the evolution of parasite life history would be an interesting route for future investigation. Importantly, there may also be genetic variation in the shape of the relationship between within‐host gametocyte densities and the probability of transmission to mosquitos. As far as we are aware, this relationship has been quantified only a few times and only for a few distinct strains (Bell et al., 2012; Huijben et al., 2010; Paul, Bonnet, Boudin, Tchuinkam, & Robert, 2007). While the qualitative shapes of these relationships remain the same, there are quantitative differences in their parametrization. We found that these differences did not alter our predictions (see Figure A.3 in Appendix A), but further empirical exploration of this relationship is warranted, as is theoretical investigation of how any quantitative changes in this relationship alter evolutionary predictions.
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| 99.94 |
While our model allows for variation across infections treated with different drug regimes and variation over time within infections, our heuristic analysis also constrains variation at both of these scales. First, to determine when evolution should favour a drug‐adapted strategy, we assumed that there were only two strategies available to parasites: the pattern of transmission investment predicted to be best in an untreated host or the one predicted to be best in the presence of a particular drug dose. In a heterogeneous host population, some intermediate parasite investment strategy may perform better than either of these two “extremes”. Second, our model does not allow for parasites to directly receive and respond to cues within infections; that is, it is not a model of plasticity. Put another way, the model implicitly assumes that parasites have perfect knowledge about the timing of drug treatment (which does not vary across treated hosts) and optimal patterns of investment may allow parasites to, in effect, prepare in advance for drug treatment. This scenario may not be too far from reality in some areas. Drug doses are standardized by WHO guidelines (WHO 2015b), and hosts likely seek treatment when symptoms appear, which generally correlates with peak parasite density (Kachur et al., 2006), although there will be variation across individual hosts in the timing of early dynamics. How much fitness could be gained by allowing parasites in our model to detect and respond to drug treatment more directly is unclear, as our results suggest that differences in investment early in infections (and, in particular, before drug treatment) have little effect on parasite fitness. Consistent with this, Greischar et al. (2016a) found that investing little in transmission at the beginning of infections is adaptive in untreated hosts, regardless of other changes to the within‐host environment. Thus, it seems unlikely that allowing parasites more flexibility in pretreatment patterns of investment would result in different life history strategies than we have predicted. On the other hand, if parasites could respond plastically to the presence of drugs in the within‐host environment (instead of through evolutionary change, as we have focused on), then this would avoid the negative consequences for host health we report.
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The evolution of classical resistance is the expected result of using chemical interventions to kill parasites (or, in evolutionary terms, reduce their fitness), but, as we have shown, failing to consider the potential for nonclassical resistance, like life history evolution, can yield overly optimistic predictions about the epidemiological or clinical effects of those interventions. Similarly, Lynch, Grimm, and Read (2008) used models to investigate the influence of different antihelminth interventions on nematode life history, finding that disease control programs may frequently select for increasingly fecund worms, with ramifications for clinical outcomes and onward transmission. In an experimental system, filarial nematodes altered their reproductive schedules in the presence of specialized immune cells, producing transmissible stages faster and in greater numbers (Babayan, Read, Lawrence, Bain, & Allen, 2010). As these are the same immune cells on which current experimental vaccines rely, this work suggests that nematodes could reduce the benefits of vaccination through plasticity in life history. Further, the mosquitoes that transmit malaria and other diseases can also respond to intervention efforts with nonclassical resistance, including, for example, changes in feeding behaviour or timing to avoid insecticide‐treated bednets (Gatton et al., 2013; Sokhna, Ndiath, & Rogier, 2013).
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| 94.0 |
An important question is how treatment recommendations would change in the light of our predictions about optimal malaria parasite life histories. Regardless of the life history shifts we predict here, parasite fitness and within‐host densities are reduced by drug treatment. This suggests that despite the evolution of nonclassical resistance, drug treatment offers epidemiological and clinical benefits. Those benefits are not as great as they would be in the absence of life history evolution and, importantly, any hosts that remained untreated could be worse off if drug‐adapted strategies became fixed in the parasite population. Further, as a result of altered patterns of transmission investment, parasites could maintain higher within‐host densities in the face of drug treatment, potentially facilitating the evolution of classical resistance. The theory developed here provides a basis for assessing the constraints and limits on parasite life history evolution in response to human interventions.
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| 99.94 |
The fungal kingdom encompasses diverse species, including a minority that have a devastating impact on human health. One of the most pervasive fungal pathogens of humans is Candida albicans 1, 2, which is a commensal on the skin and mucosal surfaces of up to 60% of healthy individuals3. As an opportunist, C. albicans can exploit a decline in host immunity or an imbalance in the host microbiome, leading to diverse pathologies such as oral thrush, vaginal candidiasis, or life-threatening bloodstream infections with mortality rates of ~40%4, 5. C. albicans thrives as a human pathogen in part due to its ability to evade host immunity by switching between yeast and filamentous morphologies, as well as due to its capacity to withstand the hostile host environment by activating robust stress responses6. The emerging paradigm is that C. albicans stress response pathways are not only critical for adaptation to host conditions, but they also enable fungal virulence and drug resistance7–11.
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| 99.75 |
The emergence of resistance to the limited arsenal of antifungal drugs impedes the effective treatment of systemic infections12–14. A poignant example is the evolution of resistance to the only new class of antifungal to be approved in decades, the echinocandins15, 16. Echinocandins block β-1,3-glucan biosynthesis in the fungal cell wall via inhibition of the glucan synthase Fks1, thereby compromising cell wall integrity. The most common mechanism of echinocandin resistance involves mutations in the drug target FKS1 17, 18; however, resistance phenotypes are also modulated by cellular stress responses8, 9, 11. Targeting core hubs in cellular circuitry that control responses to stress, such as the molecular chaperone Hsp90 or protein phosphatase calcineurin, has emerged as a powerful strategy to enhance antifungal activity against diverse fungal pathogens8, 16. A challenge in targeting Hsp90 or calcineurin in antifungal drug development is that they are conserved and important regulators in the human host.
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| 99.9 |
An expanded repertoire of potential targets is provided by effectors downstream of these cellular regulators. C. albicans mobilizes diverse stress response programs through the action of transcription factors. For example, in response to cell membrane and cell wall stress, the transcription factor Crz1 is activated by calcineurin, leading to the induction of calcineurin-dependent genes19, 20. Another example from the model yeast Saccharomyces cerevisiae is the cell wall stress-dependent activation of the transcription factor Rlm1 by the MAP kinase Mpk121. Although Rlm1 is the main transcriptional regulator of cell wall stress responses in S. cerevisiae, its function is not conserved in C. albicans 22. In C. albicans, the zinc finger transcription factor, Cas5, serves as a key transcriptional regulator of responses to cell wall stress22. Cas5 lacks an ortholog in S. cerevisiae and most other eukaryotes, and the mechanism by which it is regulated remains enigmatic.
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| 74.56 |
Activation of stress responses can induce diverse physiological changes, including modulation of cell cycle progression and remodeling of cell wall architecture23–27. The most well characterized stress response pathway involved in cell cycle regulation is controlled by the MAP kinase Hog126. In response to osmotic stress, Hog1 mediates a transient cell cycle arrest to enable cellular adaptation26. Multiple stress response pathways coordinate cell wall remodeling in response to environmental perturbations, including heat shock27, osmotic stress28, and cell wall stress29. However, little is known about whether cell cycle progression and cell wall remodeling are coordinated in response to stress in C. albicans, and whether there is a central signaling pathway that integrates these fundamental aspects of cellular biology.
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Here, we harness genome-wide approaches to elucidate the mechanism by which Cas5 orchestrates transcriptional changes in response to cell wall perturbation. We identify an unexpected role for Cas5 in coupling cell cycle dynamics to cell wall stress responses, and determine that Cas5 regulates distinct transcriptional programs under basal and stress conditions. We discover that Cas5 is activated by the type I protein phosphatase Glc7 in response to cell wall stress, and it regulates cell wall homeostasis in part through its interaction with Swi4-Swi6 cell cycle box-binding factor (SBF) complex members, Swi4 and Swi6. Finally, we describe a role for Cas5 in orchestrating nuclear division. Our work provides insight into cellular reprogramming in response to cell wall stress, and establishes regulatory circuitry that couples stress response, cell cycle regulation, and drug resistance.
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Cas5 was originally identified in a genetic screen for transcription factor mutants that are hypersensitive to the echinocandin caspofungin, and microarray analysis implicated Cas5 in the regulation of genes important for cell wall integrity in response to cell wall stress22. However, the role of Cas5 under basal conditions remained elusive. To further understand Cas5 function under basal conditions, we sought to explore the impact of Cas5 on regulation of global transcriptional profiles in rich medium. To do so, we used chromatin immunoprecipitation (ChIP) of RNA polymerase II (PolII) coupled to sequencing (ChIP-Seq) with a wild-type strain and cas5Δ/cas5Δ mutant. RNA PolII occupancy has been found to serve as a more precise measure of transcriptional regulation than steady state transcript levels, given that transcript levels depend on RNA turnover in addition to transcription factor activity and RNA PolII-mediated transcription30. As RNA PolII recruitment corresponds to transcriptional activity, an increase in RNA PolII occupancy indicates upregulation of gene expression, whereas a decrease in RNA PolII occupancy indicates downregulation30, 31. Further, strong correlation in RNA PolII binding between our biological replicates supported the reproducibility of this method (Supplementary Fig. 1). We identified 329 genes that had increased RNA PolII occupancy, and 275 genes that had reduced PolII occupancy in the cas5Δ/cas5Δ mutant as compared with the wild-type strain under standard growth conditions (Supplementary Data 1). To identify the physiological roles of Cas5, we subjected these gene sets to pathway analysis using Gene Ontology (GO). The sets that had reduced RNA PolII occupancy upon deletion of CAS5 were significantly enriched in genes with functions in diverse processes, including metabolic processes and interaction with host (Fig. 1a and Supplementary Data 1). In contrast, the gene set that had increased RNA PolII occupancy in a cas5Δ/cas5Δ mutant was enriched in genes with functions associated with rRNA processing, respiration, and amino acid biogenesis (Fig. 1a and Supplementary Data 1). We also employed Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis as a secondary approach to identify genes and pathways affected by deletion of CAS5. Consistent with our GO term analysis, many genes with RNA PolII-binding profiles that were affected by Cas5 were associated with ribosome biogenesis and metabolic pathways (Fig. 1b and Supplementary Data 1). KEGG analysis also identified a signature of differentially bound genes with functions in meiosis, cell cycle, and DNA replication (Fig. 1c and Supplementary Data 1). Thus, Cas5 governs diverse biological responses under basal conditions.Fig. 1Cas5 has a profound impact on RNA PolII binding at genes implicated in cell wall and cell cycle processes under both basal and cell wall stress conditions. a Heat maps showing the genes with increased (red) and decreased (blue) RNA PolII binding in a cas5∆/cas5∆ mutant relative to wildtype. Enriched GO processes are indicated, and were clustered using the DAVID Gene Functional Classification Tool. b Bar chart showing the number of genes differentially bound by PolII (DBGs differentially bound genes), with increased binding in red and decreased binding in blue, in a cas5∆/cas5∆ mutant relative to wildtype based on their assigned KEGG pathways. KEGG pathways with four or more genes assigned are shown. c Bar chart showing the number of genes differentially bound by PolII, with increased binding in red and decreased binding in blue, in a cas5∆/cas5∆ mutant relative to wildtype belonging to select KEGG pathways involved in cell cycle and related processes. d, e Bar charts showing the number of differentially bound genes involved in the indicated physiological pathways upon caspofungin treatment, with increased binding in red and decreased binding in blue, in a wild-type strain in response to caspofungin. KEGG pathways were grouped according to the ratio of genes with increased PolII binding to genes with decreased PolII binding in response to caspofungin, with d enriched for genes with increased PolII binding and e enriched for genes with decreased PolII binding. KEGG pathways with five or more genes assigned are shown. f Venn diagram depicting number of genes differentially bound by RNA PolII in response to caspofungin in the wild-type reference (increased, green; decreased, yellow) and cas5Δ/cas5Δ mutant (increased, red; decreased, blue) strains. See Supplementary Data 1–4 for full data sets
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Cas5 has a profound impact on RNA PolII binding at genes implicated in cell wall and cell cycle processes under both basal and cell wall stress conditions. a Heat maps showing the genes with increased (red) and decreased (blue) RNA PolII binding in a cas5∆/cas5∆ mutant relative to wildtype. Enriched GO processes are indicated, and were clustered using the DAVID Gene Functional Classification Tool. b Bar chart showing the number of genes differentially bound by PolII (DBGs differentially bound genes), with increased binding in red and decreased binding in blue, in a cas5∆/cas5∆ mutant relative to wildtype based on their assigned KEGG pathways. KEGG pathways with four or more genes assigned are shown. c Bar chart showing the number of genes differentially bound by PolII, with increased binding in red and decreased binding in blue, in a cas5∆/cas5∆ mutant relative to wildtype belonging to select KEGG pathways involved in cell cycle and related processes. d, e Bar charts showing the number of differentially bound genes involved in the indicated physiological pathways upon caspofungin treatment, with increased binding in red and decreased binding in blue, in a wild-type strain in response to caspofungin. KEGG pathways were grouped according to the ratio of genes with increased PolII binding to genes with decreased PolII binding in response to caspofungin, with d enriched for genes with increased PolII binding and e enriched for genes with decreased PolII binding. KEGG pathways with five or more genes assigned are shown. f Venn diagram depicting number of genes differentially bound by RNA PolII in response to caspofungin in the wild-type reference (increased, green; decreased, yellow) and cas5Δ/cas5Δ mutant (increased, red; decreased, blue) strains. See Supplementary Data 1–4 for full data sets
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Next, we explored the impact of Cas5 on global responses to cell wall stress. We mapped genome-wide RNA PolII occupancy during caspofungin treatment in wild-type and cas5Δ/cas5Δ strains. In a wild-type strain, 546 genes had distinct RNA PolII-binding profiles in response to caspofungin treatment, with increased binding for 294 genes and reduced binding for 252 genes (Supplementary Data 2). As expected, many genes with increased RNA PolII binding upon caspofungin exposure were important for tolerating cell wall stress (Fig. 1d and Supplementary Data 2), whereas genes with reduced RNA PolII binding were involved in diverse biological processes, including cell cycle progression (Fig. 1e and Supplementary Data 2). Strikingly, comparison of genes that were differentially bound in response to caspofungin between the wild-type strain and the cas5∆/cas5∆ mutant, revealed that >60% of caspofungin-responsive genes were dependent on Cas5. Specifically, 163 of the 294 genes with increased PolII occupancy in response to caspofungin exposure and 178 of the 252 genes with reduced occupancy were dependent on Cas5 (Fig. 1f and Supplementary Data 2–4). These findings suggest that Cas5 has a profound impact on global transcriptional responses to cell wall stress.
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Finally, we focused on those genes with Cas5-dependent differences in RNA PolII binding under basal and cell wall stress conditions. Strikingly, only 28% of genes with Cas5-dependent differences in RNA PolII binding were common to both untreated and caspofungin treatment conditions (Fig. 2a and Supplementary Data 1, 3 and 4). The Cas5-dependent genes specific to each condition had different physiological functions (Fig. 2c and Supplementary Data 4). Our analysis revealed a major overlap of genes that had Cas5-dependent increased RNA PolII occupancy under basal conditions with those that had reduced RNA PolII occupancy in a wild-type strain in response to caspofungin treatment (Fig. 2d and Supplementary Data 5), suggesting that caspofungin impedes Cas5-mediated expression of its basal-specific targets. Collectively, we identified hundreds of new Cas5-dependent transcriptional events under basal and stress conditions, implicating Cas5 in governing distinct transcriptional programs in response to different environmental conditions.Fig. 2Cas5 regulatory network is rewired in response to caspofungin treatment. a, b Heat maps comparing Cas5-dependent genes with caspofungin-regulated genes. a The left heat map shows the overlap of genes with Cas5-dependent increased RNA PolII binding with (+) and without (−) caspofungin treatment. Cas5-dependent genes are indicated by a solid blue line. The right heat map indicates those genes with increased binding (red) and decreased binding (blue) in a wild-type strain upon caspofungin treatment. b The left heat map shows the overlap of genes with Cas5-dependent reduced RNA PolII binding with (+) and without (−) caspofungin treatment. Cas5-dependent genes are indicated by a solid red line. The right heat map indicates those genes with increased (red) or decreased (blue) RNA PolII binding in a wild-type strain upon caspofungin treatment. c Bar graph showing the relative percentage of Cas5-dependent genes specific to basal conditions (blue bars), specific to caspofungin treatment (red bars), or common to both conditions (white bars) in the indicated KEGG pathways. The total number of genes belonging to each KEGG pathway are indicated. Only selected KEGG pathways are shown based on number of genes enriched for PolII binding either under basal or caspofungin conditions. See Supplementary Data 4 for complete data set. d Venn diagram depicting number of genes differentially bound in a cas5∆/cas5∆ mutant relative to wildtype under basal conditions (increased, red; decreased, blue) and genes responding to caspofungin treatment in a wild-type strain (increased, green; decreased, yellow). The 131 genes highlighted in red text represent the substantial fraction of the genes with Cas5-dependent increase in RNA PolII binding under basal conditions that have reduced PolII binding in response to caspofungin
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Cas5 regulatory network is rewired in response to caspofungin treatment. a, b Heat maps comparing Cas5-dependent genes with caspofungin-regulated genes. a The left heat map shows the overlap of genes with Cas5-dependent increased RNA PolII binding with (+) and without (−) caspofungin treatment. Cas5-dependent genes are indicated by a solid blue line. The right heat map indicates those genes with increased binding (red) and decreased binding (blue) in a wild-type strain upon caspofungin treatment. b The left heat map shows the overlap of genes with Cas5-dependent reduced RNA PolII binding with (+) and without (−) caspofungin treatment. Cas5-dependent genes are indicated by a solid red line. The right heat map indicates those genes with increased (red) or decreased (blue) RNA PolII binding in a wild-type strain upon caspofungin treatment. c Bar graph showing the relative percentage of Cas5-dependent genes specific to basal conditions (blue bars), specific to caspofungin treatment (red bars), or common to both conditions (white bars) in the indicated KEGG pathways. The total number of genes belonging to each KEGG pathway are indicated. Only selected KEGG pathways are shown based on number of genes enriched for PolII binding either under basal or caspofungin conditions. See Supplementary Data 4 for complete data set. d Venn diagram depicting number of genes differentially bound in a cas5∆/cas5∆ mutant relative to wildtype under basal conditions (increased, red; decreased, blue) and genes responding to caspofungin treatment in a wild-type strain (increased, green; decreased, yellow). The 131 genes highlighted in red text represent the substantial fraction of the genes with Cas5-dependent increase in RNA PolII binding under basal conditions that have reduced PolII binding in response to caspofungin
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Given that Cas5 is known to regulate cell wall integrity22, we sought to further evaluate the impact of Cas5 on cell wall homeostasis. We observed that Cas5 has a profound impact on RNA PolII binding to genes with cell wall functions; GO terms from process ontology revealed that 23 of the 163 genes with Cas5-dependent increases in RNA PolII binding in response to caspofungin encode proteins involved in cell wall organization and biogenesis (P-value 7.45e–06, Bonferroni Correction) (Supplementary Data 3). Among these genes were WSC1 and WSC2, which encode transmembrane sensors that respond to cell wall perturbations, yet have not been previously linked to Cas5. We confirmed the induction of these transcripts in response to caspofungin was dependent on Cas5 by quantitative RT-PCR (qRT-PCR) (Fig. 3a). We next evaluated the impact of Cas5 on tolerating cell wall stress in a wild-type strain and an echinocandin-resistant mutant. We measured susceptibility of a cas5Δ/cas5Δ mutant to two cell wall perturbing agents, caspofungin and calcofluor white. The cas5Δ/cas5Δ mutant was hypersensitive to both cell wall stressors, and the hypersensitivity phenotype was complemented by re-introduction of a wild-type allele (Fig. 3b). Given that the most common mechanism of resistance to echinocandins is mutations in the drug target gene, FKS1, we tested whether Cas5 also enables Fks-dependent caspofungin resistance. Homozygous deletion of CAS5 reduced resistance of a strain carrying the Fks1F641S substitution, suggesting Cas5 is a key regulator of target-mediated echinocandin resistance (Fig. 3b). Thus, Cas5 regulates cell wall stability during periods of cell wall stress, with a profound impact on drug resistance.Fig. 3Cas5 regulates cell wall stability under basal and cell wall stress conditions. a WSC1 and WSC2, transmembrane sensors that respond to cell wall perturbation, require Cas5 for upregulation in response to caspofungin. The transcript level of WSC1 and WSC2 were monitored by qRT-PCR and normalized to TEF1. Plotted are the log2 fold-changes in transcript levels upon caspofungin treatment relative to untreated conditions in both the wildtype (black) and cas5∆/cas5∆ mutant (gray). Error bars represent standard deviation (s.d.) from the mean of triplicate samples. Significance was measured with an unpaired t test in GraphPad Prism (**P < 0.01). b Cas5 is required for tolerance and resistance to cell wall stress. Caspofungin or calcofluor white susceptibility assays were conducted in YPD medium. Growth was measured by absorbance at 600 nm after 48 h at 30 °C. Optical densities were averaged for duplicate measurements. Data was quantitatively displayed with color using Treeview (see color bar). c Deletion of CAS5 leads to the activation of the cell wall integrity pathway in the absence of cell wall stress. A SN95 wild-type strain and a cas5Δ/cas5Δ mutant were left untreated (−) or treated for 1 h with 125 ng/ml of caspofungin (+), as indicated. Phosphorylated Mkc1 was monitored by western blot and detected with α-p44/42 antibody. Full blots are shown in Supplementary Fig. 2a. Cdc28 was detected with α-PSTAIRE antibody and used as a loading control. d RLM1, a transcription factor downstream of Mkc1 in the cell wall integrity pathway, is induced in a cas5Δ/cas5Δ mutant in the absence of cell wall tress. The transcript level of RLM1 was monitored by qRT-PCR and normalized to TEF1. Differences in transcript level upon caspofungin treatment relative to untreated conditions are plotted. Error bars represent s.d. from the mean of triplicate samples. Significance was measured with an unpaired t test in GraphPad Prism (***P < 0.001)
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Cas5 regulates cell wall stability under basal and cell wall stress conditions. a WSC1 and WSC2, transmembrane sensors that respond to cell wall perturbation, require Cas5 for upregulation in response to caspofungin. The transcript level of WSC1 and WSC2 were monitored by qRT-PCR and normalized to TEF1. Plotted are the log2 fold-changes in transcript levels upon caspofungin treatment relative to untreated conditions in both the wildtype (black) and cas5∆/cas5∆ mutant (gray). Error bars represent standard deviation (s.d.) from the mean of triplicate samples. Significance was measured with an unpaired t test in GraphPad Prism (**P < 0.01). b Cas5 is required for tolerance and resistance to cell wall stress. Caspofungin or calcofluor white susceptibility assays were conducted in YPD medium. Growth was measured by absorbance at 600 nm after 48 h at 30 °C. Optical densities were averaged for duplicate measurements. Data was quantitatively displayed with color using Treeview (see color bar). c Deletion of CAS5 leads to the activation of the cell wall integrity pathway in the absence of cell wall stress. A SN95 wild-type strain and a cas5Δ/cas5Δ mutant were left untreated (−) or treated for 1 h with 125 ng/ml of caspofungin (+), as indicated. Phosphorylated Mkc1 was monitored by western blot and detected with α-p44/42 antibody. Full blots are shown in Supplementary Fig. 2a. Cdc28 was detected with α-PSTAIRE antibody and used as a loading control. d RLM1, a transcription factor downstream of Mkc1 in the cell wall integrity pathway, is induced in a cas5Δ/cas5Δ mutant in the absence of cell wall tress. The transcript level of RLM1 was monitored by qRT-PCR and normalized to TEF1. Differences in transcript level upon caspofungin treatment relative to untreated conditions are plotted. Error bars represent s.d. from the mean of triplicate samples. Significance was measured with an unpaired t test in GraphPad Prism (***P < 0.001)
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Next, we assessed if Cas5 regulates the expression of genes important for cell wall homeostasis in the absence of cell wall stress. We found that 110 out of the 275 genes that have reduced RNA PolII binding in the cas5Δ/cas5Δ mutant under basal conditions were associated with cell membrane, cell wall, and cell periphery GO components (Supplementary Data 1). Thus, we predicted Cas5 enables cell wall stability in the absence of cell wall stress, such that deletion of CAS5 would induce cell wall stress under basal conditions. To determine if a cas5Δ/cas5Δ mutant is intrinsically defective in cell wall architecture, we monitored phosphorylation of the terminal cell wall integrity MAP kinase, Mkc1, under basal conditions32. Compared with a wild-type control, the cas5Δ/cas5Δ mutant had elevated levels of Mkc1 phosphorylation under basal conditions (Fig. 3c and Supplementary Fig. 2a). Moreover, expression of RLM1, a transcription factor downstream of Mkc1, was upregulated in the cas5Δ/cas5Δ mutant under basal conditions (Fig. 3d), suggesting strains lacking Cas5 experience intrinsic cell wall stress. Our results indicate Cas5 maintains cell wall homeostasis under basal conditions and during cell wall stress.
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We observed an intriguing role of Cas5 on governing RNA PolII binding at genes important for cell cycle, meiosis, and DNA replication (Figs. 1c and 2c), suggesting Cas5 regulates cell cycle dynamics under basal and cell wall stress conditions. If this were the case, the expression pattern of cell cycle regulated genes would be perturbed in the absence of Cas5. Using the Candida cell cycle database33, we determined that 56 out of the 604 genes that had Cas5-dependent RNA PolII binding under basal conditions, were genes whose expression peaked during the cell cycle (Supplementary Data 6). Of these, over 75% (43 out of 56) had reduced RNA PolII binding in the cas5Δ/cas5Δ mutant as compared with the wild-type strain (Fig. 4a and Supplementary Data 6). Next, we analyzed the class of genes involved in cell cycle that had reduced RNA PolII binding upon caspofungin treatment (Fig. 1e). Among these genes were all six factors of the mini-chromosome maintenance (MCM) complex (Fig. 4b), which has important roles in the formation of the pre-replicative complex (pre-RC) prior to DNA replication34. Reduced RNA PolII binding at genes encoding MCM complex components was dependent on Cas5 (Fig. 4b). As expected, qRT-PCR confirmed that reduced expression of representative genes, MCM2 and MCM3, was also dependent on Cas5 (Fig. 4c). Finally, we classified genes that were differentially bound by RNA PolII in response to caspofungin based on their peak expression throughout the cell cycle, once again using the Candida cell cycle database33. In the wild-type strain, genes with increased RNA PolII binding in response to caspofungin were predominantly G1-specific, and those with reduced RNA PolII binding were predominantly M phase-specific (Fig. 4d and Supplementary Data 6). Importantly, these trends were less apparent in a mutant lacking Cas5 (Fig. 4d and Supplementary Data 6). Overall, our results highlight the important role of Cas5 in ensuring appropriate cell cycle dynamics under basal conditions and in response to cell wall stress.Fig. 4Cas5 governs cell cycle dynamics under basal and cell wall stress conditions. a Circos plot depicting the number of genes with increased (red) and decreased (blue) RNA PolII binding in the cas5∆/cas5∆ mutant whose normal expression in the wild-type peaks at the indicated cell cycle stage. b Cas5 is required for the decreased PolII binding at genes encoding MCM complex members and CDC6 in response to caspofungin. Histogram showing the PolII ChIP-Seq signal for the indicated genes encoding MCM complex components in response to caspofungin treatment. Error bars represent standard deviation (s.d.) from the mean of duplicate samples. c Downregulation of MCM2 and MCM3 in response to caspofungin depends on Cas5. Transcript levels were monitored by qRT-PCR and normalized to TEF1. Error bars represent s.d. from the mean of triplicate samples. Significance was measured with an unpaired t test in GraphPad Prism (*P < 0.05). d Circos plots illustrating the number of genes with increased (red) or decreased (blue) PolII binding in response to caspofungin (CF) in wild-type and cas5∆/cas5∆ strains for genes whose expression is regulated throughout the cell cycle
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Cas5 governs cell cycle dynamics under basal and cell wall stress conditions. a Circos plot depicting the number of genes with increased (red) and decreased (blue) RNA PolII binding in the cas5∆/cas5∆ mutant whose normal expression in the wild-type peaks at the indicated cell cycle stage. b Cas5 is required for the decreased PolII binding at genes encoding MCM complex members and CDC6 in response to caspofungin. Histogram showing the PolII ChIP-Seq signal for the indicated genes encoding MCM complex components in response to caspofungin treatment. Error bars represent standard deviation (s.d.) from the mean of duplicate samples. c Downregulation of MCM2 and MCM3 in response to caspofungin depends on Cas5. Transcript levels were monitored by qRT-PCR and normalized to TEF1. Error bars represent s.d. from the mean of triplicate samples. Significance was measured with an unpaired t test in GraphPad Prism (*P < 0.05). d Circos plots illustrating the number of genes with increased (red) or decreased (blue) PolII binding in response to caspofungin (CF) in wild-type and cas5∆/cas5∆ strains for genes whose expression is regulated throughout the cell cycle
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Given our findings that Cas5 governs cell cycle dynamics and cell wall stress responses, we next explored the mechanisms by which it is regulated. Initially, we monitored the subcellular localization of Cas5 by indirect immunofluorescence in a strain in which both alleles of CAS5 were HA-tagged. The Cas5-HA protein was functional and sufficient to confer wild-type tolerance to caspofungin (Supplementary Fig. 3a). Under basal conditions, Cas5 appeared diffuse throughout the cytoplasm, however following treatment with caspofungin, Cas5 translocated to the nucleus (Fig. 5a). Owing to the moderate expression of Cas5 under its native promoter, we employed a strain in which we could overexpress Cas5 to facilitate its localization in the absence of stress. Overexpression of HA-tagged Cas5 confirmed that Cas5 was mostly cytoplasmic, as observed when under its native promoter (Fig. 5b). Overexpression also enabled us to observe a subpopulation of cells in which Cas5 was localized to the nucleus even in the absence of cell wall stress (Fig. 5b). Next, we assessed changes in Cas5 abundance in response to caspofungin. CAS5 transcript and protein levels were induced in response to cell wall stress, as measured by qRT-PCR and western blot analysis (Fig. 5c, d and Supplementary Fig. 2b). Thus, in response to cell wall perturbation by caspofungin, Cas5 is induced and translocates to the nucleus.Fig. 5Cas5 becomes activated by dephosphorylation in response to cell wall stress. a Cas5 localizes to the nucleus in response to caspofungin. Cells were fixed and Cas5 (red) was detected by indirect immunofluorescence using α-HA antibody and α-mouse IgG-Cy3. Nuclei (green) were visualized by DAPI staining. Scale bar represents 5 μm. b Cells were subcultured in rich medium to log phase and fixed. HA-tagged Cas5 (red) and nuclei (green) were visualized as in a. Scale bar represents 5 μm. c CAS5 is induced by caspofungin. The transcript level of CAS5 was monitored by qRT-PCR and normalized to GPD1. Error bars represent standard deviation (s.d.) from the mean of triplicate samples. Significance was measured with an unpaired t test in GraphPad Prism (****P < 0.0001). d Levels of Cas5 were monitored by western blot and detected with an α-HA antibody. Actin was detected with an α-β-actin antibody as a loading control. Full blots are shown in Supplementary Fig. 2b. e Cas5 is post-translationally modified upon cell wall stress treatment. Cells were grown to log phase and subsequently treated with 125 ng/ml of caspofungin or 50 μg/ml of calcofluor white for 1 h. Total protein was resolved by SDS-PAGE and the blot was hybridized with an α-HA to monitor Cas5 migration. Full blots are shown in Supplementary Fig. 2c. f Cas5 migration and actin detection were monitored as part d. Full blots are shown in Supplementary Fig. 2d. g Protein lysates were subjected to two-dimensional gel electrophoresis coupled with western blotting. Cas5 was detected with an α-HA antibody. h Cas5 is phosphorylated in the absence of stress. Cas5 migration was monitored by western blot and detected with an α-HA antibody. Treatment of protein lysate with lambda phosphatase resulted in a faster migrating band. Full blots are shown in Supplementary Fig. 2e. i Cas5 is required for caspofungin (CF)-induction of cell wall genes. Transcript levels of ECM331 and PGA13 were monitored by qRT-PCR and normalized to GPD1. Error bars represent s.d. from the mean of triplicate samples. Significance was measured with a Tukey’s multiple comparisons test in GraphPad Prism (****P < 0.0001, **P < 0.01)
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Cas5 becomes activated by dephosphorylation in response to cell wall stress. a Cas5 localizes to the nucleus in response to caspofungin. Cells were fixed and Cas5 (red) was detected by indirect immunofluorescence using α-HA antibody and α-mouse IgG-Cy3. Nuclei (green) were visualized by DAPI staining. Scale bar represents 5 μm. b Cells were subcultured in rich medium to log phase and fixed. HA-tagged Cas5 (red) and nuclei (green) were visualized as in a. Scale bar represents 5 μm. c CAS5 is induced by caspofungin. The transcript level of CAS5 was monitored by qRT-PCR and normalized to GPD1. Error bars represent standard deviation (s.d.) from the mean of triplicate samples. Significance was measured with an unpaired t test in GraphPad Prism (****P < 0.0001). d Levels of Cas5 were monitored by western blot and detected with an α-HA antibody. Actin was detected with an α-β-actin antibody as a loading control. Full blots are shown in Supplementary Fig. 2b. e Cas5 is post-translationally modified upon cell wall stress treatment. Cells were grown to log phase and subsequently treated with 125 ng/ml of caspofungin or 50 μg/ml of calcofluor white for 1 h. Total protein was resolved by SDS-PAGE and the blot was hybridized with an α-HA to monitor Cas5 migration. Full blots are shown in Supplementary Fig. 2c. f Cas5 migration and actin detection were monitored as part d. Full blots are shown in Supplementary Fig. 2d. g Protein lysates were subjected to two-dimensional gel electrophoresis coupled with western blotting. Cas5 was detected with an α-HA antibody. h Cas5 is phosphorylated in the absence of stress. Cas5 migration was monitored by western blot and detected with an α-HA antibody. Treatment of protein lysate with lambda phosphatase resulted in a faster migrating band. Full blots are shown in Supplementary Fig. 2e. i Cas5 is required for caspofungin (CF)-induction of cell wall genes. Transcript levels of ECM331 and PGA13 were monitored by qRT-PCR and normalized to GPD1. Error bars represent s.d. from the mean of triplicate samples. Significance was measured with a Tukey’s multiple comparisons test in GraphPad Prism (****P < 0.0001, **P < 0.01)
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When monitoring Cas5 protein levels, we observed that the Cas5 protein demonstrated a downward band shift when cells were exposed to cell wall antagonists, indicative of a post-translational modification (Fig. 5e and Supplementary Fig. 2c). To confirm this shift was a consequence of cell wall stress, we employed a strain in which we could specifically inhibit the kinase activity of the cell wall integrity regulator Pkc135. In this strain, the only allele of PKC1 carries an M850G gatekeeper mutation, rendering the kinase susceptible to inhibition by ATP analog 1-naphthyl-PP1 (1-NA-PP1), without affecting its kinase activity in the absence of the inhibitor35, 36. In the presence of 1-NA-PP1, the gatekeeper mutant was hypersensitive to caspofungin, similar to a pkc1Δ/pkc1Δ mutant (Supplementary Fig. 3b). Inhibition of Pkc1 kinase activity induced Cas5 protein levels and resulted in a downward mobility shift (Fig. 5f and Supplementary Fig. 2d), similar to what was observed in a wild-type strain treated with caspofungin (Fig. 5d). Given that a change in protein phosphorylation is a common mechanism to regulate transcription factor activity37, we assessed whether Cas5 was modified by phosphorylation. We performed two-dimensional gel electrophoresis coupled to western blot analysis. In the absence of stress, Cas5-HA resolved into a heterogeneous population of differentially charged species (Fig. 5g). In response to caspofungin, Cas5 collapsed into a less electronegative species, consistent with protein dephosphorylation. To confirm that Cas5 was phosphorylated under basal conditions, we monitored Cas5 mobility by western blot analysis following lambda phosphatase treatment. Phosphatase treatment led to a faster migrating Cas5 band, consistent with Cas5 being a phosphoprotein (Fig. 5h and Supplementary Fig. 2e). Finally, to determine if dephosphorylation of Cas5 is coupled to the activation of gene expression, we monitored transcript levels of ECM331 and PGA13, two Cas5-dependent caspofungin-responsive cell wall genes (Supplementary Data 4)22. ECM331 and PGA13 were upregulated in wild-type cells with the same caspofungin exposure required to induce Cas5 dephosphorylation, and this upregulation was partially blocked by deletion of CAS5 (Fig. 5i). Thus, Cas5 is regulated by dephosphorylation and governs the expression of caspofungin-responsive cell wall genes in response to cell wall stress.
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On the basis of our observations that Cas5 function is regulated by phosphorylation, we hypothesized that dephosphorylation of key residues is important for Cas5 activation. To map the phosphorylation residues on Cas5 important for its activity, we performed immunoprecipitation coupled to mass spectrometry with HA-tagged Cas5. Phosphorylation was detected at serine residues S462 and S476 (Supplementary Fig. 4a). To test whether these serine residues along with two other adjacent residues, S464 and S472, were functionally important for Cas5 function in regulating cell wall integrity, we mutagenized all four serines to either glutamic acid, to mimic a constitutively phosphorylated state, or alanine, to mimic a constitutively unphosphorylated state. The resulting mutants displayed wild-type tolerance to caspofungin (Supplementary Fig. 4b), with no appreciable difference in the band shift upon caspofungin treatment (Supplementary Fig. 4c), suggesting that the sites identified by mass spectrometry were not sufficient to control Cas5 function.
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To identify other potential Cas5 phosphorylation sites, we analyzed its amino acid sequence using NetPhos and followed up on S769, a highly conserved residue in one of the zinc finger domains with a phosphorylation site-prediction score of 0.980 (Fig. 6a)38, 39. To assess the functional significance of this residue, we engineered strains in which one allele of CAS5 was deleted and the other was replaced with a wild-type allele, the phosphomimetic S769E allele, or the phosphodeficient S769A allele. Introduction of the phosphomimetic S769E allele as the only source of CAS5 phenocopied homozygous deletion of CAS5 in terms of hypersensitivity to caspofungin, whereas introduction of the phosphodeficient S769A allele conferred a wild-type phenotype (Fig. 6b). Consistent with our hypothesis, the S769E mutation blocked the upregulation of ECM331 and PGA13 in response to caspofungin, whereas the S769A mutation did not (Fig. 6c). Notably, these mutations did not affect Cas5 induction or dephosphorylation in response to caspofungin (Fig. 6d and Supplementary Fig. 2f), suggesting that the S769E substitution does not affect the capacity of Cas5 to sense cell wall stress, and that additional phosphorylation sites remain to be identified. Our data supports a model in which Cas5 is activated by dephosphorylation in response to cell wall stress.Fig. 6The S769E substitution in the Cas5 DNA-binding domain phenocopies CAS5 deletion. a Schematic showing the position of S769 in the Cas5 zinc finger domain and the alignment of Cas5 orthologs in related fungal species. The alignment was performed using the Candida Genome Database (CGD), and the phosphorylation site-prediction score was generated using NetPhos. b The phosphomimetic S769E substitution in Cas5 confers hypersensitivity to caspofungin. Caspofungin susceptibility assays were conducted in YPD medium. Growth was measured by absorbance at 600 nm after 48 h at 30 °C. Optical densities were averaged for duplicate measurements. Data was quantitatively displayed with color using Treeview (see color bar in Fig. 2). c The S769E substitution blocks induction of cell wall genes in response to caspofungin treatment. The transcript levels of ECM331 and PGA13 were monitored by qRT-PCR and normalized to GPD1. Plotted are the fold-changes in transcript levels of ECM331 or PGA13 in response to caspofungin relative to untreated. Error bars represent standard deviation (s.d.) from the mean of triplicate samples. The fold change in gene expression for each mutant strain was compared with the wild-type strain using one-way ANOVA in GraphPad Prism (**P < 0.01). d Phosphomutations in CAS5 do not affect band shifts associated with activation of the cell wall stress response, as observed upon caspofungin treatment. Cas5 was monitored by western blot and detected using an α-HA antibody. Full blots are shown in Supplementary Fig. 2f
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The S769E substitution in the Cas5 DNA-binding domain phenocopies CAS5 deletion. a Schematic showing the position of S769 in the Cas5 zinc finger domain and the alignment of Cas5 orthologs in related fungal species. The alignment was performed using the Candida Genome Database (CGD), and the phosphorylation site-prediction score was generated using NetPhos. b The phosphomimetic S769E substitution in Cas5 confers hypersensitivity to caspofungin. Caspofungin susceptibility assays were conducted in YPD medium. Growth was measured by absorbance at 600 nm after 48 h at 30 °C. Optical densities were averaged for duplicate measurements. Data was quantitatively displayed with color using Treeview (see color bar in Fig. 2). c The S769E substitution blocks induction of cell wall genes in response to caspofungin treatment. The transcript levels of ECM331 and PGA13 were monitored by qRT-PCR and normalized to GPD1. Plotted are the fold-changes in transcript levels of ECM331 or PGA13 in response to caspofungin relative to untreated. Error bars represent standard deviation (s.d.) from the mean of triplicate samples. The fold change in gene expression for each mutant strain was compared with the wild-type strain using one-way ANOVA in GraphPad Prism (**P < 0.01). d Phosphomutations in CAS5 do not affect band shifts associated with activation of the cell wall stress response, as observed upon caspofungin treatment. Cas5 was monitored by western blot and detected using an α-HA antibody. Full blots are shown in Supplementary Fig. 2f
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To identify the phosphatase that regulates Cas5 activity, we leveraged insights from the model yeast S. cerevisiae. A BLASTp search of the C. albicans Cas5 protein sequence against the S. cerevisiae protein database identified Mig1, Msn4, Mig2, and Msn2 as proteins with similar sequences40. Mig1, Msn4, and Msn2 are dephosphorylated by the type I protein phosphatase Glc741, 42. As Glc7 influences cell wall maintenance in S. cerevisiae 43, we hypothesized that Glc7 was required for activating cell wall stress responses in C. albicans. We engineered a strain in which we could transcriptionally repress GLC7 by deleting one allele and replacing the native promoter of the remaining allele with a tetracycline-repressible promoter. We confirmed that GLC7 expression was repressed in the presence of the tetracycline analog doxycycline by qRT-PCR (Supplementary Fig. 5a). Depletion of GLC7 with doxycycline conferred hypersensitivity to caspofungin (Fig. 7a), confirming Glc7 is important for tolerating cell wall stress in C. albicans. Next, we examined the effect of GLC7 depletion on Cas5 expression and function. Doxycycline-mediated transcriptional repression of GLC7 did not block upregulation of Cas5 in response to caspofungin (Fig. 7b and Supplementary Fig. 2g), but did block the downward band shift associated with dephosphorylation, as was evident with equivalent Cas5 levels loaded per sample (Fig. 7c and Supplementary Fig. 2h). Depletion of GLC7 also blocked the upregulation of Cas5-dependent caspofungin-responsive genes, ECM331 and PGA13 (Fig. 7d). Thus, Glc7 activates Cas5 by dephosphorylation in response to cell wall stress, thereby enabling transcriptional upregulation of cell wall genes.Fig. 7Cas5 activation in response to caspofungin is coupled with dephosphorylation by the protein phosphatase Glc7. a Depletion of GLC7 confers sensitivity to caspofungin. Caspofungin susceptibility assays were conducted in YPD medium in the presence or absence of 1 μg/ml of doxycycline (DOX). Growth was measured by absorbance at 600 nm after 48 h at 30 °C. Optical densities were averaged for duplicate measurements. Data were quantitatively displayed with color using Treeview (see color bar in Fig. 2). b Upregulation of Cas5 expression does not depend on Glc7. CAS5-HA/CAS5 and CAS5-HA/CAS5 tetO-GLC7/glc7Δ strains were cultured in the absence or presence of doxycycline and caspofungin, as indicated. Cas5 was monitored by western blot and detected with an α-HA antibody. Hsp90 protein levels served as a loading control. Full blots are shown in Supplementary Fig. 2g. c Post-translational modification of Cas5 is absent upon GLC7 depletion. The western blot was performed as described in b, except caspofungin treated samples were diluted fivefold to achieve equal loading of Cas5. Full blots are shown in Supplementary Fig. 2h. d Dephosphorylation of Cas5 is required for the induction of cell wall genes in response to caspofungin treatment. CAS5-HA/CAS5 and CAS5-HA/CAS5 tetO-GLC7/glc7Δ strains were grown in the absence or presence of doxycycline (DOX) or caspofungin, as indicated. The transcript levels of ECM331 and PGA13 were monitored by qRT-PCR and normalized to GPD1. Plotted are the fold-changes in transcript levels in the presence of caspofungin relative to untreated. Error bars represent standard deviation (s.d.) from the mean of triplicate samples. The treatment conditions were compared using a Tukey’s multiple comparisons test in GraphPad Prism (****P < 0.0001, ***P < 0.001)
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Cas5 activation in response to caspofungin is coupled with dephosphorylation by the protein phosphatase Glc7. a Depletion of GLC7 confers sensitivity to caspofungin. Caspofungin susceptibility assays were conducted in YPD medium in the presence or absence of 1 μg/ml of doxycycline (DOX). Growth was measured by absorbance at 600 nm after 48 h at 30 °C. Optical densities were averaged for duplicate measurements. Data were quantitatively displayed with color using Treeview (see color bar in Fig. 2). b Upregulation of Cas5 expression does not depend on Glc7. CAS5-HA/CAS5 and CAS5-HA/CAS5 tetO-GLC7/glc7Δ strains were cultured in the absence or presence of doxycycline and caspofungin, as indicated. Cas5 was monitored by western blot and detected with an α-HA antibody. Hsp90 protein levels served as a loading control. Full blots are shown in Supplementary Fig. 2g. c Post-translational modification of Cas5 is absent upon GLC7 depletion. The western blot was performed as described in b, except caspofungin treated samples were diluted fivefold to achieve equal loading of Cas5. Full blots are shown in Supplementary Fig. 2h. d Dephosphorylation of Cas5 is required for the induction of cell wall genes in response to caspofungin treatment. CAS5-HA/CAS5 and CAS5-HA/CAS5 tetO-GLC7/glc7Δ strains were grown in the absence or presence of doxycycline (DOX) or caspofungin, as indicated. The transcript levels of ECM331 and PGA13 were monitored by qRT-PCR and normalized to GPD1. Plotted are the fold-changes in transcript levels in the presence of caspofungin relative to untreated. Error bars represent standard deviation (s.d.) from the mean of triplicate samples. The treatment conditions were compared using a Tukey’s multiple comparisons test in GraphPad Prism (****P < 0.0001, ***P < 0.001)
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To identify physical interactors of Cas5, we leveraged the results from our immunoprecipitation coupled to mass spectrometry analysis (Supplementary Data 7). The only interaction that passed a stringent statistical analysis using Significance Analysis of Interactome (SAINT)44 with a probability threshold of 0.9 was Swi4 (Supplementary Data 7), which interacts with Swi6 to form the SBF complex in S. cerevisiae 45. To validate the interaction between Cas5 and the SBF complex, we individually tandem affinity purification (TAP) tagged Swi4 or Swi6 at the C terminus in a strain harboring Cas5-HA to enable co-immunoprecipitation analysis. The Swi4-TAP and Swi6-TAP proteins were functional and sufficient to mediate wild-type tolerance to caspofungin (Supplementary Fig. 5b). Immunoprecipitation of Cas5 with anti-HA agarose co-purified both Swi4 and Swi6 (Fig. 8a and Supplementary Fig. 6), verifying these proteins physically interact. To determine whether the SBF complex has a role in cell wall stress responses in C. albicans, we monitored the expression of ECM331 and PGA13 in mutants lacking Swi4, Swi6, or Cas5. In all three mutants, upregulation of these cell wall genes in response to caspofungin was blocked (Fig. 8b). However, strains lacking Swi4 or Swi6 displayed intermediate caspofungin susceptibility phenotypes relative to a wild-type strain and a CAS5 homozygous deletion mutant (Fig. 8c). Altogether, our results suggest that Cas5 regulates transcriptional responses to cell wall stress in part through the SBF complex and in part through an independent mechanism.Fig. 8Cas5 regulates caspofungin tolerance in part through the interaction with components of the SBF complex, Swi4 and Swi6. a Swi4 and Swi6 co-purify with Cas5. C terminally HA-tagged Cas5 was immunoprecipitated with α-HA beads. Swi4 and Swi6 co-purification was monitored by western blot and detected with an α-TAP antibody. Cas5 pull down was confirmed by detection with an α-HA antibody. Input samples confirm the expression of tagged proteins. Full blots are shown in Supplementary Fig. 6. b Cas5, Swi4, and Swi6 are required for the upregulation of caspofungin-responsive genes. The transcript levels of ECM331 and PGA13 was monitored by qRT-PCR and normalized to GPD1. Plotted are the fold-changes in transcript levels in the presence of caspofungin relative to untreated. Error bars represent standard deviation (s.d.) from the mean of triplicate samples. The fold change in gene expression for each mutant strain was compared to the wildtype using one-way ANOVA in GraphPad Prism (****P < 0.0001). c Swi4 and Swi6 regulate cell wall stress response. Caspofungin susceptibility assays were conducted in YPD medium. Growth was measured by absorbance at 600 nm after 48 h at 30 °C. Optical densities were averaged for duplicate measurements. Data was quantitatively displayed with color using Treeview (see color bar in Fig. 2)
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Cas5 regulates caspofungin tolerance in part through the interaction with components of the SBF complex, Swi4 and Swi6. a Swi4 and Swi6 co-purify with Cas5. C terminally HA-tagged Cas5 was immunoprecipitated with α-HA beads. Swi4 and Swi6 co-purification was monitored by western blot and detected with an α-TAP antibody. Cas5 pull down was confirmed by detection with an α-HA antibody. Input samples confirm the expression of tagged proteins. Full blots are shown in Supplementary Fig. 6. b Cas5, Swi4, and Swi6 are required for the upregulation of caspofungin-responsive genes. The transcript levels of ECM331 and PGA13 was monitored by qRT-PCR and normalized to GPD1. Plotted are the fold-changes in transcript levels in the presence of caspofungin relative to untreated. Error bars represent standard deviation (s.d.) from the mean of triplicate samples. The fold change in gene expression for each mutant strain was compared to the wildtype using one-way ANOVA in GraphPad Prism (****P < 0.0001). c Swi4 and Swi6 regulate cell wall stress response. Caspofungin susceptibility assays were conducted in YPD medium. Growth was measured by absorbance at 600 nm after 48 h at 30 °C. Optical densities were averaged for duplicate measurements. Data was quantitatively displayed with color using Treeview (see color bar in Fig. 2)
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Since our RNA PolII ChIP-Seq data implicated Cas5 in proper cell cycle dynamics and DNA replication (Figs. 1c and 4), we wanted to further investigate the impact of Cas5 on nuclear division. We monitored the number of nuclei per cell in wildtype and cas5Δ/cas5Δ strains. Cells were stained with DAPI for DNA content and calcofluor white for chitin (Fig. 9a and Supplementary Fig. 7a). During log phase, ~40% of cells lacking Cas5 were multinucleated, an indication of uncontrolled DNA replication or a defect in cytokinesis (Fig. 9b). Given that Swi4 and Swi6 physically interact with Cas5 (Fig. 8a) and that the SBF complex regulates the G1/S phase progression in S. cerevisiae and C. albicans 46, 47, we also investigated their impact on nuclear division. Strikingly, <10% of mutant cells lacking either Swi4 or Swi6 showed a multinucleated phenotype, suggesting that the SBF complex has a minor role in ensuring proper nuclear division in C. albicans (Fig. 9a, b). We further investigated the defective nuclear division associated with loss of Cas5 by time-lapsed fluorescence microscopy. To monitor DNA content, we RFP-tagged histone H4, encoded by HHF1; to monitor mitotic spindle positioning, we GFP-tagged the Dam1 complex subunit DAD2 (Fig. 9c, d). We observed multiple nuclei dividing simultaneously in a single cell in a cas5Δ/cas5Δ mutant (Fig. 9d and Supplementary Fig. 7b). Importantly, each nucleus had at least one spindle pole body, suggesting that the accumulation of nuclei was due to nuclear division and not spontaneous nuclear fragmentation (Fig. 9d and Supplementary Fig. 7b).Fig. 9Cas5 regulates nuclear division largely independent of Swi4 and Swi6. a Nuclei were stained with DAPI and chitin was stained using calcofluor white. For each image, 12 Z-stack slices were taken at 0.3 μm each. Black arrows highlight cells with a single nucleus. Red arrows highlight cells with a multinucleated phenotype. b Mutants lacking Cas5 exhibit severe cell cycle defect. The histogram represents the average number of nuclei per cell in three biological replicates. The number of nuclei counted in each experiment was at least 120 cells for each strain. The nuclei counts for each strain were averaged and error bars represent standard deviation (s.d.) from the mean of biological duplicates. Nuclei counts for each strain were compared to the wildtype using one-way ANOVA in GraphPad Prism (****P < 0.0001). c, d Mitotic spindles are aligned along the mother–bud axis during cell division in wild-type cells (c) and misaligned in a mutant lacking Cas5 (d). DNA was monitored by RFP-tagged Hhf1, and mitotic spindle was monitored by GFP-tagged Dad2 using time-lapse fluorescence microscopy. e Cas5 is required for maintaining normal DNA content throughout the cell cycle. Cellular DNA content was measured by propidium iodide and flow cytometry of the wild-type diploid and the cas5Δ/cas5Δ mutant strain. The wild-type diploid has the standard G1 and G2 cell cycle peaks representing 2C and 4C DNA levels. The cas5∆/cas5∆ mutant population, in addition to the same 2C and 4C DNA levels, contained a large subpopulation of cells with DNA levels at 6C, 8C, and 12C. These DNA levels represent tetraploid (4N) and hexaploid (6N) cells. f Activation of Cas5 by dephosphorylation is required for proper cell cycle progression. The histogram represents the average number of nuclei per cell in three biological replicates. The number of nuclei counted was at least 120 cells for each strain. The nuclei counts for each strain were averaged and error bars represent s.d. from the mean of biological duplicates. Nuclei counts for each strain were compared to that of the wildtype using one-way ANOVA in GraphPad Prism (****P < 0.0001)
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Cas5 regulates nuclear division largely independent of Swi4 and Swi6. a Nuclei were stained with DAPI and chitin was stained using calcofluor white. For each image, 12 Z-stack slices were taken at 0.3 μm each. Black arrows highlight cells with a single nucleus. Red arrows highlight cells with a multinucleated phenotype. b Mutants lacking Cas5 exhibit severe cell cycle defect. The histogram represents the average number of nuclei per cell in three biological replicates. The number of nuclei counted in each experiment was at least 120 cells for each strain. The nuclei counts for each strain were averaged and error bars represent standard deviation (s.d.) from the mean of biological duplicates. Nuclei counts for each strain were compared to the wildtype using one-way ANOVA in GraphPad Prism (****P < 0.0001). c, d Mitotic spindles are aligned along the mother–bud axis during cell division in wild-type cells (c) and misaligned in a mutant lacking Cas5 (d). DNA was monitored by RFP-tagged Hhf1, and mitotic spindle was monitored by GFP-tagged Dad2 using time-lapse fluorescence microscopy. e Cas5 is required for maintaining normal DNA content throughout the cell cycle. Cellular DNA content was measured by propidium iodide and flow cytometry of the wild-type diploid and the cas5Δ/cas5Δ mutant strain. The wild-type diploid has the standard G1 and G2 cell cycle peaks representing 2C and 4C DNA levels. The cas5∆/cas5∆ mutant population, in addition to the same 2C and 4C DNA levels, contained a large subpopulation of cells with DNA levels at 6C, 8C, and 12C. These DNA levels represent tetraploid (4N) and hexaploid (6N) cells. f Activation of Cas5 by dephosphorylation is required for proper cell cycle progression. The histogram represents the average number of nuclei per cell in three biological replicates. The number of nuclei counted was at least 120 cells for each strain. The nuclei counts for each strain were averaged and error bars represent s.d. from the mean of biological duplicates. Nuclei counts for each strain were compared to that of the wildtype using one-way ANOVA in GraphPad Prism (****P < 0.0001)
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Next, we assessed the ploidy of the cas5Δ/cas5Δ mutant compared with a wild-type control by flow cytometry. During log phase growth in the absence of external stress, wild-type cells exhibited two distinct peaks at 2C and 4C (representing the G1- and G2-phases of the cell cycle, respectively), whereas the cas5Δ/cas5Δ mutant exhibited five distinct peaks at 2C, 4C, 6C, 8C, and 12C (Fig. 9e and Supplementary Fig. 7c). Notably, peaks at 1C and 3C were absent, indicating that the nuclei were not haploidizing, mating, or undergoing reductional division. Instead, these data suggest multiple rounds of DNA replication were occurring in the same cell, causing ploidy level increases similar to what is observed when C. albicans is exposed to fluconazole48.
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Finally, to determine whether the dephosphorylated form of Cas5 was required for controlled DNA replication, we quantified the number of nuclei per cell in the phosphomutants. The CAS5 mutant carrying the phosphomimetic S769E substitution phenocopied the cell cycle defects observed in the CAS5 homozygous deletion mutant, whereas the mutant carrying phosphodeficient S769A substitution was indistinguishable from the wildtype (Fig. 9f). Thus, our results support a novel role for the dephosphorylated form of Cas5 in regulating nuclear division that is largely independent of the SBF complex.
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study
| 100.0 |
The capacity to coordinate cell cycle progression with stress responses is crucial for cellular survival to give cells time to recover from a myriad of environmental perturbations, including cell wall stress49. In the present work, we uncovered a dual role for the transcription factor Cas5 in mediating cell cycle dynamics and cell wall stress responses in C. albicans (Fig. 10). We discovered that RNA PolII binding to many genes important for cell wall biosynthesis and DNA replication is dependent on Cas5 under basal conditions (Fig. 1). We also found Cas5 regulates the expression of a distinct set of cell wall and cell cycle genes in response to cell wall stress (Fig. 2). We established Cas5 function is regulated by the protein phosphatase Glc7 (Fig. 7), and Cas5 functions in concert with Swi4 and Swi6 to regulate cell wall homeostasis (Fig. 8). Our findings implicate Cas5 at the core of a novel mechanism by which cell cycle and cell wall integrity are coordinately regulated.Fig. 10Model for Cas5-mediated coupling of cell cycle progression to cell wall stress response. Cas5 is dephosphorylated by the protein phosphatase Glc7 in response to cell wall stress. Once dephosphorylated, it can translocate to the nucleus to govern the expression of a myriad of genes, including those important for cell cycle regulation and cell wall stress response. Cas5 regulates changes in gene expression through both Swi4/Swi6-dependent and independent mechanisms
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| 100.0 |
Model for Cas5-mediated coupling of cell cycle progression to cell wall stress response. Cas5 is dephosphorylated by the protein phosphatase Glc7 in response to cell wall stress. Once dephosphorylated, it can translocate to the nucleus to govern the expression of a myriad of genes, including those important for cell cycle regulation and cell wall stress response. Cas5 regulates changes in gene expression through both Swi4/Swi6-dependent and independent mechanisms
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study
| 100.0 |
The coordination of sensing and responding to cell wall stress is crucial for fungi, and precise temporal control of cell wall stress response regulators underpins the rapid mobilization of signaling cascades that govern cellular integrity. We demonstrated that Cas5 not only becomes activated in response to cell wall stress but it also maintains cell wall stability under basal conditions (Figs. 3 and 5). Although Cas5 is not broadly conserved in the fungal kingdom, its activation is regulated by a conserved protein phosphatase, Glc7, which has 91% protein identity to the S. cerevisiae ortholog. In S. cerevisiae, temperature sensitive glc7 mutants exhibit sorbitol-remediable lysis defects at the restrictive temperature, due to impaired cell wall integrity43. We demonstrated Glc7 is required for cell wall maintenance in C. albicans (Fig. 7). Glc7 substrates in S. cerevisiae include three proteins with sequence similarity to Cas5: Mig1, Msn2, and Msn4. Msn2 and Msn4 are transcription factors that are required for general stress response in S. cerevisiae, but not in C. albicans 50, 51. This highlights a divergence in signal transduction cascades required to respond to environmental stressors, and suggests that Cas5 may be the central downstream effector of Glc7 that modulates stress responses in C. albicans. Consistent with this possibility, Cas5 controls responses not only to cell wall stress, but also to cell membrane stress exerted by the azole antifungal drugs52.
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| 100.0 |
In yeast, cell wall stress originates not only through environmental perturbations but also through normal physiological changes that are coupled to cell cycle progression, as cell wall remodeling and biosynthesis are required to enable the emergence of the growing daughter bud. In S. cerevisiae cell membrane perturbation triggers cell cycle arrest via degradation of Cdc6, a component of the pre-RC53. This is required for cell survival as continued cell cycle progression in the presence of plasma membrane damage induces plasma membrane rupture and cell lysis, leading to cell death53. We found that CDC6 and genes encoding members of the MCM complex that participate in the pre-RC are transcriptionally repressed in response to caspofungin in a Cas5-dependent manner (Fig. 4). To our knowledge, this is the first mechanistic insight into the signaling events that couple cell wall stress with cell cycle arrest in C. albicans, and likely enables the cell to respond to environmental stress without succumbing to cell death. In S. cerevisiae, cell cycle genes are regulated by transcriptional complexes composed of Swi4 and Swi6, including the SBF complex, which is involved in budding and membrane/cell-wall biosynthesis54. We observed that impairment of Cas5 function manifested in more severe misregulation of nuclear division than deletion of SWI4 or SWI6 (Fig. 9), suggesting Cas5 regulates nuclear division largely independent of the SBF complex.
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| 100.0 |
Targeting regulators of cellular circuitry that are crucial for cellular stress responses may provide a powerful strategy for antifungal drug development. This strategy opens up the opportunity for combining stress response inhibitors with current antifungals for treatment of fungal infections. Promising examples of this approach include targeting the molecular chaperone Hsp90 or its downstream effector calcineurin, which are both required for drug resistance and virulence of diverse fungal pathogens8, 55. Given that both of these regulators are highly conserved in humans and have essential functions in mobilizing immune responses, the challenge in exploiting the therapeutic utility of these targets for antifungal therapies hinges on the development of fungal-selective inhibitors56. Our work further supported the role of Cas5 in governing echinocandin tolerance and established that Cas5 enables echinocandin resistance in a strain harboring a mutation in the drug target gene FKS1 (Fig. 3b). As Cas5 lacks an identifiable ortholog in humans22, but is required for drug resistance (Fig. 3b) and virulence in C. albicans 10, 57, it provides an attractive target for antifungal drug development. There is growing support for the feasibility of chemical modulation of transcription factors based on blocking transcription factor dimerization, DNA binding, or the interaction with regulatory proteins58, 59. Elucidating mechanisms that enable remodeling of transcriptional programs that control stress response and virulence traits provides opportunities to expand the antimicrobial target space in this era of antimicrobial resistance.
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| 99.94 |
All C. albicans strains were archived in 25% glycerol and stored at −80 °C. Strains were grown in YPD (1% yeast extract, 2% bactopeptone, and 2% glucose) at 30 °C unless otherwise specified. For solid media, 2% agar was added. Strains were constructed according to standard protocols. Strains used in this study are listed in Supplementary Table 1. Caspofungin was generously provided by Terry Roemer (Department of Infectious Diseases, Merck Research Laboratories) and was diluted to a 100 µg/ml stock, and used at a final concentration of 125 ng/ml. Doxycycline (DOX, BD Biosciences #631311) was formulated in a 20 mg/ml stock in water and used at a final concentration of 0.02 µg/ml or 1 µg/ml, as specified.
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study
| 100.0 |
CaLC2034: To generate a CAS5 heterozygous deletion mutant, the NAT flipper cassette (pLC49)60 was PCR amplified using primers oLC2017 and oLC2018 (4366 bp) and transformed into CaLC239 (SN95). NAT-resistant transformants were PCR tested with oLC275 in combination with oLC2034 for upstream integration and oLC274 in combination with oLC2035 for downstream integration. The SAP2 promoter was induced to drive expression of FLP recombinase to excise the NAT flipper cassette.
|
study
| 99.94 |
CaLC2056: To generate a CAS5 homozygous deletion mutant, the NAT flipper cassette (pLC49)60 was PCR amplified as described for CaLC2034 and transformed into CaLC2034. NAT-resistant transformants were PCR tested for correct integration of the cassette as described for CaLC2034. The presence of deleted allele was verified by amplification with primer pairs oLC2034 in combination with oLC2035, and absence of wild-type allele was verified by amplification with primer pairs oLC2047 in combination with oLC2048. The SAP2 promoter was induced to drive expression of FLP recombinase to excise the NAT flipper cassette.
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study
| 100.0 |
CaLC3908/CaLC3909: To generate a CAS5 heterozygous deletion mutant in a strain carrying the FKS1 F641S mutation, the NAT flipper cassette (pLC49)60 was PCR amplified as described for CaLC2034 and transformed into CaLC2087 (SN95 FKS1 F641S /FKS1). Correct upstream and downstream integration at the CAS5 locus was verified as described for CaLC2034. The SAP2 promoter was induced to drive expression of FLP recombinase to excise the NAT flipper cassette. To generate a CAS5 homozygous deletion mutant, the NAT flipper cassette (pLC49)60 was again PCR amplified and transformed into the SN95 FKS1 F641S /FKS1 CAS5/cas5::FRT strain. Correct upstream and downstream integration at the CAS5 locus was verified as described for CaLC2034. The absence of a wild-type allele was confirmed with oLC2034 in combination with oLC2164. The SAP2 promoter was induced to drive expression of FLP recombinase to excise the NAT flipper cassette.
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study
| 100.0 |
CaLC3378: To C terminally HA tag CAS5 in a mutant carrying the PKC1 M850G mutation, the CAS5-HA-HIS cassette was released from pLC790 with ApaI and transformed into SN95 PKC1 M850G /PKC1. Proper integration at the CAS5 locus was verified by amplification using primer pairs oLC2163 in combination with oLC2029 and oLC2164 in combination with oLC1645. Expression of HA-tagged Cas5 was verified by western blot.
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study
| 99.94 |
CaLC3859: To regulate the expression of CAS5, the tetracycline-repressible transactivator, the tetO promoter, and the NAT flipper cassette were PCR amplified from pLC60561 using primers oLC2088 in combination with oLC2089. The PCR amplified product was transformed into CaLC2034. Correct upstream and downstream integration at the CAS5 locus was verified by amplifying across both junctions using primer pairs oLC2034 in combination with oLC534 and oLC300 in combination with oLC2145. The absence of a wildtype CAS5 promoter was verified with oLC2034 in combination with oLC2035. To C terminally HA tag CAS5, the CAS5-HA-HIS1 cassette was released from pLC818 with SacII and transformed into the tetO-CAS5/cas5Δ. Correct upstream integration at the C terminus of CAS5 was verified by amplifying across the junction using primer pair oLC2163 in combination with oLC2164. Expression of HA-tagged Cas5 was verified by western blot.
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study
| 100.0 |
CaLC3113: To C terminally HA tag CAS5 in a mutant heterozygous for CAS5, the HA-HIS1 cassette was PCR amplified from pLC57562 using primers oLC2161 in combination with oLC2162 and transformed into CaLC2034. Transformants prototrophic for histidine were PCR tested with C2163 in combination with oLC2029 for upstream integration and oLC2164 in combination with oLC1645 for downstream integration.
|
study
| 99.94 |
CaLC3151: To C terminally HA tag CAS5 in a mutant heterozygous for CAS5, the CAS5-HA-HIS1 cassette was released from pLC790 with ApaI and transformed into CaLC2034. Transformants prototrophic for histidine were PCR tested with oLC2163 in combination with oLC2029 for upstream integration and oLC2164 in combination with oLC1645 for downstream integration. This strain shares the same genotype as CaLC3113 but was generated as a control for strains made using plasmid pLC790 as the backbone, such pLC791 and pLC800.
|
study
| 99.94 |
CaLC4285: To C terminally HA tag CAS5, the HA-ARG4 cassette was PCR amplified from pLC57662 using primers oLC2161 in combination with oLC2162 and transformed into CaLC1900. Correct integration at the C terminus of CAS5 was verified by using primer pair oLC2163 in combination with oLC2164.
|
study
| 99.9 |
CaLC2213: To C terminally HA tag CAS5 in SN95, the HA-HIS1 cassette was PCR amplified from pLC57562 using primers oLC2161 in combination with oLC2162 and transformed into CaLC239 (SN95). Transformants prototrophic for histidine were PCR tested for correct integration of the cassette with oLC2163 in combination with oLC2029 and oLC2164 in combination with oLC1645.
|
study
| 99.94 |
CaLC3044: To C terminally HA tag the both alleles of CAS5, the HA-ARG4 cassette was PCR amplified from pLC57662 as described for CaLC4285 and transformed into CaLC2213. Transformants prototrophic for both histidine and arginine were PCR tested for correct integration of the cassette as described for CaLC4285.
|
study
| 99.94 |
CaLC3209: To introduce a mutant allele of CAS5 carrying the S769E mutation, the CAS5 S769E-HA-HIS1 cassette was released from pLC791 with ApaI and transformed into CaLC2034. Transformants prototrophic for histidine were PCR tested for integration of the cassette with oLC3052 in combination with oLC2029 and oLC2164 in combination with oLC1645. The absence of a wildtype CAS5 allele was verified with oLC3052 in combination with oLC2164. The S769E mutation was sequence verified with oLC2029.
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study
| 100.0 |
CaLC3189: To introduce a mutant allele of CAS5 carrying the S769A mutation, the CAS5 S769A-HA-HIS1cassette was released from pLC800 with ApaI and transformed into CaLC2034. Transformants prototrophic for histidine were PCR tested for integration of the cassette as described for CaLC3209. The S769A mutation was sequence verified with oLC2029.
|
study
| 99.94 |
CaLC4036: To generate a SWI4 homozygous deletion mutant, the NAT flipper cassette (pLC49)60 was PCR amplified using primers oLC3472 in combination with oLC3473 and transformed into CaLC239 (SN95). NAT-resistant transformants were PCR tested with oLC275 in combination with oLC3474 and oLC274 in combination with oLC3427. The SAP2 promoter was induced to drive expression of FLP recombinase to excise the NAT flipper cassette. Once again, the NAT flipper cassette (pLC49)60 was PCR amplified using primers oLC3472 in combination with oLC3473 and transformed into SWI4/swi4::FRT. NAT-resistant transformants were PCR tested as described above. The absence of a wild-type allele was verified with oLC3426 in combination with oLC3427, and the presence of deleted allele was verified by oLC3473 in combination with oLC3427. The SAP2 promoter was induced to drive expression of FLP recombinase to excise the NAT flipper cassette.
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study
| 100.0 |
CaLC4330: To generate a SWI6 homozygous deletion mutant, the NAT flipper cassette (pLC49)60 was PCR amplified using primers oLC3641 in combination with oLC3642 and transformed into CaLC239 (SN95). NAT-resistant transformants were PCR tested with oLC275 in combination with oLC3645 and oLC274 in combination with oLC3654. The SAP2 promoter was induced to drive expression of FLP recombinase to excise the NAT flipper cassette. Once again, the NAT flipper cassette (pLC49)60 was PCR amplified using primers oLC3641 in combination with oLC3642 and transformed into SWI6/swi6::FRT. NAT-resistant transformants were PCR tested as described above. The absence of a wild-type allele and the presence of the deleted allele was verified with oLC3645 in combination with oLC3431. The SAP2 promoter was induced to drive expression of FLP recombinase to excise the NAT flipper cassette.
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study
| 100.0 |
CaLC3391: To C terminally TAP tag Swi4, the TAP-ARG4 cassette was PCR amplified from pLC57362 using primers oLC3424 in combination with oLC3425 and transformed into CaLC239. Transformants prototrophic for arginine were PCR tested with oLC3426 in combination with oLC1593 and oLC1594 in combination with oLC3427.
|
study
| 99.94 |
CaLC3393: To C terminally TAP tag Swi6, the TAP-ARG4 cassette was PCR amplified from pLC57362 using primers oLC3428 in combination with oLC3429 and transformed into CaLC239. Transformants prototrophic for arginine were PCR tested with oLC3430 in combination with oLC1593 and oLC1594 in combination with oLC3431.
|
study
| 99.94 |
CaLC3395: To C terminally TAP tag Swi4 in a strain with HA-tagged Cas5, the TAP-ARG4 cassette was PCR amplified from pLC57362 as described for CaLC3391 and transformed into CaLC3151. Transformants prototrophic for arginine and histidine were PCR tested as described for CaLC3391.
|
study
| 99.94 |
CaLC3398: To C terminally TAP tag Swi6 in a strain with HA-tagged Cas5, the TAP-ARG4 cassette was PCR amplified from pLC57362 as described for CaLC3393 and transformed into CaLC3151. Transformants prototrophic for arginine and histidine were PCR tested as described for CaLC3393.
|
study
| 99.94 |
CaLC3932: To generate a GLC7 heterozygous deletion mutant, the NAT flipper cassette (pLC49)60 was PCR amplified using primers oLC3490 in combination with oLC3491 and transformed into CaLC239 (SN95). NAT-resistant transformants were PCR tested with oLC275 in combination with oLC3558 and oLC274 in combination with oLC3561. The SAP2 promoter was induced to drive expression of FLP recombinase to excise the NAT flipper cassette. To regulate the expression of GLC7, the tetracycline-repressible transactivator, the tetO promoter, and the NAT flipper cassette were PCR amplified from pLC60561 using primers oLC3793 in combination with oLC3492 and transformed into GLC7/glc7::FRT. NAT-resistant transformants were PCR tested with oLC3794 in combination with oLC534 and oLC274 in combination with oLC3495. The absence of a wildtype GLC7 promoter was verified with oLC3794 in combination with oLC3495, and the presence of a deleted allele was verified with oLC3794 in combination with oLC3494. The SAP2 promoter was induced to drive expression of FLP recombinase to excise the NAT flipper cassette.
|
study
| 100.0 |
CaLC3952: To C terminally HA tag CAS5 in the GLC7 repressible strain, the HA-HIS1 cassette was PCR amplified from pLC57562 using primers oLC2161 in combination with oLC2162 and transformed into CaLC3932. Transformants prototrophic for histidine were PCR tested with oLC2163 in combination with oLC2164.
|
study
| 99.94 |
CaLC3693/CaLC3694/CaLC3695: To introduce a mutant allele of CAS5 carrying S462E, S464E, S472E, and S476E mutations, the CAS5S S462E/S464E/S472E/S476E -HA-HIS1 cassette was released from pLC857 with SacII and transformed into CaLC2034. Transformants prototrophic for histidine were PCR tested with oLC3052 in combination with oLC2029 and oLC2164 in combination with oLC1645. The absence of an untagged wild type CAS5 allele was verified with oLC3052 in combination with oLC2164. The S462A/S464A/S472A/S476A mutations were sequence verified with oLC3371.
|
study
| 100.0 |
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