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string | predicted_class
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|---|---|---|
Virus quantification by nano-metamaterial based THz detection. (a–d) Normalized THz spectra for various concentrations (0, 1, 0.14, 0.28 mg/ml) of H9N2 virus in the buffer solution. (e) The changes in the maximum values of the normalized transmittances (ΔT, magenta closed circle) and shifted resonance frequency (Δf, green closed triangle) are plotted for H9N2 virus in different concentrations as a function of concentration level. The red bar is error bar of buffer solution measurement. Black line and gray dashed line are linear fitting of the transmittance change and frequency shift data, respectively. (f) FDTD simulation results of transmittances for three different model samples with various composition of dielectric constants (n and κ) are shown.
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study
| 100.0 |
In order to categorize the subtype of viruses, finally, the THz transmittance spectra via the sensing chips are characterized with two important parameters; the resonance frequency shift and the decrease of normalized transmittance value. The ΔT norm is again defined with the relationship between the decrease of normalized transmittance value and a mass of virus in the sample as following: ΔT norm = \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\frac{{T}_{buffer}-{T}_{v}}{m}$$\end{document}Tbuffer−Tvm, where T buffer is a transmittance maximum value for buffer solution without any protein, T v is a transmittance maximum value for virus contained protein, and m is a mass of virus sample calculated from concentration of virus samples. Δf is the shifted frequency from the maximum value of T buffer to T v. The transmission spectra for three virus samples (H5N2, H1N1, and H9N2) were mapped with two parameters, ΔT norm and Δf (Fig. 4), showing their own frequency shift and mass-normalized transmittance change. For three virus samples, ΔT norm and Δf were extracted from Fig. 2a. Especially, the H9N2 has a same surface protein, neuraminidase, with H5N2, but the location of it in the map is farther than H1N1. This is reasonable with the fact that each same subtype of virus has a same spike shaped protein, located outside of the virus but different strain in it. Therefore, it can be explained that H9N2 is located far from H5N2 in the map, since the difference of strain in the virus may be larger than other subtype of viruses. For the complete mapping of the subtype of the viruses, further substantial experiments with various types of samples to collect vast database will be worth.Figure 4Classified map for various virus samples as functions of the frequency shift and transmittance decrement per unit mass. The three different subtype virus show their own frequency shift and mass-normalized transmittance change.
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study
| 100.0 |
In conclusion, The THz optical characteristics of several types of AI viruses were investigated using nano-metamaterial sensing chip with THz spectroscopy. Multi-resonance nano-antenna sensing chip is very useful to detect optically unknown bio samples, especially, as such viruses without their unique fingerprinting at reliable frequency range. This preliminary attempt allows us to select suitable single-resonance nano-antenna optimized for the special virus detecting. The measured THz spectra for various virus samples were analyzed in terms of the optical properties, and discussed with FDTD simulations demonstrating that spectral changes can emerge from the optical properties of samples near the nano-antenna. According to the optical properties including complex refractive index and absorption characteristics, tested viruses could be categorized with respect to their subtypes. Moreover, the virus quantification was successfully performed with a concentration dependence. Introduced nano-metamaterial based THz sensing, here, can provide a quick solution for the detection of AI viruses in non-contact and label-free manner, allowing quantification with very high accuracy additionally.
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study
| 99.94 |
Spider major ampullate (MA) silk is nature’s toughest materials . Accordingly, there is considerable interest in the creation of materials that mimic its performance . Nevertheless, attempts to recombine, amplify and spin spider silk proteins have not produced fibers with properties resembling those of naturally spun silk . One reason for the inability to produce such fibers is that the properties of MA silk are highly variable and the mechanisms inducing this variation have never been delineated from nano to macro scales .
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other
| 96.4 |
Researchers can determine the consequences of gene expression on the functional properties of structural proteins by either switching genes on and off and observing the consequences in the secreted proteins, or by observing the function of the proteins produced in different organisms with varying levels of expression of a particular gene . Indeed, using such approaches has informed us how and why certain genes cause specific ailments in humans and other animals . Progress has been recently made into our understanding of spider silk genes and their expression patterns [7–11]. Likewise, significant insights have been gained on silk production, spinning, and its engineering [11–15]. However, because no single study has holistically examined the consequences of gene expression on protein structure and silk functional properties, it is not known why spiders spin silks with such exceptional properties and, more importantly, why silk properties vary so much between and within individual spiders . Experimentally switching silk related genes on and off within individual spiders is not yet achievable, so observing the function of silk proteins in different spiders with varying levels of expression of particular genes appears the best way forward.
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review
| 99.9 |
MA silk properties have traditionally been thought to be the product of the combined expression of genes coding for two proteins (called spidroins); major ampullate spidroin 1, or MaSp1, and major ampullate spidroin 2, or MaSp2 (with the genes called MaSp1 and MaSp2). The secondary structures of the spidroins are considered critical for silk performance . MaSp1 consists of repeating polyalanine, (GA)n, (GGX)n and (A)n amino acid motifs (G = glycine, A = alanine and X = other amino acids). These motifs form cassettes that combine to promote the formation of crystalline β–sheet nanostructures in the assembled fibers . The MaSp2 protein on the other hand has been thought to consist of multiple (GPGXX)n motifs (where P = proline), and predicted to form disordered type II β-turns and similar non-crystalline nanostructures . Collectively, the various nanostructures are thought to combine and provide MA silk with its great strength and extensibility . Since MaSp2 has long been predicted to contain (GPGXX)n sequences, the proline composition of MA silk was considered a reliable indicator of MaSp2 gene expression . The ratio of MaSp1: MaSp2 expression is variable among and between spider species, presumably because the MaSp2 protein is metabolically costly to synthesize so may be differentially expressed. Variation in the ratio of the two spidroins has been traditionally thought to bring about variations in nanostructure formations leading to variations in the mechanics of the spun fibres .
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study
| 99.94 |
The recent mapping of the Nephila clavipes’ spidroin genome has caused researchers to rethink much assumed knowledge. We now understand that, at least in N. clavipes: (i) individual spiders express multiple versions of the MaSp genes at different loci, potentially leading to several MaSp1 and MaSp2 proteins appearing in the spun silks, (ii) the different spidroin cassettes and motifs are shared and partitioned among the different proteins, and (iii) other spidroins, e.g. AcSps, appear in the major ampullate gland, so can provide additional cassettes and motifs that may form components of MA silk . For these reasons, MA silk amino acid compositions may not necessarily reflect the MaSp1: MaSp2 expression, possibly explaining why nanostructure formations can vary within individual spider silks independent of MaSp1: MaSp2 expression [25–27].
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study
| 100.0 |
Any study aspiring to understand how spidroin gene expression influences MA silk nanostructure formation must reliably measure the size, density, orientation, and distances between the crystalline and non-crystalline nanostructures in the silk proteins in addition to genetic expression. Techniques such as small angle X-ray scattering (SAXS), wide angle X-ray scattering (WAXS), nuclear magnetic resonance (NMR), Fourier transform infrared (FTIR), Raman and circular dichroism (CD) spectroscopy, and transmission electron microscopy, have been used to examine the nanostructures of MA silk’s proteins [28–31]. Of these only synchrotron-derived SAXS and WAXS can explicitly and reliably measure nanoscale variability in silk crystallinity, and the size, density, orientation, and distance between individual crystalline and non-crystalline nanostructures . For instance, SAXS derived parameters such as the meridional peak and long period can be used to elucidate nanostructure alignment in the silk’s amorphous and lamellar regions [34–36]. WAXS on the other hand can be used to determine silk crystallinity, and the size, density, and orientation, of crystalline nanostructures by examining the scattering pattern and diffraction angles (θ) at specific high intensity diffraction peaks . For spider silk the diffraction peaks at the (200), (120) or (002) regions identified on two dimensional WAXS images are of particular interest as they are associated with scattering from crystalline β–sheets [37,39–41]. SAXS and WAXS accordingly are tools appropriate for measuring and classifying silks based on the size, alignment, and distances, between and within crystalline and non-crystalline nanostructures .
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review
| 92.5 |
X-ray scattering analyses of the MA silks of different spider species has found that the micro-arrangement of silks from spiders in the Araneoidea clade are relatively conserved across the group . The mechanical properties of Araneoid spider MA silks, nonetheless, may vary considerably between species and individuals across environments and loading preconditions . For instance, silk spun by spiders walking along a surface is not as stiff as that spun by free falling spiders . Much of this variability is considered a consequence of changes in the friction acting on the silk as it exits the valve during spinning since this induces the amorphous region proteins to further self-align . The properties of the crystalline nanostructures can nonetheless also vary within individual spider silks across loading conditions. For example, spiders exposed to winds of different strength produce silks differing in nanocrystal density, which affects the ultimate strength of the fibers . Exposing spiders to strong wind induces silk extensibility and ultimate strength to change in the same direction . Simulations and experiments have shown that variations in glandular pH, salts and shear stress during spinning induce the poly-alanine residues to undergo α-helix→ β-sheet phase transitions, which enhances silk strength [47–50]. These types of phase transitions could explain the enhancement of ultimate strength from spiders exposed to high wind. Moreover, it has been shown that the additional enhancement of extensibility in the silks of spiders exposed to strong wind could be a consequence of spinning under a static load, since this induces the amorphous region proteins to move more freely relative to each other .
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study
| 100.0 |
An additional experiment found that the spider Nephila pilipes produces stronger and tougher MA silk when fed a high protein diet than when deprived of protein . Subsequent WAXS analyses found that, like wind exposed spiders , changes in silk crystal density explains some of the enhanced strength . Nevertheless, unlike wind exposed spiders, a combination of changes in crystallinity and nanostructural orientation in the amorphous region were also prevalent . In both instances, the silk nanostructures seem to vary independent of MaSp1: MaSp2 expression, although only indirect measures (i.e. amino acid composition) of MaSp expression was made . Different mechanisms at different scales seem to be responsible for nutritionally induced spider silk property changes compared to wind induced property changes. However, no study has holistically examined the consequences of gene expression on silk proteins and protein structure and, ultimately, silk functional properties to understand the mechanisms inducing nutritionally induced property variation in spider silks at multiple scales.
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study
| 99.94 |
Here we performed an examination of nutritionally induced MA silk property variation by running experiments similar to those described by Blamires et al. . However, we used five species of Araneoid spiders: Argiope keyserlingi, Eriophora transmarina, Latrodectus hasselti, Nephila plumipes and Phonognatha graeffei, and directly measured spidroin expression using quantitative real-time PCR (RT-PCR) techniques. Specifically, our experiments aimed to answer two persistent and problematic questions about spider silk property variability: (1) Do the silk nanostructures and mechanical properties of different spiders respond similarly to variations in spidroin expression? And (2) what are the relative contributions of changes to amino acid compositions and nanostructures in inducing spider silk mechanical property variation? To answer the first question, we performed silk tensile tests, SAXS/WAXS analyses, amino acid determinations, and gene expression analysis, for the abovementioned spiders and compared the results across species and treatments. To answer the second question, we pooled the mechanical property, nanostructural, and amino acid compositional data across species and constructed predictive models.
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study
| 100.0 |
Ethical clearance was not required to perform this research. Capture permits were not required under New South Wales law as all collections were made outside of protected areas. We confirm that the collection locations were not privately owned, and we did not collect any endangered or protected species.
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other
| 99.94 |
We collected, per species, 40 adult female A. keyserlingi, E. transmarina, L. hasselti, N. plumipes and P. graeffei from locations between Sydney and Ballina, New South Wales, Australia, during trips made between October 2014 and January 2015. To ensure spiders of approximately equal size within species were used and that no gravid spiders were collected, we measured each spider’s body length and width to ±0.1 mm using digital Vernier calipers (Caliper Technologies Corp., Mountain View, CA, USA) and mass to ±0.001g using an electronic balance (Ohaus Corp., Pine Brook, NY, USA) upon collection, and discarded any particularly large or heavy (>50% above the mean) individuals. We returned all the required spiders to the laboratory at the University of New South Wales, Sydney, where they were placed in 115 mm (wide) x 45 mm (high) plastic circular containers. The containers had perforated wire mesh lids with a 20 mm long slit cut into them using a Stanley knife to facilitate feeding with a 50 μl micropipette. We pre-fed the spiders 20 μl (A. keyserlingi, L. hasselti and P. graeffei) or 50 μl (E. transmarina and N. plumipes) of a 30% (w/v) glucose solution daily over five days (for details see ) to standardize the diet of all spiders prior to experimentation. We reweighed the spiders after the pre-feeding treatment and any individuals who lost > 50% of their initial mass (one A. keyserlingi, one L. hasselti and three N. plumipes) were discarded.
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study
| 100.0 |
We randomly divided the remaining 40 or so spiders per species equally into two groups and fed them either one of two solutions over 10 days: a protein solution (P) or protein deprived solution (N). The protein solution used to experimentally increase protein intake was identical to that used by Blamires et al. , i.e. a mixture of 10g of a 10% albumin solution with 6g of sucrose in 60ml of water. The protein deprived solution was 8g of sucrose in 30 ml of water. We fed the spiders by placing a measured droplet of solution onto their chelicerae using a 20 μl micropipette (see ). As protein and carbohydrates contain approximately similar energy densities (~4kJ g-1), solutions of similar energy concentrations were fed to all spiders. After completing the feeding experiment we re-weighed all of the spiders and any that lost > 20% of their mass during the experiment (i.e. one A. keyserlingi, two L. hasselti and P. graeffei, and five N. plumipes) were not used for any of the subsequent experiments.
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study
| 100.0 |
After completing the feeding experiment and subsequent re-weighing, we anaesthetized each spider (N = 185 spiders; 38 A. keyserlingi, 40 E. transmarina, 37 L. hasselti, 32 N. plumipes, and 38 P. graeffei) using CO2 and carefully pulled a single MA silk fiber from their spinnerets using tweezers. We collected a thread of silk from eight spiders per treatment per species for the determination of mechanical properties as follows.
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study
| 99.94 |
We connected a revolving headframe to an electronic spool. We then attached a 240 mm long x 40 mm wide cardboard strip with six 10 mm x 10 mm square holes punched at 30 mm intervals to the headframe. Double sided sticky tape was stuck onto the cardboard at the border of the holes. A silk thread was pulled over the headframe and stuck to the sticky tape. The headframe was rotated once at 1m min-1 while ensuring the silk traversed all of the square holes and adhered to the pieces of tape. The strip was then removed from the headframe and a drop of water based glue applied at the position where the silk attached to the tape. Another frame of equal size with identically positioned holes punched into it was placed on top. The two strips were squeezed together with forceps ensuring that they were tightly stuck together. We then cut the strip in the regions between the holes perpendicular to the silk thread, thus leaving six 10mm x 10mm frames each holding parts of a single thread of silk. The procedure was repeated for every individual used from each of the five species. Accordingly, 48 frames were collected per treatment per species, i.e. 6 frames x 8 individual threads (see for details).
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study
| 100.0 |
We collected silk from a further three individuals per treatment per species for SAXS/WAXS analyses. We did this by spooling their silk onto 3 mm x 1 mm titanium frames containing 0.5 mm x 0.5 mm windows (see ) for ~1 h (A. keyserlingi, L. hasselti and P. graeffei) or ~2 h (E. transmarina and N. plumipes). We assumed that the amount of silk extracted was approximately the entire store of silk from the spider’s major ampullate glands. We collected between 1000–2000 rounds of silk across the windows of each frame. We have previously found this amount of silk to be adequate for attaining quality scattering from 5-35keV synchrotron X-ray sources .
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study
| 100.0 |
We collected silk from the remaining 75 spiders [16 A. keyerslingi (8 each from the N and P treatment), 18 E. transmarina (9 N and 9 P), 15 L. hasselti (7 N and 8 P), 10 N. plumipes (4 N and 6 P) and 16 P. graeffei (8 N and 8 P)] to determine their amino acid compositions. We wrapped the silk threads around a glass tube connected to the electronic spool spun at 1m min-1for ~1–2 h. This approximated the collection of the store of silk from the spider’s glands, so any variations in amino acid composition within individual threads were accounted for. All silks were extracted under controlled temperature (~25°C) and humidity (~50% R.H.) in still air, so reeling speed and post-spin environment did not influence the subsequent chemical or mechanical property measurements.
|
study
| 100.0 |
One randomly selected frame of silk from each thread (i.e. one of the six frames of silk collected per spider) was used to ascertain the width of the thread so we could calculate the cross-sectional area of the individual threads used in the ensuing tensile tests. We taped the frames to a microscope slide and examined and photographed them under air immersion at 1000x magnification using a polarized light microscope (CKX41, Olympus, Tokyo, Japan) connected to a SPOT Idea 5 Mp digital camera (Spot Imaging Solutions, Sterling Heights, MI, USA). The images were digitized using the program Spot Basic 4.7 (Spot Imaging Solutions, Sterling Heights, MI, USA) and the width of each thread determined as a mean of 12 measurements using the program Image J (NIH, Bethesda MD, USA).
|
study
| 99.94 |
We performed the following tensile tests under controlled temperature (~25°C) and humidity (~50% R.H.) in still air within 10 days of silk collection. We placed each of the cardboard frame-mounted silks for each species within the grips of an Instron 5543 tensile testing machine (Instron Machines, Melbourne, Australia) with a ~2μN resolution . We ensured that the grips held the silks firmly at the upper and lower frame edges. The left and right sides of the frames were cut away and the silks stretched at a rate of 0.1 mm s-1 until the fiber ruptured.
|
study
| 99.94 |
True stress (σ) and strain (ε) were derived from the following equations: σ=FA and ε=logeLL0 where F is the force applied to the specimen, A is the cross-sectional area of the thread calculated from the thread diameter assuming a constant thread volume, L is the instantaneous length of the fiber at a given extension value and L0 is the original gage length of the fiber. Stress vs strain curves were determined for each silk tested by a standard trapezoidal method from which we calculated the following mechanical properties using both Bluehill 3.0 (Instron Machines, Melbourne, Australia) and Microsoft Excel 2010: (1) ultimate strength; or the stress at rupture, (2) extensibility; or the strain at rupture, (3) toughness; the area under the stress strain curve, and (4) Young’s modulus (stiffness); the slope of the stress-strain curve during its initial elastic phase.
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study
| 100.0 |
Small-angle X-ray scattering (SAXS) procedures were performed at the end station of beamline BL23A SWAXS of the Taiwan Light Source, National Synchrotron Radiation Research Center (NSRRC), Hsinchu, Taiwan. Pre-tests were performed using polyethylene and silver behenate to calibrate the scattering intensity and wave vector sensitivity, respectively. The samples were placed 3659 mm from the incident 15 keV X-ray beam (λ = 0.8265 Å) and were exposed to the beam for 10–60 s depending on the measured signal intensity. The scattered radiation was captured using a Pilatus 1M-F area detector and two dimensional SAXS images generated. From these images the scattering intensity was obtained and intensity vs scattering vector (q) plots generated using the program Albula (Dectris, Baden-Dättwil, Switzerland). Where q was ascertained by the equation: q=4πλ−1sinθ where λ is the wavelength of the incident X-ray beam (λ = 1.033Å) and θ is the scattering angle .
|
study
| 100.0 |
We then calculated the meridional peak following Balta-Calleja and Vonk , from which we estimated the long period (L) using the equation [54–56]: L=2π/qm where the qm is the integrated position of the meridional peak ascertained by scanning the intensity vs q plots along the equatorial direction .
|
study
| 99.94 |
We performed WAXS procedures immediately upon completion of the SAXS procedures using the same 30 silk samples at the end station of beamline BL01C2 at NSRRC, Hsinchu, Taiwan. We used the same samples because we were interested in measuring a combination of nanostructural properties within the crystalline, amorphous and lamella regions of the same silk threads. We thus first performed a series of pre-tests to establish that the short exposure time (10–60 s) of the SAXS procedures was unlikely to damage the silk nanostructures and affect the WAXS measurements.
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study
| 100.0 |
We placed the samples 300 mm from the 12keV incident beam and exposed them to X-rays for 10–60 min depending on the pre-measured signal intensity. Beam size was confined by a collimator 0.5 mm in diameter. Scattered radiation was detected by a Mar 345 imaging plate and two dimensional diffraction images generated for each silk sample using the program Fit2D (ESRF, Grenoble, France). From the diffraction images we calculated the diffraction angle (θ), intensity peaks (Ix), and 2θ full width and half width maximum intensities (FWHM) of the (200) and (120) diffraction peaks and the so called amorphous halo.
|
study
| 99.94 |
We subsequently calculated: (i)The relative crystalline intensity ratios (I200/ I120) with I200 and I120 representing the sum of the intensity peaks at the (200) and (120) peaks respectively .(ii)The crystallinity index, Xc, calculated according to Grubb and Jelinski , and(iii)Herman’s orientation function, fc, using the equation : fc=(3{cos2θ}‑1)/2 where φ is the angle between the c axis and the fiber axis, {cos2φ} is the azimuthal width at the (200) and (120) diffraction peaks determined using the equation : {cos2φ}=1−A{cos2φ1}‑B{cos2φ2} where A = 0.8 and B = 1.2.
|
study
| 100.0 |
We weighed all of the silk samples designated for amino acid composition analysis to the nearest 0.001 mg on an electronic balance (Pioneer PA214C, Ohaus, Pine Brook NJ, USA), before submergence in 99% hexoflouro-isopropanol solvent (500 μl of per mg of silk) within 1 ml Eppendorf tubes. The samples were then hydrolyzed in 6 mol l-1 HCl for 24 h at 115°C. Molar percentage compositions of glutamine, serine, proline, glycine, and alanine, the amino acids representing ~90% of the total amino acids in the MA silks of most spiders , determined using an Alliance Systems (Waters, Rydalmere NSW, Australia) high performance liquid chromatography column at the Australian Proteomic Analysis Facility, Sydney.
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study
| 100.0 |
At the end of silk collecting five randomly selected spiders per treatment and species were sacrificed by lethal exposure to CO2and their major ampullate glands dissected as described by Jeffrey et al. . The glands and a sample of the remaining abdomen were immediately lysed with RNase free mini pestles in QIAzol Lysis Reagent and mRNA extracted using an RNeasy Plus Universal RNA extraction kit (Qiagen, Düsseldorf, Germany). In order to prevent any DNA contamination we used a gDNA Eliminator Solution provided with the extraction kit to remove all genomic DNA. The extracted mRNA was eluted to 30–35 μl and we measured the concentration extracted using a NanoDrop 1000 Spectrophotometer (Thermo Fisher Scientific, Wilmington, DE, USA). A mean concentration of 1376.4 ± 239.04 ng/μl RNA was extracted from all samples and did not differ substantially between species. Mean absorbance ratios of the samples were 2.24 at 260/280 nm, and ranged between 1.86 and 2.38, and 1.97 at 260/230 nm and ranged between 1.22 and 2.27. Absorbance ratios in this range are considered acceptable as ‘pure’ for single stranded RNA .
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study
| 100.0 |
All of the mRNA samples were diluted to 1000ng/μl. We took 12.5 μl subsamples of the diluted mRNA for reverse transcription to cDNA using an Advantage reverse transcription kit for PCR (Clontech, Clayton Vic, Australia). The reverse transcription and PCR activation procedures were carried using Eppindorf Mastercycler (Eppindorf, Mamburg, Germany) qPCR machines, following the recipe outlined by the reverse transcription kit handbook . A “buffers only” (i.e. no RNA and no Reverse Transcriptase) solution was included in the analyses as a negative control. We included the spider’s abdomens in the gene expression analysis for normalization against background expression of MaSp transcripts in other silk glands or abdominal tissue . All procedures were replicated three times for each individual spider.
|
study
| 100.0 |
We used the Drosophila rufa Glycerol-3-phosphate dehydrogenase (g3pdh) gene as a “housekeeping” reference gene for our RT-PCR analyses, as is common practice in gene expression analyses . However, since the g3pdh gene primers were designed from Drosophila spp., there may have been unintended amplification biases across species.
|
study
| 99.94 |
We diluted all of the cDNA eluent to 200 ng/μl, checking (using a NanoDrop 1000 Spectrophotometer) that the 260:280nm absorbance ratio were all between 1.5 and 1.8 before sending 10 μl samples to the Ramaciotti Centre for Genomics, University of New South Wales, for Fluidigm quantitative RT-PCR gene expression analyses . Fluidigm RT-PCR gene expression analysis utilizes dual-labelled probes designed to hybridize a complementary region of the cDNA for real-time amplification. These probes contain a fluorescent reporter dye on the 5' base, and a quencher on the 3' base, whose intensity increases proportional to the number of probe molecules cleaved .
|
study
| 50.6 |
We used published C-terminal domain sequences for MaSp1 and MaSp2 from Argiope trifasciata (hereon called MaSp1a and MaSp2a) and Latrodectus hesperus (hereon called MaSp1b and MaSp2b) [7,68–71] to order primers for the RT-PCR analyses (see Supporting Information, S1 Table for sequences and accession numbers) using the Fluidigm online assay designer . We converted the threshold cycle (CT) values (see S2 Table) derived by the RT-PCR analyses to 2-ΔΔCT values, which were averaged for each individual spider across the three technical replicates, following Schmittgen and Livak .
|
study
| 100.0 |
While recent genomic work has shown that some spider species may possess more than two MaSp loci, with some paralogs exhibiting different expression patterns across different silk glands , further verification of multiple MaSp loci across the Araneoid clade awaits. Since our objective here was to identify whether shifts in amino acid compositions can be attributable to shifts in MaSp1: MaSp2 expression and not verify or refute the multiple loci hypothesis, we used the abovementioned procedures to ascertain the across treatments expression patterns of just two MaSp1 and MaSp2 paralogs.
|
study
| 100.0 |
For each species we used separate single-factor (two treatment levels: protein deprived and protein fed) multivariate analyses of variance (MANOVAs) and Fisher’s Least Significant Difference post-hoc analyses to determine whether the mean (± 1 standard error): (1) mechanical properties (ultimate strength, extensibility, toughness, and Young’s modulus), (2) nanostructures (L, 2θ FWHM of the (200) and (120) diffraction peaks and amorphous halo, I200/ I120, Xc, and fc), (3) mole compositions of glutamine, serine, proline, glycine, and alanine, and (4) spidroin (MaSp1a, MaSp1b, MaSp2a, and MaSp2b) expression. We used additional univariate (treatment) ANOVAs to individually compare the MaSp1a, MaSp1b, MaSp2a, and MaSp2b 2-ΔΔCT values between treatments for each species. We log10 or arcsine (amino acid composition data) transformed any data that failed Levene’s heterogeneity tests.
|
study
| 100.0 |
To ascertain the influences of nanostructures and amino acid compositional variations on silk mechanical properties we pooled the data for all species and constructed multiple regression models. We used the mechanical properties that our MANOVAs found to differ across treatments as the response variables and any nanostructural parameters or amino acid compositions that our MANOVAs found to differ across treatments as the predictor variables. Species and treatments were assigned as continuous predictor variables. A large number of predictor variables, interactions, and intercept terms were likely so we considered linear regression models to be too complex for interpretation. We therefore derived Y = β0 + β1(x1) + β2(x2)… βn(xn) + εi additive regression models for each response variable, where β0 is the population intercept, β1, β2. βn are the regression coefficients associated with the predictor variables x1, x2… xn and εi is the random error term associated with the ith observation . We checked all data for normality, linearity, homoscedasticity and singularity using Q-Q and scatterplots prior to constructing the models.
|
study
| 100.0 |
We found that protein feeding and deprivation affected silk mechanics differently among the five species examined (see Supporting Information, S3 Table). Argiope keyserlingi’s silk was more extensible when protein deprived than when protein fed. On the other hand Eriophora transmarina’s silk was less extensible when protein fed (Supplementary Material, S3A and S3B Table). Both Nephila plumipes’ and Phonognatha graeffei’s MA silks were stronger and tougher when they were protein deprived (Supporting Information, S3D and S3E Table). Wherein we found neither protein feeding nor deprivation to effect the mechanical properties of Latrodectus hasselti’s silk (Supporting Information, S3C Table). Comparisons of the silk thread widths across treatments found a significant difference for A. keyserlingi only (means ± SE: protein deprived spiders = 3.39 ± 0.18 μm, protein fed spiders = 2.17 ± 0.15 μm, Supporting Information, S3A Table), thus thread width differences across treatments were not responsible for any of the variations silk mechanical properties found across the five species.
|
study
| 100.0 |
The silk nanostructures varied in response to protein feeding/deprivation among the five spiders (see Supporting Information, S4 Table). The SAXS images and subsequent intensity vs scattering vector (q) plots for each spider are shown in Fig 2 and Fig 3 respectively. Examples of two dimensional WAXS images are shown in Fig 4 and the subsequent intensity vs 2θ plots in Fig 5. The azimuthal angles at the (200) and (120) diffraction peaks are in the Supporting Information (see S1 Fig and S2 Fig, respectively). From the various plots we calculated a greater long period in A. keyserlingi silks when the spiders were protein deprived compared to when they were protein fed. Within species, the nanostructures generally shifted in the same direction as the mechanical properties we predicted them to affect, e.g. long period and/or FWHM of the amorphous halo varied with extensibility in A. keyerlingi and N. plumipes (Supporting Information, S4 Table). We thus expect the structural variations to explain much of the variation in mechanical properties.
|
study
| 100.0 |
The FWHM of the (200) peak was greater in A. keyserlingi silks when protein deprived compared to when protein fed. Crystallinity, on the other hand, was greater in the silks of protein fed A. keyserlingi (Supporting Information, S4A Table). These findings and the greater long period in the silk of protein deprived A. keyserlingi indicate that the crystalline nanostructures in their silks were stacked more densely when the spiders were protein fed, and were more aligned and stretched when they were protein deprived. We considered it likely that the greater alignment of the crystalline region proteins in the silks of protein deprived A. keyserlingi explains the high extensibility of their silks. FWHM at the amorphous halo was greater in the silks of protein deprived compared to protein fed N. plumipes (S4D Table). This result, and the greater ultimate strength in the silk of protein fed N. plumipes, indicated that variability in the non-crystalline nanostructures primarily influenced their silk mechanical properties. SAXS/WAXS did not detect any significant nanostructural shifts across treatment for E. transmarina, L. hasselti and P. graeffei silks (Supporting Information, S4B, S4C and S4E Table).
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Amino acid compositions of the MA silks varied across treatments and the type of variations found differed for each of the spiders. For instance, we found a reduction in the proline, alanine and glycine compositions of A. keyserlingi silk when protein deprived (Supporting Information, S5 Table). A reduction in proline composition was, however, detected in N. plumipes and P. graeffeii MA silks when they were protein fed.
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Fig 6 shows mean spidroin expression (2-ΔΔCT) values across treatments for each species. Interestingly, the across treatment expression patterns differed significantly among the five species (Wilk’s λ = 0.011,d.f. = 5,10, P <0.001). We found that A. keyserlingi upregulated the expression of MaSp1a within their MA gland when protein deprived (F1,28 = 13.911, p < 0.001). E. transmarina on the other hand downregulated the MaSp1a expression within their MA gland when protein deprived (F1,28 = 42.171, p < 0.001), but upregulated MaSp1b (F1,28 = 8.135, p = 0.005). L. hasselti upregulated their MaSp1a (F1,28 = 12.308, p < 0.001), MaSp2a (F1,28 = 27.604, p < 0.001), and MaSp2b expression (F1,28 = 25.342, p < 0.001) expressions when protein deprived, while downregulating MaSp1b (F1,28 = 54.224, p < 0.001), while N. plumipes (c.f. response to protein deprivation by N. pilipes ) downregulated both their MaSp1a (F1,28 = 21.104, p < 0.001) and MaSp2a (F1,28 = 8.358, p = 0.007) expression when protein deprived, which corresponded with an increase in proline composition. P. graeffei significantly downregulated their MaSp1a (F1,28 = 6.587, p = 0.010) and upregulated their MaSp2a (F1,28 = 8.543, p = 0.006) expression when protein deprived.
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To answer this question we derived additive regression models for three response (Y) variables pertaining to the mechanical properties: ultimate strength, extensibility and toughness, and eight predictor (xi) variables: thread width (x1), FWHM of the amorphous halo (x2), FWHM of the (200) diffraction peak (x3), crystallinity (x4), long period (x5), proline composition (x6), glycine composition (x7), and alanine composition (x8) using data from all species excluding L. hasselti. The variables chosen were those that our analyses above found to differ across treatments in at least one species. Our models showed crystallinity to predominantly influence the ultimate strength in nutritionally affected MA silks (Table 2A). Long period, which represents the nanostructure alignment across the silk’s amorphous and lamellar regions, and alanine and proline composition, were predominantly influential over extensibility (Table 2B). Long period, along with glycine, alanine and proline compositions, influenced silk toughness (Table 2C).
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Our study was the first to comprehensively examine the consequences of protein deprivation on variations in spider silk mechanics, structures, amino acid compositions and gene expressions at multiple scales across species. We concluded that: (i) MA silk properties of the spiders respond differently across multiple scales to variations in nutritional intake, and (ii) variations in spidroin expression and the crystalline and non-crystalline nanostructures play specific roles in inducing variations in the silk’s mechanical properties.
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The MaSp1: MaSp2 expression patterns we found across treatments generally did not correspond with the amino acid compositions according to our expectations under a traditional MaSp expression model. For instance, a decreased in alanine and glycine composition in the MA silk of protein deprived A. keyserlingi was not associated with any form of MaSp2 downregulation, but was associated with an upregulation of MaSp1a. The spidroin expression pattern found for E. transmarina across treatments likely explains why the amino acid compositions did not vary across treatments in this spider. The high expression of all of the MaSp1 and MaSp2 paralogs in the MA glands of L. hasselti is consistent with findings for L. hasperus . Expression of multiple MaSp paralogs in the MA glands was not common for most species we assessed, but was pronounced in L. hasselti. The increase in proline composition in protein deprived N. plumipes MA silk conflicts with the findings of Blamires et al. , who found an increase in proline composition in protein deprive N. pilipes. We would have expected the downregulation of MaSp2a expression by this spider when protein deprived to result in a decrease in silk proline composition. The downregulation of MaSp1a on the other hand was unlikely to have any influence on proline composition . An increase in the proline composition in the silks of protein deprived P. graefei, however, may be ascribed to an increased in MaSp2a expression by these spiders.
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The apparent disconnect between the spidroin expression patterns and the amino acid compositions in the silks of most species might lead us to conclude that: (1) the silk nanostructures and mechanical properties of different spiders do not respond similarly to variations in spidroin expression, and (2) the relative contributions of change in spidroin expression in inducing spider silk mechanical property variation is minimal at best. We, nevertheless, presume caution before drawing definitive conclusions about the influence of MaSp expression in light of the following. Firstly, in our RT-PCR reactions any specific amplification of MaSp paralogs could arise in any given species using a single pair of primers . This may cause falsely elevate expression levels of certain MaSp transcripts. Examination of the melt curves for the genes screened suggested that in some species there were indeed multiple amplifications. The most discernible example of this is found in the melt curves for MaSp1a and MaSp2a, particularly for Nephila plumipes (see S3 Fig). Secondly, the amplification of non-orthologous MaSp loci across species with single primer pairs could lead to falsely elevated or falsely lowered detection of expression levels . Any of the MaSp-targeting primers could thus have identified a single MaSp transcript in one species, a single non-orthologous transcript in a second species, and/or multiple non-orthologous transcripts in yet another species. Thirdly, since the full length MaSp sequences for each of the species under investigation herein is not known, we designed primers based on the MaSp sequences for closely related species (Argiope trifasciata and Latrodectus hesperus). The amplification efficiency thus may potentially be biased toward species with the greatest sequence homology (i.e. Argiope keyserlingi and Latrodectus hasselti). Indeed only in L. hasselti was expression of MaSp1b and MaSp2b comparable to that of MaSp1a and MaSp2a. Lastly, some degree of gDNA amplification cannot be ruled out as influencing our results. We, however, did not expect this to cause major expression value biases within any particular species.
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While the above caveats may tempt us to think that the RT-PCR analyses yielded largely uncertain results, we found that the expression patterns for P. graeffei aligned exceptionally close to our expectation should the spiders be regulating MaSp1 and MaSp2 expression alone. We expect that such a finding would not have been possible if our primer choices and/or amplifications were compromised in any way. Moreover, our expression values conformed with those reported for the MaSp-a, MaSp-f, and MaSp-g genes by Babb et al. (although these authors reported a wider range of values for other MaSp loci). We thus expect that the across-treatment expression patterns that we reported for each species to be reliable for the explicit purpose of checking MaSp expression against the amino acid compositions for each species.
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Our study corroborates work showing that amino acid compositional shifts confer consequences on crystalline and non-crystalline protein structures and alignments within the crystalline, amorphous and lamellar regions of the silk, which in turn influences its strength, extensibility and toughness . Our study also uncovered novel mechanisms behind the multilevel shifts in silk properties, as follows.
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While silk mechanical properties were affected by protein deprivation in four of the five species of spider (the exception being L. hasselti), the mechanisms by which the mechanics were affected differed in each instance. Our amino acid composition analyses, for instance, found a lowering of proline, alanine and glycine compositions in the silk of A. keyserlingi when protein deprived. Nevertheless the silks of N. plumipes and P. graeffei had greater proline compositions when protein deprived. Our genetic expression analyses revealed that most spiders seemed to preferentially regulate their expression of the MaSp1 genes rather than MaSp2 genes across treatments, contrary to what might be predicted, given the MaSp2 protein is expected to be the more costly of the two proteins to metabolically synthesize .
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Our SAXS measurements revealed that the spidroin regulation and consequent variability in silk proline corresponded with variations in long period (L) in A. keyserlingi, N. plumipes and P. graeffei, suggesting that spidroin expression and/or proline composition affects nanostructural alignment in the amorphous and lamellar regions. These compositional and structural variations correlated well with extensibility and toughness variations in the silks, providing first evidence of a functional link between spidroin expression, nanostructural formation, and mechanical property variations in the MA silks of different spiders.
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An explanation for the association between proline and amorphous region nanostructural alignment might lie in the capacity for proline to form cross-linkages which disrupt the hydrogen bonds between amorphous region α-helices and other structures causing slippage in the nanostructures under strain . Our finding of a lack of any change in amorphous and lamella region alignment in the silks of protein deprived P. graeffei, despite significant variations in proline composition, across treatments nonetheless suggests that proline does not necessarily directly affect amorphous region nanostructural alignment but likely provides the conditions for the breaking and re-establishment of hydrogen bonds in the region .
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Our amino acid composition and WAXS analyses, and subsequent modelling, predicted that, in contrast to extensibility and toughness, MA silk ultimate strength was primarily influenced by variations in crystallinity independent of MaSp1: MaSp2 expression or any subsequent shifts in amino acid composition. Our WAXS analyses withal suggested that variations in amorphous region nanostructural alignment and crystallinity combine to influence silk strength. Glandular pH, salts and shear stresses during spinning influence the formation of the crystalline nanostructures . Hence, we deduced that between treatment variations in glandular pH, salts and shear stress induce the crystalline region proteins to undergo α-helix→β-sheet nanostructural phase transitions within the spinning duct . These transitions result in an increase in crystalline density , which causes the crystalline region nanostructures to realign under strain . Such an increase in crystalline density should be identifiable in WAXS experiments as a reduction in 2θ and FWHM at the (200) or (120) diffraction peaks . Indeed, our analyses herein revealed such a mechanism occurred in protein deprived N. plumipes silks (S1 Table). Nonetheless, the phenomenon was not detected in the silks of any other species, leading us to conclude that the precise physiological mechanisms inducing property variation differed among the five species.
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Unlike the other four species, we found that neither protein feeding nor deprivation effected the amino acid compositions, nanostructures, or mechanical properties of L. hasselti’s MA silk. A similar lack of variability in MA silk structure and mechanical properties was found in L. hasselti collected at different times of year . Spiders of the genus Latrodectus use MA silk within their three-dimensional cobwebs as structural supports , whereas the orb web building spiders use MA silks within webs to absorb the impact of flying prey . A testable hypothesis might accordingly be that orb web spider MA silks have a greater inherent variability in order to adjust the functionality of the orb web and this variability is triggered by changes in nutrient uptake. The MA silks of cobweb spiders on the other hand do not require such property variability so are not so sensitive to changes in nutrient uptake. Our gene expression and amino acid composition analyses alluded to the possibility of the expression of multiple spidroins in L. hasselti, as has been found in L. hesperus . The differential expression of a multitude of spidroins might thus be a mechanism by which cobweb building spiders maintain silk property homeostasis across variable nutritional environments.
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In summary, we found that the MA silk properties of five species of Araneoid spiders varied in response to similar variations in protein intake. Stronger and tougher silks with greater crystallinity and amorphous region nanostructural alignments were found for protein fed P. graeffei and N. plumipes which contrasted with the findings for A. keyserlingi and those reported previously for protein fed/deprived N. pilipes . The properties and nanostructures of L. hasselti’s MA silks were unaffected by nutrient deprivation. Our analyses suggested that variations in MaSp1: MaSp2 expression were largely ineffectual over amino acid compositions. Proline and alanine composition and the crystalline and amorphous nanostructures significantly varied in all species with the exception of Latrodectus hasselti, all with subsequent effects on mechanical properties. We uncovered additional unexpected and novel findings regarding the mechanisms inducing variations at different levels in different spiders. For instance, MaSp2 genes were not as strongly regulated as we might have predicted under the current MaSp model when protein intake changed in A. keyserlingi, N. plumipes or P. graeffei. Rather MaSp1 was more likely to be up or downregulated in these spiders. Our modelling showed that variations in silk strength were associated with variations in crystallinity and the size, length and alignment of the crystalline and non-crystalline proteins independent of expressions of the MaSp genes. Extensibility and toughness on the other hand were driven by variations in the crystalline, amorphous and lamellar region nanostructural alignments, which were largely disassociated from MaSp expression.
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Here we holistically examined the consequences of gene expression on silk proteins and protein structure and, ultimately, silk functional properties and established that: (1) the MA silk properties of five species of Araneoid spiders varied differently in response to similar variations in protein intake, and (2) the roles of spidroin expression, crystalline, and amorphous region nanostructures on mechanical property variations differed across the species examined.
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While there is broad micro-scale homogeneity in the MA silks of Araneoid spiders, our measurements found that variations in the gene expression, amino acid compositions, and nanostructures, further induce mechanical property variations between and within species. Our modelling found nanostructural variations to primarily influence silk extensibility and toughness while variations in the alignment of the crystalline and non-crystalline proteins influenced ultimate strength independent of MaSp expressions. Our study provides insights into the nanoscale mechanisms of nutritionally induced spider silk property variability by showing how spidroin expression and nanostructures affect spider silk mechanical property variations in different species. These insights further our understanding of MA silk property variability at multiple levels which is imperative if materials that match the performance of naturally spun spider silks are to one day be synthesized.
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Natural killer (NK) cells are classified as members of the type 1 innate lymphoid cells (1). NK cells defend against infection, both directly and by orchestrating T cell, DC, monocyte, and macrophages (Mφ) responses (2). NK cells also may eliminate cancer cells and senescent cells (3, 4). Peripheral NK cells develop in the bone marrow and secondary lymphoid organs, where they are nurtured by multiple cell types and cytokines. IL-15, which is critical for mature NK cell development, homeostasis and function (5), signals via a trimeric receptor comprised of IL-15Rα, CD122, and CD132. IL-15 RNA is made in the bone marrow, secondary lymphoid tissues, and many nonlymphoid tissues, including skeletal muscle and adipose tissue. Although IL-15Rα is part of the IL-15 receptor, it also is required for IL-15 secretion and appearance on cell surfaces. In Mφ, DC, and other producing cells, IL-15 and IL-15Rα bind together with very high affinity. The complex is transported to the cell surface, where it stimulates neighboring NK cells in a paracrine fashion (5, 6). IL-15/IL-15Rα complexes also circulate to act on NK cells in an endocrine fashion (7). Two observations indicate that physiological IL-15 levels are dose-limiting for NK cells homeostasis: hemizygous IL-15 mice have low NK cell number and exogenous IL-15 boosts NK cell number in both normal mice and primates (8–10).
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Human NK cells are classified into two major subsets based on their CD56 surface expression. Most circulating blood NK cells are CD56dim, while 5–15% are CD56bright. CD56bright NK cells are poorly cytotoxic but secrete high levels of cytokines and chemokines in response to inflammatory cytokines. Although CD56dim NK cells respond weakly to inflammatory cytokines, they kill target cells (such as the erythroleukemia cell line K562) and secrete chemokines and cytokines in response to antibody-coated cells and tumor cells.
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| 99.7 |
Natural killer cell numbers are maintained in healthy elderly people, but NK-mediated cytotoxicity and secretion of immunoregulatory cytokines and chemokines decline with age (11, 12). Aging-related NK defects in mice are due, at least in part, to ineffective support from stromal cells (13–15). These defects could be due to decreased Mφ and dendritic cell IL-15 production and presentation (13, 15). Decreased NK cell activity in elderly people correlates with an increased incidence and severity of viral and bacterial infections and deaths (11, 16). Moreover, low NK function was found to be associated with increased cancer rates in subsequent years (17).
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The objectives of this work were to determine (1) if human muscle, subcutaneous adipose tissue (SAT), and visceral adipose tissue (VAT) are sources of IL-15 and IL-15 Rα, and (2) whether any of these tissues correlate with NK cell activity in elderly humans. We found that IL-15 and IL-15Rα RNA are expressed in muscle, SAT, and VAT, but with relatively lower IL-15Rα RNA levels in skeletal muscle. Because skeletal muscle produces high levels of IL-15 RNA, we initially hypothesized that relatively strong elderly individuals would have higher IL-15 levels and more robust NK cell response (18). Contrary to our prediction, we found that plasma IL-15 level did not associate with lean tissue mass, but rather with VAT. Additionally, NK cell response inversely correlated with muscle strength.
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In accordance with the Declaration of Helsinki (modified in 2008), all protocols were approved by the Institutional Review Board of the University of Kentucky, Lexington, KY, USA. All subjects were made aware of the design and purpose of the studies, and all subjects signed consent forms. The cohorts are summarized in Table S1 in Supplementary Material, with additional information provided in some of the figure legends. Cohort A vastus lateralis muscle and SAT biopsies from healthy research subjects were frozen in liquid nitrogen and stored at −80°C. Cohort B SAT and VAT were obtained from discarded surgery specimens, immediately put on ice for no more than 3 h, and immediately processed into stromal vascular fraction (SVF) and adipocyte fractions or stored at −80°C. Cohort C VAT, including mesenteric fat, epiploic appendages, and omentum were obtained from discarded surgery specimens, immediately snap frozen in liquid nitrogen, and stored at −80°C. Cohort D blood samples were obtained between 9:30 a.m. and 12:45 p.m. and kept at room temperature until processing within 2 h of collection.
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As described in Ref. (19), whole blood was diluted with PBS and the mononuclear cells were recovered using Lymphoprep® lymphocyte separation medium (Axis-Shield, Oslo, Norway). For antibody staining, ~0.5 × 106 fresh mononuclear cells were washed and incubated with human IgG for 15 min at room temperature to block Fc-receptor binding and then stained on ice for 30 min with combinations of fluorescently labeled mAb, including those specific for CD3, CD16, and CD56 to allow for identification of CD56bright and CD56dim subsets (19). After washing, the cells were analyzed on a LSR-II flow cytometer (BD, San Jose, CA, USA), and data were processed using FlowJo software. Fresh mononuclear cells (0.5 × 106) were rested overnight and then stimulated with 1 × 106 K562 cells for 3 h at 37°C. Alternatively, mononuclear cells were cultured overnight with 0.5 µg/L IL-12 and then transferred to polystyrene plates coated with anti-NKp46 mAb for 3 h. Cells were stained with mAb to CD3, CD16, CD56, and CD107a. Cells also were fixed in 2% paraformaldehyde solution, then permeabilized (1× Permeabilization buffer, eBioscience) and stained with anti-IFN-γ and anti-MIP-1β mAb.
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In cohort D, body composition was measured by dual X-ray absorptiometry (DXA) using a GE Lunar iDXA. Standardized methods for regional partitioning and phantom calibrations were employed to ensure data quality. Scans were analyzed using the GE Lunar software v10.0 in order to calculate fat-free mass (kg), mineral-free lean mass (kg), fat mass (kg), and percent fat. Leanness is a significant risk factor for health outcomes in the elderly (20, 21) and was calculated as appendicular lean mass divided by body mass index (BMI). aLM was calculated as the sum of lean soft tissue in both the right and left arms and legs where limbs were isolated from the trunk by using DXA pre-defined regional lines with manual adjustment. Computed tomography (CT) is described in Supplementary Material.
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Measures of strength included isometric and isokinetic knee extension testing on a Biodex System 4 dynamometer. Subjects were given a familiarization training session to acclimate to the testing protocol. During testing sessions, isometric measurements of peak torque and time to peak torque were completed with the subject seated with hip and knee angles at 85° and 90°, respectively. Peak torque was recorded as the highest torque achieved over three trials, whereas time to peak was recorded as the time in seconds to reach peak torque. Knee extensor isokinetic strength testing was completed at 90 degrees per second. We assessed peak torque, time to peak torque, total work, and average power over three trials.
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| 99.94 |
Subcutaneous adipose tissue and VAT samples were either frozen at −80°C if unfractionated, or immediately processed if they were to be separated into adipocyte and SVF. Unfractionated fat (~200 mg) or muscle (100 mg) were mixed with zirconium oxide beads and 1.0 mL of TRIzol (Thermo-Fisher) in a 1.6 ml microcentrifuge tube (Thermo-Fisher) and treated with a tissue disruptor (Bullet Blender Storm 5, Next Advance) at full speed for 2 min. RNA was purified from the homogenized samples using the RNAeasy Lipid Tissue Mini kit (Qiagen). Nucleic acid (~1.0 μg) was next treated with DNase 1 (Promega). After DNase inactivation, RNA was reverse transcribed using the RNA to cDNA high capacity kit (Thermo-Fisher), following manufacturer protocol.
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| 99.94 |
To prepare adipocyte and SVF fractions, 10–15 g of fresh SAT or VAT was washed thoroughly with Hanks balanced salt solution (HBSS, Sigma), minced, and after clots and connective tissue was removed, subdivided into two 50 mL polypropylene conical tubes. Each tube contained 25 mL of HBSS supplemented with 40 mM HEPES (pH 7.2), 1.0% BSA (Fraction V, Bioworld), 2 mM CaCl2 and 0.1% collagenase (Type 1, Worthington, OH, USA). Tubes were gently agitated on an orbital shaker for 3 h at room temperature, centrifuged at 12 × g for 5 min, and the floating adipocyte layer and undigested fat was removed. This fraction was sent through a 500 µm steel mesh filter to remove undigested fat and washed once with HBSS. Approximately 0.1–0.2 mL of this fraction was treated with 0.9 mL of TRIzol. The SVF was centrifuged at 300 × g and then treated with 1.0 mM EDTA-HBSS in at 37 C for 15 min and centrifuged at 300 × g. Pelleted cells were resuspended in HBSS in 1.0 mM EDTA, sent through a 70 µm filter to remove large adipocytes, centrifuged, and solubilized with 1.0 mL of TRIzol. For both adipocytes and SVF, RNA was purified and cDNA prepared as described above.
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Primers and primer design are described in Supplementary Material. We wished to estimate the IL-15 and IL-15Rα RNA that was made by adipocytes and SVF cells. Here, we distinguish contaminating adipocytes from all other SVF cells, which likely includes pre-adipocytes. We first used differentiated adult-derived human adipose stem cells (22) to confirm that adipocytes do not contain CD45 RNA. The SVF CD45 level was only 5% of that of blood mononuclear cells. Using this very conservative 5% level, we estimated the SVF RNA contamination of the adipocyte fraction, which had a median of 2.27%. Based on this measurement, we reasoned that the adiponectin level in the adipocyte fraction would closely estimate the percentage adipocyte RNA. We then used the adiponectin RNA level in the SVF to calculate the percentage of adipocyte RNA, which was a median of 0.5% compared to adipocytes. Using the amount of adiponectin signal in each SFV, we calculated the amount of IL-15 and IL-15Rα RNA that came from SVF cells.
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All statistical tests were run using IBM SPSS Software (version 24, Armonk, NY, USA). The strength and direction of associations were evaluated using the nonparametric Spearman rank-order correlation coefficient (Spearman’s correlation, for short) or the Pearson product-moment correlation coefficient (Pearson’s correlation, for short). Linear regression analysis was used to quantify how well two variables relate to each other. When two or more independent variables were hypothesized to affect the outcome, multiple regression analysis was used. When sample groups were not normally distributed, differences between groups were compared by related samples Wilcoxon Singed Rank Testing. All histogram charts represent single values. Significance was set at <0.05. For box-and-whisker plots, the center lines show the medians; box limits indicate the 25th and 75th percentiles, whiskers extend 1.5 times the interquartile range from the 25th and 75th percentiles. Box and whisker graphs were plotted using BoxPlotR (http://shiny.chemgrid.org/boxplotr/).
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Natural killer cell and other innate immune functions decline with age. We hypothesized that IL-15 and its chaperone, IL-15Rα, are secreted from skeletal muscle, SAT, and VAT and influence NK number and function. Therefore, we measured IL-15 and IL-15Rα RNA levels in paired samples of vastus lateralis muscle and abdominal SAT from eight healthy cohort A adult subjects (cohorts are described in Table S1 in Supplementary Material). IL-15 RNA levels in muscle and SAT were not significantly different, but SAT expressed more of the IL-15 chaperone, IL-15Rα. These data are presented as a ratio of SAT RNA level to muscle RNA level in the same individual (Figure 1). This result indicates that fat is a significant source of IL-15 and IL-15Rα RNA and that IL-15Rα RNA level is higher in SAT, compared with skeletal muscle from the same individual.
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IL-15 and IL-15Rα RNA levels in abdominal subcutaneous adipose tissue (SAT) normalized to values in paired vastus lateralis muscle samples in measured by RT-qPCR and normalized to four housekeeping genes in cohort A. Shown are means plus SEM. Each symbol for IL-15 and IL-15Rα represents an individual donor. IL-15 level did not significantly differ between tissues, but SAT expressed significantly more IL-15Rα RNA than did skeletal muscle (p = 0.012). Cohort A is described in Table S1 in Supplementary Material.
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We extended these findings by measuring the IL-15 and IL-15Rα transcript levels in SAT and VAT. Non-paired samples of SAT and VAT from surgeries were processed in cohorts B and C. The levels of IL-15 and IL-15Rα transcripts are shown in Figures 2 and 3. As indicated in the figure legend, some VAT samples were from donors with inflammatory conditions (e.g., cancer), but transcript levels from these samples did not differ from donors without inflammatory conditions, such as hernia repair (Figures 2 and 3). Both SAT and VAT produced considerable IL-15 and IL-15Rα RNA (Figure 2). VAT from the mesentery, epiploic appendages, and omentum all produced IL-15 and IL-15Rα RNAs (Figure 3). IL-15 and IL-15Rα RNA levels occasionally differed between VAT depots, but the differences were not found in all subjects. For example, IL-15Rα RNA was higher in epiploic fat than in mesenteric fat in subject 21, but the opposite was true in subject 26 (Figure 3).
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| 100.0 |
IL-15 and IL-15Rα RNA are produced in both subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT). Except for one sample, all cohort B SAT samples were from females, from surgeries for panniculectomy (7), ventral hernia (1), breast reduction (4), and mastectomy (1). VAT subjects had surgeries for hernia repair (1), nephrectomy (3), colectomy (12), and gastrectomy (1). Cohort B is described in Table S1 in Supplementary Material.
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IL-15 and IL-15Rα RNA are expressed in epiploic appendage (E), mesenteric (M), and omental (O) VAT. Cohort C subjects had surgeries for hernia repair (male #22, female #23, male #25), ostomy reversal (male #24), rectal intussusception (female #18), malfunctioning ileal conduit (male #19), large bowel obstruction (female #20), cancer (male #21, male #26, female #27, female #28), and acute appendicitis (male #29). Cohort C is described in Table S1 in Supplementary Material.
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Adipose tissue is comprised of adipocytes, pre-adipocytes, stromal cells, and a variety of leukocytes (23), any one of which could be a source of IL-15 and IL-15Rα. To understand which cells produce IL-15 and IL-15Rα transcripts, fresh SAT and VAT received from surgery were fractionated into adipocyte fraction and SVF. Figure 4 shows that SVF cells expressed more IL-15 RNA than did adipocytes from the same tissue sample. IL-15Rα RNA levels did not differ significantly between the paired samples. From these results, we propose that VAT IL-15 largely comes from SVF cells, which likely include Mφ.
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| 100.0 |
Subcutaneous adipose tissue (SAT) (A) and visceral adipose tissue (VAT) (B) stromal vascular fraction (SVF) cells produced more IL-15 RNA than did adipocytes (p = 0.012 and p = 0.043, respectively, by Wilcoxon Signed Rank Test) in cohort B. IL-15Rα RNA levels did not significantly differ between SVF and adipocytes. Adipocyte (A Adipo) and non-adipocyte (SVF Non-adipo) were calculated to exclude contaminating cells as described in Section “Materials and Methods.” All but one SAT samples were from female patients. SAT samples were from breast reductions (5), panniculectomy (4), and ventral hernia repair (1). None of the SAT cases involved cancer or other inflammatory diseases. VAT samples were from colectomies (5), exploratory laparotomy (1), and gastrectomy (1). Two VAT cases did not involve cancer. Cohort B is described in Table S1 in Supplementary Material.
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| 100.0 |
We hypothesized that non-lymphoid sources of IL-15 and IL-15Rα may influence plasma IL-15 levels and NK cell activity in the elderly. To test our hypothesis, we recruited 50 healthy adults aged >70 years old and correlated their body composition to plasma IL-15 levels and to NK cell number and function (cohort D). In these elderly subjects, IL-15 plasma levels did not differ by gender (19). IL-15 correlated strongly with CT measures of total abdominal fat and VAT (Figure 5A), but not with abdominal SAT (Table 1). The correlation between IL-15 and VAT was even stronger when analysis was limited to non-obese subjects (BMI < 30; data not shown). Because cytomegalovirus (CMV) infection is life-long and profoundly affects the human immune system (12), we tested whether the correlation between IL-15 and VAT could be explained by CMV infection status. CMV did not correlate with NK cell response to multiple different stimuli (24). Importantly, the correlation between VAT and IL-15 remained strong when CMV status was included as a factor in multifactorial analysis (Table 1). In multifactorial testing, the associations of IL-15 with other adipose depots were not significant when VAT was included as a factor (Table 1). Together, these data indicate that amount of VAT, but not other adipose depots, predicts circulating IL-15 concentration in elderly subjects.
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study
| 100.0 |
Plasma IL-15 level associates with visceral adipose tissue (VAT) (A) and inversely associates with knee extensor peak torque (B) in cohort D. Associations of IL-15 with VAT were significant in all subjects (ρ = 0.442, p = 0.001) and in males (ρ = 0.657, p < 0.001), but not in females tested separately. IL-15 inversely associated with strength in all subjects (ρ = −0.348, p = 0.014) and in females (ρ = −0.581, p = 0.004), but not in males considered separately. Cohort D is described in Table S1 in Supplementary Material.
|
study
| 100.0 |
As expected, men had significantly greater muscle mass, bone mineral content, fat-free mass, and android fat, whereas women had more gynoid and leg fat by DXA and more SAT by CT (data not shown). Men had more VAT than women by CT, but this difference was not significant. For all subjects, C-reactive protein levels were <10 mg/L, indicating a lack of marked inflammatory disease (data not shown). As expected, C-reactive protein level correlated with measures of adipose tissue and inversely with leanness. C-reactive protein correlated directly with IL-15 (data not shown), suggesting a link between systemic inflammation and circulating IL-15 level.
|
study
| 100.0 |
To investigate whether or not plasma IL-15 correlated with NK cell activity, we stimulated elderly cohort D peripheral blood mononuclear cells in vitro with K562 leukemia cells or with low-level IL-12 and immobilized anti-NKp46 antibody, which are well-known NK cell stimuli. Using flow cytometry, we measured the ability of CD56bright and CD56dim NK cells to produce IFN-γ and MIP-1β. We also measured their cytotoxic response, as assayed by the appearance of the CD107a cytotoxic granule marker on the cell surface. Plasma IL-15 level correlated with the CD56dim NK cell MIP-1β and CD107a, but not IFN-γ, responses to NKp46 crosslinking (Table S2 in Supplementary Material). The correlations between NKp46-stimulated responses and plasma IL-15 level remained significant when correcting for age and gender. The responses to NKp46 did not significantly correlate with the responses to K562 leukemia cells, indicating that these two assays measure distinct aspects of NK cell signaling (data not shown). This is not surprising because NK cell responses to K562 largely depend upon NKp30 and NKG2D (25).
|
study
| 100.0 |
We compared NK cell responses and muscle strength. Mature CD56dim NK cell responses to K562 leukemia cells showed inverse correlations with muscle strength, as measured by knee extensor peak torque (Figure 6; Table S2 in Supplementary Material), isometric peak torque, and average power (data not shown). Both CD56dim NK cell responses to K562 target cells were significant or trended against strength after factoring in age and sex (Table S2 in Supplementary Material). As expected, skeletal muscle mass correlated with strength in this elderly cohort D (data not shown). Notably, both CD56dim NK cell degranulation (as measured by CD107a) and MIP-1β responses to K562 cells robustly inversely associated with strength after factoring in either thigh muscle mass or leanness (Table S2 in Supplementary Material and data not shown). This indicates that muscle quality substantially and inversely associated with NK cell response to leukemia cells. As with other associations, CMV status did not weaken this inverse correlation (Table S2 in Supplementary Material).
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study
| 100.0 |
Strength (knee extensor peak torque) correlates inversely with measures of mature CD56dim natural killer (NK) cell responses to K562 leukemia cells in cohort D. CD56dim NK cell CD107a (A) is a measure of cytotoxic granule release and correlated inversely with peak torque for all subjects (ρ = −0.475, p = 0.001), but not for each sex considered individually. CD56dim NK cell MIP-1β chemokine production (B) correlated inversely with peak torque for all subjects (ρ = −0.384, p = 0.006), but not for each sex considered individually.
|
study
| 100.0 |
Because IL-15 is anabolic for skeletal muscle in some situations and skeletal muscle itself may be a source of IL-15, we predicted that skeletal muscle and plasma IL-15 would directly correlate (18). However, there was no significant association between skeletal muscle mass and plasma IL-15 (Table 1). Because muscle mass declines less rapidly in elderly people than does strength, we searched for an association between strength and IL-15. We found a significant inverse correlation between IL-15 and knee extensor peak torque (Table 1; Figure 5B). This inverse association was confirmed when sex and age were included in multifactorial analysis; the inverse association became even more robust when VAT was included as a factor in multifactorial analysis (Table 1), indicating that this correlation is not confounded by VAT volume. Likewise, the correlation remained strongly negative when a role for CMV infection status was tested, suggesting that this correlation is not dependent on CMV status. When both knee extensor peak torque and thigh muscle cross-sectional area were compared, muscle strength, but not muscle cross-sectional area, was significant (Table 1).
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study
| 100.0 |
The objectives of this work were to determine (1) if human muscle, SAT, and VAT are sources of IL-15 and IL-15 Rα, and (2) whether any of these tissues correlate with NK cell activity in elderly humans. We found that skeletal muscle, SAT, and VAT all produced IL-15 and IL-15Rα RNA. Of these, only VAT correlated with IL-15 plasma levels in elderly human subjects. Our findings suggest that VAT may support NK homeostasis and activity in the elderly, when IL-15-producing immune cells have declined.
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study
| 100.0 |
Other studies indicate that adipose tissue may be a significant source of IL-15. Dozio et al. (26) found that mouse epicardial fat, a type of VAT, expressed IL-15 and IL-15Rα RNA. Hickner and co-workers found that human abdominal SAT and skeletal muscle produce IL-15; and that SAT interstitial IL-15 level was higher in obese vs. lean young adults (27). Like us, this group found IL-15 blood levels did not correlate with total body mass (27). Another group found that IL-15 level was higher in VAT tissue homogenates from obese vs. lean middle-aged people (28). Liou et al. (29) showed that mouse adipocytes make significant amounts of IL-15 and that optimal NK cell development required adipocyte IL-15. Using parabiotic mouse pairs, O’Sullivan found that NK cells recirculate between the blood and the VAT compartments (30). This indicates that circulating NK cells are exposed to cytokines in the VAT (Figure 7B). Christiansen et al. found that young adult subjects placed on 12-week regimes of reduced caloric intake had significant IL-15 declines (31). This study suggests that negative caloric balance lowers IL-15 level and because caloric restriction usually leads to loss of fat, is consistent with our finding that increased VAT directly correlated with plasma IL-15 level (Figure 7B).
|
study
| 99.94 |
Observed associations (A) and proposed mechanistic relationships (B) between adipose tissue, IL-15, natural killer (NK) cells, and muscle. (A) Positive associations are represented by green arrows. Inverse associations are represented by red arrows. All arrows are double-headed, indicating that associations do not necessarily imply mechanism. Mechanistic model (B) is based on our findings and on published literature. Stimulatory mechanisms are represented by green arrows and inhibitory mechanisms are represented by the red arrow. Double-headed green arrows indicate mutually positive stimulation. We propose that adipose tissue is a significant source of IL-15, especially in aging humans. IL-15 stimulates NK cells. Importantly, IL-15 forms part of a positive feedback loop between adipose tissue NK cells, other type 1 innate lymphocytes, and macrophages, as represented by the double-headed green arrows. IL-15 and other inflammatory agents weaken muscle.
|
study
| 99.94 |
We attempted to determine if human skeletal muscle and fat derived IL-15 and IL-15Rα may correlate with NK activity in the elderly. Definitive experimental manipulation of human subjects is not possible, but we identified strong correlations between body composition parameters, IL-15 plasma levels, and NK cell function. We restricted our main analysis to elderly people because a prior study had suggested a correlation between BMI and NK cell number in elderly women, but not in young women (18). Additionally, because excess VAT and SAT carry different health risks, we separately analyzed correlations with IL-15 and NK cell activity. Our data, summarized graphically in Figure 7A, suggests that non-lymphoid tissues affect NK cells via multiple distinct interactions, but the strongest direct correlations were between IL-15 plasma levels and VAT, and an inverse correlation between NK function and muscle strength.
|
study
| 100.0 |
Our study appears to contrast with several studies of rodents and humans in which serum IL-15 level negatively associated with VAT (32–37). As mentioned above, we found that plasma IL-15 positively associated with VAT. It is useful to consider that abdominal VAT may respond to IL-15 and that VAT itself produces IL-15 (27, 29). Most of the studies in rodents involved animals exposed to high nonphysiologic IL-15 levels via transgene expression or via injection; other studies utilized mice that were IL-15 knockouts. These extremes of IL-15 exposure might not be good models of the human condition. Prior human studies involved young adults and sometimes obese and lean groups differed significantly in age (33, 38). The contrasting outcomes in our study and past human studies may reflect age-related physiological differences. One effect of IL-15 is to increase gene expression and metabolic activity in brown adipose tissue (39). Because the amount of brown adipose tissue usually declines with age (40), many elderly individuals might not respond to IL-15 by increasing brown fat metabolic activity. Another possible explanation for this apparent contradiction is the amount of skeletal muscle mass in young and elderly adults and its correlation with adipose tissue mass. Obese young adults typically have more muscle mass than do lean young adults (41). Yet, fat mass is associated with a more rapid decline in muscle mass during aging (41). In addition, muscle strength declines rapidly in the elderly, much faster than loss of muscle mass, and may be a better measure of skeletal muscle health and function (42, 43).
|
study
| 99.94 |
Macrophages and DC functions decline with aging (13, 15, 44), making non-lymphoid sources of IL-15 relatively more important. These factors are likely to influence IL-15-depedent NK cells and memory CD8 T cells in the elderly. We propose that in elderly humans, VAT is a significant incremental source of IL-15 (Figure 7B) and promotes NK cell homeostasis.
|
study
| 100.0 |
Growing evidence supports the hypothesis of a positive feedback loop between type 1 innate lymphoid cells (including NK cells) and Mφ in people with a positive energy balance (30, 45–47). Adipocyte hypertrophy, fibrosis, hypoxia, and cell death cause release of inflammatory molecules, which stimulate adipose tissue Mφ (48). Stressed adipocytes express NKp46 ligands that directly stimulate NK cells and probably other type 1 innate lymphoid cells (47). In response to the inflammatory molecules, Mφ produce IL-12 and IL-15, which stimulate NK cells and other type 1innate lymphoid cells to produce IFN-γ, TNFα, and IL-6 (30, 45). These products, in turn, stimulate Mφ, setting up a positive feedback loop. Inflammatory cytokines, including IL-6 and TNFα, which affect skeletal muscle, the vasculature, and other tissues, cause pathologies associated with frailty and the metabolic syndrome (48, 49). Our data fit into this picture (Figure 7B). We found that IL-15 is elevated in relation to human VAT mass. Plasma C-reactive protein, a measure of inflammation, correlated strongly with plasma IL-15 (data not shown). We propose that IL-15 stimulates NK cells, both in a local paracrine fashion in VAT and in an endocrine fashion (Figure 7B). To explain the negative correlation between IL-15 and muscle strength, we propose that IL-15 itself and other inflammatory factors inhibit skeletal muscle (Figure 7B).
|
study
| 100.0 |
In accordance with the Declaration of Helsinki (modified in 2008), all protocols were approved by the Institutional Review Board of the University of Kentucky, Lexington, KY, USA. All subjects were made aware of the design and purpose of the studies, and all subjects signed consent forms. The cohorts are summarized in Table S1 in Supplementary Material, including IRB approval numbers, where applicable.
|
other
| 99.94 |
Metabolomics, a rapidly developing post-genomic approach, has proven to be a powerful and indispensable tool to interrogate cellular biochemistry, investigating metabolism and its reciprocal crosstalk with cellular signaling and regulation . The metabolic profiles may be seen as functional signatures of the physiological state of the biosystem under investigation, i.e., snapshots of partially-mapped molecular landscapes, comprising effects of genetic regulation, as well as environmental factors . However, the realization of a holistic coverage of the whole metabolome, in a given biological system, is still currently not feasible (at least with a single method) at the metabolite extraction and analytical levels. Furthermore, this current “unfeasibility” can be expanded to the handling of the data from untargeted metabolomics studies: how do we maximize the value of untargeted metabolomic data with the current chemometric methods and algorithms?
|
review
| 99.9 |
Metabolomics studies, particularly liquid chromatography mass spectrometry (LC-MS)-based untargeted approaches, generate information-rich, high-dimensional, and complex datasets that remain challenging to handle and fully exploit . Thus, dedicated modeling algorithms, able to cope with the inherent complexity of these metabolomic datasets are mandatory for extracting relevant information. Various chemometric and bioinformatics tools and resources have been developed, and are utilized for this purpose, thereby integrating computer science, mathematics, and statistics . However, despite the remarkable progress in the development of tools and algorithms, as presented in the recent review , the exhaustive extraction of information from these metabolomic datasets is still a non-trivial undertaking .
|
review
| 99.9 |
It is to be noted and emphasized that the information extracted from the metabolomics raw data and the resulting outputs depend heavily on the data analysis methodology employed. Additionally, in the hierarchy of data, information, and knowledge, the logical and epistemological implication is that information is the key to knowledge formulation . In order to maximize the value of metabolomic data and generate biologically-meaningful hypotheses, particularly with regard to the regulatory mechanisms and molecular processes involved in global biological responses (such as those of a biosystem), the metabolomics raw data are to be appropriately handled and fully exploited. This will ensure the extraction of sufficient information to determine, as holistically as possible, biological components that show differential behaviors between experimental conditions .
|
study
| 98.44 |
Thus, data analysis methodology is critical for generating meaningful scientific results from these information-rich metabolomic data. The typical pipeline used in such analysis has been well detailed and described in the literature, although with some notational and semantic nuances , and can be summarized in the following steps: (i) processing (extracting features from raw instrumental data to a suitable form; normalization, scaling, centering, etc., to put all samples and variables on a comparable scale); (ii) statistical analysis/modeling (this covers understanding and visualization of data and feature selection methods, and validation/estimation of the predictive capability of the applied statistical models); (iii) annotation of the selected features; and (iv) interpretation and metabolic pathway and network analysis, leading to the generation of research hypotheses and knowledge compilation.
|
review
| 99.25 |
The data processing and statistical modeling are crucial and vital, as the information extracted from the raw data will depend on these steps. A better understanding of data processing and statistical algorithms and methods are important to achieve statistically-relevant and optimal biological information. These post-acquisition steps can be challenging and time-consuming and comprise data cleaning and generation of statistical models that are explorative and predictive. Certainly, different parameters and algorithms used in these data analyses steps would lead to different outputs and influence the extent of data mining outcomes . This observation, thus, raises a series of questions of knowing how to handle untargeted metabolomic data adequately, or if there is one single “formula” or methodological protocol to follow for maximal exploration of untargeted metabolomic datasets.
|
review
| 61.03 |
Considerable literature exists on the data analysis and mathematical description of algorithms and chemometric methods used during data analysis in metabolomics, with suggestions and requirements for a sound approach . However, questions such as (i) to what extent can an untargeted metabolomic dataset be mined; (ii) are the data analysis methodologies, applied to an untargeted metabolomic dataset, not biased by the scope of the initial biologic question; (iii) are the current chemometric tools and algorithms fit to holistically extract information from the mega-variate datasets generated by untargeted metabolomics studies ; (iv) to what extent do the different steps in a data handling methodology pipeline influence the data analysis output; and (v) what could be a methodological approach and practice that would aid to maximize the value of untargeted metabolomic dataset all remain to be explored.
|
review
| 99.9 |
Different from, but also complementing the existing literature on metabolomic data analysis, this study looks at the influence of data pre-processing (e.g., collection parameters, such as intensity threshold and mass tolerance) and pre-treatment methods (e.g., scaling and data transformation) on the statistical models generated and feature selection, using an LC-MS-based untargeted metabolomic dataset. This was in order to actually demonstrate to what extent steps in the data analysis pipeline impact on the output, which would certainly affect the downstream biological interpretation. Thus, the study clearly points out, with illustrative examples, that the methods employed in the data analysis should not be regarded as “one-size-fits-all”. As such, this study intends to make a contribution to the on-going discussions in the metabolomics community with regard to ways of maximizing the value of untargeted metabolomics data and influence/effects of data analysis steps on the downstream analyses and interpretation . Ultimately this work emphasizes the importance of understanding the structures of raw data, and exploration of various algorithms and parameters are vital and mandatory in data mining to maximize the value of generated data.
|
study
| 99.94 |
Before embarking on the details of the results, it is firstly worth noting and re-emphasizing that metabolomic data analysis does not occur in isolation, but is rather intimately linked to the other metabolomics workflow steps that are upstream thereof . Hence, a careful experimental design is mandatory, and statistical rigor, quality assurance, and proper scientific procedures must be followed and applied at every stage of the workflow so as to generate data that actually contain “objectively true” information about the biological question under investigation . Furthermore, these metabolomics workflow steps are not always to be followed linearly. Adaptation and a “forth-back” methodological approach is necessary to revise, correct (avoid distortions), validate, or to further mine the data so as to obtain a comprehensive biological answer with fewer spurious or false positive outcomes to the research question .
|
other
| 99.3 |
Untargeted LC-MS metabolomic analyses generally generate a wealth of data. The data structure is a two-way matrix (retention time and mass spectra directions), with thousands of data entries, depending on the complexity of the extracts, per sample. To make it more complicated, the data inherently contain vast amounts of noise, artifacts, unintended fragments and adducts, potentially making components of the datasets either not usable or redundant . The challenge is how to extract and create a “clean” dataset from such raw data in a way that captures as much usable information as possible for downstream pattern recognition, classification, and feature selection. Most pre-processing software pipelines share the general functions of peak detection-, alignment, and annotation. Currently, several open-source, as well as commercial software programs, have been developed to aid in metabolomics data processing (up to metabolite annotation for some). Each of these tools has its own capabilities, providing some context-dependent insights, but also limitations . The detailed description of all of these tools and algorithms is beyond the scope of this paper and the reader is referred to the cited literature.
|
review
| 99.9 |
Thus, the first step in metabolomic data analysis is to select relevant signals from the raw data, decrease redundancy and generate a data matrix for downstream analysis. In this study, for creating the data matrix (unbiased mass peak extraction, ion intensities identification and alignment of the acquired LC-MS data), an automated approach was applied, using the MarkerLynxTM Application Manager for MassLynxTM software (Waters Corporation, Manchester, UK) for data processing. As described in the experimental section, the MarkerLynxTM application uses the patented ApexTrack peak detection algorithm to perform accurate peak detection and alignment. Following the peak detection, the associated ions are analyzed (the maximum intensity, the Rt and exact m/z mass) and captured for all samples. The data matrix is then generated . The data pre-processing steps and relevant parameters’ settings are detailed in the experimental section.
|
study
| 100.0 |
Varying the mass tolerance parameter (which specifies the mass accuracy of the acquired data) and intensity threshold parameter (which specifies the threshold of a spectral peak) resulted in different data matrices from the same LC-MS raw data, and the different numbers of variables (in the multivariate X-space) and noise levels are tabulated in Table 1. Theoretically, an infinite number of combinations for sets of processing parameters with MarkerLynxTM are possible. In practice, the computational time to process one combination of a set of parameters could be in hours, depending on the size of the datasets. Furthermore, understanding of the underlying algorithms and steps involved in the data processing is essential so as to decide which parameter to vary. As indicated in the experimental section, parameters, such as mass tolerance and the intensity threshold (which define the real peak versus background noise), can be changed, within certain limits: for instance mass tolerance can be set to the mass accuracy of the acquired data (which was 4.9 mDa in this study) and twice this value; hence, in this study mass tolerance was varied in these limits (0.005 and 0.01 Da). The mass tolerance (mass accuracy) parameter is the basis by which the ApexTrack algorithm determines the regions of interest in the m/z domain, whereas the intensity threshold parameter is used in the peak removal step, defining the resultant noise level and redundancy in the data matrix. These two parameters are essential, hence this study explored the impact of these on the creation of the data matrix.
|
study
| 100.0 |
The tabulated results (Table 1), from the same LC-MS raw dataset, demonstrate that changing the mass tolerance and intensity threshold parameters affects the number of defined features (X-variables). One observation to point out is that increasing the intensity threshold (counts) led to a significant decrease in the number of X-variables and noise levels. For a novice in metabolomic data analysis or any metabolomic scientist with less expertise in statistics/chemometrics, this could raise questions with regard to the “correct” method (or set of parameters) to trust and use: e.g., the one producing a matrix with less noise. A point to consider is to what extent any decrease in the number of “defined” ion peaks (variables) would bring about information loss? As a first approach, a chemometrician or statistician would advise for a cleaner data matrix: the less noise in the created matrix the better. Methods (set of parameters or algorithms) that could reduce the noise in the data and decrease the redundancy are often advised and preferred .
|
study
| 99.9 |
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