dataesr/scientific-paragraphs-categorization huggingface dataset:
The fraction containing purified AGA (AGA representing more than 60% of total proteins of the fraction) was then used for estimation of kinetic constants and cross-linking experiments. Because purified AGAs were in limited quantities, we estimated AGA α-and β-subunits amounts using a silver stained gel method. Briefly, increasing volumes of the purified fractions were separated by SDS-PAGE under reducing conditions together with known amounts of BSA. Band intensities were obtained from digitalized images of the silver-stained gels using ImageJ software (http://imagej. nih. gov/ij/).
This work received support from the  French National Research Agency  ( PARATOXOSE  project,  ANR-09-BLAN-0243-01 ), the  INRA Division for Plant Health and Environment (SPE)  and the  "Investments for the Future" LABEX SIGNALIFE: program  reference  ANR-11-LABX-0028. Quentin Coulette PhD was funded by a grant from the  French Ministry for Research. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
First instar larvae were collected on Petri dishes 24-28 hr AEL and treated as embryos, except for sonication (2 • 12 sec). Second and third instar larvae were sexed and female fat bodies were hand-dissected. They were treated like embryos but without sonication, and fixation was in PBT 0. 4%. Gonads were observed using a Leica TCS SP5 reverse confocal microscope. Z-stacks of PGC-containing gonad optical sections were acquired and analyzed using Fiji software.
49 using Bowtie  (Langmead et al. 2009). Only 19 to 29 nt reads matching the reference sequences with 0 or 1 mismatch were retained for subsequent analysis. For global annotation of the libraries (Table  S1 ), we used release 5. 49 of FASTA reference files available in FlyBase, including transposon sequences (dmel-all-transposon_r5. 49. fasta) and release 20 of miRNA sequences from miR-Base (www. mirbase. org). Sequence length distributions, smRNA mapping, and smRNA overlap signatures were generated from Bowtie alignments using Python and R (www. r-project. org/) scripts, which were wrapped and run in a Galaxy instance publicly available at http://mississippi. fr. Tools and workflows used in this study may be downloaded from this Galaxy instance. For library comparisons, read counts were normalized (effective depth, Table  S1 ) to the total number of smRNAs that matched the D.
When RNA sequences matched those regions repeatedly, the weight was divided by the number of hits to these regions (multiple mapping). Distributions of piRNA overlaps (ping-pong signatures) were computed as first described in  Klattenhoff et al. (2009)  and detailed in  Antoniewski (2014). Thus, for each sequencing dataset, we collected all the 23 to 28 nt RNA reads matching the 42AB locus, the 3R extremity, or the RS3 transgene whose 59 ends overlapped with another 23 to 28 nt RNA read on the opposite strand. Then, for each possible overlap of 1-28 nt, the number of read pairs was counted and represented in histogram form. Sequence reads of different genotypes were also matched to known transposon sequences (Dmel_transposon_set_BDGP_v941). Antisense read counts from all libraries were normalized as previously, and the RPKM was calculated.
Half of the 23-29 nt smRNAs from the 42AB region are also rhino-dependent and thus likely come from PGCs (Figure  4A ). However, rhino-independent smRNAs were also detected and these could originate from germline or somatic tissues. Since these rhino-independent smRNAs present a strong ping-pong signature (Figure  4A ), we favor the possibility that they come from PGCs. These rhino-independent piRNAs could also represent maternally inherited piRNAs. Taken together, our data strongly suggest that bona fide piRNAs are produced in third instar larval PGCs and that the loss of repression in piRNA pathway mutant contexts (Figure  2M' ) is correlated to the loss of RS3 transgenic piRNAs. Next, we analyzed smRNAs extracted from Su(var)205 GLKD larval fat body-attached gonads that had maternally inherited RS3.
ACKNOWLEDGMENTS We thank  Toshie Kai ,  William Theurkauf , and  Jean-René Huynh  for sharing reagents. We are also very grateful to  J. R. Huynh  for helpful suggestions. We thank  Richard Schwartzmann  for help with confocal microscopy. Confocal work was carried out at the  Institute of Biology Paris-Seine Imaging Facility. We are grateful to  Christophe Antoniewski  and  Marius van den Beek for developing the Galaxy platform  ( Centre National de la Recherche Scientifique, Institut de Biologie Paris-Seine, ARTbio Bioinformatics Analysis Facility ). We thank  Ritha Zamy  for technical assistance, members of the  Ronsseray laboratory  for discussions, and  Anne-Marie Pret  and  Neel Randsholt  for critical reading of the manuscript. This work was supported by a fellowship from the  Ministère de l'Enseignement Supérieur et de la Recherche  to P.
, 2010). To sum up, the yeast cell wall is endowed with several original properties relevant for various applications in health, food and feed nutrition and food safety (reviewed in  Chen and Seviour, 2007; Kogan et al. , 2008; Braconi et al. , 2011; Pfliegler et al. , 2015). However, understanding how the biochemical composition of the cell wall dictates its nanomechanical properties and unraveling molecular cues (i. e. , genes) that are underlying these relationships could represent a major scientific advance toward a better mining and exploitation of technological properties of the yeast cell wall at an industrial level. In this work, we used an integrative approach that combined genome scale expression datasets and cell wall biochemical and biophysical measurements from four different industrial yeast strains and a laboratory strain to search for relationships between genes expression, cell wall composition and cell surface characteristics.
To this end, quantitative data on cell wall composition measurements  (i. e. , mannans, chitin, β-1,3-glucans, β-1,6-glucans) , quantitative data on biophysical properties (i. e. , cell wall elasticity, occurrence of the interaction with concanavalin A at the cell surface, length of the molecule unfolded) and transcriptome profiles of these 5 yeast strains were obtained. These datasets were incorporated into the mixOmics package (http://mixomics. org/; Gonzalez et al. , 2009; Le Cao et al. , 2009a)  that integrates multiples biological data with the aim to identify variables that are highly correlated, leading to either biological inferring explanation or pertinent biological hypotheses.
s -1. Elasticity histograms were generated by analyzing with OpenFovea software  (Roduit et al. , 2012)  the force (F) curves according to the Hertz model with an indentation (δ) of 50 nm and taking into account a conical tip geometry with an half-opening angle α of 0. 31 rad and a Poisson ratio (ν) of 0. 5: F = 2E tan α π (1 -ν 2 ). δ 2 The Young's (or Elasticity) modulus is the value obtained at the maximal height of the Gaussian curve ± σ value (corresponding to the mid height width) of this Gaussian distribution function. Prior to force spectroscopy experiments, AFM tips were functionalized with the lectin concanavalin A from Canavalia ensiformis (ConA; Sigma-Aldrich) via a dendritip as described in  Jauvert et al. (2012). The coupling with the lectin was made by immersion of the dendritip in 100 µL of ConA solution (100 µg.
mL -1 in 0. 1 M sodium carbonate buffer). After 1 h of incubation, 100 µL of NaBH 4 (3. 5 mg. mL -1 ) solution was added and incubated 15 min in order to reduce the unreacted groups. The cantilever was washed three times in acetate buffer and immediately used. Using the functionalized tip, force-distance curves were recorded on each yeast cell with a maximal applied force of 250 pN, and using a constant approach and retraction speed of 2 µm. s -. At least 8 cells of two independent cultures were analyzed for each strain, representing 8,192 force curves. All force curves were analyzed with the JPK Data processing software. All specific adhesion peaks were considered for the histograms, which were generated using Origin 8 software (OriginLab Northampton, MA, USA). The extension of polysaccharides at the surface of the cell with the AFM-tip functionalized with ConA was analyzed using either the freely-jointed chain (FJC) model for single adhesion event or the worm like chain (WLC) model for multiple adhesion events.
Data Analysis Transcriptome analyses were done in R computing environment (R Core at https://www. R-project. org/) using the Limma package available on the R repository Bioconductor (www. bioconductor. org). The estimates used for the foreground and background intensities were the median of pixels intensity. Raw data were imported into R and spot quality weights were performed assigning a weight of 1 or 0 to each spot. Low-quality spots, non-uniform spots, spots with low signal/background ratio or spots with low signal-to-noise ratio and empty or nonvalidated spots were down weighted. Data were preprocessed by base 2 logarithmic transformation and within-array normalized was performed using the weighted global median (spots with zero weight were not included in the normalization). To achieve consistency of expression values between arrays, quantile normalization across all the microarrays for each strain was performed.
After normalization, the expression of a gene was calculated by the median of replicate spots within each microarray. Gene expression data for both strains were pairwise compared using the Limma package  (Smyth, 2005). Genes with significant evidence for differential expression were identified with a modified T-test in conjunction with an empirical Bayes method to moderate the standard errors of the estimated log-fold changes. The p-values were adjusted for multiple testing by the "BH" method  (Hochberg and Benjamini, 1990). The microarray work is fully MIAME-compliant and the data have been deposited in the Gene Expression Omnibus (GEO) Database (http://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc= GSE78759), under the accession number GSE103392.
Relationships between Gene Expression, Biochemical and Biophysical Data The package mixOmics employs partial least square (PLS) based methods to unravel relationships between variables from multiple biological datasets. The tool has its own tutorial and can be easily used, for a nearly inexperienced R user, through the web site at http://mixomics. org/. Specific functions were searched on the Saccharomyces Genome Database (http://www. yeastgenome. org), Yeastract (http://www. yeastract. com/) and Funspec (http:// funspec. med. utoronto. ca/).
Inference of Genes Function That Correlate with Cell Wall Biochemical and Biophysical Properties by Multivariate Methods and transcriptome are a set of biochemical, biophysics and molecular data that must be connected in one way or another. To unravel the connection between these high dimensional data where the number of variables (e. g. , genes, proteins, metabolites) is much larger than the number of samples (i. e. , yeast strains in this work), we employed the sparse methods of the mixOmics package. Exploration of relationships and correlation between these data sets requires reducing the dimension of the variables into components, from which can be retrieved correlations that are amenable to statistical inference about novel function or biological hypotheses by employing several sparse multivariate models  (Gonzalez et al.
On the horizontal axis, a set of more than 150 genes were retrieved from the 5 transcriptome data, the expression of which was the most correlated with either a physical or a biochemical variable. Detailed description of these genes with their assignated function as obtained from SGD database (https://www. yeastgenome. org/) is reported in Table  2. The sPLS regression analysis further distributed this list into 6 groups based on their closest association with either one of these variables. Group 1 which contained 23 genes was positively correlated to β-1,6-glucans and to chitin but negatively associated with FIGURE 6 | Heat map representation of association between biophysical-biochemical variables and genes transcripts from the laboratory and industrial strains. Biophysical and biochemical variables are presented on the vertical scale whereas the gene transcripts are on the horizontal scale.
Clustering analysis made with the mixOmics tool (http://mixomics. org/) allows grouping biochemical and biophysical variables into 4 groups (horizontal) and into 6 groups with respect to gene transcripts (vertical). Red (blue) indicates high positive (negative) correlation between gene transcript and biochemical/biophysical variables. k refers to the number of genes in the category over the total number of genes in all clusters. f refers to the number of genes in the category over the total number of assigned genes in the genome. the contour length and β-1,3-glucans. Surprisingly enough, this cluster was enriched of genes that encode sulfate assimilation pathway which is required for the biosynthesis of cysteine, an amino acid that is implicated in the cysteine-rich domain of cell wall sensors  (Kock et al.
In conclusion, this single-molecule AFM study highlighted two relevant data, namely that the αmannan oligosaccharides that decorate cell wall proteins and that the anchorage of these mannoproteins in the wall can be very different between yeast strains, which may reflect their environmental adaptation. Whilst this comparative analysis of the biochemical composition and elasticity of cell wall pointed out clear differences between strains that could be in part explained by variance in gene expression, this approach does not permit to explore connections between these properties and how these properties can be molecularly explained. Use of multivariate methods such as Sparse partial least squares (sPLS) or sPLS discriminant analysis (DA) are well dedicated to extract correlations between apparently heterogeneous datasets in which the number of variables (e.
ACKNOWLEDGMENTS We thank colleagues of the  JMF laboratory  and  Anne Ortiz-Julien  from  Lallemand Inc. for critical discussions during this work. This work was supported in part by grants  INSA/SAIC 2015/020 & 2016/048  from  Lallemand SAS  to JMF. ED is researcher at the  Centre National de la Recherche Scientifique (CNRS ) and MS is project manager at Lallemand SAS.
Mycobacterium tuberculosis genetic structure, and evolutionary history have been studied for years by several genotyping approaches, but delineation of a few sublineages remains controversial and needs better characterization. This is particularly the case of T group within lineage 4 (L4) which was first described using spoligotyping to pool together a number of strains with ill-defined signatures. Although T strains were not traditionally considered as a real phylogenetic group, they did contain a few phylogenetically meaningful sublineages as shown using SNPs. We therefore decided to investigate if this observation could be corroborated using other robust genetic markers. We consequently made a first assessment of genetic structure using 24-loci MIRU-VNTRs data extracted from the SITVIT2 database (n = 607 clinical isolates collected in Russia, Albania, Turkey, Iraq, Brazil and China).
Combining Minimum Spanning Trees and Bayesian population structure analyses (using STRUCTURE and TESS softwares), we distinctly identified eight tentative phylogenetic groups (T1-T8) with a remarkable correlation with geographical origin. We further compared the present structure observed with other L4 sublineages (n = 416 clinical isolates belonging to LAM, Haarlem, X, S sublineages), and showed that 5 out of 8 T groups seemed phylogeographically well-defined as opposed to the remaining 3 groups that partially mixed with other L4 isolates. These results provide with novel evidence about phylogeographically specificity of a proportion of ill-defined T group of M. tuberculosis. The genetic structure observed will now be further validated on an enlarged worldwide dataset using Whole Genome Sequencing (WGS).
4. 2, 4. 6. 1. 1, 4. 6. 1. 2, 4. 6. 2. 1, 4. 7, 4. 8 and 4. 9  [1]. Thus even though the initial T group structuring based on spoligotyping alone was considered misleading, it nonetheless paved the way to decipher recent subdivision of T isolates into several potential clusters. We therefore consider that digging into phylogeographical specificity of well-structured T group isolates makes sense. Based on a large international dataset of 24-loci MIRU-VNTR markers (which are less subjected to homoplasy and present higher mutation rate as compared to spoligotyping), we hereby provide novel evidence regarding genetic structure of T group isolates, with a clear-cut phylogeographical specificities for 5 out of 8 sublineages.
Data collection Anonymized data on M. tuberculosis T lineage strains genotyped using spoligotyping and 24-loci MIRU-VNTRs were extracted from the SITVIT2 proprietary database of Institut Pasteur de la Guadeloupe  [15] , which is an updated version of the SITVITWEB database  [11]. Most of data were published earlier within a context focusing on M. tuberculosis population structure and/or epidemiology within a country or region  [16] [17] [18] [19] [20] [21] [22] [23] [24]. However, for data submitted to the database but not yet published by respective investigators, permission was officially sought and duly granted by following researchers: Dr. Ling Cheng (Department of Respiratory Medicine, Affiliated Hospital of Zunyi Medical College, Zunyi, Guizhou, China), Dr. Silva Tafaj (Microbiology Department, University Hospital "Shefqet Ndroqi", Tirana, Albania), and Dr Nurhan Albayrak / Dr Ry ´za Durmaz (Department of Microbiology Reference Laboratories, Ministry of Health, Public Health Agency of Turkey, Ankara, Turkey).
The T lineage strains studied (n = 607 isolates) were collected from Russia (n = 17), Albania (n = 100), Turkey (n = 72), Iraq (n = 76), Brazil (n = 90) and China (n = 252). Within China, dataset was divided into regions in Tibet (n = 13), Sichuan (n = 83), Guizhou (n = 43), Chongqing (n = 74) and Jiangsu (n = 39).
Phylogenetic inferences BioNumerics software 6. 6 (Applied Maths, Sint-Martens-Latem, Belgium) was used to visualize evolutionary relationships between the T clinical isolates by drawing Minimum Spanning Trees (MSTs) using 24-loci MIRU-VNTR and spoligotype data. MSTs are undirected graphs in which all samples are connected together with the fewest possible connections between nearest neighbors.
Bayesian population structure analyses To explore genetic structure of M. tuberculosis T isolates, two Bayesian clustering algorithms were used in parallel, implemented in the software STRUCTURE 2. 3  [25]  and in TESS 2. 3  [26, 27]. In both programs, an admixture model was implemented considering that the data originate from the admixture of k ancestral populations at some time in the past. The ancestry coefficient (or admixture proportion) in the individual Q-matrix correspond to part of the genome that each individual inherited from ancestors. Admixture models are a common feature for real data and are therefore more flexible than models without admixture. Posterior estimates for the parameters of interest are computed by using a Markov chain Monte Carlo (MCMC) algorithm. In our study, STRUCTURE was run in 10 parallel MCMC for K populations ranging from 3 to 10, with a burn-in of 100000 iterations and a run length of 10 6 iterations following the burn-in.
To estimate the right number of population among T isolates, ln P(D|K) (the logarithm of the probability of the data given K) was calculated using the program STRUC-TURE HARVESTER  [28] , as well as the delta K calculated by the Evanno method  [29]. Concerning the TESS analysis, random spatial coordinates were first generated individually (within each country or region) prior to any run. TESS was then run in 10 replicates, with K max ranging from 2 to 15 for 50000 sweeps with a burn-in period of 10000. To estimate the right number of clusters among T dataset, the deviance information criterion (DIC) was computed and plotted against K max  [30]. For both STRUCTURE and TESS Q-matrix, medians were then calculated from 10 replicates for K = 8 by using the Greedy algorithm implemented in CLUMPP 1.
1. 2 software  [31]  to guarantee the optimum clustering for each analyses. Results of admixture coefficients were then displayed spatially by an interpolation technique called universal kriging: Q-matrix were represented either on a single map (ETOPO1 map produced by NOAA freely available as indicated here: https://www. ngdc. noaa. gov/mgg/global/dem_faq. html#sec-2. 4 [32] ) using the script 'POPSutilities. r' implemented in the program R, or on separate maps for each K by using the script 'plot. admixture. r' (both scripts available through TESS website: http:// membres-timc. imag. fr/Olivier. Francois/tess. html). New MST analyses were then performed using BioNumerics software 6. 6 and identifying M. tuberculosis T strains belonging to sublineages 1 to 8 defined by STRUCTURE analysis using a cutoff of 0.
Allelic richness For analyses on allelic richness, 24-loci MIRU-VNTR data were grouped according to M. tuberculosis T sublineages defined by STRUCTURE software. Mean allelic richness was evaluated using statistical technique of rarefaction implemented in the software HP-RARE 1. 0 which compensates for sampling disparity  [35]. Results were compared based on Dunn's test for stochastic dominance  [36]  followed by multiple pairwise comparisons of the stochastic dominance among k groups using the Kruskal-Wallis test  [37].
Phylogenetic inference of T lineage isolates by MST Evolutionary relationships between M. tuberculosis T isolates were explored by MST analysis. Spoligotyping showed a non-perfect structuring of T sublineages with main central node made up of the T1 (n = 450), T2 (n = 73) and T3 (n = 40) sublineages and other sublineages mixed up in the MST without any clear structure. Furthermore, no clear-cut geographical segregation of T isolates could be highlighted from this spoligotyping data (S1 Fig) , corroborating the fact that T sublineages include many strains that do not structure together as a single phylogenetic group stricto sensu  [11]. Indeed, spoligotyping used alone may misclassify certain strains mainly due to homoplasy and weak mutation rates  [1, 7, 8]. Hence, we further explored evolutionary relationships by constructing a MST focusing on more robust 24-loci MIRU-VNTR of the studied T strains (n = 607 isolates,  Fig 1).
Bayesian population and spatial analyses To better characterize and delineate clusters revealed by 24-loci MIRU-VNTR analysis, we further performed two Bayesian clustering approaches implemented in STRUCTURE 2. 3  [25]  and TESS 2. 3 softwares  [26, 27]. STRUCTURE, which explores clusters and clines by making use of multilocus genotypes as for example MIRU-VNTR, is the most influential program since Bayesian revolution  [38]. This approach has been improved in several Bayesian spatial clustering programs (as TESS) by adding individual geographic coordinates as prior parameters. The appropriate K value was selected for the STRUCTURE analyses by the lnP(D|K) and the derived delta K calculated by the Evanno method  [29]   Classification of T isolates to each population were very similar between STRUCTURE and TESS approaches except for sublineage T8 for which higher probabilities were obtained in the STRUCTURE analysis, then allowing a better structuring of a few strains in this sublineage (detailed TESS results are available as S4 Fig and  S1 Table).