dataesr/scientific-paragraphs-categorization huggingface dataset:
Pathway Specific Chemicals In order to distribute the genes to pathways and pathway overlapping zones, log2 genes fold changes (FC) were ranked in decreasing order and examined on reduced datasets containing conditions relative to pathway specific activators. We define a pathway specific activator as a chemical where the mode of action is known, that the mode of action activates the specific pathways and that this mode of action is not expected to activate the other pathways under investigation. Thus, at relatively short exposures, to relatively low concentrations these chemicals will only act on their specific target. It is however possible at higher concentrations or longer time exposure, other targets will be affected due to increasing toxicity. As shown in Table  1 , some chemicals were not tested in all categories and tissue types.
Thus, it was not possible to find pathway specific activators able to cover the entire database. Table  2  shows the coverage of the datasets by the pathway specific activators selected as reference for analysis. Although none of the toxicogenomic databases analyzed here were designed to specifically address any of our three pathways of interest, most datasets included at least one chemical that could be considered as a specific pathway activator. Two specific chemicals were selected for AhR (Benzo(a)pyrene and Omeprazole) and Nrf2 (Potassium Bromate and Phorone) and one for ATF4 (Tunicamycin). However, within "Rat Kidney in vivo" category, no Nrf2 specific chemicals were found, and for all kidney data no ATF4 specific chemical were found either.
A pathways signature was formed by the genes whose average activation values were greater than µ + 2σ or smaller than µ -2σ for this pathway. Genes appearing in the signature of more than one pathway were set apart in "overlapping signatures. " Furthermore, we stratified signatures by original databases' categories ("Rat liver cells in vitro," "Rat liver cells in vivo," "Human liver cells in vitro" etc. ) (which correspond to primary cells), to check if there would be any species-specific or in vitro/in vivo differences among signatures. We chose to work only with liver data since more data were available for liver (602 conditions in kidney vs. 4,083 tested in liver, see Table  3 ). Following the same procedure as above, we constructed pathway signatures for AhR, Nrf2, and ATF4 in each of the following liver categories: (a) Rat liver cells in vitro, (b) Rat liver cells in vivo, and (c) Human liver cells in vitro.
All sorts of Endoplasmic Reticulum disturbances can cause an increase in unfolded proteins. Using multiple toxicogenomic databases we investigated the most appropriate activators of these three pathways, where it is expected that the chemical does not directly activate the other two pathways. These compounds were, Benzo(a)pyrene and Omeprazole for AhR, Potassium Bromate and Phorone for Nrf2 and Tunicamycin A for ATF4. All conditions up to and including 24 h were pooled to generate a list of genes allocated to the three pathways (Table  4 ). This list confirmed the majority of a priori literature based information of "Activated genes" (i. e. , upregulated). Although some genes were now reallocated to different pathways. The overlap with "Inhibited genes" (i. e. , down regulated), was much poorer.
This is too be expected as TF activated gene down regulation is much more complex and is often due to competition for auxiliary transcription facilitating proteins. Cytochrome P450 1A was the central element of the AhR pathway: CYP1A1 is the most prominent gene of this pathway, regardless of the experimental category, followed by CYP1A2. These findings are similar to previous investigations and have been implemented in a systems biology model  (Hamon et al. , 2014). For the Nrf2 pathway, the prototypical Nrf2 genes (HMOX1, SRXN1, and GCLM) appear in the Nrf2 signature of all datasets, but also in the AhR-Nrf2 overlapping signature for most liver categories. This may reflect the fact that several AhR agonists are themselves metabolized to reactive chemicals via AhR dependent CYP expression.
ACKNOWLEDGMENTS The research leading to these results has received support from the  Innovative Medicines Initiative Joint Undertaking (IMIJU)  under grant agreement number  115439 , resources of which are composed of financial contribution from the  European Union 's  Seventh Framework Programme  ( FP7/2007-2013 ) and EFPIA companies' in kind contribution. This work was supported by the 2015 CEFIC- LRI award  (AL) and partly supported by the  EU- ToxRisk  project  (An Integrated European  " Flagship" Program Driving Mechanism-Based Toxicity Testing and Risk Assessment.
Significant Batwa-Bakiga immune response differences Whole blood samples from 103 individuals (59 HG-Batwa and 44 AG-Bakiga, Supplementary Figure  1 ) were collected, and peripheral blood mononuclear cells (PBMCs) from these samples were isolated and cryopreserved. PBMCs were collected and processed for both populations simultaneously during the same field expedition to minimize technical variability. Each individual was genotyped for ~1 million genome wide SNPs  16  , with additional imputation to 10,530,212 SNP genotypes (see Materials and Methods). These data were used to estimate genome-wide levels of HG-Batwa and AG-Bakiga ancestry, using the program ADMIXTURE  17. We observed variable but considerable levels of AG-Bakiga ancestry among self-identified HG-Batwa individuals (mean = 21.
Characterization of cell type composition PBMCs were isolated from whole blood by Ficoll-Paque centrifugation and cryopreserved. Cell type composition of each PBMC sample was quantified using the following conjugated antibodies: CD3-FITC (clone UCHT1, BD Biosciences), CD20-PE (clone L27, BD Biosciences), CD8-APC (clone RPA-T8, BD Biosciences), and CD4-V450 (clone L200, BD Biosciences), CD16-PE (clone 3G8, Biolegend), CD56-APC (clone HCD-56), and CD14-Pacific Blue (clone M5E2, Biolegend). Antibodies were incubated for 20 min. Fluorescence was analyzed on a total of 30,000 cells for each population per sample with a FACSFortesa (BD Biosciences) and the FlowJo software (Treestar, Inc. , San Carlos, CA). Supplementary Figure  2  illustrates what combinations of markers were used to define each of the cellular populations we considered in this study.
2. 7). The resulting reads were then mapped to the human genome reference sequence (Ensembl GRCh37 release 75) using STAR (2. 4. 1d)  31  with an hg19 transcript annotation GTF downloaded from ENSEMBL (date: 2014-02-07). Reads matrices were computed using htseq-count 31. To ensure stringent quality control of the RNA-seq data we removed from downstream analyses samples: (i) with less than 10 million of sequencing reads, (ii) with less than 50% of reads mapping to annotated exons; and (iii) samples that in a principal component analysis appeared to be contaminated or had failed to respond to the immune challenges. After these filtering steps we were left with 229 samples (76 CTL, 83 LPS and 70 GARD, samples labeled as PopDE_set=1 in Supplementary Figure  1 ), coming from 99 individuals (42 Bakiga, 57 Batwa).
Identification of PopDE genes To estimate the effects of HG ancestry on gene expression (within each experimental condition), gene expression levels across samples were normalized using the TMM algorithm (i. e. , weighted trimmed mean of M-values), implemented in the edgeR R package  32. Afterwards, we log-transformed the data and obtained precision-weights using the voom function in the limma package  33. Only genes showing a median log2(cpm) > 2 within at least one of the experimental conditions were included in the analyses, which resulted in a total of 10,895 genes. Sequencing Flowcell batch effects were removed using the function ComBat, in the sva Bioconductor package  34. Then, expression was modelled as a function of hunter-gatherer ancestry (HG) levels, while correcting for sex (x1), proportions of CD4 + T-cells (x2), CD14 + monocytes (x3), CD20 + Bcells (x4) and the fraction of reads assigned to the transcriptome (x5).
Explicitly, let  ' ^ represent the number of positive epitopes for virus i and individual j, and _ ^`' the virus average across all individuals. Thus, the relative deviation in seropositivity for each individual gets defined, for individual i and virus j as  ' ^= b ' ^-_ ^`' c /_ ^`'. By testing for a linear association between  ' ^ and HG ancestry, we estimate the inter-population differences in seropositivity relative to the mean epitope prevalence of each virus. We conducted this analysis using the lmFit function, in the R package limma  33. Finally, false discovery rates associated to these linear models were estimated using Storey and Tibshirani's method implemented in the R package qvalue  36.
Genome-wide genotyping From the 99 individuals that were included in the sample-set used for PopDE analyses, a subset of 96 individuals (54-Batwa and 42-Bakiga, samples labelled as EQTL_set=1 in Supplementary Table  S1 ) were successfully genotyped on the Illumina HumanOmni1-Quad genotyping array (Illumina, San Diego, USA), as previously described  16. We got phased genotypes using ShapeIT v2 and obtained the imputed dataset using Impute2  37. In total, 10,524,770 autosomal and X-linked SNPs passed quality control filters.
Admixture and relatedness estimations Admixture was estimated using a nonhierarchical clustering analysis of the SNP data using the software ADMIXTURE  17  , based upon independent SNPs (LD >0. 3) from the genotyping chip dataset for the set of 96 individuals that were successfully genotyped. For the three individuals for which genotype data was not available (T15, T30 and T62, included in Pop_DE set but absent from EQTL_set), admixture values were estimated from the RNA-seq data. A pair-wise relatedness matrix among genotyped individuals was computed using Plink  38. As expected, we found that the mean relatedness within each population was modest in both cases, but significantly larger among HG-Batwa (Mean relatedness among HG-Batwa samples: 6. 9%; 0. 6% among AG-Bakiga). To ensure that our results were not impacted by the increased number of related individuals in the HG-Batwa population, we re-ran our PopDE analyses excluding strongly related individuals (i.
Mapping of cis -eQTL Cis-eQTL mapping was conducted using the R package Matrix eQTL  39. We estimated associations between SNP genotypes and changes in gene expression levels using a linear regression model where alleles affecting expression, denoted , were assumed to be additive. This was conducted for each of the conditions separately with individuals from both populations included in the analyses to increase the power to map cis-eQTL. Associations of SNPs within the gene body or 100Kb upstream and downstream of the transcript start site and transcript end site were used to map cis-eQTL. SNPs with a minor allele frequency (MAF) less that 10% were removed from the analyses resulting in 2,284,380 autosomal SNPs that were tested against a total of 10,479 protein coding genes. To account for false positives resulting from population structure, the first two principal components obtained from a PCA on the genotype data were included in the model ().
Selection Statistics We calculated the selection statistics by using the individuals used to map cis-eQTL that had an admixture less than 0. 2 or greater than 0. 8 to clearly define the two populations. This included 43 Bakiga individuals and 39 HG-Batwa individuals. We calculated the fixation indexes (Fst) using a modified version of Wright's Fst for all SNPs using VCFtools v0. 1. 12b  42. The integrated haplotype scores (iHS) were calculated using Selscan, which is a program that calculates haplotype-based scans for recent or ongoing signatures of positive selection. This method is based on the knowledge that when adaptive de novo mutations quickly increase in frequency it reduces genetic diversity around this variant faster than recombination can occur. Therefore, this score is a measure of haplotype homozygosity extending from an adaptive locus  43.
GBR -T. Bakiga. GBR) / 2 PBS. Bakiga = (T. Batwa. Bakiga + T. Bakiga. GBR -T. Batwa. GBR) / 2 Acknowledgments: The authors would like to thank the Batwa and Bakiga communities and all individuals who participated in this study, and the Batwa Development Program, Byaruhanga Julius, Magambo Michael, Byamugisha Patrick, Twesigomwe Sabastian, Safari Joseph, and Busingye Levi for expert assistance during the sample collection process in Uganda. We also thank Nanyunja Sarah for technical laboratory assistance. We thank Jenny Tung and L. B. B. lab members for critical reading of the manuscript. We thank Calcul Québec and Compute Canada for providing access to the supercomputer Briaree from the University of Montreal. This work was supported by NIH R01-GM115656 to G. H. P and L. B. B. , a fellowship from the Réseau de Médecine Génétique Appliquée (RMGA), and the Fonds de Recherche du Québec -Santé (FRQS) to G.
F. H, and 1 F32 GM125228-638 01A1 to C. M. B. RNA-seq data have been deposited in Gene Expression Omnibus (accession number GSE120502). The 1M SNP genotype data are available at the European Genome-Phenome archive, www. ebi. ac. uk/ega/ (accession numbers EGAS00001000605, and EGAS00001000908).
Cytokine measurements and detection of active caspase-1. Cytokine secretion from activated human T cells into cell supernatants was quantified using the human Th1/Th2/Th17 Cytometric Bead Array (BD Biosciences, San Jose, CA, USA) following the manufacturer's instructions. Samples were acquired on a Fortessa LSRIII supported by FACS diva software (BD Biosciences, San Jose, CA, USA). Data were analyzed by FCAP array software (Soft Flow Hungary Ltd, Pecs, Hungary). IL-1β was measured using the Human IL1B Duo Set ELISA kit (R&D system Minneapolis, MN, USA) following the manufacturer's instructions in combination with the SIGMAFAST TM OPD tablets (Sigma-Aldrich, Saint Louis, MO, USA) for substrate detection. Generation of cleaved and active caspase-1 in human cells was monitored using the Green FLICA Caspase-1 Assay Kit (ImmunoChemistry Technologies, Bloomington, MN, USA) according to the manufacturer's protocol with subsequent FACS analysis.
For intracellular stainings, cells were first fixed and permeabilized utilizing the Cytofix/Cytoperm kit (BD Biosciences, San Jose, CA, USA) and preincubated for 10 min with human FcR blocking reagent (Miltenyi Biotec, Auburn, CA, USA) before addition of the desired antibodies and incubation for 30 min on ice. If the initial staining was performed with non-conjugated antibodies, cells were washed twice with PBS and then incubated for an additional 30 min on ice with a desired fluorochrome-conjugated secondary antibody or isotype control. Cells were washed again and data acquired utilizing a Fortessa LSRIII supported by FACS diva software (BD Biosciences, San Jose, CA, USA), and analyzed using the FlowJo 10. 0. 8 software (Ashland, OR, USA).
Acknowledgements We thank the patients and the healthy donors for their support. We also thank the  Bioinformatics and Computational Biology Core as well as the DNA Sequencing and Genomics Core at NHLBI  for sample processing and help with the analyses. This work was financed by the  MRC   Centre grant   MR/J006742/1 , an  EU-funded Innovative Medicines Initiative BTCURE  (C. K. ), a  Wellcome Trust   Investigator Award  (C. K), a  Wellcome Trust Clinical Fellowship  (B. A. ), the  King's Bioscience Institute at King's College London  (G. A. ), a grant from the  Guy's and St. Thomas' Charity  (C. K. , B. A. , and N. P. ), The King's  College London BRC Genomics Facility , the  National Institute for Health Research (NIHR) Biomedical Research Centre based at Guy's and St. Thomas' NHS Foundation Trust and King's College London , [in part] by the  Intramural Research Program  of the  NIH , the  National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) , and by the  Division of Intramural Research, National Heart, Lung, and Blood Institute, NIH.
Using a   S1 ). The SEM1b consortium analyzed in this study is highlighted in yellow combined metagenomic and culture-dependent approach, two strains and a metagenome-assembled genome (MAG) affiliated to C. proteolyticus were recovered and genetically compared with the only available type strain, C. proteolyticus DSM 5265  [29]. Notable genomic differences included the acquisition of an operon (region-A) encoding carbohydrate-active enzymes (CAZymes), which inferred that C. proteolyticus has adapted to take advantage of longer polysaccharides. Enzymology was used to further support our hypothesis that the CAZymes within region-A are functionally active. We further examined the saccharolytic potential of our recovered C. proteolyticus population in a broader community context, by examining genome-resolved temporal metatranscriptomic data generated from the SEM1b consortium.
The PCR was performed as previously described  [30]  and the sequencing library was prepared using Nextera XT Index kit according to Illumina's instructions for the MiSeq system (Illumina, Inc. ). MiSeq sequencing (2 × 300 bp with paired ends) was conducted using the MiSeq Reagent Kit v3. The reads were quality filtered (Phred ≥ Q20) and USEARCH61  [34]  was used for detection and removal of chimeric sequences. Resulting sequences were clustered at 97% similarity into operational taxonomic units (OTUs) and taxonomically annotated with the pick_closed_refer-ence_otus. py script from the QIIME v1. 8. 0 toolkit  [35]  using the Greengenes database (gg_13_8). The resulting OTU table was corrected based on the predicted number of rrs operons for each taxon  [36]. D1B and D2B were also subjected to metagenomic shotgun sequencing using the Illumina HiSeq 3000 platform (Illumina, Inc.
Quality trimming, filtering, and assembly were performed as described in the aforementioned metagenomic assembly section. The raw reads were additionally mapped on assembled contigs using bowtie2 (-very-sensitive -X 1000 -I 350) and the coverage was retrieved for every nucleotide with samtool depth -a. All the contigs with an average coverage higher than 100 were selected and individually inspected for coverage discontinuity. All the contigs selected with the average coverage criterion (BWF2A: 11, SW3C: 13) looked continuous in coverage and, together with the MAGs, they were submitted to the Integrated Microbial Genomes and Microbiomes system  [44]  for genomic feature prediction and annotation (pipeline version  4. 15. 1). Resulting annotated open reading frames (ORFs) were retrieved, further annotated for CAZymes using the CAZy annotation pipeline  [45] , and subsequently used as a reference database for the metatranscriptomics (with exception of glycosyltransferases).
Phylogenetic analysis A concatenated ribosomal protein phylogeny was performed on the MAGs and the isolated strains using 16 ribosomal proteins chosen as single-copy phylogenetic marker genes (RpL2,  3, 4, 5, 6, 14, 15, 16, 18, 22, and 24, and RpS3, 8, 10, 17, and 19)   [48]. The dataset was augmented with metagenomic sequences retrieved from our previous research on the original FREVAR reactor  [4]  and with sequences from reference genomes identified during the 16S rRNA analysis. Each gene set was individually aligned using MUSCLE v3. 8. 31  [49]  and then manually curated to remove end gaps and ambiguously aligned terminal regions. The curated alignments were concatenated and a maximum likelihood phylogeny was obtained using MEGA7  [50]  with 1000 bootstrap replicates. The radial tree was visualized using iTOL  [51].
Due to low cell biomass at the initial growth stages, sampling for metatranscriptomics was performed from T2 to T8. Sample aliquots (6 ml) were treated with RNAprotect Bacteria Reagent (Qiagen, USA) following the manufacturer's instructions and the treated cell pellets were kept at -80 °C until RNA extraction. In parallel, metadata measurements including cellulose degradation rate, monosaccharide production, and protein concentration were performed over all the nine time points (T0-T8). For monosaccharide detection, 2 ml samples were taken in triplicates, centrifuged at 16,000 × g for 5 min and the supernatants were filtered with 0. 2 µm sterile filters and boiled for 15 min before being stored at -20 °C until processing. Solubilized sugars released during microbial hydrolysis were identified and quantified by HPAEC with PAD.
0. 36  [59]. Subsequently, a quality filtering was applied with an average Phred threshold of 30 over a 10 nt window and a minimum read length of 100 nt. rRNA and tRNA were removed using SortMeRNA v. 2. 1b  [60]. SortMeRNA was also used to isolate the reads originating from the pGem-3Z plasmid. These reads were mapped against the specific portion of the plasmid containing the Ampr gene using Bowtie2  [61]  with default parameters and the number of reads per transcript was quantified and scaled to match the length of the standard (x5. 08). The remaining reads were pseudoaligned against the metagenomic dataset, augmented with the annotated strains, using Kallisto pseudo -pseudobam  [62]. The resulting output was used to generate mapping files with bam2hits, which were used for expression quantification with mmseq  [63] , and the results were scaled to match the initial volume of the samples (x 10).
The number of reads generated from the internal standard molecule were calculated to be 2. 4 × 10 4 +/-2. 1 × 10 4 reads per sample out of 6. 2 × 10 9 molecules added. Using this information, the estimated number of transcript molecules per sample was computed to be 1. 0 × 10 13 +/-7. 3 × 10 12 transcripts. The resulting estimates for the sample sizes were used to scale the expression estimates from mmseq collapse and to obtain absolute expression values. During initial screening the sample T7C (time point T7, replicate C) was identified as an outlier using principle component analysis and removed from downstream analysis. The expression groups were clustered using hierarchical clustering with Euclidean distance. Clusters were identified using the Dynamic Tree Cut algorithm  [65]  with hybrid mode, deepsplit = 1, and minClusterSize = 7.
proteolyticus strains, which created significant assembly and binning related issues  [4]. In this study, multiple oligotypes of C. proteolyticus were also found (Table  S2 ). We therefore sought to isolate and recover axenic representatives to complement our meta-omic approaches, and using traditional anaerobic isolation techniques, we were successful in recovering two novel axenic strains (hereafter referred to as BWF2A and SW3C). The genomes of BWF2A and SW3C were sequenced and assembled, and subsequently incorporated into our metagenomic and metatranscriptomic analysis below. Shotgun metagenome sequencing of two SEM1b samples (D1B and D2B) generated 290 Gb (502 M paired-end reads) and 264 Gb (457 M paired-end reads) of data, respectively. Co-assembly of both datasets using straindepleted reads with Metaspades produced 20,760 contigs totalizing 27 Mbp with a maximum contig length of 603 Kbp.
thermocellum, whereas CLOS1 grouped together with another Clostridium MAG generated from the Frevar dataset and the isolate C. stercorarium (ANI: 79. 1%). Both RCLO1 and CLOS1 encoded broad plant polysaccharide-degrading capabilities, containing 297 and 139 CAZymes, respectively (Table  S4 ). RCLO1 in particular encoded cellulolytic (e. g. , glycosyl hydrolase (GH) families GH5, GH9, and GH48) and cellulosomal features (dockerins and cohesins), whereas CLOS1 appears more specialized toward hemicellulose degradation (e. g. , GH3, GH10, GH26, GH43, GH51, and GH130). Surprisingly, several CAZymes were also identified in COPR1 (n = 65), and both BWF2A (n ×= 37) and SW3C (n = 34) at levels higher than what has previously been observed in C. proteolyticus DSM 5265 (n = 29) (Table  S4 ). Several MAGs were also affiliated with other known lineages associated with biogas processes, including Tepidanaerobacter (TEPI1-2), Synergistales (SYNG1-2), Tissierellales (TISS1), and Methanothermobacter (METH1).