dataesr/scientific-paragraphs-categorization huggingface dataset:
The cohort, summarized in Fig. 1 , was organized into four groups: (A) The control group of women who underwent standard-of-care screening at 32 weeks of pregnancy and who displayed no clinical risk of premature delivery or premature membrane rupture, (B) Full-term pregnancies in which there was a premature rupture of the membrane > 24 h before childbirth, (C) Premature births defined as labor before 37 weeks of gestation and (D) Premature deliveries that also experienced membrane rupture > 24 h before childbirth. Vaginal swabs were collected from the mother at the time of delivery. Categorical electronic patient records were used to collect both anthropomorphic as well as clinical data for individuals in the cohort, with the resulting information stored in a secure database. The health status of mothers was recorded using a set of variables used to identify complication or risk factors such as gestational diabetes, smoking or alcohol use during pregnancy, or high BMI.
Bacteriological culture was performed, plating vaginal swabs on several media and allowing for the detection of Streptococcus agalactiae (group B Streptococcus, GBS) and additional potential pathogens including Escherichia coli, Klebsiella pneumonia, Proteus mirabilis, among others. Antibiotic susceptibility or resistance to a number of routine antibiotics such as macrolides (e. g. , erythromycin) and beta-lactams (e. g. , methicillin or carbapenem) were measured using both plating and PCR methods. Data collection continued after delivery for mother-infant pairs in order to track immediate or delayed pathogen infections across the four cohort arms. The initial focus of our analyses has been on the microbial composition of samples and links with risk to pregnancy outcomes and infection. A full listing of the analyzed samples in this study as well as the clinical variables used for this study are provided as supplemental data (Supplemental Table  S1 ).
Dominant species influences pathogen presence in the vaginal microbiota. We analyzed maternal vaginal swabs from 761 of the completed mother-infant pairs. Sufficient DNA was successfully extracted from 757 samples. Samples were subsequently sequenced using Illumina paired ends (2 × 150 bp) technology. An average of 3. 16 × 10 7 (SD ± 1. 4 × 10 7 ) high-quality reads were generated for each sample. Although bacteria play an important role in the vaginal environment, their biomass in relation to human cell is low. Consistent with this fact, we found that human host reads made up a mean of 95% of the total data set. Human-mapping reads were removed prior to performing downstream analysis. Taxonomic identification of filtered reads was determined using Kraken2 to map with our customized vaginal microbiota database.
An average of 1. 06 × 10 6 reads were mapped per sample. A total of 749 samples with matched patient records were retained for further analysis. Sequencing read identification and quantification for samples can be found in supplemental data (Supplemental Table  S2 ). We detected microbial communities of the vaginal microbiota mostly consistent with previously reported CSTs (community sequence types) (Fig. 2  and Supplemental Table  S3 ). Lactobacillus crispatus (CST I) was the most abundant organism in our cohort, detected in 87% of samples and representing 30. 18% of reads overall. Lactobacillus iners (CST III), 19. 69% of total reads, was detected in 90. 75% of samples and Gardnerella vaginalis (CST IV), 10. 19% of total reads, was detected in 77. 01% of samples. Lactobacillus jensenii (CST V), 3.
Discussion Here we report on a prospective cohort of pregnant women from three Parisian hospitals elucidating the link between the vaginal microbiota and pregnancy outcomes, specifically the risk of preterm delivery. An extensive array of clinical data was collected for both mothers and infants and paired with whole-genome metagenomic sequencing analysis of vaginal swabs collected at the time of delivery. The analyzed composition of the VMC agreed with previous studies as we observed distinct CSTs, primarily dominated by Lactobacillus species. In accordance with the majority of previous reports for both child-bearing age women who were not pregnant and those with ongoing pregnancies, we found that Lactobacillus crispatus (CST I, 252 out of 749 samples, 33. 6% of samples) and Lactobacillus iners (CST III, 165 samples, 22.
1 ). At d33, three fish and 150 mL of water were sampled in each aquarium. All fish were then exposed in the same aquaria for 5 days (exactly 134 h) to the highest concentration of M. aeruginosa, 100 µg. L -1 Chla (d39, Fig. 1 ). At d39, four fish, 150 mL of water and faeces were sampled in each aquarium. Along the two successive exposures, 10 mL of M. aeruginosa culture were sampled every 2 days but only one sample per week was analyzed further. Dataset S1 provides details of sampled individuals and performed analyses. Fig. 1  Experimental design and O. latipes sampling strategy. After a 14-day acclimatization period, adult male O. latipes were exposed to two successive exposures. During the first 28 days (d0 to d28), fish were exposed to five different treatments, each carried out in three 10-L aquaria.
They were fed twice daily (~ 3-5% of the fish biomass per day) with Nutra HP 0. 3 (Crude protein 57, Crude fat 17, N. F. E 7. 5, Ash 10, Crude fiber 0. 5, Phosphorus 1. 7, Vitamins A, D3, E; Skretting, Norway). Microcystin (MC) concentration was monitored on a regular basis on water samples in M. aeruginosa-containing treatments and quantified using enzyme-linked immunosorbent assay (ELISA) analyses (Microcystins-ADDA SAES ELISA, Eurofins Abraxis). Each sample was analyzed in duplicates and MC concentrations were determined according to the MC-LR response standard curve (Dataset S2).
Mass spectrometry data processing and analysis Each metabolite extract from fish livers, guts and muscles was analyzed by ultra high-performance liquid chromatography (UHPLC; ELUTE, Bruker) coupled with a high-resolution mass spectrometer (ESI-Qq-TOF Compact, Bruker) at 2 Hz speed, on simple MS mode then on broad-band Collision Ion Dissociation (bbCID) or autoMS/MS mode on the 50-1500 m/z range. Three feature peak lists were generated from MS spectra within a retention time window of 1-15 min and a filtering of 5000 counts using MetaboScape 4. 0 software (Bruker). The three peak lists consisted of the area-under-thepeaks of extracted analytes from the three tissues (gut, liver, muscle) sampled at d28, d33, and d39, resulting in 1672, 909, and 3127 analytes, respectively. The genuine metabolite content of the culture was investigated on metabolite extracts using LC-MS/MS approach, combined with molecular network analysis and metabolite annotation using a cyanobacterial metabolite reference database, as previously described  [47].
Prior to analyses, Pareto scaling was applied on the datasets  [48]. Principal component analyses (PCA) were performed to compare the metabolite composition among groups using the mix-Omics package  [49]  in R 4. 1. 0 (R Core Team, 2021). The variance among groups was compared conducting PER-MANOVA (999 permutations) based on euclidean distance with vegan  [50]  followed by Bonferroni-adjusted pairwise comparisons with RVAideMemoire  [51].
DNA extraction DNA was extracted from gut pellets resulting from the metabolites extraction, culture, biofilm, faeces, water filters and food using the ZymoBIOMICS DNA Miniprep kit (Zymo Research, California). Prior to DNA extraction, all pellets were re-suspended in Eppendorf tubes with 750 µL of the ZymoBIOMICS TM lysis solution, then the contents were transferred to the ZR BashingBead TM lysis tubes. Water filters were cut into pieces then transferred to the ZR BashingBead TM lysis tubes. All steps were conducted following the manufacturer's instructions except for mechanical lysis, achieved on a bead beater (Tissue-Lyser II, Qiagen) during 6 × 1 min. An extraction blank was sequenced as a control and the 8 ASVs that were > 1% of reads were removed from the whole dataset as potential contaminants.
Bacterial 16S rRNA gene sequencing and analyses The V4-V5 variable region of the 16S rRNA gene was amplified using 479F (5′-CAGCMGCYGCNG-TAANAC-3′) and 888R primers (5′-CCG YCA ATTC-MTTT RAG T-3′)  [52] , and sequenced (Illumina MiSeq paired-end, 2 × 250 bp, GenoScreen, France). Paired-end reads were demultiplexed, quality controlled, trimmed and assembled with FLASH  [53]. Sequence analysis was performed using the QIIME 2 2020. 11 pipeline  [54]. Chimeras were removed and sequences were trimmed to 367 pb then denoised using the DADA2 plugin, resulting in Amplicon Sequence Variants (ASVs)  [55]. ASVs were affiliated to taxa using the SILVA database release 138  [56]  using the feature-classifier plugin and classifysklearn module  [57, 58]. Sequences assigned as Eukaryota, Archaea, Mitochondria, Chloroplast and unassigned were removed from the dataset then the sample dataset was rarefied to a list of 6978 sequences.
Microbiome-metabolome integrative analysis The integration of datasets, i. e. , the area-under-the-peaks in metabolite profiles and the ASV counts describing the bacterial communities in the same sample, was performed using the mixOmics package in R. Pareto scaling was applied on the metabolome data, and a centered log-ratio transformation then a pre-filtering keeping only abundant ASVs, (i. e. , representing at least 1% of the reads in at least one sample), were applied on the microbiome data. Following unsupervised analyses on each dataset, completed to explore and visualize any similar changes according to treatments, the integration was carried out. A supervised Projection to Latent Structures Discriminant Analysis (PLS-DA) was performed using DIABLO (Data Integration Analysis for Biomarker discovery using Latent cOmponent)  [64] , enabling to identify highlycorrelated variables (metabolites and ASVs) also discriminating the different treatments.
Metagenomic sequencing and analysis Shotgun metagenome sequencing was performed on DNA from 10 gut samples collected at d28, 5 in treatment d28_0 and 5 from d28_100 (Illumina HiSeq, 2 × 150 bp, GenoScreen, France). Reads corresponding to animal sequences were identified by aligning each dataset against Oryzias latipes available at the NCBI, using BBMap/ bbsplit  [66] , and discarded. Remaining reads from each sample were assembled using metaSPAdes with default parameters  [67]. Scaffolds were first taxonomically annotated using Contig Annotation Tool (CAT)  [68]  and Kaiju  [69]  allowing to detect sequences from O. latipes retroviruses and Microcystis genome which were then discarded. All scaffolds were clustered using MyCC  [70]  (k-mer size = 4, minimal sequence size = 1000) and bins were taxonomically annotated using Bin Annotation Tool  [68].
Completeness of bins was assessed using CheckM  [71]. Relative abundance of bins in each sample were also determined using BBMap. Significant bins between the two treatments were determined using Wilcoxon ranksum test. Finally, coding sequences predicted by Prodigal  [72]  were functionally annotated using eggNOG-emapper  [73]. Resulting KEGG annotations were used as input to MinPath  [74]  in order to obtain the MetaCyc pathway information, and to Omixer-RPM  [75]  to specifically characterize gut metabolic modules (GMMs) as firstly described in  [76] , with pathway coverage threshold ≥ 80%.
Monitoring of experiments Oryzias latipes fish were maintained in suitable and stable conditions, and no fish died during the whole experimentation (Fig. 1  and Dataset S2). During the first 28 days long exposure (d0-d28), concentrations of Microcystis aeruginosa in the three treatments (d28_1, d28_10, and d28_100) corresponded to expected levels, 1. 0 ± 0. 2, 10. 0 ± 0. 8, and 100. 2 ± 7. 8 µg. L -1 Chla, respectively. Microcystin levels in water were 0. 4 ± 0. 0 and 10. 4 ± 2. 1 µg MC-LR eq. L -1 in the d28_10 and d28_100 treatments, respectively, while microcystin was below detection level (< 0. 15 µg. L -1 ) in d28_1. During the second M. aeruginosa exposure (d33-d39), fish were exposed to 102. 8 ± 2. 9 µg. L -1 Chla and 11. 2 ± 2. 8 µg MC-LR eq. L -1. The metabolic content of Microcystis aeruginosa cultures was examined (see Supplementary Note 2, Dataset S3, and Table  S1 ).
Diversity and composition of the gut bacterial microbiota after 28 days of exposure At d28, gut communities display between 42 and 219 ASVs with higher average bacterial richness (136 ± 56 ASVs) and evenness (0. 556) reported in fish guts exposed to d28_Z8, and lower average bacterial richness (82 ± 25 ASVs) and evenness (0. 391) in the treatment d28_0 (Dataset S4). Gut-associated species richness is highest in the d28_Z8 treatment (LMM, p < 0. 05), while no differences are observed among replicates within each treatment (LMM, p > 0. 05). The Shannon index increases slightly with Microcystis concentration (d28_1: 2. 09, d28_10: 2. 14, d28_100: 2. 28). Visual comparisons on individual plots of principal coordinates analyses (PCoA) based on the weighted or unweighted UniFrac distances suggest changes occur in terms of both abundances as well as community membership when fish are exposed to M.
Metagenome-based comparison of gut communities in d28_0 and d28_100 Metagenome datasets obtained from 5 fish guts from d28_0 and 5 others from d28_100 yielded negligible amounts of Archaea and non-fish Eukaryota sequences. Among the 32 bacterial bins obtained, 8 were found to be more abundant after treatment (p < 0. 05, Wilcoxon rank-sum test) (Figure  S2b ), including seven which vary concordantly with ASVs sharing the same taxonomic annotation (Flavobacterium, Aeromonas, Gemmobacter, Rhizobiales, Figure  S2a, b ). Three bins showed a decreasing abundance after treatment, including bin24, affiliated to the Firmicutes, with a majority of coding sequences associated to ZOR0006 (58. 06%), the genus corresponding to aforementioned ASV1662. Gut Metabolic Modules were also investigated, with a total of 93 found within the 32 bins (Fig.
In muscles, differences occur (p < 0. 001) especially between d28_0 and both d28_Z8 and d28_1 (p = 0. 01), and between d28_100 and all other treatments (p = 0. 01) except d28_0 (p = 0. 33) (Figure  S4b ). A joint analysis of gut metabolites and bacterial communities was performed, as described by Singh and colleagues  [64]. The resulting combined dataset consists of two matrices of the 70 abundant ASVs, i. e. , representing at least 1% of reads in at least one sample, and the 1674 metabolites, sufficiently well correlated together (76%) to explore associations between ASVs and metabolites. The relevance network illustrates correlations between the most highly associated ASVs and metabolites discriminating among the five treatments. Four ASVs (ASV1349, ASV1564, ASV475, ASV1995) appear negatively correlated with several metabolites and are the least abundant in d28_0 (Fig.
2e  and Fig. 4b ). Differently, the two other ASVs, namely ASV1543 (Epulopiscium) and ASV2350 (Barnesiellaceae), are positively correlated with few metabolites and are the most abundant in d28_0 (Fig. 4b ). Unfortunately, due to the general lack of proper metabolite databases  [77] , only few of the metabolites presenting high correlation with abundant ASVs could be annotated, and these mainly corresponded to amino acids or small peptides (Dataset S5).
Occurrence of dominant ASVs in compartments other than gut Dominant ASVs were searched for in fish food, M. aeruginosa culture, water, biofilm, and faeces (Figure  S7 ). ASV1620 (Cetobacterium) is most abundant in guts (44. 3 ± 20. 4%) then in faeces (28. 6 ± 15. 3%), congruent with its best matches in the database which are fish gastrointestinal bacteria (zebrafish, Nile Tilapia). ASV1349 (Flavobacterium) and ASV1564 (Aeromonas) are most abundant in faeces (8. 8 ± 9. 6% and 7. 8 ± 2. 8%, respectively), then guts (2. 1 ± 4. 4% and 3. 4 ± 2. 6%, respectively) and biofilms (2. 0 ± 3. 3% and 3. 4 ± 2. 5%, respectively). This is congruent with their respective BLASTN hits with bacteria from fish intestinal tracts or aquatic environments. ASV1644 (unassigned Bacteria) is also most abundant in faeces (10.
Following staining, cells were washed with permeabilization followed by two washing steps with PBS before proceeding to acquisition. For TCR-β screening, the anti-mouse TCR Vβ screening panel kit (BD Biosciences) was used according to the instructions of the manufacturer. For all FACS stainings dead cells were excluded from the analysis using a Zombie viability dye (BioLegend) prior to antibody staining and in accordance with the protocol provided by the manufacturer. Fluorescent minus one (FMO) controls were used to properly interpret flow cytometry data, identify, and gate cells, and UltraComp eBeads (eBioscience) were used as single-stain controls to allow for spectral compensation. For spectral flow cytometry, single stains were prepared on freshly isolated cells of the same tissue and data was acquired on a Cytek Aurora Northern Lights (Cytek).
1H11, BioLegend), anti-CD4 (clone SK3, BioLegend), anti-CD25 (clone BC96, BioLegend), anti-CD11c (clone Bly6, BD Biosciences), anti-CD45RA (clone HI100, BioLegend), anti-CD3 (clone UCHT1, ThermoFisher), anti-CD20 (clone L27, BD Biosciences), anti-IgD (clone IA6-2, BioLegend), anti-CD95 (clone DX2, BioLegend), anti-CD11b (clone LM2, BioLegend), anti-CD38 (clone HB7, ThermoFisher), anti-CD57 (clone HNK-1, BioLegend), anti-CD27 (clone O323, BioLegend), anti-CD14 (clone 63D3, BioLegend), anti-CD127 (clone A019D5, BioLegend), and anti-HLA-DR (clone LN3, BioLegend). Data was acquired on a Cytek Aurora Northern Lights (Cytek). Please refer to Supplementary Table  1  for further details on antibody clone, dilution factor, provider, target species and application (suppl. Table  1 ). For dimensionality reduction, samples were down-sampled using the DownSample plugin (version 3.
RNA-sequencing For quantification of Btn2a2 in thymic APCs, extra thymic epithelial cells and splenic DCs, RNA-seq count data was derived from GSE144722 and GSE67834  [28]. In this case, data was processed using Galaxy  [29]. Therefore, samples were obtained from the sequence read archive (SRA) at the National Center for Biotechnology Information (NCBI), and FASTQ files were extracted using the SRA toolkit. Adapter and sequence trimming, as well as filtering out low quality reads was done within Galaxy. Classification based read mapping was performed with salmon. Then, batches between data sets were corrected using the ComBat(GG) function in the R/Bioconductor sva package (version 3. 32. 1  [30] ) and differential expression was performed using DESeq2 (version 1. 32  [31] ) in R (version 4.
1. 0). For RNAseq analysis of Aire -/-mTEC hi cells, count data was derived from GSE140683  [32]  and re-analyzed using DESeq2 (version 1. 32  [31] ) in R (version 4. 1. 0). Analysis mTECs from CIITA -/- and wildtype as well as OT-II-Rag2 -/-and OT-II-Rag2 -/-× Rip-mO-VA tg mice was done as described (GSE144650  [33] ). ScRNA-seq data of human thymic stroma was derived from GSE147520  [34]. H5AD files were converted using SeuratDisk and read into R (version 4. 1. 0). Visualizations were performed using Seurat (version 4. 0. 3 [35] ). For scRNA-seq of TECs, wildtype and Btn2a2 -/-TEC populations were isolated by cell sorting as described, and single cell libraries of four Samples (wildtype mTEC, wildtype cTEC, Btn2a2 -/-mTEC, Btn2a2 -/- cTEC) were constructed using the dual-index kit (10 × ) according to the protocol provided with the kit.
All rights reserved. Reference data was filtered to remove CD11b expressing cells (Itgam == 0) and down-sampled to contain the same number of cells as the lacrimal gland data set. Then, reference and lacrimal gland data were integrated using Seurat's integration pipeline and further analyzed according to the standard workflow in Seurat with a clustering resolution of 0. 8. Clusters were annotated manually based on signature genes. Differential expression (DE) analysis was done using Seurat's FindMarkers (GG) function. Functional annotation was done with the "Hallmark" gene set retrieved from the Molecular Signatures Database (MsigDB), using DE genes with an adjusted p-value ≤0. 05.
Analysis of gene BTN2A2 gene associations Analysis of gene BTN2A2 gene associations. To identify relevant candidate studies involving BTN2A2 variants the online databases Online Mendelian Inheritance in Man (OMIM  [36] ), GWAS Catalog (htt ps://www. ebi. ac. uk/gwas/home  [37] ), GWAS Central (https://www. gwascentral. org/ [38] ), gnomAD browser (https://gnomad. broadinsti tute. org/ [39] ), Exome Variant Server (https://evs. gs. washington. edu/EVS/), and ClinVar (https://www. ncbi. nlm. nih. gov/clinvar/ [40] ) (all accessed last in July 2022) were searched using the term "BTN2A2". Resulting putative genetic associations were then manually curated by examining the literature sources from which the records in the databases originated.
These data show that lymphocytic infiltrations of aged Btn2a2 -/- mice display the features of a fully established TLS with B/T cell segmentation, expansion of lymphocytes and the recruitment of early differentiated T lymphocytes. To better characterize the cellular composition and clonality within TLS, we performed single-cell RNA-seq (scRNA-seq) coupled with scVDJ-seq. To this end, CD11b -CD45 + cells were sort-purified from aged Btn2a2 -/-and subjected to droplet-based scRNA-seq. To discriminate between TLS-derived lymphocytes and potential blood contaminant cells, we integrated the data with a publicly available mouse pbmc data set, which we have subset to cells lacking Itgam (encoding CD11b) expression. By clustering these data, we identified 23 clusters (Fig. 4b-c ). For example, of the nine B cell clusters identified, B cell clusters I-II and VIII-IV were present in both data sets, suggesting that these cells represent circulating B cells either newly recruited into the tissue, or blood contaminants.
We therefore analyzed T cells from cLN of aged Btn2a2 -/-and wildtype mice by flow cytometry. We found higher frequencies of CD62L -CD44 + effector/memory T cells in aged Btn2a2 -/-compared to sex-and age-matched wildtype mice (Fig. 5g ). Moreover, the effector/naïve T cell ratio of cLN-derived T cells correlated with disease severity as defined by the percentage of infiltrated area (Fig. 5h ). Interestingly, CD5 expression levels also positively correlated with the percentage of the infiltrated area (Fig. 5i ), raising the idea that CD5 hi T cells have a higher propensity to get inadequately activated and expand in response to self-antigens. Together, these data suggest that Btn2a2 modulates TCR signaling in thymocytes both at the cortical and medullary stage leading to CD5 hi -skewed T cells that may exhibit an increased autoimmune potential.
BTN2A2 is associated with autoimmunity Previously, we reported that BTN2A2 levels were dysregulated in rheumatoid arthritis (RA) patients with active disease  [45]. The resemblance of autoimmune features observed in Btn2a2 -/-with human SLE and pSS led us to investigate human BTN2A2 levels also in patients of SLE and pSS. Therefore, we first interrogated the relevance of BTN2A2 alleles in human autoimmune disorder development and queried several databases of human genome-wide association studies. Although there are various short variants reported for BTN2A2, their clinical significance appears to be uncertain, and no pathological short variants have been reported to our knowledge. Nevertheless, we cannot exclude the possibility that SNPs in gene regulatory elements controlling BTN2A2 expression levels are associated with autoimmune diseases.