dataesr/scientific-paragraphs-categorization huggingface dataset:
To test this hypothesis, we used the EpiFactors database of epigenetic regulators  43  as a reference to interrogate the T1/early and the T2/late transcriptome datasets for dysregulated chromatin modifiers. Genes comprised in the histone H2A/H2B deubiquitination (DUB) and the histone methyltransferases (HMT) complex denominations were consistently upregulated in chronic patients compared to resolvers and healthy controls (Fig. 6d ). We focused on the HMTs G9a (also known as EHMT2), which methylates lysine 9 of histone 3, and EZH2, which methylates lysine 27 of histone 3, because of their repressive effects  [44] [45] [46]. As shown in Fig. 6e , significantly higher levels of H3 K9 di-methylation were detected in HCV-specific CD8+ T cells from T2/late chronic patients compared to T2/late resolvers and healthy controls.
The PRC2 repressive complex and its catalytic subunit, the H3K27 histone methyltransferase EZH2, have been shown to regulate terminal effector (and memory) CD8+ T cell differentiation  [57] [58] [59] [60] [61]  , whereas the H3K9 methyltransferase G9a has been reported to promote the expression of inhibitory receptors during T cell exhaustion in chronic LCMV infection and to repress type I cytokine gene expression in Th2 cells  62, 63. Through EpiFactors database investigation, we found that both HMTs and additional PRC2 complex components (e. g. , EED, Suz12, RBBP4, and Jarid2)  61  are upregulated in T2/late-exhausted HCV-specific CD8+ T cells compared to the same cells from T2/ late resolved patients. Accordingly, we observed an increase in the repressive H3K9me2 mark, accompanied by reduced levels of the activating H3K9ac2 mark (mutually exclusive with the repressive H3K27me3 modification) in T2/late exhausted HCV-specific CD8+ T cells.
Methods Human subjects and samples. The following groups of patients (Supplementary Data 3) were enrolled into the study at the Unit of Infectious Diseases and Hepatology of the Azienda Ospedaliero-Universitaria of Parma, Italy: 84 T2/late treatment-naive patients with chronic active hepatitis C (aged 20-67 years; median 45 years); diagnosis was based on the finding of positive serum HCV-RNA and alanine aminotransferase (ALT) elevation for at least 1 year; 20 T2/late chronic active hepatitis C genotype 1 patients were followed through IFNα-free DAA therapy (Supplementary Data 3b). 85 T1/early patients with acute hepatitis C, 52 of them with chronic evolution of infection (aged 18-73 years; median 41 years) and 33 with a self-limited outcome (aged 23-70 years; median 40 years), with clinical, biochemical and virological evidence of acute HCV infection (transaminase levels at least ten times higher than the upper limit of the normal range, anti-HCV antibody, and HCV-RNA positive); 26 T2/late subjects who spontaneously recovered from acute HCV infection (aged 19-64 years; median 33 years), recruited 8 to 18 months after the peak of ALT elevation; 28 healthy subjects (aged 23-54 years; median, 36 years), as controls.
After thawing, CD8+ T cells were isolated from PBMC with the CD8+ T Cell Isolation Kit (Miltenyi Biotec) and labeled with the viability probe 7-AAD, anti-CD3, anti-CD8, and HLA-Class I dextramers to identify antigen-specific T cell sub-populations. CD8-dextramer-positive T cells were subsequently sorted (yield 500-2000 cells per sample) with a FACSAria III Cell Sorter (BD Biosciences). Microarray data acquisition. RNA was purified from dextramer-sorted CD8+ T cells with the Nucleospin RNA XS kit (Macherey Nagel, Duren, Germany), according to the manufacturer's instructions. Total RNA concentration was determined using a Nanodrop spectrophotometer and/or with the Ribogreen RNA quantification kit (ThermoFisher). RNA integrity was evaluated with the Bioanalyzer 2100 traces system (Agilent Technologies).
Total RNA was amplified with the Transplex Whole Transcriptome Amplification (WTA2) kit (Sigma) and purified with GenElute PCR Clean-Up silica spin-columns (Sigma) following manufacturer's instructions. cDNA was labeled with the ULS Fluorescent Labeling kit (Agilent Technologies) and hybridized to 60-mer oligonucleotide whole-humangenome microarrays (Human GE 4 × 44K v2, Agilent Technologies), as indicated in the manufacturer's protocol. Microarray slides were scanned with an Agilent dual-laser DNA microarray scanner. The Agilent Feature Extraction software with default settings (user manual version 7. 5) was used to obtain normalized expression values from the raw scans. Microarray data analysis. The GeneSpring GX v11. 5 software package (Agilent Technologies) was used for quality control checks, data normalization by the quantile method and initial microarray data analysis.
Probes detectable in at least two-thirds of the replicates for each condition were retained for further analysis. ANOVA test with Benjamini-Hochberg correction for multiple testing (FDR ≤ 0. 05) was used to select genes differentially expressed among all conditions. These were visualized as hierarchical clustering and used for principal component analysis (PCA), to which 'mean-center' and 'scale to unit standard deviation' options were applied. Heat maps were generated using the MT4 Multi-Experiment Viewer  71. Topological pathway analysis was performed separately on the T1 and T2 timepoints datasets with Clipper method  72  , a two-step empirical approach implemented in the GraphiteWeb tool  73  , that uses Gaussian graphical models to identify pathways with means or covariance matrices significantly different between two experimental conditions.
Specifically, following pathway structure conversion into nodes (i. e. , genes) and edges (i. e. , gene connections) graphical representation using biologically driven rules (KEGG and Reactome databases), mean and covariance matrices were calculated for chronic and self-limited patients at each time-point assuming a Gaussian distribution and taking into account the constraints imposed by the graph structure. After graph decomposition into cliques (i. e. , small connected components), separate likelihood ratio tests were used to compare the means and covariance matrices of the two groups and to identify the most altered (i. e. , dysregulated) portions (sub-networks) of each pathway. The p-values derived from this second step of analysis were corrected for multiple testing with the Benjamini-Hochberg method, thereby generating the corresponding q-values.
Pathways with q-values ≤ 0. 05 either in mean or covariance were deemed as significant and genes associated to individual dysregulated subnetworks were selected for further analysis. Overrepresentation analysis was performed on log 2 expression data (all detected probes) using the Gene Set Enrichment Analysis (GSEA) software  74  to identify molecular pathways significantly overrepresented among the upregulated and downregulated genes, and to compare the resulting expression profiles with those of previously published studies. To this end, we used the Molecular Signature Database v 6. 0 (C2, canonical pathways; C5, Gene Ontology, GO, gene sets) available at http://www. broadinstitute. org/gsea/msigdb/index. jsp. Gene sets smaller than 15 or larger than 500 genes were excluded; gene sets passing this filter were considered as significantly enriched if the False Discovery Rate (FDR), calculated using Signal2Noise as metric and 1000 permutations of gene sets, was ≤0.
To identify candidate regulatory genes at T1 time point, leading-edge genes present in at least five of the eight categories were retained, and the Search Tool for the Retrieval of Interacting Genes (STRING v10. 5) database was used to build the functional gene association network which was visualized by Cytoscape  76, 77. EpiFactors (http://epifactors. autosome. ru)  43 , a web-accessible, manually curated database that provides information on human proteins and complexes involved in epigenetic regulation, was used to generate expression profiles of the main chromatin-regulator complexes. In Fig. 6d  the listed complexes are: APOB mRNA editosome also known as the Apolipoprotein B mRNA editing enzyme complex; CAF-1, Chromatin Assembly Factor-1; CoREST, REST Corepressor 1; CREST-BRG1, transcription activator BRG1; DUB, histone deubiquitination complex; Exosome, RNA exosome complex; FACT, histone chaperone that FAcilitates Chromatin Transcription; HAT, Histone acetyltransferases; HDAC, histone deacetylases; HMT, Histone Methyltransferases; Ino80, chromatin-remodeling ATPase INO80; ISWI, SWI/SNF-Related Matrix-Associated Actin-Dependent Regulator of Chromatin A5; L3MBTL1, Histone Methyl-Lysine Binding Protein; MLL, Histone-lysine N-methyltransferases; PcG and PcG-like, Polycomb Group complexes; PP, dephosporylation complex phosphatases; SRCAP, multiprotein chromatin-remodeling complex; SWI/SNF, SWItch/Sucrose Non-Fermentable nucleosome remodeling complex; and SWR, SWR1-like complex.
Expression data are available at NCBI GEO: GSE111449. Quantitative PCR. Expression levels of a subset of modulated genes were independently determined by Taqman gene-expression assays (Life Technologies) using the same amplified cDNAs used for microarray analysis, as starting material. The selected genes included PDCD1 (assay Hs00169472_m1) and BATF (assay Hs00232390_m1); GAPDH (Hs02758991_g1) served as a loading and normalization control. The expression levels of nine additional genes-TBX21 (Hs. PT. 56a. 20216516), CTLA4 (5′UTR-EX1 custom: F: 5-TCCTTGATTCTGTGTGGGTTC-3, R: 5-TTTATGGGA GCGGTGTTCAG-3, probe: 5-ACACATTTCAAAGCTTCAGGATCCTGA-3), HeyL (Hs. PT. Glucose uptake and GLUT1 assay. Glucose uptake assays were done on PBMC stimulated overnight with coated anti-CD3 (10 μg/ml) and soluble anti-CD28 (2 μg/ml) for ex vivo analysis.
To study inhibition by specific chemical compounds, PBMCs were stimulated for 40 h with virus-specific peptides. After specific stimulation, PBMCs were washed with PBS 1X to eliminate endogenous glucose and stained for 30 min at 37 °C with the glucose analog 2-NBDG (2-deoxy-2-[(7-nitro-2,1,3-benzoxadiazol-4-yl) amino]-D-glucose, ThermoFisher; 40 μM) in glucose-free RPMI with 10% dialyzed FBS, and finally stained with the viability probe 7-AAD. Fluorescence generated by the glucose analog, which is proportional to glucose uptake, was measured with a FACSCANTO II multicolor flow cytometer and analyzed with DIVA (BD Biosciences) and FlowJo (Tree Star) softwares. GLUT 1 assay was performed on PBMCs, after specific stimulation as described above, by using an anti-glucose transporter GLUT1 antibody (Glut1-FITC, R&D Systems).
MMP was determined on PBMCs stimulated overnight with coated anti-CD3 (10 μg/ml) and soluble anti-CD28 (2 μg/ml) for ex vivo analysis by using the potentiometric probes JC-1 (Molecular Probes, Life Technologies). After surface staining, cells were incubated with JC-1 (2. 5 μg/ml) for 15 min at 37 °C and protected from light before flow-cytometry analysis. Samples were then stained with the viability probe 7-AAD and finally acquired on a FACSCANTO II multicolor flow cytometer and analyzed with the DIVA software (BD Biosciences). The decrease in JC-1 fluorescence caused by co-treatment (15 min at 37 °C) with the protonophores valinomycin or carbonyl cyanide m-chlorophenyl hydrazine (CCCP) (Molecular Probes) served as a positive control for MMP depolarization. Dextramer-positive virus-specific depolarized cells were quantified by subtracting the percentage of FL1high/FL2low cells (JC-1 staining) detected in the unstimulated samples from the percentage of the corresponding cell subsets detected in the stimulated samples  23.
Cells were analyzed for viability with the LIVE/DEAD Far Red Dead Cell Stain Kit (ThermoFisher), stained for cell surface markers, then incubated with the superoxide sensitive dye dihydroethidium (DHE, 1 µM, Thermo-Fisher) or with the H 2 O 2 sensitive dye 2′,7′-dichlorodihydrofluorescein diacetate (H 2 DCFDA, 2 µM, ThermoFisher), according to the manufacturer's protocol, and finally analyzed with FACSCANTO II multicolor flow cytometer. Data were analyzed with DIVA (BD Biosciences) and FlowJo (Tree Star) softwares. Phosphoprotein staining. Phosphoproteins and nuclear factors were measured on PBMC either unstimulated or incubated overnight with coated-anti-CD3 (10 μg/ml) and soluble anti-CD28 (2 μg/ml) or with NS3-/FLU-CMV-EBV-specific peptides in the presence or absence of the antioxidant resveratrol (10 μM).
For anti-phospho-ATM, anti-dimethyl-Histone H3 (Lys9), anti-acetyl-Histone H3 intracellular antibody detection, intracellular staining BD Phosflow™ Fix and Perm/Wash Buffer I (BD) was used, according to the manufacturer's instructions. For anti-phospho-p38, anti-total p53 and anti-phospho-p53 antibody detection, the intracellular staining Fixation/Permeabilization Solution Kit (Cytofix/ Cytoperm BD) was used, according to the manufacturer's instructions. Data were collected with a FACSCANTO II multicolor flow cytometer and analyzed with DIVA (BD Biosciences) and FlowJo (Tree Star) softwares. Seahorse analysis. Seahorse experiments were performed on total CD8+ T cells purified with the CD8+ T Cell Isolation Kit (Miltenyi Biotec). Multiple stimuli (i. e, Flu-/EBV-/CMV-specific and NS3-specific HCV peptides, plus an unstimulated control) were applied to CD8+ T cells from each individual patient and each culture condition was analyzed in the same experimental session in order to limit possible batch effects.
Specifically, oligomycin blocks mitochondrial complex V, providing indication of the amount of oxygen utilized for ATP synthesis; FCCP uncouples ATP synthesis from the electron transport chain by transporting protons across the inner mitochondrial membrane, thereby allowing the calculation of the maximal and the spare respiratory capacity; rotenone/antimycin A shuts down mitochondrial respiration by blocking complexes I and III, allowing to calculate the ATP production, the maximal (MRC), the spare respiratory capacity (SRC), the proton leak and the coupling efficiency of virus-specific CD8+ T cells. Results were obtained with the Agilent Seahorse XF Report Generator of the Wave 2. 3 software, according to the manufacturer's instructions. Aggresome detection (ProteoStat staining). For the detection of protein aggregates, after overnight PBMC stimulation with or without coated anti-CD3 (10 μg/ml) and soluble anti-CD28 (2 μg/ml), cells were surface stained and then the ProteoStat Aggresome Detection Kit (Enzo Life Sciences, New York, NY) was used according to the manufacturer's protocol, before flow cytometry acquisition  25.
Cells treated with 5 μM of proteasome inhibitor (MG-132) served as positive controls. Aggresome levels were quantified by subtracting ProteoStat median fluorescence intensity (MFI) in the unstimulated from the stimulated samples. Data were collected with a FACSCANTO II multicolor flow cytometer and analyzed with DIVA (BD Biosciences) and FlowJo (Tree Star) softwares. T cell expansion, inhibitory compound treatment, and cytokine production assays. Short-term T cell lines were generated by 10 days PBMC stimulation with HCV-NS3 or with HLA-A2-restricted HCV peptides (each at the final 1 μM concentration) in the presence or absence of drug inhibitors. For ex vivo analysis of inhibitor activity, PBMCs were stimulated for 40 h with HCV-NS3 peptides. FLU, CMV and EBV peptides were used as controls.
Statistical analysis. The GraphPad Prism 6. 0, JASP 0. 10. 2 (https://jasp-stats. org/) and Jamovi 1. 0. 7 (https://www. jamovi. org) softwares were used for statistical analysis. The F test of variance was applied to the different groups; normality distribution of data was tested by the Kolmogorov-Smirnov test. Differences between multiple patient groups have been evaluated by Kruskal-Wallis nonparametric test and the p-values have been calculated and corrected for pair-wise multiple comparisons, according to the methods of Dunn. Moreover, to detect differences in treatments the Friedman non-parametric test has been applied and corrected for pair-wise multiple comparisons, according to the Conover test. In the experiments with the different inhibitory compounds, the significance of the fold change (FC) upon drug treatments was evaluated by the Wilcoxon signed-rank tests (different from a theoretical median = 1).
Acknowledgements We thank  Prof. Riccardo Bonadonna  ( Unit of Endocrinology, University of Parma, Italy ) for helpful discussions and the  Microarray Facility at the University of Ferrara-Italy  (http://ltta. tecnopoloferrara. it/bioinformatica. php)for help in the initial phase of bioinformatic analysis. Access to the  CoreLab (Azienda Ospedaiero-Universitaria of Parma, Italy)  and to the  CRBa (University of Bologna, Italy ) central facility instrumentations and technical advice by  Dr. Vilma Mantovani ,  Dr. Francesca Bianchi ,  Dr. Rosalia Aloe , and  Dr. Roberta Musa  are also gratefully acknowledged. This work also benefited from support by the  Biotechnology Interuniversity Consortium (CIB)  and the bioinformatics expertize framework available within the  COMP-HUB Initiative , funded by the  'Departments of Excellence ' program of the  Italian Ministry for Education, University and Research  (MIUR, 2018(MIUR, -2022)).
This work was supported by the  European Commission  grant  HepAcute  ( FP7-HEALTH-2010 ), by a grant from  Regione Emilia-Romagna, Italy (Programma di Ricerca Regione-Università   2010-2012 ;  PRUa1RI-2012-006  to C. F. , by a  FIRB grant  ( RBAP10TPXK  to C. F. ) from the  Italian Ministry of Education, University and Research (MIUR  to C. F. ), by a grant from  "Agence Nationale pour la Recherche sur le SIDA et les hepatites virales" (ANRS)  to M. L. (n. ECTZ66014) and by a grant from the  Agence National de la Recherche (ANR@TRACTION)  to M. L.
5 million cells) and ionomycin (500 ng/ml/0. 5 million cells), along with GolgiStop for 4 h. Th1, Th17, and Treg populations were analyzed by combination of surface and intracellular staining for various markers. Surface staining was performed with fluorescence-conjugated MAbs to CD4 and CD25. After fixation and permeabilization by intracellular staining kit (eBioscience), intracellular staining with fluorescence-conjugated MAbs to IFN-γ, IL-17A, and FOXP3 were carried out. Samples were acquired using LSR-II flow cytometer (BD Biosciences) and data were analyzed by FACS-DIVA (BD Biosciences). The gating strategy for CD4 + T cells is provided in the Supplementary Fig. 2b. Treatment of DCs and mice splenocytes. DCs (0. 5 million cells/ml) were cultured in RPMI-1640 containing 10% fetal calf serum (FCS) alone or with 25 mg/ml or 15 mg/ml of IVIG or desialylated IVIG (25 mg/ml) or equimolar concentration of F(ab′) 2 fragments, Fc fragments or HSA (irrelevant protein control) for 24 h.
β-actin or GAPDH were used as a loading control. The western blot images were analyzed by myImageAnalysis software v 2. 0 (Thermo Fisher Scientific). The full western blot images are provided in the Supplementary Fig. 4. Analyses of nuclear translocation of β-catenin. DCs were cultured alone or in presence of IVIG or equimolar concentration of HSA for 24 h. Cells were harvested and pellets were washed twice with ice cold PBS and resuspended in hypotonic lysis buffer (20 mM Tris pH 7. 5, 5 mM MgCl 2 , 1 mM EDTA, and 240 mM Sucrose). After incubating for 10 min on ice, cells were centrifuged at 13,000 rpm for 10 min at 4 °C to pellet down the nuclei. Supernatant was collected and used as a cytosolic extract. Pellets were resuspended in ice cold Buffer C (20 % Glycerol, 20 mM HEPES pH7. 9, 420 mM NaCl, 1.
Acknowledgements Supported by  Institut National de la Santé et de la Recherche Médicale ,  Sorbonne Université  and  Université Paris Descartes, France ; A. K. was a recipient of fellowship from  Indo-French Center for Promotion of Advanced Research (CEFIPRA)  and  La Fondation pour la Recherche Médicale  ( FDT201805005552 ). M. B. J. was a recipient of a fellowship from  Ministère de l'enseignement supérieur et de la recherche (Ecole doctorale 394 ,  Sorbonne Université, France). We thank  Dr. C Galeotti  and  Dr. E Stephen-Victor  for the support. We also thank the staff of core facilities at  Centre de Recherche des Cordeliers  for the help:  Centre d'Histologie, d'Imagerie et de Cytométrie (CHIC), Centre d'Explorations Fonctionnelles (CEF) , and  Le Centre de Génotypage et de Biochimie (CGB).
Motivation: Siberian sturgeon is a long lived and late maturing fish farmed for caviar production in 50 countries. Functional genomics enable to find genes of interest for fish farming. In the absence of a reference genome, a reference transcriptome is very useful for sequencing based functional studies. Results: We present here a high-quality transcriptome assembly database built using RNA-seq reads coming from brain, pituitary, gonadal, liver, stomach, kidney, anterior kidney, heart, embryonic and pre-larval tissues. It will facilitate crucial research on topics such as puberty, reproduction, growth, food intake and immunology. This database represents a major contribution to the publicly available sturgeon transcriptome reference datasets.
In contrast to the economic value of caviar, only one single sturgeon species, the sterlet, Acipencer ruthenus  (9)  received enough attention to have is genome sequenced. The sterlet has one of the smallest sturgeon genome sizes of 1. 9 Gb compared to 2. 3, 3. 3 and 4. 2 Gb of Acipencer stellatus, Acipencer oxyrhynchus and Acipenser baerii, respectively. The large variation between sturgeon genome sizes comes from recent whole genome duplications. The authors of  (9)  have shown that slow genome nucleotide content evolution renders genome assembly more difficult because of the large sections which are not separated in the assembly process and have to be split afterwards. Assembly complexity and genome size are causes for the lack of a Siberian sturgeon reference genome. Reference transcriptomes have been used in many different species as for example the PhyloFish database  (10) , for fishes.
Gene expression being tissue specific, reference transcriptomes are build with a mix of tissues. This mix can directly be performed on the RNA samples or in silico by combining the reads or the contigs produced for each tissue. Several software packages enable transcriptome assembly. The most used software package these days is Trinity  (11)  but others such as rnaSPAdes  (12)  and Oases (  13 ) are also available. Once the reference is built its quality can be checked by verifying its compaction, read realignment rate and protein content. The read alignment rate should be close or over 90% for all of the good quality read sets. Protein content is usually checked with BUSCO  (14). BUSCO searches the contigs for a set of nearly universal single-copy ortholog proteins, this for a given branch of the tree of life.
For reference transcriptomes, the awaited BUSCO complete score is 90% or more. Assembly compaction is checked using contig count and length metrics as well as TransRate score  (15). Even if trinity is the most used RNA-Seq de novo assembler these days, the transcriptome assembly problem is still an open question. Several strategies can be used with different combinations of read sets, software packages, filtering procedure. The aim is to have the smallest possible number of contigs, each one harboring a unique gene or transcript, with an high read realignment rate. Several assemblies should be performed to find the one. In order to be able to understand sturgeon molecular biological processes, in the absence of a reference genome, several teams have produced transcriptomic assemblies. Eight different sturgeon species already have one publication including transcriptomic data.