dataesr/scientific-paragraphs-categorization huggingface dataset:
Discussion By combining multi-omics analyses in experimental models of myofibroblastic activation and organ fibrosis, our study has allowed to uncover BNC2 as a TF critical for MF identity and fibrogenesis. Our strategy, leveraging epigenomic and transcriptomic features of identity TF genes, allowed us to capture poorly studied TFs such as BNC2. This is in contrast with previous studies relying on the enrichment of TF-recognition motifs and regulatory network reconstruction (e. g. , refs. 56,57). Indeed, many TFs of the Zinc-finger family including BNC2 have been understudied and are consequently not yet represented in databases of known TF-binding motifs  58, 59. As an illustration, motifbased identification of TFs cooperating with the cofactor YAP1 in MFs did not identify BNC2  60. In that regard, and in line with ref.
Human liver samples Fibrotic liver samples were obtained from NASH patients from the ABOS prospective cohort ("Atlas Biologique de l'Obésité Sévère"; ClinGov NCT01129297)  69  and from patients of the TargetOH cohort ("Comparison of Inflammatory Profiles and Regenerative Potential in Alcoholic Liver Disease"; ClinGov NCT03773887; and DC-2008-642)  70, 71. Patients with obesity enrolled in the ABOS cohort were deemed eligible for weight loss surgery  69. For transcriptomic analyses of biopsies from the ABOS cohorts  36  , 53 NASH patients with a Kleiner score F3-F4 were matched for clinical parameters with 53 control patients with obesity and F0 Kleiner score (age, sex, BMI, alcohol, diabetes, statin, fenofibrate; Supplementary Data File 5) as in ref. 72. Follow-up liver biopsies from patients who underwent bariatric surgery were obtained 1 or 5 years after surgery  6.
Transcriptomic data analyses RNA-seq raw data were processed using a local instance of Galaxy  82. Data were analyzed using FastQC (http://www. bioinformatics. babraham. ac. uk/projects/fastqc) and reads were trimmed when necessary. Mapping of reads to the human genome (hg38) was achieved using Tophat 2. 0. 9  83  using the Gencode annotation (GRCh38. 84). Read counting was then performed using the Htseqcount tool v1. 0. 0  84. Normalization taking into account gene lengths and differential expression analyses were performed using EdgeR v0. 0. 3  85. Affymetrix raw data were processed through the GIANT v0. 0. 2 tools suite  86  on a local instance of Galaxy  82. Normalization was performed using the apt-probeset-summarize tool of Affymetrix Power Tools (https://www. affymetrix. com/support/developer/powertools/ changelog/index.
html) using "gc correction+scale intensity+rma". Normalization level was set to "probeset" and probesets assigned to the same gene (NetAffx Annotation Release 36) were averaged for human data while normalization level was set to "Core genes" for mouse data in order to use the "Collapse to gene symbols" function of the GSEA tool. For differential expression analysis, the LIMMA tool from GIANT v0. 0. 2 was used  87. When necessary, human gene symbols were attributed to each murine gene using the Orthologue Conversion software (https://biodbnet-abcc. ncifcrf. gov/db/dbOrtho. php).
Pathway and gene-set enrichment analyses Enrichments for biological or molecular pathways were defined using Metascape (http://metascape. org)  88 or the ToppGene Suite  89. Gene-set enrichment analyses (GSEA) were performed using the GSEA software (v3. 0) developed at the Broad Institute 50 essentially as described previously  25. We used 1000 gene-set permutations and the following settings: "weighted" as the enrichment statistic and "difference of classes" or "signal to noise" as the metric for ranking genes. Ranking was performed by the GSEA software using the gene average expression when multiple probesets were present in the microarray. The following gene sets from MSigDB (https://www. gsea-msigdb. org/ gsea/msigdb) were used: Biological process v7. 2, Reactome v. 7. 2, and KEGG v7. 2.
Single-cell and single-nuclei RNA-seq analyses Single-cell RNA-seq data of HSCs from control and CCl 4 -treated mice have been described in  31. Briefly, dissociated livers from CCl 4 or vehicle-treated mice were enriched by flow cytometry for LSECs, KCs/ MDMs (Kupffer cells/monocyte-derived macrophages), and HSCs prior to 10x Genomics Single-Cell 3' v2 library preparation and sequencing. Sequencing data were aligned and quantified using the Cell Ranger Single Cell Software Suite (ver. 2. 1), and cell types were separated using the Scanpy implementation of the Louvain algorithm. UMAPs were generated using Seurat v. 4. 0. 3. Single-nuclei RNA-seq data of liver cells obtained from mice fed a HFSC NASH diet were retrieved from https://www. livercellatlas. org/ download. php  34. Only annotated cells were used, and cell populations with less than 50 individual cells were removed.
2 (LI-COR Biosciences, Lincoln, USA). Simple western immunoassays. Protein extracts (0. 4 µg/mL) were run on a Wes system (Protein Simple) according to the manufacturer's instructions. Separation was performed using the 12-230 kDa capillary cartridges. The chemiluminescence-based electrophoretogram was autogenerated, and digitally rendered bands were produced from the chemiluminescent peaks using the Compass software (Protein Simple). Quantifications were obtained using the area under the peak of the protein of interest. Since antibodies against BNC2 had been poorly characterized in the literature, we performed experiments to demonstrate the specificity of #HPA059419 (Sigma) for western blotting (Supplementary Fig. 7 ).
Immunoprecipitation was performed using 2 µg of BNC2 antibody (55220-1-AP from Proteintech or HPA018525 from Sigma-Aldrich) or a control IgG and 500 µg of nuclear extract, which were incubated overnight at 4 °C in dilution buffer (25 mM Tris-HCl pH 8, 1 mM EDTA, 1. 5 mM MgCl2). 10 µl of a 1:1 mixture of protein A and protein G magnetic beads (Dynabeads, Thermo Fisher Scientific) blocked overnight in PBS containing 5 mg/ mL Bovine serum albumin (BSA) was then added to each sample. After incubating samples 4 h at 4 °C under agitation, beads were washed four times with cold washing buffer (25 mM Tris-HCl, pH 8. 0, 150 mM NaCl, 1 mM EDTA, 0. 2% NP40). Elution was performed in Laemmli buffer 6× (175 mM Tris-HCl pH 6. 8, 15% glycerol, 5% SDS, 300 mM DTT and 0. 01% Bromophenol Blue). All buffers were supplemented with PIC (Roche).
Chromatin immunoprecipation (ChIP) and ChIP-seq Chromatin immunoprecipitation was performed as described previously  25. LX2 cells were fixed for 10 min at room temperature with 1% formaldehyde (Thermo Fisher Scientific) followed by 5 min incubation with 125 mM glycine. After two washes with ice-cold PBS, cell pellets were incubated for 10 min in 0,25% Triton X-100, 10 mM EDTA, 10 mM HEPES and 0. 5 mM EGTA followed by 10 min incubation in 0. 2 M NaCl, 1 mM EDTA, 10 mM HEPES, 0. 5 mM EGTA. Nuclei were then resuspended in Lysis buffer (50 mM Tris-HCl pH 8. 0, 10 mM EDTA, 1% SDS) and sonicated for 30 min (three cycles 30 s ON/30 s OFF) using a Bioruptor (Diagenode). All buffers were supplemented with PIC (Roche). Chromatin was diluted tenfold in Dilution Buffer (20 mM Tris-HCl pH 8. 0, 1% Triton X-100, 2 mM EDTA, 150 mM NaCl) and incubated overnight at 4 °C with 2 μg of H3K27ac antibody (Active Motif, #39685) or 3 μg of BNC2 antibody (Sigma, HPA018525 or Sigma, HPA059419).
ChIP-seq and CoP-seq data analysis ChIP-seq and CoP-seq raw data were processed using a local instance of Galaxy 82 essentially as described previously  93. Data processing involved FastQC analysis (http://www. bioinformatics. babraham. ac. uk/projects/ fastqc) and read mapping to hg38 using Bowtie2 version 1. 0. 0  94. Genome-wide signal tracks and enriched regions (peak calling) were obtained using model-based analysis of ChIP-seq version 2 (MACS2 v2. 1. 1. 20160309)  95. Input DNA was used as control, duplicate tags and those mapping to ENCODE blacklisted regions v2  96  were removed and parameters used for peak calling were as described in ref. 93. Signal tracks corresponding to enrichment over input were obtained using MACS2 bdgcmp. For H3K4me3 ChIP-seq processing, peak calling was performed using the broad option for histone marks (q < 0.
001). Only H3K4me3 domains which could be assigned to a gene were considered in subsequent analyses, i. e. , H3K4me3 domains with predicted gene TSS (GENCODE v24 database) overlapping or within 1 kb. ChIP-seq signals and called peaks were visualized at specific loci using the Integrated Genome Browser (IGB v9. 0. 1)  97  or were analyzed at large scales using the deepTools v3. 3. 2  98. The promoters of housekeeping genes defined in Eisenberg and Levanon, 2013 were used as a control in some of these analyses. The coordinates of their TSS were retrieved using the UCSC Genome Browser  99  and RefSeq as the gene annotation. The BNC2 cistrome was compared with that of publicly available cistromes contained within the CistromeDB database using the Cis-tromeDB toolkit  41. « All peaks in each sample » was used for these analyses.
De novo motif enrichment analyses were performed using the RSAT peak-motifs tool run with default parameters (http://rsat. sbroscoff. fr/peak-motifs_form. cgi)  100. BNC2 target genes were predicted by assigning peaks to genes using GREAT4. 0. 4  45  or FOCS  49. With regards to the latest, BNC2 peaks within 2. 5 kb of a Gencode v34 TSS  101  were defined as promoter binding sites. The remaining peaks were monitored for predicted promoter interaction within the entire FOCS datasets (http://acgt. cs. tau. ac. il/focs/download. html; enhancers without assigned genes were removed). All retrieved genes were merged defining FOCS predicted BNC2 target genes. Super-enhancers were defined as in our previous study  102. First, model-based analysis of ChIP-seq version 2 (MACS2)  95  was used to call peaks in the H3K27ac ChIP-seq data (FDR < 0.
15) using input as control. Peaks matching to ENCODE blacklisted regions  96  were discarded. Super-enhancers were then called using retained MACS2-derived peaks and Rank Ordering of Super-Enhancers (stitching set at 12. 5 kb)  24, 103  leaving out regulatory regions mapping to promoters (TSS +/-2. 5 kb) issued from GREAT  45  and using Input DNA as control.
Next, the length of H3K4me3 domains associated with the 1278 genes in 76 normal human cell types and tissues defined by  26  was retrieved (Supplementary Data File 2). For each gene, the relative H3K4me3 domain length in a given cell type or tissue was obtained by dividing by the mean length of domains associated to this gene in all samples. A heatmap of relative H3K4me3 lengths was generated using the heatmap. 2 function of the R package "gplots" (https://cran. rproject. org/web/packages/gplots/) and hierarchical clustering was performed using the hclust function of the R package "Stats" using ward. D2 agglomeration method  105. This allowed to define three main clusters of genes listed in Supplementary Data File 1. TFs within these clusters were subsequently identified by comparison with the human TFs list provided by the Animal TFDB 2.
0 database  106. Further analysis of TFs from cluster 1 was performed by mining the scientific literature to identify articles linking these TFs to MF or fibrosis using the easyPubMed package (https://www. data-pulse. com/ dev_site/easypubmed/) in R. Articles with co-occurrence of a given TF gene name and "fibroblast", "myofibroblast", "fibrosis" or "extracellular matrix" in their title or abstract were retrieved. Results were visualized on a network generated using Cytoscape  107. First, the TF network was generated using String 11. 0 (https://string-db. org/; "full network", "Medium confidence")  108. The network was then exported into Cytoscape where TFs without interactions were manually added back and where nodes were colored according to the number of previously retrieved PubMed articles.
The entire lobe sections were scanned using an Axioscan (Zeiss), and ten fields were randomly chosen (7. 5-week experiment) or the entire section (12-week experiment) was used for quantification with Image J version 1. 53c software (NIH, https://imagej. nih. gov/ij/). The analysis was systematically done on two different liver sections and all histological analyses were performed blinded. Metabolic parameters. Before sacrifice, blood samples were collected from the retro-orbital sinus of mice. Plasma alanine aminotransferase (ALT) and aspartate aminotransferase (AST) activities were measured using colorimetric assays (Thermo Fisher) on a Konelab 20 (Thermo Fisher). Measurement of liver triglycerides. A weighted piece of liver was homogenized with T10 Ultra-Turrax (Ika) in PBS. Samples were transferred into glass tubes and mixed with a 2:1 chlorofrom:methanol mixture.
Rapid immunoprecipitation mass spectrometry of endogenous protein (RIME) LX2 cells were processed using the protocol described in ref. 38. Briefly, 5 × 10 6 cells were grown for 2 days before being fixed in 1% formaldehyde (Sigma-Aldrich). Nuclear proteins were extracted in 10 mM Tris-HCl pH 8. 0, 100 mM NaCl, 1 mM EDTA, 0,5 mM EGTA, 0,1% Na deoxycholate, 0. 5% N-lauroylsarcosine and PIC (Roche). Immunoprecipitation was achieved using Dynabeads (Invitrogen) conjugated with 10 µg of BNC2 antibody (#55220-1-AP, Proteintech or #HPA018525, Sigma-Aldrich) or 10 μg of a non-immune control IgG (#2729, Cell Signaling). Beads were rinsed with RIPA buffer (50 mM HEPES, 1 mM EDTA, 0. 7% Na Deoxycholate, 1% NP40 and 500 mM LiCl) and AMBIC solution (Sigma-Aldrich) before trypsin digestion. Samples were analyzed by coupling a nanoflow liquid chromatography system (nanoElute, Bruker Daltonics) online to a trapped ion mobility spectrometry-quadrupole time of flight mass spectrometer (timsTOF Pro, Bruker Daltonics) equipped with a CaptiveSpray source operating in positive mode.
u. were dynamically excluded for 0. 2 min. The quadrupole isolation width was set between 2 and 3 m/z depending on precursor m/z. The collision energies varied between 20 and 52 eV depending on precursor mass and charge. TIMS, MS operation and PASEF were controlled and synchronized using the control instrument software OtofControl 6. 0 (Bruker Daltonik). Raw MS files were converted into mascot generic files (mgf) and subjected to a Mascot (2. 6. 2) (Mascot, Matrix Science) search using a target-decoy strategy in order to evaluate the false discovery rate (FDR) of the search. The human database was created with human proteins and present in SwisProt database (created 2019-09-12, containing 20490 target sequences plus the same number of reversed decoy sequences). Search parameters were as follows: trypsin was set as the cleavage enzyme and a maximum of one miscleavage was allowed.
Cysteine carbamidomethylation was set as a fixed modification, whereas methionine oxidation, was set as a variable modification. The peptide mass tolerance (tolerance of mass measurement for precursor ion) was set to 15 ppm and the MS/MS mass tolerance (tolerance of mass measurement for fragment ion) set to 0. 05 Da. Proline pipeline (http://www. profiproteomics. fr/proline/) was used to validate the identification results. This statistical validation was performed using the Target Decoy approach, which consists in creating a "decoy" sequence that does not exist in nature. The false identifications thus distribute evenly between the real database (target) and the decoy one, and so the number of decoy hits can be used to estimate the false discovery rate (FDR = decoy hits/decoy hits + target hits) and eliminate false positives.
Peptide and protein identification validation parameters were set as follows: minimal length of seven amino acid, score ≥20, pretty rank ≤1, and FDR ≤ 1. Only proteins detected in the BNC2 RIME, at least two out of three biological replicates, but in none of the non-immune control IgG RIME experiments were considered. Common non-specifically detected proteins in affinity purification mass spectrometry data from the Contaminant Repository for Affinity Purification (human CRAPome2. 0) database  110  were also discarded. BNC2 interactors were further filtered using the list of human transcriptional regulators from the Ani-malTFDB3. 0 database 111. BNC2 interactors were clustered based on percent protein coverage in RIME assays using the heatmap. 2 function of the R package "gplots" (https://cran.
r-project. org/web/packages/gplots/) as described hereabove for H3K4me3 domain lengths. Biological triplicates were obtained using the anti-BNC2 antibody 55220-1-AP (Proteintech) and an additional experiment was performed using the anti-BNC2 antibody HPA018525 (Sigma-Aldrich). All non-filtered detected proteins for individual samples are provided in Supplementary Data Files 6 and 8.
When specified, RNA in situ hybridization was followed by immunofluorescence staining using an anti-ACTA2 antibody (ab76126, Abcam:1:50). Alcohol-related liver cirrhosis samples were analyzed using a CSU-W1 Spinning Disk (Gataca) with a 60x CFI PLAN APO LBDA objective (resolution 0. 110 µM) and images were analyzed using Ima-geJ1. 53c (https://imagej. nih. gov/ij/). For liver biopsies from NASH patients before and after bariatric surgery, the entire liver sections were scanned using the Axioscan (Zeiss) and images were analyzed using the ZEN 2 software (Zeiss). Quantification of red and white dots; illustrating the number of COL1A1 and BNC2 transcripts, respectively, was performed using ImageJ1. 53c (https://imagej. nih. gov/ij/). The number of cells in each section was obtained by counting DAPI-positive nuclei.
Structural model of the BNC2-CRBN interaction and docking analyses A structural model of the interaction between BNC2 zinc-finger 1 (BNC2ZF1) and CRBN was built using the crystallographic structure of DDB1-CRBN-pomalidomide bound to IKZF1 (Protein Data Bank ID 6h0f)  52  and used for docking of thalidomide and its derivatives. First, we chose an experimentally resolved zinc finger-CRBN complex on the basis of its resolution and co-crystallized ligand, settling for DDB1-CRBN-pomalidomide bound to IKZF1 (Protein Data Bank ID 6h0f)  52. Out of the six BNC2 zinc fingers, four were below a 25% identity threshold with the co-crystallized IKZF1 zinc finger. The two remaining BNC2 zinc fingers were the first (29% identity, 38. 7% similarity) and fifth (25. 8% identity and 38. 7% similarity). We opted to keep the first (BNC2ZF1), as it was closer to the reference.
BNC2ZF1 was modeled on the RMN structure of human zinc fingers and homeoboxes 1 (Protein Data Bank ID 2ghf; 20. 83% identity and 50% similarity with BNC2ZF1). Interestingly, even with a higher identity percentage than the template used to build BNC2ZNF1, the model resulting from a homology with IKZF1 was marginally less interesting, with a slightly higher overall deviation from the Ramachandran optimal parameters. The BNCZF1-CRBN complex was next built by superimposing the BNC2 and IKZF1 zinc fingers and exchanging the co-crystallized IKZF1 for BNCZF1 and adjusting the side chains by a quick geometry optimization in two steps after adding hydrogens and partial charges. First, the side chains were adjusted by 1000 steps of steepest descent, then the whole zinc finger and the side chains of the DDB1-CRBN-pomalidomide complex were subjected to another 1000 steps of steepest descent.
Acknowledgements The authors are indebted to  Julien Devassine  and  Kim Letten  of the EOPS2 animal facility ( Plateformes Lilloises en Biologie et Santé (PLBS)  - UMS   2014 -US 41 ,  Univ. Lille) ,  Sophie Salomé  and  Nathalie Jouy of the BioImaging Center Lille (BICeL) platform (Plateformes Lilloises en Biologie et Santé (PLBS)  - UMS   2014 -US 41 ,  Univ. Lille)  for their assistance. The authors also would like to thank members of  Inserm   U1011  including  Drs M. Johanns ,  L. L'Homme ,  N. Hennuyer ,  X. Maréchal ,  M. Rosa  and  M. Gimenez  as well as  F. Maggiotto  ( INFINITE, Lille ) for technical help or helpful discussions. This work was supported by the  Agence Nationale de la Recherche (ANR)  grants " HSCreg " ( ANR-21-CE14-0032 ), " MEdicAL " ( ANR-21-CE17-0016 ), " DeCodeNASH " ( ANR-20-CE14-0034 ), " Pre-ciNASH " ( ANR-16-RHUS-0006-PreciNASH ),  French Proteomic Infrastructure (ProFI ;  ANR-10-INBS-08-03 ) and " European Genomic Institute for Diabetes" E.
G. I. D. ( ANR-10-LABX-0046 ), a  French State  fund managed by  ANR  under the  frame program Investissements d'Avenir I-SITE ULNE/ ANR-16-IDEX-0004   ULNE. This project was also co-funded by The  European Union  under the  European Regional Development Fund (ERDF)  and by the  Hauts de France Regional Council  (contract 20000007 and contract  17003781 ), the MEL (contract_2020_ESR_02 and con-tract_2016_ESR_05) and the  French State  (contract no. 2019-R3-CTRL_IPL_Phase3  and contract no. 2017-R3-CTRL-Phase 1 ). Work in INFINITE was supported by a grant from the  Fondation Recherche Médicale (FRM)  (Equipe labellisée,  EQU202003010299 ). BS is a recipient of an  Advanced ERC Grant  ( 694717 ).
Data availability Transcriptomic and cistromic data generated in this study have been deposited into Gene Expression Omnibus under SuperSeries accession number GSE185529. The mass spectrometry proteomics data generated in this study have been deposited to the ProteomeXchange Consortium via the PRIDE  [1]  partner repository with the dataset identifier PXD003624. Datasets analyzed in this study (Supplementary Data File 11) are available through the Gene Expression Omnibus (GSE68108  18  , GSE58680  113  , GSE38103  43  , GSE61852  44  , GSE68108  18  , GSE111059  114  , GSE63626  115  , GSE145086 31 , GSE192742  34  ), ENCODE (https://www. encodeproject. org/experiments/ENCSR507UDH/)  79 , FAN-TOM5 (https://fantom. gsc. riken. jp/5/sstar)  81 , the matrisome database (http://matrisomeproject.
| Patients and controls We collected AXIN2 P/LP variants identified between 2004 and June 2020 in the French Oncogenetics laboratories from the Genetics and Cancer Group-Unicancer analyzing AXIN2 in a context of suspected predisposition to CRC. All the patients but one were originally referred to clinical geneticists because of colorectal polyposis and/or personal or family history of early CRC, leading to analysis of a panel of genes known for their implication in CRC predisposition which included AXIN2 (Figure  1 ). One patient, a 10-year-old child, presented with ectodermal dysplasia, leading to analysis of a panel of genodermatosis-causing genes, including AXIN2. Information on personal and familial history was obtained during genetic counseling and from medical records. Full informed consent for genetic analyses and the use of clinical data for research purposes was obtained from all patients or legal representatives.
15 (Sophia Genetics). Prediction of the effect on splicing was assessed using SPiP (Splicing Prediction Pipeline, https://github. com/raphaelleman/SPiP)  17 and SpliceAI 18 tools. Prediction of translation initiation sites was performed using ATGpr (Prediction of Translation initiation ATG)  19  and TIS Miner. 20 Using available evidence for their interpretation, variants were classified as benign/likely benign (B/LB), of unknown significance (VUS), and pathogenic/likely pathogenic (P/LP). 16 A consensus in the French Genetics and Cancer Group is to consider allele frequency over 1% in control populations as stand-alone evidence of benign impact (instead of 5%).
| Family history of individuals with P/LP AXIN2 variants Detailed family history data were available for 9 index cases (Figure  3 ). Only 10 relatives were tested for AXIN2 variant and 5 of them were found to carry the P/LP variant identified in their family. Nevertheless, adenomatous polyps, CRC or intestinal cancer were reported in a number of relatives. A large variety of other cancers were also observed, including a salivary gland cancer at 50 in one first-degree relative with oligodontia (Li-4 II. 2), and a head-and-neck cancer before 64 in another relative with adenomatous polyposis (Li-1 II. 2). Tooth agenesis was frequently reported (16 individuals from 5 unrelated families). Notably, oligodontia was reported in seven first-/second-degree relatives on three generations in one family (Li-4).