dataesr/scientific-paragraphs-categorization huggingface dataset:
List of Figures and Tables in Supplementary data Figure  S1 : Cell surface analysis of BY4741 and L69 strain using silicon-nitride (Si3N4) AFM cantilevers p3 Table  S1. TANGO software analysis of β-aggregation motifs in Flo1, Flo5, Flo9, Flo10 and Flo11 protein from Saccharomyces cerevisiae S288c strain p17 Table  S2. Search for intragenic repeats using EMBOSS ETANDEM software p18 Table  S3. Search for β-aggregation prone sequence in the different Flo11 proteins using TANGO software (http://tango. crg. es/ ) p19 Table  S4. Yeast strains used or constructed in this study. p20.
ACKNOWLEDGEMENTS We are grateful  Dr Jean Luc Parrou  for advice on performing RT-qPCR experiments, to  Dr Charlie Boone  of  University Toronto Canada , and  Dr Marilena Budroni  from  University of Sassari, Italy , for the kind gift of yeast strains, and to  Dr Mathieu Castex  from  Lallemand Inc. for continuous support on this work. CB is financed by  ANRT  ( Agence Nationale de la Recherche et des Technologies)  grant to carry out her PhD thesis. 19 20.
Results are from three independent experiments. h PLS in clinical liver tissues from patient cohorts. HCV-related cirrhosis (Italy, n = 216 GSE156546; US, n = 145 GSE541027), HBV-related liver cancer (n = 199, GSE14520), alcoholic hepatitis (n = 22, GSE28619), and NASH (n = 72, GSE49541). Induction of the PLS was assessed by comparing diseased tissues with nondiseased tissues. i Induction of the PLS in cell-based systems was compared to liver transcriptome profiles from clinical cohorts using Subclass Mapping. j PLS deconvolution showing mean gene expression of the poor-and the good-prognosis genes in the main cell compartment within the liver. The scRNA-Seq dataset was extracted from GSE115469  15. Source data are provided as a Source data file. Fig. 5c-e ). To further investigate cell heterogeneity, we assessed the diversity across individual HCV-infected Huh7.
5g ). These data support that HCV infection, as an example of etiologic agent to induce the PLS, does not result in cellular heterogeneity with regard to induction of the parenchymal-or NPC-associated PLS genes, i. e. , the cPLS system at least partially models the transcriptional program of both parenchymal and NPC liver cell types at comparable level across the Huh7. 5. 1 dif cells. Screening of computationally prioritized compounds in the cell-based model identifies nizatidine as a candidate chemoprevention agent. We next hypothesized that the cPLS system enables high-throughput screening of compounds that reverse the PLS poor-prognosis status to a good-prognosis status, and therefore could halt progression of liver disease. To test this hypothesis, we first performed a transcriptome-based in silico drug screening, using the PLS as a query in the chemogenomic database connectivity map (cmap, www.
broadinstitute. org/cmap) and LINCS database (https://clue. io/)  17, 18 , and computationally prioritized 54 candidate compounds (cmap: negative enrichment score with p value < 0. 05; LINCS: connectivity score < -90, see "Method"; Supplementary Tables  1  and 2 ). Next, we further narrowed down the list to 25 compounds currently approved for long-term clinical use, and/or at late stage of clinical development without severe adverse effects (LiverTox database (http:// livertox. nlm. nih. gov). Due to its simplicity and robustness, we used persistent HCV infection to induce the cPLS poor-prognosis status. The EGFR inhibitor erlotinib served as a positive control for the PLS reversion  9. To simplify the protocol, reduce the screening costs and thus increase the clinical translatability of the assay, we used a bioinformatically streamlined version of the PLS comprising 32 genes bioinformatically defined and validated in multiple patient cohorts in previous studies  7, 8  (Supplementary Data 1).
We identified 11 small molecules that significantly reversed the poor-prognosis PLS (hit rate 44%; Fig. 2a  and Supplementary Fig. 6 ). Importantly, the effect of the compounds on the PLS was not due to an antiviral effect (Fig. 2a ). The identification of known drugs with a chemopreventive effect, such as pioglitazone or resveratrol  19, 20  , confirms the validity of the system. Interestingly, the top candidate compounds target different and complementary pathways, suggesting that several signaling pathways contribute to the induction of the PLS. Next, the transcriptional targets of each identified compound were extracted from the LINCS database (the top 100 genes most modulated by the compounds) and tested for their enrichment during HCV induction of the poor-prognosis cPLS at the singlecell level (Fig.
PLS induction was determined by GSEA analysis using "mock" non-infected cells as reference control. Reversal of the PLS was assessed by comparing "HCV-infected cells treated with compounds" versus "HCV-infected cells (CTRL)". The compounds were then ranked depending on the FDR (significant FDR < 0. 25). Results from drug screening performed in triplicate are shown. b Screen hit target genes modulated by HCV infection at the single-cell level. Gene signatures comprising the targets of each specific compound were extracted from the LINCS database and tested for their enrichment in association with the HCV viral load. Heatmaps show the mean expression of the leading-edge genes for the significantly enriched signatures (p < 0. 05) in each of the single cells (columns), row normalized as z-score.
05, unpaired t test. j Nizatidine reverts the histamine-induced poor-prognosis PLS in culture of patient-derived multicellular spheroids. Source data are provided as a Source data file. catalytic subunit beta) and Creb5 was increased, following DEN liver injury, with reversal by nizatidine (Fig. 4j ). Similar results were found for protein expression as shown by immunoblot studies, corroborating our findings (Fig. 4k  and Supplementary Fig. 8 ). To address a direct relationship of nizatidine treatment and HRH2 expression, we treated cell lines with nizatidine and quantified HRH2 expression after treatment. As nizatidine had no effect on HRH2 expression in cell lines or 3D spheroids (Fig. 3i  and Supplementary Fig. 9 ), we assume that the observed downregulation of HRH2 in nizatidine-treated animals is most likely not due to a direct effect of nizatidine on HRH2 expression, but rather due to the improvement of liver disease.
Each row representing a single cell. Markers of inflammation, fibrogenesis/cancer, and antigen presentation are shown. All genes are normalized by row from their own min to max (Log2 fold; p value ≤ 0. 05). Source data are provided as a Source data file. non-hepatocyte cell types, including macrophages, which supports versatile utility of our system to screen and identify compounds targeting various liver cell types. These findings are in line with studies indicating that modeling of cellular signaling is not necessarily restricted by cell type and tissue lineage  8, 14, 18, 43. Indeed, such cell-based in vitro models (e. g. , NIH LINCS program) have revealed that cellular signaling and physiology are often shared across multiple tissues and cell types  14. As any cell culture model, the cPLS platform only partially recapitulates the cell circuits of the major liver cell types in the diseased liver in a simplified manner, and therefore the platform should be used for compound or target screening followed by validation in multicelltype experimental systems, such as patient-derived spheroids, as well as rodent models of advanced liver disease as demonstrated in this study.
Methods For reagents, primers, antibodies, and other resources, see Supplementary Table  4  Human subjects. Human liver tissues were obtained from liver disease patients undergoing liver resection with informed consent from all patients for deidentified use at the Center for Digestive and Liver Disease of the Strasbourg University Hospitals University of Strasbourg, France (DC-2016-2616 and RIPH2 LivMod IDRCB 2019-A00738-49, ClinicalTrial NCT04690972) or at Mount Sinai Hospital, New York City, NY (HS13-00159). The protocols were approved by the local Ethics Committee of the University of Strasbourg Hospitals and Mount Sinai Hospital, respectively. All material was collected during a medical procedure strictly performed within the frame of the medical treatment of the patient. Informed consent is provided according to the Declaration of Helsinki.
Detailed patient information and informed consent procedures are implemented by the Strasbourg University Hospital Biological Resources Center (HUS CRB). Patients were given an information sheet, which outlines that their leftover biological material (liver resection and blood samples) that was collected during their medical treatment is requested for research purposes. All patients received and signed an informed consent form in order to provide authorization or refuse the use of their biological samples (protocols DC-2016-2616 and RIPH2 LivMod IDRCB 2019-A00738-49 Clin-icalTrial NCT04690972). The patients maintain the right to withdraw their consent at any time and to request the destruction of their biological material, which is strictly respected. While there was clinical descriptive data available, the identity of the patients was protected by internal coding.
A brief summary of patient characteristics (diagnosis and treatments) is provided in Supplementary Table  3. Spheroids (Fig. 3 ) were generated from liver tissue from patient without history of chronic liver disease. Tumorspheroids (Fig. 8 ) were generated from tumoral tissues from HCC patients. The PLS was analyzed in clinical liver tissues from HCVrelated cirrhosis (Italy, n = 216 GSE15654 (ref. 6 ); US, n = 145 GSE54102 7 ), HBVrelated liver cancer (n = 199, GSE14520)  46  , alcoholic hepatitis (alcohol; n = 22, GSE28619)  47  , and NASH (n = 72, GSE49541)  48  cohorts. Induction of the PLS in cell-based systems was compared to liver transcriptome profiles from published clinical cohorts of HCV-related cirrhosis (n = 145, GSE54102)  7  , HBV-related liver cancer, alcoholic hepatitis, nonalcoholic fatty liver disease (NAFLD; n = 131, GSE31803)  49  , and NASH (n = 73, GSE48452)  50  , using Subclass Mapping  50.
1  and 3 ). For full PLS gene list, refer to Supplementary Data 1. PLS gene expression was normalized according to six housekeeping gene expression (Supplementary Data 1), using GenePattern genomic analysis toolkits  52, 53. Detailed PLS gene expression profiles are presented as heatmaps showing the mean expression of the 186 or 32 PLS genes, using Morpheus software (z-scores of log2-normalized data). Induction or suppression of the PLS signature was determined as previously reported by using GSEA, implemented in GenePattern genomic analysis toolkits  52, 53. PLS was always determined by using CTRL cells, CTRL animals, or CTRL patient-derived tissues as references. Results are presented as simplified heatmaps showing the classification of PLS global status as poor or good prognosis, and the significance of induction/suppression of PLS genes (log10 of false discovery rate (FDR) values).
utsouthwestern. edu/ aboutus/administrativeoffices/technology/development/about/). Transcriptomic analyses. Molecular pathway deregulations were determined in Molecular Signature Database (MSigDB, ver. 4. 0) 58 using GSEA  59. Comparison between the cell-based systems and clinical datasets was performed using Subclass Mapping algorithm 50 implemented in GenePattern. Transcriptome profiling of nizatidine-treated rat liver was performed by RNA-Seq. Sequencing libraries were generated using the TruSeq RNA Sample Prep Kit (Illumina) using 200 ng of total RNA, according to the manufacturer's protocol and sequenced, using Illumina NextSeq genome sequencer (Illumina). Data preprocessing and transcript abundance calculation (RPKM: reads per kilo bases of exons for per million mapped reads) were performed using TopHat2.
1 and Cufflinks 2. 2 software, using the rat reference genome, rn4 (ref. 60 ). Rat genes were converted into orthologous human genes using a mapping table obtained from NCBI HomoloGene database (build 68). Induction or suppression of target genes of relevant cellular signaling pathways was assessed by GSEA. Statistics and reproducibility. Individual experiments were reproduced three times in an independent manner with similar results (except otherwise stated). The precise number (n) of biologically independent samples used to derive statistics is indicated in the figure legends. The in vitro data are presented as the mean ± SD and were analyzed by the unpaired Student's t test or the two-tailed Mann-Whitney U test as indicated in figure legends, after determination of distribution by the Shapiro-Wilk normality test.
p < 0. 05 (*), p < 0. 01 (**) were considered statistically significant. Significant p values are indicated by asterisks in the individual figures. The exact p values are provided in the Source data file. The data points are presented with the bar charts. Statistical analyzes were performed with GraphPad Prism 8 software. No statistical analyzes were performed if n < 3. For in vivo experiments, the sample size estimate was based on a p value of 0. 01 at 90% power assuming a 50% difference in means in tumor burden with 33% standard deviation between control and drug-treated animals. The one-way ANOVA followed by Tukey's post hoc test was used to compare the three groups. p < 0. 05 (* or #), p < 0. 01 (** or ##) were considered statistically significant. Number of animals are indicated in figure legends.
Forty out of 42 single cells were submitted for downstream analyses after excluding samples, i. e. , one noninfected single cell that was potentially contaminated and one infected single cell with significantly lower liver cell gene detection levels. Induction or repression of PLS poor-and good-prognosis genes, in association with the HCV viral load in scRNA-Seq data was determined using the preranked GSEA module implemented in GenePattern  53, 59  , with Pearson correlation as the rank metric. The FDR values of the preranked GSEA analyses that are based on the Kolmogorov-Smirnov statistic, specifically, FDR = 0. 013 for poor-prognosis and FDR = 0. 016 for good-prognosis genes. Enrichment score of poor-and goodprognosis genes with the HCV viral load was determined using Pearson correlation tests and the p value as estimated by the corrcoef function in Matlab.
PLS induction in virus-infected cells was always determined using mockinfected cells as control references. HEV infection. HEV viral stocks were produced by electroporation of PLC3 cells (human hepatoma cell line) with gt3 Kernow C-1 p6 strain (GenBank accession number JQ679013)-capped RNAs  65. Transfected PLC3 cells were maintained at 32 °C in medium containing DMEM/M199 (1 v:1 v), 1 mg/mL of lipid-rich albumin (Albumax I), 1% of nonessential amino acids, and 1% of pyruvate sodium (Life Technologies). Supernatant of confluent T75 flasks of HEV producing cells was harvested, centrifuged for 10 min at 800 r. p. m. , and used to infect Huh7. 5. 1 dif cells in 1% DMSO-complemented DMEM. At 10 days post infection, infected Huh7. 5. 1 dif cells were either used for total RNA extraction or processed for indirect immunofluorescence.
1 % heat-decomplemented FBS for 24 h. Cells cultured in medium containing high glucose (25 mM D-glucose) and 10% FBS were used as controls. Selection of compounds for the molecular signature-based drug screening. Transcriptome-based in silico drug screening was performed using the PLS as a query in the chemogenomics database connectivity map (cmap, www. broadinstitute. org/cmap) and LINCS database (https://clue. io/), as previously described  18, 43. Cmap-predicted compounds with a negative enrichment score and a p value < 0. 05, and LINCS-predicted compounds with connectivity score < -90 were further selected with respect to their approval in clinical trials, according to www. clinical trial. gov. Compounds that have been formally licensed for clinical use, compounds currently being evaluated in phases 3-4 clinical trials and compounds with a putative relevance for liver cancer currently being evaluated in phase 2 clinical trials were selected for assessment in the cell-based model.
Known carcinogens, antibiotics, and compounds associated with severe liver toxicity according to LiverTox database (http://livertox. nlm. nih. gov) were discarded. To assess the effect of selected compounds on the noninfected and HCV-infected cells in the scRNA-Seq data, gene signatures comprising the targets of each specific compound were obtained by extracting the top 100 upregulated and top 100 downregulated Affymetrix probes as indicated in the LINCS database (BING rowspace). Enrichment of these signatures in association with the HCV viral load in scRNA-Seq data was tested using the preranked GSEA module in GenePattern with Pearson correlation as the rank metric. Probes were collapsed to unique genes by prioritizing landmark over inferred probes and selecting the probe with the highest absolute z-score in case of multiple probes.
synthego. com) and complexed with Streptococcus pyogenes Cas9 2NLS nuclease to form ribonucleoproteins before cell transfection. Edited cells were then selected by clonal selection using Sony SH800 Cell Sorter (Sony, Serial number: 0314067). Real-time qRT-PCR. cDNAs were synthetized by reverse transcription using SuperScript III First-Strand Synthesis SuperMix (Life Technologies). Expression of mouse Acta2, Col1a1, Ccl2, Cd68, Il6, Timp1, and Tgf-β1 was analyzed by quantitative real-time PCR using TaqMan Gene Expression Assays (Thermo Fisher Scientific) on Applied Bioscience 7900HT Fast Real-Time PCR system. Expression of human HDC, IL6, IL1β, TNFα, HRH2, and CREB5 was analyzed by quantitative real-time PCR using TaqMan Gene Expression Assays (Thermo Fisher Scientific) on the CFX96 Touch Real-Time PCR Detection System PCR system (Bio-Rad).
Data were acquired using Cytoflex B2R2U0 cytometer (Beckman Coulter, BA47394) and CytExpert 2. 3 software and then analyzed using FlowJo V10. 5. 3. Cells incubated without Edu, but exposed to the detection cocktail served as reference. For PHH, HRH2 was stained using rabbit polyclonal HRH2-specific antibody. Cytokeratin 18 (CK18) was used as hepatocyte marker (mouse monoclonal anti-CK18 antibody). Rabbit or mouse control IgG were used as negative controls. Primary antibodies were detected using Alexa FluorTM 647-conjugated rabbit-specific or PEconjugated mouse-specific secondary antibodies, according to manufacturer's instructions. Data were acquired using Sony SH800 Cell Sorter (Sony, Serial number: 0314067) and SH800 cell sorter software V2. 1. 5 then analyzed using FlowJo V10. 5. 3. In vitro apoptosis assay.
Apoptosis level in HRH2 KO or CTRL Huh7. 5. 1 cells was assessed by detecting cleaved caspase 3 after H 2 0 2 treatment (300 µM, 3 h) using CellEvent™ Caspase 3/7 (ThermoFischer Scientific), according to manufacturer's instructions. Immunofluorescence pictures were acquired using Axio Observer Z1 microscope. Quantification of cleaved caspase 3 levels were performed using Celigo Imaging Cytometer. Data were integrated and normalized to total cell number (DAPI staining). Histology, immunohistochemistry. Animal tissues: formalin-fixed samples were embedded in paraffin, cut in 5 µm thick sections, and stained with hematoxylin and eosin or Picrosirius red staining. The CPA was morphometrically quantified on Sirius red stained sections with image processing software (Image J, NIH). Additional sections in the DEN injury model were stained with antibody specific for lymphocyte antigen 6 complex locus G6D (Ly6G), F4/80, cluster of differentiation 3 (CD3), cluster of differentiation 4 (CD4), cluster of differentiation 8 (CD8), HRH2, and CREB5.
Then, polyclonal rabbit anti-HRH2-serum (GeneTex) was used at a dilution of 1:100 in Tris-buffered saline at pH 6. 1 overnight. After subsequent washes in PBS for 20 min, sections were incubated with the secondary antibody for 30 min, washed with PBS, and incubated with the avidin-biotin complex for 10 min. After washing with PBS, sections were incubated with the peroxidase complex for 2-10 min. Sections were mounted with Eukitt (Riedel de Haen, Seelze, Germany). Images were taken with an Olympus BX45 microscope using Plan ×20 objectives with a Gryphax Progress camera (Jenoptik) and Cell Sense software (Micro Optimal, Kirchheim / Teck). Single-cell RNA-Seq analyses of patient liver tissues. Human cells were dissociated from HCC patient liver tissues undergoing surgical resection using gentleMACS™ Octo Dissociator with Heaters and Human tumor dissociation kit (Miltenyi Biotec).
After dissociation, cells were filtered using 100 µm MACS SmartStrainers (Miltenyi Biotec) and centrifuged 5 min at 50 g to remove hepatocytes. Supernatants were then centrifuged 10 min at 800 × g and cells were washed with Hank's Balanced Salt Solution (Gibco). Erythrocytes were removed using Buffer EL (Qiagen) and washing in PBS + 1% FBS. Leukocyte CD45 + cells were separated from other cell types by flow cytometry using SH800 cell sorter (Sony). (The gating strategy is described in Supplementary Fig. 15 ). Living cells were selected using Zombie green staining according to manufacturer's instructions. Data were acquired using the Sony Cell Sorter Software. CD45 + cells were then treated with nizatidine 20 µM or DMSO vehicle control. Two days after treatment, single cells were sorted into 384 well cell capture plates (Single-Cell Discovery, https:// www.
scdiscoveries. com) using SH800 cell sorter (Sony). Sorted plates were briefly centrifuged at 4 °C, snap-frozen on dry ice and stored at -80 °C until processed. The plates contain mineral oil and droplets of poly-A primers, containing a cellspecific barcode and a unique molecular identifier, enabling to distinguish the wellspecific (and cell-specific) mRNA molecules. scRNA-Seq was performed by Single-Cell Discoveries B. V. using SORT-Seq, a modified CEL-Seq2 protocol  38. Reads were aligned to the human hG19 UCSC reference using Hisat2. R version 3. 5. 3 with package "RaceID" for clusterization, cluster analysis, and DEG calculation, as described  35. Dataset were filtered, analyzed, and normalized using RaceID3. As quality control, we removed all the cells with >80% of mitochondrial genes.
Transcripts correlating to KCNQ1OT1 with a Pearson's correlation coefficient of >0. 4 were removed. RaceID3 was run with the following parameters: mintotal = 1500, minexpr = 2, minnumber = 5, knn = 10. This step filtered out the cells with <1500 transcripts. For normalization, the total transcript counts in each cell were normalized to 1 and multiplied by the minimum total transcript count across all cells that passed the quality control threshold. Data are available at NCBI Gene Expression Omnibus database (https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi? acc=GSE173671). Culture of organotypic ex vivo patient liver slices, patient-derived spheroids, and tumorspheroids. Organotypic liver slices: fresh liver tissue sections (300 µm thick) were made from surgically resected fibrotic livers from liver disease patients, using Krumdieck Tissue Slicer MD6000 (Alabama Research and Development)  8.
Acknowledgements This work was supported by  ARC ,  Paris and Institut Hospitalo-Universitaire, Strasbourg  ( TheraHCC1. 0  and  2. 0 IHUARC IHU201301187  and  IHUARC2019  to T. F. B. ), the  European Union  ( ERC-AdG-2014-671231-HEPCIR  to T. F. B. and Y. H. ,  EU   H2020-667273-HEPCAR  to T. F. B. and M. H. , and  INTERREG-IV-Rhin Supérieur-FEDER-Hepato-Regio-Net   2012  to T. F. B. and M. B. Z),  ANRS ,  Paris  ( 2013/108  and  ECTZ103701  to T. F. B),  NIH  ( DK099558  to  Y. H. , and  CA233794  to Y. H. and T. F. B;  CA140861  to B. C. F. ; and  CA209940 ,  R21CA209940 , and  R03AI131066  to N. P. and T. F. B. ),  Cancer Prevention and Research Institute of Texas  ( RR180016  to Y. H. ),  US Department of Defense  ( W81XWH-16-1-0363  to T. F. B. and Y. H. ), the  Irma T. Hirschl/Monique Weill-Caulier Trust  (Y.