dataesr/scientific-paragraphs-categorization huggingface dataset:
4,  E-G ). In contrast, the depletion of T or B cells did not affect CCA progression in mice with PSC (Fig. 4, H-J ). Altogether, we conclude that PBC (but not PSC) specifically protects against CCA but not from other antigenically different malignancies through a T cell-dependent (and to a lesser extent B cell-dependent) mechanism. Polarity of the T cell-mediated CCA immunosurveillance upon PBC In tumor-naive hosts, deep analysis of the aforementioned liver RNA-seq dataset indicated that several genes specifically involved in Th1 and Tc1 responses were up-regulated in PBC livers with respect to controls and, in some cases, also with respect to PSC. Especially, this applies to Tc1-related genes encoding the markers CD8a, CD274 (best known as programmed death-ligand 1 [PD-L1]), UL16 binding protein 1 (ULBP1), IFNγ, G proteincoupled receptor 18 (GPR18), the transcription factor suppressor of cytokine signaling 1 (SOCS1), and the cytotoxic protease granzyme B (GzmB; Fig.
In silico histological image analysis Image-based quantification of immune cells Micrographs were analyzed using R software (https://www. rproject. org/) by means of EBImage (https://www. bioconductor. org/), RbioFormats (https://github. com/aoles/RbioFormats), and RniftyReg (CRAN) packages. After acquisition, a primary region of interest (ROI) was automatically defined by detecting tissue contours using the Otsu's thresholding method on the grayscale-converted image. Each ROI was semi-automatically refined by removing necrotic and adipose regions, and its surface was computed. Cell nuclei were detected and numbered by using the red component of the hematoxylin, eosin, safran image; briefly, the image channel was converted into grayscale, normalized following a sigmoidal transformation, and thresholded to generate a nuclei mask, thereafter cleaned by morphological operations.
Immune cells were detected and numbered using a similar approach, selecting the right image component according to the staining method. Finally, each population density was calculated as the cell count-to-tissue surface ratio. Image-based quantification of collagen proportionate area Micrographs (data not shown) were analyzed following similar procedure as for quantifying immune cells. Collagen ROI was defined as follows: each image was converted into grayscale, and a top hat filter was applied to enhance local contrast; the resulting image was thereafter normalized by a sigmoidal transformation, on which a threshold was finally applied. Collagen proportionate area was calculated by computing the ratio between collagen and total tissue surfaces. Ex vivo tumor, blood, and lymph node processing Blood sample processing Approximately 300 µl of blood was drawn from mice and collected in a heparin-containing tube.
1-PerCP-Cy5. 5 (PK136), and anti-TCRγδ-APC (GL3). Panel 4 "Lymphocytic subsets" consisted of anti-B220-APC-Vio770 (REA755), anti-CD3-BV421 (145-2C11), anti-CD4-BUV496 (GK1. 5), anti-CD8-PE (53-6. 7), anti-CD19-PerCP-Cy5. 5 (1D3), anti-CD25-BV786 (PC61), anti-CD27-PE-Dazzle594 (LG. 3A10), anti-CD44-APC (IM7), anti-CD62L-PE-Vio770 (REA828), anti-CD45-BUV661 (30F11), anti-FoxP3-FITC (FJK-16s), anti-NK1. 1-BV605 (PK136), and anti-TCRγδ-BV711 (GL3). Antibodies were purchased from BD Biosciences, Miltenyi Biotec, Thermo Fisher Scientific, and BioLegend. Finally, stained samples were run through a BD LSRFortessa X20 or BD LSR II flow cytometer (BD Biosciences). Data were acquired using BD FACSDiva 6. 1. 3 software (BD Biosciences) and analyzed using FlowJo 10. 5. 3 software (TreeStar, Inc. ) according to the gating strategies displayed in Fig.
Replacement of dTTP by dUTP during the second-strand synthesis allowed strand specificity to be achieved. Addition of a single A base to the cDNA was followed by ligation of adapters. Libraries were quantified by Qubit DNA HS Assay (Life Technologies), and library profiles were assessed using the DNA High Sensitivity LabChip kit on an Agilent Bioanalyzer. Libraries were sequenced on an Illumina Nextseq 500 instrument using 75-base-length read V2 chemistry in a paired-end mode. After sequencing, a primary analysis based on AOZAN software (ENS) was applied to demultiplex and control the quality of the raw data (based of FastQC modules/version 0. 11. 5; Perrin et al. , 2017). Obtained fastq files were then aligned using Star algorithm (version 2. 5. 2b), and quality control of the alignment was realized with Picard tools (version 2.
8. 1). Reads were then counted using Feature count (version Rsubread 1. 24. 1), and the statistical analyses of the read counts were performed with the DESeq2 package version 1. 14. 1 to determine the proportion of differentially expressed genes between two samples. In silico RNA-seq data analysis RNA-seq analysis RNA-seq data analysis was performed by GenoSplice technology (http://www. genosplice. com). Sequencing, data quality, reads repartition (e. g. , for potential ribosomal contamination), and insert size estimation were performed using FastQC, Picard-Tools, Samtools, and rseqc. Reads were mapped using STARv2. 4. 0  (Dobin et al. , 2013)  on the mm10 Mouse genome assembly. Gene expression regulation study was performed as already described  (Gacem et al. , 2020; Romagnoli et al. , 2020).
Briefly, for each gene present in the Mouse FAST DB v2018_1 annotations, reads aligning on constitutive regions (which are not prone to alternative splicing) were counted. Based on these read counts, normalization and differential gene expression were performed using DESeq2 on R (v. 3. 2. 5). Only genes expressed in at least one of the six compared experimental conditions were further analyzed. Genes were considered expressed if their fragments per kilobase of transcript per million fragments mapped (FPKM) value was >98% of the background FPKM value based on intergenic regions. Results were considered statistically significant for P values ≤0. 05 and foldchanges ≥1. 5. Significant GO terms and Kyoto Encyclopedia of Genes and Genomes pathways  (Kanehisa et al. , 2012)  were retrieved using DAVID  (Huang et al.
Heatmap for the results visualization Results were considered statistically significant for uncorrected P values ≤0. 05 and fold-changes ≥2. Hierarchical Euclidean clustering and heatmaps were performed using Morpheus (https://software. broadinstitute. org/morpheus). GO terms enrichment analysis GO terms enrichment was analyzed using the DAVID Functional Annotation tool (https://david. ncifcrf. gov/home. jsp) on genes that were up-regulated in both conditions (PBC and PSC), or upregulated in only one, or down-regulated for both cholangitis or only one  (Huang et al. , 2009a (Huang et al. , , 2009b)).
1 user guide). 10× libraries were sequenced on NovaSeq (Illumina) SP (for TCR-seq) or S1 (for gene expression). In silico scRNA-seq data analysis Single-cell 59 and V(D)J data processing Single-cell 59 and V(D)J data processing was performed by GenoSplice technology (http://www. genosplice. com). Assessment of the quality of the sequencing data was done using FastQC v0. 11. 2 on each single-cell 59 and V(D)J sample. The analysis pipelines in Cell Ranger (10× Genomics, version 5. 0. 0) were used for single-cell sequencing data processing. V(D)J sequence assembly and paired clonotype calling were performed using cellranger vdj function, with reference refdata-cellranger-vdj-GRCh38alts-ensembl-5. 0. 0 for each sample and cellranger aggr function to integrate results. Read alignment and unique molecular identifier (UMI) quantifications were performed using cellranger count function with reference refdata-gex-mm10-2020-A.
Visualization of data Single-cell 59 and V(D)J were integrated in Seurat object. Functions vlnPlot, DimPlot, and DotPlot were used for data visualization. Heatmap for gene expression visualization Z-scored expression was calculated as follows: z cluster gene expression -mean of gene expression standard deviation. Hierarchical Euclidean clustering and heatmaps have been performed using Morpheus (https://software. broadinstitute. org/morpheus). In silico statistical analyses and data graphing Linear mixed-effects models were applied for longitudinal comparison of tumor growth curves using TumGrowth web tool (https://kroemerlab. shinyapps. io/TumGrowth/; Enot et al. , 2018). Other statistical analyses were performed using the software GraphPad Prism (v8) or R (v3. 6. 3). Tests performed are indicated in the figure legends and consisted of paired Student t test, Kruskal-Wallis H test with Dunn's pairwise multiple comparisons, one-way or two-way ANOVA with Tukey's pairwise multiple comparison, Mann-Whitney U test, or Fisher's exact test.
In the figures, statistically significant P values (P < 0. 05) are indicated as follows: *, P < 0. 05; **, P < 0. 01; ***, P < 0. 001; and ****, P < 0. 0001. Exact P values are displayed in the legend when 0. 13 > P ≥ 0. 05 to illustrate a trend toward significance. Outlier values were detected with the ROUT test. R or GraphPad Prism software was used for data graphing. Spider plots were generated by means of ggradar (https://github. com/ ricardo-bion/ggradar) package. Online supplemental material Fig. S1  illustrates that PBC, but not PSC, prevents specifically the outgrowth of ectopic CCA. Fig. S2  shows the relative expression of some genes related to Tc1 immune response in the liver of mice affected with PBC and PSC. Fig. S3  displays CD8 + T cell polyfunctionality within CCA tumor-draining lymph node during cholangitis.
Data availability Raw transcriptome data reported in this paper are deposited and available from the Gene Expression Omnibus under accession no. GSE180289; GSE179993 for the RNA-Seq dataset (Fig. 2  and Fig. 5  A), and GSE180288 for the scTCR-seq/scRNA-seq dataset (Fig. 8  and Fig. 9 ). Further information and requests for resources and reagents should be directed to and will be fulfilled by the corresponding authors.
Acknowledgments We would like to thank  Estelle Devêvre  and  Hélène Forher-Ting  ( Centre d'Histologie, d'Imagerie et de Cytométrie core facility, Cordeliers Research Center, Institut National de la Santé et de la Recherche Médicale [INSERM] U1138, Paris ) and the staff of the histo-cytopathology core facility of the  Gustave Roussy cancer campus (Villejuif, France) , as well as the staff of the Genom'IC platform of the  Cochin Institute (INSERM U1016, Paris ) for their assistance in flow cytometry, histology, and transcriptomic analyses, respectively. We also thank  Pr. Raoul Poupon  for having supported this project since the very beginning. J. Paillet is supported by the Association pour la lutte contre les maladies inflammatoires du foie et des voies biliaires. S. Gujar is supported by  Dalhousie Medical Research Foundation ,  Canadian Cancer Society , and  Canadian Institutes of Health Research.
J. G. Pol is supported by the  Association Française d'Hépatologie; Site de Recherche intégrée sur le Cancer Cancer Research and Personalized Medicine; Seerave Foundation ;  Multi-Organism Institute (ITMO) Aviesan Cancer (National Alliance for Life Sciences and Health) ; and  Institut National du Cancer. G. Kroemer  is supported by the  Ligue contre le Cancer  (équipe labellisée);  Agence National de la Recherche (ANR)  -Projets blancs;  Agence National de la Recherche  under the frame of E-Rare-2, the  ERA-Net  for Research on Rare Diseases; Association pour la recherche sur le cancer;  Cancéropôle Ile-de-France ;  Chancellerie des universités de Paris  ( Legs Poix ),  Fondation pour la Recherche Médicale ; a donation by Elior;  European Research Area Network on Cardiovascular Diseases (MINOTAUR) ;  Gustave Roussy Odyssea , the  European Union   Horizon 2020 Project Oncobiome ;  Fondation Carrefour ;  High-end Foreign Expert Program in China  ( GDW20171100085  and  GDW20181100051 ),  Institut National du Cancer ;  Inserm Program Heterogeneity of Tumors & Ecosystem ;  Institut Universitaire de France ;  LeDucq Foundation ; the  LabEx Immuno-Oncology ; the  Recherche Hospitalo-Universitaire Torino Lumière ; the  Seerave Foundation ; the  Site de Recherche intégrée sur le Cancer Stratified Oncology Cell DNA Repair and Tumor Immune Elimination ; and  Cancer Research and Personalized Medicine.
Statistical analysis: Statistical analysis was performed using GraphPad Prism. Unpaired two-tailed Student's T-test or unpaired two-tailed Mann Whitney test was used for statistical comparison between two groups. For comparisons between multiple groups, one-way ANOVA followed by Bonferroni's post-hoc tests was used. For statistical analysis of cell confluence live imaging, two-way ANOVA was used. For statistical analysis of Kaplan-Meier curves, the log rank (Mantel-Cox) test was used. Results are given as mean ± SEM or mean ± SD Data and materials availability: Expression datasets that support the findings of this study have been deposited in the Gene Expression Omnibus SuperSerie record GSE171883 containing 3 distinct datasets under the following accession codes: -Dataset 1: GSE171880. Effect of miR-143-3p or miR-145-5p mimics overexpression in M238P cells (microarrays).
Real-time quantitative PCR: Gene expression: Protocol using the Step One (Applied Biosystem): 1 μg of extracted RNA was reverse transcribed into cDNA using the Multiscribe reverse transcriptase kit provided by Applied Biosystems. Primers were designed using PrimerBank or adopted from published studies. Gene expression levels were measured using Platinum SYBR Green qPCR Supermix (Fisher Scientific) and Step One thermocycler. Results from qPCR were normalized using the reference gene RPL32 and relative gene expression was quantified with the ΔΔCt method. Heatmaps describing gene expression fold changes were prepared using MeV software. Heatmaps depicting fold changes of gene expression were prepared using MeV software. miRNAs expression: 20 ng of extracted RNA was reverse transcribed into cDNA using the miRCURY LNA RT kit (Qiagen).
Fibrillar collagen imaging: Collagen in paraffin-embedded tumors was stained with picrosirius red using standard protocols. Tumor sections were analyzed by polarized light microscopy as described  [26]. Images were acquired under polarized illumination using a light transmission microscope (Zeiss PALM, at 10x magnification). Fiber thickness was analyzed by the change in polarization color. Birefringence hue and amount were quantified as a percent of total tissue area using ImageJ software.
Migration and invasion assays: Migration properties of melanoma cells were tested using Boyden chambers containing polycarbonate membranes (8 μm pores transwell from Corning). After overnight starvation, 1x10 4 cells were seeded on the upper side of the chambers placed on 24 well plates containing 10% FBS medium for 24 h, unless otherwise stated, at 37°C in 5% CO2. At the end of the experiment, cells migrated on the lower side of the chambers were fixed in 4% paraformaldehyde, stained for 15 min with Hoechst and imaged at the microscope (5 random fields per well). Nuclei counting was performed using the ImageJ software. To assess invasion properties of melanoma cells, transwells were coated with Matrigel (1 mg/mL) and cell solution was added on the top of the matrigel coating to simulate invasion through the extracellular matrix.
Reads were processed with the FLAIR pipeline (https://doi. org/10. 1038/s41467-020-15171-6). Raw reads were aligned to hg38 with minimap2 (version 2. 17-r941). Misaligned splice sites were corrected according to the GENCODE v. 35 annotations. High confidence isoforms were defined after grouping corrected reads of all samples sharing same unique splice junctions, by selecting for each group a representative isoform with confident TSS/TES and supported by more than 3 reads. Selected isoforms were quantified using minimap2 in each sample. Differential isoform expression and alternative splicing events significance were tested without replicates using ad-hoc scripts provided on the Brook's lab Github (https://github. com/BrooksLabUCSC/FLAIR). Statistical analysis and Biological Theme Analysis: Microarray data analyses were performed using R (http://www.
r-project. org/). Quality control of expression arrays was performed using the Bioconductor package arrayQualityMetrics and custom R scripts. Additional analyses of expression arrays were performed using the Bioconductor package limma. Briefly, data were normalized using the quantile method. No background subtraction was performed. Replicated probes were averaged after normalization and control probes removed. Statistical significance was assessed using the limma moderated t-statistic Quality control of RNA-seq count data was assessed using in-house R scripts. Normalization and statistical analysis were performed using Bioconductor package DESeq2. All P-values were adjusted for multiple testing using the Benjamini-Hochberg procedure, which controls the false discovery rate (FDR). Differentially expressed genes were selected based on an adjusted p-value below 0.
program reference # ANR-11-LABX-0028-01 and ANR-PRCI FIBROMIR). The financial contribution of the Conseil Général 06, Canceropôle PACA and Region Provence Alpes Côte d'Azur to the C3M and IPMC is also acknowledged. S. D. was a recipient of doctoral fellowships from the LABEX SIGNALIFE and Fondation pour la Recherche Médicale. I. B. and A. C. were recipients of a doctoral fellowship from La Ligue Contre le Cancer. The PDX work was supported by FWO (#G. 0929. 16N) and KULeuven (C1 grant) to J-C. M. Author contributions: Conception and design of the work: SD, MD, BM, ST-D Development of methodology: SD, AB, JF, ML, GV, MD, BM, ST-D Acquisition of data: SD, AB, JF, ML, OM-B, CG, LL, CL, CM, MT, MC, AC, IB, FL, GV, J-CM Analysis and interpretation of data: SD, AB, JF, MI, FL, MD, BM, ST-D Writingoriginal draft: SD, BM, ST-D Writingreview & editing: SD, CAG, GV, J-CM, MD, BM, ST-D Administrative, technical or material support: MD, BM, ST-D Study supervision: MD, BM, ST-D.
M238P melanoma cells were transduced with a lentivirus containing the sequence of the miR-143/145 cluster or a control vector. Expression profiles were determined with RNA-seq (Dataset 2). Predicted targets were obtained using the Targetscan database (conserved sites) (www. targetscan. org). Av. Exp: logarithm (base 2) of the base mean ; logFC: logarithm (base 2) of the ratio of miR-143/145 vs miR-Neg; Adj. pVal: Benjamini-Hochberg adjusted pValue. The thresholds used for the analysis are: Av. Exp >6, LogFC<-1 and adj. pVal <0. 05.
Acknowledgments: We thank  Roger Lo  for M229P/R, M238P/R and M249P/R melanoma cells,  Richard Neubig  for UACC62 parental and resistant cell lines,  Markus Bosenberg  for YUMM1. 7 cells and  Ghanem Ghanem  for short-term cultured melanoma cells. Melanoma PDX models are from the  PDX facility TRACE (KULeuven, Belgium). We thank  Patrick Brest  and  David Gilot  for helpful discussions. We also thank the C3M animal room facility and the C3M imaging facility as well as the  UCA GenomiX platform  and the  PVM Vectorology Platform.
Funding: This work was supported by funds from  Institut National de la Santé et de la Recherche Médicale (Inserm) ,  Centre National de la Recherche Scientifique" (CNRS) ,  Ligue Contre le Cancer, Institut National du Cancer (INCA_12673) ,  ITMO Cancer Aviesan (Alliance Nationale pour les Sciences de la Vie et de la Santé, National Alliance for Life Science and Health)  within the framework of the  Cancer Plan  (Plan Cancer 2018 « ARN non-codants en cancérologie: du fondamental au translationnel » n°  18CN045 ), and the  French Government (National Research Agency, ANR)  through the '' Investments for the Future '' LABEX SIGNALIFE:.
26 3. 35E-05 GMFB 8. 71 -1. 82 1. 22E-06 SWAP70 8. 11 -2. 48 8. 21E-08 Damage of chromosomes Expression profiles were determined with Agilent whole genome microarrays (Dataset 1). 6. 00 0. 00 TERT 7. 44 -1. 07 3. 18E-04 GRB10 11. 17 -1. 39 6. 25E-05 TAGLN2 10. 49 -1. 15 6. 26E-05 TPM3 Metabolism of reactive oxygen 12. 64 -2. 03 7. 16E-07 HLTF TGFBR2 9. 16 11. 53 -1. 26 1. 77E-05 -2. 13 3. 61E-07 species Predicted targets were obtained using the Targetscan database (conserved sites) 5. 44 0. 00 TUB 6. 61 -1. 64 4. 79E-06 HMGB3 7. 94 -2. 13 3. 61E-07 TPM3 12. 64 -1. 65 1. 72E-06 UBE2E3 Chromosomal aberration 9. 79 -1. 04 1. 60E-04 HS6ST1 TRIM2 5. 37 8. 38 8. 90 0. 00 -1. 29 1. 78E-05 -2. 47 8. 21E-08 Entry into interphase (www. targetscan. org). Av. Exp: logarithm (base 2) of the average intensity ; logFC: logarithm 5.
Anaeramoebid MROs have the most complete protein import machinery among metamonads Previous microscopic examinations presented evidence that anaeramoebids possess MROs 5 (Figure  1A , B insets). We therefore sought to reconstruct the mitochondrial proteome from the transcriptomic data. We identified 102 and 122 proteins that are predicted to function in the MROs of A. ignava and A. flamelloides, respectively, based on homology to known mitochondrial or MRO proteins and/or predicted mitochondrial localization (Data S1). We found no evidence of transcripts encoding proteins related to mitochondrial genome (mtDNA) maintenance or expression, suggesting that, like all other MROs from metamonads, anaeramoebid MROs lack mtDNA. Below, we focus our discussion on the pathways that have not been observed in metamonads before (Figure  2 , blue and orange shaded circles).
Some of the putatively cytoplasmic anaeramoebid Group A HYDA proteins have N-terminal extensions that encode WD40 domains that have never been reported on HYDA proteins until this study (Figure  3 , Figure  S2 ). WD40 domains are abundant in most eukaryotic proteomes and are often considered interaction hubs for protein-protein interaction networks  23. Since WD40 domain-containing proteins often interact with other WD40 domain-containing proteins  23  , we manually investigated every predicted protein that contains a WD40 domain (IPR020472) to identify potential interaction partners. We failed to detect any WD40-domain containing proteins that contain an additional domain related to electron transfer reactions (e. g. , butyryl-CoA dehydrogenase, carbon monoxide dehydrogenase). Without biochemical characterization, we cannot be confident about the direction of the reactions catalyzed by the putative HYDA enzymes.
Other pathways Oxygen and reactive oxygen species can oxidize DNA, lipids, and proteins, therefore, most eukaryotes have strategies to prevent and repair oxidative damage in all compartments of the cell, including mitochondria and MROs. In the hydrogenosomes of Trichomonas vaginalis, oxygen is metabolized directly to water via flavodiiron protein (FDP), NADH oxidase (NO), or by the concerted action of nitrate reductase (NR), superoxide dismutase (SOD), and rubrerythrin (RBR) (Figure  2F ). We identified homologues of NO, NR, SOD, and RBR in the Anaeramoeba species (Figure  2F ) and two FDPs predicted to function in the cytoplasm (FDP-Rb) and MRO (Fd-FDP) with distinct evolutionary histories and domain organization (see Figure  S3A ). We noted that the MRO-targeted FDP (Fd-FDP) had an N-terminal 2Fe-2S ferredoxin domain (IPR036010) in addition to the core FDP domains (IPR036866 and IPR029039) (Figure  S3A ).
Read decontamination, transcriptome assembly and annotation Read statistics before and after decontamination are shown in Data S1. Adaptor trimming was performed using trimmomatic v0. 32 using recommended settings (SLIDINGWINDOW:10:25 MINLEN:50)  40. Initial assemblies were performed using Trinity (trinityrnaseq_r20140717 41 ) using default parameters. The top 100 most abundant transcripts were extracted and used as queries against the nt database on GenBank to identify potential contaminants using a cut-off of 90% sequence identity. Genomes of those organisms most similar to the trinity transcripts were downloaded (Data S1) and used to decontaminate the raw reads using DeconSeq 42 (http://deconseq. sourceforge. net/). 'Cleaned' reads were reassembled using Trinity with default parameters.