dataesr/scientific-paragraphs-categorization huggingface dataset:
Automatic detection of peaks of contact with 2µ plasmid To detect contact peaks with the 2µ plasmid, we used the find_peaks function of the scipy package (with the following parameters: height = 0. 8, distance=2). Before detection, the contact signal was interpolated using the interp1d function of the scipy package. In most of the average 2µ plasmid contact profiles shown, the set of contact peaks used is the one detected under normal log-phase culture conditions from Micro-C data  (Swygert et al. , 2019)  corresponding to the 73 genomic positions given in Extended Table  1.
Genomic features of regions contacted by 2µ plasmid To plot the aggregated profile of genomic signals, we convert contact data (cool file at 200 bp resolution) into a bw file using home made python code. We then used the functions computeMatrix and plotProfile from deepTools suite  (Ramírez et al. , 2016)  to compute and plot the heatmaps. Same approach was used to plot the averaged contact signal inside long genes. Gene boxes at regions contacted by 2µ plasmid were generated using home made python code and using the coordinates of genes of SGD database (http://sgd-archive. yeastgenome. org/sequence/S288C_reference/orf_dna/). To plot the pileup plots around the pairs of genomic positions of peaks of contact with 2µ plasmid, we used the quantify mode of Chromosight  (Matthey-Doret et al. , 2020)  with the following parameters --perc-undetected=100 --perc-zero=100.
All possible pairs of peaks of contact in intra or inter configurations were generated using home made python code and the function combinations from itertools package. The computational screen uses a dataset of Chip-exo libraries for about 800 different proteins and genomic signals  (Rossi et al. , 2021). For each aligned library (bowtie2 alignment with very sensitive mode and mapping quality >0), Chip-exo signal was computed with home made python code with a binning of 2 kb. An enrichment score was computed by taking the average of the ChIP-exo signal +/-2kb signal around the bins of the positions of contact peaks detected in log phase (Extended Table  1 ). The different libraries were sorted according to the different categories identified in the UMAP analysis of  (Rossi et al. , 2021).
Acknowledgements The authors thank  M. Dobson  for sharing 2µ plasmid mutant strains and Rep1 antibody. We thank  G. Liti  and  J. Schacherer  for sharing strains and suggestions. We thank  S G Swygert  and  T Tsukiyama  for sharing strains,  Y. Barral ,  L. Baudry ,  G. Millot ,  P. Moreau ,  A. Piazza  for useful discussions;  C. Chapard ,  C. Matthey-Doret ,  J. Sérizay  for technical advice. This work used the computational and storage services (maestro cluster) provided by the  IT department at Institut Pasteur, Paris. F. G is supported by an  ENS Paris Saclay  fellowship. This work has received support under the program  Investissements d'Avenir launched  by the  French Government  and implemented by  ANR  with the references  ANR-10-LABX-54   MEMOLIFE  and  ANR-10-IDEX-0001-02   PSL Université Paris , Q-life  ANR-17-CONV-6150005  (SA).
The authors greatly acknowledge the  PICT@Pasteur imaging facility  of the  Institut Curie , member of the  France Bioimaging National Infrastructure  ( ANR-10-INBS-04 ). This research was supported by funding from the  European Research Council  under the  Horizon 2020 Program  ( ERC  grant agreement  771813 ) to R. K, from  Agence Nationale pour la Recherche   ANR-22-CE12-0013-01  to R. K. and A. T. , and from  Agence Nationale pour la Recherche JCJC 2019 (Apollo)  to A. C.
Sharks and rays (class Chondrichthyes) were excluded from the analyses due to their highly vagrant behavior and patchy distributions that challenge their population assessment via UVC  65  (only representing 6% of the species initially recorded and 0. 01% of the total abundance). Also, they have, markedly distinct life history, morphological, and ecological traits  66  that might represent outliers in the multidimensional functional space. Due to the logistical challenges of sampling at depths, some transects were not conducted, resulting in a slightly unbalanced sampling design (Table  S2 ). All survey methods were performed in accordance with relevant guidelines and regulations. Trait database. We compiled eleven functional traits for each fish species recorded from a recently published dataset on behavioural, morphological, and ecological characters of Atlantic reef fishes  66.
Ellipsoids were overlaid to depict confidence limits (0. 95) areas encompassing depth strata. We then tested for variation in fish assemblage structure among depth strata as a function of habitat (two-way interaction, 'depth x habitat'), using a model-based approach for multivariate abundance data  72. This approach overcomes shortcomings related to mean-variance relationships, typical of distance-based approaches for multivariate community data (e. g. , PERMANOVA and ANOSIM)  73. Generalised linear models (GLMs), using a negative binomial distribution for overdispersed count data, were fitted to the species abundance matrix via the 'mvabund' R package  74  , with p-values calculated using 999 iterations via PIT-trap resampling. Violation of model assumptions was visually inspected by plotting residuals vs.
e. , those in the edges of the convex hull). These three dimensions allowed us to investigate both compositional (presence/absence) and structural (abundance) changes in the functional diversity of reef fish assemblages, providing insights in community assembly and ecosystem functioning  36. Differences in functional alpha diversity among depth strata, as a function of habitat (two-way interaction, 'depth x habitat'), were then tested via Generalised Linear Mixed Effects Models (GLMMs), implemented in the 'glmmTMB' R package  76. Before analyses, we applied an exponential transformation to Feve and Fdiv, as values were highly left-skewed. Models were run using a "Gamma" error distribution with an "inverse" link function for Fric, while Feve and FDiv were fitted using a "Gaussian" distribution.
All models included "Site" nested within each habitat, as well as "Season", as random effects to control for spatial and temporal non-independence. Violation of model assumptions was visually inspected through plots of residuals vs. fitted values. Analyses of trait spaces and functional diversity were carried in the 'mFD' R package  77. Taxonomic and functional beta diversity. We investigated whether fish assemblages associated with BCFs were taxonomically and functionally more distinctive, relative to their shallower counterparts, using an attribute diversity framework  78. This framework, based on generalizations of Hill numbers, enables comparison of taxonomic (i. e. , species) and functional (i. e. , species within a pre-defined threshold of functional distinctiveness) entities shared among depth strata as a function of habitat.
e. , "q" > 0) places more weight on the distribution of abundances among common ("q" = 1) and dominant ("q" = 2) entities. Differences in the magnitude of taxonomic and functional dissimilarities among depth strata as a function of habitat (twoway interaction, 'depth × habitat') were then tested via GLMMs, implemented in the 'glmmTMB' R package  76  , for each metric and "q" parameter independently. Models were run using a "tweedie" error distribution, which is adequate for handling the high proportion of assemblages with low values of dissimilarities and overdispersion in the data, and included the same random effect structure as models for functional alpha diversity. Violation of model assumptions was visually inspected through plots of residuals vs. fitted values. Indices of beta diversity were calculated using functions in the 'mFD' R package  77.
Acknowledgements We acknowledge  Alvaro Roldán  ( Liquid Planet ) and  José Manuel Golderos Tena  for their technical diving assistance during sampling, and  Ángel Moreno Martín , technician of  Dirección General de Lucha contra el Cambio Climático y Medio Ambiente del Gobierno de Canarias (Spain)  for his assistance in mapping edition (bathymetry and sampling sites). This study has been produced with financial support from the  LIFE Programme.
SMARCD3 is a potential functional epigenetic dependency in pancreatic cancer To define epigenetic and transcriptional regulatory programs required for PDAC stem cell function, we used an RNA-sequencing (RNA-seq) dataset  8  to identify factors significantly enriched in the therapy-resistant MSI2+ stem cell fraction  7  of primary tumors from the Kras G12D/+ ; p53 f/f ; Ptf1a-Cre (KP f/f C) model of pancreatic cancer (Fig. 1a )  [21] [22] [23] [24]. To assess their impact, we conducted a targeted functional screen using primary cancer stem cells derived from Msi2-GFP reporter KP f/f C tumors (Fig. 1b )  7  , where cells were transduced with lentiviral shRNA or sgRNA, and growth was analyzed in sphere-forming conditions  25. Master transcription factors and histone deacetylases such as KLF4  26  , OCT4  27  , SOX9  28  , HDAC11  29  , and HDAC7  30  were required for the growth of PDAC stem cells, serving as controls (Fig.
i Smarcd3 knockdown blocks proliferation (BrdU incorporation) of CD133 + KP f/f C cells in vitro (n = 2 biological replicates, n = 3, mean ± SEM). j Smarcd3 knockdown blocks growth of MSI2+ KP f/f C cells in vivo, reducing flank transplant tumor growth rate (shControl slope = 43. 8 mm 3 /day; shSmarcd3 slope = 10. 08 mm 3 /day, linear regression, p = <0. 0001); mass, cell count, and number of MSI2+ tumor stem cells at endpoint (n = 4 for 3 biological replicates, ANOVA with multiple comparisons, mean ± SEM; see Supplementary Fig. 1i, j ). Source data for all experiments are provided as a Source Data file. driven by a pancreas-specific Pdx-Flp recombinase, allowing independent spatiotemporal control over Smarcd3 deletion with CRE  38. To induce deletion post-establishment in vivo, Smarcd3 f/f ; FSF-Kras G12D/+ ; p53 frt/frt ; Pdx-Flp mice were crossed to a globally expressed tamoxifen-inducible R26-CreER T2 CRE  39  (Fig.
SMARCD3 knockdown blocks tumor growth in human models of pancreatic cancer Although genetically engineered mouse models (GEMMs) are useful models that are representative of human disease, PDAC patient tumors are diverse and exhibit more complex mutational landscapes. While SMARCD3 was rarely expressed in benign inflamed tissue (pancreatitis), the frequency of nuclear SMARCD3+ epithelial cells rose in PanIN and, to a greater degree, in PDAC in a human tissue microarray (Fig. 3a ). In addition, the frequency of SMARCD3 expression was enriched within PROM1+ (CD133+ ) and MSI2+ tumor cells (1. 5-fold and 3-fold, respectively) in primary human PDAC tumors in a published single-cell RNA-seq dataset  40  (Fig. 3b , Supplementary Fig. 3a ), supporting the data from genetic models (Fig. 1a, f , Supplementary Fig.
Strikingly, four of these hubs converged on lipid metabolism (Fig. 5d  in yellow, Fig. 5e ), which was also the most significantly enriched term in the Gene Ontology analysis of the RNA-seq dataset (Supplementary Fig. 5b ). These lipid metabolism hubs encompassed functions in arachidonic acid metabolism, fatty acid metabolism, cholesterol biosynthesis, and metabolic regulation. As lipid metabolism has emerged as an important feature of aggressive cancer stem cell populations  20  , we further focused on this functional program. Within lipid-associated network hubs (Fig. 5e ), SMARCD3regulated genes were involved at almost every level of lipid homeostasis. Smarcd3 loss downregulated lipid transport and storage genes, as well as major transcriptional regulators of lipid metabolism (Supplementary Table  1 ).
In contrast to our findings that Smarcd3 is a potential cancer dependency, the cancer dependency DepMap database shows that SMARCD3 is an enrichment in cancer cell lines, further suggesting that SMARCD3 function may be context-dependent. While DepMap explores gene function in 2D cancer cell lines, our studies have used both primary mouse cell lines and more physiologically relevant patient-derived organoids in 3D culture systems as well as five distinct genetically engineered models and patientderived xenografts. It should be noted that DepMap does not accurately predict the function of several other well-known cancer-associated genes. PROM1 (CD133) is an enrichment within PDAC lines despite its function in cancer invasion  58. DepMap also predicts an opposing function for SWI/SNF component ARID1A, which appears as a slight dependency in contrast to its apparent function as a tumor suppressor in PDAC  57.
Curated screen of metabolic inhibitors conducted in Smarcd3 WT and Smarcd3 KO -KPF cells. Primary tumor cell lines were derived from (Cre-) Smarcd3 f/f -KPF tumors. Smarcd3 deletion was driven by the delivery of adenoviral GFP (WT) or Cre (KO); transduced cells were treated with inhibitors for 72 h in sphere-forming conditions and assessed for viability (representative, n = 3 biological replicates with n = 4 technical replicates per experiment; 2-way ANOVA with multiple comparisons, mean ± SEM). g Free fatty acids are reduced in tamoxifen-treated Smarcd3 f/f -KPF-R26-CreER T2 tumors. Vehicle or tamoxifen-treated Smarcd3 f/f -KPF-R26-CreER T2 EpCAM+ tumor cells were sorted for free fatty acid analysis by GC-MS (n = 3, twotailed T-test, mean ± SEM; see Supplementary Fig. 5c, d ). All Source data are provided in the Source Data file.
Mice Mice were pathogen-free, and bred and maintained in the animal care facilities at the University of California San Diego; all animals were maintained as mixed background. Animals had access to food (Teklad # 5053) and water ad libitum and were group-housed in ventilated cages under controlled temperature and humidity with a 12-h light-dark cycle. No sexual dimorphism was noted, therefore, males and females of each strain were equally used for experiments and both sexes are represented in all data sets. Littermates of the same sex were randomized into experimental groups when possible. Only female NOD/SCID and NSG mice were used, all at ages 6-8 weeks. The exact strain, age, and sex of all mice used are outlined in Supplementary Data 2. Experiments involving subcutaneous injections were monitored and sacrificed before exceeding the UCSD Institutional Animal Care and Use Committee limit of 1.
1. 3, Becton Dickinson). Data were analyzed with FlowJo software v. 10. 5. 3 (Tree Star). Antibodies and dilutions for FACS analysis are listed in Supplementary Table  2. Propidium-iodide (Life Technologies) was used to stain for dead cells. MSI2 expression was assessed by GFP expression in Msi2-GFP-KP f/f C mice. Patient-derived xenograft tumors were dissociated to single cells by adapting from Tuveson lab organoid protocols  65, 66. Tumors were washed in MEM and cut into 1-2 mm pieces, which were collected in 10 ml Gey's balanced salt solution, 5 mg Collagenase P, 0. 2 µg DNAse I, and 10. 5 μM Rho Kinase inhibitor. Samples were incubated for 10 min at 37 °C, then pipetted up and down 10 times and returned to 37 °C. After 10 more minutes, samples were pipetted up and down 5 times, then passed through a 100 µm nylon mesh (Corning).
Gels were run at 100 V for 1 h and transferred to PVDF at 90 V/250 mA for 1 h. Blots were blocked in Odyssey buffer incubated in primary antibodies diluted in Odyssey buffer 0. 1% Tween20 overnight. Blots were washed and incubated in secondary antibodies (1:10,000, Li-cor) the next day at room temperature for 1 h before images were collected (Li-cor scanner). Antibodies and dilutions are listed in Supplementary Table  2. Uncropped and unscanned images of all blots are included in the Source Data file. IP-Western, co-IP and IP-mass spectrometry. Primary Smarcd3 WT and Smarcd3 KO -KP f/f C cell lines were derived from end-stage tumors as described above and cells were collected for lysis and analysis of BAF complex composition using immunoprecipitation (IP) followed by western blot or mass spectrometry (MS).
salk. edu/ homer/) was used to process alignment files to generate ChIP-seq bed files. ChIP-seq peaks for SMARCA4 and ARID1A were found by using the findPeaks program in HOMER with the parameter "-style factor" versus the appropriate ChIP input experiments as background. ChIP-seq peaks for H3K4me, H3K4me3, and H3K27ac were called using the parameter "-style histone". SMARCA4 and ARID1A peaks were called when enriched >four-fold over input and local tag counts (FDR 0. 001, Benjamin-Hochberg). For histone ChIP-seq, peaks within a 1000 bp range were stitched together to form regions. Differential ChIP-seq peaks were found by merging peaks from shControl and shSmarcd3 groups and called using getDiffer-entialPeaks with fold change 1. 5, Poisson p value < 0. 0001. For motif enrichment analysis, sequences within 200 bp of peak centers were compared to motifs in the HOMER database using the findMotifs-Genome.
pl command using default fragment size and motif length parameters. Random GC content-matched genomic regions were used as background. Enriched motifs are statistically significant motifs in input over background by a p < 0. 05 using cumulative binomial distribution. For RNA-seq, RNA expression was quantified as raw integer counts using analyzeRepeats. pl in HOMER using the following parameters: -strand both -count exons -condenseGenes -noadj. To identify differentially expressed genes, we performed getDiffExpression. pl in HOMER, which uses the DESeq2 R package to calculate the biological variation within replicates. Cut-offs were set at log2 FC = 0. 585 and FDR at 0. 05. Software and algorithms are published and are listed in Supplementary Table  3. GSEA. Gene set enrichment analysis (GSEA) was performed with the Bioconductor GSVA data C2, C6, and C7 BroadSets gene set collections from MsigDB3.
0. Additionally, we used curated gene sets we derived from published data in context of shFoxa1 or sgFoxa1 knockdown  49  , sgKlf5 knockdown  70  , and a gene signature enriched within Msi2+KP f/f C stem cells and Msi2-KP f/f C non-stem cells  8. Software and algorithms are published and are listed in Supplementary Table  3. Network analysis. Genes down-regulated by Smarcd3 knockdown (padj < 0. 05, logFC < -0. 35) were used to construct a network using high confidence (>0. 8) interactions within the STRING mouse interactome in Cytoscape. The STRING network contained 1030 nodes connected by 7860 edges; node size was scaled to logFC by RNA-seq. To interrogate functional programs, we applied a community clustering algorithm (GLay) to the network using clusterMaker. This generated 12 network hubs of interacting proteins; we used STRING functional enrichment to identify enriched annotations for each hub.
Quantification and statistical analysis Statistical analyses were carried out using GraphPad Prism software version 8. 2. 0 (GraphPad Software Inc. ). Sample sizes for in vivo studies were determined based on the variability of pancreatic tumor models used. For flank transplant and autochthonous drug and tamoxifen studies, tumor-bearing animals within each group were randomly assigned to treatment groups. Experimental group sizes were determined based on previous studies  7, 8. Data are shown as the mean ± SEM. Two-tailed unpaired Student's t-tests with Welch's correction or 1-way or 2-way analysis of variance (ANOVA) for multiple comparisons when appropriate were used to determine statistical significance; p values were adjusted for multiple comparisons in the case of analysis by ANOVA. The level of replication for each in vitro and in vivo study is noted in the figure legends for each figure and described in detail above.
Acknowledgements We are grateful to  Christopher Wright  for providing the Ptf1a-Cre mice,  Dieter Saur  for providing FSF-Kras G12D/+ ; p53 frt/frt ; Pdx-Flp mice, the  UCSD Lipidomics Core  for GC-MS analysis of free fatty acids and the  UCSD Tissue Technology Shared Resource  for processing of tissues for histological analysis. L. P. F. received support from  T32 GM007752  and a  Ruth L. Kirschstein National Research Service Award   F31 CA247489. M. L. M. received support from  T32 GM007752. N. R. received support from the  Tobacco- Related Disease Research Program   T29FT0280. M. N. received support from an  AACR-Bayer START  Award  20-40-44-NAKA. M. H. received support from  T32 HL086344  and the  Tobacco- Related Disease Research Program   T32FT5133. J. G. received support from  T32 CA009370  and a  Ruth L.
Kirschstein National Research Service Award   F32 GM128377. D. C. H. received support from an  American Cancer   Society Scholar Award  and the  Pew-Stewart Scholars Program for Cancer Research. This work was also supported by  R01 CA186043  to A. M. L. and T. R. ,  R35 CA197699  to T. R. , and a  SU2C-CRUK-Lustgarten Foundation Pancreatic Cancer Dream Team Research Grant  ( SU2C-AACR-DT-20-16 ) to A. M. L. and T. R.
Data availability The KP f/f C RNA-seq and H3K27-acetyl, H3K4me, H3K4me3, ARID1A, KLF5, FOXA1 and SMARCA4 ChIP-seq data generated in this study have been deposited in the GEO database under accession code GSE168490. The processed RNA-seq data are available also available under accession code GSE168490. The IP-MS data generated in this study have been deposited in MassIVE under accession MSV000090744 (https://massive. ucsd. edu/ ProteoSAFe/dataset. jsp?task=63becd806f68431c90cb6455e5e7ec12) and in the ProteomeXchange dataset under the accession number PXD038216. The publicly available mouse Msi2+ and Msi2-primary KP f/f C RNAseq data  8  used in this study are available in the GEO database under accession code GSE114906. The publicly available human PDAC scRNA-seq data  40  used in this study are available at the Genome Sequence Archive under accession code GSA: CRA001160.
The MS raw files were used for peptides and proteins identification employing the Protein Pilot software (version 5. 0. 1, Sciex) and the bovine-specific Uniprot database (https://www. uniprot. org/). The false discovery rate (FDR) was set to 1% for both peptides and proteins. For relative quantification of peptides and proteins, 4 μL of peptides from each individual sample were analysed using a data-independent acquisition (IDA) method, known as SWATH (Sequential Window Acquisition of all Theoretical Mass Spectra)-MS method. Samples were analysed using the LC-MS equipment and LC gradient. A total of samples from 6 treatments (6 biological replicates per group) were analysed. The data extraction from the SWATH runs was performed by PeakView v. 2. 2 (Sciex, Framingham, MA, USA) employing the SWATH Acquisition MicroApp v.
Statistical analysis SWATH-MS analysis allowed the identification and quantification of 684 proteins and 1908 peptides at an FDR of 1%. Data processing and statistical analysis were performed using MetaboAnalyst platform (https://www. metaboanalyst. ca/). Proteins with >50% missing values were removed. For the remaining proteins, missing values were estimated using the k-nearest neighbour (KNN) algorithm. Data was normalized using a log 10 transformation and Pareto scaling approach. Pairwise (two groups) and multiple (three groups) comparisons were performed to analyse the proteomic data. In the case of pairwise comparisons, a volcano plot with a 1. 5-fold change (FC) and P-value of 0. 05 was used. For multiple comparisons, a partial least square discriminant analysis (PLS-DA) with fixed VIP (Variable Importance in Projection) threshold ≥2 was employed.
Bioinformatics analysis To benefit from the most complete annotation available, the bovine Uniprot IDs of the 684 proteins were converted into the orthologous human EntrezGeneID  (Gagaoua et al. , 2021) , using the UniprotKB database (https://www. uniprot. org/). Both bovine and human Uniprot IDs were indexed in the databases and used for bioinformatics analysis. Metascape® tool (https://metascape. org/) was used to perform the Gene Ontology (GO) enrichment analysis and render the most significant and enriched GO terms, which allowed exploring the potential functions and biological processes of the differential abundant proteins (DAPs) identified in the proteome of dry-aged beef. Bioinformatics analysis were performed as described by  Gagaoua, Terlouw, et al. (2021)  with some brief modifications.