dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGEMENTS We thank  Ahmed Haouz  and the staff from the  Institut Pasteur protein crystallogenesis facility  for their help with crystallization trials,  Fabrice Agou  and the staff from the  Chemogenomic and Biological Screening Platform  for access to the MALS equipment, the staff of the PX1 and PX2 beamlines at synchrotron SOLEIL ( St Aubin, France ) for beamline support, and  Andreas Herrmann ,  François Bontems ,  Gleyder Roman Sosa ,  Niels Volkmann  and  Dorit Hanein  for discussion. We acknowledge support from the  National French Research Agency (ANR , grant  ANR-18-CE11-0011 ), The  French "Fondation pour la Recherche Médicale" (FRM) ,  Labex IBEID  (grant  ANR-10-LABX-62-IBEID ),  Infect-ERA IMI European network program  " HantaHunt", Institut Pasteur , and  CNRS.
DATA AND CODE AVAILABILITY The atomic coordinates and structure factors of the X-ray structures have been deposited in the Protein Data Bank (www. pdb. org) with accession codes 6Y5W, 6Y5F, 6Y62, 6Y68, 6Y6Q, 6Y6P, 6YRB, and 6YRQ. The cET map has been deposited in the Electron Microscopy Data Bank (www. ebi. ac. uk/pdbe/emdb/) with accession number EMD-11236. Atomic coordinates of the tetrameric spike quasi-atomic model have been deposited in the Protein Data Bank with the accession code ZJM. The previously reported "inverted" map EMD-3364 has been redeposited with accession code EMD-4867. The scripts used to process the cryo-ET data are available at https://github. com/OPIC-Oxford/TomoPreprocess (TomoPreprocess) and https://github. com/OPIC-Oxford/PatchFinder (PatchFinder).
Conclusions Multitrait genetic signals can be decomposed into distinct pleiotropy profiles that reveal consistent with pathways databases and random control trials. We propose this method for the mapping of unannotated SNPs to putative pathways. Main Genome-wide association studies (GWAS) have identified thousands of significant genetic associations for multiple traits and diseases 1. Publicly available summary statistics from these GWAS have proven invaluable in human genetic studies, enabling a range of secondary analyses without requiring individual-level genotype data and thus, averting major practical and ethical issues 2. Among others, the estimation of phenotype heritability 3 , the derivation of polygenic risk score 4 , and the assessment of causal relations between phenotypes 5 are paragons of their critical utility.
GWAS summary statistics have also been extremely useful to investigate pleiotropy and the genetic relationship between human phenotypes. For example, recent works assessed whether significant loci for a given phenotype are also associated with other traits 6,7 while others estimated genome-wide 8,9 and regional 10 genetic correlations among phenotypes. The joint analysis of multiple traits is also an efficient way to detect variants missed by univariate screening 11-23 , especially variants with association patterns that deviate from the observed phenotypic correlation 24-26. Nevertheless, while simulation studies and examples from real data applications in best case scenarios have confirmed the relevance of multitrait association tests, there have seldom been applied to large-scale dataset.
Here, we argue that, besides the detection of new associated variants, multitrait GWAS summary statistics analysis offers a powerful framework to decipher the complex inter and intra-phenotype genetic architecture. We performed series of analyses on GWAS summary statistics from 36 phenotypes categorized into five clinically relevant sets (Immunity, Anthropometry, Metabolism, Cardiovascular and Brain) that demonstrate how such data can be used to reveal potential genetic pathways and their links to diseases. First, characterizing and comparing the relative performances of alternative multitrait association models, we found strong specificity of the signal identified by each approach, both in terms of association patterns and expressed tissue enrichment. We then used a Gaussian mixture model on the phenotypes by variants association matrix to identify potential clusters of variants displaying similar genetic multitrait association profiles.
Finally, we checked the 392 associations identified by the multitrait test only in this data against previously reported associations from the GWAS catalogue 1 for the same phenotypes. Altogether, we report a total of 322 new associations (Tables  S4 to S10 ). To understand further the relative performance of those three tests (omnibus, sumZ ica , sumZ g ) along the univariate test, we explored which multitrait signal was associated with the largest increase in detection per test. For that aim, we listed all loci found associated with at least one of the four approaches, and assigned each locus to a test based on the lowest p-value. We then derived the median chi-squared z-score by phenotype across the loci assigned to each test. As showed in Fig. 2B-H , the median pattern varied substantially across tests and phenotype sets.
Biological meaning of genetic clusters These distinct multitrait association profiles might arise because their variants belong to distinct genetic functional groups. Understanding whether those genetic functional groups are only statistical construction or correspond to meaningful biologically mechanism is critical. In the latter, it means that data-driven approach, such as the one proposed in the present study, can be used to dissect the genetic contribution of many complex human phenotypes. To assess this hypothesis, we conducted series of in silico functional analyses with the objective of mapping clusters to candidate biological functions. For each phenotype set, we evaluated two types of enrichment: tissue-specific chromatin mark enrichment per cluster (Table  S13 ), and pathway enrichment framework (Tables  S14  and S15 ) which integrates multiple databases such as Gene Ontology (GO) and KEGG.
56. Additionally to these well-known drugs, we provide a systematic listing of potential drug targets by cluster (Table  S20 ) based on the druggable genome database 57. Altogether, those results suggest that drug development might be more effective by accounting for the gene context, i. e. by selecting candidate gene not from their individual feature, but based on the disease association trend of genes displaying similar multitrait association profile. Under this working hypothesis, the proposed inference of genetic functional groups can provide a means to identify those genes and therefore to select potential candidates.
To compute the weight vector ‫ܟ‬ of the SumZ ica , for a given phenotype set, the genome wide Z-score matrix was extracted and an independent component analysis was performed with the scikit-learn python package. The component yielding the most novel association was selected as loadings. We verified that this selection procedure did not lead to an inflation under the null hypothesis by simulation (see Fig. S2 ). Performing the omnibus test requires inverting the Z-score covariance matrix ୰. When this matrix does not have a full rank, we use a pseudo inverse of the matrix based on the singular value decomposition (Supplementary Note). Briefly, as ୰ is a variance-covariance matrix, it can be written ‫۾۲۾‬ ‫ܜ‬ where ۲ ൌ ݀݅ܽ݃ሺሺߣ ሻ ୀଵ… ሻ, ሺߣ ሻ ୀଵ… are the eigenvalues of ୰ and ‫۾‬ is the orthogonal matrix whose columns correspond to the eigenvectors of ୰.
Multitrait genetic association clustering and selection of the optimal number of clusters We performed a clustering of top associated SNPs for each phenotype set using a Gaussian Mixture model (GMM). One major difficulty in applying the GMM was to deal with incomplete data. Indeed, even after imputation of some missing statistics, our datasets still contained some missing values. To solve the clustering, we implemented the statistical framework described by Ghahramani et al 81 which we recently implemented in a R package MGMM.
Functional enrichment of metabolism clusters We used FUMA 85 SNP2GENE function to associate SNPs with genes based on two criteria, the physical position (in 30kb radius of a protein coding gene) and eQTLs (all significant cis-eQTL from GTEx up to a distance of 1Mb). Note that we restrained the eQTLs to the one that were found in relevant tissue for the Immunity and Metabolism set: immune cells for Immunity and adipose, intestine, liver and brain tissues for Metabolism (see Supplementary Data 1 for complete parameters). After chaining genes to clusters based on SNPs, we performed a functional enrichment for pathways defined in KEGG 86 and GO 87 databases and derived report p-values using FUMA GENE2FUNC function. Here, cluster's gene were compared against a background of protein coding genes.
Figures Figure 1. Analysis overview The diagram below presents the overall analysis pipeline. A total of 36 GWAS were included covering several common diseases and quantitative traits. All GWAS summary statistics went through extensive pre-processing and quality control filtering, and missing single SNP statistics were imputed when possible. Multitrait approaches were then applied to all clean GWAS data and on each clinically based set (All, Immunity, Metabolism, Brain, Cardiovascular, Anthropometry, and Composite). After combining univariate and multivariate results, and merging SNPs within locus, a total of 6,767 associations were identified. After a comparison of results per approach, a clustering analysis was performed for variants within each set. Finally, we performed in-silico functional analysis of the clusters derived in the Metabolism set to assess their biological relevance.
, 2013). This analysis showed that TFIIIC binding to AEs caused a decreased in the overall nucleosome occupancy or stability around TFIIIC-bound regions (Figure  3C ). In addition, TFIIIC-bound AEs had a single or dimeric nucleosome positioned over the peak's center corresponding to the AE itself  (Figure 3C) , in agreement with a previous report  (Tanaka et al. , 2010). Notably, the central AE nucleosome signal was significantly reduced in serum-starved cells (Figure  3C  and selected exam-ples in Figure  3D ), concurring with the increased H3K18ac signal at these sites (Figure  3B ). All the results point to an in vivo role of TFIIIC in relieving chromatin-mediated repression by increasing H3K18ac and decreasing nucleosome stability at AEs. To further support this idea, we interrogated the NucMap database  (Zhao et al.
, 2019)  and used published dataset of micrococcal nuclease (MNase) digestion in T47D to plot the nucleosome profile over the AEs bound by TFIIIC in serumstarved cells. In agreement with our ATAC-seq data, these regions showed a precise pattern of nucleosome occupancy with the center of the AE barring a mono-or di-nucleosome (Figure  S3D ). The difference in the relative nucleosome density of the AE's nucleosome(s) observed between ATAC-seq and MNase digestion could simply reflect the higher accessibility to the two nucleosome free regions surrounding the AE's nucleosome(s) of the MNase enzyme ($16 kDa) compared to the Tn5 transposase ($100 kDa). Despite the differences between the two experimental approaches, the detection of a nucleosome over the AE was clear, and therefore we conclude that the H3K18ac profile (Figure  3B ) is compatible with the presence of a nucleosome at AEs bound by TFIIIC in T47D.
Remarkably, the analysis of published cancer data shows that expression of the genes regulated by TFIIIC-directed H3K18ac in response to SS (TFIIIC-associated promoters and TFIIIC-activated genes) is predictive of the clinical outcome of breast cancer patients (Figures  S6E  and S6G ) and that H3K18ac levels also correlate with cancer prognosis  (Seligson et al. , 2005; Tasselli et al. , 2016). Thus, our study points to TFIIIC and its HAT activity as potential new targets for cancer management. 1G  and S1I  were performed by adding DMEM supplemented with 10% FBS to serum-starved cells for 30 min. For siRNA-treated cells, serum re-exposure was performed by adding RPMI supplemented with 10% charcoal-treated FBS to the serum-starved cells at the indicated times.
STAR+METHODS For cell cycle profiling, cells were fixed with ethanol and DNA was stained with propidium iodide. Labeled cells were analyzed with a LSF II flow cytometer (Becton Dickinson) using the FACS Diva Software v6. 1. 2 (Becton Dickinson). The cell cycle profile was determined with the program ModFit v3. 2 (BD Bioscience).
Differences in gene expression were calculated by using a DESeq. R script for RNA analysis. The script is provided as a downloadable link with the manuscript (https://www. dropbox. com/s/026pc48kfuqr88g/RNA_analysis_deseq. 2. R?dl=0). Genes with fold change (FC) ± 1. 5 (p value < 0. 05; FDR < 0. 01) were considered as significantly regulated. Sitepro profiles were generated with the script, provided in the CEAS package.
, 2014)  were both used with default parameters to detect GO terms enrichments and association of peaks with genes, respectively. Lists of genome coordinates derived from downstream analysis of ChIP-seq data were analyzed with ChIP-seq Enrich. Parameters used: locus definition (Nearest TSS), Enrichment Method (Chip-Enrich), filter (2000 genes), adjust for the mappability of the gene locus regions (False). Gene symbols lists resulted from ChIP-seq Enrich analysis and RNA-seq downstream analysis were used as input for DAVID GO (default parameters) to generate Figure  S1D  and Figure  S4K. For all DAVID GO analysis the Bonferronicorrected p values were reported. ChIP-seq Enrich also generated a GO analysis that was used to corroborate DAVID GO terms enrichment reported in Figure  S1D  (data not shown).
External Data Sources ChIP-seq of CTCF in T47D in the absence of serum was taken from GEO: GSE53463 (Le  Dily et al. , 2014). ChIP-seq of eGFP-ADNP in K562 cells was from GEO: GSE105573 (Consortium, 2012). ChIP-seq of mouse Adnp was taken from GEO: GSE97945  (Ostapcuk et al. , 2018). T47D ATAC-seq in normal growth conditions (+S) was taken from GEO: GSM2241147, GEO: GSM2241148  (Toska et al. , 2017). ChIP-seq of p300 in T98G cells grown in normal conditions or in the absence of serum was from GEO: GSE21026  (Ramos et al. , 2010).
Hi-C library quality was assessed through ClaI digestion and low-coverage sequencing on an Illumina NextSeq500 instrument, after which every technical replicate (n = 2) of each biological replicate (n = 2) was sequenced at high coverage on an Illumina HiSeq2500 instrument. Data from technical replicates were pooled for downstream analysis. We sequenced > 18 billion reads in total to obtain 0. 78-1. 21 billion valid interactions per time point per biological replicate. In Situ Hi-C Data Processing and Normalization Hi-C data were processed by using an in-house pipeline based on TADbit and OneD algorithms  (Serra et al. , 2017; Vidal et al. , 2018). First, the quality of the reads was checked with FastQC to discard problematic samples and detect systematic artifacts. Trimmomatic  (Bolger et al.
These steps were repeated for each read in the input FASTQ files. Multiple fragments from a single uniquely mapped read resulted in a number of contacts identical to the number of possible pairs between the fragments. For example, if a single read was mapped through three fragments, a total of three contacts (all-versusall) was represented in the final contact matrix. We used the TADbit filtering module to remove non-informative contacts and to create contact matrices. The different categories of filtered reads applied were: 1. Self-circle: reads coming from a single restriction enzyme (REnz) fragment and pointing to the outside. 2. Dangling end: reads coming from a single REnz fragment and pointing to the inside. 3. Error: reads coming from a single REnz fragment and pointing in the same direction.
Auto gain control (AGC) was set to 106, dynamic exclusion was set at 60 s, and the charge-state filter disqualifying singly charged peptides for fragmentation was activated. Following each survey scan, the 10 most intense ions with multiple charged ions above a threshold ion count of 5000 were selected for fragmentation at normalized collision energy of 35%. Fragment ion spectra produced via collision-induced dissociation were acquired in the linear ion trap, AGC was set to 3$104 and isolation window of 2. 0 m/z, activation time of 30 ms, and maximum injection time of 250 ms were used. All data were acquired with Xcalibur software v2. 2. Acquired data were analyzed using the Proteome Discoverer software suite (v1. 4, Thermo Fisher Scientific), and the Mascot search engine (v2. 5, Matrix Science) was used for peptide identification.
Data were searched against the human protein database derived from the SwissProt database plus common contaminants  (April 2016; 20,200 sequences). A precursor ion mass tolerance of 7 ppm was used, and up to three missed cleavages were allowed. The fragment ion mass tolerance was set to 0. 5 Da, and oxidation (M), and acetylation (Protein N-term) were defined as variable modifications, whereas carbamidomethylation (C) was set as fixed modification. The identified peptides were filtered by FDR < 0. 01 (1%). For the assessment of protein-protein interactors, we used the Significance Analysis of INTeractome (SAINT) software  (Choi et al. , 2011) , using rabbit IgG as negative controls. Experiments were performed with samples in triplicate (see Table  S1  for results).
DATA AND CODE AVAILABILITY The accession numbers for the raw sequencing and mass spectrometry data reported in this paper are NCBI GEO: GSE120162 and PRIDE (https://www. ebi. ac. uk/pride/archive/): PXD011250. Original western blots and Coomassie gels were deposited in Mendeley Data and are available at DOI: http://dx. doi. org/10. 17632/mzjf96t3gc. 5. Custom scripts for data analysis are available upon request, other tools used are indicated in the Key Resources Table and the respective STAR Methods sections. Processed data used for analyses in this manuscript are included as Tables  S1, S2 , and S3.
Note that GTF3C1 immunopurification via the FLAG-tag pulled down the whole TFIIIC complex (lane 1). ADNP co-purifies with TFIIIC (lane 3). GTF3C5 fractionates on top of the heavy IgG chain (IgG H ). Right panel: Coomassie staining of anti-HA immunopurification of lysates expressing FLAG-TFIIIC alone (lane 4), HA-ADNP alone (lane 5), or both together (lane 6). The presence of the TFIIIC subunits is detected in the ADNP immuno-complexes. The position of the IgG heavy and light chains (IgG H and IgG L , respectively) is indicated. The identity of the bands was confirmed by mass spectrometry and western blotting with HA and GTF3C2 antibodies (FigureS2A). (C) Heatmap of ADNP-eGFP binding in K562 cells (GEO: GSE105573) spanning ±1 kb across all TFIIIC-bound AEs in T47D, ranked from high to low ADNP-eGFP enrichment.
Color bar scale with increasing shades of color stands for higher enrichment (-log10 of the Poisson p value). (D) Venn diagram with the number of TFIIIC peaks upon ADNP depletion (siADNP, yellow) compared to control cells (siCTRL, blue) in serum-starved T47D cells. (E) Sitepro profile of TFIIIC occupancy in control (siCTRL) and ADNP-depleted cells (siADNP) in serum-starved T47D cells. The graphs are plotted over the summit of TFIIIC peaks in the siCTRL cells (p value, two-tailed unpaired t test). ADNP depletion levels are shown in Figures S2E and S2F. (F) Genome browser view of representative TFIIIC-associated genes FEM1A and HELLS showing TFIIIC occupancy in ADNP-depleted (siADNP) and T47D control cells (siCTRL). ADNP binding from GEO: GSE105573 is represented by the blue track (eGFP-ADNP).
ACKNOWLEDGMENTS We thank all the members of the  Beato lab , the  CRG Gene Regulation ,  Stem Cells , and  Cancer Program , the  CRG Genome and Proteomic Facilities , the  4D Genome Unit (Synergy Program ),  Jose Luis Villanueva (CRG) ,  Yasmina Cuartero  (CRG), and  Prof. Simone Ottonello  ( University of Parma, Italy ) for invaluable insight and help. This work was supported by the  Spanish Ministry of Economy and Competitiveness Centro de Excelencia Severo Ochoa   2013-2017 SEV-2012-0208  (to CRG) and  BFU2016-76141-P  (to S. d. l. L. ); ACER (to CRG);  Italian Association for Cancer Research  ( AIRC , grant  IG16877  to G. D. ); the  Cancer Research UK Programme Foundation  ( CR-UK C47547/ A21536  to A. V. ); a  Wellcome Trust   Investigator Award  ( 200818/Z/16/Z  to A. V. ); and the  European Research Council (ERC)  under the European Union's  Seventh Framework Programme  (FP7/2007-2013/ ERC Synergy  grant agreement  609989-4DGenome  to M.
Requests for reagents, cell lines generated in this study and resource sharing should be addressed to and will be fulfilled by the lead contact, Miguel Beato (miguel. beato@crg. eu). All unique/stable reagents generated in this study are available from the Lead Contact without restriction. STAR+METHODS Continued Continued REAGENT or RESOURCE REAGENT or RESOURCE KEY RESOURCES TABLE L-Glutamine 200 mM 100x Proteomic data: PRIDE SOURCE SOURCE GIBCO This study IDENTIFIER IDENTIFIER 25030-024 PXD011250 Penicillin-Streptomycin Mendeley GIBCO This study 15140-122 https://doi. org/10. 17632/mzjf96t3gc. 3 Detailed methods are provided in the online version of this paper and include the following: REAGENT or RESOURCE SOURCE Human Insulin (Humulin regular) Lilly Experimental Models: Cell Lines EGF SIGMA T47D ATCC Antibodies rabbit polyclonal antibody anti-GTF3C2 This paper Hydrocortisone SIGMA T98G ATCC mouse monoclonal antibody anti-GTF3C2 Abcam Horse Serum Life technologies IMR90 ATCC rabbit polyclonal antibody anti-BDP1 Wang and Roeder, 1997; Weser et al.
T98G cells (ATCC: CRL-1690) were grown in DMEM with 10% FBS (referred as +S in the Figures); for serum GIBCO 42401-018 Thermo Fisher EI0001 RPMI No Phenol Red Deposited Data starvation experiments, cells were cultured in DMEM with 0. 1% FBS for 48 h (referred as -S in the Figures). IMR90 fibroblasts (ATCC: GIBCO 32404-014 DMEM Phenol Red DMEM Phenol Red DMEM/F12 Phenol Red DMEM/F12 No Phenol Red ChIP-seq, RNA-seq, Hi-C data CCL-186) were grown in EMEM with 10% FBS (referred as +S in the Figures); for serum starvation experiments, cells were cultured in GIBCO 41965-039 GIBCO 21063-029 GIBCO 11330-032 GIBCO 11039-021 This study GSE120162 Human reference genome NCBI build 38, GRCh38 Genome Reference Consortium the absence of FBS for 16 h (referred as -S in the Figures). MCF10A (ATCC: CRL-10317) were cultured in DMEM/F12 supplied with https://www.
ncbi. nlm. nih. gov/projects/ 20 ng/ml epidermal growth factor, 0. 5 mg/ml hydrocortisone, 10 mg/ml insulin, and 5% horse serum (with the addition of 100 ng/ml genome/assembly/grc/human/ CTCF ChIP-seq of T47D in -S Le Dily et al. , 2014 cholera toxin) (referred as +S in the Figures); for serum starvation experiments, cells were grown in RPMI supplemented with 10% GSE53463 charcoal-treated FBS for 48 h and in the absence of serum for 16 h (referred as -S in the Figures). All cultures were maintained EMEM Phenol Red ChIP-seq of eGFP-ADNP in K562 cells GIBCO Consortium, 2012 31095-029 GSE105573 EMEM No Phenol Red ChIP-seq of mouse Adnp GIBCO Ostapcuk et al. , 2018 51200-038 GSE97945 Fetal Bovine Serum Gene expression data of breast cancer samples GIBCO Ringner et al. , 2011 10270-106 http://co.
bmc. lu. se/gobo/ Fetal Bovine Serum, charcoal stripped EP300 ChIP-seq in T98G GIBCO Ramos et al. , 2010 12676029 GSE21026 0. 5% Trypsin-EDTA 1x GIBCO 25300-054 (Continued on next page) e1 Molecular Cell 77, 475-487. e1-e11,February 6, 2020 (Continued on next page)Molecular Cell 77, 475-487. e1-e11, February 6, 2020 e2 with antibiotics (100 u/ml penicillin, 100 mg/ml streptomycin). Experiments of serum re-exposure in Figures.