dataesr/scientific-paragraphs-categorization huggingface dataset:
As a post hoc comparison, we obtained CRC summary GWAS statistics with UK Biobank excluded and repeated MR analyses of adiposity for CRC risk. Estimates were largely consistent with or without the inclusion of UK Biobank data (Additional file 1: Table  S8 , B). Our sex-specific MR investigations were confined to effects of adiposity on overall CRC because sex-specific GWAS were unavailable for sitespecific CRC and metabolite outcomes. Sex-stratified GWAS of such outcomes would enable these in the future.
Acknowledgements ASTERISK: We are very grateful to  Dr. Bruno Buecher  without whom this project would not have existed. We also thank all those who agreed to participate in this study, including the patients and the healthy control persons, as well as all the physicians, technicians, and students. COLON and NQplus: The authors would like to thank the  COLON and NQplus investigators at Wageningen University & Research  and the involved clinicians in the participating hospitals. CCFR: The Colon CFR graciously thanks the generous contributions of their 42,505 study participants, dedication of study staff, and the financial support from the  US National Cancer Institute , without which this important registry would not exist. CORSA: We kindly thank all those who contributed to the screening project Burgenland against  CRC.
The MCCS was further supported by Australian  NHMRC  grants  509348 ,  209057 ,  251553 , and  504711  and by infrastructure provided by  Cancer Council Victoria. Cases and their vital status were ascertained through the  Victorian Cancer Registry (VCR)  and the  Australian Institute of Health and Welfare (AIHW) , including the  National Death Index  and the Australian Cancer Database. MEC:  National Institutes of Health  ( R37 CA54281 ,  P01 CA033619 , and  R01 CA063464 ). MECC: This work was supported by the  National Institutes of Health, US Department of Health and Human Services  ( R01 CA81488  to SBG and GR). MSKCC: The work at Sloan Kettering in New York was supported by the  Robert and Kate Niehaus Center for Inherited Cancer Genomics  and the  Romeo Milio Foundation. Moffitt: This work was supported by funding from the  National Institutes of Health  (grant numbers  R01 CA189184 ,  P30 CA076292 ),  Florida Department of Health Bankhead-Coley  Grant  09BN-13 , and the  University of South Florida Oehler Foundation.
The authors wish to acknowledge the contribution of  Alexandre Belisle  and the genotyping team of the  McGill University and Génome Québec Innovation Centre, Montréal, Canada , for genotyping the Sequenom panel in the NFCCR samples. Funding was provided to Michael O. Woods by the  Canadian Cancer Society Research Institute. NSHDS: Swedish Cancer Society ;  Cancer Research Foundation in Northern Sweden; Swedish Research Council; J C Kempe Memorial Fund ;  Faculty of Medicine, Umeå University, Umeå, Sweden ; and  Cutting-Edge Research Grant  from the  County Council of Västerbotten, Sweden. OFCCR: The  Ontario Familial Colorectal Cancer Registry  was supported in part by the  National Cancer Institute (NCI)  of the  National Institutes of Health (NIH)  under award  U01 CA167551  and award  U01/U24 CA074783  (to SG).
Additional funding for the OFCCR and ARCTIC testing and genetic analysis was through a  Canadian Cancer Society CaRE  ( Cancer Risk Evaluation) program  grant and  Ontario Research Fund  award  GL201-043  (to BWZ), through the  Canadian Institutes of Health Research  award  112746  (to TJH), and through generous support from the  Ontario Ministry of Research and Innovation. OSUMC: OCCPI funding was provided by  Pelotonia , and HNPCC funding was provided by the  NCI  ( CA16058  and  CA67941 ).
EEV is supported by  Diabetes UK  ( 17/0005587 ) and the  World Cancer Research Fund (WCRF UK) , as part of the  World Cancer Research Fund International  grant programme (IIG_2019_2009). GDS works in a unit funded by the  UK Medical Research Council  ( MC_UU_00011/1 ) and the  University of Bristol. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Where authors are identified as personnel of the  International Agency for Research on Cancer/World Health Organization , the authors alone are responsible for the views expressed in this article and they do not necessarily represent the decisions, policy, or views of the  International Agency for Research on Cancer/World Health Organization. Consortia funding:  Genetics and Epidemiology of Colorectal Cancer Consortium (GECCO) :  National Cancer Institute, National Institutes of Health, US Department of Health and Human Services  ( U01 CA164930 ,  U01 CA137088 ,  R01 CA059045 ,  R21 CA191312 ).
ASTERISK: A  Hospital Clinical Research Program  ( PHRC-BRD09/C ) from the  University Hospital Center of Nantes (CHU de Nantes ) and supported by the  Regional Council of Pays de la Loire , the  Groupement des Entreprises Françaises dans la Lutte contre le Cancer (GEFLUC) , and the  Association Anne de Bretagne Génétique  and the  Ligue Régionale Contre le Cancer (LRCC). The  ATBC Study  is supported by the  Intramural Research Program  of the  US National Cancer Institute, National Institutes of Health , and by  US Public Health Service  contract  HHSN261201500005C  from the  National Cancer Institute, Department of Health and Human Services. CLUE II: This research was funded by the  American Institute for Cancer Research  and the  Maryland Cigarette Restitution Fund at Johns Hopkins , the  NCI  ( U01 CA86308 ,  P30 CA006973  to  W.
The  CCFR Set-3 (Affymetrix Axiom CORECT Set  array) was supported by  NIH  award  U19 CA148107  and  R01 CA81488  (to SBG). The CCFR Set-4 (Illumina OncoArray 600 K SNP array) was supported by  NIH  award  U19 CA148107  (to SBG) and by the  Center for Inherited Disease Research (CIDR) , which is funded by the  NIH  to the  Johns Hopkins University , contract number  HHSN268201200008I. Colon Cancer Family Registry (CCFR) : The content of this manuscript does not necessarily reflect the views or policies of the  NIH  or any of the collaborating centers in the CCFR, nor does mention of trade names, commercial products, or organizations imply endorsement by the  US government , any cancer registry, or the CCFR. COLON: The COLON study is sponsored by  Wereld Kanker Onderzoek Fonds , including funds from grant  2014/1179  as part of the  World Cancer Research Fund   International Regular Grant Programme , by  Alpe d'Huzes and the Dutch Cancer Society  ( UM 2012-5653 ,  UW 2013-5927 ,  UW2015-7946 ), and by  TRANSCAN  ( JTC2012-MetaboCCC ,  JTC2013-FOCUS ).
The Nqplus study is sponsored by a  ZonMW investment grant  ( 98-10030 ); by  PREVIEW , the project  PREVention of diabetes through lifestyle intervention and population studies in Europe and around the World (PREVIEW ) project which received funding from the  European Union Seventh Framework Programme  ( FP7/ 2007-2013 ) under grant no. 312057 ; by funds from  TI Food and Nutrition  (cardiovascular health theme), a public-private partnership on precompetitive research in food and nutrition; and by  FOODBALL , the  Food Biomarker Alliance , a project from  JPI Healthy Diet for a Healthy Life. Colorectal Cancer Transdisciplinary (CORECT)  Study: The  CORECT Study  was supported by the  National Cancer Institute, National Institutes of Health (NCI/ NIH) ,  US Department of Health and Human Services  (grant numbers  U19 CA148107 ,  R01 CA81488 ,  P30 CA014089 ,  R01 CA197350 ,  P01 CA196569 ,  R01 CA201407 ), and  National Institutes of Environmental Health Sciences, National Institutes of Health  (grant number  T32 ES013678 ).
CORSA: " Österreichische Nationalbank Jubiläumsfondsprojekt " (12511) and  Austrian Research Funding Agency (FFG)  grant  829675. CPS-II: The  American Cancer Society  funds the creation, maintenance, and updating of the  Cancer Prevention Study-II (CPS-II ) cohort. This study was conducted with Institutional Review Board approval. CRCGEN:  Colorectal Cancer Genetics & Genomics, Spanish study  was supported by  Instituto de Salud Carlos III , co-funded by  FEDER  funds-a way to build Europe-(grants  PI14-613  and  PI09-1286 ),  Agency for Management of University and Research Grants (AGAUR) of the Catalan Government  (grant  2017SGR723 ), and  Junta de Castilla y León  (grant  LE22A10-2 ). The sample collection of this work was supported by the  Xarxa de Bancs de Tumors de Catalunya  sponsored by  Pla Director d'Oncología de Catalunya (XBTC) ,  Plataforma Biobancos   PT13/0010/0013 , and ICOBIOBANC, sponsored by the  Catalan Institute of Oncology.
EPICOLON: This work was supported by grants from  Fondo de Investigación Sanitaria/FEDER  ( PI08/0024 ,  PI08/1276 ,  PS09/02368 ,  P111/00219 ,  PI11/00681 ,  PI14/00173 ,  PI14/00230 ,  PI17/00509 ,  17/00878 ,  Acción Transversal de Cáncer) ,  Xunta de Galicia  ( PGIDIT07PXIB9101209PR ),  Ministerio de Economia y Competitividad  ( SAF07-64873 ,  SAF 2010-19273 ,  SAF2014-54453R ),  Fundación Científica de la Asociación Española  contra el Cáncer ( GCB13131592CAST ),  Beca Grupo de Trabajo "Oncología" AEG  ( Asociación Española de Gastroenterología ),  Fundación Privada Olga Torres ,  FP7   CHIBCHA  Consortium,  Agència de Gestió d'Ajuts Universitaris i de Recerca (AGAUR, Generalitat de Catalunya ,  2014SGR135 ,  2014SGR255 ,  2017SGR21 ,  2017SGR653 ),  Catalan Tumour Bank Network  ( Pla Director d'Oncologia, Generalitat de Catalunya ),  PERIS  ( SLT002/ 16/00398 ,  Generalitat de Catalunya) ,  CERCA Programme  ( Generalitat de Catalunya ), and  COST Action   BM1206  and  CA17118.
CIBERehd is funded by the  Instituto de Salud Carlos III. ESTHER/VERDI. This work was supported by grants from the  Baden-Württemberg Ministry of Science, Research and Arts and the German Cancer Aid. Harvard  cohorts (HPFS, NHS, PHS): HPFS is supported by the  National Institutes of Health  ( P01 CA055075 ,  UM1 CA167552 ,  U01 CA167552 ,  R01 CA137178 ,  R01 CA151993 ,  R35 CA197735 ,  K07 CA190673 , and  P50 CA127003 ), NHS by the  National Institutes of Health  ( R01 CA137178 ,  P01 CA087969 ,  UM1 CA186107 ,  R01 CA151993 ,  R35 CA197735 ,  K07CA190673 , and  P50 CA127003 ), and PHS by the  National Institutes of Health  ( R01 CA042182 ). Hawaii Adenoma Study :  NCI  grants  R01 CA72520. HCES-CRC: The  Hwasun Cancer Epidemiology Study-Colon and Rectum Cancer (HCES-CRC ; grants from  Chonnam National University Hwasun Hospital ,  HCRI15011-1 ).
Data Normalization We performed read count normalization using a size factor calculated for each sample according to the RLE (Relative Log Expression) method implemented in the R package DESeq2  (Love et al. , 2014). To identify expressed genes among different RNAseq samples coming from different batches of sequencing (strain IPO-323), we used Fragments Per Kilobase of transcript per Million mapped reads (FPKM) normalization. Coding Variant Calling From RNA-Sequencing SNP calling for each RNA-sequencing sample was performed using GTAK GenomeAnalysisTK. jar with the following parameters: -T SplitNCigarReadsrf ReassignOneMappingQuality -RMQT 60 -U ALLOW_N_CIGAR_READS and GenomeAnalysisTK. jar -T HaplotypeCaller -dontUseSoftClippedBases -stand_call_conf 20  (DePristo et al. , 2011). Custom R scripts were used to compare coding SNPs among samples.
Natural Variation of Gene Expression Level Among Fungal Isolates Gene expression levels were compared between our two ancestor clones and the reference strain IPO-323 (Dutch field strain isolated in 1984), using in vitro cultures RNA-seq data from the literature (using Yeast Malt Sucrose medium at 18 • C  (Kellner et al. , 2014) ; using PDB medium at 18 • C  (Rudd et al. , 2015) ; (fastq files SRR1167717, SRR1167718 3 ; and fastq files ERR789217, ERR789218 kindly provided by J. Rudd). Raw fastq files from both studies were analyzed the same way as for our samples. 3 https://www. ncbi. nlm. nih. gov/sra?term=SAMN02640204 The following model was fit for each gene to compare the genotypes: Y ij = µ + G i + ε ij , (DESeq2 Model 1) Y ij is the expression level of the ith genotype (i = IPO-323, MGGP01, MGGP44) and the jth replicate (j = 1, 2, 3, 4), µ is the intercept, and ε ij is the random error.
Evolution and Plasticity of Gene Expression We analyzed the transcriptome evolution and the level of plasticity of gene expression for each ancestral genotype separately. Significant differences in the interaction term between lineages and temperature could not be tested with the DESeq2 Model (2), due to model overfitting. Here, the following model which includes the gene expression data from the ancestors was fit for each gene separately with the R package DESeq2: Y ijk = µ + L i + T j + LT ij + ε ij (DESeq2 Model 3) Y ijk is the expression level of the ith lineage (i = ancestor MGGP01, 12F, 13F, 1217, 1323, or ancestor MGGP44, 441F, 443F, 44117, 44323), at the kth temperature (k = 17 • C, 23 • C), and for the jth replicate (j = 1, 2), µ is the intercept, and ε ikj is the random error. LRT tests were used to compare the full model to reduced models, and FDR threshold at 5% was used for multiple testing correction.
Gene Co-expression Network Analysis We searched for correlated patterns of expression among genes across the selection regimes. Only genes that displayed at least one significant contrast for the selection regimes from our DESeq2s Model (3) were included in this analysis (3174 and 3440 genes, for the genetic background MGGP01 and MGGP44, respectively). Scale-free co-expression networks were built using a Weighted Gene Correlation Network Analysis with the R package WGCNA (weighted correlation network analysis)  (Langfelder and Horvath, 2008). Using the log2 transformed read counts, the genes were clustered into modules according to their expression profile in response to the selection regimes and the assay temperatures. Modules were summarized by the first principal component of the gene expression data of the module (the eigengene), and the hub (most highly connected node of the module).
For the analysis of gene expression plasticity, chi-square tests with Holm's correction for multiple testing were applied to compare changes of gene number among the different selection regimes. Following the WGCNA permutation tests (10,000 bootstraps) were done to test for the significance of the ranking position of candidate genes with functional annotation related to transcription regulation or signal transduction. Two bootstraps were done, using a sampling of 24 (genes involved in transcriptional regulation) or 39 (genes involved in signal transduction) randomly chosen genes among 4786 (total number of genes in the 8 selected modules revealed by WGCNA, Additional File 11: Supplementary Table  S7 ).
Description of hub genes. Functional annotation of the 8 hub genes identified using WGCNA approach (Xlsx 13KB). FILE S11 | Supplementary Table  S7. Description of the 63 genes which function is associated to transcription regulation or signal transduction, among the top 8 WGCNA modules  (Xlsx 19KB). FILE S12 | Supplementary Table  S8. Small scale mutations in transcripts common between the two temperatures conditions compared to the reference genome IPO-323 (Xlsx 9. 6MB). FILE S13 | Supplementary Table  S9. Gene counts according to their change of plasticity among the selection regimes (Xlsx 13KB).
ACKNOWLEDGMENTS We would like to thank the reviewers for their insightful comments on the manuscript. We are grateful to  Henriette Goyeau  for sampling the two fungal isolates, to  Johann Confais  for providing help for the fungus serial transfers, and to the genotoul bioinformatics  Toulouse Midi-Pyrenees  ( Bioinfo Genotoul ) for providing computing resources. This manuscript has been released as a pre-print at www. bioRxiv. com(Jallet et al. , 2019).
ACKNOWLEDGMENTS We thank  J. Comelles ,  E. Pencreach ,  O. Pertz ,  P. Rossolillo , the  Imaging Platform of IGBMC , and the  Riveline Laboratory  for help, constructs, and discussions. We acknowledge  Raphae ¨l Voituriez  for his comments on the paper. This study with the reference  ANR-10-LABX-0030-INRT  has been also supported by a  French  state fund through the  Agence Nationale de la Recherche  under the frame programme  Investissements d'Avenir labeled   ANR-10-IDEX-0002-02. D. R. acknowledges funding from  R egion Grand Est, Alsace contre le cancer , the  University of Strasbourg , and the  CNRS. This study was partially supported by grants from the  Ministry of Science and Technology (MOST)/Taiwan  ( 106-2314-B-110 -001 -MY3 ;  106-2811-B-110 -004 ).
These data were similar to those reported previously for CTPS1-deficient patients. (6, (15) (16) (17). Ranges and numbers of the different blood cell populations were compared to established healthy pediatric standards  (18) (19) (20) (21). Blood leukocyte subset analysis in CTPS1-deficient individuals. Immunological investigations were performed in depth in 7 CTPS1-deficient patients (P1. 2, P7. 1, P7. 2, P10, P11, P12, P13, and P14). Frequencies of αβ and γδ T cells and B cells were in the normal ranges, whereas absolute numbers and frequency of NK cells were significantly diminished in all patients when compared with healthy controls (Figure  1A  and Supplemental Table  3 ). Analysis of NK cell subsets in 1 patient showed reduced proportions of CD56 dim CD16 + NK cells (Supplemental Figure  1A ).
As expected, CTPS1 and CTPS2 protein expression was preceded by a peak in CTPS1 and CTPS2 mRNA expression, respectively (Figure  4D ). The upregulation of CTPS2 could not compensate for CTPS1 deficiency in patient cells. Importantly, by using this new monoclonal CTPS1 antibody, we detected in activated CTPS1-deficient T cells from patients a weak expression with an apparent lower molecular weight (Figure  5A ). These find-Figure  1. Immunophenotyping of CTPS1-deficient patients. (A) Percentages of αβ and γδ T cells, NK cells, B cells, monocytes, myeloid dendritic cells (mDCs), and plasmacytoid dendritic cells (pDCs) in PBMCs are represented in dot plot graphs. Data were obtained from FACS analysis after cell-specific staining. Reference ranges for age-matched controls (3-18 years) correspond to 21%-98%, 0.
CTPS activity was then measured in protein lysates extracted from cells of controls and patients. CTPS activity was found to be markedly decreased but detectable in lysates from T cell blasts, LCLs, and fibroblasts derived from CTPS1-deficient patients as compared with control cells (Figure  5E ). In control T cell blasts, CTPS activity was significantly increased following TCR-CD3 stimulation, while no or a weak increase in CTPS activity was detected in T cell blasts of patients. CTPS activity in CTPS1-deficient T cells was in the order of 20% of the activity found in control cells. These data demonstrate that expression of a mutant CTPS1 protein is detected, albeit at low levels, in CTPS1-deficient cells and is associated with a marked reduction of the CTPS activity. Mutated CTPS1 protein is unstable but has a preserved CTPS activity.
Methods Genomic DNA sequencing. Genomic DNA was sequenced for CTPS1 as previously described  (6). The diagnosis of patient P10 was made via whole-genome sequencing and confirmed by Sanger sequencing. Whole-genome sequencing data sets were created through the UK 100,000 Genomes Project main/ pilot program, using Illumina X10 sequencing chemistry. Sequencing reads were aligned to build GRCh37 of the human reference genome using Issac (Aligner) and variants within the PID panel version analyzed. All collected sequences were analyzed using 4peaks software (version 1. 8; https://nucleobytes. com/4peaks/index. html). Gene expression analysis. Total RNA was isolated from resting and activated T cell blasts, EBV-transformed LCLs, and fibroblasts of patients and control donors using the RNeasy Mini kit (QIAGEN).
All data were collected on an LSRFortessa cytometer (BD Biosciences) and analyzed using Flow-Jo version 10. 4. 2 software (Tree Star). Cytokine production assay. For intracellular staining of cytokines, cells were stimulated overnight with PMA and ionomycin, anti-CD3/CD28 beads, or 1 μg/mL coated anti-CD3 (OKT3, eBioscience, Thermo Fisher Scientific) in the presence of brefeldin A (GolgiPlug, BD Biosciences). Cells were then fixed and permeabilized using the BD Cytofix/Cytoperm Plus kit (BD Pharmingen) according to the manufacturer's instructions. Cells were labeled with PE-anti-IL-2 (rat IgG2a, MHQ1-17H12), APC-anti-IFN-γ (mouse IgG1, 4S. B3), and isotype-matched monoclonal antibodies purchased from BioLegend and analyzed by flow cytometry. For measurement of cytokine secretion, cells were stimulated with PMA and ionomycin or anti-CD3/CD28 beads or 1 μg/mL coated anti-CD3 (OKT3); supernatants were collected after 36 hours of culture.
IFN-γ, TNF-α, IL-17A, IL-4, and IL-2 cytokines and granzyme B were simultaneously quantified in supernatants using coated beads array technology (BD Biosciences). All collected flow cytometry data were analyzed on FCAP Array Software (version 3. 0, BD Biosciences). Proliferation assay. For proliferation analysis, T cell blasts were washed and cultured without IL-2 for 72 hours to synchronize the cells. Cell proliferation was monitored by labeling cells with cell-tracking violet dye (Tag-it Violet, BioLegend) before stimulation, according to the manufacturer's instructions. Blasts or PBMCs were then cultured for 4 to 6 days in complete medium alone or in the presence of 1 μg/mL coated anti-CD3 antibody (clone OKT3, eBioscience) or anti-CD3/CD28-coated beads (Invitrogen, Thermo Fisher Scientific).
PBMCs were incubated with 40 μM 3-DAZ (MilliporeSigma), 200 μM of cytidine, and 100 U/mL of IL-2 in combination or separately during the time of stimulation. Then, cells were harvested and cell-tracking violet dye dilution was assessed by flow cytometry with an LSRFortessa cytometer. The index of proliferation was calculated using FlowJo software and corresponds to the average number of cell divisions per cell including the undivided peak. Degranulation assay. T cell blasts were washed and stimulated for 3 hours with different concentrations of anti-CD3 antibody in the presence of anti-CD107a and anti-CD107b antibodies (H4A3 and H4B4, BioLegend). After 3 hours of stimulation, CD107a/b membrane protein expression was quantified by flow cytometry using an LSRFortessa cytometer. Immunoblots. The following antibodies were used for immunoblotting with standard procedures: rabbit polyclonal anti-CTPS1 (SAB111072, MilliporeSigma), rabbit monoclonal anti-CTPS1 [EPR8086(B)] and rabbit anti-CTPS2 (ab235109, both from Abcam), anti-GFP (C163, Life Technologies), and rabbit anti-actin (D18C11, Cell Signaling Technology).
3 mM UTP, and 10 μM stable CTP isotope as an internal standard in 10 μL HEPES buffer, 70 mM, at pH 8. 0. The reaction mixtures were incubated at 37°C for 90 minutes, and then the enzymatic reaction was stopped by the addition of 2 volumes of HClO 4. Five microliters of sample was injected onto an Acquity HSS T3 column, 1. 8-μm particle size, 2. 1 × 100 mm (Waters), connected to an Acquity H-Class ultra-performance liquid chromatography interfaced with a Xevo TQ-S triple-quadrupole mass spectrometer (Waters), both controlled by MassLynx software (Waters). Aqueous mobile phase A was water at pH 6. 5, and organic mobile phase B was acetonitrile, both containing 5 mM dibutylamine acetate ion pair. A programmed mobile phase gradient was used for 12-minute runs: 0 minutes, 11% B; 8 minutes, 13% B; 8.