dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments RKG is supported by a  Wellcome Trust   Senior Fellowship in Clinical Science  ( WT108082AIA ). IG is a  Wellcome Senior Fellow  ( 207498/Z/17/Z ). This study was supported by the  Cambridge NIHRB Biomedical Research Centre. I. A. T. M. F. is funded by a  SANTHE award  ( DEL-15-006 ). We would like to thank  Paul Lehner  for Calu-3 cells. We would like to thank  James Voss  for  HeLa ACE2  and  Suzanne Rihn  for the A549 cells. We thank  Gavin Screaton  for the Omicron isolate M21021166. We would like to thank  Claire Cormie  for microscope training. We would like to thank  Thushan de Silva  for the Delta variant isolate. This work was supported by the  MRC (TSF ref. MR/T032413/1  to NJM),  NHSBT  (grant ref. WPA15-02  to NJM),  Addenbrooke's Charitable Trust  (grant ref. to 900239 NJM).
The authors acknowledge support from the  G2P-UK National Virology  consortium funded by  MRC/UKRI  (grant ref:  MR/W005611/1 ). This study was also supported by The  Rosetrees Trust  and the Geno2pheno UK consortium. SF acknowledges the  EPSRC  ( EP/V002910/1 ). KS is supported by  AMED Research Program on Emerging and Re-emerging Infectious Diseases  ( 20fk0108270  and  20fk0108413 ),  JST SICORP  ( JPMJSC20U1  and  JPMJSC21U5 ) and  JST CREST  ( JPMJCR20H4 ). We would like to thank all members belonging to The Genotype to  Phenotype Japan  ( G2P -Japan) Consortium. We thank  National Institute for Infectious Diseases, Japan  and  Hisashi Arase  ( Osaka University, Japan ) for providing virus isolates and reagents and  Ituro Inoue  and  Sachiko Sakamoto  ( National Institute of Genetics, Japan ) for technical supports.
1 h and the flask was then filled with 20 mL of complete growth medium and incubated for 4 days. The viral supernatant (passage 2 (P2) stock) was used for experiments. ,M:D3G,M:Q19E,M:A63T,N:P13L,N:R203K,N:G204R,ORF1a:K856R,ORF1a:L2084I ,ORF1a:A2710T,ORF1a:T3255I,ORF1a:P3395H,ORF1a:I3758V,ORF1b:P314L,ORF1b:I15 66V,ORF9b:P10S,S:A67V,S:T95I,S:Y145D,S:L212I,S:G339D,S:S371L,S:S373P,S:S375F,S :K417N,S:N440K,S:G446S,S:S477N,S:T478K,S:E484A,S:Q493R,S:G496S,S:Q498R,S:N50 1Y,S:Y505H,S:T547K,S:D614G,S:H655Y,S:N679K,S:P681H,S:N764K,S:D796Y,S:N856K, S:Q954H,S:N969K,S:L981F. Sequence was deposited in GISAID, accession: EPI_ISL_7886688.
Meanwhile, we also observed those that exhibit more strain-specific or stochastic colonization of the C. elegans gut (Figures  2A  and 2B ; Data S1AB). The assemblages and proportions of microbes observed in each worm strain were unique. To explore the potential for specific natural variation in host genetics in driving the selection of particular microbiome communities, we used the extensive genomics resources available through the Million Mutation Project for our wild C. elegans panel (>3. 8M single-nucleotide variants, ~65,000 missense mutations versus the N2 reference genome). 18 We performed genomewide association study (GWAS) analyses (STAR Methods) to identify regions of the genome associated with taxa abundance, colonization level, alpha diversity, and beta diversity as trait values per strain.
29 We observed broad increases in expression in genes involved in three main pathways: microbial and immune response, general stress response, and insulin signaling (Figures  4D-4G ). Microbial response pathway genes varied significantly between the strain groups. We found that genes more highly expressed in type 1 animals were broadly enriched in genes altered in response to a wide array of microbes (60. 4% [81/134 datasets] for Type 1 Up versus 42. 9% [27/63 datasets] for Type 3 Up; Fig-  ures 4E  and 4F ). Interestingly, Type 1 Up genes overlap with those upregulated upon exposure to pathogens (Figure  4E ) (e. g. , Bacillus thuringiensis, Serratia marcescens, Enterococcus faecalis, Pseudomonas aeruginosa, and others  [30] [31] [32]  ) whereas Type 3 Up genes overlap more with those downregulated upon pathogen exposure (Figure  4F ).
Although no pathogens are included in the BIGbiome, type 1 animals seem to be using similar responses to related microbes to exclude most everything but Ochrobactrum from the gut. Consistent with this idea, Type 1 Up genes overlap with 13 datasets of upregulated genes in response to the pathogen P. aeruginosa PA14 (162 genes in total; e. g. , 39 genes from Troemel et al. ,  33  Q = 1. 8eÀ8). Under these conditions, the twelve Pseudomonas strains in the BIGbiome are excluded from the guts of type 1 animals (1. 2% relative abundance compared to 6. 4% and 7. 4% for type 3 and lawns, respectively). Further, several canonical C. elegans immune effectors from multiple pathways  34  were expressed more highly in type 1 animals (Figure  4I ; Data S1AC)e. g. , irg-5 (2. 81-fold and Pearson of 0. 75 to Ochrobactrum; p38/MAPK and FSHR-1), lys-5 (2.
Both Type 1 Up and Type 3 Up gene sets were highly enriched for DAF-2and/or DAF-16-dependent genes, although overlap was more extensive in type 1 animals (47 datasets for Type 1 Up and 20 datasets for Type 3 Up; both high-and low-insulin conditions were observed; Figures  4C  and 4D ). In addition, the vast majority of the microbially responsive genes identified above are also associated with changes in insulin signaling pathways in the lab strain N2 (1,066-gene [80%] in type 1 versus 464-gene [46%] overlap with insulin signaling datasets; Data S1AC; Figures  4C  and 4D ). Interestingly, the enrichment observed for Type 1 Up genes has been associated with both low-and high-insulinsignaling conditions in the lab strain N2, which may reflect plasticity in gene expression driven by natural genetic variation in these wild strains.
RNAi knockdown of pqm-1 in daf-16(mgDf50) mutants also delayed development by 10% (p < 0. 001; Figure  6C ) and significantly lowered Ochrobactrum colonization compared to the empty vector (Figures  6E  and 6G ). These data suggest that pqm-1 promotes Ochrobactrum colonization independent of daf-16. Finally, we examined the transcriptional networks themselves based on promoter-binding elements for each of these transcription factors. DAF-16 activates genes that contain a DAF-16binding element (DBE; class I) under stressful or low-insulin conditions, whereas PQM-1 activates genes under favorable or high-insulin conditions containing the DAF-16-associated element (DAE; class II). 38 Analysis of the transcriptional datasets identified 170 (10. 2%) class I and 219 (12. 6%) class II genes that were differentially regulated between types 1 and 3.
05) are colored red if they are upregulated in type 1, or log 2 FC >1, or colored orange if they are upregulated in type 3, or log 2 FC <À1. See also Data S1AC for a full gene list. (C-F) Significant (false discovery rate [FDR] < 0. 05) WormExp enrichments from the Type 1 Up gene set (C) and Type 3 Up gene set (D). Barplots represent counts of unique genes for each category. ''Multiple'' category includes daf-16;daf-2 double mutants. ''Microbes'' category subset is separated into specific terms in the Type 1 Up set (E) and Type 3 Up set (F). (G) Heatmap depicting genes that are significantly differentially expressed between microbiome types and significantly correlated (Pearson correlation, Benjamini-Hochberg adjusted p value < 0. 05) with the absolute abundance of at least one BIGbiome member.
(H-J) Ternary plots illustrating the microbiome-type enrichment patterns of genes belonging to insulin-like peptides (H) and immune (I) and stress responses (J). Each dot is an individual gene and dot sizes are proportional to normalized read counts in the transcriptional dataset. Due to a large number in the immune and stress response genes, only genes with significant changes (p < 0. 05) in expression between the microbiome types are shown. Only one gene (ctl-1) is expressed almost exclusively in type 2 (J). See also Figure  S6. signaling activates PQM-1 to promote microbial specific immune response in microbiome selection from the environment, whereas lower insulin signaling levels drive DAF-16-mediated broad stress responses that suppress microbiome selection.
[67] This is also consistent with our observations of greater microbiome selectivity under high insulin signaling conditions. Through continued efforts, we believe that this system will allow for greater understanding of the interplay of host, microbial, and environmental factors that regulate microbiome impact on broad aspects of host physiology. reads were filtered and trimmed using bbmap (https://jgi. doe. gov/data-and-tools/bbtools/bb-tools-user-guide/bbmap-guide/) and bbduk, respectively (https://jgi. doe. gov/data-and-tools/bbtools/bb-tools-user-guide/bbduk-guide/; https://sourceforge. net/ projects/bbmap/). Reads that did not map to the C. elegans genome (build WBcel235) with high quality were removed from the analysis [3. 8%-5. 6% of reads for each dataset]. This was based on an internal evolutionary probability model score 'minid', which was set to 0.
Quantification of animal body size and Ochrobactrum gut colonization Microscopy-based quantification GFP expressing Ochrobactrum were used to visualize the colonization of this bacterium. Brightfield and fluorescent images taken by a Nikon TiE Inverted Microscope were imported to MATLAB based WorMachine. 76 A mask was generated for individual worms with default parameters from brightfield images. Worm length and GFP intensity for each mask were measured and compared using oneway ANOVA and Tukey HSD post hoc test in R packages. BioSorter-based quantification Animals on Day 3 adulthood were collected, washed, paralyzed, and surface bleached as described in the gut microbiome colonization steps, then transferred with 150 mL M9 buffer to a flat bottom 96 well plate (Costar 3370, Corning). Individual body size (time of flight, TOF) and level of GFP intensity were measured by a COPAS Biosorter (Union Biometrica) with a 250 micron flow cell and Sap-phire488 laser at 310 V and 1.
QUANTIFICATION AND STATISTICAL ANALYSIS RNA-seq analyses See Method details for explanation of software and programs used. In brief, reads were checked for quality with FASTQC, filtered for quality with bbmap, trimmed with bbduk, aligned with kallisto, and analyzed for differential expression with DESEQ2. 75 Genes were considered differentially expressed between microbiome types if the Benjamini-Hochberg adjusted p value was less than 0. 05. The work was completed locally using a Late 2013 Mac Pro (3. 5 GHz 6-Core Intel Xeon E5) and software including Mac Terminal (fastqc, bbmap, bbduk, kallisto) and Rstudio (DESeq2).
See also Data S1AC for a full gene list. (C-F) Significant (false discovery rate [FDR] < 0. 05) WormExp enrichments from the Type 1 Up gene set (C) and Type 3 Up gene set (D). Barplots represent counts of unique genes for each category. ''Multiple'' category includes daf-16;daf-2 double mutants. ''Microbes'' category subset is separated into specific terms in the Type 1 Up set (E) and Type 3 Up set (F). (G) Heatmap depicting genes that are significantly differentially expressed between microbiome types and significantly correlated (Pearson correlation, Benjamini-Hochberg adjusted p value < 0. 05) with the absolute abundance of at least one BIGbiome member. (H-J) Ternary plots illustrating the microbiome-type enrichment patterns of genes belonging to insulin-like peptides (H) and immune (I) and stress responses (J).
75,586,505 per dataset) were pseudoaligned to the WBcel235 genome assembly using kallisto [https://pachterlab. github. io/kallisto/ 74 with default settings [89. 0%-93. 3%alignedfor each dataset]. DESeq2 was used to estimate differential expression in an R workspace [https://bioconductor. org/packages/release/bioc/html/DESeq2. html75]. Briefly, DESeq2 models raw counts, normalizes to library depth, estimates and shrinks gene-wise dispersions, and fits a negative binomial model to estimate differential expression based on a Likelihood Ratio Test. Genes with an adjusted p value of 0. 05 or smaller and expressional change greater than two-fold were considered differentially expressed and used in further analyses. Gene set enrichment analyses WormExp [https://wormexp. zoologie. uni-kiel. de/wormexp/ 29 ] was used for gene set enrichment analyses compared to a comprehensive database of over 1700 curated gene expression datasets in C.
elegans. Significance for enrichment scores are calculated using the method developed for the program EASE 87 and reported as uncorrected p value, Bonferroni-corrected p value, and False Discovery Rate. Terms were considered significant if the WormExp-reported FDR score was less than 0. 05. WormCat [http:// wormcat. com 27 ] is a similar nematode-specific enrichment analysis and visualization tool that allows for easy categorization and interpretation of datasets based on gene ontology (GO) terms. WormCat is designed for identification of gene sets that are coexpressed or cofunctioning, allowing for drilled-down analysis of specific pathways. Significance scores are reported as Fisher's exact test p values. Terms were considered significant if the WormCat-reported P value score was less than 0.
ACKNOWLEDGMENTS This work was supported by  NIH  grants  DP2DK116645  (to B. S. S. ). This project was supported by the  Cytometry and Cell Sorting Core at Baylor College of Medicine  with funding from a  CPRIT  core facility support award ( CPRIT-RP180672 ) and the  NIH  ( CA125123  and  RR024574 ) and the assistance of  Joel M. Sederstrom , plus an instrumentation grant for the BioSorter ( NIH  grant  S10 OD025251 ). Some strains were provided by the  CGC , which is funded by the  NIH Office of Research Infrastructure Programs  ( P40 OD010440 ). Emily Troemel generously provided Ochrobactrum  BH3  and isogenic GFP-expressing strain. We also thank the  Gretchen Diehl Lab, Joseph Hyser Lab, Rachel Arey Lab , and  Meng Wang Lab, plus Houston Area Worm Group and Center for Metagenomics and Microbiome Research labs  for helpful advice at various stages of this project.
Analysis of gut microbiome composition Fastq files for each library were split by barcode and quality trimmed in the QIIME software package (v1. 9. 0)  78  with an average quality score of 30. Chimeras were removed by usearch61 and Greengenes 13. 8 database. Resulting fasta files were imported to Deblur  77  with default parameters with all sequences trimmed to 250 bp and positive filter based on 16S rRNA sequences of the 63 strains in the core microbiome. A phylogenetic tree with all Amplicon Sequence Variant (ASV) detected was generated using maximum likelihood method in Mega7 with default parameters. Diversity indices were computed in QIIME using core_diversity_analyses. py with default parameters and rarefied to 3,000 sequences. Alpha diversity was determined using Faith's phylogenetic diversity and Beta-diversity (between samples) distance matrices were computed within QIIME using default parameters.
GWAS analyses of genetic associations with gut microbiome abundance The Caenorhabditis elegans Natural Diversity Resource (CeNDR) was used to perform GWAS  53  using the EMMA algorithm via the rrBLUP package. 83, 84 The EMMA algorithm used within CeNDR takes into account prevalent linkage disequilibrium observed in C. elegans. 85 The gut microbiome taxa abundance values and C. elegans strain names were used as input for GWAS. The CeNDR version used was 1. 2. 9, with data release 20180527 and cegwas version 1. 01. Version WS263 of the worm genome was used in this data release. Representative strains for isotypes with more than one strain tested were randomly selected prior GWAS analyses in CeNDR. RNAi knockdown of C. elegans genes L1 animals were grown on NGM plates with 25 mg/ml carbenicillin and 1 mM IPTG and seeded with 30 mL (OD = 1) of E.
Debris was removed by centrifugation at 13,800 x g for 15 minutes at 4 C, and supernatants (~200 mL) were transferred into RNasefree Eppendorf tubes and stored at À80 C until extraction. Frozen supernatants were thawed at room temperature and loaded to a KingFisher flex purification system (Thermo Scientific) for automatic RNA processing using MirVANA total RNA kit (Thermo Scientific) following the manufacturer's protocol. Purified RNAs were stored at À20 C in elution buffer until use. Aliquots of RNA (0. 5-2 mg) were used for creation of RNA sequencing libraries and sequenced by Illumina HiSeq4000 (paired end 150bp reads; QuickBiology). RNA-seq processing and analysis An average of 19,638,470 reads were obtained from each dataset, and samples with less than 2 replicates were not utilized in analyses.
Chromosomal translocations are important drivers of haematological malignancies whereby proto-oncogenes are activated by juxtaposition with enhancers, often called enhancer hijacking. We analysed the epigenomic consequences of rearrangements between the super-enhancers of the immunoglobulin heavy locus (IGH) and proto-oncogene CCND1 that are common in B-cell malignancies. By integrating BLUEPRINT epigenomic data with DNA breakpoint detection, we characterised the normal chromatin landscape of the human IGH locus and its dynamics after pathological genomic rearrangement. We detected an 10.
S4 ). The cell-type exclusive H3K4me3-BDs (Supplemental Fig. S4D-F ) had proximal super-enhancer(s) more frequently than cell-type non-exclusive H3K4me3-BDs (Supplemental Fig. S4A-C ). Cold Spring Harbor Laboratory Press on June 24, 2022 -Published by genome. cshlp. org Downloaded from Cancer associated translocation events provide opportunities to investigate super-enhancer and H3K4me3-BD relationships A common translocation in B-cell malignancies involves the proto-oncogene cyclin D1 (CCND1) at 11q13 and IGH at 14q32 (IGH-CCND1). To begin to understand how this translocation event leads to CCND1 activation, we characterised the epigenomic landscape in these two regions from healthy and malignant B cells. To precisely define the location and activity of the human IGH super-enhancers and promoters and the chromatin dynamics of the CCND1 locus in B cells, we used 108 chromatin state maps for different haematopoietic cell types built with over 700 ChIP-seq datasets from BLUEPRINT  (Carrillo-de-Santa-Pau et al.
2017; Stunnenberg, International Human Epigenome Consortium, and Hirst 2016)  (Supplemental Tables  S1-S3 ). These data included samples from haematopoietic stem cells, four stages of healthy B-cell differentiation, plasma cells and both primary tumour cells and cell lines derived from four different B-cell haematological malignancies. We used seven healthy non-B-cell cell types (T cells, neutrophils, eosinophils, monocytes, macrophages, erythroblasts and megakaryocytes) as negative controls in which chromatin activity signals in the IGH locus are not predicted.
In summary, we found that the appearance of H3K4me3-BDs is associated with cancer-specific super-enhancer activation in both B-cell and T-cell derived haematological malignancies. Our data suggest that H3K4me3-BDs could be a signature of super-enhancer targets in general and a useful marker to identify deregulated genes affected by the hijacking of super-enhancers in cancer. Cold Spring Harbor Laboratory Press on June 24, 2022 -Published by genome. cshlp. org Downloaded from.
We also observed H3K4me3-BDs over additional proto-oncogenes rearranged in B-cell and T-cell malignancies including the involvement of gene specific and de novo super-enhancers. H3K4me3-BDs are associated with high levels of stable transcription of cell identity and tumour suppressor genes  (Pekowska et al. 2010 (Pekowska et al. , 2011;; Benayoun et al. 2014; K. Chen et al. 2015)  and we show here, for the first time, that they can be a feature of oncogene activation by translocated super-enhancers (as a result of a genomic rearrangement). We analysed a total of fourteen haemato-oncology samples, newly generated data for seven PDX samples and seven patient-derived cell lines. Twelve of these cases were positive for a genomic abnormality involving hijacking of super-enhancers and protooncogene activation (remaining two did not have these rearrangements).
Genome-wide H3K4me3-BDs and super-enhancers co-occurrence We used an epigenomic consensus approach to test whether H3K4me3-BDs co-occur with super enhancers at the genome-wide level in the BLUEPRINT dataset (Supplemental Tables  S2-S3 ). For this analysis, the proximity of H3K4me3-BDs (≥2 kb) to super-enhancers were compared (≥5 kb) to a control group of small promoters (1 kb, overlapping the transcription start site). Super-enhancers overlapping directly H3K4me3-BD and/or genes with H3K4me3-BD were excluded from the analysis. Also, promoters in genes with H3K4me3-BD in alternative transcription start site were excluded. Genomic distances between H3K4me3-BD/promoters and super-enhancers up to 5 Mb were analysed by 100 kb bins. Gene enrichment for biological processes was performed using R package clusterProfiler v3.
18. 0. To determine epigenomic cell type specificity, H3K4me3-BDs were tested for overlaps and coverage of an active chromatin state background for each H3K4me3-BD was calculated. A threshold <10% was used for low active chromatin state background coverage. Targeted DNA sequencing of myeloma cell lines DNA of myeloma cell lines U266, KMS11 and MM1S (Supplemental Table  S3 ) were sequenced using targeted high-throughput sequencing covering the following genomic regions: 1) 4. 2 Mb; extended coverage for chromosomal abnormality detection within IGH, IGK, IGL and MYC (4. 2 Mb); and 2) 0. 6 Mb, exonic regions of 127 myeloma specific genes for mutation analysis and 27 additional regions for efficient data normalisation. GRCh37 human genome assembly was used for sequence mapping using BWA-MEM (v0.
Myeloma patient derived xenografts Patient derived xenografts were generated by passaging primary patient CD138+ selected cells through the previously described SCID-rab myeloma mouse model  (Yata and Yaccoby 2004; Mikulasova et al. 2019 ). Detailed methods are described previously  (Mikulasova et al. 2019). Seven patient derived xenografts were used in this study (Supplemental Table  S5 ). Whole genome sequencing of patient derived xenograft samples DNA from seven patient derived xenograft samples were sequenced using phased whole genome sequencing (10x Genomics, Pleasanton, CA, USA) at Hudson Alpha (Huntsville, AL, USA). Long Ranger (10x Genomics) pipelines were used for data processing, including alignment to GRCh38 genome assembly and structural variant calling. Germline controls were used to distinguish somatic abnormalities and chromosomal breakpoints of detected translocations (Supplemental Table  S5 ) were manually inspected.
Data access The DNA sequencing data for U266, KMS11 and MM1S myeloma cell lines generated in this study have been submitted to the NCBI BioProject database (https://www. ncbi. nlm. nih. gov/bioproject/) under accession number PRJNA635269. All raw and processed ChIP-seq data for KMS11 and MM1S myeloma cell lines as well as PEER T-ALL cell line have been submitted to the NCBI GEO (https://www. ncbi. nlm. nih. gov/geo/) under accession number GSE151556. All high-throughput sequencing data for multiple myeloma patient derived xenografts have been submitted to the European Genome-phenome Archive (https://ega-archive. org/) under accession number EGAS00001005684. Cold Spring Harbor Laboratory Press on June 24, 2022 -Published by genome. cshlp. org Downloaded from.
Acknowledgements This work was supported by the  Wellcome Trust  ( 206103/Z/17/Z  to D. R. and  207556/Z/17/Z  to S. H. ), the  Leukemia & Lymphoma Society  (to G. J. M. and B. A. W. ) and  Kidscan Children's Cancer Research and The Little Princess Trust  (to L. J. R). S. H. would also like to thank the  Newcastle upon Tyne Hospitals NHS Charities , the  NIHR Newcastle Biomedical Research Centre  and the  Sir Jules Thorn Charitable Trust  ( 12/JTA ). S. S. and V. A. would also like to thank the  Institut National du Cancer  ( PLBIO018-031 INCA_12619 ). L. J. R and D. R. both had Newcastle University Research Fellowships and A. M. a  Newcastle University Faculty Fellowship.