Project description:We report the genome-wide mapping of time-series ChIP-seq data sets of human ERα cell lines (MCF-7). The 4 time points cover 0, 1, 4 and 24 hours, respectively. MCF7 cells were maintained in a hormone-free medium (phenol red–free MEM with 2 mmol/L L-glutamine, 0.1 mmol/L nonessential amino acids, 50 units/mL penicillin, 50 Ag/mL streptomycin, 6 ng/mL insulin, and 10% charcoal-stripped FBS) for three days. MCF7 cells were treated with DMSO (as 0 hour time point) or E2 (108 mol/L) for 1, 4 and 24 hours. 5 x 107 cells were cross-linked with 1% formaldehyde for 10 min, at which point 0.125 M glycine was used to stop the crosslinking. In brief, after crosslinking, cells were treated by lysis buffers and sonicated to fragment the chromatin to a size range of 500bp-1kb. Chromatin fragments were then immunoprecipitated with 10ug of antibody/magnetic beads. The antibodies against ERα were purchased from Santa Cruz Biotechnology (Santa Cruz, sc-8005 X). After immunoprecipitation, washing, and elution, ChIP DNA was purified by phenol:chloroform:isoamyl alcohol and solubilized in 70 μl of water. The ChIP DNA sample was run in 12% PAGE and the 100-300bp DNA fraction was excised and eluted from the gel slice overnight at 4 °C in 300 μl of elution buffer (5:1, LoTE buffer (3 mM Tris-HCl, pH 7.5, 0.2 mM EDTA)-7.5 M ammonium acetate) and was purified using a QIAquick purification kit (Qiagen, Cat#28104). The library was constructed using Illumina genomic DNA prep kit by following its protocol (Illumina, cat# FC-102-1002), clusters were generated on the Illumina cluster station (Illumina, cat# FC-103-1002), DNA samples (20 nM per sample) quantified by an Agilent Bioanalyzer, were loaded onto Illumina Genome Analyzer IIx (GAIIx) for sequencing according to the manufacturer’s protocol. Reads generated from the Illumina GAIIx pipeline were aligned to the Human Genome Assembly (NCBI build 36.1/hg18) using ELAND algorithm. Experiments on human ERα breat cancer cell lines (MCF-7) in 4 time points.

Project description:Crohn’s disease, which can affect any part of the gastrointestinal tract, is characterised by the formation of strictures and frequently requires surgery to manage symptoms, which increases morbidity and mortality. A better understanding of the strictured state is therefore needed to identify new targets for therapy. The aim of this study was to identify molecular signals of the strictured state. Sections from proximal, strictured and distal regions of resected colonoic tissue from Crohn’s disease patients undergoing surgery for stricturing disease, were collected in RNAlater. RNA was extracted using RNeasy columns (Qiagen) and quantified by Qubit (Life Technologies). 0.5ng RNA was used to prepare uniquely indexed cDNA QIAseq UPX 3’ Transcriptome libraries according to manufacturer’s instructions (Qiagen). Libraries were quantified and quality-controlled using the QIAseq Library Quant Assay Kit and tapestation analysis and sequenced on the Nextseq Illumina platform using the illumina sequencing primer and PhiX 15% to a depth of 1-3milion reads/sample. Read numbers were set as 101 bp (R1) x 51 bp (R2). Fastq files were obtained through BaseSpace and reads de-multiplexed, aligned to the human genome version GRCh38, quantified and normalised by the TPM method using the CLC Genomics Workbench (Qiagen). The following differential expression procedure was performed across the three sites. TPM normalised values were log-transformed with a pseudocount of 1 added. Transcripts with < 1 transformed count in any sample were excluded from further analysis, as were transcripts with low variance (defined as less than 10% unique counts across both conditions and greater than a 19:1 ratio of the most frequent count to the second most frequent across both conditions). Differential expression analysis between tissues was conducted with the limma package. Library size was estimated using reduced maximum likelihood estimator with 500 iterations. Initial fitting was performed using a robust M-estimation, and moderated test statistics computed by empirical Bayes. A FDR corrected p value <0.05 was considered and filtered for further downstream data analysis. Partial least squares discriminant analysis (PLS-DA) modelling was also performed on the genes, giving a Variable Importance in Projection (VIP) score was used to further rank the genes. VIP is a measure of a variable's importance in the PLS-DA model. To understand the biological significance and pathways, we also performed enrichment analysis using EnrichR with Gene ontology biological pathways, Wiki pathways and Kyoto Encyclopaedia of Genes and Genomes (KEGG). For those genes that showed strong statistical and biological relevance we assessed their combined ability to predict stricture vs non stricture using the Random Foreset Method of Area under the curve analysis.

Project description:The objective of the study was to understand gene expression patterns and pathways that relate to Endoscopic Remission and Histologic remission in IBD as defined by different endoscopic and histological disease activity scores. Colonic Endoscopic biopsies were collected in RNAlater from 21 UC patients undergoingcolonoscopies withthe endocytoscope CF- Y-0058-1 prototype Olympus, Japan). RNA was extracted using RNeasy columns (Qiagen) and quantified by Qubit (Life Technologies). 0.5ng RNA was used to prepare uniquely indexed cDNA QIAseq UPX 3’ Transcriptome libraries according to manufacturer’s instructions (Qiagen). Libraries were quantified and quality-controlled using the QIAseq Library Quant Assay Kit and tapestation analysis and sequenced on the Miseq and Nextseq Illumina platforms using the illumina sequencing primer and PhiX 15% to a depth of 1-3milion reads/sample. Read numbers were set as 101 bp (R1) x 51 bp (R2). Fastq files were obtained through BaseSpace and reads de-multiplexed, aligned to human genome GRCh38, quantified and normalised by the TPM method using the CLC Genomics Workbench (Qiagen). The following differential expression procedure was performed for participants classified as healed versus non-healed according different histologic and endoscpic scores which included the histological scores: Nancy and Robarts Histological Index (RHI) as well as the mayo endoscopic score (MES) and the endocytoscopy score system (ECSS). Normalised values were log-transformed with a pseudocount of 1 added. Transcripts with < 1 transformed count in any sample were excluded from further analysis, as were transcripts with low variance (defined as less than 10% unique counts across both conditions and greater than a 19:1 ratio of the most frequent count to the second most frequent across both conditions). Differential expression analysis between conditions was conducted with the limma package. Library size was estimated using reduced maximum likelihood estimator with 500 iterations. Initial fitting was performed using a robust M-estimation, and moderated test statistics computed by empirical Bayes. A FDR corrected p value <0.05 was considered and filtered for further downstream data analysis. Partial least squares discriminant analysis (PLS-DA) modelling was performed on these genes and a variable Importance in Projection (VIP) score of >1 was used to further filter the genes. To understand the biological significance and pathways, we also performed enrichment analysis using EnrichR involving three databases: Gene ontology, Wiki pathways and Kyoto Encyclopaedia of Genes and Genomes (KEGG).

Project description:The objective of the study was to understand gene expression patterns and pathways that relate to Endoscopic Remission and Histologic remission in IBD as defined by different endoscopic and histological disease activity scores. Colonic Endoscopic biopsies were collected in RNAlater from 9 Crohn's disease patients undergoing colonoscopies with the endocytoscope CF- Y-0058-1 prototype Olympus, Japan). RNA was extracted using RNeasy columns (Qiagen) and quantified by Qubit (Life Technologies). 0.5ng RNA was used to prepare uniquely indexed cDNA QIAseq UPX 3’ Transcriptome libraries according to manufacturer’s instructions (Qiagen). Libraries were quantified and quality-controlled using the QIAseq Library Quant Assay Kit and tapestation analysis and sequenced on the Miseq and Nextseq Illumina platforms using the illumina sequencing primer and PhiX 15% to a depth of 1-3milion reads/sample. Read numbers were set as 101 bp (R1) x 51 bp (R2). Fastq files were obtained through BaseSpace and reads de-multiplexed, aligned to human genome GRCh38, quantified and normalised by the TPM method using the CLC Genomics Workbench (Qiagen). The following differential expression procedure was performed for participants classified as healed versus non-healed according different histologic and endoscpic scores which included the histological scores: Nancy and Robarts Histological Index (RHI) as well as SES-CD, Modified Riley and and the endocytoscopy score system (ECSS). Normalised values were log-transformed with a pseudocount of 1 added. Transcripts with < 1 transformed count in any sample were excluded from further analysis, as were transcripts with low variance (defined as less than 10% unique counts across both conditions and greater than a 19:1 ratio of the most frequent count to the second most frequent across both conditions). Differential expression analysis between conditions was conducted with the limma package. Library size was estimated using reduced maximum likelihood estimator with 500 iterations. Initial fitting was performed using a robust M-estimation, and moderated test statistics computed by empirical Bayes. A FDR corrected p value <0.05 was considered and filtered for further downstream data analysis. Partial least squares discriminant analysis (PLS-DA) modelling was performed on these genes and a variable Importance in Projection (VIP) score of >1 was used to further filter the genes. To understand the biological significance and pathways, we also performed enrichment analysis using EnrichR involving three databases: Gene ontology, Wiki pathways and Kyoto Encyclopaedia of Genes and Genomes (KEGG).

Project description:The objective of this study was to identify pre-treatment colonic gene expression patterns in patients with inflammatory bowel disease (Ulcerative Colitis and colonic Crohn’s disease) that are predictive of a good versus poor response to treatment with the bioloic anti-TNFa. By identifying key pathways that are altered in responders versus non-responders we also aimed to propose novel targets for therapy. Pre-treatment colonic endoscopic biopsies were collected in RNAlater from 22 patients (14 CD and 8 UC) who were starting anti-TNFa therapy. RNA was extracted using RNeasy columns (Qiagen) and quantified by Qubit (Life Technologies). 0.5ng RNA was used to prepare uniquely indexed cDNA QIAseq UPX 3’ Transcriptome libraries according to manufacturer’s instructions (Qiagen). Libraries were quantified and quality-controlled using the QIAseq Library Quant Assay Kit and tapestation analysis and sequenced on the Miseq and Nextseq Illumina platforms using the illumina sequencing primer and PhiX 15% to a depth of 1-3milion reads/sample. Read numbers were set as 101 bp (R1) x 51 bp (R2). Fastq files were obtained through BaseSpace and reads de-multiplexed, aligned to human genome GRCh38, quantified and normalised by the TPM method using the CLC Genomics Workbench (Qiagen). The following differential expression procedure was performed for participants classified as responders, partial responders or non-responders according to their endoscopic presentation 12 weeks after therapy. For UC patients the Mayo score was used: Responders Mayo= 0, Partial responders 1<= Mayo <3 and Non Responders Mayo >=3. For CD patients % reduction in the SES-CD score was used: Responders >=50% of SES-CD, Partial responders 50%<SES-CD<75%, Non responders SES-CD > 75%. Normalised values were log-transformed with a pseudocount of 1 added. Transcripts with < 1 transformed count in any sample were excluded from further analysis, as were transcripts with low variance (defined as less than 10% unique counts across both conditions and greater than a 19:1 ratio of the most frequent count to the second most frequent across both conditions). Differential expression analysis between conditions was conducted with the limma package. Library size was estimated using reduced maximum likelihood estimator with 500 iterations. Initial fitting was performed using a robust M-estimation, and moderated test statistics computed by empirical Bayes. A FDR corrected p value <0.05 was considered and filtered for further downstream data analysis. Partial least squares discriminant analysis (PLS-DA) modelling was performed on these genes to calculate their variable Importance in Projection (VIP) score. To understand the biological significance and pathways represented in the DEGS, we also performed enrichment analysis using the DAVID Gene Functional Classification Tool (REF I,II). Finally, strongest indicators of response were predicted by Random Forest Area Under the Curve (AUC) analysis.

Dataset Information

RNA-seq data from 74 patients with chronic lymphocytic leukemia

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