E-MTAB-9708
Comparison of the gene expression profiles of strictured and non-srictured colonic tissue from patients with stricturing Crohn's disease
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.
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.