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Peripheral blood DNA methylation of Crohn's disease patients starting treatment with either vedolizumab of ustekinumab

ABSTRACT: In the current study we performed a longitudinal case-control study comparing the peripheral blood DNA methylome of Crohn's disease (CD) patients that were classified as responders and non-responders to vedolizumab and ustekinumab. Patients with active CD were prospectively recruited and followed up for around 26 weeks into treatment during which response assessment took place. Response was defined as a strict combination of endoscopic- (Ã¢Â‰Â¥50% reduction in SES-CD score) combined with either corticosteroid-free clinical- (Ã¢Â‰Â¥3 point drop87 in HBI or HBI Ã¢Â‰Â¤4 and no systemic steroids) and/or biochemical response (C-reactive protein (CRP) and fecal calprotectin reduction Ã¢Â‰Â¥50% or Ã¢Â‰Â¤5 g/mL and fecal calprotectin Ã¢Â‰Â¤250 Ã‚Âµg/g).

PROVIDER: EGAS00001007532 | EGA |

REPOSITORIES: EGA

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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.

Project description:The current treatment for Celiac Disease (CD) is adhering to a gluten-free diet (GFD), although its long-term molecular effects are still undescribed. New molecular features detectable in faecal samples may improve and facilitate non-invasive clinical management of CD on GFD. For this purpose, faecal small non-coding RNAs (sncRNAs) and gut microbiome profiles were concomitantly explored in CD subjects in relation to strict (or not) GFD adherence over time. In the present observational study, we performed small RNA and shotgun metagenomic sequencing in stool from 63 treated CD (tCD) subjects and 66 sex- and age-matched healthy controls. tCD included 51 individuals on strict GFD and with negative transglutaminase (TG) serology (tCD-TG-) and 12 symptomatic with not strict/short-time of GFD adherence and positive TG serology (tCD-TG+). Samples from additional 40 adult healthy individuals and from a cohort of 19 untreated paediatric CD subjects and 19 sex/age matched controls were analyzed to further test the outcomes. Several miRNA, other sncRNA (piRNA and tRNA) and microbiota profiles were altered in tCD subjects(adj.p<0.05). Findings were validated in one external group of controls. In tCD-TG-, GFD duration correlated with five miRNA levels (p<0.05): for miR-4533-3p and miR-2681-3p, the longer the diet adherence, the less the expression differed from controls. tCD-TG+ and untreated paediatric CD patients showed a similar miRNA dysregulation. Immune-response, trans-membrane transport and cell death pathways were enriched in targets of identified miRNAs. Bifidobacterium longum, Ruminococcus bicirculans and Haemophilus parainfluenzae abundances shifted (adj. p<0.05) with a progressive reduction of denitrification pathways with GFD length. Integrative analysis highlighted 121 miRNA-bacterial relationships (adj.p<0.05). Specific faecal sncRNA and microbial patterns characterise CD subjects on GFD, reflecting either the long-term effects or the gut inflammatory status, in case of a not strict/short-time adherence. Our findings suggest novel host-microbial interplays and could help the discovery of biomarkers for the clinical monitoring of GFD over time.

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Peripheral blood DNA methylation of Crohn's disease patients starting treatment with either vedolizumab of ustekinumab

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