Host transcriptome signatures in human faecal-washes predict histological remission in patients with IBD

Abstract

Background Colonoscopy is the gold standard for evaluation of inflammation in inflammatory bowel diseases (IBDs), yet entails cumbersome preparations and risks of injury. Existing non-invasive prognostic tools are limited in their diagnostic power. Moreover, transcriptomics of colonic biopsies have been inconclusive in their association with clinical features. Aims To assess the utility of host transcriptomics of faecal wash samples of patients with IBD compared with controls. Methods In this prospective cohort study, we obtained biopsies and faecal-wash samples from patients with IBD and controls undergoing lower endoscopy. We performed RNAseq of biopsies and matching faecal-washes, and associated them with endoscopic and histological inflammation status. We also performed faecal mass-spectrometry proteomics on a subset of samples. We inferred cell compositions using computational deconvolution and used classification algorithms to identify informative genes. Results We analysed biopsies and faecal washes from 39 patients (20 IBD, 19 controls). Host faecal-transcriptome carried information that was distinct from biopsy RNAseq and faecal proteomics. Transcriptomics of faecal washes, yet not of biopsies, from patients with histological inflammation were significantly correlated to one another (p=5.3×10⁻¹²). Faecal-transcriptome had significantly higher statistical power in identifying histological inflammation compared with transctiptome of intestinal biopsies (150 genes with area under the curve >0.9 in faecal samples vs 10 genes in biopsy RNAseq). These results were replicated in a validation cohort of 22 patients (10 IBD, 12 controls). Faecal samples were enriched in inflammatory monocytes, regulatory T cells, natural killer-cells and innate lymphoid cells. Conclusions Faecal wash host transcriptome is a statistically powerful biomarker reflecting histological inflammation. Furthermore, it opens the way to identifying important correlates and therapeutic targets that may be obscured using biopsy transcriptomics.