A microbial signature for Crohn's disease

By V. Pascal, M. Pozuelo, N. Borruel, F. Casellas, D. Campos, A. Santiago, Xavier Martinez, E. Varela, G. Sarrabayrouse, K. Machiels and 4 more. Gut, 2017

770 citations recorded by the source corpus.

Objective A decade of microbiome studies has linked IBD to an alteration in the gut microbial community of genetically predisposed subjects. However, existing profiles of gut microbiome dysbiosis in adult IBD patients are inconsistent among published studies, and did not allow the identification of microbial signatures for CD and UC. Here, we aimed to compare the faecal microbiome of CD with patients having UC and with non-IBD subjects in a longitudinal study. Design We analysed a cohort of 2045 non-IBD and IBD faecal samples from four countries (Spain, Belgium, the UK and Germany), applied a 16S rRNA sequencing approach and analysed a total dataset of 115 million sequences. Results In the Spanish cohort, dysbiosis was found significantly greater in patients with CD than with UC, as shown by a more reduced diversity, a less stable microbial community and eight microbial groups were proposed as a specific microbial signature for CD. Tested against the whole cohort, the signature achieved an overall sensitivity of 80% and a specificity of 94%, 94%, 89% and 91% for the detection of CD versus healthy controls, patients with anorexia, IBS and UC, respectively. Conclusions Although UC and CD share many epidemiologic, immunologic, therapeutic and clinical features, our results showed that they are two distinct subtypes of IBD at the microbiome level. For the first time, we are proposing microbiomarkers to discriminate between CD and non-CD independently of geographical regions.

Subfields: Genetics, Infectious Diseases, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/5b771bb746b52447f1ad22c65112bb1c83ef3ae0

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In Molecular Biology

Citations in the corpus

Alteration of Gut Microbiota in Inflammatory Bowel Disease (IBD): Cause or Consequence? IBD Treatment Targeting the Gut Microbiome (2019, background): “Human Faecalibacterium prausnitzii Escherichia coli, Fusobacterium Decrese Increase [150]”
Applications of Machine Learning in Human Microbiome Studies: A Review on Feature Selection, Biomarker Identification, Disease Prediction and Treatment (2021, methodology): “…of 21 individuals after probiotics consumption for 60 days and evaluated the changes in microbiome composition through 16S rRNA amplicon Crohn’s Disease (CD) BISCUIT cohort (Hansen et al., 2012; Pascal et al., 2017), CD n = 20, Controls n = 20, Validation Cohort RISK cohort (Gevers et al., 2014).”