A comparative study of the gut microbiota in immune-mediated inflammatory diseases—does a common dysbiosis exist?

• Published in: Microbiome Vol. 6; no. 1; pp. 221 - 15
• Main Authors: Forbes, Jessica D., Chen, Chih-yu, Knox, Natalie C., Marrie, Ruth-Ann, El-Gabalawy, Hani, de Kievit, Teresa, Alfa, Michelle, Bernstein, Charles N., Van Domselaar, Gary
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 13.12.2018; BioMed Central; BMC
• Subjects: 16S rRNA gene amplicon sequencing; Abundance; Adult; Ambulatory care; Arthritis; Arthritis, Rheumatoid - microbiology; Artificial intelligence; Bacteria; Bacteria - classification; Bacteria - genetics; Bacteria - isolation & purification; Biological markers; Biomarkers; Case-Control Studies; Colitis; Colitis, Ulcerative - microbiology; Comparative literature; Crohn Disease - microbiology; Crohn's disease; Diagnosis; DNA, Bacterial - genetics; DNA, Ribosomal - genetics; Dysbacteriosis; Dysbiosis; Dysbiosis - diagnosis; Etiology; Etiology (Medicine); Female; Gastrointestinal Microbiome; Genes; Genetic aspects; Genetic research; Gut microbiota; Health; Humans; Immune-mediated inflammatory disease; Immunologic diseases; Inflammation; Inflammatory bowel disease; Inflammatory bowel diseases; Inflammatory Bowel Diseases - microbiology; Inflammatory diseases; Intestinal microflora; Learning algorithms; Machine Learning; Male; Metagenomics - methods; Microbiomes; Microbiota; Microbiota (Symbiotic organisms); Middle Aged; Multiple sclerosis; Multiple Sclerosis - microbiology; Pathogenesis; Phylogeny; Rheumatoid arthritis; Rheumatoid factor; Risk factors; RNA; RNA, Ribosomal, 16S - genetics; rRNA 16S; Sequence Analysis, DNA - methods; Studies; Taxonomy; Ulcerative colitis; 16S rRNA gene amplicon sequencing; Gut microbiota; Inflammatory bowel disease; Multiple sclerosis; Immune-mediated inflammatory disease; Machine learning classifiers; Rheumatoid arthritis; Bacteria; Taxonomic biomarkers
• DOI: 10.1186/s40168-018-0603-4
• ISSN: 2049-2618

Immune-mediated inflammatory disease (IMID) represents a substantial health concern. It is widely recognized that IMID patients are at a higher risk for developing secondary inflammation-related conditions. While an ambiguous etiology is common to all IMIDs, in recent years, considerable knowledge has emerged regarding the plausible role of the gut microbiome in IMIDs. This study used 16S rRNA gene amplicon sequencing to compare the gut microbiota of patients with Crohn's disease (CD; N = 20), ulcerative colitis (UC; N = 19), multiple sclerosis (MS; N = 19), and rheumatoid arthritis (RA; N = 21) versus healthy controls (HC; N = 23). Biological replicates were collected from participants within a 2-month interval. This study aimed to identify common (or unique) taxonomic biomarkers of IMIDs using both differential abundance testing and a machine learning approach. Significant microbial community differences between cohorts were observed (pseudo F = 4.56; p = 0.01). Richness and diversity were significantly different between cohorts (pFDR < 0.001) and were lowest in CD while highest in HC. Abundances of Actinomyces, Eggerthella, Clostridium III, Faecalicoccus, and Streptococcus (pFDR < 0.001) were significantly higher in all disease cohorts relative to HC, whereas significantly lower abundances were observed for Gemmiger, Lachnospira, and Sporobacter (pFDR < 0.001). Several taxa were found to be differentially abundant in IMIDs versus HC including significantly higher abundances of Intestinibacter in CD, Bifidobacterium in UC, and unclassified Erysipelotrichaceae in MS and significantly lower abundances of Coprococcus in CD, Dialister in MS, and Roseburia in RA. A machine learning approach to classify disease versus HC was highest for CD (AUC = 0.93 and AUC = 0.95 for OTU and genus features, respectively) followed by MS, RA, and UC. Gemmiger and Faecalicoccus were identified as important features for classification of subjects to CD and HC. In general, features identified by differential abundance testing were consistent with machine learning feature importance. This study identified several gut microbial taxa with differential abundance patterns common to IMIDs. We also found differentially abundant taxa between IMIDs. These taxa may serve as biomarkers for the detection and diagnosis of IMIDs and suggest there may be a common component to IMID etiology.