A comparative study of the gut microbiota in immune-mediated inflammatory diseases-does a common dysbiosis exist? Forbes JD1,2,3,4,5, Chen CY3, Knox NC3, Marrie RA1,6, El-Gabalawy H1,7, de Kievit T8, Alfa M4, Bernstein CN1,2, Van Domselaar G9,10,11. Microbiome. 2018 Dec 13;6(1):221. doi: 10.1186/s40168-018-0603-4. |
Author information 1 Department of Internal Medicine, University of Manitoba, Winnipeg, MB, Canada. 2 University of Manitoba IBD Clinical and Research Centre, Winnipeg, MB, Canada. 3 National Microbiology Laboratory, Public Health Agency of Canada, 1015 Arlington Street, Winnipeg, MB, R3E 3R2, Canada. 4 Department of Medical Microbiology and Infectious Diseases, University of Manitoba, Winnipeg, MB, Canada. 5 Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Canada. 6 Department of Community Health Sciences, University of Manitoba, Winnipeg, MB, Canada. 7 Arthritis Centre, University of Manitoba, Winnipeg, MB, Canada. 8 Department of Microbiology, University of Manitoba, Winnipeg, MB, Canada. 9 University of Manitoba IBD Clinical and Research Centre, Winnipeg, MB, Canada. Gary.vandomselaar@canada.ca. 10 National Microbiology Laboratory, Public Health Agency of Canada, 1015 Arlington Street, Winnipeg, MB, R3E 3R2, Canada. Gary.vandomselaar@canada.ca. 11 Department of Medical Microbiology and Infectious Diseases, University of Manitoba, Winnipeg, MB, Canada. Gary.vandomselaar@canada.ca. Abstract BACKGROUND: 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. RESULTS: 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 diseasecohorts 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. CONCLUSIONS: 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. |
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