The collection of microbes and their genes that exist within and on the human body, collectively known as the microbiome has emerged as a principal factor in human health and disease. Humans and microbes have established a symbiotic association over time, and perturbations in this association have been linked to several immune-mediated inflammatory diseases (IMID) including inflammatory bowel disease, rheumatoid arthritis, and multiple sclerosis. IMID is a term used to describe a group of chronic, highly disabling diseases that affect different organ systems. Though a cornerstone commonality between IMID is the idiopathic nature of disease, a considerable portion of their pathobiology overlaps including epidemiological co-occurrence, genetic susceptibility loci and environmental risk factors. At present, it is clear that persons with an IMID are at an increased risk for developing comorbidities, including additional IMID. Advancements in sequencing technologies and a parallel explosion of 16S rDNA and metagenomics community profiling studies have allowed for the characterization of microbiomes throughout the human body including the gut, in a myriad of human diseases and in health. The main challenge now is to determine if alterations of gut flora are common between IMID or, if particular changes in the gut community are in fact specific to a single disease. Herein, we review and discuss the relationships between the gut microbiota and IMID.
BackgroundImmune-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.ResultsSignificant 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.ConclusionsThis 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.Electronic supplementary materialThe online version of this article (10.1186/s40168-018-0603-4) contains supplementary material, which is available to authorized users.
The effect of neonatal and infant feeding practices on childhood obesity is unclear. The gut microbiome is strongly influenced by feeding practices and has been linked to obesity. OBJECTIVE To characterize the association between breastfeeding, microbiota, and risk of overweight during infancy, accounting for the type and timing of supplementary feeding.
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