Dysbiosis signatures of gut microbiota in coronary artery disease

• Published in: Physiological genomics 2018-10, Vol.50 (10), p.893-903
• Main Authors: Zhu, Qi, Gao, Renyuan, Zhang, Yi, Pan, Dengdeng, Zhu, Yefei, Zhang, Xiaohui, Yang, Rong, Jiang, Rong, Xu, Yawei, Qin, Huanlong
• Format: Article
• Language: English
• Subjects: Amino acid sequence; Bioinformatics; Cardiovascular disease; Coronary artery; Coronary vessels; Dysbacteriosis; Escherichia; Faecalibacterium; Heart diseases; Intestinal microflora; Lipopolysaccharides; Metabolism; Microbiota; Patients; Phosphotransferase; Protein biosynthesis; Protein turnover; rRNA 16S; Shigella; Signatures; Tryptophan
• ISSN: 1094-8341; 1531-2267
• DOI: 10.1152/physiolgenomics.00070.2018

Gut microbiota dysbiosis has been considered to be an important risk factor that contributes to coronary artery disease (CAD), but limited evidence exists about the involvement of gut microbiota in the disease. Our study aimed to characterize the dysbiosis signatures of gut microbiota in coronary artery disease. The gut microbiota represented in stool samples were collected from 70 patients with coronary artery disease and 98 healthy controls. 16S rRNA sequencing was applied, and bioinformatics methods were used to decipher taxon signatures and function alteration, as well as the microbial network and diagnostic model of gut microbiota in coronary artery disease. Gut microbiota showed decreased diversity and richness in patients with coronary artery disease. The composition of the microbial community changed; Escherichia-Shigella [false discovery rate (FDR = 7.5*10 ] and Enterococcus (FDR = 2.08*10 ) were significant enriched, while Faecalibacterium (FDR = 6.19*10 ), Subdoligranulum (FDR = 1.63*10 ), Roseburia (FDR = 1.95*10 ), and Eubacterium rectale (FDR = 2.35*10 ) were significant depleted in the CAD group. Consistent with the taxon changes, functions such as amino acid metabolism, phosphotransferase system, propanoate metabolism, lipopolysaccharide biosynthesis, and protein and tryptophan metabolism were found to be enhanced in CAD patients. The microbial network revealed that Faecalibacterium and Escherichia-Shigella were the microbiotas that dominated in the healthy control and CAD groups, respectively. The microbial diagnostic model based on random forest also showed probability in identifying those who suffered from CAD. Our study successfully identifies the dysbiosis signature, dysfunctions, and comprehensive networks of gut microbiota in CAD patients. Thus, modulation targeting the gut microbiota may be a novel strategy for CAD treatment.