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.

AbstractObjectiveThe aim of this study is to investigate the dysbiosis characteristics of gut microbiota in patients with cerebral infarction (CI) and its clinical implications.MethodsStool samples were collected from 79 CI patients and 98 healthy controls and subjected to 16S rRNA sequencing to identify stool microbes. Altered compositions and functions of gut microbiota in CI and its correlation with clinical features were investigated. Random forest and receiver operating characteristic analysis were used to develop a diagnostic model.ResultsMicrobiota diversity and structure between CI patients and healthy controls were overall similar. However, butyrate-producing bacteria (BPB) were significantly reduced in CI patients, while lactic acid bacteria (LAB) were increased. Genetically, BPB-related functional genes were reduced in CI patients, whereas LAB-related genes were enhanced. The interbacterial correlations among BPB in CI patients were less prominent than those in healthy controls. Clinically, BPB was negatively associated with the National Institutes of Health Stroke Scale (NIHSS), while LAB was positively correlated with NIHSS. Both BPB and LAB played leading roles in the diagnostic model based on 47 bacteria.ConclusionsThe abundance and functions of BPB in CI patients were significantly decreased, while LAB were increased. Both BPB and LAB displayed promising potential in the assessment and diagnosis of CI.

The authors declare that they have no conflicts of interest. Author contributions: HQ takes responsibility for the integrity of the work as a whole, from its inception to article publication. RYG and ZGW wrote the manuscript; ZGG, HL, and HQC performed the data checking and analysis. XHZ, DDP, RRS, RY, and LY help collect all the subjects' information and samples. ZC, HZ, and ZYJ performed data visualization. NQ and TYS performed sample sequencing. ZQH and HLQ designed and guided the entire study. All authors approved the final version of the manuscript.