The Relationship Between The Number Of Stenotic Coronary Arteries And The Gut Microbiome In Coronary Heart Disease Patients

Objective: To investigate the changes of gut microbiota in patients with coronary heart disease(CHD)with different number of stenotic coronary arteries and its correlation with the severity of CHD.Methods:Feces were collected from 48 hospitalized patients who had undergone coronary angiography at the Affiliated Hospital of Southwest Medical University.Subjects were divided into three groups based on angiographic findings.Ten subjects without coronary plaques and with completely smooth intima were used as the control group(group C);subjects with more than 75%stenosis of any of coronary arteries or branches was regarded as the coronary heart disease group(CHD group): 1)12 subjects with only one coronary artery stenosis ≥ 75% and other coronary intima completely smooth were used as the single-vessel lesion group(SV group);2)26 subjects with two or more coronary arteries with ≥ 75% stenosis were used as the multi-vessel lesions group(MV group).Exclusion criteria were as follows:(1)having gastrointestinal disease or a history of gastrointestinal surgery within one year(2)having used gut microbiota preparations or antibiotics within the past 1month.On the day of admission,cubital venous blood was collected from all subjects under fasting state to complete blood routine,liver function,renal function,electrolytes,blood lipids,blood glucose and N-terminal pro-B-type natriuretic peptide(NT-pro BNP)tests,and echocardiography was completed.Five grams of fresh feces was collected from each subject on the day following coronary angiography,and all collected samples were transported to the laboratory within 1 hour and stored in a-80 ℃ freezer.Gene sequences of gut microbiota were obtained by 16 S r RNA sequencing technology.The sequencing results of the three groups of subjects were analyzed for species composition of gut microbiota,Alpha diversity analysis,Beta diversity analysis,enterotype analysis,species difference analysis between groups,functional prediction analysis,and correlation analysis between gut microbiota and clinical parameters.Gene data obtained from functional prediction analysis of gut microbiota were intersected with genes obtained from disease-related databases searching CHD to obtain gut microbiota and CHD-related gene sets.Protein-protein interaction(PPI)network was constructed using STRING database for gut microbiota and CHD-related gene sets.The protein interaction network was further analyzed with Cytoscape,and a key subnetwork consisting of 11 target genes was obtained using Cyto Nca.Two genes with the highest Cytoscape scores,heme oxygenase 1(HMOX1)and catalase(CAT),were selected,and the proteins expressed by these two target genes were found in the rcsb pdb database as protein receptors for molecular docking.Molecular docking simulation validation was performed using Autodock pairs.Results:1.There was no significant difference in baseline clinical data such as age,gender,smoking history,hypertension history,diabetes history,blood routine,liver function,renal function,blood glucose,NT-pro BNP and echocardiography among the three groups.Compared with the control group,triglyceride(TG)increased and high-density lipoprotein(HDL)decreased in the coronary heart disease group,and the above changes were more significant in the MV group,being statistically significant(P < 0.05).2.At the phylum level,the gut microbiota of all subjects was mainly classified into five phyla: Firmicutes,Bacteroidota,Proteobacteria,Actinobacteriota,and Verrucomicrobiota.At the genus level,the gut microbiota was mainly composed of Bacteroides,Blautia,Escherichia-Shigella,Faecalibacterium,and Streptococcus.3.The results showed that the Subdoligranulum and Collinsella genera were significantly more abundant in group C subjects than in the SV and MV groups(MV vs.C,P < 0.05;MV vs.SV,P < 0.01).At the same time,the abundance of the Escherichia-Shigella genus was significantly higher in subjects in the MV group than in those in the C and SV groups(C vs.MV,P <0.05;C vs.SV,P < 0.01).4.Enterotypes analysis showed that five enterotypes contributed differently at the genus level: Bacteroides resulted in enterotype 1 and enterotype 2,Megamonas resulted in enterotype 3,Lactobacillus resulted in enterotype 4,and Escherichia-Shigella resulted in enterotype 5.At the same time,enterotype 5 had a significant advantage only in the CHD group.5.The genus Escherichia-Shigella was positively correlated with plasma low density lipoprotein(LDL)and left atrial diameter(LA)and negatively correlated with total bile acids(TBA)(P<0.05).The genus Collinsella was negatively correlated with neutrophil ratio(NEU-R)and TG(P<0.05),and the genus Subdoligranulum was negatively correlated with alanine aminotransferase(ALT)and aspartate aminotransferase(AST)(P<0.05).6.Subdoligranulum and Collinsella genera were selected as a gut bacterial biomarker set to develop a predictive model.Receiver operating characteristic(ROC)curve revealed that this gut bacterial set could distinguish C from CHD,SV,and MV with area under the curve(AUC)values of 0.9,0.98,and 0.87,respectively(Figure 5A).A novel predictive model was constructed to distinguish SV and MV groups using the genera Subdoligranulum,Collinsella,Escherichia-Shigella,and three clinical features(LDL,LA,and TBA)with an AUC of 0.80.7.KEGG functional prediction showed that metabolic pathways such as biosynthesis of unsaturated fatty acids and betaine biosynthesis were significantly enriched in MV group compared with C and SV groups(P < 0.05).Metabolic pathways such as sphingolipid metabolism,glycosphingolipid biosynthesis,and primary bile acid biosynthesis were significantly enriched in group C compared with SV and MV groups(P < 0.05).8.PICRUSt functional prediction showed that metabolic pathways such as biosynthesis of unsaturated fatty acids and betaine biosynthesis were significantly enriched in MV group compared with C and SV groups(P < 0.05).Metabolic pathways such as sphingolipid metabolism,glycosphingolipid biosynthesis,and primary bile acid biosynthesis were significantly enriched in group C compared with SV and MV groups(P < 0.05).9.Trimethylamine oxide(TMAO)can well enter and bind to the active pocket of HMOX1 and CAT proteins,and has a strong binding capacity.Conclusion:This study suggests that the composition and diversity of the gut microbiota change significantly from healthy controls to CHD subgroups with different numbers of coronary lesions.At the same time,we also found several gut microbiotas associated with leading to CHD and affecting the number of coronary lesions.Our prediction model for coronary heart disease diagnosis based on these related gut microbes and plasma metabolites has good predictive power and provides a new direction for the diagnosis and prognosis of coronary heart disease.In addition,the results of functional prediction analysis and molecular docking showed that gut microbiota may affect the ability of the body to cope with oxidative stress injury and redox-dependent inflammation by producing a large amount of TMAO,which in turn leads to the occurrence and development of coronary atherosclerosis and coronary heart disease.It provides a new research idea for exploring the development of coronary heart disease and its severity.