Analysis of gut microbiota in patients with acute myocardial infarction by 16S rRNA sequencing

Sotagliflozin attenuates cardiac dysfunction and depression-like behaviors in mice with myocardial infarction through the gut-heart-brain axis

Myocardial infarction (MI) and depression are leading causes of mortality and morbidity globally, and these conditions are increasing recognized as being fundamentally interconnected. The recently recognized gut-heart-brain axis offers insights into depression following MI, but effective treatments for this comorbidity remain lacking. To address this medical need, we employed an animal model of MI to investigate the potential repurposing of sotagliflozin (SOTA), an approved sodium-glucose cotransporter 1 and 2 (SGLT1/2) inhibitor for diabetes, for managing depression following MI and identifying potential SOTA-associated microbial mechanisms. SOTA treatment improved cardiac dysfunction and alleviated depression-like behaviors induced by MI, accompanied by alterations in gut microbiota composition, such as changes in the Prevotellaceae NK3B31 group, Alloprevotella, and Prevotellaceae UCG-001. Moreover, fecal microbiota transplantation (FMT) using fecal samples from SOTA-treated MI mice demonstrated that gut microbiota contributed to the beneficial effects of SOTA on cardiac dysfunction and depression-like behaviors in MI mice. Intriguingly, FMT-based intervention and concordance analysis of gut microbiota before and after FMT suggested that Prevotellaceae NK3B31 group, Alloprevotella, and Prevotellaceae UCG-001 were associated with the beneficial effects of SOTA. Furthermore, functional prediction of gut microbiota and correlation analysis support the significance of these dynamic microbial communities. In conclusion, these findings suggest that SOTA could serve as a potential drug to ameliorate cardiac dysfunction and depressive symptoms in MI patients via through the gut-heart-brain axis.

Unravelling the Gut Microbiome Role in Cardiovascular Disease: A Systematic Review and a Meta-Analysis

A notable shift in understanding the human microbiome's influence on cardiovascular disease (CVD) is underway, although the causal association remains elusive. A systematic review and meta-analysis were conducted to synthesise current knowledge on microbial taxonomy and metabolite variations between healthy controls (HCs) and those with CVD. An extensive search encompassing three databases identified 67 relevant studies (2012-2023) covering CVD pathologies from 4707 reports. Metagenomic and metabolomic data, both qualitative and quantitative, were obtained. Analysis revealed substantial variability in microbial alpha and beta diversities. Moreover, specific changes in bacterial populations were shown, including increased Streptococcus and Proteobacteria and decreased Faecalibacterium in patients with CVD compared with HC. Additionally, elevated trimethylamine N-oxide levels were reported in CVD cases. Biochemical parameter analysis indicated increased fasting glucose and triglycerides and decreased total cholesterol and low- and high-density lipoprotein cholesterol levels in diseased individuals. This study revealed a significant relationship between certain bacterial species and CVD. Additionally, it has become clear that there are substantial inconsistencies in the methodologies employed and the reporting standards adhered to in various studies. Undoubtedly, standardising research methodologies and developing extensive guidelines for microbiome studies are crucial for advancing the field.

Microbiome interactions with different risk factors in development of myocardial infarction

Among all non-communicable diseases, Cardiovascular Diseases (CVDs) stand as the leading global cause of mortality. Within this spectrum, Myocardial Infarction (MI) strikingly accounts for over 15 % of all deaths. The intricate web of risk factors for MI, comprising family history, tobacco use, oral health, hypertension, nutritional pattern, and microbial infections, is firmly influenced by the human gut and oral microbiota, their diversity, richness, and dysbiosis, along with their respective metabolites. Host genetic factors, especially allelic variations in signaling and inflammatory markers, greatly affect the progression or severity of the disease. Despite the established significance of the human microbiome-nutrient-metabolite interplay in associations with CVDs, the unexplored terrain of the gut-heart-oral axis has risen as a critical knowledge gap. Moreover, the pivotal role of the microbiome and the complex interplay with host genetics, compounded by age-related changes, emerges as an area of vital importance in the development of MI. In addition, a distinctive disease susceptibility and severity influenced by gender-based or ancestral differences, adds a crucial insights to the association with increased mortality. Here, we aimed to provide an overview on interactions of microbiome (oral and gut) with major risk factors (tobacco use, alcohol consumption, diet, hypertension host genetics, gender, and aging) in the development of MI and therapeutic regulation.

Targeted arginine metabolomics combined with metagenomics revealed the potential mechanism of Pueraria lobata extract in treating myocardial infarction

The extraction methods for traditional Chinese medicine (TCM) may have varying therapeutic effects on diseases. Currently, Pueraria lobata (PL) is mostly extracted with ethanol, but decoction, as a TCM extraction method, is not widely adopted. In this study, we present a strategy that integrates targeted metabolomics, 16 s rDNA sequencing technology and metagenomics for exploring the potential mechanism of the water extract of PL (PLE) in treating myocardial infarction (MI). Using advanced analytical techniques like ultra-high performance liquid chromatography with quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS) and ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS), we comprehensively characterized PLE's chemical composition. Further, we tested its efficacy in a rat model of MI induced by ligation of the left anterior descending branch of the coronary artery (LAD). We assessed cardiac enzyme levels and conducted echocardiograms. UPLC-MS/MS was used to compare amino acid differences in serum. Furthermore, we investigated fecal samples using 16S rDNA sequencing and metagenomic sequencing to study intestinal flora diversity and function. This study demonstrated PLE's effectiveness in reducing cardiac injury in LAD-ligated rats. Amino acid metabolomics revealed significant improvements in serum levels of arginine, citrulline, proline, ornithine, creatine, creatinine, and sarcosine in MI rats, which are key compounds in the arginine metabolism pathway. Enzyme-linked immunosorbent assay (ELISA) results showed that PLE significantly improved arginase (Arg), nitric oxide synthase (NOS), and creatine kinase (CK) contents in the liver tissue of MI rats. 16 s rDNA and metagenome sequencing revealed that PLE significantly improved intestinal flora imbalance in MI rats, particularly in taxa such as Tuzzerella, Desulfovibrio, Fournierella, Oscillibater, Harryflintia, and Holdemania. PLE also improved the arginine metabolic pathway in the intestinal microorganisms of MI rats. The findings indicate that PLE effectively modulates MI-induced arginine levels and restores intestinal flora balance. This study, the first to explore the mechanism of action of PLE in MI treatment considering amino acid metabolism and intestinal flora, expands our understanding of the potential of PL in MI treatment. It offers fresh insights into the mechanisms of PL, guiding further research and development of PL-based medicines.

Potential biomarkers of acute myocardial infarction based on the composition of the blood microbiome

BACKGROUND

It is difficult to distinguish between acute myocardial infarction (AMI) and unstable angina (UA) due to their similar clinical features. In recent years, studies have shown that microbiomes have great potential in distinguishing diseases. The purpose of this study is to describe the composition of serum microbiome in the AMI and UA by 16S rDNA sequencing.

METHODS

Based on the high-throughput detection platform and 16S rDNA amplification sequencing technology, this study detected the blood microbial composition of 55 patients with AMI and 62 patients with UA. Alpha diversity and Beta diversity analysis were used to compare the differences in microbial composition and bacterial colony structure between AMI and UA groups. We perform PCoA (Principal Co-ordinates Analysis) based on Unweighted Unifrac distance. In addition, various statistical methods were employed to examine the significance of differences in microbial composition and genus between the two groups. PICRUSt (Phylogenetic Investigation of Communities by Reconstruction of Unobserved States) was employed to predict KEGG (Kyoto Encyclopedia of Genes and Genomes) function from 16S sequencing data. Random forest was applied to identify biomarkers and construct the diagnostic model. Subsequently, the stability of the model was verified by 10-fold cross and the diagnostic effectiveness was evaluated through ROC (Receiver Operating Characteristic).

RESULTS

In this study, we found that alpha diversity index of serum microbiome in AMI group was significantly higher than in UA group. T-test analysis demonstrated that the UA group presented a higher abundance of Ralstonia, Faecalibaculum and Gammaproteobacteria, while Bacteroides, Sphingomonas, Faecalibaculum, Haemophilus, Serratia, Bifidobacterium and Chloroplast were more abundant in the AMI group. The LefSe (LDA Effect Size) analysis showed that the Gammaproteobacteria, Proteobacteria, Ralstonia pickettli, Ralstonia, Burkholderiaceae and Burkholderiales were enriched in UA group, and Bacteroidales, Bacteroidia, Bacteroidota, Clostridia and Firmicutes were more abundant in the AMI group. Ten bacterial diagnostic models were constructed in the random forest. The area under the curve (AUC) in the training set was 88.01%, and the AUC value in the test set was 95.04%.

CONCLUSION

In this study, the composition of blood microorganisms in the groups of patients with AMI and UA has been analyzed, providing novel insights for understanding the pathogenesis of AMI; Blood microbiome may serve as novel diagnostic biomarkers of AMI.

Gut microbiome-based noninvasive diagnostic model to predict acute coronary syndromes

Background

Previous studies have shown that alterations in the gut microbiota are closely associated with Acute Coronary Syndrome (ACS) development. However, the value of gut microbiota for early diagnosis of ACS remains understudied.

Methods

We recruited 66 volunteers, including 29 patients with a first diagnosis of ACS and 37 healthy volunteers during the same period, collected their fecal samples, and sequenced the V4 region of the 16S rRNA gene. Functional prediction of the microbiota was performed using PICRUSt2. Subsequently, we constructed a nomogram and corresponding webpage based on microbial markers to assist in the diagnosis of ACS. The diagnostic performance and usefulness of the model were analyzed using boostrap internal validation, calibration curves, and decision curve analysis (DCA).

Results

Compared to that of healthy controls, the diversity and composition of microbial community of patients with ACS was markedly abnormal. Potentially pathogenic genera such as Streptococcus and Acinetobacter were significantly increased in the ACS group, whereas certain SCFA-producing genera such as Blautia and Agathobacter were depleted. In addition, in the correlation analysis with clinical indicators, the microbiota was observed to be associated with the level of inflammation and severity of coronary atherosclerosis. Finally, a diagnostic model for ACS based on gut microbiota and clinical variables was developed with an area under the receiver operating characteristic (ROC) curve (AUC) of 0.963 (95% CI: 0.925-1) and an AUC value of 0.948 (95% CI: 0.549-0.641) for bootstrap internal validation. The calibration curves of the model show good consistency between the actual and predicted probabilities. The DCA showed that the model had a high net clinical benefit for clinical applications.

Conclusion

Our study is the first to characterize the composition and function of the gut microbiota in patients with ACS and healthy populations in Southwest China and demonstrates the potential effect of the microbiota as a non-invasive marker for the early diagnosis of ACS.