Gut microbiome mediates the protective effects of exercise after myocardial infarction

Abstract

Gut microbiota plays important roles in health maintenance and diseases. Physical exercise has been demonstrated to be able to modulate gut microbiota. However, the potential role of gut microbiome in exercise protection to myocardial infarction (MI) remains unclear. We aimed to explore the potential role of gut microbiome in exercise protection to MI. Male C57BL/6 mice were subjected to MI and treated with antibiotics for 7 days (called ABX) prior to running exercise for 8 weeks. 16S ribosomal DNA profiling was used to evaluate changes in gut microbial composition. We discovered exercise mitigated cardiac dysfunction post-MI, as demonstrated by echocardiography and cardiac morphology. Besides, exercise rescued the decreased richness and increased the percentage of Bacteroidales post-MI. The abundance of 7 genera was changed in exercise after MI. Furthermore, ABX mice were subjected to MI and then were transplanted with fecal microbiota (FMT) every other day for 1 week, prior to 8-week running. We found ABX administration abolished the protective effects of exercise in MI mice and mice receiving FMT from exercised MI mice had better cardiac function compared to mice receiving FMT from MI mice. Moreover, we analyzed metabolomics in mice fecal samples from exercised animals post-MI by UPLC-MS/MS and MicrobioMET and observed 3-Hydroxyphenylacetic acid (3-HPA) and 4-Hydroxybenzoic acid (4-HBA) were significant different in exercised MI and MI compared to the control group separately. Finally, 3-HPA and 4-HBA were found to be able to protect cardiac dysfunction post-MI and mediated beneficial effects of microbiota transplants from exercised MI mice. Exercise protects against MI through gut microbiota and gut microbiota-derived 3-HPA and 4-HBA play vital roles in the protective effects of exercise post-MI. Our study offers opportunities to modulate MI by exercise, microbiome and metabolites.

Orthogonal outlier detection and dimension estimation for improved MDS embedding of biological datasets

“…The resulting data is an array which typically contains the abundance of different elements of the microbiome (typically 10 2 to 10 3 ), denoted phylotypes, measured in different human subjects. To analyze such high dimensional datasets, dimensionality reduction methods including MDS (often denoted Principal Coordinates Analysis PCoA), are typically applied and used to visualize the data [17][18][19]. To assess our method incrementally, we restricted first the analysis to a representative specific site (nose), yielding a 136 × 425 array that was further normalized to generate Euclidean pairwise distance matrices (see Material and Methods section 2.6 for more details).…”