Inflammation is a major risk factor of morbidity and mortality in older adults. Although its precise etiology is unknown, low-grade inflammation in older adults is commonly associated with increased intestinal epithelial permeability (leaky gut) and abnormal (dysbiotic) gut microbiota. The increasing older population and lack of treatments to reduce aging-related microbiota dysbiosis, leaky gut, and inflammation culminates in a rise in aging-related comorbidities, constituting a significant public health concern. Here, we demonstrate that a human-origin probiotic cocktail containing 5 Lactobacillus and 5 Enterococcus strains isolated from healthy infant gut prevented high-fat diet–induced (HFD-induced) microbiota dysbiosis, leaky gut, inflammation, metabolic dysfunctions, and physical function decline in older mice. Probiotic-modulated gut microbiota primarily reduced leaky gut by increasing tight junctions, which in turn reduced inflammation. Mechanistically, probiotics modulated microbiota in a way to increase bile salt hydrolase activity, which in turn increased taurine abundance in the gut that stimulated tight junctions and suppressed gut leakiness. Furthermore, in Caenorhabditis elegans, taurine increased life span, reduced adiposity and leaky gut, and enhanced physical function. The results suggest that such probiotic therapies could prevent or treat aging-related leaky gut and inflammation in the elderly.

Aging-related abnormalities in gut microbiota are associated with cognitive decline, depression, and anxiety, but underlying mechanisms remain unstudied. Here, our study demonstrated that transplanting old gut microbiota to young mice induced inflammation in the gut and brain coupled with cognitive decline, depression, and anxiety. We observed diminished mucin formation and increased gut permeability (“leaky gut”) with a reduction in beneficial metabolites like butyrate because of decline in butyrate-producing bacteria in the aged gut microbiota. This led to suppressed expression of butyrate receptors, free fatty acid receptors 2 and 3 (FFAR2/3). Administering butyrate alleviated inflammation, restored mucin expression and gut barriers, and corrected brain dysfunction. Furthermore, young mice with intestine-specific loss of FFAR2/3 exhibited gut and brain abnormalities akin to those in older mice. Our results demonstrate that reduced butyrate-producing bacteria in aged gut microbiota result in low butyrate levels and reduced FFAR2/3 signaling, leading to suppressed mucin formation that increases gut permeability, inflammation, and brain abnormalities. These findings underscore the significance of butyrate-FFAR2/3 agonism as a potential strategy to mitigate aged gut microbiota–induced detrimental effects on gut and brain health in older adults.

Metabolomics is an interdisciplinary area that integrates knowledge of instrumentation, data science, and biochemistry. Metabolomics studies the changes in a large number of metabolites after various treatments using analytical platforms. However, the interpretation approaches have not been completely investigated. Principal component analysis (PCA) is an unsupervised method that describes high throughput metabolite data, which is different from supervised approaches such as partial least-squares discriminant analysis (PLS-DA) which frequently has overfitting problems. The interpretation of PCA loadings, especially for studies with multiple study groups, is not well developed for metabolomics. In this study, a new method was reported that integrates PCA loading values with the commonly used statistical t-test analysis to significantly improve the convenience and efficiency of interpretation. The method was demonstrated using practical studies of NMR metabolomics on the extracts from sea anemone that were treated with six atrazine concentrations. The results indicated that the approach is suitable for multiple groups of metabolomics for early-stage discoveries, such as low concentrations and potentially longitudinal studies. In summary, this methodology may be critical in studies such as environmental metabolomics with various stimuli factors where the data interpretation was previously incompletely developed.