Abstract
BackgroundMicrobiome-metabolome studies of the human gut have been gaining popularity in recent years, mostly due to accumulating evidence of the interplay between gut microbes, metabolites, and host health. Statistical and machine learning-based methods have been widely applied to analyze such paired microbiome-metabolome data, in the hope of identifying metabolites that are governed by the composition of the microbiome. Such metabolites can be likely modulated by microbiome-based interventions, offering a route for promoting gut metabolic health. Yet, to date, it remains unclear whether findings of microbially associated metabolites in any single study carry over to other studies or cohorts, and how robust and universal are microbiome-metabolites links.ResultsIn this study, we addressed this challenge by performing a comprehensive meta-analysis to identify human gut metabolites that can be predicted based on the composition of the gut microbiome across multiple studies. We term such metabolites “robustly well-predicted”. To this end, we processed data from 1733 samples from 10 independent human gut microbiome-metabolome studies, focusing initially on healthy subjects, and implemented a machine learning pipeline to predict metabolite levels in each dataset based on the composition of the microbiome. Comparing the predictability of each metabolite across datasets, we found 97 robustly well-predicted metabolites. These include metabolites involved in important microbial pathways such as bile acid transformations and polyamines metabolism. Importantly, however, other metabolites exhibited large variation in predictability across datasets, suggesting a cohort- or study-specific relationship between the microbiome and the metabolite. Comparing taxonomic contributors to different models, we found that some robustly well-predicted metabolites were predicted by markedly different sets of taxa across datasets, suggesting that some microbially associated metabolites may be governed by different members of the microbiome in different cohorts. We finally examined whether models trained on a control group of a given study successfully predicted the metabolite’s level in the disease group of the same study, identifying several metabolites where the model was not transferable, indicating a shift in microbial metabolism in disease-associated dysbiosis.ConclusionsCombined, our findings provide a better understanding of the link between the microbiome and metabolites and allow researchers to put identified microbially associated metabolites within the context of other studies.9FDD9YR_-AdGzQQ9nr1KAwVideo abstract
Highlights
Microbiome-metabolome studies of the human gut have been gaining popularity in recent years, mostly due to accumulating evidence of the interplay between gut microbes, metabolites, and host health
One well-known example is that of trimethylamine N-oxide (TMAO), a product of microbial metabolism of nutrients found in eggs and red meat, which accelerates atherosclerotic cardiovascular diseases [5, 6]
Other examples include imidazole propionate, a metabolite over-produced by Type 2 Diabetes-associated bacteria, which was found to impair glucose tolerance [7], Akkermansia muciniphila-associated nicotinamide, which was shown to improve motor symptoms in a mouse model of ALS [8], and microbially produced short-chain fatty acids (SCFAs), which have a protective role against intestinal inflammation [9]
Summary
Microbiome-metabolome studies of the human gut have been gaining popularity in recent years, mostly due to accumulating evidence of the interplay between gut microbes, metabolites, and host health. Statistical and machine learning-based methods have been widely applied to analyze such paired microbiomemetabolome data, in the hope of identifying metabolites that are governed by the composition of the microbiome. Such metabolites can be likely modulated by microbiome-based interventions, offering a route for promoting gut metabolic health. The microbial communities that reside in the human gut wield a multitude of activities with pervasive effect on human health and disease. Among these activities, perhaps the most important and clinically relevant one is the microbiota’s metabolic activity. Microbiome-associated metabolites such as the organic acid taurine or some SCFAs have been studied as potential novel therapeutic agents, intended to “correct” negative effects of microbial dysbiosis [12]
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