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Microbiome and metabolome data integration provides insight into health and disease

      For much of our history, the most basic information about the microbial world has evaded characterization. Next-generation sequencing has led to a rapid increase in understanding of the structure and function of host-associated microbial communities in diverse diseases ranging from obesity to autism. Through experimental systems such as gnotobiotic mice only colonized with known microbes, a causal relationship between microbial communities and disease phenotypes has been supported. Now, microbiome research must move beyond correlations and general demonstration of causality to develop mechanistic understandings of microbial influence, including through their metabolic activities. Similar to the microbiome field, advances in technologies for cataloguing small molecules have broadened our understanding of the metabolites that populate our bodies. Integration of microbial and metabolomics data paired with experimental validation has promise for identifying microbial influence on host physiology through production, modification, or degradation of bioactive metabolites. Realization of microbial metabolic activities that affect health is hampered by gaps in our understanding of (1) biological properties of microbes and metabolites, (2) which microbial enzymes/pathways produce which metabolites, and (3) the effects of metabolites on hosts. Capitalizing on known mechanistic relationships and filling gaps in our understanding has the potential to enable translational microbiome research across disease contexts.

      Abbreviations:

      SCFA (Short chain fatty acids), BGC (biosynthetic gene cluster), MS/MS (tandem mass spectrometry), rRNA (ribosomal RNA), GF (germ free), KO (KEGG Orthologue), LC/MS (liquid chromatography/mass spectroscopy)
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