Gut bacteria produce natural products (NPs), bioactive molecules hypothesized to influence both microbiome composition and host physiology. Despite their significance, NPs of the human gut microbiome remain largely uncharacterized.
Genome mining represents a powerful in silico approach for discovering NP biosynthetic gene clusters (BGCs). BGCs harbor the genetic blueprints of all enzymes involved in NP biosynthetic pathways. State-of-the-art genome mining tools excel at identifying large BGCs associated with assembly line-like biosynthetic pathways. In contrast, smaller BGCs involved in alkaloid, ribosomally synthesized and post-translationally modified peptide (RiPP), or terpene biosynthesis often evade detection by existing genome mining tools. The low energy demand of the biosynthetic pathways associated with these small BGCs might be particularly suitable for the anaerobic lifestyle of human gut microbes. We aim to chart these underexplored NP BGCs and mine gut bacterial genomes for their full biosynthetic potential. Specifically, we will use TailEnzA, an artificial intelligence (AI)-based genome mining algorithm recently developed in the Helfrich lab (unpublished). TailEnzA predicts NP BGCs through the identification of genes encoding NP-modifying enzymes. We will prioritize non-canonical alkaloid, RiPP, and terpene BGCs from key members of the human gut microbiome such as Blautia species as well as others that are relevant to the SPP2474 consortium. Prioritized BGCs will undergo functional studies in the native producer or through heterologous expression, followed by the structural characterization of the associated NPs. A wide array of bioactivity assays will be employed to assess NP functions, including bacterial interaction assays, assays examining synthetic community dynamics and in vitro eukaryotic cell-based assays. The latter assays will evaluate the impact of the identified NPs on intestinal function, immune responses, host metabolism and the nervous system.