MIA: V. Pascal, Algorithms for metabolic pathway discovery in human microbiome; Primer: M. Medema

Models, Inference and Algorithms
November 6, 2024
Broad Institute of MIT and Harvard
Primer: Deciphering the Chemical Language of Microbiomes
Marnix Medema
Bioinformatics Group, Wageningen University & Research
Microorganisms produce a wealth of specialized metabolites, which play important roles in
microbiome ecology and provide a rich resource for natural product drug discovery. Genome
sequence data has revealed that only a tiny fraction of the chemical diversity of these natural
products has been unearthed. Here, I will highlight recent work performed in my research
group and with the wider community on developing and applying computational and artificial
intelligence approaches to systematically map the biosynthetic diversity and elucidate the
functionality of these microbial specialized metabolites. Specifically, I will highlight new
methods to chart biosynthetic diversity, and to predict chemical (sub)structures and functions
of metabolites from omics data. Furthermore, I will highlight approaches used to prioritize
biosynthetic space for those genes that are most likely to mediate key microbiome-associated
phenotypes of interest, with examples from human as well as plant microbiomes. All in all,
these computational approaches are facilitating smarter and more targeted automated
genome mining of microbial metabolites to uncover the hidden chemistry of life and elucidate
its roles in microbe-microbe and host-microbe interactions.
Meeting: Algorithms for metabolic pathway discovery and analysis in the human microbiome
Victoria Pascal
Epigenetics in cancer and cell differentiation group in Germans Trias i Pujol Research Institute (IGTP)
Metabolites derived from specialized primary metabolism are key players in the
crosstalk with the host and constitute major modulators of host phenotypes. Many of
these known metabolites are encoded in gene clusters. However, the options to
systematically analyze microbial genomes for specialized primary metabolic gene
clusters were limited. In this talk, I will present gutSMASH, a tool to metabolically
profile the human microbiome for known and putative gene clusters. In order to go
beyond bacteria’s functional potential, we also developed BiG-MAP an algorithm to
assess gene cluster abundance and expression by using metagenomic and
metatranscriptomic data as input. Combining different omics data can ultimately help
providing causation between microbiome host-phenotypes and get more biological
insights at different molecular levels. During the talk I will highlight the analysis we
have performed using these tools in combination with different datasets to ultimately
show our findings, which constitute a starting point to understand the role bacteria
have in the overall chemistry of the human microbiome.
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