[SS23] Unraveling microbial community dynamics through metabolic interactions

[SS23-02] Integration of Metabolic Networks with Multi-Omics Data for New Insights into Context-Dependent Microbiome Metabolism

*Hugh C McCullough¹, Hyun-Seob Song¹ (1. University of Nebraska - Lincoln (United States of America))

Keywords:

Omics Integration,Metabolic Networks,Microbiome

With the rise of high-throughput methodologies, mechanistic insights into complex microbial community dynamics are within reach. While multi-omics data for singular organisms can be informationally rich, the analysis of the same for microbial communities can increase complexity by orders of magnitude. To accelerate interpretation and aid in hypothesis generation, we introduce a new metabolic network-based multi-omics integration tool, termed Metabolite-Expression-Metabolic Network Integration for Pathway Identification and Selection (MEMPIS) (Chowdhury et al., mSystems, 2019; McClure et al., Scientific Reports, 2020). MEMPIS connects metatranscriptomics to metabolomics data to identify pathways which exhibited context-specific regulation and activity. These predictions are made by constraining metabolic networks based on condition-specific gene-expression and metabolite measurements. As a basic scaffold for this multi-omics integration, MEMPIS flexibly utilizes either a system-specific metabolic network constructed from high-quality metagenomes or a template network that represents all known biochemical reactions from public databases such as KEGG or ModelSEED. The outcome of MEMPIS can graphically be represented as a condition-specific bipartite network connecting metabolites and reactions/genes. Former implementations of MEMPIS granted valuable insights into broad-scale community function between contexts. We also highlight the utility of network visualization to serve as an interactive platform for hypothesis generation and multi-omics interpretation. Through additional mapping all measured omics data including those that are not directly included in MEMPIS predictions, our visualization technique provides a more comprehensive view on conditional trends across omics data and to identify microbiomes’ complex metabolic responses that do not strictly follow defined pathways or span multiple pathways.