Streptococcus pyogenes can cause a wide variety of acute infections throughout the body of its human host. Among more than 200 serotypes of S. pyogenes, serotype M1 strains hold the greatest clinical relevance due to their high prevalence in severe human infections.
Genome-scale models (GEMs) play a crucial role in investigating bacterial metabolism, predicting the effects of inhibiting specific metabolic genes and pathways, and aiding in the identification of potential drug targets. We recently developed the first GEM for S. pyogenes M1 serotype (mSystems. 2024. PMID: 39158303).
Independent component analysis (ICA) is a framework that decomposes a compendium of RNA-sequencing (RNA-seq) expression profiles to determine the underlying regulatory structure of a bacterial transcriptome. Using ICA, we identified 42 independently modulated gene sets (iModulons) for S. pyogenes M1 serotype and calculated their corresponding activity levels under each experimental condition (mSystems. 2023. PMID: 37278526).
In this poster presentation, we demonstrate how GEMs and iModulon are useful to analyze RNA-seq data from S. pyogenes cultured in chemically defined medium. First, we introduce novel findings derived from the integration of metabolic gene expression differences into GEMs. Then, we demonstrate how iModulons can be leveraged to generate new hypotheses from RNA-seq analysis.
These two systems biology techniques, GEMs and iModulons, provide powerful tools for uncovering the regulatory and metabolic mechanisms underlying Streptococcus pyogenes M1 serotype.