A genome-scale metabolic model of a globally disseminated hyperinvasive M1 strain of Streptococcus pyogenes
Yujiro Hirose1,2, Daniel C Zielinski3, Saugat Poudel3
1Department of Microbiology, Osaka University Graduate School of Dentistry, Suita, Osaka, Japan.
Abstract:
Streptococcus pyogenes is responsible for a range of diseases in humans contributing significantly to morbidity and mortality. 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. To enhance our understanding of pathogenesis and discovery of potential therapeutic approaches, we have developed the first genome-scale metabolic model (GEM) for a serotype M1 S. pyogenes strain, which we name iYH543. The curation of iYH543 involved cross-referencing a draft GEM of S. pyogenes serotype M1 from the AGORA2 database with gene essentiality and autotrophy data obtained from transposon mutagenesis-based and growth screens. We achieved a 92.6% (503/543 genes) accuracy in predicting gene essentiality and a 95% (19/20 amino acids) accuracy in predicting amino acid auxotrophy. Additionally, Biolog Phenotype microarrays were employed to examine the growth phenotypes of S. pyogenes, which further contributed to the refinement of iYH543. Notably, iYH543 demonstrated 88% accuracy (168/190 carbon sources) in predicting growth on various sole carbon sources. Discrepancies observed between iYH543 and the actual behavior of living S. pyogenes highlighted areas of uncertainty in the current understanding of S. pyogenes metabolism. iYH543 offers novel insights and hypotheses that can guide future research efforts and ultimately inform novel therapeutic strategies.IMPORTANCEGenome-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. Here, we have developed the first GEM for the S. pyogenes highly virulent serotype, M1, which we name iYH543. The iYH543 achieved high accuracy in predicting gene essentiality. We also show that the knowledge obtained by substituting actual measurement values for iYH543 helps us gain insights that connect metabolism and virulence. iYH543 will serve as a useful tool for rational drug design targeting S. pyogenes metabolism and computational screening to investigate the interplay between inhibiting virulence factor synthesis and growth.
Insights
We created the first genome-scale metabolic model (GEM) for virulent Streptococcus pyogenes M1, named iYH543. This model accurately predicts gene essentiality and aids in understanding metabolism for new therapeutic targets.
Area of Science:
- Microbiology and Systems Biology
- Computational Biology and Metabolic Modeling
Background:
- Streptococcus pyogenes causes significant human disease, with M1 serotype strains being particularly virulent.
- Understanding S. pyogenes metabolism is crucial for developing effective therapeutic strategies against severe infections.
- Genome-scale metabolic models (GEMs) are powerful tools for investigating bacterial metabolism and identifying drug targets.
Purpose of the Study:
- To develop the first genome-scale metabolic model (GEM) for a serotype M1 Streptococcus pyogenes strain.
- To validate the model's accuracy in predicting gene essentiality, auxotrophy, and growth phenotypes.
- To provide a computational tool for understanding S. pyogenes metabolism and guiding drug discovery.
Main Methods:
- Developed a GEM (iYH543) for S. pyogenes M1 by integrating draft models with experimental data.
- Utilized transposon mutagenesis and growth screens for gene essentiality and autotrophy data.
- Employed Biolog Phenotype microarrays to assess growth on various carbon sources for model refinement.
Main Results:
- The iYH543 model achieved high accuracy: 92.6% for gene essentiality, 95% for amino acid auxotrophy, and 88% for carbon source utilization.
- Model predictions identified discrepancies with experimental data, highlighting gaps in current knowledge of S. pyogenes metabolism.
- The model successfully connected metabolic insights with virulence, offering hypotheses for future research.
Conclusions:
- iYH543 is the first GEM for virulent S. pyogenes M1, offering novel insights into its metabolism.
- The model serves as a valuable tool for rational drug design and computational screening against S. pyogenes.
- iYH543 facilitates investigation into the interplay between metabolic pathways, virulence factor synthesis, and bacterial growth.
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