A genome-scale metabolic model of a globally disseminated hyperinvasive M1 strain of Streptococcus pyogenes
0301 basic medicine
570
Genome
Streptococcus pyogenes
0206 medical engineering
Bacterial
auxotrophy
610
carbon sources
Biological
Serogroup
essential gene
Microbiology
Models, Biological
QR1-502
metabolic modeling
Models
Streptococcal Infections
genome-scale model
Humans
Genome, Bacterial
Research Article
DOI:
10.1128/msystems.00736-24
Publication Date:
2024-08-19T13:00:41Z
AUTHORS (12)
ABSTRACT
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.
IMPORTANCE
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. 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.
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CITATIONS (3)
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