A genome-scale metabolic model of a globally disseminated hyperinvasive M1 strain of Streptococcus pyogenes

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.

Keywords: Streptococcus pyogenes; auxotrophy; carbon sources; essential gene; genome-scale model; metabolic modeling.

Fig 1

Curation strategy of the AGORA2-derived genome-scale metabolic model (draft GEM) of S. pyogenes M1 GAS (strain SF370) to produce iYH543. (A) Schematic of the network reconstruction strategy for iYH543. The experimental data from this study and previously published data from other studies were compared with the results of the simulation in the AGORA2-derived draft GEM. Based on the identified discrepancies, the model was curated and reconstructed to produce iYH543. (B) Attributes of the AGORA2-derived draft GEM and iYH543. Details regarding the genes, metabolites, and reactions included in iYH543 are listed in Table S2. (C) Accuracy of essentiality predictions in the AGORA2-derived draft GEM and iYH543. Transposon mutagenesis-based gene essentiality in S. pyogenes strain 5448 (serotype M1) was used for validation. AGORA2 (12). Transposon mutagenesis-based gene essentiality (18). CDM components and amino acid auxotrophy (11). SBML, Systems Biology Markup Language. Accuracy: (TP + TN)/all genes.

Fig 2

COG categories represented in the true positive (TP), true negative (TN), false positive (FP), and false negative (FN) groups of genes, as well as the 65 essential genes not in iYH543. COGs are colored for each function. Sixty-five genes out of 224 essential genes, which were not included in iYH543, mainly having COG categories of S (function unknown), M (cell wall/membrane/envelope biogenesis), D (cell cycle control, cell division, and chromosome partitioning), U (intracellular trafficking, secretion, and vesicular transport), and O (posttranslational modification, protein turnover, and chaperones). Transposon mutagenesis-based gene essentiality (18).

Fig 3

Sole carbon source utilization profiles and amino acid auxotrophies in iYH543 versus experimental data. (A) Biolog validation. We tested 190 carbon sources to investigate the sole carbon source utilization profiles of S. pyogenes M1 serotype (strain 5448). The results of the Biolog experiment are visualized in Fig. S1. The details of the medium for the simulation in iYH543 are shown in Table S6 (CDM1). The results of the simulation in iYH543 are in Table S4 (Carbon_sources). TP contains the carbon sources that allow S. pyogenes growth in both Biolog plates and DataS2_iYH543_CDM_1.json. TN contains (i) the carbon sources that do not allow the growth of S. pyogenes in both Biolog plates and DataS2_iYH543_CDM_1.json and (ii) the carbon sources that do not allow the growth of S. pyogenes in Biolog plates and are not exchanged in iYH543. FP contains the carbon sources that allow S. pyogenes growth in DataS2_iYH543_CDM_1.json, but they do not allow S. pyogenes growth in Biolog plates. FN contains the carbon sources that allow S. pyogenes growth in Biolog plates but do not allow growth in DataS2_iYH543_CDM_1.json due to the absence of the exchange reactions in BiGG or VMH. (B) Amino acid auxotrophies in living bacterial cells. Levering et al. reported the amino acid auxotrophies of S. pyogenes serotype M49 (strain 591) in living bacterial cells. We compared amino acid auxotrophies in iYH543 versus experimental data using DataS3_iYH543_CDM_2.json. The details of the medium for these simulations are shown in Table S6 (CDM2). The json formats (iYH543_CDM_1.json and DataS3_iYH543_CDM_2.json) can be downloaded at our lab website (http://www.dent.osaka-u.ac.jp/wp-content/uploads/2024/04/Code_Model_Files_Hirose_et_al.zip). The results of the simulations are in Table S4 (Amino_acid_Auxotrophy). CDM2 components and amino acid auxotrophy (11). Accuracy: (TP + TN)/all genes.

Fig 4

The similarity and difference of essential genes between serotype M49 NZ131 and serotype M1 SF370. (A) Overlapping genes among M49 essential genes, M1 essential genes, and 543 genes in iYH543. M49_E, 236 essential genes of serotype M49 NZ131. M1_E, 224 essential genes of serotype M1 SF370. Transposon mutagenesis-based gene essentiality is referenced (18). (B) Details of 22 essential genes of M49 NZ131 in iYH543, which are not essential in M1 SF370. COGs are colored for each function. The details of genes are listed in Table S5.

Fig 5

Curation of iYH543 using discrepancies between experimental data and the AGORA2-derived draft GEM. (A) Rationale for adding the FE2DH reaction. Fe²⁺ is a component of biomass in AGORA2-derived draft GEM; however, S. pyogenes can grow without Fe²⁺ in CDM2 (11). FE2DH reaction was referred from the VMH database (14). (B) Curated pathways for L-ascorbate degradation. Since L-ascorbate (ascb__L in GEM) is a consumable metabolite in CDM2 (11), L-ascorbate utilization reactions were added to the GEM referred from the BioCyc database (15). (C) Curated tetrahydrofolate biosynthesis pathway and its flux in iYH543 with a nutrient-rich media or CDM2. AGORA2-derived draft GEM requires folate (fol in GEM) in the medium, while folate is not contained in CDM2. Therefore, dihydroneopterin triphosphate pyrophosphatase (DNTPPA), dihydroneopterin monophosphate dephosphorylase (DNMPPA), and sink_gcald_c reactions were added to the GEM referred from the BioCyc database. Escher-FBA (22) was used to confirm whether the curated pathways actually worked in iYH543. Added reactions were referred from databases, including VMH, BiGG, EggNOG, and BioCyc. CDM2 components that make S. pyogenes growth (11). bio1, a component of biomass (a reactant in the formula for biomass, bio1 reaction); flux, estimated flux in each model.

Fig 6

Curation of iYH543 based on transposon mutagenesis-based gene essentiality. (A) The curation of NAD⁺ de novo biosynthesis pathway. Visualization of transposon mutagenesis-based gene essentiality (18) allowed us to identify that there is no NAD⁺ de novo biosynthesis pathway present in the AGORA2-derived draft GEM. Therefore, by tracing NAD⁺ de novo biosynthesis I (from aspartate) of S. pyogenes in BioCyc (15), ASPO6 and QULNS reactions were added to the model. (B) Example for the modification of biomass components based on the transposon mutagenesis-based gene essentiality. Visualization of transposon mutagenesis-based gene essentiality (18) allowed us to distinguish between essential and non-essential biomass components of living bacteria. Biomass components associated with non-essential genes (clpn180_c, pe180_c, pg180_c, and sttcaala__D_c) are deleted from the biomass reaction (bio1 formula), and the coefficients are assigned to the precursor (cdpdodecg_c, sttca1_c) in the biomass reaction. Added reactions were referred from databases, including VMH, BiGG, and BioCyc. All reactions, metabolites, and GPR rules in iYH543 are detailed in Table S2 and can be found in the json format at our lab website (http://www.dent.osaka-u.ac.jp/wp-content/uploads/2024/04/Code_Model_Files_Hirose_et_al.zip). bio1, a component of biomass (a reactant in formula for biomass, bio1 reaction). CDM2, components that make S. pyogenes growth (11); CDM1, components that make S. pyogenes growth (4); NMN, nicotinamide mononucleotide; NADP, nicotinamide adenine dinucleotide phosphate.

Fig 7

Utilization efficiency of amino acid is changed by the difference of supplemented carbon sources in CDM1. (A) Growth curves in CDM1 supplemented with indicated carbon sources. (B) Calculated differences in the efficiency of AA uptake (mmol/g*h) in CDM1 glucose (+), maltose (+), or dextrin (+) conditions. Assuming that the efficiency of AA uptake is constant regardless of carbon sources, iYH543 predicts the same growth rates in all CDM1 glucose (+), maltose (+), or dextrin (+) conditions (above, gray). The efficiency of AA uptake is adjusted according to the actual values of growth rate (1/h) and carbon uptake (mmol/g*h) (bottom). (C) Calculated differences in the efficiency of AA uptake per glucose molecule uptake in CDM1 glucose (+), maltose (+), or dextrin (+) conditions. The simulation suggested that maltose utilization tends to show inefficient amino acid uptake as compared to glucose utilization. On the other hand, the simulation suggested that glucose utilization tends to show efficient amino acid uptake as compared to glucose utilization. (D) The expression of selected genes in iYH543, associated with amino acid transport and metabolism. Bacterial RNA samples from CDM1 glucose (+), maltose (+), or dextrin (+) conditions are compared to those from CDM1 carbon (−) conditions. These data are acquired from a previous study (36). The color scale indicates enrichment (red) and depletion (blue). Genes are arranged in descending order of log2 fold change values in CDM1 glucose (+) condition. The detail of the expression levels is shown in Table S7. (E) The growth rate in proportion to the arginine uptake in iYH543 with CDM dextrin (+) condition. The actual consumption rates of dextrin 2.96 mmol g⁻¹ h⁻¹ are used as the fixed value. AA uptake, lower bounds of exchange reactions of other all AAs; arginine uptake, flux of the exchange reaction of arginine.