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Abstract

How to Utilize a Genome-Scale Metabolic Model and iModulon in the Research of Streptococcus pyogenes M1 Serotype †

by
Yujiro Hirose
1,*,
Eri Ikeda
1,
Masayuki Ono
1,
Masaya Yamaguchi
2,
Bernhard O. Palsson
3 and
Victor Nizet
4
1
Graduate School of Dentistry, Osaka University, 1-8, Yamadaoka, Suita, Osaka 565-0871, Japan
2
Microbial Research Center for Health and Medicine, National Institutes of Biomedical Innovation, Health and Nutrition, Ibaraki, Osaka 567-0085, Japan
3
Department of Bioengineering, University of California San Diego, La Jolla, CA 92093, USA
4
Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, CA 92093, USA
*
Author to whom correspondence should be addressed.
†
Presented at the 22nd Lancefield International Symposium on Streptococci and Streptococcal Diseases, Brisbane, Australia, 1–5 June 2025.
Proceedings 2025, 124(1), 2; https://doi.org/10.3390/proceedings2025124002
Published: 6 August 2025
Versions Notes

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.

Author Contributions

Y.H., B.O.P. and V.N. contributed to the conceptualization of the study. Methodology was developed by Y.H. and B.O.P., and software was implemented by B.O.P. The investigation, formal analysis, and data curation were conducted by Y.H. Supervision was provided by V.N. and B.O.P.; Visualization and original draft preparation were completed by Y.H. The manuscript was reviewed and edited by E.I., M.O. and M.Y. Funding acquisition was led by M.Y., V.N. and B.O.P., and project administration was managed by Y.H., B.O.P. and V.N. All authors have read and agreed to the published version of the manuscript.

Funding

This study was supported in part by AMED (JP23wm0325066), and JSPS KAKENHI (25K00123, 25K028040, 23KK0281).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

All RNA-seq data generated in this study are available from the DDBJ Sequence Read Archive under accession number DRA014564. The code for quality control, ICA, and regulatory analysis is available on GitHub (modulome-workflow; PyModulon). Interactive dashboards are hosted at iModulonDB under “S. pyogenes”. The curated genome-scale metabolic models (iYH543 and variants) and simulation code are available at Osaka Univ. lab website (http://www.dent.osaka-u.ac.jp/wp-content/uploads/2024/03/Code_Model_Files_Hirose_et_al.zip).

Conflicts of Interest

The authors declare no conflict of interest.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Hirose, Y.; Ikeda, E.; Ono, M.; Yamaguchi, M.; Palsson, B.O.; Nizet, V. How to Utilize a Genome-Scale Metabolic Model and iModulon in the Research of Streptococcus pyogenes M1 Serotype. Proceedings 2025, 124, 2. https://doi.org/10.3390/proceedings2025124002

AMA Style

Hirose Y, Ikeda E, Ono M, Yamaguchi M, Palsson BO, Nizet V. How to Utilize a Genome-Scale Metabolic Model and iModulon in the Research of Streptococcus pyogenes M1 Serotype. Proceedings. 2025; 124(1):2. https://doi.org/10.3390/proceedings2025124002

Chicago/Turabian Style

Hirose, Yujiro, Eri Ikeda, Masayuki Ono, Masaya Yamaguchi, Bernhard O. Palsson, and Victor Nizet. 2025. "How to Utilize a Genome-Scale Metabolic Model and iModulon in the Research of Streptococcus pyogenes M1 Serotype" Proceedings 124, no. 1: 2. https://doi.org/10.3390/proceedings2025124002

APA Style

Hirose, Y., Ikeda, E., Ono, M., Yamaguchi, M., Palsson, B. O., & Nizet, V. (2025). How to Utilize a Genome-Scale Metabolic Model and iModulon in the Research of Streptococcus pyogenes M1 Serotype. Proceedings, 124(1), 2. https://doi.org/10.3390/proceedings2025124002

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