In Silico Profiling of Small Open Reading Frames in hsdM-Deficient Group A Streptococcus and Their Predicted Role in Membrane Adaptation

https://doi.org/10.55230/mabjournal.v55i3.3857

Authors

  • Chung Yuen Khew Department of Applied Physics, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia
  • Nur Afiqah Mohd Azali Department of Biosciences and Biotechnology, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia
  • Nurnazatul Syiffa Muhammad Bahrain Department of Biosciences and Biotechnology, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia
  • Norfarhan Mohd-Assaad Department of Applied Physics, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia; Institute of Systems Biology (INBIOSIS), Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia https://orcid.org/0000-0002-7543-5805

Keywords:

Bacterial transformation, Group A Streptococcus, RNA-Seq, Small open reading frame, Streptococcus pyogenes, WGCNA

Abstract

Small open reading frames (sORFs) are emerging as key players in bacterial gene regulation, yet their roles remain underexplored in many pathogens. This study investigates the regulatory landscape of sORFs in Group A Streptococcus (GAS) by integrating transcriptomic data and weighted gene co-expression network analysis (WGCNA). We focused on the functional consequences of inactivating the Type 1 Restriction-Modification (RM) system, specifically the hsdM subunit, which is a known barrier to genetic transformation in GAS. By reanalysing the transcriptomic profile of the hyper-transformable 854ΔhsdM strain, we identified 28 sORFs localised within significantly altered co-expression modules. Notably, sORF_00369 (associated with fatty acid biosynthesis) and sORF_00282 (linked to ABC transport) were significantly downregulated. These findings suggest that the inactivation of the RM system, while improving transformation efficiency, may trigger a targeted shift in sORF-mediated metabolism and membrane integrity. Our results propose that sORFs are candidate regulators within the epigenetically mediated regulatory networks of GAS, providing new insights into how genetic recalcitrance and metabolic fitness might be linked. This in silico study lays a theoretical foundation for potentially targeting sORFs in antimicrobial strategy development and optimising GAS as a chassis for synthetic biology.

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References

Andrews, S. 2010. FastQC: A quality control tool for high throughput sequence data. Available at: https://www.scirp.org/reference/referencespapers?referenceid=2781642.

Aoyama, J.J. & Storz, G. 2023. Two-for-one: regulatory RNAs that encode small proteins. Trends in Biochemical Sciences, 48(12): 1035. DOI: https://doi.org/10.1016/j.tibs.2023.09.002

Bindea, G., Mlecnik, B., Hackl, H., Charoentong, P., Tosolini, M., Kirilovsky, A., Fridman, W.H., Pagès, F., Trajanoski, Z. & Galon, J. 2009. ClueGO: a Cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks. Bioinformatics, 25(8): 1091-1093. DOI: https://doi.org/10.1093/bioinformatics/btp101

Bolger, A.M., Lohse, M. & Usadel, B. 2014. Trimmomatic: A flexible trimmer for Illumina sequence data. Bioinformatics, 30(15): 2114-2120. DOI: https://doi.org/10.1093/bioinformatics/btu170

Brantl, S. & Ul Haq, I. 2023. Small proteins in Gram-positive bacteria. FEMS Microbiology Reviews, 47(6): fuad064. DOI: https://doi.org/10.1093/femsre/fuad064

Čavužić, M.T., Larson, B.A. & Waldrop, G.L. 2024. Insights into the methodology of acetyl-CoA carboxylase inhibition. Methods in Enzymology, 708: 67-103. DOI: https://doi.org/10.1016/bs.mie.2024.10.017

Chin, C.H., Chen, S.H., Wu, H.H., Ho, C.W., Ko, M.T. & Lin, C.Y. 2014. cytoHubba: identifying hub objects and sub-networks from complex interactome. BMC Systems Biology, 8(Suppl 4): S11. DOI: https://doi.org/10.1186/1752-0509-8-S4-S11

Conesa, A. & Götz, S. 2008. Blast2GO: A comprehensive suite for functional analysis in plant genomics. International Journal of Plant Genomics, 2008: 619832. DOI: https://doi.org/10.1155/2008/619832

Diomandé, S.E., Nguyen-The, C., Guinebretière, M.H., Broussolle, V. & Brillard, J. 2015. Role of fatty acids in Bacillus environmental adaptation. Frontiers in Microbiology, 6: 813. DOI: https://doi.org/10.3389/fmicb.2015.00813

Dobin, A., Davis, C.A., Schlesinger, F., Drenkow, J., Zaleski, C., Jha, S., Batut, P., Chaisson, M. & Gingeras, T.R. 2013. STAR: Ultrafast universal RNA-seq aligner. Bioinformatics, 29(1): 15-21. DOI: https://doi.org/10.1093/bioinformatics/bts635

Esposito, S., Masetti, M., Calanca, C., Canducci, N., Rasmi, S., Fradusco, A. & Principi, N. 2025. Recent changes in the epidemiology of Group A Streptococcus infections: Observations and implications. Microorganisms, 13(8): 1871. DOI: https://doi.org/10.3390/microorganisms13081871

Ewels, P., Magnusson, M., Lundin, S. & Käller, M. 2016. MultiQC: Summarize analysis results for multiple tools and samples in a single report. Bioinformatics, 32(19): 3047-3048. DOI: https://doi.org/10.1093/bioinformatics/btw354

Finn, M.B., Ramsey, K.M., Tolliver, H.J., Dove, S.L. & Wessels, M.R. 2021. Improved transformation efficiency of group A Streptococcus by inactivation of a type I restriction modification system. PLoS ONE, 16(4): e0248201. DOI: https://doi.org/10.1371/journal.pone.0248201

Förstner, K.U., Vogel, J. & Sharma, C.M. 2014. READemption-a tool for the computational analysis of deep-sequencing-based transcriptome data. Bioinformatics, 30(23): 3421-3423. DOI: https://doi.org/10.1093/bioinformatics/btu533

Fuchs, S. & Engelmann, S. 2023. Small proteins in bacteria - Big challenges in prediction and identification. Proteomics, 23(23-24): 2200421. DOI: https://doi.org/10.1002/pmic.202200421

Garai, P. & Blanc-Potard, A. 2020. Uncovering small membrane proteins in pathogenic bacteria: Regulatory functions and therapeutic potential. Molecular Microbiology, 114(5): 710-720. DOI: https://doi.org/10.1111/mmi.14564

Germe, T.R., Bush, N.G., Baskerville, V.M., Saman, D., Benesch, J.L. & Maxwell, A. 2024. Rapid, DNA-induced interface swapping by DNA gyrase. eLife, 12: RP86722. DOI: https://doi.org/10.7554/eLife.86722

Giess, A., Jonckheere, V., Ndah, E., Chyzyńska, K., Van Damme, P. & Valen, E. 2017. Ribosome signatures aid bacterial translation initiation site identification. BMC Biology, 15(1): 1-14. DOI: https://doi.org/10.1186/s12915-017-0416-0

Jain, N., Richter, F., Adzhubei, I., Sharp, A.J. & Gelb, B.D. 2023. Small open reading frames: a comparative genetics approach to validation. BMC Genomics, 24(1): 1-11. DOI: https://doi.org/10.1186/s12864-023-09311-7

Jones, P., Binns, D., Chang, H.Y., Fraser, M., Li, W., McAnulla, C., McWilliam, H., Maslen, J., Mitchell, A., Nuka, G., Pesseat, S., Quinn, A.F., Sangrador-Vegas, A., Scheremetjew, M., Yong, S.Y., Lopez, R. & Hunter, S. 2014. InterProScan 5: Genome-scale protein function classification. Bioinformatics, 30(9): 1236-1240. DOI: https://doi.org/10.1093/bioinformatics/btu031

Ko, S.H., Cho, B.L. & Shin, D. 2025. Microproteins in metabolic biology: Emerging functions and potential roles as nutrient-linked biomarkers. International Journal of Molecular Sciences, 26(24): 11883. DOI: https://doi.org/10.3390/ijms262411883

Langfelder, P. & Horvath, S. 2008. WGCNA: An R package for weighted correlation network analysis. BMC Bioinformatics, 9(1): 1-13. DOI: https://doi.org/10.1186/1471-2105-9-559

Leong, A.Z.X., Lee, P.Y., Mohtar, M.A., Syafruddin, S.E., Pung, Y.F. & Low, T.Y. 2022. Short open reading frames (sORFs) and microproteins: an update on their identification and validation measures. Journal of Biomedical Science, 29(1): 1-15. DOI: https://doi.org/10.1186/s12929-022-00802-5

Li, H., Handsaker, B., Wysoker, A., Fennell, T., Ruan, J., Homer, N., Marth, G., Abecasis, G. & Durbin, R. 2009. The Sequence Alignment/Map format and SAMtools. Bioinformatics, 25(16): 2078-2079. DOI: https://doi.org/10.1093/bioinformatics/btp352

Liu, Z.Y. & Yu, X.Z. 2025. Engineering Bacillus subtilis for high-value bioproduction: recent advances and applications. Microbial Cell Factories, 24(1): 182. DOI: https://doi.org/10.1186/s12934-025-02818-6

Lu, Y., Ran, Y., Li, H., Wen, J., Cui, X., Zhang, X., Guan, X. & Cheng, M. 2023. Micropeptides: origins, identification, and potential role in metabolism-related diseases. Journal of Zhejiang University Science B, 24(12): 1106-1123. DOI: https://doi.org/10.1631/jzus.B2300128

Pickering, A.C. & Fitzgerald, J.R. 2020. The role of Gram-positive surface proteins in bacterial niche and host-specialization. Frontiers in Microbiology, 11: 594737. DOI: https://doi.org/10.3389/fmicb.2020.594737

Robinson, M.D., McCarthy, D.J. & Smyth, G.K. 2009. edgeR: A Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics, 26(1): 139-140. DOI: https://doi.org/10.1093/bioinformatics/btp616

Roginski, P., Papadopoulos, C., Herman, S., Baumann, A., Grislain, A. & Lopes, A. 2026. Impact of GC content on de novo gene birth. Nature Communications, 17(1): 1268. DOI: https://doi.org/10.1038/s41467-025-68022-7

Rowland, C.E., Newman, H., Martin, T.T., Dods, R., Bournakas, N., Wagstaff, J.M., Lewis, N., Stanway, S.J., Balmforth, M., Kessler, C., van Rietschoten, K., Bellini, D., Roper, D.I., Lloyd, A.J., Dowson, C.G., Skynner, M.J., Beswick, P. & Dawson, M.J. 2025. Discovery and chemical optimisation of a potent, bi-cyclic antimicrobial inhibitor of Escherichia coli PBP3. Communications Biology, 8(1): 819. DOI: https://doi.org/10.1038/s42003-025-08246-x

Shannon, P., Markiel, A., Ozier, O., Baliga, N.S., Wang, J.T., Ramage, D., Amin, N., Schwikowski, B. & Ideker, T. 2003. Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Research, 13(11): 2498-2504. DOI: https://doi.org/10.1101/gr.1239303

Shin, S., Jiang, D., Yu, J., Yang, C., Jeong, W., Li, J., Bae, J., Shin, J., An, K., Kim, W. & Cho, N.J. 2025. Interaction dynamics of liposomal fatty acids with Gram-positive bacterial membranes. ACS Applied Materials & Interfaces, 17(16): 23666-23679. DOI: https://doi.org/10.1021/acsami.5c00787

Simoens, L., Fijalkowski, I. & Van Damme, P. 2023. Exposing the small protein load of bacterial life. FEMS Microbiology Reviews, 47(6): fuad063. DOI: https://doi.org/10.1093/femsre/fuad063

Stan, G., Lorimer, G.H. & Thirumalai, D. 2022. Friends in need: How chaperonins recognize and remodel proteins that require folding assistance. Frontiers in Molecular Biosciences, 9: 1071168. DOI: https://doi.org/10.3389/fmolb.2022.1071168

Taguchi, H. & Koike-Takeshita, A. 2023. In vivo client proteins of the chaperonin GroEL-GroES provide insight into the role of chaperones in protein evolution. Frontiers in Molecular Biosciences, 10: 1091677. DOI: https://doi.org/10.3389/fmolb.2023.1091677

Thaqi, S.K., Siani, R., Chiba, A., Peine, M., Baum, C., Witting, M., Walch, S., Leinweber, P., Schloter, M. & Schulz, S. 2025. Strain-specific strategies underlie convergent phosphate solubilization in Bacillus. ISME Communications, 5(1): ycaf208. DOI: https://doi.org/10.1093/ismeco/ycaf208

Upender, I., Yoshida, O., Schrecengost, A., Ranson, H., Wu, Q., Rowley, D.C., Kishore, S., Cywes, C., Miller, E.L. & Whalen, K.E. 2023. A marine-derived fatty acid targets the cell membrane of Gram-positive bacteria. Journal of Bacteriology, 205(11): 200310-23. DOI: https://doi.org/10.1128/jb.00310-23

Vanni, C., Schechter, M.S., Acinas, S.G., Barberán, A., Buttigieg, P.L., Casamayor, E.O., Delmont, T.O., Duarte, C.M., Eren, A.M., Finn, R.D., Kottmann, R., Mitchell, A., Sánchez, P., Sirén, K., Steinegger, M., Glöckner, F.O. & Fernandez-Guerra, A. 2022. Unifying the known and unknown microbial coding sequence space. eLife, 11: e67667. DOI: https://doi.org/10.7554/eLife.67667

Venkat, K., Hoyos, M., Haycocks, J.R., Cassidy, L., Engelmann, B., Rolle-Kampczyk, U., von Bergen, M., Tholey, A., Grainger, D.C. & Papenfort, K. 2021. A dual-function RNA balances carbon uptake and central metabolism in Vibrio cholerae. The EMBO Journal, 40(24): e108542. DOI: https://doi.org/10.15252/embj.2021108542

Wu, H.Y.L., Ai, Q., Teixeira, R.T., Nguyen, P.H.T., Song, G., Montes, C., Elmore, J.M., Walley, J.W. & Hsu, P.Y. 2024. Improved super-resolution ribosome profiling reveals prevalent translation of upstream ORFs and small ORFs in Arabidopsis. The Plant Cell, 36(3): 510-539. DOI: https://doi.org/10.1093/plcell/koad290

Xie, C., Künzel, S., Zhang, W., Hathaway, C.A., Tworoger, S.S. & Tautz, D. 2025. Patterns of extreme outlier gene expression suggest an edge of chaos effect in transcriptomic networks. Genome Biology, 26(1): 272. DOI: https://doi.org/10.1186/s13059-025-03709-0

Yang, Q., Li, N., Zheng, Y., Tian, Y., Liang, Q., Zhao, M., Chu, H., Gong, Y., Wu, T., Wei, S., Wang, H., Yan, G., Li, F. & Lei, L. 2025. Identification and characterization of ugpE associated with the full virulence of Streptococcus suis. Veterinary Research, 56(1): 82. DOI: https://doi.org/10.1186/s13567-025-01513-z

Yuan, J., Koch, H.G. & Berghoff, B.A. 2025. Functional diversity and molecular interactions of small membrane proteins in bacteria. MicroLife, 6: uqaf035. DOI: https://doi.org/10.1093/femsml/uqaf035

Yu, S.H., Vogel, J. & Förstner, K.U. 2018. ANNOgesic: A Swiss army knife for the RNA-seq based annotation of bacterial/archaeal genomes. GigaScience, 7(9): giy096. DOI: https://doi.org/10.1093/gigascience/giy096

Published

28-09-2026

How to Cite

Khew, C. Y., Mohd Azali, N. A., Muhammad Bahrain, N. S., & Mohd-Assaad, N. . (2026). In Silico Profiling of Small Open Reading Frames in hsdM-Deficient Group A Streptococcus and Their Predicted Role in Membrane Adaptation. Malaysian Applied Biology, 55(3), 159–167. https://doi.org/10.55230/mabjournal.v55i3.3857

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Section

Research Articles