Genomic analysis identifies candidate SOS-associated and EPS/envelope-remodeling islands in a drinking-water Stenotrophomonas maltophilia complex isolate.

Λεπτομέρειες βιβλιογραφικής εγγραφής
Τίτλος: Genomic analysis identifies candidate SOS-associated and EPS/envelope-remodeling islands in a drinking-water Stenotrophomonas maltophilia complex isolate.
Συγγραφείς: Hassan F; Department of Microbiology and Immunology, Faculty of Pharmacy, Modern University for Technology and Information, Cairo, Egypt., Türkyılmaz S; Department of Microbiology, Faculty of Veterinary Medicine, Aydın Adnan Menderes University, Aydın, Türkiye., El-Mahdy TS; Department of Microbiology and Immunology, Faculty of Pharmacy, Capital (formerly Helwan) University, Cairo, Egypt. taghrid-elmahdy@pharm.capu.edu.eg.
Πηγή: Scientific reports [Sci Rep] 2026 Jun 28; Vol. 16 (1). Date of Electronic Publication: 2026 Jun 28.
Τύπος έκδοσης: Journal Article
Γλώσσα: English
Στοιχεία περιοδικού: Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: MEDLINE
Imprint Name(s): Original Publication: London : Nature Publishing Group, copyright 2011-
Ιατρικοί όροι (MeSH): Stenotrophomonas maltophilia*/genetics , Stenotrophomonas maltophilia*/isolation & purification , Stenotrophomonas maltophilia*/drug effects , Stenotrophomonas maltophilia*/classification , Drinking Water*/microbiology , SOS Response, Genetics*/genetics , Extracellular Polymeric Substance Matrix*/genetics , Extracellular Polymeric Substance Matrix*/metabolism , Genomic Islands*, Biofilms/growth & development ; Biofilms/drug effects ; Genomics/methods ; Genome, Bacterial ; Phylogeny
Περίληψη: Drinking-water distribution systems (DWDS) impose ecological pressures shaped by oligotrophy, surface attachment, hydraulic fluctuation, and residual disinfectants. These conditions may favor stress-tolerant and biofilm-capable microorganisms, including members of the Stenotrophomonas maltophilia complex. Although this complex is clinically relevant because of intrinsic multidrug resistance and opportunistic pathogenic potential, the strain-level genomic features that may support persistence in disinfectant-managed water systems remain incompletely characterized. We applied an integrated genome-resolved framework to Stenotrophomonas sp. NG-SM01, a drinking-water isolate assigned to the S. maltophilia complex genomospecies Sgn4. Hybrid sequencing was used to generate a closed genome assembly, followed by phylogenomics and MLST for taxonomic placement, pangenome analysis for gene-content context, and genomic-island mapping to identify candidate accessory regions. Functional profiling included BacMet screening for metal/biocide tolerance-associated loci and genome-scale metabolic reconstruction using gapseq. Phenotypic assays assessed crystal-violet biofilm biomass and acute hydrogen peroxide (H₂O₂) survival as a general oxidative-stress proxy. NG-SM01 clustered within the Sgn4 genomospecies of the S. maltophilia complex and formed a strongly supported sister lineage to its closest available reference genome, GCF_025642255.1 (UFboot = 100). MLST confirmed a newly curated guaA allele (guaA-909), and the allelic profile was assigned ST1409. Disk diffusion showed limited inhibition by several antimicrobial agents; however, categorical interpretation using S. maltophilia-specific CLSI criteria was applied only to levofloxacin and trimethoprim-sulfamethoxazole, both of which were susceptible. Genomic-island analysis highlighted two candidate loci potentially relevant to stress adaptation: GI_6, carrying an EPS/envelope-remodeling cassette including algL and a GT26-family glycosyltransferase within a panel-restricted architecture, and GI_4, an IME-associated region located near SOS-response genes including recA, recX, and lexA. NG-SM01 formed reproducible moderate biofilm biomass under static microtiter conditions and showed plateau-like survival dynamics during acute H₂O₂ exposure. BacMet screening prioritized Tier-1 metal/biocide tolerance-associated signals, including CzcR-like and AdeL-like regulators and a YfeB-like transport component, while gapseq reconstruction predicted broad transport capacity, including 1,920 transporter entries, of which 23% were metal-related. This single-isolate study identifies genomic and phenotypic features potentially relevant to persistence in a drinking-water S. maltophilia complex Sgn4 isolate. The combined evidence supports a hypothesis-generating "Switch-Shield" framework, in which stress-response regulation and mobilome-associated plasticity may represent a candidate "switch," while EPS/envelope remodeling may represent a candidate protective "shield." However, direct causality between GI_4/GI_6 and the observed phenotypes was not demonstrated. Future validation using transcriptomics, mutant-based assays, and DWDS-mimetic free-chlorine or chloramine exposure models, including flow/pipe reactors with detachment and shedding measurements, will be required to test this model.
(© 2026. The Author(s).)
Competing Interests: Declarations. Competing interests: The authors declare no competing interests.
References: Douterelo, I., Husband, S., Loza, V. & Boxall, J. B. Dynamics of biofilm regrowth in drinking water distribution systems. Appl. Environ. Microbiol. 82 (14), 4155–4168. https://doi.org/10.1128/AEM.00109-16 (2016). (PMID: 10.1128/AEM.00109-16272081194959196)
Fish, K. E. & Boxall, J. B. Biofilm microbiome (re)growth dynamics in drinking water distribution systems are impacted by chlorine concentration. Front. Microbiol. 9, 2519. https://doi.org/10.3389/fmicb.2018.02519 (2018). (PMID: 10.3389/fmicb.2018.02519304597306232884)
Erdei-Tombor, P., Kiskó, G. & Taczman-Brückner, A. Biofilm formation in water distribution systems. Processes 12 (2), 280. https://doi.org/10.3390/pr12020280 (2024). (PMID: 10.3390/pr12020280)
World Health Organization. Guidelines for drinking-water quality: fourth edition incorporating the first addendum (World Health Organization, 2022).
National Health and Medical Research Council (NHMRC). Chlorine. Australian Drinking Water Guidelines. https://guidelines.nhmrc.gov.au/australian-drinking-water-guidelines/part-5/physical-chemical-characteristics/chlorine . Accessed 6 Feb 2026.
Queensland Health. Chlorine dosing for water-related hazards. Queensland Government. https://www.health.qld.gov.au/public-health/industry-environment/environment-land-water/water/risk-management/plan/manage/chlorine-dosing . Accessed 6 Feb 2026.
Bruno, A. et al. Changes in the drinking water microbiome: effects of water treatments along the flow of two drinking water treatment plants in an urbanized area, Milan (Italy). Front. Microbiol. 9, 2557. https://doi.org/10.3389/fmicb.2018.02557 (2018). (PMID: 10.3389/fmicb.2018.02557304298326220058)
Zhao, W. et al. Chlorination-induced spread of antibiotic resistance genes in drinking water systems. Water Res. 274, 123092. https://doi.org/10.1016/j.watres.2025.123092 (2025). (PMID: 10.1016/j.watres.2025.12309239787839)
Brooke, J. S. Advances in the microbiology of Stenotrophomonas maltophilia. Clin. Microbiol. Rev. 34 (3), e00030–e00019. https://doi.org/10.1128/CMR.00030-19 (2021). (PMID: 10.1128/CMR.00030-19340434578262804)
Gil-Gil, T., Martínez, J. L. & Blanco, P. Mechanisms of antimicrobial resistance in Stenotrophomonas maltophilia: a review of current knowledge. Expert Rev. Anti Infect. Ther. 18 (4), 335–347. https://doi.org/10.1080/14787210.2020.1730178 (2020). (PMID: 10.1080/14787210.2020.173017832052662)
Mojica, M. F. et al. Clinical challenges treating Stenotrophomonas maltophilia infections: an update. JAC Antimicrob. Resist. 4 (3), dlac040. https://doi.org/10.1093/jacamr/dlac040 (2022). (PMID: 10.1093/jacamr/dlac040355290519071536)
Bhaumik, R., Aungkur, N. Z. & Anderson, G. G. A guide to Stenotrophomonas maltophilia virulence capabilities, as we currently understand them. Front. Cell. Infect. Microbiol. 13, 1322853. https://doi.org/10.3389/fcimb.2023.1322853 (2024). (PMID: 10.3389/fcimb.2023.13228533827473810808757)
Zimmermann, J., Kaleta, C. & Waschina, S. gapseq: informed prediction of bacterial metabolic pathways and reconstruction of accurate metabolic models. Genome Biol. 22, 81. https://doi.org/10.1186/s13059-021-02295-1 (2021). (PMID: 10.1186/s13059-021-02295-1336917707949252)
LeChevallier, M. W., Prosser, T. & Stevens, M. Opportunistic pathogens in drinking water distribution systems: a review. Microorganisms 12 (5), 916. https://doi.org/10.3390/microorganisms12050916 (2024). (PMID: 10.3390/microorganisms120509163879275111124194)
Clinical and Laboratory Standards Institute (CLSI). Performance standards for antimicrobial susceptibility testing. 35th ed. CLSI supplement M100. Wayne (PA): CLSI; (2025).
Kolmogorov, M., Yuan, J., Lin, Y. & Pevzner, P. A. Assembly of long, error-prone reads using repeat graphs. Nat. Biotechnol. 37 (5), 540–546. https://doi.org/10.1038/s41587-019-0072-8 (2019). (PMID: 10.1038/s41587-019-0072-830936562)
Bouras, G. et al. How low can you go? Short-read polishing of Oxford Nanopore bacterial genome assemblies. Microb. Genom. 0, 001254. https://doi.org/10.1099/mgen.0.001254 (2024). (PMID: 10.1099/mgen.0.001254)
Gurevich, A., Saveliev, V., Vyahhi, N. & Tesler, G. QUAST: quality assessment tool for genome assemblies. Bioinformatics 29 (8), 1072–1075. https://doi.org/10.1093/bioinformatics/btt086 (2013). (PMID: 10.1093/bioinformatics/btt086234223393624806)
Chklovski, A., Parks, D. H., Woodcroft, B. J. & Tyson, G. W. CheckM2: a rapid, scalable and accurate tool for assessing microbial genome quality using machine learning. Nat. Methods. 20 (8), 1203–1212. https://doi.org/10.1038/s41592-023-01940-w (2023). (PMID: 10.1038/s41592-023-01940-w37500759)
Ondov, B. D. et al. Mash: fast genome and metagenome distance estimation using MinHash. Genome Biol. 17, 132. https://doi.org/10.1186/s13059-016-0997-x (2016). (PMID: 10.1186/s13059-016-0997-x273238424915045)
Schwengers, O. et al. Bakta: rapid and standardized annotation of bacterial genomes via alignment-free sequence identification. Microb. Genom. 7 (11), 000685. https://doi.org/10.1099/mgen.0.000685 (2021). (PMID: 10.1099/mgen.0.000685347393698743544)
Seemann, T. Prokka: rapid prokaryotic genome annotation. Bioinformatics 30 (14), 2068–2069. https://doi.org/10.1093/bioinformatics/btu153 (2014). (PMID: 10.1093/bioinformatics/btu15324642063)
Cantalapiedra, C. P., Hernández-Plaza, A., Letunic, I., Bork, P. & Huerta-Cepas, J. eggNOG-mapper v2: functional annotation, orthology assignments, and domain prediction at the metagenomic scale. Mol. Biol. Evol. 38 (12), 5825–5829. https://doi.org/10.1093/molbev/msab293 (2021). (PMID: 10.1093/molbev/msab293345974058662613)
Jones, P. et al. InterProScan 5: genome-scale protein function classification. Bioinformatics 30 (9), 1236–1240. https://doi.org/10.1093/bioinformatics/btu031 (2014). (PMID: 10.1093/bioinformatics/btu031244516263998142)
Jolley, K. A., Bray, J. E. & Maiden, M. C. J. Open-access bacterial population genomics: BIGSdb software, the PubMLST.org website and their applications. Wellcome Open. Res. 3, 124 (2018). PMID:30345391. (PMID: 10.12688/wellcomeopenres.14826.1303453916192448)
Tonkin-Hill, G. et al. Producing polished prokaryotic pangenomes with the Panaroo pipeline. Genome Biol. 21 (1), 180. https://doi.org/10.1186/s13059-020-02090-4 (2020). (PMID: 10.1186/s13059-020-02090-4326988967376924)
Brynildsrud, O., Bohlin, J., Scheffer, L. & Eldholm, V. Rapid scoring of genes in microbial pan-genome-wide association studies with Scoary. Genome Biol. 17, 238. https://doi.org/10.1186/s13059-016-1108-8 (2016). (PMID: 10.1186/s13059-016-1108-8278876425124306)
Zheng, J. et al. dbCAN3: automated carbohydrate-active enzyme and substrate annotation. Nucleic Acids Res. 51 (W1), W115–W121. https://doi.org/10.1093/nar/gkad328 (2023). (PMID: 10.1093/nar/gkad3283712564910320055)
Bertelli, C. et al. IslandViewer 4: expanded prediction of genomic islands for larger-scale datasets. Nucleic Acids Res. 45 (W1), W30–W35. https://doi.org/10.1093/nar/gkx343 (2017). (PMID: 10.1093/nar/gkx343284724135570257)
Wang, M. et al. ICEberg 3.0: functional categorization and analysis of integrative and conjugative elements in bacteria. Nucleic Acids Res. 52 (D1), D732–D737. https://doi.org/10.1093/nar/gkad935 (2024). (PMID: 10.1093/nar/gkad9353787046710767825)
Arndt, D. et al. PHASTER: a better, faster version of the PHAST phage search tool. Nucleic Acids Res. 44 (W1), W16–W21. https://doi.org/10.1093/nar/gkw387 (2016). (PMID: 10.1093/nar/gkw387271419664987931)
Bouras, G. et al. Pharokka: a fast scalable bacteriophage annotation tool. Bioinformatics 39 (1), btac776. https://doi.org/10.1093/bioinformatics/btac776 (2023). (PMID: 10.1093/bioinformatics/btac776364538619805569)
Feldgarden, M. et al. AMRFinderPlus and the Reference Gene Catalog facilitate examination of the genomic links among antimicrobial resistance, stress response, and virulence. Sci. Rep. 11, 12728. https://doi.org/10.1038/s41598-021-91456-0 (2021). (PMID: 10.1038/s41598-021-91456-0341353558208984)
Seemann, T. & ABRicate GitHub. https://github.com/tseemann/abricate . Accessed 6 Feb 2026.
Liu, B., Zheng, D., Jin, Q., Chen, L. & Yang, J. VFDB 2019: a comparative pathogenomic platform with an interactive web interface. Nucleic Acids Res. 47 (D1), D687–D692 (2019). (PMID: 10.1093/nar/gky1080303952556324032)
Alcock, B. P. et al. CARD 2020: antibiotic resistome surveillance with the comprehensive antibiotic resistance database. Nucleic Acids Res. 48 (D1), D517–D525. https://doi.org/10.1093/nar/gkz935 (2020). (PMID: 10.1093/nar/gkz935316654417145624)
O’Toole, G. A. Microtiter dish biofilm formation assay. J. Vis. Exp. (47), 2437. https://doi.org/10.3791/2437 (2011).
Macvanin, M. & Hughes, D. Assays of sensitivity of antibiotic-resistant bacteria to hydrogen peroxide and measurement of catalase activity. Methods Mol. Biol. 642, 95–103. https://doi.org/10.1007/978-1-60327-279-7_7 (2010). (PMID: 10.1007/978-1-60327-279-7_720401588)
Miles, A. A., Misra, S. S. & Irwin, J. O. The estimation of the bactericidal power of the blood. J. Hyg. (Camb). 38 (6), 732–749. https://doi.org/10.1017/S002217240001158X (1938). (PMID: 10.1017/S002217240001158X204754672199673)
Clinical and Laboratory Standards Institute (CLSI). Methods for Determining Bactericidal Activity of Antimicrobial Agents (M26-A) (CLSI, 1999).
Welch, B. L. The generalisation of Student’s problem when several different population variances are involved. Biometrika 34 (1–2), 28–35. https://doi.org/10.1093/biomet/34.1-2.28 (1947). (PMID: 10.1093/biomet/34.1-2.2820287819)
Holm, S. A simple sequentially rejective multiple test procedure. Scand. J. Stat. 6 (2), 65–70 (1979).
Hedges, L. V. Distribution theory for Glass’s estimator of effect size and related estimators. J. Educ. Stat. 6 (2), 107–128 (1981). (PMID: 10.3102/10769986006002107)
Gilchrist, C. L. M. & Chooi, Y-H. clinker & clustermap.js: automatic generation of gene cluster comparison figures. Bioinformatics 37 (16), 2473–2475. https://doi.org/10.1093/bioinformatics/btab007 (2021). (PMID: 10.1093/bioinformatics/btab00733459763)
Stothard, P. & Wishart, D. S. Circular genome visualization and exploration using CGView. Bioinformatics 21 (4), 537–539. https://doi.org/10.1093/bioinformatics/bti054 (2005). (PMID: 10.1093/bioinformatics/bti05415479716)
Gröschel, M. I. et al. The phylogenetic landscape and nosocomial spread of the multidrug-resistant opportunist Stenotrophomonas maltophilia. Nat. Commun. 11 (1), 2044. https://doi.org/10.1038/s41467-020-15123-0 (2020). (PMID: 10.1038/s41467-020-15123-0323413467184733)
Pompilio, A. et al. Biofilm formation among Stenotrophomonas maltophilia isolates has clinical relevance: the ANSELM prospective multicenter study. Microorganisms 9 (1), 49. https://doi.org/10.3390/microorganisms9010049 (2021). (PMID: 10.3390/microorganisms9010049)
Charron, R., Boulanger, M., Briandet, R. & Bridier, A. Biofilms as protective cocoons against biocides: from bacterial adaptation to One Health issues. Microbiol. (Reading). 169, 001340. https://doi.org/10.1099/mic.0.001340 (2023). (PMID: 10.1099/mic.0.001340)
McDaniel, M. S. et al. Comparative genomics of clinical Stenotrophomonas maltophilia isolates reveals regions of diversity which correlate with colonization and persistence in vivo. PubMed (preprint record).
Podlesek, Z. & Žgur Bertok, D. The DNA damage inducible SOS response is a key player in the generation of bacterial persister cells and population-wide tolerance. Front. Microbiol. 11, 1785. https://doi.org/10.3389/fmicb.2020.01785 (2020). (PMID: 10.3389/fmicb.2020.01785328494037417476)
Chong, S. Y. et al. Levofloxacin efflux and smeD in clinical isolates of Stenotrophomonas maltophilia. Microb. Drug Resist. 23 (2), 163–168. https://doi.org/10.1089/mdr.2015.0228 (2017). (PMID: 10.1089/mdr.2015.022827294684)
Huedo, P. et al. Draft genome sequence of Stenotrophomonas maltophilia strain M30, isolated from a chronic pressure ulcer in an elderly patient. Genome Announc. 2 (3), e00576–e00514. https://doi.org/10.1128/genomeA.00576-14. (2014). (PMID: 10.1128/genomeA.00576-14249260594056302)
Pal, C., Bengtsson-Palme, J., Rensing, C., Kristiansson, E. & Larsson, D. G. J. BacMet: antibacterial biocide and metal resistance genes database. Nucleic Acids Res. 42 (D1), D737–D743. https://doi.org/10.1093/nar/gkt1252 (2014). (PMID: 10.1093/nar/gkt125224304895)
Huang, C., Lin, L. & Kuo, S. Risk factors for mortality in Stenotrophomonas maltophilia bacteremia: a meta-analysis. Infect Dis (Lond). 56(5):335–347. 10.1080/23744235.2024.2324365. (2024).
Contributed Indexing: Keywords: Stenotrophomonas maltophilia complex; Biofilm adaptation; Drinking-water distribution system (DWDS); Environmental surveillance; Genomic islands; Persistence
Substance Nomenclature: 0 (Drinking Water)
Entry Date(s): Date Created: 20260628 Date Completed: 20260628 Latest Revision: 20260726
Update Code: 20260726
PubMed Central ID: PMC13310844
DOI: 10.1038/s41598-026-58581-0
PMID: 42366209
Βάση Δεδομένων: MEDLINE
Περιγραφή
ISSN:2045-2322
DOI:10.1038/s41598-026-58581-0