Skip to content
1887

Abstract

() is an important cause of healthcare-associated infections (HAIs). In low- and middle-income countries, HAI due to disproportionately affects neonates. In this study, we investigated the genomic changes that occurred during long-term circulation of a sequence type (ST) 39 clone, causing a disproportionate number of infections on the neonatal ward at a tertiary healthcare facility in Malawi in 2017.

We analysed whole-genome sequences of ST39 collected from Queen Elizabeth Central Hospital over a 20-year period, including the generation of several high-quality hybrid genomes. We compared virulence markers, antibiotic resistance determinants and mobile genetic elements, focusing on variable regions between strains from the outbreak clone in 2017 and genomes from other co-occurring ST39 lineages.

We identified eight variable genomic regions that demonstrate the plasticity of within ST, including the role of bacteriophages in shaping the genome of ST39.

The analysed ST39 lineages have a highly variable genome capable of incorporating large genomic regions during prolonged hospital circulation, which may offer a selective advantage in hospital environments and provide resistance to antimicrobial agents.

Funding
This study was supported by the:
  • Biotechnology and Biological Sciences Research Council (Award BB/V011278/1)
    • Principal Award Recipient: EvaHeinz
  • Biotechnology and Biological Sciences Research Council (Award BB/V011278/2)
    • Principal Award Recipient: EvaHeinz
  • Wellcome Trust (Award 217303/Z/19/Z)
    • Principal Award Recipient: EvaHeinz
  • Bill and Melinda Gates Foundation (Award INV-005180)
    • Principal Award Recipient: NicholasA Feasey
  • This is an open-access article distributed under the terms of the Creative Commons Attribution License. This article was made open access via a Publish and Read agreement between the Microbiology Society and the corresponding author’s institution.
Loading

Article metrics loading...

/content/journal/mgen/10.1099/mgen.0.001673
2026-04-01
2026-04-22

Metrics

Loading full text...

Full text loading...

/deliver/fulltext/mgen/12/4/mgen001673.html?itemId=/content/journal/mgen/10.1099/mgen.0.001673&mimeType=html&fmt=ahah

References

  1. Podschun R, Ullmann U. Klebsiella spp. as nosocomial pathogens: epidemiology, taxonomy, typing methods, and pathogenicity factors. Clin Microbiol Rev 1998; 11:589–603 [View Article] [PubMed]
    [Google Scholar]
  2. Tryfinopoulou K, Linkevicius M, Pappa O, Alm E, Karadimas K et al. Emergence and persistent spread of carbapenemase-producing Klebsiella pneumoniae high-risk clones in Greek hospitals, 2013 to 2022. Euro Surveill 2023; 28:2300571 [View Article] [PubMed]
    [Google Scholar]
  3. Fang C-T. Klebsiella pneumoniae meningitis: timing of antimicrobial therapy and prognosis. QJM 2000; 93:45–53 [View Article]
    [Google Scholar]
  4. Holt KE, Wertheim H, Zadoks RN, Baker S, Whitehouse CA et al. Genomic analysis of diversity, population structure, virulence, and antimicrobial resistance in Klebsiella pneumoniae, an urgent threat to public health. Proc Natl Acad Sci USA 2015; 112:E3574–81 [View Article] [PubMed]
    [Google Scholar]
  5. Sati H, Carrara E, Savoldi A, Hansen P, Garlasco J et al. The WHO bacterial priority pathogens list 2024: a prioritisation study to guide research, development, and public health strategies against antimicrobial resistance. Lancet Infect Dis 2025; 25:1033–1043 [View Article] [PubMed]
    [Google Scholar]
  6. WHO Prioritization of pathogens to guide discovery, research and development of new antibiotics for drug-resistant bacterial infections, including tuberculosis. In , 12 ed. Geneva: World Health Organization; 2017
  7. Wyres KL, Lam MMC, Holt KE. Population genomics of Klebsiella pneumoniae. Nat Rev Microbiol 2020; 18:344–359 [View Article] [PubMed]
    [Google Scholar]
  8. Sands K, Carvalho MJ, Portal E, Thomson K, Dyer C et al. Characterization of antimicrobial-resistant Gram-negative bacteria that cause neonatal sepsis in seven low- and middle-income countries. Nat Microbiol 2021; 6:512–523 [View Article] [PubMed]
    [Google Scholar]
  9. Bassat Q, Blau DM, Ogbuanu IU, Samura S, Kaluma E et al. Causes of death among infants and children in the child health and mortality prevention surveillance (CHAMPS) network. JAMA Netw Open 2023; 6:e2322494 [View Article] [PubMed]
    [Google Scholar]
  10. Heinz E, Pearse O, Zuza A, Bilima S, Msefula C et al. Longitudinal analysis within one hospital in sub-Saharan Africa over 20 years reveals repeated replacements of dominant clones of Klebsiella pneumoniae and stresses the importance to include temporal patterns for vaccine design considerations. Genome Med 2024; 16:67 [View Article] [PubMed]
    [Google Scholar]
  11. Cornick J, Musicha P, Peno C, Seager E, Iroh Tam P-Y et al. Genomic investigation of a suspected Klebsiella pneumoniae outbreak in a neonatal care unit in sub-Saharan Africa. Microb Genom 2021; 7:000703 [View Article] [PubMed]
    [Google Scholar]
  12. De Coster W, Rademakers R. NanoPack2: population-scale evaluation of long-read sequencing data. Bioinformatics 2023; 39:btad311 [View Article] [PubMed]
    [Google Scholar]
  13. Andrews S. FastQC. 0.12.0 ed2023. p. a quality control tool for high throughput sequence data.
  14. Bankevich A, Nurk S, Antipov D, Gurevich AA, Dvorkin M et al. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J Comput Biol 2012; 19:455–477 [View Article] [PubMed]
    [Google Scholar]
  15. Prjibelski A, Antipov D, Meleshko D, Lapidus A, Korobeynikov A. Using SPAdes de novo assembler. Curr Protoc Bioinformatics 2020; 70:e102 [View Article] [PubMed]
    [Google Scholar]
  16. Seemann T. Shovill: assemble bacterial isolate genomes from illumina paired-end reads. 1.1.0 ed. github2020.
  17. Wick RR, Judd LM, Cerdeira LT, Hawkey J, Méric G et al. Trycycler: consensus long-read assemblies for bacterial genomes. Genome Biol 2021; 22:266 [View Article] [PubMed]
    [Google Scholar]
  18. Kolmogorov M, Yuan J, Lin Y, Pevzner PA. Assembly of long, error-prone reads using repeat graphs. Nat Biotechnol 2019; 37:540–546 [View Article] [PubMed]
    [Google Scholar]
  19. Vaser R, Šikić M. Raven: a de novo genome assembler for long reads. bioRxiv 2021 [View Article]
    [Google Scholar]
  20. Wick RR, Holt KE. Benchmarking of long-read assemblers for prokaryote whole genome sequencing. F1000Res 2021; 8:2138 [View Article]
    [Google Scholar]
  21. Wick RR, Holt KE. Polypolish: short-read polishing of long-read bacterial genome assemblies. PLoS Comput Biol 2022; 18:e1009802 [View Article] [PubMed]
    [Google Scholar]
  22. Zimin AV, Salzberg SL. The genome polishing tool POLCA makes fast and accurate corrections in genome assemblies. PLoS Comput Biol 2020; 16:e1007981 [View Article] [PubMed]
    [Google Scholar]
  23. Seemann T. Prokka: rapid prokaryotic genome annotation. Bioinformatics 2014; 30:2068–2069 [View Article]
    [Google Scholar]
  24. Feldgarden M, Brover V, Gonzalez-Escalona N, Frye JG, Haendiges J et al. AMRFinderPlus and the reference gene catalog facilitate examination of the genomic links among antimicrobial resistance, stress response, and virulence. Sci Rep 2021; 11:12728 [View Article] [PubMed]
    [Google Scholar]
  25. Seemann T. ABRicate; 2020 https://github.com/tseemann/abricate
  26. Carattoli A, Zankari E, García-Fernández A, Voldby Larsen M, Lund O et al. In Silico detection and typing of plasmids using plasmidfinder and plasmid multilocus sequence typing. Antimicrob Agents Chemother 2014; 58:3895–3903 [View Article]
    [Google Scholar]
  27. Robertson J, Nash JHE. MOB-suite: software tools for clustering, reconstruction and typing of plasmids from draft assemblies. Microb Genom 2018; 4: [View Article]
    [Google Scholar]
  28. Arndt D, Grant JR, Marcu A, Sajed T, Pon A et al. PHASTER: a better, faster version of the PHAST phage search tool. Nucleic Acids Res 2016; 44:W16–W21 [View Article]
    [Google Scholar]
  29. Wang RH, Yang S, Liu Z, Zhang Y, Wang X et al. PhageScope: a well-annotated bacteriophage database with automatic analyses and visualizations. Nucleic Acids Res 2024; 52:D756–D761 [View Article]
    [Google Scholar]
  30. Lam MMC, Wick RR, Watts SC, Cerdeira LT, Wyres KL et al. A genomic surveillance framework and genotyping tool for Klebsiella pneumoniae and its related species complex. Nat Commun 2021; 12:4188 [View Article] [PubMed]
    [Google Scholar]
  31. Seemann T. Snippy: rapid haploid variant calling and core genome alignment; 2015 https://github.com/tseemann/snippy
  32. Croucher NJ, Page AJ, Connor TR, Delaney AJ, Keane JA et al. Rapid phylogenetic analysis of large samples of recombinant bacterial whole genome sequences using Gubbins. Nucleic Acids Res 2015; 43:e15–e [View Article] [PubMed]
    [Google Scholar]
  33. Minh BQ, Schmidt HA, Chernomor O, Schrempf D, Woodhams MD et al. IQ-TREE 2: new models and efficient methods for phylogenetic inference in the genomic era. Mol Biol Evol 2020; 37:1530–1534 [View Article]
    [Google Scholar]
  34. Nguyen L-T, Schmidt HA, von Haeseler A, Minh BQ. IQ-TREE: a fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol Biol Evol 2015; 32:268–274 [View Article] [PubMed]
    [Google Scholar]
  35. Wailan AM, Coll F, Heinz E, Tonkin-Hill G, Corander J et al. rPinecone: define sub-lineages of a clonal expansion via a phylogenetic tree. Microb Genom 2019; 5: [View Article]
    [Google Scholar]
  36. Schliep K. Phangorn: phylogenetic reconstruction and analysis; 2023
  37. Team RC. R: A Language and Environment for Statistical Computing. Vienna, Austria; 2022
  38. Yu G, Smith DK, Zhu H, Guan Y, Lam T et al. Ggtree: an r package for visualization and annotation of phylogenetic trees with their covariates and other associated data. Methods Ecol Evol 2017; 8:28–36 [View Article]
    [Google Scholar]
  39. Suchard MA, Lemey P, Baele G, Ayres DL, Drummond AJ et al. Bayesian phylogenetic and phylodynamic data integration using BEAST 1.10. Virus Evol 2018; 4:vey016 [View Article] [PubMed]
    [Google Scholar]
  40. Rambaut A, Lam TT, Max Carvalho L, Pybus OG. Exploring the temporal structure of heterochronous sequences using TempEst (formerly Path-O-Gen). Virus Evol 2016; 2:vew007 [View Article]
    [Google Scholar]
  41. Kassambara A. Ggpubr: ggplot2 based publication ready plots; 2023
  42. Wickham H, Averick M, Bryan J, Chang W, McGowan L et al. Welcome to the Tidyverse. JOSS 2019; 4:1686 [View Article]
    [Google Scholar]
  43. Hodge D. Ggblanket: simplify ggplot2 visualisation; 2025
  44. Pedersen TL. Patchwork: the composer of plots; 2024
  45. Zaborskytė G, Hjort K, Lytsy B, Sandegren L. Parallel within-host evolution alters virulence factors in an opportunistic Klebsiella pneumoniae during a hospital outbreak. Nat Commun 2025; 16:8727 [View Article] [PubMed]
    [Google Scholar]
  46. Musicha P. n.d. Transmission of extended spectrum beta-lactamase-producing escherichia coli and antimicrobial resistance gene flow across one health compartments in eastern africa: a whole-genome sequence analysis from a prospective cohort study. Lancet Microbe
    [Google Scholar]
  47. Lewis JM, Lester R, Mphasa M, Banda R, Edwards T et al. Emergence of carbapenemase-producing Enterobacteriaceae in Malawi. J Glob Antimicrob Resist 2020; 20:225–227 [View Article] [PubMed]
    [Google Scholar]
  48. Li J, Zhang H, Ning J, Sajid A, Cheng G et al. The nature and epidemiology of OqxAB, a multidrug efflux pump. Antimicrob Resist Infect Control 2019; 8:44 [View Article]
    [Google Scholar]
  49. Borodovich T, Shkoporov AN, Ross RP, Hill C. Phage-mediated horizontal gene transfer and its implications for the human gut microbiome. Gastroenterol Rep (Oxf) 2022; 10:goac012 [View Article] [PubMed]
    [Google Scholar]
  50. Pires J, Santos R, Monteiro S. Antibiotic resistance genes in bacteriophages from wastewater treatment plant and hospital wastewaters. Sci Total Environ 2023; 892:164708 [View Article] [PubMed]
    [Google Scholar]
  51. Kumwenda GP, Sugawara Y, Abe R, Akeda Y, Kasambara W et al. First Identification and genomic characterization of multidrug-resistant carbapenemase-producing Enterobacteriaceae clinical isolates in Malawi, Africa. J Med Microbiol 2019; 68:1707–1715 [View Article] [PubMed]
    [Google Scholar]
  52. Bwanali AN, Lubanga AF, Kondowe S, Nzima E, Mwale A et al. Trends and patterns of antimicrobial resistance among common pathogens isolated from adult bloodstream and urinary tract infections in public health facilities in Malawi, 2020-2024. BMC Infect Dis 2025; 25:946 [View Article] [PubMed]
    [Google Scholar]
  53. ASfL M. Malawi: National Situation of Antimicrobial Resistance and Consumption Analysis (2016–2018) Malawi: Fleming Fund Regional Grant; 2022
    [Google Scholar]
  54. Pearse O, Lester R, Zuza A, Mangochi H, Siyabu P et al. Extended-Spectrum Beta-Lactamase Klebsiella pneumoniae on a Malawian neonatal unit is amplified by neonates and transmitted by maternal hands, cots and ward surfaces. medRxiv 20252025 [View Article] [PubMed]
    [Google Scholar]
  55. Pearse O, Zuza A, Tewesa E, Siyabu P, Fraser AJ et al. High diversity of Escherichia coli causing invasive disease in neonates in Malawi poses challenges for O-antigen based vaccine approach. Commun Med (Lond) 2025; 5:298 [View Article] [PubMed]
    [Google Scholar]
/content/journal/mgen/10.1099/mgen.0.001673
Loading
/content/journal/mgen/10.1099/mgen.0.001673
Loading

Data & Media loading...

Supplements

Supplementary material 1

Supplementary material 2

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An error occurred
Approval was partially successful, following selected items could not be processed due to error