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Volume 9, Issue 11

Systematic analysis of plasmids of the complex using 142 closed genomes Open Access

Abstract

Plasmids play important roles in bacterial genome diversification. In the complex (SMC), a notable contribution of plasmids to genome diversification was also suggested by our recent analysis of >600 draft genomes. As accurate analyses of plasmids in draft genomes are difficult, in this study we analysed 142 closed genomes covering the entire complex, 67 of which were obtained in this study, and identified 132 plasmids (1.9–244.4 kb in length) in 77 strains. While the average numbers of plasmids in clinical and non-clinical strains showed no significant difference, strains belonging to clade 2 (one of the two hospital-adapted lineages) contained more plasmids than the others. Pangenome analysis revealed that of the 28 954 genes identified, 12.8 % were plasmid-specific, and 1.4 % were present in plasmids or chromosomes depending on the strain. In the latter group, while transposon-related genes were most prevalent (31.4 % of the function-predicted genes), genes related to antimicrobial resistance and heavy metal resistance accounted for a notable proportion (22.7 %). Mash distance-based clustering separated the 132 plasmids into 23 clusters and 50 singletons. Most clusters/singletons showed notably different GC contents compared to those of host chromosomes, suggesting their recent or relatively recent appearance in the SMC. Among the 23 clusters, 17 were found in only clinical or only non-clinical strains, suggesting the possible preference of their distribution on the environmental niches of host strains. Regarding the host strain phylogeny, 16 clusters were distributed in two or more clades, suggesting their interclade transmission. Moreover, for many plasmids, highly homologous plasmids were found in other species, indicating the broadness of their potential host ranges, beyond the genus, family, order, class or even phylum level. Importantly, highly homologous plasmids were most frequently found in and other species in the family , suggesting that this family, particularly , is the main source for plasmid exchanges with the SMC. These results highlight the power of closed genome-based analysis in the investigation of plasmids and provide important insights into the nature of plasmids distributed in the SMC.

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  • Accepted:
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Keyword(s): closed genome , GC content , host range of plasmid , plasmid and Serratia marcescens complex
Funding
This study was supported by the:
  • Ministry of Education, Culture, Sports, Science and Technology (Award Not available)
    • Principle Award Recipient: DeboraSatie Nagano
  • Japan Society for the Promotion of Science (Award 22H02870)
    • Principle Award Recipient: TetsuyaHayashi
  • Japan Society for the Promotion of Science (Award 19H03472)
    • Principle Award Recipient: TetsuyaHayashi
© 2023 The Authors
  • This is an open-access article distributed under the terms of the Creative Commons Attribution License.
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2023-11-15
2024-10-11
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References

  1. Iguchi A, Nagaya Y, Pradel E, Ooka T, Ogura Y et al. Genome evolution and plasticity of Serratia marcescens, an important multidrug-resistant nosocomial pathogen. Genome Biol Evol 2014; 6:2096–2110 [View Article] [PubMed]
     [Google Scholar]
  2. Moradigaravand D, Boinett CJ, Martin V, Peacock SJ, Parkhill J. Recent independent emergence of multiple multidrug-resistant Serratia marcescens clones within the United Kingdom and Ireland. Genome Res 2016; 26:1101–1109 [View Article] [PubMed]
     [Google Scholar]
  3. Abreo E, Altier N. Pangenome of Serratia marcescens strains from nosocomial and environmental origins reveals different populations and the links between them. Sci Rep 2019; 9:46 [View Article] [PubMed]
     [Google Scholar]
  4. Cristina ML, Sartini M, Spagnolo AM. Serratia marcescens infections in Neonatal Intensive Care Units (NICUs). Int J Environ Res Public Health 2019; 16:610 [View Article] [PubMed]
     [Google Scholar]
  5. 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]
  6. Ono T, Taniguchi I, Nakamura K, Nagano DS, Nishida R et al. Global population structure of the Serratia marcescens complex and identification of hospital-adapted lineages in the complex. Microb Genom 2022; 8:000793 [View Article] [PubMed]
     [Google Scholar]
  7. Tavares-Carreon F, De Anda-Mora K, Rojas-Barrera IC, Andrade A. Serratia marcescens antibiotic resistance mechanisms of an opportunistic pathogen: a literature review. PeerJ 2023; 11:e14399 [View Article] [PubMed]
     [Google Scholar]
  8. Mendes JC, Casado A. Serratia marcescens outbreak in a COVID-19 intensive care unit - are there any factors specific to COVID-19 units that facilitate bacterial cross-contamination between COVID-19 patients?. Am J Infect Control 2022; 50:223–225 [View Article] [PubMed]
     [Google Scholar]
  9. Adeolu M, Alnajar S, Naushad S, S Gupta R. Genome-based phylogeny and taxonomy of the “Enterobacteriales”: proposal for Enterobacterales ord. nov. divided into the families Enterobacteriaceae, Erwiniaceae fam. nov., Pectobacteriaceae fam. nov., Yersiniaceae fam. nov., Hafniaceae fam. nov., Morganellaceae fam. nov., and Budviciaceae fam. nov. Int J Syst Evol Microbiol 2016; 66:5575–5599 [View Article] [PubMed]
     [Google Scholar]
  10. Williams DJ, Grimont PAD, Cazares A, Grimont F, Ageron E et al. The genus Serratia revisited by genomics. Nat Commun 2022; 13:5195 [View Article] [PubMed]
     [Google Scholar]
  11. Inouye M, Dashnow H, Raven L-A, Schultz MB, Pope BJ et al. SRST2: rapid genomic surveillance for public health and hospital microbiology labs. Genome Med 2014; 6:90 [View Article] [PubMed]
     [Google Scholar]
  12. Clausen P, Zankari E, Aarestrup FM, Lund O. Benchmarking of methods for identification of antimicrobial resistance genes in bacterial whole genome data. J Antimicrob Chemother 2016; 71:2484–2488 [View Article] [PubMed]
     [Google Scholar]
  13. Orlek A, Stoesser N, Anjum MF, Doumith M, Ellington MJ et al. Plasmid classification in an era of whole-genome sequencing: application in studies of antibiotic resistance epidemiology. Front Microbiol 2017; 8:182 [View Article] [PubMed]
     [Google Scholar]
  14. Rozwandowicz M, Brouwer MSM, Fischer J, Wagenaar JA, Gonzalez-Zorn B et al. Plasmids carrying antimicrobial resistance genes in Enterobacteriaceae. J Antimicrob Chemother 2018; 73:1121–1137 [View Article] [PubMed]
     [Google Scholar]
  15. Shintani M, Sanchez ZK, Kimbara K. Genomics of microbial plasmids: classification and identification based on replication and transfer systems and host taxonomy. Front Microbiol 2015; 6:242 [View Article] [PubMed]
     [Google Scholar]
  16. Orlek A, Phan H, Sheppard AE, Doumith M, Ellington M et al. Ordering the MOB: insights into replicon and MOB typing schemes from analysis of a curated dataset of publicly available plasmids. Plasmid 2017; 91:42–52 [View Article] [PubMed]
     [Google Scholar]
  17. 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] [PubMed]
     [Google Scholar]
  18. Garcillán-Barcia MP, Redondo-Salvo S, Vielva L, de la Cruz F. MOBscan: automated annotation of MOB relaxases. Methods Mol Biol 2020; 2075:295–308 [View Article] [PubMed]
     [Google Scholar]
  19. O’Reilly LC. A method for overcoming DNA degradation during PFGE for Serratia marcescens. J Microbiol Methods 2011; 85:173–174 [View Article] [PubMed]
     [Google Scholar]
  20. De Coster W, D’Hert S, Schultz DT, Cruts M, Van Broeckhoven C. NanoPack: visualizing and processing long-read sequencing data. Bioinformatics 2018; 34:2666–2669 [View Article] [PubMed]
     [Google Scholar]
  21. Shen W, Le S, Li Y, Hu F. SeqKit: a cross-platform and ultrafast toolkit for FASTA/Q file manipulation. PLoS One 2016; 11:e0163962 [View Article] [PubMed]
     [Google Scholar]
  22. Wick RR, Judd LM, Gorrie CL, Holt KE. Unicycler: resolving bacterial genome assemblies from short and long sequencing reads. PLoS Comput Biol 2017; 13:e1005595 [View Article] [PubMed]
     [Google Scholar]
  23. Tanizawa Y, Fujisawa T, Nakamura Y. DFAST: a flexible prokaryotic genome annotation pipeline for faster genome publication. Bioinformatics 2018; 34:1037–1039 [View Article] [PubMed]
     [Google Scholar]
  24. Robertson J, Bessonov K, Schonfeld J, Nash JHE. Universal whole-sequence-based plasmid typing and its utility to prediction of host range and epidemiological surveillance. Microb Genom 2020; 6:mgen000435 [View Article] [PubMed]
     [Google Scholar]
  25. Alvarado A, Garcillán-Barcia MP, de la Cruz F. A degenerate primer MOB typing (DPMT) method to classify gamma-proteobacterial plasmids in clinical and environmental settings. PLoS One 2012; 7:e40438 [View Article] [PubMed]
     [Google Scholar]
  26. Thompson JD, Higgins DG, Gibson TJ. CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic Acids Res 1994; 22:4673–4680 [View Article] [PubMed]
     [Google Scholar]
  27. Kumar S, Stecher G, Li M, Knyaz C, Tamura K. MEGA X: Molecular Evolutionary Genetics Analysis across computing platforms. Mol Biol Evol 2018; 35:1547–1549 [View Article] [PubMed]
     [Google Scholar]
  28. R Core Team R: A language and environment for statistical computing. R Foundation 648 for Statistical Computing, Vienna, Austria; 2021 https://www.R-project.org
  29. Page AJ, Cummins CA, Hunt M, Wong VK, Reuter S et al. Roary: rapid large-scale prokaryote pan genome analysis. Bioinformatics 2015; 31:3691–3693 [View Article] [PubMed]
     [Google Scholar]
  30. Stamatakis A. RAxML-VI-HPC: maximum likelihood-based phylogenetic analyses with thousands of taxa and mixed models. Bioinformatics 2006; 22:2688–2690 [View Article] [PubMed]
     [Google Scholar]
  31. 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]
  32. Ligozzi M, Fontana R, Aldegheri M, Scalet G, Lo Cascio G. Comparative evaluation of an automated repetitive-sequence-based PCR instrument versus pulsed-field gel electrophoresis in the setting of a Serratia marcescens nosocomial infection outbreak. J Clin Microbiol 2010; 48:1690–1695 [View Article] [PubMed]
     [Google Scholar]
  33. Kumarasamy KK, Toleman MA, Walsh TR, Bagaria J, Butt F et al. Emergence of a new antibiotic resistance mechanism in India, Pakistan, and the UK: a molecular, biological, and epidemiological study. Lancet Infect Dis 2010; 10:597–602 [View Article] [PubMed]
     [Google Scholar]
  34. Ohtsubo Y, Ikeda-Ohtsubo W, Nagata Y, Tsuda M. GenomeMatcher: a graphical user interface for DNA sequence comparison. BMC Bioinformatics 2008; 9:376 [View Article] [PubMed]
     [Google Scholar]
  35. Ondov BD, Treangen TJ, Melsted P, Mallonee AB, Bergman NH et al. Mash: fast genome and metagenome distance estimation using MinHash. Genome Biol 2016; 17:132 [View Article] [PubMed]
     [Google Scholar]
  36. Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 2003; 13:2498–2504 [View Article] [PubMed]
     [Google Scholar]
  37. Hawkey J, Cottingham H, Tokolyi A, Wick RR, Judd LM et al. Linear plasmids in Klebsiella and other Enterobacteriaceae. Microb Genom 2022; 8:000807 [View Article]
     [Google Scholar]
  38. Ahsan S, Kabir MS. Linear plasmids and their replication. SJ Microbiol 2012; 2:1–5 [View Article]
     [Google Scholar]
  39. Douarre P-E, Mallet L, Radomski N, Felten A, Mistou M-Y. Analysis of COMPASS, a new comprehensive plasmid database revealed prevalence of multireplicon and extensive diversity of IncF plasmids. Front Microbiol 2020; 11:483 [View Article] [PubMed]
     [Google Scholar]
  40. Wallden K, Rivera-Calzada A, Waksman G. Type IV secretion systems: versatility and diversity in function. Cell Microbiol 2010; 12:1203–1212 [View Article] [PubMed]
     [Google Scholar]
  41. Ghoul M, Mitri S. The ecology and evolution of microbial competition. Trends Microbiol 2016; 24:833–845 [View Article] [PubMed]
     [Google Scholar]
  42. Bauer MA, Kainz K, Carmona-Gutierrez D, Madeo F. Microbial wars: competition in ecological niches and within the microbiome. Microb Cell 2018; 5:215–219 [View Article] [PubMed]
     [Google Scholar]
  43. Coulthurst S. The Type VI secretion system: a versatile bacterial weapon. Microbiology 2019; 165:503–515 [View Article] [PubMed]
     [Google Scholar]
  44. Almpanis A, Swain M, Gatherer D, McEwan N. Correlation between bacterial G+C content, genome size and the G+C content of associated plasmids and bacteriophages. Microb Genom 2018; 4:e000168 [View Article] [PubMed]
     [Google Scholar]
  45. Moore Pardo SM, Patel RH, Ramsakal A, Greene J. Disseminated Corynebacterium jeikeium infection in cancer patients. Cureus 2020; 12:e8764 [View Article] [PubMed]
     [Google Scholar]
  46. Shaw LP, Chau KK, Kavanagh J, AbuOun M, Stubberfield E et al. Niche and local geography shape the pangenome of wastewater- and livestock-associated Enterobacteriaceae. Sci Adv 2021; 7:eabe3868 [View Article] [PubMed]
     [Google Scholar]
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Systematic analysis of plasmids of the Serratia marcescens complex using 142 closed genomes
M Gen 9, 001135 (2023); https://doi.org/10.1099/mgen.0.001135
/content/journal/mgen/10.1099/mgen.0.001135
/content/journal/mgen/10.1099/mgen.0.001135
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