Plasmid Analysis Reveals Potential Horizontal Gene Transfer Events in Hospital-Associated Infections

This poster was presented at the NSCMID 2024 conference.

Introduction

Antimicrobial resistance (AMR) is a growing concern in healthcare-associated infections (HAI) (1). A key mechanism for spreading antimicrobial resistance genes (ARGs) among bacteria is horizontal gene transfer (HGT). One form of HGT is plasmid conjugation, making plasmids with ARGs a major threat to healthcare (2). Various bioinformatics tools have been developed to detect and analyse plasmids (3). Genomic surveillance with plasmid analysis can provide insights into the transmission of ARGs (4).

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We have previously developed Solu Platform (5), a tool for genomic surveillance that enables the analysis of plasmid-mediated AMR. Here, we use Solu Platform to analyse plasmids in previously published clinical whole genome sequencing (WGS) samples of Escherichia coli and Staphylococcus aureus.

Methods

We analysed three previously published studies (6-8) that did not originally include plasmid analysis. A total of 94 WGS samples were downloaded from SRA and uploaded to Solu Platform for genomic analysis. The platform’s pipeline includes de novo assembly with Shovill (9), ARG detection with AMRFinderPlus (10), plasmid analysis with MobSuite (11), and phylogeny with IQ-TREE 2 (12).

Results

We found mobilizable plasmids in 74.5% (70/94) of the samples and conjugative plasmids in 29.8% (28/94) of the samples. The majority of the mobilizable and conjugative plasmids also contained ARGs. Furthermore, in one dataset some clonally distant samples contained highly similar plasmids from the AA324 plasmid cluster, indicating a potential HGT event.

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Conclusion

This study identified several conjugative plasmids and a potential HGT event, highlighting the importance of plasmid analysis in pathogen surveillance. Including it in WGS surveillance programs enhances our ability to track and combat the spread of AMR.
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The results of this study are publicly viewable at https://platform.solugenomics.com/w/solu-plasmid-analysis-1

References

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2. Lerminiaux NA, Cameron ADS. Horizontaltransfer of antibiotic resistance genes in clinical environments. Can J Microbiol. 2019;65(1):34-44.doi:10.1139/cjm-2018-0275

3. Robertson J, Nash JHE. MOB-suite:software tools for clustering, reconstruction and typing of plasmids from draftassemblies. Microb Genom.2018;4(8):e000206. doi:10.1099/mgen.0.000206

4. Wheeler NE, Price V,Cunningham-Oakes E, et al. Innovations in genomic antimicrobial resistancesurveillance. Lancet Microbe. 2023;4(12):e1063-e1070.doi:10.1016/S2666-5247(23)00285-9

5. Moilanen TJ, Lehtinen J, Visuri K, Sihvonen S. Solu – a cloud platform for real-timegenomic pathogen surveillance. bioRxiv 2024.05.30.596434.doi:10.1101/2024.05.30.596434

6. Sonda T, Kumburu H, van Zwetselaar M, et al. Whole genome sequencingreveals high clonal diversity of Escherichia coli isolated from patients in atertiary care hospital in Moshi, Tanzania. Antimicrob Resist Infect Control. 2018;7:72.Published 2018 Jun 8. doi:10.1186/s13756-018-0361-x

7. Coll F, Harrison EM, Toleman MS, et al. Longitudinal genomicsurveillance of MRSA in the UK reveals transmission patterns in hospitals andthe community. Sci Transl Med. 2017;9(413):eaak9745.doi:10.1126/scitranslmed.aak9745

8. Durand G, Javerliat F, Bes M, et al. RoutineWhole-Genome Sequencing for Outbreak Investigations of Staphylococcus aureus ina National Reference Center. Front Microbiol. 2018;9:511. Published 2018 Mar20. doi:10.3389/fmicb.2018.00511

9. Seemann T. Shovill. Available at: https://github.com/tseemann/shovill.

10. Feldgarden M, BroverV, Gonzalez-Escalona N, et al. AMRFinderPlus and the Reference Gene Catalog facilitate examination of thegenomic links among antimicrobial resistance, stress response, and virulence.Sci Rep. 2021;11(1):12728.

11. Robertson J, Nash JHE. MOB-suite:software tools for clustering, reconstruction and typing of plasmids from draftassemblies. Microb Genom.2018;4(8):e000206. doi:10.1099/mgen.0.000206

12. Minh BQ, Schmidt HA, Chernomor O, et al. IQ-TREE 2: New Modelsand Efficient Methods for Phylogenetic Inference in the Genomic Era. Teeling E, editor. Molecular Biology andEvolution. 2020 May 1;37(5):1530–4.

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