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ToolsCrossman, Lisa, Sims, Leanne, Dean, Rachael, Felgate, Heather, Diaz Calvo, Teresa, Hill, Claire, McNamara, Iain, Webber, Mark A. and Wain, John (2024) Sticking together: Independent evolution of biofilm formation in different species of staphylococci has occurred multiple times via different pathways. BMC Genomics, 25. ISSN 1471-2164
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Abstract
Background: Staphylococci cause a wide range of infections, including implant-associated infections which are difficult to treat due to the presence of biofilms. Whilst some proteins involved in biofilm formation are known, the differences in biofilm production between staphylococcal species remains understudied. Currently biofilm formation by Staphylococcus aureus is better understood than other members of the genus as more research has focused on this species. Results: We assembled a panel of 385 non-aureus Staphylococcus isolates of 19 species from a combination of clinical sources and reference strains. We used a high-throughput crystal violet assay to assess the biofilm forming ability of all strains and assign distinct biofilm formation categories. We compared the prevalence of Pfam domains between the categories and used machine learning to identify amino acid 20-mers linked to biofilm formation. This identified some domains within proteins already linked to biofilm formation and important domains not previously linked to biofilm formation in staphylococci. RT-qPCR confirmed the expression of selected genes predicted to encode important domains within biofilms in Staphylococcus epidermidis. The prevalence and distribution of biofilm associated domains showed a link to phylogeny, suggesting different Staphylococcus species have independently evolved different mechanisms of biofilm production. Conclusions: This work has identified different routes to biofilm formation in diverse species of Staphylococcus and suggests independent evolution of biofilm has occurred multiple times across the genus. Understanding the mechanisms of biofilm formation in any given species is likely to require detailed study of relevant strains and the ability to generalise across the genus may be limited.
| Item Type: | Article |
|---|---|
| Additional Information: | Availability of data and materials: The reads representing the raw genome sequence data used in this project can be accessed as ENA BioProject PRJEB31403. Study accession ERP113963. Code used for the machine learning work is available from https://github.com/LCrossman. Funding Information: This work was supported by Heraeus Medical via a grant to IM and JW; and by funding from Action Arthritis to MW, JW and IM. HF was supported by BBSRC grant number BB/T014644/1. |
| Uncontrolled Keywords: | machine learning,prosthetic joint infection,protein domains,biotechnology,genetics ,/dk/atira/pure/subjectarea/asjc/1300/1305 |
| Faculty \ School: | Faculty of Science > School of Biological Sciences Faculty of Medicine and Health Sciences > Norwich Medical School |
| Related URLs: | |
| Depositing User: | LivePure Connector |
| Date Deposited: | 15 May 2024 13:30 |
| Last Modified: | 25 Oct 2024 15:30 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/95209 |
| DOI: | 10.1101/2024.03.01.582901 |
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