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A systematic approach to classify and characterize genomic islands driven by conjugative mobility using protein signatures
Genomic islands (GIs) play a crucial role in the spread of antibiotic resistance, virulence factors and antiviral defense systems in a broad range of bacterial species. However, the characterization and classification of GIs are challenging due to their relatively small size and considerable genetic...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10484663/ https://www.ncbi.nlm.nih.gov/pubmed/37526274 http://dx.doi.org/10.1093/nar/gkad644 |
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author | Audrey, Bioteau Cellier, Nicolas White, Frédérique Jacques, Pierre-Étienne Burrus, Vincent |
author_facet | Audrey, Bioteau Cellier, Nicolas White, Frédérique Jacques, Pierre-Étienne Burrus, Vincent |
author_sort | Audrey, Bioteau |
collection | PubMed |
description | Genomic islands (GIs) play a crucial role in the spread of antibiotic resistance, virulence factors and antiviral defense systems in a broad range of bacterial species. However, the characterization and classification of GIs are challenging due to their relatively small size and considerable genetic diversity. Predicting their intercellular mobility is of utmost importance in the context of the emerging crisis of multidrug resistance. Here, we propose a large-scale classification method to categorize GIs according to their mobility profile and, subsequently, analyze their gene cargo. We based our classification decision scheme on a collection of mobility protein motif definitions available in publicly accessible databases. Our results show that the size distribution of GI classes correlates with their respective structure and complexity. Self-transmissible GIs are usually the largest, except in Bacillota and Actinomycetota, accumulate antibiotic and phage resistance genes, and favour the use of a tyrosine recombinase to insert into a host's replicon. Non-mobilizable GIs tend to use a DDE transposase instead. Finally, although tRNA genes are more frequently targeted as insertion sites by GIs encoding a tyrosine recombinase, most GIs insert in a protein-encoding gene. This study is a stepping stone toward a better characterization of mobile GIs in bacterial genomes and their mechanism of mobility. |
format | Online Article Text |
id | pubmed-10484663 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-104846632023-09-08 A systematic approach to classify and characterize genomic islands driven by conjugative mobility using protein signatures Audrey, Bioteau Cellier, Nicolas White, Frédérique Jacques, Pierre-Étienne Burrus, Vincent Nucleic Acids Res Data Resources and Analyses Genomic islands (GIs) play a crucial role in the spread of antibiotic resistance, virulence factors and antiviral defense systems in a broad range of bacterial species. However, the characterization and classification of GIs are challenging due to their relatively small size and considerable genetic diversity. Predicting their intercellular mobility is of utmost importance in the context of the emerging crisis of multidrug resistance. Here, we propose a large-scale classification method to categorize GIs according to their mobility profile and, subsequently, analyze their gene cargo. We based our classification decision scheme on a collection of mobility protein motif definitions available in publicly accessible databases. Our results show that the size distribution of GI classes correlates with their respective structure and complexity. Self-transmissible GIs are usually the largest, except in Bacillota and Actinomycetota, accumulate antibiotic and phage resistance genes, and favour the use of a tyrosine recombinase to insert into a host's replicon. Non-mobilizable GIs tend to use a DDE transposase instead. Finally, although tRNA genes are more frequently targeted as insertion sites by GIs encoding a tyrosine recombinase, most GIs insert in a protein-encoding gene. This study is a stepping stone toward a better characterization of mobile GIs in bacterial genomes and their mechanism of mobility. Oxford University Press 2023-08-01 /pmc/articles/PMC10484663/ /pubmed/37526274 http://dx.doi.org/10.1093/nar/gkad644 Text en © The Author(s) 2023. Published by Oxford University Press on behalf of Nucleic Acids Research. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Data Resources and Analyses Audrey, Bioteau Cellier, Nicolas White, Frédérique Jacques, Pierre-Étienne Burrus, Vincent A systematic approach to classify and characterize genomic islands driven by conjugative mobility using protein signatures |
title | A systematic approach to classify and characterize genomic islands driven by conjugative mobility using protein signatures |
title_full | A systematic approach to classify and characterize genomic islands driven by conjugative mobility using protein signatures |
title_fullStr | A systematic approach to classify and characterize genomic islands driven by conjugative mobility using protein signatures |
title_full_unstemmed | A systematic approach to classify and characterize genomic islands driven by conjugative mobility using protein signatures |
title_short | A systematic approach to classify and characterize genomic islands driven by conjugative mobility using protein signatures |
title_sort | systematic approach to classify and characterize genomic islands driven by conjugative mobility using protein signatures |
topic | Data Resources and Analyses |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10484663/ https://www.ncbi.nlm.nih.gov/pubmed/37526274 http://dx.doi.org/10.1093/nar/gkad644 |
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