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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...

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Autores principales: Audrey, Bioteau, Cellier, Nicolas, White, Frédérique, Jacques, Pierre-Étienne, Burrus, Vincent
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2023
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.
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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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