Comparative genomics of 274 Vibrio cholerae genomes reveals mobile functions structuring three niche dimensions

Autor: Dutilh, Bas E, Thompson, Cristiane C, Vicente, Ana C P, Marin, Michel A, Lee, Clarence, Silva, Genivaldo G Z, Schmieder, Robert, Andrade, Bruno G N, Chimetto, Luciane, Cuevas, Daniel, Garza, Daniel R, Okeke, Iruka N, Aboderin, Aaron Oladipo, Spangler, Jessica, Ross, Tristen, Dinsdale, Elizabeth A, Thompson, Fabiano L, Harkins, Timothy T, Edwards, Robert A, Sub Bioinformatics, Theoretical Biology and Bioinformatics
Přispěvatelé: Sub Bioinformatics, Theoretical Biology and Bioinformatics
Rok vydání: 2014
Předmět:
Zdroj: BMC Genomics, 15, 1, pp. 654
BMC Genomics, 15. BioMed Central
BMC Genomics
BMC Genomics, 15, 654
ISSN: 1471-2164
Popis: Background Vibrio cholerae is a globally dispersed pathogen that has evolved with humans for centuries, but also includes non-pathogenic environmental strains. Here, we identify the genomic variability underlying this remarkable persistence across the three major niche dimensions space, time, and habitat. Results Taking an innovative approach of genome-wide association applicable to microbial genomes (GWAS-M), we classify 274 complete V. cholerae genomes by niche, including 39 newly sequenced for this study with the Ion Torrent DNA-sequencing platform. Niche metadata were collected for each strain and analyzed together with comprehensive annotations of genetic and genomic attributes, including point mutations (single-nucleotide polymorphisms, SNPs), protein families, functions and prophages. Conclusions Our analysis revealed that genomic variations, in particular mobile functions including phages, prophages, transposable elements, and plasmids underlie the metadata structuring in each of the three niche dimensions. This underscores the role of phages and mobile elements as the most rapidly evolving elements in bacterial genomes, creating local endemicity (space), leading to temporal divergence (time), and allowing the invasion of new habitats. Together, we take a data-driven approach for comparative functional genomics that exploits high-volume genome sequencing and annotation, in conjunction with novel statistical and machine learning analyses to identify connections between genotype and phenotype on a genome-wide scale. Electronic supplementary material The online version of this article (doi:10.1186/1471-2164-15-654) contains supplementary material, which is available to authorized users.
Databáze: OpenAIRE
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