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Identification of genes with fast-evolving regions in microbial genomes.

ABSTRACT: Complete sequences of multiple strains of the same microbial species provide an invaluable source for studying the evolutionary dynamics between orthologous genes over a relatively short time scale. Usually the intensity of the selection pressure is inferred from a comparison between the nonsynonymous substitution rate and the synonymous substitution rate. In this paper, we propose an alternative method for detecting genes with one or more fast-evolving regions from pairwise comparisons of orthologous genes. Our method looks for regions with overrepresented nonsynonymous mutations along the alignment, and requires a higher nonsynonymous evolution rate in those regions than the neutral evolution rate. It identifies gene targets under intensive selection pressure that are not detected from the conventional rate comparison analysis. For those identified genes with known annotations, most of them have a clear role in processes such as bacterial defense and host-pathogen interactions. Gene sets reported from our method provide a measure of the phenotypic divergence between two closely related genomes.

SUBMITTER: Zheng Y

PROVIDER: S-EPMC535660 | biostudies-literature | 2004

REPOSITORIES: biostudies-literature

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Identification of genes with fast-evolving regions in microbial genomes.

Zheng Yu Y Roberts Richard J RJ Kasif Simon S

Nucleic acids research 20041202 21

Complete sequences of multiple strains of the same microbial species provide an invaluable source for studying the evolutionary dynamics between orthologous genes over a relatively short time scale. Usually the intensity of the selection pressure is inferred from a comparison between the nonsynonymous substitution rate and the synonymous substitution rate. In this paper, we propose an alternative method for detecting genes with one or more fast-evolving regions from pairwise comparisons of ortho ...[more]

PMID: 15576679

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Project description:BACKGROUND: Mammalian genome sequence data are being acquired in large quantities and at enormous speeds. We now have a tremendous opportunity to better understand which genes are the most variable or conserved, and what their particular functions and evolutionary dynamics are, through comparative genomics. RESULTS: We chose human and eleven other high-coverage mammalian genome data-as well as an avian genome as an outgroup-to analyze orthologous protein-coding genes using nonsynonymous (Ka) and synonymous (Ks) substitution rates. After evaluating eight commonly-used methods of Ka and Ks calculation, we observed that these methods yielded a nearly uniform result when estimating Ka, but not Ks (or Ka/Ks). When sorting genes based on Ka, we noticed that fast-evolving and slow-evolving genes often belonged to different functional classes, with respect to species-specificity and lineage-specificity. In particular, we identified two functional classes of genes in the acquired immune system. Fast-evolving genes coded for signal-transducing proteins, such as receptors, ligands, cytokines, and CDs (cluster of differentiation, mostly surface proteins), whereas the slow-evolving genes were for function-modulating proteins, such as kinases and adaptor proteins. In addition, among slow-evolving genes that had functions related to the central nervous system, neurodegenerative disease-related pathways were enriched significantly in most mammalian species. We also confirmed that gene expression was negatively correlated with evolution rate, i.e. slow-evolving genes were expressed at higher levels than fast-evolving genes. Our results indicated that the functional specializations of the three major mammalian clades were: sensory perception and oncogenesis in primates, reproduction and hormone regulation in large mammals, and immunity and angiotensin in rodents. CONCLUSION: Our study suggests that Ka calculation, which is less biased compared to Ks and Ka/Ks, can be used as a parameter to sort genes by evolution rate and can also provide a way to categorize common protein functions and define their interaction networks, either pair-wise or in defined lineages or subgroups. Evaluating gene evolution based on Ka and Ks calculations can be done with large datasets, such as mammalian genomes. REVIEWERS: This article has been reviewed by Drs. Anamaria Necsulea (nominated by Nicolas Galtier), Subhajyoti De (nominated by Sarah Teichmann) and Claus O. Wilke.

Dataset Information

Identification of genes with fast-evolving regions in microbial genomes.

Publications

Identification of genes with fast-evolving regions in microbial genomes.

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