Dataset Information

Genomic analysis of hepatitis B virus reveals antigen state and genotype as sources of evolutionary rate variation.

ABSTRACT: Hepatitis B virus (HBV) genomes are small, semi-double-stranded DNA circular genomes that contain alternating overlapping reading frames and replicate through an RNA intermediary phase. This complex biology has presented a challenge to estimating an evolutionary rate for HBV, leading to difficulties resolving the evolutionary and epidemiological history of the virus. Here, we re-examine rates of HBV evolution using a novel data set of 112 within-host, transmission history (pedigree) and among-host genomes isolated over 20 years from the indigenous peoples of the South Pacific, combined with 313 previously published HBV genomes. We employ Bayesian phylogenetic approaches to examine several potential causes and consequences of evolutionary rate variation in HBV. Our results reveal rate variation both between genotypes and across the genome, as well as strikingly slower rates when genomes are sampled in the Hepatitis B e antigen positive state, compared to the e antigen negative state. This Hepatitis B e antigen rate variation was found to be largely attributable to changes during the course of infection in the preCore and Core genes and their regulatory elements.

SUBMITTER: Harrison A

PROVIDER: S-EPMC3136878 | biostudies-literature | 2011 Feb

REPOSITORIES: biostudies-literature

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Genomic analysis of hepatitis B virus reveals antigen state and genotype as sources of evolutionary rate variation.

Harrison Abby A Lemey Philippe P Hurles Matthew M Moyes Chris C Horn Susanne S Pryor Jan J Malani Joji J Supuri Mathias M Masta Andrew A Teriboriki Burentau B Toatu Tebuka T Penny David D Rambaut Andrew A Shapiro Beth B

Viruses 20110201 2

Hepatitis B virus (HBV) genomes are small, semi-double-stranded DNA circular genomes that contain alternating overlapping reading frames and replicate through an RNA intermediary phase. This complex biology has presented a challenge to estimating an evolutionary rate for HBV, leading to difficulties resolving the evolutionary and epidemiological history of the virus. Here, we re-examine rates of HBV evolution using a novel data set of 112 within-host, transmission history (pedigree) and among-ho ...[more]

PMID: 21765983

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Project description:Rates of evolution differ widely among proteins, but the causes and consequences of such differences remain under debate. With the advent of high-throughput functional genomics, it is now possible to rigorously assess the genomic correlates of protein evolutionary rate. However, dissecting the correlations among evolutionary rate and these genomic features remains a major challenge. Here, we use an integrated probabilistic modeling approach to study genomic correlates of protein evolutionary rate in Saccharomyces cerevisiae. We measure and rank degrees of association between (i) an approximate measure of protein evolutionary rate with high genome coverage, and (ii) a diverse list of protein properties (sequence, structural, functional, network, and phenotypic). We observe, among many statistically significant correlations, that slowly evolving proteins tend to be regulated by more transcription factors, deficient in predicted structural disorder, involved in characteristic biological functions (such as translation), biased in amino acid composition, and are generally more abundant, more essential, and enriched for interaction partners. Many of these results are in agreement with recent studies. In addition, we assess information contribution of different subsets of these protein properties in the task of predicting slowly evolving proteins. We employ a logistic regression model on binned data that is able to account for intercorrelation, non-linearity, and heterogeneity within features. Our model considers features both individually and in natural ensembles ("meta-features") in order to assess joint information contribution and degree of contribution independence. Meta-features based on protein abundance and amino acid composition make strong, partially independent contributions to the task of predicting slowly evolving proteins; other meta-features make additional minor contributions. The combination of all meta-features yields predictions comparable to those based on paired species comparisons, and approaching the predictive limit of optimal lineage-insensitive features. Our integrated assessment framework can be readily extended to other correlational analyses at the genome scale.

Dataset Information

Genomic analysis of hepatitis B virus reveals antigen state and genotype as sources of evolutionary rate variation.

Publications

Genomic analysis of hepatitis B virus reveals antigen state and genotype as sources of evolutionary rate variation.

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