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MBE Advance Access published online on September 14, 2005
Molecular Biology and Evolution, doi:10.1093/molbev/msj023
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© The Author 2005. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. All rights reserved. For permissions, please e-mail: journals.permissions@oxfordjournals.org
Accepted September 13, 2005

Research Article

Inferring Parameters Shaping Amino-Acid Usage in Prokaryotic Genomes via Bayesian MCMC Methods

Hugo Naya 1*, Daniel Gianola 2, Héctor Romero 3, Jorge I. Urioste 4, and Héctor Musto 5

1 Laboratorio de Organización y Evolución del Genoma, Facultad de Ciencias, Iguá 4225, Montevideo 11400, Uruguay; Departamento de Producción Animal y Pasturas, Facultad de Agronomía, Avenida. Garzón 780, Montevideo 12900, Uruguay; Department of Animal Sciences, University of Wisconsin-Madison, Madison, WI 53706, USA
2 Department of Animal Sciences, University of Wisconsin-Madison, Madison, WI 53706, USA
3 Laboratorio de Organización y Evolución del Genoma, Facultad de Ciencias, Iguá 4225, Montevideo 11400, Uruguay; Escuela Universitaria de Tecnología Médica, Facultad de Medicina, Avenida Italia (s/n), Hospital de Clínicas, Montevideo 11600, Uruguay
4 Departamento de Producción Animal y Pasturas, Facultad de Agronomía, Avenida. Garzón 780, Montevideo 12900, Uruguay
5 Laboratorio de Organización y Evolución del Genoma, Facultad de Ciencias, Iguá 4225, Montevideo 11400, Uruguay

* To whom correspondence should be addressed.
Hugo Naya, E-mail: hnaya{at}fcien.edu.uy


  Abstract

G+C and Optimal Growth Temperature (OGT) are main factors characterizing the frequency distribution of amino-acids in prokaryotes. Previous work, using multivariate exploratory methods, has emphasized ascertainment of biological factors underlying variability between genomes, but the strength of each identified factor on amino-acid content has not been quantified. We combine the flexibility of the Phylogenetic Mixed Model (PMM) with the power of Bayesian inference via MCMC methods, to obtain a novel evolutionary picture of amino-acid usage in prokaryotic genomes. We implement a Bayesian PMM which incorporates the feature that evolutionary history makes observed data interdependent. As in previous studies with PMM, we present a variance partition; however, attention is also given to the posterior distribution of "systematic effects" that may shed light about the relative importance of and relationships between evolutionary forces acting at the genomic level. In particular, we analyzed influences of G+C content, OGT and respiratory metabolism. Estimates of G+C effects were significant for amino-acids coded by G+C or A+T in first and second bases. OGT effect had an important effect on 12 amino-acids, probably reflecting complex patterns of protein modifications, to cope with varying environments. The effect of respiratory metabolism was less clear, probably due to the already reported association of G+C with aerobic metabolism. A "heritability" parameter was always high and significant, reinforcing the importance of accommodating phylogenetic relationships in these analyses. "Heritable" component correlations displayed a pattern that tended to cluster "pure" G+C (A+T) in first and second codon positions, suggesting an inherited departure from linear regression on G+C.

Keywords: Bayesian methods; MCMC; amino acid usage; genome evolution; linear models; GC content; Optimal Growth Temperature.
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