Inferring parameters shaping amino acid usage in prokaryotic genomes via Bayesian MCMC methods

Molar content of guanine plus cytosine (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 Markov Chain Monte Carlo (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, OGT, and respiratory metabolism. Estimates of G + C effects were significant for amino acids coded by G + C or molar content of adenine plus thymine (A + T) in first and second bases. OGT 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.