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Mol Biol Evol. 2011 Mar;28(3):1271-9. doi: 10.1093/molbev/msq314. Epub 2010 Nov 25.

Population proteomics: quantitative variation within and among populations in cardiac protein expression.

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  • 1Department of Biological Sciences, University of New Orleans, LA, USA.

Abstract

Population analysis of gene expression is typically achieved by quantifying levels of mRNA; however, gene expression is also a function of protein translation and turnover. Therefore, a complete understanding of population variation in gene expression requires quantitative knowledge of protein expression within and among natural populations. We used two-dimensional fluorescence difference gel electrophoresis (2D-DIGE) to quantitatively compare expression of heart ventricle proteins among 18 individuals in three populations of the teleost fish Fundulus. Among populations, expressions between orthologous proteins and mRNAs were generally positively correlated. Additionally, similar to the pattern of cardiac mRNA expression for the same populations, we found considerable variation in protein expression both within and among populations: Of 408 protein features in 2D gels, 34% are significantly different (P < 0.01) among individuals within a population, 9% differ between populations, and 12% have a pattern of expression that suggests they have evolved by natural selection. Although similar to mRNA expression, the frequency of significant differences among populations is larger for proteins. Similar to mRNA expressions, expressions of most proteins are correlated to the expressions of many other proteins. However, the correlations among proteins are more extensive than the correlation for similar RNAs. These correlations suggest a greater coordinate regulation of protein than mRNA expression. The larger frequency of significant differences among populations and the greater frequency of correlated expression among proteins versus among RNAs suggest that the molecular mechanisms affecting protein expression enhance the differences among populations, and these regulatory steps could be a source of variation for adaptation.

PMID:
21109588
[PubMed - indexed for MEDLINE]
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