Journal Article

Reduced mRNA Secondary-Structure Stability Near the Start Codon Indicates Functional Genes in Prokaryotes

Thomas E. Keller, S. David Mis, Kevin E. Jia and Claus O. Wilke

in Genome Biology and Evolution

Published on behalf of Society for Molecular Biology and Evolution

Volume 4, issue 2, pages 80-88
Published in print January 2012 |
Published online December 2011 | e-ISSN: 1759-6653 | DOI:

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Several recent studies have found that selection acts on synonymous mutations at the beginning of genes to reduce mRNA secondary-structure stability, presumably to aid in translation initiation. This observation suggests that a metric of relative mRNA secondary-structure stability, ZΔG, could be used to test whether putative genes are likely to be functionally important. Using the Escherichia coli genome, we compared the mean ZΔG of genes with known functions, genes with known orthologs, genes where function and orthology are unknown, and pseudogenes. Genes in the first two categories demonstrated similar levels of selection for reduced stability (increased ZΔG), whereas for pseudogenes stability did not differ from our null expectation. Surprisingly, genes where function and orthology were unknown were also not different from the null expectation, suggesting that many of these open reading frames are not functionally important. We extended our analysis by constructing a Bayesian phylogenetic mixed model based on data from 145 prokaryotic genomes. As in E. coli, genes with no known function had consistently lower ZΔG, even though we expect that many of the currently unannotated genes will ultimately have their functional utility discovered. Our findings suggest that functional genes tend to evolve increased ZΔG, whereas nonfunctional ones do not. Therefore, ZΔG may be a useful metric for identifying genes of potentially important function and could be used to target genes for further functional study.

Keywords: synonymous mutations; protein function; translation

Journal Article.  5847 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology ; Evolutionary Biology ; Genetics and Genomics