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Inferring gene function from evolutionary change in signatures of translation efficiency

Kriško, Anita; Copić, Tea; Gabaldon, Toni; Lehner, Ben; Supek, Fran (2014) Inferring gene function from evolutionary change in signatures of translation efficiency. Genome Biology, 15 (3). R44-1. ISSN 1465-6906

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Abstract

The genetic code is redundant, meaning that most amino acids can be encoded by more than one codon. Highly expressed genes tend to use optimal codons to increase the accuracy and speed of translation. As such, codon usage biases provide a signature of the relative expression levels of genes that can, uniquely, be quantified across the domains of life. Here we describe a general statistical framework to exploit this phenomenon and to systematically associate genes to environments and phenotypic traits through changes in codon adaptation. By inferring evolutionary signatures of translation efficiency in 911 bacterial and archaeal genomes while controlling for confounding effects of phylogeny and inter-correlated phenotypes, we link 187 gene families to 24 diverse phenotypic traits. A series of experiments in E. coli reveal that 13/15, 19/23 and 3/6 gene families with changes in codon adaptation in aerotolerant, thermophilic or halophilic microbes confer specific resistance to, respectively, hydrogen peroxide, heat, and high salinity. Further, we demonstrate experimentally that changes in codon optimality alone are sufficient to enhance stress resistance. Finally, we present evidence that multiple genes with altered codon optimality in aerobes confer oxidative stress resistance by controlling the levels of iron and NAD(P)H. Taken together, this work provides experimental evidence for a widespread connection between changes in translation efficiency and phenotypic adaptation. As the number of sequenced genomes increases, this novel genomic context method for linking genes to phenotypes from sequence alone will become increasingly useful.

Item Type: Article
Uncontrolled Keywords: codon biases; gene function prediction; prokaryote genome evolution; oxidative stress
Subjects: NATURAL SCIENCES > Biology > Microbiology
NATURAL SCIENCES > Biology > Genetics, Evolution and Phylogenetics
BIOTECHNICAL SCIENCES > Biotechnology > Bioinformatics
Divisions: Division of Electronics
Projects:
Project titleProject leaderProject codeProject type
Strojno učenje prediktivnih modela u računalnoj biologiji[136501] Tomislav Šmuc098-0000000-3168MZOS
Learning from Massive, Incompletely annotated, and Structured Data – MAESTRATomislav Šmuc612944FP7
Depositing User: Fran Supek
Date Deposited: 09 Jun 2014 13:23
Last Modified: 27 Aug 2014 07:20
URI: http://fulir.irb.hr/id/eprint/1365
DOI: 10.1186/gb-2014-15-3-r44

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