Genome-wide patterns of sequence divergence over evolutionary time provide a unique window into the fundamental metabolic costs imposed on cellular life. Purifying selection eliminates mutations that increase costs and/or promote genetic disease. This process can discern minor cost differences, even ones that may not be readily measurable by direct laboratory experiments. This project will identify and interpret the evolutionary signals imprinted into genomes by one specific cost, the cost of erroneously translating proteins. Mistranslation events lead to protein misfolding, misfolded proteins can be cytotoxic or require costly cleanup, and selection operates both on the codon and on the amino-acid level to minimize cellular exposure to misfolded proteins. The working hypothesis for this project is that among the costs linked to translation, mistranslation-induced misfolding is the dominant one, whereas other costs, including mistranslation-induced loss of function and translation at reduced speed, play a minor role. This hypothesis will be tested using a combination of bioinformatics, mathematical modeling, and computer simulation, and the relative importance of the various translation- linked costs will be quantified. There are three specific aims. 1. What makes translationally optimal codons optimal? 2. Does selection against protein misfolding shapes synonymous codon usage? 3. How does protein biophysics interact with translational selection to constrain sequence evolution?

Public Health Relevance

All organisms have to translate proteins accurately and efficiently;mutations that interfere with efficient translation impair cellular function and cause disease states in humans. This project will identify the specific costs associated with mutations that affect translation, and will provide insight into which mutations are most likely to impose meaningful costs on cellular function. This research will impact several health-related areas, including the industrial production of drug compounds in genetically modified microbes and the cause and detection of certain kinds of genetic diseases in humans.

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
Research Project (R01)
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Genetic Variation and Evolution Study Section (GVE)
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Eckstrand, Irene A
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University of Texas Austin
Schools of Arts and Sciences
United States
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Spielman, Stephanie J; Dawson, Eric T; Wilke, Claus O (2014) Limited utility of residue masking for positive-selection inference. Mol Biol Evol 31:2496-500
Shahmoradi, Amir; Sydykova, Dariya K; Spielman, Stephanie J et al. (2014) Predicting evolutionary site variability from structure in viral proteins: buriedness, packing, flexibility, and design. J Mol Evol 79:130-42
Meyer, Austin G; Sawyer, Sara L; Ellington, Andrew D et al. (2014) Analyzing machupo virus-receptor binding by molecular dynamics simulations. PeerJ 2:e266
O'Dea, Eamon B; Pepin, Kim M; Lopman, Ben A et al. (2014) Fitting outbreak models to data from many small norovirus outbreaks. Epidemics 6:18-29
Meyer, Austin G; Dawson, Eric T; Wilke, Claus O (2013) Cross-species comparison of site-specific evolutionary-rate variation in influenza haemagglutinin. Philos Trans R Soc Lond B Biol Sci 368:20120334
Meyer, Austin G; Wilke, Claus O (2013) Integrating sequence variation and protein structure to identify sites under selection. Mol Biol Evol 30:36-44
Spielman, Stephanie J; Wilke, Claus O (2013) Membrane environment imposes unique selection pressures on transmembrane domains of G protein-coupled receptors. J Mol Evol 76:172-82
Wallace, Edward W J; Airoldi, Edoardo M; Drummond, D Allan (2013) Estimating selection on synonymous codon usage from noisy experimental data. Mol Biol Evol 30:1438-53
Agashe, Deepa; Martinez-Gomez, N Cecilia; Drummond, D Allan et al. (2013) Good codons, bad transcript: large reductions in gene expression and fitness arising from synonymous mutations in a key enzyme. Mol Biol Evol 30:549-60
Keller, Thomas E; Mis, S David; Jia, Kevin E et al. (2012) Reduced mRNA secondary-structure stability near the start codon indicates functional genes in prokaryotes. Genome Biol Evol 4:80-8

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