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.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
5R01GM088344-04
Application #
8269780
Study Section
Genetic Variation and Evolution Study Section (GVE)
Program Officer
Eckstrand, Irene A
Project Start
2009-08-01
Project End
2014-05-31
Budget Start
2012-06-01
Budget End
2014-05-31
Support Year
4
Fiscal Year
2012
Total Cost
$246,313
Indirect Cost
$42,323
Name
University of Texas Austin
Department
Biology
Type
Schools of Arts and Sciences
DUNS #
170230239
City
Austin
State
TX
Country
United States
Zip Code
78712
Spielman, Stephanie J; Wilke, Claus O (2016) Extensively Parameterized Mutation-Selection Models Reliably Capture Site-Specific Selective Constraint. Mol Biol Evol 33:2990-3002
McWhite, Claire D; Meyer, Austin G; Wilke, Claus O (2016) Sequence amplification via cell passaging creates spurious signals of positive adaptation in influenza virus H3N2 hemagglutinin. Virus Evol 2:
Echave, Julian; Spielman, Stephanie J; Wilke, Claus O (2016) Causes of evolutionary rate variation among protein sites. Nat Rev Genet 17:109-21
Spielman, Stephanie J; Wan, Suyang; Wilke, Claus O (2016) A Comparison of One-Rate and Two-Rate Inference Frameworks for Site-Specific dN/dS Estimation. Genetics 204:499-511
Meyer, Austin G; Wilke, Claus O (2015) Geometric Constraints Dominate the Antigenic Evolution of Influenza H3N2 Hemagglutinin. PLoS Pathog 11:e1004940
Spielman, Stephanie J; Wilke, Claus O (2015) The relationship between dN/dS and scaled selection coefficients. Mol Biol Evol 32:1097-108
Meyer, Austin G; Wilke, Claus O (2015) The utility of protein structure as a predictor of site-wise dN/dS varies widely among HIV-1 proteins. J R Soc Interface 12:20150579
Kachroo, Aashiq H; Laurent, Jon M; Yellman, Christopher M et al. (2015) Evolution. Systematic humanization of yeast genes reveals conserved functions and genetic modularity. Science 348:921-5
Meyer, Austin G; Spielman, Stephanie J; Bedford, Trevor et al. (2015) Time dependence of evolutionary metrics during the 2009 pandemic influenza virus outbreak. Virus Evol 1:
Franks, Alexander M; Csárdi, Gábor; Drummond, D Allan et al. (2015) Estimating a structured covariance matrix from multi-lab measurements in high-throughput biology. J Am Stat Assoc 110:27-44

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