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
Jack, Benjamin R; Boutz, Daniel R; Paff, Matthew L et al. (2017) Reduced Protein Expression in a Virus Attenuated by Codon Deoptimization. G3 (Bethesda) 7:2957-2968
Brown, Colin W; Sridhara, Viswanadham; Boutz, Daniel R et al. (2017) Large-scale analysis of post-translational modifications in E. coli under glucose-limiting conditions. BMC Genomics 18:301
Caglar, Mehmet U; Houser, John R; Barnhart, Craig S et al. (2017) The E. coli molecular phenotype under different growth conditions. Sci Rep 7:45303
Jackson, Eleisha L; Spielman, Stephanie J; Wilke, Claus O (2017) Computational prediction of the tolerance to amino-acid deletion in green-fluorescent protein. PLoS One 12:e0164905
Echave, Julian; Spielman, Stephanie J; Wilke, Claus O (2016) Causes of evolutionary rate variation among protein sites. Nat Rev Genet 17:109-21
Jack, Benjamin R; Meyer, Austin G; Echave, Julian et al. (2016) Functional Sites Induce Long-Range Evolutionary Constraints in Enzymes. PLoS Biol 14:e1002452
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:
Spielman, Stephanie J; Wilke, Claus O (2016) Extensively Parameterized Mutation-Selection Models Reliably Capture Site-Specific Selective Constraint. Mol Biol Evol 33:2990-3002
Jackson, Eleisha L; Shahmoradi, Amir; Spielman, Stephanie J et al. (2016) Intermediate divergence levels maximize the strength of structure-sequence correlations in enzymes and viral proteins. Protein Sci 25:1341-53
Lipsitch, Marc; Barclay, Wendy; Raman, Rahul et al. (2016) Viral factors in influenza pandemic risk assessment. Elife 5:

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