The fitness function developed in this thesis directly links the physicochemical properties of an enzyme to evolutionary fates in a quantitative and predictive manner through a comparative study of empirical and simulated data. The success or failure of organisms during evolution is dictated by changes in molecular structure that give rise to changes in fitness revealed by evolutionary dynamics within a population. While the conceptual link between genotype, phenotype and fitness is clear, the ability to relate these in a quantitative manner remains difficult. I show here that predicting success during adaptation can depend critically upon enzyme kinetic and folding models. We used a 'weak link' method to favor mutations to an essential, but maladapted adenylate kinase gene within a microbial population that resulted in the identification of five mutants that arose nearly simultaneously and competed for success. The unique catalytic role of adenylate kinase in vivo is to maintain adenylate homeostasis by catalyzing the reaction: ATP + AMP [imaginary] ADP. The stabilizing substitutions retained this essential function and were shown to be necessary for viability at higher temperatures. Physicochemical characterization of these mutants demonstrated that, although steady-state enzyme activity is important, success within the population is critically dependent on resistance to denaturation and aggregation thus emphasizing the importance of proper folding in adaptation. In vitro activity is a product of critical catalytic and folding pathways, and hence is a valuable proxy for fitness. A fitness function relating in vitro measurements of enzyme activity and reversible and irreversible unfolding to growth rate must impose an activity threshold above which there is no added fitness benefit in order to reproduce in vivo evolutionary fates in an in silico population. The fitness function thereby links organismal adaptation to the properties of a single gene. Understanding the physical basis for adaptation of an organism is the first step in the development of approaches that can accurately model, and someday predict, the manner in which organisms would respond to new antibiotics and improve upon the current clinical regimens.
| Identifer | oai:union.ndltd.org:RICE/oai:scholarship.rice.edu:1911/70387 |
| Date | January 2012 |
| Contributors | Shamoo, Yousif |
| Source Sets | Rice University |
| Language | English |
| Detected Language | English |
| Type | Thesis, Text |
| Format | 164 p., application/pdf |
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