UC San Diego
Proteome allocation trade-offs in bacterial evolution and regulation
- Author(s): OBrien, Edward
- Advisor(s): Palsson, Bernhard O
- Bourne, Philip E
- et al.
The abundance of proteins expressed in a particular environment are primary determinants of an organism's phenotypic and fitness properties. However, protein synthesis is costly and proteome size is limited; thus, the benefit of expressing proteins also comes with costs. In this thesis, I interrogate the evolutionary and regulatory trade-offs resulting from these proteome allocation constraints. Throughout the thesis I employ a genome-scale model of metabolism and protein synthesis for Escherichia coli, which can compute condition-specific proteome allocation requirements and limitations. First, I show that microbial growth rates are quantitatively determined by the expression of unused protein. Rather than supporting growth in the current environment, large fractions of the expressed proteome enable readiness for environmental change and stress. The expression of these different proteome segments is regulated by global transcription factors and results in fitness trade-offs. Second, I show that after selecting for growth through experimental evolution, several adaptive regulatory mutations increase fitness through proteome and energy resource re-allocation. These pleiotropic mutations in the RNA Polymerase systematically re-allocate the proteome towards growth and away from stress resistance, showing that fitness trade-offs are readily modulated by global regulators during evolution. Finally, I show that the diversity present in evolving populations is predictable and due to proteome allocation trade-offs. Rather than evolving to a unique optimum, a range of near-optimal proteomic and metabolic phenotypes is apparent when strains are independently evolved in the same environment. The diversity of alternative phenotypes reflects a rate-yield trade-off due to the varying protein cost of metabolic pathways in central carbon metabolism. Thus, proteome allocation constraints have a pervasive and predictable effect on bacterial ecology, regulation, and evolution.