Improving selection tests for protein-coding genes

Bacteria adapt rapidly to their environments through the accumulation of mutations and the action of natural selection. These processes have major consequences for human health, agriculture, and industry, as harmful bacteria can quickly evolve resistance to treatments and colonize new environments. For this reason, identifying the genes that enable bacterial adaptation is of great importance. Current methods for identifying selection in bacterial genomes make overly simplistic assumptions about which mutations are harmful, beneficial, or neutral within a gene. The proposed work aims to develop improved methods for determining which mutations are truly neutral and which are most beneficial or deleterious. In addition, new datasets will be used to obtain more accurate estimates of the effects of different types of mutations on gene function. These advances will then be integrated into a new tool designed to more precisely estimate the strength and nature of selective pressures acting on individual genes. Overall, this project will improve the sensitivity of tests for selection and provide valuable new insights into bacterial evolution. The broader impacts of this project include delivering a new computational tool to the research community and that may facilitate advances in biotechnology, and providing training opportunities for students through research projects in microbiology, sequencing technologies, and high-performance computing. Selection is a core process shaping the evolution of all forms of life. Although many methods exist to detect and characterize selection on genes, most are not well suited for bacteria. Common approaches focus on protein-coding genes and compare rates of synonymous versus non-synonymous substitutions, assuming that synonymous mutations are neutral while non-synonymous ones are not. In practice, this binary view is overly simplistic: synonymous sites can still be subject to selective constraints, and different amino acid changes can vary widely in their effects on gene fitness. To address these limitations, this project proposes a new framework to better characterize neutral evolution in bacteria. By analyzing short intergenic regions, specific codons, and pseudogenes, it aims to establish more accurate baseline estimates of neutral evolution. It will also incorporate data from deep mutational scanning experiments to quantify how individual mutations influence fitness more accurately. These new measures will be integrated to a novel approach that uses continuous amino acid exchangeability matrices within a maximum likelihood framework to improve the inference of selection. Beyond providing a quantitative measure of selection, this framework can also distinguish qualitatively different substitution patterns, enabling us to separate the molecular signatures of relaxed versus positive selection. Ultimately, this approach will support the development of improved null models of evolution and enhance our ability to distinguish among different selective regimes. The broader impacts of this project include delivering a new computational tool to the community and that may facilitate advances in biotechnology, and providing training opportunities for students through research projects in microbiology, sequencing technologies, and high-performance computing. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.