Genome evolution in structured systems

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Genome evolution in structured systems

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Title: Genome evolution in structured systems
Author: Chung, Hattie ORCID  0000-0001-8417-5606
Citation: Chung, Hattie. 2016. Genome evolution in structured systems. Doctoral dissertation, Harvard University, Graduate School of Arts & Sciences.
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Abstract: The evolution of a genome is shaped by spatial interactions at multiple scales. At the angstrom level, structural constraints on both RNA molecules and proteins contribute to the evolution of a gene sequence. Such optimized genes are weaved together in a particular order, out of a near-infinite number of combinations, to result in a genome. The fate of a genome is intricately linked to the evolutionary fate of its host organism; in turn, the fate of an organism is governed by where it resides in space. In this dissertation, I investigate how structure shapes the evolution of a gene, genome content, and pathogen populations residing in a diseased human lung. Chapter 1 provides a brief historical overview of population genetics in structured environments. I motivate why it is important to determine the migration rate of new alleles. Chapter 2 investigates how pathogens evolve within the structure of the cystic fibrosis lung. I find that migration rate and mutation rate are on similar timescales. Selection, rather than spatial isolation, maintains diversity within a pathogen population. Chapter 3 presents a new method to probe how codon choice is optimized throughout a gene. I find that codon choice is dictated by preference for particular RNA secondary structures, rather than intrinsic properties of a codon. Chapter 4 describes an ongoing study of how rapidly P. aeruginosa populations evolve in short-term infections. Preliminary results show that gene duplication events can sweep through a population in just 11 days. Chapter 5 introduces ideas for future directions. I pose questions regarding how pathogens evolve molecular mimicry that can trigger autoimmune disease in the human host, and how cancer-inducing inflammation might be detected from mutational signatures in the microbiome.
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