The causes and consequences of a mutational hotspot in Pseudomonas fluorescens
: (Alternative Format Thesis)

Student thesis: Doctoral ThesisPhD


Mutational hotspots describe areas of the genome where genetic change is more likely to occur due to underlying biases. These biases are often a consequence of interacting genetic features, which raise the likelihood of DNA damage and error. When found at genomic positions under selection, such hotspots can be powerful determinants of evolutionary outcomes, as they can facilitate highly repeatable genetic evolution from the stochastic process of mutation. However our current lack of understanding means such hotspots can be difficult to identify and characterise, and as such their role in evolutionary processes is largely under-appreciated. In this thesis, I reveal the genetic causes and evolutionary consequences of a powerful mutational hotspot in the soil bacterium Pseudomonas fluorescens. Engineered immotile lines of this microbe have been found in previous work to rapidly re-evolve motility though a one-step de novo mutation. I find a prominent role for mutation bias, which is predicated on silent genetic changes, in facilitating a highly repeatable genetic outcome during the evolution of the motility phenotype. My work also reveals that the local tract of DNA that enables repeatable evolution works alongside several other genetic features, namely strand orientation, genomic position, and mismatch repair proteins to generate this nearly deterministic adaptive outcome. Finally, I examine the evolutionary history of this mutational hotspot, and suggest that the hotspot is not preserved but suppressed by selection, and so hotspots of this type may well appear transiently and throughout the genome. This model system establishes a novel framework with which we can empirically determine the nuances of hotspots and define their role in evolution. The collective work in this thesis therefore moves us closer to comprehensively understanding the mutational drivers that underpin repeatable evolution, and as such acts as a key stepping stone for future forecasts of evolution.
Date of Award17 Nov 2021
Original languageEnglish
Awarding Institution
  • University of Bath
SupervisorTiffany Taylor (Supervisor), Nicholas Priest (Supervisor) & Michael Tipping (Supervisor)

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