Abstract:
We use the term ‘mutation spectrum’ to refer to the frequency distribution of the types of mutations that an organism tends to undergo overtime. However, more often than not, this mutation spectrum tends to be somewhat biased towards either end. This phenomenon is called mutation bias. It plays a role in determining several important factors such as predictability and evolution of genetic diversity, and mutational robustness. These factors, in turn, directly influence adaptive evolution in the long run. It has been known for a long time that the occurrence, selection, and accumulation of changes in the genetic material of an organism, i.e., mutations, forms the foundation of evolutionary trajectories. Since the mutation spectrum is a representative of the range of mutations that can occur at any given point, a bias in this spectrum can, therefore, potentially dictate the evolutionary trajectory taken by a given organism under a particular selection pressure. This necessitates a systematic study of evolution
from the relatively less explored perspective of mutation biases. Recent studies have demonstrated how a shift in the mutation bias of an organism can lead to a change in the supply of beneficial or deleterious mutations to that organism. Hence, it is important to explore how an organism behaves under various selection pressures when its mutation spectrum is shifted towards either end. The aim of my work was to highlight how the combined effect of mutation bias and mutation rate shapes the evolutionary trajectory of an organism. To that effect, my results show interesting trends which do not completely align with my expectations prior to the experiments. Firstly, in terms of the effect of rate on adaptation in fluctuating environments, reversed-bias mutators did not seem to differ much from each other at any given time point in the evolution in Glucuronate. However, these results were not consistent in Succinate, where there was a noticeable difference in fitness between strains of different mutation rates at day 18. However, in the case of reinforced-bias mutators, this difference in fitness between mutators of different mutation rates was not seen in either medium. Secondly, contrary to my initial expectations, mutation bias was not found to play a strong role in bacterial adaptation in fluctuating environments. These results are in direct contradiction to the expectations which were formed on the basis of distribution of fitness effects obtained in the lab. As a result, this work necessitates further detailed investigations into the mechanistic basis of such adaptation.
