What do we need to know in order to predict evolution? For a long time we have been able to predict the fate of a known mutation within a population. However, a more difficult task is predicting which mutations are likely to emerge, and the consequences of those mutations within the context of the pre-existing genetic background. We know that there are certain biases that make some mutations more likely to occur than others, and we know that the effect of mutations on an individual's fitness can vary depending on the mutations already carried by that individual. But, we have yet to bring these pieces of information together to enable effective forecasting of likely adaptive mutations in an evolving population.
The ability to forecast adaptive evolution of populations has many important implications. It will improve our ability to predict antibiotic, herbicide and pesticide resistance; grant opportunities to optimise treatment strategies for cancers and infectious diseases; and in a rapidly changing climate, allow strategic manoeuvres to limit detrimental effects to ecosystems and at risk populations.
To address this problem, we will compare the evolutionary trajectories of two strains of bacteria of the same species that show different adaptive routes to the same selective challenge - one repeatable the other variable. My previous work has used real-time evolution of microbes in the laboratory to show that a non-motile bacteria can re-evolve motility within 96 hours. Interestingly, we found the same mutation in 90% of cases. We repeated this experiment in a different strain of bacteria of the same species: they were also able to rescue motility within 96 hours via mutations in the same genes, or within the same network of genes, but never at the same site. These bacteria are closely related enough such that they share most of their genes and these genes carry out the same functions. However, they also carry a number of differences across their genomes. By comparing these two strains under the same selective conditions, we are able to experimentally test what might be causing differences in the evolved mutations conferring motility, and the consequences of these differences in driving predictable evolutionary trajectories.
We will test whether there are factors that might be biasing which mutations are emerging. To do this we will evolve both bacterial strains, which have been genetically engineered to be non-motile, under positive selection and in the absence of selection for motility. We will look for differences in the frequency of mutations that rescue motility across the two bacterial strains. This will inform us as to whether differences in the structure or composition of the genome might be contributing to accessible mutations. Next, we will reciprocally generate mutations that confer motility in either strain and measure the fitness effect of these mutations across strains. This will tell us how differences in the genome translate into different fitness effects of the same mutations in the same genes. Finally, we will maintain selection for motility in the reciprocal motile lines across each strain, and select for faster motile bacteria. We will look to see whether mutations that confer a faster moving bacteria are dependent on the mutations that precede it, and whether certain mutations are more common in one strain compared to the other. This will tell us how accessible certain mutational routes are given differences in fitness effects across different bacteria.
By gaining an in depth understanding of the rules that determine repeatable evolution in a simple and tractable system, we can build the foundation for a more generalised ruleset. The principals discovered here will help improve our understanding of how underlying mutational biases and the wider genetic background contribute to accessible adaptive mutations. With a long term goal of improved ability to forecast adaptive evolution more generally.