Enzyme Evolution in the Context of Cellular Metabolism

Evolution is the governing principle in biology. Recent developments in gene sequencing technology mean that we can follow the pathways evolution takes on a molecular level. However, there is still a huge gap in our understanding of evolution’s role in the development of enzymes, the way they function as part of larger metabolic networks, and how they contribute to organismal fitness. This thesis investigates this mystery by looking at a highly conserved enzyme, situated at the centre of the glycolysis and gluconeogenesis, triose phosphate isomerase (TPI). I have used this well-studied enzyme to probe this central relationship between genotype and phenotype.

In the first part of this thesis, I aimed to create a near-complete library comprising all possible single amino acid substitutions of the Escherichia coli TPI. A combination of gene synthesis technologies and high-throughput cloning techniques were used to construct a library of 18,676 clones, each expressing a variant TPI, arrayed in such a way to retain positional data about each mutation. Following this, the library was used to methodically map the effects of each mutation, both in regards to enzyme activity and overall cellular fitness. This is the first time this has been done for an enzyme at the heart of central carbon catabolism. I collected growth curves for each individual mutant, as well as measured the total intracellular enzyme activity for each variant. The results demonstrated that mutations to regions involved in dimerisation and catalysis were detrimental to both TPI activity and the fitness of the cell, and also revealed regions of the enzyme that were mutationally robust.

The collection of these data then allowed me to construct a comprehensive fitness-versus-activity matrix for TPI. This matrix suggests that a higher level of enzymatic output does not always lead to the fittest organism, as well as reveals deleterious mutations in unexpected regions of the gene. The final part of this thesis used the TPI library to explore the evolutionary outcomes of each mutation in 67 different environments, the results of which were revealed by deep sequencing. This uniquely expansive examination takes into account enzyme performance, cellular growth, and environmental conditions, to create a global landscape of TPI with unprecedented breadth and depth.