Quantitatively Assessing the Effects of Mutations on Signaling Pathway Function

Cells receive diverse stimuli from their surroundings and process them into distinct physiological responses through signal transduction pathways. Mitogen-activated protein kinase pathways (MAPKs) are responsible for many cellular functions. The overarching goal of this thesis was to study the effects of mutations on MAPK signaling function in living cells using complementary systems biology and genetic engineering methods. First, I asked how mutations could rescue overexpression driven decrease in signaling. I identified several variants of the yeast MAPK, Fus3, including those containing premature stop codons (PSC) which are naturally readthrough, that rescued mating function in an overexpression background. The location of the mutated residue in the protein structure and its identity play a role in determining the impact of the PSC on pathway function. These results suggest that selection can compensate for protein overexpression by introducing PSCs at permitted positions. Next, I quantified the effects of mutations on yeast mating and HOG signaling. I used the information theory measure of mutual information to measure signaling and introduced change in mutual information as the measure of the effects of mutations on signaling. I showed that information transmission in HOG signaling is more robust to spontaneous mutations than in mating signaling. I controlled for some of the possible reasons for this difference, redundancy and mutational target size, and found that although HOG signaling mutational robustness is compromised, it is still more robust suggesting that other mechanisms play a role in maintaining this mutational robustness. Finally, I compared the effects of the mutations in a shared component on information transmission in the pathways and found that large effect mutations have large effects on information transmission in both signaling pathways and that some of these effects are opposite. Overall, in this thesis work, we found both non-intuitive and intuitive results and ways to quantify both. Applying the tools of systems biology and genetic engineering is a comprehensive and complementary approach to answering evolutionary questions.Ph.D