Evolution of protein-protein interaction networks in yeast

Abstract

Interest in the evolution of protein-protein and genetic interaction networks has been rising in recent years, but the lack of large-scale high quality comparative datasets has acted as a barrier. Here, we carried out a comparative analysis of computationally predicted protein-protein interaction (PPI) networks from five closely related yeast species. We used the Protein-protein Interaction Prediction Engine (PIPE), which uses a database of known interactions to make sequence-based PPI predictions, to generate high quality predicted interactomes. Simulated proteomes and corresponding PPI networks were used to provide null expectations for the extent and nature of PPI network evolution. We found strong evidence for conservation of PPIs, with lower than expected levels of change in PPIs for about a quarter of the proteome. Furthermore, we found that changes in predicted PPI networks are poorly predicted by sequence divergence. Our analyses identified a number of functional classes experiencing fewer PPI changes than expected, suggestive of purifying selection on PPIs. Our results demonstrate the added benefit of considering predicted PPI networks when studying the evolution of closely related organisms.

Master regulators of genetic interaction networks mediating statin drug response inSaccharomyces cerevisiaevary with genetic background

“…However, we have concluded that GINs cannot be units of heritability because GINs and their involved processes, ER-stress response to statins in this paper, were not conserved in closely related strains. Although GINs appear to be ephemeral or transitory, GINs could nonetheless have a role in evolutionary potential 57, 65 . The concept of an evolutionary ratchet of sequentially accumulating single mutations stabilized by withinprotein epistatic effects describing the evolution of the glucocorticoid receptor 66 might be instructive.…”

Aligning functional network constraint to evolutionary outcomes

“…Gresham et al [64] similarly showed that evolutionary constraint in experimentally evolved yeast populations over 200 generations is dependent on the type of selection (limiting Glucose or Phosphate vs. Sulphur), with convergence being an outcome of the system level organization of the respective metabolic pathway. To assess evolutionary outcomes (i) Rapid adaptation and (ii) Convergent evolution, as well as to address the important factor of gene expression in shaping protein-coding gene evolution, the Schoenrock et al [62] data set needs to be rearranged and expanded on. For this purpose, I obtained the data of [62] including yeast ORF ID, computationally predicted evolutionary PPI re-wiring score (γ), and substitution rate (ω).…”

“…To assess evolutionary outcomes (i) Rapid adaptation and (ii) Convergent evolution, as well as to address the important factor of gene expression in shaping protein-coding gene evolution, the Schoenrock et al [62] data set needs to be rearranged and expanded on. For this purpose, I obtained the data of [62] including yeast ORF ID, computationally predicted evolutionary PPI re-wiring score (γ), and substitution rate (ω). This data set was then integrated with data downloaded from Wall et al [38] including ORF ID, and CAI (Codon Adaptation Index, a measure of RNA expression levels, based on [65]).…”

Investigation of metal toxicity leads to the identification of novel translation associated genes in yeast (Saccharomyces cerevisiae)

“…In addition, quantitative high-throughput methods provide more precise systematic predictions that will offer novel bioengineering approaches for biotargets and drug discovery (e.g. Kholodenko et al, 2015, Pitre et al, 2006Schoenrock et al, 2017;Kazmirchuk et al, 2017). The capability of understanding and measuring genome-wide, transcriptome-wide or proteome-wide systems is perhaps the single utmost force giving rise to systems biology.…”

“…Krogan et al, 2006;Samanfar et al, 2013;2014) experiments. In addition, large-scale experiments in yeast provided a platform for prediction of protein interatomic networks and engineering of novel proteins in other organisms (Pitre et al, 2006;Schoenrock et al, 2015;Schoenrock et al, 2017;Burnside et al, 2018). The integration of data obtained from these approaches can eventually give a framework for modeling biological systems which may explain functional relationships between genes and other cellular communications.…”