Evolutionary Systems Biology in Yeast

Since the release of the yeast whole genome sequence in 1996 and the advances in high-throughput technologies, scientists have studied the evolution of the yeast Saccharomyces cerevisiae at the genomics level in two major ways: comparative genomics and systems biology. Both of these approaches have provided important insights into how the yeast genome is organized and how networks evolve to achieve phenotypic features. In this thesis, I study these issues by analyzing both genomics and networks data. The major questions I asked and the findings raised by this thesis are as follows: First, I studied why there are so many yeast non-coding transcripts (>60%) that are transcribed in bi-directional orientation with a coding gene. For some genes, notably essential genes, expression when expression is needed is vital, hence low noise in expression is favorable. For other genes, noise is necessary for coping with environmental stochasticity or for providing dice-like mechanisms to control cell fate. But how is noise in gene expression modulated? We hypothesize that gene orientation may be crucial, as for divergently organized gene pairs, expression of one gene could affect the chromatin conformation of a neighbouring gene, thereby reducing noise for that gene. Transcription of antisense non-coding RNA from a shared promoter is similarly argued to be a noise-reduction mechanism. Our stochastic simulation models confirm the expectation. The model correctly predicts: that protein coding genes with bi-promoter architecture, including those with a ncRNA partner, have lower noise than other genes; divergent gene pairs uniquely have correlated expression noise; distance between promoters predicts noise; ncRNA divergent transcripts are associated with genes that a priori would be under selection for low noise; essential genes reside in divergent orientation more than expected; bi-promoter pairs are rare subtelomerically, cluster together and are enriched in essential gene clusters. We conclude that gene orientation and transcription of ncRNAs, even if unstable, are candidate modulators of noise levels. Second, I studied whether ancestrally neighbouring genes still remain co-expressed even after they have been separated by chromosomal rearrangements. Although there is clear evidence that closely spaced gene pairs tend to be highly co-expressed, it is not clear if this co-expression is solely due to a mechanistic neighbourhood effect, or if the co-expression is selectively favorable. Thanks to the multiple fungi genome sequencing projects, it is now possible to answer this question. Using a reconstruction of gene order in an ancestral yeast based on parsimony, we found a significant co-expression signal for many separated gene pairs. Moreover, even genes that are neighbouring in other fungi but were never genomic neighbours in the evolutionary history of Saccharomyces cerevisiae show higher co-expression than expected. We conclude that co-expression of neighbouring genes is indeed often favoured by natural selection. Third, I studied how network neighbours influence the evolutionary rate of a protein and why. Recently it was shown that the level of protein expression is the main predictor of the protein evolution rate. Thus, if two genes have similar expression levels, they should also have a similar rate of evolution. We found that this can explain the fact that neighbouring gene evolve similarly in most biological networks, regardless of the different network topologies. Namely, controlling for expression level, neighbouring genes no longer show correlated evolution in almost all networks studied. But in co-expression network, even controlling for expression abundance as well as for gene essentiality and gene length, neighbouring (i.e., co-expressed) genes still co-evolve. This finding suggests that both expression level and co-expression influence the rate of protein evolution in networks. Finally, I focused on the phenotypic effect of genetic hubs. Robustness is a basic feature of biological networks, and we expect different proteins to make different contributions to the overall robustness of the network. In genetic interaction networks, when the genetic hubs function abnormally, the offspring is expected to exhibit more phenotypic variation (both genetically caused and non-genetically caused). We observed that the number of strong negative genetic interactions (synthetic lethality) is indeed positively correlated with phenotypic variation of the respective single-gene knockouts in yeast. Furthermore, there is a high correlation between haploid fitness of the knockouts and phenotypic variation. Thus, haploid fitness and genetic interactions are two predictors of phenotypic variation in mutants. This further suggests that the release of phenotypic variation in mutants is mostly not due to a specific buffering function of the mutated gene (as in the case of chaperones, e.g., Hsp90), but that compromised function of one part of the network reduces the cell’s ability to compensate for sub-optimal pathways elsewhere.