To date, most genome-scale approaches designed to explore kinase pathways have been targeted towards substrate identification for individual kinases but provide little functional information and only a limited view of the interplay of kinases and their targets in key biological processes. I attempted to tackle the complexity of kinase networks using an unbiased integrated global analysis in budding yeast. I used functional genomics screens to study the yeast kinome using combinatorial genetic perturbations. I first assessed the effects of gene overexpression on the fitness of non-essential kinase deletion mutants to generate a comprehensive view of Synthetic Dosage Lethal (SDL) interactions involving yeast kinases. These data were complemented by assessing genome-wide Synthetic Lethal (SL) interactions for kinases that gave SDL interactions. By measuring >600,000 potential interactions between kinase-gene pairs, I produced a meta-network of ~1300 dosage lethal interactions and 7500 negative and positive genetic interactions. I reasoned that by combining two complementary genetic datasets for kinases, I could: 1) characterize the unexpected phenotypic outcomes that result from the interplay of gain-of-function and loss-of-function phenotypes; 2) better comprehend the complexity of kinase signaling networks and; 3) predict the function of novel genes that arise from the combined genetic network and a gold standard list of known kinase-substrate pairs in the literature.
The SDL network alone was enriched for pathways known to be regulated by cognate kinases including phosphoproteins, and kinase targets and kinases that yielded informative SDL interactions were largely those with cell polarity roles. Condition-specific screens and analysis of kinase double mutants suggested that the apparent resistance of many kinases to genetic perturbation cannot be solely attributed to kinase redundancy but most likely reflects the requirement for many kinases in certain activating conditions.
Next, I created the first systematic gold standard for kinase-substrate pairs and generated the first kinase interaction database, specifically curated for experiments pertaining to kinase-substrate relationships, in order to analyze the SDL network for identification of kinase targets. Also, using a novel approach that combines the gold standard and the integrated SDL-genetic interaction meta-network, I found that the integrated network is more functionally informative than either SGA or SDL networks alone. Additional integration of known kinase-substrate relationships extracted from the biochemical literature into this network was key to the identification of recurring motifs that enable accurate prediction of single mutant phenotypes.
I have also identified ~2000 triplet regulatory network motifs, unraveling novel pathways regulated by kinases. I have tested several motifs using phenotypic and biochemical assays and have identified a novel gene involved in the regulation of cell wall integrity pathway, and found a new regulatory mechanism involving the mitotic exit network machinery. My study provides a general framework for predicting phenotypic outcomes from different combinations of genetic mutations, but also delineates the complexity of signaling pathways involving phosphorylation. The identified network motifs belie the simplistic notion of linear kinase cascades, and support a complex network model where multi-dimensional signaling waves dictate a phenotypic outcome. My data suggest that unraveling the complexity of network biology demands an unbiased global analysis using an integrated set of functional genomics approaches with a high quality gold standard.
| Identifer | oai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:OTU.1807/31937 |
| Date | 11 January 2012 |
| Creators | Sharifpoor, Sara |
| Contributors | Andrews, Brenda |
| Source Sets | Library and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada |
| Language | en_ca |
| Detected Language | English |
| Type | Thesis, Dataset |
Page generated in 0.0018 seconds