Mapping dynamic functional networks across environments and backgrounds

University Of Toronto, Toronto ON

Investigators

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Abstract

Genome sequencing has provided an unprecedented view into the extent of human genetic variation. Yet, our ability to link specific genetic variants to phenotypes remains limited. Moreover, genetic interactions between complex combinations of variants likely contribute to the challenge. To discover rules governing genetic interaction networks, we previously constructed all possible ~18 million yeast double mutants to generate a global yeast genetic interaction map, which reveals a functional ‘wiring diagram’ of a eukaryotic cell. In the context of the last funding cycle, we systematically analyzed how the global yeast genetic interaction network responds to different conditions, and we discovered that it is remarkably robust to environmental perturbation. On the other hand, our systematic analysis of trigenic interactions associated with triple mutants and genetic interactions involving natural variants revealed the prevalence of complex genetic interactions and their immense potential to modify phenotype. To explore gene function and genetic networks in human cells, we also established an efficient genome-wide CRSPR-Cas9 platform for mapping genetic interactions, and we constructed a ‘scaffold’ genetic network for a reference human cell line. Like the yeast genetic network, the topology of the human network is informative of gene function and suggests that general properties of genetic networks are highly conserved. Here, we propose continued systematic analysis of complex genetic interaction networks and phenotypes in yeast, and the application of the results for the cogent design of experiments to continue mapping genetic networks in human cells. Aim 1: Conditional phenotypes and genetic networks dynamics in the context of diverse genetic backgrounds. We will perform systematic phenotypic and genetic analyses in wild, genetically diverse yeast strains to identify genetic modifiers underlying background-specific gene essentiality. We will also map genetic interactions in wild yeast isolates to quantify the effect of genetic background on genetic networks and more generally, the genotype-phenotype relationship. Aim 2: Quantitative single cell read-outs for assaying the phenotypic consequences of genetic variation. We will produce quantitative cell biological phenotypic profiles associated with gene perturbation and explore the influence of cell state on the effects of genetic perturbation, using proteome dynamics as a phenotypic read-out. These projects will map genetic determinants of subcellular morphology, reveal connections between conserved compartments, and establish methods to use the proteome as a read-out for genotype-phenotype analysis. Aim 3: Mapping a global genetic interaction network for a human cell line. Based on our current human genetic interaction dataset, we will select and screen an informative set of query gene mutants, with an emphasis on essential genes, to expand our scaffold genetic network and efficiently map networks underlying a set of functionally representative protein complexes. This network will provide a powerful resource for annotating human gene function and identify conserved network properties that can be used to discover disease gene modifiers, including those underlying cancer cell genetic dependencies.