A Systems Approach to Cancer Biology
Physical interactions mediated by proteins are critical for cellular function, constituting in toto complex macromolecular "interactome" networks. Systematic mapping of protein-protein, protein-DNA, protein-RNA and protein-metabolite interactions at the scale of the whole proteome advances understanding of interactome networks. Applications range from functional characterization of single proteins to discoveries on local and global systems properties of cellular networks. We generate and improve comprehensive interactome maps for multiple organisms (currently human, the model unicellular eukaryote yeast S. cerevisiae, and the model metazoan D. melanogaster). To ensure that the interactome maps we release are of the highest possible quality we carry out all experimental steps thoroughly and carefully, verifying all interacting pairs and validating them by independent, orthogonal assays.
Classical forward genetics and modern functional genomics (i.e. reverse genetics) have assigned potential functions to thousands of genes across dozens of organisms. The availability of genome sequences and the development of automated phenotypic analyses makes reverse genetics strategies based on null or nearly null alleles a major source of gene function information. Functional interpretation of (nearly) null alleles is often complicated because gene products do not operate in isolation but instead act on each other within complex and dynamic interactome networks. In interactome graphs, knockouts or knockdowns eliminate a node and ALL its edges. We have been developing alternatives to generate alleles that perturb a single interaction, or edge at a time, while maintaining all others unperturbed. Such “edgetic” alleles allow precise evaluation of the in vivo roles of individual interactions. We have provided proof-of-principle of an integrated strategy based on reverse yeast two-hybrid to isolate edgetic alleles and functionally characterize them in vivo. This strategy could be readily implemented for other biological pathways in other model organisms.
Many mutations responsible for human disease might also be edgetic. Edgetic mutations would be different in their effects and properties than the complete losses of gene products (node removal) generally accepted as primarily responsible for disease. Conventional node removal models for disease cannot reconcile with the increasingly appreciated prevalence of complex genotype-to-phenotype associations for even simple Mendelian disorders, particularly the confounding influence of allelic heterogeneity, locus heterogeneity, incomplete penetrance, and variable expressivity. We have delineated clear distinctions of mutations corresponding to node removal versus edgetic perturbations in the full set of mutations associated with human Mendelian disorders. Mutations associated with recessive disorders are more likely node removal, whereas mutations associated with dominant disorders are more likely edgetic. We have developed and tested an experimental platform that can characterize, at high throughput, edgetic interaction profiles of mutant disease proteins. We are currently using this platform at high throughput to edgetically profile cancer mutations, both the set of mutations already identified as well as the vaster set of cancer mutations being identified by cancer genome projects.
To learn more about the Vidal Lab or the Center for Cancer Systems Biology, please visit http://ccsb.dfci.harvard.edu.