Interrogation of Mammalian Protein Complex Structure, Function, and Membership Using Genome-Scale Fitness Screens

Abstract

Summary Protein complexes are assemblies of subunits that have co-evolved to execute one or many coordinated functions in the cellular environment. Functional annotation of mammalian protein complexes is critical to understanding biological processes as well as disease mechanisms. Here, we used genetic co-essentiality derived from genome-scale RNAi- and CRISPR-Cas9- based fitness screens performed across hundreds of human cancer cell lines to assign measures of functional similarity. From these measures, we systematically built and characterized functional similarity networks which recapitulate known structural and functional features of well-studied protein complexes and resolve novel functional modules within complexes lacking structural resolution, such as the mammalian SWI/SNF complex. Finally, by integrating functional networks with large protein-protein interaction networks, we discovered novel protein complexes involving recently-evolved genes of unknown function. Taken together, these findings demonstrate the utility of genetic perturbation screens alone and in combination with large-scale biophysical data to enhance our understanding of mammalian protein complexes in normal and disease states.

High‐resolution mapping of cancer cell networks using co‐functional interactions

“…Remarkably, this holds true not only across cell types, but even in the context of expression data from a panel of lymphoblastoid cell lines (LCLs; Fig B; Pickrell et al , ), suggesting that broad mechanisms of gene regulation and not tissue specificity are responsible for the trend. As reported previously (Wang et al , , ; Pan et al , ), differences in essentiality across genes thus appear quantitative and not strictly binary in each cell line. To facilitate exploration of our co‐functional gene dataset, we have developed a shiny app that visualizes all genes co‐functional to the query gene with added functionality for overlaying interactions from STRING, published CRISPR screens, or custom files containing scores per gene.…”

“…As reported by others, the degree to which two gene essentiality profiles (rows) are correlated reflects their functional relationship. This functional relationship can reflect many gene–gene relationships, including membership in the same metabolic pathway or protein complex, and depends on the mutations present in the cell lines tested (Fig A and B; Wang et al , ; Pan et al , ).…”

“…Although the rate at which we identified the members of a protein complex co‐functional varied according to the knockout growth phenotype, we identified other factors that guided which of a complex's members were co‐essential and which were not. The Mediator complex, a transcriptional coactivator that has been previously examined using a similar parallel screening analytical approach (Pan et al , ), serves as an example. Although knockout of every Mediator complex member is associated with impaired growth, we called fewer than half of the pairs of genes comprising the Mediator complex co‐functional (Fig F).…”

“…The publication of hundreds of CRISPR screens in cell lines drawn from diverse cell lineage and mutational backgrounds has invited even broader surveys of co‐essentiality (Meyers et al , ; Data ref: Meyers et al , ). One study has dissected the composition of essential protein complexes (Pan et al , ), another has leveraged the natural occurrence of gene activating mutations to ascertain likely genetic interactions (Rauscher et al , ), and other work accessible as a preprint has focused on the organization of cancer growth pathways (preprint: Kim et al , ). In all these cases, interactions identified from correlated gene profiles operated on multiple levels of cellular regulation, validating parallel screening as a powerful tool for reconstructing cell networks.…”

“…Recent work has shown that copy number variation can underlie the strongest hits in CRISPR‐knockout screens, and multiple groups have proposed corrective algorithms to confront this problem (Pommier, ; Meyers et al , ; Data ref: Meyers et al , ; preprint: Wu et al , ). Additional heuristics aimed at increasing the quality of genetic interactions identified from parallel genetic screens have included discarding entire screens with noisy effect sizes, setting an effect size threshold for correlating genes, and capping the number of interactions per gene (Wang et al , ; preprint: Kim et al , ; Pan et al , ); however, reliance on these heuristics prevents truly unbiased genome‐wide analyses (McFarland et al , ). Furthermore, as the scale and diversity of published genetic screens grow, so will the need for new statistical techniques that can correct for technical variation while preserving even small levels of true signal.…”