A network of human functional gene interactions from knockout fitness screens in cancer cells.

Eiru Kim,Traver Hart,Sanjana Srinivasan,Gang Wang,Walter F Lenoir,Medina Colic,Merve Dede

doi:10.26508/lsa.201800278

Eiru Kim, Traver Hart + Show 5 more

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https://doi.org/10.26508/lsa.201800278

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Abstract

Genetic interactions mediate the emergence of phenotype from genotype. The systematic survey of genetic interactions in yeast showed that genes operating in the same biological process have highly correlated genetic interaction profiles, and this observation has been exploited to infer gene function in model organisms. Such assays of digenic perturbations in human cells are also highly informative, but are not scalable, even with CRISPR-mediated methods. As an alternative, we developed an indirect method of deriving functional interactions. We show that genes having correlated knockout fitness profiles across diverse, non-isogenic cell lines are analogous to genes having correlated genetic interaction profiles across isogenic query strains and similarly imply shared biological function. We constructed a network of genes with correlated fitness profiles across 276 high-quality CRISPR knockout screens in cancer cell lines into a "coessentiality network," with up to 500-fold enrichment for co-functional gene pairs, enabling strong inference of gene function and highlighting the modular organization of the cell.

Highlights

Genetic interactions govern the translation of genotype to phenotype at every level, from the function of subcellular molecular machines to the emergence of complex organismal traits
The result is that genes that operate in the same biological process tend to interact genetically with the same sets of other genes in discrete, related pathways, culminating in highly correlated genetic interaction profiles across a diverse panel of genetic backgrounds or “query strains.”
We considered CRISPR and shRNA whole-genome screen data from multiple libraries and laboratories: Avana (Doench et al, 2014; Meyers et al, 2017), GeCKOv2 (Aguirre et al, 2016), TKO (Hart et al, 2015, 2017a; Steinhart et al, 2017), Sabatini (Wang et al, 2014, 2017), the Moffat shRNA library (Koh et al, 2012; Marcotte et al, 2012, 2016; Medrano et al, 2017), and other large data sets

Summary

Introduction

Genetic interactions govern the translation of genotype to phenotype at every level, from the function of subcellular molecular machines to the emergence of complex organismal traits. The result is that genes that operate in the same biological process tend to interact genetically with the same sets of other genes in discrete, related pathways, culminating in highly correlated genetic interaction profiles across a diverse panel of genetic backgrounds or “query strains.” This observation has been exploited extensively to infer gene function in model organisms and, on a smaller scale, in human cells based on similarity of genetic interaction profiles (Lehner et al, 2006; Horn et al, 2011; Bassik et al, 2013; Kampmann et al, 2013; Roguev et al, 2013; Costanzo et al, 2016). Beyond the specific interactions themselves, a gene’s pattern of fitness phenotypes across a diverse set of backgrounds can inform our knowledge of that gene’s function

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