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

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.

SUBMITTER: Kim E

PROVIDER: S-EPMC6464042 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature

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

Kim Eiru E Dede Merve M Lenoir Walter F WF Wang Gang G Srinivasan Sanjana S Colic Medina M Hart Traver T

Life science alliance 20190412 2

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 indire ...[more]

PMID: 30979825

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Project description:The treatment of complex diseases often relies on combinatorial therapy, a strategy where drugs are used to target multiple genes simultaneously. Promising candidate genes for combinatorial perturbation often constitute epistatic genes, i.e., genes which contribute to a phenotype in a non-linear fashion. Experimental identification of the full landscape of genetic interactions by perturbing all gene combinations is prohibitive due to the exponential growth of testable hypotheses. Here we present a model for the inference of pairwise epistatic, including synthetic lethal, gene interactions from siRNA-based perturbation screens. The model exploits the combinatorial nature of siRNA-based screens resulting from the high numbers of sequence-dependent off-target effects, where each siRNA apart from its intended target knocks down hundreds of additional genes. We show that conditional and marginal epistasis can be estimated as interaction coefficients of regression models on perturbation data. We compare two methods, namely glinternet and xyz, for selecting non-zero effects in high dimensions as components of the model, and make recommendations for the appropriate use of each. For data simulated from real RNAi screening libraries, we show that glinternet successfully identifies epistatic gene pairs with high accuracy across a wide range of relevant parameters for the signal-to-noise ratio of observed phenotypes, the effect size of epistasis and the number of observations per double knockdown. xyz is also able to identify interactions from lower dimensional data sets (fewer genes), but is less accurate for many dimensions. Higher accuracy of glinternet, however, comes at the cost of longer running time compared to xyz. The general model is widely applicable and allows mining the wealth of publicly available RNAi screening data for the estimation of epistatic interactions between genes. As a proof of concept, we apply the model to search for interactions, and potential targets for treatment, among previously published sets of siRNA perturbation screens on various pathogens. The identified interactions include both known epistatic interactions as well as novel findings.

Project description:Toxoplasma virulence depends on its ability to evade or survive the toxoplasmacidal mechanisms induced by interferon gamma (IFNγ). While many Toxoplasma genes involved in the evasion of the murine IFNγ response have been identified, genes required to survive the human IFNγ response are largely unknown. In this study, we used a genome-wide loss-of-function screen to identify Toxoplasma genes important for parasite fitness in IFNγ-stimulated primary human fibroblasts. We generated gene knockouts for the top six hits from the screen and confirmed their importance for parasite growth in IFNγ-stimulated human fibroblasts. Of these six genes, three have homology to GRA32, localize to dense granules, and coimmunoprecipitate with each other and GRA32, suggesting they might form a complex. Deletion of individual members of this complex leads to early parasite egress in IFNγ-stimulated cells. Thus, prevention of early egress is an important Toxoplasma fitness determinant in IFNγ-stimulated human cells. IMPORTANCE Toxoplasma infection causes serious complications in immunocompromised individuals and in the developing fetus. During infection, certain immune cells release a protein called interferon gamma that activates cells to destroy the parasite or inhibit its growth. While most Toxoplasma parasites are cleared by this immune response, some can survive by blocking or evading the IFNγ-induced restrictive environment. Many Toxoplasma genes that determine parasite survival in IFNγ-activated murine cells are known but parasite genes conferring fitness in IFNγ-activated human cells are largely unknown. Using a Toxoplasma adapted genome-wide loss-of-function screen, we identified many Toxoplasma genes that determine parasite fitness in IFNγ-activated human cells. The gene products of four top hits play a role in preventing early parasite egress in IFNγ-stimulated human cells. Understanding how IFNγ-stimulated human cells inhibit Toxoplasma growth and how Toxoplasma counteracts this, could lead to the development of novel therapeutics.

Dataset Information

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

Publications

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

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