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


Journal

Life science alliance
ISSN: 2575-1077
Titre abrégé: Life Sci Alliance
Pays: United States
ID NLM: 101728869

Informations de publication

Date de publication:
04 2019
Historique:
received: 12 12 2018
revised: 03 04 2019
accepted: 04 04 2019
entrez: 14 4 2019
pubmed: 14 4 2019
medline: 14 4 2019
Statut: epublish

Résumé

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.

Identifiants

pubmed: 30979825
pii: 2/2/e201800278
doi: 10.26508/lsa.201800278
pmc: PMC6464042
pii:
doi:

Substances chimiques

RNA, Small Interfering 0

Types de publication

Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Subventions

Organisme : NCI NIH HHS
ID : P30 CA016672
Pays : United States
Organisme : NIGMS NIH HHS
ID : R35 GM130119
Pays : United States

Informations de copyright

© 2019 Kim et al.

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Auteurs

Eiru Kim (E)

Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.

Merve Dede (M)

Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
UTHealth Graduate School of Biomedical Sciences, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.

Walter F Lenoir (WF)

Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
UTHealth Graduate School of Biomedical Sciences, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.

Gang Wang (G)

Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.

Sanjana Srinivasan (S)

Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
UTHealth Graduate School of Biomedical Sciences, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.

Medina Colic (M)

Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
UTHealth Graduate School of Biomedical Sciences, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.

Traver Hart (T)

Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA traver@hart-lab.org.
Department of Cancer Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.

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