title: A single gene network accurately predicts phenotypic effects of gene perturbation in Caenorhabditis elegans.
abstract: The fundamental aim of genetics is to understand how an organism's phenotype is determined by its genotype, and implicit in this is predicting how changes in DNA sequence alter phenotypes. A single network covering all the genes of an organism might guide such predictions down to the level of individual cells and tissues. To validate this approach, we computationally generated a network covering most C. elegans genes and tested its predictive capacity. Connectivity within this network predicts essentiality, identifying this relationship as an evolutionarily conserved biological principle. Critically, the network makes tissue-specific predictions-we accurately identify genes for most systematically assayed loss-of-function phenotypes, which span diverse cellular and developmental processes. Using the network, we identify 16 genes whose inactivation suppresses defects in the retinoblastoma tumor suppressor pathway, and we successfully predict that the dystrophin complex modulates EGF signaling. We conclude that an analogous network for human genes might be similarly predictive and thus facilitate identification of disease genes and rational therapeutic targets.
authors.affiliation: Center for Systems and Synthetic Biology, Department of Chemistry and Biochemistry, Institute for Cellular and Molecular Biology, University of Texas, 2500 Speedway, MBB 3.210, Austin, Texas 78712, USA.
authors.forename: Insuk
authors.initials: I
authors.lastname: Lee
authors.forename: Ben
authors.initials: B
authors.lastname: Lehner
authors.forename: Catriona
authors.initials: C
authors.lastname: Crombie
authors.forename: Wendy
authors.initials: W
authors.lastname: Wong
authors.forename: Andrew G
authors.initials: AG
authors.lastname: Fraser
authors.forename: Edward M
authors.initials: EM
authors.lastname: Marcotte
mesh_terms: D000818:Animals; D001499:Bayes Theorem; D017173:Caenorhabditis elegans / Q000235:genetics* / Q000378:metabolism; D019295:Computational Biology / Q000379:methods; D030541:Databases, Genetic; D049028:Dystrophin-Associated Protein Complex / Q000235:genetics; D004815:Epidermal Growth Factor / Q000378:metabolism; D005260:Female; D018507:Gene Expression Regulation, Developmental; D053263:Gene Regulatory Networks / Q000235:genetics*; D017238:Genes, Helminth; D011905:Genes, ras; D008040:Genetic Linkage; D015801:Helminth Proteins / Q000235:genetics; D016013:Likelihood Functions; D020928:Mitogen-Activated Protein Kinases / Q000378:metabolism; D008954:Models, Biological; D008957:Models, Genetic; D010641:Phenotype; D011237:Predictive Value of Tests; D011336:Probability; D020543:Proteome / Q000235:genetics; D034622:RNA Interference; D018510:RNA, Helminth / Q000235:genetics; D012333:RNA, Messenger / Q000378:metabolism; D012372:ROC Curve; D012097:Repressor Proteins / Q000235:genetics; D015203:Reproducibility of Results; D016160:Retinoblastoma Protein / Q000037:antagonists & inhibitors / Q000235:genetics; D015398:Signal Transduction; D014844:Vulva / Q000254:growth & development
chemical_list: D049028:Dystrophin-Associated Protein Complex; D015801:Helminth Proteins; D020543:Proteome; D018510:RNA, Helminth; D012333:RNA, Messenger; D012097:Repressor Proteins; D016160:Retinoblastoma Protein; D004815:Epidermal Growth Factor; D020928:Mitogen-Activated Protein Kinases