A single gene network accurately predicts phenotypic effects of gene perturbation in Caenorhabditis elegans
Open accessAbstract
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.
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NETBAG combines multiple forms of literature-curated data in an integrated network approach that has been shown effective for predicting gene essentiality in yeast.
“The Network-Based Analysis of Genes (NETBAG)127 approach combines multiple forms of literature-curated data using an integrated network approach that has been demonstrated to be effective for predicting gene essentiality in yeast128.”
From Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders (2015) · cites this paper as [128]
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- Authors
- Lee, I., Lehner, B., Crombie, C., Wong, W., Fraser, A. G., & Marcotte, E. M.
- Affiliations
- The University of Texas at Austin (US)
- Institució Catalana de Recerca i Estudis Avançats (ES)
- Wellcome Sanger Institute (GB)
- Corresponding author
- Fraser, Andrew G
- Marcotte, Edward M
- Venue
- Nature Genetics
- Published
- 2008-01-27
- Volume
- 40
- Issue
- 2
- Pages
- 181-188
- ISSN
- 1061-4036 (ISSN-L)
- 1546-1718
- PMC
- PMC13030915
- OpenAlex
- W1976386441
- Cited by
- 281
- References
- 51
- Citations by year
- 2024: 3 · 2023: 1 · 2022: 2 · 2021: 4 · 2020: 7 · 2019: 8 · 2018: 12 · 2017: 9 · 2016: 11 · 2015: 22 · 2014: 14 · 2013: 29 · 2012: 33
- FWCI
- 17.12
- Type
- journal article
- Language
- en
- Open access
- green
- pmc.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Genetics (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (submittedVersion)
- Funders
- Wellcome Trust
- National Institute of General Medical Sciences (R01GM067779, GM 06779-01)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
- Keywords
- gene regulatory networks, Caenorhabditis elegans, gene essentiality, network connectivity, loss-of-function phenotypes, retinoblastoma pathway, dystrophin complex, EGF signaling, tissue-specific gene function, disease gene identification, therapeutic target
- MeSH terms
- Animals, Bayes Theorem, Epidermal Growth Factor, Female, Genetic Linkage, Models, Biological, Models, Genetic, Phenotype, Predictive Value of Tests, Probability, Genes, ras, Repressor Proteins, RNA, Messenger, ROC Curve, Vulva, Reproducibility of Results, Signal Transduction, Helminth Proteins, Likelihood Functions, Retinoblastoma Protein, Caenorhabditis elegans, Genes, Helminth, Gene Expression Regulation, Developmental, RNA, Helminth, Computational Biology, Proteome, Mitogen-Activated Protein Kinases, Databases, Genetic, RNA Interference, Dystrophin-Associated Protein Complex, Gene Regulatory Networks
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