Systematic profiling of Caenorhabditis elegans locomotive behaviors reveals additional components in G-protein Gαq signaling
Abstract
Genetic screens have been widely applied to uncover genetic mechanisms of movement disorders. However, most screens rely on human observations of qualitative differences. Here we demonstrate the application of an automatic imaging system to conduct a quantitative screen for genes regulating the locomotive behavior in Caenorhabditis elegans. Two hundred twenty-seven neuronal signaling genes with viable homozygous mutants were selected for this study. We tracked and recorded each animal for 4 min and analyzed over 4,400 animals of 239 genotypes to obtain a quantitative, 10-parameter behavioral profile for each genotype. We discovered 87 genes whose inactivation causes movement defects, including 50 genes that had never been associated with locomotive defects. Computational analysis of the high-content behavioral profiles predicted 370 genetic interactions among these genes. Network partition revealed several functional modules regulating locomotive behaviors, including sensory genes that detect environmental conditions, genes that function in multiple types of excitable cells, and genes in the signaling pathway of the G protein Gαq, a protein that is essential for animal life and behavior. We developed quantitative epistasis analysis methods to analyze the locomotive profiles and validated the prediction of the γ isoform of phospholipase C as a component in the Gαq pathway. These results provided a system-level understanding of how neuronal signaling genes coordinate locomotive behaviors. This study also demonstrated the power of quantitative approaches in genetic studies.
Additional Information
© 2013 National Academy of Sciences. Contributed by Paul W. Sternberg, June 4, 2013 (sent for review March 8, 2013). Published online before print July 1, 2013. We thank the Caenorhabditis Genetics Center (CGC), Erin Cram, and Shohei Mitani for strains, Illya Hicks for helpful discussions, Joaquina Nunez, Barbara Perry, and Julie Nguyen for technical assistance, Ranjana Kishore for a critical reading of the manuscript, and WormBase. This work was supported by National Institutes of Health Grant HG004724 and a Searle Scholar grant from the Kinship Foundation (to W.Z.), by National Institutes of Health Grant DA018341 (to P.W.S. and W.Z.), and by the Howard Hughes Medical Institute, with which P.W.S. is an investigator. CGC is funded by National Institutes of Health Office of Research Infrastructure Programs Grant P40 OD010440. Author contributions: H.Y., B.A.-M., P.W.S., and W.Z. designed research; H.Y., B.A.-M., S.G., and W.Z. performed research; C.J.C. contributed new reagents/analytic tools; B.A.-M., M.K.L., and W.Z. analyzed data; and P.W.S. and W.Z. wrote the paper.Attached Files
Published - PNAS-2013-Yu-11940-5.pdf
Supplemental Material - TableS1.doc
Supplemental Material - TableS2.doc
Supplemental Material - TableS3.doc
Supplemental Material - TableS4.doc
Supplemental Material - TableS5.doc
Supplemental Material - TableS6.doc
Supplemental Material - TableS7.doc
Supplemental Material - pnas.201310468SI.pdf
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Additional details
- PMCID
- PMC3718121
- Eprint ID
- 41088
- Resolver ID
- CaltechAUTHORS:20130904-135424352
- NIH
- HG004724
- Kinship Foundation Searle Scholar Grant
- NIH
- DA018341
- Howard Hughes Medical Institute (HHMI)
- NIH Office of Research Infrastructure Programs
- P40 OD010440
- Created
-
2013-09-17Created from EPrint's datestamp field
- Updated
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2021-11-10Created from EPrint's last_modified field