Complex genetic networks underlie human disease and health. The construction of genetic networks is now a standard technique in simple cells such as yeast and cultured mammalian cells. Network inference for multicellular organisms is promising especially but one challenge is to parse the network into functional pathways as opposed to just connected graphs, and a second challenge is to analyze networks for complex phenotypes such as neuronal function and behavior. Our goal is to use C. elegans as a model to learn how to accomplish this task, meanwhile generating a network that will inform human genetics. In particular, we will continue to exploit our semi-automated locomotion analysis system (WormTracker) to obtain a phenotypic profile for a large set of genes. Genes will be interrogated using available loss-of- function mutations. The genes examined will include all relevant neuronal genes, as well as genes that encode chromatin modifying proteins and transcription factors. Computational clustering of transcriptional regulators or chromatin modifying proteins with neuronal effector genes will infer regulatory relationships among genes. In addition to locomotion on food, we will also score locomotion off food, and both during crawling and swimming. We will cluster the phenotypes to infer genetic modules, and expand these modules using other available genome- scale data such as gene expression data. To obtain a drug-gene network, we will profile a representative set of drugs and compare them to gene phenotypic profiles. We will test predictions of the drug-gene network by testing particular drug-gene interactions. To refine the genetic network, we will develop additional phenotypic profiling methods, and apply to genes, drugs and gene-drug interaction to split the network into regions of phenotype space. These assays will include quantitative, automated analysis of the rate and variation in pharyngeal pumping using microfluidic devices, established assays for pharmacological effects on male tail posture and spicule protraction to sample genetic effects on the more complex male nervous system, and panels of chemoattractants and repellants to monitor sensory responses. We will leverage our results by integrating what will an extensive data set on quantitative behavioral phenotypes with existing information that allow genetic network inference (expression data, in vitro binding, Gene Ontology annotations, Chromatin immunoprecipitation data, etc.) imported from WormBase. Software and protocols for hardware construction will be freely available from laboratory websites.

Public Health Relevance

Complex genetic networks underlie human disease and health but are a challenge to elucidate. We will use the model organism C. elegans to elucidate genetic networks underlying behavior by efficiently obtaining quantitative behavioral data on mutant strains that are defective in single genes using automated, machine vision systems. The quantitative data will be used to computationally infer genetic networks including genes that function in the nervous system and those that regulate other genes. The data and inferences will be publically available through the Neuroscience Information Framework and WormBase;the software for machine vision will be freely available for download.

Agency
National Institute of Health (NIH)
Institute
National Institute on Drug Abuse (NIDA)
Type
Research Project (R01)
Project #
5R01DA018341-08
Application #
8267063
Study Section
Biodata Management and Analysis Study Section (BDMA)
Program Officer
Satterlee, John S
Project Start
2004-07-01
Project End
2015-05-31
Budget Start
2012-06-01
Budget End
2013-05-31
Support Year
8
Fiscal Year
2012
Total Cost
$391,677
Indirect Cost
$91,030
Name
California Institute of Technology
Department
None
Type
Schools of Arts and Sciences
DUNS #
009584210
City
Pasadena
State
CA
Country
United States
Zip Code
91125
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Brown, Andre E X; Yemini, Eviatar I; Grundy, Laura J et al. (2013) A dictionary of behavioral motifs reveals clusters of genes affecting Caenorhabditis elegans locomotion. Proc Natl Acad Sci U S A 110:791-6
Yemini, Eviatar; Jucikas, Tadas; Grundy, Laura J et al. (2013) A database of Caenorhabditis elegans behavioral phenotypes. Nat Methods 10:877-9
Yu, Hui; Aleman-Meza, Boanerges; Gharib, Shahla et al. (2013) Systematic profiling of Caenorhabditis elegans locomotive behaviors reveals additional components in G-protein G*q signaling. Proc Natl Acad Sci U S A 110:11940-5
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Wang, Ying; Apicella Jr, Alfonso; Lee, Sun-Kyung et al. (2008) A glial DEG/ENaC channel functions with neuronal channel DEG-1 to mediate specific sensory functions in C. elegans. EMBO J 27:2388-99
Huang, Kuang-Man; Cosman, Pamela; Schafer, William R (2008) Automated detection and analysis of foraging behavior in Caenorhabditis elegans. J Neurosci Methods 171:153-64
Kindt, Katie S; Viswanath, Veena; Macpherson, Lindsey et al. (2007) Caenorhabditis elegans TRPA-1 functions in mechanosensation. Nat Neurosci 10:568-77
Van Buskirk, Cheryl; Sternberg, Paul W (2007) Epidermal growth factor signaling induces behavioral quiescence in Caenorhabditis elegans. Nat Neurosci 10:1300-7

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