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 #
2R01DA018341-06
Application #
7890193
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
2010-06-01
Budget End
2011-05-31
Support Year
6
Fiscal Year
2010
Total Cost
$380,473
Indirect Cost
Name
California Institute of Technology
Department
Type
Schools of Arts and Sciences
DUNS #
009584210
City
Pasadena
State
CA
Country
United States
Zip Code
91125
Gonzales, Daniel L; Badhiwala, Krishna N; Vercosa, Daniel G et al. (2017) Scalable electrophysiology in intact small animals with nanoscale suspended electrode arrays. Nat Nanotechnol 12:684-691
Labocha, Marta K; Yuan, Wang; Aleman-Meza, Boanerges et al. (2017) A strategy to apply quantitative epistasis analysis on developmental traits. BMC Genet 18:42
Zhou, Ying; Loeza-Cabrera, Mario; Liu, Zheng et al. (2017) Potential Nematode Alarm Pheromone Induces Acute Avoidance in Caenorhabditis elegans. Genetics 206:1469-1478
Aleman-Meza, Boanerges; Loeza-Cabrera, Mario; Peña-Ramos, Omar et al. (2017) High-content behavioral profiling reveals neuronal genetic network modulating Drosophila larval locomotor program. BMC Genet 18:40
Ezcurra, Marina; Walker, Denise S; Beets, Isabel et al. (2016) Neuropeptidergic Signaling and Active Feeding State Inhibit Nociception in Caenorhabditis elegans. J Neurosci 36:3157-69
Jung, Sang-Kyu; Qu, Xiaolei; Aleman-Meza, Boanerges et al. (2015) Multi-endpoint, high-throughput study of nanomaterial toxicity in Caenorhabditis elegans. Environ Sci Technol 49:2477-85
Aleman-Meza, Boanerges; Jung, Sang-Kyu; Zhong, Weiwei (2015) An automated system for quantitative analysis of Drosophila larval locomotion. BMC Dev Biol 15:11
Labocha, Marta K; Jung, Sang-Kyu; Aleman-Meza, Boanerges et al. (2015) WormGender - Open-Source Software for Automatic Caenorhabditis elegans Sex Ratio Measurement. PLoS One 10:e0139724
Schwarz, Roland F; Branicky, Robyn; Grundy, Laura J et al. (2015) Changes in Postural Syntax Characterize Sensory Modulation and Natural Variation of C. elegans Locomotion. PLoS Comput Biol 11:e1004322
Cho, Julie Y; Sternberg, Paul W (2014) Multilevel modulation of a sensory motor circuit during C. elegans sleep and arousal. Cell 156:249-60

Showing the most recent 10 out of 24 publications