William R. Schafer, Ph.D. - US grants

DESCRIPTION (Applicant's Abstract): Tobacco use has been implicated in a wide range of human diseases, including heart disease, emphysema, and cancer, which together result in millions of premature deaths each year. The addictive properties of nicotine are a major cause of persistent and compulsive tobacco use. Nicotine addiction is thought to result from long-term adaptive changes in the activity and expression of nicotinic acetylcholine receptors in the brain. However, the molecular and neuronal mechanisms that underlie these adaptive processes remain poorly understood. The goal of this research is to use genetic analysis in a simple animal model, the nematode Caenorhabditis elegans, to investigate the molecular basis of nicotine adaptation. C. elegans is highly amenable to molecular analysis of nervous system function: It has a simple and well-characterized nervous system, and its short generation time, small and largely sequenced genome, and accessibility to germline transformation make it ideal for classical and molecular genetic studies. C. elegans exhibits a striking and easily measurable response to nicotine, and long-term nicotine exposure leads to both nicotine tolerance and nicotine dependence. In this R21 exploratory/developmental project, genes required for nicotine adaptation in nematodes will be identified by screening for adaptation defective mutants. Detailed characterization of mutant phenotypes will provide insight into the roles of these genes in nicotine adaptation and other aspects of nervous system function. The results of this study will be used to support an RO1 proposal to determine the molecular functions of these nicotine adaptation genes, and to characterize the cellular pathways in which they function. The ultimate goal of this work is to provide a model for the general molecular mechanisms underlying nicotine adaptation in neurons, and to identify new proteins that participate in nicotine addiction in other animals, including vertebrates.

DESCRIPTION (provided by applicant): The nematode C. elegans has powerful genetics, a well-described nervous system, and a near complete genome sequence; thus, it is well suited to analysis of behavior at the molecular and cellular levels. The ability to functionally map the influence of particular genes to specific behavioral consequences makes it possible to use genetic analysis to functionally dissect the molecular mechanisms underlying poorly understood aspects of nervous system function such as addiction. However, many genes with critical roles in neuronal function have effects on behavior that to a casual observer appear very subtle or difficult to describe precisely. Therefore, to fully realize the potential of C. elegans for the genetic analysis of nervous system functions, it is necessary to develop sophisticated methods for the rapid and consistent quantitation of behavioral phenotypes. The goal of this proposed work is to develop computer vision tools for quantitatively characterizing the behavioral patterns caused by mutations or pharmacological treatment in C. elegans. By making it possible to precisely characterize the behavioral phenotypes of uncoordinated, locomotion-abnormal mutants, these tools will be particularly useful for correlation specific neurotransmission defects with characteristic behavioral patterns. These analytical tools will also be used to precisely define the long-term effects of neuroactive drugs on behavioral, opening the possibility of genetic screening for mutants with defects in tolerance and dependence.

DESCRIPTION (provided by applicant): Learning involves long-lasting changes in the functional properties of neural circuits, and inappropriate activation of these plasticity mechanisms through chronic drug use is thought to underlie drug tolerance and dependence. Understanding how functional changes in individual neurons act within the context of a neural circuit to modify behavior therefore represents a critical challenge in the study of addiction. The goal of the proposed research is to use in vivo calcium imaging to investigate the cellular basis of a simple form of long-term learning, touch habituation, in the nematode C. elegans. We will characterize the normal response and adaptation properties of C. elegans mechanoreceptors by visualizing the activity of touch neurons in response to mechanical stimulation. In addition, by assaying the effect of cloned mechanosensory genes on touch cell activity we will define the molecular requirements for mechanotransduction in these neurons and gain insight into the roles of specific molecules in mechanotransduction. Finally, by simultaneously imaging the activity of multiple neurons in the mechanosensory circuitry, we will gain information about the cellular mechanisms underlying touch habituation. Together, these experiments will address key unanswered questions regarding the molecular mechanisms of mechanosensation and neural plasticity in C. elegans, and are likely to provide insight into analogous processes in vertebrates. These studies will also provide a proving ground for improvements in optical imaging methodologies, which could facilitate the application of this cutting-edge technology to other genetically tractable organisms.

[unreadable] DESCRIPTION (provided by applicant): The nematode C. elegans has powerful genetics, a well-described nervous system, and a complete genome sequence; thus, it is well suited to analysis of behavior and development at the molecular and cellular levels. In particular, the ability to functionally map the influence of particular genes to specific behavioral consequences makes it possible to use genetic analysis to functionally dissect the molecular mechanisms underlying poorly understood aspects of nervous system function. However, many genes with critical roles in neuronal function have effects on behavior that to a casual observer appear very subtle or difficult to describe precisely. Therefore, to fully realize the potential of C. elegans for the genetic analysis of nervous system function, it is necessary to develop sophisticated methods for the rapid and consistent quantitation of mutant phenotypes, especially those related to behavior. [unreadable] [unreadable] The goal of this proposed work is to develop computer vision tools for quantitatively characterizing the phenotypic patterns caused by mutations or pharmacological treatments in C. elegans. By making it possible to precisely characterize the behavioral phenotypes of mutants with abnormal locomotion or egglaying, these tools will be particularly useful for correlating specific neurotransmission defects with characteristic behavioral patterns. These analytical tools will also be used to generate a comprehensive database containing complex behavioral data on a large set of mutant strains. This database will make it possible to identify groups of mutants and pharmacological treatments that have similar effects on behavior or development. With the accumulation of increasing phenotypic data on known mutants, it should ultimately be possible to record from unknown mutant or drug-treated animals and make informed initial hypotheses about the functions of uncharacterized genes and the targets of uncharacterized drugs. [unreadable] [unreadable]

DESCRIPTION (provided by applicant): 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.