Wnt proteins are intercellular signals that regulate various aspects of animal development. In Caenorhabditis elegans, mutations in lin-17, a Frizzled-class Wnt receptor, and in lin-18 affect cell fate patterning in the P7.p vulval lineage. We found that lin-18 encodes a member of the Ryk/Derailed family of tyrosine kinase-related receptors, recently found to function as Wnt receptors. Members of this family have nonactive kinase domains. The LIN-18 kinase domain is dispensable for LIN-18 function, while the Wnt binding WIF domain is required. We also found that Wnt proteins LIN-44, MOM-2, and CWN-2 redundantly regulate P7.p patterning. Genetic interactions indicate that LIN-17 and LIN-18 function independently of each other in parallel pathways, and different ligands display different receptor specificities. Thus, two independent Wnt signaling pathways, one employing a Ryk receptor and the other a Frizzled receptor, function in parallel to regulate cell fate patterning in the C. elegans vulva.

The GAL4-UAS system is a powerful tool for manipulating gene expression, but its application in C. elegans has not been described. Here we systematically optimize the system’s three main components to develop a temperature-optimized GAL4-UAS system (cGAL) that robustly controls gene expression in C. elegans across 15–25°C. We demonstrate its utility in transcriptional reporter analysis, site-of-action experiments and exogenous transgene expression, and provide a basic driver and effector toolkit.

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.gene network | high-content screening | locomotion A number of neuronal signaling genes are known to regulate locomotive behaviors of animals. For example, disruption of the heterotrimeric G protein subunit Gαq in neurons caused movement disorders in Caenorhabditis elegans and mice (1, 2). The Gαq signaling pathway is composed of proteins and lipids conserved in all animals (3-5). The main target of Gαq is phospholipase C (PLC), which converts phosphatidylinositol 4,5-bisphosphate to the second messengers, diacyl glycerol (DAG) and inositol trisphosphate (3-5). In C. elegans excitatory motor neurons, DAG promotes ACh release, necessary for locomotion.Despite the wealth of information on individual signaling genes and pathways, a system-level understanding remains missing on how these genes coordinate animal behavior. For example, among all neuronal signaling genes, which ones are involved in regulating a specific stereotyped behavior? How do these genes interact with each other to form networks that process information? A successful method to uncover large-scale gene networks in metazoans is high-content phenotypic profiling. Using binary parameters to score presence and absence of multiple phenotypic details, this approach enabled computational approaches such as hierarchical clustering to infer interactions among development genes (6-8). Behavioral phenotypes such as movement disorders are intrinsically quantitative. Therefore, a quantitative m...