Commentary: Selecting synaptic partners: GRASPing the role of UNC- 6/netrin

Forming synaptic connections of the appropriate strength between specific neurons is crucial for constructing neural circuits to control behavior. A recent paper in Neural Development describes the use of a synapse-specific label in Caenorhabditis elegans to implicate local UNC-6/netrin signaling in this developmental process. Thus, as well as their well known roles in cell migration and axon guidance, UNC-6/netrin signals distinguish an appropriate synaptic partner from other potential targets

An Insulin-to-Insulin Regulatory Network Orchestrates Phenotypic Specificity in Development and Physiology

Insulin-like peptides (ILPs) play highly conserved roles in development and physiology. Most animal genomes encode multiple ILPs. Here we identify mechanisms for how the forty Caenorhabditis elegans ILPs coordinate diverse processes, including development, reproduction, longevity and several specific stress responses. Our systematic studies identify an ILP-based combinatorial code for these phenotypes characterized by substantial functional specificity and diversity rather than global redundancy. Notably, we show that ILPs regulate each other transcriptionally, uncovering an ILP-to-ILP regulatory network that underlies the combinatorial phenotypic coding by the ILP family. Extensive analyses of genetic interactions among ILPs reveal how their signals are integrated. A combined analysis of these functional and regulatory ILP interactions identifies local genetic circuits that act in parallel and interact by crosstalk, feedback and compensation. This organization provides emergent mechanisms for phenotypic specificity and graded regulation for the combinatorial phenotypic coding we observe. Our findings also provide insights into how large hormonal networks regulate diverse traits

Profiling Synaptic Proteins Identifies Regulators of Insulin Secretion and Lifespan

Cells are organized into distinct compartments to perform specific tasks with spatial precision. In neurons, presynaptic specializations are biochemically complex subcellular structures dedicated to neurotransmitter secretion. Activity-dependent changes in the abundance of presynaptic proteins are thought to endow synapses with different functional states; however, relatively little is known about the rules that govern changes in the composition of presynaptic terminals. We describe a genetic strategy to systematically analyze protein localization at Caenorhabditis elegans presynaptic specializations. Nine presynaptic proteins were GFP-tagged, allowing visualization of multiple presynaptic structures. Changes in the distribution and abundance of these proteins were quantified in 25 mutants that alter different aspects of neurotransmission. Global analysis of these data identified novel relationships between particular presynaptic components and provides a new method to compare gene functions by identifying shared protein localization phenotypes. Using this strategy, we identified several genes that regulate secretion of insulin-like growth factors (IGFs) and influence lifespan in a manner dependent on insulin/IGF signaling

Genetic dissection of neuropeptide cell biology at high and low activity in a defined sensory neuron.

Neuropeptides are ubiquitous modulators of behavior and physiology. They are packaged in specialized secretory organelles called dense core vesicles (DCVs) that are released upon neural stimulation. Unlike synaptic vesicles, which can be recycled and refilled close to release sites, DCVs must be replenished by de novo synthesis in the cell body. Here, we dissect DCV cell biology in vivo in a Caenorhabditis elegans sensory neuron whose tonic activity we can control using a natural stimulus. We express fluorescently tagged neuropeptides in the neuron and define parameters that describe their subcellular distribution. We measure these parameters at high and low neural activity in 187 mutants defective in proteins implicated in membrane traffic, neuroendocrine secretion, and neuronal or synaptic activity. Using unsupervised hierarchical clustering methods, we analyze these data and identify 62 groups of genes with similar mutant phenotypes. We explore the function of a subset of these groups. We recapitulate many previous findings, validating our paradigm. We uncover a large battery of proteins involved in recycling DCV membrane proteins, something hitherto poorly explored. We show that the unfolded protein response promotes DCV production, which may contribute to intertissue communication of stress. We also find evidence that different mechanisms of priming and exocytosis may operate at high and low neural activity. Our work provides a defined framework to study DCV biology at different neural activity levels

Training and Validation Data for Automated Head versus Tail Classification and Cell Identification in C. elegans

The data is comprised of three zip files containing both brightfield and fluorescent images of C. elegans and manual annotation data identifying the pharynx and individual cells within the worm. HeadvTailTrainingData.zip contains all the brightfield images and annotated binary images used for training of a head versus tail classifier. HeadvTailTestingData.zip contains all the brightfield images used to validate the head versus tail classifier. CellIDTrainingandTestingData.zip contains fluorescent images and annotated binary images for the training and validation of fluorescent cell identification classifiers in C. elegans.This dataset is licensed under a Creative Commons Attribution 4.0 International License. You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.We demonstrate the utility of a generalizable classification routine for biological image processing by developing two specific classifiers as a toolset for automation and cell identification in the model organism Caenorhabditis elegans. To serve a common need for automated high-resolution imaging and behavior applications in the C. elegans research community, we contribute a ready-to-use classifier for the identification of the head of the animal under bright field imaging. Furthermore, we extend our framework to address the pervasive problem of cell-specific identification under fluorescent imaging, which is critical for biological investigation in multicellular organisms or tissues. These data files contain all of the training and validation data used for the construction and validation of these classifiers.National Institutes of Health (U.S.)Wellcome Trust (London, England)Biotechnology and Biological Sciences Research Council (Great Britain)European Research CouncilNational Science Foundation (U.S.