Maps of the synapses made and neurotransmitters released by all neurons in model systems, such as Caenorhabditis elegans have left still unresolved how neural circuits integrate and respond to neurotransmitter signals. Using the egg-laying circuit of C. elegans as a model, we mapped which cells express each of the 26 neurotransmitter GPCRs of this organism and also genetically analyzed the functions of all 26 GPCRs. We found that individual neurons express many distinct receptors, epithelial cells often express neurotransmitter receptors, and receptors are often positioned to receive extrasynaptic signals.Receptor knockouts reveal few egg-laying defects under standard laboratory conditions, suggesting that the receptors function redundantly or regulate egg-laying only in specific conditions; however, increasing receptor signaling through overexpression more efficiently reveals receptor functions. This map of neurotransmitter GPCR expression and function in the egg-laying circuit provides a model for understanding GPCR signaling in other neural circuits.
Maps of the synapses made and neurotransmitters released by all neurons in model systems such as C. elegans have left still unresolved how neural circuits integrate and respond to neurotransmitter signals. Using the egg-laying circuit of C. elegans as a model, we mapped which cells express each of the 26 neurotransmitter G protein coupled receptors (GPCRs) of this organism and also genetically analyzed the functions of all 26 GPCRs. We found that individual neurons express many distinct receptors, epithelial cells often express neurotransmitter receptors, and receptors are often positioned to receive extrasynaptic signals. The egg-laying circuit appears to use redundancy and compensation to achieve functional robustness, as receptor knockouts reveal few defects; however, increasing receptor signaling through overexpression more efficiently reveals receptor functions. This map of neurotransmitter GPCR expression and function in the egg-laying circuit provides a model for understanding GPCR signaling in other neural circuits.
Animals and fungi produce cholesterol and ergosterol, respectively, while plants produce the phytosterols stigmasterol, campesterol, and β‐sitosterol in various combinations. The recent sequencing of many algal genomes allows the detailed reconstruction of the sterol metabolic pathways. Here, we characterized sterol synthesis in two sequenced Chlorella spp., the free‐living C. sorokiniana, and symbiotic C. variabilis NC64A. Chlamydomonas reinhardtii was included as an internal control and Coccomyxa subellipsoidea as a plant‐like outlier. We found that ergosterol was the major sterol produced by Chlorella spp. and C. reinhardtii, while C. subellipsoidea produced the three phytosterols found in plants. In silico analysis of the C. variabilis NC64A, C. sorokiniana, and C. subellipsoidea genomes identified 22 homologs of sterol biosynthetic genes from Arabidopsis thaliana, Saccharomyces cerevisiae, and C. reinhardtii. The presence of CAS1, CPI1, and HYD1 in the four algal genomes suggests the higher plant cycloartenol branch for sterol biosynthesis, confirming that algae and fungi use different pathways for ergosterol synthesis. Phylogenetic analysis for 40 oxidosqualene cyclases (OSCs) showed that the nine algal OSCs clustered with the cycloartenol cyclases, rather than the lanosterol cyclases, with the OSC for C. subellipsoidea positioned in between the higher plants and the eight other algae. With regard to why C. subellipsoidea produced phytosterols instead of ergosterol, we identified 22 differentially conserved positions where C. subellipsoidea CAS and A. thaliana CAS1 have one amino acid while the three ergosterol producing algae have another. Together, these results emphasize the position of the unicellular algae as an evolutionary transition point for sterols.
NeuroPAL (Neuronal Polychromatic Atlas of Landmarks) is a recently developed transgene that labels each of the 118 classes of neurons in C. elegans with various combinations of four fluorescent proteins. This neuron‐type‐specific labeling helps identify neurons that could otherwise be confused with neighboring neurons. Neuron identification enables researchers to combine new data that they generate on a C. elegans neuron with existing datasets on that same neuron, such as its synaptic connections, neurotransmitters, and transcriptome. An impediment to using NeuroPAL, however, is overcoming the steep learning curve for interpreting three‐dimensional (3D) fluorescence images of crowded neural ganglia within which different neurons may be similarly colored, some neurons are only very faintly labeled, and the positions of some neurons are variable. Here, we provide protocols that allow researchers to learn to accurately identify neurons within 3D images of NeuroPAL‐labeled animals. We provide 3D reference images that illustrate NeuroPAL labeling of each body region, and additional 3D images as training exercises to learn to accurately carry out C. elegans neuron identifications. We also provide tools to annotate images in 3D, and suggest that such 3D annotated images should be the standard for documenting C. elegans neuron identifications for publication. © 2022 Wiley Periodicals LLC. Basic Protocol 1: Using Imaris software to view and annotate images of NeuroPAL‐labeled animals in 3D Alternate Protocol: Using FIJI/ImageJ software to view and annotate images of NeuroPAL‐labeled animals in 3D Basic Protocol 2: Identifying tail neurons—an introduction to identifying neurons Basic Protocol 3: Identifying midbody neurons Basic Protocol 4: Identifying anterior head neurons Basic Protocol 5: Identifying posterior head neurons Basic Protocol 6: Identifying ventral head and retrovesicular ganglion neurons
The neural circuit for C. elegans egg laying has been studied intensively for decades, yet it is not clear that its known components can account for how egg-laying and locomotion behaviors are coordinated. We found that the two PVP neurons, which release neuropeptides that promote roaming locomotion, make previously-undescribed branches that terminate in large wing-shaped endings directly over the egg-laying apparatus. The PVP branches occur in hermaphrodites but not males and develop during the L4 larval stage when the egg-laying system also develops. The PVP wing is located at the junction between the uterus and the vulva, adjacent to neurons that control egg laying, and surrounded by cells that we found label with a glial marker. The morphology of the PVP wing and its envelopment within possible glial cells are consistent with the hypothesis that the PVP wing is a sensory cilium. Although PVP is reported to express sensory receptor homologs, we have been unable to detect PVP expression of more specific markers of neural cilia, and we have also not detected strong PVP defects in the daf-19 mutant, which does show defects in known neural cilia. The PVPs are extraordinarily sensitive to expression of transgenes, which cause developmental and possibly functional defects in these neurons. This has prevented us from recording or manipulating PVP activity to determine its functional roles. Thus, the intriguing hypothesis that PVP is a sensory neuron that might coordinate egg laying and locomotion will remain speculative until better methods to manipulate PVP can be developed.
Individual neuron or muscle cells express many G protein coupled receptors (GPCRs) for neurotransmitters and neuropeptides. It remains unclear how these cells integrate multiple GPCR signals that all must act through the same few G proteins. We investigated how two serotonin GPCRs, Gαq-coupled SER-1 and Gαs-coupled SER-7, function together on the C. elegans egg-laying muscles to promote contraction and thus cause eggs to be laid. Using receptor null mutations and cell-specific knockdowns, we found that serotonin signaling through either SER-1/Gαq or SER-7/Gαs alone does not induce egg laying, but these subthreshold signals can combine to promote egg laying. However, using designer receptors or optogenetics to artificially induce high levels of either Gαq signaling or Gαs signaling in the muscles was sufficient to induce egg laying. Conversely, knocking down both Gαq and Gαs in the egg-laying muscle cells induced egg-laying defects stronger than those of a ser-7 ser-1 double knockout. These results suggest that, in the egg-laying muscles, multiple GPCRs for serotonin and other signals each produce weak effects that individually do not result in strong behavioral outcomes. However, they can combine to produce sufficient levels of Gαq and Gαs signaling to promote muscle activity and egg laying.
Individual neurons or muscle cells express many G protein coupled receptors (GPCRs) for neurotransmitters and neuropeptides, yet it remains unclear how cells integrate multiple GPCR signals that all must activate the same few G proteins. We analyzed this issue in theC. elegansegg-laying system, where multiple GPCRS on muscle cells promote contraction and egg laying. We genetically manipulated individual GPCRs and G proteins specifically in these muscle cells within intact animals and then measured egg laying and muscle calcium activity. Two serotonin GPCRs on the muscle cells, Gαq-coupled SER-1 and Gαs-coupled SER-7, together promote egg laying in response to serotonin. We found that signals produced by either SER-1/Gαqor SER-7/Gαsalone have little effect, but these two subthreshold signals combine to activate egg laying. We then transgenically expressed natural or designer GPCRs in the muscle cells and found that their subthreshold signals can also combine to induce muscle activity. However, artificially inducing strong signaling through just one of these GPCRs can be sufficient to induce egg laying. Knocking down Gαqand Gαsin the egg-laying muscle cells induced egg-laying defects that were stronger than those of a SER-1/SER-7 double knockout, indicating that additional endogenous GPCRs also activate the muscle cells. These results show that, in the egg-laying muscles, multiple GPCRs for serotonin and other signals each produce weak effects that individually do not result in strong behavioral outcomes. However, they combine to produce sufficient levels of Gαqand Gαssignaling to promote muscle activity and egg laying.Significance Statement:How can neurons and other cells gather multiple independent pieces of information from the soup of chemical signals in their environment and compute an appropriate response? Most cells express >20 G protein couple receptors (GPCRs) that each receive one signal and transmit that information through just ∼three types of G proteins. We analyzed how this machinery generates responses by studying the egg-laying system ofC. elegans, where serotonin and multiple other signals act through GPCRs on the egg-laying muscles to promote muscle activity and egg laying. We found that individual GPCRs within an intact animal each generate effects too weak to activate egg laying. However, combined signaling from multiple GPCR types reaches a threshold capable of activating the muscle cells.
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