Neuropeptide GPCRs in C. elegans
Bibliographic record
Abstract
Like most organisms, the nematode Caenorhabditis elegans relies heavily on neuropeptidergic signaling. This tiny animal represents a suitable model system to study neuropeptidergic signaling networks with single cell resolution due to the availability of powerful molecular and genetic tools. The availability of the worm's complete genome sequence allows researchers to browse through it, uncovering putative neuropeptides and their cognate G protein-coupled receptors (GPCRs). Many predictions have been made about the number of C. elegans neuropeptide GPCRs. In this review, we report the state of the art of both verified as well as predicted C. elegans neuropeptide GPCRs. The predicted neuropeptide GPCRs are incorporated into the receptor classification system based on their resemblance to orthologous GPCRs in insects and vertebrates. Appointing the natural ligand(s) to each predicted neuropeptide GPCR (receptor deorphanization) is a crucial step during characterization. The development of deorphanization strategies resulted in a significant increase in the knowledge of neuropeptidergic signaling in C. elegans. Complementary localization and functional studies demonstrate that neuropeptides and their GPCRs represent a rich potential source of behavioral variability in C. elegans. Here, we review all neuropeptidergic signaling pathways that so far have been functionally characterized in C. elegans.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".