IDENTIFICATION of NLP-5 and NLP-6 as potential ligands for the NPR-9 receptor in Caenorhabditis elegans
Bibliographic record
Abstract
Neuropeptides in Caenorhabditis elegans regulate physiological and behavioural responses to environmental cues, influencing locomotion, feeding, and fat storage via interactions with G-protein coupled receptors (GPCRs). C. elegans expresses a diverse repertoire of neuropeptides, including FMRFamide-related peptides, neuropeptide-like peptides (NLPs), and insulin-like peptides (INSs). Among these, the galanin/allatostatin-like GPCR, NPR-9, localized in the AIB interneurons, regulates locomotory behaviours (roaming and dwelling) and fat accumulation by inhibiting AIB activity. Recent studies identified NLP-1 as a ligand for NPR-9, modulating behaviour through direct receptor interaction. However, our research explored whether other neuropeptides, specifically NLP-5 and NLP-6 (allatostatin A-type/galanin-like neuropeptides), could also function as NPR-9 ligands, despite evidence suggesting NLP-1 as the primary ligand. In this study, we characterized phenotypes associated with NPR-9 receptor function, including Omega turns, roaming, and fat accumulation. Loss-of-function mutations in nlp-5, nlp-6, and nlp-1 exhibited behavioural phenotypes similar to npr-9 mutants, suggesting that NLP-5 and NLP-6 may act as additional ligands for NPR-9 or affect NPR-9 signalling. Furthermore, double-mutant analyses with candidate ligand genes suppressed phenotypes associated with NPR-9 overexpression, reinforcing the hypothesis that these neuropeptides may regulate NPR-9-mediated signalling.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".