A functional study of all 40 Caenorhabditis elegans insulin-like peptides
Bibliographic record
Abstract
The human genome encodes 10 insulin-like genes, whereas the Caenorhabditis elegans genome remarkably encodes 40 insulin-like genes. Knockout strategies to determine the roles of all the insulin/insulin-like peptide ligands (INS) in C. elegans has been challenging due to functional redundancy. Here, we individually overexpressed each of the 40 ins genes pan-neuronally, and monitored multiple phenotypes including: L1 arrest life span, neuroblast divisions under L1 arrest, dauer formation, and fat accumulation, as readouts to characterize the functions of each INS in vivo . Of the 40 INS peptides, we found functions for 35 INS peptides and functionally categorized each as agonists, antagonists, or of pleiotropic function. In particular, we found that 9 of 16 agonistic INS peptides shortened L1 arrest life span and promoted neuroblast divisions during L1 arrest. Our study revealed that a subset of β-class INS peptides that contain a distinct F peptide sequence are agonists. Our work is the first to categorize the structures of INS peptides and relate these structures to the functions of all 40 INS peptides in vivo . Our findings will promote the study of insulin function on development, metabolism, and aging-related diseases.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".