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Record W2950915942 · doi:10.1101/336750

A functional study of all 40 <i>C. elegans</i> insulin-like peptides

2018· preprint· en· W2950915942 on OpenAlexaff
Shanqing Zheng, Hilton Chiu, Jeffrey R. Boudreau, Tony Papanicolaou, William G. Bendena, Ian D. Chin-Sang

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2018
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetics, Aging, and Longevity in Model Organisms
Canadian institutionsQueen's University
Fundersnot available
KeywordsInsulinBiologyNeuroblastGeneCaenorhabditis elegansFunction (biology)Insulin receptorPhenotypeCell biologyGeneticsEndocrinologyInsulin resistanceNeurogenesis

Abstract

fetched live from OpenAlex

Abstract The human genome encodes ten insulin-like genes, whereas the C. elegans genome remarkably encodes forty insulin-like genes. The roles of insulin/insulin-like peptide ligands (INS) in C. elegans are not well understood. The functional redundancy of the forty INS genes makes it challenging to address their functions by using knock out strategies. Here, we individually overexpressed each of the forty 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 forty INS peptides in vivo . Our findings will promote the study of insulin function on development, metabolism, and aging-related diseases. Author Summary Insulin and insulin-like growth factors are found in all animals and regulate many physiological and developmental processes. The human genome has 10 insulin-like peptides including the well characterized insulin hormone. The nematode C. elegans has 40 insulin-like (INS) peptide genes. All 40 INS peptides have been knocked out but no single INS gene knock out resembles the loss of the C. elegans insulin receptor suggesting that the other INS peptides can compensate when one INS is lost. We have used a genetic approach to overexpress each of the 40 INS peptides in C. elegans and have identified in vivo function for 35 of the 40 INS peptides. Like the human insulin and IGF-1, C. elegans INS peptides are derived from a precursor protein and we have shown that INS peptides with an associated peptide called the F peptide are strong activators of the C. elegans insulin-like receptor. We also identified several INS peptides that inhibit the insulin-like receptor and these inhibitory INS peptides may have therapeutic potential.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.016
GPT teacher head0.221
Teacher spread0.204 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2018
Admission routes1
Has abstractyes

Explore more

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