Cuticle Collagen Expression Is Regulated in Response to Environmental Stimuli by the GATA Transcription Factor ELT-3 in<i>Caenorhabditis elegans</i>
Bibliographic record
Abstract
Abstract Mesbahi et al. find that environmental factors, including diet, starvation, and population density can differentially influence the penetrance of collagen mutant phenotypes. Factors that decrease the penetrance of rolling in dominant... The nematode Caenorhabditis elegans is protected from the environment by the cuticle, an extracellular collagen-based matrix that encloses the animal. Over 170 cuticular collagens are predicted in the C. elegans genome, but the role of each individual collagen is unclear. Stage-specific specialization of the cuticle explains the need for some collagens; however, the large number of collagens suggests that specialization of the cuticle may also occur in response to other environmental triggers. Missense mutations in many collagen genes can disrupt cuticle morphology, producing a helically twisted body causing the animal to move in a stereotypical pattern described as rolling. We find that environmental factors, including diet, early developmental arrest, and population density can differentially influence the penetrance of rolling in these mutants. These effects are in part due to changes in collagen gene expression that are mediated by the GATA family transcription factor ELT-3. We propose a model by which ELT-3 regulates collagen gene expression in response to environmental stimuli to promote the assembly of a cuticle specialized to a given environment.
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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 | 0.000 | 0.000 |
| 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".