Interplay between MIG-6/papilin and TGF-β signaling promotes extracellular matrix remodeling and modulates the maintenance of neuronal architecture
Bibliographic record
Abstract
ABSTRACT Neuronal architecture laid out during embryogenesis persists lifelong, ensuring normal nervous system function. However, the mechanisms underlying the long-term maintenance of neuronal organization remain largely unknown. We previously uncovered that the conserved extracellular matrix protein MIG-6/papilin impacts collagen IV remodeling and neuronal maintenance, such that disruption of MIG-6/papilin leads to a collagen IV fibrotic state and altered tissue biomechanics, thereby stabilizing neuronal architecture. Here, we combine incisive molecular genetics and in vivo quantitative imaging to determine how this mig-6 -dependent fibrotic phenotype is modulated, by investigating the implication of the TGF-β pathway, which is well known to regulate fibrosis in mammals. Our findings highlight a mechanism whereby the interplay between MIG-6/papilin and the TGF-β pathway regulates ECM composition and neuronal maintenance, with MIG-6/papilin acting as a positive regulator of TGF-β signaling. This work provides key insights into the molecular basis of sustaining neuronal architecture and offers a foundation for understanding age-related neurodegenerative and fibrotic conditions.
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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".