Development of specialized sensory neurons engages a nuclear receptor required for functional plasticity
Bibliographic record
Abstract
During development, the nervous system generates neurons that serve highly specialized roles and, accordingly, possess unique functional attributes. The chemosensory BAG neurons of C. elegans are striking exemplars of this. BAGs sense the respiratory gas carbon dioxide (CO2) and, in a context-dependent manner, switch from mediating avoidance of CO2 to supporting CO2 attraction. To determine mechanisms that support the physiology and plasticity of BAG neurons, we used tandem ChIP-seq and cell targeted RNA-seq to identify gene targets of the transcription factor ETS-5, which is required for BAG development. A functional screen of ETS-5 targets revealed that NHR-6, the sole C. elegans NR4A-type nuclear receptor, is required for BAG-mediated avoidance of CO2 and regulates expression of a subset of BAG-specific genes. Unlike ets-5 mutants, which are defective for both attraction to and avoidance of CO2, nhr-6 mutants are fully competent for attraction. These data indicate that the remarkable ability of BAGs to adaptively assign positive or negative valence to a chemosensory stimulus requires a gene-regulatory program supported by an evolutionarily conserved type of nuclear receptor. We suggest that NHR-6 might be an example of a developmental mechanism for modular encoding of functional plasticity in the nervous system.
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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 | 0.001 |
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".