Arrestin-mediated Desensitization Enables Olfactory Discrimination in <i>C. elegans</i>
Bibliographic record
Abstract
Abstract In the mammalian olfactory system, crosstalk among diverse olfactory signals is minimized through labelled line coding: individual neurons express one or few olfactory receptors among those encoded in the genome. Labelled line coding allows for separation of stimuli during mammalian olfactory signal transduction, however, in the nematode worm Caenorhabditis elegans , 1,300 olfactory receptors are primarily expressed in only 32 neurons, precluding this strategy. Here we report genetic, pharmacological and behavioural evidence that β-arrestin-mediated desensitization of olfactory receptors, working downstream of the kinase GRK-1, enables discrimination between intra-neuronal olfactory stimuli, but that this discrimination relies on quantitative, rather than qualitative differences in signalling. Our findings suggest that C. elegans exploits β-arrestin desensitization to maximize responsiveness to novel odors, allowing for behaviourally appropriate responses to olfactory stimuli despite the large number of olfactory receptors signalling in single cells. This represents a fundamentally different solution to the problem of olfactory discrimination than that which evolved in mammals, allowing for economical use of an extremely limited number of sensory neurons.
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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".