Anti-diuretic activity of a CAPA neuropeptide can compromise <i>Drosophila</i> chill tolerance
Bibliographic record
Abstract
For insects, chilling injuries that occur in the absence of freezing are often related to a systemic loss of ion and water balance that leads to extracellular hyperkalemia, cell depolarization, and the triggering of apoptotic signalling cascades. The ability of insect ionoregulatory organs (e.g. the Malpighian tubules) to maintain ion balance in the cold has been linked to improved chill tolerance, and many neuroendocrine factors are known to influence ion transport rates of these organs. Injection of micromolar doses of CAPA (an insect neuropeptide) have been previously demonstrated to improve Drosophila cold tolerance, but the mechanisms through which it impacts chill tolerance are unclear, and low doses of CAPA have been previously demonstrated to cause anti-diuresis in insects, including dipterans. Here, we provide evidence that low (fM) and high (µM) doses of CAPA impair and improve chill tolerance, respectively, via two different effects on Malpighian tubule ion and water transport. While low doses of CAPA are anti-diuretic, reduce tubule K+ clearance rates and reduce chill tolerance, high doses facilitate K+ clearance from the haemolymph and increase chill tolerance. By quantifying CAPA peptide levels in the central nervous system, we estimated the maximum achievable hormonal titres of CAPA, and found further evidence that CAPA may function as an anti-diuretic hormone in Drosophila melanogaster. We provide the first evidence of a neuropeptide that can negatively affect cold tolerance in an insect, and further evidence of CAPA functioning as an anti-diuretic peptide in this ubiquitous insect model.
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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 | 0.001 |
| 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".