CNS serotonin content mediating food deprivation-enhanced learning is regulated by hemolymph tryptophan concentration and autophagic flux in the pond snail
Bibliographic record
Abstract
, 1 day of food deprivation enhances taste aversion learning ability by decreasing the serotonin (5-hydroxytryptamin; 5-HT) content in the central nervous system (CNS). On the other hand, after 5 days of food deprivation, learning ability and the CNS 5-HT concentration return to basal levels. How food deprivation leads to alterations of 5-HT levels in the CNS, however, is unknown. Here, we measured the concentration of the 5-HT precursor tryptophan in the hemolymph and CNS, and demonstrated that the CNS tryptophan concentration was higher in 5-day food-deprived snails than in non-food-deprived or 1-day food-deprived snails, whereas the hemolymph tryptophan concentration was not affected by the duration of food deprivation. This finding suggests the existence of a mediator of the CNS tryptophan concentration independent of food deprivation. To identify the mediator, we investigated autophagic flux in the CNS under different food deprivation conditions. We found that autophagic flux was significantly upregulated by inhibition of the tropomyosin receptor kinase (Trk)-Akt-mechanistic target of rapamycin complex 1 (MTORC1) pathway in the CNS of 5-day food-deprived snails. Moreover, when autophagy was inhibited, the CNS 5-HT content was significantly downregulated in 5-day food-deprived snails. Our results suggest that the hemolymph tryptophan concentration and autophagic flux in the CNS cooperatively regulate learning ability affected by different durations of food deprivation. This mechanism may underlie the selection of behaviors appropriate for animal survival depending on the degree of nutrition.
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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".