Genes and associated peptides involved with aestivation in a land snail
Bibliographic record
Abstract
Some animals can undergo a remarkable transition from active normal life to a dormant state called aestivation; entry into this hypometabolic state ensures that life continues even during long periods of environmental hardship. In this study, we aimed to identify those central nervous system (CNS) peptides that may regulate metabolic suppression leading to aestivation in land snails. Mass spectral-based neuropeptidome analysis of the CNS comparing active and aestivating states, revealed 19 differentially produced peptides; 2 were upregulated in active animals and 17 were upregulated in aestivated animals. Of those, the buccalin neuropeptide was further investigated since there is existing evidence in molluscs that buccalin modulates physiology by muscle contraction. The Theba pisana CNS contains two buccalin transcripts that encode precursor proteins that are capable of releasing numerous buccalin peptides. Of these, Tpi-buccalin-2 is most highly expressed within our CNS transcriptome derived from multiple metabolic states. No significant difference was observed at the level of gene expression levels for Tpi-buccalin-2 between active and aestivated animals, suggesting that regulation may reside at the level of post-translational control of peptide abundance. Spatial gene and peptide expression analysis of aestivated snail CNS demonstrated that buccalin-2 has widespread distribution within regions that control several physiological roles. In conclusion, we provide the first detailed molecular analysis of the peptides and associated genes that are related to hypometabolism in a gastropod snail known to undergo extended periods of aestivation.
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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".