Global metabolite analysis of the land snail Theba pisana hemolymph during active and aestivated states
Bibliographic record
Abstract
The state of metabolic dormancy has fascinated people for hundreds of years, leading to research exploring the identity of natural molecular components that may induce and maintain this state. Many animals lower their metabolism in response to high temperatures and/or arid conditions, a phenomenon called aestivation. The biological significance for this is clear; by strongly suppressing metabolic rate to low levels, animals minimize their exposure to stressful conditions. Understanding blood or hemolymph metabolite changes that occur between active and aestivated animals can provide valuable insights relating to those molecular components that regulate hypometabolism in animals, and how they afford adaptation to their different environmental conditions. In this study, we have investigated the hemolymph metabolite composition from the land snail Theba pisana, a remarkably resilient mollusc that displays an annual aestivation period. Using LC-MS-based metabolomics analysis, we have identified those hemolymph metabolites that show significant changes in relative abundance between active and aestivated states. We show that certain metabolites, including some phospholipids [e.g. LysoPC(14:0)], and amino acids such as l-arginine and l-tyrosine, are present at high levels within aestivated snails. Further investigation of our T. pisana RNA-sequencing data elucidated the entire repertoire of phospholipid-synthesis genes in the snail digestive gland, as a precursor towards future comparative investigation between the genetic components of aestivating and non-aestivating species. In summary, we have identified a large number of metabolites that are elevated in the hemolymph of aestivating snails, supporting their role in protecting against heat or desiccation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".