Molecular signatures of diapause in the Asian longhorned beetle: Gene expression
Bibliographic record
Abstract
We conducted RNA-seq on fat body, the supraesophageal ganglion, midgut, hindgut, and Malpighian tubules during pre-diapause, diapause maintenance, post-diapause quiescence, and post-diapause development. We observed a small, but consistent, proportion of genes within each gene expression profile that were shared among tissues, lending support for a core set of diapause-associated genes whose expression is tissue-independent. We evaluated the overarching hypotheses that diapause would be associated with cell cycle arrest, developmental arrest, and increased stress tolerance and found evidence of repressed TOR and insulin signaling, reduced cell cycle activity and increased capacity of stress response via heat shock protein expression and remodeling of the cytoskeleton. However, these processes varied among tissues, with the brain and fat body appearing to maintain higher levels of cellular activity during diapause than the midgut or Malpighian tubules. We also observed temperature-dependent changes in gene expression during diapause maintenance, particularly in genes related to the heat shock response and MAPK, insulin, and TOR signaling pathways. Additionally, we provide evidence for epigenetic reorganization during the diapause/post-diapause quiescence transition and expression of genes involved in post-translational modification, highlighting the need for investigations of the protein activity of these candidate genes and processes. We conclude that diapause development is coordinated via diverse tissue-specific gene expression profiles and that canonical diapause phenotypes vary among tissues.
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".