Multi-omics analysis reveals diapause-associated lipid remodeling in the fat body of Colorado potato beetle
Bibliographic record
Abstract
BACKGROUND: The Colorado potato beetle, Leptinotarsa decemlineata (Coleoptera: Chrysomelidae), is a major pest of potato that undergoes diapause (hibernation) to survive harsh winter conditions. Lipid metabolism plays a crucial role in diapause preparation and maintenance. However, the specific changes in lipid composition and their molecular regulation remain unclear. This study integrates lipidomics, transcriptomics, and proteomics to investigate diapause-associated metabolic shifts in the fat body of L. decemlineata. RESULTS: We identified significant increases in monounsaturated (e.g., oleic acid) and polyunsaturated (e.g., linoleic acid) fatty acids, as well as phosphatidylethanolamines (membrane lipids) enriched in unsaturated fatty acids during diapause. Transcriptomic analysis revealed the differential expression of genes related to energy metabolism and lipid processing, while qualitative proteomics identified some calcium-associated protein isoforms (regucalcin-like and annexin B9-like) that are uniquely present in diapause. Correlation analysis suggested that specific transcripts, such as carboxyl ester lipase, are associated with the regulation of key lipid molecules, including oleic acid and linoleic acid, during diapause. These findings advance our understanding of lipid regulation during diapause and highlight potential molecular targets for disrupting overwintering strategies, which could inform novel pest control approaches. CONCLUSIONS: Diapause in the Colorado potato beetle is associated with changes in lipid composition in the fat body, which is mediated through changes at the transcriptome and proteome levels.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".