Bibliographic record
Abstract
Lipids are the primary storage molecules and an essential source of energy in insects during reproduction, prolonged periods of flight, starvation, and diapause. The coordination center for insect lipid metabolism is the fat body, which is analogous to the vertebrate adipose tissue and liver. The fat body is primarily composed of adipocytes, which accumulate triacylglycerols in intracellular lipid droplets. Genomics and proteomics, together with functional analyses, such as RNA interference and CRISPR/Cas9-targeted genome editing, identified various genes involved in lipid metabolism and elucidated their functions. However, the endocrine control of insect lipid metabolism, in particular the roles of peptide hormones in lipogenesis and lipolysis are relatively less-known topics. In the current review, the neuropeptides that directly or indirectly affect insect lipid metabolism are introduced. The primary lipolytic and lipogenic peptide hormones are adipokinetic hormone and the brain insulin-like peptides (ILP2, ILP3, ILP5). Other neuropeptides, such as insulin-growth factor ILP6, neuropeptide F, allatostatin-A, corazonin, leucokinin, tachykinins and limostatin, might stimulate lipolysis, while diapause hormone-pheromone biosynthesis activating neuropeptide, short neuropeptide F, CCHamide-2, and the cytokines Unpaired 1 and Unpaired 2 might induce lipogenesis. Most of these peptides interact with one another, but mostly with insulin signaling, and therefore affect lipid metabolism indirectly. Peptide hormones are also involved in lipid metabolism during reproduction, flight, diapause, starvation, infections and immunity; these are also highlighted. The review concludes with a discussion of the potential of lipid metabolism-related peptide hormones in pest management.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".