Regulation of a Trehalose-Specific Facilitated Transporter (TRET) by Insulin and Adipokinetic Hormone in Rhodnius prolixus, a Vector of Chagas Disease
Bibliographic record
Abstract
Using the blood-sucking kissing bug, Rhodnius prolixus as an experimental model, we have studied the involvement of insulin-like peptides (ILPs) and adipokinetic hormone (AKH) signaling in carbohydrate metabolism, focusing on the regulation of the trehalose-specific facilitated transporter (Rhopr-TRET), particularly in the ovaries. We find that trehalose stores in ovaries increase after feeding, synchronously with the beginning of vitellogenesis, but that the transcript expression of enzymes involved in trehalose synthesis show no changes between unfed and blood-fed animals. However, an eightfold increase in Rhopr-TRET transcript expression is observed in the ovaries post-blood meal. In vivo and ex vivo assays using exogenous insulins and Rhopr-AKH, reveal that Rhopr-TRET is up-regulated in ovaries by both peptide families. In accordance with these results, when ILP and AKH signaling cascades are impaired using RNA interference, Rhopr-TRET transcript is down-regulated. In addition, trehalose injection induces an up-regulation of Rhopr-TRET transcript expression and suggests an activation of insulin signaling. Overall, the results support the hypothesis of a direct trehalose uptake by ovaries from the hemolymph through Rhopr-TRET, regulated by ILP and/or AKH. We also show that Rhopr-TRET may work cooperatively with AKH signaling to support the release of trehalose from the ovaries into the hemolymph during the unfed (starved) condition. In conclusion, the results indicate that in females of R. prolixus, trehalose metabolism and its hormonal regulation by ILP and AKH play critical roles in adapting to different nutritional conditions and physiological states.
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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".