Transcriptomic analysis of regulatory pathways involved in female reproductive physiology of Rhodnius prolixus under different nutritional states
Bibliographic record
Abstract
The triatomine Rhodnius prolixus, a vector of the etiological agent of Chagas disease, has long been used as model to understand important aspects of insect physiology. Despite this history, the impact of the nutritional state on regulatory pathways associated with reproductive performance in triatomines has never been studied. The insulin-like peptide/target of rapamycin (ILP/ToR) signaling pathway is typically responsible for detecting and interpreting nutrient levels. Here, we analyzed transcriptomes from the central nervous system, fat bodies and ovaries of adult females in unfed and fed conditions, with a focus on the ILP/ToR signaling. The results show an up-regulation of transcripts involved in ILP/ToR signaling in unfed insects. However, we demonstrate that this signaling is only activated in tissues from fed insects. Moreover, we report that FoxO (forkhead box O) factor, which regulates longevity via ILP signaling, is responsible for the up-regulation of transcripts related with ILP/ToR signaling in unfed insects. As a consequence, we reveal that unfed females are in a sensitized state to respond to an increase of ILP levels by rapidly activating ILP/ToR signaling. This is the first analysis that correlates gene expression and protein activation of molecules involved with ILP/ToR signaling in R. prolixus females in different nutritional 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".