A critical role for ecdysone response genes in regulating egg production in adult female Rhodnius prolixus
Bibliographic record
Abstract
Ecdysteroids control ovary growth and egg production through a complex gene hierarchy. In the female Rhodnius prolixus, a blood-gorging triatomine and the vector of Chagas disease, we have identified the ecdysone response genes in the ovary using transcriptomic data. We then quantified the expression of the ecdysone response gene transcripts (E75, E74, BR-C, HR3, HR4, and FTZ-F1) in several tissues, including the ovary, following a blood meal. These results confirm the presence of these transcripts in several tissues in R. prolixus and show that the ecdysone response genes in the ovary are mostly upregulated during the first three days post blood meal (PBM). Knockdown of E75, E74, or FTZ-F1 transcripts using RNA interference (RNAi) was used to understand the role of the ecdysone response genes in vitellogenesis and egg production. Knockdown significantly decreases the expression of the transcripts for the ecdysone receptor and Halloween genes in the fat body and the ovaries and reduces the titer of ecdysteroid in the hemolymph. Knockdown of each of these transcription factors typically alters the expression of the other transcription factors. Knockdown also significantly decreases the expression of vitellogenin transcripts, Vg1 and Vg2, in the fat body and ovaries and reduces the number of eggs produced and laid. Some of the laid eggs have an irregular shape and smaller volume, and their hatching rate is decreased. Knockdown also influences the expression of the chorion gene transcripts Rp30 and Rp45. The overall effect of knockdown is a decrease in number of eggs produced and a severe reduction in number of eggs laid and their hatching rate. Clearly, ecdysteroids and ecdysone response genes play a significant role in reproduction in R. prolixus.
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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".