RNA Interference-Mediated Knockdown of Male Fertility Genes in the Queensland Fruit Fly Bactrocera tryoni (Diptera: Tephritidae)
Bibliographic record
Abstract
The Queensland fruit fly, Bactrocera tryoni, is Australia’s most important horticultural pest. The Sterile Insect Technique (SIT) has been used to control this species for decades, using radiation to sterilize males before field-release. This method of sterilization can potentially reduce the insects’ abilities to compete for mates. In this study, RNA interference (RNAi) techniques were examined for their potential to sterilize male B. tryoni without adversely affecting mating competitiveness. B. tryoni adults were injected or fed double-stranded RNAs (dsRNAs) targeting spermatogenesis genes (tssk1, topi and trxt); quantitative reverse-transcriptase PCR analyses confirmed that transcript levels were reduced 60–80% for all three genes following injections. Feeding produced a significant gene knockdown for tssk1 and trxt after three days, but interestingly, two genes (trxt and topi) produced an excess of transcripts after 10 days of feeding. Despite these fluctuations in transcript levels, all three dsRNAs impacted the fecundity of treated males, with tssk1- and topi-dsRNA-treated males producing 75% fewer viable offspring than the negative controls. Mating competition assays demonstrated that dsRNA-treated males can actively compete with untreated males. These findings suggest that RNAi technology could serve as an alternative to radiation as a means of sterilizing these insects in an SIT program.
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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".