Genetically Modifying the Insect Gut Microbiota to Control Chagas Disease Vectors through Systemic RNAi
Bibliographic record
Abstract
Technologies based on RNA interference may be used for insect control. Sustainable strategies are needed to control vectors of Chagas disease such as Rhodnius prolixus. The insect microbiota can be modified to deliver molecules to the gut. Here, Escherichia coli HT115(DE3) expressing dsRNA for the Rhodnius heme-binding protein (RHBP) and for catalase (CAT) were fed to nymphs and adult triatomine stages. RHBP is an egg protein and CAT is an antioxidant enzyme expressed in all tissues by all developmental stages. The RNA interference effect was systemic and temporal. Concentrations of E. coli HT115(DE3) above 3.35 × 10(7) CFU/mL produced a significant RHBP and CAT gene knockdown in nymphs and adults. RHBP expression in the fat body was reduced by 99% three days after feeding, returning to normal levels 10 days after feeding. CAT expression was reduced by 99% and 96% in the ovary and the posterior midgut, respectively, five days after ingestion. Mortality rates increased by 24-30% in first instars fed RHBP and CAT bacteria. Molting rates were reduced by 100% in first instars and 80% in third instars fed bacteria producing RHBP or CAT dsRNA. Oviposition was reduced by 43% (RHBP) and 84% (CAT). Embryogenesis was arrested in 16% (RHBP) and 20% (CAT) of laid eggs. Feeding females 105 CFU/mL of the natural symbiont, Rhodococcus rhodnii, transformed to express RHBP-specific hairpin RNA reduced RHBP expression by 89% and reduced oviposition. Modifying the insect microbiota to induce systemic RNAi in R. prolixus may result in a paratransgenic strategy for sustainable vector control.
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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 | 0.000 | 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 | 0.001 |
| 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".