TROPSA and TRE31 Gene Knockouts to Prevent the Transmission of Lyme Disease from Tick to Host: A Research Protocol
Bibliographic record
Abstract
Introduction: Ixodes scapularis, the blacklegged tick, is responsible for the transmission of Lyme disease. Rising temperatures and shorter winter seasons, due to climate change, is resulting in the Northward expansion of tick range. This is correlated with the increasing prevalence of Lyme disease in Canada. This research protocol aims to address this issue by genetically mutating the blacklegged tick which is primarily responsible for the transmission of Borrelia burgdorferi, the bacterium that causes Lyme disease, in North America. The proposed mutation involves two gene knockouts: TROSPA and TRE31. The blacklegged tick mutant is predicted to be unable to transmit Lyme disease to the white-footed mouse, Peromyscus leucopus. Methods: Mutated ticks will feed on the blood of Lyme positive mice and later naïve mice. The rate of Lyme disease transmission from mutated ticks will be compared to transmission rates in positive and negative wild type control groups. The statistical significance of the difference between these groups’ transmission rates will be evaluated by Student’s t-test with Fisher’s protected least significant difference test. Results: Based on the results from literature testing each mutation independently, we predict our I. scapularis mutant, having both TROSPA and TRE31 gene knockouts, will be unable to transmit Lyme disease to the white-footed mouse. Discussion: Unsuccessful transmission of Lyme disease from mutated ticks indicates that the TROSPA and TRE31 knockouts are effective in preventing B. burgdorferi from completing its lifecycle within the tick. Based on the expected results, the combined gene-knockout model presents a novel method to hinder the transmission of Lyme disease more effectively than previously investigated single gene knockouts. Conclusion: This research protocol suggests a strategy to decrease the rate of Lyme disease amongst ticks, and thus humans. Future research could explore efficacies of knocking out other genes in combination with TROSPA or TRE31.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.026 | 0.006 |
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".