Knockdown resistance allele L1014F introduced by CRISPR/Cas9 is not associated with altered vector competence of Anopheles gambiae for o’nyong nyong virus
Bibliographic record
Abstract
Knockdown resistance (kdr) alleles conferring resistance to pyrethroid insecticides are widespread amongst vector populations. Previous research has suggested that these alleles are associated with changes in the vector competence of mosquitoes for arboviruses and Plasmodium, however non-target genetic differences between mosquito strains may have had a confounding effect. Here, to minimise genetic differences, the laboratory Anopheles gambiae Kisumu strain was compared to a CRISPR/Cas9 homozygous kdr L1014F mutant Kisumu-kdr line in order to examine associations with vector competence for o'nyong nyong virus (ONNV). Mosquitoes were infected using either blood feeds or intrathoracic microinjections. There were no significant differences in the prevalence of virus in mosquito body parts between kdr mutant and wildtype lines from either oral or intrathoracic injection routes. The ONNV titre was significantly higher in the legs of the wildtype strain at 7dpi following intrathoracic microinjection, but no other significant differences in viral titre were detected. ONNV was not detected in the saliva of mosquitoes from either strain. Our findings from per os infections suggest that the kdr L1014F allele is not associated with altered infection prevalence for ONNV, a key component of vector competence.
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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) | 0.002 | 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".