Vector microbiota manipulation by host antibodies: the forgotten strategy to develop transmission-blocking vaccines
Bibliographic record
Abstract
Human and animal pathogens that are transmitted by arthropods are a global concern, particularly those vectored by ticks (e.g. Borrelia burgdorferi and tick-borne encephalitis virus) and mosquitoes (e.g. malaria and dengue virus). Breaking the circulation of pathogens in permanent foci by controlling vectors using acaricide-based approaches is threatened by the selection of acaricide resistance in vector populations, poor management practices and relaxing of control measures. Alternative strategies that can reduce vector populations and/or vector-mediated transmission are encouraged worldwide. In recent years, it has become clear that arthropod-associated microbiota are involved in many aspects of host physiology and vector competence, prompting research into vector microbiota manipulation. Here, we review how increased knowledge of microbial ecology and vector-host interactions is driving the emergence of new concepts and tools for vector and pathogen control. We focus on the immune functions of host antibodies taken in the blood meal as they can target pathogens and microbiota bacteria within hematophagous arthropods. Anti-microbiota vaccines are presented as a tool to manipulate the vector microbiota and interfere with the development of pathogens within their vectors. Since the importance of some bacterial taxa for colonization of vector-borne pathogens is well known, the disruption of the vector microbiota by host antibodies opens the possibility to develop novel transmission-blocking vaccines.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".