Anti-Microbiota Vaccine Reduces Avian Malaria Infection Within Mosquito Vectors
Bibliographic record
Abstract
Animal and human pathogens that are transmitted by arthropods are a global concern, particularly those vectored by mosquitoes (e.g., Plasmodium spp. and dengue virus). Vector microbiota may hold the key to vector-borne pathogen control, as mounting evidence suggests that the contributions of the vector microbiota to vector physiology and pathogen life cycle are so relevant that vectorial capacity cannot be understood without considering microbial communities within the vectors. Anti-tick microbiota vaccines targeting commensal bacteria of the vector microbiota alter vector feeding and modulate the taxonomic and functional profiles of vector microbiome, but their impact on vector-borne pathogen development within the vector has not been tested. In this study, we tested whether anti-microbiota vaccination in birds targeting Enterobacteriaceae within mosquito midguts modulates the mosquito microbiota and disrupt Plasmodium relictum development in its natural vector Culex quinquefasciatus. Domestic canaries (Serinus canaria domestica) were experimentally infected with P. relictum and/or immunized with live vaccines containing different strains of Escherichia coli. Immunization of birds induced E. coli-specific antibodies. The midgut microbial communities of mosquitoes fed on Plasmodium-infected and/or E. coli-immunized birds were different from those of mosquitoes fed on control birds. Notably, mosquito midgut microbiota modulation was associated with a significant decrease in the occurrence of P. relictum oocysts and sporozoites in the midguts and salivary glands of C. quinquefasciatus, respectively. A significant reduction in the number of oocysts was also observed. These findings suggest that anti-microbiota vaccines can be used as a novel tool to control malaria transmission and potentially other vector-borne pathogens.
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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 | 0.001 |
| 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".