Anti-Microbiota Vaccines Modulate the Tick Microbiome in a Taxon-Specific Manner
Bibliographic record
Abstract
The lack of tools for the precise manipulation of the tick microbiome is currently a major limitation to achieve mechanistic insights into the tick microbiome. Anti-tick microbiota vaccines targeting keystone bacteria of the tick microbiota alter tick feeding, but their impact on the taxonomic and functional profiles of the tick microbiome has not been tested. In this study, we immunized a vertebrate host model ( Mus musculus ) with live bacteria vaccines targeting keystone (i.e., Escherichia-Shigella ) or non-keystone (i.e., Leuconostoc ) taxa of tick microbiota and tested the impact of bacterial-specific antibodies (Abs) on the structure and function of tick microbiota. We also investigated the effect of these anti-microbiota vaccines on mice gut microbiota composition. Our results showed that the tick microbiota of ticks fed on Escherichia coli -immunized mice had reduced Escherichia-Shigella abundance and lower species diversity compared to ticks fed on control mice immunized with a mock vaccine. Immunization against keystone bacteria restructured the hierarchy of nodes in co-occurrence networks and reduced the resistance of the bacterial network to taxa removal. High levels of E. coli -specific IgM and IgG were negatively correlated with the abundance of Escherichia-Shigella in tick microbiota. These effects were not observed when Leuconostoc was targeted with vaccination against Leuconostoc mesenteroides . Prediction of functional pathways in the tick microbiome using PICRUSt2 revealed that E. coli vaccination reduced the abundance of lysine degradation pathway in tick microbiome, a result validated by qPCR. In contrast, the gut microbiome of immunized mice showed no significant alterations in the diversity, composition and abundance of bacterial taxa. Our results demonstrated that anti-tick microbiota vaccines are a safe, specific and an easy-to-use tool for manipulation of vector microbiome. These results guide interventions for the control of tick infestations and pathogen infection/transmission.
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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".