Microbiota‐driven vaccination in soft ticks: Implications for survival, fitness and reproductive capabilities in <i>Ornithodoros moubata</i>
Bibliographic record
Abstract
The Ornithodoros moubata (Om) soft tick, a vector for diseases like tick-borne human relapsing fever and African swine fever, poses challenges to conventional control methods. With diminishing insecticide efficacy, harnessing the tick's microbiota through innovative approaches like microbiota-driven vaccination emerges as a promising strategy for sustainable and targeted disease control. This study investigated the intricate relationship between Pseudomonas, a keystone taxon in the Om microbiome, and its impact on tick fitness, microbiome structure and network dynamics. Utilizing in silico analyses and empirical vaccination experiments, the role of Pseudomonas within microbial networks in the tick midguts (MG) and salivary glands (SG) of Om was studied. Additionally, the consequences of anti-microbiota vaccines targeting Pseudomonas and Lactobacillus on tick fitness, microbiome diversity and community assembly were explored. The result of the study shows that in Om, Pseudomonas plays a central role in microbial networks, influencing keystone species despite being categorized as peripheral (interacting with 47 different taxa, 13 of which are keystone species). Anti-microbiota vaccination targeting Pseudomonas and Lactobacillus yields distinct effects on tick fitness, with Pseudomonas vaccination significantly impacting female tick survival, while Lactobacillus significantly reduced oviposition and fertility. Microbiome changes post-vaccination reveal diversity alterations, emphasizing the impact of vaccine choice. Community assembly dynamics and network robustness analyses highlight Pseudomonas' pivotal role, in influencing topological features and network resilience. The findings of the study provide comprehensive insights into the intricate dynamics of Om microbial networks and the potential of targeted microbiota-driven vaccines for tick control.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".