Comparison of salivary gland and midgut microbiome in the soft ticks Ornithodoros erraticus and Ornithodoros moubata
Bibliographic record
Abstract
Introduction Ornithodoros erraticus and Ornithodoros moubata are the main vectors of African swine fever virus (ASFV) and the human relapsing fever spirochetes Borrelia hispanica and Borrelia crocidurae in the Mediterranean region and Borrelia duttoni in continental Africa. Manipulation of the tick microbiome has been shown to reduce vector fitness and competence in tick vectors, suggesting that the identification of key microbial players associated with tick tissues can inform interventions such as anti-microbiota vaccines to block pathogen development in the midgut and/or salivary glands. Methods In this study, we analyzed and compared the microbiome of the salivary glands and midgut of O. erraticus and O. moubata . For the taxonomic and functional characterization of the tissue-specific microbiome, we used 16S rRNA amplicon sequencing and prediction of metabolic profiles using PICRUSt2. Co-occurrence networks were built to characterize the community assembly and identify keystone taxa in each tick species. Results Our results revealed differences in the composition, diversity, and assembly of the bacterial microbiome of salivary glands and midgut within each tick species, but differences were more noticeable in O. moubata . Differences were also found in the microbiome of each tissue, salivary gland and midgut, between species. However, the ‘Core Association Networks (CAN)’ analysis revealed conserved patterns of interacting taxa in tissues within and between tick species. Different keystone taxa were identified in O. erraticus and O. moubata tissues, but Muribaculaceae and Alistipes were found as keystone taxa in the salivary glands of both tick species which justifies their use as anti-microbiota vaccine candidates to alter the microbiome and reduce tick fitness and/or block pathogen transmission. The high similarity of predicted metabolic pathways profiles between tissues of the two tick species suggests that taxonomic variability of the microbiome is not associated with significant changes in microbial functional profiles. Conclusion We conclude that the taxonomic structure of the microbiome in O. erraticus and O. moubata is tissue-specific, suggesting niche partitioning of bacterial communities associated to these soft ticks. However, shared keystone taxa and conserved patterns of interacting taxa between tissues and tick species suggest the presence of key microbial players that could be used as anti-microbiota vaccine candidates to affect tick physiology and/or pathogen colonization.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".