Insights into the Regulatory Roles of miRNAs in the Salivary Glands of the Soft Ticks Ornithodoros moubata and Ornithodoros erraticus
Bibliographic record
Abstract
MicroRNAs (miRNAs) are small non-coding RNAs that regulate gene expression by inhibiting or degrading messenger RNAs (mRNAs). In ticks, salivary miRNAs are proposed to play key roles in modulating host–vector interactions during blood feeding. Previously, we identified salivary miRNAs in Ornithodoros moubata and Ornithodoros erraticus, major vectors of African swine fever and tick-borne human relapsing fever. In this study, we investigated the regulatory roles of salivary miRNAs in tick biology. Salivary miRNA datasets were re-analysed to identify conserved miRNAs, and putative target genes were predicted using the sialotranscriptomes of both species. In silico predictions were validated through experimental inhibition of specific miRNAs using antagomirs. Knockdown of miR-375 and miR-1 significantly reduced blood intake, oviposition, and fertility, indicating their involvement in feeding and reproductive processes. Silencing miR-252b in O. moubata led to increased mortality, suggesting a critical role in survival. Notably, Metis1 was identified as a likely target of miR-252b, and its dysregulation may underlie the observed lethality in miR-252b-silenced ticks. These findings highlight the functional relevance of salivary miRNAs in tick physiology and host interaction, offering new perspectives for the development of innovative tick control strategies.
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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 | 0.000 |
| 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 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".