Differential Impact of Simultaneous or Sequential Coinfections With <i>Borrelia afzelii</i> and Tick‐Borne Encephalitis Virus on the <i>Ixodes ricinus</i> Microbiota
Bibliographic record
Abstract
Ticks, particularly Ixodes ricinus , are significant vectors of pathogens such as Borrelia spp. and tick‐borne encephalitis virus (TBEV), which cause Lyme borreliosis (LB) and tick‐borne encephalitis (TBE), respectively. Understanding how these pathogens interact within the tick microbiome is essential for developing vector control strategies. This study investigates the impact of Borrelia afzelii and TBEV, as well as their coinfection, on the microbiota composition and structure of I. ricinus nymphs. Using a network‐based approach, we analyzed the microbial communities of ticks exposed to infected or coinfected mice. DNA extracted from newly molted nymphs was sequenced for the bacterial 16S rRNA gene, and microbial diversity metrics (alpha and beta diversity) were calculated. Our results showed that TBEV infection increased microbiome diversity compared to the uninfected and Borrelia groups. Co‐occurrence network analyses revealed that while microbial structures remained consistent across conditions, TBEV‐infected networks exhibited higher robustness to perturbations, indicating a stabilizing effect on the tick microbiome. Furthermore, the hierarchical position and associations of Borrelia varied significantly depending on the infection scenario, highlighting its adaptive role within the tick microbiota. The study demonstrates that pathogen presence alters tick microbial dynamics, with TBEV enhancing stability, suggesting virus‐mediated modifications of the microbiome. These findings advance our understanding of pathogen–tick–microbiome interactions and provide insights into the ecological mechanisms underlying pathogen coexistence within ticks. This research underscores the importance of microbial networks in ticks and offers new perspectives for targeted approaches in managing tick‐borne diseases.
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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) | 0.001 | 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".