Diversity of the Ixodes ricinus Microbiome Across Belgian Ecoregions and Its Association with Pathogen and Symbiont Presence
Bibliographic record
Abstract
Ticks are important vectors of zoonotic pathogens, and their presence can be influenced by the composition of the tick microbiome. In turn, this microbiome is shaped by environmental and ecological factors, as demonstrated in several studies conducted under controlled conditions. However, the extent of these influences under natural ecological conditions remains underexplored. In this study, we investigated the diversity of the microbiome and the prevalence of pathogens in Ixodes ricinus nymphs across three distinct Belgian ecoregions: Sandy Loam, Condroz, and Ardennes. Using real-time quantitative PCR (qPCR) and Oxford Nanopore 16S rRNA sequencing, we assessed how geography and pathogen presence influence tick-associated microbial communities. Our results revealed significant regional differences in microbiome composition and pathogen prevalence. Borrelia burgdorferi sensu lato (s.l.) was most prevalent in the Ardennes (9% (7.4-10.9) vs 3.8% (2.8-5.2) in the Condroz and 2.1% (1.4-3.2) in Sandy Loam) while Anaplasma phagocytophilum was more common in the Sandy Loam region (21.1% (18.7-23.8) vs 4% (3-5.4) in the Condroz and 3.2% (2.2-4.4) in the Ardennes). Endosymbionts such as Midichloria mitochondrii and Spiroplasma ixodetis also exhibited distinct geographic distributions. Network analysis identified potential pathogen-microbiota interactions, with certain bacterial taxa showing positive or negative associations with specific pathogens. Moreover, microbiome composition was influenced not only by ecoregion but also by microorganisms such as Rickettsia helvetica, suggesting that its colonization may actively shape microbial community structure, potentially through competition or facilitation mechanisms. Additionally, microbiome network robustness varied across ecoregions, highlighting the role of ecological context in shaping microbial interactions within ticks. These findings underscore the complex interplay between geography, pathogen presence, and microbial diversity in ticks, highlighting the importance of integrating these interactions to inform microbiome-based strategies for vector control and disease prevention.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".