Ixodes ricinus microbiome varies in response to pathogen and symbiont presence across ecoregions in Belgium
Bibliographic record
Abstract
Ticks are important vectors of zoonotic pathogens, and the presence of these pathogens can be influenced by factors such as the tick microbiome, which, conversely, is shaped by environmental conditions and the presence of tick-borne pathogens, 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, while Anaplasma phagocytophilum was more common in the Sandy Loam region. 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 pathogens 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, emphasizing the need to consider ecological variability when studying tick-borne disease dynamics. Understanding these interactions could inform microbiome-based strategies for vector control and disease prevention.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".