<i>Borrelia</i> infection in rodent host has dramatic effects on the microbiome of ticks
Bibliographic record
Abstract
Abstract Background Vector-borne diseases remain major causes of human morbidity and mortality. It is increasingly recognized that the community of microbes inhabiting arthropods can strongly affect their vector competence, but the role of the tick microbiome in Borrelia transmission – the cause of Lyme disease – remains unclear. Results Here, we use a large-scale experiment to clarify the reciprocal interactions between Borrelia afzelii and the microbiome of Ixodes ricinus , its primary vector. In contrast to other reports, we find that depletion of the bacterial microbiome in larval ticks has no effect on their subsequent acquisition of B. afzelii during blood feeding on infected mice. Rather, exposure to B. afzelii -infected hosts drives pervasive changes to the tick microbiome, decreasing overall bacterial abundance, shifting bacterial community composition, and increasing bacterial diversity. These effects appear to be independent of the acquisition of B. afzelii by ticks, suggesting they are mediated by physiological or immunological aspects of B. afzelii infection in the rodent host. Conclusions Manipulation of the microbiome of I. ricinus larvae had no effect on their ability to acquire B. afzelii . In contrast, B. afzelii infection in the mouse had dramatic effects on the composition of the gut microbiome in I. ricinus nymphs. Our study demonstrates that vector-borne infections in the vertebrate host shape the microbiome of the arthropod vector.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".