Competition between strains of <i>Borrelia afzelii</i> in the host tissues and consequences for transmission to ticks
Bibliographic record
Abstract
Pathogen species often consist of genetically distinct strains, which can establish mixed infections or coinfections in the host. In coinfections, interactions between pathogen strains can have important consequences for their transmission success. We used the tick-borne bacterium Borrelia afzelii, which is the most common cause of Lyme disease in Europe, as a model multi-strain pathogen to investigate the relationship between coinfection, competition between strains, and strain-specific transmission success. Mus musculus mice were infected with one or two strains of B. afzelii, strain transmission success was measured by feeding ticks on mice, and the distribution of each strain in six different mouse organs and the ticks was measured using qPCR. Coinfection and competition reduced the tissue infection prevalence of both strains and changed their bacterial abundance in some tissues. Coinfection and competition also reduced the transmission success of the B. afzelii strains from the infected hosts to feeding ticks. The ability of the B. afzelii strains to establish infection in the host tissues was strongly correlated with their transmission success to the tick vector. Our study demonstrates that coinfection and competition between pathogen strains inside the host tissues can have major consequences for their transmission success.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".