Resistance of Tick Gut Microbiome to Anti-Tick Vaccines, Pathogen Infection and Antimicrobial Peptides
Bibliographic record
Abstract
Ixodes scapularis ticks harbor microbial communities including pathogenic and non-pathogenic microbes. Pathogen infection increases the expression of several tick gut proteins, which disturb the tick gut microbiota and impact bacterial biofilm formation. Anaplasma phagocytophilum induces ticks to express I. scapularis antifreeze glycoprotein (IAFGP), a protein with antimicrobial activity, while Borrelia burgdorferi induces the expression of PIXR. Here, we tested the resistance of I. scapularis microbiome to A. phagocytophilum infection, antimicrobial peptide IAFGP, and anti-tick immunity specific to PIXR. We demonstrate that A. phagocytophilum infection and IAFGP affect the taxonomic composition and taxa co-occurrence networks, but had limited impact on the functional traits of tick microbiome. In contrast, anti-tick immunity disturbed the taxonomic composition and the functional profile of tick microbiome, by increasing both the taxonomic and pathways diversity. Mechanistically, we show that anti-tick immunity increases the representation and importance of the polysaccharide biosynthesis pathways involved in biofilm formation, while these pathways are under-represented in the microbiome of ticks infected by A. phagocytophilum or exposed to IAFGP. These analyses revealed that tick microbiota is highly sensitive to anti-tick immunity, while it is less sensitive to pathogen infection and antimicrobial peptides. Results suggest that biofilm formation may be a defensive response of tick microbiome to anti-tick immunity.
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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 | 0.000 |
| 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".