Differential interactions of Rickettsia species with tick microbiota in Rh. sanguineus and Rh. turanicus
Bibliographic record
Abstract
Tick-borne rickettsioses, caused by Gram-negative bacteria of the Rickettsia genus, pose a growing global threat, with various arthropod vectors contributing to their transmission. Understanding the complex interactions within tick microbiota, including the role of Rickettsia species, is crucial for elucidating the dynamics of rickettsial diseases. Here, we investigate the taxonomic profiles and co-occurrence networks of Rickettsia in Rh. sanguineus sensus lato (s.l.) and Rh. turanicus ticks, revealing significant differences in community composition and local connectivity of Rickettsia species. While the microbiota of both tick species share common taxa, distinct differences in relative abundance and network topology suggest unique ecological niches. Moreover, robustness analysis demonstrates varying resilience to perturbations, indicating different strategies for network organization. Our findings also highlight metabolic differences between tick species, suggesting potential implications for Rickettsia interactions. Overall, this study provides insights into the intricate microbial landscape within ticks, shedding light on the functional redundancy and metabolic pathways associated with Rickettsia, thus advancing our understanding of tick-borne diseases.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".