Rickettsia helvetica infection is associated with microbiome modulation in Ixodes ricinus collected from humans in Serbia
Bibliographic record
Abstract
Rickettsia helvetica is an emerging pathogen of the Spotted Fever Group Rickettsia (SFGR) causing spotted fever diseases in various European countries. This tick-borne pathogen replicates in tick tissues such as the midgut and salivary gland, but its potential interactions with the vector microbiota is poorly characterized. The vector microbiome plays a pivotal role in tick-pathogen interactions, and some microbiota members facilitate or impede tick-borne pathogen infection. Manipulations of the tick microbiome have led to reduction in pathogen colonization in the tick vector. However, translating these findings into disease control applications requires a thorough characterization of vector microbiota response to different pathogens. In this study, we analyzed and compared the microbiota of Ixodes ricinus ticks attached on humans and collected in Serbia. Ticks were either infected with R. helvetica, or uninfected with major tick-borne pathogens (referred hereafter as 'pathogen-free'). We used microbial co-occurrence network analysis to determine keystone taxa of each set of samples, and to study the interaction patterns of the microbial communities in response to pathogen infection. The inferred functional profiles of the tick microbiome in R. helvetica-positive and pathogen-free samples were also compared. Our results show that R. helvetica infection reduces significantly the diversity of the microbiota and the connectivity of the co-occurrence network. In addition, using co-occurrence network we identified bacterial taxa (i.e., Enterobacteriaceae, Comamonadaceae, and Bacillus) that were negatively associated with 'Rickettsia' in R. helvetica-infected ticks, suggesting competition between R. helvetica and some members of the tick microbiota. The reconstruction of microbial metabolic pathways shows that the presence of R. helvetica might have a major impact on the metabolic functions of the tick microbiome. These results can inform novel interventions for the prevention of R. helvetica, or other SFGR infections in humans.
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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 | 0.000 |
| 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".