Rickettsial pathogens drive microbiota assembly in <i>Hyalomma marginatum</i> and <i>Rhipicephalus bursa</i> ticks
Bibliographic record
Abstract
Most tick-borne pathogens (TBPs) are secondarily acquired by ticks during feeding on infected hosts, which imposes 'priority effect' constraints, as arrival order influences the establishment of new species in a microbial community. Here we tested whether once acquired, TBPs contribute to bacterial microbiota functioning by increasing community stability. For this, we used Hyalomma marginatum and Rhipicephalus bursa ticks collected from cattle in different locations of Corsica and combined 16S rRNA amplicon sequencing and co-occurrence network analysis, with high-throughput pathogen detection, and in silico removal of nodes to test for impact of rickettsial pathogens on network properties. Despite its low centrality, Rickettsia showed preferential connections in the networks, notably with a keystone taxon in H. marginatum, suggesting facilitation of Rickettsia colonisation by the keystone taxon. In addition, conserved patterns of community assembly in both tick species were affected by Rickettsia removal, suggesting that privileged connections of Rickettsia in the networks make this taxon a driver of community assembly. However, Rickettsia removal had minor impact on the conserved 'core bacterial microbiota' of H. marginatum and R. bursa. Interestingly, networks of the two tick species with Rickettsia have similar node centrality distribution, a property that is lost after Rickettsia removal, suggesting that this taxon drives specific hierarchical interactions between bacterial microbes in the microbiota. The study indicates that tick-borne Rickettsia play a significant role in the tick bacterial microbiota, despite their low centrality. These bacteria are influential and contribute to the conservation of the 'core bacterial microbiota' while also promoting community stability.
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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 | 0.001 |
| 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".