Babesia bovis infection alters the composition and assembly of Rhipicephalus microplus midgut microbiota
Bibliographic record
Abstract
Introduction Babesia bovis is one of the main causative agents of bovine babesiosis. Livestock farmers are constantly struggling to control the population of the tick vector and reduce babesiosis outbreaks. For this reason, the development of new control strategies is necessary. Tick microbiota consists of a diverse group of symbiotic, commensal, and pathogenic microorganisms. It has been shown that altering the microbiota population prevents the transmission of apicomplexan pathogens. This work represents a primary exploratory approach to determine the changes B. bovis infection causes in the microbiota of R. microplus. Methods Two calves were infested with R. microplus larvae; next, one of the calves was splenectomized and infected with Babesia bovis. Fifteen days after the infestation, engorged females were collected from each calf. Collected ticks were separated into two groups: 0 h and 72 h. Ticks from the 0 h group were dissected to extract their midgut the same day they were collected, while midgut dissection of the other group was done after 72 h of incubation. Thus, samples were separated into 4 experimental groups depending on their infection status and the time of the dissection. Total DNA was purified and the V4 region of the bacterial 16S rRNA gene was sequenced using Illumina MiSeq technology. Results Data analysis showed fewer complex networks with reduced connectivity in infected ticks compared to the uninfected group. In both groups, the tick microbiota networks showed reduced node density at 72 h post-repletion. Different keystone taxa were found in all groups, indicating that midgut microbiota assembly is influenced by both tick developmental stage and the infection with B. bovis. Discussion Results of this work aim to serve as a steppingstone in the development of anti-tick microbiota vaccines capable of impairing both the life cycle of R. microplus and B. bovis transmission.
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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".