Composition and co-occurrence patterns of the microbiota of different niches of the bovine mammary gland: potential associations with mastitis susceptibility, udder inflammation, and teat-end hyperkeratosis
Bibliographic record
Abstract
BACKGROUND: Within complex microbial ecosystems, microbe-microbe interrelationships play crucial roles in determining functional properties such as metabolic potential, stability and colonization resistance. In dairy cows, microbes inhabiting different ecological niches of the udder may have the potential to interact with mastitis pathogens and therefore modulate susceptibility to intramammary infection. In the present study, we investigated the co-occurrence patterns of bacterial communities within and between different niches of the bovine mammary gland (teat canal vs. milk) in order to identify key bacterial taxa and evaluate their associations with udder health parameters and mastitis susceptibility. RESULTS: Overall, teat canal microbiota was more diverse, phylogenetically less dispersed, and compositionally distinct from milk microbiota. This, coupled with identification of a large number of bacterial taxa that were exclusive to the teat canal microbiota suggested that the intramammary ecosystem, represented by the milk microbiota, acts as a selective medium that disfavors the growth of certain environmental bacterial lineages. We further observed that the diversity of milk microbiota was negatively correlated with udder inflammation. By performing correlation network analysis, we identified two groups of phylogenetically distinct hub species that were either positively (unclassified Bacteroidaceae and Phascolarctobacterium) or negatively (Sphingobacterium) correlated with biodiversity metrics of the mammary gland (MG). The latter group of bacteria also showed positive associations with the future incidence of clinical mastitis. CONCLUSIONS: Our results provide novel insights into the composition and structure of bacterial communities inhabiting different niches of the bovine MG. In particular, we identified hub species and candidate foundation taxa that were associated with the inflammatory status of the MG and/or future incidences of clinical mastitis. Further in vitro and in vivo interrogations of MG microbiota can shed light on different mechanisms by which commensal microbiota interact with mastitis pathogens and modulate udder homeostasis.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".