A longitudinal census of the bacterial community in raw milk correlated with Staphylococcus aureus clinical mastitis infections in dairy cattle
Bibliographic record
Abstract
BACKGROUND: Staphylococcus aureus is a common cause of clinical mastitis (CM) in dairy cattle. Optimizing the bovine mammary gland microbiota to resist S. aureus colonization is a growing area of research. However, the details of the interbacterial interactions between S. aureus and commensal bacteria, which would be required to manipulate the microbiome to resist infection, are still unknown. This study aims to characterize changes in the bovine milk bacterial community before, during, and after S. aureus CM and to compare bacterial communities present in milk between infected and healthy quarters. METHODS: We collected quarter-level milk samples from 698 Holstein dairy cows over an entire lactation. A total of 11 quarters from 10 cows were affected by S. aureus CM and milk samples from these 10 cows (n = 583) regardless of health status were analyzed by performing 16S rRNA gene amplicon sequencing. RESULTS: The milk microbiota of healthy quarters was distinguishable from that of S. aureus CM quarters two weeks before CM diagnosis via visual inspection. Microbial network analysis showed that 11 OTUs had negative associations with OTU0001 (Staphylococcus). A low diversity or dysbiotic milk microbiome did not necessarily correlate with increased inflammation. Specifically, Staphylococcus xylosus, Staphylococcus epidermidis, and Aerococcus urinaeequi were each abundant in milk from the quarters with low levels of inflammation. CONCLUSION: Our results show that the udder microbiome is highly dynamic, yet a change in the abundance in certain bacteria can be a potential indicator of future S. aureus CM. This study has identified potential prophylactic bacterial species that could act as a barrier against S. aureus colonization and prevent future instances of S. aureus CM.
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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 | 0.001 | 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 | 0.000 |
| 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".