Sequence characterisation and novel insights into bovine mastitis-associated Streptococcus uberis in dairy herds
Bibliographic record
Abstract
Streptococcus uberis is one of the most frequent mastitis-causing pathogens isolated from dairy cows. Further understanding of S. uberis genetics may help elucidate the disease pathogenesis. We compared the genomes of S. uberis isolates cultured from dairy cows located in distinctly different geographic regions of Australia. All isolates had novel multi locus sequence types (MLST) indicating a highly diverse population of S. uberis. Global clonal complexes (GCC) were more conserved. GCC ST86 and GCC ST143 represented 30% of the total isolates (n = 27) and were clustered within different geographic regions. Core genome phylogeny revealed low phylogenetic clustering by region, isolation source, and MLST. Identification of putative sortase (srtA) substrates and generation of a custom putative virulence factor database revealed genes which may explain the affinity of S. uberis for mammary tissue, evasion of antimicrobial efforts and disease pathogenesis. Of 27 isolates, four contained antibiotic resistance genes including an antimicrobial resistance cluster containing mel/mef(A), mrsE, vatD, lnuD, and transposon-mediated lnuC was also identified. These are novel genes for S. uberis, which suggests interspecies lateral gene transfer. The presence of resistance genes across the two geographic regions tested within one country supports the need for a careful, tailored, implementation and monitoring of antimicrobial stewardship.
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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 | 0.002 |
| 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".