Meta-Analysis of Bovine Digital Dermatitis Microbiota Reveals Distinct Microbial Community Structures Associated With Lesions
Bibliographic record
Abstract
Bovine digital dermatitis (DD) is a significant cause of infectious lameness and economic losses in cattle production across the world. There is a lack of a consensus across different 16S metagenomic studies on DD-associated bacteria that may be potential pathogens of the disease. The goal of this meta-analysis was to identify a consistent group of DD-associated bacteria in individual DD lesions across studies, regardless of experimental design choices including sample collection and preparation, hypervariable region sequenced, and sequencing platform. A total of 6 studies were included in this meta-analysis. Raw sequences and metadata were identified on the NCBI sequence read archive and European nucleotide archive. Bacterial community structures were investigated between normal skin and DD skin samples. Random forest models were generated to classify DD status based on microbial composition, and to identify taxa that best differentiate DD status. Among all samples, members of Treponema, Mycoplasma, Porphyromonas, and Fusobacterium were consistently identified in the majority of DD lesions, and were the best genera at differentiating DD lesions from normal skin. Individual study and 16S hypervariable region sequenced had significant influence on final DD lesion microbial composition (P < 0.05). These findings indicate that members of Treponema, Mycoplasma, Porphyromonas, and/or Fusobacterium may have significant roles in DD pathogenesis, and should be studied further in respect to elucidating DD etiopathogenic mechanisms and developing more effective treatment and mitigation strategies.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.013 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".