Adherence and metal-ion acquisition gene expression increases during infection with <i>Treponema phagedenis</i> strains from bovine digital dermatitis
Bibliographic record
Abstract
ABSTRACT Digital dermatitis (DD) is an ulcerative foot lesion on the heel bulbs of dairy cattle. DD is a polymicrobial disease with no precise etiology, although Treponema spirochetes are found disproportionally abundant in diseased tissue. Within Treponema, several different species are found in DD; however, the species Treponema phagedenis is uniformly found in copious quantities and deep within the skin layers of the active, ulcerative stages of disease. The pathogenic mechanisms these bacteria use to persist in the skin and the precise role they play in the pathology of DD are widely unknown. To explore the pathogenesis and virulence of Treponema phagedenis , newly isolated strains of this species were investigated in a subcutaneous murine abscess model. In the first trial, a dosage study was conducted to compare the pathogenicity of different strains across three different treponemes per inoculum (TPI) doses based on abscess volumes. In the second trial, the expression levels of 11 putative virulence genes were obtained to gain insight into their involvement in pathogenesis. During the RT-qPCR analysis, it was determined that genes encoding for two metal-ion import lipoproteins and two adherence genes were found highly upregulated during infection. Conversely, two genes involved in motility and chemotaxis were found to not be significantly upregulated or utilized during infection. These results were supported by gene expression data from natural M2 lesions of dairy cattle. This gene expression analysis could highlight the preference in strategy for T. phagedenis to persist and adhere in the host rather than engage in motility and disseminate.
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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 | 0.001 |
| 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".