Establishment of persistent enteric mycobacterial infection following streptomycin pre-treatment
Bibliographic record
Abstract
Mycobacterium avium subsp. paratuberculosis (MAP) is the causative agent of paratuberculosis, a chronic gastrointestinal disease affecting ruminants. This disease remains widespread in part due to the limitations of available diagnostics and vaccines. A representative small animal model of disease could act as a valuable tool for studying its pathogenesis and to develop new methods for paratuberculosis control, but current models are lacking. Streptomycin pre-treatment can reduce colonization resistance and has previously been shown to improve enteric infection in a Salmonella model. Here, we investigated whether streptomycin pre-treatment of mice followed by MAP gavage could act as a model of paratuberculosis which mimics the natural route of infection and disease development in ruminants. The infection outcomes of MAP were compared to M. avium subsp. hominissuis (MAH), an environmental mycobacterium, and M. bovis and M. orygis, two tuberculous mycobacteria. Streptomycin pre-treatment was shown to consistently improve bacterial infection post-oral inoculation. This model led to chronic MAP infection of the intestines and mesenteric lymph nodes (MLNs) up to 24-weeks post-gavage, however there was no evidence of inflammation or disease. These infection outcomes were found to be specific to MAP. When the model was applied to a bacterium of lesser virulence MAH, the infection was comparatively transient. Mice infected with bacteria of greater virulence, M. bovis or M. orygis, developed chronic intestinal and MLN infection with pulmonary disease similar to zoonotic TB. Our findings suggest that a streptomycin pre-treatment mouse model could be applied to future studies to improve enteric infection with MAP and to investigate other modifications underlying MAP enteritis.
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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".