Mycobacterium avium subsp. paratuberculosis Inhibits Gamma Interferon-Induced Signaling in Bovine Monocytes: Insights into the Cellular Mechanisms of Johne's Disease
Bibliographic record
Abstract
Mycobacterium avium subsp. paratuberculosis is the causative agent of Johne's disease in cattle and may have implications for human health. Establishment of chronic infection by M. avium subsp. paratuberculosis depends on its subversion of host immune responses. This includes blocking the ability of infected macrophages to be activated by gamma interferon (IFN-γ) for clearance of this intracellular pathogen. To define the mechanism by which M. avium subsp. paratuberculosis subverts this critical host cell function, patterns of signal transduction to IFN-γ stimulation of uninfected and M. avium subsp. paratuberculosis-infected bovine monocytes were determined through bovine-specific peptide arrays for kinome analysis. Pathway analysis of the kinome data indicated activation of the JAK-STAT pathway, a hallmark of IFN-γ signaling, in uninfected monocytes. In contrast, IFN-γ stimulation of M. avium subsp. paratuberculosis-infected monocytes failed to induce patterns of peptide phosphorylation consistent with JAK-STAT activation. The inability of IFN-γ to induce differential phosphorylation of peptides corresponding to early JAK-STAT intermediates in infected monocytes indicates that M. avium subsp. paratuberculosis blocks responsiveness at, or near, the IFN-γ receptor. Consistent with this hypothesis, increased expression of negative regulators of the IFN-γ receptors SOCS1 and SOCS3 as well as decreased expression of IFN-γ receptor chains 1 and 2 is observed in M. avium subsp. paratuberculosis-infected monocytes. These patterns of expression are functionally consistent with the kinome data and offer a mechanistic explanation for this critical M. avium subsp. paratuberculosis behavior. Understanding this mechanism may contribute to the rational design of more effective vaccines and/or therapeutics for Johne's disease.
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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 | 0.000 |
| 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".