Altered Toll-Like Receptor 9 Signaling in Mycobacterium avium subsp. paratuberculosis-Infected Bovine Monocytes Reveals Potential Therapeutic Targets
Bibliographic record
Abstract
Mycobacterium avium subsp. paratuberculosis is the causative agent of Johne's disease in cattle. The complex, multifaceted interaction of M. avium subsp. paratuberculosis with its host includes dampening the ability of infected cells to respond to stimuli that promote M. avium subsp. paratuberculosis clearance. By disrupting host defenses, M. avium subsp. paratuberculosis creates an intracellular environment that favors the establishment and maintenance of infection. Toll-like receptors (TLRs) are important sensors that initiate innate immune responses to microbial challenge and are also immunotherapeutic targets. For example, TLR9 contributes to host defense against M. avium subsp. paratuberculosis, and its agonists (CpG oligodeoxynucleotides [ODNs]) are under investigation for treatment of Johne's disease and other infections. Here we demonstrate that M. avium subsp. paratuberculosis infection changes the responsiveness of bovine monocytes to TLR9 stimulation. M. avium subsp. paratuberculosis inhibits classical TLR9-mediated responses despite a 10-fold increase in TLR9 expression and maintained uptake of CpG ODNs. Other TLR9-mediated responses, such as oxidative burst, which occur through noncanonical signaling, remain functional. Kinome analysis verifies that classic TLR9 signaling is blocked by M. avium subsp. paratuberculosis infection and that signaling instead proceeds through a Pyk2-mediated mechanism. Pyk2-mediated signaling does not hinder infection, as CpG ODNs fail to promote M. avium subsp. paratuberculosis clearance. Indeed, Pyk2 signaling appears to be an important aspect of M. avium subsp. paratuberculosis infection, as Pyk2 inhibitors significantly reduce the number of intracellular M. avium subsp. paratuberculosis bacteria. The actions of M. avium subsp. paratuberculosis on TLR9 signaling may represent a strategy to generate a host environment which is better suited for infection, revealing potential new targets for therapeutic intervention.
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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".