Protein Kinase R Restricts the Intracellular Survival of Mycobacterium tuberculosis by Promoting Selective Autophagy
Bibliographic record
Abstract
(Mtb). The identification of macrophage signaling proteins exploited by Mtb during infection will enable the development of alternative host-directed therapies (HDT) for TB. HDT strategies will boost host immunity to restrict the intracellular replication of Mtb and therefore hold promise to overcome antimicrobial resistance, a growing crisis in TB therapy. Protein Kinase R (PKR) is a key host sensor that functions in the cellular antiviral response. However, its role in defense against intracellular bacterial pathogens is not clearly defined. Herein, we demonstrate that expression and activation of PKR is upregulated in macrophages infected with Mtb. Immunological profiling of human THP-1 macrophages that overexpress PKR (THP-PKR) showed increased production of IP-10 and reduced production of IL-6, two cytokines that are reported to activate and inhibit IFNγ-dependent autophagy, respectively. Indeed, sustained expression and activation of PKR reduced the intracellular survival of Mtb, an effect that could be enhanced by IFNγ treatment. We further demonstrate that the enhanced anti-mycobacterial activity of THP-PKR macrophages is mediated by a mechanism dependent on selective autophagy, as indicated by increased levels of LC3B-II that colocalize with intracellular Mtb. Consistent with this mechanism, inhibition of autophagolysosome maturation with bafilomycin A1 abrogated the ability of THP-PKR macrophages to limit replication of Mtb, whereas pharmacological activation of autophagy enhanced the anti-mycobacterial effect of PKR overexpression. As such, PKR represents a novel and attractive host target for development of HDT for TB, and our data suggest value in the design of more specific and potent activators of PKR.
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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 | 0.001 |
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".