Bibliographic record
Abstract
The global antimicrobial resistance crisis poses a significant threat to humankind in the coming decades. Challenges associated with the development of novel antibiotics underscore the urgent need to develop alternative treatment strategies to combat bacterial infections. Host-directed therapy is a promising new therapeutic strategy that aims to boost the host immune response to bacteria rather than target the pathogen itself, thereby circumventing the development of antibiotic resistance. However, host-directed therapy depends on the identification of druggable host targets or proteins with key functions in antibacterial defense. Protein Kinase R (PKR) is a well-characterized human kinase with established roles in cancer, metabolic disorders, neurodegeneration, and antiviral defense. However, its role in antibacterial defense has been surprisingly underappreciated. Although the canonical role of PKR is to inhibit protein translation during viral infection, this kinase senses and responds to multiple types of cellular stress by regulating cell-signaling pathways involved in inflammation, cell death, and autophagy - mechanisms that are all critical for a protective host response against bacterial pathogens. Indeed, there is accumulating evidence to demonstrate that PKR contributes significantly to the immune response to a variety of bacterial pathogens. Importantly, there are existing pharmacological modulators of PKR that are well-tolerated in animals, indicating that PKR is a feasible target for host-directed therapy. In this review, we provide an overview of immune cell functions regulated by PKR and summarize the current knowledge on the role and functions of PKR in bacterial infections. We also review the non-canonical activators of PKR and speculate on the potential mechanisms that trigger activation of PKR during bacterial infection. Finally, we provide an overview of existing pharmacological modulators of PKR that could be explored as novel treatment strategies for bacterial infections.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".