Diet-mediated Immunometabolic Regulation Promotes Pulmonary Clearance of K. Pneumoniae
Bibliographic record
Abstract
Abstract RATIONALE: Klebsiella pneumoniae (Kp) is a major cause of healthcare-associated pneumonia, often refractory to treatment even when susceptible to antimicrobials. One emerging immune evasion strategy involves its manipulation of the host metabolism, particularly by stimulating mitochondrial oxidative phosphorylation (OXPHOS). This creates reactive oxygen species (ROS), fostering a milieu conducive to the accumulation of immunosuppressive myeloid cells that cannot kill the bacteria. We hypothesize that reducing ROS through a ketogenic diet would limit anti-inflammatory cells and promote bacterial clearance. In addition to activating the antioxidant regulator Nrf2, we postulate that diet-induced ketones would improve the bioenergetics and function of immune cells. METHODS: Using a mouse pneumonia model in both BL/6 and Nrf2-/- backgrounds, we compared bacterial burden in mice fed either a ketogenic or a control diet. We examined the airway metabolic response by semi-targeted and spatial metabolomics, and the immune response by single cell RNA-seq and flow cytometry. RESULTS: We show that BL/6 but not Nrf2-/- mice fed a ketogenic diet are protected against pulmonary Kp infection, as compared with controls, with enhanced survival and significantly reduced bacterial burden. These mice exhibited increased ketones in both the blood and airway, but decreased glucose. Notably, there was a significant decrease in metabolites associated with myeloid-derived suppressor cells including pyrimidines and polyamines in the airway. We observed an increase in monocytes, neutrophils and T cells in the lungs of the ketogenic diet-fed mice and enhanced anabolic processes such as protein synthesis in these cells. CONCLUSION: Our study suggests that the ketogenic diet protects against Kp by reducing oxidative stress and immunosuppressive monocytes. Our data not only highlight the Nrf2 signaling effect of ketones but also their role as an energy source to improve immune cell function. Therefore, manipulating the host metabolism may offer a promising strategy for treating bacterial pulmonary 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 | 0.000 | 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) | 0.002 | 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".