Diet-mediated immunometabolic regulation promotes Klebisella pneumoniae airway clearance 2722
Bibliographic record
Abstract
Abstract Description Antimicrobial-resistant and susceptible Klebsiella pneumoniae (Kp) are major causes of pneumonia and mortality in healthcare settings. One emerging immune evasion strategy of Kp involves its manipulation of the host metabolism, particularly through the induction of mitochondrial oxidative phosphorylation (OXPHOS), which generates reactive oxygen species (ROS). This promotes the accumulation of immunosuppressive myeloid cells that fail to clear bacteria. We hypothesized that reducing ROS with a ketogenic diet would limit anti-inflammatory cells and enhance bacterial clearance. In addition, we postulate that diet-induced ketones would improve immune cell bioenergetics and function. Using a mouse pneumonia model in both BL/6 and Nrf2-/- backgrounds, we compared bacterial burden in mice fed either a ketogenic or control diet. We show that BL/6, but not Nrf2-/- mice lacking antioxidative regulation, which were fed a ketogenic diet had reduced pulmonary bacterial burden and increased survival. These mice had elevated ketones in the blood and airway, along with decreased levels of metabolites associated with myeloid-derived suppressor cells. We observed increased monocytes, neutrophils, and T cells in the lungs of these mice, along with enhanced protein synthesis. Our data highlight the role of ketones in enhancing the immune response to Kp via Nrf2 activation and supporting bioenergetics, suggesting that regulating the host metabolism may help clear persistent infections. Funding Sources This abstract is funded by NIH R00HL157550 (NHLBI). Topic Categories Innate Immune Responses and Host Defense: Cellular Mechanisms (INC)
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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) | 0.005 | 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".