NLRP3 inflammasome blockade treats intestinal inflammation associated with chronic granulomatous disease
Bibliographic record
Abstract
ABSTRACT: Chronic granulomatous disease (CGD) is an inborn error of immunity associated with a 50% prevalence of inflammatory bowel disease (IBD) for which current treatments are suboptimal due to the increased risk of infections in this population. CGD results from defects in the nicotinamide adenine dinucleotide phosphate oxidase 2 complex, leading to minimal or absent phagocyte-derived reactive oxygen species production. Patients with CGD present with recurrent infections and severe inflammatory complications, especially in the gut. These inflammatory complications have been associated with the increased systemic activation of the nucleotide-binding domain and leucine-rich-repeat-containing protein 3 (NLRP3) inflammasome and dysregulation of the T-cell compartment. However, the role of the NLRP3 inflammasome at the intestinal barrier and whether it can be targeted to treat CGD-associated IBD (CGD-IBD) remain unclear. β-Hydroxybutyrate (βHB), a ketone body produced during fasting or adherence to a ketogenic diet, can inhibit the NLRP3 inflammasome and restore T-cell balance. In this preclinical study, we demonstrated that a ketogenic diet significantly improves colitis in CGD mice, to a greater extent than in wild-type mice, by reducing NLRP3 inflammasome activity, altering the microbiota, and inducing tolerogenic immune populations at the intestinal mucosal barrier. We also showed that βHB supplementation could significantly improve colitis in CGD mice and decrease systemic inflammation. We further confirmed that, in the blood cells of humans with CGD, βHB effectively reduces the levels of cytokines associated with inflammasome activation. In conclusion, our study identified that NLRP3 inflammasome blockade using a ketogenic diet or βHB supplementation is a potential novel and safer treatment for CGD-IBD.
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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) | 0.001 | 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 | 0.001 | 0.002 |
| 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".