The NADase CD38 is a central regulator in gouty inflammation and a novel druggable therapeutic target
Bibliographic record
Abstract
Abstract Objectives: Cellular NAD+ declines in inflammatory states associated with increased activity of the leukocyte-expressed NADase CD38. In this study, we tested the potential role of therapeutically targeting CD38 and NAD+ in gout. Methods: We studied cultured mouse wild type and CD38 knockout (KO) murine bone marrow derived macrophages (BMDMs) stimulated by monosodium urate (MSU) crystals and used the air pouch gout synovitis model. Results: MSU crystals induced CD38 in BMDMs in vitro, associated with NAD+ depletion, and IL-1b and CXCL1 release, effects reversed by pharmacologic CD38 inhibitors (apigenin, 78c). Mouse air pouch inflammatory responses to MSU crystals were blunted by CD38 KO and apigenin. Pharmacologic CD38 inhibition suppressed MSU crystal-induced NLRP3 inflammasome activation and increased anti-inflammatory SIRT3-SOD2 activity in macrophages. BMDM RNA-seq analysis of differentially expressed genes (DEGs) revealed CD38 to control multiple MSU crystal-modulated inflammation pathways. Top DEGs included the circadian rhythm modulator GRP176, and the metalloreductase STEAP4 that mediates iron homeostasis, and promotes oxidative stress and NF-kB activation when it is overexpressed. Conclusions: CD38 and NAD+ depletion are druggable targets controlling the MSU crystal- induced inflammation program. Targeting CD38 and NAD+ are potentially novel selective molecular approaches to limit gouty arthritis.
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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".