Inhibition of epigenetic reader proteins by apabetalone counters inflammation in activated innate immune cells from Fabry disease patients receiving enzyme replacement therapy
Bibliographic record
Abstract
Fabry disease (FD) is a rare X-linked disorder of lipid metabolism, characterized by the accumulation of globotriaosylceramide (Gb3) due to defective the lysosomal enzyme, α-galactosidase. Gb3 deposits activate immune-mediated systemic inflammation, ultimately leading to life-threatening consequences in multiple organs such as the heart and kidneys. Enzyme replacement therapy (ERT), the standard of care, is less effective with advanced tissue injury and inflammation in patients with FD. Here, we showed that MCP-1 and TNF-α cytokine levels were almost doubled in plasma from ERT-treated FD patients. Chemokine receptor CCR2 surface expression was increased by twofold on monocytes from patients with low eGFR. We also observed an increase in IL12B transcripts in unstimulated peripheral blood mononuclear cells (PBMCs) over a 2-year period of continuous ERT. Apabetalone is a clinical-stage oral bromodomain and extra terminal protein inhibitor (BETi), which has beneficial effects on cardiovascular and kidney disease related pathways including inflammation. Here, we demonstrate that apabetalone, a BD2-selective BETi, dose dependently reduced the production of MCP-1 and IL-12 in stimulated PBMCs through transcriptional regulation of their encoding genes. Reactive oxygen species production was diminished by up to 80% in stimulated neutrophils following apabetalone treatment, corresponding with inhibition of NOX2 transcription. This study elucidates that inhibition of BET proteins by BD2-selective apabetalone alleviates inflammatory processes and oxidative stress in innate immune cells in general and in FD. These results suggest potential benefit of BD2-selective apabetalone in controlling inflammation and oxidative stress in FD, which will be further investigated in clinical trials.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".