Inhibition of Epigenetic Reader BET Proteins by Apabetalone Counters Inflammation in Activated Innate Immune Cells from Fabry Disease Patients Receiving ERT
Bibliographic record
Abstract
Abstract Fabry disease (FD) is a rare genetic disorder with a deficit in the degradation pathway of a glycolipid, globotriaocylceramide (Gb3). Gb3 deposits in various tissues evoke immune-mediated systemic inflammation, driving the progression of FD complications. In particular, cardiac and renal problems, leading causes of mortality in FD patients, are insufficiently managed by enzyme replacement therapy (ERT) in the long term. Here, we examined immune profiles in unstimulated peripheral blood mononuclear cells (PBMCs) from FD patients on ERT relative to untreated patients. The results showed an upregulation of the CCR2/MCP-1 axis, particularly in the presence of renal dysfunction. We also observed an increase of IL12B transcripts in unstimulated PBMCs when tracking the immune status over two years of ERT in patients with early-stage chronic kidney disease (CKD). Interestingly, pro-inflammatory responses in LPS-stimulated PBMCs were countered by apabetalone, a clinical-stage candidate that inhibits Bromodomain and Extra Terminal (BET) proteins, epigenetic readers. Apabetalone treatment dose dependently inhibited MCP-1 and IL-12 production by up to 90%. Apabetalone downregulated the transcription of other pro-inflammatory cytokines including TNF-α, IL-6 and IL-8 by 70%, 48% and 54%, respectively. Reactive oxygen species (ROS) are an indicator of oxidative damage caused by intracellular Gb3 deposits in FD patients. Apabetalone suppressed ROS levels by up to ~70% in LPS-stimulated neutrophils. Hence, apabetalone treatment may reduce pathological inflammation and oxidative stress in FD patients and thus complement ERT to optimize patient outcomes, warranting further investigation of apabetalone as a therapeutic for FD.
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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".