MO072APABETALONE DOWNREGULATES FIBROTIC, INFLAMMATORY AND CALCIFIC PROCESSES IN RENAL MESANGIAL CELLS WHICH MAY CONTRIBUTE TO REDUCED CARDIAC EVENTS OBSERVED IN CKD PATIENTS
Bibliographic record
Abstract
Abstract Background and Aims Major adverse cardiac events (MACE) remain a leading cause of mortality in chronic kidney disease (CKD). Apabetalone is an orally available inhibitor of bromodomain & extraterminal (BET) proteins – epigenetic readers that modulate gene expression involved in fibrosis, inflammation and calcification. In the phase 3 BETonMACE trial, apabetalone treatment was associated with reduction in MACE in the subpopulation with CKD (eGFR < 60 mL/min/1.73m2; HR 0.50 95% CI 0.26,0.96 p=0.04]) implying favorable effects of apabetalone on cellular responses along the kidney-heart axis. This study examines effects of apabetalone on primary human renal mesangial cells (HRMCs) in culture on fibrosis, inflammation, reactive oxygen species (ROS) and calcification pathways that contribute to renal pathology. Method HRMCs from donors without kidney dysfunction were stimulated with TGF-β1 or lipopolysaccharide (LPS) ± 1-25µM apabetalone, 0.15-0.5µM JQ1 or 0.1µM MZ1 (BET inhibitors [BETi] with chemical scaffolds different than apabetalone). Gene expression was measured by real-time PCR and RNA-seq. Smooth muscle actin (α-SMA) was examined by immunofluorescence microscopy, and alkaline phosphatase enzyme activity in a biochemical assay. RNA-seq from TGF-β1 treated HRMC ± BETi was evaluated by Gene Ontology (GO) Enrichment and Ingenuity Pathway Analysis (IPA). Results TGF-β1 is a pro-fibrotic cytokine that activates HRMC to a fibroblast-like state which over-produces extracellular matrix (ECM). Apabetalone dose dependently suppressed TGF-β1 induced gene expression of (a) α-SMA, a marker of fibrotic activation, up to 90% p<0.001 and de novo α-SMA protein production (b) fibronectin, a key ECM component, up to 44% p<0.001 (c) NADPH oxidase 4 (NOX4), involved in production of pro-fibrotic ROS, up to 82% p<0.001 (d) tissue non-specific alkaline phosphatase (TNALP), associated with reduced glomerular function & extracellular calcification, up to 96% as well as TNALP enzyme activity up to 96% p<0.001. An inhibitor of TGF-β receptors reduced or abolished TGF-β1 responses, indicating the expected signal transduction pathways mediated its downstream effects. Apabetalone dose dependently opposed LPS stimulated expression of inflammatory genes: IL6 up to 94%, IL1B up to 95% & PTGS2 (COX2) up to 94% p<0.001, suggesting downregulation of inflammatory processes. In all studies, JQ1 and / or MZ1 had similar activity as apabetalone, confirming on-target BETi effects. In GO Enrichment analysis of RNA-seq from TGF-β1 stimulated HRMCs, multiple gene sets associated with ECM were in the top 20 affected by BETi, supporting anti-fibrotic properties. IPA predicted NfkB-RelA and NFkB (complex) were upstream regulators inhibited by apabetalone, indicating suppression of NF-kB mediated inflammation. IPA also predicted apabetalone activated canonical pathways of glucose utilization & tolerance of ROS production, including Oxidative Phosphorylation (z-score 5.7, p<0.01 at 25µM; z-score 3.5, p>0.05 at 5µM) and NRF2-Mediated Oxidative Stress Response (z score 2.3, p<0.001 at 25µM; z-score 1.6, p<0.001 at 5µM). PGC-1α, a key upstream regulator of the Oxidative Phosphorylation pathway, was also predicted to be activated by apabetalone (z score 4.2, p<0.001 at 25µM; z-score 2.3, p<0.001 at 5µM). These changes in energy metabolism pathways may allow HRMC to cope with elevated glucose. Conclusion Apabetalone downregulated responses to TGF-β1 or LPS in HRMCs that promote fibrotic, inflammatory and calcific processes which exacerbate kidney dysfunction. Changes in energy metabolism pathways predicted apabetalone facilitates adaptation to high glucose in the kidney. Together, our results provide mechanistic insight into reductions in MACE in CKD patients receiving apabetalone in the phase 3 BETonMACE trial. The effect of apabetalone on MACE in patients with diabetes and CKD will be further evaluated in the upcoming BETonMACE2 trial.
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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) | 0.003 | 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".