MO072APABETALONE DOWNREGULATES FIBROTIC, INFLAMMATORY AND CALCIFIC PROCESSES IN RENAL MESANGIAL CELLS WHICH MAY CONTRIBUTE TO REDUCED CARDIAC EVENTS OBSERVED IN CKD PATIENTS
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».