Kv1.3 Channel Inhibition Limits Uremia-Induced Calcification in Mouse and Human Vascular Smooth Muscle.
Violeta Cazaña-PérezPilar CidadJuan Francisco Navarro-GonzálezJorge Rojo-MencíaFrederic JaisserJosé R López-LópezDiego Alvarez de la RosaTeresa GiraldezMaria Teresa Pérez-GarcíaPublished in: Function (Oxford, England) (2020)
Chronic kidney disease (CKD) significantly increases cardiovascular risk. In advanced CKD stages, accumulation of toxic circulating metabolites and mineral metabolism alterations triggers vascular calcification, characterized by vascular smooth muscle cell (VSMC) transdifferentiation and loss of the contractile phenotype. Phenotypic modulation of VSMC occurs with significant changes in gene expression. Even though ion channels are an integral component of VSMC function, the effects of uremia on ion channel remodeling has not been explored. We used an in vitro model of uremia-induced calcification of human aorta smooth muscle cells (HASMCs) to study the expression of 92 ion channel subunit genes. Uremic serum-induced extensive remodeling of ion channel expression consistent with loss of excitability but different from the one previously associated with transition from contractile to proliferative phenotypes. Among the ion channels tested, we found increased abundance and activity of voltage-dependent K + channel Kv1.3. Enhanced Kv1.3 expression was also detected in aorta from a mouse model of CKD. Pharmacological inhibition or genetic ablation of Kv1.3 decreased the amount of calcium phosphate deposition induced by uremia, supporting an important role for this channel on uremia-induced VSMC calcification.
Keyphrases
- chronic kidney disease
- smooth muscle
- end stage renal disease
- high glucose
- endothelial cells
- gene expression
- diabetic rats
- poor prognosis
- mouse model
- drug induced
- dna methylation
- pulmonary artery
- oxidative stress
- magnetic resonance
- genome wide
- single cell
- binding protein
- image quality
- ms ms
- computed tomography
- coronary artery
- pluripotent stem cells
- bioinformatics analysis