Author(s): Lehrke I, Schaier M, Schade K, Morath C, Waldherr R,
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Abstract Retinoids are potent antiproliferative and anti-inflammatory compounds. We previously demonstrated that the natural pan-agonists all-trans retinoic acid (RA) and 13-cis RA efficiently preserve renal structure and function in rat mesangioproliferative glomerulonephritis. We examine effects of synthetic retinoid receptor-specific agonists 1) to identify common and receptor subtype-specific pathways in this model and 2) to characterize effects of retinoids on the renal endothelin (ET) system. Vehicle-injected control rats were compared with rats treated with daily subcutaneous injections of agonists specific for retinoid A (Ro-137410) and retinoid X (Ro-257386) receptors and the complex anti-activator protein-1 active retinoid BMS-453 7 days after induction of anti-Thy1.1 nephritis (n = 7-9/group). The different retinoids lowered glomerular ET-1 and ET type A and B receptor gene expression in control and nephritic rats with comparable efficacy. Reduction of glomerular c-Fos and GATA-2 mRNA expression levels suggests downregulation of transcription factors required for ET expression. The different retinoids were similar in their action on the glomerular capillary occlusion score, number of total glomerular cells, and glomerular infiltrating macrophage count. They differed in their ability to normalize blood pressure (Ro-257386 > BMS-453 > arotinoid), albuminuria (BMS-453 > Ro-257386 > arotinoid), and creatinine clearance (arotinoid > BMS-453 > Ro-257386). No signs of toxicity were observed. We conclude that all retinoid agonists with different subtype specificity are highly efficient in reducing renal damage and proliferation of mesangial cells. Retinoid X and A receptor-specific pathways are apparently involved in the antiproliferative, anti-inflammatory, and anti-ET action. Further studies are indicated to define the potential use of retinoid agonists in inflammatory renal disease.
This article was published in Am J Physiol Renal Physiol
and referenced in Journal of Data Mining in Genomics & Proteomics