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Abstract The fibrogenic process plays a significant pathophysiological role in the progression of chronic kidney disease. Inhibition of the renin-angiotensin system (RAS) is one strategy to delay disease progression but does not reverse established fibrosis. In this context, induced pluripotent stem cells (iPSCs) have been considered an alternative due to their regenerative potential. iPSCs exert their effects through paracrine signaling, which releases specific biomolecules into the extracellular environment, either directly or within extracellular vesicle (EVs), that can reach target cells. This study aims to evaluate the potential beneficial effects of iPSC-derived EVs (EV-iPSCs) in an in vitro model of fibrosis using mouse mesangial cells (MMCs) stimulated with TGF-β. EV-iPSCs were obtained by differentially ultracentrifuging iPSCs culture medium. MMCs were stimulated with 5 ng/mL of TGF-β and simultaneously treated with or without EV-iPSCs for 24 h. Markers of inflammation, fibrosis, and RAS components were assessed using RT-PCR, western blotting, and immunofluorescence. Under TGF-β stimulus, MMCs exhibited increased expression of inflammation markers, RAS components, and fibrosis. However, these changes were mitigated in the presence of EV-iPSCs. EV-iPSCs effectively reduced inflammation, RAS activation, and fibrogenesis in this fibrosis model involving mesangial cells, suggesting their potential as a strategy to reduce glomerular sclerosis.

Abstract Reverse transcription-quantitative polymerase chain reaction (RT-PCR) is the gold standard technique for gene expression analysis, but the choice of quantitative reference genes (housekeeping genes, HKG) remains challenging. Identify the best HKG is essential for estimating the expression level of target genes. Therefore, the aim of this study was to determine the best HKG for an in vitro model with mouse mesangial cells (MMCs) stimulated with 5 ng/mL of TGF-β. Five candidates HKG were selected: Actb, Hprt, Gapdh, 18S and Ppia. After quantitative expression, the best combination of these genes was analyzed in silico using six software programs. To validate the results, the best genes were used to normalize the expression levels of fibronectin, vimentin and α-SMA. In silico analysis revealed that Ppia, Gapdh and 18S were the most stable genes between the groups. GenEX software and Spearman's correlation determined Ppia and Gapdh as the best HKG pair, and validation of the HKG by normalizing fibronectin, vimentin and α-SMA were consistent with results from the literature. Our results established the combination of Ppia and Gapdh as the best HKG pair for gene expression analysis by RT-PCR in this in vitro model using MMCs treated with TGF-β.