Unravelling the Role of PAX2 Mutation in Human Focal Segmental Glomerulosclerosis.
Lorena LongarettiPiera TrionfiniValerio BriziChristodoulos XinarisCaterina MeleMatteo BrenoElena RomanoRoberta GiampietroGiuseppe RemuzziAriela BenigniSusanna TomasoniPublished in: Biomedicines (2021)
No effective treatments are available for familial steroid-resistant Focal Segmental Glomerulosclerosis (FSGS), characterized by proteinuria due to ultrastructural abnormalities in glomerular podocytes. Here, we studied a private PAX2 mutation identified in a patient who developed FSGS in adulthood. By generating adult podocytes using patient-specific induced pluripotent stem cells (iPSC), we developed an in vitro model to dissect the role of this mutation in the onset of FSGS. Despite the PAX2 mutation, patient iPSC properly differentiated into podocytes that exhibited a normal structure and function when compared to control podocytes. However, when exposed to an environmental trigger, patient podocytes were less viable and more susceptible to cell injury. Fixing the mutation improved their phenotype and functionality. Using a branching morphogenesis assay, we documented developmental defects in patient-derived ureteric bud-like tubules that were totally rescued by fixing the mutation. These data strongly support the hypothesis that the PAX2 mutation has a dual effect, first in renal organogenesis, which could account for a suboptimal nephron number at birth, and second in adult podocytes, which are more susceptible to cell death caused by environmental triggers. These abnormalities might translate into the development of proteinuria in vivo, with a progressive decline in renal function, leading to FSGS.
Keyphrases
- induced pluripotent stem cells
- high glucose
- diabetic nephropathy
- cell death
- endothelial cells
- case report
- healthcare
- single cell
- stem cells
- depressive symptoms
- signaling pathway
- pregnant women
- cell therapy
- electronic health record
- machine learning
- big data
- childhood cancer
- gestational age
- deep learning
- pluripotent stem cells