Alterations of slit diaphragm proteins and the pathogenesis of albuminuria and FSGS

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The majority of cases of steroid-resistant nephrotic syndrome (SRNS) and focal segmental glomerulosclerosis (FSGS) in childhood and adolescence is caused by mutations in single genes. While the probability of single-gene causes of FSGS declines with age there is a clear role for genetic susceptibility also in sporadic forms of FSGS in adulthood. Although the past two decades have witnessed spectacular breakthroughs in the understanding of the genetic basis of SRNS/FSGS, two decades after the description of gene defects in the genes encoding for the slit diaphragm (SD) proteins nephrin and podocin as a cause of SRNS/FSGS, the molecular basis of SRNS/FSGS development is far from being understood. In the first funding period we used CRISPR/Cas genome engineering to develop new mouse models that genetically and phenotypically mimick human SRNS. These models were meticulously studied by super-resolution (STED) microscopy and quantitative morphological analyses. Mathematical modeling revealed surprising discoveries that will change the way how we think about albuminuria. We showed that shortening of the slit diaphragm precedes albuminuria development and that slit diaphragm length inversely correlated with the magnitude of albumin loss, a very unexpected finding. Moreover, morphological alterations of the glomerular filtration barrier in disease appeared to impair compressive forces that counteract filtration pressure which resulted in reduced compression of the glomerular basement membrane and ultimately in albuminuria. These data resulted in the first experimentally validated model of glomerular ultrafiltration. The overall aim of this follow-up research project is to elucidate the pathogenic mechanisms that link the genetic mutation with these initial morphologic alterations and to apply deep learning algorithms to gain a deeper understanding of those changes. As such, this project continues to work on most recent breakthrough technical achievements. Specifically, we will (1) elucidate the mechanisms that determine the quantitative changes in the morphological properties of the SD and of podocytes’ foot processes, (2) decipher the biophysical mechanisms of renal filtration using deep learning algorithms and computational modelling, and (3) characterize the impact of altered SD and foot process morphology on genetic susceptibility for FSGS. We anticipate that this unique combination of innovative technologies and models applied in this project will result in potential treatment strategies in delayed-onset genetic forms of FSGS.