Autosomal dominant polycystic kidney disease (ADPKD) is a common monoallelic disorder associated with progressive cyst development and resulting in end stage renal failure (ESRD) in 50% of patients by 60y. However, there is considerable phenotypic variability, extending from in utero onset to patients with adequate renal function into old age. Autosomal dominant polycystic liver disease (ADPLD), as traditionally defined, results in PLD with minimal renal cysts. Classically there have been considered two ADPKD genes, PKD1 and PKD2, encoding PC1 and PC2, and two ADPLD genes, PRKCSH and SEC63, but in the past few years greater genetic heterogeneity has been described, with nine genes now implicated overall. Recent data also indicates an overlap in etiology and pathogenesis associated with ADPKD and ADPLD, with the efficient biogenesis and localization of the PC-complex central to both disorders. During the last funding period we identified a novel gene, GANAB, which is associated with both disorders, where the encoded protein, GII?? is involved in the maturation and trafficking of PC1. In this proposal we will take advantage of advances in next generation sequencing (NGS) methodologies, and large populations of ADPKD and ADPLD patients that have been assembled and screened for the classic genes, to hunt for novel genes for these disorders (Aim 1). The phenotype associated with these genes will be characterized (Aim 3) along with their mechanism of action (Aim 2). NGS methods will be perfected to screen the segmentally duplicated locus, PKD1, and to identify missed mutations at the known loci, including those present in just some cells due to mosaicism (Aim 1). The significance of many PKD1 nontruncating variants has been difficult to evaluate (classed as variants of unknown significance; VUS), but recently evidence that some are incompletely penetrant alleles partially explains phenotypic variability in PKD1 populations.
In Aim 2 improved in silico predictions, in combination with machine learning, will improve the understanding of the pathogenicity and penetrance of VUS. A cellular assay of the biogenesis and trafficking of this PC-complex will also be employed to quantify the penetrance of VUS. The mechanism of pathogenesis will be explored in animal models with ultralow penetrant (ULP) Pkd1 or Pkd2 alleles. Employing the large clinically, imaging, and genetically well-defined populations phenotypic groupings of patients will be defined that will then be compared to the genic and PKD1 allelic groups (Aim 3). This iterative process will allow the Variant Score (VS) associated with each PKD1 VUS to be refined. In a separate population the revised VS, alone and in combination with clinical, functional, and imaging data, will be employed to generate a comprehensive, predictive algorithm for ADPKD (Aim 3). Disease modifiers to severe disease, via biallelic ADPKD, and due to alleles at other loci will also be identified and characterized in the cellular assay and in vivo in combination with the Pkd1 hypomorphic, RC model.
The final aim will exploit the newly identified information that some PKD1 and PKD2 VUS are rescuable, folding mutations that in a maturation-fostering environment can traffic and function appropriately. A screening scheme based on the level of cell surface PC1 will be improved and new chaperone drugs specific for the PC complex will be sought in collaboration with Sanford Burnham Prebys. A second mutation group that will be explored therapeutically are nonsense mutations. A cellular assay for readthrough efficiency is being developed and will be used for screening. Identified chaperone or readthrough drugs will be tested in available mouse models. Overall this proposal will better explain the etiology and the genetic causes of phenotypic variability in ADPKD/ADPLD, develop better prognostic tools for individual selection of patients for treatment that are now becoming available, and explore allele based treatments for ADPKD.
ADPKD and ADPLD are genetic diseases associated with cyst development and loss of function in the kidney and liver cysts, where the disease severity varies greatly between patients. Here we will identify new genes that cause these disorders and evaluate mutations in silico, in a cellular assay, and in whole animals to better predict the disease course in each patient. Treatment options associated with the type of individual patient mutations will also be explored, bringing individualized medicine to these diseases.
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