Dataset Information

A high-throughput screening platform for Polycystic Kidney Disease (PKD) drug repurposing utilizing murine and human ADPKD cells.

ABSTRACT: Autosomal dominant polycystic kidney disease (ADPKD) is one of the most common inherited monogenic disorders, characterized by a progressive decline in kidney function due in part to the formation of fluid-filled cysts. While there is one FDA-approved therapy, it is associated with potential adverse effects, and all other clinical interventions are largely supportive. Insights into the cellular pathways underlying ADPKD have revealed striking similarities to cancer. Moreover, several drugs originally developed for cancer have shown to ameliorate cyst formation and disease progression in animal models of ADPKD. These observations prompted us to develop a high-throughput screening platform of cancer drugs in a quest to repurpose them for ADPKD. We screened ~8,000 compounds, including compounds with oncological annotations, as well as FDA-approved drugs, and identified 155 that reduced the viability of Pkd1-null mouse kidney cells with minimal effects on wild-type cells. We found that 109 of these compounds also reduced in vitro cyst growth of Pkd1-null cells cultured in a 3D matrix. Moreover, the result of the cyst assay identified therapeutically relevant compounds, including agents that interfere with tubulin dynamics and reduced cyst growth without affecting cell viability. Because it is known that several ADPKD therapies with promising outcomes in animal models failed to be translated to human disease, our platform also incorporated the evaluation of compounds in a panel of primary ADPKD and normal human kidney (NHK) epithelial cells. Although we observed differences in compound response amongst ADPKD and NHK cell preparation, we identified 18 compounds that preferentially affected the viability of most ADPKD cells with minimal effects on NHK cells. Our study identifies attractive candidates for future efficacy studies in advanced pre-clinical models of ADPKD.

SUBMITTER: Asawa RR

PROVIDER: S-EPMC7060218 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

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A high-throughput screening platform for Polycystic Kidney Disease (PKD) drug repurposing utilizing murine and human ADPKD cells.

Asawa Rosita R RR Danchik Carina C Zakharov Alexey A Chen Yuchi Y Voss Ty T Jadhav Ajit A Wallace Darren P DP Trott Josephine F JF Weiss Robert H RH Simeonov Anton A Martinez Natalia J NJ

Scientific reports 20200306 1

Autosomal dominant polycystic kidney disease (ADPKD) is one of the most common inherited monogenic disorders, characterized by a progressive decline in kidney function due in part to the formation of fluid-filled cysts. While there is one FDA-approved therapy, it is associated with potential adverse effects, and all other clinical interventions are largely supportive. Insights into the cellular pathways underlying ADPKD have revealed striking similarities to cancer. Moreover, several drugs origi ...[more]

PMID: 32144367

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Project description:BACKGROUND:Autosomal dominant polycystic kidney disease (ADPKD), a common of monogenetic disorder caused by the polycystic kidney disease-1 (PKD1) or PKD2 genes deficiency. In this study, we have re-analyzed a microarray dataset to generate a holistic view of this disease. METHODOLOGY:GSE7869, an expression profiling dataset was downloaded from the Gene Expression Omnibus (GEO) database. After quality control assessment, using GEO2R tool of GEO, genes with adjusted p-value???0.05 were determined as differentially expressed (DE). The expression profiles from ADPKD samples in different sizes were compared. Using CluePedia plugin of Cytoscape software, the protein-protein interaction (PPI) networks were constructed and analyzed by Cytoscape NetworkAnalyzer tool and MCODE application. Pathway enrichment analysis of clustered genes by MCODE with the high centrality parameters in PPI networks was performed using Cytoscape ClueGO plugin. Moreover, by Enrichr database, microRNAs (miRNAs) and transcription factors (TFs) targeted DE genes were identified. RESULTS:In this study to explore the molecular pathogenesis of kidney in ADPKD, mRNA expression profiles of cysts from patients in different sizes were re-analyzed. The comparisons were performed between normal with minimally cystic tissue (MCT) samples, MCTs with small cysts, and small cysts with large cysts. 512, 7024, and 655 DE genes were determined, respectively. The top central genes, e.g. END1, EGFR, and FOXO1 were identified with topology and clustering analysis. DE genes that were significantly enriched in PPI networks are critical genes and their roles in ADPKD remain to be assessed in future experimental studies beside miRNAs and TFs predicted. Furthermore, the functional analysis resulted in which most of them are expected to be associated with ADPKD pathogenesis, such as signal pathways that involved in cell growth, inflammation, and cell polarity. CONCLUSION:We have here explored systematic approaches for molecular mechanisms assay of ADPKD as a monogenic disease, which may also be used for other monogenetic diseases beside complex diseases to provide suitable therapeutic targets.

Project description:BACKGROUND:Autosomal dominant polycystic kidney disease (ADPKD) is the leading inheritable cause of end-stage renal disease (ESRD); however, the natural course of disease progression is heterogeneous between patients. This study aimed to develop a natural history model of ADPKD that predicted progression rates and long-term outcomes in patients with differing baseline characteristics. METHODS:The ADPKD Outcomes Model (ADPKD-OM) was developed using available patient-level data from the placebo arm of the Tolvaptan Efficacy and Safety in Management of ADPKD and its Outcomes Study (TEMPO 3:4; ClinicalTrials.gov identifier NCT00428948). Multivariable regression equations estimating annual rates of ADPKD progression, in terms of total kidney volume (TKV) and estimated glomerular filtration rate, formed the basis of the lifetime patient-level simulation model. Outputs of the ADPKD-OM were compared against external data sources to validate model accuracy and generalisability to other ADPKD patient populations, then used to predict long-term outcomes in a cohort matched to the overall TEMPO 3:4 study population. RESULTS:A cohort with baseline patient characteristics consistent with TEMPO 3:4 was predicted to reach ESRD at a mean age of 52 years. Most patients (85%) were predicted to reach ESRD by the age of 65 years, with many progressing to ESRD earlier in life (18, 36 and 56% by the age of 45, 50 and 55 years, respectively). Consistent with previous research and clinical opinion, analyses supported the selection of baseline TKV as a prognostic factor for ADPKD progression, and demonstrated its value as a strong predictor of future ESRD risk. Validation exercises and illustrative analyses confirmed the ability of the ADPKD-OM to accurately predict disease progression towards ESRD across a range of clinically-relevant patient profiles. CONCLUSIONS:The ADPKD-OM represents a robust tool to predict natural disease progression and long-term outcomes in ADPKD patients, based on readily available and/or measurable clinical characteristics. In conjunction with clinical judgement, it has the potential to support decision-making in research and clinical practice.

Dataset Information

A high-throughput screening platform for Polycystic Kidney Disease (PKD) drug repurposing utilizing murine and human ADPKD cells.

Publications

A high-throughput screening platform for Polycystic Kidney Disease (PKD) drug repurposing utilizing murine and human ADPKD cells.

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