#3125 Proteomic insights into the molecular mechanisms of autosomal dominant polycystic kidney disease (ADPKD) using mIMCD3 cells

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Abstract Background and Aims Autosomal dominant polycystic kidney disease (ADPKD) is the most common hereditary kidney disease, primarily caused by mutations in the PKD1 (80%) and PKD2 (15%) genes. Other cases are either genetically unresolved or due to mutations in less common cystic genes. Individuals with ADPKD develop renal cysts progressively, leading to kidney function loss and the need for renal replacement therapy. Despite scientific advances, challenges remain, such as diagnostic and therapeutic limitations and a lack of understanding of the disease's molecular pathogenesis. Our objective was to conduct a comprehensive proteomic analysis of a monoclonal cell line to gain insights into the molecular mechanisms of ADPKD. Method Protein samples from monoclonal WT mIMCD3 cells and three Pkd1 KO mIMCD3 cell clones were analyzed. Cells were cultured, and proteins were extracted using RIPA buffer. The proteomic analysis was conducted by the Proteomics Platform of the Health Research Institute of Santiago de Compostela. It involved LC-MS/MS and protein identification and quantification. Results include qualitative analysis (Data-Dependent Acquisition, DDA) and quantitative analysis (Sequential Window Acquisition of All Theoretical Mass Spectra, SWATH). DDA provides a comprehensive list of identified proteins, while SWATH includes quantified proteins common across conditions. Data was filtered to include proteins with a p-value <0.05 and a fold change >1.5. Bioinformatic tools like STRING, Heatmapper.com, and GraphPad Prism 10 were used for data analysis. mIMCD3 cell cycle was analyzed by flow cytometry. Results PCA showed clear clustering of KO and WT groups. SWATH identified 334 differentially expressed proteins: 165 upregulated, 118 downregulated, and 51 exclusive to KO. DDA detected 765 proteins, with 291 shared between techniques. STRING enrichment analysis was applied to all dysregulated, upregulated, and downregulated proteins. Gene Ontology terms revealed insights into mitochondrial metabolism and RNA-related processes. Upregulated proteins highlighted mitochondrial changes, while downregulated proteins were linked to diverse metabolic processes. Subcellular location enrichment aligned with biological processes, showing mitochondrial and RNA-related terms. Tissue expression enrichment did not find kidney-related terms but identified other organ systems. In terms of metabolism in Pkd1 KO mIMCD3 cells, glycolysis showed two glycolytic proteins differently regulated with opposite behaviors, making it difficult to determine alterations. Fatty acid oxidation was upregulated in Pkd1 KO cells. The TCA cycle was challenging to interpret due to varied enzyme activities. Oxidative phosphorylation had many upregulated proteins, indicating high energy demand, and Pkd1 KO cells were able to use oxygen under the used conditions, contrary to what has been described for ADPKD. Regarding stress and apoptosis, there was an increased stress state, particularly in the mitochondria. Pro-apoptotic proteins were upregulated, while anti-apoptotic proteins were downregulated. The cellular status studied by flow cytometry showed that WT cells exhibited a population with a Gaussian distribution, while mutants had a bimodal distribution. The cell cycle profile indicated a decrease in S phase cells and an increase in G2/M phase cells in mutants, suggesting genetic instability. Conclusion The proteomic study of mIMCD3 cells finds the mitochondria and the mitochondrial metabolism to be the most enriched terms based on the dysregulated proteins between Pkd1 KO and WT cells. Pkd1 KO cells show altered population size distribution and cell cycle, indicative of genetic instability.

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