Abstract: PO0726
Understanding Mechanisms Underlying Diabetic Kidney Disease Using Integrative Transcriptome and Proteome Profiling of Insulin-Resistant Human Cell Lines
Session Information
- Diabetic Kidney Disease: Basic - II
November 04, 2021 | Location: On-Demand, Virtual Only
Abstract Time: 10:00 AM - 12:00 PM
Category: Diabetic Kidney Disease
- 601 Diabetic Kidney Disease: Basic
Authors
- Lay, Abigail Charlotte, University of Bristol, Bristol, Bristol, United Kingdom
- Tran, Van Du Thuong, Swiss Institute of Bioinformatics, Lausanne, Vaud, Switzerland
- Mehl, Florence, Swiss Institute of Bioinformatics, Lausanne, Vaud, Switzerland
- Kryvokhyzha, Dmytro, Lunds Universitet, Lund, Sweden
- Betin, Virginie M. S., University of Bristol, Bristol, Bristol, United Kingdom
- Wilson, Marieangela C., University of Bristol, Bristol, Bristol, United Kingdom
- Heesom, Kate J., University of Bristol, Bristol, Bristol, United Kingdom
- Coward, Richard, University of Bristol, Bristol, Bristol, United Kingdom
Group or Team Name
- BEAT-DKD consortium
Background
One of the strongest metabolic features of diabetic kidney disease (DKD), in both type 1 and type 2 diabetes, is insulin resistance and it is increasingly clear that disruptions to renal cellular insulin responses can drive DKD development. The present study aims to generate a comprehensive network of molecular changes occurring in the kidney in response to insulin resistance using cell models
Methods
Conditionally immortalised human podocytes (Pod), glomerular endothelial cells (GEC), mesangial cells (MC) and proximal tubular cells (PTC) were studied. A diabetic, insulin resistant, environment was established using a combination of TNFα, IL-6, high glucose and high insulin. The cellular proteome and transcriptome were studied simultaneously using Tandem-Mass-tagged mass spectrometry and RNA sequencing. To explore the changes occurring in insulin resistance, integrated transcriptome and proteome data were analysed using univariate and multivariate statistical models and gene set enrichment analysis (GSEA) was performed to identify significantly regulated cellular processes
Results
Initial results revealed that exposure to a diabetic environment induced differential insulin resistance between human kidney cell lines. Differential expression analysis of both transcriptome and proteome found that insulin resistance had the most pronounced effect on expression in Pod and PTC and highlighted 45 consistently regulated genes/proteins. GSEA identified consistent increases in the inflammatory response, ER stress and glycoprotein metabolism and a consistent decrease in hippo signalling across all insulin resistant cells. In contrast, mitochondrial-related signatures were significantly reduced at the protein level in Pod and PTC but increased in GEC. Investigation of these gene/protein signatures in human DKD cohorts is currently ongoing
Conclusion
By performing integrated omics profiling on renal cell models, we identified conserved and cell-specific changes occurring in insulin resistance. Integration with human cohort data will highlight conserved pathways and the utility of cell models in pre-clinical investigations, aiding the identification of molecular processes underlying the development and progression of DKD