#894 Dynamic multi-omics and mechanistic modelling approach provides deeper insight into kidney fibrosis progression

Abstract

Abstract Background and Aims Chronic kidney disease (CKD) affects more than 10% of the world's population and causes millions of deaths annually. Organ fibrosis is the main driver of CKD, leading to excessive accumulation of extracellular matrix (ECM). The presence of myofibroblasts correlates with disease progression. The pro-fibrotic responses of these cells, such as ECM production, are driven by the master regulator TGF-β. Despite progress in understanding the disease, there are no treatments available, and current diagnostic indicators neither facilitate early detection of CKD nor correlate with actual renal damage [1–4] Using human kidney-derived PDGFRβ+ mesenchymal cells we aim to provide time-resolved mechanistic insight into CKD progression. Method Our goal is to understand and model how these cells contribute to fibrosis by studying their kinetic response to TGF-β. Therefore, we use multiple omics measurements: transcriptomics, secretomics and phospho-/proteomics coupled with quantitative imaging of the ECM. For each time point, we quantified differential changes in omics and estimated the activities of transcription factors, kinases and phosphatases [5]. Furthermore, we used causal reasoning to draw mechanistic relationships between omics layers within and across time points. Results For validation, we focused on a set of transcription factors that also showed importance in the network context that considers several data modalities. Using knock-down experiments, we show the relevance of these transcription factors for the ECM deposition as well as the fibrotic response to TGF-β. Conclusion Taken together, by applying mechanistic modeling and the integration of multiple datasets, we are contributing to a better understanding of the factors and mechanisms driving kidney fibrosis. Furthermore, we will publish this dataset and thus provide a valuable resource for researchers in the field of CKD.