Abstract: TH-PO1086
Integrative Multiomics Network Analysis Reveals Distinct Molecular Subtypes in CKD
Session Information
- CKD: Epidemiology, Risk Factors, and Other Conditions
November 06, 2025 | Location: Exhibit Hall, Convention Center
Abstract Time: 10:00 AM - 12:00 PM
Category: CKD (Non-Dialysis)
- 2301 CKD (Non-Dialysis): Epidemiology, Risk Factors, and Prevention
Authors
- Castro, Ricardo, Evotec International GmbH, Göttingen, NDS, Germany
- Rerra, Anna-Isavella, Evotec International GmbH, Göttingen, NDS, Germany
- Urbanke, Hendrik, Evotec International GmbH, Göttingen, NDS, Germany
- Mosblech, Alina, Evotec International GmbH, Göttingen, NDS, Germany
- Skroblin, Philipp, Evotec International GmbH, Göttingen, NDS, Germany
- Radresa, Olivier, Evotec International GmbH, Göttingen, NDS, Germany
- Andag, Uwe, Evotec International GmbH, Göttingen, NDS, Germany
- Pinkosky, Stephen L., Esperion Therapeutics Inc, Ann Arbor, Michigan, United States
Background
Chronic kidney disease (CKD) patients exhibit substantial molecular heterogeneity, making it uneasy to develop precision medicines tailored to the right populations. Although affecting kidney tissues, CKD also leaves molecular signatures across organs and biofluids. This study reports on applying powerful omics integration technologies to kidney and liquid biopsies for supporting target discovery and patient stratification. While real-world datasets rarely match patient trajectories from a timeline perspective, the approach reported here allows to address this limitation
Methods
We analyzed renal biopsy samples from the NURTuRE1 cohort using bulk mRNA sequencing. Donor-matched serum and urine samples were characterized by proteomics and metabolomics profiling. In order to enhance the resolution of the multiOmic network, we integrated publicly available single-nucleus RNA sequencing and serum proteomic datasets. Information on biological regulation at pathway-level was incorporated into the framework, allowing to connect the different omic layers. Since data alignment was often incomplete, we applied a multiOMIC inference approach to integrate the various datasets. Specifically, we used an integrative, multi-layer network model combined with machine learning embeddings to uncover shared molecular features and predict multiOMIC relationships.
Results
This new multiOMIC approach identified a subset of molecular features associated with disease progression and highlighted central genes linked to CKD severity. These features formed distinct clusters, which were used to stratify the NURTuRE cohort. Among the findings were two molecularly distinct patient groups that, despite being clinically similar, showed clear differences in their event-free survival curves. The biological relevance of these molecular signatures was further validated in mouse models of kidney disease.
Conclusion
We demonstrated that multiOMIC network inference effectively supports the integration of diverse omic datasets, also in case where direct overlap is lacking. The outcomes enhance our understanding of CKD physiology and support the identification of new therapeutic targets and responder patient populations for precision medicine.
1. https://nurturebiobank.org/