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Abstract: TH-PO914

Multi-Omics Data Integration Identifies Molecular Pathways Associated with Renal Response to Atrasentan

Session Information

Category: Diabetic Kidney Disease

  • 602 Diabetic Kidney Disease: Clinical

Authors

  • Ju, Wenjun, University of Michigan, Ann Arbor, Michigan, United States
  • Perco, Paul, Medical University Innsbruck, Innsbruck, Austria
  • Nair, Viji, University of Michigan, Ann Arbor, Michigan, United States
  • Belur nagaraj, Sunil, University Medical Center Groningen, Groningen, Netherlands
  • Burdet, Frédéric, Swiss Institute of Bioinformatics, Lausanne, Switzerland
  • Kannt, Aimo, Sanofi, Frankfurt, Germany
  • Gomez, Maria F., Lund University, Malmö, Sweden
  • Alpers, Charles E., University of Washington Medical Center, Seattle, Washington, United States
  • Kretzler, Matthias, U.Michigan, Ann Arbor, Michigan, United States
  • L Heerspink, Hiddo Jan, University Medical Center Groningen, Groningen, Netherlands

Group or Team Name

  • Biomarker Enterprise to Attack DKD (BEAt-DKD) consortium
Background

The endothelin-1 receptor antagonist atrasentan lowers urinary albumin:creatinine ratio (UACR) and reduces renal risk in patients with type 2 diabetes and chronic kidney disease (CKD). This effect markedly varies among patients. Aim of this study was to identify molecular pathways and biomarkers predicting the renoprotective effect of atrasentan.

Methods

In vivo and in vitro transcriptomics profiling was performed in kidney tissue from atrasentan treated BTBR ob/ob mice and human mesangial cell cultures respectively. A transcriptomic dataset from human diabetic kidney biopsies was used for cross-validation. Critically, microRNA, proteomics, and metabolomics profiles were generated in plasma and urine samples of a phase 2 trial (RADAR) in patients with type 2 diabetes and CKD treated for 12 weeks with atrasentan. Logistic regression analysis was performed to identify features associated with atrasentan response (UACR reduction ≥30%, n=42) versus non-response (UACR reduction <10%, n=8). Omics features were subjected to pathway analysis to identify patterns of UACR response to atrasentan across model systems and human disease.

Results

Clinical characteristics between responders and non-responders were similar. More than 1000 genes that were dysregulated in DKD mouse tissue were significantly reversed after atrasentan treatment and 513 genes in mesangial cells significantly changed after atrasentan treatment. Four miRNAs, 68 metabolites, and 210 proteins measured before atrasentan exposure in human samples predicted UACR response to atrasentan (p<0.1). Multi-omics integration revealed Sirtuin and ephrin signaling, NRF2-mediated oxidative stress response among the top ranked pathways associated with atrasentan response. A biomarker panel of 5 urinary proteins (including SOD1, CXCL10, PDGF) reflecting the identified pathways predicted UACR response to atrasentan with an area under the ROC curve of 0.82

Conclusion

We implemented a multi-omics data integration approach to derive molecular pathways associated with UACR response to atrasentan. The marker panel predicting atrasentan response in humans reflects a non-invasive surrogate of molecular pathways activated in human DKD and CKD, linking individual atrasentan response to known and novel CKD progression pathways.

Funding

  • Commercial Support