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Kidney Week

Abstract: SA-PO110

Proteomics Reveals the Principle of Transition from AKI to CKD

Session Information

Category: Acute Kidney Injury

  • 103 AKI: Mechanisms

Authors

  • Fu, Haiyan, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China
  • Liu, Silvia, University of Pittsburgh, Pittsburgh, Pennsylvania, United States
  • Zhou, Xianke, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China
  • Zhou, Dong, University of Pittsburgh, Pittsburgh, Pennsylvania, United States
Background


Chronic kidney disease (CKD) compromises renal function and occurs as a potential long-term outcome in response to acute kidney injury (AKI). Currently, lack of mechanistic understanding prevents the progression from AKI to CKD. As we enter the ‘big data’ era, multiple OMICS analyses provide a platform not only assist the field to further understand kidney disease transition, but also significantly enhance the possibility to translate novel findings from basic research into the clinic. The present study aims to systemically analyze the proteomes profiles from the onset of kidney injury to end stage renal disease and identify dynamic network biomarkers during disease progression.

Methods


We constructed renal ischemia reperfusion injury (IRI) mouse model for time series courses. Quantitative proteomics (isobaric peptide tags for relative and absolute quantification, iTRAQ) was applied in revealing the proteomes profiles in kidney tissues. Dynamic network biomarker (DNB) analysis was performed to clearly identify the critical state or tipping point during the transition of kidney disease.

Results


We identified 6146 proteins in the disease kidneys. Pearson correlation analysis indicated that the transitional process from AKI to CKD could be divided into 4 periods in mice, initiate phase (0-12h), switching phase (1d), repair phase (3-5d), and irreversible phase (after 5d). Impressively, protein signatures were completely different in the above 4 phases. Among these phases, two time points (4h and 3d) were critical in determining the prognosis of kidney disease. We then analyzed time-series protein expression data with the DNB method, and identified NCBP1 as a core DNB member. At a network level, the biological role of NCBP1 was prominent in kidney repair and regeneration.

Conclusion


Establishing disease monitoring system at appropriate time points will be greatly beneficial in understanding the pathogenesis of kidney disease transition.

Funding

  • NIDDK Support