Kidney Week

Abstract: SA-PO381

DESI-MSI Based Spatial Metabolomics Reveals Altered Metabolome and Increased Pseudouridine in Renal Proximal Tubules of Mice with Diabetic Kidney Disease

Session Information

• CKD: Risk Factors for Incidence and Progression - III
  November 04, 2017 | Location: Hall H, Morial Convention Center
  Abstract Time: 10:00 AM - 10:00 AM

Category: Chronic Kidney Disease (Non-Dialysis)

• 301 CKD: Risk Factors for Incidence and Progression

Authors

• Zhang, Guanshi, University of California San Diego, La Jolla, California, United States

Guanshi Zhang,

• Zhang, Jialing, The University of Texas at Austin, Austin, Texas, United States

Jialing Zhang,

• Dehoog, Rachel J, The University of Texas at Austin, Austin, Texas, United States

Rachel J Dehoog,

• Darshi, Manjula, University of California San Diego, La Jolla, California, United States

Manjula Darshi,

• Van espen, Benjamin Fj, University of California San Diego, La Jolla, California, United States

Benjamin Fj Van espen,

• Pennathur, Subramaniam, University of Michigan, Ann Arbor, Michigan, United States

Subramaniam Pennathur,

• Walavalkar, Vighnesh, University of California San Diego, La Jolla, California, United States

Vighnesh Walavalkar,

• Alexandrov, Theodore, European Molecular Biology Laboratory, Heidelberg, Germany

Theodore Alexandrov,

• Eberlin, Livia S., The University of Texas at Austin, Austin, Texas, United States

Livia S. Eberlin,

• Sharma, Kumar, University of California San Diego, La Jolla, California, United States

Kumar Sharma,

Background

Diabetic kidney disease (DKD) is the most prevalent complication in diabetic patients, which contributes to high morbidity and mortality. Urine and plasma metabolomics studies have reported that both blood and urinary metabolites to provide valuable insights for DKD. Spatial distributions of metabolites in kidney tissues would link circulating metabolites to actual kidney compartments but the techniques are challenging. We employed an ambient desorption electrospray ionization – mass spectrometry imaging (DESI-MSI) approach to characterize the metabolome in a mouse model of DKD coupled to a novel bioinformatics platform (METASPACE).

Methods

DESI-MSI was performed for spatial untargeted metabolomics analysis in kidneys of mouse models (F1 C57BL/6J-Ins2^Akita male mice at 17 weeks of age) of type 1 diabetes (T1D, n = 5) and heathy controls (n = 6). Metabolite annotations from MSI were conducted using METASPACE and further validated by collision induced dissociation or higher-energy collisional dissociation tandem MS analysis. MetaboAnalyst 3.0 was employed for statistical analyses.

Results

Multivariate analyses (i.e., PCA and PLS-DA (a 2000 permutation test: P < 0.001)) showed clearly separated clusters for the two groups of mice on the basis of 878 measured m/z’s in kidney cortical tissues. Specifically, mice with T1D had increased relative abundances of pseudouridine (m/z 279.039), fatty acids (FA) (e.g., FA (18:2), m/z 279.233), and glycerophosphoglycerols (PG) (e.g., PG (36:1), m/z 775.548) in cortical proximal tubules when compared with healthy controls.

Conclusion

Results from the current study further support a role for pseudouridine in DKD and the data suggests that pseudouridine accumulation might originate from cortical proximal tubules. Lipid data indicate disordered fatty acid metabolism (e.g., enhanced FA synthesis and altered FA oxidation) and glycerophospholipid metabolism in DKD mice. DESI-MSI technology is a powerful approach to shed new light on fundamental pathways in disease states.

Funding

• NIDDK Support