Abstract: FR-PO308
Spatial Metabolomics via Matrix-Assisted Laser Desorption Ionization Mass Spectrometry Imaging (MALDI-MSI) Identifies Bioenergetic and Tricarboxylic Acid (TCA) Cycle Metabolites as Pharmacodynamic Biomarkers of Losartan for Diabetic Kidney Disease
Session Information
- Diabetic Kidney Disease: Basic - 1
October 25, 2024 | Location: Exhibit Hall, Convention Center
Abstract Time: 10:00 AM - 12:00 PM
Category: Diabetic Kidney Disease
- 701 Diabetic Kidney Disease: Basic
Authors
- Hejazi, Leila, SygnaMap, San Antonio, Texas, United States
- Sharma, Shoba, SygnaMap, San Antonio, Texas, United States
- Ruiz, Aaron, SygnaMap, San Antonio, Texas, United States
- Tamayo, Ian M., The University of Texas Health Science Center at San Antonio, San Antonio, Texas, United States
- Maity, Soumya, The University of Texas Health Science Center at San Antonio, San Antonio, Texas, United States
- Zhang, Guanshi, The University of Texas Health Science Center at San Antonio, San Antonio, Texas, United States
- Tucci, Fabio C., Epigen Biosciences Inc, San Diego, California, United States
- Sharma, Kumar, The University of Texas Health Science Center at San Antonio, San Antonio, Texas, United States
Background
Spatial metabolomics is a powerful platform for drug development for identifying pharmacodynamic biomarkers and mechanism of action (MoA) of therapeutics. Herein, we used an advanced MALDI-MSI platform to identify metabolites regulated by a specific treatment losartan (an angiotensin II receptor blocker used for clinical diabetic kidney disease).
Methods
Fresh frozen kidney tissues were obtained from vehicle-treated and losartan-treated Zucker diabetic fatty (ZDF)-rats (5/group). After sectioning and DAN matrix application, MALDI-MSI was performed using a ThermoFisher Orbitrap QE-Exactive-HFX coupled with a Spectroglyph MALDI/ESI source. All imaging data was uploaded to METASPACE for metabolite annotations using HMDB. Autofluorescence- and histochemical-images were used to identify kidney margins and to correlate different kidney tissue features to the MSI data using MSI-DeepPath and obtain pixel-level annotated metabolites.
Results
Using METASPACE, an average of 300 endogenous metabolites were annotated in each kidney tissue section. To compare levels of metabolites across tissue sections and animal groups, intensity data extracted from METASPACE were co-registered to corresponding tissue sections, normalized and quantified using the MSI-DeepPath platform. Statistically significant results (p <0.02) showed that the most highly up-regulated metabolites in response to losartan treatment were uridine 5'-diphosphate (UDP), orotidylic acid (OMP), and adenosine diphosphate (ADP). The most significantly down-regulated metabolites (p <0.005) were malic acid and fumaric acid, key intermediates in the TCA cycle pathway.
Conclusion
Losartan can reduce levels of fumarate, a potential fibrogenic metabolite in the diabetic kidney, confirming a prior report that losartan can lower urine fumarate. Additionally, losartan increased levels of nucleotides such as UDP, ADP, and OMP, which may significantly impact energy levels and overall metabolic state of a cell. MSI-DeepPath identified losartan’s novel effects on fumarate and bioenergetic metabolites, thereby demonstrating the power of quantitative spatial metabolomics for pharmacodynamic biomarkers/MoA.
Funding
- NIDDK Support