Investigating the Role of Metabolic Reprogramming in the Pathogenesis of Diabetic Kidney Disease Using Novel Metabolic Mouse Models

Published: 31 July 2023| Version 1 | DOI: 10.17632/2w9bkcc6pz.1
Emily Wallace,


Metabolic disorders are involved in the pathogenesis of diabetic kidney disease (DKD), and protein lipoylation may regulate metabolic disorders and energy reprogramming in DKD. To elucidate the role of protein lipoylation in metabolic regulation and understand its underlying mechanism, we used the Ins2Akita, a diabetic mouse model, crossed with lipoic acid synthase transgenic mice to illustrate the role of metabolic regulation at the initiation and early stages of DKD. We found that enhanced protein lipoylation attenuates the early DKD phenotype by analyzing renal albuminuria, glomerular mesangial expansion, and mitochondrial damage in proximal tubular epithelial cells as the LiasH/HIns2Akita/+ mice had markedly milder DKD phenotype than LiasL/LIns2Akita/+ mice via metabolic regulation by protein lipoylation. These data suggest that enhanced protein lipoylation potentially attenuates DKD by regulating metabolism and energy reprogramming. Our study provides insight into a new therapeutic strategy for DKD. The data included is high resolution mass spectrometry metabolomics raw data (generated on a Thermo Q Exactive HF-X). A processed peak list is also included along with metabolites that were identified in the samples.


Steps to reproduce

Urine metabolomic analysis Urine was collected using metabolic cages over 24 hours then frozen until Aliquots of the frozen 24-hour urine collection using metabolic cages were thawed and analyzed analysis for creatinine content before LC-MS analysisbeing processed for analysis at the UNC-CH Mass Spectrometry Laboratory. A total of 200 µL of urine was extracted with 1 mL of 80/20 methanol/water. Samples were shaken for 10 minutes and then centrifuged for 10 min at 20,000 rcf. The supernatant was removed, dried down, and reconstituted in 100 µL of 80/20 methanol/water for analysis. Analysis was performed using a Thermo Q Exactive H-FXPlus coupled to a Waters Acquity H-Class LC. A 100 mm x 2.1 mm, 2.1 µm Waters Amide column was used for separations. The following mobile phases were used: A- 95/5 water/acetonitrile (ACN), 10 mM ammonium formate, 20 mM ammonium hydroxide B-Acetonitrile. A flow rate of 0.15 mL/min was used. Starting composition was 15% A (held for 1 min), which increased linearly to 20% A at 2.5 min and again to 43% A at 7 min. A final increase to 62% A was performed at 16 min. Starting conditions were res-established at 16.1 min and held until 25 min for re-equilibration. Samples were processed with an internal processing method based on an in house library of 230 endogenous metabolites. Identifications were conducted using accurate mass (10 ppm) and retention time (min) based on previous data from analytical grade standards. Values were normalized based on creatinine content. These values were used to generate the volcano plots which were generated using the RealStats plugin in Excel. The raw data was also processed via MZMine 2.53 to create a data matrix for untargeted analysis. Multivariate statistical analysis was processed using SIMCA 17. Quantitative enrichment analysis (QEA) was performed in MetaboAnalyst using KEGG/HMBD IDs and pathways with mean-centered data that was log-transformed. Statistical analysis All data were presented as means ± SEM. Statistical analysis of the data was conducted using GraphPad Prism 9.0 software (GraphPad, San Diego, CA). Differences between multiple groups were determined by one-way ANOVA followed by the Tukey–Kramer honestly significant difference (HSD) test. P < 0.05 was considered statistically significant.


University of North Carolina at Chapel Hill


Mass Spectrometry, Biomarkers, Diabetes, Liquid Chromatography, Statistical Analysis