Predicting Changes in Renal Metabolism after Compound Exposure with a Genome-Scale Metabolic Model
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ABSTRACT:
PROVIDER: PRJNA594206 | ENA |
REPOSITORIES: ENA
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Project description:The kidneys are metabolically active organs that are important for several physiological tasks such as the secretion of soluble wastes into urine. Other functions of the kidneys include synthesizing glucose and oxidizing fatty acids for energy in fasting (non-fed) conditions. Once damaged, the metabolic capability of the kidneys becomes altered. Here, we define metabolic tasks in a computational modeling framework to capture kidney function in an update to the iRno network reconstruction of rat metabolism using literature-based evidence. To demonstrate the utility of iRno for predicting kidney function, we exposed primary rat renal proximal tubule epithelial cells to five compounds with varying levels of nephrotoxicity (acetaminophen, carbon tetrachloride, gentamicin, 2,3,7,8-tetrachlorodibenzodioxin, and trichloroethylene for six and twenty-four hours), and collected transcriptomics and metabolomics data to measure the metabolic effects of compound exposure. We observed changes in fatty acid metabolism and amino acid metabolism, consistent with changes in existing markers of kidney function such as Lcn2 (lipocalin), Clu (clusterin), and the carnitine transporter Octn2 (solute carrier family 22, member 5). The iRno metabolic network reconstruction was able to predict alterations in these same pathways after integrating transcriptomics data, and was able to distinguish between select compound-specific effects on the proximal tubule epithelial cells. Genome-scale metabolic network reconstructions with coupled omics data can be used to predict changes in metabolism to provide a step towards identifying novel biomarkers of kidney function and dysfunction.
2021-01-05 | GSE141628 | GEO
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Project description:Diabetic nephropathy is associated with endothelial dysfunction and oxidative stress, in which nitric oxide-soluble guanylate cyclase-cyclic guanosine monophosphate (NO-sGC-cGMP) signaling pathway is impaired. We hypothesize that sGC stimulator, Compound 1, can enhance NO signaling, reduce proteinuria in diabetic nephropathy pre-clinical model with diminished NO-bioavailability and increased oxidized sGC. We therefore, evaluated the effect of sGC stimulator Compound 1 on renal effect in obese ZSF1 rats. Materials and Methods: The sGC stimulator Compound 1, the standard of care agent enalapril, and combination of Compound 1 with enalapril was administered chronically to ZSF1 rats for 6 months. Mean arterial pressure, heart rate, creatinine clearance for estimate glomerular filtration rate (eGFR), urinary protein excretion to creatinine ratio (UPCR), and urinary albumin excretion ratio (UACR) were determined during the study. Histopathology of glomerular and interstitial lesions were assessed at the completion of the study. Results: While both Compound 1 and Enalapril significantly reduced blood pressure the combination of Compound 1 and enalapril normalized blood pressure level. Compound 1 improved eGFR and reduced UPCR and UACR. Combination of enalapril and Compound 1 resulted in marked reduction in UPCR and UACR and improved GFR. Conclusion: The sGC stimulator Compound 1 as a monotherapy slowed renal disease progression and the combination of sGC stimulator with enalapril provided a greater renal protection in a rodent model of diabetic nephropathy.
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Project description:Large-scale top-down proteomics characterizes proteoforms in cells globally with high confidence and high throughput using reversed-phase liquid chromatography (RPLC)-tandem mass spectrometry (MS/MS) or capillary zone electrophoresis (CZE)-MS/MS. The false discovery rate (FDR) from the target-decoy database search is typically deployed to filter identified proteoforms to ensure high-confidence identifications (IDs). It has been demonstrated that the FDRs in top-down proteomics can be drastically underestimated. An alternative approach to the FDR can be useful for further evaluating the confidence of proteoform IDs after database search. We argue that predicting retention/migration time of proteoforms from the RPLC/CZE separation accurately and comparing their predicted and experimental separation time could be a useful and practical approach. Based on our knowledge, there is still no report in the literature about predicting separation time of proteoforms using large top-down proteomics datasets. In this pilot study, for the first time, we evaluated various semi-empirical models for predicting proteoforms’ electrophoretic mobility (µef) using large-scale top-down proteomics datasets from CZE-MS/MS. We achieved a linear correlation between experimental and predicted µef of E. coli proteoforms (R2=0.98) with a simple semi-empirical model, which utilizes the number of charges and molecular mass of each proteoform as the parameters. Our modeling data suggest that the complete unfolding of proteoforms during CZE separation benefits the prediction of their µef. Our results also indicate that N-terminal acetylation and phosphorylation both decrease proteoforms’ charge by roughly one charge unit.
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2023-03-05 | GSE216252 | GEO
Project description:Different segments of renal tubules have distinct metabolic features and functions, and defective metabolism in renal tubules is involved in the pathobiology of kidney diseases. However, the mechanisms underlying the metabolic regulation in renal tubules remain to be defined. We demonstrated that the renal proximal tubule (PT) has high expression of fatty acid and lipid metabolism enzymes, which is transcriptionally upregulated by abundantly expressed peroxisome proliferator-activated receptor (PPAR)/γ to support active lipid metabolism. In contrast, the renal distal tubule (DT) has elevated glycolytic enzyme expression corresponding with high rates of glycolysis, and this increased expression is mediated by abundantly expressed c-Myc. In addition, Nrf2 upregulated glucose-6-phosphate dehydrogenase, 6-phosphogluconate dehydrogenase, and NADP-dependent malic enzyme 1 expression for NADPH production and antioxidation in the DT. Highly expressed PPARγ transcriptionally enhanced expression of the protease iRhom2 in the PT, which suppressed epidermal growth factor (EGF) expression and secretion, inhibiting EGF receptor activation-dependent glycolytic gene expression and maintaining the low level of glycolysis. PPARγ inhibition reduced iRhom2 expression and increased EGF and GLUT1 expression in the PT in mice, resulting in hypertrophy of renal tubules and inhibited kidney functions, which can be rescued by inhibition of glycolysis via treatment with 2-deoxy-D-glucose. These findings delineate instrumental molecular mechanisms underlying the active lipid metabolism and suppressed glycolysis in the PT and active glycolysis in the DT and reveal critical roles for PPARs and c-Myc in maintaining metabolic homeostasis in the kidney
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Project description:Circadian rhythmicity in renal function suggests a requirement for circadian adaptations in renal metabolism. We studied circadian changes in renal metabolic pathways using integrated transcriptomic, proteomic and metabolomic analysis performed on control mice and mice deficient in the circadian clock gene Bmal1 in the renal tubule (cKOt mice). Proteins were extracted from whole kidneys of 60 mice. Of these, 30 were conditional knockouts of Arntl (Bmal1) and 30 were of control genotype. They were housed under 12-hours light/12-hours dark cycles and were sacrificed at six different time points: zeitgeber time ZT 0, ZT 4, ZT 8, ZT 12, ZT 16, ZT 20 ( ZT 0 being the time of light on and ZT 12 the time of light off). Five replicates per genotype and time point were analysed.
2023-03-15 | PXD036803 | Pride
Project description:Renal dysfunction and its functional regression have been known as one of major complication of type 2 diabetes (T2D). The prognosis for kidney function can be determined by estimated glomerular filtration rate (eGFR) and the known protein makers such as albumin and those parameters can classy patients with chronic kidney disease (CKD) grade 3 or higher but there are limitations in prediction. Therefore, there have been clinical unmet needs for urinary protein biomarkers to monitor or predict the patient status for precision medicine.
2020-06-23 | PXD016571 | Pride
Project description: Not available
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