In situ Metabolomics of Metabolic Reprogramming Involved in a Mouse Model of Type 2 Diabetic Kidney Disease.
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ABSTRACT:
SUBMITTER: Linnan B
PROVIDER: S-EPMC8670437 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Kidney cancer, or renal cell carcinoma (RCC), is a disease of increasing incidence that commonly is seen in the general practice of nephrology. Despite this state of affairs, this fascinating and highly morbid disease frequently is under-represented, or even absent, from the curriculum of nephrologists in training and generally is underemphasized in national nephrology meetings, both scientific as well as clinical. Although classic concepts in cancer research in general had led to the concept that cancer is a disease resulting from mutations in the control of growth-regulating pathways, reinforced by the discovery of oncogenes, more contemporary research, particularly in kidney cancer, has uncovered changes in metabolic pathways mediated by those same genes that control tumor energetics and biosynthesis. This adaptation of classic biochemical pathways to the tumor's advantage has been labeled metabolic reprogramming. For example, in the case of kidney cancer there exists a near-universal presence of von Hippel-Lindau tumor suppressor (pVHL) inactivation in the most common form, clear cell RCC (ccRCC), leading to activation of hypoxia-relevant and other metabolic pathways. Studies of this and other pathways in clear cell RCC (ccRCC) have been particularly revealing, leading to the concept that ccRCC can itself be considered a metabolic disease. For this reason, the relatively new method of metabolomics has become a useful technique in the study of ccRCC to tease out those pathways that have been reprogrammed by the tumor to its maximum survival advantage. Furthermore, identification of the nodes of such pathways can lead to novel areas for drug intervention in a disease for which such targets are seriously lacking. Further research and dissemination of these concepts, likely using omics techniques, will lead to clinical trials of therapeutics specifically targeted to tumor metabolism, rather than those generally toxic to all proliferating cells. Such novel agents are highly likely to be more effective than existing drugs and to have far fewer adverse effects. This review provides a general overview of the technique of metabolomics and then discusses how it and other omics techniques have been used to further our understanding of the basic biology of kidney cancer as well as to identify new therapeutic approaches.
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Project description:Kidney cancer [or renal cell carcinoma (RCC)] is known as "the internist's tumor" because it has protean systemic manifestations, suggesting that it utilizes complex, nonphysiologic metabolic pathways. Given the increasing incidence of this cancer and its lack of effective therapeutic targets, we undertook an extensive analysis of human RCC tissue employing combined grade-dependent proteomics and metabolomics analysis to determine how metabolic reprogramming occurring in this disease allows it to escape available therapeutic approaches. After validation experiments in RCC cell lines that were wild-type or mutant for the Von Hippel-Lindau tumor suppressor, in characterizing higher-grade tumors, we found that the Warburg effect is relatively more prominent at the expense of the tricarboxylic acid cycle and oxidative metabolism in general. Further, we found that the glutamine metabolism pathway acts to inhibit reactive oxygen species, as evidenced by an upregulated glutathione pathway, whereas the β-oxidation pathway is inhibited, leading to increased fatty acylcarnitines. In support of findings from previous urine metabolomics analyses, we also documented tryptophan catabolism associated with immune suppression, which was highly represented in RCC compared with other metabolic pathways. Together, our results offer a rationale to evaluate novel antimetabolic treatment strategies being developed in other disease settings as therapeutic strategies in RCC.
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Project description:Diabetic kidney disease and atherosclerotic disease are major causes of morbidity and mortality associated with type 2 diabetes (T2D), and diabetic kidney disease is a major cardiovascular risk factor. The black and tan, brachyury (BTBR) mouse strain with leptin deficiency (Lepob) has emerged as one of the best models of human diabetic kidney disease. However, no T2D mouse model of combined diabetic kidney disease and atherosclerosis exists. Our goal was to generate such a model. To this end, the low-density lipoprotein (LDL) receptor was targeted for degradation via inducible degrader of the LDL receptor (IDOL) overexpression, using liver-targeted adenoassociated virus serotype DJ/8 (AAV-DJ/8) in BTBR wild-type and BTBR Lepob mice. Liver-targeted IDOL-AAV-DJ/8 increased plasma LDL cholesterol compared with the control enhanced green fluorescent protein AAV-DJ/8. IDOL-induced dyslipidemia caused formation of atherosclerotic lesions of an intermediate stage, which contained both macrophages and smooth muscle cells. BTBR Lepob mice exhibited diabetic kidney disease. IDOL-induced dyslipidemia worsened albuminuria and glomerular macrophage accumulation but had no effect on mesangial expansion or podocyte numbers. Thus, by inducing hepatic degradation of the LDL receptor, we generated a T2D model of combined kidney disease and atherosclerosis. This model provides a new tool to study mechanisms, interactions, and treatment strategies of kidney disease and atherosclerosis in T2D.
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Project description:Detailed knowledge on tissue-specific metabolic reprogramming in diabetic nephropathy (DN) is vital for more accurate understanding the molecular pathological signature and developing novel therapeutic strategies. In the present study, a spatial-resolved metabolomics approach based on air flow-assisted desorption electrospray ionization (AFADESI) and matrix-assisted laser desorption ionization (MALDI) integrated mass spectrometry imaging (MSI) was proposed to investigate tissue-specific metabolic alterations in the kidneys of high-fat diet-fed and streptozotocin (STZ)-treated DN rats and the therapeutic effect of astragaloside IV, a potential anti-diabetic drug, against DN. As a result, a wide range of functional metabolites including sugars, amino acids, nucleotides and their derivatives, fatty acids, phospholipids, sphingolipids, glycerides, carnitine and its derivatives, vitamins, peptides, and metal ions associated with DN were identified and their unique distribution patterns in the rat kidney were visualized with high chemical specificity and high spatial resolution. These region-specific metabolic disturbances were ameliorated by repeated oral administration of astragaloside IV (100 mg/kg) for 12 weeks. This study provided more comprehensive and detailed information about the tissue-specific metabolic reprogramming and molecular pathological signature in the kidney of diabetic rats. These findings highlighted the promising potential of AFADESI and MALDI integrated MSI based metabolomics approach for application in metabolic kidney diseases.
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Project description:Background and purposeACE inhibitors (ACEIs) and AT1 receptor antagonists (ARBs) are first-line drugs that are believed to reduce the progression of end-stage renal disease in diabetic patients. Differences in the effects of ACEIs and ARBs are not well studied and the mechanisms responsible are not well understood.Experimental approachMale diabetic CD-1 mice were treated with ACEI, ARB, N-acetyl-seryl-aspartyl-lysyl-proline (AcSDKP), ACEI + AcSDKP, ARB + AcSDKP, glycolysis inhibitors or non-treatment. Moreover, prolyl oligopeptidase inhibitor (POPi)-injected male diabetic C57Bl6 mice were treated with ACEI, AcSDKP and ARB or non-treatment. Western blot and immunofluorescent staining were used to examine key enzymes and regulators of central metabolism.Key resultsThe antifibrotic action of ACEI imidapril is due to an AcSDKP-mediated antifibrotic mechanism, which reprograms the central metabolism including restoring SIRT3 protein and mitochondrial fatty acid oxidation and suppression of abnormal glucose metabolism in the diabetic kidney. Moreover, the POPi S17092 significantly blocked the AcSDKP synthesis, accelerated kidney fibrosis and disrupted the central metabolism. ACEI partly restored the kidney fibrosis and elevated the AcSDKP level, whereas the ARB (TA-606) did not show such effects in the POPi-injected mice. ACE inhibition and AcSDKP suppressed defective metabolism-linked mesenchymal transformations and reduced collagen-I and fibronectin accumulation in the diabetic kidneys.Conclusion and implicationsThe study envisages that AcSDKP is the endogenous antifibrotic mediator that controls the metabolic switch between glucose and fatty acid metabolism and that suppression of AcSDKP leads to disruption of kidney cell metabolism and activates mesenchymal transformations leading to severe fibrosis in the diabetic kidney.
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Project description:Background and Objectives. Diabetic kidney disease is a leading cause of chronic kidney disease and end-stage renal disease across the world. Early identification of DKD is vitally important for the effective prevention and control of it. However, the available indicators are doubtful in the early diagnosis of DKD. This study is aimed at determining novel sensitive and specific biomarkers to distinguish DKD from their counterparts effectively based on the widely targeted metabolomics approach. Materials and Method. This case-control study involved 44 T2DM patients. Among them, 24 participants with DKD were defined as the cases and another 20 without DKD were defined as the controls. The ultraperformance liquid chromatography-electrospray ionization-tandem mass spectrometry system was applied for the assessment of the serum metabolic profiles. Comprehensive analysis of metabolomics characteristics was conducted to detect the candidate metabolic biomarkers and assess their capability and feasibility. Result:A total of 11 differential metabolites, including Hexadecanoic Acid (C16:0), Linolelaidic Acid (C18:2N6T), Linoleic Acid (C18:2N6C), Trans-4-Hydroxy-L-Proline, 6-Aminocaproic Acid, L-Dihydroorotic Acid, 6-Methylmercaptopurine, Piperidine, Azoxystrobin Acid, Lysopc 20:4, and Cuminaldehyde, were determined as the potential biomarkers for the DKD early identification, based on the multivariable generalized linear regression model and receiver operating characteristic analysis. Conclusion:Serum metabolites might act as sensitive and specific biomarkers for DKD early detection. Further longitudinal studies are needed to confirm our findings.
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Project description:Our understanding of diabetic kidney disease pathogenesis has been hampered by the lack of easily generated pre-clinical animal models that faithfully recapitulate critical features of human disease. While most standard animal models develop manifestations of early stage diabetic injury such as hyperfiltration and mesangial matrix expansion, only a select few develop key late stage features such as interstitial fibrosis and reduced glomerular filtration rate. An underlying theme in these late stage disease models has been the addition of renin-angiotensin system hyperactivation, an important contributor to human disease pathogenesis. Widespread use of these models has been limited, however, as they are either labour intensive to generate, or have been developed in the rat, preventing the use of the many powerful genetic tools developed for mice. Here we describe the Akita+/- Ren+/- mouse, a new, easily generated murine model of diabetic kidney disease that develops many features of late stage human injury, including not only hyperglycemia, hypertension, and albuminuria, but also reduced glomerular filtration rate, glomerulosclerosis, and interstitial fibrosis.
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Project description:Background:The complex yet interrelated connections between cancer metabolism and oncogenic driver genes are relatively unexplored but have the potential to identify novel biomarkers and drug targets with prognostic and therapeutic value. The goal of this study was to identify global metabolic profiles of breast tumors isolated from multiple transgenic mouse models and to identify unique metabolic signatures driven by these oncogenes. Methods:Using mass spectrometry (GC-MS, LC-MS/MS, and capillary zone electrophoresis (CZE)-MS platforms), we quantified and compared the levels of 374 metabolites in breast tissue from normal and transgenic mouse breast cancer models overexpressing a panel of oncogenes (PyMT, PyMT-DB, Wnt1, Neu, and C3-TAg). We also compared the mouse metabolomics data to published human metabolomics data already linked to clinical data. Results:Through analysis of our metabolomics data, we identified metabolic differences between normal and tumor breast tissues as well as metabolic differences unique to each initiating oncogene. We also quantified the metabolic profiles of the mammary fat pad versus mammary epithelium by CZE-MS/MS. However, the differences between the tissues did not account for the majority of the metabolic differences between the normal mammary gland and breast tumor tissues. Therefore, the differences between the cohorts were unlikely due to cellular heterogeneity. Of the mouse models used in this study, C3-TAg was the only cohort with a tumor metabolic signature composed of ten metabolites that had significant prognostic value in breast cancer patients. Gene expression analysis identified candidate genes that may contribute to the metabolic reprogramming. Conclusions:This study identifies oncogene-induced metabolic reprogramming within mouse breast tumors and compares the results to that of human breast tumors, providing a unique look at the relationship between and clinical value of oncogene initiation and metabolism during breast cancer.
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