Metabonomic Evaluation of Melamine-Induced Acute Renal Toxicity in Rats
Department of Nutrition, University of North Carolina at Greensboro, North Carolina Research Campus, Kannapolis, North Carolina 28081, USA. Journal of Proteome Research
(Impact Factor: 4.25).
07/2009; 9(1):125-33. DOI: 10.1021/pr900333h
The recent outbreak of renal failure in infants in China has been determined to be caused by melamine (Mel) and derivatives adulterated in the food. A metabonomic study was performed to evaluate the global biochemical alteration triggered by Mel ingestion in parallel with the acute renal toxicity in rats. Mel at 600, 300, and 100 mg/kg, cyanuric acid (Cya) at 100 mg/kg, and mixture of Mel and Cya (50 mg/kg each) were administered in five groups of Wistar rats, respectively, via oral gavage for 15 days. Urinary metabonomic profiles indicated that Mel perturbed urinary metabolism in a dose-dependent manner, with high-dose group showing the most significant impact. Metabonomic variations also suggest that the toxicity of low-dose (50 mg/kg) Mel was greatly elevated by the presence of Cya (at 50 mg/kg), which was able to induce a significant metabolic alteration to a level equivalent to that of 600 mg/kg Mel. Histological examination and serum biochemical analysis also indicated that the low-dose Mel-Cya mixture and high-dose Mel group resulted in the greatest renal toxicity. The high-dose Mel and low-dose Mel-Cya resulted in disrupted amino acid metabolism including tryptophan, polyamine, and tyrosine metabolism, and altered TCA and gut microflora structure.
Available from: Youhe Gao
- "Metabolites in urine may reflect state of the body to some extent and serve as informative biomarkers for some diseases. Urinary metabolomics has been widely used in the studies of various diseases, including diabetic nephropathy , acute kidney injury , chronic heart failure , liver cancer , and breast cancer . "
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ABSTRACT: Biomarkers are the measurable changes associated with a physiological or pathophysiological process. Unlike blood, urine is not subject to homeostatic mechanisms. Therefore, greater fluctuations could occur in urine than in blood, better reflecting the changes in human body. The roadmap of urine biomarker era was proposed. Although urine analysis has been attempted for clinical diagnosis, and urine has been monitored during the progression of many diseases, particularly urinary system diseases, whether urine can reflect brain disease status remains uncertain. As some biomarkers of brain diseases can be detected in the body fluids such as cerebrospinal fluid and blood, there is a possibility that urine also contain biomarkers of brain diseases. This review summarizes the clues of brain diseases reflected in the urine proteome and metabolome.
Available from: europepmc.org
- "rough the analysis of one or several kinds of bio�uids including serum, urine, saliva, and tissue samples, the global and dynamic alterations in metabolism can be deciphered . erefore, metabolomics has been increasingly used in many applications such as identifying metabolite markers for clinical diagnosis and prognosis , monitoring the chemical-induced toxicity , exploring the potential mechanism of diverse diseases , and assessing therapeutic effects of treatment modalities  . Univariate and/or multivariate statistical methods are routinely used in metabolomics studies, aiming at successful classi�cation of samples with metabolic phenotypic variations and identi�cation of potential biomarkers while minimizing the technical variations. "
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ABSTRACT: Metabolomic data analysis becomes increasingly challenging when dealing with clinical samples with diverse demographic and genetic backgrounds and various pathological conditions or treatments. Although many classification tools, such as projection to latent structures (PLS), support vector machine (SVM), linear discriminant analysis (LDA), and random forest (RF), have been successfully used in metabolomics, their performance including strengths and limitations in clinical data analysis has not been clear to researchers due to the lack of systematic evaluation of these tools. In this paper we comparatively evaluated the four classifiers, PLS, SVM, LDA, and RF, in the analysis of clinical metabolomic data derived from gas chromatography mass spectrometry platform of healthy subjects and patients diagnosed with colorectal cancer, where cross-validation, R (2)/Q (2) plot, receiver operating characteristic curve, variable reduction, and Pearson correlation were performed. RF outperforms the other three classifiers in the given clinical data sets, highlighting its comparative advantages as a suitable classification and biomarker selection tool for clinical metabolomic data analysis.
Available from: Xianfu Gao
- "It is well known that the minor alteration at the level of gene or protein expression usually leads to significant change in metabolite level. Combining a robust instrumental analysis with whole metabolite information and multivariate statistical analysis, such as principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA), metabolomics has been a considerably intensive means for comprehensively evaluating toxicity of drugs or xenobiotics [6,7], early diagnosis and identifying potential biomarkers [8,9], and elucidating biological pathways . Mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy are major analytical tools for metabolomics. "
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Membranous nephropathy is an important glomerular disease characterized by podocyte injury and proteinuria, but no metabolomics research was reported as yet. Here, we performed a parallel metabolomics study, based on human urine and serum, to comprehensively profile systematic metabolic variations, identify differential metabolites, and understand the pathogenic mechanism of membranous nephropathy.
There were obvious metabolic distinctions between the membranous nephropathy patients with urine protein lower than 3.5 g/24 h (LUPM) and those higher than 3.5 g/24 h (HUPM) by Partial Least Squares Discriminant Analysis (PLS-DA) model analysis. In total, 26 urine metabolites and 9 serum metabolites were identified to account for such differences, and the majority of metabolites were significantly increased in HUPM patients for both urines and serums. Combining the results of urine with serum, all differential metabolites were classified to 5 classes. This classification helps globally probe the systematic metabolic alterations before and after blood flowing through kidney. Citric acid and 4 amino acids were markedly increased only in the serum samples of HUPM patients, implying more impaired filtration function of kidneys of HUPM patients than LUPM patients. The dicarboxylic acids, phenolic acids, and cholesterol were significantly elevated only in urines of HUPM patients, suggesting more severe oxidative attacks than LUPM patients.
Parallel metabolomics of urine and serum revealed the systematic metabolic variations associated with LUPM and HUPM patients, where HUPM patients suffered more severe injury of kidney function and oxidative stresses than LUPM patients. This research exhibited a promising application of parallel metabolomics in renal diseases.
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