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.
"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. "
[Show abstract][Hide abstract] 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.
Evidence-based Complementary and Alternative Medicine 02/2013; 2013(11):298183. DOI:10.1155/2013/298183 · 1.88 Impact Factor
"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. "
[Show abstract][Hide abstract] ABSTRACT: Background
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.
BMC Systems Biology 07/2012; 6(Suppl-S14):1. DOI:10.1186/1752-0509-6-S1-S14 · 2.44 Impact Factor
"Recent outbreak of renal failure in infants in China has been REVIEW determined to be caused by melamine and derivatives adulterated in the food. Metabonomics was performed to evaluate the global biochemical alteration triggered by melamine ingestion in parallel with the acute renal toxicity in rats (Xie et al. 2010). "
[Show abstract][Hide abstract] ABSTRACT: Metabonomics has played increasingly important roles in pharmaceutical research and development. Safety assessment of drugs is a key stage in drug development and one which represents a significant attritional hurdle. However, characterization of the molecular mechanisms of drug toxicity still remains an enormous challenge. Recent advancements in 'omics' sciences, and in particular metabonomics, has enabled some elucidation or insights into toxicological sequelae. Metabonomics is a global metabolic profiling framework which utilizes high resolution analytics together with chemometric statistical tools to derive an integrated picture of both endogenous and xenobiotic metabolism. Hepatotoxicity and nephrotoxicity are major reasons that drugs are withdrawn post-market, and hence it is of major concern to both the Food and Drug Administration and pharmaceutical companies. There is a strong need to develop reliable biomarkers that can accurately predict toxicity in the drug discovery and development process and are translatable to the clinic. A deeper understanding of global perturbations in biochemical pathways and useful biomarkers could provide valuable insights about mechanisms of toxicity. This review summarizes some current progress in the application of metabonomic in understanding drug-induced hepatotoxicity and nephrotoxicity, with an emphasis on identifying early toxicity biomarkers.
Data provided are for informational purposes only. Although carefully collected, accuracy cannot be guaranteed. The impact factor represents a rough estimation of the journal's impact factor and does not reflect the actual current impact factor. Publisher conditions are provided by RoMEO. Differing provisions from the publisher's actual policy or licence agreement may be applicable.