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Ni Y.,University of North Carolina at Charlotte | Qiu Y.,University of North Carolina at Greensboro | Jiang W.,University of North Carolina at Charlotte | Suttlemyre K.,University of North Carolina at Charlotte | And 4 more authors.
Analytical Chemistry | Year: 2012

ADAP-GC 2.0 has been developed to deconvolute coeluting metabolites that frequently exist in real biological samples of metabolomics studies. Deconvolution is based on a chromatographic model peak approach that combines five metrics of peak qualities for constructing/selecting model peak features. Prior to deconvolution, ADAP-GC 2.0 takes raw mass spectral data as input, extracts ion chromatograms for all the observed masses, and detects chromatographic peak features. After deconvolution, it aligns components across samples and exports the qualitative and quantitative information of all of the observed components. Centered on the deconvolution, the entire data analysis workflow is fully automated. ADAP-GC 2.0 has been tested using three different types of samples. The testing results demonstrate significant improvements of ADAP-GC 2.0, compared to the previous ADAP 1.0, to identify and quantify metabolites from gas chromatography/time-of-flight mass spectrometry (GC/TOF-MS) data in untargeted metabolomics studies. © 2012 American Chemical Society.

Jiang W.,University of North Carolina at Charlotte | Qiu Y.,University of North Carolina at Greensboro | Ni Y.,University of North Carolina at Charlotte | Su M.,David H Murdock Research Institute | And 2 more authors.
Journal of Proteome Research | Year: 2010

Recent technological advances have made it possible to carry out high-throughput metabonomics studies using gas chromatography coupled with time-of-flight mass spectrometry. Large volumes of data are produced from these studies and there is a pressing need for algorithms that can efficiently process and analyze data in a high-throughput fashion as well. We present an Automated Data Analysis Pipeline (ADAP) that has been developed for this purpose. ADAP consists of peak detection, deconvolution, peak alignment, and library search. It allows data to flow seamlessly through the analysis steps without any human intervention and features two novel algorithms in the analysis. Specifically, clustering is successfully applied in deconvolution to resolve coeluting compounds that are very common in complex samples and a two-phase alignment process has been implemented to enhance alignment accuracy. ADAP is written in standard C++ and R and uses parallel computing via Message Passing Interface for fast peak detection and deconvolution. ADAP has been applied to analyze both mixed standards samples and serum samples and identified and quantified metabolites successfully. ADAP is available at http://www.du-lab.org. © 2010 American Chemical Society.

Maranz S.,David H Murdock Research Institute
The Scientific World Journal | Year: 2012

Most investigations into the antimalarial activity of African plants are centered on finding an indigenous equivalent to artemisinin, the compound from which current frontline antimalarial drugs are synthesized. As a consequence, the standard practice in ethnopharmacological research is to use in vitro assays to identify compounds that inhibit parasites at nanomolar concentrations. This approach fails to take into consideration the high probability of acquisition of resistance to parasiticidal compounds since parasite populations are placed under direct selection for genetic that confers a survival advantage. Bearing in mind Africa's long exposure to malaria and extensive ethnobotanical experimentation with both therapies and diet, it is more likely that compounds not readily overcome by Plasmodium parasites would have been retained in the pharmacopeia and cuisine. Such compounds are characterized by acting primarily on the host rather than directly targeting the parasite and thus cannot be adequately explored in vitro. If Africa's long history with malaria has in fact produced effective plant therapies, their scientific elucidation will require a major emphasis on in vivo investigation. © 2012 Steven Maranz.

Wei J.,Shanghai JiaoTong University | Xie G.,University of North Carolina at Greensboro | Zhou Z.,Shanghai JiaoTong University | Shi P.,Shanghai JiaoTong University | And 6 more authors.
International Journal of Cancer | Year: 2011

Oral cancer, one of the six most common human cancers with an overall 5-year survival rate of <50%, is often not diagnosed until it has reached an advanced stage. The aim of the current study is to explore salivary metabolomics as a disease diagnostic and stratification tool for oral cancer and leukoplakia and evaluate the potential of salivary metabolome for detection of oral squamous cell carcinoma (OSCC). Saliva metabolite profiling for a group of 37 OSCC patients, 32 oral leukoplakia (OLK) patients and 34 healthy subjects was performed using ultraperformance liquid chromatography coupled with quadrupole/time-of-flight mass spectrometry in conjunction with multivariate statistical analysis. The OSCC, OLK and healthy control groups demonstrate characteristic salivary metabolic signatures. A panel of five salivary metabolites including γ-aminobutyric acid, phenylalanine, valine, n-eicosanoic acid and lactic acid were selected using OPLS-DA model with S-plot. The predictive power of each of the five salivary metabolites was evaluated by receiver operating characteristic curves for OSCC. Valine, lactic acid and phenylalanine in combination yielded satisfactory accuracy (0.89, 0.97), sensitivity (86.5% and 94.6%), specificity (82.4% and 84.4%) and positive predictive value (81.6% and 87.5%) in distinguishing OSCC from the controls or OLK, respectively. The utility of salivary metabolome diagnostics for oral cancer is successfully demonstrated in this study and these results suggest that metabolomics approach complements the clinical detection of OSCC and stratifies the two types of lesions, leading to an improved disease diagnosis and prognosis. © 2011 UICC.

Chen T.,Shanghai JiaoTong University | Xie G.,University of North Carolina at Greensboro | Wang X.,Zhongshan Hospital | Fan J.,Zhongshan Hospital | And 10 more authors.
Molecular and Cellular Proteomics | Year: 2011

Hepatocellular carcinoma (HCC) is a common malignancy in the world with high morbidity and mortality rate. Identification of novel biomarkers in HCC remains impeded primarily because of the heterogeneity of the disease in clinical presentations as well as the pathophysiological variations derived from underlying conditions such as cirrhosis and steatohepatitis. The aim of this study is to search for potential metabolite biomarkers of human HCC using serum and urine metabolomics approach. Sera and urine samples were collected from patients with HCC (n = 82), benign liver tumor patients (n = 24), and healthy controls (n = 71). Metabolite profiling was performed by gas chromatography time-of-flight mass spectrometry and ultra performance liquid chromatography-quadrupole time of flight mass spectrometry in conjunction with univariate and multivariate statistical analyses. Forty three serum metabolites and 31 urinary metabolites were identified in HCC patients involving several key metabolic pathways such as bile acids, free fatty acids, glycolysis, urea cycle, and methionine metabolism. Differentially expressed metabolites in HCC subjects, such as bile acids, histidine, and inosine are of great statistical significance and high fold changes, which warrant further validation as potential biomarkers for HCC. However, alterations of several bile acids seem to be affected by the condition of liver cirrhosis and hepatitis. Quantitative measurement and comparison of seven bile acids among benign liver tumor patients with liver cirrhosis and hepatitis, HCC patients with liver cirrhosis and hepatitis, HCC patients without liver cirrhosis and hepatitis, and healthy controls revealed that the abnormal levels of glycochenodeoxycholic acid, glycocholic acid, taurocholic acid, and chenodeoxycholic acid are associated with liver cirrhosis and hepatitis. HCC patients with alpha fetoprotein values lower than 20 ng/ml was successfully differentiated from healthy controls with an accuracy of 100% using a panel of metabolite markers. Our work shows that metabolomic profiling approach is a promising screening tool for the diagnosis and stratification of HCC patients. © 2011 by The American Society for Biochemistry and Molecular Biology, Inc.

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