High-throughput omics analysis platforms provide important new technical capabilities to analyze complex biological fluids for disease markers. A particularly important platform for metabolomics is two-dimensional gas chromatography time-of- flight mass spectrometry (GCxGC/TOF-MS) that provides both high precision and extended capacity for molecular detection. However, data analysis from this platform is not developed for high sample numbers or for detection of the very large numbers of analytes that this technology detects (more than 1500 per biofluid sample). We will develop an informatics platform for accurate and efficient analysis of data generated from GCxGC/TOF-MS analyses through the following specific aims: (1) Mass informatics for differential metabolomics, (2) Interactive visual analysis of metabolite correlation networks, and (3) Informatics analysis tools integration. The biological information that our system will deliver is regulation of altered metabolites that relate to phenotypic differences between groups of samples. This information will enable assessment of human health and wellness, and will have a direct impact on public health. In the context of existing collaborations, our initial large scale application focus will be analyses of metabolomic profiling data from plasma samples of patients with breast and prostate cancer compared with control subjects. Broadly speaking, our informatics tools will facilitate efforts in the metabolomics community to perform comparative metabolite profiling with high precision and high volume on the powerful GCXGC-TOF-MS platform. The project consists of the development of algorithms for assessment of metabolite identification accuracy, alignment of spectra, and normalization of datasets. Separate algorithms will be developed to distinguish specific molecules that are observed to be different between different sample cohorts and to provide a statistical significance context for each differentially expressed molecule. An additional group of algorithms will be developed to enable interactive visual analysis of metabolite correlation networks. Deliverables from this project will include provision of independent software modules that can be remotely invoked via web services and an integrated web-based data analysis pipeline for GCxGC/TOF-MS data.

Public Health Relevance

High-throughput molecular omics analysis platforms provide important new technical capabilities to analyze complex biological fluids for disease markers. A particularly important platform for metabolomics is two-dimensional gas chromatography time-of- flight mass spectrometry (GCxGC/TOF-MS) that provides both high precision and extended capacity for molecular detection. This approach usually uses a short polar column after the main analytical column. Typically, the second column is operated at a higher temperature than the first column. The metabolites co-eluted from the first GC column are further separated in the second column because of the difference of column temperature and the chromatography matrix. The further separated metabolites are directed to a high capacity time-of-flight mass spectrometry system for detection. Two- dimensional gas chromatography offers significant advantages for analysis of complex samples including: an order-of-magnitude increase in separation capacity, significant increase in signal-to-noise ratio and dynamic range, and improvement of mass spectral deconvolution and similarity matches. Since GCWGC/TOF-MS can provide more and accurate information, it represents powerful tool for the analysis of small molecule metabolites in complex biological systems. However, data analysis from this platform is not developed for high sample numbers or for detection of the very large numbers of analytes that this technology detects (more than 1500 per biofluid sample). We will develop a set of computational algorithms for accurate and efficient analysis of data generated from GCxGC/TOF-MS analyses through the following specific aims: (1) Mass informatics for differential metabolomics, (2) Interactive visual analysis of metabolite correlation networks, and (3) Informatics analysis tools integration. The biological information that our system will deliver is regulation of altered metabolites that relate to phenotypic differences between groups of samples. This information will enable assessment of human health and wellness, and will have a direct impact on public health. A direct application of this type of information is metabolite biomarker discovery in clinical biomedical research. In the context of existing collaborations, our initial large scale application focus of the developed mass informatics system will be analyses of metabolomic profiling data from human plasma samples of patients with breast cancer and human multiple myeloma, compared with corresponding control subjects. With the raw instrument data as input information, our informatics tools will perform a series of data mining and discover the metabolites present in the sample and also provide their regulation information. These informatics tools will facilitate efforts in the metabolomics community to perform comparative metabolite profiling with high precision and high volume on the powerful GCXGC-TOF-MS platform. The project consists of the development of algorithms for assessment of metabolite identification accuracy, alignment of spectra, and normalization of datasets. Separate algorithms will be developed to distinguish specific molecules that are observed to be different between different sample cohorts and to provide a statistical significance context for each differentially expressed molecule. An additional group of algorithms will be developed to enable interactive visual analysis of metabolite correlation networks. Deliverables from this project will include provision of independent software modules that can be remotely invoked via web services and an integrated web-based data analysis pipeline for GCxGC/TOF-MS data.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
5R01GM087735-04
Application #
8260467
Study Section
Enabling Bioanalytical and Biophysical Technologies Study Section (EBT)
Program Officer
Lyster, Peter
Project Start
2009-05-01
Project End
2014-04-30
Budget Start
2012-05-01
Budget End
2014-04-30
Support Year
4
Fiscal Year
2012
Total Cost
$323,441
Indirect Cost
$36,099
Name
University of Louisville
Department
Chemistry
Type
Schools of Arts and Sciences
DUNS #
057588857
City
Louisville
State
KY
Country
United States
Zip Code
40292
Koo, Imhoi; Wei, Xiaoli; Zhang, Xiang (2014) Analysis of metabolomic profiling data acquired on GC-MS. Methods Enzymol 543:315-24
Kim, Seongho; Ouyang, Ming; Jeong, Jaesik et al. (2014) A NEW METHOD OF PEAK DETECTION FOR ANALYSIS OF COMPREHENSIVE TWO-DIMENSIONAL GAS CHROMATOGRAPHY MASS SPECTROMETRY DATA. Ann Appl Stat 8:1209-1231
Shi, Xue; Yin, Xinmin; Zhang, Xiang (2014) Analysis of mouse liver metabolites by GC × GC-TOF MS. Methods Mol Biol 1198:99-105
Koo, Imhoi; Wei, Xiaoli; Shi, Xue et al. (2014) Constructing Metabolic Association Networks Using High-dimensional Mass Spectrometry Data. Chemometr Intell Lab Syst 138:193-202
Wei, Xiaoli; Koo, Imhoi; Kim, Seongho et al. (2014) Compound identification in GC-MS by simultaneously evaluating the mass spectrum and retention index. Analyst 139:2507-14
Koo, Imhoi; Shi, Xue; Kim, Seongho et al. (2014) iMatch2: compound identification using retention index for analysis of gas chromatography-mass spectrometry data. J Chromatogr A 1337:202-10
Shi, Xue; Wei, Xiaoli; Koo, Imhoi et al. (2014) Metabolomic analysis of the effects of chronic arsenic exposure in a mouse model of diet-induced Fatty liver disease. J Proteome Res 13:547-54
Koo, Imhoi; Yao, Sen; Zhang, Xiang et al. (2014) Comparative analysis of false discovery rate methods in constructing metabolic association networks. J Bioinform Comput Biol 12:1450018
Wei, Xiaoli; Shi, Xue; Kim, Seongho et al. (2014) Data dependent peak model based spectrum deconvolution for analysis of high resolution LC-MS data. Anal Chem 86:2156-65
Jeong, Jaesik; Zhang, Xiang; Shi, Xue et al. (2013) An efficient post-hoc integration method improving peak alignment of metabolomics data from GCxGC/TOF-MS. BMC Bioinformatics 14:123

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