4.6 Article

Metabolomics method to comprehensively analyze amino acids in different domains

Journal

ANALYST
Volume 140, Issue 8, Pages 2726-2734

Publisher

ROYAL SOC CHEMISTRY
DOI: 10.1039/c4an02386b

Keywords

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Funding

  1. Institute of Translational Health Sciences (ITHS) small pilot grant at the University of Washington (Seattle, WA) [2R01GM085291, RO1 CA57138]
  2. Cancer Care Engineering Project at Purdue University (Department of Defense, USAMRMC) [W81XWH-08-1-0065, W81XWH-10-1-0540]
  3. Chromosome Metabolism and Cancer Training grant [T32 CA009657]
  4. Chinese National Instrumentation Program [2011YQ170067]
  5. National Natural Science Foundation of China [21365001]
  6. Cancer Care Engineering Project at Purdue University (Walther Cancer Foundation)
  7. Cancer Care Engineering Project at Purdue University (Regenstrief Foundation)

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Amino acids play essential roles in both metabolism and the proteome. Many studies have profiled free amino acids (FAAs) or proteins; however, few have connected the measurement of FAA with individual amino acids in the proteome. In this study, we developed a metabolomics method to comprehensively analyze amino acids in different domains, using two examples of different sample types and disease models. We first examined the responses of FAAs and insoluble-proteome amino acids (IPAAs) to the Myc oncogene in Tet21N human neuroblastoma cells. The metabolic and proteomic amino acid profiles were quite different, even under the same Myc condition, and their combination provided a better understanding of the biological status. In addition, amino acids were measured in 3 domains (FAAs, free and soluble-proteome amino acids (FSPAAs), and IPAAs) to study changes in serum amino acid profiles related to colon cancer. A penalized logistic regression model based on the amino acids from the three domains had better sensitivity and specificity than that from each individual domain. To the best of our knowledge, this is the first study to perform a combined analysis of amino acids in different domains, and indicates the useful biological information available from a metabolomics analysis of the protein pellet. This study lays the foundation for further quantitative tracking of the distribution of amino acids in different domains, with opportunities for better diagnosis and mechanistic studies of various diseases.

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