We propose to develop a cell-specific isotope labeling technology to study cancer metabolism and metabolite exchange in the intact animal. Our technology addresses a major limitation of most metabolomic studies on cancer, namely that they have been mostly performed on simple cell-culture systems. The metabolic interactions of a cancer cell with its environment have been largely ignored and remain uncharacterized. This is because current metabolomic technologies cannot resolve metabolites from each of the various cell types of a mixed culture or tissue. Therefore, current approaches cannot measure metabolite exchange between tumors and their neighboring cells. Yet, these interactions have been suggested to define tumor phenotype. We exploit the fact that vertebrate cells do not take up or utilize the carbohydrate cellobiose. Cellobiose consists of two glucose molecules joined by a -linkage. We will genetically engineer human fibroblast and HeLa cell lines that can take up and digest 13C-cellobiose (Aim 1). Co-culturing genetically engineered fibroblasts with wildtype HeLa cells in 13C-cellobiose will enable specific loading of label into the fibroblast metabolome. Subsequent analysis of the HeLa cell metabolome for isotopic label by metabolomics will be readout of metabolite exchange (Aim 2). The converse experiment will also be performed where genetically engineered HeLa cells are co-cultured with wild type fibroblasts in 13C-cellobiose enriched media. We will extend our technology to tumors in animals by constructing transgenic zebrafish melanoma cell lines or transgenic zebrafish expressing the cellobiose-utilization genes (Aim 3). Following transplant of melanoma cells into zebrafish, we will follow metabolite exchange from tumor to stroma (or vice versa) by tracking isotope labels with both LC/MS and NIMS in situ imaging. We will further resolve the role of individual stromal cell types by using established cell-specific promoters in the zebrafish to express cellobiose-utilization genes in vasculature, connective tissue, or muscle. Our technology will provide the first platform to characterize the crosstalk between cancer cells and their nonmalignant neighbors. Future applications of the technology include culturing biopsied tumors from patients with 13C-cellobiose and genetically engineered fibroblasts. This will provide an assay for stromal feeding, which could be diagnostic of tumor phenotype. Additional future applications will include analysis of tumor metabolism in the mouse model as well as metabolite exchange. Indeed, the technology can ultimately be applied to map metabolites exchanged between any pair of animal tissues. This organismal view of metabolism will have profound impacts on our understanding of both tumor biology and basic physiology.
Due to technical limitations, the metabolic interaction of cancer cells with their neighbors has not been well characterized. We propose to develop a technology that specifically labels cancer-cell metabolites so that we can track them as they are transferred to different cells and tissues within an animal. This approach will lead to new possibilities for cancer therapeutics that do not target the tumor itself.
|Naser, Fuad J; Mahieu, Nathaniel G; Wang, Lingjue et al. (2018) Two complementary reversed-phase separations for comprehensive coverage of the semipolar and nonpolar metabolome. Anal Bioanal Chem 410:1287-1297|
|Llufrio, Elizabeth M; Wang, Lingjue; Naser, Fuad J et al. (2018) Sorting cells alters their redox state and cellular metabolome. Redox Biol 16:381-387|
|Gelman, Susan J; Naser, Fuad; Mahieu, Nathaniel G et al. (2018) Consumption of NADPH for 2-HG Synthesis Increases Pentose Phosphate Pathway Flux and Sensitizes Cells to Oxidative Stress. Cell Rep 22:512-522|
|Mahieu, Nathaniel G; Patti, Gary J (2017) Systems-Level Annotation of a Metabolomics Data Set Reduces 25?000 Features to Fewer than 1000 Unique Metabolites. Anal Chem 89:10397-10406|
|Chen, Ying-Jr; Mahieu, Nathaniel G; Huang, Xiaojing et al. (2016) Lactate metabolism is associated with mammalian mitochondria. Nat Chem Biol 12:937-943|
|Mahieu, Nathaniel G; Genenbacher, Jessica Lloyd; Patti, Gary J (2016) A roadmap for the XCMS family of software solutions in metabolomics. Curr Opin Chem Biol 30:87-93|
|Yao, Cong-Hui; Fowle-Grider, Ronald; Mahieu, Nathanial G et al. (2016) Exogenous Fatty Acids Are the Preferred Source of Membrane Lipids in Proliferating Fibroblasts. Cell Chem Biol 23:483-93|
|Yao, Cong-Hui; Liu, Gao-Yuan; Yang, Kui et al. (2016) Inaccurate quantitation of palmitate in metabolomics and isotope tracer studies due to plastics. Metabolomics 12:|
|Spalding, Jonathan L; Cho, Kevin; Mahieu, Nathaniel G et al. (2016) Bar Coding MS(2) Spectra for Metabolite Identification. Anal Chem 88:2538-42|
|Mahieu, Nathaniel G; Spalding, Jonathan L; Gelman, Susan J et al. (2016) Defining and Detecting Complex Peak Relationships in Mass Spectral Data: The Mz.unity Algorithm. Anal Chem 88:9037-46|