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Perturbational Metabolic Profiling of Human Breast Cancer Cells
Citation:
TENG, Q., W. Huang, T. W. COLLETTE, AND D. R. EKMAN. Perturbational Metabolic Profiling of Human Breast Cancer Cells. Presented at ISMRM 17th Scientific Meeting & Exposition, Honolulu, HI, April 18 - 24, 2009.
Impact/Purpose:
The mission of the ERD Metabolomics Team is to study the impact of stressors on various species using NMR and other advanced analytical approaches to characterize changes in endogenous metabolites.
Description:
A major goal of toxicity testing is to obtain toxicity data for protecting public health and the environment from adverse effects that may be caused by exposure to environmental agents in the air, water, soil and food. The current toxicological studies that target human health effects primarily rely on animal studies. Unfortunately, large-scale animal exposure studies are expensive, requiring dedicated care and handling facilities. Furthermore, the US EPA and other organizations have adopted the goal of reducing animal usage in future toxicity testing programs. One potential approach to increase the throughput of metabolomics is to use cell cultures instead of live animals in exposure studies, which provides obvious advantages. For example, there is no need to house and sacrifice animals, costs are significantly lower, and cells can be grown and exposed rapidly. Also, human cell lines can be employed in order to avoid cross-species extrapolations. To conduct cell culture based metabolomics, we have developed a novel sample preparation method using adherent mammalian cells, which is rapid, effective, and exhibits greater metabolite retention by approximately a factor of 50 compared to the conventional sample preparation method. We have applied this approach to study the metabolic changes caused by 17α-ethynyl-estradiol (EE2) in estrogen receptor (ER)-positive MCF-7 and ER-negative MDA-MB-231 human breast cancer cell lines using NMR spectroscopy. Presented here are the details of sample preparation, NMR spectroscopy, metabolite identification, statistical analysis and intracellular metabolic changes.