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Metabotypes of breast cancer cell lines revealed by non-targeted metabolomics
Institution:1. Institute of Molecular Systems Biology, ETH Zurich, CH-8093 Zurich, Switzerland;2. Zurich Life Science Graduate School, CH-8057 Zurich, Switzerland;3. Gene Expression Analysis Laboratory, Cancer Research UK, London Research Institute, 44 Lincoln''s Inn Fields, London WC2A 3LY, UK;1. Institute for Research in Biomedicine, Università della Svizzera italiana, Bellinzona 6500, Switzerland;2. Institute of Microbiology, ETH Zurich, Zurich 8093, Switzerland;3. Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried 82152, Germany;4. Institute of Biochemistry, ETH Zurich, Zurich 8093, Switzerland;5. Institute of Molecular Systems Biology, ETH Zurich, Zurich 8093, Switzerland;6. Center of Medical Immunology, Institute for Research in Biomedicine, Università della Svizzera italiana, Bellinzona 6500, Switzerland;1. The Scripps Research Institute, Scripps Center for Metabolomics and Mass Spectrometry, 10550 North Torrey Pines Road, La Jolla, CA 92037, USA;2. University of Vienna, Faculty of Chemistry, Department of Food Chemistry and Toxicology, Währingerstraße 38, 1090 Vienna, Austria;3. Vienna Metabolomics Center (VIME), University of Vienna, 1090 Vienna, Austria;4. Department of Molecular Medicine, The Scripps Research Institute, La Jolla, CA, USA;5. Nanyang Technological University, School of Civil and Environmental Engineering, 50 Nanyang Avenue, Singapore 639798, Singapore;6. Department of Surgery, Scripps Clinic Medical Group, La Jolla, CA 92037, USA;7. Department of Environmental Health Sciences, Yale School of Public Health, Yale University, 60 College Street, New Haven, CT 06520, USA;8. The Scripps Research Institute, Department of Integrative and Computational Biology, 10550 North Torrey Pines Road, La Jolla, CA 92037, USA;1. Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, Mich;2. Department of Physiology, Michigan State University, East Lansing, Mich;3. Department of Pharmacology and Toxicology, Michigan State University, East Lansing, Mich;4. Department of Chemical Engineering and Materials Science, Michigan State University, East Lansing, Mich;1. State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources, Animal Reproduction Institute, Guangxi University, Nanning, PR China;2. Guangxi Key Laboratory of Buffalo Genetics, Guangxi Buffalo Research Institute, Chinese Academy of Agricultural Science, Ministry of Agriculture, Nanning, PR China
Abstract:We present an analysis of intracellular metabolism by non-targeted, high-throughput metabolomics profiling of 18 breast cell lines. We profiled >900 putatively annotated metabolite ions for >100 samples collected under both normoxic and hypoxic conditions and revealed extensive heterogeneity across all metabolic pathways and cell lines. Cell line–specific metabolome profiles dominated over patterns associated with malignancy or with the clinical nomenclature of breast cancer cells. Such characteristic metabolome profiles were reproducible across different laboratories and experiments and exhibited mild to robust changes with change in experimental conditions. To extract a functional overview of cell line heterogeneity, we devised an unsupervised metabotyping procedure that for each pathway automatically recognized metabolic types from metabolome data and assigned cell lines. Our procedure provided a condensed yet global representation of cell line metabolism, revealing the fine structure of metabolic heterogeneity across all tested pathways and cell lines. In follow-up experiments on selected pathways, we confirmed that different metabolic types correlated to differences in the underlying fluxes and difference sensitivity to gene knockdown or pharmacological inhibition. Thus, the identified metabotypes recapitulated functional differences at the pathway level. Metabotyping provides a powerful compression of multi-dimensional data that preserves functional information and serves as a resource for reconciling or understanding heterogeneous metabolic phenotypes or response to inhibition of metabolic pathways.
Keywords:Breast cancer  Metabolism  Metabolomics  Typing  Cell line  Modeling
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