Metabolic profiling of cancer cells reveals genome-wide crosstalk between transcriptional regulators and metabolism
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Abstract
Transcriptional reprogramming of cellular metabolism is a hallmark of cancer. However, systematic approaches to study the role of transcriptional regulators (TRs) in mediating cancer metabolic rewiring are missing. Here, we chart a genome-scale map of TR-metabolite associations in human cells using a combined computational-experimental framework for large-scale metabolic profiling of adherent cell lines. By integrating intracellular metabolic profiles of 54 cancer cell lines with transcriptomic and proteomic data, we unraveled a large space of associations between TRs and metabolic pathways. We found a global regulatory signature coordinating glucose- and one-carbon metabolism, suggesting that regulation of carbon metabolism in cancer may be more diverse and flexible than previously appreciated. Here, we demonstrate how this TR-metabolite map can serve as a resource to predict TRs potentially responsible for metabolic transformation in patient-derived tumor samples, opening new opportunities in understanding disease etiology, selecting therapeutic treatments and in designing modulators of cancer-related TRs.
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Cites
Comprehensive molecular characterization of human colon and rectal cancer
The Cancer Genome Atlas Network (2012) · Nature · doi:10.1038/nature11252 · Cited by 8,499 · see citing works
The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity
Barretina, J., Caponigro, G., Stransky, N., Venkatesan, K., Margolin, A. A., Kim, S., Wilson, C. J., Lehár, J., Kryukov, G. V., Sonkin, D., et al. (2012) · Nature · doi:10.1038/nature11003 · Cited by 7,950 · see citing works
The emerging hallmarks of cancer metabolism
Pavlova, N. N., & Thompson, C. B. (2016) · Cell Metabolism · doi:10.1016/j.cmet.2015.12.006 · Cited by 5,926 · see citing works
Comprehensive molecular profiling of lung adenocarcinoma
The Cancer Genome Atlas Research Network (2014) · Nature · doi:10.1038/nature13385 · Cited by 5,732 · see citing works
Cancer-associated IDH1 mutations produce 2-hydroxyglutarate
Dang, L., White, D. W., Gross, S., Bennett, B. D., Bittinger, M. A., Driggers, E. M., Fantin, V. R., Jang, H. G., Jin, S., Keenan, M. C., et al. (2009) · Nature · doi:10.1038/nature08617 · Cited by 4,154 · see citing works
A Next Generation Connectivity Map: L1000 Platform and the First 1,000,000 Profiles
Subramanian, A., Narayan, R., Corsello, S. M., Peck, D. D., Natoli, T. E., Lu, X., Gould, J., Davis, J. F., Tubelli, A. A., Asiedu, J. K., et al. (2017) · Cell · doi:10.1016/j.cell.2017.10.049 · Cited by 3,515 · see citing works
Crosstalk of reactive oxygen species and NF-κB signaling
Morgan, M. J., & Liu, Z.-G. (2011) · Cell Research · doi:10.1038/cr.2010.178 · Cited by 3,505 · see citing works
The first 30 years of p53: growing ever more complex.
Levine, A. J., & Oren, M. (2009) · Nature Reviews Cancer · doi:10.1038/nrc2723 · Cited by 1,967 · see citing works
A census of human transcription factors: function, expression and evolution
Vaquerizas, J. M., Kummerfeld, S. K., Teichmann, S. A., & Luscombe, N. M. (2009) · Nature Reviews Genetics · doi:10.1038/nrg2538 · Cited by 1,573 · see citing works
P53 in survival, death and metabolic health: a lifeguard with a licence to kill.
Kruiswijk, F., Labuschagne, C. F., & Vousden, K. H. (2015) · Nature Reviews Molecular Cell Biology · doi:10.1038/nrm4007 · Cited by 1,080 · see citing works
A community-driven global reconstruction of human metabolism
Thiele, I., Swainston, N., Fleming, R. M. T., Hoppe, A., Sahoo, S., Aurich, M. K., Haraldsdottir, H., Mo, M. L., Rolfsson, O., Stobbe, M. D., et al. (2013) · Nature Biotechnology · doi:10.1038/nbt.2488 · Cited by 1,019 · see citing works
Direct inhibition of the NOTCH transcription factor complex
Moellering, R. E., Cornejo, M., Davis, T. N., Bianco, C. D., Aster, J. C., Blacklow, S. C., Kung, A. L., Gilliland, D. G., Verdine, G. L., & Bradner, J. E. (2009) · Nature · doi:10.1038/nature08543 · Cited by 752 · see citing works
Milo, R. (2013) · BioEssays · doi:10.1002/bies.201300066 · Cited by 643 · see citing works
The metabolic profile of tumors depends on both the responsible genetic lesion and tissue type
Yuneva, M. O., Fan, T. W. M., Allen, T. D., Higashi, R. M., Ferraris, D. V., Tsukamoto, T., Matés, J. M., Alonso, F. J., Wang, C., Seo, Y., et al. (2012) · Cell Metabolism · doi:10.1016/j.cmet.2011.12.015 · Cited by 639 · see citing works
Fumarate is an epigenetic modifier that elicits epithelial-to-mesenchymal transition
Sciacovelli, M., Gonçalves, E., Johnson, T. I., Zecchini, V. R., da Costa, A. S. H., Gaude, E., Drubbel, A. V., Theobald, S. J., Abbo, S. R., Tran, M. G. B., et al. (2016) · Nature · doi:10.1038/nature19353 · Cited by 584 · see citing works
Single-cell chromatin modification profiling reveals increased epigenetic variations with aging
Cheung, P., Vallania, F., Warsinske, H. C., Donato, M., Schaffert, S., Chang, S. E., Dvorak, M., Dekker, C. L., Davis, M. M., Utz, P. J., et al. (2018) · Cell · doi:10.1016/j.cell.2018.03.079 · Cited by 323 · see citing works
Chen, J. C., Alvarez, M. J., Talos, F., Dhruv, H., Rieckhof, G. E., Iyer, A., Diefes, K. L., Aldape, K., Berens, M., Shen, M. M., et al. (2014) · Cell · doi:10.1016/j.cell.2014.09.021 · Cited by 188 · see citing works
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Role of Glutathione in Cancer: From Mechanisms to Therapies
Kennedy, L., Sandhu, J. K., Harper, M.-E., & Cuperlovic-Culf, M. (2020) · Biomolecules · doi:10.3390/biom10101429 · Cited by 880 · see citing works
Hu, L., Liu, J., Zhang, W., Wang, T., Zhang, N., Lee, Y. H., & Lu, H. (2020) · Mass Spectrometry Reviews · doi:10.1002/mas.21611 · Cited by 81 · see citing works
- Authors
- Ortmayr, K., Dubuis, S., & Zampieri, M.
- Affiliations
- ETH Zurich (CH)
- Corresponding author
- Zampieri, Mattia
- Venue
- Nature Communications
- Published
- 2019-04-23
- Volume
- 10
- Issue
- 1
- Pages
- 1841-1841
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC6478870
- OpenAlex
- W2941275129
- Cited by
- 142
- References
- 17
- Citations by year
- 2026: 4 · 2025: 17 · 2024: 20 · 2023: 16 · 2022: 30 · 2021: 22 · 2020: 25 · 2019: 7 · 2018: 1
- FWCI
- 6.65
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Nature Communications (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Repository for Publications and Research Data (ETH Zurich) (submittedVersion, cc-by)
- ETH Zürich Research Collection (cc-by)
- Funders
- Austrian Science Fund (FWF P26603, FWF; W1224, P26603, W 1224)
- National Cancer Institute
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer, Hypoxia, and Metabolism
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Metabolism, Diabetes, and Cancer
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Microbial Metabolic Engineering and Bioproduction
- Keywords
- cancer metabolism, transcription factors, cancer cell lines, metabolite profiling, glucose metabolism, one-carbon metabolism, carbon metabolism, metabolic reprogramming, transcriptomics, proteomics, patient-derived tumors
- MeSH terms
- Cell Transformation, Neoplastic, Humans, Neoplasms, Transcription Factors, Genome, Human, Gene Expression Regulation, Neoplastic, Gene Expression Profiling, Protein Interaction Mapping, Proteomics, Cell Line, Tumor, Metabolic Networks and Pathways, Metabolomics, Metabolome, Transcriptome, Protein Interaction Maps
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