Abstract
The alteration of metabolic pathways is a critical strategy for cancer cells to attain the traits necessary for metastasis in disease progression. Here, we find that dysregulation of propionate metabolism produces a pro-aggressive signature in breast and lung cancer cells, increasing their metastatic potential. This occurs through the downregulation of methylmalonyl coenzyme A epimerase (MCEE), mediated by an extracellular signal-regulated kinase 2-driven transcription factor Sp1/early growth response protein 1 transcriptional switch driven by metastatic signalling at its promoter level. The loss of MCEE results in reduced propionate-driven anaplerotic flux and intracellular and intratumoral accumulation of methylmalonic acid, a by-product of propionate metabolism that promotes cancer cell invasiveness. Altogether, we present a previously uncharacterized dysregulation of propionate metabolism as an important contributor to cancer and a valuable potential target in the therapeutic treatment of metastatic carcinomas.
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Data availability
Source data information for the metabolomics experiment can be found in Supplementary Table 1. RNA-seq data that support the findings of this study have been deposited in the GEO under accession no. GSE161108 and provided as summary information in Supplementary Table 2. Source data are provided with this paper. For the RNA-seq analysis, the hg38 reference genome database was obtained from iGenomes and the GSEA analysis was done with gene sets derived from the GO biological processes gene sets in the MSigDB collection v.6.2, which can be accessed at https://www.gsea-msigdb.org/gsea/msigdb/index.jsp.
Code availability
The Fiji/ImageJ macro for the automation of the quantification of transwell migration and invasion assays is not a standalone code but is available from the corresponding authors upon reasonable request.
References
Dillekås, H., Rogers, M. S. & Straume, O. Are 90% of deaths from cancer caused by metastases? Cancer Med. 8, 5574–5576 (2019).
Wang, H. et al. Global, regional, and national life expectancy, all-cause mortality, and cause-specific mortality for 249 causes of death, 1980–2015: a systematic analysis for the Global Burden of Disease Study 2015. Lancet 388, 1459–1544 (2016).
DeBerardinis, R. J. & Chandel, N. S. Fundamentals of cancer metabolism. Sci. Adv. 2, e1600200 (2016).
Corrado, M., Scorrano, L. & Campello, S. Changing perspective on oncometabolites: from metabolic signature of cancer to tumorigenic and immunosuppressive agents. Oncotarget 7, 46692–46706 (2016).
Gomes, A. P. et al. Age-induced accumulation of methylmalonic acid promotes tumour progression. Nature 585, 283–287 (2020).
Tao, K., Fang, M., Alroy, J. & Sahagian, G. G. Imagable 4T1 model for the study of late stage breast cancer. BMC Cancer 8, 228 (2008).
Rinaldi, G. et al. In vivo evidence for serine biosynthesis-defined sensitivity of lung metastasis, but not of primary breast tumors, to mTORC1 inhibition. Mol. Cell 81, 386–397.e7 (2021).
Ngo, B. et al. Limited environmental serine and glycine confer brain metastasis sensitivity to PHGDH inhibition. Cancer Discov. 10, 1352–1373 (2020).
Spinelli, J. B. et al. Metabolic recycling of ammonia via glutamate dehydrogenase supports breast cancer biomass. Science 358, 941–946 (2017).
Aslakson, C. J. & Miller, F. R. Selective events in the metastatic process defined by analysis of the sequential dissemination of subpopulations of a mouse mammary tumor. Cancer Res. 52, 1399–1405 (1992).
Padua, D. & Massagué, J. Roles of TGFβ in metastasis. Cell Res. 19, 89–102 (2009).
Liu, J., Lin, P. C. & Zhou, B. P. Inflammation fuels tumor progress and metastasis. Curr. Pharm. Des. 21, 3032–3040 (2015).
Gomes, A. P. et al. Dynamic incorporation of histone H3 variants into chromatin is essential for acquisition of aggressive traits and metastatic colonization. Cancer Cell 36, 402–417.e13 (2019).
Iwamoto, T. et al. Distinct gene expression profiles between primary breast cancers and brain metastases from pair-matched samples. Sci. Rep. 9, 13343 (2019).
Shin, S., Dimitri, C. A., Yoon, S.-O., Dowdle, W. & Blenis, J. ERK2 but not ERK1 induces epithelial-to-mesenchymal transformation via DEF motif-dependent signaling events. Mol. Cell 38, 114–127 (2010).
Shin, S. et al. ERK2 regulates epithelial-to-mesenchymal plasticity through DOCK10-dependent Rac1/FoxO1 activation. Proc. Natl Acad. Sci. USA 116, 2967–2976 (2019).
Vashi, P., Edwin, P., Popiel, B., Lammersfeld, C. & Gupta, D. Methylmalonic acid and homocysteine as indicators of vitamin B-12 deficiency in cancer. PLoS ONE 11, e0147843 (2016).
Minn, A. J. et al. Genes that mediate breast cancer metastasis to lung. Nature 436, 518–524 (2005).
Miller, F. R., Miller, B. E. & Heppner, G. H. Characterization of metastatic heterogeneity among subpopulations of a single mouse mammary tumor: heterogeneity in phenotypic stability. Invasion Metastasis 3, 22–31 (1983).
Broekaert, D. & Fendt, S.-M. Measuring in vivo tissue metabolism using 13C glucose infusions in mice. Methods Mol. Biol. 1862, 67–82 (2019).
Yuan, M., Breitkopf, S. B., Yang, X. & Asara, J. M. A positive/negative ion-switching, targeted mass spectrometry-based metabolomics platform for bodily fluids, cells, and fresh and fixed tissue. Nat. Protoc. 7, 872–881 (2012).
Huttlin, E. L. et al. The BioPlex network: a systematic exploration of the human interactome. Cell 162, 425–440 (2015).
Bos, P. D. et al. Genes that mediate breast cancer metastasis to the brain. Nature 459, 1005–1009 (2009).
Mootha, V. K. et al. PGC-1α-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat. Genet. 34, 267–273 (2003).
Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).
Oskarsson, T. et al. Breast cancer cells produce tenascin C as a metastatic niche component to colonize the lungs. Nat. Med. 17, 867–874 (2011).
Acknowledgements
We thank members of the Blenis and Cantley laboratories for critical input on this project. We also thank W. Schiemann for the 4T1 clones and M. Planque for experimental assistance. The Gomes laboratory is supported by a Pathway to Independence Award to A.P.G. from the National Cancer Institute (no. R00CA218686), a New Innovator Award from the Office of the Director/National Institutes of Health (NIH) (no. DP2 AG0776980) to A.P.G., the American Lung Association, Florida Health Department Bankhead-Coley Research Program, Florida Breast Cancer Foundation and George Edgecomb Society of Moffitt Cancer Center. T.S. is supported by the NIH F31 predoctoral fellowship no. F31CA220750. This research was supported by the NIH grant no. R01CA46595 and a research agreement with Highline Therapeutics to J.B. S.-M.F. is funded by the European Research Council (ERC) under the ERC Consolidator Grant Agreement no. 711486-MetaRegulation, Research Foundation–Flanders research grants and projects, Katholieke Universiteit Leuven Methusalem Co-Funding and Fonds Baillet Latour.
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Authors and Affiliations
Contributions
A.P.G. and J.B. conceived the project. A.P.G. and D.I. performed all the molecular biology, EMT-related and invasion and migration experiments, prepared the RNA for the RNA-seq experiments and assisted on all the other experiments. V.L. and T.S. performed all the mouse experiments and assisted on all the other experiments. S.D. assisted with the MCEE analysis of patient samples and performed the proliferation assays. A.P.M. and B.E.S. quantified the migration and invasion experiments. A.R. produced the viral particles, generated the genetically modified cell lines, performed the qPCR analysis of MCEE and assisted with the metabolite extractions and MMA measurements. J.H. generated the constructs and assisted in the EMT-related experiments. D.B. and I.E. collected the tumour and metastases tissues and prepared the samples for the metabolomics analysis. T.S. and E.M. prepared and analysed the 13C tracing analysis and assisted on all other metabolite measurements. M.N. and J.B.N. optimized the ERK2 D319N mutant. J.M.A. performed the metabolomics analysis. A.P.G., J.M.A., L.C.C., S.-M.F. and J.B. supervised the project. A.P.G., D.I., V.L., A.P.M., B.E.S., E.M. and J.B. analysed the data. The manuscript was written by A.P.G., V.L. and J.B. and edited by D.I., T.S., I.E., B.E.S. and S.-M.F. All authors discussed the results and approved the manuscript.
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Competing interests
S.-M.F. has received funding from Bayer, Merck and Black Belt Therapeutics and has consulted for Fund+. L.C.C. owns equity in, receives compensation from and serves on the board of directors and scientific advisory board of Agios Pharmaceuticals and Petra Pharma Corporation. The other authors declare no competing interests.
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Nature Metabolism thanks Edward Chambers, Sara Zanivan and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editors: Alfredo Giménez-Cassina and George Caputa, in collaboration with the Nature Metabolism team.
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Extended data
Extended Data Fig. 1 Methylmalonic acid and MCEE levels are altered by metastatic signalling in different cancer cell models.
(a) Propionate metabolism-related enzyme levels evaluated by immunoblots in 4T1-derived clones of cells with different metastatic potential; representative images (n = 4). (b) MMA levels in A549 cells treated with TGFβ + TNFα for 3 days (n = 4, two-tailed t-test). c, Propionate metabolism-related enzyme levels evaluated by immunoblots in A549 cells treated with TGFβ + TNFα for 3 days; representative images (n = 4). d, MCEE-luciferase promoter activity in A549 cells treated with TGFβ + TNFα for 3 days (n = 4, two-tailed t-test). e, MMA levels in non-metastatic and metastatic triple negative breast cancer human cell lines (n = 4). f, Kaplan-Meyer survival curve of breast cancer patients as a function of MCEE expression. g, Kaplan-Meyer survival curve of lymph node positive triple negative breast cancer patients as a function of MCEE expression. All values are expressed as mean ± SEM.
Extended Data Fig. 2 Knockdown of MCEE induces a pro-aggressive reprogramming.
a, b, MMA levels in HCC1806 (a) and MCF-10A (b) cells with MCEE knockdown for 2 days (n = 4, one-way ANOVA with Tukey’s multiple comparison test). c, Immunoblots for EMT and aggressiveness markers in HCC1806, MCF-10A and A549 cells with MCEE knockdown for 10 days; representative images (n = 4). All values are expressed as mean ± SEM.
Extended Data Fig. 3 Suppression of MUT induces a pro-aggressive reprogramming.
a, MMA levels in MCF-10A cell with MUT knockdown for 3 days (n = 3, one-way ANOVA with Tukey’s multiple comparison test). b, c, Immunoblots for EMT and aggressiveness markers in MCF-10A (b) and A549 (c) cells with MUT knockdown for 10 days; representative images (n = 4). d, e, f, g, mRNA levels of SOX4 (d), TGFB1 (e), TGFBR1 (f), and TGFBR3 (g) evaluated by RNA sequencing in A549 cells with MUT knockdown for 3 days (n = 3, one-way ANOVA with Tukey’s multiple comparison test). h, MMA levels in MDA-MB-231-LM2 versus MDA-MB-231-luciferase parental cells (n = 8, two-tailed t-test). All values are expressed as mean ± SEM.
Extended Data Fig. 4 Vitamin B12 deficiency induces a pro-aggressive reprogramming.
a, MMA levels in MCF-10A cells grown in complete or Vitamin B12-depleted media for 9 days (n = 3, two-tailed t-test). b, Immunoblots for EMT and aggressiveness markers in HCC1806, MCF-10A and A549 cells grown in complete or Vitamin B12-depleted media for 10 days; representative images (n = 4). c, d, MMA levels in HCC1806 (n = 4) (c) and MCF-10A (n = 4) (d) cells with MMAB knockdown for 3 days (one-way ANOVA with Tukey’s multiple comparison test). e, Immunoblots for EMT and aggressiveness markers in HCC1806, MCF-10A and A549 cells with MMAB knockdown for 10 days; representative images (n = 4). All values are expressed as mean ± SEM.
Extended Data Fig. 5 Overexpression of PCC induces a pro-aggressive reprogramming.
a, b, Propionyl-CoA (a) and MMA (b) levels in MCF-10A cells overexpressing PCCA and PCCB for 5 days (n = 3, two-tailed t-test). c-f, TCA cycle intermediates succinate (c), fumarate (d), malate (e), oxaloacetate (f) in MCF-10A cells overexpressing PCCA and PCCB for 5 days (n = 3, two-tailed t-test). g, Immunoblots for EMT and aggressiveness markers in HCC1806, MCF-10A and A549 cells overexpressing PCCA and PCCB for 10 days; representative images (n = 4). h, i, Transwell migration (h) and invasion (i) assays of MDA-MB-231-luciferase parental cells overexpressing PCCA and PCCB for 6 days (n = 4, two-tailed t-test). j, k, Lung colonization assay of MDA-MB-231-luciferase parental cells injected after 6 days of PCCA and PCCB overexpression, imaged at 6 weeks; representative images (j) and quantification (k) (n = 10, two-tailed t-test). All values are expressed as mean ± SEM.
Extended Data Fig. 6 Knockdown of PCCA does not induce EMT.
a, b, Immunoblots for EMT markers in MCF-10A (a), and A549 (b) cells with PCCA knockdown for 10 days; representative images (n = 4). c, Immunoblots for EMT markers in MCF-10A and A549 cells with PCCA knockdown and treated with 5 mM MMA for 10 days; representative images (n = 4). d, Immunoblots for EMT markers in A549 cells with PCCA knockdown and treated with TGFβ + TNFα for 5 days; representative images (n = 4). e, MMA levels in Hs578T cells with PCCA knockdown for 5 days (n = 4, one-way ANOVA with Tukey’s multiple comparison test). f, g, Transwell migration (f) and invasion (g) assays of Hs578T with PCCA knockdown for 5 days (n = 4, one-way ANOVA with Tukey’s multiple comparison test). h, i, Proliferation of Hs578T (h) and MDA-MB-231-LM2 (i) with PCCA knockdown for 5 days (n = 4, two-way repeated measures ANOVA test based on general linear model (GLM) with Tukey’s multiple comparison test, p values only shown for end point). All values are expressed as mean ± SEM.
Supplementary information
Supplementary Information
Legends for Supplementary Tables 1–3.
Supplementary Tables
Summary data for metabolomics (1), RNA-seq analyses (2) and qPCR primer sequences (3).
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Gomes, A.P., Ilter, D., Low, V. et al. Altered propionate metabolism contributes to tumour progression and aggressiveness. Nat Metab 4, 435–443 (2022). https://doi.org/10.1038/s42255-022-00553-5
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DOI: https://doi.org/10.1038/s42255-022-00553-5
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