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Cancer-cell-secreted exosomal miR-105 promotes tumour growth through the MYC-dependent metabolic reprogramming of stromal cells

Nature Cell Biologyvolume 20pages597609 (2018) | Download Citation

Abstract

Cancer and other cells residing in the same niche engage various modes of interactions to synchronize and buffer the negative effects of environmental changes. Extracellular microRNAs (miRNAs) have recently been implicated in the intercellular crosstalk. Here we show a mechanistic model involving breast-cancer-secreted, extracellular-vesicle-encapsulated miR-105, which is induced by the oncoprotein MYC in cancer cells and, in turn, activates MYC signalling in cancer-associated fibroblasts (CAFs) to induce a metabolic program. This results in the capacity of CAFs to display different metabolic features in response to changes in the metabolic environment. When nutrients are sufficient, miR-105-reprogrammed CAFs enhance glucose and glutamine metabolism to fuel adjacent cancer cells. When nutrient levels are low and metabolic by-products accumulate, these CAFs detoxify metabolic wastes, including lactic acid and ammonium, by converting them into energy-rich metabolites. Thus, the miR-105-mediated metabolic reprogramming of stromal cells contributes to sustained tumour growth by conditioning the shared metabolic environment.

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Acknowledgements

This work was supported by the National Institutes of Health (NIH)/National Cancer Institute (NCI) grants R01CA218140 (S.E.W.), R01CA166020 (S.E.W.), R01CA206911 (S.E.W.) and R01CA163586 (S.E.W.), California Breast Cancer Research Program grant 20IB-0118 (S.E.W.), Breast Cancer Research Foundation-AACR grant 12-60-26-WANG (S.E.W.) and the National Key Technology R&D Program of China Grant 2015BAI12B12 (X.R.). Research reported in this publication included work performed in Core facilities supported by the NIH/NCI under grant number P30CA23100 (UCSD Cancer Center) and P30CA33572 (City of Hope Cancer Center). We acknowledge the ENCODE Consortium and the ENCODE production laboratories generating the data sets used herein for the analysis.

Author information

Affiliations

  1. Department of Pathology, University of California San Diego, La Jolla, CA, USA

    • Wei Yan
    • , Miranda Y. Fong
    • , Minghui Cao
    • , Oluwole Fadare
    • , Donald P. Pizzo
    • , Jiawen Wu
    • , Andrew R. Chin
    •  & Shizhen Emily Wang
  2. Department of Molecular and Cellular Biology, Beckman Research Institute of City of Hope, Duarte, CA, USA

    • Xiwei Wu
  3. Department of Cancer Biology, Beckman Research Institute of City of Hope, Duarte, CA, USA

    • Weiying Zhou
    •  & Miranda Y. Fong
  4. School of Pharmacy, Chongqing Medical University, Chongqing, China

    • Weiying Zhou
  5. Department of Pharmacology and Cancer Biology, Duke University, Durham, NC, USA

    • Juan Liu
    • , Xiaojing Liu
    •  & Jason W. Locasale
  6. Department of Molecular Medicine, Beckman Research Institute of City of Hope, Duarte, CA, USA

    • Chih-Hong Chen
    •  & Yuan Chen
  7. Department of Immunology and Biotherapy, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China

    • Liang Liu
    •  & Xiubao Ren
  8. City of Hope Irell & Manella Graduate School of Biological Sciences, Duarte, CA, USA

    • Xuxiang Liu
    •  & Andrew R. Chin
  9. Moores Cancer Center, University of California San Diego, La Jolla, CA, USA

    • Shizhen Emily Wang

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Contributions

S.E.W. and W.Y. conceived ideas, and J.W.L., Y.C. and X.R. contributed to project planning. W.Y. and S.E.W. designed and performed most of the experiments. X.W. performed all bioinformatics analyses. W.Z. performed some experiments with the MCF10A-derived lines. M.Y.F., M.C., J.W. and L.L. assisted with cell line construction and mouse experiments. Xuxiang L. and A.R.C. assisted with the nanoparticle tracking analysis and EV gradient separation. J.L., Xiaojing L. and J.W.L. performed LC/HRMS and data analysis. C.-H.C. and Y.C. assisted with NMR analysis. M.Y.F., D.P.P. and O.F. performed IHC and pathological evaluation. S.E.W. and W.Y. wrote the manuscript.

Competing interests

The authors declare no competing interests.

Corresponding author

Correspondence to Shizhen Emily Wang.

Integrated supplementary information

  1. Supplementary Figure 1 Characterization of primary CAFs.

    (a-b) CAFs in culture were examined by immunofluorescence (a) and flow cytometry (b; gating performed according to bead-based compensation controls) for the positivity of PDGFβ and vimentin and negativity of EpCAM and CD31. Bar = 100 μm. The experiment was repeated independently for three times with similar results.

  2. Supplementary Figure 2 Characterization of EVs.

    (a) EVs pelleted at 110,000 ×g were analysed by nanoparticle tracking analysis (n = 3 independent experiments). The black line indicates mean, whereas the red shaded area indicates SEM. (b) Numbers of secreted EVs determined by nanoparticle tracking analysis (n = 3 independent experiments). (c) A schematic representation of the gradient separation procedure to further characterize the 110,000 ×g pellets. (d) Western blot, density measurement, and RT-qPCR of fractions collected from gradient separation indicating miR-105 enrichment in fractions containing EV markers (n = 3 independent experiments). (e) RT-qPCR-determined levels of miR-105 and miR-155 in equal amounts of EVs from indicated cells (normalized to cel-miR-39-3p spike-in control) and in cell extracts (normalized to U6) (one-way ANOVA, n = 3 independent experiments). For the entire figure, data are shown as mean ± SD; **P < 0.01, ***P < 0.001. †undetectable. Unprocessed original scans of blots are shown in Supplementary Figure 9. Source data are shown in Supplementary Table 5.

  3. Supplementary Figure 3 Overexpression of miR-105 and MYC induce overlapping gene profiles.

    (a) GSEA demonstrating the enrichment of MYC target gene sets in MCF10A cells overexpressing miR-105 vs. control cells overexpressing empty vector or GFP (n = 1 biologically independent miR-105-overexpressing cell line and n = 2 biologically independent control cell lines, i.e., empty-vector- and GFP-expressing cell lines). Genes were ranked by signed P value score from edgeR and subjected to GSEA interrogation, which generated the indicated P value, q value and normalized enrichment score (NES) for each gene set based on 1,000 random permutations. (b) A Venn diagram showing the numbers of genes whose levels are altered by ≥2-fold by overexpression of miR-105 or MYC in MCF10A cells.

  4. Supplementary Figure 4 Schematic representatives of the constructs.

    (a) Sequence of the putative miR-105 binding sites in MXI1 3’UTR and schematic representatives of the reporter constructs. (b) Sequence of the putative E-box in GABRA3/hsa-mir-105/mir-767 gene promoter and schematic representatives of the reporter constructs. (c) Schematic representatives of the genetic deletion of hsa-mir-105-1 and hsa-mir-105-2 genes and the confirmed genomic DNA sequence in MDA-MB-231ΔmiR-105 cells (the deleted sequences are indicated by strikethrough).

  5. Supplementary Figure 5 Cancer-secreted miR-105 enhances glycolysis and glutaminolysis in NIH3T3 cells.

    (a) ECAR and OCR assays in NIH3T3 cells expressing anti-miR-105 or control and treated with indicated EVs or PBS for 48 h (n = 3 independent experiments). **ECAR P < 0.01, ***ECAR P < 0.001, †OCR P < 0.001 (when individually compared to both PBS and MCF10A/vec EV control groups). (b) Changes of metabolite levels in the medium over 72 h by NIH3T3 cells that have been transduced with anti-miR-105 or control and treated with indicated EVs or PBS (n = 3 independent experiments). (c) Levels of metabolites in NIH3T3 cells pretreated as indicated and cultured in regular medium containing 3 g/L glucose and 4 mM glutamine were measured by 1D NMR. Data was normalized to the PBS group (n = 3 independent experiments). (d) Relative RNA levels in EV-treated NIH3T3 cells were detected by RT-qPCR and compared to PBS-treated cells (n = 3 independent experiments). (e) Western blots showing indicated protein levels in EV-treated NIH3T3 cells. For the entire figure, data are shown as mean ± SD; statistical significance was assessed using one-way ANOVA. *P < 0.05, **P < 0.01, ***P < 0.001 (when individually compared to both PBS and MCF10A/vec EV control groups unless indicated). Unprocessed original scans of blots are shown in Supplementary Figure 9. Source data are shown in Supplementary Table 5.

  6. Supplementary Figure 6 miR-105-reprogrammed NIH3T3 cells have an enhanced ability to consume extracellular LA.

    (a) LDH activity assays to measure the interconversion between pyruvate and lactate in EV-treated NIH3T3 cells under high-glucose (3 g/L glucose; no LA) or high-LA (1 g/L glucose; 25 mM LA) conditions (n = 3 independent experiments). (b) Extracellular (in the non-buffered conditioned medium) and intracellular pH of treated NIH3T3 cells following 24-h incubation in medium containing indicated levels of glucose and LA (n = 3 independent experiments). (c) NIH3T3 cells pretreated with EVs were cultured in medium containing indicated levels of glucose and LA. Cell numbers were determined after 24 h and compared to those of PBS-treated cells (n = 3 independent experiments). (d) MDA-MB-231 cells were cultured in high-LA medium (1 g/L glucose; 25 mM LA) that had been conditioned by NIH3T3 cells treated as indicated. Survival of MDA-MB-231 cancer cells in the CM was determined after 24 h by comparing to cells grown in high-glucose medium (3 g/L glucose; no LA) (n = 3 independent experiments). For the entire figure, data are shown as mean ± SD; statistical significance was assessed using one-way ANOVA. *P < 0.05, **P < 0.01, ***P < 0.001. Source data are shown in Supplementary Table 5.

  7. Supplementary Figure 7 ROS levels in CAFs, migration of CM-treated cancer cells, and GLUD1 expression.

    (a) Fluorescence intensity of the ROS indicator CM-H2DCFDA in CAFs treated as indicated in glutamine/glutamate-free medium containing 5 mM NH4Cl (n = 3 independent experiments). H2O2 was used as a positive control to induce ROS. (b) MDA-MB-231 cells were cultured in NH4+-containing medium (3 g/L glucose; glutamine/glutamate-free; 5 mM NH4Cl) that had been conditioned by CAFs treated as indicated. After 24 h, MDA-MB-231 cells were subjected to transwell migration assay and cells that had migrated within 16 h were quantified (n = 3 independent experiments). (c) Left: Relative RNA levels of GLUD1 in EV-treated CAFs grown in regular medium (3 g/L glucose; 4 mM glutamine) (n = 3 independent experiments). Right: Western blots showing protein levels of GLUD1 in EV-treated CAFs grown in regular medium. For the entire figure, data are shown as mean ± SD; statistical significance was assessed using one-way ANOVA. **P < 0.01, ***P < 0.001. Unprocessed original scans of blots are shown in Supplementary Figure 9. Source data are shown in Supplementary Table 5.

  8. Supplementary Figure 8 miR-105-reprogrammed NIH3T3 cells promote tumour growth.

    (a) Female NSG mice received mammary fat pad injection of 1 × 106 MDA-MB-231 cells mixed with 1 × 106 NIH3T3 cells stably expressing anti-miR-105 or control. Tumour volume was measured over the indicated time course (two-way ANOVA, n = 7 mice per condition). (b) Representative IHC images showing Ki67 staining and the overall percentage of tumour cells positive for human Ki67 (two-sided t-test, n = 7 mice per condition). Bar = 100 μm. For the entire figure, data are shown as mean ± SD; ***P < 0.001. Source data are shown in Supplementary Table 5.

  9. Supplementary Figure 9 Unprocessed original scans of blots.

    Unprocessed images of all Western blots as indicated. Molecular size markers in kDa.

Supplementary information

  1. Supplementary Information

    Supplementary Figures 1–9 and Supplementary Table legends.

  2. Reporting Summary.

  3. Supplementary Table 1

    Predicted lists of miRNA targeting MXI1.

  4. Supplementary Table 2

    IPA and ENCODE analyses of genes associated with miR-105 overexpression.

  5. Supplementary Table 3

    Lists of primers, antibodies and other key reagents.

  6. Supplementary Table 4

    LC/HRMS metabolomics data summary.

  7. Supplementary Table 5

    Source data.

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