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Adipocytes reprogram cancer cell metabolism by diverting glucose towards glycerol-3-phosphate thereby promoting metastasis

Nature Metabolism volume 5, pages 1563–1577 (2023)Cite this article

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Abstract

In the tumor microenvironment, adipocytes function as an alternate fuel source for cancer cells. However, whether adipocytes influence macromolecular biosynthesis in cancer cells is unknown. Here we systematically characterized the bidirectional interaction between primary human adipocytes and ovarian cancer (OvCa) cells using multi-platform metabolomics, imaging mass spectrometry, isotope tracing and gene expression analysis. We report that, in OvCa cells co-cultured with adipocytes and in metastatic tumors, a part of the glucose from glycolysis is utilized for the biosynthesis of glycerol-3-phosphate (G3P). Normoxic HIF1α protein regulates the altered flow of glucose-derived carbons in cancer cells, resulting in increased glycerophospholipids and triacylglycerol synthesis. The knockdown of HIF1α or G3P acyltransferase 3 (a regulatory enzyme of glycerophospholipid synthesis) reduced metastasis in xenograft models of OvCa. In summary, we show that, in an adipose-rich tumor microenvironment, cancer cells generate G3P as a precursor for critical membrane and signaling components, thereby promoting metastasis. Targeting biosynthetic processes specific to adipose-rich tumor microenvironments might be an effective strategy against metastasis.

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Fig. 1: Adipocytes increase glucose metabolism and GPLs in OvCa cells.
Fig. 2: Adipocytes increase glycolysis but not oxidative phosphorylation in OvCa cells.
Fig. 3: GPAT3 regulates GPL synthesis.
Fig. 4: Adipocyte-mediated HIF1α expression regulates glucose utilization in cancer cells.
Fig. 5: Reduction of HIF1a expression inhibits omental metastasis.
Fig. 6: Cancer cells adjacent to adipocytes have increased PC.

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Data availability

Metabolomic data are deposited in the metabolomics workbench (https://www.metabolomicsworkbench.org/) and can be queried using the project https://doi.org/10.21228/M82S3K. Raw and processed microarray data are available in the Gene Expression Omnibus database (accession number GSE235641). Unique biological materials will be provided upon reasonable request. Source data are provided with this paper.

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Acknowledgements

We thank all patients at The University of Chicago for donating tissue to this study. We thank F. Coscia and M. Mann (Max Plank Institute of Biochemistry, Martinsried, Bavaria) for help with proteomic analysis, and J. Andrade at the Center for Research Informatics (University of Chicago) for assistance with microarray analysis. This work was supported by the Ann Sol Schreiber award (372898), Colleen’s dream foundation and the DOD pilot award (W81XWH2110376) to A.M., NIH grants (R01CA169604, R35CA264619) awarded to E.L. D.B. is supported by NIH grant F31CA239330. R.J.D. is supported by grants from NIH (R35CA22044901) and by the Howard Hughes Medical Institute. We thank G. Isenberg for editing the paper. Figure 2b and Supplementary Figs. 2e,f, 5f and 6a,f were created using BioRender.com.

Author information

Authors and Affiliations

  1. Department of Obstetrics and Gynecology/Section of Gynecologic Oncology—Center for Integrative Sciences, University of Chicago, Chicago, IL, USA

    Abir Mukherjee, Malu Zandbergen, Chun-Yi Chiang, Medine Tasdemir & Ernst Lengyel

  2. Children’s Medical Center Research Institute, University of Texas Southwestern Medical Center, Dallas, TX, USA

    Divya Bezwada, Hieu S. Vu & Ralph J. DeBerardinis

  3. Fondazione Pisana per la Scienza ONLUS, San Giuliano Terme, Italy

    Francesco Greco

  4. Institute of Life Sciences, Sant’Anna School of Advanced Studies, Pisa, Italy

    Francesco Greco & Liam A. McDonnell

  5. NIH West Coast Metabolomics Center, University of California, Davis, CA, USA

    Tong Shen, Dmitry Grapov, Michael R. La Frano, John W. Newman & Oliver Fiehn

  6. Department of Clinical Cancer Prevention, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

    Johannes Fahrmann

  7. Department of Medicine/Section of Hematology and Oncology, University of Chicago, Chicago, IL, USA

    Brandon Faubert

  8. Department of Experimental Oncology, IRCCS European Institute of Oncology, Milano, Italy

    Luigi Nezi

  9. 9Howard Hughes Medical Institute, University of Texas Southwestern Medical Center, Dallas, TX, USA

    Ralph J. DeBerardinis

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Contributions

A.M. and E.L. supervised the study, and wrote and edited the paper. Data acquisition and analysis were carried out by all A.M., D.B., F.G., C.-Y.C., M.Z., T.S., H.S.V., B.F., M.T., J.F., D.G., M.R.L.F., J.W.N., O.F., R.J.D. and E.L. Adipocyte isolation, co-culture, Seahorse, gene and protein expression analysis (A.M. and C.-Y.C.), metabolomics (A.M., J.F., M.R.L.F., D.G. and T.S.), omics data integrations (D.G. and A.M.), IMS (F.G., A.M., L.A.M. and L.N.), [13C]-glucose isotope tracing (A.M., D.B., H.S.V., B.F. and R.J.D.), xenograft studies (A.M.), animal studies (A.M., M.Z. and M.T.) and proteomics (A.M.). All authors edited and approved the final paper.

Corresponding author

Correspondence to Ernst Lengyel.

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Competing interests

There are no potential conflicts of interest. E.L. received funding from Abbvie and Arsenal Bioscience for preclinical research studies unrelated to the submitted paper.

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Nature Metabolism thanks Rugang Zhang and the other, anonymous, reviewers for their contribution to the peer review of this work. Alfredo Giménez-Cassina, in collaboration with the Nature Metabolism team.

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Extended data

Extended Data Fig. 1 Untargeted metabolomic analysis of adipocytes after co-culture with ovarian cancer cells.

(a) Network map showing altered levels (red = increased; green = decreased) of biochemically and structurally similar metabolites in adipocytes after co-culture with SKOV3ip1 OvCa cells for 18 h. Biochemically and structurally similar metabolites are clustered. Metabolites in the same biochemical pathway are connected by orange lines. Structurally similar metabolites are connected by blue lines. (b) Principle component analysis (PCA) showing lipidomic changes in SKOV3ip1 cells induced by conditioned media derived from patient matched adipocytes (Adi CM), fibroblasts (NOF CM) and preadipocytes (Pre-adi CM). (c) Venn diagram showing number of metabolites altered in adipocytes, cancer cells, and co-culture derived media (p < 0.05, two-way ANOVA, mixed model post hoc Tukey HSD). (d) Changes in metabolites in adipocytes, cancer cells, and co-culture derived media. C24-ceramide, C16-ceramide and 18:1 sphingosine were primarily secreted by adipocytes; endocannabinoids such as 2-Arachidonylglycerol and 2-Linoleoylglycerol were increased in co-culture conditions (p < 0.05, one-way ANOVA). (e) Changes in phosphatidylcholine levels in the secretome of cancer cells (blue), adipocytes (green), and cancer cells co-cultured with adipocytes (red). Data extracted from metabolomics described in Fig. 1c. Statistical significance was calculated using two-way ANOVA, * p < 0.05, ** p < 0.01, *** p < 0.001.

Source data

Extended Data Fig. 2 Joint gene-metabolite analysis of adipocytes co-cultured with cancer cells.

Gene expression analysis. Heat map showing clustering of (a) cancer cells +/− adipocyte co-culture, (b) adipocytes +/− co-culture with SKOV3ip1 cells for 12 h. Gene Set Enrichment Analysis (cell cycle) based on the gene expression (microarray) of (c) cancer cells +/− adipocyte co-culture and (d) adipocytes +/− SKOV3ip1 co-culture. (One-way ANOVA, FDR, q value < 0.05). Integration of gene expression and metabolites using IMPala, depicting changes in the (e) cancer cells +/− adipocyte co-culture and (f) adipocytes +/− co-culture with SKOV3ip1 cells. Genes and metabolites increased with co-culture are in red, those reduced are in green. Fold changes of significantly altered genes from microarray analysis are shown in a table on left (One-way ANOVA, FDR, q value < 0.05). (E, F Created with BioRender.com (2023)).

Extended Data Fig. 3 Seahorse and metabolomics of ovarian cancer cells co-cultured with adipocytes.

(a) Glycolytic (ECAR) profiles of SKOV3ip1 cells treated with adipocyte conditioned media (Adi CM) for 18 h measured using a Seahorse XFe96 analyzer (n = 3 independent experiments, mean +/− SEM are plotted as bar graphs, two-way ANOVA, ** p < 0.05). (b) ECAR profile of OVCAR5 cells treated with Adi CM for 18 h (n = 3 independent experiments). (c–d), Ex-vivo culture of omental tumor explants. Stable isotope tracing using [13C]-glucose (data from Fig. 2c) for 24 h. Isotopologue prolife of (C) TCA cycle intermediates and (D) Lysophosphocholine (LPC) 18:1 for three patient tumors is plotted (n = 3 independent biological samples). Mean +/− SEM are plotted as bar graphs. (e) Stable isotope tracing. SKOV3ip1 cells were treated with either control or Adi CM for 6 h and glucose utilization traced using uniformly labeled [13C]-glucose. Bar graph depict contribution of 13C vs 12C carbon to the total pool of glycerol-3-phosphate under specific treatment conditions (n = 3 independent experiments). Mean +/− SEM are plotted as bar graphs. (p = 0.11, two-way ANOVA; and p = 0.001 unpaired T-test).

Source data

Extended Data Fig. 4 Knockdown of GPAT3 reduces metastatic tumor burden in mice.

Scrambled (Control shRNA) or GPAT3 targeting shRNA (GPAT3 shRNA) transduced, luciferase expressing, HeyA8 cancer cells were injected intraperitoneally into athymic nude mice and tumor burden visualized after 2 weeks using the IVIS spectrum in vivo imaging system. Images show end-point difference in tumor burden.

Extended Data Fig. 5 Adipocyte-induced HIF1α regulates glycolysis.

(a) Immunoblot of HIF1α in ovarian cancer cell lines co-cultured with Adi for 16 h (n = 3 independent experiments). (b) Immunoblot for HIF1α, showing stable knockdown of HIF1α in SKOV3ip1 cells (n = 3 independent experiments). (c) Principal component analysis using adipocyte co-cultured samples (16 h) stably expressing either control shRNA or HIF1α shRNA cells. (d) qPCR analysis to determine glycerol-3-phosphate dehydrogenase (GPD1) mRNA expression in stable HIF1α knockdown SKOV3ip1 cells after 12 h adipocyte co-culture. Mean +/− SEM are plotted as bar graphs. (n = 3 independent experiments, two-tailed t-test, ** p < 0.005). (e-f) SKOV3ip1 HIF1α knockdown cells were treated with Adi CM for 18 h, followed by Seahorse analysis to determine changes in ECAR (n = 3 independent experiments). (Mean +/− SEM, two-way ANOVA, *** p = 0.0005, **** p < 0.0001). g) Adi CM was fractioned into metabolite and non-metabolite fractions based on size (3 kd). SKOV3ip1 cells were treated with both fractions for 6 h and immunoblot carried out (n = 3 independent experiments). (h) Immunoblot for HIF1α after treatment of SKOV3i1p cells with 10 ng/ml of recombinant human cytokines (IL-6, IL-8, and MCP1) for 6 hr (n = 3 independent experiments. (i) Immunoblot of HIF1α expression in SKOV3ip1 cells treated with Adi CM (6 h) +/− neutralizing antibodies against human IL-6, IL-8, or MCP-1(n = 3 independent experiments. (j) SKOV3ip1 cells were pretreated with the STAT-3 inhibitor STATTIC (10 µM), the JAK2 inhibitor AZD-1480 (10 µM), or a MEK inhibitor, Trametinib (1 µM) for 30 min, followed by incubation with Adi CM for 6 h. Immunoblot of HIF1α expression (n = 3 independent experiments).

Source data

Extended Data Fig. 6 Adipocyte-induced HIF1α alters the lipidome of ovarian cancer cells.

Lipidomics. HIF1α shRNA or control shRNA transduced SKOV3ip1 cells were co-cultured with primary omental adipocytes for 18 h, and mass spectrometry performed. The heat map depicts fold changes of significantly altered lipids (two-tailed t-test, p value of <s0.05) with adipocyte co-culture and HIF1α knockdown. Lipids: Triacylglycerol, TG; Phosphatidylethanolamine, PE; Lysophosphatidylcholine, LPC; phosphatidylcholine, PC; carnitine, CAR; ceramide, Cer; fatty acid, FA; diacylglycerol, DG; phosphatidylglycerol, PG; phosphatidylinositol, PI; phosphatidylserine, PS; sphingomyelin, SM.

Source data

Extended Data Fig. 7 Effect of HIF1α knockdown on ovarian cancer cells.

(a, b) Explant assay. SKOV3ip1 cells transduced with either control shRNA or HIF1α shRNA were cultured with non-cancerous human omentum for (A) 18 h and (B) 72 h to measure cellular adherence capacity and proliferation, respectively (n = 3 biologically independent samples). Mean +/− SEM are plotted as bar graphs. (c) C11 Bodipy stained cells (control and HIF1α) were treated with adipocyte-derived conditioned media and mean fluorescent intensity measured after 24 h using Incucyte (data extracted from Fig. 5f). (n = 3 independent experiments, Mean +/− SEM are plotted as bar graphs, one-way ANOVA * p < 0.05, *** p = 0.0002, p < 0.0001.) (D-E) Lipid ROS measurements. HeyA8 cells transduced with either scrambled shRNA (Control shRNA) or GPAT3 shRNA was labelled with C11-Bodipy dye was treated adipocyte-derived conditioned media (Adi CM). (d) Images were taken every 2 h using Incucyte and total green fluorescence intensity plotted. (n = 3 independent experiments, Mean +/− SEM are plotted). (e) Bar graph depicts green fluorescence intensity measured at the 24 h time point. (n = 3 independent experiments, two-way ANOVA, ** p = 0.003, mean +/− SEM are plotted). (f) Immunohistochemistry for 4-HNE adducts in serial sections of xenograft omental tumors (from Fig. 3d). Staining intensities of the images (left) were quantified using ImageJ (right). (n = 3 independent experiments, two-tailed t-test, mean +/− SEM are plotted as bar graphs). (g) MTT assay to determine the viability of HeyA8 cells after treatment with the indicated compounds. (n = 3 independent experiments, two-way ANOVA, p < 0.05, mean +/− SEM are plotted as bar graphs).

Source data

Supplementary information

Reporting Summary

Supplementary Table 1

Significantly altered (two-tailed t-test, P < 0.05) metabolites in SKOV3ip1 cells treated with conditioned medium derived from patient-matched primary adipocytes, fibroblasts and pre-adipocytes.

Supplementary Table 2

Differentially expressed genes in both adipocyte and SKOV3i1p OvCa cells after co-culture.

Supplementary Table 3

Joint gene–metabolite analysis using IMPaLa.

Supplementary Table 4

Proteomics to determine the effect of HIF1α knockdown on adipocyte-induced protein changes.

Supplementary Table 5

Lipidome of cancer cells co-cultured with primary human adipocytes after HIF1α knockdown.

Supplementary Table 6

IMS.

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Unprocessed western blots.

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Unprocessed western blots.

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Mukherjee, A., Bezwada, D., Greco, F. et al. Adipocytes reprogram cancer cell metabolism by diverting glucose towards glycerol-3-phosphate thereby promoting metastasis. Nat Metab 5, 1563–1577 (2023). https://doi.org/10.1038/s42255-023-00879-8

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