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An artificial metabzyme for tumour-cell-specific metabolic therapy

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Abstract

Metabolic dysregulation constitutes a pivotal feature of cancer progression. Enzymes with multiple metal active sites play a major role in this process. Here we report the first metabolic-enzyme-like FeMoO4 nanocatalyst, dubbed ‘artificial metabzyme’. It showcases dual active centres, namely, Fe2+ and tetrahedral Mo4+, that mirror the characteristic architecture of the archetypal metabolic enzyme xanthine oxidoreductase. Employing spatially dynamic metabolomics in conjunction with the assessments of tumour-associated metabolites, we demonstrate that FeMoO4 metabzyme catalyses the metabolic conversion of tumour-abundant xanthine into uric acid. Subsequent metabolic adjustments orchestrate crosstalk with immune cells, suggesting a potential therapeutic pathway for cancer. Our study introduces an innovative paradigm in cancer therapy, where tumour cells are metabolically reprogrammed to autonomously modulate and directly interface with immune cells through the intervention of an artificial metabzyme, for tumour-cell-specific metabolic therapy.

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Fig. 1: Schematic of metabolic-enzyme-like FeMoO4 metabzyme for tumour-cell-specific metabolic therapy.
Fig. 2: Designing a multimetal metabzyme with tetrahedral catalytic frameworks.
Fig. 3: Physicochemical characterizations of FeMoO4 metabzyme.
Fig. 4: DFT studies on the XOR-like catalysis mechanism of FeMoO4 metabzyme.
Fig. 5: FeMoO4-metabzyme-mediated metabolic modulation.
Fig. 6: FeMoO4 metabzyme for tumour-cell-specific metabolic therapy.

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Data availability

The metallic structure models of MoO3, FeMoO4 and Fe3O4 are available at the Materials Project Database (https://materialsproject.org; mp-18856, mp-505526 and mp-19306). The structure model of XOR metabolic enzyme is available at the RCSB Protein Databank (https://rcsb.org; PDB 1FIQ). AFADESI-MSI imaging data have been deposited in the Metaspace database, enabling the visualization of MS imaging results. All the MS imaging data can be directly downloaded from https://metaspace2020.eu/project/metabzyme2024. Source data are provided with this paper.

Code availability

DFT calculations were conducted with Quantum Espresso, which is an open-source package and available at https://www.quantum-espresso.org/.

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Acknowledgements

We acknowledge financial support from the National Key Research and Development Program of China (2022YFB3203801 and 2022YFB3203800 to D.L.; 2022YFB3203804 and 2023YFF0724101 to F.L.), National Natural Science Foundation of China (32071374 to F.L.), Leading Talent of ‘Ten Thousand Plan’-National High-Level Talents Special Support Plan (to D.L.), Anzhong Scholars Outstanding Talents Plan (to X.H.), Shanghai Municipal Health Commission Traditional Chinese Medicine Research Project (2024PT009 to D.L.), Program of Shanghai Academic Research Leader under the Science and Technology Innovation Action Plan (21XD1422100 to D.L.), Explorer Program of Science and Technology Commission of Shanghai Municipality (22TS1400700 to D.L.), Zhejiang Provincial Natural Science Foundation of China (LR22C100001 to F.L.) and the innovative research team of high-level local universities in Shanghai (SHSMU-ZDCX20210900 to D.L.). We thank the beamline BL14W1 of the Shanghai Synchrotron Radiation Facility (SSRF, China) for providing the beamtime. We thank Z. Wu from the Center of Electron Microscopy at Zhejiang University for help with TEM and energy-dispersive spectroscopy measurements. We thank Shanghai Luming Biological Technology for the AFADESI spatially resolved metabolomics used in this study.

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Author notes

  1. These authors contributed equally: Xi Hu, Bo Zhang, Miao Zhang, Wenshi Liang.

Authors and Affiliations

  1. Frontiers Science Center for Transformative Molecules, School of Chemistry and Chemical Engineering, School of Biomedical Engineering, National Center for Translational Medicine, Zhangjiang Institute for Advanced Study, Shanghai Jiao Tong University, Shanghai, China

    Xi Hu, Bo Zhang, Zeyu Liang, Chunhai Fan & Daishun Ling

  2. Songjiang Hospital and Songjiang Research Institute, Shanghai Key Laboratory of Emotions and Affective Disorders, Shanghai Jiao Tong University School of Medicine, Shanghai, China

    Xi Hu & Fangyuan Li

  3. School of Pharmacy, Anhui Province Key Laboratory of Pharmaceutical Preparation Technology and Application, Anhui University of Chinese Medicine, Hefei, China

    Xi Hu, Bangzhen Hong & Tianqi Liu

  4. WLA Laboratories, Shanghai, China

    Bo Zhang & Daishun Ling

  5. Institute of Pharmaceutics, Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, the First Affiliated Hospital, Key Laboratory of Precision Diagnosis and Treatment for Hepatobiliary and Pancreatic Tumour of Zhejiang Province, Zhejiang University, Hangzhou, China

    Miao Zhang, Wenshi Liang, Zhiyuan Ma, Jianpeng Sheng, Shengfei Yang & Fangyuan Li

  6. Shanghai Synchrotron Radiation Facility, Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai, China

    Jichao Zhang

  7. Institute of Molecular Medicine, Shanghai Key Laboratory for Nucleic Acids Chemistry and Nanomedicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China

    Chunhai Fan

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  1. Xi Hu
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Contributions

D.L. and F.L. conceived and designed the project. X.H. designed the experiments. X.H., B.Z., M.Z., W.L., B.H., Z.M., J.S., T.L., S.Y., Z.L. and J.Z. performed the experiments and analysed the data. X.H. drafted the manuscript. D.L., F.L. and C.F. provided constructive advice for data analysis and manuscript writing. All authors reviewed and/or revised the paper.

Corresponding authors

Correspondence to Fangyuan Li or Daishun Ling.

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Nature Nanotechnology thanks Thanh Loc Nguyen and Shiren Wang for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 FeMoO4 metabzyme promotes the phagocytosis of tumour cells by macrophage.

a, CLSM images of B16 cells after incubation with FITC-FeMoO4 metabzyme. Scale bar, 100 μm. The experiment was repeated three times. b,c, Cell viabilities of B16 cells (b) and RAW264.7 cells (c) after incubation with FeMoO4 metabzyme. All data are presented as means ± s.e.m., n = 3 independent experiments. d, Representative images of RAW264.7 cells after incubation with FeMoO4 metabzyme-treated B16-GFP cells. Scale bar, 10 μm. Green, B16-GFP cells. Red arrow, phagocytosis of B16-GFP cells by macrophage. The experiment was repeated three times.

Source data

Extended Data Fig. 2 FeMoO4 metabzyme-mediated tumour cell-specific metabolism regulation.

a, In vivo T2-weighted MR images of B16 melanoma-bearing mouse. Tumour regions are marked with white dashed lines. The experiment was repeated three times. b, Biodistribution of FeMoO4 metabzyme in B16 melanoma-bearing mouse after i.v. administration. c-e, The UA levels of tumours (c), para-carcinoma muscle tissues (d) and serum (e) after different treatments. f, Schematic illustration of tumour-cell-specific metabolic modulation. Data are presented as means ± s.e.m. in (b-e), n = 3 independent experiments. Statistical analysis was performed by one-way ANOVA for comparison in (b-e).

Extended Data Fig. 3 FeMoO4 metabzyme boosts anti-tumor cellular responses.

a-c, The percentage of CD4+ T cells (a), NK cells (b), MDSCs (c) of tumour tissues after different treatments by mass cytometry. d-f, The relative proportion of DCs (d), CD8+ T cells (e) and CD4+ T cells (f) in the tumour-draining lymph nodes. All data are presented as means ± s.e.m., n = 3 independent experiments. Statistical analysis was performed by unpaired t-test (two tailed) for comparison in (a-f). g, Staining pictures of tumour tissues. Scale bar, 50 μm. h, HE staining images of main organs. Scale bar, 100 μm. The experiment was repeated three times. i, Pattern diagram of FeMoO4 metabzyme-mediated cancer metabolic therapy. Upon reaching tumour cells marked by elevated xanthine substrates, FeMoO4 metabzyme catalyses the metabolic-level conversion of xanthine into UA, as a “attack me” signal, facilitating direct communication with macrophages for proinflammatory M1 phenotype polarization. Meanwhile, UA and proinflammatory cytokines could be generated by macrophages to induce DC maturation, thus eliciting antigen-specific T cell activation to directly recognize and attack tumour cells.

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Hu, X., Zhang, B., Zhang, M. et al. An artificial metabzyme for tumour-cell-specific metabolic therapy. Nat. Nanotechnol. (2024). https://doi.org/10.1038/s41565-024-01733-y

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