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The antitumour effects of caloric restriction are mediated by the gut microbiome

Nature Metabolism volume 5, pages 96–110 (2023)Cite this article

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Abstract

Calorie restriction (CR) and intermittent fasting (IF) without malnutrition reduce the risk of cancer development. Separately, CR and IF can also lead to gut microbiota remodelling. However, whether the gut microbiota has a role in the antitumour effect related to CR or IF is still unknown. Here we show that CR, but not IF, protects against subcutaneous MC38 tumour formation through a mechanism that is dependent on the gut microbiota in female mice. After CR, we identify enrichment of Bifidobacterium through 16S rRNA sequencing of the gut microbiome. Moreover, Bifidobacterium bifidum administration is sufficient to rescue the antitumour effect of CR in microbiota-depleted mice. Mechanistically, B. bifidum mediates the CR-induced antitumour effect through acetate production and this effect is also dependent on the accumulation of interferon-γ+CD8+ T cells in the tumour microenvironment. Our results demonstrate that CR can modulate the gut taxonomic composition, which should be of oncological significance in tumour growth kinetics and cancer immunosurveillance.

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Fig. 1: The gut microbiota mediates the antitumour effect of CR but not IF.
Fig. 2: Actinobacteria and its genus Bifidobacterium are over-represented in the gut microbiome of CR-treated mice.
Fig. 3: B. bifidum mimics the antitumour effect of CR.
Fig. 4: CR and B. bifidum suppress primary tumour growth and reduce the burden of metastasis.
Fig. 5: CR increases IFNγ+CD8+ T cells in a gut microbiota-dependent manner.
Fig. 6: CD8+ T cells have a critical role in the B. bifidum-mediated antitumour effect of CR.
Fig. 7: The supernatant of B. bifidum facilitates the antitumour effect of CR.
Fig. 8: Acetate facilitates the antitumour effect of CR.

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Data availability

All data supporting the findings of this study are available within the article and its Supplementary Information files. The data for the 16S amplicon sequencing have been deposited at the Sequence Read Archive and are publicly available under accession no. PRJNA900444. Source data are provided with this paper.

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Acknowledgements

This work was supported by grants from the National Natural Science Foundation of China (no. 81872245 to L.-S.W. and no. 81803601 to B.H.), the Fundamental Research Funds for Minhang Hospital (no. 2020MHJC12 to Y.-Q.M. and no. 2022MHBJ01 to B.H.), the Research Project of Shanghai Municipal Health Commission (no. 20214Y0328 to Y.-Q.M. and 2022YQ052 to B.H.) and the Open Research Fund of State Key Laboratory of Genetic Engineering, Fudan University (no. SKLGE-2112 to L.-S.W.).

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  1. These authors contributed equally: Yu-Qin Mao, Jia-Ting Huang.

Authors and Affiliations

  1. Center for Traditional Chinese Medicine and Gut Microbiota, Minhang Hospital, Fudan University, Shanghai, China

    Yu-Qin Mao, Jia-Ting Huang, Shi-Long Zhang, Chao Kong, Zhan-Ming Li, Hui Jing, Hui-Ling Chen, Chao-Yue Kong, Sheng-Hui Huang, Pei-Ran Cai, Bing Han & Li-Shun Wang

  2. Institute of Fudan-Minhang Academic Health System, Minhang Hospital, Fudan University, Shanghai, China

    Yu-Qin Mao, Jia-Ting Huang, Shi-Long Zhang, Chao Kong, Zhan-Ming Li, Hui Jing, Hui-Ling Chen, Chao-Yue Kong, Sheng-Hui Huang, Pei-Ran Cai, Bing Han & Li-Shun Wang

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Contributions

Y.-Q.M., B.H. and L.-S.W. designed the experiments. Y.-Q.M., J.-T.H., S.-L.Z., H.J., S.-H.H. and C.K. performed the experiments and the statistical analysis. H.-L.C., C.-Y.K., Z.-M.L. and B.H. contributed to the scientific discussion. Y.-Q.M. and L.-S.W. wrote the manuscript. Y.-Q.M., J.-T.H., C.K. and L.-S.W. revised the manuscript. Y.-Q.M., B.H. and L.-S.W. provided funding support.

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Correspondence to Bing Han or Li-Shun Wang.

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Nature Metabolism thanks Bertrand Routy, Laurence Zitvogel and Jay H Chung for their contribution to the peer review of this work. Primary Handling Editor: Ashley Castellanos-Jankiewicz, in collaboration with the Nature Metabolism team.

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Extended data

Extended Data Fig. 1 Structural rearrangement of gut microbiota in mice under CR and IF treatment.

(a, b) Effects of ABX treatment on diversity and richness of gut microbiota revealed by Shannon index (a) and Chao1 index (b). n = 5 mice. (c,d) Change in body weight trajectories (c) and average body weight at the end of the study (d). AL, n = 8 mice; CR, n = 8 mice; IF, n = 8 mice; ABX + AL, n = 10 mice; CR + ABX, n = 10 mice; ABX + IF, n = 10 mice. (e-h) Comparison of relative abundance of four phyla Bacteroidetes(e), Firmicutes(f), Proteobacteria(g), and Verrucomicrobia(h) of gut microbiota from mice under AL, CR and IF treatment. n = 5 mice. Data were mean ± s.e.m. Statistical analysis was performed using unpaired two-tailed t-test.

Source data

Extended Data Fig. 2 The α- and β-diversity of gut microbiota in mice under CR and IF treatment.

(a) Comparison of phylum-level proportional abundance of feces from mice under AL, CR and IF treatment. n = 5 mice. (b-d)Effects of CR and IF treatment on diversity and richness of fecal microbiota revealed respectively by Observed_species (b), Simpson index (c) and ACE index (d), n = 5 mice. (e) Changes in genus levels of related gut microbiota shown by heatmap. n = 5 mice. Data were mean ± s.e.m. Statistical analysis was performed using unpaired two-tailed t-test.

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Extended Data Fig. 3 The abundance of Bifidobacterium were dynamically over-represented in CR group.

(a) Stool samples were collected from the CR, IF and AL groups at D5 (the beginning time of CR as baseline), D15 (10 days after CR and IF treatment) and D35 (30 days after CR and IF treatment). Comparison of phylum-level proportional abundance of feces from mice under AL, CR and IF treatment. n = 5 mice. (b-d) Composition of gut microbiota in CR groups at different time D5 (b), D15 (c) and D35 (d) were shown in pie chart. n = 5 mice. (e-g) The abundance of Bifidobacterium at different time in AL (e), CR (f) and IF (g) group, each line represents a mouse. n = 5 mice. Data were mean ± s.e.m.

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Extended Data Fig. 4 CR and B.bifidum treatment reshaped the tumor immune environment.

Representative images of IHC staining for CD4 + , CD8 + T cell, CD11c, NK1.1 and PD-1. Data were representative of one independent experiments and three independent samples.

Extended Data Fig. 5 Gut microbiome from CR treatment exerted anti-tumor effect depended on CD8.

(a) MC38 tumor growth kinetics. The experimental design: Mice were transplanted the gut microbiome from CR treated mice, followed by anti-CD8 antibody treatment. FMT: Fecal microbiota transplantion. n = 5 mice. Data are mean ± s.e.m. Statistical analysis was performed using two-way ANOVA. (b,c) Representative images of dissected tumors (b) and tumor weights (c) on the 26th day. n = 5 mice. (d) Representative flow cytometry plots for one mouse/group were shown. (e) The percentage of tumor-infiltrating IFNγ+CD8+T cells in the tumor tissues. Data were mean ± s.e.m. (c,e) Statistical analysis was performed using unpaired two-tailed t-test.

Source data

Extended Data Fig. 6 CR and B.bifidum treatment elevated the level of acetate in plasma and tumor.

(a-c) Serum SCFA profile (a), Tumor SCFA profile (b) and Liver SCFA profile (c) of mice which were treated with CR or ABX plus with DH5α, B.bifidum or acetate followed by CR treatment. CR, n = 4 mice; ABX + CR + DH5α, n = 4 mice; ABX + CR + B.bifidum, n = 4 mice; ABX + CR + acetate, n = 4 mice. Data were mean ± s.e.m. Statistical analysis was performed using unpaired two-tailed t-test.

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Mao, YQ., Huang, JT., Zhang, SL. et al. The antitumour effects of caloric restriction are mediated by the gut microbiome. Nat Metab 5, 96–110 (2023). https://doi.org/10.1038/s42255-022-00716-4

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