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A dietary commensal microbe enhances antitumor immunity by activating tumor macrophages to sequester iron

Nature Immunology volume 25, pages 790–801 (2024)Cite this article

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Abstract

Innate immune cells generate a multifaceted antitumor immune response, including the conservation of essential nutrients such as iron. These cells can be modulated by commensal bacteria; however, identifying and understanding how this occurs is a challenge. Here we show that the food commensal Lactiplantibacillus plantarum IMB19 augments antitumor immunity in syngeneic and xenograft mouse tumor models. Its capsular heteropolysaccharide is the major effector molecule, functioning as a ligand for TLR2. In a two-pronged manner, it skews tumor-associated macrophages to a classically active phenotype, leading to generation of a sustained CD8+ T cell response, and triggers macrophage ‘nutritional immunity’ to deploy the high-affinity iron transporter lipocalin-2 for capturing and sequestering iron in the tumor microenvironment. This process induces a cycle of tumor cell death, epitope expansion and subsequent tumor clearance. Together these data indicate that food commensals might be identified and developed into ‘oncobiotics’ for a multi-layered approach to cancer therapy.

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Fig. 1: LpMB19 suppresses tumor growth and enhances anti-PD-L1 efficacy.
Fig. 2: LpIMB19 augments CD8+ T cell cytotoxicity and clonal expansion via CPS.
Fig. 3: LpIMB19 activates and induces iron sequestration in tumor macrophages.
Fig. 4: LpIMB19-induced macrophages deploy lipocalin-2 for iron sequestration.
Fig. 5: LpIMB19 suppresses a human urinary bladder carcinoma xenograft growth.

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Data availability

Sequencing data for this study have been deposited at the NCBI BioProject database under accession no. PRJNA991251. TCRβ sequencing data can be accessed at immuneACCESS (https://doi.org/10.21417/GS2024NI). IMvigor210 data investigated in this study are previously reported and can be accessed in European Genome–phenome Archive under accession no. EGAS00001002556. Source data are provided with this paper.

Code availability

Codes used for data analyses are available at https://github.com/CB-postech/nature-immunology-tumor-microbiome/ (ref. 71).

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Acknowledgements

We thank the POSTECH Biotech Center flow cytometry, animal research and electron microscopy core research facilities for technical assistance. Cartoons in Supplementary Fig. 7g and Extended Data Fig. 10 were created with BioRender.com. This research was supported, in part, by a grant from the Korea Basic Science Institute (National Research Facilities and Equipment Center), funded by the Ministry of Education, for using a diversity of instruments. A.M. was funded by the Italian Ministry of Health POS 5 Project Mediterranean Diet for Human Health Lab (MeDiHealthLab) No. T5-AN-07 and by the National Recovery and Resilience Plan, European Union—NextGenerationEU (ONFoods—Research and Innovation Network on Food and Nutrition Sustainability, Safety and Security—Working on Foods; code PE0000003). The funding bodies had no role in the design of the study, analysis, interpretation of data and writing of the manuscript.

Author information

Author notes

  1. These authors contributed equally: Garima Sharma, Amit Sharma.

Authors and Affiliations

  1. Department of Life Sciences, Pohang University of Science and Technology (POSTECH), Pohang, Republic of Korea

    Garima Sharma, Amit Sharma, Dong Gon Cha, Somi Kim, Eun Seo Park, Jae Gyun Noh, Yoon Ha Choi, JungHo Kong, Haena Lee, Haeun Ko, Sanguk Kim, Jong Kyoung Kim & Sin-Hyeog Im

  2. ImmunoBiome, Bio Open Innovation Center, Pohang, Republic of Korea

    Garima Sharma, Inhae Kim, Juhun Lee, Dipayan Rudra & Sin-Hyeog Im

  3. Innovation Research Center for Bio-future Technology (B-IRC), Pohang University of Science and Technology (POSTECH), Pohang, Republic of Korea

    Amit Sharma

  4. Department of New Biology, Daegu Gyeongbuk Institute of Science and Technology (DGIST), Daegu, Republic of Korea

    Dong Gon Cha, Eun Seo Park, Yoon Ha Choi & Jong Kyoung Kim

  5. Institute of Molecular Biology and Genetics, Seoul National University, Seoul, Republic of Korea

    Juhee Lee & Kunyoo Shin

  6. Department of Urology, College of Medicine, Seoul National University Hospital, Seoul, Republic of Korea

    Ja Hyeon Ku

  7. Department of Chemical Sciences, University of Napoli Federico II Complesso Universitario Monte Santangelo, Via Cintia 4, I-80126, Naples, Italy

    Anna Notaro, Cristina De Castro & Antonio Molinaro

  8. Clinical Vaccine R&D Center and Combinatorial Tumor Immunotherapy MRC, Chonnam National University, Hwasun-gun, Republic of Korea

    Seol Hee Hong & Joon Haeng Rhee

  9. College of Pharmacy and Integrated Research Institute for Drug Development, Dongguk University, Seoul, Republic of Korea

    Sang Geon Kim

  10. Department of Biological Sciences, College of Natural Sciences, Seoul National University, Seoul, Republic of Korea

    Kunyoo Shin

  11. School of Life Science and Technology, ShanghaiTech University, Shanghai, China

    Dipayan Rudra

  12. Institute for Convergence Research and Education in Advanced Technology, Yonsei University, Seoul, Republic of Korea

    Sin-Hyeog Im

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  1. Garima Sharma
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Contributions

G.S., A.S., D.R. and S.-H.I. devised the project, the main conceptual ideas and the project outline. G.S. and A.S. did the experimentation planning. G.S., A.S., H.K. and H.L. performed experiments. J.G.N. processed tissues for immunohistochemistry. Juhee Lee isolated and cultured the patient tumor organoids under the supervision of K.S. J.H.K. collected and provided the patient tumor samples for preparing tumor organoids. Y.H.C. and D.G.C. processed the samples for scRNA-seq and prepared the libraries. S.K., E.S.P. and D.G.C. analyzed the scRNA-seq data. J.K.K. supervised the scRNA-seq study and data analysis. A.N., C.D.C. and A.M. evaluated the purity and structure of RHP. S.H.H., J.H.R. and S.G.K. provided the mice strains. I.K., Juhun Lee and J.K. coordinated the analysis of RNA-seq and genomics data under the supervision of S.K. G.S, A.S. and I.K. analyzed the data and contributed to the interpretation of the data. G.S. and A.S. wrote the final paper in collaboration with I.K., D.G.C., S.K. and E.S.P. D.R. and S.-H.I. co-edited the paper and supervised the research work. All authors provided intellectual input and critical feedback throughout the project, which helped shape the paper.

Corresponding authors

Correspondence to Dipayan Rudra or Sin-Hyeog Im.

Ethics declarations

Competing interests

G.S., I.K. and Juhun Lee are employees of ImmunoBiome. S.-H.I. is the founder and major shareholder of ImmunoBiome. G.S., A.S. and S.-H.I. are inventors of patent applications related to work on L. plantarum IMB19. The other authors declare no competing interests.

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Peer review information

Nature Immunology thanks the anonymous reviewers for their contribution to the peer review of this work. Peer reviewer reports are available. Primary Handling Editor: N. Bernard, in collaboration with the Nature Immunology team.

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Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Selection of Lactiplantibacillus plantarum IMB19 (LpIMB19).

a, Bubble plot showing IFN-γ and IL-10 levels in culture supernatants derived from murine total splenocytes treated with different strains of human commensal bacteria and control L. plantarum. b, Flow cytogram (left) and quantification (right) of LpIMB19 concentration-dependent generation of IFNγ+CD8 T cells in bacteria stimulated CD11C+ APCs and CD8 T cell co-culture for 72 hrs followed by 3.5 hrs stimulation with PMA/ionomycin and stained intracellularly, n = 5 for PBS and n = 6 for LpIMB19 biological replicates. c, Bubble plot showing IFN-γ and IL-10 levels in culture supernatants derived from human PBMCs treated with different strains of human commensal bacteria. d, LpIMB19-mediated generation of IFNγ+CD8 T cells in bacteria stimulated human APCs, differentiated from CD14+ blood monocytes, and CD8 T cell co-culture (n = 4 biological replicates). e-f, Dendrogram depicting similarity of LpIMB19 with other L. plantarum strains as assessed by average nucleotide distance (e) and conserved capsular polysaccharide related genes (f). Results are from one (a, c) or are pooled from three (b) or two (d) independent experiments. Data are Mean ± SEM. Statistical analyses were performed by one-way ANOVA with Tukey’s multiple comparison test (b), or unpaired two-tailed Student’s t-test (d).

Source data

Extended Data Fig. 2 LpIMB19’s antitumor immunity is mediated by the activation of intratumoral CD8 T cells.

a, Gating strategy for flowcytometric analysis of tumor-infiltrating CD8 and CD4 T cells. b-c, Quantitation of frequency of intratumoral CD8 (b) and CD4 (c) T cells in B16F10 mouse melanoma treated with PBS (n = 13) or LpIMB19 (n = 14). d-i, Representative histograms (top) and tabulations (bottom) of expression of cytokines TNF (d) (n = 6), IL-2 (e) (n = 6), activation marker CD44 (f) (n = 8 for PBS, n = 9 for LpIMB19), and cytotoxicity markers CD107 (g) (n = 5), Granzyme B (h) (n = 8 for PBS, n = 7 for LpIMB19), and Perforin (i) (n = 8 for PBS, n = 7 for LpIMB19) in TIL CD8 T cells isolated from PBS or LpIMB19-treated mice. j-k, Representative flow cytogram (top) and tabulation (bottom) of expression of activation marker CD44 (j) and frequency of CD4+Foxp3+ regulatory T cells (k) in TIL CD4 T cells (n = 8 for PBS, n = 9 for LpIMB19). l, Kinetics of tumor growth in mice heterotopically implanted with EMT-6 breast carcinoma and treated with PBS or LpIMB19 (n = 5). m, Kaplan–Meier survival curve of mice heterotopically implanted with EMT-6 breast carcinoma and treated with PBS or LpIMB19 (n = 11). n, B16F10 melanoma progression upon depletion of CD8 T cells and treatment with PBS or LpIMB19, scheme of treatment (left), histogram showing the efficiency of CD8 deletion (middle), and tumor growth curve (right) are shown (n = 5). Results are pooled from three (b, c) or two (d-m) or one (n) independent experiments. Data are Mean ± SEM. n, represents mice. Two-tailed unpaired Student’s t-test (b-k), two-way ANOVA (l, n), and log-rank (Mantel–Cox) test (m) were used for statistical analyses.

Source data

Extended Data Fig. 3 LpIMB19 feeding does not alter the gut microbiota composition in tumor-bearing mice.

2×105 B16F10 cells were injected subcutaneously in C57/BL6 mice and LpIMB19 (1×109 CFU/mouse) were administered by gastric gavage, once every three days from day 0 onwards. Intestinal samples were collected at the end of the study (Day 20). a, b, Relative abundance of microbial phyla (a) and families (b) in intestinal contents of PBS and LpIMB19-treated B16F10 melanoma bearing mice (n = 5). Each bar depicts gut microbial composition of one mouse. c-e, Diversity analysis of intestinal microbes as depicted by total number of Operational Taxonomic Units (OTUs) (c), Shannon diversity index (d), and Simpson’s diversity index (e). Results are compiled from one experiment with n = 5 mice per treatment. The box plots display median and interquartile range (25–75%) and the whiskers extend from the box to the farthest data point lying within 1.5x IQR from the box. (c-e). Two-tailed unpaired Student’s t-test (c-e) was used for statistical analyses.

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Extended Data Fig. 4 Capsular polysaccharides of LpIMB19 are primary effector molecules.

a, Frequency of IFNγ+CD8 T cells co-cultured with PBS or live or heat-killed LpIMB19-treated APCs. Naïve CD8 T cells were co-cultured, for 72 hrs with CD11c+ splenic APCs treated as indicated. (n = 3 biological replicates). b, Tumor progression in mice treated with PBS (n = 6 mice) or Live LpIMB19 (n = 6 mice) or heat-killed LpIMB19 (n = 7 mice). c, Transmission electron micrographs of LpIMB19. The presence of a thick capsule around the bacillus is shown. d-f, Frequency of IFN-γ+CD8 T cells, co-cultured with APCs treated with different concentrations of LpIMB19 derived capsular polysaccharide (CPS), membrane-associated proteins, and cell wall peptidoglycans (d) (n = 4 biological replicates); or secreted exo-polysaccharides (EPS) (e) (n = 4 biological replicates); and increasing concentrations of CPS (f) (n = 3 biological replicates). g, CPS biosynthesis gene cluster depicting genes present in LpIMB19 in comparison to type strain L. plantarum WCSF1. LpIMB19 harbors partial cluster 1 with rhamnose synthesis gene rmlC, rmlB, and rmlD and cluster 4, evolutionarily conserved in L. plantarum. Results are pooled from three (a), two (b, d, f) or four (e) independent experiments. Data are Mean ± SEM (a, b, d-f). Statistical analyses were performed by one-way ANOVA with Tukey’s multiple comparison test (a, d-f) or two-way ANOVA (b).

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Extended Data Fig. 5 LpIMB19 and RHP enhance infiltration and activation of melanoma-specific CD8 T cells.

Rag1 mice were inoculated with 2x105 B16F10 melanoma cells subcutaneously and engrafted intravenously with 1:1 mixture of antigen-specific Pmel-1Thy1.1 and OT-1Thy1.1 CD8 T cells. a, Experimental scheme (top) and tumor progression of B16F10 melanoma in mice treated with PBS (n = 5) or LpIMB19 (n = 6) or RHP (n = 8). b, Flow cytometry diagram (top) and quantitation of frequency of Pmel-1 and OT-1 CD8 T cells in melanomas (n = 5 for PBS, n = 6 for LpIMB19, and n = 7 for RHP). c–f, Flow cytogram (top) and quantitation of IFN-γhi (c), TNFhi (d), PD-1hi (e), and Tim3hi (f) CD8 T cells in tumor-infiltrating melanoma antigen-specific Pmel-1 TCRVβ13+ CD8 T cells (n = 5 for PBS, n = 6 for LpIMB19, and n = 7 for RHP). Results are pooled from two independent experiments. Data are mean ± SEM. Statistical analyses were performed using two-way ANOVA (a) or two-tailed paired Student’s t-test (b) or two-tailed unpaired Student’s t-test (c-f).

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Extended Data Fig. 6 LpIMB19 induces a mild inflammatory phenotype in small intestinal macrophages.

B16F10 mouse melanoma cells (2x105/mouse) were implanted subcutaneously and LpIMB19 was administered (1x109 CFU/mouse, oral gavage) once every 3 days, starting from day 0 till the end of the study. Intestine and tumor-infiltrated immune cells were isolated on day 20 and subjected to flow cytometry analysis. a-c, Frequency of IFN-γ+, IL-2+ and TNF+ cells in CD8 T cells, isolated from SI-LP (a), Colon (b), and melanoma (c) (n = 7 mice for PBS and n = 8 mice for LpIMB19). d-e, Representative flow diagram of CX3CR1−Ly6Cint-lo macrophages (d) and frequency of CX3CR1−Ly6Cint-lo, CD86+, iNOS2+, and CD206+CX3CR1−Ly6Cint-lo macrophages (e) in SI-LP (n = 11 mice). f-g, Representative flow diagram of CX3CR1−Ly6Cint-lo macrophages (f) and frequency of CX3CR1−Ly6Cint-lo, CD86+, iNOS2+, and CD206+CX3CR1−Ly6Cint-lo macrophages (g) in colon (n = 11 mice). h, Representative flow diagram of CX3CR1−Ly6Cint-lo macrophages in tumors. i, Representative flow histograms (top) and frequencies of iNOS2+, CD86+, and CD206+ in CX3CR1−Ly6Cint-lo macrophages in melanoma (n = 7 mice for PBS and n = 8 mice for LpIMB19). Results are pooled from two (a-c, i) or three (e, g) independent experiments. Data are mean ± SEM (a-c; e, g, i). Statistical analyses were performed using two-tailed unpaired Student’s t-test (a-c; e, g, i). SI-LP, small intestine lamina propria.

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Extended Data Fig. 7 Inflammatory and iron sequestering gene signature of LpIMB19-induced macrophages.

scRNA sequencing of flow cytometry-based sorted macrophages isolated from B16F10 melanoma-engrafted mice, 40 hrs after subcutaneous implantation of 5x106 B16F10 cells. a, Heat map showing relative expression of marker genes used for annotating three distinct subtypes of tumor-infiltrating macrophages. b, UMAP plots showing expression of inflammatory genes Nos2 (left) and Il1b (right) in macrophage subtype clusters. c, UMAP plots showing expression of anti- inflammatory genes Arg1 (left) and Mrc1 (right) in macrophage subtype clusters. d-j, Total n = 1062 cells for PBS and n = 1071 cells for RHP treatment group were analyzed. Cells were further categorized into subtypes based on marker genes expression as in a. For PBS control n = 399 regulatory TAM, n = 179 inflammatory macrophages, and n = 484 monocytic macrophages were analyzed; for RHP treatment n = 425 regulatory TAM, n = 357 inflammatory macrophages, and n = 289 monocytic macrophages were analyzed. d, Violin plots comparing expression of anti- inflammatory genes Arg1 (top) and Mrc1 (bottom) in macrophage subtypes treated with PBS or RHP. e-f, Violin plots comparing Tlr2 expression (e) and module score of Tlr2 signaling pathway (f) in macrophage subtypes. g-j, Violin plots showing module scores of signature genes in respective iron-related gene ontology biological processes (GOBP) within each condition for each macrophage subtype. k, LCN2 secretion by mouse peritoneal macrophages or bone marrow-derived dendritic cells, stimulated in vitro with LpIMB19 or RHP. n = 4 biological replicates, each representing co-culture setup with cells from one mouse. l, Western blot analysis of HO-1 in mouse peritoneal macrophages treated with LpIMB19 (MOI 1:30) and RHP (50 μg/ml) for 24, 48, 72 and 96 hrs. Results are pooled from two experiments (k) or are representative of two independent experiments (l). Statistical analyses were performed using two-tailed unpaired Student’s t-test (d-j) or one-way ANOVA with Tukey’s multiple comparison test (k).

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Extended Data Fig. 8 Efficacy of LpIMB19 and RHP in suppressing tumor growth is mediated through TLR2.

a, Intracellular iron levels in competitive iron uptake assay between B16F10 cells and LpIMB19 or RHP-treated bio-gel-elicited peritoneal macrophages; numbers on top represent average MFI of SiRhoNox in macrophages (M) and tumor (T) cells, n = 4 biological replicates, each representing co-culture setup with cells from one mouse. b, Macrophage depletion by DT treatment in Cd11bDTR mice. c-j, B16F10 melanoma engrafted in Cd11bDTR mice adoptively transferred with Tlr2−/− or Tlr2+/+ macrophages (c-h) or Lcn2−/− or Lcn2+/+ macrophages (i-j) and treated with PBS, LpIMB19 or RHP, each in conjugation with diphtheria toxin. c, Tumor growth kinetics of melanoma (n = 11 mice). d-e, Quantitation of frequencies of inflammatory markers iNOS2 (d) and CD86 (e) in tumoral macrophages treated as in (c), (n = 5 mice for each experimental group, and n = 4 mice for Tlr2+/+ RHP). f-g, Quantitation of frequencies of inflammatory cytokines IFN-γ (f) and TNF (g) producing tumor-infiltrated CD8 T cells upon restimulation, (n = 5 mice). h, Representative histogram (top) and tabulation (bottom) of iron uptake as measured by SiRhoNox MFI in tumor-infiltrating macrophages in mice treated as in (c), (n = 5 mice). i, Gating strategy (top) and frequency (bottom) of tumor-infiltrating CD11b+F4/80+ macrophages, (n = 8 mice). j, Flow cytometry analysis of activated caspase 3/7 expression in melanoma cells, histogram (top) and quantification (bottom), (n = 4 mice). k, FACS representation of deletion of ferroportin in macrophages from FpnF/FLysMCre mice. Results are pooled from two (a, c-i) or from one (j) independent experiment. Data are Mean ± SEM. Statistical analyses were performed using paired two-tailed Student’s t-test (a), two-way ANOVA (c), unpaired two-tailed Student’s t-test (d-e, i), and one-way ANOVA with Tukey’s multiple comparison test (f-h).

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Extended Data Fig. 9 Correlation between RHP-induced murine gene orthologues and cancer patient survival.

a, Overall survival of TCGA patients stratified by human orthologues of signature genes expressed in RHP-treated intratumoral macrophages. The difference in 5-year restricted mean survival time (RMST) is shown regardless of the statistical significance from the weighted log-rank test. b, Prediction of response to PD-L1 blockade using 5-fold with Monte-Carlo sampling. For each sampling, the performance was measured by the area under the receiver operating characteristic curve (AUROC). c, The difference of AUROCs in 100 samplings. d, Strategy for PBMC humanization and patient tumor-derived organoid xenotransplantation bladder carcinoma model in NOD-SCID mice. e, Fluorescent multiplex immunohistochemistry representative images (left) and IFNγ-AF488 mean fluorescence intensity quantification (right) (counter stained with DAPI) of tumor-infiltrated human CD8 T cells, CD3+ (magenta fluorescence) CD8+ (red fluorescence) and IFNγ+ (green fluorescence), (n = 26 cells for PBS, n = 80 cells for LpIMB19, and n = 49 cells for RHP treatment were analyzed from a minimum of 3 independent tumor-bearing urinary bladder tissue sections of mice). f, Fluorescence multiplex immunohistochemistry representative images (left) and iNOS2-PE mean fluorescence intensity quantification (right) (counter stained with DAPI) of tumor-infiltrated human macrophages, CD68+ (green fluorescence, macrophage marker) and iNOS2 (red fluorescence), (n = 94 cells for PBS, n = 89 cells for LpIMB19, and n = 60 cells for RHP treatment were analyzed from a minimum of 3 independent tumor-bearing urinary bladder tissue sections of mice). Data are Mean ± SEM (c, e, f). The box plots display median and interquartile range (25–75%) and the whiskers extend from the box to the farthest data point lying within 1.5x the IQR from the box. (c). Statistical analyses were performed using two-sided Fisher’s exact test (c) and one-way ANOVA with Tukey’s multiple comparison test (e, f, right panels).

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Extended Data Fig. 10 Model of LpIMB19/RHP-induced anti-cancer immunity.

LpIMB19/RHP induce TLR2 mediated NF-kB activation in macrophages leading them toward an inflammatory phenotype with higher expression of iNOS2, which further activates the cytotoxic CD8 T cells for effective killing of cancer cells. In macrophages, NF-kB activation also induces LCN2 secretion leading to capture of extracellular iron, and its intracellular sequestration using ferritin. This leads to iron deprivation of cancer cells. Therefore, both cytotoxic CD8 T cells and macrophages induce increased cancer cell death and subsequent epitope expansion, presumably leading to a vicious cycle of macrophage and CD8 T cell activation, and cancer cell killing.

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Supplementary Data 1

Statistical source data for Supplementary Fig. 3a,b,d–f.

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Statistical source data for Supplementary Fig. 6d and P values for Fig. 6e.

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Statistical source data for Supplementary Fig. 7a–e.

Supplementary Tables 2 and 3

Supplementary Table 2: PICRUSt2 analysis of 16s rRNA-seq data. Supplementary Table 3: Top 100 human genes mapped from the mouse signatures for TCGA patient transcriptome.

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Sharma, G., Sharma, A., Kim, I. et al. A dietary commensal microbe enhances antitumor immunity by activating tumor macrophages to sequester iron. Nat Immunol 25, 790–801 (2024). https://doi.org/10.1038/s41590-024-01816-x

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