The gut microbiome switches mutant p53 from tumour-suppressive to oncogenic

Abstract

Somatic mutations in p53, which inactivate the tumour-suppressor function of p53 and often confer oncogenic gain-of-function properties, are very common in cancer1,2. Here we studied the effects of hotspot gain-of-function mutations in Trp53 (the gene that encodes p53 in mice) in mouse models of WNT-driven intestinal cancer caused by Csnk1a1 deletion3,4 or ApcMin mutation5. Cancer in these models is known to be facilitated by loss of p533,6. We found that mutant versions of p53 had contrasting effects in different segments of the gut: in the distal gut, mutant p53 had the expected oncogenic effect; however, in the proximal gut and in tumour organoids it had a pronounced tumour-suppressive effect. In the tumour-suppressive mode, mutant p53 eliminated dysplasia and tumorigenesis in Csnk1a1-deficient and ApcMin/+ mice, and promoted normal growth and differentiation of tumour organoids derived from these mice. In these settings, mutant p53 was more effective than wild-type p53 at inhibiting tumour formation. Mechanistically, the tumour-suppressive effects of mutant p53 were driven by disruption of the WNT pathway, through preventing the binding of TCF4 to chromatin. Notably, this tumour-suppressive effect was completely abolished by the gut microbiome. Moreover, a single metabolite derived from the gut microbiota—gallic acid—could reproduce the entire effect of the microbiome. Supplementing gut-sterilized p53-mutant mice and p53-mutant organoids with gallic acid reinstated the TCF4–chromatin interaction and the hyperactivation of WNT, thus conferring a malignant phenotype to the organoids and throughout the gut. Our study demonstrates the substantial plasticity of a cancer mutation and highlights the role of the microenvironment in determining its functional outcome.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: Mutant p53 counteracts dysplasia and tumorigenesis in the proximal gut without regaining wild-type transcriptional activity.
Fig. 2: Mutant p53 suppresses the expression of WNT targets in the proximal gut.
Fig. 3: Mutant p53 promotes normally balanced growth and differentiation of intestinal tumour organoids.
Fig. 4: Treatment with antibiotics unleashes tumour-suppressive effects of mutant p53 in the distal gut.
Fig. 5: Gallic acid induces WNT-driven dysplasia and tumorigenesis when p53 is mutated.

Data availability

The source gels for immunoblots are provided in Supplementary Fig. 1. All sequencing data of the study are available at Array Express: the ChIP–seq results and enrichment data are available with accession code E-MTAB-7858 and the RNA-seq results and gene expression data are available with accession code E-MTAB-7859.Source data are provided with this paper.

References

  1. 1.

    Sabapathy, K. & Lane, D. P. Therapeutic targeting of p53: all mutants are equal, but some mutants are more equal than others. Nat. Rev. Clin. Oncol. 15, 13–30 (2018).

    CAS  Article  Google Scholar 

  2. 2.

    Oren, M. & Rotter, V. Mutant p53 gain-of-function in cancer. Cold Spring Harb. Perspect. Biol. 2, a001107 (2010).

    Article  Google Scholar 

  3. 3.

    Elyada, E. et al. CKIα ablation highlights a critical role for p53 in invasiveness control. Nature 470, 409–413 (2011).

    ADS  CAS  Article  Google Scholar 

  4. 4.

    Pribluda, A. et al. A senescence-inflammatory switch from cancer-inhibitory to cancer-promoting mechanism. Cancer Cell 24, 242–256 (2013).

    CAS  Article  Google Scholar 

  5. 5.

    Moser, A. R., Pitot, H. C. & Dove, W. F. A dominant mutation that predisposes to multiple intestinal neoplasia in the mouse. Science 247, 322–324 (1990).

    ADS  CAS  Article  Google Scholar 

  6. 6.

    Halberg, R. B. et al. Tumorigenesis in the multiple intestinal neoplasia mouse: redundancy of negative regulators and specificity of modifiers. Proc. Natl Acad. Sci. USA 97, 3461–3466 (2000).

    ADS  CAS  Article  Google Scholar 

  7. 7.

    Freed-Pastor, W. A. & Prives, C. Mutant p53: one name, many proteins. Genes Dev. 26, 1268–1286 (2012).

    CAS  Article  Google Scholar 

  8. 8.

    Stiewe, T. & Haran, T. E. How mutations shape p53 interactions with the genome to promote tumorigenesis and drug resistance. Drug Resist. Updat. 38, 27–43 (2018).

    Article  Google Scholar 

  9. 9.

    Dittmer, D. et al. Gain of function mutations in p53. Nat. Genet. 4, 42–46 (1993).

    CAS  Article  Google Scholar 

  10. 10.

    Olive, K. P. et al. Mutant p53 gain of function in two mouse models of Li-Fraumeni syndrome. Cell 119, 847–860 (2004).

    CAS  Article  Google Scholar 

  11. 11.

    Lang, G. A. et al. Gain of function of a p53 hot spot mutation in a mouse model of Li-Fraumeni syndrome. Cell 119, 861–872 (2004).

    CAS  Article  Google Scholar 

  12. 12.

    Petrova, T. V. et al. Transcription factor PROX1 induces colon cancer progression by promoting the transition from benign to highly dysplastic phenotype. Cancer Cell 13, 407–419 (2008).

    CAS  Article  Google Scholar 

  13. 13.

    Fearon, E. F. & Vogelstein, B. A genetic model for colorectal tumorigenesis. Cell 61, 759–767 (1990).

    CAS  Article  Google Scholar 

  14. 14.

    Vousden, K. H. & Prives, C. Blinded by the light: the growing complexity of p53. Cell 137, 413–431 (2009).

    CAS  Article  Google Scholar 

  15. 15.

    Bullock, A. N. & Fersht, A. R. Rescuing the function of mutant p53. Nat. Rev. Cancer 1, 68–76 (2001).

    CAS  Article  Google Scholar 

  16. 16.

    Nusse, R. & Clevers, H. Wnt/β-catenin signaling, disease, and emerging therapeutic modalities. Cell 169, 985–999 (2017).

    CAS  Article  Google Scholar 

  17. 17.

    Schulz-Heddergott, R. et al. Therapeutic ablation of gain-of-function mutant p53 in colorectal cancer inhibits Stat3-mediated tumor growth and invasion. Cancer Cell 34, 298–314 (2018).

    CAS  Article  Google Scholar 

  18. 18.

    Lynch, S. V. & Pedersen, O. The human intestinal microbiome in health and disease. N. Engl. J. Med. 375, 2369–2379 (2016).

    CAS  Article  Google Scholar 

  19. 19.

    Rooks, M. G. & Garrett, W. S. Gut microbiota, metabolites and host immunity. Nat. Rev. Immunol. 16, 341–352 (2016).

    CAS  Article  Google Scholar 

  20. 20.

    Muir, R. M. et al. Mechanism of gallic acid biosynthesis in bacteria (Escherichia coli) and walnut (Juglans regia). Plant Mol. Biol. 75, 555–565 (2011).

    CAS  Article  Google Scholar 

  21. 21.

    Chen, H. et al. The microbiota is essential for the generation of black tea theaflavins-derived metabolites. PLoS ONE 7, e51001 (2012).

    ADS  CAS  Article  Google Scholar 

  22. 22.

    Díaz-Quiroz, D. C. et al. Synthesis, biological activity and molecular modelling studies of shikimic acid derivatives as inhibitors of the shikimate dehydrogenase enzyme of Escherichia coli. J. Enzyme Inhib. Med. Chem. 33, 397–404 (2018).

    Article  Google Scholar 

  23. 23.

    Laurent-Puig, P. et al. Genetic alterations associated with hepatocellular carcinomas define distinct pathways of hepatocarcinogenesis. Gastroenterology 120, 1763–1773 (2001).

    CAS  Article  Google Scholar 

  24. 24.

    Yizhak, K. et al. RNA sequence analysis reveals macroscopic somatic clonal expansion across normal tissues. Science 364, eaaw0726 (2019).

    CAS  Article  Google Scholar 

  25. 25.

    Martincorena, I. et al. Somatic mutant clones colonize the human esophagus with age. Science 362, 911–917 (2018).

    ADS  CAS  Article  Google Scholar 

  26. 26.

    Yokoyama, A. et al. Age-related remodelling of oesophageal epithelia by mutated cancer drivers. Nature 565, 312–317 (2019).

    ADS  CAS  Article  Google Scholar 

  27. 27.

    Clément, G., Braunschweig, R., Pasquier, N., Bosman, F. T. & Benhattar, J. Alterations of the Wnt signaling pathway during the neoplastic progression of Barrett’s esophagus. Oncogene 25, 3084–3092 (2006).

    Article  Google Scholar 

  28. 28.

    Campisi, J. Aging, cellular senescence, and cancer. Annu. Rev. Physiol. 75, 685–705 (2013).

    CAS  Article  Google Scholar 

  29. 29.

    Sato, T. et al. Single Lgr5 stem cells build crypt-villus structures in vitro without a mesenchymal niche. Nature 459, 262–265 (2009).

    ADS  CAS  Article  Google Scholar 

  30. 30.

    Haber, A. L. et al. A single-cell survey of the small intestinal epithelium. Nature 551, 333–339 (2017).

    ADS  CAS  Article  Google Scholar 

  31. 31.

    Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

    Article  Google Scholar 

  32. 32.

    Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).

    CAS  Article  Google Scholar 

  33. 33.

    Bolyen, E. et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol. 37, 852–857 2019).

    CAS  Article  Google Scholar 

  34. 34.

    Sklarz, M., Levin, L., Gordon, M. & Chalifa-Caspi, V. NeatSeq-Flow: a lightweight high-throughput sequencing workflow platform for non-programmers and programmers alike. Preprint at https://www.biorxiv.org/content/10.1101/173005v3 (2018).

  35. 35.

    Callahan, B. J. et al. DADA2: high-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).

    CAS  Article  Google Scholar 

  36. 36.

    Mandal, S. et al. Analysis of composition of microbiomes: a novel method for studying microbial composition. Microb. Ecol. Health Dis. 26, 27663 (2015).

    PubMed  Google Scholar 

  37. 37.

    Morton, J. T. et al. Balance trees reveal microbial niche differentiation. mSystems 2, e00162-16 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. 38.

    Huang, Y. et al. A UPLC-MS/MS method for simultaneous determination of free and total forms of a phenolic acid and two flavonoids in rat plasma and its application to comparative pharmacokinetic studies of Polygonum capitatum extract in rats. Molecules 22, 353 (2017).

    Article  Google Scholar 

Download references

Acknowledgements

We thank N. Cohen-Saban and N. Amsalem for assistance with mouse models; M. Biton for project discussions in the initial phase of this study; the Mass Spectrometry Unit of the Institute for Drug Research, School of Pharmacy, The Hebrew University of Jerusalem (HUJI) for MS analysis; and the Genomic Applications Laboratory, Core Research Facility and Faculty of Medicine at HUJI and I. Plaschkes (Bioinformatics Unit, The Robert H. Smith Faculty of Agriculture, Food and Environment at HUJI) for assistance with 16S rRNA sequence analysis. This work was supported by the Israel Science Foundation (ISF) Centers of Excellence (2084/15) to Y.B.-N., M.O. and E.P., the ISF (3165/19) within the Israel Precision Medicine Program to Y.B.-N., the European Research Council within the FP-7 to Y.B.-N. (294390 PICHO) and E.P (281738 LIVERMICROENV) and the Israel Cancer Research Fund Professorship to Y.B.-N.

Author information

Affiliations

Authors

Contributions

E.K., I.S.-A. and Y.B.-N. planned the study and Y.B-N. supervised the research. E.K. prepared the mouse models, performed the molecular, cellular and mouse studies and analysed the data related to Figs. 1a–f, 2a–d, 3a–c, 4a–c, 5a–f and Extended Data Figs. 1a–d, 2a–e, 3a–c, 4a–g, 5a–g, 6a–i, 7a–g, 8a–e, 9a–o, 10a–f; I.S.-A. contributed to Fig. 3b, c and Extended Data Figs. 6h, i, 8d, 9a and independently reproduced similar data to Figs. 1a, 2b, 5f and Extended Data Figs. 5a, b, 9n, o; A.V. contributed to Fig. 4a, Extended Data Figs. 7c, 9a, d, 10a, b and Supplementary Table 1 and independently reproduced similar data to Fig. 5f; S.M. contributed to Figs. 1e, 5a and Extended Data Figs. 1b, 2a, c, e, 3a, 6b, 10c, e, f and independently reproduced similar data to Figs. 2b, 3a, 4b and Extended Data Figs. 6g, 7d, e, g, 8b, 9m; A.L. contributed to Figs. 1d, 4a and Extended Data Figs. 1b, 2c, 7c; E.E. generated the original CKIafl/fl mouse model of CKIaΔgut and CKIaΔgutp53Δgut, contributed to Extended Data Fig. 1b and independently reproduced similar data to Fig. 1a and Extended Data Fig. 1a; A.Z. contributed to Fig. 4a and Extended Data Figs. 7c, 9d; M.S. contributed to Extended Data Fig. 9a and independently reproduced similar data to Figs. 1a, 2b, 5f and Extended Data Figs. 5a, b, 9n, o; G.V. contributed to Extended Data Fig. 9b, c; M.M. and T.S. contributed to Figs. 1d, 5e, Extended Data Fig. 4f, g and Supplementary Table 1; and T.S., E.P. and M.O. provided discussions and advice on the study. E.K., I.S.-A., A.V., T.S., E.P., M.O. and Y.B.-N. wrote the paper.

Corresponding author

Correspondence to Yinon Ben-Neriah.

Ethics declarations

Competing interests

A US provisional patent application (no. 62/987,058) entitled ‘A method for the diagnosis and treatment of cancer’ was filed on 9 March 2020. The inventors are Y.B.-N., E.K. and I.S.-A. The invention is based on the finding that gut microbiota, in particular polyphenol-producing microbiota, promote colorectal cancer and have a pro-tumorigenic effect both in vitro and in vivo. The invention concerns a method for the prevention and therapy of cancer in patients who have a tumour with an oncogenic p53 mutation. The method involves therapy with agents that reduce the levels of polyphenols, in particular the levels of gallic acid in the gut.

Additional information

Peer review information Nature thanks Christian Jobin, Carol Prives and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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

Extended data figures and tables

Extended Data Fig. 1 Mutant p53 counteracts dysplasia and proliferation in the proximal mouse gut.

a, IHC of p53 in the jejunum, ileum and colon of p53WT and p53R172H mice before and after CKIa deletion, and in CKIaΔgutp53Δgut mice. Scale bars, 100 μm. b, H&E-stained sections of different mouse gut segments. Inserts show high-magnification images of duodenal and jejunal villi, demonstrating the grade of dysplasia. Scale bars, 100 μm. c, IHC of PROX1 and Ki67 in mouse colon. Scale bars, 100 μm. d, Jejunal crypt length. Overall average of the mean values for each mouse ± s.e.m (n, number of crypts pooled from three mice), one-way ANOVA with Tukey’s test. Representative data from six independent experiments (ac). Source data

Extended Data Fig. 2 Mutant p53 exerts oncogenic GOF at the distal gut and tumour-suppressive effects at the proximal gut.

a, H&E-stained sections and IHC of CKIα and p53 in mouse ileum and colon, three days after knockout induction. Inserts (H&E) show high-magnification images of colon and ileum, demonstrating the grade of dysplasia. Scale bars, 100 μm. b, Colon thickness of mice described in (a). Overall average of the mean values for each mouse ± s.e.m (n, number of colon thickness measurements), one-sided Student’s t-test. c, H&E-stained sections and IHC of p53, PROX1 and Ki67 in indicated mouse gut segments. Inserts (H&E) show high-magnification images of jejunal villi, demonstrating the grade of dysplasia. Scale bars, 100 μm. d, RT–qPCR of p53 targets in mouse jejunal enterocytes indicating that mutant p53 has not regained wild-type transcriptional activity. Mean ± s.e.m. (n, number of mice) relative to CKIafl/fl (normalized to 1), one-way ANOVA with Tukey’s test. e, IHC of p21 in indicated mouse gut segments. Scale bars, 100 μm. Representative data from two (a) or six (c, e) independent experiments. Source data

Extended Data Fig. 3 Mutant p53 counteracts tumorigenesis in the proximal mouse gut.

a, Representative images of different mouse bowel segments. Arrowheads indicate visible tumours. b, Tumour size (each mouse is colour-coded). Overall average of the mean values for each mouse ± s.e.m (n, number of mice), one-sided Student’s t-test. c, Quantification of visible tumours per mouse. Mean ± s.e.m. (n, number of mice), one-sided Student’s t-test. Representative data from three independent experiments (a). Source data

Extended Data Fig. 4 Mutant p53 suppresses the expression of WNT targets exclusively in the proximal gut.

a, IHC of WNT targets in mouse jejunum. Scale bars, 100 μm. b, c, RT–qPCR of WNT targets in mouse jejunal (b) and ileal (c) enterocytes. Mean ± s.e.m. (n, number of mice) relative to CKIafl/fl (normalized to 1), one-way ANOVA with Tukey’s test. d, e, IHC of WNT targets in mouse ileum (d) and colon (e). Scale bars, 100 μm. f, g, Over-representation analysis of enriched gene sets differentially expressed in jejunum (f) and ileum (g) of CKIaΔgutp53R172H versus CKIaΔgutp53Δgut mice; negative logarithm of Benjamini–Hochberg-corrected P values (data derived from three mice of each group). Red lines indicate the significance threshold for α = 0.05. Representative data from four independent experiments (a, d, e). Source data

Extended Data Fig. 5 Suppression of WNT target gene expression by mutant p53 is mediated by preventing the association of TCF4 with WNT promoters.

a, Immunoblot of mouse jejunal enterocytes. PP2Ac, loading control. For gel source data, see Supplementary Fig. 1. b, Immunoblot of cytoplasmic and nuclear extracts from mouse jejunal enterocytes. GAPDH and fibrillarin, cytoplasmic and nuclear markers, respectively. For gel source data, see Supplementary Fig. 1. c, d, TCF4 (c) and H3K4me3 (d) ChIP of WNT target promoters in mouse jejunal enterocytes. Mean enrichment ± s.e.m. (n, number of mice), one-way ANOVA with Tukey’s test. e, f, TCF4 (e) and H3K4me3 (f) ChIP of WNT target promoters in mouse ileal enterocytes. Mean enrichment ± s.e.m. (n, number of mice), one-sided Student’s t-test. g, H3K4me3 ChIP of WNT target promoters in crypt and villus fractions of mouse jejunum. Mean enrichment ± s.e.m. (n, number of mice), one-sided Student’s t-test. Representative data from three independent experiments (a, b). Source data

Extended Data Fig. 6 Mutant p53 promotes balanced growth and differentiation of intestinal organoids.

a, RT–qPCR of Csnk1a1 and Trp53 in jejunal organoids. Mean ± s.e.m. (n, number of mice that were used as a source for organoid cultures) relative to CKIafl/fl (normalized to 1), one-way ANOVA with Tukey’s test. b, e, Immunofluorescent staining of p53 (b) and H&E (e) of jejunal organoids; different levels of dysplasia are evident in e. Nuclear counterstain (immunofluorescence), Hoechst (blue). Scale bars, 100 μm (b) and 50 μm (e). c, d, Bright-field imaging of jejunal (c) and ileal (d) organoids that express p53WT or p53R172H, with or without CKIa knockout induction, and of CKIa/p53 DKO organoids. Inserts show a high-magnification image of a representative organoid. Scale bars, 500 μm. f, RT–qPCR of p53 targets in jejunal organoids. Mean ± s.e.m. (n, number of independent experiments performed with organoid cultures of two different mice), relative to CKIafl/fl (normalized to 1), one-way ANOVA with Tukey’s test. g, Immunofluorescent staining of Ki67 and WNT targets in jejunal organoids. Nuclear counterstain, Hoechst (blue). Scale bars, 100 μm. h, Bright-field imaging and merged immunofluorescence of GFP with p53 or Ki67 in jejunal CKIa/p53 DKO organoids transduced with the indicated lentiviruses. Inserts (bright-field) show a GFP and bright-field merged image of a representative organoid. Nuclear counterstain (immunofluorescence), Hoechst (blue). Scale bars, 500 μm (top); 100 μm (bottom). i, Immunoblot of wild-type and ApcMin/Min organoids. Tubulin, loading control. For gel source data, see Supplementary Fig. 1. Representative data from two or three (b, gi) or five (ce) independent experiments. Source data

Extended Data Fig. 7 Treatment with antibiotics unleashes tumour-suppressive effects of mutant p53 in the distal gut.

a, RT–qPCR of the 16S subunit of microbial rRNA in the ileum of vehicle- or antibiotic-treated mice. Mean ± s.e.m. (n, number of mice) relative to vehicle-treated CKIafl/fl (normalized to 1), one-sided Student’s t-test. The y axis is represented as log10-transformed expression. b, Length of crypts in mouse ileum. Overall average of the mean values for each mouse ± s.e.m (n, number of crypts), one-sided Student’s t-test. c, d, g, H&E-stained sections (c) and IHC of Ki67 (d) and WNT targets (g) in mouse colon. Scale bars, 100 μm. e, IHC of WNT targets in mouse ileum. Scale bars, 100 μm. f, RT–qPCR of WNT targets in ileal enterocytes from vehicle- or antibiotic-treated mice. Mean ± s.e.m. (n, number of mice) relative to vehicle-treated CKIafl/fl mice (normalized to 1), one-sided Student’s t-test. Representative data from four independent experiments (c, d, e, g). Source data

Extended Data Fig. 8 Mutant p53 induces WNT suppression and differentiation of intestinal tumour organoids, an effect which is reversibly blocked by gallic acid treatment.

a, Bright-field and immunofluorescent imaging of WNT targets and Ki67 in CKIaKOp53R172H jejunal organoids treated with different bacterial metabolites. Nuclear counterstain (immunofluorescence), Hoechst (blue). Inserts (bright-field) show a high-magnification image of a representative organoid. Scale bars, 500 μm (bright-field); 100 μm (immunofluorescence). b, RT–qPCR of WNT targets in jejunal organoids. Mean ± s.e.m. (n, number of independent experiments performed with organoid cultures from two different mice) relative to CKIafl/fl (normalized to 1), one-way ANOVA with Tukey’s test. c, Bright-field imaging of non-treated and gallic-acid-treated jejunal organoids. Inserts show a high-magnification image of a representative organoid. Scale bar, 500 μm. d, Merged immunofluorescence of GFP and Ki67 in non-treated and gallic-acid-treated jejunal CKIa/p53 DKO and ApcMin/Min organoids transduced with the indicated lentiviruses; three representative organoid fields. Expression of p53R175H-GFP in CKIa/p53 DKO organoids and p53R273H-GFP in ApcMin/Min organoids produced a similar effect. Nuclear counterstain, Hoechst (blue). Scale bars, 100 μm. e, Bright-field and immunofluorescent imaging of WNT targets and Ki67 in CKIaKOp53R172H jejunal organoids grown with gallic acid for 9 days continuously, or 5 days with and then 4 days without gallic acid. Nuclear counterstain (immunofluorescence), Hoechst (blue). Inserts (bright-field) show a high-magnification image of a representative organoid. Scale bars, 500 μm (top); 100 μm (bottom). Representative data: a, c-e from two to three independent experiments. Source data

Extended Data Fig. 9 Gallic acid reverses mutant-p53-induced WNT suppression and promotes dysplasia and tumorigenesis across the entire gut.

a, Levels of gallic acid in the jejunum and ileum of gallic-acid-treated and non-treated CKIaΔgutp53R172H mice. Mean ± s.e.m. (n, number of samples, each pooled from two mice), one-way ANOVA with Tukey’s test. b, qPCR of SDH-coding genes and 16S rRNA genes from mouse stool. Mean ± s.e.m. (n, number of mice from which stool was collected), relative to CKIafl/fl (normalized to 1), one-way ANOVA with Tukey’s test. c, qPCR of SDH-coding genes from the mucosa of different segments of the mouse gut. Mean ± s.e.m. (n, number of mice) relative to CKIafl/fl jejunum (normalized to 1), Kruskal–Wallis with Conover’s test. The y axis is represented as a power of 10. d, H&E-stained sections of mouse jejunum. Inserts show high-magnification images of jejunal villi, demonstrating the grade of dysplasia. Scale bar, 100 μm. e, Average nuclear size in mouse jejunal villi. Overall average of the mean values for each mouse ± s.e.m (n, number of microscope fields), one-sided Student’s t-test. f, Dysplasia score (see Methods) of mouse jejunal villi. Overall average of the mean values for each mouse ± s.e.m (n, number of villi), one-sided Student’s t-test. g, h, Representative images of different segments of the mouse bowel. Arrowheads indicate visible tumours. i, Quantification of visible tumours per mouse. Mean ± s.e.m. (n, number of mice), one-sided Student’s t-test. j, Tumour size (each mouse is colour-coded). Overall average of the mean values for each mouse ± s.e.m (n, number of mice), one-way ANOVA with Tukey’s test. k, H3K4me3 ChIP of WNT target promoters in mouse jejunal enterocytes. Mean enrichment ± s.e.m. (n, number of mice), one-sided Student’s t-test. l, RT–qPCR of WNT targets in mouse jejunal enterocytes. Mean ± s.e.m. (n, number of mice) relative to non-treated CKIafl/fl (normalized to 1), one-sided Student’s t-test. m, IHC of WNT targets in mouse jejunum. Scale bars, 100 μm. n, o, Immunoblot of mouse jejunal enterocytes. PP2Ac, loading control. For gel source data, see Supplementary Fig. 1. Representative data from two or three independent experiments (d, g, h, mo). Source data

Extended Data Fig. 10 Gallic acid reverses the effect of antibiotic treatment in the distal gut of CKIaΔgutp53R172H mice.

a, b, H&E-stained sections of mouse ileum (a) and colon (b). Scale bars, 100 μm. c, IHC of Ki67 in mouse ileum and colon. Scale bars, 100 μm. d, RT–qPCR of WNT targets in mouse ileal enterocytes. Mean ± s.e.m. (n, number of mice) relative to antibiotic-treated CKIafl/fl (normalized to 1), one-sided Student’s t-test. e, f, IHC of WNT targets in mouse ileum (e) and colon (f). Scale bars, 100 μm. Representative data from two or three independent experiments (ac, e, f). Source data

Supplementary information

Supplementary Figure

Supplementary Figure 1. Source Data of Immunoblots. The full scanned images show the uncropped version with molecular weight markers, loading controls and the order of rehybridization. Blots are labeled according to the corresponding Figure panel within the main or Extended Data Figures.

Reporting Summary

Supplementary Table

Supplementary Table 1. Mutual exclusivity analysis of TP53 and WNT activating genes in hepatocellular carcinoma. cBioPortal mutual exclusivity analysis of TP53 (n = 308) and WNT activating genes (CTNNB1: n = 305; APC: n = 33; AXIN1: n = 74; AXIN2: n = 10) in 1089 hepatocellular carcinoma samples from 1085 patients, Fisher’s exact test. P-value for each comparison is specified in the table above.

Source data

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Kadosh, E., Snir-Alkalay, I., Venkatachalam, A. et al. The gut microbiome switches mutant p53 from tumour-suppressive to oncogenic. Nature (2020). https://doi.org/10.1038/s41586-020-2541-0

Download citation

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.