Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

High-fat-diet-mediated dysbiosis promotes intestinal carcinogenesis independently of obesity

Subjects

Abstract

Several features common to a Western lifestyle, including obesity and low levels of physical activity, are known risk factors for gastrointestinal cancers1. There is substantial evidence suggesting that diet markedly affects the composition of the intestinal microbiota2. Moreover, there is now unequivocal evidence linking dysbiosis to cancer development3. However, the mechanisms by which high-fat diet (HFD)-mediated changes in the microbial community affect the severity of tumorigenesis in the gut remain to be determined. Here we demonstrate that an HFD promotes tumour progression in the small intestine of genetically susceptible, K-rasG12Dint, mice independently of obesity. HFD consumption, in conjunction with K-ras mutation, mediated a shift in the composition of the gut microbiota, and this shift was associated with a decrease in Paneth-cell-mediated antimicrobial host defence that compromised dendritic cell recruitment and MHC class II molecule presentation in the gut-associated lymphoid tissues. When butyrate was administered to HFD-fed K-rasG12Dint mice, dendritic cell recruitment in the gut-associated lymphoid tissues was normalized, and tumour progression was attenuated. Importantly, deficiency in MYD88, a signalling adaptor for pattern recognition receptors and Toll-like receptors, blocked tumour progression. The transfer of faecal samples from HFD-fed mice with intestinal tumours to healthy adult K-rasG12Dint mice was sufficient to transmit disease in the absence of an HFD. Furthermore, treatment with antibiotics completely blocked HFD-induced tumour progression, suggesting that distinct shifts in the microbiota have a pivotal role in aggravating disease. Collectively, these data underscore the importance of the reciprocal interaction between host and environmental factors in selecting a microbiota that favours carcinogenesis, and they suggest that tumorigenesis is transmissible among genetically predisposed individuals.

This is a preview of subscription content

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Figure 1: An HFD accelerates cancer progression.
Figure 2: Diet-induced tumour progression is associated with an altered microbial community.
Figure 3: Butyrate supplementation, but not prebiotics, confers protection against HFD-induced tumorigenesis.
Figure 4: Disease-associated bacteria can be transmitted to healthy K-rasG12Dint animals, and antibiotic treatment abolishes tumorigenesis.

Accession codes

Primary accessions

Gene Expression Omnibus

Sequence Read Archive

Data deposits

Microarray data were generated in accordance with the MIAME guidelines and have been deposited in the Gene Expression Omnibus (GEO) database under accession number GSE56257. Sequence data have been deposited in the NCBI Sequence Read Archive (SRA) database under BioProject PRJNA242565 (SRA project accession number, SRP040736; sample accession numbers, SRS584259SRS584323, SRS584325).

References

  1. 1

    Giovannucci, E. & Michaud, D. The role of obesity and related metabolic disturbances in cancers of the colon, prostate, and pancreas. Gastroenterology 132, 2208–2225 (2007)

    CAS  Article  Google Scholar 

  2. 2

    Wu, G. D. et al. Linking long-term dietary patterns with gut microbial enterotypes. Science 334, 105–108 (2011)

    CAS  ADS  Article  Google Scholar 

  3. 3

    Schwabe, R. F. & Jobin, C. The microbiome and cancer. Nature Rev. Cancer 13, 800–812 (2013)

    CAS  Article  Google Scholar 

  4. 4

    Turnbaugh, P. J., Backhed, F., Fulton, L. & Gordon, J. I. Diet-induced obesity is linked to marked but reversible alterations in the mouse distal gut microbiome. Cell Host Microbe 3, 213–223 (2008)

    CAS  Article  Google Scholar 

  5. 5

    Zimmet, P., Alberti, K. G. & Shaw, J. Global and societal implications of the diabetes epidemic. Nature 414, 782–787 (2001)

    CAS  ADS  Article  Google Scholar 

  6. 6

    Ley, R. E., Turnbaugh, P. J., Klein, S. & Gordon, J. I. Microbial ecology: human gut microbes associated with obesity. Nature 444, 1022–1023 (2006)

    CAS  ADS  Article  Google Scholar 

  7. 7

    Turnbaugh, P. J. et al. An obesity-associated gut microbiome with increased capacity for energy harvest. Nature 444, 1027–1031 (2006)

    ADS  Article  Google Scholar 

  8. 8

    Bennecke, M. et al. Ink4a/Arf and oncogene-induced senescence prevent tumor progression during alternative colorectal tumorigenesis. Cancer Cell 18, 135–146 (2010)

    CAS  Article  Google Scholar 

  9. 9

    Arkan, M. C. et al. IKK-β links inflammation to obesity-induced insulin resistance. Nature Med. 11, 191–198 (2005)

    CAS  Article  Google Scholar 

  10. 10

    Khasawneh, J. et al. Inflammation and mitochondrial fatty acid β-oxidation link obesity to early tumor promotion. Proc. Natl Acad. Sci. USA 106, 3354–3359 (2009)

    CAS  ADS  Article  Google Scholar 

  11. 11

    Clevers, H. C. & Bevins, C. L. Paneth cells: maestros of the small intestinal crypts. Annu. Rev. Physiol. 75, 289–311 (2013)

    CAS  Article  Google Scholar 

  12. 12

    Shan, M. et al. Mucus enhances gut homeostasis and oral tolerance by delivering immunoregulatory signals. Science 342, 447–453 (2013)

    CAS  ADS  Article  Google Scholar 

  13. 13

    Maslowski, K. M. & Mackay, C. R. Diet, gut microbiota and immune responses. Nature Immunol. 12, 5–9 (2011)

    CAS  Article  Google Scholar 

  14. 14

    Kawai, T. & Akira, S. The role of pattern-recognition receptors in innate immunity: update on Toll-like receptors. Nature Immunol. 11, 373–384 (2010)

    CAS  Article  Google Scholar 

  15. 15

    Slack, E. et al. Innate and adaptive immunity cooperate flexibly to maintain host–microbiota mutualism. Science 325, 617–620 (2009)

    CAS  ADS  Article  Google Scholar 

  16. 16

    Larsson, E. et al. Analysis of gut microbial regulation of host gene expression along the length of the gut and regulation of gut microbial ecology through MyD88. Gut 61, 1124–1131 (2012)

    CAS  Article  Google Scholar 

  17. 17

    Ubeda, C. et al. Familial transmission rather than defective innate immunity shapes the distinct intestinal microbiota of TLR-deficient mice. J. Exp. Med. 209, 1445–1456 (2012)

    CAS  Article  Google Scholar 

  18. 18

    Redgwell, R. J. & Fischer, M. Dietary fiber as a versatile food component: an industrial perspective. Mol. Nutr. Food Res. 49, 521–535 (2005)

    Article  Google Scholar 

  19. 19

    Macfarlane, G. T., Steed, H. & Macfarlane, S. Bacterial metabolism and health-related effects of galacto-oligosaccharides and other prebiotics. J. Appl. Microbiol. 104, 305–344 (2008)

    CAS  PubMed  Google Scholar 

  20. 20

    Furusawa, Y. et al. Commensal microbe-derived butyrate induces the differentiation of colonic regulatory T cells. Nature 504, 446–450 (2013)

    CAS  ADS  Article  Google Scholar 

  21. 21

    Brestoff, J. R. & Artis, D. Commensal bacteria at the interface of host metabolism and the immune system. Nature Immunol. 14, 676–684 (2013)

    CAS  Article  Google Scholar 

  22. 22

    Zoetendal, E. G. et al. The human small intestinal microbiota is driven by rapid uptake and conversion of simple carbohydrates. ISME J. 6, 1415–1426 (2012)

    CAS  Article  Google Scholar 

  23. 23

    Gautier, L., Cope, L., Bolstad, B. M. & Irizarry, R. A. affy—analysis of Affymetrix GeneChip data at the probe level. Bioinformatics 20, 307–315 (2004)

    CAS  Article  Google Scholar 

  24. 24

    Wettenhall, J. M. & Smyth, G. K. limmaGUI: a graphical user interface for linear modeling of microarray data. Bioinformatics 20, 3705–3706 (2004)

    CAS  Article  Google Scholar 

  25. 25

    Gentleman, R. C. et al. Bioconductor: open software development for computational biology and bioinformatics. Genome Biol. 5, R80 (2004)

    Article  Google Scholar 

  26. 26

    Meyer, S., Nolte, J., Opitz, L., Salinas-Riester, G. & Engel, W. Pluripotent embryonic stem cells and multipotent adult germline stem cells reveal similar transcriptomes including pluripotency-related genes. Mol. Hum. Reprod. 16, 846–855 (2010)

    CAS  Article  Google Scholar 

  27. 27

    Irizarry, R. A. et al. Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics 4, 249–264 (2003)

    Article  Google Scholar 

  28. 28

    Smyth, G. K. Linear models and empirical Bayes methods for assessing differential expression in microarray experiments. Stat. Appl. Genet. Mol. Biol. http://dx.doi.org/10.2202/1544-6115.1027 (12 February 2004)

  29. 29

    Klipper-Aurbach, Y. et al. Mathematical formulae for the prediction of the residual β cell function during the first two years of disease in children and adolescents with insulin-dependent diabetes mellitus. Med. Hypotheses 45, 486–490 (1995)

    CAS  Article  Google Scholar 

  30. 30

    Quince, C., Lanzen, A., Davenport, R. J. & Turnbaugh, P. J. Removing noise from pyrosequenced amplicons. BMC Bioinformatics 12, 38 (2011)

    Article  Google Scholar 

  31. 31

    Caporaso, J. G. et al. QIIME allows analysis of high-throughput community sequencing data. Nature Methods 7, 335–336 (2010)

    CAS  Article  Google Scholar 

  32. 32

    Schloss, P. D. et al. Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl. Environ. Microbiol. 75, 7537–7541 (2009)

    CAS  Article  Google Scholar 

  33. 33

    DeSantis, T. Z. et al. Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl. Environ. Microbiol. 72, 5069–5072 (2006)

    CAS  Article  Google Scholar 

  34. 34

    Caporaso, J. G. et al. PyNAST: a flexible tool for aligning sequences to a template alignment. Bioinformatics 26, 266–267 (2010)

    CAS  Article  Google Scholar 

  35. 35

    Wang, Q., Garrity, G. M., Tiedje, J. M. & Cole, J. R. Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl. Environ. Microbiol. 73, 5261–5267 (2007)

    CAS  Article  Google Scholar 

  36. 36

    Segata, N. et al. Metagenomic biomarker discovery and explanation. Genome Biol. 12, R60 (2011)

    Article  Google Scholar 

  37. 37

    Hothorn, T., Bretz, F. & Westfall, P. Simultaneous inference in general parametric models. Biom. J. 50, 346–363 (2008)

    MathSciNet  Article  Google Scholar 

Download references

Acknowledgements

We thank K. Burmeister and J. Khasawneh for technical assistance and H. Wagner for generously providing Myd88−/− mice. We are thankful to K. Offe and the PhD program ‘Medical Life Sciences and Technology’ for providing a fellowship to J.H. for one year. This work was supported in part by the LOEWE Center for Cell and Gene Therapy Frankfurt (CGT, III L 4-518/17.004) and institutional funds from the Georg-Speyer-Haus, as well as grants from the Deutsche Forschungsgemeinschaft (DFG) (Gr1916/5-1), the Deutsche Krebshilfe (108872) and the ERC (ROSCAN-281967) to F.R.G. Computational infrastructure made available to S.W.P. by the University of Delaware Center for Bioinformatics and Computational Biology Core Facility and the Delaware Biotechnology Institute was supported by grants from the US National Institutes of Health National Institute of General Medical Sciences (8 P20 GM103446-12) and the US National Science Foundation EPSCoR (EPS-081425). This work was supported by grants from the Deutsche Krebshilfe (107977) and the DFG (AR710/2-1) to M.C.A.

Author information

Affiliations

Authors

Contributions

M.D.S., Ç.A., J.H. and M.C.A. performed experimental work and managed data analyses. F.K.R., S.S., B.A., P.K.Z., J.V., W.R. and F.R.G. assisted with sample collection, bone marrow transplantation and flow cytometric analyses. C.P. and G.S.-R. carried out sample preparation and microarray analyses. A.B. conducted statistical analyses. C.A. and M.B. performed gas chromatography. L.B. and T.K. provided human samples and evaluated all histological sections. S.C.P. and S.W.P. conducted 16S rRNA gene sequencing computational analyses. M.C.A. designed the study and prepared the manuscript.

Corresponding author

Correspondence to Melek C. Arkan.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Extended data figures and tables

Extended Data Figure 1 An HFD accelerates carcinogenesis independently of obesity and insulin resistance.

a, Primary tumours metastasized to the liver, pancreas and spleen in K-rasG12Dint mice maintained on an HFD for 43 weeks. b, Immunohistochemistry staining of duodenal samples using an antibody specific for 5-bromodeoxyuridine (BrdU) showed increased proliferation in K-rasG12Dint mice. c, Weight curves for LSL-K-rasG12D/+ controls (n = 5) and K-rasG12Dint (n = 6) littermate animals showed no difference in weight gain under the normal diet (ND) condition, although K-rasG12Dint mice (n = 7) remained significantly leaner when fed on an HFD (LSL-K-rasG12D/+ controls n = 4). P values were determined by t-test and adjusted for multiple testing. The error bars indicate s.e.m. *, P ≤ 0.05; **P ≤ 0.01. d, In accordance with the weight curves, the response to glucose overload and insulin secretion during a glucose tolerance test (GTT) remained similar between the two groups under the ND condition; however, K-rasG12Dint mice remained insulin sensitive on an HFD (ND, LSL-K-rasG12D/+ controls n = 5, K-rasG12Dint mice n = 3; HFD, LSL-K-rasG12D/+ controls n = 8, K-rasG12Dint mice n = 5). P values were determined by t-test and adjusted for multiple testing. The error bars indicate s.e.m. *, P ≤ 0.05; **, P ≤ 0.01. The results are representative of two to three independent experiments. e, Together with resistance to diet-induced obesity, K-rasG12Dint mice showed microvesicular steatosis, in contrast to the littermate controls (which had macrovesicular steatosis), suggesting decreased lipid accumulation in the liver of K-rasG12Dint mice. f, Messenger RNA expression levels of F4/80 and TNF-α were analysed by reverse transcriptase (RT)–PCR (HFD, LSL-K-rasG12D/+ controls n = 3, K-rasG12Dint mice n = 6). Plasma TNF-α levels determined by enzyme-linked immunosorbent assay (ELISA) showed decreased levels in K-rasG12Dint mice (HFD, LSL-K-rasG12D/+ controls n = 7, K-rasG12Dint mice n = 11). P values were determined by t-test. The error bars indicate s.e.m. *, P ≤ 0.05.

Source data

Extended Data Figure 2 The host immune response is dampened in K-rasG12Dint mice.

a, Relative mRNA expression levels of genes involved in the immune response were analysed by RT–PCR in duodenal samples from mice under the ND or the HFD regimen (ND, LSL-K-rasG12D/+ controls n = 3, K-rasG12Dint mice n = 3; HFD, LSL-K-rasG12D/+ controls n = 3, K-rasG12Dint mice n = 6). P values were determined by one-way analysis of variance (ANOVA) and adjusted for the number of comparisons by using the Bonferroni method. The error bars indicate s.e.m. **, P ≤ 0.01. #, P ≤ 0.05 and ##, P ≤ 0.01 compared with littermate controls. b, Azure eosin staining of duodenal samples showed decreased amounts of granules with antimicrobial peptides in the crypts of K-rasG12Dint mice. c, The expression of differentiation markers for Paneth cells (cryptdin), epithelial cells (sucrase-isomaltase) and enteroendocrine cells (synaptophysin), as well as mucins in the duodenum, was analysed by RT–PCR under the ND or the HFD regimen (ND, LSL-K-rasG12D/+ controls n = 3, K-rasG12Dint mice n = 3; HFD, LSL-K-rasG12D/+ controls n = 3, K-rasG12Dint mice n = 4). P values were determined by one-way ANOVA and adjusted for the number of comparisons by using the Bonferroni method. The error bars indicate s.e.m. *, P ≤ 0.05. ###, P ≤ 0.001 compared with littermate controls. d, Flow cytometric analysis of cells from the lamina propria (LP) and Peyer’s Patches (PP) indicated the presence of CD11c+ dendritic cells (DCs) and the expression of MHC class II molecules in K-rasG12Dint mice and littermate controls on the ND or the HFD regimen (n = 2). P values were determined by one-way ANOVA and adjusted for the number of comparisons by using the Bonferroni method. The error bars indicate s.e.m. *, P ≤ 0.05; **, P ≤ 0.01. #, P ≤ 0.05 compared with littermate controls.

Source data

Extended Data Figure 3 An HFD leads to community changes in the gut microbiota.

a, b, Linear discriminant analysis (LDA) effect size (LEfSe) results showed bacteria that were significantly different in abundance among K-rasG12Dint mice and littermate controls on the HFD and indicated the effect size of each differentially abundant bacterial taxon in the small intestine (LSL-K-rasG12D/+ controls, n = 3; K-rasG12Dint mice, n = 8) (a) and colon (LSL-K-rasG12D/+ controls, n = 5; K-rasG12Dint mice, n = 7) (b). c, The relative abundance of Escherichia/Shigella spp. and Helicobacter spp. in the small intestine of K-rasG12Dint mice on the ND or the HFD.

Extended Data Figure 4 Systemic deletion of Myd88 prevents tumour progression in K-rasG12Dint mice.

a, Histological scores for the small intestine showed complete lack of tumour progression in HFD-fed K-rasG12Dint mice with systemic Myd88 deletion. However, tissue-specific deletion of Myd88 did not confer any protection against tumour progression in K-rasG12Dint mice (Myd88−/− LSL-K-rasG12D/+ controls, n = 19; Myd88−/− K-rasG12Dint mice, n = 15; Myd88fl/fl LSL-K-rasG12D/+ controls, n = 5; Myd88IEC K-rasG12Dint mice, n = 7; K-rasG12Dint+WT BM mice, n = 7; K-rasG12Dint+Myd88−/−BM mice, n = 8). Each point represents one animal, and the lines indicate means. A two-sided Fisher test was applied. Adjustments on pairwise t-tests were made using the single-step method. ***, P ≤ 0.001; NS, not significant. Myd88IEC K-rasG12Dint, K-rasG12Dint mice with IEC-specific deletion of Myd88; K-rasG12Dint+WT BM, K-rasG12Dint mice transplanted with wild-type bone marrow; K-rasG12Dint+Myd88−/−BM, K-rasG12Dint mice transplanted with Myd88-deficient bone marrow. b, LEfSe results showed bacteria that were significantly different in abundance among HFD-fed K-rasG12Dint mice with (n = 8) or without (n = 4) systemic Myd88 deletion. Peptostreptococcaceae, Deferribacteraceae and Ruminococcaceae in the small intestine, as well as Peptostreptococcaceae and Deferribacteraceae in the colon, became abundant after Myd88 deficiency (K-rasG12Dint mice, n = 7; Myd88−/− K-rasG12Dint mice, n = 4). c, Flow cytometric analysis of LP cells indicated that the decreased recruitment of, and surface antigen presentation by, CD11c+ DCs following the HFD was partially attenuated after Myd88 deletion (Myd88−/− LSL-K-rasG12D/+ controls, n = 8; Myd88−/− K-rasG12Dint mice, n = 4; LSL-K-rasG12D/+ controls, n = 2; K-rasG12Dint mice, n = 2). P values were determined by one-way ANOVA and adjusted for the number of comparisons by using the Bonferroni method. The error bars indicate s.e.m. *, P ≤ 0.05; **, P ≤ 0.01.

Source data

Extended Data Figure 5 Bacterial composition differs between K-rasG12Dint mice with tissue-specific and systemic deletion of Myd88.

LEfSe results showed that the composition of the small intestinal microbiota was significantly different between HFD-fed K-rasG12Dint mice with and without Myd88 deletion in the IECs (a) or the haematopoietic cells (b, c) (Myd88IEC K-rasG12Dint mice, n = 4; K-rasG12Dint+WT BM mice, n = 4; K-rasG12Dint+MyD88–/–BM mice, n = 4; K-rasG12Dint mice, n = 8).

Extended Data Figure 6 An HFD decreases SCFA concentrations.

a, The HFD decreased the acetate, butyrate and propionate concentrations in stool samples from K-rasG12Dint mice and littermate controls, whereas the isovaleric acid and valeric acid concentrations increased (ND, LSL-K-rasG12D/+ controls n = 6, K-rasG12Dint mice n = 8; HFD, LSL-K-rasG12D/+ controls n = 7, K-rasG12Dint mice n = 11). P values were determined by one-way ANOVA and adjusted for the number of comparisons by using the Bonferroni method. The error bars indicate s.e.m. *, P ≤ 0.05; **, P ≤ 0.01; ***, P ≤ 0.001. b, SCFA concentrations of small intestinal samples (LSL-K-rasG12D/+ controls, n = 4; K-rasG12Dint mice, n = 4) and colonic samples (LSL-K-rasG12D/+ controls, n = 3; K-rasG12Dint mice, n = 5) from K-rasG12Dint and littermate controls on the HFD supplemented with arabinogalactan. P values were determined by one-way ANOVA followed by Bonferroni’s multiple comparison test. The error bars indicate s.e.m. *, P ≤ 0.05; **, P ≤ 0.01; ***, P ≤ 0.001. c, Flow cytometric analysis of cells from the LP and the MLNs revealed compromised DC recruitment and decreased surface antigen presentation in mice fed the HFD supplemented with GOS (HFD, LSL-K-rasG12D/+ controls n = 2, K-rasG12Dint mice n = 2; HFD + GOS, LSL-K-rasG12D/+ controls n = 5, K-rasG12Dint mice n = 3). P values were determined by one-way ANOVA and adjusted for the number of comparisons by using the Bonferroni method. The error bars indicate s.e.m. The differences are not significant.

Source data

Extended Data Figure 7 Prebiotic supplementation does not protect K-rasG12Dint mice against HFD-induced tumorigenesis.

a, Relative mRNA expression levels for genes involved in the immune response and those that encode mucins and differentiation markers for Paneth, enteroendocrine and epithelial cells in duodenal samples (HFD + GOS, LSL-K-rasG12D/+ controls n = 5, K-rasG12Dint mice n = 6; HFD + antibiotics (Abx), LSL-K-rasG12D/+ controls n = 5, K-rasG12Dint mice n = 6). P values were determined by one-way ANOVA and adjusted for the number of comparisons by using the Bonferroni method. The error bars indicate s.e.m. *, P ≤ 0.05. #, P ≤ 0.05, ##, P ≤ 0.01 and ###, P ≤ 0.001 compared with littermate controls. b, Prebiotic supplementation had little or no effect on stool SCFA concentrations (HFD, LSL-K-rasG12D/+ controls n = 7, K-rasG12Dint mice n = 11; HFD + GOS, LSL-K-rasG12D/+ controls n = 5, K-rasG12Dint mice n = 6). P values were determined by one-way ANOVA and adjusted for the number of comparisons by using the Bonferroni method. The error bars indicate s.e.m. *, P ≤ 0.05, ***, P ≤ 0.001. ##, P ≤ 0.01 and ###, P ≤ 0.001 compared with littermate controls.

Source data

Extended Data Figure 8 Butyrate attenuates K-Ras signalling and has systemic effects on metabolic parameters.

a, Sodium butyrate treatment only slightly affected butyrate and propionate concentrations in the stool compared with prebiotic supplementation (HFD, LSL-K-rasG12D/+ controls n = 7, K-rasG12Dint mice n = 11; HFD + butyrate, LSL-K-rasG12D/+ controls n = 6, K-rasG12Dint mice n = 5). P values were determined by one-way ANOVA and adjusted for the number of comparisons by using the Bonferroni method. The error bars indicate s.e.m. *, P ≤ 0.05; **, P ≤ 0.01; ***, P ≤ 0.001. #, P ≤ 0.05, ##, P ≤ 0.01 and ###, P ≤ 0.001 compared with littermate controls. b, The higher proliferation levels observed in the duodenum of K-rasG12Dint mice were decreased following butyrate supplementation. c, The expression of differentiation markers and mucins in the duodenum (HFD, LSL-K-rasG12D/+ controls n = 3, K-rasG12Dint mice n = 4; HFD + butyrate, LSL-K-rasG12D/+ controls n = 3, K-rasG12Dint mice n = 3). P values were determined by one-way ANOVA and adjusted for the number of comparisons by using the Bonferroni method. The error bars indicate s.e.m. *, P ≤ 0.05; **, P ≤ 0.01. #, P ≤ 0.05 and ##, P ≤ 0.01 compared with littermate controls. d, Butyrate had systemic effects and protected K-rasG12Dint mice and littermate controls against HFD-induced hyperinsulinaemia (n = 5 per group). Data were assessed by t-test, and P values were adjusted for multiple testing. The error bars indicate s.e.m. *, P ≤ 0.05; **, P ≤ 0.01; ***, P ≤ 0.001. e, Butyrate treatment did not affect H3 or H4 acetylation (n = 3–8). The data were assessed by one-way ANOVA, and P values were adjusted for the number of comparisons by using the Bonferroni method. The error bars indicate s.e.m. The differences are not significant.

Source data

Extended Data Figure 9 HFD-induced dysbiosis and the associated cancer risk can be transferred to K-rasG12Dint mice on a normal diet.

a, Following 1 week of antibiotic cocktail treatment, K-rasG12Dint mice and littermate controls (approximately 7 weeks of age) on the ND regimen were gavaged three times a week with fresh stool pellets from HFD-fed mutants (which had been HFD fed for 24 weeks on the day of first transfer) for a total of 15 weeks. Haematoxylin and eosin staining of duodenal samples from three gavaged K-rasG12Dint mice show mSA-LGD and mSA-HGD, as well as invasive carcinoma development. b, LEfSe results showed bacteria that were significantly different in abundance between ND-fed K-rasG12Dint mice that had been gavaged with stool samples from HFD-fed K-rasG12Dint donors and ND-fed K-rasG12Dint mice that had not been gavaged. Helicobacteraceae, Enterococcaceae and Deferribacteraceae became more abundant in the colon after stool transfer (K-rasG12Dint mice + ND, n = 3; K-rasG12Dint mice + ND + stool, n = 6). c, Flow cytometric analysis of cells from the PP and MLNs showed antigen presentation by CD11c+ DCs (ND, LSL-K-rasG12D/+ controls n = 2, K-rasG12Dint mice n = 2; ND + stool, LSL-K-rasG12D/+ controls n = 7, K-rasG12Dint mice n = 10). P values were determined by one-way ANOVA and adjusted for the number of comparisons by using the Bonferroni method. The error bars indicate s.e.m. ***, P ≤ 0.001. #, P ≤ 0.05 and ##, P ≤ 0.01 compared with littermate controls. d, Glucose clearance during a GTT in ND-fed K-rasG12Dint mice and littermate controls (n = 5 per group) that had received stool samples from HFD-fed K-rasG12Dint donors. The results were analysed by t-test. P values were adjusted for multiple testing. The error bars indicate s.e.m. The differences are not significant.

Source data

Extended Data Figure 10 Antibiotic treatment blocks HFD-induced tumorigenesis in K-rasG12Dint mice.

a, The expression profiles of selected genes involved in antigen recognition, immune response, immune cell recruitment, differentiation markers and mucins in duodenal samples from LSL-K-rasG12D/+ and K-rasG12Dint littermate animals (ND, LSL-K-rasG12D/+ controls n = 3, K-rasG12Dint mice n = 3; ND + stool, LSL-K-rasG12D/+ controls n = 3, K-rasG12Dint mice n = 5). P values were determined by one-way ANOVA and adjusted for the number of comparisons with the Bonferroni method. The error bars indicate s.e.m. ***, P ≤ 0.001. #, P ≤ 0.05, ##, P ≤ 0.01 and ###, P ≤ 0.001 compared with littermate controls. b, Fluorescence-activated cell sorting (FACS) analysis of MLN cells indicates recruitment of, and antigen presentation by, CD11c+ dendritic cells following treatment with antibiotics (HFD, LSL-K-rasG12D/+ controls n = 2, K-rasG12Dint mice n = 2; HFD + Abx, LSL-K-rasG12D/+ controls n = 5, K-rasG12Dint mice n = 7). P values were determined by one-way ANOVA and adjusted for the number of comparisons with the Bonferroni method. The error bars indicate s.e.m. ***, P ≤ 0.001. The results are not significant. c, The mechanistic scheme suggests that HFD-induced changes in the bacterial community, SCFA levels and mucin profiles cooperate with an oncogene-associated decrease in host immunity, collectively enhancing carcinogenesis in the small intestine.

Source data

Supplementary information

Supplementary Information

This file contains Supplementary Tables 1 and 2. (PDF 204 kb)

PowerPoint slides

Source data

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Schulz, M., Atay, Ç., Heringer, J. et al. High-fat-diet-mediated dysbiosis promotes intestinal carcinogenesis independently of obesity. Nature 514, 508–512 (2014). https://doi.org/10.1038/nature13398

Download citation

Further reading

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.

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing