Healthy infants harbor intestinal bacteria that protect against food allergy


There has been a striking generational increase in life-threatening food allergies in Westernized societies1,2. One hypothesis to explain this rising prevalence is that twenty-first century lifestyle practices, including misuse of antibiotics, dietary changes, and higher rates of Caesarean birth and formula feeding have altered intestinal bacterial communities; early-life alterations may be particularly detrimental3,4. To better understand how commensal bacteria regulate food allergy in humans, we colonized germ-free mice with feces from healthy or cow’s milk allergic (CMA) infants5. We found that germ-free mice colonized with bacteria from healthy, but not CMA, infants were protected against anaphylactic responses to a cow’s milk allergen. Differences in bacterial composition separated the healthy and CMA populations in both the human donors and the colonized mice. Healthy and CMA colonized mice also exhibited unique transcriptome signatures in the ileal epithelium. Correlation of ileal bacteria with genes upregulated in the ileum of healthy or CMA colonized mice identified a clostridial species, Anaerostipes caccae, that protected against an allergic response to food. Our findings demonstrate that intestinal bacteria are critical for regulating allergic responses to dietary antigens and suggest that interventions that modulate bacterial communities may be therapeutically relevant for food allergy.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Fig. 1: Transfer of healthy, but not CMA, infants’ microbiota protects against an allergic response to food.
Fig. 2: Analysis of fecal samples from eight human infant donors reveals taxonomic signatures that correlate with allergic phenotype.
Fig. 3: Unique ileal transcriptome signatures distinguish healthy- and CMA-colonized mice.
Fig. 4: Correlation of ileal OTUs with DEGs in the ileum of healthy-colonized mice identifies a clostridial species, A. caccae, that protects against an allergic response to food.

Code availability

The open-source analysis software used in this study is publicly available and referenced as appropriate. Custom codes are available from the corresponding author upon request.

Data availability

The data that support the findings of this study are available from the corresponding author upon request. The 16S rRNA and RNA-seq raw FastQ sequencing files were deposited into the National Center for Biotechnology Information Sequence Read Archive and are available under the accession numbers SRP130620 and SRP130644, respectively. Additional processed data reported in this study are available upon request.

Change history

  • 25 January 2019

    In the version of the article originally published, there was a hyperlinking error in the callout to Extended Data Fig. 7 at the end of the ‘16s RNA-targeted sequencing section' of the Methods. The hyperlink led to Extended Data Fig. 6 instead of Extended Data Fig. 7. The error has been corrected online.


  1. 1.

    Sicherer, S. et al. Critical issues in food allergy: a National Academies consensus report. Pediatrics 140, e20170194 (2017).

    Article  Google Scholar 

  2. 2.

    Iweala, O. I. & Burks, A. W. Food allergy: our evolving understanding of its pathogenesis, prevention, and treatment. Curr. Allergy Asthma Rep. 16, 37 (2016).

    Article  Google Scholar 

  3. 3.

    Wesemann, D. R. & Nagler, C. R. The microbiome, timing, and barrier function in the context of allergic disease. Immunity 44, 728–738 (2016).

    CAS  Article  Google Scholar 

  4. 4.

    Plunkett, C. H. & Nagler, C. R. The influence of the microbiome on allergic sensitization to food. J. Immunol. 198, 581–589 (2017).

    CAS  Article  Google Scholar 

  5. 5.

    Berni Canani, R. et al. Lactobacillus rhamnosus GG-supplemented formula expands butyrate-producing bacterial strains in food allergic infants. ISME J. 10, 742–750 (2016).

    CAS  Article  Google Scholar 

  6. 6.

    Bunyavanich, S. et al. Early-life gut microbiome composition and milk allergy resolution. J. Allergy Clin. Immunol. 138, 1122–1130 (2016).

    CAS  Article  Google Scholar 

  7. 7.

    Stefka, A. T. et al. Commensal bacteria protect against food allergen sensitization. Proc. Natl Acad. Sci. USA 111, 13145–13150 (2014).

    CAS  Article  Google Scholar 

  8. 8.

    Dominguez-Bello, M. G. et al. Delivery mode shapes the acquisition and structure of the initial microbiota across multiple body habitats in newborns. Proc. Natl Acad. Sci. USA 107, 11971–11975 (2010).

    Article  Google Scholar 

  9. 9.

    Mueller, N. T., Bakacs, E., Combellick, J., Grigoryan, Z. & Dominguez-Bello, M. G. The infant microbiome development: mom matters. Trends Mol. Med. 21, 109–117 (2015).

    Article  Google Scholar 

  10. 10.

    Blanton, L. V. et al. Gut bacteria that prevent growth impairments transmitted by microbiota from malnourished children. Science 351, aad3311 (2016).

    Article  Google Scholar 

  11. 11.

    Cahenzli, J., Koller, Y., Wyss, M., Geuking, M. B. & McCoy, K. D. Intestinal microbial diversity during early-life colonization shapes long-term IgE levels. Cell Host Microbe 14, 559–570 (2013).

    CAS  Article  Google Scholar 

  12. 12.

    Pabst, O. & Mowat, A. M. Oral tolerance to food protein. Mucosal Immunol. 5, 232–239 (2012).

    CAS  Article  Google Scholar 

  13. 13.

    Honda, K. & Littman, D. R. The microbiota in adaptive immune homeostasis and disease. Nature 535, 75–84 (2016).

    CAS  Article  Google Scholar 

  14. 14.

    Thaiss, C. A., Zmora, N., Levy, M. & Elinav, E. The microbiome and innate immunity. Nature 535, 65–74 (2016).

    CAS  Article  Google Scholar 

  15. 15.

    Yanez, A. J. et al. Broad expression of fructose-1,6-bisphosphatase and phosphoenolpyruvate carboxykinase provide evidence for gluconeogenesis in human tissues other than liver and kidney. J. Cell. Physiol. 197, 189–197 (2003).

    CAS  Article  Google Scholar 

  16. 16.

    Ostroukhova, M. et al. The role of low-level lactate production in airway inflammation in asthma. Am. J. Physiol. Lung Cell. Mol. Physiol. 302, L300–L307 (2012).

    CAS  Article  Google Scholar 

  17. 17.

    Zhu, Y. et al. NPM1 activates metabolic changes by inhibiting FBP1 while promoting the tumorigenicity of pancreatic cancer cells. Oncotarget 6, 21443–21451 (2015).

    PubMed  PubMed Central  Google Scholar 

  18. 18.

    Berger, C. N. et al. Citrobacter rodentium subverts ATP flux and cholesterol homeostasis in intestinal epithelial cells in vivo. Cell Metab. 26, 738–752 (2017).

    CAS  Article  Google Scholar 

  19. 19.

    Zhang, M., Zola, H., Read, L. & Penttila, I. Identification of soluble transforming growth factor-β receptor III (sTβIII) in rat milk. Immunol. Cell Biol. 79, 291–297 (2001).

    CAS  Article  Google Scholar 

  20. 20.

    Miyoshi, H., Ajima, R., Luo, C. T., Yamaguchi, T. P. & Stappenbeck, T. S. Wnt5a potentiates TGF-β signaling to promote colonic crypt regeneration after tissue injury. Science 338, 108–113 (2012).

    CAS  Article  Google Scholar 

  21. 21.

    Planer, J. D. et al. Development of the gut microbiota and mucosal IgA responses in twins and gnotobiotic mice. Nature 534, 263–266 (2016).

    CAS  Article  Google Scholar 

  22. 22.

    Schwiertz, A. et al. Anaerostipes caccae gen. nov., sp. nov., a new saccharolytic, acetate-utilising, butyrate-producing bacterium from human faeces. Syst. Appl. Microbiol. 25, 46–51 (2002).

    CAS  Article  Google Scholar 

  23. 23.

    Duncan, S. H., Louis, P. & Flint, H. J. Lactate-utilizing bacteria, isolated from human feces, that produce butyrate as a major fermentation product. Appl. Environ. Microbiol. 70, 5810–5817 (2004).

    CAS  Article  Google Scholar 

  24. 24.

    Kurakawa, T. et al. Diversity of intestinal Clostridium coccoides group in the Japanese population, as demonstrated by reverse transcription-quantitative PCR. PLoS ONE 10, e0126226 (2015).

    Article  Google Scholar 

  25. 25.

    Donohoe, D. R. et al. The microbiome and butyrate regulate energy metabolism and autophagy in the mammalian colon. Cell Metab. 13, 517–526 (2011).

    CAS  Article  Google Scholar 

  26. 26.

    Byndloss, M. X. et al. Microbiota-activated PPAR-γ signaling inhibits dysbiotic Enterobacteriaceae expansion. Science 357, 570–575 (2017).

    CAS  Article  Google Scholar 

  27. 27.

    Donohoe, D. R., Wali, A., Brylawski, B. P. & Bultman, S. J. Microbial regulation of glucose metabolism and cell-cycle progression in mammalian colonocytes. PLoS ONE 7, e46589 (2012).

    CAS  Article  Google Scholar 

  28. 28.

    Atarashi, K. et al. Induction of colonic regulatory T cells by indigenous Clostridium species. Science 331, 337–341 (2011).

    CAS  Article  Google Scholar 

  29. 29.

    Atarashi, K. et al. Treg induction by a rationally selected mixture of Clostridia strains from the human microbiota. Nature 500, 232–236 (2013).

    CAS  Article  Google Scholar 

  30. 30.

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

    CAS  Article  Google Scholar 

  31. 31.

    Yano, J. M. et al. Indigenous bacteria from the gut microbiota regulate host serotonin biosynthesis. Cell 161, 264–276 (2015).

    CAS  Article  Google Scholar 

  32. 32.

    Kim, Y. G. et al. Neonatal acquisition of Clostridia species protects against colonization by bacterial pathogens. Science 356, 315–319 (2017).

    CAS  Article  Google Scholar 

  33. 33.

    Noval Rivas, M. et al. A microbiota signature associated with experimental food allergy promotes allergic sensitization and anaphylaxis. J. Allergy Clin. Immunol. 131, 201–212 (2013).

    CAS  Article  Google Scholar 

  34. 34.

    Caporaso, J. G. et al. Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms. ISME J. 6, 1621–1624 (2012).

    CAS  Article  Google Scholar 

  35. 35.

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

    CAS  Article  Google Scholar 

  36. 36.

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

    CAS  Article  Google Scholar 

  37. 37.

    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 

  38. 38.

    Edgar, R. C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461 (2010).

    CAS  Article  Google Scholar 

  39. 39.

    Oksanen, J., et al. Package ‘vegan’: Community Ecology Package. R package v.2.4.5 (2017).

  40. 40.

    Jiang, L. et al. Discrete false-discovery rate improves identification of differentially abundant microbes. mSystems 2, e00092-17 (2017).

    Article  Google Scholar 

  41. 41.

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

    Article  Google Scholar 

  42. 42.

    Bashir, M. E., Louie, S., Shi, H. N. & Nagler-Anderson, C. Toll-like receptor 4 signaling by intestinal microbes influences susceptibility to food allergy. J. Immunol. 172, 6978–6987 (2004).

    CAS  Article  Google Scholar 

  43. 43.

    Nik, A. M. & Carlsson, P. Separation of intact intestinal epithelium from mesenchyme. Biotechniques 55, 42–44 (2013).

    CAS  Article  Google Scholar 

  44. 44.

    Andrew, S. FastQC: a quality control application for high throughput sequence data. Babraham Institute (2016).

  45. 45.

    Bray, N. L., Pimentel, H., Melsted, P. & Pachter, L. Near-optimal probabilistic RNA-seq quantification. Nat. Biotechnol. 34, 525–527 (2016).

    CAS  Article  Google Scholar 

  46. 46.

    Soneson, C., Love, M. I. & Robinson, M. D. Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences. F1000Research 4, 1521 (2015).

    Article  Google Scholar 

  47. 47.

    Law, C. W., Chen, Y., Shi, W. & Smyth, G. K. voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol. 15, R29 (2014).

    Article  Google Scholar 

  48. 48.

    Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B 57, 289–300 (1995).

    Google Scholar 

  49. 49.

    Yu, G., Wang, L. G., Han, Y. & He, Q. Y. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS 16, 284–287 (2012).

    CAS  Article  Google Scholar 

  50. 50.

    Upadhyay, V. et al. Lymphotoxin regulates commensal responses to enable diet-induced obesity. Nat. Immunol. 13, 947–953 (2012).

    CAS  Article  Google Scholar 

  51. 51.

    Liu, X. et al. Warburg effect revisited: an epigenetic link between glycolysis and gastric carcinogenesis. Oncogene 29, 442–450 (2010).

    CAS  Article  Google Scholar 

  52. 52.

    Roelen, B. A., Lin, H. Y., Knezevic, V., Freund, E. & Mummery, C. L. Expression of TGF-βs and their receptors during implantation and organogenesis of the mouse embryo. Dev. Biol. 166, 716–728 (1994).

    CAS  Article  Google Scholar 

  53. 53.

    Ellis, J. M., Bowman, C. E. & Wolfgang, M. J. Metabolic and tissue-specific regulation of acyl-CoA metabolism. PLoS ONE 10, e0116587 (2015).

    Article  Google Scholar 

  54. 54.

    Al-Dwairi, A., Pabona, J. M., Simmen, R. C. & Simmen, F. A. Cytosolic malic enzyme 1 (ME1) mediates high fat diet-induced adiposity, endocrine profile, and gastrointestinal tract proliferation-associated biomarkers in male mice. PLoS ONE 7, e46716 (2012).

    CAS  Article  Google Scholar 

  55. 55.

    Pinheiro, J. C. & Bates, D. M. Mixed Effects in Models S and S Plus (Springer, New York, 2000).

  56. 56.

    Kuznetsova, A., Brockhoff, P. B., Rune, H. & Christensen, B. lmertest package: tests in linear mixed effects models. J. Stat. Softw. 82, 1–26 (2017).

    Article  Google Scholar 

Download references


We thank the children and families for their participation in this study. We are grateful to D. Wesemann, E. Forbes-Blom, G. Nunez, M. Rothenberg, M. Alegre and J. Colson for discussion. We thank S. Wang, M. Bauer and A. Kemter for assistance with some experiments, M. Jarsulic for technical assistance with computing infrastructure, K. Hernandez for discussion of the statistical results and C. Weber for histopathological evaluation of all intestinal sections. Statistical consultation was also provided by M. Giurcanu of the University of Chicago Biostatistics Laboratory. We are grateful to B. Theriault and her staff at the University of Chicago Gnotobiotic Research Animal Facility for superb animal care and experimental support. This work was supported by the Sunshine Charitable Foundation (C.R.N.), a pilot award from the University of Chicago Institute for Translational Medicine (CTSA ULI TR000430, C.R.N.), National Institutes of Health (NIH) grants AI134923 (C.R.N.), DK42086 (D.A.A.) and an Italian Ministry of Health grant PE-2011-02348447 (R.B.C.). The Center for Research Informatics is funded by the Biological Sciences Division at the University of Chicago with additional support provided by the Institute for Translational Medicine/Clinical and Translational Award (NIH 5UL1TR002389-02), and the University of Chicago Comprehensive Cancer Center Support Grant (NIH P30CA014599). Bioinformatics analysis was performed on Gardner High-Performance Computing clusters at the Center for Research Informatics at the University of Chicago. A provisional US patent application (62/755,945) was filed on 5 November 2018.

Author information




T.F., C.H.P., R.B., R.B.C., and C.R.N. designed the study. C.H.P. and T.F. performed mouse experiments with help from P.B.-F., R.A., E.Culleen, E.Campbell, and S.M.C.H. R.B., P.B.-F., and J.A. performed bioinformatics analysis. T.F., C.H.P., R.B., P.B.F., and C.R.N. analyzed results. R.B.C., R.N., and L.A. cared for patients and provided donor fecal samples. D.A.A. provided protocols and assisted with the colonization of germ-free mice with human feces and A. caccae. T.F., C.H.P., R.B., R.B.C., and C.R.N. wrote the manuscript. All authors read and commented on the manuscript.

Corresponding author

Correspondence to Cathryn R. Nagler.

Ethics declarations

Competing interests

C.R.N. is president and co-founder of ClostraBio, Inc. The other authors declare no competing interests.

Additional information

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

Extended data

Extended Data Fig. 1 Sensitization of healthy- or CMA-colonized mice with BLG plus cholera toxin does not result in intestinal pathology.

Representative images of histological samples from BLG plus cholera toxin-sensitized healthy- or CMA-colonized mice 24 h post-challenge for donors 1 (healthy) and 5 (CMA; see Supplementary Table 1). All sections stained with H&E or PAS, as indicated. Scale bars, 100 μm.

Extended Data Fig. 2 Long-term colonization of germ-free mice with feces from healthy or CMA infants does not lead to intestinal pathology.

Representative images of histological samples from unsensitized healthy- or CMA-colonized mice collected 5 to 6 months post-colonization for donors described in Supplementary Table 1. All sections stained with H&E or PAS, as indicated. Scale bars, 100 μm.

Extended Data Fig. 3 Diversity analysis of fecal samples from healthy- or CMA-colonized mice.

Shannon diversity index (a) and Pielou’s evenness index (b) in feces from healthy-colonized (orange) and CMA-colonized (blue) mice from Fig. 2a. n = 1–4 mice per colonized mouse group with feces taken at 2 and 3 weeks post-colonization; see Online Methods). Each circle represents one fecal sample; bars represent mean  +s.e.m. The eight human formula-fed fecal donors are described in Supplementary Table 1.

Extended Data Fig. 4 Transfer of a healthy, exclusively breast-fed infant microbiota protects against an anaphylactic response to sensitization with BLG plus  cholera toxin.

a, Change in core body temperature at indicated time points following first challenge with BLG of mice colonized with feces from breast-fed healthy or CMA infant donors (n = 13 mice per group, collected from at least 2 independent experiments). bd, Serum BLG-specific IgE (b), BLG-specific IgG1 (c) and mMCPT-1 (d) from mice in a. Four of the BLG plus cholera toxin-sensitized CMA-colonized mice died of anaphylaxis following challenge. For a, symbols represent mean, and bars represent s.e.m. For bd, symbols represent individual mice, and bars represent mean + s.e.m. Linear mixed-effect models were used to compare groups in a and two-sided Student’s t-test in b after log transformation. The two human breast-fed fecal donors are described in Supplementary Table 2. *P < 0.05.

Extended Data Fig. 5 Continuous exposure to cow’s milk does not induce tolerance to BLG in germ-free mice fed with water or Enfamil and sensitized with BLG plus cholera toxin.

a, Change in core body temperature at indicated time points following first challenge with BLG of mice fed with water (n = 12) or Enfamil (n = 10) collected from 3 independent experiments. bd, serum BLG-specific IgE (b), BLG-specific IgG1 (c) and mMCPT-1 (d) from mice in a. For a, circles represent mean, and error bars represent s.e.m. For bd, circles represent individual mice, and bars represent mean + s.e.m. Linear mixed-effect models were used to compare groups in a and two-sided Student’s t-test in bd after log transformation. **P < 0.01. n.s. = not significant (P = 0.36).

Extended Data Fig. 6 Binary representation of protective and non-protective OTUs in CMA and healthy donors and colonized mouse groups.

a, Binary map of the presence/absence ratio of protective/non-protective OTUs in CMA and healthy donors with the same layout as Fig. 2a. Columns depict each donor (D) or colonized mouse group (m). n = 2–3 technical replicates per donor and n = 1–4 mice per colonized mouse group, with feces taken at 2 and 3 weeks post-colonization; see Online Methods). Rows show 58 OTUs FDR controlled at 0.10 (see Online Methods) in human CMA versus healthy donor comparison, present in at least 4 human fecal samples and at least 2 groups of colonized mice (see Supplementary Table 3). The bar graphs above the grid map represent the total number of potentially protective (more abundant in healthy donors; orange) and potentially non-protective (more abundant in CMA donors; blue) OTUs in each individual donor or mouse group. The grid map represents presence (green) or absence (white) of protective and non-protective OTUs in each sample. b, A protective/non-protective OTU ratio was computed per individual donor or mouse group from a, taking into consideration the presence or absence of 58 OTUs. The donors and their murine transfer recipients are shown in squares and circles, respectively. The vertical dashed line represents a ratio of 2.6.

Extended Data Fig. 7 Validation of protective/non-protective OTU ratio using a larger, independent cohort of healthy and CMA infant donors.

Box plots showing the protective/non-protective OTU ratio (see Fig. 2 and Extended Data Fig. 6) in fecal samples from healthy (n = 19) and CMA (n = 19) infants from ref. 5. The horizontal center line indicates the median, the boxes represent the 25th and 75th percentiles, and the whiskers extend to the farthest data point within a maximum of 1.5 times the interquartile range (IQR). All individual points are shown, with each circle denoting a subject. Out of the 58 OTUs shown in Fig. 2a, 55 OTUs were assigned with known reference IDs and 3 with new reference IDs. The new reference OTU IDs are not comparable across the different analysis cohorts, so we focused on the OTUs with known reference IDs. Among the 55 known OTUs, 52 (29 protective OTUs and 23 non-protective OTUs) were detected in this cohort and were used for the ratio calculation (see Online Methods). The other 3 were not detected. Two-sided Wilcoxon rank sum test was used. *P < 0.05.

Extended Data Fig. 8 The healthy versus CMA OTU abundance ratio is significantly correlated between mouse fecal and ileal samples.

a, Bubble plots show a similar pattern in fecal (n = 8 mice in healthy group, n = 9 mice in CMA group, with fecal samples collected at 2 and 3 weeks post-colonization, same as in Fig. 2a) and ileal samples (n = 22 mice in healthy group, n = 25 mice in CMA group) from healthy- and CMA-colonized mice; 58 OTUs significantly differentially abundant between CMA and healthy donors are shown in the same order as in Fig. 2a. The size of the circle indicates the magnitude of relative abundance enrichment towards either CMA or healthy. Color intensity indicates the statistical significance computed using the DS-FDR permutation test (see Online Methods). b,c, The healthy versus CMA OTU abundance ratio is significantly correlated between mouse fecal and ileal samples. Each dot represents one individual OTU. For b, for each OTU, its average abundance was calculated at the group level among 8 healthy-colonized and 9 CMA-colonized mice for the fecal samples, and among 22 healthy-colonized and 25 CMA-colonized mice for the ileal samples. The ratios of OTU abundance in the feces are plotted on the x axis with the ratio of OTU abundance in the ileum on the y axis. For c, n = 35 (15 healthy-colonized and 20 CMA-colonized) mice collected from at least 2 independent experiments were used for the calculation of both the fecal and ileal OTU abundance ratio, where fecal and ileal samples were collected from the same individual mice. For further details, see the Online Methods.

Extended Data Fig. 9 Abundance of OTU259772 (Lachnospiraceae) and A. caccae are correlated in fecal samples from healthy- and CMA-colonized mice.

a,b, Abundance of OTU259772 (Lachnospiraceae) from the 16S dataset (a) and abundance of A. caccae by qPCR (b) in fecal samples from healthy-colonized (n = 7) and CMA-colonized (n = 8) mice from Fig. 2. For each individual mouse, 1–2 fecal samples were collected at 2 and 3 weeks post-colonization. LD indicates samples that were below the limit of detection for the assay. c, Spearman’s correlation between abundance of OTU259772 (Lachnospiraceae; 16S sequencing) and abundance of A. caccae (qPCR) in fecal samples from healthy- and CMA-colonized mice from Fig. 2. Fecal samples that were above LD in both 16S and qPCR experiments are shown (n = 13). Each circle represents one fecal sample. For a and b, bars show mean + s.e.m. For c, shaded bands indicate 95% confidence interval fitted by linear regression. The DS-FDR method was used to compare groups in a and two-sided Student’s t-test in b. ***P < 0.001.

Extended Data Fig. 10 Abundance of A. caccae in ileal samples correlates with gene expression in ileal IECs.

Spearman’s correlation between abundance of A. caccae by qPCR and RNA-seq gene expression of Ror2, Fbp1, Tgfbr3, Acot12 and Me1 in ileal IECs (see Fig. 3a). Circles show individual mice, and shaded bands indicate 95% confidence interval fitted by linear regression. n = 36 (18 healthy and 18 CMA-colonized) mice collected from at least 2 independent experiments. Samples with values above the limit of detection are shown (A. caccae abundance > 0).

Supplementary information

Supplementary Tables

Supplementary Tables 1–8

Reporting Summary

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Feehley, T., Plunkett, C.H., Bao, R. et al. Healthy infants harbor intestinal bacteria that protect against food allergy. Nat Med 25, 448–453 (2019).

Download citation

Further reading


Sign up for the Nature Briefing newsletter for a daily update on COVID-19 science.
Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing