Microbiota therapy acts via a regulatory T cell MyD88/RORγt pathway to suppress food allergy - Nature Medicine
- ️Chatila, Talal A.
- ️Mon Jun 24 2019
Data availability
Data presented in the manuscript, including de-identified patient results, will be made available to investigators following request. All requests for raw and analyzed data and materials will be promptly reviewed by the Boston Children’s Hospital Technology & Innovation Development Office to verify whether the request is subject to any intellectual property or confidentiality obligations. Any data and materials that can be shared will be released via a Material Transfer Agreement. The 16S bacterial rRNA datasets generated in the course of this project have been deposited at the National Center for Biotechnology Information Sequence Read Archive under BioProject ID: PRJNA525231. Detailed age and disease attributes of the deposited BioProject sample data will be made available on request.
Change history
16 August 2019
An amendment to this paper has been published and can be accessed via a link at the top of the paper.
References
Branum, A. M. & Lukacs, S. L. Food allergy among children in the United States. Pediatrics 124, 1549–1555 (2009).
Wills-Karp, M., Santeliz, J. & Karp, C. L. The germless theory of allergic disease: revisiting the hygiene hypothesis. Nat. Rev. Immunol. 1, 69–75 (2001).
Ly, N. P., Litonjua, A., Gold, D. R. & Celedon, J. C. Gut microbiota, probiotics, and vitamin D: interrelated exposures influencing allergy, asthma, and obesity? J. Allergy Clin. Immunol. 127, 1087–1094 (2011).
Koplin, J. et al. Is caesarean delivery associated with sensitization to food allergens and IgE-mediated food allergy? A systematic review. Pediatr. Allergy Immunol. 19, 682–687 (2008).
Bjorksten, B. Disease outcomes as a consequence of environmental influences on the development of the immune system. Curr. Opin. Allergy Clin. Immunol. 9, 185–189 (2009).
Rachid, R. & Chatila, T. A. The role of the gut microbiota in food allergy. Curr. Opin. Pediatr. 28, 748–753 (2016).
Azad, M. B. et al. Infant gut microbiota and food sensitization: associations in the first year of life. Clin. Exp. Allergy 45, 632–643 (2015).
Sudo, N. et al. The requirement of intestinal bacterial flora for the development of an IgE production system fully susceptible to oral tolerance induction. J. Immunol. 159, 1739–1745 (1997).
Fritz, J. H. et al. Acquisition of a multifunctional IgA+plasma cell phenotype in the gut. Nature 481, 199–203 (2012).
Geuking, M. B. et al. Intestinal bacterial colonization induces mutualistic regulatory T cell responses. Immunity 34, 794–806 (2011).
Stefka, A. T. et al. Commensal bacteria protect against food allergen sensitization. Proc. Natl Acad. Sci. USA 111, 13145–13150 (2014).
Atarashi, K. et al. Induction of colonic regulatory T cells by indigenous Clostridium species. Science 331, 337–341 (2011).
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).
Wesemann, D. R. & Nagler, C. R. The microbiome, timing, and barrier function in the context of allergic disease. Immunity 44, 728–738 (2016).
Noval Rivas, M. & Chatila, T. A. Regulatory T cells in allergic diseases. J. Allergy Clin. Immunol. 138, 639–652 (2016).
Abdel-Gadir, A., Massoud, A. H. & Chatila, T. A. Antigen-specific Treg cells in immunological tolerance: implications for allergic diseases. F1000Res. 7, 38 (2018).
Furusawa, Y. et al. Commensal microbe-derived butyrate induces the differentiation of colonic regulatory T cells. Nature 504, 446–450 (2013).
Smith, P. M. et al. The microbial metabolites, short-chain fatty acids, regulate colonic Treg cell homeostasis. Science 341, 569–573 (2013).
Arpaia, N. et al. Metabolites produced by commensal bacteria promote peripheral regulatory T-cell generation. Nature 504, 451–455 (2013).
Tan, J. et al. Dietary fiber and bacterial SCFA enhance oral tolerance and protect against food allergy through diverse cellular pathways. Cell Rep. 15, 2809–2824 (2016).
Ohnmacht, C. et al. MUCOSAL IMMUNOLOGY. The microbiota regulates type 2 immunity through RORγt +T cells. Science 349, 989–993 (2015).
Sefik, E. et al. MUCOSAL IMMUNOLOGY. Individual intestinal symbionts induce a distinct population of RORγ+ regulatory T cells. Science 349, 993–997 (2015).
Burton, O. T. et al. Immunoglobulin E signal inhibition during allergen ingestion leads to reversal of established food allergy and induction of regulatory T cells. Immunity 41, 141–151 (2014).
Noval Rivas, M., Burton, O. T., Oettgen, H. C. & Chatila, T. IL-4 production by group 2 innate lymphoid cells promotes food allergy by blocking regulatory T-cell function. J. Allergy Clin. Immunol. 138, 801–811.e9 (2016).
Noval Rivas, M. et al. Regulatory T cell reprogramming toward a Th2-cell-like lineage impairs oral tolerance and promotes food allergy. Immunity 42, 512–523 (2015).
Fagarasan, S. et al. Critical roles of activation-induced cytidine deaminase in the homeostasis of gut flora. Science 298, 1424–1427 (2002).
Kubinak, J. L. et al. MyD88 signaling in T cells directs IgA-mediated control of the microbiota to promote health. Cell Host Microbe 17, 153–163 (2015).
Wang, S. et al. MyD88 adaptor-dependent microbial sensing by regulatory T cells promotes mucosal tolerance and enforces commensalism. Immunity 43, 289–303 (2015).
Donaldson, G. P. et al. Gut microbiota utilize immunoglobulin A for mucosal colonization. Science 360, 795–800 (2018).
Macpherson, A. J., Yilmaz, B., Limenitakis, J. P. & Ganal-Vonarburg, S. C. IgA function in relation to the intestinal microbiota. Annu. Rev. Immunol. 36, 359–381 (2018).
Mathias, C. B. et al. IgE-mediated systemic anaphylaxis and impaired tolerance to food antigens in mice with enhanced IL-4 receptor signaling. J. Allergy Clin. Immunol. 127, e791–e796 (2011).
Kalia, V. C. et al. Analysis of the unexplored features of rrs (16S rDNA) of the genus Clostridium. BMC Genom. 12, 18 (2011).
Rajilic-Stojanovic, M. & de Vos, W. M. The first 1000 cultured species of the human gastrointestinal microbiota. FEMS Microbiol. Rev. 38, 996–1047 (2014).
Blander, J. M., Longman, R. S., Iliev, I. D., Sonnenberg, G. F. & Artis, D. Regulation of inflammation by microbiota interactions with the host. Nat. Immunol. 18, 851–860 (2017).
Narushima, S. et al. Characterization of the 17 strains of regulatory T cell-inducing human-derived Clostridia. Gut Microbes 5, 333–339 (2014).
Walker, A. W. & Lawley, T. D. Therapeutic modulation of intestinal dysbiosis. Pharm. Res. 69, 75–86 (2013).
Kim, K. S. et al. Dietary antigens limit mucosal immunity by inducing regulatory T cells in the small intestine. Science 351, 858–863 (2016).
Lathrop, S. K. et al. Peripheral education of the immune system by colonic commensal microbiota. Nature 478, 250–254 (2011).
Russler-Germain, E. V., Rengarajan, S. & Hsieh, C. S. Antigen-specific regulatory T-cell responses to intestinal microbiota. Mucosal Immunol. 10, 1375–1386 (2017).
Thornton, A. M. et al. Expression of Helios, an Ikaros transcription factor family member, differentiates thymic-derived from peripherally induced Foxp3. +T regulatory cells. J. Immunol. 184, 3433–3441 (2010).
Weiss, J. M. et al. Neuropilin 1 is expressed on thymus-derived natural regulatory T cells, but not mucosa-generated induced Foxp3. +T reg cells. J. Exp. Med. 209, S1721 (2012).
Yadav, M. et al. Neuropilin-1 distinguishes natural and inducible regulatory T cells among regulatory T cell subsets in vivo. J. Exp. Med. 209, S1711–S1719 (2012).
Bilate, A. M. & Lafaille, J. J. Induced CD4 +Foxp3+ regulatory T cells in immune tolerance. Annu Rev. Immunol. 30, 733–758 (2012).
Xu, M. et al. c-MAF-dependent regulatory T cells mediate immunological tolerance to a gut pathobiont. Nature 554, 373–377 (2018).
Abdel-Gadir, A. et al. Oral immunotherapy with omalizumab reverses the Th2 cell-like programme of regulatory T cells and restores their function. Clin. Exp. Allergy 48, 825–836 (2018).
Bartnikas, L. M. et al. Epicutaneous sensitization results in IgE-dependent intestinal mast cell expansion and food-induced anaphylaxis. J. Allergy Clin. Immunol. 131, e451–e456 (2013).
Round, J. L. et al. The Toll-like receptor 2 pathway establishes colonization by a commensal of the human microbiota. Science 332, 974–977 (2011).
Kverka, M. et al. Oral administration of Parabacteroides distasonis antigens attenuates experimental murine colitis through modulation of immunity and microbiota composition. Clin. Exp. Immunol. 163, 250–259 (2011).
Geva-Zatorsky, N. et al. Mining the human gut microbiota for immunomodulatory organisms. Cell 168, 928–943 e911 (2017).
Mangalam, A. et al. Human gut-derived commensal bacteria suppress CNS inflammatory and demyelinating disease. Cell Rep. 20, 1269–1277 (2017).
Haribhai, D. et al. A requisite role for induced regulatory T cells in tolerance based on expanding antigen receptor diversity. Immunity 35, 109–122 (2011).
Rios-Covian, D., Salazar, N., Gueimonde, M. & de Los Reyes-Gavilan, C. G. Shaping the metabolism of intestinal Bacteroides population through diet to improve human health. Front. Microbiol. 8, 376 (2017).
Atarashi, K. et al. Treg induction by a rationally selected mixture of Clostridia strains from the human microbiota. Nature 500, 232–236 (2013).
Britton, G. J. et al. Microbiotas from humans with inflammatory bowel disease alter the balance of gut Th17 and RORγt +regulatory T cells and exacerbate colitis in mice.Immunity 50, 212–224 e214 (2019).
Dethlefsen, L. & Relman, D. A. Incomplete recovery and individualized responses of the human distal gut microbiota to repeated antibiotic perturbation. Proc. Natl Acad. Sci. USA 108 (Suppl. 1), 4554–4561 (2011).
Gerber, G. K., Onderdonk, A. B. & Bry, L. Inferring dynamic signatures of microbes in complex host ecosystems. PLoS Comput. Biol. 8, e1002624 (2012).
Bucci, V. et al. MDSINE: microbial dynamical systems inference engine for microbiome time-series analyses. Genome Biol. 17, 121 (2016).
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).
Excoffier, L., Smouse, P. E. & Quattro, J. M. Analysis of molecular variance inferred from metric distances among DNA haplotypes: application to human mitochondrial DNA restriction data. Genetics 131, 479–491 (1992).
Lozupone, C., Hamady, M. & Knight, R. UniFrac–an online tool for comparing microbial community diversity in a phylogenetic context. BMC Bioinforma. 7, 371 (2006).
Lozupone, C. & Knight, R. UniFrac: a new phylogenetic method for comparing microbial communities. Appl. Environ. Microbiol. 71, 8228–8235 (2005).
McMurdie, P. & Holmes, S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE 8, e61217 (2013).
McMurdie, P. & Holmes, S. Waste not, want not: why rarefying microbiome data is inadmissible. PLoS Comput. Biol. 10, e1003531 (2014).
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).
Matsen, F., Kodner, R. B. & Armbrust, E. V. pplacer: linear time maximum-likelihood and Bayesian phylogenetic placement of sequences onto a fixed reference tree. BMC Bioinforma. 11, 538 (2010).
Cole, J. R. et al. Ribosomal Database Project: data and tools for high throughput rRNA analysis. Nucleic acids Res. 42, D633–642 (2014).
Massoud, A. H. et al. An asthma-associated IL4R variant exacerbates airway inflammation by promoting conversion of regulatory T cells to TH17-like cells. Nat. Med. 22, 1013–1022 (2016).
Rubtsov, Y. P. et al. Regulatory T cell-derived interleukin-10 limits inflammation at environmental interfaces. Immunity 28, 546–558 (2008).
Pinciroli, R. et al. Endotracheal Tubes cleaned with a novel mechanism for secretion removal: a randomized controlled clinical study. Respir. Care 61, 1431–1439 (2016).
Acknowledgements
We thank H. Oettgen for the provision of Igh7–/–Il4raF709 mice, L.-M. Charbonnier for critical review of the manuscript and M. Delaney for support with microbiology. This work was supported by NIH NIAID grants Nos. 1R56AI117983 and 1R01AI126915 (to T.A.C.), NIDDK grant No. P30DK056338 (to L.B.), the Clinical and Translational Science Center/Harvard Catalyst, the Bunning Food Allergy Fund, the Jasmine and Paul Mashikian Fund, the Massachusetts Life Sciences Center and a Partners Healthcare Innovations Development grant.
Author information
Author notes
These authors contributed equally: Azza Abdel-Gadir, Emmanuel Stephen-Victor, Georg K. Gerber.
These authors jointly supervised this work: Lynn Bry, Rima Rachid, Talal A. Chatila.
Authors and Affiliations
Division of Immunology, Boston Children’s Hospital, Boston, MA, USA
Azza Abdel-Gadir, Emmanuel Stephen-Victor, Sen Wang, Hani Harb, Leighanne Wang, Elena Crestani, Sara Spielman, William Secor, Heather Biehl, Rima Rachid & Talal A. Chatila
Department of Pediatrics, Harvard Medical School, Boston, MA, USA
Azza Abdel-Gadir, Emmanuel Stephen-Victor, Sen Wang, Hani Harb, Elena Crestani, Rima Rachid & Talal A. Chatila
Massachusetts Host-Microbiome Center, Department of Pathology, Brigham & Women’s Hospital, Harvard Medical School, Boston, MA, USA
Georg K. Gerber, Ning Li, Nicholas DiBenedetto, Xiaoxi Dong & Lynn Bry
Division of Pediatric Infectious Diseases and Immunology, Department of Pediatrics, Infectious and Immunologic Diseases Research Center, Cedars-Sinai Medical Center, Los Angeles, CA, USA
Magali Noval Rivas
Genentech, South San Francisco, CA, USA
Dale T. Umetsu
Authors
- Azza Abdel-Gadir
You can also search for this author in PubMed Google Scholar
- Emmanuel Stephen-Victor
You can also search for this author in PubMed Google Scholar
- Georg K. Gerber
You can also search for this author in PubMed Google Scholar
- Magali Noval Rivas
You can also search for this author in PubMed Google Scholar
- Sen Wang
You can also search for this author in PubMed Google Scholar
- Hani Harb
You can also search for this author in PubMed Google Scholar
- Leighanne Wang
You can also search for this author in PubMed Google Scholar
- Ning Li
You can also search for this author in PubMed Google Scholar
- Elena Crestani
You can also search for this author in PubMed Google Scholar
- Sara Spielman
You can also search for this author in PubMed Google Scholar
- William Secor
You can also search for this author in PubMed Google Scholar
- Heather Biehl
You can also search for this author in PubMed Google Scholar
- Nicholas DiBenedetto
You can also search for this author in PubMed Google Scholar
- Xiaoxi Dong
You can also search for this author in PubMed Google Scholar
- Dale T. Umetsu
You can also search for this author in PubMed Google Scholar
- Lynn Bry
You can also search for this author in PubMed Google Scholar
- Rima Rachid
You can also search for this author in PubMed Google Scholar
- Talal A. Chatila
You can also search for this author in PubMed Google Scholar
Contributions
T.A.C., R.R. and D.T.U. conceived the human microbiota studies, and T.A.C. conceived the mechanistic studies and directed the overall project. L.B. conceived the bacterial consortia and oversaw their development for use as a therapeutic. G.K.G., N.L. and X.D. carried out the bioinformatic analyses of human fecal microbiota composition. N.D. designed multiplex probes for the consortia and carried out the persistence studies. A.A.-G., E.S.-V., M.N.R., S.W., H.H. and L.W. carried out the experiments and evaluated the data. R.R. oversaw the design and execution of the human studies. S.S., W.S., E.C. and H.B. were involved in human subject recruitment and/or the collection of fecal samples. T.A.C. and A.A.-G. wrote the manuscript.
Corresponding authors
Correspondence to Rima Rachid or Talal A. Chatila.
Ethics declarations
Competing interests
L.B., G.K.G., T.A.C., R.R. and A.A.-G. are inventors on published US patent application No. 15/801,811, submitted by The Brigham and Women’s Hospital, Inc. and Children’s Medical Center Corporation, that covers methods and compositions for the prevention and treatment of food allergy using microbial treatments. T.A.C., R.R., A.A.-G. and E.S.-V. have pending patent applications related to the use of probiotics in enforcing oral tolerance in food allergy (Nos. 62/758, 161, and 62/823,866). L.B., G.K.G. and T.A.C. are founders of, and have equity in, Consortia Tx. R.R. has equity in Consortia Tx. A.A.-G. is currently an employee of, and owns shares in, Seed Health Inc. The rest of the 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 FMT from WT mice protects against FA in GF Il4raF709 mice.
a, Temperature changes in GF Il4raF709 mice that were either left uncolonized or reconstituted with FMT from WT or Il4raF709 mice, then sensitized with OVA/SEB and challenged with OVA (n = 15 WT and 14 Il4raF709 mice). b,c, Total and OVA-specific serum IgE (n = 15 WT and 14 Il4raF709 mice). d, MMCP-1 concentrations post-OVA challenge (n = 6 per group). e,f, Analysis of ROR-γt and GATA3 expression in MLN Helios–NRP1– and Helios+NRP1+ Treg cells (n = 6 per group). Each dot represents one mouse. Data represent mean ± s.e.m. from two or three independent experiments. P values were derived by repeat measures two-way ANOVA (a) or by Student’s unpaired two-tailed t-test with Welch correction (b–f).
Extended Data Fig. 2 Analysis of IgA- and IgE-bound bacteria in fecal samples.
a,c, Representative FACS plots showing the gating strategy for human (a) and mouse (c) fecal bacteria. Bacteria present in the feces were identified by gating on SYTO-BC+ events (right-side panels). b,d, Frequencies of IgA- and IgE-bound bacteria as assessed by gating on bacteria bound with the respective PE-labeled anti-IgA and anti-IgE antibodies, as shown in a and c. e,f, Analysis of sIgA+ (e) and IgE+ (f) fecal bacteria from Il4raF709 mice sensitized with OVA/SEB without or with bacterial therapy. Fecal pellets from Rag2–/– and Igh7–/–Il4raF709 mice were used as negative controls. Each symbol in the scatter plots represents one mouse (no treatment: n = 11 per group; Clostridiales: n = 8 per group; Proteobacteria: n = 9 and 7). Data represent mean ± s.e.m. from two independent experiments. Flow panels in c,d are representative of two two independent experiments. P values were derived by one-way ANOVA with Dunnett’s post hoc analysis.
Extended Data Fig. 3 Antibiotic therapy potentiates the therapeutic efficacy of the Clostridiales consortium in Il4raF709 mice.
a, Temperature changes in the respective OVA/SEB-sensitized and OVA-challenged SPF Il4raF709 mouse groups treated as follows: no antibiotics (n = 6), Clostridiales (n = 5) and antibiotics without or with Clostridiales (n = 5 per group). P values were derived by two-way ANOVA. b,c, Total and OVA-specific IgE: no antibiotics (n = 4 per group), Clostridiales (n = 5 per group) and antibiotics without (n = 5 per group) or with Clostridiales (n = 4 per group). d, MMCP-1 concentrations: no antibiotics (n = 5), Clostridiales (n = 5) and antibiotics without (n = 4) or with Clostridiales (n = 4). e, Frequencies of total CD4+Foxp3+, Helios–NRP1–Foxp3+, ROR-γt+CD4+Foxp3+ and IL-4+ CD4+Foxp3+ Treg cells in the MLN of the respective mouse group: no antibiotics (n = 6), Clostridiales (n = 5) and antibiotics without (n = 5) or with Clostridiales (n = 5). Each dot represents one mouse. Throughout, data represent mean ± s.e.m. from two independent experiments. Unless otherwise indicated, P values were derived by one-way ANOVA with Dunnett’s post hoc analysis.
Extended Data Fig. 4 Bacteriotherapy with S. variabile protects against FA.
a, Temperature changes in SPF Il4raF709 mice that were antibiotic-treated then sensitized with OVA/SEB while receiving no treatment (n = 8) or treatment with S. variabile (n = 11), and thereafter challenged with OVA. P values were derived by two-way ANOVA. b,c, Total and OVA-specific IgE (no bacteria: n = 8; S. variabile: n = 11). d, MMCP-1 concentrations (no bacteria: n = 8; S. variabile: n = 11). e,f, Analysis of MLN ROR-γt+ and GATA3+ cells among Helios–NRP1– and Helios+NRP1+ Foxp3+ Treg cells, respectively (no bacteria: n = 8; S. variabile: n = 5). g, Analysis of MLN IL-4+ CD4+Foxp3+ Treg cells and IL-4+ CD4+Foxp3– Teff cells (no bacteria: n = 8; S. variabile: n = 5). Each dot represents one mouse. Throughout, data represent mean ± s.e.m. from two independent experiments. For b–g, P values were derived by Student’s unpaired two-tailed t-test with Welch correction.
Extended Data Fig. 5 Clostridiales protects against percutaneous sensitization-induced FA.
a, Temperature changes in SPF WT BALB/c mice that were antibiotic-treated then percutaneously sensitized with OVA/SEB while receiving either no treatment (n = 14) or treatment with Clostridales (n = 11), and thereafter challenged with OVA. P values were derived by two-way ANOVA. b,c, Total and OVA-specific IgE concentrations (n = 7 per group). d, MMCP-1 concentrations (n = 7 per group). e, Analysis of small intestinal LPL ROR-γt+ CD4+Foxp3+ Treg cells (n = 7 per group). Each dot represents one mouse. Data represent mean ± s.e.m. from two independent experiments. For b–e, P values were derived by Student’s unpaired two-tailed t-test with Welch correction.
Extended Data Fig. 6 A Bacteroidales consortium prevents FA.
a, Left: experimental schema; right: temperature changes in GF Il4raF709 mice that were colonized and sensitized as indicated then challenged with OVA (n = 5 per group). b,c, Total and OVA-specific IgE (b) and MMCP-1 concentrations (c). GF, OVA/SEB (n = 5 per group), Bacteroidales, PBS (n = 6, 7 and 7), Bacteroidales, OVA/SEB (n = 6, 6 and 7). d, Frequencies of MLN CD4+Foxp3+, IL-4+Foxp3+ and GATA3+Foxp3+ T cells. GF, OVA/SEB (n = 5 per group), Bacteroidales, PBS (n = 4, 8 and 5), Bacteroidales, OVA/SEB (n = 6, 5 and 6). e, Frequencies of Helios–Nrp1–Foxp3+ and ROR-γt+Foxp3+ T cells. GF, OVA/SEB (n = 5 and 7), Bacteroidales, PBS (n = 5 per group), Bacteroidales, OVA/SEB (n = 6 per group). f, Left: experimental schema; right: temperature changes in Il4raF709 mice sensitized and treated as indicated. OVA/SEB (n = 6), OVA/SEB, Bacteroidales, (n = 5). g, Total and OVA-specific IgE and MMCP-1 concentrations: OVA/SEB (n = 7, 9 and 9), OVA/SEB, Bacteroidales, (n = 5, 10 and 5). h,i, Frequencies of MLN CD4+Foxp3+, IL-4+Foxp3+ and GATA3+Foxp3+ (h) and Helios–Nrp1–Foxp3+ and ROR-γt+Foxp3+ T cells (i). OVA/SEB (n = 5, 8 and 3, 5 and 5), OVA/SEB, Bacteroidales (n = 5, 10, 4, 6 and 9). j, IgE and IgA staining of fecal bacteria. OVA/SEB (n = 11 per group), OVA/SEB, Bacteroidales (n = 8 per group). Each dot represents one mouse. Data represent mean ± s.e.m. from two independent experiments. P values were derived by repeat measures two-way ANOVA (a,f), by one-way ANOVA with Dunnett’s post hoc analysis (b–e) or by Student’s unpaired two-tailed t-test (h–j).
Extended Data Fig. 7 Depletion of Treg cells abrogates protection by the microbiota.
a, Experimental schema. b, Temperature changes in the respective OVA/SEB-sensitized and OVA-challenged mouse groups: Il4raF709Foxp3EGFP/DTR– (n = 6), Il4raF709Foxp3EGFP/DTR–+Clostridiales+DT (n = 7), Il4raF709Foxp3EGFP/DTR++Clostridiales+DT (n = 8), Il4raF709Foxp3EGFP/DTR–+Bacteroidales+DT (n = 9), Il4raF709Foxp3EGFP/DTR++Bacteroidales+DT (n = 7). c, Total and OVA-specific IgE in the groups listed in b (total IgE: n = 9, 6, 8, 7 and 5; OVA-specific IgE: 6, 5, 5, 5 and 5). d, MMCP-1 concentrations (n = 12 for Il4raF709Foxp3EGFP/DTR–, and n = 8 per group for all other groups). e,f, Frequencies of MLN CD4+Foxp3+ and IL-4+Foxp3+ T cells: Il4raF709Foxp3EGFP/DTR– (n = 6 and 10), Il4raF709Foxp3EGFP/DTR–+Clostridiales+DT (n = 8 and 7), Il4raF709Foxp3EGFP/DTR++Clostridiales+DT (n = 8 and 7), Il4raF709Foxp3EGFP/DTR–+Bacteroidales+DT (n = 8 and 7), Il4raF709Foxp3EGFP/DTR++Bacteroidales+DT (n = 8 and 7). g, Frequencies of ROR-γt+Foxp3+ and GATA3+Foxp3+ T cells: Il4raF709Foxp3EGFP/DTR– (n = 5 per group), Il4raF709Foxp3EGFP/DTR–+Clostridiales+DT (n = 7 and 9), Il4raF709Foxp3EGFP/DTR++Clostridiales+DT (n = 8 per group), Il4raF709Foxp3EGFP/DTR–+Bacteroidales+DT (n = 6 per group), Il4raF709Foxp3EGFP/DTR++Bacteroidales+DT (n = 8 pre group). Each dot represents one mouse. Data represent mean ± s.e.m. from two independent experiments. P values were derived by repeat measures two-way ANOVA (b) or by one-way ANOVA with Dunnett’s post hoc analysis or Student’s unpaired two-tailed t-test (c–f).
Extended Data Fig. 8 Oral SCFA supplementation does not protect against FA.
a, SCFA in fecal samples of PBS or OVA/SEB-sensitized WT and Il4raF709 mice. Acetate, propionate and butyrate: WT, PBS or OVA/SEB (n = 5 per group); Il4raF709, PBS (n = 5 per group) or OVA/SEB (n = 10 per group). Isovalerate: WT, PBS or OVA/SEB (n = 5 and 4); Il4raF709, PBS or OVA/SEB (n = 5 and 8). Valerate: WT, PBS or OVA/SEB (n = 5 and 3); Il4raF709, PBS or OVA/SEB (n = 4 and 7). b, Temperature changes in OVA-challenged WT and Il4raF709 mice sensitized with PBS or OVA/SEB without or with SCFA supplementation: WT, PBS+SCFA (n = 10), WT, OVA/SEB (n = 11), WT, OVA/SEB+SCFA (n = 24); Il4raF709, PBS+SCFA (n = 12), Il4raF709, OVA/SEB (n = 7), Il4raF709, OVA/SEB+SCFA (n = 17). c, Total and OVA-specific IgE: WT, PBS+SCFA (n = 5 per group), WT, OVA/SEB (n = 6 per group), WT, OVA/SEB+SCFA (n = 9 per group); Il4raF709, PBS+SCFA (n = 4 per group), Il4raF709, OVA/SEB (n = 8 per group), Il4raF709, OVA/SEB+SCFA (n = 9 per group). d, Frequencies of MLN CD4+Foxp3+ROR-γt+ and CD4+Foxp3–ROR-γt+ T cells: WT, PBS+SCFA (n = 5 per group), WT, OVA/SEB (n = 4 per group), WT, OVA/SEB+SCFA (n = 4 per group); Il4raF709, PBS+SCFA (n = 5 per group), Il4raF709, OVA/SEB (n = 5 per group), Il4raF709, OVA/SEB+SCFA (n = 7 per group). Each dot represents one mouse. Data represent mean ± s.e.m. from two independent experiments. P values were derived by the Kolmogorov–Smirnov test (a), by Student’s unpaired two-tailed t-test (c,d) or by two-way ANOVA (b).
Extended Data Fig. 9 Analysis of ROR-γt+ expression in human subjects and mutant mice.
a, Gating strategy for CD4+Foxp3+ (G1) and CD4+Foxp3– T (G2) cells ex vivo. b, Gating strategy for the expression of ROR-γt in Teff cells (G2) from patients with FA, healthy controls (HC) and atopic subjects (atopy), as compared to an isotype control. c, Flow plots and frequencies of peripheral blood CD4+Foxp3+ROR-γt+ T cells in WT and Il4raF709 mice (n = 7 mice per group). d, Flow plots and frequencies of peripheral blood CD4+Foxp3+Helios–NRP1–ROR-γt+ T cells in WT and Il4raF709 mice (n = 7 mice per group). e,f, Flow plots and frequencies of MLN CD4+Foxp3+ROR-γt+ T cells from Foxp3YFPCre mice sensitized with OVA/SEB, and Foxp3YFPCreRorcΔ/Δ either sham-sensitized (PBS) or sensitized with OVA/SEB, as indicated (n = 5 mice per group). g, Quantitative PCR with reverse transcription of Rorc gene expression in MLN CD4+Foxp3+ Treg and CD4+Foxp3– Teff cells from Foxp3YFPCre, Foxp3YFPCreRorcΔ/Δ and Il4raF709Foxp3YFPCreRorcΔ/Δ mice. Data were normalized to the endogenous Hprt transcripts (n = 5 mice per group). Each dot represents one mouse. Results represent means ± s.e.m. collated from two independent experiments. P values were derived by Student’s unpaired two-tailed t-test with Welch correction (c,d), or by one-way ANOVA with Dunnett’s post hoc analysis (f,g).
Extended Data Fig. 10 Treg cell-specific deletion of Rorc and Myd88 impairs mucosal tolerance.
a–d. Analysis of sIgA+ (a,b) and IgE+ (c,d) fecal bacteria in OVA/SEB-sensitized Foxp3YFPCre, Il4raF709Foxp3YFPCre and Foxp3YFPCreRorcΔ/Δ mice. Fecal pellets from Rag2–/– and Igh7–/–Il4raF709 mice were used as negative controls: Foxp3YFPCre (n = 6 per group), Il4raF709Foxp3YFPCre (n = 11 and 7) and Foxp3YFPCreRorcΔ/Δ mice (n = 10 and 8). e–f, Analysis of GATA3+Foxp3+ Treg cells in the following OVA/SEB-sensitized mice that were either untreated or treated with Clostridiales or Bacteroidales consortium: Il4raF709Foxp3YFPCre (n = 9, 5 and 5) and Il4raF709Foxp3YFPCreRorcΔ/Δ (n = 4, 5 and 8). g,h, Analysis of GATA3+Foxp3+ Treg cells in OVA/SEB-sensitized Il4raF709Foxp3YFPCre mice treated with the Bacteroidales consortium (n = 9), and in OVA/SEB-sensitized Il4raF709Foxp3YFPCreMyd88Δ/Δ mice otherwise untreated or treated with the Clostridiales or Bacteroidales consortium (n = 8, 9 and 8). Each symbol represents one mouse. Results represent means ± s.e.m. collated from two independent experiments. P values were derived by one-way ANOVA with Dunnett’s post hoc analysis (b,d,f,h).
Supplementary information
Source data
Rights and permissions
About this article
Cite this article
Abdel-Gadir, A., Stephen-Victor, E., Gerber, G.K. et al. Microbiota therapy acts via a regulatory T cell MyD88/RORγt pathway to suppress food allergy. Nat Med 25, 1164–1174 (2019). https://doi.org/10.1038/s41591-019-0461-z
Received: 02 August 2018
Accepted: 18 April 2019
Published: 24 June 2019
Issue Date: July 2019
DOI: https://doi.org/10.1038/s41591-019-0461-z