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Exacerbation of allergic rhinitis by the commensal bacterium Streptococcus salivarius

Abstract

Allergic rhinitis (AR)—commonly called hay fever—is a widespread condition that affects the quality of life of millions of people. The pathophysiology of AR remains incompletely understood. In particular, it is unclear whether members of the colonizing nasal microbiota contribute to AR. Here, using 16S ribosomal RNA sequencing, we show that the nasal microbiome of patients with AR (n = 55) shows distinct differences compared with that from healthy individuals (n = 105), including decreased heterogeneity and the increased abundance of one species, Streptococcus salivarius. Using ex vivo and in vivo models of AR, we demonstrate that this commensal bacterium contributes to AR development, promoting inflammatory cytokine release and morphological changes in the nasal epithelium that are characteristic of AR. Our data indicate that this is due to the ability of S. salivarius to adhere to the nasal epithelium under AR conditions. Our study indicates the potential of targeted antibacterial approaches for AR therapy.

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Fig. 1: Nasal microbiome composition in patients with AR.
Fig. 2: S. salivarius exacerbates pathophysiology of AR in a mouse model.
Fig. 3: S. salivarius promotes cytokine gene expression in allergen-induced epithelial cells.
Fig. 4: S. salivarius shows strong adhesion to nasal epithelia under allergen-induced conditions.

Data availability

Raw microbiome sequencing data (related to data shown in Fig. 1, Table 1 and Extended Data Figs. 16) and genome sequencing data have been deposited in NCBI’s Sequencing Read Archive (SRA) database under Bioproject numbers PRJNA796497 and PRJNA874865, respectively. All other data are presented in this paper. Bacterial strains are available from M.L. Source data are provided with this paper.

References

  1. Agnihotri, N. T. & McGrath, K. G. Allergic and nonallergic rhinitis. Allergy Asthma Proc. 40, 376–379 (2019).

    Article  CAS  Google Scholar 

  2. Greiner, A. N., Hellings, P. W., Rotiroti, G. & Scadding, G. K. Allergic rhinitis. Lancet 378, 2112–2122 (2011).

    Article  Google Scholar 

  3. Skoner, D. P. Allergic rhinitis: definition, epidemiology, pathophysiology, detection, and diagnosis. J. Allergy Clin. Immunol. 108, S2–S8 (2001).

    Article  CAS  Google Scholar 

  4. Wheatley, L. M. & Togias, A. Clinical practice. Allergic rhinitis. N. Engl. J. Med. 372, 456–463 (2015).

    Article  Google Scholar 

  5. Cheng, L. et al. Chinese Society of Allergy guidelines for diagnosis and treatment of allergic rhinitis. Allergy Asthma Immunol. Res. 10, 300–353 (2018).

    Article  Google Scholar 

  6. Corren, J. Allergic rhinitis and asthma: how important is the link? J. Allergy Clin. Immunol. 99, S781–S786 (1997).

    Article  CAS  Google Scholar 

  7. Brozek, J. L. et al. Allergic rhinitis and its impact on asthma (ARIA) guidelines—2016 revision. J. Allergy Clin. Immunol. 140, 950–958 (2017).

    Article  Google Scholar 

  8. Small, P., Keith, P. K. & Kim, H. Allergic rhinitis. Allergy Asthma Clin. Immunol. 14, 51 (2018).

    Article  Google Scholar 

  9. Cho, D. Y., Hunter, R. C. & Ramakrishnan, V. R. The microbiome and chronic rhinosinusitis. Immunol. Allergy Clin. North Am. 40, 251–263 (2020).

    Article  Google Scholar 

  10. Koeller, K. et al. Microbiome and culture based analysis of chronic rhinosinusitis compared to healthy sinus mucosa. Front. Microbiol. 9, 643 (2018).

    Article  Google Scholar 

  11. Gan, W. et al. The difference in nasal bacterial microbiome diversity between chronic rhinosinusitis patients with polyps and a control population. Int. Forum Allergy Rhinol. 9, 582–592 (2019).

    Article  Google Scholar 

  12. Rom, D. et al. The association between disease severity and microbiome in chronic rhinosinusitis. Laryngoscope 129, 1265–1273 (2019).

    Article  Google Scholar 

  13. Hoggard, M. et al. Evidence of microbiota dysbiosis in chronic rhinosinusitis. Int. Forum Allergy Rhinol. 7, 230–239 (2017).

    Article  Google Scholar 

  14. Lal, D. et al. Mapping and comparing bacterial microbiota in the sinonasal cavity of healthy, allergic rhinitis, and chronic rhinosinusitis subjects. Int. Forum Allergy Rhinol. 7, 561–569 (2017).

    Article  Google Scholar 

  15. Gan, W. et al. Comparing the nasal bacterial microbiome diversity of allergic rhinitis, chronic rhinosinusitis and control subjects. Eur. Arch. Otorhinolaryngol. 278, 711–718 (2021).

    Article  Google Scholar 

  16. Choi, C. H. et al. Seasonal allergic rhinitis affects sinonasal microbiota. Am. J. Rhinol. Allergy 28, 281–286 (2014).

    Article  Google Scholar 

  17. Hyun, D. W. et al. Dysbiosis of inferior turbinate microbiota is associated with high total IgE levels in patients with allergic rhinitis. Infect. Immun. 86, e00934-17 (2018).

    Article  Google Scholar 

  18. Quillen, D. M. & Feller, D. B. Diagnosing rhinitis: allergic vs. nonallergic. Am. Fam. Physician 73, 1583–1590 (2006).

    Google Scholar 

  19. Borish, L. C. & Steinke, J. W. 2. Cytokines and chemokines. J. Allergy Clin. Immunol. 111, S460–S475 (2003).

    Article  CAS  Google Scholar 

  20. Liu, Q. et al. Staphylococcus epidermidis contributes to healthy maturation of the nasal microbiome by stimulating antimicrobial peptide production. Cell Host Microbe 27, 68–78 e65 (2020).

    Article  CAS  Google Scholar 

  21. Nakajima, T. et al. Population structure and characterization of viridans group streptococci (VGS) isolated from the upper respiratory tract of patients in the community. Ulster Med. J. 82, 164–168 (2013).

    Google Scholar 

  22. Pearce, C. et al. Identification of pioneer viridans streptococci in the oral cavity of human neonates. J. Med. Microbiol. 42, 67–72 (1995).

    Article  CAS  Google Scholar 

  23. Wilson, M. et al. Clinical and laboratory features of Streptococcus salivarius meningitis: a case report and literature review. Clin. Med. Res. 10, 15–25 (2012).

    Article  Google Scholar 

  24. Wescombe, P. A., Hale, J. D., Heng, N. C. & Tagg, J. R. Developing oral probiotics from Streptococcus salivarius. Future Microbiol. 7, 1355–1371 (2012).

    Article  CAS  Google Scholar 

  25. Ramakrishnan, V. R. et al. The microbiome of the middle meatus in healthy adults. PLoS ONE 8, e85507 (2013).

    Article  Google Scholar 

  26. Bowman, F. W. Test organisms for antibiotic microbial assays. Antibiot. Chemother. 7, 639–640 (1957).

    CAS  Google Scholar 

  27. Salo, P. M. et al. Exposure to multiple indoor allergens in US homes and its relationship to asthma. J. Allergy Clin. Immunol. 121, 678–684.e672 (2008).

    Article  CAS  Google Scholar 

  28. Gabriel, M. F., Postigo, I., Tomaz, C. T. & Martinez, J. Alternaria alternata allergens: markers of exposure, phylogeny and risk of fungi-induced respiratory allergy. Environ. Int. 8990, 71–80 (2016).

    Article  Google Scholar 

  29. Dinarello, C. A. A clinical perspective of IL-1beta as the gatekeeper of inflammation. Eur. J. Immunol. 41, 1203–1217 (2011).

    Article  CAS  Google Scholar 

  30. Heinrich, P. C. et al. Principles of interleukin (IL)-6-type cytokine signalling and its regulation. Biochem. J. 374, 1–20 (2003).

    Article  CAS  Google Scholar 

  31. Akdis, M. et al. Interleukins (from IL-1 to IL-38), interferons, transforming growth factor beta, and TNF-alpha: receptors, functions, and roles in diseases. J. Allergy Clin. Immunol. 138, 984–1010 (2016).

    Article  CAS  Google Scholar 

  32. Xu, M. & Dong, C. IL-25 in allergic inflammation. Immunol. Rev. 278, 185–191 (2017).

    Article  CAS  Google Scholar 

  33. Martinez-Moczygemba, M. & Huston, D. P. Biology of common beta receptor-signaling cytokines: IL-3, IL-5, and GM-CSF. J. Allergy Clin. Immunol. 112, 653–665 (2003). quiz 666.

    Article  CAS  Google Scholar 

  34. Russo, R. C., Garcia, C. C., Teixeira, M. M. & Amaral, F. A. The CXCL8/IL-8 chemokine family and its receptors in inflammatory diseases. Expert Rev. Clin. Immunol. 10, 593–619 (2014).

    Article  CAS  Google Scholar 

  35. Schmitz, J. et al. IL-33, an interleukin-1-like cytokine that signals via the IL-1 receptor-related protein ST2 and induces T helper type 2-associated cytokines. Immunity 23, 479–490 (2005).

    Article  CAS  Google Scholar 

  36. Cianferoni, A. & Spergel, J. The importance of TSLP in allergic disease and its role as a potential therapeutic target. Expert Rev. Clin. Immunol. 10, 1463–1474 (2014).

    Article  CAS  Google Scholar 

  37. Rankin, S. M., Conroy, D. M. & Williams, T. J. Eotaxin and eosinophil recruitment: implications for human disease. Mol. Med. Today 6, 20–27 (2000).

    Article  CAS  Google Scholar 

  38. Martin, L. B., Kita, H., Leiferman, K. M. & Gleich, G. J. Eosinophils in allergy: role in disease, degranulation, and cytokines. Int. Arch. Allergy Immunol. 109, 207–215 (1996).

    Article  CAS  Google Scholar 

  39. Bidossi, A. et al. Probiotics Streptococcus salivarius 24SMB and Streptococcus oralis 89a interfere with biofilm formation of pathogens of the upper respiratory tract. BMC Infect. Dis. 18, 653 (2018).

    Article  CAS  Google Scholar 

  40. Srikham, K., Daengprok, W., Niamsup, P. & Thirabunyanon, M. Characterization of Streptococcus salivarius as new probiotics derived from human breast milk and their potential on proliferative inhibition of liver and breast cancer cells and antioxidant activity. Front. Microbiol. 12, 797445 (2021).

    Article  Google Scholar 

  41. Henderson, B., Poole, S. & Wilson, M. Microbial/host interactions in health and disease: who controls the cytokine network. Immunopharmacology 35, 1–21 (1996).

    Article  CAS  Google Scholar 

  42. Kumar, H., Kawai, T. & Akira, S. Pathogen recognition by the innate immune system. Int. Rev. Immunol. 30, 16–34 (2011).

    Article  CAS  Google Scholar 

  43. Rogers, D. F. Airway hypersecretion in allergic rhinitis and asthma: new pharmacotherapy. Curr. Allergy Asthma Rep. 3, 238–248 (2003).

    Article  Google Scholar 

  44. Evans, C. M. et al. The polymeric mucin Muc5ac is required for allergic airway hyperreactivity. Nat. Commun. 6, 6281 (2015).

    Article  CAS  Google Scholar 

  45. Dominguez-Bello, M. G., Godoy-Vitorino, F., Knight, R. & Blaser, M. J. Role of the microbiome in human development. Gut 68, 1108–1114 (2019).

    Article  CAS  Google Scholar 

  46. Zhao, L. The gut microbiota and obesity: from correlation to causality. Nat. Rev. Microbiol. 11, 639–647 (2013).

    Article  CAS  Google Scholar 

  47. Holmstrom, K., Collins, M. D., Moller, T., Falsen, E. & Lawson, P. A. Subdoligranulum variabile gen. nov., sp. nov. from human feces. Anaerobe 10, 197–203 (2004).

    Article  CAS  Google Scholar 

  48. Van Hul, M. et al. From correlation to causality: the case of Subdoligranulum. Gut Microbes 12, 1–13 (2020).

    Google Scholar 

  49. Doern, C. D. & Burnham, C. A. It’s not easy being green: the viridans group streptococci, with a focus on pediatric clinical manifestations. J. Clin. Microbiol. 48, 3829–3835 (2010).

    Article  Google Scholar 

  50. Escapa, I. F. et al. New insights into human nostril microbiome from the expanded Human Oral Microbiome Database (eHOMD): a resource for the microbiome of the human aerodigestive tract. mSystems 3, e00187-18 (2018).

    Article  Google Scholar 

  51. Couvigny, B. et al. Three glycosylated serine-rich repeat proteins play a pivotal role in adhesion and colonization of the pioneer commensal bacterium, Streptococcus salivarius. Environ. Microbiol. 19, 3579–3594 (2017).

    Article  CAS  Google Scholar 

  52. Amies, C. R. A modified formula for the preparation of Stuart’s transport medium. Can. J. Public Health 58, 296–300 (1967).

    CAS  Google Scholar 

  53. Magoc, T. & Salzberg, S. L. FLASH: fast length adjustment of short reads to improve genome assemblies. Bioinformatics 27, 2957–2963 (2011).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  55. Seow, W. K., Lam, J. H., Tsang, A. K., Holcombe, T. & Bird, P. S. Oral Streptococcus species in pre-term and full-term children—a longitudinal study. Int. J. Paediatr. Dent. 19, 406–411 (2009).

    Article  Google Scholar 

  56. Vandecasteele, S. J., Peetermans, W. E., Merckx, R. & Van Eldere, J. Quantification of expression of Staphylococcus epidermidis housekeeping genes with Taqman quantitative PCR during in vitro growth and under different conditions. J. Bacteriol. 183, 7094–7101 (2001).

    Article  CAS  Google Scholar 

  57. Blin, K. et al. antiSMASH 6.0: improving cluster detection and comparison capabilities. Nucleic Acids Res. 49, W29–W35 (2021).

    Article  CAS  Google Scholar 

  58. Liu, Y. et al. Skin microbiota analysis-inspired development of novel anti-infectives. Microbiome 8, 85 (2020).

    Article  CAS  Google Scholar 

  59. Ma, M., Redes, J. L., Percopo, C. M., Druey, K. M. & Rosenberg, H. F. Alternaria alternata challenge at the nasal mucosa results in eosinophilic inflammation and increased susceptibility to influenza virus infection. Clin. Exp. Allergy 48, 691–702 (2018).

    Article  CAS  Google Scholar 

  60. Lu, L. et al. Loss of natural resistance to schistosome in T cell deficient rat. PLoS Negl. Trop. Dis. 14, e0008909 (2020).

    Article  CAS  Google Scholar 

  61. Lu, L. et al. Excessive immunosuppression by regulatory T cells antagonizes T cell response to schistosome infection in PD-1-deficient mice. PLoS Pathog. 18, e1010596 (2022).

    Article  CAS  Google Scholar 

  62. Chamanza, R. & Wright, J. A. A review of the comparative anatomy, histology, physiology and pathology of the nasal cavity of rats, mice, dogs and non-human primates. Relevance to inhalation toxicology and human health risk assessment. J. Comp. Pathol. 153, 287–314 (2015).

    Article  CAS  Google Scholar 

  63. Dixon, P. VEGAN, a package of R functions for community ecology. J. Veg. Sci. 14, 927–930 (2003).

    Article  Google Scholar 

  64. Lozupone, C. & Knight, R. UniFrac: a new phylogenetic method for comparing microbial communities. Appl. Environ. Microbiol. 71, 8228–8235 (2005).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

This work was supported by the Clinical Research Plan of the Shanghai Shenkang Hospital Development Center (SHDC, grant number SHDC2020CR3006A to M.L.), the National Natural Science Foundation of China (grant numbers 81873957 and 82172325, to M.L.) and the Intramural Research Program of Allergy and Infectious Diseases (NIAID), US National Institutes of Health (NIH) (project number ZIA AI000904, to M.O.).

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Conceptualization, M.O. and M.L. Methodology, P.M. and M.L. Investigation, P.M., Y. Jiang, J.S., Y.L., P.P., Y.Z., G.Y.C.C., Y. Jian and Q.L. Funding acquisition, M.L. and M.O. Supervision, M.L. and M.O. Writing, M.O.

Corresponding authors

Correspondence to Michael Otto or Min Li.

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The authors declare no competing interests.

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Nature Microbiology thanks Cathryn Nagler and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Analysis of group composition.

a, Relative species abundance and evenness by rank abundance (Whittaker) plots. b, Total OTU in HR and AC groups. Statistical analysis is by two-tailed Mann-Whitney test. Error bars show the mean ± SD. n = 55 (AR), n = 102 (HC).

Extended Data Fig. 2 Relative abundance in single individuals of main detected phyla in AR patients versus HC.

Statistical analysis is by two-tailed Mann-Whitney tests. Error bars show the mean ± SD. n = 55 (AR), n = 102 (HC).

Extended Data Fig. 3 Analysis of absolute bacterial abundance.

a, Bacterial CFU obtained from nasal swabs from n = 18 individuals/group, grown in aerobic and anaerobic conditions. Error bars show the mean ± SD. Statistical analysis is by two-tailed Mann-Whitney tests. All comparisons were not significant (p≥0.05). b, Absolute abundance according to 16S rRNA sequencing analysis.

Source data

Extended Data Fig. 4 Relative abundance in single individuals of main detected genera in AR patients versus HC.

Statistical analysis is by two-tailed Mann-Whitney tests. Error bars show the mean ± SD. n = 55 (AR), n = 102 (HC).

Extended Data Fig. 5 Microbiome analysis by type of AR (severity, seasonality, frequency).

a, α-diversity (Shannon indices). Statistical analysis is by Mann-Whitney tests. Error bars show the mean ± SD. b, β-diversity (PCoA analyses). Statistical analysis is by Adonis. c, Relative abundances on the phylum level. d, Relative abundances on the genus level. a-d, n = 55 (all AR).

Extended Data Fig. 6 Microbiome analysis by sex and age of participants.

a, α-diversity (Shannon indices). Statistical analysis is by two-tailed Mann-Whitney tests. Error bars show the mean ± SD. b, β-diversity (PCoA analyses). Statistical analysis is by Adonis. c, Relative abundances on the phylum level. d, Relative abundances on the genus level. a-d, n = 55 (all AR).

Extended Data Fig. 7 Absolute abundance of Staphylococcus and S. epidermidis in AR patients versus HC.

a, Absolute abundance by OTUs. b, Abundance by qPCR. n = 52 (AR), n = 58 (HR) (all samples with sufficient DNA for qRT-PCR analysis). a,b, Statistical analysis is by two-tailed Mann-Whitney tests. Error bars show the mean ± SD.

Source data

Extended Data Fig. 8 Agar diffusion test of antibacterial activity of S. salivarius nasal isolates obtained from AR patients.

All obtained 14 nasal isolates from AR patients were spotted on agar plates with embedded M. luteus, S. epidermidis, C. acnes, or C. accolens. Asterisks designate strains with detected bacteriocin genes in the genome. Vancomycin (Van) and supernatant obtained from the micrococcin MP1 producer S. hominis S34-1 were used as positive controls.

Extended Data Fig. 9 Repeat of experiment shown in Fig. 2 of the main manuscript.

a, Cytokine expression data. b, Epithelial thickness. n = 10. See legend to Fig. 2 for further details. Statistical analysis is by 1-way ANOVAs with Tukey’s post-tests. Error bars show the mean ± SD.

Source data

Extended Data Table 1 Cytokines in the blood of AR patients

Supplementary information

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Supplementary Table 1 and Fig. 1

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Miao, P., Jiang, Y., Jian, Y. et al. Exacerbation of allergic rhinitis by the commensal bacterium Streptococcus salivarius. Nat Microbiol (2023). https://doi.org/10.1038/s41564-022-01301-x

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