Infant airway microbiota and topical immune perturbations in the origins of childhood asthma

Jonathan Thorsen, Morten A Rasmussen, Johannes Waage, Martin Mortensen, Asker Brejnrod, Klaus Bønnelykke, Bo L Chawes, Susanne Brix, Søren J Sørensen, Jakob Stokholm, Hans Bisgaard, Jonathan Thorsen, Morten A Rasmussen, Johannes Waage, Martin Mortensen, Asker Brejnrod, Klaus Bønnelykke, Bo L Chawes, Susanne Brix, Søren J Sørensen, Jakob Stokholm, Hans Bisgaard

Abstract

Asthma is believed to arise through early life aberrant immune development in response to environmental exposures that may influence the airway microbiota. Here, we examine the airway microbiota during the first three months of life by 16S rRNA gene amplicon sequencing in the population-based Copenhagen Prospective Studies on Asthma in Childhood 2010 (COPSAC2010) cohort consisting of 700 children monitored for the development of asthma since birth. Microbial diversity and the relative abundances of Veillonella and Prevotella in the airways at age one month are associated with asthma by age 6 years, both individually and with additional taxa in a multivariable model. Higher relative abundance of these bacteria is furthermore associated with an airway immune profile dominated by reduced TNF-α and IL-1β and increased CCL2 and CCL17, which itself is an independent predictor for asthma. These findings suggest a mechanism of microbiota-immune interactions in early infancy that predisposes to childhood asthma.

Conflict of interest statement

The authors declare no competing interests regarding the content of this manuscript.

Figures

Fig. 1
Fig. 1
Differential abundance and asthma. Hazard ratios and corresponding p values from Cox proportional hazards regression models using log-transformed relative abundances for each genus as a predictor for asthma by age 6 years. Dashed line indicates 5% false discovery rate (FDR) cutoff. Colored by taxonomic phylum. n = 573
Fig. 2
Fig. 2
Bacterial asthma score and asthma. Sparse partial least-squares (sPLS) model between genus-level relative abundances at 1 month of age and asthma by age 6 years. Kaplan–Meier curve showing cumulative risk of asthma by bacterial asthma score, divided into tertiles. n = 573 (191 in each tertile group). Adjusted hazard ratio (aHR) and 95% confidence interval corresponds to each standard deviation (SD) of the continuous score from the sPLS model, adjusted for paternal asthma, season of birth, and siblings in a Cox regression (n = 554). The displayed percentages are the Kaplan–Meier estimates of asthma risk at 6 years in each tertile group
Fig. 3
Fig. 3
Airway immune profile and bacterial asthma score. Associations between the bacterial asthma score, based on Veillonella, Prevotella, Gemella, Bacilli incertae sedis, Bacillales incertae sedis, Streptococcus, and Lactobacillus, and upper airway mucosal immune mediators. Linear models show that the bacterial asthma score is associated with several immune mediators, expressed as relative concentration ratios of immune mediators per standard deviation (SD) increase in bacterial asthma score, n = 499. Error bars indicate 95% confidence intervals. Associations are adjusted for collinearity with other bacteria

References

    1. Bach J-F. The EFFECT OF INFECTIONS ON SUSCEPTIBILITY TO AUTOIMMUNE AND ALLERGIC DISEases. N. Engl. J. Med. 2002;347:911–920. doi: 10.1056/NEJMra020100.
    1. Martinez FD, Vercelli D. Asthma. Lancet. 2013;382:1360–1372. doi: 10.1016/S0140-6736(13)61536-6.
    1. Eder W, Ege MJ, von Mutius E. The asthma epidemic. N. Engl. J. Med. 2006;355:2226–2235. doi: 10.1056/NEJMra054308.
    1. Arrieta, M. -C., Stiemsma, L. T., Amenyogbe, N., Brown, E. M. & Finlay, B. The intestinal microbiome in early life: health and disease. Front. Immunol. 5, 427 (2014).
    1. Prescott SL. Early-life environmental determinants of allergic diseases and the wider pandemic of inflammatory noncommunicable diseases. J. Allergy Clin. Immunol. 2013;131:23–30. doi: 10.1016/j.jaci.2012.11.019.
    1. Bisgaard H, et al. Fish oil-derived fatty acids in pregnancy and wheeze and asthma in offspring. N. Engl. J. Med. 2016;375:2530–2539. doi: 10.1056/NEJMoa1503734.
    1. Gensollen T, Iyer SS, Kasper DL, Blumberg RS. How colonization by microbiota in early life shapes the immune system. Science. 2016;352:539–544. doi: 10.1126/science.aad9378.
    1. Vatanen T, et al. Variation in icrobiome LPS immunogenicity contributes to autoimmunity in humans. Cell. 2016;165:842–853. doi: 10.1016/j.cell.2016.04.007.
    1. Holt PG, Strickland DH, Sly PD. Virus infection and allergy in the development of asthma: what is the connection? Curr. Opin. Allergy Clin. Immunol. 2012;12:151–157. doi: 10.1097/ACI.0b013e3283520166.
    1. Arrieta M-C, et al. Early infancy microbial and metabolic alterations affect risk of childhood asthma. Sci. Transl. Med. 2015;7:307ra152. doi: 10.1126/scitranslmed.aab2271.
    1. Cox LM, et al. Altering the intestinal microbiota during a critical developmental window has lasting metabolic consequences. Cell. 2014;158:705–721. doi: 10.1016/j.cell.2014.05.052.
    1. Illi S, et al. Protection from childhood asthma and allergy in Alpine farm environments—the GABRIEL advanced studies. J. Allergy Clin. Immunol. 2012;129:1470–1477.e6. doi: 10.1016/j.jaci.2012.03.013.
    1. Ball TM, et al. Siblings, day-care attendance, and the risk of asthma and wheezing during childhood. N. Engl. J. Med. 2000;343:538–543. doi: 10.1056/NEJM200008243430803.
    1. Sevelsted A, Stokholm J, Bønnelykke K, Bisgaard H. Cesarean section and chronic immune disorders. Pediatrics. 2015;135:e92–e98. doi: 10.1542/peds.2014-0596.
    1. Thavagnanam S, Fleming J, Bromley A, Shields MD, Cardwell CR. A meta-analysis of the association between Caesarean section and childhood asthma. Clin. Exp. Allergy. 2008;38:629–633. doi: 10.1111/j.1365-2222.2007.02780.x.
    1. Human Microbiome Project Consortium. Structure, function and diversity of the healthy human microbiome. Nature. 2012;486:207–214. doi: 10.1038/nature11234.
    1. Segal LN, Blaser MJ. A brave new world: the lung microbiota in an era of change. Ann. Am. Thorac. Soc. 2014;11:S21–S27. doi: 10.1513/AnnalsATS.201306-189MG.
    1. Kong HH, et al. Temporal shifts in the skin microbiome associated with disease flares and treatment in children with atopic dermatitis. Genome Res. 2012;22:850–859. doi: 10.1101/gr.131029.111.
    1. Ravel J, et al. Vaginal microbiome of reproductive-age women. Proc. Natl. Acad. Sci. USA. 2011;108(Suppl 1):4680–4687. doi: 10.1073/pnas.1002611107.
    1. Aagaard K, et al. The placenta harbors a unique microbiome. Sci. Transl. Med. 2014;6:237ra65–237ra65. doi: 10.1126/scitranslmed.3008599.
    1. Mortensen MS, et al. The developing hypopharyngeal microbiota in early life. Microbiome. 2016;4:70. doi: 10.1186/s40168-016-0215-9.
    1. Yan M, et al. Nasal microenvironments and interspecific interactions influence nasal microbiota complexity and S. aureus carriage. Cell Host Microbe. 2013;14:631–640. doi: 10.1016/j.chom.2013.11.005.
    1. Charlson ES, et al. Topographical Continuity of bacterial populations in the healthy human respiratory tract. Am. J. Respir. Crit. Care Med. 2011;184:957–963. doi: 10.1164/rccm.201104-0655OC.
    1. Dickson RP, et al. Spatial variation in the healthy human lung microbiome and the adapted island model of lung biogeography. Ann. Am. Thorac. Soc. 2015;12:821–830. doi: 10.1513/AnnalsATS.201501-029OC.
    1. Bisgaard H, et al. Childhood asthma after bacterial colonization of the airway in neonates. N. Engl. J. Med. 2007;357:1487–1495. doi: 10.1056/NEJMoa052632.
    1. Følsgaard NV, et al. Pathogenic bacteria colonizing the airways in asymptomatic neonates stimulates topical inflammatory mediator release. Am. J. Respir. Crit. Care Med. 2013;187:589–595. doi: 10.1164/rccm.201207-1297OC.
    1. Bisgaard H, et al. Deep phenotyping of the unselected COPSAC2010 birth cohort study. Clin. Exp. Allergy J. Br. Soc. Allergy Clin. Immunol. 2013;43:1384–1394. doi: 10.1111/cea.12213.
    1. Teo SM, et al. The infant nasopharyngeal microbiome impacts severity of lower respiratory infection and risk of asthma development. Cell Host Microbe. 2015;17:704–715. doi: 10.1016/j.chom.2015.03.008.
    1. Igartua C, et al. Host genetic variation in mucosal immunity pathways influences the upper airway microbiome. Microbiome. 2017;5:16. doi: 10.1186/s40168-016-0227-5.
    1. Luna PN, et al. The association between anterior nares and nasopharyngeal microbiota in infants hospitalized for bronchiolitis. Microbiome. 2018;6:2. doi: 10.1186/s40168-017-0385-0.
    1. Dickson RP, Erb-Downward JR, Martinez FJ, Huffnagle GB. The microbiome and the respiratory tract. Annu. Rev. Physiol. 2016;78:481–504. doi: 10.1146/annurev-physiol-021115-105238.
    1. Franzosa EA, et al. Sequencing and beyond: integrating molecular ‘omics’ for microbial community profiling. Nat. Rev. Microbiol. 2015;13:360–372. doi: 10.1038/nrmicro3451.
    1. Lê Cao, K. -A., Rossouw, D., Robert-Granié, C. & Besse, P. A sparse PLS for variable selection when integrating omics data. Stat. Appl. Genet. Mol. Biol. 7, 10.2202/1544-6115.1390 (2008).
    1. McDade TW. Early environments and the ecology of inflammation. Proc. Natl. Acad. Sci. USA. 2012;109(Suppl. 2):17281–17288. doi: 10.1073/pnas.1202244109.
    1. Segal LN, et al. Enrichment of the lung microbiome with oral taxa is associated with lung inflammation of a Th17 phenotype. Nat. Microbiol. 2016;1:16031. doi: 10.1038/nmicrobiol.2016.31.
    1. Erb-Downward JR, et al. Analysis of the lung microbiome in the “healthy” smoker and in COPD. PLoS ONE. 2011;6:e16384. doi: 10.1371/journal.pone.0016384.
    1. Segal LN, et al. Enrichment of lung microbiome with supraglottic taxa is associated with increased pulmonary inflammation. Microbiome. 2013;1:19. doi: 10.1186/2049-2618-1-19.
    1. Brix S, Eriksen C, Larsen JM, Bisgaard H. Metagenomic heterogeneity explains dual immune effects of endotoxins. J. Allergy Clin. Immunol. 2015;135:277–280. doi: 10.1016/j.jaci.2014.09.036.
    1. Larsen JM, et al. Divergent pro-inflammatory profile of human dendritic cells in response to commensal and pathogenic bacteria associated with the airway microbiota. PLoS ONE. 2012;7:e31976. doi: 10.1371/journal.pone.0031976.
    1. Leung T, et al. Plasma concentration of thymus and activation-regulated chemokine is elevated in childhood asthma. J. Allergy Clin. Immunol. 2002;110:404–409. doi: 10.1067/mai.2002.126378.
    1. Prescott SL. Early origins of allergic disease: a review of processes and influences during early immune development. Curr. Opin. Allergy Clin. Immunol. 2003;3:125–132. doi: 10.1097/00130832-200304000-00006.
    1. Bosch AATM, et al. Maturation of the infant respiratory microbiota, environmental drivers, and health consequences. A prospective cohort study. Am. J. Respir. Crit. Care Med. 2017;196:1582–1590. doi: 10.1164/rccm.201703-0554OC.
    1. Chang W-C, et al. Close correlation between season of birth and the prevalence of bronchial asthma in a Taiwanese population. PLoS ONE. 2013;8:e80285. doi: 10.1371/journal.pone.0080285.
    1. Chawes BL, et al. Effect of vitamin D3 supplementation during pregnancy on risk of persistent wheeze in the offspring: a randomized clinical trial. JAMA. 2016;315:353–361. doi: 10.1001/jama.2015.18318.
    1. Bisgaard H, Pipper CB, Bønnelykke K. Endotyping early childhood asthma by quantitative symptom assessment. J. Allergy Clin. Immunol. 2011;127:1155–1164.e2. doi: 10.1016/j.jaci.2011.02.007.
    1. Martinez FD, et al. Asthma and wheezing in the first six years of life. The Group Health Medical Associates. N. Engl. J. Med. 1995;332:133–138. doi: 10.1056/NEJM199501193320301.
    1. Bischoff AL, et al. Altered response to A(H1N1)pnd09 vaccination in pregnant women: a single blinded randomized controlled trial. PLoS ONE. 2013;8:e56700. doi: 10.1371/journal.pone.0056700.
    1. Bischoff AL, et al. Airway mucosal immune-suppression in neonates of mothers receiving A(H1N1)pnd09 vaccination during pregnancy. Pediatr. Infect. Dis. J. 2015;34:84–90. doi: 10.1097/INF.0000000000000529.
    1. Stokholm J, et al. Azithromycin for episodes with asthma-like symptoms in young children aged 1–3 years: a randomised, double-blind, placebo-controlled trial. Lancet Respir. Med. 2016;4:19–26. doi: 10.1016/S2213-2600(15)00500-7.
    1. Hansen, M. A. . . Accessed 5 Oct 2015.
    1. Edgar RC. Search and clustering orders of magnitude faster than BLAST. Bioinforma. Oxf. Engl. 2010;26:2460–2461. doi: 10.1093/bioinformatics/btq461.
    1. Edgar RC. UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nat. Methods. 2013;10:996–998. doi: 10.1038/nmeth.2604.
    1. Haas BJ, et al. Chimeric 16S rRNA sequence formation and detection in Sanger and 454-pyrosequenced PCR amplicons. Genome Res. 2011;21:494–504. doi: 10.1101/gr.112730.110.
    1. Mukherjee S, et al. Genomes OnLine Database (GOLD) v.6: data updates and feature enhancements. Nucleic Acids Res. 2017;45:D446–D456. doi: 10.1093/nar/gkw992.
    1. Schloss PD, et al. Introducing Mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl. Environ. Microbiol. 2009;75:7537–7541. doi: 10.1128/AEM.01541-09.
    1. Cole JR, et al. Ribosomal Database Project: data and tools for high throughput rRNA analysis. Nucleic Acids Res. 2014;42:D633–D642. doi: 10.1093/nar/gkt1244.
    1. DeSantis TZ, et al. Greengenes, a chimera-checked 16S rRNA Gene Database and workbench compatible with ARB. Appl. Environ. Microbiol. 2006;72:5069–5072. doi: 10.1128/AEM.03006-05.
    1. Price MN, Dehal PS, Arkin AP. FastTree: computing large minimum evolution trees with profiles instead of a distance matrix. Mol. Biol. Evol. 2009;26:1641–1650. doi: 10.1093/molbev/msp077.
    1. Chawes BLK, et al. A novel method for assessing unchallenged levels of mediators in nasal epithelial lining fluid. J. Allergy Clin. Immunol. 2010;125:1387–1389.e3. doi: 10.1016/j.jaci.2010.01.039.
    1. Wolsk H. M., Chawes B. L., Følsgaard N. V., Rasmussen M. A., Brix S., Bisgaard H. Siblings Promote a Type 1/Type 17-oriented immune response in the airways of asymptomatic neonates. Allergy. 2016;71(6):820–828. doi: 10.1111/all.12847.
    1. R. Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2015).
    1. Bray JR, Curtis JT. An ordination of the upland forest communities of Southern Wisconsin. Ecol. Monogr. 1957;27:325–349. doi: 10.2307/1942268.
    1. Lozupone CA, Hamady M, Kelley ST, Knight R. Quantitative and qualitative β diversity measures lead to different insights into factors that structure microbial communities. Appl. Environ. Microbiol. 2007;73:1576–1585. doi: 10.1128/AEM.01996-06.
    1. McMurdie PJ, Holmes S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE. 2013;8:e61217. doi: 10.1371/journal.pone.0061217.
    1. Kuznetsova A, Brockhoff PBB, Christensen RH. lmerTest Package: tests in linear mixed effects models. J. Stat. Softw. 2017;82:1–26. doi: 10.18637/jss.v082.i13.
    1. Oksanen, J. et al. vegan: Community Ecology Package. (2015).
    1. Rohart, F., Gautier, B., Singh, A. & Cao, K.-A. L. mixOmics: An R package for ‘omics feature selection and multiple data integration. PLOS Computational Biology13, e1005752 (2017).
    1. Torgo, L. Data Mining with R, Learning with Case Studies (Chapman & Hall/CRC, 2010).
    1. Tingley D, Yamamoto T, Hirose K, Keele L, Imai K. mediation: R package for causal mediation analysis. J. Stat. Softw. 2014;59:1–38. doi: 10.18637/jss.v059.i05.

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