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Farm-like indoor microbiota in non-farm homes protects children from asthma development

Nature Medicine volume 25, pages 1089–1095 (2019)Cite this article

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An Author Correction to this article was published on 17 July 2019

This article has been updated

Abstract

Asthma prevalence has increased in epidemic proportions with urbanization, but growing up on traditional farms offers protection even today1. The asthma-protective effect of farms appears to be associated with rich home dust microbiota2,3, which could be used to model a health-promoting indoor microbiome. Here we show by modeling differences in house dust microbiota composition between farm and non-farm homes of Finnish birth cohorts4 that in children who grow up in non-farm homes, asthma risk decreases as the similarity of their home bacterial microbiota composition to that of farm homes increases. The protective microbiota had a low abundance of Streptococcaceae relative to outdoor-associated bacterial taxa. The protective effect was independent of richness and total bacterial load and was associated with reduced proinflammatory cytokine responses against bacterial cell wall components ex vivo. We were able to reproduce these findings in a study among rural German children2 and showed that children living in German non-farm homes with an indoor microbiota more similar to Finnish farm homes have decreased asthma risk. The indoor dust microbiota composition appears to be a definable, reproducible predictor of asthma risk and a potential modifiable target for asthma prevention.

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Fig. 1: Differences between farm and non-farm rural home indoor microbiota.
Fig. 2: Farm home-like indoor microbiota is associated with asthma protection in non-farm children.
Fig. 3: Replication of the asthma-protective effect of growing up in a home with a farm home-like indoor bacterial microbiota.

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Data availability

The bacterial and fungal sequences from LUKAS have been deposited in the European Bioinformatics Institute European Nucleotide Archive database under accession number PRJEB29081. Other data supporting the findings of this study are available through direct communication with the corresponding author. Limitations apply to variables where too small subgroups may compromise research participant privacy/consent. In these cases amendment to the ethical approval will be required before data transfer.

Code availability

All codes used in the study are available on the public repository https://github.com/PirkkaKirjavainen/FaRMI. Contact the corresponding author for more information.

Change history

  • 17 July 2019

    An amendment to this paper has been published and can be accessed via a link at the top of the paper.

References

  1. von Mutius, E. & Vercelli, D. Farm living: effects on childhood asthma and allergy. Nat. Rev. Immunol. 10, 861–868 (2010).

    Article  CAS  Google Scholar 

  2. Ege, M. J. et al. Exposure to environmental microorganisms and childhood asthma. New Engl. J. Med. 364, 701–709 (2011).

    Article  CAS  PubMed  Google Scholar 

  3. Schuijs, M. J. et al. Farm dust and endotoxin protect against allergy through A20 induction in lung epithelial cells. Science 349, 1106–1110 (2015).

    Article  CAS  PubMed  Google Scholar 

  4. Karvonen, A. M. et al. Confirmed moisture damage at home, respiratory symptoms and atopy in early life: a birth-cohort study. Pediatrics 124, e329–e338 (2009).

    Article  PubMed  Google Scholar 

  5. Liu, A. H. Revisiting the hygiene hypothesis for allergy and asthma. J. Allergy Clin. Immunol. 136, 860–865 (2015).

    Article  PubMed  Google Scholar 

  6. Reynolds, L. A. & Finlay, B. B. Early life factors that affect allergy development. Nat Rev. Immunol. 17, 518–528 (2017).

  7. Stein, M. M. et al. Innate immunity and asthma risk in Amish and Hutterite farm children. New Engl. J. Med. 375, 411–421 (2016).

    Article  CAS  PubMed  Google Scholar 

  8. Debarry, J. et al. Acinetobacter lwoffii and Lactococcus lactis strains isolated from farm cowsheds possess strong allergy-protective properties. J. Allergy Clin. Immunol. 119, 1514–1521 (2007).

    Article  PubMed  Google Scholar 

  9. Karvonen, A. M. et al. Quantity and diversity of environmental microbial exposure and development of asthma: a birth cohort study. Allergy 69, 1092–1101 (2014).

    Article  CAS  PubMed  Google Scholar 

  10. Child-Specific Exposure Factors Handbook (Final Report) (US EPA, 2008).

  11. Hyytiainen, H. K. et al. Crawling-induced floor dust resuspension affects the microbiota of the infant breathing zone. Microbiome 6, 25 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  12. Janssen, P. H. & Kirs, M. Structure of the archaeal community of the rumen. Appl. Environ. Microbiol. 74, 3619–3625 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Birzele, L. T. et al. Environmental and mucosal microbiota and their role in childhood asthma. Allergy 72, 109–119 (2017).

    Article  CAS  PubMed  Google Scholar 

  14. Braun-Fahrlander, C. et al. Environmental exposure to endotoxin and its relation to asthma in school-age children. New Engl. J. Med. 347, 869–877 (2002).

    Article  PubMed  Google Scholar 

  15. Lynch, S. V. et al. Effects of early-life exposure to allergens and bacteria on recurrent wheeze and atopy in urban children. J. Allergy Clin. Immunol. 134, 593–601.e12 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  16. Valkonen, M. et al. Microbial characteristics in homes of asthmatic and non-asthmatic adults in the ECRHS cohort. Indoor Air 28, 16–27 (2018).

    Article  CAS  PubMed  Google Scholar 

  17. Ottman, N. et al. Soil exposure modifies the gut microbiota and supports immune tolerance in a mouse model. J. Allergy Clin. Immunol. 143, 1198–1206.e12 (2018).

    Article  CAS  PubMed  Google Scholar 

  18. Genuneit, J. et al. The GABRIEL Advanced Surveys: study design, participation and evaluation of bias. Paediatr. Perinat. Epidemiol. 25, 436–447 (2011).

    Article  PubMed  Google Scholar 

  19. Leppanen, H. K. et al. Quantitative assessment of microbes from samples of indoor air and dust. J. Expo. Sci. Environ. Epidemiol. 28, 231–241 (2017).

    Article  CAS  PubMed  Google Scholar 

  20. Adams, R. I., Bateman, A. C., Bik, H. M. & Meadow, J. F. Microbiota of the indoor environment: a meta-analysis. Microbiome 3, 49 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  21. Barberan, A. et al. Continental-scale distributions of dust-associated bacteria and fungi. Proc. Natl Acad. Sci. USA 112, 5756–5761 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Natarajan, S., Kim, J., Bouchard, J., Cruikshank, W. & Remick, D. G. Pulmonary endotoxin tolerance protects against cockroach allergen-induced asthma-like inflammation in a mouse model. Int. Arch. Allergy Immunol. 158, 120–130 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Kumar, S. & Adhikari, A. Dose-dependent immunomodulating effects of endotoxin in allergic airway inflammation. Innate Immun. 23, 249–257 (2017).

    Article  CAS  PubMed  Google Scholar 

  24. Rochat, M. K. et al. Maternal vitamin D intake during pregnancy increases gene expression of ILT3 and ILT4 in cord blood. Clin. Exp. Allergy 40, 786–794 (2010).

    Article  CAS  PubMed  Google Scholar 

  25. Wu, J. & Horuzsko, A. Expression and function of ILTs on tolerogenic dendritic cells. Hum. Immunol. 70, 353–356 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. de Heer, H. J. et al. Essential role of lung plasmacytoid dendritic cells in preventing asthmatic reactions to harmless inhaled antigen. J. Exp. Med. 200, 89–98 (2004).

    Article  PubMed  PubMed Central  Google Scholar 

  27. Rosenblueth, M., Martinez-Romero, J. C., Reyes-Prieto, M., Rogel, M. A. & Martinez-Romero, E. Environmental mycobacteria: a threat to human health? DNA Cell Biol. 30, 633–640 (2011).

    Article  CAS  PubMed  Google Scholar 

  28. Deng, W. et al. Assembly, structure, function and regulation of type III secretion systems. Nat. Rev. Microbiol. 15, 323–337 (2017).

    Article  CAS  PubMed  Google Scholar 

  29. Wallden, K., Rivera-Calzada, A. & Waksman, G. Type IV secretion systems: versatility and diversity in function. Cell. Microbiol. 12, 1203–1212 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Shrivastava, R. & Miller, J. F. Virulence factor secretion and translocation by Bordetella species. Curr. Opin. Microbiol. 12, 88–93 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Pizarro-Cerdá, J. & Cossart, P. Bacterial adhesion and entry into host cells. Cell 124, 715–727 (2006).

    Article  CAS  PubMed  Google Scholar 

  32. Shan, L., He, P. & Sheen, J. Intercepting host MAPK signaling cascades by bacterial type III effectors. Cell Host Microbe 1, 167–174 (2007).

    Article  CAS  PubMed  Google Scholar 

  33. Bakowski, M. A., Cirulis, J. T., Brown, N. F., Finlay, B. B. & Brumell, J. H. SopD acts cooperatively with SopB during Salmonella enterica serovar Typhimurium invasion. Cell. Microbiol. 9, 2839–2855 (2007).

    Article  CAS  PubMed  Google Scholar 

  34. Shao, F., Merritt, P. M., Bao, Z., Innes, R. W. & Dixon, J. E. A Yersinia effector and a Pseudomonas avirulence protein define a family of cysteine proteases functioning in bacterial pathogenesis. Cell 109, 575–588 (2002).

    Article  CAS  PubMed  Google Scholar 

  35. Needham, B. D. & Trent, M. S. Fortifying the barrier: the impact of lipid A remodelling on bacterial pathogenesis. Nat. Rev. Microbiol. 11, 467 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Bisgaard, H. et al. Childhood asthma after bacterial colonization of the airway in neonates. New Engl. J. Med. 357, 1487–1495 (2007).

    Article  CAS  PubMed  Google Scholar 

  37. O’Connor, G. T. et al. Early-life home environment and risk of asthma among inner-city children. J. Allergy Clin. Immunol. 141, 1468–1475 (2018).

    Article  PubMed  Google Scholar 

  38. Oksanen, J. et al. vegan: Community Ecology Package (R Package, R Foundation for Statistical Computing, 2016).

  39. Schram-Bijkerk, D. et al. Bacterial and fungal agents in house dust and wheeze in children: the PARSIFAL study. Clin. Exp. Allergy 35, 1272–1278 (2005).

    Article  CAS  PubMed  Google Scholar 

  40. Caporaso, J. G. et al. Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. Proc. Natl Acad. Sci. USA 108, 4516–4522 (2011).

    Article  PubMed  Google Scholar 

  41. Smith, D. P. & Peay, K. G. Sequence depth, not PCR replication, improves ecological inference from next generation DNA sequencing. PloS One 9, e90234 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Rideout, J. R. et al. Consistent, comprehensive and computationally efficient OTU definitions. PeerJ Preprints 2, e411v411 (2014).

    Google Scholar 

  45. Amir, A. et al. Deblur rapidly resolves single-nucleotide community sequence patterns. mSystems 2, e00191-16 (2017).

  46. Herzum, I., Blumer, N., Kersten, W. & Renz, H. Diagnostic and analytical performance of a screening panel for allergy. Clin. Chem. Lab. Med. 43, 963–966 (2005).

    Article  CAS  PubMed  Google Scholar 

  47. Mustonen, K. et al. Moisture damage in home associates with systemic inflammation in children. Indoor Air 26, 439–447 (2016).

    Article  CAS  PubMed  Google Scholar 

  48. Bomert, M. et al. Analytical performance of a multiplexed, bead-based cytokine detection system in small volume samples. Clin. Chem. Lab. Med. 49, 1691–1693 (2011).

    Article  CAS  PubMed  Google Scholar 

  49. Martikainen, M. V. et al. Farm exposures are associated with lower percentage of circulating myeloid dendritic cell subtype 2 at age 6. Allergy 70, 1278–1287 (2015).

    Article  CAS  PubMed  Google Scholar 

  50. Chen, J. et al. Associating microbiome composition with environmental covariates using generalized UniFrac distances. Bioinformatics 28, 2106–2113 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Paradis, E., Claude, J. & Strimmer, K. APE: Analyses of phylogenetics and evolution in R language. Bioinformatics 20, 289–290 (2004).

    Article  CAS  PubMed  Google Scholar 

  52. Mandal, S. et al. Analysis of composition of microbiomes: a novel method for studying microbial composition. Microb. Ecol. Health Dis. 26, 27663 (2015).

    PubMed  Google Scholar 

  53. Pedregosa, F. et al. Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12, 2825–2830 (2011).

    Google Scholar 

  54. Eren, A. M., Borisy, G. G., Huse, S. M. & Mark Welch, J. L. Oligotyping analysis of the human oral microbiome. Proc. Natl Acad. Sci. USA 111, E2875–E2884 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Gonzalez, A. et al. Qiita: rapid, web-enabled microbiome meta-analysis. Nat. Methods 15, 796–798 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Langille, M. G. et al. Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences. Nat. Biotechnol. 31, 814–821 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Parks, D. H., Tyson, G. W., Hugenholtz, P. & Beiko, R. G. STAMP: statistical analysis of taxonomic and functional profiles. Bioinformatics 30, 3123–3124 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. White, J. R., Nagarajan, N. & Pop, M. Statistical methods for detecting differentially abundant features in clinical metagenomic samples. PLoS Comput. Biol. 5, e1000352 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Cohen, R. A. Introducing the GLMSELECT PROCEDURE for Model Selection Paper 207-31 (SAS Institute Inc., 2006).

  61. Firth, D. Bias reduction of maximum likelihood estimates. Biometrika 80, 27–38 (1993).

    Article  Google Scholar 

  62. Uh, H. W., Hartgers, F. C., Yazdanbakhsh, M. & Houwing-Duistermaat, J. J. Evaluation of regression methods when immunological measurements are constrained by detection limits. BMC Immunol. 9, 59 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We thank J. Kauttio for the processing of LUKAS dust samples and P. Tiittanen and T. Kauppila for LUKAS data management; G. Humphrey and J. Gaffney for the processing of GABRIELA samples; A. Amir for assistance in bioinformatics; U. Naukkarinen and R. Tiihonen for cytokine stimulations; S. Illi for cytokine data processing; M. Martikainen for dendritic cell analyses; the participating families of the LUKAS and GABRIELA studies; the field workers in the LUKAS and GABRIELA studies; and the LUKAS and GABRIELA study groups. We acknowledge funding by the Academy of Finland grants 139021 (J.P.), 256375 (M.R.), 287675 (A.M.K.), 296814 (J.P.), 296817 (M.T.) and 308254 (P.V.K.); the Juho Vainio Foundation (P.V.K., M.T.); the Päivikki and Sakari Sohlberg Foundation (P.V.K., J.P.); The Finnish Cultural Foundation (J.P.); the Yrjö Jahnsson Foundation (P.V.K., J.P.); Kuopion seudun hengityssäätiö (B.J.); European Union QLK4-CT-2001-00250 (J.P., H.R., P.I.P., R.L., E.v.M); the National Institute for Health and Welfare, Finland (P.V.K., A.M.K., M.T., B.J., A.H., J.P.); Alfred P. Sloan Foundation G-2016-7076 R.A.; Deutsche Zentrum für Lungenforschung grants (M.J.E.) and 82DZL00502 (H.R.); Deutsche Forschungsgemeinschaft (DFG)-funded SFB 1021 (H.R.); SCHA 997/8-1 (B.S.); GILKUJ-39 (B.S.); Kühne Foundation, Schindellegi, Switzerland (R.L.); MU 891/5-1 Leibniz Prize, German Research Foundation (E.v.M.); ERC2009-AdG_20090506_250268 (E.v.M.); and LSHB-CT-2006-018996 (E.v.M.).

Author information

Author notes

  1. The authors contributed equally: Erika von Mutius, Juha Pekkanen.

Authors and Affiliations

  1. Environmental Health Unit, National Institute for Health and Welfare, Kuopio, Finland

    Pirkka V. Kirjavainen, Anne M. Karvonen, Martin Täubel, Pauli Tuoresmäki, Balamuralikrishna Jayaprakash, Anne Hyvärinen & Juha Pekkanen

  2. Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio, Finland

    Pirkka V. Kirjavainen

  3. California Department of Public Health, Richmond, CA, USA

    Rachel I. Adams

  4. Department of Environmental and Biological Sciences, University of Eastern Finland, Kuopio, Finland

    Marjut Roponen

  5. Department of Public Health, University of Helsinki, Helsinki, Finland

    Pauli Tuoresmäki & Juha Pekkanen

  6. Department of Pediatrics, School of Medicine, University of California, San Diego, San Diego, CA, USA

    Georg Loss

  7. Institute for Asthma and Allergy Prevention, Helmholtz Center Munich, German Research Center for Environmental Health, Neuherberg, Germany

    Martin Depner & Erika von Mutius

  8. Dr von Hauner Children’s Hospital, Ludwig Maximilians University Munich, Munich, Germany

    Markus Johannes Ege, Bianca Schaub & Erika von Mutius

  9. Comprehensive Pneumology Center Munich, Member of the German Center for Lung Research, Munich, Germany

    Markus Johannes Ege, Bianca Schaub & Erika von Mutius

  10. Institute of Laboratory Medicine, Philipps University Marburg, Marburg, Germany

    Harald Renz

  11. Comprehensive Biomaterial Bank Marburg (CBBMR), Member of the German Center for Lung Research, Philipps-University Marburg, Marburg, Germany

    Petra Ina Pfefferle

  12. Children’s Hospital of Eastern Switzerland, St Gallen, Switzerland

    Roger Lauener

  13. University of Zurich, Zurich, Switzerland

    Roger Lauener

  14. School of Medicine, University of St Gallen, St Gallen, Switzerland

    Roger Lauener

  15. Christine Kühne-Center for Allergy Care and Education, CK-CARE, Davos, Switzerland

    Roger Lauener

  16. Center for Microbiome Innovation and Departments of Pediatrics and Computer Science & Engineering, University of California San Diego, San Diego, CA, USA

    Rob Knight

  17. Division of Environmental Epidemiology, Institute for Risk Assessment Sciences, Utrecht University, Utrecht, the Netherlands

    Dick J. J. Heederik

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  1. Pirkka V. Kirjavainen
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Contributions

The study approach was conceived by P.V.K., while P.V.K., E.v.M., D.J.J.H. and J.P. all contributed to refining of the concept and the study design. Statistical modeling was performed by P.V.K. Statistical analyses were performed by P.V.K., A.M.K. and P.T. Bioinformatics and related computational analyses were performed by R.I.A., M.T., G.L., P.T. and B.J. M.T., A.H. and R.K. contributed to the supervision and infrastructure of the microbiological laboratory work. M.R., H.R., P.I.P., B.S. and R.L. contributed to the supervision, coordination and infrastructure of the immunological laboratory work; P.V.K. wrote the manuscript with important contributions to intellectual content from all authors, especially A.M.K., R.I.A., M.T., M.R., M.D., M.J.E., B.S., R.K., D.J.J.H., E.v.M. and J.P.

Corresponding author

Correspondence to Pirkka V. Kirjavainen.

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Competing interests

P.V.K., A.M.K., R.I.A., M.T., M.R., P.T., G.L., B.J., M.D., H.R., P.I.P., B.S., R.L., A.H., D.J.J.H. and J.P. do not have competing interests to disclose. M.J.E. and E.v.M. report patents EP2361632B1 and EP1964570B1 held by their institution LMU. E.v.M. reports being the recipient of funds from the European Commission for the conduct of the LUKAS (EFRAIM) and GABRIEL study and declares personal fees from Pharma Ventures, Peptinnovate Ltd, OM Pharma SA, the European Commission/European Research Council Executive Agency, Tampereen Yliopisto, the University of Turku, HAL Allergie GmbH, Ökosoziales Forum Oberösterreich and Mundipharma Deutschland GmbH & Co. KG; E.v.M. is an inventor on the patents EP1637147, EP1964570, LU101064 and EP1411977; E.v.M. is an inventor on and has received royalties from the patent EP2361632. R.K. is on the Scientific Advisory Board of Commense, Inc.

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Extended Data

Extended Data Fig. 1 Bacterial and fungal diversity in farm and non-farm homes.

In the LUKA1 farm homes, the bacterial/archaeal richness (n = 107), with median 652 OTUs (IQR 567–708), and Shannon entropy, with median 7.8 (IQR 7.2–8.2), were consistently higher than in the majority of the rural control homes (n = 96) where the respective values were 449 (IQR 384–555) for richness and 6.7 (IQR 5.9–7.2) for Shannon entropy (Wilcoxon, two-sided P < 0.0001). In fungal microbiota, there was a tendency for higher richness, with median 263 (217–306) fungal OTUs, and Shannon entropy, with median 3.9 (3.4–4.3), in the rural control (n = 97) than farm homes (n = 101) where the respective values were 252 (195–301; P = 0.12) for richness and 3.7 (3.3–4.1; Wilcoxon, two-sided P = 0.07) for Shannon entropy. The boxes represent the IQR with the median marked within the box, the whiskers represent the minimum/maximum value within 1.5 IQR below the lower quartile/above the upper quartile, respectively, and the dots represent outliers.

Extended Data Fig. 2 Key microbial sources in floor dust microbiota in farm and non-farm homes.

a,b, The relative abundance of bovine-associated (a) and human-associated (b) bacterial/archaeal OTUs in living room floor dust in farm (n = 107) and non-farm homes (n = 96) within LUKAS1 as determined by source tracking. Both comparisons (a,b) were significantly different with the Wilcoxon test at two-sided P < 0.0001. c, Relative abundance of soil-associated bacterial/archaeal OTUs was higher in the LUKAS (LUKAS1 and 2) non-farm homes that resembled more LUKAS1 farm homes (n = 179) than non-farm homes (n = 215) as defined by FaRMI (Wilcoxon test, two-sided P = 0.0003). The boxes represent IQR with median marked within the box, the whiskers represent minimum/maximum value within 1.5 IQR below the lower quartile/above the upper quartile, respectively, and the dots represent outliers.

Extended Data Fig. 3 Fungal microbiota in farm and non-farm homes.

Fungal taxa with significantly higher relative abundance in LUKAS1 farm (n = 101) than non-farm (orange circles) or in non-farm (n = 96) than farm homes (blue circles) as determined with ANCOM. Clades are colored up to genus level. Names are given for all phyla and for all taxa with significantly different relative abundance between farm and non-farm homes that have taxonomic assignment. The name of the highest taxonomic level is given for clades where the relative abundance between farm and non-farm homes is significantly different at several taxonomic levels. o, order.

Extended Data Fig. 4 Classification accuracy of the LUKAS1 farm-like microbiota model in the data it was trained in (LUKAS1).

Based on the receiver operating characteristics (ROC) curve, FaRMI had only moderate classification accuracy with area under the curve 0.74. This is a critical feature of FaRMI as it enables the detection of farm-like features also in non-farm homes.

Extended Data Fig. 5 Taxa included in models of LUKAS1 and GABRIELA farm-like indoor microbiota.

That is, variables contributing to FaRMI and FaRMIGABRIELA, respectively (see also Supplementary Table 9). Taxa in both models are marked with yellow, taxa in LUKAS alone are marked with blue and taxa in GABRIELA alone are marked with red triangles. The direction of the triangle indicates negative (down-pointing triangle) or positive (up-pointing triangle) association with FaRMI and FaRMIGABRIELA. The size of the triangles is proportional to the variance explained by the taxa (adjusted partial R2); the size of the yellow triangles is proportional to the higher adjusted R2 in the FaRMI or FaRMIGABRIELA model. In three cases, the direction was opposite between the two models; also in these cases, the triangles represent the model where the taxa had higher adjusted R2. Clades are colored where the adjusted R2 was >1%.

Extended Data Fig. 6 Cytokine responses and serum CRP levels associated with farm-like indoor microbiota.

a,b, Quantile process plots of the quantile regression analysis showing the estimated change in cytokine concentration (pg ml−1) at a given percentile per IQR change in FaRMI at year 1 (a) and year 6 (b). The shaded areas show the 95% confidence intervals based on resampling with 1,000 repetitions and where these do not overlap with the horizontal zero-change line the decrease/increase at that percentile is statistically significant (two-sided P value without correction for multiple testing < 0.05). Plots are presented for all cytokines that show tendency for significant (P < 0.1) association with FaRMI between the 25th and 80th percentile without correction for multiple testing. Cytokines were measured from blood cultures stimulated with PI, LPS and PPG. CRP was measured from serum.

Extended Data Fig. 7 Proportion of ILT 4-expressing plasmacytoid dendritic cells is correlated with FaRMI.

The proportion of ILT 4-expressing plasmacytoid dendritic cells increased with increasing FaRMI within LUKAS1 children living in a non-farm home who were not diagnosed with asthma by 6 years of age (n = 26). In those children who had been diagnosed with asthma (n = 16) such a correlation did not exist. In logistic regression analysis modeling of the association between the ILT4 expression on plasmacytoid dendritic cells and FaRMI, the interaction term asthma ever by 6 years of age × FaRMI was significant (P = 0.03). The scatterplots were fitted with simple linear regression lines.

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Kirjavainen, P.V., Karvonen, A.M., Adams, R.I. et al. Farm-like indoor microbiota in non-farm homes protects children from asthma development. Nat Med 25, 1089–1095 (2019). https://doi.org/10.1038/s41591-019-0469-4

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