Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

The infant gut virome is associated with preschool asthma risk independently of bacteria

Abstract

Bacteriophage (also known as phage) communities that inhabit the gut have a major effect on the structure and functioning of bacterial populations, but their roles and association with health and disease in early life remain unknown. Here, we analyze the gut virome of 647 children aged 1 year from the Copenhagen Prospective Studies on Asthma in Childhood2010 (COPSAC2010) mother–child cohort, all deeply phenotyped from birth and with longitudinally assessed asthma diagnoses. Specific temperate gut phage taxa were found to be associated with later development of asthma. In particular, the joint abundances of 19 caudoviral families were found to significantly contribute to this association. Combining the asthma-associated virome and bacteriome signatures had additive effects on asthma risk, implying an independent virome–asthma association. Moreover, the virome-associated asthma risk was modulated by the host TLR9 rs187084 gene variant, suggesting a direct interaction between phages and the host immune system. Further studies will elucidate whether phages, alongside bacteria and host genetics, can be used as preclinical biomarkers for asthma.

This is a preview of subscription content, access via your institution

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Infant gut virome classes, lifestyle versus preschool asthma.
Fig. 2: Comparison of 1-year-old infant virome compositions at the virus family-level, between infants with and without preschool asthma.
Fig. 3: Signature viral families within the infant temperate virome versus preschool asthma and associations with host bacteria.
Fig. 4: Congruence between temperate virome and bacterial compositions.
Fig. 5: Virome signature scores in relation to bacteriome signature scores in the associations with preschool asthma.
Fig. 6: Virome asthma signature score associations with early life exposures.

Similar content being viewed by others

Data availability

Viral sequence files can be accessed through the European Nucleotide Archive (https://ebi.ac.uk) under project number PRJEB46943. The 16 S rRNA sequences are deposited at the Sequence Read Archive (SRA) repository under accession number PRJNA417357.

Participant-level personally identifiable data are protected under the Danish Data Protection Act and European Regulation 2016/679 of the European Parliament and of the Council (GDPR) that prohibit distribution even in pseudo-anonymized form. However, participant-level data can be made available under a data transfer agreement as part of a collaboration effort.

Code availability

Data analyses were carried out using R (v.4.2.1) as specified in the Methods. Code is available on GitHub (https://github.com/crlero/vir2asth).

References

  1. Bisgaard, H. & Szefler, S. Prevalence of asthma-like symptoms in young children. Pediatr. Pulmon. 42, 723–728 (2007).

    Article  Google Scholar 

  2. Holgate, S. T. The epidemic of allergy and asthma. Nature 402, 2–4 (1999).

    Article  Google Scholar 

  3. Gensollen, T., Iyer, S. S., Kasper, D. L. & Blumberg, R. S. How colonization by microbiota in early life shapes the immune system. Science 352, 539–544 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Vatanen, T. et al. Variation in microbiome LPS immunogenicity contributes to autoimmunity in humans. Cell 165, 1551 (2016).

    Article  CAS  PubMed  Google Scholar 

  5. Depner, M. et al. Maturation of the gut microbiome during the first year of life contributes to the protective farm effect on childhood asthma. Nat. Med. 26, 1766–1775 (2020).

    Article  CAS  PubMed  Google Scholar 

  6. Stokholm, J. et al. Maturation of the gut microbiome and risk of asthma in childhood. Nat. Commun. 9, 141 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  7. Bisgaard, H. et al. Reduced diversity of the intestinal microbiota during infancy is associated with increased risk of allergic disease at school age. J. Allergy Clin. Immunol. 128, 646–52.e1–5 (2011).

    Article  PubMed  Google Scholar 

  8. Penders, J. et al. Gut microbiota composition and development of atopic manifestations in infancy: the KOALA Birth Cohort Study. Gut 56, 661–667 (2007).

    Article  CAS  PubMed  Google Scholar 

  9. Vijay, A. & Valdes, A. M. Role of the gut microbiome in chronic diseases: a narrative review. Eur. J. Clin. Nutr. 76, 489–501 (2022).

    Article  CAS  PubMed  Google Scholar 

  10. Shkoporov, A. N. & Hill, C. Bacteriophages of the human gut: the ‘known unknown’ of the microbiome. Cell Host Microbe 25, 195–209 (2019).

    Article  CAS  PubMed  Google Scholar 

  11. Sausset, R., Petit, M. A., Gaboriau-Routhiau, V. & De Paepe, M. New insights into intestinal phages. Mucosal Immunol. 13, 205–215 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Liang, G. & Bushman, F. D. The human virome: assembly, composition and host interactions. Nat. Rev. Microbiol. 19, 514–527 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Duerkop, B. A. et al. A composite bacteriophage alters colonization by an intestinal commensal bacterium. Proc. Natl Acad. Sci. USA 109, 17621–17626 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Barr, J. J. et al. Bacteriophage adhering to mucus provide a non–host-derived immunity. Proc. Natl Acad. Sci. 110, 10771–10776 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Van Belleghem, J. D., Dąbrowska, K., Vaneechoutte, M., Barr, J. J. & Bollyky, P. L. Interactions between bacteriophage, bacteria, and the mammalian immune system. Viruses 11, 10 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  16. Sweere, J. M. et al. Bacteriophage trigger antiviral immunity and prevent clearance of bacterial infection. Science 363, eaat9691 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Focà, A. et al. Gut inflammation and immunity: what is the role of the human gut virome? Mediat. Inflamm. 2015, 326032 (2015).

    Article  Google Scholar 

  18. Bisgaard, H. et al. Deep phenotyping of the unselected COPSAC2010 birth cohort study. Clin. Exp. Allergy 43, 1384–1394 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Shah, S. A. et al. Expanding known viral diversity in the healthy infant gut. Nat. Microbiol. 8, 986–998 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Manrique, P. et al. Healthy human gut phageome. Proc. Natl Acad. Sci. USA 113, 10400–10405 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Zhang, Y. et al. The influence of early life exposures on the infant gut virome. Preprint at https://www.biorxiv.org/content/10.1101/2023.03.05.531203v1 (2023)

  22. Górski, A. et al. Bacteriophage translocation. FEMS Immunol. Med. Microbiol. 46, 313–319 (2006).

    Article  PubMed  Google Scholar 

  23. Bichet, M. C. et al. Bacteriophage uptake by mammalian cell layers represents a potential sink that may impact phage therapy. iScience 24, 102287 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Shkoporov, A. N. et al. Viral biogeography of the mammalian gut and parenchymal organs. Nat. Microbiol. 7, 1301–1311 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Kumagai, Y., Takeuchi, O. & Akira, S. TLR9 as a key receptor for the recognition of DNA. Adv. Drug Deliv. Rev. 60, 795–804 (2008).

    Article  CAS  PubMed  Google Scholar 

  26. Wagner, H. The immunobiology of the TLR9 subfamily. Trends Immunol. 25, 381–386 (2004).

    Article  CAS  PubMed  Google Scholar 

  27. Wagner, H. Interactions between bacterial CpG-DNA and TLR9 bridge innate and adaptive immunity. Curr. Opin. Microbiol. 5, 62–69 (2002).

    Article  CAS  PubMed  Google Scholar 

  28. Hochrein, H. et al. Herpes simplex virus type-1 induces IFN-α production via Toll-like receptor 9-dependent and -independent pathways. Proc. Natl Acad. Sci. 101, 11416–11421 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Gogokhia, L. et al. Expansion of bacteriophages is linked to aggravated intestinal inflammation and colitis. Cell Host Microbe 25, 285–299.e8 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Susi, M. D. et al. Toll-like receptor 9 polymorphisms and Helicobacter pylori influence gene expression and risk of gastric carcinogenesis in the Brazilian population. World J. Gastrointest. Oncol. 11, 998–1010 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  31. Chen, X. et al. A genetic variant in the promoter region of Toll-like receptor 9 and cervical cancer susceptibility. DNA Cell Biol. 31, 766–771 (2012).

    Article  CAS  PubMed  Google Scholar 

  32. Tao, K. et al. Genetic variations of Toll-like receptor 9 predispose to systemic lupus erythematosus in Japanese population. Ann. Rheum. Dis. 66, 905–909 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Leoratti, F. M. S. et al. Variants in the toll-like receptor signaling pathway and clinical outcomes of malaria. J. Infect. Dis. 198, 772–780 (2008).

    Article  CAS  PubMed  Google Scholar 

  34. Bank, S. et al. Polymorphisms in the inflammatory pathway genes TLR2, TLR4, TLR9, LY96, NFKBIA, NFKB1, TNFA, TNFRSF1A, IL6R, IL10, IL23R, PTPN22, and PPARG are associated with susceptibility of inflammatory bowel disease in a Danish cohort. PLoS ONE 9, e98815 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  35. Kormann, M. S. D. et al. Toll-like receptor heterodimer variants protect from childhood asthma. J. Allergy Clin. Immunol. 122, 86–92, 92.e1–8 (2008).

    Article  CAS  PubMed  Google Scholar 

  36. Genuneit, J. et al. A multi-centre study of candidate genes for wheeze and allergy: the International Study of Asthma and Allergies in Childhood Phase 2. Clin. Exp. Allergy 39, 1875–1888 (2009).

    Article  CAS  PubMed  Google Scholar 

  37. Nuolivirta, K. et al. Post-bronchiolitis wheezing is associated with toll-like receptor 9 rs187084 gene polymorphism. Sci. Rep. 6, 31165 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Norman, J. M. et al. Disease-specific alterations in the enteric virome in inflammatory bowel disease. Cell 160, 447–460 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Clooney, A. G. et al. Whole-virome analysis sheds light on viral dark matter in inflammatory bowel disease. Cell Host Microbe 26, 764–778.e5 (2019).

    Article  CAS  PubMed  Google Scholar 

  40. Yang, K. et al. Alterations in the gut virome in obesity and type 2 diabetes mellitus. Gastroenterology 161, 1257–1269.e13 (2021).

    Article  CAS  PubMed  Google Scholar 

  41. Jiang, L. et al. Intestinal virome in patients with alcoholic hepatitis. Hepatology 72, 2182–2196 (2020).

    Article  CAS  PubMed  Google Scholar 

  42. Johnson, C. H. et al. Metabolism links bacterial biofilms and colon carcinogenesis. Cell Metab. 21, 891–897 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Khan Mirzaei, M. et al. Bacteriophages isolated from stunted children can regulate gut bacterial communities in an age-specific manner. Cell Host Microbe 27, 199–212.e5 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Tisza, M. J. & Buck, C. B. A catalog of tens of thousands of viruses from human metagenomes reveals hidden associations with chronic diseases. Proc. Natl Acad. Sci. USA 118, e2023202118 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Zhao, G. et al. Intestinal virome changes precede autoimmunity in type I diabetes-susceptible children. Proc. Natl Acad. Sci. USA 114, E6166–E6175 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Megremis, S. et al. Respiratory eukaryotic virome expansion and bacteriophage deficiency characterize childhood asthma. Sci. Rep. 13, 8319 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Choi, S. et al. Lung virome: new potential biomarkers for asthma severity and exacerbation. J. Allergy Clin. Immunol. 148, 1007–1015.e9 (2021).

    Article  CAS  PubMed  Google Scholar 

  48. Olo Ndela, E. et al. Reekeekee- and roodoodooviruses, two different clades constituted by the smallest DNA phages. Virus Evol. 9, veac123 (2023).

    Article  PubMed  Google Scholar 

  49. Shkoporov, A. N. et al. The human gut virome is highly diverse, stable, and individual specific. Cell Host Microbe 26, 527–541.e5 (2019).

    Article  CAS  PubMed  Google Scholar 

  50. Zuo, T. et al. Human-gut-DNA virome variations across geography, ethnicity, and urbanization. Cell Host Microbe 28, 741–751.e4 (2020).

    Article  CAS  PubMed  Google Scholar 

  51. Lim, E. S. et al. Early life dynamics of the human gut virome and bacterial microbiome in infants. Nat. Med. 21, 1228–1234 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Liang, G. et al. The stepwise assembly of the neonatal virome is modulated by breastfeeding. Nature 581, 470–474 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Vicente, C. T., Revez, J. A. & Ferreira, M. A. R. Lessons from ten years of genome-wide association studies of asthma. Clin. Transl. Immunol. 6, e165 (2017).

    Article  Google Scholar 

  54. Spycher, B. D. et al. Genome-wide prediction of childhood asthma and related phenotypes in a longitudinal birth cohort. J. Allergy Clin. Immunol. 130, 503–9.e7 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  55. Bushman, F. & Liang, G. Assembly of the virome in newborn human infants. Curr. Opin. Virol. 48, 17–22 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Neil, J. A. & Cadwell, K. The intestinal virome and immunity. J. Immunol. 201, 1615–1624 (2018).

    Article  CAS  PubMed  Google Scholar 

  57. Adiliaghdam, F. et al. Human enteric viruses autonomously shape inflammatory bowel disease phenotype through divergent innate immunomodulation. Sci. Immunol. 7, eabn6660 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Lester, S. N. & Li, K. Toll-like receptors in antiviral innate immunity. J. Mol. Biol. 426, 1246–1264 (2014).

    Article  CAS  PubMed  Google Scholar 

  59. Rifkin, I. R., Leadbetter, E. A., Busconi, L., Viglianti, G. & Marshak-Rothstein, A. Toll-like receptors, endogenous ligands, and systemic autoimmune disease. Immunol. Rev. 204, 27–42 (2005).

    Article  CAS  PubMed  Google Scholar 

  60. Marshak-Rothstein, A. Toll-like receptors in systemic autoimmune disease. Nat. Rev. Immunol. 6, 823–835 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Fischer, J. et al. Sex-specific effects of TLR9 promoter variants on spontaneous clearance of HCV infection. Gut 66, 1829–1837 (2017).

    Article  CAS  PubMed  Google Scholar 

  62. Almqvist, C., Ekberg, S., Rhedin, S. & Fang, F. Season of birth, childhood asthma and allergy in a nationwide cohort–Mediation through lower respiratory infections. Clin. Exp. Allergy 50, 222–230 (2020).

    Article  PubMed  Google Scholar 

  63. Schoos, A.-M. M. et al. Season of birth impacts the neonatal nasopharyngeal microbiota. Children 7, 45 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  64. Thorsen, J. et al. Infant airway microbiota and topical immune perturbations in the origins of childhood asthma. Nat. Commun. 10, 5001 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  65. Strachan, D. P. Hay fever, hygiene, and household size. BMJ Brit. Med. J. 299, 1259–1260 (1989).

    Article  CAS  PubMed  Google Scholar 

  66. Rook, G. A. W. & Brunet, L. R. Microbes, immunoregulation, and the gut. Gut 54, 317–320 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Christensen, E. D. et al. The developing airway and gut microbiota in early life is influenced by age of older siblings. Microbiome 10, 106 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Bertolini, V. et al. Temporal variability and effect of environmental variables on airborne bacterial communities in an urban area of Northern Italy. Appl. Microbiol. Biotechnol. 97, 6561–6570 (2013).

    Article  CAS  PubMed  Google Scholar 

  69. Prussin, A. J. et al. Seasonal dynamics of DNA and RNA viral bioaerosol communities in a daycare center. Microbiome 7, 53 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  70. Brum, J. R., Hurwitz, B. L., Schofield, O., Ducklow, H. W. & Sullivan, M. B. Seasonal dynamics of DNA and RNA viral bioaerosol communities in a daycare center. ISME J 10, 437–449 (2016).

    Article  CAS  PubMed  Google Scholar 

  71. Hevroni, G., Flores-Uribe, J., Béjà, O. & Philosof, A. Seasonal and diel patterns of abundance and activity of viruses in the Red Sea. Proc. Natl Acad. Sci. USA 117, 29738–29747 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Shkoporov, A. N., Turkington, C. J. & Hill, C. Mutualistic interplay between bacteriophages and bacteria in the human gut. Nat. Rev. Microbiol. https://doi.org/10.1038/s41579-022-00755-4 (2022).

    Article  PubMed  Google Scholar 

  73. Minot, S. et al. The human gut virome: inter-individual variation and dynamic response to diet. Genome Res. 21, 1616–1625 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Sutcliffe, S. G., Shamash, M., Hynes, A. P. & Maurice, C. F. Common oral medications lead to prophage induction in bacterial isolates from the human gut. Viruses 13, 455 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Heida, F. H. et al. Weight shapes the intestinal microbiome in preterm infants: results of a prospective observational study. BMC Microbiol. 21, 219 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Tun, H. M. et al. Exposure to household furry pets influences the gut microbiota of infants at 3–4 months following various birth scenarios. Microbiome 5, 1–14 (2017).

    Article  Google Scholar 

  77. Bisgaard, H., Hermansen, M. N., Loland, L., Halkjaer, L. B. & Buchvald, F. Intermittent inhaled corticosteroids in infants with episodic wheezing. N. Engl. J. Med. 354, 1998–2005 (2006).

    Article  CAS  PubMed  Google Scholar 

  78. Bisgaard, H. et al. Fish oil-derived fatty acids in pregnancy and wheeze and asthma in offspring. N. Engl. J. Med. 375, 2530–2539 (2016).

    Article  CAS  PubMed  Google Scholar 

  79. Deng, L. et al. A protocol for extraction of infective viromes suitable for metagenomics sequencing from low volume fecal samples. Viruses 11, e2023202118 (2019).

    Article  Google Scholar 

  80. Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 17, 10 (2011).

    Article  Google Scholar 

  81. Callahan, B. J. et al. DADA2: high-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Bolyen, E. et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol. 37, 852–857 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Alishum, A. DADA2 formatted 16S rRNA gene sequences for both bacteria & archaea. Zenodo https://doi.org/10.5281/ZENODO.3188334 (2019)

  84. R Core Team. R: a language and environment for statistical computing. The R Project for Statistical Computing https://www.R-project.org (2018).

  85. McMurdie, P. J. & Holmes, S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE 8, e61217 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. Roux, S., Emerson, J. B., Eloe-Fadrosh, E. A. & Sullivan, M. B. Benchmarking viromics: an in silico evaluation of metagenome-enabled estimates of viral community composition and diversity. PeerJ 5, e3817 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  87. Oksanen, J., Kindt, R., Legendre, P. & O’Hara, B. vegan: community ecology package. R package Version 2.4-3. https://CRAN.R-project.org/package=vegan (2017)

  88. Tingley, D., Yamamoto, T., Hirose, K., Keele, L. & Imai, K. mediation: R package for causal mediation analysis. J. Stat. Softw. 59, 1–38 (2014).

    Article  Google Scholar 

  89. Højsgaard, S., Halekoh, U. & Yan, J. The R Package geepack for Generalized Estimating Equations. J. Stat. Softw. 15, 1–11 (2006).

    Google Scholar 

Download references

Acknowledgements

We thank the children and families of the COPSAC2010 cohort study for their support and commitment. We acknowledge and appreciate the unique efforts of the clinicians involved in the COPSAC2010 study who followed up on the children, collecting samples and establishing diagnoses. Finally, we acknowledge the work of the late Professor Hans Bisgaard, founder of COPSAC and head of the clinical research center for more than 25 years. He was a dedicated, innovative physician–scientist who pushed the asthma research field forward and contributed immensely to pediatric research. His work and ideas live on in the studies conducted in the birth cohort, and we thank him for being an inspiration to us all.

All funding received by COPSAC is listed at https://copsac.com. The Lundbeck Foundation (R16-A1694), the Ministry of Health (903516), the Danish Council for Strategic Research (0603-00280B) and the Capital Region Research Foundation have provided core support to the COPSAC research center. This work is supported by the Joint Programming Initiative ‘Healthy Diet for a Healthy Life’, specifically here, the Danish Agency for Science and Higher Education, Institut National de la Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE) and the Canadian Institutes of Health Research (Team Grant in Intestinal Microbiomics, Institute of Nutrition, Metabolism, and Diabetes, 143924). S.A.S. and M.A.R. are recipients of a Novo Nordisk Foundation project grant in basic bioscience (grant NNF18OC0052965). J.S. and D.S.N. are recipients of Novo Nordisk Foundation grant NNF20OC0061029. J.S. received funding from the Danish Council for Independent Research (8045-00081B). J.T. is supported by the BRIDGE Translational Excellence Program at the Faculty of Health and Medical Sciences, University of Copenhagen, funded by the Novo Nordisk Foundation (grant NNF18SA0034956). C.-E.T.P. is funded by the Lundbeck Foundation (grant R322-2019-2735). S.M. holds the Canada Research Chair in Bacteriophages. Y.Z. is recipient of a PhD scholarship from the Chinese Scholarship Council (CSC). B.C. is funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant agreement no. 946228).

Author information

Authors and Affiliations

Authors

Contributions

J.S., S.M., M.A.P. and D.S.N. contributed to the conception of the study. J.S. and C.L.R. were responsible for acquisition of data. C.L.R. performed statistical analyses, and generated tables and figures. C.L.R. has written the first draft of the manuscript with contributions from J.S., S.A.S., J.T. and M.A.R. J.C.L., L.D., Y.Z. and D.S.N. generated data. All co-authors have provided important intellectual input and interpretation of the results. All authors guarantee that the accuracy and integrity of any part of the work have been appropriately investigated and resolved. All authors have approved the final version of the manuscript. The corresponding author had full access to the data and had final responsibility for the decision to submit for publication. No honorarium, grant or other form of payment was given to any of the authors to produce this manuscript.

Corresponding author

Correspondence to Jakob Stokholm.

Ethics declarations

Competing interests

J.T. has received speaking fee from AstraZeneca. S.A.S. has been a consultant for Profluent. D.S.N. has been a consultant for Pfizer and Sniprbiome. All other authors have no competing interests. The funding agencies did not have any role in the design and conduct of the study; collection, management, and interpretation of the data; or preparation, review or approval of the manuscript. No pharmaceutical company was involved in the study.

Peer review

Peer review information

Nature Medicine thanks Nikolaos Papadopoulos, Peter Vuillermin and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Sonia Muliyil, in collaboration with the Nature Medicine team.

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 Incidence of preschool asthma in COPSAC2010.

Distribution of age at first asthma diagnosis for 133 children with preschool asthma by five years of age at the COPSAC2010 cohort. Each colored background panel represents 1 year.

Extended Data Fig. 2 Individual virome profiles.

a Proportion of core, common, and rare bacteriophages among children across different taxonomic levels (top: species (that is vOTUs or viral contig representatives); bottom: families (that is viral family-level clades)) shared by >50% (core), 21-50% (common), 1-20% (rare) and <1% (very rare), respectively. Each bar in the x-axis represents one child. b Lifestyle differences (that is temperate and virulent) between the core, common, and rare virome components for all children (n = 631). P-values (P) were two-sided and correspond to the Wilcoxon rank-sum test. The center of the boxes represents the median, their bounds represent the 25th and 75th centile and the lower and upper ends of whiskers represent the smallest/largest value, no further than 1.5 × IQR from the box-plot respective end.

Extended Data Fig. 3 Phage classes abundance and prevalence.

Mean relative abundance (mra) and prevalence distributions for the three different classes of bacteriophages.

Extended Data Fig. 4 Taxonomy and lifestyle in asthma.

Stratified analysis comparing relative viral abundances between asthmatic and healthy children (n = 133/498) according to the viral classes and lifestyles. The center of the boxes represents the median, their bounds represent the 25th and 75th centile and the lower and upper ends of whiskers represent the smallest/largest value, no further than 1.5 × IQR from the box-plot respective end.

Extended Data Fig. 5 Alpha diversity analyses.

Alpha diversity analyses (observed richness and Shannon index) for overall virome and according to lifestyle (temperate, virulent or unknown) between asthmatic and healthy children (n = 133/498). Two-sided P-values (P) correspond to logistic regression. P* denotes FDR-adjusted P. The center of the boxes represents the median, their bounds represent the 25th and 75th centile and the lower and upper ends of whiskers represent the smallest/largest value, no further than 1.5 × IQR from the box-plot respective end.

Extended Data Fig. 6 Beta-diversity analyses.

Reproducibility of beta diversity analyses across presence-absence metrics (that is Jaccard and UniFrac), abundance metrics (that is Aitchison and Canberra), combined metrics (that is weighted UniFrac), and across high-low taxonomic resolutions (that is vOTUs and VFCs). VFC: viral family clades; vOTUs: viral contigs. P-values (P) were two-sided and derived from PERMANOVA tests.

Extended Data Fig. 7 Virome asthma signature scores and asthmatic episodes.

Associations between virome signature scores and number of asthmatic episodes in the first three years of life were investigated univariately by generalised linear regressions with a quasi-poisson distribution and P-values (P) were two-sided. Points indicate the point estimate for incidence risk ratios with horizontal lines indicating the 95% confidence intervals on the number of asthmatic episodes by virome signature scores. Incidence risk ratios (y-axis) are on logarithmic scale (base 10). The incidence risk rate of asthma troublesome symptoms can be interpreted as the risk for each standard deviation increases in the scores. Results are presented as Total (all episodes in the entire 3-year window) and stratified for each year separately for the entire cohort (n = 631).

Extended Data Fig. 8 Bacterial hosts of the virome asthma fingerprint.

Chord diagram showing the relationship between sequence-predicted bacterial hosts (genus) and the main 19 viral family-clades (VFCs) making up the virome signature scores for asthma. Only bacterial hosts present in more than one vOTU of the elected VFCs are shown. Ribbons width indicates the number of vOTUs and ribbons color specifies the bacterial genus. Colored outer bands of the VFCs indicate the viral order cluster (VOC). Bacterial hosts predicted by sequence similarity are sorted based on the number of phage vOTUs belonging to the asthma signature VFCs (highest-top to lowest-bottom) and tagged with a red asterisk (*) if the bacterial genus was predictive for asthma as reported by Stokholm et al.6. Asthma signature VFCs are sorted based on the loadings (strongest-top to weakest-bottom).

Extended Data Fig. 9 Virome scores sensitivity analysis.

Full replica of Fig. 5 using the virome scores derived from the sensitivity analysis performed in a nested cross-validation model. For the full caption refer to main Fig. 5.

Extended Data Fig. 10 Signature VFCs from the nested 5-fold cross-validation.

Comparison between the signature VFCs contributing to the optimal set of loadings for the 10-times repeated 10-fold CV SPLs model (nmodels=100) using the entire cohort (n = 631) and the corresponding 5 outer-folds (nmodel=1) from the nested cross-validation model derived from the five partitions of the entire cohort (n1 = 505, n2 = 504, n3 = 506, n4 = 505, n5 = 504). Bars depict the median ± SD. VFCs: viral-family clades.

Supplementary information

Supplementary Information

Supplementary Tables 1 and 2

Reporting Summary

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Leal Rodríguez, C., Shah, S.A., Rasmussen, M.A. et al. The infant gut virome is associated with preschool asthma risk independently of bacteria. Nat Med 30, 138–148 (2024). https://doi.org/10.1038/s41591-023-02685-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41591-023-02685-x

This article is cited by

Search

Quick links

Nature Briefing Microbiology

Sign up for the Nature Briefing: Microbiology newsletter — what matters in microbiology research, free to your inbox weekly.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing: Microbiology