Yoshihiko Raita1, Marcos Pérez-Losada2,3, Robert J Freishtat4,5,6, Andrea Hahn4,6,7, Eduardo Castro-Nallar8, Ignacio Ramos-Tapia8, Nathaniel Stearrett9, Yury A Bochkov10, James E Gern10,11, Jonathan M Mansbach12, Zhaozhong Zhu13, Carlos A Camargo13, Kohei Hasegawa13. 1. Dept of Emergency Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA yraita1@alumni.jh.edu. 2. Dept of Biostatistics and Bioinformatics and Computational Biology Institute, The George Washington University, Washington, DC, USA. 3. CIBIO-InBIO, Centro de Investigação em Biodiversidade e Recursos Genéticos, Universidade do Porto, Vairão, Portugal. 4. Center for Genetic Medicine Research, Children's National Research Institute, Washington, DC, USA. 5. Division of Emergency Medicine, Children's National Hospital, Washington, DC, USA. 6. Dept of Pediatrics, The George Washington University School of Medicine and Health Sciences, Washington, DC, USA. 7. Division of Infectious Diseases, Children's National Hospital, Washington, DC, USA. 8. Centro de Bioinformática y Biología Integrativa, Universidad Andres Bello, Santiago, Chile. 9. Computational Biology Institute, The George Washington University, Washington, DC, USA. 10. Dept of Pediatrics, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA. 11. Dept of Medicine, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA. 12. Dept of Pediatrics, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA. 13. Dept of Emergency Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Abstract
BACKGROUND: Bronchiolitis is not only the leading cause of hospitalisation in US infants but also a major risk factor for asthma development. Growing evidence supports clinical heterogeneity within bronchiolitis. Our objectives were to identify metatranscriptome profiles of infant bronchiolitis, and to examine their relationship with the host transcriptome and subsequent asthma development. METHODS: As part of a multicentre prospective cohort study of infants (age <1 year) hospitalised for bronchiolitis, we integrated virus and nasopharyngeal metatranscriptome (species-level taxonomy and function) data measured at hospitalisation. We applied network-based clustering approaches to identify metatranscriptome profiles. We then examined their association with the host transcriptome at hospitalisation and risk for developing asthma. RESULTS: We identified five metatranscriptome profiles of bronchiolitis (n=244): profile A: virusRSVmicrobiomecommensals; profile B: virusRSV/RV-Amicrobiome H.influenzae ; profile C: virusRSVmicrobiome S.pneumoniae ; profile D: virusRSVmicrobiome M.nonliquefaciens ; and profile E: virusRSV/RV-Cmicrobiome M.catarrhalis . Compared with profile A, profile B infants were characterised by a high proportion of eczema, Haemophilus influenzae abundance and enriched virulence related to antibiotic resistance. These profile B infants also had upregulated T-helper 17 and downregulated type I interferon pathways (false discovery rate (FDR) <0.005), and significantly higher risk for developing asthma (17.9% versus 38.9%; adjusted OR 2.81, 95% CI 1.11-7.26). Likewise, profile C infants were characterised by a high proportion of parental asthma, Streptococcus pneumoniae dominance, and enriched glycerolipid and glycerophospholipid metabolism of the microbiome. These profile C infants had an upregulated RAGE signalling pathway (FDR <0.005) and higher risk of asthma (17.9% versus 35.6%; adjusted OR 2.49, 95% CI 1.10-5.87). CONCLUSIONS: Metatranscriptome and clustering analysis identified biologically distinct metatranscriptome profiles that have differential risks of asthma.
BACKGROUND: Bronchiolitis is not only the leading cause of hospitalisation in US infants but also a major risk factor for asthma development. Growing evidence supports clinical heterogeneity within bronchiolitis. Our objectives were to identify metatranscriptome profiles of infant bronchiolitis, and to examine their relationship with the host transcriptome and subsequent asthma development. METHODS: As part of a multicentre prospective cohort study of infants (age <1 year) hospitalised for bronchiolitis, we integrated virus and nasopharyngeal metatranscriptome (species-level taxonomy and function) data measured at hospitalisation. We applied network-based clustering approaches to identify metatranscriptome profiles. We then examined their association with the host transcriptome at hospitalisation and risk for developing asthma. RESULTS: We identified five metatranscriptome profiles of bronchiolitis (n=244): profile A: virusRSVmicrobiomecommensals; profile B: virusRSV/RV-Amicrobiome H.influenzae ; profile C: virusRSVmicrobiome S.pneumoniae ; profile D: virusRSVmicrobiome M.nonliquefaciens ; and profile E: virusRSV/RV-Cmicrobiome M.catarrhalis . Compared with profile A, profile B infants were characterised by a high proportion of eczema, Haemophilus influenzae abundance and enriched virulence related to antibiotic resistance. These profile B infants also had upregulated T-helper 17 and downregulated type I interferon pathways (false discovery rate (FDR) <0.005), and significantly higher risk for developing asthma (17.9% versus 38.9%; adjusted OR 2.81, 95% CI 1.11-7.26). Likewise, profile C infants were characterised by a high proportion of parental asthma, Streptococcus pneumoniae dominance, and enriched glycerolipid and glycerophospholipid metabolism of the microbiome. These profile C infants had an upregulated RAGE signalling pathway (FDR <0.005) and higher risk of asthma (17.9% versus 35.6%; adjusted OR 2.49, 95% CI 1.10-5.87). CONCLUSIONS: Metatranscriptome and clustering analysis identified biologically distinct metatranscriptome profiles that have differential risks of asthma.
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