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      Serum Metabolome Is Associated With the Nasopharyngeal Microbiota and Disease Severity Among Infants With Bronchiolitis

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          Abstract

          Emerging evidence suggests relationships between the nasopharyngeal metabolome and both the microbiota and severity of bronchiolitis. However, the influence of host systemic metabolism on disease pathobiology remains unclear. We aimed to examine metabolome profiles and their association with more-severe disease, defined by use of positive pressure ventilation (PPV), in infants hospitalized for bronchiolitis. In 140 infants with bronchiolitis, metabolomic profiling was performed on serum; samples from 70 were in a training data set, and samples from 70 were in an independent test data set. We also profiled the nasopharyngeal airway microbiota and examined its association with the serum metabolites. Serum metabolome profiles differed by bronchiolitis severity ( P < .001). In total, 20 metabolites in the training data set were significantly associated with the risk of PPV, of which 18 remained significant following adjustment for confounders (false-discovery rate [FDR], < 0.10). Phosphatidylcholine metabolites were associated with higher risks of PPV use, while metabolites from the plasmalogen subpathway were associated with lower risks. The test data set validated these findings (FDR < 0.05). Streptococcus abundance was positively associated with metabolites that are associated with higher risks of PPV. Serum metabolomic signatures were associated with both the nasopharyngeal microbiota and the severity of bronchiolitis. Our findings advance research into the complex interrelations between the airway microbiome, host systemic response, and pathobiology of bronchiolitis. We investigated the interrelationships between serum metabolome, airway microbiome, and bronchiolitis severity, showing that the serum metabolome profiles differed by disease severity. Plasmalogen metabolites were associated with less severe disease, and Streptococcus was negatively correlated with these plasmalogen pathway metabolites.

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          Most cited references24

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          Is Open Access

          integrOmics: an R package to unravel relationships between two omics datasets

          Motivation: With the availability of many ‘omics’ data, such as transcriptomics, proteomics or metabolomics, the integrative or joint analysis of multiple datasets from different technology platforms is becoming crucial to unravel the relationships between different biological functional levels. However, the development of such an analysis is a major computational and technical challenge as most approaches suffer from high data dimensionality. New methodologies need to be developed and validated. Results: integrOmics efficiently performs integrative analyses of two types of ‘omics’ variables that are measured on the same samples. It includes a regularized version of canonical correlation analysis to enlighten correlations between two datasets, and a sparse version of partial least squares (PLS) regression that includes simultaneous variable selection in both datasets. The usefulness of both approaches has been demonstrated previously and successfully applied in various integrative studies. Availability: integrOmics is freely available from http://CRAN.R-project.org/ or from the web site companion (http://math.univ-toulouse.fr/biostat) that provides full documentation and tutorials. Contact: k.lecao@uq.edu.au Supplementary information: Supplementary data are available at Bioinformatics online.
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            Trends in bronchiolitis hospitalizations in the United States, 2000-2009.

            To examine temporal trend in the national incidence of bronchiolitis hospitalizations, use of mechanical ventilation, and hospital charges between 2000 and 2009.
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              Association of nasopharyngeal microbiota profiles with bronchiolitis severity in infants hospitalised for bronchiolitis.

              Little is known about the relationship between the specific airway microbiota composition and severity of bronchiolitis. We aimed to identify nasopharyngeal microbiota profiles and link these profiles to acute severity in infants hospitalised for bronchiolitis.We conducted a multicentre prospective cohort study of 1005 infants (age <1 year) hospitalised for bronchiolitis over three winters, 2011-2014. By applying a 16S rRNA gene sequence and clustering approach to the nasopharyngeal aspirates collected within 24 h of hospitalisation, we determined nasopharyngeal microbiota profiles and their association with bronchiolitis severity. The primary outcome was intensive care use, i.e. admission to an intensive care unit or use of mechanical ventilation.We identified four nasopharyngeal microbiota profiles: three profiles were dominated by one of Haemophilus, Moraxella or Streptococcus, while the fourth profile had the highest bacterial richness. The rate of intensive care use was highest in infants with a Haemophilus-dominant profile and lowest in those with a Moraxella-dominant profile (20.2% versus 12.3%; unadjusted OR 1.81, 95% CI 1.07-3.11, p=0.03). After adjusting for 11 patient-level confounders, the rate remained significantly higher in infants with Haemophilus-dominant profiles (OR 1.98, 95% CI 1.08-3.62, p=0.03). These findings were externally validated in a separate cohort of 307 children hospitalised for bronchiolitis.
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                Author and article information

                Journal
                The Journal of Infectious Diseases
                Oxford University Press (OUP)
                0022-1899
                1537-6613
                June 15 2019
                May 24 2019
                January 10 2019
                June 15 2019
                May 24 2019
                January 10 2019
                : 219
                : 12
                : 2005-2014
                Affiliations
                [1 ]Alkek Center for Metagenomics and Microbiome Research, Department of Molecular Virology and Microbiology, Baylor College of Medicine, Houston, Texas
                [2 ]Institute of Cellular Medicine, Newcastle University, Newcastle Upon Tyne, United Kingdom
                [3 ]Department of Medicine, Boston Children’s Hospital, Harvard Medical School
                [4 ]Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, United States
                [5 ]Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, United States
                [6 ]Department of Emergency Medicine, Massachusetts General Hospital, Harvard Medical School
                Article
                10.1093/infdis/jiz021
                6534192
                30629185
                771f1180-5e8c-4380-838f-3d603891d62a
                © 2019

                https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model

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