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Association between rhinovirus species and nasopharyngeal microbiota in infants with severe bronchiolitis

2019; Elsevier BV; Volume: 143; Issue: 5 Linguagem: Inglês

10.1016/j.jaci.2018.12.1004

1097-6825

Laura Toivonen, Carlos A. Camargo, James E. Gern, Yury A. Bochkov, Jonathan M. Mansbach, Pedro A. Piedra, Kohei Hasegawa,

Tracheal and airway disorders

Bronchiolitis is the leading cause of hospitalization in US infants.1Hasegawa K. Mansbach J.M. Camargo Jr., C.A. Infectious pathogens and bronchiolitis outcomes.Expert Rev Anti Infect Ther. 2014; 12: 817-828Crossref PubMed Scopus (65) Google Scholar Rhinovirus (RV) is the second most common cause of severe bronchiolitis (ie, bronchiolitis requiring hospitalization) following respiratory syncytial virus (RSV).1Hasegawa K. Mansbach J.M. Camargo Jr., C.A. Infectious pathogens and bronchiolitis outcomes.Expert Rev Anti Infect Ther. 2014; 12: 817-828Crossref PubMed Scopus (65) Google Scholar RVs are RNA viruses consisting of more than 160 genotypes that are classified into 3 species (RV-A, RV-B, and RV-C).2Bochkov Y.A. Gern J.E. Rhinoviruses and their receptors: implications for allergic disease.Curr Allergy Asthma Rep. 2016; 16: 30Crossref PubMed Scopus (61) Google Scholar RV-A and RV-C are more frequently found than RV-B in children with acute respiratory infections (ARIs) and wheezing illnesses.3Iwane M.K. Prill M.M. Lu X. Miller E.K. Edwards K.M. Hall C.B. et al.Human rhinovirus species associated with hospitalizations for acute respiratory illness in young US children.J Infect Dis. 2011; 204: 1702-1710Crossref PubMed Scopus (200) Google Scholar, 4Cox D.W. Bizzintino J. Ferrari G. Khoo S.K. Zhang G. Whelan S. et al.Human rhinovirus species C infection in young children with acute wheeze is associated with increased acute respiratory hospital admissions.Am J Respir Crit Care Med. 2013; 188: 1358-1364Crossref PubMed Google Scholar Emerging evidence suggests a complex interplay between viral infection, airway microbes, and host immune response in the pathobiology of ARI. Studies have shown that RV infection in children is associated with increased detection of pathogenic bacteria in the airways.5Karppinen S. Terasjarvi J. Auranen K. Schuez-Havupalo L. Siira L. He Q. et al.Acquisition and transmission of Streptococcus pneumoniae are facilitated during rhinovirus infection in families with children.Am J Respir Crit Care Med. 2017; 196: 1172-1180Crossref PubMed Scopus (28) Google Scholar, 6Kloepfer K.M. Lee W.M. Pappas T.E. Kang T.J. Vrtis R.F. Evans M.D. et al.Detection of pathogenic bacteria during rhinovirus infection is associated with increased respiratory symptoms and asthma exacerbations.J Allergy Clin Immunol. 2014; 133 (7.e1-3): 1301-1307Abstract Full Text Full Text PDF PubMed Scopus (221) Google Scholar Furthermore, detection of RV together with specific airway pathogens (eg, Moraxella catarrhalis) is associated with increased ARI and asthma symptoms.6Kloepfer K.M. Lee W.M. Pappas T.E. Kang T.J. Vrtis R.F. Evans M.D. et al.Detection of pathogenic bacteria during rhinovirus infection is associated with increased respiratory symptoms and asthma exacerbations.J Allergy Clin Immunol. 2014; 133 (7.e1-3): 1301-1307Abstract Full Text Full Text PDF PubMed Scopus (221) Google Scholar Recently, RV-A and RV-C were reported to differentially associate with detection of pathogenic bacteria in school-age children.7Bashir H. Grindle K. Vrtis R. Vang F. Kang T. Salazar L. et al.Association of rhinovirus species with common cold and asthma symptoms and bacterial pathogens.J Allergy Clin Immunol. 2018; 141: 822-824.e9Abstract Full Text Full Text PDF PubMed Scopus (35) Google Scholar However, no study has investigated the relationships between rhinovirus species and airway microbiota in infants, let alone infants with bronchiolitis. To address the knowledge gap, we examined the association between rhinovirus species and the nasopharyngeal airway microbiota determined by 16S rRNA gene sequencing in 774 infants with severe bronchiolitis. This was a post hoc analysis of data from the 35th Multicenter Airway Research Collaboration (MARC-35) cohort study—a multicenter prospective cohort study of infants hospitalized for bronchiolitis. The details of the study design, setting, virus and microbiota measurements, and analysis are described in this article's Online Repository at www.jacionline.org. Briefly, 1016 infants (age <1 year) hospitalized for bronchiolitis were enrolled in 17 sites across 14 US states (see Table E1 in this article's Online Repository at www.jacionline.org). Bronchiolitis was defined according to the American Academy of Pediatrics guidelines. The institutional review boards at participating sites approved the study. Informed consent was obtained from the infants' parent or legal guardian. Nasopharyngeal samples were collected within 24 hours of hospitalization and stored at −80°C locally. These samples were processed and tested for 17 respiratory pathogens by real-time PCR and for microbiota using 16S rRNA gene sequencing at Baylor College of Medicine (Houston, Tex). Singleplex real-time PCR was used to detect RV, and positive specimens were further genotyped by using molecular typing assay at the University of Wisconsin (Madison, Wis). By using partitioning around medoids unsupervised clustering with the use of weighted UniFrac distance, 4 distinct nasopharyngeal microbiota profiles were derived as previously described.8Hasegawa K. Mansbach J.M. Ajami N.J. Espinola J.A. Henke D.M. Petrosino J.F. et al.Association of nasopharyngeal microbiota profiles with bronchiolitis severity in infants hospitalised for bronchiolitis.Eur Respir J. 2016; 48: 1329-1339Crossref PubMed Scopus (53) Google Scholar In the current analysis, we grouped infants into 4 mutually exclusive virus categories: solo RSV (reference), RV-A, RV-B, and RV-C. We tested the association between these virus categories and nasopharyngeal microbiota profiles by constructing multinomial logistic regression model adjusting for 8 covariates. Data were analyzed using R version 3.4.4. Of 1016 enrolled infants, 774 were in 1 of the 4 prespecified virus categories (580 RSV-only, 91 RV-A, 12 RV-B, and 91 RV-C) and had high-quality microbiota data; they comprised the analytic sample. Overall, the median age was 2.9 months (interquartile range, 1.6-5.3), 60% were male, and 16% infants received intensive care therapy. Compared with infants with RSV-only, those with RV-A or RV-C were older and more likely to have previous breathing problems (P < .001; see Table E2 in this article's Online Repository at www.jacionline.org). Across the virus categories, there was a significant difference in the likelihood of nasopharyngeal microbiota profiles (P < .001; see Table E3 in this article's Online Repository at www.jacionline.org). For example, while infants with RSV had the highest likelihood of Streptococcus-dominant profile (the reference virus and microbiota profile), those with RV-A had the highest likelihood of Haemophilus-dominant profile (Fig 1, A), corresponding to an adjusted relative rate ratio of 5.67 (95% CI, 2.76-11.67; P < .001; Table I). In contrast, infants with RV-C were more likely to have Moraxella-dominant profile than Streptococcus-dominant profile (adjusted relative rate ratio, 2.69; 95% CI, 1.39-5.20; P = .003). Similarly, at the genus-level (Fig 1, B; see Table E3 in this article's Online Repository at www.jacionline.org), compared with infants with RSV-only, those with any RV species had lower relative abundance of Streptococcus (P = .002) and those with RV-A had a higher abundance of Haemophilus (P = .002).Table IUnadjusted and adjusted associations of respiratory viruses (exposure) with nasopharyngeal microbiota profiles (outcome) in infants hospitalized for bronchiolitisModel and virus categoryMicrobiota profileHaemophilus-dominant (n = 133)P valueMoraxella-dominant (n = 167)P valueMixed profile (n = 239)P valueStreptococcus-dominant (n = 235)RRR (95% CI)RRR (95% CI)RRR (95% CI)RRR (95% CI)Unadjusted model RSV-only (n = 580)ReferenceReferenceReferenceReference RV-A (n = 91)5.62 (2.79-11.30)<.0012.22 (1.04-4.73).042.74 (1.39-5.39).004Reference RV-B (n = 12)7.30 (0.75-71.21).096.79 (0.75-61.46).094.59 (0.51-41.50).17Reference RV-C (n = 91)2.18 (1.08-4.40).032.41 (1.29-4.53).0061.69 (0.91-3.13).09ReferenceAdjusted model∗Multinomial logistic regression model adjusting for 8 patient-level covariates (age, sex, race/ethnicity, gestational age, siblings in the household, breast-feeding, history of breathing problems, and lifetime history of systemic antibiotic use). RSV-only infection was used as the reference of exposure (virus category), and Streptococcus-dominant microbiota profile was used as the reference for the outcome (nasopharyngeal microbiota profile). RSV-only (n = 580)ReferenceReferenceReferenceReference RV-A (n = 91)5.67 (2.76-11.67)<.0012.26 (1.05-4.89).042.74 (1.38-5.44).004Reference RV-B (n = 12)7.50 (0.74-76.08).095.72 (0.62-52.71).124.73 (0.52-43.04).17Reference RV-C (n = 91)1.81 (0.86-3.81).122.69 (1.39-5.20).0031.57 (0.83-2.96).17ReferenceRRR, Relative rate ratio.∗ Multinomial logistic regression model adjusting for 8 patient-level covariates (age, sex, race/ethnicity, gestational age, siblings in the household, breast-feeding, history of breathing problems, and lifetime history of systemic antibiotic use). RSV-only infection was used as the reference of exposure (virus category), and Streptococcus-dominant microbiota profile was used as the reference for the outcome (nasopharyngeal microbiota profile). Open table in a new tab RRR, Relative rate ratio. Earlier studies reported that RV-C infection is associated with higher risks of subsequent ARI in young children4Cox D.W. Bizzintino J. Ferrari G. Khoo S.K. Zhang G. Whelan S. et al.Human rhinovirus species C infection in young children with acute wheeze is associated with increased acute respiratory hospital admissions.Am J Respir Crit Care Med. 2013; 188: 1358-1364Crossref PubMed Google Scholar and that enrichment of Moraxella abundance in the upper airways is related to higher frequency of ARIs.9Bosch A.A. de Steenhuijsen Piters W.A. van Houten M.A. Chu M. Biesbroek G. Kool J. 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: 1582-1590Crossref PubMed Scopus (58) Google Scholar Furthermore, a recent analysis from RhinoGen study (310 children [aged 4-12 years] with or without asthma, using quantitative PCR for 3 bacteria) reported that RV-A and RV-C are differentially associated with increased quantity of H influenzae, M catarrhalis, and S pneumoniae.7Bashir H. Grindle K. Vrtis R. Vang F. Kang T. Salazar L. et al.Association of rhinovirus species with common cold and asthma symptoms and bacterial pathogens.J Allergy Clin Immunol. 2018; 141: 822-824.e9Abstract Full Text Full Text PDF PubMed Scopus (35) Google Scholar Our observations—for example, the association between RV-C and higher likelihood of Moraxella-dominant microbiota— corroborate these earlier findings, and extend them by applying 16S rRNA gene sequencing to the airway samples of a large multicenter prospective cohort of infants with severe bronchiolitis. The underlying mechanisms of the virus-microbiota relationships are beyond the scope of our data. The observed associations may be causal—that is, specific respiratory virus species (eg, RV-C) alters the airway microbiota.6Kloepfer K.M. Lee W.M. Pappas T.E. Kang T.J. Vrtis R.F. Evans M.D. et al.Detection of pathogenic bacteria during rhinovirus infection is associated with increased respiratory symptoms and asthma exacerbations.J Allergy Clin Immunol. 2014; 133 (7.e1-3): 1301-1307Abstract Full Text Full Text PDF PubMed Scopus (221) Google Scholar Alternatively, unique microbiota profiles in conjunction with airway immune response might have contributed to susceptibility to specific virus infection. These potential mechanisms are not mutually exclusive. Despite this complexity, the identification of the association between specific virus species and airway microbiota in infants with bronchiolitis is important given their relation to subsequent respiratory health in children. Our study has potential limitations. First, the study design precluded us from examining the relation between the temporal pattern of airway microbiota and respiratory health in children. To address this question, the cohort is currently being followed longitudinally for 6+ years with serial examinations of microbiota. Second, the current study did not have healthy controls. However, the study aim was to determine the association of virus species with microbiota among infants with bronchiolitis. Finally, although the study cohort comprised a racially/ethnically diverse US sample of infants, we must generalize the inferences cautiously beyond infants with severe bronchiolitis. Regardless, our data are highly relevant for 130,000 US children hospitalized with bronchiolitis each year.1Hasegawa K. Mansbach J.M. Camargo Jr., C.A. Infectious pathogens and bronchiolitis outcomes.Expert Rev Anti Infect Ther. 2014; 12: 817-828Crossref PubMed Scopus (65) Google Scholar In summary, on the basis of this multicenter prospective cohort study of infants with severe bronchiolitis, we observed that compared with infants with RSV-only infection, infants with RV-A or RV-C infection had distinct nasopharyngeal microbiota profiles—for example, those with RV-C infection had a higher likelihood of Moraxella-dominant microbiota profile, whereas those with RV-A infection had a higher likelihood of Haemophilus-dominant profile. Although causal inferences remain premature, our data should advance research into delineating the complex interrelations between respiratory viruses, airway microbiome, and respiratory outcomes in children. We thank the MARC-35 study hospitals and research personnel for their ongoing dedication to bronchiolitis and asthma research (Table E1), and Janice A. Espinola, MPH, Ashley F. Sullivan, MS, MPH, and Courtney N. Tierney, MPH (Massachusetts General Hospital, Boston, Mass), for their many contributions to the MARC-35 study. We also thank Joseph F. Petrosino, PhD, at Alkek Center for Metagenomics and Microbiome Research, Department of Molecular Virology and Microbiology, Baylor College of Medicine (Houston, Tex) for 16S rRNA gene sequencing analysis and Alkis Togias, MD, at the National Institutes of Health (Bethesda, Md) for helpful comments about the study results. This was a post hoc analysis of data from the 35th Multicenter Airway Research Collaboration (MARC-35)E1Hasegawa K. Mansbach J.M. Ajami N.J. Espinola J.A. Henke D.M. Petrosino J.F. et al.Association of nasopharyngeal microbiota profiles with bronchiolitis severity in infants hospitalised for bronchiolitis.Eur Respir J. 2016; 48: 1329-1339Crossref PubMed Scopus (135) Google Scholar—a multicenter prospective cohort study of infants hospitalized with bronchiolitis (ie, severe bronchiolitis). MARC-35 is coordinated by the Emergency Medicine Network (EMNet), a collaboration of 245 participating hospitals.E2Emergency Medicine Network.http://www.emnet-usa.org/Google Scholar Site investigators enrolled infants (age <1 year) hospitalized with bronchiolitis at 17 sites across 14 US states (Table E1) using a standardized protocol during 3 consecutive bronchiolitis seasons (from November 1 through April 30) during the period 2011 to 2014. Bronchiolitis was defined by the American Academy of Pediatrics guidelines as an acute respiratory illness with some combination of rhinitis, cough, tachypnea, wheezing, crackles, and retractionsE3Ralston S.L. Lieberthal A.S. Meissner H.C. Alverson B.K. Baley J.E. Gadomski A.M. et al.Clinical practice guideline: the diagnosis, management, and prevention of bronchiolitis.Pediatrics. 2014; 134: e1474-e1502Crossref PubMed Scopus (1172) Google Scholar and was diagnosed by an attending physician. We excluded infants who were transferred to a participating hospital more than 24 hours after the original hospitalization, those for whom consent was provided more than 24 hours after hospitalization, or those with known heart-lung disease, immunodeficiency, immunosuppression, or gestational age of less than 32 weeks. All patients were treated at the discretion of the treating physicians. The institutional review board at each of the participating hospitals approved the study. Written informed consent was obtained from the parent or guardian. Clinical data (patients' demographic characteristics and family, environmental, and medical history, and details of the acute illness) were collected via structured interview and chart reviews.E1Hasegawa K. Mansbach J.M. Ajami N.J. Espinola J.A. Henke D.M. Petrosino J.F. et al.Association of nasopharyngeal microbiota profiles with bronchiolitis severity in infants hospitalised for bronchiolitis.Eur Respir J. 2016; 48: 1329-1339Crossref PubMed Scopus (135) Google Scholar All data were reviewed at the EMNet Coordinating Center (Boston, Mass), and site investigators were queried about missing data and discrepancies identified by manual data checks. In addition to the clinical data, blood and nasopharyngeal airway samples were collected within 24 hours of hospitalization and stored at −80°C using standardized protocols.E1Hasegawa K. Mansbach J.M. Ajami N.J. Espinola J.A. Henke D.M. Petrosino J.F. et al.Association of nasopharyngeal microbiota profiles with bronchiolitis severity in infants hospitalised for bronchiolitis.Eur Respir J. 2016; 48: 1329-1339Crossref PubMed Scopus (135) Google Scholar Nasopharyngeal samples were analyzed for 17 respiratory pathogens using real-time PCR assaysE4Mansbach J.M. Piedra P.A. Stevenson M.D. Sullivan A.F. Forgey T.F. Clark S. et al.Prospective multicenter study of children with bronchiolitis requiring mechanical ventilation.Pediatrics. 2012; 130: e492-e500Crossref PubMed Scopus (124) Google Scholar as well as for microbiota using 16S rRNA gene sequencing at Baylor College of Medicine (Houston, Tex). Singleplex real-time PCR was used to detect RV at Baylor College of Medicine. Of RV-positive specimens, their species and genotypes were identified by using molecular typing assay that targets a variable fragment in 5′ untranslated region of the viral genome flanked by highly conserved motifs at the University of Wisconsin (Madison, Wis).E5Bochkov Y.A. Grindle K. Vang F. Evans M.D. Gern J.E. Improved molecular typing assay for rhinovirus species A, B, and C.J Clin Microbiol. 2014; 52: 2461-2471Crossref PubMed Scopus (72) Google Scholar 16S rRNA gene sequencing methods were adapted from the methods developed for the National Institutes of Health Human Microbiome Project.E6Human Microbiome Project ConsortiumA framework for human microbiome research.Nature. 2012; 486: 215-221Crossref PubMed Scopus (1944) Google Scholar, E7Human Microbiome Project ConsortiumStructure, function and diversity of the healthy human microbiome.Nature. 2012; 486: 207-214Crossref PubMed Scopus (8015) Google Scholar All samples were processed with a low-biomass extraction protocol to avoid sample loss and degradation and to maximize yield. Bacterial genomic DNA was extracted using MO BIO PowerSoil DNA Isolation Kit (Mo Bio Laboratories; Carlsbad, Calif). The 16S rDNA V4 region was amplified by PCR and sequenced in the MiSeq platform (Illumina; San Diego, Calif) using the 2 × 250 bp paired-end protocol yielding pair-end reads that overlap almost completely. The primers used for amplification contain adapters for MiSeq sequencing and single-end barcodes allowing pooling and direct sequencing of PCR products.E8Caporaso J.G. Kuczynski J. Stombaugh J. Bittinger K. Bushman F.D. Costello E.K. et al.QIIME allows analysis of high-throughput community sequencing data.Nat Methods. 2010; 7: 335-336Crossref PubMed Scopus (27287) Google Scholar, E9Caporaso J.G. Lauber C.L. Walters W.A. Berg-Lyons D. Huntley J. Fierer N. et al.Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms.ISME J. 2012; 6: 1621-1624Crossref PubMed Scopus (6160) Google Scholar Sequencing read pairs were demultiplexed on the basis of unique molecular barcodes, and reads were merged using USEARCH v7.0.1090,E10Edgar R.C. Search and clustering orders of magnitude faster than BLAST.Bioinformatics. 2010; 26: 2460-2461Crossref PubMed Scopus (15483) Google Scholar allowing 0 mismatches and a minimum overlap of 50 bases. Rarefaction curves of bacterial operational taxonomic units (OTUs) were constructed using sequence data for each sample to ensure coverage of the bacterial diversity present. Samples with suboptimal amounts of sequencing reads were resequenced to ensure that most bacterial taxa were encompassed in our analyses. 16S rRNA gene sequences were clustered into OTUs at a similarity cutoff value of 97% using the UPARSE algorithm.E11Edgar R.C. UPARSE: highly accurate OTU sequences from microbial amplicon reads.Nat Methods. 2013; 10: 996-998Crossref PubMed Scopus (11594) Google Scholar OTUs were determined by mapping the centroids to the SILVA databaseE12Quast C. Pruesse E. Yilmaz P. Gerken J. Schweer T. Yarza P. et al.The SILVA ribosomal RNA gene database project: improved data processing and web-based tools.Nucleic Acids Res. 2013; 41: D590-D596Crossref PubMed Scopus (18031) Google Scholar containing only the 16S V4 region to determine taxonomies. A custom script constructed a rarefied OTU table (rarefaction was performed at only 1 sequence depth) from the output files generated in the previous 2 steps for downstream analyses of alpha-diversity (eg, Shannon index) and beta-diversity (eg, weighted UniFrac).E13Lozupone C. Knight R. UniFrac: a new phylogenetic method for comparing microbial communities.Appl Environ Microbiol. 2005; 71: 8228-8235Crossref PubMed Scopus (5910) Google Scholar, E14Lozupone C. Lladser M.E. Knights D. Stombaugh J. Knight R. UniFrac: an effective distance metric for microbial community comparison.ISME J. 2011; 5: 169-172Crossref PubMed Scopus (1873) Google Scholar The processes involving microbial DNA extraction, 16S rRNA gene amplification, and amplicon sequencing included a set of controls that enabled us to evaluate the potential introduction of contamination or off-target amplification. Nontemplate controls (extraction chemistries) were included in the microbial DNA extraction process and the resulting material was subsequently used for PCR amplification. In addition, at the step of amplification, another set of nontemplate controls (PCR-mix) was included to evaluate the potential introduction of contamination at this step. Similarly, a positive control composed of known and previously characterized microbial DNA was included at this step to evaluate the efficiency of the amplification process. Before samples (unknowns) were pooled together, sequencing controls were evaluated and the rejection criteria were the presence of amplicons in any of the nontemplate controls or the absence of amplicons in the positive control. In the present study, no amplicons were observed in the nontemplate controls and a negligible amount of raw reads was recovered after sequencing. 16S rRNA gene sequencing of the nasopharyngeal airway samples from the enrolled infants (n = 1016) obtained 17,399,260 high-quality merged sequences, of which 16,685,637 (95.9%) were mapped to the database. Of 1016 infant samples, 1005 (98.9%) had sufficient sequence depth (rarefaction cutoff, 2128 reads per sample). The primary outcome was nasopharyngeal microbiota profile. As previously described,E1Hasegawa K. Mansbach J.M. Ajami N.J. Espinola J.A. Henke D.M. Petrosino J.F. et al.Association of nasopharyngeal microbiota profiles with bronchiolitis severity in infants hospitalised for bronchiolitis.Eur Respir J. 2016; 48: 1329-1339Crossref PubMed Scopus (135) Google Scholar by using the partitioning around medoids methodE15Wu G.D. Chen J. Hoffmann C. Bittinger K. Chen Y.Y. Keilbaugh S.A. et al.Linking long-term dietary patterns with gut microbial enterotypes.Science. 2011; 334: 105-108Crossref PubMed Scopus (4629) Google Scholar with weighted UniFrac distance, an unsupervised clustering method, we derived 4 distinct microbiota profiles: (1) Haemophilus-dominant profile, (2) Moraxella-dominant profile, (3) mixed profile, and (4) Streptococcus-dominant profile. The number of clusters was determined using the average silhouette score.E16Rousseeuw P. Silhouettes: a graphical aid to the interpretation and validation of cluster analysis.J Comput Appl Math. 1987; 20: 53-65Crossref Scopus (12055) Google Scholar To examine the association of respiratory viruses with nasopharyngeal microbiota profiles, we first grouped infants into 4 mutually exclusive virus categories: RSV-only (reference), RV-A, RV-B, and RV-C. Infections with multiple RV species were excluded. We compared the patient characteristics and clinical presentation between the virus categories, by using chi-square test and Wilcoxon-Mann-Whitney test, as appropriate. We also compared the relative abundances of 10 most abundant genera between the virus categories by using 1-way ANOVA, adjusting for multiple comparisons with the use of the Benjamini-Hochberg false- discovery rate method.E17Benjamini Y. Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing.J Royal Stat Soc (Methodological). 1995; 57: 289-300Google Scholar We then tested the association of these virus categories with nasopharyngeal microbiota profiles by constructing a multinomial logistic regression model adjusting for 8 potential confounders (ie, age, sex, race/ethnicity, gestational age, siblings in the household, breast-feeding, history of breathing problems, and lifetime history of systemic antibiotic use). These potential confounders were chosen on the basis of clinical plausibility and a priori knowledge.E18Hasegawa K. Linnemann R.W. Mansbach J.M. Ajami N.J. Espinola J.A. Fiechtner L.G. et al.Household siblings and nasal and fecal microbiota in infants.Pediatr Int. 2017; 59: 473-481Crossref PubMed Scopus (28) Google Scholar, E19Bosch A.A. de Steenhuijsen Piters W.A. van Houten M.A. Chu M. Biesbroek G. Kool J. 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: 1582-1590Crossref PubMed Scopus (200) Google Scholar, E20Teo S.M. Mok D. Pham K. Kusel M. Serralha M. Troy N. et al.The infant nasopharyngeal microbiome impacts severity of lower respiratory infection and risk of asthma development.Cell Host Microbe. 2015; 17: 704-715Abstract Full Text Full Text PDF PubMed Scopus (642) Google Scholar, E4Mansbach J.M. Piedra P.A. Stevenson M.D. Sullivan A.F. Forgey T.F. Clark S. et al.Prospective multicenter study of children with bronchiolitis requiring mechanical ventilation.Pediatrics. 2012; 130: e492-e500Crossref PubMed Scopus (124) Google Scholar We reported all P values as 2-tailed, with P < .05 considered statistically significant. The data were analyzed with the use of R version 3.4.4 (R Foundation, Vienna, Austria) with the phyloseq package for the microbiota analysis.Table E1Principal investigators at the 17 participating sites in MARC-35Amy D. Thompson, MDAlfred I. duPont Hospital for Children, Wilmington, DelFederico R. Laham, MD, MSArnold Palmer Hospital for Children, Orlando, FlaJonathan M. Mansbach, MD, MPHBoston Children's Hospital, Boston, MassVincent J. Wang, MD, MHAChildren's Hospital of Los Angeles, Los Angeles, CalifMichelle B. Dunn, MDChildren's Hospital of Philadelphia, Philadelphia, PaJuan C. Celedon, MD, DrPHChildren's Hospital of Pittsburgh, Pittsburgh, PaMichael Gomez, MD, MS-HCA, and Nancy Inhofe, MDThe Children's Hospital at St Francis, Tulsa, OklaBrian M. Pate, MD, and Henry T. Puls, MDThe Children's Mercy Hospital & Clinics, Kansas City, MoStephen J. Teach, MD, MPHChildren's National Medical Center, Washington, DCRichard T. Strait, MDCincinnati Children's Hospital and Medical Center, Cincinnati, OhioIlana Waynik, MDConnecticut Children's Medical Center, Hartford, ConnSujit Iyer, MDDell Children's Medical Center of Central Texas, Austin, TexMichelle D. Stevenson, MD, MSKosair Children's Hospital, Louisville, KyWayne G. Schreffler, MD, PhD, and Ari R. Cohen, MDMassachusetts General Hospital, Boston, MassAnne K Beasley, MDPhoenix Children's Hospital, Phoenix, ArizThida Ong, MDSeattle Children's Hospital, Seattle, WashCharles G. Macias, MD, MPHTexas Children's Hospital, Houston, Tex Open table in a new tab Table E2Characteristics of 774 infants hospitalized for bronchiolitis by RV categoryCharacteristicRVP valueRSV-only (n = 580)RV-A (n = 91)RV-B (n = 12)RV-C (n = 91)Age (mo), median (IQR)2.7 (1.5-4.8)3.1 (1.9-5.7)3.0 (2.1-4.7)4.4 (2.3-7.4)<.001Female sex249 (42.9)30 (33.0)6 (50.0)27 (29.7).04Race/ethnicity.38 Non-Hispanic white267 (46.0)33 (36.3)5 (41.7)36 (39.6) Non-Hispanic black126 (21.7)20 (22.0)4 (33.3)26 (28.6) Hispanic162 (27.9)36 (39.6)3 (25.0)25 (27.5) Other25 (4.3)2 (2.2)0 (0)4 (4.4)Parental history of asthma190 (32.8)35 (38.5)4 (33.3)35 (38.5).54Maternal smoking during pregnancy84 (14.5)14 (15.4)1 (8.3)13 (14.3).48C-section delivery214 (36.9)25 (27.5)3 (25.0)34 (37.4).07Prematurity (32-37 wk)104 (17.9)18 (19.8)1 (8.3)21 (23.1).51Low birth weight (<2.3 kg)33 (5.7)8 (8.8)0 (0)8 (8.8).58Sibling in the household457 (78.8)80 (87.9)12 (100)67 (73.6).03Mostly breast-fed during the first 3 mo of life248 (42.8)37 (40.7)7 (58.3)33 (36.3).57History of a breathing problem76 (13.1)21 (23.1)2 (16.7)31 (34.1)<.001Lifetime history of systemic antibiotic use153 (26.4)27 (29.7)3 (25.0)32 (35.2).36Lifetime history of corticosteroid use62 (10.7)11 (12.1)3 (25.0)21 (23.1).005Detected pathogens RSV580 (100)52 (57.1)10 (83.3)47 (51.6)<.001 RV0 (0)91 (100)12 (100.0)91 (100)<.001 Other pathogen∗Adenovirus, bocavirus, Bordetella pertussis, enterovirus, human coronavirus NL63, OC43, 229E, or HKU1, human metapneumovirus, influenza A or B virus, Mycoplasma pneumoniae, parainfluenza virus 3.0 (0)22 (24.2)2 (16.7)24 (26.4)<.001Clinical outcomes Intensive care therapy†Defined as admission to intensive care unit or use of mechanical ventilation (continuous positive airway pressure or intubation).93 (16.0)13 (14.3)3 (25.0)13 (14.3).78 Hospital length of stay (d), median (IQR)2.0 (1.0-3.0)2.0 (1.0-3.0)1.5 (1.0-3.3)2.0 (1.0-2.5).09IQR, Interquartile range.Data are n (%) of infants unless otherwise indicated.∗ Adenovirus, bocavirus, Bordetella pertussis, enterovirus, human coronavirus NL63, OC43, 229E, or HKU1, human metapneumovirus, influenza A or B virus, Mycoplasma pneumoniae, parainfluenza virus 3.† Defined as admission to intensive care unit or use of mechanical ventilation (continuous positive airway pressure or intubation). Open table in a new tab Table E3Nasopharyngeal microbiota of infants hospitalized for bronchiolitis by respiratory virus categoryCharacteristicRVP valueRSV-only (n = 580)RV-A (n = 91)RV-B (n = 12)RV-C (n = 91)Richness No. of genera, median (IQR)17 (10-25)13 (7-23)14 (7-24)15 (8-22).10Alpha-diversity Shannon index, median (IQR)0.96 (0.58-1.49)0.94 (0.41-1.39)0.67 (0.14-1.34)0.91 (0.57-1.28).20Microbiota profiles<.001 Haemophilus-dominant profile83 (14.3)30 (33.0)3 (25.0)17 (18.7) Moraxella-dominant profile119 (20.5)17 (18.7)4 (33.3)27 (29.7) Mixed profile176 (30.3)31 (34.1)4 (33.3)28 (30.8) Streptococcus-dominant profile202 (34.8)13 (14.3)1 (8.3)19 (20.9)Relative abundance of 10 most abundant genera, mean ± SD Streptococcus0.35 ± 0.310.23 ± 0.260.17 ± 0.250.29 ± 0.30.002∗Benjamini-Hochberg false-discovery rate–adjusted P value accounting for multiple comparisons. Moraxella0.27 ± 0.330.31 ± 0.330.42 ± 0.420.36 ± 0.36.10∗Benjamini-Hochberg false-discovery rate–adjusted P value accounting for multiple comparisons. Haemophilus0.16 ± 0.280.30 ± 0.360.29 ± 0.390.19 ± 0.29.002∗Benjamini-Hochberg false-discovery rate–adjusted P value accounting for multiple comparisons. Prevotella0.03 ± 0.070.02 ± 0.060.01 ± 0.010.02 ± 0.06.77∗Benjamini-Hochberg false-discovery rate–adjusted P value accounting for multiple comparisons. Neisseria0.02 ± 0.070.02 ± 0.070.02 ± 0.040.02 ± 0.10.99∗Benjamini-Hochberg false-discovery rate–adjusted P value accounting for multiple comparisons. Staphylococcus0.03 ± 0.100.01 ± 0.070.01 ± 0.020.02 ± 0.08.72∗Benjamini-Hochberg false-discovery rate–adjusted P value accounting for multiple comparisons. Corynebacterium0.02 ± 0.080.00 ± 0.010.00 ± 0.010.00 ± 0.02.10∗Benjamini-Hochberg false-discovery rate–adjusted P value accounting for multiple comparisons. Alloprevotella0.01 ± 0.050.02 ± 0.060.00 ± 0.010.01 ± 0.06.75∗Benjamini-Hochberg false-discovery rate–adjusted P value accounting for multiple comparisons. Veillonella0.01 ± 0.030.01 ± 0.020.00 ± 0.000.01 ± 0.02.17∗Benjamini-Hochberg false-discovery rate–adjusted P value accounting for multiple comparisons. Gemella0.01 ± 0.040.01 ± 0.020.00 ± 0.000.01 ± 0.02.61∗Benjamini-Hochberg false-discovery rate–adjusted P value accounting for multiple comparisons.IQR, Interquartile range.∗ Benjamini-Hochberg false-discovery rate–adjusted P value accounting for multiple comparisons. Open table in a new tab Table E4Characteristics of 774 infants hospitalized for bronchiolitis by nasopharyngeal microbiota profilesCharacteristicMicrobiota profileP valueHaemophilus-dominant (n = 133)Moraxella-dominant (n = 167)Mixed profile (n = 239)Streptococcus-dominant (n = 235)Age (mo), median (IQR)3.9 (2.1-7.6)2.9 (1.5-5.4)2.9 (1.7-4.8)2.5 (1.3-4.3)<.001Female sex53 (39.8)71 (42.5)95 (39.7)93 (39.6).93Race/ethnicity Non-Hispanic white49 (36.8)70 (41.9)100 (41.8)122 (51.9).20 Non-Hispanic black29 (21.8)42 (25.1)60 (25.1)45 (19.1) Hispanic49 (36.8)48 (28.7)68 (28.5)61 (26.0) Other6 (4.5)7 (4.2)11 (4.6)7 (3.0)Parental history of asthma44 (33.1)49 (29.3)80 (33.5)91 (38.7).18Maternal smoking during pregnancy17 (12.8)21 (12.6)38 (15.9)36 (15.3).43C-section delivery47 (35.3)60 (35.9)82 (34.3)87 (37.0).60Prematurity (32-37 wk)26 (19.5)27 (16.2)48 (20.1)43 (18.3).78Low birth weight (<2.3 kg)9 (6.8)12 (7.2)13 (5.4)15 (6.4).94Sibling in the household98 (73.7)142 (85.0)188 (78.7)188 (80.0).11Mostly breast-fed during the first 3 mo of life59 (44.4)79 (47.3)88 (36.8)99 (42.1).43History of a breathing problem27 (20.3)22 (13.2)44 (18.4)37 (15.7).34Lifetime history of systemic antibiotic use52 (39.1)29 (17.4)67 (28.0)67 (28.5).001Lifetime history of corticosteroid use20 (15.0)19 (11.4)30 (12.6)28 (11.9).79Virus category∗Of these, 48 had coinfection with a non-RSV/non-RV, which did not have statistically significant association with the microbiota profiles (P = .06). RSV-only83 (62.4)119 (71.3)176 (73.6)202 (86.0)<.001 RV-A30 (22.6)17 (10.2)31 (13.0)13 (5.5) RV-B3 (2.3)4 (2.4)4 (1.7)1 (0.4) RV-C17 (12.8)27 (16.2)28 (11.7)19 (8.1)IQR, Interquartile range.Data are n (%) of infants unless otherwise indicated.∗ Of these, 48 had coinfection with a non-RSV/non-RV, which did not have statistically significant association with the microbiota profiles (P = .06). Open table in a new tab Table E5Full results of the multivariable analysis on associations of RVs (exposure) with nasopharyngeal microbiota profiles (outcome) in infants hospitalized for bronchiolitis∗Multinomial logistic regression model adjusting for 8 patient-level covariates. RSV-only infection was used as the reference of exposure (virus category), and Streptococcus-dominant microbiota profile was used as the reference for the outcome (nasopharyngeal microbiota profile).VariableMicrobiota profileHaemophilus-dominant (n = 133)Moraxella-dominant (n = 167)Mixed profile (n = 239)Streptococcus-dominant (n = 235)RRR (95% CI)P valueRRR (95% CI)P valueRRR (95% CI)P valueRRR (95% CI)Virus category RSV-only (n = 580)ReferenceReferenceReferenceReference RV-A (n = 91)5.67 (2.76-11.67)<.0012.26 (1.05-4.89).042.74 (1.38-5.44).004Reference RV-B (n = 12)7.50 (0.74-76.08).095.72 (0.62-52.71).124.73 (0.52-43.04).17Reference RV-C (n = 91)1.81 (0.86-3.81).122.69 (1.39-5.20).0031.57 (0.83-2.96).17ReferenceAge ≥ 6 mo2.82 (1.58-5.02)<.0012.09 (1.18-3.72).011.34 (0.78-2.29).29ReferenceFemale (vs male) sex1.08 (0.68-1.71).741.11 (0.73-1.69).631.01 (0.69-1.48)ReferenceRace/ethnicity.95 Non-Hispanic whiteReferenceReferenceReferenceReference Non-Hispanic black1.75 (0.96-3.22).071.58 (0.92-2.69).101.56 (0.96-2.53).07Reference Hispanic1.94 (1.14-3.30).011.41 (0.86-2.33).171.28 (0.82-2.00).28Reference Other2.19 (0.67-7.11).191.74 (0.57-5.31).331.98 (0.73-5.34).18ReferencePrematurity (32-37 wk)1.04 (0.59-1.83).900.83 (0.48-1.43).491.06 (0.67-1.70).80ReferenceSibling in the household0.65 (0.38-1.11).111.49 (0.86-2.58).160.89 (0.56-1.40).61ReferenceBreast-feeding during the first 3 mo of life1.13 (0.70-1.84).621.26 (0.81-1.97).310.80 (0.53-1.20).28ReferenceHistory of breathing problems before the index hospitalization0.80 (0.43-1.49).490.65 (0.35-1.22).181.05 (0.62-1.76).86ReferenceLifetime history of systemic antibiotic use1.39 (0.86-2.27).180.49 (0.29-0.82).0070.94 (0.62-1.44).79ReferenceRRR, Relative rate ratio.∗ Multinomial logistic regression model adjusting for 8 patient-level covariates. RSV-only infection was used as the reference of exposure (virus category), and Streptococcus-dominant microbiota profile was used as the reference for the outcome (nasopharyngeal microbiota profile). Open table in a new tab IQR, Interquartile range. Data are n (%) of infants unless otherwise indicated. IQR, Interquartile range. IQR, Interquartile range. Data are n (%) of infants unless otherwise indicated. RRR, Relative rate ratio.