Acute wheeze in preschool children accounts for more than 75% of pediatric health care attendances for wheeze/asthma in the United Kingdom.1Brand P.L. Caudri D. Eber E. Gaillard E.A. Garcia-Marcos L. Hedlin G. et al.Classification and pharmacological treatment of preschool wheezing: changes since 2008.Eur Respir J. 2014; 43: 1172-1177Crossref PubMed Scopus (151) Google Scholar The high morbidity suggests current therapeutic strategies are failing. Preschool wheezers are heterogeneous; many have a disease course clinically distinct from that of childhood type 2–mediated allergic asthma.2Brand P.L. Baraldi E. Bisgaard H. Boner A.L. Castro-Rodriguez J.A. Custovic A. et al.Definition, assessment and treatment of wheezing disorders in preschool children: an evidence-based approach.Eur Respir J. 2008; 32: 1096-1110Crossref PubMed Scopus (571) Google Scholar However, the pathologic mechanisms underpinning preschool wheeze are poorly understood. Treatment is currently determined by clinical phenotype.1Brand P.L. Caudri D. Eber E. Gaillard E.A. Garcia-Marcos L. Hedlin G. et al.Classification and pharmacological treatment of preschool wheezing: changes since 2008.Eur Respir J. 2014; 43: 1172-1177Crossref PubMed Scopus (151) Google Scholar Episodic viral wheezers (EVWs) are predominantly prescribed intermittent therapies, and multiple-trigger wheezers (MTWs) are predominantly prescribed maintenance inhaled corticosteroids.1Brand P.L. Caudri D. Eber E. Gaillard E.A. Garcia-Marcos L. Hedlin G. et al.Classification and pharmacological treatment of preschool wheezing: changes since 2008.Eur Respir J. 2014; 43: 1172-1177Crossref PubMed Scopus (151) Google Scholar However, clinical phenotypes can overlap and change with time.1Brand P.L. Caudri D. Eber E. Gaillard E.A. Garcia-Marcos L. Hedlin G. et al.Classification and pharmacological treatment of preschool wheezing: changes since 2008.Eur Respir J. 2014; 43: 1172-1177Crossref PubMed Scopus (151) Google Scholar No studies have related lower airway inflammation or infection to clinical wheeze phenotype in children.1Brand P.L. Caudri D. Eber E. Gaillard E.A. Garcia-Marcos L. Hedlin G. et al.Classification and pharmacological treatment of preschool wheezing: changes since 2008.Eur Respir J. 2014; 43: 1172-1177Crossref PubMed Scopus (151) Google Scholar We hypothesized that lower airway inflammation and microbial dysbiosis during stable disease in preschool children with recurrent severe wheeze (requiring hospitalization or systemic corticosteroids) would be related to clinical wheeze phenotype.1Brand P.L. Caudri D. Eber E. Gaillard E.A. Garcia-Marcos L. Hedlin G. et al.Classification and pharmacological treatment of preschool wheezing: changes since 2008.Eur Respir J. 2014; 43: 1172-1177Crossref PubMed Scopus (151) Google Scholar We prospectively determined clinical phenotype and analyzed lower airway inflammation and the microbiota in children aged 1 to 6 years with severe wheeze undergoing a clinically indicated bronchoscopy (see Table E1 in this article's Online Repository at www.jacionline.org). Wheeze was doctor diagnosed and confirmed by using a video questionnaire.3Saglani S. McKenzie S.A. Bush A. Payne D.N. A video questionnaire identifies upper airway abnormalities in preschool children with reported wheeze.Arch Dis Child. 2005; 90: 961-964Crossref PubMed Scopus (0) Google Scholar Wheezers were categorized as MTWs or EVWs based on parental report (see the Methods section in this article's Online Repository at www.jacionline.org).2Brand P.L. Baraldi E. Bisgaard H. Boner A.L. Castro-Rodriguez J.A. Custovic A. et al.Definition, assessment and treatment of wheezing disorders in preschool children: an evidence-based approach.Eur Respir J. 2008; 32: 1096-1110Crossref PubMed Scopus (571) Google Scholar Infection was assessed by using bacterial culture and viral PCR, whereas microbiota profiling was determined by using 16S-based amplicon sequencing. Fiberoptic bronchoscopy and bronchoalveolar lavage (BAL) were performed under general anesthesia.4Saglani S. Payne D.N. Zhu J. Wang Z. Nicholson A.G. Bush A. et al.Early detection of airway wall remodeling and eosinophilic inflammation in preschool wheezers.Am J Respir Crit Care Med. 2007; 176: 858-864Crossref PubMed Scopus (343) Google Scholar, 5Saglani S. Payne D.N. Nicholson A.G. Scallan M. Haxby E. Bush A. The safety and quality of endobronchial biopsy in children under five years old.Thorax. 2003; 58: 1053-1057Crossref PubMed Scopus (36) Google Scholar BAL fluid and blood differential leukocyte counts were performed, whole BAL fluid was processed for bacterial culture and viral PCR, and BAL fluid supernatants were processed for microbiota analysis (see the Methods section in this article's Online Repository).6Bernasconi E. Pattaroni C. Koutsokera A. Pison C. Kessler R. Benden C. et al.Airway microbiota determines innate cell inflammatory or tissue remodeling profiles in lung transplantation.Am J Respir Crit Care Med. 2016; 194: 1252-1263Crossref PubMed Scopus (41) Google Scholar Ethical approval was obtained, and all patients' caregivers provided informed consent. Thirty-five wheezers (19 male and 16 female patients; median age, 34 months [range, 13.6-70.2 months]) were included (Table I). MTWs had more BAL fluid macrophages than EVWs. Although there were no other significant differences in blood or BAL fluid leukocytes between MTWs and EVWs (Table I), EVWs had an approximately 6 times greater median BAL neutrophil count than MTWs, but there was a wide range within the groups and marked overlap between the groups. The percentage of EVWs and MTWs with atopy and their serum IgE levels were similar (Table I). When all wheezers, regardless of clinical phenotype, were grouped according to atopic status, blood and BAL fluid eosinophil counts were significantly greater in atopic wheezers compared with those in nonatopic wheezers (Fig 1, A).Table IPatients' demographics and inflammation dataMTWs (n = 21)EVW (n = 14)P valueMale sex, no. (%)14 (66.7)5 (35.7).09Age (mo), median (range)29.2 (13.6-70.2)33.8 (17.6-57.6).90Weight (kg), median (range)14.0 (9.2-25.5)14.0 (10.6-17.6).54Height (cm), median (range)92.0 (78-116.2)92.5 (84-115).61Gestational age (wk), median (range)38 + 5 (35-41)40 + 0 (36-42).06Birth weight (kg), median (range [n])3.18 (2.0-3.91 [20])3.62 (1.41-4.0 [11]).08Atopy, no. (%)6 (28.6)1 (7.1).20Passive smoking, no. (%)5 (25.0)3 (21.4).99Inhaled corticosteroids, no. (%)12 (57.1)8 (57.1).99OCS ever, no. (%)16 (76.2)9 (64.3).47No. of OCS courses, median (range [n])1.5 (0-30 [20])2 (0-16).57Inhaled salbutamol, no. (%)17 (81.0%)11 (78.6%).99Oral montelukast, no. (%)9 (42.9)7 (50.0).74Oral antibiotics (ever), no. (%)19 (90.5)14 (100).51No. of antibiotic courses, median (range)2 (0-21)3 (1-12).46Confirmed wheeze, no. (%)17 (81.0)8 (57.1).27TRACK score, median (range [n])50 (10-85 [13])62.5 (30-75 [6]).27PACQLQ score, median (range [n])4.50 (2.3-7 [16])5.16 (1.2-7 [8]).95Hospitalizations for wheeze, median (range [n])5 (0-100 [20])3 (0-16 [13]).19Peripheral blood eosinophils (%), median (range [n])3.4 (0.7-15.5 [20])5.0 (1.3-14.0).48Peripheral blood neutrophils (%), median (range [n])39.8 (14.0-68.3 [20])37.6 (16.0-51.1).46Peripheral blood lymphocytes (%), median (range [n])44.1 (20.0-67.4 [20])46.69 (33.3-74.0).44Peripheral blood monocytes (%), median (range [n])7.1 (4.6-16.9 [20])6.5 (4.9-11.5).57BAL fluid eosinophils (%), median (range [n])0.6 (0.0-26.4 [18])0.9 (0.0-5.7 [13]).88BAL fluid neutrophils (%), median (range [n])7.1 (0.5-67.1 [18])42.1 (1.4-84.5 [13]).06BAL fluid lymphocytes (%), median (range [n])9.2 (4.4-51.3 [18])7.4 (0.7-53.4 [13]).65BAL macrophages (%), median (range [n])79.7 (16.5-91.2 [18])37.8 (9.3-92.8 [13]).03Serum IgE (IU/mL), median (range [n])10.5 (1-2589 [20])34 (2-495 [13]).33Positive bacterial culture, no. (%)9 (42.9)7 (50.0).79Positive viral PCR, no. (%)8 (38.1)6 (42.9).99Passive smoking is defined by parental report of either parent currently smoking. See the Methods section in this article's Online Repository for details of statistical tests.OCS, Oral corticosteroid; PACQLQ, Paediatric Asthma Caregiver's Quality of Life Questionnaire; TRACK, Test for Respiratory and Asthma Control in Kids. Open table in a new tab Passive smoking is defined by parental report of either parent currently smoking. See the Methods section in this article's Online Repository for details of statistical tests. OCS, Oral corticosteroid; PACQLQ, Paediatric Asthma Caregiver's Quality of Life Questionnaire; TRACK, Test for Respiratory and Asthma Control in Kids. Numbers of EVWs and MTWs with positive BAL fluid bacterial culture or viral detection results by using routine clinical analysis were similar (Table I). Sixty percent of all wheezers had either positive bacterial culture or viral detection results, and 26% had both. The most common bacteria cultured were Streptococcus pneumoniae, Moraxella catarrhalis, Staphylococcus aureus, and Haemophilus influenzae, and the most common viruses were rhinovirus, bocavirus, and adenovirus. To examine the relationship between infection and cellular inflammation, we compared BAL fluid granulocytes with culture results and wheeze phenotype. Greater BAL fluid neutrophil counts were associated with positive BAL fluid bacterial cultures in MTWs but not EVWs (Fig 1, B). There was no relationship between BAL fluid neutrophil counts and the presence of a virus or BAL fluid eosinophilia and infection. Surplus BAL fluid remaining after clinically indicated tests (leukocyte differential, bacterial culture, and viral PCR) was used for microbiome analysis in 26 patients. Six of 26 were insufficient for 16S amplification for sequencing, and a further 6 of 26 were sequenced but were of very low quality, leaving BAL fluid from 14 patients (7 MTWs and 7 EVWs) with high-quality microbiota profiling. Unsupervised analysis of the microbiota in all wheezers revealed 2 distinct profiles referred to hereafter as “mixed” or “Moraxella” (Fig 1, C). Indeed, Moraxella species was the most discriminant genus between the 2 groups (mean relative abundance, 47.5% compared with 1.2%; Fig 1, D). Interaction networks of the most represented genera (Fig 1, E) revealed 5 significant positive and 5 negative co-occurrences. Porphyromonas, Veillonella, Haemophilus, Granulicatella, and Prevotella species showed significant co-occurrence. The majority of negative co-occurrences were associated with Moraxella species, which is indicative of microbial dysbiosis in its presence. The Moraxella species profile was characterized by lower proportions of BAL fluid macrophages and lymphocytes (Fig 1, F and G) but significantly higher neutrophil counts (Fig 1, H) when compared with the mixed microbiota profile. There were no differences in BAL fluid eosinophil or blood leukocyte counts between the microbiota profiles. Microbiota profiles did not significantly associate with MTW or EVW clinical phenotypes (Fig 1, I) or atopy (Fig 1, J). However, the majority of EVWs had a Moraxella profile (Fig 1, I), and EVWs had a trend toward greater BAL neutrophil counts (Table I), suggesting an association between EVW status, microbiota profile, and neutrophilia. BAL fluid microbiota and granulocyte profiles did not correlate with the subject's age. All wheezers with a Moraxella species–predominant microbiota profile also had positive BAL fluid cultures of M catarrhalis by using traditional techniques, but 3 of 7 additional wheezers who had not grown M catarrhalis were identified by using 16S rDNA gene amplicon sequencing. To our knowledge, this is the first study investigating lower airway inflammation and microbiota composition in preschool patients with severe wheeze. We found that stratification of patients as EVWs or MTWs did not consistently reflect underlying lower airways inflammatory status or microbiology (viral and bacterial). In contrast, microbiota-based clustering of wheezers correlated with local inflammation. Specifically, the overrepresentation of Moraxella taxa and subsequent reduced bacterial diversity reflective of microbiota dysbiosis were linked with neutrophilia. This is consistent with adult studies reporting lower bacterial diversity and increased prevalence of Moraxella species in patients with severe asthma with neutrophilia.7Green B.J. Wiriyachaiporn S. Grainge C. Rogers G.B. Kehagia V. Lau L. et al.Potentially pathogenic airway bacteria and neutrophilic inflammation in treatment resistant severe asthma.PLoS One. 2014; 9: e100645Crossref PubMed Scopus (210) Google Scholar, 8Taylor S.L. Leong L.E.X. Choo J.M. Wesselingh S. Yang I.A. Upham J.W. et al.Inflammatory phenotypes in patients with severe asthma are associated with distinct airway microbiology.J Allergy Clin Immunol. 2018; 141: 94-103.e15Abstract Full Text Full Text PDF PubMed Scopus (183) Google Scholar By contrast, the mixed microbiota profile was characterized by increased diversity, notably in the abundance of commensal bacterial genera, such as Streptococcus, Prevotella, Neisseria, and Porphyromonas species. The latter are known constituents of the healthy adult lung microbiota. Only a limited number of samples could be analyzed, which is reflective of the difficulty in obtaining these rare lower respiratory tract samples using invasive BAL procedures and the technical considerations surrounding isolating high-quality microbial DNA for sequence analysis. Confirmation in larger numbers is needed, and analysis of mild wheezers would be valuable. Nevertheless, it is striking that even with a modest number, our microbiota and cellular analysis clearly defines 2 distinct patient groups, which was not possible when using standard approaches. In summary, we have shown there is no consistent relationship between lower airway inflammation or infection and clinical preschool wheeze phenotypes,2Brand P.L. Baraldi E. Bisgaard H. Boner A.L. Castro-Rodriguez J.A. Custovic A. et al.Definition, assessment and treatment of wheezing disorders in preschool children: an evidence-based approach.Eur Respir J. 2008; 32: 1096-1110Crossref PubMed Scopus (571) Google Scholar suggesting that use of clinical phenotype alone to guide maintenance therapy to prevent wheeze attacks is not appropriate. Assessment of the lower airway microbiota revealed 2 groups; a Moraxella species dysbiotic microbiota cluster that associated with airway neutrophilia and a mixed microbiota cluster with a macrophage- and lymphocyte-predominant inflammatory profile. We speculate that antibiotics might be beneficial only for the Moraxella species cluster. We thank Royal Brompton Hospital Consultants Dr Ian Balfour-Lynn, Dr Mark Rosenthal, Dr Claire Hogg, Dr Siobhan Carr, Dr Hui-Leng Tan, and Professor Jane Davies for their help in patient recruitment and sample collection. We thank the parents and children who agreed to take part in the research. Children aged 1 to 6 years undergoing elective clinically indicated bronchoscopy for severe recurrent wheeze were recruited prospectively between September 2015 and August 2016.E1Saglani S. Nicholson A.G. Scallan M. Balfour-Lynn I. Rosenthal M. Payne D.N. et al.Investigation of young children with severe recurrent wheeze: any clinical benefit?.Eur Respir J. 2006; 27: 29-35Crossref PubMed Scopus (80) Google Scholar Wheeze was doctor diagnosed and confirmed by using a validated video questionnaire.E2Saglani S. McKenzie S.A. Bush A. Payne D.N. A video questionnaire identifies upper airway abnormalities in preschool children with reported wheeze.Arch Dis Child. 2005; 90: 961-964Crossref PubMed Scopus (48) Google Scholar Wheezers were categorized as MTWs or EVWs based on parental report.E3Brand P.L. Baraldi E. Bisgaard H. Boner A.L. Castro-Rodriguez J.A. Custovic A. et al.Definition, assessment and treatment of wheezing disorders in preschool children: an evidence-based approach.Eur Respir J. 2008; 32: 1096-1110Crossref PubMed Scopus (679) Google Scholar Ethics approval was obtained from the National Research Ethics Committee, Royal Brompton Hospital (RBH) Research and Design Department (15/LO/1885, 08/H0708/3, and 15/SC/0569). All patients' caregivers provided written informed consent. Inclusion and exclusion criteria are detailed in Table E1. Caregivers completed a clinical questionnaire. This included questions on symptom pattern used to phenotype children as either EVWs who wheeze only during acute attacks in association with a clinically diagnosed viral upper respiratory tract infection or MTWs who wheeze both during and between attacks.E3Brand P.L. Baraldi E. Bisgaard H. Boner A.L. Castro-Rodriguez J.A. Custovic A. et al.Definition, assessment and treatment of wheezing disorders in preschool children: an evidence-based approach.Eur Respir J. 2008; 32: 1096-1110Crossref PubMed Scopus (679) Google Scholar Symptom control was assessed by using the Test for Respiratory and Asthma Control in Kids,E4Murphy K.R. Zeiger R.S. Kosinski M. Chipps B. Mellon M. Schatz M. et al.Test for respiratory and asthma control in kids (TRACK): a caregiver-completed questionnaire for preschool-aged children.J Allergy Clin Immunol. 2009; 123: 833-839.e9Abstract Full Text Full Text PDF PubMed Scopus (105) Google Scholar and symptom effect was assessed by using the Paediatric Caregivers Asthma Quality of Life Questionnaire.E5Juniper E.F. Guyatt G.H. Feeny D.H. Ferrie P.J. Griffith L.E. Townsend M. Measuring quality of life in the parents of children with asthma.Qual Life Res. 1996; 5: 27-34Crossref PubMed Scopus (499) Google Scholar The questionnaire also included early-life events (gestation, mode of delivery, birth weight, neonatal resuscitation and need for respiratory support, and mode of feeding); family demographics (siblings, atopy, smoking, pets, and urban or rural location); past medical history, including comorbidities and atopy; and current and previous medication use. Retrospective data were collected from electronic medical records at the RBH, where patients were treated. Vaccination status, including for Haemophilus influenzae B, was not recorded. Fiberoptic bronchoscopy was performed after achievement of general anesthesia.E6Saglani S. Payne D.N. Zhu J. Wang Z. Nicholson A.G. Bush A. et al.Early detection of airway wall remodeling and eosinophilic inflammation in preschool wheezers.Am J Respir Crit Care Med. 2007; 176: 858-864Crossref PubMed Scopus (402) Google Scholar Three aliquots of 1 mL/kg 0.9% sodium chloride were used for BAL.E1Saglani S. Nicholson A.G. Scallan M. Balfour-Lynn I. Rosenthal M. Payne D.N. et al.Investigation of young children with severe recurrent wheeze: any clinical benefit?.Eur Respir J. 2006; 27: 29-35Crossref PubMed Scopus (80) Google Scholar BAL fluid was first processed for clinical indications. BAL cytospin preparations were prepared, and differential leukocyte counts were performed by the RBH laboratory.E7Jochmann A. Artusio L. Robson K. Nagakumar P. Collins N. Fleming L. et al.Infection and inflammation in induced sputum from preschool children with chronic airways diseases.Pediatr Pulmonol. 2016; 51: 778-786Crossref PubMed Scopus (41) Google Scholar Differential leukocyte counts were performed, as previously described.E7Jochmann A. Artusio L. Robson K. Nagakumar P. Collins N. Fleming L. et al.Infection and inflammation in induced sputum from preschool children with chronic airways diseases.Pediatr Pulmonol. 2016; 51: 778-786Crossref PubMed Scopus (41) Google Scholar Human tissue was processed in accordance with the Human Tissue Act (2004). Whole BAL fluid was processed at the RBH laboratory for bacterial culture and viral PCR (FTD Respiratory pathogens 21 multiplex kit; Fast Track Diagnostics, Sliema, Malta). Any surplus BAL fluid was stored at −80°C for microbiota analysis. Because BAL fluid was first sent for clinically indicated tests, only BAL fluid that was left over after cytospin had been prepared, and samples sent for bacterial culture and viral PCR could be used for microbiota profiling. To ensure high-quality data, samples that did not meet the quality controls listed below were excluded. Blood was collected for differential leukocyte counts and specific IgE to aeroallergens (house dust mite, cat, dog, and grass pollen). Atopy was defined as more than 1 positive specific IgE level (≥0.35 kU/L) or more than 1 positive skin prick test response. Because children were undergoing a clinically indicated procedure, not all recruited patients had blood or BAL fluid samples taken, and thus the sample size for each parameter is stated. Bacterial DNA was isolated from BAL fluid supernatants, as previously described.E8Bernasconi E. Pattaroni C. Koutsokera A. Pison C. Kessler R. Benden C. et al.Airway microbiota determines innate cell inflammatory or tissue remodeling profiles in lung transplantation.Am J Respir Crit Care Med. 2016; 194: 1252-1263Crossref PubMed Scopus (79) Google Scholar In addition, DNA extraction and PCR-negative controls were added. Amplification of the 16S rDNA gene was carried out with custom-barcoded primers targeting the V1 to V2 region before pooling at equimolar amounts and sequencing on an Illumina MiSeq platform (Illumina, San Diego, Calif), as previously described.E9Rapin A. Pattaroni C. Marsland B.J. Harris N.L. Microbiota analysis using an Illumina MiSeq platform to sequence 16S rRNA genes.Curr Protoc Mouse Biol. 2017; 7: 100-129Crossref PubMed Scopus (25) Google Scholar Sequences were quality filtered (quality Phred score Q < 20, >3 low-quality base calls, and >75% of their original length), and chimeras were removed by using usearch61, clustered into operational taxonomic units, and assigned taxonomy by using Quantitative Insights into Microbial Ecology (QIIME, version 1.9)E10Caporaso 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 (24698) Google Scholar software with usearch61 with the Greengenes (version 13.5) database.E11DeSantis T.Z. Hugenholtz P. Larsen N. Rojas M. Brodie E.L. Keller K. et al.Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB.Appl Environ Microbiol. 2006; 72: 5069-5072Crossref PubMed Scopus (7757) Google Scholar Diversity was estimated with the Shannon index, as calculated in QIIME. All downstream analyses were performed with R statistical software by using an operational taxonomic unit table rarefied at a sequencing depth of 50,000 reads. Samples with less than 5,000 quality reads or clustering with extraction or PCR-negative controls were removed from the data set. β-Diversity was estimated by using principal coordinate analysis on the Bray-Curtis dissimilarity matrix at the genus level. Samples were clustered by using the partitioning around medoids algorithm. A heat map showing relative abundance of the most abundant genera (>1% relative abundance) and a dendrogram were drawn by using Ward hierarchical clustering (Bray-Curtis distance, genus level). A co-occurrence network was built with the Sparse Correlations for Compositional (SparCC) data algorithmE12Friedman J. Alm E.J. Inferring correlation networks from genomic survey data.PLoS Comput Biol. 2012; 8: e1002687Crossref PubMed Scopus (1154) Google Scholar on the most abundant genera (>1% relative abundance). Significant associations were defined as positive SparCC data correlations with a P value of less than .05 (pseudo-P values were calculated by using a bootstrap procedure with 999 random permutations and 999 iterations for each SparCC data calculation). Network visualization (undirected positive or negative co-occurrence) was carried out with the Fruchterman-Reingold layout algorithm and the igraph package. Statistical analysis was performed with GraphPad Prism software (version 7; GraphPad Software, La Jolla, Calif). A power calculation was unable to be performed because of insufficient data to inform it. Distribution of data was assessed by using the D'Agostino-Pearson test. For normally distributed continuous data, differences between groups were assessed by using the Student t test. For nonparametric data, intergroup differences were analyzed by using the Mann-Whitney test. Categorical data were analyzed by using the 2-tailed Fisher exact test. Values are expressed as medians with interquartile ranges. Statistical significance was accepted at P values of less than .05 and less than .01. For histologic counts, intraobserver variability was calculated by analyzing a subset of samples 3 times with a minimum of 2 weeks between counts and presented as the coefficient of variation. A subset of samples was counted by a minimum of 2 investigators, and interobserver variability was analyzed with Bland-Altman plots for 2 observers or as the coefficient of variation for 3 observers. Because participants were undergoing clinically indicated tests, not all patients had blood and BAL fluid samples taken. Additionally, a subset of samples did not meet the RBH laboratory's quality control standards and were excluded. Hence the sample size for each parameter is stated in the table or figure legend. Fig 1. Airway microbial dysbiosis in patients with severe preschool wheeze. A, Eosinophil percentage according to atopic status in all wheezers in peripheral blood (data available for atopic [n = 7; black circles] and nonatopic [n = 27; gray circles] subjects) and BAL fluid (data available for atopic [n = 7] and nonatopic [n = 24] subjects). Each point represents an individual child. B, For microbiology in MTWs, BAL fluid neutrophil percentages related to BAL fluid positive for bacterial culture. Black circles represent positive microbiological results, and gray circles represent negative microbiological results (data are available for positive [n = 7] and negative [n = 11] results). For microbiology in EVWs, data are available for positive (n = 7) and negative (n = 6) results. Each point represents an individual child. C, Principal coordinate analysis score plot on Bray-Curtis distance at the genus level, with each dot representing an individual patient. Colors indicate differential mixed (gray) and Moraxella (black) microbiota profiles, as determined by using an unbiased partitioning around medoids clustering algorithm, with the rectangle showing the cluster centroid. D, Heat map of the most abundant genera (>1% mean relative abundance), with each column representing an individual patient. The color-scale bar indicates operational taxonomic units (OTU) relative abundance from 0% to 100%. The dendrogram represents Ward hierarchical clustering on Bray-Curtis distance at the genus level. E, Taxonomic co-occurrence network of the most abundant genera (>1% mean relative abundance) on the total data set using the SparCC data algorithm. Thick edges indicate the extent to which bacterial genera positively (green) or negatively (red) co-occur, and node size is proportional to genus mean relative abundance. Only significant correlations (1-sided P < .05) are represented in color. F-H, Results of cytospin cell differentials showing percentages of macrophages/monocytes, lymphocytes, and neutrophils in BAL fluid and peripheral blood in relation to microbiota profiles. Each point represents an individual child. I and J, Distribution of microbiota profiles (gray, mixed profile; black, Moraxella profile) by clinical wheeze phenotype (Fig 1, I) and atopy status (Fig 1, J). Statistics represent results of the D’Agostino and Pearson normality test, followed by the Mann-Whitney test for nonparametric data (Fig 1, A, B, and F-H). ∗P < .05 and ∗∗P < .01.Table E1Inclusion and exclusion criteria for patient recruitmentInclusion criteriaExclusion criteriaAge 12-72 moUndergoing elective and clinically indicated bronchoscopy between September 2015 and August 2016Known diagnosis of chronic lung diseaseCystic fibrosisGestation of 36 wk or greater at birthPrimary ciliary dyskinesiaRecurrent episodes of wheeze and difficulty breathing severe enough to merit referral to a pediatric pulmonologistInterstitial lung diseaseDysfunctional breathingPsychosocial problemsParent/guardian able to provide written informed consentAble to give age-appropriate assentPanic attacksAcute illness or recent exacerbation Open table in a new tab