Airway microbiota and bronchial hyperresponsiveness in patients with suboptimally controlled asthma

Article Outline

• Abstract
• Methods
  • Subjects
  • Sample processing
  • Quantitative PCR analysis
  • Data analysis
  • 16S rRNA clone library sequencing
  • Phylogenetic tree construction
• Results
  • Airway bacterial burden
  • Airway microbiota structure and clinical features of asthma
  • Airway bacterial diversity, community composition, and bronchial hyperresponsiveness
  • 16S rRNA clone library-sequencing validation
• Discussion
• Acknowledgment
• Methods
  • Subject characterization and study procedures
  • Sample preparations and array processing
  • Q-PCR
  • Microarray data analysis
  • 16S rRNA clone library-sequencing
• Appendix E1
  • Collaborators
• Fig E1. 
• Supplementary data
• References
• References
• Copyright

Background

Improvement in lung function after macrolide antibiotic therapy has been attributed to reduction in bronchial infection by specific bacteria. However, the airway might be populated by a more diverse microbiota, and clinical features of asthma might be associated with characteristics of the airway microbiota present.

Objective

We sought to determine whether relationships exist between the composition of the airway bacterial microbiota and clinical features of asthma using culture-independent tools capable of detecting the presence and relative abundance of most known bacteria.

Methods

In this pilot study bronchial epithelial brushings were collected from 65 adults with suboptimally controlled asthma participating in a multicenter study of the effects of clarithromycin on asthma control and 10 healthy control subjects. A combination of high-density 16S ribosomal RNA microarray and parallel clone library-sequencing analysis was used to profile the microbiota and examine relationships with clinical measurements.

Results

Compared with control subjects, 16S ribosomal RNA amplicon concentrations (a proxy for bacterial burden) and bacterial diversity were significantly higher among asthmatic patients. In multivariate analyses airway microbiota composition and diversity were significantly correlated with bronchial hyperresponsiveness. Specifically, the relative abundance of particular phylotypes, including members of the Comamonadaceae, Sphingomonadaceae, Oxalobacteraceae, and other bacterial families were highly correlated with the degree of bronchial hyperresponsiveness.

Conclusion: The composition of bronchial airway microbiota is associated with the degree of bronchial hyperresponsiveness among patients with suboptimally controlled asthma. These findings support the need for further functional studies to examine the potential contribution of members of the airway microbiota in asthma pathogenesis.

Key words: Microbiome, bacteria, asthma, 16S ribosomal RNA, PhyloChip

Abbreviations used: ICS, Inhaled corticosteroid, iNKT, Invariant natural killer T, MIA, Macrolides in Asthma, MRPP, Multiresponse Permutation Procedure and Indicator Species Analysis, NMDS, Nonmetric multidimensional scaling, Q-PCR, Quantitative PCR, 16S rRNA, 16S ribosomal RNA

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Interest in the potential role of bacterial colonization or infection of the bronchial mucosa in the pathogenesis of asthma has been raised by several recent reports: associations between bacterial colonization of neonatal airways and subsequent asthma development,¹ evidence of bronchial infection by specific intracellular bacteria among adult asthmatic patients,² and the efficacy of prolonged macrolide antibiotic treatment in subsets of asthmatic patients.³, ⁴, ⁵ Although the role of microbial infection in asthma pathogenesis remains unclear, studies to date have primarily applied culture-based or targeted molecular approaches to identify specific bacterial species of interest in the airways. However, recent developments in defining the human microbiome have demonstrated that the composition of bacterial communities colonizing mucosal surfaces, rather than simply the presence of individual species, can be important in defining states of health or disease.⁶, ⁷, ⁸, ⁹, ¹⁰, ¹¹

Culture-independent microbiota profiling based on sequence polymorphisms in the 16S ribosomal RNA (16S rRNA) gene, which is present in all bacteria, has been widely applied in environmental ecologic studies¹², ¹³, ¹⁴ and in studies of the human “superorganism.”⁹, ¹⁰, ¹¹ 16S rRNA–based phylogenetic analysis through sequencing or phylogenetic arrays permits detection of uncultured or fastidious bacteria, providing information on community composition without a priori knowledge of those present.¹⁵, ¹⁶, ¹⁷ One such platform, the 16S rRNA PhyloChip, has been applied in a number of environmental¹², ¹⁴, ¹⁵, ¹⁸ and clinical⁶, ¹⁹, ²⁰ studies. This high-density array contains approximately 500,000 probes that can differentiate approximately 8,500 bacterial taxa (defined as groups of organisms sharing ≥97% 16S rRNA sequence homology)¹⁵, ²¹ and demonstrates greater resolution of complex bacterial communities than traditional 16S rRNA clone library-sequencing approaches.⁶, ¹⁸, ²¹ It provides an ideal tool to examine relationships between microbiota composition, including the presence and relative abundance of members of the “rare biosphere,”²² and clinical characteristics of the disease.

Recognizing the frequency with which the tracheobronchial tree is exposed to the external environment and to secretions from the oropharynx or upper gastrointestinal tract, we hypothesized that complex bacterial communities might colonize asthmatic airway mucosa and exhibit relationships with clinical features of disease. Therefore in conjunction with a prospective study of the effects of extended clarithromycin therapy in adults with suboptimally controlled asthma,²³ we conducted a pilot study of the airway microbiota in 65 adult asthmatic patients and 10 healthy control subjects using the PhyloChip and parallel clone library sequencing. Bronchial airway samples from asthmatic patients possessed greater bacterial burden and diversity than those from healthy subjects. Furthermore, the degree of bronchial hyperresponsiveness exhibited by subjects was related to community composition and the relative abundance of specific bacterial families comprising the airway microbiota. Portions of these results have previously been presented in abstract form.²⁴, ²⁵

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Methods 

Subjects 

Bronchial epithelial samples for microbial analysis were obtained from a subset of subjects enrolled in the Macrolides in Asthma (MIA) study²³ (NCT00318708, clinicaltrials.gov) conducted by the National Heart, Lung, and Blood Institute–sponsored Asthma Clinical Research Network. Briefly, adults with clinically stable but suboptimally controlled asthma, defined as persistent symptoms on the Asthma Control Questionnaire²⁶ after 4 weeks of standardized treatment with inhaled fluticasone, were studied. Relevant inclusion/exclusion criteria and study procedures are described in the Methods section in this article’s Online Repository at www.jacionline.org, including exclusion for symptoms of respiratory tract infection or asthma exacerbation within 6 weeks of the bronchoscopy visit. Bronchoscopy was performed before randomization to clarithromycin or placebo therapy. Three protected specimen bronchial brushings for microbiome analysis were obtained and sent to the University of California, San Francisco, where 10 healthy, nonsmoking, nonatopic adults without asthma were also enrolled. All protocols were approved by the institutional review board at each center.

Sample processing 

All samples first underwent screening by using PCR for the presence of the 16S rRNA gene with extracted total DNA (100 ng) and the universal 16S rRNA primers Bact-27F and Bact-1492R²⁷ in a 40-cycle reaction. Samples were deemed positive or negative for bacteria based on the presence or absence of a visible 16S rRNA PCR product. Samples with any evidence of a 16S rRNA PCR product were subsequently subjected to 8 PCR reactions (per sample) by using the same primer set across a temperature gradient (48°C to 58°C) to maximize bacterial diversity captured. Amplicons from each sample were then pooled, purified, and gel quantified with E-gels (Invitrogen, Carlsbad, Calif) before hybridization of a standardized quantity to the array for each sample, as previously described.⁶, ¹⁵

Quantitative PCR analysis 

Quantitative PCR (Q-PCR) was performed for 21 samples to determine the total 16S rRNA copy number normalized to β-actin copy numbers. Further details are provided in the Methods section of this article’s Online Repository.

Data analysis 

Relationships between microbiota structure and study variables were analyzed by using multivariate statistics in combination with the Multi-response Permutation Procedure and Indicator Species Analysis (MRPP) and nonmetric multidimensional scaling (NMDS) ordination.²⁸ Data were log transformed before analysis, where appropriate. Sorensen distance-based dissimilarity matrices were calculated and used for subsequent NMDS analysis to examine the relatedness of samples based on their microbiota composition. Three samples from asthmatic patients with mean relative Sorensen distances of greater than 2 deviations from the pooled mean were excluded for this analysis. Pearson correlations were performed to determine relationships between the relative abundance of all detected phylotypes and study variables with q value corrections for false discovery.²⁹ Significance was conservatively based on a false discovery rate of less than 3% and a probability of no more than 2 taxa being falsely identified as significant for each test. Community diversity was estimated by using Shannon indices³⁰ based on the number of taxa present (community “richness”) and their relative abundance (community “evenness”). Statistical analyses were conducted with PC-ORD version 5,³¹ JMP version 7 (SAS Institute, Inc, Chicago, Ill), and R software version 2.7.1 (http://www.r-project.org).

16S rRNA clone library sequencing 

Clone libraries for a subset of patients were constructed (by using the same 16S rRNA amplicon pool hybridized to the array) and sequenced at the Department of Energy’s Joint Genome Institute (299-374 clones per library). Quality-filtered sequences were aligned and chimera checked with NAST³² and Bellerophon,³³ respectively, and then classified by means of G2-chip taxonomy (www.greengenes.lbl.gov).³⁴

Phylogenetic tree construction 

Representative 16S rRNA sequences for taxa of interest were exported from the Greengenes database. A neighbor-joining tree with nearest-neighbor interchange was produced with FastTree.³⁵ Trees were annotated by using the Interactive Tree of Life (http://www.itol.embl.de/).³⁶

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Results 

Airway bacterial burden 

Bronchial brushings were obtained from 75 subjects: 65 asthmatic patients and 10 healthy control subjects. Subjects’ characteristics are summarized in Table I. All asthmatic patients fulfilled entry criteria for the parent MIA trial,²³ and samples for this pilot study were processed as they were received with no additional selection criteria applied. On initial screening, 54 (83%) of 65 asthmatic patients and 8 (80%) of 10 control subjects exhibited a visible 16S rRNA PCR product. However, not all PCR-positive samples subsequently produced sufficient 16S rRNA amplicon for PhyloChip analysis. Although very small amplicon concentrations can be hybridized to the array, in our experience application of 250 ng provides good characterization of the respiratory microbiota.⁸, ¹⁹ A greater proportion of asthmatic patients (37/65 [56.9%]) than healthy subjects (3/10 [30%]) produced 250 ng of amplicon, although this difference was not significant (P = .17). To increase the number of samples for comparative analyses, we analyzed the community composition detected by using the PhyloChip in 6 paired samples with both 100 and 250 ng of amplicon. Finding no significant difference in the community composition detected in paired samples (P_MRPP > 0.1), 7 additional subjects (5 asthmatic patients and 2 healthy subjects) were assayed by using PhyloChip with 100 ng of amplicon. Therefore array data from a total of 47 subjects (42 asthmatic patients and 5 healthy control subjects) were obtained for the subsequent analyses (see Fig E1 in this article’s Online Repository at www.jacionline.org).

Table I. Baseline characteristics of subjects

Values are presented as means ± SDs, except where noted.

NA, Not applicable; NS, not significant.

To confirm that 16S rRNA amplicon concentrations (in nanograms per microliter) reported by means of gel quantification accurately reflected bacterial burden, we performed total 16S rRNA Q-PCR and regression analysis on these independent measurements, which demonstrated strong concordance (r = 0.69, P < .001). These data also confirmed that samples with sufficient amplicon for array analysis had significantly greater 16S rRNA copy numbers compared with those unable to be analyzed by using PhyloChip (P = .002). Therefore gel-quantified 16S rRNA amplicon concentrations were used as a proxy for bacterial burden to examine potential relationships between airway bacterial burden and clinical variables. Of the samples analyzed by using PhyloChip, bacterial burden was significantly higher in asthmatic samples compared with that seen in control samples (P = .008, Fig 1). Bacterial burden appeared to decrease with increasing methacholine PC₂₀ measurements, although this did not reach statistical significance (r = −0.21, P = .09), suggesting that community composition rather than actual bacterial burden might be more related to this clinical feature.

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• Fig 1. 

  16S rRNA bacterial burden in bronchial brushings from asthmatic patients and healthy subjects. Of subjects analyzed by using PhyloChip (total n = 47), asthmatic patients exhibited greater bacterial burden (measured concentration of 16S rRNA PCR product) than healthy subjects (P = .008, Welch t test with log-transformed data). Median and interquartile ranges are noted.

Airway microbiota structure and clinical features of asthma 

Phylogenetic analysis identified 1941 bacterial taxa (detected in at least 1 subject) representing 161 bacterial families (see Table E1 in this article’s Online Repository at www.jacionline.org). As a form of validation, we compared the phylogenetic distribution of airway microbiota detected by using the array with reference trees for airway bacteria reported in the Human Microbiome Project (http://www.hmpdacc.org/reference_genomes.php; accessed May 13, 2010)³⁷ and observed good agreement in the types of bacteria reported by both (data not shown). Community richness across samples from asthmatic patients was variable and ranged from 48 to 1240 taxa. Of the 5 healthy subjects, 3 exhibited low community richness (200-253 taxa), whereas 2 demonstrated greater richness (844 and 1121 taxa), suggesting that variability in airway microbiota composition was not exclusive to asthmatic patients.

We found no association between study center and variation in microbiota composition, indicating that sample collection by different operators in distinct geographic regions was not a major influence on community composition in this study. Multivariate analyses also showed no association between microbiota composition and spirometric measurements (FEV₁ and forced vital capacity), sputum eosinophil or neutrophil percentages, history of exacerbations, or systemic corticosteroid treatment within the previous 2 years. However, microbiota structure differed significantly based on asthmatic or healthy group assignment (P_MRPP = .028), and NMDS analysis demonstrated that variability in airway microbiota composition was correlated with both the degree of airway hyperresponsiveness (methacholine PC₂₀ concentrations; r = 0.56, P < .001; Fig 2, A) and bacterial burden (r = −0.36, P = .015; Fig 2, B). This suggests that the types and relative abundance of bacteria present in the airways are associated with these 2 variables.

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• Fig 2. 

  Relationships between variability in airway microbiota composition and community diversity with bronchial hyperresponsiveness. Each circle represents the microbiota present in a single sample (n = 44). A and B, NMDS ordination, based on Sorensen dissimilarity matrices, demonstrates that variability in community composition is most strongly related to bronchial hyperresponsiveness and bacterial burden. The NMDS axes have no inherent units and indicate spatial relationships of samples based on their phylogenetic (dis)similarity. Three samples from asthmatic patients were excluded due to extreme mean relative Sorensen distances from the pooled mean. C, Increased diversity (higher Shannon indices) is correlated with greater bronchial hyperresponsiveness (Pearson correlations with log-transformed data).

Airway bacterial diversity, community composition, and bronchial hyperresponsiveness 

Asthmatic patients possessed greater airway microbiota diversity than control subjects (P = .012). Methacholine PC₂₀ concentrations were inversely correlated with diversity (r = −0.46, P < .003; Fig 2, C), suggesting a strong relationship between increased airway bacterial diversity and greater bronchial hyperresponsiveness.

Relationships between PC₂₀ and relative abundance of all taxa detected were analyzed to determine which of the airway microbiota were most associated with bronchial hyperresponsiveness. After false discovery correction, approximately 100 taxa demonstrated a significant (P < .01, q ≤ 0.015) linear relationship between increasing relative abundance and greater bronchial hyperresponsiveness (Fig 3 and see Table E2 in this article’s Online Repository at www.jacionline.org). These taxa, primarily belonging to the Proteobacteria, represented 31 bacterial families, such as the Comamonadaceae, Sphingomonadaceae, Nitrosomonadaceae, Oxalobacteraceae, and Pseudomonadaceae, and included a number of potentially interesting organisms. Although samples were collected in a manner to minimize oral secretion contamination (eg, use of triple-lumen protected specimen brushes and sample collection early in the procedure preferentially from upper lobes), we recognized the potential for organisms associated with the oropharynx to reside in the lower airways. Thus we investigated whether these approximately 100 phylotypes have previously been identified in the oral cavity by interrogating both the extensive human oral microbiome sequence database³⁸ and PubMed using species or genus names and the search terms “oral,” “oropharynx,” “mouth,” or “tongue.” The majority (87.5%) of these phylotypes were not found referenced to the oral cavity in either database.

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• Fig 3. 

  Phylogenetic tree based on 16S rRNA representative gene sequences (GenBank accession numbers) of the approximately 100 bacterial taxa highly correlated with greater bronchial hyperresponsiveness (P < .01, q < 0.015). Colors represent different bacterial families. Asterisks denote taxa with member species previously associated with clinical disease or possessing notable functional features (see also Table E2 in this article’s Online Repository).

Posttreatment bronchoscopy was not performed in the parent study, and therefore examination of relationships between clarithromycin-induced changes in airway microbiota composition and treatment outcomes was not possible. Instead, we explored whether relationships existed between pretreatment airway microbiota and posttreatment clinical outcomes. Airway bacterial diversity was significantly greater in asthmatic subjects who demonstrated a significant reduction in bronchial reactivity in response to clarithromycin treatment (defined as at least a doubling in methacholine PC₂₀ dose before and after treatment) than those with a smaller reduction (P = .03, Fig 4). This pattern was not evident in patients who received placebo intervention (P = .64).

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• Fig 4. 

  Baseline airway microbiota diversity and change in bronchial hyperresponsiveness with clarithromycin treatment. Sixteen asthmatic patients with pretreatment samples analyzed by means of microarray received clarithromycin therapy. Subjects with significant improvement in bronchial hyperresponsiveness after clarithromycin (n = 9) possessed higher pretreatment airway bacterial diversity than those with no significant response (n = 3). Four subjects with incomplete data because of study discontinuation were not included.

16S rRNA clone library-sequencing validation 

The PhyloChip, like other array technologies, is potentially susceptible to cross-hybridization, despite features of the array design intended to minimize this. 16S rRNA clone libraries were constructed with the same amplicon pool hybridized to the array for a subset of asthmatic patients (n = 6) to validate array-based findings. An average of 333 clones per sample were analyzed, yielding approximately 2000 nonchimeric sequences representing 197 taxa, the majority of which were present in more than 1 sample. The majority of bacterial subfamilies identified by means of sequencing were also present in the corresponding array dataset, validating large numbers of array-based positive hits (Table II). Two subfamilies (the Fusobacteriaceae subfamily 3 and Prevotellaceae subfamily 1) were detected by means of sequencing only in 2 samples (1 subfamily per sample). However, taxa within these subfamilies were very close (positive-fraction value = 0.84 and 0.75, respectively) to the conservative arbitrary cutoff (positive-fraction value ≥ 0.9) used to define array-based presence or absence.¹⁵ Clone library analysis also indicated that 2 samples (AB01 and AB04) were each dominated by a specific bacterial family. The majority of sequences for sample AB01 were classified as Pasteurellaceae subfamily 1 (this sample also exhibited the lowest array-reported community richness [48 taxa]), whereas for subject AB04, a majority of the sequences belonged to the Cellulomonadaceae.

Table II. Bacterial families identified by using both 16S rRNA clone library sequencing and 16S rRNA PhyloChip

Phylotypes reported at the subfamily level classified by using Greengenes G2 chip taxonomy (number of clones sequenced per library indicated in parentheses). Subfamilies in boldface were undetected by means of array based on conservative criteria for determining taxon presence.

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Discussion 

To our knowledge, this is the first study to examine whether specific aspects of the airway microbiota are related to relevant clinical or physiologic features of asthma. Our findings suggest that bacterial diversity, variations in community composition, and the relative abundance of specific phylotypes are associated with the degree of bronchial hyperresponsiveness in asthmatic patients administered inhaled corticosteroids (ICSs). This culture-independent study also expands the repertoire of organisms of potential relevance to asthma pathogenesis within this cohort beyond those previously implicated.¹, ² These data, together with recent studies in other asthmatic patients,⁷ suggest that the microbiome of the airways, as with other discrete host niches, might be an important contributor to asthma pathophysiology and to the heterogeneity of disease.³⁹, ⁴⁰, ⁴¹

Overall, these findings highlight 2 key aspects in defining airway health. The existence of a bronchial microbiota suggests that the bronchial tree is unlikely to be completely sterile and that the composition of its microbiome might directly or indirectly modulate airway function. The role of airway microbiota in airway disease is underinvestigated compared with other human niches, such as the intestinal microbiota, which has a demonstrated role in chronic inflammatory gastrointestinal diseases.¹⁰, ⁴² That a bronchial epithelium–associated microbiota exists is not entirely unexpected, particularly in the setting of airway disease, ICS therapy, or both. Specific bacterial communities persist in the airways despite antimicrobial treatment in patients with chronic obstructive pulmonary disease and cystic fibrosis.⁸, ¹⁹ Recently, Hilty et al⁷ also described airway microbiota in patients with mild-to-moderate asthma (all receiving ICS therapy), patients with chronic obstructive pulmonary disease, and healthy subjects. Those with airway disease had an increased prevalence of pathogenic Proteobacteria compared with control subjects, although relationships to clinical measurements were not examined in their study.

Complex microbial communities are now recognized to reside in various host mucosal sites⁴³ where perturbations of community structure are associated with disease.⁶, ⁸, ¹⁰, ⁴² Moreover, the airway epithelium is increasingly recognized as important in immunologic responses to environmental and microbial exposures.⁴⁴ Thus the airway microbiome could potentially influence presentations of asthma. As such, our findings of specific community relationships with bronchial hyperresponsiveness in the context of a well-characterized asthmatic cohort receiving standardized baseline treatment implicate additional bacterial groups in asthma. These include a Nitrosomonas species possessing a functional nitric oxide reductase.⁴⁵ Their relative abundance might be a microbial indicator of airway concentrations of nitric oxide that, although not assessed in this study, reflect airway inflammation⁴⁶ and correlate with measures of airway hyperresponsiveness.⁴⁷ Also notable is Oxalobacter formigenes (Oxalobacteraceae), an anaerobic bacterium known to be susceptible to macrolide antibiotics.⁴⁸ Other bacteria of interest include the Comamonadaceae and Sphingomonadaceae. Members of the Comamonadaceae, previously identified in patients with cystic fibrosis,⁴⁹ possess steroid-responsive degradation pathways,⁵⁰, ⁵¹ and their presence could plausibly be related to the selective pressure of ICS therapy. This raises the intriguing possibility that steroid nonresponsiveness, as observed in some asthmatic patients despite adherence to therapy,⁵² might be due in part to the presence of airway bacteria with steroid-degrading capacity.

Given the existence of airway microbiota, it is conceivable that the coincident presence of multiple potentially pathogenic bacteria contributes to persistent airway inflammation in asthma. This possibility is supported by the finding of neutrophilia in bronchial airway samples of some patients with severe asthma despite regular use of high-dose ICSs.⁵³ Such inflammation could represent an appropriate response by the host to inappropriate airway microbial colonization. For example, Sphingomonadaceae, members of which were significantly correlated with bronchial hyperresponsiveness, are characterized by the presence of cell-membrane glycosphingolipids, which are recognized by and can activate invariant natural killer T (iNKT) cells, resulting in induction of IL-4 and IL-13.⁵⁴, ⁵⁵ Glycolipid activation of iNKT cells can also induce airway hyperreactivity independent of conventional CD4⁺ T cells.⁵⁶ Although the presence of iNKT cells in asthmatic airways remains controversial,⁵⁷, ⁵⁸ our findings suggest a possible role for Sphingomonadaceae in airway pathophysiology and the possibility that the observed variability of iNKT cell populations in asthmatic patients might depend in part on the relative abundance of these species.

We recognize that this pilot study is limited by the relatively small number of subjects, especially healthy control subjects, and by the absence of asthmatic patients not taking an ICS. Nonetheless, the data indicate that airway bacterial burden in subjects without airway disease is much lower than in asthmatic patients requiring ICS therapy. Because all asthmatic patients were required to be taking standardized ICSs for the parent study,²³ we are unable to infer whether the higher bacterial burden among asthmatic patients is a function of the disease itself or ICS treatment, although the relationships of bacterial burden and the composition and diversity of the microbiota to bronchial hyperresponsiveness are robust. Determining the effects of ICS use on the airway microbiome will require further investigation and is an important question given the wide use of ICS therapies in airway diseases, which, among patients with chronic obstructive pulmonary disease, has been associated with an increased risk of pneumonia.⁵⁹

The array-based analysis in this study identified a greater diversity of airway microbiota than has previously been described, although to our knowledge, there has been only one other study involving asthmatic patients that applied 16S rRNA phylogenetic analysis.⁷ In addition to clone library-sequencing validations, our results also agreed with those of Hilty et al⁷ in that we detected all bacterial phyla and genera identified in their study of the lower airways of 11 adult and 13 pediatric asthmatic patients on ICS therapy. The PhyloChip platform permits in-depth analysis of relatively large numbers of samples by using a standardized assay that applies several levels of stringent criteria for determining the microbiota profile in a given sample. It is impractical to perform sequence corroboration for every array-based positive hit. Moreover, the extent of microbial diversity detected is dependent on the community structure and the depth of sequencing performed.⁶⁰ Our study included the largest number of clones sequenced to date for asthmatic airway samples and confirmed the presence of many array-detected phylotypes, indicating that complex microbiota do exist in this niche.

Finally, we observed that asthmatic patients exhibiting a significant decrease in bronchial hyperresponsiveness after clarithromycin treatment in the parent study possessed greater pretreatment airway bacterial diversity than nonresponders. Despite the small number of subjects in this exploratory analysis, the trend is interesting given that prior studies have also observed decreased bronchial responsiveness after prolonged macrolide therapy.⁴, ⁶¹ Furthermore, although the antimicrobial susceptibility of the entire airway microbiota is unknown, conceivably, a multitude of community members might be sensitive to macrolides, resulting in a reduction in bacterial diversity, burden, or both on treatment and manifesting clinically in a reduction in bronchial responsiveness. Thus although the efficacy of macrolides in airway disease is often attributed to their anti-inflammatory properties, their effectiveness could also reflect an extended spectrum of antibacterial activity against members of the airway microbiota, the composition of which, as in the setting of ICSs, might influence outcomes.

In summary, several features of the airway microbial community are significantly associated with the degree of bronchial hyperresponsiveness among patients with suboptimally controlled asthma. Despite limitations, these study findings are notable in providing the first evidence for the potential functional, physiologic, and clinical relevance of the airway microbiome in asthma. Our results suggest that variations in airway microbiome structure and function might exert distinct effects that contribute to asthma heterogeneity and provide novel targets and hypotheses for future studies on disease mechanisms.

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We thank the Steering Committee of the Asthma Clinical Research Network, which permitted and indeed encouraged our use of clinical data from the MIA trial for our analysis of relationships with bronchial microbial community composition, and we also thank the Asthma Clinical Research Network investigators (see the Acknowledgments section) and coordinators who obtained and provided the protected bronchial brush samples from asthmatic patients. We also thank Yvette Piceno and Gary Andersen at the Lawrence Berkeley National Laboratory for their PhyloChip expertise and advice.

The Asthma Clinical Research Network investigators were as follows: Bill T. Ameredes, Eugene Bleecker, William J. Calhoun, Mario Castro, Reuben Cherniack, Vernon M. Chinchilli, Timothy J. Craig, Loren Denlinger, Emily DiMango, John Fahy, Elliot Israel, Nizar Jarjour, Monica Kraft, Stephen C. Lazarus, Robert F. Lemanske, Stephen Peters, Joe Ramsdell, Robert A. Smith, Christine A. Sorkness, Stanley J. Szefler, Michael J. Walter, Stephen Wasserman, and Michael E. Wechsler.

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Methods 

Subject characterization and study procedures 

For the MIA clinical trial, adult men and women 18 to 60 years of age meeting the diagnostic criteria for asthma were enrolled through 10 Asthma Clinical Research Network clinical research centers. Subjects had to have suboptimal asthma control, which was defined as persistent symptoms at least as severe as those of mild persistent asthma, as reflected by an Asthma Control Questionnaire score of greater than 1.25, after 4 weeks of standardized treatment with 88 μg of fluticasone proprionate administered twice daily through a metered-dose inhaler (GlaxoSmithKline, London, United Kingdom). Exclusion criteria included a smoking history of greater than 10 pack-years, unstable asthma, respiratory tract infection or exacerbation less than 6 weeks before enrollment, postbronchodilator FEV₁ of less than 60% of predicted value, and other pulmonary disease. The institutional review board at each center approved all study protocols. After the run-in treatment period with inhaled fluticasone, sputum induction was performed for differential cell counts, followed by flexible fiberoptic bronchoscopy 3 to 10 days later after achievement of local anesthesia, as previously described.^E1 This was performed before randomization to treatment with clarithromycin or placebo in the parent trial. During bronchoscopy, 3 bronchial brushings with triple-lumen protected specimen brushes were obtained for microbiome analysis, primarily from the upper or middle lobes of the lung, and immediately placed into 2 mL of RNALater. Samples were placed at 4°C for at least 24 hours before shipping on blue ice to the University of California, San Francisco. In addition, healthy nonsmoking adults were enrolled separately at the University of California, San Francisco, and ruled out for asthma and atopy based on the presence of normal spirometric results, normal bronchial reactivity to methacholine (PC₂₀ >16 mg/mL), and negative responses to skin testing with 10 mixes of allergens common to northern California.

Sample preparations and array processing 

Sterilized forceps were used to transfer 3 protected specimen bronchial brushes to a single lysing matrix B tube containing 0.1-mm silica spheres (MP Biomedicals, Solon, Ohio). Original collection tubes were centrifuged at 12,000g, RNALater was removed, and Buffer RLT (650 μL, AllPrep kit; Qiagen, Valencia, Calif) was used to resuspend the pellet before transfer into the lysing matrix B tube containing the brushes. Bead beating at 5.5 m/s for 30 seconds was carried out with the FastPrep system (MP Biomedicals). Tubes were then centrifuged at 2,000g for 2 minutes, and the supernatant was transferred to a DNA spin column of the AllPrep Kit and further processed according to kit instructions.

Samples were screened by means of PCR for the presence of a 16S rRNA gene product with 100 ng of extracted DNA and 15 μmol each of the universal 16S rRNA primers^E2 Bact-27F (5′-AGAGTTTGATCCTGGCTCAG-3′) and Bact-1492R (5′-GGTTACCTTGTTACGACTT-3′) in 50-μL volume reactions at an annealing temperature of 53°C for 40 cycles in an Eppendorf MasterCycler gradient PCR machine. In addition to negative controls, parallel poison control PCR reactions (with Pseudomonas aeruginosa DNA) were performed to rule out PCR inhibition for those samples with no evidence of 16S rRNA PCR product on a 1% ethidium bromide–stained agarose gel. Those with a PCR-positive screen for a 16S rRNA PCR product then proceeded to sample preparation for array hybridization and processing (Fig E1). For this, eight 25-μL PCR reactions, each containing 100 ng of extracted sample DNA and 8 μmol of each of Bact-27F and Bact-1492R primers, were performed in 30 cycles of amplification across a gradient of annealing temperatures (48°C to 58°C). 16S rRNA amplicons for each sample were then pooled, purified, and gel quantified before array hybridization (E-gel 2% and E-gel Low Range Quantitative Ladder, Invitrogen). A standard concentration of 250 ng (or 100 ng in 7 samples, as discussed in the Results section) of DNAse-fragmented and biotin-labeled 16S rRNA amplicons from each sample spiked with known concentrations of synthetic 16S rRNA gene fragments and non-16S rRNA gene fragments as internal controls for interarray normalization was hybridized to each array, followed by washing, staining, and scanning of arrays (GeneArray scanner; Affymetrix, Santa Clara, Calif) as previously described.^E3., ^E4.

Q-PCR 

Triplicate 25-μL reactions were performed in 21 samples by using the QuantiTect SYBR Green PCR master mix kit (Qiagen), 10 ng of extracted DNA, and the 16S rRNA Q-PCR primers^E5 338-F (5′-ACTCCTACGGGAGGCAGCAG-3′) and 518-R (5′- ATTACCGCGGCTGCTGG-3′) at a final concentration of 0.3 μmol/L under the following conditions: 95°C for 10 minutes, followed by 40 cycles of denaturation at 95°C for 30 seconds, annealing at 53°C for 30 seconds, extension at 72°C for 30 seconds, and final extension at 72°C for 10 minutes. Standard curves at serial log concentrations of 1 × 10² to 1 × 10⁸ were generated from a PCR product obtained by using the universal 16S rRNA primers Bact-27F and Bact-1492R to calculate total 16S rRNA gene copy numbers. Regression coefficients (r²) for all standard curves were greater than 0.99. Total 16S rRNA copy numbers for each sample were normalized to mammalian β-actin copy numbers in each sample, which were determined by means of Q-PCR with β-actin primers, as previously described.^E6

Microarray data analysis 

After scaling and normalization of the raw data, relatively conservative criteria were applied to determine the presence or absence of bacterial taxa by using previously described methods.^E3., ^E4. Briefly, taxon presence was determined based on a positive-fraction value corresponding to 90% of the probe sets for an individual taxon. Each taxon probe set is comprised of a minimum of 11 probe pairs (a perfect match and a mismatch probe differing at a single central oligonucleotide) to distinguish taxa on the basis of at least 2 discriminatory loci on the 16S rRNA gene. Relative abundance estimates were based on averaged, log-transformed fluorescence intensities for each taxon. Sorenson distance measures were calculated by using the software package PC-ORD version 5. Three samples from asthmatic patients were identified as statistical outliers with a mean relative Sorensen distance of greater than 2 SDs from the pooled mean and were not included in the array-based analyses of community structure and diversity.

16S rRNA clone library-sequencing 

Clone libraries were constructed by using the same 16S rRNA amplicon pool that was hybridized to the array. 16S rRNA PCR products were gel purified, and 3′ adenine overhangs were reintroduced after amplification through Taq DNA polymerase for improved cloning efficiency. Purified amplicons were ligated to the pCR4-TOPO vector by using the TOPO TA Cloning Kit (Invitrogen). Ligation mixtures were electroporated into One Shot TOP10 Electrocomp Escherichia coli cells (Invitrogen) and plated on selective agar. Two hundred ninety-nine to 374 clones per library were sequenced on an ABI PRISM 3730 capillary DNA sequencer (Applied Biosystems, Foster City, Calif) at the Department of Energy’s Joint Genome Institute (Walnut Creek, Calif), according to standard Joint Genome Institute protocols (www.jgi.doe.gov). Bidirectional sequence reads were end paired, trimmed for vector and PCR primer sequence, and filtered for quality. This yielded 1,997 sequences for 6 asthmatic libraries, which were aligned by using the NAST algorithm for multiple sequence alignment, checked for chimeras with Bellepheron, and classified by means of G2-chip taxonomy, all available as Greengenes Web-based tools (http://greengenes.lbl.gov). Taxa identified by means of clone library sequencing and by means of corresponding array data in these subjects were compared to determine the number of phylotypes detected by using the array, by sequencing alone, or by both methods.

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Appendix E1 

Collaborators 

The following Asthma Clinical Research Network sites and investigators participated in the parent MIA trial, which obtained the clinical data analyzed in this study and additionally obtained the bronchial brushings analyzed in this study only:

Brigham & Women’s Hospital, Boston, Mass: E. Israel, M. E. Wechsler

National Jewish Health, Denver, Colo: R. J. Martin, R. M. Cherniack, S. J. Szefler, E. R. Sutherland

University of Wisconsin School of Medicine and Public Health, Madison, Wis: R. F. Lemanske, C. A. Sorkness, N. N. Jarjour, L. Denlinger.

University of California, San Francisco: H. A. Boushey, J. V. Fahy, S. C. Lazarus

Columbia University College of Physicians and Surgeons, New York, NY: E. DiMango

Duke University, Durham, NC: M. Kraft,

University of Texas Medical Branch, Galveston, Tex: W. J. Calhoun, B. T. Ameredes

Washington University, School of Medicine, St Louis, Mo: M. Castro, M. Walter

University of California, San Diego Medical Center, Calif: J. Ramsdell, S. I. Wasserman

Wake Forest University, School of Medicine, Winston-Salem, NC: E. Bleecker, D. Meyers, S. P. Peters, W. C. Moore, R. Pascual

Pennsylvania State College of Medicine, Hershey, Pa: V. M. Chinchilli, T. J. Craig, N. Icitovic, T. S. King

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Fig E1. 

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• Diagram showing the sample evaluation process for PhyloChip analysis.

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Supplementary data 

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