- Email: [email protected]
Features of the bronchial bacterial microbiome associated with atopy, asthma, and responsiveness to inhaled corticosteroid treatment
Accepted Manuscript Features of the bronchial bacterial microbiome associated with atopy, asthma and responsiveness to inhaled corticosteroid treatment Juliana Durack, PhD, Susan V. Lynch, PhD, Snehal Nariya, BS, Nirav R. Bhakta, MD, PhD, Avraham Beigelman, MD, Mario Castro, MD, Anne-Marie Dyer, MS, Elliot Israel, MD, Monica Kraft, MD, Richard J. Martin, MD, David T. Mauger, PhD, Sharon R. Rosenberg, MD, MS, Tonya Sharp-King, PhD, Steven R. White, MD, Prescott G. Woodruff, MD, MPH, Pedro C. Avila, MD, MPH, Loren C. Denlinger, MD, PhD, Fernando Holguin, MD, Stephen C. Lazarus, MD, Njira Lugogo, MD, Wendy C. Moore, MD, Stephen P. Peters, MD, Loretta Que, MD, Lewis J. Smith, MD, Christine A. Sorkness, PharmD, Michael Wechsler, MD, MMSc, Sally E. Wenzel, MD, Homer A. Boushey, MD, Yvonne J. Huang, MD PII:
S0091-6749(16)31283-0
DOI:
10.1016/j.jaci.2016.08.055
Reference:
YMAI 12438
To appear in:
Journal of Allergy and Clinical Immunology
Received Date: 21 March 2016 Revised Date:
2 August 2016
Accepted Date: 12 August 2016
Please cite this article as: Durack J, Lynch SV, Nariya S, Bhakta NR, Beigelman A, Castro M, Dyer AM, Israel E, Kraft M, Martin RJ, Mauger DT, Rosenberg SR, Sharp-King T, White SR, Woodruff PG, Avila PC, Denlinger LC, Holguin F, Lazarus SC, Lugogo N, Moore WC, Peters SP, Que L, Smith LJ, Sorkness CA, Wechsler M, Wenzel SE, Boushey HA, Huang YJ, for the National Heart, Lung and Blood Institute’s “AsthmaNet”, Features of the bronchial bacterial microbiome associated with atopy, asthma and responsiveness to inhaled corticosteroid treatment, Journal of Allergy and Clinical Immunology (2016), doi: 10.1016/j.jaci.2016.08.055. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please
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Features of the bronchial bacterial microbiome associated with atopy, asthma
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and responsiveness to inhaled corticosteroid treatment.
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Avraham Beigelman, MD4., Mario Castro, MD,4,5., Anne-Marie Dyer, MS6., Elliot Israel, MD7., Monica Kraft, MD ., Richard J. Martin, MD ., David T. Mauger, PhD ., Sharon R. Rosenberg, MD, 10
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MS ., Tonya Sharp-King, PhD ., Steven R. White, MD ., Prescott G. Woodruff, MD, MPH ., 10
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Pedro C. Avila, MD, MPH ., Loren C. Denlinger, MD, PhD ., Fernando Holguin, MD ., Stephen 3
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C. Lazarus, MD ., Njira Lugogo, MD ., Wendy C. Moore, MD ., Stephen P. Peters, MD ., 14
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Loretta Que, MD ., Lewis J. Smith, MD ., Christine A. Sorkness, PharmD ., Michael Wechsler, 13
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MD, MMSc ., Sally E. Wenzel, MD ., Homer A. Boushey, MD ., and Yvonne J. Huang, MD ., for the National Heart, Lung and Blood Institute’s “AsthmaNet”.
University of California, Dept. of Medicine, Division of Gastroenterology, San Francisco, CA; 2 University of Michigan, Division of Pulmonary & Critical Care Medicine, Ann Arbor, MI; 3 University of California, Dept. of Medicine, San Francisco, CA; 4 Washington University School of Medicine, Division of Pediatrics, St Louis, MO; 5 Washington University School of Medicine, Division of Pulmonary and Critical Care Medicine, St Louis, MO; 6 Penn State University, Department of Public Health Sciences, Hershey, PA; 7 Brigham & Women's Hospital, Dept. of Medicine, Boston, MA; 8University of Arizona, Health Sciences, Tucson, AZ; 9 National Jewish Hospital, Dept. of Medicine, Denver, CO; 10 Northwestern University, Dept. of Medicine, Chicago, IL; 11 University of Chicago, Dept. of Medicine, Chicago, IL; 12 University of Wisconsin-Madison, Dept. of Medicine, Madison, WI; 13 The University of Pittsburgh Asthma Institute at UPMC/UPSOM, Pittsburgh, PA; 14 Duke University School of Medicine, Duke Asthma, Allergy & Airway Center, Durham, NC; 15 Wake Forest School of Medicine, Winston-Salem, NC;
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Corresponding author: Homer A. Boushey,
Professor of Medicine, Div. of Pulmonary/Critical Care Medicine University of California, San Francisco 505 Parnassus Avenue M1292, Box 0130 San Francisco, CA 94143 Phone: 415-476-8019 Fax: 415-502-6235 Email: [email protected]
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Juliana Durack, PhD ., Susan V. Lynch, PhD ., Snehal Nariya, BS ., Nirav R. Bhakta, MD, PhD .,
Running title: Bronchial microbiome in asthma, atopy, and health.
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Abstract
38 Background: Compositional differences in bronchial bacterial microbiota have been
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associated with asthma, but it remains unclear whether the findings are attributable to
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asthma, to aeroallergen sensitization or to inhaled corticosteroid treatment.
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Objectives: To compare the bronchial bacterial microbiota in adults with steroid-naive
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atopic asthma (AA), with atopy but no asthma (ANA), and non-atopic healthy subjects
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(HC), and determine relationships of bronchial microbiota to phenotypic features of
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asthma.
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Methods: Bacterial communities in protected bronchial brushings from 42 AA, 21 ANA,
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and 21 HC subjects were profiled by 16S rRNA gene sequencing. Bacterial composition
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and community-level functions inferred from sequence profiles were analyzed for
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between-group differences. Associations with clinical and inflammatory variables were
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examined, including markers of type 2-related inflammation and change in airway hyper-
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responsiveness following six weeks of fluticasone treatment.
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Results: The bronchial microbiome differed significantly among the three groups.
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Asthmatic subjects were uniquely enriched in members of the Haemophilus, Neisseria,
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Fusobacterium, Porphyromonas and Sphingomonodaceae, and depleted in members of
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the Mogibacteriaceae and Lactobacillales. Asthma-associated differences in predicted
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bacterial functions included involvement of amino acid and short-chain fatty acid
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metabolism pathways. Subjects with type 2-high asthma harbored significantly lower
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bronchial bacterial burden. Distinct changes in specific microbiota members were seen
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following fluticasone treatment. Steroid-responsiveness was linked to differences in
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baseline compositional and functional features of the bacterial microbiome.
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Conclusion: Even in mild steroid-naive asthma subjects, differences in the bronchial
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microbiome are associated with immunologic and clinical features of the disease. The
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specific differences identified suggest possible microbiome targets for future approaches
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to asthma treatment or prevention.
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Key messages: •
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The bronchial bacterial microbiota of both mild atopic-asthma (steroid-naïve) and atopy-alone differ from that of healthy controls and also differ from each other.
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Asthma is associated with enrichment in members of the Haemophilus,
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Neisseria, Fusobacterium, Porphyromonas and Sphingomonodaceae and with
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depletion of Lactobacillus. •
The T2-high asthma phenotype is associated with low bronchial bacterial burden.
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ICS-response is linked to a distinct bacterial community composition and functional profile prior to steroid exposure.
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Capsule summary: Mild atopic asthma is associated with distinct differences in the
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composition and inferred functional capacities of bronchial bacterial microbiota, which
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further associate with type 2-low airway inflammation and with responsiveness to inhaled
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corticosteroid treatment.
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Key words: Asthma, atopy, microbiome, corticosteroids, 16S ribosomal RNA, bacteria,
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Th2 inflammation, Three-gene mean, metabolic pathways, SCFAs.
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Abbreviations:
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AA – Atopic asthmatic subjects ANA – Atopic non-asthmatic subjects HC – Healthy controls ICS – Inhaled corticosteroid ACQ – Asthma Control Questionnaire OW – Oral wash BB – Bronchial brush OTU – Operational taxonomic unit SCFAs – Short chain fatty acids TGM – Three-gene mean PICRUSt - Phylogenetic Reconstruction of Unobserved States KEGG - Kyoto Encyclopedia of Genes and Genomes
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Introduction:
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Recent culture-independent studies have documented that the composition of
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commensal lower respiratory tract bacteria (microbiota) differs between asthmatic and
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healthy adults (1-6). Additionally, phenotypic features of asthma, such as measures of
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airway hyper-responsiveness, asthma control, and transcriptional response to steroids,
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correlate with patterns of bronchial microbiota composition (4, 5). Though different
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studies have reported asthma-associated enrichment (higher relative abundance) of
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different taxa [i.e. bacterial-derived 16S ribosomal RNA gene sequences that exhibit an
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operator-defined level of sequence homology (typically 97%)], enrichment in members of
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the phylum Proteobacteria is a repeating signature. Asthmatic subjects in most previous
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studies were treated with inhaled corticosteroids (ICS), casting some uncertainty on
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whether the findings reflect the effects of ICS treatment or of asthma itself. Similarly,
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many asthmatic patients are atopic (7, 8), raising the question as to whether asthma-
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associated differences in respiratory microbiota are related to underlying atopy, itself
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associated with altered mucosal immune function (9, 10). Collectively, these
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considerations indicate a need to elucidate differences in bronchial microbiota
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associated with asthma versus atopy, and with important phenotypic features of this
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disease, such as the level of T2-type inflammation and responsiveness to ICS treatment.
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Accordingly, we compared the bronchial bacterial microbiome among adults with mild
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steroid-naive atopic asthma, with atopy without asthma, and healthy non-atopic non-
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asthmatic controls. We hypothesized that specific compositional and functional
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differences in bronchial microbiota are associated with asthma and with distinguishing
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phenotypic features of the disease, including evidence of Th2 inflammation and
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responsiveness to ICS treatment.
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Methods:
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Study Population and Sample Collection.
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This study was conducted at nine sites in the NHLBI AsthmaNet, using a standardized
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bronchoscopy protocol for sample collection. Of 186 adults screened, 84 subjects were
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enrolled (Figure S1A-B): 42 atopic asthmatics (AA), 21 atopic non-asthmatics (ANA)
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and 21 non-atopic healthy control subjects (HC). Atopy was defined by serologic
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evidence (>0.35 kU/l) of sensitivity to ≥1 of 12 aeroallergens (specific IgE by
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ImmunoCap; Thermo-Scientific; Table S1). Asthma was confirmed by airway hyper-
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responsiveness (methacholine PC20 ≤8 mg/mL or FEV1 improvement ≥12% post-
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albuterol). At enrollment, asthmatics had been clinically stable for three months, and had
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an Asthma Control Questionnaire (ACQ) score of <1.5 (11) without the use of a
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controller medication. Exclusion criteria included a history of smoking, respiratory
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infection within six weeks or antibiotic use within 3 months of enrollment
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(Supplementary Methods).
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Samples processed for microbiota analysis included oral wash (OW) (12), a saline flush
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(10 mL) of the bronchoscope suction channel (“scope-flush”) and protected bronchial
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brushings (BB; Figure S1C; Supplemental Methods). AA were further randomized in a
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2:1 ratio to treatment with inhaled fluticasone propionate (250 mcg, GlaxoSmithKline) or
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placebo twice daily for six weeks and re-assessed post-treatment. Each subject signed
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informed consent approved by their center’s IRB; an NHLBI-appointed Data Safety
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Monitoring Board (DSMB) oversaw the study conduct.
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Nucleic acid extraction and quantitation of 16S rRNA gene copy number
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Nucleic acids from OW, and 3 BB were extracted as previously described (5, 12) using a
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modified bead-beating protocol and the AllPrep kit (Qiagen). 16S rRNA gene copy
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number was assessed by quantitative PCR using universal primers (Supplemental
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Methods).
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16S rRNA-based sequencing and raw data processing.
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For BB and OW samples, variable region 4 (V4) of the 16S rRNA gene was amplified
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using the primer combination 515F/806R (13, 14) and sequenced on the Illumina MiSeq
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platform. Using chimera-checked and quality-filtered sequence reads (Supplemental
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Methods), a 97% sequence homology cut-off was used to define bacterial taxa (also
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referred to as operational taxonomic units; OTUs) and classified using the Greengenes
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database (15). A phylogenetic tree was built using FastTree (16) and used to compute
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Faith’s Phylogenetic Diversity (17) of samples using an OTU table multiply-rarefied to
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52,317 sequences per sample (Supplemental Methods).
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164 “Three-gene mean” bronchial epithelial signature of type 2 inflammation.
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Expression levels of three bronchial epithelial genes (CLCA1, SERPINB2 and POSTN)
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previously shown to be induced by IL-13 (18, 19), were measured using RNA extracted
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in parallel with DNA in this study to calculate the “three-gene mean” (TGM) score for
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each participant (20). Type 2 (T2) – high asthma was defined by TGM scores ≥1.117
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(two standard deviations above the average TGM-score in HC), confirmed by
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unsupervised cluster and principal component analysis.
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172 Statistical analyses:
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We applied principal coordinates analyses (PCoA) on an unweighted UniFrac distance
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matrix and PERMANOVA (21) to identify the determinants of variation in bacterial
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community beta-diversity. Linear mixed-effects model (LME) (22) was employed to
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examine relationships between paired OW and BB microbiota; negative binomial (NB)
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and zero-inflated negative binomial (ZINB) regression models (23-26) corrected for false
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discovery (Benjamini-Hochberg, q-value <0.1) were used to identify OTUs differentially
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abundant between subject groups and paired-samples, respectively. We applied
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Phylogenetic Reconstruction of Unobserved States (PICRUSt) to predict functional
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capacities of the microbiota (27), and NB regression to compare inferred functional
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pathway predictions across groups. Procrustes analysis (13) was used to explore the
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strength of relationships between paired samples in the AA group (Supplemental
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Methods).
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Results:
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Study group characteristics.
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AA had mild well-controlled disease, significantly higher serum total IgE and blood and
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sputum eosinophil cell counts (Table 1, Figure S2A-C) than HC. Compared to the ANA,
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the asthmatics had significantly higher serum IgE, were sensitized to more of the
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aeroallergens tested (Table 1), were more likely to be sensitive to cat, dog, and mouse
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(Table S1), and to report a history of allergic rhinitis and eczema (Table S2). However,
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they did not exhibit significant differences in environmental exposures as assessed by
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questionnaire (Table S3).
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Bacterial microbiota in bronchial brushings are compositionally distinct from oral
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wash.
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To evaluate mucosa-associated bronchial microbiota, we focused on protected BB.
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Sequence-based bacterial community analysis could be performed in the same
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proportion (67%) of samples collected in each of the three groups (Figure S1D-E).
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Samples that could not be sequenced had lower bacterial burden as indicated from 16S
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rRNA (p<0.0001; Figure S2D), despite recovery of similar mammalian cell burden as
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assessed by quantifying β-actin copy number (Supplemental Methods). Subjects with
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insufficient 16S rRNA amplicon for microbiota profiling were younger [median 28 (22-37)
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vs. 34 yrs. (25-44); p=0.03] but did not differ in any other characteristic measured.
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Potential oral contamination of BB was evaluated by comparing a random subset of 30
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BB-paired scope-flushes. Bacterial burden of scope-flushes for BB with sufficient 16S
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rRNA for sequencing was indistinguishable from those unable to be sequenced (p>0.1;
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Figure S2E). Additionally, PCoA analysis of bacterial community composition in paired
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OW and BB samples showed these two niches to be compositionally distinct (LME β
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=0.31, p<0.0001; Figure 1A), with BB samples exhibiting lower phylogenetic diversity
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(Faith’s index; p<0.0001; Figure 1B).
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Features of the bronchial bacterial microbiota and distinct combinations of
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specific bacterial taxa are associated with atopic asthma or atopy alone.
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Alpha-diversity indices, such as richness (number of observed taxa), Shannon’s
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diversity, and evenness, as well as bacterial burden did not differ amongst the groups
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(Figure S2F-I). However, phylogenetic diversity (Faith’s index) tended to be higher in AA
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compared with HC (p=0.06; Figure 1C). Since this index weights phylogenetic
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relatedness of the bacteria detected, this observation suggests that the bronchial
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airways of AA harbor more phylogenetically diverse bacterial communities. Although
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phylogenetic diversity in the ANA also appeared to be greater than in HC (Figure 1C),
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the difference fell short of significance. Overall, inter-subject bacterial community
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composition (beta-diversity) was highly heterogeneous across subjects (Figure S3A).
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This was significantly related to bacterial richness independent of the study group
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(Figure S3B-E), but was not associated with any of the clinical measures or
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environmental exposures evaluated in this study (Tables 1 and S1-3). Compositional
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variability in bronchial bacterial microbiota was significantly greater within the asthmatic
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group (weighted UniFrac distance F-test p<0.001; Figure S3F), indicating a greater
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degree of bacterial community heterogeneity in this group.
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Following these initial assessments for differences in overall bacterial community
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composition, we conducted OTU-level analyses to determine if the relative abundance of
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specific taxa differed significantly across groups; 76 taxa were found to do so in the AA
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vs HC comparison (Table S4). Phylum-level differences in AA (Figures 2A and S4A)
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included significant enrichment for members of the Bacteroidetes (Prevotella),
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Fusobacteria
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(Haemophilus and Neisseria), the latter previously associated with more severe, ICS-
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requiring asthma (2-6, 28). Conversely, asthmatic subjects showed reduced relative
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abundance
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(Actinobacillus), and Firmicutes (Lactobacillus). ANA subjects also demonstrated
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significant differences in the relative abundance of 100 taxa compared to HC (Table
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S5B). The taxa most relatively enriched in ANA included members of the Proteobacteria
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(Aggregatibacter,
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(Corynebacterium) phyla (Figures 2B and S4B); those depleted included members of
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the phylum Bacteroidetes (Porphyromonas and Prevotella).
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members
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the
(Actinomyces)
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(Leptotrichia),
Proteobacteria
Proteobacteria
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of
Haemophilus),
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(Fusobacterium), Actinobacteria
Firmicutes
(Granulicatella)
and
Actinobacteria
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We noted that the ANA shared 26% and 29% of taxa that were also relatively enriched
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or depleted in the AA, when compared to HC. Despite these similarities, OTU-level
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analysis identified 103 taxa that differed significantly between the AA and ANA groups
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(Table S6; Figures 2C and S4C). To pinpoint bacteria discretely associated with
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asthma, we evaluated which taxa among those that distinguished AA from HC, also
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distinguished AA from ANA (Figure 3). By this approach, asthma-associated taxa
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included members of the Haemophilus (OTU4406393), Fusobacterium (OTU4405869;
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OTU2438396),
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Sphingomonodaceae (OTU8331815); while taxa negatively associated with asthma
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included members of the Mogibacteriaceae (OTU4335578) and Lactobacillales
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(OTU4480189; OTU4469032). The same approach also identified taxa specific to atopy-
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alone, which included enrichment in Aggregatibacter (OTU4432431), Corynebacterium
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(OTU1015518; OTU495067) and Prevotella (OTU4372058; OTU134265) (Figure 3). Of
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the taxa identified as specifically associated with either asthma or atopy-alone, a subset
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exhibited strong, significant correlations with features of atopy (IgE, blood and sputum
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eosinophil counts); these associations were distinct in the two atopic groups. For
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example, specific taxa uniquely enriched in AA, belonging to Sphingomonodaceae and
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Fusobacteria, were positively associated with all three markers of atopy, while of those
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specifically enriched in ANA, Sharpea and Prevotella (OTU1052181; OTU134265)
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correlated with IgE and blood eosinophil counts, respectively (Figure 3). These data
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indicate that while taxonomic overlap exists between AA and ANA subjects, discrete
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bacterial enrichments characterize these groups, a subset of which are associated with
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biomarkers of atopic disease.
Porphyromonas
(OTU495451)
and
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(OTU1304),
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Neisseria
We further explored whether predicted functions of the bronchial bacterial microbiome
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differed among the three groups using PICRUSt (27), an algorithm that predicts bacterial
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metagenomes in silico from 16S rRNA sequence. This analysis revealed significant
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differences across groups (Figure 4). Predicted bacterial gene functions enriched in AA
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included those involved in metabolism of amino acids and carbohydrates, especially of
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short chain fatty acids (SCFAs) such as butanoate and propanoate (more commonly
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known as butyrate and propionate, respectively). Also noteworthy was a relative
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depletion in predicted bacterial functions involved in lipopolysaccharide biosynthesis
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among asthma-associated bacterial communities.
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Type 2-high asthma is associated with reduced bronchial bacterial burden.
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TGM scores were higher in AA (Figure 5A) and, consistent with prior reports (20, 29),
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correlated positively with serum IgE (rSpearman=0.36, p<0.01), blood and sputum
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eosinophil counts (rS=0.32 and rS=0.37, respectively, p≤0.01), and negatively with FEV1
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(rS= –0.45, p<0.005) and PC20 (rS= –0.41, p<0.005). Ten of 40 AA had T2-high asthma
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and exhibited significantly higher ACQ scores, serum IgE, blood and sputum eosinophil
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counts, and younger age, than T2-low asthmatics (Table S7). T2-high asthmatics
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demonstrated significantly lower bronchial bacterial burden than T2-low asthmatics
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(Figure 5C); bacterial burden also was negatively correlated with TGM scores across all
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asthmatic subjects (rS= –0.43, p<0.01) independent of age (GLM p=0.03). Because of
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their low bacterial burden BBs from only four T2-high asthmatics, could be sequenced
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for profiling, an insufficient sample size to allow meaningful assessment of differences in
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bacterial microbiota composition between T2-high and T2-low subjects.
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Inhaled corticosteroid-responsiveness is associated with distinct features of the
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bronchial bacterial microbiota present before treatment.
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We hypothesized that differences in baseline airway microbiota characteristics may be
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associated with ICS-responsiveness, defined as ≥2-fold increase in PC20Mch after ICS-
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treatment. Of the asthmatics included, 15 were classified as ICS-responders, 10 as non-
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responders, and three were excluded as PC20 was not performed (Figure S5A-B).
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Compared to non-responders, ICS-responders did not differ in PC20, serum IgE, blood
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and sputum eosinophil counts, or bronchial burden at baseline but did have lower FEV1
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and a trend toward a higher TGM score (p<0.08) (Table S8; Figure S5C), which
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decreased following treatment (p<0.05; Figure S4D).
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The baseline composition of profiled bronchial bacterial microbiota in ICS-responders
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(n=10) differed from that in the non-responders (n=5; unweighted UniFrac PERMANOVA
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R2=0.13, p=0.01), and was significantly more similar to that of HC (Figure 6A). Bacterial
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families enriched at baseline in ICS non-responders included Microbacteriaceae,
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Pasteurellaceae (e.g. asthma-associated Haemophilus OTU4406393) and several
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others (Figure 6C and Table S9). Conversely, bacterial families enriched at baseline in
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ICS-responders
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Sphingomonodaceae. Compared with ICS-responders, the predicted functions of
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bacterial communities of non-responders were enriched in xenobiotic biodegradation
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pathways (Figure 6D and Table S10), implicating potential enhanced capacity for
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synthetic chemical degradation.
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included
Streptococcaceae,
Fusobacteriaceae
and
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Following six weeks of ICS vs. placebo treatment, we found no significant changes in
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bacterial burden or phylogenetic diversity in the AA group (Figure S6A-B). Bacterial
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community analysis in paired samples before and after treatment was limited, as several
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subjects did not have sufficient 16S rRNA to obtain sequence data at both time points.
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Nonetheless, we reasoned that ICS exposure might have distinct effects on the
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microbiotas of non-responders and ICS-responders (2) and explored ICS-induced
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compositional changes in the latter, more predominant group. No significant differences
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were seen in the magnitude of changes (beta-diversity assessed by unweighted Unifrac
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distance) between paired samples (n=8 each in placebo or ICS-treated responders;
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Figure S6C-E). At the taxon level, however, ICS treatment resulted in increased relative
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abundance of Microbacteriaceae, Neisseria and Moraxella and depletion of a specific
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Fusobacterium, which was not observed with the placebo treatment (Figure 6D, Tables
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S11-12). We unfortunately could not analyze compositional changes in ICS-non-
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responders, as only 2 subjects in this subgroup had sufficient 16S rRNA in both pre- and
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post- treatment samples.
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Discussion:
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Our findings show compositional and predicted functional differences in the bronchial
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bacterial microbiomes of atopic asthmatic, atopic non-asthmatic, and healthy individuals.
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An important implication of these findings is that control for allergic sensitization is
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necessary in studies aimed at understanding differences in the respiratory microbiome
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associated with asthma. Despite overlap in bacterial genera significantly associated with
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both atopic groups, our study identified specific bacterial taxa whose relative enrichment
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or depletion were discretely associated with asthma. Analyses based on metagenomic
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inference further suggest that genes for pathways involved in the metabolism of short-
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chain fatty acids and amino acids are enriched in the asthmatic bronchial microbiome.
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We additionally observed that the bronchial microbiome among asthmatics at baseline
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differed compositionally and functionally according to their responsiveness to treatment
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with inhaled corticosteroids.
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Commonly reported microbial community metrics (e.g., richness, evenness, and burden)
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did not differ significantly among our three groups. This is not unexpected due to the
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broad characterization of microbial composition provided by such measures, the inter-
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individual heterogeneity of microbiota composition found in all human niches, including
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the lung (30), and the mild disease severity in our subjects. Considering the clinical
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homogeneity of our asthmatic group, their heterogeneity in bronchial bacterial
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composition is striking. Moreover, this heterogeneity was predominantly observed
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among T2-low asthma subjects, in whom bronchial bacterial burden was significantly
359
greater than in T2-high subjects. The trend towards higher bacterial phylogenetic
360
diversity in our mild asthmatic subjects suggests their lower airways are receptive to
361
colonization by a wider variety of bacteria. Although we could not pinpoint any specific
362
clinical features associated with this finding, additional contributing factors might include
363
differences in previous environmental exposures, in clearance of microorganisms, or in
364
other immune function parameters not identified here.
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Reasons for the low bronchial bacterial burden in T2-high asthma are unclear, but the
367
finding echoes a recent observation in severe asthma, of an inverse relationship
368
between bronchial bacterial burden and numbers of bronchial biopsy eosinophils (4). A
369
possible explanation is the bactericidal activity of more numerous airway eosinophils (31,
370
32) in T2-high airway inflammation. Given prior reports of inverse relationships between
371
bacterial and fungal richness (33, 34), the low bacterial burden in T2-high asthma could
372
also reflect increased fungal or bacteriophage burden. Indeed microbial interactions are
373
not limited to bacteria, as bacterial-viral interactions have been associated with asthma
374
exacerbations and risk for asthma development (35, 36). Such inter-kingdom
375
interactions (37) should be considered in future studies.
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This study expands the list of bacterial groups previously associated with asthma, further
378
supporting the idea that alterations in microbial composition from a healthy state, is a
379
characteristic of asthma. Enrichment in certain Proteobacteria members (e.g.
380
Haemophilus and Neisseria) among our ICS-naïve asthmatics resembles prior findings
381
in ICS-using patients (2-6, 28). However, we found other taxa discretely associated with
382
asthma including Fusobacterium and Porphyromonas, two oral-associated anaerobes
383
capable of augmenting pathogenic behavior of opportunistic respiratory pathogens, such
384
as Pseudomonas aeruginosa (38). Additionally, both Fusobacterium (39) and
385
Haemophilus
386
Sphingomonodaceae represented another asthma-specific taxon whose abundance, like
387
that of Fusobacterium, correlated with sputum eosinophilia. This finding echoed the
388
reported
389
Sphingomonodaceae) in activating NKT-cells to produce T2-cytokines (41-43) and the
390
reported association of this bacterial family with bronchial reactivity (5).
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(40)
activity
induce
of
MUC5AC
expression
glycosphingolipids
(present
in
in
bronchial
the
cell
epithelial
cells.
membrane
of
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391 Asthma-related alterations in bronchial microbiota composition also involve relative
393
depletion in certain taxa, including members of the Lactobacillales. Numerous studies
394
have demonstrated protective effects of certain Lactobacillus strains against atopy by
395
various mechanisms including alteration of gastrointestinal (44) and respiratory tract
396
permeability (45, 46). Our finding that the predicted functions of asthma-associated
397
microbiota were relatively depleted in machinery for LPS biosynthesis suggests another
398
possibility, for continuous stimulation of airways with LPS has been shown to suppress
399
T2 immune activation (47). We propose, though, that any “pro-asthmatic” activity of a
400
bronchial microbiome is likely dependent on functional effects consequent to interactions
401
among many microbiota members in the airway microenvironment, rather than the
402
activity of any one species. Microbiome-related functions indicated by our analyses as
403
potentially enhanced in asthma include increased capacity for metabolism of butyrate
404
and propionate, SCFAs that maintain epithelial barrier function and immune tolerance in
405
the gut (48-50). We speculate that utilization of anti-inflammatory SCFAs by members
406
of the asthmatic airway microbiota may contribute to atopic asthma by reducing their
407
bioavailability and the consequent capacity to down-regulate host inflammatory
408
responses to aeroallergens and pathogens.
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Microbiome-related functions might also affect responsiveness to corticosteroid
411
treatment. We found that pre-treatment enrichment in an asthma-associated
412
Haemophilus, a genus with species previously shown to reduce response of BAL
413
macrophages to corticosteroids (2), was associated with diminished response to six
414
weeks of treatment with inhaled fluticasone. Analysis of the predicted metagenome of
415
pre-treatment bronchial microbiota present in ICS-non-responders also indicated
416
enhancement of xenobiotic degradation capacity, which we hypothesize may contribute
417
to their diminished response. In contrast, ICS-induced changes to the bronchial
418
microbiota in ICS-responsive asthmatics showed enrichment of previously reported
419
asthma-associated taxa such as Neisseria and Moraxella (2, 3, 6). Community shift
420
detected in response to lactose-containing placebo inhaler was not surprising, as an
421
influx of an additional sugar would be expected to alter the composition of microbial
422
communities in the airways, favoring those species with the metabolic capacity to utilize
423
such carbon sources. As importantly, these results emphasize the need to consider the
424
effects of repeated inhalation of particles, especially of an ICS, as a selective pressure
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425
and nutritional source for airway microbiome members with the catalytic capacity to
426
degrade such xenobiotics.
427 Perturbations to the bronchial microbiota associated with atopy-alone is also a novel
429
finding of this study. ANA subjects were less sensitized than the AA subjects and
430
showed primarily low T2 inflammation of bronchial epithelium. While the greater severity
431
of allergy in the AA group cannot be ruled out as contributing to the difference in
432
bacterial signature observed between AA and ANA subjects, distinct taxa associations
433
with different markers of atopy and allergic inflammation were observed, suggesting that
434
distinct microbial interactions with the host immune system occur in these two patient
435
groups. These observations in ANA subjects support an association between airway
436
colonization and atopy-related altered mucosal immune functions (9, 10). Atopy-specific
437
taxa included members of the Pasteurellaceae (specifically Aggregatibacter and
438
Haemophilus). Members of Aggregatibacter are associated with periodontal disease
439
driven by high pro-inflammatory cytokines (e.g., TNF-a, IL1B, IL-6 and IL-8;) and
440
reduced levels of IL-10 (51, 52). It is tempting to speculate that airway enrichment of this
441
genus in ANA subjects could contribute to asthma protection through activation of the T1
442
arm of the immune system. We also highlight that specific taxa belonging to certain
443
genera (e.g., Prevotella and Haemophilus) were associated with atopy-only or asthma.
444
This underscores the likely importance of species- or strain-level functional differences in
445
microbial interactions related to disease status.
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The limitations of our study include exclusive analysis of bacterial communities, the
448
absence of non-atopic asthmatic subjects and the relatively narrow breadth of asthma
449
phenotype captured in this cohort. Moreover, our sample size was small in some
450
comparisons, particularly in assessing changes in the bronchial microbiota in paired
451
samples before and after ICS vs. placebo treatment. However, strengths include the
452
large number of subjects who were characterized and underwent invasive bronchoscopy
453
to collect samples for analysis. Our study protocol also attended carefully to procedural
454
and sampling methods to reduce contamination and analyze for possible contribution of
455
non-bronchial sources of bacterial DNA to the dataset.
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Conclusion:
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Our findings highlight the complexity of bacterial relationships to asthma in a background
459
of atopy, conditions that are associated with distinct alterations in the airway
460
microbiota. To achieve a comprehensive understanding of microbial factors involved in
461
the induction of, management of, or protection against asthma, there is an important
462
need to better understand functions collectively expressed by consortia of airway
463
microbes, which could have a profound influence on asthma.
464
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Acknowledgements:
466
We additionally would like to thank the following coordinators and staff, and to identify
467
the grants supporting the study: Robert Pedicini, BS1; Kathy Zheng, MPH1; Duanny
468
Alva, MPH2; Assel Biyasheva, PhD2; Jenny Hixon, BS, CCRC2; Lucius Robinson III,
469
BS, CMA, CCRC2; Mary Gill, RN, BSN3; James T. Good, MD3; Christena Kolakowski,
470
MS3; Allen Stevens, CCRC, NREMT3; E. Rand Sutherland, MD3; Julia Bach, RN4; Rich
471
Cornwell, MD4; Holly Eversoll, RN4; Tiffany Huard4; Keith Meyer, MD4; Barbara Miller,
472
RN4; Ann Sexton, MPH4; Michele Wolff, RN4; Merritt Fajt, MD5; Sherri Hill, BS5; Lisa
473
Lane, BS5; Russell Traister, MD5; Cathy Vitari, RN, BSN AE-C5; Vanessa Curtis, RRT6;
474
Brenda Patterson, RN, APN6; Cheryl Shelton, RN, BSN6; Kelly Norsworthy, BA, CPT7;
475
Kelsey Wollen, BA7; Eugene Bleeker, MD8; Christopher Barrios, MD8; Suzan Farris,
476
CCRP8; Jeffrey Krings, FNP8; Victor Ortega, MD8; Cheryl Wilmoth, CCRP8; Matthew
477
Bowman, BS9; Linda Engle, BS9; Jennifer Lucier, BS9; Aimee J. Merchlinski, MS9;
478
Kathryn Trasatt, BS9; Angela Updegrave9; Rachel Weber, BS9; Ronald R. Zimmerman,
479
Jr., MPA9. We also wish to thank Elizabeth F. Juniper of McMaster’s University,
480
Canada, for permitting our use of her Asthma Control Questionnaire, and Andrew
481
Manies of UCSF for assistance with manuscript preparation.
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References: 1.
RI PT
2.
Denner DR, Sangwan N, Becker JB, Hogarth DK, Oldham J, Castillo J, Sperling AI, Solway J, Naureckas ET, Gilbert JA, White SR. 2015. Corticosteroid therapy and airflow obstruction influence the bronchial microbiome, which is distinct from that of bronchoalveolar lavage in asthmatic airways. Journal of Allergy and Clinical Immunology. Goleva E, Jackson LP, Kirk Harris J, Robertson CE, Sutherland ER, Hall CF, Good JTJ, Gelfand EW, Martin RJ, Leung D, Y. M. 2013. The Effects of Airway Microbiome on Corticosteroid Responsiveness in Asthma. American Journal of Respiratory and Critical Care Medicine 188:1193-1201. Hilty M, Burke C, Pedro H, Cardenas P, Bush A, Bossley C, Davies J, Ervine A, Poulter L, Pachter L, Moffatt MF, Cookson WOC. 2010. Disordered Microbial Communities in Asthmatic Airways. PLOSone 5:e8578. Huang YJ, Nariya S, Harris JM, Lynch SV, Choy DF, Arron JR, Boushey HA. 2015. The airway microbiome in patients with severe asthma: Associations with disease features and severity. The Journal of allergy and clinical immunology 136:874-884. Huang YJ, Nelson CE, Brodie EL, Desantis TZ, Baek MS, Liu J, Woyke T, Allgaier M, Bristow J, Wiener-Kronish JP, Sutherland ER, King TS, Icitovic N, Martin RJ, Calhoun WJ, Castro M, Denlinger LC, Dimango E, Kraft M, Peters SP, Wasserman SI, Wechsler ME, Boushey HA, Lynch SV. 2011. Airway microbiota and bronchial hyperresponsiveness in patients with suboptimally controlled asthma. The Journal of allergy and clinical immunology 127:372-381.e371-373. Marri PR, Stern DA, Wright AL, Billheimer D, Martinez FD. 2013. Asthma-associated differences in microbial composition of induced sputum. The Journal of allergy and clinical immunology 131:346-352.e341-343. Burrows B, Martinez FD, Halonen M, Barbee RA, Cline MG. 1989. Associations of asthma with serum IgE levels and skin-test reactivity to allergens. N Engl J Med 320:271277. Pearce N, Pekkanen J, Beasley R. 1999. How much asthma is really attributable to atopy? Thorax 54:268-272. Kariyawasam HH, Rotiroti G. 2013. Allergic rhinitis, chronic rhinosinusitis and asthma: unravelling a complex relationship. Current Opinion in Otolaryngology & Head and Neck Surgery 21:79-86. Tomazic PV, Birner-Gruenberger R, Leitnera A, Obrist B, Spoerkc S, Lang-Loidolt D. 2014. Nasal mucus proteomic changes reflect altered immune responses and epithelial permeability in patients with allergic rhinitis. . The Journal of allergy and clinical immunology 133:741-750. Juniper EF, O'Byrne PM, Guyatt GH, Ferrier PJ, King DR. 1999. Development and validation of a questionnaire to measure asthma control. Eur Respir J. 14:902-907. Iwai S, Fei M, Huang D, Fong S, Subramanian A, Grieco K, Lynch SV, Huang L. 2012. Oral and Airway Microbiota in HIV-Infected Pneumonia Patients. J. Clin. Microbiol. 50:2995-3002. Caporaso JG, Lauber CL, Walters WA, Berg-Lyons D, Huntley J, Fierer N, Owens SM, Betley J, Fraser L, Bauer M, Gormley N, Gilbert JA, Smith G, Knight R. 2012. Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms. The ISME Journal 6:1621-1624. Caporaso JG, Lauber CL, Walters WA, Berg-Lyons D, Lozupone CA, Turnbaugh PJ, Fierer N, Knight R. 2011. Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. Proceedings of the National Academy of Sciences of the United States of America 108 Suppl 1:4516-4522. DeSantis TZ, Hugenholtz P, Larsen N, Rojas M, Brodie EL, Keller K, Huber T, Dalevi D, Hu P, Andersen GL. 2006. Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Applied and environmental microbiology 72:5069-5072.
3.
SC
4.
7.
8. 9.
10.
11. 12.
EP
6.
TE D
M AN U
5.
AC C
482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534
13.
14.
15.
16
ACCEPTED MANUSCRIPT
21. 22. 23. 24. 25.
26. 27.
28.
29.
30.
RI PT
20.
SC
19.
M AN U
18.
TE D
17.
Price MN, Dehal PS, Arkin AP. 2009. FastTree: Computing Large Minimum Evolution Trees with Profiles instead of a Distance Matrix. Molecular Biology and Evolution 26:1641-1650. Faith DP. 1992. Conservation evaluation and phylogenetic diversity. Biological Conservation 61:1-10. Woodruff PG, Boushey HA, Dolganov GM, Barker CS, Yang YH, Donnelly S, Ellwanger A, Sidhu SS, Dao-Pick TP, Pantoja C, Erle DJ, Yamamoto KR, Fahy JV. 2007. Genome-wide profiling identifies epithelial cell genes associated with asthma and with treatment response to corticosteroids. Proc Natl Acad Sci USA 104:15858-15868. Woodruff PG, Modrek B, Choy DF, Jia J, Abbas AR, Ellwanger A, Koth LL, Arron JR, Fahy JV. 2009. T-helper type 2-driven inflammation defines major subphenotypes of asthma. Am J Respir Crit Care Med. 180:388-395. Bhakta NR, Solberg OD, Nguyen CP, Nguyen CN, Arron JR, Fahy JV, Woodruff PG. 2013. A qPCR-based metric of Th2 airway inflammation in asthma. Clin. Transl. Allergy 3:24. Anderson MJ. 2001. A new method for non-parametric multivariate analysis of variance. Austral Ecology 26:32-46. Pinheiro JC. 2005. Linear Mixed Effects Models for Longitudinal Data, Encyclopedia of Biostatistics. John Wiley & Sons, Ltd. Gerber GK, Onderdonk AB, Bry L. 2012. Inferring dynamic signatures of microbes in complex host ecosystems. PLoS computational biology 8:e1002624. McMurdie PJ, Holmes S. 2014. Waste Not, Want Not: Why Rarefying Microbiome Data Is Inadmissible. PLoS computational biology 10:e1003531. Kassahun W, Neyens T, Molenberghs G, Faes C, Verbeke G. 2014. Marginalized multilevel hurdle and zero-inflated models for overdispersed and correlated count data with excess zeros. Statistics in medicine 33:4402-4419. Min Y, Agresti A. 2005. Random effect models for repeated measures of zero-inflated count data. Statistical Modelling 5:1-19. Langille MGI, Zaneveld J, Caporaso JG, McDonald D, Knights D, Reyes JA, Clemente JC, Burkepile DE, Vega Thurber RL, Knight R, Beiko RG, Huttenhower C. 2013. Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences. Nat Biotech 31:814-821. Simpson JL, Daly J, Baines KJ, Yang IA, Upham JW, Reynolds PN, Hodge S, James AL, Hugenholtz P, Willner D, Gibson PG. 2015. Airway dysbiosis: Haemophilus influenzae and Tropheryma in poorly controlled asthma. The European respiratory journal. Peters MC, Mekonnen ZK, Yuan S, Bhakta NR, Woodruff PG, Fahy JV. 2014. Measures of gene expression in sputum cells can identify TH2-high and TH2-low subtypes of asthma. The Journal of allergy and clinical immunology 133:388-394. Ursell LK, Clemente JC, Rideoutb JR, Gevers D, Caporaso JG, Knight R. 2012. The interpersonal and intrapersonal diversity of human-associated microbiota in key body sites. J. Allergy Clin. Immunol. 129:1204–1208. Hogan SP, Waddell A, Fulkerson PC. 2013. Eosinophils in Infection and Intestinal Immunity. . Current Opinion in Gastroenterology 29:7–14. Travers J, Rothenberg ME. 2015. Eosinophils in mucosal immune responses. Mucosal Immunol 8:464-475. Dollive S, Chen Y-Y, Grunberg S, Bittinger K, Hoffmann C, Vandivier L, Cuff C, Lewis JD, Wu GD, Bushman FD. 2013. Fungi of the Murine Gut: Episodic Variation and Proliferation during Antibiotic Treatment. PLoS ONE 8:e71806. Peleg AY, Hogan DA, Mylonakis E. 2010. Medically important bacterial–fungal interactions. Nat Rev Micro 8:340-349. Kloepfer KM, Lee WM, Pappas TE, Kang TJ, Vrtis RF, Evans MD, Gangnon RE, Bochkov YA, Jackson DJ, Lemanske RF, Jr., Gern JE. 2014. Detection of pathogenic bacteria during rhinovirus infection is associated with increased respiratory symptoms and asthma exacerbations. The Journal of allergy and clinical immunology 133:13011307, 1307.e1301-1303.
EP
16.
AC C
535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590
31. 32. 33.
34. 35.
17
ACCEPTED MANUSCRIPT
41.
42.
43. 44. 45.
46.
47.
48.
RI PT
40.
SC
39.
M AN U
38.
TE D
37.
Teo Shu M, Mok D, Pham K, Kusel M, Serralha M, Troy N, Holt Barbara J, Hales Belinda J, Walker Michael L, Hollams E, Bochkov Yury A, Grindle K, Johnston Sebastian L, Gern James E, Sly Peter D, Holt Patrick G, Holt Kathryn E, Inouye M. The Infant Nasopharyngeal Microbiome Impacts Severity of Lower Respiratory Infection and Risk of Asthma Development. Cell Host & Microbe 17:704-715. Tarkka MT, Sarniguet A, Frey-Klett P. 2009. Inter-kingdom encounters: recent advances in molecular bacterium-fungus interactions. Current genetics 55:233-243. Pana Y, Tenga D, Burkea AC, Haasea EM, Scannapieco FA. 2008. Oral bacteria modulate invasion and induction of apoptosis in HEp-2 cells by Pseudomonas aeruginosa. Microbial Pathogenesis 46:73-79. Nagaoka K, Yanagihara K, Harada Y, Yamada K, Migiyama Y, Morinaga Y, Hasegawa H, Izumikawa K, Kakeya H, Nishimura M, Kohno S. 2013. Macrolides inhibit Fusobacterium nucleatum-induced MUC5AC production in human airway epithelial cells. Antimicrobial agents and chemotherapy 57:1844-1849. Araki N, Yanagihara K, Morinaga Y, Yamada K, Nakamura S, Yamada Y, Kohno S, Kamihira S. 2010. Azithromycin inhibits nontypeable Haemophilus influenzae-induced MUC5AC expression and secretion via inhibition of activator protein-1 in human airway epithelial cells. European journal of pharmacology 644:209-214. Akbari O, Stock P, Meyer E, Kronenberg M, Sidobre S, Nakayama T, Taniguchi M, Grusby MJ, DeKruyff RH, Umetsu DT. 2003. Essential role of NKT cells producing IL-4 and IL-13 in the development of allergen-induced airway hyperreactivity. Nat Med 9:582588. Kinjo Y, Wu D, Kim G, Xing GW, Poles MA, Ho DD, Tsuji M, Kawahara K, Wong CH, Kronenberg M. 2005. Recognition of bacterial glycosphingolipids by natural killer T cells. Nature 434:520-525. Zajonc DM, Girardi E. 2015. Recognition of Microbial Glycolipids by Natural Killer T Cells. Frontiers in immunology 6:400. Özdemir O. 2010. Various effects of different probiotic strains in allergic disorders: an update from laboratory and clinical data. Clin Exp Immunol 160:295-304. Abreu NA, Nagalingam NA, Song Y, Roediger FC, Pletcher SD, Goldberg AN, Lynch SV. 2012. Sinus microbiome diversity depletion and Corynebacterium tuberculostearicum enrichment mediates rhinosinusitis. Sci Transl Med. 12:151ra124. Fujimura KE, Demoor T, Rauch M, Faruqi AA, Jang S, Johnson CC, Boushey HA, Zoratti E, Ownby D, Lukacs NW, Lynch SV. 2014. House dust exposure mediates gut microbiome Lactobacillus enrichment and airway immune defense against allergens and virus infection. Proc Natl Acad Sci USA 111:805-810. Schuijs MJ, Willart MA, Vergote K, Gras D, Deswarte K, Ege MJ, Branco Madeira F, Beyaert R, van Loo G, Bracher F, von Mutius E, Chanez P, Lambrecht BN, Hammad H. 2015. Farm dust and endotoxin protect against allergy through A20 induction in lung epithelial cells. Science 349:1106-1110. Arrieta MC, Stiemsma LT, Dimitriu PA, Thorson L, Russell S, Yurist-Doutsch S, Kuzeljevic B, Gold MJ, Britton HM, Lefebvre DL, Subbarao P, Mandhane P, Becker A, McNagny KM, Sears MR, Kollmann T, Mohn WW, Turvey SE, Brett Finlay B. 2015. Early infancy microbial and metabolic alterations affect risk of childhood asthma. Science translational medicine 7:307ra152. Kelly CJ, Zheng L, Campbell EL, Saeedi B, Scholz CC, Bayless AJ, Wilson KE, Glover LE, Kominsky DJ, Magnuson A, Weir TL, Ehrentraut SF, Pickel C, Kuhn KA, Lanis JM, Nguyen V, Taylor CT, Colgan SP. 2015. Crosstalk between MicrobiotaDerived Short-Chain Fatty Acids and Intestinal Epithelial HIF Augments Tissue Barrier Function. Cell Host Microbe 17:662-671. Trompette A, Gollwitzer ES, Yadava K, Sichelstiel AK, Sprenger N, Ngom-Bru C, Blanchard C, Junt T, Nicod LP, Harris NL, Marsland BJ. 2014. Gut microbiota metabolism of dietary fiber influences allergic airway disease and hematopoiesis. Nat Med 20:159-166.
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591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644
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50.
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51.
EP
TE D
M AN U
SC
RI PT
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Bostanci N, Akgül B, Tsakanika V, Allaker RP, Hughes FJ, McKay IJ. 2011. Effects of low-dose doxycycline on cytokine secretion in human monocytes stimulated with Aggregatibacter actinomycetemcomitans. Cytokine 56:656-661. Hirose M, Ishihara K, Saito A, Nakagawa T, Yamada S, Okuda K. 2001. Expression of cytokines and inducible nitric oxide synthase in inflamed gingival tissue. J Periodontol. 72:590-597.
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Table 1. Study cohort characteristics p-value#
28 (23 - 46)
NS
-
-
-
-
-
-
48%
43%
NS€
52%
71%
NS€
25 (21 - 28)
26 (22 - 28)
NS
100 (93 - 108) 102 (98 - 116) 5.0 (2.5 - 5.0)
<0.01&
<0.0001&
Age (yrs)
33 (25 - 41)
28 (24 - 44)
Age of asthma diagnosis (yrs)
9 (5 - 22)
-
Duration of Asthma (yrs)
21 (14 - 27)
-
ACQ Score*
0.3 (0 - 1.3)
-
% Male
45%
% White
60%
M AN U
26 (23 - 29)
Atopic, non-asthmatic (ANA) (n = 21)
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Atopic asthmatic (AA) (n = 42)**
BMI (kg/m2)
89 (76 - 97) 100 (84 - 106)
7.5 (5.0 - 14.3)
98 (89 - 109) 104 (97 - 109) 3.0 (-0.5 - 5.5)
Methacholine PC20
1.2 (0.3 - 3.2)
>32$
>32$
-
Blood eosinophils (absolute) Blood eosinophils (%)
200 (100 - 393) 3.3 (1.5 - 5.6) 55.5 (49.3 - 62.0) 54.6 (31.8 - 64.9) 0.4 (0.0 - 1.1)
100 (87 - 200) 2.0 (1.4 - 3.0) 56.9 (51.0 - 63.8) 39.5 (26.5 - 50.0) 0.0 (0.0 - 0.6)
100 (60 - 200) 1.4 (1.0 - 3.0)
<0.01
58.4 (52.7 63.5) 41.8 (29.2 76.5) 0.0 (0.0 - 0.4)
169.5 (56.3 - 321.3) 6 (2-7)
64.0 (22.0 - 164.5) 3 (2-5)
15.0 (5.0 31.0) -
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FEV1 % predicted (pre-albuterol) FEV1 % predicted (post-albuterol) Change in FEV1%
Blood neutrophils (%)
Sputum neutrophils (%)
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Sputum eosinophils (%) Serum IgE (IU/mL)
¢
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Number of positive sIgE
654 655 656 657 658 659 660 661 662
Healthy control (HC) (n = 21)
Variable
SC
652 653
NS&
<0.01¥ NS NS NS¥ <0.01&¥ <0.05&
All values are medians (IQR).*ACQ, Asthma Control Questionnaire ** Number of exacerbations requiring oral steroids in the past 5 years: zero exacerbations (38 subjects); one exacerbation (3 subjects); two $ exacerbations (one subject). Methacholine challenge was stopped at 32 mg/dL and PC20 for these subjects ¥ ¢ was censored. For between group statistical comparisons see Supplementary Figure S2A-C. Number of positive specific IgE (sIgE >0.35 kU/l) from a total of 12 aeroallergens tested by ImmunoCap assay (for breakdown of specific aeroallergens see Supplementary Table S1). Statistical significance was determined # € & using Kruskal-Wallis or Chi-square or Mann-Whitney test for AA vs. ANA comparison.
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Figure Legends:
664
Figure 1: (A) Principal coordinate analysis (unweighted UniFrac) shows compositional
665
dissimilarity between paired BB and OW samples (LME p<0.0001). (B) Phylogenetic
666
diversity (Faith’s index) in BB and paired OW samples (& Wilcoxon matched-pairs signed
667
rank test). (C) Phylogenetic diversity (Faith’s index) in BB samples for the three subject
668
groups (Welch’s corrected t-test).
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Figure 2: Bacterial taxa, significantly enriched or depleted in relative abundance (at
671
least 2 fold; NB regression, q<0.1) in (A) AA (n=28) compared to HC (n=13); (B) ANA
672
(n=15) compared to HC; (C) AA compared to ANA. The OTUs indicated represent the
673
most abundant representatives within the indicated genus.
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Figure 3: Mean difference in specific bacterial taxa between groups. Asthma-specific
676
taxa are similarly abundant in AA compared to HC and ANA (NB regression, q<0.1).
677
Atopy-only taxa are similarly abundant in ANA vs. both HC and AA. Taxa positively
678
correlated (rPearson≥0.5, q<0.1) with blood eosinophil counts (BEO), serum IgE (IgE) or
679
sputum eosinophil counts (SEO) in AA (*) or ANA (#) subjects.
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Figure 4. Mean difference in predicted KEGG orthologs (KOs) between groups. Asthma-
682
specific pathways are similarly abundant in AA compared to HC and ANA. Atopy-only
683
pathways are similarly abundant in ANA compared to HC and AA. Statistical significance
684
was determined using NB regression model corrected for false discovery rate (q<0.1).
EP
685
TE D
681
Figure 5. (A) AA displayed greater expression of epithelial genes induced by type 2
687
cytokines compared to non-asthmatic subjects. T2-high AA, with a TGM ≥1.117 (cut-off
688
value indicated by a dashed line) are colored in maroon red. (B) Significantly lower
689
bacterial burden was observed among T2-high asthma subjects. Statistical significance
690
was determined using Wilcoxon rank sum test.
691
AC C
686
692
Figure 6. (A) Mean distance to HC (unweighted UniFrac; Bonferroni-corrected t-test).
693
(B) Taxa significantly enriched or depleted in relative abundance (at least 2 fold; NB
694
regression, q<0.1) in ICS-responders vs. non-responders. (C) Predicted KEGG
695
pathways associated with xenobiotic biodegradation and metabolism in ICS-responders
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and non-responders. (D) Taxa, differentially expressed (at least 2-fold; ZINB regression,
697
q<0.1) in asthmatics following ICS or placebo treatment.
AC C
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SUPPLEMENTAL FIGURES AND TABLES:
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Figure S1: Schematic diagrams summarizing the breakdown of participants enrolled in this study. (A) Of the 102 asthmatic adults screened for this study, 42 atopic asthmatics (AAs) were enrolled. These subjects underwent specimen collection at baseline (prior to any treatment) and were then randomized to receive inhaled placebo or 250 mcg of fluticasone (ICS) twice daily for six weeks. (B) Of the 84 atopic non-asthmatic (ANAs) and non-atopic non-asthmatic healthy control (HCs) subjects screened, 21 from each group were enrolled in this study. (C) Enrolled participants were seen at multiple visits (indicated by arrows) in nine participating study centers, during which they underwent spirometry (S), methacholine provocation (MP), sputum induction (IS) and bronchoscopy (B). Following baseline assessment (visits 1 and 2) asthmatic patients underwent additional assessment post six weeks twice-daily treatment with ICS or placebo inhaler (post-treatment assessment visits 3 and 4). (D) A schematic diagram of the total bronchial brush samples (BBs) collected during bronchoscopy at baseline (visit 2) indicating the breakdown of subjects with sufficient bacterial 16S rRNA for bacterial profile. The proportion of sequenced to not-sequenced BB samples did not vary by subject group or clinical study site (#Chi-square test; p>0.5). (E) Breakdown of BBs collected from asthmatics post-treatment (visit 4) showing the proportion of sequenced to not sequenced samples, which did not vary between type of inhaler used (&Fishers exact test; p>0.5).
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Figure S2: (A) Total serum IgE concentrations differentiate the three groups. (B, C) Atopic asthmatics show evidence of systemic eosinophilia. (D) Bacterial burden (normalized to ß-actin expression) in sequenced samples was significantly higher than in non-sequenced samples. (E) Bacterial burden in the second scope flush was indistinguishable between a random subset of sequenced and non-sequenced samples, while their paired brush 16S rRNA burden was significantly higher for the sequenced samples. (F) Bacterial burden (normalized to ß-actin expression) for sequenced samples did not significantly vary between study groups. (G) Bacterial richness did not significantly vary between study groups. (H) Bacterial diversity based on Shannon Index did not significantly vary between study groups. (I) Bacterial evenness based on Pielou’s Index did not significantly vary between study groups. Statistical significance was determined using Wilcoxon rank sum test, & Wilcoxon matched-pairs signed rank test or $Welch’s corrected t-test.
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Figure S3. The bronchial microbiota of all groups is highly heterogeneous, but compositional variability is higher in the atopic asthmatic subjects. Principal coordinate analysis plots based on unweighted UniFrac distance, where bronchial brush samples (A) from all subjects (28 atopic asthmatics – AA; 15 atopic non asthmatics – ANA; 13 healthy controls – HC) are colored by group, which does not explain variation in community composition (PERMANOVA p>0.05); (B) from all subjects are colored by richness or (C) from AAs only colored by richness or (D) ANAs only colored by richness or (E) HC only colored by richness. Community richness (Observed species ranging from 219 to 1122 operational taxonomic units (OTUs), significantly explained variability in composition between samples in all groups (PERMANOVA, p≤0.01). (F) Distance box plots based on weighted UniFrac distance matrix show greater compositional variability in the AA compared to both ANAs and HC subjects (F-test p<0.05).
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Figure S4: Summary of bacterial taxa at a phylum level, significantly enriched or depleted in relative abundance (at least 2 fold) in (A) atopic asthmatic (AAs) patients (n=28) compared to healthy control subjects (HC, n=13); (B) atopic non-asthmatic (ANAs) subjects (n=15) compared to HC subjects; (C) AAs patients compared to ANAs subjects. The number of operational taxonomic units (OTU) included in each phyla are indicated by #OTU. Statistical significance was determined using Negative binomial regression model corrected for false discovery rate (q<0.1).
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Figure S5: (A) ICS-responders showed increase in the dose of methacholine in the PC20 challenge after 6 weeks of treatment with fluticasone, but not ICS non-responders or asthmatics taking a placebo inhaler. (B) ICS-responders showed at least one doubling in the dose of methacholine after 6 weeks of treatment with fluticasone. (C) ICS-responders exhibited slightly higher T2-type inflammation at baseline compared to ICS non-responders. Subjects identified as T2-high asthmatics are colored in maroon. (D) ICS-responders showed significant decline in the three-gene signature after treatment with fluticasone compared to both ICS non-responders and placebo controls. Statistical significance was determined using Wilcoxon rank sum test and & Wilcoxon matched-pairs rank sum test. Figure S6: (A) Treatment with ICS had no significant effect on the change in bacterial burden compared to placebo inhaler. Statistical significance was determined using Wilcoxon rank sum test. (B) Treatment with ICS had no significant effect on the change in Faith’s phylogenetic diversity compared to placebo inhaler. Statistical significance was determined using Wilcoxon rank sum test. (C) Procrustes analysis plot based on unweighted UniFrac distance of asthmatics taking placebo treatment (n = 8) shows no significant similarity in community composition between pre- and post-treatment paired samples (M = 0.635, p>0.05). (D) Procrustes analysis plot based on unweighted UniFrac distance shows no significant similarity in community composition between pre- and post-treatment paired samples (M = 0.504, p>0.05) in ICSresponders (n = 8). (E) Analysis of paired unweighted UniFrac distance shows no significant difference in the magnitude of the community shift identified with treatment using fluticasone or a lactose containing placebo inhaler. Statistical significance was determined using Wilcoxon rank sum test.
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Table S1: Breakdown of sensitivities to specific aeroallergens in the atopic asthmatics and atopic non-asthmatic subjects. p-value#
Healthy controls (n = 21) -
NS NS <0.05 0.05 0.05 NS NS NS NS NS NS NS
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sIgE (Mite-1) sIgE (Mite-2) sIgE (Cat) sIgE (Dog) sIgE (Mouse) sIgE (Grass mix) sIgE (Cockroach) sIgE (Mold mix) sIgE (Tree mix 1) sIgE (Tree mix 2) sIgE (Weed) sIgE (Weed mix)
Atopic non-asthmatics (n = 21) 50% 60% 40% 32% 0% 37% 11% 16% 21% 21% 18% 29%
SC
Variable
Atopic asthmatics (n = 42) 56% 54% 68% 59% 17% 58% 28% 38% 42% 34% 34% 47%
#
M AN U
Percentage of positive specific IgE (sIgE >0.35 kU/l) as tested by ImmunoCap assay. Statistical significance was determined using Chi-square test.
Table S2: Responses to Prior clinical conditions questionnaire for atopic participants. Questions about Prior conditions
Atopic Asthmatics (n=42)
Atopic Nonasthmatics (n=21)
#
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Have you ever had eczema/allergic dermatitis? Yes 31% 5% No 69% 95% Have you ever had allergic rhinitis (hay fever)? Yes 53% 24% No 48% 76% Do you have chronic or recurrent sinusitis (treated with antibiotics and/or surgery)? Yes 7% 0% No 93% 100% Have you ever had pneumonia? Yes 29% 10% No 71% 91% Have you been diagnosed with sleep disordered breathing (sleep apnea)? Yes 5% 0% No 95% 100% Do you have gastroesophageal reflux disease (GERD)? Yes 10% 5% No 91% 95%
p-value#
0.02
0.06
NS
0.11
NS
NS
Statistical significance was determined using Chi-square test, where NS = p≥0.2.
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Table S3: Responses to Environmental exposure questionnaire for atopic participants. Atopic Asthmatics (n = 42)
Environmental exposure questions
Atopic Nonasthmatics (n = 21)
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Do you live within a mile of a farm? Yes 17% 10% No 83% 91% Do you live within a mile of a major highway? Yes 67% 48% No 33% 52% Does the home you live in have a yard? Yes 69% 57% No 31% 43% Have you been around animals outside your home at least 2 days/week in the past 3 months? Yes 36% 29% No 64% 71% Are you frequently exposed (2 or more days/week) to tobacco smoke outside of your home? Yes 17% 10% No 83% 91% Has there been any mold or mildew, on any surfaces inside your house in the past 12 months? Yes 35% 35% No 65% 65% Does your household have any pets? Yes 33% 43% No 67% 57% In general, and on a regular basis, are you exposed to cats? Yes 29% 43% No 71% 57% In general, and on a regular basis, are you exposed to dogs? Yes 50% 43% No 50% 57% In general, and on a regular basis, are you exposed to other furry pets? Yes 7% 5% No 93% 95% In general, and on a regular basis, are you exposed to birds? Yes 7% 5% No 93% 95% In general, and on a regular basis, are you exposed to farm animals? Yes 5% 0% No 95% 100% Do you ever see cockroaches in your house? Yes 14% 19% No 86% 81%
p-value#
Do you ever see rodents or rodent droppings in your house? Yes 14% No 86% Do you ever see cockroaches OR rodents/rodent droppings in your house? Yes 29% No 71% #
NS
0.18
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
19% 81%
NS
33% 67%
NS
Statistical significance was determined using Chi-square test, where NS = p≥0.2.
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Table S4: Specific bacterial taxa that differ in relative abundance between atopic asthmatic (AA) and healthy control (HC) subjects. #
AA 356.8
#
HC 61.5
AA-HC 295.3
q-value
Phylum
Class
0.0610
Bacteroidetes
Bacteroidia
Order Bacteroidales
Proteobacteria
Gammaproteobacteria
Pasteurellales
Fusobacteria
Fusobacteriia
Fusobacteriales
Proteobacteria
Betaproteobacteria
Neisseriales
Family Paraprevotellaceae
Genus Prevotella
Species tannerae
Pasteurellaceae
Haemophilus
-
Fusobacteriaceae
Fusobacterium
-
Neisseriaceae
Neisseria
-
Fusobacteriaceae
Fusobacterium
-
RI PT
OTU ID 2714267
243.8
1.0
242.8
2438396
192.5
1.7
190.8
4440404
176.8
0.2
176.7
3.00E106 1.66E-21
4405869
196.0
47.4
148.6
0.0347
Fusobacteria
Fusobacteriia
Fusobacteriales
100.2
0.0759
Actinobacteria
Actinobacteria
Actinomycetales
Actinomycetaceae
Actinomyces
Bacteroidetes
Bacteroidia
Bacteroidales
Prevotellaceae
Prevotella
-
Bacteroidales
Prevotellaceae
Prevotella
-
Spirochaetales
Spirochaetaceae
Treponema
-
Actinomycetales
Actinomycetaceae
Actinomyces
-
Clostridiales
Veillonellaceae
Veillonella
dispar
Clostridiales
Mogibacteriaceae
Anaerovorax
-
Bacteroidales
Prevotellaceae
Prevotella
-
Bacteroidales
Prevotellaceae
Prevotella
-
137.9
37.8
240049
48.2
0.8
47.3
0.0011
1541939
47.9
2.0
45.9
0.0011
Bacteroidetes
Bacteroidia
71146
43.4
1.4
42.0
0.0231
Spirochaetes
Spirochaetes
4448211
32.6
6.5
26.1
0.0476
Actinobacteria
Actinobacteria
Firmicutes
Clostridia
Firmicutes
Clostridia Bacteroidia
31.8
6.8
24.9
4468500
24.4
2.5
21.9
1.02E-34
642465
21.4
0.3
21.1
0.0041
Bacteroidetes
70671
87.4
67.5
19.9
2.66E-09
Bacteroidetes Spirochaetes
Bacteroidia
TE D
4388775
0.0297
M AN U
4296424
SC
4406393
0.0041
Spirochaetes
25.5
7.4
18.1
41911
17.7
0.2
17.4
0.0027
Bacteroidetes
Spirochaetales
Spirochaetaceae
Treponema
-
Bacteroidia
Bacteroidales
Prevotellaceae
Prevotella
-
4297695
16.1
2.5
13.6
0.0667
Proteobacteria
Epsilonproteobacteria
Campylobacterales
Campylobacteraceae
Campylobacter
-
495451
11.3
0.4
10.9
4.53E-12
Bacteroidetes
Bacteroidia
Bacteroidales
Porphyromonadaceae
Porphyromonas
-
250288
14.1
5.4
8.8
5.95E-12
Firmicutes
Clostridia
Clostridiales
Peptostreptococcaceae
-
-
Fusobacteria
Fusobacteriia
EP
73875
4.02E-28
9.8
1.1
8.7
Fusobacteriales
Leptotrichiaceae
Leptotrichia
-
4307228
9.1
0.8
8.3
0.0550
Spirochaetes
Spirochaetes
Spirochaetales
Spirochaetaceae
Treponema
-
1398
8.2
0.4
7.8
0.0277
Bacteroidetes
Bacteroidia
Bacteroidales
Paraprevotellaceae
Prevotella
-
949816
9.2
1.7
7.5
0.0622
Firmicutes
Bacilli
Lactobacillales
Streptococcaceae
Streptococcus
-
Proteobacteria
Betaproteobacteria
AC C
1033013
6.48E-14
4480775
8.4
1.0
7.4
0.0231
Neisseriales
Neisseriaceae
Kingella
-
4331815
7.5
0.5
7.0
3.60E-10
Proteobacteria
Alphaproteobacteria
Sphingomonadales
Sphingomonadaceae
-
-
3859
11.6
4.9
6.7
1.42E-08
Spirochaetes
Spirochaetes
Spirochaetales
Spirochaetaceae
Treponema
amylovorum
855912
6.6
1.2
5.5
3.46E-09
Fusobacteria
Fusobacteriia
Fusobacteriales
Fusobacteriaceae
Fusobacterium
-
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#
AA
HC
#
AA-HC
q-value
Phylum
Class
Order
Family
Genus
Species
Proteobacteria
Alphaproteobacteria
Sphingomonadales
Sphingomonadaceae
Sphingopyxis
alaskensis
Bacteroidetes
Bacteroidia
Bacteroidales
Prevotellaceae
Prevotella
-
Firmicutes
Bacilli
Lactobacillales
Lactobacillaceae
Lactobacillus
-
Porphyromonadaceae
Porphyromonas
-
Prevotellaceae
Prevotella
-
Mogibacteriaceae
-
-
Prevotellaceae
Prevotella
copri
267852
5.9
0.6
5.3
1.96E-08
1033687
5.1
0.3
4.8
0.0914
292057
6.3
1.5
4.7
1.07E-07
328
4.9
0.4
4.5
0.0498
Bacteroidetes
Bacteroidia
Bacteroidales
Bacteroidetes
Bacteroidia
Bacteroidales
RI PT
OTU ID
4.5
0.1
4.4
4093791
14.5
10.7
3.8
0.0469
Firmicutes
Clostridia
Clostridiales
4410166
3.1
0.1
3.0
0.0073
Bacteroidetes
Bacteroidia
Bacteroidales
1304
3.0
0.1
2.9
0.0082
Proteobacteria
Betaproteobacteria
Neisseriales
Neisseriaceae
Neisseria
subflava
2377731
3.2
0.5
2.6
0.0002
Firmicutes
Clostridia
Clostridiales
Veillonellaceae
Selenomonas
-
Fusobacteria
Fusobacteriia
Fusobacteriales
0.8
2.3
2.7
0.4
2.3
0.0009
Firmicutes
Bacilli
29566
2.3
0.1
2.2
0.0200
Fusobacteria
Fusobacteriia
1062051
2.6
0.8
1.8
0.0083
Actinobacteria
Actinobacteria
Bacteroidetes
Cytophagia
Leptotrichiaceae
Leptotrichia
-
Lactobacillales
Lactobacillaceae
Lactobacillus
iners
Fusobacteriales
Leptotrichiaceae
Sneathia
-
Actinomycetales
Corynebacteriaceae
Corynebacterium
-
M AN U
3.1
130864
2.4
0.6
1.8
Cytophagales
Cytophagaceae
-
-
1135830
2.6
1.1
1.5
0.0603
Proteobacteria
Betaproteobacteria
Burkholderiales
Comamonadaceae
-
-
4375000
0.5
1.5
-0.9
0.0713
Proteobacteria
Gammaproteobacteria
Enterobacteriales
Enterobacteriaceae
-
-
165489
0.5
1.5
-1.0
0.0333
Proteobacteria
Gammaproteobacteria
Enterobacteriales
Enterobacteriaceae
-
-
-1.0
0.0108
Actinobacteria
Actinobacteria
Actinomycetales
Micrococcaceae
Kocuria
palustris
Firmicutes
Bacilli
Lactobacillales
Streptococcaceae
Streptococcus
-
Gammaproteobacteria
Pasteurellales
Pasteurellaceae
Haemophilus
-
Fusobacteriia
Fusobacteriales
Leptotrichiaceae
-
-
Proteobacteria
Gammaproteobacteria
Enterobacteriales
Enterobacteriaceae
-
-
Proteobacteria
Gammaproteobacteria
Enterobacteriales
Enterobacteriaceae
-
-
Firmicutes
Bacilli
Lactobacillales
Streptococcaceae
Streptococcus
-
160203
0.3
1.3
0.5
1.8
-1.3
4469627
0.9
2.2
-1.3
0.0204
Proteobacteria
4335450
1.4
3.3
-1.9
0.0041
Fusobacteria
51646
0.4
2.6
-2.2
2.96E-06
EP
538185
0.0165
TE D
1116069
0.0072
AC C
832
0.0010
SC
851668
0.0017
3799784
0.9
3.2
-2.3
1.43E-05
521996
0.3
2.8
-2.6
0.0252
710275
0.8
3.5
-2.6
8.49E-07
Proteobacteria
Gammaproteobacteria
Pseudomonadales
Moraxellaceae
Acinetobacter
-
849642
0.2
4.0
-3.8
1.67E-09
Proteobacteria
Betaproteobacteria
Burkholderiales
Comamonadaceae
-
-
Proteobacteria
Betaproteobacteria
4417996
1.4
5.5
-4.1
2.22E-10
Burkholderiales
Comamonadaceae
-
-
399903
2.4
6.7
-4.3
1.20E-08
Spirochaetes
Spirochaetes
Spirochaetales
Spirochaetaceae
Treponema
-
242070
1.7
6.2
-4.4
1.53E-10
Proteobacteria
Gammaproteobacteria
Pseudomonadales
Pseudomonadaceae
Pseudomonas
-
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OTU ID
HC
#
AA-HC
q-value
Phylum
Class
Order
Family
Genus
Species
Bacteroidetes
Bacteroidia
1.9
7.3
-5.5
Bacteroidales
Prevotellaceae
Prevotella
-
114510
2.8
11.6
-8.9
6.38E-23
Proteobacteria
Gammaproteobacteria
Enterobacteriales
Enterobacteriaceae
-
-
100791
2.7
12.2
-9.6
1.98E-25
Proteobacteria
Betaproteobacteria
Burkholderiales
Comamonadaceae
-
-
4333897
3.6
15.9
-12.3
3.51E-32
Proteobacteria
Gammaproteobacteria
Enterobacteriales
Enterobacteriaceae
-
-
Firmicutes
Clostridia
Clostridiales
-
-
-
Prevotellaceae
Prevotella
-
Methylobacteriaceae
Methylobacterium
-
RI PT
851961
1.38E-13
3.8
17.7
-13.9
74407
1.3
16.9
-15.6
1.69E-44
Bacteroidetes
Bacteroidia
Bacteroidales
4396717
2.5
25.6
-23.1
8.72E-68
Proteobacteria
Alphaproteobacteria
Rhizobiales
4469032
21.9
45.3
-23.4
2.20E-34
Firmicutes
Bacilli
Lactobacillales
-
-
-
70628
0.4
24.3
-24.0
2.55E-37
Fusobacteria
Fusobacteriia
Fusobacteriales
Leptotrichiaceae
Leptotrichia
-
Bacteroidetes
Bacteroidia
Bacteroidales
55854
0.2
24.9
-24.7
0.0610
98258
0.7
26.7
-26.0
0.0186
Cyanobacteria
4C0d-2
269930
22.8
81.1
-58.3
Proteobacteria
684
1.6
61.8
-60.3
8.82E139 0.0001
114813
1.0
66.9
-66.0
4404577
372.8
441.8
-69.0
4480189
10.0
98.2
-88.2
4335578
221.3
332.8
4404220
336.1
4400260
2.3
Prevotella
-
-
-
-
Gammaproteobacteria
Pseudomonadales
Pseudomonadaceae
-
-
Firmicutes
Bacilli
Lactobacillales
-
-
-
Bacteroidetes
Bacteroidia
Bacteroidales
Prevotellaceae
Prevotella
-
Firmicutes
Clostridia
452.3
-116.2
5.61E-69
Proteobacteria
123.2
-120.9
0.0333
-235.4
0
mean relative abundance
Fusobacteria
Peptostreptococcaceae
Peptostreptococcus
-
Lactobacillales
Lactobacillaceae
Lactobacillus
zeae
Clostridia
Clostridiales
Mogibacteriaceae
-
-
Gammaproteobacteria
Pasteurellales
Pasteurellaceae
Actinobacillus
-
Fusobacteriia
Fusobacteriales
Leptotrichiaceae
Leptotrichia
-
Bacteroidales
Prevotellaceae
Prevotella
-
EP
246.5
Firmicutes
Clostridiales
Bacilli
TE D
Firmicutes
-111.6
1.23E259 6.19E-92
M AN U
Prevotellaceae
MLE1-12
Bacteroidetes
Bacteroidia
AC C
11.1
6.31E102 4.03E-23
SC
4477971
1.85E-37
4307006 #
#
AA
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Table S5: Specific bacterial taxa that differ in relative abundance between atopic non-asthmatic (ANA) and healthy control (HC) subjects. HC
#
ANAHC
ANA
4432431
963.7
55.5
908.3
828676
315.3
0.1
q-value
Phylum
Class
Order
0.0029
Proteobacteria
Gammaproteobacteria
Pasteurellales
315.3
0.0036
Fusobacteria
Fusobacteriia
Fusobacteriales
0.0302
Proteobacteria
Betaproteobacteria
Neisseriales
Family
Genus
Species
Pasteurellaceae
Aggregatibacter
segnis
Fusobacteriaceae
Fusobacterium
-
Neisseriaceae
-
-
Carnobacteriaceae
Granulicatella
-
RI PT
#
OTU ID
292.5
12.1
280.4
218.4
18.0
200.4
0.0143
Firmicutes
Bacilli
Lactobacillales
4306587
158.2
11.7
146.5
0.0758
Firmicutes
Clostridia
Clostridiales
Veillonellaceae
Selenomonas
-
134265
119.2
7.2
112.0
2.37E-152
Bacteroidetes
Bacteroidia
Bacteroidales
Prevotellaceae
Prevotella
-
4296424
141.9
37.8
104.1
0.0613
Actinobacteria
Actinobacteria
Actinomycetales
Actinomycetaceae
Actinomyces
-
Actinomycetales
Corynebacteriaceae
Corynebacterium
-
Bacteroidales
Prevotellaceae
Prevotella
melaninogenica
Bacteroidales
Porphyromonadaceae
Porphyromonas
-
M AN U
SC
978067 4483015
103.4
3.6
99.8
1.74E-111
Actinobacteria
Actinobacteria
105.2
5.6
99.6
0.0007
Bacteroidetes
Bacteroidia
4459993
102.7
4.5
98.2
0.0854
Bacteroidetes
Bacteroidia
976838
98.5
29.9
68.5
5.39E-95
Proteobacteria
Epsilonproteobacteria
Campylobacterales
Helicobacteraceae
Flexispira
2466322
65.7
0.2
65.6
0.0092
Firmicutes
Clostridia
Clostridiales
Peptostreptococcaceae
-
-
4388775
70.5
6.8
63.7
0.0047
Firmicutes
Clostridia
Clostridiales
Veillonellaceae
Veillonella
dispar
1015518
55.8
1.8
54.0
4.86E-58
Actinobacteria
Actinobacteria
Actinomycetales
Corynebacteriaceae
Corynebacterium
-
4469627
55.3
2.2
53.0
8.15E-63
Proteobacteria
Gammaproteobacteria
Pasteurellales
Pasteurellaceae
Haemophilus
-
4297222
47.9
4.3
43.6
1.36E-65
Firmicutes
Bacilli
Lactobacillales
Streptococcaceae
Streptococcus
-
995
44.7
2.5
42.2
0.0242
Bacteroidetes
Bacteroidia
Bacteroidales
Prevotellaceae
Prevotella
melaninogenica
1053321
75.5
36.6
38.9
3.63E-38
Proteobacteria
Gammaproteobacteria
Pseudomonadales
Moraxellaceae
Moraxella
-
4364176
47.9
11.3
36.6
1.91E-55
Firmicutes
Erysipelotrichi
Erysipelotrichales
Erysipelotrichaceae
Sharpea
-
267852
33.8
0.6
33.2
1.47E-27
AC C
EP
TE D
495067 4372058
Proteobacteria
Alphaproteobacteria
Sphingomonadales
Sphingomonadaceae
Sphingopyxis
alaskensis
4419634
33.3
2.3
31.0
0.0302
Fusobacteria
Fusobacteriia
Fusobacteriales
Leptotrichiaceae
Leptotrichia
-
1102
33.7
3.8
29.9
0.0583
Bacteroidetes
Bacteroidia
Bacteroidales
Prevotellaceae
Prevotella
nanceiensis
575
28.0
0.5
27.5
0.0014
Bacteroidetes
Bacteroidia
Bacteroidales
Prevotellaceae
Prevotella
melaninogenica
2438396
28.2
1.7
26.5
4.60E-36
Fusobacteria
Fusobacteriia
Fusobacteriales
Fusobacteriaceae
Fusobacterium
-
1044611
24.1
0.8
23.3
0.0434
Firmicutes
Clostridia
Clostridiales
Peptostreptococcaceae
-
-
4353757
24.0
1.3
22.7
0.0043
Proteobacteria
Gammaproteobacteria
Pasteurellales
Pasteurellaceae
Aggregatibacter
-
8
ACCEPTED MANUSCRIPT
787709
#
ANA
24.9
HC
#
ANAHC
q-value
Phylum
Class
Order
Family
Genus
Species
Actinomycetaceae
Actinomyces
-
Porphyromonadaceae
3.7
21.2
0.0192
Actinobacteria
Actinobacteria
Actinomycetales
0.0132
Bacteroidetes
Bacteroidia
Bacteroidales
4412630
22.1
1.3
20.8
876114
24.1
3.8
20.3
2.50E-32
Fusobacteria
Fusobacteriia
Fusobacteriales
71146
18.1
1.4
16.7
0.0714
Spirochaetes
Spirochaetes
Spirochaetales
4333897
32.1
15.9
16.2
1.12E-15
Proteobacteria
Gammaproteobacteria
Enterobacteriales
3859
20.6
4.9
15.7
9.42E-24
Spirochaetes
Spirochaetes
Spirochaetales
Porphyromonas
-
Leptotrichiaceae
-
-
Spirochaetaceae
Treponema
-
Enterobacteriaceae
-
-
Spirochaetaceae
Treponema
amylovorum
RI PT
OTU ID
14.7
1.9
12.8
2.17E-20
Proteobacteria
Betaproteobacteria
Neisseriales
Neisseriaceae
Kingella
-
18.9
7.0
11.9
4.85E-15
Proteobacteria
Betaproteobacteria
Neisseriales
Neisseriaceae
Neisseria
-
89
11.4
0.1
11.3
1.40E-05
Bacteroidetes
Bacteroidia
Bacteroidales
Prevotellaceae
Prevotella
-
1124080
11.5
1.0
10.5
7.55E-16
Bacteroidetes
Bacteroidia
Bacteroidales
-
-
-
M AN U
SC
63117 4373910
161677
10.4
0.3
10.1
1.07E-10
Proteobacteria
Gammaproteobacteria
Pseudomonadales
Moraxellaceae
Acinetobacter
-
4468500
12.4
2.5
9.9
1.12E-15
Firmicutes
Clostridia
Clostridiales
Mogibacteriaceae
Anaerovorax
-
124932
10.9
1.5
9.4
4.42E-15
Spirochaetes
Spirochaetes
Spirochaetales
Spirochaetaceae
Treponema
-
3931
10.7
2.1
8.6
1.20E-13
Spirochaetes
Spirochaetes
Spirochaetales
Spirochaetaceae
Treponema
-
1.11E-12
Proteobacteria
Gammaproteobacteria
Pasteurellales
Pasteurellaceae
Haemophilus
influenzae
Dethiosulfovibrionaceae
TG5
-
9.1
1.2
7.9
10.2
3.3
6.9
1.93E-09
Synergistetes
Synergistia
Synergistales
41911
6.6
0.2
6.4
0.0562
Bacteroidetes
Bacteroidia
Bacteroidales
Prevotellaceae
Prevotella
-
114510
17.7
11.6
6.1
0.0007
Proteobacteria
Gammaproteobacteria
Enterobacteriales
Enterobacteriaceae
-
-
4309323
6.8
1.0
5.8
0.0164
Proteobacteria
Gammaproteobacteria
Pasteurellales
Pasteurellaceae
Haemophilus
-
EP
TE D
956702 4460505
5.5
0.1
5.5
0.0005
Proteobacteria
Betaproteobacteria
Burkholderiales
Oxalobacteraceae
-
-
11.3
6.0
5.3
7.30E-05
Bacteroidetes
Bacteroidia
Bacteroidales
-
-
-
4351304
5.9
0.5
5.3
3.12E-08
Actinobacteria
Actinobacteria
Actinomycetales
Actinomycetaceae
Actinomyces
-
1052181
6.8
1.9
4.9
3.64E-07
Bacteroidetes
Bacteroidia
Bacteroidales
Prevotellaceae
Prevotella
nanceiensis
4374322
5.8
0.9
4.9
6.14E-08
Proteobacteria
Gammaproteobacteria
Pseudomonadales
Moraxellaceae
Acinetobacter
-
4419380
5.6
1.1
4.5
2.81E-07
Firmicutes
Erysipelotrichi
Erysipelotrichales
Erysipelotrichaceae
Bulleidia
p-1630-c5
4331815
4.8
0.5
4.3
7.69E-07
Proteobacteria
Alphaproteobacteria
Sphingomonadales
Sphingomonadaceae
-
-
517
4.3
0.2
4.1
0.0462
Bacteroidetes
Bacteroidia
Bacteroidales
Porphyromonadaceae
Porphyromonas
-
544313
4.3
0.3
4.0
7.71E-06
Proteobacteria
Gammaproteobacteria
Pseudomonadales
Pseudomonadaceae
Pseudomonas
-
328
4.0
0.4
3.6
0.0478
Bacteroidetes
Bacteroidia
Bacteroidales
Porphyromonadaceae
Porphyromonas
-
AC C
520222 4313608
9
ACCEPTED MANUSCRIPT
3799784
#
ANA
6.5
HC
#
ANAHC
q-value
Phylum
Class
Order
Family
Genus
Species
Enterobacteriaceae
-
-
Comamonadaceae
3.2
3.2
0.0031
Proteobacteria
Gammaproteobacteria
Enterobacteriales
0.0007
Proteobacteria
Betaproteobacteria
Burkholderiales
325027
5.1
2.0
3.1
43339
7.0
4.4
2.6
0.0562
Firmicutes
Clostridia
Clostridiales
2119418
2.7
0.1
2.6
0.0075
Proteobacteria
Gammaproteobacteria
Enterobacteriales
3600504
2.5
0.5
2.0
0.0031
Bacteroidetes
Bacteroidia
Bacteroidales
165489
3.4
1.5
1.9
0.0241
Proteobacteria
Gammaproteobacteria
Enterobacteriales
4454619
2.3
0.8
1.5
0.0353
Firmicutes
Clostridia
Clostridiales
4412991
2.0
0.5
1.5
0.0247
Proteobacteria
Betaproteobacteria
Neisseriales
4432889
2.1
0.6
1.5
0.0302
Actinobacteria
Actinobacteria
Actinomycetales
495451
1.5
0.4
1.2
0.0613
Bacteroidetes
Bacteroidia
Comamonas
-
Mogibacteriaceae
-
-
Enterobacteriaceae
-
-
Bacteroidaceae
Bacteroides
-
Enterobacteriaceae
-
-
Veillonellaceae
Selenomonas
-
Neisseriaceae
-
-
Intrasporangiaceae
-
-
Bacteroidales
Porphyromonadaceae
Porphyromonas
-
M AN U
SC
RI PT
OTU ID
51646
1.2
2.6
-1.4
0.0854
Proteobacteria
Gammaproteobacteria
Enterobacteriales
Enterobacteriaceae
-
-
1010329
0.9
2.6
-1.7
0.0173
Bacteroidetes
Flavobacteriia
Flavobacteriales
Flavobacteriaceae
Capnocytophaga
-
4426165
0.9
2.8
-1.9
0.0055
Bacteroidetes
Bacteroidia
Bacteroidales
Prevotellaceae
Prevotella
-
1595
1.4
3.6
-2.2
0.0051
Bacteroidetes
Bacteroidia
Bacteroidales
Porphyromonadaceae
Porphyromonas
-
8.94E-04
Fusobacteria
Fusobacteriia
Fusobacteriales
Fusobacteriaceae
Fusobacterium
-
Actinomycetaceae
Actinomyces
-
1.0
3.4
-2.4
0.2
3.0
-2.8
0.0001
Actinobacteria
Actinobacteria
Actinomycetales
4401957
1.9
6.2
-4.2
0.0920
Proteobacteria
Epsilonproteobacteria
Campylobacterales
Campylobacteraceae
Campylobacter
-
851961
0.3
7.3
-7.0
4.95E-10
Bacteroidetes
Bacteroidia
Bacteroidales
Prevotellaceae
Prevotella
-
245523
9.7
17.8
-8.0
3.05E-07
Bacteroidetes
Bacteroidia
Bacteroidales
-
-
-
EP
TE D
941024 4306356
0.1
8.2
-8.1
0.0908
Fusobacteria
Fusobacteriia
Fusobacteriales
Leptotrichiaceae
Leptotrichia
-
6.3
17.7
-11.4
1.35E-15
Firmicutes
Clostridia
Clostridiales
-
-
-
100791
0.1
12.2
-12.1
5.90E-09
Proteobacteria
Betaproteobacteria
Burkholderiales
Comamonadaceae
-
-
4443201
12.3
29.4
-17.1
9.85E-21
Bacteroidetes
Bacteroidia
Bacteroidales
Porphyromonadaceae
Tannerella
-
4396717
8.2
25.6
-17.4
1.91E-25
Proteobacteria
Alphaproteobacteria
Rhizobiales
Methylobacteriaceae
Methylobacterium
-
579608
0.1
18.7
-18.6
1.03E-10
Firmicutes
Bacilli
Lactobacillales
Streptococcaceae
Streptococcus
-
615020
15.9
36.7
-20.8
3.31E-24
Tenericutes
Mollicutes
Mycoplasmatales
Mycoplasmataceae
Mycoplasma
-
4344371
35.3
57.7
-22.4
1.86E-16
Proteobacteria
Alphaproteobacteria
Sphingomonadales
Sphingomonadaceae
Sphingomonas
-
70628
0.2
24.3
-24.1
4.42E-15
Fusobacteria
Fusobacteriia
Fusobacteriales
Leptotrichiaceae
Leptotrichia
-
4297119
3.7
39.8
-36.2
0.0968
Fusobacteria
Fusobacteriia
Fusobacteriales
Leptotrichiaceae
Leptotrichia
-
AC C
923032 4477971
10
ACCEPTED MANUSCRIPT
OTU ID
#
ANA
1048420
q-value
Phylum
Class
Order
Family
Genus
Species
36.8
-36.8
7.58E-09
Firmicutes
Bacilli
Lactobacillales
Streptococcaceae
Streptococcus
-
60.8
98.2
-37.4
1.28E-26
Firmicutes
Bacilli
Lactobacillales
Lactobacillaceae
7.0
46.4
-39.4
0.0149
Firmicutes
Bacilli
Lactobacillales
28118
0.1
52.9
-52.8
0.0029
Firmicutes
Clostridia
Clostridiales
684
1.9
61.8
-59.9
0.0103
Firmicutes
Bacilli
Lactobacillales
4376637
8.1
73.5
-65.4
0.0785
Spirochaetes
Spirochaetes
Spirochaetales
269930
0.1
81.1
-80.9
0.0065
Proteobacteria
Gammaproteobacteria
557974
10.1
96.8
-86.8
0.0812
Proteobacteria
Gammaproteobacteria
69782
1.7
122.8
-121.0
6.05E-101
Proteobacteria
Epsilonproteobacteria
3581175
3.0
133.8
-130.8
1.90E-137
Firmicutes
Clostridia
93.5
260.4
-166.9
7.86E-226
Bacteroidetes
Bacteroidia
4331006
30.5
205.6
-175.1
0
Bacteroidetes
Bacteroidia
4377418
215.8
413.2
-197.4
4.13E-185
Firmicutes
Clostridia
4307006
3.5
246.5
-242.9
5.60E-204
Bacteroidetes
Bacteroidia
2613485
4.7
428.8
-424.2
0
Bacteroidetes
Bacteroidia
252843 8.1 450.7 -442.6 mean relative abundance
0
Fusobacteria
Fusobacteriia
zeae
Streptococcus
-
Veillonellaceae
Selenomonas
-
-
-
-
Spirochaetaceae
Treponema
-
Pseudomonadales
Pseudomonadaceae
-
-
Pseudomonadales
Pseudomonadaceae
Pseudomonas
-
Campylobacterales
Helicobacteraceae
Helicobacter
-
Clostridiales
Veillonellaceae
-
-
Bacteroidales
Prevotellaceae
Prevotella
-
Bacteroidales
Prevotellaceae
Prevotella
-
Clostridiales
Tissierellaceae
Parvimonas
-
Bacteroidales
Prevotellaceae
Prevotella
-
Bacteroidales
Porphyromonadaceae
Porphyromonas
-
Fusobacteriales
Leptotrichiaceae
Leptotrichia
-
EP AC C
#
TE D
1004910
Lactobacillus
Streptococcaceae
RI PT
4480189
ANAHC
SC
0.1
#
M AN U
123320
HC
11
ACCEPTED MANUSCRIPT
Table S6: Specific bacterial taxa which differ in relative abundance between atopic asthmatic (AA) and atopic non-asthmatic (ANA) subjects. OTU ID
#
AA
#
AA-ANA
Class
Order
Family
Genus
Species
503.7
8.1
495.6
0.0255
Fusobacteria
Fusobacteriia
Fusobacteriales
Leptotrichiaceae
Leptotrichia
-
4307790
518.8
119.1
399.7
0.0255
Firmicutes
Clostridia
Clostridiales
Veillonellaceae
Selenomonas
-
4338372
363.6
86.0
277.6
0.0132
Bacteroidetes
Bacteroidia
Bacteroidales
Prevotellaceae
Prevotella
-
4406393
243.8
1.8
242.0
0.0014
Proteobacteria
Gammaproteobacteria
Pasteurellales
Pasteurellaceae
Haemophilus
-
2438396
192.5
28.2
164.3
0
Fusobacteria
Fusobacteriia
Fusobacteriales
Fusobacteriaceae
Fusobacterium
-
4405869
196.0
57.4
138.6
0.0711
Fusobacteria
Fusobacteriia
Fusobacteriales
Fusobacteriaceae
Fusobacterium
-
4393181
141.3
3.4
137.9
0.0004
Tenericutes
Mollicutes
Mycoplasmatales
Mycoplasmataceae
Mycoplasma
-
4382476
126.9
7.0
119.9
0.0014
Bacteroidetes
Bacteroidia
Bacteroidales
Prevotellaceae
Prevotella
-
SC
q-value
RI PT
Phylum
252843
M AN U
ANA
4297119
93.6
3.7
89.9
0.0014
Fusobacteria
Fusobacteriia
Fusobacteriales
Leptotrichiaceae
Leptotrichia
-
525942
94.6
16.9
77.6
0.0194
Bacteroidetes
Bacteroidia
Bacteroidales
Prevotellaceae
Prevotella
melaninogenica
4426165
59.0
0.9
58.1
0.0373
Bacteroidetes
Bacteroidia
Bacteroidales
Prevotellaceae
Prevotella
-
4318284
60.2
6.1
54.1
0.0599
Firmicutes
Clostridia
Clostridiales
Veillonellaceae
Dialister
-
923032
31.0
0.1
30.9
0.0255
Fusobacteria
Fusobacteriia
269930
22.8
0.1
22.6
0.0256
Proteobacteria
4452538
24.3
4.5
19.7
0.0077
Fusobacteria
4307309
19.5
7.5
12.0
1.93E-18
Fusobacteria
4468500
24.4
12.4
12.0
1.75E-14
Firmicutes
495451
11.3
1.5
9.7
1.92E-18
Bacteroidetes
4346863
10.9
1.2
9.7
0.0094
Bacteroidetes
245523
18.9
9.7
9.1
6.69E-11
69782
10.4
1.7
8.7
1.25E-16
Leptotrichiaceae
Leptotrichia
-
Pseudomonadales
Pseudomonadaceae
-
-
Fusobacteriia
Fusobacteriales
Fusobacteriaceae
Fusobacterium
-
Fusobacteriia
Fusobacteriales
Fusobacteriaceae
Fusobacterium
-
Clostridia
Clostridiales
Mogibacteriaceae
Anaerovorax
-
Bacteroidia
Bacteroidales
Porphyromonadaceae
Porphyromonas
-
Bacteroidia
Bacteroidales
Prevotellaceae
Prevotella
-
Bacteroidetes
Bacteroidia
Bacteroidales
-
-
-
Proteobacteria
Epsilonproteobacteria
Campylobacterales
Helicobacteraceae
Helicobacter
-
AC C
EP
TE D
Fusobacteriales
Gammaproteobacteria
28118
7.7
0.1
7.5
0.0255
Firmicutes
Clostridia
Clostridiales
Veillonellaceae
Selenomonas
-
1580
7.1
0.6
6.5
0.0808
Bacteroidetes
Bacteroidia
Bacteroidales
Prevotellaceae
Prevotella
melaninogenica
4426163
7.7
1.6
6.1
0.0425
Bacteroidetes
Bacteroidia
Bacteroidales
Prevotellaceae
Prevotella
-
1033687
5.1
0.3
4.8
0.0689
Bacteroidetes
Bacteroidia
Bacteroidales
Prevotellaceae
Prevotella
-
92316
6.0
1.5
4.5
3.52E-08
Firmicutes
Clostridia
Clostridiales
Lachnospiraceae
-
-
62513
4.4
0.1
4.3
3.52E-05
Firmicutes
Clostridia
Clostridiales
Lachnospiraceae
Moryella
-
12
ACCEPTED MANUSCRIPT
OTU ID
#
AA
#
ANA
AA-ANA
q-value
Phylum
Class
Order
Family
Genus
Species
Firmicutes
Bacilli
Lactobacillales
Streptococcaceae
Streptococcus
-
Flavobacteriaceae
Capnocytophaga
-
Neisseriaceae
Neisseria
subflava
4.6
0.9
3.7
989074
3.4
0.1
3.2
0.0002
Bacteroidetes
Flavobacteriia
Flavobacteriales
1304
3.0
0.1
2.9
0.0044
Proteobacteria
Betaproteobacteria
Neisseriales
4331815
7.5
4.8
2.7
0.0212
Proteobacteria
Alphaproteobacteria
Sphingomonadales
Sphingomonadaceae
-
-
100791
2.7
0.1
2.5
0.0009
Proteobacteria
Betaproteobacteria
Burkholderiales
Comamonadaceae
-
-
55917
3.6
1.1
2.5
0.0004
Bacteroidetes
Bacteroidia
Bacteroidales
-
-
-
831
2.5
0.3
2.2
0.0897
Bacteroidetes
Bacteroidia
Bacteroidales
Prevotellaceae
Prevotella
-
2377731
3.2
1.1
2.0
0.0029
Firmicutes
Clostridia
Clostridiales
Veillonellaceae
Selenomonas
-
1465
2.1
0.1
2.0
0.0033
Bacteroidetes
Bacteroidia
Bacteroidales
Prevotellaceae
Prevotella
intermedia
4353264
3.8
1.8
2.0
0.0135
Proteobacteria
Alphaproteobacteria
Caulobacterales
Caulobacteraceae
-
-
4414261
2.1
0.5
1.6
0.0044
Firmicutes
Bacilli
Lactobacillales
Lactobacillaceae
Lactobacillus
-
1066621
2.2
0.6
1.6
0.0925
Bacteroidetes
Bacteroidia
Bacteroidales
Prevotellaceae
Prevotella
melaninogenica
1188
1.4
0.1
1.4
0.0449
Fusobacteria
Fusobacteriia
Fusobacteriales
Leptotrichiaceae
Leptotrichia
-
104313
1.2
0.3
0.9
0.0696
Proteobacteria
Gammaproteobacteria
Pseudomonadales
Pseudomonadaceae
Pseudomonas
-
SC
M AN U
0.0787
RI PT
523025
0.6
1.7
-1.1
0.0190
Firmicutes
Bacilli
Bacillales
Exiguobacteraceae
-
-
0.1
1.3
-1.2
0.0894
Proteobacteria
Gammaproteobacteria
Pasteurellales
Pasteurellaceae
Aggregatibacter
segnis
57758
0.5
1.7
-1.2
0.0047
Bacteroidetes
Flavobacteriia
Flavobacteriales
Flavobacteriaceae
Flavobacterium
columnare
4483174
0.7
2.0
-1.3
0.0092
Fusobacteria
Fusobacteriia
Fusobacteriales
Leptotrichiaceae
Leptotrichia
-
TE D
540269 4423236
0.4
2.1
-1.7
8.43E-05
Actinobacteria
Actinobacteria
Actinomycetales
Gordoniaceae
Gordonia
-
1.5
3.3
-1.8
0.0043
Cyanobacteria
Chloroplast
Streptophyta
-
-
-
4375000
0.5
2.7
-2.2
2.70E-06
Proteobacteria
Gammaproteobacteria
Enterobacteriales
Enterobacteriaceae
-
-
470724
0.4
3.8
-3.4
2.37E-10
Proteobacteria
Betaproteobacteria
Neisseriales
Neisseriaceae
Neisseria
-
3.65E-11
Betaproteobacteria
Burkholderiales
Comamonadaceae
Hydrogenophaga
-
Streptococcaceae
Streptococcus
-
AC C
EP
671258 3359884
339015
0.3
4.1
-3.8
669486
0.8
5.3
-4.4
2.19E-13
Firmicutes
Bacilli
Lactobacillales
1052181
1.8
6.8
-5.0
4.58E-13
Bacteroidetes
Bacteroidia
Bacteroidales
Prevotellaceae
Prevotella
nanceiensis
4335450
1.4
6.5
-5.1
2.75E-14
Fusobacteria
Fusobacteriia
Fusobacteriales
Leptotrichiaceae
-
-
4343627
0.3
5.7
-5.4
1.07E-14
Bacteroidetes
Bacteroidia
Bacteroidales
Bacteroidaceae
Bacteroides
fragilis
Proteobacteria
903686
2.8
8.3
-5.5
0.0689
Proteobacteria
Gammaproteobacteria
Pasteurellales
Pasteurellaceae
Aggregatibacter
segnis
4440670
19.8
25.3
-5.5
0.0055
Firmicutes
Clostridia
Clostridiales
Veillonellaceae
Veillonella
-
13
ACCEPTED MANUSCRIPT
OTU ID
#
AA
#
ANA
AA-ANA
q-value
Phylum
Class
Order
Family
Genus
Species
Proteobacteria
Gammaproteobacteria
Enterobacteriales
Enterobacteriaceae
-
-
Pasteurellaceae
Haemophilus
-
0.9
6.5
-5.6
4309323
1.1
6.8
-5.7
0.0588
Proteobacteria
Gammaproteobacteria
Pasteurellales
242070
1.7
8.2
-6.5
2.34E-18
Proteobacteria
Gammaproteobacteria
Pseudomonadales
Pseudomonadaceae
Pseudomonas
-
521996
0.3
7.1
-6.9
0.0007
Firmicutes
Bacilli
Lactobacillales
Streptococcaceae
Streptococcus
-
571873
0.4
7.6
-7.2
1.32E-18
Proteobacteria
Betaproteobacteria
Burkholderiales
Comamonadaceae
-
-
Spirochaetaceae
Treponema
-
Micrococcaceae
-
-
6.52E-17
RI PT
3799784
1.4
8.9
-7.5
1.36E-22
Spirochaetes
Spirochaetes
Spirochaetales
0.3
8.1
-7.8
6.36E-19
Actinobacteria
Actinobacteria
Actinomycetales
898611
1.0
9.3
-8.3
3.23E-25
Firmicutes
Clostridia
Clostridiales
Lachnospiraceae
Butyrivibrio
-
4428313
6.5
15.3
-8.8
3.16E-16
Firmicutes
Bacilli
Lactobacillales
Lactobacillaceae
Lactobacillus
-
SC
95168 543491
11.6
20.6
-9.0
3.10E-11
Spirochaetes
Spirochaetes
Spirochaetales
Spirochaetaceae
Treponema
amylovorum
0.1
9.1
-9.0
0.0615
Proteobacteria
Gammaproteobacteria
Pasteurellales
Pasteurellaceae
Haemophilus
influenzae
4303688
4.1
14.5
-10.4
8.09E-26
Proteobacteria
Betaproteobacteria
Burkholderiales
Burkholderiaceae
Lautropia
-
561636
0.1
10.9
-10.8
0.0232
Firmicutes
Bacilli
Lactobacillales
Streptococcaceae
Streptococcus
-
63117
1.9
14.7
-12.9
3.41E-39
Proteobacteria
Betaproteobacteria
Neisseriales
Neisseriaceae
Kingella
-
4386920
0.0
15.0
-15.0
0.0255
Proteobacteria
Gammaproteobacteria
Pseudomonadales
Pseudomonadaceae
Pseudomonas
-
2510538
1.6
17.5
-15.9
0.0254
Firmicutes
Bacilli
Lactobacillales
Carnobacteriaceae
-
-
4430639
7.5
24.6
-17.1
0.0254
Proteobacteria
Gammaproteobacteria
Pasteurellales
Pasteurellaceae
Haemophilus
parainfluenzae
353459
0.0
17.4
-17.4
0.087
Bacteroidetes
Bacteroidia
Bacteroidales
Prevotellaceae
Prevotella
copri
4443200
0.8
19.3
-18.5
8.79E-45
Bacteroidetes
Flavobacteriia
Flavobacteriales
Flavobacteriaceae
Capnocytophaga
ochracea
4412630
2.0
22.1
-20.1
0.0271
Bacteroidetes
Bacteroidia
Bacteroidales
Porphyromonadaceae
Porphyromonas
-
171157
8.6
31.3
-22.7
1.72E-57
Proteobacteria
Epsilonproteobacteria
Campylobacterales
Helicobacteraceae
Flexispira
-
259631
0.0
23.0
-23.0
0.0686
Firmicutes
Clostridia
Clostridiales
Lachnospiraceae
Ruminococcus
gnavus
0.0043
Fusobacteriia
Fusobacteriales
Fusobacteriaceae
Fusobacterium
-
Bacteroidetes
Bacteroidia
Bacteroidales
Prevotellaceae
Prevotella
melaninogenica
AC C
EP
TE D
M AN U
3859 956702
245
0.2
25.1
-25.0
575
2.5
28.0
-25.5
0.032
4301566
10.6
37.3
-26.7
0.0172
Firmicutes
Bacilli
Lactobacillales
Streptococcaceae
Streptococcus
-
886735
21.0
47.9
-26.9
1.18E-47
Firmicutes
Bacilli
Lactobacillales
Aerococcaceae
Alloiococcus
-
267852
5.9
33.8
-27.9
1.99E-82
Proteobacteria
Alphaproteobacteria
Sphingomonadales
Sphingomonadaceae
Sphingopyxis
alaskensis
4469032
21.9
50.2
-28.3
1.74E-50
Firmicutes
Bacilli
Lactobacillales
-
-
-
995
4.0
44.7
-40.6
0.010
Bacteroidetes
Bacteroidia
Bacteroidales
Prevotellaceae
Prevotella
melaninogenica
Fusobacteria
14
ACCEPTED MANUSCRIPT
OTU ID
#
ANA
AA-ANA
q-value
Phylum
Class
Order
Family
Genus
Species
2.03E-123
Firmicutes
Bacilli
Lactobacillales
Streptococcaceae
Streptococcus
-
Erysipelotrichaceae
Sharpea
-
Lactobacillaceae
Lactobacillus
zeae
7.1
47.9
-40.9
4364176
1.3
47.9
-46.6
1.99E-97
Firmicutes
Erysipelotrichi
Erysipelotrichales
4480189
10.0
60.8
-50.8
1.45E-151
Firmicutes
Bacilli
Lactobacillales
1015518
3.1
55.8
-52.7
5.83E-143
Actinobacteria
Actinobacteria
Actinomycetales
Corynebacteriaceae
Corynebacterium
-
4466150
5.5
63.3
-57.9
5.08E-05
Proteobacteria
Gammaproteobacteria
Pasteurellales
Pasteurellaceae
Haemophilus
-
4337755
30.6
94.8
-64.2
0.0255
Firmicutes
Bacilli
Gemellales
1925094
8.2
75.5
-67.4
5.19E-204
Actinobacteria
Actinobacteria
Actinomycetales
260397
3.7
77.7
-74.0
2.51E-191
Firmicutes
Erysipelotrichi
Erysipelotrichales
4372058
17.6
105.2
-87.6
0.026
Bacteroidetes
Bacteroidia
Bacteroidales
2.41E-263 1.94E-70
4459993
3.5
102.7
-99.1
0.0010
495067
1.8
103.4
-101.7
134265
1.0
119.2
4306587
12.7
4400869
10.9
891034
31.5
-
-
Micrococcaceae
-
-
Erysipelotrichaceae
Allobaculum
-
Prevotellaceae
Prevotella
melaninogenica
SC
-91.2 -92.2
Proteobacteria
Epsilonproteobacteria
Campylobacterales
Helicobacteraceae
Flexispira
-
Firmicutes
Clostridia
Clostridiales
Mogibacteriaceae
-
-
Bacteroidetes
Bacteroidia
Bacteroidales
Porphyromonadaceae
Porphyromonas
-
2.20E-171
Actinobacteria
Actinobacteria
Actinomycetales
Corynebacteriaceae
Corynebacterium
-
-118.2
9.40E-137
Bacteroidetes
Bacteroidia
Bacteroidales
Prevotellaceae
Prevotella
-
158.2
-145.5
0.0156
Firmicutes
Clostridia
Clostridiales
Veillonellaceae
Selenomonas
-
167.7
-156.8
0.0960
SR1
-
-
-
-
-
270.0
-238.5
0.0806
Proteobacteria
Gammaproteobacteria
Pasteurellales
Pasteurellaceae
Aggregatibacter
-
-872.4
0.0006
Proteobacteria
Gammaproteobacteria
Pasteurellales
Pasteurellaceae
Aggregatibacter
segnis
4432431 91.3 963.7 mean relative abundance
M AN U
98.5 313.5
TE D
7.3 221.3
Gemellaceae
EP
976838 4335578
RI PT
4297222
AC C
#
#
AA
15
ACCEPTED MANUSCRIPT
0.3 (0.0 - 1.0) 0.0 (0.0 - 0.2) -0.2 (-0.6 - 0.0) 121.5 (49.0 - 231.3) 6 (3-7) 27% 39%
<0.05 NS NS <0.05 € NS € NS € NS
SC
<0.05 NS NS <0.05 € NS € NS NS NS NS <0.05 NS NS NS NS NS NS <0.01 <0.01 <0.001 NS NS NS NS
M AN U
1.3 (0.4 - 3.8) 0.0 (0.0 - 0.4) 0.0 (-0.7 - 0.1) 323.5 (184.3 - 560.5) 5 (2-10) 40% 60%
p-value#
TE D
Sputum eosinophils (% Baseline) Sputum eosinophils (% post-ICS) Change in sputum eosinophils Serum IgE (IU/mL) Number of positive sIgE¢ % Eczema/Allergic dermatitis§ % Allergic rhinitis§
T2-low asthmatics (n = 30) 37 (27-43) 9 (5-22) 23 (14-29) 0.5 (0.2 - 0.7) 47% 60% 26 (23 - 29) 86 (76 - 96) 105 (83 - 105) 7 (5 - 12) 95 (90 - 106) 103 (98 - 115) 1 (-6 - 4) 0 (-5 - 6) 1.3 (0.4 - 2.7) 1.9 (0.3 - 5.8) 0.3 (-0.2 - 1.5) 130 (100 - 293) 2.7 (1.3 - 4.2) 56.1 (49.7 - 62.0) 55.8 (32.5 - 65.2) 49.4 (25.1 - 60.3) -9.6 (-18-1.8)
EP
Age (yrs) Age of Asthma diagnosis (yrs) Duration of Asthma (yrs) ACQ Score (Baseline)* % Male % White BMI FEV1 % pre-Ab (Baseline) FEV1 % post-Ab (Baseline) Change in FEV % (Baseline) FEV1 % pre-Ab (post-ICS) FEV1 % post-Ab (post-ICS) Change in FEV % pre-Ab Change in FEV % post-Ab PC20 (Baseline) PC20 (post ICS) Change in PC20 Blood eosinophils (absolute) Blood eosinophils (%) Blood neutrophils (%) Sputum neutrophils (% Baseline) Sputum neutrophils (% post-ICS) Change in sputum neutrophils
T2-high asthmatics (n = 10) 28 (24-33) 9 (5-24) 18 (9-23) 1.0 (0.8 - 1.3) 50% 60% 26 (23 - 30) 91 (68 - 110) 102 (93 - 114) 15 (7 - 25) 88 (79 - 97) 98 (91 - 105) 3 (-4 - 13) 1 (-3 - 5) 0.9 (0.2 - 3.8) 6.1 (1.6 - 12.4) 4.9 (1.2 - 11.5) 400 (245 - 600) 6.9 (4.6 - 10.3) 53.5 (43.7 - 60.5) 53.6 (33.9 - 64.9) 49.5 (32.8 - 59.9) 0.0 (-24.9 - 16.0)
AC C
Variable
RI PT
Table S7: Clinical and immunological comparison of T2-high compared to T2-low asthma patients.
¢
All values are medians (IQR). *ACQ, Asthma Control Questionnaire. Number of positive specific IgE (sIgE >0.35 kU/l) from a total of 12 aeroallergens tested by § # ImmunoCap assay. Subjects with a positive history of allergic conditions specified (self-reported). Statistical significance was determined using Mann-Whitney or € Fisher’s exact test.
16
ACCEPTED MANUSCRIPT
Table S8: Clinical and immunological comparison of ICS-responders compared to ICS non-responders. p-value#
RI PT
NS NS NS NS NS € NS € NS NS <0.05 NS <0.05 NS NS NS NS NS NS NS NS <0.01 NS NS NS NS NS NS NS NS NS NS
M AN U
SC
ICS non-responders (n = 10) 29 (20-39) 5 (2-9) 21 (13-26) 0.6 (0.1 - 1.0) 0.2 (0.0 - 0.7) 40% 70% 26 (24 - 28) 99 (94 - 110) 105 (100 - 114) 6 (5 - 7) 95 (90 - 106) 103 (98 - 115) 10 (5 - 14) 1 (-6 - 4) 0 (-5 - 6) 0.4 (-5.3 – 3.3) 1.9 (0.3 - 4.1) 1.9 (0.3 - 5.8) 0.3 (-0.2 - 1.5) 155 (100 - 315) 3.0 (1.0 - 5.1) 58.0 (50.4 - 64.3) 56.1 (35.0 - 66.7) 49.4 (25.1 - 60.3) -9.6 (-18-1.8) 0.4 (0.0 - 0.7) 0.0 (0.0 - 0.2) -0.2 (-0.6 - 0.0) 188.0 (66.8 - 310.8)
TE D
AC C
Age (yrs) Age of Asthma diagnosis (yrs) Duration of Asthma (yrs) ACQ Score (Baseline)* ACQ Score (post ICS)* % Male % White BMI FEV1 % pre-Ab (Baseline) FEV1 % post-Ab (Baseline) Change in FEV with Ab % (Baseline) FEV1 % pre-Ab (post-ICS) FEV1 % post-Ab (post-ICS) Change in FEV % with Ab (post-ICS) Change in FEV % pre-Ab Change in FEV % post-Ab % Change from baseline in pre-Alb FEV1 PC20 (Baseline) PC20 (post ICS) Change in PC20 Blood eosinophils (absolute) Blood eosinophils (%) Blood neutrophils (%) Sputum neutrophils (% Baseline) Sputum neutrophils (% post-ICS) Change in sputum neutrophils Sputum eosinophils (% Baseline) Sputum eosinophils (% post-ICS) Change in sputum eosinophils Serum IgE (IU/mL)
ICS responders (n = 15) 32 (27-43) 8 (5-22) 25 (18-27) 0.8 (0.2 - 1.2) 0.3 (0.0 - 0.5) 40% 47% 26 (23 - 29) 86 (71 - 95) 101 (85 - 105) 12 (8 - 15) 88 (79 - 97) 98 (91 - 105) 12 (5 - 14) 3 (-4 - 13) 1 (-3 - 5) 3.2 (-4.7 – 11.7) 0.8 (0.3 - 1.2) 6.1 (1.6 - 12.4) 4.9 (1.2 - 11.5) 200 (100 - 400) 3.8 (1.3 - 6.7) 53.3 (49.8 - 61.0) 51.5 (25.6 - 64.2) 49.5 (32.8 - 59.9) 0.0 (-24.9 - 16.0) 0.2 (0.0 - 1.0) 0.0 (0.0 - 0.4) 0.0 (-0.7 - 0.1) 153.0 (93.0 - 261.0)
EP
Variable
#
€
All values are medians (IQR). *ACQ, Asthma Control Questionnaire. Statistical significance was determined using Mann-Whitney or Fisher’s exact test.
Table S9: Specific taxa differentially expressed in ICS non-responders (ICS non-R) compared to ICS responders (ICS-R) at baseline.
18
ACCEPTED MANUSCRIPT
OTU ID
ICS # non-R
#
ICS-R
ICS nonR- R
q-value
Phylum
Class
Order
Family
Genus
Species
9157.6
0.5
9157.1
1.05E-104
Actinobacteria
Actinobacteria
Actinomycetales
Microbacteriaceae
-
-
1111.8
0.7
1111.1
1.29E-82
Proteobacteria
Gammaproteobacteria
Pasteurellales
Pasteurellaceae
Haemophilus
-
4459021
221
7.1
213.9
0.0658
Bacteroidetes
Bacteroidia
Bacteroidales
Porphyromonadaceae
Tannerella
-
4465803
210.2
3.7
206.5
1.06E-126
Bacteroidetes
Bacteroidia
Bacteroidales
Porphyromonadaceae
Porphyromonas
-
RI PT
12297 4406393
164
3.8
160.2
7.45E-112
Fusobacteria
Fusobacteriia
Fusobacteriales
Leptotrichiaceae
Leptotrichia
-
154
8.7
145.3
3.64E-140
Bacteroidetes
Bacteroidia
Bacteroidales
Prevotellaceae
Prevotella
-
2654263
116.4
4.5
111.9
0.0129
Proteobacteria
Gammaproteobacteria
Pasteurellales
Pasteurellaceae
Haemophilus
parainfluenzae
4437399
137.4
58.4
79
2.55E-50
Spirochaetes
Spirochaetes
Spirochaetales
Spirochaetaceae
Treponema
-
4404220
410.2
333.7
76.5
5.54E-12
Proteobacteria
Gammaproteobacteria
Pasteurellales
Pasteurellaceae
Actinobacillus
-
14920
60.6
0.2
60.4
2.48E-14
Firmicutes
Bacilli
Gemellales
Gemellaceae
Gemella
-
73875
84.6
26.9
57.7
4.06E-47
Spirochaetes
Spirochaetes
Spirochaetales
Spirochaetaceae
Treponema
-
71146
108.6
52.6
56
1.06E-30
Spirochaetes
Spirochaetes
Spirochaetales
Spirochaetaceae
Treponema
-
2613485
60.8
6.3
54.5
2.20E-58
Bacteroidetes
Bacteroidia
Bacteroidales
Porphyromonadaceae
Porphyromonas
-
Flavobacteriia
M AN U
SC
923032 4426165
54.4
0.6
53.8
4.08E-26
Bacteroidetes
Flavobacteriales
Flavobacteriaceae
Capnocytophaga
ochracea
548730
114.4
62.3
52.1
3.75E-24
Bacteroidetes
Bacteroidia
Bacteroidales
Prevotellaceae
Prevotella
-
949816
37.4
2.6
34.8
0.0389
Firmicutes
Bacilli
Lactobacillales
Streptococcaceae
Streptococcus
-
4376637
82.6
47.8
34.8
1.24E-14
Spirochaetes
Spirochaetes
Spirochaetales
Spirochaetaceae
Treponema
-
968873
33.8
0.1
33.7
1.63E-07
Bacteroidetes
Flavobacteriia
325027
33.6
1.4
32.2
0.0454
4459265
42.8
12.9
29.9
1013316
29.8
0.9
28.9
4480189
27.2
0.4
26.8
4468500
58.2
36.4
21.8
4446973
21.8
0.1
21.7
4313722
20.4
0.4
20
4449458
23.6
5.6
4422512
19.6
886735
20.2
TE D
4419376
Weeksellaceae
-
-
Betaproteobacteria
Burkholderiales
Comamonadaceae
Comamonas
-
2.28E-25
Bacteroidetes
Bacteroidia
Bacteroidales
Paraprevotellaceae
Prevotella
-
8.09E-23
Firmicutes
Clostridia
Clostridiales
Lachnospiraceae
-
-
3.05E-15
Firmicutes
Bacilli
Lactobacillales
Lactobacillaceae
Lactobacillus
zeae
6.44E-08
AC C
EP
Flavobacteriales
Proteobacteria
Firmicutes
Clostridia
Clostridiales
Mogibacteriaceae
Anaerovorax
-
0.0716
Bacteroidetes
Bacteroidia
Bacteroidales
Porphyromonadaceae
Paludibacter
-
0.0059
Fusobacteria
Fusobacteriia
Fusobacteriales
Fusobacteriaceae
Fusobacterium
-
18
0.0545
Proteobacteria
Gammaproteobacteria
Pseudomonadales
Moraxellaceae
Acinetobacter
-
2.1
17.5
5.90E-19
Tenericutes
Mollicutes
Mycoplasmatales
Mycoplasmataceae
Mycoplasma
-
3.6
16.6
2.32E-17
Firmicutes
Bacilli
Lactobacillales
Aerococcaceae
Alloiococcus
-
19
ACCEPTED MANUSCRIPT
OTU ID
ICS # non-R
ICS nonR- R
#
ICS-R
q-value
Phylum
Class
Order
Family
Genus
Species
17.6
1.3
16.3
6.34E-17
Actinobacteria
Actinobacteria
Actinomycetales
Actinomycetaceae
Actinomyces
-
4329788
33.2
19.3
13.9
6.48E-06
Firmicutes
Clostridia
Clostridiales
-
-
-
269930
43.2
32.5
10.7
0.0181
1157
11
1.1
9.9
8.65E-11
4299087
8.2
0.3
7.9
1609
7.6
0.3
1076969
11.8
4306773
9.8
4329957
5.8
137183
RI PT
46
Gammaproteobacteria
Pseudomonadales
Pseudomonadaceae
-
-
Bacilli
Lactobacillales
Streptococcaceae
Streptococcus
-
0.0389
Bacteroidetes
Flavobacteriia
Flavobacteriales
Flavobacteriaceae
Capnocytophaga
ochracea
7.3
1.60E-06
Bacteroidetes
Bacteroidia
Bacteroidales
Prevotellaceae
Prevotella
melaninogenica
4.5
7.3
2.31E-05
Firmicutes
Bacilli
Lactobacillales
Streptococcaceae
Streptococcus
-
3.3
6.5
2.74E-05
Fusobacteria
Fusobacteriia
Fusobacteriales
Leptotrichiaceae
Leptotrichia
-
0.2
5.6
8.21E-05
Bacteroidetes
Bacteroidia
Bacteroidales
Prevotellaceae
Prevotella
-
6.8
1.4
5.4
1.37E-05
Actinobacteria
Actinobacteria
Bifidobacteriales
Bifidobacteriaceae
-
-
886264
2.6
0.6
2
0.0398
Actinobacteria
Actinobacteria
Actinomycetales
Micrococcaceae
Rothia
mucilaginosa
M AN U
SC
Proteobacteria Firmicutes
0.8
3.6
-2.8
0.0550
Firmicutes
Clostridia
Clostridiales
Mogibacteriaceae
-
-
0.6
3.6
-3
0.0390
Fusobacteria
Fusobacteriia
Fusobacteriales
Fusobacteriaceae
Fusobacterium
-
968954
0.6
4.2
-3.6
0.0389
Firmicutes
Bacilli
Lactobacillales
Streptococcaceae
Streptococcus
-
1015518
1.2
4.9
-3.7
0.0176
Actinobacteria
Actinobacteria
Actinomycetales
Corynebacteriaceae
Corynebacterium
-
TE D
43339 645055
2.2
6
-3.8
0.0325
Actinobacteria
Actinobacteria
Actinomycetales
Actinomycetaceae
Actinomyces
-
4399781
1.2
5.1
-3.9
0.0130
Bacteroidetes
Bacteroidia
Bacteroidales
Prevotellaceae
Prevotella
-
989074
1.2
6.4
-5.2
0.0016
Bacteroidetes
Flavobacteriia
Flavobacteriales
Flavobacteriaceae
Capnocytophaga
-
1580
2.2
7.6
-5.4
0.0022
Bacteroidetes
Bacteroidia
Bacteroidales
Prevotellaceae
Prevotella
melaninogenica
0.0545
Firmicutes
Bacilli
Lactobacillales
Streptococcaceae
Streptococcus
-
Clostridia
Clostridiales
Lachnospiraceae
-
-
591583
0.2
5.6
-5.4
4341205
3.8
9.4
-5.6
965500
2.4
8.9
-6.5
254888
3.6
10.5
-6.9
1935279
0.8
9.1
-8.3
851935
0.4
9
-8.6
0.0916
610111
3.4
13.1
-9.7
3.71E-06
851824
1.2
12.1
-10.9
7.99E-07
124932
1.8
12.8
-11
3.19E-07
4306587
0.2
11.5
-11.3
0.0325
0.0056
EP
4306356
Firmicutes
Firmicutes
Bacilli
Lactobacillales
Streptococcaceae
Streptococcus
-
0.0005
Proteobacteria
Betaproteobacteria
Burkholderiales
Comamonadaceae
-
-
0.0125
Firmicutes
Bacilli
Lactobacillales
Streptococcaceae
Streptococcus
-
Bacteroidetes
Bacteroidia
Bacteroidales
Prevotellaceae
Prevotella
-
Bacteroidetes
Bacteroidia
Bacteroidales
Prevotellaceae
Prevotella
-
Firmicutes
Erysipelotrichi
Erysipelotrichales
Erysipelotrichaceae
Bulleidia
-
Spirochaetes
Spirochaetes
Spirochaetales
Spirochaetaceae
Treponema
-
Firmicutes
Clostridia
Clostridiales
Veillonellaceae
Selenomonas
-
AC C 0.0004
20
ACCEPTED MANUSCRIPT
OTU ID
#
ICS-R
ICS nonR- R
q-value
Phylum
Class
Order
Family
Genus
Species
Bacteroidetes
Bacteroidia
Bacteroidales
Prevotellaceae
Prevotella
-
Fusobacteria
Fusobacteriia
Fusobacteriales
Leptotrichiaceae
Leptotrichia
-
6.4
18.2
-11.8
1.15E-06
4297119
0.2
15
-14.8
0.0131
4373910
0.8
16
-15.2
8.52E-08
Proteobacteria
Betaproteobacteria
Neisseriales
Neisseriaceae
Neisseria
-
4440670
2.6
19.9
-17.3
3.30E-11
Firmicutes
Clostridia
Clostridiales
Veillonellaceae
Veillonella
-
1044611
1.6
23.9
-22.3
1.61E-12
Firmicutes
Clostridia
Clostridiales
Peptostreptococcaceae
-
-
537098
125.6
150.9
-25.3
0.0020
Firmicutes
Clostridia
Clostridiales
Lachnospiraceae
-
-
0.0155
Bacteroidetes
Bacteroidia
Bacteroidales
Paraprevotellaceae
Prevotella
-
Firmicutes
Clostridia
Clostridiales
Lachnospiraceae
Oribacterium
-
Actinobacteria
Actinobacteria
Actinomycetales
Micrococcaceae
Rothia
dentocariosa
RI PT
22951
1.4
28.5
-27.1
25.8
57.8
-32
3.94E-15
4466006
5.2
46.9
-41.7
0.0863
4313608
12.8
56.4
-43.6
1.16E-27
Bacteroidetes
Bacteroidia
Bacteroidales
-
-
-
245523
0.2
47.7
-47.5
1.07E-06
Bacteroidetes
Bacteroidia
Bacteroidales
-
-
-
M AN U
SC
86 4310395
0.2
52.1
-51.9
0.0233
Bacteroidetes
Bacteroidia
Bacteroidales
Prevotellaceae
Prevotella
-
21.2
98.8
-77.6
1.93E-49
Fusobacteria
Fusobacteriia
Fusobacteriales
Fusobacteriaceae
Fusobacterium
-
4330849
6.6
92.4
-85.8
2.08E-48
SR1
-
-
-
-
-
4344371
2
94.1
-92.1
4.52E-32
Proteobacteria
Alphaproteobacteria
Sphingomonadales
Sphingomonadaceae
Sphingomonas
-
1053321
17
110.2
-93.2
5.59E-60
Proteobacteria
Gammaproteobacteria
Pseudomonadales
Moraxellaceae
Moraxella
-
4335578
117.4
236
-118.6
6.17E-50
Firmicutes
Clostridia
Clostridiales
Mogibacteriaceae
-
-
4404577
253.4
388.7
-135.3
3.41E-38
Firmicutes
Clostridia
Clostridiales
Peptostreptococcaceae
Peptostreptococcus
-
4455767
56.8
206.1
-149.3
0.0597
Firmicutes
Bacilli
Lactobacillales
Streptococcaceae
Streptococcus
-
2.22E-99
Fusobacteriia
Fusobacteriales
Leptotrichiaceae
Leptotrichia
-
Bacteroidia
Bacteroidales
Paraprevotellaceae
Prevotella
tannerae
54.4
215.3
-160.9
0.8
165.9
-165.1
4308793
35.2
270.2
-235
4363066
9.8
333.7
-323.9
2469654
41.2
399.4
-358.2
4439603 1030 2727.2 mean relative abundance
-1697.2
0.0006
EP
252843 4448497
TE D
642465 2438396
Fusobacteria
Bacteroidetes
0.0389
Fusobacteria
Fusobacteriia
Fusobacteriales
Fusobacteriaceae
Fusobacterium
-
0.0568
Proteobacteria
Gammaproteobacteria
Pasteurellales
Pasteurellaceae
Aggregatibacter
-
Bacteroidetes
Bacteroidia
Bacteroidales
Paraprevotellaceae
Prevotella
-
Firmicutes
Bacilli
Lactobacillales
Streptococcaceae
Streptococcus
-
AC C
#
ICS # non-R
1.68E-219 0.0127
Table S10: Predicted KEGG pathways enriched in ICS non-responders compared to ICS responders at baseline.
21
ACCEPTED MANUSCRIPT
ICS nonresponders
ICSresponders
ICS non-responders responders
KEGG Super-pathways
KEGG pathways
6133
90535
Amino Acid Metabolism
Alanine, aspartate and glutamate metabolism
163683
182438
Amino Acid Metabolism
Amino acid related enzymes
67400
48235
19165
Amino Acid Metabolism
171954
78691
93263
Amino Acid Metabolism
231976
121154
110822
Amino Acid Metabolism
54604
16710
37894
Amino Acid Metabolism
46299
268
46031
Amino Acid Metabolism
159693
49782
109911
Amino Acid Metabolism
44
12
32
Amino Acid Metabolism
RI PT
96668 346121
Arginine and proline metabolism Cysteine and methionine metabolism Glycine, serine and threonine metabolism
Phenylalanine metabolism Phenylalanine, tyrosine and tryptophan biosynthesis Tryptophan metabolism
20667
31017
Amino Acid Metabolism
Tyrosine metabolism
205019
112275
Amino Acid Metabolism
Valine, leucine and isoleucine biosynthesis
276968
119360
157608
Amino Acid Metabolism
Valine, leucine and isoleucine degradation
46198
546
45652
Biosynthesis of Other Secondary Metabolites
Novobiocin biosynthesis
4388
18964
-14576
Biosynthesis of Other Secondary Metabolites
Penicillin and cephalosporin biosynthesis
9285
36231
-26946
Biosynthesis of Other Secondary Metabolites
Phenylpropanoid biosynthesis
149876
160184
-10308
Carbohydrate Metabolism
Amino sugar and nucleotide sugar metabolism
93433
4694
88739
Carbohydrate Metabolism
Ascorbate and aldarate metabolism
413978
211294
202684
Carbohydrate Metabolism
Butanoate metabolism
151818
133288
18530
Carbohydrate Metabolism
C5-Branched dibasic acid metabolism
510250
246694
263556
Carbohydrate Metabolism
Citrate cycle (TCA cycle)
91488
115716
-24228
Carbohydrate Metabolism
Fructose and mannose metabolism
26877
108183
-81306
Carbohydrate Metabolism
Galactose metabolism
542521
327948
214573
Carbohydrate Metabolism
Glycolysis / Gluconeogenesis
83375
82142
1233
Carbohydrate Metabolism
Inositol phosphate metabolism
116722
70965
45757
Carbohydrate Metabolism
Pentose and glucuronate interconversions
309090
182603
126487
Carbohydrate Metabolism
Pentose phosphate pathway
96668
6133
90535
Carbohydrate Metabolism
Propanoate metabolism
455675
229990
225685
Carbohydrate Metabolism
Pyruvate metabolism
AC C
TE D
51684 317294
EP
M AN U
SC
Lysine degradation
22
ACCEPTED MANUSCRIPT
ICS nonresponders
ICSresponders
ICS non-responders responders
KEGG Super-pathways
KEGG pathways
64173
-49729
Carbohydrate Metabolism
Starch and sucrose metabolism
42064
67886
Cell Motility
Bacterial motility proteins
62488
39219
23269
Cellular Processes and Signaling
2
57
-55
Cellular Processes and Signaling
57202
31502
25700
Cellular Processes and Signaling
29671
112498
-82827
Cellular Processes and Signaling
79239
102277
-23038
Cellular Processes and Signaling
46318
902
45416
Cellular Processes and Signaling
101147
21454
79693
Cellular Processes and Signaling
RI PT
14444 109950
Electron transfer carriers Germination
Inorganic ion transport and metabolism
M AN U
SC
Membrane and intracellular structural molecules Other ion-coupled transporters Other transporters Signal transduction mechanisms
37
155
-118
Cellular Processes and Signaling
Sporulation
85232
82785
2447
Energy Metabolism
Carbon fixation in photosynthetic organisms
48202
4851
43351
Energy Metabolism
64518
40288
24230
Energy Metabolism
Carbon fixation pathways in prokaryotes
38894
22677
Energy Metabolism
9573
364303
Energy Metabolism
1540
420
1120
Energy Metabolism
Photosynthesis
396
108
288
Energy Metabolism
Photosynthesis - antenna proteins
54942
11504
43438
Energy Metabolism
Photosynthesis proteins
332715
262004
70711
Enzyme Families
Peptidases
63873
37500
26373
Enzyme Families
Protein kinases
188379
99634
88745
Folding, Sorting and Degradation
Protein export
67735
42062
25673
Folding, Sorting and Degradation
RNA degradation
103067
21437
81630
Folding, Sorting and Degradation
Sulfur relay system
48173
2656
45517
Genetic Information Processing
Protein folding and associated processing
120672
63480
57192
Genetic Information Processing
Replication, recombination and repair proteins
166228
69731
96497
Genetic Information Processing
Translation proteins
10512
43144
-32632
Glycan Biosynthesis and Metabolism
Glycosaminoglycan degradation
62858
48757
14101
Glycan Biosynthesis and Metabolism
Glycosyltransferases
AC C
TE D
61571 373876
EP
Methane metabolism Nitrogen metabolism Oxidative phosphorylation
23
ACCEPTED MANUSCRIPT
ICS nonresponders
ICSresponders
ICS non-responders responders
KEGG Super-pathways
KEGG pathways
22647
-17127
Glycan Biosynthesis and Metabolism
Lipopolysaccharide biosynthesis proteins
126883
64237
Glycan Biosynthesis and Metabolism
Peptidoglycan biosynthesis
50687
12248
38439
Lipid Metabolism
51658
20661
30997
Lipid Metabolism
44
12
32
Lipid Metabolism
-136
Lipid Metabolism
12
13
Lipid Metabolism
1036463
799357
237106
Membrane Transport
188379
99634
88745
Membrane Transport
42102
177864
-135762
Membrane Transport
480439
264215
216224
Membrane Transport
1233749
1031431
202318
Membrane Transport
7503
30877
-23374
Metabolism
Fatty acid metabolism Glycerolipid metabolism Lipid biosynthesis proteins
SC
140
Fatty acid biosynthesis
M AN U
4 25
RI PT
5520 191120
Steroid biosynthesis ABC transporters Bacterial secretion system Phosphotransferase system (PTS)
Secretion system Transporters Amino acid metabolism
12
32
Metabolism
15503
38017
Metabolism
70507
69838
669
Metabolism
Energy metabolism
88
24
64
Metabolism
Lipid metabolism
3010
11274
-8264
Metabolism
Metabolism of cofactors and vitamins
Biosynthesis and biodegradation of secondary metabolites Carbohydrate metabolism
21
32
Metabolism
Nucleotide metabolism
248847
66227
Metabolism
Others
127273
82607
44666
Metabolism of Cofactors and Vitamins
Folate biosynthesis
121296
59194
62102
Metabolism of Cofactors and Vitamins
Lipoic acid metabolism
22645
Metabolism of Cofactors and Vitamins
Nicotinate and nicotinamide metabolism
46360
Metabolism of Cofactors and Vitamins
One carbon pool by folate
41407
Metabolism of Cofactors and Vitamins
Pantothenate and CoA biosynthesis
288859
Metabolism of Cofactors and Vitamins
Porphyrin and chlorophyll metabolism
AC C
53 315074
EP
TE D
44 53520
61527
38882
128513
82153
209922
168515
448003
159144
51658
20661
30997
Metabolism of Cofactors and Vitamins
Retinol metabolism
404632
189043
215589
Metabolism of Cofactors and Vitamins
Riboflavin metabolism
24
ACCEPTED MANUSCRIPT
ICS nonresponders
ICSresponders
ICS non-responders responders
KEGG Super-pathways
KEGG pathways
642
346
Metabolism of Cofactors and Vitamins
Thiamine metabolism
15689
38121
Metabolism of Cofactors and Vitamins
Ubiquinone and other terpenoid-quinone biosynthesis
96668
6133
90535
Metabolism of Other Amino Acids
9285
36231
-26946
Metabolism of Other Amino Acids
244909
142175
102734
Metabolism of Other Amino Acids
RI PT
988 53810
beta-Alanine metabolism Cyanoamino acid metabolism
602
1129
Metabolism of Other Amino Acids
57393
11496
Metabolism of Other Amino Acids
53634
15641
37993
Metabolism of Terpenoids and Polyketides
Biosynthesis of siderophore group nonribosomal peptides
195
50
145
Metabolism of Terpenoids and Polyketides
Carotenoid biosynthesis
319432
117227
202205
Metabolism of Terpenoids and Polyketides
Prenyltransferases
368339
126713
241626
Metabolism of Terpenoids and Polyketides
Terpenoid backbone biosynthesis
409304
360629
48675
Nucleotide Metabolism
Purine metabolism
170310
80320
89990
Nucleotide Metabolism
Pyrimidine metabolism
306381
206945
99436
Replication and Repair
Base excision repair
306381
206945
99436
Replication and Repair
DNA repair and recombination proteins
44
12
32
Signal Transduction
Calcium signaling pathway
180412
87533
92879
Signal Transduction
Two-component system
10512
43144
-32632
Signaling Molecules and Interaction
Bacterial toxins
257807
183480
74327
Transcription
Transcription factors
642256
442093
200163
Translation
Aminoacyl-tRNA biosynthesis
270199
206858
63341
Translation
Ribosome Biogenesis
54816
18151
36665
Translation
Ribosome biogenesis in eukaryotes
2 146
26
6
51658
20661
51658 51685
M AN U
TE D
AC C
79 578
Phosphonate and phosphinate metabolism
SC
1731 68889
EP
Glutathione metabolism
Selenocompound metabolism
77
Xenobiotics Biodegradation and Metabolism
Aminobenzoate degradation
432
Xenobiotics Biodegradation and Metabolism
Benzoate degradation
20
Xenobiotics Biodegradation and Metabolism
Caprolactam degradation
30997
Xenobiotics Biodegradation and Metabolism
Chloroalkane and chloroalkene degradation
20661
30997
Xenobiotics Biodegradation and Metabolism
Drug metabolism - cytochrome P450
21169
30516
Xenobiotics Biodegradation and Metabolism
Metabolism of xenobiotics by cytochrome P450
25
ACCEPTED MANUSCRIPT
ICS nonresponders
ICSresponders
ICS non-responders responders
KEGG Super-pathways
KEGG pathways
20661
30997
Xenobiotics Biodegradation and Metabolism
Naphthalene degradation
121
27
94
Xenobiotics Biodegradation and Metabolism
Polycyclic aromatic hydrocarbon degradation
26
6
20
Xenobiotics Biodegradation and Metabolism
26
6
20
Xenobiotics Biodegradation and Metabolism
RI PT
51658
Toluene degradation Xylene degradation
#
Post - Pre
195.3
0.5
194.8
241.0
71.6
4401186 4404731
Pre-
q-value
Phylum
Class
Order
Family
Genus
Species
0.0013
Proteobacteria
Betaproteobacteria
Neisseriales
Neisseriaceae
Eikenella
-
169.4
3.27E-140
Actinobacteria Proteobacteria
Actinobacteria
Actinomycetales
Corynebacteriaceae
Corynebacterium
-
Betaproteobacteria
Burkholderiales
Comamonadaceae
Delftia
-
Proteobacteria
Gammaproteobacteria
Pseudomonadales
Pseudomonadaceae
-
-
Tenericutes
Mollicutes
Mycoplasmatales
Mycoplasmataceae
Mycoplasma
-
Proteobacteria
Betaproteobacteria
Neisseriales
Neisseriaceae
Eikenella
-
4469492
183.6
16.4
167.3
0.0009
269930
129.9
12.0
117.9
0.0051
615020
39.4
0.9
38.5
7.57E-31
87506
31.3
4.1
27.1
0.0904
4366487
23.4
2.6
20.8
0.0951
Bacteroidetes Proteobacteria
M AN U
Post-
Flavobacteriia
Flavobacteriales
Flavobacteriaceae
Capnocytophaga
-
Gammaproteobacteria
Pseudomonadales
Pseudomonadaceae
Pseudomonas
-
Bacteroidia
Bacteroidales
Porphyromonadaceae
Porphyromonas
-
Bacteroidia
Bacteroidales
-
-
-
TE D
#
OTU ID
SC
Table S11: Specific taxa differentially expressed in placebo treated asthmatics post-treatment compared to baseline (pre-treatment) visit.
16.8
0.8
16.0
495451
19.5
10.6
8.9
0.0009
Bacteroidetes
245523
14.0
6.3
7.8
0.0004
Bacteroidetes
63117
10.1
2.6
7.5
0.0010
Proteobacteria
Betaproteobacteria
Neisseriales
Neisseriaceae
Kingella
-
Proteobacteria
Alphaproteobacteria
Rhodospirillales
Acetobacteraceae
Acidocella
-
Bacteroidetes
Bacteroidia
Bacteroidales
Prevotellaceae
Prevotella
nanceiensis
EP
1566691
0.0040
7.6
0.6
7.0
1052181
7.8
2.9
4.9
1.47E-09
763967
5.0
0.1
4.9
0.0658
Bacteroidetes
Bacteroidia
Bacteroidales
Prevotellaceae
Prevotella
-
4305935
5.9
1.4
4.5
0.0066
Bacteroidetes
Bacteroidia
Bacteroidales
Porphyromonadaceae
Paludibacter
-
399903
5.4
0.9
4.5
0.0010
Spirochaetes
Spirochaetes
Spirochaetales
Spirochaetaceae
Treponema
-
937813
4.5
0.3
4.3
0.0241
Firmicutes
Clostridia
Clostridiales
Tissierellaceae
Anaerococcus
-
458
4.9
0.8
4.1
0.0099
Proteobacteria
Gammaproteobacteria
Pasteurellales
Pasteurellaceae
Aggregatibacter
-
886735
6.0
2.1
3.9
0.0154
Firmicutes
Bacilli
Lactobacillales
Aerococcaceae
Alloiococcus
-
676367
3.8
0.3
3.5
0.0423
Firmicutes
Clostridia
Clostridiales
Veillonellaceae
Schwartzia
-
AC C
240252
2.40E-05
26
ACCEPTED MANUSCRIPT
OTU ID
#
Post-
Pre-
#
Post - Pre
q-value
Phylum
Class
Order
Family
Genus
Species
Betaproteobacteria
Neisseriales
Neisseriaceae
-
-
839
3.4
0.3
3.1
0.0013
Proteobacteria
4365143
3.1
0.1
3.0
0.0792
Actinobacteria
Actinobacteria
Actinomycetales
Corynebacteriaceae
Corynebacterium
-
Firmicutes
Clostridia
Clostridiales
4.4
1.8
2.6
Lachnospiraceae
Oribacterium
-
4312347
2.4
0.6
1.8
0.0350
Proteobacteria
Gammaproteobacteria
Cardiobacteriales
Cardiobacteriaceae
Cardiobacterium
-
92721
1.9
0.6
1.3
0.0029
Actinobacteria
Actinobacteria
Actinomycetales
Actinomycetaceae
Actinomyces
-
887744
1.9
0.6
1.3
0.0404
Bacteroidetes
Flavobacteriia
Flavobacteriales
Weeksellaceae
-
-
Cyanobacteria
Chloroplast
Streptophyta
-
RI PT
686900
0.0059
1.8
1.6
0.1
-
-
844535
0.8
2.0
-1.3
0.0350
Proteobacteria
Alphaproteobacteria
Caulobacterales
Caulobacteraceae
Asticcacaulis
-
2758
0.4
2.1
-1.8
0.0137
Fusobacteria
Fusobacteriia
Fusobacteriales
Leptotrichiaceae
Leptotrichia
-
299830
0.8
3.0
-2.3
0.0904
Bacteroidetes
Bacteroidia
Bacteroidales
Prevotellaceae
Prevotella
copri
506
0.3
3.3
-3.0
0.0668
Bacteroidetes
Bacteroidia
Bacteroidales
Prevotellaceae
Prevotella
-
Proteobacteria
Alphaproteobacteria
Caulobacterales
Caulobacteraceae
Asticcacaulis
-
Bacteroidales
Prevotellaceae
Prevotella
melaninogenica
Bacteroidales
Prevotellaceae
Prevotella
melaninogenica
M AN U
SC
4420570
0.0902
0.3
3.4
-3.1
1066621
1.0
4.3
-3.3
0.0951
Bacteroidetes
Bacteroidia
425
0.3
3.6
-3.4
0.0409
Bacteroidetes
Bacteroidia
1536
0.5
4.3
-3.8
0.0423
Firmicutes
Bacilli
Gemellales
-
-
-
Fusobacteria
Fusobacteriia
Fusobacteriales
Leptotrichiaceae
Leptotrichia
-
Bacteroidetes
Bacteroidia
Bacteroidales
Prevotellaceae
Prevotella
melaninogenica
BD1-5
-
-
-
-
943
0.3
4.0
-3.8
0.0608
1643
0.4
4.8
-4.4
0.0404
4457085
0.5
5.0
-4.5
2.30E-05
301
0.1
4.6
-4.5
0.0792
Bacteroidetes
-4.6
0.0340
Bacteroidetes
0.1
4.8
Bacteroidia
Bacteroidales
Prevotellaceae
Prevotella
-
Bacteroidia
Bacteroidales
Prevotellaceae
Prevotella
-
Proteobacteria
Gammaproteobacteria
Pseudomonadales
Moraxellaceae
Acinetobacter
-
Firmicutes
Clostridia
Clostridiales
Peptostreptococcaceae
-
-
Proteobacteria
Gammaproteobacteria
Pseudomonadales
Pseudomonadaceae
Pseudomonas
-
EP
68416
GN02
TE D
551109
0.0658
0.3
6.0
-5.8
250288
5.8
11.5
-5.8
6.95E-12
133961
0.5
7.3
-6.8
9.90E-05
105
0.8
8.4
-7.6
0.0608
Bacteroidetes
Bacteroidia
Bacteroidales
Prevotellaceae
Prevotella
-
Proteobacteria
Gammaproteobacteria
Enterobacteriales
Enterobacteriaceae
-
-
Bacteroidetes
Bacteroidia
Bacteroidales
Paraprevotellaceae
Prevotella
-
Actinobacteria
Actinobacteria
Actinomycetales
Actinomycetaceae
Actinomyces
-
Firmicutes
Clostridia
Clostridiales
Mogibacteriaceae
-
-
Bacteroidetes
Bacteroidia
Bacteroidales
Prevotellaceae
Prevotella
-
AC C
3530625
0.0071
274365
0.3
9.5
-9.3
0.0010
4459265
2.3
11.6
-9.4
1.49E-23
734
2.4
13.8
-11.4
0.0404
43339
4.5
24.9
-20.4
4.64E-11
-63.3
0.0552
594026
2.6
65.9
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#
mean relative abundance
#
Post-
#
Pre-
Post Pre
q-value
Phylum
Class
Order
Family
Genus
Species
5944.5
0.6
5943.9
1.08E-09
Actinobacteria
Actinobacteria
Actinomycetales
Microbacteriaceae
-
-
233.9
46.3
187.6
0.0071
Proteobacteria
Betaproteobacteria
Neisseriales
Neisseriaceae
Neisseria
-
855912
34.5
1.4
33.1
8.01E-23
Fusobacteria
Fusobacteriia
Fusobacteriales
Fusobacteriaceae
Fusobacterium
-
246528
103.4
73.0
30.4
1.67E-08
Proteobacteria
Gammaproteobacteria
Pseudomonadales
Moraxellaceae
Moraxella
-
3931
17.8
1.8
16.0
3.35E-14
Spirochaetes
Spirochaetes
Spirochaetales
Spirochaetaceae
Treponema
-
3860
13.5
0.8
12.8
1.13E-09
Spirochaetes
Spirochaetes
Spirochaetales
Spirochaetaceae
Treponema
amylovorum
930422
13.0
1.9
11.1
4.45E-10
Proteobacteria
Gammaproteobacteria
Pseudomonadales
Moraxellaceae
Moraxella
-
114813
11.3
3.3
8.0
2.30E-06
Bacteroidetes
Bacteroidia
Bacteroidales
Prevotellaceae
Prevotella
-
4472050
7.3
2.0
5.3
1.51E-14
Actinobacteria
Actinobacteria
Actinomycetales
Actinomycetaceae
-
-
Mycoplasmatales
Mycoplasmataceae
Mycoplasma
-
Erysipelotrichales
Erysipelotrichaceae
Sharpea
-
Clostridia
Clostridiales
Veillonellaceae
Dialister
-
BD1-5
-
-
-
-
Bacteroidia
Bacteroidales
-
-
-
Actinobacteria
Bifidobacteriales
Bifidobacteriaceae
Scardovia
-
5.6
1.4
4.3
5.83E-08
Tenericutes
Mollicutes
2.4
1.5
0.9
0.0821
Firmicutes
Erysipelotrichi
1105876
1.3
2.1
-0.9
0.0922
Firmicutes
148695
0.8
1.8
-1.0
0.0848
GN02
0.3
1.5
-1.3
0.0520
Bacteroidetes
5.3
6.6
-1.4
0.0020
Actinobacteria
1465
1.3
3.5
-2.3
1.34E-06
62513
0.9
3.4
-2.5
EP
23709 4411875
TE D
773109 4364176
SC
12297 4318672
M AN U
OTU ID
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Table S12: Specific taxa differentially expressed in ICS treated asthmatics post-treatment compared to baseline (pre-treatment) visit.
Bacteroidetes
Bacteroidia
Bacteroidales
Prevotellaceae
Prevotella
intermedia
0.0699
Firmicutes
Clostridia
Clostridiales
Lachnospiraceae
Moryella
-
0.0539
Actinobacteria
Actinobacteria
Actinomycetales
Corynebacteriaceae
Corynebacterium
-
Neisseriaceae
Kingella
-
0.8
3.5
-2.8
1.0
3.9
-2.9
0.0358
Proteobacteria
Betaproteobacteria
Neisseriales
645055
1.4
4.5
-3.1
0.0293
Fusobacteria
Fusobacteriia
Fusobacteriales
Fusobacteriaceae
Fusobacterium
-
43339
1.0
4.5
-3.5
0.0159
Firmicutes
Clostridia
Clostridiales
Mogibacteriaceae
-
-
422455
0.8
4.8
-4.0
0.0020
Tenericutes
Mollicutes
Acholeplasmatales
Acholeplasmataceae
Acholeplasma
-
AC C
495067 4380570
1580
1.4
5.6
-4.3
0.0111
Bacteroidetes
Bacteroidia
Bacteroidales
Prevotellaceae
Prevotella
melaninogenica
399903
2.4
7.5
-5.1
0.0009
Spirochaetes
Spirochaetes
Spirochaetales
Spirochaetaceae
Treponema
-
4004098
1.8
7.6
-5.9
5.69E-05
Tenericutes
RF3
ML615J-28
-
-
-
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3.3
9.3
-6.0
5.11E-12
Firmicutes
Bacilli
Lactobacillales
Lactobacillaceae
Lactobacillus
iners
92316
1.3
9.6
-8.4
0.0006
Firmicutes
Clostridia
Clostridiales
Lachnospiraceae
-
-
4426165
2.3
10.9
-8.6
1.25E-07
Bacteroidetes
Bacteroidia
Bacteroidales
Prevotellaceae
Prevotella
-
1.9
10.9
-9.0
4.04E-08
Firmicutes
Clostridia
Clostridiales
10.4
29.9
-19.5
3.10E-14
Firmicutes
Clostridia
Clostridiales
-
-
-
Peptostreptococcaceae
-
-
4421864
5.1
26.3
-21.1
0.0597
Firmicutes
Clostridia
Clostridiales
Veillonellaceae
Selenomonas
-
398192
0.3
23.1
-22.9
2.93E-06
Bacteroidetes
Bacteroidia
Bacteroidales
Paraprevotellaceae
Prevotella
-
4440670
1.5
24.9
-23.4
1.74E-18
Firmicutes
Clostridia
Clostridiales
Veillonella
-
4318284
3.9
69.1
-65.3
0.0359
Firmicutes
Clostridia
Clostridiales
Veillonellaceae
Dialister
-
2438396 12.5 123.5 mean relative abundance
-111.0
1.93E-102
Fusobacteria
Fusobacteriia
Fusobacteriales
Fusobacteriaceae
Fusobacterium
-
RI PT
893048 1044611
EP
TE D
M AN U
SC
Veillonellaceae
AC C
#
130864
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SUPPLEMENTAL METHODS:
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Study Population and Sample Collection: Of 186 adults screened for eligibility at nine “AsthmaNet” clinical centers, 84 subjects were enrolled (Figure S1A-C). Each participating clinical center (Brigham and Women’s Hospital, Boston, MA; Northwestern University, Chicago, IL; National Jewish Health, Denver, CO; University of Wisconsin, Madison, WI; University of Pittsburgh, Pittsburgh, PA; Washington University, St. Louis, MO; University of California San Francisco, San Francisco, CA; Duke University, Durham, NC; Duke University, Durham, NC and Wake Forest University, WinstonSalem, NC) enrolled a median of 10 (IQR of 8-11) subjects of whom 50% (IQR of 39-64) were asthmatic. The study center was not a significant contributor to any of the differences in bacterial community composition reported in this study. Each subject signed informed consent approved by the center’s IRB, and an NHLBI-appointed Data Safety Monitoring Board (DSMB) oversaw the study conduct.
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Adult subjects with mild atopic asthma (AA; n=42) were enrolled who had demonstrated serologic evidence of sensitivity (>0.35 kU/l) to at least one of 12 aeroallergens (Table S1) identified using ImmunoCAP specific IgE test (Thermo scientific); and airway hyper-responsiveness (methacholine PC20 ≤8 mg/mL), or bronchodilator reversibility (FEV1 improvement ≥12% in response to albuterol). Asthmatic subjects had stable asthma for preceding 3 months, an Asthma Control Questionnaire (ACQ) score of <1.5 (1) and no use of a controller medication (such as an inhaled corticosteroid) in the preceding 6 months. Atopic non-asthmatic subjects (ANA; n=21) had sensitivity to at least one aeroallergen but no evidence of airway hyper-responsiveness, (PC20 >16 mg/mL) or bronchodilator reversibility, and no history of chronic sinusitis (for responses to additional prior clinical conditions and environmental exposure for AA and ANA subjects see Tables S2-S3). Healthy control subjects (HC; n=21) had no history of atopic symptoms, negative serologic tests for all aeroallergens tested, and no history of chronic respiratory or other disease. Exclusion criteria for all subjects included smoking, symptoms of respiratory tract infection (including acute sinusitis and bronchitis) in the previous 6 weeks, and antibiotic use in the previous 3 months.
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All subjects underwent spirometry, blood sampling, and sputum induction at visit 1 (Figure S1C). At visit 2, prior to bronchoscopy, oral wash samples (OW) consisting of a tongue scraping followed by oral rinse and gargle with sterile saline (10 mL), were collected as previously described (2). To evaluate for instrument carryover of oral bacterial DNA contamination during sample collection, a 10mL flush of sterile saline through the suction channel of the bronchoscope (“scope flush”) was collected following application of topical anesthesia to the vocal cords when the instrument was withdrawn before proceeding to collection of 4 protected bronchial brush (BB) samples as described previously (3). Asthmatic subjects were subsequently randomized in a 2:1 ratio to treatment with inhaled fluticasone propionate (250 mcg; GlaxoSmithKline) or placebo (lactose alone) from a dry powder inhaler twice daily for six weeks. Repeat assessments were then performed (visits 3 and 4). All samples intended for microbial analysis were stored in RNAlater (Ambion, Inc. Austin, TX) at -80°C until processing. Nucleic acid extraction: For the focus of this study, nucleic acids from OW and 3 BB’s were extracted as previously described (2, 3) using a bead-beating protocol and the AllPrep kit (Qiagen, CA), to purify DNA and RNA in parallel. Reagent controls including 10mL aliquots of the lidocaine and sterile saline used during bronchoscopy, and from “scope flush” samples were extracted for DNA and tested by quantitative PCR (Q-PCR) using universal bacterial primers (see below) for bacterial contamination, with all reagents demonstrating no evidence of significant bacterial contamination
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(<35 16S rRNA gene copy number). Each extraction batch also included a blank control; these went through all the steps in preparation for 16S rRNA sequencing (as described below) and were sequenced. DNA and RNA were quantified on a NanoDrop2000 (ThermoFisher, CA).
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Quantitation of 16S rRNA and β-actin copy number: 16S rRNA gene copy number was assessed by quantitative PCR (Q-PCR) using the 16S rRNA universal primers P891F, P1033R and TaqMan UniProbe as previously described (4) in triplicate using TaqMan Universal Master Mix (Life Technologies). Mammalian β-actin copy numbers were evaluated using primers bActin-F (5’- CCTGGCACCCAGCACAAT-3’) and bActin-R (5’GCCGATCCACACGGAGTACT-3’) and bActin-TMP probe (5-FAM/ TCAAGATCATTGCTCCTCCTGAGCGC/3BHQ) (5). Total 16S rRNA and β-actin copy number was calculated against a standard curve of known 16SrRNA or β-actin copy numbers (1x102 – 1x108). Regression coefficients for all standard curves were > 0.98.
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16S rRNA-based airway and oral microbiota profiling using Illumina MiSeq: The variable region 4 (V4) of the 16S rRNA gene was amplified using 10 ng/l of OW and 100 ng/l of BB DNA template and 515F/806R primer combination as previously described (6, 7). Those samples without visible evidence of 16s rRNA PCR product on the first screen underwent repeat PCR screening with a bacterial-DNA control to rule out presence of sample-specific PCR inhibition. Amplicons were purified using SPRI beads (Beckman Coulter) or in the presence of multiple bands, gel-extracted with a Qiagen Gel Extraction kit (Qiagen) per manufacturer protocol, analyzed on Bioanalyzer (Aligent), quantified using the Qubit HS dsDNA kit (Invitrogen). Samples with amplicons <10ng were not sequenced. Blank control DNA extracts were also amplified, bead purified and included in each MiSeq run. Samples with sufficient 16S rRNA amplicon were pooled at 50 ng per sample, with 30-35 samples and a blank control. The barcoded, pooled library was quantified using the Qubit HS dsDNA kit (Invitrogen), denatured and 5pM was loaded onto the Illumina MiSeq cartridge (V3) in combination with a 15% (v/v) of denatured 12.5pM PhiX for sequencing.
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Paired-end sequences were combined using FLASh version 1.2.7 (8). Sequence analysis was performed using the Quantitative Insights into Microbial Ecology (QIIME) pipeline version 1.8.0 (9). Raw sequences were de-multiplexed by barcode and quality filtered by removing low quality sequences. Sequences with three or more consecutive bases with a Q score < 30 were truncated and discarded if the length was less than 75% of the original 250 bp read length. Sequences were aligned using PyNAST (10) and operational taxonomic units (OTUs) were picked at 97% sequence identity using uclust against the latest release of the Greengenes database (13_5) (11, 12). Reads that failed to hit the reference sequence collection were retained and clustered de novo. PyNAST-aligned sequences were chimera checked using ChimeraSlayer and putative chimeras as well as OTUs identified in negative controls or where not from kingdom Bacteria were removed from the OTU table. A phylogenetic tree was built using FastTree (13) and used to compute Faith’s Phylogenetic Diversity on OTU table multiply rarefied 100 times to 52,317 sequences per sample. Statistical analysis: All statistical analysis was performed as indicated in QIIME (9), R environment or using PRISM software. Welch’s corrected t-test, Kruskal-Wallis or Wilcoxon rank sum test and Chi-square or Fisher’s exact test where appropriate were used to determine significant differences in metadata between study groups conducted in PRISM. Ordination was visualized using Principle Coordinates Analysis (PCoA) on unweighted UniFrac distance matrix and plotted using Emperor in QIIME (14). Significant differences in beta-diversity between paired OW and BB samples were calculated on unweighted UniFrac PC1 coordinates (as a response variable) using Linear mixed
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effects (LME) model ((15) using lmerTest package in R). PERMANOVA ((16); vegan package function Adonis in R) was used to evaluate significant differences in beta-diversity between groups of independent samples. Negative binomial (NB) regression model (17, 18) was used to identify specific OTUs (present in at least 3 samples) that differed in relative abundance between groups of subjects. Only OTUs having a false discovery (FDR) corrected using Benjamini Hochberg method, q-value of <0.1, were retained and declared as significantly differentially enriched taxa. Pearson correlations were performed to determine relationships between relative abundance of OTUs and study variables with q-value correction for false discovery. Phylogenetic tree constructed with taxa of interest in QIIME were annotated using Interactive tree of Life (19). Metagenome prediction from normalized 16S rRNA representative sequences was performed using PICRUSt (20). NB regression model with Benjamini Hochberg FDR correction for multiple comparisons was used to determine Kyoto Encyclopedia of Genes and Genomes (KEGG) gene orthologs (KO’s) that were significantly enriched in each group of interest. Zero-inflated negative binomial (ZINB) model (21,22) corrected for false discovery (Benjamini Hochberg, q-value of <0.1) was used to identify specific OTUs in paired samples before and after treatment with ICS or placebo inhaler. Procrustes analysis (23) based on unweighted UniFrac distance was used to compare community composition between paired samples in each treatment group.
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ACKNOWLEGEMENTS: We additionally would like to thank the following coordinators and staff, and to identify the grants supporting the study: Robert Pedicini, BS1; Kathy Zheng, MPH1; Duanny Alva, MPH2; Assel Biyasheva, PhD2; Jenny Hixon, BS, CCRC2; Lucius Robinson III, BS, CMA, CCRC2; Mary Gill, RN, BSN3; James T. Good, MD3; Christena Kolakowski, MS3; Allen Stevens, CCRC, NREMT3; E. Rand Sutherland, MD3; Julia Bach, RN4; Rich Cornwell, MD4; Holly Eversoll, RN4; Tiffany Huard4; Keith Meyer, MD4; Barbara Miller, RN4; Ann Sexton, MPH4; Michele Wolff, RN4; Merritt Fajt, MD5; Sherri Hill, BS5; Lisa Lane, BS5; Russell Traister, MD5; Cathy Vitari, RN, BSN AE-C5; Vanessa Curtis, RRT6; Brenda Patterson, RN, APN6; Cheryl Shelton, RN, BSN6; Kelly Norsworthy, BA, CPT7; Kelsey Wollen, BA7; Eugene Bleeker, MD8; Christopher Barrios, MD8; Suzan Farris, CCRP8; Jeffrey Krings, FNP8; Victor Ortega, MD8; Cheryl Wilmoth, CCRP8; Matthew Bowman, BS9; Linda Engle, BS9; Jennifer Lucier, BS9; Aimee J. Merchlinski, MS9; Kathryn Trasatt, BS9; Angela Updegrave9; Rachel Weber, BS9; Ronald R. Zimmerman, Jr., MPA9 2
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Brigham & Women’s Hospital, Boston, MA (Grant: HL098102); Northwestern University, Chicago, IL (Grant: 3 4 HL098096); National Jewish Health; Denver, CO (Grant: HL098075 & TR001082); University of Wisconsin Madison, 5 6 Madison, WI (Grant: HL098090); University of Pittsburgh, Pittsburgh, PA (Grant: HL098177); Washington University, 7 St. Louis, MO (Grant: HL098098 & TR000448); University of California San Francisco, San Francisco, CA (Grant: 8 9 HL098107 & HL105572); Wake Forest University, Winston-Salem, NC (Grant: HL098103 & TR000454); Pennsylvania State University, Hershey, PA (Grant: HL098115)
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SUPPLEMENTAL MATERIAL REFERENCES:
6.
7.
8. 9.
10. 11.
12. 13. 14. 15. 16. 17. 18. 19. 20.
RI PT
SC
5.
M AN U
4.
TE D
3.
EP
2.
Juniper EF, O'Byrne PM, Guyatt GH, Ferrier PJ, King DR. 1999. Development and validation of a questionnaire to measure asthma control. Eur Respir J. 14:902-907. Iwai S, Fei M, Huang D, Fong S, Subramanian A, Grieco K, Lynch SV, Huang L. 2012. Oral and Airway Microbiota in HIV-Infected Pneumonia Patients. J. Clin. Microbiol. 50:2995-3002. Huang YJ, Nelson CE, Brodie EL, Desantis TZ, Baek MS, Liu J, Woyke T, Allgaier M, Bristow J, Wiener-Kronish JP, Sutherland ER, King TS, Icitovic N, Martin RJ, Calhoun WJ, Castro M, Denlinger LC, Dimango E, Kraft M, Peters SP, Wasserman SI, Wechsler ME, Boushey HA, Lynch SV. 2011. Airway microbiota and bronchial hyperresponsiveness in patients with suboptimally controlled asthma. J. Allergy Clin. Immunol. 127:372-381.e371-373. Iwai S, Huang D, Fong S, Jarlsberg LG, Worodria W, Yoo S, Cattamanchi A, Davis JL, Kaswabuli S, Segal M, Huang L, Lynch SV. 2014. The Lung Microbiome of Ugandan HIV-Infected Pneumonia Patients Is Compositionally and Functionally Distinct from That of San Franciscan Patients. PLOSone 9:e95726. Bhakta NR, Solberg OD, Nguyen CP, Nguyen CN, Arron JR, Fahy JV, Woodruff PG. 2013. A qPCR-based metric of Th2 airway inflammation in asthma. Clin. Transl. Allergy 3:24. Caporaso JG, Lauber CL, Walters WA, Berg-Lyons D, Huntley J, Fierer N, Owens SM, Betley J, Fraser L, Bauer M, Gormley N, Gilbert JA, Smith G, Knight R. 2012. Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms. The ISME Journal 6:1621-1624. Caporaso JG, Lauber CL, Walters WA, Berg-Lyons D, Lozupone CA, Turnbaugh PJ, Fierer N, Knight R. 2011. Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. Proceedings of the National Academy of Sciences of the United States of America 108 Suppl 1:4516-4522. Magoc T, Salzberg SL. 2011. FLASH: fast length adjustment of short reads to improve genome assemblies. Bioinformatics 27:2957-2963. Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, Fierer N, Pena AG, Goodrich JK, Gordon JI, Huttley GA, Kelley ST, Knights D, Koenig JE, Ley RE, Lozupone CA, McDonald D, Muegge BD, Pirrung M, Reeder J, Sevinsky JR, Turnbaugh PJ, Walters WA, Widmann J, Yatsunenko T, Zaneveld J, Knight R. 2010. QIIME allows analysis of high-throughput community sequencing data. Nature methods 7:335-336. Caporaso JG, Bittinger K, Bushman FD, DeSantis TZ, Andersen GL, Knight R. 2010. PyNAST: a flexible tool for aligning sequences to a template alignment. Bioinformatics 26:266-267. DeSantis TZ, Hugenholtz P, Larsen N, Rojas M, Brodie EL, Keller K, Huber T, Dalevi D, Hu P, Andersen GL. 2006. Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Applied and environmental microbiology 72:5069-5072. Edgar RC. 2010. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26:2460-2461. Price MN, Dehal PS, Arkin AP. 2009. FastTree: Computing Large Minimum Evolution Trees with Profiles instead of a Distance Matrix. Molecular Biology and Evolution 26:1641-1650. Vazquez-Baeza Y, Pirrung M, Gonzalez A, Knight R. 2013. EMPeror: a tool for visualizing highthroughput microbial community data. GigaScience 2:16. Pinheiro JC. 2005. Linear Mixed Effects Models for Longitudinal Data, Encyclopedia of Biostatistics. John Wiley & Sons, Ltd. Anderson MJ. 2001. A new method for non-parametric multivariate analysis of variance. Austral Ecology 26:32-46. Gerber GK, Onderdonk AB, Bry L. 2012. Inferring dynamic signatures of microbes in complex host ecosystems. PLoS computational biology 8:e1002624. McMurdie PJ, Holmes S. 2014. Waste Not, Want Not: Why Rarefying Microbiome Data Is Inadmissible. PLoS computational biology 10:e1003531. Letunic I, Bork P. 2007. Interactive Tree Of Life (iTOL): an online tool for phylogenetic tree display and annotation. Bioinformatics 23:127-128. Langille MGI, Zaneveld J, Caporaso JG, McDonald D, Knights D, Reyes JA, Clemente JC, Burkepile DE, Vega Thurber RL, Knight R, Beiko RG, Huttenhower C. 2013. Predictive functional
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profiling of microbial communities using 16S rRNA marker gene sequences. Nat Biotech 31:814821. Kassahun W, Neyens T, Molenberghs G, Faes C, Verbeke G. 2014. Marginalized multilevel hurdle and zero-inflated models for overdispersed and correlated count data with excess zeros. Statistics in medicine 33:4402-4419. Min Y, Agresti A. 2005. Random effect models for repeated measures of zero-inflated count data. Statistical Modelling 5:1-19. Caporaso JG, Lauber CL, Walters WA, Berg-Lyons D, Huntley J, Fierer N, Owens SM, Betley J, Fraser L, Bauer M, Gormley N, Gilbert JA, Smith G, Knight R. 2012. Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms. ISME J 6: 1621-1624.
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S0091-6749(16)31283-0
DOI:
10.1016/j.jaci.2016.08.055
Reference:
YMAI 12438
To appear in:
Journal of Allergy and Clinical Immunology
Received Date: 21 March 2016 Revised Date:
2 August 2016
Accepted Date: 12 August 2016
Please cite this article as: Durack J, Lynch SV, Nariya S, Bhakta NR, Beigelman A, Castro M, Dyer AM, Israel E, Kraft M, Martin RJ, Mauger DT, Rosenberg SR, Sharp-King T, White SR, Woodruff PG, Avila PC, Denlinger LC, Holguin F, Lazarus SC, Lugogo N, Moore WC, Peters SP, Que L, Smith LJ, Sorkness CA, Wechsler M, Wenzel SE, Boushey HA, Huang YJ, for the National Heart, Lung and Blood Institute’s “AsthmaNet”, Features of the bronchial bacterial microbiome associated with atopy, asthma and responsiveness to inhaled corticosteroid treatment, Journal of Allergy and Clinical Immunology (2016), doi: 10.1016/j.jaci.2016.08.055. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please
note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
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Features of the bronchial bacterial microbiome associated with atopy, asthma
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and responsiveness to inhaled corticosteroid treatment.
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Avraham Beigelman, MD4., Mario Castro, MD,4,5., Anne-Marie Dyer, MS6., Elliot Israel, MD7., Monica Kraft, MD ., Richard J. Martin, MD ., David T. Mauger, PhD ., Sharon R. Rosenberg, MD, 10
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MS ., Tonya Sharp-King, PhD ., Steven R. White, MD ., Prescott G. Woodruff, MD, MPH ., 10
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Pedro C. Avila, MD, MPH ., Loren C. Denlinger, MD, PhD ., Fernando Holguin, MD ., Stephen 3
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C. Lazarus, MD ., Njira Lugogo, MD ., Wendy C. Moore, MD ., Stephen P. Peters, MD ., 14
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Loretta Que, MD ., Lewis J. Smith, MD ., Christine A. Sorkness, PharmD ., Michael Wechsler, 13
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MD, MMSc ., Sally E. Wenzel, MD ., Homer A. Boushey, MD ., and Yvonne J. Huang, MD ., for the National Heart, Lung and Blood Institute’s “AsthmaNet”.
University of California, Dept. of Medicine, Division of Gastroenterology, San Francisco, CA; 2 University of Michigan, Division of Pulmonary & Critical Care Medicine, Ann Arbor, MI; 3 University of California, Dept. of Medicine, San Francisco, CA; 4 Washington University School of Medicine, Division of Pediatrics, St Louis, MO; 5 Washington University School of Medicine, Division of Pulmonary and Critical Care Medicine, St Louis, MO; 6 Penn State University, Department of Public Health Sciences, Hershey, PA; 7 Brigham & Women's Hospital, Dept. of Medicine, Boston, MA; 8University of Arizona, Health Sciences, Tucson, AZ; 9 National Jewish Hospital, Dept. of Medicine, Denver, CO; 10 Northwestern University, Dept. of Medicine, Chicago, IL; 11 University of Chicago, Dept. of Medicine, Chicago, IL; 12 University of Wisconsin-Madison, Dept. of Medicine, Madison, WI; 13 The University of Pittsburgh Asthma Institute at UPMC/UPSOM, Pittsburgh, PA; 14 Duke University School of Medicine, Duke Asthma, Allergy & Airway Center, Durham, NC; 15 Wake Forest School of Medicine, Winston-Salem, NC;
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Corresponding author: Homer A. Boushey,
Professor of Medicine, Div. of Pulmonary/Critical Care Medicine University of California, San Francisco 505 Parnassus Avenue M1292, Box 0130 San Francisco, CA 94143 Phone: 415-476-8019 Fax: 415-502-6235 Email: [email protected]
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Juliana Durack, PhD ., Susan V. Lynch, PhD ., Snehal Nariya, BS ., Nirav R. Bhakta, MD, PhD .,
Running title: Bronchial microbiome in asthma, atopy, and health.
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Abstract
38 Background: Compositional differences in bronchial bacterial microbiota have been
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associated with asthma, but it remains unclear whether the findings are attributable to
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asthma, to aeroallergen sensitization or to inhaled corticosteroid treatment.
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Objectives: To compare the bronchial bacterial microbiota in adults with steroid-naive
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atopic asthma (AA), with atopy but no asthma (ANA), and non-atopic healthy subjects
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(HC), and determine relationships of bronchial microbiota to phenotypic features of
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asthma.
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Methods: Bacterial communities in protected bronchial brushings from 42 AA, 21 ANA,
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and 21 HC subjects were profiled by 16S rRNA gene sequencing. Bacterial composition
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and community-level functions inferred from sequence profiles were analyzed for
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between-group differences. Associations with clinical and inflammatory variables were
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examined, including markers of type 2-related inflammation and change in airway hyper-
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responsiveness following six weeks of fluticasone treatment.
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Results: The bronchial microbiome differed significantly among the three groups.
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Asthmatic subjects were uniquely enriched in members of the Haemophilus, Neisseria,
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Fusobacterium, Porphyromonas and Sphingomonodaceae, and depleted in members of
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the Mogibacteriaceae and Lactobacillales. Asthma-associated differences in predicted
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bacterial functions included involvement of amino acid and short-chain fatty acid
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metabolism pathways. Subjects with type 2-high asthma harbored significantly lower
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bronchial bacterial burden. Distinct changes in specific microbiota members were seen
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following fluticasone treatment. Steroid-responsiveness was linked to differences in
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baseline compositional and functional features of the bacterial microbiome.
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Conclusion: Even in mild steroid-naive asthma subjects, differences in the bronchial
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microbiome are associated with immunologic and clinical features of the disease. The
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specific differences identified suggest possible microbiome targets for future approaches
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to asthma treatment or prevention.
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Key messages: •
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The bronchial bacterial microbiota of both mild atopic-asthma (steroid-naïve) and atopy-alone differ from that of healthy controls and also differ from each other.
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Asthma is associated with enrichment in members of the Haemophilus,
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Neisseria, Fusobacterium, Porphyromonas and Sphingomonodaceae and with
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depletion of Lactobacillus. •
The T2-high asthma phenotype is associated with low bronchial bacterial burden.
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•
ICS-response is linked to a distinct bacterial community composition and functional profile prior to steroid exposure.
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Capsule summary: Mild atopic asthma is associated with distinct differences in the
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composition and inferred functional capacities of bronchial bacterial microbiota, which
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further associate with type 2-low airway inflammation and with responsiveness to inhaled
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corticosteroid treatment.
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Key words: Asthma, atopy, microbiome, corticosteroids, 16S ribosomal RNA, bacteria,
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Th2 inflammation, Three-gene mean, metabolic pathways, SCFAs.
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Abbreviations:
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AA – Atopic asthmatic subjects ANA – Atopic non-asthmatic subjects HC – Healthy controls ICS – Inhaled corticosteroid ACQ – Asthma Control Questionnaire OW – Oral wash BB – Bronchial brush OTU – Operational taxonomic unit SCFAs – Short chain fatty acids TGM – Three-gene mean PICRUSt - Phylogenetic Reconstruction of Unobserved States KEGG - Kyoto Encyclopedia of Genes and Genomes
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Introduction:
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Recent culture-independent studies have documented that the composition of
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commensal lower respiratory tract bacteria (microbiota) differs between asthmatic and
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healthy adults (1-6). Additionally, phenotypic features of asthma, such as measures of
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airway hyper-responsiveness, asthma control, and transcriptional response to steroids,
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correlate with patterns of bronchial microbiota composition (4, 5). Though different
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studies have reported asthma-associated enrichment (higher relative abundance) of
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different taxa [i.e. bacterial-derived 16S ribosomal RNA gene sequences that exhibit an
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operator-defined level of sequence homology (typically 97%)], enrichment in members of
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the phylum Proteobacteria is a repeating signature. Asthmatic subjects in most previous
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studies were treated with inhaled corticosteroids (ICS), casting some uncertainty on
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whether the findings reflect the effects of ICS treatment or of asthma itself. Similarly,
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many asthmatic patients are atopic (7, 8), raising the question as to whether asthma-
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associated differences in respiratory microbiota are related to underlying atopy, itself
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associated with altered mucosal immune function (9, 10). Collectively, these
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considerations indicate a need to elucidate differences in bronchial microbiota
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associated with asthma versus atopy, and with important phenotypic features of this
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disease, such as the level of T2-type inflammation and responsiveness to ICS treatment.
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Accordingly, we compared the bronchial bacterial microbiome among adults with mild
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steroid-naive atopic asthma, with atopy without asthma, and healthy non-atopic non-
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asthmatic controls. We hypothesized that specific compositional and functional
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differences in bronchial microbiota are associated with asthma and with distinguishing
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phenotypic features of the disease, including evidence of Th2 inflammation and
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responsiveness to ICS treatment.
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Methods:
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Study Population and Sample Collection.
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This study was conducted at nine sites in the NHLBI AsthmaNet, using a standardized
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bronchoscopy protocol for sample collection. Of 186 adults screened, 84 subjects were
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enrolled (Figure S1A-B): 42 atopic asthmatics (AA), 21 atopic non-asthmatics (ANA)
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and 21 non-atopic healthy control subjects (HC). Atopy was defined by serologic
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evidence (>0.35 kU/l) of sensitivity to ≥1 of 12 aeroallergens (specific IgE by
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ImmunoCap; Thermo-Scientific; Table S1). Asthma was confirmed by airway hyper-
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responsiveness (methacholine PC20 ≤8 mg/mL or FEV1 improvement ≥12% post-
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albuterol). At enrollment, asthmatics had been clinically stable for three months, and had
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an Asthma Control Questionnaire (ACQ) score of <1.5 (11) without the use of a
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controller medication. Exclusion criteria included a history of smoking, respiratory
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infection within six weeks or antibiotic use within 3 months of enrollment
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(Supplementary Methods).
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Samples processed for microbiota analysis included oral wash (OW) (12), a saline flush
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(10 mL) of the bronchoscope suction channel (“scope-flush”) and protected bronchial
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brushings (BB; Figure S1C; Supplemental Methods). AA were further randomized in a
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2:1 ratio to treatment with inhaled fluticasone propionate (250 mcg, GlaxoSmithKline) or
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placebo twice daily for six weeks and re-assessed post-treatment. Each subject signed
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informed consent approved by their center’s IRB; an NHLBI-appointed Data Safety
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Monitoring Board (DSMB) oversaw the study conduct.
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Nucleic acid extraction and quantitation of 16S rRNA gene copy number
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Nucleic acids from OW, and 3 BB were extracted as previously described (5, 12) using a
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modified bead-beating protocol and the AllPrep kit (Qiagen). 16S rRNA gene copy
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number was assessed by quantitative PCR using universal primers (Supplemental
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Methods).
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16S rRNA-based sequencing and raw data processing.
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For BB and OW samples, variable region 4 (V4) of the 16S rRNA gene was amplified
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using the primer combination 515F/806R (13, 14) and sequenced on the Illumina MiSeq
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platform. Using chimera-checked and quality-filtered sequence reads (Supplemental
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Methods), a 97% sequence homology cut-off was used to define bacterial taxa (also
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referred to as operational taxonomic units; OTUs) and classified using the Greengenes
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database (15). A phylogenetic tree was built using FastTree (16) and used to compute
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Faith’s Phylogenetic Diversity (17) of samples using an OTU table multiply-rarefied to
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52,317 sequences per sample (Supplemental Methods).
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164 “Three-gene mean” bronchial epithelial signature of type 2 inflammation.
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Expression levels of three bronchial epithelial genes (CLCA1, SERPINB2 and POSTN)
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previously shown to be induced by IL-13 (18, 19), were measured using RNA extracted
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in parallel with DNA in this study to calculate the “three-gene mean” (TGM) score for
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each participant (20). Type 2 (T2) – high asthma was defined by TGM scores ≥1.117
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(two standard deviations above the average TGM-score in HC), confirmed by
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unsupervised cluster and principal component analysis.
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172 Statistical analyses:
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We applied principal coordinates analyses (PCoA) on an unweighted UniFrac distance
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matrix and PERMANOVA (21) to identify the determinants of variation in bacterial
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community beta-diversity. Linear mixed-effects model (LME) (22) was employed to
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examine relationships between paired OW and BB microbiota; negative binomial (NB)
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and zero-inflated negative binomial (ZINB) regression models (23-26) corrected for false
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discovery (Benjamini-Hochberg, q-value <0.1) were used to identify OTUs differentially
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abundant between subject groups and paired-samples, respectively. We applied
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Phylogenetic Reconstruction of Unobserved States (PICRUSt) to predict functional
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capacities of the microbiota (27), and NB regression to compare inferred functional
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pathway predictions across groups. Procrustes analysis (13) was used to explore the
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strength of relationships between paired samples in the AA group (Supplemental
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Methods).
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Results:
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Study group characteristics.
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AA had mild well-controlled disease, significantly higher serum total IgE and blood and
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sputum eosinophil cell counts (Table 1, Figure S2A-C) than HC. Compared to the ANA,
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the asthmatics had significantly higher serum IgE, were sensitized to more of the
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aeroallergens tested (Table 1), were more likely to be sensitive to cat, dog, and mouse
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(Table S1), and to report a history of allergic rhinitis and eczema (Table S2). However,
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they did not exhibit significant differences in environmental exposures as assessed by
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questionnaire (Table S3).
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Bacterial microbiota in bronchial brushings are compositionally distinct from oral
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wash.
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To evaluate mucosa-associated bronchial microbiota, we focused on protected BB.
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Sequence-based bacterial community analysis could be performed in the same
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proportion (67%) of samples collected in each of the three groups (Figure S1D-E).
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Samples that could not be sequenced had lower bacterial burden as indicated from 16S
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rRNA (p<0.0001; Figure S2D), despite recovery of similar mammalian cell burden as
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assessed by quantifying β-actin copy number (Supplemental Methods). Subjects with
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insufficient 16S rRNA amplicon for microbiota profiling were younger [median 28 (22-37)
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vs. 34 yrs. (25-44); p=0.03] but did not differ in any other characteristic measured.
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Potential oral contamination of BB was evaluated by comparing a random subset of 30
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BB-paired scope-flushes. Bacterial burden of scope-flushes for BB with sufficient 16S
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rRNA for sequencing was indistinguishable from those unable to be sequenced (p>0.1;
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Figure S2E). Additionally, PCoA analysis of bacterial community composition in paired
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OW and BB samples showed these two niches to be compositionally distinct (LME β
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=0.31, p<0.0001; Figure 1A), with BB samples exhibiting lower phylogenetic diversity
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(Faith’s index; p<0.0001; Figure 1B).
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Features of the bronchial bacterial microbiota and distinct combinations of
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specific bacterial taxa are associated with atopic asthma or atopy alone.
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Alpha-diversity indices, such as richness (number of observed taxa), Shannon’s
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diversity, and evenness, as well as bacterial burden did not differ amongst the groups
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(Figure S2F-I). However, phylogenetic diversity (Faith’s index) tended to be higher in AA
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compared with HC (p=0.06; Figure 1C). Since this index weights phylogenetic
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relatedness of the bacteria detected, this observation suggests that the bronchial
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airways of AA harbor more phylogenetically diverse bacterial communities. Although
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phylogenetic diversity in the ANA also appeared to be greater than in HC (Figure 1C),
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the difference fell short of significance. Overall, inter-subject bacterial community
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composition (beta-diversity) was highly heterogeneous across subjects (Figure S3A).
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This was significantly related to bacterial richness independent of the study group
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(Figure S3B-E), but was not associated with any of the clinical measures or
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environmental exposures evaluated in this study (Tables 1 and S1-3). Compositional
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variability in bronchial bacterial microbiota was significantly greater within the asthmatic
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group (weighted UniFrac distance F-test p<0.001; Figure S3F), indicating a greater
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degree of bacterial community heterogeneity in this group.
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Following these initial assessments for differences in overall bacterial community
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composition, we conducted OTU-level analyses to determine if the relative abundance of
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specific taxa differed significantly across groups; 76 taxa were found to do so in the AA
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vs HC comparison (Table S4). Phylum-level differences in AA (Figures 2A and S4A)
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included significant enrichment for members of the Bacteroidetes (Prevotella),
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Fusobacteria
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(Haemophilus and Neisseria), the latter previously associated with more severe, ICS-
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requiring asthma (2-6, 28). Conversely, asthmatic subjects showed reduced relative
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abundance
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(Actinobacillus), and Firmicutes (Lactobacillus). ANA subjects also demonstrated
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significant differences in the relative abundance of 100 taxa compared to HC (Table
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S5B). The taxa most relatively enriched in ANA included members of the Proteobacteria
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(Aggregatibacter,
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(Corynebacterium) phyla (Figures 2B and S4B); those depleted included members of
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the phylum Bacteroidetes (Porphyromonas and Prevotella).
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(Actinomyces)
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(Leptotrichia),
Proteobacteria
Proteobacteria
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Haemophilus),
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(Fusobacterium), Actinobacteria
Firmicutes
(Granulicatella)
and
Actinobacteria
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We noted that the ANA shared 26% and 29% of taxa that were also relatively enriched
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or depleted in the AA, when compared to HC. Despite these similarities, OTU-level
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analysis identified 103 taxa that differed significantly between the AA and ANA groups
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(Table S6; Figures 2C and S4C). To pinpoint bacteria discretely associated with
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asthma, we evaluated which taxa among those that distinguished AA from HC, also
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distinguished AA from ANA (Figure 3). By this approach, asthma-associated taxa
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included members of the Haemophilus (OTU4406393), Fusobacterium (OTU4405869;
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OTU2438396),
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Sphingomonodaceae (OTU8331815); while taxa negatively associated with asthma
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included members of the Mogibacteriaceae (OTU4335578) and Lactobacillales
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(OTU4480189; OTU4469032). The same approach also identified taxa specific to atopy-
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alone, which included enrichment in Aggregatibacter (OTU4432431), Corynebacterium
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(OTU1015518; OTU495067) and Prevotella (OTU4372058; OTU134265) (Figure 3). Of
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the taxa identified as specifically associated with either asthma or atopy-alone, a subset
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exhibited strong, significant correlations with features of atopy (IgE, blood and sputum
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eosinophil counts); these associations were distinct in the two atopic groups. For
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example, specific taxa uniquely enriched in AA, belonging to Sphingomonodaceae and
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Fusobacteria, were positively associated with all three markers of atopy, while of those
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specifically enriched in ANA, Sharpea and Prevotella (OTU1052181; OTU134265)
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correlated with IgE and blood eosinophil counts, respectively (Figure 3). These data
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indicate that while taxonomic overlap exists between AA and ANA subjects, discrete
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bacterial enrichments characterize these groups, a subset of which are associated with
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biomarkers of atopic disease.
Porphyromonas
(OTU495451)
and
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Neisseria
We further explored whether predicted functions of the bronchial bacterial microbiome
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differed among the three groups using PICRUSt (27), an algorithm that predicts bacterial
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metagenomes in silico from 16S rRNA sequence. This analysis revealed significant
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differences across groups (Figure 4). Predicted bacterial gene functions enriched in AA
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included those involved in metabolism of amino acids and carbohydrates, especially of
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short chain fatty acids (SCFAs) such as butanoate and propanoate (more commonly
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known as butyrate and propionate, respectively). Also noteworthy was a relative
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depletion in predicted bacterial functions involved in lipopolysaccharide biosynthesis
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among asthma-associated bacterial communities.
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Type 2-high asthma is associated with reduced bronchial bacterial burden.
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TGM scores were higher in AA (Figure 5A) and, consistent with prior reports (20, 29),
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correlated positively with serum IgE (rSpearman=0.36, p<0.01), blood and sputum
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eosinophil counts (rS=0.32 and rS=0.37, respectively, p≤0.01), and negatively with FEV1
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(rS= –0.45, p<0.005) and PC20 (rS= –0.41, p<0.005). Ten of 40 AA had T2-high asthma
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and exhibited significantly higher ACQ scores, serum IgE, blood and sputum eosinophil
290
counts, and younger age, than T2-low asthmatics (Table S7). T2-high asthmatics
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demonstrated significantly lower bronchial bacterial burden than T2-low asthmatics
292
(Figure 5C); bacterial burden also was negatively correlated with TGM scores across all
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asthmatic subjects (rS= –0.43, p<0.01) independent of age (GLM p=0.03). Because of
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their low bacterial burden BBs from only four T2-high asthmatics, could be sequenced
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for profiling, an insufficient sample size to allow meaningful assessment of differences in
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bacterial microbiota composition between T2-high and T2-low subjects.
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Inhaled corticosteroid-responsiveness is associated with distinct features of the
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bronchial bacterial microbiota present before treatment.
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We hypothesized that differences in baseline airway microbiota characteristics may be
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associated with ICS-responsiveness, defined as ≥2-fold increase in PC20Mch after ICS-
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treatment. Of the asthmatics included, 15 were classified as ICS-responders, 10 as non-
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responders, and three were excluded as PC20 was not performed (Figure S5A-B).
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Compared to non-responders, ICS-responders did not differ in PC20, serum IgE, blood
305
and sputum eosinophil counts, or bronchial burden at baseline but did have lower FEV1
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and a trend toward a higher TGM score (p<0.08) (Table S8; Figure S5C), which
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decreased following treatment (p<0.05; Figure S4D).
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The baseline composition of profiled bronchial bacterial microbiota in ICS-responders
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(n=10) differed from that in the non-responders (n=5; unweighted UniFrac PERMANOVA
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R2=0.13, p=0.01), and was significantly more similar to that of HC (Figure 6A). Bacterial
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families enriched at baseline in ICS non-responders included Microbacteriaceae,
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Pasteurellaceae (e.g. asthma-associated Haemophilus OTU4406393) and several
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others (Figure 6C and Table S9). Conversely, bacterial families enriched at baseline in
315
ICS-responders
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Sphingomonodaceae. Compared with ICS-responders, the predicted functions of
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bacterial communities of non-responders were enriched in xenobiotic biodegradation
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pathways (Figure 6D and Table S10), implicating potential enhanced capacity for
319
synthetic chemical degradation.
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included
Streptococcaceae,
Fusobacteriaceae
and
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Following six weeks of ICS vs. placebo treatment, we found no significant changes in
322
bacterial burden or phylogenetic diversity in the AA group (Figure S6A-B). Bacterial
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community analysis in paired samples before and after treatment was limited, as several
324
subjects did not have sufficient 16S rRNA to obtain sequence data at both time points.
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Nonetheless, we reasoned that ICS exposure might have distinct effects on the
326
microbiotas of non-responders and ICS-responders (2) and explored ICS-induced
327
compositional changes in the latter, more predominant group. No significant differences
328
were seen in the magnitude of changes (beta-diversity assessed by unweighted Unifrac
329
distance) between paired samples (n=8 each in placebo or ICS-treated responders;
330
Figure S6C-E). At the taxon level, however, ICS treatment resulted in increased relative
331
abundance of Microbacteriaceae, Neisseria and Moraxella and depletion of a specific
332
Fusobacterium, which was not observed with the placebo treatment (Figure 6D, Tables
333
S11-12). We unfortunately could not analyze compositional changes in ICS-non-
334
responders, as only 2 subjects in this subgroup had sufficient 16S rRNA in both pre- and
335
post- treatment samples.
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Discussion:
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Our findings show compositional and predicted functional differences in the bronchial
339
bacterial microbiomes of atopic asthmatic, atopic non-asthmatic, and healthy individuals.
340
An important implication of these findings is that control for allergic sensitization is
341
necessary in studies aimed at understanding differences in the respiratory microbiome
342
associated with asthma. Despite overlap in bacterial genera significantly associated with
343
both atopic groups, our study identified specific bacterial taxa whose relative enrichment
344
or depletion were discretely associated with asthma. Analyses based on metagenomic
345
inference further suggest that genes for pathways involved in the metabolism of short-
346
chain fatty acids and amino acids are enriched in the asthmatic bronchial microbiome.
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We additionally observed that the bronchial microbiome among asthmatics at baseline
348
differed compositionally and functionally according to their responsiveness to treatment
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with inhaled corticosteroids.
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Commonly reported microbial community metrics (e.g., richness, evenness, and burden)
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did not differ significantly among our three groups. This is not unexpected due to the
353
broad characterization of microbial composition provided by such measures, the inter-
354
individual heterogeneity of microbiota composition found in all human niches, including
355
the lung (30), and the mild disease severity in our subjects. Considering the clinical
356
homogeneity of our asthmatic group, their heterogeneity in bronchial bacterial
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composition is striking. Moreover, this heterogeneity was predominantly observed
358
among T2-low asthma subjects, in whom bronchial bacterial burden was significantly
359
greater than in T2-high subjects. The trend towards higher bacterial phylogenetic
360
diversity in our mild asthmatic subjects suggests their lower airways are receptive to
361
colonization by a wider variety of bacteria. Although we could not pinpoint any specific
362
clinical features associated with this finding, additional contributing factors might include
363
differences in previous environmental exposures, in clearance of microorganisms, or in
364
other immune function parameters not identified here.
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Reasons for the low bronchial bacterial burden in T2-high asthma are unclear, but the
367
finding echoes a recent observation in severe asthma, of an inverse relationship
368
between bronchial bacterial burden and numbers of bronchial biopsy eosinophils (4). A
369
possible explanation is the bactericidal activity of more numerous airway eosinophils (31,
370
32) in T2-high airway inflammation. Given prior reports of inverse relationships between
371
bacterial and fungal richness (33, 34), the low bacterial burden in T2-high asthma could
372
also reflect increased fungal or bacteriophage burden. Indeed microbial interactions are
373
not limited to bacteria, as bacterial-viral interactions have been associated with asthma
374
exacerbations and risk for asthma development (35, 36). Such inter-kingdom
375
interactions (37) should be considered in future studies.
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This study expands the list of bacterial groups previously associated with asthma, further
378
supporting the idea that alterations in microbial composition from a healthy state, is a
379
characteristic of asthma. Enrichment in certain Proteobacteria members (e.g.
380
Haemophilus and Neisseria) among our ICS-naïve asthmatics resembles prior findings
381
in ICS-using patients (2-6, 28). However, we found other taxa discretely associated with
382
asthma including Fusobacterium and Porphyromonas, two oral-associated anaerobes
383
capable of augmenting pathogenic behavior of opportunistic respiratory pathogens, such
384
as Pseudomonas aeruginosa (38). Additionally, both Fusobacterium (39) and
385
Haemophilus
386
Sphingomonodaceae represented another asthma-specific taxon whose abundance, like
387
that of Fusobacterium, correlated with sputum eosinophilia. This finding echoed the
388
reported
389
Sphingomonodaceae) in activating NKT-cells to produce T2-cytokines (41-43) and the
390
reported association of this bacterial family with bronchial reactivity (5).
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(40)
activity
induce
of
MUC5AC
expression
glycosphingolipids
(present
in
in
bronchial
the
cell
epithelial
cells.
membrane
of
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391 Asthma-related alterations in bronchial microbiota composition also involve relative
393
depletion in certain taxa, including members of the Lactobacillales. Numerous studies
394
have demonstrated protective effects of certain Lactobacillus strains against atopy by
395
various mechanisms including alteration of gastrointestinal (44) and respiratory tract
396
permeability (45, 46). Our finding that the predicted functions of asthma-associated
397
microbiota were relatively depleted in machinery for LPS biosynthesis suggests another
398
possibility, for continuous stimulation of airways with LPS has been shown to suppress
399
T2 immune activation (47). We propose, though, that any “pro-asthmatic” activity of a
400
bronchial microbiome is likely dependent on functional effects consequent to interactions
401
among many microbiota members in the airway microenvironment, rather than the
402
activity of any one species. Microbiome-related functions indicated by our analyses as
403
potentially enhanced in asthma include increased capacity for metabolism of butyrate
404
and propionate, SCFAs that maintain epithelial barrier function and immune tolerance in
405
the gut (48-50). We speculate that utilization of anti-inflammatory SCFAs by members
406
of the asthmatic airway microbiota may contribute to atopic asthma by reducing their
407
bioavailability and the consequent capacity to down-regulate host inflammatory
408
responses to aeroallergens and pathogens.
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Microbiome-related functions might also affect responsiveness to corticosteroid
411
treatment. We found that pre-treatment enrichment in an asthma-associated
412
Haemophilus, a genus with species previously shown to reduce response of BAL
413
macrophages to corticosteroids (2), was associated with diminished response to six
414
weeks of treatment with inhaled fluticasone. Analysis of the predicted metagenome of
415
pre-treatment bronchial microbiota present in ICS-non-responders also indicated
416
enhancement of xenobiotic degradation capacity, which we hypothesize may contribute
417
to their diminished response. In contrast, ICS-induced changes to the bronchial
418
microbiota in ICS-responsive asthmatics showed enrichment of previously reported
419
asthma-associated taxa such as Neisseria and Moraxella (2, 3, 6). Community shift
420
detected in response to lactose-containing placebo inhaler was not surprising, as an
421
influx of an additional sugar would be expected to alter the composition of microbial
422
communities in the airways, favoring those species with the metabolic capacity to utilize
423
such carbon sources. As importantly, these results emphasize the need to consider the
424
effects of repeated inhalation of particles, especially of an ICS, as a selective pressure
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425
and nutritional source for airway microbiome members with the catalytic capacity to
426
degrade such xenobiotics.
427 Perturbations to the bronchial microbiota associated with atopy-alone is also a novel
429
finding of this study. ANA subjects were less sensitized than the AA subjects and
430
showed primarily low T2 inflammation of bronchial epithelium. While the greater severity
431
of allergy in the AA group cannot be ruled out as contributing to the difference in
432
bacterial signature observed between AA and ANA subjects, distinct taxa associations
433
with different markers of atopy and allergic inflammation were observed, suggesting that
434
distinct microbial interactions with the host immune system occur in these two patient
435
groups. These observations in ANA subjects support an association between airway
436
colonization and atopy-related altered mucosal immune functions (9, 10). Atopy-specific
437
taxa included members of the Pasteurellaceae (specifically Aggregatibacter and
438
Haemophilus). Members of Aggregatibacter are associated with periodontal disease
439
driven by high pro-inflammatory cytokines (e.g., TNF-a, IL1B, IL-6 and IL-8;) and
440
reduced levels of IL-10 (51, 52). It is tempting to speculate that airway enrichment of this
441
genus in ANA subjects could contribute to asthma protection through activation of the T1
442
arm of the immune system. We also highlight that specific taxa belonging to certain
443
genera (e.g., Prevotella and Haemophilus) were associated with atopy-only or asthma.
444
This underscores the likely importance of species- or strain-level functional differences in
445
microbial interactions related to disease status.
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The limitations of our study include exclusive analysis of bacterial communities, the
448
absence of non-atopic asthmatic subjects and the relatively narrow breadth of asthma
449
phenotype captured in this cohort. Moreover, our sample size was small in some
450
comparisons, particularly in assessing changes in the bronchial microbiota in paired
451
samples before and after ICS vs. placebo treatment. However, strengths include the
452
large number of subjects who were characterized and underwent invasive bronchoscopy
453
to collect samples for analysis. Our study protocol also attended carefully to procedural
454
and sampling methods to reduce contamination and analyze for possible contribution of
455
non-bronchial sources of bacterial DNA to the dataset.
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Conclusion:
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Our findings highlight the complexity of bacterial relationships to asthma in a background
459
of atopy, conditions that are associated with distinct alterations in the airway
460
microbiota. To achieve a comprehensive understanding of microbial factors involved in
461
the induction of, management of, or protection against asthma, there is an important
462
need to better understand functions collectively expressed by consortia of airway
463
microbes, which could have a profound influence on asthma.
464
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Acknowledgements:
466
We additionally would like to thank the following coordinators and staff, and to identify
467
the grants supporting the study: Robert Pedicini, BS1; Kathy Zheng, MPH1; Duanny
468
Alva, MPH2; Assel Biyasheva, PhD2; Jenny Hixon, BS, CCRC2; Lucius Robinson III,
469
BS, CMA, CCRC2; Mary Gill, RN, BSN3; James T. Good, MD3; Christena Kolakowski,
470
MS3; Allen Stevens, CCRC, NREMT3; E. Rand Sutherland, MD3; Julia Bach, RN4; Rich
471
Cornwell, MD4; Holly Eversoll, RN4; Tiffany Huard4; Keith Meyer, MD4; Barbara Miller,
472
RN4; Ann Sexton, MPH4; Michele Wolff, RN4; Merritt Fajt, MD5; Sherri Hill, BS5; Lisa
473
Lane, BS5; Russell Traister, MD5; Cathy Vitari, RN, BSN AE-C5; Vanessa Curtis, RRT6;
474
Brenda Patterson, RN, APN6; Cheryl Shelton, RN, BSN6; Kelly Norsworthy, BA, CPT7;
475
Kelsey Wollen, BA7; Eugene Bleeker, MD8; Christopher Barrios, MD8; Suzan Farris,
476
CCRP8; Jeffrey Krings, FNP8; Victor Ortega, MD8; Cheryl Wilmoth, CCRP8; Matthew
477
Bowman, BS9; Linda Engle, BS9; Jennifer Lucier, BS9; Aimee J. Merchlinski, MS9;
478
Kathryn Trasatt, BS9; Angela Updegrave9; Rachel Weber, BS9; Ronald R. Zimmerman,
479
Jr., MPA9. We also wish to thank Elizabeth F. Juniper of McMaster’s University,
480
Canada, for permitting our use of her Asthma Control Questionnaire, and Andrew
481
Manies of UCSF for assistance with manuscript preparation.
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References: 1.
RI PT
2.
Denner DR, Sangwan N, Becker JB, Hogarth DK, Oldham J, Castillo J, Sperling AI, Solway J, Naureckas ET, Gilbert JA, White SR. 2015. Corticosteroid therapy and airflow obstruction influence the bronchial microbiome, which is distinct from that of bronchoalveolar lavage in asthmatic airways. Journal of Allergy and Clinical Immunology. Goleva E, Jackson LP, Kirk Harris J, Robertson CE, Sutherland ER, Hall CF, Good JTJ, Gelfand EW, Martin RJ, Leung D, Y. M. 2013. The Effects of Airway Microbiome on Corticosteroid Responsiveness in Asthma. American Journal of Respiratory and Critical Care Medicine 188:1193-1201. Hilty M, Burke C, Pedro H, Cardenas P, Bush A, Bossley C, Davies J, Ervine A, Poulter L, Pachter L, Moffatt MF, Cookson WOC. 2010. Disordered Microbial Communities in Asthmatic Airways. PLOSone 5:e8578. Huang YJ, Nariya S, Harris JM, Lynch SV, Choy DF, Arron JR, Boushey HA. 2015. The airway microbiome in patients with severe asthma: Associations with disease features and severity. The Journal of allergy and clinical immunology 136:874-884. Huang YJ, Nelson CE, Brodie EL, Desantis TZ, Baek MS, Liu J, Woyke T, Allgaier M, Bristow J, Wiener-Kronish JP, Sutherland ER, King TS, Icitovic N, Martin RJ, Calhoun WJ, Castro M, Denlinger LC, Dimango E, Kraft M, Peters SP, Wasserman SI, Wechsler ME, Boushey HA, Lynch SV. 2011. Airway microbiota and bronchial hyperresponsiveness in patients with suboptimally controlled asthma. The Journal of allergy and clinical immunology 127:372-381.e371-373. Marri PR, Stern DA, Wright AL, Billheimer D, Martinez FD. 2013. Asthma-associated differences in microbial composition of induced sputum. The Journal of allergy and clinical immunology 131:346-352.e341-343. Burrows B, Martinez FD, Halonen M, Barbee RA, Cline MG. 1989. Associations of asthma with serum IgE levels and skin-test reactivity to allergens. N Engl J Med 320:271277. Pearce N, Pekkanen J, Beasley R. 1999. How much asthma is really attributable to atopy? Thorax 54:268-272. Kariyawasam HH, Rotiroti G. 2013. Allergic rhinitis, chronic rhinosinusitis and asthma: unravelling a complex relationship. Current Opinion in Otolaryngology & Head and Neck Surgery 21:79-86. Tomazic PV, Birner-Gruenberger R, Leitnera A, Obrist B, Spoerkc S, Lang-Loidolt D. 2014. Nasal mucus proteomic changes reflect altered immune responses and epithelial permeability in patients with allergic rhinitis. . The Journal of allergy and clinical immunology 133:741-750. Juniper EF, O'Byrne PM, Guyatt GH, Ferrier PJ, King DR. 1999. Development and validation of a questionnaire to measure asthma control. Eur Respir J. 14:902-907. Iwai S, Fei M, Huang D, Fong S, Subramanian A, Grieco K, Lynch SV, Huang L. 2012. Oral and Airway Microbiota in HIV-Infected Pneumonia Patients. J. Clin. Microbiol. 50:2995-3002. Caporaso JG, Lauber CL, Walters WA, Berg-Lyons D, Huntley J, Fierer N, Owens SM, Betley J, Fraser L, Bauer M, Gormley N, Gilbert JA, Smith G, Knight R. 2012. Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms. The ISME Journal 6:1621-1624. Caporaso JG, Lauber CL, Walters WA, Berg-Lyons D, Lozupone CA, Turnbaugh PJ, Fierer N, Knight R. 2011. Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. Proceedings of the National Academy of Sciences of the United States of America 108 Suppl 1:4516-4522. DeSantis TZ, Hugenholtz P, Larsen N, Rojas M, Brodie EL, Keller K, Huber T, Dalevi D, Hu P, Andersen GL. 2006. Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Applied and environmental microbiology 72:5069-5072.
3.
SC
4.
7.
8. 9.
10.
11. 12.
EP
6.
TE D
M AN U
5.
AC C
482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534
13.
14.
15.
16
ACCEPTED MANUSCRIPT
21. 22. 23. 24. 25.
26. 27.
28.
29.
30.
RI PT
20.
SC
19.
M AN U
18.
TE D
17.
Price MN, Dehal PS, Arkin AP. 2009. FastTree: Computing Large Minimum Evolution Trees with Profiles instead of a Distance Matrix. Molecular Biology and Evolution 26:1641-1650. Faith DP. 1992. Conservation evaluation and phylogenetic diversity. Biological Conservation 61:1-10. Woodruff PG, Boushey HA, Dolganov GM, Barker CS, Yang YH, Donnelly S, Ellwanger A, Sidhu SS, Dao-Pick TP, Pantoja C, Erle DJ, Yamamoto KR, Fahy JV. 2007. Genome-wide profiling identifies epithelial cell genes associated with asthma and with treatment response to corticosteroids. Proc Natl Acad Sci USA 104:15858-15868. Woodruff PG, Modrek B, Choy DF, Jia J, Abbas AR, Ellwanger A, Koth LL, Arron JR, Fahy JV. 2009. T-helper type 2-driven inflammation defines major subphenotypes of asthma. Am J Respir Crit Care Med. 180:388-395. Bhakta NR, Solberg OD, Nguyen CP, Nguyen CN, Arron JR, Fahy JV, Woodruff PG. 2013. A qPCR-based metric of Th2 airway inflammation in asthma. Clin. Transl. Allergy 3:24. Anderson MJ. 2001. A new method for non-parametric multivariate analysis of variance. Austral Ecology 26:32-46. Pinheiro JC. 2005. Linear Mixed Effects Models for Longitudinal Data, Encyclopedia of Biostatistics. John Wiley & Sons, Ltd. Gerber GK, Onderdonk AB, Bry L. 2012. Inferring dynamic signatures of microbes in complex host ecosystems. PLoS computational biology 8:e1002624. McMurdie PJ, Holmes S. 2014. Waste Not, Want Not: Why Rarefying Microbiome Data Is Inadmissible. PLoS computational biology 10:e1003531. Kassahun W, Neyens T, Molenberghs G, Faes C, Verbeke G. 2014. Marginalized multilevel hurdle and zero-inflated models for overdispersed and correlated count data with excess zeros. Statistics in medicine 33:4402-4419. Min Y, Agresti A. 2005. Random effect models for repeated measures of zero-inflated count data. Statistical Modelling 5:1-19. Langille MGI, Zaneveld J, Caporaso JG, McDonald D, Knights D, Reyes JA, Clemente JC, Burkepile DE, Vega Thurber RL, Knight R, Beiko RG, Huttenhower C. 2013. Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences. Nat Biotech 31:814-821. Simpson JL, Daly J, Baines KJ, Yang IA, Upham JW, Reynolds PN, Hodge S, James AL, Hugenholtz P, Willner D, Gibson PG. 2015. Airway dysbiosis: Haemophilus influenzae and Tropheryma in poorly controlled asthma. The European respiratory journal. Peters MC, Mekonnen ZK, Yuan S, Bhakta NR, Woodruff PG, Fahy JV. 2014. Measures of gene expression in sputum cells can identify TH2-high and TH2-low subtypes of asthma. The Journal of allergy and clinical immunology 133:388-394. Ursell LK, Clemente JC, Rideoutb JR, Gevers D, Caporaso JG, Knight R. 2012. The interpersonal and intrapersonal diversity of human-associated microbiota in key body sites. J. Allergy Clin. Immunol. 129:1204–1208. Hogan SP, Waddell A, Fulkerson PC. 2013. Eosinophils in Infection and Intestinal Immunity. . Current Opinion in Gastroenterology 29:7–14. Travers J, Rothenberg ME. 2015. Eosinophils in mucosal immune responses. Mucosal Immunol 8:464-475. Dollive S, Chen Y-Y, Grunberg S, Bittinger K, Hoffmann C, Vandivier L, Cuff C, Lewis JD, Wu GD, Bushman FD. 2013. Fungi of the Murine Gut: Episodic Variation and Proliferation during Antibiotic Treatment. PLoS ONE 8:e71806. Peleg AY, Hogan DA, Mylonakis E. 2010. Medically important bacterial–fungal interactions. Nat Rev Micro 8:340-349. Kloepfer KM, Lee WM, Pappas TE, Kang TJ, Vrtis RF, Evans MD, Gangnon RE, Bochkov YA, Jackson DJ, Lemanske RF, Jr., Gern JE. 2014. Detection of pathogenic bacteria during rhinovirus infection is associated with increased respiratory symptoms and asthma exacerbations. The Journal of allergy and clinical immunology 133:13011307, 1307.e1301-1303.
EP
16.
AC C
535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590
31. 32. 33.
34. 35.
17
ACCEPTED MANUSCRIPT
41.
42.
43. 44. 45.
46.
47.
48.
RI PT
40.
SC
39.
M AN U
38.
TE D
37.
Teo Shu M, Mok D, Pham K, Kusel M, Serralha M, Troy N, Holt Barbara J, Hales Belinda J, Walker Michael L, Hollams E, Bochkov Yury A, Grindle K, Johnston Sebastian L, Gern James E, Sly Peter D, Holt Patrick G, Holt Kathryn E, Inouye M. The Infant Nasopharyngeal Microbiome Impacts Severity of Lower Respiratory Infection and Risk of Asthma Development. Cell Host & Microbe 17:704-715. Tarkka MT, Sarniguet A, Frey-Klett P. 2009. Inter-kingdom encounters: recent advances in molecular bacterium-fungus interactions. Current genetics 55:233-243. Pana Y, Tenga D, Burkea AC, Haasea EM, Scannapieco FA. 2008. Oral bacteria modulate invasion and induction of apoptosis in HEp-2 cells by Pseudomonas aeruginosa. Microbial Pathogenesis 46:73-79. Nagaoka K, Yanagihara K, Harada Y, Yamada K, Migiyama Y, Morinaga Y, Hasegawa H, Izumikawa K, Kakeya H, Nishimura M, Kohno S. 2013. Macrolides inhibit Fusobacterium nucleatum-induced MUC5AC production in human airway epithelial cells. Antimicrobial agents and chemotherapy 57:1844-1849. Araki N, Yanagihara K, Morinaga Y, Yamada K, Nakamura S, Yamada Y, Kohno S, Kamihira S. 2010. Azithromycin inhibits nontypeable Haemophilus influenzae-induced MUC5AC expression and secretion via inhibition of activator protein-1 in human airway epithelial cells. European journal of pharmacology 644:209-214. Akbari O, Stock P, Meyer E, Kronenberg M, Sidobre S, Nakayama T, Taniguchi M, Grusby MJ, DeKruyff RH, Umetsu DT. 2003. Essential role of NKT cells producing IL-4 and IL-13 in the development of allergen-induced airway hyperreactivity. Nat Med 9:582588. Kinjo Y, Wu D, Kim G, Xing GW, Poles MA, Ho DD, Tsuji M, Kawahara K, Wong CH, Kronenberg M. 2005. Recognition of bacterial glycosphingolipids by natural killer T cells. Nature 434:520-525. Zajonc DM, Girardi E. 2015. Recognition of Microbial Glycolipids by Natural Killer T Cells. Frontiers in immunology 6:400. Özdemir O. 2010. Various effects of different probiotic strains in allergic disorders: an update from laboratory and clinical data. Clin Exp Immunol 160:295-304. Abreu NA, Nagalingam NA, Song Y, Roediger FC, Pletcher SD, Goldberg AN, Lynch SV. 2012. Sinus microbiome diversity depletion and Corynebacterium tuberculostearicum enrichment mediates rhinosinusitis. Sci Transl Med. 12:151ra124. Fujimura KE, Demoor T, Rauch M, Faruqi AA, Jang S, Johnson CC, Boushey HA, Zoratti E, Ownby D, Lukacs NW, Lynch SV. 2014. House dust exposure mediates gut microbiome Lactobacillus enrichment and airway immune defense against allergens and virus infection. Proc Natl Acad Sci USA 111:805-810. Schuijs MJ, Willart MA, Vergote K, Gras D, Deswarte K, Ege MJ, Branco Madeira F, Beyaert R, van Loo G, Bracher F, von Mutius E, Chanez P, Lambrecht BN, Hammad H. 2015. Farm dust and endotoxin protect against allergy through A20 induction in lung epithelial cells. Science 349:1106-1110. Arrieta MC, Stiemsma LT, Dimitriu PA, Thorson L, Russell S, Yurist-Doutsch S, Kuzeljevic B, Gold MJ, Britton HM, Lefebvre DL, Subbarao P, Mandhane P, Becker A, McNagny KM, Sears MR, Kollmann T, Mohn WW, Turvey SE, Brett Finlay B. 2015. Early infancy microbial and metabolic alterations affect risk of childhood asthma. Science translational medicine 7:307ra152. Kelly CJ, Zheng L, Campbell EL, Saeedi B, Scholz CC, Bayless AJ, Wilson KE, Glover LE, Kominsky DJ, Magnuson A, Weir TL, Ehrentraut SF, Pickel C, Kuhn KA, Lanis JM, Nguyen V, Taylor CT, Colgan SP. 2015. Crosstalk between MicrobiotaDerived Short-Chain Fatty Acids and Intestinal Epithelial HIF Augments Tissue Barrier Function. Cell Host Microbe 17:662-671. Trompette A, Gollwitzer ES, Yadava K, Sichelstiel AK, Sprenger N, Ngom-Bru C, Blanchard C, Junt T, Nicod LP, Harris NL, Marsland BJ. 2014. Gut microbiota metabolism of dietary fiber influences allergic airway disease and hematopoiesis. Nat Med 20:159-166.
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591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644
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50.
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51.
EP
TE D
M AN U
SC
RI PT
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Bostanci N, Akgül B, Tsakanika V, Allaker RP, Hughes FJ, McKay IJ. 2011. Effects of low-dose doxycycline on cytokine secretion in human monocytes stimulated with Aggregatibacter actinomycetemcomitans. Cytokine 56:656-661. Hirose M, Ishihara K, Saito A, Nakagawa T, Yamada S, Okuda K. 2001. Expression of cytokines and inducible nitric oxide synthase in inflamed gingival tissue. J Periodontol. 72:590-597.
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Table 1. Study cohort characteristics p-value#
28 (23 - 46)
NS
-
-
-
-
-
-
48%
43%
NS€
52%
71%
NS€
25 (21 - 28)
26 (22 - 28)
NS
100 (93 - 108) 102 (98 - 116) 5.0 (2.5 - 5.0)
<0.01&
<0.0001&
Age (yrs)
33 (25 - 41)
28 (24 - 44)
Age of asthma diagnosis (yrs)
9 (5 - 22)
-
Duration of Asthma (yrs)
21 (14 - 27)
-
ACQ Score*
0.3 (0 - 1.3)
-
% Male
45%
% White
60%
M AN U
26 (23 - 29)
Atopic, non-asthmatic (ANA) (n = 21)
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Atopic asthmatic (AA) (n = 42)**
BMI (kg/m2)
89 (76 - 97) 100 (84 - 106)
7.5 (5.0 - 14.3)
98 (89 - 109) 104 (97 - 109) 3.0 (-0.5 - 5.5)
Methacholine PC20
1.2 (0.3 - 3.2)
>32$
>32$
-
Blood eosinophils (absolute) Blood eosinophils (%)
200 (100 - 393) 3.3 (1.5 - 5.6) 55.5 (49.3 - 62.0) 54.6 (31.8 - 64.9) 0.4 (0.0 - 1.1)
100 (87 - 200) 2.0 (1.4 - 3.0) 56.9 (51.0 - 63.8) 39.5 (26.5 - 50.0) 0.0 (0.0 - 0.6)
100 (60 - 200) 1.4 (1.0 - 3.0)
<0.01
58.4 (52.7 63.5) 41.8 (29.2 76.5) 0.0 (0.0 - 0.4)
169.5 (56.3 - 321.3) 6 (2-7)
64.0 (22.0 - 164.5) 3 (2-5)
15.0 (5.0 31.0) -
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FEV1 % predicted (pre-albuterol) FEV1 % predicted (post-albuterol) Change in FEV1%
Blood neutrophils (%)
Sputum neutrophils (%)
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Sputum eosinophils (%) Serum IgE (IU/mL)
¢
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Number of positive sIgE
654 655 656 657 658 659 660 661 662
Healthy control (HC) (n = 21)
Variable
SC
652 653
NS&
<0.01¥ NS NS NS¥ <0.01&¥ <0.05&
All values are medians (IQR).*ACQ, Asthma Control Questionnaire ** Number of exacerbations requiring oral steroids in the past 5 years: zero exacerbations (38 subjects); one exacerbation (3 subjects); two $ exacerbations (one subject). Methacholine challenge was stopped at 32 mg/dL and PC20 for these subjects ¥ ¢ was censored. For between group statistical comparisons see Supplementary Figure S2A-C. Number of positive specific IgE (sIgE >0.35 kU/l) from a total of 12 aeroallergens tested by ImmunoCap assay (for breakdown of specific aeroallergens see Supplementary Table S1). Statistical significance was determined # € & using Kruskal-Wallis or Chi-square or Mann-Whitney test for AA vs. ANA comparison.
20
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Figure Legends:
664
Figure 1: (A) Principal coordinate analysis (unweighted UniFrac) shows compositional
665
dissimilarity between paired BB and OW samples (LME p<0.0001). (B) Phylogenetic
666
diversity (Faith’s index) in BB and paired OW samples (& Wilcoxon matched-pairs signed
667
rank test). (C) Phylogenetic diversity (Faith’s index) in BB samples for the three subject
668
groups (Welch’s corrected t-test).
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Figure 2: Bacterial taxa, significantly enriched or depleted in relative abundance (at
671
least 2 fold; NB regression, q<0.1) in (A) AA (n=28) compared to HC (n=13); (B) ANA
672
(n=15) compared to HC; (C) AA compared to ANA. The OTUs indicated represent the
673
most abundant representatives within the indicated genus.
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Figure 3: Mean difference in specific bacterial taxa between groups. Asthma-specific
676
taxa are similarly abundant in AA compared to HC and ANA (NB regression, q<0.1).
677
Atopy-only taxa are similarly abundant in ANA vs. both HC and AA. Taxa positively
678
correlated (rPearson≥0.5, q<0.1) with blood eosinophil counts (BEO), serum IgE (IgE) or
679
sputum eosinophil counts (SEO) in AA (*) or ANA (#) subjects.
M AN U
675
680
Figure 4. Mean difference in predicted KEGG orthologs (KOs) between groups. Asthma-
682
specific pathways are similarly abundant in AA compared to HC and ANA. Atopy-only
683
pathways are similarly abundant in ANA compared to HC and AA. Statistical significance
684
was determined using NB regression model corrected for false discovery rate (q<0.1).
EP
685
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681
Figure 5. (A) AA displayed greater expression of epithelial genes induced by type 2
687
cytokines compared to non-asthmatic subjects. T2-high AA, with a TGM ≥1.117 (cut-off
688
value indicated by a dashed line) are colored in maroon red. (B) Significantly lower
689
bacterial burden was observed among T2-high asthma subjects. Statistical significance
690
was determined using Wilcoxon rank sum test.
691
AC C
686
692
Figure 6. (A) Mean distance to HC (unweighted UniFrac; Bonferroni-corrected t-test).
693
(B) Taxa significantly enriched or depleted in relative abundance (at least 2 fold; NB
694
regression, q<0.1) in ICS-responders vs. non-responders. (C) Predicted KEGG
695
pathways associated with xenobiotic biodegradation and metabolism in ICS-responders
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and non-responders. (D) Taxa, differentially expressed (at least 2-fold; ZINB regression,
697
q<0.1) in asthmatics following ICS or placebo treatment.
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SUPPLEMENTAL FIGURES AND TABLES:
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Figure S1: Schematic diagrams summarizing the breakdown of participants enrolled in this study. (A) Of the 102 asthmatic adults screened for this study, 42 atopic asthmatics (AAs) were enrolled. These subjects underwent specimen collection at baseline (prior to any treatment) and were then randomized to receive inhaled placebo or 250 mcg of fluticasone (ICS) twice daily for six weeks. (B) Of the 84 atopic non-asthmatic (ANAs) and non-atopic non-asthmatic healthy control (HCs) subjects screened, 21 from each group were enrolled in this study. (C) Enrolled participants were seen at multiple visits (indicated by arrows) in nine participating study centers, during which they underwent spirometry (S), methacholine provocation (MP), sputum induction (IS) and bronchoscopy (B). Following baseline assessment (visits 1 and 2) asthmatic patients underwent additional assessment post six weeks twice-daily treatment with ICS or placebo inhaler (post-treatment assessment visits 3 and 4). (D) A schematic diagram of the total bronchial brush samples (BBs) collected during bronchoscopy at baseline (visit 2) indicating the breakdown of subjects with sufficient bacterial 16S rRNA for bacterial profile. The proportion of sequenced to not-sequenced BB samples did not vary by subject group or clinical study site (#Chi-square test; p>0.5). (E) Breakdown of BBs collected from asthmatics post-treatment (visit 4) showing the proportion of sequenced to not sequenced samples, which did not vary between type of inhaler used (&Fishers exact test; p>0.5).
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Figure S2: (A) Total serum IgE concentrations differentiate the three groups. (B, C) Atopic asthmatics show evidence of systemic eosinophilia. (D) Bacterial burden (normalized to ß-actin expression) in sequenced samples was significantly higher than in non-sequenced samples. (E) Bacterial burden in the second scope flush was indistinguishable between a random subset of sequenced and non-sequenced samples, while their paired brush 16S rRNA burden was significantly higher for the sequenced samples. (F) Bacterial burden (normalized to ß-actin expression) for sequenced samples did not significantly vary between study groups. (G) Bacterial richness did not significantly vary between study groups. (H) Bacterial diversity based on Shannon Index did not significantly vary between study groups. (I) Bacterial evenness based on Pielou’s Index did not significantly vary between study groups. Statistical significance was determined using Wilcoxon rank sum test, & Wilcoxon matched-pairs signed rank test or $Welch’s corrected t-test.
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Figure S3. The bronchial microbiota of all groups is highly heterogeneous, but compositional variability is higher in the atopic asthmatic subjects. Principal coordinate analysis plots based on unweighted UniFrac distance, where bronchial brush samples (A) from all subjects (28 atopic asthmatics – AA; 15 atopic non asthmatics – ANA; 13 healthy controls – HC) are colored by group, which does not explain variation in community composition (PERMANOVA p>0.05); (B) from all subjects are colored by richness or (C) from AAs only colored by richness or (D) ANAs only colored by richness or (E) HC only colored by richness. Community richness (Observed species ranging from 219 to 1122 operational taxonomic units (OTUs), significantly explained variability in composition between samples in all groups (PERMANOVA, p≤0.01). (F) Distance box plots based on weighted UniFrac distance matrix show greater compositional variability in the AA compared to both ANAs and HC subjects (F-test p<0.05).
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Figure S4: Summary of bacterial taxa at a phylum level, significantly enriched or depleted in relative abundance (at least 2 fold) in (A) atopic asthmatic (AAs) patients (n=28) compared to healthy control subjects (HC, n=13); (B) atopic non-asthmatic (ANAs) subjects (n=15) compared to HC subjects; (C) AAs patients compared to ANAs subjects. The number of operational taxonomic units (OTU) included in each phyla are indicated by #OTU. Statistical significance was determined using Negative binomial regression model corrected for false discovery rate (q<0.1).
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Figure S5: (A) ICS-responders showed increase in the dose of methacholine in the PC20 challenge after 6 weeks of treatment with fluticasone, but not ICS non-responders or asthmatics taking a placebo inhaler. (B) ICS-responders showed at least one doubling in the dose of methacholine after 6 weeks of treatment with fluticasone. (C) ICS-responders exhibited slightly higher T2-type inflammation at baseline compared to ICS non-responders. Subjects identified as T2-high asthmatics are colored in maroon. (D) ICS-responders showed significant decline in the three-gene signature after treatment with fluticasone compared to both ICS non-responders and placebo controls. Statistical significance was determined using Wilcoxon rank sum test and & Wilcoxon matched-pairs rank sum test. Figure S6: (A) Treatment with ICS had no significant effect on the change in bacterial burden compared to placebo inhaler. Statistical significance was determined using Wilcoxon rank sum test. (B) Treatment with ICS had no significant effect on the change in Faith’s phylogenetic diversity compared to placebo inhaler. Statistical significance was determined using Wilcoxon rank sum test. (C) Procrustes analysis plot based on unweighted UniFrac distance of asthmatics taking placebo treatment (n = 8) shows no significant similarity in community composition between pre- and post-treatment paired samples (M = 0.635, p>0.05). (D) Procrustes analysis plot based on unweighted UniFrac distance shows no significant similarity in community composition between pre- and post-treatment paired samples (M = 0.504, p>0.05) in ICSresponders (n = 8). (E) Analysis of paired unweighted UniFrac distance shows no significant difference in the magnitude of the community shift identified with treatment using fluticasone or a lactose containing placebo inhaler. Statistical significance was determined using Wilcoxon rank sum test.
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Table S1: Breakdown of sensitivities to specific aeroallergens in the atopic asthmatics and atopic non-asthmatic subjects. p-value#
Healthy controls (n = 21) -
NS NS <0.05 0.05 0.05 NS NS NS NS NS NS NS
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sIgE (Mite-1) sIgE (Mite-2) sIgE (Cat) sIgE (Dog) sIgE (Mouse) sIgE (Grass mix) sIgE (Cockroach) sIgE (Mold mix) sIgE (Tree mix 1) sIgE (Tree mix 2) sIgE (Weed) sIgE (Weed mix)
Atopic non-asthmatics (n = 21) 50% 60% 40% 32% 0% 37% 11% 16% 21% 21% 18% 29%
SC
Variable
Atopic asthmatics (n = 42) 56% 54% 68% 59% 17% 58% 28% 38% 42% 34% 34% 47%
#
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Percentage of positive specific IgE (sIgE >0.35 kU/l) as tested by ImmunoCap assay. Statistical significance was determined using Chi-square test.
Table S2: Responses to Prior clinical conditions questionnaire for atopic participants. Questions about Prior conditions
Atopic Asthmatics (n=42)
Atopic Nonasthmatics (n=21)
#
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Have you ever had eczema/allergic dermatitis? Yes 31% 5% No 69% 95% Have you ever had allergic rhinitis (hay fever)? Yes 53% 24% No 48% 76% Do you have chronic or recurrent sinusitis (treated with antibiotics and/or surgery)? Yes 7% 0% No 93% 100% Have you ever had pneumonia? Yes 29% 10% No 71% 91% Have you been diagnosed with sleep disordered breathing (sleep apnea)? Yes 5% 0% No 95% 100% Do you have gastroesophageal reflux disease (GERD)? Yes 10% 5% No 91% 95%
p-value#
0.02
0.06
NS
0.11
NS
NS
Statistical significance was determined using Chi-square test, where NS = p≥0.2.
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Table S3: Responses to Environmental exposure questionnaire for atopic participants. Atopic Asthmatics (n = 42)
Environmental exposure questions
Atopic Nonasthmatics (n = 21)
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Do you live within a mile of a farm? Yes 17% 10% No 83% 91% Do you live within a mile of a major highway? Yes 67% 48% No 33% 52% Does the home you live in have a yard? Yes 69% 57% No 31% 43% Have you been around animals outside your home at least 2 days/week in the past 3 months? Yes 36% 29% No 64% 71% Are you frequently exposed (2 or more days/week) to tobacco smoke outside of your home? Yes 17% 10% No 83% 91% Has there been any mold or mildew, on any surfaces inside your house in the past 12 months? Yes 35% 35% No 65% 65% Does your household have any pets? Yes 33% 43% No 67% 57% In general, and on a regular basis, are you exposed to cats? Yes 29% 43% No 71% 57% In general, and on a regular basis, are you exposed to dogs? Yes 50% 43% No 50% 57% In general, and on a regular basis, are you exposed to other furry pets? Yes 7% 5% No 93% 95% In general, and on a regular basis, are you exposed to birds? Yes 7% 5% No 93% 95% In general, and on a regular basis, are you exposed to farm animals? Yes 5% 0% No 95% 100% Do you ever see cockroaches in your house? Yes 14% 19% No 86% 81%
p-value#
Do you ever see rodents or rodent droppings in your house? Yes 14% No 86% Do you ever see cockroaches OR rodents/rodent droppings in your house? Yes 29% No 71% #
NS
0.18
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
19% 81%
NS
33% 67%
NS
Statistical significance was determined using Chi-square test, where NS = p≥0.2.
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Table S4: Specific bacterial taxa that differ in relative abundance between atopic asthmatic (AA) and healthy control (HC) subjects. #
AA 356.8
#
HC 61.5
AA-HC 295.3
q-value
Phylum
Class
0.0610
Bacteroidetes
Bacteroidia
Order Bacteroidales
Proteobacteria
Gammaproteobacteria
Pasteurellales
Fusobacteria
Fusobacteriia
Fusobacteriales
Proteobacteria
Betaproteobacteria
Neisseriales
Family Paraprevotellaceae
Genus Prevotella
Species tannerae
Pasteurellaceae
Haemophilus
-
Fusobacteriaceae
Fusobacterium
-
Neisseriaceae
Neisseria
-
Fusobacteriaceae
Fusobacterium
-
RI PT
OTU ID 2714267
243.8
1.0
242.8
2438396
192.5
1.7
190.8
4440404
176.8
0.2
176.7
3.00E106 1.66E-21
4405869
196.0
47.4
148.6
0.0347
Fusobacteria
Fusobacteriia
Fusobacteriales
100.2
0.0759
Actinobacteria
Actinobacteria
Actinomycetales
Actinomycetaceae
Actinomyces
Bacteroidetes
Bacteroidia
Bacteroidales
Prevotellaceae
Prevotella
-
Bacteroidales
Prevotellaceae
Prevotella
-
Spirochaetales
Spirochaetaceae
Treponema
-
Actinomycetales
Actinomycetaceae
Actinomyces
-
Clostridiales
Veillonellaceae
Veillonella
dispar
Clostridiales
Mogibacteriaceae
Anaerovorax
-
Bacteroidales
Prevotellaceae
Prevotella
-
Bacteroidales
Prevotellaceae
Prevotella
-
137.9
37.8
240049
48.2
0.8
47.3
0.0011
1541939
47.9
2.0
45.9
0.0011
Bacteroidetes
Bacteroidia
71146
43.4
1.4
42.0
0.0231
Spirochaetes
Spirochaetes
4448211
32.6
6.5
26.1
0.0476
Actinobacteria
Actinobacteria
Firmicutes
Clostridia
Firmicutes
Clostridia Bacteroidia
31.8
6.8
24.9
4468500
24.4
2.5
21.9
1.02E-34
642465
21.4
0.3
21.1
0.0041
Bacteroidetes
70671
87.4
67.5
19.9
2.66E-09
Bacteroidetes Spirochaetes
Bacteroidia
TE D
4388775
0.0297
M AN U
4296424
SC
4406393
0.0041
Spirochaetes
25.5
7.4
18.1
41911
17.7
0.2
17.4
0.0027
Bacteroidetes
Spirochaetales
Spirochaetaceae
Treponema
-
Bacteroidia
Bacteroidales
Prevotellaceae
Prevotella
-
4297695
16.1
2.5
13.6
0.0667
Proteobacteria
Epsilonproteobacteria
Campylobacterales
Campylobacteraceae
Campylobacter
-
495451
11.3
0.4
10.9
4.53E-12
Bacteroidetes
Bacteroidia
Bacteroidales
Porphyromonadaceae
Porphyromonas
-
250288
14.1
5.4
8.8
5.95E-12
Firmicutes
Clostridia
Clostridiales
Peptostreptococcaceae
-
-
Fusobacteria
Fusobacteriia
EP
73875
4.02E-28
9.8
1.1
8.7
Fusobacteriales
Leptotrichiaceae
Leptotrichia
-
4307228
9.1
0.8
8.3
0.0550
Spirochaetes
Spirochaetes
Spirochaetales
Spirochaetaceae
Treponema
-
1398
8.2
0.4
7.8
0.0277
Bacteroidetes
Bacteroidia
Bacteroidales
Paraprevotellaceae
Prevotella
-
949816
9.2
1.7
7.5
0.0622
Firmicutes
Bacilli
Lactobacillales
Streptococcaceae
Streptococcus
-
Proteobacteria
Betaproteobacteria
AC C
1033013
6.48E-14
4480775
8.4
1.0
7.4
0.0231
Neisseriales
Neisseriaceae
Kingella
-
4331815
7.5
0.5
7.0
3.60E-10
Proteobacteria
Alphaproteobacteria
Sphingomonadales
Sphingomonadaceae
-
-
3859
11.6
4.9
6.7
1.42E-08
Spirochaetes
Spirochaetes
Spirochaetales
Spirochaetaceae
Treponema
amylovorum
855912
6.6
1.2
5.5
3.46E-09
Fusobacteria
Fusobacteriia
Fusobacteriales
Fusobacteriaceae
Fusobacterium
-
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#
AA
HC
#
AA-HC
q-value
Phylum
Class
Order
Family
Genus
Species
Proteobacteria
Alphaproteobacteria
Sphingomonadales
Sphingomonadaceae
Sphingopyxis
alaskensis
Bacteroidetes
Bacteroidia
Bacteroidales
Prevotellaceae
Prevotella
-
Firmicutes
Bacilli
Lactobacillales
Lactobacillaceae
Lactobacillus
-
Porphyromonadaceae
Porphyromonas
-
Prevotellaceae
Prevotella
-
Mogibacteriaceae
-
-
Prevotellaceae
Prevotella
copri
267852
5.9
0.6
5.3
1.96E-08
1033687
5.1
0.3
4.8
0.0914
292057
6.3
1.5
4.7
1.07E-07
328
4.9
0.4
4.5
0.0498
Bacteroidetes
Bacteroidia
Bacteroidales
Bacteroidetes
Bacteroidia
Bacteroidales
RI PT
OTU ID
4.5
0.1
4.4
4093791
14.5
10.7
3.8
0.0469
Firmicutes
Clostridia
Clostridiales
4410166
3.1
0.1
3.0
0.0073
Bacteroidetes
Bacteroidia
Bacteroidales
1304
3.0
0.1
2.9
0.0082
Proteobacteria
Betaproteobacteria
Neisseriales
Neisseriaceae
Neisseria
subflava
2377731
3.2
0.5
2.6
0.0002
Firmicutes
Clostridia
Clostridiales
Veillonellaceae
Selenomonas
-
Fusobacteria
Fusobacteriia
Fusobacteriales
0.8
2.3
2.7
0.4
2.3
0.0009
Firmicutes
Bacilli
29566
2.3
0.1
2.2
0.0200
Fusobacteria
Fusobacteriia
1062051
2.6
0.8
1.8
0.0083
Actinobacteria
Actinobacteria
Bacteroidetes
Cytophagia
Leptotrichiaceae
Leptotrichia
-
Lactobacillales
Lactobacillaceae
Lactobacillus
iners
Fusobacteriales
Leptotrichiaceae
Sneathia
-
Actinomycetales
Corynebacteriaceae
Corynebacterium
-
M AN U
3.1
130864
2.4
0.6
1.8
Cytophagales
Cytophagaceae
-
-
1135830
2.6
1.1
1.5
0.0603
Proteobacteria
Betaproteobacteria
Burkholderiales
Comamonadaceae
-
-
4375000
0.5
1.5
-0.9
0.0713
Proteobacteria
Gammaproteobacteria
Enterobacteriales
Enterobacteriaceae
-
-
165489
0.5
1.5
-1.0
0.0333
Proteobacteria
Gammaproteobacteria
Enterobacteriales
Enterobacteriaceae
-
-
-1.0
0.0108
Actinobacteria
Actinobacteria
Actinomycetales
Micrococcaceae
Kocuria
palustris
Firmicutes
Bacilli
Lactobacillales
Streptococcaceae
Streptococcus
-
Gammaproteobacteria
Pasteurellales
Pasteurellaceae
Haemophilus
-
Fusobacteriia
Fusobacteriales
Leptotrichiaceae
-
-
Proteobacteria
Gammaproteobacteria
Enterobacteriales
Enterobacteriaceae
-
-
Proteobacteria
Gammaproteobacteria
Enterobacteriales
Enterobacteriaceae
-
-
Firmicutes
Bacilli
Lactobacillales
Streptococcaceae
Streptococcus
-
160203
0.3
1.3
0.5
1.8
-1.3
4469627
0.9
2.2
-1.3
0.0204
Proteobacteria
4335450
1.4
3.3
-1.9
0.0041
Fusobacteria
51646
0.4
2.6
-2.2
2.96E-06
EP
538185
0.0165
TE D
1116069
0.0072
AC C
832
0.0010
SC
851668
0.0017
3799784
0.9
3.2
-2.3
1.43E-05
521996
0.3
2.8
-2.6
0.0252
710275
0.8
3.5
-2.6
8.49E-07
Proteobacteria
Gammaproteobacteria
Pseudomonadales
Moraxellaceae
Acinetobacter
-
849642
0.2
4.0
-3.8
1.67E-09
Proteobacteria
Betaproteobacteria
Burkholderiales
Comamonadaceae
-
-
Proteobacteria
Betaproteobacteria
4417996
1.4
5.5
-4.1
2.22E-10
Burkholderiales
Comamonadaceae
-
-
399903
2.4
6.7
-4.3
1.20E-08
Spirochaetes
Spirochaetes
Spirochaetales
Spirochaetaceae
Treponema
-
242070
1.7
6.2
-4.4
1.53E-10
Proteobacteria
Gammaproteobacteria
Pseudomonadales
Pseudomonadaceae
Pseudomonas
-
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OTU ID
HC
#
AA-HC
q-value
Phylum
Class
Order
Family
Genus
Species
Bacteroidetes
Bacteroidia
1.9
7.3
-5.5
Bacteroidales
Prevotellaceae
Prevotella
-
114510
2.8
11.6
-8.9
6.38E-23
Proteobacteria
Gammaproteobacteria
Enterobacteriales
Enterobacteriaceae
-
-
100791
2.7
12.2
-9.6
1.98E-25
Proteobacteria
Betaproteobacteria
Burkholderiales
Comamonadaceae
-
-
4333897
3.6
15.9
-12.3
3.51E-32
Proteobacteria
Gammaproteobacteria
Enterobacteriales
Enterobacteriaceae
-
-
Firmicutes
Clostridia
Clostridiales
-
-
-
Prevotellaceae
Prevotella
-
Methylobacteriaceae
Methylobacterium
-
RI PT
851961
1.38E-13
3.8
17.7
-13.9
74407
1.3
16.9
-15.6
1.69E-44
Bacteroidetes
Bacteroidia
Bacteroidales
4396717
2.5
25.6
-23.1
8.72E-68
Proteobacteria
Alphaproteobacteria
Rhizobiales
4469032
21.9
45.3
-23.4
2.20E-34
Firmicutes
Bacilli
Lactobacillales
-
-
-
70628
0.4
24.3
-24.0
2.55E-37
Fusobacteria
Fusobacteriia
Fusobacteriales
Leptotrichiaceae
Leptotrichia
-
Bacteroidetes
Bacteroidia
Bacteroidales
55854
0.2
24.9
-24.7
0.0610
98258
0.7
26.7
-26.0
0.0186
Cyanobacteria
4C0d-2
269930
22.8
81.1
-58.3
Proteobacteria
684
1.6
61.8
-60.3
8.82E139 0.0001
114813
1.0
66.9
-66.0
4404577
372.8
441.8
-69.0
4480189
10.0
98.2
-88.2
4335578
221.3
332.8
4404220
336.1
4400260
2.3
Prevotella
-
-
-
-
Gammaproteobacteria
Pseudomonadales
Pseudomonadaceae
-
-
Firmicutes
Bacilli
Lactobacillales
-
-
-
Bacteroidetes
Bacteroidia
Bacteroidales
Prevotellaceae
Prevotella
-
Firmicutes
Clostridia
452.3
-116.2
5.61E-69
Proteobacteria
123.2
-120.9
0.0333
-235.4
0
mean relative abundance
Fusobacteria
Peptostreptococcaceae
Peptostreptococcus
-
Lactobacillales
Lactobacillaceae
Lactobacillus
zeae
Clostridia
Clostridiales
Mogibacteriaceae
-
-
Gammaproteobacteria
Pasteurellales
Pasteurellaceae
Actinobacillus
-
Fusobacteriia
Fusobacteriales
Leptotrichiaceae
Leptotrichia
-
Bacteroidales
Prevotellaceae
Prevotella
-
EP
246.5
Firmicutes
Clostridiales
Bacilli
TE D
Firmicutes
-111.6
1.23E259 6.19E-92
M AN U
Prevotellaceae
MLE1-12
Bacteroidetes
Bacteroidia
AC C
11.1
6.31E102 4.03E-23
SC
4477971
1.85E-37
4307006 #
#
AA
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Table S5: Specific bacterial taxa that differ in relative abundance between atopic non-asthmatic (ANA) and healthy control (HC) subjects. HC
#
ANAHC
ANA
4432431
963.7
55.5
908.3
828676
315.3
0.1
q-value
Phylum
Class
Order
0.0029
Proteobacteria
Gammaproteobacteria
Pasteurellales
315.3
0.0036
Fusobacteria
Fusobacteriia
Fusobacteriales
0.0302
Proteobacteria
Betaproteobacteria
Neisseriales
Family
Genus
Species
Pasteurellaceae
Aggregatibacter
segnis
Fusobacteriaceae
Fusobacterium
-
Neisseriaceae
-
-
Carnobacteriaceae
Granulicatella
-
RI PT
#
OTU ID
292.5
12.1
280.4
218.4
18.0
200.4
0.0143
Firmicutes
Bacilli
Lactobacillales
4306587
158.2
11.7
146.5
0.0758
Firmicutes
Clostridia
Clostridiales
Veillonellaceae
Selenomonas
-
134265
119.2
7.2
112.0
2.37E-152
Bacteroidetes
Bacteroidia
Bacteroidales
Prevotellaceae
Prevotella
-
4296424
141.9
37.8
104.1
0.0613
Actinobacteria
Actinobacteria
Actinomycetales
Actinomycetaceae
Actinomyces
-
Actinomycetales
Corynebacteriaceae
Corynebacterium
-
Bacteroidales
Prevotellaceae
Prevotella
melaninogenica
Bacteroidales
Porphyromonadaceae
Porphyromonas
-
M AN U
SC
978067 4483015
103.4
3.6
99.8
1.74E-111
Actinobacteria
Actinobacteria
105.2
5.6
99.6
0.0007
Bacteroidetes
Bacteroidia
4459993
102.7
4.5
98.2
0.0854
Bacteroidetes
Bacteroidia
976838
98.5
29.9
68.5
5.39E-95
Proteobacteria
Epsilonproteobacteria
Campylobacterales
Helicobacteraceae
Flexispira
2466322
65.7
0.2
65.6
0.0092
Firmicutes
Clostridia
Clostridiales
Peptostreptococcaceae
-
-
4388775
70.5
6.8
63.7
0.0047
Firmicutes
Clostridia
Clostridiales
Veillonellaceae
Veillonella
dispar
1015518
55.8
1.8
54.0
4.86E-58
Actinobacteria
Actinobacteria
Actinomycetales
Corynebacteriaceae
Corynebacterium
-
4469627
55.3
2.2
53.0
8.15E-63
Proteobacteria
Gammaproteobacteria
Pasteurellales
Pasteurellaceae
Haemophilus
-
4297222
47.9
4.3
43.6
1.36E-65
Firmicutes
Bacilli
Lactobacillales
Streptococcaceae
Streptococcus
-
995
44.7
2.5
42.2
0.0242
Bacteroidetes
Bacteroidia
Bacteroidales
Prevotellaceae
Prevotella
melaninogenica
1053321
75.5
36.6
38.9
3.63E-38
Proteobacteria
Gammaproteobacteria
Pseudomonadales
Moraxellaceae
Moraxella
-
4364176
47.9
11.3
36.6
1.91E-55
Firmicutes
Erysipelotrichi
Erysipelotrichales
Erysipelotrichaceae
Sharpea
-
267852
33.8
0.6
33.2
1.47E-27
AC C
EP
TE D
495067 4372058
Proteobacteria
Alphaproteobacteria
Sphingomonadales
Sphingomonadaceae
Sphingopyxis
alaskensis
4419634
33.3
2.3
31.0
0.0302
Fusobacteria
Fusobacteriia
Fusobacteriales
Leptotrichiaceae
Leptotrichia
-
1102
33.7
3.8
29.9
0.0583
Bacteroidetes
Bacteroidia
Bacteroidales
Prevotellaceae
Prevotella
nanceiensis
575
28.0
0.5
27.5
0.0014
Bacteroidetes
Bacteroidia
Bacteroidales
Prevotellaceae
Prevotella
melaninogenica
2438396
28.2
1.7
26.5
4.60E-36
Fusobacteria
Fusobacteriia
Fusobacteriales
Fusobacteriaceae
Fusobacterium
-
1044611
24.1
0.8
23.3
0.0434
Firmicutes
Clostridia
Clostridiales
Peptostreptococcaceae
-
-
4353757
24.0
1.3
22.7
0.0043
Proteobacteria
Gammaproteobacteria
Pasteurellales
Pasteurellaceae
Aggregatibacter
-
8
ACCEPTED MANUSCRIPT
787709
#
ANA
24.9
HC
#
ANAHC
q-value
Phylum
Class
Order
Family
Genus
Species
Actinomycetaceae
Actinomyces
-
Porphyromonadaceae
3.7
21.2
0.0192
Actinobacteria
Actinobacteria
Actinomycetales
0.0132
Bacteroidetes
Bacteroidia
Bacteroidales
4412630
22.1
1.3
20.8
876114
24.1
3.8
20.3
2.50E-32
Fusobacteria
Fusobacteriia
Fusobacteriales
71146
18.1
1.4
16.7
0.0714
Spirochaetes
Spirochaetes
Spirochaetales
4333897
32.1
15.9
16.2
1.12E-15
Proteobacteria
Gammaproteobacteria
Enterobacteriales
3859
20.6
4.9
15.7
9.42E-24
Spirochaetes
Spirochaetes
Spirochaetales
Porphyromonas
-
Leptotrichiaceae
-
-
Spirochaetaceae
Treponema
-
Enterobacteriaceae
-
-
Spirochaetaceae
Treponema
amylovorum
RI PT
OTU ID
14.7
1.9
12.8
2.17E-20
Proteobacteria
Betaproteobacteria
Neisseriales
Neisseriaceae
Kingella
-
18.9
7.0
11.9
4.85E-15
Proteobacteria
Betaproteobacteria
Neisseriales
Neisseriaceae
Neisseria
-
89
11.4
0.1
11.3
1.40E-05
Bacteroidetes
Bacteroidia
Bacteroidales
Prevotellaceae
Prevotella
-
1124080
11.5
1.0
10.5
7.55E-16
Bacteroidetes
Bacteroidia
Bacteroidales
-
-
-
M AN U
SC
63117 4373910
161677
10.4
0.3
10.1
1.07E-10
Proteobacteria
Gammaproteobacteria
Pseudomonadales
Moraxellaceae
Acinetobacter
-
4468500
12.4
2.5
9.9
1.12E-15
Firmicutes
Clostridia
Clostridiales
Mogibacteriaceae
Anaerovorax
-
124932
10.9
1.5
9.4
4.42E-15
Spirochaetes
Spirochaetes
Spirochaetales
Spirochaetaceae
Treponema
-
3931
10.7
2.1
8.6
1.20E-13
Spirochaetes
Spirochaetes
Spirochaetales
Spirochaetaceae
Treponema
-
1.11E-12
Proteobacteria
Gammaproteobacteria
Pasteurellales
Pasteurellaceae
Haemophilus
influenzae
Dethiosulfovibrionaceae
TG5
-
9.1
1.2
7.9
10.2
3.3
6.9
1.93E-09
Synergistetes
Synergistia
Synergistales
41911
6.6
0.2
6.4
0.0562
Bacteroidetes
Bacteroidia
Bacteroidales
Prevotellaceae
Prevotella
-
114510
17.7
11.6
6.1
0.0007
Proteobacteria
Gammaproteobacteria
Enterobacteriales
Enterobacteriaceae
-
-
4309323
6.8
1.0
5.8
0.0164
Proteobacteria
Gammaproteobacteria
Pasteurellales
Pasteurellaceae
Haemophilus
-
EP
TE D
956702 4460505
5.5
0.1
5.5
0.0005
Proteobacteria
Betaproteobacteria
Burkholderiales
Oxalobacteraceae
-
-
11.3
6.0
5.3
7.30E-05
Bacteroidetes
Bacteroidia
Bacteroidales
-
-
-
4351304
5.9
0.5
5.3
3.12E-08
Actinobacteria
Actinobacteria
Actinomycetales
Actinomycetaceae
Actinomyces
-
1052181
6.8
1.9
4.9
3.64E-07
Bacteroidetes
Bacteroidia
Bacteroidales
Prevotellaceae
Prevotella
nanceiensis
4374322
5.8
0.9
4.9
6.14E-08
Proteobacteria
Gammaproteobacteria
Pseudomonadales
Moraxellaceae
Acinetobacter
-
4419380
5.6
1.1
4.5
2.81E-07
Firmicutes
Erysipelotrichi
Erysipelotrichales
Erysipelotrichaceae
Bulleidia
p-1630-c5
4331815
4.8
0.5
4.3
7.69E-07
Proteobacteria
Alphaproteobacteria
Sphingomonadales
Sphingomonadaceae
-
-
517
4.3
0.2
4.1
0.0462
Bacteroidetes
Bacteroidia
Bacteroidales
Porphyromonadaceae
Porphyromonas
-
544313
4.3
0.3
4.0
7.71E-06
Proteobacteria
Gammaproteobacteria
Pseudomonadales
Pseudomonadaceae
Pseudomonas
-
328
4.0
0.4
3.6
0.0478
Bacteroidetes
Bacteroidia
Bacteroidales
Porphyromonadaceae
Porphyromonas
-
AC C
520222 4313608
9
ACCEPTED MANUSCRIPT
3799784
#
ANA
6.5
HC
#
ANAHC
q-value
Phylum
Class
Order
Family
Genus
Species
Enterobacteriaceae
-
-
Comamonadaceae
3.2
3.2
0.0031
Proteobacteria
Gammaproteobacteria
Enterobacteriales
0.0007
Proteobacteria
Betaproteobacteria
Burkholderiales
325027
5.1
2.0
3.1
43339
7.0
4.4
2.6
0.0562
Firmicutes
Clostridia
Clostridiales
2119418
2.7
0.1
2.6
0.0075
Proteobacteria
Gammaproteobacteria
Enterobacteriales
3600504
2.5
0.5
2.0
0.0031
Bacteroidetes
Bacteroidia
Bacteroidales
165489
3.4
1.5
1.9
0.0241
Proteobacteria
Gammaproteobacteria
Enterobacteriales
4454619
2.3
0.8
1.5
0.0353
Firmicutes
Clostridia
Clostridiales
4412991
2.0
0.5
1.5
0.0247
Proteobacteria
Betaproteobacteria
Neisseriales
4432889
2.1
0.6
1.5
0.0302
Actinobacteria
Actinobacteria
Actinomycetales
495451
1.5
0.4
1.2
0.0613
Bacteroidetes
Bacteroidia
Comamonas
-
Mogibacteriaceae
-
-
Enterobacteriaceae
-
-
Bacteroidaceae
Bacteroides
-
Enterobacteriaceae
-
-
Veillonellaceae
Selenomonas
-
Neisseriaceae
-
-
Intrasporangiaceae
-
-
Bacteroidales
Porphyromonadaceae
Porphyromonas
-
M AN U
SC
RI PT
OTU ID
51646
1.2
2.6
-1.4
0.0854
Proteobacteria
Gammaproteobacteria
Enterobacteriales
Enterobacteriaceae
-
-
1010329
0.9
2.6
-1.7
0.0173
Bacteroidetes
Flavobacteriia
Flavobacteriales
Flavobacteriaceae
Capnocytophaga
-
4426165
0.9
2.8
-1.9
0.0055
Bacteroidetes
Bacteroidia
Bacteroidales
Prevotellaceae
Prevotella
-
1595
1.4
3.6
-2.2
0.0051
Bacteroidetes
Bacteroidia
Bacteroidales
Porphyromonadaceae
Porphyromonas
-
8.94E-04
Fusobacteria
Fusobacteriia
Fusobacteriales
Fusobacteriaceae
Fusobacterium
-
Actinomycetaceae
Actinomyces
-
1.0
3.4
-2.4
0.2
3.0
-2.8
0.0001
Actinobacteria
Actinobacteria
Actinomycetales
4401957
1.9
6.2
-4.2
0.0920
Proteobacteria
Epsilonproteobacteria
Campylobacterales
Campylobacteraceae
Campylobacter
-
851961
0.3
7.3
-7.0
4.95E-10
Bacteroidetes
Bacteroidia
Bacteroidales
Prevotellaceae
Prevotella
-
245523
9.7
17.8
-8.0
3.05E-07
Bacteroidetes
Bacteroidia
Bacteroidales
-
-
-
EP
TE D
941024 4306356
0.1
8.2
-8.1
0.0908
Fusobacteria
Fusobacteriia
Fusobacteriales
Leptotrichiaceae
Leptotrichia
-
6.3
17.7
-11.4
1.35E-15
Firmicutes
Clostridia
Clostridiales
-
-
-
100791
0.1
12.2
-12.1
5.90E-09
Proteobacteria
Betaproteobacteria
Burkholderiales
Comamonadaceae
-
-
4443201
12.3
29.4
-17.1
9.85E-21
Bacteroidetes
Bacteroidia
Bacteroidales
Porphyromonadaceae
Tannerella
-
4396717
8.2
25.6
-17.4
1.91E-25
Proteobacteria
Alphaproteobacteria
Rhizobiales
Methylobacteriaceae
Methylobacterium
-
579608
0.1
18.7
-18.6
1.03E-10
Firmicutes
Bacilli
Lactobacillales
Streptococcaceae
Streptococcus
-
615020
15.9
36.7
-20.8
3.31E-24
Tenericutes
Mollicutes
Mycoplasmatales
Mycoplasmataceae
Mycoplasma
-
4344371
35.3
57.7
-22.4
1.86E-16
Proteobacteria
Alphaproteobacteria
Sphingomonadales
Sphingomonadaceae
Sphingomonas
-
70628
0.2
24.3
-24.1
4.42E-15
Fusobacteria
Fusobacteriia
Fusobacteriales
Leptotrichiaceae
Leptotrichia
-
4297119
3.7
39.8
-36.2
0.0968
Fusobacteria
Fusobacteriia
Fusobacteriales
Leptotrichiaceae
Leptotrichia
-
AC C
923032 4477971
10
ACCEPTED MANUSCRIPT
OTU ID
#
ANA
1048420
q-value
Phylum
Class
Order
Family
Genus
Species
36.8
-36.8
7.58E-09
Firmicutes
Bacilli
Lactobacillales
Streptococcaceae
Streptococcus
-
60.8
98.2
-37.4
1.28E-26
Firmicutes
Bacilli
Lactobacillales
Lactobacillaceae
7.0
46.4
-39.4
0.0149
Firmicutes
Bacilli
Lactobacillales
28118
0.1
52.9
-52.8
0.0029
Firmicutes
Clostridia
Clostridiales
684
1.9
61.8
-59.9
0.0103
Firmicutes
Bacilli
Lactobacillales
4376637
8.1
73.5
-65.4
0.0785
Spirochaetes
Spirochaetes
Spirochaetales
269930
0.1
81.1
-80.9
0.0065
Proteobacteria
Gammaproteobacteria
557974
10.1
96.8
-86.8
0.0812
Proteobacteria
Gammaproteobacteria
69782
1.7
122.8
-121.0
6.05E-101
Proteobacteria
Epsilonproteobacteria
3581175
3.0
133.8
-130.8
1.90E-137
Firmicutes
Clostridia
93.5
260.4
-166.9
7.86E-226
Bacteroidetes
Bacteroidia
4331006
30.5
205.6
-175.1
0
Bacteroidetes
Bacteroidia
4377418
215.8
413.2
-197.4
4.13E-185
Firmicutes
Clostridia
4307006
3.5
246.5
-242.9
5.60E-204
Bacteroidetes
Bacteroidia
2613485
4.7
428.8
-424.2
0
Bacteroidetes
Bacteroidia
252843 8.1 450.7 -442.6 mean relative abundance
0
Fusobacteria
Fusobacteriia
zeae
Streptococcus
-
Veillonellaceae
Selenomonas
-
-
-
-
Spirochaetaceae
Treponema
-
Pseudomonadales
Pseudomonadaceae
-
-
Pseudomonadales
Pseudomonadaceae
Pseudomonas
-
Campylobacterales
Helicobacteraceae
Helicobacter
-
Clostridiales
Veillonellaceae
-
-
Bacteroidales
Prevotellaceae
Prevotella
-
Bacteroidales
Prevotellaceae
Prevotella
-
Clostridiales
Tissierellaceae
Parvimonas
-
Bacteroidales
Prevotellaceae
Prevotella
-
Bacteroidales
Porphyromonadaceae
Porphyromonas
-
Fusobacteriales
Leptotrichiaceae
Leptotrichia
-
EP AC C
#
TE D
1004910
Lactobacillus
Streptococcaceae
RI PT
4480189
ANAHC
SC
0.1
#
M AN U
123320
HC
11
ACCEPTED MANUSCRIPT
Table S6: Specific bacterial taxa which differ in relative abundance between atopic asthmatic (AA) and atopic non-asthmatic (ANA) subjects. OTU ID
#
AA
#
AA-ANA
Class
Order
Family
Genus
Species
503.7
8.1
495.6
0.0255
Fusobacteria
Fusobacteriia
Fusobacteriales
Leptotrichiaceae
Leptotrichia
-
4307790
518.8
119.1
399.7
0.0255
Firmicutes
Clostridia
Clostridiales
Veillonellaceae
Selenomonas
-
4338372
363.6
86.0
277.6
0.0132
Bacteroidetes
Bacteroidia
Bacteroidales
Prevotellaceae
Prevotella
-
4406393
243.8
1.8
242.0
0.0014
Proteobacteria
Gammaproteobacteria
Pasteurellales
Pasteurellaceae
Haemophilus
-
2438396
192.5
28.2
164.3
0
Fusobacteria
Fusobacteriia
Fusobacteriales
Fusobacteriaceae
Fusobacterium
-
4405869
196.0
57.4
138.6
0.0711
Fusobacteria
Fusobacteriia
Fusobacteriales
Fusobacteriaceae
Fusobacterium
-
4393181
141.3
3.4
137.9
0.0004
Tenericutes
Mollicutes
Mycoplasmatales
Mycoplasmataceae
Mycoplasma
-
4382476
126.9
7.0
119.9
0.0014
Bacteroidetes
Bacteroidia
Bacteroidales
Prevotellaceae
Prevotella
-
SC
q-value
RI PT
Phylum
252843
M AN U
ANA
4297119
93.6
3.7
89.9
0.0014
Fusobacteria
Fusobacteriia
Fusobacteriales
Leptotrichiaceae
Leptotrichia
-
525942
94.6
16.9
77.6
0.0194
Bacteroidetes
Bacteroidia
Bacteroidales
Prevotellaceae
Prevotella
melaninogenica
4426165
59.0
0.9
58.1
0.0373
Bacteroidetes
Bacteroidia
Bacteroidales
Prevotellaceae
Prevotella
-
4318284
60.2
6.1
54.1
0.0599
Firmicutes
Clostridia
Clostridiales
Veillonellaceae
Dialister
-
923032
31.0
0.1
30.9
0.0255
Fusobacteria
Fusobacteriia
269930
22.8
0.1
22.6
0.0256
Proteobacteria
4452538
24.3
4.5
19.7
0.0077
Fusobacteria
4307309
19.5
7.5
12.0
1.93E-18
Fusobacteria
4468500
24.4
12.4
12.0
1.75E-14
Firmicutes
495451
11.3
1.5
9.7
1.92E-18
Bacteroidetes
4346863
10.9
1.2
9.7
0.0094
Bacteroidetes
245523
18.9
9.7
9.1
6.69E-11
69782
10.4
1.7
8.7
1.25E-16
Leptotrichiaceae
Leptotrichia
-
Pseudomonadales
Pseudomonadaceae
-
-
Fusobacteriia
Fusobacteriales
Fusobacteriaceae
Fusobacterium
-
Fusobacteriia
Fusobacteriales
Fusobacteriaceae
Fusobacterium
-
Clostridia
Clostridiales
Mogibacteriaceae
Anaerovorax
-
Bacteroidia
Bacteroidales
Porphyromonadaceae
Porphyromonas
-
Bacteroidia
Bacteroidales
Prevotellaceae
Prevotella
-
Bacteroidetes
Bacteroidia
Bacteroidales
-
-
-
Proteobacteria
Epsilonproteobacteria
Campylobacterales
Helicobacteraceae
Helicobacter
-
AC C
EP
TE D
Fusobacteriales
Gammaproteobacteria
28118
7.7
0.1
7.5
0.0255
Firmicutes
Clostridia
Clostridiales
Veillonellaceae
Selenomonas
-
1580
7.1
0.6
6.5
0.0808
Bacteroidetes
Bacteroidia
Bacteroidales
Prevotellaceae
Prevotella
melaninogenica
4426163
7.7
1.6
6.1
0.0425
Bacteroidetes
Bacteroidia
Bacteroidales
Prevotellaceae
Prevotella
-
1033687
5.1
0.3
4.8
0.0689
Bacteroidetes
Bacteroidia
Bacteroidales
Prevotellaceae
Prevotella
-
92316
6.0
1.5
4.5
3.52E-08
Firmicutes
Clostridia
Clostridiales
Lachnospiraceae
-
-
62513
4.4
0.1
4.3
3.52E-05
Firmicutes
Clostridia
Clostridiales
Lachnospiraceae
Moryella
-
12
ACCEPTED MANUSCRIPT
OTU ID
#
AA
#
ANA
AA-ANA
q-value
Phylum
Class
Order
Family
Genus
Species
Firmicutes
Bacilli
Lactobacillales
Streptococcaceae
Streptococcus
-
Flavobacteriaceae
Capnocytophaga
-
Neisseriaceae
Neisseria
subflava
4.6
0.9
3.7
989074
3.4
0.1
3.2
0.0002
Bacteroidetes
Flavobacteriia
Flavobacteriales
1304
3.0
0.1
2.9
0.0044
Proteobacteria
Betaproteobacteria
Neisseriales
4331815
7.5
4.8
2.7
0.0212
Proteobacteria
Alphaproteobacteria
Sphingomonadales
Sphingomonadaceae
-
-
100791
2.7
0.1
2.5
0.0009
Proteobacteria
Betaproteobacteria
Burkholderiales
Comamonadaceae
-
-
55917
3.6
1.1
2.5
0.0004
Bacteroidetes
Bacteroidia
Bacteroidales
-
-
-
831
2.5
0.3
2.2
0.0897
Bacteroidetes
Bacteroidia
Bacteroidales
Prevotellaceae
Prevotella
-
2377731
3.2
1.1
2.0
0.0029
Firmicutes
Clostridia
Clostridiales
Veillonellaceae
Selenomonas
-
1465
2.1
0.1
2.0
0.0033
Bacteroidetes
Bacteroidia
Bacteroidales
Prevotellaceae
Prevotella
intermedia
4353264
3.8
1.8
2.0
0.0135
Proteobacteria
Alphaproteobacteria
Caulobacterales
Caulobacteraceae
-
-
4414261
2.1
0.5
1.6
0.0044
Firmicutes
Bacilli
Lactobacillales
Lactobacillaceae
Lactobacillus
-
1066621
2.2
0.6
1.6
0.0925
Bacteroidetes
Bacteroidia
Bacteroidales
Prevotellaceae
Prevotella
melaninogenica
1188
1.4
0.1
1.4
0.0449
Fusobacteria
Fusobacteriia
Fusobacteriales
Leptotrichiaceae
Leptotrichia
-
104313
1.2
0.3
0.9
0.0696
Proteobacteria
Gammaproteobacteria
Pseudomonadales
Pseudomonadaceae
Pseudomonas
-
SC
M AN U
0.0787
RI PT
523025
0.6
1.7
-1.1
0.0190
Firmicutes
Bacilli
Bacillales
Exiguobacteraceae
-
-
0.1
1.3
-1.2
0.0894
Proteobacteria
Gammaproteobacteria
Pasteurellales
Pasteurellaceae
Aggregatibacter
segnis
57758
0.5
1.7
-1.2
0.0047
Bacteroidetes
Flavobacteriia
Flavobacteriales
Flavobacteriaceae
Flavobacterium
columnare
4483174
0.7
2.0
-1.3
0.0092
Fusobacteria
Fusobacteriia
Fusobacteriales
Leptotrichiaceae
Leptotrichia
-
TE D
540269 4423236
0.4
2.1
-1.7
8.43E-05
Actinobacteria
Actinobacteria
Actinomycetales
Gordoniaceae
Gordonia
-
1.5
3.3
-1.8
0.0043
Cyanobacteria
Chloroplast
Streptophyta
-
-
-
4375000
0.5
2.7
-2.2
2.70E-06
Proteobacteria
Gammaproteobacteria
Enterobacteriales
Enterobacteriaceae
-
-
470724
0.4
3.8
-3.4
2.37E-10
Proteobacteria
Betaproteobacteria
Neisseriales
Neisseriaceae
Neisseria
-
3.65E-11
Betaproteobacteria
Burkholderiales
Comamonadaceae
Hydrogenophaga
-
Streptococcaceae
Streptococcus
-
AC C
EP
671258 3359884
339015
0.3
4.1
-3.8
669486
0.8
5.3
-4.4
2.19E-13
Firmicutes
Bacilli
Lactobacillales
1052181
1.8
6.8
-5.0
4.58E-13
Bacteroidetes
Bacteroidia
Bacteroidales
Prevotellaceae
Prevotella
nanceiensis
4335450
1.4
6.5
-5.1
2.75E-14
Fusobacteria
Fusobacteriia
Fusobacteriales
Leptotrichiaceae
-
-
4343627
0.3
5.7
-5.4
1.07E-14
Bacteroidetes
Bacteroidia
Bacteroidales
Bacteroidaceae
Bacteroides
fragilis
Proteobacteria
903686
2.8
8.3
-5.5
0.0689
Proteobacteria
Gammaproteobacteria
Pasteurellales
Pasteurellaceae
Aggregatibacter
segnis
4440670
19.8
25.3
-5.5
0.0055
Firmicutes
Clostridia
Clostridiales
Veillonellaceae
Veillonella
-
13
ACCEPTED MANUSCRIPT
OTU ID
#
AA
#
ANA
AA-ANA
q-value
Phylum
Class
Order
Family
Genus
Species
Proteobacteria
Gammaproteobacteria
Enterobacteriales
Enterobacteriaceae
-
-
Pasteurellaceae
Haemophilus
-
0.9
6.5
-5.6
4309323
1.1
6.8
-5.7
0.0588
Proteobacteria
Gammaproteobacteria
Pasteurellales
242070
1.7
8.2
-6.5
2.34E-18
Proteobacteria
Gammaproteobacteria
Pseudomonadales
Pseudomonadaceae
Pseudomonas
-
521996
0.3
7.1
-6.9
0.0007
Firmicutes
Bacilli
Lactobacillales
Streptococcaceae
Streptococcus
-
571873
0.4
7.6
-7.2
1.32E-18
Proteobacteria
Betaproteobacteria
Burkholderiales
Comamonadaceae
-
-
Spirochaetaceae
Treponema
-
Micrococcaceae
-
-
6.52E-17
RI PT
3799784
1.4
8.9
-7.5
1.36E-22
Spirochaetes
Spirochaetes
Spirochaetales
0.3
8.1
-7.8
6.36E-19
Actinobacteria
Actinobacteria
Actinomycetales
898611
1.0
9.3
-8.3
3.23E-25
Firmicutes
Clostridia
Clostridiales
Lachnospiraceae
Butyrivibrio
-
4428313
6.5
15.3
-8.8
3.16E-16
Firmicutes
Bacilli
Lactobacillales
Lactobacillaceae
Lactobacillus
-
SC
95168 543491
11.6
20.6
-9.0
3.10E-11
Spirochaetes
Spirochaetes
Spirochaetales
Spirochaetaceae
Treponema
amylovorum
0.1
9.1
-9.0
0.0615
Proteobacteria
Gammaproteobacteria
Pasteurellales
Pasteurellaceae
Haemophilus
influenzae
4303688
4.1
14.5
-10.4
8.09E-26
Proteobacteria
Betaproteobacteria
Burkholderiales
Burkholderiaceae
Lautropia
-
561636
0.1
10.9
-10.8
0.0232
Firmicutes
Bacilli
Lactobacillales
Streptococcaceae
Streptococcus
-
63117
1.9
14.7
-12.9
3.41E-39
Proteobacteria
Betaproteobacteria
Neisseriales
Neisseriaceae
Kingella
-
4386920
0.0
15.0
-15.0
0.0255
Proteobacteria
Gammaproteobacteria
Pseudomonadales
Pseudomonadaceae
Pseudomonas
-
2510538
1.6
17.5
-15.9
0.0254
Firmicutes
Bacilli
Lactobacillales
Carnobacteriaceae
-
-
4430639
7.5
24.6
-17.1
0.0254
Proteobacteria
Gammaproteobacteria
Pasteurellales
Pasteurellaceae
Haemophilus
parainfluenzae
353459
0.0
17.4
-17.4
0.087
Bacteroidetes
Bacteroidia
Bacteroidales
Prevotellaceae
Prevotella
copri
4443200
0.8
19.3
-18.5
8.79E-45
Bacteroidetes
Flavobacteriia
Flavobacteriales
Flavobacteriaceae
Capnocytophaga
ochracea
4412630
2.0
22.1
-20.1
0.0271
Bacteroidetes
Bacteroidia
Bacteroidales
Porphyromonadaceae
Porphyromonas
-
171157
8.6
31.3
-22.7
1.72E-57
Proteobacteria
Epsilonproteobacteria
Campylobacterales
Helicobacteraceae
Flexispira
-
259631
0.0
23.0
-23.0
0.0686
Firmicutes
Clostridia
Clostridiales
Lachnospiraceae
Ruminococcus
gnavus
0.0043
Fusobacteriia
Fusobacteriales
Fusobacteriaceae
Fusobacterium
-
Bacteroidetes
Bacteroidia
Bacteroidales
Prevotellaceae
Prevotella
melaninogenica
AC C
EP
TE D
M AN U
3859 956702
245
0.2
25.1
-25.0
575
2.5
28.0
-25.5
0.032
4301566
10.6
37.3
-26.7
0.0172
Firmicutes
Bacilli
Lactobacillales
Streptococcaceae
Streptococcus
-
886735
21.0
47.9
-26.9
1.18E-47
Firmicutes
Bacilli
Lactobacillales
Aerococcaceae
Alloiococcus
-
267852
5.9
33.8
-27.9
1.99E-82
Proteobacteria
Alphaproteobacteria
Sphingomonadales
Sphingomonadaceae
Sphingopyxis
alaskensis
4469032
21.9
50.2
-28.3
1.74E-50
Firmicutes
Bacilli
Lactobacillales
-
-
-
995
4.0
44.7
-40.6
0.010
Bacteroidetes
Bacteroidia
Bacteroidales
Prevotellaceae
Prevotella
melaninogenica
Fusobacteria
14
ACCEPTED MANUSCRIPT
OTU ID
#
ANA
AA-ANA
q-value
Phylum
Class
Order
Family
Genus
Species
2.03E-123
Firmicutes
Bacilli
Lactobacillales
Streptococcaceae
Streptococcus
-
Erysipelotrichaceae
Sharpea
-
Lactobacillaceae
Lactobacillus
zeae
7.1
47.9
-40.9
4364176
1.3
47.9
-46.6
1.99E-97
Firmicutes
Erysipelotrichi
Erysipelotrichales
4480189
10.0
60.8
-50.8
1.45E-151
Firmicutes
Bacilli
Lactobacillales
1015518
3.1
55.8
-52.7
5.83E-143
Actinobacteria
Actinobacteria
Actinomycetales
Corynebacteriaceae
Corynebacterium
-
4466150
5.5
63.3
-57.9
5.08E-05
Proteobacteria
Gammaproteobacteria
Pasteurellales
Pasteurellaceae
Haemophilus
-
4337755
30.6
94.8
-64.2
0.0255
Firmicutes
Bacilli
Gemellales
1925094
8.2
75.5
-67.4
5.19E-204
Actinobacteria
Actinobacteria
Actinomycetales
260397
3.7
77.7
-74.0
2.51E-191
Firmicutes
Erysipelotrichi
Erysipelotrichales
4372058
17.6
105.2
-87.6
0.026
Bacteroidetes
Bacteroidia
Bacteroidales
2.41E-263 1.94E-70
4459993
3.5
102.7
-99.1
0.0010
495067
1.8
103.4
-101.7
134265
1.0
119.2
4306587
12.7
4400869
10.9
891034
31.5
-
-
Micrococcaceae
-
-
Erysipelotrichaceae
Allobaculum
-
Prevotellaceae
Prevotella
melaninogenica
SC
-91.2 -92.2
Proteobacteria
Epsilonproteobacteria
Campylobacterales
Helicobacteraceae
Flexispira
-
Firmicutes
Clostridia
Clostridiales
Mogibacteriaceae
-
-
Bacteroidetes
Bacteroidia
Bacteroidales
Porphyromonadaceae
Porphyromonas
-
2.20E-171
Actinobacteria
Actinobacteria
Actinomycetales
Corynebacteriaceae
Corynebacterium
-
-118.2
9.40E-137
Bacteroidetes
Bacteroidia
Bacteroidales
Prevotellaceae
Prevotella
-
158.2
-145.5
0.0156
Firmicutes
Clostridia
Clostridiales
Veillonellaceae
Selenomonas
-
167.7
-156.8
0.0960
SR1
-
-
-
-
-
270.0
-238.5
0.0806
Proteobacteria
Gammaproteobacteria
Pasteurellales
Pasteurellaceae
Aggregatibacter
-
-872.4
0.0006
Proteobacteria
Gammaproteobacteria
Pasteurellales
Pasteurellaceae
Aggregatibacter
segnis
4432431 91.3 963.7 mean relative abundance
M AN U
98.5 313.5
TE D
7.3 221.3
Gemellaceae
EP
976838 4335578
RI PT
4297222
AC C
#
#
AA
15
ACCEPTED MANUSCRIPT
0.3 (0.0 - 1.0) 0.0 (0.0 - 0.2) -0.2 (-0.6 - 0.0) 121.5 (49.0 - 231.3) 6 (3-7) 27% 39%
<0.05 NS NS <0.05 € NS € NS € NS
SC
<0.05 NS NS <0.05 € NS € NS NS NS NS <0.05 NS NS NS NS NS NS <0.01 <0.01 <0.001 NS NS NS NS
M AN U
1.3 (0.4 - 3.8) 0.0 (0.0 - 0.4) 0.0 (-0.7 - 0.1) 323.5 (184.3 - 560.5) 5 (2-10) 40% 60%
p-value#
TE D
Sputum eosinophils (% Baseline) Sputum eosinophils (% post-ICS) Change in sputum eosinophils Serum IgE (IU/mL) Number of positive sIgE¢ % Eczema/Allergic dermatitis§ % Allergic rhinitis§
T2-low asthmatics (n = 30) 37 (27-43) 9 (5-22) 23 (14-29) 0.5 (0.2 - 0.7) 47% 60% 26 (23 - 29) 86 (76 - 96) 105 (83 - 105) 7 (5 - 12) 95 (90 - 106) 103 (98 - 115) 1 (-6 - 4) 0 (-5 - 6) 1.3 (0.4 - 2.7) 1.9 (0.3 - 5.8) 0.3 (-0.2 - 1.5) 130 (100 - 293) 2.7 (1.3 - 4.2) 56.1 (49.7 - 62.0) 55.8 (32.5 - 65.2) 49.4 (25.1 - 60.3) -9.6 (-18-1.8)
EP
Age (yrs) Age of Asthma diagnosis (yrs) Duration of Asthma (yrs) ACQ Score (Baseline)* % Male % White BMI FEV1 % pre-Ab (Baseline) FEV1 % post-Ab (Baseline) Change in FEV % (Baseline) FEV1 % pre-Ab (post-ICS) FEV1 % post-Ab (post-ICS) Change in FEV % pre-Ab Change in FEV % post-Ab PC20 (Baseline) PC20 (post ICS) Change in PC20 Blood eosinophils (absolute) Blood eosinophils (%) Blood neutrophils (%) Sputum neutrophils (% Baseline) Sputum neutrophils (% post-ICS) Change in sputum neutrophils
T2-high asthmatics (n = 10) 28 (24-33) 9 (5-24) 18 (9-23) 1.0 (0.8 - 1.3) 50% 60% 26 (23 - 30) 91 (68 - 110) 102 (93 - 114) 15 (7 - 25) 88 (79 - 97) 98 (91 - 105) 3 (-4 - 13) 1 (-3 - 5) 0.9 (0.2 - 3.8) 6.1 (1.6 - 12.4) 4.9 (1.2 - 11.5) 400 (245 - 600) 6.9 (4.6 - 10.3) 53.5 (43.7 - 60.5) 53.6 (33.9 - 64.9) 49.5 (32.8 - 59.9) 0.0 (-24.9 - 16.0)
AC C
Variable
RI PT
Table S7: Clinical and immunological comparison of T2-high compared to T2-low asthma patients.
¢
All values are medians (IQR). *ACQ, Asthma Control Questionnaire. Number of positive specific IgE (sIgE >0.35 kU/l) from a total of 12 aeroallergens tested by § # ImmunoCap assay. Subjects with a positive history of allergic conditions specified (self-reported). Statistical significance was determined using Mann-Whitney or € Fisher’s exact test.
16
ACCEPTED MANUSCRIPT
Table S8: Clinical and immunological comparison of ICS-responders compared to ICS non-responders. p-value#
RI PT
NS NS NS NS NS € NS € NS NS <0.05 NS <0.05 NS NS NS NS NS NS NS NS <0.01 NS NS NS NS NS NS NS NS NS NS
M AN U
SC
ICS non-responders (n = 10) 29 (20-39) 5 (2-9) 21 (13-26) 0.6 (0.1 - 1.0) 0.2 (0.0 - 0.7) 40% 70% 26 (24 - 28) 99 (94 - 110) 105 (100 - 114) 6 (5 - 7) 95 (90 - 106) 103 (98 - 115) 10 (5 - 14) 1 (-6 - 4) 0 (-5 - 6) 0.4 (-5.3 – 3.3) 1.9 (0.3 - 4.1) 1.9 (0.3 - 5.8) 0.3 (-0.2 - 1.5) 155 (100 - 315) 3.0 (1.0 - 5.1) 58.0 (50.4 - 64.3) 56.1 (35.0 - 66.7) 49.4 (25.1 - 60.3) -9.6 (-18-1.8) 0.4 (0.0 - 0.7) 0.0 (0.0 - 0.2) -0.2 (-0.6 - 0.0) 188.0 (66.8 - 310.8)
TE D
AC C
Age (yrs) Age of Asthma diagnosis (yrs) Duration of Asthma (yrs) ACQ Score (Baseline)* ACQ Score (post ICS)* % Male % White BMI FEV1 % pre-Ab (Baseline) FEV1 % post-Ab (Baseline) Change in FEV with Ab % (Baseline) FEV1 % pre-Ab (post-ICS) FEV1 % post-Ab (post-ICS) Change in FEV % with Ab (post-ICS) Change in FEV % pre-Ab Change in FEV % post-Ab % Change from baseline in pre-Alb FEV1 PC20 (Baseline) PC20 (post ICS) Change in PC20 Blood eosinophils (absolute) Blood eosinophils (%) Blood neutrophils (%) Sputum neutrophils (% Baseline) Sputum neutrophils (% post-ICS) Change in sputum neutrophils Sputum eosinophils (% Baseline) Sputum eosinophils (% post-ICS) Change in sputum eosinophils Serum IgE (IU/mL)
ICS responders (n = 15) 32 (27-43) 8 (5-22) 25 (18-27) 0.8 (0.2 - 1.2) 0.3 (0.0 - 0.5) 40% 47% 26 (23 - 29) 86 (71 - 95) 101 (85 - 105) 12 (8 - 15) 88 (79 - 97) 98 (91 - 105) 12 (5 - 14) 3 (-4 - 13) 1 (-3 - 5) 3.2 (-4.7 – 11.7) 0.8 (0.3 - 1.2) 6.1 (1.6 - 12.4) 4.9 (1.2 - 11.5) 200 (100 - 400) 3.8 (1.3 - 6.7) 53.3 (49.8 - 61.0) 51.5 (25.6 - 64.2) 49.5 (32.8 - 59.9) 0.0 (-24.9 - 16.0) 0.2 (0.0 - 1.0) 0.0 (0.0 - 0.4) 0.0 (-0.7 - 0.1) 153.0 (93.0 - 261.0)
EP
Variable
#
€
All values are medians (IQR). *ACQ, Asthma Control Questionnaire. Statistical significance was determined using Mann-Whitney or Fisher’s exact test.
Table S9: Specific taxa differentially expressed in ICS non-responders (ICS non-R) compared to ICS responders (ICS-R) at baseline.
18
ACCEPTED MANUSCRIPT
OTU ID
ICS # non-R
#
ICS-R
ICS nonR- R
q-value
Phylum
Class
Order
Family
Genus
Species
9157.6
0.5
9157.1
1.05E-104
Actinobacteria
Actinobacteria
Actinomycetales
Microbacteriaceae
-
-
1111.8
0.7
1111.1
1.29E-82
Proteobacteria
Gammaproteobacteria
Pasteurellales
Pasteurellaceae
Haemophilus
-
4459021
221
7.1
213.9
0.0658
Bacteroidetes
Bacteroidia
Bacteroidales
Porphyromonadaceae
Tannerella
-
4465803
210.2
3.7
206.5
1.06E-126
Bacteroidetes
Bacteroidia
Bacteroidales
Porphyromonadaceae
Porphyromonas
-
RI PT
12297 4406393
164
3.8
160.2
7.45E-112
Fusobacteria
Fusobacteriia
Fusobacteriales
Leptotrichiaceae
Leptotrichia
-
154
8.7
145.3
3.64E-140
Bacteroidetes
Bacteroidia
Bacteroidales
Prevotellaceae
Prevotella
-
2654263
116.4
4.5
111.9
0.0129
Proteobacteria
Gammaproteobacteria
Pasteurellales
Pasteurellaceae
Haemophilus
parainfluenzae
4437399
137.4
58.4
79
2.55E-50
Spirochaetes
Spirochaetes
Spirochaetales
Spirochaetaceae
Treponema
-
4404220
410.2
333.7
76.5
5.54E-12
Proteobacteria
Gammaproteobacteria
Pasteurellales
Pasteurellaceae
Actinobacillus
-
14920
60.6
0.2
60.4
2.48E-14
Firmicutes
Bacilli
Gemellales
Gemellaceae
Gemella
-
73875
84.6
26.9
57.7
4.06E-47
Spirochaetes
Spirochaetes
Spirochaetales
Spirochaetaceae
Treponema
-
71146
108.6
52.6
56
1.06E-30
Spirochaetes
Spirochaetes
Spirochaetales
Spirochaetaceae
Treponema
-
2613485
60.8
6.3
54.5
2.20E-58
Bacteroidetes
Bacteroidia
Bacteroidales
Porphyromonadaceae
Porphyromonas
-
Flavobacteriia
M AN U
SC
923032 4426165
54.4
0.6
53.8
4.08E-26
Bacteroidetes
Flavobacteriales
Flavobacteriaceae
Capnocytophaga
ochracea
548730
114.4
62.3
52.1
3.75E-24
Bacteroidetes
Bacteroidia
Bacteroidales
Prevotellaceae
Prevotella
-
949816
37.4
2.6
34.8
0.0389
Firmicutes
Bacilli
Lactobacillales
Streptococcaceae
Streptococcus
-
4376637
82.6
47.8
34.8
1.24E-14
Spirochaetes
Spirochaetes
Spirochaetales
Spirochaetaceae
Treponema
-
968873
33.8
0.1
33.7
1.63E-07
Bacteroidetes
Flavobacteriia
325027
33.6
1.4
32.2
0.0454
4459265
42.8
12.9
29.9
1013316
29.8
0.9
28.9
4480189
27.2
0.4
26.8
4468500
58.2
36.4
21.8
4446973
21.8
0.1
21.7
4313722
20.4
0.4
20
4449458
23.6
5.6
4422512
19.6
886735
20.2
TE D
4419376
Weeksellaceae
-
-
Betaproteobacteria
Burkholderiales
Comamonadaceae
Comamonas
-
2.28E-25
Bacteroidetes
Bacteroidia
Bacteroidales
Paraprevotellaceae
Prevotella
-
8.09E-23
Firmicutes
Clostridia
Clostridiales
Lachnospiraceae
-
-
3.05E-15
Firmicutes
Bacilli
Lactobacillales
Lactobacillaceae
Lactobacillus
zeae
6.44E-08
AC C
EP
Flavobacteriales
Proteobacteria
Firmicutes
Clostridia
Clostridiales
Mogibacteriaceae
Anaerovorax
-
0.0716
Bacteroidetes
Bacteroidia
Bacteroidales
Porphyromonadaceae
Paludibacter
-
0.0059
Fusobacteria
Fusobacteriia
Fusobacteriales
Fusobacteriaceae
Fusobacterium
-
18
0.0545
Proteobacteria
Gammaproteobacteria
Pseudomonadales
Moraxellaceae
Acinetobacter
-
2.1
17.5
5.90E-19
Tenericutes
Mollicutes
Mycoplasmatales
Mycoplasmataceae
Mycoplasma
-
3.6
16.6
2.32E-17
Firmicutes
Bacilli
Lactobacillales
Aerococcaceae
Alloiococcus
-
19
ACCEPTED MANUSCRIPT
OTU ID
ICS # non-R
ICS nonR- R
#
ICS-R
q-value
Phylum
Class
Order
Family
Genus
Species
17.6
1.3
16.3
6.34E-17
Actinobacteria
Actinobacteria
Actinomycetales
Actinomycetaceae
Actinomyces
-
4329788
33.2
19.3
13.9
6.48E-06
Firmicutes
Clostridia
Clostridiales
-
-
-
269930
43.2
32.5
10.7
0.0181
1157
11
1.1
9.9
8.65E-11
4299087
8.2
0.3
7.9
1609
7.6
0.3
1076969
11.8
4306773
9.8
4329957
5.8
137183
RI PT
46
Gammaproteobacteria
Pseudomonadales
Pseudomonadaceae
-
-
Bacilli
Lactobacillales
Streptococcaceae
Streptococcus
-
0.0389
Bacteroidetes
Flavobacteriia
Flavobacteriales
Flavobacteriaceae
Capnocytophaga
ochracea
7.3
1.60E-06
Bacteroidetes
Bacteroidia
Bacteroidales
Prevotellaceae
Prevotella
melaninogenica
4.5
7.3
2.31E-05
Firmicutes
Bacilli
Lactobacillales
Streptococcaceae
Streptococcus
-
3.3
6.5
2.74E-05
Fusobacteria
Fusobacteriia
Fusobacteriales
Leptotrichiaceae
Leptotrichia
-
0.2
5.6
8.21E-05
Bacteroidetes
Bacteroidia
Bacteroidales
Prevotellaceae
Prevotella
-
6.8
1.4
5.4
1.37E-05
Actinobacteria
Actinobacteria
Bifidobacteriales
Bifidobacteriaceae
-
-
886264
2.6
0.6
2
0.0398
Actinobacteria
Actinobacteria
Actinomycetales
Micrococcaceae
Rothia
mucilaginosa
M AN U
SC
Proteobacteria Firmicutes
0.8
3.6
-2.8
0.0550
Firmicutes
Clostridia
Clostridiales
Mogibacteriaceae
-
-
0.6
3.6
-3
0.0390
Fusobacteria
Fusobacteriia
Fusobacteriales
Fusobacteriaceae
Fusobacterium
-
968954
0.6
4.2
-3.6
0.0389
Firmicutes
Bacilli
Lactobacillales
Streptococcaceae
Streptococcus
-
1015518
1.2
4.9
-3.7
0.0176
Actinobacteria
Actinobacteria
Actinomycetales
Corynebacteriaceae
Corynebacterium
-
TE D
43339 645055
2.2
6
-3.8
0.0325
Actinobacteria
Actinobacteria
Actinomycetales
Actinomycetaceae
Actinomyces
-
4399781
1.2
5.1
-3.9
0.0130
Bacteroidetes
Bacteroidia
Bacteroidales
Prevotellaceae
Prevotella
-
989074
1.2
6.4
-5.2
0.0016
Bacteroidetes
Flavobacteriia
Flavobacteriales
Flavobacteriaceae
Capnocytophaga
-
1580
2.2
7.6
-5.4
0.0022
Bacteroidetes
Bacteroidia
Bacteroidales
Prevotellaceae
Prevotella
melaninogenica
0.0545
Firmicutes
Bacilli
Lactobacillales
Streptococcaceae
Streptococcus
-
Clostridia
Clostridiales
Lachnospiraceae
-
-
591583
0.2
5.6
-5.4
4341205
3.8
9.4
-5.6
965500
2.4
8.9
-6.5
254888
3.6
10.5
-6.9
1935279
0.8
9.1
-8.3
851935
0.4
9
-8.6
0.0916
610111
3.4
13.1
-9.7
3.71E-06
851824
1.2
12.1
-10.9
7.99E-07
124932
1.8
12.8
-11
3.19E-07
4306587
0.2
11.5
-11.3
0.0325
0.0056
EP
4306356
Firmicutes
Firmicutes
Bacilli
Lactobacillales
Streptococcaceae
Streptococcus
-
0.0005
Proteobacteria
Betaproteobacteria
Burkholderiales
Comamonadaceae
-
-
0.0125
Firmicutes
Bacilli
Lactobacillales
Streptococcaceae
Streptococcus
-
Bacteroidetes
Bacteroidia
Bacteroidales
Prevotellaceae
Prevotella
-
Bacteroidetes
Bacteroidia
Bacteroidales
Prevotellaceae
Prevotella
-
Firmicutes
Erysipelotrichi
Erysipelotrichales
Erysipelotrichaceae
Bulleidia
-
Spirochaetes
Spirochaetes
Spirochaetales
Spirochaetaceae
Treponema
-
Firmicutes
Clostridia
Clostridiales
Veillonellaceae
Selenomonas
-
AC C 0.0004
20
ACCEPTED MANUSCRIPT
OTU ID
#
ICS-R
ICS nonR- R
q-value
Phylum
Class
Order
Family
Genus
Species
Bacteroidetes
Bacteroidia
Bacteroidales
Prevotellaceae
Prevotella
-
Fusobacteria
Fusobacteriia
Fusobacteriales
Leptotrichiaceae
Leptotrichia
-
6.4
18.2
-11.8
1.15E-06
4297119
0.2
15
-14.8
0.0131
4373910
0.8
16
-15.2
8.52E-08
Proteobacteria
Betaproteobacteria
Neisseriales
Neisseriaceae
Neisseria
-
4440670
2.6
19.9
-17.3
3.30E-11
Firmicutes
Clostridia
Clostridiales
Veillonellaceae
Veillonella
-
1044611
1.6
23.9
-22.3
1.61E-12
Firmicutes
Clostridia
Clostridiales
Peptostreptococcaceae
-
-
537098
125.6
150.9
-25.3
0.0020
Firmicutes
Clostridia
Clostridiales
Lachnospiraceae
-
-
0.0155
Bacteroidetes
Bacteroidia
Bacteroidales
Paraprevotellaceae
Prevotella
-
Firmicutes
Clostridia
Clostridiales
Lachnospiraceae
Oribacterium
-
Actinobacteria
Actinobacteria
Actinomycetales
Micrococcaceae
Rothia
dentocariosa
RI PT
22951
1.4
28.5
-27.1
25.8
57.8
-32
3.94E-15
4466006
5.2
46.9
-41.7
0.0863
4313608
12.8
56.4
-43.6
1.16E-27
Bacteroidetes
Bacteroidia
Bacteroidales
-
-
-
245523
0.2
47.7
-47.5
1.07E-06
Bacteroidetes
Bacteroidia
Bacteroidales
-
-
-
M AN U
SC
86 4310395
0.2
52.1
-51.9
0.0233
Bacteroidetes
Bacteroidia
Bacteroidales
Prevotellaceae
Prevotella
-
21.2
98.8
-77.6
1.93E-49
Fusobacteria
Fusobacteriia
Fusobacteriales
Fusobacteriaceae
Fusobacterium
-
4330849
6.6
92.4
-85.8
2.08E-48
SR1
-
-
-
-
-
4344371
2
94.1
-92.1
4.52E-32
Proteobacteria
Alphaproteobacteria
Sphingomonadales
Sphingomonadaceae
Sphingomonas
-
1053321
17
110.2
-93.2
5.59E-60
Proteobacteria
Gammaproteobacteria
Pseudomonadales
Moraxellaceae
Moraxella
-
4335578
117.4
236
-118.6
6.17E-50
Firmicutes
Clostridia
Clostridiales
Mogibacteriaceae
-
-
4404577
253.4
388.7
-135.3
3.41E-38
Firmicutes
Clostridia
Clostridiales
Peptostreptococcaceae
Peptostreptococcus
-
4455767
56.8
206.1
-149.3
0.0597
Firmicutes
Bacilli
Lactobacillales
Streptococcaceae
Streptococcus
-
2.22E-99
Fusobacteriia
Fusobacteriales
Leptotrichiaceae
Leptotrichia
-
Bacteroidia
Bacteroidales
Paraprevotellaceae
Prevotella
tannerae
54.4
215.3
-160.9
0.8
165.9
-165.1
4308793
35.2
270.2
-235
4363066
9.8
333.7
-323.9
2469654
41.2
399.4
-358.2
4439603 1030 2727.2 mean relative abundance
-1697.2
0.0006
EP
252843 4448497
TE D
642465 2438396
Fusobacteria
Bacteroidetes
0.0389
Fusobacteria
Fusobacteriia
Fusobacteriales
Fusobacteriaceae
Fusobacterium
-
0.0568
Proteobacteria
Gammaproteobacteria
Pasteurellales
Pasteurellaceae
Aggregatibacter
-
Bacteroidetes
Bacteroidia
Bacteroidales
Paraprevotellaceae
Prevotella
-
Firmicutes
Bacilli
Lactobacillales
Streptococcaceae
Streptococcus
-
AC C
#
ICS # non-R
1.68E-219 0.0127
Table S10: Predicted KEGG pathways enriched in ICS non-responders compared to ICS responders at baseline.
21
ACCEPTED MANUSCRIPT
ICS nonresponders
ICSresponders
ICS non-responders responders
KEGG Super-pathways
KEGG pathways
6133
90535
Amino Acid Metabolism
Alanine, aspartate and glutamate metabolism
163683
182438
Amino Acid Metabolism
Amino acid related enzymes
67400
48235
19165
Amino Acid Metabolism
171954
78691
93263
Amino Acid Metabolism
231976
121154
110822
Amino Acid Metabolism
54604
16710
37894
Amino Acid Metabolism
46299
268
46031
Amino Acid Metabolism
159693
49782
109911
Amino Acid Metabolism
44
12
32
Amino Acid Metabolism
RI PT
96668 346121
Arginine and proline metabolism Cysteine and methionine metabolism Glycine, serine and threonine metabolism
Phenylalanine metabolism Phenylalanine, tyrosine and tryptophan biosynthesis Tryptophan metabolism
20667
31017
Amino Acid Metabolism
Tyrosine metabolism
205019
112275
Amino Acid Metabolism
Valine, leucine and isoleucine biosynthesis
276968
119360
157608
Amino Acid Metabolism
Valine, leucine and isoleucine degradation
46198
546
45652
Biosynthesis of Other Secondary Metabolites
Novobiocin biosynthesis
4388
18964
-14576
Biosynthesis of Other Secondary Metabolites
Penicillin and cephalosporin biosynthesis
9285
36231
-26946
Biosynthesis of Other Secondary Metabolites
Phenylpropanoid biosynthesis
149876
160184
-10308
Carbohydrate Metabolism
Amino sugar and nucleotide sugar metabolism
93433
4694
88739
Carbohydrate Metabolism
Ascorbate and aldarate metabolism
413978
211294
202684
Carbohydrate Metabolism
Butanoate metabolism
151818
133288
18530
Carbohydrate Metabolism
C5-Branched dibasic acid metabolism
510250
246694
263556
Carbohydrate Metabolism
Citrate cycle (TCA cycle)
91488
115716
-24228
Carbohydrate Metabolism
Fructose and mannose metabolism
26877
108183
-81306
Carbohydrate Metabolism
Galactose metabolism
542521
327948
214573
Carbohydrate Metabolism
Glycolysis / Gluconeogenesis
83375
82142
1233
Carbohydrate Metabolism
Inositol phosphate metabolism
116722
70965
45757
Carbohydrate Metabolism
Pentose and glucuronate interconversions
309090
182603
126487
Carbohydrate Metabolism
Pentose phosphate pathway
96668
6133
90535
Carbohydrate Metabolism
Propanoate metabolism
455675
229990
225685
Carbohydrate Metabolism
Pyruvate metabolism
AC C
TE D
51684 317294
EP
M AN U
SC
Lysine degradation
22
ACCEPTED MANUSCRIPT
ICS nonresponders
ICSresponders
ICS non-responders responders
KEGG Super-pathways
KEGG pathways
64173
-49729
Carbohydrate Metabolism
Starch and sucrose metabolism
42064
67886
Cell Motility
Bacterial motility proteins
62488
39219
23269
Cellular Processes and Signaling
2
57
-55
Cellular Processes and Signaling
57202
31502
25700
Cellular Processes and Signaling
29671
112498
-82827
Cellular Processes and Signaling
79239
102277
-23038
Cellular Processes and Signaling
46318
902
45416
Cellular Processes and Signaling
101147
21454
79693
Cellular Processes and Signaling
RI PT
14444 109950
Electron transfer carriers Germination
Inorganic ion transport and metabolism
M AN U
SC
Membrane and intracellular structural molecules Other ion-coupled transporters Other transporters Signal transduction mechanisms
37
155
-118
Cellular Processes and Signaling
Sporulation
85232
82785
2447
Energy Metabolism
Carbon fixation in photosynthetic organisms
48202
4851
43351
Energy Metabolism
64518
40288
24230
Energy Metabolism
Carbon fixation pathways in prokaryotes
38894
22677
Energy Metabolism
9573
364303
Energy Metabolism
1540
420
1120
Energy Metabolism
Photosynthesis
396
108
288
Energy Metabolism
Photosynthesis - antenna proteins
54942
11504
43438
Energy Metabolism
Photosynthesis proteins
332715
262004
70711
Enzyme Families
Peptidases
63873
37500
26373
Enzyme Families
Protein kinases
188379
99634
88745
Folding, Sorting and Degradation
Protein export
67735
42062
25673
Folding, Sorting and Degradation
RNA degradation
103067
21437
81630
Folding, Sorting and Degradation
Sulfur relay system
48173
2656
45517
Genetic Information Processing
Protein folding and associated processing
120672
63480
57192
Genetic Information Processing
Replication, recombination and repair proteins
166228
69731
96497
Genetic Information Processing
Translation proteins
10512
43144
-32632
Glycan Biosynthesis and Metabolism
Glycosaminoglycan degradation
62858
48757
14101
Glycan Biosynthesis and Metabolism
Glycosyltransferases
AC C
TE D
61571 373876
EP
Methane metabolism Nitrogen metabolism Oxidative phosphorylation
23
ACCEPTED MANUSCRIPT
ICS nonresponders
ICSresponders
ICS non-responders responders
KEGG Super-pathways
KEGG pathways
22647
-17127
Glycan Biosynthesis and Metabolism
Lipopolysaccharide biosynthesis proteins
126883
64237
Glycan Biosynthesis and Metabolism
Peptidoglycan biosynthesis
50687
12248
38439
Lipid Metabolism
51658
20661
30997
Lipid Metabolism
44
12
32
Lipid Metabolism
-136
Lipid Metabolism
12
13
Lipid Metabolism
1036463
799357
237106
Membrane Transport
188379
99634
88745
Membrane Transport
42102
177864
-135762
Membrane Transport
480439
264215
216224
Membrane Transport
1233749
1031431
202318
Membrane Transport
7503
30877
-23374
Metabolism
Fatty acid metabolism Glycerolipid metabolism Lipid biosynthesis proteins
SC
140
Fatty acid biosynthesis
M AN U
4 25
RI PT
5520 191120
Steroid biosynthesis ABC transporters Bacterial secretion system Phosphotransferase system (PTS)
Secretion system Transporters Amino acid metabolism
12
32
Metabolism
15503
38017
Metabolism
70507
69838
669
Metabolism
Energy metabolism
88
24
64
Metabolism
Lipid metabolism
3010
11274
-8264
Metabolism
Metabolism of cofactors and vitamins
Biosynthesis and biodegradation of secondary metabolites Carbohydrate metabolism
21
32
Metabolism
Nucleotide metabolism
248847
66227
Metabolism
Others
127273
82607
44666
Metabolism of Cofactors and Vitamins
Folate biosynthesis
121296
59194
62102
Metabolism of Cofactors and Vitamins
Lipoic acid metabolism
22645
Metabolism of Cofactors and Vitamins
Nicotinate and nicotinamide metabolism
46360
Metabolism of Cofactors and Vitamins
One carbon pool by folate
41407
Metabolism of Cofactors and Vitamins
Pantothenate and CoA biosynthesis
288859
Metabolism of Cofactors and Vitamins
Porphyrin and chlorophyll metabolism
AC C
53 315074
EP
TE D
44 53520
61527
38882
128513
82153
209922
168515
448003
159144
51658
20661
30997
Metabolism of Cofactors and Vitamins
Retinol metabolism
404632
189043
215589
Metabolism of Cofactors and Vitamins
Riboflavin metabolism
24
ACCEPTED MANUSCRIPT
ICS nonresponders
ICSresponders
ICS non-responders responders
KEGG Super-pathways
KEGG pathways
642
346
Metabolism of Cofactors and Vitamins
Thiamine metabolism
15689
38121
Metabolism of Cofactors and Vitamins
Ubiquinone and other terpenoid-quinone biosynthesis
96668
6133
90535
Metabolism of Other Amino Acids
9285
36231
-26946
Metabolism of Other Amino Acids
244909
142175
102734
Metabolism of Other Amino Acids
RI PT
988 53810
beta-Alanine metabolism Cyanoamino acid metabolism
602
1129
Metabolism of Other Amino Acids
57393
11496
Metabolism of Other Amino Acids
53634
15641
37993
Metabolism of Terpenoids and Polyketides
Biosynthesis of siderophore group nonribosomal peptides
195
50
145
Metabolism of Terpenoids and Polyketides
Carotenoid biosynthesis
319432
117227
202205
Metabolism of Terpenoids and Polyketides
Prenyltransferases
368339
126713
241626
Metabolism of Terpenoids and Polyketides
Terpenoid backbone biosynthesis
409304
360629
48675
Nucleotide Metabolism
Purine metabolism
170310
80320
89990
Nucleotide Metabolism
Pyrimidine metabolism
306381
206945
99436
Replication and Repair
Base excision repair
306381
206945
99436
Replication and Repair
DNA repair and recombination proteins
44
12
32
Signal Transduction
Calcium signaling pathway
180412
87533
92879
Signal Transduction
Two-component system
10512
43144
-32632
Signaling Molecules and Interaction
Bacterial toxins
257807
183480
74327
Transcription
Transcription factors
642256
442093
200163
Translation
Aminoacyl-tRNA biosynthesis
270199
206858
63341
Translation
Ribosome Biogenesis
54816
18151
36665
Translation
Ribosome biogenesis in eukaryotes
2 146
26
6
51658
20661
51658 51685
M AN U
TE D
AC C
79 578
Phosphonate and phosphinate metabolism
SC
1731 68889
EP
Glutathione metabolism
Selenocompound metabolism
77
Xenobiotics Biodegradation and Metabolism
Aminobenzoate degradation
432
Xenobiotics Biodegradation and Metabolism
Benzoate degradation
20
Xenobiotics Biodegradation and Metabolism
Caprolactam degradation
30997
Xenobiotics Biodegradation and Metabolism
Chloroalkane and chloroalkene degradation
20661
30997
Xenobiotics Biodegradation and Metabolism
Drug metabolism - cytochrome P450
21169
30516
Xenobiotics Biodegradation and Metabolism
Metabolism of xenobiotics by cytochrome P450
25
ACCEPTED MANUSCRIPT
ICS nonresponders
ICSresponders
ICS non-responders responders
KEGG Super-pathways
KEGG pathways
20661
30997
Xenobiotics Biodegradation and Metabolism
Naphthalene degradation
121
27
94
Xenobiotics Biodegradation and Metabolism
Polycyclic aromatic hydrocarbon degradation
26
6
20
Xenobiotics Biodegradation and Metabolism
26
6
20
Xenobiotics Biodegradation and Metabolism
RI PT
51658
Toluene degradation Xylene degradation
#
Post - Pre
195.3
0.5
194.8
241.0
71.6
4401186 4404731
Pre-
q-value
Phylum
Class
Order
Family
Genus
Species
0.0013
Proteobacteria
Betaproteobacteria
Neisseriales
Neisseriaceae
Eikenella
-
169.4
3.27E-140
Actinobacteria Proteobacteria
Actinobacteria
Actinomycetales
Corynebacteriaceae
Corynebacterium
-
Betaproteobacteria
Burkholderiales
Comamonadaceae
Delftia
-
Proteobacteria
Gammaproteobacteria
Pseudomonadales
Pseudomonadaceae
-
-
Tenericutes
Mollicutes
Mycoplasmatales
Mycoplasmataceae
Mycoplasma
-
Proteobacteria
Betaproteobacteria
Neisseriales
Neisseriaceae
Eikenella
-
4469492
183.6
16.4
167.3
0.0009
269930
129.9
12.0
117.9
0.0051
615020
39.4
0.9
38.5
7.57E-31
87506
31.3
4.1
27.1
0.0904
4366487
23.4
2.6
20.8
0.0951
Bacteroidetes Proteobacteria
M AN U
Post-
Flavobacteriia
Flavobacteriales
Flavobacteriaceae
Capnocytophaga
-
Gammaproteobacteria
Pseudomonadales
Pseudomonadaceae
Pseudomonas
-
Bacteroidia
Bacteroidales
Porphyromonadaceae
Porphyromonas
-
Bacteroidia
Bacteroidales
-
-
-
TE D
#
OTU ID
SC
Table S11: Specific taxa differentially expressed in placebo treated asthmatics post-treatment compared to baseline (pre-treatment) visit.
16.8
0.8
16.0
495451
19.5
10.6
8.9
0.0009
Bacteroidetes
245523
14.0
6.3
7.8
0.0004
Bacteroidetes
63117
10.1
2.6
7.5
0.0010
Proteobacteria
Betaproteobacteria
Neisseriales
Neisseriaceae
Kingella
-
Proteobacteria
Alphaproteobacteria
Rhodospirillales
Acetobacteraceae
Acidocella
-
Bacteroidetes
Bacteroidia
Bacteroidales
Prevotellaceae
Prevotella
nanceiensis
EP
1566691
0.0040
7.6
0.6
7.0
1052181
7.8
2.9
4.9
1.47E-09
763967
5.0
0.1
4.9
0.0658
Bacteroidetes
Bacteroidia
Bacteroidales
Prevotellaceae
Prevotella
-
4305935
5.9
1.4
4.5
0.0066
Bacteroidetes
Bacteroidia
Bacteroidales
Porphyromonadaceae
Paludibacter
-
399903
5.4
0.9
4.5
0.0010
Spirochaetes
Spirochaetes
Spirochaetales
Spirochaetaceae
Treponema
-
937813
4.5
0.3
4.3
0.0241
Firmicutes
Clostridia
Clostridiales
Tissierellaceae
Anaerococcus
-
458
4.9
0.8
4.1
0.0099
Proteobacteria
Gammaproteobacteria
Pasteurellales
Pasteurellaceae
Aggregatibacter
-
886735
6.0
2.1
3.9
0.0154
Firmicutes
Bacilli
Lactobacillales
Aerococcaceae
Alloiococcus
-
676367
3.8
0.3
3.5
0.0423
Firmicutes
Clostridia
Clostridiales
Veillonellaceae
Schwartzia
-
AC C
240252
2.40E-05
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OTU ID
#
Post-
Pre-
#
Post - Pre
q-value
Phylum
Class
Order
Family
Genus
Species
Betaproteobacteria
Neisseriales
Neisseriaceae
-
-
839
3.4
0.3
3.1
0.0013
Proteobacteria
4365143
3.1
0.1
3.0
0.0792
Actinobacteria
Actinobacteria
Actinomycetales
Corynebacteriaceae
Corynebacterium
-
Firmicutes
Clostridia
Clostridiales
4.4
1.8
2.6
Lachnospiraceae
Oribacterium
-
4312347
2.4
0.6
1.8
0.0350
Proteobacteria
Gammaproteobacteria
Cardiobacteriales
Cardiobacteriaceae
Cardiobacterium
-
92721
1.9
0.6
1.3
0.0029
Actinobacteria
Actinobacteria
Actinomycetales
Actinomycetaceae
Actinomyces
-
887744
1.9
0.6
1.3
0.0404
Bacteroidetes
Flavobacteriia
Flavobacteriales
Weeksellaceae
-
-
Cyanobacteria
Chloroplast
Streptophyta
-
RI PT
686900
0.0059
1.8
1.6
0.1
-
-
844535
0.8
2.0
-1.3
0.0350
Proteobacteria
Alphaproteobacteria
Caulobacterales
Caulobacteraceae
Asticcacaulis
-
2758
0.4
2.1
-1.8
0.0137
Fusobacteria
Fusobacteriia
Fusobacteriales
Leptotrichiaceae
Leptotrichia
-
299830
0.8
3.0
-2.3
0.0904
Bacteroidetes
Bacteroidia
Bacteroidales
Prevotellaceae
Prevotella
copri
506
0.3
3.3
-3.0
0.0668
Bacteroidetes
Bacteroidia
Bacteroidales
Prevotellaceae
Prevotella
-
Proteobacteria
Alphaproteobacteria
Caulobacterales
Caulobacteraceae
Asticcacaulis
-
Bacteroidales
Prevotellaceae
Prevotella
melaninogenica
Bacteroidales
Prevotellaceae
Prevotella
melaninogenica
M AN U
SC
4420570
0.0902
0.3
3.4
-3.1
1066621
1.0
4.3
-3.3
0.0951
Bacteroidetes
Bacteroidia
425
0.3
3.6
-3.4
0.0409
Bacteroidetes
Bacteroidia
1536
0.5
4.3
-3.8
0.0423
Firmicutes
Bacilli
Gemellales
-
-
-
Fusobacteria
Fusobacteriia
Fusobacteriales
Leptotrichiaceae
Leptotrichia
-
Bacteroidetes
Bacteroidia
Bacteroidales
Prevotellaceae
Prevotella
melaninogenica
BD1-5
-
-
-
-
943
0.3
4.0
-3.8
0.0608
1643
0.4
4.8
-4.4
0.0404
4457085
0.5
5.0
-4.5
2.30E-05
301
0.1
4.6
-4.5
0.0792
Bacteroidetes
-4.6
0.0340
Bacteroidetes
0.1
4.8
Bacteroidia
Bacteroidales
Prevotellaceae
Prevotella
-
Bacteroidia
Bacteroidales
Prevotellaceae
Prevotella
-
Proteobacteria
Gammaproteobacteria
Pseudomonadales
Moraxellaceae
Acinetobacter
-
Firmicutes
Clostridia
Clostridiales
Peptostreptococcaceae
-
-
Proteobacteria
Gammaproteobacteria
Pseudomonadales
Pseudomonadaceae
Pseudomonas
-
EP
68416
GN02
TE D
551109
0.0658
0.3
6.0
-5.8
250288
5.8
11.5
-5.8
6.95E-12
133961
0.5
7.3
-6.8
9.90E-05
105
0.8
8.4
-7.6
0.0608
Bacteroidetes
Bacteroidia
Bacteroidales
Prevotellaceae
Prevotella
-
Proteobacteria
Gammaproteobacteria
Enterobacteriales
Enterobacteriaceae
-
-
Bacteroidetes
Bacteroidia
Bacteroidales
Paraprevotellaceae
Prevotella
-
Actinobacteria
Actinobacteria
Actinomycetales
Actinomycetaceae
Actinomyces
-
Firmicutes
Clostridia
Clostridiales
Mogibacteriaceae
-
-
Bacteroidetes
Bacteroidia
Bacteroidales
Prevotellaceae
Prevotella
-
AC C
3530625
0.0071
274365
0.3
9.5
-9.3
0.0010
4459265
2.3
11.6
-9.4
1.49E-23
734
2.4
13.8
-11.4
0.0404
43339
4.5
24.9
-20.4
4.64E-11
-63.3
0.0552
594026
2.6
65.9
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#
mean relative abundance
#
Post-
#
Pre-
Post Pre
q-value
Phylum
Class
Order
Family
Genus
Species
5944.5
0.6
5943.9
1.08E-09
Actinobacteria
Actinobacteria
Actinomycetales
Microbacteriaceae
-
-
233.9
46.3
187.6
0.0071
Proteobacteria
Betaproteobacteria
Neisseriales
Neisseriaceae
Neisseria
-
855912
34.5
1.4
33.1
8.01E-23
Fusobacteria
Fusobacteriia
Fusobacteriales
Fusobacteriaceae
Fusobacterium
-
246528
103.4
73.0
30.4
1.67E-08
Proteobacteria
Gammaproteobacteria
Pseudomonadales
Moraxellaceae
Moraxella
-
3931
17.8
1.8
16.0
3.35E-14
Spirochaetes
Spirochaetes
Spirochaetales
Spirochaetaceae
Treponema
-
3860
13.5
0.8
12.8
1.13E-09
Spirochaetes
Spirochaetes
Spirochaetales
Spirochaetaceae
Treponema
amylovorum
930422
13.0
1.9
11.1
4.45E-10
Proteobacteria
Gammaproteobacteria
Pseudomonadales
Moraxellaceae
Moraxella
-
114813
11.3
3.3
8.0
2.30E-06
Bacteroidetes
Bacteroidia
Bacteroidales
Prevotellaceae
Prevotella
-
4472050
7.3
2.0
5.3
1.51E-14
Actinobacteria
Actinobacteria
Actinomycetales
Actinomycetaceae
-
-
Mycoplasmatales
Mycoplasmataceae
Mycoplasma
-
Erysipelotrichales
Erysipelotrichaceae
Sharpea
-
Clostridia
Clostridiales
Veillonellaceae
Dialister
-
BD1-5
-
-
-
-
Bacteroidia
Bacteroidales
-
-
-
Actinobacteria
Bifidobacteriales
Bifidobacteriaceae
Scardovia
-
5.6
1.4
4.3
5.83E-08
Tenericutes
Mollicutes
2.4
1.5
0.9
0.0821
Firmicutes
Erysipelotrichi
1105876
1.3
2.1
-0.9
0.0922
Firmicutes
148695
0.8
1.8
-1.0
0.0848
GN02
0.3
1.5
-1.3
0.0520
Bacteroidetes
5.3
6.6
-1.4
0.0020
Actinobacteria
1465
1.3
3.5
-2.3
1.34E-06
62513
0.9
3.4
-2.5
EP
23709 4411875
TE D
773109 4364176
SC
12297 4318672
M AN U
OTU ID
RI PT
Table S12: Specific taxa differentially expressed in ICS treated asthmatics post-treatment compared to baseline (pre-treatment) visit.
Bacteroidetes
Bacteroidia
Bacteroidales
Prevotellaceae
Prevotella
intermedia
0.0699
Firmicutes
Clostridia
Clostridiales
Lachnospiraceae
Moryella
-
0.0539
Actinobacteria
Actinobacteria
Actinomycetales
Corynebacteriaceae
Corynebacterium
-
Neisseriaceae
Kingella
-
0.8
3.5
-2.8
1.0
3.9
-2.9
0.0358
Proteobacteria
Betaproteobacteria
Neisseriales
645055
1.4
4.5
-3.1
0.0293
Fusobacteria
Fusobacteriia
Fusobacteriales
Fusobacteriaceae
Fusobacterium
-
43339
1.0
4.5
-3.5
0.0159
Firmicutes
Clostridia
Clostridiales
Mogibacteriaceae
-
-
422455
0.8
4.8
-4.0
0.0020
Tenericutes
Mollicutes
Acholeplasmatales
Acholeplasmataceae
Acholeplasma
-
AC C
495067 4380570
1580
1.4
5.6
-4.3
0.0111
Bacteroidetes
Bacteroidia
Bacteroidales
Prevotellaceae
Prevotella
melaninogenica
399903
2.4
7.5
-5.1
0.0009
Spirochaetes
Spirochaetes
Spirochaetales
Spirochaetaceae
Treponema
-
4004098
1.8
7.6
-5.9
5.69E-05
Tenericutes
RF3
ML615J-28
-
-
-
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3.3
9.3
-6.0
5.11E-12
Firmicutes
Bacilli
Lactobacillales
Lactobacillaceae
Lactobacillus
iners
92316
1.3
9.6
-8.4
0.0006
Firmicutes
Clostridia
Clostridiales
Lachnospiraceae
-
-
4426165
2.3
10.9
-8.6
1.25E-07
Bacteroidetes
Bacteroidia
Bacteroidales
Prevotellaceae
Prevotella
-
1.9
10.9
-9.0
4.04E-08
Firmicutes
Clostridia
Clostridiales
10.4
29.9
-19.5
3.10E-14
Firmicutes
Clostridia
Clostridiales
-
-
-
Peptostreptococcaceae
-
-
4421864
5.1
26.3
-21.1
0.0597
Firmicutes
Clostridia
Clostridiales
Veillonellaceae
Selenomonas
-
398192
0.3
23.1
-22.9
2.93E-06
Bacteroidetes
Bacteroidia
Bacteroidales
Paraprevotellaceae
Prevotella
-
4440670
1.5
24.9
-23.4
1.74E-18
Firmicutes
Clostridia
Clostridiales
Veillonella
-
4318284
3.9
69.1
-65.3
0.0359
Firmicutes
Clostridia
Clostridiales
Veillonellaceae
Dialister
-
2438396 12.5 123.5 mean relative abundance
-111.0
1.93E-102
Fusobacteria
Fusobacteriia
Fusobacteriales
Fusobacteriaceae
Fusobacterium
-
RI PT
893048 1044611
EP
TE D
M AN U
SC
Veillonellaceae
AC C
#
130864
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SUPPLEMENTAL METHODS:
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Study Population and Sample Collection: Of 186 adults screened for eligibility at nine “AsthmaNet” clinical centers, 84 subjects were enrolled (Figure S1A-C). Each participating clinical center (Brigham and Women’s Hospital, Boston, MA; Northwestern University, Chicago, IL; National Jewish Health, Denver, CO; University of Wisconsin, Madison, WI; University of Pittsburgh, Pittsburgh, PA; Washington University, St. Louis, MO; University of California San Francisco, San Francisco, CA; Duke University, Durham, NC; Duke University, Durham, NC and Wake Forest University, WinstonSalem, NC) enrolled a median of 10 (IQR of 8-11) subjects of whom 50% (IQR of 39-64) were asthmatic. The study center was not a significant contributor to any of the differences in bacterial community composition reported in this study. Each subject signed informed consent approved by the center’s IRB, and an NHLBI-appointed Data Safety Monitoring Board (DSMB) oversaw the study conduct.
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Adult subjects with mild atopic asthma (AA; n=42) were enrolled who had demonstrated serologic evidence of sensitivity (>0.35 kU/l) to at least one of 12 aeroallergens (Table S1) identified using ImmunoCAP specific IgE test (Thermo scientific); and airway hyper-responsiveness (methacholine PC20 ≤8 mg/mL), or bronchodilator reversibility (FEV1 improvement ≥12% in response to albuterol). Asthmatic subjects had stable asthma for preceding 3 months, an Asthma Control Questionnaire (ACQ) score of <1.5 (1) and no use of a controller medication (such as an inhaled corticosteroid) in the preceding 6 months. Atopic non-asthmatic subjects (ANA; n=21) had sensitivity to at least one aeroallergen but no evidence of airway hyper-responsiveness, (PC20 >16 mg/mL) or bronchodilator reversibility, and no history of chronic sinusitis (for responses to additional prior clinical conditions and environmental exposure for AA and ANA subjects see Tables S2-S3). Healthy control subjects (HC; n=21) had no history of atopic symptoms, negative serologic tests for all aeroallergens tested, and no history of chronic respiratory or other disease. Exclusion criteria for all subjects included smoking, symptoms of respiratory tract infection (including acute sinusitis and bronchitis) in the previous 6 weeks, and antibiotic use in the previous 3 months.
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All subjects underwent spirometry, blood sampling, and sputum induction at visit 1 (Figure S1C). At visit 2, prior to bronchoscopy, oral wash samples (OW) consisting of a tongue scraping followed by oral rinse and gargle with sterile saline (10 mL), were collected as previously described (2). To evaluate for instrument carryover of oral bacterial DNA contamination during sample collection, a 10mL flush of sterile saline through the suction channel of the bronchoscope (“scope flush”) was collected following application of topical anesthesia to the vocal cords when the instrument was withdrawn before proceeding to collection of 4 protected bronchial brush (BB) samples as described previously (3). Asthmatic subjects were subsequently randomized in a 2:1 ratio to treatment with inhaled fluticasone propionate (250 mcg; GlaxoSmithKline) or placebo (lactose alone) from a dry powder inhaler twice daily for six weeks. Repeat assessments were then performed (visits 3 and 4). All samples intended for microbial analysis were stored in RNAlater (Ambion, Inc. Austin, TX) at -80°C until processing. Nucleic acid extraction: For the focus of this study, nucleic acids from OW and 3 BB’s were extracted as previously described (2, 3) using a bead-beating protocol and the AllPrep kit (Qiagen, CA), to purify DNA and RNA in parallel. Reagent controls including 10mL aliquots of the lidocaine and sterile saline used during bronchoscopy, and from “scope flush” samples were extracted for DNA and tested by quantitative PCR (Q-PCR) using universal bacterial primers (see below) for bacterial contamination, with all reagents demonstrating no evidence of significant bacterial contamination
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(<35 16S rRNA gene copy number). Each extraction batch also included a blank control; these went through all the steps in preparation for 16S rRNA sequencing (as described below) and were sequenced. DNA and RNA were quantified on a NanoDrop2000 (ThermoFisher, CA).
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Quantitation of 16S rRNA and β-actin copy number: 16S rRNA gene copy number was assessed by quantitative PCR (Q-PCR) using the 16S rRNA universal primers P891F, P1033R and TaqMan UniProbe as previously described (4) in triplicate using TaqMan Universal Master Mix (Life Technologies). Mammalian β-actin copy numbers were evaluated using primers bActin-F (5’- CCTGGCACCCAGCACAAT-3’) and bActin-R (5’GCCGATCCACACGGAGTACT-3’) and bActin-TMP probe (5-FAM/ TCAAGATCATTGCTCCTCCTGAGCGC/3BHQ) (5). Total 16S rRNA and β-actin copy number was calculated against a standard curve of known 16SrRNA or β-actin copy numbers (1x102 – 1x108). Regression coefficients for all standard curves were > 0.98.
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16S rRNA-based airway and oral microbiota profiling using Illumina MiSeq: The variable region 4 (V4) of the 16S rRNA gene was amplified using 10 ng/l of OW and 100 ng/l of BB DNA template and 515F/806R primer combination as previously described (6, 7). Those samples without visible evidence of 16s rRNA PCR product on the first screen underwent repeat PCR screening with a bacterial-DNA control to rule out presence of sample-specific PCR inhibition. Amplicons were purified using SPRI beads (Beckman Coulter) or in the presence of multiple bands, gel-extracted with a Qiagen Gel Extraction kit (Qiagen) per manufacturer protocol, analyzed on Bioanalyzer (Aligent), quantified using the Qubit HS dsDNA kit (Invitrogen). Samples with amplicons <10ng were not sequenced. Blank control DNA extracts were also amplified, bead purified and included in each MiSeq run. Samples with sufficient 16S rRNA amplicon were pooled at 50 ng per sample, with 30-35 samples and a blank control. The barcoded, pooled library was quantified using the Qubit HS dsDNA kit (Invitrogen), denatured and 5pM was loaded onto the Illumina MiSeq cartridge (V3) in combination with a 15% (v/v) of denatured 12.5pM PhiX for sequencing.
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Paired-end sequences were combined using FLASh version 1.2.7 (8). Sequence analysis was performed using the Quantitative Insights into Microbial Ecology (QIIME) pipeline version 1.8.0 (9). Raw sequences were de-multiplexed by barcode and quality filtered by removing low quality sequences. Sequences with three or more consecutive bases with a Q score < 30 were truncated and discarded if the length was less than 75% of the original 250 bp read length. Sequences were aligned using PyNAST (10) and operational taxonomic units (OTUs) were picked at 97% sequence identity using uclust against the latest release of the Greengenes database (13_5) (11, 12). Reads that failed to hit the reference sequence collection were retained and clustered de novo. PyNAST-aligned sequences were chimera checked using ChimeraSlayer and putative chimeras as well as OTUs identified in negative controls or where not from kingdom Bacteria were removed from the OTU table. A phylogenetic tree was built using FastTree (13) and used to compute Faith’s Phylogenetic Diversity on OTU table multiply rarefied 100 times to 52,317 sequences per sample. Statistical analysis: All statistical analysis was performed as indicated in QIIME (9), R environment or using PRISM software. Welch’s corrected t-test, Kruskal-Wallis or Wilcoxon rank sum test and Chi-square or Fisher’s exact test where appropriate were used to determine significant differences in metadata between study groups conducted in PRISM. Ordination was visualized using Principle Coordinates Analysis (PCoA) on unweighted UniFrac distance matrix and plotted using Emperor in QIIME (14). Significant differences in beta-diversity between paired OW and BB samples were calculated on unweighted UniFrac PC1 coordinates (as a response variable) using Linear mixed
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effects (LME) model ((15) using lmerTest package in R). PERMANOVA ((16); vegan package function Adonis in R) was used to evaluate significant differences in beta-diversity between groups of independent samples. Negative binomial (NB) regression model (17, 18) was used to identify specific OTUs (present in at least 3 samples) that differed in relative abundance between groups of subjects. Only OTUs having a false discovery (FDR) corrected using Benjamini Hochberg method, q-value of <0.1, were retained and declared as significantly differentially enriched taxa. Pearson correlations were performed to determine relationships between relative abundance of OTUs and study variables with q-value correction for false discovery. Phylogenetic tree constructed with taxa of interest in QIIME were annotated using Interactive tree of Life (19). Metagenome prediction from normalized 16S rRNA representative sequences was performed using PICRUSt (20). NB regression model with Benjamini Hochberg FDR correction for multiple comparisons was used to determine Kyoto Encyclopedia of Genes and Genomes (KEGG) gene orthologs (KO’s) that were significantly enriched in each group of interest. Zero-inflated negative binomial (ZINB) model (21,22) corrected for false discovery (Benjamini Hochberg, q-value of <0.1) was used to identify specific OTUs in paired samples before and after treatment with ICS or placebo inhaler. Procrustes analysis (23) based on unweighted UniFrac distance was used to compare community composition between paired samples in each treatment group.
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ACKNOWLEGEMENTS: We additionally would like to thank the following coordinators and staff, and to identify the grants supporting the study: Robert Pedicini, BS1; Kathy Zheng, MPH1; Duanny Alva, MPH2; Assel Biyasheva, PhD2; Jenny Hixon, BS, CCRC2; Lucius Robinson III, BS, CMA, CCRC2; Mary Gill, RN, BSN3; James T. Good, MD3; Christena Kolakowski, MS3; Allen Stevens, CCRC, NREMT3; E. Rand Sutherland, MD3; Julia Bach, RN4; Rich Cornwell, MD4; Holly Eversoll, RN4; Tiffany Huard4; Keith Meyer, MD4; Barbara Miller, RN4; Ann Sexton, MPH4; Michele Wolff, RN4; Merritt Fajt, MD5; Sherri Hill, BS5; Lisa Lane, BS5; Russell Traister, MD5; Cathy Vitari, RN, BSN AE-C5; Vanessa Curtis, RRT6; Brenda Patterson, RN, APN6; Cheryl Shelton, RN, BSN6; Kelly Norsworthy, BA, CPT7; Kelsey Wollen, BA7; Eugene Bleeker, MD8; Christopher Barrios, MD8; Suzan Farris, CCRP8; Jeffrey Krings, FNP8; Victor Ortega, MD8; Cheryl Wilmoth, CCRP8; Matthew Bowman, BS9; Linda Engle, BS9; Jennifer Lucier, BS9; Aimee J. Merchlinski, MS9; Kathryn Trasatt, BS9; Angela Updegrave9; Rachel Weber, BS9; Ronald R. Zimmerman, Jr., MPA9 2
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Brigham & Women’s Hospital, Boston, MA (Grant: HL098102); Northwestern University, Chicago, IL (Grant: 3 4 HL098096); National Jewish Health; Denver, CO (Grant: HL098075 & TR001082); University of Wisconsin Madison, 5 6 Madison, WI (Grant: HL098090); University of Pittsburgh, Pittsburgh, PA (Grant: HL098177); Washington University, 7 St. Louis, MO (Grant: HL098098 & TR000448); University of California San Francisco, San Francisco, CA (Grant: 8 9 HL098107 & HL105572); Wake Forest University, Winston-Salem, NC (Grant: HL098103 & TR000454); Pennsylvania State University, Hershey, PA (Grant: HL098115)
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SUPPLEMENTAL MATERIAL REFERENCES:
6.
7.
8. 9.
10. 11.
12. 13. 14. 15. 16. 17. 18. 19. 20.
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M AN U
4.
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3.
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Juniper EF, O'Byrne PM, Guyatt GH, Ferrier PJ, King DR. 1999. Development and validation of a questionnaire to measure asthma control. Eur Respir J. 14:902-907. Iwai S, Fei M, Huang D, Fong S, Subramanian A, Grieco K, Lynch SV, Huang L. 2012. Oral and Airway Microbiota in HIV-Infected Pneumonia Patients. J. Clin. Microbiol. 50:2995-3002. Huang YJ, Nelson CE, Brodie EL, Desantis TZ, Baek MS, Liu J, Woyke T, Allgaier M, Bristow J, Wiener-Kronish JP, Sutherland ER, King TS, Icitovic N, Martin RJ, Calhoun WJ, Castro M, Denlinger LC, Dimango E, Kraft M, Peters SP, Wasserman SI, Wechsler ME, Boushey HA, Lynch SV. 2011. Airway microbiota and bronchial hyperresponsiveness in patients with suboptimally controlled asthma. J. Allergy Clin. Immunol. 127:372-381.e371-373. Iwai S, Huang D, Fong S, Jarlsberg LG, Worodria W, Yoo S, Cattamanchi A, Davis JL, Kaswabuli S, Segal M, Huang L, Lynch SV. 2014. The Lung Microbiome of Ugandan HIV-Infected Pneumonia Patients Is Compositionally and Functionally Distinct from That of San Franciscan Patients. PLOSone 9:e95726. Bhakta NR, Solberg OD, Nguyen CP, Nguyen CN, Arron JR, Fahy JV, Woodruff PG. 2013. A qPCR-based metric of Th2 airway inflammation in asthma. Clin. Transl. Allergy 3:24. Caporaso JG, Lauber CL, Walters WA, Berg-Lyons D, Huntley J, Fierer N, Owens SM, Betley J, Fraser L, Bauer M, Gormley N, Gilbert JA, Smith G, Knight R. 2012. Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms. The ISME Journal 6:1621-1624. Caporaso JG, Lauber CL, Walters WA, Berg-Lyons D, Lozupone CA, Turnbaugh PJ, Fierer N, Knight R. 2011. Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. Proceedings of the National Academy of Sciences of the United States of America 108 Suppl 1:4516-4522. Magoc T, Salzberg SL. 2011. FLASH: fast length adjustment of short reads to improve genome assemblies. Bioinformatics 27:2957-2963. Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, Fierer N, Pena AG, Goodrich JK, Gordon JI, Huttley GA, Kelley ST, Knights D, Koenig JE, Ley RE, Lozupone CA, McDonald D, Muegge BD, Pirrung M, Reeder J, Sevinsky JR, Turnbaugh PJ, Walters WA, Widmann J, Yatsunenko T, Zaneveld J, Knight R. 2010. QIIME allows analysis of high-throughput community sequencing data. Nature methods 7:335-336. Caporaso JG, Bittinger K, Bushman FD, DeSantis TZ, Andersen GL, Knight R. 2010. PyNAST: a flexible tool for aligning sequences to a template alignment. Bioinformatics 26:266-267. DeSantis TZ, Hugenholtz P, Larsen N, Rojas M, Brodie EL, Keller K, Huber T, Dalevi D, Hu P, Andersen GL. 2006. Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Applied and environmental microbiology 72:5069-5072. Edgar RC. 2010. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26:2460-2461. Price MN, Dehal PS, Arkin AP. 2009. FastTree: Computing Large Minimum Evolution Trees with Profiles instead of a Distance Matrix. Molecular Biology and Evolution 26:1641-1650. Vazquez-Baeza Y, Pirrung M, Gonzalez A, Knight R. 2013. EMPeror: a tool for visualizing highthroughput microbial community data. GigaScience 2:16. Pinheiro JC. 2005. Linear Mixed Effects Models for Longitudinal Data, Encyclopedia of Biostatistics. John Wiley & Sons, Ltd. Anderson MJ. 2001. A new method for non-parametric multivariate analysis of variance. Austral Ecology 26:32-46. Gerber GK, Onderdonk AB, Bry L. 2012. Inferring dynamic signatures of microbes in complex host ecosystems. PLoS computational biology 8:e1002624. McMurdie PJ, Holmes S. 2014. Waste Not, Want Not: Why Rarefying Microbiome Data Is Inadmissible. PLoS computational biology 10:e1003531. Letunic I, Bork P. 2007. Interactive Tree Of Life (iTOL): an online tool for phylogenetic tree display and annotation. Bioinformatics 23:127-128. Langille MGI, Zaneveld J, Caporaso JG, McDonald D, Knights D, Reyes JA, Clemente JC, Burkepile DE, Vega Thurber RL, Knight R, Beiko RG, Huttenhower C. 2013. Predictive functional
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profiling of microbial communities using 16S rRNA marker gene sequences. Nat Biotech 31:814821. Kassahun W, Neyens T, Molenberghs G, Faes C, Verbeke G. 2014. Marginalized multilevel hurdle and zero-inflated models for overdispersed and correlated count data with excess zeros. Statistics in medicine 33:4402-4419. Min Y, Agresti A. 2005. Random effect models for repeated measures of zero-inflated count data. Statistical Modelling 5:1-19. Caporaso JG, Lauber CL, Walters WA, Berg-Lyons D, Huntley J, Fierer N, Owens SM, Betley J, Fraser L, Bauer M, Gormley N, Gilbert JA, Smith G, Knight R. 2012. Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms. ISME J 6: 1621-1624.
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