A severe asthma phenotype of excessive airway Haemophilus influenzae relative abundance associated with sputum neutrophilia

Ali Versi , Adnan Azim , Fransiskus Xaverius Ivan , Mahmoud I Abdel-Aziz , Stewart Bates , John Riley , Mohib Uddin , Nazanin Zounemat Kermani , Anke-H Maitland-Van Der Zee , Sven-Eric Dahlen , Ratko Djukanovic , Sanjay H Chotirmall , Peter Howarth , Ian M Adcock , Kian Fan Chung

Clinical and Translational Medicine ›› 2024, Vol. 14 ›› Issue (9) : e70007

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Clinical and Translational Medicine ›› 2024, Vol. 14 ›› Issue (9) : e70007 DOI: 10.1002/ctm2.70007
RESEARCH ARTICLE

A severe asthma phenotype of excessive airway Haemophilus influenzae relative abundance associated with sputum neutrophilia

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Abstract

Background: Severe asthma (SA) encompasses several clinical phenotypes with a heterogeneous airway microbiome. We determined the phenotypes associated with a low α-diversity microbiome.

Methods: Metagenomic sequencing was performed on sputum samples from SA participants. A threshold of 2 standard deviations below the mean of α-diversity of mild-moderate asthma and healthy control subjects was used to define those with an abnormal abundance threshold as relative dominant species (RDS).

Findings: Fifty-one out of 97 SA samples were classified as RDSs with Haemophilus influenzae RDS being most common (n = 16), followed by Actinobacillus unclassified (n = 10), Veillonella unclassified (n = 9), Haemophilus aegyptius (n = 9), Streptococcus pseudopneumoniae (n = 7), Propionibacterium acnes (n = 5), Moraxella catarrhalis (= 5) and Tropheryma whipplei (n = 5). Haemophilus influenzae RDS had the highest duration of disease, more exacerbations in previous year and greatest number on daily oral corticosteroids. Hierarchical clustering of RDSs revealed a C2 cluster (n = 9) of highest relative abundance of exclusively Haemophilus influenzae RDSs with longer duration of disease and higher sputum neutrophil counts associated with enrichment pathways of MAPK, NF-κB, TNF, mTOR and necroptosis, compared to the only other cluster, C1, which consisted of 7 Haemophilus influenzae RDSs out of 42. Sputum transcriptomics of C2 cluster compared to C1 RDSs revealed higher expression of neutrophil extracellular trap pathway (NETosis), IL6-transignalling signature and neutrophil activation.

Conclusion: We describe a Haemophilus influenzae cluster of the highest relative abundance associated with neutrophilic inflammation and NETosis indicating a host response to the bacteria. This phenotype of severe asthma may respond to specific antibiotics.

Keywords

α-diversity / Haemophilus influenzae / metagenome / Moraxella catarrhalis / neutrophils / severe asthma / Tropheryma whipplei

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Ali Versi, Adnan Azim, Fransiskus Xaverius Ivan, Mahmoud I Abdel-Aziz, Stewart Bates, John Riley, Mohib Uddin, Nazanin Zounemat Kermani, Anke-H Maitland-Van Der Zee, Sven-Eric Dahlen, Ratko Djukanovic, Sanjay H Chotirmall, Peter Howarth, Ian M Adcock, Kian Fan Chung. A severe asthma phenotype of excessive airway Haemophilus influenzae relative abundance associated with sputum neutrophilia. Clinical and Translational Medicine, 2024, 14(9): e70007 DOI:10.1002/ctm2.70007

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References

[1]

Schofield JPR, Burg D, Nicholas B, et al. Stratification of asthma phenotypes by airway proteomic signatures. J Allergy Clin Immunol. 2019; 144(1): 70-82.

[2]

Kuo CHS, Pavlidis S, Loza M, et al. T-helper cell type 2 (Th2) and non-Th2 molecular phenotypes of asthma using sputum transcriptomics in U-BIOPRED. Eur Respir J. 2017; 49(2): 1602135.

[3]

Abdel-Aziz MI, Vijverberg SJH, Neerincx AH, et al. A multi-omics approach to delineate sputum microbiome-associated asthma inflammatory phenotypes. Eur Respir J. 2022; 59(1): 2102603.

[4]

Taylor SL, Leong LEX, Choo JM. Inflammatory phenotypes in patients with severe asthma are associated with distinct airway microbiology. J Allergy Clin Immunol. 2018; 141(1): 94-103.

[5]

Versi A, Ivan FX, Abdel-Aziz MI, et al. Haemophilus influenzae and Moraxella catarrhalis in sputum of severe asthma with inflammasome and neutrophil activation. Allergy. 2023; 78(11): 2906-2920.

[6]

Simpson JL, Daly J, Baines KJ, et al. Airway dysbiosis: Haemophilus influenzae and Tropheryma in poorly controlled asthma. Eur Respir J. 2016; 47(3): 792-800.

[7]

Diver S, Richardson M, Haldar K, et al. Sputum microbiomic clustering in asthma and chronic obstructive pulmonary disease reveals a Haemophilus-predominant subgroup. Allergy. 2020; 75(4): 808-817.

[8]

Juneau RA, Pang B, Armbruster CE, Murrah KA, Perez AC, Swords WE. Peroxiredoxin-glutaredoxin and catalase promote resistance of nontypeable Haemophilus influenzae 86-028NP to oxidants and survival within neutrophil extracellular traps. Infect Immun. 2015; 83(1): 239-246.

[9]

Shaw DE, Sousa AR, Fowler SJ, Fleming LJ, Roberts G, Corfield J. Clinical and inflammatory characteristics of the European U-BIOPRED adult severe asthma cohort. Eur Respir J. 2015; 46(5): 1308-1321.

[10]

Avalos-Fernandez M, Alin T, Métayer C, Thiébaut R, Enaud R, Delhaes L. The respiratory microbiota alpha-diversity in chronic lung diseases: first systematic review and meta-analysis. Respir Res. 2022; 23(1): 214.

[11]

Irizarry RA, Hobbs B, Collin F, et al. Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostat Oxf Engl. 2003; 4(2): 249-264.

[12]

Truong DT, Franzosa EA, Tickle TL, Scholz M, Weingart G, Pasolli E. MetaPhlAn2 for enhanced metagenomic taxonomic profiling. Nat Methods. 2015; 12(10): 902-903.

[13]

Franzosa EA, McIver LJ, Rahnavard G, et al. Species-level functional profiling of metagenomes and metatranscriptomes. Nat Methods. 2018; 15(11): 962-968.

[14]

Morgat A, Coissac E, Coudert E, et al. UniPathway: a resource for the exploration and annotation of metabolic pathways. Nucleic Acids Res. 2012; 40: D761-D769.

[15]

UniProt: a hub for protein information. Nucleic Acids Res. 2015; 43(Database issue): D204-D212.

[16]

Aitchison J. The statistical analysis of compositional data. Journal of the Royal Statistical Society. Series B (Methodological), 1982;44:139–177.

[17]

Gloor GB, Macklaim JM, Pawlowsky-Glahn V, Egozcue JJ. Microbiome datasets are compositional: and this is not optional. Front Microbiol. 2017; 8: 2224.

[18]

Rousseeuw PJ. Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J Comput Appl Math. 1987; 20: 53-65.

[19]

Caliński T, Harabasz J. A dendrite method for cluster analysis. Commun Stat. 1974; 3(1): 1-27.

[20]

Monti S, Tamayo P, Mesirov J, Golub T. Consensus clustering: a resampling-based method for class discovery and visualization of gene expression microarray data. Machine Learn. 2003; 52: 91-118.

[21]

Wilkerson MD, Hayes DN. ConsensusClusterPlus: a class discovery tool with confidence assessments and item tracking. Bioinformatics. 2010; 26(12): 1572-1573.

[22]

Hänzelmann S, Castelo R, Guinney J. GSVA: gene set variation analysis for microarray and RNA-Seq data. BMC Bioinformatics. 2013; 14(1): 1-15.

[23]

team T pandas development. pandas-dev/pandas: Pandas [Internet]. Zenodo; 2020. doi:10.5281/zenodo.3509134

[24]

Van Rossum G, Drake Jr FL. Python reference manual. Centrum voor Wiskunde en Informatica Amsterdam; 1995.

[25]

R Core Team. R: A Language and Environment for Statistical Computing [Internet]. R Foundation for Statistical Computing; 2021. Available from: https://www.R-project.org/

[26]

Lin H, Peddada SD. Analysis of compositions of microbiomes with bias correction. Nat Commun. 2020; 11(1): 3514.

[27]

Kuo CS, Pavlidis S, Loza M, Baribaud F, Rowe A. Pandis I. A transcriptome-driven analysis of epithelial brushings and bronchial biopsies to define asthma phenotypes in U-BIOPRED. Am J Respir Crit Care Med. 2017; 195(4): 443-455.

[28]

Smyth GK. limma: linear models for microarray data. In: Gentleman R, Carey VJ, Huber W, Irizarry RA, Dudoit S, eds. Bioinformatics and Computational Biology Solutions Using R and Bioconductor [Internet]. Springer; 2005: 397-420. doi:10.1007/0-387-29362-0_23. [cited 2022 Sep 26]. p.. (Statistics for Biology and Health). Available from:.

[29]

Yu G, Wang LG, Han Y, He QY. clusterProfiler: an R package for comparing biological themes among gene clusters. Omics J Integr Biol. 2012; 16(5): 284-287.

[30]

de Bont CM, Boelens WC, Pruijn GJM. NETosis, complement, and coagulation: a triangular relationship. Cell Mol Immunol. 2019; 16(1): 19-27.

[31]

Jevnikar Z, Östling J, Ax E, et al. Epithelial IL-6 trans-signaling defines a new asthma phenotype with increased airway inflammation. J Allergy Clin Immunol. 2019; 143(2): 577-590.

[32]

Zhang H, Nestor CE, Zhao S, et al. Profiling of human CD4+ T-cell subsets identifies the TH2-specific noncoding RNA GATA3-AS1. J Allergy Clin Immunol. 2013; 132(4): 1005-1008.

[33]

Russell GJ, Brooks TM, McKinney MM, Anderson CG. Present and future taxonomic selectivity in bird and mammal extinctions. Conserv Biol. 1998; 12(6): 1365-1376.

[34]

Purvis A, Agapow PM, Gittleman JL, Mace GM. Nonrandom extinction and the loss of evolutionary history. Science. 2000; 288(5464): 328-330.

[35]

Leitão RP, Zuanon J, Villéger S, et al. Rare species contribute disproportionately to the functional structure of species assemblages. Proc Biol Sci. 2016; 283(1828): 20160084.

[36]

Bracken MES, Low NHN. Realistic losses of rare species disproportionately impact higher trophic levels. Ecol Lett. 2012 May; 15(5): 461-467.

[37]

Baddal B, Muzzi A, Censini S, et al. Dual RNA-seq of nontypeable Haemophilus influenzae and host cell transcriptomes reveals novel insights into host-pathogen cross talk. mBio. 2015; 6(6). e01765-15.

[38]

Andrews CS, Miyata M, Susuki-Miyata S, Lee BC, Komatsu K, Li JD. Nontypeable Haemophilus influenzae-induced MyD88 short expression is regulated by positive IKKβ and CREB pathways and negative ERK1/2 pathway. PLoS One. 2015; 10(12): e0144840.

[39]

Brinkmann V, Reichard U, Goosmann C, et al. Neutrophil extracellular traps kill bacteria. Science. 2004; 303(5663): 1532-1535.

[40]

Uddin M, Watz H, Malmgren A, Pedersen F. NETopathic inflammation in chronic obstructive pulmonary disease and severe asthma. Front Immunol. 2019; 10: 47.

[41]

Winslow S, Odqvist L, Diver S, et al. Multi-omics links IL-6 trans-signalling with neutrophil extracellular trap formation and Haemophilus infection in COPD. Eur Respir J. 2021; 58(4): 2003312.

[42]

Pedersen F, Waschki B, Marwitz S, et al. Neutrophil extracellular trap formation is regulated by CXCR2 in COPD neutrophils. Eur Respir J. 2018; 51(4): 1700970.

[43]

López-López N, Euba B, Hill J, et al. Haemophilus influenzae glucose catabolism leading to production of the immunometabolite acetate has a key contribution to the host airway-pathogen interplay. ACS Infect Dis. 2020; 6(3): 406-421.

[44]

Garnett JP, Nguyen TT, Moffatt JD, et al. Proinflammatory mediators disrupt glucose homeostasis in airway surface liquid. J Immunol Baltim Md 1950. 2012; 189(1): 373-380.

[45]

de Vries SPW, Eleveld MJ, Hermans PWM, Bootsma HJ. Characterization of the molecular interplay between Moraxella catarrhalis and human respiratory tract epithelial cells. PloS One. 2013; 8(8): e72193.

[46]

Slevogt H, Schmeck B, Jonatat C, et al. Moraxella catarrhalis induces inflammatory response of bronchial epithelial cells via MAPK and NF-κB activation and histone deacetylase activity reduction. Am J Physiol-Lung Cell Mol Physiol. 2006; 290(5): L818-L826.

[47]

Rigauts C, Aizawa J, Taylor SL, et al. R othia mucilaginosa is an anti-inflammatory bacterium in the respiratory tract of patients with chronic lung disease. Eur Respir J. 2022; 59(5): 2101293.

[48]

Arrieta MC, Stiemsma LT, Dimitriu PA, et al. Early infancy microbial and metabolic alterations affect risk of childhood asthma. Sci Transl Med. 2015; 7(307): 307ra152.

[49]

Brown MA, Jabeen M, Bharj G, Hinks TSC. Non-typeable Haemophilus influenzae airways infection: the next treatable trait in asthma? Eur Respir Rev. 2022; 31(165): 220008.

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2024 The Author(s). Clinical and Translational Medicine published by John Wiley & Sons Australia, Ltd on behalf of Shanghai Institute of Clinical Bioinformatics.

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