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

Background: Eosinophilic and neutrophilic asthma defined by high levels of blood and sputum eosinophils and neutrophils exemplifies the inflammatory heterogeneity of asthma, particularly severe asthma. We analysed the serum and sputum proteome to identify biomarkers jointly associated with these different phenotypes., Methods: Proteomic profiles (N = 1129 proteins) were assayed in sputum (n = 182) and serum (n = 574) from two cohorts (U-BIOPRED and ADEPT) of mild-moderate and severe asthma by SOMAscan. Using least absolute shrinkage and selection operator (LASSO)-penalised logistic regression in a stability selection framework, we sought sparse sets of proteins associated with either eosinophilic or neutrophilic asthma with and without adjustment for established clinical factors including oral corticosteroid use and forced expiratory volume., Findings: We identified 13 serum proteins associated with eosinophilic asthma, including 7 (PAPP-A, TARC/CCL17, ALT/GPT, IgE, CCL28, CO8A1, and IL5-Rα) that were stably selected while adjusting for clinical factors yielding an AUC of 0.84 (95% CI: 0.83-0.84) compared to 0.62 (95% CI: 0.61-0.63) for clinical factors only. Sputum protein analysis selected only PAPP-A (AUC = 0.81 [95% CI: 0.80-0.81]). 12 serum proteins were associated with neutrophilic asthma, of which 5 (MMP-9, EDAR, GIIE/PLA2G2E, IL-1-R4/IL1RL1, and Elafin) complemented clinical factors increasing the AUC from 0.63 (95% CI: 0.58-0.67) for the model with clinical factors only to 0.89 (95% CI: 0.89-0.90). Our model did not select any sputum proteins associated with neutrophilic status., Interpretation: Targeted serum proteomic profiles are a non-invasive and scalable approach for subtyping of neutrophilic and eosinophilic asthma and for future functional understanding of these phenotypes., Funding: U-BIOPRED has received funding from the Innovative Medicines Initiative (IMI) Joint Undertaking under grant agreement no. 115010, resources of which are composed of financial contributions from the European Union's Seventh Framework Programme (FP7/2007-2013), and European Federation of Pharmaceutical Industries and Associations (EFPIA) companies' in-kind contributions (www.imi.europa.eu). ADEPT was funded by Johnson & Johnson/Janssen pharmaceutical Company., Competing Interests: Declaration of interests Prof M Chadeau-Hyam holds shares in the O-SMOSE company; consulting activities conducted by the company are independent of the present work. Prof Adcock reports consulting fees from GSK, Sanofi, Chiesi and Kinaset; speaker fees from AZ, Sanofi, Eurodrug and Sunovion; travel support from AZ; grants from GSK, MRC, EPSRC, Sanofi and NIEHS, which were independent of the present work. Dr. Dahlén reports consulting fees from Affiboby, AZ, Cayman Chemicals, GSK and Regeneron, and speaker fees from AZ, GSK and Sanofi, outside the submitted work. Prof Chung has received speaker fees from Novartis, AZ and Merck; honoraria for participating in Advisory Board meetings of GSK, Novartis, Roche, Merck, Trevi, Rickett-Beckinson, Nocion and Shionogi; and has received grants from MRC, EPSRC and GSK. Prof Chung is a member of the Scientific Advisory Board of the Clean Breathing Institute supported by Haleon. Dr Djukanovic declares consulting fees from Synairgen plc and lecture fees from GSK, ZenasBio and Celltrion. He holds shares from Synairgen and is Chair of the European Respiratory Society's Clinical collaboration on severe asthma (SHARP). The other authors have no conflict of interest to disclose., (Copyright © 2023 The Author(s). Published by Elsevier B.V. All rights reserved.)

Multi-omics data integration is a complex and challenging task in biomedical research. Consensus clustering, also known as meta-clustering or cluster ensembles, has become an increasingly popular downstream tool for phenotyping and endotyping using multiple omics and clinical data. However, current consensus clustering methods typically rely on ensembling clustering outputs with similar sample coverages (mathematical replicates), which may not reflect real-world data with varying sample coverages (biological replicates). To address this issue, we propose a new consensus clustering with missing labels (ccml) strategy termed ccml, an R protocol for two-step consensus clustering that can handle unequal missing labels (i.e. multiple predictive labels with different sample coverages). Initially, the regular consensus weights are adjusted (normalized) by sample coverage, then a regular consensus clustering is performed to predict the optimal final cluster. We applied the ccml method to predict molecularly distinct groups based on 9-omics integration in the Karolinska COSMIC cohort, which investigates chronic obstructive pulmonary disease, and 24-omics handprint integrative subgrouping of adult asthma patients of the U-BIOPRED cohort. We propose ccml as a downstream toolkit for multi-omics integration analysis algorithms such as Similarity Network Fusion and robust clustering of clinical data to overcome the limitations posed by missing data, which is inevitable in human cohorts consisting of multiple data modalities. The ccml tool is available in the R language (https://CRAN.R-project.org/package=ccml, https://github.com/pulmonomics-lab/ccml, or https://github.com/ZhoulabCPH/ccml)., (© The Author(s) 2024. Published by Oxford University Press.)

Rationale: Patients with severe asthma are dependent upon treatment with high doses of inhaled corticosteroids (ICS) and often also oral corticosteroids (OCS). The extent of endogenous androgenic anabolic steroid (EAAS) suppression in asthma has not previously been described in detail. The objective of the present study was to measure urinary concentrations of EAAS in relation to exogenous corticosteroid exposure., Methods: Urine collected at baseline in the U-BIOPRED (Unbiased Biomarkers for the Prediction of Respiratory Disease outcomes) study of severe adult asthmatics (SA, n=408) was analysed by quantitative mass spectrometry. Data were compared to that of mild-to-moderate asthmatics (MMA, n=70) and healthy subjects (HC, n=98) from the same study., Measurements and Main Results: The concentrations of urinary endogenous steroid metabolites were substantially lower in SA than in MMA or HC. These differences were more pronounced in SA patients with detectable urinary OCS metabolites. Their dehydroepiandrosterone sulfate (DHEA-S) concentrations were <5% of those in HC, and cortisol concentrations were below the detection limit in 75% of females and 82% of males. The concentrations of EAAS in OCS-positive patients, as well as patients on high-dose ICS only, were more suppressed in females than males (p<0.05). Low levels of DHEA were associated with features of more severe disease and were more prevalent in females (p<0.05). The association between low EAAS and corticosteroid treatment was replicated in 289 of the SA patients at follow-up after 12-18 months., Conclusion: The pronounced suppression of endogenous anabolic androgens in females might contribute to sex differences regarding the prevalence of severe asthma., Competing Interests: Conflict of interest: V. Yasinska reports participation in advisory boards for AZ and GSK, and lecture honoraria from Sanofi and GSK. Conflict of interest: C. Gómez has nothing to disclose. Conflict of interest: J. Kolmert has nothing to disclose. Conflict of interest: M. Ericsson has nothing to disclose. Conflict of interest: A. Pohanka has nothing to disclose. Conflict of interest: A. James reports personal grant from Swedish Heart-Lung Foundation. Conflict of interest: L.I. Andersson has nothing to disclose. Conflict of interest: M. Sparreman-Mikus has nothing to disclose. Conflict of interest: A.R. Sousa reports employment and stocks or stock options from GSK. Conflict of interest: J.H. Riley has nothing to disclose. Conflict of interest: S. Bates has nothing to disclose. Conflict of interest: P.S. Bakke reports lecture honoraria from AstraZeneca and Boehringer Ingelheim. Conflict of interest: N. Zounemat Kermani has nothing to disclose. Conflict of interest: M. Caruso has nothing to disclose. Conflict of interest: P. Chanez reports participation in advisory boards, honoraria for consultancy, lectures fees and support for attending and/or travel from ALK, Almirall, AZ, Chiesi, GSK, Menarini, Novartis and Sanofi. Conflict of interest: S.J. Fowler has nothing to disclose. Conflict of interest: T. Geiser has nothing to disclose. Conflict of interest: P.H. Howarth reports employment and stocks or stock options from GSK. Conflict of interest: I. Horváth reports participation on an advisory board for AZ and Chiesi, honoraria for lectures from Chiesi and Roche, and support for attending and/or travel from Roche. Conflict of interest: N. Krug has nothing to disclose. Conflict of interest: P. Montuschi has nothing to disclose. Conflict of interest: M. Sanak has nothing to disclose. Conflict of interest: A. Behndig has nothing to disclose. Conflict of interest: D.E. Shaw has nothing to disclose. Conflict of interest: R.G. Knowles has nothing to disclose. Conflict of interest: B. Dahlén reports grant from GSK and Novartis. Conflict of interest: A-H. Maitland-van der Zee reports grants from BI, Vertex Innovation Award, Dutch Lung Foundation, Stichting Astma Bestrijding, IMI/3TR, EU grant ONELAB and EUROSTARS grant with Respiq, consulting fees from AZ and BI, and lecture honoraria from GSK. Conflict of interest: P.J. Sterk reports a grant from the Innovative Medicines Initiative. Conflict of interest: R. Djukanovic reports consulting fees from Synairgen, lecture honoraria from Regeneron, GSK and Kymab, and an advisory board for Synairgen. Conflict of interest: I.M. Adcock reports grant from EU-IMI, grants from GSK, MRK, EPSRC and Sanofi, consulting fees from GSK, Sanofi, Chiesi and Kinaset, lecture honoraria from AZ, Sanofi and Eurodrug, and payment for an educational event from Sunovion. Conflict of interest: K.F. Chung reports lectures honoraria from Novartis, AZ and Merck, advisory boards for GSK, AZ, Novartis, Roche, Merck, Rickett-Beckinson, Nocion and Shionogi, the Scientific Advisory Board of the Clean Breathing Institute supported by Haleon, grants from GSK, MRC and EPSRC, and support for travel from AZ. Conflict of interest: C.E. Wheelock has nothing to disclose. Conflict of interest: S-E. Dahlén reports research grants, consulting fees or lecture honoraria from AZ, Cayman Chemicals, GSK, Regeneron, Sanofi and Teva. Conflict of interest: E. Wikström Jonsson reports a research grant and expert assignment by Region Stockholm, and an expert appointment by the Swedish Medical Product Agency., (Copyright ©The authors 2023.)