. 2022 Jun 30;59(6):2101733.

doi: 10.1183/13993003.01733-2021. Print 2022 Jun.

Urinary metabotype of severe asthma evidences decreased carnitine metabolism independent of oral corticosteroid treatment in the U-BIOPRED study

Stacey N Reinke^{1

2

3}, Shama Naz^{1

3}, Romanas Chaleckis^{1

4}, Hector Gallart-Ayala¹, Johan Kolmert^{1

5}, Nazanin Z Kermani⁶, Angelica Tiotiu^{6

7}, David I Broadhurst², Anders Lundqvist⁸, Henric Olsson⁹, Marika Ström^{10

11}, Åsa M Wheelock^{10

11}, Cristina Gómez^{1

5}, Magnus Ericsson¹², Ana R Sousa¹³, John H Riley¹³, Stewart Bates¹³, James Scholfield¹⁴, Matthew Loza¹⁵, Frédéric Baribaud¹⁵, Per S Bakke¹⁶, Massimo Caruso¹⁷, Pascal Chanez¹⁸, Stephen J Fowler¹⁹, Thomas Geiser²⁰, Peter Howarth¹⁴, Ildikó Horváth²¹, Norbert Krug²², Paolo Montuschi²³, Annelie Behndig²⁴, Florian Singer²⁵, Jacek Musial²⁶, Dominick E Shaw²⁷, Barbro Dahlén¹¹, Sile Hu²⁸, Jessica Lasky-Su²⁹, Peter J Sterk³⁰, Kian Fan Chung⁶, Ratko Djukanovic¹⁴, Sven-Erik Dahlén^{5

11}, Ian M Adcock⁶, Craig E Wheelock^{31

4

11}; U-BIOPRED Study Group

Affiliations

¹ Division of Physiological Chemistry 2, Dept of Medical Biochemistry and Biophysics, Karolinska Institute, Stockholm, Sweden.
² Centre for Integrative Metabolomics and Computational Biology, School of Science, Edith Cowan University, Perth, Australia.
³ Equal contribution.
⁴ Gunma Initiative for Advanced Research (GIAR), Gunma University, Maebashi, Japan.
⁵ The Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden.
⁶ National Heart and Lung Institute, Imperial College, London, UK.
⁷ Dept of Pulmonology, University Hospital of Nancy, Nancy, France.
⁸ DMPK, Research and Early Development, Respiratory and Immunology, BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden.
⁹ Translational Science and Experimental Medicine, Research and Early Development, Respiratory and Immunology, BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden.
¹⁰ Respiratory Medicine Unit, K2 Dept of Medicine Solna and Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden.
¹¹ Dept of Respiratory Medicine and Allergy, Karolinska University Hospital, Stockholm, Sweden.
¹² Dept of Clinical Pharmacology, Karolinska Institutet and Karolinska University Hospital, Stockholm, Sweden.
¹³ GlaxoSmithKline, London, UK.
¹⁴ Faculty of Medicine, Southampton University and NIHR Southampton Respiratory Biomedical Research Centre, University Hospital Southampton, Southampton, UK.
¹⁵ Janssen Research and Development, High Wycombe, UK.
¹⁶ Institute of Medicine, University of Bergen, Bergen, Norway.
¹⁷ Dept of Biomedical and Biotechnological Sciences and Dept of Clinical and Experimental Medicine, University of Catania, Catania, Italy.
¹⁸ Assistance Publique des Hôpitaux de Marseille, Clinique des Bronches, Allergies et Sommeil, Aix Marseille Université, Marseille, France.
¹⁹ Division of Infection, Immunity and Respiratory Medicine, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, and Manchester Academic Health Science Centre and NIHR Biomedical Research Centre, Manchester University Hospitals NHS Foundation Trust, Manchester, UK.
²⁰ Dept of Pulmonary Medicine, University Hospital, University of Bern, Bern, Switzerland.
²¹ Dept of Pulmonology, Semmelweis University, Budapest, Hungary.
²² Fraunhofer Institute for Toxicology and Experimental Medicine, Hannover, Germany.
²³ Pharmacology, Catholic University of the Sacred Heart, Rome, Italy.
²⁴ Dept of Public Health and Clinical Medicine, Section of Medicine, Umeå University, Umeå, Sweden.
²⁵ Division of Paediatric Respiratory Medicine and Allergology, Dept of Paediatrics, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.
²⁶ Dept of Medicine, Jagiellonian University Medical College, Krakow, Poland.
²⁷ Nottingham NIHR Biomedical Research Centre, University of Nottingham, Nottingham, UK.
²⁸ Data Science Institute, Imperial College, London, UK.
²⁹ Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
³⁰ Dept of Respiratory Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands.
³¹ Division of Physiological Chemistry 2, Dept of Medical Biochemistry and Biophysics, Karolinska Institute, Stockholm, Sweden craig.wheelock@ki.se.

PMID: 34824054
PMCID: PMC9245194
DOI: 10.1183/13993003.01733-2021

Urinary metabotype of severe asthma evidences decreased carnitine metabolism independent of oral corticosteroid treatment in the U-BIOPRED study

Stacey N Reinke et al. Eur Respir J. 2022.

. 2022 Jun 30;59(6):2101733.

doi: 10.1183/13993003.01733-2021. Print 2022 Jun.

Authors

Affiliations

¹ Division of Physiological Chemistry 2, Dept of Medical Biochemistry and Biophysics, Karolinska Institute, Stockholm, Sweden.
² Centre for Integrative Metabolomics and Computational Biology, School of Science, Edith Cowan University, Perth, Australia.
³ Equal contribution.
⁴ Gunma Initiative for Advanced Research (GIAR), Gunma University, Maebashi, Japan.
⁵ The Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden.
⁶ National Heart and Lung Institute, Imperial College, London, UK.
⁷ Dept of Pulmonology, University Hospital of Nancy, Nancy, France.
⁸ DMPK, Research and Early Development, Respiratory and Immunology, BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden.
⁹ Translational Science and Experimental Medicine, Research and Early Development, Respiratory and Immunology, BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden.
¹⁰ Respiratory Medicine Unit, K2 Dept of Medicine Solna and Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden.
¹¹ Dept of Respiratory Medicine and Allergy, Karolinska University Hospital, Stockholm, Sweden.
¹² Dept of Clinical Pharmacology, Karolinska Institutet and Karolinska University Hospital, Stockholm, Sweden.
¹³ GlaxoSmithKline, London, UK.
¹⁴ Faculty of Medicine, Southampton University and NIHR Southampton Respiratory Biomedical Research Centre, University Hospital Southampton, Southampton, UK.
¹⁵ Janssen Research and Development, High Wycombe, UK.
¹⁶ Institute of Medicine, University of Bergen, Bergen, Norway.
¹⁷ Dept of Biomedical and Biotechnological Sciences and Dept of Clinical and Experimental Medicine, University of Catania, Catania, Italy.
¹⁸ Assistance Publique des Hôpitaux de Marseille, Clinique des Bronches, Allergies et Sommeil, Aix Marseille Université, Marseille, France.
¹⁹ Division of Infection, Immunity and Respiratory Medicine, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, and Manchester Academic Health Science Centre and NIHR Biomedical Research Centre, Manchester University Hospitals NHS Foundation Trust, Manchester, UK.
²⁰ Dept of Pulmonary Medicine, University Hospital, University of Bern, Bern, Switzerland.
²¹ Dept of Pulmonology, Semmelweis University, Budapest, Hungary.
²² Fraunhofer Institute for Toxicology and Experimental Medicine, Hannover, Germany.
²³ Pharmacology, Catholic University of the Sacred Heart, Rome, Italy.
²⁴ Dept of Public Health and Clinical Medicine, Section of Medicine, Umeå University, Umeå, Sweden.
²⁵ Division of Paediatric Respiratory Medicine and Allergology, Dept of Paediatrics, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.
²⁶ Dept of Medicine, Jagiellonian University Medical College, Krakow, Poland.
²⁷ Nottingham NIHR Biomedical Research Centre, University of Nottingham, Nottingham, UK.
²⁸ Data Science Institute, Imperial College, London, UK.
²⁹ Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
³⁰ Dept of Respiratory Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands.
³¹ Division of Physiological Chemistry 2, Dept of Medical Biochemistry and Biophysics, Karolinska Institute, Stockholm, Sweden craig.wheelock@ki.se.

PMID: 34824054
PMCID: PMC9245194
DOI: 10.1183/13993003.01733-2021

Abstract

Introduction: Asthma is a heterogeneous disease with poorly defined phenotypes. Patients with severe asthma often receive multiple treatments including oral corticosteroids (OCS). Treatment may modify the observed metabotype, rendering it challenging to investigate underlying disease mechanisms. Here, we aimed to identify dysregulated metabolic processes in relation to asthma severity and medication.

Methods: Baseline urine was collected prospectively from healthy participants (n=100), patients with mild-to-moderate asthma (n=87) and patients with severe asthma (n=418) in the cross-sectional U-BIOPRED cohort; 12-18-month longitudinal samples were collected from patients with severe asthma (n=305). Metabolomics data were acquired using high-resolution mass spectrometry and analysed using univariate and multivariate methods.

Results: A total of 90 metabolites were identified, with 40 significantly altered (p<0.05, false discovery rate <0.05) in severe asthma and 23 by OCS use. Multivariate modelling showed that observed metabotypes in healthy participants and patients with mild-to-moderate asthma differed significantly from those in patients with severe asthma (p=2.6×10^-20), OCS-treated asthmatic patients differed significantly from non-treated patients (p=9.5×10^-4), and longitudinal metabotypes demonstrated temporal stability. Carnitine levels evidenced the strongest OCS-independent decrease in severe asthma. Reduced carnitine levels were associated with mitochondrial dysfunction via decreases in pathway enrichment scores of fatty acid metabolism and reduced expression of the carnitine transporter SLC22A5 in sputum and bronchial brushings.

Conclusions: This is the first large-scale study to delineate disease- and OCS-associated metabolic differences in asthma. The widespread associations with different therapies upon the observed metabotypes demonstrate the need to evaluate potential modulating effects on a treatment- and metabolite-specific basis. Altered carnitine metabolism is a potentially actionable therapeutic target that is independent of OCS treatment, highlighting the role of mitochondrial dysfunction in severe asthma.

Trial registration: ClinicalTrials.gov NCT01976767.

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Conflict of interest statement

Conflict of interest: S.N. Reinke reports grants from Canadian Institutes of Health Research, during the conduct of the study. Conflict of interest: S. Naz has nothing to disclose. Conflict of interest: R. Chaleckis has nothing to disclose. Conflict of interest: H. Gallart-Ayala has nothing to disclose. Conflict of interest: J. Kolmert reports personal fees for consultancy from Gesynta Pharma AB, outside the submitted work. Conflict of interest: N.Z. Kermani has nothing to disclose. Conflict of interest: A. Tiotiu has nothing to disclose. Conflict of interest: D.I. Broadhurst has nothing to disclose. Conflict of interest: A. Lundqvist has nothing to disclose. Conflict of interest: H. Olsson is an employee and shareholder of AstraZeneca. Conflict of interest: M. Ström has nothing to disclose. Conflict of interest: Å.M. Wheelock has nothing to disclose. Conflict of interest: C. Gómez has nothing to disclose. Conflict of interest: M. Ericsson has nothing to disclose. Conflict of interest: A.R. Sousa has nothing to disclose. Conflict of interest: J.H. Riley works for and own shares in GlaxoSmithKline. Conflict of interest: S. Bates is an employee of Johnson & Johnson and has previously worked for and holds stock in GlaxoSmithKline. Conflict of interest: J. Scholfield reports grants from Innovative Medicines Initiative, during the conduct of the study; and is director and employee of TopMD Precision Medicine Ltd. Conflict of interest: M. Loza is an employee of and owns stock in Johnson & Johnson. Conflict of interest: F. Baribaud is a shareholder of Johnson & Johnson and a current employee of Bristol Myers Squibb. Conflict of interest: P.S. Bakke reports personal fees for advisory board work and lectures from AstraZeneca, and personal fees for lectures from Novartis and Boehringer Ingelheim, outside the submitted work. Conflict of interest: M. Caruso has nothing to disclose. Conflict of interest: P. Chanez reports grants and personal fees from AstraZeneca, ALK, Boehringer Ingelheim, Chiesi, Sanofi-Aventis, Novartis and GlaxoSmithKline, outside the submitted work. Conflict of interest: S.J. Fowler reports personal fees from AstraZeneca, Novartis, TEVA and Chiesi, outside the submitted work. Conflict of interest: T. Geiser has nothing to disclose. Conflict of interest: P. Howarth has nothing to disclose. Conflict of interest: I. Horvath has nothing to disclose. Conflict of interest: N. Krug has nothing to disclose. Conflict of interest: P. Montuschi has nothing to disclose. Conflict of interest: A. Behndig has nothing to disclose. Conflict of interest: F. Singer reports personal fees from Vertex Pharmaceuticals (CH) and Novartis, outside the submitted work. Conflict of interest: J. Musial has nothing to disclose. Conflict of interest: D.E. Shaw has nothing to disclose. Conflict of interest: B. Dahlén reports personal fees for advisory board work and lectures from AstraZeneca, TEVA and Sanofi, and grants from Novartis and GlaxoSmithKline, outside the submitted work. Conflict of interest: S. Hu has nothing to disclose. Conflict of interest: J. Lasky-Su has nothing to disclose. Conflict of interest: P.J. Sterk reports a public private grant from the Innovative Medicines Initiative (IMI) covered by the EU and EFPIA, during the conduct of the study. Conflict of interest: K.F. Chung has received honoraria for participating in advisory board meetings of GlaxoSmithKline, AstraZeneca, Roche, Novartis, Merck, Nocion and Shionogi regarding treatments for asthma, COPD and chronic cough and has also been remunerated for speaking engagements. Conflict of interest: R. Djukanovic reports receiving fees for lectures at symposia organised by Novartis, AstraZeneca and TEVA, consultation for TEVA and Novartis as member of advisory boards, and participation in a scientific discussion about asthma organised by GlaxoSmithKline; and is a co-founder and current consultant, and has shares in Synairgen, a University of Southampton spin out company. Conflict of interest: S-E. Dahlén reports personal fees for consultancy from AstraZeneca, Cayman Chemical, GlaxoSmithKline, Novartis, Merck, Regeneron, Sanofi and TEVA, outside the submitted work. Conflict of interest: I.M. Adcock has nothing to disclose. Conflict of interest: C.E. Wheelock has nothing to disclose.

Figures

**FIGURE 1**
Hierarchical cluster analysis (HCA) of metabolite abundances. HCA was performed using multivariate Spearman correlation distance metric and Ward's group linkage. a) Resulting metabolite clusters are presented as a polar dendrogram (differentially coloured and labelled as a to g). Black text: metabolites not significant in either univariate or multivariate analysis; red text: metabolites significant in univariate and/or multivariate analysis. *: p<0.05 univariate analysis; ^#: p<0.05, canonical variate 1 (CV1) (supplementary figure E1C). b) The mean (95% CI) of log-transformed and z-scaled data of the resulting clusters plotted against the clinical groups. HC: healthy controls; MMA: mild-to-moderate asthma; SAns: severe asthma non-smokers; SAs: severe asthma ex/smokers; _L: longitudinal data.

**FIGURE 2**
Principal components–canonical variate analysis (PC-CVA) with non-smoking patients with severe asthma stratified by oral corticosteroid (OCS) use. Cross validation showed that five principal components were the optimal number to use in the CVA model (supplementary figure E2). a) Scores plot of baseline data, labelled by clinical class and b) longitudinal data for severe asthma groups projected into the baseline model. Red: healthy controls (HC); yellow: mild-to-moderate asthma (MMA); green: severe asthma non-smokers (SAns); blue: severe asthma non-smokers taking OCS treatment (SAns+OCS); _L: longitudinal data; black cross: mean of each baseline group; black dot: mean of each longitudinal group; solid circles: 95% CI of the mean of baseline groups; dashed circles: 95% CI of the mean of longitudinal groups. c) Loadings plot displaying metabolites that significantly (p<0.05) contributed to the model. Metabolite position displays the magnitude and direction of effect in canonical variate (CV) 1 (x-axis) and CV2 (y-axis). The quadrant positions of metabolites are related to those of the clinical groups in the scores plots. In other words, metabolites are most abundant in the clinical groups with which they share a quadrant. Metabolites are colour-coded based on the corresponding cluster as identified in figure 1 and according to the figure legend.

**FIGURE 3**
Individual canonical variate (CV) loadings for the principal components–canonical variate analysis (PC-CVA) with non-smoking patients with severe asthma stratified by oral corticosteroid (OCS) use. Loadings plots for CV1 (left panel) and CV2 (right panel) are shown. Clinical group labels at the top of each panel reflect the group position along the CV axis, as described by the model; clinical groups were not combined for this analysis. Red: metabolites that significantly (p<0.05) contributed to separation in the CV based on 500 iterations of bootstrap resampling/remodelling; blue: metabolites that did not significantly contribute to the separation in the CV. Metabolites are ordered and colour-coded by cluster (figure 1). The cluster label is presented on the left side of the figure. SAns: severe asthma non-smokers; HC: healthy controls; MMA: mild-to-moderate asthma.

**FIGURE 4**
Molecular signatures of carnitine metabolism. Scatter-overlaid boxplots stratified by clinical class. a) Urinary carnitine composite variable. Relative abundances of carnitine, acetylcarnitine and propionylcarnitine were log-transformed, z-scaled and summed (p=4×10⁻⁹). b) Sputum fatty acid β-oxidation gene set variance analysis (GSVA) enrichment score (ES) (p=8.02×10⁻⁶). c) Sputum fatty acid metabolism GSVA ES (p=6.29×10⁻⁶). d) Sputum *SLC22A5* expression levels (p=5.69×10⁻⁵). e) Bronchial brushings (BB) *SLC22A5* expression levels (p=0.0583). Open circles: observations; box: median and interquartile range (IQR); whiskers: range of data up to 1.5 times of IQR above Q3 or below Q1; black cross: outliers. Kruskal–Wallis p-values are reported with *post hoc* pairwise comparisons shown on the figure. HC: healthy controls; MMA: mild-to-moderate asthma; SAns: severe asthma non-smokers; SAs: severe asthma ex/smokers; _L: longitudinal data; *: p<0.05; **: p<0.01; ***: p<0.001.

**FIGURE 5**
Relationship of *SLC22A5* gene expression levels with lung function and genotype. a) Correlation between the forced expiratory volume in 1 s (FEV₁)/forced vital capacity (FVC) ratio pre-salbutamol and sputum *SLC22A5* gene expression levels. b) Correlation between FEV₁ % predicted and sputum *SLC22A5* gene expression levels. All assumptions for parametric analysis were verified, thus Pearson correlation was used. Dots: observations; solid line: regression; dashed lines: 95% CIs of the regression. A weak linear correlation was also observed between the urinary carnitine composite and FEV₁ % predicted (r=0.15, p=1.43×10⁻⁴), but not FEV₁/FVC ratio pre-salbutamol (p=0.90). c) Relationship between sputum *SLC22A5* gene expression levels and genotype (effect allele C: β=0.234, sd=0.148, p=0.119; n=91). d) Relationship between bronchial brushing (BB) *SLC22A5* gene expression levels and genotype (effect allele C: β=0.138, sd=0.057, p=0.028; n=118). The p-value of the effect size/coefficient of genotype in the regression model was used to test if the single nucleotide polymorphism was significantly associated with gene expression. Open circles: observations; box: median and interquartile range (IQR); whiskers: range of data up to 1.5 times of IQR above Q3 or below Q1; black cross: outliers; HC: healthy controls; MMA: mild-to-moderate asthma; SAns: severe asthma non-smokers; SAs: severe asthma ex/smokers.

**FIGURE 6**
Biochemical pathways underlying severe asthma observed in the current study. a) Metabolism associated with oral corticosteroid (OCS) use. b) Carnitine metabolism. Green: OCS-associated alteration in severe asthma; pink: OCS-independent alteration in asthma; blue: OCS-associated, disease-independent alteration; orange: no change observed; grey: metabolites not detected in the current study; black: notes on metabolic reactions; yellow boxes: known pathogenic mechanisms of asthma. Arrows indicate direction of change. SAM: S-adenosylmethionine; AMD1: adenosylmethionine decarboxylase; dcSAM: decarboxylated SAM; ASM: airway smooth muscle; NMDA: N-methyl-D-aspartate; OCTN: organic cation transporter novel; CoA: conenzyme A; CPT: carnitine palmitoyltransferase; CAC: carnitine-acylcarnitine carrier. ^#: shift observed *via* multivariate analysis only.

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References

1. Tyler SR, Bunyavanich S. Leveraging -omics for asthma endotyping. J Allergy Clin Immunol 2019; 144: 13–23. doi: 10.1016/j.jaci.2019.05.015 - DOI - PMC - PubMed
1. Papi A, Brightling C, Pedersen SE, et al. Asthma. Lancet 2018; 391: 783–800. doi: 10.1016/S0140-6736(17)33311-1 - DOI - PubMed
1. Kaur R, Chupp G. Phenotypes and endotypes of adult asthma: moving toward precision medicine. J Allergy Clin Immunol 2019; 144: 1–12. doi: 10.1016/j.jaci.2019.05.031 - DOI - PubMed
1. Kolmert J, Gomez C, Balgoma D, et al. Urinary leukotriene E₄ and prostaglandin D₂ metabolites increase in adult and childhood severe asthma characterised by type 2 inflammation. A clinical observational study. Am J Respir Crit Care Med 2021; 203: 37–53. doi: 10.1164/rccm.201909-1869OC - DOI - PMC - PubMed
1. Kelly RS, Dahlin A, McGeachie MJ, et al. Asthma metabolomics and the potential for integrative omics in research and the clinic. Chest 2017; 151: 262–277. doi: 10.1016/j.chest.2016.10.008 - DOI - PMC - PubMed

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Urinary metabotype of severe asthma evidences decreased carnitine metabolism independent of oral corticosteroid treatment in the U-BIOPRED study

Affiliations

Urinary metabotype of severe asthma evidences decreased carnitine metabolism independent of oral corticosteroid treatment in the U-BIOPRED study

Authors

Affiliations

Abstract

Conflict of interest statement

Figures

References

Publication types

MeSH terms

Substances

Associated data

Grants and funding

LinkOut - more resources

Full Text Sources

Medical