Oxidative stress is believed to be a major driver of inflammation in smoking asthmatics. The U-BIOPRED project recruited a cohort of Severe Asthma smokers/ex-smokers (SAs/ex) and non-smokers (SAn) with extensive clinical and biomarker information enabling characterization of these subjects. We investigated oxidative stress in severe asthma subjects by analysing urinary 8-iso-PGF2α and the mRNA-expression of the main pro-oxidant (NOX2; NOSs) and anti-oxidant (SODs; CAT; GPX1) enzymes in the airways of SAs/ex and SAn. All the severe asthma U-BIOPRED subjects were further divided into current smokers with severe asthma (CSA), ex-smokers with severe asthma (ESA) and non-smokers with severe asthma (NSA) to deepen the effect of active smoking. Clinical data, urine and sputum were obtained from severe asthma subjects. A bronchoscopy to obtain bronchial biopsy and brushing was performed in a subset of subjects. The main clinical data were analysed for each subset of subjects (urine-8-iso-PGF2α; IS-transcriptomics; BB-transcriptomics; BBr-transcriptomics). Urinary 8-iso-PGF2α was quantified using mass spectrometry. Sputum, bronchial biopsy and bronchial brushing were processed for mRNA expression microarray analysis. Urinary 8-iso-PGF2α was increased in SAs/ex, median (IQR) = 31.7 (24.5–44.7) ng/mmol creatinine, compared to SAn, median (IQR) = 26.6 (19.6–36.6) ng/mmol creatinine (p< 0.001), and in CSA, median (IQR) = 34.25 (24.4–47.7), vs. ESA, median (IQR) = 29.4 (22.3–40.5), and NSA, median (IQR) = 26.5 (19.6–16.6) ng/mmol creatinine (p = 0.004). Sputum mRNA expression of NOX2 was increased in SAs/ex compared to SAn (probe sets 203922_PM_s_at fold-change = 1.05 p = 0.006; 203923_PM_s_at fold-change = 1.06, p = 0.003; 233538_PM_s_at fold-change = 1.06, p = 0.014). The mRNA expression of antioxidant enzymes were similar between the two severe asthma cohorts in all airway samples. NOS2 mRNA expression was decreased in bronchial brushing of SAs/ex compared to SAn (fold-change = -1.10; p = 0.029). NOS2 mRNA expression in bronchial brushing correlated with FeNO (Kendal’s Tau = 0.535; p< 0.001). From clinical and inflammatory analysis, FeNO was lower in CSA than in ESA in all the analysed subject subsets (p< 0.01) indicating an effect of active smoking. Results about FeNO suggest its clinical limitation, as inflammation biomarker, in severe asthma active smokers. These data provide evidence of greater systemic oxidative stress in severe asthma smokers as reflected by a significant changes of NOX2 mRNA expression in the airways, together with elevated urinary 8-iso-PGF2α in the smokers/ex-smokers group.
Trial registration ClinicalTrials.gov—Identifier: NCT01976767
Citation: Emma R, Bansal AT, Kolmert J, Wheelock CE, Dahlen S-E, Loza MJ, et al. (2018) Enhanced oxidative stress in smoking and ex-smoking severe asthma in the U-BIOPRED cohort. PLoS ONE 13(9): e0203874. https://doi.org/10.1371/journal.pone.0203874
Editor: Stelios Loukides, National and Kapodistrian University of Athens, GREECE
Received: March 15, 2018; Accepted: August 29, 2018; Published: September 21, 2018
Copyright: © 2018 Emma et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All relevant data are within the paper and its Supporting Information files. The U-BIOPRED data sets are published in GEO website: GSE76262 (IS data); GSE76225 (BB data); GSE76226 (BBr data).
Funding: U-BIOPRED is supported through an Innovative Medicines Initiative Joint Undertaking under grant agreement no. 115010, resources of which are composed of financial contribution from the European Union’s Seventh Framework Programme (FP7/2007–2013) and EFPIA companies’ in kind contribution (www.imi.europa.eu). We would also like to acknowledge help from the IMI funded eTRIKS project (EU Grant Code No.115446 to R.E.). Funding information for this article has been deposited with FundRef. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Acclarogen Ltd., Janssen Research & Development, LLC., GSK, Astra Zeneca R&D and Areteva R&D provided support in the form of salaries for authors ATB, MJL, ARS, AND JC but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the ‘author contributions’ section.
Competing interests: M. Caruso, R. Emma, A.T. Bansal, C.E. Wheelock, M.J. Loza, L. Fabbella, T. Sandstrom, F. Singer, A.R. Sousa, D.E. Shaw, R. Lutter, S.J. Fowler, I. Horvath, I. Pandis, P. Montuschi declare non conflict of interest. J. Kolmert, B. De Meulder, D. Lefaudeux, C. Auffray, P.J. Sterk, N. Krug, M. Sanak, J. Corfield declare grants from IMI (Innovative Medicine Initiatives) during the conduct of the study. S.E. Dahlen declares financial supporting by Swedish Research Academic Foundations, RSPS Incentive, AZ, Teva and GSK consultancies. B. Dahlen declares consultancies for TEVA. P. Chanez reports grants and personal fees from Almirall, BI, Centocor, GSK, MSD, AstraZeneca, Novartis, Teva, Chiesi, Schering Plough, outside the submitted work. P.S. Bakke reports personal fees from GlaxoSmithKline, Boehringer-Ingelheim, AstraZeneca, Orionpharma, Mundipharma, outside the submitted work. R. Djukanovic reports receiving fees for lectures at symposia organised by Novartis and TEVA and consultation for these two companies as member of advisory boards. He is a co-founder and current consultant, and has shares in Synairgen, a University of Southampton spin out company. P.H. Howarth reports fees from GSK, outside the submitted work. L. Fleming reports personal fees and other from Vectura, Novartis, Boehringer ingelheim, outside the submitted work. I. Adcock reports grants from EU-IMI, during the conduct of the study; grants from MRC, BHF, Dunhill Medical Trust, personal fees from Chiesi, GSK, Boehringer Ingelheim, outside the submitted work. K.F. Chung reports personal fees from Advisory Board membership, grants for research, and personal fees from payments for lectures, outside the submitted work. ATB is employed by Acclarogen Ltd., MJL by Janssen Research & Development, LLC., ARS by GSK and JC by Astra Zeneca R&D and Areteva R&D. There are no patents, products in development or marketed products to declare. This does not alter our adherence to all the PLOS ONE policies on sharing data and materials, as detailed online in the guide for authors.
Abbreviations: 8-iso-PGF2α, 8-Isoprostaglandin F2α; BB, Bronchial Biopsy; BBr, Bronchial Brushing; CAT, catalase; CSA, current smokers with severe asthma; ESA, ex-smokers with severe asthma; FC, Fold change; GPX1, glutathione peroxidase 1; IS, Induced Sputum; LOD, limit of detection; NOS, nitric oxide synthase; NOX2, NADPH oxidase 2; NSA, non smokers with severe asthma; RNS, reactive nitrogen species; ROS, reactive oxygen species; SA, severe asthma/asthmatic; SAn, severe asthma non smokers; SAs/ex, severe asthma smokers/ex-smokers; SOD, superoxide dismutase; U-BIOPRED, Unbiased BIOmarkers for the PREDiction of Respiratory Disease Outcomes
Asthma is a heterogeneous inflammatory syndrome of the airways characterized by several clinical and molecular phenotypes [1–3]. In severe asthma (SA), genetic, immunologic and environmental factors interact contributing to airway chronic inflammation . Cigarette smoke is a key factor implicated in modulation of asthma. Data on asthmatic smokers suggest marked impairment in asthma control, accelerated decline in lung function, increased airflow obstruction and increase in disease severity .
Human airways are normally exposed to oxidative products present in environmental pollutants. But, the inflammatory state in the airways of asthmatic patients may also promote oxidative stress with increased levels of reactive oxygen species (ROS) and reactive nitrogen species (RNS), which may further contribute to maintenance and progression of the inflammatory response and disease exacerbation . Activated inflammatory cells produce anion superoxide (O2•-) through the NADPH oxidase pathway. The O2•- is neutralized by superoxide dismutase enzymes (SODs), catalase (CAT) and glutathione peroxidase (GPX) activity . Furthermore, nitric oxide synthase (NOS) enzymes generate nitric oxide (NO), another common free radical, that in the presence of ROS rapidly forms RNS . An excess of ROS and RNS has been shown to lead to membrane lipids peroxidation, nicotinamide nucleotides depletion, enhanced intracellular Ca2+, cytoskeleton breakage and DNA damage [8,10]. The peroxidative breakdown of membrane fatty acids by ROS leads to F2-Isoprostanes production. 8-Isoprostaglandin F2α (8-iso-PGF2α) is therefore considered a useful marker of oxidative stress .
Tobacco smoke is a major exogenous source of oxidative stress, contributing to subsistence and progression of the inflammatory response and disease chronicity in asthma. Many oxidant compounds are present in cigarette smoke, which may induce direct and/or indirect oxidative damage . Very little is known about the role of oxidative stress in SA and even less about the combined impact with cigarette smoking. We tested the hypothesis that oxidative stress and inflammatory biomarkes differs in SA subjects with and without a significant smoking history, as well as in SA current-, ex- and non-smokers.
We therefore investigated the level of the lipid peroxidation marker 8-iso-PGF2α in the urine and the mRNA expression profile of key pro-oxidant (NADPH oxidase 2, NOX2; inducible NOS, NOS2; constitutive NOSs, NOS1 and NOS3) and anti-oxidant (superoxide dismutases, SOD1, -2 and -3; catalase, CAT; and glutathione peroxidase 1, GPX1) enzymes in the airways, in particular in bronchial biopsy (BB), bronchial brushing (BBr) and induced sputum (IS) samples.
Materials and methods
Materials and methods section is fully described in the online S1 File. Material and Methods.
The Severe Asthma U-BIOPRED participants were enrolled in two groups :
- Severe non-smoking asthma (SAn): subjects in this group refrained from smoking for at least 12 months prior to the study, with a less than five pack-years smoking history. They had uncontrolled symptoms as defined according to GINA guidelines  and/or frequent exacerbations (more than two per year) despite high-dose inhaled corticosteroids (ICS ≥ 1000 μg fluticasone propionate/day or equivalent dose).
- Smokers and ex-smokers with severe asthma (SAs/ex): this group was defined as for the SAn group except that they were either current smokers or ex-smokers with at least five pack-years smoking history.
In order to deepen potential effect of current smoking, we further divided all the severe asthma U-BIOPRED subjects into three subgroups by smoking status: Current smokers with Severe Asthma (CSA); Ex-smokers with Severe Asthma (ESA); Non-smokers with Severe Asthma (NSA).
All enrolled subjects underwent a baseline visit during which clinical data, and urine and sputum samples were collected. An optional broncoscopy visit was carried out only in specialist centres. Clinical and omic data of severe asthma cohorts were downloaded on June 2016 from U-BIOPRED database (tranSMART system) . We obtained urine 8-iso-PGF2α data from 411 severe asthma subjects, and IS, BB, BBr transcriptomincs data from 84, 53, 67 severe asthma subjects, respectively. There is a good overlap, in term of patient coverage between BB, BBr and urine, and between sputum and urine. In contrast, there is little overlap between patients who had IS and BB/BBr.
The study protocol was approved by the Ethics Review Board of the Academic Medical Centre of the University of Amsterdam (The Netherlands) and subsequently by the Ethics Boards of all other clinical centres in the study (see S1 File. Material and Methods). The study adhered to the standards set by International Conference on Harmonisation and Good Clinical Practice. All participants signed a written informed consent.
8-iso-PGF2α was extracted from spot urine samples using solid phase extraction (SPE) and quantified via liquid chromatography coupled to mass spectrometry (LC-MS/MS) using an Acquity UPLC coupled to a Xevo TQ-S mass spectrometer (Waters, Milford, MA). Optimal extraction volumes for SPE were calculated using individual UV absorbance (λ = 300 nm) measurements to minimize matrix effects, and levels of 8-iso-PGF2α were normalized to urinary creatinine concentrations as previously published .
RNA from RNAlater-preserved IS cell pellet, BB, and BBr samples were extracted using Qiagen miRNeasy kit and amplified with Nugen ovation pico WTA kit (NuGen Technologies; San Carlos, CA). The cDNA was analysed using the Affymetrix HG-U133+PM microarray platform (Affymetrix, Santa Clara, CA). The primary raw data images (DAT files) were processed into numerical CEL files. CEL files were normalized, assessed for quality control to exclude technical outliers, and re-normalized using the robust multi-array (RMA) method. Batch effects from RNA processing sets were observed for the sputum and BBr datasets, with the batch effect adjusted in the data matrices using linear modeling of batch (as a random factor) and cohort in the ComBat R programme. For the sputum dataset, 3 subjects had duplicate samples, of which the mean of the log2 intensities, after RNA processing set batch adjustment, were used in the final analysis dataset. The limit of reliable quantification (LOD) was established from the inflection point of maximum variance with decreasing signal in a standard deviation vs. mean intensity plot across all probe sets and nonspecific probesets distribution. The cut-offs (number of probe sets included) were 5.5 (23496), 5.0 (18697), and 4.75 (21363) for sputum, BB, and BBr, respectively. The probe sets used in this study were as follow: NOX2 (203922_PM_s_at, 203923_PM_s_at, 217431_PM_x_at, 233538_PM_s_at); NOS1 (1560974_PM_s_at, 207309_PM_at, 207310_PM_s_at, 231916_PM_at, 239132_PM_at, 240911_PM_at); NOS2 (210037_PM_s_at); NOS3 (205581_PM_s_at); SOD1 (200642_PM_at); SOD2 (215078_PM_at, 215223_PM_s_at, 216841_PM_s_at, 221477_PM_s_at); SOD3 (205236_PM_x_at); CAT (201432_PM_at, 211922_PM_s_at, 215573_PM_at); GPX1 (200736_PM_s_at). Usually, probes are selected to represent genes and are designed to match particular mRNA transcripts, often based on deposited NCBI sequences. However, those sequences might be incorrect, partially inaccurate or incomplete due to different problems. Moreover a gene can have multiple splice variants . Thus, multiple probe sets assigned to a common gene were studied separately in order to keep an unbiased reporting of all relevant probe sets. Public repository for microarray data are published in GEO website: GSE76262 (IS data); GSE76225 (BB data); GSE76226 (BBr data).
Categorical data were summarized by counts and percentages; continuously distributed data exhibiting approximate symmetry of distribution were summarized using the mean (standard error; SE); continuously distributed data exhibiting skewness were summarized using the median (inter-quartile range; IQR). The latter all exhibited positive skewness and were log-transformed prior to parametric association testing. P-values were calculated by applying ANOVA to a generalised linear model. ANOVA was performed using logistic regression with adjustment for age and gender. All analyses were considered significant with a p-value of less than 5%. Acknowledging the modest sample size of the current exploratory study, adjustments for multiple-testing were not applied. Moreover, a negative result should be considered inconclusive. In order to characterise further some of the observed associations, rank correlation was assessed by calculation of Kendall’s Tau. A p-value was calculated in a test of the null hypothesis of zero correlation. Analyses of clinical and inflammatory data were performed using R version 2.15.2 (R Core Team, 2012).
The clinical and inflammatory characteristics of all U-BIOPRED patients have been previously described . Here, we compared the main clinical data between SAn and SAs/ex groups, as well as between all SA subjects when stratified by smoking status. i.e., CSA, ESA and NSA groups. These results are reported in S1–S4 Tables and Tables 1–4, respectively, for each subject subset (urine-8-iso-PGF2α; IS-transcriptomics; BB-transcriptomics; BBr-transcriptomics). In brief, the onset of asthma occurred later in SAs/ex than in SAn for urine-8-iso-PGF2α (p< 0.001) and IS-transcriptimics (p< 0.031) subsets, although the subjects had a similar degree of airway obstruction (spirometry data). An older age at diagnosis was also observed in the CSA and/or ESA than NSA, but only in the urine-8-iso-PGF2α subset (p< 0.001). Gastro-esophageal reflux disease (GERD) was increased in SAs/ex compared to SAn in the urine-8-iso-PGF2α subset (p = 0.006), and this trend was also observed in the other subsets (p> 0.05). GERD was also increased in CSA for the urine-8-iso-PGF2α subset (p = 0.009) compared to ESA and NSA. FEV1/FVC ratio was decreased in CSA than ESA and NSA with significant p-value in the urine-8-iso-PGF2α subset (p = 0.033). FeNO levels were significantly lower in SAs/ex than SAn for those subjects for whom BB and BBr transcriptomic data was available, but this was not seen across all subsets. However, when compared SA smoking subgroups we observed that FeNO levels were significantly lower in CSA than ESA and NSA for all subject subsets. Regular OCS use was increased in ESA than CSA and NSA with significant p value in the urine-8-iso-PGF2α (p = 0.002) and BBr transcriptomics (p = 0.009) subsets.
We analysed 8-iso-PGF2α, a specific biomarker of lipid peroxidation, in spot urine samples. The median (IQR) concentration of 8-iso-PGF2α was significantly increased in the urine of SAs/ex compared to SAn, 31.7 (24.5–44.7) vs. 26.6 (19.6–36.6) ng/mmol creatinine respectively (FC = 1.19; p< 0.001) (Fig 1A), Furthermore, we assessed levels of 8-iso-PGF2α taking into consideration smoking status within the two combined SA cohorts (SAn+SAs/ex). A significant difference was observed between the smoking subgroups (p = 0.004) with increased median (IQR) concentration of urinary 8-iso-PGF2α in CSA, 34.25 (24.4–47.7), vs ESA, 29.4 (22.3–40.5), and NSA, 26.5 (19.6–16.6) ng/mmol creatinine (Fig 1B).
(A) Comparison of 8-iso-PGF2α in urine between SAn and SAs/ex. (B) Comparison of urinary 8-iso-PGF2α between severe asthma smoking subgroups. CSA: current smokers with severe asthma; ESA: ex-smokers with severe asthma; NSA: non smokers with severe asthma; SAn: Severe Asthma non smokers; SAs/ex: Severe Asthma smokers/ex-smokers.
mRNA expression of pro-/anti-oxidant enzymes
All the results about mRNA expression of pro-/antioxidant enzymes are reported in Table 5. Sputum mRNA levels of the pro-oxidant enzymes NOX2 and NOS3 were examined. The mRNA levels of NOX2 were increased in SAs/ex compared to SAn. In particular the probe sets 203922_PM_s_at (FC = 1.05 p = 0.006), 203923_PM_s_at (FC = 1.06; p = 0.003), 233538_PM_s_at (FC = 1.06; p = 0.014) were over-expressed in SAs/ex. The NOX2 probe set 217431_PM_x_at is less abundant than the other probe sets, and was not significantly different between SAn and SAs/ex (FC = 1.00; p = 0.950). The expression of NOS3 mRNA in IS was similar between SAs/ex and SAn, while the levels of NOS1, NOS2 probe sets, together with the anti-oxidative SOD3, were below the limit of detection (LOD). CAT mRNA levels were over-expressed in IS of SAs/ex compared to SAn for the probe set 201432_PM_at (FC = 1.07; p = 0.028), but the probe set 211922_PM_s_at did not show a significant difference (p = 0.359). By contrast, IS mRNA expression of SOD1, SOD2, SOD3 and GPX1 were not statistically different between SAn and SAs/ex.
All of the pro-oxidant enzymes were excluded in BB analyses because their expression levels were below the LOD. The expression of all antioxidant enzymes in BB was similar among SAn and SAs/ex.
NOS2 mRNA levels were significantly decreased in BBr of SAs/ex compared to SAn (FC = -1.10; p = 0.029). BBr NOS3 mRNA expression was similar between SAn and SAs/ex (FC = -1.01; p = 0.310). In addition, the mRNA expression levels of anti-oxidant enzymes in BBr were similar among the two study groups. NOX2, NOS1 and SOD3 were excluded from the analysis in BBr subset because their mRNA expression levels were below the LOD.
When stratification was made based on smoking status a low number of subjects were available for which oxidant/anti-oxidant enzyme mRNA expression data could be used with sufficient statistical power and therefore this analysis was not performed.
Two NOX2 probe sets in sputum correlated significantly with macrophage numbers (203922_PM_s_at Kendall’s Tau = 0.49, p< 0.001; 203923_PM_s_at Kendall’s Tau = 0.45 p< 0.001) and percentages (203922_PM_s_at Kendall’s Tau = 0.48, p< 0.001; 203923_PM_s_at Kendall’s Tau = 0.43 p< 0.001). Inversely correlations were observed between three NOX2 probe sets and eosinophil numbers (203922_PM_s_at Kendall’s Tau = -0.19, p = 0.01; 203923_PM_s_at Kendall’s Tau = -0.24, p = 0.002; 233538_PM_s_at Kendall’s Tau = -0.22, p = 0.004), and percentages (203922_PM_s_at Kendall’s Tau = -0.20, p = 0.008; 203923_PM_s_at Kendall’s Tau = -0.24, p = 0.001; 233538_PM_s_at Kendall’s Tau = -0.22, p = 0.004). One NOX2 probe set in sputum inversely correlated with neutrophil numbers (203922_PM_s_at Kendall’s Tau = -0.18, p = 0.014), while two NOX2 probe sets inversely correlated with neutrophil percentages (203922_PM_s_at Kendall’s Tau = -0.18, p = 0.014; 203923_PM_s_at Kendall’s Tau = -0.15, p = 0.038) However, there was no correlation between NOX2 mRNA in sputum and 8-iso-PGF2α in urine (all the four probe sets had p> 0.10). Moreover, a strong correlation between NOS2 in BBr and FeNO was observed with a Kendall’s Tau = 0.535 (p< 0.001) (Fig 2). The correlation analysis between NOS2 in BBr and FeNO was also performed for each smoking status group (i.e., NSA, ESA, CSA): where we observed a significant correlation in 36 NSA (Kendall’s Tau = 0.551; p< 0.001); in 20 ESA (Kendall’s Tau = 0.394; p = 0.085); the number of CSA subjects was six and therefore correlation analysis was considered not appropriate.
We used data from the U-BIOPRED severe asthma cohorts to assess the effect of cigarette smoke on oxidative stress markers in severe asthma subjects with a significat smoking history. We found an increased systemic oxidative stress in SAs/ex, and in particular among severe asthma current smokers, who exhibited the highest concentration of urinary 8-iso-PGF2α. In sputum, NOX2 mRNA expression was increased in SAs/ex compared to SAn, while NOS2 mRNA expression was decresead in bronchial brushing of SAs/ex. Moreover, levels of FeNO were decresed in severe asthma current smokers, and was correlated with NOS2 mRNA expression in bronchial brushing.
Tobacco smoke is one of the major environmental sources of oxidative stress and can lead to greater lipid peroxidation. It has been shown that 8-iso-PGF2α levels are elevated with asthma severity  and further enhanced during acute exacerbations  and allergen challenge in asthmatics . Our data provide additional support for the findings in other studies, showing the ability of cigarette smoke to increase isoprostane levels, by the increased level of urinary 8-iso-PGF2α in SAs/ex, and in particular, in the current smokers subgroup [19,20]. However, our data extended the previous findings showing the incresase of this oxidative stress biomarker in smokers with severe asthma. F2 isoprostanes have a potent smooth muscle and vascular constrictive action which increases airway hyperresponsiveness and obstruction, and induces plasma exudation and inflammation . Thus, the increased formation of 8-iso-PGF2α in smoking asthmatics, as induced by cigarette smoke, may enhance disease progression and asthma symptoms.
The NOX2 isoform is primarily present in macrophages, neutrophils and eosinophils and is most highly abundant in IS. NOX2 activity is normally required for phagocyte respiratory burst and regulation of cell signaling [22,23]. Furthermore, the enzyme NOX2 is activated by cigarette smoke through the phosphorylation of c-Src , a tyrosine kinase protein, leading to a higher production of ROS [24,25]. In our study, NOX2 mRNA is over-expressed in sputum of SAs/ex compared to SAn, supporting the hypothesis that exposure to smoke in asthmatic subjects is able to amplify NOX2 mRNA expression with a consequent elevation of O2•- production. However, we could not establish a significant correlation between NOX2 over-expression in IS and urinary 8-iso-PGF2α. Certainly, NOX2 is just one of the several factors contributing to the increase of oxidative stress. The positive correlation of NOX2 mRNA with macrophages in sputum and the inverse relationship with phagocytes may highlight the importance of distinct cellular phenotypes in regulating the inflammatory process in severe asthmatics who smoke [26,27]. This area deserves further investigation.
NOS enzymes are important pro-oxidants producing NO, an endogenous regulator involved in homeostatic and immunological functions with a role in asthma pathogenesis . We observed no differences in mRNA expression of constitutive NOS3 between SAn and SAs/ex, which suggests that cigarette smoke has a weak relation to NOS3 expression, if any. NOS2 is the inducible isoform (iNOS), which produces high levels of NO and its activity persists for many days after induction [28,29], leading to cell death and tissue damage . This enzyme is mainly expressed in lung epithelium , so it is mainly detectable in BBr. NOS2 mRNA expression is induced by pro-inflammatory cytokines [9,30], and is increased in asthma in proportion to the severity of the disease , and in particular by allergen provocation . In this study, levels of NOS2 mRNA from BBr of SAs/ex was lower than that of SAn. Furthermore, we demonstrated a strong correlation between FeNO levels and BBr NOS2 mRNA expression in SA cohorts, as also have been reported previously . Another study has shown that NO generated by NOS2 is able to attenuate its own expression through the negative regulation of NF-κB . Therefore, we hypothesise that active exposure to cigarette smoke can lead to inhibition of NOS2 mRNA expression in a negative feedback manner.
Evidence exists of lower FeNO levels in asthmatic smokers [35,36], which has also been shown in this U-BIOPRED adult SAs/ex cohort , indicating a possible effect of active smoking on levels of exhaled NO. Further analysis of FeNO, in relation to the cigarette smoking status of patients (current-, ex- and never-smokers), showed decreased FeNO levels in current smokers confirming the effect of active smoking on exhaled NO . Despite the fact that superoxide concentration is known to be high in cigarette smoke, and thereby it can enhance the reduction of FeNO by reacting with available NO, we demonstrated a significant correlation between NOS2 in bronchial brushings and FeNO in severe asthmatic subjects. Moreover, the peroxynitrite product of this reaction is a very strong oxidant species [8–10]. Thus, knowledge of current smoking status is important when using FeNO measurements in assessing asthma control and severity.
The anti-oxidant enzymes—SODs, CAT and GPX1 —catalyze reactions to neutralize oxidative toxic intermediates. SODs are the primary enzymes able to dismantle superoxide anion to form H2O2. Whereas, CAT and GPX1 are key antioxidant enzymes for the degradation of reactive H2O2 to H2O and O2 . We observed no significant differences in SOD1, SOD2 and SOD3 mRNA expression, as well as for CAT and GPX1 mRNA expression, in airways of SAn and SAs/ex. To our knowledge, there are no reports concerning antioxidant enzyme status in severe asthma smokers. However, some evidences relating to expression of SODs, CAT and GPX1 enzymes in relation to asthma or cigarette smoke were found in the literature. Several studies reported that SOD1 has low activity and expression in asthmatic airways [10,37] and, in addiction, SOD2 was found to be inactivated and down regulated in asthmatic patients [37–39]. Furthermore, the expression of SOD3 seems to be decreased in vitro by TNF-α, TGF-β and IL1-α, while it is enhanced by IFN-γ [10,40]. Cigarette smoke enhanced the expression  and activity  of SODs in rat airways. Conversely, the levels of SOD were found increased in blood and saliva of subjects who smoke , and prolonged cigarette smoke exposure was found to increase the mRNA level of SOD2 in human bronchial epithelial cells . Therefore, it is conceivable that whilst SOD mRNA expression is decreased in SA, smoking enhances SOD mRNA expression, resulting in a balance between the two actions and overall no difference between SAn and SAs/ex. Catalase (protein and mRNA levels) was previously reported as decreased in the bronchiolar epithelium of smokers with COPD . Furthermore, the activity of CAT and GPX was previously found reduced in asthmatic patients . Further studies are required to determine whether the decreased expression reported by Betsuyaku et al.  was a result of smoking or of COPD.
Limitations of the study
Due to the explorative nature of this study, there are several limitations. One limitation is the absence of a healthy smoker and a healthy non-smokers control groups, which do not allow us to establish for sure the influence of cigarette smoking and/or severe asthma. Moreover, the measurement of whole body excretion of 8-iso-PGF2α in urine cannot determine the source (airways or systemic source). Given that the patients included in this study have the same degree of asthma severity it is however likely that the observed changes are predominantly due to smoking.
Few patients provided samples from each compartment within the U-BIOPRED study. Particularly, the number of SAs/ex samples is low for the sputum, BB and BBr transcriptomics set, thus we cannot didived SAs/ex group into current and ex-smokers for the mRNA expression analysis.
We evaluated the mRNA expression of pro-/anti-oxidant enzymes, but the mRNA levels do not necessarily correlate with the activity of the corresponding enzymes or their products’ concentration. Moreover, mRNA-expression of several enzymes could not be accurately assessed due to their low expression levels on the microarray. Although we report significant differences in oxidant gene expression of SAs/ex, the changes are small and other mechanisms driving asthma severity in these patients may be present. Independent replications of these findings are warranted.
In conclusion, our results indicate that severe asthmatics who smoke have evidence for increased systemic oxidative stress. The increased mRNA expression of NOX2 in the airway lumen could contribute to this phenomenon. However, this is the first study in severe asthma showing a clear relationship between cigarette smoking and reduced NOS2 expression together with lower levels of FeNO. Future studies are needed to investigate this complex mechanism in the framework of smoking related to severe asthma.
S1 Table. Clinical and inflammatory characteristics of subjects present in the urinary 8-iso-PGF2α subset.
S2 Table. Clinical and inflammatory characteristics of subjects present in the induced sputum subset.
S3 Table. Clinical and inflammatory characteristics of subjects present in the bronchial biopsy subset.
S4 Table. Clinical and inflammatory characteristics of subjects present in the bronchial brushing subset.
On behalf of The U-BIOPRED Study Group with input from the U-BIOPRED Patient Input Platform and patient representatives from the Ethics Board and Safety Management Board.
The U-BIOPRED consortium wishes to acknowledge the help and expertise of the following individuals and groups without whom, the study would not have been possible. Full list and definition of U-Biopred supplementary author is provided in S5 Table U-BIOPRED Consortium Information.
- 1. Holgate ST, Polosa R. Treatment strategies for allergy and asthma. Nat Rev Immunol. 2008;8: 218–30. pmid:18274559
- 2. Wenzel SE. Asthma phenotypes: the evolution from clinical to molecular approaches. Nat Med. 2012;18: 716–25. pmid:22561835
- 3. Chung KF. Defining phenotypes in asthma: a step towards personalized medicine. Drugs. 2014;74: 719–28. pmid:24797157
- 4. Chung KF, Wenzel SE, Brozek JL, Bush A, Castro M, Sterk PJ, et al. International ERS/ATS guidelines on definition, evaluation and treatment of severe asthma. Eur Respir J. 2014;43: 343–373. pmid:24337046
- 5. Polosa R, Thomson NC. Smoking and asthma: dangerous liaisons. Eur Respir J. 2013;41: 716–26. pmid:22903959
- 6. Kelly FJ, Mudway I, Blomberg A, Frew A, Sandström T. Altered lung antioxidant status in patients with mild asthma. Lancet (London, England). 1999;354: 482–3.
- 7. Zuo L, Otenbaker NP, Rose BA, Salisbury KS. Molecular mechanisms of reactive oxygen species-related pulmonary inflammation and asthma. Mol Immunol. 2013;56: 57–63. pmid:23665383
- 8. Holguin F. Oxidative stress in airway diseases. Ann Am Thorac Soc. 2013;10 Suppl: S150–7.
- 9. Bogdan C. Nitric oxide synthase in innate and adaptive immunity: an update. Trends Immunol. 2015;36: 161–178. pmid:25687683
- 10. Rahman I, Biswas SK, Kode A. Oxidant and antioxidant balance in the airways and airway diseases. Eur J Pharmacol. 2006;533: 222–39. pmid:16500642
- 11. Wood LG, Gibson PG, Garg ML. Biomarkers of lipid peroxidation, airway inflammation and asthma. Eur Respir J. 2003;21: 177–86. pmid:12570126
- 12. Valavanidis A, Vlachogianni T, Fiotakis K. Tobacco smoke: involvement of reactive oxygen species and stable free radicals in mechanisms of oxidative damage, carcinogenesis and synergistic effects with other respirable particles. Int J Environ Res Public Health. 2009;6: 445–62. pmid:19440393
- 13. Shaw DE, Sousa AR, Fowler SJ, Fleming LJ, Roberts G, Corfield J, et al. Clinical and inflammatory characteristics of the European U-BIOPRED adult severe asthma cohort. Eur Respir J. 2015;
- 14. Bousquet J. Global initiative for asthma (GINA) and its objectives. Clin Exp Allergy. 2000;30 Suppl 1: 2–5.
- 15. Athey BD, Braxenthaler M, Haas M, Guo Y. tranSMART: An Open Source and Community-Driven Informatics and Data Sharing Platform for Clinical and Translational Research. AMIA Jt Summits Transl Sci proceedings AMIA Jt Summits Transl Sci. 2013;2013: 6–8.
- 16. Balgoma D, Larsson J, Rokach J, Lawson JA, Daham K, Dahlén B, et al. Quantification of lipid mediator metabolites in human urine from asthma patients by electrospray ionization mass spectrometry: controlling matrix effects. Anal Chem. American Chemical Society; 2013;85: 7866–74.
- 17. Liu H, Bebu I, Li X. Microarray probes and probe sets. Front Biosci (Elite Ed). 2010;2: 325–38.
- 18. Mak JCW, Ho SP, Ho ASS, Law BKW, Cheung AHK, Ho JCM, et al. Sustained elevation of systemic oxidative stress and inflammation in exacerbation and remission of asthma. ISRN Allergy. 2013;2013: 561831. pmid:24073339
- 19. Papaioannou AI, Koutsokera A, Tanou K, Kiropoulos TS, Tsilioni I, Oikonomidi S, et al. The acute effect of smoking in healthy and asthmatic smokers. Eur J Clin Invest. 2010;40: 103–109. pmid:19912318
- 20. MONTUSCHI P, CORRADI M, CIABATTONI G, NIGHTINGALE J, KHARITONOV SA, BARNES PJ. Increased 8-Isoprostane, a Marker of Oxidative Stress, in Exhaled Condensate of Asthma Patients. Am J Respir Crit Care Med. 1999;160: 216–220. pmid:10390403
- 21. Rahman I, Adcock IM. Oxidative stress and redox regulation of lung inflammation in COPD. Eur Respir J. 2006;28: 219–42. pmid:16816350
- 22. Lee I-T, Yang C-M. Role of NADPH oxidase/ROS in pro-inflammatory mediators-induced airway and pulmonary diseases. Biochem Pharmacol. 2012;84: 581–90. pmid:22587816
- 23. Bedard K, Krause K-H. The NOX family of ROS-generating NADPH oxidases: physiology and pathophysiology. Physiol Rev. 2007;87: 245–313. pmid:17237347
- 24. Cheng S-E, Lee I-T, Lin C-C, Kou YR, Yang C-M. Cigarette smoke particle-phase extract induces HO-1 expression in human tracheal smooth muscle cells: role of the c-Src/NADPH oxidase/MAPK/Nrf2 signaling pathway. Free Radic Biol Med. 2010;48: 1410–22. pmid:20188821
- 25. Yao H, Yang S-R, Kode A, Rajendrasozhan S, Caito S, Adenuga D, et al. Redox regulation of lung inflammation: role of NADPH oxidase and NF-kappaB signalling. Biochem Soc Trans. 2007;35: 1151–5. pmid:17956299
- 26. Bruijnzeel PLB, Uddin M, Koenderman L. Targeting neutrophilic inflammation in severe neutrophilic asthma: can we target the disease-relevant neutrophil phenotype? J Leukoc Biol. 2015;98: 549–56. pmid:25977288
- 27. Xue J, Schmidt S V, Sander J, Draffehn A, Krebs W, Quester I, et al. Transcriptome-based network analysis reveals a spectrum model of human macrophage activation. Immunity. 2014;40: 274–88. pmid:24530056
- 28. Batra J, Chatterjee R, Ghosh B. Inducible nitric oxide synthase (iNOS): role in asthma pathogenesis. Indian J Biochem Biophys. 2007;44: 303–9. pmid:18341205
- 29. Mattila JT, Thomas AC. Nitric oxide synthase: non-canonical expression patterns. Front Immunol. 2014;5: 478. pmid:25346730
- 30. Voraphani N, Gladwin MT, Contreras AU, Kaminski N, Tedrow JR, Milosevic J, et al. An airway epithelial iNOS-DUOX2-thyroid peroxidase metabolome drives Th1/Th2 nitrative stress in human severe asthma. Mucosal Immunol. 2014;7: 1175–85. pmid:24518246
- 31. Yamamoto M, Tochino Y, Chibana K, Trudeau JB, Holguin F, Wenzel SE. Nitric oxide and related enzymes in asthma: relation to severity, enzyme function and inflammation. Clin Exp Allergy. 2012;42: 760–8. pmid:22092728
- 32. Roos AB, Mori M, Grönneberg R, Österlund C, Claesson H-E, Wahlström J, et al. Elevated exhaled nitric oxide in allergen-provoked asthma is associated with airway epithelial iNOS. PLoS One. 2014;9: e90018. pmid:24587191
- 33. Lane C, Knight D, Burgess S, Franklin P, Horak F, Legg J, et al. Epithelial inducible nitric oxide synthase activity is the major determinant of nitric oxide concentration in exhaled breath. Thorax. 2004;59: 757–60. pmid:15333851
- 34. Kelleher ZT, Matsumoto A, Stamler JS, Marshall HE. NOS2 regulation of NF-kappaB by S-nitrosylation of p65. J Biol Chem. 2007;282: 30667–72. pmid:17720813
- 35. Horváth I, Donnelly LE, Kiss A, Balint B, Kharitonov SA, Barnes PJ. Exhaled nitric oxide and hydrogen peroxide concentrations in asthmatic smokers. Respiration. 2004;71: 463–468. pmid:15467323
- 36. Thomson NC, Chaudhuri R, Heaney LG, Bucknall C, Niven RM, Brightling CE, et al. Clinical outcomes and inflammatory biomarkers in current smokers and exsmokers with severe asthma. J Allergy Clin Immunol. 2013;131: 1008–16. pmid:23419540
- 37. Kinnula VL, Crapo JD. Superoxide dismutases in the lung and human lung diseases. Am J Respir Crit Care Med. 2003;167: 1600–19. pmid:12796054
- 38. Comhair SAA, Ricci KS, Arroliga M, Lara AR, Dweik RA, Song W, et al. Correlation of systemic superoxide dismutase deficiency to airflow obstruction in asthma. Am J Respir Crit Care Med. 2005;172: 306–13. pmid:15883124
- 39. Comhair SAA, Xu W, Ghosh S, Thunnissen FBJM, Almasan A, Calhoun WJ, et al. Superoxide dismutase inactivation in pathophysiology of asthmatic airway remodeling and reactivity. Am J Pathol. 2005;166: 663–74. pmid:15743779
- 40. Marklund SL. Regulation by cytokines of extracellular superoxide dismutase and other superoxide dismutase isoenzymes in fibroblasts. J Biol Chem. 1992;267: 6696–701. pmid:1551878
- 41. Gilks CB, Price K, Wright JL, Churg A. Antioxidant gene expression in rat lung after exposure to cigarette smoke. Am J Pathol. 1998;152: 269–78. pmid:9422544
- 42. York GK, Peirce TH, Schwartz LW, Cross CE. Stimulation by cigarette smoke of glutathione peroxidase system enzyme activities in rat lung. Arch Environ Health. 31: 286–90. pmid:999340
- 43. Jenifer HD, Bhola S, Kalburgi V, Warad S, Kokatnur VM. The influence of cigarette smoking on blood and salivary super oxide dismutase enzyme levels among smokers and nonsmokers—A cross sectional study. J Tradit Complement Med. 2015;5: 100–105. pmid:26151019
- 44. Hoffmann RF, Zarrintan S, Brandenburg SM, Kol A, de Bruin HG, Jafari S, et al. Prolonged cigarette smoke exposure alters mitochondrial structure and function in airway epithelial cells. Respir Res. 2013;14: 97. pmid:24088173
- 45. Betsuyaku T, Fuke S, Inomata T, Kaga K, Morikawa T, Odajima N, et al. Bronchiolar epithelial catalase is diminished in smokers with mild COPD. Eur Respir J. 2013;42: 42–53. pmid:23100509