Article pubs.acs.org/jpr
Metabolomics Reveals Inflammatory-Linked Pulmonary Metabolic Alterations in a Murine Model of House Dust Mite-Induced Allergic Asthma Wanxing Eugene Ho,†,‡,§,+ Yong-Jiang Xu,†,¶,+ Chang Cheng,§,∥ Hong Yong Peh,§,⊥ Steven R. Tannenbaum,‡,# W. S. Fred Wong,*,§,⊥ and Choon Nam Ong*,†,□ †
Saw Swee Hock School of Public Health and §Department of Pharmacology, Yong Loo Lin School of Medicine, National University Health System, Singapore 119228 ‡ Singapore-MIT Alliance for Research and Technology (SMART), Singapore 138602 ¶ Key Laboratory of Insect Development and Evolutionary Biology, Chinese Academy of Sciences, Shanghai 200032, China ∥ Department of Gastroenterology & Hepatology, Singapore General Hospital, Singapore 169608 ⊥ Immunology Program, Life Science Institute and □NUS Environmental Research Institute, National University of Singapore, Singapore 117597 # Department of Biological Engineering and Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States S Supporting Information *
ABSTRACT: Although the house dust mite (HDM) is a major environmental aeroallergen that promotes the pathogenesis and severity of allergic asthma, it remains elusive if HDM exposures can induce global metabolism aberrations during allergic airway inflammation. Using an integrated gas and liquid chromatography mass spectrometry-based metabolomics and multiplex cytokine profile analysis, metabolic alterations and cytokine changes were investigated in the bronchoalveolar lavage fluid (BALF), serum, and lung tissues in experimental HDM-induced allergic asthma. Allergic pulmonary HDM exposures lead to pronounced eosinophilia, neutrophilia, and increases in inflammatory cytokines. Metabolomics analysis of the BALF, serum, and lung tissues revealed distinctive compartmental metabolic signatures, which included depleted carbohydrates, increased energy metabolites, and consistent losses of sterols and phosphatidylcholines. Pearson correlation analysis uncovered strong associations between specific metabolic alterations and inflammatory cells and cytokines, linking altered pulmonary metabolism to allergic airway inflammation. The clinically prescribed glucocorticoid prednisolone could modulate airway inflammation but was ineffective against the reversal of many HDM-induced metabolic alterations. Collectively, metabolomics reveal comprehensive pulmonary metabolic signatures in HDM-induced allergic asthma, with specific alterations in carbohydrates, lipids, sterols, and energy metabolic pathways. Altered pulmonary metabolism may be a major underlying molecular feature involved during HDM-induced allergic airway inflammation, linked to inflammatory cells and cytokines changes. KEYWORDS: gas chromatography, liquid chromatography, mass spectrometry, allergy, inflammation, corticosteroids, metabolome
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INTRODUCTION Allergic asthma is a respiratory inflammatory disorder induced by recurrent aeroallergen exposures, with a characteristic hallmark of airway inflammation. At present, a substantial proportion of asthmatics are observed to be poorly controlled by existing treatments, resulting in over 250,000 deaths annually worldwide.1 These clinical observations imply that current understanding of the molecular processes in the pathogenesis of allergic asthma are incomprehensive and fuel a continual drive to discover potential disease-linked pathogenic molecules and disease mechanisms. © XXXX American Chemical Society
Metabolomics is an emerging scientific discipline that integrates advanced analytical technologies, such as mass spectrometry, alongside bioinformatics to study global small molecule metabolite changes in biological systems.2,3 Alterations in the metabolome, the extensive assortment of endogenous and exogenous metabolites, can be differentially influenced by a variety of external stimuli, drug exposures, genetic modifications, and disease pathways, which results in distinctive shifts in Received: April 9, 2014
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the metabolic profile.4 Metabolomics is well-demonstrated to be a competent and reproducible platform technology, capable of capturing disease-relevant metabolic profile changes and molecular signatures of disease processes.5−9 For research on asthma, metabolomics has been highlighted to be an upcoming and promising analytical technology to identify novel metabolic biomarkers and disease mechanisms.10 Utilization of metabolomics to study allergic asthma has uncovered altered metabolism and metabolic profiles in serum, urine, and exhaled breath condensate samples from experimental and clinical asthma.11−16 Correspondingly, our recent metabolomics investigation of the bronchoalveolar lavage fluid (BALF) has further revealed promising inflammatory-related metabolic pathway modifications directly in allergic-inflamed lungs of experimental asthma.17 Complementing ovalbumin (OVA)-induced models of allergic asthma,18,19 there is increasing utilization of more biologically relevant experimental models, especially house dust mite (HDM)-induced disease models, to better elucidate relevant disease mechanisms and biomarkers.20 HDM is a multipart environmental aeroallergen, and exposures to HDM have been clinically proven to promote pathogenesis and increased severity of allergic asthma.21 Experimental models of HDM-induced asthma are well-accepted to better emulate the human disease, due to the use of biologically relevant HDM allergen extracts and physiologically relevant routes of intratracheal sensitization while avoiding the use of adjuvants.22 Till now, metabolomics have not been used to study HDM-induced asthma and thus may offer useful new information to identify aeroallergeninduced molecular processes. In this study, we hypothesized that there are inflammatoryassociated metabolic perturbations in the BALF, lung tissues, and serum of HDM-induced allergic asthma. A liquid chromatography−mass spectrometry (LC−MS) and gas chromatography−mass spectrometry (GC−MS) metabolomics approach was used to identify HDM-altered metabolite changes. In complement, inflammatory cell and cytokine profiles were analyzed to identify potential biological associations between inflammatory cells and cytokines to specific metabolites. Prednisolone, a clinically prescribed drug, was included to study corticosteroid-induced metabolic effects in allergic asthma. Our collective results demonstrate for the first time comprehensive inflammatory-associated alterations of the BALF, lung tissues, and serum metabolomes in HDM-induced allergic asthma.
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adhered to the Institutional guidelines for Animal Care and Use Committee of the National University of Singapore. Chemicals
Prednisolone, N-(9-fluorenylmethoxycarbonyl)-glycine (FMOC-glycine), and N-methyl-N-trimethylsilyl-trifluoroacetamide (MSTFA) were purchased from Sigma-Aldrich (St. Louis, MO, USA). Formic acid, pyridine, HPLC methanol, and ethanol were purchased from Merck (Darmstadt, Germany). Sample Collection
Cardiac puncture were performed on anesthetized mice 24 h after the last HDM challenge.18,23 Collected blood was incubated for 1 h prior to serum collection. Bronchoalveolar lavage was performed following cardiac puncture, using a cannula surgically inserted into the trachea. Ice-cold PBS (3× 500 μL) was instilled into the airways and a consistent volume of BALF (1400− 1450 μL) was recovered from each animal. Excised lung tissues were rinsed in ice-cold PBS and snap-frozen in liquid nitrogen. Samples were kept at −80 °C prior further analysis. Inflammatory Cell and Multiplex Cytokine Profile Analysis
Total and differential cell counts were determined from BALF.19,23 BALF cytokines were assayed using a murine 23-cytokines Luminex bead-based suspension array system coupled to a BioPlex Array Reader, according to manufacturer’s instructions (Biorad Laboratories, Inc., CA, USA). Sample Pretreatment
A 400 μL sample of BALF was concentrated using a freeze-dryer (Labconco Corporation, Kansas City, USA) at −85 °C and reconstituted using 200 μL ice-cold methanol spiked with 10 μg/mL FMOC-glycine as internal standard. Lung tissues were similarly lyophilized using the same freeze-dryer settings. A 2 mg sample of lyophilized powdered lung tissues was homogenized in 200 μL ice-cold methanol spiked with 10 μg/mL FMOC-glycine as internal standard by a bead-based homogenizer (Tissuelyser LT, Qiagen). The solution (BALF or lung tissues) was vortexed (3 min) and ultrasonically extracted (15 min, 4 °C). After two centrifugations (16000 rcf × 10 min, 4 °C), the supernatant was divided into two portions: 70 μL for LC−MS analysis directly and another 70 μL for GC−MS analysis. GC−MS Metabolomics Analysis
GC−MS derivatization was based on our earlier method.24 In essence, 70 μL supernatant was dried under nitrogen and derivatized by 70 μL methoxyamine (50 μg/mL in pyridine, 37 °C × 2 h) followed by 70 μL MSTFA (37 °C × 16 h). After centrifugation (6000 rcf × 1 min, 4 °C), 1.0 μL derivatized supernatant was injected splitlessly with an Agilent 7683 Series autosampler into an Agilent 6890 GC system equipped with a HP-5MSI column. The inlet temperature was set at 250 °C. Helium was used as the carrier gas at a constant flow rate of 1.0 mL/min. The column temperature was initially maintained at 70 °C for 1 min, then increased to 250 °C at a rate of 10 °C/min, and further increased at 25 °C/min to 300 °C where it remained for 5 min. The column effluent was introduced into the ion source of an Agilent Mass selective detector. The transfer line temperature was set at 280 °C, and the ion source temperature at 230 °C. The mass spectrometer was operated in electron impact (EI) mode (70 eV). Data acquisition was performed in full scan mode from m/z 50 to 550 with a scan time of 0.5 s. Compounds were identified by
MATERIALS AND METHODS
Animals and Treatment Groups
Female BALB/c mice, 6 to 8 weeks old (Canning Vale, Western Australia, Australia), were allowed to acclimatize for a week, prior to being sensitized and challenged using commercial HDM extracts (Dermatophagoides pteronyssinus, Greer Laboratories, Lenoir, NC, USA) to develop allergic airway inflammation.23 Isofluorane-anesthetized mice received intratracheal HDM exposures (100 μg in 40 μL saline) on days 0, 7, and 14. Saline controls (Saline) received a corresponding regime of ̈ (N) animals were included as baseline saline exposures. Naive controls. Prednisolone (5 mg/kg), dissolved in saline, was administrated intraperitoneally to N and HDM groups (N/Pred and HDM/Pred) on days 8, 10, 12, and 14. On day 14, Pred was administered 1 h prior to HDM challenge, consistent with our previous study.17 Animal experiments were approved and B
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comparison of mass spectra and retention time with those of reference standards and in libraries (NIST 5.0). LC−MS Metabolomics Analysis
LC−MS analysis was performed on an Agilent 1200 HPLC system equipped with 6410 QQQ mass detector.17 A 5 μL sample of the pretreated supernatant was injected into an Agilent 1200 HPLC system (Waldbronn, Germany) equipped with 6410 QQQ mass detector and managed by a MassHunter workstation. The column used for the separation was an Agilent rapid resolution HT zorbax SB-C18 (2.1 mm × 50 mm, 1.8 μm) (Agilent Technologies, Santa Clara, CA). The oven temperature was set at 50 °C. The gradient elution involved a mobile phase consisting of (A) 0.1% formic acid in water and (B) 0.1% formic acid in methanol. The initial condition was set at 5% B. The following solvent gradient was applied: from 95% A and 5% B to 10% A and 90% B within 10 min, hold for 10 min, then to 100% B within 5 min, and hold for 10 min. Flow rate was set at 0.2 mL/min, and 5 μL of samples was injected. The ESI-MS were acquired in positive and negative ion mode, respectively. The ion spray voltage was set at 3,000 V. The heated capillary temperature was maintained at 350 °C. The drying gas and nebulizer nitrogen gas flow rates were 10 L/min and 30 psi, respectively. For full scan mode analysis spectra were stored from m/z 100 to 1000. Compounds showing significant differences between groups were identified by high resolution and tandem mass spectrometry using an Agilent 6500 QTOF mass detector and validated by reference spectra using mass-to-charge ratio and by either MS/MS fragmentation patterns from Human Metabolome Database (HMDB, www. hmdb.ca) or reference standards. Data Preprocessing
Figure 1. HDM-induced allergic airway inflammatory cell recruitment and cytokine profile changes. (A) Total and differential inflammatory ̈ (N) and saline cell counts were analyzed in BALF obtained from naive controls (Saline) 24 h after the last intratracheal saline or HDM exposure (HDM) without and with 5 mg/kg intraperitoneal Prednisolone (N/Pred and HDM/Pred). Cell count data are presented as means ± SEM; # indicates statistical significance as compared to Saline, p < 0.05; * indicates statistical significance as compared to HDM, p < 0.05. n = 8 for N, n = 6 for N/Pred, n = 8 for Saline, n = 10 for HDM, and n = 8 for HDM/Pred. (B) BALF profiles of N, Saline, and HDM groups were analyzed using multiplex 23-cytokine murine cytokine assay and expressed as a heatmap, denoting fold change over normalized means. Red squares indicate increases up to 2-fold, white squares indicate little or no significant change, and blue squares indicate reductions up to 2-fold. # indicates statistical significance of HDM group as compared to Saline group, p < 0.05. n = 7 for N, n = 8 for Saline, and n = 11 for HDM.
Each chromatogram obtained from GC−MS and LC−MS analysis was processed for baseline correction and area calculation using MZmine 2.0 software package.25 Data were combined into a single matrix by aligning peaks with the same mass and retention time for GC−MS and LC−MS data, respectively. The area of each peak was normalized to that of internal standard (FMOC-glycine) in each data set. The missing values were replaced with a half of the minimum value found in the data set.6 A 90% filtering was applied to the raw data to include only metabolites that were detectable in 90% of the subjects in at least one of the treatment groups to ensure selection of relevant metabolites. Statistical Analysis
One-way ANOVA followed by Dunnett’s test was used to determine significant differences in cell counts and cytokines between groups. Principal component analysis (PCA) was initially carried out to generate an overview (Supplementary Figures S1−S3). Metabolic profile discriminations between groups were modeled by score plots using orthogonal projections to latent structures discriminant analysis (OPLS-DA). For OPLS-DA, data were mean-centered and unit variance scaled. Multivariate statistics (OPLS-DA) and a nonparametric test (Kruskal−Wallis) were employed to identify treatment group differences in mass abundance of metabolites using SIMCA-P V11.0 (Umetrics AB, Umea, Sweden). CV-ANOVA (for validation of OPLS-DA plots) was computed to verify OPLSDA model significance with a high statistical significance for the F-test using an updated version of SIMCA-P V13.0 (Umetrics AB, Umea, Sweden). Colored circles have been illustrated on respective OPLS-DA plots to provide better visualization of
respective treatment groups. Heatmaps were visualized, and fold changes over normalized mean (overall mean for each metabolite) were calculated using MultiExperiment View V4.6.1 (www.tm4.org). Statistical correlations were determined using Pearson’s correlation analysis. Metabolite relationships were derived from HMDB, Small Molecule Pathway Database (SMPDB, www.smpdb.com) and existing literature.
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RESULTS
Allergic HDM Exposures Induced Airway Inflammation and Cytokine Profile Alterations
Total and differential inflammatory cell counts were analyzed in the BALF collected 24 h following the last intratracheal ̈ and saline controls HDM exposure. As compared to naive C
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̈ mice (N), saline Figure 2. HDM-induced metabolic perturbations in BALF. OPLS-DA of global BALF metabolite profiles in the BALF of naive controls (Saline), and HDM exposure (HDM), detected by (A) LC−MS analysis and (B) GC−MS analysis. OPLS-DA comparing BALF metabolite ̈ mice (N), treated with Prednisolone treatment (N/Pred), and HDM exposure (HDM) treated with Prednisolone (HDM/Pred), profiles in naive detected by (C) LC−MS analysis and (D) GC−MS analysis. The x axis, t[1], and y axis, t[2], indicate the first and second principle components, respectively. (E) Significant fold level changes in BALF metabolites are represented as a heatmap depicting metabolite changes in all treatment groups detected by either LC−MS or GC−MS. Kruskal−Wallis test: * indicates statistical significant difference of HDM group vs Saline, p < 0.05; + indicates statistical significant difference of HDM/Pred group vs HDM, p < 0.05. n = 8 for N, n = 6 for N/Pred, n = 8 for saline, n = 10 for HDM, and n = 8 for HDM/Pred. Colored circles are illustrated on the OPLS-DA plots to provide better visualization of respective treatment groups.
and HDM were analyzed, using a multiplex murine cytokine profile assay (Figure 1B). Recurrent HDM airway exposures led to concomitant increases in G-CSF, IL-12(p40), IL-4, IL-5, KC, IL-10, MCP-1, MIP-1α, and MIP-1β, as compared to saline controls.
(N and Saline), recurrent airway exposures to HDM significantly promoted robust increases in total cell counts, macrophage, and lymphocyte numbers, with pronounced eosinophilia and neutrophilia (Figure 1A). Administration of prednisolone ̈ mice (5 mg/kg, i.p.) did not affect BALF cell counts in naive (N/Pred) but was found effective in abating HDM-induced increases in total inflammatory cells, especially eosinophils and macrophages (HDM/Pred). However, prednisolone was ineffective against neutrophil recruitment and promoted increases in pulmonary neutrophils. BALF cytokine profiles of N, Saline,
Pulmonary Exposures to HDM Lead to Altered BALF Metabolome
BALF samples were analyzed using LC−MS and GC−MS, and global metabolite profiles were investigated using PCA D
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Figure 3. Pearson correlation analysis reveals statistical associations among BALF metabolites. A correlation heatmap is used to represent significant statistical correlation values (r) among BALF metabolites in N, Saline, and HDM groups (n = 26). Blue squares indicate significant negative correlations (−0.390 to −0.721, p < 0.05), white squares indicate nonsignificant correlations (p > 0.05), gray squares indicate nonapplicable correlations, and red squares indicate significant positive correlations (0.390 to 0.997, p < 0.05).
Statistical Correlations between BALF Metabolites and Associations with Inflammatory Cells and Cytokines
(Supplementary Figure S1), supervised clustering, and OPLSDA (Figure 2A−D) and validated using pairwise OPLS-DA (Supplementary Figure S4) and CV-ANOVA (Supplementary Table S1, p < 0.05). In OPLS-DA analysis, the value of R2Y describes that fraction of Y variance explained by a specific model component, while the value of Q2 describes the predictive accuracy of the model. Typically, models with Q2 values greater than or equal to 0.5 are generally considered to have good predictive capability.26,27 BALF metabolic profiles of HDM-exposed mice were observed to differ distinctively from metabolic profiles of Saline and N, by LC−MS (Figure 2A) and GC−MS (Figure 2B). The R2Y scores (LC−MS, 0.946; GC− MS, 0.933), accompanied by predictive Q2 values (LC−MS, 0.808; GC−MS, 0.708) implied effective differentiation of HDM-exposed group from Saline and N controls by metabolomics. Although prednisolone could substantially suppress airway inflammation (Figure 1A), the corticosteroid was observably ineffective in modulating HDM-induced alterations of BALF metabolome (Figure 2C and D). HDM exposures induced consistent increases in energy metabolites (urea, lysylarginine, creatine, and malate) and losses in sugars (mannose, galactose, glucose, and gluconic acid). The aeroallergen further caused reductions in sterols and bile acids (cholesterol, cortol, 3-keto-4-methylzymosterol, cholic acid, and 12b-hydroxy-5b-cholanoic acid) and considerable losses in 13 phosphatidylcholines (PCs) and corresponding increase in choline.
Pearson correlation analysis was used to identify potential links between altered BALF metabolites and associations with inflammatory cells and cytokines. Significant positive correlations were observed between BALF sugars, amino acids, sterols, and PCs (Figure 3), implying metabolites from the same chemical classes are modulated in the same fashion by HDM exposures. Choline, creatine, and malate levels were found to be negatively related to glucose, amino acids, sterols, and PCs levels. Inflammatory cells, IL-4, IL-5, IL-10, IL-12p40, IL-13, and KC were determined to correlate negatively with sugars, sterols, and PCs (Figure 4). These inflammatory cells and cytokines were also statistically linked positively to choline, malate, methyl-hippuric acid, and lysyl-arginine levels. Lung Metabolome Is Modified by HDM-Induced Allergic Inflammation
To provide first-hand metabolic understanding of allergicinflamed lung tissues, we used metabolomics to identify potential novel HDM-induced metabolic changes in the lungs. Preliminary PCA (Supplementary Figure S2) indicated no distinctive outliers. OPLS-DA of global metabolite changes in lung tissues revealed consistent separation of HDM-exposed lung metabolic profiles, by LC−MS (Figure 5A) and GC−MS analysis (Figure 5B), as compared to N and Saline controls. Satisfactory R2Y values (LC−MS, 0.962; GC−MS, 0.849) and predictive Q2 scores (LC−MS, 0.857; GC−MS, 0.587) similarly E
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metabolism of PCs in HDM-induced allergic inflammation. BALF energy metabolites, especially malate, were noted to correlate strongly to many lung metabolite changes. Lung mannose was also noted to significantly associate with many BALF metabolic changes, further implying that these two metabolites and others highlighted may be biologically important molecules involved in HDM-induced airway inflammation. HDM-Induced Lung Inflammation Promotes Serum Metabolome Changes
To complement BALF and lung tissues metabolome alterations, we looked for corresponding differences in serum metabolic profiles of N, Saline, and HDM mice using PCA (Supplementary Figure S3) and OPLS-DA (Figure 7A−D). Our results indicate observable separation of serum metabolite profiles between HDM, N, and Saline animals, detected by LC−MS and GC−MS (Figure 7A and B), as indicated by good modeling R2Y scores, 0.859 (LC−MS) and 0.928 (GC−MS). Poorer predictive Q2 values (LC−MS, 0.436; GC−MS, 0.318) suggest metabolomics analysis of serum profiles is comparatively weaker than metabolomics investigations of BALF or lung profiles to discriminate allergic-inflamed animals from Saline and N controls. Metabolomics analysis of serum profiles was able to discriminate corticosteroid-induced metabolic differences between HDM/Pred and HDM, only by LC−MS (Figure 7C), as affirmed by agreeable values of R2Y (0.889) and Q2 (0.623), further validated using pairwise OPLS-DA (Supplementary Figure S6) and CV-ANOVA (Supplementary Table S1, p < 0.05). Numerous serum metabolites could be potentially linked to BALF and lung metabolites. Losses in pulmonary carbohydrates corresponded to reduction in serum mannose and talose, alongside increases in glucose and galactopyranose (Figure 7E). Increases in lung urea-arginine metabolites, urea, creatine, and lysyl-arginine also resonated with increases in serum urea, creatine, symmetric dimethylarginine (SDMA), and proline. HDM-induced alterations in lung lipids were potentially linked to losses in serum PCs, LPCs, choline, and succinylcholine and increases in fatty acids, hexadecanoic acid, and octadecanoic acid.
Figure 4. Statistical associations of BALF metabolites to inflammatory cell and cytokines. A correlation heatmap is used to represent significant statistical correlation values (r) of inflammatory cells and cytokines to BALF metabolites in N, Saline, and HDM groups (n = 25). Total = total cell counts, Mac = macrophages, Eos = eosinophils, Lym = lymphocytes, and Neu = neutrophils. Blue squares indicate significant negative correlations (−0.435 to −0.847, p < 0.05), white squares indicate nonsignificant correlations (p > 0.05), and red squares indicate significant positive correlations (0.435 to 0.937, p < 0.05).
implied good discrimination of HDM lung metabolic profiles from controls, further validated using pairwise OPLS-DA (Supplementary Figure S5) and CV-ANOVA (Supplementary Table S1, p < 0.05). Prednisolone was ineffective against modulating lung metabolic profiles despite exerting antiinflammatory effects against pulmonary eosinophilia (Figure 5C and D). Notably, prednisolone could modify lung metabolite ̈ animals as indicated by LC−MS analysis profiles in naive (Figure 5C). Multivariate statistical analysis revealed 39 specific lung metabolites were significantly altered in HDM-exposed mice, as compared to saline controls (Figure 5E). Prednisolone could reverse 8 HDM-induced metabolite changes of amino acids and lipids (homoserine, tetradecanoyl-carnitine, glucosaminide, PC (28:0), PC (36:6), PC (37:5), DF (36:8)) and independently reduced lung metabolite levels of N-acetyllactosamine and PC (31:1).
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DISCUSSION Existing metabolomics studies have consistently identified promising metabolic profile changes in asthmatic serum, urine, and exhaled breath condensate samples11−14,16 but have not directly investigated relevant lung metabolome changes. Our recent study has identified promising metabolic markers and pathways directly in allergic-inflamed airways, derived from an OVA-induced asthma model.17 Despite established evidence that HDM is a major pro-inflammatory allergen that directly promotes disease pathogenesis and severity in most asthmatics, there are no existing metabolomics investigations that have explored metabolic changes specific to HDM-induced allergic asthma. To provide first-hand experimental understanding of HDM-specific metabolic alterations in allergic asthma, we employed an integrated LC−MS and GC−MS metabolomics analysis to identify potential diseaserelevant metabolite changes in the BALF, lung tissues, and serum of HDM-induced allergic asthma. In complement, the BALF inflammatory cell profile and cytokine profiles were also investigated to provide potential biological associations between pulmonary metabolites and inflammatory responses. We acknowledge that there are some inconsistencies in the sample sizes in the Bioplex and metabolomics analyses due to individual experimental assay constraints, and thus some samples (N8 or HDM11) were not included in the respective analyses.
Statistical Associations between Lung and BALF Metabolites Altered by Airway Inflammation
Pearson correlation analysis was employed to uncover potential biological links between lung and BALF metabolites (Figure 6). Our correlation analysis identified multiple significant associations between HDM-altered BALF and lung metabolites. BALF sugars and sterols were negatively related to lung amino acids and lipids but were positively linked to lung mannose. Interestingly, BALF PCs were negatively related to lung PCs, suggesting possible differences in intracellular and extracellular F
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Figure 5. HDM exposures induced metabolic alterations in lung tissues. OPLS-DA of global lung metabolite profiles detected in the lung tissues of ̈ mice (N), saline exposure (Saline), and HDM exposure (HDM), detected by (A) LC−MS analysis and (B) GC−MS analysis. OPLS-DA naive ̈ mice (N), treated with Prednisolone treatment (N/Pred), and HDM exposure (HDM) treated with comparing lung metabolite profiles in naive Prednisolone (HDM/Pred), detected by (C) LC−MS analysis and (D) GC−MS analysis. The x axis, t[1], and y axis, t[2], indicate the first and second principle components, respectively. (E) Significant fold level changes in lung metabolites are expressed as a heatmap showing metabolite ̈ (N), treated with Prednisolone (N/Pred), saline exposure (Saline), HDM-induced airway inflammation (HDM), and treated with changes in Naive Prednisolone (HDM/Pred), detected by either LC−MS or GC−MS. Kruskal−Wallis test: * indicates statistical significant difference of HDM group vs Saline, p < 0.05; + indicates statistical significant difference of HDM/Pred group vs HDM, p < 0.05. n = 8 for N, n = 6 for N/Pred, n = 8 for Saline, n = 10 for HDM and n = 8 for HDM/Pred. Colored circles are illustrated on the OPLS-DA plots to provide better visualization of respective treatment groups.
From BALF and lung tissues, we identified several consistent metabolic signatures in HDM-induced allergic asthma, which corroborated strongly our previous report of OVA-induced asthma.17 Here, our investigation reveals comprehensive inflammatory-associated metabolism changes relating to choline-phosphatidylcholines and sterols metabolism, urea
Our present investigation successfully uncovered concurrent HDM-induced metabolic profile changes in the BALF, serum, and lung tissues. Many altered metabolites share manifold statistical associations to inflammatory cells counts and cytokines, implicating that they may be biologically relevant metabolic changes linked to airway inflammation. G
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Figure 6. Pearson correlation analysis uncovers statistical associations of altered lung metabolites to BALF metabolites levels. A correlation heatmap is used to represent correlation values (r) of statistical associations between lung metabolites to BALF metabolite levels in N, Saline, and HDM groups (n = 26). Blue squares indicate significant negative correlations (−0.390 to −0.809, p < 0.05). white squares indicate nonsignificant correlations (p > 0.05), and red squares indicate significant positive correlations (0.390 to 0.750, p < 0.05).
increases in energy metabolites and related metabolic pathways. We have detected reduction of carbohydrates, such as glucose, mannose, and galactose, in HDM-induced allergic airways, which could be strongly related to increased energy burden in the inflamed lungs.17 Mannose and galactose have been identified to possess anti-inflammatory properties in inflamed airways.31,32 Exclusive in this study, mannose was detected to be synchronously depleted in the BALF, lung, and serum samples of HDM-exposed mice. Our correlation analyses have also identified strong negative correlations of mannose to many inflammatory cells and cytokines (Figure 4), while sharing strong positive correlations to many altered metabolites in the airways (Figure 6). This evidence suggests strong biological roles for mannose in allergic asthma and highlights its potential to be an indicative metabolic biomarker. While additional sugars (inositol, gluconic acid, talose) were observed to be affected in allergic-inflamed airways, their biological functions in allergic airway diseases have not been well-elucidated. For the first time, we identified the involvement of the glucose-alanine metabolic pathway in allergic-inflamed lungs, with decreases in upstream glucose and corresponding increases in downstream alanine and byproducts, urea, L-glutamate, and glutamine (Figure 8). The glucose-alanine cycle is an alternative energy-producing metabolic pathway that is activated during anaerobic cellular respiration.33 Activation of the glucose-alanine
cycle-arginine metabolism, carbohydrates, and glucose-alanine cycle (Figure 8). Phosphatidylcholines (PCs) have been linked to lung surfactant levels, and losses of PCs can lead to reduced lung functions in asthmatic airways.28 A recent metabolomics study of asthmatic patients’ serum profile has similarly reported deregulated PCs levels, which correlated to asthma-risk alleles.15 Consistent with our previous report,17 we observed substantial reductions in BALF PCs, with corresponding adaptive increases in choline. Interestingly, we report here for the first time an increase in PCs in allergic-inflamed lung tissues, revealing distinctive differences in lipid metabolism between intracellular (lung) and extracellular (BALF) PCs in HDM-induced allergic asthma. Our hypothesis is affirmed by statistical correlation analysis showing multiple strong negative statistical correlations between BALF and lung PCs. Adaptive increases in choline, observed in this present study, corroborated with our previous observation.17 Choline possesses protective anti-inflammatory actions in allergic-inflamed airways, and reports have demonstrated that excessive supplement of choline is clinically beneficial to allergic airway inflammation.29,30 We further noted choline to be reduced in asthmatic serum, which corroborated with clinical observations.11 Another inflammatory-linked metabolic signature identified is the depletion of pulmonary carbohydrates, alongside H
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Figure 7. Serum metabolite profile is modified by pulmonary exposures of HDM. OPLS-DA of global serum metabolite profiles detected in the lung ̈ mice (N), saline exposure (Saline), and HDM exposure (HDM), detected by (A) LC−MS analysis and (B) GC−MS analysis. tissues of naive ̈ mice (N), treated with Prednisolone treatment (N/Pred), HDM exposure (HDM), and OPLS-DA comparing serum metabolite profiles in naive treated with Prednisolone (HDM/Pred), detected by (C) LC−MS analysis and (D) GC−MS analysis. The x axis, t[1], and y axis, t[2], indicate the first and second principle components, respectively. (E) Significant fold level changes in serum metabolites are expressed as a heatmap showing ̈ (N), treated with Prednisolone (N/Pred), saline exposure (Saline), HDM-induced airway inflammation (HDM), and metabolite changes in Naive treated with Prednisolone (HDM/Pred), detected by either LC−MS or GC−MS. Kruskal−Wallis test: # indicates statistical difference of N/Pred group vs N, p < 0.05; * indicates statistical significant difference of HDM group vs Saline, p < 0.05; + indicates statistical significant difference of HDM/Pred group vs HDM, p < 0.05. n = 8 for N, n = 6 for N/Pred, n = 8 for Saline, n = 10 for HDM, and n = 8 for HDM/Pred. Colored circles are illustrated on the OPLS-DA plots to provide better visualization of respective treatment groups.
broad anti-inflammatory effects in allergic asthma.36 Here, we also detected dampening of sterol metabolism, specifically, reduction in 3-keto-4-methylzymosterol, cholesterol, cholic acid, 12bhydroxy-5b-cholanoic acid, and cortol. 3-Keto-4-methylzymosterol and cholesterol are major upstream intermediary metabolites of steroidogenesis, which leads to endogenous cortisol formation. Cortol is a downstream metabolite formed from the metabolism of cortisol and related steroids.37 Indeed, we observed strong positive intercorrelations between BALF sterols (Figure 3), which suggest that these sterols are modulated concomitantly in HDM-induced lung inflammation. We also noted strong negative statistical associations with the increase of inflammatory cells and inflammatory cytokines
cycle will lead to concurrent increase in the deamination process via the urea cycle, due to the excessive production of ammonia (NH3) from the metabolism of alanine and L-glutamate.34 The urea cycle has been implicated in previous clinical reports, via increased arginine metabolism, which promotes the asthmatic phenotype.35 Correspondingly, we noted increases in multiple arginine-related metabolites, urea, lsysl-arginine, SDMA, proline, and creatine. Increases in malate, an energy metabolite produced by the citric acid cycle (TCA cycle), have also been consistently observed in this present and earlier investigations,17 indicating definitive increases in pulmonary energy respiration via the TCA cycle. Sterol biosynthesis is a vital biological process that synthesizes regulatory steroidal hormones, such as cortisol, which modulates I
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Figure 8. Altered lung metabolic pathways in HDM-induced allergic asthma. Overview of altered key lung metabolite changes in HDM-induced allergic asthma, detected by LC−MS and GC−MS metabolomics analysis of allergic-inflamed BALF and lung tissues. Metabolite relationships are derived from HMDB, SMPDB, and existing literature. Black arrows indicate direct relationships between two metabolites, dotted arrows indicate indirect relationships between two metabolites, red block arrows indicate trend changes in HDM-induced allergic-inflamed lungs, and green block arrows indicate trend changes by Prednisolone treatment in HDM-induced allergic lungs.
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CONCLUSIONS Our collective results have revealed for the first time comprehensive HDM-specific inflammatory-associated metabolic signatures in the BALF, lung tissues, and serum of HDMinduced experimental allergic asthma. These metabolic signatures include modifications in the sterol metabolism and choline-phosphatidylcholines metabolism and reduction of pulmonary carbohydrates with corresponding increases in energy metabolism (Figure 8). This present study, complemented by our recent report of altered BALF metabolome in experimental asthma,17 demonstrates considerable and corroborative evidence that altered pulmonary metabolism may be a major underlying molecular feature involved during HDMinduced allergic airway inflammation that is broadly linked to inflammatory cells and cytokines changes. Succeeding the earlier study, this metabolomics study identified a comprehensive collection of compartmental profiles of HDM-specific metabolite alterations in the BALF, lung, and serum samples. This report further demonstrates that metabolomics investigations of BALF, serum, and lung tissues of allergic asthma can potentially provide invaluable molecular understanding of inflammatory airway diseases and offer prospective diseaserelevant metabolic biomarkers for clinical diagnosis.
alongside decreasing levels of respective pulmonary sterols (Figure 4). While dampened cholesterol metabolism can also result in reduced bile acids biosynthesis, specifically, decreases in cholic acid and 12b-hydroxy-5b-cholanoic acid, the biological significance of reduced bile acids in allergic-inflamed airways are presently unclear. Prednisolone was effective against HDM-induced airway eosinophilia but was unable to suppress neutrophilic inflammation (Figure 1A). Indeed, corticosteroids are reported to be ineffective against neutrophil recruitment and can lead to pronounced neutrophilic inflammation via inhibition of apoptosis in neutrophils.38 This finding is consistently validated in this investigation and in previous reports.17,19As opposed to our earlier report on pronounced effects of dexamethasone,17 prednisolone demonstrated considerably weaker reversal effects against HDM-induced metabolic alterations. This could be attributed to the unresolved neutrophilic inflammation, promoted by robust increases in pro-neutrophilic cytokines, especially G-CSF and IL-12(p40).39,40 Correspondingly, many prednisolone-unaltered metabolites share strong statistical correlations to neutrophil counts and related inflammatory cytokines (Figure 4). Apart from reversing changes in HDMinduced metabolites, choline, creatine, and malate, prednisolone was observed to promote increases in amino acids, homoserine, tyrosine, methionine, and thymidine. However, the biological relevance of these amino acids in airway inflammation is not well-understood at present. Therefore, this study demonstrates that current glucocorticoid-based therapeutics may be ineffective against reversing specific major inflammatory-linked metabolite changes in HDM-allergic asthma and advocates the potential development of pharmacological agents that target these metabolic alterations as an alternative therapeutic strategy against allergic asthma.
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ASSOCIATED CONTENT
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AUTHOR INFORMATION
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Author Contributions
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These authors contributed equally to this work.
Notes
The authors declare no competing financial interest.
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ACKNOWLEDGMENTS W.E.H. is a recipient of the Singapore-MIT Alliance (SMA) Graduate Fellowship. H.Y.P. is a recipient of the NUS-Industry Relevant Programme (IRP) Research Scholarship. The authors would like to thank NERI-Agilent Research Alliance for their technical support. This research work was supported in part by NMRC/CBRG/0027/2012 (W.S.F.W.) from the National Medical Research Council of Singapore, NUS Environmental Research Institute and SPH Centre for Environmental and Occupational Health Research.
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ABBREVIATIONS BALF, bronchoalveolar lavage fluid; FMOC-glycine, N-(9fluorenylmethoxycarbonyl)-glycine; GC−MS, gas chromatography−mass spectrometry; HDM, house dust mite; HMDB, human metabolome database; IL, interleukin; LPCs, lysophos̈ OPLS-DA, orthogonal projections phatidylcholines; N, naive; to latent structures discriminant analysis; OVA, ovalbumin; PBS, phosphate-buffered saline; PCs, phosphatidylcholines; PCA, principle component analysis; Pred, prednisolone; SDMA, symmetric dimethylarginine; SMPDB, small molecule pathway database
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