The Effect of Chinese Herbal Medicine Formula

International Journal of

Molecular Sciences Article

The Effect of Chinese Herbal Medicine Formula mKG on Allergic Asthma by Regulating Lung and Plasma Metabolic Alternations Meng Yu, Hong-Mei Jia, Feng-Xia Cui, Yong Yang, Yang Zhao, Mao-Hua Yang and Zhong-Mei Zou * Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100193, China; [email protected] (M.Y.); [email protected] (H.-M.J.); [email protected] (F.-X.C.); [email protected] (Y.Y.); [email protected] (Y.Z.); [email protected] (M.-H.Y.) * Correspondence: [email protected]; Tel./Fax: +86-10-5783-3290 Academic Editor: Maurizio Battino Received: 8 January 2017; Accepted: 4 March 2017; Published: 10 March 2017

Abstract: Asthma is a chronic inflammatory disorder of the airway and is characterized by airway remodeling, hyperresponsiveness, and shortness of breath. Modified Kushen Gancao Formula (mKG), derived from traditional Chinese herbal medicines (TCM), has been demonstrated to have good therapeutic effects on experimental allergic asthma. However, its anti-asthma mechanism remains currently unknown. In the present work, metabolomics studies of biochemical changes in the lung tissue and plasma of ovalbumin (OVA)-induced allergic asthma mice with mKG treatment were performed using ultra high-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF/MS). Partial least squares–discriminate analysis (PLS−DA) indicated that the metabolic perturbation induced by OVA was reduced after mKG treatment. A total of twenty-four metabolites involved in seven metabolic pathways were identified as potential biomarkers in the development of allergic asthma. Among them, myristic acid (L3 or P2), sphinganine (L6 or P4), and lysoPC(15:0) (L12 or P16) were detected both in lung tissue and plasma. Additionally, L-acetylcarnitine (L1), thromboxane B2 (L2), 10-HDoHE (L10), and 5-HETE (L11) were first reported to be potential biomarkers associated with allergic asthma. The treatment of mKG mediated all of those potential biomarkers except lysoPC(15:0) (P16). The anti-asthma mechanism of mKG can be achieved through the comprehensive regulation of multiple perturbed biomarkers and metabolic pathways. Keywords: allergic asthma; mKG; Sophora flavescens; Glycyrrhiza uralensis; Angelica oil; metabolomics; biomarkers; UPLC-Q-TOF/MS

1. Introduction Asthma is a chronic inflammatory disorder of the airway characterized by airway remodeling, hyperresponsiveness, and shortness of breath [1,2]. Currently, three types of prescription medications, including inhaled short and long-acting β2 agonists, inhaled and oral corticosteroids, and leukotriene antagonists are usually adopted for the treatment of asthma patients [3]. However, these treatments are not effective for all patients and also demonstrate significant, reproducible, undesirable side effects of systemic corticosteroids in the therapeutic response [4]. Accordingly, the search for new anti-asthma drugs is an area of intense research activity. More recently, Chinese medicines and their formulas have aroused much interest especially due to their low side effects in the treatment of asthma [5]. Modified Kushen Gancao Formula (mKG), derived from traditional Chinese herbal medicines (TCM), has been demonstrated to show prominent anti-inflammatory effects and good therapeutic Int. J. Mol. Sci. 2017, 18, 602; doi:10.3390/ijms18030602

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effects on pulmonary fibrosis in our previous study [6]. It contains three herbs: viz., the roots of Sophora flavescens Ait. (Ku-Shen), the rhizome of Glycyrrhiza uralensis Fisch. (Gan-Cao), and Angelica oil. Ku-Shen is a commonly used TCM, used mainly in combination with other medicinal plants in prescriptions to treat fever, dysentery, jaundice, eczema, asthma, and inflammatory disorders [7]. Recent studies have revealed that the extract of Ku-Shen suppresses both helper T cell 2 (Th2) and tumor necrosis factor-α (TNF-α) associated inflammation in murine asthma models [8]. The primary effective components of Ku-Shen are matrine and oxymatrine, and both have been demonstrated to show significant anti-asthmatic effects in OVA-induced allergic asthma mouse models [9,10]. Gan-Cao is a very common herbal medicine used in almost half of Chinese herbal formulas to reduce toxicity, enhance effectiveness, and improve the flavor of other herbs [11]. It has been reported that Gan-Cao extract has obvious anti-asthma effects, which is associated with the inhibition of the production of IgE in OVA-sensitized asthma mice [12]. The primary constituents of Gan-Cao are glycyrrhizin, and a mixture of calcium and potassium salts of glycyrrhizinic acid which represent the efficacy on asthmatic features via attenuating inflammation effects in asthma mouse models [13,14]. In addition, Ku-Shen and Gan-Cao are also the major herbs in ASHMI™ (Anti-asthma the Herbal Medicine Intervention), which can prevent allergic asthma airway hyper-reactivity in mice and inhibits acetylcholine (ACh) induced airway smooth muscle (ASM) contraction in tracheal rings from allergic asthmatic mice [15]. Angelica oil is considered to be the main biologically active component in the roots of Angelica sinensis (Oliv.) Diels and exhibits many bioactivities, especially anti-inflammatory, immunostimulatory, and anti-cancer effects [16]. Preclinical studies have indicated that Angelica oil suppresses airway smooth muscle contraction and is often used to treat asthma and other allergic diseases [17]. The main chemical constituents of Angelica oil include Z-ligustilide, butylphthalide, and senkyunolide A. Combination therapies of TCM prescription have been validated and show potential clinical benefits. To achieve a better therapeutic effect, the three Chinese herbs are combined in a single prescription mKG to treat allergic asthma. More research is needed to investigate the effectiveness and the mechanism of the herbal formula. Metabolomics, the comprehensive analysis of small biochemicals in biological systems, can provide insight into complex biochemical processes and enable the identification of biomarkers that may serve as therapeutic targets [18]. Successful applications of the metabolomic approach have been located at active herb molecules, single herb preparations, and multiple herb derived prescriptions [19,20]. Therefore, utilizing a metabolomics platform to explore disease-relevant metabolic profile changes and discover potential biomarkers can provide valuable information for a deeper understanding of the pathological mechanisms of disease and drug treatment, especially in complex diseases. The OVA-induced allergic asthma mouse model is becoming increasingly popular as an animal model of allergic asthma to elucidate asthma pathology and evaluate new therapeutic agents. Correspondingly, our recent metabolomics investigation of the plasma has further revealed promising allergic asthma-related metabolic biomarkers from the mouse model of OVA-induced allergic asthma [21]. Metabolites in lung tissue could directly reflect the pathophysiological state, and potentially generate unique perspectives for understanding disease mechanisms. Therefore, in this paper the metabolic profiles of the lung tissue and plasma in the mouse model of OVA-induced allergic asthma with and without treatment by mKG were investigated using UPLC-Q-TOF/MS to provide a comprehensive understanding of the underlying mechanism of mKG against allergic asthma. 2. Results 2.1. Identification of the Chemical Constituents in mKG Extract The mKG is composed of Kushen Gancao extract and Angelica oil. The Kushen Gancao extract samples were analyzed using the UPLC-Q-TOF/MS method. The base peak intensity (BPI) chromatograms of mKG, Kushen, and Gancao extracts in positive ion electrospray ionization (ESI)

samples were analyzed using the UPLC-Q-TOF/MS method. The base peak intensity (BPI) chromatograms of mKG, Kushen, and Gancao extracts in positive ion electrospray ionization (ESI) mode are shown in Figures 1A and S1. The MS data showed high precision with all the mass accuracies within 5 ppm, which provided valuable information for confirming the accurate Int. J. Mol. Sci.weight 2017, 18, 602 3 of 18 molecular and composition of the constituents. Forty-nine compounds were tentatively E identified on the basis of their retention behaviors, accurate molecular weight, and MS fragment data, or by comparison with reference substances or literature data (chemical structures are shown mode are shown in Figure 1A and Figure S1. The MS data showed high precision withE all the mass in Figure S2, corresponding quasi-molecular ions and their fragment ions in the MS spectra are accuracies within 5 ppm, which provided valuable information for confirming the accurate molecular listed in Table S1). weight and composition of the constituents. Forty-nine compounds were tentatively identified on The Angelica oil was analyzed by the gas chromatography-mass spectrometer (GC-MS) the basis of their retention behaviors, accurate molecular weight, and MSE fragment data, or by method. The total ion chromatograms (TIC) of Angelica oil is shown in Figure 1B. A total of 15 comparison with reference substances or literature data (chemical structures are shown in Figure S2, compounds were identified based on reference substances or literature data (Figure S3 and Table corresponding quasi-molecular ions and their fragment ions in the MSE spectra are listed in Table S1). S2).

Figure Figure 1. BPI chromatogram of mKG extract (A) in positive ion mode analyzed by UPLC-Q-TOF/MS, UPLC-Q-TOF/MS, and TIC TIC chromatogram chromatogram of of Angelica Angelica oil oil (B) (B) analyzed analyzed by by using using GC-MS. GC-MS.

2.2. Histopathology The Angelica oil was analyzed by the gas chromatography-mass spectrometer (GC-MS) method. The total ion chromatograms (TIC)tissue of Angelica oil is shown in Figure 1B. A total of 15 compounds Pathological changes of lung were assessed by hematoxylin-eosin (HE) staining of the were identified based on reference substances or literature data (Figure S3 and Table S2). paraffin embedded section (Figure 2). All pathological abnormalities in OVA-induced allergic asthma mice, such as infiltration of inflammatory cells into perivascular and connective tissues, 2.2. Histopathology lumen narrowing, and mucosa thickening, were markedly ameliorated by both mKG and Pathological pretreatments. changes of lungThe tissue were assessed byresults hematoxylin-eosin (HE) staining the dexamethasone histopathological demonstrated that mKGofand paraffin embedded section (Figure 2). All pathological abnormalities in OVA-induced allergic asthma mice, such as infiltration of inflammatory cells into perivascular and connective tissues, lumen narrowing, and mucosa thickening, were markedly ameliorated by both mKG and dexamethasone pretreatments. The histopathological results demonstrated that mKG and dexamethasone showed a similar anti-asthma effect on the pathological changes with OVA-challenged allergic asthma mice (Figure 2C,D).

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dexamethasone showed a similar anti-asthma effect on the pathological changes with dexamethasone a similar anti-asthma OVA-challenged showed allergic asthma mice (Figure 2C,D). effect on the pathological changes 4with Int. J. Mol. Sci. 2017, 18, 602 of 18 OVA-challenged allergic asthma mice (Figure 2C,D).

Figure 2. Example of pathological changes of the lung tissue observed by Hematoxylin and Eosin Figure 2. Example of of pathological pathological changes changes of of the the lung lung tissue tissue observed observed by by Hematoxylin Hematoxylin and and Eosin Eosin Figure 2. Example (HE) staining (Light microscopy, ×400): (A) (control); (B) (model); (C) (Dexamethasone (2 mg·kg−−11)); (HE) staining (Light microscopy, × 400): (A) (A) (control); (control); (B) (B) (model); ·kg −1)); )); (HE) staining ×400): (model); (C) (C) (Dexamethasone (Dexamethasone (2 (2 mg mg·kg −1microscopy, (D) (mKG (9.7(Light g·kg− 1)). (D) (mKG (mKG (9.7 ·kg−1)). )). (D) (9.7 g g·kg

2.3. Lung Tissue Metabolic Profiles of OVA-Induced Allergic Asthma with mKG Treatment 2.3. Lung Tissue Metabolic Profiles of OV A-Induced Allergic OVA-Induced AllergicAsthma Asthmawith withmKG mKGTreatment Treatment Metabolic profiles of the lung tissue in each group were analyzed by UPLC-Q-TOF/MS in Metabolic profiles ofofthethe lung tissue in each groupgroup were analyzed by UPLC-Q-TOF/MS in positive profilesmodes. lung tissue in chromatograms each were analyzed by UPLC-Q-TOF/MS in positive and negative The typical BPI of all experimental groups are shown and negative modes. The typical BPI chromatograms of all experimental groups are shown inare Figure S4. positive and negative modes. The typical BPI chromatograms of all experimental groups shown in Figure S4. A supervised PLS−DA model was performed for the analysis of the normalization A supervised −DA model was performed for the analysis of spectral data from in Figuredata S4. PLS A supervised PLS−DA model was performed forthe thenormalization analysis of cross-validation the normalization spectral from all the experimental groups. Meanwhile, we used seven-fold to all the experimental groups. Meanwhile, we used seven-fold cross-validation to validate the statistical spectral from all the experimental groups. Meanwhile, we used seven-fold cross-validation validate data the statistical significance for the PLS−DA model. The parameters for the classification to of significance for the PLS −DA model. ThePLS−DA parameters for the classification of the the classification lung metabolic validate statistical significance the model. Themodes parameters for of the lungthe metabolic profiles both infor positive and negative ion are shown in Table 1, which profiles both in positive and both negative ion modes are shown in Table 1, which support good prediction the lung metabolic profiles in positive and negative ion modes are shown in Table 1, which support good prediction analysis. From the PLS−DA score plot (Figure 3A,B), it was demonstrated analysis.good Fromprediction the PLS−DA score From plot (Figure 3A,B), score it wasplot demonstrated that itthe metabolic profile support the PLS−DA (Figure was demonstrated that the metabolic profileanalysis. of the OVA-induced mice was far from that 3A,B), of normal mice, suggesting of the OVA-induced mice was farOVA-induced from that of normal mice, suggesting that significant biochemical that the metabolic profile of the mice was far from that of normal mice, suggesting that significant biochemical changes were induced by OVA. Meanwhile, a clear separation among changes were induced by OVA. Meanwhile, a clear separation among the control, model, mKG, and that significant biochemical changes were induced by OVA. Meanwhile, a clear separation among the control, model, mKG, and dexamethasone treated groups were observed. The treatments of dexamethasone treated groups were observed. The treatments of mKG and dexamethasone could the control, model, mKG, and dexamethasone treated groups were observed. of mKG and dexamethasone could regulate the metabolic deviations induced byThe the treatments experimental regulate thedexamethasone metabolic deviations induced by experimental asthma. However, their regulations mKG and could regulate thethe metabolic deviations induced experimental asthma. However, their regulations were different. The mKG group was closerby to the the control group were different. The mKG group was closer to the control group than the dexamethasone group in the asthma. their regulations different. Thecomponent mKG groupinwas to the than theHowever, dexamethasone group in were the first principal thecloser positive ioncontrol mode,group while first principal component in the in positive ionprincipal mode, while closer tointhe group the second than dexamethasone the first component thecontrol positive ion in mode, while closerthe to the control groupgroup in the second principal component in the negative ion mode. principal component in thein negative ion mode. closer to the control group the second principal component in the negative ion mode.

Figure −DA model model classifying classifying the the control control group group ((●), ), model model group group ((■ ),), Figure 3. 3. Score Score plots plots from from the the PLS PLS−DA Figure 3. Score plots from the PLS−DA model classifying the control group(A) (●),and model group (■ ), mKG group ( H ), and Dex group ( N ) with mice lung tissue samples in positive negative (B) ion mKG group (▼), and Dex group (▲) with mice lung tissue samples in positive (A) and negative (B) mKG group ( ▼ ), and Dex group ( ▲ ) with mice lung tissue samples in positive (A) and negative (B) modes by UPLC-Q-TOF/MS. ion modes by UPLC-Q-TOF/MS. ion modes by UPLC-Q-TOF/MS.

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for assessing the modeling quality of PLS−DA in positive and negative5ion of 17 modes of plasma and lung tissue samples.

Table The parameters Int. J. Mol.1.Sci. 2017, 18, 602

Table 1. The parameters for assessing the modeling quality of PLS−DA in positive and negative ion PLS−DA (Negative Ion Mode) modes of plasma and lung tissue samples. PLS−DA (Positive Ion Mode)

Sample

Group

Components

R2 X

SampleControl vs. Model Group Plasma Model vs. mKG samples Model vs. Dex Control vs. Model

3Components 0.684 3 0.579 3 0.679 3

vs. mKG LungsamplesControl Model vs. Model Model vs. Dex tissue Model vs. mKG samples Control vs. Model Model vs. Dex

3 3 3

Plasma

Lung tissue samples

Model vs. mKG Model vs. Dex

3 3 3 3 3

0.417 0.613 0.464

R2 Y

Q2 (cum)

PLS−DA 0.998 Ion Mode) 0.939 (Positive 2 (cum) R2X 0.992 R2Y Q0.891 0.943 0.684 0.998 0.998 0.939 0.579 0.992 0.992 0.891 0.894 0.679 0.991 0.998 0.943 0.848 0.417 0.987 0.992 0.894 0.836 0.613 0.991 0.848 0.464 0.987 0.836

R2 X

R2 Y

Q2 (cum)

PLS−DA 0.823 (Negative 0.991 Ion Mode) 0.973 0.558 0.898 R2X R20.963 Y Q2 (cum) 0.801 0.989 0.9730.884 0.823 0.991 0.558 0.963 0.558 0.990 0.8980.919 0.801 0.989 0.753 0.985 0.8840.939 0.558 0.990 0.587 0.970 0.9190.977 0.753 0.985 0.939 0.587 0.970 0.977

2.4. Identification of Potential Lung Tissue Biomarkers Associated with OVA-Induced Asthma

2.4. Identification Potential Lung Tissue Biomarkers Associated with OVA-Induced Asthmawas employed to The orthogonalofpartial least squares–discriminate analysis (OPLS −DA) method

sharpenThe an already established separation between theanalysis model and control method groups was in PLS −DA. The orthogonal partial least squares–discriminate (OPLS−DA) employed S-plot and variable importance of project (VIP) were used to select potential biomarkers (Figure 4). The to sharpen an already established separation between the model and control groups in PLS−DA. The S-plots based on lung importance metabolic profiles between the used control grouppotential and model group indicated S-plot and variable of project (VIP) were to select biomarkers (Figure 4).that The contributed S-plots basedtoon lung metabolic profiles between control groupofand model group4.15_393.2245, indicated 13 ions the clustering, with retention timethe and m/z pairs 0.63_204.1233, that 13 ions5.19_355.2626, contributed to the clustering, with retention time and m/z of 0.63_204.1233, 4.79_246.2432, 5.20_373.2735, 6.41_302.3056, 7.12_522.3552, andpairs 8.95_256.2637 in positive 4.15_393.2245, 4.79_246.2432, 5.19_355.2626, 5.20_373.2735, 6.41_302.3056, 7.12_522.3552, and ion mode and 5.19_407.2790, 6.93_343.2262, 7.04_319.2264, 7.04_480.3081, and 8.91_327.2315 in negative 8.95_256.2637 in positive ion mode and 5.19_407.2790, 6.93_343.2262, 7.04_319.2264, 7.04_480.3081, ion mode. Their VIP values are listed in Table 2. Among those ions, some of them had close retention and 8.91_327.2315 negative ion mode. Their VIP 5.19_355.2626 values are listed in5.20_373.2735. Table 2. Among those ions, of times and signified theinsame metabolites such as ions and Identification some of them had close retention times and signified the same metabolites such as ions E the metabolites was performed based on the accurate mass and the collected MS spectra measurements 5.19_355.2626 and 5.20_373.2735. Identification of the metabolites was performed based on the via Q-TOF/MS and comparison of Ethe data was performed with authentic reference materials accurate mass and the collected MS spectra measurements via Q-TOF/MS and comparison of the and database resources. Databases, such as Human Metabolome Database (HMDB), METLIN, data was performed with authentic reference materials and database resources. Databases, such as MassBank, and Kyoto Encyclopedia of Genes and Genomes (KEGG) were used for confirmation. Human Metabolome Database (HMDB), METLIN, MassBank, and Kyoto Encyclopedia of Genes As aand result, 11 metabolites in the lungfortissue samples were identified as potentialinbiomarkers related Genomes (KEGG) were used confirmation. As a result, 11 metabolites the lung tissue to OVA-induced They are: L-acetylcarnitine (L1), thromboxane B2 (L2), myristic samples were asthma. identified as potential biomarkers related to OVA-induced asthma. Theyacid are:(L3), cholic acid fragments (L4,L5), dihydrosphinganine (L6), LysoPC(18:1(11Z)) (L7), palmitic amide L-acetylcarnitine (L1), thromboxane B2 (L2), myristic acid (L3), cholic acid fragments (L4,L5),(L8), cholic acid (L9), 10-HDoHE 5-HETE (L11), LysoPC(15:0) (L12), andcholic docosahexaenoic acid (DHA) dihydrosphinganine (L6),(L10), LysoPC(18:1(11Z)) (L7), palmitic amide (L8), acid (L9), 10-HDoHE (L10), 5-HETE (L11), LysoPC(15:0) (L12), and were docosahexaenoic acid (DHA) (L13) (Table 2). Among (L13) (Table 2). Among them, 10 metabolites identified using MS spectral databases and one them, 10(L9) metabolites were identified MS spectral databases and Moreover, one metabolite was(L1, metabolite was confirmed using theusing reference substance (Figure S5). four (L9) of them confirmed using the reference substance (Figure S5). Moreover, four of them (L1, L2, L10, and L11) L2, L10, and L11) were first reported to have potential relevance to the pathogenesis of allergic asthma. were first reported to have of potential relevancewhile to the pathogenesis of asthma. mKG on mKG mediated the deviations all biomarkers, dexamethasone didallergic not show any effects mediated the deviations of all biomarkers, while dexamethasone did not show any effects on LysoPC(15:0) (L12). LysoPC(15:0) (L12).

Figure 4. OPLS−DA score plots plots and onon lung tissue of the control and model groups in Figure 4. OPLS −DA score and S-plots S-plotsbased based lung tissue of the control and model groups 2X = 0.554, R2Y 2 (cum) 2X = 0.558, 2Y = 0.990, 2 (cum) 2 2 2 2 2 = 1.000, Q = 0.907) positive and (C,D) R R Q (A,B) R in (A,B) R X = 0.554, R Y = 1.000, Q (cum) = 0.907) positive and (C,D) R X = 0.558, R Y = 0.990, Q2 = 0.953) negative ion modes. (cum) = 0.953) negative ion modes.

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Table 2. Potential biomarkers characterized in the lung tissue profile and their change trends in different groups. NO.

Metabolites

RT (min)

m/z

Formula

VIP

M/C

Y/M

mKG/M

Pathways

3.59 3.39 13.30 5.41 3.24 14.52 6.20 4.01

↑ ** ↓ ** ↑* ↓ ** ↓ ** ↑ ** ↓ ** ↓*

↓ ** ↑* ↓ ** ↑ ** ↑ ↓ ** ↑ ↑*

↓ ↑ ↓ ↑ ** ↑ ↓* ↑ ↑

Fatty acid metabolism Arachidonicacid metabolism Fatty acid metabolism Bile Acid Biosynthesis Bile Acid Biosynthesis Sphingolipid metabolism Glycerophospholipid metabolism Fatty acid metabolism

4.04 1.71 7.90 2.33 3.39

↓ ** ↓* ↓ ** ↑* ↓*

↑ ** ↑* ↑ ** ↑ ↑

↑ ** ↑ ↑ ** ↓ ↑

Bile Acid Biosynthesis Fatty acid metabolism Arachidonicacid metabolism Glycerophospholipid metabolism Fatty acid metabolism

Positive ion mode L1 L2 L3 L4 L5 L6 L7 L8

L -Acetylcarnitine

b

Thromboxane B2 b Myristic acid b Cholic acid fragment a Cholic acid fragment a Sphinganine b LysoPC(18:1(11Z)) b Palmitic amide b

0.63 4.15 4.79 5.19 5.2 6.41 7.12 8.95

204.1233 393.2245 246.2432 355.2626 373.2735 302.3056 522.3552 256.2637

C9 H17 NO4 C20 H34 O6 C14 H28 O2 C24 H34 O2 C24 H36 O3 C18 H39 NO2 C26 H52 NO7 P C16 H33 NO Negative ion mode

L9 L10 L11 L12 L13

a

Cholic acid 10-HDoHE b 5-HETE b LysoPC(15:0) b Docosahexaenoic acid (DHA) b

5.19 6.93 7.04 7.04 8.91

407.279 343.2262 319.2264 480.3081 327.2315

C24 H40 O5 C22 H32 O3 C20 H32 O3 C23 H48 NO7 P C21 H31 O2

The M/C represents the change trend in the model group compared with the control group, Y/M and mKG/M represent the change trends in the positive group and mKG group compared with the model group, respectively. a Metabolites validated by reference standards; b Metabolites identified by databases. ↑: up-regulated, ↓: down-regulated; * p < 0.05, ** p < 0.01.

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2.5. Plasma Metabolic Profiles of OVA-Induced Allergic Asthma with mKG Treatment 2.5. Plasma Metabolic Profiles of OVA-Induced Allergic Asthma with mKG Treatment The metabolic profiles of the plasma samples from OVA-induced allergic asthma mice with and The metabolic profiles of the plasma samples from OVA-induced allergic asthma mice with and without mKG treatment were characterized using UPLC-Q-TOF/MS in both positive and negative without mKG treatment were characterized using UPLC-Q-TOF/MS in both positive and negative ion ion scan modes. PLS−DA was used to evaluate the metabolic patterns of OVA-induced mice with scan modes. PLS−DA was used to evaluate the metabolic patterns of OVA-induced mice with mKG mKG treatment (Figure 5A,B). The parameters of the PLS−DA models for the classification and the treatment (Figure 5A,B). The parameters of the PLS−DA models for the classification and the change change trends of potential biomarkers in the plasma samples are shown in Tables 1 and 3. Among trends of potential biomarkers in the plasma samples are shown in Tables 1 and 3. Among these plasma these plasma metabolites, 12 metabolites were identified by the MS spectral databases and four metabolites, 12 metabolites were identified by the MS spectral databases and four metabolites (P12–15) metabolites (P12–15) were confirmed using the reference substances (Figures S6–S9). The metabolic were confirmed using the reference substances (Figures S6–S9). The metabolic profile of mice in the profile of mice in the mKG treated group differed from the model group and were closer to the mKG treated group differed from the model group and were closer to the control group, indicating control group, indicating that the deviations induced by OVA were significantly improved after that the deviations induced by OVA were significantly improved after mKG treatment. The results mKG treatment. The results showed that mKG was much closer to the control group than that of the showed that mKG was much closer to the control group than that of the dexamethasone treated group dexamethasone treated group in positive ion mode. in positive ion mode.

Figure −DA model model classifying classifying the the control control group group ((●), ), model model group Figure 5. 5. Score Score plots plots from from the the PLS PLS−DA group (( ■),), mKG group ( H ), and Dex group ( N ) with mice plasma samples in positive (A) and negative (B) ion mKG group (▼), and Dex group (▲) with mice plasma samples in positive (A) and negative (B) ion modes modes by by UPLC-Q-TOF/MS. UPLC-Q-TOF/MS.

2.6. Perturbed Metabolic Pathways in Response to Allergic Asthma and mKG Treatment Treatment Based on biomarkers of OVA-induced allergic asthma from from the lung onthe theidentified identifiedpotential potential biomarkers of OVA-induced allergic asthma thetissue lung and plasma of mice, the metabolic network affected by the OVA-induced mice was depicted according tissue and plasma of mice, the metabolic network affected by the OVA-induced mice was depicted to the KEGG and the HMDB (http://www.hmdb.ca/) (Figure 6). according to (http://www.genome.ad.jp/kegg/) the KEGG (http://www.genome.ad.jp/kegg/) and the HMDB (http://www.hmdb.ca/) The results that seven metabolic pathways were disturbedwere in OVA-induced asthma (Figure 6). indicated The results indicated that seven metabolic pathways disturbed inallergic OVA-induced mice, including fatty acid metabolism (P1, P2, L1, L3, L8, L10 and L13), sphingolipid metabolism (P3, allergic asthma mice, including fatty acid metabolism (P1, P2, L1, L3, L8, L10 and L13), sphingolipid P4, and L6), glycerophospholipid metabolism (P5–P11, P16, L7, and L12), purine metabolism (P12 and metabolism (P3, P4, and L6), glycerophospholipid metabolism (P5–P11, P16, L7, and L12), purine P13), tryptophan metabolism (P14), bile acid metabolism (P15, L4, L5, and L9), and arachidonic acid metabolism (P12 and P13), tryptophan metabolism (P14), bile acid metabolism (P15, L4, L5, and L9), metabolism (L2 and and arachidonic acidL11). metabolism (L2 and L11).

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Table 3. Potential biomarkers characterized in the plasma profile and their change trends in different groups. NO.

Metabolites

RT (min)

m/z

Formula

VIP

M/C

Y/M

mKG/M

Pathways

↓ ** ↓ ** ↓ ** ↓ ** ↓ ** ↓ ** ↓ ** ↓* ↓ ** ↑ ** ↑ **

↑ ** ↑ ** ↑ ** ↑ ** ↑ ** ↑ ** ↓ ↑* ↑ ** ↓* ↓*

↑ ** ↑ ** ↑ ** ↑ ** ↑ ↑ ** ↑ ** ↑ ↑* ↓* ↓*

Fatty acid metabolism Fatty acid metabolism Sphingolipid metabolism Sphingolipid metabolism Glycerophospholipid metabolism Glycerophospholipid metabolism Glycerophospholipid metabolism Glycerophospholipid metabolism Glycerophospholipid metabolism Glycerophospholipid metabolism Glycerophospholipid metabolism

↓ ** ↑ ** ↓ ** ↓ ** ↓ ** ↓ ** ↓ ** ↓* ↓ **

↑ ** ↓ ** ↑* ↑ ↑ ** ↑ ** ↓ ↑* ↑

↑ ** ↓ ** ↑ ↑ ↑ ↑ ** ↑ ** ↑ ↓

Purine metabolism Purine metabolism Tryptophan metabolism Bile acid metabolism Glycerophospholipid metabolism Glycerophospholipid metabolism Glycerophospholipid metabolism Glycerophospholipid metabolism Glycerophospholipid metabolism

Positive ion mode P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11

b

Dodecanoic acid Myristic acid b Phytosphingosine b Sphinganine b LysoPC(22:6) b LysoPC(18:2) b LysoPC(20:4) b LysoPC(16:0) b LysoPC(18:1) b PS(18:0/18:1(9Z)) b PS(18:2(9Z,12Z)/18:0) b

2.25 2.87 4.13 6.06 6.85 6.90 6.92 7.85 8.46 13.57 13.67

218.2116 246.2427 318.3002 302.3053 568.3402 520.3405 544.3402 496.3404 522.3554 790.5587 788.5428

C12 H24 O2 C14 H28 O2 C18 H39 NO3 C18 H39 NO2 C30 H50 NO7 P C26 H50 NO7 P C28 H50 NO7 P C24 H50 NO7 P C26 H52 NO7 P C42 H80 NO10 P C42 H78 NO10 P

2.60 2.57 5.00 4.70 2.45 8.34 5.95 5.56 4.01 1.79 3.64

Negative ion mode P12 P13 P14 P15 P5 P6 P7 P8 P16

a

Uric acid Inosine a L -tryptophan a Taurocholic acid a LysoPC(22:6) b LysoPC(18:2) b LysoPC(20:4) b LysoPC(16:0) b LysoPC(15:0) b

0.61 0.66 1.28 2.40 6.86 6.90 6.92 7.86 7.87

167.0123 267.0561 203.0728 514.2839 612.3295 564.3299 588.3292 540.3302 480.3091

C5 H4 N4 O3 C10 H12 N4 O5 C11 H12 N2 O2 C26 H45 NO7 S C30 H50 NO7 P C26 H50 NO7 P C28 H50 NO7 P C24 H50 NO7 P C23 H48 NO7 P

1.50 1.02 1.29 1.74 1.74 3.08 2.44 3.37 4.33

The M/C represents the change trend in model group compared with control group, Y/M and mKG/M represent the change trends in the positive group and mKG group compared with the model group, respectively. a Metabolites validated by reference standards; b Metabolites identified by databases. ↑: up-regulated, ↓: down-regulated; * p < 0.05, ** p < 0.01.

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Figure 6. The perturbed network of th Figure 6. The perturbed network of the potential biomarker associated allergic asthma and the modulation Figure 6. The perturbed network of the potential biomarker associated allergic asthma and of themKG according to the K Figure 6. The perturbed network of the potential allergic xanthine asthma and the modulation of mKG according to biomarker the KEGG associated database. 1.17.1.4: dehydrogenase; 1.17.3.2: xanthineoxidase; ADA: Adenosine dea modulation of mKG according to the KEGG database. 1.17.1.4: xanthine dehydrogenase; 1.17.3.2: modulation of mKG according to the KEGG database.deaminase; 1.17.1.4: xanthine dehydrogenase; xanthineoxidase; ADA: Adenosine 1.13.11.11: tryptophan 1.17.3.2: 2,3-dioxygenase (TDO); 1.13.11.52: indoleamine 2,3-dioxygenase ADA: Adenosine deaminase; 1.13.11.11: tryptophan (TDO); 2,3-dioxygenase (TDO); xanthineoxidase; xanthineoxidase; ADA: Adenosine deaminase; 1.13.11.11: tryptophan 2,3-dioxygenase 1.13.11.52: indoleamine 2,3-dioxygenase (IDO); 1.13.11.34: arachidonate 5-lipoxygenase; 1.14.99.1: prostaglandin-endoperoxide synthase; 1.13.11.52: indoleamine 2,3-dioxygenase (IDO); 1.13.11.34: arachidonate 5-lipoxygenase; 1.14.99.1: 1.13.11.52: indoleamine 2,3-dioxygenase (IDO);synthase; 1.13.11.34: arachidonate 5-lipoxygenase; reductase 1.14.99.1: (DSR); 5.3.99.5: prostaglandin-endoperoxide 1.1.1.102: dehydrosphinganine prostaglandin-endoperoxide synthase; 1.1.1.102: dehydrosphinganine reductase thromboxane-A (DSR); 5.3.99.5: synthase; 2.7.1.91: sphing prostaglandin-endoperoxide synthase; 1.1.1.102: reductase (DSR); 5.3.99.5: thromboxane-A synthase; 2.7.1.91:dehydrosphinganine sphingosine kinase; 1.14.13.169: sphingolipid C4-monooxygenase thromboxane-A synthase; 2.7.1.91: sphingosine kinase; 1.14.13.169: sphingolipid C4-monooxygenase (SUR2); 2.3.1.65: bile acid-CoA:amino thromboxane-A synthase; 2.7.1.91: sphingosine kinase; 1.14.13.169: sphingolipid C4-monooxygenase (SUR2); 2.3.1.65: bile acid-CoA:amino acid N-acyltransferase (BAT); 3.5.1.24: choloylglycine (SUR2); 2.3.1.65: bile acid-CoA:amino acid N-acyltransferase (BAT); 3.5.1.24: choloylglycine hydrolase. hydrolase. Metabolites measured in lung (SUR2); 2.3.1.65: hydrolase. bile acid-CoA:amino acid N-acyltransferase (BAT); 3.5.1.24: choloylglycine Metabolites measured in lung tissue were labeled in red, metabolites labeled in blue were Metabolites measured in lung tissue were labeled in red, metabolites labeled in blue were measured in measured in plasma, and metabolite nam hydrolase. Metabolites measured in lung tissue were labeled metabolites labeled in blue were measured in plasma, and metabolite namesininred, green mean they were measured both in plasma and : down plasma, and metabolite names in green mean they were measured both in plasmaand andlung lungtissue. tissue. :: up-regulated, measured in plasma, metabolite names in green they were measured both plasma lungand tissue. : up-regulated, : mean down-regulated; * p < 0.05, ** p