Dynamic analysis of the endogenous metabolites in depressed patients treated with TCM formula Xiaoyaosan using urinary 1H NMR-based metabolomics

Dynamic analysis of the endogenous metabolites in depressed patientstreated with TCM formula Xiaoyaosan using urinary1H NMR-based metabolomics

Jun-sheng Tian a, Guo-jiang Peng a,b,1, Xiao-xia Gao a, Yu-zhi Zhou a, Jie Xing a,Xue-mei Qin a,n, Guan-hua Duc,a,nnQ1

a Modern Research Center for Traditional Chinese Medicine of Shanxi University, Taiyuan 030006, PR Chinab College of Chemistry and Chemical Engineering of Shanxi University, Taiyuan 030006, PR Chinac Institute of Materia Medica, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100050, PR China

a r t i c l e i n f o

Article history:Received 26 June 2014Received in revised form22 September 2014Accepted 4 October 2014

Keywords:XiaoyaosanMetabolomicsDynamic analysisDepressed patientsNuclear magnetic resonance

Chemical compounds studied in this article::Lactate (PubChem CID: 5460161)Alanine (PubChem CID: 5950)Glutamine (PubChem CID: 5961)Citrate (PubChem CID: 31348)Dimethylamine (PubChem CID: 674)Timethylamine (PubChem CID: 1146)Trimethylamine N-oxide(PubChem CID: 1145)Taurine (PubChem CID: 1123)Glycine (PubChem CID: 750)(PubChem CID: 31348)Xanthurenic acid (PubChem CID: 5699)

a b s t r a c t

Ethnophamacological relevance: Xiaoyaosan (XYS), one of the best-known traditional Chinese medicineprescriptions with a long history of use, is composed of Bupleurum chinense DC., Paeonia lactiflora Pall.,Poria cocos (Schw.) Wolf, Angelica sinensis (Oliv.) Diels, Zingiber officinale Rosc., Atractylodes macrocephalaKoidz., Glycyrrhiza uralensis Fisch., and Mentha haplocalyx Briq. For centuries, XYS has been widely usedin China for the treatment of mental disorders such as depression. However, the complicated mechanismunderlying the antidepressant activity of XYS is not yet well-understood. This understanding iscomplicated by the sophisticated pathophysiology of depression and by the complexity of XYS, whichhas multiple constituents acting on different metabolic pathways. The variations of endogenousmetabolites in depressed patients after administration of XYS may help elucidate the anti-depressanteffect and mechanism of action of XYS. The aim of this study is to establish the metabolic profile ofdepressive disorder and to investigate the changes of endogenous metabolites in the depressed patientsbefore and after the treatment of Xiaoyaosan using the dynamic analysis of urine metabolomics profilesbased on 1H NMR.Materials and methods: Twenty-one depressed patients were recruited from the Traditional ChineseMedicine Department of the First Affiliated Hospital of Shanxi Medical University. Small endogenousmetabolites present in urine samples were measured by nuclear magnetic resonance (NMR) andanalyzed by multivariate statistical methods. The patients then received XYS treatment for six weeks,after which their Hamilton Depression Scale (HAMD) scores were significantly decreased compared withtheir baseline scores (pr0.01). Eight components in urine specimens were identified that enableddiscrimination between the pre- and post-XYS-treated samples.Results: Urinary of creatinine, taurine, 2-oxoglutarate and xanthurenic acid increased significantly afterXYS treatment (pr0.05), while the urinary levels of citrate, lactate, alanine and dimethylaminedecreased significantly (pr0.05) compared with pre-treatment urine samples. These statisticallysignificant perturbations are involved in energy metabolism, gut microbes, tryptophan metabolismand taurine metabolism.Conclusions: The symptoms of depression had been improved after 6 weeks' treatment of XYS accordingto evaluation of HAMD scores. The dynamic tendency of the 8 metabolites that changed significantlyduring the treatment of XYS ias consistent with the improvement in symptoms of depression. Thesemetabolites may be used as biomarkers for the diagnosis of depressive disorders or the evaluation of theantidepressant as well as the exploration of the mechanism of depression.

& 2014 Published by Elsevier Ireland Ltd.

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Contents lists available at ScienceDirect

journal homepage: www.elsevier.com/locate/jep

Journal of Ethnopharmacology

http://dx.doi.org/10.1016/j.jep.2014.10.0050378-8741/& 2014 Published by Elsevier Ireland Ltd.

n Correspondence to: Modern Research Center for Traditional Chinese Medicine of Shanxi University; No. 92 WuCheng Road, XiaoDian District, Tanyuan, PR China.Tel./Fax: þ86 351 7018379; fax: þ86 351 7018379.

nn Corresponding author at: Institute of Materia Medica, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100050, PR China.Tel.: þ86 351 7019297; fax: þ86 351 7018379.

E-mail addresses: [email protected] (, [email protected] (. Guan-hua).1 Co-first author.

Please cite this article as: Tian, J.-s., et al., Dynamic analysis of the endogenous metabolites in depressed patients treated with TCMformula Xiaoyaosan using urinary 1H NMR-based.... Journal of Ethnopharmacology (2014), http://dx.doi.org/10.1016/j.jep.2014.10.005i

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1. Introduction

Depressive disorder is a prevalent serious mental disorder witha pathophysiological mechanism that is extremely complicatedand poorly understood (Dai et al., 2010). Currently depressivedisorder is an escalating worldwide problem, creating an enor-mous burden on the whole society, including increased healthcareexpenditures and suicide rate (Simon, 2003). Usually, depressivedisorder has the following features: depressed mood, feelings ofguilt, low self-worth, loss of interest or pleasure and disturbedappetite or sleep (Serretti et al., 2004). According to the WorldHealth Organization, depression is predicted to be the secondhighest disabling disease by 2020, second only to heart disease(Liu et al., 2012a). Despite the abundance of research into thisdisease that has been published, the etiology of depressivedisorder is still not completely understood. To date several typesof synthetic chemical antidepressants have been introduced, suchas tricyclic antidepressants (TCAs), selective serotonin reuptakeinhibitors (SSRIs) and monoamine oxidase inhibitors (MAOIs).However, their therapeutic effects are encumbered by variousside effects such as psychomotor impairment and dependenceliability (Sarko, 2000). Therefore, studies looking for moreeffective antidepressant therapies and/or therapies with fewer orno adverse effects are badly needed. In this regard, TraditionalChinese Medicines (TCMs) have drawn increasing attention inrecent years owing to their specific theories and long history ofclinical practice.

Xiaoyaosan (XYS), one of the best-known Traditional ChineseMedicine prescriptions with a long history of use, originates from“Taiping Huimin Heji Jufang” in the Song Dynasty (960-1127 A.D.)and has been used mainly for the treatment of liver stagnation andspleen deficiency syndrome (Chen et al., 2008). XYS compriseseight herbal medicines: Radix Bupleuri (Bupleurum chinense DC.),Radix Paeoniae Alba (Paeonia lactiflora Pall.), Poria (Poria cocos(Schw.) Wolf), Radix Angelicae Sinensis (Angelica sinensis (Oliv.)Diels), Rhizoma Zingiberis Recens (Zingiber officinale Rosc.), Rhi-zoma Atractylodis Macrocephalae (Atractylodes macrocephalaKoidz.), Radix Glycyrrhizae (Glycyrrhiza uralensis Fisch.) and HerbaMenthae Haplocalycis (Mentha haplocalyx Briq.). Furthermore, XYShas been commonly recognized as a safe and effective treatmentfor various depressive disorders in China. Several studies on theanti-depressant effects of XYS on the rat CUMS model have beenreported, and the results have suggested that XYS produces anobvious antidepressant effect (Liu et al., 2012b). However, little isknown about the mechanism by which XYS ameliorates MDD.

As an important component of systems biology, metabolomicsis a newly developed technology for the quantitative measure-ment of dynamic multiparametric metabolic changes of livingsystems exposed to pathophysiological stimuli or genetic modifi-cations. The cornerstone of metabolomics is its reliable measure-ment of as many potential metabolites as possible in biologicalsamples, including body fluids (e.g., urine, serum, amniotic fluid,and cerebrospinal fluid), single cells and tissues from givenorganisms or plants, using modern high-throughput analyticalinstruments (Tao et al., 2008). Metabonomic approaches arebecoming more prevalent in many fields, such as animal husban-dry Nie and Zhang, 2011, food chemistry (Van Dorsten et al., 2006),plant genetic engineering (Li et al., 2014), diseases pathology(Xuan et al., 2011), and so on.

Analytical spectroscopic methods, such as nuclear magneticresonance (NMR), liquid chromatography–mass spectrometry (LC–MS) or gas chromatography–mass spectrometry (GC–MS), arealways combined with chemometric data analysis and patternrecognition, respectively, for generating and interpreting profilesof metabolism in complex biological systems (Nicholson et al.,1985). 1H NMR, which is non-invasive and nondestructive, offers

vital information on metabolite structure (Jiang et al., 2012). It isalso an unbiased method that reveals the overall metabolic profileof biofluids or tissue extracts (Ghosh et al., 2012). 1H NMR-basedmetabolomics is a powerful technique, which not only enables theparallel assessment of the levels of a broad range of metabolitesbut also plays an important role in the investigation of physiolo-gical or pathological states and their relevant pathways.

Multivariate data analysis discriminates samples by comparingmetabolites in part by visualization of clustering between differentgroups. Principal component analysis (PCA), partial least-square-discriminate analysis (PLS-DA) and orthogonal projection to latentstructure-discriminate analysis (OPLS-DA) are the primary types ofmultivariate data analysis. First, PCA was used to investigategeneral interrelation between groups, including clustering andoutliers among the samples. Next, PLS-DA was applied to max-imize the differences between metabolic profiles and to facilitatethe detection of metabolites that were consistently present in thebiological samples. The supervised OPLS-DA can separate samplesinto two blocks to achieve the best biomarker discovery and toobtain better discrimination (Wang et al., 2012a). To our knowl-edge, few reports of 1H NMR urine metabolomics have examineddepressed patients before and after XYS treatment. In this study,urinary metabolomics based on 1H NMR spectroscopy was appliedto investigate the metabolic profiles and potential biomarkers indepressed patients before and after treatment with XYS, anddynamic changes of metabolites during drug treatment weremonitored, which may facilitate the understanding of the patho-logical changes in depression and the anti-depressant mechanismsof XYS.

2. Materials and methods

2.1. Raw herbal medicines and XYS extract

Poria (Poria cocos(Schw.) Wolf), Radix Paeoniae Alba (Paeonialactiflora Pall.), Radix Bupleuri (Bupleurum chinense DC.), RadixAngelicae Sinensis (Angelica sinen-sis(Oliv.) Diels), Rhizoma Atrac-tylodis Macrocephalae (Atractylodes macrocephala Koidz.), RadixGlycyrrhizae (Glycyrrhiza uralensis Fisch.), Herba Menthae (Men-tha haplocalyx Briq.), and Rhizoma Zingiberis Recens (Zingiberofficinale Rosc.) were purchased from Shanxi Huayang Pharmaceu-tical Company and authenticated by Prof. Xue-mei Qin, ModernResearch Center for Traditional Chinese Medicine of Shanxi Uni-versity, before preparation. The XYS extract was prepared accord-ing to the traditional method with slight modifications. The eightherbs mentioned above were mixed in the ratio 6:6:6:6:6:3:2:2 byweight and crushed into small pieces. The crushed herbs weresoaked together in water (1:8, w/v) for 30 min at room tempera-ture and refluxed for 1.5 h. After collecting the filtrate, water wasadded to the residue (1:6, w/v) and the mixture was refluxed foranother 1.5 h. Finally, the filtrates were mixed and concentratedunder vacuum.

2.2. Clinical samples

The protocol of this study was approved by the EthicalCommittee of the First Affiliated Hospital at Shanxi MedicalUniversity and the Modern Research Center for Traditional ChineseMedicine of Shanxi University. Written informed consents wereacquired from all recruited participants in this study. Depressedpatients were enrolled into the traditional Chinese MedicineDepartment of the First Affiliated Hospital of Shanxi MedicalUniversity. Patients with a single depression episode wereassessed according to a structured clinical interview employingDiagnostic and Statistical Manual of Mental Disorders-IV (DSM-IV)

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criteria, and the 17-item Hamilton Rating Scale for Depression(HAMD) was applied to evaluate the severity of depression, whereonly depressed patients with HAMD scores higher than 17 wererecruited. Exclusion criteria included dementia, bipolar disorder,schizophrenia, any anxiety disorder or the use of substances andalcohol, and current suicidal and/or psychotic ideation. Additionalexclusion criteria included pregnancy, breast-feeding and historyof relevant medical comorbidities. In total, 21 depressed patientswith HAMD scores higher than 17 were recruited into this study.During the experiment, XYS decoctions were prepared for thepatients every day. 21 patients were assigned to take XYS decoc-tion for 6 weeks and 8 patients for 8 weeks, with 2 weeksconsidered as a course. The clinical characteristics of the recruitedpatients are shown in Table 1.

2.3. Sample collection and preparation

Morning urine samples were collected in Eppendorf tubes fromall subjects every 2 weeks. Specifically, 5 batches of samples wereindividually obtained before drug administration (0 week) andthen after 2 weeks, after 4 weeks, after 6 weeks and after 8 weeksof XYS treatment and the volume of each sample was approxi-mately 8 mL. The samples were centrifuged at 3000 rpm for10 min at 4 1C. Subsequently, the supernatant was aliquoted at avolume of 600 μL and stored at �80 1C until analysis. For NMRanalysis, urine samples were thawed at 0 1C and then centrifugedat 3000 rpm for 15 min. A 500-μL sample of urine was mixed with200 μL of phosphate buffer in D2O (0.2 M Na2HPO4 �12H2O/0.2 MNaH2PO4 �2H2O, 81:19, v/v, pH 7.4), containing 0.015% TSP (used aschemical shift reference) (Carrola et al., 2010; Kim et al., 2013).After centrifugation at 13,000 rpm for 20 min, 600 μL of super-natant was transferred into 5-mm NMR tubes.

2.4. 1H NMR data acquisition

The 1H NMR spectra of the urine samples were recorded on aBruker 600-MHz AVANCE III NMR spectrometer (Bruker Biospin,Rheinstetten, Germany) operating at a 1H NMR frequency of600 MHz and a temperature of 298 K. A one-dimensional (1D)Nuclear Overhauser Effect Spectroscopy (NOESY, RD-901-t1-90º-tm-90º-acquire) pulse sequence with water suppression was usedfor the urine sample analysis. Each NMR spectrum consisted of 64scans requiring 5 min acquisition time with the following para-meters: spectral width¼12,345.7 Hz, spectral size¼65,536 points,pulse width (PW)¼301 (12.7 μs), relaxation delay (RD)¼1.0 s, andmixing time (tm, 0.1 s); FIDs were Fourier transformed withLB¼0.3 Hz.

For signal assignment purposes, two-dimensional (2D) NMRspectra were also acquired using a 298 k on Bruker 600-MHzAVANCE III NMR spectrometer for selected samples including1H–1H correlation spectroscopy (COSY) and 1H–13C heteronuclearsingle quantum coherence spectroscopy (HSQC) in the samefashion as previously reported (Gronwald et al., 2008; Xu et al.,2012). The COSY spectra were acquired with 1.5 s relaxation delay;

48 transients were collected into 1024 data points with thespectral width of 12 ppm for both dimensions. For the 2D 1H–13CHSQC spectra, water suppressionwas achieved using presaturationduring the relaxation delay of 1.5 s. For each 2D spectrum,2048�128 data points were collected using 8 scans per incre-ment. The spectral widths were set to 12 and 240 ppm in theproton and carbon dimensions, respectively.

2.5. Data processing

The 1H NMR spectra were processed using MestReNova soft-ware (Mestrelab Research, Santiago de Compostella, Spain). All thespectra were manually corrected for phase and baseline distor-tions. The spectra of urine were referenced to the chemical shift ofTSP at δ 0.00 ppm. The spectra were divided and the signal integralcomputed in 0.01 ppm intervals across the region δ 0.50–9.50 ppm. The regions of δ 3.34–3.38 ppm and δ 4.50–6.40 ppmwere removed to eliminate the influence of methanol, water andurea. The remaining spectral segments in each NMR spectrumwere normalized to the total sum of the spectral intensities topartially compensate for differences in concentrations among thenumerous metabolites.

2.6. Statistical analysis

The normalized integral values were imported into SIMCA-P13.0 software (Umetrics, Umeå, Sweden) as variables. Partial least-square-discrimination analysis (PLS-DA) and orthogonal projec-tion to latent structure-discriminate analysis (OPLS-DA) wereemployed. PLS-DA is a multivariate classification method basedon partial least squares (PLS) and is performed by a PLS regressionagainst a “dummy matrix” (Y) that describes variation according toclass. Orthogonal signal correction (OSC) was chosen as aneffective approach for filtering unrelated variations. The OSCdetermines components that explain the maximum varianceorthogonal to Y. The use of the residual data from this orthogonalmodel effectively filters out variations in the dataset (Wang et al.,2012b). The results were presented in the form of a scores plot,where each point represented an individual sample (to show thegroup clusters), and a loadings plot, where each coordinaterepresented one 1H NMR spectral region (to identify the variablescontributing to the classification). According to the criterion of theloadings plot, the further the plot sits from the origin, the greaterthe contribution of the metabolite to the separation of the groupsinvolved. Each of the metabolites was identified, and the variablesthat demonstrated large contributions to the anti-depressanteffects of the XYS treatment were selected as major metabolites.A paired t-test was further used to investigate alterations inendogenous metabolites using SPSS 17.0 (Chicago, IL, USA).Po0.05 was considered to be the threshold for statisticallysignificant difference.

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Table 1The information of clinical samples with the treatment of XYS.

0 week 2nd week 4th week 6th week 8th week

Sample size 21 21 21 21 8Age (years) 4878.9 4878.9 4878.9 4878.9 45.779.9Sex (M/F) 6/15 6/15 6/15 6/15 2/6HAMD scores 19.6772.05 11.8173.25n 7.5773.43n# 5.4772.62n# 4.8673.41n

M: male; F: female; HAMD: Hamilton Depression Scale.n po0.01, compare to 0 week.# po0.01, compare to the week before.

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3. Results

3.1. Clinical sample information

21 patients obtained effective treatment, and their clinicalcharacteristics are shown in Table 1. The samples were investi-gated at 0 week, 2nd week, 4th week and 6th week from the 21depressed patients, 8 of whom participated at 8th week. The meanbaseline (SD) HAMD scores of the depressed patients were 19.67(2.05). The depressed subjects had a mean baseline HAMD scoresgreater than 17. After weeks of XYS treatment, the patients' HAMDscores were significantly decreased compared with their baselinescores (pr0.01). The mean HAMD scores of the 6th week were5.47. A paired t-test analysis indicated that the post-treatmentHAMD scores were significantly lower than those before the XYStreatment except for the 8th week (po0.01).

3.2. 1H NMR spectra

Typical NOESY 1H NMR spectra of urine from depressed patientsare depicted in Fig. 1. Resonance assignments were performedaccording to the literature Tian et al. (2013), Zheng et al. (2013), andDawiskiba et al. (2014) and the Chenomx NMR suite (Chenomx Inc.,Edmonton, AB, Canada) as well as public NMR databases (HMDB,http://www.hmdb.ca/) and 2D NMR experiments. Urine spectra weredominated by amines and various organic acids, including citrate,succinate and lactate, as well as a range of gut microbial-host co-metabolites. In total, 29 metabolites were marked on the spectra, andthe identification of the metabolites is shown in Table 2.

3.3. Multivariate statistical analysis

With a metabolomics platform, the patterns of metabolites in urinefrom depressed patients at 0 week, 2nd week, 4th week, 6th week and8th week after treatment with XYS were plotted by the PCA, PLS-DAand three-dimensional PLS-DA patterns (Fig. 2). First, PCA was used toinvestigate general interrelations between groups. Then, PLS-DA andthree-dimensional PLS-DAwere applied to maximize the difference ofmetabolic profiles and facilitate the detection of metabolites consis-tently present in the biological samples. The scores plot from PCAshows inter-group metabolic differences, where each point representsa patient's urine metabonome, and the distance between data pointsreflects the scale of their metabolic differences.

According to the significantly changed patients' HAMD scores from0 week to 8th week, depression has been greatly relieved with XYStreatment. As shown in Fig. 2, when investigated with multivariatestatistical analysis, it can be suggested that metabolic turnoverand significant pathobiological changes could be induced by XYS

treatment. During the XYS treatment period the metabolic patterns ofthe depressed patients at the 2nd week were closest to that of thepatients before treatment (Fig. 2A and B), indicating that the effectof XYS required a period of time before appearing, which agreeswith the change in HAMD scores in the depressed patients' clinicalcharacteristics.

The scores plot from three-dimensional PLS-DA (Fig. 2C and D)showed obvious separation among the urine samples at 0 weekand after the 2nd week, 4th week, 6th week and 8th week of XYStreatment, suggesting that XYS significantly altered levels ofendogenous biomarkers in the urine of depressed patients.

To find changed metabolites in urine samples collected atdifferent time points, OPLS-DA was applied to filter out variationsand missing values unrelated to the classification. The supervisedOPLS-DA can improve biomarker discovery effects and separatesamples into two blocks to obtain better discrimination betweenurine samples from patients before and after XYS treatment. Auto-matic modeling parameters indicated a poor explanatory and pre-dicative performance of the models at 0 week compared with the2nd week, as shown in Table 3. The failure of the modeling resultedfrom the small physiological variations in metabolite levels betweenurine samples at 0 week and at the 2nd week, as well as from theslow efficacy of Traditional Chinese Medicine. The OPLS-DA scoresplot of metabolites in urine at 0 week and 4th week, at 0 week and6th week, and at 0 week and 8th week are shown in Fig. 3A, C, and E,respectively. To identify the metabolites contributing to the anti-depressive effects of XYS, OPLS-DA loadings plot of urine at 0 weekand 4th week, at 0 week and 6th week, and at 0 week and 8th weekare also shown in Fig. 3B, D, and F, respectively. To refine this analysis,we also calculated the variable importance for the projection (VIP)values, which reflected the importance of the chemical shifts withrespect to both class segregation and loading. Variations with VIPvalues exceeding 1.0 for the selection of the chemical shift of the binsrelevant to class segregation were first selected for further investiga-tion because these bins were subsequently analyzed by paired t-testanalysis. Therefore, the significance of the differences in the levels ofmetabolites was checked and considered to be significant when po0.05. In total, 8 metabolites were identified as potential biomarkersin this study. Creatinine, taurine, xanthurenic acid and 2-oxoglutaratewere significantly increased in the urine samples from patientsundergoing XYS treatment for 8 weeks compared with pre-XYStreatment samples, while citrate, alanine, lactate and dimethylaminelevels were significantly decreased. Moreover, creatinine, lactate,xanthurenic acid, citrate, alanine and dimethylamine shared thesame trend change at the 4th week and 6th week (po0.05) oftreatment. At the 2nd week of treatment only the creatinine levelsignificantly increased (p o0.05), and the dimethylamine and lactatelevels significantly decreased (po0.05) compared with the 0 weeksamples (Fig. 4).

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100101102103104105106107108109110111112113114115116117118119120121122123124125126127128129130131132Fig. 1. Representative 600 MHz one-dimensional NOESY 1H NMR spectra of urine samples from a depressed patient.






4. Discussion

Non-invasive 1H NMR metabolomics is a powerful approach toascertain metabolic signatures from complex biofluid samples andshould aid in the discovery of diagnostic biomarkers and patho-physiological mechanisms underlying disease states. In this study,a 1H NMR metabolomics approach was applied to analyze meta-bolites in the urine of depressed patients at different times after

XYS administration. Metabolomic data were collected at five timepoints: at 0 week, and at the 2nd week, the 4th week, the 6thweek and the 8th week, and the later collections were comparedwith the 0 week samples. In terms of the concentrations of theendogenous metabolites in depressed patients' urine, creatinine,xanthurenic acid, taurine and 2-oxoglutarate were up-regulatedwhile alanine, citrate, lactate and dimethylamine were down-regulated (Fig. 4). However, among these eight metabolites, only

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Table 2Peak attribution of the main marked metabolites in 1H NMR spectra of urine samples.

No. metabolites Group δ1H (multiplicity) δ 13C Assigned with

1 Leucine –CH3 0.95(t,J¼5.89 Hz) COSY–CH– 1.70(m)–CH– 3.72(m)

2 Isoleucine –CH3 0.93(t,J¼7.41 Hz) COSY–NH2 0.98(d,J¼7.00 Hz)–CH– 1.24(m)–OH 1.46(m)

3 Valine –CH3 0.97(d,J¼7.01 Hz) 31.68 COSY HSQC–CH3 1.02(d,J¼7.05 Hz)–CH– 2.26(m)–CH– 3.60(d,J¼4.33 Hz)

4 Isobutyrate –CH3 1.08(d,J¼7.02 Hz) COSY–CH– 2.59(m)

5 3-aminoisobutyrate –CH3 1.17(d,J¼7.48 Hz) 17.85 COSY HSQC–CH– 2.58(m)–CH2– 3.01(dd,J¼5.28 Hz)

6 Methylmalonate –CH3 1.21(d,J¼7.35 Hz) 17.60 COSY HSQC3.16(q,J¼7.27 Hz)

7 Lactate –CH3 1.33(d,J¼6.96 Hz) 27.64 COSY HSQC–CH– 4.10(q,J¼6.93 Hz)

8 Alanine –CH3 1.48(d,J¼8.56 Hz) COSY–CH– 3.76(q,J¼8.64 Hz)

9 Acetate –CH3 1.92(s) 27.07 HSQC10 Glycoprotein –CH3 1.97(s)11 Succinate –CH2– 2.39(s)12 Glutamine –CH2– 2.12(m) 34.7 COSY HSQC

–CH2– 2.45(m)–CH– 3.76(t,J¼6.18 Hz)

13 Citrate –CH2– 2.53(d,J¼15.16) 45.82 COSY HSQC–CH2– 2.65(d,J¼15.16) 45.82

14 Methylamine –CH3 2.61(s) 29.78 HSQC15 Dimethylamine –CH3 2.71(s) 34.70 HSQC16 Trimethylamine –CH3 2.91(s) 50.05 HSQC17 Dimethylglycine –CH3 2.99(s)

–CH2– 3.71(s)18 2-oxoglutarate –CH2– 2.43(t,J¼6.95 Hz) 36.86 HSQC

–CH2– 3.01(t,J¼6.84 Hz)19 Creatinine –CH3 3.03(s) 30.72 COSY HSQC

–CH2– 4.04(s) 56.7020 Trimethylamine N-oxide –CH3 3.26(s) 59.58 HSQC21 Taurine –CH2– 3.25(t,J¼6.57 Hz) 38.74 COSY HSQC

–CH2– 3.41(t,J¼6.62 Hz)22 Glycine –CH2– 3.55(s) 41.89 HSQC23 Betaine –CH2– 3.93(s) 53.46 HSQC

–CH3 3.25(s)24 Hippurate –CH2– 3.95(d,J¼5.84 Hz) 44.40 COSY HSQC

–CH¼ 7.54(m) 128.78–CH¼ 7.62(m) 132.80–CH¼ 7.82(m) 127.35

25 Phenylacetylglycine –CH2– 3.68(s) COSY HSQC–CH2– 3.76(d,J¼5.81 Hz)–CH¼ 7.34(m) 127.73–CH¼ 7.41(s) 129.01

26 Formate –CHO 8.44(s)27 Histidine –CH2– 3.16(dd) COSY HSQC

–CH– 3.98(dd)–N–CH¼ 7.08(d,J¼0.58 Hz) 117.96–CH¼N 7.90(d,J¼1.13 Hz)

28 Indoxylsulfate –CH¼ 7.49(d,J¼8.24 Hz) 111.87 HSQC–CH¼ 7.69(d,J¼7.96 Hz)

29 Xanthurenic acid –CH¼N 7.89(s) 121.87 HSQC

The words in bold are signed peaks of 1H NMR in Fig. 1.






creatinine, lactate and dimethylamine changed significantly at the2nd week, and taurine showed a significant change beginning atthe 6th week, and 2-oxoglutarate levels were significantly affectedby drug administration only at the 8th week. The tendency ofthese metabolite levels to vary showed a good agreement withprevious studies using animal models (Liu et al., 2012a; Zhou et al.,2011) or MDD patients (Zheng et al., 2013), which indicated thatafter the administration of the XYS decoction, depressed patientsexperienced improvement after some abnormal metabolite levelssignificantly changed, although at a slower rate. These metabolitesare discussed in further detail below.

These findings demonstrated the variation in the metabolicpathway induced by XYS treatment, which mainly regulatedenergy metabolism, gut microbes, tryptophan metabolism andother metabolic pathways (Fig. 5).

4.1. Energy metabolism

Citrate and 2-oxoglutarate are dominant products of thetricarboxylic acid cycle (TCA) associated with glycometabolismand energy metabolism. As an important biological compound aswell as key intermediate in the TCA cycle, 2-oxoglutarate occursnaturally within cells and can combine with ammonia to formglutamate and finally convert into glutamine. It also combineswith nitrogen released in the cell to prevent nitrogen overload.The higher levels of citrate and lower levels of 2-oxoglutarate inthe urine of depressed patients at 0 week are indicative of TCAcycle dysfunction. The energy deficiency, one of the most repre-sented depressive symptoms, results in reduced activity (Raisonand Miller, 2013). Acetyl-CoA, whose input is cyclical, is anactivated form of acetate generated by the degradation of carbo-hydrates, fats and proteins and mainly stems from pyruvate, whichis generated by glycolysis and then converted into acetyl-CoA bythe pyruvate dehydrogenase complex. Energy is deficient whenthe oxygen supply cannot meet its consumption, which will resultin an organism being forced to produce ATP in many differentways to adapt to their environment. As a part of the adaptiveresponse, lactate can be used to assess the severity of the supply/demand imbalance. Lactate measurement in the critically ill hasbeen traditionally used to stratify patients with poor outcome(Valenza et al., 2005). Potentially, an increase in lactate in criticallyill patients may be viewed as an early reversible marker. Alanine isa nonessential amino acid for humans and, as a major energy

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Fig. 2. PCA scores plot of urine samples from depressed patients at 0 week and 2nd week, 4th week, 6th week and 8th week with XYS treatment (A). PLS-DA scores plot ofurine samples from depressed patients at 0 week and 2nd week, 4th week and 6th week with XYS treatment (B). Three dimensional PLS-DA scores plot of urine samplesfrom depressed patients at 0 week and 2nd week, 4th week, and 6th week of XYS treatment (C). Three dimensional PLS-DA scores plot of urine samples from depressedpatients at 0 week and 2nd week, 4th week, 6th week and 8th week of XYS treatment (D).

Table 3Automatic modeling parameters for the classification of depressed patients atdifferent time points.

Before XYS treatment After XYS treatment R2X R2Y Q2

Week 0 Week 2 0.064 0.490 �0.204Week 4 0.431 0.573 0.446Week 6 0.535 0.595 0.439Week 8 0.574 0.851 0.466






source, it is also one of the most important amino acids releasedby muscle (Odessey et al., 1974). Creatinine, a breakdown productof creatine phosphate in muscle, is transferred to the kidneys byblood, where it is eliminated from the body through glomerularfiltration and partial tubular excretion. It originates from the slowspontaneous degradation of creatine-phosphate, while creatine-phosphate is always viewed as a “battery” that stores the energy ofexcess ATP (Zhou et al., 2011). Generally, in this study, there wasgood agreement with the variation of these metabolites indepressed patients or animal models in previous studies. Thesignificantly changed metabolites, such as citrate, 2-oxoglutarate,lactate, alanine and creatinine, and the HAMD scores from MDDpatients at different times, indicated that the depressed patientshad an improved condition at 6 weeks or 8 weeks after XYStreatment. It is evident that XYS can prevent both the increased

levels of citrate, lactate, alanine and creatinine, and decreasedtendency of 2-oxoglutarate in depressed patients. That is to say,the therapeutic effects of XYS may be based on the regulation ofthe dysfunction in energy metabolism.

4.2. Gut microbe metabolism

Loss of appetite is a common symptom in depression patients;previous urinary metabonomic analysis in a depressed animalmodel has shown that depressed behavior is associated withchanges in gut microflora (Zheng et al., 2010); also several clinicalstudies have demonstrated that depressed patients display ahigh comorbidity of gut microflora along with the concertrationchanges of metabolites such as dimethylamine (DMA), dimethyl-glycine, and trimethylamine N-oxide (TMAO) (Gros et al., 2009;

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Fig. 3. The OPLS-DA scores plot and loadings plot of urine samples at different time points. (A: Scores plot: 0 week vs. 4th week; B: loadings plot: 0 week vs. 4th week;C: scores plot: 0 week vs. 6th week; D: loadings plot: 0 week vs. 6th week; E: scores plot: 0 week vs. 8th week; F: loadings plot: 0 week vs. 8th week).






Ladep et al., 2006; Zheng et al., 2013). These combined findingshighlight the potential involvement of gut microbiotic variation inthe development of depression. Trimethylamine (TMA) is a volatiletertiary aliphatic amine that is derived from the diet either directlyfrom the consumption of foods containing TMA or derived fromthe degradation of choline and carnitine by bacterial enzymes inthe intestine, and it is normally oxidized to TMAO by the flavinmono-oxygenase system in the liver (Bain et al., 2005; Liu et al.,

2012b; O’Mahony et al., 2009). TMAO subsequently undergoesexcretion in the urine. As an organic secondary amine, DMA isabundantly present in human urine and is a colorless, liquefiedand flammable gas with an ammonia and fish-like odor. DMA is aproduct of choline and related precursors processed by gutmicrobiota Q2(Ross et al., 2013), and it has been reported that TMAOhas been identified as a major source of urinary dimethylaminein man (Zhang et al., 1993). Urinary excretion of methylamines

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Fig. 4. Mean peak area (mean7S.D.) of the representative metabolites at 2nd week, 4th week, 6th week and 8th week compared with that at 0 week: npo0.05; nnpo0.01.

Fig. 5. An overview of the metabolic pathways related to XYS treatment. The notations are as follows: ↓, higher than 0 week and ↓, lower than 0 week.






(TMA, TMAO, and DMA) is directly related to gut microbiotametabolism (Dumas et al., 2006). The variation of dimethylaminein the urine of depressed patients may be evidence of a relation-ship between depression and gut microbe metabolism and XYSmay play an important role in regulating the gut microbe meta-bolism of depression.

4.3. Tryptophan metabolism

Tryptophan is the least prevalent essential amino acid inmammals; it is required for the biosynthesis of proteins and it isa precursor of 5-hydroxytryptamine (5-HT). 5-HT could be foundin organs throughout the body, is a brain neurotransmitter,platelet clotting factor and neurohormone. Assessment of trypto-phan deficiency was performed through studying the excretion oftryptophan metabolites in the urine or blood. Xanthurenic acid is aphotochemically active tryptophan metabolite (Shen and Ji, 2008).It is a substrate of the enzyme methyltransferases in the trypto-phan metabolic pathway. It was reported that there is a down-regulation of xanthurenic acid in the urine of rats in a model ofdepression model induced by CVS (Su et al., 2011). However XYSwas observed to increase the levels of urinary xanthurenic acid indepressed patients in this study, indicating that the therapeuticeffects of XYS may be based on regulating a dysfunction oftryptophan metabolism.

4.4. Other metabolism

Taurine, abundant in the brain, heart, breast, gallbladder andkidney, is a sulfur-containing amino acid, similar to methionine,cystine, cysteine and homocysteine. Furthermore, taurine hasmany diverse biological functions, including acting as a neuro-transmitter in the brain, a stabilizer of cell membranes and afacilitator in the transport of ions such as sodium, potassium,calcium and magnesium (Okamoto et al., 1983; Sturman, 1988).The earliest and most prominent symptom of taurine deficiency ismental depression that is not responsive to antidepressant drugsor electroconvulsive therapy, whose obvious features consist ofsleep disturbances, exhaustion and marked weight loss. To ourknowledge the amino acids alanine, glutamate and pantothenicacid inhibit taurine metabolism, whereas vitamin A, vitamin B6,zinc and manganese can help produce taurine (Wu and Prentice,2010). Taurine involves the detoxification of hypochlorous acid,biosynthesis of certain mitochondria encoded proteins dramati-cally declines, respiratory function falls, ATP generation decreasesand the generation of oxidants by the respiratory chain increases(Schaffer et al., 2014a, 2014b). In the central nervous system,taurine specifically functions as a neuromodulator, interacting andaltering the actions of the GABAA receptor and the glycine receptor(Menzie et al., 2014). It was reported that taurine was deficient innearly all depressed patients (Caletti et al., 2012; Shealy et al.,1992) and taurine might represent a new adjuvant drug for thetreatment of depression (Caletti et al., 2012). In our research, lowlevel of alanine and high level of taurine were observed in theurine of patients after XYS administration suggesting that the anti-depressive effect of XYS may be based on inhibiting the synthesisof alanine and promoting the synthesis of taurine. Additionalstudies are necessary to clarify the mechanisms involved in theantidepressant and metabolic effects of taurine.

In this study, we have investigated the biomarkers of depres-sion and the relative concentration changes of metabolites duringthe administration of XYS using 1H NMR-based metabolomics.According to the metabolic pathways of these significantly chan-ged metabolites with the treatment of XYS, we try to find thepossible mechanism of antidepressant effect. Therefore, it isnecessary to find the controlling genes and the key enzymes of

changed metabolic pathways through genomics and proteomicstechnologies to confirm these mechanisms.

5. Conclusions

The results of our study were obtained by application of 1HNMR spectroscopy and analyzing crucial urinary metabolites indepressed patients treated with XYS at different time points.According to HAMD scores, symptoms of depression had beencontrolled after 6 weeks' treatment of XYS. The 8 metabolites,which were changed significantly after administration of XYS,were related to the energy metabolism, gut microbes, tryptophanmetabolism and taurine metabolism. The changing trend of thesemetabolites during the treatment of XYS was consistent with theimprovement in symptoms of depression. In summary, thesemetabolites may be used as biomarkers for the diagnosis ofdepressive disorders or the evaluation of the antidepressant aswell as the exploration of the mechanism of depression.

Acknowledgment

This study was financially supported Q3by the National NaturalScience Foundation of China (Nos. 81102833 and 81173366),the Program of International S&T Cooperation of China (No.2011DFA32630), and Science and Technology Innovation Team ofShanxi Province (Nos. 2013131015 and 2012021031-2)

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Glossary

CUMS: chronic unpredictable mild stress;DSM-IV: Diagnostic and Statistical Manual of Mental Disorders-IV;HAMD: Hamilton Depression Scale;MAOIs: monoamine oxidase inhibitors;NMR: nuclear magnetic resonance;OPLS-DA: orthogonal-projection to latent structure-discriminate analysis;OSC: orthogonal signal correction;PCA: principal component analysis;PLS-DA: partial least squares-discriminate analysis;SSRIs: selective serotonin reuptake inhibitors;TCA: tricarboxylic acid cycle tricarboxylic acid cycle;TCAs: tricyclic antidepressants;TCM: traditional Chinese medicines;XYS: xiaoyaosan.

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Dynamic analysis of the endogenous metabolites in depressed patients treated with TCM formula Xiaoyaosan using urinary 1H NMR-based metabolomics