Metabolomic Study of Biochemical Changes in the Plasma and

Metabolomic Study of Biochemical Changes in the Plasma and Urineof Primary Dysmenorrhea Patients Using UPLC−MS Coupled with aPattern Recognition ApproachShulan Su,† Jinao Duan,*,† Peijuan Wang,‡ Pei Liu,† Jianming Guo,† Erxin Shang,† Dawei Qian,†

Yuping Tang,† and Zongxiang Tang†

†Jiangsu Key Laboratory for High Technology Research of TCM Formulae, Nanjing University of Chinese Medicine,Nanjing 210046, P.R. China‡Department of Endocrinology and Gynecology, Jiangsu Provincial Hospital of Integrated Traditional and Western Medicine,Nanjing 210028, P.R. China

*S Supporting Information

ABSTRACT: Primary dysmenorrhea (PD) is characterized by painful menstrual cramps withoutany organic pathology and has a prevalence of up to 90% in adolescents. Recent advances in itsetiology and pathogenesis are providing more speculative hypotheses focused on integral systems.Using a targeted tandem mass spectrometry (MS/MS)-based metabolomic platform, we exploredthe changes of metabolic profiling in plasma/urine simultaneously between PD patients andhealthy controls before and after a 3-month herbal medicine (namely Shaofu Zhuyu formulaconcentrated-granule, SFZYFG) therapy. To detect and identify potential biomarkers associatedwith PD and SFZYFG treatment, we also performed a combined UPLC−QTOF-MS/MS-basedmetabolomic profiling of the plasma/urine samples, indicating a further deviation of the patients’global metabolic profile from that of controls. The total thirty-five metabolites (nineteen in plasma andsixteen in urine), up-regulated or down-regulated (p < 0.05 or 0.01), were identified and contributedto PD progress. These promising identified biomarkers underpinning the metabolic pathway includingsphingolipid metabolism, steroid hormone biosynthesis, and glycerophospholipid metabolism aredisturbed in PD patients, which were identified by using pathway analysis with MetPA. Twenty-four altered metabolites and fourteenbiochemical indicators were restored back to the control-like level after the treatment of SFZYFG and could be potential biomarkersfor monitoring therapeutic efficacy. These findings may be promising to yield a valuable insight into the pathophysiology of PD and toadvance the approaches of treatment, diagnosis, and prevention of PD and related syndromes.

KEYWORDS: primary dysmenorrhea (PD), metabolomics, potential biomarkers, UPLC−QTOF-MS/MS,Shaofu Zhuyu formula concentrated-granule (SFZYFG)

■ INTRODUCTION

Primary dysmenorrhea (PD) is characterized by painful men-strual cramps without any organic pathology. Its prevalence isestimated to be of 25% of women and up to 90% of adolescentsworldwide, and approximately 15% patients describe the pain assevere.1 Although PD is not life-threatening, it has a profoundlynegative impact on a woman’s daily life. In addition to physicalpain, the life quality of PD patients is often affected. Moreover,dysmenorrhea is responsible for a significant absenteeism fromwork and is the most common reason for school absence amongadolescents.1,2

Theories proposed by previous data have converged onthe excessive secretion of uterine prostaglandins (PGs) for theunderlying cause of PD. Women suffering painful periods havehigh levels of prostaglandin F2 (PGF2) in their menstrual fluid,which stimulates myometrial contractions, ischemia, and en-hanced sensitization of nerve endings.3 PD has also been sug-gested to be a sex-hormone related disorder accompanied by adecrease of progesterone before menstruation. This is followedby a cascade of response of PGs and leukotrienes, which subsequently

causes hyperalgesia, inflammatory pain, vasoconstriction,ischemia, and myometrial contraction.4,5 The level of vaso-pressin and oxytocin may also play similar roles.6 However, thepathogeneses behind the symptoms are largely limited. It isincreasingly apparent that the pathologies of PD are betterassociated with multiple factors in the neuro-endocrine-immune(N-E-I) network.7,8

Currently, the first-line therapies for treating PD are non-steroidal anti-inflammatory drugs (NSAIDs) and hormonalcontraceptives. However, NSAIDs have been associated with anincreasing risk for serious gastrointestinal adverse effects, suchas ulceration and bleeding.9,10 While the hormonal contra-ceptives (estrogen and progestin) could reduce menstrual fluidvolume and the amount of PGs in the fluid, the reduction couldbe below a normal range, which disturbs the whole endocrinemetabolism.11−13 Biomarkers are very useful for diagnosing andmonitoring disease progression and are important for patients

Received: October 5, 2012Published: January 4, 2013

Article

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© 2013 American Chemical Society 852 dx.doi.org/10.1021/pr300935x | J. Proteome Res. 2013, 12, 852−865

pubs.acs.org/jpr

to select appropriate treatment, monitor side-effects, and helpfind a new drug discovery.14 However, until now, there are noprevious reports of metabolic profiling or biomarkers researcheson PD.Metabonomics, based on the dynamic changes of low molec-

ular weight metabolites in organisms, indicates the overallphysiological status in responding to pathophysiological stimulior genetic, environmental, or lifestyle factors.15 The metabolitesoften mirror the end result of genomic and protein perturba-tions in disease, and they are closely associated with phenotypicchanges. Furthermore, the pathogenesis of diseases and theaction mechanisms of therapy would be elucidated by identify-ing the biomarkers, analyzing the metabolic pathway, dis-covering the drug−target interactions, and so on. Recently,metabolic profiling has attracted an interest for biomarkerdiscovery and for assessing holistic therapeutic effects of manyTCMs.16−21 These studies reflected the drug-induced effects onglobal metabolites and indeed valuable results were achieved.The Chinese herbal formula, Shaofu Zhuyu decoction (SFZYD),

is considered as an effective prescription for treating PD. Thisprescription originally came from “Correction of Errors inMedical Classics” compiled by Qing-ren Wang in Qing dynasty(A.D. 1830), which has been utilized in clinic to treat bloodstasis syndrome of gynecology diseases such as dysmenorrhea

and amenorhhea for about 200 years. The efficacy of SFZYDtreating PD was reported to be above 90%.22,23 Our previousstudies also indicated that SFZYD showed an inhibition ofuterine smooth muscle constriction and manifested an anti-inflammatory activity.24 The recent study also showed thatSFZYD could significantly improve hemorheological indexes ofrat model with blood stasis.25 SFZYD contained diverse bio-active compounds, including organic acids, alkaloids, flavonoidsand polysaccharides.26 Sixteen bioactive constituents and nineof their metabolites in rat plasma have been reported by usingUPLC−QTOF-MS.24 However, the therapeutic mechanisms ofSFZYD is still needed to have a comprehensive investigation.By using a metabolomics analysis of plasma and urine of PD

patients after intake of SFZYFG, the aims are (1) to elucidatethe metabolic profiling and phenotype changes between PDpatients and healthy controls; (2) to identify the potential bio-markers; and (3) to explore the intervention effects and actionmechanisms.

■ MATERIALS AND METHODS

Experimental Design

We conducted a prospective, controlled trial in Department ofEndocrinology and Gynecology, Jiangsu Provincial Hospital ofIntegrated Traditional and Western Medicine from Sep. 2010to Dec. 2011. The protocol was reviewed and approved byJiangsu Provincial Hospital of Integrated Traditional andWestern Medicine Ethics Committee (No. 2010 SL-028) andthe protocol was conducted in accordance with the Guidelinefor Good Clinical Practice (GCP). After the clinical trial hadbeen fully explained to the patients, the written consent wasobtained from all subjects. The clinical trial protocol wasdescribed in supplementary Text S1, Supporting Information.

Subjects

We previously recruited twenty-four patients with self-reportedprimary dysmenorrheal and twelve healthy volunteers from NanjingUniversity of Chinese Medicine in Nanjing, Jiangsu, China, toinvestigate their metabolites variations in response to SFZYFGtherapy. The selection criteria were briefly as follows: (1) age

Table 1. Pre-randomization Characteristics of twenty-fourPatients and twelve Healthy Volunteers

characteristicsShaofu Zhuyu

decoction (n = 24)controls(n = 12) P

Age mean (SD), years 23.8 (3.03) 24.3 (2.12) 0.87College education, n (%) 24 (100) 14 (100) 1.00Weight mean (SD), kg 50.8 (6.02) 51.6 (3.02) 0.46Body-mass indexa mean(SD), kg/m2

19.6 (2.27) 20.7 (1.27) 0.53

Menstruation durationmean (SD), days

4.8 (0.98) 5.2 (0.92) 0.98

Menstrual cycle lengthmean (SD), days

28.1 (3.23) 27.8 (2.75) 0.65

aBody mass index was calculated as the weight in kilograms divided bythe square of height in meters.

Table 2. Level of Biochemical Indicators in Serum of PD Patients before and after the Treatment by SFZYFG (mean ± SD, n = 24)

indicators control group before treatment after treatment p valuea

T (nmol/L) 7.90 ± 3.70 11.15 ± 1.66 7.63 ± 4.03 0.001

P (nmol/L) 57.40 ± 17.27 14.29 ± 13.84 39.67 ± 27.72 0.043

E2 (pmol/L) 11.17 ± 8.26 40.97 ± 30.51 12.17 ± 10.62 0.035

FSH (mIU/mL) 3.68 ± 4.23 5.54 ± 1.58 3.88 ± 2.50 0.000

LTB4 (pg/mL) 822.36 ± 215.56 871.52 ± 153.72 821.06 ± 125.69 0.017

S-LP(a) (mg/L) 237.62 ± 82.80 266.53 ± 260.79 223.08 ± 317.03 0.000

GC (pg/mL) 329.93 ± 116.23 432.10 ± 217.83 376.88 ± 146.55 0.010

ET-1 (pg/mL) 1.99 ± 0.53 2.67 ± 0.98 2.26 ± 0.49 0.026

NO (μmol/L) 105.37 ± 36.25 73.31 ± 26.72 93.84 ± 26.75 0.048

PGE2 (pg/mL) 170.34 ± 75.68 168.19 ± 57.26 171.55 ± 56.14 0.035

PGF2α (pg/mL) 13383.29 ± 578 13422 ± 638 13264 ± 632 0.033

OT (pg/mL) 240.04 ± 227.16 286.4 ± 279.96 248.74 ± 127.36 0.000

AVP (pg/mL) 220.36 ± 109.78 270.05 ± 165.64 258.65 ± 127.29 0.030

5-HT (ng/mL) 128.25 ± 78.00 97.26 ± 87.66 125.10 ± 110.61 0.010

ET-1 (pg/mL) 1.99 ± 0.53 2.67 ± 0.98 2.26 ± 0.49 0.026

NO (μmol/L) 105.37 ± 36.25 73.31 ± 26.72 93.84 ± 26.75 0.048

PGE2 (pg/mL) 170.34 ± 75.68 168.19 ± 57.26 171.55 ± 56.14 0.035aP-values were calculated from two-tailed ANOVA test with a threshold of 0.05. Before the treatment of PD vs after the treatment bySFZYFG.

Journal of Proteome Research Article

dx.doi.org/10.1021/pr300935x | J. Proteome Res. 2013, 12, 852−865853

18−40 years; (2) dysmenorrhea in the last three periods withan average score higher than 4 on Visual-Analog Scale (VAS)for pain (VAS assessment method shown in SupplementaryText S2, Supporting Information);27 (3) regular menstrualcycles for at least 3 months; and (4) no history of gynecologicdisease. All patients must met for Diagnostic Code of PrimaryDysmenorrhea (1993 Edition, the National Ministry of Health,

China) (Supplementary Text S3, Supporting Information) andCriteria of Diagnosis and therapeutic effect of traditional Chinesemedicine disease (State Administration of TCM approved)(Supplementary Text S4, Supporting Information) and litera-ture.27 Exclusion criteria for all patients were endometriosis,leiomyomata (fibroids), adenomyosis, pelvic inflammatory disease,and intrauterine device (IUD); age above 40 years; not belonging

Figure 1. Typical BPI chromatogram of plasma (A) control, (B) PD patient, and urine (C) healthy control, (D) PD patients at positive ESI mode.



to the syndrome of cold coagulation and blood stasis; withcardiovascular, liver, kidney, hyperlipidemia, hypertension, ormental diseases through history or evaluation at the screeningvisit. The subjects completed a detailed medical questionnaireto ensure that they were eligible to participate in this study.The characteristics of twenty-four patients and twelve healthyvolunteers are listed in Table 1. The progress of subjects ateach stage of the clinical trial was shown in Supplementary FigureS1, Supporting Information.

Drug Administration

In our study, Shaofu Zhuyu formula concentrated-granule(SFZYFG), a modified form of preparation of SFZYD, wasused and approved by State Food and Drug Administration ofChina (SFDA). It was produced by Jiangsu Tianjiang Pharma-ceutical Co., Ltd. and composed of 10 herbal concentrated-granule of Angelicae sinensis Radix (No. 1008064), Chuanxiong

Rhizoma (No. 1007151), Cinnamomi Cortex (No. 1005038),Foeniculi Fructus (No. 0908048), Zingiberis Rhizoma (No.1007044), Myrrha (No. 1002085), Trogopteri Feces (No.1005144), Typhae Pollen (No. 1003002), Paeoniae Radix Rubra(No. 1008034), and Corydalis Rhizoma (No. 1008073) accord-ing to the herbs weight ratio of 3:1:2:1:0.5:2:1:3:1:1. Each herbalconcentrate-granule was obtained by refluxing with water 2 timesand the combined filtrates were dried through spraying. In addi-tion, the granule was prepared according to the approved pre-paration technology. The quality and quantity analysis of chem-ical components in SFZYFG were analyzed (SupplementaryText S5, Supporting Information).All patients were followed to monitor for four menstrual

cycles and began receiving a treatment with SFZYFG on thefirst day of the first period, twice daily (200 mL each time) for5 days. In the second and third menstrual cycle, SFZYFG was

Figure 2. PCA model results between PD patients and healthy controls on the first day of period in positive mode. (A, 2-D plot of plasma; B, 2-Dplot of urine). A 3D PLS-DA scores plot of LC−MS spectral data between PD patients and healthy controls on the first day of period in positivemode. (C, plasma, R2 = 0.81, Q2 = 0.86; D, urine, R2 = 0.91, Q2 = 0.82).



administrated with the same dosage for 10 days (beginning 5days before the menstruation).

Plasma and Urine Samples

Blood and urine samples were taken from all participants onthe seventh day before her expected menses and labeled assample 1. Sample 2 (Pre-2) of blood and urine were collectedon the first day of first menstrual cycle before administration ofSFZYFG. Blood and urine collections on the first day of theirsecond, third and forth periods were labeled as sample 3, 4, and5 (Post-3, -4, and -5), respectively. The urine samples fromtwelve patients without being contaminated by menstrual fluid atall time points were selected for a metabolomics study, theselection could meet that there was the same amount ofsamples at each time point to obtain objective and preciseresults of variation trends during the total treatment cycle fromthe same patients at all time points.

Biochemical Indicators Measurements and UnivariateStatistical Analysis

The therapeutic efficacy was evaluated for the levels of5-hydroxytryptamine (5-HT), oxytocin (OT), arg8-Vasopressin(AVP), estradiol (E2), progesterone (P), follicle-stimulatinghormone (FSH), testosterone (T), glucocorticoid (GC), prosta-glandin E2 (PGE2), prostaglandin F2α apha (PGF2α), leukotriene

B4 (LTB4), nitric Oxide (NO), endothelin-1 (ET-1), and lipo-protein (a) [S-LP(a)] in plasma. The test methods of thesebiochemical indicators were listed in Supplementary Text S6,Supporting Information.Biochemical analysis data were presented as the mean ± SD.

Statistical significance was assessed by ANOVA test andShapiro-Wilk statistics was adopted to test normal distributionby SPSS 16.0 version (Inc., Chicago, IL). In all experiments,confidence level was set at 95% to determine the significance ofdifference (P < 0.05).

Plasma and Urine Samples Preparation

Each blood sample was anticoagulated by natrium citricum andcentrifuged at 15 000× g for 10 min to obtain plasma samplesin a randomized order. Two-hundred microliters of plasma wasadded with 400 μL of acetonitrile and vortex mixed for 30 s,then centrifuged at 15 000× g for 10 min to obtain the super-natant. Prior to analysis, urine samples were thawed at roomtemperature and centrifuged at 15 000× g for 10 min. Thesupernatant liquid 1 mL was added 3 mL acetonitrile and vor-tex mixed for 30 s, then centrifuged at 15 000× g for 10 min toobtain the supernatant in a randomized order. The supernatantfrom plasma or urine was removed and evaporated to drynessin a 40 °C water bath under a gentle stream of nitrogen,

Figure 3. S-plot of OPLS-DA model for PD vs control group. (A, plasma, R2 = 0.89, Q2 = 0.86; B, urine, R2 = 0.98, Q2 = 0.82). VIP- plot of OPLS-DA model for PD vs healthy controls. (C, plasma R2 = 0.88, Q2 = 0.89; D, urine, R2 = 0.98, Q2 = 0.82).



Table

3.Identified

Differential

Metabolites

Accou

ntable

fortheDiscrim

inationbetweenPD

PatientsandHealth

yCon

trolsin

Plasm

a

no.

t R(m

in)

metabolites

obsd

[M+H]+

calcd[M

+H]+

MS/MS

VIPa

PbFC

ccontent

varianced

pathway

(KEG

G)

1(M1)

9.56

PC(18:2(9Z

,12Z

)/16:0)

758.5735

758.5694

748.4868,5

44.0000,

464.2797,3

18.3612

1.79

0.0086

1.31

↑Glycerophospholipid

metabolism

2(M2)

11.71

Phytosphingosine

318.3016

318.3003

256.2173,1

84.0717,

161.5589,1

29.4796

1.67

0.0106

−2.01

↓Sphingolipid

metabolism

3(M3)

13.67

LysoPC

(18:3(9Z

,12Z

,15Z

))518.3262

518.3241

500.2929,4

67.7113,

357.2786,2

64.2601,

184.0585,1

24.9547

1.09

0.0416

−1.24

↓Glycerophospholipid

metabolism

4(M4)

14.04

LysoPC

(16:1(9Z

))494.3290

494.3298

476.3173,3

18.2872,

184.0680,1

24.9903,

103.9807

4.89

0.0253

−1.56


metabolism

5(M5)

14.84

LysoPC

(20:4

(8Z,11Z

,14Z

,17Z

))544.3367

544.3398

526.2919,5

20.3394,

291.7539,2

61.1359,

184.0739,

1.63

0.0342

−2.25


metabolism

6(M6)

16.12

2-Arachidonylglycerol

544.3367

544.3398

520.3394,2

79.7625,

104.0851

1.67

0.0428

2.01

↑Sphingolipid

metabolism

7(M7)

15.67

LysoPC

(18:1(11Z))

522.3577

522.3554

504.3532,4

02.3223,

184.0718

4.01

0.0312

3.35


metabolism

8(M8)

16.05

PC(18:1(9Z

)/0:0)

522.3577

522.3554

504.3532,4

45.2400,

184.0718

4.02

0.0127

4.56


metabolism

918.89

Palmiticam

ide

256.2633

256.2635

217.1028,1

84.0719,

103.9557

2.59

0.0298

2.78

↑Sphingolipid

metabolism

1012.34

Sphinganine

302.3072

302.3054

284.294,

261.1236,2

17.1038,

146.0377

1.18

0.0068

5.60

↑Sphingolipid

metabolism

114.02

17-phenoxy

trinor

PGF2

αethyl

amide

432.2808

432.2745

415.2510,3

62.2432,

217.1066,1

46.0120,

117.9561

1.56

0.0092

1.23


metabolism

1218.08

LysoPC

(18:0)

524.3736

524.3711

506.3636,3

41.2591,

281.8056,2

17.1051,

184.0784,1

04.0698

3.72

0.0321

5.20


metabolism

1315.97

LysoPC

(16:0)e

496.3383

496.3398

478.3290,4

19.2279,

313.1615,1

84.0786,

125.9252,1

04.1071

2.67

0.0432

−3.05


metabolism

140.82

Choline

104.1069

104.1070

-10.39

0.0378

1.37


metabolism

157.18

LysoPC

(18:2(9Z

,12Z

))520.3389

520.3398

502.3165,3

61.2709,

279.6420,2

17.1042,

184.0719

1.92

0.0329

−4.00


metabolism

1619.71

2-Ph

enylacetam

ide

282.2819

282.2791

261.1248,2

17.1059,

184.0792,1

03.9593

1.28

0.0451

−1.45

↓Sphingolipid

metabolism

1716.70

LysoPC

(17:0)

510.3588

510.3554

330.3187,3

05.1661,

261.1286,2

17.1066

2.18

0.0256

−1.68


metabolism

1818.31

LysoPC

(20:0)

510.3588

510.3554

466.3296,3

58.3694,

261.1317,2

17.1071,

184.0796,1

03.9582

2.34

0.0218

−3.18


metabolism

1920.37

SM(d18:1/16:0)

703.5751

703.5748

689.5698,6

37.7039,

550.6163,5

02.4437,

353.2712,2

61.1337,

217.1055,1

84.0746,

3.12

0.0091

2.89

↑Sphingolipid

metabolism

aVariableimportance

intheprojectio

n(V

IP)values

wereobtained

from

cross-validated

PLS-DAmodelswith

athresholdof

1.bP-values

werecalculated

from

two-tailedMann−

Whitney

U-testwith

athresholdof

0.05.cFo

ldchange

wascalculated

astheratio

ofthemeanmetabolite

levelsbetweentwogroups.A

positivevalueof

foldchange

indicatesarelativelyhigherconcentrationofmetaboliteswhile

anegativevalueof

fold

change

indicatesarelativelylower

concentrationin

PDpatientsas

comparedto

healthycontrols.d↑,

contentincreased;

↓,contentdecreased.

e Confirm

edby

standard

samples.



Table

4.Identified

Differential

Metabolites

Accou

ntable

fortheDiscrim

inationbetweenPD

PatientsandHealth

yCon

trolsin

Urine

no.

t R(m

in)

metabolites

obsd

[M+H]+

calcd

[M+H]+

MS/MS

VIPa

PbFC

ccontent

varianced

pathway

(KEGG)

1(M9)

0.9

Uric

acid

169.0348

169.0356

153.0489,1

24.9869,

110.9935

1.88

0.0086

1.29


metabolism

2(M10)

0.63

Creatinine

114.0658

114.0662

1.87

0.0106

−1.59

↓Sphingolipid

metabolism

3(M11)

8.18

LysoPC

(18:2(9Z

,12Z

))520.3346

520.3398

502.3165,361.2709,279.6420,217.1042,184.0719

2.58

0.0416

1.81


metabolism

4(M12)

7.88

PC(18:1(9Z

)/2:0)

564.3633

564.3660

547.3376,386.2143,173.0795

1.21

0.0253

2.53


metabolism

5(M13)

9.25

Estronee

271.1661

271.1692

253.1530,1

47.0433,

130.0508

1.48

0.0342

1.40

↑Steroidhorm

onebiosynthesis

6(M14)

10.87

PE(18:2(9Z

,12Z

)/15:0)

702.5073

702.5068

679.5154,340.3844,328.2107,170.0593,146.1234

1.38

0.0428

−1.70


metabolism

7(M15)

14.13

Cyclic

AMP

330.0612

330.0603

305.1726,261.1335,

217.1033,133.0868

11.23

0.0312

−2.79


metabolism

8(M16)

14.98

Sphinganine

302.3060

302.2687

268.2392,2

11.5184,

162.5414,1

18.5106

13.26

0.0127

1.42

↑Sphingolipid

metabolism

9(M17)

14.51

Phytosphingosine

318.3006

318.3003

300.2314,2

56.2327,

230.2393,1

46.0639

1.81

0.0298

1.23

↑Sphingolipid

metabolism

10(M

18)

6.34

Palmiticam

ide

256.2646

256.2635

206.5815,1

32.5265

2.28

0.0068

−1.01

↓Sphingolipid

metabolism

11(M

19)

5.44

Phosphorylcholine

170.0603

170.0577

158.9926,146.9436,130.9662,115.0543

2.56

0.0092

10.0


metabolism

12(M

20)

12.78

Palmito

ylglycine

314.2328

314.2326

304.0785,2

55.1856

2.18

0.0321

−10.0


metabolism

13(M

21)

13.67

Sphingosine

300.2824

300.2897

283.1796,2

64.5628,

171.9893,1

63.1417

2.14

0.0432

2.64

↑Sphingolipid

metabolism

14(M

22)

6.71

LysoPC

(14:0)

413.2567

413.2537

391.2856,279.1606,184.0788,167.0341,149.0215

1.88

0.0378

1.48


metabolism

15(M

23)

4.04

Dihydrocortisole

365.2338

365.2322

347.2228,329.2151,285.1794,2

23.1023,

150.5119

1.58

0.0329

2.5

↑Steroidhorm

onebiosynthesis

16(M

24)

11.81

17-H

ydroxyprogesterone

331.2362

331.2267

261.1319,217.1052,

133.0842

1.25

0.0451

−2.79

↓Steroidhorm

onebiosynthesis

aVariableimportance

intheprojectio

n(V

IP)values

wereobtained

from

cross-validated

PLS-DAmodelswith

athresholdof

1.bP-values

werecalculated

from

two-tailedMann−

Whitney

U-testwith

athresholdof

0.05.cFo

ldchange

wascalculated

astheratio

ofthemeanmetabolite

levelsbetweentwogroups.A

positivevalueof

foldchange

indicatesarelativelyhigherconcentrationofmetaboliteswhile

anegativevalueof

fold

change

indicatesarelativelylower

concentrationin

PDpatientsas

comparedto

healthycontrols.d↑,

contentincreased;

↓,contentdecreased.

e Confirm

edby

standard

samples.



respectively. The residues were reconstituted in 200 μL mobilephase of 70% acetonitrile−water solution and centrifugation at15 000× g for 5 min and filtered through 0.22 μm membranefilter, respectively. The filtrates were transferred to autosamplervial kept at 4 °C and an aliquot (5 μL) of plasma and urinesamples was injected for LC−MS analysis, respectively, and thesamples were run in a randomized order.

UPLC−QTOF-MS and UPLC−QqQ-MS Analysis

The UPLC analysis was performed on a Waters ACQUITY UPLCsystem (Waters Corporation, Milford, MA). Acquity UPLCBEH-C18 column (2.1 mm × 50 mm, 1.7 μm) was applied forall analyses. Mass spectrometry was carried out on a SynaptQ-TOF mass spectrometer (Waters, Manchester, U.K.). UPLC−QqQ/MS was used for relative quantification analysis of thepotential biomarkers. The chromatographic conditions and MSspectra analytical methods are listed in Supplementary Text S7,Supporting Information.

Data Processing and Multivariate Analysis

Centroided and integrated raw mass spectrometric data of plasmaand urine were processed by MassLynx V4.1 and MarkerLynxsoftware (Waters Corp., Milford, MA). The intensity of eachion was normalized with respect to the total ion count togenerate a data matrix that consisted of the retention time, m/zvalue, and the normalized peak area. The multivariate data ma-trix was analyzed by EZinfo software (Waters Corp., Milford, MA).The unsupervised segregation was checked by principalcomponents analysis (PCA) using Pareto-scaled data. Thepartial least squared discriminant analysis (PLS-DA) andorthogonal partial least-squared discriminant analysis (OPLS-DA)were used to identify the varied metabolites responsible forthe separation between PD patients and healthy controls.S-plots were calculated to visualize the relationship betweencovariance and correlation within the OPLS-DA results.Variables that had significant contributions to discriminationbetween groups were considered as potential biomarkers and

subjected to further identification of the molecular formula.The variable importance in the projection (VIP) values was alsoused for the selection of biomarkers. Variables, with a VIP valuelarger than 1, showed that a higher than average influence onthe classification.28 An independent t-test indicated that thesevariables between the healthy controls and patients group werestatistically significant difference (p < 0.05). Those variableswere eventually selected as potential biomarkers. An internal 5-fold cross-validation was carried out to estimate the perform-ance of PLS-DA models. The calculated R2Y(cum) estimates thegoodness of fit of the model that represents the fraction ofexplained Y-variation, and Q2

(cum) estimates the ability ofprediction. Excellent models are obtained when the cumulativevalues of R2Y and Q2 are above 0.8. In addition to cross-validation, model validation was also performed by 200 timespermutation tests. Ultimately, differential metabolic featuresassociated with PD and SFZYFG treatment were obtained inaccordance with the cutoff points of both VIP values andcritical p-values from univariate analysis. In addition, thecorresponding fold change was calculated to show the degree ofvariation in metabolite levels between groups.

Biomarkers Identification

The identities of the potential biomarkers were confirmedby comparing their mass spectra and chromatographic reten-tion times with the available reference standards. A full spectrallibrary, containing MS/MS data, was obtained in the positiveand/or negative ion modes. The Mass Fragment applicationmanager (Waters MassLynx v4.1, Waters corp., Milford, MA)was used to facilitate the MS/MS fragment ion analysis processby way of chemically intelligent peak-matching algorithms. Thisinformation was then submitted for database searching, eitherin-house or using the online ChemSpider database (www.chemspider.com), and Mass Bank (http://www.massbank.jp/),PubChem (http://ncbi.nim.nih.gov/), and MetFrag (http://msbi.ipb-halle.de/MetFrag/) data source.

Figure 4. PCA analytical results from PD patients treated with SFZYFG in different periods at positive mode. (A) For plasma; (B) for urine (Pre-2:before administration of SFZYFG at the first period; Post-3: after administration of SFZYFG for one period; Post-4: after administration of SFZYFGfor two periods; Post-5: after administration of SFZYFG for three periods).



www.chemspider.com

www.chemspider.com

http://www.massbank.jp/

http://ncbi.nim.nih.gov/

http://msbi.ipb-halle.de/MetFrag/

http://msbi.ipb-halle.de/MetFrag/

Construction of Metabolic Pathway

The construction, interaction, and pathway analysis of potentialbiomarkers was performed with MetPA (http://metpa.metabolomics.ca./MetPA/faces/Home.jsp) based on databasesource including the KEGG (http://www.genome.jp/kegg/),Human Metabolome Database (http://www.hmdb.ca/),SMPD (http://www.smpdb.ca/), and METLIN (http://metlin.scripps.edu/) for identification of the affected metabolicpathways and visualization.

■ RESULTS

Demographic and Clinical Characteristics

The enrolled patients and controls were well-matched in termsof age, gender and ethnicity. The clinical characteristics of thesubjects are shown in Table 1. No significant changes frombaseline in weight and body mass index (BMI) were observedat the end of SFZYFG treatment. Twenty-four PD patients,mean age 23.8 ± 3.03 (SD) and 12 healthy volunteers (aged24.3 ± 2.12) participated in the study. No significant dis-crepancies were found between the two groups in other base-line features.

Clinical and General Biochemistry Analysis

Biochemistry data was presented in Table 2. The levels ofindicators were changed significantly after the treatment ofSFZYFG (p < 0.05 or p < 0.01). For hormone indicators,SFZYFG reduced significantly the levels of T, E2, FSH, and GC(P < 0.05 or P < 0.01), and SFZYFG obviously increased thelevels of P (p < 0.05). The elevated inflammatory factors levelsof PGF2α, LTB4, and ET-1 were inhibited significantly, and thatof PGE2 and NO were increased significantly after thetreatment of SFZYFG (p < 0.05 or p < 0.01). The elevatedneuro-transmitters of OT and AVP were reduced significantlywhereas NA and 5-HT were increased in serum (p < 0.05 orp < 0.01). These data implied that SFZYFG regulated multiplebiochemical indicators related to neuro-endocrine-immune(N-E-I) system and exhibited an integral efficacy.

Metabolic Profiling Analysis

Typical based peak intensity (BPI) chromatograms of plasmaand urine samples, collected from PD patients and healthy con-trols in positive modes were shown in Figure 1. The subtlechanges could be found using a pattern recognition approach,including, for example, PCA and PLS-DA. The unsupervisedPCA model was used to separate plasma or urine sample into

Figure 5. Changes in the relative quantities of target metabolites identified by PCA in different groups. A two- tailed, parametric t-test was used todetermine the significance of the change in relative quantities for each metabolite. Bars represent the mean relative quantities and standarddeviations. * p < 0.05; ** p < 0.01; *** p < 0.001: after treatment by SFZYFG vs before treatment; # p < 0.05; ## p < 0.01 PD patients vs healthycontrols; Δ p > 0.05: after treatment by SFZYFG vs healthy controls. (A) Eight metabolites in the plasma; (B) 16 metabolites in the urine.



http://metpa.metabolomics.ca./MetPA/faces/Home.jsp

http://metpa.metabolomics.ca./MetPA/faces/Home.jsp

http://www.genome.jp/kegg/

http://www.hmdb.ca/

http://www.smpdb.ca/

http://metlin.scripps.edu/

http://metlin.scripps.edu/

two blocks between PD patients and healthy controls on thefirst day of menstrual cycle before the treatment of SFZYFG.A total of 528 ions in plasma samples and 606 ions in urinesamples at positive modes were detected from PD patientsand healthy controls. PCA scores plots showed clearclustering of PD samples versus healthy controls samples(Figure 2A−D).The supervised OPLS-DA divided samples into two blocks:

this method was applied to obtain a better discrimination be-tween the two groups. Based on the differences in their meta-bolic profiles, the OPLS-DA score plot analysis distinguishedthe plasma and/or urine samples of PD patients and controls.OPLS-DA distinguished PD patients and controls’ cohorts with100% sensitivity and no less than 95% specificity using a leaveone out algorithm, which indicated that the OPLS-DA modelswere reliable. The different pattern recognition therefore sug-gested that the endogenous metabolites were changed as aresult of PD.From the loading plots of OPLS-DA, 96 ions in plasma samples

and 108 ions in urine samples at positive modes were deemed dis-criminatory (p < 0.05), and identified as being responsible for theseparation between patients and control groups (Figure 3A−D).Finally, thirty-five potential biomarkers (nineteen in plasma andsixteen in urine) were tentatively identified (Table 3).

Identification and Quantification of Potential Biomarkers

In the plasma, nineteen endogenous metabolites, contributing tothe separation between the groups, were identified by compar-ing with authentic standards or based on their molecular ioninformation as well as the fragments of corresponding production (Table 3). The precise molecular mass was determinedwithin measurement errors (<5 ppm) by Q-TOF. To illustratethe identification of metabolites, we took the ion attR = 15.97 min, m/z 496.3383) as an example to be describedbelow. This ion may contain an odd number of nitrogen atomsbecause its precise molecular weight is 496.3383, and itsmolecular formula was speculated to be C24H50NO7P based onthe analysis of its elemental composition and fractional isotopeabundance. In the positive ion spectrum, the main fragmentions were observed at m/z 478.3290, 419.2279, 313.1615,184.0786, 125.9252, and 104.1071, which could be the [M + H]+ oflost -H2O, -C3H10NO, -C5H13NO4P, -C19H37NO2, -C22H44NO3,and -C20H42NO4P, respectively. Finally, to define its struc-ture, according to the HMDB database, the metabolite wastentatively identified as lysophosphatidylcholine (16:0) [LPC(16:0)].Compared with healthy volunteers, there are up regulated

metabolites in PD patients, including PC(18:2(9Z,12Z)/16:0),2-arachidonylglycerol, LysoPC (18:1 (11Z)), PC(18:1 (9Z)/0:0), palmitic amide, sphinganine, 17-phenoxy trinor PGF2αethyl amide, LysoPC(18:0), choline, and SM(d18:1/16:0)(p < 0.05). While the significantly down regulated ones werephytosphingosine, LysoPC (18:3(9Z,12Z,15Z)), LysoPC(16:1-(9Z)), LysoPC (20:4(8Z,11Z,14Z,17Z)), LysoPC (16:0),LysoPC (18:2(9Z,12Z)), 2-phenylacetamide, LysoPC(17:0),and LysoPC(20:0) (p < 0.05) (Table 3).In the urine, the detected significant variables in the positive

ion mode are summarized in Table 4. Sixteen endogenousmetabolites were tentatively identified by the methodsdescribed above. The metabolites of estrone, uric acid,sphinganine, phytosphingosine, phosphorylcholine, sphingo-sine, LysoPC(14:0), dihydrocortisol, PC(18:1(9Z)/0:0),LysoPC(18:2(9Z,12Z)) were observed to be up regulated

significantly (p < 0.05), whereas the metabolites of creatinine,PE(18:2 (9Z, 12Z)/15:0), cyclic AMP, palmitic amide,palmitoylglycine, and 17-hydroxyprogesterone were downregulated obviously (p < 0.05). These differences in plasmaand urine might denote the potential targeted biomarkers fordifferentiating PD pathological and normal states.Intervention Effects of SFZYFG

In order to characterize the clinical efficacy of SFZYFG fortreating PD, PCA analysis was carried out to obtain the changesduring the three therapeutic cycles. The variations of plasmaand urine metabolic profiling of SFZYGH-treated patients wasrestored back to the levels more control-like at the end of therapy(Figure 4A and B). Furthermore, the relative quantities of twenty-four target metabolites were obtained using UPLC-QqQ/MS andeight endogenous metabolites in plasma and sixteen endogenousmetabolites in urine were significantly affected by SFZYFG(p < 0.05 or p < 0.01). And all of them were restored back to acontrol-like level and there is no significant difference betweenthe SFZYFG- treated group and healthy controls (p > 0.05)except for M18 and M19, which were shown in Figure 5A andB. The mass spectrum and proposed chemical structures of

Figure 6. Summary of pathway analysis with MetPA. (A) Sphingolipidmetabolism, (B) glycerophospholipid metabolism, and (C) steroidhormone biosynthesis (A, plasma; B, urine).



these twenty-four SFZYFG-regulated targeted biomarkers aredisplayed in supplementary Figure S2, Supporting Information.

Metabolic Pathway and Function Analysis

Here, the metabolic pathway analysis with MetPA revealed thatthe identified biomarkers in plasma were critical for the hostresponding to PD, which was responsible for sphingolipidmetabolism, and glycerophospholipid metabolism (Figure 6 A).The urine metabolic pathway analysis with MetPA revealed thatsphingolipid metabolism, glycerophospholipid metabolism, andsteroid hormone biosynthesis were disturbed when PDoccurred (Figure 6 B). The pathway impact value calculatedfrom pathway topology analysis above 0.1 was filtered outas potential target pathway and is listed in supplementaryTable S1 and 2, Supporting Information.

Correlation Analysis between Biomarkers andBiochemistry Indicators

A Pearson correlation matrix analysis was used to discover thecorrelations between potential biomarkers and biochemicalindicators (Figure 7). The results indicated that the level ofT was strong positive associated with metabolites of M10(creatinine) (r = 0.801) and negative associated withmetabolites of M16 (sphinganine) (r < −0.6). E2 and P weresignificantly positive associated with M4 (lysoPC(16:1(9Z)))and M12 (PC(18:1(9Z)/0:0)) (r > 0.7), respectively. The levelof GC was significantly positive associated with metabolites ofM13 (estrone), M19 (phosphorylcholine) (r = 0.8) and M1(PC (18:2(9Z,12Z)/0:0)) (r > 0.5). An obvious negative cor-relation was discovered between inflammatory mediators ofPGs and M1, and M17 (phytosphingosine) (r < −0.5) andpositive associated with M6 (2-arachidonylglycerol) (r > 0.5),respectively. The level of LTB4 was highly negative correlation

with metabolites of lysophospholipid. Moreover, the neuro-transmitters of 5-HT, OT, and VAP were significantlyassociated with metabolites of phospholipid. These correlation-ships could be valuable for understanding the PD related withlipid metabolism and steroid hormone disorder.

■ DISCUSSION

The pathogenesis of PD associated with metabolic disturbancewas proposed in Figure 8 based on our researches and litera-tures.3−8 Metabolomic technology has been suggested as anefficient means for the identification and relative quantitiesof metabolites, which are altered in response to disease ortherapeutic intervention. It was implied that the pathways ofsphingolipid metabolism (impact value 0.15, 0.23), glycer-ophospholipid metabolism (impact value 0.12, 0.18), andsteroid hormone biosynthesis (impact value 0.127) in plasmaand urine were disturbed, respectively. After the treatment withSFZYG, these disturbed metabolic profiling was restored backto the control-like levels. In addition, twenty-four SFZYFG-regulated specific metabolites were identified, including phytos-phingosine, sphinganine, sphingosine 1-phosphate, sphingosine,LysoPC, estrone, dihydrocortisol, 17-hydroxyl-progesterone,etc. These promising biomarkers candidates verified that thepathogenesis of PD is closely related to multiple etiologies andpathogenesis. On the basis of these findings, further studieshave to be performed in order to validate the changes andtargeted metabolites.Previous studies also indicated that PD patients had an

enhanced pain perception, possibly a result of both peripheral andcentral sensitization. The ongoing menstrual pain in PD patients isaccompanied by abnormal brain metabolism.29 By using anuntargeted metabolomics study, the sphingomyelin - ceramide

Figure 7. Correlation analysis between biomarkers and biochemistry data in plasma before administration of SFZYFG at the first menstrual period.Rows, metabolites; Columns, biochemical indicators; Color key indicates correlation value: blue, lowest; red, highest. The correlation heat-map wasbuilt and optimized by MATLAB software (MathWorks, US).



metabolism was shown to be altered in dorsal horn of rats withneuropathic pain and the up-regulated endogenous metabolite,N,N-dimethylsphingosine, induced a mechanical hypersensitiv-ity in vivo.30 These discoveries agreed with our results of meta-bolic disorder of sphingolipid metabolism and the alteredendogenous metabolites of phytosphingosine, sphinganine,sphingosine 1-phosphate (S1P), and sphingosine. Actually,S1P plays an important role during development, particularly invascular maturation and has been implicated in pathophysiol-ogy of cancer, wound healing, and atherosclerosis.31 Plasmasphingolipids level has been found to be a risk factor for poly-cystic ovary syndrome and endometriosis.16

PD has been suggested to be a sex-hormone related disorder:there is a buildup of fatty acids in the phospholipids of the cellmembranes.32,33 After the onset of progesterone withdrawalbefore menstruation, these omega-6 fatty acids, particularlyarachidonic acid, are released, and a cascade of prostaglandins(PG) and leukotrienes (LT) is initiated in uterus. The inflam-matory response, mediated by these PGs and LTs, producesboth cramps and systemic symptoms such as nausea, vomiting,bloating and headaches. The PGF2α, cyclooxygenase (COX)metabolite of arachidonic acid, caused potent vasoconstrictionand myometrial contractions, which was leading to ischemiaand pain.34,5

Our results agreed well with previous studies that showedthe hub genes, chemical messengers, in Cold ZHENG

(Cold Syndrome) network were characterized as hypothalamus-pituitary hormones (POMC/ACTH, CRH/CRH, TRH/TRHand CORT/CORT) or neurotransmitters (AVP/AVP).35,36 Inour study, three unique metabolic pathway of sphingolipid,glycerolphospholipid metabolism, and steroid hormone biosyn-thesis were identified from PD patients. Furthermore, potentialbiomarkers of lysophosphatidylcholine (LPCs) were identified.The LPC is in turn enzymatically converted to lysophosphatidicacid (LPA). It has been found to have some functions in cellsignaling and specific receptors (coupled to G proteins).37 LPAsignaling can also have pathological consequences, influencingaspects of endometriosis and ovarian cancer.38

The correlation analysis results showed that the levels ofT, P, and E2 were closely related to the metabolites of uric acidand creatinine and which agreed with the reports of litera-tures.39,40 The levels of 5-HT, OT, and AVP were also closelyrelated to the metabolites of phospholipid from our study.It was reported that 5-HT2A activated phospholipase A2 andcoupled with second messenger, which could bring in theneurophysiological effects.41 The correlations of biomarkersand biochemical indicators could be significant for the elucida-tion of PD pathogenesis. More importantly, these promisingbiomarkers could provide guidance for clinical diagnosis andtherapy of PD. Here, we found that the multiple biochemicalindicators and potential biomarkers were regulated by SFZYFG,which consist with our previous results partly.42,43 So, it was

Figure 8. Proposed pathogenesis of PD associated with metabolic disturbance.



stated that SFZYFG produced an integral regulation byintervening special metabolites in global metabolic network.More experiments will be investigated to discover the complexaction mechanisms.

■ CONCLUSIONMetabolomics is a useful tool for discriminating metabolicpathways change between patients and controls and forpredicting and discovering drug action mechanisms, especiallyfor investigating the therapeutic effects and mechanisms ofTCMs. By using an UPLC−QTOF-MS/MS-based metabolo-mics study, we can obtain more detailed information aboutthe metabolic changes that in patients. In this study, UPLC−QTOF-MS spectroscopy coupled with multivariate statisticalanalysis showed that endogenous metabolites have changed in PDpatients. The identified thirty-five potential metabolites, associatedwith sphingolipid metabolism, glycerophospholipid metabo-lism, and steroid hormone biosynthesis, were contributed toPD progress. In addition, twenty-four altered metabolites wererestored back to the control-like level, and fourteen clinicalbiochemical indicators were regulated significantly after thetreatment of SFZYFG. Further refinement and validation ofthese biomarkers in larger cohorts of patients would be of con-siderable interest.

■ ASSOCIATED CONTENT*S Supporting Information

Text S1 displays the clinical trial protocol of Shaofu Zhuyuformula for treating primary dysmenorrheal. Text S2 describesthe method of assessment of pain intensity. Text S3 listed theclinical diagnosis criteria of primary dysmenorrhea by Westernmedicine. Text S4 presents the clinical diagnosis criteria ofPrimary dysmenorrhea with syndrome of cold coagulation andblood stasis by traditional Chinese medicine. Text S5 displayedthe quality and quantity analysis of chemical components inSFZYFG. Text S6 listed the test methods of clinicalbiochemical indicators. Text S7 listed the chromatographicconditions and MS spectra analytical conditions. Figure S1showed the flow diagram of the progress of subjects at eachstage of the clinical trial. Figure S2 displays the MS spectrum of24 SFZYFG-regulated promising biomarker candidates.Table S1 shows the results from pathway analysis with MetPAfrom plasma, and Table S2 lists the results from pathway analysiswith MetPA from urine. This material is available free of chargevia the Internet at http://pubs.acs.org.

■ AUTHOR INFORMATIONCorresponding Author

*Tel./fax: +86 25 85811116. E-mail: [email protected]

The authors declare no competing financial interest.

■ ACKNOWLEDGMENTSWe thank the study doctors of Guifang Sun, and Qihu Dong,Dept. of Endocrinology and Gynecology, Jiangsu ProvincialHospital of Integrated Traditional and Western Medicine,Nanjing, China, who have recruited the patients and collectedblood samples, urine samples, and clinical data. This work wassupported by Key Research Project in Basic Science of JiangsuCollege and University (No. 06KJA36022; 11KJA360002),National Natural Science Foundation of China (No.

30973885). And this work was supported by ConstructionProject for Jiangsu Key Laboratory for High TechnologyResearch of TCM Formulas (BM2010576), ConstructionProject for Jiangsu Engineering Center of Innovative Drugfrom Blood-conditioning TCM Formulas and a Project Fundedby the Priority Academic Program Development of JiangsuHigher Education Institutions (ysxk-2010).

■ ABBREVIATIONS

PD, primary dysmenorrhea; SFZYFG, Shaofu Zhuyu formulaconcentrated - granule; VAS, Visual-Analog Scale; MS/MS,tandem mass spectrometry; OPLS, orthogonal partial least-squares; PCA, principal components analysis; PLS-DA, partialleast-squares discriminant analysis; RT, retention time;TOFMS, time-of-flight mass spectrometry; UPLC, ultra-performance liquid chromatography; 5-HT, 5-hydroxytrypt-amine; OT, oxytocin; AVP, arg8-Vasopressin; E2, estradiol; P,progesterone; FSH, follicle-stimulating hormone; T, testoster-one; GC, glucocorticoid; PGE2, prostaglandin E2; PGF2α,prostaglandin F2α; LTB4, leukotriene B4; NO, nitric Oxide; ET-1, endothelin-1; [S-LP(a)], lipoprotein (a)

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Metabolomic Study of Biochemical Changes in the Plasma and