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A Metabolomic Study of Biochemical Changes in the Plasma and Urine of Primary Dysmenorrhea Patients Using UPLC - MS Coupled with Pattern Recognition Approach Shulan Su, Jin-Ao Duan, Peijuan Wang, Pei Liu, Jianming Guo, Erxin Shang, Dawei Qian, Yuping Tang, and Zongxiang Tang J. Proteome Res., Just Accepted Manuscript • DOI: 10.1021/pr300935x • Publication Date (Web): 04 Jan 2013 Downloaded from http://pubs.acs.org on January 7, 2013

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Journal of Proteome Research

A Metabolomic Study of Biochemical Changes in the Plasma and Urine of Primary Dysmenorrhea Patients Using UPLC - MS Coupled with Pattern Recognition Approach Shulan Su1, Jinao Duan1*, Peijuan Wang2, Pei Liu1, Jianming Guo1, Erxin Shang1, Dawei Qian1, Yuping Tang1, Zongxiang Tang1 1

Jiangsu Key Laboratory for High Technology Research of TCM Formulae, Nanjing

University of Chinese Medicine, Nanjing 210046, PR China 2

Department of Endocrinology and Gynecology, Jiangsu Provincial Hospital of

Integrated Traditional and Western Medicine, Nanjing 210028, PR China *Corresponding author. Prof. Jin-ao Duan Jiangsu Key Laboratory for High Technology Research of TCM Formulae, Nanjing University of Chinese Medicine, Nanjing 210046, PR China Tel. / fax: +86 25 85811116 E-mail: [email protected] Running head: Metabolomic Study of an Herbal Medicine for PD Abbreviations: PD, primary dysmenorrhea; SFZYFG, Shaofu Zhuyu formula concentrated granule; VAS, Visual-Analog Scale; MS/MS, tandem mass spectrometry; OPLS, orthogonal partial least squares; PCA, principal components analysis; PLS-DA, partial least-squares discriminant analysis; RT, retention time; TOFMS, time-of-flight mass spectrometry; UPLC, ultra-performance liquid chromatography; 5-HT, 5-hydroxytryptamine; OT, oxytocin; AVP, arg8-Vasopressin; E2, estradiol; P, progesterone; FSH, follicle-stimulating hormone; T, testosterone; GC, glucocorticoid; PGE2, prostaglandin E2; PGF2α, prostaglandin F2α; LTB4, leukotriene B4; NO, nitric Oxide; ET-1, endothelin-1; [S-LP(a)], lipoprotein (a).

Name of the registry: Chinese Clinical Trial Registry (ChiCTR) http://www.chictr.org/usercenter/project/listbycreater.aspx Registration number: ChiCTR-ONC-12002278 1

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Abstract Primary dysmenorrhea (PD) is characterized by painful menstrual cramps without any organic pathology and has a prevalence of up to 90% in the adolescents. Recent advances in its etiology and pathogenesis are providing more speculative hypotheses focused on integral systems. Using a targeted tandem mass spectrometry (MS/MS) based metabolomic platform, we explored the changes of metabolic profiling in plasma / urine simultaneously between PD patients and healthy controls before and after a 3- month herbal medicine (namely Shaofu Zhuyu formula concentrated granule, SFZYFG) therapy. To detect and identify potential biomarkers associated with PD and SFZYFG treatment, we also performed a combined UPLC − QTOF-MS/MS based metabolomic profiling of the plasma/urine samples, indicating a further deviation of the patients’ global metabolic profile from that of controls. The total 35 metabolites (19 in plasma and 16 in urine), up regulated or down regulated (p  0.05 or 0.01), were identified and contributed to PD progress. These promising identified biomarkers underpin the metabolic pathway including sphingolipid metabolism, steroid hormone biosynthesis, and glycerophospholipid metabolism are disturbed in PD patients, which were identified by using pathway analysis with MetPA. 24 altered metabolites and 14 biochemical indicators were restored back to the control- like level after the treatment of SFZYFG and could be potential biomarkers for monitoring therapeutic efficacy. These findings may be promise to yield a valuable insight into the pathophysiology of PD and advance the approaches of treatment, diagnosis and prevention of PD and related syndromes. Key Words: primary dysmenorrhea (PD); metabolomics; potential biomarkers; UPLC-QTOF-MS/MS; Shaofu Zhuyu formula concentrated-granule (SFZYFG) 2


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INTRODUCTION Primary dysmenorrhea (PD) is characterized by painful menstrual cramps without any organic pathology. Its prevalence is estimated to be at 25% of women and up to 90% of adolescents worldwide and approximately 15% patients describe the pain as severe. 1 Although PD is not life-threatening, it has a profoundly negative impact on woman's daily life. In addition to physical pain, the life quality of PD patients is often affected. Moreover, dysmenorrhea is responsible for a significant absenteeism from work, and which is the most common reason for school absence amongst the adolescents. 1,2 Theories proposed by previous data have converged on the excessive secretion of uterine prostaglandins (PGs) for the underlying cause of PD. Women suffering painful periods have high levels of prostaglandin F2 (PGF2) in their menstrual fluid, which stimulates myometrial contractions, ischemia, and enhanced sensitization of nerve endings.

3

PD has also been suggested to be a sex-hormone related disorder

accompanied by a decrease of progesterone before menstruation. This is followed by a cascade of response of PGs and leukotrienes, and which subsequently causes hyperalgesia, inflammatory pain, vasoconstriction, ischemia, and myometrial contraction.

4,5

The level of vasopressin and oxytocin may also play similar roles.

6

However, the pathogeneses behind the symptoms are largely limited. It is increasingly apparent that the pathologies of PD are better associated with multiple factors in neuro - endocrine - immune (N-E-I) network. 7,8 Currently,

the

first-line

therapies

for

treating

3


PD

are

non-steroidal


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anti-inflammatory drugs (NSAIDs) and hormonal contraceptives. However, NSAIDs have been associated with an increasing risk for serious gastrointestinal adverse effects, such as ulceration and bleeding.

9,10

While the hormonal contraceptives

(estrogen and progestin) could reduce menstrual fluid volume and the amount of PGs in the fluid: the reduction could below a normal range, which disturbs the whole endocrine metabolism.

11-13

Biomarkers are very useful for diagnosing and

monitoring disease progression and are important for patients to select appropriate treatment, monitor side-effects, and help a new drug discovery. 14 But till now, there are no previous reports of metabolic profiling or biomarkers researches on PD. Metabonomics, based on the dynamic changes of low molecular weight metabolites in organism, indicates the overall physiological status in responding to pathophysiological stimuli or genetic, environmental, or lifestyle factors.

15

The

metabolites often mirror the end result of genomic and protein perturbations in disease, and they are closely associated with phenotypic changes. Furthermore, the pathogenesis of diseases and the action mechanisms of therapy would be elucidated by identifying the biomarkers, analyzing the metabolic pathway, discovering the drug-target interactions, and so on. Recently, metabolic profiling has attracted an interest for biomarker discovery and for assessing holistic therapeutic effects of many TCMs.

16-21

These studies reflected the drug-induced effects on global

metabolites and indeed the valuable results were achieved. The Chinese herbal formula, Shaofu Zhuyu decoction (SFZYD), is considered as an effective prescription for treating PD. This prescription originally came from

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“Correction of Errors in Medical Classics” compiled by Qing-ren Wang in Qing dynasty (A.D. 1830), which has been utilized in clinic to treat blood stasis syndrome of gynecology diseases such as dysmenorrhea, and amenorhhea for about 200 years. The efficacy of SFZYD treating PD was reported to be above 90%.

22, 23

Our

previous studies also indicated that SFZYD showed an inhibition of uterine smooth muscle constriction and manifested an anti-inflammatory activity. 24 The recent study also showed that SFZYD could significantly improve hemorheological indexes of rat model with blood stasis.

25

SFZYD contained diverse bioactive compounds,

including organic acids, alkaloids, flavonoids and polysaccharides.

26

Sixteen

bioactive constituents and nine their metabolites in rat plasma have been reported by using UPLC-QTOF/MS.

24

However, the therapeutic mechanisms of SFZYD 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 elucidate the metabolic profiling and phenotype changes between PD patients and healthy controls; (2) to identify the potential biomarkers; and (3) to explore the intervention effects and action mechanisms. MATERIALS AND METHODS Experimental Design We conducted a prospective, controlled trial in Department of Endocrinology and Gynecology, Jiangsu Provincial Hospital of Integrated Traditional and Western Medicine from Sep. 2010 to Dec. 2011. The protocol was reviewed and approved by Jiangsu Provincial Hospital of Integrated Traditional and Western Medicine Ethics

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Committee (No. 2010 SL-028) and the protocol was conducted in accordance with the Guideline for Good Clinical Practice (GCP). After the clinical trial had been fully explained to the patients, the written consent was obtained from all subjects. The clinical trial protocol was described in supplementary Text S1, Supporting Information. Subjects We previously recruited 24 patients with self-reported primary dysmenorrheal and 12 healthy volunteers from Nanjing University of Chinese Medicine in Nanjing, Jiangsu, China, to investigate their metabolites variations in response to SFZYFG therapy. The selection criteria were briefly as follows: (1) age 18-40 years; (2) dysmenorrhea in the last three periods with an average score higher than 4 on Visual-Analog Scale (VAS) for pain (VAS assessment method shown in Supplementary Text S2, Supporting Information); 27 (3) regular menstrual cycles for at least 3 months; and (4) no history of gynecologic disease. All patients must met for Diagnostic Code of Primary Dysmenorrhea (1993 Edition, the National Ministry of Health, China) (Supplementary Text S3, Supporting Information) and Criteria of Diagnosis and therapeutic effect of traditional Chinese medicine disease (State Administration of TCM approved) (Supplementary Text S4, Supporting Information) and literature.

27

Exclusion criteria for all patients were endometriosis, leiomyomata

(fibroids), adenomyosis, pelvic inflammatory disease, and intrauterine device (IUD); age above 40 years; not belonging to the syndrome of cold coagulation and blood stasis; with cardiovascular, liver, kidney, hyperlipidemia, hypertension, or mental

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diseases through history or evaluation at the screening visit. The subjects completed a detailed medical questionnaire to ensure that they were eligible to participate in this study. The characteristics of 24 patients and 12 healthy volunteers were listed in Table 1. The progress of subjects at each stage of the clinical trial was shown in Supplementary Figure S1, Supporting Information. Drug Administration In our study, Shaofu Zhuyu formula concentrated - granule (SFZYFG), a modified form of preparation of SFZYD, was used and approved by State Food and Drug Administration of China (SFDA). It was produced by Jiangsu Tianjiang Pharmaceutical Co., Ltd. and composed of ten 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) according to the herbs weight ratio of 3:1:2:1:0.5:2:1:3:1:1. Each herbal concentrate - granule was obtained by refluxing with water for 2 times and the combined filtrates were dried through spraying. In addition, the granule was prepared according to the approved preparation technology. The quality and quantity analysis of chemical components in SFZYFG were analyzed (Supplementary Text S5, Supporting Information). All patients were followed to monitor for four menstrual cycles and began receiving a treatment with SFZYFG on the first day of the first period, twice daily

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(200 ml each time) for 5 days. In the second and third menstrual cycle, SFZYFG was administrated with the same dosage for 10 days (beginning 5 days before the menstruation). Plasma and Urine Samples Blood and urine samples were taken from all participants on the 7th day before her expected menses and labeled as sample 1. Sample 2 (Pre-2) of blood and urine were collected on the first day of first menstrual cycle before administration of SFZYFG. Blood and urine collections on the first day of their second, third and forth periods were labeled as sample 3, 4, and 5 (Post-3, 4, and 5), respectively. The urine samples from 12 patients without being contaminated by menstrual fluid at all time points were selected for a metabolomics study, the selection could meet that there was the same amount of samples at each time point to obtain objective and precise results of variation trends during the total treatment cycle from the same patients at all time points. Biochemical Indicators Measurements and Univariate statistical Analysis The therapeutic efficacy was evaluated for the levels of 5-hydroxytryptamine (5-HT), oxytocin (OT), arg8-Vasopressin (AVP), estradiol (E2), progesterone (P), follicle-stimulating hormone (FSH), testosterone (T), glucocorticoid (GC), prostaglandin E2 (PGE2), prostaglandin F2α apha (PGF2α), leukotriene B4 (LTB4), nitric Oxide (NO), endothelin-1 (ET-1), and lipoprotein (a) [S-LP(a)] in plasma. The test methods of these biochemical indicators were listed in Supplementary Text S6, Supporting Information.

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Biochemical analysis data were presented as the mean ± SD. Statistical significance was assessed by ANOVA test and Shapiro-Wilk statistics was adopted to test normal distribution by SPSS 16.0 version (Inc., Chicago, IL). In all experiments, confidence level was set at 95% to determine the significance of difference (P  0.05). Plasma and Urine Samples Preparation Each blood sample was anti-coagulated by natrium citricum and centrifuged at 15, 000 g for 10 min to obtain plasma samples in a randomized order. 200 μL plasma was added with 400 μL acetonitrile and vortex mixed for 30 s, then centrifuged at 15,000 g for 10 min to obtain the supernatant. Prior to analysis, urine samples were thawed at room temperature and centrifuged at 15, 000 g for 10 min. The supernatant liquid 1 mL was added 3 mL acetonitrile and vortex mixed for 30 s, then centrifuged at 15, 000 g for 10 min to obtain the supernatant in a randomized order. The supernatant from plasma or urine was removed and evaporated to dryness in a 40 ºC water bath under a gentle stream of nitrogen, respectively. The residues were reconstituted in 200 μL mobile phase of 70% acetonitrile-water solution and centrifugation at 15, 000 g for 5 min and filtered through 0.22 µm membrane filter, respectively. The filtrates were transferred to auto-sampler vial kept at 4 C and an aliquot (5 µL) of plasma and urine samples was injected for LC/MS analysis, respectively, and the samples were run in a randomized order. UPLC – QTOF/MS and UPLC-QqQ/MS Analysis The UPLC analysis was performed on a Waters ACQUITY UPLC system (Waters

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Corporation, Milford, USA). Acquity UPLC BEH-C18 column（2.1 mm × 50 mm, 1.7 μm）was applied for all analyses. Mass spectrometry was carried out on a SynaptTM Q-TOF mass spectrometer (Waters, Manchester, UK). UPLC - QqQ/MS was used for relative quantification analysis of the potential biomarkers. The chromatographic conditions and MS spectra analytical methods are listed in Supplementary Text S7, Supporting Information. Data Processing and Multivariate Analysis Centroided and integrated raw mass spectrometric data of plasma and urine were processed by MassLynx V4.1 and MarkerLynx software (Waters Corp., Milford, USA). The intensity of each ion was normalized with respect to the total ion count to generate a data matrix that consisted of the retention time, m/z value, and the normalized peak area. The multivariate data matrix was analyzed by EZinfo software (Waters Corp., Milford, USA). The unsupervised segregation was checked by principal components analysis (PCA) using Pareto-scaled data. The partial least squared discriminant analysis (PLS-DA) and orthogonal partial least-squared discriminant analysis (OPLS-DA) were used to identify the varied metabolites responsible for the separation between PD patients and healthy controls. S-plots were calculated to visualize the relationship between covariance and correlation within the OPLS-DA results. Variables that had significant contributions to discrimination between groups were considered as potential biomarkers and subjected to further identification of the molecular formula. The variable importance in the projection (VIP) values was also used for the selection of biomarkers.

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Variables, with a VIP value larger than 1, showed that a higher than average influence on the classification. 28 An independent t-test indicated that these variables between the healthy controls and patients group were statistically significant difference (p < 0.05). Those variables were eventually selected as potential biomarkers. An internal 5-fold cross-validation was carried out to estimate the performance of PLS-DA models. The calculated R2Y(cum) estimates the goodness of fit of the model that represents the fraction of explained Y-variation, and Q2

(cum)

estimates the ability of prediction. Excellent models are obtained when the cumulative values of R2Y and Q2 are above 0.8. In addition to cross-validation, model validation was also performed by 200 times permutation tests. Ultimately, differential metabolic features associated with PD and SFZYFG treatment were obtained in accordance with the cutoff points of both VIP values and critical p-values from univariate analysis. In addition, the corresponding fold change was calculated to show the degree of variation in metabolite levels between groups. Biomarkers Identification The identities of the potential biomarkers were confirmed by comparing their mass spectra and chromatographic retention times with the available reference standards. A full spectral library, containing MS/MS data, was obtained in the positive and/or negative ion modes. The Mass Fragment application manager (Waters MassLynx v4.1, Waters corp., Milford, USA) was used to facilitate the MS/MS fragment ion analysis process by way of chemically intelligent peak-matching algorithms. This information was then submitted for database

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searching, either in-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. Construction of Metabolic Pathway The construction, interaction, and pathway analysis of potential biomarkers was performed with MetPA (http://metpa.metabolomics.ca./MetPA/faces/Home.jsp) based on database source 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 metabolic pathways and visualization. RESULTS Demographic and Clinical Characteristics The enrolled patients and controls were well-matched in terms of age, gender and ethnicity. The clinical characteristics of the subjects are shown in Table 1. No significant changes from baseline in weight and body mass index (BMI) were observed at the end of SFZYFG treatment. 24 PD patients, mean age 23.8 ± 3.03 (SD) and 12 healthy volunteers (aged 24.3 ± 2.12) participated in the study. No significant discrepancies were found between the two groups in other baseline features. Clinical and General Biochemistry Analysis Biochemistry data was presented in Table 2. The levels of indicators were changed significantly after the treatment of SFZYFG (p  0.05 or p  0.01). For

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hormone indicators, SFZYFG reduced significantly the levels of T, E2, FSH, and GC (P < 0.05 or P < 0.01), and SFZYFG obviously increased the levels of P (p < 0.05). The elevated inflammatory factors levels of PGF2α, LTB4, and ET-1 were inhibited significantly, and that of PGE2 and NO were increased significantly after the treatment of SFZYFG (p < 0.05 or p < 0.01). The elevated neuro-transmitters of OT and AVP were reduced significantly whereas NA and 5-HT were increased in serum (p < 0.05 or p < 0.01). These data implied that SFZYFG regulated multiple biochemical 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 plasma and urine samples, collected from PD patients and healthy controls in positive modes were shown in Figure 1. The subtle changes could be found using pattern recognition approach, including e.g. PCA and PLS-DA. The unsupervised PCA model was used to separate plasma or urine sample into two blocks between PD patients and healthy controls on the first day of menstrual cycle before the treatment of SFZYFG. A total of 528 ions in plasma samples and 606 ions in urine samples at positive modes were detected from PD patients and healthy controls. PCA scores plots showed clear clustering of PD samples versus healthy controls samples (Figures. 2A, B, C and D). The supervised OPLS-DA divided samples into two blocks: this method was applied to obtain a better discrimination between the two groups. Based on the differences in their metabolic profiles, the OPLS-DA score plot analysis

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distinguished the plasma and/or urine samples of PD patients and controls. OPLS-DA distinguished PD patients and controls’ cohorts with 100% sensitivity and no less than 95% specificity using a leave one out algorithm, which indicated that the OPLS-DA models were reliable. The different pattern recognition therefore suggested that the endogenous metabolites were changed as a result 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 discriminatory (p < 0.05), and identified as being responsible for the separation between patients and control groups (Figs. 3 A, B, C, and D). Finally, 35 potential biomarkers (19 in plasma and 16 in urine) were tentatively identified (Table 3). Identification and Quantification of Potential Biomarkers In the plasma, 19 endogenous metabolites, contributing to the separation between the groups, were identified by comparing with authentic standards or based on their molecular ion information as well as the fragments of corresponding product ion (Table 3). The precise molecular mass was determined within measurement errors ( 0.7), respectively. The level of GC was significantly positive associated with metabolites of M13 (estrone), M19 (phosphorylcholine) (r = 0.8) and M1 (PC (18:2(9Z, 12Z)/0:0)) (r > 0.5). An obvious negative correlation was discovered between inflammatory mediators of PGs and M1, and M17 (phytosphingosine) (r  -0.5) and positive associated with M6 (2arachidonylglycerol) (r > 0.5), respectively. The level of LTB4 was highly negative correlation with metabolites of lysophospholipid. Moreover, the neurotransmitters of 5-HT, OT, and VAP were significantly associated with metabolites of phospholipid. These correlationships could be valuable for understanding the PD related with lipid metabolism and steroid hormone disorder. DISCUSSION The pathogenesis of PD associated with metabolic disturbance was proposed in Figure 8 based on our researches and literatures.

3-8

Metabolomic technology has

been suggested as an efficient means for the identification and relative quantities of metabolites, which are altered in response to disease or therapeutic intervention. It was implied that the pathways of sphingolipid metabolism (impact value 0.15, 0.23),

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glycerophospholipid metabolism (impact value 0.12, 0.18), and steroid hormone biosynthesis (impact value 0.127) in plasma and urine were disturbed, respectively. After the treatment with SFZYG, these disturbed metabolic profiling was restored back to the control-like levels. In addition, 24 SFZYFG-regulated specific metabolites were identified, including phytosphingosine, sphinganine, sphingosine 1-phosphate, sphingosine, LysoPC, estrone, dihydrocortisol, 17- hydroxylprogesterone, etc. These promising biomarkers candidates verified that the pathogenesis of PD is closely related to multiple etiologies and pathogenesis. Based on these findings, further studies have to be performed in order to validate the changes and targeted metabolites. Previous studies also indicated that PD patients had an enhanced pain perception, possibly a result of both peripheral and central sensitization. The ongoing menstrual pain in PD patients is accompanied by abnormal brain metabolism.

29

By using an

untargeted metabolomics study, the sphingomyelin - ceramide metabolism was shown to be altered in dorsal horn of rats with neuropathic pain and the up regulated endogenous metabolite, N, N - dimethylsphingosine, induced a mechanical hypersensitivity in vivo.

30

These discoveries agreed with our results of metabolic

disorder of sphingolipid metabolism and the altered endogenous metabolites of phytosphingosine, sphinganine, sphingosine 1-phosphate (S1P), and sphingosine. Actually, S1P plays an important role during development, particularly in vascular maturation and has been implicated in pathophysiology of cancer, wound healing, and atherosclerosis.

31

Plasma sphingolipids level has been found to be a risk factor

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for polycystic 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 cell membranes.

32,33

After the onset of

progesterone withdrawal before menstruation, these omega-6 fatty acids, particularly arachidonic acid, are released, and a cascade of prostaglandins (PG) and leukotrienes (LT) is initiated in uterus. The inflammatory response, mediated by these PGs and LTs, produces both cramps and systemic symptoms such as nausea, vomiting, bloating and headaches. The PGF2α, cyclooxygenase (COX) metabolite of arachidonic acid, caused potent vasoconstriction and myometrial contractions, which was leading to ischemia and pain. 34,5 Our results agreed well with previous studies that showed the hub genes, chemical messengers, in Cold ZHENG (Cold Syndrome) network were characterized as hypothalamus - pituitary hormones (POMC / ACTH, CRH / CRH, TRH / TRH and CORT / CORT) or neuro - transmitters (AVP / AVP). 35, 36 In our study, three unique metabolic pathway of sphingolipid, glycerolphospholipid metabolism, and steroid hormone biosynthesis were identified from PD patients. Furthermore, potential biomarkers of lysophosphatidylcholine (LPCs) were identified. The LPC is in turn enzymatically converted to lysophosphatidic acid (LPA). It has been found to have some functions in cell signaling and specific receptors (coupled to G proteins). 37 LPA signaling can also have pathological consequences, influencing aspects of endometriosis and ovarian cancer. 38 The correlation analysis results showed that the levels of T, P, and E2 were closely

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related to the metabolites of uric acid and creatinine and which agreed with the reports of literatures.

39, 40

The levels of 5-HT, OT, and AVP were also closely

related to the metabolites of phospholipid from our study. It was reported that 5-HT2A activated phospholipase A2 and coupled with second messenger, which could bring in the neurophysiological effects.

41

The correlations of biomarkers and

biochemical indicators could be significant for the elucidation of PD pathogenesis. More importantly, these promising biomarkers could provide guidance for clinical diagnosis and therapy of PD. Here, we found that the multiple biochemical indicators and potential biomarkers were regulated by SFZYFG, which consist with our previous results partly. 42, 43 So, it was stated that SFZYFG produced an integral regulation by intervening special metabolites in global metabolic network. And more experiments will be investigated to discover the complex action mechanisms. CONCLUSION Metabolomics is a useful tool for discriminating metabolic pathways change between patients and controls and for predicting and discovering drug action mechanisms, especially for investigating the therapeutic effects and mechanisms of TCMs. By using an UPLC-QTOF/MS/MS-based metabolomics study we can obtain more detailed information about the metabolic changes that in patients. In this study, UPLC-QTOF/MS spectroscopy coupled with multivariate statistical analysis showed that endogenous metabolites have changed in PD patients. The identified 35 potential metabolites, associated with sphingolipid metabolism, glycerophospholipid metabolism, and steroid hormone biosynthesis, were contributed to PD progress. In

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addition, 24 altered metabolites were restored back to the control-like level, and 14 clinical biochemical indicators were regulated significantly after the treatment of SFZYFG. Further refinement and validation of these biomarkers in larger cohorts of patients would be of considerable interest.

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Supporting Information Available Text S1 displays the clinical trial protocol of Shaofu Zhuyu formula for treating primary dysmenorrheal. Text S2 describes the method of assessment of pain intensity. Text S3 listed the clinical diagnosis criteria of primary dysmenorrhea by Western medicine. Text S4 presents the clinical diagnosis criteria of Primary dysmenorrhea with syndrome of cold coagulation and blood stasis by traditional Chinese medicine. Text S5 displayed the quality and quantity analysis of chemical components in SFZYFG. Text S6 listed the test methods of clinical biochemical indicators. Text S7 listed the chromatographic conditions and MS spectra analytical conditions. Figure S1 showed the flow diagram of the progress of subjects at each stage of the clinical trial. Figure S2 displays the MS spectrum of 24 SFZYFG-regulated promising biomarker candidates. Table S1 shows the results from pathway analysis with MetPA from plasma, and Table S2 lists the results from pathway analysis with MetPA from urine. These material is available free of charge via the Internet at http://pubs.acs.org.

Acknowledgements We thank the study doctors of Guifang Sun, and Qihu Dong, Dept. of Endocrinology and Gynecology, Jiangsu Provincial Hospital of Integrated Traditional and Western Medicine, Nanjing, China, have recruited the patients, collected blood samples, urine samples, and clinical data.

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Grant support This work was supported by Key Research Project in Basic Science of Jiangsu College and University (No. 06KJA36022; 11KJA360002), National Natural Science Foundation of China (No. 30973885). And this work was supported by Construction Project for Jiangsu Key Laboratory for High Technology Research of TCM Formulae (BM2010576), Construction Project for Jiangsu Engineering Center of Innovative Drug from Blood-conditioning TCM Formulae and a Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions (ysxk-2010).

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(38) Ye, X.; Chun, J. Lysophosphatidic acid (LPA) signaling in vertebrate reproduction. Trends Endocrinol. Metab. 2010, 21 (1), 17-24. (39) Schumacher, M.; Guennoun, R.; Ghoumari, A.; Massaad, C.; Robert, F.; El-Etr, M.; Akwa, Y.; Rajkowski, K.; Baulieu, E.E. Novel perspectives for progesterone in hormone replacement therapy, with special reference to the nervous system. Endocr. Rev. 2007, 28 (4), 387-439. (40) Marinello, E.; Riario-Sforza, G.; & Marcolongo, R. Plasma follicle- stimulating hormone, luteinizing hormone, and sex hormones in patients with gout. Arthritis Rheum. 1985, 28 (2), 127-131. (41) Sudhir, K. Clinical review: Lipoprotein-associated phospholipase A2, a novel inflammatory biomarker and independent risk predictor for cardiovascular disease. J. Clin. Endocrin. Metab. 2005, 90 (5), 3100-31005. (42) Hua, Y.Q.; Su, S.L.; Duan, J.A.; Wang, Q.J.; Lu, Y.; Chen, L. Danggui-Shaoyao-San, a traditional Chinese prescription, suppresses PGF2α production in endometrial epithelial cells by inhibiting COX-2 expression and activity. Phytomedicine 2008, 15 (12), 1046-1052. (43) Su, S.L.; Duan, J.A.; Wang, T.J.; Yu, L., Hua, Y.Q.; Tang, Y.P. Shaofu Zhuyu decoction on hemorheology and ovarian function in rat model of Han-Ning blood stasis. Chin. J. Exper. Trad. Med. Form. 2008, 14 (12), 14-16.

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

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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-D plot 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 positive mode. (C, plasma, R2 = 0.81, Q2 = 0.86; D, urine, R2 = 0.91, Q2 = 0.82).

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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).

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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 SFZYFG for two periods; Post-5: after administration of SFZYFG for three periods)

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Figure 5 Changes in the relative quantities of target metabolites identified by PCA in different groups. A two- tailed, parametric t-test was used to determine the significance of the change in relative quantities for each metabolite. Bars represent the mean relative quantities and standard deviations. *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. healthy controls;

p  0.05: after treatment by SFZYFG vs. healthy

△

controls. (A) 8 metabolites in the plasma; (B) 16 metabolites in the urine.

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Figure 6 Summary of pathway analysis with MetPA. a. sphingolipid metabolism b. glycerophospholipid metabolism c. steroid hormone biosynthesis (A, plasma; B, urine)

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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 was built and optimized by MATLAB software (MathWorks, US).

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Figure 8 The proposed pathogenesis of PD associated with metabolic disturbance.

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Table captions Table 1 Pre-randomization Characteristics of 24 Patients and 12 Healthy Volunteers

Characteristics

P

Shaofu Zhuyu

Controls

Decoction (n=24)

(n=12)

Age mean (SD), years

23.8 (3.03)

24.3 (2.12)

0.87

College education, n (%)

24 (100)

14 (100)

1.00

Weight mean (SD), kg

50.8 (6.02)

51.6 (3.02)

0.46

Body-mass index* mean (SD),

19.6 (2.27)

20.7 (1.27)

0.53

4.8 (0.98)

5.2 (0.92)

0.98

28.1 (3.23)

27.8 (2.75)

0.65

kg/m2 Menstruation duration mean (SD), days Menstrual cycle length mean (SD), days * Body mass index was calculated as the weight in kilograms divided by the square of height in meters

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

Control group

Before treatment

After treatment

p value a

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

0.035

Indicators

a

P-values were calculated from two-tailed ANOVA test with a threshold of 0.05. Before the treatment of PD vs. after the treatment by SFZYFG.

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Table 3 Identified Differential Metabolites Accountable for the Discrimination between PD Patients and Healthy Controls in plasma No.

1(M1)

tR (min)

9.56

metabolites

PC(18:2(9Z,12Z)/16:0)

Obsd

Calcd

[M+H]+

[M+H]+

758.5735

758.5694

MS/MS

VIP a

Pb

FC c

content variance d

748.4868, 544.0000,

1.79

0.0086

1.31

↑

464.2797, 318.3612 2(M2)

11.71

Phytosphingosine

318.3016

318.3003

256.2173, 184.0717,

13.67

LysoPC(18:3(9Z,12Z,1

518.3262

518.3241

5Z))

500.2929, 467.7113,

Glycerophospholipid metabolism

1.67

0.0106

-2.01

↓

161.5589, 129.4796 3(M3)

pathway (KEGG)

Sphingolipid metabolism

1.09

0.0416

-1.24

↓

357.2786, 264.2601,


184.0585, 124.9547 4(M4)

14.04

LysoPC(16:1(9Z))

494.3290

494.3298

476.3173, 318.2872,

4.89

0.0253

-1.56

↓

184.0680, 124.9903,


103.9807 5(M5)

14.84

LysoPC(20:4(8Z,11Z,1

544.3367

544.3398

4Z,17Z))

526.2919, 520.3394,

1.63

0.0342

-2.25

↓

291.7539, 261.1359,


184.0739, 6(M6)

16.12

2-Arachidonylglycerol

544.3367

544.3398

520.3394, 279.7625,

1.67

0.0428

2.01

↑

104.0851 7(M7)

15.67

LysoPC(18:1(11Z))

522.3577

522.3554

504.3532, 402.3223, 184.0718

40



4.01

0.0312

3.35

↑


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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47


8(M8)

16.05

PC(18:1(9Z)/0:0)

522.3577

522.3554

504.3532, 445.2400,

4.02

0.0127

4.56

↑

184.0718 9

18.89

Palmitic amide

256.2633

256.2635

metabolism

217.1028, 184.0719,

2.59

0.0298

2.78

↑

103.9557 10

12.34

Sphinganine

302.3072

302.3054

4.02

17-phenoxy trinor

432.2808

432.2745

PGF2α ethyl amide


284.294, 261.1236,

1.18

0.0068

5.60

↑

217.1038, 146.0377 11

Glycerophospholipid


415.2510, 362.2432,

1.56

0.0092

1.23

↑

217.1066, 146.0120,


117.9561 12

18.08

LysoPC(18:0)

524.3736

524.3711

506.3636, 341.2591,

3.72

0.0321

5.20

↑

281.8056, 217.1051,


184.0784, 104.0698 13

15.97

LysoPC(16:0) e

496.3383

496.3398

478.3290, 419.2279,

2.67

0.0432

-3.05

↓

313.1615, 184.0786,


125.9252, 104.1071 14

0.82

Choline

104.1069

104.1070

-

10.39

0.0378

1.37

↑


15

7.18

LysoPC(18:2(9Z,12Z))

520.3389

520.3398

502.3165, 361.2709,

1.92

0.0329

-4.00

↓

279.6420, 217.1042,


184.0719 16

19.71

2-Phenylacetamide

282.2819

282.2791

261.1248, 217.1059, 184.0792, 103.9593

41


1.28

0.0451

-1.45

↓



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47

17

16.70

LysoPC(17:0)

510.3588

510.3554

330.3187, 305.1661,

Page 42 of 45

2.18

0.0256

-1.68

↓

261.1286, 217.1066 18

18.31

LysoPC(20:0)

510.3588

510.3554

466.3296, 358.3694,


2.34

0.0218

-3.18

↓

261.1317, 217.1071,


184.0796, 103.9582 19

20.37

SM(d18:1/16:0)

703.5751

703.5748

689.5698, 637.7039, 550.6163, 502.4437,

3.12

0.0091

2.89

↑


353.2712, 261.1337, 217.1055, 184.0746, a

Variable importance in the projection (VIP) values were obtained from cross-validated PLS-DA models with a threshold of 1. P-values were calculated from two-tailed Mann-Whitney U-test with a threshold of 0.05. c Fold change was calculated as the ratio of the mean metabolite levels between two groups. A positive value of fold change indicates a relatively higher concentration of metabolites while a negative value of fold change indicates a relatively lower concentration in PD patients as compared to healthy controls. d ↑, content increased; ↓, content decreased. e Confirmed by standard samples. b

42


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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47


Table 4 Identified Differential Metabolites Accountable for the Discrimination between PD Patients and Healthy Controls in urine No.

tR

metabolites

(min) 1(M9)

0.9

Uric acid

Obsd

Calcd

[M+H]+

[M+H]+

169.0348

169.0356

MS/MS

VIP a

Pb

FC c

variance d 153.0489, 124.9869,

1.88

0.0086

1.29

110.9935 2(M10)

3(M11)

0.63

8.18

Creatinine

LysoPC(18:2(9Z,12Z))

114.0658

520.3346

114.0662

520.3398

content

-

1.87

502.3165, 361.2709,

2.58

0.0106

0.0416

-1.59

↑ ↓

1.81 ↑

279.6420, 217.1042, 184.0719 4(M12)

7.88

PC(18:1(9Z)/2:0)

564.3633

564.3660

547.3376,386.2143,

1.21

0.0253

2.53

173.0795 5(M13)

9.25

Estrone

e

271.1661

271.1692

253.1530, 147.0433,

1.48

0.0342

1.40

130.0508 6(M14)

10.87

PE(18:2(9Z,12Z)/15:0)

702.5073

702.5068

679.5154,340.3844,

1.38

0.0428

↑ ↑

-1.70 ↓

328.2107,170.0593, 146.1234 7(M15)

14.13

Cyclic AMP

330.0612

330.0603

305.1726,261.1335,

11.23

0.0312

-2.79

217.1033,133.0868 8(M16)

14.98

Sphinganine

302.3060

302.2687

268.2392, 211.5184,

13.26

0.0127

1.42

162.5414, 118.5106 9(M17)

14.51

Phytosphingosine

318.3006

318.3003

300.2314, 256.2327, 230.2393, 146.0639

43


1.81

0.0298

1.23

↓ ↑ ↑

pathway (KEGG) Glycerophospholipid metabolism Sphingolipid metabolism Glycerophospholipid metabolism Glycerophospholipid metabolism Steroid hormone biosynthesis Glycerophospholipid metabolism Glycerophospholipid metabolism Sphingolipid metabolism Sphingolipid metabolism


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47

10(M18)

11(M19)

6.34

5.44

Palmitic amide

Phosphorylcholine

256.2646

170.0603

256.2635

170.0577

206.5815, 132.5265

158.9926,146.9436,

Page 44 of 45

2.28

2.56

0.0068

0.0092

-1.01

10.0

130.9662,115.0543 12(M20)

13(M21)

12.78

13.67

Palmitoylglycine

Sphingosine

314.2328

300.2824

314.2326

300.2897

304.0785, 255.1856

283.1796, 264.5628,

2.18

2.14

0.0321

0.0432

-10.0

2.64

171.9893, 163.1417 14(M22)

6.71

LysoPC(14:0)

413.2567

413.2537

391.2856, 279.1606,

1.88

0.0378

↓ ↑ ↓ ↑

1.48 ↑

184.0788, 167.0341, 149.0215 15(M23)

4.04

Dihydrocortisol e

365.2338

365.2322

347.2228,329.2151,

1.58

0.0329

2.5 ↑

285.1794, 223.1023, 150.5119 16(M24)

11.81

17-Hydroxyprogesterone

331.2362

331.2267

261.1319,217.1052, 133.0842

a

1.25

0.0451

-2.79

↓

Sphingolipid metabolism Glycerophospholipid metabolism Glycerophospholipid metabolism Sphingolipid metabolism Glycerophospholipid metabolism

Steroid hormone biosynthesis Steroid hormone biosynthesis

Variable importance in the projection (VIP) values were obtained from cross-validated PLS-DA models with a threshold of 1. P-values were calculated from two-tailed Mann-Whitney U-test with a threshold of 0.05. c Fold change was calculated as the ratio of the mean metabolite levels between two groups. A positive value of fold change indicates a relatively higher concentration of metabolites while a negative value of fold change indicates a relatively lower concentration in PD patients as compared to healthy controls. d ↑, content increased; ↓, content decreased. e Confirmed by standard samples. b

44


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Graphical Abstract 190x254mm (96 x 96 DPI)


Metabolomic Study of Biochemical Changes in the Plasma and

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