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High-throughput metabolomic approach revealed the acupuncture exerting intervention effects by perturbed signatures and pathways†
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Yingzhi Zhang, Aihua Zhang, Guangli Yan, Weiping Cheng,* Hui Sun, Xiangcai Meng, Li Liu, Ning Xie and Xijun Wang* Metabolomics can capture global changes and the overall physiological status in biochemical networks and pathways in order to elucidate sites of perturbations. High-throughput metabolomics and acupuncturology have similar characteristics such as entirety, comprehensiveness and dynamic changes, and can identify potential candidates for acupuncture effects and provide valuable information towards understanding therapy mechanisms. Saliva has recently gained popularity as a potential tool for biomarker monitoring, as its composition may potentially reflect plasma metabolite levels and, therefore, may be used as an indicator of the physiological state. However, the underlying mechanism of acupuncture, remains largely unknown, which hinders its widespread use. Acupuncture would produce unique characterization of metabolic perturbations. In this study, UPLC/ESI-HDMS in high-accuracy mode coupled with pattern recognition analysis was carried out to investigate the mechanism and saliva metabolite biomarkers for acupuncture treatment at ‘Zusanli’ acupoint (ST-36) as a case study. Putative metabolite identifications for these ions were obtained through a mass-based database search. As a result, the top canonical pathways including phenylalanine metabolism, alanine, aspartate and glutamate metabolism,
D-glutamine
and
D-glutamate
metabolism, and steroid hormone biosynthesis pathways
were acutely perturbed. 26 differential metabolites were identified by chemical profiling, and may be Received 19th August 2013, Accepted 30th September 2013
useful to clarify the physiological basis and mechanism of ST-36. More importantly, network
DOI: 10.1039/c3mb70352e
pathways. These results provide useful insights into biomarker discovery utilizing metabolomics as an
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efficient and cost effective platform. This study opens new possibilities for the selection of saliva as a source of metabolite biomarkers representative of specific disorders.
construction has led to the integration of metabolites associated with the multiple perturbation
Introduction Metabolomics can allow the measurement of the comprehensively small molecules that are endogenous metabolites in easily accessible biofluids and biomarker discovery.1–6 Analysis of these key metabolites in body fluids has become an important method to monitor the state of biological organisms.7 Human saliva is an attractive diagnostic fluid because it has several key advantages for disease diagnosis and prognosis.8 It is easily collected and stored and ideal for early detection of disease as it contains specific biological biomarkers.9 Analysis of the metabolome of human saliva may contribute to the understanding National TCM Key Lab of Serum Pharmacochemistry, Key Lab of Metabolomics and Chinmedomics, First Affiliated Hospital, Heilongjiang University of Chinese Medicine, Heping Road 24, Harbin 150040, China. E-mail: [email protected], [email protected]; Fax: +86-0451-82110818; Tel: +86-0451-82110818 † Electronic supplementary information (ESI) available. See DOI: 10.1039/ c3mb70352e
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of pathophysiology and provide a foundation for the recognition of potential biomarkers.10 Acupuncture is an ancient Chinese medical therapy that is used widely around the world.11 Metabolomics is a rapidly developing field that has given new hope in the treatment of acupuncture. Metabolomic technologies used to study acupuncture will help us to identify potential biomarkers and to have a more in-depth understanding of the pathophysiological processes. Acupuncture medicine can take advantage of metabolomic frameworks to provide the healthcare system with useful tools that can optimize the effectiveness of treatment.12 Nowadays, the common applications of acupuncture include heart disease, smoking cessation, and the treatment of inflammatory diseases and psychological disorders etc.13–15 In traditional Chinese medicine (TCM), Zusanli (also known as ST-36) point of ‘‘The Stomach Meridian of Foot-Yangming’’ is commonly used in human acupuncture to treat a wide range of health conditions including gastrointestinal disorders and others,16 however, little is known about its biological mechanism.
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Saliva composition is affected by physiological, pathological and environmental factors that may affect its metabolic profile.17 Acupuncture may cause changes in the flow and composition of saliva. Herein, this study illustrates the power of accurate mass measurement by UPLC/MS, combined with the multivariate modelling such as Principal Component Analysis (PCA) and Orthogonal Projection to Latent Structures Discriminant Analysis (OPLS-DA) for a comprehensive saliva metabolomic analysis to investigate changes in metabolite levels. The scope of this work was to gain a better insight into acupuncture-treated ST-36, to detect the low molecular-weight metabolite data, to determine the perturbation pathways, and to infer the biological processes.
Experimental procedures Chemicals and reagents Acetonitrile (HPLC grade) was purchased from Merck (Germany); leucine enkephalin was purchased from Sigma-Aldrich (St. Louis, MO, USA); the distilled water was produced by a Milli-Q Ultra-pure water system (Millipore, Billerica, USA); formic acid was obtained from (Kermel Chemical Reagent Co., Ltd., China). All other reagents were of analytical grade. Ethical statement and volunteers The present work was approved by the Ethical Committee of Heilongjiang University of Chinese Medicine (No: HLJZY-20120198) and was carried out in accordance with The Code of Ethics of the World Medical Association (Declaration of Helsinki) for experiments. A total of twenty healthy volunteers (males, mean age 25.4 4.2 years) were enrolled in this study following informed consent. Volunteers in the control group were included on the basis of a physician’s assessment of their general health status. Acupuncture treatment was performed on bilateral ST-36 for 30 min, once a day for two consecutive weeks. The whole unstimulated saliva was collected between 15:00 and 15:30 p.m. using standard techniques done by Weiping Cheng.
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UPLC/MS analysis Chromatography was carried out with an ACQUITY UPLC HSS T3 C18 column (100 mm 2.1 mm i.d., 1.8 mm) using an ACQUITY UPLCt system (Waters Corp., Milford, USA). A ‘‘purge–wash–purge’’ cycle was employed on the auto-sampler, 10% (v/v) formic acid aqueous solution was used as the wash solvent and 0.1% (v/v) formic acid aqueous solution was used as the purge solvent, to ensure that the carry-over between injections was minimized. The mobile phases were composed of 0.1% formic acid in acetonitrile (solvent A) and 0.1% formic acid in water (solvent B). The gradient was as follows: a linear gradient of 0–1 min, 2–6% A; 1–3 min, 6–20% A; 5–7 min, 30–35% A; 7–8 min, 35–50% A; 8–10 min, 50–99% A; 10–11 min, 99% A; 11–11.5 min, 99–2% A; 11.5–13 min, 2% A. The flow rate was 0.50 ml min 1 and The column compartment was kept at a temperature of 45 1C, and the sample injection volume was 5 mL. The eluent was introduced to the mass spectrometer directly, i.e. without a split. After every 10 sample runs, quality control and blank samples were injected in order to ensure consistent performance of the system. In separate runs, detection was achieved in both positive and negative ion modes. The m/z range was set to be 100–1000 in centroid mode with a scan time of 0.2 s and an inter-scan delay of 0.01 s. The eluent was introduced into the Synaptt High Definition MS (Waters Corp., Milford, USA) analysis, and the optimal conditions were as follows: desolvation temperature of 300 1C, source temperature of 110 1C, sample cone voltage of 30 V, extraction cone voltage of 3.5 V for positive ion mode, collision energy of 4 eV, microchannel plate voltage of 2400 V, cone gas flow of 50 L h 1 and desolvation gas flow of 700 L h 1, capillary voltage of 3.0 kV for positive ion mode and 2.6 kV for negative ion mode. For accurate mass acquisition, a lock-mass of leucine enkephalin at a concentration of 200 pg ml 1 was used via a lock spray interface at a flow rate of 100 ml min 1 monitoring for positive ion mode ([M + H]+ = 556.2771) and negative ion mode ([M – H] = 554.2615) to ensure accuracy during the MS analysis.
Sample preparation protocols
Multivariate data processing and visualization
Saliva samples were obtained from volunteers in the Heilongjiang University of Chinese Medicine. Control saliva samples were obtained before the acupuncture stimulation at day 0 points. The subjects were given insulated ice packs in which they were asked to store the saliva samples immediately until they were received by the study investigator. On arrival at the laboratory, the samples were centrifuged at 10 000 rpm for 10 min at 4 1C to remove any solid debris. The supernatant was transferred and immediately frozen and stored at 80 1C until analysis. Before analysis, the samples were thawed. Methanol (1.5 ml) was added to 0.5 ml of saliva and shaken vigorously (5 min), and the mixture was allowed to stand for 10 min, centrifuged at 13 000 rpm for 15 min. The supernatant was dried by nitrogen, and then 100 ml of 50% methanol were added and vortexed 5 min. The supernatants were centrifuged at 13 000 rpm for 15 min at 4 1C, and then filtered through a syringe filter (0.22 mm), 5 mL of the supernatant were injected into the UPLC-MS.
Data acquisition and collection were performed by MassLynx v4.1 (Waters Corporation). MassLynx version 4.1 software (Waters Corporation, Milford, MA, USA) with an added statistical programme for multivariate data analysis, was used to analyse the UPLC/MS data where ESI positive and negative raw data was extracted. Chromatographic peak picking and raw data deconvolution, including noise filtering, peak detection, removal of isotope masses and alignment of retention time (rt) and mass (m/z), were performed by Markerlynx software. The dataset obtained from MarkerLynxt processing was exported to the EZinfo software version 2.0 programme in order to perform PCA and OPLS-DA models. Data processing was based on PCA which provides an overview of any patterns and groupings, and OPLS-DA to find the most influential differentiating features separating sample groups in PCA. Potential markers of interest were extracted from S-plots constructed following analysis with OPLS-DA, and markers were chosen based on their contribution to the variation and correlation
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within the data set. Discriminating features at VIP (Variable Importance for Projection) values Z2 were considered and highlighted in an S-plot from the OPLS-DA model.
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Marker identification and related pathways Significant features (markers) selected using the OPLS-DA models were identified based on the proposed minimum reporting standards of metabolomics data. The MassFragmentt application manager was used to facilitate the MS/MS fragment ion analysis process by way of chemically intelligent peak-matching algorithms. Fragmentation patterns, using MS and MS/MS modes in QTOF/MS, were used for structural elucidation of marker features. Where possible, marker identification was eventually confirmed by matching fragmentation patterns to authentic standards. Subsequently, a database and information search were performed. The database search was conducted in Metlin (http://metlin.scripps.edu/), online ChemSpider database and MassBank (www.massbank.jp). Significant metabolic features were identified by matching the accurate mass of observed peaks to metabolites in the selected databases within a mass accuracy window of 10 ppm. Metabolic pathways associated to metabolic classifiers were studied and performed with MetPA and MetaboAnalyst based on the database source. Thus, all the metabolites were highlighted and mapped into KEGG pathways to elucidate the metabolic processes more affected by the progression of the acupuncture intervention. Statistical analyses. SPSS 17.0 for Windows was used for the statistical analysis and validation. The data were analysed using the Wilcoxon Mann–Whitney Test, with p o 0.05 set as the level of statistical significance.
Results and discussion Metabolic profiling Procedures of establishing the proposed model involved three stages. Firstly, metabolic profiling was performed on saliva samples; then, features were extracted from the obtained multivariate dimensional metabonomic data; and finally, the diagnostic model was constructed on data of training set by mathematic modeling. For UPLC-MS analysis, aliquots were separated using a Waters Acquity UPLC (Waters, Millford, MA) and analyzed using a Q-TOF/HDMS, which consisted of an electrospray ionization source. Fig. 1 lists typical UPLC-MS BPI profiles of the representative saliva samples, respectively. In the first stage, LC/MS analysis was carried out to acquire metabolic profiles. Operating conditions were optimized to elute as many metabolites as possible in a single injection. Both positive and negative modes of analysis were used to allow much more metabolite information to be gained. A large number of chromatographic peaks were eluted in each metabolic profile, while their intensity and the number of peaks were rather different. Pattern recognition analysis For the purpose of analyzing changes in the metabolic pattern, which are associated with the recovery process, 2 typical analytical
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procedure steps were used (as often used in metabolomics): first, an unsupervised PCA technique to find trajectories and clustering, and second, PLS-DA on datasets. PCA is a nonsupervised mathematical procedure which reduces the dimensionality of data without altering the data itself. The OPLS-DA model was adapted only when there was group separation in PCA. Finding the differentiating features directly from the loadings plot in the PCA model of the whole dataset is not always possible, and therefore OPLS-DA was used for this purpose. The obtained PCA and PLS-DA models can be used to extract the metabolomic information and, in particular, to identify the metabolites and their correlations in order to determinate the progression of disease. PCA plots showed that the trajectory profiles of the 0, 7 and 14 day samples significantly changed as a result of acupuncture-treatment (Fig. 2A and 3A). By PCA, loading plots can be applied to suggest potential biomarkers. From the panels in Fig. 3, samples can be clearly separated in one principal component direction (PC1 or PC2), and biomarkers were discovered by investigating their loadings in the according directions using Hotelling’s T2 statistic (significance level was set at 0.05). Subsequently, supervised OPLS-DA was performed to reveal detailed and statistically significant metabolite changes that were caused by acupuncture intervention at 0 and 14 days (Fig. 2B and 3B). The ions that showed significant difference in abundance between the 0 day and 14 day treated humans contributed to the observed separation (Fig. 2C and 3C) and were selected from the respective S-plots as potential markers in positive and negative mode (Fig. 2D and 3D). The VIP-value threshold cutoff of the metabolites was set to 2.0, above this threshold, were viewed as potential biomarkers. Finally, the markers of significant contribution were characterized by 17 in positive mode and 9 in negative mode (Table 1). Identification of important metabolites The UPLC/MS analysis platform provides the retention time, precise molecular mass and MS/MS data for the structural identification of biomarkers. To identify these biomarkers, KEGG, Metlin, and PubChem databases were utilized and analysis was performed by time-of-flight mass spectrometry (TOF/MS). A list of candidates was generated by searching from the database, and the most probable biomarkers would be found according to the possible fragment mechanisms carried out by TOF/MS analysis. In order to elucidate the salivary metabolome, a final putative identification step was carried out for which the extracted features were queried against the METLIN database, which includes endogenous metabolites found in plasma, besides common human drugs and their metabolites.18 The database search results are summarized in Table 1, which include the molecular formula, the detected adduct mass, matching tolerance, and the numeric reference in METLIN. Finally, 26 acupuncture intervention related-biomarkers were discovered from a statistical point of view. According to the protocol detailed above, 26 endogenous metabolites contributing to the separation of the acupuncture intervention group and control
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Fig. 1 Typical BPI chromatogram obtained from saliva in positive and negative ionization mode. 0 days (A) and 14 days (B) in positive mode. 0 days (C) and 14 days (D) in negative mode.
group were detected in the samples (Table 1). Although some of them are known to be common components of saliva, most of them have not been previously reported in bibliography. Metabolic pathway and function analysis More detailed analysis of pathways and networks influenced by acupuncture was performed by the MetPA tool. It revealed that metabolites, which were identified together as being important for the host’s response to acupuncture-treatment, were responsible for alanine, aspartate and glutamate metabolism, phenylalanine metabolism, D-glutamine and D-glutamate metabolism, steroid
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hormone biosynthesis, pyrimidine metabolism, phenylalanine, tyrosine and tryptophan biosynthesis, arachidonic acid metabolism, arginine and proline metabolism etc. ESI† Table S1 suggests that these pathways showed marked perturbations over the entire time-course of acupuncture intervention. Potential biomarkers were also identified from these relevant pathways. Some significantly changed metabolites have been found and used to explain the phenylalanine metabolism, alanine, aspartate and glutamate metabolism. The detailed construction of the phenylalanine metabolism, alanine, aspartate and glutamate metabolism pathways with a higher score is shown in Fig. 4. Below is the detailed analysis
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Fig. 3 Representative PCA score plot (A) based on the UPLC/MS chromatograms in negative mode (A). PCA model results for 0 days and 14 days in negative mode (B). VIP-plot of OPLS-DA of samples in negative mode (C). Fig. 2 Trajectory analysis of PCA score plots obtained from the saliva samples in positive mode (A). PCA model results for 0 days and 14 days in positive mode (B). VIP-plot of OPLS-DA of samples in positive mode (C).
of the phenylalanine metabolism, alanine, aspartate and glutamate metabolism pathways. Phenylalanine and tyrosine metabolism. Phenylalanine is an essential amino acid which must be supplied by dietary proteins. Once in the body, phenylalanine may follow any of three paths. It may be (1) incorporated into cellular proteins, (2) converted to phenylpyruvic acid, or (3) converted to tyrosine. Tyrosine can be converted into L-DOPA, which is further converted into dopamine, norepinephrine (noradrenaline), and epinephrine (adrenaline). Depicted in this pathway is the
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conversion of phenylalanine to phenylpyruvate, the incorporation of phenylalanine and/or tyrosine into polypeptides and the conversion of phenylalanine to tyrosine via phenylalanine hydroxylase. This reaction functions both as the first step in tyrosine/phenylalanine catabolism by which the body disposes of excess phenylalanine, and as a source of the amino acid tyrosine. The decomposition of L-tyrosine begins with an a-ketoglutarate dependent transamination through the tyrosine transaminase to para-hydroxyphenylpyruvate. The next oxidation step catalyzed by p-hydroxylphenylpyruvatedioxygenase creates homogentisate. Alanine metabolism. Alanine is most commonly produced by the reductive amination of pyruvate via alanine transaminase. This reversible reaction involves the interconversion of alanine
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Paper Table 1
Molecular BioSystems Potential marker metabolites in acupuncture-treated humans’ saliva samples identified by UPLC/MS in positive and negative mode
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Rt m/z m/z Error Ion No. (min) Determined Calculated (mDa) form 1 2 3 4 5 6 7 8 9 10
1.09 1.47 2.13 2.15 2.22 2.73 2.81 2.99 8.54 8.59
165.0525 294.1753 279.0892 486.1857 526.2897 486.1827 492.2237 316.2081 230.2447 318.2483
165.0552 294.1719 279.0892 486.1876 526.2910 486.1876 492.2247 316.2124 230.2484 318.2433
2.7 3.4 0.0 1.9 1.3 4.9 1.0 4.3 3.7 5.0
[M [M [M [M [M [M [M [M [M [M
+ + + + + + + + + +
11 12 13 14 15 16 17 18 19 20 21 22 23
8.65 8.71 8.76 8.88 9.28 9.29 9.29 0.44 0.46 0.56 0.84 0.84 9.3
406.2917 494.3303 288.2853 172.1698 415.1441 437.1307 437.1882 249.0185 216.9791 335.1279 167.0623 335.1242 459.2032
406.2933 494.3304 288.2903 172.1701 415.1465 437.1349 437.1859 249.0156 216.9807 335.1283 167.0609 335.1243 459.2019
1.6 0.1 5.0 0.3 2.4 4.2 2.3 2.9 1.6 0.4 1.4 0.1 1.0
[M [M [M [M [M [M [M [M [M [M [M [M [M
+ + + + + + +
24 25 26
9.81 9.92 9.95
295.2273 293.2110 319.2257
295.2273 293.2117 319.2273
0.0 0.7 1.6
[M [M [M
Formula H]+ H]+ Na]+ H]+ Na]+ H]+ H]+ H]+ H]+ Na]+
Na]+ H]+ H]+ H]+ H]+ H]+ H]+ H] H] H] H] H] H] H] H] H]
C9H8O3 C17H19N5 C10H16N4O2S C24H27N3O8 C25H46NO7P C24H27N3O8 C26H29N5O5 C16H29NO5 C14H31NO C20H31NO2
Phenylpyruvic acid Anastrozole Buthidazole 7-Hydroxyondansetron glucuronide LysoPE(0 : 0/20 : 3(8Z,11Z,14Z)) 6-Hydroxyondansetron glucuronide Thr Trp Trp Butoctamide hydrogen succinate Xestoaminol C 17beta-Hydroxy-4,17-dimethyl4-azaandrost-5-en-3-one C22H41NO4 N-Oleoyl threonine C28H47NO4S Tiamulin C17H37NO2 C17 Sphinganine C10H21NO N,2,3-Trimethyl-2-(1-methylethyl)butanamide C16H22N4O9 Clavamycin A C23H20N2O7 Phe-Tyr-OH C20H28N4O5S Unknown C11H10N2OS2 (S)-Spirobrassinin C7H6O6S 5-Sulfosalicylic acid C21H20O4 Dorspoinsettifolin C11H8N2 Norharman C16H20N2O6 Semilepidinoside A C25H32O8 17,21-Dihydroxypregn-4-ene-3,11,20-trione 21-(hydrogensuccinate) C18H32O3 6-Hydroxy-9Z,12Z-octadecadienoic acid C18H30O3 8-Hydroxy-11Z-octadecen-9-ynoic acid C20H32O3 5-Hydroxyeicosatetraenoic acid
and pyruvate, coupled to the interconversion of alphaketoglutarate (2-oxoglutarate) and glutamate. Because transamination reactions are readily reversible and pyruvate is widespread, alanine can be easily formed in most tissues. Another route to the production of alanine is through the enzyme called alanine-glyoxylate transaminase. This reaction involves the interconversion of alanine and pyruvate, coupled to the interconversion of glyoxylate and glycine. Once synthesized, alanine can be coupled to alanyl tRNA via alanyl-tRNA synthetase and used by the body in protein synthesis. Aspartate metabolism. Aspartate synthesis involves the generation of aspartate from oxaloacetate via transamination by aspartate aminotransferase or amino acid oxidase. Once synthesized, aspartate can be coupled to aspartyl tRNA via aspartyl-tRNA synthetase and used by the body in protein synthesis. Aspartate can be converted to another polar amino acid, asparagine, via asparagine synthase. Aspartate is a precursor to many other cofactors or compounds involved in cellular signaling including N-acetyl-aspartate, beta-alanine, adenylsuccinate, arginino-succinate and N-carbamoylaspartate. Aspartate is also a metabolite in the urea cycle and participates in gluconeogenesis. Glutamate metabolism. This pathway depicts the diverse metabolic fates and roles that glutamate plays in the body. Glutamate can also be oxidatively deaminated through glutamate dehydrogenase to produce 2-oxoglutarate and ammonia. Additionally, glutamate can be generated from alanine or aspartate in combination with 2-oxoglutarate using the enzyme transaminase. The resulting byproducts (pyruvate and oxaloacetate) are key components in glycolysis, gluconeogenesis and the TCA cycle. Glutamate and
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VIP value Trend P-value
Metabolite name
4.2 5.1 4.8 11.2 4.2 6.5 5.9 10.3 4.8 31.7
m k m m k m m k m m
0.0093 0.0175 0.0034 0.0064 0.0132 0.0084 0.0117 0.0129 0.0270 0.0350
10.7 4.9 11.0 4.4 12.4 6.4 6.1 2.6 2.8 3.8 5.4 6.5 3.2
m m k m m m k m m m m m k
0.0144 0.0291 0.0110 0.0246 0.0296 0.0146 0.0070 0.0088 0.0122 0.0273 0.0140 0.0000 0.0426
4.0 2.4 2.5
k k k
0.0133 0.0198 0.0000
proline metabolism are also connected. Glutamate also plays an important role in the body’s disposal of excess nitrogen through the reaction catalyzed by glutamate dehydrogenase, which converts glutamate and NADP into 2-oxoglutarate, NADPH and ammonia. Glutamate also serves as the precursor for the synthesis of the neurotransmitter known as GABA (gamma-aminobutyric acid) in GABA-ergic neurons. This reaction is catalyzed by glutamate decarboxylase, which is most abundant in the cerebellum and pancreas. Glutamate also serves as the most abundant fast excitatory neurotransmitter in the mammalian nervous system. Acupuncture has been used to treat various diseases for thousand years worldwide, and has been gradually accepted in western countries as an alternative or complementary treatment.19 More recently, the practice of acupuncture has become prevalent in the United States, with over 2 million Americans reporting its recent use.20 However, metabolomics analyses evaluating the physiological basis of acupuncture-treated humans have yet to be examined. This study was therefore designed to further elucidate the underlying mechanism of acupuncture-treated men from the metabolic pathways in a global view. In this paper, a salivary metabolomics method was used to study the acupuncture treatment effect by identifying a subset of important molecules from high-throughput metabolic data. For this aim, UPLC/MS was utilized to investigate the effects of acupuncture at ST-36 meridian points on saliva metabolites. Then metabolite profiles were generated from a collection of case samples (with acupuncture at the meridian point) and control samples (without acupuncture). Generally speaking,
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Fig. 4 Schematic diagram of the disturbed metabolic pathway in acupuncture-treated humans. The map was generated using the reference map by KEGG (http://www.genome.jp/kegg/). Map illustrating the most predominant disturbed metabolic pathways and the biochemical linkages among the biomarker metabolites identified. The green boxes: enzymatic activities with putative cases of analogy in acupuncture-treated human. (B) Phenylalanine metabolism; (C) alanine, aspartate and glutamate metabolism.
those molecules are usually termed as biomarkers and have been widely deployed in clinical settings. Relevant salivary metabolites may provide important insights into the mechanisms responsible for acupuncture treatment.21,22 In this study, metabolomic analysis of key salivary biomarkers in acupuncture-treated men was successfully investigated by UPLC/MS combined with multivariate statistical analysis. To examine the effect of 0 day acupuncture on ST-36, global metabolomic profiles were compared between 0 days and 14 days. In total, 26 metabolites were significantly changed. These metabolite biomarkers for acupuncture treatment serve as the candidates for further mechanism investigation. Five unique metabolic pathways were also indicated to be differentially affected in acupuncture-treated humans. The significantly downregulated and upregulated biomarkers were observed following 14 days of treatment. Many metabolic pathways were disrupted as a result of
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acupuncture on ST-36. Thus, salivary metabolomics will advance in-depth investigations of acupuncture. In a few years, emerging as a promising biofocus, saliva metabolomics will drive saliva analysis and offer great benefits for public health in the long-term.
Conclusions In order to realize the potential role of acupuncture and elucidate the mechanisms of acupuncture, metabolomics has been increasingly used in acupuncture medicine. UPLC/MS fingerprinting combined with validated multivariate analysis and ingenuity pathways analysis firstly proved to be effective for the analysis of saliva composition to investigate the effects of acupuncture intervention, to address this important problem.
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Supervised multivariate methods like PLS-DA and OPLS-DA are commonly used in feature-based data analysis for selecting discriminative features. Clear metabolic differences were observed between acupuncture intervention and healthy controls. These variations involved significant perturbations in phenylalanine and tyrosine metabolism, alanine metabolism, aspartate metabolism and glutamate metabolism. The identified target metabolites were found to encompass a variety of pathways. The power of salivary metabolomics to elucidate metabolic characters of the ST-36 has been successfully revealed in this study. In summary, our study indicated the importance of salivary metabolomics as a powerful tool in providing valuable biochemical insights into metabolic alterations.
Competing financial interests The authors declare no competing financial interests.
Acknowledgements This work was supported by grants from the Key Program of Natural Science Foundation of State (Grant No. 90709019, 81173500, 81302905, 81102556, 81202639), National Key Technology Research and Development Program of the Ministry of Science and Technology of China (Grant No. 2011BAI03B03, 2011BAI03B06, 2011BAI03B08), Key Science and Technology Program of Heilongjiang Province, China (Grant No. GC06C501, GA08C303, GA06C30101), Foundation of Heilongjiang University of Chinese Medicine (Grant no. 201209), National Key Subject of Drug Innovation (Grant No. 2009ZX09502-005).
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6 A. H. Zhang, H. Sun, Y. Han, G. L. Yan, Y. Yuan, G. C. Song, X. X. Yuan, N. Xie and X. J. Wang, Ultraperformance liquid chromatography-mass spectrometry based comprehensive metabolomics combined with pattern recognition and network analysis methods for characterization of metabolites and metabolic pathways from biological data sets, Anal. Chem., 2013, 85(15), 7606–7612. 7 A. H. Zhang, P. Wang, H. Sun, G. L. Yan, Y. Han and X. J. Wang, High-throughput ultra-performance liquid chromatography-mass spectrometry characterization of metabolites guided by a bioinformatics program, Mol. BioSyst., 2013, 9(9), 2259–2265. 8 J. Zheng, R. A. Dixon and L. Li, Development of isotope labeling LC-MS for human salivary metabolomics and application to profiling metabolome changes associated with mild cognitive impairment, Anal. Chem., 2012, 84(24), 10802–10811. 9 A. Zhang, H. Sun and X. Wang, Saliva metabolomics opens door to biomarker discovery, disease diagnosis, and treatment, Appl. Biochem. Biotechnol., 2012, 168(6), 1718–1727. ´lvarez-Sa ´nchez, F. Priego-Capote and M. D. Luque de 10 B. A Castro, Study of sample preparation for metabolomic profiling of human saliva by liquid chromatography-time of flight/mass spectrometry, J. Chromatogr., A, 2012, 1248, 178–181. 11 J. Jia, Y. Yu, J. H. Deng, N. Robinson, M. Bovey, Y. H. Cui, H. R. Liu, W. Ding, H. G. Wu and X. M. Wang, A review of Omics research in acupuncture: the relevance and future prospects for understanding the nature of meridians and acupoints, J. Ethnopharmacol., 2012, 140(3), 594–603. 12 A. Zhang, H. Sun, G. Yan, W. Cheng and X. Wang, Systems biology approach opens door to essence of acupuncture, Complementary Ther. Med., 2013, 21(3), 253–259. 13 Y. Maeda, N. Kettner, J. Lee, J. Kim, S. Cina, C. Malatesta, J. Gerber, C. McManus, J. Im, A. Libby, P. Mezzacappa, L. R. Morse, K. Park, J. Audette and V. Napadow, Acupuncture-evoked response in somatosensory and prefrontal cortices predicts immediate pain reduction in carpal tunnel syndrome, J. Evidence-Based Complementary Altern. Med., 2013, 2013, 795906. 14 Y. R. Seo, W. S. Jung, S. U. Park, S. K. Moon, J. M. Park and J. Y. Park, The effect of ouhyul herbal acupuncture point injections on shoulder pain after stroke, J. Evidence-Based Complementary Altern. Med., 2013, 2013, 504686. 15 F. G. Miller and T. J. Kaptchuk, Acupuncture for chronic low back pain, N. Engl. J. Med., 2010, 363(18), 1776. 16 G. Yan, A. Zhang, H. Sun, W. Cheng, X. Meng, L. Liu, Y. Zhang, N. Xie and X. Wang, Dissection of Biological Property of Chinese Acupuncture Point Zusanli Based on Long-Term Treatment via Modulating Multiple Metabolic Pathways, J. Evidence-Based Complementary Altern. Med., 2013, 2013, 429703. 17 A. Zhang, H. Sun, P. Wang, Y. Han and X. Wang, Recent and potential developments of biofluid analyses in metabolomics, J. Proteomics, 2012, 75(4), 1079–1088.
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for profiling of both urine and plasma metabolic perturbation, Am. J. Chin. Med., 2011, 39(2), 287–300. 21 M. Tsuruoka, J. Hara, A. Hirayama, M. Sugimoto, T. Soga, W. R. Shankle and M. Tomita, CE-MS-based metabolome analysis of serum and saliva from neurodegenerative dementia patients, Electrophoresis, 2013, 34(19), 2865–2872. 22 L. Zhuang, Z. Yang, X. Zeng, X. Zhua, Z. Chen, L. Liu and Z. Meng, The preventive and therapeutic effect of acupuncture for radiation-induced xerostomia in patients with head and neck cancer: a systematic review, Integr. Cancer Ther., 2013, 12(3), 197–205.
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18 Z. J. Zhu, A. W. Schultz, J. Wang, C. H. Johnson, S. M. Yannone, G. J. Patti and G. Siuzdak, Liquid chromatography quadrupole time-of-flight mass spectrometry characterization of metabolites guided by the METLIN database, Nat. Protoc., 2013, 8(3), 451–460. 19 L. Zhao, J. Chen, C. Z. Liu, Y. Li, D. J. Cai, Y. Tang, J. Yang and F. R. Liang, A review of acupoint specificity research in china: status quo and prospects, J. Evidence-Based Complementary Altern. Med., 2012, 2012, 543943. 20 S. L. Wen, Y. J. Liu, H. L. Yin, L. Zhang, J. Xiao, H. Y. Zhu, J. T. Xue and L. M. Ye, Effect of acupuncture on rats with acute gouty arthritis inflammation: a metabonomic method
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High-throughput metabolomic approach revealed the acupuncture exerting intervention effects by perturbed signatures and pathways†
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Yingzhi Zhang, Aihua Zhang, Guangli Yan, Weiping Cheng,* Hui Sun, Xiangcai Meng, Li Liu, Ning Xie and Xijun Wang* Metabolomics can capture global changes and the overall physiological status in biochemical networks and pathways in order to elucidate sites of perturbations. High-throughput metabolomics and acupuncturology have similar characteristics such as entirety, comprehensiveness and dynamic changes, and can identify potential candidates for acupuncture effects and provide valuable information towards understanding therapy mechanisms. Saliva has recently gained popularity as a potential tool for biomarker monitoring, as its composition may potentially reflect plasma metabolite levels and, therefore, may be used as an indicator of the physiological state. However, the underlying mechanism of acupuncture, remains largely unknown, which hinders its widespread use. Acupuncture would produce unique characterization of metabolic perturbations. In this study, UPLC/ESI-HDMS in high-accuracy mode coupled with pattern recognition analysis was carried out to investigate the mechanism and saliva metabolite biomarkers for acupuncture treatment at ‘Zusanli’ acupoint (ST-36) as a case study. Putative metabolite identifications for these ions were obtained through a mass-based database search. As a result, the top canonical pathways including phenylalanine metabolism, alanine, aspartate and glutamate metabolism,
D-glutamine
and
D-glutamate
metabolism, and steroid hormone biosynthesis pathways
were acutely perturbed. 26 differential metabolites were identified by chemical profiling, and may be Received 19th August 2013, Accepted 30th September 2013
useful to clarify the physiological basis and mechanism of ST-36. More importantly, network
DOI: 10.1039/c3mb70352e
pathways. These results provide useful insights into biomarker discovery utilizing metabolomics as an
www.rsc.org/molecularbiosystems
efficient and cost effective platform. This study opens new possibilities for the selection of saliva as a source of metabolite biomarkers representative of specific disorders.
construction has led to the integration of metabolites associated with the multiple perturbation
Introduction Metabolomics can allow the measurement of the comprehensively small molecules that are endogenous metabolites in easily accessible biofluids and biomarker discovery.1–6 Analysis of these key metabolites in body fluids has become an important method to monitor the state of biological organisms.7 Human saliva is an attractive diagnostic fluid because it has several key advantages for disease diagnosis and prognosis.8 It is easily collected and stored and ideal for early detection of disease as it contains specific biological biomarkers.9 Analysis of the metabolome of human saliva may contribute to the understanding National TCM Key Lab of Serum Pharmacochemistry, Key Lab of Metabolomics and Chinmedomics, First Affiliated Hospital, Heilongjiang University of Chinese Medicine, Heping Road 24, Harbin 150040, China. E-mail: [email protected], [email protected]; Fax: +86-0451-82110818; Tel: +86-0451-82110818 † Electronic supplementary information (ESI) available. See DOI: 10.1039/ c3mb70352e
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of pathophysiology and provide a foundation for the recognition of potential biomarkers.10 Acupuncture is an ancient Chinese medical therapy that is used widely around the world.11 Metabolomics is a rapidly developing field that has given new hope in the treatment of acupuncture. Metabolomic technologies used to study acupuncture will help us to identify potential biomarkers and to have a more in-depth understanding of the pathophysiological processes. Acupuncture medicine can take advantage of metabolomic frameworks to provide the healthcare system with useful tools that can optimize the effectiveness of treatment.12 Nowadays, the common applications of acupuncture include heart disease, smoking cessation, and the treatment of inflammatory diseases and psychological disorders etc.13–15 In traditional Chinese medicine (TCM), Zusanli (also known as ST-36) point of ‘‘The Stomach Meridian of Foot-Yangming’’ is commonly used in human acupuncture to treat a wide range of health conditions including gastrointestinal disorders and others,16 however, little is known about its biological mechanism.
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Saliva composition is affected by physiological, pathological and environmental factors that may affect its metabolic profile.17 Acupuncture may cause changes in the flow and composition of saliva. Herein, this study illustrates the power of accurate mass measurement by UPLC/MS, combined with the multivariate modelling such as Principal Component Analysis (PCA) and Orthogonal Projection to Latent Structures Discriminant Analysis (OPLS-DA) for a comprehensive saliva metabolomic analysis to investigate changes in metabolite levels. The scope of this work was to gain a better insight into acupuncture-treated ST-36, to detect the low molecular-weight metabolite data, to determine the perturbation pathways, and to infer the biological processes.
Experimental procedures Chemicals and reagents Acetonitrile (HPLC grade) was purchased from Merck (Germany); leucine enkephalin was purchased from Sigma-Aldrich (St. Louis, MO, USA); the distilled water was produced by a Milli-Q Ultra-pure water system (Millipore, Billerica, USA); formic acid was obtained from (Kermel Chemical Reagent Co., Ltd., China). All other reagents were of analytical grade. Ethical statement and volunteers The present work was approved by the Ethical Committee of Heilongjiang University of Chinese Medicine (No: HLJZY-20120198) and was carried out in accordance with The Code of Ethics of the World Medical Association (Declaration of Helsinki) for experiments. A total of twenty healthy volunteers (males, mean age 25.4 4.2 years) were enrolled in this study following informed consent. Volunteers in the control group were included on the basis of a physician’s assessment of their general health status. Acupuncture treatment was performed on bilateral ST-36 for 30 min, once a day for two consecutive weeks. The whole unstimulated saliva was collected between 15:00 and 15:30 p.m. using standard techniques done by Weiping Cheng.
Molecular BioSystems
UPLC/MS analysis Chromatography was carried out with an ACQUITY UPLC HSS T3 C18 column (100 mm 2.1 mm i.d., 1.8 mm) using an ACQUITY UPLCt system (Waters Corp., Milford, USA). A ‘‘purge–wash–purge’’ cycle was employed on the auto-sampler, 10% (v/v) formic acid aqueous solution was used as the wash solvent and 0.1% (v/v) formic acid aqueous solution was used as the purge solvent, to ensure that the carry-over between injections was minimized. The mobile phases were composed of 0.1% formic acid in acetonitrile (solvent A) and 0.1% formic acid in water (solvent B). The gradient was as follows: a linear gradient of 0–1 min, 2–6% A; 1–3 min, 6–20% A; 5–7 min, 30–35% A; 7–8 min, 35–50% A; 8–10 min, 50–99% A; 10–11 min, 99% A; 11–11.5 min, 99–2% A; 11.5–13 min, 2% A. The flow rate was 0.50 ml min 1 and The column compartment was kept at a temperature of 45 1C, and the sample injection volume was 5 mL. The eluent was introduced to the mass spectrometer directly, i.e. without a split. After every 10 sample runs, quality control and blank samples were injected in order to ensure consistent performance of the system. In separate runs, detection was achieved in both positive and negative ion modes. The m/z range was set to be 100–1000 in centroid mode with a scan time of 0.2 s and an inter-scan delay of 0.01 s. The eluent was introduced into the Synaptt High Definition MS (Waters Corp., Milford, USA) analysis, and the optimal conditions were as follows: desolvation temperature of 300 1C, source temperature of 110 1C, sample cone voltage of 30 V, extraction cone voltage of 3.5 V for positive ion mode, collision energy of 4 eV, microchannel plate voltage of 2400 V, cone gas flow of 50 L h 1 and desolvation gas flow of 700 L h 1, capillary voltage of 3.0 kV for positive ion mode and 2.6 kV for negative ion mode. For accurate mass acquisition, a lock-mass of leucine enkephalin at a concentration of 200 pg ml 1 was used via a lock spray interface at a flow rate of 100 ml min 1 monitoring for positive ion mode ([M + H]+ = 556.2771) and negative ion mode ([M – H] = 554.2615) to ensure accuracy during the MS analysis.
Sample preparation protocols
Multivariate data processing and visualization
Saliva samples were obtained from volunteers in the Heilongjiang University of Chinese Medicine. Control saliva samples were obtained before the acupuncture stimulation at day 0 points. The subjects were given insulated ice packs in which they were asked to store the saliva samples immediately until they were received by the study investigator. On arrival at the laboratory, the samples were centrifuged at 10 000 rpm for 10 min at 4 1C to remove any solid debris. The supernatant was transferred and immediately frozen and stored at 80 1C until analysis. Before analysis, the samples were thawed. Methanol (1.5 ml) was added to 0.5 ml of saliva and shaken vigorously (5 min), and the mixture was allowed to stand for 10 min, centrifuged at 13 000 rpm for 15 min. The supernatant was dried by nitrogen, and then 100 ml of 50% methanol were added and vortexed 5 min. The supernatants were centrifuged at 13 000 rpm for 15 min at 4 1C, and then filtered through a syringe filter (0.22 mm), 5 mL of the supernatant were injected into the UPLC-MS.
Data acquisition and collection were performed by MassLynx v4.1 (Waters Corporation). MassLynx version 4.1 software (Waters Corporation, Milford, MA, USA) with an added statistical programme for multivariate data analysis, was used to analyse the UPLC/MS data where ESI positive and negative raw data was extracted. Chromatographic peak picking and raw data deconvolution, including noise filtering, peak detection, removal of isotope masses and alignment of retention time (rt) and mass (m/z), were performed by Markerlynx software. The dataset obtained from MarkerLynxt processing was exported to the EZinfo software version 2.0 programme in order to perform PCA and OPLS-DA models. Data processing was based on PCA which provides an overview of any patterns and groupings, and OPLS-DA to find the most influential differentiating features separating sample groups in PCA. Potential markers of interest were extracted from S-plots constructed following analysis with OPLS-DA, and markers were chosen based on their contribution to the variation and correlation
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within the data set. Discriminating features at VIP (Variable Importance for Projection) values Z2 were considered and highlighted in an S-plot from the OPLS-DA model.
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Marker identification and related pathways Significant features (markers) selected using the OPLS-DA models were identified based on the proposed minimum reporting standards of metabolomics data. The MassFragmentt application manager was used to facilitate the MS/MS fragment ion analysis process by way of chemically intelligent peak-matching algorithms. Fragmentation patterns, using MS and MS/MS modes in QTOF/MS, were used for structural elucidation of marker features. Where possible, marker identification was eventually confirmed by matching fragmentation patterns to authentic standards. Subsequently, a database and information search were performed. The database search was conducted in Metlin (http://metlin.scripps.edu/), online ChemSpider database and MassBank (www.massbank.jp). Significant metabolic features were identified by matching the accurate mass of observed peaks to metabolites in the selected databases within a mass accuracy window of 10 ppm. Metabolic pathways associated to metabolic classifiers were studied and performed with MetPA and MetaboAnalyst based on the database source. Thus, all the metabolites were highlighted and mapped into KEGG pathways to elucidate the metabolic processes more affected by the progression of the acupuncture intervention. Statistical analyses. SPSS 17.0 for Windows was used for the statistical analysis and validation. The data were analysed using the Wilcoxon Mann–Whitney Test, with p o 0.05 set as the level of statistical significance.
Results and discussion Metabolic profiling Procedures of establishing the proposed model involved three stages. Firstly, metabolic profiling was performed on saliva samples; then, features were extracted from the obtained multivariate dimensional metabonomic data; and finally, the diagnostic model was constructed on data of training set by mathematic modeling. For UPLC-MS analysis, aliquots were separated using a Waters Acquity UPLC (Waters, Millford, MA) and analyzed using a Q-TOF/HDMS, which consisted of an electrospray ionization source. Fig. 1 lists typical UPLC-MS BPI profiles of the representative saliva samples, respectively. In the first stage, LC/MS analysis was carried out to acquire metabolic profiles. Operating conditions were optimized to elute as many metabolites as possible in a single injection. Both positive and negative modes of analysis were used to allow much more metabolite information to be gained. A large number of chromatographic peaks were eluted in each metabolic profile, while their intensity and the number of peaks were rather different. Pattern recognition analysis For the purpose of analyzing changes in the metabolic pattern, which are associated with the recovery process, 2 typical analytical
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procedure steps were used (as often used in metabolomics): first, an unsupervised PCA technique to find trajectories and clustering, and second, PLS-DA on datasets. PCA is a nonsupervised mathematical procedure which reduces the dimensionality of data without altering the data itself. The OPLS-DA model was adapted only when there was group separation in PCA. Finding the differentiating features directly from the loadings plot in the PCA model of the whole dataset is not always possible, and therefore OPLS-DA was used for this purpose. The obtained PCA and PLS-DA models can be used to extract the metabolomic information and, in particular, to identify the metabolites and their correlations in order to determinate the progression of disease. PCA plots showed that the trajectory profiles of the 0, 7 and 14 day samples significantly changed as a result of acupuncture-treatment (Fig. 2A and 3A). By PCA, loading plots can be applied to suggest potential biomarkers. From the panels in Fig. 3, samples can be clearly separated in one principal component direction (PC1 or PC2), and biomarkers were discovered by investigating their loadings in the according directions using Hotelling’s T2 statistic (significance level was set at 0.05). Subsequently, supervised OPLS-DA was performed to reveal detailed and statistically significant metabolite changes that were caused by acupuncture intervention at 0 and 14 days (Fig. 2B and 3B). The ions that showed significant difference in abundance between the 0 day and 14 day treated humans contributed to the observed separation (Fig. 2C and 3C) and were selected from the respective S-plots as potential markers in positive and negative mode (Fig. 2D and 3D). The VIP-value threshold cutoff of the metabolites was set to 2.0, above this threshold, were viewed as potential biomarkers. Finally, the markers of significant contribution were characterized by 17 in positive mode and 9 in negative mode (Table 1). Identification of important metabolites The UPLC/MS analysis platform provides the retention time, precise molecular mass and MS/MS data for the structural identification of biomarkers. To identify these biomarkers, KEGG, Metlin, and PubChem databases were utilized and analysis was performed by time-of-flight mass spectrometry (TOF/MS). A list of candidates was generated by searching from the database, and the most probable biomarkers would be found according to the possible fragment mechanisms carried out by TOF/MS analysis. In order to elucidate the salivary metabolome, a final putative identification step was carried out for which the extracted features were queried against the METLIN database, which includes endogenous metabolites found in plasma, besides common human drugs and their metabolites.18 The database search results are summarized in Table 1, which include the molecular formula, the detected adduct mass, matching tolerance, and the numeric reference in METLIN. Finally, 26 acupuncture intervention related-biomarkers were discovered from a statistical point of view. According to the protocol detailed above, 26 endogenous metabolites contributing to the separation of the acupuncture intervention group and control
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Fig. 1 Typical BPI chromatogram obtained from saliva in positive and negative ionization mode. 0 days (A) and 14 days (B) in positive mode. 0 days (C) and 14 days (D) in negative mode.
group were detected in the samples (Table 1). Although some of them are known to be common components of saliva, most of them have not been previously reported in bibliography. Metabolic pathway and function analysis More detailed analysis of pathways and networks influenced by acupuncture was performed by the MetPA tool. It revealed that metabolites, which were identified together as being important for the host’s response to acupuncture-treatment, were responsible for alanine, aspartate and glutamate metabolism, phenylalanine metabolism, D-glutamine and D-glutamate metabolism, steroid
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hormone biosynthesis, pyrimidine metabolism, phenylalanine, tyrosine and tryptophan biosynthesis, arachidonic acid metabolism, arginine and proline metabolism etc. ESI† Table S1 suggests that these pathways showed marked perturbations over the entire time-course of acupuncture intervention. Potential biomarkers were also identified from these relevant pathways. Some significantly changed metabolites have been found and used to explain the phenylalanine metabolism, alanine, aspartate and glutamate metabolism. The detailed construction of the phenylalanine metabolism, alanine, aspartate and glutamate metabolism pathways with a higher score is shown in Fig. 4. Below is the detailed analysis
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Fig. 3 Representative PCA score plot (A) based on the UPLC/MS chromatograms in negative mode (A). PCA model results for 0 days and 14 days in negative mode (B). VIP-plot of OPLS-DA of samples in negative mode (C). Fig. 2 Trajectory analysis of PCA score plots obtained from the saliva samples in positive mode (A). PCA model results for 0 days and 14 days in positive mode (B). VIP-plot of OPLS-DA of samples in positive mode (C).
of the phenylalanine metabolism, alanine, aspartate and glutamate metabolism pathways. Phenylalanine and tyrosine metabolism. Phenylalanine is an essential amino acid which must be supplied by dietary proteins. Once in the body, phenylalanine may follow any of three paths. It may be (1) incorporated into cellular proteins, (2) converted to phenylpyruvic acid, or (3) converted to tyrosine. Tyrosine can be converted into L-DOPA, which is further converted into dopamine, norepinephrine (noradrenaline), and epinephrine (adrenaline). Depicted in this pathway is the
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conversion of phenylalanine to phenylpyruvate, the incorporation of phenylalanine and/or tyrosine into polypeptides and the conversion of phenylalanine to tyrosine via phenylalanine hydroxylase. This reaction functions both as the first step in tyrosine/phenylalanine catabolism by which the body disposes of excess phenylalanine, and as a source of the amino acid tyrosine. The decomposition of L-tyrosine begins with an a-ketoglutarate dependent transamination through the tyrosine transaminase to para-hydroxyphenylpyruvate. The next oxidation step catalyzed by p-hydroxylphenylpyruvatedioxygenase creates homogentisate. Alanine metabolism. Alanine is most commonly produced by the reductive amination of pyruvate via alanine transaminase. This reversible reaction involves the interconversion of alanine
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Paper Table 1
Molecular BioSystems Potential marker metabolites in acupuncture-treated humans’ saliva samples identified by UPLC/MS in positive and negative mode
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Rt m/z m/z Error Ion No. (min) Determined Calculated (mDa) form 1 2 3 4 5 6 7 8 9 10
1.09 1.47 2.13 2.15 2.22 2.73 2.81 2.99 8.54 8.59
165.0525 294.1753 279.0892 486.1857 526.2897 486.1827 492.2237 316.2081 230.2447 318.2483
165.0552 294.1719 279.0892 486.1876 526.2910 486.1876 492.2247 316.2124 230.2484 318.2433
2.7 3.4 0.0 1.9 1.3 4.9 1.0 4.3 3.7 5.0
[M [M [M [M [M [M [M [M [M [M
+ + + + + + + + + +
11 12 13 14 15 16 17 18 19 20 21 22 23
8.65 8.71 8.76 8.88 9.28 9.29 9.29 0.44 0.46 0.56 0.84 0.84 9.3
406.2917 494.3303 288.2853 172.1698 415.1441 437.1307 437.1882 249.0185 216.9791 335.1279 167.0623 335.1242 459.2032
406.2933 494.3304 288.2903 172.1701 415.1465 437.1349 437.1859 249.0156 216.9807 335.1283 167.0609 335.1243 459.2019
1.6 0.1 5.0 0.3 2.4 4.2 2.3 2.9 1.6 0.4 1.4 0.1 1.0
[M [M [M [M [M [M [M [M [M [M [M [M [M
+ + + + + + +
24 25 26
9.81 9.92 9.95
295.2273 293.2110 319.2257
295.2273 293.2117 319.2273
0.0 0.7 1.6
[M [M [M
Formula H]+ H]+ Na]+ H]+ Na]+ H]+ H]+ H]+ H]+ Na]+
Na]+ H]+ H]+ H]+ H]+ H]+ H]+ H] H] H] H] H] H] H] H] H]
C9H8O3 C17H19N5 C10H16N4O2S C24H27N3O8 C25H46NO7P C24H27N3O8 C26H29N5O5 C16H29NO5 C14H31NO C20H31NO2
Phenylpyruvic acid Anastrozole Buthidazole 7-Hydroxyondansetron glucuronide LysoPE(0 : 0/20 : 3(8Z,11Z,14Z)) 6-Hydroxyondansetron glucuronide Thr Trp Trp Butoctamide hydrogen succinate Xestoaminol C 17beta-Hydroxy-4,17-dimethyl4-azaandrost-5-en-3-one C22H41NO4 N-Oleoyl threonine C28H47NO4S Tiamulin C17H37NO2 C17 Sphinganine C10H21NO N,2,3-Trimethyl-2-(1-methylethyl)butanamide C16H22N4O9 Clavamycin A C23H20N2O7 Phe-Tyr-OH C20H28N4O5S Unknown C11H10N2OS2 (S)-Spirobrassinin C7H6O6S 5-Sulfosalicylic acid C21H20O4 Dorspoinsettifolin C11H8N2 Norharman C16H20N2O6 Semilepidinoside A C25H32O8 17,21-Dihydroxypregn-4-ene-3,11,20-trione 21-(hydrogensuccinate) C18H32O3 6-Hydroxy-9Z,12Z-octadecadienoic acid C18H30O3 8-Hydroxy-11Z-octadecen-9-ynoic acid C20H32O3 5-Hydroxyeicosatetraenoic acid
and pyruvate, coupled to the interconversion of alphaketoglutarate (2-oxoglutarate) and glutamate. Because transamination reactions are readily reversible and pyruvate is widespread, alanine can be easily formed in most tissues. Another route to the production of alanine is through the enzyme called alanine-glyoxylate transaminase. This reaction involves the interconversion of alanine and pyruvate, coupled to the interconversion of glyoxylate and glycine. Once synthesized, alanine can be coupled to alanyl tRNA via alanyl-tRNA synthetase and used by the body in protein synthesis. Aspartate metabolism. Aspartate synthesis involves the generation of aspartate from oxaloacetate via transamination by aspartate aminotransferase or amino acid oxidase. Once synthesized, aspartate can be coupled to aspartyl tRNA via aspartyl-tRNA synthetase and used by the body in protein synthesis. Aspartate can be converted to another polar amino acid, asparagine, via asparagine synthase. Aspartate is a precursor to many other cofactors or compounds involved in cellular signaling including N-acetyl-aspartate, beta-alanine, adenylsuccinate, arginino-succinate and N-carbamoylaspartate. Aspartate is also a metabolite in the urea cycle and participates in gluconeogenesis. Glutamate metabolism. This pathway depicts the diverse metabolic fates and roles that glutamate plays in the body. Glutamate can also be oxidatively deaminated through glutamate dehydrogenase to produce 2-oxoglutarate and ammonia. Additionally, glutamate can be generated from alanine or aspartate in combination with 2-oxoglutarate using the enzyme transaminase. The resulting byproducts (pyruvate and oxaloacetate) are key components in glycolysis, gluconeogenesis and the TCA cycle. Glutamate and
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VIP value Trend P-value
Metabolite name
4.2 5.1 4.8 11.2 4.2 6.5 5.9 10.3 4.8 31.7
m k m m k m m k m m
0.0093 0.0175 0.0034 0.0064 0.0132 0.0084 0.0117 0.0129 0.0270 0.0350
10.7 4.9 11.0 4.4 12.4 6.4 6.1 2.6 2.8 3.8 5.4 6.5 3.2
m m k m m m k m m m m m k
0.0144 0.0291 0.0110 0.0246 0.0296 0.0146 0.0070 0.0088 0.0122 0.0273 0.0140 0.0000 0.0426
4.0 2.4 2.5
k k k
0.0133 0.0198 0.0000
proline metabolism are also connected. Glutamate also plays an important role in the body’s disposal of excess nitrogen through the reaction catalyzed by glutamate dehydrogenase, which converts glutamate and NADP into 2-oxoglutarate, NADPH and ammonia. Glutamate also serves as the precursor for the synthesis of the neurotransmitter known as GABA (gamma-aminobutyric acid) in GABA-ergic neurons. This reaction is catalyzed by glutamate decarboxylase, which is most abundant in the cerebellum and pancreas. Glutamate also serves as the most abundant fast excitatory neurotransmitter in the mammalian nervous system. Acupuncture has been used to treat various diseases for thousand years worldwide, and has been gradually accepted in western countries as an alternative or complementary treatment.19 More recently, the practice of acupuncture has become prevalent in the United States, with over 2 million Americans reporting its recent use.20 However, metabolomics analyses evaluating the physiological basis of acupuncture-treated humans have yet to be examined. This study was therefore designed to further elucidate the underlying mechanism of acupuncture-treated men from the metabolic pathways in a global view. In this paper, a salivary metabolomics method was used to study the acupuncture treatment effect by identifying a subset of important molecules from high-throughput metabolic data. For this aim, UPLC/MS was utilized to investigate the effects of acupuncture at ST-36 meridian points on saliva metabolites. Then metabolite profiles were generated from a collection of case samples (with acupuncture at the meridian point) and control samples (without acupuncture). Generally speaking,
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Fig. 4 Schematic diagram of the disturbed metabolic pathway in acupuncture-treated humans. The map was generated using the reference map by KEGG (http://www.genome.jp/kegg/). Map illustrating the most predominant disturbed metabolic pathways and the biochemical linkages among the biomarker metabolites identified. The green boxes: enzymatic activities with putative cases of analogy in acupuncture-treated human. (B) Phenylalanine metabolism; (C) alanine, aspartate and glutamate metabolism.
those molecules are usually termed as biomarkers and have been widely deployed in clinical settings. Relevant salivary metabolites may provide important insights into the mechanisms responsible for acupuncture treatment.21,22 In this study, metabolomic analysis of key salivary biomarkers in acupuncture-treated men was successfully investigated by UPLC/MS combined with multivariate statistical analysis. To examine the effect of 0 day acupuncture on ST-36, global metabolomic profiles were compared between 0 days and 14 days. In total, 26 metabolites were significantly changed. These metabolite biomarkers for acupuncture treatment serve as the candidates for further mechanism investigation. Five unique metabolic pathways were also indicated to be differentially affected in acupuncture-treated humans. The significantly downregulated and upregulated biomarkers were observed following 14 days of treatment. Many metabolic pathways were disrupted as a result of
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acupuncture on ST-36. Thus, salivary metabolomics will advance in-depth investigations of acupuncture. In a few years, emerging as a promising biofocus, saliva metabolomics will drive saliva analysis and offer great benefits for public health in the long-term.
Conclusions In order to realize the potential role of acupuncture and elucidate the mechanisms of acupuncture, metabolomics has been increasingly used in acupuncture medicine. UPLC/MS fingerprinting combined with validated multivariate analysis and ingenuity pathways analysis firstly proved to be effective for the analysis of saliva composition to investigate the effects of acupuncture intervention, to address this important problem.
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Supervised multivariate methods like PLS-DA and OPLS-DA are commonly used in feature-based data analysis for selecting discriminative features. Clear metabolic differences were observed between acupuncture intervention and healthy controls. These variations involved significant perturbations in phenylalanine and tyrosine metabolism, alanine metabolism, aspartate metabolism and glutamate metabolism. The identified target metabolites were found to encompass a variety of pathways. The power of salivary metabolomics to elucidate metabolic characters of the ST-36 has been successfully revealed in this study. In summary, our study indicated the importance of salivary metabolomics as a powerful tool in providing valuable biochemical insights into metabolic alterations.
Competing financial interests The authors declare no competing financial interests.
Acknowledgements This work was supported by grants from the Key Program of Natural Science Foundation of State (Grant No. 90709019, 81173500, 81302905, 81102556, 81202639), National Key Technology Research and Development Program of the Ministry of Science and Technology of China (Grant No. 2011BAI03B03, 2011BAI03B06, 2011BAI03B08), Key Science and Technology Program of Heilongjiang Province, China (Grant No. GC06C501, GA08C303, GA06C30101), Foundation of Heilongjiang University of Chinese Medicine (Grant no. 201209), National Key Subject of Drug Innovation (Grant No. 2009ZX09502-005).
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