Article pubs.acs.org/ac
Pattern Recognition Approaches and Computational Systems Tools for Ultra Performance Liquid Chromatography−Mass SpectrometryBased Comprehensive Metabolomic Profiling and Pathways Analysis of Biological Data Sets Xijun Wang,*,† Bo Yang,† Hui Sun,† and Aihua Zhang† †
National TCM Key Lab of Serum Pharmacochemistry, Heilongjiang University of Chinese Medicine, and Key Pharmacometabolomics Platform of Chinese Medicines, Heping Road 24, Harbin 150040, China ABSTRACT: Metabolomics represents an emerging and powerful discipline that provides an accurate and dynamic picture of the phenotype of biosystems through the study of potential metabolites that could be used for therapeutic targets and discovery of new drugs. Metabolomic network construction has led to the integration of metabolites associated with the caused perturbation of multiple pathways. Herein, we present a method for the construction of efficient networks with regard to that Jujuboside B (JuB) protects against insomnia as a case study. UPLC/ESI-SYNAPT-HDMS coupled with pattern recognition methods including PCA, PLS-DA, OPLS-DA, and computational systems analysis were integrated to obtain comprehensive metabolomic profiling and pathways of the large biological data sets. Among the regulated pathways, twelve biomarkers were identified and tryptophan metabolism, phenylalanine, tyrosine, tryptophan biosynthesis, arachidonic acid metabolism, and phenylalanine metabolism related network were acutely perturbed. Results not only supplied a systematic view of the development and progression of insomnia but also were used to analyze the therapeutic effects of JuB, a widely used anti-insomina medicine in clinics. The results showed that JuB administration could provide satisfactory effects on insomnia through partially regulating the perturbed pathway. We have constructed a metabolomic feature network of JuB to protect against insomnia. The most promising use in the near future would be to clarify pathways for the drugs and get biomarkers for these pathways, to help guide testable predictions, provide insights into drug action mechanisms, and enable us to increase research productivity toward metabolomic drug discovery.
M
of drug-development process, the metabolic profiling could provide a global changes of endogenous metabolites in perturbations of drug treatments.5,6 Biomarker metabolites can also be therapeutic targets.7 Precise identification and accurate quantification of metabolites facilitate downstream pathway and network analysis for the drug discovery. Metabolomics can make an impact at several points in the drug-development process: target identification, lead discovery, and optimization. Identification of drug targets is one of the major tasks in drug discovery, and metabolomics could be used to infer drug-target interactions. Insomnia is a serious health problem, and enhancing sleep quality is an issue of significant importance to public health.8 Newer, it is estimated that more than three-fourths of the general population suffer from insomnia, and it frequently occurs at a rate of approximately 10% in the whole world.9 Until now, there has been no effective treatment for insomnia.
etabolomics, an omic science in systems biology, is the comprehensive and simultaneous profling of metabolic changes occurring in living systems in response to genetic, environmental, or lifestyle factors. This approach offers a global analysis of low molecular weight metabolite level changes in biological samples, attempts to capture global changes, and overall physiological status in biochemical networks and pathways in order to elucidate sites of perturbations and has shown great promise as a means to identify biomarkers of drug efficacy.1 Metabolomics adopts a ‘top-down’ strategy to reflect the function of organisms from terminal symptoms of metabolic network and understand drug-target networks caused by interventions in holistic context.2 It has played increasingly important roles in many fields such as responses to environmental stress, toxicology, nutrition, studying global effects of genetic manipulation, cancer, comparing different growth stages, diabetes, gut functional ecology, disease diagnosis, drug metabolism, and natural product discovery.3,4 Recent years have seen an explosion in the amount of ‘omics’ data, which has influenced all areas of life sciences including that of drug mechanism and development, new target discovery. One area of considerable interest in the field of metabolomics is that © 2011 American Chemical Society
Received: October 26, 2011 Accepted: November 30, 2011 Published: November 30, 2011 428
dx.doi.org/10.1021/ac202828r | Anal. Chem. 2012, 84, 428−439
Analytical Chemistry
Article
Similarly, almost no available therapies drug can prevent its occurrence. The prognosis of patients with insomnia remains very poor because the molecular mechanism underlying it is not fully understood. Especially, few reports are available on the identifcation of key metabolites characterizing insomnia. While existing evidence consistently also demonstrates that insomnia is associated with significantly higher healthcare and productivity costs, studies in this area have had a number of limitations. Sedative-hypnotic drugs are increasingly prescribed for the insomniac patients; however, a growing body of evidence now suggests that these drugs do not exert satisfactory therapeutic effects.10 The addiction, dependence, and side effects of these medications have drawn much attention and result in a variety of problems, such as tolerance of the hypnosedative effects, pharmacological dependence, anterograde amnesia, cognitive and psychomotor impairment, abuse potential, and respiratory depression. In this context, there is an urgent need to find out novel biomarkers of practical value for clinical intervention. Therefore, there is a substantial interest in the discovery and use of newer biomarkers, to complement the best existing ones and to identify persons who are at risk for the development of insomnia disease and who could be targeted for preventive measures. Currently, a new focus on the pursuit of natural products is being sought for the treatment of insomnia. Increasing evidence supports that many Chinese medicinal products have been used for the treatment of insomnia, and their therapeutic effects have also been verifed by a host of clinical studies. Amusingly, a lot of steroidlike compounds like triterpenoids, steroids, and saponins are found in many Chinese medicinal products used for promoting sleep regulation and regarded as the active ingredients responsible for their therapeutic effects.11 Suanzaoren decoction (SZRD), a famous Chinese herbal remedy, has been efficiently and widely used to treat insomnia for thousands of years in Asia.12−15 The decoction is composed of five herbs, Semen ziziphi spinosae, Rhizoma chuanxiong, Poria, Rhizoma anemarrhenae, and Radixglycyrrhizae.16 The major ingredient of SZRD is suanzaoren (Semen ziziphi spinosae), which is the dried seed of Ziziphus jujuba Mill var. spinosa (Bunge) Hu ex. H.F. Chou (Rhamnaceae). Modern pharmacological studies have shown that suanzaoren possesses multiple activities such as hypnotic-sedative, hypotensive, antihypoxia, and antihyperlipidemia effects mediated through GABA-A receptors.17,18 Jujuboside B (JuB), a classic natural product, extracted from suanzaoren is considered to be the major pharmacological active compounds responsible for insomnia treatment (see Figure 1).19 It has been efficiently used for insomnia relief in Asia; however, its detailed metabolic mechanism for hypnotics function is poorly understood. Metabolomic network analyses combined with system-level resources can contribute to modern drug discovery.20 Traditional approaches to drug target identification include literature search-based target prioritization and in vitro binding assays which are both time-consuming and labor intensive. Computational integration of different knowledge sources is a more effective approach. A wealth of metabolomic data provides unprecedent opportunities for drug target identification.21−26 The specific and unique biochemical pathways of drug efficacy can be identified, when coupled with multivariate data analysis techniques or machine learning algorithms. Therefore, metabolomic technologies facilitate the systematic characterization of a drug targets, thereby helping to reduce the typically high attrition rates in discovery projects. Moreover, pinpointing
Figure 1. Chemical structure of Jujuboside B.
new drug targets has proven to be more complex than anticipated and has revealed large holes in our understanding of metabolic pathways and their integrated regulation. In aiming to gain get a better insight into insomnia metabolism and identify possible biomarkers with potential diagnostic values for predicting insomnia, we have developed a method based on metabolic networks to identify potential targets, which may become an effective strategy for the discovery of new drugs for insomnia. Metabolite data were analyzed to detect the enriched clusters, to determine the possible pathways of detected targets, and to infer the biological processes. The metabolite network of insomnia was predicted via Ingenuity Pathway Analysis (IPA) methods. We give an illustrative example to show that the drug target identification problem can be solved effectively by our method and then apply it to a JuB-related metabolic pathway.
■
MATERIALS AND METHODS
Chemicals and Reagents. Acetonitrile, HPLC grade, was obtained from Merck (Darmstadt, Germany); methanol (HPLC grade) was purchased from Fisher Scientific Corporation (Loughborough, UK); water was produced by a Milli-Q Ultrapure water system (Millipore, Billerica, USA); formic acid was of HPLC grade and was obtained from Honeywell Company (Morristown, New Jersey, USA); leucine enkephalin was purchased from Sigma-Aldrich (St. Louis, MO, USA). All other reagents were of analytical grade. JuB was obtained from Mansite Pharmaceutical CO. LTD (Chendu, China; batch number, 20100427; Purity ≥98%). All other reagents were HPLC grade. Nutrient medium preparation: Maizena (14 g), saccharobiose (10.3 g), agar (1 g), and distilled water (153 mL) are stirred and boiled for 2−3 min, then add in yeast powder (1 g) and propionic acid (0.8 mL) miscing. Animals. The wild-type Drosophila melanogaster (Canton S) was provided by Beijing university. Drosophila melanogaster adult of the wild-type strain Canton-S were collected within 24 h after eclosion and maintained at 25 °C, humidity (55 ± 5%), under 12-h/12-h light/dark (L/D) cycle on a diet containing agar (1%), sugar (5%), yeast (4%), cornmeal (8%), and methylparaben (0.2%). When they were approximately 5 days old for experiments, individual flies were acclimated in a vial (40 mL) containing 5 mL of standard diet, for 24 h (one L/D cycle). The locomotor activity was measured using the Drosophila Activity Monitoring System (DAMS, Trikinetics Inc. USA).
429
dx.doi.org/10.1021/ac202828r | Anal. Chem. 2012, 84, 428−439
Analytical Chemistry
Article
Animal Treatments and Sample Preparation. All animal studies were approved by the Animal Experimental Ethical Committee of Heilongjiang University of Chinese Medicine. All efforts were made to ameliorate suffering of animals. Seven-day drosophila were randomly divided into 3 groups with 10 drosophila in each (male, 5; female, 5): the Control; Model and JuB groups. The drosophila in the model and JuB groups were performed by light sleep deprivation method, holding for 3 consecutive days. The drosophila in the control and model groups were administrated with normal nutrient medium in the whole procedure for 10 consecutive days and anaesthetized by CO2 and killed on day 10 (8:00 a.m.). Drosophila was shattered and extracted by adding four volumes of methanol in 1.0 mL microtubes at room temperature. After brief vortex mixing the samples were incubated overnight at −20 °C. Supernatants were collected after centrifugation at 13,000 rpm for 10 min and transferred to vials for metabolomic analysis. Metabolic Profiling. Chromatography. Chromatography was performed on a 2.1 mm i.d. × 100 mm ACQUITY 1.7 μm C18 BEH column (Waters Corp., Milford, USA) using an ACQUITY UPLC system (Waters Corp., Milford, USA). A “purge-wash-purge” cycle was employed on the autosampler, with 90% aqueous formic acid used for the wash solvent and 0.1% aqueous formic acid used as the purge solvent; this ensured that the carry-over between injections was minimized. The column was maintained at 40 °C, and subsequently, a gradient of 0.1% formic acid in acetonitrile (solvent A) and 0.1% formic acid in water (solvent B) used as follows: a linear gradient of 1−50% B over initial−2.0 min, 50−70% B over 2.0−7.0 min, 70−99% B over 7.0−11.0 min, and then 99% B kept for 2.0 min, returned to 1% B for 0.5 min, and then held for 1.5 min. The flow rate was 0.40 mL/min, and 5 μL aliquot of each sample was injected onto the column. The eluent was introduced to the mass spectrometry directly, i.e. without a split. After every 10 sample injections a pooled sample followed by a blank were injected in order to ensure consistent performance of the system. Mass Spectrometry. The eluent was introduced into the synapt high-definition mass spectrometer (Waters Corp., Milford, USA) analysis, and the optimal conditions were as follows: desolvation temperature of 350 °C, source temperature of 100 °C, sample cone voltage of 40 V, extraction cone voltage of 4.0 V, capillary voltage of 1500 V, collision energy of 4 eV, microchannel plate voltage of 2400 V, cone gas flow of 20 L/h, and desolvation gas flow of 700 L/h for positive ion mode and negative ion mode. The data acquisition rate was set to 0.4 s/ scan, with a 0.1 s interscan delay. Data were collected in centroid mode from 100 to 1000 Da. For accurate mass acquisition, a lock-mass of leucine enkephalin at a concentration of 0.2 ng/mL was used via a lock spray interface at a flow rate of 100 μL·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. Data Processing. All data were processed using the MarkerLynx application manager for MassLynx 4.1 software (Waters Corp., Milford, USA). The UPLC/MS data are detected and noise-reduced in both the UPLC and MS domains such that only true analytical peaks are further processed by the software (e.g., noise spikes are rejected). A list of intensities (chromatographic peak areas) of the peaks detected is then generated for the first chromatogram, using the Rt-m/z data pairs as identifiers. This process is repeated for
each UPLC/MS analysis and the data sorted such that the correct peak intensity data for each Rt-m/z pair are aligned in the final data table. The ion intensities for each peak detected are then normalized, within each sample, to the sum of the peak intensities in that sample. There was no significant correlation between the total intensities used for normalization and the sample groups being compared in the study. The resulting normalized peak intensities form a single matrix with Rt-m/z pairs for each file in the data set. All processed data were mean centered and pareto scaled during multivariate data analysis. Multivariate Data Analysis. Centroided and integrated raw mass spectrometric data were processed using 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. With PCA data were visualized by plotting the PC scores where each point in the scores plot represents an individual sample and the PC loadings where each point represents one mass/retention time pair. Thus, the loadings plot gives an indication of the metabolites that most strongly influence the patterns in the scores plot. From the loading plots of OPLS-DA, various metabolites could be identified as being responsible for the separation between control and model groups and were therefore viewed as potential biomarkers. Appropriate filtration of the loading profile associated with the OPLS-DA predictive components resulted in a set of candidate biomarkers that were further evaluated by calculating group percentage changes and parametric t-test. 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 within the data set. With the completion of the PLS-DA analysis, we can try computational systems analysis with MetaboAnalyst (http://www.metaboanalyst.ca/MetaboAnalyst/faces/Home. jsp) data annotation approach including hierarchical clustering, PCA, and heatmap to select interesting or significant features that distinguish between controls and models. Hierarchical clustering is commonly used for unsupervised clustering. Agglomerative hierarchical clustering begins with each sample as a separate cluster and then proceeds to combine them until all samples belong to one cluster. The result is usually presented as a dendrogram or heatmap; both have been implemented in MetaboAnalyst. Biomarkers Identification. Exact molecular mass data from redundant m/z peaks corresponding to the formation of different parent and product ions were first used to help confirm the metabolite molecular mass. MS/MS data analysis highlights neutral losses or product ions, which are characteristic of metabolite groups and can serve to discriminate between database hits. The MassFragment 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. The identities of the specific metabolites were confirmed by comparison of their mass spectra and chromatographic retention times with those obtained using commercially available reference standards. A full spectral library, containing MS/MS data obtained in the positive and negative ion modes, for all metabolites reported in this work is available on request from the authors. 430
dx.doi.org/10.1021/ac202828r | Anal. Chem. 2012, 84, 428−439
Analytical Chemistry
Article
Figure 2. Based peak intensity (BPI) chromatograms of drosophila samples derived from UPLC-HDMS. A: positive mode; B: negative mode.
collinear, and possibly incomplete data. Typically, the metabolic profiles of disease cases and controls are compared with the aim of identifying spectral features, and ultimately metabolites, which discriminate the classes. With PLS-DA, identification of discriminatory variables proceeds from an analysis of the PLS weights. The supervised OPLS-DA can improve biomarker discovery efforts and separate samples into two blocks was applied to obtain better discrimination between the control and model groups. The OPLS-DA score plots analysis of the chromatographic data identified the control and the model groups based on the differences in their metabolic profiles, suggesting the metabolic profiles have significantly changed as a result of sleep deprivation (Figures3A and 4A). From the loading plots, various metabolites could be identified as being responsible for the separation between control and model groups and were therefore viewed as potential biomarkers (Figure 3B and 4B). The ions that showed a significant difference in abundance between the control and treated animals and contributed to the observed separation were selected from the respective S-plots and VIP-plot as potential markers in positive and negative mode (Figures 3C and 4C). Combining the results of the OPLS-DA analysis with S-plots and VIP-value plots, the UPLC-HDMS analysis platform provided the retention time, precise molecular mass, and MS/MS data for the structural identification of biomarkers. The VIP-value threshold cutoff of the metabolites was set to 13, above this threshold, and were filtered out as potential target biomarkers. Finally, potential biomarkers of significant contribution was characterizated 5 in positive mode and 7 in negative mode (Table 1). Hence, these ions presumably represent the metabolic pathways that are differentially affected animals on sleep deprivation treatment. Trajectory analysis of
This information was then submitted for database searching, either in-house or using the online ChemSpider database (www.chemspider.com), and MassBank (http://www. massbank.jp/), 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 IPA (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/) to identify the affected metabolic pathways analysis and visualization. The possible biological roles were evaluated by the enrichment analysis using the MetaboAnalyst.
■
RESULTS Metabolomic Profiling of Samples. UPLC-HDMS representative BPI chromatograms of biosamples derived from the normal, model, and JuB group in positive and negative modes are presented in Figure 2. Three BPI profles of consecutively injected samples of the same aliquot showed stable retention time with no drift in all of the peaks. The stable BPI reflected the stability of UPLC-HDMS analysis and reliability of the metabolomic data with UPLC-HDMS, per sample using mass spectral deconvolution software for peak detection. Low molecular mass metabolites could be separated well in the short time of 12 min due to the minor particles (sub-1.7 μm) of UPLC. Pattern Recognition Analysis. Both multivariate projection approaches such as PCA and PLS-DA often can be taken, because of their ability to cope with highly multivariate, noisy, 431
dx.doi.org/10.1021/ac202828r | Anal. Chem. 2012, 84, 428−439
Analytical Chemistry
Article
Figure 3. OPLS-DA model results for control and model group in positive mode (A). Loading plot of OPLS-DA of insomnia drosophila in positive mode (B). VIP-plot of OPLS-DA of insomnia drosophila in positive mode (C). Trajectory analysis of PCA Score plots (3-D) for the insomnia drosophila after Jujuboside B treatment in positive model (D).
significantly upregulated serotonin, dopamine, arachidonic acid, glutathionylspermidine, and the downregulated S-Adenosyl methionine, phenylalanine, adenosine monophosphate, 5hydroxy-L-tryptophan, melatonin, adenosine, tryptophan, and trypanothione were observed in the model group following sleep deprivation compared with control group (Figure 6). This difference of metabolites may denote their potential as targeted biomarkers for differentiating insomnia and normal states. Monitoring changes of these metabolites may predict the development of insomnia. Additionally, in order to more clearly characterize JuB treatment for sleep, changes in the relative concentrations of target metabolites identified by PCA of different groups were analyzed, and we have found that content of these key markers is closer to the normal group. In this study, the relative concentration of 12 endogenous metabolites was significantly affected by JuB treatment. Interestingly, according to the parametric t-test, we found that the relative concentration of these metabolites could be reversed after taking the JuB. Compared with the alterations of insomniarelated metabolites, most of them were reset to a healthier level after JuB administration. Thus, JuB may regulate metabolism of these markers to be efficiently used for sleep disorder. Metabolic Pathway and Function Analysis. With pattern recognition analysis of metabolites, a clear separation of a model group and a healthy control group was achieved, and the JuB dose group was located between the model group and the healthy control group showing a tendency of recovering to a healthy control group. Metabolite profiling focuses on the
PCA score plots (3-D) for the insomnia drosophila after Jujuboside B treatment showed clear segregation (Figures 3D and 4D). Acquired data were subjected to computational systems analysis to further investigate the effects of JuB on the insomnia drosophila metabolite profiles. The parallels PCA score trajectory plots (Figure 5A), hierarchical clustering analysis (Figure 5B), and heatmap visualization (Figure 5C) in computational systems analysis for the insomnia drosophila of intervention effects of Jujuboside B showed distinct segregation. These models were capable of distinguishing models from healthy subjects and showed that JuB exhibited preventive efficacy against insomnia by adjusting multiple metabolic pathways to their normal state. Identification and Quantitation of Potential Metabolites. The robust UPLC-HDMS analysis platform provides the retention time, precise molecular mass, and MS/MS data for the structural identification of biomarkers. The precise molecular mass was determined within measurement errors ( 0.10) were also indicated to be differentially affected in somnia animals due to sleep-deprivation treatment. Of note, we found that JuB activated an array of factors involved in tryptophan metabolism,
phenylalanine, tyrosine, tryptophan biosynthesis, arachidonic acid metabolism, and phenylalanine metabolism metabolic pathways. The significantly downregulated serotonin, dopamine, arachidonic acid, and glutathionylspermidine and upregulated S-Adenosyl methionine, phenylalanine, adenosine monophosphate, 5-hydroxy-L-tryptophan, melatonin, adenosine, tryptophan, and trypanothione were observed in the model group following JuB treatment. These metabolites demonstrated that abnormal metabolism occurred in the insomnia animals. Metabolic analysis of insomnia was inferred from changes in the intermediates during substance metabolism. Further experiments can be done to validate these targetbiomarkers. Drugs can be designed to modify the functioning of the pathway in the diseased state by inhibiting a key molecule or to enhance the normal pathway by promoting specific molecules that may have been affected in the diseased state and can influence the whole metabolic system by target which catalyze metabolic reactions.29 The identification and validation of targeted drug-metabolites relationships is a key area in metabolomic drug discovery. Discovery of drug targets through metabolic pathway analysis promises to be a useful and novel approach in this direction. However, pathway analysis of large and highly entangled metabolic networks meets the problem of combinatorial explosion of possible routes across the networks. In this article, we have characterized drug-target interaction networks involve receptors (melatonin receptor, serotonin receptor, dopamine receptor), neurotransmitter, enzymes, signal transduction, and electron carrier. Serotonin (5hydroxytryptamine, 5-HT), widely distributed in the nervous system of vertebrates and invertebrates, is a biochemical messenger and regulator and acts as a local transmitter at synapses, synthesized from the essential amino acid Ltryptophan.30 5-HT has three different modes of action in the nervous system: as transmitter, acting locally at synaptic boutons; upon diffusion at a distance from its release sites, producing hormonal effects. The three modes can affect a 435
dx.doi.org/10.1021/ac202828r | Anal. Chem. 2012, 84, 428−439
Analytical Chemistry
Article
Figure 5. Computational systems analysis with MetaboAnalyst’s data annotation tools. PCA Score trajectory plots (A), hierarchical clustering analysis (B), and heatmap visualization (C) of intervention effects of Jujuboside B for insomnia drosophila. Heatmap, implemented in MetaboAnalyst, is commonly used for unsupervised clustering. Rows: samples; columns: metabolites; color key indicates metabolite expression value, blue: lowest, red: highest.
vitamin B6, niacin, and glutathione. Some disorders of excess metabolism of tryptophan in the body may contribute to mental retardation. Increased tryptophan fragments correlates with increased tryptophan degradation, which occurs with depression, mental retardation, hypertension, and anxiety states. Dopamine is a member of the catecholamine (neurotransmitters) family in the brain and is a precursor to epinephrine (adrenaline) and norepinephrine (noradrenaline). Dopamine is a major transmitter in the extrapyramidal system
single neuronal circuit. 5-HT has been claimed to help alleviate insomnia, depression, and headaches. 5-Hydroxy-L-tryptophan is the immediate precursor of the neurotransmitter serotonin and frequently seen in sleep disruption, nightly restlessness, sundowning, and other circadian disturbances disease.31 Tryptophan is an essential amino acid which is the precursor of serotonin. Serotonin is a brain neurotransmitter, platelet clotting factor, and neurohormone found in organs throughout the body. of tryptophan to serotonin requires nutrients such as 436
dx.doi.org/10.1021/ac202828r | Anal. Chem. 2012, 84, 428−439
Analytical Chemistry
Article
Figure 6. Changes in the relative concentrations of target metabolites identified by PCA of different groups. The corresponding markers are represented in Table 1. A two tailed, parametric t test was used to determine the significance of the change in relative concentrations for each metabolite. Bars represent the mean relative metabolite concentration and standard deviations. *, p < 0.05; **, p < 0.01; ***, p < 0.001.
regulation of mood, learning and memory, immune activity, dreaming, fertility, and reproduction. In particular, most of the actions of melatonin are mediated through the binding and activation of melatonin receptors. Adenosine plays a role in signal transduction as cyclic adenosine monophosphate, cAMP. NADPH serves as an electron carrier in a number of reactions, being alternately oxidized and reduced. Enkephalin is an opioid peptide and can typically modulate immune activity and cell proliferation. Major enkephalin pathways in the brain involve the extrapyramidal system, including motor pathways controlled by the basal ganglia, the limbic system that governs emotional and behavioral control, and the hypothalamicneuroendocrine axis. Indeed, future metabolomic studies in human populations with insomnia will be needed to validate the biomarkers found in the animal model. These results implicate the JuB effects may be mediated through receptor, neurotransmitter, enzymes, signal transduction, and electron carrier. It provided strong evidence that the hypnotic effect of JuB occurred at the level of global metabolomics. Metabolomics for the screening of biomarker patterns and elucidation of biochemical processes during the postgenomic era has increased contemporaneously with progress in global systems biology.33,34 Application of metabolomic technologies to the study of insomnia will increase our understanding of the pathophysiological processes involved, and this should help us to identify potential biomarkers to develop new therapeutic strategies.35 Indeed, analysis and construction of metabolomic feature profiling of Jujuboside B protects against insomnia using UPLC/MS combined with pattern recognition methods and computational systems analysis provides a unified platform to integrate all the biological information on genes, proteins, metabolites, drugs, and drug targets for a comprehensive system level study of the relationship between metabolism and disease. System analysis of metabolic networks that are a central paradigm in biology will help us in identifying new drug targets which in turn will generate more in-depth understanding of the insomnia mechanism and thus provide better guidance for drug discovery. Thus network-based pathways of special interest are
Figure 7. Summary of pathway analysis with MetPA. a, tryptophan metabolism; b, phenylalanine, tyrosine, and tryptophan biosynthesis; c, arachidonic acid metabolism; d, phenylalanine metabolism.
of the brain and is important in regulating movement. A family of receptors (dopamine receptors) mediates its action. Phenylalanine is an essential amino acid and highly concentrated in the brain. Adenosine monophosphate, also known as 5′-adenylic acid and abbreviated AMP, is a nucleotide. It is an ester of phosphoric acid with the nucleoside adenosine. AMP consists of the phosphate group, the pentose sugar ribose, and the nucleobase adenine. AMP can be produced during ATP synthesis by the enzyme adenylate kinase. Melatonin is produced by the pineal gland which is located in the center of the brain but outside the blood-brain barrier and regulates the circadian rhythms of several biological functions, including the sleep-wake cycle.32 Melatonin is also implicated in the 437
dx.doi.org/10.1021/ac202828r | Anal. Chem. 2012, 84, 428−439
Analytical Chemistry
Article
Table 2. Result from Ingenuity Pathway Analysis with MetPAa no.
pathway name
total cmpd
expected
hits
raw p
−log(p)
impact
1 2 3 4 5 6 7 8 9 10
tryptophan metabolism phenylalanine, tyrosine, tryptophan biosynthesis arachidonic acid metabolism phenylalanine metabolism purine metabolism aminoacyl-tRNA biosynthesis cysteine and methionine metabolism glutathione metabolism tyrosine metabolism arginine and proline metabolism
23 4 10 10 64 67 25 26 30 37
0.27 0.05 0.12 0.12 0.76 0.80 0.30 0.31 0.36 0.44
3 1 1 1 2 2 1 1 1 1
0.0020 0.0466 0.1128 0.1128 0.1726 0.1856 0.2603 0.2693 0.3042 0.3617
2.7052 1.3315 0.9476 0.9476 0.7631 0.7315 0.5845 0.5698 0.5168 0.44161
0.3572 0.3333 0.2000 0.4286 0.0976 0.0417 0.0909 0.0370 0.0455 0.0278
a
Total is the total number of compounds in the pathway; the hits is the actually matched number from the user uploaded data; the raw p is the original p value calculated from the enrichment analysis; the impact is the pathway impact value calculated from pathway topology analysis.
Figure 8. Construction of the tryptophan metabolism pathways in drosophila. The map was generated using the reference map by KEGG (http:// www.genome.jp/kegg/). The green boxes: enzymatic activities with putative cases of analogy in drosophila.
arachidonic acid metabolism, and phenylalanine metabolism. Compared with the alterations of insomnia-related metabolites, most of them were reset to a healthier level after JuB administration. Our findings also show that JuB exhibited preventive efficacy against insomnia by adjusting these multiple metabolic pathways to their normal state and may be mediated through receptor, neurotransmitter, enzymes, signal transduction, and electron carrier. We have first constructed the metabolomic feature profiling and metabolite interaction network of Jujuboside B against insomnia using pattern recognition methods and IPA approach. This validated strategy may augment established drug discovery methodologies, for this and possibly other diseases, with a relatively low additional investment of time and resources and enable us to suggest many potential drug−target interactions and to increase research productivity toward metabolomic drug discovery.
emerging as an important paradigm for analysis of biological systems.
■
CONCLUSIONS Metabolic networks provide what is, arguably, the best context for interpretation of metabolomics data. Classical approaches consist of highlighting metabolites within the represented global network. Our study highlights the importance of metabolomics as a potential tool for uncovering metabolic pathways to predict and discover drug action mechanisms and enable us to increase research productivity toward metabolomics throughout the drug discovery and development process. The power of metabolomics to capture and elucidate metabolic characters of the insomnia and the therapeutic effects of JuB has been demonstrated in this study. Analyzing the topology of the network, we have detected 12 potential biomarkers and predicted the major metabolites network of insomnia by using the validated pattern recognition methods and computational systems analysis. Combining the results from these methods, we have calculated 4 high confidence networks. The identified target metabolites were found to encompass a variety of pathways related to tryptophan metabolism, phenylalanine, tyrosine, tryptophan biosynthesis,
■
AUTHOR INFORMATION
Corresponding Author *Phone/Fax: +86-451-82110818. E-mail: phar_research@ hotmail.com,
[email protected]. 438
dx.doi.org/10.1021/ac202828r | Anal. Chem. 2012, 84, 428−439
Analytical Chemistry
■
Article
(27) Duffy, J. F.; Cain, S. W.; Chang, A. M.; Phillips, A. J.; Münch, M. Y.; Gronfier, C; Wyatt, J. K.; Dijk, D. J.; Wright, K. P. Jr.; Czeisler, C. A. Proc. Natl. Acad. Sci. U. S. A. 2011, 108, 15602−15608, [PubMed:21536890]. (28) Carr, P. W.; Stoll, D. R.; Wang, X. Anal. Chem. 2011, 83, 1890− 900, [PubMed: 21341777]. (29) Denery, J. R.; Nunes, A. A.; Dickerson, T. J. Anal. Chem. 2011, 83, 1040−1047, [PubMed:21175165]. (30) Lee, P. T.; Lin, H. W.; Chang, Y. H.; Fu, T. F.; Dubnau, J.; Hirsh, J.; Lee, T.; Chiang, A. S. Proc. Natl. Acad. Sci. U. S. A. 2011, 108, 13794−13799, [PubMed: 21808003]. (31) Zhang, Z.; Alfonta, L.; Tian, F.; Bursulaya, B.; Uryu, S.; King, D. S.; Schultz, P. G. Proc. Natl. Acad. Sci. U. S. A. 2004, 101, 8882−8887, [PubMed: 15187228]. (32) Shimomura, K.; Lowrey, P. L.; Vitaterna, M. H.; Buhr, E. D.; Kumar, V.; Hanna, P.; Omura, C.; Izumo, M.; Low, S. S.; Barrett, R. K.; LaRue, S. I.; Green, C. B.; Takahashi, J. S. Proc. Natl. Acad. Sci. U. S. A. 2010, 107, 8399−8403, [PubMed: 20404168]. (33) Boughton, B. A.; Callahan, D. L.; Silva, C.; Bowne, J.; Nahid, A.; Rupasinghe, T.; Tull, D. L.; McConville, M. J.; Bacic, A.; Roessner, U. Anal. Chem. 2011, 83, 7523−30, [PubMed:21879760]. (34) Yuliana, N. D.; Khatib, A.; Verpoorte, R.; Choi, Y. H. Anal. Chem. 2011, 83, 6902−6, [PubMed: ]21800886]. (35) Nicholson, J. K.; Lindon, J. C. Nature 2008, 455, 1054−1056, [PubMed: 18948945].
ACKNOWLEDGMENTS This work was supported by grants from the Key Program of Natural Science Foundation of State (Grant No. 90709019), the National Specific Program on the Subject of Public Welfare (Grant No. 200807014), National Key Subject of Drug Innovation (Grant No. 2009ZX09502-005), and National Program on Key Basic Research Project of China (Grant No. 2005CB523406).
■
REFERENCES
(1) Nicholson, J. K.; Wilson, I. D. Nat. Rev. 2003, 2, 668−676, [PubMed: 12904817]. (2) Holmes, E.; Loo, R. L.; Stamler, J.; Bictash, M.; Yap, I. K.; Chan, Q.; Ebbels, T.; De Iorio, M.; Brown, I. J.; Veselkov, K. A.; Daviglus, M. L.; Kesteloot, H.; Ueshima, H.; Zhao, L.; Nicholson, J. K.; Elliott, P. Nature 2008, 453, 396−401, [PubMed: 18425110]. (3) Wang, X.; Sun, H.; Zhang, A.; Sun, W.; Wang, P.; Wang, Z. J. Pharm. Biomed. Anal. 2011, 55, 859−868, [PubMed: 21353755]. (4) Zhang, A.; Sun, H.; Wang, Z.; Sun, W.; Wang, P.; Wang, X. Planta Med. 2010, 76 (17), 2026−2035, [PubMed: 21058239]. (5) Chan, E. C.; Pasikanti, K. K.; Nicholson, J. K. Nat Protoc. 2011, 6, 1483−1499, [PubMed: 21959233]. (6) Yang, B.; Zhang, A.; Sun, H.; Dong, W.; Yan, G.; Li, T.; Wang, X. J. Pharm Biomed Anal. 2012, 58, 113−124, [PubMed: 22019702]. (7) Arakaki, A. K.; Skolnick, J.; McDonald, J. F. Nature 2008, 456, 443 [PubMed: 19037294]. (8) Mahowald, M. W.; Schenck, C. H. Nature 2005, 437, 1279−1285, [PubMed: 16251953]. (9) Pigeon, W. R. Indian J. Med. Res. 2010, 131, 321−332, [PubMed: 20308757]. (10) Richey, S. M.; Krystal, A. D. Curr. Pharm. Des. 2011, 17, 1471− 1475, [PubMed: 21476952]. (11) Sarris, J.; Byrne, G. J. Sleep Med. Rev. 2011, 15, 99−106, [PubMed: 20965131]. (12) Chen, C. Y.; Chen, Y. F.; Tsai, H.Y. J. Biomol. Struct. Dyn. 2008, 26, 57−64, [PubMed: 18533726]. (13) Zhang, Z. J. Prescription of Jinkuiyaolue. People’s Medical Publishing House: Beijing, 2002. (14) Chen, C. Y.; Chen, Y. F.; Tsai, H. Y. J. Biomol Struct. Dyn. 2008, 26, 57−64, [PubMed: 18533726]. (15) Zhang, S. J.; Chen, Z. X.; Lin, Y. W.; Cheng, Y. H.; Liu, S. L.; Wang, C. J. Zhong Yao Cai 2007, 27, 303−305, [PubMed: 17526166]. (16) Li, Y. J.; Bi, K. S. Chem. Pharm. Bull. (Tokyo) 2006, 54, 847− 851, [PubMed: 16755056]. (17) Fang, X. S.; Hao, J. F.; Zhou, H. Y.; Zhu, L. X.; Wang, J. H.; Song, F. Q. Phytomedicine 2010, 17, 75−80, [PubMed: 19682877]. (18) Yi, P. L.; Tsai, C. H.; Chen, Y. C.; Chang, F. C. J. Biomed Sci. 2007, 14, 285−297, [PubMed:17151826]. (19) ZhaoJ.LiS. P.YangF. Q.LiP.WangY. T.J. Chromatogr., A2006 [PubMed:16458908] (20) Masson, P.; Alves, A. C.; Ebbels, T. M.; Nicholson, J. K.; Want, E. J. Anal. Chem. 2010, 82, 7779−7786, [PubMed: 20715759]. (21) Sands, C. J.; Coen, M.; Ebbels, T. M.; Holmes, E.; Lindon, J. C.; Nicholson, J. K. Anal. Chem. 2011, 83, 2075−2082, [PubMed: 21323345]. (22) Spagou, K.; Wilson, I. D.; Masson, P.; Theodoridis, G.; Raikos, N.; Coen, M.; Holmes, E.; Lindon, J. C.; Plumb, R. S.; Nicholson, J. K.; Want, E. J. Anal. Chem. 2011, 83, 382−390, [PubMed: 21142126]. (23) Spagou, K; Wilson, I. D.; Masson, P; Theodoridis, G; Raikos, N; Coen, M; Holmes, E; Lindon, J. C.; Plumb, R. S.; Nicholson, J. K.; Want, E. J. Anal. Chem. 2011, 83, 382−390, [PubMed: 21142126]. (24) Yanes, O.; Tautenhahn, R.; Patti, G. J.; Siuzdak, G. Anal. Chem. 2011, 83, 2152−2161, [PubMed: 21329365]. (25) Sekiyama, Y.; Chikayama, E.; Kikuchi, J. Anal. Chem. 2011, 83, 719−726, [PubMed: 21208007]. (26) Chylla, R. A.; Hu, K.; Ellinger, J. J.; Markley, J. L. Anal. Chem. 2011, 83, 4871−4880, [PubMed: 21526800]. 439
dx.doi.org/10.1021/ac202828r | Anal. Chem. 2012, 84, 428−439
