Pattern Recognition Approaches and Computational Systems Tools for Ultra Performance Liquid Chromatography���Mass Spectrometry- Based 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 con- struction 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 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 environ- mental 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 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. Received: October 26, 2011 Accepted: November 30, 2011 Published: November 30, 2011 Article pubs.acs.org/ac �� 2011 American Chemical Society 428 dx.doi.org/10.1021/ac202828r | Anal. Chem. 2012, 84, 428���439

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, anterog- rade 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 pharmaco- logical 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 Tradi- tional approaches to drug target identification include literature search-based target prioritization and in vitro binding assays which are both time-consuming and labor intensive. Computa- tional 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 character- ization of a drug targets, thereby helping to reduce the typically high attrition rates in discovery projects. Moreover, pinpointing 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 Corpo- ration (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). Figure 1. Chemical structure of Jujuboside B. Analytical Chemistry Article dx.doi.org/10.1021/ac202828r | Anal. Chem. 2012, 84, 428���439 429