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Current Opinion in
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ELSEVIER
Metabolomics for phytomedicine research and drug development
Lie-Fen Shyur and Ning-Sun Yang
Metabolomics, including both targeted and global metabolite profiling strategies, is fast becoming the approach of choice across a broad range of sciences including systems biology, drug discovery, molecular and cell biology, and other medical and agricultural sciences. New analytical and bioinformatics technologies and techniques are continually being created or optimized, significantly increasing the crossdisciplinary capabilities of this new biology. The metabolomes of medicinal plants are particularly a valuable natural resource for the evidence-based development of new phytotherapeutics and nutraceuticals. Comparative metabolomics platforms are evolving into novel technologies for monitoring disease development, drug metabolism, and chemical toxicology. An efficient multidisciplinary marriage of these emerging metabolomics techniques with agricultural biotechnology will greatly benefit both basic and applied medical research.
Addresses
Agricultural Biotechnology Research Center, Academia Sinica, No. 128, Sec. 2, Academia Road, Nankang, Taipei 115, Taiwan, ROC
Corresponding author: Shyur, Lie-Fen (lfshyur@ccvax.sinica.edu.tw) and Yang, Ning-Sun (nsyang@gate.sinica.edu.tw)
Current Opinion in Chemical Biology 2008, 12:66-71
This review comes from a themed issue on
Proteomics and Genomics
Edited by Natalie Ahn and Andrew H.-J. Wang
Available online 21st February 2008
1367-5931/$ - see front matter
© 2008 Elsevier Ltd. All rights reserved.
DOI 10.1016/j.cbpa.2008.01.032
Introduction
Metabolomics is an emerging and rapidly evolving science and technology system of comprehensive experimental analysis of metabolite profiles, either as a targeted subset of related chemicals or more globally, for diverse applications in diagnosis, toxicology, disease development and animal disease models, genetic modification of specific organisms, drug discovery and development, and phytomedicines [1**,2,3*,4,5**,6—8]. Metabolomics is also a vital component of the systems biology approach, in which it at once reflects and connects the genotype with the diverse and yet specific phenotypes of cells, tissues, or organs [9]. Pioneering research in the metabolomics approach involved the quantitative metabolite profiling of urine or urinary drug metabolites of humans [10,11]. In the plant research field, metabolite profiling was first used
as a diagnostic technique to determine the mode of action of herbicides on barley seedlings [12]. The concept of the 'metabolome' was first reported in 1998 as a way to quantitatively and qualitatively measure specific or defined phenotypes to assess gene function in yeast [13] and to discuss the interplay between the global metabolite pool and specific environmental conditions in Escherichia coli [14].
In the decade since these early beginnings, the use of metabolomics technologies in biological research expanded exponentially. If one just looks quickly at PubMed literature search results, metabolomics-related research articles have increased from some 40 published articles in 2002 to 100, 170, 200 and >250 articles in the years 2004, 2005, 2006 and 2007, respectively. Owing to its great utility in a variety of basic and applied research fields, metabolomics has quickly become a universal tool and a key component for systems biology studies in medical research.
Systems biology research using genomics, proteomics, and metabolomics approaches is investigating characteristic molecular signatures for disease diagnosis, prognosis, and therapeutics [15]. Recent developments in technology platforms and experimental approaches for metabolomics studies in drug development are outlined in this review. We also urge the greater use of metabolomics in the development of active secondary metabolites from medicinal plants as novel or improved phytotherapeutic agents.
Technology development and experimental approaches
Metabolomics is defined as a comprehensive quantitative and qualitative analysis of all metabolites present in a specific cell, tissue, or organism. The term 'metabolomics' is sometimes used synonymously with metabolite profiling, mainly because at present, unlike in genomics or proteomics, the one-step analysis and exhibition of all metabolites in a metabolome is not possible, because of the enormous complexity of chemicals in biological systems especially in plants [16]. Metabonomics, commonly substituted by the term metabolomics, is the term more properly used for the measurement of metabolite profiles, activities, and reactions toward the environment, medication, or disease, of a given tissue or biological fluid [17]. Metabolic fingerprinting, like metabonomics, usually refers to high-throughput global analysis of metabolites with minimal sample preparation. The two major approaches in metabolomics are the targeted and the global   (or   un-biased)   metabolite   analyses.   Targeted
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Metabolomics for phytomedicine research Shyur and Yang   67
metabolite analysis or metabolite profiling, as the name implies, targets a subset of metabolites in a sample, instead of a complete metabolome analysis, using a particular set of analytic technique(s) such as gas chroma-tography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS), together with an estimate of quantity. Various other techniques including thin layer chromatography (TLC), Fourier transform infrared spectroscopy (FT-IR), Raman spectroscopy, and nuclear magnetic resonance (NMR) are also part of the metabolite analysis arsenal.
Remarkable recent developments in analytical chemistry for small molecular mass compound detection and characterization, such as MS and high-field NMR, coupled with user-friendly multivariate statistics, have led to a highly efficient system for comprehensive analysis of the metabolite data matrices generated by metabolomics experiments [2]. NMR-based metabolite profiling/ metabolomics was first used in a pioneering study for the rapid multicomponent analysis of urine [18]. This approach has since been successfully used to examine the structure of metabolic pathways and network modules in yeast metabolism [19], and in understanding the pathways involved in adaptation to hypoxia in Drosophila flight muscle [20]. One-dimensional (ID) NMR spectrometry has shown its utility for high-throughput analysis and classification of similar chemical groups of samples; however, the large numbers of overlapping peaks generated by this method may hinder the accurate identification of specific metabolites. Recently, replacing the ID H NMR spectroscopic technology, a two-dimensional (2D) H- C NMR strategy (fast metabolite quantification, FMQ, by NMR), for the analysis of metabolites as multivariate statistical objects has been developed [21]. The new 'hyphenated' techniques, coupling various forms of liquid chromatography with NMR, such as HPLC-SPE-NMR, have improved the sensitivity of NMR analyses and are capable of characterizing both the high and low abundance metabolites in complex crude plant extracts [22,23].
MS is currently the most widely applied technology in metabolomics. Among a variety of MS techniques, GC-MS has long been used in metabolite profiling of human body fluids or plant extracts [10-12,24]. Rapid and high-resolution 2D GC x GC-TOF MS was applied in the phenotyping of natural rice variants [25] and for efficient quality control of herbal medicines [26]. Recently, capillary electrophoresis-MS has also been developed as a metabolomics tool, capable of simultaneously analyzing over 1000 charged chemical species, a technique with obvious utility in biological samples [27]. A shotgun approach using MALDI-TOF/TOF MS has been used for the rapid analysis of negatively charged metabolites in mammalian tissues to facilitate the detection of low abundance metabolites (e.g. cAMP, cGMP, and IP3) and discriminate isomeric molecular species [28].
To efficiently link the flood of experimental data and metabolite information to biology and metabolism, traditional bioinformatics is being combined with che-minformatics to generate a basic computational infrastructure of metabolomics [29**,30]. A number of metabolomics databases, some covering only chemical spectral data, some based on both chemical and biological/biochemical data, have recently been made publicly available [30]. The Human Metabolome Database (HMDB) is currently the largest and most complete database in breadth and depth, offering spectral, physico-chemical, clinical, biochemical and genomic, and metabolism information for a library of >2500 known human metabolites [31]. Other databases include the BioMagResBank (BMRB) with an emphasis on NMR data (>270 pure compounds), the Madison Metabolomics Consortium Database (MMCD) which presents MS and/ or NMR data of more than 10 000 metabolites [32], MassBank.jp for high-field mass spectral data, the Golm Metabolome Database (GMD) specific for plants and focusing on GC-MS data [33], and BiGG for human, yeast and bacterial metabolites, pathways and reactions. Algorithmic development and informatics innovations in data reduction, normalization, and alignment that offer sufficient biological insight into metabolic profiles are the future of development in computational metabolomics, as recently reviewed by Wishart [29**].
Metabolomics in phytomedicine research
Plant metabolomes are expected to be much more complex than their mammalian counterparts: by some estimates, over 200 000 molecules will eventually be detected [34]. The diversity of plant secondary metabolites evolved through the continuous interaction with challenging and predominantly hostile environments, coupled with characteristic species and agronomic differences, and these metabolites generally confer a specific bioactivity related to their biochemical structures [35]. A whole range of well-known cancer chemotherapeutic drugs are derived from plant secondary metabolites, such as paclitaxel (taxol), camptothecin (irinotecan, topotecan), and podophyllotox-ins (etoposide, teniposide). The great potential of plant secondary metabolites or natural products to serve as health care products or lead compounds for new drug development have renewed interest in pharmaceutical and nutra-ceutical research. Only a few cases of successful development of novel drugs have so far stemmed from de novo combinational chemistry, and natural products or their derivatives are still considered by many scientists as the most productive source of leads for drug development [36,37**]. The use of whole plants or extracts as medicines gave way to the isolation of active compounds, beginning in the early 19th century with the isolation of morphine from opium; however, in the reductionistic approach, single active phytocompounds are sometimes barely able to be identified because of their low abundance in plants, or a spectrum    of    pharmacological     efficacy    traditionally
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68   Proteomics and Genomics
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Current Opinion in Chemical Biology
Key features of the technologies used in metabolomics for herbal medicine research.
observed arises only as a synergistic action of multiple ingredients in a single plant or from a multiple medicinal plant formulation, as in traditional Chinese medicine (TCM) [38].
The application of systems biology technologies and approaches, that is, genomics, proteomics, and metabolomics, to phytomedicine research may greatly assist evidence-based phytotherapeutics, and such research may also lead to a change of paradigm in the development and application of complex plant/phytocompound mixtures in modern medicine [5**]. Wang et al. [39] pointed out that metabolomics could provide the needed links between the complex chemical mixtures used in TCM and molecular pharmacology. Phytocompound-speciric signatures in gene and/or protein expression profiles can also be highly useful in pharmacological standardization, such as their use in 'biological fingerprinting' of medicinal plant extracts (i.e. bioactivity spectra of phytoextracts or phyto-compounds versus their medicinal efficacy in test animal or human systems) [5**,40,41]. Metabolomics approaches using GC-MS, LC-MS, or 2D NMR are an effective tool for quality control of medicinal plants or herb medicinal products [26,42,43]. In addition, they may provide proof of the toxicology/safety measures of specific phytopreparations after metabolism in test animals/humans [5**]. Some key aspects of the technology used in a metabolomics approach to herbal medicine research are outlined in Figure 1.
Recently, we have employed a comparative metabolomics approach for characterizing candidate phytomedi-cines from a traditional Western herbal remedy, by integrating special plant extract preparation, on-line GC-MS or LC-ESI/MS/MS, and metabolite clustering systems to analyze extracts of the three medicinal Echinacea species that are frequently mislabeled in commercial products, that is, E. purpurea, E. pallida, and E. angustifolia [44]. The metabolomics strategy was quite useful in profiling unique groups of plant secondary metabolites that can be used for improved classification of the three Echinacea species, and for better quality control of medicinal Echinacea extracts under various pharmacological considerations (Shyur et al, unpublished). Hyphenated NMR techniques are also a highly valuable tool for the elucidation of the chemical composition of a complex, bioactive fraction of natural origin [45]. As an alternative to the reductionist approach, plant metabolomics strategies are providing new and important insights for medicinal herb research, linking putative bioactivity with the constituent phytocompounds of herbal medicines [37**,45].
Metabolomics, metabolic diseases, and drug metabolism
Much current metabolomics research in medical science concerns the comparison of small-metabolite portraits to distinguish health and disease states [46]. MS and NMR technology systems are being increasingly used in tumor
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Metabolomics for phytomedicine research Shyur and Yang   69
metabolomics, in vitro, in animal models and clinically, for tumor diagnosis and assessment of prognosis and response to treatment [7]. For instance, an NMR-based metabolomics study of the drug rosiglitazone (RSG) in Type 2 diabetes mellitus (T2DM) patients demonstrated that a broad array of early-responding biomarkers (fingerprints) can readily be detected, and this information can then expedite the clinical development of novel drugs (e.g. thiazolidinediones) for T2DM [47]. Focused or targeted metabolomics platforms, for example, lipid profiling or lipidomics, are being applied in the drug development process, especially for lipid-related metabolic disorders and inflammatory diseases [48,49].
Recently, metabolomics has been used as a high-throughput tool to characterize and monitor the disease process in mouse models [8]. In oncological metabolomics, NMR was used to target biomarkers for prostate cancer by analyzing the global variation of metabolites involved in the development and progression of this cancer for better future management [50,51]. Metabolomics approaches to monitor tumor metabolism have also been used in animal models of human brain tumors, lymphomas, liver tumors, and colon cancers [7]. Currently, the grouping of molecularly distinct diseases into different clinical classes is mainly based on morphology; however, cases with the same clinical and morphological diagnoses are known to result in markedly different clinical outcomes [52]. H NMR spectrometry has been successfully applied to distinguish benign and malignant breast tumors, and identify the pathological features of nodal involvement and vascular invasion [52]. By providing metabolic tumor fingerprints [4] with advanced metabolomics technologies, we may expect greatly improved taxonomie classification of cancers.
Comparative metabolomics approaches using LC-MS-based techniques are a useful technique in drug or xeno-biotic metabolism and chemical toxicology research. For instance, the comparison of metabolite profiles in mouse urine following vehicle and xenobiotic treatment, or in unlabeled versus stable isotope-labeled xenobiotic treatment have led to the identification of novel xenobiotic metabolites [53]. The combination of genetically modified mouse models with LC-MS-based metabolomics is a powerful new tool for mechanistic studies of drug metabolism, especially genotype-dependent metabolism [3*,53].
Conclusion, future challenges, and perspectives
Unlike other 'omics' technologies, there is currently not a single preferred or generic method for metabolome identification at the global level, despite the introduction of more and more sophisticated technologies for targeted or focused metabolomics, and this is a key hurdle in current metabolomics research [1**,17]. To overcome the problems of metabolite complexity and allow a deeper mining
of the metabolome, the development of combinations of multidimensional chromatographic separations with multidimensional MS or NMR spectrometric analysis is urgently needed to achieve maximal output of existing technologies. Further optimization of chromatographic separations and better sensitivity of NMR or MS instruments, with higher resolution capability and greater mass accuracy, will be essential for future improvements in metabolomics [54]. However, metabolomics studies using hyphenated separation and analytical methods already face the problem of lack of reproducibility of results, because of batch variability or when performed in different laboratories, especially with regard to the use of metabolomics in herbal medicine and clinical studies [5**]. It is therefore essential to establish Standard Operating Protocols (SOPs) to minimize and limit technical variations in extraction procedures across a broad base of metabolomics data sets. A continuous effort to develop computational metabolomics to allow it to simulate biologically meaningful metabolic networking is crucial to the routine adoption of metabolomics in medical, agricultural, and pharmaceutical research, as classical bioinformatics has already turned genomics and proteomics into routine and reliable methods in modern biology.
Herbal medicines, in addition to their traditional values, also hold great public and medical interest worldwide as sources of nutraceuticals or novel lead compounds for drug development. A thorough integration of information from genomics, proteomics, and metabolomics is expected to provide solid evidence-based scientific rationales for the development of modern phytomedicines. The search for active phytocompounds will be greatly advanced by the combination of various metabolomics approaches with an array of bioactivity assays in mammalian systems to differentiate between plant species, tissues, or phytopreparations, and to identify novel lead compound candidates for future development. In a complementary development, the use of metabolome-refined herbal extracts with other biochemical components in combination, rather than as isolated single compound(s), may prove to be very useful as broader and holistic therapeutical or pharmacological agents for a variety of human health care applications.
References and recommended reading
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