Anal Bioanal Chem DOI 10.1007/s00216-012-6117-1
REVIEW
Serum metabolomics as a novel diagnostic approach for disease: a systematic review Aihua Zhang & Hui Sun & Xijun Wang
Received: 19 March 2012 / Revised: 5 May 2012 / Accepted: 15 May 2012 # Springer-Verlag 2012
Abstract Metabolomics is a promising “omics” field in systems biology; its objective is comprehensive analysis of lowmolecular-weight endogenous metabolites in a biological sample. It could enable mapping of perturbations of early biochemical changes in diseases and hence provide an opportunity to develop predictive biomarkers that could result in earlier intervention and provide valuable insights into the mechanisms of diseases. Because of the possible discovery of clinically relevant biomarkers, metabolomics has potential advantages that routine approaches to clinical diagnosis do not. Monitoring specific metabolite levels in serum, the most commonly used biofluid in metabolomics, has become an important way of detecting the early stages of a disease. Serum is a readily accessible and informative biofluid, making it ideal for early detection of a wide range of diseases, and analysis of serum has several advantages over analysis of other biofluids. Metabolite profiles of serum can be regarded as important indicators of physiological and pathological states and may aid understanding of the mechanism of disease occurrence and progression on the metabolic level, and provide A. Zhang : H. Sun National TCM Key Lab of Serum Pharmacochemistry, Key Pharmacometabolomics Platform of Chinese Medicines, and Heilongjiang University of Chinese Medicine, Heping Road 244, Harbin 150040, China X. Wang (*) National TCM Key Lab of Serum Pharmacochemistry, Key Pharmacometabolomics Platform of Chinese Medicines, and Heilongjiang University of Chinese Medicine, Heping Road 24, Harbin 150040, China e-mail:
[email protected] X. Wang e-mail:
[email protected]
information enabling identification of early and differential metabolic markers of disease. Analysis of these crucial metabolites in serum has become important in monitoring the state of biological organisms and is widely used for diagnosis of disease. Emerging metabolomics will drive serum analysis, facilitate and improve the development of disease treatments, and provide great benefits for public health in the long-term. Keywords Metabolomics . System biology . Serum analysis . Metabolites . Biomarkers . Disease diagnostics Abbreviations AFP Alpha fetal protein CE Capillary electrophoresis CRC Colorectal cancer EAC Esophageal adenocarcinoma FT-IR Fourier-transform infrared spectroscopy GC Gas chromatography HCC Hepatocarcinoma HPLC High-performance liquid chromatography KEGG Kyoto Encyclopedia of Genes and Genomes mCRC Metastatic colorectal cancer MS Mass spectrometry NMR Nuclear magnetic resonance PCA Principal-components analysis PLS-DA Partial least-squares discriminant analysis RCC Renal cell carcinoma SLE Systemic lupus erythematosus
Introduction Metabolomics is a recent “omic” technique, defined as a complete overview of metabolic status, which could provide
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new insight into pathophysiologic mechanisms in diseases. It is a powerful technique that enables comprehensive measurement of small molecules in easily accessible biofluids and diagnostic biomarker discovery in order to distinguish between diseased and non-diseased status [1]. The small molecule endogenous metabolites, including lipids, amino acids, peptides, nucleic acids, organic acids, vitamins, thiols and carbohydrates, are important in biological systems and are attractive candidates for understand disease phenotypes [2, 3]. Metabolite changes observed in diseased individuals as primary indicators have been an important part of clinical practice. The biofluid serum has several advantages that have ensured the widespread use of metabolites as a diagnostic tool [4]. Most clinical chemistry tests available today rely on old technology, and these tests are neither sensitive nor specific for any particular disease, and traditional markers only increase significantly after substantial disease injury. Therefore, more sensitive markers of disease are urgently needed, particularly, for early detection of disease. Highly sensitive and specific biomarkers as primary indicators in serum are more useful [5]. Metabolomics, the objective of which is complete characterization of the entire metabolome, irrespective of molecular size, offers a complete approach to systems medicine, with the promise of enhancing clinical chemistry diagnostics in specific physiological or pathologic conditions [6, 7]. It has become promising means of studying disease, and its benefits have been demonstrated in such diverse clinical areas as development of diagnostics, therapeutics, and drug development [8]. Metabolomics also has potential advantages that classical approaches to diagnosis do not, based on the discovery of clinically relevant biomarkers that are simultaneously affected by the disease. Serum metabolomic analysis has great potential as a useful diagnostic technique and can facilitate monitoring of both disease progression and effects of therapeutic treatment. These advantages have ensured the widespread use of serum as a diagnostic tool in clinical practice. Metabolomics has been shown to be a promising method for evaluating the efficacy of diagnosis and has demonstrated the capability for early detection of response to therapy in wider clinical settings [9]. Moreover, novel metabolomic approaches are likely to be used to screen for potential diagnostic and prognostic biomarkers and have the potential to provide more information about the pathophysiological status of an organism and to distinguish between disease stages. Monitoring specific metabolite levels in serum, the most commonly used biofluid in metabolomics, has become an important way of detecting the early stages of disease [10]. Serum metabolomic approaches have great diagnostic potential to be used to screen for earlier diagnostic and prognostic biomarkers of disease [11]. Thus, this review examines different aspects of serum analysis, reviews recent developments in metabolomics, and discusses their significance in the postgenomic era. Especially, this review
highlights the potential use of endogenous small-molecule metabolites in serum metabolomics.
Recent developments in metabolomics Over the last decade metabolomics, the systematic study of the full complement of metabolites in a biological sample, has become increasingly popular and significant in the life sciences. Together with genomics, transcriptomics, and proteomics, metabolomics provides additional information on specific reactions occurring in humans, enabling us to understand some of the metabolic pathways in pathological processes. Recently, it has been used in many fields, for example in the study of responses to environmental stress [12], toxicology [13], nutrition [14], and cancer [15–17], for comparison of different growth stages [18], in the study of diabetes [19], for disease diagnosis [20], for natural product discovery [21], and in traditional medicine [22–24]. As a novel strategy for discovery of markers of interest, serum metabolomic analysis has been successfully used in physiology, diagnostics, functional genomics, pharmacology, toxicology, and nutrition [25, 26], because of its sensitivity and quantitative reproducibility. This approach is noninvasive, efficient, and low-cost, and can be developed as a promising method for understanding of disease by development of robust, sensitive, and reproducible diagnostic tests. Metabolite profiles of serum fluids can be regarded as important indicators of physiological or pathological states and may aid understanding of the mechanism of disease occurrence and progression on the metabolic level and provide information for identification of early and differential metabolic markers for disease [27]. Metabolomics is a relatively new science, and unlike the human genome, the human metabolome has not been fully recorded. The main challenge of metabolomics is the complexity of the metabolome, because the metabolome contains a wide variety of chemically diverse compounds, for example carbohydrates, amino acids, lipids, steroids organic acids, and nucleotides, among others.
Serum sampling Serum as a primary carrier of small molecules in the body contains an enormous amount of information. Its usefulness in diagnostics cannot be overestimated. Most of today’s clinical tests are based on the analysis of serum. Serum is a readily accessible and informative biofluid, making it ideal for the early detection of a wide range of diseases [28]. Use of serum as an analytical tool has several advantages over use of other biofluids. It can be obtained in large quantities sampling and repeat sampling is not a problem. Serum samples are normally collected as random samples, timed
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samples or 24-h samples. Stability and sample integrity during storage are important considerations, as in any analysis. Sample handling and processing could greatly affect the measured variability of the serum metabolome. Serum samples must be stable to provide valid data. Samples have been processed either immediately by freezing at −80 °C or stored at 4 °C for 24 h before being frozen, and then thawed and profiled to reveal unique metabolite peaks. A wealth of information in serum sample has been accumulated with global profiling tools and several candidate techniques. A task has been undertaken to systematically characterize the human serum metabolome [29]. Combined targeted and non-targeted NMR, GC–MS, and LC–MS methods have been used with computer-aided literature mining to identify and quantify a comprehensive, complete set of metabolites (4) commonly detected and quantified in the human serum metabolome. Therefore, the serum metabolome is now regarded as the most predictive phenotype. Consequently comprehensive and quantitative study of metabolites is a desirable tool for diagnosing disease, identifying new therapeutic targets, and enabling appropriate treatment. Use of several metabolomics techniques could substantially enhance the level of metabolome coverage of serum samples.
Analytical techniques The development and application of techniques for detailed analysis of serum has led to the discovery of numerous disease biomarkers, and could enable separation, detection, characterization, and quantification of the serum metabolome. Recently, the combination of advanced analytical technology with multivariate statistics has enabled differential replicate analysis of low-molecular-weight analytes [30, 31]. Global serum profiles measured by use of NMR or mass spectrometry (MS)-based methods distinguish, for example, individuals, health status, and hormonal changes. MS, NMR, and multivariate statistical techniques have been used to profile changes in small molecules associated with the onset and progression of human diseases [32]. The objective of these efforts is to identify metabolites that are uniquely correlated with a specific human disease in order to accurately diagnose and treat the malady. NMR-based metabonomic research has the potential to generate novel noninvasive diagnostic tests, based on biomarkers of disease, which are simple and cost effective yet highly sensitive and specific [33]. The NMR spectrum of serum contains peaks from a large number of detectable and quantifiable metabolites and hence serum metabolite profiling is potentially useful for the study of systems biology and the discovery of biomarkers for clinical applications. Owing to the complexity of metabolome and the diverse properties of metabolites, no single analytical device can be used to detect
all metabolites in a biological sample. The combination of these different analytical techniques has important advantages when analyzing the complete metabolome [34, 35]. A combination of different analytical techniques could be used to obtain a broad perspective of the metabolome. Integrated analytical techniques have frequently been used to enable sensitive and reliable detection of thousands of metabolites in serum and accelerate integration of metabolomics into systems biology [36]. Continued development of these analytical techniques will accelerate widespread use and integration of metabolomics into disease diagnostics research.
Robust data analysis and bioinformatic tools Only the use of sensitive and modern analytical techniques in connection with bioinformatic methods can facilitate the interpretation of complex metabolomics data and therefore make it possible to identify the significant function of biomarkers [37, 38]. By analyzing differences between metabolomes by use of biostatistics, metabolites relevant to a specific phenotypic characteristic can be identified. Pattern recognition, and multivariate and multidimensional statistical software have been developed to facilitate and filter large amounts of untargeted LC–MS and GC–MS data [39]. One approach involves chemometric techniques such as principal-components analysis (PCA) and partial least squares-discriminant analysis (PLSDA) to identify the spectral pattern and intensities of the metabolites [40]. The second approach attempts to identify and quantify each metabolite in the sample, then uses multivariate statistical analysis to determine which metabolites are differently expressed among the experimental groups [41]. The largest and most popular human metabolite databases are HMDB (http://www.hmdb.ca/) and METLIN (http://metlin. scripps.edu); others include MMCD (http://mmcd.nmrfam. wisc.edu/) and KEGG (http://www.genome.jp/kegg/). LIPID maps (http://www.lipidmaps.org/) is a useful database for searching for lipid metabolites. One way of identifying compounds by GC–MS is by spectral matching, in which experimental mass spectrum of the unknown is compared with those in a spectral library, for example the National Institute of Standards and Technology (http://www.webbook.nist.gov/ chemistry) and Wiley Registry (http://www.wileyregistry. com) libraries.
Recent and potential developments of serum analyses in metabolomics Serum contains a wide range of small molecule metabolites which are increasingly gaining attention for use in the diagnosis of human disease [42]. Soga and colleague used serum metabolomics to analyze low-molecular-weight metabolites
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and discover noninvasive and reliable biomarkers for rapidscreening diagnosis of liver diseases [43]. It was found that γglutamyl dipeptides are key biomarkers for liver diseases, and different levels of these peptides have the power to distinguish patient with nine types of liver disease from healthy controls. Serum metabolic profiles may be useful for more accurate disease detection and for gaining more insight into disease mechanisms. Oral cancer is the eighth most common cancer worldwide and is a significant disease burden. If oral cancer is detected at an early stage, 5-year survival is better than 90 % whereas for late-stage disease it is only 30 % [44]. Therefore, there is an obvious clinical need for novel metabolic markers that aid diagnosis of oral cancer at an early stage and enable monitoring of treatment response. Serum metabolomics-based diagnosis has the potential to monitor the progression of oral cancer and to provide an essential means of fighting this disease [45]. Serum samples from ovarian tumors have been analyzed by LC–MS-based nontargeted metabolomics [46]. Six important metabolites were regarded as potential biomarker candidates, ready for early stage detection. The serum metabolomic profile could be useful for distinguishing benign from malignant pancreatic lesions and would facilitate diagnosis and, potentially, prevent unnecessary surgery [47]. NMR-based metabolomic analysis of serum could be used to predict exercise-inducible ischemia in patients with suspected coronary artery disease [48]. This capability could be useful for screening and risk stratification of patients with coronary risk factors. The pathogenic mechanism of ulcerative colitis, a dextran sulfate sodium-induced acute colitis model, was successfully examined by serum metabolomic analysis [49]. These studies demonstrate the feasibility of highthroughput serum metabolomics for identifying disease changes at omics levels. Serum metabolomics approach will promote the translation of biomarkers with clinical value into routine clinical practice. Colorectal cancer (CRC) is the third most common cancer worldwide, and its prognosis at early stages is poor [50]. A panel of novel biomarkers is urgently needed for early diagnosis of CRC. Therefore, a metabolomics approach was performed to define biomarkers in CRC by metabolite profiling of serum samples from CRC patients [51]. Data from supervised predictive models enabled separation of 93.5 % of CRC patients from healthy controls by use of the metabolites 3hydroxybutyric acid, L-valine, L-threonine, 1-deoxyglucose, and glycine. Late diagnosis of hepatocarcinoma (HCC) is one of the primary reasons for poor survival of patients. Identification of sensitive and specific biomarkers for early diagnosis of HCC is of great importance. Serum metabolites of the HCC patients were investigated by use of LC–MS coupled with cluster analysis [52]. The serum metabolite 1methyladenosine, was identified as a characteristic metabolite for HCC. Moreover, a larger area under the curve value was measured for a 1-methyladenosine group than for an alpha fetal
protein (AFP) group (0.802 vs. 0.592); this diagnostic model had significant improved sensitivity over other models, suggesting that serum metabonomics is a potent and promising strategy for identifying novel biomarkers of HCC. Tan et al. found potential biomarkers from serum metabolic profiling of HCC by use of a non-target metabolomics method, and tested their usefulness for early human HCC diagnosis [53]. Three metabolites, taurocholic acid, lysophosphoethanolamine 16:0, and lysophosphatidylcholine 22:5, were defined as “marker metabolites”, which were effective for discrimination of HCC patients, achieving sensitivity of 80.5 % and specificity of 80.1 %. The indicated that serum metabolomics has the potential to find biomarkers for the early diagnosis of HCC. Earlier detection of patients with metastatic colorectal cancer (mCRC) might improve their treatment and survival. NMR serum profiling can provide a strong metabolomic signature of mCRC, and analysis of the signature may result in an independent tool for predicting overall survival [54]. Lung cancer is one of the most common cancers in the world, but no good clinical markers that can be used to diagnose the disease at an early stage and predict its prognosis. Therefore, the discovery of novel clinical markers is required. In one study, serum metabolomic analysis of lung cancer patients was performed by use of GC–MS [55]. A total of 23 serum metabolites were significantly changed in all lung cancer patients compared with healthy volunteers. Results demonstrated that changes in the metabolite pattern are useful for assessing the clinical characteristics of lung cancer; hopefully these will lead to the establishment of novel diagnostic tools. The combination of LC– MS analysis with multivariate statistical analysis can be used for renal cell carcinoma (RCC) diagnosis [56]. MS–MS experiments have been conducted to identify the biomarkers in the patterns that made a great contribution to the discrimination. As a result, 30 potential biomarkers were identified for RCC. To further elucidate the pathophysiology of RCC, related metabolic pathways have also been studied. RCC was found to be closely related to disturbed phospholipid catabolism, sphingolipid metabolism, phenylalanine metabolism, tryptophan metabolism, fatty acid beta-oxidation, cholesterol metabolism, and arachidonic acid metabolism. These results illustrate the potential of serum-based metabolomics in combination with multivariate statistical analysis to reveal the metabolic phenotype of a particular disease and help investigate the disease from a new perspective. The metabolites identified may be used as metabolic markers for early detection of pancreatitis. Profiling of serum metabolites was an effective method for patients with pancreatitis, and for analysis of the characteristic smallmolecule metabolites of pancreatitis [57]. It was found that that 3-hydroxybutyrate, trimethylamine-N-oxide, acetate, and acetone levels were significantly lower in the pancreatitis group than in the control group. Isoleucine, acetylglycine, triglyceride, and inosine levels were higher in the
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pancreatitis group than in the control group. Recent evidence from serum metabolomics indicates that specific metabolic disturbances precede β-cell autoimmunity in humans and can be used to identify type 1 diabetes children [58]. These findings suggest alternative metabolism-related pathways as therapeutic targets to prevent diabetes. Metabolomic profiling of serum is a powerful approach to discover diagnostic and therapeutic biomarkers by analyzing global changes in an individual’s metabolic profile in response to pathophysiological stimuli. Serum metabolomic profiling has been used to detect potential biomarkers associated with schizophrenia and risperidone treatment [59]. Twenty-two marker metabolites contributing to complete separation of schizophrenic patients from matched healthy controls were identified, with citrate, palmitic acid, myo-inositol, and allantoin having the best combined classification performance. Further study of these metabolites may facilitate the development of noninvasive biomarkers and more efficient therapeutic strategies for treatment of schizophrenia. Systemic lupus erythematosus (SLE) is a chronic inflammatory disease characterized by multi-system involvement, diverse clinical presentation, and alterations in circulating metabolites. In one study, an NMR metabolomics approach was used to establish a human SLE serum metabolic profile [60]. The OPLS-DA model was able to diagnose SLE with sensitivity of 60.9 % and specificity of 97.1 %. The results indicated not only that serum metabolomic methods had sufficient sensitivity and specificity to distinguish SLE from healthy controls but also have the potential to be developed into a clinically useful diagnostic tool, and could also contribute to further understanding of the mechanisms of the disease. Reliable metabolite markers for patients with esophageal adenocarcinoma (EAC) have been detected and evaluated by use of a metabolomics approach [61]. Metabolites profiles of serum were constructed by use of NMR and statistical analysis. This was an effective approach to differentiating between patients with EAC and healthy subjects. Good sensitivity, selectivity, and specificity were obtained by use of the eight metabolite markers discovered to predict the classification of samples from the healthy group and the patients. This demonstrated that serum metabolic profiling may have potential for early diagnosis of EAC and may enhance our understanding of its mechanisms. Consequently, these results provide convincing evidence of the power of serum metabolomics for identifying functional “omics” changes at many levels.
Concluding remarks and future directions for serum metabolomics Metabolomics is a powerful means of large-scale identification of endogenous and exogenous metabolites and has been
shown to be highly effective in investigation of physiological status, discovery of biomarkers, identification of perturbed pathways, and diagnosis of diseases [62]. The emerging field of application of serum metabolic analysis for study of diseases has great potential for biomarker discovery, especially in disease diagnosis. Serum metabolic profiling revealing homeostatic imbalances in biological systems is capable of providing comprehensive information about serum and will enhance the feasibility of highthroughput patient screening for diagnosis of disease state or for risk evaluation. It also has the potential to enable recording of early biochemical changes in disease, and hence provides an opportunity to develop predictive biomarkers that can lead to earlier intervention. It could also provide valuable insights into the mechanisms of disease. Indeed, identification of clinically relevant changes in metabolites that may be regarded as potential new biomarkers will also help with evaluation of prognosis and contribute to the development of new therapeutic strategies. The significance of recent advances in the potential application of metabolomic profile analysis of serum is highlighted again. We have delineated and evaluated the current status of serum analysis in metabolomics, with emphasis on specific high-throughput noninvasive biomarkers. Despite significant advances there are several limitations of current technology including lack of a single method for comprehensive analysis of the metabolome, incomplete spectral libraries and databases, and flaws in current software for data extraction and analysis. Further research is still needed before proposing an ideal method for serum metabolite analysis that can replace conventional diagnosis in clinical practice. Future technology development combined with more robust data analysis and bioinformatic tools will help overcome current limitations and fully integrate small molecule biochemistry with systems biology. Because metabolomics is complementary to genomics, transcriptomics, and proteomics, full integration of these will ultimately lead to personalized molecular diagnosis and treatment of diseases. Further improvements in the sensitivity and selectivity of analytical techniques and the development and routine use of novel methods with demonstrated potential are certain to lead to the discovery of novel targets in the near future.
Acknowledgments This work was supported by grants from the Key Program of the Natural Science Foundation of the State (grant no. 90709019), the National Key Program on the Subject of Drug Innovation (grant no. 2009ZX09502-005), the National Specific Program on the Subject of Public Welfare (grant no. 200807014), and the National Program for Key Basic Research Projects in China (grant no. 2005CB523406). Competing financial interests The authors declare no competing financial interests.
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References 1. Nicholson JK, Lindon JC (2008) Systems biology: Metabonomics. Nature 455:1054–1056 2. Arakaki AK, Skolnick J, McDonald JF (2008) Marker metabolites can be therapeutic targets as well. Nature 456:443 3. Pan Z, Raftery D (2007) Comparing and combining NMR spectroscopy and mass spectrometry in metabolomics. Anal Bioanal Chem 387:525–527 4. Suhre K, Shin SY, Petersen AK, Mohney RP, Meredith D, Wägele B, Altmaier E, CARDIoGRAM, Deloukas P, Erdmann J, Grundberg E, Hammond CJ, de Angelis MH, Kastenmüller G, Köttgen A, Kronenberg F, Mangino M, Meisinger C, Meitinger T, Mewes HW, Milburn MV, Prehn C, Raffler J, Ried JS, Römisch-Margl W, Samani NJ, Small KS, Wichmann HE, Zhai G, Illig T, Spector TD, Adamski J, Soranzo N, Gieger C, Assimes TL, Deloukas P, Erdmann J, Holm H, Kathiresan S, König IR, McPherson R, Reilly MP, Roberts R, Samani NJ, Schunkert H, Stewart AF (2011) Human metabolic individuality in biomedical and pharmaceutical research. Nature 477:54–60 5. Koulman A, Lane GA, Harrison SJ, Volmer DA (2009) From differentiating metabolites to biomarkers. Anal Bioanal Chem 394:663–670 6. Wang X, Yang B, Sun H, Zhang A (2012) 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. Anal Chem 84:428–439 7. Zhang A, Sun H, Wang P, Han Y, Wang X (2012) Recent and potential developments of biofluid analyses in metabolomics. J Proteomics 75:1079–1088 8. Wang X, Wang H, Zhang A, Lu X, Sun H, Dong H, Wang P (2012) Metabolomics study on the toxicity of aconite root and its processed products using ultraperformance liquid-chromatography/ electrospray-ionization synapt high-definition mass spectrometry coupled with pattern recognition approach and ingenuity pathways analysis. J Proteome Res 11:1284–1301 9. Wang X, Zhang A, Han Y, Wang P, Sun H, Song G, Dong T, Yuan Y, Yuan X, Zhang M, Ning X, Zhang H, Dong H, Dong W (2012) Urine metabolomics analysis for biomarker discovery and detection of jaundice syndrome in patients with liver disease. Mol Cell Proteomics. doi:10.1074/mcp.M111.016006 mcp.M111.016006 10. Yang B, Zhang A, Sun H, Dong W, Yan G, Li T, Wang X (2012) Metabolomic study of insomnia and intervention effects of Suanzaoren decoction using ultra-performance liquid-chromatography/ electrospray-ionization synapt high-definition mass spectrometry. J Pharm Biomed Anal 58:113–124 11. Wang X, Sun H, Zhang A, Sun W, Wang P, Wang Z (2011) Potential role of metabolomics apporoaches in the area of traditional Chinese medicine: as pillars of the bridge between Chinese and Western medicine. J Pharm Biomed Anal 55:859–868 12. Prince EK, Pohnert G (2010) Searching for signals in the noise: metabolomics in chemical ecology. Anal Bioanal Chem 396:193–197 13. Zhang A, Sun H, Wang P, Han Y, Wang X (2012) Metabonomics for discovering biomarkers of hepatotoxicity and nephrotoxicity. Pharmazie 67(2):99–105 14. Lee R, West D, Phillips SM, Britz-McKibbin P (2010) Differential metabolomics for quantitative assessment of oxidative stress with strenuous exercise and nutritional intervention: thiol-specific regulation of cellular metabolism with N-acetyl-L-cysteine pretreatment. Anal Chem 82:2959–2968 15. Zhou L, Wang Q, Yin P, Xing W, Wu Z, Chen S, Lu X, Zhang Y, Lin X, Xu G (2012) Serum metabolomics reveals the deregulation of fatty acids metabolism in hepatocellular carcinoma and chronic liver diseases. Anal Bioanal Chem 403(1):203–213
16. Wu H, Xue R, Tang Z, Deng C, Liu T, Zeng H, Sun Y, Shen X (2010) Metabolomic investigation of gastric cancer tissue using gas chromatography/mass spectrometry. Anal Bioanal Chem 396 (4):1385–1395 17. Sreekumar A, Poisson LM, Rajendiran TM, Khan AP, Cao Q, Yu J, Laxman B, Mehra R, Lonigro RJ, Li Y, Nyati MK, Ahsan A, Kalyana-Sundaram S, Han B, Cao X, Byun J, Omenn GS, Ghosh D, Pennathur S, Alexander DC, Berger A, Shuster JR, Wei JT, Varambally S, Beecher C, Chinnaiyan AM (2009) Metabolomic profiles delineate potential role for sarcosine in prostate cancer progression. Nature 457:910–914 18. Catchpole GS, Beckmann M, Enot DP, Mondhe M, Zywicki B, Taylor J, Hardy N, Smith A, King RD, Kell DB, Fiehn O, Draper J (2009) Hierarchical metabolomics demonstrates substantial compositional similarity between genetically modified and conventional potato crops. Proc Natl Acad Sci U S A 102:14458–14462 19. Xu J, Zhang J, Cai S, Dong J, Yang JY, Chen Z (2009) Metabonomics studies of intact hepatic and renal cortical tissues from diabetic db/db mice using high-resolution magic-angle spinning 1H NMR spectroscopy. Anal Bioanal Chem 393(6–7):1657–1668 20. Kuhara T, Ohse M, Inoue Y, Cooper AJ (2011) A GC/MS-based metabolomic approach for diagnosing citrin deficiency. Anal Bioanal Chem 400(7):1881–1894 21. Wang X, Sun H, Zhang A, Sun W, Wang P, Wang Z (2010) Potential role of metabolomics apporoaches in the area of traditional Chinese medicine: as pillars of the bridge between Chinese and Western medicine. J Pharm Biomed Anal 55:859–868 22. Zhang A, Sun H, Wang Z, Sun W, Wang P, Wang X (2010) Metabolomics: towards understanding traditional Chinese medicine. Planta Med 76:2026–2035 23. Sun H, Zhang A, Wang X (2012) Potential Role of Metabolomic Approaches for Chinese Medicine Syndromes and Herbal Medicine. Phytother Res. doi:10.1002/ptr.4613 24. Drexler DM, Reily MD, Shipkova PA (2011) Advances in mass spectrometry applied to pharmaceutical metabolomics. Anal Bioanal Chem 399:2645–2653 25. Zhang A, Sun H, Wang P, Han Y, Wang X (2012) Future perspectives of personalized medicine in traditional Chinese medicine: a systems biology approach. Complement Ther Med 20:93–99 26. Winder CL, Cornmell R, Schuler S, Jarvis RM, Stephens GM, Goodacre R (2011) Metabolic fingerprinting as a tool to monitor whole-cell biotransformations. Anal Bioanal Chem 399:387–401 27. Psychogios N, Hau DD, Peng J, Guo AC, Mandal R, Bouatra S, Sinelnikov I, Krishnamurthy R, Eisner R, Gautam B, Young N, Xia J, Knox C, Dong E, Huang P, Hollander Z, Pedersen TL, Smith SR, Bamforth F, Greiner R, McManus B, Newman JW, Goodfriend T, Wishart DS (2011) The human serum metabolome. PLoS One 6(2):e16957 28. Amantonico A, Urban PL, Zenobi R (2010) Analytical techniques for single-cell metabolomics: state of the art and trends. Anal Bioanal Chem 398:2493–2504 29. Aberg KM, Alm E, Torgrip RJ (2009) The correspondence problem for metabonomics datasets. Anal Bioanal Chem 394:151–162 30. Neumann S, Böcker S (2010) Computational mass spectrometry for metabolomics: identification of metabolites and small molecules. Anal Bioanal Chem 398(7–8):2779–2788 31. Vallejo M, García A, Tuñón J, García-Martínez D, Angulo S, Martin-Ventura JL, Blanco-Colio LM, Almeida P, Egido J, Barbas C (2009) Plasma fingerprinting with GC–MS in acute coronary syndrome. Anal Bioanal Chem 394(6):1517–1524 32. Graham SF, Ruiz-Aracama A, Lommen A, Cannizzo FT, Biolatti B, Elliott CT, Mooney MH (2012) Use of NMR metabolomic plasma profiling methodologies to identify illicit growthpromoting administrations. Anal Bioanal Chem 403(2):573–582 33. Zhang A, Sun H, Wang P, Han Y, Wang X (2012) Modern analytical techniques in metabolomics analysis. Analyst 137:293–300
Serum metabolomics as a diagnostic approach for disease 34. Wu H, Liu T, Ma C, Xue R, Deng C, Zeng H, Shen X (2011) GC/MSbased metabolomic approach to validate the role of urinary sarcosine and target biomarkers for human prostate cancer by microwaveassisted derivatization. Anal Bioanal Chem 401(2):635–646 35. Dong H, Zhang A, Sun H, Wang H, Lu X, Wang M, Ni B, Wang X (2012) Ingenuity pathways analysis of urine metabolomics phenotypes toxicity of Chuanwu in Wistar rats by UPLC–Q-TOF-HDMS coupled with pattern recognition methods. Mol Biosyst 8:1206–1221 36. Wang X, Yang B, Zhang A, Sun H, Yan G (2012) Potential drug targets on insomnia and intervention effects of Jujuboside A through metabolic pathway analysis as revealed by UPLC/ESI– SYNAPT-HDMS coupled with pattern recognition approach. J Proteomics 75:1411–1427 37. Neumann S, Böcker S (2010) Computational mass spectrometry for metabolomics: identification of metabolites and small molecules. Anal Bioanal Chem 398:2779–2788 38. Li M, Zhou Z, Nie H, Bai Y, Liu H (2011) Recent advances of chromatography and mass spectrometry in lipidomics. Anal Bioanal Chem 399:243–249 39. Schlage WK, Westra JW, Gebel S, Catlett NL, Mathis C, Frushour BP, Hengstermann A, Van Hooser A, Poussin C, Wong B, Lietz M, Park J, Drubin D, Veljkovic E, Peitsch MC, Hoeng J, Deehan R (2011) A computable cellular stress network model for non-diseased pulmonary and cardiovascular tissue. BMC Syst Biol 5:168 40. Wang X, Sun H, Zhang A, Wang P, Han Y (2011) Ultraperformance liquid chromatography coupled to mass spectrometry as a sensitive and powerful technology for metabolomic studies. J Sep Sci 34(24):3451–3459 41. Liu JY, Li N, Yang J, Li N, Qiu H, Ai D, Chiamvimonvat N, Zhu Y, Hammock BD (2010) Metabolic profiling of murine plasma reveals an unexpected biomarker in rofecoxib-mediated cardiovascular events. Proc Natl Acad Sci U S A 107:17017–17022 42. Beckwith-Hall BM, Thompson NA, Nicholson JK, Lindon JC, Holmes E (2003) A metabonomic investigation of hepatotoxicity using diffusion-edited 1H NMR spectroscopy of blood serum. Analyst 128:814–818 43. Soga T, Sugimoto M, Honma M, Mori M, Igarashi K, Kashikura K, Ikeda S, Hirayama A, Yamamoto T, Yoshida H, Otsuka M, Tsuji S, Yatomi Y, Sakuragawa T, Watanabe H, Nihei K, Saito T, Kawata S, Suzuki H, Tomita M, Suematsu M (2011) Serum metabolomics reveals γ-glutamyl dipeptides as biomarkers for discrimination among different forms of liver disease. J Hepatol 55:896–905 44. Tiziani S, Lopes V, Günther UL (2009) Early stage diagnosis of oral cancer using 1H NMR-based metabolomics. Neoplasia 11:269–276 45. Denery JR, Nunes AA, Hixon MS, Dickerson TJ, Janda KD (2010) Metabolomics-based discovery of diagnostic biomarkers for onchocerciasis. PLoS Negl Trop Dis 4:e834 46. Chen J, Zhang X, Cao R, Lu X, Zhao S, Fekete A, Huang Q, SchmittKopplin P, Wang Y, Xu Z, Wan X, Wu X, Zhao N, Xu C, Xu G (2011) Serum 27-nor-5β-cholestane-3,7,12,24,25 pentol glucuronide discovered by metabolomics as potential diagnostic biomarker for epithelium ovarian cancer. J Proteome Res 10:2625–2632 47. Bathe OF, Shaykhutdinov R, Kopciuk K, Weljie AM, McKay A, Sutherland FR, Dixon E, Dunse N, Sotiropoulos D, Vogel HJ (2011) Feasibility of identifying pancreatic cancer based on serum metabolomics. Cancer Epidemiol Biomarkers Prev 20:140–147 48. Barba I, de León G, Martín E, Cuevas A, Aguade S, Candell-Riera J, Barrabés JA, Garcia-Dorado D (2008) Nuclear magnetic
49.
50.
51.
52.
53.
54.
55.
56.
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58.
59.
60.
61.
62.
resonance-based metabolomics predicts exercise-induced ischemia in patients with suspected coronary artery disease. Magn Reson Med 60:27–32 Chen C, Shah YM, Morimura K, Krausz KW, Miyazaki M, Richardson TA, Morgan ET, Ntambi JM, Idle JR, Gonzalez FJ (2008) Metabolomics reveals that hepatic stearoyl-CoA desaturase 1 downregulation exacerbates inflammation and acute colitis. Cell Metab 7:135–147 McMurray HR, Sampson ER, Compitello G, Kinsey C, Newman L, Smith B, Chen SR, Klebanov L, Salzman P, Yakovlev A, Land H (2008) Synergistic response to oncogenic mutations defines gene class critical to cancer phenotype. Nature 453(7198):1112–1116 Ma Y, Zhang P, Wang F, Liu W, Yang J, Qin H (2012) An integrated proteomics and metabolomics approach for defining oncofetal biomarkers in the colorectal cancer. Ann Surg 255:720–730 Chen F, Xue J, Zhou L, Wu S, Chen Z (2011) Identification of serum biomarkers of hepatocarcinoma through liquid chromatography/mass spectrometry-based metabonomic method. Anal Bioanal Chem 401:1899–1904 Tan Y, Yin P, Tang L, Xing W, Huang Q, Cao D, Zhao X, Wang W, Lu X, Xu Z, Wang H, Xu G (2012) Metabolomics study of stepwise hepatocarcinogenesis from the model rats to patients: potential biomarkers effective for small hepatocellular carcinoma diagnosis. Mol Cell Proteomics 11:M111.010694 Bertini I, Cacciatore S, Jensen BV, Schou JV, Johansen JS, Kruhøffer M, Luchinat C, Nielsen DL, Turano P (2012) Metabolomic NMR fingerprinting to identify and predict survival of patients with metastatic colorectal cancer. Cancer Res 72:356–364 Hori S, Nishiumi S, Kobayashi K, Shinohara M, Hatakeyama Y, Kotani Y, Hatano N, Maniwa Y, Nishio W, Bamba T, Fukusaki E, Azuma T, Takenawa T, Nishimura Y, Yoshida M (2011) A metabolomic approach to lung cancer. Lung Cancer 74:284–292 Lin L, Huang Z, Gao Y, Yan X, Xing J, Hang W (2011) LC–MS based serum metabonomic analysis for renal cell carcinoma diagnosis, staging, and biomarker discovery. J Proteome Res 10:1396–1405 Ouyang D (2012) Metabolomic Characterization of Human Pancreatitis by 1H-NMR Spectroscopy. Hepatogastroenterology 59 (119). doi:10.5754/hge11839 Sysi-Aho M, Ermolov A, Gopalacharyulu PV, Tripathi A, SeppänenLaakso T, Maukonen J, Mattila I, Ruohonen ST, Vähätalo L, Yetukuri L, Härkönen T, Lindfors E, Nikkilä J, Ilonen J, Simell O, Saarela M, Knip M, Kaski S, Savontaus E, Orešič M (2011) Metabolic regulation in progression to autoimmune diabetes. PLoS Comput Biol 7:e1002257 Xuan J, Pan G, Qiu Y, Yang L, Su M, Liu Y, Chen J, Feng G, Fang Y, Jia W, Xing Q, He L (2011) Metabolomic profiling to identify potential serum biomarkers for schizophrenia and risperidone action. J Proteome Res 10:5433–5443 Ouyang X, Dai Y, Wen JL, Wang LX (2011) ¹H NMR-based metabolomic study of metabolic profiling for systemic lupus erythematosus. Lupus 20:1411–1420 Zhang J, Liu L, Wei S, Nagana Gowda GA, Hammoud Z, Kesler KA, Raftery D (2011) Metabolomics study of esophageal adenocarcinoma. J Thorac Cardiovasc Surg 141:469–475 Weckwerth W (2011) Unpredictability of metabolism—the key role of metabolomics science in combination with next-generation genome sequencing. Anal Bioanal Chem 400:1967–1978
