Urine Metabolomics Analysis for Biomarker Discovery and Detection of Jaundice Syndrome in Patients With Liver Disease

Metabolomics is a powerful new technology that allows for the assessment of global metabolic profiles in easily accessible biofluids and biomarker discovery in order to distinguish between diseased and nondiseased status information. Deciphering the molecular networks that distinguish diseases may lead to the identification of critical biomarkers for disease aggressiveness. However, current diagnostic methods cannot predict typical Jaundice syndrome (JS) in patients with liver disease and little is known about the global metabolomic alterations that characterize JS progression. Emerging metabolomics provides a powerful platform for discovering novel biomarkers and biochemical pathways to improve diagnostic, prognostication, and therapy. Therefore, the aim of this study is to find the potential biomarkers from JS disease by using a nontarget metabolomics method, and test their usefulness in human JS diagnosis. Multivariate data analysis methods were utilized to identify the potential biomarkers. Interestingly, 44 marker metabolites contributing to the complete separation of JS from matched healthy controls were identified. Metabolic pathways (Impact-value≥0.10) including alanine, aspartate, and glutamate metabolism and synthesis and degradation of ketone bodies were found to be disturbed in JS patients. This study demonstrates the possibilities of metabolomics as a diagnostic tool in diseases and provides new insight into pathophysiologic mechanisms. Metabolomics is a powerful new technology that allows for the assessment of global metabolic profiles in easily accessible biofluids and biomarker discovery in order to distinguish between diseased and nondiseased status information. Deciphering the molecular networks that distinguish diseases may lead to the identification of critical biomarkers for disease aggressiveness. However, current diagnostic methods cannot predict typical Jaundice syndrome (JS) in patients with liver disease and little is known about the global metabolomic alterations that characterize JS progression. Emerging metabolomics provides a powerful platform for discovering novel biomarkers and biochemical pathways to improve diagnostic, prognostication, and therapy. Therefore, the aim of this study is to find the potential biomarkers from JS disease by using a nontarget metabolomics method, and test their usefulness in human JS diagnosis. Multivariate data analysis methods were utilized to identify the potential biomarkers. Interestingly, 44 marker metabolites contributing to the complete separation of JS from matched healthy controls were identified. Metabolic pathways (Impact-value≥0.10) including alanine, aspartate, and glutamate metabolism and synthesis and degradation of ketone bodies were found to be disturbed in JS patients. This study demonstrates the possibilities of metabolomics as a diagnostic tool in diseases and provides new insight into pathophysiologic mechanisms. Metabolomics, an omic science in systems biology, is the comprehensive profiling of metabolic changes occurring in living systems (1Nicholson J.K. Lindon J.C. Systems biology: Metabonomics.Nature. 2008; 455: 1054-1056Crossref PubMed Scopus (1488) Google Scholar). It 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 diseases (2Kim K. Aronov P. Zakharkin S.O. Anderson D. Perroud B. Thompson I.M. Weiss R.H. Urine metabolomics analysis for kidney cancer detection and biomarker discovery.Mol. Cell Proteomics. 2009; 8: 558-570Abstract Full Text Full Text PDF PubMed Scopus (235) Google Scholar, 3Tohge T. Fernie A.R. Combining genetic diversity, informatics and metabolomics to facilitate annotation of plant gene function.Nat. Protoc. 2010; 5: 1210-1227Crossref PubMed Scopus (174) Google Scholar). One area of considerable interest in the field of metabolomics is the detection of potential biomarkers associated with diseases, and the metabolic profiling could provide global changes of endogenous metabolites of patients. Metabolomics is the study of metabolic pathways and the measurement of unique biochemical molecules generated in a living system. It could facilitate biomarker discovery by distinguishing between diseased and nondiseased patients. Biomarker metabolites can also be therapeutic targets (4Arakaki A.K. Skolnick J. McDonald J.F. Marker metabolites can be therapeutic targets as well.Nature. 2008; 456: 443Crossref PubMed Scopus (94) Google Scholar). Detecting changes in metabolite concentrations reveals the range of biochemical effects induced by a disease condition. Metabolic profiling of urine is particularly attractive because urine collection is noninvasive, and urine contains metabolic signatures of many biochemical pathways. The advantages of urine include its noninvasive collection and wide availability, its low protein and cellular levels, and its richness in metabolites. Monitoring certain metabolite levels in urine fluid has become an important way to detect early stages in disease (5Cai Z. Zhao J.S. Li J.J. Peng D.N. Wang X.Y. Chen T.L. Qiu Y.P. Chen P.P. Li W.J. Xu L.Y. Li E.M. Tam J.P. Qi R.Z. Jia W. Xie D. A combined proteomics and metabolomics profiling of gastric cardia cancer reveals characteristic dysregulations in glucose metabolism.Mol. Cell. Proteomics. 2010; 9: 2617-2628Abstract Full Text Full Text PDF PubMed Scopus (102) Google Scholar). Urinary metabolomic approaches have been used to screen for potentially earlier diagnostic and prognostic biomarkers of disease (6Veselkov K.A. Vingara L.K. Masson P. Robinette S.L. Want E. Li J.V. Barton R.H. Boursier-Neyret C. Walther B. Ebbels T.M. Pelczer I. Holmes E. Lindon J.C. Nicholson J.K. Optimized preprocessing of ultra-performance liquid chromatography/mass spectrometry urinary metabolic profiles for improved information recovery.Anal. Chem. 2011; 83: 5864-5872Crossref PubMed Scopus (211) Google Scholar). Metabolite changes observed in diseased individuals as a primary indicator have become possible, and hence the measurement of metabolites have been an important part of clinical practice. Traditional markers used in conventional clinical chemistry and histopathology methods are not region-specific and only increase significantly after substantial disease injury. Therefore, more sensitive markers of disease are needed. The ideal biomarkers will identify disease early, resulting in safer drugs. Metabolomics, is an emerging and powerful discipline, which has become a promising player in the disease arena, and its benefits have been demonstrated in diverse clinical areas (7Chadeau-Hyam M. Ebbels T.M. Brown I.J. Chan Q. Stamler J. Huang C.C. Daviglus M.L. Ueshima H. Zhao L. Holmes E. Nicholson J.K. Elliott P. De Iorio M. Metabolic profiling and the metabolome-wide association study: significance level for biomarker identification.J. Proteome Res. 2010; 9: 4620-4627Crossref PubMed Scopus (101) Google Scholar, 8Wang T.J. Larson M.G. Vasan R.S. Cheng S. Rhee E.P. McCabe E. Lewis G.D. Fox C.S. Jacques P.F. Fernandez C. O'Donnell C.J. Carr S.A. Mootha V.K. Florez J.C. Souza A. Melander O. Clish C.B. Gerszten R.E. Metabolite profiles and the risk of developing diabetes.Nat. Med. 2011; 17: 448-453Crossref PubMed Scopus (2127) Google Scholar, 9Beckonert O. Coen M. Keun H.C. Wang Y. Ebbels T.M. Holmes E. Lindon J.C. Nicholson J.K. High-resolution magic-angle-spinning NMR spectroscopy for metabolic profiling of intact tissues.Nat. Protoc. 2010; 5: 1019-1032Crossref PubMed Scopus (306) Google Scholar). Recent methodology, whose aim for complete characterization of the entire metabolome regardless of molecular size, are distinguishable from traditional tests on one or two components. The metabolomics approach has substantial impact on the development of diagnostics, therapeutics, and drug development (10Clayton T.A. Lindon J.C. Cloarec O. Antti H. Charuel C. Hanton G. Provost J.P. Le Net J.L. Baker D. Walley R.J. Everett J.R. Nicholson J.K. Nicholson. Pharmaco-metabonomic phenotyping and personalized drug treatment.Nature. 2006; 440: 1073-1077Crossref PubMed Scopus (730) Google Scholar, 11Holmes 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. Human metabolic phenotype diversity and its association with diet and blood pressure.Nature. 2008; 453: 396-400Crossref PubMed Scopus (883) Google Scholar, 12Sreekumar A. Poisson L.M. Rajendiran T.M. Khan A.P. Cao Q. Yu J. Laxman B. Mehra R. Lonigro R.J. Li Y. Nyati M.K. Ahsan A. Kalyana-Sundaram S. Han B. Cao X. Byun J. Omenn G.S. Ghosh D. Pennathur S. Alexander D.C. Berger A. Shuster J.R. Wei J.T. Varambally S. Beecher C. Chinnaiyan A.M. Metabolomic profiles delineate potential role for sarcosine in prostate cancer progression.Nature. 2009; 457: 910-914Crossref PubMed Scopus (1741) Google Scholar). Particularly, for the early detection of disease, highly sensitive and specific biomarkers as primary indicators in bio-fluids are relatively more useful because these can be used for nonbiopsy tests. By analyzing and verifying the specificity of early biomarkers of a disease, metabolomics enables us to better understand pathological processes and metabolic pathways. Compared with traditional diagnostic methods, even small changes of metabolites can help to detect early pathologic changes earlier. Metabolic profiling has also been used as a diagnostic tool in the setting of liver disease. Studies of metabolites from patient urine that were discovered using metabolomics technology have recently come into focus as possible biomarkers for liver disease (13Yu K. Sheng G. Sheng J. Chen Y. Xu W. Liu X. Cao H. Qu H. Cheng Y. Li L. A metabonomic investigation on the biochemical perturbation in liver failure patients caused by hepatitis b virus.J. Proteome Res. 2007; 6: 2413-2419Crossref PubMed Scopus (78) Google Scholar, 14Chen J. Wang W. Lv S. Yin P. Zhao X. Lu X. Zhang F. Xu G. Metabonomics study of liver cancer based on ultra performance liquid chromatography coupled to mass spectrometry with HILIC and RPLC separations.Anal. Chim. Acta. 2009; 650: 3-9Crossref PubMed Scopus (185) Google Scholar, 15Manna S.K. Patterson A.D. Yang Q. Krausz K.W. Idle J.R. Fornace A.J. Gonzalez F.J. UPLC-MS-based urine metabolomics reveals indole-3-lactic acid and phenyllactic acid as conserved biomarkers for alcohol-induced liver disease in the Ppara-null mouse model.J. Proteome Res. 2011; 10: 4120-4133Crossref PubMed Scopus (61) Google Scholar). Understanding syndromes is a core research to develop more efficient therapeutic strategies, classification, and diagnostic criteria for patients. and clinical have shown that patients with liver disease are by syndrome (JS) used to used to X. Yang C. Y. C. Zhang X. Wang Y. Zhang J. Li S. J. for the and specificity of hepatitis S. A. 2011; PubMed Scopus Google Scholar, J. of the with and J. Med. 2010; PubMed Scopus Google Scholar, C. R.L. 2011; Full Text Full Text PDF PubMed Scopus Google Scholar, S. R. and Full Text Full Text PDF PubMed Scopus Google Scholar, I.M. of and liver Full Text Full Text PDF PubMed Scopus Google Scholar, A. F. S. W. of and in Full Text Full Text PDF PubMed Scopus Google Scholar). a and disease early and two of and diagnostic methods, levels of and in blood have been the diagnostic markers for JS and can be as the current for and However, the of these markers is relatively are prognostic indicators and are not particularly in of JS from the development of metabolomics technology has been used to the diagnostic and prognostic and pathways of the syndrome to provide a for a of the of the syndrome from the of systems X. Yang B. H. Zhang A. approaches and systems for ultra performance liquid comprehensive metabolomic profiling and pathways analysis of data Chem. PubMed Scopus Google Scholar). this to a comprehensive metabolome of JS by ultra-performance mass spectrometry combined with methods and pathways in order to a specific metabolite phenotype and the diagnostic new potential and a better of the Jaundice syndrome analysis to Jaundice syndrome analysis to were from The were and by the of of and to the in the of were from the of of for the between and JS and patients and were in this The of in patients with and the controls were and the clinical information including mass syndromes of disease, and of liver were in criteria or diseases that will the clinical and a of JS and patients and were to be and using from from by a acid of and from from were The were in which were to the urine were by the study the the were for to of the urine were urine were after for and the to a and a of the were into the The analysis using a ultra-performance coupled with mass spectrometry from with an chromatography The and of A with and A with used to the molecules in the biofluids The for the urine as B. 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I.M. J. A. C. A. A. S. A. A. E. A. T. J. S. C. J. M. I. L. P. D. T. P. C. F. P. M.L. D. J. B. J. A. G. D. M. S. C. K. E. B. G. J. S. P. O. E. R. A.J. D. C. Chen Z. L. C. Systems and to Med. 2011; PubMed Scopus Google Scholar). of molecular markers and metabolic pathways has the potential to improve diagnostic, prognostication, and of interest G.S. M. M. B. J. A. O. J. metabolomics demonstrates substantial between and conventional S. A. 2009; Scopus Google Scholar, Li Yang J. Li Qiu H. D. Y. Metabolic profiling of reveals an biomarker in S. A. 2010; PubMed Scopus Google Scholar, K. A.K. D. B. E. P. J. E. C.J. G. A. F. M. C. T. C. J. J.S. W. K. G. T. J. C. T.L. P. J. H. S. R. R. H. Human metabolic in and 2010; Scopus Google Scholar, of a in S. A. 2011; PubMed Scopus Google Scholar). The of metabolic profiles may provide a biomarker for the of diseases as as of healthy from patients. diagnostic methods, levels of and in blood have been the diagnostic markers for JS and can be as the current for and However, the of these markers is relatively low and is to an novel approaches for the detection of JS are needed. The nontarget metabolomics provides a global of the and can be used to the metabolic alterations that in pathological processes Z. L. Y. Chen Y. X. J. W. cancer two urinary a biomarker Cell Proteomics. 2011; Full Text Full Text PDF Scopus Google Scholar, W.J. S. J. J.R. R. and of liver in early Cell. Proteomics. 2010; 9: Full Text Full Text PDF PubMed Scopus Google Scholar). diagnostic methods in clinical are for the metabolite biomarkers. Metabolomics is a powerful new technology that allows for the assessment of global metabolic profiles in easily accessible biofluids and biomarker discovery in order to distinguish between diseased and nondiseased status information L. T. S. A.M. L. K. C. A. B. R.L. R. T. A.J. T.M. proteomics and metabolomics analysis of human fluid Cell Proteomics. 2010; 9: Full Text Full Text PDF PubMed Scopus Google Scholar). a noninvasive the field of metabolomics has the potential to provide new diagnostic Recent have with the to or even of metabolites in as little as a the way for to diseases as JS M. in 2010; 6: PubMed Scopus Google Scholar, T. Xie G. Wang X. J. Qiu Y. X. Qi X. Cao Y. M. Wang X. Xu Y. Liu P. Jia W. and urine metabolite profiling reveals potential biomarkers of human Cell Proteomics. 2011; Scholar). or of JS be for and Metabolomics is particularly to liver urine is the for tests. gene and protein have been in human disease, little is known about the global metabolomic alterations that characterize disease. molecular characterize disease development and progression. Deciphering the molecular networks that distinguish disease from liver disease may lead to the identification of critical biomarkers for disease aggressiveness. utilized the metabolomics approach in a study in urine of JS patients from It demonstrated that based on of urine with could be to distinguish between diseased and nondiseased The global metabolic profiling and analysis JS patients from matched shown in and an and separation between the JS and that JS metabolites an role in glutamate and degradation of ketone alanine, and which are with the of and in the metabolites By using metabolomics 44 important with were Interestingly, of the 44 metabolites from these many are found in the stages of of study of these metabolites may facilitate the development of noninvasive biomarkers and more efficient therapeutic for and metabolism also the by for changes associated with disease, as metabolite changes in controls were for metabolites that are potential for biomarkers. primary acid and metabolism were with It is that metabolites are important for the to JS metabolism pathways. a new way to metabolome information of typical patients A separation using analysis for the between JS controls as as between patients systems analysis with tool provides a powerful approach to metabolic profiling of urine to patients from The patients with JS were easily from by the approach using these metabolic markers which were using metabolomics a using a of patients and which were that the a and specificity for the clinical JS as shown in metabolite analysis have shown that alterations of critical JS metabolic as glutamate synthesis and degradation of ketone and are associated with JS changes are to be in metabolic which may in be for potential biomarkers for JS assessment and metabolic pathways networks of and are to provide information on of disease and have become a and the of biochemical information for and a wide of molecular and occurring in the living in a and using on the a of the the and primary acid pathways with is shown in that these pathways the the entire of JS and could to the development of the more patients and the metabolomic biomarkers in the significance of could be are also to the JS induced these Therefore, metabolomics that is a great potential for metabolite biomarker metabolite signatures may also have the potential to be used as diagnostic biomarkers. research will focus on the discovery of biomarkers using metabolomics and the of the biomarkers. more will be to the will be which pathways were in the biochemical changes associated with the and of and these changes are the and or changes of the stages of metabolomics provides a powerful approach to diagnostic and therapeutic biomarkers by analyzing global changes in an metabolic for the a comprehensive analysis of metabolic of JS and have significantly metabolites associated with JS and 44 metabolomic signatures in and degradation of ketone and metabolism found that the associated with JS to The not only that metabolomic methods and specificity to distinguish JS from healthy also have the potential to be into a useful diagnostic and could also to a of disease mechanisms. that be to a for disease pathological of the the between the syndrome and with