Clinical metabolomics: The new metabolic window for inborn errors of metabolism investigations in the post-genomic era

International Journal of Molecular Sciences

Volumn 17, Issue 7, 2016, Pages

  Tebani, Abdellah ^a,b,c   Abily Donval, Lenaig ^a,b   Afonso, Carlos ^c   Marret, Stéphane ^a,b   Bekri, Soumeya ^a,b  

^a ROUEN UNIVERSITY HOSPITAL   (France)

^b NORMANDIE UNIVERSITÉ   (France)

^c UNIVERSITÉ DE ROUEN   (France)

Author keywords

Diagnosis; Inborn errors of metabolism; Metabolomics; Precision medicine; Screening; Systems medicine

Indexed keywords

ISOVALERIC ACID;

ACIDURIA; GENE SEQUENCE; GENETIC VARIABILITY; HUMAN; INBORN ERROR OF METABOLISM; MASS SPECTROMETRY; MATHEMATICAL ANALYSIS; METABOLOMICS; NUCLEAR MAGNETIC RESONANCE SPECTROSCOPY; PERSONALIZED MEDICINE; REVIEW; TRANSCRIPTOMICS; GENETICS; GENOMICS; METABOLISM; PROCEDURES;

GENOMICS; HUMANS; METABOLISM, INBORN ERRORS; METABOLOMICS;

EID: 84979022687     PISSN: 16616596     EISSN: 14220067     Source Type: Journal    
DOI: 10.3390/ijms17071167     Document Type: Review

References (177)

1. Collins, F.S.; Varmus, H. A new initiative on precision medicine. N. Engl. J. Med. 2015, 372, 793-795
2. Lanpher, B.; Brunetti-Pierri, N.; Lee, B. Inborn errors of metabolism: The flux from mendelian to complex diseases. Nat. Rev. Genet. 2006, 7, 449-460
3. Vernon, H.J. Inborn errors of metabolism: Advances in diagnosis and therapy. JAMA Pediatr. 2015, 169, 778-782
4. Garrod, A. The incidence of alkaptonuria: A study in chemical individuality. Lancet 1902, 160, 1616-1620
5. Ahn, A.C.; Tewari, M.; Poon, C.S.; Phillips, R.S. The limits of reductionism in medicine: Could systems biology offer an alternative? PLoS Med. 2006, 3, e208
6. Regenmortel, M.H.V.V. Reductionism and complexity in molecular biology. EMBO Rep. 2004, 5, 1016-1020
7. Aon, M.A.; Lloyd, D.; Saks, V. From physiology, genomes, systems, and self-organization to systems biology: The historical roots of a twenty-first century approach to complexity. In Systems Biology of Metabolic and Signaling Networks: Energy, Mass and Information Transfer; Aon, A.M., Saks, V., Schlattner, U., Eds.; Springer: Berlin & Heidelberg, Germany, 2014; pp. 3-17.
8. Aon, M.A. Complex systems biology of networks: The riddle and the challenge. In Systems Biology of Metabolic and Signaling Networks; Springer: Berlin & Heidelberg, Germany, 2014; pp. 19-35.
9. Kitano, H. Computational systems biology. Nature 2002, 420, 206-210
10. Aon, M.A.; Cortassa, S. Systems biology of the fluxome. Processes 2015, 3, 607-618
11. Weston, A.D.; Hood, L. Systems biology, proteomics, and the future of health care: Toward predictive, preventative, and personalized medicine. J. Proteome Res. 2004, 3, 179-196
12. Ehrenberg, M.; Elf, J.; Aurell, E.; Sandberg, R.; Tegner, J. Systems biology is taking off. Genome Res. 2003, 13, 2377-2380
13. Kitano, H. Looking beyond the details: A rise in system-oriented approaches in genetics and molecular biology. Curr. Genet. 2002, 41, 1-10
14. Kitano, H. Systems biology: A brief overview. Science 2002, 295, 1662-1664
15. Tenenbaum, J.D.; Avillach, P.; Benham-Hutchins, M.; Breitenstein, M.K.; Crowgey, E.L.; Hoffman, M.A.; Jiang, X.; Madhavan, S.; Mattison, J.E.; Nagarajan, R.; et al. An informatics research agenda to support precision medicine: Seven key areas. JAMIA 2016, 23, 791-795
16. McMurry, J.; Kohler, S.; Balhoff, J.; Borromeo, C.; Brush, M.; Carbon, S.; Conlin, T.; Dunn, N.; Engelstad, M.; Foster, E. Navigating the phenotype frontier: The monarch initiative. bioRxiv 2016
17. Ritchie, M.D.; Holzinger, E.R.; Li, R.; Pendergrass, S.A.; Kim, D. Methods of integrating data to uncover genotype-phenotype interactions. Nat. Rev. Genet. 2015, 16, 85-97
18. Sperisen, P.; Cominetti, O.; Martin, F.-P.J. Longitudinal omics modeling and integration in clinical metabonomics research: Challenges in childhood metabolic health research. Front. Mol. Biosci. 2015
19. Hood, L.; Balling, R.; Auffray, C. Revolutionizing medicine in the 21st century through systems approaches. Biotechnol. J. 2012, 7, 992-1001
20. Cho, D.-Y.; Kim, Y.-A.; Przytycka, T.M. Chapter 5: Network biology approach to complex diseases. PLoS Comput. Biol. 2012, 8, e1002820
21. Argmann, C.A.; Houten, S.M.; Zhu, J.; Schadt, E.E. A next generation multiscale view of inborn errors of metabolism. Cell Metab. 2016, 23, 13-26
22. Nicholson, J.K.; Holmes, E.; Kinross, J.M.; Darzi, A.W.; Takats, Z.; Lindon, J.C. Metabolic phenotyping in clinical and surgical environments. Nature 2012, 491, 384-392
23. Chen, R.; Mias, G.I.; Li-Pook-Than, J.; Jiang, L.; Lam, H.Y.K.; Chen, R.; Miriami, E.; Karczewski, K.J.; Hariharan, M.; Dewey, F.E.; et al. Personal omics profiling reveals dynamic molecular and medical phenotypes. Cell 2012, 148, 1293-1307
24. Nicholson, J.K.; Lindon, J.C. Systems biology: Metabonomics. Nature 2008, 455, 1054-1056
25. Oliver, S.G.; Winson, M.K.; Kell, D.B.; Baganz, F. Systematic functional analysis of the yeast genome. Trends Biotechnol. 1998, 16, 373-378
26. Nicholson, J.K.; Lindon, J.C.; Holmes, E. “Metabonomics”: Understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data. Xenobiotica 1999, 29, 1181-1189
27. Dunn, W.B.; Broadhurst, D.I.; Atherton, H.J.; Goodacre, R.; Griffin, J.L. Systems level studies of mammalian metabolomes: The roles of mass spectrometry and nuclear magnetic resonance spectroscopy. Chem. Soc. Rev. 2011, 40, 387-426
28. Fiehn, O. Metabolomics-The link between genotypes and phenotypes. Plant Mol. Biol. 2002, 48, 155-171
29. Holmes, E.; Wilson, I.D.; Nicholson, J.K. Metabolic phenotyping in health and disease. Cell 2008, 134, 714-717
30. Clayton, 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.; et al. Pharmaco-metabonomic phenotyping and personalized drug treatment. Nature 2006, 440, 1073-1077
31. Kaddurah-Daouk, R.; Kristal, B.S.; Weinshilboum, R.M. Metabolomics: A global biochemical approach to drug response and disease. Annu. Rev. Pharmacol. Toxicol. 2008, 48, 653-683
32. James, L.P. Metabolomics: Integration of a new “omics” with clinical pharmacology. Clin. Pharmacol. Ther. 2013, 94, 547-551
33. Kaddurah-Daouk, R.; Weinshilboum, R.M. Pharmacometabolomics: Implications for clinical pharmacology and systems pharmacology. Clin. Pharmacol. Ther. 2014, 95, 154-167
34. Kell, D.B.; Goodacre, R. Metabolomics and systems pharmacology: Why and how to model the human metabolic network for drug discovery. Drug Discov. Today 2014, 19, 171-182
35. Everett, J.R. Pharmacometabonomics in humans: A new tool for personalized medicine. Pharmacogenomics 2015, 16, 737-754
36. Dunn, W.B.; Broadhurst, D.; Begley, P.; Zelena, E.; Francis-McIntyre, S.; Anderson, N.; Brown, M.; Knowles, J.D.; Halsall, A.; Haselden, J.N.; et al. Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry. Nat. Protoc. 2011, 6, 1060-1083
37. Nunes de Paiva, M.J.; Menezes, H.C.; de Lourdes Cardeal, Z. Sampling and analysis of metabolomes in biological fluids. Analyst 2014, 139, 3683-3694
38. Want, E.J.; Masson, P.; Michopoulos, F.; Wilson, I.D.; Theodoridis, G.; Plumb, R.S.; Shockcor, J.; Loftus, N.; Holmes, E.; Nicholson, J.K. Global metabolic profiling of animal and human tissues via UPLC-MS. Nat. Protoc. 2013, 8, 17-32
39. Want, E.J.; Wilson, I.D.; Gika, H.; Theodoridis, G.; Plumb, R.S.; Shockcor, J.; Holmes, E.; Nicholson, J.K. Global metabolic profiling procedures for urine using UPLC-MS. Nat. Protoc. 2010, 5, 1005-1018
40. Graham, S.F.; Chevallier, O.P.; Roberts, D.; Hölscher, C.; Elliott, C.T.; Green, B.D. Investigation of the human brain metabolome to identify potential markers for early diagnosis and therapeutic targets of Alzheimer’s disease. Anal. Chem. 2013, 85, 1803-1811
41. Wuolikainen, A.; Hedenstrom, M.; Moritz, T.; Marklund, S.L.; Antti, H.; Andersen, P.M. Optimization of procedures for collecting and storing of CSF for studying the metabolome in ALS. Amyotroph. Lateral Scler. 2009, 10, 229-236
42. Dame, Z.; Aziat, F.; Mandal, R.; Krishnamurthy, R.; Bouatra, S.; Borzouie, S.; Guo, A.; Sajed, T.; Deng, L.; Lin, H.; et al. The human saliva metabolome. Metabolomics 2015, 11, 1864-1883
43. Kawasaki, G.; Ichikawa, Y.; Yoshitomi, I.; Umeda, M. Metabolomics of salivary biomarkers in yusho patients. Hukuoka Acta Med. 2015, 106, 144-148
44. Mikkonen, J.J.; Singh, S.P.; Herrala, M.; Lappalainen, R.; Myllymaa, S.; Kullaa, A.M. Salivary metabolomics in the diagnosis of oral cancer and periodontal diseases. J. Periodontal Res. 2015
45. Bach, J.-P.; Gold, M.; Mengel, D.; Hattesohl, A.; Lubbe, D.; Schmid, S.; Tackenberg, B.; Rieke, J.; Maddula, S.; Baumbach, J.I.; et al. Measuring compounds in exhaled air to detect Alzheimer’s disease and parkinson? S disease. PLoS ONE 2015, 10, e0132227
46. Pijls, K.E.; Smolinska, A.; Jonkers, D.M.A.E.; Dallinga, J.W.; Masclee, A.A.M.; Koek, G.H.; van Schooten, F.-J. A profile of volatile organic compounds in exhaled air as a potential non-invasive biomarker for liver cirrhosis. Sci. Rep. 2016
47. Koulman, A.; Prentice, P.; Wong, M.C.; Matthews, L.; Bond, N.J.; Eiden, M.; Griffin, J.L.; Dunger, D.B. The development and validation of a fast and robust dried blood spot based lipid profiling method to study infant metabolism. Metabolomics 2014, 10, 1018-1025
48. Wilson, I. Global metabolic profiling (metabonomics/metabolomics) using dried blood spots: Advantages and pitfalls. Bioanalysis 2011, 3, 2255-2257
49. Michopoulos, F.; Theodoridis, G.; Smith, C.J.; Wilson, I.D. Metabolite profiles from dried blood spots for metabonomic studies using UPLC combined with orthogonal acceleration TOF-MS: Effects of different papers and sample storage stability. Bioanalysis 2011, 3, 2757-2767
50. Prentice, P.; Turner, C.; Wong, M.C.Y.; Dalton, R.N. Stability of metabolites in dried blood spots stored at different temperatures over a 2-year period. Bioanalysis 2013, 5, 1507-1514
51. Denes, J.; Szabo, E.; Robinette, S.L.; Szatmari, I.; Szonyi, L.; Kreuder, J.G.; Rauterberg, E.W.; Takats, Z. Metabonomics of newborn screening dried blood spot samples: A novel approach in the screening and diagnostics of inborn errors of metabolism. Anal. Chem. 2012, 84, 10113-10120
52. Wagner, M.; Tonoli, D.; Varesio, E.; Hopfgartner, G. The use of mass spectrometry to analyze dried blood spots. Mass Spectrom. Rev. 2014, 35, 361-438
53. Oliveira, R.V.; Henion, J.; Wickremsinhe, E.R. Automated high-capacity on-line extraction and bioanalysis of dried blood spot samples using liquid chromatography/high-resolution accurate mass spectrometry. Rapid Commun. Mass Spectrom. 2014, 28, 2415-2426
54. Do, K.T.; Kastenmüller, G.; Mook-Kanamori, D.O.; Yousri, N.A.; Theis, F.J.; Suhre, K.; Krumsiek, J. Network-based approach for analyzing intra-and interfluid metabolite associations in human blood, urine, and saliva. J. Proteome Res. 2015, 14, 1183-1194
55. Torell, F.; Bennett, K.; Cereghini, S.; Rannar, S.; Lundstedt-Enkel, K.; Moritz, T.; Haumaitre, C.; Trygg, J.; Lundstedt, T. Multi-organ contribution to the metabolic plasma profile using hierarchical modelling. PLoS ONE 2015, 10, e0129260
56. Athersuch, T. Metabolome analyses in exposome studies: Profiling methods for a vast chemical space. Arch. Biochem. Biophys. 2016, 589, 177-186
57. Pauling, L.; Robinson, A.B.; Teranishi, R.; Cary, P. Quantitative analysis of urine vapor and breath by gas-liquid partition chromatography. Proc. Natl. Acad. Sci. USA 1971, 68, 2374-2376
58. Jimenez, B.; Montoliu, C.; MacIntyre, D.A.; Serra, M.A.; Wassel, A.; Jover, M.; Romero-Gomez, M.; Rodrigo, J.M.; Pineda-Lucena, A.; Felipo, V. Serum metabolic signature of minimal hepatic encephalopathy by (1) h-nuclear magnetic resonance. J. Proteome Res. 2010, 9, 5180-5187
59. Wijeyesekera, A.; Selman, C.; Barton, R.H.; Holmes, E.; Nicholson, J.K.; Withers, D.J. Metabotyping of long-lived mice using 1 h NMR spectroscopy. J. Proteome Res. 2012, 11, 2224-2235
60. Larive, C.K.; Barding, G.A., Jr.; Dinges, M.M. NMR spectroscopy for metabolomics and metabolic profiling. Anal. Chem. 2015, 87, 133-146
61. Auray-Blais, C.; Boutin, M. Novel GB(3) isoforms detected in urine of fabry disease patients: A metabolomic study. Curr. Med. Chem. 2012, 19, 3241-3252
62. Manwaring, V.; Boutin, M.; Auray-Blais, C. A metabolomic study to identify new globotriaosylceramide-related biomarkers in the plasma of fabry disease patients. Anal. Chem. 2013, 85, 9039-9048
63. Emwas, A.-H.; Salek, R.; Griffin, J.; Merzaban, J. Nmr-based metabolomics in human disease diagnosis: Applications, limitations, and recommendations. Metabolomics 2013, 9, 1048-1072
64. Chan, E.C.Y.; Pasikanti, K.K.; Nicholson, J.K. Global urinary metabolic profiling procedures using gas chromatography-mass spectrometry. Nat. Protoc. 2011, 6, 1483-1499
65. Ramautar, R.; Somsen, G.W.; de Jong, G.J. CE-MS for metabolomics: Developments and applications in the period 2012-2014. Electrophoresis 2015, 36, 212-224
66. Hill, H.H., Jr.; Siems, W.F.; st Louis, R.H.; McMinn, D.G. Ion mobility spectrometry. Anal. Chem. 1990, 62, 1201A-1209A
67. Paglia, G.; Angel, P.; Williams, J.P.; Richardson, K.; Olivos, H.J.; Thompson, J.W.; Menikarachchi, L.; Lai, S.; Walsh, C.; Moseley, A.; et al. Ion mobility-derived collision cross section as an additional measure for lipid fingerprinting and identification. Anal. Chem. 2015, 87, 1137-1144
68. Maldini, M.; Natella, F.; Baima, S.; Morelli, G.; Scaccini, C.; Langridge, J.; Astarita, G. Untargeted metabolomics reveals predominant alterations in lipid metabolism following light exposure in broccoli sprouts. Int. J. Mol. Sci. 2015, 16, 13678-13691
69. Paglia, G.; Williams, J.P.; Menikarachchi, L.; Thompson, J.W.; Tyldesley-Worster, R.; Halldórsson, S.; Rolfsson, O.; Moseley, A.; Grant, D.; Langridge, J.; et al. Ion mobility derived collision cross sections to support metabolomics applications. Anal. Chem. 2014, 86, 3985-3993
70. Wickramasekara, S.I.; Zandkarimi, F.; Morre, J.; Kirkwood, J.; Legette, L.; Jiang, Y.; Gombart, A.F.; Stevens, J.F.; Maier, C.S. Electrospray quadrupole travelling wave ion mobility time-of-flight mass spectrometry for the detection of plasma metabolome changes caused by xanthohumol in obese zucker (fa/fa) rats. Metabolites 2013, 3, 701-717
71. Dwivedi, P.; Schultz, A.J.; Hill, H.H. Metabolic profiling of human blood by high resolution ion mobility mass spectrometry (IM-MS). Int. J. Mass Spectrom. 2010, 298, 78-90
72. Hauschild, A.C.; Frisch, T.; Baumbach, J.I.; Baumbach, J. Carotta: Revealing hidden confounder markers in metabolic breath profiles. Metabolites 2015, 5, 344-363
73. Smolinska, A.; Hauschild, A.C.; Fijten, R.R.; Dallinga, J.W.; Baumbach, J.; van Schooten, F.J. Current breathomics-A review on data pre-processing techniques and machine learning in metabolomics breath analysis. J. Breath Res. 2014
74. Fenn, L.; Kliman, M.; Mahsut, A.; Zhao, S.; McLean, J. Characterizing ion mobility-mass spectrometry conformation space for the analysis of complex biological samples. Anal. Bioanal. Chem. 2009, 394, 235-244
75. Fenn, L.; McLean, J. Biomolecular structural separations by ion mobility-mass spectrometry. Anal. Bioanal. Chem. 2008, 391, 905-909
76. Kliman, M.; May, J.C.; McLean, J.A. Lipid analysis and lipidomics by structurally selective ion mobility-mass spectrometry. Biochim. Biophys. Acta 2011, 1811, 935-945
77. Tebani, A.; Schmitz-Afonso, I.; Rutledge, D.N.; Gonzalez, B.J.; Bekri, S.; Afonso, C. Optimization of a liquid chromatography ion mobility-mass spectrometry method for untargeted metabolomics using experimental design and multivariate data analysis. Anal. Chim. Acta 2016, 913, 55-62
78. May, J.C.; Goodwin, C.R.; McLean, J.A. Ion mobility-mass spectrometry strategies for untargeted systems, synthetic, and chemical biology. Curr. Opin. Biotechnol. 2015, 31, 117-121
79. Sherrod, S.D.; McLean, J.A. Systems-wide high-dimensional data acquisition and informatics using structural mass spectrometry strategies. Clin. Chem. 2015, 62, 77-83
80. Junot, C.; Madalinski, G.; Tabet, J.C.; Ezan, E. Fourier transform mass spectrometry for metabolome analysis. Analyst 2010, 135, 2203-2219
81. Twohig, M.; Shockcor, J.P.; Wilson, I.D.; Nicholson, J.K.; Plumb, R.S. Use of an atmospheric solids analysis probe (ASAP) for high throughput screening of biological fluids: Preliminary applications on urine and bile. J. Proteome Res. 2010, 9, 3590-3597
82. Eberlin, L.S.; Norton, I.; Orringer, D.; Dunn, I.F.; Liu, X.; Ide, J.L.; Jarmusch, A.K.; Ligon, K.L.; Jolesz, F.A.; Golby, A.J.; et al. Ambient mass spectrometry for the intraoperative molecular diagnosis of human brain tumors. Proc. Natl. Acad. Sci. USA 2013, 110, 1611-1616
83. Ferreira, C.R.; Jarmusch, A.K.; Pirro, V.; Alfaro, C.M.; Gonzalez-Serrano, A.F.; Niemann, H.; Wheeler, M.B.; Rabel, R.A.; Hallett, J.E.; Houser, R.; et al. Ambient ionisation mass spectrometry for lipid profiling and structural analysis of mammalian oocytes, preimplantation embryos and stem cells. Reprod. Fertil. Dev. 2015, 27, 621-637
84. Kerian, K.S.; Jarmusch, A.K.; Pirro, V.; Koch, M.O.; Masterson, T.A.; Cheng, L.; Cooks, R.G. Differentiation of prostate cancer from normal tissue in radical prostatectomy specimens by desorption electrospray ionization and touch spray ionization mass spectrometry. Analyst 2015, 140, 1090-1098
85. Balog, J.; Sasi-Szabo, L.; Kinross, J.; Lewis, M.R.; Muirhead, L.J.; Veselkov, K.; Mirnezami, R.; Dezso, B.; Damjanovich, L.; Darzi, A.; et al. Intraoperative tissue identification using rapid evaporative ionization mass spectrometry. Sci. Transl. Med. 2013
86. Balog, J.; Kumar, S.; Alexander, J.; Golf, O.; Huang, J.; Wiggins, T.; Abbassi-Ghadi, N.; Enyedi, A.; Kacska, S.; Kinross, J.; et al. In vivo endoscopic tissue identification by rapid evaporative ionization mass spectrometry (REIMS). Angew. Chem. Int. Ed. Engl. 2015, 54, 11059-11062
87. Ifa, D.R.; Eberlin, L.S. Ambient ionization mass spectrometry for cancer diagnosis and surgical margin evaluation. Clin. Chem. 2016, 62, 111-123
88. Annesley, T.; Diamandis, E.; Bachmann, L.; Hanash, S.; Hart, B.; Javahery, R.; Singh, R.; Smith, R. A spectrum of views on clinical mass spectrometry. Clin. Chem. 2016, 62, 30-36
89. Wishart, D.S.; Jewison, T.; Guo, A.C.; Wilson, M.; Knox, C.; Liu, Y.; Djoumbou, Y.; Mandal, R.; Aziat, F.; Dong, E.; et al. Hmdb 3.0-The human metabolome database in 2013. Nucleic Acids Res. 2013, 41, D801-D807
90. Broadhurst, D.I.; Kell, D.B. Statistical strategies for avoiding false discoveries in metabolomics and related experiments. Metabolomics 2006, 2, 171-196
91. Benjamini, Y.; Hochberg, Y. Controlling the false discovery rate: A practical and powerful approach to multiple testing. J. R. Stat. Soc. 1995, 57, 289-300.
92. Hogeweg, P. The roots of bioinformatics in theoretical biology. PLoS Comput. Biol. 2011, 7, e1002021
93. Luscombe, N.M.; Greenbaum, D.; Gerstein, M. What is bioinformatics? A proposed definition and overview of the field. Methods Inf. Med. 2001, 40, 346-358
94. Brereton, R.G. A short history of chemometrics: A personal view. J. Chemom. 2014, 28, 749-760
95. Hotelling, H. Analysis of a Complex of Statistical Variables into Principal Components; Warwick & York: Oradell, NJ, USA, 1933.
96. Hartigan, J.A.; Wong, M.A. Algorithm as 136: A k-means clustering algorithm. J. R. Stat. Soc. 1979, 28, 100-108
97. Johnson, S.C. Hierarchical clustering schemes. Psychometrika 1967, 32, 241-254
98. Kohonen, T. The self-organizing map. Proc. IEEE 1990, 78, 1464-1480
99. Wold, S.; Sjöström, M.; Eriksson, L. PLS-regression: A basic tool of chemometrics. Chemom. Intell. Lab. Syst. 2001, 58, 109-130
100. Trygg, J.; Wold, S. Orthogonal projections to latent structures (O-PLS). J. Chemom. 2002, 16, 119-128
101. Cortes, C.; Vapnik, V. Support-vector networks. Mach. Learn. 1995, 20, 273-297
102. Offroy, M.; Duponchel, L. Topological data analysis: A promising big data exploration tool in biology, analytical chemistry and physical chemistry. Anal. Chim. Acta 2016, 910, 1-11
103. Ren, S.; Hinzman, A.; Kang, E.; Szczesniak, R.; Lu, L. Computational and statistical analysis of metabolomics data. Metabolomics 2015, 11, 1492-1513
104. Gromski, P.S.; Muhamadali, H.; Ellis, D.I.; Xu, Y.; Correa, E.; Turner, M.L.; Goodacre, R. A tutorial review: Metabolomics and partial least squares-discriminant analysis-A marriage of convenience or a shotgun wedding. Anal. Chim. Acta 2015, 879, 10-23
105. Misra, B.B.; van der Hooft, J.J. Updates in metabolomics tools and resources: 2014-2015. Electrophoresis 2016, 37, 86-110
106. Thiele, I.; Swainston, N.; Fleming, R.M.; Hoppe, A.; Sahoo, S.; Aurich, M.K.; Haraldsdottir, H.; Mo, M.L.; Rolfsson, O.; Stobbe, M.D.; et al. A community-driven global reconstruction of human metabolism. Nat. Biotechnol. 2013, 31, 419-425
107. Cazzaniga, P.; Damiani, C.; Besozzi, D.; Colombo, R.; Nobile, M.S.; Gaglio, D.; Pescini, D.; Molinari, S.; Mauri, G.; Alberghina, L.; et al. Computational strategies for a system-level understanding of metabolism. Metabolites 2014, 4, 1034-1087
108. Garcia-Campos, M.A.; Espinal-Enriquez, J.; Hernandez-Lemus, E. Pathway analysis: State of the art. Front. Physiol. 2015
109. Khatri, P.; Sirota, M.; Butte, A.J. Ten years of pathway analysis: Current approaches and outstanding challenges. PLoS Comput. Biol. 2012, 8, e1002375
110. Kanehisa, M.; Sato, Y.; Kawashima, M.; Furumichi, M.; Tanabe, M. Kegg as a reference resource for gene and protein annotation. Nucleic Acids Res. 2016, 44, D457-D462
111. Romero, P.; Wagg, J.; Green, M.L.; Kaiser, D.; Krummenacker, M.; Karp, P.D. Computational prediction of human metabolic pathways from the complete human genome. Genome Biol. 2005
112. Caspi, R.; Foerster, H.; Fulcher, C.A.; Kaipa, P.; Krummenacker, M.; Latendresse, M.; Paley, S.; Rhee, S.Y.; Shearer, A.G.; Tissier, C.; et al0The metacyc database of metabolic pathways and enzymes and the biocyc collection of pathway/genome databases. Nucleic Acids Res. 2008, 36, D623-D631
113. Vastrik, I.; D’Eustachio, P.; Schmidt, E.; Gopinath, G.; Croft, D.; de Bono, B.; Gillespie, M.; Jassal, B.; Lewis, S.; Matthews, L. Reactome: A knowledge base of biologic pathways and processes. Genome Biol. 2007
114. Jewison, T.; Su, Y.; Disfany, F.M.; Liang, Y.; Knox, C.; Maciejewski, A.; Poelzer, J.; Huynh, J.; Zhou, Y.; Arndt, D.; et al. Smpdb 2.0: Big improvements to the small molecule pathway database. Nucleic Acids Res. 2014, 42, D478-D484
115. Kelder, T.; van Iersel, M.P.; Hanspers, K.; Kutmon, M.; Conklin, B.R.; Evelo, C.T.; Pico, A.R. Wikipathways: Building research communities on biological pathways. Nucleic Acids Res. 2012, 40, D1301-D1307
116. Xia, J.; Wishart, D.S. MSEA: A web-based tool to identify biologically meaningful patterns in quantitative metabolomic data. Nucleic Acids Res. 2010, 38, W71-W77
117. Adjaye, J.; Huntriss, J.; Herwig, R.; BenKahla, A.; Brink, T.C.; Wierling, C.; Hultschig, C.; Groth, D.; Yaspo, M.L.; Picton, H.M.; et al. Primary differentiation in the human blastocyst: Comparative molecular portraits of inner cell mass and trophectoderm cells. Stem Cells 2005, 23, 1514-1525
118. Goeman, J.J.; van de Geer, S.A.; de Kort, F.; van Houwelingen, H.C. A global test for groups of genes: Testing association with a clinical outcome. Bioinformatics 2004, 20, 93-99
119. Hummel, M.; Meister, R.; Mansmann, U. Globalancova: Exploration and assessment of gene group effects. Bioinformatics 2008, 24, 78-85
120. Xia, J.; Sinelnikov, I.V.; Han, B.; Wishart, D.S. Metaboanalyst 3.0-Making metabolomics more meaningful. Nucleic Acids Res. 2015, 43, W251-W257
121. Xia, J.; Wishart, D.S. METPA: A web-based metabolomics tool for pathway analysis and visualization. Bioinformatics 2010, 26, 2342-2344
122. Steuer, R. Review: On the analysis and interpretation of correlations in metabolomic data. Brief. Bioinform. 2006, 7, 151-158
123. Krumsiek, J.; Suhre, K.; Illig, T.; Adamski, J.; Theis, F.J. Gaussian graphical modeling reconstructs pathway reactions from high-throughput metabolomics data. BMC Syst. Biol. 2011
124. Valcarcel, B.; Wurtz, P.; Seich al Basatena, N.K.; Tukiainen, T.; Kangas, A.J.; Soininen, P.; Jarvelin, M.R.; Ala-Korpela, M.; Ebbels, T.M.; de Iorio, M. A differential network approach to exploring differences between biological states: An application to prediabetes. PLoS ONE 2011, 6, e24702
125. Bartel, J.; Krumsiek, J.; Schramm, K.; Adamski, J.; Gieger, C.; Herder, C.; Carstensen, M.; Peters, A.; Rathmann, W.; Roden, M.; et al. The human blood metabolome-transcriptome interface. PLoS Genet. 2015, 11, e1005274
126. Shin, S.Y.; Fauman, E.B.; Petersen, A.K.; Krumsiek, J.; Santos, R.; Huang, J.; Arnold, M.; Erte, I.; Forgetta, V.; Yang, T.P.; et al. An atlas of genetic influences on human blood metabolites. Nat. Genet. 2014, 46, 543-550
127. Li, S.; Park, Y.; Duraisingham, S.; Strobel, F.H.; Khan, N.; Soltow, Q.A.; Jones, D.P.; Pulendran, B. Predicting network activity from high throughput metabolomics. PLoS Comput. Biol. 2013, 9, e1003123
128. Caspi, R.; Billington, R.; Ferrer, L.; Foerster, H.; Fulcher, C.A.; Keseler, I.M.; Kothari, A.; Krummenacker, M.; Latendresse, M.; Mueller, L.A.; et al. The metacyc database of metabolic pathways and enzymes and the biocyc collection of pathway/genome databases. Nucleic Acids Res. 2016, 44, D471-D480
129. Yamada, T.; Letunic, I.; Okuda, S.; Kanehisa, M.; Bork, P. Ipath2.0: Interactive pathway explorer. Nucleic Acids Res. 2011, 39, W412-W415
130. Karnovsky, A.; Weymouth, T.; Hull, T.; Tarcea, V.G.; Scardoni, G.; Laudanna, C.; Sartor, M.A.; Stringer, K.A.; Jagadish, H.V.; Burant, C.; et al. Metscape 2 bioinformatics tool for the analysis and visualization of metabolomics and gene expression data. Bioinformatics 2012, 28, 373-380
131. Garcia-Alcalde, F.; Garcia-Lopez, F.; Dopazo, J.; Conesa, A. Paintomics: A web based tool for the joint visualization of transcriptomics and metabolomics data. Bioinformatics 2011, 27, 137-139
132. Leader, D.P.; Burgess, K.; Creek, D.; Barrett, M.P. Pathos: A web facility that uses metabolic maps to display experimental changes in metabolites identified by mass spectrometry. Rapid Commun. Mass Spectrom. 2011, 25, 3422-3426
133. Kutmon, M.; van Iersel, M.P.; Bohler, A.; Kelder, T.; Nunes, N.; Pico, A.R.; Evelo, C.T. Pathvisio 3: An extendable pathway analysis toolbox. PLoS Comput. Biol. 2015, 11, e1004085
134. Rohn, H.; Junker, A.; Hartmann, A.; Grafahrend-Belau, E.; Treutler, H.; Klapperstück, M.; Czauderna, T.; Klukas, C.; Schreiber, F. Vanted v2: A framework for systems biology applications. BMC Syst. Biol. 2012, 6, 1-13
135. Kamburov, A.; Cavill, R.; Ebbels, T.M.D.; Herwig, R.; Keun, H.C. Integrated pathway-level analysis of transcriptomics and metabolomics data with impala. Bioinformatics 2011, 27, 2917-2918
136. Lopez-Ibanez, J.; Pazos, F.; Chagoyen, M. Mbrole 2.0-functional enrichment of chemical compounds. Nucleic Acids Res. 2016, 44, W201-W204
137. Kankainen, M.; Gopalacharyulu, P.; Holm, L.; Oresic, M. Mpea-Metabolite pathway enrichment analysis. Bioinformatics 2011, 27, 1878-1879
138. Tautenhahn, R.; Patti, G.J.; Rinehart, D.; Siuzdak, G. Xcms online: A web-based platform to process untargeted metabolomic data. Anal. Chem. 2012, 84, 5035-5039
139. Winkler, R. An evolving computational platform for biological mass spectrometry: Workflows, statistics and data mining with massypup64. PeerJ 2015, 3, e1401
140. Giacomoni, F.; le Corguille, G.; Monsoor, M.; Landi, M.; Pericard, P.; Petera, M.; Duperier, C.; Tremblay-Franco, M.; Martin, J.F.; Jacob, D.; et al. Workflow4metabolomics: A collaborative research infrastructure for computational metabolomics. Bioinformatics 2015, 31, 1493-1495
141. Mak, T.D.; Laiakis, E.C.; Goudarzi, M.; Fornace, A.J., Jr. Metabolyzer: A novel statistical workflow for analyzing postprocessed LC-MS metabolomics data. Anal. Chem. 2014, 86, 506-513
142. Cascante, M.; Marin, S. Metabolomics and fluxomics approaches. Essays Biochem. 2008, 45, 67-82
143. Cortassa, S.; Aon, M.A. Computational modeling of mitochondrial function. Methods Mol. Biol. 2012, 810, 311-326
144. Winter, G.; Kromer, J.O. Fluxomics-Connecting “omics” analysis and phenotypes. Environ. Microbiol. 2013, 15, 1901-1916
145. Aurich, M.K.; Thiele, I. Computational modeling of human metabolism and its application to systems biomedicine. Methods Mol. Biol. 2016, 1386, 253-281
146. Cortassa, S.; Caceres, V.; Bell, L.N.; O’Rourke, B.; Paolocci, N.; Aon, M.A. From metabolomics to fluxomics: A computational procedure to translate metabolite profiles into metabolic fluxes. Biophys. J. 2015, 108, 163-172
147. Cho, I.; Blaser, M.J. The human microbiome: At the interface of health and disease. Nat. Rev. Genet. 2012, 13, 260-270
148. 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.; et al. Human metabolic phenotype diversity and its association with diet and blood pressure. Nature 2008, 453, 396-400
149. Garrod, A.E. The Inborn Factors in Disease; Clarendon Press: Oxford, UK, 1931.
150. Beebe, K.; Kennedy, A.D. Sharpening precision medicine by a thorough interrogation of metabolic individuality. Comput. Struct. Biotechnol. J. 2016, 14, 97-105
151. Auray-Blais, C.; Maranda, B.; Lavoie, P. High-throughput tandem mass spectrometry multiplex analysis for newborn urinary screening of creatine synthesis and transport disorders, triple H syndrome and otc deficiency. Clin. Chim. Acta 2014, 436, 249-255
152. Pitt, J.J. Principles and applications of liquid chromatography-mass spectrometry in clinical biochemistry. Clin. Biochem. Rev. 2009, 30, 19-34
153. Pitt, J.J. Newborn screening. Clin. Biochem. Rev. 2010, 31, 57-68
154. Pitt, J.J.; Eggington, M.; Kahler, S.G. Comprehensive screening of urine samples for inborn errors of metabolism by electrospray tandem mass spectrometry. Clin. Chem. 2002, 48, 1970-1980
155. Spacil, Z.; Tatipaka, H.; Barcenas, M.; Scott, C.R.; Turecek, F.; Gelb, M.H. High-throughput assay of 9 lysosomal enzymes for newborn screening. Clin. Chem. 2013, 59, 502-511
156. Therrell, B.L.; Padilla, C.D.; Loeber, J.G.; Kneisser, I.; Saadallah, A.; Borrajo, G.J.; Adams, J. Current status of newborn screening worldwide: 2015. Semin. Perinatol. 2015, 39, 171-187
157. Janeckova, H.; Hron, K.; Wojtowicz, P.; Hlidkova, E.; Baresova, A.; Friedecky, D.; Zidkova, L.; Hornik, P.; Behulova, D.; Prochazkova, D.; et al. Targeted metabolomic analysis of plasma samples for the diagnosis of inherited metabolic disorders. J. Chromatogr. A 2012, 1226, 11-17
158. Dercksen, M.; Koekemoer, G.; Duran, M.; Wanders, R.J.A.; Mienie, L.J.; Reinecke, C.J. Organic acid profile of isovaleric acidemia: A comprehensive metabolomics approach. Metabolomics 2013, 9, 765-777
159. Ostermann, K.M.; Dieplinger, R.; Lutsch, N.M.; Strupat, K.; Metz, T.F.; Mechtler, T.P.; Kasper, D.C. Matrix-assisted laser desorption/ionization for simultaneous quantitation of (acyl-)carnitines and organic acids in dried blood spots. Rapid Commun. Mass Spectrom. 2013, 27, 1497-1504
160. Fan, M.; Sidhu, R.; Fujiwara, H.; Tortelli, B.; Zhang, J.; Davidson, C.; Walkley, S.U.; Bagel, J.H.; Vite, C.; Yanjanin, N.M.; et al. Identification of niemann-pick c1 disease biomarkers through sphingolipid profiling. J. Lipid Res. 2013, 54, 2800-2814
161. Reinecke, C.J.; Koekemoer, G.; Westhuizen, F.H.; Louw, R.; Lindeque, J.Z.; Mienie, L.J.; Smuts, I. Metabolomics of urinary organic acids in respiratory chain deficiencies in children. Metabolomics 2011, 8, 264-283
162. Smuts, I.; Westhuizen, F.H.; Louw, R.; Mienie, L.J.; Engelke, U.F.H.; Wevers, R.A.; Mason, S.; Koekemoer, G.; Reinecke, C.J. Disclosure of a putative biosignature for respiratory chain disorders through a metabolomics approach. Metabolomics 2012, 9, 379-391
163. Venter, L.; Lindeque, Z.; Jansen van Rensburg, P.; van der Westhuizen, F.; Smuts, I.; Louw, R. Untargeted urine metabolomics reveals a biosignature for muscle respiratory chain deficiencies. Metabolomics 2014, 11, 111-121
164. Wikoff, W.R.; Gangoiti, J.A.; Barshop, B.A.; Siuzdak, G. Metabolomics identifies perturbations in human disorders of propionate metabolism. Clin. Chem. 2007, 53, 2169-2176
165. Auray-Blais, C.; Boutin, M.; Gagnon, R.; Dupont, F.O.; Lavoie, P.; Clarke, J.T. Urinary globotriaosylsphingosine-related biomarkers for fabry disease targeted by metabolomics. Anal. Chem. 2012, 84, 2745-2753
166. Shlomi, T.; Cabili, M.N.; Ruppin, E. Predicting metabolic biomarkers of human inborn errors of metabolism. Mol. Syst. Biol. 2009
167. Mutze, U.; Beblo, S.; Kortz, L.; Matthies, C.; Koletzko, B.; Bruegel, M.; Rohde, C.; Thiery, J.; Kiess, W.; Ceglarek, U. Metabolomics of dietary fatty acid restriction in patients with phenylketonuria. PLoS ONE 2012, 7, e43021
168. Pan, Z.; Gu, H.; Talaty, N.; Chen, H.; Shanaiah, N.; Hainline, B.E.; Cooks, R.G.; Raftery, D. Principal component analysis of urine metabolites detected by nmr and desi-ms in patients with inborn errors of metabolism. Anal. Bioanal. Chem. 2007, 387, 539-549
169. Gertsman, I.; Gangoiti, J.A.; Barshop, B.A. Validation of a dual LC-HRMS platform for clinical metabolic diagnosis in serum, bridging quantitative analysis and untargeted metabolomics. Metabolomics 2014, 10, 312-323
170. Miller, M.; Kennedy, A.; Eckhart, A.; Burrage, L.; Wulff, J.; Miller, L.D.; Milburn, M.; Ryals, J.; Beaudet, A.; Sun, Q.; et al. Untargeted metabolomic analysis for the clinical screening of inborn errors of metabolism. J. Inherit. Metab. Dis. 2015, 1-11
171. Aygen, S.; Dürr, U.; Hegele, P.; Kunig, J.; Spraul, M.; Schäfer, H.; Krings, D.; Cannet, C.; Fang, F.; Schütz, B.; et al. NMR-based screening for inborn errors of metabolism: Initial results from a study on turkish neonates. JIMD Rep. 2014, 16, 101-111
172. Goodacre, R.; Broadhurst, D.; Smilde, A.K.; Kristal, B.S.; Baker, J.D.; Beger, R.; Bessant, C.; Connor, S.; Capuani, G.; Craig, A. Proposed minimum reporting standards for data analysis in metabolomics. Metabolomics 2007, 3, 231-241
173. Chitayat, S.; Rudan, J.F. Chapter 10-Phenome centers and global harmonization. In Metabolic Phenotyping in Personalized and Public Healthcare; Academic Press: Boston, MA, USA, 2016; pp. 291-315.
174. Kohler, I.; Verhoeven, A.; Derks, R.J.; Giera, M. Analytical pitfalls and challenges in clinical metabolomics. Bioanalysis 2016, 8, 1509-1532
175. Alyass, A.; Turcotte, M.; Meyre, D. From big data analysis to personalized medicine for all: Challenges and opportunities. BMC Med. Genom. 2015, 8, 1-12
176. Tarailo-Graovac, M.; Shyr, C.; Ross, C.J.; Horvath, G.A.; Salvarinova, R.; Ye, X.C.; Zhang, L.H.; Bhavsar, A.P.; Lee, J.J.; Drogemoller, B.I.; et al. Exome sequencing and the management of neurometabolic disorders. N. Engl. J. Med. 2016, 374, 2246-2255
177. Sahoo, S.; Franzson, L.; Jonsson, J.J.; Thiele, I. A compendium of inborn errors of metabolism mapped onto the human metabolic network. Mol. Biosyst. 2012, 8, 2545-2558