Metabolomics in multiple sclerosis

Abstract

Multiple sclerosis (MS) is a chronic demyelinating disorder of the central nervous system with inflammatory and degenerative components. The cause of MS remains unknown although genetic and environmental factors appear to play a role in its etiopathogenesis. Metabolomics is a new “omics” technology that aims at measuring small molecules in various biological matrices and can provide information that is not readily obtained from genomics, transcriptomics, or proteomics. Currently, several different analytical platforms exist for metabolomics, and both untargeted and targeted approaches are being employed. Methods of analysis of metabolomics data are also being developed and no consensus currently exists on the optimal approach to analysis and interpretation of these data. Metabolomics has the potential to provide putative biomarkers, insights into the pathophysiology of the disease, and to aid in precision medicine for patients with MS.

Keywords

Metabolomics multiple sclerosis mass spectrometry nuclear magnetic resonance biomarkers

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References

Compston

Coles

Multiple sclerosis. Lancet 2008; 372(9648): 1502–1517, http://www.ncbi.nlm.nih.gov/pubmed/18970977 (accessed 11 December 2013).

O’Connell

Kelly

Fogarty

. Economic costs associated with an MS relapse. Mult Scler Relat Disord 2014; 3(6): 678–683, http://www.ncbi.nlm.nih.gov/pubmed/25891546 (accessed 11 October 2015).

Adelman

Rane

Villa

KF.

The cost burden of multiple sclerosis in the United States: A systematic review of the literature. J Med Econ 2013; 16(5): 639–647, http://www.ncbi.nlm.nih.gov/pubmed/23425293 (accessed 30 August 2015).

Beecham

Patsopoulos

Xifara

. Analysis of immune-related loci identifies 48 new susceptibility variants for multiple sclerosis. Nat Genet 2013; 45(11): 1353–1360, http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3832895…tool=pmcentrez…rendertype=abstract (accessed 18 March 2015).

Isobe

Madireddy

Khankhanian

. An ImmunoChip study of multiple sclerosis risk in African Americans. Brain 2015;138(Pt 6): 1518–1530, http://www.ncbi.nlm.nih.gov/pubmed/25818868 (accessed 11 October 2015).

Kroksveen

Jaffe

Aasebø

. Quantitative proteomics suggests decrease in the secretogranin-1 cerebrospinal fluid levels during the disease course of multiple sclerosis. Proteomics 2015; 15(19): 3361–3369, http://www.ncbi.nlm.nih.gov/pubmed/26152395 (accessed 11 October 2015).

Huynh

Garg

Thin

. Epigenome-wide differences in pathology-free regions of multiple sclerosis-affected brains. Nat Neurosci 2014; 17(1): 121–130, http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3934491…tool=pmcentrez…rendertype=abstract (accessed 6 August 2015).

Duarte

Diaz

Gil

AM.

NMR metabolomics of human blood and urine in disease research. J Pharm Biomed Anal 2014; 93: 17–26, http://www.sciencedirect.com/science/article/pii/S0731708513004500 (accessed 6 September 2015).

Scrivo

Casadei

Valerio

. Metabolomics approach in allergic and rheumatic diseases. Curr Allergy Asthma Rep 2014; 14(6): 445, http://www.ncbi.nlm.nih.gov/pubmed/24744271 (accessed 6 September 2015).

10.

Manna

Patterson

Yang

. 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(9): 4120–4133, http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3170755…tool=pmcentrez…rendertype=abstract (accessed 19 January 2014).

11.

Blanchet

Smolinska

Attali

. Fusion of metabolomics and proteomics data for biomarkers discovery: Case study on the experimental autoimmune encephalomyelitis. BMC Bioinformatics 2011; 12: 254, http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3225201…tool=pmcentrez…rendertype=abstract (accessed 6 September 2015).

12.

Guo

Milburn

Ryals

. Plasma metabolomic profiles enhance precision medicine for volunteers of normal health. Proc Natl Acad Sci 2015; 112(35): E4901–E4910, http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=4568216…tool=pmcentrez…rendertype=abstract (accessed 18 August 2015).

13.

Sas

Karnovsky

Michailidis

. Metabolomics and diabetes: Analytical and computational approaches. Diabetes 2015; 64(3): 718–732, http://diabetes.diabetesjournals.org.ezp.welch.jhmi.edu/content/64/3/718.long (accessed 5 September 2015).

14.

Dunn

Wilson

Nicholls

. The importance of experimental design and QC samples in large-scale and MS-driven untargeted metabolomic studies of humans. Bioanalysis 2012; 4(18): 2249–2264, http://www.ncbi.nlm.nih.gov/pubmed/23046267 (accessed 14 October 2015).

15.

Zamboni

Saghatelian

Patti

GJ.

Defining the metabolome: Size, flux, and regulation. Mol Cell 2015; 58(4): 699–706, http://www.sciencedirect.com/science/article/pii/S1097276515003032 (accessed 25 May 2015).

16.

Want

Wilson

Gika

. Global metabolic profiling procedures for urine using UPLC-MS. Nat Protoc 2010; 5(6): 1005–1018, http://dx.doi.org/10.1038/nprot.2010.50 (accessed 24 July 2015).

17.

Want

Masson

Michopoulos

. Global metabolic profiling of animal and human tissues via UPLC-MS. Nat Protoc 2013; 8(1): 17–32, http://dx.doi.org/10.1038/nprot.2012.135 (accessed 14 October 2015).

18.

Smolinska

Posma

Blanchet

. Simultaneous analysis of plasma and CSF by NMR and hierarchical models fusion. Anal Bioanal Chem 2012; 403(4): 947–959, http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3336062…tool=pmcentrez…rendertype=abstract (accessed 6 September 2015).

19.

Alonso

Marsal

Julià

Analytical methods in untargeted metabolomics: State of the art in 2015. Front Bioeng Biotechnol 2015; 3: 23, http://journal.frontiersin.org/article/10.3389/fbioe.2015.00023/abstract (accessed 17 September 2015).

20.

Cao

Lamichhane

Lundgren

. Metabolic characterization of triple negative breast cancer. BMC Cancer 2014; 14: 941, http://pmc/articles/PMC4295321/?report=abstract (accessed 7 July 2015).

21.

Smith

O’Maille

Want

. METLIN: A metabolite mass spectral database. Ther Drug Monit 2005; 27(6): 747–751, http://www.ncbi.nlm.nih.gov/pubmed/16404815 (accessed 22 September 2015).

22.

Horai

Arita

Kanaya

. MassBank: A public repository for sharing mass spectral data for life sciences. J Mass Spectrum 2010; 45(7): 703–714, http://www.ncbi.nlm.nih.gov/pubmed/20623627 (accessed 21 September 2015).

23.

Benjamini

Hochberg

Controlling the false discovery rate: A practical and powerful approach to multiple testing. J R Stat Soc Ser B Methodol 57(1): 289–300, http://cat.inist.fr/?aModele=afficheN…cpsidt=3457006 (accessed 15 October 2015).

24.

Valcárcel

Würtz

Seich

Basatena

N-K

. A differential network approach to exploring differences between biological states: An application to prediabetes. PLoS ONE 2011; 6(9): e24702, http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0024702 (accessed 15 October 2015).

25.

Langfelder

Horvath

WGCNA: An R package for weighted correlation network analysis. BMC Bioinformatics 2008; 9: 559, http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2631488…tool=pmcentrez…rendertype=abstract (accessed 11 July 2014).

26.

Demchak

Hull

Reich

. Cytoscape: The network visualization tool for GenomeSpace workflows. F1000Res 2014; 3: 151, http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=4133763…tool=pmcentrez…rendertype=abstract (accessed 15 October 2015).

27.

Gao

Tarcea

Karnovsky

. Metscape: A Cytoscape plug-in for visualizing and interpreting metabolomic data in the context of human metabolic networks. Bioinformatics 2010; 26(7): 971–973, http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2844990…tool=pmcentrez…rendertype=abstract (accessed 15 October 2015).

28.

Rohn

Junker

Hartmann

. VANTED v2: A framework for systems biology applications. BMC Syst Biol 2012; 6: 139, http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3610154…tool=pmcentrez…rendertype=abstract (accessed 8 October 2015).

29.

Xia

Sinelnikov

Han

. MetaboAnalyst 3.0—making metabolomics more meaningful. Nucleic Acids Res 2015; 43(W1): W251–W257, http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=4489235…tool=pmcentrez…rendertype=abstract (accessed 21 April 2015).

30.

Patel

Ahmed

Emerging field of metabolomics: Big promise for cancer biomarker identification and drug discovery. J Pharm Biomed Anal 2014; 107: 63–74, http://www.ncbi.nlm.nih.gov/pubmed/25569286 (accessed 2 January 2015).

31.

Noga

Dane

Shi

. Metabolomics of cerebrospinal fluid reveals changes in the central nervous system metabolism in a rat model of multiple sclerosis. Metabolomics 2012; 8(2): 253–263,http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3291832…tool=pmcentrez…rendertype=abstract (accessed 14 January 2014).

32.

Mangalam

Poisson

Nemutlu

. Profile of circulatory metabolites in a relapsing-remitting animal model of multiple sclerosis using global metabolomics. J Clin Cell Immunol 2013; 4, http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3837296…tool=pmcentrez…rendertype=abstract (accessed 30 December 2013).

33.

‘t Hart

Vogels

JTWE

Spijksma

. 1H-NMR spectroscopy combined with pattern recognition analysis reveals characteristic chemical patterns in urines of MS patients and non-human primates with MS-like disease. J Neurol Sci 2003; 212(1–2): 21–30, http://www.ncbi.nlm.nih.gov/pubmed/12809995 (accessed 23 September 2015).

34.

Gebregiworgis

Massilamany

Gangaplara

. Potential of urinary metabolites for diagnosing multiple sclerosis. ACS Chem Biol 2013; 8(4): 684–690, http://www.ncbi.nlm.nih.gov/pubmed/23369377

35.

Lynch

Peeling

Auty

. Nuclear magnetic resonance study of cerebrospinal fluid from patients with multiple sclerosis. Can J Neurol Sci 1993; 20(3): 194–198, http://www.ncbi.nlm.nih.gov/pubmed/8221382 (accessed 24 September 2015).

36.

Simone

Federico

Trojano

. High resolution proton MR spectroscopy of cerebrospinal fluid in MS patients. Comparison with biochemical changes in demyelinating plaques. J Neurol Sci 1996; 144(1–2): 182–190, http://www.ncbi.nlm.nih.gov/pubmed/8994122 (accessed 24 September 2015).

37.

Aasly

Gårseth

Sonnewald

. Cerebrospinal fluid lactate and glutamine are reduced in multiple sclerosis. Acta Neurol Scand 1997; 95(1): 9–12, http://www.ncbi.nlm.nih.gov/pubmed/9048978 (accessed 24 September 2015).

38.

Sinclair

Viant

Ball

. NMR-based metabolomic analysis of cerebrospinal fluid and serum in neurological diseases—A diagnostic tool? NMR Biomed 2010; 23(2): 123–132, http://www.ncbi.nlm.nih.gov/pubmed/19691132 (accessed 6 September 2015).

39.

Reinke

Broadhurst

Sykes

. Metabolomic profiling in multiple sclerosis: Insights into biomarkers and pathogenesis. Mult Scler 2014; 20(10): 1396–1400, http://www.ncbi.nlm.nih.gov/pubmed/24468817 (accessed 26 August 2015).

40.

Pieragostino

D’Alessandro

di Ioia

. An integrated metabolomics approach for the research of new cerebrospinal fluid biomarkers of multiple sclerosis. Mol Biosyst 2015; 11(6): 1563–1572, http://www.ncbi.nlm.nih.gov/pubmed/25690641 (accessed 1 September 2015).

41.

Dickens

Larkin

Griffin

. A type 2 biomarker separates relapsing-remitting from secondary progressive multiple sclerosis. Neurology 2014; 83(17): 1492–1499, http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=4222850…tool=pmcentrez…rendertype=abstract (accessed 6 September 2015).

42.

Mehrpour

Kyani

Tafazzoli

. A metabonomics investigation of multiple sclerosis by nuclear magnetic resonance. Magn Reson Chem 2013; 51(2): 102–109, http://www.ncbi.nlm.nih.gov/pubmed/23255426 (accessed 6 September 2015).

43.

Moussallieh

F-M

Elbayed

Chanson

. Serum analysis by 1H nuclear magnetic resonance spectroscopy: A new tool for distinguishing neuromyelitis optica from multiple sclerosis. Mult Scler 2014; 20(5): 558–565, http://www.ncbi.nlm.nih.gov/pubmed/24080986 (accessed 5 September 2015).