Utility of an Untargeted Metabolomics Approach Using a 2D GC-GC-MS Platform to Distinguish Relapsing and Progressive Multiple Sclerosis

Abstract

Multiple sclerosis (MS) is the most common inflammatory neurodegenerative disease of the central nervous system (CNS) in young adults and results in progressive neurological defects. The relapsing-remitting phenotype (RRMS) is the most common disease course in MS, which ultimately progresses to secondary progressive MS (SPMS), while primary progressive MS (PPMS) is a type of MS that worsens gradually over time without remissions. There is a gap in knowledge regarding whether the relapsing form can be distinguished from the progressive course, or healthy subjects (HS) based on an altered serum metabolite profile. In this study, we performed global untargeted metabolomics with the 2D GC-GC-MS platform to identify altered metabolites between RRMS, PPMS, and HS. We profiled 235 metabolites in the serum of patients with RRMS (n = 41), PPMS (n = 31), and HS (n = 91). A comparison of RRMS and HS patients revealed 22 significantly altered metabolites at p < 0.05 (false-discovery rate [FDR] = 0.3). The PPMS and HS comparisons revealed 28 altered metabolites at p < 0.05 (FDR = 0.2). Pathway analysis using MetaboAnalyst revealed enrichment of four metabolic pathways in both RRMS and PPMS (hypergeometric test p < 0.05): (1) galactose metabolism; (2) amino sugar and nucleotide sugar metabolism; (3) phenylalanine, tyrosine, and tryptophan biosynthesis; and (4) aminoacyl-tRNA biosynthesis. The Qiagen IPA enrichment test identified the sulfatase 2 (SULF2) (p = 0.0033) and integrin subunit beta 1 binding protein 1 (ITGB1BP1) (p = 0.0067) genes as upstream regulators of altered metabolites in the RRMS vs. HS groups. However, in the PPMS vs. HS comparison, valine was enriched in the neurodegeneration of brain cells (p = 0.05), and heptadecanoic acid, alpha-ketoisocaproic acid, and glycerol participated in inflammation in the CNS (p = 0.03). Overall, our study suggests that RRMS and PPMS may contribute metabolic fingerprints in the form of unique altered metabolites for discriminating MS disease from HS, with the potential for constructing a metabolite panel for progressive autoimmune diseases such as MS.

Keywords: GC-GC-MS; PPMS; RRMS; metabolomics; multiple sclerosis.

Figure 1

Partial least-squares discriminant analysis (PLS-DA). PLSDA plot showing the distribution of metabolite profiles between (A) RRMS and HS and (B) PPMS and HS for the X and Y matrices.

Figure 2

Metabolite profile across RRMS vs. HS and PPMS vs. HS. (A) Heatmap of differential metabolites between RRMS patients and HS. (B) Heatmap of differential metabolites between PPMS and HS. (C) Overlap between differential metabolites in RRMS vs. HS and PPMS vs. HS patients. (D) Intensity plots depicting the differences among the HS, RRMS, and PPMS.

Figure 3

Pathway analysis using KEGG and IPA. (A,B) KEGG pathway analysis in which the pathways and raw p-values are shown for the RRMS and PPMS patients. (C,D) IPA of the molecules SULF2 3 and ITGB1BP1 8 in RRMS and PPMS. Associations of differentially expressed metabolites between RRMS and HS with biological processes and upstream regulators determined via IPA. In addition, SULF2 and ITGB1BP1, which are the primary regulators activated for activation based on the different levels of metabolites in the chain below, exhibited connectivity. The prediction of activation is shown by the broken orange lines, whereas the prediction inhibition is represented by the broken blue line. The red color shows the upregulation of RRMS. The last layer shows the different metabolites in RRMS and HS (The networks were generated through the use of IPA (QIAGEN Inc., https://digitalinsights.qiagen.com/products-overview/discovery-insights-portfolio/analysis-and-visualization/qiagen-ipa/?cmpid=PromoTile_Discovery_2B3_WP, accessed on 12 July 2024)).

Figure 4

Heatmap and signature of metabolites. (A) Different tiers of selected features with PLS-DA and SVM random forest to binary classifiers on a scale of red to green showing their intensities to choose a smaller subset of metabolites with the highest predictive accuracies. (B) S signatures of different metabolites were identified through the classifiers for control and disease patients.

Figure 5

S-signature across the HS, RRMS, and PPMS cohorts. (A) The intensity trend of the ‘S’- signature metabolites from 3 binary classifiers is shown for all the groups. (B) The intensity trend of the ‘S’ RRMS signature metabolites is shown between RRMS and PPMS only. (PP primary progressive; Ma: matched controls for PPMS; CT controls for RRMS; RR relapsing-remitting).