Blood metabolomic and transcriptomic signatures stratify patient subgroups in multiple sclerosis according to disease severity

Alexandra E Oppong; Leda Coelewij; Georgia Robertson; Lucia Martin-Gutierrez; Kirsty E Waddington; Pierre Dönnes; Petra Nytrova; Rachel Farrell; Inés Pineda-Torra; Elizabeth C Jury

doi:10.1016/j.isci.2024.109225

Blood metabolomic and transcriptomic signatures stratify patient subgroups in multiple sclerosis according to disease severity

iScience. 2024 Feb 15;27(3):109225. doi: 10.1016/j.isci.2024.109225. eCollection 2024 Mar 15.

Affiliations

¹ Division of Medicine, Department of Inflammation, University College London, London WC1E 6JF, UK.
² Scicross AB, Skövde, Sweden.
³ Department of Neurology and Centre of Clinical, Neuroscience, First Faculty of Medicine, General University Hospital and First Faculty of Medicine, Charles University in Prague, 500 03 Prague, Czech Republic.
⁴ Department of Neuroinflammation, University College London and Institute of Neurology and National Hospital of Neurology and Neurosurgery, London WC1N 3BG, UK.

Abstract

There are no blood-based biomarkers distinguishing patients with relapsing-remitting (RRMS) from secondary progressive multiple sclerosis (SPMS) although evidence supports metabolomic changes according to MS disease severity. Here machine learning analysis of serum metabolomic data stratified patients with RRMS from SPMS with high accuracy and a putative score was developed that stratified MS patient subsets. The top differentially expressed metabolites between SPMS versus patients with RRMS included lipids and fatty acids, metabolites enriched in pathways related to cellular respiration, notably, elevated lactate and glutamine (gluconeogenesis-related) and acetoacetate and bOHbutyrate (ketone bodies), and reduced alanine and pyruvate (glycolysis-related). Serum metabolomic changes were recapitulated in the whole blood transcriptome, whereby differentially expressed genes were also enriched in cellular respiration pathways in patients with SPMS. The final gene-metabolite interaction network demonstrated a potential metabolic shift from glycolysis toward increased gluconeogenesis and ketogenesis in SPMS, indicating metabolic stress which may trigger stress response pathways and subsequent neurodegeneration.

Keywords: Classification Description; Machine learning; Metabolomics; Molecular network; Transcriptomics.