Machine learning approach identifies new pathways associated with demyelination in a viral model of multiple sclerosis

Abstract

Theiler’s murine encephalomyelitis is an experimentally virus-induced inflammatory demyelinating disease of the spinal cord, displaying clinical and pathological similarities to chronic progressive multiple sclerosis. The aim of this study was to identify pathways associated with chronic demyelination using an assumption-free combined microarray and immunohistology approach. Movement control as determined by rotarod assay significantly worsened in Theiler’s murine encephalomyelitis -virus-infected SJL/J mice from 42 to 196 days after infection (dpi). In the spinal cords, inflammatory changes were detected 14 to 196 dpi, and demyelination progressively increased from 42 to 196 dpi. Microarray analysis revealed 1001 differentially expressed genes over the study period. The dominating changes as revealed by k-means and functional annotation clustering included up-regulations related to intrathecal antibody production and antigen processing and presentation via major histocompatibility class II molecules. A random forest machine learning algorithm revealed that down-regulated lipid and cholesterol biosynthesis, differentially expressed neurite morphogenesis and up-regulated toll-like receptor-4-induced pathways were intimately associated with demyelination as measured by immunohistology. Conclusively, although transcriptional changes were dominated by the adaptive immune response, the main pathways associated with demyelination included up-regulation of toll-like receptor 4 and down-regulation of cholesterol biosynthesis. Cholesterol biosynthesis is a rate limiting step of myelination and its down-regulation is suggested to be involved in chronic demyelination by an inhibition of remyelination.