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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.

SUBMITTER: Ulrich R

PROVIDER: S-EPMC3837619 | biostudies-literature | 2010 Jan

REPOSITORIES: biostudies-literature

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

Ulrich Reiner R Kalkuhl Arno A Deschl Ulrich U Baumgärtner Wolfgang W

Journal of cellular and molecular medicine 20090114 1-2

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-infect ...[more]

PMID: 19183246

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Project description:ObjectivesTo stratify patients with multiple sclerosis (pwMS) based on brain MRI-derived volumetric features using unsupervised machine learning.MethodsThe 3-T brain MRIs of relapsing-remitting pwMS including 3D-T1w and FLAIR-T2w sequences were retrospectively collected, along with Expanded Disability Status Scale (EDSS) scores and long-term (10 ± 2 years) clinical outcomes (EDSS, cognition, and progressive course). From the MRIs, volumes of demyelinating lesions and 116 atlas-defined gray matter regions were automatically segmented and expressed as z-scores referenced to external populations. Following feature selection, baseline MRI-derived biomarkers entered the Subtype and Stage Inference (SuStaIn) algorithm, which estimates subgroups characterized by distinct patterns of biomarker evolution and stages within subgroups. The trained model was then applied to longitudinal MRIs. Stability of subtypes and stage change over time were assessed via Krippendorf's α and multilevel linear regression models, respectively. The prognostic relevance of SuStaIn classification was assessed with ordinal/logistic regression analyses.ResultsWe selected 425 pwMS (35.9 ± 9.9 years; F/M: 301/124), corresponding to 1129 MRI scans, along with healthy controls (N = 148; 35.9 ± 13.0 years; F/M: 77/71) and external pwMS (N = 80; 40.4 ± 11.9 years; F/M: 56/24) as reference populations. Based on 11 biomarkers surviving feature selection, two subtypes were identified, designated as "deep gray matter (DGM)-first" subtype (N = 238) and "cortex-first" subtype (N = 187) according to the atrophy pattern. Subtypes were consistent over time (α = 0.806), with significant annual stage increase (b = 0.20; p < 0.001). EDSS was associated with stage and DGM-first subtype (p ≤ 0.02). Baseline stage predicted long-term disability, transition to progressive course, and cognitive impairment (p ≤ 0.03), with the latter also associated with DGM-first subtype (p = 0.005).ConclusionsUnsupervised learning modelling of brain MRI-derived volumetric features provides a biologically reliable and prognostically meaningful stratification of pwMS.Key points• The unsupervised modelling of brain MRI-derived volumetric features can provide a single-visit stratification of multiple sclerosis patients. • The so-obtained classification tends to be consistent over time and captures disease-related brain damage progression, supporting the biological reliability of the model. • Baseline stratification predicts long-term clinical disability, cognition, and transition to secondary progressive course.

Project description:Cortical disease has emerged as a critical aspect of the pathogenesis of multiple sclerosis, being associated with disease progression and cognitive impairment. Most studies of cortical lesions have focused on autopsy findings in patients with long-standing, chronic, progressive multiple sclerosis, and the noninflammatory nature of these lesions has been emphasized. Magnetic resonance imaging studies indicate that cortical damage occurs early in the disease.We evaluated the prevalence and character of demyelinating cortical lesions in patients with multiple sclerosis. Cortical tissues were obtained in passing during biopsy sampling of white-matter lesions. In most cases, biopsy was done with the use of stereotactic procedures to diagnose suspected tumors. Patients with sufficient cortex (138 of 563 patients screened) were evaluated for cortical demyelination. Using immunohistochemistry, we characterized cortical lesions with respect to demyelinating activity, inflammatory infiltrates, the presence of meningeal inflammation, and a topographic association between cortical demyelination and meningeal inflammation. Diagnoses were ascertained in a subgroup of 77 patients (56%) at the last follow-up visit (at a median of 3.5 years).Cortical demyelination was present in 53 patients (38%) (104 lesions and 222 tissue blocks) and was absent in 85 patients (121 tissue blocks). Twenty-five patients with cortical demyelination had definite multiple sclerosis (81% of 31 patients who underwent long-term follow-up), as did 33 patients without cortical demyelination (72% of 46 patients who underwent long-term follow-up). In representative tissues, 58 of 71 lesions (82%) showed CD3+ T-cell infiltrates, and 32 of 78 lesions (41%) showed macrophage-associated demyelination. Meningeal inflammation was topographically associated with cortical demyelination in patients who had sufficient meningeal tissue for study.In this cohort of patients with early-stage multiple sclerosis, cortical demyelinating lesions were frequent, inflammatory, and strongly associated with meningeal inflammation. (Funded by the National Multiple Sclerosis Society and the National Institutes of Health.).

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

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

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