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Dynamics and heterogeneity of brain damage in multiple sclerosis

Authors :
Ekaterina Kotelnikova
Leonidas G. Alexopoulos
Inna Pertsovskaya
Tomas Olsson
Roland Martin
Jordi Garcia-Ojalvo
Irene Pulido-Valdeolivas
Pablo Villoslada
Jesper Tegnér
Elena Abad
Friedemann Paul
Elena H. Martinez-Lapiscina
Irati Zubizarreta
Magi Andorra
Narsis A. Kiani
University of Zurich
Villoslada, Pablo
Source :
PLoS Computational Biology, Vol 13, Iss 10, p e1005757 (2017), Recercat. Dipósit de la Recerca de Catalunya, instname, PLoS Computational Biology
Publication Year :
2017
Publisher :
Public Library of Science (U.S.A.), 2017.

Abstract

Multiple Sclerosis (MS) is an autoimmune disease driving inflammatory and degenerative processes that damage the central nervous system (CNS). However, it is not well understood how these events interact and evolve to evoke such a highly dynamic and heterogeneous disease. We established a hypothesis whereby the variability in the course of MS is driven by the very same pathogenic mechanisms responsible for the disease, the autoimmune attack on the CNS that leads to chronic inflammation, neuroaxonal degeneration and remyelination. We propose that each of these processes acts more or less severely and at different times in each of the clinical subgroups. To test this hypothesis, we developed a mathematical model that was constrained by experimental data (the expanded disability status scale [EDSS] time series) obtained from a retrospective longitudinal cohort of 66 MS patients with a long-term follow-up (up to 20 years). Moreover, we validated this model in a second prospective cohort of 120 MS patients with a three-year follow-up, for which EDSS data and brain volume time series were available. The clinical heterogeneity in the datasets was reduced by grouping the EDSS time series using an unsupervised clustering analysis. We found that by adjusting certain parameters, albeit within their biological range, the mathematical model reproduced the different disease courses, supporting the dynamic CNS damage hypothesis to explain MS heterogeneity. Our analysis suggests that the irreversible axon degeneration produced in the early stages of progressive MS is mainly due to the higher rate of myelinated axon degeneration, coupled to the lower capacity for remyelination. However, and in agreement with recent pathological studies, degeneration of chronically demyelinated axons is not a key feature that distinguishes this phenotype. Moreover, the model reveals that lower rates of axon degeneration and more rapid remyelination make relapsing MS more resilient than the progressive subtype. Therefore, our results support the hypothesis of a common pathogenesis for the different MS subtypes, even in the presence of genetic and environmental heterogeneity. Hence, MS can be considered as a single disease in which specific dynamics can provoke a variety of clinical outcomes in different patient groups. These results have important implications for the design of therapeutic interventions for MS at different stages of the disease.<br />Author summary Multiple Sclerosis (MS) is an autoimmune disease in which inflammatory and degenerative processes damage the brain. We tested the hypothesis that the variability in disease progression and the clinical heterogeneity observed in MS is driven by a single mechanism, namely the autoimmune attack on the CNS that provokes this chronic inflammation and degeneration. As such, it is the difference in the intensity of these processes at distinct times that is responsible for establishing each of the disease subtypes defined to date. Mathematical models of brain damage and disease course were generated that were fitted to clinical data. We found that the phenotypes of the different MS subtypes were reproduced by the model, supporting the concept of a common pathogenesis and thus, that of a single disease in which specific dynamics can produce a variety of clinical outcomes in different groups of patients. These results are likely to be helpful when designing new therapies for this disease.

Subjects

Subjects :
Central Nervous System
0301 basic medicine
Databases, Factual
Epidemiology
2804 Cellular and Molecular Neuroscience
Degeneration (medical)
Disease
Pathology and Laboratory Medicine
600 Technik, Medizin, angewandte Wissenschaften::610 Medizin und Gesundheit
Nervous System
Nerve Fibers
0302 clinical medicine
Animal Cells
Medicine and Health Sciences
Image Processing, Computer-Assisted
Prospective Studies
Biology (General)
10. No inequality
Immune Response
Neurons
Ecology
Simulation and Modeling
Brain
Neurodegenerative Diseases
Magnetic Resonance Imaging
3. Good health
medicine.anatomical_structure
Neurology
Computational Theory and Mathematics
Modeling and Simulation
Cellular Types
Anatomy
medicine.symptom
Function and Dysfunction of the Nervous System
Research Article
QH301-705.5
Inflammatory Diseases
Immunology
Central nervous system
610 Medicine & health
Brain damage
Research and Analysis Methods
Autoimmune Diseases
Multiple sclerosis
03 medical and health sciences
Cellular and Molecular Neuroscience
Signs and Symptoms
1311 Genetics
Diagnostic Medicine
1312 Molecular Biology
Genetics
medicine
Humans
Disease Dynamics
Remyelination
Molecular Biology
Ecology, Evolution, Behavior and Systematics
Inflammation
Autoimmune disease
Expanded Disability Status Scale
business.industry
Biology and Life Sciences
Computational Biology
Cell Biology
medicine.disease
Demyelinating Disorders
Axons
10040 Clinic for Neurology
1105 Ecology, Evolution, Behavior and Systematics
030104 developmental biology
Cellular Neuroscience
Clinical Immunology
Clinical Medicine
business
2303 Ecology
Neuroscience
030217 neurology & neurosurgery
2611 Modeling and Simulation
1703 Computational Theory and Mathematics

Details

Language :
English
Database :
OpenAIRE
Journal :
PLoS Computational Biology, Vol 13, Iss 10, p e1005757 (2017), Recercat. Dipósit de la Recerca de Catalunya, instname, PLoS Computational Biology
Accession number :
edsair.doi.dedup.....541bd2433fba94a5d4ae4688594a2b84