Kotelnikova Ekaterina, Kiani Narsis A, Abad Elena, Martinez-Lapiscina Elena H, Andorra Magi, Zubizarreta Irati, Pulido-Valdeolivas Irene, Pertsovskaya Inna, Alexopoulos Leonidas G, Olsson Tomas, Martin Roland, Paul Friedemann, Tegnér Jesper, Garcia-Ojalvo Jordi, Villoslada Pablo
Center for Neuroimmunology, Institut d'Investigacions Biomèdiques August Pi Sunyer (IDIBAPS), University of Barcelona, Barcelona, Spain.
Unit of Computational Medicine, Department of Medicine & Science for Life Laboratory, Karolinska Institute, Stockholm, Sweden.
PLoS Comput Biol. 2017 Oct 26;13(10):e1005757. doi: 10.1371/journal.pcbi.1005757. eCollection 2017 Oct.
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.
多发性硬化症（MS）是一种自身免疫性疾病，会引发炎症和退行性病变，损害中枢神经系统（CNS）。然而，目前尚不清楚这些事件如何相互作用和演变，从而引发这种高度动态且异质性的疾病。我们提出了一个假说，即MS病程的变异性是由导致该疾病的相同致病机制驱动的，即针对CNS的自身免疫攻击，导致慢性炎症、神经轴突退变和再髓鞘化。我们认为，这些过程在每个临床亚组中或多或少地在不同时间以不同程度发挥作用。为了验证这一假说，我们开发了一个数学模型，该模型受从66名进行长期随访（长达20年）的MS患者的回顾性纵向队列中获得的实验数据（扩展残疾状态量表[EDSS]时间序列）的约束。此外，我们在第二个前瞻性队列的120名MS患者中对该模型进行了验证，该队列进行了三年的随访，可获得EDSS数据和脑容量时间序列。通过使用无监督聚类分析对EDSS时间序列进行分组，减少了数据集中的临床异质性。我们发现，通过调整某些参数，尽管在其生物学范围内，数学模型能够再现不同的疾病病程，支持动态CNS损伤假说以解释MS的异质性。我们的分析表明，进展性MS早期产生的不可逆轴突退变主要是由于有髓轴突退变率较高，再加上再髓鞘化能力较低。然而，与最近的病理学研究一致，慢性脱髓鞘轴突的退变并不是区分该表型的关键特征。此外，该模型表明，较低的轴突退变率和更快的再髓鞘化使复发型MS比进展型亚型更具恢复力。因此，我们的结果支持不同MS亚型具有共同发病机制的假说，即使存在遗传和环境异质性。因此，MS可被视为一种单一疾病，其中特定的动态变化可在不同患者群体中引发多种临床结果。这些结果对MS疾病不同阶段的治疗干预设计具有重要意义。
