Molecular mechanisms associated with multiple sclerosis progression, severity and phenotype

Abstract

While current treatments of multiple sclerosis (MS) effectively inhibit formation of focal lesions and relapses, most patients experience progression independent of relapse activity (PIRA). To understand PIRA, we analyzed nine prospectively acquired clinical and imaging outcomes in 176 relapsing-remitting and 215 progressive MS patients and 45 healthy volunteers, along with matched cellular and >5000 protein data in 1,042 cerebrospinal fluid (CSF) samples. Regressing out physiological aging and sex effects identified MS-related processes. Among these, compartmentalized inflammation and its effector mechanisms such as pyroptosis showed the strongest association with MS severity, irrespective of clinical categorization of patients. However, molecular processes affected localization of CNS injury: patients with predominant brain damage had proportionally higher neuroinflammation, while fibrosis and tissue hypoxia were linked to principal involvement of spinal cord. We did not identify inflammation-unrelated neurodegeneration; instead, CNS-related processes were beneficial, such as synaptogenesis. Machine learning-based CSF biomarker models predicted nine clinical and volumetric imaging outcomes in the independent cohort with accuracy exceeding published MS models. These data show intra-individual diversity of putative disease mechanisms in MS and implicate processes related to compartmentalized neuroinflammation as leading candidate mechanisms of PIRA. Future drug development should include CNS-penetrant anti-inflammatory agents.

Clemastine fumarate accelerates accumulation of disability in progressive multiple sclerosis by enhancing pyroptosis

“…Nevertheless, the largest MS genetic study did not link P2RX7 polymorphism to MS severity 43 . The heterogeneity and multiplicity of candidate pathogenic mechanisms 4,21,44,45 and their evolution during MS natural history, compounded by inadequate accuracy of traditional MS severity outcomes make studies of MS severity extremely challenging. Thus, this issue needs more research.…”

“…Neurological exams were performed by MS-trained clinician and recorded into NeurEx TM App 51 that automatically generates MS disability scores, such as EDSS. CombiWISE 23 , Brain Damage severity 44 , and MS Disease Severity Score (MS-DSS) 31 were calculated as described. Briefly, CombiWISE is a machine-learning derived progression outcome that combines disability levels measured by EDSS, Scripps Neurological Rating Scale 52 , timed 25 foot walk and non-dominant hand nine hole peg test.…”

“…To calculate Pyroptosis activation score in CSF, first, we intersected known genes participating in pyroptosis signaling based on the knowledge base of Ingenuity Pathway Analysis (IPA®) platform with CSF proteins measured by the SOMAScan assay. Out of those, 10 proteins demonstrated statistically significant correlation with global MS severity outcome (sum of scaled values of Brain damage severity 44 , CombiWISE severity 44 , and MS-DSS 31 ) and were selected for the Pyroptosis activation score calculation (Table S3). The CSF Pyroptosis activation score was then calculated as a sum of HD-scaled Somamer levels of the selected 10 proteins, multiplied by the correlation coefficient of each protein with the global MS severity outcome.…”

Whole-blood methylation signatures are associated with and accurately classify multiple sclerosis disease severity

“…These findings add to a growing body of evidence implicating neuronal response to injury in disease severity. Jokubaitis et al recently identified genetic variants associated with disease severity based on longitudinal ARMSS scores overrepresented in synaptic plasticity processes [ 6 ], while Kosa et al identified synaptogenesis as a pathway differentially activated between patients with different levels of disability based on spinal cord damage [ 30 ].…”

“…For model 2, elastic net regression identified whole-blood methylation levels at 1708 CpGs to be associated with disease severity, of which five overlapped with the main differential methylation analysis at the CpG level and 100 CpGs overlapped at the gene level. These genes were enriched in neuronal components and processes, adding to a growing evidence that disease severity is driven by neuronal, rather than immune, mechanisms [ 6 , 30 ]. The ability of the machine learning-trained elastic net regression model to identify 1702 additional DMPs to the main analysis demonstrate the presence of small, correlated and potentially cumulative effects of methylation on disease severity.…”