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Multiple sclerosis (MS) is a chronic autoimmune disease with huge heterogeneity in terms of clinical course, disease severity and treatment response. The need for a tailored treatment approach has emerged over the last few years. The present observational study aims to assess fingolimod (FTY) effectiveness in RRMS patients, stratifying them according to the disease-modifying treatments used before FTY, to identify subjects who could benefit more from this treatment.We prospectively included 554 RRMS patients who started FTY at San Raffaele Hospital between 2012 and 2018. We classified them into three categories according to previous treatments: naïve patients, subjects previously treated with first-line drugs, and patients previously treated with second-line drugs. We compared disease activity during a 2-years follow-up using No-Evidence-of-Disease-Activity (NEDA-3) and Time-To-First-Relapse (TTFR) outcomes, applying logistic and Cox proportional hazard regression respectively.The proportion of patients who maintained NEDA-3 status was higher in the naïve group despite a higher level of baseline disease activity (naïve versus first-line p = 0.025, naïve versus second-line p 0.001). In the multivariable analyses, patients switching to FTY from first- and second-line treatments showed a higher risk of disease reactivation (p = 0.041, OR = 1.86 and p = 0.002, OR = 2.92, respectively) and a shorter TTFR (p = 0.017, HR = 4.35 and p = 0.001, HR = 8.19, respectively).Naïve patients showed a better response to FTY compared to patients switching to FTY from other drugs. Our findings support the early use of FTY in patients with active MS.

A personalized approach is strongly advocated for treatment selection in Multiple Sclerosis patients due to the high number of available drugs. Machine learning methods proved to be valuable tools in the context of precision medicine. In the present work, we applied machine learning methods to identify a combined clinical and genetic signature of response to fingolimod that could support the prediction of drug response. Two cohorts of fingolimod-treated patients from Italy and France were enrolled and divided into training, validation, and test set. Random forest training and robust feature selection were performed in the first two sets respectively, and the independent test set was used to evaluate model performance. A genetic-only model and a combined clinical–genetic model were obtained. Overall, 381 patients were classified according to the NEDA-3 criterion at 2 years; we identified a genetic model, including 123 SNPs, that was able to predict fingolimod response with an AUROC= 0.65 in the independent test set. When combining clinical data, the model accuracy increased to an AUROC= 0.71. Integrating clinical and genetic data by means of machine learning methods can help in the prediction of response to fingolimod, even though further studies are required to definitely extend this approach to clinical applications

While the role of common genetic variants in multiple sclerosis (MS) has been elucidated in large genome-wide association studies, the contribution of rare variants to the disease remains unclear. Herein, a whole-genome sequencing study in four affected and four healthy relatives of a consanguineous Italian family identified a novel missense c.1801T > C (p.S601P) variant in the GRAMD1B gene that is shared within MS cases and resides under a linkage peak (LOD: 2.194). Sequencing GRAMD1B in 91 familial MS cases revealed two additional rare missense and two splice-site variants, two of which (rs755488531 and rs769527838) were not found in 1000 Italian healthy controls. Functional studies demonstrated that GRAMD1B, a gene with unknown function in the central nervous system (CNS), is expressed by several cell types, including astrocytes, microglia and neurons as well as by peripheral monocytes and macrophages. Notably, GRAMD1B was downregulated in vessel-associated astrocytes of active MS lesions in autopsied brains and by inflammatory stimuli in peripheral monocytes, suggesting a possible role in the modulation of inflammatory response and disease pathophysiology.

Multiple sclerosis (MS) is a complex disease of the central nervous system for which human leukocyte antigen (HLA) alleles are major contributors to susceptibility. Several investigations have focused on the relationship between HLA and clinical parameters, while few studies have evaluated its correlation with brain magnetic resonance imaging (MRI) measures. We investigated the association between the HLA genetic burden (HLAGB), originating from the most updated HLA alleles associated with MS, and neuroimaging endophenotypes, with a specific focus on brain atrophy metrics. A monocentric Italian cohort of 334 MS patients with imputed HLA alleles and cross-sectional volumetric measures of white matter (WM), gray matter (GM), hippocampus, thalamus and T2-hyperintense lesions was investigated. Linear regression models with covariate adjustment were fitted for each metric. We detected no effect of HLAGB on WM and GM volumes. Interestingly, we found a marginal correlation between higher HLAGB and lower hippocampal volume (β = −0.142, p = 0.063) and a nominal association between higher HLAGB and lower thalamic volume (β = −0.299, p = 0.047). No association was found with T2 lesion volumes. The putative impact of higher HLAGB on hippocampus and thalamus suggests, if replicated in independent cohorts, a possible cumulative contribution of HLA risk loci on brain volumetric traits linked to clinical deficits in MS.

Background and ObjectivesThe major histocompatibility complex (MHC) locus has a predominant role in the genetic predisposition to multiple sclerosis (MS), with 32 associations found to be involved. We aimed to investigate the impact of MHC MS-risk alleles on T-cell repertoire in patients with MS.MethodsWe studied 161 untreated patients with relapsing-remitting MS for whom Class I and II human leukocyte antigen (HLA) alleles were inferred from whole-genome genotyping data, and T-cell receptor (TCR) CDR3 sequences were obtained through next-generation sequencing. T-cell repertoire features including diversity, public clones, and architecture were evaluated.ResultsWe identified 5 MS-risk loci associated with TCR diversity: HLA-DRB1*15:01 (7.65 × 10−3), rs9271366 (1.96 × 10−3), rs766848979 A (1.89 × 10−2), rs9277626 (2.95 × 10−2), and rs11751659 (1.92 × 10−2), with evidence of expanded clonotypes in carriers of risk alleles. Moreover, HLA-DRB1*15:01 (4.99 × 10−3), rs9271366 (6.54 × 10−3), rs1049079 C (4.37 × 10−2), AA DQΒ1 position −5 L (1.05 × 10−3), and AA DQΒ1 position 221 Q (9.39 × 10−4) showed an association with the CDR3 aminoacidic sequence architecture, suggesting an impact on the antigen recognition breadth as well. Evaluating the sharing of clones across MS-risk allele carrier individuals revealed the presence of highly shared clonotypes predicted to target viral antigens, including Epstein-Barr virus.DiscussionOur study supports the association between MHC-risk alleles and macrofeatures of the T-cell repertoire in the context of MS. Further studies are needed to understand the underlying molecular mechanisms.

Multiple sclerosis (MS) is an inflammatory neurodegenerative disorder of the central nervous system (CNS). The migration of immune cells into the CNS is essential for its development, and plasma membrane molecules play an important role in triggering and maintaining the inflammation. We previously identified ninjurin2, a plasma membrane protein encoded by NINJ2 gene, as involved in the occurrence of relapse under Interferon-β treatment in MS patients. The aim of the present study was to investigate the involvement of NINJ2 in inflammatory conditions and in the migration of monocytes through the blood–brain barrier (BBB). We observed that NINJ2 is downregulated in monocytes and in THP-1 cells after stimulation with the pro-inflammatory cytokine LPS, while in hCMEC/D3 cells, which represent a surrogate of the BBB, LPS stimulation increases its expression. We set up a transmigration assay using an hCMEC/D3 transwell-based model, finding a higher transmigration rate of monocytes from MS subjects compared to healthy controls (HCs) in the case of an activated hCMEC/D3 monolayer. Moreover, a positive correlation between NINJ2 expression in monocytes and monocyte migration rate was observed. Overall, our results suggest that ninjurin2 could be involved in the transmigration of immune cells into the CNS in pro-inflammatory conditions. Further experiments are needed to elucidate the exact molecular mechanisms.