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Multiple sclerosis (MS) is an immune-mediated, neuroinflammatory disease of the central nervous system and in industrialised countries is the most common cause of progressive neurological disability in working age persons. However, there is significant inter-individual heterogeneity in disease activity and response to treatment. Currently, the ability to predict at diagnosis who will have a benign, intermediate, or aggressive disease course is very limited. There is therefore a need for integrated predictive tools to inform individualised treatment decision-making. FutureMS is a nationally representative, prospective observation cohort study comprising of 440 participants with a new diagnosis of relapsing remitting MS living in Scotland between May 2016 and March 2019. Established with the aim of addressing this need for individualised predictive tools, the cohort is designed to combine detailed clinical phenotyping with imaging, genetic and biomarker metrics of disease activity and progression. Recruitment, baseline assessment and follow up at year one is complete and longer term follow up is planned, beginning at five years after first visit. The study aims to explore the pathobiology and determinants of disease heterogeneity in MS. Here we describe the cohort design and present a profile of the participants at baseline and one year of follow up.

Objective To identify short-term changes in gene expression in peripheral blood mononuclear cells (PBMCs) associated with treatment response to dimethyl fumarate (DMF, Tecfidera) in patients with relapsing-remitting MS (RRMS). Methods Blood samples were collected from 24 patients with RRMS (median Expanded Disability Status Scale score, 2.0; range 1–7) at baseline, 6 weeks, and 15 months after the initiation of treatment with DMF (BG-12; Tecfidera). Seven healthy controls were also recruited, and blood samples were collected over the same time intervals. PBMCs were extracted from blood samples and sequenced using next-generation RNA sequencing. Treatment responders were defined using the composite outcome measure “no evidence of disease activity” (NEDA-4). Time-course and cross-sectional differential expression analyses were performed to identify transcriptomic markers of treatment response. Results Treatment responders (NEDA-4 positive, 8/24) over the 15-month period had 478 differentially expressed genes (DEGs) 6 weeks after the start of treatment. These were enriched for nuclear factor (erythroid-derived 2)-like 2 (Nrf2) and inhibition of nuclear factor κB (NFκB) pathway transcripts. For patients who showed signs of disease activity, there were no DEGs at 6 weeks relative to their (untreated) baseline. Contrasting transcriptomes expressed at 6 weeks with those at 15 months of treatment, 0 and 1,264 DEGs were found in the responder and nonresponder groups, respectively. Transcripts in the nonresponder group (NEDA-4 negative, 18/24) were enriched for T-cell signaling genes. Conclusion Short-term PBMC transcriptome changes reflecting activation of the Nrf2 and inhibition of NFκB pathways distinguish patients who subsequently show a medium-term treatment response with DMF. Relative stabilization of gene expression patterns may accompany treatment-associated suppression of disease activity.

Background: Severe coronavirus disease 2019 (COVID-19) is associated with multiple comorbidities and is characterized by an auto-aggressive inflammatory state leading to massive collateral damage. To identify preventive and therapeutic strategies against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), it is important to ascertain the molecular interactions between virus and host, and how they translate into disease pathophysiology. Methods: We matched virus-human protein interactions of human coronaviruses and other respiratory viruses with lists of genes associated with autoimmune diseases and comorbidities associated to worse COVID-19 course. We then selected the genes included in the statistically significant intersection between SARS-CoV-2 network and disease associated gene sets, identifying a meta-interactome. We analyzed the meta-interactome genes expression in samples derived from lungs of infected humans, and their regulation by IFN-β. Finally, we performed a drug repurposing screening to target the network’s most critical nodes. Results: We found a significant enrichment of SARS-CoV-2 interactors in immunological pathways and a strong association with autoimmunity and three prognostically relevant conditions (type 2 diabetes, coronary artery diseases, asthma), that present more independent physiopathological subnetworks. We observed a reduced expression of meta-interactome genes in human lungs after SARS-CoV-2 infection, and a regulatory potential of type I interferons. We also underscored multiple repurposable drugs to tailor the therapeutic strategies. Conclusions: Our data underscored a plausible genetic background that may contribute to the distinct observed pathophysiologies of severe COVID-19. Also, these results may help identify the most promising therapeutic targets and treatments for this condition.