Sex differences in the molecular basis of multiple sclerosis: meta-analysis of transcriptomic data

Multiple sclerosis (MS), an auto-immune, inflammatory, and degenerative disorder of the central nervous system, affects both males and females; however, females are at an increased risk of developing MS (2-3:1 ratio compared to males). The factors behind these sex differences are not still clear. Therefore, the aim of this work has been to explore the role of sex in MS to identify potential molecular mechanisms underlying sex-based differences. To this end, we performed a systematic review in public databases of transcriptomic studies, in nervous tissue and blood. Next, we performed 3 meta-analyses that allowed us to detect MS alterations in females, in males and between both sexes. As a result of our work, we selected 9 studies (4 of nerve tissue and 5 of blood) containing 474 individuals. Our meta-analyses identified some genes and functions altered on a sex-specific or between-sex basis. Among them, highlight 15 genes that showed a significantly different expression pattern between sexes in some of the tissues analyzed: (KIR2DL3 in blood; ARL17B, CECR7, CEP78, IFFO2, LOC401127, NUDT18, RNF10, SLC17A5, STEMP1, TRAF3IP2-AS1, UBXN2B, ZNF117, ZNF488 in nervous tissue; LOC 102723701 in both tissues). This work evidences the existence of sex differences in MS at the transcriptomic level and, moreover, open the door to future applications leading to more sex-specific treatments.</p

Molecular and functional atlas of sex-differences in multiple sclerosis subtypes analising single cell and single nucleus transcriptomic data

Multiple sclerosis (MS) is the commonest cause of non-traumatic disability among young adults. MS hallmark underlies myelin damage induced by defective autoimmune responses, leading to the neurodegeneration of the central nervous system. Sex differences in MS have been reported at several epidemiological and clinical levels as prevalence, progression and response to treatment. However, the molecular mechanisms underneath those differences remain poorly understood.  To exhaustively characterise sex bias in MS by cell type, we performed an in silico analysis of scRNA-seq and snRNA-seq data using R programming language. Firstly, we performed a systematic review implementing PRISMA guidelines [PMID:33780438] in public repositories. Then, we processed each selected dataset through quality control filtering, normalisation, high variable genes selection, dimensionality reduction, clustering and cell type annotation. Finally, we characterise each cell type by differential gene expression and functional profiling analyses, evaluating for the latter biological functions from the Gene Ontology [PMID:10802651] and pathways from the KEGG [PMID:10592173] databases. Three datasets, each representing a different subtype of MS, were spotted. Nervous tissue dataset (n=1) stored astrocytes, microglia, neurons, oligodendrocytes, and oligodendrocyte precursor cells, whilst blood datasets (n=2) included diverse types of lymphocytes, dendritic cells and monocytes. We found significant sex-differential and sex-specific gene expression patterns, biological functions, and pathways for almost all cell types in each dataset. Some significant features were shared among cell types with similar or opposite patterns, whilst others were cell type exclusive. Therefore, this atlas enhances personalised medicine by unveiling molecular and functional sex-dependent prospective biomarkers.</p