Comparing In Silico and Sequencing-Based miRNA Expression in Multiple Sclerosis

Abstract

Multiple sclerosis (MS) is a chronic inflammatory demyelinating disease of the central nervous system characterized by considerable clinical heterogeneity. Its diagnosis and prognosis remain challenging – although clinical, radiological, and laboratory criteria are applied, reliable biomarkers capable of enabling earlier detection, predicting relapses, or assessing therapeutic efficacy are still lacking. Moreover, treatment effectiveness is often limited by inter-individual variability in therapeutic response and the complexity of disease progression. One promising class of biomarkers is microRNAs (miRNAs), small non-coding RNA molecules that regulate gene expression at the transcriptional or post-transcriptional level. However, due to their broad diversity, identifying biologically relevant miRNAs requires integration of in silico and experimental approaches. Our study consisted of two major stages: (1) in silico miRNA modeling and (2) an experimental sequencing phase. In the silico phase, we computationally derived predicted miRNA expression profiles in the context of MS. MiRNA sequencing was performed using the Illumina platform on extracellular vesicle samples isolated from blood (9 MS patients and 13 healthy controls). Differential expression analysis was conducted using the DESeq2 package to evaluate miRNA expression profiles between MS patients and healthy individuals. Overlapping miRNAs identified through both in silico prediction and sequencing were compared to assess their biological relevance to MS. Four miRNAs predicted in silico were confirmed in sequencing data. Among them, hsa-miR-19a-3p showed the highest expression change (log₂FoldChange > 1), while hsa-miR-17-5p, hsa-miR-139-5p, and hsa-miR-20a-5p exhibited moderate but significant alterations. KEGG analysis revealed enrichment in pathways of neurodegeneration – multiple diseases, amyotrophic lateral sclerosis (ALS), and the MAPK signaling pathway, suggesting shared molecular mechanisms between MS and other neurodegenerative disorders. The integration of in silico modeling with high-throughput sequencing analysis effectively identifies biologically relevant miRNAs in multiple sclerosis and highlights their potential as biomarkers involved in neurodegenerative and inflammatory signaling pathways.