Role of Ocrelizumab in modulating gene and microRNA expression in multiple sclerosis

Abstract

Multiple sclerosis is an autoimmune neurodegenerative disease and one of the most significant challenges in modern neurology, impacting approximately 2.8 million people globally. As a multifactorial condition, susceptibility to MS can result from a combination of genetic and environmental factors. Current treatment strategies aim to prevent acute attacks, slow disease progression, and alleviate symptoms. Ocrelizumab, a monoclonal antibody targeting CD20, has demonstrated efficacy in clinical trials by reducing both disease activity and frequency of relapses. Given the recent approval of this treatment, we investigated whether Ocrelizumab alters the expression of key miRNAs and genes involved in neuroinflammation, such as let-7a-5p, miR-14a-5p, miR-21a-5p, miR-338-3p, IL-1, IL-6, NEAT1, NEFL, NESTIN, SLC16A10 and TNF-alpha, by comparing their expression in patients' blood before and after one year of treatment with Ocrelizumab. Additionally, we explored potential inverse correlations and direct or indirect interactions among the genes and miRNAs that showed significant changes in expression. Lastly, we conducted a pathway analysis to understand the overall effects potentially exerted by the drug. Results revealed a significant decrease in the expression of TNF-alpha, SLC16A10, NEFL and IL-6, and an increase in let-7a-5p expression. There was an inverse correlation between let-7a-5p and the four genes, while the genes positively correlated with each other, suggesting let-7a-5p as a common modulator. These findings indicate that further investigation is needed to determine if the drug directly upregulates let-7a-5p, thereby downregulating the four genes, or if these expression changes are an indication of an overall reduction in inflammation.

Keywords: Inflammation; Interaction network; Multiple sclerosis; Ocrelizumab; Pathway analysis; miRNAs.

Fig. 1

Column histograms displaying the expression levels in fold change of the miRNAs and genes on the y-axis and the sample label on the x-axis. Significance is marked as follows: not significant (ns) for p-values over 0.05; ∗ for p-values from 0.05 to 0.01; ∗∗ for p-values from 0.01 to 0.001; ∗∗∗ for p-values from 0.001 to 0.0001; and ∗∗∗∗ for p-values less than 0.0001.

Fig. 2

Heatmap of gene correlations. Blue color indicates an inverse correlation, while the red indicates a direct correlation, Intensity of the color is proportional to the robustness of the relationship. (a) Visualization of the Correlation coefficient between SLC16A10, NEFL, TNF-alpha and IL-6 with let-7. (b) Matrix of the correlation among all the significant genes and let-7.

Fig. 3

Interaction Network of let-7a-5pand NEFL (highlighted in yellow), in the center of the network are visible their shared interactors: APP, PDE4DIP and NEFM. To enhance readability, only the interactors of let-7a-5pwith a score of 3 or higher are displayed, aside from the three shared interactors.

Fig. 4

Outcome of the pathway analysis displayed as a network. The width of each node is directly related to the count of its connected genes, miRNA or interactors. Pathways that share interactors are linked by lines (or edges), where the line thickness reflects the number of shared interactors. Pathways associated with NEFL, SLC16A10, TNF-alpha and IL-1 are highlighted in green, whereas those connected to let-7a-5pfeature a red color.