Unlocking the Hidden "Causal" Factors of Multiple Sclerosis
Multiple Sclerosis (MS) is a complex immune-mediated disease where genetic factors play a massive role, yet identifying the specific functional genes within susceptibility loci remains a significant hurdle for researchers. While Genome-Wide Association Studies (GWAS) have successfully pinpointed hundreds of genomic loci associated with MS, prioritizing the biologically relevant genes within these regions is difficult using GWAS data alone. Addressing this gap, a 2019 study published in the Journal of Neurology utilized an integrative analysis to highlight potential causal genes and epigenetic factors for MS.
The research team employed a statistical method known as Summary data-based Mendelian Randomization (SMR) to integrate large-scale MS GWAS data with quantitative trait locus (QTL) studies. By treating genetic variants as instrumental variables, the researchers tested whether specific exposures—such as DNA methylation levels (mQTL) and gene expression levels (eQTL)—have a causal effect on MS. This multi-omics approach allowed for the prioritization of functionally relevant genes by combining data from expression profiles, plasma protein levels, and metabolite studies to map the complex regulatory mechanisms underlying the disease.
A key finding of the study was the identification of epigenetic factors contributing to disease risk. The analysis revealed 178 DNA methylation sites across 23 loci that were causally associated with MS. Notably, the study observed that most of the identified gene regions contained CpG islands in their promoters, which often exhibit long-range spatial interactions with other enhancers or promoters. This architecture suggests a regulatory potential where DNA methylation influences gene expression, which in turn contributes to MS susceptibility.
In terms of gene expression, the researchers identified 29 genes causally associated with MS, enriched in pathways such as antigen processing and interferon-gamma mediated signaling. Among the non-MHC (Major Histocompatibility Complex) genes identified, a strong connectivity was observed between METTL21B, METTL1, and TSFM. This interaction suggests a novel pathway in MS pathogenesis, potentially involving the regulation of translation elongation factors by methyltransferases.
The study also shed light on the role of plasma proteins. It was discovered that MS-associated SNPs in the DDR1 gene were strongly associated with the levels of plasma MICB (MHC class I polypeptide-related sequence B) and Granzyme A. Furthermore, the analysis indicated that plasma levels of MICB and Granzyme A are causally associated with MS, providing novel evidence for their involvement in the disease's etiology. Specifically, the causal association between plasma MICB levels and MS remained significant even after correcting for outliers.
To validate the regulatory mechanisms, the researchers investigated N 6-methyladenosine (m6A) associated SNPs, as m6A is a reversible RNA methylation playing a crucial role in biological regulation. They validated that the SNP rs923829 is significantly associated with METTL21B expression levels in peripheral blood mononuclear cells from a Chinese Han population. The data showed that carriers of the minor allele A had higher mRNA levels of METTL21B compared to non-carriers, supporting the hypothesis that these genetic variants regulate gene expression to influence MS risk.
In conclusion, this integrative study successfully utilized SMR to prioritize a list of genes and DNA methylation sites as important risk factors for Multiple Sclerosis. By combining GWAS with multi-omics data, the authors demonstrated that the interaction among DDR1, MICB, and GZMA, as well as the network involving METTL21B, METTL1, and TSFM, may actively participate in MS pathogenesis. These findings offer a robust framework for future research to disentangle the complex genetic and epigenetic architecture of autoimmune diseases.
Disclaimer: This blog post is based on the provided research article and is intended for informational purposes only. It is not intended to provide medical advice. Please consult with a healthcare professional for any health concerns.
References:
Mo, X. B., Lei, S. F., Qian, Q. Y., Guo, Y. F., Zhang, Y. H., & Zhang, H. (2019). Integrative analysis revealed potential causal genetic and epigenetic factors for multiple sclerosis. Journal of neurology, 266(11), 2699-2709.
