A systematic analysis of gene-gene interaction in multiple sclerosis

Association of Epistatic Effects of lncRNA GAS5, miR-146a, IRAK-1, and miR-155 Genetic Variants with Multiple Sclerosis Risk and Severity

The complex genetic architecture of heritability in multiple sclerosis (MS) remains undisclosed mainly. Epistasis (gene-gene interaction) substantially impacts MS; however, it is largely unexplored, especially among the non-coding RNA genes and their targets. The long non-coding RNA GAS5 exacerbates demyelination and sponges miR-146a and miR-155, impeccable contributors to MS pathogenesis. miR-146a negatively regulates the immune responses by targeting IRAK-1. We investigated the association of epistatic effects and haplotypes of five single nucleotide polymorphisms (SNPs), GAS5 rs2067079, miR-146a rs2910164 and rs57095329, IRAK-1 rs3027898, and miR-155 rs767649, with the risk of MS and its phenotypes. The expression quantitative trait locus (eQTL) associated with these variants was explored through bioinformatics analysis. The study enrolled 116 MS patients and 120 healthy controls. No strong linkage disequilibrium (D' ≥ 0.8) was detected among the studied SNPs. SNP-SNP interactions overlaid an overall magnified risk of MS and its phenotypes compared to the single-locus effects. After adjustment for multiple comparisons, the most significant interactions associated with the risk of overall MS and secondary-progressive MS were rs2067079-rs2910164, rs2910164-rs57095329, and rs3027898-rs767649. The last two former SNP-SNP interactions were highly associated with relapsing-remitting MS risk. The same pattern of interactions, as observed in association with MS risk, was female-specific. The CCAAA haplotype (alleles in the order of rs2067079, rs2910164, rs57095329, rs3027898, and rs767649) was protective against MS risk (CCAAA vs. CGAAT, adjusted OR = 0.14, 95% CI = 0.03-0.69, P = 0.009). Among MS patients, harboring the CGACT and CGAAT haplotypes was more prevalent in females and males, respectively. MS patients having EDSS ≥ 6 had a significantly higher frequency of the CCGCA haplotype than those with EDSS < 6. Functional analysis revealed rs2067079, rs57095329, and rs767649 as strong cis-eQTL regulating multiple genes, particularly in the brain and immune system. We propose that a magnified combined effect of GAS5, miR-146a, IRAK-1, and miR-155 genetic variants via epistatic interactions might impact the risk of MS and its phenotypes and could help in the risk stratification of MS patients.

Heterogeneous network approaches to protein pathway prediction

Understanding protein-protein interactions (PPIs) and the pathways they comprise is essential for comprehending cellular functions and their links to specific phenotypes. Despite the prevalence of molecular data generated by high-throughput sequencing technologies, a significant gap remains in translating this data into functional information regarding the series of interactions that underlie phenotypic differences. In this review, we present an in-depth analysis of heterogeneous network methodologies for modeling protein pathways, highlighting the critical role of integrating multifaceted biological data. It outlines the process of constructing these networks, from data representation to machine learning-driven predictions and evaluations. The work underscores the potential of heterogeneous networks in capturing the complexity of proteomic interactions, thereby offering enhanced accuracy in pathway prediction. This approach not only deepens our understanding of cellular processes but also opens up new possibilities in disease treatment and drug discovery by leveraging the predictive power of comprehensive proteomic data analysis.

MR-GGI: accurate inference of gene-gene interactions using Mendelian randomization

BACKGROUND

Researchers have long studied the regulatory processes of genes to uncover their functions. Gene regulatory network analysis is one of the popular approaches for understanding these processes, requiring accurate identification of interactions among the genes to establish the gene regulatory network. Advances in genome-wide association studies and expression quantitative trait loci studies have led to a wealth of genomic data, facilitating more accurate inference of gene-gene interactions. However, unknown confounding factors may influence these interactions, making their interpretation complicated. Mendelian randomization (MR) has emerged as a valuable tool for causal inference in genetics, addressing confounding effects by estimating causal relationships using instrumental variables. In this paper, we propose a new statistical method, MR-GGI, for accurately inferring gene-gene interactions using Mendelian randomization.

RESULTS

MR-GGI applies one gene as the exposure and another as the outcome, using causal cis-single-nucleotide polymorphisms as instrumental variables in the inverse-variance weighted MR model. Through simulations, we have demonstrated MR-GGI's ability to control type 1 error and maintain statistical power despite confounding effects. MR-GGI performed the best when compared to other methods using the F1 score on the DREAM5 dataset. Additionally, when applied to yeast genomic data, MR-GGI successfully identified six clusters. Through gene ontology analysis, we have confirmed that each cluster in our study performs distinct functional roles by gathering genes with specific functions.

CONCLUSION

These findings demonstrate that MR-GGI accurately inferences gene-gene interactions despite the confounding effects in real biological environments.

Elevated genetic risk for multiple sclerosis emerged in steppe pastoralist populations

Multiple sclerosis (MS) is a neuro-inflammatory and neurodegenerative disease that is most prevalent in Northern Europe. Although it is known that inherited risk for MS is located within or in close proximity to immune-related genes, it is unknown when, where and how this genetic risk originated1. Here, by using a large ancient genome dataset from the Mesolithic period to the Bronze Age2, along with new Medieval and post-Medieval genomes, we show that the genetic risk for MS rose among pastoralists from the Pontic steppe and was brought into Europe by the Yamnaya-related migration approximately 5,000 years ago. We further show that these MS-associated immunogenetic variants underwent positive selection both within the steppe population and later in Europe, probably driven by pathogenic challenges coinciding with changes in diet, lifestyle and population density. This study highlights the critical importance of the Neolithic period and Bronze Age as determinants of modern immune responses and their subsequent effect on the risk of developing MS in a changing environment.

Subtypes of relapsing-remitting multiple sclerosis identified by network analysis

We used network analysis to identify subtypes of relapsing-remitting multiple sclerosis subjects based on their cumulative signs and symptoms. The electronic medical records of 113 subjects with relapsing-remitting multiple sclerosis were reviewed, signs and symptoms were mapped to classes in a neuro-ontology, and classes were collapsed into sixteen superclasses by subsumption. After normalization and vectorization of the data, bipartite (subject-feature) and unipartite (subject-subject) network graphs were created using NetworkX and visualized in Gephi. Degree and weighted degree were calculated for each node. Graphs were partitioned into communities using the modularity score. Feature maps visualized differences in features by community. Network analysis of the unipartite graph yielded a higher modularity score (0.49) than the bipartite graph (0.25). The bipartite network was partitioned into five communities which were named fatigue, behavioral, hypertonia/weakness, abnormal gait/sphincter, and sensory, based on feature characteristics. The unipartite network was partitioned into five communities which were named fatigue, pain, cognitive, sensory, and gait/weakness/hypertonia based on features. Although we did not identify pure subtypes (e.g., pure motor, pure sensory, etc.) in this cohort of multiple sclerosis subjects, we demonstrated that network analysis could partition these subjects into different subtype communities. Larger datasets and additional partitioning algorithms are needed to confirm these findings and elucidate their significance. This study contributes to the literature investigating subtypes of multiple sclerosis by combining feature reduction by subsumption with network analysis.