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Ayeldeen G, Shaker OG, Gomaa M, Magdy MM, Elsamaloty N, Kamel AS, Senousy MA. Association of Epistatic Effects of lncRNA GAS5, miR-146a, IRAK-1, and miR-155 Genetic Variants with Multiple Sclerosis Risk and Severity. Mol Neurobiol 2025:10.1007/s12035-025-04876-8. [PMID: 40234289 DOI: 10.1007/s12035-025-04876-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2024] [Accepted: 03/20/2025] [Indexed: 04/17/2025]
Abstract
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.
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Affiliation(s)
- Ghada Ayeldeen
- Department of Medical Biochemistry and Molecular Biology, Faculty of Medicine, Cairo University, Cairo, Egypt
| | - Olfat G Shaker
- Department of Medical Biochemistry and Molecular Biology, Faculty of Medicine, Cairo University, Cairo, Egypt
| | - Mohammed Gomaa
- Department of Neurology, Faculty of Medicine, Fayoum University, Fayoum, Egypt
| | - Mostafa M Magdy
- Department of Neurology, Faculty of Medicine, Fayoum University, Fayoum, Egypt
| | - Nourhan Elsamaloty
- Department of Biochemistry, Faculty of Pharmacy and Drug Technology, Egyptian Chinese University, Cairo, 11786, Egypt
| | - Ahmed S Kamel
- Department of Pharmacology and Toxicology, Faculty of Pharmacy, Cairo University, Cairo, 11562, Egypt
- Department of Pharmacology and Toxicology, Faculty of Pharmacy and Drug Technology, Egyptian Chinese University, Gesr El Suez St, Cairo, PO 11786, Egypt
| | - Mahmoud A Senousy
- Department of Biochemistry, Faculty of Pharmacy and Drug Technology, Egyptian Chinese University, Cairo, 11786, Egypt.
- Department of Biochemistry, Faculty of Pharmacy, Cairo University, Cairo, 11562, Egypt.
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Oh W, Jung J, Joo JWJ. MR-GGI: accurate inference of gene-gene interactions using Mendelian randomization. BMC Bioinformatics 2024; 25:192. [PMID: 38750431 PMCID: PMC11094870 DOI: 10.1186/s12859-024-05808-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Accepted: 05/09/2024] [Indexed: 05/19/2024] Open
Abstract
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.
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Affiliation(s)
- Wonseok Oh
- Department of Industrial Pharmacy, Dongguk University-Seoul, Seoul, 04620, South Korea
| | - Junghyun Jung
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Hollywood, CA, USA
| | - Jong Wha J Joo
- Department of Computer Science and Engineering, Dongguk University-Seoul, Seoul, 04620, South Korea.
- Division of AI Software Convergence, Dongguk University-Seoul, Seoul, 04620, South Korea.
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Barrie W, Yang Y, Irving-Pease EK, Attfield KE, Scorrano G, Jensen LT, Armen AP, Dimopoulos EA, Stern A, Refoyo-Martinez A, Pearson A, Ramsøe A, Gaunitz C, Demeter F, Jørkov MLS, Møller SB, Springborg B, Klassen L, Hyldgård IM, Wickmann N, Vinner L, Korneliussen TS, Allentoft ME, Sikora M, Kristiansen K, Rodriguez S, Nielsen R, Iversen AKN, Lawson DJ, Fugger L, Willerslev E. Elevated genetic risk for multiple sclerosis emerged in steppe pastoralist populations. Nature 2024; 625:321-328. [PMID: 38200296 PMCID: PMC10781639 DOI: 10.1038/s41586-023-06618-z] [Citation(s) in RCA: 48] [Impact Index Per Article: 48.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Accepted: 09/06/2023] [Indexed: 01/12/2024]
Abstract
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.
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Affiliation(s)
- William Barrie
- Department of Zoology, University of Cambridge, Cambridge, UK
- Department of Genetics, University of Cambridge, Cambridge, UK
| | - Yaoling Yang
- Department of Statistical Sciences, School of Mathematics, University of Bristol, Bristol, UK
- MRC Integrative Epidemiology Unit, Population Health Sciences, University of Bristol, Bristol, UK
| | - Evan K Irving-Pease
- Lundbeck Foundation GeoGenetics Centre, Globe Institute, University of Copenhagen, Copenhagen, Denmark
| | - Kathrine E Attfield
- Oxford Centre for Neuroinflammation, Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, UK
| | - Gabriele Scorrano
- Lundbeck Foundation GeoGenetics Centre, Globe Institute, University of Copenhagen, Copenhagen, Denmark
| | - Lise Torp Jensen
- Oxford Centre for Neuroinflammation, Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, UK
- Department of Clinical Medicine, Aarhus University Hospital, Aarhus, Denmark
| | - Angelos P Armen
- Oxford Centre for Neuroinflammation, Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, UK
| | | | - Aaron Stern
- Departments of Integrative Biology and Statistics, University of California, Berkeley, Berkeley, CA, USA
| | - Alba Refoyo-Martinez
- Lundbeck Foundation GeoGenetics Centre, Globe Institute, University of Copenhagen, Copenhagen, Denmark
| | - Alice Pearson
- Department of Genetics, University of Cambridge, Cambridge, UK
| | - Abigail Ramsøe
- Lundbeck Foundation GeoGenetics Centre, Globe Institute, University of Copenhagen, Copenhagen, Denmark
| | - Charleen Gaunitz
- Lundbeck Foundation GeoGenetics Centre, Globe Institute, University of Copenhagen, Copenhagen, Denmark
| | - Fabrice Demeter
- Lundbeck Foundation GeoGenetics Centre, Globe Institute, University of Copenhagen, Copenhagen, Denmark
- Eco-anthropologie (EA), Muséum National d'Histoire Naturelle, CNRS, Université de Paris, Musée de l'Homme, Paris, France
| | - Marie Louise S Jørkov
- Laboratory of Biological Anthropology, Department of Forensic Medicine, University of Copenhagen, Copenhagen, Denmark
| | | | | | - Lutz Klassen
- Museum Østdanmark-Djursland og Randers, Randers, Denmark
| | | | | | - Lasse Vinner
- Lundbeck Foundation GeoGenetics Centre, Globe Institute, University of Copenhagen, Copenhagen, Denmark
| | | | - Morten E Allentoft
- Lundbeck Foundation GeoGenetics Centre, Globe Institute, University of Copenhagen, Copenhagen, Denmark
- Trace and Environmental DNA (TrEnD) Laboratory, School of Molecular and Life Sciences, Curtin University, Perth, Western Australia, Australia
| | - Martin Sikora
- Lundbeck Foundation GeoGenetics Centre, Globe Institute, University of Copenhagen, Copenhagen, Denmark
| | - Kristian Kristiansen
- Lundbeck Foundation GeoGenetics Centre, Globe Institute, University of Copenhagen, Copenhagen, Denmark
- Department of Historical Studies, University of Gothenburg, Gothenburg, Sweden
| | - Santiago Rodriguez
- MRC Integrative Epidemiology Unit, Population Health Sciences, University of Bristol, Bristol, UK
| | - Rasmus Nielsen
- Lundbeck Foundation GeoGenetics Centre, Globe Institute, University of Copenhagen, Copenhagen, Denmark
- Departments of Integrative Biology and Statistics, University of California, Berkeley, Berkeley, CA, USA
| | - Astrid K N Iversen
- Oxford Centre for Neuroinflammation, Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, UK.
- Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, UK.
| | - Daniel J Lawson
- Department of Statistical Sciences, School of Mathematics, University of Bristol, Bristol, UK.
- MRC Integrative Epidemiology Unit, Population Health Sciences, University of Bristol, Bristol, UK.
| | - Lars Fugger
- Oxford Centre for Neuroinflammation, Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, UK.
- Department of Clinical Medicine, Aarhus University Hospital, Aarhus, Denmark.
- MRC Human Immunology Unit, John Radcliffe Hospital, University of Oxford, Oxford, UK.
| | - Eske Willerslev
- Department of Zoology, University of Cambridge, Cambridge, UK.
- Lundbeck Foundation GeoGenetics Centre, Globe Institute, University of Copenhagen, Copenhagen, Denmark.
- MARUM Center for Marine Environmental Sciences and Faculty of Geosciences, University of Bremen, Bremen, Germany.
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Howlett-Prieto Q, Oommen C, Carrithers MD, Wunsch DC, Hier DB. Subtypes of relapsing-remitting multiple sclerosis identified by network analysis. Front Digit Health 2023; 4:1063264. [PMID: 36714613 PMCID: PMC9874946 DOI: 10.3389/fdgth.2022.1063264] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Accepted: 12/22/2022] [Indexed: 01/12/2023] Open
Abstract
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.
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Affiliation(s)
- Quentin Howlett-Prieto
- Department of Neurology and Rehabilitation, University of Illinois at Chicago, Chicago, IL, United States
| | - Chelsea Oommen
- Department of Neurology and Rehabilitation, University of Illinois at Chicago, Chicago, IL, United States
| | - Michael D. Carrithers
- Department of Neurology and Rehabilitation, University of Illinois at Chicago, Chicago, IL, United States
| | - Donald C. Wunsch
- Department of Electrical and Computer Engineering, Missouri University of Science and Technology, Rolla, MO, United States
| | - Daniel B. Hier
- Department of Neurology and Rehabilitation, University of Illinois at Chicago, Chicago, IL, United States
- Department of Electrical and Computer Engineering, Missouri University of Science and Technology, Rolla, MO, United States
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