Combining Gene Expression Microarrays and Mendelian Randomization: Exploring Key Immune-related Genes in Multiple Sclerosis

Overview

Journal Front Neurol

Specialty Neurology

Date 2024 Dec 12

PMID 39664749

Authors

Shuangfeng Ding

Yunyun Zhang

Yunzhe Tang

Ying Zhang

Mingyuan Liu

Affiliations

Soon will be listed here.

Abstract

Objective: Multiple Sclerosis (MS) is an autoimmune disorder characterized by demyelination occurring within the white matter of the central nervous system. While its pathogenesis is intricately linked with the body's immune response, the precise underlying mechanisms remain elusive. This study aims to explore potential immune-related genes associated with MS and assess the causal relationship between these genes and the risk of developing MS.

Methods: We retrieved expression datasets of peripheral blood mononuclear cells from MS patients from the Gene Expression Omnibus (GEO) database. Immune-related differentially expressed genes (IM-DEGs) were identified using the ImmPort database. GO and KEGG analyses were subsequently performed to elucidate the functions and pathways associated with the IM-DEGs. To visualize protein-protein interactions (PPIs), we used STRING, Cytoscape, and Cytohubba to construct networks of PPIs and hub genes. The diagnostic efficacy of hub genes was assessed using the nomogram model and ROC curve. The correlation of these hub genes was further validated in the mouse EAE model using quantitative PCR (qPCR). Finally, Mendelian randomization (MR) was performed to ascertain the causal impact of hub genes on MS.

Results: Twenty-eight IM-DEGs were selected from the intersection of DEGs and immune genes. These genes are involved mainly in antigen receptor-mediated signaling pathways, B cell differentiation, B cell proliferation, and B cell receptor signaling pathways. Using Cytoscape software for analysis, the top 10 genes with the highest scores were identified as PTPRC, CD19, CXCL8, CD79A, IL7, CR2, CD22, BLNK, LCN2, and LTF. Five hub genes (PTPRC, CD19, CXCL8, CD79A, and IL7) are considered to have strong diagnostic potential. In the qPCR validation, the relative expression of these five genes showed significant differences between the control and EAE groups, indicating that these genes may play a potential role in the pathogenesis of MS. The MR results indicate that elevated levels of CD79A (OR = 1.106, 95% CI 1.002-1.222,  = 0.046) are causally positively associated with the risk of developing MS.

Conclusion: This study integrated GEO data mining with MR to pinpoint pivotal immune genes linked to the onset of MS, thereby offering novel strategies for the treatment of MS.

References

Marcus R . What Is Multiple Sclerosis?. JAMA. 2022; 328(20):2078. DOI: 10.1001/jama.2022.14236. View

Bhatta P, Whale K, Sawtell A, Thompson C, Rapecki S, Cook D . Bispecific antibody target pair discovery by high-throughput phenotypic screening using in vitro combinatorial Fab libraries. MAbs. 2021; 13(1):1859049. PMC: 7849716. DOI: 10.1080/19420862.2020.1859049. View

Liu S, Deng B, Yin Z, Lin Y, An L, Liu D . Combination of CD19 and CD22 CAR-T cell therapy in relapsed B-cell acute lymphoblastic leukemia after allogeneic transplantation. Am J Hematol. 2021; 96(6):671-679. DOI: 10.1002/ajh.26160. View

Boardman A, Salles G . CAR T-cell therapy in large B cell lymphoma. Hematol Oncol. 2023; 41 Suppl 1:112-118. PMC: 10348487. DOI: 10.1002/hon.3153. View

Emdin C, Khera A, Kathiresan S . Mendelian Randomization. JAMA. 2017; 318(19):1925-1926. DOI: 10.1001/jama.2017.17219. View

Zeng R, Jiang R, Huang W, Wang J, Zhang L, Ma Y . Dissecting shared genetic architecture between obesity and multiple sclerosis. EBioMedicine. 2023; 93:104647. PMC: 10363440. DOI: 10.1016/j.ebiom.2023.104647. View

Flach A, Litke T, Strauss J, Haberl M, Gomez C, Reindl M . Autoantibody-boosted T-cell reactivation in the target organ triggers manifestation of autoimmune CNS disease. Proc Natl Acad Sci U S A. 2016; 113(12):3323-8. PMC: 4812745. DOI: 10.1073/pnas.1519608113. View

Li R, Patterson K, Bar-Or A . Reassessing B cell contributions in multiple sclerosis. Nat Immunol. 2018; 19(7):696-707. DOI: 10.1038/s41590-018-0135-x. View

Park S . Nomogram: An analogue tool to deliver digital knowledge. J Thorac Cardiovasc Surg. 2018; 155(4):1793. DOI: 10.1016/j.jtcvs.2017.12.107. View

10.

Rodriguez Murua S, Farez M, Quintana F . The Immune Response in Multiple Sclerosis. Annu Rev Pathol. 2021; 17:121-139. DOI: 10.1146/annurev-pathol-052920-040318. View

11.

Baranzini S, Oksenberg J . The Genetics of Multiple Sclerosis: From 0 to 200 in 50 Years. Trends Genet. 2017; 33(12):960-970. PMC: 5701819. DOI: 10.1016/j.tig.2017.09.004. View

12.

Kemppinen A, Kaprio J, Palotie A, Saarela J . Systematic review of genome-wide expression studies in multiple sclerosis. BMJ Open. 2011; 1(1):e000053. PMC: 3191406. DOI: 10.1136/bmjopen-2011-000053. View

13.

Chin C, Chen S, Wu H, Ho C, Ko M, Lin C . cytoHubba: identifying hub objects and sub-networks from complex interactome. BMC Syst Biol. 2014; 8 Suppl 4:S11. PMC: 4290687. DOI: 10.1186/1752-0509-8-S4-S11. View

14.

Lin J, Zhou J, Xu Y . Potential drug targets for multiple sclerosis identified through Mendelian randomization analysis. Brain. 2023; 146(8):3364-3372. PMC: 10393411. DOI: 10.1093/brain/awad070. View

15.

Musette P, Bouaziz J . B Cell Modulation Strategies in Autoimmune Diseases: New Concepts. Front Immunol. 2018; 9:622. PMC: 5908887. DOI: 10.3389/fimmu.2018.00622. View

16.

Hardy I, Anceriz N, Rousseau F, Seefeldt M, Hatterer E, Irla M . Anti-CD79 antibody induces B cell anergy that protects against autoimmunity. J Immunol. 2014; 192(4):1641-50. PMC: 3941979. DOI: 10.4049/jimmunol.1302672. View

17.

Lenk L, Carlet M, Vogiatzi F, Spory L, Winterberg D, Cousins A . CD79a promotes CNS-infiltration and leukemia engraftment in pediatric B-cell precursor acute lymphoblastic leukemia. Commun Biol. 2021; 4(1):73. PMC: 7810877. DOI: 10.1038/s42003-020-01591-z. View

18.

Wilkerson M, Hayes D . ConsensusClusterPlus: a class discovery tool with confidence assessments and item tracking. Bioinformatics. 2010; 26(12):1572-3. PMC: 2881355. DOI: 10.1093/bioinformatics/btq170. View

19.

Kulasingam V, Diamandis E . Strategies for discovering novel cancer biomarkers through utilization of emerging technologies. Nat Clin Pract Oncol. 2008; 5(10):588-99. DOI: 10.1038/ncponc1187. View

20.

Browne P, Chandraratna D, Angood C, Tremlett H, Baker C, Taylor B . Atlas of Multiple Sclerosis 2013: A growing global problem with widespread inequity. Neurology. 2014; 83(11):1022-4. PMC: 4162299. DOI: 10.1212/WNL.0000000000000768. View