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This study provides novel insights into the interactions among hypoxia, ferroptosis, and immune infiltration in the pathogenesis of multiple sclerosis (MS) via bioinformatics analysis. By employing weighted gene coexpression network analysis (WGCNA) and protein-protein interaction (PPI) analysis, we identified three pivotal hub genes (ITGB1, ITGB8, and VIM).
Multiple sclerosis (MS) is a chronic inflammatory disorder characterized by demyelination, with failed remyelination leading to progressive axon loss in chronic stages. Oligodendrocyte precursor cells (OPCs) are critical for remyelination. Recent studies suggest that both hypoxia and ferroptosis play crucial roles in the dysfunctional differentiation of OPCs. This research seeks to identify key genes linked to hypoxia and ferroptosis and immune infiltration characteristics in OPCs derived from induced pluripotent stem cells (iPSCs) of MS patients and to construct a diagnostic model centered on these pivotal genes.
We analyzed gene expression data from the GSE196575 and GSE147315 datasets and compared MS patients with healthy individuals. Using weighted gene coexpression network analysis (WGCNA), we pinpointed primary module genes and essential genes associated with hypoxia, ferroptosis, and MS. The ferroptosis Z score and the hypoxia Z score calculated via gene set variation analysis (GSVA) were greater in the iPSC-derived OPCs of MS patients than those of the control group. The implicated genes are predominantly linked to the PI3K/Akt/mTOR pathway, as identified through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses.
A protein-protein interaction (PPI) network of crucial genes revealed 10 central hub genes (COL4A1, COL4A2, ITGB5, ITGB1, ITGB8, ITGAV, VIM, FLNA, VCL, and SPARC). The robust expression of ITGB1, ITGB8, and VIM was validated in the GSE151306 dataset, supporting their role as key hub genes. Additionally, an interaction network between transcription factors (TFs) and hub genes was established via Transcriptional Regulatory Relationships Unraveled by Sentence-based Text (TRRUST), which identified five key TFs. The results of this study could help elucidatenovel biomarkers or therapeutic targets for MS.
Multiple sclerosis (MS) is a chronic inflammatory condition characterized by demyelination, that affects approximately 2.5 million individuals globally. The majority of those diagnosed with MS exhibit a relapsing-remitting (RR) disease course. During the relapsing phase, acute inflammation leads to the inevitable loss of myelin and axons. Conversely, during remission, demyelination lesions can be repaired by remyelination, providing trophic support to axons and preventing progressive axon loss1. Remyelination failure occurs in the chronic stages of MS and leads to progressive axonal degeneration2.
The process of remyelination is critically correlated with oligodendrocyte precursor cells (OPCs), involving the proliferation and migration of OPCs to differentiate into mature oligodendrocytes (OLs), which are the myelin-forming cells in the central nervous system (CNS)3. In the initial disease stages, the number of new OLs generated by OPCs around the demyelinated lesions is relatively preserved and can successfully promote remyelination4. However, during advanced MS stages, the inadequate migration and differentiation of OPCs lead to a reduction in new OLs and impaired remyelination5, thus leading to nerve degeneration and accumulation of disability.
Two hypotheses have been proposed to explain the neurodegeneration in MS. The extrinsic hypothesis suggests that the immune response initiated by activated T cells causes demyelination as well as neurodegeneration6. The intrinsic model, however, suggests that the intrinsic abnormalities in OPCs7, OLs8, and other cells in the CNS may contribute to neurodegeneration. The intrinsic model was previously considered only applicable in more advanced stages of MS, such as primary or secondary progressive MS (PPMS and SPMS). Nevertheless, neurodegeneration independent of inflammation or relapse has recently been observed in RRMS9,10, suggesting that intrinsic cellular abnormalities may be involved throughout all disease stages, including RRMS.
Furthermore, ferroptosis, a distinctive cell death pathway linked to iron-mediated lipid metabolic disturbances, plays a pivotal role in neurodegeneration. This pathway involves an imbalance of intracellular redox states driven by excess iron, leading to lipid peroxide accumulation and reactive oxygen species (ROS) production, ultimately resulting in oxidative cell death11. Neurodegenerative diseases such as Alzheimer's disease, Parkinson's disease, and Huntington's disease often originate from oxidative damage to neuronal cells, which is frequently triggered by unusually high iron concentrations within lesions. In MS, increased vulnerability to oxidative damage, combined with mitochondrial dysfunction due to the high lipid content and oxygen consumption of CNS cells, promotes lipid peroxidation, a critical factor in ferroptosis. OLs are sensitive to lipid peroxidation, an essential feature of ferroptosis12. Iron deposition near spinal inflammatory lesions13 and the vulnerability of OLs to lipid peroxidation14 and free radicals15 highlight the susceptibility of MS to ferroptosis.
Hypoxia is another critical factor in MS pathogenesis contributing to oligodendrocyte loss. Evidence of hypoxia-like damage and the generation of ROS and nitrogen oxide (NO) in acute MS lesions indicates that such stressors may precipitate mitochondrial dysfunction and subsequent energy deficits16. This metabolic stress not only affects OLs but also impairs neighboring axons through disrupted energy transfer17, as myelinic channels transmit energy between the myelin sheath and peri-axonal spaces.
Primary human OPCs and OLs of the CNS are extremely difficult to access in MS patients. Hence, induced pluripotent stem cell (iPSCs)-derived human OPCs and OLs have emerged as promising tools for studying the intrinsic disorders of MS. In light of the crucial roles of ferroptosis and hypoxia in MS pathogenesis and their impact on the oligodendrocyte lineage, this study employed weighted gene coexpression network analysis (WGCNA) to extract module information18 and to elucidate gene expression patterns associated with these phenomena in MS. By screening the correlation coefficients between genes, we are able to identify the same or similar coexpression networks or modules, shedding light on novel biomarkers or potential therapeutic targets for MS. In addition, by focusing on the transcription factors (TFs) that regulate critical genes, this study provides a foundation for further exploration of the mechanisms and potential intervention strategies for MS.
1. Data download and preprocessing
2. Differential expressed gene analysis
3. Functional enrichment analysis (GO and KEGG)
4. Gene set variation analysis for ferroptosis and hypoxia (GSVA)
5. Weighted Gene Co-expression Network Analysis (WGCNA)
6. Identification of differentially expressed genes related to ferroptosis and hypoxia in MS patients
7. Protein-protein interaction (PPI) network analysis
8. Validation with the GSE151306 dataset
9. ROC curve plotting for hub genes
10. Prediction of transcription factor-hub gene regulatory networks
The merged dataset consisting of four healthy individuals as controls and nine people with MS (PwMS), was analyzed and then validated in another dataset of four PwMS and four healthy controls. The analysis protocol is shown in Figure 1, and the detailed information of all the samples is listed in Supplementary Table S1. Through the analysis, 706 differentially expressed genes (DEGs, p < 0.01) were identified, 378 genes of which were upregulated and 328 g...
Given its pivotal role in the remyelination process, the migration, differentiation, and death of OPCs have long been determined to be crucial factors in MS pathogenesis and therapeutic targets of MS. Inflammation-independent progressive nerve degeneration has been observed in all three types of MS19, and a pronounced loss of oligodendrocyte was noted at the center of demyelinated lesions20, suggesting that primary disorders of oligodendrocyte lineage cells could accelerate...
The authors have no competing interests to declare that are relevant to the content of this article.
This study was supported and funded by National High Level Hospital Clinical Research Funding (2022-PUMCH-B-103). The authors would like to thank Dr. Shuang Song, Department of Biostatistics, Harvard T. H. Chan School of Public Health, Harvard University, for her valuable advice and guidance in the revision stage of this article.
Name | Company | Catalog Number | Comments |
BioInfoTools | / | online analysis website http://biowinford.site:3838/patrick_wang87/ | |
Cytoscape | / | bioinformatics network analysis software | |
GSE196575,GSE147315 and GSE151306 | / | RNA-seq from GEO dataset | |
Omicshare | GENE DENOVO | online analysis tools https://www.omicshare.com/tools/Home/Soft/getsoft | |
R-studio | RStudio, Inc | R integrated development environment software |
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