Identification of Ferroptosis- and Hypoxia-related Genes in iPSC-derived Oligodendrocyte Precursor Cells from Multiple Sclerosis Patients

Summary

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).

Abstract

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.

Introduction

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 RRMS^9,10, 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.

Protocol

1. Data download and preprocessing

1. Download the datasets GSE196575 and GSE147315 from the Gene Expression Omnibus (GEO).
2. Merge the two datasets and remove batch effects, using an online analysis platform (see Table of Materials).
   1. Choose the Expression module | data merge module. Upload the two expression matrix files as input files and click Run to automatically output the merged matrix.
   2. Create a sample annotation file as a spreadsheet.
   3. Choose the Expression module | Remove batch effect module. Input the annotation file and merged matrix and click on Run to create the merged matrix with batch effect removed (merged matrix 2).
3. Normalize the expression data using the merge datasets, batch effect, and Normalize features available on an online analysis platform. Choose expression module and normalize module. Input merged matrix 2 created in step 1.2.3. Click the Run button. (see the Table of Materials).
4. Output the expression matrix file after running the whole process described above. Extract the grouping information according to the intervention provided on the GEO website. Input the above information in a ".txt" format document to create the grouping information file for the merged dataset.

2. Differential expressed gene analysis

1. Perform differential gene expression analysis using the limma package in R. Input the expression matrix file and grouping information file. Set the filtering criteria to |logFC| = 1 and p < 0.05 to identify differentially expressed genes (DEGs) related to multiple sclerosis.

3. Functional enrichment analysis (GO and KEGG)

1. Conduct functional enrichment analysis for the DEGs via the GO and KEGG modules on online platforms.
   1. Set the model organism was set to Homo sapiens, with the Ensemble_109 version as the background gene file and genes as the data type.
   2. Avoid broad GO terms; refine the analysis using ToppFun. Apply filters to limit terms to 500 and 1,000 associated genes.
      NOTE: The results from the 500-gene filter are presented in the main manuscript. The full GO results are available in Supplementary Figure S2. The online KEGG platform is shown in Supplementary Figure S3.

4. Gene set variation analysis for ferroptosis and hypoxia (GSVA)

1. Retrieve a total of 538 ferroptosis-related genes from FerrDb and 200 hypoxia-related genes from MsigDB.
2. Create GMT format files for both gene sets.
3. Use the GSVA module to calculate the ferroptosis Z score and hypoxia Z score for each sample in the merged dataset, and use the expression matrix file, grouping information file, and GMT files as inputs.
4. Compare the Z scores of different groups (Supplementary Figure S4).

5. Weighted Gene Co-expression Network Analysis (WGCNA)

1. Extract the Z score of ferroptosis and hypoxia of each sample calculated in step 4.4. Use the above Z scores of ferroptosis and hypoxia as trait files alongside the expression matrix files to identify key gene modules related to ferroptosis and hypoxia.
2. Use the WGCNA module and set R² = 0.9 and soft threshold β = 18 to construct a weighted gene coexpression network. Focus on modules significantly associated with both ferroptosis and hypoxia (Supplementary Figure S4).

6. Identification of differentially expressed genes related to ferroptosis and hypoxia in MS patients

1. Intersect the disease-related DEGs with ferroptosis and hypoxia-related gene modules via a Venn diagram tool on an online analysis platform (Supplementary Figure S3). Identify the genes associated with both ferroptosis and hypoxia in MS.

7. Protein-protein interaction (PPI) network analysis

1. Input the intersected genes into the STRING database's Multiple Proteins module.
2. Set the organism to Homo sapiens and click Search.
3. Use the CONTINUE option in the center of the webpage to generate the PPI network.
4. Export the interaction network information in TSV format and import it into bioinformatics network analysis software (Supplementary Figure S5).
5. Use the Cytohubba plugin to identify the top 10 hub genes on the basis of the MCC algorithm.

8. Validation with the GSE151306 dataset

1. Download the GSE151306 dataset from GEO.
2. Use the limma package in R studio software (R language codes are provided in GitHub: https://github.com/Drxiazhang/Identification-of-Ferroptosis--and-Hypoxia-Related-Genes-in-iPSC-Derived-OPCs-from-MS) to validate the expression of the hub genes across different groups, and input the expression matrix file, the grouping file of the validation set, and the hub gene list.

9. ROC curve plotting for hub genes

1. Use the pROC package in R to plot ROC curves for the differentially expressed hub genes.
2. Validate the diagnostic value of these hub genes in the validation dataset (R language codes are provided in GitHub: https://github.com/Drxiazhang/Identification-of-Ferroptosis--and-Hypoxia-Related-Genes-in-iPSC-Derived-OPCs-from-MS).

10. Prediction of transcription factor-hub gene regulatory networks

1. Access the TRRUST v2 database at https://www.grnpedia.org/trrust/.
2. Use the Find key regulators for query genes module to identify transcription factors regulating the 10 hub genes.
   1. Set Human for the species. Input the transcription factors and hub genes into STRING's Multiple Proteins module, set Homo sapiens for organisms, and click CONTINUE to generate a regulatory network.

Results

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 ge...

Discussion

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...

Materials

List of materials used in 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