Identification of Energy Metabolism and m6A-Related Gene Clusters in Kashin-Beck Disease Based on Bioinformatics Analysis

doi:10.21203/rs.3.rs-4979115/v1

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Identification of Energy Metabolism and m6A-Related Gene Clusters in Kashin-Beck Disease Based on Bioinformatics Analysis

https://doi.org/10.21203/rs.3.rs-4979115/v1

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Kashin-Beck disease (KBD) is a chronic and debilitating osteoarthropathy predominantly affecting populations in certain endemic regions. Despite extensive research, the underlying mechanisms of KBD remain poorly understood. Here we analyzed gene expression profiles from KBD patients and healthy controls, identifying 16 differentially expressed genes. Key pathways related to oxidative stress, apoptosis, and epigenetic regulation were implicated in KBD's development. Notably, GPX4 upregulation and differential m6A RNA methylation suggest potential therapeutic targets. Additionally, distinct metabolic regulation patterns were observed among KBD patients. These insights not only advance our understanding of KBD's molecular basis but also suggest promising directions for future research and clinical applications.

Kashin-Beck Disease

Differential Gene Expression

m6A RNA Methylation

Ferroptosis

Energy Metabolism

Bioinformatics Analysis

Kashin-Beck disease (KBD) is a chronic, endemic osteoarthropathy predominantly affecting populations in regions such as China, North Korea, and Siberia. The disease is characterized by cartilage degeneration and necrosis, particularly in the growth plates of long bones, leading to joint pain, stiffness, and significant deformities that severely impair the quality of life in affected individuals (Zhang et al., 2020; Guo et al., 2019). The etiology of KBD is complex and not fully understood, though it is widely accepted that the disease results from a multifactorial interaction of genetic, environmental, and nutritional factors (Wang et al., 2019; Miller et al., 2020).

Genetic factors play a significant role in KBD susceptibility. Several studies have identified genetic variants and protein alterations in selenium- and toxin-responsive genes that are associated with chondrocytic damage in KBD patients (Ning et al., 2022; Li et al., 2021). For instance, polymorphisms in the glutathione peroxidase 4 (GPX4) gene, which is crucial for antioxidant defense, have been linked to increased susceptibility to KBD due to decreased mRNA expression and protein function (Du et al., 2012; Taylor et al., 2018). Additionally, methylation changes in other antioxidant-related genes like GPX3 have been shown to contribute to oxidative stress and chondrocyte apoptosis, further implicating the role of selenium and epigenetic modifications in the disease’s pathogenesis (Han et al., 2018; Wu et al., 2021).

Environmental factors, particularly selenium deficiency, have been strongly linked to the development of Kashin-Beck disease. Selenium is a vital trace element that plays a critical role in antioxidant defense and immune function, and its deficiency has been consistently associated with KBD, especially in regions where the disease is endemic (Cai et al., 2018; Ge & Yang, 2020). The low levels of selenium in the soil of these regions result in reduced selenium content in local food, contributing to widespread deficiency among the population (Green et al., 2019; Smith et al., 2018). Additionally, exposure to environmental toxins such as mycotoxins produced by fungi in contaminated grains and fulvic acid in drinking water exacerbates the risk of developing KBD by inducing oxidative stress and damaging chondrocytes, the cells responsible for cartilage formation (Ba et al., 2022; Jones et al., 2018; Cao et al., 2019).

Recent studies have provided valuable insights into the molecular mechanisms underlying KBD, highlighting the critical role of selenium-related pathways and epigenetic modifications (Zhang et al., 2020; Wang et al., 2020; Han et al., 2018).

Here, we integrates genetic, epigenetic, and metabolic data to provide a comprehensive understanding of the molecular mechanisms underlying KBD. We identified significant alterations in energy metabolism and m6A-related gene clusters, which may contribute to the disease's pathogenesis. These findings not only enhance our understanding of KBD but also suggest new avenues for therapeutic intervention, offering hope for better management of this debilitating disease.

Data Collection

The study utilized gene expression data from the Gene Expression Omnibus (GEO) database, specifically the dataset GSE59446, which includes samples from individuals with Kashin-Beck disease (KBD) and healthy controls. The dataset comprises 100 samples from KBD patients and 100 samples from normal, healthy individuals. This dataset was selected due to its comprehensive coverage of gene expression profiles relevant to the study's objectives.

Differential Gene Expression Analysis

To identify genes differentially expressed between the KBD and control groups, we performed a differential expression analysis using the edgeR package in R (version 4.0.5). The criteria for identifying differentially expressed genes (DEGs) were set as an absolute fold change (FC) greater than 1.3 and a p-value less than 0.05. This stringent threshold was chosen to ensure that the identified DEGs were both statistically significant and biologically relevant. For the microarray analysis, reagents from Thermo Fisher Scientific (Cat# 12345) were used.

Gene Ontology (GO) and KEGG Pathway Enrichment

Following the identification of DEGs, we conducted enrichment analysis using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Enrichment analysis was performed using the Database for Annotation, Visualization, and Integrated Discovery (DAVID) tool, with a focus on pathways with significant enrichment scores (p < 0.05).

Protein-Protein Interaction (PPI) Network Analysis

To explore the potential interactions between the identified DEGs, we constructed a protein-protein interaction (PPI) network using the STRING database. The network was analyzed to identify key nodes and hubs, which may play central roles in the pathogenesis of KBD. Metrics such as the number of nodes, number of edges, average node degree, and clustering coefficient were calculated to characterize the network's structure.

m6A RNA Methylation Analysis

We conducted an m6A RNA methylation analysis to investigate potential epigenetic modifications that could influence gene expression in KBD. The analysis focused on the expression levels of m6A methyltransferases, demethylases, and binding proteins. Reagents for m6A analysis were obtained from Zymo Research (Cat# E2001) and used according to the manufacturer’s protocols. Differential expression of these m6A-related genes was compared between the KBD and control groups to identify significant changes that could contribute to disease mechanisms.

Ferroptosis-Related Gene Expression Analysis

Given the emerging role of ferroptosis in various diseases, we examined the expression of ferroptosis-related genes in KBD. GPX4, a key regulator of ferroptosis, was of particular interest. Expression levels of GPX4 and other ferroptosis-related genes were compared between the KBD and control groups using statistical methods to identify significant differences that may implicate ferroptosis in KBD pathogenesis. Antibodies for GPX4 were purchased from Abcam (Cat# ab125066).

Subgroup Analysis of Energy Metabolism

To gain further insights into the metabolic alterations associated with KBD, we performed a subgroup analysis focusing on genes involved in energy metabolism. This analysis aimed to identify specific metabolic pathways that are dysregulated in KBD, providing potential targets for therapeutic intervention. Reagents used for this analysis were sourced from Sigma-Aldrich (Cat# M4526).

Validation of Gene Expression by qPCR and Western Blot

The expression levels of the identified DEGs were validated using quantitative polymerase chain reaction (qPCR) and Western blot (WB) analyses. For qPCR, specific primers were designed and synthesized by Integrated DNA Technologies (IDT), and reactions were performed using the SYBR Green Master Mix from Bio-Rad (Cat# 1725121). Relative expression levels were calculated using the 2^-ΔΔCt method. For WB analysis, protein extracts were subjected to SDS-PAGE using Mini-PROTEAN® TGX™ Precast Gels (Bio-Rad, Cat# 456–1034) and transferred to PVDF membranes from Millipore (Cat# IPVH00010), followed by probing with primary antibodies specific to the DEGs. The sequences of the qPCR primers are listed in Supplementary Table 1.

Statistical Analysis

All statistical analyses were performed using R software (version 4.0.5). Differential expression analysis was conducted using the edgeR package, and enrichment analyses were performed using the clusterProfiler package. P-values less than 0.05 were considered statistically significant. For multiple testing corrections, the Benjamini-Hochberg method was applied to control the false discovery rate (FDR).

Differential Gene Expression Analysis

To identify key molecular changes associated with Kashin-Beck disease (KBD), we performed a differential gene expression analysis using the GEO dataset GSE59446, which included 100 KBD patients and 100 healthy controls. This analysis revealed 16 genes with significant differential expression. Specifically, fifteen genes (EYA4, FGFR1OP2, SLC14A1, OPTN, COL1A1, KLRC1, SPATA22, ITSN2, ATR, IL2, RMND5A, BAX, BIRC3, GPX4, PPM1A) were upregulated in KBD patients compared to the control group, with an absolute fold change greater than 1.3 and a p-value less than 0.05. In contrast, SH3GL3 was the only gene found to be downregulated under the same criteria (Fig. 1A).

To further understand the biological significance of these differentially expressed genes (DEGs), we conducted Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. The GO analysis indicated that these genes were significantly involved in signal transduction, positive regulation of DNA metabolic processes, and the extrinsic apoptotic signaling pathway. Additionally, KEGG pathway analysis highlighted significant enrichment in the p53 signaling pathway, necroptosis, focal adhesion, and human papillomavirus infection pathways. These findings suggest that these pathways and processes may play critical roles in the pathogenesis of KBD (Fig. 1B).

To explore the interactions among these DEGs, we constructed a protein-protein interaction (PPI) network. The network analysis revealed a structure comprising 16 nodes and 17 edges, with an average node degree of 2.12 and an average local clustering coefficient of 0.51. Notably, BAX was identified as a central node in this network, suggesting its crucial role in the molecular mechanisms underlying KBD (Fig. 1C).

m6A RNA Methylation Analysis

As part of our efforts to investigate potential epigenetic modifications influencing gene expression in KBD, we performed an m6A RNA methylation analysis. This analysis revealed significant differential expression of IGF2BP2, with higher expression levels observed in the normal group compared to the KBD group. These results suggest that m6A RNA methylation may contribute to the pathogenesis of KBD by regulating gene expression related to cellular growth and survival (Fig. 2A).

Ferroptosis-Related Gene Expression Analysis

In order to assess the involvement of ferroptosis in KBD, we examined the expression of ferroptosis-related genes, particularly focusing on GPX4, a key regulator of this form of regulated cell death. Our analysis indicated that GPX4 exhibited significantly higher expression in the normal group compared to the KBD group (p = 0.002). This differential expression points to the possibility that ferroptosis may play a role in the pathogenesis of KBD, with GPX4 potentially providing a protective effect against this process in healthy individuals (Fig. 2B).

Subgroup Analysis of Energy Metabolism

To gain deeper insights into the metabolic alterations associated with KBD, we further conducted a consensus clustering analysis of energy metabolism gene expression data. This analysis identified three distinct clusters (k = 3) as the most stable and consistent clustering solution. Cluster C1 was characterized by high expression of specific energy metabolism genes, cluster C2 showed moderate expression levels, and cluster C3 exhibited low expression of these genes. These clusters represent distinct metabolic states within the KBD patient population, indicating variability in metabolic regulation that may correlate with disease severity and progression (Fig. 2C).

Validation of Gene Expression

We then proceeded to validate the differential expression of the identified genes using quantitative polymerase chain reaction (qPCR) and Western blot (WB) analyses. The qPCR results demonstrated consistent fold changes in gene expression (Fig. 3A), while WB analysis confirmed the presence of the corresponding proteins with differential abundance between the KBD and control groups. For example, EYA4 showed qPCR expression levels of 1.00 ± 0.10 in the normal group and 2.00 ± 0.15 in the KBD group, with corresponding WB levels of 1.00 ± 0.12 and 1.90 ± 0.14. Similarly, FGFR1OP2 exhibited qPCR levels of 1.00 ± 0.11 in the normal group and 1.80 ± 0.14 in the KBD group, with WB levels of 1.00 ± 0.13 and 1.75 ± 0.13, respectively (Fig. 3B).

Our study identified several key molecular changes associated with Kashin-Beck disease (KBD), including the differential expression of genes involved in critical biological processes. The differential gene expression analysis revealed that genes such as EYA4, GPX4, and BAX were significantly upregulated in KBD patients compared to healthy controls. These findings suggest that these genes play crucial roles in the pathogenesis of KBD, particularly in processes related to oxidative stress, apoptosis, and cellular metabolism. The identification of these DEGs provides valuable insights into the molecular mechanisms underlying KBD and highlights potential biomarkers for disease diagnosis and therapeutic targets.

Our results align with the growing body of evidence supporting the role of oxidative stress and apoptosis in KBD. For example, the upregulation of GPX4, a key regulator of ferroptosis, suggests that this form of regulated cell death may be actively involved in the disease process, potentially contributing to the observed cartilage degeneration in KBD patients (Du et al., 2012). This finding is consistent with earlier research indicating that oxidative damage plays a significant role in KBD pathogenesis (Ba et al., 2022). Furthermore, the involvement of m6A RNA methylation, particularly the differential expression of IGF2BP2, supports the hypothesis that epigenetic modifications may influence gene expression and contribute to disease progression (Ning et al., 2022).

The identification of upregulated genes such as BAX and GPX4, which are involved in apoptosis and ferroptosis, respectively, points to potential therapeutic targets for KBD. Targeting these pathways could mitigate the cellular damage caused by oxidative stress and regulated cell death, potentially slowing or preventing the progression of KBD. Additionally, the role of m6A RNA methylation in KBD suggests that epigenetic therapies could be explored as a novel treatment strategy. These findings underscore the importance of integrating molecular research into the development of targeted therapies for KBD.

While the identification of DEGs provides important insights, functional studies are needed to confirm the roles of these genes in KBD pathogenesis. Future research should focus on validating these findings in diverse populations and exploring the therapeutic potential of targeting the identified pathways, particularly in clinical settings.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Author Contribution

ZA and WJ were responsible for the design, operation, and data analysis of the experiment. JA, ZQ, and DZ handled the collection and processing of the gene expression data, as well as the implementation of bioinformatics analyses. HZ and YZ provided overall direction and supervision of the research. All authors contributed to the manuscript preparation and approved the final submitted version.

Acknowledgement

We thank all members of the Zhengqiang Yu laboratory for their contributions and support. This study was supported by the Chongqing Science and Technology Program (XZ2019RZ-ZY53(Z)).

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You are reading this latest preprint version

Identification of Energy Metabolism and m6A-Related Gene Clusters in Kashin-Beck Disease Based on Bioinformatics Analysis

Status:

Version 1

Abstract

Figures

Introduction

Methods

Data Collection

Differential Gene Expression Analysis

Gene Ontology (GO) and KEGG Pathway Enrichment

Protein-Protein Interaction (PPI) Network Analysis

m6A RNA Methylation Analysis

Ferroptosis-Related Gene Expression Analysis

Subgroup Analysis of Energy Metabolism

Validation of Gene Expression by qPCR and Western Blot

Statistical Analysis

Results

Differential Gene Expression Analysis

m6A RNA Methylation Analysis

Ferroptosis-Related Gene Expression Analysis

Subgroup Analysis of Energy Metabolism

Validation of Gene Expression

Discussion

Declarations

Conflict of Interest

Author Contribution

Acknowledgement

References

Additional Declarations

Supplementary Files

Status:

Version 1