Identification of HK3 as a Potential Key Biomarker in the Progression of Temporomandibular Joint Osteoarthritis via RNA Sequencing

TL;DR

This study identifies HK3 as a key gene in TMJOA progression and suggests it as a potential biomarker and drug target for treatment.

Contribution

First study to link HK3 with TMJOA cartilage degeneration and propose small-molecule inhibitors targeting HK3.

Findings

RNA-seq identified 257 differentially expressed genes in a rat TMJOA model.

HK3 was confirmed as significantly upregulated in damaged cartilage via qPCR.

Three small-molecule inhibitors (MK-8719, LY3372689, and thiamet-G) showed high binding affinity to HK3.

Abstract

Temporomandibular joint osteoarthritis (TMJOA) is a prevalent degenerative disorder with incompletely elucidated mechanisms. Identifying key biomarkers and pathways driving TMJOA progression is crucial for developing targeted therapies. In a rat TMJOA model induced via intra-articular MIA, RNA-seq analysis identified 257 differentially expressed genes. Network analysis pinpointed the glycolytic enzyme HK3 as a critical driver of disease progression, with qPCR confirming its significant up-regulation in damaged cartilage. Integrating the drug–gene interaction database with molecular-docking simulations, we identified three small-molecule inhibitors (MK-8719, LY3372689, and thiamet-G) with high binding affinity for HK3, predicted to inhibit its catalytic activity. To our knowledge, this study is the first to establish a link between HK3 and TMJOA cartilage degeneration, offering a metabolically relevant biomarker for early diagnosis as well as potential disease-modifying therapeutic candidates.

The pathogenesis of temporomandibular joint osteoarthritis (TMJOA) is poorly understood. This study aims to identify key biomarkers involved in TMJOA progression and explore potential therapeutic drugs through transcriptome analysis. A rat TMJOA model was established by bilateral injection of monosodium iodoacetate (MIA) into the TMJ cavities. Model validation was conducted using hematoxylin-eosin (HE) and Safranin O-Fast Green (SO-FG) staining. Differentially expressed genes (DEGs) were identified through RNA sequencing. Key pathways were explored using Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Ontology (GO), and Reactome pathway analyses. DEGs were clustered using MCODE analysis, and Hexokinase 3 (HK3) was identified as a key gene, which was further validated by qPCR. Potential drugs targeting HK3 were selected using the DGIdb database, and molecular docking was conducted to confirm drug-HK3 binding affinity. The TMJOA model was successfully established. RNA-seq analysis revealed 160 upregulated and 97 downregulated DEGs. KEGG, GO, and Reactome pathways analysis identified dysregulated pathways. The top five clusters of DEGs were identified, with HK3 emerging as the key gene. qPCR validation confirmed upregulated HK3 mRNA expression in TMJOA cartilage compared to the control group. Three drugs (MK8719, LY3372689, and Thiamet-G) targeting HK3 were identified through the Drug-Gene Interaction Database (DGIdb) screening, and molecular docking demonstrated high binding affinity between these drugs and HK3. This study suggests that HK3 may play a role in TMJOA progression and could serve as a potential biomarker for inflammatory progression in TMJOA. Targeting HK3 may offer new diagnostic and therapeutic strategies for TMJOA management.

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