Identification of the osteoarthritis signature gene PDK1 by machine learning and its regulatory mechanisms on chondrocyte autophagy and apoptosis

Advancing osteoarthritis research: the role of AI in clinical, imaging and omics fields

Osteoarthritis (OA) is a degenerative joint disease with significant clinical and societal impact. Traditional diagnostic methods, including subjective clinical assessments and imaging techniques such as X-rays and MRIs, are often limited in their ability to detect early-stage OA or capture subtle joint changes. These limitations result in delayed diagnoses and inconsistent outcomes. Additionally, the analysis of omics data is challenged by the complexity and high dimensionality of biological datasets, making it difficult to identify key molecular mechanisms and biomarkers. Recent advancements in artificial intelligence (AI) offer transformative potential to address these challenges. This review systematically explores the integration of AI into OA research, focusing on applications such as AI-driven early screening and risk prediction from electronic health records (EHR), automated grading and morphological analysis of imaging data, and biomarker discovery through multi-omics integration. By consolidating progress across clinical, imaging, and omics domains, this review provides a comprehensive perspective on how AI is reshaping OA research. The findings have the potential to drive innovations in personalized medicine and targeted interventions, addressing longstanding challenges in OA diagnosis and management.

Innate Immunity and Synovitis: Key Players in Osteoarthritis Progression

Osteoarthritis (OA) is a chronic progressive disease of the joint. Although representing the most frequent cause of disability in the elderly, OA remains partly obscure in its pathogenic mechanisms and is still the orphan of resolutive therapies. The concept of what was once considered a "wear and tear" of articular cartilage is now that of an inflammation-related disease that affects over time the whole joint. The attention is increasingly focused on the synovium. Even from the earliest clinical stages, synovial inflammation (or synovitis) is a crucial factor involved in OA progression and a major player in pain onset. The release of inflammatory molecules in the synovium mediates disease progression and worsening of clinical features. The activation of synovial tissue-resident cells recalls innate immunity cells from the bloodstream, creating a proinflammatory milieu that fuels and maintains a damaging condition of low-grade inflammation in the joint. In such a context, cellular and molecular inflammatory behaviors in the synovium could be the primum movens of the structural and functional alterations of the whole joint. This paper focuses on and discusses the involvement of innate immunity cells in synovitis and their role in the progression of OA.

New insights into the mechanisms and therapeutic strategies of chondrocyte autophagy in osteoarthritis

Osteoarthritis (OA) is a chronic joint disease with an unclear cause characterized by secondary osteophytes and degenerative changes in the articular cartilage. More than 250 million people are expected to be affected by it by 2050, putting a tremendous socioeconomic strain on the entire world. OA cannot currently be treated with any effective medications that change the illness. Over time, chondrocytes undergo gradual metabolic, structural, and functional changes as a result of aging or abuse. The degenerative progression of osteoarthritis is significantly influenced by the imbalance of chondrocyte homeostasis. By continuously recycling and rebuilding macromolecules or organelles, autophagy functions as a crucial regulatory system to maintain homeostasis during an individual's growth and development. This review uses chondrocytes as its starting point and establishes a strong connection between autophagy and osteoarthritis in order to thoroughly examine the mechanisms behind chondrocyte autophagy in osteoarthritis. Biomarkers of chondrocyte autophagy will be identified, and prospective targeted medications and novel treatment approaches for slowing or preventing the course of OA will be developed based on chondrocyte senescence, autophagy, and apoptosis in OA. KEY MESSAGES: Currently, OA has not been treated with any drugs that can effectively cure it. We hope that by exploring specific targets in the course of osteoarthritis, we can promote the progress of treatment strategies. The degenerative progression of osteoarthritis is significantly influenced by the imbalance of chondrocyte balance. Through the continuous recovery and reconstruction of macromolecules or organelles, autophagy is an important regulatory system for maintaining homeostasis during individual growth and development. In this paper, the close relationship between autophagy and osteoarthritis was established with chondrocytes as the starting point, in order to further explore the mechanism of chondrocyte autophagy in osteoarthritis. The development process of osteoarthritis was studied from the perspective of chondrocytes, and the change of autophagy level had a significant impact on osteoarthritis. Chondrocyte autophagy is mainly determined by intracellular mitochondrial autophagy, so we are committed to finding relevant molecules. Through PI3K/AKT- and MAPK-related pathways, the biomarkers of chondrocyte autophagy were identified, and chondrocyte senescence, autophagy, and apoptosis based on osteoarthritis provided a constructive idea for the development of prospective targeted drugs and new therapies to slow down or prevent the progression of osteoarthritis.

Connecting the dots: Computational network analysis for disease insight and drug repurposing

Network biology is a powerful framework for studying the structure, function, and dynamics of biological systems, offering insights into the balance between health and disease states. The field is seeing rapid progress in all of its aspects: data availability, network synthesis, network analytics, and impactful applications in medicine and drug development. We review the most recent and significant results in network biomedicine, with a focus on the latest data, analytics, software resources, and applications in medicine. We also discuss what in our view are the likely directions of impactful development over the next few years.

A Novel BD2-Selective Inhibitor of BRDs Mitigates ROS Production and OA Pathogenesis

Bromodomain and extra-terminal domain (BET) family proteins regulate transcription and recognize lysine residues in histones. Selective BET inhibitors targeting one domain have attracted attention because they maintain normal physiological activities, whereas pan (nonselective) BET inhibitors do not. Osteoarthritis (OA) is a joint disorder characterized by cartilage degeneration for which no treatment currently exists. Here, we investigated whether the selective inhibition of BET proteins is an appropriate therapeutic strategy for OA. We focused on the development and characterization of 2-(4-(2-(dimethylamino)ethoxy)-3,5-dimethylphenyl)-5,7-dimethoxyquinazolin-4(3H)-one (BBC0906), a novel bromodomain 2 (BD2)-specific inhibitor designed to suppress OA progression. Using a DNA-encoded chemical library (DEL) screening approach, BBC0906 was identified because of its high affinity with the BD2 domain of BET proteins. BBC0906 effectively reduced reactive oxygen species (ROS) production and suppressed catabolic factor expression in chondrocytes in vitro. Moreover, in an OA mouse model induced by the destabilization of the medial meniscus (DMM), BBC0906 intra-articular injection attenuated cartilage degradation and alleviated OA. Importantly, BBC0906 selectively inhibits the BD2 domain, thus minimizing its potential side effects. We highlighted the therapeutic potential of targeting BET proteins to modulate oxidative stress and suppress cartilage degradation in OA. BBC0906 is a promising candidate for OA treatment, offering improved safety and efficacy.

Identification of the shared genes in type 2 diabetes mellitus and osteoarthritis and the role of quercetin

This study investigated the underlying comorbidity mechanism between type 2 diabetes mellitus (T2DM) and osteoarthritis (OA), while also assessing the therapeutic potential of quercetin for early intervention and treatment of these two diseases. The shared genes were obtained through GEO2R, limma and weighted gene co-expression network analysis (WGCNA), and validated using clinical databases and the area under the curves (ROC). Functional enrichment analysis was conducted to elucidate the underlying mechanisms of comorbidity between T2DM and OA. The infiltration of immune cells was analysed using the CIBERSORT algorithm in conjunction with ESTIMATE algorithm. Subsequently, transcriptional regulation analysis, potential chemical prediction, gene-disease association, relationships between the shared genes and ferroptosis as well as immunity-related genes were investigated along with molecular docking. We identified the 12 shared genes (EPHA3, RASIP1, PENK, LRRC17, CEBPB, EFEMP2, UBAP1, PPP1R15A, SPEN, MAFF, GADD45B and KLF4) across the four datasets. Our predictions suggested that targeting these shared genes could potentially serve as therapeutic interventions for both T2DM and OA. Specifically, they are involved in key signalling pathways such as p53, IL-17, NF-kB and MAPK signalling pathways. Furthermore, the regulation of ferroptosis and immunity appears to be interconnected in both diseases. Notably, in this context quercetin emerges as a promising drug candidate for treating T2DM and OA by specifically targeting the shared genes. We conducted a bioinformatics analysis to identify potential therapeutic targets, mechanisms and drugs for T2DM and OA, thereby offering novel insights into molecular therapy for these two diseases.

Exploration of the potential mechanism of Duhuo Jisheng Decoction in osteoarthritis treatment by using network pharmacology and molecular dynamics simulation

In this study, the active ingredients of 15 Chinese herbal medicines of Duhuo Jisheng Decoction and their corresponding targets were obtained from the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database. The microarray data of Osteoarthritis (OA) were obtained through the GEO database for differential analysis and then a drug target-OA-related gene protein-protein interaction (PPI) network was established. The potential targets of Duhuo Jisheng Decoction in the treatment of OA were acquired by intersecting the OA-associated genes with the target genes of active ingredients. Random walk with restart (RWR) analysis of PPI networks was performed using potential targets as seed, and the top 50 genes of affinity coefficients were used as key action genes of Duhuo Jisheng Decoction in the treatment of OA. A drug-active ingredient-gene interaction network was established. AKT1, a key target of Duhuo Jisheng Decoction in the treatment of OA, was obtained by topological analysis of the gene interaction network. Molecular docking and molecular dynamics verified the binding of AKT1 to its corresponding drug active ingredients. CETSA assay demonstrated that the combination of luteolin and AKT1 increased the stability of AKT1, and the combination efficiency was high. In conclusion, the molecular mechanism of Duhuo Jisheng Decoction in treating OA featured by multiple components, targets, and pathways had been further investigated in this study, which is of significance for discovering as well as developing new drugs for this disease. The findings can also offer personalized diagnosis and treatment strategies for patients with OA in clinical practice.

Global research trends and hotspots of PI3K/Akt signaling pathway in the field of osteoarthritis: A bibliometric study

The phosphatidylinositol 3-kinase/protein kinase B (PI3K/Akt) signaling pathway has gradually become a new target for the treatment of osteoarthritis (OA). Numerous studies of PI3K/Akt signaling in OA have been published in the past few years. By analyzing these research characteristics and qualities, we aimed to reveal the current research focus and emerging trends in PI3K/Akt signaling in OA. We searched the Web of Science database for relevant articles concerning the PI3K/Akt signaling pathway in OA published from inception to October 31, 2022. The following data were extracted: author name, article title, keywords, topic, publication country/region, institution, publication journal, journal impact factor, number of times cited, and H-index. VOSviewer and Excel 2019 were used to conduct the bibliometric study and visualize the analysis. A total of 374 publications were included in this study. In all selected articles, "orthopedics" was the dominant topic (252 of 374, 67.38%). The most productive year was 2021. Frontiers in Pharmacology published the most articles. The People's Republic of China has published the most articles worldwide. The top 5 keywords were "OA," "expression," "apoptosis," "chondrocytes," and "inflammation." The keywords "autophagy," "mitochondrial dysfunction," "inflammatory response," "cartilage degeneration," and "network pharmacology" have increased in recent years. Our study showed a growing trend in published articles related to the PI3K/Akt signaling pathway in OA. Inflammatory response, cartilage degeneration, and apoptosis remain central topics in the field. Research on autophagy, mitochondrial dysfunction, and network pharmacology is on the rise, and the focus on PI3K/Akt will continue to increase.

Identification of Immune-Related Risk Genes in Osteoarthritis Based on Bioinformatics Analysis and Machine Learning

In this research, we aimed to perform a comprehensive bioinformatic analysis of immune cell infiltration in osteoarthritic cartilage and synovium and identify potential risk genes. Datasets were downloaded from the Gene Expression Omnibus database. We integrated the datasets, removed the batch effects and analyzed immune cell infiltration along with differentially expressed genes (DEGs). Weighted gene co-expression network analysis (WGCNA) was used to identify the positively correlated gene modules. LASSO (least absolute shrinkage and selection operator)-cox regression analysis was performed to screen the characteristic genes. The intersection of the DEGs, characteristic genes and module genes was identified as the risk genes. The WGCNA analysis demonstrates that the blue module was highly correlated and statistically significant as well as enriched in immune-related signaling pathways and biological functions in the KEGG and GO enrichment. LASSO-cox regression analysis screened 11 characteristic genes from the hub genes of the blue module. After the DEG, characteristic gene and immune-related gene datasets were intersected, three genes, PTGS1, HLA-DMB and GPR137B, were identified as the risk genes in this research. In this research, we identified three risk genes related to the immune system in osteoarthritis and provide a feasible approach to drug development in the future.