Is RANKL a potential molecular target in osteoarthritis?

Muratovic, Dzenita; Atkins, Gerald J.; Findlay, David M.

doi:10.1016/j.joca.2023.10.010

Cited by 5 publications

(2 citation statements)

References 71 publications

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“…In the past few decades, the pathogenesis of OA has been extensively studied [ 43 ], but the complex ion channels and signaling pathways involved in the pathogenesis and development of OA are still not fully understood. Previous studies have confirmed that the signaling pathways involved in OA include the Wnt pathway [ 44 , 45 , 46 , 47 , 48 ], Notch pathway [ 49 , 50 , 51 ], AMPK pathway [ 52 , 53 , 54 ], nuclear factor-κb (NF-κB) pathway [ 55 , 56 , 57 , 58 , 59 ], MAPKs pathway [ 15 , 60 , 61 , 62 ], Hippo-YAP pathway [ 63 ], TGF-β/Smad pathway [ 4 , 64 , 65 ], mTOR pathway [ 66 , 67 , 68 ], and the OPG/RANK/RANKL pathway [ 69 , 70 ]. In addition, the ion channel theory has been increasingly investigated in the pathogenesis of arthritis.…”

Section: Signal Pathways and Ion Channels Related To Osteoarthritismentioning

confidence: 99%

TRPV Channels in Osteoarthritis: A Comprehensive Review

Chen,

Yang,

Chen

et al. 2024

Biomolecules

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Osteoarthritis (OA) is a debilitating joint disorder that affects millions of people worldwide. Despite its prevalence, our understanding of the underlying mechanisms remains incomplete. In recent years, transient receptor potential vanilloid (TRPV) channels have emerged as key players in OA pathogenesis. This review provides an in-depth exploration of the role of the TRPV pathway in OA, encompassing its involvement in pain perception, inflammation, and mechanotransduction. Furthermore, we discuss the latest research findings, potential therapeutic strategies, and future directions in the field, shedding light on the multifaceted nature of TRPV channels in OA.

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Section: Signal Pathways and Ion Channels Related To Osteoarthritismentioning

confidence: 99%

TRPV Channels in Osteoarthritis: A Comprehensive Review

Chen,

Yang,

Chen

et al. 2024

Biomolecules

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“…Some of the properties associated with E. sessiliflorus include immune-stimulating, anti-inflammatory, and anticancer activities [19]. It has been found to inhibit receptor activator of nuclear factor kappa-B ligand (RANKL)-induced osteoclast differentiation and has been evaluated for its potential to prevent osteoporosis [20,21]. The chemical constituents of E. sessiliflorus include triterpenoids, phenolic compounds, alkaloids, flavonoids, and other bioactive compounds.…”

Section: Introductionmentioning

confidence: 99%

Antifungal Effects of Fermented Sophora flavescens and Eleutherococcus sessiliflorus Extract

Kim,

Chae,

Son

et al. 2024

Applied Sciences

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In this study, a microbial strain was isolated from humus soil to ferment Sophora flavescens and Eleutherococcus sessiliflorus extracts. The isolated microbial was identified as the Bacillus genus by 16S rRNA sequence analysis. The fermented plant extracts exhibited antifungal effects against four types plant pathogen, P. carotorum, B. cinerea, C. fructicola Sau-3, and C. gloeosporioides, according to incubation time. In particular, the fermented plant extracts showed the most activity for Colletotrichum genus in inhibiting mycelium growth. Metabolite changes in fermented S. flavescens and E. sessiliflorus extracts were confirmed through LC-Q-TOF/MS. Flavonoid and peptide derivatives were improved in fermented S. flavescens and E. sessiliflorus extracts compared to their unfermented counterparts. This study suggested that isolated Bacillus microbial fermentation could be a valuable tool in improving the bioactivity of S. flavescens and E. sessiliflorus extracts, with the potential to form more environmentally friendly antifungal agents.

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Identification of therapeutic targets in osteoarthritis by combining heterogeneous transcriptional datasets, drug-induced expression profiles, and known drug-target interactions

Costa,

Angelini,

Franzese

et al. 2024

J Transl Med

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Background Osteoarthritis (OA) is a multifactorial, hypertrophic, and degenerative condition involving the whole joint and affecting a high percentage of middle-aged people. It is due to a combination of factors, although the pivotal mechanisms underlying the disease are still obscure. Moreover, current treatments are still poorly effective, and patients experience a painful and degenerative disease course. Methods We used an integrative approach that led us to extract a consensus signature from a meta-analysis of three different OA cohorts. We performed a network-based drug prioritization to detect the most relevant drugs targeting these genes and validated in vitro the most promising candidates. We also proposed a risk score based on a minimal set of genes to predict the OA clinical stage from RNA-Seq data. Results We derived a consensus signature of 44 genes that we validated on an independent dataset. Using network analysis, we identified Resveratrol, Tenoxicam, Benzbromarone, Pirinixic Acid, and Mesalazine as putative drugs of interest for therapeutics in OA for anti-inflammatory properties. We also derived a list of seven gene-targets validated with functional RT-qPCR assays, confirming the in silico predictions. Finally, we identified a predictive subset of genes composed of DNER, TNFSF11, THBS3, LOXL3, TSPAN2, DYSF, ASPN and HTRA1 to compute the patient’s risk score. We validated this risk score on an independent dataset with a high AUC (0.875) and compared it with the same approach computed using the entire consensus signature (AUC 0.922). Conclusions The consensus signature highlights crucial mechanisms for disease progression. Moreover, these genes were associated with several candidate drugs that could represent potential innovative therapeutics. Furthermore, the patient’s risk scores can be used in clinical settings.

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Is RANKL a potential molecular target in osteoarthritis?

Cited by 5 publications

References 71 publications

TRPV Channels in Osteoarthritis: A Comprehensive Review

TRPV Channels in Osteoarthritis: A Comprehensive Review

Antifungal Effects of Fermented Sophora flavescens and Eleutherococcus sessiliflorus Extract

Identification of therapeutic targets in osteoarthritis by combining heterogeneous transcriptional datasets, drug-induced expression profiles, and known drug-target interactions

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