Mitoquinone alleviates osteoarthritis progress by activating the NRF2-Parkin axis

Summary: Osteoarthritis (OA) is a prevalent degenerative disease of the elderly. The NRF2 antioxidant system plays a critical role in maintaining redox balance. Mitoquinone (MitoQ) is a mitochondria-targeted antioxidant. This research aimed to determine whether MitoQ alleviated OA and the role of the NRF2/Parkin axis in MitoQ-mediated protective effects. In interleukin (IL)-1β-induced OA chondrocytes, MitoQ activated the NRF2 pathway, reducing extracellular matrix (ECM) degradation and inflammation. MitoQ also increased glutathione peroxidase 4 (GPX4) expression, leading to decreased levels of reactive oxygen species (ROS) and lipid ROS. Silencing NRF2 weakened MitoQ’s protective effects, while knockdown of Parkin upregulated the NRF2 pathway, inhibiting OA progression. Intra-articular injection of MitoQ mitigated cartilage destruction in destabilized medial meniscus (DMM)-induced OA mice. Our study demonstrates that MitoQ maintains cartilage homeostasis in vivo and in vitro through the NRF2/Parkin axis. We supplemented the negative feedback regulation mechanism between NRF2 and Parkin. These findings highlight the therapeutic potential of MitoQ for OA treatment

IRF1 regulation of ZBP1 links mitochondrial DNA and chondrocyte damage in osteoarthritis

Abstract Background Z-DNA binding protein 1 (ZBP1) is a nucleic acid sensor that is involved in multiple inflammatory diseases, but whether and how it contributes to osteoarthritis (OA) are unclear. Methods Cartilage tissues were harvested from patients with OA and a murine model of OA to evaluate ZBP1 expression. Subsequently, the functional role and mechanism of ZBP1 were examined in primary chondrocytes, and the role of ZBP1 in OA was explored in mouse models. Results We showed the upregulation of ZBP1 in articular cartilage originating from OA patients and mice with OA after destabilization of the medial meniscus (DMM) surgery. Specifically, knockdown of ZBP1 alleviated chondrocyte damage and protected mice from DMM-induced OA. Mechanistically, tumor necrosis factor alpha induced ZBP1 overexpression in an interferon regulatory factor 1 (IRF1)-dependent manner and elicited the activation of ZBP1 via mitochondrial DNA (mtDNA) release and ZBP1 binding. The upregulated and activated ZBP1 could interact with receptor-interacting protein kinase 1 and activate the transforming growth factor-beta-activated kinase 1-NF-κB signaling pathway, which led to chondrocyte inflammation and extracellular matrix degradation. Moreover, inhibition of the mtDNA-IRF1-ZBP1 axis with Cyclosporine A, a blocker of mtDNA release, could delay the progression of DMM-induced OA. Conclusions Our data revealed the pathological role of the mtDNA-IRF1-ZBP1 axis in OA chondrocytes, suggesting that inhibition of this axis could be a viable therapeutic approach for OA

Inhibition of SAT1 alleviates chondrocyte inflammation and ferroptosis by repressing ALOX15 expression and activating the Nrf2 pathway

Aims: Osteoarthritis (OA) is the most common chronic pathema of human joints. The pathogenesis is complex, involving physiological and mechanical factors. In previous studies, we found that ferroptosis is intimately related to OA, while the role of Sat1 in chondrocyte ferroptosis and OA, as well as the underlying mechanism, remains unclear. Methods: In this study, interleukin-1β (IL-1β) was used to simulate inflammation and Erastin was used to simulate ferroptosis in vitro. We used small interfering RNA (siRNA) to knock down the spermidine/spermine N1-acetyltransferase 1 (Sat1) and arachidonate 15-lipoxygenase (Alox15), and examined damage-associated events including inflammation, ferroptosis, and oxidative stress of chondrocytes. In addition, a destabilization of the medial meniscus (DMM) mouse model of OA induced by surgery was established to investigate the role of Sat1 inhibition in OA progression. Results: The results showed that inhibition of Sat1 expression can reduce inflammation, ferroptosis changes, reactive oxygen species (ROS) level, and lipid-ROS accumulation induced by IL-1β and Erastin. Knockdown of Sat1 promotes nuclear factor-E2-related factor 2 (Nrf2) signalling. Additionally, knockdown Alox15 can alleviate the inflammation-related protein expression induced by IL-1β and ferroptosis-related protein expression induced by Erastin. Furthermore, knockdown Nrf2 can reverse these protein expression alterations. Finally, intra-articular injection of diminazene aceturate (DA), an inhibitor of Sat1, enhanced type II collagen (collagen II) and increased Sat1 and Alox15 expression. Conclusion: Our results demonstrate that inhibition of Sat1 could alleviate chondrocyte ferroptosis and inflammation by downregulating Alox15 activating the Nrf2 system, and delaying the progression of OA. These findings suggest that Sat1 provides a new approach for studying and treating OA. Cite this article: Bone Joint Res 2024;13(3):110–123

Deferoxamine Alleviates Osteoarthritis by Inhibiting Chondrocyte Ferroptosis and Activating the Nrf2 Pathway

Objective: Osteoarthritis (OA) is a common disease with a complex pathology including mechanical load, inflammation, and metabolic factors. Chondrocyte ferroptosis contributes to OA progression. Because iron deposition is a major pathological event in ferroptosis, deferoxamine (DFO), an effective iron chelator, has been used to inhibit ferroptosis in various degenerative disease models. Nevertheless, its OA treatment efficacy remains unknown. We aimed to determine whether DFO alleviates chondrocyte ferroptosis and its effect on OA and to explore its possible mechanism.Methods: Interleukin-1β (IL-1β) was used to simulate inflammation, and chondrocyte ferroptosis was induced by erastin, a classic ferroptosis inducer. A surgical destabilized medial meniscus mouse model was also applied to simulate OA in vivo, and erastin was injected into the articular cavity to induce mouse knee chondrocyte ferroptosis. We determined the effects of DFO on ferroptosis and injury-related events: chondrocyte inflammation, extracellular matrix degradation, oxidative stress, and articular cartilage degradation.Results: IL-1β increased the levels of ROS, lipid ROS, and the lipid peroxidation end product malondialdehyde (MDA) and altered ferroptosis-related protein expression in chondrocytes. Moreover, ferrostatin-1 (Fer-1), a classic ferroptosis inhibitor, rescued the IL-1β–induced decrease in collagen type II (collagen II) expression and increase in matrix metalloproteinase 13 (MMP13) expression. Erastin promoted MMP13 expression in chondrocytes but inhibited collagen II expression. DFO alleviated IL-1β– and erastin-induced cytotoxicity in chondrocytes, abrogated ROS and lipid ROS accumulation and the increase in MDA, improved OA-like changes in chondrocytes, and promoted nuclear factor E2–related factor 2 (Nrf2) antioxidant system activation. Finally, intra-articular injection of DFO enhanced collagen II expression in OA model mice, inhibited erastin-induced articular chondrocyte death, and delayed articular cartilage degradation and OA progression.Conclusion: Our research confirms that ferroptosis occurs in chondrocytes under inflammatory conditions, and inhibition of chondrocyte ferroptosis can alleviate chondrocyte destruction. Erastin-induced chondrocyte ferroptosis can stimulate increased MMP13 expression and decreased collagen II expression in chondrocytes. DFO can suppress chondrocyte ferroptosis and promote activation of the Nrf2 antioxidant system, which is essential for protecting chondrocytes. In addition, ferroptosis inhibition by DFO injection into the articular cavity may be a new OA treatment

Cancer-Risk Module Identification and Module-Based Disease Risk Evaluation: A Case Study on Lung Cancer

<div><p>Gene expression profiles have drawn broad attention in deciphering the pathogenesis of human cancers. Cancer-related gene modules could be identified in co-expression networks and be applied to facilitate cancer research and clinical diagnosis. In this paper, a new method was proposed to identify lung cancer-risk modules and evaluate the module-based disease risks of samples. The results showed that thirty one cancer-risk modules were closely related to the lung cancer genes at the functional level and interactional level, indicating that these modules and genes might synergistically lead to the occurrence of lung cancer. Our method was proved to have good robustness by evaluating the disease risk of samples in eight cancer expression profiles (four for lung cancer and four for other cancers), and had better performance than the WGCNA method. This method could provide assistance to the diagnosis and treatment of cancers and a new clue for explaining cancer mechanisms.</p></div

The robustness of our method and comparison with the WGCNA method.

<p><b>a</b>) X-axis is samples. Y-axis is the lung cancer risk score of individual samples using our method, and it is ranked from the smallest to the largest. Blue represents GSE10072; green represents GSE21933; red represents GSE27262; and brown represents GSE4079. Full lines represent lung cancer samples; and dashed lines represent normal samples. The different experiment data sets have different numbers of the normal samples and the disease samples. In order to show the disease risk of every sample in four expression profiles intuitively, all samples of each expression profiles are distributed uniformly throughout x-axis. <b>b</b>) The figure is plotted the same way as a). The lung cancer risk of each sample is evaluated by the WGCNA method. <b>c</b>) Receiver operator characteristic curve using our method for the four lung cancer expression profiles (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0092395#pone-0092395-g007" target="_blank">Figure 7a</a>). The areas under curve provided at lower right of each diagram. <b>d</b>) Receiver operator characteristic curve using the WGCNA method for the four lung cancer expression profiles (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0092395#pone-0092395-g007" target="_blank">Figure 7b</a>).</p

The lung cancer risk of each sample in GSE7670 by the WGCNA method.

<p>The lung cancer risk of each sample in GSE7670 by the WGCNA method.</p

The number of the tumor samples and the normal samples in the expression profiles.

<p>The number of the tumor samples and the normal samples in the expression profiles.</p

Cancer-risk Modules Identification and Module-based Disease risk Evaluation.

<p>Cancer-risk Modules Identification and Module-based Disease risk Evaluation.</p