Over 30 million people suffer from the consequences of ischemic stroke. The precise molecular mechanism of neuronal damage during ischemic stroke remains unclear; therefore, the effective treatment of post-ischemic stroke remains a critical challenge.Recently, iron has emerged as a crucial factor in post-reperfusion injuries, participating in cell peroxidation, excitotoxicity, and a distinctive cell death pathway, namely, ferroptosis. Since iron is tightly regulated in the brain and important for brain functions, the imbalance of its metabolism, including its overload and deficiency, has been shown to impact ischemic stroke outcomes. This review summarizes the current understanding of pathological events associated with iron in ischemic stroke and discusses relevant drug development.
Patients of Alzheimer’s disease (AD) frequently have lower bone mineral density and higher rate of hip fracture. Tg2576, a well characterized AD animal model that ubiquitously express Swedish mutant amyloid precursor protein (APPswe), displays not only AD-relevant neuropathology, but also age-dependent bone deficits. However, the underlying mechanisms remain poorly understood. As APP is implicated as a regulator of iron export, and the metal chelation is considered as a potential therapeutic strategy for AD, we examined iron chelation’s effect on the osteoporotic deficit in Tg2576 mice. Remarkably, in vivo treatment with iron chelator, clinoquinol (CQ), increased both trabecular and cortical bone-mass, selectively in Tg2576, but not wild type (WT) mice. Further in vitro studies showed that low concentrations of CQ as well as deferoxamine (DFO), another iron chelator, selectively inhibited osteoclast (OC) differentiation, without an obvious effect on osteoblast (OB) differentiation. Intriguingly, both CQ and DFO’s inhibitory effect on OC was more potent in bone marrow macrophages (BMMs) from Tg2576 mice than that of wild type controls. The reduction of intracellular iron levels in BMMs by CQ was also more dramatic in APPswe-expressing BMMs. Taken together, these results demonstrate a potent inhibition on OC formation and activation in APPswe-expressing BMMs by iron chelation, and reveal a potential therapeutic value of CQ in treating AD-associated osteoporotic deficits.
Introduction: Cuproptosis seems to promote the progression of diverse diseases. Hence, we explored the cuproptosis regulators in human spermatogenic dysfunction (SD), analyzed the condition of immune cell infiltration, and constructed a predictive model.Methods: Two microarray datasets (GSE4797 and GSE45885) related to male infertility (MI) patients with SD were downloaded from the Gene Expression Omnibus (GEO) database. We utilized the GSE4797 dataset to obtain differentially expressed cuproptosis-related genes (deCRGs) between SD and normal controls. The correlation between deCRGs and immune cell infiltration status was analyzed. We also explored the molecular clusters of CRGs and the status of immune cell infiltration. Notably, weighted gene co-expression network analysis (WGCNA) was used to identify the cluster-specific differentially expressed genes (DEGs). Moreso, gene set variation analysis (GSVA) was performed to annotate the enriched genes. Subsequently, we selected an optimal machine-learning model from four models. Finally, nomograms, calibration curves, decision curve analysis (DCA), and the GSE45885 dataset were utilized to verify the predictions’ accuracy.Results: Among SD and normal controls, we confirmed that there are deCRGs and activated immune responses. Through the GSE4797 dataset, we obtained 11 deCRGs. ATP7A, ATP7B, SLC31A1, FDX1, PDHA1, PDHB, GLS, CDKN2A, DBT, and GCSH were highly expressed in testicular tissues with SD, whereas LIAS was lowly expressed. Additionally, two clusters were identified in SD. Immune-infiltration analysis showed the existing heterogeneity of immunity at these two clusters. Cuproptosis-related molecular Cluster2 was marked by enhanced expressions of ATP7A, SLC31A1, PDHA1, PDHB, CDKN2A, DBT, and higher proportions of resting memory CD4+ T cells. Furthermore, an eXtreme Gradient Boosting (XGB) model based on 5-gene was built, which showed superior performance on the external validation dataset GSE45885 (AUC = 0.812). Therefore, the combined nomogram, calibration curve, and DCA results demonstrated the accuracy of predicting SD.Conclusion: Our study preliminarily illustrates the relationship between SD and cuproptosis. Moreover, a bright predictive model was developed.
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