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Different types of cell death and their interactions in myocardial ischemia–reperfusion injury
Myocardial ischemia–reperfusion (I/R) injury is a multifaceted process observed in patients with coronary artery disease when blood flow is restored to the heart tissue following ischemia-induced damage. Cardiomyocyte cell death, particularly through apoptosis, necroptosis, autophagy, pyroptosis, and ferroptosis, is pivotal in myocardial I/R injury. Preventing cell death during the process of I/R is vital for improving ischemic cardiomyopathy. These multiple forms of cell death can occur simultaneously, interact with each other, and contribute to the complexity of myocardial I/R injury. In this review, we aim to provide a comprehensive summary of the key molecular mechanisms and regulatory patterns involved in these five types of cell death in myocardial I/R injury. We will also discuss the crosstalk and intricate interactions among these mechanisms, highlighting the interplay between different types of cell death. Furthermore, we will explore specific molecules or targets that participate in different cell death pathways and elucidate their mechanisms of action. It is important to note that manipulating the molecules or targets involved in distinct cell death processes may have a significant impact on reducing myocardial I/R injury. By enhancing researchers’ understanding of the mechanisms and interactions among different types of cell death in myocardial I/R injury, this review aims to pave the way for the development of novel interventions for cardio-protection in patients affected by myocardial I/R injury.
Iron homeostasis and ferroptosis in muscle diseases and disorders: mechanisms and therapeutic prospects
The muscular system plays a critical role in the human body by governing skeletal movement, cardiovascular function, and the activities of digestive organs. Additionally, muscle tissues serve an endocrine function by secreting myogenic cytokines, thereby regulating metabolism throughout the entire body. Maintaining muscle function requires iron homeostasis. Recent studies suggest that disruptions in iron metabolism and ferroptosis, a form of iron-dependent cell death, are essential contributors to the progression of a wide range of muscle diseases and disorders, including sarcopenia, cardiomyopathy, and amyotrophic lateral sclerosis. Thus, a comprehensive overview of the mechanisms regulating iron metabolism and ferroptosis in these conditions is crucial for identifying potential therapeutic targets and developing new strategies for disease treatment and/or prevention. This review aims to summarize recent advances in understanding the molecular mechanisms underlying ferroptosis in the context of muscle injury, as well as associated muscle diseases and disorders. Moreover, we discuss potential targets within the ferroptosis pathway and possible strategies for managing muscle disorders. Finally, we shed new light on current limitations and future prospects for therapeutic interventions targeting ferroptosis.
Extravascular coagulation regulates haemostasis independently of activated platelet surfaces in an in vivo mouse model
While the conventional understanding of haemostatic plug formation is that coagulation proceeds efficiently on the surface of activated platelets at the vascular injury site to form a robust haemostatic plug, this understanding does not explain the clinical reality that platelet dysfunction results in a mild bleeding phenotype, whereas coagulation disorders exhibit severe bleeding phenotypes, particularly in deep tissues. Here, we introduce an in vivo imaging method to observe internal bleeding and subsequent haemostatic plug formation in mice and report that haemostatic plug formation after internal bleeding, coagulation occurs primarily outside the blood vessel rather than on platelets. Experiments in mice with impaired platelet surface coagulation, depleted platelets, haemophilia A or reduced tissue factor expression suggest that this extravascular coagulation triggers and regulates haemostatic plug formation. Our discovery of the important role of extravascular coagulation in haemostasis may contribute to refining the treatment of haemostatic abnormalities and advancing antithrombotic therapy.
Regulatory T cells-related gene in primary sclerosing cholangitis: evidence from Mendelian randomization and transcriptome data
The present study utilized large-scale genome-wide association studies (GWAS) summary data (731 immune cell subtypes and three primary sclerosing cholangitis (PSC) GWAS datasets), meta-analysis, and two PSC transcriptome data to elucidate the pivotal role of Tregs proportion imbalance in the occurrence of PSC. Then, we employed weighted gene co-expression network analysis (WGCNA), differential analysis, and 107 combinations of 12 machine-learning algorithms to construct and validate an artificial intelligence-derived diagnostic model (Tregs classifier) according to the average area under curve (AUC) (0.959) in two cohorts. Quantitative real-time polymerase chain reaction (qRT-PCR) verified that compared to control, Akap10, Basp1, Dennd3, Plxnc1, and Tmco3 were significantly up-regulated in the PSC mice model yet the expression level of Klf13, and Scap was significantly lower. Furthermore, immune cell infiltration and functional enrichment analysis revealed significant associations of the hub Tregs-related gene with M2 macrophage, neutrophils, megakaryocyte-erythroid progenitor (MEP), natural killer T cell (NKT), and enrichment scores of the autophagic cell death, complement and coagulation cascades, metabolic disturbance, Fc gamma R-mediated phagocytosis, mitochondrial dysfunction, potentially mediating PSC onset. XGBoost algorithm and SHapley Additive exPlanations (SHAP) identified AKAP10 and KLF13 as optimal genes, which may be an important target for PSC.
Identifying perturbations that boost T-cell infiltration into tumours via counterfactual learning of their spatial proteomic profiles
Cancer progression can be slowed down or halted via the activation of either endogenous or engineered T cells and their infiltration of the tumour microenvironment. Here we describe a deep-learning model that uses large-scale spatial proteomic profiles of tumours to generate minimal tumour perturbations that boost T-cell infiltration. The model integrates a counterfactual optimization strategy for the generation of the perturbations with the prediction of T-cell infiltration as a self-supervised machine learning problem. We applied the model to 368 samples of metastatic melanoma and colorectal cancer assayed using 40-plex imaging mass cytometry, and discovered cohort-dependent combinatorial perturbations (CXCL9, CXCL10, CCL22 and CCL18 for melanoma, and CXCR4, PD-1, PD-L1 and CYR61 for colorectal cancer) that support T-cell infiltration across patient cohorts, as confirmed via in vitro experiments. Leveraging counterfactual-based predictions of spatial omics data may aid the design of cancer therapeutics.
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