New treatment methods for myocardial infarction

Identification of mitophagy-related key genes and their correlation with immune cell infiltration in acute myocardial infarction via bioinformatics analysis

Background

Acute myocardial infarction (AMI), a subset of acute coronary syndrome, remains the major cause of mortality worldwide. Mitochondrial dysfunction is critically involved in AMI progression, and mitophagy plays a vital role in eliminating damaged mitochondria. This study aimed to explore mitophagy-related biomarkers and their potential molecular basis in AMI.

Methods

AMI datasets (GSE24519 and GSE34198) from the Gene Expression Omnibus database were combined and the batch effects were removed. Differentially expressed genes (DEGs) in AMI were selected, intersected with mitophagy-related genes for mitophagy-related DEGs (MRDEGs), and then subjected to enrichment analyses. Next, the MRDEGs were screened using machine learning methods (logistic regression analysis, RandomForest, least absolute shrinkage and selection operator) to construct a diagnostic risk model and select the key genes in AMI. The diagnostic efficacy of the model was evaluated using a nomogram. Moreover, the infiltration patterns of different immune cells in two risk groups were compared. We also explored the interactions between the key genes themselves or with miRNAs/transcription factors (TFs) and drug compounds and visualized the protein structure of the key genes. Finally, we explored and validated the expression of key genes in plasma samples of patients with an AMI and healthy individuals.

Results

We screened 28 MRDEGs in AMI. Based on machine learning methods, 12 key genes were screened for the diagnostic risk model, including AGPS, CA2, CAT, LTA4H, MYO9B, PRDX6, PYGB, SIRT3, TFEB, TOM1, UBA52, and UBB. The nomogram further revealed the accuracy of the model for AMI diagnosis. Moreover, we found a lower abundance of immune cells such as gamma delta T and natural killer cells in the high-risk group, and the expression of key genes showed a significant correlation with immune infiltration levels in both groups. Finally, 64 miRNA-mRNA pairs, 75 TF-mRNA pairs, 119 RNA-binding protein-mRNA pairs, and 32 drug-mRNA pairs were obtained in the interaction networks.

Conclusions

In total, 12 key MRDEGs were identified and a risk model was constructed for AMI diagnosis. The findings of this study might provide novel biomarkers for improving the detection of AMI.

Exercise mediates myocardial infarction via non-coding RNAs

Myocardial infarction (MI), a widespread cardiovascular issue, mainly occurs due to blood clot formation in the coronary arteries, which reduces blood flow to the heart muscle and leads to cell death. Incorporating exercise into a lifestyle can significantly benefit recovery and reduce the risk of future cardiac events for MI patients. Non-coding RNAs (ncRNAs) play various roles in the effects of exercise on myocardial infarction (MI). ncRNAs regulate gene expression, influence cardiac remodeling, angiogenesis, inflammation, oxidative stress, apoptosis, cardioprotection, and cardiac electrophysiology. The expression of specific ncRNAs is altered by exercise, leading to beneficial changes in heart structure, function, and recovery after MI. These ncRNAs modulate molecular pathways that contribute to improved cardiac health, including reducing inflammation, enhancing angiogenesis, promoting cell survival, and mitigating oxidative stress. Furthermore, they are involved in regulating changes in cardiac remodeling, such as hypertrophy and fibrosis, and can influence the electrical properties of the heart, thereby decreasing the risk of arrhythmias. Knowledge on MI has entered a new phase, with investigations of ncRNAs in physical exercise yielding invaluable insights into the impact of this therapeutic modality. This review compiled research on ncRNAs in MI, with an emphasis on their applicability to physical activity.

Role of deubiquitinase USP47 in cardiac function alleviation and anti-inflammatory immunity after myocardial infarction by regulating NLRP3 inflammasome-mediated pyroptotic signal pathways

Myocardial infarction (MI) is an event of heart attack due to the formation of plaques in the interior walls of the arteries. This study is conducted to explore the role of ubiquitin-specific peptidase 47 (USP47) in cardiac function and inflammatory immunity. MI mouse models were established, followed by an appraisal of cardiac functions, infarct size, pathological changes, and USP47 and NLRP3 levels. MI cell models were established in HL-1 cells using anoxia. Levels of cardiac function-associated proteins, USP7, interferon regulatory factor 1 (IRF1), platelet factor-4 (CXCL4), pyroptotic factors, and neutrophil extracellular traps (NETs) were determined. The bindings of IRF1 to USP47 and the CXCL4 promoter and the ubiquitination of IRF1 were analyzed. USP47 was upregulated in myocardial tissues of MI mice. USP47 inhibition alleviated cardiac functions, and decreased infarct size, pro-inflammatory cytokines, NETs, NLRP3, and pyroptosis. The ubiquitination and expression levels of IRF1 were increased by silencing USP47, and IRF1 bound to the CXCL4 promoter to promote CXCL4. Overexpression of IRF1 or CXCL4 in vitro and injection of Nigericin in vivo reversed the effect of silencing USP47 on alleviating pyroptosis and cardiac functions. Collectively, USP47 stabilized IRF1 and promoted CXCL4, further promoting pyroptosis, impairing cardiac functions, and aggravating immune inflammation through NLRP3 pathways.

Role of aptamer technology in extracellular vesicle biology and therapeutic applications

Extracellular vesicles (EVs) are cell-derived nanosized membrane-bound vesicles that are important intercellular signalling regulators in local cell-to-cell and distant cell-to-tissue communication. Their inherent capacity to transverse cell membranes and transfer complex bioactive cargo reflective of their cell source, as well as their ability to be modified through various engineering and modification strategies, have attracted significant therapeutic interest. Molecular bioengineering strategies are providing a new frontier for EV-based therapy, including novel mRNA vaccines, antigen cross-presentation and immunotherapy, organ delivery and repair, and cancer immune surveillance and targeted therapeutics. The revolution of EVs, their diversity as biocarriers and their potential to contribute to intercellular communication, is well understood and appreciated but is ultimately dependent on the development of methods and techniques for their isolation, characterization and enhanced targeting. As single-stranded oligonucleotides, aptamers, also known as chemical antibodies, offer significant biological, chemical, economic, and therapeutic advantages in terms of their size, selectivity, versatility, and multifunctional programming. Their integration into the field of EVs has been contributing to the development of isolation, detection, and analysis pipelines associated with bioengineering strategies for nano-meets-molecular biology, thus translating their use for therapeutic and diagnostic utility.

Advancements in tissue engineering for cardiovascular health: a biomedical engineering perspective

Myocardial infarction (MI) stands as a prominent contributor to global cardiovascular disease (CVD) mortality rates. Acute MI (AMI) can result in the loss of a large number of cardiomyocytes (CMs), which the adult heart struggles to replenish due to its limited regenerative capacity. Consequently, this deficit in CMs often precipitates severe complications such as heart failure (HF), with whole heart transplantation remaining the sole definitive treatment option, albeit constrained by inherent limitations. In response to these challenges, the integration of bio-functional materials within cardiac tissue engineering has emerged as a groundbreaking approach with significant potential for cardiac tissue replacement. Bioengineering strategies entail fortifying or substituting biological tissues through the orchestrated interplay of cells, engineering methodologies, and innovative materials. Biomaterial scaffolds, crucial in this paradigm, provide the essential microenvironment conducive to the assembly of functional cardiac tissue by encapsulating contracting cells. Indeed, the field of cardiac tissue engineering has witnessed remarkable strides, largely owing to the application of biomaterial scaffolds. However, inherent complexities persist, necessitating further exploration and innovation. This review delves into the pivotal role of biomaterial scaffolds in cardiac tissue engineering, shedding light on their utilization, challenges encountered, and promising avenues for future advancement. By critically examining the current landscape, we aim to catalyze progress toward more effective solutions for cardiac tissue regeneration and ultimately, improved outcomes for patients grappling with cardiovascular ailments.

Cardiovascular Diseases Diagnosis Using an ECG Multi-Band Non-Linear Machine Learning Framework Analysis

BACKGROUND

cardiovascular diseases (CVDs), which encompass heart and blood vessel issues, stand as the leading cause of global mortality for many people.

METHODS

the present study intends to perform discrimination between seven well-known CVDs (bundle branch block, cardiomyopathy, myocarditis, myocardial hypertrophy, myocardial infarction, valvular heart disease, and dysrhythmia) and one healthy control group, respectively, by feeding a set of machine learning (ML) models with 10 non-linear features extracted every 1 s from electrocardiography (ECG) lead signals of a well-known ECG database (PTB diagnostic ECG database) using multi-band analysis performed by discrete wavelet transform (DWT). The ML models were trained and tested using a leave-one-out cross-validation approach, assessing the individual and combined capabilities of features, per each lead or combined, to distinguish between pairs of study groups and for conducting a comprehensive all vs. all analysis.

RESULTS

the Accuracy discrimination results ranged between 73% and 100%, the Recall between 68% and 100%, and the AUC between 0.42 and 1.

CONCLUSIONS

the results suggest that our method is a good tool for distinguishing CVDs, offering significant advantages over other studies that used the same dataset, including a multi-class comparison group (all vs. all), a wider range of binary comparisons, and the use of classical non-linear analysis under ECG multi-band analysis performed by DWT.