Untargeted metabolomics identified kynurenine as a predictive prognostic biomarker in acute myocardial infarction

Phenylacetyl glutamine (PAGln) enhances cardiomyocyte death after myocardial infarction through β1 adrenergic receptor

This study aims to explore the roles of phenylacetyl glutamine (PAGln) on myocardial infarction (MI) pathogenesis. Here, using targeted metabolomics analysis, it was found that the plasma metabolite PAGln was upregulated in coronary artery disease (CAD) patients and MI mice and could be an independent risk factor for CAD. In vivo and in vitro functional experiments revealed that PAGln pretreatment enhanced MI-induced myocardial injury and cardiac fibrosis, as evident by the increased infarct size, cardiomyocyte death, and the upregulated expression of cardiac fibrosis markers (Col1a1 and α-SMA). Combined with RNA-sequencing analysis and G protein-coupled receptor (GPCR) inhibitor, we found that the GPCR signaling activation is essential for PAGln-mediated effects on cardiomyocyte death. Furthermore, drug affinity responsive target stability and cellular thermal shift assay demonstrated that PAGln could interact with β1-adrenergic receptor (AR). Moreover, β1-AR blocker treatment indeed extended the cardiac remodeling after PAGln-enhanced MI. These results suggest that PAGln might be a potential therapeutic target for extending the cardiac remodeling window in MI patients that signals via β1-AR.

Advances in Mass Spectrometry-Based Blood Metabolomics Profiling for Non-Cancer Diseases: A Comprehensive Review

Blood metabolomics profiling using mass spectrometry has emerged as a powerful approach for investigating non-cancer diseases and understanding their underlying metabolic alterations. Blood, as a readily accessible physiological fluid, contains a diverse repertoire of metabolites derived from various physiological systems. Mass spectrometry offers a universal and precise analytical platform for the comprehensive analysis of blood metabolites, encompassing proteins, lipids, peptides, glycans, and immunoglobulins. In this comprehensive review, we present an overview of the research landscape in mass spectrometry-based blood metabolomics profiling. While the field of metabolomics research is primarily focused on cancer, this review specifically highlights studies related to non-cancer diseases, aiming to bring attention to valuable research that often remains overshadowed. Employing natural language processing methods, we processed 507 articles to provide insights into the application of metabolomic studies for specific diseases and physiological systems. The review encompasses a wide range of non-cancer diseases, with emphasis on cardiovascular disease, reproductive disease, diabetes, inflammation, and immunodeficiency states. By analyzing blood samples, researchers gain valuable insights into the metabolic perturbations associated with these diseases, potentially leading to the identification of novel biomarkers and the development of personalized therapeutic approaches. Furthermore, we provide a comprehensive overview of various mass spectrometry approaches utilized in blood metabolomics research, including GC-MS, LC-MS, and others discussing their advantages and limitations. To enhance the scope, we propose including recent review articles supporting the applicability of GC×GC-MS for metabolomics-based studies. This addition will contribute to a more exhaustive understanding of the available analytical techniques. The Integration of mass spectrometry-based blood profiling into clinical practice holds promise for improving disease diagnosis, treatment monitoring, and patient outcomes. By unraveling the complex metabolic alterations associated with non-cancer diseases, researchers and healthcare professionals can pave the way for precision medicine and personalized therapeutic interventions. Continuous advancements in mass spectrometry technology and data analysis methods will further enhance the potential of blood metabolomics profiling in non-cancer diseases, facilitating its translation from the laboratory to routine clinical application.

Metabolic signatures in post-myocardial infarction heart failure, including insights into prediction, intervention, and prognosis

Heart failure (HF) is a prevalent long-term complication of myocardial infarction (MI). The incidence of post-MI HF is high, and patients with the condition have a poor prognosis. Accurate identification of individuals at high risk for post-MI HF is crucial for implementation of a protective and ideally personalized strategy to prevent fatal events. Post-MI HF is characterized by adverse cardiac remodeling, which results from metabolic changes in response to long-term ischemia. Moreover, various risk factors, including genetics, diet, and obesity, can influence metabolic pathways in patients. This review focuses on the metabolic signatures of post-MI HF that could serve as non-invasive biomarkers for early identification in high-risk populations. We also explore how metabolism participates in the pathophysiology of post-MI HF. Furthermore, we discuss the potential of metabolites as novel targets for treatment of post-MI HF and as biomarkers for prognostic evaluation. It is expected to provide valuable suggestions for the clinical prevention and treatment of post-MI HF from a metabolic perspective.

From multi-omics approaches to personalized medicine in myocardial infarction

Myocardial infarction (MI) is a prevalent cardiovascular disease characterized by myocardial necrosis resulting from coronary artery ischemia and hypoxia, which can lead to severe complications such as arrhythmia, cardiac rupture, heart failure, and sudden death. Despite being a research hotspot, the etiological mechanism of MI remains unclear. The emergence and widespread use of omics technologies, including genomics, transcriptomics, proteomics, metabolomics, and other omics, have provided new opportunities for exploring the molecular mechanism of MI and identifying a large number of disease biomarkers. However, a single-omics approach has limitations in understanding the complex biological pathways of diseases. The multi-omics approach can reveal the interaction network among molecules at various levels and overcome the limitations of the single-omics approaches. This review focuses on the omics studies of MI, including genomics, epigenomics, transcriptomics, proteomics, metabolomics, and other omics. The exploration extended into the domain of multi-omics integrative analysis, accompanied by a compilation of diverse online resources, databases, and tools conducive to these investigations. Additionally, we discussed the role and prospects of multi-omics approaches in personalized medicine, highlighting the potential for improving diagnosis, treatment, and prognosis of MI.