PRKAR1A and SDCBP Serve as Potential Predictors of Heart Failure Following Acute Myocardial Infarction

From Cell to Gene: Deciphering the Mechanism of Heart Failure With Single-Cell Sequencing

Heart failure (HF) is a prevalent cardiovascular disease with significant morbidity and mortality rates worldwide. Due to the intricate structure of the heart, diverse cell types, and the complex pathogenesis of HF, further in-depth investigation into the underlying mechanisms  is required. The elucidation of the heterogeneity of cardiomyocytes and the intercellular communication network is particularly important. Traditional high-throughput sequencing methods provide an average measure of gene expression, failing to capture the "heterogeneity" between cells and impacting the accuracy of gene function knowledge. In contrast, single-cell sequencing techniques allow for the amplification of the entire genome or transcriptome at the individual cell level, facilitating the examination of gene structure and expression with unparalleled precision. This approach offers valuable insights into disease mechanisms, enabling the identification of changes in cellular components and gene expressions during hypertrophy associated with HF. Moreover, it reveals distinct cell populations and their unique roles in the HF microenvironment, providing a comprehensive understanding of the cellular landscape that underpins HF pathogenesis. This review focuses on the insights provided by single-cell sequencing techniques into the mechanisms underlying HF and discusses the challenges encountered in current cardiovascular research.

Differentially expressed platelet activation-related genes in dogs with stage B2 myxomatous mitral valve disease

BACKGROUND

Peripheral blood carries a reservoir of mRNAs that regulate cardiac structure and function potential. Although it is well recognized that the typical symptoms of Myxomatous Mitral Valve Disease (MMVD) stage B2 are long-standing hemodynamic disorder and cardiac structure remodeling caused by mitral regurgitation, the transcriptomic alterations in blood from such dogs are not understood.

RESULTS

In the present study, comparative high-throughput transcriptomic profiling of blood was performed from normal control (NC) and naturally-occurring MMVD stage B2 (MMVD) dogs. Using Weighted Gene Co-expression Network Analyses (WGCNA), Gene Ontology (GO), and Kyoto Encyclopedia of Gene and Genomes (KEGG), we identified that the turquoise module was the most highly correlated with echocardiographic features and found 64 differentially expressed genes (DEGs) that were significantly enriched in platelet activation related pathways. Therefore, from the turquoise module, we selected five DEGs (MDM2, ROCK1, RIPK1, SNAP23, and ARHGAP35) that, according to real-time qPCR, exhibited significant enrichment in platelet activation related pathways for validation. The results showed that the blood transcriptional abundance of MDM2, ROCK1, RIPK1, and SNAP23 differed significantly (P < 0.01) between NC and MMVD dogs. On the other hand, Correlation Analysis revealed that MDM2, ROCK1, RIPK1, and SNAP23 genes negatively regulated the heart structure parameters, and followed the same trend as observed in WGCNA.

CONCLUSION

We screened four platelet activation related genes, MDM2, ROCK1, RIPK1, and SNAP23, which may be considered as the candidate biomarkers for the diagnosis of MMVD stage B2. These findings provided new insights into MMVD pathogenesis.

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.

Development and comparison of machine learning-based models for predicting heart failure after acute myocardial infarction

AIMS

Heart failure (HF) is one of the common adverse cardiovascular events after acute myocardial infarction (AMI), but the predictive efficacy of numerous machine learning (ML) built models is unclear. This study aimed to build an optimal model to predict the occurrence of HF in AMI patients by comparing seven ML algorithms.

METHODS

Cohort 1 included AMI patients from 2018 to 2019 divided into HF and control groups. All first routine test data of the study subjects were collected as the features to be selected for the model, and seven ML algorithms with screenable features were evaluated. Cohort 2 contains AMI patients from 2020 to 2021 to establish an early warning model with external validation. ROC curve and DCA curve to analyze the diagnostic efficacy and clinical benefit of the model respectively.

RESULTS

The best performer among the seven ML algorithms was XgBoost, and the features of XgBoost algorithm for troponin I, triglycerides, urine red blood cell count, γ-glutamyl transpeptidase, glucose, urine specific gravity, prothrombin time, prealbumin, and urea were ranked high in importance. The AUC of the HF-Lab9 prediction model built by the XgBoost algorithm was 0.966 and had good clinical benefits.

CONCLUSIONS

This study screened the optimal ML algorithm as XgBoost and developed the model HF-Lab9 will improve the accuracy of clinicians in assessing the occurrence of HF after AMI and provide a reference for the selection of subsequent model-building algorithms.

FTH1- and SAT1-Induced Astrocytic Ferroptosis Is Involved in Alzheimer’s Disease: Evidence from Single-Cell Transcriptomic Analysis

Objectives: Despite significant advances in neuroscience, the mechanisms of AD are not fully understood. Single-cell RNA sequencing (scRNA-seq) techniques provide potential solutions to analyze cellular composition of complex brain tissue and explore cellular and molecular biological mechanisms of AD. Methods: We investigated cellular heterogeneity in AD via utilization of bioinformatic analysis of scRNA-seq in AD patients and healthy controls from the Gene Expression Omnibus (GEO) database. The “GOplot” package was applied to explore possible biological processes in oligodendrocytes, astrocytes, and oligodendrocyte progenitor cells (OPCs). Expression patterns and biological functions of differentially expressed genes (DEGs) from scRNA-seq data were validated in RNA sequencing data. DEGs in astrocytes interacted with ferroptosis-related genes in FerrDb. CCK-8 and EdU assays were performed to measure cell proliferation ability. ROS, Fe²⁺ level, mitochondrial membrane potentials, iron concentrations, and total iron binding capacity (TIBC) in serum were evaluated. Y-maze and elevated maze were used to measure anxiety-like behavior. Autonomous and exploration behaviors or learning and memory ability in mice were analyzed using open field test and novel object recognition test. Results: Multiple clusters were identified, including oligodendrocytes, astrocytes, OPCs, neurons, microglia, doublets, and endothelial cells. Astrocytes were significantly decreased in AD, while oligodendrocytes and OPCs increased. Cell-to-cell ligand–receptor interaction analysis revealed that astrocytes, neurons, and OPCs mainly established contacts with other cells via the NRG3–ERBB4 ligand–receptor pair. GO and KEGG analyses found that astrocytes were enriched in the ferroptosis pathway. FTH1 and SAT1 in astrocytes were identified as hub mRNAs associated with ferroptosis. Serum iron concentration of 5xFAD mice was higher than that of WT, and emotional and cognitive function were significantly impaired as compared to WT. Serum iron concentration was negatively correlated with number of astrocytes and percentage of time spent entering the novelty arm in the Y-maze test, while it was positively correlated with percentage of time spent in the central area. Meanwhile, number of astrocytes was negatively correlated with percentage of time spent in the central area, while it was positively correlated with percentage of time spent entering the novelty arm. Conclusions: Through scRNA-seq analysis, we found that ferroptosis was activated in astrocytes and may contribute to the pathophysiological process in the entorhinal cortex. FTH1 and SAT1 were identified to impact astrocyte ferroptosis. Emotional and cognitive impairment in AD was associated with astrocyte ferroptosis. Our findings provide clues to reveal the pathophysiological processes following AD at the cellular level and highlight potential drug targets for the treatment of AD.