Peripheral Blood RNA Levels of QSOX1 and PLBD1 Are New Independent Predictors of Left Ventricular Dysfunction After Acute Myocardial Infarction

Serum IGFBP5 as a predictor of major adverse cardiac events in patients with acute myocardial infarction

BACKGROUND

Acute myocardial infarction (AMI) is a serious condition with high mortality rates. Early risk stratification is of significant importance to assess the prognosis. Insulin-like growth factor-binding protein 5 (IGFBP5) levels in AMI patients and its potential as a prognosis biomarker were unclear.

OBJECTIVE

To investigate serum IGFBP5 levels in AMI and its prognostic value for short-term major adverse cardiovascular events (MACE).

METHODS

We collected serum IGFBP5 levels from 200 patients with new-onset AMI and 71 coronary heart disease (CAD) patients without AMI. Linear regression was used to analyze the relationship between IGFBP5 and baseline variables. AMI patients were followed up, and the risk of major adverse cardiovascular events (MACE) was assessed using Kaplan-Meier curve, multivariate Cox models and restricted cubic spline (RCS) analysis.

RESULTS

During a median follow-up of 217 days, 40 patients developed MACE. Serum IGFBP5 was associated with serum cardiac troponin T (cTnT) and C-reactive protein (CRP) (P = 0.013 and P = 0.013). In multivariable survival analyses, higher IGFBP5 was associated with an increased risk of MACE [HR = 1.183, 95%CI (1.104, 1.268), P < 0.001)]. There was a positive and linear association between IGFBP5 levels and the occurrence of MACE (P for nonlinearity = 0.283). The positive association between IGFBP5 and MACE risk consist across subgroups characterized by demographics and comorbidities.

CONCLUSION

Serum IGFBP5 was highly expressed in patients with AMI and positively associated with the short-term risk of MACE. Circulating IGFBP5 may be a diagnostic and prognostic indicator for AMI, and further studies with larger sample and longer follow-up are warranted.

Identification and Validation of Diagnostic Model Based on Angiogenesis- and Epithelial Mesenchymal Transition-Related Genes in Myocardial Infarction

Background

Myocardial infarction (MI) is a chronic cardiovascular disease. This study aims to discern potentially angiogenesis- and epithelial mesenchymal transition (EMT)-related genes as biomarkers for MI diagnosis through bioinformatics.

Methods

All datasets and angiogenesis- and EMT-related genes were collected from the public database. The differentially expressed genes (DEGs) of MI and MI-related genes were acquired. DEGs, MI-related genes, and angiogenesis- and EMT-related genes were intersected to obtain hub genes. Functional enrichment, immune microenvironment, and transcription factors (TFs)-hub genes regulatory network analysis were performed. The diagnostic markers and models were developed and validated. Drug prediction and molecular docking were performed. Finally, diagnostic markers expressions were validated using RT-qPCR.

Results

A total of 224 angiogenesis- and EMT-related genes, 2,897 DEGs, 1,217 MI-related genes, and 9 hub genes were acquired. The immune infiltration levels of plasma cells, T cells CD4 memory activated, monocytes, macrophages M0, mast cells resting, and neutrophils were higher in patients with MI. LRPAP1, COLGALT1, QSOX1, THBD, VCAN, PLOD1, and PLAUR as the diagnostic markers were identified and used to construct diagnostic models, which can distinguish MI from controls well. Then, 9 drugs were screened, and the binding energies ranged from -7.08 to -5.21 kcal/mol. RT-qPCR results showed that the expression of LRPAP1, PLAUR, and PLOD1 was significantly increased in the MI group.

Conclusion

The 7 diagnostic markers may play potential roles in MI and could contribute to improved future diagnostics.

Unveiling the Potential of Migrasomes: A Machine-Learning-Driven Signature for Diagnosing Acute Myocardial Infarction

BACKGROUND

Recent studies have demonstrated that the migrasome, a newly functional extracellular vesicle, is potentially significant in the occurrence, progression, and diagnosis of cardiovascular diseases. Nonetheless, its diagnostic significance and biological mechanism in acute myocardial infarction (AMI) have yet to be fully explored.

METHODS

To remedy this gap, we employed an integrative machine learning (ML) framework composed of 113 ML combinations within five independent AMI cohorts to establish a predictive migrasome-related signature (MS). To further elucidate the biological mechanism underlying MS, we implemented single-cell RNA sequencing (scRNA-seq) of cardiac Cd45+ cells from AMI-induced mice. Ultimately, we conducted mendelian randomization (MR) and molecular docking to unveil the therapeutic effectiveness of MS.

RESULTS

MS demonstrated robust predictive performance and superior generalization, driven by the optimal combination of Stepglm and Lasso, on the expression of nine migrasome genes (BMP1, ITGB1, NDST1, TSPAN1, TSPAN18, TSPAN2, TSPAN4, TSPAN7, TSPAN9, and WNT8A). Notably, ITGB1 was found to be predominantly expressed in cardiac macrophages in AMI-induced mice, mechanically regulating macrophage transformation between anti-inflammatory and pro-inflammatory. Furthermore, we showed a positive causality between genetic predisposition towards ITGB1 expression and AMI risk, positioning it as a causative gene. Finally, we showed that ginsenoside Rh1, which interacts closely with ITGB1, could represent a novel therapeutic approach for repressing ITGB1.

CONCLUSIONS

Our MS has implications in forecasting and curving AMI to inform future diagnostic and therapeutic strategies for AMI.

Leveraging a neutrophil-derived PCD signature to predict and stratify patients with acute myocardial infarction: from AI prediction to biological interpretation

BACKGROUND

Programmed cell death (PCD) has recently been implicated in modulating the removal of neutrophils recruited in acute myocardial infarction (AMI). Nonetheless, the clinical significance and biological mechanism of neutrophil-related PCD remain unexplored.

METHODS

We employed an integrative machine learning-based computational framework to generate a predictive neutrophil-derived PCD signature (NPCDS) within five independent microarray cohorts from the peripheral blood of AMI patients. Non-negative matrix factorization was leveraged to develop an NPCDS-based AMI subtype. To elucidate the biological mechanism underlying NPCDS, we implemented single-cell transcriptomics on Cd45+ cells isolated from the murine heart of experimental AMI. We finally conducted a Mendelian randomization (MR) study and molecular docking to investigate the therapeutic value of NPCDS on AMI.

RESULTS

We reported the robust and superior performance of NPCDS in AMI prediction, which contributed to an optimal combination of random forest and stepwise regression fitted on nine neutrophil-related PCD genes (MDM2, PTK2B, MYH9, IVNS1ABP, MAPK14, GNS, MYD88, TLR2, CFLAR). Two divergent NPCDS-based subtypes of AMI were revealed, in which subtype 1 was characterized as inflammation-activated with more vibrant neutrophil activities, whereas subtype 2 demonstrated the opposite. Mechanically, we unveiled the expression dynamics of NPCDS to regulate neutrophil transformation from a pro-inflammatory phase to an anti-inflammatory phase in AMI. We uncovered a significant causal association between genetic predisposition towards MDM2 expression and the risk of AMI. We also found that lidoflazine, isotetrandrine, and cepharanthine could stably target MDM2.

CONCLUSION

Altogether, NPCDS offers significant implications for prediction, stratification, and therapeutic management for AMI.

Targeting NPM1 Epigenetically Promotes Postinfarction Cardiac Repair by Reprogramming Reparative Macrophage Metabolism

BACKGROUND

Reparative macrophages play a crucial role in limiting excessive fibrosis and promoting cardiac repair after myocardial infarction (MI), highlighting the significance of enhancing their reparative phenotype for wound healing. Metabolic adaptation orchestrates the phenotypic transition of macrophages; however, the precise mechanisms governing metabolic reprogramming of cardiac reparative macrophages remain poorly understood. In this study, we investigated the role of NPM1 (nucleophosmin 1) in the metabolic and phenotypic shift of cardiac macrophages in the context of MI and explored the therapeutic effect of targeting NPM1 for ischemic tissue repair.

METHODS

Peripheral blood mononuclear cells were obtained from healthy individuals and patients with MI to explore NPM1 expression and its correlation with prognostic indicators. Through RNA sequencing, metabolite profiling, histology, and phenotype analyses, we investigated the role of NPM1 in postinfarct cardiac repair using macrophage-specific NPM1 knockout mice. Epigenetic experiments were conducted to study the mechanisms underlying metabolic reprogramming and phenotype transition of NPM1-deficient cardiac macrophages. The therapeutic efficacy of antisense oligonucleotide and inhibitor targeting NPM1 was then assessed in wild-type mice with MI.

RESULTS

NPM1 expression was upregulated in the peripheral blood mononuclear cells from patients with MI that closely correlated with adverse prognostic indicators of MI. Macrophage-specific NPM1 deletion reduced infarct size, promoted angiogenesis, and suppressed tissue fibrosis, in turn improving cardiac function and protecting against adverse cardiac remodeling after MI. Furthermore, NPM1 deficiency boosted the reparative function of cardiac macrophages by shifting macrophage metabolism from the inflammatory glycolytic system to oxygen-driven mitochondrial energy production. The oligomeric NPM1 recruited histone demethylase KDM5b to the promoter of Tsc1 (TSC complex subunit 1), the mTOR (mechanistic target of rapamycin kinase) complex inhibitor, reduced histone H3K4me3 modification, and inhibited TSC1 expression, which then facilitated mTOR-related inflammatory glycolysis and antagonized the reparative function of cardiac macrophages. The in vivo administration of antisense oligonucleotide targeting NPM1 or oligomerization inhibitor NSC348884 substantially ameliorated tissue injury and enhanced cardiac recovery in mice after MI.

CONCLUSIONS

Our findings uncover the key role of epigenetic factor NPM1 in impeding postinfarction cardiac repair by remodeling metabolism pattern and impairing the reparative function of cardiac macrophages. NPM1 may serve as a promising prognostic biomarker and a valuable therapeutic target for heart failure after MI.

The prognostic and immune significance of PLBD1 in pan-cancer and its roles in proliferation and invasion of glioma

Cancer is a destructive disease and is currently the leading cause of major threats to human health. PLBD1 is a transcription factor that regulates phospholipid metabolism, but its role in tumors is unknown. We assessed pan-cancer expression, methylation, and mutation data of PLBD1 by multiple databases to investigate its clinical prognostic value. In addition, we examined the pan-cancer immunological signature of PLBD1, particularly in gliomas. Furthermore, we assessed the impact of PLBD1 knockdown on the proliferation and invasive capacity of glioma cells by in vitro experiments. Our results suggest that PLBD1 is highly expressed in multiple types of cancers, and it can serve as an independent prognostic factor for gliomas. In addition, we found that the epigenetic alterations of PLBD1 were highly heterogeneous in a variety of cancers, including gliomas, and that its high methylation was associated with poor prognosis in a broad range of cancers. Immunological profiling demonstrated that PLBD1 was significantly associated with immune cell infiltration and multiple immune checkpoints in gliomas and is a potential biomarker for gliomas. Furthermore, cellular experiments showed that knockdown of PLBD1 significantly inhibited the proliferation and invasive ability of glioma cells. In conclusion, PLBD1 is a potential tumor prognostic biomarker and immunotherapeutic target that plays a crucial role in glioma cell proliferation, invasion and immunotherapy.

Identification of key biomarkers for predicting CAD progression in inflammatory bowel disease via machine-learning and bioinformatics strategies

The study aimed to identify the biomarkers for predicting coronary atherosclerotic lesions progression in patients with inflammatory bowel disease (IBD). Related transcriptome datasets were seized from Gene Expression Omnibus database. IBD-related modules were identified via Weighted Gene Co-expression Network Analysis. The 'Limma' was applied to screen differentially expressed genes between stable coronary artery disease (CAD) and acute myocardial infarction (AMI). Subsequently, we employed protein-protein interaction (PPI) network and three machine-learning strategies to further screen for candidate hub genes. Application of the receiver operating characteristics curve to quantitatively evaluate candidates to determine key diagnostic biomarkers, followed by a nomogram construction. Ultimately, we performed immune landscape analysis, single-gene GSEA and prediction of target-drugs. 3227 IBD-related module genes and 570 DEGs accounting for AMI were recognized. Intersection yielded 85 shared genes and mostly enriched in immune and inflammatory pathways. After filtering through PPI network and multi-machine learning algorithms, five candidate genes generated. Upon validation, CTSD, CEBPD, CYP27A1 were identified as key diagnostic biomarkers with a superior sensitivity and specificity (AUC > 0.8). Furthermore, all three genes were negatively correlated with CD4+ T cells and positively correlated with neutrophils. Single-gene GSEA highlighted the importance of pathogen invasion, metabolism, immune and inflammation responses during the pathogenesis of AMI. Ten target-drugs were predicted. The discovery of three peripheral blood biomarkers capable of predicting the risk of CAD proceeding into AMI in IBD patients. These identified biomarkers were negatively correlated with CD4+ T cells and positively correlated with neutrophils, indicating a latent therapeutic target.

RNA in cardiovascular disease: A new frontier of personalized medicine

Personalized medicine has witnessed remarkable progress with the emergence of RNA therapy, offering new possibilities for the treatment of various diseases, and in particular in the context of cardiovascular disease (CVD). The ability to target the human genome through RNA manipulation offers great potential not only in the treatment of cardiac pathologies but also in their diagnosis and prevention, notably in cases of hyperlipidemia and myocardial infarctions. While only a few RNA-based treatments have entered clinical trials or obtained approval from the US Food and Drug Administration, the growing body of research on this subject is promising. However, the development of RNA therapies faces several challenges that must be overcome. These include the efficient delivery of drugs into cells, the potential for immunogenic responses, and safety. Resolving these obstacles is crucial to advance the development of RNA therapies. This review explores the newest developments in medical studies, treatment plans, and results related to RNA therapies for heart disease. Furthermore, it discusses the exciting possibilities and difficulties in this innovative area of research.

Myocardial infarction complexity: A multi-omics approach

Myocardial infarction (MI), a prevalent cardiovascular disease, is fundamentally precipitated by thrombus formation in the coronary arteries, which subsequently decreases myocardial perfusion and leads to cellular necrosis. The intricacy of MI pathogenesis necessitates extensive research to elucidate the disease's root cause, thereby addressing the limitations present in its diagnosis and prognosis. With the continuous advancement of genomics technology, genomics, proteomics, metabolomics and transcriptomics are widely used in the study of MI, which provides an excellent way to identify new biomarkers that elucidate the complex mechanisms of MI. This paper provides a detailed review of various genomics studies of MI, including genomics, proteomics, transcriptomics, metabolomics and multi-omics studies. The metabolites and proteins involved in the pathogenesis of MI are investigated through integrated protein-protein interactions and multi-omics analysis by STRING and Metascape platforms. In conclusion, the future of omics research in myocardial infarction offers significant promise.

Proteomic Profiling in Patients With Peripartum Cardiomyopathy: A Biomarker Study of the ESC EORP PPCM Registry

BACKGROUND

Peripartum cardiomyopathy (PPCM) remains an important cause of maternal morbidity and mortality globally. The pathophysiology remains incompletely understood, and the diagnosis is often missed or delayed.

OBJECTIVES

This study explored the serum proteome profile of patients with newly diagnosed PPCM, as compared with matched healthy postpartum mothers, to unravel novel protein biomarkers that would further an understanding of the pathogenesis of PPCM and improve diagnostic precision.

METHODS

Study investigators performed untargeted serum proteome profiling using data-independent acquisition-based label-free quantitative liquid chromatography-tandem mass spectrometry on 84 patients with PPCM, as compared with 29 postpartum healthy controls (HCs). Significant changes in protein intensities were determined with nonpaired Student's t-tests and were further classified by using the Boruta algorithm. The proteins' diagnostic performance was evaluated by area under the curve (AUC) and validated using the 10-fold cross-validation.

RESULTS

Patients with PPCM presented with a mean left ventricular ejection fraction of 33.5% ± 9.3% vs 57.0% ± 8.8% in HCs (P < 0.001). Study investigators identified 15 differentially up-regulated and 14 down-regulated proteins in patients with PPCM compared with HCs. Seven of these proteins were recognized as significant by the Boruta algorithm. The combination of adiponectin, quiescin sulfhydryl oxidase 1, inter-α-trypsin inhibitor heavy chain, and N-terminal pro-B-type natriuretic peptide had the best diagnostic precision (AUC: 0.90; 95% CI: 0.84-0.96) to distinguish patients with PPCM from HCs.

CONCLUSIONS

Salient biologic themes related to immune response proteins, inflammation, fibrosis, angiogenesis, apoptosis, and coagulation were predominant in patients with PPCM compared with HCs. These newly identified proteins warrant further evaluation to establish their role in the pathogenesis of PPCM and potential use as diagnostic markers.

Atherosclerotic plaque vulnerability quantification system for clinical and biological interpretability

Acute myocardial infarction dominates coronary artery disease mortality. Identifying bio-signatures for plaque destabilization and rupture is important for preventing the transition from coronary stability to instability and the occurrence of thrombosis events. This computational systems biology study enrolled 2,235 samples from 22 independent bulks cohorts and 14 samples from two single-cell cohorts. A machine-learning integrative program containing nine learners was developed to generate a warning classifier linked to atherosclerotic plaque vulnerability signature (APVS). The classifier displays the reliable performance and robustness for distinguishing ST-elevation myocardial infarction from chronic coronary syndrome at presentation, and revealed higher accuracy to 33 pathogenic biomarkers. We also developed an APVS-based quantification system (APVSLevel) for comprehensively quantifying atherosclerotic plaque vulnerability, empowering early-warning capabilities, and accurate assessment of atherosclerosis severity. It unraveled the multidimensional dysregulated mechanisms at high resolution. This study provides a potential tool for macro-level differential diagnosis and evaluation of subtle genetic pathological changes in atherosclerosis.

Identification of biomarkers and immune infiltration in acute myocardial infarction and heart failure by integrated analysis

The mortality of heart failure after acute myocardial infarction (AMI) remains high. The aim of the present study was to analyze hub genes and immune infiltration in patients with AMI and heart failure (HF). The study utilized five publicly available gene expression datasets from peripheral blood in patients with AMI who either developed or did not develop HF. The unbiased patterns of 24 immune cell were estimated by xCell algorithm. Single-cell RNA sequencing data were used to examine the immune cell infiltration in heart failure patients. Hub genes were validated by quantitative reverse transcription-PCR (RT-qPCR). In comparison with the coronary heart disease (CHD) group, immune infiltration analysis of AMI patients showed that macrophages M1, macrophages, monocytes, natural killer (NK) cells, and NKT cells were the five most highly activated cell types. Five common immune-related genes (S100A12, AQP9, CSF3R, S100A9, and CD14) were identified as hub genes associated with AMI. Using RT-qPCR, we confirmed FOS, DUSP1, CXCL8, and NFKBIA as the potential biomarkers to identify AMI patients at risk of HF. The study identified several transcripts that differentiate between AMI and CHD, and between HF and non-HF patients. These findings could improve our understanding of the immune response in AMI and HF, and allow for early identification of AMI patients at risk of HF.

Identification and validation of diagnostic biomarkers of coronary artery disease progression in type 1 diabetes via integrated computational and bioinformatics strategies

OBJECTIVE

Our study aimed to identify early peripheral blood diagnostic biomarkers and elucidate the immune mechanisms of coronary artery disease (CAD) progression in patients with type 1 diabetes mellitus (T1DM).

METHODS

Three transcriptome datasets were retrieved from the Gene Expression Omnibus (GEO) database. Gene modules associated with T1DM were selected with weighted gene co-expression network analysis. Differentially expressed genes (DEGs) between CAD and acute myocardial infarction (AMI) peripheral blood tissues were identified using limma. Candidate biomarkers were selected with functional enrichment analysis, node gene selection from a constructed protein-protein interaction (PPI) network, and 3 machine learning algorithms. Candidate expression was compared, and the receiver operating characteristic curve (ROC) and nomogram were constructed. Immune cell infiltration was assessed with the CIBERSORT algorithm.

RESULTS

A total of 1283 genes comprising 2 modules were detected as the most associated with T1DM. In addition, 451 DEGs related to CAD progression were identified. Among them, 182 were common to both diseases and mainly enriched in immune and inflammatory response regulation. The PPI network yielded 30 top node genes, and 6 were selected using the 3 machine learning algorithms. Upon validation, 4 genes (TLR2, CLEC4D, IL1R2, and NLRC4) were recognized as diagnostic biomarkers with the area under the curve (AUC) > 0.7. All 4 genes were positively correlated with neutrophils in patients with AMI.

CONCLUSION

We identified 4 peripheral blood biomarkers and provided a nomogram for early diagnosing CAD progression to AMI in patients with T1DM. The biomarkers were positively associated with neutrophils, indicating potential therapeutic targets.

Inflammation and Syndecan-4 Shedding from Cardiac Cells in Ischemic and Non-Ischemic Heart Disease

Circulating biomarkers reflecting cardiac inflammation are needed to improve the diagnostics and guide the treatment of heart failure patients. The cardiac production and shedding of the transmembrane proteoglycan syndecan-4 is upregulated by innate immunity signaling pathways. Here, we investigated the potential of syndecan-4 as a blood biomarker of cardiac inflammation. Serum syndecan-4 was measured in patients with (i) non-ischemic, non-valvular dilated cardiomyopathy (DCM), with (n = 71) or without (n = 318) chronic inflammation; (ii) acute myocarditis (n = 15), acute pericarditis (n = 3) or acute perimyocarditis (23) and (iii) acute myocardial infarction (MI) at day 0, 3 and 30 (n = 119). Syndecan-4 was investigated in cultured cardiac myocytes and fibroblasts (n = 6–12) treated with the pro-inflammatory cytokines interleukin (IL)-1β and its inhibitor IL-1 receptor antagonist (IL-1Ra), or tumor necrosis factor (TNF)α and its specific inhibitor infliximab, an antibody used in treatment of autoimmune diseases. The levels of serum syndecan-4 were comparable in all subgroups of patients with chronic or acute cardiomyopathy, independent of inflammation. Post-MI, syndecan-4 levels were increased at day 3 and 30 vs. day 0. IL-1Ra attenuated IL-1β-induced syndecan-4 production and shedding in vitro, while infliximab had no effect. In conclusion, syndecan-4 shedding from cardiac myocytes and fibroblasts was attenuated by immunomodulatory therapy. Although its circulating levels were increased post-MI, syndecan-4 did not reflect cardiac inflammatory status in patients with heart disease.

From 'Omics to Multi-omics Technologies: the Discovery of Novel Causal Mediators

PURPOSE OF REVIEW

'Omics studies provide a comprehensive characterisation of a biological entity, such as the genome, epigenome, transcriptome, proteome, metabolome, or microbiome. This review covers the unique properties of these types of 'omics and their roles as causal mediators in cardiovascular disease. Moreover, applications and challenges of integrating multiple types of 'omics data to increase predictive power, improve causal inference, and elucidate biological mechanisms are discussed.

RECENT FINDINGS

Multi-omics approaches are growing in adoption as they provide orthogonal evidence and overcome the limitations of individual types of 'omics data. Studies with multiple types of 'omics data have improved the diagnosis and prediction of disease states and afforded a deeper understanding of underlying pathophysiological mechanisms, beyond any single type of 'omics data. For instance, disease-associated loci in the genome can be supplemented with other 'omics to prioritise causal genes and understand the function of non-coding variants. Alternatively, techniques, such as Mendelian randomisation, can leverage genetics to provide evidence supporting a causal role for disease-associated molecules, and elucidate their role in disease pathogenesis. As technologies improve, costs for 'omics studies will continue to fall and datasets will become increasingly accessible to researchers. The intrinsically unbiased nature of 'omics data is well-suited to exploratory analyses that discover causal mediators of disease, and multi-omics is an emerging discipline that leverages the strengths of each type of 'omics data to provide insights greater than the sum of its parts.

Integrated whole-genome gene expression analysis reveals an atlas of dynamic immune landscapes after myocardial infarction

Introduction

Myocardial infarction (MI) is a deadly medical condition leading to irreversible damage to the inflicted cardiac tissue. Elevated inflammatory response marks the severity of MI and is associated with the development of heart failure (HF), a long-term adverse outcome of MI. However, the efficacy of anti-inflammatory therapies for MI remains controversial. Deciphering the dynamic transcriptional signatures in peripheral blood mononuclear cells (PBMCs) is a viable and translatable route to better understand post-MI inflammation, which may help guide post-MI anti-inflammatory treatments.

Methods

In this work, integrated whole-genome gene expression analysis was performed to explore dynamic immune landscapes associated with MI.

Results

GSEA and GSVA showed that pathways involved in the inflammatory response and metabolic reprogramming were significantly enriched in PBMCs from MI patients. Based on leukocyte profiles generated by xCell algorithm, the relative abundance of monocytes and neutrophils was significantly increased in PBMCs from MI patients and had positive correlations with typical inflammation-associated transcripts. Mfuzz clustering revealed temporal gene expression profiles of PBMCs during the 6-month post-MI follow-up. Analysis of DEGs and gene sets indicated that PBMCs from HF group were characterized by elevated and lasting expression of genes implicated in inflammation and coagulation. Consensus clustering generated 4 metabolic subtypes of PBMCs with molecular heterogeneity in HF patients.

Discussion

In summary, integrated whole-genome gene expression analysis here outlines a transcriptomic framework that may improve the understanding of dynamic signatures present in PBMCs, as well as the heterogeneity of PBMCs in MI patients with or without long-term clinical outcome of HF. Moreover, the work here uncovers the diversity and heterogeneity of PBMCs from HF patients, providing novel bioinformatic evidence supporting the mechanistic implications of metabolic reprogramming and mitochondrial dysfunction in the post-MI inflammation and HF. Therefore, our work here supports the notion that individualized anti-inflammatory therapies are needed to improve the clinical management of post-MI patients.

Peripheral blood RNA biomarkers for cardiovascular disease from bench to bedside: a position paper from the EU-CardioRNA COST action CA17129

Despite significant advances in the diagnosis and treatment of cardiovascular diseases, recent calls have emphasized the unmet need to improve precision-based approaches in cardiovascular disease. Although some studies provide preliminary evidence of the diagnostic and prognostic potential of circulating coding and non-coding RNAs, the complex RNA biology and lack of standardization have hampered the translation of these markers into clinical practice. In this position paper of the CardioRNA COST action CA17129, we provide recommendations to standardize the RNA development process in order to catalyse efforts to investigate novel RNAs for clinical use. We list the unmet clinical needs in cardiovascular disease, such as the identification of high-risk patients with ischaemic heart disease or heart failure who require more intensive therapies. The advantages and pitfalls of the different sample types, including RNAs from plasma, extracellular vesicles, and whole blood, are discussed in the sample matrix, together with their respective analytical methods. The effect of patient demographics and highly prevalent comorbidities, such as metabolic disorders, on the expression of the candidate RNA is presented and should be reported in biomarker studies. We discuss the statistical and regulatory aspects to translate a candidate RNA from a research use only assay to an in-vitro diagnostic test for clinical use. Optimal planning of this development track is required, with input from the researcher, statistician, industry, and regulatory partners.

Transcriptomic Profiling Unravels Novel Deregulated Gene Signatures Associated with Acute Myocardial Infarction: A Bioinformatics Approach

Acute myocardial infarction (AMI) is a severe disease with elevated morbidity and mortality rate worldwide. This is attributed to great losses of cardiomyocytes, which can trigger the alteration of gene expression patterns. Although several attempts have been made to assess the AMI biomarkers, to date their role in rescuing myocardial injury remains unclear. Therefore, the current study investigated three independent microarray-based gene expression datasets from AMI patients (n = 85) and their age-sex-matched healthy controls (n = 70), to identify novel gene signatures that might be involved in cardioprotection. The differentially expressed genes (DEGs) were analyzed using 'GEO2R', and weighted gene correlation network analysis (WGCNA) was performed to identify biomarkers/modules. We found 91 DEGs, of which the number of upregulated and downregulated genes were 22 and 5, respectively. Specifically, we found that the deregulated genes such as ADOR-A3, BMP6, VPS8, and GPx3, may be associated with AMI. WGCNA revealed four highly preserved modules among all datasets. The 'Enrichr' unveiled the presence of miR-660 and STAT1, which is known to affect AMI severity. Conclusively, these genes and miRNA might play a crucial role the rescue of cardiomyocytes from severe damage, which could be helpful in developing appropriate therapeutic strategies for the management of AMI.

Identification of PFKFB2 as a key gene for the transition from acute to old myocardial infarction in peripheral blood

Objective

This study aims to analyze the gene expression profile of peripheral blood in different stages of myocardial infarction (MI) by transcriptome sequencing, and to study the gene expression characteristics of peripheral blood after MI.

Methods

Differentially expressed genes (DEGs) and weighted gene co-expression network analysis (WGCNA) were used to identify genes and modules associated with old myocardial infarction (OMI). Gene Ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway annotation were applied to analyze the potential functions of genes. Hub genes were identified by Random Forest Classifier. CIBERSORT was used to provide an estimate of the abundance of 22 immune cells in peripheral blood. Quantitative polymerase chain reaction (qPCR) was used to detect gene expression levels in clinical samples. The cellular components (CC) of peripheral blood were counted by an automatic hematology analyzer.

Results

Through differential gene analysis and co-expression network analysis, 11 candidate genes were obtained. A random forest classifier identified 10 hub genes. Immune cell distribution of peripheral blood was found that T cell CD4 memory resting, NK cells resting, Dendritic cells activated, Mast cells resting, Monocytes and Neutrophils were correlated with OMI. Spearman correlation analysis found that PFKFB2 is related to the above immune cells. Low expression of PFKFB2 in peripheral blood of OMI was detected in clinical samples, and the relationship between PFKFB2 and peripheral blood immune cell counts was analyzed, which showed monocytes were associated with PFKFB2 in our study.

Conclusion

PFKFB2 was low expressed in OMI, and related to the distribution of immune cells. PFKFB2 may play a key role in reflecting the transition from AMI to OMI, and predicting the distribution of immune cells, which provided a new perspective for improving myocardial fibrosis and adverse remodeling.

Integrated Analysis of circRNA-miRNA-mRNA-Mediated Network and Its Potential Function in Atrial Fibrillation

Background

Atrial fibrillation (AF) is one of the most prevalent arrhythmias, characterized by a high risk of heart failure and embolic stroke. Competing endogenous RNA network has been reported to play an important role in cardiovascular diseases. The main objective of the present study was to construct a circRNA–miRNA–mRNA-mediated network and explore the potential function in AF.

Methods

The microarray data of circRNA, miRNA, and mRNA in AF were downloaded from the Gene Expression Omnibus database. The RobustRankAggreg method was used to screen the different expression circRNAs(DECs). Then the circRNA–miRNA–mRNA-mediated network was constructed by using the CircInteractome database and the miRWalk online tool. A quantitative real-time polymerase chain reaction was used to detect the circRNA expression level in plasma. The left atrial fibrosis was evaluated with the left atrial low voltage area (LVA) by using left atrial voltage matrix mapping.

Results

Three DECs (hsa_circRNA_102461, hsa_circRNA_103693, and hsa_circRNA_059880) and 4 miRNAs were screened. Then a circRNA–miRNA–mRNA-mediated network was constructed, which included 2 circRNAs, 4 miRNAs, and 83 genes. Furthermore, the plasma’s hsa_circ_0070391 expression level was confirmed to be upregulated and positively correlated with left atrial fibrosis in AF (r = 0.88, P < 0.001), whereas hsa_circ_0003935 was downregulated. Moreover, the ROC curve analysis revealed hsa_circ_0070391 and hsa_circ_0003935 could differentiate AF from the healthy controls with an AUC of 0.95 (95% sensitivity and 90% specificity) and 0.86 (70% sensitivity and 75% specificity), respectively. Finally, the free of atrial tachyarrhythmia rate was dramatically lower in the hsa_circ_0070391 high expression group than in the low expression group post catheter ablation (70.0 vs. 90.0%, p = 0.04).

Conclusion

This study provides a novel insight to further understand the AF pathogenesis from the perspective of the circRNA–miRNA–mRNA network, suggesting that plasma circRNAs could serve as a novel atrial fibrosis and prognosis biomarker for AF.

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

Background and Objectives

Early diagnosis of patients with acute myocardial infarction (AMI) who are at a high risk of heart failure (HF) progression remains controversial. This study aimed at identifying new predictive biomarkers of post-AMI HF and at revealing the pathogenesis of HF involving these marker genes.

Methods and Results

A transcriptomic dataset of whole blood cells from AMI patients with HF progression (post-AMI HF, n = 16) and without progression (post-AMI non-HF, n = 16) was analyzed using the weighted gene co-expression network analysis (WGCNA). The results indicated that one module consisting of 720 hub genes was significantly correlated with post-AMI HF. The hub genes were validated in another transcriptomic dataset of peripheral blood mononuclear cells (post-AMI HF, n = 9; post-AMI non-HF, n = 8). PRKAR1A, SDCBP, SPRED2, and VAMP3 were upregulated in the two datasets. Based on a single-cell RNA sequencing dataset of leukocytes from heart tissues of normal and infarcted mice, PRKAR1A was further verified to be upregulated in monocytes/macrophages on day 2, while SDCBP was highly expressed in neutrophils on day 2 and in monocytes/macrophages on day 3 after AMI. Cell-cell communication analysis via the "CellChat" package showed that, based on the interaction of ligand-receptor (L-R) pairs, there were increased autocrine/paracrine cross-talk networks of monocytes/macrophages and neutrophils in the acute stage of MI. Functional enrichment analysis of the abovementioned L-R genes together with PRKAR1A and SDCBP performed through the Metascape platform suggested that PRKAR1A and SDCBP were mainly involved in inflammation, apoptosis, and angiogenesis. The receiver operating characteristic (ROC) curve analysis demonstrated that PRKAR1A and SDCBP, as well as their combination, had a promising prognostic value in the identification of AMI patients who were at a high risk of HF progression.

Conclusion

This study identified that PRKAR1A and SDCBP may serve as novel biomarkers for the early diagnosis of post-AMI HF and also revealed their potentially regulatory mechanism during HF progression.

Proteomic Profiling and T Cell Receptor Usage of Abacavir Susceptible Subjects

Type B adverse drug reactions (ADRs) represent a significant threat as their occurrence arises unpredictable and despite proper application of the drug. The severe immune reaction Abacavir Hypersensitivity Syndrome (AHS) that arises in HIV⁺ patients treated with the antiretroviral drug Abacavir (ABC) strongly correlates to the presence of the human leukocyte antigen (HLA) genotype HLA-B*57:01 and discriminates HLA-B*57:01⁺ HIV⁺ patients from ABC treatment. However, not all HLA-B*57:01⁺ HIV⁺ patients are affected by AHS, implying the involvement of further patient-specific factors in the development of AHS. The establishment of a reliable assay to classify HLA-B*57:01 carriers as ABC sensitive or ABC tolerant allowed to investigate the T cell receptor (TCR) Vβ chain repertoire of effector cells and revealed Vβ6 and Vβ24 as potential public TCRs in ABC sensitive HLA-B*57:01 carriers. Furthermore, distinct effects of ABC on the cellular proteome of ABC sensitive and tolerant volunteers were observed and suggest enhanced activation and maturation of dentritic cells (DC) in ABC sensitive volunteers. Analysis of ABC-naïve cellular proteomes identified the T cell immune regulator 1 (TCIRG1) as a potential prognostic biomarker for ABC susceptibility and the involvement of significantly upregulated proteins, particularly in peptide processing, antigen presentation, interferon (IFN), and cytokine regulation.

Dissecting the transcriptome in cardiovascular disease

The human transcriptome comprises a complex network of coding and non-coding RNAs implicated in a myriad of biological functions. Non-coding RNAs exhibit highly organized spatial and temporal expression patterns and are emerging as critical regulators of differentiation, homeostasis, and pathological states, including in the cardiovascular system. This review defines the current knowledge gaps, unmet methodological needs, and describes the challenges in dissecting and understanding the role and regulation of the non-coding transcriptome in cardiovascular disease. These challenges include poor annotation of the non-coding genome, determination of the cellular distribution of transcripts, assessment of the role of RNA processing and identification of cell-type specific changes in cardiovascular physiology and disease. We highlight similarities and differences in the hurdles associated with the analysis of the non-coding and protein-coding transcriptomes. In addition, we discuss how the lack of consensus and absence of standardized methods affect reproducibility of data. These shortcomings should be defeated in order to make significant scientific progress and foster the development of clinically applicable non-coding RNA-based therapeutic strategies to lessen the burden of cardiovascular disease.

Somatic Genetic Mosaicism in the Apolipoprotein E-null Mouse Aorta

In addition to genetic and epigenetic inheritance, somatic variation may contribute to cardiovascular disease (CVD) risk. CVD-associated somatic mutations have been reported in human clonal hematopoiesis, but evidence in the atheroma is lacking. To probe for somatic variation in atherosclerosis, we sought single-nucleotide private variants (PVs) in whole-exome sequencing (WES) data of aorta, liver, and skeletal muscle of two C57BL/6J coisogenic male ApoE null/wild-type (WT) sibling pairs, and RNA-seq data of one of the two pairs. Relative to the C57BL/6 reference genome, we identified 9 and 11 ApoE null aorta- and liver-specific PVs that were shared by all WES and RNA-seq datasets. Corresponding PVs in WT sibling aorta and liver were 1 and 0, respectively, and not overlapping with ApoE null PVs. Pyrosequencing analysis of 4 representative PVs in 17 ApoE null aortas and livers confirmed tissue-specific shifts toward the alternative allele, in addition to significant deviations from mendelian allele ratios. Notably, all aorta and liver PVs were present in the dbSNP database and were predominantly transition mutations within atherosclerosis-related genes. The majority of PVs were in discrete clusters approximately 3 Mb and 65 to 73 Mb away from hypermutable immunoglobin loci in chromosome 6. These features were largely shared with previously reported CVD-associated somatic mutations in human clonal hematopoiesis. The observation that SNPs exhibit tissue-specific somatic DNA mosaicism in ApoE null mice is potentially relevant for genetic association study design. The proximity of PVs to hypermutable loci suggests testable mechanistic hypotheses.

The autoimmune signature of hyperinflammatory multisystem inflammatory syndrome in children

Multisystem inflammatory syndrome in children (MIS-C) manifests as a severe and uncontrolled inflammatory response with multiorgan involvement, occurring weeks after SARS-CoV-2 infection. Here, we utilized proteomics, RNA sequencing, autoantibody arrays, and B cell receptor (BCR) repertoire analysis to characterize MIS-C immunopathogenesis and identify factors contributing to severe manifestations and intensive care unit admission. Inflammation markers, humoral immune responses, neutrophil activation, and complement and coagulation pathways were highly enriched in MIS-C patient serum, with a more hyperinflammatory profile in severe than in mild MIS-C cases. We identified a strong autoimmune signature in MIS-C, with autoantibodies targeted to both ubiquitously expressed and tissue-specific antigens, suggesting autoantigen release and excessive antigenic drive may result from systemic tissue damage. We further identified a cluster of patients with enhanced neutrophil responses as well as high anti-Spike IgG and autoantibody titers. BCR sequencing of these patients identified a strong imprint of antigenic drive with substantial BCR sequence connectivity and usage of autoimmunity-associated immunoglobulin heavy chain variable region (IGHV) genes. This cluster was linked to a TRBV11-2 expanded T cell receptor (TCR) repertoire, consistent with previous studies indicating a superantigen-driven pathogenic process. Overall, we identify a combination of pathogenic pathways that culminate in MIS-C and may inform treatment.

LRG1 promotes hypoxia-induced cardiomyocyte apoptosis and autophagy by regulating hypoxia-inducible factor-1α

Cardiomyocyte apoptosis and autophagy play important roles in acute myocardial infarction (AMI), but the effect of leucine-rich alpha-2-glycoprotein 1 (LRG1) on the apoptosis and autophagy of H9c2 has not yet been reported. It was found through differential gene analysis and LASSO analysis that LRG1 was the key gene in AMI. In this study, western blot was applied to detect the protein expression of Bax, Bcl2, LC3, p62, LRG1 and hypoxia-inducible factor-1α (HIF-1α); CCK-8 assay was employed to detect cell viability; Annexin V-FITC/PI staining was adopted to evaluate apoptosis, and immunofluorescence assay was applied to detect autophagy. Under hypoxia conditions in H9c2 cells, LRG1 protein levels were increased, the cell activity was decreased, and apoptosis and autophagy were promoted; the downregulated LRG1 significantly enhanced cell viability but inhibited apoptosis and autophagy. When knocking down HIF-1α in the overexpressed LRG1 cells, the effects of LRG1 were reversed under hypoxia condition. In conclusion, LRG1/HIF-1α promoted H9c2 cell apoptosis and autophagy in hypoxia, potentially providing new ideas for the determination and treatment of AMI.

Abbreviation: LRG1: Leucine-rich alpha-2-glycoprotein 1; LRR: leucine-rich repeat; HIF-1α: Hypoxia-inducible factor-1α; AMI: acute myocardial infarction

Long Non-Coding RNA Regulation of Epigenetics in Vascular Cells

The vascular endothelium comprises the interface between the circulation and the vessel wall and, as such, is under the dynamic regulation of vascular signalling, nutrients, and hypoxia. Understanding the molecular drivers behind endothelial cell (EC) and vascular smooth muscle cell (VSMC) function and dysfunction remains a pivotal task for further clinical progress in tackling vascular disease. A newly emerging era in vascular biology with landmark deep sequencing approaches has provided us with the means to profile diverse layers of transcriptional regulation at a single cell, chromatin, and epigenetic level. This review describes the roles of major vascular long non-coding RNA (lncRNAs) in the epigenetic regulation of EC and VSMC function and discusses the recent progress in their discovery, detection, and functional characterisation. We summarise new findings regarding lncRNA-mediated epigenetic mechanisms—often regulated by hypoxia—within the vascular endothelium and smooth muscle to control vascular homeostasis in health and disease. Furthermore, we outline novel molecular techniques being used in the field to delineate the lncRNA subcellular localisation and interaction with proteins to unravel their biological roles in the epigenetic regulation of vascular genes.

Clec4e-Receptor Signaling in Myocardial Repair After Ischemia-Reperfusion Injury

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The role of the CLEC4E during myocardial healing after ischemia-reperfusion injury is unknown.

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CLEC4E deletion is associated with reduced cardiac injury, inflammation, and left ventricular structural and functional remodeling.

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CLEC4E is a promising target to modulate myocardial inflammation and enhance repair after ischemia-reperfusion injury.

The bacterial C-type lectin domain family 4 member E (CLEC4E) has an important role in sterile inflammation, but its role in myocardial repair is unknown. Using complementary approaches in porcine, murine, and human samples, we show that CLEC4E expression levels in the myocardium and in blood correlate with the extent of myocardial injury and left ventricular (LV) functional impairment. CLEC4E expression is markedly increased in the vasculature, cardiac myocytes, and infiltrating leukocytes in the ischemic heart. Loss of Clec4e signaling is associated with reduced acute cardiac injury, neutrophil infiltration, and infarct size. Reduced myocardial injury in Clec4e^–/– translates into significantly improved LV structural and functional remodeling at 4 weeks’ follow-up. The early transcriptome of LV tissue from Clec4e^–/– mice versus wild-type mice reveals significant upregulation of transcripts involved in myocardial metabolism, radical scavenging, angiogenesis, and extracellular matrix organization. Therefore, targeting CLEC4E in the early phase of ischemia-reperfusion injury is a promising therapeutic strategy to modulate myocardial inflammation and enhance repair after ischemia-reperfusion injury.

Uncovering Potential lncRNAs and mRNAs in the Progression From Acute Myocardial Infarction to Myocardial Fibrosis to Heart Failure

Background: Morbidity and mortality of heart failure (HF) post-myocardial infarction (MI) remain elevated. The aim of this study was to find potential long non-coding RNAs (lncRNAs) and mRNAs in the progression from acute myocardial infarction (AMI) to myocardial fibrosis (MF) to HF.

Methods: Firstly, blood samples from AMI, MF, and HF patients were used for RNA sequencing. Secondly, differentially expressed lncRNAs and mRNAs were obtained in MF vs. AMI and HF vs. MF, followed by functional analysis of shared differentially expressed mRNAs between two groups. Thirdly, interaction networks of lncRNA-nearby targeted mRNA and lncRNA-co-expressed mRNA were constructed in MF vs. AMI and HF vs. MF. Finally, expression validation and diagnostic capability analysis of selected lncRNAs and mRNAs were performed.

Results: Several lncRNA-co-expressed/nearby targeted mRNA pairs including AC005392.3/AC007278.2-IL18R1, AL356356.1/AL137145.2-PFKFB3, and MKNK1-AS1/LINC01127-IL1R2 were identified. Several signaling pathways including TNF and cytokine–cytokine receptor interaction, fructose and mannose metabolism and HIF-1, hematopoietic cell lineage and fluid shear stress, and atherosclerosis and estrogen were selected. IL1R2, IRAK3, LRG1, and PLAC4 had a potential diagnostic value for both AMI and HF.

Conclusion: Identified AC005392.3/AC007278.2-IL18R1, AL356356.1/AL137145.2-PFKFB3, and MKNK1-AS1/LINC01127-IL1R2 lncRNA-co-expressed/nearby targeted mRNA pairs may play crucial roles in the development of AMI, MF, and HF.

Identification of Key Genes Associated with Changes in the Host Response to Severe Burn Shock: A Bioinformatics Analysis with Data from the Gene Expression Omnibus (GEO) Database

Background

Patients with severe burns continue to display a high mortality rate during the initial shock period. The precise molecular mechanism underlying the change in host response during severe burn shock remains unknown. This study aimed to identify key genes leading to the change in host response during burn shock.

Methods

The GSE77791 dataset, which was utilized in a previous study that compared hydrocortisone administration to placebo (NaCl 0.9%) in the inflammatory reaction of severe burn shock, was downloaded from the Gene Expression Omnibus (GEO) database and analyzed to identify the differentially expressed genes (DEGs). Functional enrichment analyses of Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were performed. The protein–protein interaction (PPI) network of DEGs was constructed using the Search Tool for the Retrieval of Interacting Genes (STRING) database and then visualized in Cytoscape. In addition, important modules in this network were selected using the Molecular Complex Detection (MCODE) algorithm, and hub genes were identified in cytoHubba, a Cytoscape plugin.

Results

A total of 1059 DEGs (508 downregulated genes and 551 upregulated genes) were identified from the dataset. The DEGs enriched in GO terms and KEGG pathways were related to immune response. The PPI network contained 439 nodes and 2430 protein pairs. Finally, important modules and hub genes were identified using the different Cytoscape plugins. The key genes in burn shock were identified as arginase 1 (ARG1), cytoskeleton-associated protein (CKAP4), complement C3a receptor (C3AR1), neutrophil elastase (ELANE), gamma-glutamyl hydrolase (GGH), orosomucoid (ORM1), and quiescin sulfhydryl (QSOX1).

Conclusion

The DEGs, functional terms and pathways, and hub genes identified in the present study can help shed light on the molecular mechanism underlying the changes in host response during burn shock and provide potential targets for early detection and treatment of burn shock.

Identification of Post-myocardial Infarction Blood Expression Signatures Using Multiple Feature Selection Strategies

Myocardial infarction (MI) is a type of serious heart attack in which the blood flow to the heart is suddenly interrupted, resulting in injury to the heart muscles due to a lack of oxygen supply. Although clinical diagnosis methods can be used to identify the occurrence of MI, using the changes of molecular markers or characteristic molecules in blood to characterize the early phase and later trend of MI will help us choose a more reasonable treatment plan. Previously, comparative transcriptome studies focused on finding differentially expressed genes between MI patients and healthy people. However, signature molecules altered in different phases of MI have not been well excavated. We developed a set of computational approaches integrating multiple machine learning algorithms, including Monte Carlo feature selection (MCFS), incremental feature selection (IFS), and support vector machine (SVM), to identify gene expression characteristics on different phases of MI. 134 genes were determined to serve as features for building optimal SVM classifiers to distinguish acute MI and post-MI. Subsequently, functional enrichment analyses followed by protein-protein interaction analysis on 134 genes identified several hub genes (IL1R1, TLR2, and TLR4) associated with progression of MI, which can be used as new diagnostic molecules for MI.

A Year in the Life of the EU-CardioRNA COST Action: CA17129 Catalysing Transcriptomics Research in Cardiovascular Disease

The EU-CardioRNA Cooperation in Science and Technology (COST) Action is a European-wide consortium established in 2018 with 31 European country members and four associate member countries to build bridges between translational researchers from academia and industry who conduct research on non-coding RNAs, cardiovascular diseases and similar research areas. EU-CardioRNA comprises four core working groups (WG1-4). In the first year since its launch, EU-CardioRNA met biannually to exchange and discuss recent findings in related fields of scientific research, with scientific sessions broadly divided up according to WG. These meetings are also an opportunity to establish interdisciplinary discussion groups, brainstorm ideas and make plans to apply for joint research grants and conduct other scientific activities, including knowledge transfer. Following its launch in Brussels in 2018, three WG meetings have taken place. The first of these in Lisbon, Portugal, the second in Istanbul, Turkey, and the most recent in Maastricht, The Netherlands. Each meeting includes a scientific session from each WG. This meeting report briefly describes the highlights and key take-home messages from each WG session in this first successful year of the EU-CardioRNA COST Action.