Transcriptomics of cardiac biopsies reveals differences in patients with or without diagnostic parameters for heart failure with preserved ejection fraction

Prognostic Value of a Multi-mRNA Signature for 1-Year All-Cause Death in Hospitalized Patients With Heart Failure With a Preserved Ejection Fraction

BACKGROUND

Heart failure with preserved ejection fraction is a major global public health problem, while effective risk stratification tools are still lacking. We sought to construct a multi-mRNA signature to predict 1-year all-cause death.

METHODS

We selected 30 patients with heart failure with preserved ejection fraction who died during 1-year follow-up and 30 who survived in the discovery set. One hundred seventy-one and 120 patients with heart failure with preserved ejection fraction were randomly selected as a test set and a validation set, respectively. We performed mRNA microarrays in all patients.

RESULTS

We constructed a 5-mRNA signature for predicting 1-year all-cause death. The scores of the 5-mRNA signature were significantly associated with the 1-year risk of all-cause death in both the test set (hazard ratio, 2.72 [95% CI, 1.98-3.74]; P<0.001) and the validation set (hazard ratio, 3.95 [95% CI, 2.40-6.48]; P<0.001). Compared with a reference model, which included sex, ASCEND-HF (Acute Study of Clinical Effectiveness of Nesiritide in Decompensated Heart Failure) score, history of HF and NT-proBNP (N-terminal pro-B-type natriuretic peptide), the 5-mRNA signature had a better discrimination capability, with an increased area under the curve from 0.696 to 0.813 in the test set and from 0.712 to 0.848 in the validation set. A composite model integrating the 5-mRNA risk score and variables in the reference model demonstrated an excellent discrimination capability, with an area under the curve of 0.861 (95% CI, 0.784-0.939) in the test set and an area under the curve of 0.859 (95% CI, 0.755-0.963) in the validation set. The net reclassification improvement and integrated discrimination improvement indicated that the composite model significantly improved patient classification compared with the reference model in both sets (P<0.001).

CONCLUSIONS

The 5-mRNA signature is a promising predictive tool for 1-year all-cause death and shows improved prognostic power over the established risk scores and NT-proBNP in patients with heart failure with preserved ejection fraction.

The JMJD family of histone demethylase and their intimate links to cardiovascular disease

The incidence rate and mortality rate of cardiovascular disease rank first in the world. It is associated with various high-risk factors, and there is no single cause. Epigenetic modifications, such as DNA methylation or histone modification, actively participate in the initiation and development of cardiovascular diseases. Histone lysine methylation is a type of histone post-translational modification. The human Jumonji C domain (JMJD) protein family consists of more than 30 members. JMJD proteins participate in many key nuclear processes and play a key role in the specific regulation of gene expression, DNA damage and repair, and DNA replication. Importantly, increasing evidence shows that JMJD proteins are abnormally expressed in cardiovascular diseases, which may be a potential mechanism for the occurrence and development of these diseases. Here, we discuss the key roles of JMJD proteins in various common cardiovascular diseases. This includes histone lysine demethylase, which has been studied in depth, and less-studied JMJD members. Furthermore, we focus on the epigenetic changes induced by each JMJD member, summarize recent research progress, and evaluate their relationship with cardiovascular diseases and therapeutic potential.

Cardiac biopsies reveal differences in transcriptomics between left and right ventricle in patients with or without diagnostic signs of heart failure

New or mild heart failure (HF) is mainly caused by left ventricular dysfunction. We hypothesised that gene expression differ between the left (LV) and right ventricle (RV) and secondly by type of LV dysfunction. We compared gene expression through myocardial biopsies from LV and RV of patients undergoing elective coronary bypass surgery (CABG). Patients were categorised based on LV ejection fraction (EF), diastolic function and NT-proBNP into pEF (preserved; LVEF ≥ 45%), rEF (reduced; LVEF < 45%) or normal LV function. Principal component analysis of gene expression displayed two clusters corresponding to LV and RV. Up-regulated genes in LV included natriuretic peptides NPPA and NPPB, transcription factors/coactivators STAT4 and VGLL2, ion channel related HCN2 and LRRC38 associated with cardiac muscle contraction, cytoskeleton, and cellular component movement. Patients with pEF phenotype versus normal differed in gene expression predominantly in LV, supporting that diastolic dysfunction and structural changes reflect early LV disease in pEF. DKK2 was overexpressed in LV of HFpEF phenotype, potentially leading to lower expression levels of β-catenin, α-SMA (smooth muscle actin), and enhanced apoptosis, and could be a possible factor in the development of HFpEF. CXCL14 was down-regulated in both pEF and rEF, and may play a role to promote development of HF.

Left Ventricular Gene Expression in Heart Failure With Preserved Ejection Fraction-Profibrotic and Proinflammatory Pathways and Genes

BACKGROUND

Heart failure with preserved ejection fraction (HFpEF) is increasingly prevalent and has few treatments. The molecular mechanisms and resultant signaling pathways that underlie the development of HFpEF are poorly defined. It has been proposed that activation of proinflammatory pathways plays a role in the development of cardiac fibrosis. The signature of gene expression (transcriptome) of previously validated left ventricular biopsies obtained from patients with HFpEF and matched referent controls allows for an unbiased assessment of proinflammatory and profibrotic signaling pathways and genes.

METHODS

Epicardial left ventricular biopsies from stringently selected HFpEF patients (HFpEF, n=16) and referent control patients (CTR, n=14) were obtained during aortocoronary bypass surgery. The subepicardial myocardium was flash-frozen to build a repository that was parallel-processed for RNA sequencing to allow for an unsupervised in-depth comparison of the left ventricular transcriptome.

RESULTS

The average patient age was 67±10 years. When compared with controls, patients with HFpEF were hypertensive with a higher body mass index (kg/m2: 30±5 versus 37±6; P<0.01) and elevated NT-proBNP levels (pg/mL: 155 [89-328] versus 1554 [888-2178]; P<0.001). The transcriptome analysis revealed differential expression of 477 genes many of which were involved in profibrotic pathways including extracellular matrix production and posttranslational modification but no proinflammatory signature.

CONCLUSIONS

The transcriptome analysis of left ventricular myocardial samples from patients with HFpEF confirms an overabundant extracellular matrix gene expression, the basis of myocardial fibrosis, without a signature of activated proinflammatory pathways or genes.

Myocardial RNA Sequencing Reveals New Potential Therapeutic Targets in Heart Failure with Preserved Ejection Fraction

Heart failure with preserved ejection fraction (HFpEF) represents a global health challenge, with limited therapies proven to enhance patient outcomes. This makes the elucidation of disease mechanisms and the identification of novel potential therapeutic targets a priority. Here, we performed RNA sequencing on ventricular myocardial biopsies from patients with HFpEF, prospecting to discover distinctive transcriptomic signatures. A total of 306 differentially expressed mRNAs (DEG) and 152 differentially expressed microRNAs (DEM) were identified and enriched in several biological processes involved in HF. Moreover, by integrating mRNA and microRNA expression data, we identified five potentially novel miRNA-mRNA relationships in HFpEF: the upregulated hsa-miR-25-3p, hsa-miR-26a-5p, and has-miR4429, targeting HAPLN1; and NPPB mRNA, targeted by hsa-miR-26a-5p and miR-140-3p. Exploring the predicted miRNA-mRNA interactions experimentally, we demonstrated that overexpression of the distinct miRNAs leads to the downregulation of their target genes. Interestingly, we also observed that microRNA signatures display a higher discriminative power to distinguish HFpEF sub-groups over mRNA signatures. Our results offer new mechanistic clues, which can potentially translate into new HFpEF therapies.

A plasma proteomic approach in patients with heart failure after acute myocardial infarction: insights into the pathogenesis and progression of the disease

Aims

The pathogenesis of disease progression targets for patients with heart failure after acute myocardial infarction was investigated by using plasma proteomics.

Methods

The plasma proteomes of acute myocardial infarction patients with (MI-HF) and without (MI-WHF) heart failure were compared. Each group consisted of 10 patients who were matched for age and sex. The peptides were analyzed by 2-dimensional liquid chromatography coupled to tandem mass spectrometry in a high definition mode. Parallel reaction monitoring (PRM) verified the selected target proteins.

Results

We identified and quantified 2,589 and 2,222 proteins, respectively, and found 117 differentially expressed proteins (DEPs) (≥1.5-fold), when the MI-HF and MI-WHF groups were compared. Of these 51 and 66 were significantly up-regulated and down-regulated, respectively. The significant DEPs was subjected to protein-protein interaction network analysis which revealed a central role of the NF-κB signaling pathway in the MI-HF patients. PRM verified that MB, DIAPH1, VNN1, GOT2, SLC4A1, CRP, CKM, SOD3, F7, DLD, PGAM2, GOT1, UBA7 and HYOU1 were 14 proteins which were highly expressed in MI-HF patients.

Conclusions

These findings showed a group of proteins related to the NF-κB signaling pathway in the pathogenesis of patients with poor outcomes after experiencing MI-HF. These proteins may be useful candidate markers for the diagnosis of MI-HF as well as help to elucidate the pathophysiology of this major cause of mortality in older patients.

Functional and Metabolic Imaging in Heart Failure with Preserved Ejection Fraction: Promises, Challenges, and Clinical Utility

Heart failure with preserved ejection fraction (HFpEF) is recognised as an increasingly prevalent, morbid and burdensome condition with a poor outlook. Recent advances in both the understanding of HFpEF and the technological ability to image cardiac function and metabolism in humans have simultaneously shone a light on the molecular basis of this complex condition of diastolic dysfunction, and the inflammatory and metabolic changes that are associated with it, typically in the context of a complex patient. This review both makes the case for an integrated assessment of the condition, and highlights that metabolic alteration may be a measurable outcome for novel targeted forms of medical therapy. It furthermore highlights how recent technological advancements and advanced medical imaging techniques have enabled the characterisation of the metabolism and function of HFpEF within patients, at rest and during exercise.

Relationship between iron deficiency and expression of genes involved in iron metabolism in human myocardium and skeletal muscle

BACKGROUND

Iron deficiency (ID) is associated with adverse prognosis in patients with heart failure. This study aims to investigate the relationship between ID and expression of genes involved in iron metabolism in human myocardium and skeletal muscle, focusing on Transferrin 1 receptor (TfR1), the main pathway of cellular iron uptake.

METHODS

Patients undergoing elective CABG were assessed prior to surgery with echocardiography and serum iron parameters. Core needle biopsies were collected from the left and right ventricle (LV, RV), the right atrium and intercostal skeletal muscle (SM). Gene expression analyses were done by mRNA sequencing.

RESULTS

Of 69 patients (median age 69 years, 91% men), 28% had ID. 26% had HFrEF, 25% had HFpEF physiology according to echocardiographic findings and NT-proBNP levels, and 49% had normal LV function. The expression of TfR1 was increased in patients with ID compared to patients without ID in ventricular tissue (p = 0.04) and in intercostal SM (p = 0.01). The increase in TfR1 expression in LV and RV was more pronounced when analysing patients with absolute ID (S-Ferritin<100 μg/L). Analysing the correlation between various iron parameters, S-Ferritin levels showed the strongest correlation with TfR1 expression. There was no correlation with NT-proBNP levels and no difference in TfR1 expression between different HF phenotypes.

CONCLUSIONS

In patients undergoing elective CABG we found an association between ID and increased TfR1 expression in myocardium regardless of LV function, indicating physiologically upregulated TfR1 expression in the presence of ID to restore intracellular iron needs.

CLINICAL TRIAL REGISTRATION

Clinicaltrials.govNCT03671122.

Using Artificial Intelligence to Better Predict and Develop Biomarkers

Advancements in technology have improved biomarker discovery in the field of heart failure (HF). What was once a slow and laborious process has gained efficiency through use of high-throughput omics platforms to phenotype HF at the level of genes, transcripts, proteins, and metabolites. Furthermore, improvements in artificial intelligence (AI) have made the interpretation of large omics data sets easier and improved analysis. Use of omics and AI in biomarker discovery can aid clinicians by identifying markers of risk for developing HF, monitoring care, determining prognosis, and developing druggable targets. Combined, AI has the power to improve HF patient care.

TRIM35-mediated degradation of nuclear PKM2 destabilizes GATA4/6 and induces P53 in cardiomyocytes to promote heart failure

Pyruvate kinase M2 (PKM2) is a glycolytic enzyme that translocates to the nucleus to regulate transcription factors in different tissues or pathologic states. Although studied extensively in cancer, its biological role in the heart remains unresolved. PKM1 is more abundant than the PKM2 isoform in cardiomyocytes, and thus, we speculated that PKM2 is not genetically redundant to PKM1 and may be critical in regulating cardiomyocyte-specific transcription factors important for cardiac survival. Here, we showed that nuclear PKM2 ( S37 P-PKM2) in cardiomyocytes interacts with prosurvival and proapoptotic transcription factors, including GATA4, GATA6, and P53. Cardiomyocyte-specific PKM2-deficient mice ( Pkm2 Mut Cre + ) developed age-dependent dilated cardiac dysfunction and had decreased amounts of GATA4 and GATA6 (GATA4/6) but increased amounts of P53 compared to Control Cre +  hearts. Nuclear PKM2 prevented caspase-1–dependent cleavage and degradation of GATA4/6 while also providing a molecular platform for MDM2-mediated reduction of P53. In a preclinical heart failure mouse model, nuclear PKM2 and GATA4/6 were decreased, whereas P53 was increased in cardiomyocytes. Loss of nuclear PKM2 was ubiquitination dependent and associated with the induction of the E3 ubiquitin ligase TRIM35. In mice, cardiomyocyte-specific TRIM35 overexpression resulted in decreased S37 P-PKM2 and GATA4/6 along with increased P53 in cardiomyocytes compared to littermate controls and similar cardiac dysfunction to Pkm2 Mut Cre +  mice. In patients with dilated left ventricles, increase in TRIM35 was associated with decreased S37 P-PKM2 and GATA4/6 and increased P53. This study supports a previously unrecognized role for PKM2 as a molecular platform that mediates cell signaling events essential for cardiac survival.

Sex-specific responses to slow progressive pressure overload in a large animal model of HFpEF

Approximately 50% of all heart failure (HF) diagnoses can be classified as HF with preserved ejection fraction (HFpEF). HFpEF is more prevalent in females compared with males, but the underlying mechanisms are unknown. We previously showed that pressure overload (PO) in male felines induces a cardiopulmonary phenotype with essential features of human HFpEF. The goal of this study was to determine if slow progressive PO induces distinct cardiopulmonary phenotypes in females and males in the absence of other pathological stressors. Female and male felines underwent aortic constriction (banding) or sham surgery after baseline echocardiography, pulmonary function testing, and blood sampling. These assessments were repeated at 2 and 4 mo postsurgery to document the effects of slow progressive pressure overload. At 4 mo, invasive hemodynamic studies were also performed. Left ventricle (LV) tissue was collected for histology, myofibril mechanics, extracellular matrix (ECM) mass spectrometry, and single-nucleus RNA sequencing (snRNAseq). The induced pressure overload (PO) was not different between sexes. PO also induced comparable changes in LV wall thickness and myocyte cross-sectional area in both sexes. Both sexes had preserved ejection fraction, but males had a slightly more robust phenotype in hemodynamic and pulmonary parameters. There was no difference in LV fibrosis and ECM composition between banded male and female animals. LV snRNAseq revealed changes in gene programs of individual cell types unique to males and females after PO. Based on these results, both sexes develop cardiopulmonary dysfunction but the phenotype is somewhat less advanced in females.NEW & NOTEWORTHY We performed a comprehensive assessment to evaluate the effects of slow progressive pressure overload on cardiopulmonary function in a large animal model of heart failure with preserved ejection fraction (HFpEF) in males and females. Functional and structural assessments were performed at the organ, tissue, cellular, protein, and transcriptional levels. This is the first study to compare snRNAseq and ECM mass spectrometry of HFpEF myocardium from males and females. The results broaden our understanding of the pathophysiological response of both sexes to pressure overload. Both sexes developed a robust cardiopulmonary phenotype, but the phenotype was equal or a bit less robust in females.

Transcriptomic and ChIP-seq Integrative Analysis Identifies KDM5A-Target Genes in Cardiac Fibroblasts

Cardiac fibrosis is a common pathological feature in cardiac remodeling. This study aimed to explore the role of KDM5A in cardiac fibrosis via bioinformatics analysis. Cardiac fibroblasts (CFs) were harvested and cultured from 10 dilated cardiomyopathy (DCM) patients who underwent heart transplantation. Western blotting was applied to verify that KDM5A is regulated by angiotensin II (Ang II) via the PI3k/AKT signaling pathway. The differentially expressed genes (DEGs) were analyzed by transcriptomics. ChIP-seq and ChIP-qPCR were used to identify the genes bound by KDM5A. In integrative analysis, weighted gene coexpression network analysis (WGCNA) was performed to identify highly relevant gene modules. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed for the key genes in modules. The STRING database, Cytoscape, and MCODE were applied to construct the protein–protein interaction (PPI) network and screen hub genes. To verify the expression of DEGs regulated by KDM5A, Western blotting and immunofluorescence were performed in myocardial tissue samples. Immunofluorescence verified the vimentin positivity of CFs. Ang II upregulated the expression of KDM5A in CFs via the PI3K/AKT signaling pathway. GO analysis of DEGs indicated that regulation of vasoconstriction, extracellular region, and calcium ion binding were enriched when KDM5A interfered with CPI or Ang II. KEGG analysis of the DEGs revealed the involvement of ECM-receptor interaction, focal adhesion, PI3K-Akt signaling pathway, cell adhesion, and arrhythmogenic right ventricular cardiomyopathy pathways. Three hub genes (IGF1, MYH11, and TGFB3) were identified via four different algorithms. Subsequent verification in patient samples demonstrated that the hub genes, which were regulated by KDM5A, were downregulated in DCM samples. KDM5A is a key regulator in the progression of cardiac fibrosis. In this successful integrative analysis, IGF1, MYH11, and TGFB3 were determined to be coordinately expressed to participate in cardiac fibrosis.

STAT4 regulates cardiomyocyte apoptosis in rat models of diabetic cardiomyopathy

OBJECTIVE

This study aimed to investigate the protective role of the signal transducer and activator of transcription 4 (STAT4) in diabetic cardiomyopathy.

MATERIALS AND METHODS

Male Sprague-Dawley (SD) rats (6-8 weeks old) were purchased from the Experimental Animal Center of Zhengzhou University. The rats were randomly divided into the control and diabetic cardiomyopathy groups. Rat models of diabetic cardiomyopathy were established by a high-sugar and high-fat diet combined with a peritoneal injection of streptozocin. Pathological changes in the heart were visualized using Hematoxylin-eosin (HE) staining and Masson's staining. Moreover, cell apoptosis was detected using terminal deoxyribonucleotidyl transferase (TdT)-mediated biotin-16-dUTP nick-end labeling (TUNEL) staining and Annexin V apoptosis detection kit. Furthermore, H9C2 cells were transfected with lentivirus overexpressing STAT4 and treated with high glucose. The CCK-8 assay was performed to determine cell viability. Finally, Western blotting was used to determine the expression of STAT4, Bax, and Bcl-2.

RESULTS

The myocardial tissue of the diabetic cardiomyopathy models showed hypertrophy, myocardial fibrosis and collagen deposition. Furthermore, TUNEL staining showed increased apoptosis and decreased expression of STAT4 in the myocardial cells. Moreover, the myocardial tissues of the DCM models showed increased expression of Bax/Bcl-2 and a high percentage of Annexin V positive cells. The H9C2 cells showed decreased expression of STAT4 following high glucose treatment. However, the H9C2 cells overexpressing STAT4 showed decreased expression of Bax/Bcl-2 and reduced percentage of Annexin V positive cells.

CONCLUSION

The DCM group had decreased myocardial expression of STAT4. Furthermore, overexpression of STAT4 was shown to reduce high glucose-induced apoptosis.

Baseline characteristics of 547 new onset heart failure patients in the PREFERS heart failure study

Aim

Methods and results

Conclusions

Using Artificial Intelligence to Better Predict and Develop Biomarkers

Advancements in technology have improved biomarker discovery in the field of heart failure (HF). What was once a slow and laborious process has gained efficiency through use of high-throughput omics platforms to phenotype HF at the level of genes, transcripts, proteins, and metabolites. Furthermore, improvements in artificial intelligence (AI) have made the interpretation of large omics data sets easier and improved analysis. Use of omics and AI in biomarker discovery can aid clinicians by identifying markers of risk for developing HF, monitoring care, determining prognosis, and developing druggable targets. Combined, AI has the power to improve HF patient care.

Inhibition of Jumonji demethylases reprograms severe dilated cardiomyopathy and prolongs survival

Hypertrophic/dilated cardiomyopathy, often a prequel to heart failure, is accompanied by maladaptive transcriptional changes that contribute to arrythmias and contractile misfunction. Transgenic mice constitutively expressing high levels of calcineurin are known to develop extreme heart hypertrophy, which progresses to dilated cardiomyopathy, and to die several weeks after birth. Here, we characterized aberrant transcriptional and epigenetic pathways in this mouse model and established a pharmacological approach to treat established cardiomyopathy. We found that H3K4me3 (trimethyl histone 3 lysine 4) and H3K9me3 (trimethyl histone 3 lysine 9) Jumonji histone demethylases are markedly increased at the protein level and show enhanced enzymatic activity in diseased hearts. These epigenetic regulators continued to increase with time, further affecting cardiac gene expression. Our findings parallel the lower H3K4me3 and H3K9me3 levels seen in human patients. Inhibition of Jumonji demethylase activities in vivo results in lower histone demethylase enzymatic function in the heart and higher histone methylation levels and leads to partial reduction of heart size, reversal of maladaptive transcriptional programs, improved heart function, and prolonged survival. At the molecular level, target genes of transcription factor myocyte enhancer factor 2 are specifically regulated in response to pharmacological or genetic inhibition of Jumonji demethylases. Similar transcriptional reversal of disease-associated genes is seen in a second disease model based on cardiac mechanical overload. Our findings validate pharmacological inhibitors of Jumonji demethylases as potential therapeutics for the treatment of cardiomyopathies across disease models and provide evidence of the reversal of maladaptive transcriptional reprogramming leading to partial restoration of cardiac function. In addition, this study defines pathways of therapeutic resistance upregulated with disease progression.

Fortilin inhibits p53, halts cardiomyocyte apoptosis, and protects the heart against heart failure

Heart failure (HF) has reached epidemic proportions in developed countries, affecting over 20 million people worldwide. Despite modern medical and device therapies, 60–70% of HF patients still die within 5 years of diagnosis as it relentlessly progresses through pervasive apoptotic loss of cardiomyocytes. Although fortilin, a 172-amino-acid anti-p53 molecule, is one of the most expressed proteins in the heart, its precise role there has remained unknown. Also unclear is how cardiomyocytes are protected against apoptosis. Here, we report that failing human hearts express less fortilin than do non-failing hearts. We also found that mice lacking fortilin in the heart (fortilin^KO-heart) die by 9 weeks of age due to extensive cardiomyocyte apoptosis and severe HF, which suggests that fortilin sustains cardiomyocyte viability. The lack of fortilin is also associated with drastic upregulation of p53 target genes in the hearts. The heart-specific deletion of p53 in fortilin^KO-heart mice extends their life spans from 9 to 18 weeks by mitigating cardiomyocyte apoptosis. Our data suggest that fortilin is a novel cardiac p53 inhibitor and that its inadequate expression in failing hearts and subsequent overactivation of the p53 apoptosis pathway in cardiomyocytes exacerbates HF.

Gold based nano-photonic approach for point-of-care detection of circulating long non-coding RNAs

Development of a rapid, sensitive and easy to use point of care assay for detection of circulating long non-coding RNAs (lncRNAs) is of great importance. These biomolecules possess the ability to regulate vital cellular processes and act as biomarkers for various human non-communicable diseases. The present work aimed to develop a simplified and reliable cytometric fluorescence-based approach for precise recognition of circulating lncRNAs in a given sample using biotinylated uracil-modified oligonucleotide tethered AlexaFluor488-labeled streptavidin gold colloidal (BiO-StrAG) nano-conjugates. The fluorophores in close proximity to the gold nanoparticles result in quenching of fluorescence; however, specific recognition of target lncRNAs increases this distance which causes plasmonic enhancement of fluorescence. As per the flow cytometry and fluorometry investigations, the developed methodology provides a precise and sensitive approach for detection of the target lncRNAs (up to 5 nM in any given sample). With advantages of high selectivity and feasibility, our strategy offers great potential of being developed as a promising tool for interrogating aberrant regulation of lncRNAs functions, especially indicated in various diseased states.

Decoding empagliflozin's molecular mechanism of action in heart failure with preserved ejection fraction using artificial intelligence

The use of sodium-glucose co-transporter 2 inhibitors to treat heart failure with preserved ejection fraction (HFpEF) is under investigation in ongoing clinical trials, but the exact mechanism of action is unclear. Here we aimed to use artificial intelligence (AI) to characterize the mechanism of action of empagliflozin in HFpEF at the molecular level. We retrieved information regarding HFpEF pathophysiological motifs and differentially expressed genes/proteins, together with empagliflozin target information and bioflags, from specialized publicly available databases. Artificial neural networks and deep learning AI were used to model the molecular effects of empagliflozin in HFpEF. The model predicted that empagliflozin could reverse 59% of the protein alterations found in HFpEF. The effects of empagliflozin in HFpEF appeared to be predominantly mediated by inhibition of NHE1 (Na⁺/H⁺ exchanger 1), with SGLT2 playing a less prominent role. The elucidated molecular mechanism of action had an accuracy of 94%. Empagliflozin’s pharmacological action mainly affected cardiomyocyte oxidative stress modulation, and greatly influenced cardiomyocyte stiffness, myocardial extracellular matrix remodelling, heart concentric hypertrophy, and systemic inflammation. Validation of these in silico data was performed in vivo in patients with HFpEF by measuring the declining plasma concentrations of NOS2, the NLPR3 inflammasome, and TGF-β1 during 12 months of empagliflozin treatment. Using AI modelling, we identified that the main effect of empagliflozin in HFpEF treatment is exerted via NHE1 and is focused on cardiomyocyte oxidative stress modulation. These results support the potential use of empagliflozin in HFpEF.

The Function of KDEL Receptors as UPR Genes in Disease

The KDEL receptor retrieval pathway is essential for maintaining resident proteins in the endoplasmic reticulum (ER) lumen. ER resident proteins serve a variety of functions, including protein folding and maturation. Perturbations to the lumenal ER microenvironment, such as calcium depletion, can cause protein misfolding and activation of the unfolded protein response (UPR). Additionally, ER resident proteins are secreted from the cell by overwhelming the KDEL receptor retrieval pathway. Recent data show that KDEL receptors are also activated during the UPR through the IRE1/XBP1 signaling pathway as an adaptive response to cellular stress set forth to reduce the loss of ER resident proteins. This review will discuss the emerging connection between UPR activation and KDEL receptors as it pertains to ER proteostasis and disease states.

Integrative transcriptomic analysis of tissue-specific metabolic crosstalk after myocardial infarction

Myocardial infarction (MI) promotes a range of systemic effects, many of which are unknown. Here, we investigated the alterations associated with MI progression in heart and other metabolically active tissues (liver, skeletal muscle, and adipose) in a mouse model of MI (induced by ligating the left ascending coronary artery) and sham-operated mice. We performed a genome-wide transcriptomic analysis on tissue samples obtained 6- and 24 hr post MI or sham operation. By generating tissue-specific biological networks, we observed: (1) dysregulation in multiple biological processes (including immune system, mitochondrial dysfunction, fatty-acid beta-oxidation, and RNA and protein processing) across multiple tissues post MI and (2) tissue-specific dysregulation in biological processes in liver and heart post MI. Finally, we validated our findings in two independent MI cohorts. Overall, our integrative analysis highlighted both common and specific biological responses to MI across a range of metabolically active tissues.

The human body is like a state-of-the-art car, where each part must work together with all the others. When a car breaks down, most of the time the problem is not isolated to only one part, as it is an interconnected system. Diseases in the human body can also have systemic effects, so it is important to study their implications throughout the body. Most studies of heart attacks focus on the direct impact on the heart and the cardiovascular system. Learning more about how heart attacks affect rest of the body may help scientists identify heart attacks early or create improved treatments.

Arif and Klevstig et al. show that heart attacks affect the metabolism throughout the body. In the experiments, mice underwent a procedure that mimics either a heart attack or a fake procedure. Then, Arif and Klevstig et al. compared the activity of genes in the heart, muscle, liver and fat tissue of the two groups of mice 6- and 24-hours after the operations. This revealed disruptions in the immune system, metabolism and the production of proteins. The experiments also showed that changes in the activity of four important genes are key to these changes. This suggests that this pattern of changes could be used as a way to identify heart attacks.

The experiments show that heart attacks have important effects throughout the body, especially on metabolism. These discoveries may help scientists learn more about the underlying biological processes and develop new treatments that prevent the harmful systemic effects of heart attacks and boost recovery.

Subtypes identification on heart failure with preserved ejection fraction via network enhancement fusion using multi-omics data

Heart failure with preserved ejection fraction (HFpEF) is associated with multiple etiologic and pathophysiologic factors. HFpEF leads to significant cardiovascular morbidity and mortality. There are various reasons that fail to identify effective therapeutic interventions for HFpEF, primarily due to its clinical heterogeneity causing significant difficulties in determining physiologic and prognostic implications for this syndrome. Thus, identifying clinical subtypes using multi-omics data has great implications for efficient treatment and prognosis of HFpEF patients. Here we proposed to integrate mRNA, DNA methylation and microRNA (miRNA) expression data of HFpEF with a similarity network fusion (SNF) method following a network enhancement (ne-SNF) denoising technique to form a fused network. A spectral clustering method was then used to obtain clusters of patient subtypes. Experiments on HFpEF datasets demonstrated that ne-SNF significantly outperforms single data subtype analysis and other integrated methods. The identified subgroups were shown to have statistically significant differences in survival. Two HFpEF subtypes were defined: a high-risk group (16.8%) and a low-risk group (83.2%). The 5-year mortality rates were 63.3% and 33.0% for the high- and low-risk group, respectively. After adjusting for the effects of clinical covariates, HFpEF patients in the high-risk group were 2.43 times more likely to die than the low-risk group. A total of 157 differentially expressed (DE) mRNAs, 2199 abnormal methylations and 121 DE miRNAs were identified between two subtypes. They were also enriched in many HFpEF-related biological processes or pathways. The ne-SNF method provides a novel pipeline for subtype identification in integrated analysis of multi-omics data.

Myocardial expression of somatotropic axis, adrenergic signalling, and calcium handling genes in heart failure with preserved ejection fraction and heart failure with reduced ejection fraction

Aims

Limited data are available regarding cardiac expression of molecules involved in heart failure (HF) pathophysiology. The majority of the studies have focused on end‐stage HF with reduced ejection fraction (HFrEF) without comparison with healthy subjects, while no data are available with regard to HF with preserved ejection fraction (HFpEF). HFpEF is a condition whose multiple pathophysiological mechanisms are still not fully defined, with many proposed hypotheses remaining speculative due to limited access to human heart tissue. This study aimed at evaluating cardiac expression levels of key genes of interest in human biopsy samples from patients affected with HFrEF and HFpEF in order to possibly point out distinct phenotypes.

Methods and results

Total RNA was extracted from left ventricular cardiac biopsies collected from stable patients with HFrEF (n = 6) and HFpEF (n = 7) and healthy subjects (n = 9) undergoing elective cardiac surgery for valvular replacement, mitral valvuloplasty, aortic surgery, or coronary artery bypass. Real‐time PCR was performed to evaluate the mRNA expression levels of genes involved in somatotropic axis regulation [IGF‐1, IGF‐1 receptor (IGF‐1R), and GH receptor (GHR)], in adrenergic signalling (GRK2, GRK5, ADRB1, and ADRB2), in myocardial calcium handling (SERCA2), and in TNF‐α. Patients with HFrEF and HFpEF showed reduced serum IGF‐1 circulating levels when compared with controls (102 ± 35.6, 138 ± 11.5, and 160 ± 13.2 ng/mL, P < 0.001, respectively). At myocardial level, HFrEF showed significant decreased GHR and increased IGF‐1R expressions when compared with HFpEF and controls (0.54 ± 0.27, 0.94 ± 0.25, and 0.84 ± 0.2, P < 0.05 and 1.52 ± 0.9, 1.06 ± 0.21, and 0.72 ± 0.12, P < 0.05, respectively), while no differences in the local expression of IGF‐1 mRNA were detected among the groups (0.80 ± 0.45, 0.97 ± 0.18, and 0.63 ± 0.23, P = 0.09, respectively). With regard to calcium handling and adrenergic signalling, HFrEF displayed significant decreased levels of SERCA2 (0.19 ± 0.39, 0.82 ± 0.15, and 0.87 ± 0.32, P < 0.01) and increased levels of GRK2 (3.45 ± 2.94, 0.93 ± 0.12, and 0.80 ± 0.14, P < 0.01) and GRK5 (1.32 ± 0.70, 0.71 ± 0.14, and 0.77 ± 0.15, P < 0.05), while no significant difference was found in ADRB1 (0.66 ± 0.4, 0.83 ± 0.3, and 0.86 ± 0.4) and ADRB2 mRNA expression (0.65 ± 0.3, 0.66 ± 0.2, and 0.68 ± 0.1) when compared with HFpEF and controls. Finally, no changes in the local expression of TNF‐α were detected among groups.

Conclusions

Heart failure with reduced ejection fraction and HFpEF patients with stable clinical condition display a distinct molecular milieu of genes involved in somatotropic axis regulation, calcium handling, and adrenergic derangement at a myocardial level. The unique opportunity to compare these results with a control group, as reference population, may contribute to better understand HF pathophysiology and to identify novel potential therapeutic targets that could be modulated to improve ventricular function in patients with HF.

Construction and analysis of a joint diagnosis model of random forest and artificial neural network for heart failure

Heart failure is a global health problem that affects approximately 26 million people worldwide. As conventional diagnostic techniques for heart failure have been in practice with various limitations, it is necessary to develop novel diagnostic models to supplement existing methods. With advances and improvements in gene sequencing technology in recent years, more heart failure-related genes have been identified. Using existing gene expression data in the Gene Expression Omnibus (GEO) database, we screened differentially expressed genes (DEGs) of heart failure and identified six key genes (HMOX2, SERPINA3, LCN6, CSDC2, FREM1, and ZMAT1) by random forest classifier. Of these genes, CSDC2, FREM1, and ZMAT1 have never been associated with heart failure. We also successfully constructed a new diagnostic model of heart failure using an artificial neural network and verified its diagnostic efficacy in public datasets.

Fibrosis Distinguishes Critical Limb Ischemia Patients from Claudicants in a Transcriptomic and Histologic Analysis

Most patients with critical limb ischemia (CLI) from peripheral arterial disease (PAD) do not have antecedent intermittent claudication (IC). We hypothesized that transcriptomic analysis would identify CLI-specific pathways, particularly in regards to fibrosis. Derivation cohort data from muscle biopsies in PAD and non-PAD (controls) was obtained from the Gene Expression Omnibus (GSE120642). Transcriptomic analysis indicated CLI patients (N = 16) had a unique gene expression profile, when compared with non-PAD controls (N = 15) and IC (N = 20). Ninety-eight genes differed between controls and IC, 2489 genes differed between CLI and controls, and 2783 genes differed between CLI and IC patients. Pathway enrichment analysis showed that pathways associated with TGFβ, collagen deposition, and VEGF signaling were enriched in CLI but not IC. Receiver operating curve (ROC) analysis of nine fibrosis core gene expression revealed the areas under the ROC (AUC) were all >0.75 for CLI. Furthermore, the fibrosis area (AUC = 0.81) and % fibrosis (AUC = 0.87) in validation cohort validated the fibrosis discrimination CLI from IC and control (all n = 12). In conclusion, transcriptomic analysis identified fibrosis pathways, including those involving TGFβ, as a novel gene expression feature for CLI but not IC. Fibrosis is an important characteristic of CLI, which we confirmed histologically, and may be a target for novel therapies in PAD.

Myocardial Gene Expression Signatures in Human Heart Failure With Preserved Ejection Fraction

BACKGROUND

Heart failure (HF) with preserved ejection fraction (HFpEF) constitutes half of all HF but lacks effective therapy. Understanding of its myocardial biology remains limited because of a paucity of heart tissue molecular analysis.

METHODS

We performed RNA sequencing on right ventricular septal endomyocardial biopsies prospectively obtained from patients meeting consensus criteria for HFpEF (n=41) contrasted with right ventricular septal tissue from patients with HF with reduced ejection fraction (HFrEF, n=30) and donor controls (n=24). Principal component analysis and hierarchical clustering tested for transcriptomic distinctiveness between groups, effect of comorbidities, and differential gene expression with pathway enrichment contrasted HF groups and donor controls. Within HFpEF, non-negative matrix factorization and weighted gene coexpression analysis identified molecular subgroups, and the resulting clusters were correlated with hemodynamic and clinical data.

RESULTS

Patients with HFpEF were more often women (59%), African American (68%), obese (median body mass index 41), and hypertensive (98%), with clinical HF characterized by 65% New York Heart Association Class III or IV, nearly all on a loop diuretic, and 70% with a HF hospitalization in the previous year. Principal component analysis separated HFpEF from HFrEF and donor controls with minimal overlap, and this persisted after adjusting for primary comorbidities: body mass index, sex, age, diabetes, and renal function. Hierarchical clustering confirmed group separation. Nearly half the significantly altered genes in HFpEF versus donor controls (1882 up, 2593 down) changed in the same direction in HFrEF; however, 5745 genes were uniquely altered between HF groups. Compared with controls, uniquely upregulated genes in HFpEF were enriched in mitochondrial adenosine triphosphate synthesis/electron transport, pathways downregulated in HFrEF. HFpEF-specific downregulated genes engaged endoplasmic reticulum stress, autophagy, and angiogenesis. Body mass index differences largely accounted for HFpEF upregulated genes, whereas neither this nor broader comorbidity adjustment altered pathways enriched in downregulated genes. Non-negative matrix factorization identified 3 HFpEF transcriptomic subgroups with distinctive pathways and clinical correlates, including a group closest to HFrEF with higher mortality, and a mostly female group with smaller hearts and proinflammatory signaling. These groupings remained after sex adjustment. Weighted gene coexpression analysis yielded analogous gene clusters and clinical groupings.

CONCLUSIONS

HFpEF exhibits distinctive broad transcriptomic signatures and molecular subgroupings with particular clinical features and outcomes. The data reveal new signaling targets to consider for precision therapeutics.

Transcriptomic Research in Heart Failure with Preserved Ejection Fraction: Current State and Future Perspectives

Heart failure with preserved ejection fraction (HFpEF) is increasing in incidence and has a higher prevalence compared with heart failure with reduced ejection fraction. So far, no effective treatment of HFpEF is available, due to its complex underlying pathophysiology and clinical heterogeneity. This article aims to provide an overview and a future perspective of transcriptomic biomarker research in HFpEF. Detailed characterisation of the HFpEF phenotype and its underlying molecular pathomechanisms may open new perspectives regarding early diagnosis, improved prognostication, new therapeutic targets and tailored therapies accounting for patient heterogeneity, which may improve quality of life. A combination of cross-sectional and longitudinal study designs with sufficiently large sample sizes are required to support this concept.

Big Data Approaches in Heart Failure Research

PURPOSE OF REVIEW

The goal of this review is to summarize the state of big data analyses in the study of heart failure (HF). We discuss the use of big data in the HF space, focusing on "omics" and clinical data. We address some limitations of this data, as well as their future potential.

RECENT FINDINGS

Omics are providing insight into plasmal and myocardial molecular profiles in HF patients. The introduction of single cell and spatial technologies is a major advance that will reshape our understanding of cell heterogeneity and function as well as tissue architecture. Clinical data analysis focuses on HF phenotyping and prognostic modeling. Big data approaches are increasingly common in HF research. The use of methods designed for big data, such as machine learning, may help elucidate the biology underlying HF. However, important challenges remain in the translation of this knowledge into improvements in clinical care.

Matrix Metalloproteinases and Tissue Inhibitors of Metalloproteinases in Extracellular Matrix Remodeling during Left Ventricular Diastolic Dysfunction and Heart Failure with Preserved Ejection Fraction: A Systematic Review and Meta-Analysis

Matrix metalloproteinases (MMPs) and tissue inhibitors of metalloproteinases (TIMPs) are pivotal regulators of extracellular matrix (ECM) composition and could, due to their dynamic activity, function as prognostic tools for fibrosis and cardiac function in left ventricular diastolic dysfunction (LVDD) and heart failure with preserved ejection fraction (HFpEF). We conducted a systematic review on experimental animal models of LVDD and HFpEF published in MEDLINE or Embase. Twenty-three studies were included with a total of 36 comparisons that reported established LVDD, quantification of cardiac fibrosis and cardiac MMP or TIMP expression or activity. LVDD/HFpEF models were divided based on underlying pathology: hemodynamic overload (17 comparisons), metabolic alteration (16 comparisons) or ageing (3 comparisons). Meta-analysis showed that echocardiographic parameters were not consistently altered in LVDD/HFpEF with invasive hemodynamic measurements better representing LVDD. Increased myocardial fibrotic area indicated comparable characteristics between hemodynamic and metabolic models. Regarding MMPs and TIMPs; MMP2 and MMP9 activity and protein and TIMP1 protein levels were mainly enhanced in hemodynamic models. In most cases only mRNA was assessed and there were no correlations between cardiac tissue and plasma levels. Female gender, a known risk factor for LVDD and HFpEF, was underrepresented. Novel studies should detail relevant model characteristics and focus on MMP and TIMP protein expression and activity to identify predictive circulating markers in cardiac ECM remodeling.

Tunica-Specific Transcriptome of Abdominal Aortic Aneurysm and the Effect of Intraluminal Thrombus, Smoking, and Diameter Growth Rate

OBJECTIVE

There is no medical treatment to prevent abdominal aortic aneurysm (AAA) growth and rupture, both of which are linked to smoking. Our objective was to map the tunica-specific pathophysiology of AAA with consideration of the intraluminal thrombus, age, and sex, and to subsequently identify which mechanisms were linked to smoking and diameter growth rate. Approach and Results: Microarray analyses were performed on 246 samples from 76 AAA patients and 13 controls. In media and adventitia, there were 5889 and 2701 differentially expressed genes, respectively. Gene sets related to adaptive and innate immunity were upregulated in both tunicas. Media-specific gene sets included increased matrix disassembly and angiogenesis, as well as decreased muscle cell development, contraction, and differentiation. Genes implicated in previous genome-wide association studies were dysregulated in media. The intraluminal thrombus had a pro-proteolytic and proinflammatory effect on the underlying media. Active smoking resulted in increased inflammation, oxidative stress, and angiogenesis in all tissues and enriched lipid metabolism in adventitia. Processes enriched with active smoking in control aortas overlapped to a high extent with those differentially expressed between AAAs and controls. The AAA diameter growth rate (n=24) correlated with T- and B-cell expression in media, as well as lipid-related processes in the adventitia.

CONCLUSIONS

This tunica-specific analysis of gene expression in a large study enabled the detection of features not previously described in AAA disease. Smoking was associated with increased expression of aneurysm-related processes, of which adaptive immunity and lipid metabolism correlated with growth rate.

Beyond Genes: Re-Identifiability of Proteomic Data and Its Implications for Personalized Medicine

The increasing availability of high throughput proteomics data provides us with opportunities as well as posing new ethical challenges regarding data privacy and re-identifiability of participants. Moreover, the fact that proteomics represents a level between the genotype and the phenotype further exacerbates the situation, introducing dilemmas related to publicly available data, anonymization, ownership of information and incidental findings. In this paper, we try to differentiate proteomics from genomics data and cover the ethical challenges related to proteomics data sharing. Finally, we give an overview of the proposed solutions and the outlook for future studies.