Integrated Multiomics Approach to Identify Genetic Underpinnings of Heart Failure and Its Echocardiographic Precursors

Novel Identification of Ankyrin-R in Cardiac Fibroblasts and a Potential Role in Heart Failure

Altered ankyrin-R (AnkR; encoded by ANK1) expression is associated with diastolic function, left ventricular remodeling, and heart failure with preserved ejection fraction (HFpEF). First identified in erythrocytes, the role of AnkR in other tissues, particularly the heart, is less studied. Here, we identified the expression of both canonical and small isoforms of AnkR in the mouse myocardium. We demonstrate that cardiac myocytes primarily express small AnkR (sAnkR), whereas cardiac fibroblasts predominantly express canonical AnkR. As canonical AnkR expression in cardiac fibroblasts is unstudied, we focused on expression and localization in these cells. AnkR is expressed in both the perinuclear and cytoplasmic regions of fibroblasts with considerable overlap with the trans-Golgi network protein 38, TGN38, suggesting a potential role in trafficking. To study the role of AnkR in fibroblasts, we generated mice lacking AnkR in activated fibroblasts (Ank1-ifKO mice). Notably, Ank1-ifKO mice fibroblasts displayed reduced collagen compaction, supportive of a novel role of AnkR in normal fibroblast function. At the whole animal level, in response to a heart failure model, Ank1-ifKO mice displayed an increase in fibrosis and T-wave inversion compared with littermate controls, while preserving cardiac ejection fraction. Collagen type I fibers were decreased in the Ank1-ifKO mice, suggesting a novel function of AnkR in the maturation of collagen fibers. In summary, our findings illustrate the novel expression of AnkR in cardiac fibroblasts and a potential role in cardiac function in response to stress.

ELM2-SANT Domain-Containing Scaffolding Protein 1 Regulates Differentiation and Maturation of Cardiomyocytes Derived From Human-Induced Pluripotent Stem Cells

BACKGROUND

ELMSAN1 (ELM2-SANT domain-containing scaffolding protein 1) is a newly identified scaffolding protein of the MiDAC (mitotic deacetylase complex), playing a pivotal role in early embryonic development. Studies on Elmsan1 knockout mice showed that its absence results in embryo lethality and heart malformation. However, the precise function of ELMSAN1 in heart development and formation remains elusive. To study its potential role in cardiac lineage, we employed human-induced pluripotent stem cells (hiPSCs) to model early cardiogenesis and investigated the function of ELMSAN1.

METHODS AND RESULTS

We generated ELMSAN1-deficient hiPSCs through knockdown and knockout techniques. During cardiac differentiation, ELMSAN1 depletion inhibited pluripotency deactivation, decreased the expression of cardiac-specific markers, and reduced differentiation efficiency. The impaired expression of genes associated with contractile sarcomere structure, calcium handling, and ion channels was also noted in ELMSAN1-deficient cardiomyocytes derived from hiPSCs. Additionally, through a series of structural and functional assessments, we found that ELMSAN1-null hiPSC cardiomyocytes are immature, exhibiting incomplete sarcomere Z-line structure, decreased calcium handling, and impaired electrophysiological properties. Of note, we found that the cardiac-specific role of ELMSAN1 is likely associated with histone H3K27 acetylation level. The transcriptome analysis provided additional insights, indicating maturation reduction with the energy metabolism switch and restored cell proliferation in ELMSAN1 knockout cardiomyocytes.

CONCLUSIONS

In this study, we address the significance of the direct involvement of ELMSAN1 in the differentiation and maturation of hiPSC cardiomyocytes. We first report the impact of ELMSAN1 on multiple aspects of hiPSC cardiomyocyte generation, including cardiac differentiation, sarcomere formation, calcium handling, electrophysiological maturation, and proliferation.

Using Omics to Identify Novel Therapeutic Targets in Heart Failure

Omics refers to the measurement and analysis of the totality of molecules or biological processes involved within an organism. Examples of omics data include genomics, transcriptomics, epigenomics, proteomics, metabolomics, and more. In this review, we present the available literature reporting omics data on heart failure that can inform the development of novel treatments or innovative treatment strategies for this disease. This includes polygenic risk scores to improve prediction of genomic data and the potential of multiomics to more efficiently identify potential treatment targets for further study. We also discuss the limitations of omic analyses and the barriers that must be overcome to maximize the utility of these types of studies. Finally, we address the current state of the field and future opportunities for using multiomics to better personalize heart failure treatment strategies.

Exploring machine learning strategies for predicting cardiovascular disease risk factors from multi-omic data

BACKGROUND

Machine learning (ML) classifiers are increasingly used for predicting cardiovascular disease (CVD) and related risk factors using omics data, although these outcomes often exhibit categorical nature and class imbalances. However, little is known about which ML classifier, omics data, or upstream dimension reduction strategy has the strongest influence on prediction quality in such settings. Our study aimed to illustrate and compare different machine learning strategies to predict CVD risk factors under different scenarios.

METHODS

We compared the use of six ML classifiers in predicting CVD risk factors using blood-derived metabolomics, epigenetics and transcriptomics data. Upstream omic dimension reduction was performed using either unsupervised or semi-supervised autoencoders, whose downstream ML classifier performance we compared. CVD risk factors included systolic and diastolic blood pressure measurements and ultrasound-based biomarkers of left ventricular diastolic dysfunction (LVDD; E/e' ratio, E/A ratio, LAVI) collected from 1,249 Finnish participants, of which 80% were used for model fitting. We predicted individuals with low, high or average levels of CVD risk factors, the latter class being the most common. We constructed multi-omic predictions using a meta-learner that weighted single-omic predictions. Model performance comparisons were based on the F1 score. Finally, we investigated whether learned omic representations from pre-trained semi-supervised autoencoders could improve outcome prediction in an external cohort using transfer learning.

RESULTS

Depending on the ML classifier or omic used, the quality of single-omic predictions varied. Multi-omics predictions outperformed single-omics predictions in most cases, particularly in the prediction of individuals with high or low CVD risk factor levels. Semi-supervised autoencoders improved downstream predictions compared to the use of unsupervised autoencoders. In addition, median gains in Area Under the Curve by transfer learning compared to modelling from scratch ranged from 0.09 to 0.14 and 0.07 to 0.11 units for transcriptomic and metabolomic data, respectively.

CONCLUSIONS

By illustrating the use of different machine learning strategies in different scenarios, our study provides a platform for researchers to evaluate how the choice of omics, ML classifiers, and dimension reduction can influence the quality of CVD risk factor predictions.

Integrated omics analysis of coronary artery calcifications and myocardial infarction: the Framingham Heart Study

Gene function can be described using various measures. We integrated association studies of three types of omics data to provide insights into the pathophysiology of subclinical coronary disease and myocardial infarction (MI). Using multivariable regression models, we associated: (1) single nucleotide polymorphism, (2) DNA methylation, and (3) gene expression with coronary artery calcification (CAC) scores and MI. Among 3106 participants of the Framingham Heart Study, 65 (2.1%) had prevalent MI and 60 (1.9%) had incident MI, median CAC value was 67.8 [IQR 10.8, 274.9], and 1403 (45.2%) had CAC scores > 0 (prevalent CAC). Prevalent CAC was associated with AHRR (linked to smoking) and EXOC3 (affecting platelet function and promoting hemostasis). CAC score was associated with VWA1 (extracellular matrix protein associated with cartilage structure in endomysium). For prevalent MI we identified FYTTD1 (down-regulated in familial hypercholesterolemia) and PINK1 (linked to cardiac tissue homeostasis and ischemia-reperfusion injury). Incident MI was associated with IRX3 (enhancing browning of white adipose tissue) and STXBP3 (controlling trafficking of glucose transporter type 4 to plasma). Using an integrative trans-omics approach, we identified both putatively novel and known candidate genes associated with CAC and MI. Replication of findings is warranted.

Association of rs35006907 Polymorphism with Risk of Dilated Cardiomyopathy in Han Chinese Population

Background

Several investigations have demonstrated the association of MTSS1 with left ventricular (LV) structure and function. A recently published study has even revealed that rs35006907 was associated with both MTSS1 expression and the risk of dilated cardiomyopathy (DCM).

Objective

Our study intended to investigate the relationship between rs35006907 and the risk of DCM in the Han Chinese population.

Methods

A total of 529 DCM and 600 healthy controls were recruited. We conducted genotyping for rs35006907 in all participants. Gene association studies were performed to assess the association between rs35006907 and the risk of DCM. A series of functional assays including western blot, realtime PCR and firefly luciferase reporter gene assays were conducted to illuminate the underlying mechanism.

Results

We found that rs35006907-A allele was significantly associated with reduced risk of DCM in additive (p= 0.004; OR=0.78; 95% CI=0.66-0.93) and recessive models (p= 0.0005; OR=0.56; 95%CI=0.41-0.78) when compared with the rs35006907-C allele. There were significant differences in the left ventricular end-diastolic diameter (LVEDD) and left ventricular ejection fraction (LVEF) between rs35006907-CC/AC and AA genotypes. Furthermore, the variant rs35006907-A allele presented lower reporter gene activity, reduced mRNA and protein expression levels when compared with the C allele.

Conclusions

Our findings demonstrated that rs35006907-C allele increased the risk of DCM in Han Chinese population. Besides, rs35006907-C displayed higher reporter gene activity and increased MTSS1 expression in human samples.

DNA methylation and cardiovascular disease in humans: a systematic review and database of known CpG methylation sites

BACKGROUND

Cardiovascular disease (CVD) is the leading cause of death worldwide and considered one of the most environmentally driven diseases. The role of DNA methylation in response to the individual exposure for the development and progression of CVD is still poorly understood and a synthesis of the evidence is lacking.

RESULTS

A systematic review of articles examining measurements of DNA cytosine methylation in CVD was conducted in accordance with PRISMA (preferred reporting items for systematic reviews and meta-analyses) guidelines. The search yielded 5,563 articles from PubMed and CENTRAL databases. From 99 studies with a total of 87,827 individuals eligible for analysis, a database was created combining all CpG-, gene- and study-related information. It contains 74,580 unique CpG sites, of which 1452 CpG sites were mentioned in ≥ 2, and 441 CpG sites in ≥ 3 publications. Two sites were referenced in ≥ 6 publications: cg01656216 (near ZNF438) related to vascular disease and epigenetic age, and cg03636183 (near F2RL3) related to coronary heart disease, myocardial infarction, smoking and air pollution. Of 19,127 mapped genes, 5,807 were reported in ≥ 2 studies. Most frequently reported were TEAD1 (TEA Domain Transcription Factor 1) and PTPRN2 (Protein Tyrosine Phosphatase Receptor Type N2) in association with outcomes ranging from vascular to cardiac disease. Gene set enrichment analysis of 4,532 overlapping genes revealed enrichment for Gene Ontology molecular function "DNA-binding transcription activator activity" (q = 1.65 × 10^-11) and biological processes "skeletal system development" (q = 1.89 × 10^-23). Gene enrichment demonstrated that general CVD-related terms are shared, while "heart" and "vasculature" specific genes have more disease-specific terms as PR interval for "heart" or platelet distribution width for "vasculature." STRING analysis revealed significant protein-protein interactions between the products of the differentially methylated genes (p = 0.003) suggesting that dysregulation of the protein interaction network could contribute to CVD. Overlaps with curated gene sets from the Molecular Signatures Database showed enrichment of genes in hemostasis (p = 2.9 × 10^-6) and atherosclerosis (p = 4.9 × 10^-4).

CONCLUSION

This review highlights the current state of knowledge on significant relationship between DNA methylation and CVD in humans. An open-access database has been compiled of reported CpG methylation sites, genes and pathways that may play an important role in this relationship.

Rewiring of 3D Chromatin Topology Orchestrates Transcriptional Reprogramming and the Development of Human Dilated Cardiomyopathy

BACKGROUND

Transcriptional reconfiguration is central to heart failure, the most common cause of which is dilated cardiomyopathy (DCM). The effect of 3-dimensional chromatin topology on transcriptional dysregulation and pathogenesis in human DCM remains elusive.

METHODS

We generated a compendium of 3-dimensional epigenome and transcriptome maps from 101 biobanked human DCM and nonfailing heart tissues through highly integrative chromatin immunoprecipitation (H3K27ac [acetylation of lysine 27 on histone H3]), in situ high-throughput chromosome conformation capture, chromatin immunoprecipitation sequencing, assay for transposase-accessible chromatin using sequencing, and RNA sequencing. We used human induced pluripotent stem cell-derived cardiomyocytes and mouse models to interrogate the key transcription factor implicated in 3-dimensional chromatin organization and transcriptional regulation in DCM pathogenesis.

RESULTS

We discovered that the active regulatory elements (H3K27ac peaks) and their connectome (H3K27ac loops) were extensively reprogrammed in DCM hearts and contributed to transcriptional dysregulation implicated in DCM development. For example, we identified that nontranscribing NPPA-AS1 (natriuretic peptide A antisense RNA 1) promoter functions as an enhancer and physically interacts with the NPPA (natriuretic peptide A) and NPPB (natriuretic peptide B) promoters, leading to the cotranscription of NPPA and NPPB in DCM hearts. We revealed that DCM-enriched H3K27ac loops largely resided in conserved high-order chromatin architectures (compartments, topologically associating domains) and their anchors unexpectedly had equivalent chromatin accessibility. We discovered that the DCM-enriched H3K27ac loop anchors exhibited a strong enrichment for HAND1 (heart and neural crest derivatives expressed 1), a key transcription factor involved in early cardiogenesis. In line with this, its protein expression was upregulated in human DCM and mouse failing hearts. To further validate whether HAND1 is a causal driver for the reprogramming of enhancer-promoter connectome in DCM hearts, we performed comprehensive 3-dimensional epigenome mappings in human induced pluripotent stem cell-derived cardiomyocytes. We found that forced overexpression of HAND1 in human induced pluripotent stem cell-derived cardiomyocytes induced a distinct gain of enhancer-promoter connectivity and correspondingly increased the expression of their connected genes implicated in DCM pathogenesis, thus recapitulating the transcriptional signature in human DCM hearts. Electrophysiology analysis demonstrated that forced overexpression of HAND1 in human induced pluripotent stem cell-derived cardiomyocytes induced abnormal calcium handling. Furthermore, cardiomyocyte-specific overexpression of Hand1 in the mouse hearts resulted in dilated cardiac remodeling with impaired contractility/Ca2+ handling in cardiomyocytes, increased ratio of heart weight/body weight, and compromised cardiac function, which were ascribed to recapitulation of transcriptional reprogramming in DCM.

CONCLUSIONS

This study provided novel chromatin topology insights into DCM pathogenesis and illustrated a model whereby a single transcription factor (HAND1) reprograms the genome-wide enhancer-promoter connectome to drive DCM pathogenesis.

Comprehensive Landscape of Modules-Dysregulated Functions Reveals a Profound Role of ceRNAs in Coronary Heart Disease

Coronary heart disease (CHD) is one of the most common severe cardiovascular diseases. Competitive endogenous RNAs (ceRNA) play critical roles in complex diseases. However, our understanding of the dysregulated functions of ceRNAs in CHD remains limited. Here, we systematically analyzed the alterations of ceRNAs and identified the specific functions based on dysregulated modules from the ceRNA network. A total of 2457 significantly differential expressed genes and 212 differential expressed lncRNAs were identified. We got 76679 regulator relationship between different expression genes and miRNAs and 336 regulator relationship between differential expressed lncRNAs and miRNAs. We constructed the ceRNA network and selected five dysregulated modules. Furthermore, CHD specific functions based on dysregulated modules from the ceRNA network were identified, including histone acetylation, platelet degranulation, cAMP-dependent protein kinase complex, xenobiotic transport and so on. Our results will provide novel insight for a better understanding of the mechanism of ceRNAs and facilitate the identification of novel diagnostic and therapeutic biomarkers in CHD.

Progress and Research Priorities in Imaging Genomics for Heart and Lung Disease: Summary of an NHLBI Workshop

Imaging genomics is a rapidly evolving field that combines state-of-the-art bioimaging with genomic information to resolve phenotypic heterogeneity associated with genomic variation, improve risk prediction, discover prevention approaches, and enable precision diagnosis and treatment. Contemporary bioimaging methods provide exceptional resolution generating discrete and quantitative high-dimensional phenotypes for genomics investigation. Despite substantial progress in combining high-dimensional bioimaging and genomic data, methods for imaging genomics are evolving. Recognizing the potential impact of imaging genomics on the study of heart and lung disease, the National Heart, Lung, and Blood Institute convened a workshop to review cutting-edge approaches and methodologies in imaging genomics studies, and to establish research priorities for future investigation. This report summarizes the presentations and discussions at the workshop. In particular, we highlight the need for increased availability of imaging genomics data in diverse populations, dedicated focus on less common conditions, and centralization of efforts around specific disease areas.

Identification of transcriptomic signatures and crucial pathways involved in non-alcoholic steatohepatitis

PURPOSE

Our study aimed to uncover the crucial genes and functional pathways involved in the development of non-alcoholic steatohepatitis (NASH).

METHODS

Liver transcriptome datasets were integrated with Robust rank aggregation (RRA) method, and transcriptomic signatures for NASH progression and fibrosis severity in NAFLD were developed. The functions of transcriptomic signatures were explored by multiple bioinformatic analyses, and their diagnostic role was also evaluated.

RESULTS

RRA analyses of 12 transcriptome datasets comparing NASH with non-alcoholic fatty liver (NAFL) identified 116 abnormally up-regulated genes in NASH patients. RRA analyses of five transcriptome datasets focusing fibrosis severity identified 78 abnormally up-regulated genes in NAFLD patients with advanced fibrosis. The functions of those transcriptomic signatures of NASH development or fibrosis progression were similar, and were both characterized by extracellular matrix (ECM)-related pathways (Adjusted P < 0.05). The transcriptomic signatures could effectively differentiate NASH from NAFL, and could help to identify NAFLD patients with advanced fibrosis. Gene set enrichment analysis and weighted gene co-expression network analysis further validated the key role of ECM-related pathways in NASH development. The top 10 up-regulated genes in NASH patients were SPP1, FBLN5, CHI3L1, CCL20, CD24, FABP4, GPNMB, VCAN, EFEMP1, and CXCL10, and their functions were mainly related to either ECM-related pathways or immunity-related pathways. Single cell RNA-sequencing analyses revealed that those crucial genes were expressed by distinct cells such as hepatocytes, macrophages, and hepatic stellate cells.

CONCLUSIONS

Transcriptomic signatures related to NASH development and fibrosis severity of NAFLD patients are both characterized by ECM-related pathways, and fibrosis is a main player during NASH progression. This study uncovers some novel key genes involved in NASH progression, which may be promising therapeutic targets.

Framingham Heart Study: JACC Focus Seminar, 1/8

The Framingham Heart Study is the longest-running cardiovascular epidemiological study, starting in 1948. This paper gives an overview of the various cohorts, collected data, and most important research findings to date. In brief, the Framingham Heart Study, funded by the National Institutes of Health and managed by Boston University, spans 3 generations of well phenotyped White persons and 2 cohorts comprised of racial and ethnic minority groups. These cohorts are densely phenotyped, with extensive longitudinal follow-up, and they continue to provide us with important information on human cardiovascular and noncardiovascular physiology over the lifespan, as well as to identify major risk factors for cardiovascular disease. This paper also summarizes some of the more recent progress in molecular epidemiology and discusses the future of the study.

Cardiovascular disease risk and pathophysiology in South Asians: can longitudinal multi-omics shed light?

Cardiovascular disease (CVD) is the leading cause of mortality in South Asia, with rapidly increasing prevalence of hypertension, type 2 diabetes (T2DM) and hyperlipidemia over the last two decades. Atherosclerotic CVD (ASCVD) affects South Asians earlier in life and at lower body weights, which is not fully explained by differential burden of conventional risk factors. Heart failure (HF) is a complex clinical syndrome of heterogeneous structural phenotypes including two major clinical subtypes, HF with preserved (HFpEF) and reduced ejection fraction (HFrEF). The prevalence of HF in South Asians is also rising with other metabolic diseases, and HFpEF develops at younger age and leaner body mass index in South Asians than in Whites. Recent genome-wide association studies, epigenome-wide association studies and metabolomic studies of ASCVD and HF have identified genes, metabolites and pathways associated with CVD traits. However, these findings were mostly driven by samples of European ancestry, which may not accurately represent the CVD risk at the molecular level, and the unique risk profile of CVD in South Asians. Such bias, while formulating hypothesis-driven research studies, risks missing important causal or predictive factors unique to South Asians. Importantly, a longitudinal design of multi-omic markers can capture the life-course risk and natural history related to CVD, and partially disentangle putative causal relationship between risk factors, multi-omic markers and subclinical and clinical ASCVD and HF. In conclusion, combining high-resolution untargeted metabolomics with epigenomics of rigorous, longitudinal design will provide comprehensive unbiased molecular characterization of subclinical and clinical CVD among South Asians. A thorough understanding of CVD-associated metabolomic profiles, together with advances in epigenomics and genomics, will lead to more accurate estimates of CVD progression and stimulate new strategies for improving cardiovascular health.

Multiomics, virtual reality and artificial intelligence in heart failure

Aim: Multiomics delivers more biological insight than targeted investigations. We applied multiomics to patients with heart failure (HF) and reduced ejection fraction (HFrEF), with machine learning applied to advanced ECG (AECG) and echocardiography artificial intelligence (Echo AI). Patients & methods: In total, 46 patients with HFrEF and 20 controls underwent metabolomic profiling, including liquid/gas chromatography-mass spectrometry and solid-phase microextraction volatilomics in plasma and urine. HFrEF was defined using left ventricular (LV) global longitudinal strain, EF and N-terminal pro hormone BNP. AECG and Echo AI were performed over 5 min, with a subset of patients undergoing a virtual reality mental stress test. Results: A-ECG had similar diagnostic accuracy as N-terminal pro hormone BNP for HFrEF (area under the curve = 0.95, 95% CI: 0.85-0.99), and correlated with global longitudinal strain (r = -0.77, p < 0.0001), while Echo AI-generated measurements correlated well with manually measured LV end diastolic volume r = 0.77, LV end systolic volume r = 0.8, LVEF r = 0.71, indexed left atrium volume r = 0.71 and indexed LV mass r = 0.6, p < 0.005. AI-LVEF and other HFrEF biomarkers had a similar discrimination for HFrEF (area under the curve AI-LVEF = 0.88; 95% CI: -0.03 to 0.15; p = 0.19). Virtual reality mental stress test elicited arrhythmic biomarkers on AECG and indicated blunted autonomic responsiveness (alpha 2 of RR interval variability, p = 1 × 10^-4) in HFrEF. Conclusion: Multiomics-related machine learning shows promise for the assessment of HF.

Association study of hypertension susceptibility genes ITGA9, MOV10, and CACNB2 with preeclampsia in Chinese Han population

OBJECTIVE

Preeclampsia (PE) is a disorder that occurs during the pregnancy and could affect the maternal and perinatal mortality as well as morbidity. The aim of our study is to investigate the associations between the hypertension susceptibility genes ITGA9, MOV10 and CACNB2 with PE in Chinese Han population.

METHODS

A case-control study including 178 PE patients and 202 healthy controls was conducted to assess the associations between three loci (ITGA9 rs155524, MOV10 rs2932538 and CACNB2 rs4373814) and PE. The TaqMan probe assay was applied for genotyping in our study. Quantitative real-time PCR was performed to detect the mRNA expression levels of ITGA9, MOV10 and CACNB2. ELISA was carried out to detect the concentration of serum sFlt-1 or PLGF.

RESULTS

Our study detected no significant differences in allelic frequencies of three SNPs between PE patients and healthy controls. In the genetic model, the results showed that the patients with ITGA9 rs155524 GA or AA genotypes had a higher risk of PE development compared to those with GG genotype in codominant model. And PE patients had a higher frequency of GA + AA genotypes based on the dominant model. Subgroup analysis showed ITGA9 rs155524 was associated with early-onset PE but not with late-onset PE. No association was observed between MOV10 and CACNB2 with PE in any genetic model and subgroup analysis. Quantitative real-time PCR results showed that ITGA9 mRNA expression level was apparently increased in the placental tissues of PE patients. In addition, ITGA9 expression levels of GA + AA subjects were apparently higher than that in the genotype GG of placental tissues. sFlt-1/PLGF ratio was higher in GA + AA subjects than that in GG subjects. Regression analysis revealed that ratio of sFlt-1/PLGF was positively correlated with ITGA9 mRNA expression level.

CONCLUSION

This study has identified ITGA9 is a promising candidate susceptibility gene for early-onset PE. Our findings demonstrated that the high expression of ITGA9 might be associated with an increased risk of PE.

Integration of gene expression and DNA methylation identifies epigenetically controlled modules related to PM2.5 exposure

Air pollution has been associated with adverse health effects across the life-course. Although underlying mechanisms are unclear, several studies suggested pollutant-induced changes in transcriptomic profiles. In this meta-analysis of transcriptome-wide association studies of 656 children and adolescents from three European cohorts participating in the MeDALL Consortium, we found two differentially expressed transcript clusters (FDR p < 0.05) associated with exposure to particulate matter < 2.5 µm in diameter (PM_2.5) at birth, one of them mapping to the MIR1296 gene. Further, by integrating gene expression with DNA methylation using Functional Epigenetic Modules algorithms, we identified 9 and 6 modules in relation to PM_2.5 exposure at birth and at current address, respectively (including NR1I2, MAPK6, TAF8 and SCARA3). In conclusion, PM_2.5 exposure at birth was linked to differential gene expression in children and adolescents. Importantly, we identified several significant interactome hotspots of gene modules of relevance for complex diseases in relation to PM_2.5 exposure.

The Human Explanted Heart Program: A translational bridge for cardiovascular medicine

The progression of cardiovascular research is often impeded by the lack of reliable disease models that fully recapitulate the pathogenesis in humans. These limitations apply to both in vitro models such as cell-based cultures and in vivo animal models which invariably are limited to simulate the complexity of cardiovascular disease in humans. Implementing human heart tissue in cardiovascular research complements our research strategy using preclinical models. We established the Human Explanted Heart Program (HELP) which integrates clinical, tissue and molecular phenotyping thereby providing a comprehensive evaluation into human heart disease. Our collection and storage of biospecimens allow them to retain key pathogenic findings while providing novel insights into human heart failure. The use of human non-failing control explanted hearts provides a valuable comparison group for the diseased explanted hearts. Using HELP we have been able to create a tissue repository which have been used for genetic, molecular, cellular, and histological studies. This review describes the process of collection and use of explanted human heart specimens encompassing a spectrum of pediatric and adult heart diseases, while highlighting the role of these invaluable specimens in translational research. Furthermore, we highlight the efficient procurement and bio-preservation approaches ensuring analytical quality of heart specimens acquired in the context of heart donation and transplantation.

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.