Integrative Functional Annotation of 52 Genetic Loci Influencing Myocardial Mass Identifies Candidate Regulatory Variants and Target Genes

Histone Modifications and miRNA Perturbations Contribute to Transcriptional Dysregulation of Hypertrophy in Obstructive Hypertrophic Cardiomyopathy

Background

Recently, we demonstrated transcriptional downregulation of hypertrophy pathways in myectomy tissue derived from patients with obstructive hypertrophic cardiomyopathy (HCM) despite translational activation of hypertrophy pathways. The mechanisms and modifiers of this transcriptional dysregulation in HCM remain unexplored. We hypothesized that miRNA and post-translational modifications of histones contribute to transcriptional dysregulation in HCM.

Methods

First, miRNA-sequencing and chromatin immunoprecipitation sequencing (ChIP-seq) were performed on HCM myectomy tissue and control donor hearts to characterize miRNA and differential histone marks across the genome. Next, the differential miRNA and histone marks were integrated with RNA-sequencing (RNA-seq) data. Finally, the effects of miRNA and histones were removed in silico to determine their necessity for transcriptional dysregulation of pathways.

Results

miRNA-analysis identified 19 differentially expressed miRNA. ChIP-seq analysis identified 2,912 (7%) differential H3K4me3 peaks, 23,339 (21%) differential H3K9ac peaks, 33 (0.05%) differential H3K9me3 peaks, 58,837 (42%) differential H3K27ac peaks, and 853 (3%) differential H3K27me3 peaks. Univariate analysis of concordance between H3K9ac with RNA-seq data showed activation of cardiac hypertrophy signaling, while H3K27me showed downregulation of cardiac hypertrophy signaling. Similarly, miRNAs were predicted to result in downregulation of cardiac hypertrophy signaling. In silico knock-out that effects either miRNA or histones attenuated transcriptional downregulation while knocking out both abolished downregulation of hypertrophy pathways completely.

Conclusion

Myectomy tissue from patients with obstructive HCM shows transcriptional dysregulation, including transcriptional downregulation of hypertrophy pathways mediated by miRNA and post-translational modifications of histones. Cardiac hypertrophy loci showed activation via changes in H3K9ac and a mix of activation and repression via H3K27ac.

Molecular mechanisms contributing to the development of beef sensory texture and flavour traits and related biomarkers: Insights from early post-mortem muscle using label-free proteomics

Beef sensory quality comprises a suite of traits, each of which manifests its ultimate phenotype through interaction of muscle physiology with environment, both in vivo and post-mortem. Understanding variability in meat quality remains a persistent challenge, but omics studies to uncover biological connections between natural variability in proteome and phenotype could provide validation for exploratory studies and offer new insights. Multivariate analysis of proteome and meat quality data from Longissimus thoracis et lumborum muscle samples taken early post-mortem from 34 Limousin-sired bulls was conducted. Using for the first-time label-free shotgun proteomics combined with liquid chromatography-tandem mass spectrometry (LC-MS/MS), 85 proteins were found to be related with tenderness, chewiness, stringiness and flavour sensory traits. The putative biomarkers were classified in five interconnected biological pathways; i) muscle contraction, ii) energy metabolism, iii) heat shock proteins, iv) oxidative stress, v) regulation of cellular processes and binding. Among the proteins, PHKA1 and STBD1 correlated with all four traits, as did the GO biological process 'generation of precursor metabolites and energy'. Optimal regression models explained a high level (58-71%) of phenotypic variability with proteomic data for each quality trait. The results of this study propose several regression equations and biomarkers to explain the variability of multiple beef eating quality traits. Thanks to annotation and network analyses, they further suggest protein interactions and mechanisms underpinning the physiological processes regulating these key quality traits. SIGNIFICANCE: The proteomic profiles of animals with divergent quality profiles have been compared in numerous studies; however, a wide range of phenotypic variation is required to better understand the mechanisms underpinning the complex biological pathways correlated with beef quality and protein interactions. We used multivariate regression analyses and bioinformatics to analyse shotgun proteomics data to decipher the molecular signatures involved in beef texture and flavour variations with a focus on multiple quality traits. We developed multiple regression equations to explain beef texture and flavour. Additionally, potential candidate biomarkers correlated with multiple beef quality traits are suggested, which could have utility as indicators of beef overall sensory quality. This study explained the biological process responsible for determining key quality traits such as tenderness, chewiness, stringiness, and flavour in beef, which will provide support for future beef proteomics studies.

Panomics: New Databases for Advancing Cardiology

The multifactorial nature of cardiology makes it challenging to separate noisy signals from confounders and real markers or drivers of disease. Panomics, the combination of various omic methods, provides the deepest insights into the underlying biological mechanisms to develop tools for personalized medicine under a systems biology approach. Questions remain about current findings and anticipated developments of omics. Here, we search for omic databases, investigate the types of data they provide, and give some examples of panomic applications in health care. We identified 104 omic databases, of which 72 met the inclusion criteria: genomic and clinical measurements on a subset of the database population plus one or more omic datasets. Of those, 65 were methylomic, 59 transcriptomic, 41 proteomic, 42 metabolomic, and 22 microbiomic databases. Larger database sample sizes and longer follow-up are often better suited for panomic analyses due to statistical power calculations. They are often more complete, which is important when dealing with large biological variability. Thus, the UK BioBank rises as the most comprehensive panomic resource, at present, but certain study designs may benefit from other databases.

A Proteomic Study for the Discovery of Beef Tenderness Biomarkers and Prediction of Warner-Bratzler Shear Force Measured on Longissimus thoracis Muscles of Young Limousin-Sired Bulls

Beef tenderness is of central importance in determining consumers’ overall liking. To better understand the underlying mechanisms of tenderness and be able to predict it, this study aimed to apply a proteomics approach on the Longissimus thoracis (LT) muscle of young Limousin-sired bulls to identify candidate protein biomarkers. A total of 34 proteins showed differential abundance between the tender and tough groups. These proteins belong to biological pathways related to muscle structure, energy metabolism, heat shock proteins, response to oxidative stress, and apoptosis. Twenty-three putative protein biomarkers or their isoforms had previously been identified as beef tenderness biomarkers, while eleven were novel. Using regression analysis to predict shear force values, MYOZ3 (Myozenin 3), BIN1 (Bridging Integrator-1), and OGN (Mimecan) were the major proteins retained in the regression model, together explaining 79% of the variability. The results of this study confirmed the existing knowledge but also offered new insights enriching the previous biomarkers of tenderness proposed for Longissimus muscle.

Multi-omics integration identifies key upstream regulators of pathomechanisms in hypertrophic cardiomyopathy due to truncating MYBPC3 mutations

BACKGROUND

Hypertrophic cardiomyopathy (HCM) is the most common genetic disease of the cardiac muscle, frequently caused by mutations in MYBPC3. However, little is known about the upstream pathways and key regulators causing the disease. Therefore, we employed a multi-omics approach to study the pathomechanisms underlying HCM comparing patient hearts harboring MYBPC3 mutations to control hearts.

RESULTS

Using H3K27ac ChIP-seq and RNA-seq we obtained 9310 differentially acetylated regions and 2033 differentially expressed genes, respectively, between 13 HCM and 10 control hearts. We obtained 441 differentially expressed proteins between 11 HCM and 8 control hearts using proteomics. By integrating multi-omics datasets, we identified a set of DNA regions and genes that differentiate HCM from control hearts and 53 protein-coding genes as the major contributors. This comprehensive analysis consistently points toward altered extracellular matrix formation, muscle contraction, and metabolism. Therefore, we studied enriched transcription factor (TF) binding motifs and identified 9 motif-encoded TFs, including KLF15, ETV4, AR, CLOCK, ETS2, GATA5, MEIS1, RXRA, and ZFX. Selected candidates were examined in stem cell-derived cardiomyocytes with and without mutated MYBPC3. Furthermore, we observed an abundance of acetylation signals and transcripts derived from cardiomyocytes compared to non-myocyte populations.

CONCLUSIONS

By integrating histone acetylome, transcriptome, and proteome profiles, we identified major effector genes and protein networks that drive the pathological changes in HCM with mutated MYBPC3. Our work identifies 38 highly affected protein-coding genes as potential plasma HCM biomarkers and 9 TFs as potential upstream regulators of these pathomechanisms that may serve as possible therapeutic targets.

Epigenetic and Transcriptional Networks Underlying Atrial Fibrillation

Genome-wide association studies have uncovered over a 100 genetic loci associated with atrial fibrillation (AF), the most common arrhythmia. Many of the top AF-associated loci harbor key cardiac transcription factors, including PITX2, TBX5, PRRX1, and ZFHX3. Moreover, the vast majority of the AF-associated variants lie within noncoding regions of the genome where causal variants affect gene expression by altering the activity of transcription factors and the epigenetic state of chromatin. In this review, we discuss a transcriptional regulatory network model for AF defined by effector genes in Genome-wide association studies loci. We describe the current state of the field regarding the identification and function of AF-relevant gene regulatory networks, including variant regulatory elements, dose-sensitive transcription factor functionality, target genes, and epigenetic states. We illustrate how altered transcriptional networks may impact cardiomyocyte function and ionic currents that impact AF risk. Last, we identify the need for improved tools to identify and functionally test transcriptional components to define the links between genetic variation, epigenetic gene regulation, and atrial function.

A comparison of two workflows for regulome and transcriptome-based prioritization of genetic variants associated with myocardial mass

A typical task arising from main effect analyses in a Genome Wide Association Study (GWAS) is to identify single nucleotide polymorphisms (SNPs), in linkage disequilibrium with the observed signals, that are likely causal variants and the affected genes. The affected genes may not be those closest to associating SNPs. Functional genomics data from relevant tissues are believed to be helpful in selecting likely causal SNPs and interpreting implicated biological mechanisms, ultimately facilitating prevention and treatment in the case of a disease trait. These data are typically used post GWAS analyses to fine-map the statistically significant signals identified agnostically by testing all SNPs and applying a multiple testing correction. The number of tested SNPs is typically in the millions, so the multiple testing burden is high. Motivated by this, in this study we investigated an alternative workflow, which consists in utilizing the available functional genomics data as a first step to reduce the number of SNPs tested for association. We analyzed GWAS on electrocardiographic QRS duration using these two workflows. The alternative workflow identified more SNPs, including some residing in loci not discovered with the typical workflow. Moreover, the latter are corroborated by other reports on QRS duration. This indicates the potential value of incorporating functional genomics information at the onset in GWAS analyses.