Network-Based Identification and Prioritization of Key Regulators of Coronary Artery Disease Loci

N6-methyladenosine-associated genetic variants in NECTIN2 and HPCAL1 are risk factors for abdominal aortic aneurysm

Although N6-methyladenosine (m6A) modification has been implicated in the pathogenesis of abdominal aortic aneurysm (AAA), the relationship between m6A-associated single nucleotide polymorphisms (m6A-SNPs) and AAA remains unknown. This study used integrative multi-omics analysis and clinical validation approaches to systematically identify potential m6A-SNPs connected with AAA risk. We found that rs6859 and rs10198139 could modulate the expression of local genes, NECTIN2 and HPCAL1, respectively, which exhibited upregulation in AAA tissues, and their risk variants were significantly correlated with an increased susceptibility to AAA. Incorporating rs6859 and rs10198139 improved the efficiency of AAA risk prediction compared to the model considering only conventional risk factors. Additionally, these two SNPs were predicted to be located within the regulatory sequences, and rs6859 showed a substantial impact on m6A modification levels. Our findings suggest that m6A-SNPs rs6859 and rs10198139 confer an elevated risk of AAA, possibly by promoting local gene expression through an m6A-mediated manner.

Probiotics, gut microbiome, and cardiovascular diseases: An update

Cardiovascular diseases (CVD) are one of the most challenging diseases and many factors have been demonstrated to affect their pathogenesis. One of the major factors that affect CVDs, especially atherosclerosis, is the gut microbiota (GM). Genetics play a key role in linking CVDs with GM, in addition to some environmental factors which can be either beneficial or harmful. The interplay between GM and CVDs is complex due to the numerous mechanisms through which microbial components and their metabolites can influence CVDs. Within this interplay, the immune system plays a major role, mainly based on the immunomodulatory effects of microbial dysbiosis and its resulting metabolites. The resulting modulation of chronic inflammatory processes was found to reduce the severity of CVDs and to maintain cardiovascular health. To better understand the specific roles of GM-related metabolites in this interplay, this review presents an updated perspective on gut metabolites related effects on the cardiovascular system, highlighting the possible benefits of probiotics in therapeutic strategies.

Shared and distinct pathways and networks genetically linked to coronary artery disease between human and mouse

Mouse models have been used extensively to study human coronary artery disease (CAD) or atherosclerosis and to test therapeutic targets. However, whether mouse and human share similar genetic factors and pathogenic mechanisms of atherosclerosis has not been thoroughly investigated in a data-driven manner. We conducted a cross-species comparison study to better understand atherosclerosis pathogenesis between species by leveraging multiomics data. Specifically, we compared genetically driven and thus CAD-causal gene networks and pathways, by using human GWAS of CAD from the CARDIoGRAMplusC4D consortium and mouse GWAS of atherosclerosis from the Hybrid Mouse Diversity Panel (HMDP) followed by integration with functional multiomics human (STARNET and GTEx) and mouse (HMDP) databases. We found that mouse and human shared >75% of CAD causal pathways. Based on network topology, we then predicted key regulatory genes for both the shared pathways and species-specific pathways, which were further validated through the use of single cell data and the latest CAD GWAS. In sum, our results should serve as a much-needed guidance for which human CAD-causal pathways can or cannot be further evaluated for novel CAD therapies using mouse models.

Gene Regulatory Networks in Coronary Artery Disease

PURPOSE OF REVIEW

Coronary artery disease is a complex disorder and the leading cause of mortality worldwide. As technologies for the generation of high-throughput multiomics data have advanced, gene regulatory network modeling has become an increasingly powerful tool in understanding coronary artery disease. This review summarizes recent and novel gene regulatory network tools for bulk tissue and single cell data, existing databases for network construction, and applications of gene regulatory networks in coronary artery disease.

RECENT FINDINGS

New gene regulatory network tools can integrate multiomics data to elucidate complex disease mechanisms at unprecedented cellular and spatial resolutions. At the same time, updates to coronary artery disease expression data in existing databases have enabled researchers to build gene regulatory networks to study novel disease mechanisms. Gene regulatory networks have proven extremely useful in understanding CAD heritability beyond what is explained by GWAS loci and in identifying mechanisms and key driver genes underlying disease onset and progression. Gene regulatory networks can holistically and comprehensively address the complex nature of coronary artery disease. In this review, we discuss key algorithmic approaches to construct gene regulatory networks and highlight state-of-the-art methods that model specific modes of gene regulation. We also explore recent applications of these tools in coronary artery disease patient data repositories to understand disease heritability and shared and distinct disease mechanisms and key driver genes across tissues, between sexes, and between species.

Systems genetics approaches for understanding complex traits with relevance for human disease

Quantitative traits are often complex because of the contribution of many loci, with further complexity added by environmental factors. In medical research, systems genetics is a powerful approach for the study of complex traits, as it integrates intermediate phenotypes, such as RNA, protein, and metabolite levels, to understand molecular and physiological phenotypes linking discrete DNA sequence variation to complex clinical and physiological traits. The primary purpose of this review is to describe some of the resources and tools of systems genetics in humans and rodent models, so that researchers in many areas of biology and medicine can make use of the data.

Alterations in gut microbiota and host transcriptome of patients with coronary artery disease

BACKGROUND

Coronary artery disease (CAD) is a widespread heart condition caused by atherosclerosis and influences millions of people worldwide. Early detection of CAD is challenging due to the lack of specific biomarkers. The gut microbiota and host-microbiota interactions have been well documented to affect human health. However, investigation that reveals the role of gut microbes in CAD is still limited. This study aims to uncover the synergistic effects of host genes and gut microbes associated with CAD through integrative genomic analyses.

RESULTS

Herein, we collected 52 fecal and 50 blood samples from CAD patients and matched controls, and performed amplicon and transcriptomic sequencing on these samples, respectively. By comparing CAD patients with health controls, we found that dysregulated gut microbes were significantly associated with CAD. By leveraging the Random Forest method, we found that combining 20 bacteria and 30 gene biomarkers could distinguish CAD patients from health controls with a high performance (AUC = 0.92). We observed that there existed prominent associations of gut microbes with several clinical indices relevant to heart functions. Integration analysis revealed that CAD-relevant gut microbe genus Fusicatenibacter was associated with expression of CAD-risk genes, such as GBP2, MLKL, and CPR65, which is in line with previous evidence (Tang et al., Nat Rev Cardiol 16:137-154, 2019; Kummen et al., J Am Coll Cardiol 71:1184-1186, 2018). In addition, the upregulation of immune-related pathways in CAD patients were identified to be primarily associated with higher abundance of genus Blautia, Eubacterium, Fusicatenibacter, and Monoglobus.

CONCLUSIONS

Our results highlight that dysregulated gut microbes contribute risk to CAD by interacting with host genes. These identified microbes and interacted risk genes may have high potentials as biomarkers for CAD.

Shared and distinct pathways and networks genetically linked to coronary artery disease between human and mouse

Mouse models have been used extensively to study human coronary artery disease (CAD) or atherosclerosis and to test therapeutic targets. However, whether mouse and human share similar genetic factors and pathogenic mechanisms of atherosclerosis has not been thoroughly investigated in a data-driven manner. We conducted a cross-species comparison study to better understand atherosclerosis pathogenesis between species by leveraging multiomics data. Specifically, we compared genetically driven and thus CAD-causal gene networks and pathways, by using human GWAS of CAD from the CARDIoGRAMplusC4D consortium and mouse GWAS of atherosclerosis from the Hybrid Mouse Diversity Panel (HMDP) followed by integration with functional multiomics human (STARNET and GTEx) and mouse (HMDP) databases. We found that mouse and human shared >75% of CAD causal pathways. Based on network topology, we then predicted key regulatory genes for both the shared pathways and species-specific pathways, which were further validated through the use of single cell data and the latest CAD GWAS. In sum, our results should serve as a much-needed guidance for which human CAD-causal pathways can or cannot be further evaluated for novel CAD therapies using mouse models.

Cell-autonomous regulation of complement C3 by factor H limits macrophage efferocytosis and exacerbates atherosclerosis

Complement factor H (CFH) negatively regulates consumption of complement component 3 (C3), thereby restricting complement activation. Genetic variants in CFH predispose to chronic inflammatory disease. Here, we examined the impact of CFH on atherosclerosis development. In a mouse model of atherosclerosis, CFH deficiency limited plaque necrosis in a C3-dependent manner. Deletion of CFH in monocyte-derived inflammatory macrophages propagated uncontrolled cell-autonomous C3 consumption without downstream C5 activation and heightened efferocytotic capacity. Among leukocytes, Cfh expression was restricted to monocytes and macrophages, increased during inflammation, and coincided with the accumulation of intracellular C3. Macrophage-derived CFH was sufficient to dampen resolution of inflammation, and hematopoietic deletion of CFH in atherosclerosis-prone mice promoted lesional efferocytosis and reduced plaque size. Furthermore, we identified monocyte-derived inflammatory macrophages expressing C3 and CFH in human atherosclerotic plaques. Our findings reveal a regulatory axis wherein CFH controls intracellular C3 levels of macrophages in a cell-autonomous manner, evidencing the importance of on-site complement regulation in the pathogenesis of inflammatory diseases.

Advances and Challenges for GWAS Analysis in Cardiac Diseases: A Focus on Coronary Artery Disease (CAD)

The achievement of genome-wide association studies (GWAS) has rapidly progressed our understanding of the etiology of coronary artery disease (CAD). It unlocks new strategies to strengthen the stalling of CAD drug development. In this review, we highlighted the recent drawbacks, mainly pointing out those involved in identifying causal genes and interpreting the connections between disease pathology and risk variants. We also benchmark the novel insights into the biological mechanism behind the disease primarily based on outcomes of GWAS. Furthermore, we also shed light on the successful discovery of novel treatment targets by introducing various layers of "omics" data and applying systems genetics strategies. Lastly, we discuss in-depth the significance of precision medicine that is helpful to improve through GWAS analysis in cardiovascular research.

Lipid metabolism patterns and relevant clinical and molecular features of coronary artery disease patients: an integrated bioinformatic analysis

Background

Hyperlipidaemia is an important factor that induces coronary artery disease (CAD). This study aimed to explore the lipid metabolism patterns and relevant clinical and molecular features of coronary artery disease patients.

Methods

In the current study, datasets were fetched from the Gene Expression Omnibus (GEO) database and nonnegative matrix factorization clustering was used to establish a new CAD classification based on the gene expression profile of lipid metabolism genes. In addition, this study carried out bioinformatics analysis to explore intrinsic biological and clinical characteristics of the subgroups.

Results

Data for a total of 615 samples were extracted from the Gene Expression Omnibus database and were associated with clinical information. Then, this study used nonnegative matrix factorization clustering for RNA sequencing data of 581 lipid metabolism relevant genes, and the 296 patients with CAD were classified into three subgroups (NMF1, NMF2, and NMF3). Subjects in subgroup NMF2 tended to have an increased severity of CAD. The CAD index and age of group NMF1 were similar to those of group NMF3, but their intrinsic biological characteristics exhibited significant differences. In addition, weighted gene coexpression network analysis (WGCNA) was used to determine the most important modules and screen lipid metabolism related genes, followed by further analysis of the DEGs in which the significant genes were identified based on clinical information. The progression of coronary atherosclerosis may be influenced by genes such as PTGDS and DGKE.

Conclusion

Different CAD subgroups have their own intrinsic biological characteristics, indicating that more personalized treatment should be provided to patients in each subgroup, and some lipid metabolism related genes (PDGTS, DGKE and so on) were related significantly with clinical characteristics.

Connections for Matters of the Heart: Network Medicine in Cardiovascular Diseases

Cardiovascular diseases (CVD) are diverse disorders affecting the heart and vasculature in millions of people worldwide. Like other fields, CVD research has benefitted from the deluge of multiomics biomedical data. Current CVD research focuses on disease etiologies and mechanisms, identifying disease biomarkers, developing appropriate therapies and drugs, and stratifying patients into correct disease endotypes. Systems biology offers an alternative to traditional reductionist approaches and provides impetus for a comprehensive outlook toward diseases. As a focus area, network medicine specifically aids the translational aspect of in silico research. This review discusses the approach of network medicine and its application to CVD research.

Genetic Analysis of Coronary Artery Disease Using Tree-Based Automated Machine Learning Informed By Biology-Based Feature Selection

Machine Learning (ML) approaches are increasingly being used in biomedical applications. Important challenges of ML include choosing the right algorithm and tuning the parameters for optimal performance. Automated ML (AutoML) methods, such as Tree-based Pipeline Optimization Tool (TPOT), have been developed to take some of the guesswork out of ML thus making this technology available to users from more diverse backgrounds. The goals of this study were to assess applicability of TPOT to genomics and to identify combinations of single nucleotide polymorphisms (SNPs) associated with coronary artery disease (CAD), with a focus on genes with high likelihood of being good CAD drug targets. We leveraged public functional genomic resources to group SNPs into biologically meaningful sets to be selected by TPOT. We applied this strategy to data from the U.K. Biobank, detecting a strikingly recurrent signal stemming from a group of 28 SNPs. Importance analysis of these SNPs uncovered functional relevance of the top SNPs to genes whose association with CAD is supported in the literature and other resources. Furthermore, we employed game-theory based metrics to study SNP contributions to individual-level TPOT predictions and discover distinct clusters of well-predicted CAD cases. The latter indicates a promising approach towards precision medicine.

Vascular Tissue Specific miRNA Profiles Reveal Novel Correlations with Risk Factors in Coronary Artery Disease

Cardiovascular disease (CVD) is the leading cause of morbidity and mortality worldwide. Non-coding RNAs have already been linked to CVD development and progression. While microRNAs (miRs) have been well studied in blood samples, there is little data on tissue-specific miRs in cardiovascular relevant tissues and their relation to cardiovascular risk factors. Tissue-specific miRs derived from Arteria mammaria interna (IMA) from 192 coronary artery disease (CAD) patients undergoing coronary artery bypass grafting (CABG) were analyzed. The aims of the study were 1) to establish a reference atlas which can be utilized for identification of novel diagnostic biomarkers and potential therapeutic targets, and 2) to relate these miRs to cardiovascular risk factors. Overall, 393 individual miRs showed sufficient expression levels and passed quality control for further analysis. We identified 17 miRs-miR-10b-3p, miR-10-5p, miR-17-3p, miR-21-5p, miR-151a-5p, miR-181a-5p, miR-185-5p, miR-194-5p, miR-199a-3p, miR-199b-3p, miR-212-3p, miR-363-3p, miR-548d-5p, miR-744-5p, miR-3117-3p, miR-5683 and miR-5701-significantly correlated with cardiovascular risk factors (correlation coefficient >0.2 in both directions, p-value (p < 0.006, false discovery rate (FDR) <0.05). Of particular interest, miR-5701 was positively correlated with hypertension, hypercholesterolemia, and diabetes. In addition, we found that miR-629-5p and miR-98-5p were significantly correlated with acute myocardial infarction. We provide a first atlas of miR profiles in IMA samples from CAD patients. In perspective, these miRs might play an important role in improved risk assessment, mechanistic disease understanding and local therapy of CAD.

Using "-omics" Data to Inform Genome-wide Association Studies (GWASs) in the Osteoporosis Field

PURPOSE OF REVIEW

Osteoporosis constitutes a major societal health problem. Genome-wide association studies (GWASs) have identified over 1100 loci influencing bone mineral density (BMD); however, few of the causal genes have been identified. Here, we review approaches that use "-omics" data and genetic- and systems genetics-based analytical strategies to facilitate causal gene discovery.

RECENT FINDINGS

The bone field is beginning to adopt approaches that are commonplace in other disease disciplines. The slower progress has been due in part to the lack of large-scale "omics" data on bone and bone cells. This is however changing, and approaches such as eQTL colocalization, transcriptome-wide association studies (TWASs), network, and integrative approaches are beginning to provide significant insight into the genes responsible for BMD GWAS associations. The use of "-omics" data to inform BMD GWASs has increased in recent years, leading to the identification of novel regulators of BMD in humans. The ultimate goal will be to use this information to develop more effective therapies to treat and ultimately prevent osteoporosis.

Molecular evidence of herbal formula: a network-based analysis of Si-Wu decoction

INTRODUCTION

Emerging network pharmacology (NP) combines phytochemical information with bioinformatics tools allowing herbal formulae to be illustrated holistically in the context of phytochemical basis and therapeutic mechanisms.

OBJECTIVE

This study attempted to explore the holistic molecular evidence of herbal formula Si-Wu decoction (SWD) by using the method of NP.

MATERIAL AND METHOD

Databases of traditional medicines combined with PubChem, SciFinder, SEA, STRING, and KEGG were employed to gather information for establishing the "compound similarity" (CS) network and the "target-(pathway)-target" (TPT) network. Gephi software was applied to visualise the networks, with further module-based and node-based network topological analysis. Moreover, the approved drugs and shortest path analysis were used to validate the TPT network.

RESULTS

The CS network presented the phytochemical profile of SWD, including the major compound groups of iridoid glycosides, glycosides, phthalide lactones, phenylpropanoids, and monoterpenoids. Furthermore, the topological analysis of TPT network depicted the holistic property of SWD in interpretable neuroendocrine immunomodulation (NIM) perspective, and the node degree analysis indicated a closer connection of SWD with endocrine or metabolism system. Moreover, by combing the analysis of the CS network and TPT network, potential active ingredients could be primarily identified.

CONCLUSION

The phytochemical profile and molecular target profile, which might pave the way for an understanding of SWD in modern science and provide a reference for relevant quality research and evaluation, were demonstrated by network analysis. Moreover, the methods could be further applied to discover the phytochemical or biomolecular evidence with distinct advantages in dealing with the tremendous separated information.

APLP2 gene polymorphisms are associated with high TC and LDL-C levels in Chinese population in Xinjiang, China

Hyperlipidemia is one of the main risk factors for coronary artery disease (CAD). In the present study, we aimed to explore whether the single-nucleotide polymorphisms (SNPs) in amyloid precursor-like protein (APLP) 2 (APLP2) gene were associated with high lipid levels in Chinese population in Xinjiang, China. We recruited 1738 subjects (1187 men, 551 women) from the First Affiliated Hospital of Xinjiang Medical University, and genotyped three SNPs (rs2054247, rs3740881 and rs747180) of APLP2 gene in all subjects by using the improved multiplex ligation detection reaction (iMLDR) method. Our study revealed that the rs2054247 SNP was associated with serum total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C) levels, and high-density lipoprotein cholesterol (HDL-C) in additive model (all P<0.05). The rs747180 SNP was associated with serum TC and LDL-C levels in additive model (all P<0.05). Our study revealed that both rs2054247 and rs747180 SNPs of the APLP2 gene were associated with high TC and LDL-C levels in Chinese subjects in Xinjiang.

Transcription Factor MAFF (MAF Basic Leucine Zipper Transcription Factor F) Regulates an Atherosclerosis Relevant Network Connecting Inflammation and Cholesterol Metabolism

BACKGROUND

Coronary artery disease (CAD) is a multifactorial condition with both genetic and exogenous causes. The contribution of tissue-specific functional networks to the development of atherosclerosis remains largely unclear. The aim of this study was to identify and characterize central regulators and networks leading to atherosclerosis.

METHODS

Based on several hundred genes known to affect atherosclerosis risk in mouse (as demonstrated in knockout models) and human (as shown by genome-wide association studies), liver gene regulatory networks were modeled. The hierarchical order and regulatory directions of genes within the network were based on Bayesian prediction models, as well as experimental studies including chromatin immunoprecipitation DNA-sequencing, chromatin immunoprecipitation mass spectrometry, overexpression, small interfering RNA knockdown in mouse and human liver cells, and knockout mouse experiments. Bioinformatics and correlation analyses were used to clarify associations between central genes and CAD phenotypes in both human and mouse.

RESULTS

The transcription factor MAFF (MAF basic leucine zipper transcription factor F) interacted as a key driver of a liver network with 3 human genes at CAD genome-wide association studies loci and 11 atherosclerotic murine genes. Most importantly, expression levels of the low-density lipoprotein receptor (LDLR) gene correlated with MAFF in 600 CAD patients undergoing bypass surgery (STARNET [Stockholm-Tartu Atherosclerosis Reverse Network Engineering Task]) and a hybrid mouse diversity panel involving 105 different inbred mouse strains. Molecular mechanisms of MAFF were tested in noninflammatory conditions and showed positive correlation between MAFF and LDLR in vitro and in vivo. Interestingly, after lipopolysaccharide stimulation (inflammatory conditions), an inverse correlation between MAFF and LDLR in vitro and in vivo was observed. Chromatin immunoprecipitation mass spectrometry revealed that the human CAD genome-wide association studies candidate BACH1 (BTB domain and CNC homolog 1) assists MAFF in the presence of lipopolysaccharide stimulation with respective heterodimers binding at the MAF recognition element of the LDLR promoter to transcriptionally downregulate LDLR expression.

CONCLUSIONS

The transcription factor MAFF was identified as a novel central regulator of an atherosclerosis/CAD-relevant liver network. MAFF triggered context-specific expression of LDLR and other genes known to affect CAD risk. Our results suggest that MAFF is a missing link between inflammation, lipid and lipoprotein metabolism, and a possible treatment target.

Integrated Network and Gene Ontology Analysis Identifies Key Genes and Pathways for Coronary Artery Diseases

BACKGROUND

The prevalence of Coronary Artery Disease (CAD) in developing countries is on the rise, owing to rapidly changing lifestyle. Therefore, it is imperative that the underlying genetic and molecular mechanisms be understood to develop specific treatment strategies. Comprehensive disease network and Gene Ontology (GO) studies aid in prioritizing potential candidate genes for CAD and also give insights into gene function by establishing gene and disease pathway relationships.

METHODS

In the present study, CAD-associated genes were collated from different data sources and protein-protein interaction network was constructed using STRING. Highly interconnected network clusters were inferred and GO analysis was performed.

RESULTS

Interrelation between genes and pathways were analyzed on ClueGO and 38 candidates were identified from 1475 CAD-associated genes, which were significantly enriched in CAD-related pathways such as metabolism and regulation of lipid molecules, platelet activation, macrophage derived foam cell differentiation, and blood coagulation and fibrin clot formation.

DISCUSSION

Integrated network and ontology analysis enables biomarker prioritization for common complex diseases such as CAD. Experimental validation and future studies on the prioritized genes may reveal valuable insights into CAD development mechanism and targeted treatment strategies.

An integrative multi-omics network-based approach identifies key regulators for breast cancer

Although genome-wide association studies (GWASs) have successfully identified thousands of risk variants for human complex diseases, understanding the biological function and molecular mechanisms of the associated SNPs involved in complex diseases is challenging. Here we developed a framework named integrative multi-omics network-based approach (IMNA), aiming to identify potential key genes in regulatory networks by integrating molecular interactions across multiple biological scales, including GWAS signals, gene expression-based signatures, chromatin interactions and protein interactions from the network topology. We applied this approach to breast cancer, and prioritized key genes involved in regulatory networks. We also developed an abnormal gene expression score (AGES) signature based on the gene expression deviation of the top 20 rank-ordered genes in breast cancer. The AGES values are associated with genetic variants, tumor properties and patient survival outcomes. Among the top 20 genes, RNASEH2A was identified as a new candidate gene for breast cancer. Thus, our integrative network-based approach provides a genetic-driven framework to unveil tissue-specific interactions from multiple biological scales and reveal potential key regulatory genes for breast cancer. This approach can also be applied in other complex diseases such as ovarian cancer to unravel underlying mechanisms and help for developing therapeutic targets.

In silico identification of common and specific signatures in coronary heart diseases

Coronary heart disease (CHD) is on the increase in developing countries, where lifestyle choices such as smoking, bad diet, and no exercise contribute and increase the incidence of high blood pressure and high cholesterol levels to cause CHD. Through utilization of a biomarker-based approach for developing interventions, the aim of the study was to identify differentially expressed genes (DEGs) and their association and impact on various bio-targets. The microarray datasets of both healthy and CHD patients were analyzed to identify the DEGs and their interactions using Gene Ontology, PANTHER, Reactome, and STRING (for the possible associated genes with multiple targets). Our data mining approach suggests that the DEGs were associated with molecular functions, including protein binding (75%) and catalytic activity (56%); biological processes such as cellular process (83%), biological regulation (57%), and metabolic process (44%); and cellular components such as cell (65%) and organelle (58%); as well as other associations including apoptosis, inflammatory, cell development and metabolic pathways. The molecular functions were further analyzed, and protein binding in particular was analyzed using network analysis to determine whether there was a clear association with CHD and disease. The ingenuity pathway analysis revealed pathways related to cell cholesterol biosynthesis, the immune system including cytokinin signaling, in which, the understanding of DEGs is crucial to predict the advancement of preventive strategies. Results of the present study showed that, there is a need to validate the top DEGs to rule out their molecular mechanism in heart failure caused by CHD.

Interactions of antithrombotic herbal medicines with Western cardiovascular drugs

Thrombotic events act as a critical factor that interferes with Cardiovascular Diseases (CVDs), and antithrombotic herbal medicine is a long-standing controversial issue. Although a dispute is involved in their clinical application, all parties unanimously agree that herbal products have been widely used in folk medicine, and their interactions with conventional drugs are of high concern. This study aims to investigate how antithrombotic herbal medicines interact with Western cardiovascular drugs on the molecular level by taking an example of the most frequently used herbal pair, Danshen-Chuanxiong (DS-CX), and to discover more scientific evidence on their potential herb-drug interactions. Network pharmacology (NP), as an analytical approach of a complex system, is used to visualize and compare target profiles of DS-CX and Western cardiovascular drugs, which can be applied to predict common herb-drug targets and to construct a solid context for discussing herb-drug interactions. These interactions are further validated by in vitro assays, while in vivo zebrafish model employed for evaluating an overall pharmacological efficacy of herbal pairs in specific combination ratios. The study finds that DS could react directly to the Western cardiovascular drug targets relevant to antithrombotic pathways (i.e., thrombin, coagulation factor Xa and cyclooxygenase-1), whereas CX could not react directly and can synergistically affect antithrombotic effects with DS in specific combination ratios. Moreover, it is indicated that DS-CX may generate wide biological functions by a complicated mechanism of "neuro-immune-metabolism/endocrine" (NIM), which can further cause multiple direct and indirect interactions with Western cardiovascular drugs. From the clinical perspective, herb-drug interactions should be given high attention, especially when multiple herbs are used simultaneously.

The Integrated Landscape of Biological Candidate Causal Genes in Coronary Artery Disease

Background

Genome-wide association studies (GWASs) have identified more than 150 genetic loci that demonstrate robust association with coronary artery disease (CAD). In contrast to the success of GWAS, the translation from statistical signals to biological mechanism and exploration of causal genes for drug development remain difficult, owing to the complexity of gene regulatory and linkage disequilibrium patterns. We aim to prioritize the plausible causal genes for CAD at a genome-wide level.

Methods

We integrated the latest GWAS summary statistics with other omics data from different layers and utilized eight different computational methods to predict CAD potential causal genes. The prioritized candidate genes were further characterized by pathway enrichment analysis, tissue-specific expression analysis, and pathway crosstalk analysis.

Results

Our analysis identified 55 high-confidence causal genes for CAD, among which 15 genes (LPL, COL4A2, PLG, CDKN2B, COL4A1, FES, FLT1, FN1, IL6R, LPA, PCSK9, PSRC1, SMAD3, SWAP70, and VAMP8) ranked the highest priority because of consistent evidence from different data-driven approaches. GO analysis showed that these plausible causal genes were enriched in lipid metabolic and extracellular regions. Tissue-specific enrichment analysis revealed that these genes were significantly overexpressed in adipose and liver tissues. Further, KEGG and crosstalk analysis also revealed several key pathways involved in the pathogenesis of CAD.

Conclusion

Our study delineated the landscape of CAD potential causal genes and highlighted several biological processes involved in CAD pathogenesis. Further studies and experimental validations of these genes may shed light on mechanistic insights into CAD development and provide potential drug targets for future therapeutics.

The Brain-Skin Connection and the Pathogenesis of Psoriasis: A Review with a Focus on the Serotonergic System

Psoriasis is a common non-communicable chronic immune-mediated skin disease, affecting approximately 125 million people in the world. Its pathogenesis results from a combination of genetic and environmental factors. The pathogenesis of psoriasis seems to be driven by the interaction between innate immune cells, adaptive immune cells and keratinocytes, in a process mediated by cytokines (including interleukins (IL)-6, IL-17 and IL-22, interferon and tumor necrosis factor) and other signaling molecules. This leads to an inflammatory process with increased proliferation of epidermal cells, neo-angiogenesis and infiltration of dendritic cells in the skin. Dysfunctional de novo glucocorticoid synthesis in psoriatic keratinocytes and the skin microbiome have also been suggested as mediators in the pathogenesis of this disease. To understand psoriasis, it is essential to comprehend the processes underlying the skin immunity and neuroendocrinology. This review paper focuses on the skin as a neuroendocrine organ and summarizes what is known about the skin immune system, the brain-skin connection and the role played by the serotonergic system in skin. Subsequently, the alterations of neuroimmune processes and of the serotonergic system in psoriatic skin are discussed, as well as, briefly, the genetic basis of psoriasis.

Genetic Insights Into Smooth Muscle Cell Contributions to Coronary Artery Disease

Coronary artery disease is a complex cardiovascular disease involving an interplay of genetic and environmental influences over a lifetime. Although considerable progress has been made in understanding lifestyle risk factors, genetic factors identified from genome-wide association studies may capture additional hidden risk undetected by traditional clinical tests. These genetic discoveries have highlighted many candidate genes and pathways dysregulated in the vessel wall, including those involving smooth muscle cell phenotypic modulation and injury responses. Here, we summarize experimental evidence for a few genome-wide significant loci supporting their roles in smooth muscle cell biology and disease. We also discuss molecular quantitative trait locus mapping as a powerful discovery and fine-mapping approach applied to smooth muscle cell and coronary artery disease-relevant tissues. We emphasize the critical need for alternative genetic strategies, including cis/trans-regulatory network analysis, genome editing, and perturbations, as well as single-cell sequencing in smooth muscle cell tissues and model organisms, under both normal and disease states. By integrating multiple experimental and analytical modalities, these multidimensional datasets should improve the interpretation of coronary artery disease genome-wide association studies and molecular quantitative trait locus signals and inform candidate targets for therapeutic intervention or risk prediction.

PR3 levels are impaired in plasma and PBMCs from Arabs with cardiovascular diseases

Cardiovascular disease (CVD) risks persist in patients despite treatment. CVD susceptibility also varies with sex and ethnicity and is not entirely explained by conventional CVD risk factors. The aim of the present study was to identify novel CVD candidate markers in circulating Peripheral blood mononuclear cells (PBMCs) and plasma from Arab obese subjects with and without CVD using proteomic approaches. Human adults with confirmed CVD (n = 208) and matched non-CVD controls (n = 152) living in Kuwait were examined in the present cross-sectional study. Anthropometric and classical biochemical parameters were determined. We employed a shotgun proteomic profiling approach on PBMCs isolated from a subset of the groups (n = 4, each), and differentially expressed proteins selected between the two groups were validated at the mRNA level using RT-PCR (n = 6, each). Plasma levels of selected proteins from the proteomics profiling: Proteinase-3 (PR3), Annexin-A3 (ANX3), Defensin (DEFA1), and Matrix Metalloproteinase-9 (MMP9), were measured in the entire cohort using human enzyme-linked immunosorbent assay kits and were subsequently correlated with various clinical parameters. Out of the 1407 we identified and quantified from the proteomics profiling, 47 proteins were dysregulated with at least twofold change between the two subject groups. Among the differentially expressed proteins, 11 were confirmed at the mRNA levels. CVD influenced the levels of the shortlisted proteins (MMP9, PR3, ANX3, and DEFA1) in the PBMCs and plasma differentially. Despite the decreased levels of both protein and mRNA in PBMCs, PR3 circulating levels increased significantly in patients with CVD and were influenced by neither diabetes nor statin treatment. No significant changes were; however, observed in the DEFA1, MMP9, and ANX3 levels in plasma. Multivariate logistic regression analysis revealed that only PR3 was independently associated with CVD. Our results suggest that the dysregulation of PR3 levels in plasma and PBMCs reflects underlying residual CVD risks even in the treated population. More prospective and larger studies are required to establish the role of PR3 in CVD progression.

Genomic profiling of human vascular cells identifies TWIST1 as a causal gene for common vascular diseases

Genome-wide association studies have identified multiple novel genomic loci associated with vascular diseases. Many of these loci are common non-coding variants that affect the expression of disease-relevant genes within coronary vascular cells. To identify such genes on a genome-wide level, we performed deep transcriptomic analysis of genotyped primary human coronary artery smooth muscle cells (HCASMCs) and coronary endothelial cells (HCAECs) from the same subjects, including splicing Quantitative Trait Loci (sQTL), allele-specific expression (ASE), and colocalization analyses. We identified sQTLs for TARS2, YAP1, CFDP1, and STAT6 in HCASMCs and HCAECs, and 233 ASE genes, a subset of which are also GTEx eGenes in arterial tissues. Colocalization of GWAS association signals for coronary artery disease (CAD), migraine, stroke and abdominal aortic aneurysm with GTEx eGenes in aorta, coronary artery and tibial artery discovered novel candidate risk genes for these diseases. At the CAD and stroke locus tagged by rs2107595 we demonstrate colocalization with expression of the proximal gene TWIST1. We show that disrupting the rs2107595 locus alters TWIST1 expression and that the risk allele has increased binding of the NOTCH signaling protein RBPJ. Finally, we provide data that TWIST1 expression influences vascular SMC phenotypes, including proliferation and calcification, as a potential mechanism supporting a role for TWIST1 in CAD.

Integrative genomic analysis identified common regulatory networks underlying the correlation between coronary artery disease and plasma lipid levels

Background

Coronary artery disease (CAD) and plasma lipid levels are highly correlated, indicating the presence of common pathways between them. Nevertheless, the molecular pathways underlying the pathogenic comorbidities for both traits remain poorly studied. We sought to identify common pathways and key driver genes by performing a comprehensive integrative analysis based on multi-omic datasets.

Methods

By performing a pathway-based analysis of GWAS summary data, we identified that lipoprotein metabolism process-related pathways were significantly associated with CAD risk. Based on LD score regression analysis of CAD-related SNPs, significant heritability enrichments were observed in the cardiovascular and digestive system, as well as in liver and gastrointestinal tissues, which are the main regulators for lipid level.

Results

We found there existed significant genetic correlation between CAD and other lipid metabolism related traits (the smallest P value < 1 × 10^− 16). A total of 13 genes (e.g., LPA, APOC1, APOE and SLC22A3) was found to be overlapped between CAD and plasma lipid levels. By using the data-driven approach that integrated transcriptome information, we discovered co-expression modules associated prominently with both CAD and plasma lipids. With the detailed topology information on gene-gene regulatory relationship, we illustrated that the identified hub genes played important roles in the pathogenesis of CAD and plasma lipid turbulence.

Conclusion

Together, we identified the shared molecular mechanisms underlying the correlation between CAD and plasma lipid levels.

Network Modeling Approaches and Applications to Unravelling Non-Alcoholic Fatty Liver Disease

Non-alcoholic fatty liver disease (NAFLD) is a progressive condition of the liver encompassing a range of pathologies including steatosis, non-alcoholic steatohepatitis (NASH), cirrhosis, and hepatocellular carcinoma. Research into this disease is imperative due to its rapid growth in prevalence, economic burden, and current lack of FDA approved therapies. NAFLD involves a highly complex etiology that calls for multi-tissue multi-omics network approaches to uncover the pathogenic genes and processes, diagnostic biomarkers, and potential therapeutic strategies. In this review, we first present a basic overview of disease pathogenesis, risk factors, and remaining knowledge gaps, followed by discussions of the need and concepts of multi-tissue multi-omics approaches, various network methodologies and application examples in NAFLD research. We highlight the findings that have been uncovered thus far including novel biomarkers, genes, and biological pathways involved in different stages of NAFLD, molecular connections between NAFLD and its comorbidities, mechanisms underpinning sex differences, and druggable targets. Lastly, we outline the future directions of implementing network approaches to further improve our understanding of NAFLD in order to guide diagnosis and therapeutics.

GEREDB: Gene expression regulation database curated by mining abstracts from literature

Understanding how genes are expressed and regulated in different biological processes are fundamental and challenging issues. Considerable progress has been made in studying the relationship between the expression and regulation of human genes. However, it is difficult to use these resources productively to analyze gene expression data. GEREDB (www.thua45.cn/geredb) has been developed to facilitate analyses that will provide insights into the regulation of genes that govern specific biological responses. GEREDB is a publicly available, manually curated biological database that stores the data regarding relationships between expression and regulation of human genes. To date, more than 39,000 Links have been contextually annotated by reviewing more than 53,000 abstracts. GEREDB can be searched using the official NCBI gene symbol as a query, and it can be downloaded along with the GEREA software package. GEREDB has the ability to analyze user-supplied gene expression data in a causal analysis oriented manner using the GEREA bioinformatics tool.

Druggability of Coronary Artery Disease Risk Loci

BACKGROUND

Genome-wide association studies have identified multiple loci associated with coronary artery disease and myocardial infarction, but only a few of these loci are current targets for on-market medications. To identify drugs suitable for repurposing and their targets, we created 2 unique pipelines integrating public data on 49 coronary artery disease/myocardial infarction-genome-wide association studies loci, drug-gene interactions, side effects, and chemical interactions.

METHODS

We first used publicly available genome-wide association studies results on all phenotypes to predict relevant side effects, identified drug-gene interactions, and prioritized candidates for repurposing among existing drugs. Second, we prioritized gene product targets by calculating a druggability score to estimate how accessible pockets of coronary artery disease/myocardial infarction-associated gene products are, then used again the genome-wide association studies results to predict side effects, excluded loci with widespread cross-tissue expression to avoid housekeeping and genes involved in vital processes and accordingly ranked the remaining gene products.

RESULTS

These pipelines ultimately led to 3 suggestions for drug repurposing: pentolinium, adenosine triphosphate, and riociguat (to target CHRNB4, ACSS2, and GUCY1A3, respectively); and 3 proteins for drug development: LMOD1 (leiomodin 1), HIP1 (huntingtin-interacting protein 1), and PPP2R3A (protein phosphatase 2, regulatory subunit b-double prime, α). Most current therapies for coronary artery disease/myocardial infarction treatment were also rediscovered.

CONCLUSIONS

Integration of genomic and pharmacological data may prove beneficial for drug repurposing and development, as evidence from our pipelines suggests.

Candidate Genes and MiRNAs Linked to the Inverse Relationship Between Cancer and Alzheimer's Disease: Insights From Data Mining and Enrichment Analysis

The incidence of cancer and Alzheimer’s disease (AD) increases exponentially with age. A growing body of epidemiological evidence and molecular investigations inspired the hypothesis of an inverse relationship between these two pathologies. It has been proposed that the two diseases might utilize the same proteins and pathways that are, however, modulated differently and sometimes in opposite directions. Investigation of the common processes underlying these diseases may enhance the understanding of their pathogenesis and may also guide novel therapeutic strategies. Starting from a text-mining approach, our in silico study integrated the dispersed biological evidence by combining data mining, gene set enrichment, and protein-protein interaction (PPI) analyses while searching for common biological hallmarks linked to AD and cancer. We retrieved 138 genes (ALZCAN gene set), computed a significant number of enriched gene ontology clusters, and identified four PPI modules. The investigation confirmed the relevance of autophagy, ubiquitin proteasome system, and cell death as common biological hallmarks shared by cancer and AD. Then, from a closer investigation of the PPI modules and of the miRNAs enrichment data, several genes (SQSTM1, UCHL1, STUB1, BECN1, CDKN2A, TP53, EGFR, GSK3B, and HSPA9) and miRNAs (miR-146a-5p, MiR-34a-5p, miR-21-5p, miR-9-5p, and miR-16-5p) emerged as promising candidates. The integrative approach uncovered novel miRNA-gene networks (e.g., miR-146 and miR-34 regulating p62 and Beclin1 in autophagy) that might give new insights into the complex regulatory mechanisms of gene expression in AD and cancer.

The novel coronary artery disease risk gene JCAD/KIAA1462 promotes endothelial dysfunction and atherosclerosis

AIMS

Recent genome-wide association studies (GWAS) have identified that the JCAD locus is associated with risk of coronary artery disease (CAD) and myocardial infarction (MI). However, the mechanisms whereby candidate gene JCAD confers disease risk remain unclear. We addressed whether and how JCAD affects the development of atherosclerosis, the common cause of CAD.

METHODS AND RESULTS

By mining data in the Genotype-Tissue Expression (GTEx) database, we found that CAD-associated risk variants at the JCAD locus are linked to increased JCAD gene expression in human arteries, implicating JCAD as a candidate causal CAD gene. We therefore generated global and endothelial cell (EC) specific-JCAD knockout mice, and observed that JCAD deficiency attenuated high fat diet-induced atherosclerosis in ApoE-deficient mice. JCAD-deficiency in mice also improved endothelium-dependent relaxation. Genome-wide transcriptional profiling of JCAD-depleted human coronary artery ECs showed that JCAD depletion inhibited the activation of YAP/TAZ pathway, and the expression of downstream pro-atherogenic genes, including CTGF and Cyr61. As a result, JCAD-deficient ECs attracted fewer monocytes in response to lipopolysaccharide (LPS) stimulation. Moreover, JCAD expression in ECs was decreased under unidirectional laminar flow in vitro and in vivo. Proteomics studies suggest that JCAD regulates YAP/TAZ activation by interacting with actin-binding protein TRIOBP, thereby stabilizing stress fiber formation. Finally, we observed that endothelial JCAD expression was increased in mouse and human atherosclerotic plaques.

CONCLUSION

The present study demonstrates that the GWAS-identified CAD risk gene JCAD promotes endothelial dysfunction and atherosclerosis, thus highlighting the possibility of new therapeutic strategies for CAD by targeting JCAD.

A decade of genome-wide association studies for coronary artery disease: the challenges ahead

In this review, we summarize current knowledge on the genetics of coronary artery disease, based on 10 years of genome-wide association studies. The discoveries began with individual studies using 200K single nucleotide polymorphism arrays and progressed to large-scale collaborative efforts, involving more than a 100 000 people and up to 40 Mio genetic variants. We discuss the challenges ahead, including those involved in identifying causal genes and deciphering the links between risk variants and disease pathology. We also describe novel insights into disease biology based on the findings of genome-wide association studies. Moreover, we discuss the potential for discovery of novel treatment targets through the integration of different layers of 'omics' data and the application of systems genetics approaches. Finally, we provide a brief outlook on the potential for precision medicine to be enhanced by genome-wide association study findings in the cardiovascular field.

Beyond genome-wide significance: integrative approaches to the interpretation and extension of GWAS findings for alcohol use disorder

Alcohol use disorder (AUD) is a heritable complex behavior. Due to the highly polygenic nature of AUD, identifying genetic variants that comprise this heritable variation has proved to be challenging. With the exception of functional variants in alcohol metabolizing genes (e.g. ADH1B and ALDH2), few other candidate loci have been confidently linked to AUD. Genome-wide association studies (GWAS) of AUD and other alcohol-related phenotypes have either produced few hits with genome-wide significance or have failed to replicate on further study. These issues reinforce the complex nature of the genetic underpinnings for AUD and suggest that both GWAS studies with larger samples and additional analysis approaches that better harness the nominally significant loci in existing GWAS are needed. Here, we review approaches of interest in the post-GWAS era, including in silico functional analyses; functional partitioning of single nucleotide polymorphism heritability; aggregation of signal into genes and gene networks; and validation of identified loci, genes and gene networks in postmortem brain tissue and across species. These integrative approaches hold promise to illuminate our understanding of the biological basis of AUD; however, we recognize that the main challenge continues to be the extremely polygenic nature of AUD, which necessitates large samples to identify multiple loci associated with AUD liability.

Multi-omics integration reveals molecular networks and regulators of psoriasis

BACKGROUND

Psoriasis is a complex multi-factorial disease, involving both genetic susceptibilities and environmental triggers. Genome-wide association studies (GWAS) and epigenome-wide association studies (EWAS) have been carried out to identify genetic and epigenetic variants that are associated with psoriasis. However, these loci cannot fully explain the disease pathogenesis.

METHODS

To achieve a comprehensive mechanistic understanding of psoriasis, we conducted a systems biology study, integrating multi-omics datasets including GWAS, EWAS, tissue-specific transcriptome, expression quantitative trait loci (eQTLs), gene networks, and biological pathways to identify the key genes, processes, and networks that are genetically and epigenetically associated with psoriasis risk.

RESULTS

This integrative genomics study identified both well-characterized (e.g., the IL17 pathway in both GWAS and EWAS) and novel biological processes (e.g., the branched chain amino acid catabolism process in GWAS and the platelet and coagulation pathway in EWAS) involved in psoriasis. Finally, by utilizing tissue-specific gene regulatory networks, we unraveled the interactions among the psoriasis-associated genes and pathways in a tissue-specific manner and detected potential key regulatory genes in the psoriasis networks.

CONCLUSIONS

The integration and convergence of multi-omics signals provide deeper and comprehensive insights into the biological mechanisms associated with psoriasis susceptibility.

Functional regulatory mechanism of smooth muscle cell-restricted LMOD1 coronary artery disease locus

Recent genome-wide association studies (GWAS) have identified multiple new loci which appear to alter coronary artery disease (CAD) risk via arterial wall-specific mechanisms. One of the annotated genes encodes LMOD1 (Leiomodin 1), a member of the actin filament nucleator family that is highly enriched in smooth muscle-containing tissues such as the artery wall. However, it is still unknown whether LMOD1 is the causal gene at this locus and also how the associated variants alter LMOD1 expression/function and CAD risk. Using epigenomic profiling we recently identified a non-coding regulatory variant, rs34091558, which is in tight linkage disequilibrium (LD) with the lead CAD GWAS variant, rs2820315. Herein we demonstrate through expression quantitative trait loci (eQTL) and statistical fine-mapping in GTEx, STARNET, and human coronary artery smooth muscle cell (HCASMC) datasets, rs34091558 is the top regulatory variant for LMOD1 in vascular tissues. Position weight matrix (PWM) analyses identify the protective allele rs34091558-TA to form a conserved Forkhead box O3 (FOXO3) binding motif, which is disrupted by the risk allele rs34091558-A. FOXO3 chromatin immunoprecipitation and reporter assays show reduced FOXO3 binding and LMOD1 transcriptional activity by the risk allele, consistent with effects of FOXO3 downregulation on LMOD1. LMOD1 knockdown results in increased proliferation and migration and decreased cell contraction in HCASMC, and immunostaining in atherosclerotic lesions in the SMC lineage tracing reporter mouse support a key role for LMOD1 in maintaining the differentiated SMC phenotype. These results provide compelling functional evidence that genetic variation is associated with dysregulated LMOD1 expression/function in SMCs, together contributing to the heritable risk for CAD.

Tissue-specific pathways and networks underlying sexual dimorphism in non-alcoholic fatty liver disease

BACKGROUND

Non-alcoholic fatty liver disease (NAFLD) encompasses benign steatosis and more severe conditions such as non-alcoholic steatohepatitis (NASH), cirrhosis, and liver cancer. This chronic liver disease has a poorly understood etiology and demonstrates sexual dimorphisms. We aim to examine the molecular mechanisms underlying sexual dimorphisms in NAFLD pathogenesis through a comprehensive multi-omics study. We integrated genomics (DNA variations), transcriptomics of liver and adipose tissue, and phenotypic data of NAFLD derived from female mice of ~ 100 strains included in the hybrid mouse diversity panel (HMDP) and compared the NAFLD molecular pathways and gene networks between sexes.

RESULTS

We identified both shared and sex-specific biological processes for NAFLD. Adaptive immunity, branched chain amino acid metabolism, oxidative phosphorylation, and cell cycle/apoptosis were shared between sexes. Among the sex-specific pathways were vitamins and cofactors metabolism and ion channel transport for females, and phospholipid, lysophospholipid, and phosphatidylinositol metabolism and insulin signaling for males. Additionally, numerous lipid and insulin-related pathways and inflammatory processes in the adipose and liver tissue appeared to show more prominent association with NAFLD in male HMDP. Using data-driven network modeling, we identified plausible sex-specific and tissue-specific regulatory genes as well as those that are shared between sexes. These key regulators orchestrate the NAFLD pathways in a sex- and tissue-specific manner. Gonadectomy experiments support that sex hormones may partially underlie the sexually dimorphic genes and pathways involved in NAFLD.

CONCLUSIONS

Our multi-omics integrative study reveals sex- and tissue-specific genes, processes, and networks underlying sexual dimorphism in NAFLD and may facilitate sex-specific precision medicine.

Context-enriched interactome powered by proteomics helps the identification of novel regulators of macrophage activation

The role of pro-inflammatory macrophage activation in cardiovascular disease (CVD) is a complex one amenable to network approaches. While an indispensible tool for elucidating the molecular underpinnings of complex diseases including CVD, the interactome is limited in its utility as it is not specific to any cell type, experimental condition or disease state. We introduced context-specificity to the interactome by combining it with co-abundance networks derived from unbiased proteomics measurements from activated macrophage-like cells. Each macrophage phenotype contributed to certain regions of the interactome. Using a network proximity-based prioritization method on the combined network, we predicted potential regulators of macrophage activation. Prediction performance significantly increased with the addition of co-abundance edges, and the prioritized candidates captured inflammation, immunity and CVD signatures. Integrating the novel network topology with transcriptomics and proteomics revealed top candidate drivers of inflammation. In vitro loss-of-function experiments demonstrated the regulatory role of these proteins in pro-inflammatory signaling.

When human cells or tissues are injured, the body triggers a response known as inflammation to repair the damage and protect itself from further harm. However, if the same issue keeps recurring, the tissues become inflamed for longer periods of time, which may ultimately lead to health problems. This is what could be happening in cardiovascular diseases, where long-term inflammation could damage the heart and blood vessels.

Many different proteins interact with each other to control inflammation; gaining an insight into the nature of these interactions could help to pinpoint the role of each molecular actor. Researchers have used a combination of unbiased, large-scale experimental and computational approaches to develop the interactome, a map of the known interactions between all proteins in humans. However, interactions between proteins can change between cell types, or during disease. Here, Halu et al. aimed to refine the human interactome and identify new proteins involved in inflammation, especially in the context of cardiovascular disease.

Cells called macrophages produce signals that trigger inflammation whey they detect damage in other cells or tissues. The experiments used a technique called proteomics to measure the amounts of all the proteins in human macrophages. Combining these data with the human interactome made it possible to predict new links between proteins known to have a role in inflammation and other proteins in the interactome. Further analysis using other sets of data from macrophages helped identify two new candidate proteins – GBP1 and WARS – that may promote inflammation. Halu et al. then used a genetic approach to deactivate the genes and decrease the levels of these two proteins in macrophages, which caused the signals that encourage inflammation to drop.

These findings suggest that GBP1 and WARS regulate the activity of macrophages to promote inflammation. The two proteins could therefore be used as drug targets to treat cardiovascular diseases and other disorders linked to inflammation, but further studies will be needed to precisely dissect how GBP1 and WARS work in humans.

GWAS Reveal Targets in Vessel Wall Pathways to Treat Coronary Artery Disease

Coronary artery disease (CAD) is the leading cause of mortality worldwide and poses a considerable public health burden. Recent genome-wide association studies (GWAS) have revealed >100 genetic loci associated with CAD susceptibility in humans. While a number of these loci harbor gene targets of currently approved therapies, such as statins and PCSK9 inhibitors, the majority of the annotated genes at these loci encode for proteins involved in vessel wall function with no known drugs available. Importantly many of the associated genes linked to vascular (smooth muscle, endothelial, and macrophage) cell processes are now organized into distinct functional pathways, e.g., vasodilation, growth factor responses, extracellular matrix and plaque remodeling, and inflammation. In this mini-review, we highlight the most recently identified loci that have predicted roles in the vessel wall and provide genetic context for pre-existing therapies as well as new drug targets informed from GWAS. With the development of new modalities to target these pathways, (e.g., antisense oligonucleotides, CRISPR/Cas9, and RNA interference) as well as the computational frameworks to prioritize or reposition therapeutics, there is great opportunity to close the gap from initial genetic discovery to clinical translation for many patients affected by this common disease.

EBF1 gene polymorphism and its interaction with smoking and drinking on the risk of coronary artery disease for Chinese patients

Objective: Early B-cell factor 1 (EBF1) is a transcription factor that is expressed in early B-cells, adipocytes, and olfactory neurons, and is essential for the maturation of early B lymphocytes. The present study analyzes the influence of EBF1 gene polymorphism and its interaction with smoking and drinking on the risk of coronary artery disease (CAD). Methods: In the present study, 243 CAD cases were enrolled as the CAD group and 215 non-CAD patients as the control group by case-control study. We analyzed their genotypes of the rs987401919, rs36071027, and rs1056065671 loci of the EBF1 gene by Sanger sequencing and detected their content of HDL-C, LDL-C, and TG. Results: The C allele at the rs987401919 and rs36071027 loci of EBF1 was found to be the risk factor for CAD (Odds ratio, OR = 1.233; 95% confidence interval, CI: 1.039-1.421; P=0.017; OR = 1.487; 95% CI: 1.015-1.823; P=0.042). The interaction between single nucleotide polymorphisms (SNP) of the rs987401919 and rs36071027 loci and smoking and drinking were distinctly associated with the incidence of CAD (P<0.05). The content of systolic blood pressure (SBP), diastolic blood pressure (DBP), HDL-C, LDL-C, and TG was distinctly changed after gene mutation at the rs987401919 and rs36071027 loci (P<0.05). Conclusion: The results of the present study show that the mutation (CT+TT) at the rs987401919 and rs36071027 loci of EBF1 and its interaction with smoking and drinking are risk factors for CAD, and that the mechanism may be related to the changes in blood pressure and blood lipid content.

Translating GWAS Findings to Novel Therapeutic Targets for Coronary Artery Disease

The success of genome-wide association studies (GWAS) has significantly advanced our understanding of the etiology of coronary artery disease (CAD) and opens new opportunities to reinvigorate the stalling CAD drug development. However, there exists remarkable disconnection between the CAD GWAS findings and commercialized drugs. While this could implicate major untapped translational and therapeutic potentials in CAD GWAS, it also brings forward extensive technical challenges. In this review we summarize the motivation to leverage GWAS for drug discovery, outline the critical bottlenecks in the field, and highlight several promising strategies such as functional genomics and network-based approaches to enhance the translational value of CAD GWAS findings in driving novel therapeutics

Integration of Multi-omics Data from Mouse Diversity Panel Highlights Mitochondrial Dysfunction in Non-alcoholic Fatty Liver Disease

The etiology of non-alcoholic fatty liver disease (NAFLD), the most common form of chronic liver disease, is poorly understood. To understand the causal mechanisms underlying NAFLD, we conducted a multi-omics, multi-tissue integrative study using the Hybrid Mouse Diversity Panel, consisting of ∼100 strains of mice with various degrees of NAFLD. We identified both tissue-specific biological processes and processes that were shared between adipose and liver tissues. We then used gene network modeling to predict candidate regulatory genes of these NAFLD processes, including Fasn, Thrsp, Pklr, and Chchd6. In vivo knockdown experiments of the candidate genes improved both steatosis and insulin resistance. Further in vitro testing demonstrated that downregulation of both Pklr and Chchd6 lowered mitochondrial respiration and led to a shift toward glycolytic metabolism, thus highlighting mitochondria dysfunction as a key mechanistic driver of NAFLD.

Integrative gene ontology and network analysis of coronary artery disease associated genes suggests potential role of ErbB pathway gene EGFR

Coronary artery disease (CAD) is a major cause of mortality in India, more importantly the young Indians. Combinatorial and integrative approaches to evaluate pathways and genes to gain an improved understanding and potential biomarkers for risk assessment are required. Therefore, 608 genes from the CADgene database version 2.0, classified into 12 functional classes representing the atherosclerotic disease process, were analyzed. Homology analysis of the unique list of gene ontologies (GO) from each functional class gave 8 GO terms represented in 11 and 10 functional classes. Using disease ontology analysis 80 genes belonging to 8 GO terms, using FunDO suggested that 29 of them were identified to be associated with CAD. Extended network analysis of these genes using STRING version 9.1 gave 328 nodes and 4,525 interactions of which the top 5% had a node degree of ≥75 associated with pathways including the ErbB signaling pathway with epidermal growth factor receptor (EGFR) gene as the central hub. Evaluation of EFGR protein levels in age and gender-matched 342 CAD patients vs. 342 control subjects demonstrated significant differences [controls=149.76±2.47 pg/ml and CAD patients stratified into stable angina (SA)=161.65±3.40 pg/ml and myocardial infarction (MI)=171.51±4.26 pg/ml]. Logistic regression analysis suggested that increased EGFR levels exhibit 3-fold higher risk of CAD [odds ratio (OR) 3.51, 95% confidence interval [CI] 1.96–6.28, P≤0.001], upon adjustment for hypertension, diabetes and smoking. A unit increase in EGFR levels increased the risk by 2-fold for SA (OR 2.58, 95% CI 1.25–5.33, P=0.01) and 3.8-fold for MI (OR 3.82, 95% CI 1.94–7.52, P≤0.001) following adjustment. Thus, the use of ontology mapping and network analysis in an integrative manner aids in the prioritization of biomarkers of complex disease.

Shared genetic regulatory networks for cardiovascular disease and type 2 diabetes in multiple populations of diverse ethnicities in the United States

Cardiovascular diseases (CVD) and type 2 diabetes (T2D) are closely interrelated complex diseases likely sharing overlapping pathogenesis driven by aberrant activities in gene networks. However, the molecular circuitries underlying the pathogenic commonalities remain poorly understood. We sought to identify the shared gene networks and their key intervening drivers for both CVD and T2D by conducting a comprehensive integrative analysis driven by five multi-ethnic genome-wide association studies (GWAS) for CVD and T2D, expression quantitative trait loci (eQTLs), ENCODE, and tissue-specific gene network models (both co-expression and graphical models) from CVD and T2D relevant tissues. We identified pathways regulating the metabolism of lipids, glucose, and branched-chain amino acids, along with those governing oxidation, extracellular matrix, immune response, and neuronal system as shared pathogenic processes for both diseases. Further, we uncovered 15 key drivers including HMGCR, CAV1, IGF1 and PCOLCE, whose network neighbors collectively account for approximately 35% of known GWAS hits for CVD and 22% for T2D. Finally, we cross-validated the regulatory role of the top key drivers using in vitro siRNA knockdown, in vivo gene knockout, and two Hybrid Mouse Diversity Panels each comprised of >100 strains. Findings from this in-depth assessment of genetic and functional data from multiple human cohorts provide strong support that common sets of tissue-specific molecular networks drive the pathogenesis of both CVD and T2D across ethnicities and help prioritize new therapeutic avenues for both CVD and T2D.

Genetics: Implications for Prevention and Management of Coronary Artery Disease

An exciting new era has dawned for the prevention and management of coronary artery disease (CAD) utilizing genetic risk variants. The recent identification of over 60 susceptibility loci for CAD confirms not only the importance of established risk factors, but also the existence of many novel causal pathways that are expected to improve our understanding of the genetic basis of CAD and facilitate the development of new therapeutic agents over time. Concurrently, Mendelian randomization studies have provided intriguing insights on the causal relationship between CAD-related traits, and highlight the potential benefits of long-term modifications of risk factors. Last, genetic risk scores of CAD may serve not only as prognostic, but also as predictive markers, and carry the potential to considerably improve the delivery of established prevention strategies. This review will summarize the evolution and discovery of genetic risk variants for CAD and their current and future clinical applications.

Multidimensional Integrative Genomics Approaches to Dissecting Cardiovascular Disease

Elucidating the mechanisms of complex diseases such as cardiovascular disease (CVD) remains a significant challenge due to multidimensional alterations at molecular, cellular, tissue, and organ levels. To better understand CVD and offer insights into the underlying mechanisms and potential therapeutic strategies, data from multiple omics types (genomics, epigenomics, transcriptomics, metabolomics, proteomics, microbiomics) from both humans and model organisms have become available. However, individual omics data types capture only a fraction of the molecular mechanisms. To address this challenge, there have been numerous efforts to develop integrative genomics methods that can leverage multidimensional information from diverse data types to derive comprehensive molecular insights. In this review, we summarize recent methodological advances in multidimensional omics integration, exemplify their applications in cardiovascular research, and pinpoint challenges and future directions in this incipient field.

Association in a Chinese population of a genetic variation in the early B-cell factor 1 gene with coronary artery disease

Background

Early B-cell factor 1 (EBF1) is a transcription factor expressed primarily during early B cell development. Previous studies have shown EBF1 regulates blood glucose and lipid metabolism in mice with diabetes and central adiposity. Recently, a genetic variation (rs36071027) located in an EBF1 gene intron was associated with carotid artery intima-media thickness. However, whether this polymorphism is actually linked with coronary artery disease (CAD) and its severity remains unclear.

Methods

This study includes 293 CAD cases and 262 controls without CAD. All participants were devided into two groups based on their coronary angiography results. A polymerase chain reaction-ligase detection reaction was used to identify genotypes at rs36071027, and CAD patients were further divided into subgroups with one-, two-, or three-vessel stenosis reflective of CAD severity.

Results

The frequency of the rs36071027 TT genotype was significantly higher in CAD cases versus controls (4.8% vs. 1.5%, 95% CI: 1.13-10.81 P = 0.029). Subjects with a variant genotype T allele had an increased risk of CAD compared to C allele carriers (additive model: 95% CI: 1.13-2.23, P = 0.008). After adjustment for cardiovascular risk factors, analysis of the additive and dominant models involving rs36071027 also revealed that T allele carriers had a significantly higher risk for CAD than C allele carriers (additive model: OR 1.56, 95% CI 1.10–2.22, P = 0.013; dominant model: OR 1.60, 95% CI 1.07–2.41, P = 0.023). Furthermore, both diabetes and the CT + TT rs36071027 genotype were significantly associated with three-vessel stenosis.

Conclusion

Our results in a Chinese population suggest that the TT genotype and T alleles in rs36071027 in the EBF1 gene are associated with an increased risk of CAD and its severity.

Electronic supplementary material

The online version of this article (doi:10.1186/s12872-017-0489-2) contains supplementary material, which is available to authorized users.

Applications and Limitations of Mouse Models for Understanding Human Atherosclerosis

Most of the biological understanding of mechanisms underlying coronary artery disease (CAD) derives from studies of mouse models. The identification of multiple CAD loci and strong candidate genes in large human genome-wide association studies (GWASs) presented an opportunity to examine the relevance of mouse models for the human disease. We comprehensively reviewed the mouse literature, including 827 literature-derived genes, and compared it to human data. First, we observed striking concordance of risk factors for atherosclerosis in mice and humans. Second, there was highly significant overlap of mouse genes with human genes identified by GWASs. In particular, of the 46 genes with strong association signals in CAD GWASs that were studied in mouse models, all but one exhibited consistent effects on atherosclerosis-related phenotypes. Third, we compared 178 CAD-associated pathways derived from human GWASs with 263 from mouse studies and observed that the majority were consistent between the species.