Systems genetics analysis of gene-by-environment interactions in human cells

Detecting genetic effects on phenotype variability to capture gene-by-environment interactions: a systematic method comparison

Genetically associated phenotypic variability has been widely observed across organisms and traits, including in humans. Both gene-gene and gene-environment interactions can lead to an increase in genetically associated phenotypic variability. Therefore, detecting the underlying genetic variants, or variance Quantitative Trait Loci (vQTLs), can provide novel insights into complex traits. Established approaches to detect vQTLs apply different methodologies from variance-only approaches to mean-variance joint tests, but a comprehensive comparison of these methods is lacking. Here, we review available methods to detect vQTLs in humans, carry out a simulation study to assess their performance under different biological scenarios of gene-environment interactions, and apply the optimal approaches for vQTL identification to gene expression data. Overall, with a minor allele frequency (MAF) of less than 0.2, the squared residual value linear model (SVLM) and the deviation regression model (DRM) are optimal when the data follow normal and non-normal distributions, respectively. In addition, the Brown-Forsythe (BF) test is one of the optimal methods when the MAF is 0.2 or larger, irrespective of phenotype distribution. Additionally, a larger sample size and more balanced sample distribution in different exposure categories increase the power of BF, SVLM, and DRM. Our results highlight vQTL detection methods that perform optimally under realistic simulation settings and show that their relative performance depends on the phenotype distribution, allele frequency, sample size, and the type of exposure in the interaction model underlying the vQTL.

Unraveling the Role of Sex in Endothelial Cell Dysfunction: Evidence From Lineage Tracing Mice and Cultured Cells

BACKGROUND

Biological sex differences play a vital role in cardiovascular diseases, including atherosclerosis. The endothelium is a critical contributor to cardiovascular pathologies since endothelial cells (ECs) regulate vascular tone, redox balance, and inflammatory reactions. Although EC activation and dysfunction play an essential role in the early and late stages of atherosclerosis development, little is known about sex-dependent differences in EC.

METHODS

We used human and mouse aortic EC as well as EC-lineage tracing (Cdh5-CreERT2 Rosa-YFP [yellow fluorescence protein]) atherosclerotic Apoe-/- mice to investigate the biological sexual dimorphism of the EC functions in vitro and in vivo. Bioinformatics analyses were performed on male and female mouse aortic EC and human lung and aortic EC.

RESULTS

In vitro, female human and mouse aortic ECs showed more apoptosis and higher cellular reactive oxygen species levels than male EC. In addition, female mouse aortic EC had lower mitochondrial membrane potential (ΔΨm), lower TFAM (mitochondrial transcription factor A) levels, and decreased angiogenic potential (tube formation, cell viability, and proliferation) compared with male mouse aortic EC. In vivo, female mice had significantly higher lipid accumulation within the aortas, impaired glucose tolerance, and lower endothelial-mediated vasorelaxation than males. Using the EC-lineage tracing approach, we found that female lesions had significantly lower rates of intraplaque neovascularization and endothelial-to-mesenchymal transition within advanced atherosclerotic lesions but higher incidents of missing EC lumen coverage and higher levels of oxidative products and apoptosis. RNA-seq analyses revealed that both mouse and human female EC had higher expression of genes associated with inflammation and apoptosis and lower expression of genes related to angiogenesis and oxidative phosphorylation than male EC.

CONCLUSIONS

Our study delineates critical sex-specific differences in EC relevant to proinflammatory, pro-oxidant, and angiogenic characteristics, which are entirely consistent with a vulnerable phenotype in females. Our results provide a biological basis for sex-specific proatherosclerotic mechanisms.

Single-cell RNA-sequencing of peripheral blood mononuclear cells reveals widespread, context-specific gene expression regulation upon pathogenic exposure

The host's gene expression and gene regulatory response to pathogen exposure can be influenced by a combination of the host's genetic background, the type of and exposure time to pathogens. Here we provide a detailed dissection of this using single-cell RNA-sequencing of 1.3M peripheral blood mononuclear cells from 120 individuals, longitudinally exposed to three different pathogens. These analyses indicate that cell-type-specificity is a more prominent factor than pathogen-specificity regarding contexts that affect how genetics influences gene expression (i.e., eQTL) and co-expression (i.e., co-expression QTL). In monocytes, the strongest responder to pathogen stimulations, 71.4% of the genetic variants whose effect on gene expression is influenced by pathogen exposure (i.e., response QTL) also affect the co-expression between genes. This indicates widespread, context-specific changes in gene expression level and its regulation that are driven by genetics. Pathway analysis on the CLEC12A gene that exemplifies cell-type-, exposure-time- and genetic-background-dependent co-expression interactions, shows enrichment of the interferon (IFN) pathway specifically at 3-h post-exposure in monocytes. Similar genetic background-dependent association between IFN activity and CLEC12A co-expression patterns is confirmed in systemic lupus erythematosus by in silico analysis, which implies that CLEC12A might be an IFN-regulated gene. Altogether, this study highlights the importance of context for gaining a better understanding of the mechanisms of gene regulation in health and disease.

Integrative analysis of liver-specific non-coding regulatory SNPs associated with the risk of coronary artery disease

Genetic factors underlying coronary artery disease (CAD) have been widely studied using genome-wide association studies (GWASs). However, the functional understanding of the CAD loci has been limited by the fact that a majority of GWAS variants are located within non-coding regions with no functional role. High cholesterol and dysregulation of the liver metabolism such as non-alcoholic fatty liver disease confer an increased risk of CAD. Here, we studied the function of non-coding single-nucleotide polymorphisms in CAD GWAS loci located within liver-specific enhancer elements by identifying their potential target genes using liver cis-eQTL analysis and promoter Capture Hi-C in HepG2 cells. Altogether, 734 target genes were identified of which 121 exhibited correlations to liver-related traits. To identify potentially causal regulatory SNPs, the allele-specific enhancer activity was analyzed by (1) sequence-based computational predictions, (2) quantification of allele-specific transcription factor binding, and (3) STARR-seq massively parallel reporter assay. Altogether, our analysis identified 1,277 unique SNPs that display allele-specific regulatory activity. Among these, susceptibility enhancers near important cholesterol homeostasis genes (APOB, APOC1, APOE, and LIPA) were identified, suggesting that altered gene regulatory activity could represent another way by which genetic variation regulates serum lipoprotein levels. Using CRISPR-based perturbation, we demonstrate how the deletion/activation of a single enhancer leads to changes in the expression of many target genes located in a shared chromatin interaction domain. Our integrative genomics approach represents a comprehensive effort in identifying putative causal regulatory regions and target genes that could predispose to clinical manifestation of CAD by affecting liver function.

Leveraging phenotypic variability to identify genetic interactions in human phenotypes

Although thousands of loci have been associated with human phenotypes, the role of gene-environment (GxE) interactions in determining individual risk of human diseases remains unclear. This is partly because of the severe erosion of statistical power resulting from the massive number of statistical tests required to detect such interactions. Here, we focus on improving the power of GxE tests by developing a statistical framework for assessing quantitative trait loci (QTLs) associated with the trait means and/or trait variances. When applying this framework to body mass index (BMI), we find that GxE discovery and replication rates are significantly higher when prioritizing genetic variants associated with the variance of the phenotype (vQTLs) compared to when assessing all genetic variants. Moreover, we find that vQTLs are enriched for associations with other non-BMI phenotypes having strong environmental influences, such as diabetes or ulcerative colitis. We show that GxE effects first identified in quantitative traits such as BMI can be used for GxE discovery in disease phenotypes such as diabetes. A clear conclusion is that strong GxE interactions mediate the genetic contribution to body weight and diabetes risk.

Genome-wide association study of intracranial aneurysms identifies 17 risk loci and genetic overlap with clinical risk factors

Rupture of an intracranial aneurysm leads to subarachnoid hemorrhage, a severe type of stroke. To discover new risk loci and the genetic architecture of intracranial aneurysms, we performed a cross-ancestry, genome-wide association study in 10,754 cases and 306,882 controls of European and East Asian ancestry. We discovered 17 risk loci, 11 of which are new. We reveal a polygenic architecture and explain over half of the disease heritability. We show a high genetic correlation between ruptured and unruptured intracranial aneurysms. We also find a suggestive role for endothelial cells by using gene mapping and heritability enrichment. Drug-target enrichment shows pleiotropy between intracranial aneurysms and antiepileptic and sex hormone drugs, providing insights into intracranial aneurysm pathophysiology. Finally, genetic risks for smoking and high blood pressure, the two main clinical risk factors, play important roles in intracranial aneurysm risk, and drive most of the genetic correlation between intracranial aneurysms and other cerebrovascular traits.

Characterization of the 5'-flanking region of the human and mouse CHAC1 genes

The Unfolded Protein Response pathway is a conserved signaling mechanism having important roles in cellular physiology and is perturbed accompanying disease. We previously identified the novel UPR target gene CHAC1, a direct target of ATF4, downstream of PERK-EIF2A and activated by the UPR pathway. CHAC1 enzyme directs catalysis of γ-linked glutamate bonds within specific molecular targets. CHAC1 is the first enzyme characterized that can catalyze intracellular glutathione degradation in eukaryotes, having implications for regulation of oxidative stress. DDIT3 (CHOP) is a terminal UPR transcription factor, regulated by ATF4 and an output promoting cell death signaling. Herein we examine the relationship of CHOP controlling CHAC1 transcription in humans and mice. We note parallel induction of CHOP and CHAC1 in human cells after agonist induced UPR. Expanding upon previous reports, we define transcriptional induction of CHAC1 in humans and mice driven by ATF4 through a synergistic relationship with conserved ATF/CRE and CARE DNA sequences of the CHAC1 promoter. Using this system, we also tested effects of CHOP on CHAC1 transcription, and binding at the CHAC1 ATF/CRE using IM-EMSA. These data indicate a novel inhibitory effect of CHOP on CHAC1 transcription, which was ablated in the absence of the ATF/CRE control element. While direct binding of ATF4 to CHAC1 promoter sequences was confirmed, binding of CHOP to the CHAC1 ATF/CRE was not evident at baseline or after UPR induction. These data reveal CHAC1 as a novel CHOP inhibited target gene, acting through an upstream ATF/CRE motif via an indirect mechanism.

The microscopic anatomy of endothelial cells in human atherosclerosis: Focus on ER and mitochondria

Once regarded merely as a bland lipid storage disease consequence of aging, atherosclerosis is currently considered a slow and continuous inflammatory process (partially controllable by treatment) with complex etiology involving a multitude of genetic and environmental risk factors which ultimately result in the formation of the plaque. The vascular endothelium, a monolayer of endothelial cells (ECs), is an important regulatory "organ" critical for cardiovascular homeostasis in health which also contributes significantly to the pathomechanisms of several disease states, including atherosclerosis. Over the years, there has been evidence highlighting the central role of endoplasmic reticulum (ER) in the maintenance of endothelial function and perturbations in ER biology have been proposed to adversely affect a diverse range of endothelial functions. Of particular interest is the evidence that under certain pathophysiological circumstances, abnormal ER ultrastructure correlates with altered ER function and signaling and can contribute to cell injury and apoptosis. Therefore, the ultrastructural traits of ER membranes can have important implications not only for their functional bearings but also for the etiology and pathophysiology of diverse human disorders. With regard to atherosclerosis, the focus of ER research has been centered on the molecular signals originated from the ER to manage conditions of stress, leaving the fine structure of this organelle an almost unexplored (but promising) area of studies. There is, also, increasing evidence that mitochondrial dysfunction plays a critical role in promoting cell apoptosis, inflammation, and oxidative stress, thereby contributing to atheroma growth. It is within this context that the present study has been undertaken to investigate the microscopic architecture of ECs in human atherosclerosis and to determine whether the potential structural abnormalities of ER and mitochondria may play a central pathogenic role in atherogenesis or may merely reflect the condition of a tissue whose integrity has already been disturbed or destroyed. For this purpose, transmission electron microscopy (TEM) remains a powerful technique that can not only provide information about the ultrastructural state of cell organelles but also allow the correlation between different subcellular alterations indicative of a certain pathophysiological condition and cellular response. The present study expands the spectrum of ultrastructural defects known to exist in human atherosclerosis and suggests that ER alterations may be of great importance in the pathogenesis of the disease. The architectural changes of ER may be considered early pathological events that precede any overt histologic abnormalities in the vascular endothelium and its subcellular organelles, primarily the mitochondrial pool.

Systems Genetics in Human Endothelial Cells Identifies Non-coding Variants Modifying Enhancers, Expression, and Complex Disease Traits

The identification of causal variants and mechanisms underlying complex disease traits in humans is important for the progress of human disease genetics; this requires finding strategies to detect functional regulatory variants in disease-relevant cell types. To achieve this, we collected genetic and transcriptomic data from the aortic endothelial cells of up to 157 donors and four epigenomic phenotypes in up to 44 human donors representing individuals of both sexes and three major ancestries. We found thousands of expression quantitative trait loci (eQTLs) at all ranges of effect sizes not detected by the Gene-Tissue Expression Project (GTEx) in human tissues, showing that novel biological relationships unique to endothelial cells (ECs) are enriched in this dataset. Epigenetic profiling enabled discovery of over 3,000 regulatory elements whose activity is modulated by genetic variants that most frequently mutated ETS, AP-1, and NF-kB binding motifs, implicating these motifs as governors of EC regulation. Using CRISPR interference (CRISPRi), allele-specific reporter assays, and chromatin conformation capture, we validated candidate enhancer variants located up to 750 kb from their target genes, VEGFC, FGD6, and KIF26B. Regulatory SNPs identified were enriched in coronary artery disease (CAD) loci, and this result has specific implications for PECAM-1, FES, and AXL. We also found significant roles for EC regulatory variants in modifying the traits pulse pressure, blood protein levels, and monocyte count. Lastly, we present two unlinked SNPs in the promoter of MFAP2 that exhibit pleiotropic effects on human disease traits. Together, this supports the possibility that genetic predisposition for complex disease is manifested through the endothelium.

Role of Heparin-Binding Epidermal Growth Factor-Like Growth Factor in Oxidative Stress-Associated Metabolic Diseases

Heparin-binding EGF-like growth factor (HB-EGF) is an EGF family member that interacts with epidermal growth factor receptor (EGFR) and ERBB4. Since HB-EGF was first identified as a novel growth factor secreted from a human macrophage cell line, numerous pathological and physiological functions related to cell proliferation, migration, and inflammation have been reported. Notably, the expression of HB-EGF is sensitively upregulated by oxidative stress in the endothelial cells and functions for auto- and paracrine-EGFR signaling. Overnutrition and obesity cause elevation of HB-EGF expression and EGFR signaling in the hepatic and vascular systems. Modulations of HB-EGF signaling showed a series of protections against phenotypes related to metabolic syndrome and advanced metabolic diseases, suggesting HB-EGF as a potential target against metabolic diseases.

Integration of eQTL and a Single-Cell Atlas in the Human Eye Identifies Causal Genes for Age-Related Macular Degeneration

Age-related macular degeneration (AMD) is a leading cause of vision loss. To better understand disease pathogenesis and identify causal genes in GWAS loci for AMD risk, we present a comprehensive database of human retina and retinal pigment epithelium (RPE). Our database comprises macular and non-macular RNA sequencing (RNA-seq) profiles from 129 donors, a genome-wide expression quantitative trait loci (eQTL) dataset that includes macula-specific retina and RPE/choroid, and single-nucleus RNA-seq (NucSeq) from human retina and RPE with subtype resolution from more than 100,000 cells. Using NucSeq, we find enriched expression of AMD candidate genes in RPE cells. We identify 15 putative causal genes for AMD on the basis of co-localization of genetic association signals for AMD risk and eye eQTL, including the genes TSPAN10 and TRPM1. These results demonstrate the value of our human eye database for elucidating genetic pathways and potential therapeutic targets for ocular diseases.

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.

Novel Treatment of Hypertension by Specifically Targeting E2F for Restoration of Endothelial Dihydrofolate Reductase and eNOS Function Under Oxidative Stress

We have shown that hydrogen peroxide (H₂O₂) downregulates tetrahydrobiopterin salvage enzyme DHFR (dihydrofolate reductase) to result in eNOS (endothelial NO synthase) uncoupling and elevated blood pressure. Here, we aimed to delineate molecular mechanisms underlying H₂O₂ downregulation of endothelial DHFR by examining transcriptional pathways hypothesized to modulate DHFR expression and effects on blood pressure regulation of targeting these novel mechanisms. H₂O₂ dose and time dependently attenuated DHFR mRNA and protein expression and enzymatic activity in endothelial cells. Deletion of E2F-binding sites, but not those of Sp1 (specificity protein 1), abolished H₂O₂ attenuation of DHFR promoter activity. Overexpression of E2F1/2/3a activated DHFR promoter at baseline and alleviated the inhibitory effect of H₂O₂ on DHFR promoter activity. H₂O₂ treatment diminished mRNA and protein expression of E2F1/2/3a, whereas overexpression of E2F isoforms increased DHFR protein levels. Chromatin immunoprecipitation assay indicated direct binding of E2F1/2/3a to the DHFR promoter, which was weakened by H₂O₂. E2F1 RNA interference attenuated DHFR protein levels, whereas its overexpression elevated tetrahydrobiopterin levels and tetrahydrobiopterin/dihydrobiopterin ratios in vitro and in vivo. In Ang II (angiotensin II)-infused mice, adenovirus-mediated overexpression of E2F1 markedly abrogated blood pressure to control levels, by restoring endothelial DHFR function to improve NO bioavailability and vasorelaxation. Bioinformatic analyses confirmed a positive correlation between E2F1 and DHFR in human endothelial cells and arteries, and downregulation of both by oxidized phospholipids. In summary, endothelial DHFR is downregulated by H₂O₂ transcriptionally via an E2F-dependent mechanism, and that specifically targeting E2F1/2/3a to restore DHFR and eNOS function may serve as a novel therapeutic option for the treatment of hypertension.

Systems genetics applications in metabolism research

The common forms of metabolic diseases are highly complex, involving hundreds of genes, environmental and lifestyle factors, age-related changes, sex differences and gut-microbiome interactions. Systems genetics is a population-based approach to address this complexity. In contrast to commonly used 'reductionist' approaches, such as gain or loss of function, that examine one element at a time, systems genetics uses high-throughput 'omics' technologies to quantitatively assess the many molecular differences among individuals in a population and then to relate these to physiologic functions or disease states. Unlike genome-wide association studies, systems genetics seeks to go beyond the identification of disease-causing genes to understand higher-order interactions at the molecular level. The purpose of this review is to introduce the systems genetics applications in the areas of metabolic and cardiovascular disease. Here, we explain how large clinical and omics-level data and databases from both human and animal populations are available to help researchers place genes in the context of pathways and networks and formulate hypotheses that can then be experimentally examined. We provide lists of such databases and examples of the integration of reductionist and systems genetics data. Among the important applications emerging is the development of improved nutritional and pharmacological strategies to address the rise of metabolic diseases.

Endothelial senescence-associated secretory phenotype (SASP) is regulated by Makorin-1 ubiquitin E3 ligase

BACKGROUND

Disturbed flow (d-flow)-induced senescence and activation of endothelial cells (ECs) have been suggested to have critical roles in promoting atherosclerosis. Telomeric repeat-binding factor 2 (TERF2)-interacting protein (TERF2IP), a member of the shelterin complex at the telomere, regulates the senescence-associated secretory phenotype (SASP), in which EC activation and senescence are engendered simultaneously by p90RSK-induced phosphorylation of TERF2IP S205 and subsequent nuclear export of the TERF2IP-TERF2 complex. In this study, we investigated TERF2IP-dependent gene expression and its role in regulating d-flow-induced SASP.

METHODS

A principal component analysis and hierarchical clustering were used to identify genes whose expression is regulated by TERF2IP in ECs under d-flow conditions. Senescence was determined by reduced telomere length, increased p53 and p21 expression, and increased apoptosis; EC activation was detected by NF-κB activation and the expression of adhesion molecules. The involvement of TERF2IP S205 phosphorylation in d-flow-induced SASP was assessed by depletion of TERF2IP and mutation of the phosphorylation site.

RESULTS

Our unbiased transcriptome analysis showed that TERF2IP caused alteration in the expression of a distinct set of genes, including rapamycin-insensitive companion of mTOR (RICTOR) and makorin-1 (MKRN1) ubiquitin E3 ligase, under d-flow conditions. In particular, both depletion of TERF2IP and overexpression of the TERF2IP S205A phosphorylation site mutant in ECs increased the d-flow and p90RSK-induced MKRN1 expression and subsequently inhibited apoptosis, telomere shortening, and NF-κB activation in ECs via suppression of p53, p21, and telomerase (TERT) induction.

CONCLUSIONS

MKRN1 and RICTOR belong to a distinct reciprocal gene set that is both negatively and positively regulated by p90RSK. TERF2IP S205 phosphorylation, a downstream event of p90RSK activation, uniquely inhibits MKRN1 expression and contributes to EC activation and senescence, which are key events for atherogenesis.

Flexible Non-Negative Matrix Factorization to Unravel Disease-Related Genes

Recently, non-negative matrix factorization (NMF) has been shown to perform well in the analysis of omics data. NMF assumes that the expression level of one gene is a linear additive composition of metagenes. The elements in metagene matrix represent the regulation effects and are restricted to non-negativity. However, according to the real biological meaning, there are two kinds of regulation effects, i.e., up-regulation and down-regulation. Few methods based on NMF have considered this biological meaning. Therefore, we designed a flexible non-negative matrix factorization (FNMF) algorithm by further considering the biological meaning of gene expression data. It allows negative numbers in the metagene matrix, and negative numbers represent down-regulation effects. We separated gene expression data into disease-driven gene expression and background gene expression. Subsequently, we computed disease-driven gene relative expression, and a ranked list of genes was obtained. The top ranked genes are considered to be involved in some disease-related biological processes. Experimental results on two real-world gene expression data demonstrate the feasibility and effectiveness of FNMF. Compared with conventional disease-related gene identification algorithms, FNMF has superior performance in analyzing gene expression data of diseases with complex pathology.

Proatherogenic stimuli induce HuR in atherosclerosis through MAPK/ErK pathway

Atherosclerosis is a chronic inflammatory disease inflicting the arterial wall, and endothelial activation and dysfunction play an important role in its pathogenesis. The RNA-binding protein HuR has been associated with events of inflammation and activation in endothelial cells, however, its connection with atherosclerosis remains unclear. Here, we show that the expression and RNA-binding activity of HuR are upregulated in human and mouse atherosclerotic lesions. In addition, proatherogenic stimuli, such as inflammatory lipids (Ox-PAPC) and cytokines (TNF-α and IL-1β), induce HuR in human aortic endothelial cells (HAECs) in vitro. Moreover, HuR is also induced in mouse aorta ECs fed a high-fat diet, and the inducible degree is correlated with proatherogenic hyperlipidemia. We further show that the MAPK/ErK pathway in ECs is activated by proatherogenic stimuli in vitro and by high-fat diet in vivo. Finally, we demonstrate that the MAPK/ErK pathway is required for HuR induction by proatherogenic stimuli. Altogether, our study uncovers the inducible effect of proatherogenic stimuli on HuR in ECs, and connects this effect to the activated MAPK/ErK pathway.

Heparin-binding EGF-like growth factor (HB-EGF) antisense oligonucleotide protected against hyperlipidemia-associated atherosclerosis

BACKGROUND AND AIMS

Heparin-binding EGF-like growth factor (HB-EGF) is a representative EGF family member that interacts with EGFR under diverse stress environment. Previously, we reported that the HB-EGF-targeting using antisense oligonucleotide (ASO) effectively suppressed an aortic aneurysm in the vessel wall and circulatory lipid levels. In this study, we further examined the effects of the HB-EGF ASO administration on the development of hyperlipidemia-associated atherosclerosis using an atherogenic mouse model.

METHODS AND RESULTS

The male and female LDLR deficient mice under Western diet containing 21% fat and 0.2% cholesterol content were cotreated with control and HB-EGF ASOs for 12 weeks. We observed that the HB-EGF ASO administration effectively downregulated circulatory VLDL- and LDL-associated lipid levels in circulation; concordantly, the HB-EGF targeting effectively suppressed the development of atherosclerosis in the aorta. An EGFR blocker BIBX1382 administration suppressed the hepatic TG secretion rate, suggesting a positive role of the HB-EGF signaling for the hepatic VLDL production. We newly observed that there was a significant improvement of the insulin sensitivity by the HB-EGF ASO administration in a mouse model under the Western diet as demonstrated by the improvement of the glucose and insulin tolerances.

CONCLUSION

The HB-EGF ASO administration effectively downregulated circulatory lipid levels by suppressing hepatic VLDL production rate, which leads to effective protection against atherosclerosis in the vascular wall.

Genetic variant at coronary artery disease and ischemic stroke locus 1p32.2 regulates endothelial responses to hemodynamics

Biomechanical cues dynamically control major cellular processes, but whether genetic variants actively participate in mechanosensing mechanisms remains unexplored. Vascular homeostasis is tightly regulated by hemodynamics. Exposure to disturbed blood flow at arterial sites of branching and bifurcation causes constitutive activation of vascular endothelium contributing to atherosclerosis, the major cause of coronary artery disease (CAD) and ischemic stroke (IS). Conversely, unidirectional flow promotes quiescent endothelium. Genome-wide association studies (GWAS) have identified chromosome 1p32.2 as strongly associated with CAD/IS; however, the causal mechanism related to this locus remains unknown. Using statistical analyses, assay of transposase accessible chromatin with whole-genome sequencing (ATAC-seq), H3K27ac/H3K4me2 ChIP with whole-genome sequencing (ChIP-seq), and CRISPR interference in human aortic endothelial cells (HAECs), our results demonstrate that rs17114036, a common noncoding polymorphism at 1p32.2, is located in an endothelial enhancer dynamically regulated by hemodynamics. CRISPR-Cas9-based genome editing shows that rs17114036-containing region promotes endothelial quiescence under unidirectional shear stress by regulating phospholipid phosphatase 3 (PLPP3). Chromatin accessibility quantitative trait locus (caQTL) mapping using HAECs from 56 donors, allelic imbalance assay from 7 donors, and luciferase assays demonstrate that CAD/IS-protective allele at rs17114036 in PLPP3 intron 5 confers increased endothelial enhancer activity. ChIP-PCR and luciferase assays show that CAD/IS-protective allele at rs17114036 creates a binding site for transcription factor Krüppel-like factor 2 (KLF2), which increases the enhancer activity under unidirectional flow. These results demonstrate that a human SNP contributes to critical endothelial mechanotransduction mechanisms and suggest that human haplotypes and related cis-regulatory elements provide a previously unappreciated layer of regulatory control in cellular mechanosensing mechanisms.

Ensemble Consensus-Guided Unsupervised Feature Selection to Identify Huntington's Disease-Associated Genes

Due to the complexity of the pathological mechanisms of neurodegenerative diseases, traditional differentially-expressed gene selection methods cannot detect disease-associated genes accurately. Recent studies have shown that consensus-guided unsupervised feature selection (CGUFS) performs well in feature selection for identifying disease-associated genes. Since the random initialization of the feature selection matrix in CGUFS results in instability of the final disease-associated gene set, for the purposes of this study we proposed an ensemble method based on CGUFS-namely, ensemble consensus-guided unsupervised feature selection (ECGUFS) in order to further improve the accuracy of disease-associated genes and the stability of feature gene sets. We also proposed a bagging integration strategy to integrate the results of CGUFS. Lastly, we conducted experiments with Huntington's disease RNA sequencing (RNA-Seq) data and obtained the final feature gene set, where we detected 287 disease-associated genes. Enrichment analysis on these genes has shown that postsynaptic density and the postsynaptic membrane, synapse, and cell junction are all affected during the disease's progression. However, ECGUFS greatly improved the accuracy of disease-associated gene prediction and the stability of the disease-associated gene set. We conducted a classification of samples with labels based on the linear support vector machine with 10-fold cross-validation. The average accuracy is 0.9, which suggests the effectiveness of the feature gene set.

Oxidized phospholipids regulate amino acid metabolism through MTHFD2 to facilitate nucleotide release in endothelial cells

Oxidized phospholipids (oxPAPC) induce endothelial dysfunction and atherosclerosis. Here we show that oxPAPC induce a gene network regulating serine-glycine metabolism with the mitochondrial methylenetetrahydrofolate dehydrogenase/cyclohydrolase (MTHFD2) as a causal regulator using integrative network modeling and Bayesian network analysis in human aortic endothelial cells. The cluster is activated in human plaque material and by atherogenic lipoproteins isolated from plasma of patients with coronary artery disease (CAD). Single nucleotide polymorphisms (SNPs) within the MTHFD2-controlled cluster associate with CAD. The MTHFD2-controlled cluster redirects metabolism to glycine synthesis to replenish purine nucleotides. Since endothelial cells secrete purines in response to oxPAPC, the MTHFD2-controlled response maintains endothelial ATP. Accordingly, MTHFD2-dependent glycine synthesis is a prerequisite for angiogenesis. Thus, we propose that endothelial cells undergo MTHFD2-mediated reprogramming toward serine-glycine and mitochondrial one-carbon metabolism to compensate for the loss of ATP in response to oxPAPC during atherosclerosis.

Interactions between genetic variation and cellular environment in skeletal muscle gene expression

From whole organisms to individual cells, responses to environmental conditions are influenced by genetic makeup, where the effect of genetic variation on a trait depends on the environmental context. RNA-sequencing quantifies gene expression as a molecular trait, and is capable of capturing both genetic and environmental effects. In this study, we explore opportunities of using allele-specific expression (ASE) to discover cis-acting genotype-environment interactions (GxE)—genetic effects on gene expression that depend on an environmental condition. Treating 17 common, clinical traits as approximations of the cellular environment of 267 skeletal muscle biopsies, we identify 10 candidate environmental response expression quantitative trait loci (reQTLs) across 6 traits (12 unique gene-environment trait pairs; 10% FDR per trait) including sex, systolic blood pressure, and low-density lipoprotein cholesterol. Although using ASE is in principle a promising approach to detect GxE effects, replication of such signals can be challenging as validation requires harmonization of environmental traits across cohorts and a sufficient sampling of heterozygotes for a transcribed SNP. Comprehensive discovery and replication will require large human transcriptome datasets, or the integration of multiple transcribed SNPs, coupled with standardized clinical phenotyping.

Advances in the Genetics of Hypertension: The Effect of Rare Variants

Worldwide, hypertension still represents a serious health burden with nine million people dying as a consequence of hypertension-related complications. Essential hypertension is a complex trait supported by multifactorial genetic inheritance together with environmental factors. The heritability of blood pressure (BP) is estimated to be 30-50%. A great effort was made to find genetic variants affecting BP levels through Genome-Wide Association Studies (GWAS). This approach relies on the "common disease-common variant" hypothesis and led to the identification of multiple genetic variants which explain, in aggregate, only 2-3% of the genetic variance of hypertension. Part of the missing genetic information could be caused by variants too rare to be detected by GWAS. The use of exome chips and Next-Generation Sequencing facilitated the discovery of causative variants. Here, we report the advances in the detection of novel rare variants, genes, and/or pathways through the most promising approaches, and the recent statistical tests that have emerged to handle rare variants. We also discuss the need to further support rare novel variants with replication studies within larger consortia and with deeper functional studies to better understand how new genes might improve patient care and the stratification of the response to antihypertensive treatments.

Identify Huntington's disease associated genes based on restricted Boltzmann machine with RNA-seq data

BACKGROUND

Predicting disease-associated genes is helpful for understanding the molecular mechanisms during the disease progression. Since the pathological mechanisms of neurodegenerative diseases are very complex, traditional statistic-based methods are not suitable for identifying key genes related to the disease development. Recent studies have shown that the computational models with deep structure can learn automatically the features of biological data, which is useful for exploring the characteristics of gene expression during the disease progression.

RESULTS

In this paper, we propose a deep learning approach based on the restricted Boltzmann machine to analyze the RNA-seq data of Huntington's disease, namely stacked restricted Boltzmann machine (SRBM). According to the SRBM, we also design a novel framework to screen the key genes during the Huntington's disease development. In this work, we assume that the effects of regulatory factors can be captured by the hierarchical structure and narrow hidden layers of the SRBM. First, we select disease-associated factors with different time period datasets according to the differentially activated neurons in hidden layers. Then, we select disease-associated genes according to the changes of the gene energy in SRBM at different time periods.

CONCLUSIONS

The experimental results demonstrate that SRBM can detect the important information for differential analysis of time series gene expression datasets. The identification accuracy of the disease-associated genes is improved to some extent using the novel framework. Moreover, the prediction precision of disease-associated genes for top ranking genes using SRBM is effectively improved compared with that of the state of the art methods.

Transcriptional networks specifying homeostatic and inflammatory programs of gene expression in human aortic endothelial cells

Endothelial cells (ECs) are critical determinants of vascular homeostasis and inflammation, but transcriptional mechanisms specifying their identities and functional states remain poorly understood. Here, we report a genome-wide assessment of regulatory landscapes of primary human aortic endothelial cells (HAECs) under basal and activated conditions, enabling inference of transcription factor networks that direct homeostatic and pro-inflammatory programs. We demonstrate that 43% of detected enhancers are EC-specific and contain SNPs associated to cardiovascular disease and hypertension. We provide evidence that AP1, ETS, and GATA transcription factors play key roles in HAEC transcription by co-binding enhancers associated with EC-specific genes. We further demonstrate that exposure of HAECs to oxidized phospholipids or pro-inflammatory cytokines results in signal-specific alterations in enhancer landscapes and associate with coordinated binding of CEBPD, IRF1, and NFκB. Collectively, these findings identify cis-regulatory elements and corresponding trans-acting factors that contribute to EC identity and their specific responses to pro-inflammatory stimuli.

DOI:http://dx.doi.org/10.7554/eLife.22536.001

Disease-related gene module detection based on a multi-label propagation clustering algorithm

Detecting disease-related gene modules by analyzing gene expression data is of great significance. It is helpful for exploratory analysis of the interaction mechanisms of genes under complex disease phenotypes. The multi-label propagation algorithm (MLPA) has been widely used in module detection for its fast and easy implementation. The accuracy of MLPA greatly depends on the connections between nodes, and most existing research focuses on measuring the similarity between nodes. However, MLPA does not perform well with loose connections between disease-related genes. Moreover, the biological significance of modules obtained by MLPA has not been demonstrated. To solve these problems, we designed a double label propagation clustering algorithm (DLPCA) based on MLPA to study Huntington's disease. In DLPCA, in addition to category labels, we introduced pathogenic labels to supervise the process of multi-label propagation clustering. The pathogenic labels contain pathogenic information about disease genes and the hierarchical structure of gene expression data. Experimental results demonstrated the superior performance of DLPCA compared with other conventional gene-clustering algorithms.

Genetic regulation of gene expression in the epileptic human hippocampus

Epilepsy is a serious and common neurological disorder. Expression quantitative loci (eQTL) analysis is a vital aid for the identification and interpretation of disease-risk loci. Many eQTLs operate in a tissue- and condition-specific manner. We have performed the first genome-wide cis-eQTL analysis of human hippocampal tissue to include not only normal (n = 22) but also epileptic (n = 22) samples. We demonstrate that disease-associated variants from an epilepsy GWAS meta-analysis and a febrile seizures (FS) GWAS are significantly more enriched with epilepsy-eQTLs than with normal hippocampal eQTLs from two larger independent published studies. In contrast, GWAS meta-analyses of two other brain diseases associated with hippocampal pathology (Alzheimer's disease and schizophrenia) are more enriched with normal hippocampal eQTLs than with epilepsy-eQTLs. These observations suggest that an eQTL analysis that includes disease-affected brain tissue is advantageous for detecting additional risk SNPs for the afflicting and closely related disorders, but not for distinct diseases affecting the same brain regions. We also show that epilepsy eQTLs are enriched within epilepsy-causing genes: an epilepsy cis-gene is significantly more likely to be a causal gene for a Mendelian epilepsy syndrome than to be a causal gene for another Mendelian disorder. Epilepsy cis-genes, compared to normal hippocampal cis-genes, are more enriched within epilepsy-causing genes. Hence, we utilize the epilepsy eQTL data for the functional interpretation of epilepsy disease-risk variants and, thereby, highlight novel potential causal genes for sporadic epilepsy. In conclusion, an epilepsy-eQTL analysis is superior to normal hippocampal tissue eQTL analyses for identifying the variants and genes underlying epilepsy.

Time-dependent genetic effects on gene expression implicate aging processes

Gene expression is dependent on genetic and environmental factors. In the last decade, a large body of research has significantly improved our understanding of the genetic architecture of gene expression. However, it remains unclear whether genetic effects on gene expression remain stable over time. Here, we show, using longitudinal whole-blood gene expression data from a twin cohort, that the genetic architecture of a subset of genes is unstable over time. In addition, we identified 2213 genes differentially expressed across time points that we linked with aging within and across studies. Interestingly, we discovered that most differentially expressed genes were affected by a subset of 77 putative causal genes. Finally, we observed that putative causal genes and down-regulated genes were affected by a loss of genetic control between time points. Taken together, our data suggest that instability in the genetic architecture of a subset of genes could lead to widespread effects on the transcriptome with an aging signature.

Asthma Genetics in the Post-GWAS Era

Genome-wide association studies (GWAS) of asthma have yielded exciting results and identified novel risk alleles and loci. But, like other common complex diseases, asthma-associated alleles have small effect sizes and account for little of the prevalence of asthma. In this review, I discuss the limitations of GWAS approaches and the major challenges facing geneticists in the post-GWAS era and propose alternative strategies to address these challenges. In particular, I propose that focusing on genetic variations that influences gene expression and using cell models of gene-environment interactions in cell types that are relevant to asthma will allow us to more completely characterize the genetic architecture of asthma.

Cardiac Fibroblasts Adopt Osteogenic Fates and Can Be Targeted to Attenuate Pathological Heart Calcification

Mammalian tissues calcify with age and injury. Analogous to bone formation, osteogenic cells are thought to be recruited to the affected tissue and induce mineralization. In the heart, calcification of cardiac muscle leads to conduction system disturbances and is one of the most common pathologies underlying heart blocks. However the cell identity and mechanisms contributing to pathological heart muscle calcification remain unknown. Using lineage tracing, murine models of heart calcification and in vivo transplantation assays, we show that cardiac fibroblasts (CFs) adopt an osteoblast cell-like fate and contribute directly to heart muscle calcification. Small-molecule inhibition of ENPP1, an enzyme that is induced upon injury and regulates bone mineralization, significantly attenuated cardiac calcification. Inhibitors of bone mineralization completely prevented ectopic cardiac calcification and improved post injury heart function. Taken together, these findings highlight the plasticity of fibroblasts in contributing to ectopic calcification and identify pharmacological targets for therapeutic development.

Capture Hi-C identifies a novel causal gene, IL20RA, in the pan-autoimmune genetic susceptibility region 6q23

BACKGROUND

The identification of causal genes from genome-wide association studies (GWAS) is the next important step for the translation of genetic findings into biologically meaningful mechanisms of disease and potential therapeutic targets. Using novel chromatin interaction detection techniques and allele specific assays in T and B cell lines, we provide compelling evidence that redefines causal genes at the 6q23 locus, one of the most important loci that confers autoimmunity risk.

RESULTS

Although the function of disease-associated non-coding single nucleotide polymorphisms (SNPs) at 6q23 is unknown, the association is generally assigned to TNFAIP3, the closest gene. However, the DNA fragment containing the associated SNPs interacts through chromatin looping not only with TNFAIP3, but also with IL20RA, located 680 kb upstream. The risk allele of the most likely causal SNP, rs6927172, is correlated with both a higher frequency of interactions and increased expression of IL20RA, along with a stronger binding of both the NFκB transcription factor and chromatin marks characteristic of active enhancers in T-cells.

CONCLUSIONS

Our results highlight the importance of gene assignment for translating GWAS findings into biologically meaningful mechanisms of disease and potential therapeutic targets; indeed, monoclonal antibody therapy targeting IL-20 is effective in the treatment of rheumatoid arthritis and psoriasis, both with strong GWAS associations to this region.

From integrative genomics to systems genetics in the rat to link genotypes to phenotypes

Complementary to traditional gene mapping approaches used to identify the hereditary components of complex diseases, integrative genomics and systems genetics have emerged as powerful strategies to decipher the key genetic drivers of molecular pathways that underlie disease. Broadly speaking, integrative genomics aims to link cellular-level traits (such as mRNA expression) to the genome to identify their genetic determinants. With the characterization of several cellular-level traits within the same system, the integrative genomics approach evolved into a more comprehensive study design, called systems genetics, which aims to unravel the complex biological networks and pathways involved in disease, and in turn map their genetic control points. The first fully integrated systems genetics study was carried out in rats, and the results, which revealed conserved trans-acting genetic regulation of a pro-inflammatory network relevant to type 1 diabetes, were translated to humans. Many studies using different organisms subsequently stemmed from this example. The aim of this Review is to describe the most recent advances in the fields of integrative genomics and systems genetics applied in the rat, with a focus on studies of complex diseases ranging from inflammatory to cardiometabolic disorders. We aim to provide the genetics community with a comprehensive insight into how the systems genetics approach came to life, starting from the first integrative genomics strategies [such as expression quantitative trait loci (eQTLs) mapping] and concluding with the most sophisticated gene network-based analyses in multiple systems and disease states. Although not limited to studies that have been directly translated to humans, we will focus particularly on the successful investigations in the rat that have led to primary discoveries of genes and pathways relevant to human disease.

Role of lipid phosphate phosphatase 3 in human aortic endothelial cell function

AIMS

Lipid phosphate phosphatase 3; type 2 phosphatidic acid phosphatase β (LPP3; PPAP2B) is a transmembrane protein dephosphorylating and thereby terminating signalling of lipid substrates including lysophosphatidic acid (LPA) and sphingosine-1-phosphate (S1P). Human LPP3 possesses a cell adhesion motif that allows interaction with integrins. A polymorphism (rs17114036) in PPAP2B is associated with coronary artery disease, which prompted us to investigate the possible role of LPP3 in human endothelial dysfunction, a condition promoting atherosclerosis.

METHODS AND RESULTS

To study the role of LPP3 in endothelial cells we used human primary aortic endothelial cells (HAECs) in which LPP3 was silenced or overexpressed using either wild type or mutated cDNA constructs. LPP3 silencing in HAECs enhanced secretion of inflammatory cytokines, leucocyte adhesion, cell survival, and migration and impaired angiogenesis, whereas wild-type LPP3 overexpression reversed these effects and induced apoptosis. We also demonstrated that LPP3 expression was negatively correlated with vascular endothelial growth factor expression. Mutations in either the catalytic or the arginine-glycine-aspartate (RGD) domains impaired endothelial cell function and pharmacological inhibition of S1P or LPA restored it. LPA was not secreted in HAECs under silencing or overexpressing LPP3. However, the intra- and extra-cellular levels of S1P tended to be correlated with LPP3 expression, indicating that S1P is probably degraded by LPP3.

CONCLUSIONS

We demonstrated that LPP3 is a negative regulator of inflammatory cytokines, leucocyte adhesion, cell survival, and migration in HAECs, suggesting a protective role of LPP3 against endothelial dysfunction in humans. Both the catalytic and the RGD functional domains were involved and S1P, but not LPA, might be the endogenous substrate of LPP3.

A missense variant in FGD6 confers increased risk of polypoidal choroidal vasculopathy

Polypoidal choroidal vasculopathy (PCV), a subtype of 'wet' age-related macular degeneration (AMD), constitutes up to 55% of cases of wet AMD in Asian patients. In contrast to the choroidal neovascularization (CNV) subtype, the genetic risk factors for PCV are relatively unknown. Exome sequencing analysis of a Han Chinese cohort followed by replication in four independent cohorts identified a rare c.986A>G (p.Lys329Arg) variant in the FGD6 gene as significantly associated with PCV (P = 2.19 × 10(-16), odds ratio (OR) = 2.12) but not with CNV (P = 0.26, OR = 1.13). The intracellular localization of FGD6-Arg329 is distinct from that of FGD6-Lys329. In vitro, FGD6 could regulate proangiogenic activity, and oxidized phospholipids increased expression of FGD6. FGD6-Arg329 promoted more abnormal vessel development in the mouse retina than FGD6-Lys329. Collectively, our data suggest that oxidized phospholipids and FGD6-Arg329 might act synergistically to increase susceptibility to PCV.

Accounting for Population Structure in Gene-by-Environment Interactions in Genome-Wide Association Studies Using Mixed Models

Although genome-wide association studies (GWASs) have discovered numerous novel genetic variants associated with many complex traits and diseases, those genetic variants typically explain only a small fraction of phenotypic variance. Factors that account for phenotypic variance include environmental factors and gene-by-environment interactions (GEIs). Recently, several studies have conducted genome-wide gene-by-environment association analyses and demonstrated important roles of GEIs in complex traits. One of the main challenges in these association studies is to control effects of population structure that may cause spurious associations. Many studies have analyzed how population structure influences statistics of genetic variants and developed several statistical approaches to correct for population structure. However, the impact of population structure on GEI statistics in GWASs has not been extensively studied and nor have there been methods designed to correct for population structure on GEI statistics. In this paper, we show both analytically and empirically that population structure may cause spurious GEIs and use both simulation and two GWAS datasets to support our finding. We propose a statistical approach based on mixed models to account for population structure on GEI statistics. We find that our approach effectively controls population structure on statistics for GEIs as well as for genetic variants.

Autoimmune diseases - connecting risk alleles with molecular traits of the immune system

Genome-wide strategies have driven the discovery of more than 300 susceptibility loci for autoimmune diseases. However, for almost all loci, understanding of the mechanisms leading to autoimmunity remains limited, and most variants that are likely to be causal are in non-coding regions of the genome. A critical next step will be to identify the in vivo and ex vivo immunophenotypes that are affected by risk variants. To do this, key cell types and cell states that are implicated in autoimmune diseases will need to be defined. Functional genomic annotations from these cell types and states can then be used to resolve candidate genes and causal variants. Together with longitudinal studies, this approach may yield pivotal insights into how autoimmunity is triggered.

Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases

Complex and dynamic networks of molecules are involved in human diseases. High-throughput technologies enable omics studies interrogating thousands to millions of makers with similar biochemical properties (eg, transcriptomics for RNA transcripts). However, a single layer of "omics" can only provide limited insights into the biological mechanisms of a disease. In the case of genome-wide association studies, although thousands of single nucleotide polymorphisms have been identified for complex diseases and traits, the functional implications and mechanisms of the associated loci are largely unknown. Additionally, the genomic variants alone are not able to explain the changing disease risk across the life span. DNA, RNA, protein, and metabolite often have complementary roles to jointly perform a certain biological function. Such complementary effects and synergistic interactions between omic layers in the life course can only be captured by integrative study of multiple molecular layers. Building upon the success in single-omics discovery research, population studies started adopting the multi-omics approach to better understanding the molecular function and disease etiology. Multi-omics approaches integrate data obtained from different omic levels to understand their interrelation and combined influence on the disease processes. Here, we summarize major omics approaches available in population research, and review integrative approaches and methodologies interrogating multiple omic layers, which enhance the gene discovery and functional analysis of human diseases. We seek to provide analytical recommendations for different types of multi-omics data and study designs to guide the emerging multi-omic research, and to suggest improvement of the existing analytical methods.

Endothelial NOTCH1 is suppressed by circulating lipids and antagonizes inflammation during atherosclerosis

Briot et al. show that inflammatory lipids deriving from a high-fat diet suppress NOTCH1 expression and signaling in adult arterial endothelium and propose that reduction of endothelial NOTCH1 is a predisposing factor in the onset of atherosclerosis.

Although much progress has been made in identifying the mechanisms that trigger endothelial activation and inflammatory cell recruitment during atherosclerosis, less is known about the intrinsic pathways that counteract these events. Here we identified NOTCH1 as an antagonist of endothelial cell (EC) activation. NOTCH1 was constitutively expressed by adult arterial endothelium, but levels were significantly reduced by high-fat diet. Furthermore, treatment of human aortic ECs (HAECs) with inflammatory lipids (oxidized 1-palmitoyl-2-arachidonoyl-sn-glycero-3-phosphocholine [Ox-PAPC]) and proinflammatory cytokines (TNF and IL1β) decreased Notch1 expression and signaling in vitro through a mechanism that requires STAT3 activation. Reduction of NOTCH1 in HAECs by siRNA, in the absence of inflammatory lipids or cytokines, increased inflammatory molecules and binding of monocytes. Conversely, some of the effects mediated by Ox-PAPC were reversed by increased NOTCH1 signaling, suggesting a link between lipid-mediated inflammation and Notch1. Interestingly, reduction of NOTCH1 by Ox-PAPC in HAECs was associated with a genetic variant previously correlated to high-density lipoprotein in a human genome-wide association study. Finally, endothelial Notch1 heterozygous mice showed higher diet-induced atherosclerosis. Based on these findings, we propose that reduction of endothelial NOTCH1 is a predisposing factor in the onset of vascular inflammation and initiation of atherosclerosis.

Exploiting genomics and natural genetic variation to decode macrophage enhancers

The mammalian genome contains on the order of a million enhancer-like regions that are required to establish the identities and functions of specific cell types. Here, we review recent studies in immune cells that have provided insight into the mechanisms that selectively activate certain enhancers in response to cell lineage and environmental signals. We describe a working model wherein distinct classes of transcription factors define the repertoire of active enhancers in macrophages through collaborative and hierarchical interactions, and discuss important challenges to this model, specifically providing examples from T cells. We conclude by discussing the use of natural genetic variation as a powerful approach for decoding transcription factor combinations that play dominant roles in establishing the enhancer landscapes, and the potential that these insights have for advancing our understanding of the molecular causes of human disease.

Human CHAC1 Protein Degrades Glutathione, and mRNA Induction Is Regulated by the Transcription Factors ATF4 and ATF3 and a Bipartite ATF/CRE Regulatory Element

Using an unbiased systems genetics approach, we previously predicted a role for CHAC1 in the endoplasmic reticulum stress pathway, linked functionally to activating transcription factor 4 (ATF4) following treatment with oxidized phospholipids, a model for atherosclerosis. Mouse and yeast CHAC1 homologs have been shown to degrade glutathione in yeast and a cell-free system. In this report, we further defined the ATF4-CHAC1 interaction by cloning the human CHAC1 promoter upstream of a luciferase reporter system for in vitro assays in HEK293 and U2OS cells. Mutation and deletion analyses defined two major cis DNA elements necessary and sufficient for CHAC1 promoter-driven luciferase transcription under conditions of ER stress or ATF4 coexpression: the -267 ATF/cAMP response element (CRE) site and a novel -248 ATF/CRE modifier (ACM) element. We also examined the ability of the CHAC1 ATF/CRE and ACM sequences to bind ATF4 and ATF3 using immunoblot-EMSA and confirmed ATF4, ATF3, and CCAAT/enhancer-binding protein β binding at the human CHAC1 promoter in the proximity of the ATF/CRE and ACM using ChIP. To further validate the function of CHAC1 in a human cell model, we measured glutathione levels in HEK293 cells with enhanced CHAC1 expression. Overexpression of CHAC1 led to a robust depletion of glutathione, which was alleviated in a CHAC1 catalytic mutant. These results suggest an important role for CHAC1 in oxidative stress and apoptosis with implications for human health and disease.

Genome-wide significant loci: how important are they? Systems genetics to understand heritability of coronary artery disease and other common complex disorders

Genome-wide association studies (GWAS) have been extensively used to study common complex diseases such as coronary artery disease (CAD), revealing 153 suggestive CAD loci, of which at least 46 have been validated as having genome-wide significance. However, these loci collectively explain <10% of the genetic variance in CAD. Thus, we must address the key question of what factors constitute the remaining 90% of CAD heritability. We review possible limitations of GWAS, and contextually consider some candidate CAD loci identified by this method. Looking ahead, we propose systems genetics as a complementary approach to unlocking the CAD heritability and etiology. Systems genetics builds network models of relevant molecular processes by combining genetic and genomic datasets to ultimately identify key "drivers" of disease. By leveraging systems-based genetic approaches, we can help reveal the full genetic basis of common complex disorders, enabling novel diagnostic and therapeutic opportunities.

Dissecting dynamic genetic variation that controls temporal gene response in yeast

Inter-individual variation in regulatory circuits controlling gene expression is a powerful source of functional information. The study of associations among genetic variants and gene expression provides important insights about cell circuitry but cannot specify whether and when potential variants dynamically alter their genetic effect during the course of response. Here we develop a computational procedure that captures temporal changes in genetic effects, and apply it to analyze transcription during inhibition of the TOR signaling pathway in segregating yeast cells. We found a high-order coordination of gene modules: sets of genes co-associated with the same genetic variant and sharing a common temporal genetic effect pattern. The temporal genetic effects of some modules represented a single state-transitioning pattern; for example, at 10-30 minutes following stimulation, genetic effects in the phosphate utilization module attained a characteristic transition to a new steady state. In contrast, another module showed an impulse pattern of genetic effects; for example, in the poor nitrogen sources utilization module, a spike up of a genetic effect at 10-20 minutes following stimulation reflected inter-individual variation in the timing (rather than magnitude) of response. Our analysis suggests that the same mechanism typically leads to both inter-individual variation and the temporal genetic effect pattern in a module. Our methodology provides a quantitative genetic approach to studying the molecular mechanisms that shape dynamic changes in transcriptional responses.

Impact of 60-GHz millimeter waves and corresponding heat effect on endoplasmic reticulum stress sensor gene expression

Emerging high data rate wireless communication systems, currently under development, will operate at millimeter waves (MMW) and specifically in the 60 GHz band for broadband short-range communications. The aim of this study was to investigate potential effects of MMW radiation on the cellular endoplasmic reticulum (ER) stress. Human skin cell lines were exposed at 60.4 GHz, with incident power densities (IPD) ranging between 1 and 20 mW/cm(2) . The upper IPD limits correspond to the ICNIRP local exposure limit for the general public. The expression of ER-stress sensors, namely BIP and ORP150, was then examined by real-time RT-PCR. Our experimental data demonstrated that MMW radiations do not change BIP or ORP150 mRNA basal levels, whatever the cell line, the exposure duration or the IPD level. Co-exposure to the well-known ER-stress inducer thapsigargin (TG) and MMW were then assessed. Our results show that MMW exposure at 20 mW/cm(2) inhibits TG-induced BIP and ORP150 over expression. Experimental controls showed that this inhibition is linked to the thermal effect resulting from the MMW exposure.

Integrative genomics reveals novel molecular pathways and gene networks for coronary artery disease

The majority of the heritability of coronary artery disease (CAD) remains unexplained, despite recent successes of genome-wide association studies (GWAS) in identifying novel susceptibility loci. Integrating functional genomic data from a variety of sources with a large-scale meta-analysis of CAD GWAS may facilitate the identification of novel biological processes and genes involved in CAD, as well as clarify the causal relationships of established processes. Towards this end, we integrated 14 GWAS from the CARDIoGRAM Consortium and two additional GWAS from the Ottawa Heart Institute (25,491 cases and 66,819 controls) with 1) genetics of gene expression studies of CAD-relevant tissues in humans, 2) metabolic and signaling pathways from public databases, and 3) data-driven, tissue-specific gene networks from a multitude of human and mouse experiments. We not only detected CAD-associated gene networks of lipid metabolism, coagulation, immunity, and additional networks with no clear functional annotation, but also revealed key driver genes for each CAD network based on the topology of the gene regulatory networks. In particular, we found a gene network involved in antigen processing to be strongly associated with CAD. The key driver genes of this network included glyoxalase I (GLO1) and peptidylprolyl isomerase I (PPIL1), which we verified as regulatory by siRNA experiments in human aortic endothelial cells. Our results suggest genetic influences on a diverse set of both known and novel biological processes that contribute to CAD risk. The key driver genes for these networks highlight potential novel targets for further mechanistic studies and therapeutic interventions.

Sudden death due to heart attack ranks among the top causes of death in the world, and family studies have shown that genetics has a substantial effect on heart disease risk. Recent studies suggest that multiple genetic factors each with modest effects are necessary for the development of CAD, but the genes and molecular processes involved remain poorly understood. We conducted an integrative genomics study where we used the information of gene-gene interactions to capture groups of genes that are most likely to increase heart disease risk. We not only confirmed the importance of several known CAD risk processes such as the metabolism and transport of cholesterol, immune response, and blood coagulation, but also revealed many novel processes such as neuroprotection, cell cycle, and proteolysis that were not previously implicated in CAD. In particular, we highlight several genes such as GLO1 with key regulatory roles within these processes not detected by the first wave of genetic analyses. These results highlight the value of integrating population genetic data with diverse resources that functionally annotate the human genome. Such integration facilitates the identification of novel molecular processes involved in the pathogenesis of CAD as well as potential novel targets for the development of efficacious therapeutic interventions.

Genetic architecture of ethanol-responsive transcriptome variation in Saccharomyces cerevisiae strains

Natural variation in gene expression is pervasive within and between species, and it likely explains a significant fraction of phenotypic variation between individuals. Phenotypic variation in acute systemic responses can also be leveraged to reveal physiological differences in how individuals perceive and respond to environmental perturbations. We previously found extensive variation in the transcriptomic response to acute ethanol exposure in two wild isolates and a common laboratory strain of Saccharomyces cerevisiae. Many expression differences persisted across several modules of coregulated genes, implicating trans-acting systemic differences in ethanol sensing and/or response. Here, we conducted expression QTL mapping of the ethanol response in two strain crosses to identify the genetic basis for these differences. To understand systemic differences, we focused on "hotspot" loci that affect many transcripts in trans. Candidate causal regulators contained within hotspots implicate upstream regulators as well as downstream effectors of the ethanol response. Overlap in hotspot targets revealed additive genetic effects of trans-acting loci as well as "epi-hotspots," in which epistatic interactions between two loci affected the same suites of downstream targets. One epi-hotspot implicated interactions between Mkt1p and proteins linked to translational regulation, prompting us to show that Mkt1p localizes to P bodies upon ethanol stress in a strain-specific manner. Our results provide a glimpse into the genetic architecture underlying natural variation in a stress response and present new details on how yeast respond to ethanol stress.

A vascular biology network model focused on inflammatory processes to investigate atherogenesis and plaque instability

Background

Numerous inflammation-related pathways have been shown to play important roles in atherogenesis. Rapid and efficient assessment of the relative influence of each of those pathways is a challenge in the era of “omics” data generation. The aim of the present work was to develop a network model of inflammation-related molecular pathways underlying vascular disease to assess the degree of translatability of preclinical molecular data to the human clinical setting.

Methods

We constructed and evaluated the Vascular Inflammatory Processes Network (V-IPN), a model representing a collection of vascular processes modulated by inflammatory stimuli that lead to the development of atherosclerosis.

Results

Utilizing the V-IPN as a platform for biological discovery, we have identified key vascular processes and mechanisms captured by gene expression profiling data from four independent datasets from human endothelial cells (ECs) and human and murine intact vessels. Primary ECs in culture from multiple donors revealed a richer mapping of mechanisms identified by the V-IPN compared to an immortalized EC line. Furthermore, an evaluation of gene expression datasets from aortas of old ApoE^-/- mice (78 weeks) and human coronary arteries with advanced atherosclerotic lesions identified significant commonalities in the two species, as well as several mechanisms specific to human arteries that are consistent with the development of unstable atherosclerotic plaques.

Conclusions

We have generated a new biological network model of atherogenic processes that demonstrates the power of network analysis to advance integrative, systems biology-based knowledge of cross-species translatability, plaque development and potential mechanisms leading to plaque instability.

HDL inhibits the effects of oxidized phospholipids on endothelial cell gene expression via multiple mechanisms

Oxidized 1-palmitoyl-2-arachidonyl-sn-glycero-3-phospholcholine (OxPAPC) and its component phospholipids accumulate in atherosclerotic lesions and regulate the expression of >1,000 genes, many proatherogenic, in human aortic endothelial cells (HAECs). In contrast, there is evidence in the literature that HDL protects the vasculature from inflammatory insult. We have previously shown that in HAECs, HDL attenuates the expression of several proatherogenic genes regulated by OxPAPC and 1-palmitoyl-2-(5,6-epoxyisoprostane E2)-sn-glycero-3-phosphocholine. We now demonstrate that HDL reverses >50% of the OxPAPC transcriptional response. Genes reversed by HDL are enriched for inflammatory and vascular development pathways, while genes not affected by HDL are enriched for oxidative stress response pathways. The protective effect of HDL is partially mimicked by cholesterol repletion and treatment with apoA1 but does not require signaling through scavenger receptor class B type I. Furthermore, our data demonstrate that HDL protection requires direct interaction with OxPAPC. HDL-associated platelet-activating factor acetylhydrolase (PAF-AH) hydrolyzes short-chain bioactive phospholipids in OxPAPC; however, inhibiting PAF-AH activity does not prevent HDL protection. Our results are consistent with HDL sequestering specific bioactive lipids in OxPAPC, thereby preventing their regulation of select target genes. Overall, this work implicates HDL as a major regulator of OxPAPC action in endothelial cells via multiple mechanisms.

From genome to function by studying eQTLs

Genome-wide association studies (GWASs) have shown a large number of genetic variants to be associated with complex diseases. The identification of the causal variant within an associated locus can sometimes be difficult because of the linkage disequilibrium between the associated variants and because most GWAS loci contain multiple genes, or no genes at all. Expression quantitative trait locus (eQTL) mapping is a method used to determine the effects of genetic variants on gene expression levels. eQTL mapping studies have enabled the prioritization of genetic variants within GWAS loci, and have shown that trait-associated single nucleotide polymorphisms (SNPs) often function in a tissue- or cell type-specific manner, sometimes having downstream effects on completely different chromosomes. Furthermore, recent RNA-sequencing (RNA-seq) studies have shown that a large repertoire of transcripts is available in cells, which are actively regulated by (trait-associated) variants. Future eQTL mapping studies will focus on broadening the range of available tissues and cell types, in order to determine the key tissues and cell types involved in complex traits. Finally, large meta-analyses will be able to pinpoint the causal variants within the trait-associated loci and determine their downstream effects in greater detail. This article is part of a Special Issue entitled: From Genome to Function.

Expression quantitative trait analyses to identify causal genetic variants for type 2 diabetes susceptibility

Type 2 diabetes (T2D) is a common metabolic disorder which is caused by multiple genetic perturbations affecting different biological pathways. Identifying genetic factors modulating the susceptibility of this complex heterogeneous metabolic phenotype in different ethnic and racial groups remains challenging. Despite recent success, the functional role of the T2D susceptibility variants implicated by genome-wide association studies (GWAS) remains largely unknown. Genetic dissection of transcript abundance or expression quantitative trait (eQTL) analysis unravels the genomic architecture of regulatory variants. Availability of eQTL information from tissues relevant for glucose homeostasis in humans opens a new avenue to prioritize GWAS-implicated variants that may be involved in triggering a causal chain of events leading to T2D. In this article, we review the progress made in the field of eQTL research and knowledge gained from those studies in understanding transcription regulatory mechanisms in human subjects. We highlight several novel approaches that can integrate eQTL analysis with multiple layers of biological information to identify ethnic-specific causal variants and gene-environment interactions relevant to T2D pathogenesis. Finally, we discuss how the eQTL analysis mediated search for “missing heritability” may lead us to novel biological and molecular mechanisms involved in susceptibility to T2D.