Gene expression signatures of coronary heart disease

Network Analysis of Gene Expression

Studies have pointed out that the expression of genes are highly regulated, which result in a cascade of distinct patterns of coexpression forming a network. Identifying and understanding such patterns is crucial in deciphering molecular mechanisms that underlie the pathophysiology of diseases. With the advance of high throughput assay of messenger RNA (mRNA) and high performance computing, reconstructing such network from molecular data such as gene expression is now possible. This chapter discusses an overview of methods of constructing such networks, practical considerations, and an example.

Transcriptome-wide association study of inflammatory biologic age

Chronic low grade inflammation is a fundamental mechanism of aging. We estimated biologic age using nine biomarkers from diverse inflammatory pathways and we hypothesized that genes associated with inflammatory biological age would provide insights into human aging. In Framingham Offspring Study participants at examination 8 (2005 to 2008), we used the Klemera-Doubal method to estimate inflammatory biologic age and we computed the difference (∆Age) between biologic age and chronologic age. Gene expression in whole blood was measured using the Affymetrix Human Exon 1.0 ST Array. We used linear mixed effect models to test associations between inflammatory ∆Age and gene expression (dependent variable) adjusting for age, sex, imputed cell counts, and technical covariates. Our study sample included 2386 participants (mean age 67A±9 years, 55% women). There were 448 genes significantly were associated with inflammatory ∆Age (P<2.8x10^-6), 302 genes were positively associated and 146 genes were negatively associated. Pathway analysis among the identified genes highlighted the NOD-like receptor signaling and ubiquitin mediated proteolysis pathways. In summary, we identified 448 genes that were significantly associated with inflammatory biologic age. Future functional characterization may identify molecular interventions to delay aging and prolong healthspan in older adults.

High expression of long chain acyl-coenzyme A synthetase 1 in peripheral blood may be a molecular marker for assessing the risk of acute myocardial infarction

The current study aimed to investigate whether the increased expression of long chain acyl-coenzymeA synthetase 1 (ACSL1) in peripheral blood leukocytes (PBL) may be a molecular marker for the genetic evaluation of acute myocardial infarction (AMI). The mechanism of action of ACSL1 in the pathogenesis of AMI was also investigated. A total of 75 patients with AMI and 70 individuals without coronary heart disease were selected to participate in the present study. The demographic and clinical information of the enrolled subjects was recorded. Reverse transcription quantitative polymerase chain reaction and western blot analysis were applied to measure the expression of ACSL1 at the mRNA and protein levels. It was demonstrated that the expression of ACSL1 mRNA and protein in PBL was increased in patients with AMI compared with controls. Logistic regression analysis indicated that ACSL1 expression in PBL was an independent risk factor of AMI. There was a significant positive association between the level of ACSL1 expression and the degree of atherosclerosis in the coronary artery. Furthermore, patients with AMI exhibited an increased risk of atherosclerosis due to increased fasting blood glucose, total cholesterol, triglyceride and lipoprotein levels and decreased high-density lipoprotein levels, compared with controls. Therefore, the current study demonstrated that ACSL1 expression was increased in the PBLs of patients with AMI. The elevated expression of ACSL1 acts an independent risk factor of AMI and may act as a potential biomarker when determining the risk of AMI.

MicroRNA Signature of Cigarette Smoking and Evidence for a Putative Causal Role of MicroRNAs in Smoking-Related Inflammation and Target Organ Damage

BACKGROUND

Cigarette smoking increases risk for multiple diseases. MicroRNAs regulate gene expression and may play a role in smoking-induced target organ damage. We sought to describe a microRNA signature of cigarette smoking and relate it to smoking-associated clinical phenotypes, gene expression, and lung inflammatory signaling.

METHODS AND RESULTS

Expression profiling of 283 microRNAs was conducted on whole blood-derived RNA from 5023 Framingham Heart Study participants (54.0% women; mean age, 55±13 years) using TaqMan assays and high-throughput reverse transcription quantitative polymerase chain reaction. Associations of microRNA expression with smoking status and associations of smoking-related microRNAs with inflammatory biomarkers and pulmonary function were tested with linear mixed effects models. We identified a 6-microRNA signature of smoking. Five of the 6 smoking-related microRNAs were associated with serum levels of C-reactive protein or interleukin-6; miR-1180 was associated with pulmonary function measures at a marginally significant level. Bioinformatic evaluation of smoking-associated genes coexpressed with the microRNA signature of cigarette smoking revealed enrichment for immune-related pathways. Smoking-associated microRNAs altered expression of selected inflammatory mediators in cell culture gain-of-function assays.

CONCLUSIONS

We characterized a novel microRNA signature of cigarette smoking. The top microRNAs were associated with systemic inflammatory markers and reduced pulmonary function, correlated with expression of genes involved in immune function, and were sufficient to modulate inflammatory signaling. Our results highlight smoking-associated microRNAs and are consistent with the hypothesis that smoking-associated microRNAs serve as mediators of smoking-induced inflammation and target organ damage. These findings call for further mechanistic studies to explore the diagnostic and therapeutic use of smoking-related microRNAs.

Whole blood gene expression and white matter Hyperintensities

Background

White matter hyperintensities (WMH) are an important biomarker of cumulative vascular brain injury and have been associated with cognitive decline and an increased risk of dementia, stroke, depression, and gait impairments. The pathogenesis of white matter lesions however, remains uncertain. The characterization of gene expression profiles associated with WMH might help uncover molecular mechanisms underlying WMH.

Methods

We performed a transcriptome-wide association study of gene expression profiles with WMH in 3248 participants from the Framingham Heart Study using the Affymetrix Human Exon 1.0 ST Array.

Results

We identified 13 genes that were significantly associated with WMH (FDR < 0.05) after adjusting for age, sex and blood cell components. Many of these genes are involved in inflammation-related pathways.

Conclusion

Thirteen genes were significantly associated with WMH. Our study confirms the hypothesis that inflammation might be an important factor contributing to white matter lesions. Future work is needed to explore if these gene products might serve as potential therapeutic targets.

Electronic supplementary material

The online version of this article (10.1186/s13024-017-0209-5) contains supplementary material, which is available to authorized users.

Transcriptomic Signature of Atherosclerosis in the Peripheral Blood: Fact or Fiction?

The notion that gene expression signatures in blood can serve as biomarkers of disease states is not new. In the case of atherosclerosis, and coronary artery disease in particular, whether changes in gene expression in peripheral blood mononuclear cells reflects disease processes occurring in the vessel wall remains controversial. When comparing 15 studies that identified 706 differentially expressed genes, only 23 genes were replicated in 2 to 3 studies, at most. This low level of replication may reflect sample sizes too small to overcome heterogeneity in the response to disease. Genetic differences affect how each person responds to disease and what genes are altered. Recent studies with larger cohorts (over 5000 individuals) that considered the effect of common genetic variants still could not claim disease signature genes as biomarkers suggesting that even larger case-control studies will be required to achieve the required statistical power. On the other hand, out of 7 studies that identified 58 microRNAs, 12 were concordant in 2 or more studies, suggesting that microRNAs may be less affected by genetic differences and more accurately reflect the disease process. Here, we review the current state of knowledge on expression profiling and its utility for predicting coronary artery disease status and mortality.

Cross-sectional relations of whole-blood miRNA expression levels and hand grip strength in a community sample

MicroRNAs (miRNAs) regulate gene expression with emerging data suggesting miRNAs play a role in skeletal muscle biology. We sought to examine the association of miRNAs with grip strength in a community-based sample. Framingham Heart Study Offspring and Generation 3 participants (n = 5668 54% women, mean age 55 years, range 24, 90 years) underwent grip strength measurement and miRNA profiling using whole blood from fasting morning samples. Linear mixed-effects regression modeling of grip strength (kg) versus continuous miRNA 'Cq' values and versus binary miRNA expression was performed. We conducted an integrative miRNA-mRNA coexpression analysis and examined the enrichment of biologic pathways for the top miRNAs associated with grip strength. Grip strength was lower in women than in men and declined with age with a mean 44.7 (10.0) kg in men and 26.5 (6.3) kg in women. Among 299 miRNAs interrogated for association with grip strength, 93 (31%) had FDR q value < 0.05, 54 (18%) had an FDR q value < 0.01, and 15 (5%) had FDR q value < 0.001. For almost all miRNA-grip strength associations, increasing miRNA concentration is associated with increasing grip strength. miR-20a-5p (FDR q 1.8 × 10-6 ) had the most significant association and several among the top 15 miRNAs had links to skeletal muscle including miR-126-3p, miR-30a-5p, and miR-30d-5p. The top associated biologic pathways included metabolism, chemokine signaling, and ubiquitin-mediated proteolysis. Our comprehensive assessment in a community-based sample of miRNAs in blood associated with grip strength provides a framework to further our understanding of the biology of muscle strength.

Invited Commentary: The Framingham Offspring Study-A Pioneering Investigation Into Familial Aggregation of Cardiovascular Risk

Launched in 1948, the Framingham Heart Study was a seminal prospective cohort study of 5,209 adult residents of Framingham, Massachusetts, that was designed to uncover the determinants and natural history of coronary heart disease. Data from this original cohort established the cardiac threat posed by high blood pressure, high cholesterol, smoking, obesity, physical inactivity, diabetes, and other factors. In the late 1960s, investigators conceived the innovative idea of assembling a second cohort that comprised the adult children of the original study population (and these children's spouses). From 1971 to 1975, a total of 5,124 individuals were recruited to form the Offspring Cohort. Studying successive generations in this fashion provided an efficient method for examining secular trends in cardiovascular disease and its risk factors, as well as an opportunity to assess familial aggregation of risk without the threat of recall bias. In a paper published in the September 1979 issue of the Journal, then study director William Kannel et al. (Am J Epidemiol. 1979;110(3):281-290) described the sampling design of the Offspring Study and presented selected baseline characteristics of the cohort. The scientific questions addressed by this research provided the impetus for a decades-long effort-still in full force today both within the Framingham Study itself and in the broader cardiovascular epidemiologic community-to quantify the independent and synergistic effects of genetic, lifestyle, and other environmental factors on cardiovascular outcomes.

Genome-Wide Analysis of DNA Methylation and Acute Coronary Syndrome

Rationale:

Acute coronary syndrome (ACS) is a leading cause of death worldwide. Immune functions play a vital role in ACS development; however, whether epigenetic modulation contributes to the regulation of blood immune cells in this disease has not been investigated.

Objective:

We conducted an epigenome-wide analysis with circulating immune cells to identify differentially methylated genes in ACS.

Methods and Results:

We examined genome-wide methylation of whole blood in 102 ACS patients and 101 controls using HumanMethylation450 array, and externally replicated significant discoveries in 100 patients and 102 controls. For the replicated loci, we further analyzed their association with ACS in 6 purified leukocyte subsets, their correlation with the expressions of annotated genes, and their association with cardiovascular traits/risk factors. We found novel and reproducible association of ACS with blood methylation at 47 cytosine-phosphoguanine sites (discovery: false discovery rate <0.005; replication: Bonferroni corrected P <0.05). The association of methylation levels at these cytosine-phosphoguanine sites with ACS was further validated in at least 1 of the 6 leukocyte subsets, with predominant contributions from CD8 + T cells, CD4 + T cells, and B cells. Blood methylation of 26 replicated cytosine-phosphoguanine sites showed significant correlation with expressions of annotated genes (including IL6R , FASLG , and CCL18 ; P <5.9×10 −4 ), and differential gene expression in case versus controls corroborated the observed differential methylation. The replicated loci suggested a role in ACS-relevant functions including chemotaxis, coronary thrombosis, and T-cell–mediated cytotoxicity. Functional analysis using the top ACS-associated methylation loci in purified T and B cells revealed vital pathways related to atherogenic signaling and adaptive immune response. Furthermore, we observed a significant enrichment of the replicated cytosine-phosphoguanine sites associated with smoking and low-density lipoprotein cholesterol ( P enrichment ≤1×10 −5 ).

Conclusions:

Our study identified novel blood methylation alterations associated with ACS and provided potential clinical biomarkers and therapeutic targets. Our results may suggest that immune signaling and cellular functions might be regulated at an epigenetic level in ACS.

Genome-wide physical activity interactions in adiposity - A meta-analysis of 200,452 adults

Physical activity (PA) may modify the genetic effects that give rise to increased risk of obesity. To identify adiposity loci whose effects are modified by PA, we performed genome-wide interaction meta-analyses of BMI and BMI-adjusted waist circumference and waist-hip ratio from up to 200,452 adults of European (n = 180,423) or other ancestry (n = 20,029). We standardized PA by categorizing it into a dichotomous variable where, on average, 23% of participants were categorized as inactive and 77% as physically active. While we replicate the interaction with PA for the strongest known obesity-risk locus in the FTO gene, of which the effect is attenuated by ~30% in physically active individuals compared to inactive individuals, we do not identify additional loci that are sensitive to PA. In additional genome-wide meta-analyses adjusting for PA and interaction with PA, we identify 11 novel adiposity loci, suggesting that accounting for PA or other environmental factors that contribute to variation in adiposity may facilitate gene discovery.

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.

Blood-based omic profiling supports female susceptibility to tobacco smoke-induced cardiovascular diseases

We recently reported that differential gene expression and DNA methylation profiles in blood leukocytes of apparently healthy smokers predicts with remarkable efficiency diseases and conditions known to be causally associated with smoking, suggesting that blood-based omic profiling of human populations may be useful for linking environmental exposures to potential health effects. Here we report on the sex-specific effects of tobacco smoking on transcriptomic and epigenetic features derived from genome-wide profiling in white blood cells, identifying 26 expression probes and 92 CpG sites, almost all of which are affected only in female smokers. Strikingly, these features relate to numerous genes with a key role in the pathogenesis of cardiovascular disease, especially thrombin signaling, including the thrombin receptors on platelets F2R (coagulation factor II (thrombin) receptor; PAR1) and GP5 (glycoprotein 5), as well as HMOX1 (haem oxygenase 1) and BCL2L1 (BCL2-like 1) which are involved in protection against oxidative stress and apoptosis, respectively. These results are in concordance with epidemiological evidence of higher female susceptibility to tobacco-induced cardiovascular disease and underline the potential of blood-based omic profiling in hazard and risk assessment.

Messenger RNA and MicroRNA transcriptomic signatures of cardiometabolic risk factors

Background

Cardiometabolic (CM) risk factors are heritable and cluster in individuals. We hypothesized that CM risk factors are associated with multiple shared and unique mRNA and microRNA (miRNA) signatures. We examined associations of mRNA and miRNA levels with 6 CM traits: body mass index, HDL-cholesterol and triglycerides, fasting glucose, and systolic and diastolic blood pressures through cross-sectional analysis of 2812 Framingham Heart Study who had whole blood collection for RNA isolation for mRNA and miRNA expression studies and who consented to genetic research. We excluded participants taking medication for hypertension, dyslipidemia, or diabetes. We measured mRNA (n = 17,318; using the Affymetrix GeneChip Human Exon 1.0 ST Array) and miRNA (n = 315; using qRT-PCR) expression in whole blood. We used linear regression for mRNA analyses and a combination of linear and logistic regression for miRNA analyses. We conducted miRNA-mRNA coexpression and gene ontology enrichment analyses to explore relations between pleiotropic miRNAs, mRNA expression, and CM trait clustering.

Results

We identified hundreds of significant associations between mRNAs, miRNAs, and individual CM traits. Four mRNAs (FAM13A, CSF2RB, HIST1H2AC, WNK1) were associated with all 6 CM traits (FDR < 0.001) and four miRNAs (miR-197-3p, miR-328, miR-505-5p, miR-145-5p) were associated with four CM traits (FDR < 0.05). Twelve mRNAs, including WNK1, that were coexpressed with the four most pleiotropic miRNAs, were also miRNA targets. mRNAs coexpressed with pleiotropic miRNAs were enriched for RNA metabolism (miR-505-5p), ubiquitin-dependent protein catabolism (miR-197-3p, miR-328) and chromatin assembly (miR-328).

Conclusions

We identified mRNA and miRNA signatures of individual CM traits and their clustering. Implicated transcripts may play causal roles in CM risk or be downstream consequences of CM risk factors on the transcriptome. Studies are needed to establish whether or not pleiotropic circulating transcripts illuminate causal pathways for CM risk.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-017-3533-9) contains supplementary material, which is available to authorized users.

Methylome-wide Association Study of Atrial Fibrillation in Framingham Heart Study

Atrial fibrillation (AF) is the most common cardiac arrhythmia, but little is known about the molecular mechanisms associated with AF arrhythmogenesis. DNA methylation is an important epigenetic mechanism that regulates gene expression and downstream biological processes. We hypothesize that DNA methylation might play an important role in the susceptibility to develop AF. A total of 2,639 participants from the Offspring Cohort of Framingham Heart Study were enrolled in the current study. These participants included 183 participants with prevalent AF and 220 with incident AF during up to 9 years follow up. Genome-wide methylation was profiled using the Illumina Infinium HumanMethylation450 BeadChip on blood-derived DNA collected during the eighth examination cycle (2005-2008). Two CpG sites were significantly associated with prevalent AF, and five CpGs were associated with incident AF after correction for multiple testing (FDR < 0.05). Fourteen previously reported genome-wide significant AF-related SNP were each associated with at least one CpG site; the most significant association was rs6490029 at the CUX2 locus and cg10833066 (P = 9.5 × 10^-279). In summary, we performed genome-wide methylation profiling in a community-based cohort and identified seven methylation signatures associated with AF. Our study suggests that DNA methylation might play an important role in AF arrhythmogenesis.

Non-linear interactions between candidate genes of myocardial infarction revealed in mRNA expression profiles

BACKGROUND

Alterations in gene expression are key events in disease etiology and risk. Poor reproducibility in detecting differentially expressed genes across studies suggests individual genes may not be sufficiently informative for complex diseases, such as myocardial infarction (MI). Rather, dysregulation of the 'molecular network' may be critical for pathogenic processes. Such a dynamic network can be built from pairwise non-linear interactions.

RESULTS

We investigate non-linear interactions represented in mRNA expression profiles that integrate genetic background and environmental factors. Using logistic regression, we test the association of individual GWAS-based candidate genes and non-linear interaction terms (between these mRNA expression levels) with MI. Based on microarray data in CATHGEN (CATHeterization in GENetics) and FHS (Framingham Heart Study), we find individual genes and pairs of mRNAs, encoded by 41 MI candidate genes, with significant interaction terms in the logistic regression model. Two pairs replicate between CATHGEN and FHS (CNNM2|GUCY1A3 and CNNM2|ZEB2). Analysis of RNAseq data from GTEx (Genotype-Tissue Expression) shows that 20 % of these disease-associated RNA pairs are co-expressed, further prioritizing significant interactions. Because edges in sparse co-expression networks formed solely by the 41 candidate genes are unlikely to represent direct physical interactions, we identify additional RNAs as links between network pairs of candidate genes. This approach reveals additional mRNAs and interaction terms significant in the context of MI, for example, the path CNNM2|ACSL5|SCARF1|GUCY1A3, characterized by the common themes of magnesium and lipid processing.

CONCLUSIONS

The results of this study support a role for non-linear interactions between genes in MI and provide a basis for further study of MI systems biology. mRNA expression profiles encoded by a limited number of candidate genes yield sparse networks of MI-relevant interactions that can be expanded to include additional candidates by co-expression analysis. The non-linear interactions observed here inform our understanding of the clinical relevance of gene-gene interactions in the pathophysiology of MI, while providing a new strategy in developing clinical biomarker panels.

Omics for prediction of environmental health effects: Blood leukocyte-based cross-omic profiling reliably predicts diseases associated with tobacco smoking

The utility of blood-based omic profiles for linking environmental exposures to their potential health effects was evaluated in 649 individuals, drawn from the general population, in relation to tobacco smoking, an exposure with well-characterised health effects. Using disease connectivity analysis, we found that the combination of smoking-modified, genome-wide gene (including miRNA) expression and DNA methylation profiles predicts with remarkable reliability most diseases and conditions independently known to be causally associated with smoking (indicative estimates of sensitivity and positive predictive value 94% and 84%, respectively). Bioinformatics analysis reveals the importance of a small number of smoking-modified, master-regulatory genes and suggest a central role for altered ubiquitination. The smoking-induced gene expression profiles overlap significantly with profiles present in blood cells of patients with lung cancer or coronary heart disease, diseases strongly associated with tobacco smoking. These results provide proof-of-principle support to the suggestion that omic profiling in peripheral blood has the potential of identifying early, disease-related perturbations caused by toxic exposures and may be a useful tool in hazard and risk assessment.

Blood transcriptome changes after stroke in an African American population

Objective

Molecular diagnostic medicine holds much promise to change point of care treatment. An area where additional diagnostic tools are needed is in acute stroke care, to assist in diagnosis and prognosis. Previous studies using microarray‐based gene expression analysis of peripheral blood following stroke suggests this approach may be effective. Next‐generation sequencing (NGS) approaches have expanded genomic analysis and are not limited to previously identified genes on a microarray chip. Here, we report on a pilot NGS study to identify gene expression and exon expression patterns for the prediction of stroke diagnosis and prognosis.

Methods

We recruited 28 stroke patients and 28 age‐ and sex‐matched hypertensive controls. RNA was extracted from 3 mL blood samples, and RNA‐Seq libraries were assembled and sequenced.

Results

Bioinformatical analysis of the aligned RNA data reveal exonic (30%), intronic (36%), and novel RNA components (not currently annotated: 33%). We focused our study on patients with confirmed middle cerebral artery occlusion ischemic stroke (n = 17). On the basis of our observation of differential splicing of gene transcripts, we used all exonic RNA expression rather than gene expression (combined exons) to build prediction models using support vector machine algorithms. Based on model building, these models have a high predicted accuracy rate >90% (spec. 88% sen. 92%). We further stratified outcome based on the improvement in NIHss scores at discharge; based on model building we observe a predicted 100% accuracy rate.

Interpretation

NGS‐based exon expression analysis approaches have a high potential for patient diagnosis and outcome prediction, with clear utility to aid in clinical patient care.

Cohort Profile: The Framingham Heart Study (FHS): overview of milestones in cardiovascular epidemiology

The Framingham Heart Study (FHS) has conducted seminal research defining cardiovascular disease (CVD) risk factors and fundamentally shaping public health guidelines for CVD prevention over the past five decades. The success of the Original Cohort, initiated in 1948, paved the way for further epidemiological research in preventive cardiology. Due to the keen observations suggesting the role of shared familial factors in the development of CVD, in 1971 the FHS began enroling the second generation cohort, comprising the children of the Original Cohort and the spouses of the children. In 2002, the third generation cohort, comprising the grandchildren of the Original Cohort, was initiated to additionally explore genetic contributions to CVD in greater depth. Additionally, because of the predominance of White individuals of European descent in the three generations of FHS participants noted above, the Heart Study enrolled the OMNI1 and OMNI2 cohorts in 1994 and 2003, respectively, aimed to reflect the current greater racial and ethnic diversity of the town of Framingham. All FHS cohorts have been examined approximately every 2-4 years since the initiation of the study. At these periodic Heart Study examinations, we obtain a medical history and perform a cardiovascular-focused physical examination, 12-lead electrocardiography, blood and urine samples testing and other cardiovascular imaging studies reflecting subclinical disease burden.The FHS has continually evolved along the cutting edge of cardiovascular science and epidemiological research since its inception. Participant studies now additionally include study of cardiovascular imaging, serum and urine biomarkers, genetics/genomics, proteomics, metabolomics and social networks. Numerous ancillary studies have been established, expanding the phenotypes to encompass multiple organ systems including the lungs, brain, bone and fat depots, among others. Whereas the FHS was originally conceived and designed to study the epidemiology of cardiovascular disease, it has evolved over the years with staggering expanded breadth and depth that have far greater implications in the study of the epidemiology of a wide spectrum of human diseases. The FHS welcomes research collaborations using existing or new collection of data. Detailed information regarding the procedures for research application submission and review are available at [http://www.framinghamheartstudy.org/researchers/index.php].

Gene expression markers of age-related inflammation in two human cohorts

INTRODUCTION

Chronically elevated circulating inflammatory markers are common in older persons but mechanisms are unclear. Many blood transcripts (>800 genes) are associated with interleukin-6 protein levels (IL6) independent of age. We aimed to identify gene transcripts statistically mediating, as drivers or responders, the increasing levels of IL6 protein in blood at older ages.

METHODS

Blood derived in-vivo RNA from the Framingham Heart Study (FHS, n=2422, ages 40-92 yrs) and InCHIANTI study (n=694, ages 30-104 yrs), with Affymetrix and Illumina expression arrays respectively (>17,000 genes tested), were tested for statistical mediation of the age-IL6 association using resampling techniques, adjusted for confounders and multiple testing.

RESULTS

In FHS, IL6 expression was not associated with IL6 protein levels in blood. 102 genes (0.6% of 17,324 expressed) statistically mediated the age-IL6 association of which 25 replicated in InCHIANTI (including 5 of the 10 largest effect genes). The largest effect gene (SLC4A10, coding for NCBE, a sodium bicarbonate transporter) mediated 19% (adjusted CI 8.9 to 34.1%) and replicated by PCR in InCHIANTI (n=194, 35.6% mediated, p=0.01). Other replicated mediators included PRF1 (perforin, a cytolytic protein in cytotoxic T lymphocytes and NK cells) and IL1B (Interleukin 1 beta): few other cytokines were significant mediators.

CONCLUSIONS

This transcriptome-wide study on human blood identified a small distinct set of genes that statistically mediate the age-IL6 association. Findings are robust across two cohorts and different expression technologies. Raised IL6 levels may not derive from circulating white cells in age related inflammation.

Gene Expression Signatures and the Spectrum of Coronary Artery Disease

Over the past 10-15 years, developments in gene expression profiling have opened new arenas for the discovery of important factors in the pathogenesis of numerous disease processes, including coronary artery disease. Messenger RNA and microRNA are differentially expressed in patients with coronary plaques, acute plaque rupture, and response to well-established treatments for acute coronary syndromes. In this review, we will explore recent developments in messenger RNA and microRNA technology at each stage of a patient's progression through the natural history of cardiovascular disease, including evaluation of risk factors, prediction and detection of coronary artery disease and acute coronary syndromes, and finally, response to treatments for coronary artery disease and its sequelae including congestive heart failure.

Importance of substantial weight loss for altering gene expression during cardiovascular lifestyle modification

OBJECTIVE

To examine relationships between weight loss through changes in lifestyle and peripheral blood gene expression profiles.

METHODS

A prospective nonrandomized trial was conducted over 1 year in participants undergoing intensive lifestyle modification to reverse or stabilize progression of coronary artery disease. Cardiovascular risk factors, inflammatory biomarkers, and gene expression as a function of weight loss were assessed in 89 lifestyle participants and 71 retrospectively matched controls undergoing usual care.

RESULTS

Substantial weight loss (-15.2 ± 3.8%) in lifestyle participants (n = 33) was associated with improvement in selected cardiovascular risk factors and significant changes in peripheral blood gene expression from pre- to post-intervention: 132 unique genes showed significant expression changes (false discovery rate corrected P-value <0.05 and fold-change ≥1.4). Altered molecular pathways were related to immune function and inflammatory responses involving endothelial activation. In contrast, participants losing minimal weight (-3.1 ± 2.5%, n = 32) showed only minor changes in cardiovascular risk factors and markers of inflammation and no changes in gene expression compared to non intervention controls after 1 year.

CONCLUSIONS

Weight loss (≥10%) during lifestyle modification is associated with down-regulation of genetic pathways governing interactions between circulating immune cells and the vascular endothelium and may be required to successfully reduce CVD risk.

Integrative network analysis reveals molecular mechanisms of blood pressure regulation

Genome‐wide association studies (GWAS) have identified numerous loci associated with blood pressure (BP). The molecular mechanisms underlying BP regulation, however, remain unclear. We investigated BP‐associated molecular mechanisms by integrating BP GWAS with whole blood mRNA expression profiles in 3,679 individuals, using network approaches. BP transcriptomic signatures at the single‐gene and the coexpression network module levels were identified. Four coexpression modules were identified as potentially causal based on genetic inference because expression‐related SNPs for their corresponding genes demonstrated enrichment for BP GWAS signals. Genes from the four modules were further projected onto predefined molecular interaction networks, revealing key drivers. Gene subnetworks entailing molecular interactions between key drivers and BP‐related genes were uncovered. As proof‐of‐concept, we validated SH2B3, one of the top key drivers, using Sh2b3^−/− mice. We found that a significant number of genes predicted to be regulated by SH2B3 in gene networks are perturbed in Sh2b3^−/− mice, which demonstrate an exaggerated pressor response to angiotensin II infusion. Our findings may help to identify novel targets for the prevention or treatment of hypertension.

Multi-omic signature of body weight change: results from a population-based cohort study

BACKGROUND

Excess body weight is a major risk factor for cardiometabolic diseases. The complex molecular mechanisms of body weight change-induced metabolic perturbations are not fully understood. Specifically, in-depth molecular characterization of long-term body weight change in the general population is lacking. Here, we pursued a multi-omic approach to comprehensively study metabolic consequences of body weight change during a seven-year follow-up in a large prospective study.

METHODS

We used data from the population-based Cooperative Health Research in the Region of Augsburg (KORA) S4/F4 cohort. At follow-up (F4), two-platform serum metabolomics and whole blood gene expression measurements were obtained for 1,631 and 689 participants, respectively. Using weighted correlation network analysis, omics data were clustered into modules of closely connected molecules, followed by the formation of a partial correlation network from the modules. Association of the omics modules with previous annual percentage weight change was then determined using linear models. In addition, we performed pathway enrichment analyses, stability analyses, and assessed the relation of the omics modules with clinical traits.

RESULTS

Four metabolite and two gene expression modules were significantly and stably associated with body weight change (P-values ranging from 1.9 × 10(-4) to 1.2 × 10(-24)). The four metabolite modules covered major branches of metabolism, with VLDL, LDL and large HDL subclasses, triglycerides, branched-chain amino acids and markers of energy metabolism among the main representative molecules. One gene expression module suggests a role of weight change in red blood cell development. The other gene expression module largely overlaps with the lipid-leukocyte (LL) module previously reported to interact with serum metabolites, for which we identify additional co-expressed genes. The omics modules were interrelated and showed cross-sectional associations with clinical traits. Moreover, weight gain and weight loss showed largely opposing associations with the omics modules.

CONCLUSIONS

Long-term weight change in the general population globally associates with serum metabolite concentrations. An integrated metabolomics and transcriptomics approach improved the understanding of molecular mechanisms underlying the association of weight gain with changes in lipid and amino acid metabolism, insulin sensitivity, mitochondrial function as well as blood cell development and function.

Integromic analysis of genetic variation and gene expression identifies networks for cardiovascular disease phenotypes

BACKGROUND

Cardiovascular disease (CVD) reflects a highly coordinated complex of traits. Although genome-wide association studies have reported numerous single nucleotide polymorphisms (SNPs) to be associated with CVD, the role of most of these variants in disease processes remains unknown.

METHODS AND RESULTS

We built a CVD network using 1512 SNPs associated with 21 CVD traits in genome-wide association studies (at P≤5×10(-8)) and cross-linked different traits by virtue of their shared SNP associations. We then explored whole blood gene expression in relation to these SNPs in 5257 participants in the Framingham Heart Study. At a false discovery rate <0.05, we identified 370 cis-expression quantitative trait loci (eQTLs; SNPs associated with altered expression of nearby genes) and 44 trans-eQTLs (SNPs associated with altered expression of remote genes). The eQTL network revealed 13 CVD-related modules. Searching for association of eQTL genes with CVD risk factors (lipids, blood pressure, fasting blood glucose, and body mass index) in the same individuals, we found examples in which the expression of eQTL genes was significantly associated with these CVD phenotypes. In addition, mediation tests suggested that a subset of SNPs previously associated with CVD phenotypes in genome-wide association studies may exert their function by altering expression of eQTL genes (eg, LDLR and PCSK7), which in turn may promote interindividual variation in phenotypes.

CONCLUSIONS

Using a network approach to analyze CVD traits, we identified complex networks of SNP-phenotype and SNP-transcript connections. Integrating the CVD network with phenotypic data, we identified biological pathways that may provide insights into potential drug targets for treatment or prevention of CVD.

Dissecting the roles of microRNAs in coronary heart disease via integrative genomic analyses

OBJECTIVE

The roles of microRNAs (miRNAs) in coronary heart disease (CHD) have not been well characterized. This study sought to systematically characterize the complex genomic architecture of CHD by integrating whole blood miRNA and mRNA expression with genetic variation in 186 CHD cases and 186 controls.

APPROACH AND RESULTS

At false discovery rate <0.2, 15 miRNAs were differentially expressed between CHD cases and controls. To explore regulatory mechanisms, we integrated miRNA and mRNA expression with genome-wide genotype data to investigate miRNA and mRNA associations and relationships of genetic variation with miRNAs. We identified a large number of correlated miRNA-mRNA pairs and genetic loci that seem to regulate miRNA levels. Subsequently, we explored the relationships of these complex molecular associations with CHD status. We identified a large difference in miRNA-mRNA associations between CHD cases and controls, as demonstrated by a significantly higher proportion of inversely correlated miRNA-mRNA pairs in cases versus controls (80% versus 30%; P<1×10(-16)), suggesting a genome-wide shift in the regulatory structure of the transcriptome in CHD. The differentially coexpressed miRNA-mRNA pairs showed enrichment for CHD risk genetic variants affecting both miRNA and mRNA expression levels, implicating a putatively causal role in CHD. Furthermore, 3 miRNAs (miR-1275, miR-365a-3p, and miR-150-5p) were associated with an mRNA coexpression module that was causally linked to CHD and reflected the dysregulation of B-cell centered immune function.

CONCLUSIONS

Our results provide novel evidence that miRNAs are important regulators of biological processes involved in CHD via genetic control and via their tight coexpression with mRNAs.

A systematic heritability analysis of the human whole blood transcriptome

Genome-wide expression quantitative trait locus (eQTL) mapping may reveal common genetic variants regulating gene expression. In addition to mapping eQTLs, we systematically evaluated the heritability of the whole blood transcriptome in 5,626 participants from the Framingham Heart Study. Of all gene expression measurements, about 40 % exhibit evidence of being heritable [hgeneExp(2) > 0, (p < 0.05)], the average heritability was estimated to be 0.13, and 10 % display hgeneExp(2) > 0.2. To identify the role of eQTLs in promoting phenotype differences and disease susceptibility, we investigated the proportion of cis/trans eQTLs in different heritability categories and discovered that genes with higher heritability are more likely to have cis eQTLs that explain large proportions of variance in the expression of the corresponding genes. Single cis eQTLs explain 0.33-0.53 of variance in transcripts on average, whereas single trans eQTLs only explain 0.02-0.07. The top cis eQTLs tend to explain more variance in the corresponding gene when its hgeneExp(2) is greater. Taking body mass index (BMI) as a case study, we cross-linked cis/trans eQTLs with both GWAS SNPs and differentially expressed genes for BMI. We discovered that BMI GWAS SNPs in 16p11.2 (e.g., rs7359397) are associated with several BMI differentially expressed genes in a cis manner (e.g. SULT1A1, SPNS1, and TUFM). These BMI signature genes explain a much larger proportion of variance in BMI than do the GWAS SNPs. Our results shed light on the impact of eQTLs on the heritability of the human whole blood transcriptome and its relations to phenotype differences.

The shared crosstalk of multiple pathways involved in the inflammation between rheumatoid arthritis and coronary artery disease based on a digital gene expression profile

Rheumatoid arthritis (RA) and coronary artery disease (CAD) are both complex inflammatory diseases, and an increased prevalence of CAD and a high rate of mortality have been observed in RA patients. But the molecular mechanism of inflammation that is shared between the two disorders is unclear. High-throughput techniques, such as transcriptome analysis, are becoming important tools for genetic biomarker discovery in highly complex biological samples, which is critical for the diagnosis, prognosis, and treatment of disease. In the present study, we reported one type of transcriptome analysis method: digital gene expression profiling of peripheral blood mononuclear cells of 10 RA patients, 10 CAD patients and 10 healthy people. In all, 213 and 152 differently expressed genes (DEGs) were identified in RA patients compared with normal controls (RA vs. normal) and CAD patients compared with normal controls (CAD vs. normal), respectively, with 73 shared DEGs between them. Using this technique in combination with Ingenuity Pathways Analysis software, the effects on inflammation of four shared canonical pathways, three shared activated predicted upstream regulators and three shared molecular interaction networks were identified and explored. These shared molecular mechanisms may provide the genetic basis and potential targets for optimizing the application of current drugs to more effectively treat these diseases simultaneously and for preventing one when the other is diagnosed.

Whole blood gene expression and interleukin-6 levels

BACKGROUND

Circulating interleukin-6 levels increase with advancing age and are a risk factor for various diseases and mortality. The characterization of gene expression profiles associated with interleukin-6 levels might suggest important molecular events underlying its regulation.

METHODS AND RESULTS

We studied the association of transcriptional profiles with interleukin-6 levels in 2422 participants from the Framingham Heart Study Offspring Cohort using Affymetrix Human Exon 1.0 ST Array. We identified 4139 genes that were significantly associated with interleukin-6 levels (FDR<0.05) after adjusting for age, sex and blood cell components. We then replicated 807 genes in the InCHIANTI study with 694 participants. Many of the top genes are involved in inflammation-related pathways or erythrocyte function, including JAK/Stat signaling pathway and interleukin-10 signaling pathway.

CONCLUSION

We identified and replicated 807 genes that were associated with circulating interleukin-6 levels. Future characterization of interleukin-6 regulation networks may facilitate the identification of additional potential targets for treating inflammation-related diseases.

Aging and cardiovascular diseases: the role of gene-diet interactions

In the study of longevity, increasing importance is being placed on the concept of healthy aging rather than considering the total number of years lived. Although the concept of healthy lifespan needs to be defined better, we know that cardiovascular diseases (CVDs) are the main age-related diseases. Thus, controlling risk factors will contribute to reducing their incidence, leading to healthy lifespan. CVDs are complex diseases influenced by numerous genetic and environmental factors. Numerous gene variants that are associated with a greater or lesser risk of the different types of CVD and of intermediate phenotypes (i.e., hypercholesterolemia, hypertension, diabetes) have been successfully identified. However, despite the close link between aging and CVD, studies analyzing the genes related to human longevity have not obtained consistent results and there has been little coincidence in the genes identified in both fields. The APOE gene stands out as an exception, given that it has been identified as being relevant in CVD and longevity. This review analyzes the genomic and epigenomic factors that may contribute to this, ranging from identifying longevity genes in model organisms to the importance of gene-diet interactions (outstanding among which is the case of the TCF7L2 gene).

Integrative DNA, RNA, and protein evidence connects TREML4 to coronary artery calcification

Coronary artery calcification (CAC) is a heritable and definitive morphologic marker of atherosclerosis that strongly predicts risk for future cardiovascular events. To search for genes involved in CAC, we used an integrative transcriptomic, genomic, and protein expression strategy by using next-generation DNA sequencing in the discovery phase with follow-up studies using traditional molecular biology and histopathology techniques. RNA sequencing of peripheral blood from a discovery set of CAC cases and controls was used to identify dysregulated genes, which were validated by ClinSeq and Framingham Heart Study data. Only a single gene, TREML4, was upregulated in CAC cases in both studies. Further examination showed that rs2803496 was a TREML4 cis-eQTL and that the minor allele at this locus conferred up to a 6.5-fold increased relative risk of CAC. We characterized human TREML4 and demonstrated by immunohistochemical techniques that it is localized in macrophages surrounding the necrotic core of coronary plaques complicated by calcification (but not in arteries with less advanced disease). Finally, we determined by von Kossa staining that TREML4 colocalizes with areas of microcalcification within coronary plaques. Overall, we present integrative RNA, DNA, and protein evidence implicating TREML4 in coronary artery calcification. Our findings connect multimodal genomics data with a commonly used clinical marker of cardiovascular disease.

Epigenome-wide association study of fasting blood lipids in the Genetics of Lipid-lowering Drugs and Diet Network study

BACKGROUND

Genetic research regarding blood lipids has largely focused on DNA sequence variation; few studies have explored epigenetic effects. Genome-wide surveys of DNA methylation may uncover epigenetic factors influencing lipid metabolism.

METHODS AND RESULTS

To identify whether differential methylation of cytosine-(phosphate)-guanine dinucleotides (CpGs) correlated with lipid phenotypes, we isolated DNA from CD4+ T cells and quantified the proportion of sample methylation at >450 000 CpGs by using the Illumina Infinium HumanMethylation450 Beadchip in 991 participants of the Genetics of Lipid Lowering Drugs and Diet Network. We modeled the percentage of methylation at individual CpGs as a function of fasting very-low-density lipoprotein cholesterol and triglycerides (TGs) by using mixed linear regression adjusted for age, sex, study site, cell purity, and family structure. Four CpGs (cg00574958, cg17058475, cg01082498, and cg09737197) in intron 1 of carnitine palmitoyltransferase 1A (CPT1A) were strongly associated with very-low low-density lipoprotein cholesterol (P=1.8×10(-21) to 1.6×10(-8)) and TG (P=1.6×10(-26) to 1.5×10(-9)). Array findings were validated by bisulfite sequencing. We performed quantitative polymerase chain reaction experiments demonstrating that methylation of the top CpG (cg00574958) was correlated with CPT1A expression. The association of cg00574958 with TG and CPT1A expression were replicated in the Framingham Heart Study (P=4.1×10(-14) and 3.1×10(-13), respectively). DNA methylation at CPT1A cg00574958 explained 11.6% and 5.5% of the variation in TG in the discovery and replication cohorts, respectively.

CONCLUSIONS

This genome-wide epigenomic study identified CPT1A methylation as strongly and robustly associated with fasting very-low low-density lipoprotein cholesterol and TG. Identifying novel epigenetic contributions to lipid traits may inform future efforts to identify new treatment targets and biomarkers of disease risk.

Whole blood gene expression and atrial fibrillation: the Framingham Heart Study

BACKGROUND

Atrial fibrillation (AF) involves substantial electrophysiological, structural and contractile remodeling. We hypothesize that characterizing gene expression might uncover important pathways related to AF.

METHODS AND RESULTS

We performed genome-wide whole blood transcriptomic profiling (Affymetrix Human Exon 1.0 ST Array) of 2446 participants (mean age 66 ± 9 years, 55% women) from the Offspring cohort of Framingham Heart Study. The study included 177 participants with prevalent AF, 143 with incident AF during up to 7 years follow up, and 2126 participants with no AF. We identified seven genes statistically significantly up-regulated with prevalent AF. The most significant gene, PBX1 (P = 2.8 × 10(-7)), plays an important role in cardiovascular development. We integrated differential gene expression with gene-gene interaction information to identify several signaling pathways possibly involved in AF-related transcriptional regulation. We did not detect any statistically significant transcriptomic associations with incident AF.

CONCLUSION

We examined associations of gene expression with AF in a large community-based cohort. Our study revealed several genes and signaling pathways that are potentially involved in AF-related transcriptional regulation.

A module of human peripheral blood mononuclear cell transcriptional network containing primitive and differentiation markers is related to specific cardiovascular health variables

Peripheral blood mononuclear cells (PBMCs), including rare circulating stem and progenitor cells (CSPCs), have important yet poorly understood roles in the maintenance and repair of blood vessels and perfused organs. Our hypothesis was that the identities and functions of CSPCs in cardiovascular health could be ascertained by analyzing the patterns of their co-expressed markers in unselected PBMC samples. Because gene microarrays had failed to detect many stem cell-associated genes, we performed quantitative real-time PCR to measure the expression of 45 primitive and tissue differentiation markers in PBMCs from healthy and hypertensive human subjects. We compared these expression levels to the subjects' demographic and cardiovascular risk factors, including vascular stiffness. The tested marker genes were expressed in all of samples and organized in hierarchical transcriptional network modules, constructed by a bottom-up approach. An index of gene expression in one of these modules (metagene), defined as the average standardized relative copy numbers of 15 pluripotency and cardiovascular differentiation markers, was negatively correlated (all p<0.03) with age (R² = −0.23), vascular stiffness (R² = −0.24), and central aortic pressure (R² = −0.19) and positively correlated with body mass index (R² = 0.72, in women). The co-expression of three neovascular markers was validated at the single-cell level using mRNA in situ hybridization and immunocytochemistry. The overall gene expression in this cardiovascular module was reduced by 72±22% in the patients compared with controls. However, the compactness of both modules was increased in the patients' samples, which was reflected in reduced dispersion of their nodes' degrees of connectivity, suggesting a more primitive character of the patients' CSPCs. In conclusion, our results show that the relationship between CSPCs and vascular function is encoded in modules of the PBMCs transcriptional network. Furthermore, the coordinated gene expression in these modules can be linked to cardiovascular risk factors and subclinical cardiovascular disease; thus, this measure may be useful for their diagnosis and prognosis.

Intensive Cardiovascular Risk Reduction Induces Sustainable Changes in Expression of Genes and Pathways Important to Vascular Function

Background—

Healthy lifestyle changes are thought to mediate cardiovascular disease risk through pathways affecting endothelial function and progression of atherosclerosis; however, the extent, persistence, and clinical significance of molecular change during lifestyle modification are not well known. We examined the effect of a rigorous cardiovascular disease risk reduction program on peripheral blood gene expression profiles in 63 participants and 63 matched controls to characterize molecular responses and identify regulatory pathways important to cardiovascular health.

Methods and Results—

Dramatic changes in dietary fat intake (−61%; P <0.001 versus controls) and physical fitness (+34%; P <0.001) led to significant improvements in cardiovascular disease risk factors. Analysis of variance with false discovery rate correction for multiple testing ( P <0.05) identified 26 genes after 12 weeks and 143 genes after 52 weeks that were differentially expressed from baseline in participants. Controls showed little change in cardiovascular disease risk factors or gene expression. Quantitative reverse transcription polymerase chain reaction validated differential expression for selected transcripts. Lifestyle modification effectively reduced expression of proinflammatory genes associated with neutrophil activation and molecular pathways important to vascular function, including cytokine production, carbohydrate metabolism, and steroid hormones. Prescription medications did not significantly affect changes in gene expression.

Conclusions—

Successful and sustained modulation of gene expression through lifestyle changes may have beneficial effects on the vascular system not apparent from traditional risk factors. Healthy lifestyles may restore homeostasis to the leukocyte transcriptome by downregulating lactoferrin and other genes important in the pathogenesis of atherosclerosis.

Clinical Trial Registration—

URL: www.clinicaltrials.gov . Unique identifier: NCT01805492

microRNA profiling in patients with abdominal aortic aneurysms: the significance of miR-155

Our finding of high levels of miR-155 in patients with abdominal aortic aneurysm identifies this miRNA as a potential therapeutic target with implications for clinical management of this vascular disease.

A systems biology framework identifies molecular underpinnings of coronary heart disease

OBJECTIVE

Genetic approaches have identified numerous loci associated with coronary heart disease (CHD). The molecular mechanisms underlying CHD gene-disease associations, however, remain unclear. We hypothesized that genetic variants with both strong and subtle effects drive gene subnetworks that in turn affect CHD.

APPROACH AND RESULTS

We surveyed CHD-associated molecular interactions by constructing coexpression networks using whole blood gene expression profiles from 188 CHD cases and 188 age- and sex-matched controls. Twenty-four coexpression modules were identified, including 1 case-specific and 1 control-specific differential module (DM). The DMs were enriched for genes involved in B-cell activation, immune response, and ion transport. By integrating the DMs with gene expression-associated single-nucleotide polymorphisms and with results of genome-wide association studies of CHD and its risk factors, the control-specific DM was implicated as CHD causal based on its significant enrichment for both CHD and lipid expression-associated single-nucleotide polymorphisms. This causal DM was further integrated with tissue-specific Bayesian networks and protein-protein interaction networks to identify regulatory key driver genes. Multitissue key drivers (SPIB and TNFRSF13C) and tissue-specific key drivers (eg, EBF1) were identified.

CONCLUSIONS

Our network-driven integrative analysis not only identified CHD-related genes, but also defined network structure that sheds light on the molecular interactions of genes associated with CHD risk.