Blood-based gene expression tests: promises and limitations

Oxidative Stress Mediates Epigenetic Modifications and the Expression of miRNAs and Genes Related to Apoptosis in Diabetic Retinopathy Patients

Knowledge on the underlying mechanisms and molecular targets for managing the ocular complications of type 2 diabetes mellitus (T2DM) remains incomplete. Diabetic retinopathy (DR) is a major cause of irreversible visual disability worldwide. By using ophthalmological and molecular-genetic approaches, we gathered specific information to build a data network for deciphering the crosslink of oxidative stress (OS) and apoptosis (AP) processes, as well as to identify potential epigenetic modifications related to noncoding RNAs in the eyes of patients with T2DM. A total of 120 participants were recruited, being classified into two groups: individuals with T2MD (T2MDG, n = 67), divided into a group of individuals with (+DR, n = 49) and without (-DR, n = 18) DR, and a control group (CG, n = 53). Analyses of compiled data reflected significantly higher plasma levels of malondialdehyde (MDA), superoxide dismutase (SOD), and glutathione peroxidase (GPx) and significantly lower total antioxidant capacity (TAC) in the +DR patients compared with the -DR and the CG groups. Furthermore, the plasma caspase-3 (CAS3), highly involved in apoptosis (AP), showed significantly higher values in the +DR group than in the -DR patients. The microRNAs (miR) hsa-miR 10a-5p and hsa-miR 15b-5p, as well as the genes BCL2L2 and TP53 involved in these pathways, were identified in relation to DR clinical changes. Our data suggest an interaction between OS and the above players in DR pathogenesis. Furthermore, potential miRNA-regulated target genes were identified in relation to DR. In this concern, we may raise new diagnostic and therapeutic challenges that hold the potential to significantly improve managing the diabetic eye.

Identifying the RNA signatures of coronary artery disease from combined lncRNA and mRNA expression profiles

Coronary artery disease (CAD) is the most common cardiovascular disease. CAD research has greatly progressed during the past decade. mRNA is a traditional and popular pipeline to investigate various disease, including CAD. Compared with mRNA, lncRNA has better stability and thus may serve as a better disease indicator in blood. Investigating potential CAD-related lncRNAs and mRNAs will greatly contribute to the diagnosis and treatment of CAD. In this study, a computational analysis was conducted on patients with CAD by using a comprehensive transcription dataset with combined mRNA and lncRNA expression data. Several machine learning algorithms, including feature selection methods and classification algorithms, were applied to screen for the most CAD-related RNA molecules. Decision rules were also reported to provide a quantitative description about the effect of these RNA molecules on CAD progression. These new findings (CAD-related RNA molecules and rules) can help understand mRNA and lncRNA expression levels in CAD.

A Genomic Approach to Characterize the Vulnerable Patient – a Clinical Update

Atherosclerosis is the elemental precondition for any cardiovascular disease and the predominant cause of ischemic heart disease that often leads to myocardial infarction. Systemic risk factors play an important role in the starting and progression of atherosclerosis. The complexity of the disease is caused by its multifactorial origin. Besides the traditional risk factors, genetic predisposition is also a strong risk factor. Many studies have intensively researched cardioprotective drugs, which can relieve myocardial ischemia and reperfusion injury, thereby reducing infarct size. A better understanding of abnormal epigenetic pathways in the myocardial pathology may result in new treatment options. Individualized therapy based on genome sequencing is important for an effective future medical treatment. Studies based on multiomics help to better understand the pathophysiological mechanism of several diseases at a molecular level. Epigenomic, transcriptomic, proteomic, and metabolomic research may be essential in detecting the pathological phenotype of myocardial ischemia and ischemic heart failure.

C/VDdb: A multi-omics expression profiling database for a knowledge-driven approach in cardiovascular disease (CVD)

The cardiovascular disease (C/VD) database is an integrated and clustered information resource that covers multi-omic studies (microRNA, genomics, proteomics and metabolomics) of cardiovascular-related traits with special emphasis on coronary artery disease (CAD). This resource was built by mining existing literature and public databases and thereafter manual biocuration was performed. To enable integration of omic data from distinct platforms and species, a specific ontology was applied to tie together and harmonise multi-level omic studies based on gene and protein clusters (CluSO) and mapping of orthologous genes (OMAP) across species. CAD continues to be a leading cause of death in the population worldwide, and it is generally thought to be an age-related disease. However, CAD incidence rates are now known to be highly influenced by environmental factors and interactions, in addition to genetic determinants. With the complexity of CAD aetiology, there is a difficulty in research studies to elucidate general elements compared to other cardiovascular diseases. Data from 92 studies, covering 13945 molecular entries (4353 unique molecules) is described, including data descriptors for experimental setup, study design, discovery-validation sample size and associated fold-changes of the differentially expressed molecular features (p-value<0.05). A dedicated interactive web interface, equipped with a multi-parametric search engine, data export and indexing menus are provided for a user-accessible browsing experience. The main aim of this work was the development of a data repository linking clinical information and molecular differential expression in several CVD-related traits from multi-omics studies (genomics, transcriptomics, proteomics and metabolomics). As an example case of how to query and identify data sets within the database framework and concomitantly demonstrate the database utility, we queried CAD-associated studies and performed a systems-level integrative analysis. URL: www.padb.org/cvd

Integrating personalized gene expression profiles into predictive disease-associated gene pools

Gene expression data are routinely used to identify genes that on average exhibit different expression levels between a case and a control group. Yet, very few of such differentially expressed genes are detectably perturbed in individual patients. Here, we develop a framework to construct personalized perturbation profiles for individual subjects, identifying the set of genes that are significantly perturbed in each individual. This allows us to characterize the heterogeneity of the molecular manifestations of complex diseases by quantifying the expression-level similarities and differences among patients with the same phenotype. We show that despite the high heterogeneity of the individual perturbation profiles, patients with asthma, Parkinson and Huntington's disease share a broadpool of sporadically disease-associated genes, and that individuals with statistically significant overlap with this pool have a 80-100% chance of being diagnosed with the disease. The developed framework opens up the possibility to apply gene expression data in the context of precision medicine, with important implications for biomarker identification, drug development, diagnosis and treatment.

Identification of gene expression signature for cigarette smoke exposure response--from man to mouse

Gene expression profiling data can be used in toxicology to assess both the level and impact of toxicant exposure, aligned with a vision of 21st century toxicology. Here, we present a whole blood-derived gene signature that can distinguish current smokers from either nonsmokers or former smokers with high specificity and sensitivity. Such a signature that can be measured in a surrogate tissue (whole blood) may help in monitoring smoking exposure as well as discontinuation of exposure when the primarily impacted tissue (e.g., lung) is not readily accessible. The signature consisted of LRRN3, SASH1, PALLD, RGL1, TNFRSF17, CDKN1C, IGJ, RRM2, ID3, SERPING1, and FUCA1. Several members of this signature have been previously described in the context of smoking. The signature translated well across species and could distinguish mice that were exposed to cigarette smoke from ones exposed to air only or had been withdrawn from cigarette smoke exposure. Finally, the small signature of only 11 genes could be converted into a polymerase chain reaction-based assay that could serve as a marker to monitor compliance with a smoking abstinence protocol.

Association of Cross-Sectional and Longitudinal Change in Arterial Stiffness With Gene Expression in the Twins UK Cohort

We investigated whether expression of genes previously implicated in arterial stiffening associates with cross-sectional and longitudinal measures of arterial stiffness. Women from the Twins UK cohort (n=470, aged 39-81 years) had gene expression in lymphoblastoid cell lines measured using an Illumina microarray. Arterial stiffness was measured by carotid-femoral pulse wave velocity and carotid distensibility. A subsample (n=121) of women had repeat vascular measures after a mean±SD follow-up of 4.3±1.4 years. Associations of arterial phenotypes with gene expression levels were examined for 52 genes identified from previous association studies. The gene transcript most closely associated with pulse wave velocity in cross-sectional analysis was ectonucleotide pyrophosphatase/phosphodiesterase (P=0.012). Pleiotropic genetic effects accounted for 14% of the phenotypic correlation between ectonucleotide pyrophosphatase/phosphodiesterase expression and pulse wave velocity. Progression of pulse wave velocity during the follow-up period best related to expression of ectonucleotide pyrophosphatase/phosphodiesterase (β=0.19, P=0.008) and collagen type IV α 1 (β=0.32, P<0.0001). Gene transcripts most closely related to change in carotid distensibility during the follow-up period were endothelial nitric oxide synthase (β=-0.20, P=0.005), angiotensin-converting enzyme (β=-0.15, P=0.035), and B-cell CLL/lymphoma11B (β=0.18, P=0.010). Expression levels of angiotensin-converting enzyme also related to progression in carotid diameter (β=0.21, P=0.012). Expression levels of ectonucleotide pyrophosphatase/phosphodiesterase, involved in arterial calcification, and collagen type IV α 1, involved in collagen formation, correlate with aortic stiffening. These genes may be functional mediators of arterial stiffening.

BiomarCaRE: rationale and design of the European BiomarCaRE project including 300,000 participants from 13 European countries

Biomarkers are considered as tools to enhance cardiovascular risk estimation. However, the value of biomarkers on risk estimation beyond European risk scores, their comparative impact among different European regions and their role towards personalised medicine remains uncertain. Biomarker for Cardiovascular Risk Assessment in Europe (BiomarCaRE) is an European collaborative research project with the primary objective to assess the value of established and emerging biomarkers for cardiovascular risk prediction. BiomarCaRE integrates clinical and epidemiological biomarker research and commercial enterprises throughout Europe to combine innovation in biomarker discovery for cardiovascular disease prediction with consecutive validation of biomarker effectiveness in large, well-defined primary and secondary prevention cohorts including over 300,000 participants from 13 European countries. Results from this study will contribute to improved cardiovascular risk prediction across different European populations. The present publication describes the rationale and design of the BiomarCaRE project.

Biological and analytical stability of a peripheral blood gene expression score for obstructive coronary artery disease in the PREDICT and COMPASS studies

A gene expression score (GES) for obstructive coronary artery disease (CAD) has been validated in two multicenter studies. Receiver-operating characteristics (ROC) analysis of the GES on an expanded Personalized Risk Evaluation and Diagnosis in the Coronary Tree (PREDICT) cohort (NCT no. 00500617) with CAD defined by quantitative coronary angiography (QCA) or clinical reads yielded similar performance (area under the curve (AUC) = 0.70, N = 1,502) to the original validation cohort (AUC = 0.70, N = 526). Analysis of 138 non-Caucasian and 1,364 Caucasian patients showed very similar performance (AUCs = 0.72 vs. 0.70). To assess analytic stability, stored samples of the original validation cohort (N = 526) was re-tested after 5 years, and the mean score changed from 20.3 to 19.8 after 5 years (N = 501, 95 %). To assess patient scores over time, GES was determined on samples from 173 Coronary Obstruction Detection by Molecular Personalized Gene Expression (COMPASS) study (NCT no. 01117506) patients at approximately 1 year post-enrollment. Mean scores increased slightly from 15.9 to 17.3, corresponding to a 2.5 % increase in obstructive CAD likelihood. Changes in cardiovascular medications did not show a significant change in GES.

Biomarkers of coronary artery disease: the promise of the transcriptome

The last years have witnessed tremendous technical advances in the field of transcriptomics that enable the simultaneous assessment of nearly all transcripts expressed in a tissue at a given time. These advances harbor the potential to gain a better understanding of the complex biological systems and for the identification and development of novel biomarkers. This article will review the current knowledge of transcriptomics biomarkers in the cardiovascular field and will provide an overview about the promises and challenges of the transcriptomics approach for biomarker identification.