Vascular Gene Expression in Atherosclerotic Plaque-Prone Regions Analyzed by Representational Difference Analysis

PANDORA-Seq unveils the hidden small noncoding RNA landscape in atherosclerosis of LDL receptor-deficient mice

Small noncoding RNAs (sncRNAs) play diverse roles in numerous biological processes. While the widely used RNA sequencing (RNA-Seq) method has advanced sncRNA discovery, RNA modifications can interfere with the complementary DNA library construction process, preventing the discovery of highly modified sncRNAs including transfer RNA-derived small RNAs (tsRNAs) and ribosomal RNA-derived small RNAs (rsRNAs) that may have important functions in disease development. To address this technical obstacle, we recently developed a novel PANDORA-Seq (Panoramic RNA Display by Overcoming RNA Modification Aborted Sequencing) method to overcome RNA modification-elicited sequence interferences. To identify novel sncRNAs associated with atherosclerosis development, LDL receptor-deficient (LDLR-/-) mice were fed a low-cholesterol diet or high-cholesterol diet (HCD) for 9 weeks. Total RNAs isolated from the intima were subjected to PANDORA-Seq and traditional RNA-Seq. By overcoming RNA modification-elicited limitations, PANDORA-Seq unveiled an rsRNA/tsRNA-enriched sncRNA landscape in the atherosclerotic intima of LDLR-/- mice, which was strikingly different from that detected by traditional RNA-Seq. While microRNAs were the dominant sncRNAs detected by traditional RNA-Seq, PANDORA-Seq substantially increased the reads of rsRNAs and tsRNAs. PANDORA-Seq also detected 1,383 differentially expressed sncRNAs induced by HCD feeding, including 1,160 rsRNAs and 195 tsRNAs. One of HCD-induced intimal tsRNAs, tsRNA-Arg-CCG, may contribute to atherosclerosis development by regulating the proatherogenic gene expression in endothelial cells. Overall, PANDORA-Seq revealed a hidden rsRNA and tsRNA population associated with atherosclerosis development. These understudied tsRNAs and rsRNAs, which are much more abundant than microRNAs in the atherosclerotic intima of LDLR-/- mice, warrant further investigations.

The Transcriptomic Toolbox: Resources for Interpreting Large Gene Expression Data within a Precision Medicine Context for Metabolic Disease Atherosclerosis

As one of the most widespread metabolic diseases, atherosclerosis affects nearly everyone as they age; arteries gradually narrow from plaque accumulation over time reducing oxygenated blood flow to central and periphery causing heart disease, stroke, kidney problems, and even pulmonary disease. Personalized medicine promises to bring treatments based on individual genome sequencing that precisely target the molecular pathways underlying atherosclerosis and its symptoms, but to date only a few genotypes have been identified. A promising alternative to this genetic approach is the identification of pathways altered in atherosclerosis by transcriptome analysis of atherosclerotic tissues to target specific aspects of disease. Transcriptomics is a potentially useful tool for both diagnostics and discovery science, exposing novel cellular and molecular mechanisms in clinical and translational models, and depending on experimental design to identify and test novel therapeutics. The cost and time required for transcriptome analysis has been greatly reduced by the development of next generation sequencing. The goal of this resource article is to provide background and a guide to appropriate technologies and downstream analyses in transcriptomics experiments generating ever-increasing amounts of gene expression data.

Genome-Wide Expression Studies of Atherosclerosis

During the past 6 years, gene expression profiling of atherosclerosis has been used to identify genes and pathways relevant in vascular (patho)physiology. This review discusses some critical issues in the methodology, analysis, and interpretation of the data of gene expression studies that have made use of vascular specimens from animal models and humans. Analysis of gene expression studies has evolved toward the genome-wide expression profiling of large series of individual samples of well-characterized donors. Despite the advances in statistical and bioinformatical analysis of expression data sets, studies have not yet fully exploited the potential of gene expression data sets to obtain novel insights into the molecular mechanisms underlying atherosclerosis. To assess the potential of published expression data, we compared the data of a CC chemokine gene cluster between 18 murine and human gene expression profiling articles. Our analysis revealed that an adequate comparison is mainly hindered by the incompleteness of available data sets. The challenge for future vascular genomic profiling studies will be to further improve the experimental design, statistical, and bioinformatical analysis and to make data sets freely accessible.

Molecular diversity of cardiac endothelial cells in vitro and in vivo

In addition to a number of common features, cardiovascular endothelium displays structural, functional, and genetic differences according to its position in the cardiovascular tree. In the heart, endocardial and cardiac microvascular endothelia (CMVE) interact directly with surrounding cardiomyocytes, whereas the endothelium within blood vessels interacts with smooth muscle cells. In this study, we investigated whether cardiac endothelial cells were distinct from aortic endothelial (AE) cells at the transcriptional level. Using Affymetrix microarray technology and subsequent real-time PCR analyses for validation, we identified sets of genes with marked preferential expression in cultured endocardial endothelium (EE) compared with cultured AE and vice versa. Among the genes preferentially expressed in EE, some were also expressed in cultured CMVE. Immunohistochemical staining of cardiac and aortic tissue revealed that the endothelial genetic diversity observed in culture reflects, in part, a physiological diversity existing in vivo. The identification of a set of genes preferentially expressed in EE provides new insights in the functional adaptations of this endothelial subtype to its intracavitary localization and to its role in the control of ventricular performance.