Analysis of Genes Related to Angiotensin II-Induced Arterial Injury Using a Time Series Microarray

Background/Aims: Angiotensin II (Ang II)-mediated hypertension is a major risk factor for cardiovascular diseases. Ang II induces changes in vessel structure and function through the activation of genes related to signaling pathways. However, the changes in the gene expression profiles of blood vessels in response to Ang II remain unclear. Methods: Wild-type C57BL/6 mice were infused with Ang II (1500 ng/kg/min) using an osmotic pump for 1, 3, and 7 days. Vascular wall inflammation and remodeling were evaluated by pathological examination. Time-series microarray and quantitative PCR analyses were performed. Bioinformatics analyses were conducted to identify key genes, pathways, and biological processes. Results: After Ang II infusion, blood pressure and aortic remodeling were increased over time. Microarray analysis identified a totally of 3631 differentially expressed genes in aortas at days 1, 3, and 7 of Ang II infusion. These genes were involved in multiple biological processes, including cell adhesion, angiogenesis, cell migration, protein phosphorylation, immune system, and cell cycle, which may play important roles in regulating Ang II-induced arterial injury during hypertension. The genes were classified into 50 profiles by hierarchical cluster analysis, and finally, 14 significant profiles were identified. Among these genes, protein kinase cAMP-activated catalytic subunit alpha (Prkaca), a gene that directly regulated 137 neighboring genes, was located at the center of the gene network in Ang II-infused aortas. Further, Prkaca protein expression and cAMP level were downregulated in a time-dependent manner in Ang II-infused aortas. Conclusions: The combined use of DNA microarrays and cluster and gene network analyses identified Prkaca as a key Ang II-responsive gene that may mediate early vascular injury and hypertension.

MapReduce Algorithms for Inferring Gene Regulatory Networks from Time-Series Microarray Data Using an Information-Theoretic Approach

Gene regulation is a series of processes that control gene expression and its extent. The connections among genes and their regulatory molecules, usually transcription factors, and a descriptive model of such connections are known as gene regulatory networks (GRNs). Elucidating GRNs is crucial to understand the inner workings of the cell and the complexity of gene interactions. To date, numerous algorithms have been developed to infer gene regulatory networks. However, as the number of identified genes increases and the complexity of their interactions is uncovered, networks and their regulatory mechanisms become cumbersome to test. Furthermore, prodding through experimental results requires an enormous amount of computation, resulting in slow data processing. Therefore, new approaches are needed to expeditiously analyze copious amounts of experimental data resulting from cellular GRNs. To meet this need, cloud computing is promising as reported in the literature. Here, we propose new MapReduce algorithms for inferring gene regulatory networks on a Hadoop cluster in a cloud environment. These algorithms employ an information-theoretic approach to infer GRNs using time-series microarray data. Experimental results show that our MapReduce program is much faster than an existing tool while achieving slightly better prediction accuracy than the existing tool.