Integrative Bioinformatics Analysis of Genomic and Proteomic Approaches to Understand the Transcriptional Regulatory Program in Coronary Artery Disease Pathways

Patients with cardiovascular disease show a panel of differentially regulated serum biomarkers indicative of modulation of several pathways from disease onset to progression. Few of these biomarkers have been proposed for multimarker risk prediction methods. However, the underlying mechanism of the expression changes and modulation of the pathways is not yet addressed in entirety. Our present work focuses on understanding the regulatory mechanisms at transcriptional level by identifying the core and specific transcription factors that regulate the coronary artery disease associated pathways. Using the principles of systems biology we integrated the genomics and proteomics data with computational tools. We selected biomarkers from 7 different pathways based on their association with the disease and assayed 24 biomarkers along with gene expression studies and built network modules which are highly regulated by 5 core regulators PPARG, EGR1, ETV1, KLF7 and ESRRA. These network modules in turn comprise of biomarkers from different pathways showing that the core regulatory transcription factors may work together in differential regulation of several pathways potentially leading to the disease. This kind of analysis can enhance the elucidation of mechanisms in the disease and give better strategies of developing multimarker module based risk predictions.

心血管疾病患者体内存在一组差异调控的血清生物标志物，此类标志物可反映从疾病发作至进展阶段多条通路的调控状态。目前仅有少数此类生物标志物被应用于多标志物风险预测模型的构建。然而，关于这些生物标志物的表达变化及通路调控的潜在分子机制，目前尚未得到全面阐释。本研究旨在通过识别调控冠状动脉疾病相关通路的核心特异性转录因子，解析疾病的转录层面调控机制。本研究依托系统生物学（systems biology）原理，将基因组学（genomics）与蛋白质组学（proteomics）数据与计算工具相结合：基于生物标志物与疾病的关联强度，从7条不同通路中筛选目标标志物，并对24种标志物开展基因表达检测，最终构建了受5种核心调控因子（PPARG、EGR1、ETV1、KLF7及ESRRA）高度调控的网络模块。此类网络模块涵盖了来自不同通路的生物标志物，表明核心调控转录因子可协同参与多条通路的差异调控，进而可能推动疾病的发生发展。此类分析可强化对疾病潜在机制的阐释，并为开发基于多标志物模块的风险预测策略提供更优路径。