Cross-tissue Regulatory Gene Networks Shed Light on Coronary Atherosclerosis
Coronary artery disease (CAD) continues to be a leading cause of heart attacks and strokes, accounting for nearly 30% of global deaths. Understanding the complex nature of this disease requires studying the interplay between genetic and environmental risk factors across multiple tissues. In a groundbreaking study, researchers from the Stockholm Atherosclerosis Gene Expression (STAGE) project have leveraged systems genetics to gain valuable insights into CAD and atherosclerosis regression.
By integrating DNA and RNA data from metabolic and vascular tissues with phenotype information from the STAGE study participants, the researchers have successfully identified key drivers of CAD and its associated risk factors. This novel approach, known as systems genetics, allows for a comprehensive analysis of gene networks involved in the development and progression of CAD.
The study’s findings, published across several papers, showcase the significance of cross-tissue regulatory gene networks (RGNs). In Paper I, the researchers developed a computational pipeline that reconstructed RGNs highlighting key drivers in CAD. By examining the expression quantitative traits of these RGNs and integrating genotype data from multiple genome-wide association studies (GWAS), the team identified 30 CAD-related RGNs that interconnected within blood, vascular, and metabolic tissues. This multidimensional approach provides a comprehensive understanding of the intricate mechanisms underlying CAD.
Seeking further validation, the researchers turned to gene expression and phenotype data from the Hybrid Mouse Diversity Panel in Paper I. Twelve of the identified RGNs were successfully confirmed, solidifying their role in the development of CAD. Notably, the team targeted four key drivers—AIP, DRAP1, POLR2I, and PQBP1—in an arterial-wall RGN that involved RNA-processing genes. This targeting allowed them to re-identify the RGN in THP-1 foam cells and independent gene expression data from CAD macrophages and carotid lesions, providing additional evidence of its role in CAD development.
In Paper II, the researchers introduced a notable computational method called cross-tissue weighted gene co-expression network analysis (X-WGCNA). This approach effectively captures gene activities both within and across multiple tissues. The development of X-WGCNA aims to facilitate future research efforts in understanding the complex nature of diseases like CAD.
Moreover, in Paper III, the team delved into transcription factor (TF) RGNs associated with the regression of early, mature, and advanced atherosclerosis in mice. By performing in vitro experiments using THP-1 cells, they validated three key drivers—PPARG, MLL5, and SRSF10/XRN2—as contributors to atherosclerosis regression in different stages. These key findings shed light on potential therapeutic targets and provide a deeper understanding of the biological processes involved in atherosclerosis regression.
In Paper IV, the team focused on exploring the effect of multi-tissue expression quantitative trait loci (eQTLs) on CAD susceptibility. By identifying subsets of eQTLs that correlated strongly with CAD according to GWAS data, they gained important insights into the pathophysiological role of these regulatory elements. This analysis enriched our understanding of the gene sets associated with CAD and paves the way for new diagnostic and therapeutic approaches.
This groundbreaking research introduces a repository of regulatory gene networks associated with CAD, its risk factors, and atherosclerosis regression. The computational pipeline, including the X-WGCNA method and the identified RGNs, provides a powerful resource for future studies aiming to explore the underlying mechanisms of CAD beyond traditional genetic loci identified by GWAS. The potential for novel diagnostics and therapies is promising, bringing hope for improved management and treatment of this prevalent disease.
In conclusion, the integration of systems genetics and cross-tissue analysis has unveiled new insights into the complex nature of coronary atherosclerosis. By deciphering the regulatory gene networks associated with CAD, researchers have taken a significant step towards a deeper understanding of the disease’s development and progression. This knowledge opens the door to novel diagnostic methods and potential therapeutic targets, sparking optimism in the fight against CAD and its devastating consequences.
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