Dataset Information

System Genetics Including Causal Inference Identify Immune Targets for Coronary Artery Disease and the Lifespan.

ABSTRACT:

Background

Randomized clinical trials indicate that the immune response plays a significant role in coronary artery disease (CAD), a disorder impacting the lifespan potential. However, the identification of targets critical to the immune response in atheroma is still hampered by a lack of solid inference.

Methods

Herein, we implemented a system genetics approach to identify causally associated immune targets implicated in atheroma. We leveraged genome-wide association studies to perform mapping and Mendelian randomization to assess causal associations between gene expression in blood cells with CAD and the lifespan. Expressed genes (eGenes) were prioritized in network and in single-cell expression derived from plaque immune cells.

Results

Among 840 CAD-associated blood eGenes, 37 were predicted causally associated with CAD and 6 were also associated with the parental lifespan in Mendelian randomization. In multivariable Mendelian randomization, the impact of eGenes on the lifespan potential was mediated by the CAD risk. Predicted causal eGenes were central in network. FLT1 and CCR5 were identified as targets of approved drugs, whereas 22 eGenes were deemed tractable for the development of small molecules and antibodies. Analyses of plaque immune single-cell expression identified predicted causal eGenes enriched in macrophages (GPX1, C4orf3) and involved in ligand-receptor interactions (CCR5).

Conclusions

We identified 37 blood eGenes predicted causally associated with CAD. The predicted expression for 6 eGenes impacted the lifespan potential through the risk of CAD. Prioritization based on network, annotations, and single-cell expression identified targets deemed tractable for the development of drugs and for drug repurposing.

SUBMITTER: Bon-Baret V

PROVIDER: S-EPMC8284374 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

System Genetics Including Causal Inference Identify Immune Targets for Coronary Artery Disease and the Lifespan.

Bon-Baret Valentin V Chignon Arnaud A Boulanger Marie-Chloé MC Li Zhonglin Z Argaud Deborah D Arsenault Benoit J BJ Thériault Sébastien S Bossé Yohan Y Mathieu Patrick P

Circulation. Genomic and precision medicine 20210224 2

<h4>Background</h4>Randomized clinical trials indicate that the immune response plays a significant role in coronary artery disease (CAD), a disorder impacting the lifespan potential. However, the identification of targets critical to the immune response in atheroma is still hampered by a lack of solid inference.<h4>Methods</h4>Herein, we implemented a system genetics approach to identify causally associated immune targets implicated in atheroma. We leveraged genome-wide association studies to p ...[more]

PMID: 33625251

Similar Datasets

Project description:BackgroundCoronary artery disease (CAD) is a complex illness with unknown pathophysiology. Peripheral biomarkers are a non-invasive method required to track the onset and progression of CAD and have unbeatable benefits in terms of early identification, prognostic assessment, and categorization of the diagnosis. This study aimed to identify and validate the diagnostic and therapeutic potential of differentially expressed immune-related genes (DE-IRGs) in CAD, which will aid in improving our knowledge on the etiology of CAD and in forming genetic predictions.MethodsFirst, we searched coronary heart disease in the Gene Expression Omnibus (GEO) database and identified GSE20680 (CAD = 87, Normal = 52) as the trial set and GSE20681 (CAD = 99, Normal = 99) as the validation set. Functional enrichment analysis using protein-protein interactions (PPIs), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) was carried out on the identified differentially expressed genes. Optimal feature genes (OFGs) were generated using the support vector machine recursive feature elimination algorithm and the least absolute shrinkage and selection operator (LASSO) algorithm. Furthermore, immune infiltration in CAD patients and healthy controls was compared using CIBERSORT, and the relationship between immune cells and OFGs was examined. In addition, we constructed potential targeted drugs for this model through the Drug-Gene Interaction database (DGIdb) database. Finally, we verify the expression of S100A8-dominated OFGs in the GSE20681 dataset to confirm the universality of our study.ResultsWe identified the ten best OFGs for CAD from the DE-IRGs. Functional enrichment analysis showed that these marker genes are crucial for receptor-ligand activity, signaling receptor activator activity, and positive control of the response to stimuli from the outside world. Additionally, CIBERSORT revealed that S100A8 could be connected to alterations in the immune microenvironment in CAD patients. Furthermore, with the help of DGIdb and Cytoscape, a total of 64 medicines that target five marker genes were subsequently discovered. Finally, we verified the expression of the OFGs genes in the GSE20681 dataset between CAD patients and normal patients and found that there was also a significant difference in the expression of S100A8.ConclusionWe created a 10-gene immune-related prognostic model for CAD and confirmed its validity. The model can identify potential biomarkers for CAD prediction and more accurately gauge the progression of the disease.

Dataset Information

System Genetics Including Causal Inference Identify Immune Targets for Coronary Artery Disease and the Lifespan.

Background

Methods

Results

Conclusions

Publications

System Genetics Including Causal Inference Identify Immune Targets for Coronary Artery Disease and the Lifespan.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets