Weighted Gene Co-expression Network Analysis Identifies FKBP11 as a Key Regulator in Acute Aortic Dissection through a NF-kB Dependent Pathway

Abstract

Acute aortic dissection (AAD) is a life-threatening disease. Despite the higher risk of mortality, currently there are no effective therapies that can ameliorate AAD development or progression. Identification of meaningful clusters of co-expressed genes or representative biomarkers for AAD may help to identify new pathomechanisms and foster development of new therapies. To this end, we performed a weighted gene co-expression network analysis (WGCNA) and calculated module-trait correlations based on a public microarray dataset (GSE 52093) and discovered 9 modules were found to be related to AAD. The module which has the strongest positive correlation with AAD was further analyzed and the top 10 hub genes SLC20A1, GINS2, CNN1, FAM198B, MAD2L2, UBE2T, FKBP11, SLMAP, CCDC34, and GALK1 were identified. Furthermore, we validated the data by qRT-PCR in an independent sample set originated from our study center. Overall, the qRT-PCR results were consistent with the results of the microarray analysis. Intriguingly, the highest change was found for FKBP11, a protein belongs to the FKBP family of peptidyl-prolyl cis/trans isomerases, which catalyze the folding of proline-containing polypeptides. In congruent with the gene expression analysis, FKBP11 expression was induced in cultured endothelial cells by angiotensin II treatment and endothelium of the dissected aorta. More importantly we show that FKBP11 provokes inflammation in endothelial cells by interacting with NF-kB p65 subunit, resulting in pro-inflammatory cytokines production. Accordingly, siRNA mediated knockdown of FKBP11 in cultured endothelial cells suppressed angiotensin II induced monocyte transmigration through the endothelial monolayer. Based on these data, we hypothesize that pro-inflammatory cytokines elicited by FKBP11 overexpression in the endothelium under AAD condition could facilitate transendothelial migration of the circulating monocytes into the aorta, where they differentiate into active macrophages and secrete MMPs and other extracellular matrix (ECM) degrading proteins, contributing to sustained inflammation and AAD. Taken together, our data identify important role of FKBP11 which can serve as biomarker and/or therapeutic target for AAD.

The Role of Macrophages in Aortic Dissection

“…Weighted gene co-expression network analysis has identified FKBP11 as a key regulator in Ang II-induced AD. FKBP11 operates through a nuclear factor-kB-dependent pathway, and is hypothesized to promote macrophage infiltration and M1 differentiation (Wang et al, 2017). Ang II also promotes the infiltration of macrophages and the secretion of matrix metalloproteinases (MMPs) via the axis of Kruppel-like factor 6 and granulocyte macrophage-colonystimulating factor (GM-CSF) (Son et al, 2015), as well as a disintegrin and metalloproteinase with thrombospondin motifs 1 (ADAMTS-1) (Gao et al, 2016), to cause local inflammation and tissue destruction.…”

Identification of Four Potential Biomarkers Associated With Coronary Artery Disease in Non-diabetic Patients by Gene Co-expression Network Analysis

“…We can find clusters (modules) of highly correlated genes using WGCNA. The hub genes of modules, described as the most closely associated with disease, often have more biological significance compared with the other genes of global networks (Goh et al, 2007;Wang et al, 2017). We can use WGCNA to identify the specific modules and hub genes that are correlated with phenotypes (Zhang and Horvath, 2005) and then to explore candidate biomarkers or therapeutic targets.…”

“…We can use WGCNA to identify the specific modules and hub genes that are correlated with phenotypes (Zhang and Horvath, 2005) and then to explore candidate biomarkers or therapeutic targets. Recently, WGCNA has been comprehensively applied in multiple diseases, such as breast cancer (Clarke et al, 2013), schizophrenia (de Jong et al, 2012), idiopathic pulmonary arterial hypertension (Wang et al, 2019), acute aortic dissection (Wang et al, 2017), and intracranial aneurysm (Zheng et al, 2015). Compared with the traditional microarray that is based on a microarray expression profiling data analysis, WGCNA takes the interaction of the transcriptome into account via constructing co-expression modules.…”

Identification of key immune regulatory genes in HIV–1 Progression

“…Gene co-expression network analysis is a popular technique, widely used in several articles to model dependency structure among gene expression, such as: used to investigate the changes of expression patterns across the disease progression stages [5], [6], [7], [8], [9], [10], [11], used to identify hub genes and relevant pathways [12], [13], used to distinguish cancer risk modules [14] and utilized for biomarker selection in cancer prognosis [15]. Gene co-expression analysis in different stages of HIV progression is noticed to be carried out in literatures [5], [7], [16].…”