Identification of crucial genes in intracranial aneurysm based on weighted gene coexpression network analysis

A Transcriptomic Comparative Study of Cranial Vasculature

In genetic studies of cerebrovascular diseases, the optimal vessels to use as controls remain unclear. Our goal is to compare the transcriptomic profiles among 3 different types of control vessels: superficial temporal artery (STA), middle cerebral arteries (MCA), and arteries from the circle of Willis obtained from autopsies (AU). We examined the transcriptomic profiles of STA, MCA, and AU using RNAseq. We also investigated the effects of using these control groups on the results of the comparisons between aneurysms and the control arteries. Our study showed that when comparing pathological cerebral arteries to control groups, all control groups presented similar responses in the activation of immunological processes, the regulation of intracellular signaling pathways, and extracellular matrix productions, despite their intrinsic biological differences. When compared to STA, AU exhibited upregulation of stress and apoptosis genes, whereas MCA showed upregulation of genes associated with tRNA/rRNA processing. Moreover, our results suggest that the matched case-control study design, which involves control STA samples collected from the same subjects of matched aneurysm samples in our study, can improve the identification of non-inherited disease-associated genes. Given the challenges associated with obtaining fresh intracranial arteries from healthy individuals, our study suggests that using MCA, AU, or paired STA samples as controls are feasible strategies for future large-scale studies investigating cerebral vasculopathies. However, the intrinsic differences of each type of control should be taken into consideration when interpreting the results. With the limitations of each control type, it may be most optimal to use multiple tissues as controls.

Comprehensive analysis of mitochondrial dysfunction and necroptosis in intracranial aneurysms from the perspective of predictive, preventative, and personalized medicine

Mitochondrial dysfunction and necroptosis are closely associated, and play vital roles in the medical strategy of multiple cardiovascular diseases. However, their implications in intracranial aneurysms (IAs) remain unclear. In this study, we aimed to explore whether mitochondrial dysfunction and necroptosis could be identified as valuable starting points for predictive, preventive, and personalized medicine for IAs. The transcriptional profiles of 75 IAs and 37 control samples were collected from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs), weighted gene co-expression network analysis, and least absolute shrinkage and selection operator (LASSO) regression were used to screen key genes. The ssGSEA algorithm was performed to establish phenotype scores. The correlation between mitochondrial dysfunction and necroptosis was evaluated using functional enrichment crossover, phenotype score correlation, immune infiltration, and interaction network construction. The IA diagnostic values of key genes were identified using machine learning. Finally, we performed the single-cell sequencing (scRNA-seq) analysis to explore mitochondrial dysfunction and necroptosis at the cellular level. In total, 42 IA-mitochondrial DEGs and 15 IA-necroptosis DEGs were identified. Screening revealed seven  key genes invovled in mitochondrial dysfunction (KMO, HADH, BAX, AADAT, SDSL, PYCR1, and MAOA) and five genes involved in necroptosis (IL1B, CAMK2G, STAT1, NLRP3, and BAX). Machine learning confirmed the high diagnostic value of these key genes for IA. The IA samples showed  higher expression of mitochondrial dysfunction and necroptosis. Mitochondrial dysfunction and necroptosis exhibited a close association. Furthermore, scRNA-seq indicated that mitochondrial dysfunction and necroptosis were preferentially up-regulated in monocytes/macrophages and vascular smooth muscle cells (VSMCs) within IA lesions. In conclusion, mitochondria-induced necroptosis was involved in IA formation, and was mainly up-regulated in monocytes/macrophages and VSMCs within IA lesions. Mitochondria-induced necroptosis may be a novel potential target for diagnosis, prevention, and treatment of IA.

Transcriptomic Studies on Intracranial Aneurysms

Intracranial aneurysm (IA) is a relatively common vascular malformation of an intracranial artery. In most cases, its presence is asymptomatic, but IA rupture causing subarachnoid hemorrhage is a life-threating condition with very high mortality and disability rates. Despite intensive studies, molecular mechanisms underlying the pathophysiology of IA formation, growth, and rupture remain poorly understood. There are no specific biomarkers of IA presence or rupture. Analysis of expression of mRNA and other RNA types offers a deeper insight into IA pathobiology. Here, we present results of published human studies on IA-focused transcriptomics.

FOS gene associated immune infiltration signature in perivascular adipose tissues of abdominal aortic aneurysm

Abdominal aortic aneurysms (AAA) are pathological dilations in local aortic wall. The inflammatory infiltrates of the perivascular adipose tissue (PAT) surrounding AAAs were associated with AAAs and have been shown to contribute vascular pathology. However, the mechanism by which PAT inflammation contributes to vascular pathology in AAA remains to be clarified. This study aimed to explore the association between immune cell infiltration and key gene expression profile in PAT of AAA. For that, a gene expression dataset of human dilated perivascular adipose tissue (dPAT), non-dilated perivascular adipose tissue (ndPAT), subcutaneous abdominal fat (SAF) and omental-visceral fat (OVF) samples, as well as another microarray dataset of the abdominal perivascular adipose tissue in peripheral artery disease patients were downloaded from GEO database for analysis in this study. The CIBERSORT algorithm, weighted gene co-expression network analysis (WGCNA) and LASSO algorithm were used for the identification of immune infiltration, immune-related genes and the development of diagnostic signature. Our data discovered a significant higher proportion of activated mast cells and follicular helper T (Tfh) cells in dPAT than ndPAT, OVT and SAF samples. Moreover, AP-1 family members (FOS, FOSB, ATF3, JUN and JUNB) were found to compose the hub genes of purple module in WGCNA. Among them, FOS gene acts as a higher efficient marker to discriminate dPAT from ndPAT, OVT and SAF in AAA. Meanwhile, the expression profiles of the AP-1 family members are all significantly positive correlated with activated mast cell, plasma cell and Tfh cell infiltration in dPAT of AAA. Therefore, in the PAT surrounding AAA, the signature of inflammatory infiltration might be represented by a FOS-dominated cell network consist of activated mast cell, plasma cell and Tfh cell. Given the complicated etiology of AAA, our results are likely to shed new light on the pathophysiologic mechanism of AAA influenced by the local dPAT.

Weighted Co-Expression Network Analysis Identifies RNF181 as a Causal Gene of Coronary Artery Disease

Background: Coronary artery disease (CAD) exerts a global challenge to public health. Genetic heritability is one of the most vital contributing factors in the pathophysiology of CAD. Co-expression network analysis is an applicable and robust method for the interpretation of biological interaction from microarray data. Previous CAD studies have focused on peripheral blood samples since the processes of CAD may vary from tissue to blood. It is therefore necessary to find biomarkers for CAD in heart tissues; their association also requires further illustration.

Materials and Methods: To filter for causal genes, an analysis of microarray expression profiles, GSE12504 and GSE22253, was performed with weighted gene co-expression network analysis (WGCNA). Co-expression modules were constructed after batch effect removal and data normalization. The results showed that 7 co-expression modules with 8,525 genes and 1,210 differentially expressed genes (DEGs) were identified. Furthermore, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted. Four major pathways in CAD tissue and hub genes were addressed in the Hybrid Mouse Diversity Panel (HMDP) and Human Protein Atlas (HPA), and isoproterenol (ISO)/doxycycline (DOX)-induced heart toxicity models were used to validate the hub genes. Lastly, the hub genes and risk variants were verified in the CAD cohort and in genome-wide association studies (GWAS).

Results: The results showed that RNF181 and eight other hub genes are perturbed during CAD in heart tissues. Additionally, the expression of RNF181 was validated using RT-PCR and immunohistochemistry (IHC) staining in two cardiotoxicity mouse models. The association was further verified in the CAD patient cohort and in GWAS.

Conclusion: Our findings illustrated for the first time that the E3 ubiquitination ligase protein RNF181 may serve as a potential biomarker in CAD, but further in vivo validation is warranted.

High-Throughput In Vitro Gene Expression Profile to Screen of Natural Herbals for Breast Cancer Treatment

Breast cancer has surpassed lung cancer as the most commonly diagnosed cancer in women worldwide. Some therapeutic drugs and approaches could cause side effects and weaken the immune system. The combination of conventional therapies and traditional Chinese medicine (TCM) significantly improves treatment efficacy in breast cancer. However, the chemical composition and underlying anti-tumor mechanisms of TCM still need to be investigated. The primary aim of this study is to provide unique insights to screen the natural components for breast cancer therapy using high-throughput transcriptome analysis. Differentially expressed genes were identified based on two conditions: single samples and groups were classified according to their pharmaceutical effect. Subsequently, the sample treated with E. cochinchinensis Lour. generated the most significant DEGs set, including 1,459 DEGs, 805 upregulated and 654 downregulated. Similarly, group 3 treatment contained the most DEGs (414 DEGs, 311 upregulated and 103 downregulated). KEGG pathway analyses showed five significant pathways associated with the inflammatory and metastasis processes in cancer, which include the TNF, IL−17, NF-kappa B, MAPK signaling pathways, and transcriptional misregulation in cancer. Samples were classified into 13 groups based on their pharmaceutical effects. The results of the KEGG pathway analyses remained consistent with signal samples; group 3 presents a high significance. A total of 21 genes were significantly regulated in these five pathways, interestingly, IL6, TNFAIP3, and BRIC3 were enriched on at least two pathways, seven genes (FOSL1, S100A9, CXCL12, ID2, PRS6KA3, AREG, and DUSP6) have been reported as the target biomarkers and even the diagnostic tools in cancer therapy. In addition, weighted correlation network analysis (WGCNA) was used to identify 18 modules. Among them, blue and thistle2 were the most relevant modules. A total of 26 hub genes in blue and thistle2 modules were identified as the hub genes. In conclusion, we screened out three new TCM (R. communis L., E. cochinchinensis Lour., and B. fruticosa) that have the potential to develop natural drugs for breast cancer therapy, and obtained the therapeutic targets.

Identification and validation of key genes mediating intracranial aneurysm rupture by weighted correlation network analysis

Background

Rupture of intracranial aneurysm (IA) is the leading cause of subarachnoid hemorrhage. However, there are few pharmacological therapies available for the prevention of IA rupture. Therefore, exploring the molecular mechanisms which underlie IA rupture and identifying the potential molecular targets for preventing the rupture of IA is of vital importance.

Methods

We used the Gene Expression Omnibus (GEO) datasets GSE13353, GSE15629, and GSE54083 in our study. The 3 datasets were merged and normalized. Differentially expressed gene (DEG) screening and weighted correlation network analysis (WGCNA) were conducted. The co-expression patterns between ruptured IA samples and unruptured IA samples were compared. Then, the DEGs were mapped into the whole co-expression network of ruptured IA samples, and a DEG co-expression network was generated. Molecular Complex Detection (MCODE) (http://baderlab.org/Software/MCODE) was used to identify key genes based on the DEG co-expression network. Finally, key genes were validated using another GEO dataset (GSE122897), and their potential diagnostic values were shown using receiver operating characteristic (ROC) analysis.

Results

In our study, 49 DEGs were screened while 8 and 6 gene modules were detected based on ruptured IA samples and unruptured IA samples, respectively. Pathways associated with inflammation and immune response were clustered in the salmon module of ruptured IA samples. The DEG co-expression network with 35 nodes and 168 edges was generated, and 14 key genes were identified based on this DEG co-expression network. The gene with the highest degree in the key gene cluster was CXCR4. All key genes were validated using GSE122897, and they all showed the potential diagnostic value in predicting IA rupture.

Conclusions

Using a weighted gene co-expression network approach, we identified 8 and 6 modules for ruptured IA and unruptured IA, respectively. After that, we identified the hub genes for each module and key genes based on the DEG co-expression network. All these key genes were validated by another GEO dataset and might serve as potential targets for pharmacological therapies and diagnostic markers in predicting IA rupture. Further studies are needed to elucidate the detailed molecular mechanisms and biological functions of these key genes which underlie the rupture of IA.

Weighted gene co-expression network analysis revealed key biomarkers associated with the diagnosis of hypertrophic cardiomyopathy

Objective

To reveal the molecular mechanism underlying the pathogenesis of HCM and find new effective therapeutic strategies using a systematic biological approach.

Methods

The WGCNA algorithm was applied to building the co-expression network of HCM samples. A sample cluster analysis was performed using the hclust tool and a co-expression module was constructed. The WGCNA algorithm was used to study the interactive connection between co-expression modules and draw a heat map to show the strength of interactions between modules. The genetic information of the respective modules was mapped to the associated GO terms and KEGG pathways, and the Hub Genes with the highest connectivity in each module were identified. The Wilcoxon test was used to verify the expression level of hub genes between HCM and normal samples, and the “pROC” R package was used to verify the possibility of hub genes as biomarkers. Finally, the potential functions of hub genes were analyzed by GSEA software.

Results

Seven co-expression modules were constructed using sample clustering analysis. GO and KEGG enrichment analysis judged that the turquoise module is an important module. The hub genes of each module are RPL35A for module Black, FH for module Blue, PREI3 for module Brown, CREB1 for module Green, LOC641848 for module Pink, MYH7 for module Turquoise and MYL6 for module Yellow. The results of the differential expression analysis indicate that MYH7 and FH are considered true hub genes. In addition, the ROC curves revealed their high diagnostic value as biomarkers for HCM. Finally, in the results of the GSEA analysis, MYH7 and FH highly expressed genes were enriched with the “proteasome” and a “PPAR signaling pathway,” respectively.

Conclusions

The MYH7 and FH genes may be the true hub genes of HCM. Their respective enriched pathways, namely the “proteasome” and the “PPAR signaling pathway,” may play an important role in the development of HCM.

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

Background

Coronary artery disease (CAD) is a type of cardiovascular disease that greatly hurts the health of human beings. Diabetic status is one of the largest clinical factors affecting CAD-associated gene expression changes. Most of the studies focus on diabetic patients, whereas few have been done for non-diabetic patients. Since the pathophysiological processes may vary among these patients, we cannot simply follow the standard based on the data from diabetic patients. Therefore, the prognostic and predictive diagnostic biomarkers for CAD in non-diabetic patient need to be fully recognized.

Materials and Methods

To screen out candidate genes associated with CAD in non-diabetic patients, weighted gene co-expression network analysis (WGCNA) was constructed to conduct an analysis of microarray expression profiling in patients with CAD. First, the microarray data GSE20680 and GSE20681 were downloaded from NCBI. We constructed co-expression modules via WGCNA after excluding the diabetic patients. As a result, 18 co-expression modules were screened out, including 1,225 differentially expressed genes (DEGs) that were obtained from 152 patients (luminal stenosis ≥50% in at least one major vessel) and 170 patients (stenosis of <50%). Subsequently, a Pearson's correlation analysis was conducted between the modules and clinical traits. Then, a functional enrichment analysis was conducted, and we used gene network analysis to reveal hub genes. Last, we validated the hub genes with peripheral blood samples in an independent patient cohort using RT-qPCR.

Results

The results showed that the midnight blue module and the yellow module played vital roles in the pathogenesis of CAD in non-diabetic patients. Additionally, CD40, F11R, TNRC18, and calcium/calmodulin-dependent protein kinase type II gamma (CAMK2G) were screened out and validated using enzyme-linked immunosorbent assay (ELISA) in an independent patient cohort and immunohistochemical (IHC) staining in an atherosclerosis mouse model.

Conclusion

Our findings demonstrate that hub genes, CD40, F11R, TNRC18, and CAMK2G, are surrogate diagnostic biomarkers and/or therapeutic targets for CAD in non-diabetic patients and require deeper validation.

Expression profile analysis of differentially expressed genes in ruptured intracranial aneurysms: In search of biomarkers

Intracranial aneurysms (IAs) result from the bulging of arterial walls secondary to several factors such as flow, vessel morphology, and genetics. Subarachnoid hemorrhage occurs when such walls rupture, leading to high disability and mortality. Despite numerous investigations pertaining to the relationship between geometric characteristics and IA rupture, only a few have obtained consistent results. This study aimed to further identify the potential genes associated with the pathogenesis of IAs, which may provide novel molecular biomarkers. We downloaded and reanalyzed six datasets, which were divided into four groups. IA walls and blood samples were screened for differentially expressed genes (DEGs); then, functional and pathway enrichment analyses were conducted. In total, 158 common DEGs were identified from Groups 1-3 and 396 genes (187 upregulated and 209 downregulated genes) were differentially expressed in Group 4. The functional analysis revealed that the DEGs were mainly associated with the major histocompatibility complex class II protein complex and antigen processing and presentation. Finally, we identified nine key genes, both in aneurysm tissue samples and blood samples, of which three were mostly associated with the progression and rupture of IAs. Bioinformatics was used to analyze the datasets of the ruptured IAs and identify potential biomarkers, which may provide information for the early detection and treatment of IAs.

Integrated bioinformatic analysis reveals YWHAB as a novel diagnostic biomarker for idiopathic pulmonary arterial hypertension

Idiopathic pulmonary arterial hypertension (IPAH) is a severe cardiovascular disease that is a serious threat to human life. However, the specific diagnostic biomarkers have not been fully clarified and candidate regulatory targets for IPAH have not been identified. The aim of this study was to explore the potential diagnostic biomarkers and possible regulatory targets of IPAH. We performed a weighted gene coexpression network analysis and calculated module-trait correlations based on a public microarray data set (GSE703) and six modules were found to be related to IPAH. Two modules which have the strongest correlation with IPAH were further analyzed and the top 10 hub genes in the two modules were identified. Furthermore, we validated the data by quantitative real-time polymerase chain reaction (qRT-PCR) in an independent sample set originated from our study center. Overall, the qRT-PCR results were consistent with most of the results of the microarray analysis. Intriguingly, the highest change was found for YWHAB, a gene encodes a protein belonging to the 14-3-3 family of proteins, members of which mediate signal transduction by binding to phosphoserine-containing proteins. Thus, YWHAB was subsequently selected for validation. In congruent with the gene expression analysis, plasma 14-3-3β concentrations were significantly increased in patients with IPAH compared with healthy controls, and 14-3-3β expression was also positively correlated with mean pulmonary artery pressure ( R ²  = 0.8783; p < 0.001). Taken together, using weighted gene coexpression analysis, YWHAB was identified and validated in association with IPAH progression, which might serve as a biomarker and/or therapeutic target for IPAH.

Effects of threshold on the topology of gene co-expression networks

Several developments regarding the analysis of gene co-expression profiles using complex network theory have been reported recently. Such approaches usually start with the construction of an unweighted gene co-expression network, therefore requiring the selection of a suitable threshold defining which pairs of vertices will be connected. We aimed at addressing such an important problem by suggesting and comparing five different approaches for threshold selection. Each of the methods considers a respective biologically-motivated criterion for electing a potentially suitable threshold. A set of 21 microarray experiments from different biological groups was used to investigate the effect of applying the five proposed criteria to several biological situations. For each experiment, we used the Pearson correlation coefficient to measure the relationship between each gene pair, and the resulting weight matrices were thresholded considering several values, generating respective adjacency matrices (co-expression networks). Each of the five proposed criteria was then applied in order to select the respective threshold value. The effects of these thresholding approaches on the topology of the resulting networks were compared by using several measurements, and we verified that, depending on the database, the impact on the topological properties can be large. However, a group of databases was verified to be similarly affected by most of the considered criteria. Based on such results, it can be suggested that when the generated networks present similar measurements, the thresholding method can be chosen with greater freedom. If the generated networks are markedly different, the thresholding method that better suits the interests of each specific research study represents a reasonable choice.

Identification of key gene modules for human osteosarcoma by co-expression analysis

BACKGROUND

Osteosarcoma is a type of bone cancer casting huge threat to the human health worldwide. Previously, gene expression analyses were performed to identify biomarkers for cancer; however, systemic co-expression analysis for osteosarcoma is still in need. The aim of this study was to construct a gene co-expression network that predicts clusters of candidate genes associated with the pathogenesis of osteosarcoma.

METHODS

Here, we extracted the large scale of datasets from the GEO database. With systematical approaches, we identified the co-expression modules by using weighted gene co-expression network analysis (WGCNA) and investigated the functional enrichments of important modules at GO and KEGG terms.

RESULTS

First, seven co-expression modules, which contain different genes, were conducted for 2228 genes in the 22 human osteosarcoma samples. Then, correlation study showed that the hub genes between pairwise modules displayed great differences. Lastly, functional enrichments of the co-expression modules showed that the module 5 enriched in immune response, antigen processing, and presentation, which is in consistence with GO result. Therefore, we speculated that the module 5 may play a key role in the pathogenesis of osteosarcoma.

CONCLUSIONS

Here, we speculated that genes of the module 5 were the essential genes that were associated to human osteosarcoma. Together, our findings not only provided outline of co-expression gene modules for human osteosarcoma, but also promoted the understanding of these modules at functional aspects.

Highly interwoven communities of a gene regulatory network unveil topologically important genes for maize seed development

The complex interactions between transcription factors (TFs) and their target genes in a spatially and temporally specific manner are crucial to all cellular processes. Reconstruction of gene regulatory networks (GRNs) from gene expression profiles can help to decipher TF-gene regulations in a variety of contexts; however, the inevitable prediction errors of GRNs hinder optimal data mining of RNA-Seq transcriptome profiles. Here we perform an integrative study of Zea mays (maize) seed development in order to identify key genes in a complex developmental process. First, we reverse engineered a GRN from 78 maize seed transcriptome profiles. Then, we studied collective gene interaction patterns and uncovered highly interwoven network communities as the building blocks of the GRN. One community, composed of mostly unknown genes interacting with opaque2, brittle endosperm1 and shrunken2, contributes to seed phenotypes. Another community, composed mostly of genes expressed in the basal endosperm transfer layer, is responsible for nutrient transport. We further integrated our inferred GRN with gene expression patterns in different seed compartments and at various developmental stages and pathways. The integration facilitated a biological interpretation of the GRN. Our yeast one-hybrid assays verified six out of eight TF-promoter bindings in the reconstructed GRN. This study identified topologically important genes in interwoven network communities that may be crucial to maize seed development.

Identification of Key Gene Modules of Neuropathic Pain by Co-Expression Analysis

Neuropathic pain (NP) is a substantial clinical problem causing great injury to people word-widely. Although gene expression analyses had been performed previously, the mechanisms underlying the etiology and development of NP are still poorly understood. To understand the function genes involved in the etiology and development of NP, we built the co-expression modules and performed function enrichment analysis for neuropathic pain. In the present study, from a public microarray data set (GSE69901) from NCBI, gene co-expression modules were contributed with the help of WGCNA for 12 neuropathic pain samples and 13 control samples, respectively. And functional enrichment analyses were followed by DAVID database. Firstly, we established 21 co-expression modules and 19 co-expression modules out of 5,000 high-express genes in NP and control samples, respectively. Then, it showed great difference in interaction relationships of total genes and hub-genes between pairwise modules, which indicated the high confidence of gene co-expression modules. Finally, functional enrichment analysis of the top five co-expression modules in NP exhibited great differences and significant enrichment in transcription regulation of RNA polymerase II promoter and ubiquitin mediated proteolysis pathway. RNA polymerase II promoter and ubiquitin-mediated proteolysis pathway played important role in etiology and development of NP. Anyhow, our findings provided the framework of gene co-expression modules of NP and furthered the understanding of these modules from functional aspect. J. Cell. Biochem. 118: 4436-4443, 2017. © 2017 Wiley Periodicals, Inc.

Identification of Key Gene Modules in Human Osteosarcoma by Co-Expression Analysis Weighted Gene Co-Expression Network Analysis (WGCNA)

Osteosarcoma is the eighth-most common form of childhood cancer, comprising about 20% of all primary bone cancers. To date, systemic co-expression analysis for this cancer is still insufficient to explain the pathogenesis of poorly understood OC. The objective of this study was to construct a gene co-expression network to predict clusters of candidate genes involved in the pathogenesis of osteosarcoma. First, we contributed co-expression modules via weighted gene co-expression network analysis (WGCNA) and investigated the functional enrichment analysis of co-expression genes in terms of GO and KEGG. In result, seven co-expression modules were identified, containing 2,228 differentially expressed genes identified from the 22 human osteosarcoma samples. Subsequently, correlation study showed that the hub-genes between pair-wise modules displayed significant differences. Lastly, functional enrichment analysis of the co-expression modules showed that the module 5 enriched in progresses of immune response, antigen processing, and presentation. In conclusion, we identified essential genes in module 5 which were associated to human osteosarcoma. The key genes in our findings might provide the framework of co-expression gene modules of human osteosarcoma. Further, the functional analysis of these associated genes provides references to understand the mechanism of Osteosarcoma. J. Cell. Biochem. 118: 3953-3959, 2017. © 2017 Wiley Periodicals, Inc.

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

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.

Integrative Analysis of Genomics and Transcriptome Data to Identify Potential Functional Genes of BMDs in Females

Osteoporosis is known to be highly heritable. However, to date, the findings from more than 20 genome-wide association studies (GWASs) have explained less than 6% of genetic risks. Studies suggest that the missing heritability data may be because of joint effects among genes. To identify novel heritability for osteoporosis, we performed a system-level study on bone mineral density (BMD) by weighted gene coexpression network analysis (WGCNA), using the largest GWAS data set for BMD in the field, Genetic Factors for Osteoporosis Consortium (GEFOS-2), and a transcriptomic gene expression data set generated from transiliac bone biopsies in women. A weighted gene coexpression network was generated for 1574 genes with GWAS nominal evidence of association (p ≤ 0.05) based on dissimilarity measurement on the expression data. Twelve distinct gene modules were identified, and four modules showed nominally significant associations with BMD (p ≤ 0.05), but only one module, the yellow module, demonstrated a good correlation between module membership (MM) and gene significance (GS), suggesting that the yellow module serves an important biological role in bone regulation. Interestingly, through characterization of module content and topology, the yellow module was found to be significantly enriched with contractile fiber part (GO:044449), which is widely recognized as having a close relationship between muscle and bone. Furthermore, detailed submodule analyses of important candidate genes (HOMER1, SPTBN1) by all edges within the yellow module implied significant enrichment of functional connections between bone and cytoskeletal protein binding. Our study yielded novel information from system genetics analyses of GWAS data jointly with transcriptomic data. The findings highlighted a module and several genes in the model as playing important roles in the regulation of bone mass in females, which may yield novel insights into the genetic basis of osteoporosis. © 2016 American Society for Bone and Mineral Research.