Objective: The study was aimed to explore the potential mechanism underlying atherosclerosis development induced by familial hypercholesterolemia (FHC) and identify the potential target genes for atherosclerosis treatment by bioinformatics analysis.
Methods: The microarray data of GSE13985 was downloaded from Gene Expression Omnibus (GEO) database which were developed by 5 blood samples from FHC samples and 5 ones from controls. The differentially expressed genes (DEGs) between FHC and normal samples were analyzed. The protein-protein interaction (PPI) network was constructed and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis was performed. Additionally, module analysis and Gene ontology (GO) analysis for module genes were performed.
Results: Total 394 DEGs were obtained, including 125 up- and 269 down-regulated genes. Ribosomal proteins, such as ribosomal protein L9 (RPL9), ribosomal protein L35 (RPL35) and ribosomal protein S7 (RPS7), were hub nodes in PPI network. KEGG pathways results showed that the DEGs were significantly enriched in ribosome and oxidative phosphorylation pathways. Ribosome proteins related genes were identified in ribosome pathway. Cytochrome c oxidase genes, including cytochrome c oxidase (COX) subunit VIIa polypeptide 2 (COX7A2), COX subunit VIIb (COX7B), COX subunit VIIc (COX7C) and COX subunit VIc (COX6C), were enriched in oxidative phosphorylation pathway. Additionally, module analysis and GO enrichment analysis results showed that ribosome proteins were the important proteins in FHC.
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Conclusion: The ribosome and oxidative phosphorylation pathways may be closely associated with atherosclerosis development induced by FHC. Ribosome proteins related genes and cytochrome c oxidase genes may be potential therapeutic target genes for atherosclerosis.
Keywords: atherosclerosis; familial hypercholesterolemia; differentially expressed genes; bioinformatics analysis
Atherosclerosis is a chronic disease, which is caused by the deposition of lipid (mainly cholesterol and cholesterol esters) in the intima of the arterial wall, leading to the arterial wall thickness (Moghadasian 2002). It is proved that one of the leading risk factors for development of atherosclerosis is familial hypercholesterolemia (FHC) (Cheung & Lam 2010). FHC is an autosomal dominant disorder, which is characterized with high cholesterol levels, specially a high level of low-density lipoprotein (LDL) (Aliev et al. 2003). FHC atherosclerosis is one of the leading causes for death among the population worldwide. Therefore, an improved understanding mechanism on the pathogenesis of atherosclerosis induced by FHC would supply new insights for the diagnosis and treatment of atherosclerosis.
Many studies in molecular biology have been done to decipher the pathogenesis of atherosclerosis caused by FHC High LDL-cholesterol is a risk factor for atherosclerosis and deletion in the gene for the LDL receptor (LDLR) is associated with FHC (Hobbs et al. 1987). Smilde et al. reported that aggressive LDL-cholesterol reduction was accompanied by reducing the atherosclerosis progression in FHC patients (Smilde et al. 2001). Based on the previous research at gene level, the over-expression of cholesteryl ester transfer protein (CETP) was found to be able to reduce the level of cholesterol (Ishibashi et al. 1993). In addition, decrease in interleukin-12 production linking the 12/15-lipoxygenase pathway was associated with reduced atherosclerosis in a mouse model of FHC (Zhao et al. 2002). However, the molecular mechanism of atherosclerosis is not fully understood and the gene therapy for atherosclerosis is insufficient.
In this study, we applied the bioinformatics methods to download the microarray data (GSE13985) and analyze the differentially expressed genes (DEGs) of white blood cells between FHC and normal samples. Protein-protein interaction (PPI) network was constructed for DEGs and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis was performed. Besides, module analysis and Gene ontology (GO) analysis for module genes were performed to estimate the significant genes and their corresponding functions. The purpose of this work was to explore the potential mechanism underlying atherosclerosis development induced by FHC and uncover the target gene for atherosclerosis treatment.
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Affymetrix microarray data
The RNA microarray data[A1] of GSE13985 was obtained from the Gene Expression Omnibus (GEO) database in National Center for Biotechnology Information (NCBI, http://www.ncbi.nlm.nih.gov/geo/), which was deposited by ReÅ¾en et al. on December 18, 2008.. Total 10 white blood samples were used for expression patterns development, including 5 samples from patients with FHC and 5 normal samples. The raw data and probe annotation information were downloaded based on the platform of GPL570 (Affymetrix Human Genome U133 Plus 2.0 Array) (Affymetrix Inc., Santa Clara, California, USA).
Data preprocessing and differentially expressed gene analysis
The probe-leveldata in .CEL files were converted into expression measures. The data were firstly preprocessed using the Affy package (http://www.bioconductor.org/packages/release/bioc/html/affy.html) (Team 2012) in R language. The probe-level data were converted into the gene expression measures. If multiple probes corresponded to a same gene, the mean expression value was calculated to represent the gene expression level. The missing values were imputed using the K-nearest Neighbors (KNN) method (Troyanskaya et al. 2001). Then the data were quantile normalized with the Affy package (Team 2012) in R.
The differentially expressed genes (DEGs) between FHC and normal samples were analyzed with the application of limma package (http://master.bioconductor.org/packages/release/bioc/html/limma.html) (Smyth 2004) in R. P-value < 0.05 and |fold change (FC)| > 1.5 [A2](|log2FC| > 0.585) were defined as the threshold value.
Protein-protein interaction network
Search Tool for the Retrieval of Interacting Genes (STRING, http://www.string-db.org/) (Szklarczyk et al. 2011) is a public database for predicting gene interactions with a confidence score. The up-regulated gene and down-regulated ones were analyzed to construct the protein-protein interaction (PPI) network using STRING. Connectivity degree represents the number of edges linked to a given node. The important nodes with high degree in the network were obtained, namely hub nodes. The interactions with confidence score > 0.9 were collected as candidates for further analysis. The PPI network was visualized by Cytoscape software (http://cytoscape.org/plugins.html) (Smoot et al. 2011).
KEGG knowledge database (http://www.kegg.jp/) (Altermann & Klaenhammer 2005) is a major database for pathways analysis, which identified the significantly enriched metabolic or signal pathways for target genes. The Database for Annotation, Visualization and integrated discovery (DAVID, http://david.abcc.ncifcrf.gov/) (Huang da et al. 2009) is an online tool that provides a comprehensive set of functional annotation for large list of genes. The KEGG pathway enrichment analysis was carried out for DEGs in PPI network using DAVID. P-value < 0.05 was the cutoff criterion for the functional enrichment analysis.
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Gene products with the similar function can be clustered in the same module to regulate biological process (BP). Molecular Complex Detection (MCODE) (Bader & Hogue 2003) is a method to detect densely connected regions in PPI networks. The module analysis of the PPI network was performed by the MCODE. The module with degree cutoff ≥ 2 (each node in module with at least 2 degree) and K-core ≥ 2 (each node with at least 2 neighbouring nodes) were screened.
In order to explore the functions of modules, the DEGs of the module were subjected to Gene ontology (GO) analysis by the Bingo plugin of Cytoscape (Maere et al. 2005). In this study, GO categories with fold discovery rate (FDR) < 0.05 were considered as significant.
As shown in Figure 1, the raw expression data were normalized after preprocessed. A total of 394 DEGs were obtained, including 125 up-regulated genes and 269 down-regulated genes.
PPI network analysis
As shown in Figure 2, the PPI network was constructed with 94 nodes and 220 edges. In these nodes, there were 25 up- and 69 down-regulated genes.[A3] In this network, ribosomal protein L9 (RPL9, degree = 22), ribosomal protein L35 (RPL35, degree = 20), ribosomal protein S7 (RPS7, degree = 19) and ribosomal protein L23 (RPL23, degree = 18) were selected as hub nodes with the high connectivity degree. All of them were down-regulated genes.
KEGG pathways analysis
Total 2 pathways were obtained in KEGG pathways analysis (Table 1). The DEGs were significantly enriched in ribosome (p-value = 4.89E-21) and oxidative phosphorylation (p-value = 2.96E-05) pathways. 21 DEGs, such as RPL9, RPL35, RPS7 and RPL23, were identified in ribosome pathway and all of them were ribosome proteins related genes. Besides, cytochrome c oxidase genes such as cytochrome c oxidase subunit VIIa polypeptide 2 (COX7A2), cytochrome c oxidase subunit VIIb (COX7B), cytochrome c oxidase subunit VIIc (COX7C) and cytochrome c oxidase subunit VIc (COX6C) were identified in oxidative phosphorylation pathway.
Only one module was selected in PPI network (Figure 3). The module network was constructed with 15 nodes. All of DEGs in this module were ribosome proteins with high connectivity degree.
The results of GO functional annotation for the DEGs in this module were shown in Table 2. The most significantly enriched GO BP term was translational elongation (p-value = 8.03E-34). Other significant GO BP terms included translation, cellular macromolecule biosynthetic process and macromolecule biosynthetic process and so on.
Atherosclerosis is a major cause of death worldwide and FHC is an important risk factor for atherosclerosis (Cheung & Lam 2010). Understanding the molecular mechanism of atherosclerosis induced by FHC is of critical importance for management policy. In this study, the research was based on the bioinformatics analysis of the gene expression profile data of GSE13985 downloaded from GEO database to identify DEGs between FHC and normal samples. Totally, 394 DEGs including 125 up- and 269 down-regulated genes were selected. PPI network analysis showed that the ribosomal proteins, such as RPL9, RPL35 and RPS7, were hub nodes. KEGG pathways results showed that the DEGs were significantly enriched in ribosome and oxidative phosphorylation pathways. Ribosome proteins related genes and cytochrome c oxidase genes (COX7A2, COX7B, COX7C and COX6C) were identified in these pathways. Additionally, module analysis were also showed that ribosome proteins were the important proteins in FHC. These DEGs and their related functions may be involved in atherosclerosis development induced by FHC.
Ribosome is a large and complex organelle, which was found within all living cells. In this study, the ribosome pathway was the significant function. Ribosome proteins related genes, such as RPL9, RPL35 and RPS7, were identified in this pathway. It has been reported that ribosome proteins are associated with important biological progresses, such as protein synthesis (Ruvinsky & Meyuhas 2006), cell proliferation (VolareviÄ‡ et al. 2000) and cell apoptosis (Khanna et al. 2003). Dzau et al. reported that vascular cell proliferation contributed to the pathobiology of atherosclerosis (Dzau et al. 2002). RPL17 (belongs to ribosome proteins family) acted as a vascular smooth muscle cell (VAMC) growth inhibitor and represented a potential therapeutic target to limit carotid intima-media thickening (Smolock et al. 2012). Limiting carotid intima-media thickening can reduce the risk to atherosclerosis (Lorenz et al. 2006). What’s more, ribosomal protein S6 kinase (RPS6K) is the key mammalian target of rapamycin (mTOR) effector and mTOR pathway is a key regulator of cell growth via the regulation of protein synthesis (Jastrzebski et al. 2007). Mueller et al. reported that mTOR inhibitor strongly inhibited atherosclerosis development in LDLR-/- mice (Mueller et al. 2008). In this study, ribosome proteins related genes were down-expressed in FHC samples and they were hub nodes in PPI network, suggesting that these genes may play important roles in atherosclerosis development induced by FHC through regulating ribosome pathway.
Apart from ribosome proteins related genes and their function, oxidative phosphorylation pathway was another important function. Oxidative phosphorylation is the metabolic pathway to release energy to reform adenosine triphosphate (ATP) in mitochondria cells (Hatefi 1985). The accumulation of reactive oxygen species resulting from the imbalance of the development and elimination of the oxygen free radicals or excessive intake of exogenous oxidative substance can lead to the cytotoxicity (Delbosc et al. 2005). It has been demonstrated that oxidative stress played a causal role in vascular diseases including hypercholesterolemia and atherosclerosis (Magenta et al. 2013). In this study, cytochrome c oxidase genes (COX7A2, COX7B, COX7C and COX6C) were identified in this function. It has been reported that cytochrome c oxidase regulates the oxidative phosphorylation in eukaryotic enzyme (Ludwig et al. 2001). Dutta et al. reported that COX7B as a mitochondrial electron transport chain gene was down-expressed in multiple sclerosis patients, which reduced ATP production and caused mitochondria dysfunction (Dutta et al. 2006). The dysfunction of mitochondria contributes to cardiovascular diseases by inducing apoptosis and changes in mitochondrial morphology (Williamson et al. 2010). Therefore, we speculated that cytochrome c oxidases played a key role in mitochondrial function. In this study, cytochrome c oxidase genes were down-expressed in FHC samples, suggesting that aberrant expression of these genes may contribute to the development of atherosclerosis by regulation oxidative phosphorylation pathway.
In summary, our study shows that the ribosome and oxidative phosphorylation pathways may be closely associated with atherosclerosis development induced by FHC. Ribosome proteins related genes and cytochrome c oxidase genes may be potential therapeutic target genes for atherosclerosis. However, further experiments are still required to confirm our findings.