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Free PDF Download Eur opean Rev iew for Med ical and Pharmacol ogical Sci ences 2015; 19: 4139-4145 Cytochrome c oxidase subunit VIIb as a potential target in familial hypercholesterolemia by bioinformatical analysis G. LI 1,2 , X.-J. WU 1, X.-Q. KONG 1, L. WANG 3, X. JIN 1 1Department of Vascular Surgery, Shandong Provincial Hospital Aaffiliated to Shandong University, Jinan, P.R. China 2Department of Cardiovascular Surgery, The Central Hospital of Taian, Taian, P.R. China 3Department of Urology, The Central Hospital of Taian, Taian, P.R. China Abstract. – OBJECTIVE: The aim of the study changes in blood vessels and might result in col - is to explore the potential familial hypercholes - orectal cancer (CRC) 1. Familial hypercholes - terolemia markers by comparing with healthy terolemia (FH) is an autosomal disorder charac - controls. MATERIAL AND METHODS: We downloaded terized by increased levels of total cholesterol the gene expression profile GSE13985 from Gene and low density lipoprotein cholesterol, and in - Expression Omnibus database including five pa - creased risk of premature coronary heart dis - tients diagnosed with familial hypercholes - ease 2,3 . Nowadays, with a prevalence of about terolemia (FH) and five age, sex, status matched one in 500 individuals, FH remains the most controls. We applied t-test, Wilcox test and Fish - common monogenic disorder of lipoprotein me - er test in Multtest package of R language to iden - 4 tify the differentially expressed genes (DEGs) tabolism . In the United States, only 34% of pa - with p < 0.05 and |logFC| > 1, and constructed tients with familial hypercholesterolemia were the interaction network of the top 3 up- and diagnosed 5. And in Dutch, the incidence of FH down-regulated genes using STRING. Besides, among children is every 400 births 6. the modules of network were analyzed with Cy - It is known that FH can result from mutations toscape and screened out with Mcode plugin, and the functional annotation of the genes in - in the low density lipoprotein receptor gene volved in the modules was analyzed with BiNGO (LDLR), apolipoprotein B-100 gene (APOB), (Biological Networks Gene Ontology). and proprotein convertase subtilisin/kexin type RESULTS: Firstly, totally 101 differentially ex - 9 gene (PCSK9) 7. LDLR mutation databases pressed genes were identified in FH samples currently list more than 800 different muta - compared with control samples, the genes ranked tions 8,9 . Many different types of LDLR mutation in top 3 up- and down-regulated genes were se - lected. Then, basing on the interaction network of have been identified in patients with FH world - these selected genes, ribosomal L24 domain con - wide, such as large rearrangements and muta - taining 1 (RSL24D1) and cytochrome c oxidase tions i n the promoter region that affect gene tran - subunit VIIb (COX7B) showed a central position in scription 10 . For the therapy of FH, some drugs the interaction network, and also exited in the are widely used, for example, ezetimibe as a cho - modules of the network. The functional annotation lesterol-absorption inhibitor 11 , torcetrapib as an of the genes in modules showed that COX7B was 12 associated with oxidative phosphorylation. inhibitor of cholesteryl ester transfer protein , 13 CONCLUSIONS: COX7B might play vital roles and pravastatin . However, the using of these in FH via oxidative phosphorylation system, and drugs in treating for FM can reduce the levels of might be potential target in the treatment of FH. LDL cholesterol, but it cannot eliminate the dis - ease and prevent the occurrence of carotid ather - Key Words: Potential familial hypercholesterolemia markers, osclerosis completely. COX7B, Oxidative phosphorylation system. In present study, in order to detect the potential key genes in FH and their possible functions, we analyzed the differentially expressed genes (DEGs) Introduction in FH samples with t-test, Wilcox test and Fisher test by comparing with control samples, and the Hypercholesterolemia, which is a higher top genes were selected to construct the interaction serum total cholesterol level, causes sclerotic network with their potential target genes. Then, the Corresponding Author: Xing Jin, MD; e-mail: [email protected] 4139 G. Li, X.-J. Wu, X.-Q. Kong, L. Wang, X. Jin modules in the network were analyzed, and the the modules were mined with MCODE (Molecu - functional annotation of the genes involved in the lar Complex Detection) 21 , and the functional an - modules were performed. We expected this re - notation of the modules were performed with Bi - search would provide more understanding and use - ological Networks Gene Ontology tool (BiNGO) ful information for the treatment of FH. plugin 22 . The threshold of hypergeometric distri - bution of functional annotation was 0.05. Materials and Methods Results Data Sources The expression profile of GSE13985 was down - Data Pre-Treatment loaded form Gene Expression Omnibus (GEO) The primary data of chips exist some problems, database (http://www.ncbi.nlm.nih.gov/geo/), in - such as background and probe design, so there are cluding five patients diagnosed with FH and five great differences between data of chips, and nor - age, sex, BMI and smoking status matched con - malization is necessary for analysis. After normal - trols. The corresponding platform was GPL570 ization, the data had better correlation shown in [HG-U133_Plus_2] Affymetrix Human Genome Figure 1-A, the spots with different color represent - U133 Plus 2.0 Array. ed the sample data of various groups. Besides, the fitting curves to data was almost near to standard Pre-processing of Data and diagonal, and the correlation coefficient of data in Screening the DEGs different samples was equal to 0.97. Affy package 14 in R language was used to trans - form the raw data into the recognizable expression Identification of the Differentially profile data. Then, the missing parts of data were Expressed Genes imputed 15 , and the complete data were standard - T-test, Wilcox test and Fisher test were ap - ized with Median standardization 16 . Next, we ap - plied to test the expression data of genes, the plied t-test, Wilcox test and Fisher test in Multtest genes with p < 0.05 and |logFC| > 1 were identi - package of R language 17 to identify the differen - fied as the DEGs, including up- and down-regu - tially expressed genes (DEGs) between the familial lated genes. We selected top 3 gene expression hypercholesterolemia blood samples and control values listed in Table I. In order to prove the p samples, with the p value < 0.05 and |logFC| > 1. value and |logFC| whether conform to logic with And among the DEGs, the up-regulated genes and different test, the VOLCANO plot was drawn down-regulated genes, whose fold-change ranked (Figure 1-B). We could see that smaller p value within top 3, were screened out for further study. was corresponding to larger |logFC|, this was logical according to the previous studies 23,24 . Constructing the Interaction Network Single gene does not often play roles in organ - Interaction Network ism alone. In fact, the gene can interact with oth - The interaction network was constructed by er genes to accomplish several function 18 . In this combining with STRING database, according to study, the up- and down-regulated genes stood in the top 3 up- and down-regulated genes we ob - the front of the rank were screened, and the inter - tained before, the result was shown in Figure 2. action network of these genes were constructed Then, the node degree was counted, and the with STRING (Search for the Retrieval of Inter - genes ribosomal L24 domain containing 1 acting Genes/Proteins) software 19 to mine their (RSL24D1) and cytochrome c oxidase subunit target genes. This software provides uniquely VIIb (COX7B) located on the top of the genes comprehensive coverage, all interactions are pro - involved in the network, this suggested that the vided with a probabilistic confidence score. connective function of RSL24D1 and COX7B Then, we counted the node degree of the genes in were important in the network. the interaction network, and selected the genes with high connectivity. Modules Analysis in Network and Functional Annotation Analyzing the Modules in the Network Cytoscape software was applied to analyze the The modules of the whole network we ob - modules of the network we obtained before. Af - tained was analyzed with Cytoscape software 20 , ter being mined with MCODE and annotated 4140 COX7B in familial hypercholesterolemia After normalization Volcano plot s t e n u l e i a t v a g P I - Normal Log fold change Figure 1. The expression association graph of the differentially expressed genes (A) and the Volcano plot of the differentially expressed genes (B) . Table I. The top 3 up- and down-regulated genes among the differentially expressed genes (DEGs). Gene symbol ID_REF t-test Wilcox Fisher logFC RSL24D1 222465_at 0.012339 0.036145 2.76E-17 -1.65193 COX7B 202110_at 0.038268 0.036145 1.28E-21 -1.64387 FLCN 235250_at 0.017768 0.021177 4.45E-18 -1.45301 IGHD 230877_at 0.034049 0.011925 5.60E-14 1.451809 TCL1A 39318_at 0.02652 0.036145 2.76E-17 1.471415 IGHD 213674_x_at 0.026028 0.036145 1.90E-17 1.57356 Figure 2. The interaction network of the selected top genes with their potential target genes. Inverse triangular represent - ed the down-regulated genes, and regular triangle displayed the up-regulated genes. 4141 G. Li, X.-J. Wu, X.-Q. Kong, L. Wang, X. Jin with BiNGO, the modules we mined were seen COX7B were the top of the genes involved in the in Figure 3, and the functional annotation of the network according to the node degree of the genes involved in the modules were analyzed genes. In addition, the modules were mined and shown in Table II.
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