BIF731 Handouts

Computational Analysis of Functional Single Nucleotide

Polymorphisms Associated with the CYP11B2 Gene

Minyue Jia1, Boyun Yang1, Zhongyi Li2, Huiling Shen1, Xiaoxiao Song1*, Wei Gu1*

1 Department of Endocrinology and Metabolism, the Second Affiliated Hospital Zhejiang University School of Medicine, Hangzhou, China, 2 Department of Urology, the Second Affiliated Hospital (Binjiang Branch) Zhejiang University School of Medicine, Hangzhou Binjiang Hospital, Hangzhou, China

Abstract

Single nucleotide polymorphisms (SNPs) are the most common type of genetic variations in humans and play a major role in the genetics of human phenotype variation and the genetic basis of human complex diseases. Recently, there is considerable interest in understanding the possible role of the CYP11B2 gene with corticosterone methyl oxidase deficiency, primary aldosteronism, and cardio-cerebro-vascular diseases. Hence, the elucidation of the function and molecular dynamic behavior of CYP11B2 mutations is crucial in current genomics. In this study, we investigated the pathogenic effect of 51 nsSNPs and 26 UTR SNPs in the CYP11B2 gene through computational platforms. Using a combination of SIFT, PolyPhen, I-Mutant Suite, and ConSurf server, four nsSNPs (F487V, V129M, T498A, and V403E) were identified to potentially affect the structure, function, and activity of the CYP11B2 protein. Furthermore, molecular dynamics simulation and structure analyses also confirmed the impact of these nsSNPs on the stability and secondary properties of the CYP11B2 protein. Additionally, utilizing the UTRscan, MirSNP, PolymiRTS and miRNASNP, three SNPs in the 39UTR region were predicted to exhibit a pattern change in the upstream open reading frames (uORF), and eight microRNA binding sites were found to be highly affected due to 39UTR SNPs. This cataloguing of deleterious SNPs is essential for narrowing down the number of CYP11B2 mutations to be screened in genetic association studies and for a better understanding of the functional and structural aspects of the CYP11B2 protein.

Citation: Jia M, Yang B, Li Z, Shen H, Song X, et al. (2014) Computational Analysis of Functional Single Nucleotide Polymorphisms Associated with the CYP11B2 Gene. PLoS ONE 9(8): e104311. doi:10.1371/journal.pone.0104311 Editor: Junwen Wang, The University of Hong Kong, Hong Kong Received February 27, 2014; Accepted July 7, 2014; Published August 7, 2014 Copyright:  2014 Jia et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Funding: This work was supported by grants from Science Technology Department of Zhejiang Province of China [grant number 2011C23018], and grants from Research Fund for the Doctoral Program of Higher Education of China [grant number 20130101120038]. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing Interests: The authors have declared that no competing interests exist. * Email: guwei_1957 (WG); xsong103 (XS)

Introduction

Single nucleotide polymorphisms (SNPs) are the most abundant class of genetic variations in the human genome with a frequency of approximately every 100 to 300 base pairs [1]. Given that there are millions of SNPs in the entire human genome, SNPs are important as markers for constructing genetic maps and have potential as direct functional variants associated with common and genetically complex diseases and drug responses. The vast majority of SNPs are neutral allelic variants; thus, one of the main goals of SNP research is the identification of functional SNPs, which is a crucial step for understanding the molecular basis of complex traits and diseases in humans [2]. However, the identification of these functional sets of SNPs may be a daunting task. Although experimental techniques will provide the strongest evidence for the functional role of a genetic variant [3], it is not feasible to perform laboratory experiments for all SNPs in the human genome or even in a single gene. Hence, theoretical and/ or computational methods are becoming indispensable for the identification and prioritization of SNPs with functional significance from an enormous number of non-risk alleles [4]. Computational methods are sufficiently fast and flexible to provide reliable predictions of functionally significant SNPs with a high accuracy of 80–85% [5–9] when combined with sequence, structure, and phylogenetic relationships. The aldosterone synthase (CYP11B2) gene is situated on chromosome 8q24.3 and encodes aldosterone synthase, which is the key rate-limiting enzyme for the terminal steps of aldosterone biosynthesis [10]. Previously, Strushkevich N and his research group determined the CYP11B2 structure by means of X-ray crystallography [11]. In recent years, there is considerable interest in understanding the possible role of the CYP11B2 gene for assessing the risk associated with corticosterone methyl oxidase deficiency (including CMO I and CMO II), primary aldosteronism, and cardio-cerebro-vascular diseases [12–17]. However, most disease association studies have focused on just a few SNPs, particularly T-344C (rs1799998). Other SNPs in the CYP11B2 gene have not been studied, and the in silico investigations of SNPs in the CYP11B2 gene remain scarce. Lately, Hui E et al. described a 33-year old Chinese man who was compatible with type 2 aldosterone synthase deficiency carried a heterozygous mutation c.977C . A (p.Thr326Lys) in exon 3 and computational analysis also confirmed the missense variant nocuity [18]. Hence one can see that bioinformatics has its unique advantages in understanding the relationship between genes and diseases. In this study, we performed computational analyses of non-synonymous SNPs (nsSNPs) and UTR-region SNPs in the CYP11B2 gene to identify all of the possible deleterious mutations and propose a modeled structure for the mutant protein. We are confident that the results of our study will provide a further understanding of the CYP11B2 gene in human diseases, as well as a guide for future experimental work.

Materials and Methods

Dataset collection The SNP information [SNP ID, amino acid position, mRNA accession number NM_000498.3, and Protein accession number NP_000489.3] of the human CYP11B2 gene used in our computational analyses was retrieved from the National Center for Biotechnology Information (NCBI) database of SNPs (dbSNP ( [19]. The workflow, tools, and databases used to identify the potential functional SNPs in the human CYP11B2 gene are shown in Figure 1.

Assessment of nsSNP functionality The functional context of nsSNPs was predicted using SIFT, PolyPhen and I-Mutant Suite. SIFT ( is a sequencehomology-based tool to predict whether an amino acid substitution in a protein would be tolerated or damaging [20]. We performed SIFT by submitting the query in the form of SNP IDs or chromosome positions and alleles in nsSNVs tool. Variants at the position with tolerance index score #0.05 are considered to be deleterious. A lower tolerance index indicates that the particular amino acid substitution likely has a more functional impact [21,22]. PolyPhen ( is an automatic tool that predicts the possible impact of an amino acid substitution on a number of features, including the sequence, phylogenetic, and structural information [23]. The query was submitted in the form of protein sequence with mutational position and substitution. The PolyPhen output comprises a score that ranges from 0 to 1, with zero indicating a neutral effect of amino acid substitutions on protein function. Conversely, a high score represents a variant that is more likely to be damaging. I-Mutant Suite is a suite of support vector machine (SVM)- based predictors of protein stability changes according to Gibbs free energy change, enthalpy change, heat capacity change, and transition temperature [24]. The analyses were performed based on protein sequence combined with mutational position and correlated new residue. And the output result of the predicted free energy change (DDG) classifies the prediction into one of three classes: largely unstable (DDG , 20.5 kcal/mol), largely stable (DDG.0.5 kcal/mol), or neutral (-0.5# DDG#0.5 kcal/mol). IMutant Suite is available at predictors/I-Mutant3.0/I-Mutant3.0.cgi.

Evolutionary conservation analysis of nsSNPs An amino acid that plays an essential role, e.g., in enzymatic catalysis, is likely to remain unaltered despite random evolutionary drift. Hence, the level of evolutionary conservation is often indicative of the importance of the position for maintaining the protein’s structure and/or function. The ConSurf server is a bioinformatics tool for estimating the evolutionary conservation of amino/nucleic acid positions in a protein/DNA/RNA molecule based on the phylogenetic relationships between homologous sequences [25]. After entering the 3D structure of the query protein, the conservation scores are calculated based on the evolutionary relationships among the protein and its homologs

Figure 1. Workflow, tools, and databases used to identify potential functional SNPs in CYP11B2. [26,27]. A conservation score between 1 and 4 is considered variable, whereas a score of 5–6 is intermediate, and a score in the range of 7 to 9 indicates conserved. Using the empirical Bayesian method, the accuracy of the conservation score estimation was significantly improved, particularly when a small number of sequences are used for the calculations [26]. ConSurf is available

Evaluation of the functional context of SNPs in the UTR region The 59and 39 untranslated regions of eukaryotic mRNAs (UTRs) play crucial roles in the post-transcriptional regulation of gene expression through the modulation of nucleocytoplasmic mRNA transport, translation efficiency, subcellular localization, and message stability [28–30]. The functional impacts of UTR SNPs were analyzed using UTRScan [30], MirSNP [31], PolymiRTS [32] and miRNASNP[33]. The program UTRscan looks for UTR functional elements by searching through user submitted sequence data for the patterns defined in the UTRsite collection. And UTRsite is a collection of regulatory elements located in the 59 and 39UTRs whose function and structure have been experimentally determined and published. If different sequences for each UTR SNP are found to have different functional patterns, that particular UTR SNP is predicted to have functional significance. The pattern change included two directions by the influence of SNPs at the UTR regions, either from ‘‘have pattern’’ to ‘‘no pattern’’, or ‘‘no pattern’’ to ‘‘have pattern’’. UTRscan is available at utrscan. MirSNP is a database of SNPs used for the prediction of whether an SNP within the target site would decrease/break or enhance/create a microRNA-mRNA binding site based on information from dbSNP135 and miRBase 18. Its output of single search by entering the gene name includes mirSVR score, the effect of different alleles, the predicted score, conservative information and Start & End & Binding information. Combined with GWAS or eQTL data sets, MirSNP is highly sensitive and covers most experiments confirmed SNPs that affect miRNA function. MirSNP is available at mirsnp. PolymiRTS is a database of naturally occurring DNA variations in microRNA seed regions and microRNA target sites. Integrated data from CLASH (cross linking, ligation and sequencing of hybrids) experiments, PolymiRTS database provides more complete and accurate microRNA–mRNA interactions. The polymorphic microRNA target sites are assigned into four classes: ‘D’ (the derived allele disrupts a conserved microRNA site), ‘N’ (the derived allele disrupts a nonconserved microRNA site), ‘C’ (the derived allele creates a new microRNA site) and ‘O’ (other cases when the ancestral allele cannot be determined unambiguously). The class ‘C’ may cause abnormal gene repression and class ‘D’ may cause loss of normal repression control. So these two classes of PolymiRTS are most likely to have functional impacts. PolymiRTS is available at Figure 2. Distribution of SNPs.