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In Silico Analysis of SNPs in Coding Region of Human c-Myc Gene.

Byline: Mamoona Noreen, Sheeba Murad and Hayat Khan

Abstract

Deregulation and over expression of human c-Myc proto-oncogene protein can induce cancer. Presence of genetic variations is a key player among many others which affect susceptibility and progression of disease. Single nucleotide polymorphisms (SNPs) are the most frequent variations in human genome. Non synonymous single nucleotide polymorphisms (nsSNPs) leading to change in amino acids may result in altered protein structure, function and molecular morphology. We used SIFT, PolyPhen and SNPeffect algorithms to predict tolerant and deleterious SNPs in coding region of c-Myc gene and their possible structural, molecular and phenotypic effects on c-Myc protein. Effect of four nsSNPs of coding region; rs114570780 (Tyr47His), rs150308400 (Cys148Tyr), rs137906262 (Leu159Ile) and rs200431478 (Ser362Phe) is predicted to be damaging. These results may form the basis for further large-scale population based association studies.

Key Words: SNPs; c-Myc gene, single nucleotide polymorphism, SIFT, PolyPhen.

INTRODUCTION

Human Myc proto-oncogene protein is a basic dimerization motif helix-loop-helix leucine zipper (HLH-LZ) containing transcription factor that was initially discovered to be the cellular homologue of v-myc myelocytomatosis viral oncogene (Vennstrom et al., 1982). In mammals three proteins with reasonable structural resemblance; c-Myc, N-Myc and L-Myc are encoded by three different genes and their over expression is reported to lead to tumor induction (Oster et al., 2002). In humans the c-Myc gene is located on 8q24.21 chromosomal position and is reported to have three exons. N-terminal region contains four virtually identical and highly conserved domains, identified as Myc Box I (MBI), MBII, MBIII and MBIV (Oster et al., 2002; Cowling et al., 2006). N-terminal region contains several well characterized phosphorylation and ubiquitination sites (Fig. 1).

The basic region (BR) of c-Myc mediates the DNA while the helix-loop-helix (HLH) and leucine zipper (LZ) domains mediate heterodimerization with other proteins (Hann, 2006).

On the basis of discrete translational initiation sites, c-Myc gene has been reported to encode three distinct isoforms; c-Myc1, c-Myc2 (Hann et al., 1988) and c-MycS (Hann, 1995) with identical carboxy terminal domain having BR-HLH-LZ and different N-terminal regions. Major isoform i.e. c-Myc2 is a 439 amino acids long 64 kDa protein and is encoded when translation starts at AUG start site in the second exon. Translational start at a non canonical CUG codon located 15 codons upstream of c-Myc2 start site adds a short extension of additional 15 amino acids at N-terminal region of protein yielding a longer isoform of 454 amino acids and 67 kDa called c-Myc1 or p67 (Hann et al., 1988). Considerably shorter, N-terminally-truncated c-MycS isoform requires translational initiation at AUG site located 100 codons downstream of c-Myc2 start site. Such translation deprives the protein of well conserved motif MBI lying in 100 amino acids region of the trans-activation domain at N-terminus (Spotts et al., 1997).

Myc family of proto-oncogenes has been considered to be one of the most studied oncogenes to date (Meyer and Penn, 2008). Crucial involvement of Myc proto-oncogene protein in the regulation of other gene's expression (Lee and Dang, 2006), cellular proliferation (Lemaitre et al., 1996), growth (Henriksson and Luscher, 1996), apoptosis (Thompson, 1998), metabolic transformation and oncogenesis (Miller et al., 2012) has become evident during last two decades. Lower expression of c-Myc in normal resting cell becomes enormously high in the transformed cell stimulated via a broad range of signaling pathways, designating it to be an early response gene. It is not clear whether c-Myc over expression is the cause or the consequence of cell transformation (Miller et al., 2012).

Commonly, disease risk is influenced by the existing polymorphisms in the genome. One of the most frequent types of polymorphisms found in human genome is single nucleotide polymorphisms (SNP) (Noreen et al., 2012). Usually the SNPs associated with disease risk are missense or non-synonymous SNPs (nsSNPs) involving substitution of amino acid (Gul et al., 2015). These SNPs are likely to bring structural as well as functional changes in the protein. Genetic variations in cis-regulators of transcription at 8q24 can considerably change the germline expression levels of c-Myc thus contribute to cancer susceptibility (Sole et al., 2008). Many studies are conducted to find association of a well documented SNP; rs13281615 harboring the non-coding chromosomal region 8q24 near c-Myc and susceptibility of breast cancer (Easton et al., 2007; Pei et al., 2013), prostate cancer (Meyer et al., 2009; Haiman et al., 2007) and colorectal cancer (Tomlinson et al., 2007). However, the results are inconsistent.

A meta-analysis including more than 100,000 subjects was performed to evaluate the role of this SNP in risk of breast cancer development. Increased risk of breast cancer development was found to be significantly associated with presence of allele G (Odds Ratio (OR): 1.10, 95% Confidence interval (CI): 1.061.14, p value: 0.001) and genotype GG" (OR: 1.20, 95% CI: 1.121.29, p value: 0.001), while the genotype AA was found to be a protective factor against breast cancer development (OR: 0.89, 95% CI: 0.840.93, p value: 0.001) (Gong et al., 2013).

Chromosomal region 8q24.21 contains another discussable cancer risk SNP; rs6983267. Presence of allele G is variably frequent among all the populations ranging from 31% in Native Hawaiians to 85% in African American population. In Europeans the allele frequency is ~50% (Wang et al., 2014). The rs6983267 is reported to up-regulate c-Myc transcription (Takatsuno et al., 2013) and alter the physical interactions of c-Myc by affecting binding of transcription factor 7-like 2 (TCF7L2) and TCF4 (Pomerantz et al., 2009). Presence of rs6983267 is associated with susceptibility to inflammatory breast cancer (Bertucci et al., 2012), gastric cancer (Guo et al., 2011), prostate cancer (Os'kina et al., 2012), colorectal cancer and colorectal adenoma (Wang et al., 2014).

A cluster of four pathogenetically valuable nsSNPs; VAR_063384 (Glu39Asp), VAR_063385 (Pro57Ser), VAR_063386 (Pro59Ala) and VAR_063387 (Asn86Thr) was reported to harbor 2nd and 3rd exon of c-Myc2 trans-activation domain in Burkitt lymphoma samples (Rabbitts et al., 1983; Bhatia et al., 1993). A rare nsSNP (rs4645959) lying in N-terminal trans-activation domain of c-Myc2 was reported to be putatively functional.

Heterozygous carriers of this variant among Polish and German subjects were observed to have an increased risk of familial breast cancer (Wirtenberger et al., 2005). Later on Figueiredo et al. (2007) conducted a large population based centralized pathology review revealing no biological relevance of rs4645959 with risk, tumor characteristics or endurance of breast cancer (Figueiredo et al., 2007).

Molecular epidemiological association studies mainly focused on the nsSNPs lying in coding region of the gene (Ramensky et al., 2002; Savas et al., 2004; Zhu et al., 2004). Studying the structural and the functional impact of the nsSNPs on the protein can help in the selection of functionally important nsSNPs. We have used some sequence and structural homology based algorithms to find those nsSNPs which can play potential role in structural and functional alteration of c-Myc.

MATERIALS AND METHODS

Dataset compilation

Neucleotide and protein sequence in FASTA format were obtained from NCBI (http://www.ncbi. nlm.nih.gov). Longest isoform of c-Myc was selected for analysis. Public SNP databases named dbSNP (http://www. ncbi.nlm.nih.gov/SNP), SNP500cancer (http://snp500cancer.nci.nih.gov/ home.cfm), GeneCards (http://www.genecards.org/) and UniProt (http://www.uniprot.org) were used to identify SNPs related to longest isoform of human c-Myc proto-oncogene protein. Being in gene region, 165 SNPs were found to be related to c-Myc (gene ID: 4609, NCBI Reference Sequence: NP_002458.2). We kept 27 nsSNPs into analytical consideration, out of 43 lying in coding region.

Prediction of tolerated and deleterious SNPs and functional consequences of nsSNPs using SIFT

We used Sorting Intolerant from Tolerant (SIFT version 2) algorithm which is able to predict if an amino acid substitution can influence protein function. SIFT is known to make a distinction between tolerated and deleterious amino acid changes in proteins (http://blocks.fhcrc.org/sift/ SIFT.html) (Ng and Henikoff, 2001). It works on basic principle that substitution of some fundamentally conserved amino acids in a protein can have a potential impact on protein function and can be deleterious (Ng and Henikoff, 2003).

Protein sequence in FASTA format was submitted as input to SIFT in order to predict possible effect of any of the other amino acids substitution at every position. For this analysis FIST searches for homologous sequences using SWISS-PORT version 51.3 and TrEMBL 34.3 databases and performs multiple alignments of available sequences until a median sequence conservation score (MSCS) for the protein sequence is reached to 3.00. Tolerant and intolerant effect of nsSNPs of coding region was predicted as tolerance index (TI) score which is normalized probability that the amino acid substitution is tolerated or not. It ranges from 0.0 to 1.0 while threshold for intolerance is =0.05. (de Alencar and Lopes, 2010) found the level of functional influence of a specific amino acid substitution to be inversely proportional to TI score. Deleterious predictions with median score of 3.25 or less were made with low confidence due to lack of sequence diversity for protein alignment.

Prediction of functional consequences of nsSNPs using PolyPhen

We used PolyPhen-2 (Polymorphism Phenotyping version 2) (http://genetics.bwh. harvard.edu) in order to predict detrimental outcome of nsSNPs on structural and functional basis. Protein sequence in FASTA format, position of SNP in protein sequence, wild type amino acid and its substitute for each of the 27 variations in coding region of c-Myc were endowed with as an input to the algorithm. PolyPhen-2 uses three structure based and eight sequence based predictive features out of total thirteen structure based and nineteen sequence based candidate features which are automatically selected by iterative greedy algorithm (Adzhubei et al., 2010).

Homologues of the submitted sequence were searched using BLAST+ (Camacho et al., 2009) and aligned using MAFFT (Katoh et al., 2002). Quality of resulting alignments was made better by use of LEON software (Thompson et al., 2004). Secator algorithm formerly executed in ClusPack software was used to gather reliably aligned sequences (Wicker et al., 2001). Analyzed sequences along with its homologues of compact cluster were taken into further consideration. PolyPhen also searches for 3D protein structures and contact information about amino acids in a number of structure based databases in order to firstly calculate the position-specific independent counts (PSIC) scores for allelic variants of particular SNP and then to evaluate PSIC score difference (Adzhubei et al., 2010). On basis of all the information the effect of nsSNP on protein is predicted.

If the score shows high reliability that the nsSNP should influence structure or/and function of protein, then it will be predicted as possibly damaging'', if there is an option of may or may not", the nsSNP will be assigned as probably damaging'' while being unlikely to phenotypic alteration in protein can designate a nsSNP as benign'' (Ramensky et al., 2002).

Prediction of molecular phenotypic effects of nsSNPs using SNPeffect

SNPeffect (De Baets et al., 2012) was used as a platform to forecast the molecular phenotypic influence of nsSNPs lying in coding region of c-Myc protein. It works beyond the scores got on conservational basis and mainly emphasizes to map the effect of SNPs on the capability of cells to uphold suitable concentration of the properly folded proteins in appropriate cellular region i-e protein homeostasis landscape (Powers et al., 2009). For this evaluation, wild type protein sequence in FASTA format was provided to the SNPeffect server (http://snpeffect.switchlab.org) along with each of its variants to be analyzed. Homology hreshold was adjusted to 90%. SNPeffect uses TANGO (Fernandez-Escamilla et al., 2004) which predicts the regions in given protein sequence that are prone to aggregation and measures TANGO score with wild and variant amino acid. Based on difference of TANGO score (dTANGO) the server evaluates effect of these variants on protein aggregation.

Aggregate morphology is more specifically determined by WALTZ server (Maurer-Stroh et al., 2010) that predicts amyloid-forming regions in given protein sequence with accuracy and specificity on basis of dWALTZ score. Chaperone binding propensity is predicted by LIMBO (Van Durme et al., 2009) for the Hsp70 chaperones and effect of variant is determined by dLIMBO score. SNPeffect also uses high resolution crystal structure of proteins from Protein Data Bank (PDB) (Deshpande et al., 2005) and models the variants using the empirical force eld FoldX (version 2.5) (Schymkowitz et al., 2005) to evaluate possible effects on stability and binding properties of given protein.

RESULTS

Dataset compilation

Our study included retrieval of total 165 human c-Myc SNPs from the dbSNP database which were further scrutinized. GeneCards database included four additional pathologically important SNPs of c-Myc2 proto-oncogene reported in Burkitt lymphoma samples; VAR_063384 (Glu39Asp), VAR_063385 (Pro57Ser), VAR_063386 and VAR_063387 (Asn86Thr). The non-conding region of gene contains 122 SNPs of the total SNPs, out of which 38 (23%) are in the region of 5' near gene, 13 (8%) are in 5' UTR, 51 (31%) in intronic region, 11 (7%) in 3' UTR and 9 (5%) in region of 3' near gene. Coding region harbors 43 SNPs out of which 29 (17%) are non-synonymous and 14 (9%) are synonymous (Fig. 2). As nsSNPs are supposed to be involved in structural and functional alteration of proteins, thus those can result in disease development. As two SSNPs in coding region of c-Myc protein are frame shift mutations thus we investigated the probable damage caused by 27 nsSNPs.

Prediction of tolerated and deleterious SNPs and functional consequences of nsSNPs using SIFT

The SIFT program predicts the tolerant or deleterious effect of amino acid change on protein function. The analysis mainly considers the alignment of homologous sequences, physical properties of amino acids and the effect of natural nsSNPs on phenotypic alterations by aligning paralogous and orthologous protein sequences. The prediction of possible substitutions for positions 1-454 amino acids was made. Possible substitutions for those nsSNPs are shown in Figure 3 which were predicted to have some damaging effect by SIFT, PolyPhen or SNPeffect algorithms. Seq-Rep' is actually the fraction of sequences that has one of the fundamental amino acids. Low fraction shows that the position is unalienable or is severely gapped providing little information which results in poor prediction. Amino acids are shown in color codes i.e., basic (red), acidic (blue) non polar (black); uncharged polar (green).

Capital letters signifies the amino acids which come into view during the alignment while lower case letters are the consequence of prediction.

Sequence homology-based tool of SIFT was used to predict tolerant and deleterious effect of 27 nsSNPs present in coding region of c-Myc protein. As a result 8 nsSNPs were found to be deleterious in total, out of which 2 had low confidence due to median score greater than 3.25. (Table I, Fig. 4). Out of these eight nsSNPs, rs139294902 (Pro12Arg), rs4645959 (Asn26Ser), rs114570780 (Tyr47His), rs150308400 (Cys148Tyr) and rs137906262 (Leu159Ile) are located within N-terminal trans-activation domain while rs145561065 (Leu434Phe) and rs143501729 (Ala454Glu) harbor the C-terminal domain of c-Myc1 protein (Fig. 1).

Table I.-SIFT analysis of nsSNPs in coding region of human c-Myc1 protein.

dbSNP id###Chr.###mRNA###dbSNP###Codon###Amino acid variant###Amino###SIFT###Tolerance

###Position###Pos.###Allele###Pos.###acid Pos.###prediction###Index

rs139294902###128750498###560###C/G###2###Pro [P]/Arg [R]###12###Intolerant###0.00

rs146505192###128750527###589###T/C###1###Phe [F]/Leu [L]###22###Tolerant###1.00

rs148915481###128750534###596###A/G###2###Asn [N]/Ser [S]###24###Tolerant###0.99

rs4645959###128750540###602###A/G###2###Asn [N]/Ser [S]###26###Intolerant###0.01

rs114570780###128750602###664###T/C###1###Tyr [Y]/His [H]###47###Intolerant###0.00

rs148228388###128750735###797###C/A###2###Ala [A]/Glu [E]###91###Tolerant###0.06

rs199561469###128750773###835###G/C###1###Gly [G]/Arg [R]###104###Tolerant###0.36

rs150308400###128750906###968###G/A###2###Cys [C]/Tyr [Y]###148###Intolerant###0.00

rs137906262###128750938###1000###C/A###1###Leu [L]/Ile [I]###159###Intolerant###0.02

rs61755060###128750967###1029###G/T###3###Gln [Q]/His [H]###168###Tolerant###0.45

rs147329312###128750977###1039###A/C###1###Lys [K]/Gln [Q]###172###Tolerant###0.11

rs4645960###128750986###1048###G/T###1###Gly [G]/Cys [C]###175###Tolerant###0.12

rs4645960###128750986###1048###G/C###1###Gly [G]/Arg [R]###175###Tolerant###0.13

rs4645961###128751016###1078###G/A###1###Val [V]/Ile [I]###185###Tolerant###0.26

rs112602073###128751121###1183###C/T###1###Pro [P]/Ser [S]###220###Tolerant###0.93

rs147506213###128751160###1222###C/A###1###Pro [P]/Thr [T]###233###Tolerant###0.64

rs186663828###128751185###1247###C/T###2###Ser [S]/Leu [L]###241###Tolerant###0.19

rs148544254###128751200###1262###C/T###2###Pro [P]/Leu [L]###246###Tolerant###0.26

rs150629172###128752729###1415###C/A###2###Pro [P]/His [H]###297###Tolerant###0.15

rs139697494###128752745###1431###C/A###3###His [H]/Gln [Q]###302###Tolerant###0.15

rs146971340###128752799###1485###T/A###3###His [H]/Gln [Q]###320###Tolerant###0.74

rs4645968###128752849###1535###C/T###2###Ala [A]/Val [V]###337###Tolerant###1.00

rs200431478###128752924###1610###C/T###2###Ser [S]/Phe [F]###362###Intolerant###0.01

rs141095253###128753029###1715###C/T###2###Pro [P]/Leu [L]###397###Tolerant###0.05

rs145561065###128753141###1827###G/C###3###Leu [L]/Phe [F]###434###Intolerant###0.01

rs148863193###128753155###1841###G/T###2###Arg [R]/Leu [L]###439###Tolerant###0.12

rs201337668###128753196###1882###T/G###1###Cys [C]/Gly [G]###453###Tolerant###0.64

rs143501729###128753200###1886###C/A###2###Ala [A]/Glu [E]###454###Intolerant###0.00

Prediction of functional consequences of nsSNPs using PolyPhen

PolyPhen-2 server was used to predict structural and functional effects of 27 nsSNPs reported in the coding region of c-Myc proto oncogene protein. Using structure and sequence based predictive features PolyPhen searched for homologous protein sequences for multiple alignment and 3D protein structures to find probability score and predicted possible influence of variants on the protein. Out of 27 mutations, 7 were predicted to be probably damaging with probabilistic scores above 0.85, one variant was forecasted to be possibly damaging with probabilistic score above 0.15 while all others were considered to be benign (Table II, Fig. 4). Out of these eight nsSNPs, rs114570780 (Tyr47His), rs148228388 (Ala91Glu), rs199561469 (Gly104Arg), rs150308400 (Cys148Tyr), rs137906262 (Leu159Ile) are located within N-terminal trans-activation domain while rs200431478 (Ser362Phe), rs141095253 (Pro397Leu) harbor the C-terminal domain of c-Myc1 protein (Fig. 1).

Prediction of molecular phenotypic effects of nsSNPs using SNPeffect

The SNP effect server was used to predict the effect of molecular phenotype of nsSNPs present in coding region of c-Myc protein. dTANGO, dWALTZ and dLIMBO scores were calculated to find effect of 27 nsSNPs on aggregation tendency, amyloid propensity and chaperone binding tendency respectively. Threshold for some significant effect was ranging from -50 to 50. The dLIMBO score for Pro/Leu variation at position 397 (rs141095253) equals -277.70, which means that the mutation decreases the chaperone binding tendency of our protein. The scores for rest of the variants were not observed to be more than 50 or less than -50. The result shows that none of these nsSNPs other than rs141095253 is able to bring significant change in the aggregation tendency, amyloid propensity and chaperone binding tendency of c-Myc protein (Table III). The difference in free energy of the mutation was calculated using empirical protein design force field (FoldX).

The submitted sequence had 98.80 percent homology with 1NKP. Using FoldX a homology model was built starting from this PDB. The difference in free energy due to Pro/Leu variation at position 397 was of 2.68 Kcal/mol. This entails that the mutation reduces the protein stability (Fig.5). For rest of the variations SNPeffect could not predict possible changes in the stability of protein which could have been brought by variants due to the unavailability of adequate structural information of c-Myc in the form of PDB model.

1NKP presents the X-ray structure of the c-Myc protein in complex with the Max protein and DNA. Both proteins form a fork-like structure and bind the major groove of the DNA. It includes 353 to 434 residues of c-Myc2. Three SNPs; rs141095253 (Pro397Leu), rs145561065 (Leu434Phe) and rs148863193 (Arg439Leu) of longest isoform i.e. c-Myc1 are illustrated in Figure 6.

DISCUSSION

Myc proto-oncogene protein family comprises of very crucial and extensively studied transcription factors characterized by containing basic helix-loop-helix leucine zipper (bHLH LZ) motif (Vennstrom et al., 1982). Over expression and deregulation of c-Myc, N-Myc and L-Myc can become basic cause of tumor development (Oster et al., 2002) and polymorphisms especially SNPs may play an important role in it. SNPs may influence the disease susceptibility, disease progression as well as the treatment responses (De Baets et al., 2012; Noreen et al., 2015). Molecular association studies emphasize on nsSNPs involving substitution of amino acids. These nsSNPs of coding region draw more attention of researchers as these can result in structural and functional alterations of proteins. Detailed information in this concern can be obtained using sequence and structure based tools of bioinformatics to select crucial and functional nsSNPs of c-Myc to get into deeply detailed research.

Table II.-PolyPhen analysis of nsSNPs in coding region of human c-Myc1 protein

dbSNP id###Amino acid variant###Amino acid position###PolyPhen prediction###PolyPhen score

rs139294902###Pro [P]/Arg [R]###12###Benign###0.027

rs146505192###Phe [F]/Leu [L]###22###Benign###0.008

rs148915481###Asn [N]/Ser [S]###24###Benign###0.007

rs4645959###Asn [N]/Ser [S]###26###Benign###0.160

rs114570780###Tyr [Y]/His [H]###47###Probably Damaging###0.955

rs148228388###Ala [A]/Glu [E]###91###Possibly damaging###0.452

rs199561469###Gly [G]/Arg [R]###104###Probably Damaging###0.915

rs150308400###Cys [C]/Tyr [Y]###148###Probably Damaging###0.999

rs137906262###Leu [L]/Ile [I]###159###Probably Damaging###0.997

rs61755060###Gln [Q]/His [H]###168###Benign###0.155

rs147329312###Lys [K]/Gln [Q]###172###Probably Damaging###0.971

rs4645960###Gly [G]/Cys [C]###175###Benign###0.048

rs4645960###Gly [G]/Arg [R]###175###Benign###0.269

rs4645961###Val [V]/Ile [I]###185###Benign###0.248

rs112602073###Pro [P]/Ser [S]###220###Benign###0.135

rs147506213###Pro [P]/Thr [T]###233###Benign###0.002

rs186663828###Ser [S]/Leu [L]###241###Benign###0.270

rs148544254###Pro [P]/Leu [L]###246###Benign###0.273

rs150629172###Pro [P]/His [H]###297###Benign###0.001

rs139697494###His [H]/Gln [Q]###302###Benign###0.041

rs146971340###His [H]/Gln [Q]###320###Benign###0.327

rs4645968###Ala [A]/Val [V]###337###Benign###0.009

rs200431478###Ser [S]/Phe [F]###362###Probably Damaging###0.914

rs141095253###Pro [P]/Leu [L]###397###Probably Damaging###0.999

rs145561065###Leu [L]/Phe [F]###434###Benign###0.100

rs148863193###Arg [R]/Leu [L]###439###Benign###0.193

rs201337668###Cys [C]/Gly [G]###453###Benign###0.00

rs143501729###Ala [A]/Glu [E]###454###Benign###0.014

In Burkitt lymphoma samples the c-Myc2 proto-oncogene contained a cluster of four discussable SNPs in 2nd and 3rd exon (Rabbitts et al., 1983; Bhatia et al., 1993). VAR_063384 (Glu39Asp) involves substitution of Glutamate (E) to Aspartate (D) at position 39. Both amino acids are medium sized and acidic with similar physico-chemical properties. VAR_063385 (Pro57Ser) involves substitution of medium sized and hydrophobic Proline (P) to small sized and polar Serine (S) at position 57. VAR_063386 (Pro59Ala) involves substitution of medium sized and hydrophobic Proline (P) to small sized and hydrophobic Alanine (A) at 59th amino acid. VAR_063387 (Asn86Thr) involves substitution Asparagine (N) to Threonine (T) at position 86. Both amino acid residues are of medium size and polar with similar physico-chemical properties. VAR_063385 (Pro57Ser) and VAR_063386 (Pro59Ala) at position 57 and 59 respectively are of more importance because these have an adjacent threonine residue at position 58.

Phosphorylation of serine at position 62 by extracellular signal regulated kinase (ERK) or CDK kinases increases Myc cellular stability. Then phosphorylation of threonine at position 58 is brought about by GSK3-AY which triggers dephosphorylation of S62 by protein phosphatase 2A (PP2A) and subsequently ubiquitin-mediated degradation is brought about through SCF-Fbwx7 (ubiquitin ligases) (Hann, 2006). Thus any variation in this domain can gain some biological importance affecting this series of phosphorylation, dephosphorylation and ubiquitinations.

Table III.-SNPeffect analysis of nsSNPs in coding region of human c-Myc1 protein

dbSNP id###dTANGO###Aggregation###dWALTZ###Amyloid###dLIMBO###Chaperone binding

###tendency###propensity###tendency

rs139294902###0.38###No effect###-0.05###No effect###0.00###No effect

rs146505192###-1.21###No effect###-0.93###No effect###0.00###No effect

rs148915481###-0.01###No effect###-0.36###No effect###0.00###No effect

rs4645959###0.00###No effect###-0.06###No effect###0.00###No effect

rs114570780###0.04###No effect###-10.16###No effect###0.00###No effect

rs148228388###-14.97###No effect###0.00###No effect###0.00###No effect

rs199561469###0.41###No effect###-0.05###No effect###0.00###No effect

rs150308400###9.73###No effect###0.24###No effect###0.00###No effect

rs137906262###0.00###No effect###8.63###No effect###0.00###No effect

rs61755060###0.04###No effect###-0.28###No effect###0.00###No effect

rs147329312###-0.41###No effect###0.05###No effect###0.00###No effect

rs4645960###0.00###No effect###0.00###No effect###0.00###No effect

rs4645960###0.41###No effect###-0.05###No effect###0.00###No effect

rs4645961###0.00###No effect###-0.00###No effect###0.00###No effect

rs112602073###0.00###No effect###0.00###No effect###0.00###No effect

rs147506213###0.00###No effect###0.07###No effect###0.00###No effect

rs186663828###0.00###No effect###0.04###No effect###0.00###No effect

rs148544254###0.00###No effect###0.00###No effect###0.00###No effect

rs150629172###0.04###No effect###-0.00###No effect###0.00###No effect

rs139697494###-0.05###No effect###0.01###No effect###0.00###No effect

rs146971340###-0.05###No effect###1.21###No effect###0.00###No effect

rs4645968###0.00###No effect###0.00###No effect###0.00###No effect

rs200431478###0.00###No effect###0.00###No effect###0.00###No effect

rs141095253###1.17###No effect###0.06###No effect###-277.70###Decrease

rs145561065###0.00###No effect###0.16###No effect###-0.05###No effect

rs148863193###-0.68###No effect###-0.02###No effect###0.02###No effect

rs201337668###0.00###No effect###-0.00###No effect###0.00###No effect

rs143501729###0.00###No effect###0.00###No effect###0.00###No effect

Table IV.-Combined score of SIFT, PolyPhen and SNPeffect prediction

dbSNP id###Amino acid position###SIFT prediction###PolyPhen prediction###SNPeffect prediction###Combined score

rs139294902###12###1###0###0###1

rs4645959###26###1###0###0###1

rs114570780###47###1###1###0###2

rs148228388###91###0###1###0###1

rs199561469###104###0###1###0###1

rs150308400###148###1###1###0###2

rs137906262###159###1###1###0###2

rs147329312###172###0###1###0###1

rs200431478###362###1###1###0###2

rs141095253###397###0###1###1###2

rs145561065###434###1###0###0###1

rs143501729###454###1###0###0###1

Out of total 165 SNPs related to longest isoform of human c-Myc, 27 were nsSNPs found in coding region along with two frame shift mutations; rs35631115 and rs67856294. Our main emphasis was to evaluate the possible role of nsSNP in the coding region. One SNP dbSNPid: rs4645960 found at position 175 has Gly [G] amino acid in wild type protein, which can be substituted by either Cys [C] or by Arg [R]. In our study we considered both variants separately to find if they have different effects.

In order to find tolerant or deleterious effect of amino acid substitution on protein function, we used the SIFT algorithm (Ng and Henikoff, 2001) which mainly aligns homologous sequences, considers physical properties of amino acids and possible effects of naturally occurring non synonymous polymorphisms on protein. Total 8 nsSNPs were found to be deleterious (Fig. 4), out of which 2 had low confidence due to scarcity of available sequences for alignment (Table I). PolyPhen-2 server (Adzhubei et al., 2010) was used to forecast structural and functional effects of the SNPs of interest. PolyPhen searched for homologous protein sequences and 3D protein structures to predict the possible potential influence of variants on protein. Among total 27 variations, 7 were predicted to be probably damaging, one variant as possibly damaging while all others were considered to be benign (Table II, Fig. 4).

Effect of four nsSNPs in coding region; rs114570780 (Tyr47His), rs150308400 (Cys148Tyr), rs137906262 (Leu159Ile) and rs200431478 (Ser362Phe) were predicted by SIFT algorithm to be intolerant and by PolyPhen-2 server to be probably damaging (Table IV). Four nsSNPs; rs4645959 (Asn26Ser), rs114570780 (Tyr47His), rs150308400 (Cys148Tyr) and rs137906262 (Leu159Ile) are located within N-terminal trans-activation domain of c-Myc1 containing 2 highly conserved domains called MBI and MBII. In c-Myc1 the MBI will range approximately 61-78 amino acids and MBII will span region of 144-158 amino acids (Fig. 1). This region harboring MBI and MBII is essential for transactivation of target genes involved in process of apoptosis, cell proliferation and transformation (Wirtenberger et al., 2005). In MBI domain a series of potential phosphorylations by MAPK, CDKs or GSK3AY take place in cell cycle dependent manner.

These phosphorylation events regulate transcription and transformation. MBII encodes the hydrophobic part of the c-Myc protein. One transcription activation domains (TAD) is located upstream of MBI and the other is upstream and overlapping region of MBII. A repression element is identified between the MBI and MBII domains while other overlaps MBII itself (Sakamuro and Prendergast, 1999). N-terminal trans-activation domain is also reported to interact with TBP, a-Tubulin, p107 as well as adaptor proteins; TRRAP, AMY-1, Bin1, Pam, and MM-1 (Sakamuro and Prendergast, 1999; Beijersbergen et al., 1994; Gu et al,. 1994; Hateboer et al., 1993). Thus, interaction of these proteins and presence of sequences related to activation or repression activity in N-terminal trans-activation domain suggests its key role in apoptosis, chromatin modeling, cell cycle and transcriptional regulation (Sakamuro and Prendergast, 1999). Presence of genetic variants in this region can effect this regulation significantly.

The rs4645959 involves substitution of medium sized Asparagine (N) to small sized Serine (S) residue. According to SIFT Prediction this variant is intolerant (Tolerance index = 0.01). This nsSNP was reported to be putatively functional being capable of altering the secondary structure of protein. Heterozygous carriers of this variant among Polish and German individuals were observed to have an increased risk of familial breast cancer (Wirtenberger et al., 2005). Two nsSNPs; rs141095253 (Pro397Leu) and rs145561065 (Leu434Phe) are found within while rs200431478 (Ser362Phe) is located near the C-terminal domain which spans 375-452 amino acid region of c-Myc1 (Fig. 1). This C-terminal DNA binding segment contains BR-HLH-LZ domain. Another BR-HLH-LZ domain containing protein; Max is identified as a closely related, obligate and physiological heterodimerization partener of c-Myc (Blackwood and Eisenman, 1991).

The HLH-LZ domains of both proteins form strong heterodimers and recognize the E-Box containing promoters to regulate transcriptional activation, cellular transformation, cell cycle progression and apoptosis (Amati et al., 1992, 1993). Other than Max some proteins like Nmi, TFII-I, YY-1, AP-2, BRCA1 and Miz-1 are also reported to interact with C-terminal region of c-Myc. Based on these interactions, C-terminal region is supposed to control the access of the N-terminal trans-activation domain to specific genetic loci (Sakamuro and Prendergast, 1999). Presence of nsSNPs in C-terminal region can alter these interactions and consequently can lead to an impaired functioning of c-Myc protein in cell.

However, when nsSNPs were subjected to SNPeffect server to evaluate their possible effect on molecular phenotype of protein, most of the variations were neither predicted to effect aggregation tendency and amyloid propensity nor the chaperone binding tendency of c-Myc protein. It seems as if these variations are really capable of bringing some damaging alterations to c-Myc, then these are certainly other than the aggregation tendency, amyloid propensity and the chaperone binding tendency of the protein as per prediction of SNPeffect. Only the Pro/Leu variation at position 397 (rs141095253) was shown to decreases the chaperone binding tendency and protein stability of c-Myc (Table III). 1NKP presents the X-ray structure of the c-Myc protein in complex with the Max protein and DNA including 353 to 434 residues of c-Myc2. Three SNPs; rs141095253 (Pro397Leu), rs145561065 (Leu434Phe) and rs148863193 (Arg439Leu) of longest isoform i.e. c-Myc1 are shown (Fig. 6).

Further analysis can be performed using some other computational tools and availability of PDB structure can help in making more precise and reliable evaluations.

Our results can be helpful and interesting especially to those epidemiologists who are interested in large-scale population based studies. Moreover, on basis of our predictions, the real effect of nsSNPs particularly rs114570780 (Tyr47His), rs150308400 (Cys148Tyr), rs137906262 (Leu159Ile) and rs200431478 (Ser362Phe) on c-Myc can be assured by conducting well defined in vitro and in vivo studies.

ACKNOWLEDGEMENT

We would like to acknowledge National University of Sciences and Technology (NUST) and Higher Education Commission (HEC) Islamabad Pakistan for providing assistance.

Conflict of interest

Authors do not declare any potential conflict of interest.

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