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Computational Analysis of Single Nucleotide Polymorphism (SNPs) in Human GRM4 Gene
Afra Abd Elhamid Fadlalla Elshaikh1,
, Mosab Mohamed Gasemelseed1, 2, Marwa Mohamed Osman1, Samah Ahmed Ibrahim Shokri1, Samia Othman massad1, Nahid Ebrahim Merghani Fadl1, Arafa Habiballa Ibrahim3, Selma Shiekh Idris4, Maab Mohamed Abdalla5, Mohamed Ahmed Salih6
1Department of Bioinformatics, Africa city of technology, Sudan
2Alneelain University Faculty of Medical Laboratory Sciences, Sudan
3Department of haeatology, Elnehoud teaching Hospital, Sudan
4Medical Lab Manager, Tyba Hospital, Sudan
5Institute of Endemic Diseases, Khartoum University, Sudan
6Head Department of Biotechnology, Biotechnology Park, Africa city of technology, Sudan
Abstract
Background: L-glutamate is one of the most common amino acid in nature and it acts as excitatory neurotransmitter in the central nervous system. GRM4 is a large gene located in chromosome 6 and consists of 7217 bp (NCBI) divided into 10 exons. The location of GRM4 (chromosomal segment 6p21.3) is tentative susceptibility loci for Juvenile Myoclonic Epilepsy so many studies investigate the association of GRM4 polymorphism with myoclonic epilepsy juvenile. Design and methods: GRM4 gene was investigated in dbSNP/NCBI database NCBI and we used computational analysis approach. Deleterious nsSNPs were predicted by SIFT and Polyphen soft wares then the damaging nsSNPs were submitted to I mutant tool. Protein structural analysis of amino acid variants was performed by Chimera 1.8 and Project Hope. Results: We analyze 29854 SNPs from NCBI; 8561 of them found on homosapiens; of which 330 were missense, of which 208 were in the coding region, 334 were non-synonymous SNPs (nsSNPs), 232 were in the 3'un-translated region. These SNPs were analyzed using different soft wares; SIFT, Polyphen-2, Imutant3.0, PhD-SNP, PolymiRTs, Project Hope and GENEMAIA to investigate the effect of SNPs mutations on GRM4 protein structure and function. Conclusions: Computational tools were used to analyze deleterious SNPs in GRM4 gene. Out of 8561 SNPs, the SNPs (rs184636998), (rs199744441), (rs199744441), (rs149277708), (rs144660534), (rs139236496) were identified as highly damaging in coding region confirmed by using bioinformatics tools . In 3'un-translated region two SNPs (rs188406833) and (rs192860479) contained (C) allele had 4 miRSite as target binding site can be disrupts a conserved miRNA and one SNPs (rs77415386) contained (D) allele had 6 miRSite as derived allele that disrupts a conserved miRNA site. Further study must be done to detect the effect of these SNPs on the protein structure and function.
Keywords: glutamate, neurotransmitter, ionotropic, metabotropic, Myoclonic Epilepsy, SNPs
Copyright © 2016 Science and Education Publishing. All Rights Reserved.Cite this article:
- Afra Abd Elhamid Fadlalla Elshaikh, Mosab Mohamed Gasemelseed, Marwa Mohamed Osman, Samah Ahmed Ibrahim Shokri, Samia Othman massad, Nahid Ebrahim Merghani Fadl, Arafa Habiballa Ibrahim, Selma Shiekh Idris, Maab Mohamed Abdalla, Mohamed Ahmed Salih. Computational Analysis of Single Nucleotide Polymorphism (SNPs) in Human GRM4 Gene. American Journal of Biomedical Research. Vol. 4, No. 3, 2016, pp 61-73. https://pubs.sciepub.com/ajbr/4/3/2
- Elshaikh, Afra Abd Elhamid Fadlalla, et al. "Computational Analysis of Single Nucleotide Polymorphism (SNPs) in Human GRM4 Gene." American Journal of Biomedical Research 4.3 (2016): 61-73.
- Elshaikh, A. A. E. F. , Gasemelseed, M. M. , Osman, M. M. , Shokri, S. A. I. , massad, S. O. , Fadl, N. E. M. , Ibrahim, A. H. , Idris, S. S. , Abdalla, M. M. , & Salih, M. A. (2016). Computational Analysis of Single Nucleotide Polymorphism (SNPs) in Human GRM4 Gene. American Journal of Biomedical Research, 4(3), 61-73.
- Elshaikh, Afra Abd Elhamid Fadlalla, Mosab Mohamed Gasemelseed, Marwa Mohamed Osman, Samah Ahmed Ibrahim Shokri, Samia Othman massad, Nahid Ebrahim Merghani Fadl, Arafa Habiballa Ibrahim, Selma Shiekh Idris, Maab Mohamed Abdalla, and Mohamed Ahmed Salih. "Computational Analysis of Single Nucleotide Polymorphism (SNPs) in Human GRM4 Gene." American Journal of Biomedical Research 4, no. 3 (2016): 61-73.
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1. Introduction
L-glutamate is one of the most common amino acid in nature and it acts as excitatory neurotransmitter in the central nervous system. Glutamatergic neurotransmission is involved in most aspects of normal brain function and can be perturbed in many neuropathologic conditions. The metabotropic glutamate receptors are a family of G protein-coupled receptors that have been divided into 3 groups on the basis of sequence homology, putative signal transduction mechanisms, and pharmacologic properties. Group I includes GRM1 and GRM5 and these receptors have been shown to activate phospholipase C. Group II includes GRM2 and GRM3 while Group III includes GRM4, GRM6, GRM7 and GRM8. [8, 9, 14]
GRM4 is encoded to Metabotropic glutamate receptor 4 (mGluR4) which is belongs to group III of the metabotropic glutamate receptor family. [9, 14] group III receptors are coupled to Gi/o which inhibits adenylyl cyclase, decreasing the formation of Camp and they are involved in spatial learning and memory, and umami taste (mGlu4), stimulation of photoreceptors (mGlu6), long term depression (mGlu7) and presynaptic inhibition in the perforant pathway (mGlu8). [7, 8]
GRM4 is a large gene located in chromosome 6 and consists of 7217 bp (NCBI) divided into 10 exons [7, 8].
The location of GRM4 (chromosomal segment 6p21.3) is tentative susceptibility loci for Juvenile Myoclonic Epilepsy so many studies investigate the association of GRM4 polymorphism with myoclonic epilepsy juvenile [10, 13]. There are also some studies investigate the association of GRM4 polymorphism with schizophrenia, depression, Colon cancer and tumors [7, 8].
Single-nucleotide polymorphism (SNPs) most commonly refer to single-base differences in DNA among individuals. SNPs of various types can change the function or the regulation and expression of a protein. There is no studies on GRM4 single nucleotide polymorphism. In this study we will analyze GRM4 SNPs using bioinformatics prediction tools to check the effect of the mutation on the protein structure and function.
2. Material and Methods
GRM4 gene was investigated in dbSNP/NCBI database NCBI (https://www.ncbi.nlm.nih.gov/snp). GRM4 gene contained a total of 29854 SNPs; 8561 of them found on homosapiens; of which 330 were missense, of which 208 were in the coding region, 334 were non-synonymous SNPs (nsSNPs), 232 were in the 3'un-translated region. Predictions of deleterious nsSNPs was performed by SIFT and Polyphen softwares. Damaging nsSNPs by two these servers were submitted to I mutant tool, then the functional impact of the deleterious SNPS was analyzed by project hope. The FASTA format of the protein was obtained from Uniprot at Expassy database. The 3D structure of a 65% identical to protein was retrieved from database by using BAST/NCBI. The protein used as a template is called “Metabotropic glutamate receptor 4 [Homo sapiens]" with ID pdb|3MQ4|. The SNPS at the 3UTR region were analyzed by Polymirt software.
3. Bioinformatics Data Analysis
3.1. Deleterious nsSNP Analyze by the SIFT ProgramSorting Intolerant from Tolerant (SIFT, version 2) [3]. The SIFT prediction, as previously described is based on perform multiple alignments of a number of peptide sequences and it predicts whether substitution with any of the other amino acids is tolerated or deleterious for every position in the submitted sequence. The SIFT prediction was given as a tolerance index (TI) score ranging from 0.0 to 1.0, which was the normalized probability that the amino acid change was tolerated. The SNP with a TI score of <0.05 was considered to be deleterious i.e. an amino acids with probabilities < 0.05 were predicted to be deleterious. (https://blocks.fhcrc.org/sift/SIFT.html).
3.2. PolyPhen -2Polymorphism Phenotyping v2, [6] this software has position-specific independent count (PSIC) the score of 1.0 is considered to be damaging, and the nsSNP affect protein structure. (PolyPhen-2, v.2.2.2; https://genetics.bwh.harvard.edu/pph).
3.3. Investigation of Mutant Protein Stability by I-Mutant 3.0I- Mutant 3.0 is a Support Vector Machine-based web server was used to predict protein stability changes upon single-site mutations. The input FASTA sequence of protein along with the residues change was provided for analysis of DDG value (kcal/mol) Also the RI value (reliability index) was computed [4]. (I-Mutant 3.0/I-Mutant3.0.cgi).
3.3. PhD-SNPAn Online support vector machine (SVM) based classifier, which can predict if the new phenotype produced after nsSNP can be related to a genetic disease or as a neutral polymorphism. (https://snps.biofold.org/phd-snp/phd-snp.html [2]
3.4. PolymiRTSPolymiRTS database [20] (https://compbio.uthsc.edu/miRSNP/) [1] were used to predict the effect of SNPs in the GRm4 mRNA 3′-UTR on the mRNA.
3.5. Project HOPEProject Hope software [11] revealed many differences between wild and mutant residues as shown in Table 2. Also the project results of the mutation analyses are illustrated with figures of the amino acids. (https://www.cmbi.ru.nl/hope/home).
3.6. ChimeraChimera [5] is a software for analysis of molecular structures, this software can give the 3D structure of the protein and then changing between native and mutant amino acids with the candidate to display the impact that can be produced. Chimera accepts the input in the form of pdp ID or pdp file. (https://www.cgl.ucsf.edu/chimera/).
3.7. Gene MANIAGene MANIA an online server gives the function, interaction and the network of the gene based on genomics and proteomics data with a large database and high accuracy [12]. (https://www.genemania.org)
4. Result
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Table 1. shows of nonsynonymous SNPs predicted with SIFT, Polyphen and I mutant programs, chosen SNPs with PSIC SD equal 1 and TOLERANCE INDEX rang (0 – 0.001)
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Table 2. shows the SNPs predicted by Polymirt to induce disruption or formation of mirRNA binding site
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5. Discussion
5.1. Investigating the Desired Gene Using dbSNP/NCBI:GRM4 gene was investigated in dbSNP/NCBI (https://www.ncbi.nlm.nih.gov/snp). This gene containing a total of 29854 SNPs, 8561 SNPs of them found on Homo sapiens; of which 313 were missense, 192 synonymous, 3 nonsense, 7 frame shift and 289 were in the non-coding region (3'un-translated region) and the rest of the SNPs lies at 5'UTR and in the introns.
5.2. Predicting Damaging Amino Acid Substitutions Using SIFT (v5.1) and Prediction of Functional Modification Using Polyphen-2 (Polymorphism Phenotyping v2)Only SNPs that are found on the coding region and 3'UTR SNPs were selected for computational analysis. SNPs lies on coding region are Predicted by SIFT and Polyphen: Predictions of deleterious nsSNPs was performed by SIFT and Polyphen software; only 225 were predicted to be deleterious by sift. While 168 were predicted to be damaging by both servers. First, we submitted batch nsSNPs (rs SNPs) to SIFT server; then the resultant damaging nsSNPs were submitted to Polyphen as query sequences in FASTA Format, it traced 142 probably damaging nsSNPs, the other 25 nsSNPs were scored as possibly damaging are reported in Table 1.
5.3. Prediction of Change in Stability due to Mutation Used I-Mutant 2.0 ServerI mutant 2.0 (https://folding.biofold.org/i-mutant/i-mutant2.0.html) results demonstrated that protein stability with related free energy had changed due to mutation. The nine mutations (P→L, R→C, R→H, R→S, A→P, A→T, V→M, D→H, R→C) in GRM4 gene decrease effective stability of the protein, five of them with PSIC SD equal 1 and TOLERANCE INDEX rang ( 0 – 0.001) listed in Table 1, but the mutation (S→L,) increases effect stability. (Table 6).
For modeling by project hope we choose SNPs with PSIC SD equal 1 and TOLERANCE INDEX rang ( 0 – 0.001) chosen SNPs are listed in the Table 1.
5.4. Association of nsSNPs to DiseasePHD-snp software was used to predict if the SNP is disease- related (disease) or neutral polymorphism. We found that only SNP in position 334 R → C was predicted to be neutral polymorphism. All other SNP V737M, P680L, P849L, P733L, P709L, R507H, R623H, R483H, R490H, R454H, V144F, V73F were predicted to be disease related. (Table 1)
5.5. Prediction of SNPS at the 3UTR Region58 functional SNPs was predicted, among the 58 SNPs, 23 allele disrupted a conserved miRNA site and 35 derived allele created a new site of miRNA. RS188406833 and RS192860479 SNPs contained (C) allele had 4 miRSite as target binding site can be disrupts a conserved miRNA. RS77415386 SNP contained (D) allele had 6 miRSite as derived allele that disrupts a conserved miRNA site.
The table below demonstrates the SNPs predicted by Polymirt to induce disruption or formation of mirRNA binding site. (Table 2)
5.6. Protein Modeling using Project Hope:Project Hope (https://www.cmbi.ru.nl/hope/input) revealed the 3D structure for the truncated proteins with its new candidates; in addition, it described the reaction and physiochemical properties of these candidates. Here we present the results upon each candidate and discus the conformational variations and interactions with the neighboring amino acids:
C/A mutation (rs139236496) caused mutation of a valine into a phenylalanine at position 144. The wild-type and mutant amino acids differ in size. The mutant residue is bigger, this might lead to bumps. (Figure 1)
G/A mutation (rs199744441) caused mutation of a proline into a leucine at position 680, 733, 709 and 849. The wild-type and mutant amino acids differ in size. The mutant residue is bigger, this might lead to bumps. Prolines are known to have a very rigid structure, sometimes forcing the backbone in a specific conformation. Possibly, the mutation changes a proline with such a function into another residue, thereby disturbing the local structure. (Figure 2)
C/T mutation (rs149277708) caused mutation of arginine into a histidine at position 507, 623, 438, 490 and 454. There is a difference in charge between the wild-type and mutant amino acid. The charge of the wild-type residue is lost by this mutation. This can cause loss of interactions with other molecules. The wild-type and mutant amino acids differ in size. The mutant residue is smaller than the wild-type residue. This will cause a possible loss of external interactions. (Figure 3)
G/A mutation (rs139236496) caused mutation of arginine into a cysteine at position 334. There is a difference in charge between the wild-type and mutant amino acid. The charge of the wild-type residue is lost by this mutation. This can cause loss of interactions with other molecules. The wild-type and mutant amino acids differ in size. The mutant residue is smaller than the wild-type residue. This will cause a possible loss of external interactions. The hydrophobicity of the wild-type and mutant residue differs. (Figure 4)
C/T mutation (rs184636998) caused mutation of a valine into a methionine at position 737. The wild-type and mutant amino acids differ in size. The mutant residue is bigger than the wild-type residue. The residue is located on the surface of the protein; mutation of this residue can disturb interactions with other molecules or other parts of the protein. (Figure 5)
C/A mutation (rs144660534) cased mutation of arginine into a histidine at position 454. There is a difference in charge between the wild-type and mutant amino acid. The charge of the wild-type residue is lost by this mutation. This can cause loss of interactions with other molecules. The wild-type and mutant amino acids differ in size. The mutant residue is smaller than the wild-type residue. This will cause a possible loss of external interactions. (Figure 6)
5.7. Functions and Interaction of GRM4 with Functional Similar GeneIt was predicted that GRM4 gene shares the same protein domain with 11 genes (SDCBP, GRIP1, GRK7, ADRBK2, GRK5, GRK4, GRM2, GRM5, GRM1, GRK6 and GRID2). GRM4 gene is similar in its expression level with many genes most of them are glutamate receptors. (Table 3)
6. Conclusions
GRM4 SNPs were analyzed using bioinformatics prediction tools to check the effect of the mutation on the protein structure and function. Five SNPs were detected to be highly damaging in coding region while three SNPs were detected to be deleterious in 3'un-translated region.
Acknowledgements
First of all, we would like to express my special thanks to Allah who gave us the strength and patient to accomplish this work.
I would like also to express my appreciation and thanks to Dr. Mohamed Ahmed Salih the head department of biotechnology, Biotechnology Park, Africa city of technology who supported us with valuable help and encouragement throughout the period of this study.
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