In silico evaluation of single-nucleotide polymorphisms in CHRNA7 and GRIN1 genes related to Alzheimer's disease

Arash Rezaeirad; Ömer Faruk Karasakal; Ebru Özkan Oktay; Mesut Karahan

doi:10.4103/jnbs.jnbs_31_22

ORIGINAL ARTICLE

Year : 2023 | Volume : 10 | Issue : 1 | Page : 22-28

In silico evaluation of single-nucleotide polymorphisms in CHRNA7 and GRIN1 genes related to Alzheimer's disease

Arash Rezaeirad¹, Ömer Faruk Karasakal², Ebru Özkan Oktay³, Mesut Karahan⁴
¹ Molecular Biology Master's Degree, Institute of Science, Üsküdar University, Istanbul, Türkiye
² Medical Laboratory Techniques, Istanbul, Türkiye
³ Laboratory Technology, Istanbul, Türkiye
⁴ Biomedical Device Technology, Vocational School of Health Services, Üsküdar University, Istanbul, Türkiye

Date of Submission	14-Nov-2022
Date of Acceptance	02-Dec-2022
Date of Web Publication	28-Mar-2023

Correspondence Address:
Ömer Faruk Karasakal
Mimar Sinan Mah. Selman-I Pak Cad, Üsküdar Üniversitesi Çarşı, Yerleşkesi, PK: 34664, Istanbul
Türkiye

Source of Support: None, Conflict of Interest: None

DOI: 10.4103/jnbs.jnbs_31_22

Abstract

Aim: The purpose of this study is to predict the possible impact of missense single-nucleotide polymorphisms (SNPs) in CHRNA7 and GRIN1 genes associated with AD on protein structure, function, and stabilization and to analyze gene–gene interactions via in silico methods. Materials and Methods: SIFT, PolyPhen-2, SNPsandGO, PROVEAN, SNAP2, PhD-SNP, and Meta-SNP were used to estimate high-risk SNPs. The impact of SNPs on protein stabilization was evaluated with I-Mutant 3.0 and MUpro software. Three-dimensional models of amino acid changes were determined with the Project HOPE software. Furthermore, the gene–gene interactions were analyzed via GeneMANIA. Results: According to the results of 603 missense SNPs in the CHRNA7 gene, rs142728508 (Y233C), rs12899798 (W77G), rs138222088 (R227H), rs140316734 (R227C), rs199633275 (P322R), rs199819119 (L29F), rs200147286 (Q49P), rs200908085 (Y115C), rs201094833 (Q61R), rs201473594 (N69D), rs201210785 (E195K), and rs368352998 (S48W) polymorphisms were predicted as deleterious. Similarly, rs193920837 (P117 L), rs3181457 (I540M), and rs201764643 (R217P) polymorphisms in the GRIN1 were estimated as deleterious. Conclusion: It is thought that the results of this study will provide useful information to guide future diagnostic and experimental strategies for AD.

Keywords: Alzheimer's disease, CHRNA7, GRIN1, in silico, single-nucleotide polymorphism

How to cite this article:
Rezaeirad A, Karasakal &F, Oktay E&, Karahan M. In silico evaluation of single-nucleotide polymorphisms in CHRNA7 and GRIN1 genes related to Alzheimer's disease. J Neurobehav Sci 2023;10:22-8

How to cite this URL:
Rezaeirad A, Karasakal &F, Oktay E&, Karahan M. In silico evaluation of single-nucleotide polymorphisms in CHRNA7 and GRIN1 genes related to Alzheimer's disease. J Neurobehav Sci [serial online] 2023 [cited 2023 May 31];10:22-8. Available from: http://www.jnbsjournal.com/text.asp?2023/10/1/22/372725

Introduction

Alzheimer's disease (AD) is known as a neurodegenerative disease (ND) that causes neurochemical deficiency in some parts of the brain, accumulation of amyloid-β, decreased cholinergic neurons, and formation of neurofibrillary tangles. Furthermore, pathologically, it causes amyloid-β accumulation outside the cell, while accumulation of tau proteins is observed inside the cell.^[1],[2],[3] Lately, a range of research has reported that the amyloid-β peptide binds to the alpha 7 nicotinic acetylcholine receptor (α7nAChR) on neuronal cell surfaces, which results in the precipitate of amyloid plaque formation in AD.^[4],[5] CHRNA7 gene encodes α7nAChR which are ligand-gated ion channels and substantially expressed in neuronal tissues.^[6],[7] Furthermore, the N-methyl-D-aspartate (NMDA) receptor is a subtype of glutamate receptors and has been reported to be closely related to neuronal activities. GRIN1 (glutamate ionotropic receptor NMDA type subunit 1) gene encodes the GluN1 of NMDA receptors.^[8]

Single-nucleotide polymorphisms (SNPs) are significant in investigating the risk of susceptibility to diseases and in detecting drug responses. Therefore, SNPs have a key role in the detection of ND.^[9],[10] Among the SNP types, missense SNPs cause amino acid substitution. Depending on its location, this change can have significant impacts on protein structure, function, and stabilization. The possible deleterious effects of missense SNPs in CHRNA7 and GRIN1 genes that lead to amino acid changes on protein function and structure can be estimated with the help of in silico analysis software with different algorithms, and the results can further guide diagnostic and experimental strategies.

The aim of this study is to predict the possible impact of missense SNPs in CHRNA7 ve GRIN1 genes associated with AD on protein structure, function, and stabilization and to analyze gene–gene interactions using in silico methods.

Materials and Methods

There is no need for ethics committee approval.

First, the SNPs in the CHRNA7 and GRIN1 genes were obtained from the NCBI dbSNP (https://www.ncbi.nlm.nih.gov/snp/). The sequences of the protein encoded by the CHRNA7 and GRIN1 genes were obtained from the UniProt (https://www.uniprot.org/). Second, publicly available software such as SIFT, PolyPhen-2 (HumDiv-HumVar), SNPs and GO, PROVEAN, SNAP2, PHD-SNP, and Meta-SNP were used to predict potentially harmful SNPs in CHRNA7 and GRIN1 genes. After, I-Mutant 3.0 and MuPro were used to estimate its effect on protein stabilization. Furthermore, three-dimensional (3D) modeling of proteins was created by the Project HOPE. Finally, the gene–gene interactions were determined with the GeneMANIA (https://genemania.org/) [Figure 1].

Figure 1: In silico analyses tools

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SIFT (Sorting Intolerant From Tolerant) predicts the impact of an amino acid substitution on the function of a protein according to the sequence similarity and physical features of amino acids.^[11] PolyPhen-2 (HumDiv, HumVar) is a software that estimates the effects of an amino acid replacement on the structure and function of a given protein based on physical and comparative features.^[12] SNPsandGO predicts whether a SNP is associated with diseases based on protein functional annotation.^[13] PROVEAN is a software that makes a prediction on the impact of an amino acid change on the protein function.^[14] SNAP2 predicts the functional effects of amino acid substitution based on a “neural network.”^[15] PhD-SNP (Predictor of human Deleterious SNP) is defined as a predictor of harmful SNPs in humans. The PhD-SNP algorithm was used for estimating the effect of human SNPs in both coding and noncoding sites.^[16] The Meta-SNP was used to estimate whether a particular protein variation can be identified as disease-associated.^[17]

MUpro^[18] and I-Mutant 3.0^[19] are support vector machine-based tools that estimate protein stability alterations due to SNPs. 3D modeling of proteins is created by Project HOPE. It also reports data on features of residues at polymorphism sites.^[20] The interactions of CHRNA7 and GRIN1 genes with other genes were determined with the GeneMANIA software tool.^[21]

Results

Results of gene–gene interaction

It was determined that there were 161 links between them when the interaction of the CHRNA7 gene with 20 genes was examined. The maximum associated five genes with CHRNA7 were MAPK15, ADCY6, MAPKAPK5, MAPK4, and MAPK6. Similarly, 624 links were determined between GRIN1 and 20 genes examined. GRIN2A, GRIN2B, FBXO2, GRIN3A, and DRD1 genes were determined as the top five genes which have the maximum association with GRIN1 [Figure 2] (GeneMANIA).

Figure 2: Gene–gene interaction of CHRNA7 and GRIN1 genes

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Results of in silico analysis of CHRNA7 and GRIN1 genes

SNPs information for the CHRNA7 and GRIN1 genes was accessed from the NCBI dbSNP in September 2021. The total number of SNPs belonging to the CHRNA7 gene was 51693 and the number of missense SNPs was 603. A total of 913 different amino acid changes of these missense SNPs were determined. Among them, twelve missense SNPs were determined to be possibly harmful and the results of the analysis are showed in [Table 1]. For the GRIN1 gene, 591 missense SNPs were determined among 13914 SNPs and 751 different amino acid changes were detected. Among them, three missense SNPs were determined to be harmful, and the analysis results are given in [Table 1].

Table 1: Prediction results of SNPs in the CHRNA7 and GRIN1 genes

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Results of protein stability

Stability analysis of proteins was performed with the I-Mutant 3.0 and MUpro software tools for variants that all online software tools predicted to be functionally harmful. The prediction results of are shown in [Table 2].

Table 2: Stability results of CHRNA7 and GRIN1

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Results of amino acids at polymorphism sites and three-dimensional models

The features of amino acid changes caused by variants in CHRNA7 and GRIN1 genes on protein structure and function were obtained with Project HOPE. The size, hydrophobicity, and charge differences between wild and variant amino acids as well as 3D structures of the protein were estimated. The results are given in [Table 3] and [Table 4], respectively.

Table 3: Features of amino acids at polymorphism sites

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Table 4: Project HOPE results of proteins encoded by CHRNA7 and GRIN1 genes

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Discussion

In recent years, polymorphisms in the CHRNA7 and GRIN1 genes, which are associated with AD, have been the focus of attention. For example, the roles of polymorphisms in the CHRNA7 gene in response to inhibitors in AD^[22],[23] and polymorphisms in the CHRNA7 gene in AD^[24] have been reported. Furthermore, the association studies between variations in the GRIN1 gene and in various diseases such as type 2 diabetes mellitus,^[25] paranoid schizophrenia,^[26] and Parkinson's disease^[27] have been reported. In this study, the possible effects of polymorphisms in these genes were determined by bioinformatics approach based on their roles on various diseases. The high-risk SNPs predicted using bioinformatics tools are rs142728508 (Y233C), rs12899798 (W77G), rs138222088 (R227H), rs140316734 (R227C), rs199633275 (P322R), rs199819119 (L29F) rs200147286 (Q49P), rs200908085 (Y115C), rs201094833 (Q61R), rs201210785 (E195K), and rs368352998 (S48W) in the CHRNA7 gene and rs193920837 (P117 L), rs3181457 (I540M), and rs201764643 (R217P) in the GRIN1 gene in this study.

The differences of features between wild and variant type amino acids of amino acid substitutions were investigated via Project HOPE [Table 3]. The protein stability changes caused by amino acid substitutions were estimated via I-Mutant and MUpro [Table 2]. Amino acid changes can affect the folding rate of a protein and depend mainly on the location and type of mutations.^[28] Amino acid substitutions can alter the function of a protein with disruption of hydrogen bonds or salt bridges, changing of the physicochemical effects, and geometric constraint changes. These changes may cause destabilization of protein or some abnormal biological functions.^[29]

The investigation of gene–gene interactions is significant in the etiology of some diseases such as cancer, cardiovascular, and immune system.^[30] For this reason, gene–gene interaction map was determined in terms of genetic interaction, physical interaction, coexpression, colocalization, shared protein domains, pathways, and predicted interaction in CHRNA7 and GRIN1 genes [Figure 2].^[31]

Conclusion

Consequently, it is recommended that SNPs, which are predicted to be high risk in CHRNA7 and GRIN1 genes as a result of bioinformatic analyses carried out, should be primarily evaluated and investigated in experimental and clinical studies related to AD. For this reason, it is thought that the findings obtained from the study will provide important data for future experimental studies.

Patient informed consent

There is no need for patient informed consent

Ethics committee approval

There is no need for ethics committee approval.

Financial support and sponsorship

No funding was received.

Conflicts of interest

There are no conflicts of interest to declare.

Author contribution subject and rate

Arash Rezaeirad (40%): Data collection, in silico analysis, writing—original draft preparation
Ömer Faruk Karasakal (30%): Organizing the research, designing the research and methodology, writing (review and editing).
Ebru Özkan Oktay (15%): Writing (review and editing), contributed with comments on methodology.
Mesut Karahan (15%): Writing (review and editing), contributed with comments on methodology.

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