Int J Biol Med Res.2019 ;10(3):6790-6795 Int J Biol Med Res www.biomedscidirect.com Volume 10, Issue 3, July 2019

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Original article In Silico Identification of potential Drug Target and Analysis of Effective Single Nucleotide Polymorphisms for Autism Spectrum Disorder Manisha Raya, Jagruti Mishraa, Abhishikta Priyadarshinib, Souvagyalaxmi Sahoo* Tectona Biotech Resource Center, Bhubaneswar­751002, Odisha.

A R T I C L E I N F O A B S T R A C T

Keywords: Autism or Autism spectrum disorder is one of the most heritable neurophyschitric disorders; GWAS its underlying genetic architecture has largely eluded description. However the advances in Autism Spectrum Disorder Target genetics and genomics science and technology have the potential to revolutionize health care, SNPs so the number of reports investigating disease susceptibility based on the carriage of low- AMPD1 penetrance, high frequency SNPs has increased in recent years as SNPs are the most important and basic form of variation in the genome, and they are responsible for genetic effects that produce susceptibility to most autoimmune diseases. Here, the identification of novel target genes of ASD were retrieved and validated in subjected to Autdb, GeneCards, MalaCards, STRING and many others, which resulted total 86 numbers of target genes for ASD. Thereafter the collection of associated Single Nucleotide Polymorphisms of the target genes became efficaciously carried out with the involvement of SIFT, PolyPhen2, PROVEAN and SNAP2, which results the most deleterious SNP with rsID35859650, rs34526199 and rs61752478 of selected target AMPD1 against ASD. The finding of this current study followed by GWAS not solely revealed the foremost necessary target genes for ASD, however conjointly it provides the associated single nucleotide polymorphisms can take one step nearer or forward to unravel the complicated structure of common deadly diseases especially mental disorders like Autism Spectrum Disorder. c Copyright 2010 BioMedSciDirect Publications IJBMR - ISSN: 0976:6685. All rights reserved.

1. Introduction Autism Spectrum disease is a really heritable neuro Progress in genomics and the associated technological, statistical developmental sickness that impacts the brain, the immune and bioinformatics advances have facilitated the successful device, the gastrointestinal tract and other organ structures [1, 2]. implementation of genome-wide association studies (GWAS) It characterized with the aid of restricted interests, repetitive towards understanding the genetic foundation of common behaviour, impairments in social communication etc [3, 4]. diseases [8]. Genome wide association research on individuals Autism is widely taken into consideration to be a multi factorial with ASD and their families revealed several threat genes that may sickness that effects from genetic in addition to non genetic be the common molecular targets in Autism. However, the list of hazard elements, but the exact cause of autism is unknown, autism threat genes affords us a path to understand the however a strong genetic component has been identified from a potentially more prone pathways in neurons that may be family and twin studies. Moreover genetic studies discovered that therapeutic objectives to develop extra efficient interventions for alternation inside the developmental pathways of neuronal and ASD [9]. There were many genes recognized as target genes and axonal systems are strongly involved in synaptogenesis emerge reported to cause ASD with mutation including, GABRG3 [10], from single gene mutations [5]. ASD is a highly prevalent neuro MTOR [11], FMRP [4], CHD8, EHMT1, SATB2, DLX1 [12], FMR1, disease condition with no current treatment available. As TSC1/2, MECP2, CHD2, ARID1, TBR1, DYRK1A, GRIN2B [13], experimental proof emerges in current years, advances in GABRB3, CC2D1A, UBE3B, AMT, PEX7, VPS13B [14], PTEN, genetics and genomics have identified autism risk genes [6]. CNTNAP2, SLC9A9, PEX7, CC2D1A, BCKDK, SYNGAP1, DYRK1A, Complex genetic interactions appear responsible for a high SCN2A [6] etc. The considerable advances in genetic and genomics degree of heterogeneity of the clinical symptoms in ASD. The study has been rapid, more work is needed to fully understand the identification of the genetic components of autism spectrum capability for impacting disease treatment and prevention, disorder has advanced rapidly in current years, particularly with specially the mental health [5]. Thus to satisfy the unconventional the demonstration of de novo mutations as a critical source of treatment of genetic diseases, the Genome wide association causality [7]. research (GWAS) with hundreds of lots of SNPs are popular techniques which reveal the genetic basis of human complex * Corresponding Author : Dr. Souvagyalaxmi Sahoo disorders [16]. Senior Scientist, Tectona Biotech Resource Centre, Odisha, Bhubaneswar, 751002. E.Mail: [email protected] Mobile No. 9337524676 c Copyright 2011. CurrentSciDirect Publications. IJBMR - All rights reserved. Manisha Ray et.al/Int J Biol Med Res.10(3):6790-6795 6791

Single nucleotide polymorphisms (SNPs) are the most common dbSNP form of source of variations in the . Among all of the SNPs, nonsynonymous SNPs (nsSNPs) are vital as those can modify The dbSNP (https://www.ncbi.nlm.nih.gov/snp/?term) database, the amino acid residues and may create damage or modify established through National Centre for Biotechnology Information coding sites and those effects or deleterious results of SNPs are (NCBI) (https://www.ncbi.nlm.nih.gov/) serve as a crucial generally attributed to their impact on the protein structures and repository for each single base nucleotide substitutions and quick functions [17, 18]. deletion and insertion polymorphisms, which emphasizes the gathering of SNPs for amassed target genes of Autism to complete Thus, this present study retrieved some vital target genes for this present work. Autism Spectrum Disorder and analyzed the associated deleterious nsSNPs in account of having a new path to deal with and cure the SIFT Autism Spectrum Disorder by following the related tools and databases of Genome Wide Association Study, such as human After the collection of target SNPs those were subjected to predicts disease gene databases like Autdb, GeneCards, MalaCards, whether an amino acid substitution affects protein function based additionally the use of STRING database became worried to on and the physical properties of amino acids conclude the target gene identification manner. Then the SNP through SIFT (http://sift.bii.a-star.edu.sg/). retrieval was completed by way of using SIFT, PolyPhen2, PROVEAN PolyPhen­2 and SNAP2, followed by identified target genes to carry out the whole study by figuring out the most deleterious nsSNPs, which The tested SNPs through SIFT these were once more tested via results the novel drug target with most effective SNPs for the PolyPhen-2 (http://genetics.bwh.harvard.edu/pph2/) as it predicts development of latest drugs towards ASD. feasible impact of an amino acid substitution on the structure and Material and Methods function of a human protein using straightforward physical and comparative considerations. AutDB PROVEAN AutDB (http://autism.mindspec.org/autdb/Welcome.do) is an evolving modular database for the autism research network that is PROVEAN (http://provean.jcvi.org/index.php) is likewise called targeted on genes implicated in autism susceptibility. Thus this Protein Variation Effect Analyzer, that is useful for filtering sequence examines retrieved related Autism genes and achieved for further versions to become aware of non-synonymous or indel variations evaluation. which can be expected to be functionally important, so the established SNPs after PolyPhen-2 evaluation have been analyzed in GeneCards PROVEAN and consequences have been achieved for further GeneCards is also known as Human Gene Database observance. (https://www.genecards.org/), is routinely integrates gene-centric SNAP2 information from ~a hundred twenty five net sources, which encompass genomic, transcriptomic, proteomic, genetic, clinical The demonstrated SNPs from PROVEAN have been once more and practical information. Here, the Autism associated genes were analyzed in SNAP2 (https://www.rostlab.org/services/SNAP/) retrieved from GeneCards database via key-word search. device as it has the set of rules to are expecting the impact or effect of single amino acid substitutions on protein function. MalaCards Results MalaCards (https://www.malacards.org/) is an included database Retrieval of Target Genes of human sickness and problems. It also analysing disease associated There are a number of genes are involved in different ways to gene units through Gene Analytics to yield affiliated pathways, cause the autism spectrum disorder or Autism. So this presented phenotypes, compounds, and GO terms. Here on this database the work was gone through to search the autism associated target genes amassed target genes of autism have been confirmed with MalaCard from distinctive databases like Autdb, GeneCards and MalaCards and ID and the disease class. so forth. The AutDB is a molecular database for autism research, STRING which contained 6321 numbers of genes of Autism, however in those genes lists there had been many repetitions of genes had been befell, STRING (https://string-db.org/) is the practical protein association so by eliminating the repeat genes there had been total 856 numbers community, also a database of recognized and anticipated protein- of target genes for autism were accumulated from AutDB. protein interactions. So, because of its potentiality of evaluation, the Thereafter the collection of target genes from AutDB, another collected Autism genes had been interpreted through functionally human disease gene database GeneCards, was taken in to attention community via STRING. and looked for the autism target genes, which gave 4390 numbers of genes for autism. Manisha Ray et.al/Int J Biol Med Res.10(3):6790-6795 6792

Then there was a comparison made between the collected target screened out the high risk SNP as either with neutral or deleterious genes of autism from these above two considered databases, i.e. the effects, and this server predicted 4 deleterious effect nsSNPs for 2 common genes from both databases were screened out and results genes (CACNA1D, AMPD1) (Table 1) out of those 9 on the basis of its 482 numbers of genes, which were taken for another analysis like screening criteria i.e the prediction value with > -2.5 is shows neutral validation process in MalaCards database. As MalaCards is an effect and the value with < -2.5 shows deleterious effects on protein. integrative database of human diseases, the 482 amassed genes for Then at last those 4 nsSNPs submitted to SNAP2 tool to examine the autism from AutDB and GeneCards had been tested via MalaCards i.e. impact of amino acid variation which gave one final high risk 3 nsSNP from 482 genes, which genes have been also present as target to with rsIDs rsID35859650, rs34526199 and rs61752478 as effective cause autism in MalaCards were once more screened out and on target gene AMPD1 to cause Autism Spectrum Disorder (Fig. eventually were given 195 numbers of target genes for autism (Fig. 1(B)), (Table 1). 1(A)). Table­1: Predicted nsSNPs with respective prediction values Functional Network Analysis and effects from SIFT, PolyPhen2, PROVEAN and SNAP2 for collected Target Genes of ASD The collected 195 numbers of target genes were subjected to network analysis through STRING, which enrich the functional network between the 195 genes based on highest confidence score for interaction 0.900 and resulted 86 numbers of genes (Fig. 1(A), Fig. 2), were distinctly interacted functionally and subjected for further analysis. The built network contains 189 numbers of nodes, 182 numbers of edges and the PPI enrichment value was turned into < 1.0e-16.

Collection of SNP data

The data set of SNPs for 86 numbers of target genes of Autism have been gathered from database of SNP or dbSNP based totally on some criteria like non synonymous SNPs with having protein annotation, pathogenic and/or benign clinical significance, functional annotations including 3'UTR, 5'UTR, frameshift, stop gained, missence, nonsence etc and validation status by using 1000 genome, which provides 31 numbers of genes with total 94 numbers of nsSNPs for Autism among 86 target genes (Fig. 1(B)).

Functional Mutation Analysis of Target Genes

For the accuracy of computational techniques, prediction of maximum deleterious SNPs can be extended by means of the usage of a mixture of numerous exceptional in silico algorithms through different tools. Therefore by means of furnishing the accuracy to predict the most deleterious or damaged SNP, right here a few tools had been used like SIFT, which predicts only 167 nsSNPs of 31 target genes from 94 nsSNPs of 31 genes respectively as tolerated and deleterious followed by the prediction scores like; prediction score with > 0.05 was taken to be tolerated and score with < 0.05 was considered to be deleterious, according to their functional influence of amino acid substitution; but in the present study only the deleterious nsSNPs were taken into consideration for further analysis, so only SIFT predicted 17 deleterious nsSNPs of 11 (SHANK3, DLG4, NRXN1, CACNA1D, LRP2, TSC2, DRD2, AMPD1, ADSL, LAMB1, GPHN) (Table 1) drug target genes were subjected to validation through PolyPhen 2, which result 9 nsSNPs for 6 (CACNA1D, LRP2, TSC2, DRD2, AMPD1, ADSL) (Table 1) genes with possible damaging out of 17 nsSNPs by means of the use of some algorithms and prediction scores i.e. 0.000 indicates probably benign and 0.999 indicates probably damaging. Further those 9 nsSNPs had been once more subjected to analyze in PROVEAN tool to Manisha Ray et.al/Int J Biol Med Res.10(3):6790-6795 6793

Fig. 2

Discussion

In the field of computational genomic research, determination of high-risk nsSNPs which may induce disease associated phenomena is getting importance. So this present study involved identification of important drug targets of autism from different human disease databases and uses multiple computational methods to detect the high-risk pathogenic mutation in the important target genes of autism by analysing single nucleotide polymorphism.

Thus as the resulted data mentioned above that total 482 numbers of common target genes for autism were screened out by comparing the two databases i.e. Autdb, that encompasses detailed annotation of both rare and common genetic variants associated with ASD for candidate gene prioritization [19] and GeneCards, a web based compendium of human disease associated genes. These genes were validated again through MalaCards database which is effectively addresses some of the major challenges facing disease bioinformatics i.e. the gene-disease relationship [20] and got 195 final target genes. The overall retrieval process for target genes was done to get more reliable, important and more authenticated targets which might be helpful for further drug design research. Further after the database search the genes were again validated through Fig. 1 functional network association in STRING, which is one of the repositories and provides opportunity for building physical and functional networks between related genes and/ or as per the such as molecular function, biological process and cellular component [21], thus because of its potentiality towards the generation and accuracy of functional networks between the genes or proteins this study use the functional network generated through STRING and got 86 genes out of 195. Manisha Ray et.al/Int J Biol Med Res.10(3):6790-6795 6794

After the completion of target identification process this study Acknowledgement gone through the collection of single nucleotide polymorphisms (SNPs) of related target genes to isolate the most deleterious or We really thanks to Tectona Biotech Resource Centre (TBRC), highly risk SNP, because the common genetic mutation is the single Odisha, for providing necessary facilities, as well as we present our nucleotide polymorphism in human and about 93% SNPs are heartily gratitude to Dr. Shovan Kumar Mishra, Director of TBRC, for present in human genes and nsSNPs have more effect on proteins his endless support throughout this work. [22], so this search used dbSNP of NCBI [23] to collect the nsSNPs for 6. References selected target genes of Autism, as dbSNP includes disease-causing [1]. Chow ML, Pramparo T, Winn ME, Barnes CC, Li HR, Weiss L. et al. Age- clinical mutations as well as neutral polymorphisms, [24]. 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