Bioscience Biotechnology Research Communications

An Open Access International Journal

Bioscience Biotechnology Research Communications

An Open Access International Journal

Anas Hussain Maly Majeed, Hani Mohammed Ali, Yasir Anwar Ihsan Ullah and Ahmad Firoz*  

Department of Biological Science, Faculty of Science, King Abdulaziz University, Jeddah, Kingdom of Saudi Arabia

Corresponding author email: ahmadfirozbin@gmail.com

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ABSTRACT:

The proprotein convertases (PCs) are involved in variety of cellular precursors in the secretory pathway. Polymorphisms in proprotein convertase subtilisin/kexin type 1 (PCSK1) have been associated with adult and childhood obesity. In this work non synonymous SNPs of the PCSK1 gene were retrieved from the dbSNP database. In order to predict the damaging or deleterious nsSNPs, multiple consensus tools were employed by using online tool VEP. Further we also employed SNP-GO tools to predict pathogenic nonsynonymous SNPs. Mutants  like D176Y, E345A, G228V, G308E, G310R, G440E, G442R, R110C, S382L, W130S and W404R  have shown deleterious and highest pathogenicity. These predicted deleterious and pathogenic nsSNPs are expected to have impending functional influence and may contribute in understanding the functional roles of PCSK1 gene associated with obesity.

KEYWORDS:

 nsSNP, Proprotein convertase subtilisin/kexin type 1, Neuroendocrine convertase 1, In Silico Analysis, PCSK1

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Majeed. A. H. M, Ali H. M , Anwar. Y, Ullah. I, Firoz. A. In-Silico Analysis of Nonsynonymous Single Nucleotide Polymorphism in Human PCSK1 gene. Biosc.Biotech.Res.Comm. 2021;14(1).


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Majeed. A. H. M, Ali H. M , Anwar. Y, Ullah. I, Firoz. A. In-Silico Analysis of Nonsynonymous Single Nucleotide Polymorphism in Human PCSK1 gene. Biosc.Biotech.Res.Comm. 2021;14(1). Available from: <a href=”https://bit.ly/3s2zAiL“>https://bit.ly/3s2zAiL</a>


INTRODUCTION

An obesity increasing worldwide and polymorphisms in proprotein convertase subtilisin/kexin type 1 (PCSK1) gene have been associated with adult and childhood obesity. Body mass index variation (risk of common obesity) is associated with more than 60 single-nucleotide polymorphisms (SNPs), identified by genome-wide association studies (Philippe  2015).  The proprotein convertases (PCs) are involved in variety of cellular precursors in the secretory pathway and due to homology of their catalytic domains to bacterial subtilisin and yeast kexin, the genes are known as subtilisin and kexin-like proprotein convertases (PCSKs) (Stijnen, 2016 Loffler , 2016).  Human PCSK1 gene consists of 14 exons located on chromosome 5 (Ramos-Molina, 2016), and its promoter contains transcriptional elements CRE-1 and CRE-2 which can be transactivated by CREB-1 and ATF1 transcription factors (Espinosa, 2008; Stijnen,2016).

Analysis of human tissues and cells revealed the presence of a dominant transcript and the major sites of expression being endocrine pancreas, pituitary and brain (Stijnen, 2016). 71% of PCSK1 variant were located in coding region of the catalytic domain and 21% are located on the P domain (Akinci 2019). Many studies show a strong evidence about rs6232 and rs6235 involving with obesity (Jackson ,2003). association with three variants are found in PCSK1 gene rs6232 encoding by N221D substitution involve in reduce the activity of PC1/3 while rs234 encodes by Q665E compatible with rs6235 that encodes by S690T are essential to form a linkage between PC1/3 and its sorting in secretory granules (Stijnen, 2016; Frank 2013),

These variants have a significantly role in reducing the level of plasma glucose rapidly and increase serum insulin levels causing a hyperglycemia as type 2 diabetes (T2D) by increasing in glocuse production, insulin resistance and a dysfunction in β cell that found in pancreatic cells (Gjesing, 2011), along with the effect of blood pressure and energy ratio, causing a hypertension in the blood vessels which lead to a cardiovascular (Heni, 2010 Pepin et al 2019).The R405X mutation cause a deletion of P and C-terminal tail domain (Bandsma, 2013).

Identified N309K a deleterious in PCSK1 gene which make C-terminal domain incapable of cleave in intermolecular interaction (Wilschanski, 2014). K26E is located before the signal peptide cleavage site, M125I, T175M, N180S, Y181H, G226R and S325N are located in the catalytic domain and the T558A is located in the middle domain. These mutations have an impact on PC1/3 folding and its stability. also, G209R and G593R mutation might affect on the PC1/3 misfolding due to their enzymatic activation (Blanco, 2015).In addition, T175M was defined as induce the inhibition in N-glycosylation site which is responsible for cellular signal and altering the protein maturation (Creemers, 2012 Pepin et al 2019).

MATERIAL AND METHODS

Datasets:The SNPs of the PCSK1 gene were retrieved from the dbSNP database (Sherry, 2001). Keyword “Human PCSK1” used as our search term. Furthermore, it is filtered by selecting variation class as SNV, function class as missense. The protein sequences (P29120) were retrieved from the UniProt (https://www.uniprot.org)

Predicting deleterious and damaging nsSNPs: In order to predict the damaging or deleterious nsSNPs, multiple consensus tools were employed by using online tool VEP (http://www.ensembl.org/Tools/VEP). VEP advantages include: it uses latest human genome assembly GRCh38.p10, and can predict thousands of SNPs from multiple tools including SIFT, PROVEAN, Condel, and PolyPhen-2, at a time. nsSNP rs-ids were uploaded to VEP tool to get the prediction results

SIFT: The algorithm predicted that the tolerant and intolerant coding base substitution based upon properties of amino acids and homology of sequence (Choi Y, 2015). The tool considered that vital positions in the protein sequence have been conserved throughout evolution and therefore substitutions at conserved alignment position is expected to be less tolerated and affect protein function than those at diverse positions., SIFT predicted substituted amino acid as damaging at default threshold score <0.05, while score ³  0.05 is predicted as tolerated.

PolyPhen-2: This tool is predicting the structural and functional consequences of a particular amino acid substitution in human protein (Adzhubei, 2010). Prediction of PolyPhen-2 is based on a number of features including information of structural and sequence comparison. The PolyPhen-2 score varies between 0.0 (benign) to 10.0 (damaging). The PolyPhen-2 prediction output categorizes the SNPs into three basic categories, benign (score < 0.2), possibly damaging, (score between 0.2 and0.96), or probably damaging (score >0.96).

Provean: This tool (http://provean.jcvi.org/) uses an alignment-based scoring method for predicting the functional consequences of single and multiple amino acid substitutions, and in-frame  deletions and insertions (Choi, 2015). The tool has a default threshold score, i.e. -2.5, below which a protein variant is predicted as deleterious, and above that threshold, a protein variant is neutral.

CONDEL (CONsensus DELeteriousness): This tool evaluates the probability of missense single nucleotide variants (SNVs) deleterious. it computes a weighted average of the scores of SIFT, PolyPhen2, MutationAssessor and FatHMM (Hecht , 2015). 

Predicting disease associated nsSNPs

SNPs &GO: A web server predicting whether an amino acid substitution is associated to a disease or not (http://snps.biofold.org/snps-and-go) (). It is a SVM (Support Vector Machine) based tool which takes features of protein sequence, evolutionary information, and functional annotation according to Gene Ontology terms. We input isoform 1 of Swiss-Prot Code of LSP1 (P33241) and provided the list of amino acid mutations. The results predicted the probability for the polymorphisms of helicase whether being disease- associated or not by three methods: (a) SNPs&GO, (b) PhD-SNP, and (c) PANTHER. Probability score >0.5 is predicted as disease associated variation (Calabrese , 2015).

RESULTS AND DISCUSSION

473 nsSNP ids of human PCSK1 gene was downloaded from dbSNP database of NCBI (Supplementary Table 1), after filtering variation class SNV, function class missense, there were 473 SNP mapped to missense, 226 SNPs mapped to synonymous and 6 SNPs mapped to inframe deletion, while 11135 mapped to total SNPs of different variation class (Figure 1).

Figure 1: Number of SNPs in different function class of PCSK1 gene of human from dbSNP database 

Predicting deleterious and damaging pathogenic nsSNPs: In order to predict the damaging or deleterious nsSNPs multiple consensus tools were employed. Initially, online tool VEP was used (McLaren, 2016). VEP advantages include: it uses latest human genome assembly GRCh38.p10, and can predict thousands of SNPs from multiple tools including SIFT, Condel, and PolyPhen-2, at a time. 473 nsSNP rsids were uploaded to VEP tool and the prediction results were taken on default scores of consensus tools based on sequence and structure homology methods:  (a) SIFT (score <-0.5) (b) Polyphen (score >0.96) (c) PROVEAN (score< 2.5) and Condel (score >0.522).

In order to get a very high confident nsSNPs impacting structure and function of PCSK1 gene, 46 nsSNPs out of 473 nsSNP (Table 1) were found to be deleterious by all four tools and predicted disease by panther tools, and these eleven nsSNPs rs552958813 of mutation D176Y, rs864309557, of mutation E345A, rs747169606 of mutation G228V, rs990328651 of mutation G308E, rs748808191 of mutation G310R, rs865777271 of mutation G440E, rs761336991 of mutation G442R, rs774036542 of mutation R110C, rs1561368007 of mutation S382L, rs1434467255 of mutation W130S and rs1180593976 of  mutation W404R were predicted highly pathogenic with more than 9 RI score (Table-1).

Table 1. 46 Predicted deleterious and pathogenic missense SNPs of PCSK1 gene using prediction tools such as SIFT, Condel, Polyphen and PROVEAN and PANTHER.

SNP-ids AA-change SIFT (score) PolyPhen (score) Condel score) PROVEAN (score) PANTHER Prediction RI
rs759379849 D193G *(0) #(0.999) *(0.935) * (0.92173) Disease 8
rs1561374455 D195G *(0) #(0.96) *(0.848) *(0.91956) Disease 8
rs752416942 D272G *(0) #(1) *(0.945) *(0.93175) Disease 8
rs749888385 T353I *(0.02) #(0.967) *(0.792) *(0.78636) Disease 8
rs762403860 A213V *(0) #(0.998) *(0.919) *(0.71639) Disease 9
rs1475050973 C212R *(0) #(1) *(0.945) *(0.99425) Disease 9
rs552958813 D176N *(0) #(1) *(0.945) *(0.80172) Disease 9
rs752416942 D272V *(0) #(1) *(0.945) *(0.9783) Disease 9
rs1363728113 G155D *(0) #(0.98) *(0.869) *(0.90023) Disease 9
rs1382566997 G155S *(0) #(0.986) *(0.879) *(0.83899) Disease 9
rs1490377137 G158A *(0) #(0.999) *(0.935) *(0.873) Disease 9
rs768031892 G209R *(0) #(1) *(0.945) *(0.95336) Disease 9
rs142673134 G279A *(0.04) #(0.959) *(0.752) *(0.86296) Disease 9
rs1312543959 G298A *(0) #(0.999) *(0.935) *(0.87223) Disease 9
rs778681269 G311R *(0) #(1) *(0.945) *(0.95276) Disease 9
rs567641208 G390S *(0) #(0.999) *(0.935) *(0.88839) Disease 9
rs1389330621 N180K *(0) #(0.999) *(0.935) *(0.85994) Disease 9
rs1269967613 N429K *(0) #(0.994) *(0.897) *(0.87835) Disease 9
rs1246203022 P280S *(0) #(1) *(0.945) *(0.96058) Disease 9
rs775618000 P341L *(0) #(1) *(0.945) *(0.98437) Disease 9
rs775136858 P386L *(0) #(0.998) *(0.919) *(0.98692) Disease 9
rs1332430207 Q408R *(0) #(1) *(0.945) *(0.73267) Disease 9
rs748072514 R110H *(0) #(0.999) *(0.935) *(0.76822) Disease 9
rs768934109 R296I *(0) #(1) *(0.945) *(0.95246) Disease 9
rs1421014042 S186N *(0) #(0.996) *(0.906) *(0.59873) Disease 9
rs137852824 S307L *(0) #(0.999) *(0.935) *(0.86222) Disease 9
rs1166018774 T210S *(0) #(0.999) *(0.935) *(0.71762) Disease 9
rs1303515025 T276I *(0) #(0.996) *(0.906) *(0.84742) Disease 9
rs766414747 T375K *(0) #(0.998) *(0.919) *(0.88839) Disease 9
rs766414747 T375M *(0) #(0.993) *(0.895) *(0.88839) Disease 9
rs1346360455 T381I *(0) #(1) *(0.945) *(0.88839) Disease 9
rs1434467255 W130L *(0) #(1) *(0.945) *(0.99433) Disease 9
rs868424536 W152L *(0) #(0.985) *(0.877) *(0.99023) Disease 9
rs1245583638 W342G *(0) #(0.998) *(0.919) *(0.99704) Disease 9
rs1246742230 W98R *(0.02) #(0.994) *(0.835) *(0.99587) Disease 9
rs552958813 D176Y *(0) #(1) *(0.945) *(0.97364) Disease 10
rs864309557 E345A *(0) #(1) *(0.945) *(0.873) Disease 10
rs747169606 G228V *(0) #(1) *(0.945) *(0.97617) Disease 10
rs990328651 G308E *(0) #(1) *(0.945) *(0.95246) Disease 10
rs748808191 G310R *(0) #(1) *(0.945) *(0.95276) Disease 10
rs865777271 G440E *(0) #(1) *(0.945) *(0.95665) Disease 10
rs761336991 G442R *(0) #(0.999) *(0.935) *(0.95665) Disease 10
rs774036542 R110C *(0) #(1) *(0.945) *(0.93582) Disease 10
rs1561368007 S382L *(0) #(0.998) *(0.919) *(0.88839) Disease 10
rs1434467255 W130S *(0) #(1) *(0.945) *(0.99699) Disease 10
rs1180593976 W404R *(0) #(0.998) *(0.919) *(0.99969) Disease 10

 

(*Deleterious, #Probably Damaging)

Studies show a strong evidence about variants are found in PCSK1 gene involving with obesity,  association with variants N221D, S690T and Q665E substitutions found in PCSK1 gene involve in reduce the activity of PC1/3, linkage between PC1/3 and its sorting in secretory granules ( Jackson 2003, Stijnen  2016; Frank  2013), Identified N309K a deleterious in PCSK1 gene which make C-terminal domain incapable of cleave in intermolecular interaction (Wilschanski, 2014). K26E is located before the signal peptide cleavage site, M125I, T175M, N180S, Y181H, G226R and S325N are located in the catalytic domain and the T558A is located in the middle domain.

These mutations have an impact on PC1/3 folding and its stability. also, G209R and G593R mutation might affect on the PC1/3 misfolding due to their enzymatic activation (Blanco EH, 2015). In addition, T175M was defined as induce the inhibition in N-glycosylation site which is responsible for cellular signal and altering the protein maturation (Creemers, 2012). Pickett had proposed  that R80Q have the most influence part in PC1/3 maturation and its activity (Pickett, 2013). In another report, the S357G mutant that low the calcium dependence and highly resistance the peptide inhibitors (Blanco, 2015).

CONCLUSION

Our investigation shows mutants D176Y, E345A, G228V, G308E, G310R, G440E, G442R, R110C, S382L, W130S and W404R with deleterious and highest pathogenicity, and may offer valuable information in selecting SNPs that are expected to have impending functional influence and pathogenicity also eventually may contribute in understanding the functional roles of PCSK1 gene associated with obesity.

ACKNOWLEDGMENTS

This work was not supported by any funding agency. We acknowledge with thanks Bioinformatics and Computational Biology Unit at Department  of Biological Sciences King Abdulaziz University, Jeddah, KSA for providing their support and facilities.

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