Bioscience Biotechnology Research Communications

An Open Access International Journal

P-ISSN: 0974-6455 E-ISSN: 2321-4007

Bioscience Biotechnology Research Communications

An Open Access International Journal

Sowmya Dhawan1* and Usha Chouhan2

Department of Bioinformatics, Maulana Azad National Institute of Technology, Bhopal, M.P. 452051, India

Article Publishing History

Received: 11/08/2016

Accepted After Revision: 20/09/2016

ABSTRACT:

Presence of genetic variations is a key player among many others which affect susceptibility and progression of the disease. Single nucleotide polymorphisms are the most frequent variations in human genome. Ubiquitin carboxyl- terminal esterase L1 (UCHL1) located on chromosome 4p14 is one of the potential candidate neuropathogenic protein involved in Parkinson’s Disease. The aim of this study was to investigate the functional consequences of UCHL 1 single nucleotide polymorphisms (SNPs) to understand the biological basis of complex traits and diseases as the Genetics of human phenotypic variation could be understood by knowing the functions of SNPs derived from the data available in dbsSNP data base and different computer applications are used. Nonsynymous SNPs are relevant in many of the human inherited disease since they change the aminoacid sequence of the protein. Few common single –nucleotide polymorphisms (SNPs) of the UCHL1 genes were analyzed by using different bioinformatics tools based on evolutionary analysis- sequence homology based, structure based approach. Protein structural analysis was also performed by using I- Mutant. It was recognized that rs6063 and rs74315205 SNPs of UCHL1 gene were found to be more damaging in PD and is responsible for the alteration in the levels of expression. Conclusion: It has been concluded that among the entire SNPs of UCHL1 gene, the mutation in rs6063 and rs74315205 have the most significant effect on functional variation. The study suggested that G191R, G199 R, G88R and R231G variants of UCHL1 could directly or indirectly destabilize the amino acid interactions and hydrogen bond networks thus explaining the functional deviations of protein to some extent. These results may further form the basis of large- scale population based association studies.

KEYWORDS:

Parkinson’s Disease, Single Nucleotide Polymorhisms, Snp, Uchl 1 Gene

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Dhawan S, Chouhan U. Computational Analysis of Polymorphisms of Ubiquitin Carboxyl–Terminal Esterase L1 (UCHL1) Gene Involved in Parkinson’s Disease. Biosc.Biotech.Res.Comm. 2016;9(3).


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Dhawan S, Chouhan U. Computational Analysis of Polymorphisms of Ubiquitin Carboxyl–Terminal Esterase L1 (UCHL1) Gene Involved in Parkinson’s Disease. Biosc.Biotech.Res.Comm. 2016;9(3). Available from: https://bit.ly/2JgtsPV


Introduction

Parkinson’s disease (PD) is the second most common neurodegenerative disorder after Alzheimer’s affecting approximately 1–2% of the population over the age of 65 and reaching a prevalence of almost 4% in those aged above 85. Resting tremor, bradykinesia, rigidity, and postural instability are the main clinical symptoms of the disease often accompanied by nonmotor symptoms including autonomic insufficiency, cognitive impairment, and sleep disorders (Gómez et al. 2015).

There are two forms of the disease, the sporadic and familial forms. The patients with familial PD are distinguished from the ones who suffer from sporadic PD because of the early onset, greater consanguinity rate, and greater frequency of similar disease in their parents Familial PD cases are of 10% of the total no of cases and are based on the genetic component of the disease (Christine and Ana 2012). There is a life risk of 1.3% for women and 2% for men as per the study of Olmstead country. The disease is going to increase in the future to come due to the medical expenses and other reasons (Prasad et al. 2016).

UCHL1 /Park 5 gene is a compelling candidate gene for PD (Maraganore et al. 2004) on biological grounds because the protein it encodes plays a pivotal role in the ubiquitin proteasome system (UPS), displays neuron- specific expression and is found in Lewy bodies, the neuropathologic hallmark of PD .The ubiquitin proteasome system regulates the degradation of key regulatory proteins as well as misfolded and damaged proteins (Aaron & Yong 2014). Ubiquitin carboxy-terminal hydrolase L1 (UCHL1) is a 223-a.a. protein which is a component of the UPS, which cleaves the carboxy-terminal peptide bond of polyubiquitine chains, working as a deubiquitinating enzyme (Liu et al. 2002). It encodes for one of the most abundant proteins in the brain. Mutations in this target were found to be responsible for a genetic form of PD. It is thought a mutation at amino acid position 93 for methionine may decrease UCHL1 hydrolase activity, leading to accumulation of proteins that should have been degraded, and subsequently the progression of PD (Contu et al. 2014).

One of the important gene specific mutations described for the familial forms of PD, include autosomal dominant mutations of UCHL1 (PARK5). However, the pathogenic mechanisms underlying mitochondrial dysfunction in familial PD require further detailed investigation at the molecular level. The loss of dopaminergic neurons in PD is preceded by the formation of Lewy Bodies, insoluble proteinaceous inclusions enriched with ubiquitinated aggregates, as well as displaying extensive protein oxidative modification, (Hyo and Sun 2015).

The structure of UCH-L1 contains a central â-sheet that is flanked on either side by á-helices as shown in figure 1. In the crystal structure, UCH-L1 is an asymmetric dimer; however, equilibrium sedimentation analysis showed that the protein is monomeric in solution. The catalytic triad comprises Cys90, His161, and Asp176; in the crystal structure, the side chains of these residues are not close enough for catalytic activity, suggesting that in the absence of substrate, UCH-L1 is in an inactive form. In addition, the active site is covered by a loop (L8) that has been suggested to restrict the size of substrates that can access the active-site cleft ( Bishop et al. 2016).

Figure 1: Schematic of UCH-L1 structure, Schematic illustrating the á-helical and â-strand structure of UCH-L1. The residues 1–11 at the N-terminus, 220–223 at the C-terminus and residues Ile93 and Cys152 are highlighted. It has been proposed that modification at these points can affect the hydrophobic core of â-strands that are otherwise protected from solution. Figure 1: Schematic of UCH-L1 structure, Schematic illustrating the á-helical and â-strand structure of UCH-L1. The residues 1–11 at the N-terminus, 220–223 at the C-terminus and residues Ile93 and Cys152 are highlighted. It has been proposed that modification at these points can affect the hydrophobic core of â-strands that are otherwise protected from solution.

A single nucleotide polymorphism (SNP) is a source of variance in a genome. A SNP is a single base mutation in DNA. SNPs are the most simple form and most common source of individual genetic polymorphism in the human genome (90% of human DNA polymorphisms). A SNP in a coding region may have two different effects on the resulting protein: Synonymous, the substitution causes no amino acid change to the protein it produces; non synonymous, the substitution results in an alteration of the encoded amino acid. One half of all coding sequence SNPs result in non- synonymous codon changes (Smith 2002). A non- synonymous single nucleotide polymorphism (nsSNP) occurring in a coding gene may cause an amino acid substitution in the corresponding protein product, thus affecting the phenotype of the host organism .Non synonymous variants constitute more than 50% of the mutations known to be involved in human inherited diseases Single nucleotide polymorphisms (SNPs) (Kumar 2009). Computational methods are sufficiently fast and flexible to provide reliable predictions of functionally significant SNPs with a high accuracy of 80–85% when combined with sequence, structure, and phylogenetic relationships (Minyue et al., 2014). Here we are trying to consider computationally a suitable protocol for missense mutation (point mutation/single amino acid polymorphism) analysis before wet lab experimentation and provided an optimal path for further clinical and experimental studies.

Material And Methods

The data on protein sequence and variants (single amino acid polymorphisms/missense mutations/point mutations) for UCHL1 gene were collected from NCBI database (http://www.ncbi.nlm.nih.gov/snp/)of SNP by applying appropriate limits like homo-sapiens, Chromosome 4, cited in Pubmed etc. to detect the detrimental point mutants.

Further deleterious SNP analysis were performed using the computational tools sorting intolerant from tolerant (SIFT) and Polyphen 2 for nsSNPs and FASTSNP and UTRscan for UTR SNPs.

Sequence Homology Based Method (SIFT)

We have used the program SIFT (http://sift.bii.a-star.edu.sg/index.html) to detect deleterious coding nonsynonymous SNPs. SIFT is a sequence homology-based tool to predict whether an amino acid substitution in a protein would be tolerated or damaging (Pauline et al., 2003). We performed SIFT by submitting the query in the form of SNP IDs or chromosome positions and alleles in nsSNVs tool. Variants at the position with tolerance index score #0.05 were considered as deleterious. A lower tolerance index indicates that the particular amino acid substitution likely has a more functional impact (Pauline et al., 2001).

Structure Homology Based Method (Polyphen)

Analyzing the damaged coding nonsynonymous SNPs at the structural level is considered to be very important to understand the functional activity of the protein of concern. We have used PolyPhen server (http://genetics.bwh.harvard.edu/pph2/) for this purpose. This is an automatic tool that predicts the possible impact of an amino acid substitution on a number of features, including the sequence, phylogenetic, and structural information. The query was submitted in the form of protein sequence with mutational position and substitution. The PolyPhen output comprises a score that ranges from 0 to 1, with zero indicating a neutral effect of amino acid substitutions on protein function. Conversely, a high score represents a variant that is more likely to be damaging (Ramensky et al., 2002).

Functional Significance Of Noncoding Snps In Regulatory Untranslated Regions

The Web server FastSNP (http://fastsnp.ibms.sinica.edu.tw) was used for predicting the functional significance of the 5’ and 3’ UTRs of the UCHL 1 gene (Hsiang et al., 2006). The FastSNP server follows the decision tree principle with external Web service access to TF Search, which predicts whether a noncoding SNP alters the transcription factor-binding site of a gene. The score was given by this server on the basis of levels of risk with a ranking of 0, 1, 2, 3, 4, or 5. This signifies the levels of no, very low, low, medium, high, and very high effect, respectively.

Scanning Of Utr Snps In Utr Site

The 5’ and 3’ UTRs are involved in various biological processes such as posttranscriptional regulatory pathways, stability, and translational efficiency. We used the program UTRscan (http://itbtools.ba.itb.cnr.it/utrscan) which allows one to search the user-submitted sequences for any of the patterns collected in the UTR site (Graziano and Sabino 1999). UTRsite is a collection of functional sequence patterns located in 5’ or 3’ UTR sequences. Briefly, two or three sequences of each UTR SNP that have a different nucleotide at an SNP position are analyzed by UTRscan, which looks for UTR functional elements by searching through user-submitted sequence data for the patterns defined in the UTRsite and UTR databases. If different sequences for each UTR SNP are found to have different functional patterns, this UTR SNP is predicted to have functional significance. The Internet resources for UTR analysis are UTRdb and UTRsite. UTRdb contains experimentally proven biological activity of functional patterns of UTR sequence from eukaryotic mRNAs (Graziano et al., 2002). The UTRsite has the data collected from UTRdb and also is continuously enriched with new functional patterns.

Support Vector Machine (I-Mutant 3.0 And Fold- X)

The analyses were also conducted by using I-Mutant Suite is a suite of support vector machine (SVM)- based predictors of protein stability changes according to Gibbs free energy change, enthalpy change, heat capacity change, and transition temperature (Capriotti et al., 2005).The analysis was performed based on protein sequence combined with mutational position and correlated new residue. And the output result of the predicted free energy change (DDG) classifies the prediction into one of three classes: largely unstable (DDG, 20.5 kcal/mol), largely stable (DDG.0.5 kcal/mol), or neutral (-0.5# DDG#0.5 kcal/mol). IMutant Suite is available at (http://gpcr2.biocomp.unibo.it/cgi/predictors/I-Mutant3.0/I-Mutant3.0.cgi).

The FASTA sequence of protein retrieved from UniProt was used as an input to predict the mutational effect on protein stability. I-Mutant also provides the scores for free energy alterations, calculated with the FOLD-X energy based web server (Schymkowitz et al., 2005). FOLD-X is a computer algorithm for quantitative estimation of interactions facilitating the stability of proteins. The FOLD-X tool was used to provide the comparison between wild type and mutant models in the form of van der Waals clashes, which greatly influence the energy decomposition.

Results And Discussion

Single Amino Acid Polymorphism Dataset From Ncbi Dbsnp Database

The dbSNP database contains both validated and non-validated polymorphisms. In spite of this drawback, we opted to avail the dbSNP because the allelic frequency of most of nsSNPs of UCHL 1 has been recorded there and that is the most extensive SNP database. We selected 15 SNPs, out of which 2 were nsSNPs, as shown in Fig. 2.

Figure 2: A graphical representation of distribution of nonsynonymous, SNPs for UCHL 1 (based on the dbSNP database). Figure 2: A graphical representation of distribution of nonsynonymous, SNPs for UCHL 1 (based on the dbSNP database).


Deleterious Single Point Mutants Identified By The Sift Program

The conservation level of a particular position in a protein was determined by using a sequence homology-based tool, SIFT. The protein sequences of 64 variants were submitted independently to the SIFT program to determine the tolerance index. The higher the tolerance index, the less functional impact a particular amino acid substitution is likely to have, and vice versa. Among the 64variants, 24 were found to be deleterious, having a tolerance index score of ≤0.05. The results are shown in Table 2.

Table 1: SIFT classification
Ranking Risk Division
0 No effect
1 Very low
2 Low
3 Medium
4 High
5 Very high
Table 2: SIFT analysis of SNPs, Variants with tolerance index ≤0.05 score are considered as deleterious while others are taken to be tolerant.
SNPs Amino acid Change Score Prediction
rs6063 G191R 0.002 DELETERIOUS
G191R 0.002 DELETERIOUS
G199R 0.002 DELETERIOUS
G199R 0.002 DELETERIOUS
G88R 0.003 DELETERIOUS
G88R 0.003 DELETERIOUS
rs1799895 R231G 0.017 DELETERIOUS
rs45454496 E3931K 0.037 DELETERIOUS
E3898K 0.037 DELETERIOUS
E941K 0.055 TOLERATED
E22K 0.057 TOLERATED
E1022K 0.147 DELETERIOUS
E1837K 0.186 TOLERATED
E1846K 0.188 TOLERATED
E529K 0.233 TOLERATED
E878K 0.368 TOLERATED
rs62625014 S389F 0.054 TOLERATED
S320F 0.101 TOLERATED
S320F 0.101 TOLERATED
S320F 0.101 TOLERATED
S320F 0.101 TOLERATED
rs63749888 E47Q 0.102 TOLERATED
E37Q 0.248 TOLERATED
rs66785829 V3601D 0.011 DELETERIOUS
V3634D 0.012 DELETERIOUS
V644D 0.063 TOLERATED
V201D 0.067 TOLERATED
V725D 0.193 TOLERATED
V550D 0.209 TOLERATED
V1549D 0.28 TOLERATED
V1540D 0.282 TOLERATED
rs74315205 E864K 0 DELETERIOUS
rs75353611 D25V 0.003 DELETERIOUS
D25V 0.003 DELETERIOUS
D27V 0.003 DELETERIOUS
D25V 0.003 DELETERIOUS
D25V 0.027 DELETERIOUS
rs112534524 G261A 0.27 TOLERATED
G261A 0.274 TOLERATED
G261A 0.277 TOLERATED
G261D 0.114 TOLERATED
G261D 0.119 TOLERATED
G261D 0.126 TOLERATED
rs121912705 T754N 0.039 DELETERIOUS
T3744N 0.053 TOLERATED
T3711N 0.055 TOLERATED
T311N 0.302 TOLERATED
T660N 0.386 TOLERATED
T835N 0.45 TOLERATED
T1659N 0.535 TOLERATED
T1650N 0.556 TOLERATED
rs121912706 R3873W 0.001 DELETERIOUS
R3906W 0.001 DELETERIOUS
R916W 0.002 DELETERIOUS
R1821W 0.003 DELETERIOUS
R997W 0.003 DELETERIOUS
R1812W 0.004 DELETERIOUS
R853W 0.049 DELETERIOUS
R504W 0.06 TOLERATED
rs180843436 E137K 0.014 DELETERIOUS
E486K 0.015 DELETERIOUS
E3537K 0.021 DELETERIOUS
E3570K 0.021 DELETERIOUS
E580K 0.06 TOLERATED
E661K 0.061 TOLERATED
E1485K 0.062 TOLERATED
E1476K 0.063 TOLERATED
rs199473343 L1622M 0.168 TOLERATED
L1655M 0.169 TOLERATED
T854N 0.105 TOLERATED
T3844N 0.147 TOLERATED
T3811N 0.148 TOLERATED
T411N 0.267 TOLERATED
T935N 0.356 TOLERATED
T1759N 0.432 TOLERATED
T1750N 0.434 TOLERATED
T760N 0.476 TOLERATED
rs386833750 CC2D2A 0 DELETERIOUS
rs386833752 T1065M 0.001 DELETERIOUS
T1114M 0.001 DELETERIOUS


Utrscan Analysis

Functional SNPs in UTR found by the UTRscan server Polymorphisms in the 3’ UTR affect gene expression by affecting the ribosomal translation of mRNA or by influencing the RNA half-life. Table 3 shows the list of SNPs in the 3 that are predicted to be damaging because of the presence of regulatory elements and are of functional significance. We used the UTRscan server for this purpose. We analyzed the same 64 variants in UTRscan that were analyzed by the SIFT. The UTRscan server finds patterns of regulatory region motifs from the UTR database and gives information about whether the matched pattern is damaged. Various studies have shown that the transcriptional regulation is biologically important and the alteration in the transcriptional compo-nents leads to disease.

Table 3: UTRScan analysis of the SNPs where Uorf – Upstream open reading frame.IRES- Internal ribosome entry site, MBE-Mushashi Binding site
SL.No SNPs No of signal Matches Regulatory Elements
1 rs6063  4 uORF MBE GY-BOX  ARE2
2 rs6533526  4 uORF MBE IRES K-BOX
3 rs62625014  4 uORF MBE IRES BRD-BOX
4 rs35530544  1 uORF
5 rs36210415  1 uORF
6 rs45570339  2 uORF MBE
7 rs63749888  4 uORF MBE IRES PAS
8 rs66785829  4 uORF MBE IRES  SXL
9 rs72544141  3 uORF MBE PAS
10 rs72556370  2 uORF  PAS
11 rs74315205  2 uORF IRES
12 rs74821926  4 uORF MBE IRES PAS
13 rs75353611  4 uORF MBE IRES PAS
14 rs77335374  3 uORF MBE IRES
15 rs77408163  4 uORF MBE IRES PAS
16 rs77449454  4 uORF MBE IRES GY-BOX
17 rs79228041  5 uORF MBE ADH_DRE SXL_BS GY-BOX
18 rs112534524  2 uORF MBE
19 rs121912705  1 uORF
20 rs121912706  2 uORF IRES
21 rs121913101  3 uORF IRES DMRT1_RE
22 rs121913103  2 uORF  DMRT1_RE
23 rs121913105  1 uORF
24 rs121918124  1 uORF
25 rs121918125  1 uORF
26 rs121965070  1 uORF
27 rs140126678  2 uORF MBE  PAS
28 rs143228029  4 uORF MBE IRES SXL_BS
29 rs148654834  3 uORF MBE PAS
30 rs148654834  3 uORF MBE PAS
31 rs199473643  1 MBE
32 rs202247811  1  IRES
33 rs386833750  1 uORF
34 rs386833751  1 IRES
35 rs386833752  1 IRES
36 rs386833757  2 TOP IRES
37 rs386833760  2 MBE IRES
38 rs386833761  2 MBE IRES
39 rs587778769  1 IRES
40 rs587778773  1 IRES
41 rs587778775  2 TOP IRES
42 rs587778776  1 IRES
43 rs587778801  1 IRES
44 rs587778809  3 uORF IRES  PAS
45 rs587778811  1 IRES
46 rs796051882  1 BRD-BOX  


Damaging Single Point Mutations Identified By The Polyphen Server

The structural levels of alteration were determined by applying the PolyPhen program.64 protein sequences of nsSNPs investigated in this work were submitted as input to the PolyPhen server and the results are shown in Table 4. A PSIC score difference of 0.5 and above was considered to be damaging. we could infer that the results obtained on the basis of sequence details (SIFT) were in good correlation with the results obtained for structural details (PolyPhen), as can be seen from Tables 2 and 4. Interestingly, some of the deleterious variants identified by SIFT also were seen to be less stable by the Polyphen server. It is predicted that the rs6063 mutation effect is the damaging one among the SNPs identified. Hence the mutations occurring with this nsSNP would be of prime importance in the identification of UCHL 1 induced Parkinson’s disease according to SIFT and PolyPhen results.

Table 4: PolyPhen analysis
SNP Mutation effect Scoring
rs6063 Probably Damaging 1
rs1799895 Bengin 0.067


Functional Snps In Utr Found By The Fastsnp Server

By the use of Fast SNP server functionally significant variants were predicted as shown in table 5. According to this server, the functional information derived about rs6063 predicted it as damaging with a score of 0.741. Studies show that SNPs have functional effects on protein structure by a single change in the amino acid (Cargill et al., 1999 & Sunyaev et al., 2000) and on transcriptional regulation ( Prokunina et al., 2002 & Prokunina et al., 2004).

Table 5: Fast SNP analysis
Functional Category Prediction Tool Prediction Result Prediction Detail
protein coding PolyPhen probably damaging rs6063.html
SIFT damaging rs6063.html
SNPeffect deleterious rs6063.1.html
LS-SNP deleterious destabilizing.html
destabilizing.html
SNPs3D deleterious SNPs3D.html
Ensembl-NS nonsynonymous rs6063.html
splicing_regulation ESEfinder changed rs6063.A.html
rs6063.G.html
ESRSearch changed rs6063.A.html


Structural Analysis Of Mutant Structures

Out of all the above methods the SNPs predicted to be deleterious i.e., rs6063 and rs74315205 were mapped to the native structure by I mutant 2.0 server to understand its structural stability.

Prediction Of Protein Structural Stability

I-Mutant is a neural network based routine tool used in the analysis of protein stability alterations by considering the single-site mutation. I-Mutant also provides the scores for free energy alterations, calculated with the FOLD-X energy based web server. By assimilating the FOLD-X estimations with those of I-Mutant, the 93% precision can achieved. The mutations of UCHL 1 gene have been selected on the basis of prediction scores of Poly Phen. These variants were given to I-Mutant web server to predict the DDG stability and reliability index (RI) upon mutation. Out of the 4 variants 2 were found to be less stable as shown in Table 6.

Table 6: Protein structural stability based on standard free energy change Where, “WT” is the amino acid in native protein, “New” is mutant amino acid and DDG is the stability (DDG b 0: decrease stability, DDG N 0: increase stability).
Mutation Position WT New PH Temperature Stability DDG
G191R 191 G R 7.0 25 Decrease -0.25
G199R 199 G R 7.0 25 Decrease -0.83
G88R 88 G R 7.0 25 Increase 0.38
R231 G 231 R G 7.0 25 Increase 0.48


Rational Consideration Of Sift, Utr Scan, Polyphen-2, Fast Snp And I-Mutant 3.0

We considered the 64 most potential hindering point changes for further course of examinations in light of the fact that they were generally discovered to be less steady, injurious, and harming by the I-Mutant 3.0, SIFT and Poly Phen-2 servers individually. The most commonly affected among the 6 computational tools has been taken for further studies i.e. 2 variants as shown in Figure 3.

Figure 3: Status of SNPS derived through various algorithms Figure 3: Status of SNPS derived through various algorithms


Conclusion

Hence the combined approach using SIFT, UTRscan and Polyphen 2 predicts the mutation rs6063 and rs74315205 are most deleterious among the mutations for UCHL1 gene causing Parkinson’s disease characterized. The recognition of these SNPs as deleterious ones provides insight into PD biology and presents as anti Parkinson’s disease therapeutic targets and diagnostic markers .Since missense mutations are nucleotide substitutions that change an amino acid in a protein, the deleterious effects of these mutations are commonly attributed to their impact on primary amino acid sequence and protein structure. Structural analysis results showed that the amino acid residue substitutions which had the greatest impact on the stability of the UCHL 1 protein were mutations in rs6063 and rs74315205 and the variants like G191R, G199 R, G88R and R231G. Based on our results, we conclude that these SNPs should be considered important candidates in UCHL1 related P.D. Based on our results we conclude that these SNPs should be considered as important candidates in causing Parkinson’s disease.

Acknowledgement

The authors are highly thankful to the Department of Biotechnology, Delhi, India for providing support in the form of Bioinformatics infrastructure facility to carry out this work.

Disclosure Statement

No competing financial interests exist.

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