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. 2015 Oct 5;5:14731. doi: 10.1038/srep14731

Phenotype Prediction of Pathogenic Nonsynonymous Single Nucleotide Polymorphisms in WFS1

Xuli Qian 1, Luyang Qin 1, Guangqian Xing 2, Xin Cao 1,a
PMCID: PMC4592972  PMID: 26435059

Abstract

Wolfram syndrome (WS) is a rare, progressive, neurodegenerative disorder that has an autosomal recessive pattern of inheritance. The gene for WS, wolfram syndrome 1 gene (WFS1), is located on human chromosome 4p16.1 and encodes a transmembrane protein. To date, approximately 230 mutations in WFS1 have been confirmed, in which nonsynonymous single nucleotide polymorphisms (nsSNPs) are the most common forms of genetic variation. Nonetheless, there is poor knowledge on the relationship between SNP genotype and phenotype in other nsSNPs of the WFS1 gene. Here, we analysed 395 nsSNPs associated with the WFS1 gene using different computational methods and identified 20 nsSNPs to be potentially pathogenic. Furthermore, to identify the amino acid distributions and significances of pathogenic nsSNPs in the protein of WFS1, its transmembrane domain was constructed by the TMHMM server, which suggested that mutations outside of the TMhelix could have more effects on protein function. The predicted pathogenic mutations for the nsSNPs of the WFS1 gene provide an excellent guide for screening pathogenic mutations.

Wolfram syndrome (WS) (MIM 222300), also known as DIDMOAD (diabetes insipidus, insulin-deficient diabetes mellitus, optic atrophy and deafness), is a rare neurodegenerative disorder of autosomal recessive inheritance, characterised by diabetes insipidus, insulin-deficient diabetes mellitus, optic atrophy and deafness. Of these symptoms, diabetes mellitus is the most common manifestation of WS with a median onset age of 6 years1 and always presents before the age of 162. The prevalence of WS is approximately 1/700,000 individuals in the UK, and 1/100,000 individuals in North America3. Since the first report for WS by Wolfram and Wagener in 19384, progressively more cases have been observed. Many studies have been performed to investigate the genetic basis of this hereditary disease and have identified that loss-of-function mutations in the WFS1 gene are the main cause of the syndrome5.

WFS1, located on human chromosome 4p16.1, is composed of eight exons, of which only the first exon is a noncoding exon, and most mutations in WFS1 have been identified in exon 8 but also in exons 3, 4, 5 and 66,7,8. WFS1 encodes the protein wolframin, which is abundantly expressed in pancreas, brain, heart, and muscle and is thought to be a novel endoplasmic reticulum (ER) calcium channel or a regulator of channel activity9,10. Additionally, wolframin appears to be involved in membrane trafficking, protein processing11, regulation of intracellular Ca2+ homeostasis12 and β-cell dysfunction13,14. Mutations in the WFS1 gene may result in instability and a significantly reduced half-life of wolframin in the endoplasmic reticulum and then may cause disease15.

To date, approximately 230 mutations in WFS1 have been reported (https://lovd.euro-wabb.org/home.php?select_db=WFS1). Although nsSNPs are the most common form of genetic variation in these mutations, the relationship between the genotype and phenotype of other nsSNPs in the WFS1 gene is unclear. Given the large number of nsSNPs in the WFS1 gene, it is expensive and time-consuming to experimentally explore the functional effects of these SNPs. The prediction of the phenotypic effects of nsSNPs based on different computational methods has become a well-known methodology16,17, and several research articles have cited its effectiveness in identifying deleterious, disease-related mutations18,19. In those methods, predicting pathogenic nsSNPs is based on identifying structural and functional damaging properties. This study will facilitate the investigation of the role of nsSNPs in WFS1 and identify pathogenic nsSNPs associated with the WFS1 gene based on different computational methods. Among these methods, the prediction of deleterious and damaging nsSNPs was performed by SIFT and PolyPhen-2. A support vector machine (SVM) along with the SIFT algorithm, PhD-SNP and MutPred were used to detect disease-associated nsSNPs. In addition, to identify the amino acid distributions and significances of pathogenic nsSNPs in the protein of WFS1, we constructed the transmembrane domain by the TMHMM server v2.0.

Results

SNP dataset from databases

The nsSNPs were collected from the NCBI dbSNP, HGMD, Deafness Variation Databases and the Locus Specific Database, in which the NCBI dbSNP database was the primary source, containing approximately 1,500 SNPs, and the other three were as supplemental. After filtering, a total of 395 nsSNPs were identified.

NsSNP prediction results of WFS1

To identify deleterious mutations from the nsSNPs in the WFS1 gene, the SIFT and PolyPhen-2 server were used to predict whether the mutations were deleterious/damaging. The SIFT server was used to calculate the tolerance index of all 395 collected nsSNPs with evolutionary conservation analysis, and a SIFT score value of <0.05 was considered to be deleterious. Meanwhile, we subjected all 395 nsSNPs to the PolyPhen-2 structure-based analysis server to further analyze the effects of amino acid substitutions (AAS) on the structures and functions. Of the 395 nsSNPs in the WFS1 gene, 174 nsSNPs were predicted to be deleterious by SIFT and the remaining nsSNPs were tolerated except for nonsense mutations for which SIFT provided no score. Among these deleterious nsSNPs, 32 mutations (P7L, G154A, W314R, P346L, Y351C, S353C, R375C, E394V, E394K, S430L, S430W, Y528D, P533S, A684V, A684T, A684G, C690R, C690G, G695V, Y699H, Y699C, Y699S, G702S, G702D, R708C, N714T, G736R, G736D, G736S, G834S, L842F and P885L) were reported to be highly deleterious with SIFT scores of 0.000. Obviously, in these highly deleterious nsSNPs, the mutation frequencies in the amino acid loci 394, 430, 684, 690, 699, 702 and 736 were higher than other loci. In PolyPhen-2, 235 nsSNPs were predicted to be damaging to protein structure and function, of which 89 mutations were predicted to be highly deleterious with PolyPhen-2 scores of 1.000. A total of 156 nsSNPs were predicted to be deleterious and damaging by both SIFT and PolyPhen-2 (Table 1) after excluding all nonsense mutations. Additionally, of these 156 nsSNPs, 28 nsSNPs (P346L, Y351C, S353C, R375C, E394V, E394K, S430L, S430W, Y528D, P533S, Y669H, Y669C, Y669S, A684T, A684G , A684V, C690R, C690G, G695V, G702D, G702S, R708C, G736D, G736R, G736S, G834S, L842F and P885L) were predicted to be highly deleterious and damaging by both algorithms with SIFT scores of 0.000 and PolyPhen-2 scores of 1 (Table 1).

Table 1. Deleterious and damaging nsSNPs of WFS1 prioritised using SIFT and PolyPhen-2 scores.

Amino Acid Change Nucleotide Variation SIFT Score PolyPhen-2 Score SNP ID*
R24H G/A 0.011 0.999 rs71524364
T104I C/T 0.021 0.992  
G107E G/A 0.004 1 rs71530914
G107R G/A 0.003 1 WFS1_00227
Y110N T/A 0.023 0.999 CM050353
D118A A/C 0.004 0.999 rs71524349
A126T G/A 0.007 1 rs145639028
G154A G/C 0 0.996 rs71530927
T156M C/T 0.002 1  
D171N G/A 0.049 0.953  
R177P G/C 0.010 1 CM083208
A198V C/T 0.047 0.875 rs142687752
E202G A/G 0.043 0.998 WFS1_00230
D211N G/A 0.017 0.813 rs138682654
R228H G/A 0.037 1 rs150771247
E273K G/A 0.018 0.904 rs142428158
P292S C/T 0.008 1 CM992981
I296S T/G 0.003 0.688 CM992982
W314R T/A 0 0.999 WFS1_00229
L327I C/A 0.013 1 rs71537678
F329I T/A 0.031 0.99 rs188848517
P346L C/T 0 1 CM073420
F350V T/G 0.045 0.999  
Y351C A/G 0 1 rs181988441
S353C C/G 0 1 rs143547567
C360Y G/A 0.001 0.999 rs147157374
T361I C/T 0.002 1 WFS1_00075
R375C C/T 0 1 rs200095753
R375H G/A 0.003 1 rs142671083
T378N C/A 0.007 0.999 WFS1_00097
D389E T/G 0.007 0.978 rs201282601
E394K G/A 0 1 rs373146435
E394V A/T 0 1 rs146563951
L402P T/C 0.001 1 CM112216
H407R A/G 0.010 0.684 rs140407862
V412A T/C 0.021 0.981 rs144951440
F417S T/C 0.002 0.95 rs111570388
I427S T/G 0.005 0.903 CM073419
S430L C/T 0 1 WFS1_00218
S430W C/G 0 1 WFS1_00194
L432V C/G 0.027 1 rs35031397
F439C T/G 0.002 0.913 rs141585847
S443I G/T 0.002 0.997 CM015195
T455M C/T 0.027 1 rs139361521
R456C C/T 0.010 0.689 rs144452795
E462G A/G 0.016 0.99 rs398123066
E462G A/G 0.016 0.99  
C505Y G/A 0.001 0.998 CM031397
L506R T/G 0.003 0.95 CM043878
L511P T/C 0.001 0.949  
Y513S A/C 0.036 0.98  
R517H G/A 0.024 0.986 rs150394063
R517P G/C 0.022 0.904  
M518I G/A 0.013 0.978 rs138232538
A519V C/T 0.047 1 rs201557396
Y528D T/G 0 1 CM087003
P533S C/T 0 1 rs146132083
C537Y G/A 0.003 0.999 rs199910987
L543R T/G 0.003 1 CM031400
V545M G/A 0.038 0.992 rs201993978
V546D T/A 0.004 0.999 CM031401
R558C C/T 0.001 1 rs199946797
R558H G/A 0.002 1 CM031402
A575G C/G 0.018 0.528 rs71524360
G576S G/A 0.031 0.882 rs1805069
V582M G/A 0.009 0.916 rs377677092
R587W C/T 0.005 0.999 rs138968466
L594R T/G 0.001 0.999 rs200288171
A602E C/A 0.011 0.74 rs2230720
A602G C/G 0.001 0.74  
P607L C/T 0.040 0.999 rs373862003
P607R C/G 0.010 1 CM033825
R611C C/T 0.008 0.999 rs144993516
L637P T/C 0.002 1 WFS1_00215
T641M C/T 0.018 0.985 rs376626985
R653C C/T 0.007 1 rs201064551
E655G A/G 0.006 0.999 CM024439
E655K G/A 0.015 0.995 CM108408
S662P T/C 0.004 1 rs376341411
L664R T/G 0.001 1 CM090453
T665I C/T 0.002 0.976  
T665N C/A 0.005 0.544 rs138258392
T665P A/C 0.004 0.544 rs369656458
Y669C A/G 0 1 CM983479
Y669H T/C 0 1 CM072120
Y669S A/C 0 1 CM090454
L672P T/C 0.026 0.998 CM056420
G674E G/A 0.029 1 CM020990
G674R G/A 0.024 1 rs200672755
G674V G/T 0.013 1 CM020991
R676C C/T 0.030 1 rs201623184
W678L G/T 0.008 0.999 CM073425
A684G C/G 0 1  
A684T G/A 0 1  
A684V C/T 0 1 rs387906930
R685C C/T 0.003 1 rs112967046
R685P G/C 0.023 0.999 CM081852
R685P G/C 0.023 0.999  
I688T T/C 0.002 0.999  
C690G T/G 0 1 CM087004
C690R T/C 0 1 CM992988
G695V G/T 0 1 rs28937891
T699M C/T 0.001 1 rs28937894
W700C G/T 0.001 1 CM992989
G702D G/A 0 1 CM090455
G702S G/A 0 1 rs71532862
R703C C/T 0.024 1 rs201888856
K705N G/C 0.032 0.997 CM032680
R708C C/T 0 1 rs200099217
R708H G/A 0.003 1 rs369062548
D713G A/G 0.012 0.999 rs143280847
N714T A/C 0 0.998 rs397517196
L723P T/C 0.001 1  
P724L C/T 0.002 1 rs28937890
P724S C/T 0.043 1  
R732C C/T 0.007 1 rs71526458
R732H G/A 0.018 1 rs149013740
G736D G/A 0 1 rs71530912
G736R G/C 0 1  
G736S G/A 0 1 rs71532864
Y739D T/G 0.006 1 rs367737581
C742R T/C 0.010 1 rs71532865
C742W C/G 0.002 1 rs71532866
R756C C/T 0.002 1 rs138127684
A761V C/T 0.031 0.818 rs71526459
H763P A/C 0.014 0.995  
D771G A/G 0.011 1 CM015267
D771H G/C 0.003 1 CM052942
R772C C/T 0.005 1 rs149540655
E776V A/T 0.001 1 rs56002719
G780R G/C 0.046 0.989 CM012813
G780S G/A 0.049 0.896 rs387906931
R791C C/T 0.019 0.982 rs200528166
K800E A/G 0.038 0.958 rs55674815
L804P T/C 0.001 1 WFS1_00226
S807R A/C 0.012 0.973 CM020992
E809K G/A 0.042 0.999 rs71539673
R818C C/T 0.014 1 rs35932623
L829P T/C 0.001 1 rs104893883
G831D G/A 0.012 1 rs28937895
R832C C/T 0.010 1 rs148089728
G834S G/A 0 1 rs398124214
L842F C/T 0 1 rs71530915
A844T G/A 0.047 0.973 CM053436
A844V C/T 0.036 0.999 rs200192011
R859P G/C 0.004 1 CM052943
R859W C/T 0.001 1 rs372298367
H860D C/G 0.007 0.96 CM043881
I863M C/G 0.003 0.977 rs71524393
E864K G/A 0.045 1 rs74315205
R868C C/T 0.008 1 rs148611943
R868H G/A 0.031 1 rs56393026
A874T G/A 0.006 1 rs200775335
K876T A/C 0.006 0.98 rs144900514
P885L C/T 0 1 rs372855769
A889V C/T 0.024 0.855 rs147934586
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*In the SNP ID column, the nsSNPs with the prefix “rs” are from dbSNP, and those with the prefix “CM” and “WFS1_” are from HGMD and Locus Specific Database, respectively, and the remaining with no SNP ID are in the Deafness Variation Database. The nsSNPs highlighted in bold are predicted to be highly deleterious and damaging, with a SIFT score of 0, and PolyPhen-2 score of 1.

For further study, we used PhD-SNP and MutPred to investigate whether these 156 filtered deleterious and damaging nsSNPs were associated with disease. PhD-SNP is optimised to classify disease-causing point mutations from the given datasets, and MutPred is also a web application tool developed to classify an AAS as either disease-associated or neutral in humans but also predicts the molecular cause of disease/deleterious AASs. Of the 156 nsSNPs, 97 diseased-associated nsSNPs were predicted by PhD-SNP and 91 nsSNPs were predicted to be disease-associated by MutPred tools. But it is worth noting that some of the 28 mutations with scores of 0.000 for SIFT and 1.000 for Polyphen-2 in Table 1 like P346L, Y351C, G834S or L842F were not predicted as diseased-associated by both PhD-SNP and MutPred, this might be because the loci of these amino acid were conserved, but the mutants on these loci could not cause the molecular changes or affect the whole protein structure. Finally, 70 nsSNPs were predicted to be diseased-associated using both PhD-SNP and MutPred, in which the numbers of mutations predicted as very confident hypotheses, confident hypotheses and actionable hypotheses were 16, 33 and 21, respectively. The most common changes in the molecular mechanisms in the mutants predicted by MutPred were gains or losses of helixes and sheets. Representative diseased-associated nsSNPs and the corresponding AAS of nsSNPs in the WFS1 gene are provided in Table 2. After inspecting these mutations in their reference sources, most of the nsSNPs predicted have also been reported, demonstrating that the nsSNPs predicted were credible from multiple computational methods. Finally, we predicted 20 mutations (F329I, S353C, R375H, R375C, E394K, F439C, R517P, L594R, P607L, S662P, T665I, R732C, R732H, G736D, Y739D, C742R, R832C, R859W, R868C and A874T) to be potentially pathogenic mutations, and 50 other mutations had been previously published or cited (Table 2).

Table 2. Diseased-associated nsSNPs of WFS1 predicted using the PhD-SNP and MutPred servers.

Amino Acid Change g Value p Value Molecular Change Prediction Reliability SNP ID* Reported or not
Y110N 0.849 0.0133 Gain of disorder Confident Hypotheses CM050353 Y41
R177P 0.817 0.0021 Loss of MoRF binding Very Confident Hypotheses CM083208 Y42
P292S 0.942 0.0093 Gain of helix Very Confident Hypotheses CM992981 Y20
I296S 0.867 0.0051 Gain of loop Very Confident Hypotheses CM992982 Y20
W314R 0.884 0.0162 Gain of methylation at W314 Confident Hypotheses WFS1_00229 Y43
F329I 0.774 0.0344 Gain of sheet Actionable Hypotheses rs188848517 N
S353C 0.502 0.0266 Gain of sheet Actionable Hypotheses rs143547567 N
R375H 0.670 0.0444 Loss of helix Actionable Hypotheses rs142671083 N
R375C 0.669 0.0444 Loss of helix Actionable Hypotheses rs200095753 N
E394V 0.811 0.0425 Gain of helix Confident Hypotheses rs146563951 Y44
E394K 0.826 0.0176 Gain of methylation at E394 Confident Hypotheses rs373146435 N
L402P 0.679 0.0215 Gain of relative solvent accessibility Actionable Hypotheses CM112216 Y23
I427S 0.828 0.0082 Gain of disorder Very Confident Hypotheses CM073419 Y45
S430L 0.793 0.0203 Loss of loop Confident Hypotheses WFS1_00218 Y22
S430W 0.790 0.0266 Gain of sheet Confident Hypotheses WFS1_00194 Y23
F439C 0.835 0.0357 Loss of sheet Confident Hypotheses rs141585847 N
S443I 0.836 0.0221 Gain of sheet Confident Hypotheses CM015195 Y21
C505Y 0.975 0.0062 Loss of catalytic residue at P504 Very Confident Hypotheses CM031397 Y46
L506R 0.858 0.0196 Loss of helix Confident Hypotheses CM043878 Y47
L511P 0.748 0.0016 Gain of sheet Actionable Hypotheses   Y25
R517P 0.534 0.0072 Loss of helix Actionable Hypotheses   N
Y528D 0.939 0.0037 Loss of sheet Very Confident Hypotheses CM087003 Y48
P533S 0.886 0.0228 Loss of sheet Confident Hypotheses rs146132083 Y44
L543R 0.768 0.0228 Loss of sheet Actionable Hypotheses CM031400 Y46
V546D 0.828 0.0037 Loss of sheet Very Confident Hypotheses CM031401 Y46
R558C 0.890 0.0296 Loss of methylation at R558 Confident Hypotheses rs199946797 Y49
R558H 0.950 0.0296 Loss of methylation at R558 Confident Hypotheses CM031402 Y46
L594R 0.688 0.0344 Gain of sheet Actionable Hypotheses rs200288171 N
P607L 0.748 0.0022 Gain of helix Actionable Hypotheses rs373862003 N
P607R 0.954 0.0005 Gain of MoRF binding Very Confident Hypotheses CM033825 Y50
L637P 0.683 0.0072 Loss of helix Actionable Hypotheses WFS1_00215 Y51
E655G 0.756 0.0187 Loss of solvent accessibility Actionable Hypotheses CM024439 Y44
E655K 0.811 0.0049 Gain of MoRF binding Very Confident Hypotheses CM108408 Y52
S662P 0.816 0.0312 Gain of loop Confident Hypotheses rs376341411 N
L664R 0.926 0.0090 Gain of MoRF binding Very Confident Hypotheses CM090453 Y53
T665I 0.821 0.0117 Gain of helix Confident Hypotheses   N
L672P 0.874 0.0076 Loss of helix Very Confident Hypotheses CM056420 Y54
G674R 0.964 0.0328 Gain of MoRF binding Confident Hypotheses rs200672755 Y55
G674V 0.958 0.0325 Gain of helix Confident Hypotheses CM020991 Y56
W678L 0.933 0.0132 Loss of catalytic residue at A677 Confident Hypotheses CM073425 Y57
A684V 0.755 0.0104 Loss of helix Actionable Hypotheses rs387906930 Y21
R685P 0.859 0.0033 Loss of helix Very Confident Hypotheses   Y58
C690R 0.945 0.0008 Gain of MoRF binding Very Confident Hypotheses CM992988 Y20
C690G 0.955 0.0115 Gain of disorder Confident Hypotheses CM087004 Y48
G695V 0.911 0.0036 Gain of sheet Very Confident Hypotheses rs28937891 Y6
H696Y 0.764 0.0390 Gain of sheet Actionable Hypotheses WFS1_00098 Y59
W700C 0.942 0.0157 Loss of MoRF binding Confident Hypotheses CM992989 Y20
G702S 0.887 0.0315 Loss of sheet Confident Hypotheses rs71532862 Y23
G702D 0.96 0.0315 Loss of sheet Confident Hypotheses CM090455 Y53
R708C 0.921 0.0182 Loss of MoRF binding Confident Hypotheses rs200099217 Y21
L723P 0.731 0.0045 Gain of loop Actionable Hypotheses   Y23
P724L 0.926 0.0336 Loss of catalyticresi due at P724 Confident Hypotheses rs28937890 Y6
R732H 0.855 0.0444 Loss of helix Confident Hypotheses rs149013740 N
R732C 0.848 0.0376 Loss of helix Confident Hypotheses rs71526458 N
G736D 0.934 0.0425 Gain of helix Confident Hypotheses rs71530912 N
G736R 0.965 0.0117 Gain of helix Confident Hypotheses   Y60
Y739D 0.736 0.0332 Gain of disorder Actionable Hypotheses rs367737581 N
C742R 0.814 0.013 Gain of disorder Confident Hypotheses rs71532865 N
E776V 0.939 0.050 Gain of MoRF binding Confident Hypotheses rs56002719 Y47
L804P 0.768 0.0063 Loss of sheet Actionable Hypotheses WFS1_00226 Y26
L829P 0.928 0.0079 Gain of loop Very Confident Hypotheses rs104893883 Y61
G831D 0.923 0.0143 Gain of helix Confident Hypotheses rs28937895 Y61
R832C 0.505 0.0228 Loss of sheet Actionable Hypotheses rs148089728 N
R859W 0.596 0.0152 Loss of disorder Actionable Hypotheses rs372298367 N
R859P 0.853 0.0315 Loss of sheet Confident Hypotheses CM052943 Y27
H860D 0.769 0.0104 Loss of sheet Actionable Hypotheses CM043881 Y47
E864K 0.901 0.0016 Gain of MoRF binding Very Confident Hypotheses rs74315205 Y62
R868C 0.843 0.0179 Loss of disorder Confident Hypotheses rs148611943 N
A874T 0.769 0.0061 Gain of sheet Actionable Hypotheses rs200775335 N
P885L 0.953 0.0117 Gain of helix Confident Hypotheses rs372855769 Y20
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*In the SNP ID column, the nsSNPs with the prefix “rs” are from dbSNP, and those with the prefix “CM” and “WFS1_” are from HGMD and Locus Specific Database, respectively, and the remaining with no SNP ID are in the Deafness Variation Database.The nsSNPs highlighted in bold are potential pathogenic nsSNPs which have not been reported.

Additionally, to better understand how the pathogenic nsSNPs affect protein conformation and result in disease states, we constructed wild type and mutant proteins via the Robetta and SWISS-MODEL tools (Fig. 1, Supplementary file 1–4). And the geometric evaluations of the modeled 3D structure were performed using PROCHECK by calculating the Ramachandran plot (Fig. 2). The wild type protein showed 99.4% of residues in most favoured and allowed region and the overall average of G factors was 0.27 which showed the structure was usual. In this step, we randomly selected three predicted nsSNPs (P292S, S443I and G695V) that have been reported to be pathogenic6,20,21 and compared the structures between the wild type and mutant proteins. We observed that after mutation, not only did the amino acid change, but it also affected the entire protein structure. All of the three protein structures (P292S, S443I and G695V) representing different mutations gained or lost some α-helixes, suggesting a potential molecular mechanism resulting in WS.

Figure 1. Protein structure predicted by the SWISS-MODEL server.

Figure 1

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(A,B) indicate the changes between wild type and mutant wolframin with the amino acid change P292S, (C,D) depict the structural changes between wild type and mutant S443I, and E and F illustrate the effects of G695V. (A,C,E) are protein structures of the wild type wolframin, and (B,D,F) are structures of the mutant proteins (created by SWISS-MODEL and illustrated with VMD). The arrows in yellow and the circles in red indicate the differences between the wild type and the mutant.

Figure 2. Ramachandran Plot of the wild type wolframin protein structure evaluated by PROCHECK.

Figure 2

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Amino acid distribution in the transmembrane domain

To elucidate the amino acid distributions and significances of predicted pathogenic nsSNPs in wolframin, we constructed its transmembrane domain using the TMHMM server v2.0 (Fig. 3). In this analysis, the transmembrane domain of wolframin was divided into 9 TMhelixes, with each TMhelix being approximately 23-amino acids long. Except for the third and seventh TMhelix, 18 pathogenic mutations were distributed across the other seven TMhelixes, accounting for 25.71% of all 70 pathogenic mutations, of which 13 were previously known. Notably, most pathogenic mutations in our study were not located in the transmembrane domain but in the C-terminal domain of wolframin (Table 3). In all 70 pathogenic mutations, approximately 52 were not located in the TMhelix (74.29%), 39 of which were located in the C-terminal domain. Thirty-seven pathogenic mutations have been previously reported in the 52 mutations not located in the TMhelix, and only 15 mutations were predicted to be potentially pathogenic.

Figure 3. Transmembrane domain structure of wolframin and its distribution of mutations22.

Figure 3

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The 70 predicted pathogenic mutations are highlighted with green/red coloured circles compared to “normal” sequence with blue circles Inline graphic. The 50 known pathogenic mutations are depicted in green Inline graphic and the 20 predicted potentially pathogenic mutations are in red Inline graphic. The transmembrane domain is depicted in yellow Inline graphic. The circle with green and red Inline graphic denotes that the locus has a known and predicted mutation.

Table 3. NsSNP distributions of the transmembrane domain of wolframin from the TMHMM server.

Distribution of Transmembrane Domain Range of Amino Acid Number of Reported Pathogenic nsSNPs Number of Predicted Pathogenic nsSNPs Total Number of nsSNPs in Each Domain Ratio of Each Domain (%)
Outside 1–310 4 0 4 5.714
TMhelix 1 311–333 1 1 2 2.857
Inside 334–339 0 0 0 0
TMhelix 2 340–362 0 1 1 1.429
Outside 363–404 2 3 5 7.142
TMhelix 3 405–422 0 0 0 0
Inside 423–428 1 0 1 1.429
TMhelix 4 429–451 3 1 4 5.714
Outside 452–492 0 0 0 0
TMhelix 5 493–515 3 0 3 4.286
Inside 516–526 0 1 1 1.429
TMhelix 6 527–549 4 0 4 5.714
Outside 550–558 2 0 2 2.857
TMhelix 7 559–581 0 0 0 0
Inside 582–587 0 0 0 0
TMhelix 8 588–610 1 2 3 4.286
Outside 611–629 0 0 0 0
TMhelix 9 630–652 1 0 1 1.429
Inside* 653–890 28 11 39 55.714
Total 890-amino acids 50 20 70 100
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*The domain highlighted in bold is the distribution of the C terminal domain.

Discussion

WS is a rare autosomal recessive disorder with a number of loss-of-function mutations of the WFS1, both within and between most affected patients/families. Wide tissue distribution of wolframin and many mutations in WFS1 resulting in WS may contribute to different phenotypes. Growing evidences have presented many clinical signs and possible correlations between the genotype and the development of the neurologic manifestations, the age at onset of diabetes mellitus, hearing defects, and diabetes insipidus in WS on the cohort of WS patients22,23. So far, although a large number of variants of the WFS1 gene have been identified, novel mutations are continuously found in this gene. Furthermore, the pathogenic role of different mutations, polymorphisms and sequencing variants of the gene remains largely unknown. Phenotypic prediction of the effects of nsSNPs might identify meaningful changes in genes that alter protein function to induce phenotypic consequences. The sheer number of SNPs in online databases provides an abundant resource to predict the phenotypic effects of nsSNPs, and known pathogenic mutations from the literature provide us an opportunity to inspect prediction accuracy, which indicates whether the relationships between nsSNP prediction results and known pathogenic mutations are confirmed by in vivo and in vitro experiments.

In the present study, we predicted 20 potentially pathogenic mutations and 50 known pathogenic mutations using in silico methods, and combined the results of the most common changes by MutPred and the predictions of the three protein structures by the SWISS-MODEL to determine that the most probable mutational effects causing WS might be the gains or losses of α-helixes. It is worth to consider that some predicted pathogenic nsSNPs have been confirmed by in vitro functional studies and genetic analysis for WS families, which could indirectly verify the accuracy of our methods. For example, p.P724L(c.2171C>T) and p.G695V(c.2084G>T) of WFS1 have been reported to lead to WS and which cause the formation of detergent-insoluble aggregates of wolframin when was expressed in COS-7 cells24; the p.A684V(c.2051C>T) and p.L511P (c.1532T>C) were ectopically expressed in HEK293 cells which showed reduced protein levels compared to wild type wolframin, strongly indicating that the mutation is disease-causing21,25. Meanwhile, by direct DNA sequencing and linkage analysis, p.L804P (c.2411T>C) and p.R859P (c.2576G>C) were identified after screening the entire coding region of the WFS1 gene in a Chinese WS family and in a US family with the nonsyndromic hearing loss, respectively26,27.

WFS1 spanning approximately 33.4 kb of genomic DNA, consists of eight exons and produces a peptide product which is 890-amino acid long (wolframin). The amino acid distribution results of wolframin suggest that wolframin contains 9 transmembrane domains. These results are consistent with the previous research which provides experimental evidence that wolframin contains 9 transmembrane segments and is embedded in the membrane in an Ncyt/Clum topology15. However, the prediction for wolframin available at UniProt database gives 11 transmembrane domains (http://www.uniprot.org/uniprot/O76024) (Table 4), and the difference between the two predicted results was mainly in the TMhelix 5, TMhelix 6 and TMhelix 11. In our result, the 493–515 amino acids are located in TMhelix5; while in UniProt, this region has been divided into TMhelix 5 and TMhelix 6 domains, respectively; the 653–890 amino acids have also been predicted as two TMhelixes in the same way in the UniProt. With reference to most researches, the wolframin were considered as 9 transmembrane domains with some evidences, and this is due to the differences in the execution of algorithm. Additionally, our results also indicate that the mutations outside of the TMhelix could have more pronounced functional effects, especially in the C-terminal with 39 predicted mutations. Many of the reported missense mutations are located in the C-terminal hydrophilic part of the protein15, and the experiments also support these predictions. Just as de Heredia et al. found that besides the transmembrane domains, the mutations identified in WS patients also concentrate in the last 100 amino acids in the C-terminal1. Using yeast two-hybrid analysis, Zatyka et al. identified that the C-terminal domain of wolframin, which is positioned in the ER lumen, bound the C-terminal domain (amino acids 652–890) of the ER-localized Na+/K+ ATPase beta-1 subunit (ATP1B1)28. And the Na+/K+ ATPase deficiency has a crucial role in apoptosis and in neural degenerative disease which can be induced by mutations in WFS1, leading to the development of WS29.

Table 4. The prediction results to the transmembrane domain of wolframin from the TMHMM server and UniProt database.

TMHMM server
 UniProt database
Distribution of Transmembrane Domain Range of Amino Acid Distribution of Transmembrane Domain Range of Amino Acid
Outside 1–310 Outside 1–313
TMhelix-1 311–333 TMhelix-1 314–334
Inside 334–339 Inside 335–339
TMhelix-2 340–362 TMhelix-2 340–360
Outside 363–404 Outside 361–401
TMhelix-3 405–422 TMhelix-3 402–422
Inside 423–428 Inside 423–426
TMhelix-4 429–451 TMhelix-4 427–447
Outside 452–492 Outside 448–464
TMhelix-5 493–515 TMhelix-5 465–485
    Inside 486–495
    TMhelix-6 496–516
Inside 516–526 Outside 517–528
TMhelix-6 527–549 TMhelix-7 529–549
Outside 550–558 Inside 550–562
TMhelix-7 559–581 TMhelix-8 563–583
Inside 582–587 Outside 584–588
TMhelix-8 588–610 TMhelix-9 589–609
Outside 611–629 Inside 610–631
TMhelix-9 630–652 TMhelix-10 632–652
Inside* 653–890 Topological domain 653–869
    TMhelix-11 870–890
Total 890-amino acids Total 890-amino acids
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*The domains highlighted in bold are the distributions of the C terminal domain.

In summary, we used extensive functional and structural level analyses to predict potentially pathogenic mutations for nsSNPs in the WFS1 gene and analysed the amino acid distributions of wolframin to provide a guide for screening pathogenic mutations and investigating the function of wolframin. Furthermore, we provide information for predicting the effects of nsSNPs in genes encoding transmembrane proteins and for further research in variant effect prediction.

Materials and Methods

Dataset collection

NsSNP datasets of the WFS1 gene were obtained from the NCBI dbSNP database (http://www.ncbi.nlm.nih.gov/projects/SNP/)30, HGMD (http://www.hgmd.cf.ac.uk/ac)31, Deafness Variation Database (http://deafnessvariationdatabase.org) and the Locus Specific Database (https://lovd.euro-wabb.org/home.php?select_db=WFS1). The amino acid sequence of wolframin was retrieved from the UniProt database (http://www.uniprot.org/). Data for the WFS1 gene were collected from Entrez Gene on the NCBI web site (http://www.ncbi.nlm.nih.gov/genbank/), and the literature search was performed using PubMed, Science Direct, and Web of Science.

Filtering and mining of nsSNPs

Because SNPs from the databases were not initially nsSNPs, we needed to perform some manual filtering. In this process, we eliminated SNPs in 3′ or 5′UTRs and synonymous SNPs. For prediction and analysis, SNP ID, gene name, protein accession, amino acid residue 1 (wild type), amino acid position, and amino acid residue 2 (missense) for all nsSNPs were collected from the NCBI dbSNP database, HGMD, and Deafness Variation Databases.

Predicting the phenotype of nsSNPs with the SIFT and PolyPhen-2 tools

After filtering the nsSNPs, we predicted their functional effects with the SIFT (http://sift-dna.org) and PolyPhen-2 (http://genetics.bwh.harvard.edu/pph2/) tools. In SIFT server, a highly conserved position is more likely to be deleterious with a SIFT score <0.05, whereas a tolerant mutation will have a SIFT score >0.0532,33. PolyPhen-2 extracts various sequence- and structure-based features of the substitution site and inputs them into a probabilistic classifier based on a given AAS and protein accession. The mutation is appraised qualitatively, as benign, possibly damaging, or most likely damaging34.

Identifying disease-associated nsSNPs using the PhD-SNP and MutPred tools

PhD-SNP (http://snps.biofold.org/phd-snp) and MutPred (http://mutpred.mutdb.org/) were based on a support vector machine (SVM) and the SIFT algorithm. To PhD-SNP, in briefly, after inputting the protein sequence, position and new residue, the substitution from the wild type residue to the mutant is encoded in a 20-element vector that is −1 in position relative to the wild type residue, 1 in the position relative to the mutant residues and 0 in the remaining 18 positions. Next, a second 20-element vector encoding the sequence environment is constructed to report the occurrence of residues in a window of 19 residues around the mutated residue. With this supervised learning approach, a given mutation is classified as disease or neutral35,36.

MutPred is based on SIFT scores, the gain or loss of 14 different structural and functional properties. Two important scores are contained in the output of MutPred: a general score (g), and top 5 property score (p). The general score (g) indicates the probability that the AAS is deleterious/disease-associated, whereas the top 5 property score (p) is the P-value that indicates whether certain structural and functional properties are affected. The combinations of high general scores and low property scores are referred to as actionable hypotheses, confident hypotheses, and very confident hypotheses37.

Protein structure prediction of pathogenic nsSNPs via Robetta and SWISS-MODEL tools

As the structure of wolframin is not available and there is not suitable template for modelling, so we choose the Robetta server (http://robetta.bakerlab.org/) to construct the protein structure. The Robetta server is a full chain protein structure prediction server for ab initio and comparative modeling, and the SWISS-MODEL (http://swissmodel.expasy.org/) is a fully automated, dedicated protein structure homology-modelling server38,39. The amino acid sequence of wolframin was retrieved from NCBI (accession number: NP_005996.2). 3D-structure of wolframin was performed using Robetta server. And the mutant proteins were constructed by SWISS-MODEL with the template performed using Robetta server (Sup.file S). The quality of the modelled structure of native and mutant protein was evaluated by the PROCHECK (http://services.mbi.ucla.edu/SAVES/).

Analysis of the transmembrane domain by the TMHMM server v2.0

TMHMM server v2.0 (http://www.cbs.dtu.dk/services/TMHMM/), based on a hidden Markov model (HMM) with an architecture that corresponds closely to the biological system, is a membrane protein topology prediction method. Compared with other servers, TMHMM server v2.0, which is thought to be currently the best performing transmembrane prediction program, can model and predict the location and orientation of alpha helices in membrane-spanning proteins with high accuracy40.

Additional Information

How to cite this article: Qian, X. et al. Phenotype Prediction of Pathogenic Nonsynonymous Single Nucleotide Polymorphisms in WFS1. Sci. Rep. 5, 14731; doi: 10.1038/srep14731 (2015).

Supplementary Material

Supplementary Information
srep14731-s1.doc (33.5KB, doc)
Supplementary PDB files
srep14731-s2.zip (529.2KB, zip)

Acknowledgments

This research was supported in part by the National Natural Science Foundation of China (No. 31171217) to XC, the Grant from Jiangsu Health Administration of China (LJ201120) and the Research Special Fund for Public Welfare Industry of Health, Ministry of Health of China (No. 201202005) to GX.

Footnotes

Author Contributions Conceived and designed the experiments: X.C. Analyzed the data: X.Q. and G.X. Wrote the first draft of the manuscript: X.Q. L.Q. partially modified the manuscript in the later phases of revised versions. Reviewed, edited and approved the manuscript: X.C.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Information
srep14731-s1.doc (33.5KB, doc)
Supplementary PDB files
srep14731-s2.zip (529.2KB, zip)

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