Advances in Bioinformatics
Volume 2019 (2019), Article ID 1651587, 13 pages
https://doi.org/10.1155/2019/1651587

Research Article

Novel Deleterious nsSNPs within MEFV Gene that Could Be Used as Diagnostic Markers to Predict Hereditary Familial Mediterranean Fever: Using Bioinformatics Analysis

Mujahed I. Mustafa,1,2 Tebyan A. Abdelhameed,² Fatima A. Abdelrhman,² Soada A. Osman,² and Mohamed A. Hassan²

¹Department of Biochemistry, University of Bahri, Sudan
²Department of Biotechnology, Africa City of Technology, Sudan

Correspondence should be addressed to Mujahed I. Mustafa; mujahedibrahim44@gmail.com

Received 29 September 2018; Revised 2 January 2019; Accepted 21 January 2019; Published 4 June 2019

Academic Editor: Nurit Haspel

Copyright © 2019 Mujahed I. Mustafa et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

Background. Familial Mediterranean Fever (FMF) is the most common autoinflammatory disease (AID) affecting mainly the ethnic groups originating from Mediterranean basin. We aimed to identify the pathogenic SNPs in MEFV by computational analysis software. Methods. We carried out in silico prediction of structural effect of each SNP using different bioinformatics tools to predict substitution influence on protein structure and function. Result. 23 novel mutations out of 857 nsSNPs are found to have deleterious effect on the MEFV structure and function. Conclusion. This is the first in silico analysis of MEFV gene to prioritize SNPs for further genetic mapping studies. After using multiple bioinformatics tools to compare and rely on the results predicted, we found 23 novel mutations that may cause FMF disease and it could be used as diagnostic markers for Mediterranean basin populations.

1. Introduction

Familial Mediterranean Fever is an autosomal recessive inherited inflammatory disease [1–3] (however, it has been observed that a substantial number of patients with clinical FMF possess only one demonstrable MEFV mutation [4, 5]) that is principally seen in different countries [6–10]. However, patients from different ethnicities (such as Japan) are being increasingly recognized [2, 11], and the carrier frequency for MEFV genetic variants in the population in the Mediterranean basin is about 8% [12]. Most cases of FMF usually present with acute abdominal pain and fever [1, 3, 7], both of which are also the main causes of referral in the emergency department [13]. All these factors may help in medical treatment. Colchicine is the first line therapy [14], but in resistant cases (<10% of patients) [15], it affects the responsiveness to Colchicine [16]; other anti-inflammatory drugs can be used for extra anti-inflammatory effect [17]. If FMF is not treated, it may be an etiologic factor for colonic LNH in children [18]. MEFV gene is localized on 16p13.3 of chromosome 16 at position 13.3 which consists of 10 exons with 21600 bp [3, 19]. The disease is characterized by recurrent febrile episodes and inflammation in the form of sterile polyserositis. Amyloid protein involved in inflammatory amyloidosis was named AA (amyloid‐associated) protein and its circulating precursor was named SAA (serum amyloid‐associated). Amyloidosis of the AA type is the most severe complication of the disease. The gene responsible for FMF, MEFV, encodes a protein called pyrin or marenostrin and is expressed mainly in neutrophils [3, 19].

The definition of the MEFV gene has permitted genetic diagnosis of the disease. Nevertheless, as studies have unwrapped molecular data, problems have arisen with the clinical definitions of the disease [20]. FMF is caused by mutations in the MEFV missense SNPs (we were focusing on SNPs which are located in the coding region because it is much important in disease causing potential, which are responsible for amino acid residue substitutions resulting in functional diversity of proteins in humans) [20] coding for pyrin, which is a component of inflammasome functioning in inflammatory response and production of interleukin-1β (IL-1β). Recent studies have shown that pyrin recognizes bacterial modifications in Rho GTPases, which results in inflammasome activation and increase in IL-1β. Pyrin does not directly recognize Rho modification but probably is affected by Rho effector kinase, which is a downstream event in the actin cytoskeleton pathway [19, 21, 22].

The aim of this study was to identify the pathogenic SNPs in MEFV using in silico prediction software and to determine the structure, function, and regulation of their respective proteins. This is the first in silico analysis in MEFV gene to prioritize SNPs for further genetic mapping studies. The usage of in silico approach has strong impact on the identification of candidate SNPs since they are easy and less costly and can facilitate future genetic studies [23].

2. Method

2.1. Data Mining

The data on human MEFV gene was collected from National Center for Biological Information (NCBI) website [24]. The SNP information (protein accession number and SNP ID) of the MEFV gene was retrieved from the NCBI dbSNP (http://www.ncbi.nlm.nih.gov/snp/) and the protein sequence was collected from Swiss Prot databases (http://expasy.org/) [25].

2.2. SIFT

SIFT is a sequence homology-based tool [26] that sorts intolerant from tolerant amino acid substitutions and predicts whether an amino acid substitution in a protein will have a phenotypic effect. It considers the position at which the change occurred and the type of amino acid change. Given a protein sequence, SIFT chooses related proteins and obtains an alignment of these proteins with the query. Based on the amino acids appearing at each position in the alignment, SIFT calculates the probability that an amino acid at a position is tolerated conditional on the most frequent amino acid being tolerated. If this normalized value is less than a cutoff, the substitution is predicted to be deleterious. SIFT scores <0.05 are predicted by the algorithm to be intolerant or deleterious amino acid substitutions, whereas scores >0.05 are considered tolerant. It is available at (http://sift.bii.a-star.edu.sg/).

2.3. PolyPhen-2

It is a software tool [27] to predict possible impact of an amino acid substitution on both structure and function of a human protein by analysis of multiple sequence alignment and protein 3D structure; in addition, it calculates position-specific independent count scores (PSIC) for each of the two variants and then calculates the PSIC scores difference between the two variants. The higher a PSIC score difference is, the higher the functional impact a particular amino acid substitution is likely to have. Prediction outcomes could be classified as probably damaging, possibly damaging or benign according to the value of PSIC as it ranges from (0_1); values closer to zero were considered benign while values closer to 1 were considered probably damaging and also it can be indicated by a vertical black marker inside a color gradient bar, where green is benign and red is damaging. nsSNPs that is predicted to be intolerant by SIFT has been submitted to PolyPhen as protein sequence in FASTA format obtained from UniproktB/Expasy after submitting the relevant ensemble protein (ESNP) there, and then we entered position of mutation, native amino acid, and the new substituent for both structural and functional predictions. PolyPhen version 2.2.2 is available at http://genetics.bwh.harvard.edu/pph2/index.shtml.

2.4. Provean

Provean is a software tool [28] which predicts whether an amino acid substitution or indel has an impact on the biological function of a protein. It is useful for filtering sequence variants to identify nonsynonymous or indel variants that are predicted to be functionally important. It is available at (https://rostlab.org/services/snap2web/).

2.5. SNAP2

Functional effects of mutations are predicted with SNAP2 [29]. SNAP2 is a trained classifier that is based on a machine learning device called “neural network”. It distinguishes between effect and neutral variants/nonsynonymous SNPs by taking a variety of sequence and variant features into account. The most important input signal for the prediction is the evolutionary information taken from an automatically generated multiple sequence alignment. Also structural features such as predicted secondary structure and solvent accessibility are considered. If available also annotation (i.e., known functional residues, pattern, regions) of the sequence or close homologs are pulled in. In a cross-validation over 100,000 experimentally annotated variants, SNAP2 reached sustained two-state accuracy (effect/neutral) of 82% (at an AUC of 0.9). In our hands this constitutes an important and significant improvement over other methods. It is available at (https://rostlab.org/services/snap2web/).

2.6. PHD-SNP

An online Support Vector Machine (SVM) based classifier is optimized to predict if a given single point protein mutation can be classified as disease related or as a neutral polymorphism. It is available at (http://snps.biofold.org/phd-snp/phd-snp.html).

2.7. SNP&Go

SNPs&GO is an algorithm developed in the Laboratory of Biocomputing at the University of Bologna directed by Prof. Rita Casadio. SNPs&GO is an accurate method that, starting from a protein sequence, can predict whether a variation is disease related or not by exploiting the corresponding protein functional annotation. SNPs&GO collects in unique framework information derived from protein sequence, evolutionary information, and function as encoded in the Gene Ontology terms and outperforms other available predictive methods [30]. It is available at (http://snps.biofold.org/snps-and-go/snps-and-go.html).

2.8. P-Mut

P-MuT, a web-based tool [31] for the annotation of pathological variants on proteins, allows the fast and accurate prediction (approximately 80% success rate in humans) of the pathological character of single point amino acidic mutations based on the use of neural networks. It is available at (http://mmb.irbbarcelona.org/PMut).

2.9. I-Mutant 3.0

I-Mutant 3.0 is a neural network based tool [32] for the routine analysis of protein stability and alterations by taking into account the single-site mutations. The FASTA sequence of protein retrieved from UniProt is used as an input to predict the mutational effect on protein stability. It is available at (http://gpcr2.biocomp.unibo.it/cgi/predictors/I-Mutant3.0/I-Mutant3.0.cgi).

2.10. Modeling nsSNP Locations on Protein Structure

Project hope is a new online web-server to search protein 3D structures (if available) by collecting structural information from a series of sources, including calculations on the 3D coordinates of the protein, sequence annotations from the UniProt database, and predictions by DAS services. Protein sequences were submitted to project hope server in order to analyze the structural and conformational variations that have resulted from single amino acid substitution corresponding to single nucleotide substitution. It is available at (http://www.cmbi.ru.nl/hope).

2.11. GeneMANIA

We submitted genes and selected from a list of data sets that they wish to query. GeneMANIA’s [33] approach is to know protein function prediction integrating multiple genomics and proteomics data sources to make inferences about the function of unknown proteins. It is available at (http://www.genemania.org/).

3. Results and Discussion

3.1. Result

See Tables 1–5 and Figure 1.

Table 1: Damaging or deleterious effect nsSNPs associated variations predicted by various softwares.

Amino Acid Change
SIFT
Polyphen
PROVEAN
SNAP2

prediction
Score
Prediction
score
Score
Prediction (cutoff= -2.5)
prediction
score

S749Y
DAMAGING
0
PROBABLY DAMAGING
1.000
-3.116
Deleterious
effect
64

F743S
DAMAGING
0
PROBABLY DAMAGING
1.000
-5.563
Deleterious
effect
68

Y741C
DAMAGING
0
PROBABLY DAMAGING
1.000
-6.035
Deleterious
effect
77

F731V
DAMAGING
0
PROBABLY DAMAGING
1.000
-5.159
Deleterious
effect
81

I720T
DAMAGING
0
PROBABLY DAMAGING
1.000
-3.639
Deleterious
effect
58

L709R
DAMAGING
0
PROBABLY DAMAGING
1.000
-4.311
Deleterious
effect
77

V691G
DAMAGING
0
PROBABLY DAMAGING
1.000
-4.667
Deleterious
effect
66

W689R
DAMAGING
0
PROBABLY DAMAGING
1.000
-10.132
Deleterious
effect
89

G668R
DAMAGING
0
PROBABLY DAMAGING
1.000
-6.287
Deleterious
effect
92

V659F
DAMAGING
0
PROBABLY DAMAGING
1.000
-3.811
Deleterious
effect
64

F636C
DAMAGING
0
PROBABLY DAMAGING
1.000
-6.49
Deleterious
effect
79

R461W
DAMAGING
0
PROBABLY DAMAGING
1.000
-5.456
Deleterious
effect
68

H407Q
DAMAGING
0
PROBABLY DAMAGING
1.000
-7.335
Deleterious
effect
41

H407R
DAMAGING
0
PROBABLY DAMAGING
1.000
-7.332
Deleterious
effect
51

H404R
DAMAGING
0
PROBABLY DAMAGING
1.000
-7.349
Deleterious
effect
75

C398Y
DAMAGING
0
PROBABLY DAMAGING
1.000
-10.314
Deleterious
effect
51

C395Y
DAMAGING
0
PROBABLY DAMAGING
1.000
-10.262
Deleterious
effect
19

C395F
DAMAGING
0
PROBABLY DAMAGING
1.000
-10.315
Deleterious
effect
27

C395R
DAMAGING
0
PROBABLY DAMAGING
1.000
-11.074
Deleterious
effect
27

H378Q
DAMAGING
0
PROBABLY DAMAGING
1.000
-5.886
Deleterious
effect
38

H378Y
DAMAGING
0
PROBABLY DAMAGING
1.000
-4.884
Deleterious
effect
45

C375R
DAMAGING
0
PROBABLY DAMAGING
1.000
-8.429
Deleterious
effect
66

L86P
DAMAGING
0
PROBABLY DAMAGING
1.000
-4.1
Deleterious
effect
19

Table 2: Disease effect nsSNPs associated variations predicted by various softwares.

Amino Acid Change
SNP&GO
PHD-SNP
P-Mut

Prediction
RI
Probability
Prediction
RI
Score
Score
Prediction

S749Y
Disease
1
0.573
Disease
3
0.649
0.67 (85%)
Disease

F743S
Disease
2
0.617
Disease
4
0.696
0.82 (90%)
Disease

Y741C
Disease
6
0.797
Disease
7
0.869
0.61 (83%)
Disease

F731V
Disease
6
0.79
Disease
8
0.899
0.93 (94%)
Disease

I720T
Disease
6
0.811
Disease
5
0.769
0.81 (89%)
Disease

L709R
Disease
3
0.672
Disease
4
0.695
0.66 (85%)
Disease

V691G
Disease
1
0.55
Disease
3
0.675
0.92 (93%)
Disease

W689R
Disease
7
0.841
Disease
8
0.924
0.93 (94%)
Disease

G668R
Disease
6
0.778
Disease
7
0.84
0.93 (94%)
Disease

V659F
Disease
6
0.805
Disease
7
0.84
0.82 (90%)
Disease

F636C
Disease
6
0.809
Disease
7
0.86
0.60 (82%)
Disease

R461W
Disease
3
0.644
Disease
1
0.572
0.63 (84%)
Disease

H407Q
Disease
6
0.788
Disease
4
0.705
0.79 (89%)
Disease

H407R
Disease
5
0.769
Disease
3
0.673
0.86 (91%)
Disease

H404R
Disease
5
0.744
Disease
5
0.734
0.80 (89%)
Disease

C398Y
Disease
7
0.864
Disease
8
0.912
0.86 (91%)
Disease

C395Y
Disease
7
0.864
Disease
8
0.912
0.91 (93%)
Disease

C395F
Disease
7
0.859
Disease
8
0.914
0.92 (94%)
Disease

C395R
Disease
7
0.842
Disease
8
0.892
0.92 (94%)
Disease

H378Q
Disease
4
0.714
Disease
4
0.698
0.88 (92%)
Disease

H378Y
Disease
5
0.732
Disease
5
0.728
0.80 (89%)
Disease

C375R
Disease
6
0.784
Disease
6
0.822
0.92 (94%)
Disease

L86P
Disease
5
0.729
Disease
6
0.801
0.51 (79%)
Disease

Table 3: Stability analysis predicted by I-Mutant version 3.0 (also show the 23 novel mutations).

Amino Acid Change
SVM2 Prediction Effect
RI
DDG Value Prediction

S749Y
Decrease
0
-0.2

F743S
Decrease
6
-1.16

Y741C
Decrease
8
-2.5

F731V
Decrease
6
-1.52

I720T
Decrease
4
-0.92

L709R
Decrease
3
-0.56

V691G
Decrease
7
-1.25

W689R
Decrease
7
-0.73

G668R
Decrease
4
-0.37

V659F
Decrease
2
-0.19

F636C
Decrease
8
-1.28

R461W
Increase
1
-0.01

H407Q
Decrease
8
-1.48

H407R
Decrease
5
-1.1

H404R
Decrease
1
-0.06

C398Y
Decrease
2
-0.09

C395Y
Increase
4
0.26

C395F
Increase
1
0.04

C395R
Increase
4
0.13

H378Q
Decrease
4
-0.68

H378Y
Decrease
3
-0.26

C375R
Increase
2
-0.01

L86P
Decrease
2
-0.56

Table 4: The MEFV gene functions and its appearance in network and genome.

Function
FDR
Genes in network
Genes in genome

nucleotide-binding domain, leucine rich repeat containing receptor signaling pathway
1.42E-07
6
47

regulation of interleukin-1 beta production
0.000129
4
26

interleukin-1 beta production
0.000129
4
30

regulation of interleukin-1 production
0.000129
4
30

interleukin-1 production
0.000196
4
35

intracellular receptor signaling pathway
0.000201
6
207

positive regulation of cysteine-type endopeptidase activity
0.010438
4
101

positive regulation of endopeptidase activity
0.010663
4
105

positive regulation of peptidase activity
0.011
4
109

inflammatory response
0.018246
5
283

regulation of chemokine production
0.018246
3
39

chemokine production
0.022338
3
44

regulation of cysteine-type endopeptidase activity
0.033238
4
160

tumor necrosis factor production
0.033238
3
54

regulation of tumor necrosis factor production
0.033238
3
54

tumor necrosis factor superfamily cytokine production
0.0407
3
59

regulation of I-kappaB kinase/NF-kappaB signaling
0.046902
4
185

I-kappaB kinase/NF-kappaB signaling
0.057722
4
198

positive regulation of cytokine production
0.065004
4
207

positive regulation of cysteine-type endopeptidase activity involved in apoptotic process
0.099763
3
93

positive regulation of interleukin-1 beta secretion
0.099763
2
15

defense response to Gram-negative bacterium
0.099763
2
16

cysteine-type endopeptidase activator activity involved in apoptotic process
0.099763
2
17

regulation of endopeptidase activity
0.099763
4
251

glycosaminoglycan binding
0.099763
3
88

regulation of extrinsic apoptotic signaling pathway
0.099763
3
92

regulation of peptidase activity
0.099763
4
258

positive regulation of interleukin-1 secretion
0.099763
2
16

regulation of interleukin-1 beta secretion
0.099763
2
17

FDR: false discovery rate is greater than or equal to the probability that this is a false positive.

Table 5: The gene coexpression, shared domain, and interaction with MEFV gene network.

Gene 1
Gene 2
Weight
Network group

PF4
CEBPB
0.01083
Co-expression

NLRP14
MEFV
0.014663
Co-expression

EPX
PADI4
0.01094
Co-expression

CASP1
PYCARD
0.012291
Co-expression

TINAGL1
MEFV
0.021529
Co-expression

ZNF747
MEFV
0.032075
Co-expression

ZNF747
TINAGL1
0.01915
Co-expression

EPX
MEFV
0.019982
Co-expression

EPX
ZNF747
0.01848
Co-expression

MRPL44
MEFV
0.02576
Co-expression

RPL27A
MEFV
0.023047
Co-expression

TCTN2
MEFV
0.02049
Co-expression

TCTN2
ZNF747
0.021219
Co-expression

TCTN2
RPL27A
0.019574
Co-expression

ZNF528
RPL27A
0.021843
Co-expression

ZNF528
TCTN2
0.020287
Co-expression

PF4
TINAGL1
0.018596
Co-expression

PF4
EPX
0.016477
Co-expression

CASP1
PYCARD
0.005924
Co-expression

NLRP3
CEBPB
0.01342
Co-expression

CASP1
PYCARD
0.005896
Co-expression

AZU1
MEFV
0.01109
Co-expression

MAP1LC3C
NLRP14
0.011062
Co-expression

PADI4
MEFV
0.003094
Co-expression

AZU1
MEFV
0.003152
Co-expression

AZU1
PADI4
0.004853
Co-expression

ZNF747
MEFV
0.004908
Co-expression

PADI4
MEFV
0.023362
Co-expression

AZU1
MEFV
0.012616
Co-expression

AZU1
PADI4
0.014322
Co-expression

NLRP14
MEFV
0.01623
Co-expression

EPX
PADI4
0.01024
Co-expression

EPX
AZU1
0.007038
Co-expression

ZNF528
MEFV
0.039375
Co-expression

PF4
MEFV
0.017902
Co-expression

PF4
AZU1
0.012247
Co-expression

PF4
EPX
0.007715
Co-expression

TINAGL1
MEFV
0.027084
Co-expression

MRPL44
MEFV
0.011927
Co-expression

TCTN2
MEFV
0.014192
Co-expression

TCTN2
TINAGL1
0.014867
Co-expression

TCTN2
ZNF747
0.010889
Co-expression

TCTN2
MAP1LC3C
0.006994
Co-expression

TCTN2
MRPL44
0.010528
Co-expression

ZNF528
TCTN2
0.012167
Co-expression

RPL27A
MEFV
0.016846
Co-expression

TCTN2
RPL27A
0.018021
Co-expression

CASP1
PSTPIP1
0.009518
Co-expression

EPX
AZU1
0.01909
Co-localization

PADI4
MEFV
0.012301
Co-localization

PADI4
PSTPIP1
0.008748
Co-localization

AZU1
MEFV
0.011852
Co-localization

AZU1
PSTPIP1
0.008052
Co-localization

AZU1
PADI4
0.006025
Co-localization

EPX
MEFV
0.011933
Co-localization

EPX
PSTPIP1
0.008374
Co-localization

EPX
PADI4
0.006323
Co-localization

EPX
AZU1
0.006061
Co-localization

FBXO9
MEFV
0.022287
Co-localization

FBXO9
PADI4
0.009957
Co-localization

FBXO9
AZU1
0.009656
Co-localization

FBXO9
EPX
0.009948
Co-localization

PF4
MEFV
0.012063
Co-localization

PF4
PSTPIP1
0.007583
Co-localization

PF4
PADI4
0.005603
Co-localization

PF4
AZU1
0.005356
Co-localization

PF4
EPX
0.005651
Co-localization

PF4
FBXO9
0.009449
Co-localization

CEBPB
MEFV
0.159581
Pathway

RELA
MEFV
0.078321
Pathway

PSTPIP1
MEFV
0.953023
Pathway

PYCARD
MEFV
0.037199
Pathway

CASP1
PYCARD
0.037199
Pathway

CASP1
MEFV
0.469715
Physical Interactions

NLRP3
PYCARD
0.570819
Physical Interactions

PYCARD
MEFV
0.03673
Physical Interactions

PYCARD
PSTPIP1
0.028273
Physical Interactions

CASP1
PYCARD
0.017772
Physical Interactions

CASP1
CEBPB
0.010941
Physical Interactions

RELA
CEBPB
0.00247
Physical Interactions

COG5
MEFV
0.211887
Physical Interactions

NLRP3
MEFV
0.111467
Physical Interactions

MAP1LC3C
MEFV
0.104412
Physical Interactions

PYCARD
MEFV
0.292858
Physical Interactions

NLRP3
PYCARD
0.189095
Physical Interactions

PSTPIP1
MEFV
0.260595
Physical Interactions

PYCARD
MEFV
0.204673
Physical Interactions

CASP1
PYCARD
0.042335
Physical Interactions

RELA
CEBPB
0.007591
Physical Interactions

COG5
MEFV
0.387501
Physical Interactions

NLRP3
PYCARD
0.304828
Physical Interactions

NLRP3
PYCARD
1
Predicted

PYCARD
MEFV
0.455503
Predicted

CASP1
PYCARD
0.043769
Predicted

RELA
CEBPB
0.024601
Predicted

NLRP3
PYCARD
0.25852
Predicted

CASP1
CEBPB
0.445416
Predicted

CASP1
CEBPB
0.707107
Predicted

PYCARD
MEFV
0.00952
Shared protein domains

CASP1
PYCARD
0.013543
Shared protein domains

NLRP3
MEFV
0.009339
Shared protein domains

NLRP3
PYCARD
0.018527
Shared protein domains

NLRP14
MEFV
0.009512
Shared protein domains

NLRP14
PYCARD
0.018871
Shared protein domains

NLRP14
NLRP3
0.036989
Shared protein domains

ZNF528
ZNF747
0.002699
Shared protein domains

PYCARD
MEFV
0.011528
Shared protein domains

CASP1
PYCARD
0.031451
Shared protein domains

NLRP3
MEFV
0.009427
Shared protein domains

NLRP3
PYCARD
0.015448
Shared protein domains

NLRP14
MEFV
0.009815
Shared protein domains

NLRP14
PYCARD
0.016085
Shared protein domains

NLRP14
NLRP3
0.019774
Shared protein domains

ZNF528
ZNF747
0.002759
Shared protein domains

Figure 1: Diagrammatic representation of MEFV gene in silico work flow.

4. Discussion

23 novel mutations have been found (see Table 3) which affected the stability and function of the MEFV gene using bioinformatics tools. The methods used were based on different aspects and parameters describing the pathogenicity and provided clues on the molecular level about the effect of mutations. It was not easy to predict the pathogenic effect of SNPs using single method. Therefore, multiple methods were used to compare and rely on the results predicted. In this study we used different in silico prediction algorithms: SIFT, PolyPhen-2, Provean, SNAP2, SNP&GO, PHD-SNP, P-MuT, and I-Mutant 3.0 (see Figure 1).

This study identified the total number of nsSNP in Homo sapiens located in coding region of MEFV gene, which were investigated in dbSNP/NCBI Database [24]. Out of 2369, there are 856 nsSNPs (missense mutations) submitted to SIFT server, PolyPhen-2 server, Provean sever, and SNAP2, respectively, and 392 SNPs were predicted to be deleterious in SIFT server. In PolyPhen-2 server, the result showed that 453 were found to be damaging (147 possibly damaging and 306 probably damaging showing deleterious). In Provean server our result showed that 244 SNPs were predicted to be deleterious. While in SNAP2 server the result showed that 566 SNPs were predicted to have effect. The differences in prediction capabilities refer to the fact that every prediction algorithm uses different sets of sequences and alignments. In Table 2 we submitted four positive results from SIFT, PolyPhen-2, Provean, and SNAP2 (see Table 1) to observe the disease causing one by SNP&GO, PHD-SNP, and P-Mut servers.

In SNP&GO, PHD-SNP and P-Mut softwares were used to predict the association of SNPs with disease. According to SNP&GO, PHD-SNP and P-Mut (70, 91 and 58 SNPs respectively) were found to be disease-related SNPs. We selected the triple disease-related SNPs only in 3 softwares for further analysis by I-Mutant 3.0, Table 3. I-Mutant result revealed that the protein stability decreased which destabilizes the amino acid interaction (S749Y, F743S, Y741C, F731V, I720T, L709R, V691G, W689R, G668R, V659F, F636C, H407Q, H407R, H404R, C398Y, H378Q, H378Y, and L86P). C375R, C395F, C395R, C395Y, and R461W were found to increase the protein stability (see Table 3).

BioEdit software was used to align 10 amino acid sequences of MEFV demonstrating that the residues predicted to be mutated in our band (indicated by red arrow) are evolutionarily conserved across species (see Figure 2). While Project HOPE software was used to submit the 23 most deleterious and damaging nsSNPs (see Figures 3–25), L86P: Proline (the mutant residue) is smaller than Leucine (the wild-type residue); this might lead to loss of interactions. The wild-type and mutant amino acids differ in size. The mutation is located within a domain, annotated in UniProt as Pyrin. The mutation introduces an amino acid with different properties, which can disturb this domain and abolish its function. The wild-type residue is located in a region annotated in UniProt to form an α-helix. Proline disrupts an α-helix when not located at one of the first 3 positions of that helix. In case of the mutation at hand, the helix will be disturbed and this can have severe effects on the structure of the protein.

Figure 2: Alignments of 10 amino acid sequences of MEFV demonstrating that the residues predicted to be mutated in our band (indicated by red arrow) are evolutionarily conserved across species. Sequences Alignment was done by BioEdit (v7.2.5).

Figure 3: (L86P): change in the amino acid Leucine (green box) into Proline (red box) at position 86.

Figure 4: (C375R): change in the amino acid Cysteine (green box) into Arginine (red box) at position 375.

Figure 5: (H378Y): change in the amino acid Histidine (green box) into Tyrosine (red box) at position 378.

Figure 6: (H378Q): change in the amino acid Histidine (green box) into Glutamine (red box) at position 378.

Figure 7: (C395R): change in the amino acid Cysteine (green box) into Arginine (red box) at position 395.

Figure 8: (C395F): change in the amino acid Cysteine (green box) into Phenylalanine (red box) at position 395.

Figure 9: (C395Y): change in the amino acid Cysteine (green box) into Tyrosine (red box) at position 395.

Figure 10: (C398Y): change in the amino acid Cysteine (green box) into Tyrosine (red box) at position 398.

Figure 11: (H404R): change in the amino acid Histidine (green box) into Arginine (red box) at position 404.

Figure 12: (H407R): change in the amino acid Histidine (green box) into Arginine (red box) at position 407.

Figure 13: (H407Q): change in the amino acid Histidine (green box) into Glutamine (red box) at position 407.

Figure 14: (R461W): change in the amino acid Arginine (green box) into Tryptophan (red box) at position 461.

Figure 15: (F636C): change in the amino acid Phenylalanine (green box) into Cysteine (red box) at position 636.

Figure 16: (V659F): change in the amino acid Valine (green box) into Phenylalanine (red box) at position 636.

Figure 17: (G668R): change in the amino acid Glycine (green box) into Arginine (red box) at position 668.

Figure 18: (W689R): change in the amino acid Tryptophan (green box) into Arginine (red box) at position 689.

Figure 19: (V691G): change in the amino acid Valine (green box) into Glycine (red box) at position 691.

Figure 20: (L709R): change in the amino acid Leucine (green box) into Arginine (red box) at position 709.

Figure 21: (I720T): change in the amino acid Isoleucine (green box) into Threonine (red box) at position 720.

Figure 22: (F731V): change in the amino acid Phenylalanine (green box) into Valine (red box) at position 731.

Figure 23: (Y741C): change in the amino acid Tyrosine (green box) into Cysteine (red box) at position 731.

Figure 24: (F743S): change in the amino acid Phenylalanine (green box) into Serine (red box) at position 743.

Figure 25: (S749Y): change in the amino acid Serine (green box) into Tyrosine (red box) at position 749.

GeneMANIA revealed that MEFV has many vital functions: chemokine production, inflammatory response, interleukin-1 beta production, interleukin-1 production, intracellular receptor signaling pathway, nucleotide-binding domain, Leucine rich repeat containing receptor signaling pathway, positive regulation of cysteine-type endopeptidase activity, positive regulation of endopeptidase activity, positive regulation of peptidase activity, regulation of chemokine production, regulation of cysteine-type endopeptidase activity, regulation of endopeptidase activity, regulation of interleukin-1 beta production, regulation of interleukin-1 production, and regulation of peptidase activity. The genes coexpressed with, sharing similar protein domain, or participated to achieve similar function were shown in (see Figure 26) Tables 4 and 5.

Figure 26: Interaction between MEFV and its related genes.

In this study we also retrieved all these SNPs as untested (V659F, L709R, F743S, S749Y). We found it to be all damaging. Our study is the first in silico analysis of MEFV gene which was based on functional analysis while all previous studies [34, 35] were based on frequency. This study revealed that 23 novel pathological mutations have a potential functional impact and may thus be used as diagnostic markers for Mediterranean basin populations.

5. Conclusion

In this work the influence of functional SNPs in the MEFV gene was investigated through various computational methods, which determined that S749Y, F743S, Y741C, F731V, I720T, L709R, V691G, W689R, G668R, V659F, F636C, R461W, H407Q,, H407R, H404R, C398Y, C395Y, C395F, C395R, H378Q, H378Y, C375R, and L86P are new SNPs having a potential functional impact and can thus be used as diagnostic markers. They constitute possible candidates for further genetic epidemiological studies with a special consideration of the large heterogeneity of MEFV SNPs among the different populations.

Data Availability

The data which support our findings in this study are available from the corresponding author upon reasonable request.

Conflicts of Interest

The authors declare that there are no conflicts of interest regarding the publication of this paper.

Authors’ Contributions

Mujahed I. Mustafa wrote Abstract, Methodology, and Result & Discussion. Fatima A. Abdelrhman did Introduction. Conclusion was written by Soada A. Osman. Writing the original draft was carried out by Mujahed I. Mustafa.

Acknowledgments

The authors wish to acknowledge the enthusiastic cooperation of Africa City of Technology, Sudan.

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