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

Authors: Mujahed I. Mustafa; Tebyan A. Abdelhameed; Fatima A. Abdelrhman; Soada A. Osman; Mohamed A. Hassan
Journal: Advances in Bioinformatics
 (2019)

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## 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.


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## Body

## 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 demonstrableMEFV 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 forMEFV 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 theMEFV 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 theMEFV 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 inMEFV using in silico prediction software and to determine the structure, function, and regulation of their respective proteins. This is the first in silico analysis inMEFV 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 humanMEFV gene was collected from National Center for Biological Information (NCBI) website [24]. The SNP information (protein accession number and SNP ID) of theMEFV 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/).


## 2.1. Data Mining

The data on humanMEFV gene was collected from National Center for Biological Information (NCBI) website [24]. The SNP information (protein accession number and SNP ID) of theMEFV 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 Tables1–5 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 19Table 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%) DiseaseTable 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 -0.56Table 4
TheMEFV 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 withMEFV 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 domainsFigure 1
Diagrammatic representation ofMEFV gene in silico work flow.


## 3.1. Result

See Tables1–5 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 19Table 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%) DiseaseTable 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 -0.56Table 4
TheMEFV 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 withMEFV 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 domainsFigure 1
Diagrammatic representation ofMEFV gene in silico work flow.


## 4. Discussion

23 novel mutations have been found (see Table3) which affected the stability and function of theMEFV 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 ofMEFV 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, Table3. 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 Figure2). 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 ofMEFV 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 thatMEFV 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 betweenMEFV 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 ofMEFV 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 theMEFV 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 ofMEFV SNPs among the different populations.



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*Source: 1651587-2019-06-04.xml*