The Bioinformatics Report of Mutation Outcome on NADPH Flavin Oxidoreductase Protein Sequence in Clinical Isolates of H. pylori Nasrin Mirzaei, Farkhondeh Poursina, Sharareh Moghim, Abdol Majid Ghaempanah & Hajieh Ghasemian Safaei Current Microbiology ISSN 0343-8651 Curr Microbiol DOI 10.1007/s00284-016-0992-1
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Author's personal copy Curr Microbiol DOI 10.1007/s00284-016-0992-1
The Bioinformatics Report of Mutation Outcome on NADPH Flavin Oxidoreductase Protein Sequence in Clinical Isolates of H. pylori Nasrin Mirzaei1 • Farkhondeh Poursina2 • Sharareh Moghim2 Abdol Majid Ghaempanah3 • Hajieh Ghasemian Safaei2
•
Received: 8 September 2015 / Accepted: 14 December 2015 Springer Science+Business Media New York 2016
Abstract frxA gene has been implicated in the metronidazole nitro reduction by H. pylori. Alternatively, frxA is expected to contribute to the protection of urease and to the in vivo survival of H. pylori. The aim of present study is to report the mutation effects on the frxA protein sequence in clinical isolates of H. pylori in our community. Metronidazole resistance was proven in 27 of 48 isolates. glmM and frxA genes were used for molecular confirmation of H. pylori isolates. The primer set for detection of whole sequence of frxA gene for the effect of mutation on protein sequence was used. DNA and protein sequence evaluation and analysis were done by blast, Clustal Omega, and T COFFEE programs. Then, FrxA protein sequences from six metronidazole-resistant clinical isolates were analyzed by web-based bioinformatics tools. The result of six metronidazole-resistant clinical isolates in comparison with strain 26695 showed ten missense mutations. The result with the STRING program revealed that no change was seen after alterations in these sequences. According to consensus data involving four methods, residue substitutions at 40, 13, and 141 increase the stability of protein sequence after mutation, while other alterations decrease. Residue substitutions at 40, 43, 141, 138, 169, and 179 are deleterious, while, & Hajieh Ghasemian Safaei
[email protected] Nasrin Mirzaei
[email protected] 1
Department of Microbiology, Tonekabon Branch, Islamic Azad University, Tonekabon, Iran
2
Department of Microbiology, Isfahan University of Medical Sciences, Isfahan, Iran
3
Department of Pathology, Zanjan University of Medical Sciences, Zanjan, Iran
V7I, Q10R, V34I, and V96I alterations are neutral. As FrxA contribute to survival of bacterium and in regard to the effect of mutations on protein function, it might affect the survival and bacterium phenotype and it need to be studied more. Also, none of the stability prediction tool is perfect; iStable is the best predictor method among all methods.
Introduction Metronidazole is an important component of current antimicrobial therapies for H. pylori infection [1]. Goodwin et al. (1998) reported that oxygen-insensitive NADPH nitroreductase (rdxA) was a putative metronidazole nitroreductase encoding gene involved in metronidazole resistance [2]. Whilst Jenks et al. (1999) found metronidazole-resistant H. pylori isolates without any alteration in the rdxA gene, Kwon et al. (2000) present evidence that NADPH flavin oxidoreductase encoding gene (frxA) may also be involved in metronidazole resistance of H. pylori [1]. Bacterium frxA (hp0642) gene has been implicated in the metronidazole nitro reduction by H. pylori. Furthermore, this enzyme can transform the anthelmintic drug to products such as hydroxylamine that are both bactericidal and mutagenic. Mukhopadyay et al. (2003) reported low levels of FrxA in most H. pylori strain isolates (designated type I strains) but higher levels in others (type II strains) [3]. FrxA performs a role in protection of H. pylori against reactive nitrogen metabolites, as the protein has vital formally GSH-dependent formaldehyde dehydrogenase (GSNOR) activity. Because of its GSNOR activity, FrxA is expected to contribute to the protection of urease and to the in vivo survival of H. pylori. In fact, deletion of frxA is seen to reduce the resistance of the bacterium during macrophage
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infection and to compromise the pathogen’s ability to sustain mouse stomach colonization [4]. frxA gene, which encodes a NADPH flavin oxidoreductase, may act indirectly by affecting the cellular reductive potential [5]. The data also explain the rare incident of FrxA mutations in metronidazole-resistant clinical strains, as inactivation of its activity is expected to be harmful to H. pylori survival. FrxA contributes to the survival of H. pylori during macrophage infection and mouse gastric colonization. Ergo, FrxA constitutes an H. pylori defence against the S-nitrosothiols (SNO) stresses imposed by the host mammalian immunity to which the pathogen is exposed during its lifelong colonization [4]. It has been widely accepted that a protein’s primary sequence determines its three-dimensional structure, which in turn determines its function [6]. Amino acid substitutions can cause a series of changes to normal protein function, such as geometric constraint changes, physicochemical effects, and disruption of salt bridges or hydrogen bonds. These alterations may lead to protein destabilization or some abnormal biological functions. While the majority of non-synonymous single-nucleotide polymorphisms (nsSNPs) appear to be functionally neutral, the others affect protein function and may cause or influence diseases [7]. Point mutations can have a hefty effect on the thermodynamic stability of proteins. Deliberately increasing the stability of a protein or keeping it stable while changing certain other protein properties is often a goal in biotechnology, e.g., to optimize industrial processes, or also in drug design or basic research. Analysis of the stability upon point mutations can also be used to identify a wide spectrum of drug resistance conferring mutations [8, 9]. The effect of alterations varies by the type of the mutation and the sequence and structure context. A mutation may also lead into gain of function effects [10]. The measure of the protein stability change upon single-point mutations is a thermodynamic quantity that the accurate prediction is an important problem of structural bioinformatics. In the last years, a considerable number of different methods have been developed to predict the stability in protein when one residue is mutated [11]. Interestingly, other factors such as interaction need take into account. Due to the protein central role in biological function, protein interactions control the mechanisms leading to healthy and diseased states in organisms. Diseases are often caused by mutations affecting the binding interface or leading to biochemically dysfunctional allosteric changes in proteins. It is estimated that each person is heterozygous for 24,000–40,000 amino acid-altering substitutions. Also, predicting substitutions at these sites as deleterious or neutral may help identify disease-associated alleles [12, 13]. When engineering proteins, an influential problem to be considered is to which extent a mutation will affect the stability of the new protein with respect to the wild-type [14].
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For this reason, the field of amino acid mutation analysis has rapidly grown into an area of active research in computational studies of proteins today. This development is largely attributable to the availability of ever increasing amounts of protein sequences during the post-genomic era, swiftly accumulating information about protein variations, along with many novel bioinformatics tools that are applied to study the effects and consequences of the amino acid mutation [15]. Nevertheless, to date the complete mechanisms by which a nucleotide variant may result in a phenotypic change are for the most part unknown. But, in silico analysis using powerful software tools can facilitate predicting the phenotypic effect of non-synonymous coding SNPs on the physic-chemical properties of the concerned proteins. Such information is crucial for genotype–phenotype correlations and also to understand disease biology [16]. Besides, predicting the phenotypic consequence of nsSNPs using computational algorithms provides a better understanding of genetic differences in susceptibility to diseases and drug/nutrient response [17]. Nowadays, a number of algorithms based on sequence- and structurebased approach have been improved to predict the impact of different kinds of mutations on protein sequence. The aim of present study is to analyze the mutation on the protein sequence in clinical isolates of H. pylori in our community. The effect of these mutations on protein interaction, stability, and function is critical to predict.
Materials and Methods Sampling and DNA Extraction Hundred and ten biopsy specimens were obtained from patients in 2011. The biopsy specimens were cultured on brucella agar into a microaerophilic atmosphere. Then, MIC values of metronidazole-resistant H. pylori clinical isolates were determined by the epsilometer test (E-test, Biomerieux, France). Isolates were considered resistant to metronidazole if the MIC was C8 lg/ml. Forty-eight H. pylori isolates were grown up from 110 specimens. Metronidazole resistance was proven in 27 of 48 isolates. The reference strain in this study was H. pylori 26695 which is metronidazole sensitive. DNA was extracted from all H. pylori resistant isolates and reference strain by QIAamp tissue kit (Qiagen, Germany) according to the manufacturer’s instruction [18, 19]. Molecular Confirmation of H. pylori Isolates We designed two set of oligonucleotide primers including glmM (housekeeping gene) and frxA (detection of gene) (Table 1). The Allele ID version 6 software and MEGA5
Author's personal copy N. Mirzaei et al.: The Bioinformatics Report of Mutation Outcome on NADPH Flavin… Table 1 Primers used in this study
Primer pair
Encoded protein
Nucleotide sequence
Size (bp)
glmM-F
Phosphoglucose amine mutase
F:5-TGCTTGCTTTCTAACACTAACG-3
355
glmM-R
R:5-TTGATGGCGATGCTGATAGG-3
frxA-F
NADPH flavin
F:5-CGTAATCGCTGTCATAAGTAAC-3
frxA-R
oxidoreductase
R:5-ATCCTAATCGTCGTATTTCCC-3
frxA-F
F:5-GGATATGGCAGCCGTTTATCATT-3
frxA-R
R:5-GAATAGGCATCATTTAAGAGATTA-3
multiple alignment tools were used for primer designing and alignment, respectively. The MEGA5 was used to align all glmM and frxA sequences of H. pylori that were in NCBI database and identify conserved areas for the primer design. Then the designed primers were confirmed using by primer blast tool (http://www.ncbi.nlm.nih.gov/ tools/primer-blast/). The thermodynamics characteristics were assessed by Gene runner software. The expected PCR products for glmM and frxA were 355 and 228 bp, respectively. The cycling program for both genes was 1 cycle at 95 C for 10 min; 30 cycles of 95 C for 10 s, 60 C for 30 s, and 72 C for 30 s and a final elongation step at 72 C for 1 min [19]. PCR Amplification of frxA Gene The primer set for detection of whole sequence (654 bp) of frxA gene for the effect of mutation on protein sequence was used. The cycling program was 1 cycle at 94 C for 2 min; 30 cycles of 94 C for 40 s, 54 C for 40 s, and 72 C for 1 min; and a final elongation step at 72 C for 10 min. The expected PCR product was a single 780 bp band for the frxA gene [20–22]. DNA Sequence Analysis PCR products were purified and sequenced by Bioneer Korea Company. The DNA sequence analysis was done by using the nucleotide blast program (http://blast.ncbi.nlm. nih.gov/Blast.cgi) for assessing the silent alterations. The nucleotide sequences referred to this paper are deposited at GenBank under accession numbers KT232260 to KT232265. Protein Sequence Evaluation and Analysis The protein sequence analysis and comparison or similarity were done by using the blastx program. Other comparisons on protein sequences were done by programs such as Clustal Omega (http://www.ebi.ac.uk/Tools/msa/clustalo/) and T COFFEE (http://tcoffee.crg.cat/) for alignment. These programs are suitable for fast and accurate multiple sequence alignment [23, 24].
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Assessment of Mutation on Protein–Protein Interaction The Search Tool for the Retrieval of Interacting Genes (STRING) is the only site to cover hundreds organisms— ranging from Bacteria and Archaea to humans. This large number of organisms, represented by their fully sequenced genomes, also enables STRING to periodically execute interaction prediction algorithms that depend on exhaustive genome sequence information. The resource also transfers interaction information between organisms where applicable, thereby significantly increasing coverage particularly for poorly studied organisms [25]. The tool is accessible from the link http://string-db.org/. This server is recommended by UniProt (http://www.uniprot.org/) for this gene and organism. Assessment of Mutation on Protein Stability The Cologne University Protein Stability Analysis Tool (CUPSAT) is a web tool to analyze and predict protein stability changes upon point mutations (single amino acid mutations). This program uses structural environment specific atom potentials and torsion angle potentials to predict DDG, the difference in free energy of unfolding between wild-type and mutant proteins [9]. This program predicts stability from existing PDB structures and custom protein structures. The first one has two options such as thermal and denaturants. The tool is accessible from the link http://cupsat.tu-bs.de/. The PDB ID for FrxA protein is 2H0U that can be found from PDB site (http://www.rcsb. org/pdb/home/home.do). The schematic structure of this protein is shown in Fig. 1. The I-Mutant 2 is a support vector machine (SVM)based tool for the automatic prediction of protein stability changes upon single-point mutations. It can predict the stability change of the mutated protein structure, and, for the first time, it can predict to which extent a mutation in a protein sequence will or will not affect the stability of the folded protein [14]. This program is performed starting either from the protein structure or protein sequence. The tool is accessible from the link http://folding.biofold.org/imutant/.
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Fig. 1 Crystal structure of FrxA (2H0U)
The iStable which uses a support vector machine (SVM) to predict protein stability changes upon single amino acid residue mutations and it will determine whether the mutation is a stabilizing or destabilizing mutant [26]. This program is available with two different input types: structural and sequential information. The tool is accessible from the link http://predictor.nchu.edu.tw/iStable. The MUpro which uses support vector machines to predict protein stability changes for single amino acid mutations are applying for both sequence and structural information. This method can precisely predict protein stability changes using primary sequence information only; it is applicable to many situations where the tertiary structure is unknown [27]. The tool is accessible from the link http://mupro.proteomics.ics.uci.edu/.
sample was devoid of any kind of mutation in spite of having the highest MIC (256 lg/ml). A similar pattern is repeated in fourth sample with MIC of 128 lg/ml, but it should be mentioned that fourth sample had some silent alterations and sixth sample had the same sequence as reference strain (AE000511.1). While alteration of valine to isoleucine was seen in three different positions (7, 34, and 96) in three isolates, other substitution was seen just for once in these sequences. Silent substitutions in the first sample were as follows: A114G, G210A, G222A, G255A, A261G, A291G, T336C, T414C, C444T, A534G, T591C, C618T, and G624A. Also, it had three missense mutations including A29G, G286A, and G505A. Silent alterations in second sample were as follows: A114G, A117G, G162A, A165G, G222A, A291G, T330C, C444T, C561G, G564A, T591C, and C618T. Besides too many silent mutations, G19A and C119T substitutions were seen as missense mutations. Silent substitutions in the third sample were C78A, G99A, A114G, G162A, A165G, G222A, G255A, C285T, A291G, T330C, T336C, T414C, and T591C, and their missense mutations were G100A, C128T, and C421A. While silent alterations in fourth sample were G222A, A291G, A312G, T330C, T336C, and T591C, no missense mutation was seen. Silent mutations in fifth sample were as follows: C78A, G222A, C285T, A291G, T336C, T399C, and T591C, and it had two missense mutations including G412A and G536A. A similar pattern about missense mutations is repeated in sixth sample in comparison with fourth one. Effect of Mutation on Protein–Protein Interaction
Assessment of Mutation on Protein Function Protein variation effect analyzer (PROVEAN), a new algorithm, predicts the functional impact for all classes of protein sequence variations not only single amino acid substitutions but also insertions, deletions, and multiple substitutions. The method can be tested on human and nonhuman protein variations [28]. The tool is accessible from the link http://provean.jcvi.org/index.php.
Results Mutational Analysis of frxA Gene in Comparison with 26695 Strain
In spite of the fact that mutation can make impressive change on protein interaction, the result with the STRING program revealed that no change was seen after alterations in these sequences. NADPH flavin oxidoreductase (HP_0642) sequences with substitutions had the same interaction similar to reference strain. According to data, HP_0642 have interaction with Hp_1508 (Ferrodoxin like protein), Hp_0073 (Urease subunit Alpha), Hp_0277 (ferredoxin), Hp_0875 (catalase), Hp_0602 (3-methyl adenine DNA glycosylase), Hp_1184 (hypothetical protein), Hp_0224 (bifunctional methionine sulfoxide reductase A/B), Hp_0641 (hypothetical protein), Hp_0639 (hypothetical protein), and Hp_0640 (Poly A polymerase). Effect of Mutation on Protein Stability
As it is mentioned above, Metronidazole resistance was proven in 27 of 48 H. pylori positive isolates. Among these metronidazole-resistant clinical isolates, the most resistant specimens including six isolates were sent for sequencing and other processing. The results of sequencing are listed in Table 2. Among these resistant clinical isolates, sixth
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Changes in the protein stability of missense variants were examined using CUPSAT, I-Mutant 2, iStable, and MUpro which are free and on-line programs. It is crucial to know that prediction model in CUPSAT has been developed using (existing PDB structures) DDG
Author's personal copy N. Mirzaei et al.: The Bioinformatics Report of Mutation Outcome on NADPH Flavin… Table 2 Missense mutations in FrxA sequences in comparison with reference strain
Strain
MIC (lg/ml)
Amino acid position and change in FrxA sequences 7
10
34
40
43
96
138
141
169
179
26695
S
V
Q
V
A
S
V
A
Q
E
G
F-1
192
.a
R
.
.
.
I
.
.
K
.
F-2
128
I
.
.
V
.
.
.
.
.
.
F-3
192
.
.
I
.
L
.
.
K
.
.
F-4
128
.
.
.
.
.
.
.
.
.
.
F-5
192
.
.
.
.
.
.
Tb
.
.
D
F-6
256
.
.
.
.
.
.
.
.
.
.
MIC minimum inhibitory concentration a
The amino acid is the same as that of the strain 26695 (AE000511.1)
b
A missense mutation resulted in amino acid substitution
or DDGH2O values, derived either from thermal or chemical denaturation experiments, respectively. So, mutation is selected to deploy either of these models for predicting mutant stability. The predictions of web-based tools are shown in Table 3 for each missense mutation studied in this work. By taking a look at the table, it can be inferred that mutations have different effects on protein sequence in thermal and chemical denaturation. For example, when the effect of mutation is studied by thermal option, the majority of missense mutation destabilizes our clinical isolates. All mutations made protein stabilize, and have favorable torsion angles. Torsion angle potentials were derived from the distribution of angles / and w for all the amino acids. On the other hand, separate prediction models were developed for mutations that were derived from chemical (denaturants such as urea or guanidine hydrochloride) denaturation experiments (with DDGH2O). For instance, the majority of missense mutation stabilizes our clinical isolates which have favorable torsion angles except two positions (A138T and G179D). These two alterations are hydrophobic—polar and hydrophobic—charged converts, respectively. The amino acid characterizations can be found from protein structure and structural bioinformatics program (http://proteinstructures.com). Substitutions such as V34I, A40V, S43L, and E169K led to destabilize protein. The negative- and positive-predicted DDG values mean the destabilizing and stabilizing effect, respectively. It is noted that overall stability is calculated from atom potentials and torsion angle potentials. In case of unfavorable torsion angles, the atom potentials may have higher impact on stability which results in a stabilizing mutation (according to the program instruction). Residue substitutions at 96 and 138 have the minimum and position 169 have the maximum solvent accessibility.
Second method for prediction was done by I-Mutant2 (using structure) that the result of this part is shown in Table 3. By taking a more attentive look at the table, it can be deduced that alterations such as V34I, A40V, and S43L led to increase the stability of protein (DDG [ 0) while others decrease the stability (DDG \ 0). The figures for solvent accessibility are negligible for residue substitutions at A40V and Q141K with 5.4 and 10.1, respectively. No RSA was seen for V96I and A138T substitutions which decrease the stability of protein sequence. According to the program instruction, DDG \ 0 and DDG [ 0 can decrease and increase the protein stability, respectively. But an exception can be seen in residue 40. While A40V substitution had DDG \ 0 (-0.05), it increased the stability of protein. This alteration is a hydrophobic–hydrophobic convert. Third and fourth methods for prediction of stability change in our study were done by iStable and MUpro. According to these results which are the same as each other, all missense mutation decreased the stability of protein sequence except three substitutions including A40V, S43L, and Q141K. According to program instruction in MUpro, confidence score near 0 means unchanged stability, score near -1 means high confidence in decreased stability (including E169K, V96I, A138T, and G179D) and score near ?1 means high confidence in increased stability (including S43L) [29]. A consensus is achieved after comparing these programs with each other. According to its relevant data, residue substitutions at 40, 13, and 141 increase the stability of protein sequence after mutation, while other alterations decrease. Effect of Mutation on Protein Function Another stage for assessing the impact of mutation is whether mutation is neutral or deleterious. PROVEAN
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0.89 Unfavorable : Unfavorable
-1.83
1.44
;
;
-0.73
Decrease -0.91 ; 78.78
Decrease
Increase 0.18
-1 0.85 ;
;
0.5 :
-0.34
; 10.1
Favorable ;
0.49 Favorable :
0.17 Favorable
Favorable
-0.06
-0.57
-1.59
:
; 86.4
Decrease
Decrease -0.84 0.81 ; Unfavorable : Unfavorable
-0.37
3.2
0.0
;
-1.40
;
Increase 0.61
-1 0.80 ;
;
0.54 : 0.64
-0.40 ; 0.0
26.5
Favorable :
-0.51 Unfavorable ;
0.64 Favorable
Unfavorable
-0.33
1.05
:
0.63 : 5.4 Unfavorable ; Unfavorable
-2.7
-0.23
37.1 Favorable ; Favorable
0.99
-0.53
:
-0.05
:
Decrease
Increase 0.26
Decrease
0.73 ; 0.35
:
-0.62
0.71 ; -0.08
:
0.64 ; -0.43 ;
; 44.8 Favorable : -0.38
0.38 Favorable : -0.13
Favorable
Favorable
Torsion Stability
The scarce occurrence of frxA mutations in metronidazoleresistant clinical isolates is expected to be harmful to H. pylori survival. Hence, it may be conserved that targeting FrxA with drugs or antibodies could constitute an adjunct to the existing antibiotics by facilitating host dependent clearance of the pathogen [4]. There has been disagreement about the quantitative contributions of rdxA and frxA to metronidazole susceptibility and resistance. On the one hand, Kwon et al. (2000) had concluded that inactivation of either gene by itself could make any typical Table 4 Comparison of PROVEAN function analysis and stability consensus PROVEAN Site V7I Q10R
; 78.78 G179D
:
; 10.08
87.0 E169K
Q141K
; 0.0 A138T
:
; 28.16
0.0 V96I
S43L
; 6.35 A40V
;
: 37.74 V34I
56.6
43.11 Q10R
;
V34I
V7I
Stability
Torsion
DDG
DDG
54.7
RSA Denaturants Thermal RSA
0.3
Stability
Conference
;
Decrease
Discussion
Stability
;
-0.45
-0.26
Conference Stability
program can help us understand its effect. The score thresholds have considered for prediction in this program. According to program instruction (Table 4), default threshold is -2.5, that is, variants with a score equal to or below -2.5 are considered deleterious and variants with a score above -2.5 are considered neutral. So, substitutions in position V7I, Q10R, V34I, and V96I are neutral and have no impact on protein sequence while others including A40V, S43L, A138T, Q141K, E169K, and G179D are deleterious, and these are important for identifying resistance associated alleles in NADPH flavin oxidoreductase protein sequence. By taking a more attentive look at these data and compare them with consensus result, it can be inferred that residue substitutions at 40, 43, and 141 which increase the stability are deleterious and they have equal proportion with those substitutions (138, 169, and 179) are deleterious, which decrease the stability. On the other hand, alterations such as V7I, Q10R, V34I, and V96I which decrease the stability are neutral and have no impact on protein function.
DDG
MUpro iStable I-Mutant CUPSAT Site
Table 3 Comparison of stability changes by using CUPSAT (thermal and denaturants), I-Mutant 2, iStable and MUpro in mutated sequences
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;
Consensus
N. Mirzaei et al.: The Bioinformatics Report of Mutation Outcome on NADPH Flavin…
Consensus
PROVEAN score
Prediction (cutoff = -2.5)
0.730
Neutral
Decrease
-0.436
Neutral
Decrease
0.767
Neutral
Decrease Increase
A40V
-2.938
Deleterious
S43L
-5.726
Deleterious
Increase
V96I A138T
0.364 -2.860
Neutral Deleterious
Decrease Decrease
Q141K
-3.712
Deleterious
Increase
E169K
-2.874
Deleterious
Decrease
G179D
-5.470
Deleterious
Decrease
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H. pylori strain resistant to metronidazole, and that following frxA inactivation, growth on metronidazole-containing agar was not associated with mutation of rdxA [21]. The results shown in Marais et al. (2003) suggest that alterations of frxA gene (hp0642) alone conferred resistance in clinical isolates that is in agreement with the findings of Kwon et al. (2000) study. Also, other studies validate the role of frxA gene in metronidazole-resistant H. pylori clinical isolates [1, 20, 30, 31]. According to previously Mirzaei [19] work on rdxA structure of the same clinical isolates of H. pylori strains, it can be inferred that in spite of having high MIC (256 lg/ml), no changes were seen as opposed to 26695 strain in sixth isolate as the same as fourth and sixth isolates in present study. It is supposed that other mechanisms have a main part in this resistant isolate [19]. Since metronidazole is not the de novo substrate for H. pylori nitroreductases, theoretically proteins that possess or inhibit metronidazole nitroreductase activity could be involved in metronidazole susceptibility [1]. In the study done by Kwon et al. (2001), frame shift mutations appeared to be the major cause for the premature truncation of FrxA [2], while no frame shift mutation was seen in our study. In fact, missense mutations were the only kind of mutations in our study that was seen in all of our clinical isolates except fourth and sixth sample in spite of having high MIC (128 and 256 lg/ml, respectively). These results present the point that this is not the only mechanism of resistance and there may be some other mechanisms involved that is in accordance with other studies [1, 22, 31– 35]. Evaluation and verification of the bioinformatics approach for the analysis of mutational effects are of highest importance, so that computational prediction could acquire a more established role in the study of disease, accelerating for instance, the identification of pharmaceutical targets for relevant treatments. It should be noted that despite being beneficial in providing information about the nature of mutations as such, bioinformatics analyses could also be helpful in guiding the design of further experimental research [36]. The combinatorial and complementary greedy search methods and association search-based susceptibility prediction algorithms are very encouraging techniques that can possibly help discover gene interactions causing common diseases and to create diagnostic tools for genetic epidemiology of common diseases [37]. It has proven that the resulted association-based combinatorial prediction algorithm significantly outperforms existing prediction methods [37, 38] and considering all the evaluation measures, no single method could be rated as the best [39]. So, various groups of methods complement each other and are suited for different types of tasks [40]. Amino acid substitutions can cause a series of changes to normal protein function, such as geometric constraint
changes, physico-chemical effects, and disruption of salt bridges or hydrogen bonds. These changes may lead to protein destabilization or some abnormal biological functions [7]. The amino acid properties are often regarded as a consequential characteristic, which could play a critical role in protein reactions, although sometimes they may be misleading. Hence, the use of computational-based tools with various algorithms significantly helps to overpower the difficulty of selection and prioritizing pathogenic variants from a torrent of data. A number of algorithms based on sequence- and structure-based approach have been improved to predict the effect of missense mutations on protein interaction, stability, and function [41, 42]. For the first purpose in our bioinformatics study, protein–protein interactions (PPIs) can be strengthened or weakened by missense variants, which can cause loss of salt bridges, steric clashes or changes to post-translational modifications, amongst other effects and understanding the effects of missense variants on PPIs and interactome is helpful in determining how these variants can lead to disease [43]. The main strengths of STRING method, we used it in our study, are lying in its unique comprehensiveness, its confidence scoring, and it’s interactive and intuitive user interface [25] and cover more than 2000 organisms, which has necessitated novel, scalable algorithms for transferring interaction information between organisms [44]. According to the advantage and accuracy of this program, we used it in our study, no distinctive change was seen. Variants were tested and the type of PPI was the same as wild-type. Ye et al. (2006) showed that many disease associated mutations from Swiss-Prot are likely involved in disrupting protein interactions which are in against of our results that no change in PPI was seen in our resistant clinical isolates. On the other hand, similar to our study, Fu and Liang study revealed no change in the interactions between the R120G mutant and cC-crystallin or Hsp27 [45, 46]. Because there are many new and improved amino acid substitution (AAS) prediction methods with complementary strengths, better accuracy should be possible by combining prediction methods [47]. So, for the second purpose when exploring the effects of mutations on protein stability, several aspects should be taken into account. The measure of the protein stability change upon single point mutations is a thermodynamic quantity whose precise prediction is a key problem of structural bioinformatics [11]. In the last years, a significant number of different methods have been developed to predict the stability when one residue is mutated. In regard to many prediction tools applied for stability change and many studied done according to accuracy level and standard error, we chose publicly online programs which are easy to use and comprehensible such as CUPSAT (Parthiban et al., 2006), I-Mutant 2 (Capriotti et al. 2005a, b), iStable (Chen et al.
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2013), and MUpro (Cheng et al. 2006). The CUPSAT algorithm method predicted the stability of protein mutants with accuracy in the range of 80–87 % [9]. On the other side, I-Mutant 2 properly predicts 80 or 77 % of the data set, depending on the input of structural or sequence information, respectively. Hence, the accuracy of the structure-based prediction is 0.80 and it is higher than sequence based (0.77) [14]. Because of this reason, we analyzed our sequence with structure based. While the standard error values for I-Mutant are 1.30 and 1.45 kcal/mol, when the prediction is structure or sequence based, respectively, this figure for CUPSAT is 0.78–1.15 kcal/mol which is comparable with other methods [9, 14]. Although accuracy of iStable is 85.7 %, this figure for MUpro was 84 % [26, 27]. In addition, CUPSAT is relatively faster than many of the currently available algorithms, whilst iStable is the predictor not only with the best performance among other predictors, but also the slowest to finish the prediction task. Since iStable has the properties of program reusability and computing resources reduction, it was the best choice for predicting protein stability changes [9, 26] that is in agreement with our study. According to the time, fastness, more importantly the quality of results, accuracy, etc., we concluded that the best predictor in our study was iStable which outperform noticeably better than other models we used in our study and the following places are MUpro, I-Mutant, and CUPSAT(thermal and denaturant), respectively. The predictions acquired with CUPSAT are the most obscure, since the effect of thermal and denaturant stabilities is calculated to be just opposite and that is in accordance with our study and Zhang et al. [48]. While V34I, V96I, and E169K made protein to stabilize in thermal, positions of 34 and 169 led protein to destabilize in denaturant condition. We understood that CUPSAT is not able to reliably recognize stabilizing and destabilizing mutations that is in agreement with Bloom et al. (2009) study who had conducted a research on Influenza Hemagglutinin [49]. On the contrary, Khan et al. (2010) reported the CUPSAT, as the most reliable predictor, as the highest accuracy, and sensitivity in their study [50]. According to our study, the correlation between two programs was best for MUpro and iStable, which overlap each other; relatively well for I-Mutant 2 and the most questionable for CUPSAT. In general, it should be noted that computational analyses rely heavily on the quality of the data under scrutiny and the computational methods used to evaluate these data [51]. For third purpose, residues that are conserved completely in the protein family are expected to be important for function, and even a conservative substitution at one of
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these residues may affect protein function. At some positions, any amino acid change can be tolerated in the protein if these positions are not involved in protein function or structure. Because these are expected to be neutral substitutions, one might expect amino acids in these positions of a protein alignment to be diverse [13]. Further analysis for assessing the impact of mutation on protein function revealed many programs such as PROVEAN and SIFTS. Some of these programs have been applied to human variants database such as SIFT [52]. So, we chose PROVEAN (Choi et al. 2012) which uses human and non-human including viruses, fungi, bacteria, and plants. The balanced accuracy of PROVEAN was about 77 % for nonhuman protein [28]. Yue and Moult (2006) investigated the effect of amino acid substitutions on protein stability, and estimated that approximately 25 % of nsSNPs in the human population might be detrimental to protein function [7], whilst we found that 60 % (six from ten mutations) of our mutations, in spite of small volume of samples, are harmful. The result of Wang and Moult study was that many of the mutations that affect stability would not be expected to have a remarkable impact on function [53], that is in against of our study and 60 % of our mutations, underwent stability decrease or increase, affect the protein function. On the other hand, these investigators believed that these missense mutations seem to decrease stability by up to 80 % [7, 53] that is in agreement with our study and 70 % of our missense mutations decreased the protein stability. Despite residue substitutions at 7, 10, 34, and 96 which decrease stability of protein according to consensus result, they are neutral and had no impact on protein function. Although A40V, S43L, and Q141K enhanced the stability of protein, A138T, E169K, and G179D substitutions reduced and both of these groups are deleterious and influenced the protein function.
Conclusion Understanding how mutations cause changes in protein interaction, stability, and function is important to infer the molecular causes of clinical phenotypes. Owing to this reason, experimental data are complemented by bioinformatics tools. In view of the fact that FrxA contributes to survival of bacterium and in regard to the effect of mutations on protein function, it might affect the survival and bacterium phenotype and it is necessary to study more. There are many web-based prediction programs available and each of them has its own capabilities and merits, but none of the stability prediction tool is perfect, flawless, and in general, better results are acquired if the predictions from a variety of tools and different algorithms are
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combined and then interpreted. We concluded that iStable is the best predictor method among all methods and covers all criteria. We feel that it is vital to assess the molecular consequences of missense mutations in the cellular context. To understand the molecular basis of the effects of genetic variations on phenotype in all H. pylori genes that are relevant to metronidazole resistance such as nitroreductases, in general, rpsU (HP0562, 30S ribosomal protein S21) [54], recently known in metronidazole-resistant isolates, needs further investigations. It is noteworthy that our work also provides the first evidence that mutation in frxA gene causes changes on stability and function of protein. Acknowledgment This study was supported by Grant No: 290054 from Isfahan University of Medical Sciences, Isfahan, Iran. Compliance with Ethical Standards Conflict of Interest
There is no conflict of interest.
References 1. Kwon DH, KatoM El-Zaatari FA, Osato MS, Graham DY (2000) Frame-shift mutations in NAD (P) H flavin oxidoreductase encoding gene (frxA) from metronidazole resistant Helicobacter pylori ATCC43504 and its involvement in metronidazole resistance. FEMS Microbiol Lett 188:197–202 2. Kwon DH, Hulten K, Kato M, Kim JJ, Lee M, El-Zaatari FAK, Osato MS, Graham DY (2001) DNA sequence analysis of rdxA and frxA from 12 pairs of metronidazole-sensitive and-resistant clinical Helicobacter pylori isolates. Antimicrob Agents Chemother 45:2609–2615 3. Salzar SC, Lopez IP, Mejia AV, Carranza RC, Pinzon SG, Aguirre E (2005) Promutagen activation by Helicobacter pylori lysate. Rev Int Contam Ambient 21:91–96 4. Justino MC, Parente MR, Boneca IG, Saraiva LM (2014) FrxA is an S-nitrosoglutathione reductase enzyme that contributes to Helicobacter pylori pathogenicity. FEBS J 281:4495–4505 5. De Oliveira IM, Bonatto D, Henriques JAP (2010) Nitroreductases: enzymes with environmental, biotechnological and clinical importance. Current research, technology and education topics in applied microbiology and microbial biotechnology, vol 6. Formatex, Badajoz, pp 1008–1019 6. Yang ZR, Thomson R, McNeil Esnouf RM (2005) RONN: the bio-basis function neural network technique applied to the detection of natively disordered regions in proteins. Bioinformatics 2:3369–3376 7. Teng S, Srivastava AK, Wang L (2010) Sequence feature-based prediction of protein stability changes upon amino acid substitutions. BMC Genom 1:S5 8. Laimer J, Hofer H, Fritz M, Wegenkittl S, Lackner P (2015) MAESTRO-multi agent stability prediction upon point mutations. BMC Bioinformatics 16:116 9. Parthiban V, Gromiha MM, Schomburg D (2006) CUPSAT: prediction of protein stability upon point mutations. Nucleic Acids Res 34:239–242 10. Thusberg J, Vihinen M (2006) Bioinformatic analysis of protein structure-function relationships: case study of leukocyte elastase (ELA2) missense mutations. Hum Mutat 27:1230–1243
11. Capriotti E, Fariselli P, Rossi I, Casadio R (2008) A three-state prediction of single point mutations on protein stability changes. BMC Bioinform 9:S6 12. Gonzalez MW, Kann MG (2012) Chapter 4: protein interactions and disease. PLoS Comput Biol 8:e1002819 13. Ng PC, Henikoff S (2001) Predicting deleterious amino acid substitutions. Genome Res 11:863–874 14. Capriotti E, Fariselli P, Casadio R (2005) I-Mutant2.0: predicting stability changes upon mutation from the protein sequence or structure. Nucleic Acids Res 33:306–310 15. Chen J, Shen B (2009) Computational analysis of amino acid mutation: a proteome wide perspective. Curr Proteom 6:228–234 16. Hussain MRM, Shaik NA, Al-Aama JY, Asfour HZ, Khan FS, Masoodi TA, Khan MA, Shaik NS (2012) In silico analysis of single nucleotide polymorphisms (SNPs) in human BRAF gene. Gene 508:188–196 17. Marı´n-Martı´n FR, Soler-Rivas C, Martı´n-Herna´ndez R, Rodriguez-Casado A (2014) A comprehensive in silico analysis of the functional and structural impact of nonsynonymous SNPs in the ABCA1 transporter gene. Cholesterol. doi:10.1155/2014/ 639751 18. Mirzaei N, Poursina F, Faghri J, Talebi M, Khataminezhad MR, Hasanzadeh A, Safaei HG (2013) Prevalence of resistance of Helicobacter pylori strains to selected antibiotics in Isfahan, Iran. Jundishapur J Microbiol 6:e6342 19. Mirzaei N, Poursina F, Moghim S, Rahimi E, Safaei HG (2014) The mutation of the rdxA gene in metronidazole-resistant Helicobacter pylori clinical isolates. Adv Biomed Res 3:90 20. Gerrits MM, Van der Wouden EJ, Bax DA, Van Zwet AA, Van Vliet AH, de Jong A, Kusters JG, Thijs JC, Kuipers EJ (2004) Role of the rdxA and frxA genes in oxygen-dependent metronidazole resistance of Helicobacter pylori. J Med Microbiol 53:1123–1128 21. Jeong JY, Mukhopadhyay AK, Akada JK, Dailidiene D, Hoffman PS, Berg DE (2001) Roles of FrxA and RdxA nitroreductases of Helicobacter pylori in susceptibility and resistance to metronidazole. J Bacteriol 183:5155–5162 22. Yang YJ, Wu JJ, Sheu BS, Kao AW, Huang AH (2004) The rdxA gene plays a more major role than frxA gene mutation in highlevel metronidazole resistance of Helicobacter pylori in Taiwan. Helicobacter 9:400–407 23. Notredame C, HigginsDG Heringa J (2000) T-Coffee: a novel method for fast and accurate multiple sequence alignment. J Mol Biol 302:205–217 24. Sievers F, Wilm A, Dineen D, Gibson TJ, Karplus K, Li W, Lopez R, McWilliam H, Remmert M, So¨ding J, Thompson JD, Higgins DG (2011) Fast, scalable generation of high-quality protein multiple sequence alignments using Clustal Omega. Mol Syst Biol 7:539 25. Szklarczyk D, Franceschini A, Kuhn M, Simonovic M, Roth A, Minguez P, Doerks T, Stark M, Muller J, Bork P, Jensen LJ, Mering CV (2011) The STRING database in 2011: functional interaction networks of proteins, globally integrated and scored. Nucleic Acids Res 39:561–568 26. Chen CW, Lin J, Chu YW (2013) iStable: off-the-shelf predictor integration for predicting protein stability changes. BMC Bioinform 14:S5 27. Cheng J, Randall A, Baldi P (2006) Prediction of protein stability changes for single-site mutations using support vector machines. Proteins 62:1125–1132 28. Choi Y, Sims GE, Murphy S, Miller JR, Chan AP (2012) Predicting the functional effect of amino acid substitutions and indels. PLoS One 7:e46688 29. Cheng J, Randall AZ, Sweredoski MJ, Baldi P (2005) SCRATCH: a protein structure and structural feature prediction server. Nucleic Acids Res 33:72–76
123
Author's personal copy N. Mirzaei et al.: The Bioinformatics Report of Mutation Outcome on NADPH Flavin… 30. Binh TT, Suzuki R, Trang TTH, Kwon DH, Yamaoka Y (2015) Search for novel candidate mutations for metronidazole resistance in Helicobacter pylori using next-generation sequencing. Antimicrob Agents Chemother 59:2343–2348 31. Marais A, Bilardi C, Cantet F, Mendz GL, Me´graud F (2003) Characterization of the genes rdxA and frxA involved in metronidazole resistance in Helicobacter pylori. Res Microbiol 154:137–144 32. Abdollahi H, Savari M, Zahedi MJ, Darvish-Moghadam S, Hayat-Bakhah Abasi M (2011) Study of rdxA gene deletion in metronidazole resistant and sensitive Helicobacter pylori isolates in Kerman, Iran. Jundishapur J Microbiol 4(2):99–104 33. Chisholm SA, Owen RJ (2003) Mutations in Helicobacter pylori rdxA gene sequences may not contribute to metronidazole resistance. J Antimicrob Chemother 5:995–999 34. Jenks PJ, Ferrero RL, Labigne A (1999) The role of the rdxA gene in the evolution of metronidazole resistance in Helicobacter pylori. J Antimicrob Chemother 43:753–758 35. Solca` NM, Bernasconi MV, Piffaretti JC (2000) Mechanism of metronidazole resistance in Helicobacter pylori: comparison of the rdxA gene sequences in 30 strains. Antimicrob Agents Chemother 44:2207–2210 36. Thusberg J (2010) Molecular effects of missense mutations— bioinformatics analysis of genetic defects. Dissertation, University of Tampere 37. Brinza D, Zelikovsky A (2006) Combinatorial methods for disease association search and susceptibility prediction. Algorithms in bioinformatics. Springer, Berlin, pp 286–297 38. Brinza D (2007) Discrete algorithms for analysis of genotype data. Dissertation, Georgia State University 39. Rodriguez-Casado A (2012) In silico investigation of functional nsSNPs an approach to rational drug design. Res Reports Med Chem 2:31–42 40. Stefl S, Nishi H, Petukh M, Panchenko AR, Alexov E (2013) Molecular mechanisms of disease-causing missense mutations. J Mol Biol 425(21):3919–3936 41. Doss CGP, Rajith B, Garwasis N, Mathew PR, Raju AS, Apoorva K, William D, Sadhana NR, Himani T, Dike IP (2012) Screening of mutations affecting protein stability and dynamics of FGFR1A simulation analysis. Appl Transl Genom 1:37–43
123
42. Zhang Z, Miteva MA, Wang L, Alexov E (2012) Analyzing effects of naturally occurring missense mutations. Comput Math Methods Med. doi:10.1155/2012/805827 43. Yates CM, Sternberg MJ (2014) Impact of missense variants on protein–protein interactions. eLS 44. Szklarczyk D, Franceschini A, Wyder S, Forslund K, Heller D, Huerta-Cepas J, Simonovic M, Roth A, Santos A, Tsafou KP, Kuhn M, Bork P, Jensen LJ, Mering CV (2014) STRING v10: protein–protein interaction networks, integrated over the tree of life. Nucleic Acids Res 43:447–452 45. Chen JY, Youn E, Mooney SD (2009) Connecting protein interaction data, mutations, and disease using bioinformatics. Methods Mol Biol 541:449–461 46. Fu L, Liang JJN (2003) Alteration of protein–protein interactions of congenital cataract crystallin mutants. Invest Ophthalmol Vis Sci 44:1155–1159 47. Ng PC, Henikoff S (2006) Predicting the effects of amino acid substitutions on protein function. Annu Rev Genom Hum Genet 7:61–80 48. Zhang Z, Teng S, Wang L, Schwartz CE, Alexov E (2010) Computational analysis of missense mutations causing SnyderRobinson syndrome. Hum Mutat 31:1043–1049 49. Bloom JD, Glassman MJ (2009) Inferring stabilizing mutations from protein phylogenies: application to influenza hemagglutinin. PLoS Comput Biol 5:e1000349 50. Khan S, Vihinen M (2010) Performance of protein stability predictors. Hum Mutat 31:675–684 51. Reumers J, Schymkowitz J, Rousseau F (2009) Using structural bioinformatics to investigate the impact of non synonymous SNPs and disease mutations: scope and limitations. BMC Bioinform 10:S9 52. Ng PC, Henikoff S (2003) SIFT: Predicting amino acid changes that affect protein function. Nucleic Acids Res 31:3812–3814 53. Wang Z, Moult J (2001) SNPs, protein structure, and disease. Hum Mutat 17:263–270 54. Binh TT, Suzuki R, Kwon DH, Yamaoka Y (2015) Complete genome sequence of a metronidazole-resistant Helicobacter pylori strain. Genome Announc 3:e00051-15
