Escherichia coli DnaE Polymerase Couples Pyrophosphatase Activity to DNA Replication.
RESEARCH ARTICLE Escherichia coli DnaE Polymerase Couples Pyrophosphatase Activity to DNA Replication Fabio Lapenta1, Alejandro Montón Silva1, Renato Brandimarti1, Massimiliano Lanzi2, Fabio Lino Gratani1, Perceval Vellosillo Gonzalez1, Sofia Perticarari1, Alejandro Hochkoeppler1,3* 1 Department of Pharmacy and Biotechnology, University of Bologna, Viale Risorgimento 4, 40136, Bologna, Italy, 2 Department of Industrial Chemistry, University of Bologna, Viale Risorgimento 4, 40136, Bologna, Italy, 3 CSGI, University of Firenze, Via della Lastruccia 3, 50019, Sesto Fiorentino, FI, Italy * firstname.lastname@example.org Abstract OPEN ACCESS Citation: Lapenta F, Montón Silva A, Brandimarti R, Lanzi M, Gratani FL, Vellosillo Gonzalez P, et al. (2016) Escherichia coli DnaE Polymerase Couples Pyrophosphatase Activity to DNA Replication. PLoS ONE 11(4): e0152915. doi:10.1371/journal. pone.0152915 Editor: Giovanni Maga, Institute of Molecular Genetics IMG-CNR, ITALY Received: January 29, 2016 Accepted: March 21, 2016 Published: April 6, 2016 Copyright: © 2016 Lapenta et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: All the data are contained in the paper. Funding: The authors have no support or funding to report. Competing Interests: The authors have declared that no competing interests exist. DNA Polymerases generate pyrophosphate every time they catalyze a step of DNA elongation. This elongation reaction is generally believed as thermodynamically favoured by the hydrolysis of pyrophosphate, catalyzed by inorganic pyrophosphatases. However, the specific action of inorganic pyrophosphatases coupled to DNA replication in vivo was never demonstrated. Here we show that the Polymerase-Histidinol-Phosphatase (PHP) domain of Escherichia coli DNA Polymerase III α subunit features pyrophosphatase activity. We also show that this activity is inhibited by fluoride, as commonly observed for inorganic pyrophosphatases, and we identified 3 amino acids of the PHP active site. Remarkably, E. coli cells expressing variants of these catalytic residues of α subunit feature aberrant phenotypes, poor viability, and are subject to high mutation frequencies. Our findings indicate that DNA Polymerases can couple DNA elongation and pyrophosphate hydrolysis, providing a mechanism for the control of DNA extension rate, and suggest a promising target for novel antibiotics. Introduction DNA Polymerases (DNA Pols) catalyze the extension of primers annealed to DNA template strands [1,2]. These enzymes promote the nucleophilic attack by the 3’-OH of the primer to the α-phosphate of an incoming deoxynucleotide triphosphate (dNTP), releasing pyrophosphate . DNA Pols also catalyze the reverse reaction, denoted as pyrophosphorolysis, consisting in the shortening of DNA and the release of a dNTP . Therefore, DNA replication is favoured by the concomitant hydrolysis of pyrophosphate, the ΔG of which is strongly negative . This was early recognized , and inorganic pyrophosphatases (PPases) were claimed to be responsible for pushing the equilibrium of the reaction towards DNA extension . However, no evidence was provided supporting the idea that the action of inorganic PPase(s) is coupled to DNA replication in vivo. DNA Pols feature a conserved molecular architecture, consisting of 3 main domains, i.e. thumb, palm and fingers. The overall shape of these enzymes resembles an open right hand, PLOS ONE | DOI:10.1371/journal.pone.0152915 April 6, 2016 1 / 17 Pyrophosphatase Activity Coupled to DNA Replication with the catalytic site universally located in the palm. Quite recently, it was reported that some DNA Pols contain an additional domain, i.e. the Polymerase-Histidinol-Phosphatase (PHP) domain , the presence of which is frequently observed in the enzymes responsible for genome replication . In E. coli, the organism whose DNA Pols are the best characterized, the only DNA replicase containing a PHP domain is the α subunit of DNA Pol III, the assembly of which requires 10 different subunits . In addition, it was demonstrated that: i) E. coli is able to express 5 different DNA Pols (I-V) [8–13]; ii) DNA Pol III is essential for genome replication [14,15]; iii) DNA Pols II, IV, and V are dispensable [16–18]; iv) the 5’-3’ exonuclease domain of Pol I is essential to remove the primers generated during the replication of E. coli chromosome ; v) the Polymerase domain of Pol I is dispensable . Therefore, the presence of PHP in the only essential DNA Pol suggests a functional role for this domain, although its presence could be due to structural reasons . Favouring the functional role, Aravind and Koonin suggested that the PHP domain could feature pyrophosphatase activity . Quite recently, the tertiary structure of a truncated form of E. coli DNA Pol III α subunit was determined, and, intriguingly, a phosphate ion associated to the PHP domain was detected . The tertiary structure of PHP consists of a distorted α/β barrel, containing 6 but one parallel β strands. Remarkably, the phosphate ion bound to PHP is located at the C-terminal side of the β-strands, where it would be expected the PHP active site. Here we show that the PHP domain of E. coli DNA Pol III α subunit features fluoride-sensitive pyrophosphatase activity. We also identified the PHP active site and, using purified sitespecific variants of α subunit, we revealed a strong coupling between the rates of DNA elongation and pyrophosphatase activities. Considering the defective phenotypes linked to these variants of α subunit, we propose that the pyrophosphatase activity of DNA replicases represents a regulatory point of proper genome replication. Materials and Methods Bacterial Cultures Escherichia coli TOP10 (genotype: F- mcrA Δ(mrr-hsdRMS-mcrBC) ϕ80lacZΔM15 ΔlacX74 recA1 araD139 Δ(ara-leu)7697 galU galK rpsL endA1 nupG) was used to overexpress all the proteins considered. Full-length wt, H12A, and D19A α-subunits, along with the wt τ3α3ε3θ3 and the τ3α(D201A)3ε3θ3 complex were overexpressed as previously described [23,24]. To overexpress the PHP domain, E. coli TOP10 was transformed with the pBAD-α187 vector . The transformants accordingly obtained were pre-cultured at 37°C for 15 h under shaking (180 rpm), using LB medium supplemented with ampicillin at 0.1 mg/mL. The pre-cultures were diluted (1:500) in fresh LB-ampicillin medium and grown, under the same conditions, for 5 h. Cultures were then induced to overexpress the PHP domain by the addition of 1 mM arabinose, and the induction was maintained for 15 h. Finally, cells were collected by centrifugation (5,000xg, 20 min, 4°C), and stored at -20°C. Using this overexpression procedure, inclusion bodies containing the PHP domain were produced. Construction of pGOOD-τγless-ε-θ The dnaX-γless gene was synthesized by Entelechon GmbH (Bad Abbach, Germany). To avoid overexpression of the γ subunit, the sequence responsible for the translational frameshift yielding γ was mutated [25–27], i.e. the wild type codons 429–431 were changed to AAG-AAA-AGC. The pGOOD-τγless-ε-θ vector, previously described , was used to co-transform E. coli TOP10 with the pBAD-α or with the pBAD-αD201A plasmid. The co-transformants accordingly obtained were then utilized to overexpress the wt τ3α3ε3θ3 and the τ3α(D201A)3ε3θ3 complex. PLOS ONE | DOI:10.1371/journal.pone.0152915 April 6, 2016 2 / 17 Pyrophosphatase Activity Coupled to DNA Replication Protein Purification Full-length wt, H12A, and D19A α subunits, the τ3α3ε3θ3 and the τ3α(D201A)3ε3θ3 complex were purified as previously described . To purify the PHP domain, frozen cells containing the overexpressed protein were gently thawed, and resuspended in 1/10 of the original culture volume using 50 mM Tris-HCl (pH 8), 50 mM NaCl, 1 mM EDTA (buffer A). The cells suspension was homogenized with a cold potter, and 1 mM phenyl-methyl-sulfonyl fluoride (PMSF) was added. Inclusion bodies were extracted by 3 sonication cycles, using a Misonix 3000 sonifier (Farmingdale, NY, USA) at an output level of 6 W. Each sonication cycle consisted of 15 s of pulse, followed by a 15 s cooling interval for a total time of 2 min. Inclusion bodies were then recovered by centrifugation (10,000xg, 20 min), and resuspended in buffer A containing 0.1% (v/v) Triton-X-100 (buffer B). The suspension was centrifuged, and the washing step with buffer B was repeated twice. Finally, the pellet (1.5 g) containing the inclusion bodies was solubilized in 300 mL of 50 mM Tris-HCl (pH 8), 50 mM NaCl, 1 mM EDTA, 5 mM DTT, 6 M urea. After incubation for 2 h at room temperature under stirring, the solubilized pellet was diluted (1:2) with buffer A supplemented with 5 mM DTT and 20% (v/v) glycerol (buffer C). Finally, the sample was concentrated to 100 mL with an Amicon ultrafiltration cell equipped with a YM30 membrane, and dialyzed (3 cycles) against buffer C. After dialysis, the solution was centrifuged (10,000xg, 20 min at 4°C), the pellet was discarded and the supernatant, containing 1.4 mg of proteins/mL, was concentrated to 35 mL. The sample was again centrifuged (10,000xg, 20 min at 4°C), and the supernatant, featuring 1.6 mg of proteins/mL, was loaded onto a Q-Sepharose FF column (1.6x25 cm, GE Healthcare, Piscataway, USA) previously equilibrated with buffer C. After sample loading, the column was washed with 5 column volumes of buffer C, and elution was performed applying a linear NaCl gradient (from 50 mM to 1 M). PHP domain was eluted at about 0.45 M NaCl. The best fractions, according to SDS-PAGE analysis, were pooled, concentrated, and loaded onto a Superdex 200 column (GE Healthcare, 1.6x70 cm), equilibrated with 50 mM Tris–HCl pH 8, 150 mM NaCl, 1 mM EDTA, 5 mM DTT, 20% glycerol (v/v). Elution was performed at 0.6 mL/min, and fractions of 0.9 mL were collected. The column was calibrated with LMW Gel Filtration Calibration kit (GE Healthcare). Pure PHP was eluted in dimeric and monomeric form, according to column calibration (S1 Fig). Monomeric PHP was concentrated, and stored at– 20°C until used. Determination of Protein Concentration Protein concentration was determined according to Bradford . Bovine serum albumin was used as standard. Enzyme Activity Assays Steady-state activity assays were performed using a Perkin-Elmer (Waltham, MA, USA) λ19 spectrophotometer. Pyrophosphatase activity was assayed in the presence of 100 mM Tris-HCl (pH 8), 1 mM sodium pyrophosphate, 10 mM MgCl2, and 0.25 mM MnCl2. The reaction mixture did also contain 0.25 mM inosine, 50 mU/mL of Purine Nucleoside Phosphorylase (PNPase), and 500 mU/mL of Xanthine Oxidase (XOD) . By this means, the released phosphate is used by PNPase for the phosphorolysis of inosine, generating hypoxanthine and ribose-1-phosphate. Hypoxanthine is then converted by XOD to uric acid, which can be conveniently monitored at 293 nm. For all the assays relying on the detection of uric acid we assumed 12,600 M-1cm-1 as the molar extinction coefficient for uric acid at 293 nm . DNA Polymerase was assayed by detecting the pyrophosphate/phosphate released, in the presence or in the absence of 40 mU/mL of E. coli inorganic pyrophosphatase. For these assays, a dsDNA PLOS ONE | DOI:10.1371/journal.pone.0152915 April 6, 2016 3 / 17 Pyrophosphatase Activity Coupled to DNA Replication consisting of a 40mer template and a 15mer primer was used; this dsDNA features a 25mer polyA overhang . Elongation of DNA (1 μM final concentration) was triggered by the addition of 100 μM dTTP, and the reaction kinetics was detected by monitoring uric acid at 293 nm. Steady-state 3’-5’ exonuclease activity was determined according to Hamdan et al. , using the 5’-p-nitrophenyl ester of thymidine monophosphate (pNP-TMP) as substrate. Reaction mixtures contained 3 mM pNP-TMP, 100 mM Tris-HCl (pH 8), 50 mM NaCl, 1 mM DTT, and 5 mM of a divalent cation (e.g. Mg2+), as indicated. Organic phosphatase activity was assayed in 100 mM Tris-HCl (pH 8), 50 mM NaCl, 1 mM DTT, using 1 mM p-nitrophenyl phosphate (PNP) as substrate, in the presence of 5 mM of a divalent cation, as indicated. Kinetic Analysis of Fluoride Inhibition of Pyrophosphatase Activity To analyze the inhibition of fluoride towards E. coli inorganic PPase and PHP domain, a simple model was assumed: i) the binding of fluoride to enzyme is reversible, with k1 and k2 denoting the binding and dissociation rate constants, respectively; ii) the enzyme-F- complex is inactive; iii) in each assay, the concentration of fluoride is constant (enzymes were used at concentrations at least 3 orders of magnitude lower than inhibitor concentration). Accordingly, the concentration of inactive enzyme ([EI]) is given by: [EI] = ([Et] k1 [F] (1 –e-(k1 [F] + k2) t)/(k1 [F] + k2), where [Et] is the total enzyme concentration. Pyrophosphate concentration (1 mM) in the assays was assumed to be compatible with zero-order kinetics. Therefore, reaction velocity (d[P]/dt) is equal to kcat([Et]–[EI]). Upon integration, the following equation, used to fit the observed data, is obtained: [P] = Vmax k2 t/(k1 [F] + k2) + (Vmax k1 [F]/ (k1 [F] + k2)2) (1 –e- (k1 [F] + k2) t). FTIR Spectroscopy Samples for FTIR spectroscopy were previously dried at 105°C for 48 h. Anhydrous KBr was added, and the mixture was homogenized with mortar and pestle. The samples in KBr were then subjected to 735 MPa, using a Perkin-Elmer hydraulic press. FTIR spectra were recorded with a Perkin-Elmer Spectrum One FTIR spectrometer. Microscopy Pre-cultures of E. coli transformed with pBAD-α, pBAD-αH12A, or pBAD-αD201A were grown by picking single colonies from LB-ampicillin plates (0.1 mg/mL of antibiotic), and inoculating 2 mL of liquid LB-ampicillin medium. After overday growth at 37°C under shaking (180 rpm), the pre-cultures were diluted (1:500) in fresh LB-ampicillin medium, in the presence or in the absence of 1 mM arabinose. After 12 h of growth at 37°C, cells were collected by centrifugation, washed in PBS, and finally resuspended in the same buffer. To detect the fluorescence of nucleoids, Hoechst 33342 was added to the cells suspension. Micrographs were obtained with a Nikon Eclipse 600 microscope. To determine the size distribution of cells populations, the bright-field images were converted to 32-bit and processed with the ImageJ software . In particular, each micrograph was subjected to bandpass filtering (2–40 pixel), and finally converted in binary format. The size of cells was determined, and, to avoid artifacts, individuals featuring an area smaller than 50 pixel2 were discarded. For each sample, 500 individuals were analyzed. Growth Kinetics and Mutations Frequency Pre-cultures of the populations to be tested were obtained as described for the samples used for microscopy analysis. After overday growth at 37°C under shaking (180 rpm), pre-cultures were diluted (1:500) in Erlenmeyer flasks provided with a side-arm, in the presence or in the absence PLOS ONE | DOI:10.1371/journal.pone.0152915 April 6, 2016 4 / 17 Pyrophosphatase Activity Coupled to DNA Replication of 1 mM arabinose. Upon dilution, the Absorbance of the cells suspensions was determined at predetermined time intervals with a Biolog 21907 (Biolog, Hayward, CA, USA) turbidimeter. Mutations frequency was evaluated by plating aliquots of the bacterial populations on LB and on LBrifampicin (500 μg/mL) plates. After incubation of the Petri dishes for 24 h, colonies were counted and the number of total and rifampicin-resistant individuals per unit volume was determined. Results and Discussion Pyrophosphatase Activity of the PHP Domain As a first test, we attempted to detect pyrophosphatase activity in the PHP domain. To this aim, we overexpressed from pBAD-α287  the first N-terminal 287 residues of E. coli DNA Pol III α subunit, containing the whole PHP domain (amino acids 1–281). Using standard chromatographic techniques, we were able to isolate pure PHP in monomeric form (Fig 1A and S1 Fig), and we subjected the purified protein to pyrophosphatase activity assays. Fig 1. Purification and pyrophosphatase activity of PHP domain. A) SDS-PAGE of fractions collected during the purification of the E. coli PHP domain. M: molecular mass markers. Lane 1: soluble proteins isolated after refolding of the inclusion bodies containing PHP; lanes 2 and 3: purified PHP domain after ion exchange and gel filtration chromatography, respectively. B) Hydrolysis of pyrophospate by PHP domain. The release of orthophosphate as a function of time is reported for a complete assay mixture (green dots, 176 nM PHP and 1 mM pyrophosphate), and for assay solutions lacking pyrophosphate or enzyme (blue and red dots, respecitvely). C) Pyrophosphatase activity of 0.45 nM E. coli inorganic pyrophosphatase in the absence (green dots) or in the presence of 40, 100, 200, or 800 μM NaF (blue, red, cyano, and dark red dots, respectively). D) Pyrophosphatase activity of 30 nM full-length α subunit in the absence (green dots) or in the presence of 50, 200, or 800 μM NaF (blue, red, and cyano dots, respectively). doi:10.1371/journal.pone.0152915.g001 PLOS ONE | DOI:10.1371/journal.pone.0152915 April 6, 2016 5 / 17 Pyrophosphatase Activity Coupled to DNA Replication These assays were performed according to our previously published procedure , relying on (Fig 2): i) the conversion of phosphate and inosine into ribose-1-phosphate and hypoxanthine, catalyzed by purine nucleoside phosphorylase (PNPase); ii) the oxidation of hypoxanthine into uric acid, catalyzed by xanthine oxidase (XOD); iii) the continuous detection of uric acid at 293 nm. By this equilibrium value of MeCpG steps (,+14 deg.) [31,44]. In comparison, methylation has a significantly lower stability cost when happening at major groove positions, such as 211 and 21 base pair from dyad (mutations 9 and 12), where the roll of the nucleosome bound conformation (+10 deg.) is more compatible with the equilibrium geometry of MeCpG steps. The nucleosome destabilizing effect of cytosine methylation increases with the number of methylated cytosines, following the same position dependence as the single methylations. The multiple-methylation case reveals that each major groove meth- PLOS Computational Biology | www.ploscompbiol.org 3 November 2013 | Volume 9 | Issue 11 | e1003354 DNA Methylation and Nucleosome Positioning ylation destabilizes the nucleosome by around 1 kJ/mol (close to the average estimate of 2 kJ/mol obtained for from individual methylation studies), while each minor groove methylation destabilizes it by up to 5 kJ/mol (average free energy as single mutation is around 6 kJ/mol). This energetic position-dependence is the reverse of what was observed in a recent FRET/SAXS study . The differences can be attributed to the use of different ionic conditions and different sequences: a modified Widom-601 sequence of 157 bp, which already contains multiple CpG steps in mixed orientations, and which could assume different positioning due to the introduction of new CpG steps and by effect of the methylation. The analysis of our trajectories reveals a larger root mean square deviation (RMSD) and fluctuation (RMSF; see Figures S2– S3 in Text S1) for the methylated nucleosomes, but failed to detect any systematic change in DNA geometry or in intermolecular DNA-histone energy related to methylation (Fig. S1B, S1C, S4–S6 in Text S1). The hydrophobic effect should favor orientation of the methyl group out from the solvent but this effect alone is not likely to justify the positional dependent stability changes in Figure 2, as the differential solvation of the methyl groups in the bound and unbound states is only in the order of a fraction of a water molecule (Figure S5 in Text S1). We find however, a reasonable correlation between methylation-induced changes in hydrogen bond and stacking interactions of the bases and the change in nucleosome stability (see Figure S6 in Text S1). This finding suggests that methylation-induced nucleosome destabilization is related to the poorer ability of methylated DNA to fit into the required conformation for DNA in a nucleosome. Changes in the elastic deformation energy between methylated and un-methylated DNA correlate with nucleosomal differential binding free energies To further analyze the idea that methylation-induced nucleosome destabilization is connected to a worse fit of methylated DNA into the required nucleosome-bound conformation, we computed the elastic energy of the nucleosomal DNA using a harmonic deformation method [36,37,44]. This method provides a rough estimate of the energy required to deform a DNA fiber to adopt the super helical conformation in the nucleosome (full details in Suppl. Information Text S1). As shown in Figure 2, there is an evident correlation between the increase that methylation produces in the elastic deformation energy (DDE def.) and the free energy variation (DDG bind.) computed from MD/TI calculations. Clearly, methylation increases the stiffness of the CpG step , raising the energy cost required to wrap DNA around the histone octamers. This extra energy cost will be smaller in regions of high positive roll (naked DNA MeCpG steps have a higher roll than CpG steps ) than in regions of high negative roll. Thus, simple elastic considerations explain why methylation is better tolerated when the DNA faces the histones through the major groove (where positive roll is required) that when it faces histones through the minor groove (where negative roll is required). Nucleosome methylation can give rise to nucleosome repositioning We have established that methylation affects the wrapping of DNA in nucleosomes, but how does this translate into chromatin structure? As noted above, accumulation of minor groove methylations strongly destabilizes the nucleosome, and could trigger nucleosome unfolding, or notable changes in positioning or phasing of DNA around the histone core. While accumulation of methylations might be well tolerated if placed in favorable positions, accumulation in unfavorable positions would destabilize the nucleosome, which might trigger changes in chromatin structure. Chromatin could in fact react in two different ways in response to significant levels of methylation in unfavorable positions: i) the DNA could either detach from the histone core, leading to nucleosome eviction or nucleosome repositioning, or ii) the DNA could rotate around the histone core, changing its phase to place MeCpG steps in favorable positions. Both effects are anticipated to alter DNA accessibility and impact gene expression regulation. The sub-microsecond time scale of our MD trajectories of methylated DNAs bound to nucleosomes is not large enough to capture these effects, but clear trends are visible in cases of multiple mutations occurring in unfavorable positions, where unmethylated and methylated DNA sequences are out of phase by around 28 degrees (Figure S7 in Text S1). Due to this repositioning, large or small, DNA could move and the nucleosome structure could assume a more compact and distorted conformation, as detected by Lee and Lee , or a slightly open conformation as found in Jimenez-Useche et al. . Using the harmonic deformation method, we additionally predicted the change in stability induced by cytosine methylation for millions of different nucleosomal DNA sequences. Consistently with our calculations, we used two extreme scenarios to prepare our DNA sequences (see Fig. 3): i) all positions where the minor grooves contact the histone core are occupied by CpG steps, and ii) all positions where the major grooves contact the histone core are occupied by CpG steps. We then computed the elastic energy required to wrap the DNA around the histone proteins in unmethylated and methylated states, and, as expected, observed that methylation disfavors DNA wrapping (Figure 3A). We have rescaled the elastic energy differences with a factor of 0.23 to match the DDG prediction in figure 2B. In agreement with the rest of our results, our analysis confirms that the effect of methylation is position-dependent. In fact, the overall difference between the two extreme methylation scenarios (all-in-minor vs all-in-major) is larger than 60 kJ/mol, the average difference being around 15 kJ/ mol. We have also computed the elastic energy differences for a million sequences with CpG/MeCpG steps positioned at all possible intermediate locations with respect to the position (figure 3B). The large differences between the extreme cases can induce rotations of DNA around the histone core, shifting its phase to allow the placement of the methylated CpG steps facing the histones through the major groove. It is illustrative to compare the magnitude of CpG methylation penalty with sequence dependent differences. Since there are roughly 1.5e88 possible 147 base pairs long sequence combinations (i.e., (4n+4(n/2))/2, n = 147), it is unfeasible to calculate all the possible sequence effects. However, using our elastic model we can provide a range of values based on a reasonably large number of samples. If we consider all possible nucleosomal sequences in the yeast genome (around 12 Mbp), the energy difference between the best and the worst sequence that could form a nucleosome is 0.7 kj/mol per base (a minimum of 1 kJ/mol and maximum of around 1.7 kJ/mol per base, the first best and the last worst sequences are displayed in Table S3 in Text S1). We repeated the same calculation for one million random sequences and we obtained equivalent results. Placing one CpG step every helical turn gives an average energetic difference between minor groove and major groove methylation of 15 kJ/ mol, which translates into ,0.5 kJ/mol per methyl group, 2 kJ/ mol per base for the largest effects. Considering that not all nucleosome base pair steps are likely to be CpG steps, we can conclude that the balance between the destabilization due to CpG methylation and sequence repositioning will depend on the PLOS Computational Biology | www.ploscompbiol.org 4 November 2013 | Volume 9 | Issue 11 | e1003354 DNA Methylation and Nucleosome Positioning Figure 3. Methylated and non-methylated DNA elastic deformation energies. (A) Distribution of deformation energies for 147 bplong random DNA sequences with CpG steps positioned every 10 base steps (one helical turn) in minor (red and dark red) and major (light and dark blue) grooves respectively. The energy values were rescaled by the slope of a best-fit straight line of figure 2, which is 0.23, to and controls. HLA-C allele Definite MD* patients (n=23) (%)# Probable MD* patients (n=24) (%)# Control group (n=91) (%) # P value (Definite vs. probable) P value (Definit vs. Control) P value (probable vs. Control) Cw*01 Cw*02 Cw*03 Cw*04 2 (4.3%) 0(0%) 1 (2.2%) 8(17.4%) 0(0%) 0(0%) 1(2.1%) 9(18.8%) 7 (3.8%) 7 (3.8%) 9 (4.9%) 2 (1.1%) 0.23 N/A >0.99ǂ 0.86 >0.99 0.35ǂ 0.69ǂ 10-4 0.35 0.35 0.69ǂ 10-4 Cw*06 7(15.2%) 3(6.3%) 33 (18.1%) 0.15 0.64 0.46 Cw*07 Cw*08 8(17.4%) 0(0%) 5(10.4%) 4(8.3%) 25 (13.7%) 4(2.2%) 0.32 0.11ǂ 0.52 0.58ǂ 0.54 0.06ǂ Cw*12 Cw*13 5(10.9%) 0(0%) 13(27.1%) 0(0%) 43(23.6%) 3(1.6%) 0.04 N/A 0.05 >0.99ǂ 0.62 >0.99ǂ Cw*14 Cw*15 3(6.5%) 1(2.2%) 4(8.3%) 0(0%) 17(9.3%) 0(0%) >0.99 0.48ǂ 0.77 0.48ǂ >0.99 N/A Cw*16 Cw*17 Cw*18 9(19.6%) 2(4.3%) 0(0%) 8(16.7%) 1(2.1%) 0(0%) 9(4.9%) 7(3.8%) 16(8.8%) 0.71 0.61ǂ N/A 0.001 >0.99ǂ 0.04ǂ >0.99 0.02ǂ 0.06ǂ Total #46(100%) #48(100%) #182(100%) *Meniere’s Disease, ǂ analyzed with fisher exact test, others are analyzed using chi square #The number of alleles is twice the number of patients, as each person has two alleles—one maternal and one paternal. Frequencies of HLA-Cw*04 (P<0.001) and HLA-Cw*16 (P=0.001) were found to be significantly higher in definite Meniere’s disease patients compared to the control subjects. Though, the allele frequency of HLA-Cw*18 was significantly lower in definite Meniere’s disease compared to the controls (P= 0.047) (Table. 3). Discussion The frequency of HLA-Cw alleles in 3 groups of definite Meniere’s disease patients, probable Meniere’s disease patients, and healthy controls has been appraised in this study. A significant association between HLA-Cw*04 and HLA-Cw*16 alleles with both probable and definite Meniere’s disease was observed in our patients. This was in agreement with the study of Koyama and colleagues in which they found significant association between HLA-Cw4 and HLADRB1*1602 in a Japanese sample. (16) Khorsandi et al. also found that HLA-Cw4 is found more in Iranian definite Meniere’s disease patients (13). The frequency of HLA-Cw*18 allele was significantly higher in the control group. HLA-Cw*12 allele frequency was significantly higher in probable Meniere’s disease group compared to the patients with definite Meniere’s. The observed association of HLA-Cw alleles in our patients suggested an autoimmune mechanism in both definite and probable Meniere’s disease. This is in agreement with Greco’s study who discussed Meniere’s autoimmunity in literature (4). A mechanism based approach to the disease diagnosis and treatment warrants more accurate diagnostic methods and more straightforward and powerful treatment approaches. Earlier studies have identified an association between the HLA-Cw7 antigen and Meniere’s disease in a British population (17). A significant increase in the frequency of HLA-Cw7 alleles in patients with Meniere’s disease was observed compared to other inner ear diseases and healthy subjects (18). Some studies have found an association between HLA-DRB1 alleles and Meniere’s disease. However the results had not been repeated in other populations (19,20). Yeo et al. have found that frequency of HLA-Cw*0303 and HLA-Cw*0602 alleles 264 Iranian Journal of Otorhinolaryngology, Vol.28(4), Serial No.87, Jul 2016 HLA-CW Alleles in Meniere disease were significantly increased in Korean patients with MD compared to healthy controls; on the other hand, HLA-B44 and HLA-Cw*0102 were significantly decreased in their patients (19). This can support the idea of the association between probable Meniere’s disease and the HLA Alleles. In another study on 80 Mediterranean patients with bilateral Meniere’s disease, an association between HLA-DRB1 *1101 and bilateral Meniere’s disease was observed; but there was no association between HLACw and bilateral Meniere’s disease (20). The number of patients with bilateral Meniere’s disease was very low in our studied population. The discrepancies in results are probably due to the ethnic or geographic background or also might be as a result of differences in phenotypic or clinical manifestations of patients in different studies which require careful interpretation. To the best of our knowledge, this is the first study which compares HLA-Cw allele frequencies in definite Meniere’s disease and probable Meniere’s disease patients in an Iranian sample. Some HLA loci are inherited as haplotype blocks. Therefore the association found between HLA-Cw and Meniere’s disease in our study might be due to an association with another unknown gene in linkage with the HLACw allele, which was responsible for Meniere’s disease susceptibility. It will be very helpful to evaluate the presence of accompanied autoimmune disorders in our patients, which has not been performed in our study. In addition, population heterogeneity is a very important factor in genetic association studies as our patients were collected from a referral clinic from various ethnic and geographic origins. Conclusion In conclusion, this study confirmed the association between HLA-Cw*04 and HLA-Cw*16 alleles with both definite and probable MD. HLA-Cw*12 was found as a discriminating allele between definite and probable Meniere’s disease which requires more attention in replication studies. A protective role for HLA-Cw*18 also was proposed in this study. Further larger studies and more comprehensive studies on the role of other HLA genes in susceptibility to Meniere’s are warranted. Acknowledgements This study was part of MD thesis Dr Fatemeh Ghadimi and also was supported by Tehran University of Medical Sciences (TUMS).The grant number is:90-03-4815264. References 1. Wright T. Meniere’s disease. BMJ Clin Evid. 2015(9636):406–14. 2. Arweiler DJ, Jahnke K, Grosse-Wilde H. 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