Ph.D Jaroslaw Polanski
email: polanski@us.edu.pl

Pawel Mazur

Tomasz Magdziarz

Andrzej Bak

Zdzislaw Chilmonczyk

Teresa Kasprzycka-Guttman

Irena Misiewicz

Jadwiga Skupińska

Does

Keywords: oxidoreductase, sulforaphane, indoquinoline, docking, molecular dynamics, isothiocyanates molecular docking reveal alternative chemopreventive Oxidoreductase Sulforaphane Indoquinoline Docking Molecular dynamics Isothiocyanates

Isothiocyanates (ITC) are well-known chemopreventive agents extracted from vegetables. This activity results from the activation of human oxidoreductase. In this letter, the uncompetitive activatory mechanism of ITC was investigated using docking and molecular dynamics simulations. This indicates that NAD(P)H:quinone oxidoreductase can efficiently improve enzyme-substrate recognition within the catalytic site if the ITC activator supports the interaction in the uncompetitive binding site.

Fig. 5 in the revised version represents the resulting histograms of RMSD of alpha carbon (CA) of TYR128 calculated against its reference position taken from the original 1D4A file for the whole trajectory. The red color indicates TYR128 CA of the 1D4A reference structure, while the blue one shows TYR128 CA of the modified representations. Taking into account the imposed constraints on residues 45÷47 minor but noticeable change of TYR128 CA behavior during the simulations might be noticed. The detailed analysis of the histogram revealed that TYR128 CA occupied approximately 5% more time in the mean aberration. Additionally, it seems that TYR128 is less prone to the high deviation as it is shown in the subplot with logarithmic scale. The above conclusion might prove our previous observations. We observed that the constraints of 45÷47 residues either by the presence of the activators or by manual modifications makes the position of TYR128 resulting from MD simulations more similar to its reference coordinates in the initial 1D4A file. Moreover, quite similar findings were obtained for HIST161, which plays pivotal role in the substrate binding in the CBS.

Reviewer #3: In the paper of Polanski et al. the authors describe interesting MD and docking studies for alternative chemopreventive mechanism of activation of oxidoreductase by sulforaphane isothiocyanates. Altogether they provide interesting results that might be of general relevance for anti-cancer research related to oxidoreductases. Concerning the applied methods, the mixed force field approach (AMBER and MMFF94x) seems to be questionable to reflect true alterations in protein conformation. However, being aware of the difficulties for correct and rather expensive parameterization and partial charge assignment of ligands for consistent AMBER calculations the approach might be accepted as crude and fast approach. 3.a Nevertheless, the paper needs some essential improvements. In general, the paper is somewhat difficult to understand and follow for readers not directly involved in this field. E.g., a "simple" scheme showing the catalytic mechanisms exhibited by the enzyme would help.

Taking into account the comments we have improved the layout of "Result and discussion" section of our paper. We hope that this form of the paper will be better understandable for readers. A schematic plot introducing the catalytic mechanism exhibited by the enzyme has also been included as recommended.

3.b But much more relevant would be to show and explain, illustrated with corresponding detailed structural models (not only the interactions plots (Fig. 2 and3) of the active site (or ligand binding site), what are the molecular reasons for the different Tyr128 conformational changes initiated by the ligands. Only then the paper will be of any value to gain new insights in structure function (activity) relationships.

Following your remarks some additional analyses have been carried out. It seems that the movements of several amino acids in the USB are constrained by the presence of the activator resulting in some changes of the CBS. Further testing of this hypothesis has been performed using the reduced dynamic approach (RedMD), which is based on the coarse-grained model of the protein. Thus, two representations of NAD(P)H:quinine oxidoreductase have been prepared by changing each amino acid with artificial alpha carbon atoms heaving appropriate properties to fake replaced amino acids. The first representation mimics the original 1D4A crystal structure, whereas the second one has modified properties of ASN45÷47 of chain B and D, respectively. Generally, it reflects the constrains caused by the presents of activators in the UBS. Moreover, the applied modifications resulted in the increase of masses and harmonic constant. Afterwards, both representations have been used in Langevin's dynamics simulations repeated 10 times with different random seeds. The whole trajectory time was 20 ns with temperature of 300K. The atomic coordinates of the system were sampled every 20fs. Then, the entire set of the obtained frames has been superimposed by ASN45÷47 of chain B and D, respectively. Fig. 5 in the revised version represents the resulting histograms of RMSD of alpha carbon (CA) of TYR128 calculated against its reference position taken from the original 1D4A file for the whole trajectory. The red color indicates TYR128 CA of the 1D4A reference structure, while the blue one shows TYR128 CA of the modified representations. Taking into account the imposed constraints on residues 45÷47 minor but noticeable change of TYR128 CA behavior during the simulations might be noticed. The detailed analysis of the histogram revealed that TYR128 CA occupied approximately 5% more time in the mean aberration. Additionally, it seems that TYR128 is less prone to the high deviation as it is shown in the subplot with logarithmic scale. The above conclusion might prove our previous observations. We observed that the constraints of 45÷47 residues either by the presence of the activators or by manual modifications makes the position of TYR128 resulting from MD simulations more similar to its reference coordinates in the initial 1D4A file. Moreover, quite similar findings were obtained for HIST161, which plays pivotal role in the substrate binding in the CBS.

Introduction

Over the last years there has been increasing interest in the role of sulforaphane (SP) compounds as cancer chemoprotective agents. SPs, phytochemical isothiocyanates (ITC), protected against chemically induced tumors in a variety of animal organs [START_REF] Lingxiang | Total intracellular accumulation levels of dietary isothiocyanates determines their activity in elevation of cellular glutathione and induction of Phase 2 detoxification enzymes[END_REF][START_REF] Kwak | Chemoprevention through the Keap1-Nrf2 signaling pathway by phase 2 enzyme inducers[END_REF]. ITCs are found in a large number of edible plants, particularly those in the crucifer family [START_REF] Chiao | Sulforaphane and its metabolite mediate growth arrest and apoptosis in human prostate cancer cells[END_REF][START_REF] Iori | Formation of glucoraphanin by chemoselective oxidation of natural glucoerucin: a chemoenzymatic route to sulforaphane[END_REF][START_REF] Shapiro | Chemoprotective glucosinolates and isothiocyanates of broccoli sprouts: metabolism and excretion in humans[END_REF]. The epidemiological studies have repeatedly shown that the consumption of these vegetables reduces the risk of many types of cancer in humans [START_REF] Morimitsui | A sulforaphane analogue that potently activates the Nrf2-dependent detoxification pathway[END_REF][START_REF] Herman | Sulforaphane causes autophagy to inhibit release of cytochrome C and apoptosis in human prostate cancer cells[END_REF]. ITCs were found to elevate phase II enzyme activities, like glutathione S-transferases and DT diaphorase in a variety of cancer cell lines [START_REF] Lingxiang | Quantitative determination of dithiocarbamates in human plasma, serum, erythrocytes and urine: pharmacokinetics of broccoli sprout isothiocyanates in humans[END_REF][START_REF] Zhang | Reversible conjugation of isothiocyanates with glutathione catalyzed by human glutathione transferases[END_REF][START_REF] Zhang | Cancer-preventive isothiocyanates: measurement of human exposure and mechanism of action[END_REF]. It was observed that ITCs blocking DNA damage by both inhibition of carcinogen activation through inhibition of phase I enzymes, mainly cytochromes P450 and detoxification of reactive carcinogens through induction of phase II enzymes. ITCs removed premalignant and malignant cells through the activation of apoptosis [START_REF] Lingxiang | Total intracellular accumulation levels of dietary isothiocyanates determines their activity in elevation of cellular glutathione and induction of Phase 2 detoxification enzymes[END_REF][START_REF] Lingxiang | Quantitative determination of dithiocarbamates in human plasma, serum, erythrocytes and urine: pharmacokinetics of broccoli sprout isothiocyanates in humans[END_REF][START_REF] Zhang | Reversible conjugation of isothiocyanates with glutathione catalyzed by human glutathione transferases[END_REF], which has been shown to inhibit both initiation and post-initiation phases in animal models of chemical carcinogenesis [START_REF] Zhang | Molecular mechanism of rapid cellular accumulation of anticarcinogenic isothiocyanates[END_REF][START_REF] Zhang | Mechanism of differential potencies of isothiocyanates as inducers of anticarcinogenic Phase 2 enzymes[END_REF][START_REF] Zhang | A major inducer of anticarcinogenic protective enzymes from broccoli: isolation and elucidation of structure[END_REF][START_REF] Jiang | Differential responses from seven mammalian cell lines to the treatments of detoxifying enzyme inducers[END_REF] and the role of NAD(P)H:quinone oxidoreductase seems to be of key importance here.

Human NAD(P)H:quinone oxidoreductase is a homodimeric enzyme overexpressed in a variety of solid tumors, which makes it an interesting target for antitumor drugs. This enzyme plays a protective antioxidant role It catalyzes reduction of quinones to hydroquinones -see Fig. 1. At the same time it is capable of bioactivation of various prodrugs of the E09 or RH1 to their cytotoxic species. Several novel inhibitor series of this enzyme have been reported recently [START_REF] Reigan | Development of indolequinone mechanism-based inhibitors of NAD(P)H:quinone oxidoreductase 1 (NQO1): NQO1 inhibition and growth inhibitory activity in human pancreatic MIA PaCa-2 cancer cells[END_REF]. Moreover, a virtual screening was performed in the library of more than 700,000 molecules to identify potential NAD(P)H:quinone oxidoreductase ligands by molecular docking [START_REF] Nolan | In silico identification and biochemical characterization of novel inhibitors of NQO1[END_REF].

We have previously reported the synthesis of new ITC analogues activating NAD(P)H:quinone oxidoreductase [START_REF] Misiewicz | Sulforaphane-mediated induction of a phase 2 detoxifying enzyme NAD(P)H:quinone reductase and apoptosis in human lymphoblastoid cells[END_REF][START_REF] Misiewicz | Differential response of human healthy lymphoblastoid and CCRF-SB leukemia cells to sulforaphane and its two analogues: 2-oxohexyl isothiocyanate and alyssin[END_REF]. We described also the differential response of human healthy lymphoblastoid and CCRF-SB leukemia cells to these compounds [START_REF] Misiewicz | Differential response of human healthy lymphoblastoid and CCRF-SB leukemia cells to sulforaphane and its two analogues: 2-oxohexyl isothiocyanate and alyssin[END_REF]. We also attempted to explain the induction of apoptosis by ITC in cell lines carrying various inherited BRCA1 mutations [START_REF] Misiewicz | Inhibition of cell cycle and induction of apoptosis by sulforaphane in cell lines carrying various inherited BRCA1 mutations[END_REF]. In the previous studies we were not able to indicate the molecular basis for ITC activation; however; the previously described mechanism of NQO1 activation involves signaling pathways Keap1/Nrf2/ARE and AhR/XRE. Thus, sulforaphane can bring about an increase in transcriptional activity resulting also in the increase of NQO1 level, which activates the enzyme activity [START_REF] Kwak | Chemoprevention through the Keap1-Nrf2 signaling pathway by phase 2 enzyme inducers[END_REF].

On the other hand, we have recently observed that sulforaphane and its analogues can efficiently bind proteins, e.g. albumin, CYP1A1 and CYP1A2 [START_REF] Kasprzycka-Guttman | Receptor independent and receptor dependent CoMSA modeling with IVE-PLS: application to CBG benchmark steroids and reductase activators[END_REF]. We also observed that these molecules can be efficiently docked in the catalytic site of the oxidoreductase structure.

However, an attempt to model quantitatively this mechanism by molecular docking within the binding site of the enzyme followed by the CoMSA analysis provided 3D QSAR model only slightly better than this resulted from the receptor independent ligand based superimposition [21].

Therefore, we attempted here to thoroughly probe the potential activation of the oxidoreductase by sulforaphane binding. The aim of present study was to use molecular docking to search for the alternative uncompetitive binding site (UBS) in the oxidoreductase. Thus, we used this method to observe the pattern of the distribution of the large population of ITC conformers within the enzyme. Basing on this pattern we decided to indicate the most highly populated site as a possible UBS. Then, using the molecular dynamic simulations (MDs) we attempted to reveal the influence of this effect on the hypothetical interaction of the second series compounds interacting with the oxidoreductase in the active site, as reported previously [START_REF] Reigan | Development of indolequinone mechanism-based inhibitors of NAD(P)H:quinone oxidoreductase 1 (NQO1): NQO1 inhibition and growth inhibitory activity in human pancreatic MIA PaCa-2 cancer cells[END_REF].

Data sets and methodology

The chemical structures of ITC analogues a1-a10 and their quinone oxidoreductase activation rate were reported according to the references [START_REF] Misiewicz | Sulforaphane-mediated induction of a phase 2 detoxifying enzyme NAD(P)H:quinone reductase and apoptosis in human lymphoblastoid cells[END_REF][START_REF] Misiewicz | Differential response of human healthy lymphoblastoid and CCRF-SB leukemia cells to sulforaphane and its two analogues: 2-oxohexyl isothiocyanate and alyssin[END_REF][START_REF] Posner | Design and synthesis of bifunctional isothiocyanate analogs of sulforaphane: correlation between structure and potency as inducers of anticarcinogenic detoxication enzymes[END_REF][START_REF] Zhang | Anticarcinogenic activities of sulforaphane and structurally related synthetic norbornyl isothiocyanates[END_REF] and are shown in Table 1.

Table 2 contains structures and inhibition potency data of a series of indoquinolinone reductase inhibitors (i1-i9) extracted from the literature [START_REF] Reigan | Development of indolequinone mechanism-based inhibitors of NAD(P)H:quinone oxidoreductase 1 (NQO1): NQO1 inhibition and growth inhibitory activity in human pancreatic MIA PaCa-2 cancer cells[END_REF][START_REF] Zhou | Kinetic and docking studies of the interaction of quinones with the quinone reductase active site[END_REF]. This series is supplemented with diqumarol dq which is a known oxidoreductase inhibitor of the high activity [START_REF] Cullen | Dicumarol inhibition of NADPH:quinone oxidoreductase induces growth inhibition of pancreatic cancer via a superoxide-mediated mechanism[END_REF].

Protomers, tautomers and conformers built for compounds a1-a10; i1-i9 and dq were optimized and partial atomic charges were calculated using the MMFF94x forcefield.

The structure of NAD(P)H:quinone oxidoreductase was taken from 1D4A PDB entry containing four amino acid chains complexed with four molecules of flavin adenine dinucleotide (FAD) [START_REF] Faig | Structures of recombinant human and mouse NAD(P)H:quinone oxidoreductases: species comparison and structural changes with substrate binding and release[END_REF]. The protein structure was prepared for docking as described previously. This included the addition of missing hydrogens, protein solvatation, the calculation of the atomic partial charges (AMBER99) and the protonation of the protein at physiological pH 7.4 using the PROPKA related method [START_REF] Li | Very fast empirical prediction and rationalization of protein pKa values[END_REF].

During docking a series of poses (ligand-protein complexes of particular conformation and mutual orientation) were generated for each molecule. The algorithm for the optimization of the ligand-protein orientation works by the alignment of triplets of ligand atoms on triplets of site points which are the centers of alpha spheres [START_REF] Edelsbrunner | Proceedings of the 28th Hawaii International Conference on Systems Science[END_REF] Further investigation were completed using coarse-grained protein representation (reduced dynamics approach). We applied the one-bead force field for HIV-1 protease described previously by Tozzini et al. [START_REF] Tozzini | A coarse grained model for the dynamics of flap opening in HIV-1 protease[END_REF][START_REF] Tozzini | Flap opening dynamics in HIV-1 protease explored with a coarse-grained model[END_REF] and Langevin dynamics [START_REF] Pastor | An analysis of the accuracy of Langevin and molecular dynamics algorithms[END_REF][START_REF] Pastor | Techniques and applications of Langevin dynamics simulations[END_REF].

Software

Molecular modeling and molecular dynamics simulations were conducted using the Sybyl/Tripos and CCG MOE software packages and reduced molecular dynamics were conducted using ICM RedMD package running on the Intel Pentium PC with the GNU/Linux operating system.

Results and discussion

It was discovered and described in the literature that the catalytic core of quinone oxidoreductase is located in the vicinity of TYR128 [START_REF] Faig | Structures of recombinant human and mouse NAD(P)H:quinone oxidoreductases: species comparison and structural changes with substrate binding and release[END_REF]. We have reported previously that ITC molecules can be docked within this place, however, both the scoring functions for docking and the resulted 3D QSAR provided only poor quantitative models [21]. In order to reveal other possible binding sites for these compounds we generated alpha spheres all over the protein surface but in the catalytic binding site (CBS) (Fig. 2a). The docking provided 300 poses scattered on the protein (Fig. 2b). The analysis of this indicates that the majority of poses (>58%) concentrated in one site on the enzyme surface, in the proximity of the CBS. This suggests that the ITC activation mechanism may be of the uncompetitive nature involving the UBS (Fig. 2c). Additionally we tested some other potential UBSs (>7%, >6%) using above procedure but the SF increase or interaction improvement was not noticed.

A visual inspection of activator-protein interactions in UBS allowed us to observe frequent interactions of activators a1-a10 with ASN45 and ASN47 residues. This involves the -NCS, -CO-, -SO-ITC functionalities and can occur either directly or via water molecules (Fig. 3).

As binding activator in the UBS could result in the CBS changes we attempted to model this effect using the MD simulations. Thus, prior to MDs, each activator-1D4A complex was minimized to energy gradient 0,01 kcal mol -1 and then molecular dynamics were performed using forcefields AMBER99 and MMFF94x with the Nosé-Poincaré-Andersen [START_REF] Sturgeon | Symplectic algorithm for constant-pressure molecular dynamics using a Nosé-Poincaré thermostat[END_REF][START_REF] Bond | The Nosé-Poincaré Method for Constant Temperature Molecular Dynamics[END_REF] algorithm under constant temperature and volume. Although there were several controversies over the application of MMFF94 to MDs [START_REF] Kaminski | Performance of the AMBER94, MMFF94, and OPLS-AA Force Fields for Modeling Organic Liquids[END_REF] we decided to use the MMFF94x variant in our simulations. MMFF94 is reported as the efficient forcefield for minimizing ligand-protein complexes [START_REF] Kaminski | Performance of the AMBER94, MMFF94, and OPLS-AA Force Fields for Modeling Organic Liquids[END_REF][START_REF] Halgren | Maximally diagonal force constants in dependent angle-bending coordinates. II. Implications for the design of empirical force fields[END_REF], furthermore, the MMFF94x variant ensures planar geometry of the peptide bonds [START_REF] Halgren | MMFF VI. MMFF94s option for energy minimization studies[END_REF]. Moreover, parametrization problems in AMBER99 made this approach unfeasible. The simulation time was 1 ns with the step 2 fs under 300K. The partial charges (AMBER99) were recalculated after the MD simulation. In order to control possible artifacts we additionally performed a blind test using strictly the same protocol in which activatorenzyme structure was replaced by the enzyme without the activator. As these were expensive and time consuming processes we selected only several representative protein-activator complexes, namely, with compounds a1, a2, a3, a7, a9, a10. This selection was made on the basis of the activity values and structural diversity of the compounds. The results were analyzed by the RMSD values of the chain D and TYR128 in the 1D4A and the 1D4Aactivator complexes (Table 3), if compared to 1D4A in the initial and after MD blind test states. The main differences can be observed in the RMSD of TYR128. A large RMSD (TYR128) of 5.2 Å exists between the 1D4A initial state and 1D4A after blind test. Similarly the large RMSD (TYR128) values describe the differences between the 1D4A-activator complexes in relation to the 1D4A after blind test. In contrary, the activator if present, makes the position of TYR128 in 1D4A after MD more similar to the position TYR128 in the initial 1D4A (lower values of TYR128 RMSD in 1D4A-activator in relation to 1D4A). The importance of the TYR128 in the substrate binding suggests that activator binding in UBS can affect substrate activity in CBS. Indeed, Fig. 4 shows changes in the TYR128 position as resulted in MDs discussed above. The most active activators (green and blue) bend the TYR128 loop into the CBS whereas the medium or low effective activators in UBS (blind test) causes the TYR128 loop bends outside the CBS. The latter effect can also be observed in the absence of the activator.

In order to test this hypothesis we performed further experiment in which the activator-ligand complexes and blind test systems as obtained form the MDs were used as the target for the hypothetical further docking of i1-i9 and dq in the CBS. The alignment process was done using alpha spheres located in the direct neighborhood of TYR128. Docking algorithm was essentially the same as in the case of activators though additional refinement step was performed to minimize each pose (MMFF94x) and then recalculate the SF. Table 4 presents the SF values for the best docked poses of each inhibitor with in the presence of activator in the UBS and for the blind test. The lower a value of the SF the better is a fit of the inhibitor. Thus, in the majority of cases the activator, if present, in the UBS, enhances binding as indicated by the decrease of the SF value for the substrates docked in the CBS. Only several activator/substrate/enzyme systems do not follow this rule. These are i2/a1; i10/a3, i9/a7 and i7/a10.

It seems that the movements of several amino acids in the USB are constrained by the presence of the activator resulting in some changes of the CBS. Further testing of this hypothesis have been performed using the reduced dynamic approach (RedMD), which utilizes the coarse-grained models of the proteins. Thus, two representations of NAD(P)H:quinine oxidoreductase have been prepared by changing each amino acid with artificial alpha carbon atoms heaving appropriate properties to fake replaced amino acids. The first representation mimics the original 1D4A crystal structure, whereas the second one has modified properties of ASN45÷47 of chain B and D, respectively. Generally, it reflects the constrains caused by the presents of activators in the UBS. Moreover, the applied modifications resulted in the increase of masses and harmonic constant. Afterward, both representations have been used in Langevin's dynamics simulations repeated 10 times with different random seeds. The whole trajectory time was 10 times 20 ns with temperature of 300K. The atomic coordinates of the system were sampled every 20fs. Then, the entire set of the obtained frames has been superimposed by ASN45÷47 of chain B and D, respectively. Fig. 5 represents the resulting histograms of RMSD of alpha carbon (CA) of TYR128 calculated against its reference position taken from the original 1D4A file for the whole trajectory. The red color indicates TYR128 CA of the 1D4A reference structure, while the blue one shows TYR128 CA of the modified representations. Taking into account the imposed constraints on residues 45÷47 minor but noticeable change of TYR128 CA behavior during the simulations might be noticed. The detailed analysis of the histogram revealed that TYR128 CA occupied approximately 5% more time in the mean aberration. Additionally, it seems that TYR128 is less prone to the high deviation as it is shown in the subplot with logarithmic scale. The above conclusion might prove our previous observations. We observed that the constraints of 45÷47 residues either by the presence of the activators or by manual modifications makes the position of TYR128 resulting from MD simulations more similar to its reference coordinates in the initial 1D4A file. Moreover, quite similar findings were obtained for HIST161, which plays the pivotal role in the substrate binding in the CBS.

On the other hand we also observed the changes in behavior of other amino acids CAs.

However, observed changes were of low importance.

In many recent papers authors claim that the scoring functions describing molecular docking experiments cannot be quantitatively correlated to biological activity [START_REF] Zentgraf | How Reliable Are Current Docking Approaches for Structure-Based Drug Design? Lessons from Aldose Reductase[END_REF]. Thus, in Fig. 6 we presented the two-dimensional ligand-receptor interaction diagrams which allows one to qualitatively observe binding in the presence of activators. This depicts relatively strong connections or hydrogen bonds as well as electrostatic or charge-transfer interactions between a ligand and the respective amino acid residues. The comparison of the individual poses for the activator/enzyme (Fig. 6bc) with those resulted for the plane enzyme (Fig. 6a) indicates that the activator enables substrate to get easier in the catalytic core expanding a molecular volume inside receptor and simultaneously improves the solvent exposure of TYR128.

Conclusions

In conclusion the activatory effect on NAD(P)H:quinone oxidoreductase enzyme was investigated using molecular dynamics and molecular docking. This allowed us to find a hypothetical UBS for the series of ITC compounds. The ASN45 and ASN47 amino acid residues were recognized as the most probable binding residues in the UBS where ITC analogues were bound. Using the MDs for the enzyme/activator/substrate systems allowed us to indicate the enhancement of the substrate binding within the CBS, and thus directly simulate the protein activation effect. This effect can indicate an alternative mechanism of the oxidoreductase activation. We are now performing the series of experiments to prove this experimentally.

Figure captions

  created in the potential binding sites. During the simulation the conformations were generated progressively from a single conformer (protomer, tautomer) by applying a collection of preferred torsions angles to the rotatable bonds. The quality of each alignment was further assessed by the London dG (LdG) scoring function (SF) which estimates the free energy of binding of the ligand and a set of the highest scored poses were chosen for each molecule (pose) docked [29].

Fig. 1 Fig. 2 AFig. 3 Fig. 4 Fig. 5 Fig. 6

 123456 Fig. 1Schematic reaction catalyzed by Human NAD(P)H:quinone oxidoreductase

  Figure 1 Click here to download high resolution image

Table 1

 1 ITC activators of NAD(P)H:quinone oxidoreductase. Data according to publications[START_REF] Misiewicz | Sulforaphane-mediated induction of a phase 2 detoxifying enzyme NAD(P)H:quinone reductase and apoptosis in human lymphoblastoid cells[END_REF][START_REF] Misiewicz | Differential response of human healthy lymphoblastoid and CCRF-SB leukemia cells to sulforaphane and its two analogues: 2-oxohexyl isothiocyanate and alyssin[END_REF] 21,[START_REF] Posner | Design and synthesis of bifunctional isothiocyanate analogs of sulforaphane: correlation between structure and potency as inducers of anticarcinogenic detoxication enzymes[END_REF]

	Compound	Structure	Activity
	s		pAC 50
	a1	SCN(CH 2 ) 5 CH 3	-1.18
	a2	SCN(CH 2 ) 4 (S=O)CH 3	0.70
	a3	SCNCH 2 (C=O)CH 3	0.70
	a4	SCN(CH 2 ) 4 (S=O)(CH 2 ) 3 CH 3	-0.30
	a5	SCN(CH 2 ) 4 (S=O)CH 3	0.40
	a6	SCN(CH 2 ) 4 (C=O)SCH 3	-0.45
	a7	SCN(CH 2 ) 4 (C=O)OCH 3	-0.45
	a8	SCN(CH 2 ) 4 CN	-0.30
	a9	SCN(CH 2 ) 5 (S=O)CH 3	-0.20
	a10	SCN(CH 2 ) 4 (C=O)CH 3	0.30

Table 2

 2 Indoquinoline inhibitors of reductase. Data according to reference[START_REF] Reigan | Development of indolequinone mechanism-based inhibitors of NAD(P)H:quinone oxidoreductase 1 (NQO1): NQO1 inhibition and growth inhibitory activity in human pancreatic MIA PaCa-2 cancer cells[END_REF]

			R
	O		O
	X
	Y	N
	O
	Compounds Structure		Activity
					IC 50 [µM]
		X	Y	R	4h 72h
	i1	OMe H	p-nitrophenyl 0.63 0.51
	i2	H	OMe p-nitrophenyl 0.64 0.35
	i3	OMe H	phenyl	1.39 0.96
	i4	H	OMe phenyl	4.56 0.41
	i5	OMe H	4-pirydyl	2.01 2.17
	i6	H	OMe 4-pirydyl	2.56 3.12
	i7	OMe H	acetyl	2.42 2.16
	i8	H	OMe H	2.15 1.99
	i9	OMe H	H	2.17 2.16

Table 3

 3 RMSD (Å) values of α carbons of chain D and TYR128. Two references were used, the 1D4A plain enzyme model and the blind test model resulted in MD. Details in text

		Chain D		Chain D TYR128 only
	1D4A	In relation	In relation	In relation	In relation
	model	to 1D4A	to blind	to 1D4A	to blind
			test		test
	blind test 1.3	(0)	5.2	(0)
	with a1	1.3	1.5	2.3	4.2
	with a2	1.3	1.5	1.3	5.6
	with a3	1.2	1.3	1.2	4.1
	with a7	1.4	1.2	4.0	2.0
	with a9	1.5	1.4	2.5	3.7
	with a10 1.4	1.2	2.2	4.6

Table 4

 4 The LdG scoring functions of best docked poses of the indoquinolinone inhibitors depending of the presence of activator and the binding site. The blind test was performed using all steps applied in the activity-mechanism simulations

	Inhibitor	London dG scoring functions between the enzyme and enzyme/activator/substrate a/ 1D4A b/ with a1 c/ With a2 c/ with a3 c/ with a7 c/ with a9 c/ with a10 c/
	i1	-13.75	-19.03	-17.95	-20.70	-15.33	-19.69	-16.62
	i2	-14.08	-13.89	-18.25	-18.78	-14.44	-20.31	-19.32
	i3	-12.39	-18.69	-17.19	-14.81	-14.88	-18.52	-16.23
	i4	-12.06	-16.31	-15.58	-18.09	-13.14	-17.88	-20.25
	i5	-12.45	-17.28	-15.84	-19.03	-13.93	-18.83	-17.69
	i6	-11.36	-17.70	-15.21	-16.24	-12.54	-17.68	-19.60
	i7	-11.64	-17.07	-15.88	-16.30	-11.73	-17.06	-8.86
	i8	-11.71	-17.00	-12.84	-17.70	-11.85	-14.88	-15.81
	i9	-13.32	-15.64	-13.81	-17.18	-11.44	-15.75	-16.56
	dq	-11.41	-14.78	-18.78	-12.01	-15.72	-19.97	-16.87
	a/ LdG/ [kcal mol -1 ]

b/ blind test for the enzyme without any activator c/ activators were additionally optimized before the calculations of the parameters reported.

Acknowledgments

This study is supported by a grant from the Polish Ministry of Science N405 178735. PM thanks the UPGOW (in Polish Uniwersytet Partnerem Gospodarki Opartej na Wiedzy) grant founded by European Social Fund for partially financing his PhD studies.


Professor Irena Roterman

Katarzyna Prymula

Kinga Salapa

Kinga Sałapa

Fuzzy Oil Drop" Model Applied to Individual Small Proteins Built of 70 Amino

Keywords: Biological activity, Active site recognition, Hydrophobicity deficiency, Small proteins, Proteins of unknown function Biological activity  Active site recognition  Hydrophobicity deficiency 

The proteins composed of short polypeptides (about 70 amino acid residues) representing the following functional groups (according to PDB notation): growth hormones, serine protease inhibitors, antifreeze proteins, chaperones and proteins of unknown function, were selected for structural and functional analysis. Classification based on the distribution of hydrophobicity in terms of deficiency/excess as the measure of structural and functional specificity is presented. The experimentally observed distribution of hydrophobicity in the protein body is compared to the idealized one expressed by a three-dimensional Gauss function. The differences between these two distributions reveal the specificity of structural/functional characteristics of the protein. The residues of hydrophobicity deficiency versus the idealized distribution are assumed to indicate cavities with the potential to bind ligands, while the residues of hydrophobicity excess are interpreted as potentially participating in protein-protein complexation. The distribution of hydrophobicity irregularity seems to be specific for particular structures and functions of proteins. A comparative analysis of such profiles is carried out to identify the potential biological activity of proteins of unknown function.

Introduction

Techniques aimed at engineering proteins for pharmacological use, with enhanced stability or improved functions, are being widely developed nowadays [START_REF] Rosenberg | Computational protein design: a novel path to future protein drugs[END_REF][START_REF] Deng | Protein design of a bacterially expressed HIV-1 gp41 fusion inhibitor[END_REF][START_REF] Antikainen | Altering protein specificity:techniques and applications[END_REF]. The investigation of proteins exhibiting desirable activity among the proteins present in organisms, as well as the generation of new proteins resulted in the development of polypeptide sequence libraries [START_REF] Patrick | Strategies and computational tools for improving randomized protein libraries[END_REF][START_REF] Chaparro-Riggers | Better library design:datadriven protein engineering[END_REF]. Designing polypeptides that modify the biological activity of other proteins [START_REF] Fowler | Rational design of aggregation-resistant bioactive peptides: Reengineering human calcitonin[END_REF], exhibit new or altered catalytic properties [START_REF] Shao | Engineering new functions and altering existing functions[END_REF] and high stability [START_REF] Eijsink | Rational engineering of enzyme stability[END_REF] is one of the goals of pharmaceutical research. The calculation of the number of all possible sequences containing 50 amino acid residues composed of 20 standard amino acids revealed that only a minority of these sequences occur in nature [START_REF] Chiarabelli | Investigation of de novo totally random biosequences. Part I: A general method for in vitro selection of folded domains from a random polypeptide library displayed on phage[END_REF]. The huge number of protein sequences not observed in nature seems to be the space hiding many biological functions not exploited in the biochemical pathways developed so far by living organisms. Attempts focused on the investigation of protein folds within libraries containing totally random sequences have been made [START_REF] Chiarabelli | Investigation of de novo totally random biosequences. Part I: A general method for in vitro selection of folded domains from a random polypeptide library displayed on phage[END_REF][START_REF] Chiarabelli | Investigation of de novo totally random biosequences. Part II: On the folding frequency in a totally random library of de novo proteins obtained by phage display[END_REF]. The search for pharmacologically active proteins among Never Born Proteins (NBPs) is one of the aims of the EUChinaGRID project [START_REF]EUChinaGRID project[END_REF]. In the project, the search for biologically active proteins that have not been created during evolution is performed in silico using two complementary methods: ROSETTA [START_REF] Bonneau | De novo prediction of three-dimensional structures for major protein families[END_REF] and the "fuzzy oil drop" (FOD) model [START_REF] Konieczny | Gauss-function-based model of hydrophobicity density in proteins[END_REF][START_REF] Brylinski | Fuzzy-oil-drop hydrophobic force field-a model to represent late-stage folding (in silico) of lysozyme[END_REF][START_REF] Brylinski | Hydrophobic collapse in (in silico) protein folding[END_REF][START_REF] Brylinski | Hydrophobic collapse in late-stage folding (in silico) of bovine pancreatic trypsin inhibitor. Biochimie 17. Brylinski M, Konieczny L, Roterman I[END_REF][17]. The results of this investigation are presented elsewhere [START_REF] Prymula | In silico structural study of random amino acid sequence proteins not present in nature[END_REF].

Before the space of NBPs can be exploited, the existing proteins of defined chain length were taken under consideration with respect to their active-site properties. The characterization of biological activity of known proteins can help to determine the possible biological functions of NBPs. In our study, the position of potential active sites (understood also as the ligandbinding sites) is assigned on the basis of hydrophobicity deficiency in particular proteins. The biological activity has been identified based on the FOD model [START_REF] Brylinski | Ligation site in proteins recognized in silico[END_REF][START_REF] Brylinski | Sequence-structurefunction relation characterized in silico[END_REF][START_REF] Brylinski | Localization of ligand binding site in proteins identified in silico[END_REF][START_REF] Brylinski | Prediction of functional sites based on the fuzzy oil drop model[END_REF]. The number of sequences having 70 amino acid residues and a known 3-D structure is relatively large. It is impossible to present their characteristics in one paper. Proteins participating in the formation of large complexes (ribosomes) and those interacting with DNA or RNA as well as proteins with bound ligands are described elsewhere [START_REF] Prymula | Functional characteristics of small proteins (70 amino acid residues) forming protein-nucleic acid complexes[END_REF][START_REF] Prymula | Structural entropy to characterize small proteins (70 aa) and their interactions[END_REF].

Materials and methods

Data

The tool available on the Protein Data Bank [START_REF] Berman | The Protein Data Bank[END_REF] webpage aimed at searching for proteins that satisfy particular criteria was used to select proteins according to the defined polypeptide chain length (70 amino acid residues). Proteins containing less than 72 and more than 68 amino acids were pre-selected from the group of entries found in PDB (July 2007). In the next step, the structures which lacked the coordinates of main chain atoms in the middle of the sequence were rejected. The proteins chosen for analysis and presented here are a subset of the selected entries and belong to one of the following functional groups: growth factors, serine protease inhibitors, antifreeze proteins, chaperones and proteins of unknown function (Table 1). The data for the analyzed structures were taken from investigations carried out using X-ray crystallography [START_REF] Jia | Structural basis for the binding of a globular antifreeze protein to ice[END_REF][START_REF] Wisniewska | The 1.1 A resolution crystal structure of the p130cas SH3 domain and ramifications for ligand selectivity[END_REF][START_REF] Bourenkov | Structure and function of Tim14 and Tim16, the J and J-like components of the mitochondrial protein import motor[END_REF] and NMR techniques [START_REF] Werner | Three-dimensional structure of soybean trypsin/chymotrypsin Bowman-Birk inhibitor in solution[END_REF][START_REF] Cierpicki | Determination of a high precision structure of a novel protein, Linum usitatissimum trypsin inhibitor (LUTI), using computer-aided assignment of NOESY cross-peaks[END_REF][START_REF] Hyberts | The solution structure of eglin c based on measurements of many NOEs and coupling constants and its comparison with X-ray structures[END_REF][START_REF] Christendat | Structural proteomics of an archaeon[END_REF][START_REF] Kohda | Solution structure of the sh3 domain of phospholipase c-gamma[END_REF][START_REF] Sönnichsen | Refined solution structure of type III antifreeze protein:hydrophobic groups may be involved in the energetics of the protein-ice interaction[END_REF][START_REF] Schaffer | Complex with a phage display-derived peptide provides insight into the function of insulin-like growth factor I[END_REF][START_REF] Yee | An NMR approach to structural proteomics[END_REF][START_REF] Pineda-Lucena | NMR structure of the hypothetical protein encoded by the YjbJ gene from Escherichia coli[END_REF][START_REF] Abajian | Yeast cox17 solution structure and copper(I) binding[END_REF][START_REF] Arnesano | Folding studies of cox17 reveal an important interplay of cysteine oxidation and copper binding[END_REF][START_REF] Cooke | Solution structure of human insulin-like growth factor 1: a nuclear magnetic resonance and restrained molecular dynamics study[END_REF]. Additionally, the SH3 domain is discussed with respect to the various proteins that contain it and their different biological functions [START_REF] Dalgarno | SH3 domains and drug design:ligands, structure, biological function[END_REF].

Sequence and structure analysis

Protein sequences and structures were compared within the set of selected chains (Table 2).

Sequence alignment was performed using the LALIGN program [START_REF] Huang | A time-efficient, linear-space local similarity algorithm[END_REF] from the FASTA package [START_REF] Pearson | Improved tools for biological sequence comparison[END_REF], version 35, with scoring matrix PAM250 and gap open/extension penalties equal to -10/-2. Additionally, for sequences from the SH3 domain family multiple sequence alignment was produced with the aid of ClustalW [START_REF] Larkin | Clustal W and Clustal X, version 2.0[END_REF]. Structure similarity was measured using DaliLite [START_REF] Holm | DaliLite workbench for protein structure comparison[END_REF] for pairwise structure comparison and resulted RMS-D values calculated for the C  atoms were taken for further analysis (see the Statistical section for details).

Binding site recognition

The FOD model was applied to identify the distribution of hydrophobicity in the selected proteins [17,[START_REF] Brylinski | Localization of ligand binding site in proteins identified in silico[END_REF]. The idealized hydrophobicity distribution is represented by the three dimensional Gauss function, according to Eq. 1:

      2 2 2 2 2 2 1 exp exp exp 2 2 2 j j j j sum x y z x y z Ht Ht                                  (1)
j Ht  represents the theoretical hydrophobicity at j-th point (x j , y j , z j )position of the effective atom (averaged side chain position or C α atom for GLY), while the inverse of sum Ht 

is the normalizing constant. The point at which the Gauss function reaches its maximum is located in the geometric center of the macromolecule. Theoretical hydrophobicity is calculated after the translation of a molecule, so that its centroid coincides with the origin (this is why all mean values are equal to 0). The values of x y z σ ,σ ,σ (traditionally interpreted as standard deviation), calculated separately for each dimension, represent the size of the "drop", which depends on the length of the polypeptide chain (and the size of a macromolecule) under consideration [START_REF] Brylinski | Fuzzy-oil-drop hydrophobic force field-a model to represent late-stage folding (in silico) of lysozyme[END_REF].

The empirical hydrophobicity distribution (as observed based on the hydrophobic properties of residues in real proteins) is calculated by applying the Levitt [START_REF] Levitt | A simplified representation of protein conformations for rapid simulation of protein folding[END_REF] function, according to Eq. 2:

2 4 6 8 1 1 7 9 5 for 1 2 0 for ij ij ij ij ij rr j i j i sum ij r r r r rc c c c c Ho H H Ho rc                                                 
(2) j Ho  represents the empirical hydrophobicity of the j-th amino acid residue; r i H  , r j H  are the hydrophobicity characteristics for the i-th and j-th amino acid residues, respectively; r ij is the distance between the j-th and i-th effective atoms representing the side chain position of a particular amino acid; c expresses the cut-off distance, which has a fixed value equal to 9.0 Å, as stated in the original paper [START_REF] Levitt | A simplified representation of protein conformations for rapid simulation of protein folding[END_REF]; finally, the inverse of sum Ho  is the normalizing constant. j Ho  collects the hydrophobic interactions of the j-th residue with others localized within the specified distance c. Any hydrophobicity scale [START_REF] Kyte | A simple method for displaying the hydropathic character of a protein[END_REF][START_REF] Eisenberg | Hydrophobic moments and protein structure[END_REF][START_REF] Engelman | Bacteriorhodopsin is an inside-out protein[END_REF][START_REF] Hopp | Prediction of protein antigenic determinants from amino acid sequences[END_REF][START_REF] Rose | Hydrophobicity of amino acid residues in globular proteins[END_REF][START_REF] Wimley | Experimentally determined hydrophobicity scale for proteins at membrane interfaces[END_REF][START_REF] Wolfender | Affinities of amino acids side chains for solvent water[END_REF] may be applied to calculate the observed distribution of hydrophobicity. Despite some differences between hydrophobicity parameters in these scales, it was found that these differences do not significantly influence the final results [START_REF] Prymula | In silico structural study of random amino acid sequence proteins not present in nature[END_REF]. The hydrophobicity scale applied for the calculations presented in this paper is based on the "fuzzy oil drop" model. The hydrophobicity parameter is estimated according to the relative position in the three-dimensional Gauss function. This approach makes possible the calculation of the hydrophobicity of ligand molecules. Since the model ("fuzzy oil drop" model) is assumed to be used for folding process simulation in the presence of ligand molecule [START_REF] Brylinski | Hydrophobic collapse in (in silico) protein folding[END_REF]17] the self-consistency of the hydrophobicity scales for amino acids and ligand molecules is of high importance [17]. The quantitative comparison of this scale with commonly used ones is also shown in [START_REF] Brylinski | Hydrophobic collapse in (in silico) protein folding[END_REF].

Since both values of hydrophobicity (theoretical and empirical) are normalized, the difference between these values, calculated according to Eq. 3, represents the deviation of the empirical hydrophobicity versus the theoretical one, at a particular point in space (effective atom of the j-th amino acid residue). The level of predictability can be easily measured using the information theoryand information entropy in particular. The amount of information carried by a particular system (represented by j H   profile in our example) may be calculated.

j j j H Ht Ho       (3)
The first case can be treated as deterministic (residues of one fragment with high j H   are determined to meet in the space). The second case represents a random case (with randomness understood as equal probability for each fragment to participate in a common structural element).

The case with many j H   maxima can also represent a protein with one or more binding sites.

However, small polypeptides (around 70 amino acid residues) are expected to contain at best one well-defined binding site.

The characterization of a binding site can be expressed quantitatively using the probability theory.

The entropy of a binding site involving residues close in sequence is low (first case), compared with the entropy of a binding site formed by residues evenly distributed in sequence (second case). Information entropy (SE) calculated for fragments with positive

where K denotes the number of fragments with positive j H   , and

1 N p ij i j p i= t ΔH p= ΔH    (5)
where N ij represents the number of positive The SE + characterizes a particular protein and may describe an active site (fragments with positive values). Another parameter used in our study of binding sites is the information necessary to localize residues creating a binding site (I [bit]). The participation of particular residues in active site creation is understood as the probability of the conjunction of events (close mutual localization) and can be calculated according to Eq. 6:

2 1 log K j j= I = p   (6)
where K has the same meaning as in Eq. 4.

Both parameters (SE, I) describe similar characteristics of the active site formed by selected residues participating in the organization of a particular function-related structure

The SE max (the highest uncertainty) describes the situation when all solutions are equally probable (all positive j H   fragments are equally probable -discussed as the second case). 11 log

K j SE KK     (7)
The SE max calculated for equal p j values describes the random situation in which each fragment is equally represented (in the scale of p). The difference between SE max and SE and may be used to measure the distance in the probability between a particular solution (active site construction) and a completely random result [START_REF] Brylinski | Sequence-structurefunction relation characterized in silico[END_REF].

The same analysis may be applied to negative

j H   values (SE -).

Statistical analysis

The parameters describing the similarity of proteins under consideration were analyzed from a statistical point of view. The correlation coefficient measuring the relation between sequence similarity (expressed by Waterman-Eggert score) and structural similarity (expressed by RMS-D), and biological function similarity (expressed by differences between SE and I parameters introduced in section 2.5, and shown in Table 2) was calculated using the

Statistica Program [54].
All pairs of chains listed in Table 2 were taken into account in this analysis. The level of significance was selected at α=0.05. The Spearman's correlation coefficient [START_REF] Spearman | General intelligence, objectively determined and measured[END_REF] was calculated due to the absence of normal distribution. It is a nonparametric measure of correlation between two variables, without making any other assumptions about the particular nature of the relationship between the variables and no preliminary distribution analysis (for normal distribution of the variables the calculation of the Pearson correlation coefficient is applicable).

The correlation coefficient was calculated also pair-wise for proteins representing similar j H   profiles to measure the degree of similarity between them.

Results and discussion

The set of proteins fulfilling the polypeptide chain length criterion (approx. 70 amino acid residues) is quite large and diverse with regard to their biological function. The analysis of proteins crystallized as individual molecules is presented in this paper.

Growth factors

The group of insulin-like growth factors I (IGF-1) represented in PDB by four entries (Table 1) which appeared to be of identical sequence. This is why solely 1BQT is presented in this paper. The j H   profile shown in Fig. 1a together with the spatial distribution of j H   values (Fig. 1b,c) is quantitatively characterized in Table 2. Other proteins belonging to this group represent some differences in j H   profile although they are not of significant importance (results not shown here). Thorough analysis, however, reveals that the

j H   profile of 1PMX
differs the most from the others. This observation coincides with the fact that only 1PMX exhibits the structure of IGF-1 complexed with a peptide inhibitor.

Both the structure of IGF-1 complexed with the peptide identified from the phage display library [START_REF] Schaffer | Complex with a phage display-derived peptide provides insight into the function of insulin-like growth factor I[END_REF], and the structure of the same protein complexed with a detergent [START_REF] Vajdos | Crystal structure of human insulin-like growth factor-1:detergent binding inhibits binding protein interactions[END_REF] revealed that the binding sites are located in fragments B (residues 1-29) and A (residues . The interactions between IGF-1 and peptides are hydrophobic and formed by residues 3,4,13,17 and 54. The

Serine protease inhibitors

The comparison of the j H   profiles (Fig. 2a) as well as the SE values (Table 2) for proteins representing serine protease inhibitors indicates that although these proteins share similar function, their structures display some differences. the area potentially responsible for the interaction with other proteins (Fig. 2a), which was actually confirmed experimentally when the complex structure of the inhibitor with bovine trypsin was resolved [START_REF] Koepke | Crystal structure of cancer chemopreventive bowman-birk inhibitor in ternary complex with bovine trypsin at 2.3 a resolution. Structural basis of janus-faced serine protease inhibitor specificity[END_REF]. The following residues form a protein-protein interface: 11, 12, 14-18, 20, 21, 23, 52 and 57. The entropy of such a functional site should therefore be small, and this is confirmed by the SE -values (Table 2), which are lower than those for the overwhelming majority of structures analyzed in this study. The j H   values of these residues do not suggest the recognition of these residues to be responsible for biological function.

Neither local maxima nor local minima are represented by these residues.

The correlation coefficient calculated for j H   values appeared significant only for the pair 1EGL and 1DWM suggesting high similarity between these two proteins. The 1BBI molecule differs versus the others (Fig. 2c) what can also be seen taking S scale as the criterion.

Antifreeze proteins

This functional class is represented by proteins belonging to the group responsible for protection against the ordering of water molecules, and the subsequent formation of ice.

The antifreeze protein from the North Atlantic ocean pout (1MSI) and its mutated counterpart (1KDE), which exhibit 97.14% sequence identity, display very similar (although not identical) j H   profiles (Fig. 3a) and hydrophobicity distribution (Fig. 3b). In this case, sequence identity is reflected in structure similarity (RMS-D=0.8 Å). The SE values are very close to each other (Table 2). The comparison of the SE with respect to the sequence/structure similarity may be used to visualize the mutual relation between these two characteristics and

shows to what extent the SE can be treated as a similarity measure. These examples were selected to show how the sequence and structural similarity influences the Antifreeze proteins lower the temperature of ice growth with respect to the bulk of the solvent. This phenomenon, known as thermal hysteresis, is supposed to be the result of the adsorption of antifreeze proteins on the surface of ice [START_REF] Raymond | Freezing behavior of fish blood glycoproteins with antifreeze properties[END_REF][START_REF] Raymond | Adsorption inhibition as a mechanism of freezing resistance in polar fishes[END_REF][START_REF] Li | The kinetic theory of thermal hysteresis of a macromolecule solution[END_REF][START_REF] Li | Further discussion on the thermal hysteresis of the ice growth inhibitor[END_REF][START_REF] Hall | Phenomenology and mechanism of antifreeze peptide activity[END_REF]. The mechanism by which antifreeze proteins contribute to thermal hysteresis in fish is not completely understood. Some explanations of this phenomenon suggest the reduction of the solubility of the antifreeze proteins in the solution as one of the causes of hysteretic activity [START_REF] Kristiansen | The mechanism by which fish antifreeze proteins cause thermal hysteresis[END_REF]. Other studies [START_REF] Jia | Structural basis for the binding of a globular antifreeze protein to ice[END_REF][START_REF] Chao | Structure-function relationship in the globular type iii antifreeze protein: identification of a cluster of surface residues required for binding to ice[END_REF] identify N14, T18 and Q44 as the key residues for antifreeze protein (type III)ice interaction and reveal the amphipathic character of the ice-binding site. Interaction with ice is of low specificity character. This is why the detailed interpretation of their The antifreeze proteins display very short fragments (only one residue in 1KDE) of high hydrophobicity deficiency. These fragments are almost entirely "buried" in the central part of the molecule (and inaccessible on the protein surface), which suggests these molecules are unlikely to bind ligands (no hydrophilic cavity in this protein). The protein molecule is covered with residues with j H   close to 0 (Fig. 3b, yellow color) and some surface fragments exhibiting excess hydrophobicity (Fig. 3b, orange), which may indicate the potential location of an area responsible for protein-protein interactions, as observed in other proteins [START_REF] Prymula | In silico structural study of random amino acid sequence proteins not present in nature[END_REF]. On the other hand, the presence of a hydrophobicity deficiency area on the surface is in agreement with the experimental observation which suggests that the possible disorder of water molecules prevents the structuring of water molecules during the freezing process. The residues suspected of being responsible for the antifreeze activity [START_REF] Chao | Structure-function relationship in the globular type iii antifreeze protein: identification of a cluster of surface residues required for binding to ice[END_REF] belong to the hydrophilic fragment on the surface (Fig. 3c).

The high value of correlation coefficient (Fig. 3a) confirms high similarity of j H   profiles noted after visual inspection of them.

Chaperones

Proteins responsible for controlling the folding process are represented by four structures although three of them (1U96, 1U97, 1Z2G) have identical sequences (1U96 and 1Z2G were excluded from the discussion). The protein 2GUZ does not exhibit any significant sequence similarity to 1U97. The SE parameters for proteins representing chaperones are given in Table 2. The very well-defined and long fragments of hydrophobicity deficiency can be recognized based on low SE + . The j H   profiles (Fig. 4a) with a few marked minima, such as those observed for these structures, are characteristic of compact shapes with long unstructured loops that stick out (Fig. 4b,c).

The correlation coefficient (Fig. 4a) seems to express quantitatively the relatively low similarity of j H   profiles for proteins in this group.

The lack of accordance between SE parameters and between j H   profiles (low value of correlation coefficient) despite of quite high structural similarity seems to be due to the long extended N-and C-terminal polypeptide fragments in 1U97 which are absent in 2GUZ (Fig. 4b,c). This makes the approximation of 1U97 to the sphere (ellipsoid described by 3-D Gauss function) not appropriate.

Proteins of unknown biological function

The proteins belonging to this group are of particular interest. The comparison of j H   profiles suggests that there are no proteins of mutual similarity in this group, a result confirmed by the SE parameter values (Table 2) The comparison of profiles for the proteins of this group with those of proteins of known biological function may be the way to recognize similar proteins and, possibly, their biological function. This is a difficult challenge for the FOD model.

Taking into account all the selected structures containing 70 amino acid residues in the polypeptide chain, some similarities may be found. The j H   profile of a human protein of unknown function (2CRE) appeared to be similar to the profiles of two structures which belong to the SH3 domain. Namely, these are fragments of the human cytoplasmic protein NCK2 (1U5S:A), classified as a metal-binding protein, and the kinase-binding protein 1 (2DA9) found in micea regulator of ubiquitous kinase (Ruk), which regulates apoptosis (Fig. 5a). The ribbon models of these three proteins (2CRE, 1U5S:A and 2DA9) and their surface representations with highlighted areas of hydrophobicity excess/deficiency are shown in Fig. 5b. The sequence similarity between these proteins calculated using the LALING program is summarized in Table 3. The gaps suggested in Fig. 5a appeared to be in agreement with the gap localized using sequence similarity estimation making the 2CRE and 2DA9 j H   profiles more similar.

The 2CRE and 1U5S:A can be found as proteins of highest similarity in terms of SE parameters (Table 2), although higher sequence similarity (according to LALIGN) has been found for 2CRE and 2DA9.

According to the recognition procedure based on j H   profile analysis, the protein 2CRE of unknown biological function can possibly be a metal-binding protein (as 1U5S-A) or a kinase-binding protein 1 (as 2DA9). Another common characteristic of these three proteins is that they share the SH3 domain fold. The group of SH3 domains will be presented in the next part of this paper.

The comparison of correlation coefficient for j H   profiles suggests the high similarity between 2CRE and 1U5S:A what is in accordance with the SE parameters (Table 2).

The SH3 domains

The SH3 domains constitute a family of proteins characterized by different functions.

Nevertheless, they are taken into consideration in the analyzed dataset because of the similarity found between contain fragments characterized by high SD (Fig. 6d). Higher variability is, however, observed within areas on fragments with high j H   (Fig. 6c), therefore suggesting that they coincide with areas of possible biological activity as SH3 domains differ with regard to their function depending on the systems they are part of.

The pair-wise comparison of j H   profiles by means of correlation coefficient is given in Table 4.

Statistical analysis

In order to quantify to what extent the SE and I parameters are able to measure the structural/functional similarity the correlation coefficients between parameters describing similarity of sequence, 3-D structure and j H   profiles were calculated. Sequence alignment scores (Waterman-Eggert), RMS-D values and differences of SE and I parameters shown in Table 2 were variables used in correlation analysis.

The calculation of correlation coefficients was used to describe relationship between measures of sequence, 3-D structure and j H   profile similarities. The results are summarized in Table 5. For undisputable similar structures low RMS-D and high Waterman-Eggert score was obtained and negative (as expected) significant correlation between RMS-D and sequence similarity measure was obtained.

The proteins belonging to the category of "unknown function" were different since their structure were not classified according to the function related criteria.

Since no normal distribution has been observed for all variables, the Spearman coefficient was calculated to measure the degree of mutual relation the significance of which was additionally tested. The results are shown in Table 5. The most interesting are the significant correlations between

Conclusions

Prior to the analysis of the biological activity of NBPs, real proteins were characterized with respect to their structural characteristics and active-site architecture. The FOD model was applied to identify the location of possible ligand-binding sites. The ligand binding sites, ion binding sites, protein-protein interactions area can be recognized on the basis of the hydrophobicity excess/deficiency distribution all over the protein body [START_REF] Konieczny | Gauss-function-based model of hydrophobicity density in proteins[END_REF][START_REF] Brylinski | Fuzzy-oil-drop hydrophobic force field-a model to represent late-stage folding (in silico) of lysozyme[END_REF][START_REF] Brylinski | Hydrophobic collapse in (in silico) protein folding[END_REF][START_REF] Brylinski | Hydrophobic collapse in late-stage folding (in silico) of bovine pancreatic trypsin inhibitor. Biochimie 17. Brylinski M, Konieczny L, Roterman I[END_REF][17][START_REF] Brylinski | Ligation site in proteins recognized in silico[END_REF][START_REF] Brylinski | Sequence-structurefunction relation characterized in silico[END_REF][START_REF] Brylinski | Localization of ligand binding site in proteins identified in silico[END_REF][START_REF] Brylinski | Prediction of functional sites based on the fuzzy oil drop model[END_REF][START_REF] Prymula | Functional characteristics of small proteins (70 amino acid residues) forming protein-nucleic acid complexes[END_REF][START_REF] Prymula | Structural entropy to characterize small proteins (70 aa) and their interactions[END_REF]. The FOD model applied to other proteins (including proteins participating in protein-protein complexes) showed that this model can be used as a tool for active site localization in proteins (including ligand-binding sites). The applicability and reliability of the FOD model depends on the class of enzymes analyzed, as shown in [START_REF] Brylinski | Prediction of functional sites based on the fuzzy oil drop model[END_REF].

The Tables Table 1 A

  are related to hydrophobicity deficiency, which is expected to indicate a potential binding site. The potential ligand may bind in this area as a complementary element compensating the hydrophobicity deficiency and producing a smoothed hydrophobicity distribution. Negative values of j H   represent an area of excess hydrophobicity. When located on the protein surface, an area with such characteristics is expected to represent a potential area responsible for protein-protein interactions. The similarity estimation on the basis of the j H   profile The spatial distribution of amino acid residues and particularly distribution of their specific hydrophobicity in a protein structure is assumed to influence the biological function. The j H   profiles also appeared sequence-and structure-dependent. Consequently, the j H   profiles expressing the irregularities (deficiency or excess) of hydrophobicity are used to compare and measure the specificity of particular proteins. The j H   distribution in the protein structure, represented for example in the form of ribbon model, reveals a remarkable discrepancy of hydrophobicity (deficiency/excess) between clusters of residues, which may be treated as potential functional sites. Information entropy Screening the hydrophobicity discrepancy along the polypeptide chain (presented in the form of a j H   profile) reveals fragments of sequence characterized by high values of j H   . Two extreme instances of hydrophobicity discrepancy profile may be distinguished. The first one occurs when there is exactly one well-defined sequence fragment (consecutive residues) with high positive j H   values, suggesting that this fragment forms a binding site. The second case takes place when there is more than one sequence fragment with positive j H   , evenly distributed along the polypeptide chain. The degree of predictability of the formation of such functional sites is high in the first case, while the second case represents low predictability.

  . 4 measures the amount of predictability (according to original definitionuncertainty) of the organization of residues forming a binding site (see the examples discussed in

  of the whole polypeptide chain.

  All proteins in the presented set are characterized based on j H   profiles and SE calculations. The similarity between j H   profiles is analyzed in respect to structural similarity and to the results of sequence alignment. The same interpretation is assumed in respect to the SE values. If the SE based comparison appears compatible with structure and sequence comparison it could be used as equivalent to the other methods requiring the superposition or alignment.

  j H   values of these resides represent local maxima or are in their close neighborhood (Fig. 1b, c). The correlation coefficients calculated pair-wise for j H   profiles of proteins that belong to the group of growth factors appeared to be significant (very low values of p). It suggests that the j H   profile similarity is able to represent the structural (functional) similarity of proteins under consideration.

  profiles (Fig.2a) and the structure representation (Fig. 2b) visualize to what extent the j H   similarity influences the values of SE parameters. The local j H   minimum close to residue 15 significantly suggests a common localization of

  These two proteins are good examples for the FOD model. The three-dimensional Gauss function taken to represent the hydrophobic environment during the protein folding process directs the hydrophilic residues toward the protein surface and the hydrophobic residues toward the center of the molecule in order to form a spherical shape. In the case of these structures it has succeeded.

  was selected intentionally to visualize the high accordance of the protein structure to the theoretical model suggesting the mechanism of the folding process to be accordant with the assumed model.

  Fig.6b, calculated for each position of multiple sequence alignment taking into account a non-redundant data set (identical profiles were eliminated). Loops, as well as  strands,

  j H   profile based parameters and those traditionally used for similarity estimation: sequence and RMS-D values. The significant correlation was found to be present between sequence and SE + |and between sequence and SE -|. On the other hand the significant correlation between RMS-D (structural similarity) and  max SE   and between RMS-D and SE -|. It suggests that other j H   based parameters are applicable for sequence and for structure. It may be concluded that the sequence and structure comparison can be performed using j H   although more extended analysis shall be performed. The technique of multiple alignment (such as multiple sequence alignment -MSA) applicable for the set of j H   profiles comparison is under consideration. It could make possible large scale comparison of structures introducing the structural insertion/deletion operation.

  oil-drop model introduced by Kauzman[START_REF] Kauzmann | Some factors in the interpretation of protein denaturation[END_REF] expresses the characteristics of a protein molecule as a construction based on the hydrophobic core in discrete form. The FOD model based on the three-dimensional Gauss function makes this model suitable for the differentiation of protein molecule character. Many proteins presenting irregularities and characteristics deviating from the idealized hydrophobicity distribution were presented elsewhere[START_REF] Brylinski | Hydrophobic collapse in (in silico) protein folding[END_REF] 17]. The antifreeze proteins which show structures highly in agreement with the idealized model represent very interesting examples of the applicability of the FOD model. The functional characteristics of these proteins, highly soluble in aquatic environments may be seen in Fig.3. The minimal differences between the theoretical and the observed hydrophobicity throughout almost the entire protein body, as expressed by the j H   values, which are near zero, confirm the reliability of the model. The irregularity of hydrophobicity observed in many proteins has been the subject of extensive analysis. The relationship between hydrophobicity profiles and biological function, expressed in the hypothesis on the obligatory presence of the ligand during the protein folding process to ensure high specificity towards specific ligands (including substrates), is also currently being explored[START_REF] Minervini | Never born proteins as a test case for ab initio protein structures prediction[END_REF]. The second example of the applicability of the FOD model is the search for similarity. Two SH3 domains -1U5S:A and 2DA9appeared to exhibit SE parameters similar to those of 2CREa protein of unknown biological function. For this reason, it is possible to put forward a hypothesis that this protein may also have a similar function as the above-mentioned proteins, although such similarity in SE parameters should not be taken for granted among SH3 domains. The j H   profiles seem to be rather function-dependent. For SH3 domains, the location of biological activity (generally represented in red on the j H   profiles) is in line with the highest variability of j H   for large fragments with low differentiation, which suggests common structural and different functional characteristics. This observation is understandable given the relatively varied biological activity of the proteins containing SH3 domains.

Figure captions 5 Fig. 1 jFig. 2 j 23 Fig. 3 j 30 Fig. 4 j 38 Fig. 5 j 47 Fig. 6 j

 512233304385476 Figure captions

  Fig.1 Click here to download high resolution image

  list of proteins selected for analysis, divided into six groups: growth factors,

	serine protease inhibitors, antifreeze proteins, chaperones, proteins of unknown function and
	SH3 domains (the PDB IDs in parentheses show deposited structures of identical amino acid
	sequence excluded from the discussion)
	Group	Protein name	PDB ID
			1BQT, (1PMX, 2GF1,
	Growth factors	Insulin-like growth factor 1	3GF1)
	Serine protease	Trypsin/chymotripsin Bowman-Birk inhibitor	1BBI, (2BBI)
	inhibitors	Eglin C	1DWM, 1EGL
	Antifreeze proteins	Antifreeze protein type III	1KDE, 1MSI
		Protein MTH_1184	1GH9
		Putative uncharacterized protein	1RYJ
	Proteins of unknown function	UPF0337 protein yjbJ Putative uncharacterized protein	1RYK 1YVC
		HEF-like protein	2CRE
		Zinc finger CW-type PWWP domain protein 1	2E61
		UPF0165 protein AF_2212	2NWT
			1U97,
	Chaperones	Cytochrome c oxidase copper chaperone Mitochondrial import inner membrane translocase	(1U96, 1Z2G) 2GUZ
		subunit TIM14
		Phospholipase C-gamma	1HSQ
		Myosin-3 isoform	1RUW (1VA7)
		Obscurin	1V1C
	SH3 domains	CRK-associated substrate	1WYX
		SH3-domain kinase binding protein 1	2DA9
		Pro-Ser-Thr phosphatase-interacting protein	2DIL
		SH3 multiple domains 1	2DNU

Table 3

 3 Sequence similarity for two pairs of proteins. Two proteins (2DA9, 1U5S) with known biological activity are compared to the one (2CRE) of unknown biological function

		2CRE -2DA9 2CRE -1U5SA
	Identity	42.3 %	31.9 %
	Similarity	77.5 %	75.4 %
	Number of gaps	1	3
	Sequence alignment score
	(Waterman-Eggert)	139	121
	RMS-D [Å]	3.5	2.2

Table 4

 4 Correlation coefficients for

	j   profiles of proteins from the group of SH3 H

Table 5

 5 Correlation coefficients measuring relationship between similarities of sequences and 3-D structures, and

	j   profiles. R -H

Acknowledgments

The Authors are very grateful to Prof. Leszek Konieczny (Institute of Medical Biochemistry -Collegium Medicum -Jagiellonian University -Krakow -Poland) for our fruitful discussion. This research was supported by Collegium Medicum grants 501/P/266/L. This study has also been financially supported by the European Commission in the frame of the EUChinaGRID project (contract number: 026634)

Availability

The tools applied for the calculations presented in this paper are available at http://www.bioinformatics.cm-uj.krakow.pl/activesite.


MD. MSc, DSc Matthew E Falagas
email: m.falagas@aibs.gr

MD, MSc, DSc George Peppas

PhD Dimitrios K Matthaiou

MD Drosos E Karageorgopoulos

MD Nikolas Karalis

MD George Theocharis

Effect of meteorological variables on the incidence of lower urinary tract infections

Keywords: cystitis, epidemiology, weather, environment, cold, heat, aged cystitis, epidemiology, weather, environment, cold, heat, aged

Athens, November 17, 2008 To: Editor-in-Chief, European Journal of Clinical Microbiology and Infectious Diseases

Re: Submission of a revision Dear Editor, We are pleased to submit a revised version of our original article entitled "Effect of meteorological variables on the incidence of lower urinary tract infections" for your consideration for publication in the European Journal of Clinical Microbiology and Infectious Diseases.

We revised our manuscript according to your suggestions. Specifically, we formatted the article to fit to the "Brief Report" type of article (deleted the abstract, decreased the length of the text, deleted the headings within the text, decreased the number of references to 10, and unified Figures 1 and2 The influence of environmental factors on human health has long been an intriguing issue. In fact, scientific data support the notion that certain diseases are related to environmental conditions. A seasonal variation has been observed for diverse clinical entities such as myocardial infarction and cervical cancer, and particularly so for several infectious diseases, typically for those with a respiratory mode of transmission [START_REF] Falagas | Effect of meteorological variables on the incidence of respiratory tract infections[END_REF]. The seasonal variation in the incidence of infectious diseases could generally be attributed to factors like the epidemiology of the culprit pathogens, changes in environmental and meteorological parameters, alterations in human behavior, and even seasonal changes in host physiology.

Urinary tract infections (UTIs) represent a major category of community-acquired infections. Most aspects of the epidemiology of these infections have been clarified, including the fact that they are encountered more frequently in women, due to anatomical reasons, and adolescents, due to the increased sexual activity in this age group [START_REF] Falagas | Urinary tract infections: Practice guidelines[END_REF]. Still, whether environmental parameters are associated with the occurrence of these infections has not been adequately studied.

In this context, we aimed to assess the effect of meteorological variables, such as environmental temperature and relative humidity, on the incidence of lower urinary tract infections. Specifically, we performed a retrospective study that analyzes the association between meteorological variables and the relative frequency of lower UTIs in a primary care setting. Both meteorological and clinical data were recorded in a systematic prospective manner.

Analyzed meteorological data consisted of average weekly temperature (measured in degrees Celsius) and average weekly humidity (expressed as percentage), regarding the metropolitan area of Athens, Attica, Greece, in the time period between 1 st November 2000 to 18 th January 2005 (a total of 1540 days or 220 weeks). These data were recorded at the meteorological station of the National Technical University of Athens, which is at the municipality of Zografos. The university grants public access to these data (http://www.meteo.ntua.gr/e/histdata/). Analyzed clinical data referred to the types of diagnoses recorded by specialized physicians after house call visits. Patients' demographic characteristics, such as age and sex, were also assessed. The study population included all patients examined after house calls in the region of Attica, Greece, during the time period between 1 st November 2000 and 18 th January 2005. All of the above mentioned clinical data were provided by "SOS Doctors," which is a private health-care organization that offers urgent medical services. The study was approved by the Ethics Committee of "SOS Doctors," Greece.

The detailed process from the triage of house calls to the subsequent physician's visit at the patient's residence, for the provision of medical services, has been previously reported [START_REF] Peppas | An analysis of patient house calls in the area of Attica, Greece[END_REF]. Findings regarding the clinical evaluation of the patients were systematically recorded by the physicians performing the house call visits immediately in a specially designed case report form. Variables included in these forms referred to the presenting symptoms, past and preset medical history, primary clinical findings, most probable diagnosis selected among specific coded diagnostic categories, and suggested treatment. Diagnoses were reached on the basis of clinical criteria, with the additional use of simple bedside tests, at the physicians' discretion.

Lower UTIs were primarily diagnosed on the basis of characteristic clinical manifestations. A urine dipstick test was used at the physician's discretion.

Pyelonephritis, prostatitis, or epididymitis were recorded as separate diagnostic categories.

The primary focus of this study was the temporal association between average weekly temperature as well as average weekly humidity with the weekly percentage of house call visits resulting in a diagnosis of lower urinary tract infection, relatively to all house call visits with the exclusion of those performed for respiratory tract infections.

Weekly values were elected over daily values to adjust for high frequency variability in the measured variables. A time lag of 3 days between meteorological measurements and the house call visits was arbitrarily elected to adjust for the estimated time interval between the occurrence of a facilitating event and the subsequent development of clinical symptoms/signs of a lower urinary tract infection to the degree that an urgent house call visit by a physician would be performed. The percentage of house call visits for lower urinary tract infections, rather than the absolute number of these infections, was included in the analysis to adjust for the temporal variation in the number of house calls for "SOS Doctors", potentially attributed to factors such as vacation periods, environmental conditions facilitating or precluding the use of other types of primary care services, or the availability of other primary care services. House calls visits for upper and lower respiratory tract infections were not taken into account for the calculation of the percentage of house call visits performed for urinary tract infections, to correct for the recognized association between the occurrence of the former type of infections and meteorological parameters. In secondary analyses we evaluated the same as above associations without excluding house call visits for respiratory tract infections from the calculation of the percentage of house call visits performed for UTIs as well as with the use of a 2-day instead of a 3-day time lag between meteorological and clinical measurements.

Variables included in the analysis were examined for normality of data with the one sample Kolmogorov-Smirnov test. Bivariate correlations between meteorological variables and the percentage of house call visits for UTIs were obtained using the Spearman's rho, in the case of variables without a normal distribution, or using the Pearson's correlation coefficient, in the case of variables with a normal distribution.

In the time period of the study 3221 house calls visits of "SOS Doctors" performed for lower UTIs were recorded. The mean age (± standard deviation) of affected patients was 62.9 (± 21.0) years. Female patients constituted the majority (53.77%).

The outcomes of the analyses are reported in Table 1. In the primary analysis, the weekly percentage of house call visits performed for lower urinary tract infections, relatively to all house call visits excluding those performed for respiratory tract infections, recorded 3 days after meteorological measurements, was found to have a significant positive correlation with average weekly temperature (Spearman's rho = +0.468, p<0.001), and a significant negative correlation with average weekly humidity (Spearman's rho = -0.394, p<0.001) (Figure 1).

In the secondary analysis of the correlation of the weekly percentage of house call visits performed for lower urinary tract infections, relatively to all house call visits including those performed for respiratory tract infections, recorded 3 days after meteorological measurements, and the meteorological variables of average weekly temperature and average weekly humidity, similar significant associations (Spearman's rho for temperature= 0.491, Spearman's rho for humidity= -0.406, p values <0.001) were found as in the primary analysis (Table 1).

In the secondary analysis of the correlation between the weekly percentage of house call performed for lower urinary tract infections, relatively to all house call visits excluding those performed for respiratory tract infections, recorded 2 days after meteorological measurements, and the meteorological variables of the average weekly temperature and the average weekly humidity, similar significant associations (Spearman's rho for temperature= 0.468, Spearman's rho for humidity= -0.386, p values <0.001) were found as in the above analyses (Table 1).

The findings of our study suggest that, in a wide metropolitan area with a temperate climate, higher environmental temperature and decreased humidity are associated with an increase in house call visits for lower urinary tract infections. Due to the large number of observations included in the analysis, statistical correlations are of high significance. It should be mentioned, however, that the strengths of the observed associations are not of a great degree. Nonetheless, strong correlations between the examined parameters would be rather unexpected, considering the multifactorial nature of the pathogenesis of lower urinary tract infections.

The association of temperature and humidity with the development of urinary tract infections could be explained considering that these meteorological parameters affect mood in humans (4), and therefore they may affect social behavior including sexual activity, which is a factor strongly linked to the development of lower urinary tract infections, particularly in women. It should be mentioned, though, that the great majority of patients included in our analysis belonged to sexually less active age groups, so the above presumptive associations could only partly account for the findings of our study. This consideration may explain why women only marginally outweighed men in our study population, whereas in a younger age group it would be expected that considerably more lower urinary tract infections would be observed in female patients.

Another potential relation between increased environmental temperature and the development of lower urinary tract infections could be attributed to the fact that higher temperature may relate to decreased diuresis, which in turn is one of the principal factors associated with the development of urinary tract infections. In detail, increased temperature enhances perspiration and consequently total body water loss [START_REF] Melin | Comparison of passive heat or exerciseinduced dehydration on renal water and electrolyte excretion: the hormonal involvement[END_REF]. Furthermore, peripheral vasodilation and pooling of blood in the skin that ensue upon exposure to heat can cause a decrease in effective blood volume. All of these factors may lead, especially in individuals with compromised physiologic responses, to diminished urine output [START_REF] Melin | Comparison of passive heat or exerciseinduced dehydration on renal water and electrolyte excretion: the hormonal involvement[END_REF]. On the other hand, the physiologic thermoregulatory response to cold includes adrenergic stimulation, which induces peripheral vasoconstriction, along with a rise in cardiac output and blood pressure. These factors in concert with a rise in the concentration of atrial natriuretic peptide may account for the increased diuresis that is frequently observed upon cold exposure. Thus, exposure to cold may facilitate the clearance of potential bacterial contaminants of the urinary tract.

Data in the literature regarding the subject of this study are not conclusive.

Specifically, a study performed in Canada has shown a seasonal variation in hospital admissions due to urinary tract infections, although the specific characteristics of the seasonal pattern of these infections were not reported [START_REF] Upshur | Simplicity within complexity: Seasonality and predictability in the province of Ontario 1998-2001, a populationbased analysis[END_REF]. Another study in South Korea has shown that acute pyelonephritis occurs more commonly during the summer season [START_REF] Ki | The Epidemiology of Acute Pyelonephritis in South Korea, 1997-1999[END_REF]. To the contrary, there are some reports in children that indicate a temporal association between urinary tract infections and those of the respiratory tract. [START_REF] Woodruff | Prognosis in childhood urinary tract infections in girls[END_REF][START_REF] Burke | Pyelonephritis in infancy and childhood[END_REF] Whether this association can be translated into a seasonal variation in the incidence of urinary tract infections in children, considering that respiratory tract infections are known to occur more frequently in winter, is controversial [START_REF] Stansfeld | Clinical observations relating to incidence and aetiology of urinarytract infections in children[END_REF].

A number of potential limitations should be taken into account in the interpretation of the findings of our study. First, the diagnosis of lower urinary tract infections was not relied in strict diagnostic criteria, such as urine culture, and therefore some cases may have been misclassified. Yet, in routine clinical practice obtaining urine culture for the diagnosis of acute uncomplicated cystitis is not deemed necessary. The inherent limitations regarding the methodological aspects of retrospective studies should also be considered. Nevertheless, the data included in the analyses of our study were recorded in a prospective manner and should be regarded as of a high quality.

Although no selection criterion was applied, the majority of the population included in our study consisted of patients belonging in advanced age groups. This may be attributed to the fact that younger individuals may be more likely to seek primary medical care in other types of services than house call visits. Accordingly, the findings of our study may not be representative of the whole population in the community and therefore should be extrapolated with caution in younger age groups.

In the same manner, it may not be appropriate to extrapolate the findings of our study in populations residing in areas with substantially diverse climate.

Another shortcoming of our study may be that data regarding the meteorological variables were retrieved from a single meteorological station. It is expected that slight differences in meteorological variables should have existed in other locations within the study area. However, these differences could not have appreciably influenced the findings of the analysis, since variations in the meteorological parameters in different locations of relatively close proximity are expected to be highly correlated.

Despite the afore mentioned limitations, the findings of our study may aid in the design and institution of specific public health preventive measures, such as measures to draw public attention on the need to consume more fluids and to avoid exposure to heat during days of warm and dry weather. This may particularly important for elderly patients, such as the population of our study, who often have underlying diseases, and thus, a concomitant infection could cause clinical decompensation.

In conclusion, our study indicates that the occurrence of lower urinary tract infections in the community may be partly associated with meteorological parameters, such as temperature and humidity. Irrespective of the potential interpretation regarding the underlying factors that are responsible for this association, the findings of our study clearly imply that elderly individuals, who constituted the majority of the included patients, may run an increased risk, albeit not of a high degree, for lower urinary tract infections at times of higher environmental temperature or lower humidity. From a public health standpoint the awareness of this association may facilitate potential preventive strategies.

  to one Figure). The manuscript has not been published or considered for publication elsewhere. There are not any financial or other relationships that might lead to a conflict of interest. The manuscript has been read and approved by all authors. Sincerely, Matthew E. Falagas, MD, MSc, DSc Director, Alfa Institute of Biomedical Sciences, Athens, Greece Adjunct Associate Professor of Medicine, Tufts University School of Medicine, Boston, Massachusetts Director, Infectious Diseases Clinic, "Henry Dunant" Hospital, Athens, Greece

Figure .

 . Figure. Scatter plots depicting the correlation of weekly percentage of house call visits (after exclusion of respiratory tract infections) for urinary tract infections (UTIs) (Y axis of the figures) versus the average weekly temperature (degrees Celsius) observed 3 days earlier (X axis -upper figure) and the average weekly relative humidity observed 3 days earlier (X axis -lower figure).

Figure

  Figure

Table 1 . Correlation between weekly percentage of house call visits for urinary tract infections and average weekly meteorological variables.

 1

	Correlation examined	Spearman's rho	p-value
	Main analysis
	% of house calls for urinary tract infections excluding visits for respiratory tract infections vs. temperature 3 days earlier	0.468	<0.001
	% of house calls for urinary tract infections excluding visits for
	respiratory tract infections vs. average relative humidity 3 days	-0.394	<0.001
	earlier
	Secondary analysis
	% of house calls for urinary tract infections including visits for respiratory tract infections vs. average temperature 3 days earlier	0.491	<0.001
	% of house calls for urinary tract infections including visits for
	respiratory tract infections vs. average relative humidity 3 days	-0.406	<0.001
	earlier
	% of house calls for urinary tract infections excluding visits for respiratory tract infections vs. temperature 2 days earlier	0.468	<0.001
	% of house calls for urinary tract infections excluding visits for
	respiratory tract infections vs. average relative humidity 2 days	-0.386	<0.001
	earlier