Su

Pa

b

c

a

ARRAA

KiFMc

1

tfgqb8b51hdisisab

0d

Analytica Chimica Acta 683 (2010) 69–77

Contents lists available at ScienceDirect

Analytica Chimica Acta

journa l homepage: www.e lsev ier .com/ locate /aca

pectrometric study of the folding process of i-motif-forming DNA sequencespstream of the c-kit transcription initiation site

avel Buceka, Raimundo Gargallob, Andrei Kudrevc,∗

Department of Analytical Chemistry, Masaryk University, Kotlárská 2, 611 37 Brno, Czech RepublicSolution Equilibria and Chemometrics Group (Associated Unit UB-CSIC), Department of Analytical Chemistry, University of Barcelona, Diagonal 647, E-08028 Barcelona, SpainDepartment of Chemistry, St.-Petersburg State University, University pr. 26, 198504 St.-Petersburg, Russia

r t i c l e i n f o

rticle history:eceived 19 February 2010eceived in revised form 4 October 2010ccepted 6 October 2010vailable online 14 October 2010

eywords:-motif

a b s t r a c t

The c-kit oncogene shows a cytosine-rich DNA region upstream of the transcription initiation site whichforms an i-motif structure at slightly acidic pH values (Bucek et al. [5]). In the present study, the pH-induced formation of i-motif – forming sequences 5′-CCC CTC CCT CGC GCC CGC CCG-3′ (ckitC1, native),5′-CCC TTC CCT TGT GCC CGC CCG-3′ (ckitC2) and 5′-CCCTT CCC TTTTT CCC T CCC T-3′ (ckitC3) wasstudied by spectroscopic techniques, such as UV molecular absorption and circular dichroism (CD), intandem with two multivariate data analysis methods, the hard modelling-based matrix method and thesoft modelling-based MCR-ALS approach. Use of the hard chemical modelling enabled us to propose the

oldingultivariate curve resolution

-kit

equilibrium model, which describes spectral changes as functions of solution acidity. Additionally, theintrinsic protonation constant, Kin, and the cooperativity parameters, ωc, and ωa, were calculated from thefitting procedure of the coupled CD and molecular absorption spectra. In the case of ckitC2 and ckitC3, thehard model correctly reproduced the spectral variations observed experimentally. The results indicatedthat folding was accompanied by a cooperative process, i.e. the enhancement of protonated structurestability upon protonation. In contrast, unfolding was accompanied by an anticooperative process. Finally,

ence

folding of the native sequ

. Introduction

The G-quadruplex is a complex DNA structure formed fromhe planar association of four guanine bases. This structure can beound at the telomeres and near the transcription site of severalenes. Among them, c-kit is an oncogene which shows two G-uadruplex-forming regions in its sequence. The structure adoptedy the sequence 5′-AGG GAG GGC GCT GGG AGG AGG G-3′ (located7 nucleotides upstream of the transcription initiation site) haseen resolved with the aid of NMR [1,2]. Similarly, the sequence′-CGG GCG GGC GCG AGG GAG GGG-3′ (ckitG1), which is located40 nucleotides upstream of the transcription initiation site [3,4],as been shown to form a major parallel G-quadruplex with aouble-reversal loop. In vivo, the ckitG1 sequence coexists with

ts cytosine-rich complementary strand [5]. Cytosine-rich DNAequences can also form a highly ordered structure known as the

-motif in slightly acidic solutions. These structures consist of fourtrands arranged in two parallel duplexes “zipped” together in anntiparallel orientation, where the C+C base pair form the linketween two parallel strands (Scheme 1). The exact conditions

∗ Corresponding author. Tel.: +7 812 4284068; fax: +7 812 4286939.E-mail address: kudrevandrei@mail.ru (A. Kudrev).

003-2670/$ – see front matter © 2010 Elsevier B.V. All rights reserved.oi:10.1016/j.aca.2010.10.008

, ckitC1, seemed to follow a more complex mechanism.© 2010 Elsevier B.V. All rights reserved.

under which the i-motif structure is stable at a given pH valuedepend on the DNA sequence and the nature and concentration ofcations. Mainly through NMR investigations, it has been establishedthat the same cytosine-rich strand can form intercalated structuralentities of i-motif that differ essentially in their intercalation andloop topologies [6]. Recent interest has been shown in the study ofi-motif structures due to their potential application in nanotech-nology [7,8] and their possible roles in the transcription of somegenes [9–11]. Apart from possible applications of non-modified i-motifs in oligonucleotide-based therapeutics, an increasing interestis shown in the study of modified i-motifs, such as locked nucleicacids, LNA [12]. Hence, it has been proposed their use as chemicaloscillators to sense the changes in environmental proton concen-trations and as a nano-motor for conformational motions whichmay allow controlled-release of drugs [13].

Recently, the pH-modulated Watson–Crick duplex–quadruplexequilibria of ckitG1 and its complementary strand ckitC1 (5′-CCCCTC CCT CGC GCC CGC CCG-3′) have been studied [5]. ckitC1 wasshown to form a stable i-motif structure throughout the pH range

7–4, approximately, with a pH-transition midpoint around 6.5. Thatstudy, however, did not pursue the role of guanine and cytosinebases located at the loops in the folding mechanism of the wholesequence. In the present study, two ckitC1-mutated sequences,ckitC2 (5′-CCC TTC CCT TGT GCC CGC CCG-3′) and ckitC3 (5′-CCC TT

70 P. Bucek et al. / Analytica Chimica Acta 683 (2010) 69–77

S d by thc

Ces

sdgonmrasiocoocmlRat

ssar

2

2“

s

cheme 1. (a) Hemiprotonated C+C base pair. (b) Putative i-motif structure adopteytosine bases represents C+C base pairs. (c) DNA sequences studied in this work.

CC TTTTT CCC T CCC T-3′) were analysed with the aim of uncov-ring the sequence effects on pH-induced formation of i-motiftructures adopted by ckitC1.

Knowledge of the pH range for i-motif structure formationhould help to select the optimal conditions for effective i-motifetection, which in turn requires binding of small molecules to aiven DNA structural target. In order to describe the interactionf a small molecule (in this study, H+) with infinite polymers, aumber of mathematical models [14–18] has been developed. Theatrix method [19] has been proposed to calculate the equilib-

ium constants which govern the binding of small molecules toshort sequence of equivalent sites (oligomer). This method can

imultaneously calculate equilibrium constants and cooperativ-ty parameters for equilibria involving small molecules and DNAligomers, such as the DNA [d(T4G4)]4 tetraplex [20]. The aboveited method belongs to the so-called “hard modelling”, a classf methods where the conformational and acid–base equilibriaf biopolymers are explained in terms of an explicit physico-hemical model. Alongside “hard” methods, the widely used “softodelling” methods [21] can be used to unravel complex equi-

ibria. Among them, the MCR-ALS procedure (Multivariate Curveesolution-Alternating Least Squares) has been shown to be usefulnd versatile due to its ability to admit different constraints alonghe calculation [22].

The cooperative binding of H+ with three selected DNAequences leads to their intramolecular folding into an i-motiftructure. Spectroscopic data recorded along this folding have beennalyzed by means of MCR-ALS and of the matrix-method, and theesults are reported here.

. Computational details

.1. Decomposition of the experimental data matrix usingsoft-modelling” MCR-ALS

Hard modelling-based programs are especially suitable for thetudy of chemical equilibria involving monomers or short DNA

e sequence 5′-CCC TT CCC TTTTT CCC T CCC T-3′ (ckitC3). The double line between

sequences which do not show secondary effects related to poly-meric structures, such as polyelectrolytic effects, polyfunctionaleffects or conformational changes. In contrast, for large DNAsequences or when analyzing data from melting experiments,the equilibrium constants vary as the studied reaction or confor-mational change progresses. In these cases, application of hardmodelling-based methods is challenging since it can be difficult topropose a simple species model describing the spectral behaviourobserved. In these cases, multivariate data can be analysed byapplying soft modelling-based methods, as they require neither theprevious proposal nor compliance of any species model. MCR-ALShas already been widely applied in the study of acid–base and con-formational transitions of DNAs [23,24]. This approach calculatesdistribution diagrams and pure spectra for all spectroscopicallyactive species present in the system from the decomposition of theexperimental data matrix.

In addition to the common constraints used in MCR-ALS ofnon-negativity, unimodality, closure and the equality linked toselectivity or to the use of known profiles, physicochemical mod-els have been recently introduced into the MCR-ALS algorithm asnew constraints [25]. The main advantage is that the physicochem-ical model can be used that describe only part of the variation ofthe data set whereas the rest of the system can be soft-modelled.These constraints have allowed the calculation of physicochem-ical parameters, even in the presence of interferences. Examplesof hard-modelling constraints include the incorporation of kinetic,enzymatic or chemical equilibria models into the calculation of thedistribution diagrams [25,26].

In this work, the distribution diagram previously calculatedwith the soft-modelling MCR-ALS method has been later fittedto a physicochemical model. It has been shown for an infinitehomogeneous polymer [27] that, in the presence of concentrationselectivity, the equilibrium parameters can be calculated directly

from the MCR-ALS distribution diagram. This procedure is per-formed in the same way as in the previously described calculationsof equilibrium constants and stoichiometric coefficients for ordi-nary non-polymeric equilibria systems from concentration profilesdeduced by using the MCR-ALS [28,29]. The same approach has

himic

bpto

2m

tissfbtuccl

iw[plTwt

B

(soptapiωd

csfimtmfitia

babeaaoufwt

P. Bucek et al. / Analytica C

een successfully used to evaluate the protonation constants ofoly(inosinic)–poly(cytidylic) acid [30] and has been used to modelhe pH-induced intramolecular folding of a single-stranded DNAligomer [31].

.1.1. Decomposition of the experimental data matrix using theatrix method.

In this work a modification of a previously proposed methodo calculate the binding constants governing the interaction of anon with an oligomer has been used [19,31]. In the case under con-ideration, the process comprised the binding of a proton by anpecific functional group on a DNA oligomer. This molecule has Nree vacancies (sites) in fixed positions, each of which is capable ofinding a single ion. In accordance with previous studies, we haveaken into consideration the secondary structures for oligomersnder investigation (the highly ordered i-motif and the randomoil). Obviously, each type of structure contains a wide variety ofonformations which are governed by the configuration (mutualocation) of protonated sites.

Assuming that the oligomer can form a reversible bond to H+,ts protonated form will occur as a result of H+ binding to each site

ith equal probabilities. Further assuming the row of complexesPnHn] (n = 1,2,. . .,N) to be present in a solution, the sum of sim-le combinations of n bound protons among N binding sites will

ead to k = 2N − 1 possible configurations, the so called microstates.he formation constant for a complex in each configuration can beritten as a product of equilibrium constants of protons binding to

he sites involved in the configuration.

k =N∏

i=1

ˇki =N∏

i=1

(MkiKωki − Mki + 1) = Kpωp−2q

c ω2qa (1)

The equilibrium constant for the binding of a proton to the siteˇki) was calculated as the product of the intrinsic equilibrium con-tants (K ≡ Kin) for the first proton binding reaction, the elementsf the configurations matrix Mki, and the interaction correctionarameters ωki for the sites involved in the particular configura-ion which, in this case, have been defined as cooperativity ωc andnticooperativity ωa parameters. Finally, p is the number of boundrotons and q is the number of protons bound to a site already

nvolved into the C+C base pair. The non-linear parameters Kin,c and ωa were calculated using the non-linear squares methodescribed below.

The binding of ions to a multidentate macromolecule can beharacterized by means of stepwise equilibrium constants. Eachingle stepwise species represents a set of chemically distinct con-gurations of the ion in the binding sites of the macromolecule. Theatrix of configurations M has been introduced in order to describe

he detailed set of configurations adopted by the macromolecule inicrostates. Each line of the matrix M displays a microstates con-

guration through a sequence of zeroes in those positions wherehe sites of multidentate macromolecule are not occupied by theon, and with a sequence of ones in those positions where the sitesre bound to the ion.

In the course of the macromolecule sites saturation, previouslyound ions can affect the binding of following ions, which is knowns polyelectrolyte effect. As a rule, it can be explained as a result ofound ion–ion interactions and/or as a result of a particular prop-rty of the formed macromolecular configurations. Accordingly,n additional parameter has been introduced into the model toccount for the formation of secondary structures. For the sake

f simplicity, four basic DNA structures of interest are defined, anprotonated helix, a single protonated helix, a semi protonatedorm with secondary structure (i-motif), and a protonated formith loss of i-motif (random coil). Note that in the neutral solution,

he DNA molecule displays a helix conformation, whereas lowering

a Acta 683 (2010) 69–77 71

the pH leads to the folding of the molecule into an i-motif, followedby the appearance of a random coil structure. i-Motif formationappears following the uptake of two or more protons. For theseconfigurations, each equilibrium constant for ion binding appearsas a product of Kin and parameter ωc. When the computationalprocedure detects that a proton has bonded to a C+C base pair,the equilibrium constant is further multiplied by ωa. The randomcoil structure was assigned to the species in which the number ofjoined protons exceeds the number of C+C complexes. To calculateconcentrations [PnHn] in the various configurations of the form inquestion, the matrix P(2N − 1, 4) was entered. The elements of P areequal to N in the column, corresponding to the chosen form; otherelements are equal to zero. Now, with a defined pH value it is pos-sible to compute a matrix of polynucleotide distribution betweenthe equilibrium species [PnHn].

Cform =

((K

sω)

T[H]s

)T

(Ksω)[H]s

P =[

1SS

;B1[H]

SS; ....;

Bi[H]i

SS; ...

]P (2)

where SS = (Ksω)[H]s; K

s =[

1 Kin K2in

· · · KNin

]; [H]s =[

1 [H] [H]2 · · · [H]N]T

; s =[

0 1 2 · · · N]; T – sign of

a matrix transpose. Successive application of Eqs. (1) and (2) foreach from full set of studied equilibrium concentration [H] givesconcentration matrix Cf.

2.1.2. The fitting procedureAccording to Lambert–Beer’s law, a spectrometrically measured

UV–CD (circular dichroism) data matrix Aexp can be decomposedinto the product of a concentration matrix Cf and a matrix E of molarabsorptivities and molar ellipticities. The corresponding generalmatrix equation is given as:

Aexp = CfE + R (3)

where the number of rows in data matrix Aexp is equal to the num-ber of studied solutions with different component concentrations;the number of columns in data matrix Aexp is equal to the num-ber of wavelengths; and R is the residual matrix with unexplaineddata variance. The transposed data matrix Aexp can be representedgraphically by the set of experimentally measured UV and CD spec-tra. Multiplication of both sides of Eq. (3) on a pseudoinverse matrix[32] of Cf, computed with an initial estimate of the required param-eters, gives a current estimation E* of the true matrix of molarparameters E. The best set of linear parameters E* for systems underinvestigation can be calculated explicitly in a linear least-squarescalculation by:

E∗ = C+f Aexp (4)

C+f – is the pseudoinverse concentration matrix [32]. To minimise

the ambiguity of the mathematical solution, the iterative calcu-lation of C and E is always subject to constraints [33]. In orderto evaluate linear as well as non-linear parameters for a chemi-cal model such as that described above, a decomposition of theexperimental data matrix Aexp is executed under a non-negativityconstraint. The product of E* and C∗

f gives Acalc at the current valuesfor non-linear parameters. Consequently, the iteration formula forrefinement of the required non-linear parameters Kin, ωc and ωa

can be written as:

Acalc = [C+f Aexp]>0C∗

f (5)

where [ · · · ]>0 – is the operator that substituted negative values to

zero values. To fit CD data we used absolute value of experimentalsignals. When optimal non-linear parameters were found the purespecies spectra has been calculated in accordance with Eq. (4).

The Levenberg–Marquard non-linear least square algorithm isused for refinement of parameters [34,35] because it has been

7 Chimic

plio

S

ieFpdeAtESHb

P

w

3

3

TCswBsAtF2wimaK(

3

aaptw(wc

3

f1i

An acid–base titration of ckitC3 was carried out from pH 8.34to pH 2.78, and CD and molecular absorption spectra were simul-taneously recorded at each pH step along the titration (Fig. 1). Theformation of an i-motif structure is denoted by the intense CD pos-itive signal around 288 nm. Spectra of 29 solutions were measured

Table 1Tm values determined from absorbance traces at 295 nm for meltings of ckitC3,ckitC2 and ckitC1 at several pH values. The incertitude associated with the mea-surement of Tm value is ±1 ◦C. Experimental details are described in the text.

pH Tm ckitC3 (◦C) Tm ckitC2 (◦C) Tm ckitC1 (◦C) [5]

6.8 Not observed 28 33

2 P. Bucek et al. / Analytica

reviously shown be robust and efficient in the calculation of equi-ibrium constants from spectrophotometric titration data [36]. Theterative procedure described above is used to find the minimumf the objective function:

=∑

(Fexp − Fcalc)2 (6)

Eq. (6) was used to calculate the parameters Kin, ωc and ωa

n two different ways. First, the parameters were calculated fromxperimental absorbance and CD data. In this case, Fexp = Aexp andcalc = Acalc. Second, Eq. (6) was used to refine the chemical modelarameters from the previously calculated MCR-ALS distributioniagrams. In this case, the sum of squares of deviations of all thelements of a concentration matrix were calculated using MCR-LS (C(ALS) = Fexp), from the corresponding matrix considered on

he current iterative step (Cf = Fcalc), calculated in turn by using ofq. (2). Refinement was stopped when the relative difference inbetween consecutive iterations fell below a threshold value. Theamilton factor were used in the present work to compare matricesy applying the following equation:

E(Fexp, Fcalc) =√

trace[(Fexp − Fcalc)(Fexp − Fcalc)T ]

trace[FexpFTexp]

× 100% (7)

here trace is the sum of the diagonal elements.

. Experimental

.1. Reagents

Sequences 5′-CCC CTC CCT CGC GCC CGC CCG-3′ (ckitC1), CCCTC CCT TGT GCC CGC CCG-3′ (ckitC2) and 5′-CCC TT CCC TTTTTCC T CCC T-3′ (ckitC3) were prepared on a 1 �mol scale usingtandard 2-cyanoethyl phosphoramidites (Cruachem Ltd.). Thisas accomplished with an automatic DNA synthesizer (Appliediosystems Mod. 392). The sequences were deprotected usingtandard protocols (concentrated ammonia, 55 ◦C, and overnight).fter deprotection, oligonucleotides were purified using purifica-

ion cartridges in accordance with the manufacturer’s instructions.inally, purified oligonucleotides were desalted using Sephadex G-5 columns. The length and homogeneity of the oligonucleotidesere verified by 8 M urea PAGE stained with Stains-all. After melt-

ng, DNA strand concentrations were determined by absorbanceeasurements (260 nm) using calculated extinction coefficients,

nd the nearest-neighbour method [37]. KCl, MgCl2·6H2O, KH2PO4,2HPO4, HAcO, HCl and NaOH (a.r.) were purchased from Panreac

Spain). MilliQ® water was used in all experiments.

.2. Instrumental

Absorbance spectra were recorded on an Agilent HP8453 dioderray spectrophotometer. The temperature was controlled vian 89090A Agilent peltier device. Hellma quartz cells (1.0 cmath length, 1500 �L or 3000 �L volume) were used. CD spec-ra were recorded on a Jasco J-810 spectropolarimeter equippedith a Peltier-based temperature control unit. Hellma quartz cell

1.0 cm path length, 3000 �L volume) was used. pH measurementsere determined with an Orion SA 720 pH/ISE meter and micro-

ombination pH electrode (Thermo).

.3. Procedures

Experimental conditions for the acid–base titrations were asollows: 25 ◦C and 150 mM ionic strength (147 mM K+, as KCl, andmM Mg2+, as MgCl2·6H2O). Titrations were carried out by adjust-

ng the pH of solutions containing the oligonucleotides. This was

a Acta 683 (2010) 69–77

done by adding small volumes of HCl or NaOH stock solutions.Absorbance or CD spectra were recorded in a pH stepwise fash-ion. Melting experiments were conducted in a temperature rangeof 25–95 ◦C with a linear temperature ramp of 0.5 ◦C min−1. Buffersolutions were 10 mM phosphate or acetate, 1 mM Mg2+ and wereadjusted to 150 mM ionic strength with KCl. Each sample wasallowed to equilibrate at the initial temperature for 30 min beforethe melting experiment was initiated. Tm values were determinedfrom the midpoint of the absorbance trace at 295 nm, where unfold-ing of i-motif structures is accompanied by clear hypochromism ata pH higher than the pKa of free cytosine [38].

4. Results and discussion

The acid–base equilibria of ckitC1 were already studied by spec-troscopic techniques and soft-modelling MCR-ALS [5]. ckitC1 wasshown to form a stable intramolecular i-motif structure throughoutthe pH range 7–4, approximately, with a pH-transition midpointaround 6.5. The ckitC1 sequence contains three tracks of three cyto-sine bases and one track of four cytosine bases. Accordingly, ckitC1may fold into an i-motif structure with a core formed by up to sixC+C base pairs (Scheme 1). This sequence, however, contains sev-eral bases located at the potential loops which could have a certaininfluence on the folding mechanism. Hence, there are four guaninebases which can form GC Watson–Crick base pairs. Additionally,there are several cytosine bases which would not be involved in ani-motif core, being protonated by an independent mechanism.

In an attempt to elucidate the role of these guanine and cyto-sine bases, two additional ckitC1-mutated sequences were studied:ckitC3, which only contains four tracks of three cytosines; andckitC2, which contains four tracks of three cytosines and threeguanine present at the loops.

The different structures adopted by these three sequences arereflected in their Tm values. (Table 1). In neutral solution the high-est stability corresponds to ckitC1, and the lowest to ckitC3, whichreflects the formation of additional GC base pairs at neutral pH.At a pH of around 4.5, the stabilities of all three sequences aresimilar, since this pH value corresponds to the maximum stabil-ity of i-motif structures [38]. Melting experiments done at 0.3, 2and 6 �M strand concentration at pH 6.0 revealed that Tm valuefor ckitC2 and ckitC3 was also concentration independent, indicat-ing the formation of intramolecular i-motif structures throughoutthis concentration range. This point was also confirmed by nativePAGE shown in the Supporting Information. It was observed thatmobility of all the considered sequences at pH 6 was higher thanthe unfolded dT21, suggesting intramolecular folding.

4.1. ckitC3 sequence

6.0a 28 37 415.0 58 56 544.5 63 65 69

a Folding was observed to be concentration independent in the range 0.3–6 �Mat pH 6.0.

P. Bucek et al. / Analytica Chimica Acta 683 (2010) 69–77 73

F ar abse

tdlpt

gt(saoAr

wctftbao

TM

ig. 1. Acid–base titration of ckitC3 sequence. Experimental CD (a) and moleculxperimental conditions are described in the text.

hroughout this pH range, and analyzed as data matrix AexpC3, theimensions of which comprised 29 rows and 1002 columns. Singu-

ar value decomposition (SVD) of data matrix AexpC3 indicated theresence of three or four main components, which could be relatedo distinct spectroscopically active acid–base species.

When MCR-ALS was applied, the best results (distribution dia-rams and pure spectra which are chemically meaningful) werehose obtained when only three components were consideredFig. 2a and b and Table 2). From Fig. 2b we can note that the CDpectrum of species at pH 4.5–6 distinctly resembles that tradition-lly related to the i-motif of DNA structure. Despite the good shapef the distribution diagram and pure spectra resolved with MCR-LS, these results must be considered critically as it is clear that theesolution of spectra measured at around a pH of 3 is ambiguous.

Fig. 2c and d shows the results obtained when hard-modellingas applied. Four DNA structures were postulated in the hard

hemical model. The chemical assignment of corresponding spec-ral species in the distribution diagram could be explained as

ollows. The species (I) predominantly present pH 7 can be relatedo a partially stacked helix structure. In this species (I), cytosineases are in neutral form, i.e., deprotonated at the N3 position. Inddition to the three species model, the four species hard modelf ckit3 protonation superinduces species (II). When pH decreases,

able 2CR-ALS figures of merit for analysis of data matrices AexpC3 and AexpC2.

Components PCA lack of fit (%) Lack of fit (%, exp.) Std. Dev.

ckitC33 1.178 1.219 0.1214 0.960 0.999 0.099

ckitC23 1.848 1.946 0.1444 1.378 1.501 0.1115 0.991 1.152 0.085

orption spectra (b). CckitC3 = 3 �M, T = 25 ◦C, pH ranged from 8.34 to 2.78. Other

first, the species (I) absorbs one proton to yield a first protonatedstructure (II) which, in turn, evolves to an i-motif structure (III) bythe uptake of an additional proton. Now, the i-motif structure con-tains at least two C+C base pairs. At a pH lower than 4.5, the uptakeof protons clearly disrupts i-motif structure. Finally, at a pH lowerthan 3, the random coil species (IV), formed by protonation of allcytosine bases, appears. Proposed parameters (Kin, ωc, and ωa) forfour species hard models are given in Table 3. The cooperativity ofthe protonation stage of i-motif structure formation was found tobe implicit or slightly cooperative, whereas i-motif transformationto random coil was unquestionably anticooperative.

The main difference between the MCR-ALS distribution diagramand those calculated using the hard model was the presence of asingle protonated form (II). However if we assume as single speciessum of form (II) and form (I) we acquire three species hard model.Accordingly, also the hard model was applied considering threespecies. The parameters (Kin, ωc, and ωa) for three species hardmodels are given in Table 3. The second difference between MCR-ALS and hard model diagrams observed in the pH 4–6 range wasspecies (III) predominance. The presence of species (IV) or specieswith a highly correlated spectrum in the whole of this pH range wasobserved with MCR-ALS. This observation may indicate that in thecase of ckitC3, a part of the DNA molecule folds directly into a ran-dom coil from the very beginning of protonation, which kineticallyhinders complete transition to i-motif.

Using MCR-ALS as well as hard modelling the entire analy-sis was repeated twice, using independent data matrices built upfrom molecular absorption or CD data alone. The results obtained(distribution diagram and pure molecular absorption and CD spec-

tra) agreed with those described above. However, it is essential tonote that the pure molecular absorption spectra calculated fromMCR-ALS analysis were different depending on whether CD datawere included in the analysis. The reason may be that CD mea-surements usually show a higher variability than absorption ones.

74 P. Bucek et al. / Analytica Chimica Acta 683 (2010) 69–77

3 4 5 6 7 8

0.2

0.4

0.6

0.8

1

Frac

tion

pH

I

II

III

IV

c

200 400 600 800 1000

UV

-10

-5

0

5

10

15

20

CD

number of wavelength channel

Nor

mal

ized

elli

ptic

ity a

nd a

bsor

ptiv

ity (a

.u.)

I

II

III

IV

IV

II

I III

d

200 400 600 800 1000

-10

-5

0

5

10

15

20

Nor

mal

ized

elli

ptic

ity a

nd a

bsor

ptiv

ity (a

.u.)

number of wavelength channel

III

IV

I+II

I+II

IV

III

b

3 4 5 6 7 8

0.2

0.4

0.6

0.8

1

Frac

tion

pH

IV

IIII+IIa

F ) distrd expC3 m( – the

TatnCtfipgriotdscartp

TP

Kb

ig. 2. MCR-ALS resolved (a and b) and resolved by hard chemical model ((c) and (d)etected species present along the ckitC3 titration sequence, from the analysis of AII) – single protonated structures, (III) – denotes the species related to i-motif, (IV)

he uncertainty associated with a CD measurement was evalu-ted as ±0.4 mdeg, from the measurement of blank solutions. Onhe other hand, it could be expected that absorbance data wouldeed a greater number of species for a reasonable fitting thanD data. The analysis of CD data may provide different results tohose obtained from the analysis of absorbance data because in therst case, experimental spectra mainly reflect changes in structure,rimarily due to stacking and sin/anti conformations around thelycosidic bond. In the second case, experimental spectra mainlyeflect changes of the electronic environment of bases, due to stack-ng, protonation/deprotonation process, hydrogen bonding, etc. Anligonucleotide chain would provide a similar CD spectrum, evenhough the number of protonated bases along the chain would beifferent. On the other hand, the absorbance spectrum of two DNAamples may be quite similar even though their structures were

ompletely different (for instance, B- and Z-DNA). Simultaneousnalysis (CD + absorbance data) has been show to be useful to breakank deficiency problems and, hence, provide a set of concentra-ion profiles and pure spectra which incorporate the informationrovided by both techniques [39,40].

able 3rotonation parameters calculated with the hard-modelling matrix-method for the studi

Number ofconsidered species

Target matrix N log

ckitC3 AexpC3 12 4.84 AexpC3 12 5.0

ckitC3 Cf(ALS) 12 4.84 AexpC2 10 12 4.8

in – the protonation constant of free cytosine site; ωc – cooperativity, ωa – anticooperatiinding sites. AexpC2, AexpC3 – matrices of experimentally measured values of CD signals a

ibution diagrams (a and c) and pure spectra (b and d) for the main spectroscopicallyatrix. (I) – denotes the neutral form (probably a partially stacked helix structure),

random coil single strand species formed by protonation of all cytosine bases.

4.2. ckitC2 sequence

According to the number of cytosines and their arrangement inthis sequence (5′-CCC TTC CCT TGT GCC CGC CCG-3′) we expectedthe formation of up to six C+C base pairs. It should be taken intoaccount, however, that four guanine bases were present, whichcould hinder the formation of some C+C base pairs due to theformation of GC Watson–Crick base pairs. The presence of theseadditional GC base pairs may explain the observed Tm at pH 6.8and 6.0 (Table 1). The acid–base equilibria of a cytosine-rich regionlocated upstream of the P1 promoter at the bcl-2 gene were studiedin a previous paper [23]. This sequence (5′-CCC GCC CCC TTC CTCCCG CGC CCG-3′) also contained guanine bases, and NMR clearlyshowed the presence of GC base pairs at neutral pH values (from 5to 35 ◦C).

An acid–base titration of ckitC2 was performed from pH 7.45 topH 2.36 (Fig. 3). CD and absorbance data were ordered in a singledata matrix AexpC2, the dimensions of which were 25 rows and 520columns. The SVD analysis of matrix AexpC2 revealed that data vari-ance can be explained using minimum three main components, to

ed DNA sequences.

Kin ωc ωa PE (%)

39 1.44 0.0052 3.347 1.31 0.0093 3.36

25 1.24 0.023 11.39 4.93 1.29 1.19 0.021 0.033 4.22 4.23

vity parameters; for PE – see formula (7) in the text; N – the number of considerednd UV absorbance; Cf(ALS) – concentration matrix computed by MCR-ALS.

P. Bucek et al. / Analytica Chimica Acta 683 (2010) 69–77 75

F sorptic

wAsp

sa

dapicFtdrdtom

tppsasasttc4ang

atl

ig. 3. Acid–base titration of ckitC2 sequence. Experimental CD (a) and molecular abonditions are described in the text.

ithin the usual experimental accuracy. The MCR-ALS analysis ofexpC2 was carried out considering three and four components. Ithould be noted that the use of four components did not lead toroblems of rank deficiency.

Fig. 4a shows distribution diagrams for ckitC2, considering fourpectroscopically active components, were obtained from MCR-ALSnalysis of AexpC2.

For this sequence a direct fit to the MCR-ALS diagram was con-ucted using the hard model. Fig. 4c shows result of such fittingnd Fig. 4d illustrates corresponding spectra of pure species. Theresence of the random coil species (IV) in the pH range where the

-motif species (III) predominates is less significant in the case ofkitC2 than in the case of ckitC3 as can be seen from comparingigs. 2a and 4a. At the same time, the MCR-ALS-resolved distribu-ion diagram is more in agreement with the hard model-resolvedistribution diagram than in the case of ckitC3. The numericalesults of fitting the chemical model to the MCR-ALS distributioniagram Cf(ALS) are given in Table 3. In this case, we also reporthat the parameters of fitting were very similar to the parametersbtained when the direct fitting of hard model to experimental dataatrix AexpC2 was performed.The chemical assignment of corresponding spectral species in

he distribution diagram can be explained as follows. The species (I)redominantly present at neutral pH values can be related to a hair-in structure maintained by several GC base pairs or to a partiallytacked single strand structure. When pH decreases, the species (I)bsorbs one proton to yield a first protonated structure (II). Theingle protonated structure may still involve an internal core ofdditional GC base pairs, resulting in the adoption of the hairpintructure. The structure (II) evolves to an i-motif structure (III) byhe uptake of an additional proton. Now, the i-motif structure con-ains at least two C+C base pairs. It is probable that the other twoytosine are involved in GC base pairs. Again, at a pH lower than.5, the uptake of protons clearly disrupts i-motif structure. Finally,t a pH lower than 3, the random coil species (IV) formed by proto-ation of all cytosine bases appears. The calculated parameters are

iven in Table 3.

The shape of the CD spectrum for the species (I) is that usu-lly assigned to a partially stacked single strand. It must be notedhat the wavelength corresponding to the positive CD band wasocated at 279 nm, whereas that corresponding to species (II) in

on (b) data. CckitC2 = 3 �M, T = 25 ◦C, pH ranged from 7.45 to 2.36. Other experimental

ckitC2 was located at 285 nm. The pure CD spectrum for species (II)significantly different from spectrum for the species (III) as well asshifted relative to the spectrum of the species (I) (Fig. 5). Therefore,species (II) could be explained as an intermediate step between thepartially stacked single strand and an i-motif. The pure CD spec-trum of species (III) shows a characteristic i-motif structure, with apositive and negative band located around 288 and 263 nm, respec-tively. The intensity of the positive band is around twice that of thenegative band.

4.3. Comparing the calculated parameters for the acid–baseequilibria for the sequences ckitC2 and ckitC3

The calculated protonation constant Kin and cooperativityparameters ωc, ωa are given in Table 3. The values of log Kinfor ckitC2 and ckitC3 are nearly the same. A study of parame-ter sensitivity to random noise showed that parameters cannotbe distinguished at the level of ordinary experimental accuracy.This observation confirms the hypothesis that vacant cytosine sites,which are quite similar in unfolded DNA chains, uptake H+ in thefirst protonation step of ckitC2 as well as ckitC3. Based on the abovedescription, it may be postulated that, upon protonation, neutralDNA first leads to a single protonated species (II) by simple sta-tistical occupation of the free cytosine bases as a result of randomdistribution. The observed protonation constant value was compa-rable to that of cytosine protonation in poly(C) [41]. Subsequentsuccessive protonations involve cooperative as well as anticoop-erative stages. Similarly to ckitC3, in the case of ckitC2 the ratherslight cooperativity and anticooperativity is well-defined. Despitethe fact that ckitC2 contains twelve cytosine bases, it is possibleto consider only the protonation of up to ten cytosines. The addi-tional two cytosine bases may not be protonated at a pH of around2.5 because they may still be occupied by guanine bases. In orderto test this hypothesis, we performed data fitting at various hypo-thetical numbers of binding sites on the central molecule. In thecases of the single data matrix UV or CD, as well as when the aug-

mented data matrix was analysed, insignificant differences werefound between models with N = 12 and N = 10. In accordance withthe statistical tests, the verified models were equal. This resultindicates that, at the current level of experimental accuracy, itnot possible to state unambiguously that all cytosines bases are

76 P. Bucek et al. / Analytica Chimica Acta 683 (2010) 69–77

I

3 4 5 6 7

III

0.2

0.4

0.6

0.8

1

II

IV

pH

Frac

tion

I

3 4 5 6 7

III

0.2

0.4

0.6

0.8

1

II

IV

Frac

tion

I

100 200 300 400 500

-10

-5

0

5

10

15

20

25

I

III

III

IV

IVII II

Nor

mal

ized

elli

ptic

ity a

nd a

bsor

ptiv

ity (a

.u.)

100 200 300 400 500

-10

-5

0

5

10

15

20

25

Nor

mal

ized

elli

ptic

ity a

nd a

bsor

ptiv

ity (a

.u.)

IVIII

III

IV

number of wavelength channel

IIII

I

I

F distribd xpC2 m

i2

scTipaaocdfbbmvmfgbn

4

l

along an acid–base titration of ckitC1 from pH 7.95 to pH 2.57 wereordered in a single data matrix AexpC1, the dimensions of whichwere 28 rows and 560 columns [5]. The distribution diagrams cal-culated using the matrix method for four components not matchnicely with the pattern predicted by MCR-ALS as can be seen in

pH3 4 5 6 7

0.2

0.4

0.6

0.8

1

Fraction

pH

ig. 4. MCR-ALS resolved (a and b) and resolved by hard chemical model (c and d)etected species present along the ckitC2 titration sequence from the analysis of Ae

nvolved in protonation when solution pH is lowered from 7 to.

Comparing calculated parameters of cooperativity for studiedequences, the ωc value was slightly lower for ckitC2 than forkitC3, i.e., cooperativity seems to be higher in the second case.his fact could be explained by the presence of four guanine basesn ckitC2, which may promote the formation of GC base pairs atH 7. These base pairs would add extra stability to ckitC2 (prob-bly a hairpin structure) over the ckitC3 structure (which is onlypartially stacked single strand). Because of this, the tendency

f ckitC3 to form i-motif structures is greater than in the case ofkitC2. Melting experiments (Table 1) confirmed that Tm valuesetermined at pH values equal to 6.8 and 6.0 were slightly higheror ckitC2 than for ckitC3, confirming that the structure adoptedy the first sequence was slightly more stable than that adoptedy ckitC3. At pH values equal to 5.0 and 4.5, where i-motif is theajor structure, the stabilities of both structures were similar. The

alue for ωa was noticeably lower in the case of ckitC3, i.e., the i-otif formed by ckitC3 was more difficult to protonate than that

ormed by ckitC2. Again, this could be related to the presence ofuanine bases in ckitC2. In acidic media, GC+ bases (two hydrogenonds) could be formed, giving additional stability to the proto-ated oligonucleotide. This would not be the case for ckitC3.

.4. ckitC1 sequence

Data of spectrometric study of an acid–base titration ckitC1 pub-ished in [5] has been used in the present investigation to compare

number of wavelength channel

ution diagrams (a and c) and pure spectra (b and d) for the main spectroscopicallyatrix. The chemical assignment of species I–IV is the same as in Fig. 2.

with ckitC3 and ckitC2. Absorbance and CD spectra was recorded

Fig. 5. Distribution plot for the 4 spectroscopically detected species present alongthe titration of ckitC1 sequence from the analysis of AexpC1 matrix. The dottedlines were calculated by MCR-ALS, solid lines represent best fits to the AexpC1 datamatrix obtained by numerical simulations based on postulated for ckitC2 and ckitC3sequences hard model.

himic

Frwe(i

tdors

5

tscsTtdegcpao

A

ePBh

A

t

R

[

[

[[[[[

[[

[[[[[

[[

[

[[[[[[

[[[[

[[

[

[40] J. Jaumot, R. Eritja, R. Tauler, R. Gargallo, Nucleic Acids Res. 34 (2006) 206.

P. Bucek et al. / Analytica C

ig. 5, with the main differences being observed throughout the pHange 4–6, approximately. The PE(AexpC1, Acalc) value obtained (7%)as not acceptable taking into account the expected experimental

rror. This result suggested that the proposed protonation modelwhich had been successful for ckitC3 and ckitC2) cannot be appliedn the case of ckitC1.

Finally, we can conclude that sequences mutation has alteredhe protonation mechanism governing the concentration depen-encies of the detectable spectral forms. Most probably the foldingf ckitC1 does not follow the same pattern as ckitC2 or ckitC3. Theeason for this could lie in the formation of more than one i-motiftructure, depending on the number of C+C base pairs [42,43].

. Conclusions

The technique chosen for the mathematical analysis of absorp-ion and CD spectra changes versus pH of ckitC1, ckitC2 and ckitC3equences has shed light on the chemical stages of protonation pro-ess. The hard chemical model governing the pH interval of foldingtudied sequences into an i-motif has been proposed and verified.he results of the numerical simulations carried out suggest thathe protonation mechanism of the studied compounds displays aifferent pattern. The combination of soft and hard modelling hasnabled us not only to define spectral forms and the abstract dia-rams of their concentration distribution, but also to establish theirhemical nature and to calculate spectra of the given chemical com-onents of the system analysed. This strongly indicates that thepproach suggested may be effectively extended to the descriptionf similar systems.

cknowledgements

This study was partially supported by the Spanish Dirección Gen-ral de Investigación Científica y Técnica (grant CTQ2009-11572).rof. Ramon Eritja and Dra. Anna Avinó (Institute for Research iniomedicine, IQAC-CSIC, Barcelona, Spain) are thanked for theirelp in synthesizing DNA sequences and PAGE experiments.

ppendix A. Supplementary data

Supplementary data associated with this article can be found, inhe online version, at doi:10.1016/j.aca.2010.10.008.

eferences

[1] A.T. Phan, V. Kuryavyi, S. Burge, S. Neidle, D.J. Patel, J. Am. Chem. Soc. 129 (2007)4386–4392.

[

[[

a Acta 683 (2010) 69–77 77

[2] A.K. Todd, S.M. Haider, G.N. Parkinson, S. Neidle, Nucl. Acids Res. 35 (2007)5799–5808.

[3] P.S. Shirude, B. Okumus, L. Ying, T. Ha, S. Balasubramanian, J. Am. Chem. Soc.129 (2007) 7484–7485.

[4] J. Dash, P.S. Shirude, S.-T.D. Hsu, S. Balasubramanian, J. Am. Chem. Soc. 130(2008) 15950–15956.

[5] P. Bucek, J. Jaumot, A. Avino, R. Eritja, R. Gargallo, Chem. Eur. J. 15 (2009)12663–12671.

[6] M. Gueron, J.L. Leroy, Curr. Opin. Struct. Biol. 10 (2000) 326–331.[7] Y. Peng, X. Wang, Y. Xiao, L. Feng, C. Zhao, J. Ren, X. Qu, J. Am. Chem. Soc. 131

(2009) 13813–13818.[8] F. Seela, S. Budow, Helv. Chim. Acta 89 (2006) 1978–1985.[9] D. Sun, L.H. Hurley, J. Med. Chem. 52 (2009) 2863–2874.10] K. Halder, V. Mathur, D. Chugh, A. Verma, S. Chowdhury, Biochem. Biophys. Res.

Commun. 327 (2005) 49–56.11] T. Simonsson, M. Pribylova, M. Vorlickova, Biochem. Biophys. Res. Commun.

278 (2000) 158–166.12] N. Kumar, M. Petersenb, S. Maiti, Chem. Commun. (2009) 1532–1534.13] F.C. Simmel, Nanomedicine 2 (2007) 817.14] J.D. McGhee, P.H. von, Hippel, J. Mol. Biol. 86 (1974) 469–489.15] D.M. Crothers, Biopolymers 6 (1968) 575–584.16] T.L. Hill, Cooperativity Theory in Biochemistry. Steady State and Equilibrium

Systems, Springer Verlag, New York, 1985, p. 459.17] Yu.D. Nechipurenko, G.V. Gursky, Biofizika 48 (2003) 773–796.18] J.L. Garcés, C. Rey-Castro, C. David, S. Madurga, F. Mas, I. Pastor, J. Puy, J. Phys.

Chem. B 113 (2009) 15145–15155.19] A.G. Kudrev, Polym. Sci. Ser. A 42 (2000) 527–532.20] A.G. Kudrev, Russ. J. Gen. Chem. 72 (2002) 1501–1507.21] J. Jaumot, M. Vives, R. Gargallo, Anal. Biochem. 327 (2004) 1–13.22] A. de Juan, R. Tauler, Anal. Chim. Acta 500 (2003) 195–210.23] N. Khan, A. Avinó, R. Tauler, C. González, R. Eritja, R. Gargallo, Biochimie 89

(2007) 1562–1572.24] J. Jaumot, R. Eritja, R. Tauler, R. Gargallo, Nucleic Acids Res. 34 (2006) 206–216.25] R. Tauler, A. de Juan, In Practical Guide to Chemometrics, second ed., Taylor &

Francis Group, LC, 2006, p. 453 (Chapter 11).26] J. Diewok, A. de Juan, M. Maeder, R. Tauler, B. Lendl, Anal. Chem. 76 (2003)

641–647.27] A.G. Kudrev, Russ. J. Gen. Chem. 73 (2003) 20–25.28] A.G. Kudrev, Russ. J. Coord. Chem. 25 (1999) 141–147.29] A.G. Kudrev, Russ. J. Inorg. Chem. 46 (2001) 758–763.30] A.G. Kudrev, Macromolecules: An Indian Journal 6 (2010) 30–34.31] R. Gargallo, R. Eritja, A.G. Kudrev, Russ. J. Gen. Chem. 80 (2010) 485–492.32] G.H. Golub, C.F. van Loan, Matrix Computations, The Johns Hopkins Univ. Press,

London, 1989, p. 476.33] R. Tauler, A. Smilde, B.J. Kowalski, Chemometrics 9 (1995) 31–35.34] D.W. Marquardt, J. Soc. Ind. Appl. Math. 2 (1963) 431.35] M. Maeder, A.D. Zuberbühler, Anal. Chem. 62 (1990) 2220–2224.36] R. Dyson, S. Kaderli, G.A. Lawrence, M. Maeder, A.D. Zuberbühler, Anal. Chim.

Acta 353 (1997) 381–393.37] H.T. Allawi, J. SantaLucia, Biochemistry 36 (1997) 10581–10594.38] J.L. Mergny, L. Lacroix, H. Xiaogang, J.L. Leroy, C. Leroy, C. Hélène, J. Am. Chem.

Soc. 117 (1995) 8887–8898.39] J. Jaumot, N. Escaja, R. Gargallo, C. González, E. Pedroso, R. Tauler, Nucleic Acids

Res. 30 (2002) e92.

41] V.A. Bloomfield, D.M. Crothers, I. Tinoco Jr., Nucleic Acids: Structures, Propertiesand Functions, University Science Books, Sausalito, CA, USA, 2000.

42] S. Nonin-Lecomte, J.L. Leroy, J. Mol. Biol. 309 (2001) 491–506.43] P. Kumar, A. Verma, S. Maiti, R. Gargallo, S. Chowdhury, Biochemistry 44 (2005)

16426–16434.