Przemyslaw Piotr ; Cysewski
email: piotr.cysewski@cm.umk.pl

Prof Piotr Cysewski

PhD Przemysław Czeleń

Przemysław Czeleń

Keywords: stacking, telomere, oxidative damage, 8-oxoguanine, TRF1, many-body contributions Stacking  Nucleobases interactions  Telomere  Oxidative damage  8oxoguanine

come

Structural and energetic consequences of oxidation of d(ApGpGpGpTpT) telomere repeat unit in complex with TRF1 protein

1. Authors used a slightly different sequence for isolated and complexed DNA systems -the initial structure of the complex is from NMR, but no information is about an origin of the free oligomer. Was this structure prepared directly in AMBER or is it based on any experimental structure? Why authors did not use the same structure? Indeed oligomers denoted as S1 and S2 (free B-DNA) had not exactly the same sequence as S3 and S4 (TRF1 in complex with B-DNA). Reason of this was strictly technical. Before finding NMR structures deposited in NDB database we have modeled non-complexed structures comprising canonical and oxidized telomere repeat unit in water solution. We did not expect that conclusions are sensitive to oligomer sequence especially that such differences are in the region outside of our interest. Nevertheless all computations on both molecular dynamics and quantum chemistry levels were repeated for exactly the same sequences. Thus, solution structures of B-DNA bound to human TRF1 that are provided by 1IV6.pdb file (20 conformations) were used as the source of al four sets of data (S1, S2, S3 and S4). In the previous version of our paper just one structure was chosen as a source of initial conformation of S3 chain. This time we have performed independent molecular dynamics runs for all 20 available conformations. Based on data provided in 1IV6.pdb file new initial structures were prepared for S1 (after removing all atoms belonging to protein) and S2 (by replacement of guanine with 8-oxo-guanine in the central telomeric triad of S1) in each of 20 conformations. Thus, the whole paper covers 40ns dynamics for each of studied oligomer (S1-S4). However, independent 2.5ns runs were performed for every initial structure This provide broad range of conformations that were starting from different experimental structures. F rthermore, in order to check adequateness of such short MD runs the time autocorrelation functions were estimated according to specification in NIST document (www.itl.nist.gov/div898/handbook). Below there are provide typical plots:

2. There is no information about a total number of simulations. There are four types of studied systems (free, complex, free+oxoG, complex+oxoG) but it seems to me that there is only one simulation for each type -is it true? … The most critical point is the length of simulations which is -in my opinioninsufficient. Today, a common length of MD simulations is in tens of nanoseconds, frequently exceeding 50-100 ns. I am rather skeptic if the system (mainly in case of the complex, which contains ~1800 atoms and water box) is fully equilibrated after 0.5 ns and is able to show characteristic dynamical behavior in a few nanoseconds. To make qualified decisions from 2 ns of MD simulations is not possible and is necessary to extend simulations (m nimally to 10 ns) and (if it is possible) to carry out multiple simulations to improve sampling. Only in this case it is possible to make meaningful conclusions. Previous paper utilized just one short simulation. Now we performed 20x2.5ns molecular dynamics and as long as 40nps trajectories were used for averaging and most representative structures extraction. Fortunately after extension of trajectories the main concussions were almost not affected by the length of simulation. This is due to fortunate selection of initial structures. Below, it is presented demonstration of this "lucky" selection of starting points for MD runs. The correlation between median values of shift and slide for 20 distinct trajectories are plotted for S3 and S4 systems. Red symbol denotes structure that was used in previous version of our paper. Thus it is not surprising that essential structural and dynamic features were inclu ed in previous MD simulations. I would like also to add one more comment. Previously we have selected every 20ps snapshot for ab initio computations. This resulted in 100 structures and was quite feasible for our computer cluster. After extension form 2000 points up to 40000 structures it is completely impractical such way of selection and we have proposed alternative approach described in the methodology part. In addition I would like to present here figure that is not included in the paper, but probably will be interesting for Referee. The workflow of proposed algorithm is presented in supplementary material within footnote to Fig. S1 so I will not to repeat it here. The main criterion for most representative structures selection is similarity of IIE histograms obtained f r probing set and one that was averaged over all structures used for all probing sets. In figure presented below all gray lines represent IIE distributions obtained within MM framework for different probing sets. Each probing set comprised 36 conformations of dinucleotide steps representing the most frequently occurring ranges of base step parameters . Smoothed histogram presented as red line represents averaging over all 360 structures coming from 10 distinct probing sets. Dotted line corresponds to histogram estimated based on 2500 dinucleotide conformations. It is clearly visible that averaging over 10 probing sets is as good as over thousand of dinucleotide conformations.

3. Do authors know about the parmbsc0 version of the Amber force field which is today commonly used for MD simulations of DNA molecules? This force field has modified backbone torsion profiles to avoid irreversible backbone changes (α/γ torsion angle switches) observed in long MD simulations. Although this is not this case I recommend authors to use this force field version in their future simulations. Thank you for this comment. We are aware of Barcelona corrections to the Amber forcefield (Pérez et al. Biophys. J. 2007, 92(11): 3817-3829) and we use this set of parameters in all of our simulations. Just for ensuring this the citation is added in the reference list.

4. There is no figure showing DNA-TRF1 complex -it would be useful to see where the mutation is taking place in the complex. I am sorry not to providing such figure but I am ready to fulfill this requirement if Referee will insist. Since in Ref.8 there were presented molecular details showing contacts between TRF1 and B-DNA, I do not think it is really important. Paper is quite overloaded with figures and data, so please forgive me not providing such figure .   5. The list of references is rather limited -I am missing more oxoG and oxoG/MD papers. Thank you more references and comments are provided now in the introductory section.

Introduction

Telomeres are important biological complexes playing crucial role in preserving genome stability [1]. Their main function is stabilization of chromosome ends and protecting them from being recognized as B-DNA double strand breaks [2]. In humans all telomeric B-DNA fragments consist of several kilobases of six nucleotides sequences (AGGGTT) constituting so-called telomere repeat unit (TRU) [3]. These chromosomes tails are shortened with every cell division by about 20-200 base pairs, therefore often they are called "miotic clocks" [5][6][7].

There are specific protein ligands called TRF1 [3] and TRF2 [4] that can attach to telomere B-DNA in cells. Both these proteins interact with telomeric AGGGTT sequences by binding to major grove via strong contacts of different types [8]. Among many stabilization contributions there are produced distinctive hydrogen bonds between amino acids residues and G-C base pairs in GGG triplet [8]. Also direct interactions with ribose phosphate backbone on both sides of the major groove and N-terminal arm [9] are important. Stability of this complexes is highly sensitive to mild oxidative stress-induced by hyperoxia, mitochondrial dysfunction, presence of arsenic or exposure to UV irradiation [10,11].

Occurrence of oxidative lesions in the telomeric fragment significantly affects process of DNA-protein complexes formation. Presence of one 8-oxoguanine molecule in the central telomeric triad (CTT) reduces the affinity of protein binding by factor 2 compared with standard non-damaged DNA [12]. Such profound reduction of complex instability can be addressed to significant changes in structural characteristics affecting dynamic properties of oxidized CTT [13]. Besides, observed alterations of electrostatic properties imposed by oxidation is an important factor contributing to TRF binding. In our previous works [13,14] there were discussed some important consequences of telomere repeat unit oxidation. The application of MD/QM quenching method revealed significant stiffness of CTT as a results of CTT oxidation. This was related to skewness decrease of roll and shift distributions, increase of skewness and kurtosis of opening distributions and increase of kurtosis of roll distributions characterizing oxidized CTT with respect of canonical one. These structural changes imposed the increase of intermolecular interaction energies (IIE) characterizing stacking interactions of 8-oxoguanine with neighboring guanine molecules and hydrogen bonding of 8-oxoguanine with cytosine. The aim of this paper is to analyze consequences of TRF1 binding to canonical and oxidized telomere B-DNA oligomers on structure and energetics of telomere repeat unit. Although there were published important studies on dynamic and structural properties of oxidized B-DNA to our best knowledge consequences of TRF1 complexation and/or oxidation were not described so far. For example nowadays Naômé et. al [15] characterized hydration and ion binding patterns around double-stranded B-DNA containing 8-oxoguanine supporting existence of early recognition modes utilized by hOGG1 enzyme. Molecular dynamics simulation of clustered DNA damage sites that contain 8-oxoguanine were studied by Fuhimoto et. al. [16]. Dodson and Lloyd [17] studied mechanism of substrate recognition for BER glycosylases via base flipping of DNA sequences containing 8-oxoguanine. The generated trajectories were used for identification of such properties of oligonucleotides in the vicinity of the altered site that are responsible for recognition. Dynamic behavior of DNA oligomers containing 8-oxoguanine was also studied by Cheng et.al. [18]. Besides, short 1ns molecular dynamics simulations were performed for elucidation of the effect of guanine lesion presence in B-DNA [19]. Much larger MD simulation were carried out by Fujimoto et al. combined with biomolecular visualization software [20]. However, most of theoretical studies on 8-oxoguanine in B-DNA are related to explanation [21,22] f activities of human oxoguanine glycosylase (hOGG1) [23][24][25][26] and TRF1 binding was analyzed with much lower intensity [27][28][29][30]. This paper concentrates on energetic stabilities of CTT characterized on post-SCF quantum chemistry level including not only BSSE corrections [31,32] and necessary electron correlation contributions [33][34][35] but also many-body terms [35] for all of analyzed systems. Applied here methodology was successfully tested in our previous investigations [36][37][38][39] for polymorphism-related heterogeneities of guanine stacking in B-and A-DNA forms [37], characteristics of inter-and intra-strand stacking interactions in d(CpG) and d(GpC) steps found in B-DNA, A-DNA and Z-DNA crystals [38], description of energetic heterogeneities in canonical and oxidized central guanine triad of B-DNA telomeric fragments [13,14] and quantification of all possible intra-strand stacking interactions between nucleobases [39]. The essential feature of applied here approach is characteristics of statistically significant probe of conformations and consequently obtaining of intermolecular interaction energies (IIE) distributions coming from quite long (40ns) molecular dynamics sampling of analyzed oligomers. The averaged IIE values characterizing intermolecular interactions within CTT can then be supplied with corresponding standard deviations giving an impression of magnitude of structural and energetic fluctuations.

Methods

The meaningful conformations of any molecular system require adequate sampling over configurational hyperspace using one of many available theoretical approaches [40][41][42][43][44].

Among them molecular dynamics (MD) is widely accepted as an accurate tool for probing dynamics of many models of diverse biological systems within picoseconds or even microseconds time scales [41][42][43][44]. Along with increase of computer power time of simulations also continues to increase providing large amounts of data. This ensures that sampling provide adequate insight into the dynamics of interactions. However, this is accomplished with interpretation problems since selection of the most representative structures (MRS) used for characteristics of analyzed system is not a trivial task. One of commonly applied approach of extraction such representative probe is clustering of molecular configurations according to their structural and energetic similarities. Unfortunately, there is no one ideal data-mining technique [45] providing univocal way of MRS selection and different algorithms lead to different descriptions [45][46][47][48]. As it was demonstrated by Shao et al. [45] some algorithms tend to produce homogeneously sized clusters, whereas others have a tendency to produce singleton clusters. For these reasons in this paper we adopted another strategy for selection of MRS characterizing structural and energetic heterogeneities of canonical and oxidized B-DNA CTT present in free oligonucleotides and complexed with TRF1 protein in water solution. The central telomeric triad comprising d(G 2 pZ 3 pG 4 ) sequence seems to be crucial from the perspective of TRF1 binding and this fragment was analyzed in details. Since conformations of dinucleotide steps are univocally characterized by set of six values of helical parameters [49,50] the distributions of these quantities instead of actual atomic coordinates were used for MRS selection. Using trajectories generated during 40ns sampling with 1ps step there were extracted shift, slide, rise, roll, tilt and twist values using X3DNA package [51]. Then after normalization of all distributions there were prepared histograms by division of the whole span into six equal intervals. Based on derived histograms the weighting coefficients were obtained for each of base pair step parameter.

Then there were randomly selected structures in number corresponding to estimated weights for each interval. This led to 36 structures for each probing distribution of each analyzed dinucleotide step. Typically this procedure was repeated ten times for generation of distinct probing distribution sets. The intermolecular interaction energies (IIE) were estimated using Amber forcefield [52,[START_REF] Case | AMBER 10[END_REF] for all dinucleotide step belonging to each probing distributions.

For this purpose from selected pdb files corresponding to each MD trajectory snapshot there were cut dinucleotide steps for which sugar phosphate backbones was replaced with hydrogen atoms. Position of N-glycosidic hydrogen atoms were optimized according to geometric criterions. Obtained values of dinucleotide stabilization energies were used for histograms plotting. There were also prepared histograms corresponding to all analyzed structures for given dinucleotide step (360 values). This lead to series of histograms of different shapes. For actual quantum chemistry computations the most representative set was selected by comparison to averaged histogram. The RMSD of histograms points were computed and the smallest value indicated the most representative set of conformations (MRS). The whole procedure was automatized by program written by authors. Initially different length of intervals in histograms, different number of selected structures for each interval and different number of probing sets were considered but the resulting IIE distributions were hardly dependent on these parameters. More details please find in supplementary materials (Fig. S1 and footnote). Only most representative sets of structures were used for precise intermolecular interaction energies (IIE) estimation at DF-MP2/aug-cc-pvDZ (aDZ) level of theory using MolPro package [START_REF] Werner | MOLPRO, a package of ab initio programs designed by Werner HJ and Knowles PJ[END_REF]. Since dinucleotide steps comprising stacked guanine molecules can exhibit significant non-negligible many-body contributions [35,36] all IIE values were corrected using linear relationships proposed elsewhere [START_REF] Cysewski | J Mol Model (JMMO1145 -accepted for publication in this issue) 57. Azbel' MY[END_REF]. In all cases where quantum chemistry calculations were applied the simplified model was used in which sugar-phosphate backbones were replaced with hydrogen atoms. However, the conformations of nucleobases exactly matching to ones occurring in MD runs were prepared according to procedure applied in our previous investigations [13,14,[36][37][38][39]. All IIE values were also corrected for BSSE error according to the counterpoise scheme [31,32].

As it was mentioned beforehand the subject of our interest is the telomere repeat unit (AGGGTT). The water solution structures of d(GpTpTpA 1 pG 2 pG 3 pG 4 pT 5 pT 6 pApGpGpG) oligomer in B-DNA form complexed with human TRF1 are available in NDB database [50] denoted as 1IV6 [START_REF] Nishikawa | [END_REF]. It comprises 20 distinct conformation of this protein-DNA tandem.

These conformations were used as the source of initial structures of four studied systems (S1, S2, S3 and S4). Symbol S1 stands for free non-modified B-DNA oligomer, S2 represents oxidized analogue d(GpTpTpA 1 pG 2 pZ 3 pG 4 pT 5 pT 6 pApGpGpG), S3 denotes non-oxidized telomere oligomer in complex with TRF1 and S4 refers to oxidized telomeric fragment bound to TRF1. Symbol Z stands for guanine (in S1 or S3) or 8-oxoguanine (in S2 or S4). Central telomeric triad was marked using bold style. The available 20 conformations coming from NMR experiment were then the source of all S3 initial conformations. Preliminary structures of S1 were prepared by removing of all atoms belonging to protein from each of S3 conformation. Then S2 and S4 were build by replacement of G 3 with 8-oxoguanine in S1 and S3 respectively. Independent 2.5ns molecular dynamics runs were performed for all 20 available conformations. Last 2ns of all trajectory were used as the source of structural parameters collected every 1ps. The time autocorrelation functions [57,58] were plotted for ensuring that such short trajectories can still provide an adequate sampling of B-DNA dynamical structure. Since relaxation times of helicoidal parameters are proven to be shorten then 0.5ns [58] their distributions appear to be well stabilized within applied time scale.

Additionally different starting points ensure proper sampling of dynamic features of analyzed systems. The X3DNA package [51] was used for analysis of B-DNA conformation. Thus, trajectories corresponding to 40ns dynamics for each of studied oligomer (S1-S4) were obtained. Each oligomer was immersed in TIP3P water box and neutralized by 26 sodium cations. The periodic boundary conditions were imposed. All molecular dynamic simulations were done at room temperature with an aid of AMBER package [START_REF] Case | AMBER 10[END_REF] with Barcelona corrected forcefield [59].

Results and discussion

Trajectories generated during molecular dynamics simulations allow for quantitative characteristics of structural diversities imposed by oxidation and/or TRF1 complexation of analyzed telomere repeat units. Besides, statistically significant probe of conformations taken for quantum chemistry calculations offers unique insight into heterogeneity of intermolecular interaction imposed by these two important factors. Due to commonly known inaccuracies of any available force-field parameterizations the reliable energetic analysis cannot be achieved using MM level. This is especially important for stacking interactions for which attractions are granted from dispersion forces and inclusion of electron correlation is a must [32,34]. To our best knowledge this is the first such comprehensive attempt to characterize structural and energetic changes imposed by protein-DNA complexation using quantum chemistry approach.

Structural diversities of telomere repeat unit

Description of oligonucleotide chain flexibility and its sensitivity to external factors poses some challenge on the way of presentation. In order not to be obscured by very details coming from time evolution of so many parameters, the analysis of the whole distributions was done in terms of three statistical measures. Obviously the meadian value is the first choice representing the averages of particular distribution. The comparison of medians supplemented with values of standard deviation can directly reveal structural consequences of particular factor acting on the oligomer. Since not all distributions are of normal type one can find such distributions which are characterized by almost the same median values (along with similar standard deviations) and still differences between them can be statistically significant. In such cases two additional statistical measures, namely skewness and kurtosis, are very useful since departures from normality are displayed by combinations of their values. The first quantity (skewness) simply evaluates the population asymmetry. Positive values of skewness indicate that right part of distribution is more extended than the left one. Consequently existence of left tail on distribution plots is indicated by negative values of this parameter. Thus, the nonzero values of skewness reveal changes in preferences of particular conformations adopted by analyzed oligomers. The second quantity used here is kurtosis, which offers very convenient way for characterizing shape of distributions in sense of the degree of peakedness. In case of normal distribution kurtosis is equal to zero and represent so-called mesokurtic distribution. A pure leptokurtic distribution has a higher peak than the normal distribution and has heavier tails. On the contrary a platykurtic distribution has a lower peak than a normal distribution and lighter tails. There is simple and direct structural interpretation of deviation from mezokurtic distributions, namely leptokurtic character indicates stiffness of analyzed oligomer structure with respect of particular parameter. On the other hand flexibility with respect of particular parameter is correlated with platykurticity and negative values of kurtosis. The distributions of all base step and base pair parameters characterizing flexibility of the whole telomeric repeat unit d(A 1 pG 2 pZ 3 pG 4 pT 5 pT 6 ) were analyzed. Each of dinucleotide steps is to be characterized by 18 parameters distributions for each of analyzed chains. In supplementary materials detailed characteristics of obtained distributions are collected in Tables S1 andS2 along with graphical presentation (Fig. S2 andS3) of median values variations for all telomeric repeat units belonging to four analyzed oligomers.

Additionally in Fig. 1 there are provided the most representative plots. <Figure 1 here> Although there are observed some alterations of median values, the majority of base pair distributions differ by less than standard deviations. This suggests that only modest changes are enforced on such base pair parameters as shear, stretch, stagger, propeller and opening by either of analyzed factors (see Table S1, S2 and Fig. S1,S2). The only exceptions are visible for buckle. In Fig. 1 it is demonstrated that G 4 guanine (third one in CTT) is characterized by higher values of buckle in case of S4 oligomer. Also T 5 and T 6 pairs are affected on the same way by TRF1 binding to both canonical and oxidized telomere fragments. Values of buckle represent bending of hydrogen bonded pairs along shorter molecular axis (perpendicular to DNA chain in major-minor groove direction). Besides, the median value of propeller describing mutual rotation of molecular planes around shorter molecular axis is lower for bases inside CTT of S1 and S2 systems. However, the difference between non-complexed and complexed dinucleotide chains is of order of standard deviations. This suggest that rotation of H-bonded bases in pairs constituting CTT is restricted in some extend after TRF binding.

Reduction of this parameter suggest better coplanarity of nucleobases belonging to opposite chains and is the highest in case of S3. Interestingly, kurtosis and skewness are rather small and almost identical for all distributions of all base pair parameters except of stretch and opening. In both cases skewness exceeds unity only for T 5 suggesting that there are significant portion of structures adopting higher values than average of this parameter. The higher values of stretch the longer distance between hydrogen bonded pairs, while higher values of opening indicate asymmetrical trends of hydrogen bond breathing. Very high values of kurtosis were noticed in case of Z 3 opening for S2 system and T 5 stretching for S3 and S4 oligomers. Thus, oxidized guanine in CTT is the source of high stiffness if compared to nonmodified oligomer. On the other hand binding of TRF1 to both oxidized and non-oxidized chains reduces this kind of rigidity to the degree typical for free non-altered oligomer.

Furthermore, TRF1 complex formation imposes strong stiffness of T 5 stretching in S3 and S4 systems. Thus, these few mentioned alterations imposed on base pair parameters are the main structural consequences of by oxidation or/and TFR1 binding in analyzed systems. In addition some interesting alterations are induced also on distributions of base pair parameters. In Fig. 1 there are presented three such spectacular instances. For example shift and slide are quite suggesting tendency of increasing of distance between stacked bases on right side of major grove. Besides, there is observed significant twisting of Z 3 /G 4 base step pair of S3 and S4 that is much larger if compared with S2 and S3 oligomers. Above statements confirms that binding of TRF1 to canonical or oxidized telomere repeat unit is important factor contributing to structure and dynamics of analyzed oligomers. The most important alterations imposed on geometry of CTT's that were discussed above are summarized in Fig. 2. Length of vectors and directions of angles schematically represents magnitude of differences.

<Figure 2 here>

Purines rings overlapping

In the central telomeric triad (CTT) three purine rings can overlap forming two pairs of intrastrand stacking. It is reasonable to expect that any changes imposed on structure and dynamics of CTT can lead to alterations of base-over-base displacement. This feature can be expressed qualitative by the analysis of fluctuations of overlapping area (in Å 2 ) between polygons defined by atoms on successive bases within the same strand. Polygons are projected on the mean plane of the analyzed base-pair step. Such values are the measure of base ring overlaying excluding exocyclic atoms. Observed fluctuations of overlapping areas for two successive purines belonging to the first strand were plotted in Fig. 3 for all analyzed oligomers. Very interesting observation can be made from both presented plots and provided medians. First of all, native B-DNA telomere is very flexible structure and overlapping area of the first two guanine molecules in CTT changes in wide range (from 0 to 6 Å 2 ). Supplied median values as well as histogram plots in Fig. 3 support quite flexible nature of GGG triad in native telomere repeat. The comparable values of medians and standard deviations of G 2 /G 3 and G 3 /G 4 suggest symmetrical movements of both G-C pairs. Replacement of G 3 with 8oxogunaine has profound consequence on the area of overlapping. Momentous increase of G 2 /8oxoG 3 stacking contacts and slight decrease of 5'-Z 3 /G 4 -3' overlaying is observed in case of S2 system. Hence, symmetrical GGG triad become much more asymmetrical after oxidation of central guanine. Interestingly, similar consequences on overlapping areas are observed as a result of TRF1 binding to S1 structure. In the case of S3 oligomer the contacts of G 2 /G 3 pair are significantly increased with unchanged G 3 /G 4 overlapping. Oxidation of guanine in CTT in S4 complex even enhance this effect. Additionally reduction of overlapping areas in d(Z 3 pG 4 ) dinucleotide steps is to be noticed. One can expect that the intra-strand interactions should be significantly affected by such changes of CTT structure. This aspect will be discussed in the next section. Thus, both oxidation and TRF1 binding can have significant influence on the overlapping of aromatic rings in central telomeric guanine triad.

<Figure 3 here>

Energetic consequence of oxidation and TRF1 binding

Structural alterations imposed by oxidation and complexation with TRF1 must affect intermolecular interactions between bases constituting central telomeric triad. Three distinct types of contacts determine IIE within central telomeric triad, namely hydrogen bonding, intra-and inter-strands stacking. They are systematically studied below for population comprising most representative structures extracted according to procures described above.

As it was previously demonstrated [34][35][36] interactions in d(GpG) dinucleotide steps can exhibit significant non-additive character reaching several kcal mol -1 . Furthermore, manybody contributions to stabilization of dinucleotide steps comprising 8-oxoguanine are also non-negligible [START_REF] Cysewski | J Mol Model (JMMO1145 -accepted for publication in this issue) 57. Azbel' MY[END_REF]. Fortunately, sum of all many body terms scale linearly with non-additive part and simple correction is possible. Regression analysis presented in Fig. 4 demonstrate that many-body contributions are not very high and in average are equal to -1.8 kcal mol -1 for non-oxidized dinucleotide steps and -2.4 and -2.8 kcal mol -1 for d(GpZ) and d(ZpG), respectively. Since many body contributions can change depending of nucleobases conformations, all additive IIE values were corrected according to regression formula presented in Fig. 4.

<Figure 4 here>

Base-base interactions in CTT

Results of IIE calculations were presented in Fig. 5 and Table 1. In case of purine/purine intra-strand stacking width of IIE distribution is about 2 kcal mol -1 in terms of half of the most often occurring IIE values (differences between bottom and top 25% of values). In case of G 2 /G 3 in S1 oligomers the range of observed intra-stacking starts from -5.6 and -0. This significant increase of stacking of oxidized guanine is the main consequence of observed structural differences between modified and non modified chains of telomere repeat unit.

Interestingly, irrespectively of the presence or absence of TRF1 protein the intra-strand stacking of 8-oxoguanine with guanine is significant context-dependence since IIE MFO of G 2 /Z 3 and Z 3 /G 4 sequences differ by more than values of standard deviations. Purines involved in the former pair interact much stronger than the latter. This rationale the observed discrepancies in overlapping areas. The intra-strand stacking of C 2 /C 3 cytosine molecules are sensitive to TRF1 interactions with oxidized telomere repeat unit. Statistically significant decrease of IIE MFO by about 2 kcal mol -1 is noticed in case of C 2 /C 3 stacking. For the rest of intra-strand stacking pairs of two cytosine molecules there is no observed significant influence of guanine oxidation and TRF1 binding. Besides, inter-strand stacking occurring in CTT is sequence dependent. For example 5'-Z 3 |C 4 -5' (in d(ZpG)) is about 30% weaker than 3'-Z 3 |C 2 -3' (in d(GpZ)) in all oligomers except S4. In this case both these inter-strand interactions are almost identical. Presence of the oxidized guanine also influences inter-strand stacking of guanine with cytosine making 3'-G 4 |C 3 -3' interactions about 1.4 kcal mol -1 stronger than for 5'-G 2 |C 3 -5' in S2. No differences in strength of stronger hydrogen bonds are observed for all canonical and oxidize Z-C pairs. It is also interesting to know if any compensation effects take place of previously discussed changes in base-base interactions for higher ordered structures. Thus, dinucleotide steps were considered. The corresponding IIE values were estimated assuming pair-wise additivity supplemented with linear correlation corrections for many-body contributions (see Fig. 4). The most straightforward conclusion drawn from data collected in Table 1 is that the overall stabilization energy of all oxidized dinucleotide steps are significantly higher compared to canonical ones. The gain in IIE MFO interaction energies is about 9 kcal mol -1 and significantly exceeds values of standard deviations. These changes were proved to be statistically significant. Thus, for dinucleotide steps oxidation has profound stabilization effect for all pairs in CTT originating from increase of inter-and intra-strand stacking of 8-oxoguanine. On the other hand interaction with TRF1 does not change significantly stabilities of dinucleotide steps constituting central telomeric triad.

<Figure 5 here> <Table 1 here>

Structure to energy relationships

In previous sections there were discussed significant structural and energetic alterations as a consequence of oxidation and/or TRF1 protein binding to telomeric repeat unit. It is interesting to see if there are any direct structure to energy relationships not only on the level of base-base interactions but on bigger scale. Those parameters, which distributions were nonchanged by two analyzed factors were excluded from the analysis, while for those distributions, which were proven to be statistically significant two dimensional contour maps were prepared. In Fig. 6 there are presented scattering plots characterizing actual correlation between the stabilization energy in the whole tetramer that forms central telomeric triad d(GpZpG) and values of buckle, twist, shift and slide. These distributions were over imposed on the contour map representing percentage of particular structure with given IIE value. For this reason the whole spectrum of values was divided into ten equal intervals and twodimensional histograms were estimated and presented as smoothed contour maps. From such graphs it is clearly visible almost perfect energetical separation of d(G 2 pG 3 pG 4 ) and d(G 2 pZ 3 pG 4 ) trinucleotides. Besides, structural heterogeneities can also be seen. For example buckle can adopt values from interval (-36°,+36°). The range of most frequently occurring buckle values is quite similar for canonical and oxidized trinucleotides, but there are structures characterized occasionally by values outside of this region. It is also visible that buckle distributions of S4 system are significantly shifted toward higher values with respect of other oligomers. This is in good accord with conclusions drawn from Fig. 1. Twist distributions presented in Fig. 6 posses different properties. The most probable structures found in the range between 25° and 35° come from non-oxidized systems (S1 and S3). On the contrary much higher flexibilities with respect of twist values are observed for S2 and S4. Also the fluctuations of shift and slide can be momentous for all analyzed structures and cover range of more than 4Å for both of these step parameters. Interestingly, the discrepancies between shift distributions of S3 and S4 are well documented in Fig. 6. Much higher stiffness of this parameter is to be related to S1 and S3 structures. Similar conclusion can be drawn from slide distributions. In this case however, significantly lower values are observed for S3.

Finally there is spectacular consequence of TRF1 binding to oxidized telomere repeat unit.

Significant population of CTT structures found in S4 adopts positive values of slide and negative values of shift. This is observed only in the case of S4. The rest of populations are characterized by negative values of slide (<-1.0Å) and smaller than 1Å absolute values of shift. This is most direct consequence of TRF1 binding to oxidized telomere repeat unit, which distinguishes univocally such complex from canonical ones. <Figure 6 here> <Figure 7 here>

Conclusions

The B-DNA of d(A 1 pG 2 pG 3 pG 4 pT 5 pT 6 ) sequence constituting telomeric repeat unit is significantly affected by both oxidation and binding of TRF1 protein. Presented data clearly identified structural and energetic consequences imposed on central telomeric triad by these two factor. The occurrence of 8-oxoguanine in CTT is the source of high stiffness if compared to non-modified oligomer. The origin of this property comes from significantly alteration of intermolecular interactions of 8-oxoguanie. Presence of this lesion in CTT leads to important increase of intra-strand stacking involving 8-oxoguanine in G 2 /Z 3 pair by about 3.5 kcal mol -1 if compared to G 2 /G 3 pair. Also oxidized guanine influenced inter-strand stacking of guanine with cytosine making 3'-G 4 |C 3 -3' interactions about 1.4 kcal mol -1 stronger than for 5'-G 2 |C 3 -5' in oxidizes oligonucleotides. The sequence dependent inter-and intra stacking involving 8oxoguanine are observed. In the case of 8-oxoguanine present in d(ZpG) sequence significant reduces of base-base interactions if compared with d(GpZ) dinucleotide steps. The increased stability observed for base-base interactions has cumulative character and is typical also for di-and tri-nucleotides. Although for some conformations there are observed non-negligible contributions coming from many-body terms still dinucleotides and the whole CTT are significantly more stable with respect of their canonical equivalents. The observed changes in the intermolecular interactions originate from structural alterations imposed by TRF1 binding to canonical and oxidized telomere B-DNA. First and most direct consequence of TRF1 binding to oxidized telomere repeat unit is alteration of shift-slide correlations. Only in case of TRF1 complex formed with oxidized CTT significant population adopts positive values of slide and negative values of shift. This in turn leads to large differences in purine-purine overlapping in oxidized structures. Thus, oxidized structures of telomere repeat unit are more sensitivity to interactions with protein ligands especially for Z 3 /G 4 stacked pairs. Histogram plots for all distributions were supplied in edges of corresponding axis

Supplementary material available

Figure captions

Fig. 4

Regression analysis between intermolecular interactions in dinucleotide steps estimated using pair-wise additivity (IIE(additive)) and full inclusion of all many body contributions (IIE(total)) [START_REF] Cysewski | J Mol Model (JMMO1145 -accepted for publication in this issue) 57. Azbel' MY[END_REF] Fig. 5 Distributions of intra-strand stacking interactions in CTT of analyzed oligomers. In legends there are provided probabilities that the difference in the median values between the two groups (S3 with respect of S1 and S4 against S2) is statistically significant. In case of P<0.001 it is excluded the possibility that the difference is merely due to random sampling variability Figure 5. Distributions of intra-strand stacking interactions in CTT of analyzed oligomers. In legends there are provided probabilities that the difference in the median values between the two groups (S3 with respect of S1 and S4 against S2) is statistically significant. In case of P<0.001 it is excluded the possibility that the difference is merely due to random sampling variability.

  sensitive parameters affected by interactions of oxidized oligomer with analyzed protein. The median values of shifting of two base pairs along shorter molecular axis are reduced for Z 3 /G 4 and increased for G 2 /Z 3 pairs in S4 oligomer. This is accomplished by the significant reduction of skewness of shift populations of G 2 /Z 3 . This suggests that S4 system exhibits strong tendency toward reducing shift values to ones typical for S1 oligomer. Also increased sliding of Z 3 /G 4 and G 2 /Z 3 pairs found for S4 system suggests significant movement along longer molecular axis of these two pairs imposed by TRF1. Furthermore, significant reduction of base pairs twisting is observed for S2 and S4 oligonucleotides indicating much smaller rotation of base pairs within d(G 2 /pZ 3 ) dinucleotide steps. Also, interactions with protein change also roll values characterizing S4 structure. Rolling of base step pairs describes mutual rotation of base pairs around shorter molecular axis. Negative roll values of Z 3 /G 4 indicate increasing distance between stacked base pairs from major grove side. These changes are also accomplished with changes in tilt values describing Z 3 /G 4 base pair of S4 structures

7 kcal mol - 1 .

 1 The inter-strand stacking of complementary cytosine molecules ranges between -6.1 and +1.3 kcal mol -1 . This give impression of the influence of structural fluctuations generated during MD run on the heterogeneities of actual values of intermolecular interactions. Due to non-normal distributions of intermolecular interaction energies instead of mean or median values the most frequently occurring energies (IIE MFO ) are used as the main energetic characteristics of intermolecular interactions. It simply corresponds to localization of maximum on smoothed histograms. Plots characterizing distributions of intra-strand stacking of two purines or two pyrimidines are exemplified in Fig. 5. From these data interesting trends can be concluded. First of all intra-strand stacking involving 8-oxoguanine in G 2 /Z 3 pairs (of S2 and S4) is in average 3.5 kcal mol -1 stronger than in G 2 /G 3 pairs (of S1 and S3).

Fig. 1 Fig. 2 Fig. 3

 123 Fig. 1Mean values of selected parameters defining conformations of nucleobases in telomere repeat unit

Fig. 6

 6 Fig. 6Distributions of the sum of intermolecular interactions energies in d(GpZpG) central telomeric triad as a function of values of selected parameters for all analyzed system. The color spectrum representing the total percentage of populations obtained from two-dimensional histograms has the same scale for all contour maps

Fig. 7 6 -Figure 1 .

 761 Fig. 7Correlations between shift and slide values characterizing Z 3 /G 4 step for all analyzed oligonucleotide systems. Notation is the same as in Fig.6

Figure 1 Figure 2 .

 12 Figure 1 Click here to download line figure: Fig1.doc

2 Fig. 3 .

 23 Figure 2 Click here to download line figure: Fig2.doc

Figure 4 .

 4 Figure 3 Click here to download line figure: Fig3.doc

Figure 4

 4 Figure 4 Click here to download line figure: Fig4.doc

Figure 5 Figure 6 .

 56 Figure 5 Click here to download line figure: Fig5.doc

Figure 6 Figure 7 .

 67 Figure 6 Click here to download colour figure: Fig6.doc

Figure 7 Figure S2 .Figure S3 .

 7S2S3 Figure 7 Click here to download colour figure: Fig7.doc

Table S1 .

 S1 Statistical characteristics of base pair parameters distributions for all pairs constituting telomere repeat unit in analyzed oligonucleotide chains. For notation refer to Fig.S1

				S1			S2			S3			S4
			M±SD	K	S	M±SD	K	S	M±SD	K	S	M±SD	K	S
	A1		4.06 ± 11.25 0.04	-0.08	4.81 ± 11.10 0.09	-0.14	-1.57 ± 11.74 -0.05	-0.09	-3.72 ± 12.41 0.09	-0.10
	G2 Z3 G4	Buckle	11.82 ± 9.59 3.88 ± 10.27 0.00 0.06 -11.23 ± 9.88 0.04	-0.11 -0.02 0.20	10.80 ± 9.08 7.56 ± 10.28 -0.03 0.10 -7.24 ± 10.83 -0.14	-0.08 0.03 0.12	6.37 ± 9.51 5.40 ± 10.79 0.23 0.12 -1.40 ± 10.85 -0.15	-0.09 -0.29 0.09	4.56 ± 9.50 5.67 ± 10.96 0.02 0.02 -0.11 ± 11.16 0.01	-0.08 -0.10 -0.09
	T5		-13.00 ± 9.86	-0.21	0.05	-10.95 ± 9.97	-0.04	0.06	-5.26 ± 10.01 0.07	0.06	-6.28 ± 9.43	0.13	0.08
	T6		-8.25 ± 10.64 0.21	-0.03	-8.33 ± 10.82 0.23	0.08	0.54 ± 12.39 0.12	-0.04	-0.91 ± 11.52 0.11	0.08
	A1		2.15 ± 6.14	2.28	0.52	2.62 ± 6.38	4.28	0.95	0.87 ± 6.14	1.09	0.15	2.97 ± 6.62	1.90	0.49
	G2 Z3 G4 T5	Opening	1.97 ± 3.29 2.28 ± 3.32 2.24 ± 5.64 0.51 ± 6.38	1.02 3.04 7.96 2.20	0.25 0.60 -2.07 -0.79	2.25 ± 3.33 1.37 ± 3.43 2.73 ± 3.51 1.02 ± 5.15	0.89 5.67 6.31 0.51	0.30 0.78 0.85 0.15	1.19 ± 3.58 0.96 ± 3.39 1.30 ± 3.33 1.91 ± 5.15	1.87 0.39 0.44 0.47	0.09 0.10 0.09 0.05	1.87 ± 3.68 0.20 ± 3.40 1.50 ± 3.58 1.77 ± 5.48	0.86 2.01 0.76 0.63	0.26 -0.07 0.12 0.12
	T6		1.71 ± 6.30	1.67	-0.11	2.33 ± 5.86	3.95	0.80	3.13 ± 5.14	1.34	0.31	3.37 ± 5.12	1.31	0.51
	A1		-5.95 ± 8.13	0.30	-0.08	-5.89 ± 8.49	0.60	-0.06	-9.81 ± 8.50	0.00	-0.10	-10.34 ± 8.73	0.11	-0.12
	G2 Z3 G4 T5	Propeller	-6.54 ± 7.81 -11.76 ± 7.88 -15.91 ± 7.77 -14.28 ± 7.59	0.13 0.27 0.26 0.23	0.06 0.10 0.19 0.10	-8.15 ± 7.51 -11.63 ± 8.25 -15.42 ± 7.74 -15.37 ± 7.66	0.08 0.15 0.19 0.18	0.02 0.17 0.15 0.13	-9.17 ± 8.29 -7.82 ± 9.50 -11.80 ± 8.70 -13.29 ± 8.32	-0.03 0.45 -0.05 -0.08	0.00 0.45 0.21 -0.01	-11.38 ± 8.35 -7.32 ± 9.72 -14.76 ± 7.31 -15.20 ± 7.90	0.00 -0.01 0.06 0.05	0.05 0.13 0.00 0.03
	T6		-8.02 ± 8.57	0.19	0.08	-10.07 ± 8.47	0.10	-0.01	-7.37 ± 10.65 -0.13	0.09	-6.86 ± 9.20	0.17	-0.01
	A1		-0.05 ± 0.29	0.04	0.08	-0.06 ± 0.29	0.08	0.05	-0.06 ± 0.30	0.03	0.13	-0.08 ± 0.30	0.05	0.05
	A1		-0.19 ± 0.32	0.22	-0.03	-0.18 ± 0.32	0.19	-0.07	-0.16 ± 0.31	-0.01	0.01	-0.15 ± 0.32	0.10	-0.02
	Z3 G4	Shear	-0.29 ± 0.32 -0.28 ± 0.32	0.66 0.42	-0.07 -0.09	-0.26 ± 0.33 -0.24 ± 0.32	0.95 1.53	-0.04 -0.22	-0.28 ± 0.34 -0.17 ± 0.33	0.10 0.90	0.13 0.18	-0.27 ± 0.34 -0.13 ± 0.32	0.20 1.16	0.07 0.28
	T5		0.01 ± 0.27	0.06	-0.06	0.01 ± 0.27	-0.02	-0.06	-0.05 ± 0.28	0.20	-0.08	-0.06 ± 0.28	0.11	-0.05
	T6		0.05 ± 0.28	0.01	-0.05	0.05 ± 0.28	0.01	-0.04	-0.07 ± 0.27	0.07	-0.06	-0.07 ± 0.27	-0.01	-0.06
	A1		0.08 ± 0.42	0.15	0.07	0.12 ± 0.42	0.09	0.01	0.09 ± 0.43	0.12	0.09	0.18 ± 0.45	0.01	0.03
	G2 Z3 G4	Stagger	0.27 ± 0.40 0.02 ± 0.41 -0.08 ± 0.37	0.08 0.23 0.36	0.00 0.18 0.10	0.28 ± 0.38 0.09 ± 0.41 -0.04 ± 0.38	0.19 0.38 0.22	0.07 0.23 0.12	0.21 ± 0.38 0.08 ± 0.45 0.12 ± 0.44	0.09 0.22 0.29	0.00 0.11 0.03	0.18 ± 0.37 0.00 ± 0.45 0.16 ± 0.43	0.08 0.29 0.49	0.05 0.16 0.27
	T5		0.11 ± 0.39	0.21	0.08	0.09 ± 0.40	0.07	0.05	0.15 ± 0.44	0.04	0.01	0.12 ± 0.43	0.11	0.09
	T6		-0.06 ± 0.40	0.18	-0.01	-0.10 ± 0.42	0.17	0.05	-0.15 ± 0.44	0.10	-0.08	-0.17 ± 0.43	0.11	0.04
	A1		0.05 ± 0.13	6.52	1.25	0.05 ± 0.14	6.75	1.29	0.02 ± 0.13	2.86	0.75	0.03 ± 0.14	4.21	0.98
	G2 Z3 G4	Stretch	0.00 ± 0.12 -0.05 ± 0.12 -0.03 ± 0.16	0.97 2.17 6.35	0.37 0.49 -1.05	0.00 ± 0.12 -0.05 ± 0.12 -0.02 ± 0.12	1.16 3.13 4.49	0.46 0.56 0.79	-0.03 ± 0.12 -0.06 ± 0.12 -0.04 ± 0.12	1.32 0.65 0.78	0.45 0.17 0.24	-0.01 ± 0.13 -0.08 ± 0.12 -0.03 ± 0.12	1.37 0.77 1.26	0.57 0.06 0.38
	T5		0.06 ± 0.14	2.13	0.12	0.05 ± 0.12	1.07	0.49	0.04 ± 0.12	1.72	0.55	0.04 ± 0.13	2.48	0.76
	T6		0.05 ± 0.14	3.13	0.31	0.05 ± 0.14	14.47	1.61	0.04 ± 0.13	3.82	0.79	0.04 ± 0.12	1.95	0.59
			M -median value, SD -standard deviation, K -kurtosis, S -skewness

Table S2 .

 S2 Statistical characteristics of base step parameters distributions for all pairs constituting telomere repeat unit in analyzed oligonucleotide chains. For notation refer to Fig.S1

				S1			S2			S3			S4
			M±SD	K	S	M±SD	K	S	M±SD	K	S	M±SD	K	S
	A 1 /G 2		3.15 ± 0.30	0.53	0.33	3.17 ± 0.28	0.42	0.28	3.16 ± 0.30	0.31	0.31	3.15 ± 0.29	0.35	0.25
	G 2 /Z 3 Z 3 /G 4	Rise	3.48 ± 0.31 3.64 ± 0.32	0.20 0.10	0.24 0.12	3.39 ± 0.32 3.59 ± 0.31	0.15 0.20	0.22 0.22	3.39 ± 0.32 3.52 ± 0.36	0.38 0.13	0.36 0.21	3.36 ± 0.32 3.50 ± 0.36	0.30 0.60	0.32 0.39
	G 4 /T 5		3.38 ± 0.27	0.71	0.29	3.41 ± 0.28	0.36	0.26	3.47 ± 0.28	0.25	0.31	3.48 ± 0.29	0.80	0.36
	T 5 /T 6		3.20 ± 0.27	0.42	0.34	3.23 ± 0.28	5.42	0.79	3.20 ± 0.29	2.41	0.48	3.20 ± 0.27	0.44	0.33

Tables

Table 1 Values of the most frequently occurring intermolecular interactions IIE MFO [kcal mol -1 ] in central d(G 2 pZ 3 pG 4 ) fragment of telomere repeat unit, where Z stands for G or 8oxoG. The values of standard deviation were estimated for populations comprising 100 snapshots. Symbol S1 represent canonical double strand, S2 designates oxidized one, S3

indicates canonical strand with bounded TRF1 and S4 denotes oxidized B-DNA in complex


D Gillet
email: denis.gillet@epfl.ch

P Scott

R Sutherland
email: ros.sutherland@bristol.ac.uk

STELLAR European Research Network of Excellence in Technology Enhanced Learning

Starting February 1st, 2009, the European Union is supporting a new Network of Excellence in Technology Enhanced Learning called STELLAR (Sustaining Technology Enhanced Learning Large-scale multidisciplinary Research). STELLAR represents the effort of the leading European institutions and projects in Technology-Enhanced Learning (TEL) to unify their diverse community. This Network of Excellence is motivated by the need for European research on TEL to build upon, synergize and extend the valuable work we have started by significantly building capacity in TEL research within Europe, which is required to allow the European Union to achieve its goals via the Bologna Agreement and the execution of the Lisbon Agenda. The Network is executed via a series of integration instruments designed to increase the research capacity of European TEL at all levels. STELLAR's instruments act upon the backbone of an interlocking set of three Grand Research Challenge actions, themed as Connecting Learners, Orchestrating Learning, and Contextualizing Environments & Instrumentalizing Contexts. In this paper, we describe the major educational and technological research challenges tackled in the framework of the STELLAR network of Excellence, its accompanying instruments and its potential to impact higher education, and especially engineering education.

Introduction

In the framework of its Seventh Framework Programme for Research and Technological Development (FP7) in Information and Communication Technologies (ICT), the European Union is currently funding five Large-scale Integrated Projects (IP) seven Small or Medium-scale Focused Research Projects (STREP) and a single Network of Excellence (NoE) for a total of € 51 millions [START_REF] Telelearn | [END_REF]]. These projects are listed in the next subsections. Their main research topics are in accordance with the European priorities described in the yearly-updated ICT Work Programme [ICT 2009]. From an engineering education point of view, advances are especially expected in terms of personalized learning content, environments and methodologies, serious games, social learning, support for creative thinking and innovation processes, competences management, as well as reducing the gaps between professional and educational practices from knowledge and community management perspectives.

LTfLL: Language Technologies for Lifelong Learning. This project will provide semiautomatic services for feedback that require no or very limited tutor-based support and are able to analyze interactions between students and textual output. A knowledge-sharing infrastructure (combining domain ontologies and social tagging) will allow for comparing and exchanging of individual knowledge, leading to new common knowledge. The project will make extensive use of language technologies to situate the learners in their domain.

xDELIA: Boosting Deliberate Practice and Handling Biases through Immersive Cognitive and Emotional Reinforcement

Strategies & Tools. xDELIA will explore novel technologysupported approaches to training and support for non-formal and informal learning tested in the domain of financial decision making. This research will increase the understanding of emotional states in decision-making processes and help handling biases.

Network of Excellence

A single Network of Excellence in Technology Enhanced Learning called STELLAR (Sustaining Technology Enhanced Learning Large-scale multidisciplinary Research) is supported by the European Union. STELLAR (http://www.stellarnet.eu/) represents the effort of the leading European institutions and projects in Technology-Enhanced Learning (TEL) to unify their diverse community. This Network of Excellence is motivated by the need for European research on TEL to build upon, synergize and extend the current achievements by significantly building capacity in TEL research within Europe, which is required to allow the European Union to achieve its goals via the Bologna Agreement [START_REF] Bologna | The Bologna Declaration of the European Space for Higher Education: An Explanation[END_REF]] and the execution of the Lisbon Agenda [Lisbon 2004]. The European TEL agenda has been set for the last 4 years by the Kaleidoscope network (http://www.noe-kaleidoscope.org/pub/) -with a huge strength in pedagogy and scientific excellence, and the Prolearn network (http://www.prolearn-project.org/) -with a complimentary strength in technical and professional excellence. Integrating this excellence and moving on to the higher strategic formation of policy based in leading research is the key challenge for the next stage. STELLAR will move beyond the earlier networks by setting a new and critical foresight agenda for Technology Enhanced Learning. The Network is executed via a series of integration instruments designed to increase the research capacity of European TEL at all levels. STELLAR's instruments act upon the backbone of an interlocking set of three Grand Research Challenge actions, themed as Connecting Learners, Orchestrating Learning, and Contextualizing Environments & Instrumentalizing Contexts.

STELLAR brings together researchers from psychology, education, cognitive science, computer science, human factors, organizational and management science with the overall aim of focusing on advances in technology-enhanced learning that engage learners and teachers in new ways of learning in order to radically change both what it means to learn and what it is possible to learn. The emphasis is on both adapting learning to local cultural and personal situations and transforming learning into permanent and valuable knowledge assets. An important focus is the shift from learning as a focused, individual activity to lifelong learning that is interwoven in daily activities and that involves collaborative knowledge sharing and building. This type of learning has become commonplace, but is not yet sufficiently understood and supported by technology -most current learning management systems are based on individual, formal learning.

The work of STELLAR is based on the view that in today's knowledge society, people are not only confronted with classical transitions from school to university, from university to a company and so on. They are also faced with additional transitions, for example, between companies, between formal institutional learning and informal learning, between, learning for personal growth and learning for work.

The objectives of the 16 partners of the STELLAR Consortium can be summarized as follows:

• To unite the disjoint scientific communities with a virtual and distributed centre of excellence that expands the capacity of each research unit and that fits the "Grand Challenge" for the future of TEL, and that will be sustained though valuable instruments.

• To connect with policy-makers to provide a strategic direction for the integration of TEL excellence to 2012 using a grand challenge framework that explicitly relates to improving learning and educational systems.

• To reduce discipline fragmentation by promoting the integration of key European research teams through collaborative projects, research exchanges, sharing of tools, models, concepts, methods and agendas.

• To reduce community fragmentation by bringing together the key stakeholders in European TEL and stimulate ongoing knowledge exchange between them.

• To look beyond the Network partnership and actively solicit the exchange of views, knowledge, feedback and visions of key stakeholders in TEL: researchers, developers, endusers both in education and in industry.

STELLAR Grand Challenge in Technology Enhanced Learning

Informal learning is a lifelong process in which individuals learn values, skills and knowledge from daily experience and the influences and resources in their environment, from family and neighbors, from work and play, from the market place, the library and the mass media. Workers often report that they have learnt their skills from their co-workers rather than from companyprovided training. By contrast formal learning relates to learning within institutional systems from primary school through to University and also within work-based technical and professional training. The challenge now is to connect formal and informal learning practices and to 'legitimize' informal, situated learning by providing the means for virtual teams, communities and organizations to make visible their impact upon learning.

Connecting learners

On the Web, we can see that self-directed, self-managed and self-maintained communities create successful new forms of collaboration. A wide range of tools is used by these communities for knowledge sharing and building, communication, collaboration and networking. In engineering education, new schemes have to be investigated using Web 2.0 technologies and social networks to enable better interaction between students at various levels, between students and educators, as well as between students and alumni or community members already engaged in professional practices.

Orchestrating learning

Situated, collaborative learning clearly demands a new approach to pedagogy, didactics and assessment. Collaborative competence for using, generating and exchanging knowledge in a peer-to-peer manner is increasingly becoming an integrated part of TEL environments. Different perspectives are to be considered. For example, from the pedagogical perspectives, concrete problems and possible solutions offered by teaching and learning situations mediated by the use of technology are to be considered. While, from the cognitive perspective, the focus should be on what the individual can learn under certain conditions and on the new skills needed. The necessity to personalize and analyze the new key abilities and skills required in the knowledge society has become a critical issue in education. The specific characteristics induced by new technologies in the teaching and learning of disciplinary content are also important issues to be studied.

Within this theme key research questions include: What is the role of the teacher/more knowledgeable other in orchestrating learning? How can we design collaborative learning models with innovative technology in order to scaffold productive collaborative activities? From the point of view of the learner what is the relationship between higher-order skills and learning of a particular knowledge domain?

In engineering education, competences management, serious games, flexible curricula design, and personal coaching from online communities have to be further investigated in order to better align educational offers with individual and economical expectations. The trade-off between selfish university branding and philanthropic resources sharing has also to be revisited.

Contextualizing virtual learning environments and instrumentalizing learning contexts

As learning has become an integrative part of our life, and as it takes place in different learner communities, the tools, resources and systems that are used need to be contextualized. Complementarily, the interplay between formal and informal learning in formal and informal contexts has to be instrumentalized through the use of physical artifacts, mobile devices and the configuration of physical and virtual space, in order to create learning opportunities beyond the traditional institutional boundaries. Technologies for learning must be designed for culturally mediated settings, which include the co-design of technology and pedagogy for situated learning, simulated environments and support for mobility. Traditional classroom learning is founded on an illusion of context stability, by setting up a fixed location with common resources, a single teacher, and an agreed curriculum, which allows a semblance of common ground. But if these are removed, a fundamental challenge is how to form islands of temporarily stable context to enable meaning making from the flow of everyday activity.

Within this theme key research questions include: How can new forms of contextualized learning enable novel experiences for learners and for development of human competences? How to support the mobility of the learner in distributed and multi environment learning settings, like the transition between real and virtual contexts? Which standards are needed to achieve interoperability and reusability of learning resources in this field? How to harmonize the existing learning standards?

In engineering education, the transition from closed learning management systems (LMS) to open personal learning environments (PLE) better supporting, creativity, the versatility of the new learning schemes and the disappearing IT boundaries have to be investigated. In such environments, the support for professional-like knowledge management services, concurrent design facility accesses and opening to the whole Internet of Things (sensors, agents, labs) will be made easier and will better prepare the learners for the real professional life and for lifelong learning.

STELLAR Integration Instruments in Technology Enhanced Learning

The critical intervention strategy of STELLAR involves matching a need for long-term integration via capacity building at a level of TEL research with a set of sustainable "instruments" designed for that level. Instruments have been selected to allow the building of research capacity at a level matching the STELLAR objectives and which can be sustained beyond the funding of the Network. Table 1 illustrates a set of capacity building and integration instruments that can operate to reduce fragmentation on four levels of TEL research.

The first group of STELLAR instruments (Meeting of Minds and Podcasts) aims at providing opportunities for national, European, International, governmental, institutional and industrial policy makers to point out grand challenge research themes and share their prospective vision and strategic direction at the highest level.

The second group of STELLAR instruments (Theme Teams, Incubator Programme and Rendezvous) aims at building a Distributed Center of Excellence unifying the TEL scientific community to refine, tackle, and inform on the TEL grand challenge activity.

The third group of STELLAR instruments (Doctoral Community of Practice and Doctoral Consortia, Doctoral Schools and Doctoral Scholarships) aims at expending the critical mass and the excellence of the TEL doctoral scientific community with future TEL faculty members and entrepreneurs able to work closely with the stakeholders outside the classical academic silos.

The fourth group of STELLAR instruments (Community Channels, with and the Open Archive & Science 2.0 Mash-up Platform) aims at ensuring the cross-fertilization between the policy makers, the scientific community and the stakeholders representing the all knowledge society (from the bottom up). Unlike the previous European networks of excellence in TEL, STELLAR will aim to harmonize the views of the more technological and the more pedagogical communities. STELLAR will provide a new Science 2.0 platform to support research integration that will be sustained beyond its funded work. Its management and leadership will find a consensus in research strategy at each capacity layer that has been missing in the field; and will bring in many new STELLAR 'Associate Partners' into the work via its interconnected instruments.

Concluding Remarks

From teacher oration to social learning through constructivism, from the library of Alexandria to Google, from the native tom-tom drums to today smart phones, advances in education and communication are impressive. However, classroom teaching is still the main activity at all school levels and most citizens are immerged in technologies for their daily activities, but learning. STELLAR is one of the current European tentative to integrate the fragmented research community in Technology Enhanced Learning as a single and strong actor for engaging all stakeholders in closing the many existing social, institutional, political, methodological, and technical gaps for (re)appropriation of education by people (as individuals and communities) in our knowledge society.

  From the above, three initial research themes have been identified to advance technologyenhanced learning (Fig.1): Connecting Learners (Connect), Orchestrating Learning (Orchestrate), and Contextualizing Environments & Instrumentalizing Contexts(Context). These research themes are investigated concurrently, but with a strongest focus first on connecting learners, then on orchestrating learning, and finally on contextualizing environments & instrumentalizing contexts, taking into account the current maturation state of the related research.

Figure 1 :

 1 Figure 1: The initial research themes of the STELLAR Grand Challenge.

  Knowledge sharing and building is facilitated by open and closed forums, Wiki pages and personal or shared blogs. Multimedia material is shared using popular tools such as Flickr and YouTube. Communication takes place using forums, annotation, tagging, chat rooms, instant messaging and video conferences. Collaboration is facilitated by shared media repositories, version management systems and collaborative text editing systems such as Google Docs. Networking portals, such as Facebook and LinkedIn, allow professionals to find, contact and keep in touch with like-minded. In a Web 2.0 world new communities bring together self-directed, self-managed and self-maintained users and, thereby, create successful new forms of collaboration. These new communities are typically open to all learners at any point in their life of learning. Within successful communities, inherent incentive mechanisms to motivate and encourage participation exist. The heart of learning and knowledge consists of people. Replacing the current centralized, static technology-push models with new interactive models that reflect the continuous, social nature of learning requires a radical shift from a focus on knowing what to a focus on knowing how and knowing who.

Within this theme key research questions are: What are key enabling and success factors for learner networks? How can individuals be supported to move between networks and simultaneously participate in several networks? How can learner networks profit from its members' participation in and expertise derived from other networks?

Table 1 .

 1 Increasing Capacity in European TEL Research.

	Capacities	Instruments	Target Groups
	Leadership	Meeting of Minds	Learning executives, policy
			makers, senior
			researchers
		Podcasts	Integrative
	Researcher	Theme Teams	Mid tier research staff
		Incubator Programme	Early stage researchers
		Rendezvous	Integrative
	Doctoral Academy	Doctoral Community of Practice	Mid stage PhD students
		and Doctoral Consortia
		Doctoral Schools and Doctoral	Early stage PhD students
		Scholarships
	Community Level	Community Channels	Stakeholder Network
		Communication infrastructure:	Integrative
		Open Archive & Science 2.0
		Mash-up Platform

Acknowledgements

This paper integrates large parts of the STELLAR project description with contributions from (in alphabetic order) of N. Balacheff, S. Barnes, R.M. Bottino, D. Burgos Solans, U. Cress, P. Dillenbourg, E. Duval, F. Fischer, E. Herder, B. Kieslinger, M. Kravcik, S. Lindstaedt, W. Nejdl, M. Ney, M. Sharples, H. Spada, M. Specht, P. Tchounikine, C. Windrum, and K. Wodzicki.