The C‐terminal domain (CTD) of tumor suppressor protein p53 is an intrinsically disordered region that binds to various partner proteins, where lysine of CTD is acetylated/nonacetylated and histidine neutralized/non‐neutralized. Because of the flexibility of the unbound CTD, a free‐energy landscape (FEL) is a useful quantity for determining its statistical properties. We conducted enhanced conformational sampling of CTD in the unbound state via virtual system coupled multicanonical molecular dynamics, in which the lysine was acetylated or nonacetylated and histidine was charged or neutralized. The fragments were expressed by an all‐atom model and were immersed in an explicit solvent. The acetylation and charge‐neutralization varied FEL greatly, which might be convenient to exert a hub property. The acetylation slightly enhanced alpha‐helix structures that are more compact than sheet/loop conformations. The charge‐neutralization produced hairpins. Additionally, circular dichroism experiments confirmed the computational results. We propose possible binding mechanisms of CTD to partners by investigating FEL. © 2016 The Authors. Journal of Computational Chemistry Published by Wiley Periodicals, Inc. Consequently, IDR is not characterized by a single tertiary structure but by a conformational ensemble. post‐tracommon sequence [380–386] to bind to these four partners. In this sense, this short common binding region has the hub property. Furthermore, the states of histidine 380 (H380) and lysine 382 (K382) in CTD vary depending on the binding partner as follows: when binding to S100B, K382 is acetylated, although H380 can take either a positively charged or neutralized state depending on the pH condition.Tumor suppressor protein p53 consists of four functional domains: the transactivation domain (TAD) residues 1–43], the DNA binding domain (DBD) [100–300], the tetramerization domain (TET) 320–360], and the C‐terminal negative regulatory domain (CTD) [363–393]. Both TAD and CTD are IDRs that interact with many molecules. These domains possess a hub property.–360, and–43, the Actually, CTD and its partner have been studied using molecular simulations: Allen et al. demonstrated trends of fluctuations around CTD binding sites of partners.A multicanonical simulation has been introduced to enhance the sampling of complicated systems.For this study, we examined four CTD fragments in an unbound state  using TTP‐V‐McMD, where K382 was nonacetylated or acetylated and H380 was positively charged or neutralized. The system was treated with an all‐atom model in an explicit solvent. The free‐energy landscapes were computed from the sampled conformations. Results show that the free‐energy landscape of the single‐chain state varies by the K382 acetylation and the H380 neutralization. In fact, the IDR‐partner binding is controlled not only by the finally formed complex structure but also by the conformational distribution of the unbound state. We suggest possible binding mechanisms of CTD to their partner molecules, S100B, Sir2, CBP, and Cyclin A with investigation of the single‐chain free‐energy landscapes.i is computed as fi=−gradiER(r), where ER(r) is a potential energy of the system as a function of the coordinates of the constituent atoms r=, and where Natom is the number of atoms (polypeptide atoms plus solvent atoms) and ri= is the position vector of atom i, that is, the x, y, and z coordinates. An energy distribution obtained from the canonical MD converges to the following canonical distribution asRgas represents the gas constant, nR(ER) denotes the density of states (DOS) of the system at the potential energy ER, T stands for temperature, and Zc=∫nR(ER)exp[−ER/RgasT] dER represents a partition function  at T.In a conventional  MD simulation of a biomolecular system, the force acting on an atom Emc instead of ER, which is defined formally asMcMD uses a modified potential energy i is represented as The force acting on the atom Pmc(ER) from the McMD simulation at T is given simply by replacing ER by Emc in eq. Zmc=∫n(ER)exp[−Emc/RgasT] dER is a partition function at T. This equation guarantees that the energy distribution from a longtime McMD converges to a flat function. Then, the flatness of Pmc(ER) is a measure to judge the convergence of sampling.An energy distribution Etot=ER+Ev, where Ev is the virtual system's potential energy, a function of a virtual system's coordinate v. Accordingly, DOS of the entire system is represented as ntot=nR(ER)nv(Ev), where nv is DOS of the virtual system. Using the arbitrary property of the virtual system, we simply set this DOS as nv(Ev)=1. Then, the multicanonical energy Evmc of the entire system is given as Virtual system coupled McMD (V‐McMD) has been developed to increase the sampling efficiency of McMD, where a “virtual system” is introduced with setting its physical quantities arbitrarily.Zvmc=∫ntotexp[−Evmc/RgasT]dERdEv is a partition function of the entire system at T. As with eqs. Pvmc converges to an even function after a long V‐McMD simulation.From eq. v to reduce the sampling space as v=vi, where i=1,…,nvs and nvs is the number of the virtual states allowed. We introduce the term “virtual state” to specify the state of the discretized virtual system. The ith virtual state corresponds to vi. Accordingly, Ev is discretized as Evi. Therefore, the time‐evolution of the virtual system is done using the Metropolis Monte Carlo scheme.Generally, it is more difficult to achieve convergence of the two‐dimensional distribution than that of one‐dimensional one. The virtual system can be set arbitrarily. Then, we discretize coordinate ER and vi. We set the action of the virtual system on the biomolecular system so that ER is confined in a zone Zi=[ERmin(vi)<ER<ERmax(vi)] when its virtual state is in vi. The method for the confinement is explained in an earlier reportZi overlaps with Zi−1 and Zi+1, although Zi−1 and Zi+1 have no mutual overlap as presented in Figure ER is involved in Zi in the virtual state vi, then, this potential energy is also involved in Zi+1. Therefore, the state vi can transition to vi+1 using the Monte Carlo method. The molecular configuration r is not changed  in this transition. The transition from vi to vi−1 might occur because ER is involved in Zi and Zi−1 if the system is at the open‐circle position of Figure vi and vi±1 as shown below.Now, the system is specified by two quantities: Nint steps. Then, r moves according to MD scheme from the ith to the (i+Nint) th steps without changing the virtual state vi. Consequently, vi might transition to vi−1 or vi+1 at the (i+Nint) th step without changing r. The actual value for the transition interval Nint is given later. Because of eq. Equation H+‐KKLMFKTE‐NH2, (ii) Ace‐KKGQSTSR‐H+‐K‐aK‐LMFKTE‐NH2, (iii) Ace‐KKGQSTSRHKKLMFKTE‐NH2, and (iv) Ace‐KKGQSTSRHK‐aK‐LMFKTE‐NH2, where aK, H+, Ace, and NH2, respectively, represent acetylated lysine, positively charged histidine, acetyl cap, and amine cap. As described in this article, we refer, respectively, to these fragments as (i) NonAc(H+), (ii) Ac(H+), (iii) NonAc, and (iv) Ac. These fragments include in common the binding regions (residues 380–386) to the four partners S100B, Sir2, CBP, and Cyclin A. We designate the regions as “common binding regions” in this study, which are shown as underlined in the sequences above. Remember that Table We constructed four fragment systems of p53 CTD, all of which consist of 17 amino‐acid residues (residues 372–388 in UniProt3) filled with water molecules and ions, where the number of ions is set so that the net charge of the whole system is zero and the ionic concentration is set to a physiological salt one. The conformations of the CTD fragments were the helical structure taken from the S100B‐CTD complex. As explained later, these helical structures were randomized quickly in a preparative canonical MD simulation at a high temperature. Table We put each of the fragments in a periodic box  simulation at 300 K and 1 atm to ascertain the periodic box size for each system. The resultant box sizes are presented in Table nR(ER)) is required to perform multicanonical sampling . Then, as reported in the literature,Evmc for the first V‐McMD simulations , where qi is a distance for a Cα atomic pair and Npair is the number of the pairs. Remember that the number of residues is 17 for all four systems. Consequently, Npair is 136 (=17×(17−1)/2) for all systems. Then, we calculated a variance–covariance matrix A with elements  expressed as Aij=〈qiqj〉−〈qi〉〈qj〉, where brackets are the ensemble average over conformations in Qsum. Diagonalizing this matrix, we obtained Npair eigenvectors  and eigenvalues , where vi and λi are paired satisfying an equation Avi=λivi. The eigenvectors satisfy an orthogonal and normalized relation: vi⋅vj=δij. We presume that the eigenvalues are arranged in descending order as λ1>λ2>….To analyze the conformational ensemble, we generate a two‐dimensional (2D) free‐energy landscape using principal component analysis (PCA) as follows: First, we compute inter‐C v1 and v2 to construct the 2D space (2D PCA space) by setting the coordinate axes to v1 and v2, and to generate a conformational distribution by projecting conformations in QSYS to the 2D PCA space. The coordinate axis vi is designated as a principal component (PC) axis i. We denote the kth conformation in QSYS as q(k) (=). Then, the projection of q(k) to the axis vi is done by a scalar product: xPC i(k)=q(k)⋅vi . The position of q(k) in the 2D PCA space is given by 2D coordinates . Repeating this procedure for all conformations in QSYS, we obtain the distribution of conformations in the 2D PCA space: PSYS, where conformation of energy ER contributes to the distribution with the weight PcSYS. Finally, the potential of mean force (PMF) is computed as FSYS≡−RgasTln[PSYS] with spatial patterns that are called the free‐energy landscape (FEL) in this study. From comparison of the spatial patterns of FEL among the four systems, we can discuss the difference of the architecture of FEL among the systems.We use i to the whole standard deviation is expressed asThe ratio of contribution from the PC component i becomes, the greater the contribution: SD1>SD2>…. Therefore, the 2D PCA space constructed by v1 and v2 is suitable to overview the conformational distribution. The contribution ratio by the PC components 1 and 2 is given simply as rc1+rc2.In PCA, the larger the eigenvalue assigned to the PC components 2+: 1038.8.CTD fragment with nonacetyl lysine (K382) was synthesized by the 9‐fluorenylmethoxycarbonyl (Fmoc) method at a 0.05 mmol scale using a peptide synthesizer . After the completion of the peptide chain assembly, the obtained resin was treated with TFA containing 2.5% triisopropylsilane and 2.5% distilled water for 2 h. The crude peptide was concentrated by a nitrogen stream, precipitated by ether and purified by the reversed‐phase HPLC to obtain nonacetyl peptide. ESI mass, found: 1038.7, calcd. for [M + 2H]O‐benzotriazol‐1‐yl‐N,N,N′,N′‐tetramethyluronium hexafluorophosphate (HBTU)/N,N‐diisopropylethylamine (DIEA). After the completion of the chain elongation, the solution of tetrakis(triphenylphosphine)palladium(0), phenylsilane, acetic anhydride in dichloromethane was added to the resin for 45 min to acetylate the side chain amino group of Lys selectively 2+: 1059.8.The synthesis of CTD fragment with acetyl‐lysine (aK382) was also performed by the synthesizer, except that LysThe nonacetyl and acetyl CTD fragments were dissolved in Milli‐Q water at 200 μM. The peptide solutions were diluted to 40 μM with the desired concentration of trifluoroethanol (TFE) for circular dichroism (CD) measurements. To detect the helix propensity, we varied TFE concentrations of 0–50%. It is noteworthy that the TFE concentrations in this article are volume per volume percentages. The peptide solutions also contained 25 mM sodium phosphate (pH 7.0 without TFE) and 0.1 M sodium chloride. Because pH is 7.0, the histidine state is neutral. Consequently, the fragments treated in this CD measurement are also designated as NonAc and Ac.2 dmol−1). All spectra were estimated iteratively 16 times. Furthermore, this procedure was repeated three times to compute the average and standard deviation of the spectrum.We performed far‐UV CD measurements of the peptide solutions. Far‐UV CD spectra were recorded on a spectropolarimeter  at 25°C using a quartz cuvette with 1‐mm path length. The spectra were expressed as the mean residue ellipticity and [θ] (deg cmQSYS for the four CTD systems (SYS = NonAc(H+), Ac(H+), NonAc, and Ac) in the unbound state. Figure The V‐McMD production run generated conformational ensemble rc1 and rc2 from Qsum were, respectively, 41.4% and 18.8%. Then, the contributions from the PC 1 and 2 axes (rc1+rc2) were 60.2%. In Figure GkSYS, where superscript SYS specifies the computed system and the subscript k is a label assigned to the clusters. Figure G1SYS is assigned to the cluster of the global minimum PMF, which corresponds to a nearly complete helix . In other words, the CTD fragments are disordered.Figure G1SYS) but also partially helical ones. The tertiary structures are shown in G2NonAc(H+), G6NonAc(H+), G7NonAc(H+), and G8NonAc(H+) in Figure G2Ac(H+) and G4Ac(H+) in Figure G2NonAc, G5NonAc, G6NonAc, and G7NonAc in Figure G4Ac, G5Ac, and G7Ac in Figure G4NonAc(H+) in Figure G3Ac(H+) in Figure G3NonAc, G4NonAc, and G5NonAc in Figure G3Ac in Figure All FELs involved not only the complete‐helix cluster (+) and Ac systems was excluded from the computation of radius of gyration for the strict comparison because the side‐chain tip does not exist in NonAc(H+) and NonAc. Table Rg), which demonstrates that the acetylation induces a compact hydrophobic core. We discuss this point further by viewing the tertiary structures of the CTD fragments below.Although all the CTD fragments exhibited conformational diversity, the FEL shape is considerably different as shown in Figure QSYS using the DSSP program.We computed the secondary‐structure propensity of each residue in G1Ac(H+), where the aK382 and L383 side‐chains form a hydrophobic contact in the helix. Similarly, a conformation taken from G1Ac shows that a hydrophobic contact is formed between aK382 and S378 . Unless K382 is acetylated, a repulsive force acts between these two lysine residues. Furthermore, in this conformation, the oxygen atom in the aK382 side‐chain and the nitrogen atom in the K381 side‐chain interact electro‐statistically. Figure G5Ac, where a hydrophobic core is formed by side‐chain tips of aK382 and M384 and side‐chain stem of R379. Consequently, the acetylation induces the hydrophobic core formation in helices. Then, the radius of gyration becomes small. If Lys382 is nonacetyl form, then repulsion interactions take place between Lys382 and the other positively charged residues in the helix because each sequence of the fragments includes six or seven positively charged residues. Consequently, acetyl‐lysine stabilizes helical structures more than nonacetyl lysine does.Figure 378 Fig. b. FigureG4NonAc, where H380 and K381 form a hydrogen bond. Repulsive interaction acts between H380 and K381 if H380 is positively charged. Similarly, Figure G4NonAc, where the side‐chain of H380 and main‐chain of K382 form a hydrogen bond. If H380 is positively charged, then this distorted hairpin becomes unstable because a repulsive interaction between the positively charged H380 and K382 might break the hydrogen bond. Furthermore, a repulsive interaction between the positively charged H380 and R379 might also destabilize the β‐hairpin structure. Consequently, the charge neutralization of H380 is necessary for stabilizing the hairpins in Figures G3Ac, where H380 and T377 form a hydrogen bond. These tertiary structures exemplify that the charge neutralized H380 serves the hydrogen bonds to stabilize turns in the hairpins.As described above, comparison between Figures +) system might have the narrowest structural varieties among the four systems. To elucidate this feature quantitatively, we computed the standard deviation σSYS of the conformational distribution for Cα atoms for each system asQSYS weighted at 300 K. The resultant values are: σNonAc(H+)=37.6 Å, σAc(H+)=30.6 Å, σNonAc=36.5 Å, and σAc=36.9 Å. These values of structural fluctuations are consistent with the radii of gyration in Table +) system has the smallest standard deviation. This reduction of the broadening results from the acetylation of K382. One might expect the Ac system to have a narrow distribution because K382 is also acetylated in this system. However, as shown above, the neutralization of H380 induces hairpins, which prevents reduction of the distribution.Data shown in Figure +) fragment, and binds to the S100B molecule with adoption of a helical conformation. It is particularly interesting that, in these studies, the ensembles involved a helical fraction, which is consistent with our result for QNonAc(H+). In contrast, recent CD measurement of two 32‐residue p53 CTD fragments, which involve our NonAc and Ac segments, showed that the conformations of the CTD fragments are randomized.QNonAc and QAc contain a helical fraction and that the helical content of QAc is larger than that of QNonAc. This apparent inconsistency between the computational results and the CD‐experimental observation should be analyzed. We note, however, that the fragment length for the CD experiment is considerably longer than ours.A temperature replica exchange MD simulationTo link the computation and experiment, we conducted CD experiments of the NonAc and Ac fragments of 17 residues long. TFE enhances formation of secondary structures, especially of helix.+) binds to S100B with adopting helical conformation and Ac(H+) does not adopt a helical conformation to bind to a partner (Table QNonAc(H+) contains a smaller helix content than QAc(H+). We discuss possible binding mechanisms of the CTD fragments to their partner molecules in the next section.As described earlier, NonAc has the broadest distribution of the four ensembles. Many clusters are found in FEL ; partial helices are S2NonAc(H+), S3NonAc(H+), S4NonAc(H+), S6NonAc(H+), S10NonAc(H+), S11NonAc(H+), and S12NonAc(H+); β hairpins are S7NonAc(H+) and S8NonAc(H+); and random‐coiled structures are S5NonAc(H+) and S9NonAc(H+). Inter‐cluster transitions can take place readily because free‐energy barriers among the clusters are low.Apparently, the ensemble FEL Fig. a. The teFEL Fig. a: The coQAc(H+), no β hairpin cluster exists, although three partial helix clusters (S1Ac(H+), S3Ac(H+), and S4Ac(H+)) do  and three β hairpin clusters  exist  exist, but no hairpin cluster exists . The closeness of the two conformations results from the similarity of the Cα atomic pair distances. Remember that the PCA space is generated based on the inter‐Cα atomic distances. Therefore, sCBP can convert to sS100B by minor rearrangements of the Cα atomic pair distances.The bound forms of the common binding region in the experimentally determined complex structures are also assigned to Figure +) to S100B is the population selection because sS100B is located at a fringe of the most stable cluster S1NonAc(H+)  prepares the bound form in advance. Furthermore, because the free‐energy barriers from the other clusters to S1NonAc(H+) are low as described above, the bound form is recruited quickly when the bound form is exhausted to bind to S100B. The helical content of QNonAc(H+) is smaller than that of QAc(H+). Figure QNonAc(H+) contains helical conformations sufficient for use for binding to S100B even if the helical content of QNonAc(H+) is less than that of QAc(H+).Figure H+) Fig. a. In othS1Ac can transition readily to the conformation sCBP  binding to S100B if the population‐selection mechanism occurs.The population‐selection mechanism might take place when the Ac fragment binds to CBP, where the conformations in the most stable cluster CBP Fig. d. Howeve+) fragment to Sir2, the conformation sSir2 is located at a high free‐energy site in Figure sSir2, by which an encounter complex is formed. Then, the intermolecular interactions bring the fragment to the genuine complex structure. Therefore, we presume that the binding mechanism of this fragment belongs to the induced folding mechanism.For binding of the Ac(H+) fragment binding to S100B. The structure sCyclin is located at a site with free energy of about 3 kcal/mol in Figure QNonAc is a conformation close to sCyclin. Consequently, the binding mechanism of this fragment to Cyclin A might belong to the population selection. However, the main fraction of QNonAc is far from sCyclin. Therefore, different conformations might be used to bind to Cyclin A. Consequently, the induced folding mechanism is also possible.The NonAc fragment can bind to either S100B or Cyclin A . We proposed possible binding mechanisms from the free‐energy landscape of the unbound state. To judge whether the proposed mechanisms are correct or not, sampling of systems consisting of CTD and their partners is necessary for the next stage of research. However, as discussed in the Supporting InformationClick here for additional data file.


In this work, the fabrication process flow of ZnO/NiO heterojunction device on a PET substrate, optical properties, physical properties and photoresponses presented   Specifications TableValue of the data•Photograph of the prepared NiO/ZnO/ITO/PET devices for the transparent feature and scissor cut design.•Absorption coefficient and Tauc plots of ZnO and NiO samples.•Surface morphology of large area grown ZnO, NiO and ITO films on the PET substrate.•Transient ultraviolet photoresponses and its bias dependents of NiO/ZnO/ITO/PET device.122.1Commercial PET was used as the flexible substrate and was cleaned according to Ref. NiO film was formed using the DC reactive sputtering. Conditions for preparing NiO sample are as follows.2.2τr) and fall time (τf) of the photoresponse as shown in τr and τf values.Optical properties of ZnO and NiO films fabricated on the PET substrate were measured using the UV-visible spectrophotometer. The measured transmittance and reflectance spectra were used to estimate the absorption coefficient as well described in


The bi-enzymatic system of cytochrome P450  and cytochrome P450 reductase  mediate the redox metabolism of diverse indigenous and xenobiotic molecules in various cellular and organ systems, using oxygen and NADPH. Curiously, when a 1∶1 ratio is seen to be optimal for metabolism, the ubiquitous CYP:CPR distribution ratio is 10 to 100∶1 or higher. Further, the NADPH equivalents consumed in these in vitro or in situ assemblies usually far exceeded the amount of substrate metabolized. We aimed to find the rationale to explain for these two oddities. We report here that CPR is capable of activating molecular oxygen on its own merit, generating diffusible reduced oxygen species (DROS). Also, in the first instance for a flavoprotein, CPR is shown to deplete peroxide via diffusible radical mediated process, thereby leading to the formation of water (but without significant evolution of oxygen). We also quantitatively demonstrate that the rate of oxygen activation and peroxide depletion by CPR accounts for the major reactivity in the CYP+CPR mixture. We show unambiguously that CPR is able to regulate the concentration of diffusible reduced oxygen species in the reaction milieu. These findings point out that CPR mediated processes are bound to be energetically ‘wasteful’ and potentially ‘hazardous’ owing to the unavoidable nature of the CPR to generate and deplete DROS. Hence, we can understand that CPR is distributed at low densities in cells. Some of the activities that were primarily attributed to the heme-center of CYP are now established to be a facet of the flavins of CPR. The current approach of modeling drugs to minimize “uncoupling” on the basis of erstwhile hypothesis stands questionable, considering the ideas brought forth in this work. One would find it difficult to imagine that an iron-peroxo intermediate purported to be formed en route to Compound I normally, could not form  the catalytic intermediate required for peroxide depletion and water formation in the CYP + peroxide setup.It seems highly unlikely that the wide variety of CYPs' highly electrophilic Compound I radical would “opt” for the relatively slower two-electron and two-proton transfers to form water. If the hitherto held hypothesis were operative, then CYPs' Compound I should have been easily isolated, like that of the peroxidases Discovery of CPR's novel peroxide depletion function (and rediscovery of CPR's oxygen activation) now affords us an effective alternate hypothesis, where CPR is the more pivotal role-player in DROS production and regulation.d electrons would be capable of activating the ground state oxygen. This was because “normal” diatomic (molecular) oxygen is in triplet state and its reaction with a singlet (which flavin is in!) to finally give a singlet product is essentially a spin forbidden process. In the 1970s, Steven Aust's research had originally shown that cytochrome c reductase  generated DROS in reaction milieu It was held for long that only catalysts employing transition metal elements with unpaired a slow reaction with triplet oxygen (E°'∼−160 mV), giving superoxide &a fast reaction with singlet oxygen (first electron transfer E°'∼+640 mV), giving peroxide.The auto-dismutation of superoxide, originally produced by CPR, could also account for a significant part of peroxide formed in CYP+CPR reaction milieu. Sometimes, a higher peroxide concentration in the presence of CYPs could be due to- (1) the ability of the heme metal atom to facilitate the peroxide generation by the reducible metal center serving as a superoxide dismutase & (2) a lesser probable route could be that the metal center could catalyze the endothermic process of singlet oxygen generation, which could react faster with CPR giving peroxide. The presence of singlet oxygen in such reaction mixtures have been documented earlier 2/H2O2m7(E =  +270 mV) versus that of oxidized/reduced flavin m(E = −209 mV), the overall 2-electron oxidation reaction, Flred H2+O2 → Flox+H2O2, is essentially irreversible.” However, we have seen here that the flavoenzyme CPR could also indulge in the very same peroxide depleting activity as its more illustrious heme-counterparts. In the heme-based classical peroxidase/catalase enzyme systems, the activation with peroxide leads to a two electron jump into an oxidized enzyme complex, which could undergo either a ‘two-steps single-electron’ or a ‘single-step two-electrons’ pathway, for the enzyme to recycle. In CPR, the activation by peroxide is highly unlikely to yield a two-electron reduced CPR, owing to the reason quoted by Massey. But it could lead to a one-electron reduced semiquinone enzyme and a diffusible radical by-product. This reaction (H2O2 → O2−+2H++e−) has a redox potential of −890 mV. This low value makes the reaction quite favorable with the redox ranges of the two flavins of CPR, which has standard potentials ranging from −110 mV to −365 mV for its various one electron reactions In 1994, Vincent Massey had said The probable reactions that could occur in the milieu are shown in Oxygen evolution was much lower for a corresponding amount of peroxide depleted; which underplays the catalase type equation (vii).Incorporation of indigo sulfonate (a reactant probe for ozone) gave only a marginal activity in the initial time frame; which underplays equation (viii).CPR did not show a Michaelis-Menten hyperbolic relation with peroxide as substrate.Radical intermediates are involved in the reaction.Exact stoichiometry could not be established even when reaction compositions/environments were varied only on a very low note.Therefore, we could infer that the outcome of the overall CPR catalyzed reaction can be seen as a combination of the basic enzymatic and several non-enzymatic reactions within the milieu . A commercial hydrogen peroxide stock solution (∼8.8 M) was diluted and calibrated around several millimolar ranges to determine the absolute concentration by titanium oxalate complexation method 2 value >0.99 for OD at 560 nm for the colored product. NADPH was determined by spectrophotometry at 340 nm using an extinction coefficient of 6220 M−1cm−1. Peroxide is taken as the index of DROS in milieu because the thermodynamic and equilibrium considerations favor peroxide over superoxide by many folds Purified human CYP2C9 and CPR were lab preparations 2 values greater than 0.997. Rates for NADPH consumption had less than 5% SEM. For the NADH reaction, the initial concentrations of components, when present, were- (NADH)  = 150 µM. (CPR)  = 25 or 100 or 500 nM, (CYP2C9)  = 100 nM, (diclofenac)  = 100 µM.A control of 100 nM CYP with 200 µM NADPH gave no observable peroxide at 15 min and depleted NADPH at the rate of ∼0.2 µM/min, which is only slightly higher than the auto-degradation rate of NADPH under these conditions. Depletion rate of NADPH  was determined from the slope of linear fit of 340 nm OD at 16, 26 and 36 minutes. The linear fit had R2O2)  = 500 µM taken in 100 mM phosphate buffer , which also contained DLPC/lecithin vesicles ∼500 µg/ml. L-ascorbic acid palmitate (AAP), when present, was at a concentration of 1 mM. 50 µl of the reaction sample was added to 600 µl of peroxoquant reagent and incubated for 30 minutes and the samples were read at 560 nm.The reaction mixture had an initial concentration of (CPR)  = 250 nM and  of catalase reaction. These spread in readings were extrapolated to dissolved oxygen concentrations, using standard values accounting for the temperature and altitude compensation. Temperature and pH were 29°C and 7.1 respectively. A relatively high initial concentration of 10 mM peroxide was necessary because the oxygen evolved with micromolar concentrations were too low to be measured by the electrode. Concentration of enzymes was ∼50 nM. The X-axis is broken to show initial and later time oxygen evolution profile.


Heparin dodecasaccharides were found to be the minimal chain length required to efficiently bind CXCL12 . These oligosaccharides also significantly inhibited CXCL12-induced migration of CXCR4-expressing LMD MDA-MB 231 breast cancer cells. In addition, heparin dodecasaccharides were found to have less anticoagulant activity than either a smaller quantity of polymeric heparin or a similar amount of the low molecular weight heparin pharmaceutical product, Tinzaparin. When given subcutaneously in a SCID mouse model of human breast cancer, heparin dodecasaccharides had no effect on the number of lung metastases, but did however inhibit (P<0.05) tumour growth (lesion area) compared to control groups. In contrast, polymeric heparin significantly inhibited both the number (P<0.001) and area of metastases, suggesting a differing mechanism for the action of polymeric and heparin-derived oligosaccharides in the inhibition of tumour growth and metastases.Expression of the chemokine receptor CXCR4 allows breast cancer cells to migrate towards specific metastatic target sites which constitutively express CXCL12. In this study, we determined whether this interaction could be disrupted using short-chain length heparin oligosaccharides. Radioligand competition binding assays were performed using a range of heparin oligosaccharides to compete with polymeric heparin or heparan sulphate binding to I Breast cancer is the most common malignancy in women and a major cause of morbidity and mortality in many parts of the world. The main cause of these deaths is metastatic spread of the disease  components of cell surface or extracellular matrix proteoglycan molecules. Binding occurs between anionic, sulphated GAG residues and basic amino-acid domains within the chemokine primary structure  was the shortest length oligosaccharide able to significantly compete for CXCL12 binding against heparan sulphate in vitro. Dodecasaccharides exerted less anticoagulation activity than either polymeric heparin or the low molecular weight heparin Tinzaparin. Heparin significantly inhibited the formation of lung metastases in vivo. However, dp12 did not reduce the number of lung metastases, but did reduce the area of individual lesions.The current study was designed to determine the minimal chain length of a heparin oligosaccharide that could significantly inhibit CXCL12-mediated breast cancer metastasis, while abrogating the potential anticoagulation problems associated with the use of heparin as an antimetastatic treatment. A series of M NH4HCO3 at a flow rate of 9 ml h−1. Tinzaparin is produced through the enzymatic degradation of polymeric heparin using the enzyme heparinase I . The samples were centrifuged, the supernatant removed and the pellets left to dry overnight at room temperature before being weighed. This procedure typically yielded around 10 mg of oligosaccharide and was repeated as necessary to obtain sufficient quantities of oligosaccharide material. Fractions pooled to obtain dp24 and dp26 oligosaccharides are approximations as no clear peak corresponding to these size pools was directly detected within the size-exclusion profile of Tinzaparin.Three 2 ml vials of the low molecular weight heparin product Tinzaparin , were pooled and applied to two Bio-Gel P10 columns  linked in series and eluted with 0. 25 rinase I . Fractioμl of 0.25 mg ml−1. Poly-L-lysine hydrobromide  in PBS. Following washing with 0.01 M PBS, the wells were incubated with 250 μl of heparin  or heparan sulphate (Sigma) at a concentration of 50 μg ml−1 in PBS for 2 h at 37°C; negative control wells contained only PBS. The wells were then washed twice in binding buffer . Soluble competitor was added in a volume of 150 μl per well at desired concentrations followed by the addition of 5 nM unlabelled CXCL12  with a tracer concentration of 100 pM125I-CXCL12 (PerkinElmer) in a volume of 50 μl in binding buffer. The plates were incubated at room temperature for 3 h with shaking and the solution was removed from each well. After washing three times with 200 μl of binding buffer the bound radioactivity was measured in a scintillation counter .Scintillant-coated 96-well plates  were treated overnight with 200 μm pore diameter filter membrane  towards 12.5 nM CXCL12. 200 000 cells were placed in the upper chamber and the lower chamber was loaded with medium containing 12.5 nM CXCL12 and varying concentrations of dp12. The assay was incubated for 8 h at 37°C before removal of excess cells and medium from both chambers. The upper surface of each filter was gently swabbed before removal and fixation in methanol at 0°C for 1 h. The filters were then stained with 0.1% Mayer's haematoxylin (Sigma) for 10 min, followed by a 10 min wash in Scott's tap water . Finally, the filters were sequentially dehydrated with 50, 75, 90 and 100% ethanol, air-dried and mounted. Migrant cells on each filter were identified by high-power microscopy (× 40) and counted in nine randomly selected fields; the operator was blinded to the treatments.LMD MDA-MB 231 cells were assayed for their ability to migrate through an 8 μg ml−1), Tinzaparin , and dp12 oligosaccharides (0–34 μg ml−1) were examined in plasma. Whole blood mixed with an appropriate heparinoid was added to citrated blood collection tubes and immediately analysed. Briefly, the plasma was centrifuged at 3000 g for 20 min and transferred to fresh tubes. The anti-Xa level of each sample was then assessed using the Biophen heparin 6 kit  and the COAG-A-MATE MTX II  coagulation analyser. Each batch of samples was analysed in conjunction with Biophen low molecular weight heparin (LMWH) control plasma and Biophen heparin control plasma (Hyphen Biomed) as standards.The anti-coagulation effects (anti-Xa activity) of heparin  approval. All reasonable efforts were made to minimise the suffering likely to be caused and the number of animals to be used. All animals were inspected daily by a qualified technician and weighed twice weekly to ensure thorough assessment of the health of all animals involved.−1 day−1 (600 IU kg−1 day−1) of heparin in PBS every 12 h, once a day with dp12 in PBS (4.0 mg kg−1 day−1) or with PBS alone. Seven mice were treated in each group. Four hours after the first PBS/heparinoid injection, mice were injected with 2 × 105 LMD MDA-MB 231 breast carcinoma cells into the tail vein. Lungs were collected on day 28 and fixed for histopathology. Twenty sections were cut from each mouse lung at intervals of 50 μm in depth.CB-17 severe combined immunodeficient (SCID) mice  were injected subcutaneously with 3.3 mg kgLung sections were assessed blindly at × 10 magnification (Leica DMR) and the area of metastasis was calculated using Leica Q-Win software. Both the number and area of the metastatic lesions were assessed blindly by two separate investigators (Paul Mellor and James Harvey).μm thick) of mouse lung were fixed in acetone for 10 min at room temperature and then air dried. Following a wash for 5 min in tris-buffered saline (TBS), endogenous peroxidase activity was blocked by incubating for a further 5 min in 0.6% H2O2 in methanol. Sections were then rinsed in distilled water and washed for a further 5 min in TBS; antigen retrieval was not necessary. Endogenous avidin and biotin were blocked . Non-specific binding of the secondary antibody was prevented by incubation of the sections with 20% rabbit's serum in TBS for 1 h at 4 °C. Sections were then incubated overnight at 4°C with primary antibody anti-CXCL12  at a concentration of 1/20 in 20% rabbit's serum/TBS solution. Two control sections per tumour were also incubated in every run of immunohistochemistry; these sections were incubated overnight with no primary antibody . Following washing in TBS, sections were incubated for 1 h at room temperature with a rabbit-anti-mouse biotinylated secondary antibody . After a further wash step in TBS, sections were incubated for 30 min in the dark with a Streptavidin ABC complex  at room temperature. A final wash step was performed before incubation with NiDAB  for visualisation.Serial sections (5 Light counterstaining with Haematoxylin (Sigma) was performed on all specimens before mounting; this enabled visualisation of the tissue architecture without masking of the NiDAB staining. Sections unstained with antibody were dual stained with Haematoxylin and Eosin (H&E) to demonstrate the tissue architecture in serial sections. Slides were dehydrated through a series of alcohol baths , cleared in xylene and mounted using dibutylphthalate xylene. Sections were compared with controls, where lungs were stained with an isotype control instead of an anti-CXCL12 primary antibody.Total RNA was isolated from murine lung tissue using Trizol  and cDNA was generated using oligo-dT primers with the Superscript II RNase reverse transcription system . Real-time TaqMan PCR was performed according to Applied Biosystems protocols. All samples were loaded in triplicate wells and fluorescence emission detected by the ABI Prism 7000 . Primers and probes for CXCR4 were purchased from Applied Biosystems. All results were normalised to the expression of endogenous glyceraldehydes-3-phosphate dehydrogenase mRNA and expressed as a relative increase or decrease in expression compared to PBS controls.in vivo metastasis model were expressed as means±s.e.m. Statistical analysis was performed using an unpaired two-tailed Student's t-test or using a one-way analysis of variance when multiple data sets are being compared; P<0.05 was considered significant.Data values for radioligand binding, chemotaxis assays and 125I-CXCL12. Initially, polymeric heparin and the oligosaccharides dp26–dp8 were used at a constant concentration of 7 μg ml−1 , determined from the IC50 of the dp26 oligosaccharides (results not shown), to compete CXCL12 from a solid phase of heparin. Polymeric heparin and the oligosaccharides dp26–dp16 all competed significantly . As the length of the oligosaccharides decreased so did the level of competition seen. Unexpectedly, the oligosaccharides dp10 and 8 caused a small increase in binding of CXCL12 to solid-phase heparin.Radioligand binding assays were performed to determine the potential of varying concentrations of soluble heparin and heparin oligosaccharides to compete with polymeric heparin or heparan sulphate to bind P<0.001;  with solμg ml−1; P<0.001), whereas the dp10 and 8 oligosaccharides were required at much higher concentrations (50 μg ml−1 and in excess of 100 μg ml−1 respectively) to achieve similar levels of competition , but higher concentration of the dp10 oligosaccharides were required to exert the same level of competition  of the dp12 oligosaccharide .Following definition of dp12 as the shortest oligosaccharide to significantly bind CXCL12, this size pool was assayed for its effect on breast cancer cells P<0.001; . In the μg ml−1), Tinzaparin, a LMWH with a similar average molecular weight to dp12 (3.4 μg ml−1) and dp12  (−1) that correspond to what is clinically known as a therapeutic dose, with therapeutic anti-Xa levels known to be in the range 0.5–1.0 anti-Xa IU ml−1. As there was no known therapeutic dose of dp12 the same concentration (weight) as that used for Tinzaparin was used. Both heparin and Tinzaparin-treated blood showed anti-Xa IU ml−1 values within the therapeutic range (0.64 and 0.71 anti-Xa IU ml−1) respectively. However, the anti-Xa value for dp12-treated blood was 0.29 IU ml−1, which is below therapeutic values. As the concentration of dp12 increased so did the anti-Xa IU ml−1 values, indicating that dp12 is an effective anticoagulant at high concentrations  . The dostrations .5) were administered intravenously to three groups of seven SCID mice on day 1. The mice were independently treated from days 0 to 28 with either subcutaneous doses of heparin  given twice daily, 0.1 ml PBS given once daily, or dp12 (4.0 mg or 1.2 μmol kg−1 day−1) also given once daily. This dose of heparin is known to produce therapeutic anticoagulation in a clinical setting. Treatment of SCID mice with a single daily dose of Tinzaparin at 175 IU kg−1 day−1 (1.8 mg or 0.6 μmol kg−1 day−1) produces a level of anticoagulation, which is just within the therapeutic range (0.51 anti-Xa IU ml−1); data not shown. Given that Tinzaparin has an anticoagulant activity, which is approximately 2.5 times greater than that of dp12, a sub-anticoagulant dose of dp12 was chosen to dissociate anticoagulant activity from any putative anticancer effect. The half-life of LMWHs is two to four times that of unfractionated heparin; for this reason, typical clinical regimes involve administration of LMWHs once a day and heparin twice a day (LMD MDA-MB 231 cells (2 × 10ce a day . As bothP<0.05) than those found in the PBS control mice (P<0.0001) compared to the PBS-treated group (n=7), therefore giving a large s.e.m. for the average size of the lesions identified.A total of 20 sections from each mouse lung were assessed for both the presence and the area of each metastatic lesion . Dp12 harol mice . Heparined group . No signed group . Only thP>0.05), decrease in the expression of CXCR4 mRNA in comparison with PBS-treated animals  of defined-length GAG was used to compete with heparin for chemokine binding, suggesting that oligosaccharides containing 14 or fewer monosaccharide units have a reduced potential to bind CXCL12. However, heparinoids below dp14 did show some potential to bind CXCL12 when present at greatly increased concentrations. The most likely explanation for this observation is a reduction in the number of the chemokine binding domains as the chain length of the heparinoid molecule is reduced.The potential of defined-length heparin oligosaccharides to compete with solid-phase heparin to bind CXCL12 was found to decrease as the chain length decreased. Soluble heparin and the oligosaccharides ranging from dp26 down to 16 in length all competed significantly with solid-phase heparin, while oligosaccharides below 16 monomeric sugar units in length failed to compete. In all cases, a similar amount (7 CXCL12 exists as a monomer in solution but forms dimers when interacting with heparin or heparan sulphate (in vitro.Cell surface GAGs play important roles in the presentation of chemokines to their specific receptors; these include protection of chemokine molecules from degradation and the maintenance of the stable concentration gradients through the tissues which are required for vectorial cell migration . HoweverThe dp12 oligosaccharide had significantly less anticoagulant activity than either heparin or Tinzaparin. As a consequence, higher doses of dp12 were required to produce a similar level of anti-Xa activity in blood. This can be explained in terms of the relative length of the molecules. It is known that specific pentasaccharide sequences are required for AT binding; these motifs are present in about a third of heparin molecules (in vivo breast cancer model was used to determine the ability of heparin to prevent the metastasis of human breast cancer cells from peripheral blood to the lung tissue of SCID mice; importantly, heparin powerfully reduced the number of metastatic lesions observed in the lung while treatment with the dp12 oligosaccharides had no effect on the number of lesions. Further experiments were performed to investigate the possible role of chemokine antagonism in this model. Specifically, it was found that treatment with heparin significantly reduced the expression of CXCL12 on the endothelial and epithelial surfaces in the lung, while dp12 only produced a small decrease in CXCL12 expression. This suggests that heparin therapy reduces the potential of CXCL12 to ‘capture’ cancer cells within breast tissues. However, no reduction in the ratio between mRNA sequences encoding human CXCR4 and GAPDH was observed in any treatment group suggesting that the absence of immobilised CXCL12 in heparin-treated animals did not select for non-CXCR4-expressing variants of the cancer.An in vivo (in vivo to inhibit the development of new vasculature (The tumours in the dp12-treated group were significantly smaller in size than those in the PBS-treated control group. Tumour growth is dependent not only on growth factors produced by the cancer cells themselves but also those present in the surrounding microenvironment. Heparinoids are very anionic molecules that can bind and modulate the function of a wide range of cytokines and adhesion molecules (However, it is important to stress that the action of heparin in inhibiting breast cancer metastasis is potentially multifactorial, and not solely the result of the inhibition of CXCL12/heparan sulphate interactions. Studies examining the role of soluble heparin in breast cancer have demonstrated a number of mechanisms vital for breast cancer metastasis that can be disrupted by heparin. These include the inhibition of the adhesion and migration of breast cancer cells by heparin-mediated inhibition of selectin binding and proteases These processes are influenced to a greater degree by heparin than LMWHs and may potentially explain the differences seen between heparin and dp12 . In addiin vitro, but only inhibit the growth and not the number of lung metastases which form in vivo. However, anticoagulant concentrations of heparin can significantly inhibit both in vitro migration and the metastatic spread of breast cancer cells in vivo. As viable cancer cells are liberated into the vasculature during surgical resection, heparin may offer a potential antimetastatic therapy. Although clinically dp12 would not be applicable as an antimetastatic therapy it may prove useful in inhibiting the growth of established tumours, offering a potential treatment following diagnosis and before surgery. The oligosaccharide may also prove beneficial to patients with non-operable tumours by extending survival times.In conclusion, these data show that relatively non-anticoagulant concentrations of a soluble dp12 oligosaccharide can significantly inhibit the migration of breast cancer cells


Escherichia coli aer regulatory region contains a single promoter that is recognized by RNA polymerase containing the flagellar sigma factor, σ28. Expression from this promoter is dependent on direct activation by the cyclic AMP receptor protein, which binds to a target centred 49.5 base pairs upstream from the transcript start. Activator-dependent transcription from the aer promoter was reconstituted in vitro, and a tethered inorganic nuclease was used to find the position of the C-terminal domains of the RNA polymerase α subunits in transcriptionally competent open complexes. We report that the ternary activator–RNA polymerase–aer promoter open complex is organized differently from complexes at previously characterized promoters. Among other E. coli promoters recognized by RNA polymerase containing σ28, only the trg promoter is activated directly by the cyclic AMP receptor protein. The organization of the different promoter elements and the activator binding site at the trg promoter is the same as at the aer promoter, suggesting a common activation mechanism.The  Escherichia coli and other enteric bacteria  is a global transcription factor, which plays a central role in the control of metabolism in bacteria . At Clasbacteria .E. coli K-12 genome. One of these was located in the regulatory region of the aer gene, which encodes an aerotaxis sensor protein that controls movement of bacterial cells in response to the availability of oxygen and other electron acceptors in the environment  drives the transcription of genes with ‘housekeeping’ functions, while a number of alternative σ factors direct transcription of particular sets of genes in response to environmental signals or stresses, or function to control development . Here, we report the first investigation into the direct regulation by CRP of transcription by RNA polymerase containing σ28 (Eσ28). We show that aer is transcribed from a single σ28-dependent promoter that is activated by CRP binding at a location different from any previously characterized CRP-activated promoter. We also show that CRP directly activates transcription from a second σ28-dependent promoter that has a similar organization.Most studies on transcription activation by CRP have been concerned with promoters recognized by RNA holoenzyme containing σ28 on expression of aer, we cloned a DNA fragment covering the aer gene regulatory region , except that the increase in promoter activity occurs later in growth, once the culture begins to enter stationary phase. This correlates with a delayed increase in σ28 protein levels in this background . In the absence of CRP, promoter activity remains low throughout the growth cycle, showing that the requirement for CRP for expression from the aer regulatory region is independent of the effect of CRP on σ28 levels. We conclude that CRP must function directly at the aer regulatory region, and this is consistent with the previous observation that introducing mutations into the DNA target for CRP upstream of aer prevents CRP-dependent activation of the aer200::lacZ fusion , although some transcript is generated in the absence of CRP as the RNA polymerase concentration is increased (lanes 7–12). At even higher concentrations of Eσ28, transcription becomes completely independent of CRP (data not shown). The aer transcript generated by Eσ28 is not detected in reactions using Eσ70 , KMnO4-reactive bands appeared from positions −10 to +4, indicative of promoter melting around the −10 element of the σ28-dependent promoter highlighted in 28 is independent of CRP in vitro at the high RNA polymerase concentrations used in these reactions. Incubation with Eσ70 did not result in promoter melting around the aer transcript start site, either in the presence or in the absence of CRP  results in a twofold decrease in promoter activity, but, while the residual promoter activity is dependent on CRP, it is independent of σ28. The most likely explanation for this is that, here, CRP is activating transcription from an alternative cryptic σ28-independent promoter. For example, in the aer211 fragment, a 6 bp sequence, 5′-TAAAGA-3′, is located 32 bp downstream of the DNA site for CRP and this may well generate a weak Class II CRP-dependent promoter served by Eσ70 .Mutational analysis showed that CRP-dependent activation of the aer200::aer promoter. Both the deletion, which moved the DNA site for CRP to position −44.5 (aer226), and the insertion, which moved the DNA site for CRP to position −54.5 (aer227), resulted in a reduction in promoter activity in the CRP+ FliA+ strain to the basal level observed in the absence of CRP  and αCTD. Previous work showed that CRP-dependent activation at the aer promoter also requires AR1  on a single cysteine residue at position 302 in the αCTDs . Transcriptionally competent open complexes were formed using the end-labelled aer200 promoter fragment, purified CRP and FeBABE-tagged Eσ28, and DNA cleavage by FeBABE was triggered. Analysis of the pattern of DNA cleavage by gel electrophoresis reveals the location of the αCTDs at the aer promoter. Note that, in this assay, in most cases, a single Fe-BABE-labelled αCTD will give rise to cleavages in two adjacent minor grooves, as a wave of hydroxyl radicals generated from the Fe-BABE impinges on the target DNA (Activation by CRP at both Class I and Class II σ28 (lane 3), DNA cleavage on the template strand of the aer promoter is enhanced around positions −72 and −64 upstream of the DNA site for CRP, and around positions −38 and −30 downstream of the CRP site. This indicates that the αCTDs can contact the DNA both upstream and downstream of the bound CRP dimer. In the presence of Eσ28, but in the absence of CRP (lane 2), the pattern of DNA cleavage is similar to the background detected in the absence of any protein (lane 1). This suggests that the two αCTDs are positioned at their targets on the DNA only in the presence of CRP. Interestingly, the spacing between the centre of the DNA site for CRP and the downstream FeBABE-induced DNA cleavage at the aer promoter is identical to that observed by 70 . In contrast, alignment of the DNA sequences of the trg and aer regulatory regions revealed that the spacing between the predicted DNA sites for CRP and the different elements of the two σ28-dependent promoters is identical . As a result of leaky expression, genes cloned under the control of the T7 promoter in pET21a are expressed even in strains that do not produce T7 RNA polymerase, including M182  were constructed by megaprimer PCR. In a first-round PCR reaction, a megaprimer was synthesized from pSR/aer200 as a template, using a mutagenic primer carrying the desired mutation, and a flanking primer , and are averages from at least three independent experiments.β-Galactosidase levels in cells carrying promoter::Transcript start sites were mapped by primer extension as described in E. coli core RNA polymerase was purchased from Epicentre Technologies . His-tagged RNA polymerase α subunits containing a single cysteine residue at position 302 were prepared and labelled with FeBABE as described by 28 and σ70 proteins were prepared from BL21(DE3) cells carrying the overexpression plasmid pKXH100, as described by 28 and Eσ70 holoenyzmes were made by mixing wild type or FeBABE-labelled core RNA polymerase with an equimolar amount of σ28 or σ70, and incubating for 20 min at room temperature.Purified CRP protein was donated by David Grainger , and wild type in vitro transcription assays, as described by 2, 1 mM dithiothreitol, 100 mM KCl, 100 μg ml−1 bovine serum albumin, 200 μM GTP, 200 μM ATP, 200 μM CTP, 10 μM UTP and 5 μCi [α32P]-UTP. Where indicated, CRP was included at 100 nM and cAMP at 0.2 mM. Reactions were started by adding Eσ28 or Eσ70. RNA products were analysed on a denaturing 5.5% polyacrylamide gel and visualized using a Fuji phosphor screen and Bio-Rad Molecular Imager FX.Caesium chloride preparations of pSR carrying the aer200 promoter fragment served as a template for multiple-round 4 and FeBABE footprinting experiments were performed on PstI-HindIII fragments prepared from caesium chloride preparations of pSR carrying aer200. Fragments were labelled at the HindIII end with [γ-32P]-ATP using polynucleotide kinase. KMnO4 footprints were performed following the protocol of 2, 50 mM potassium glutamate, 1 mM DTT and 0.5 mg ml−1 BSA. KMnO4 footprinting reactions contained 0.2 mM cAMP, 100 nM CRP and 50 nM Eσ28 or Eσ70, as required. FeBABE footprinting reactions contained 0.2 mM cAMP, 100 nM CRP and 200 nM FeBABE-labelled Eσ28. The products of KMnO4 and FeBABE footprinting reactions were analysed on denaturing 6% polyacrylamide sequencing gels, calibrated with Maxam-Gilbert ‘G + A’ sequencing reactions.KMnO32P] ATP and polynucleotide kinase. EMSA reactions were carried out as described by The EMSA experiments were performed using EcoRI-HindIII fragments prepared from pSR derivatives, and end-labelled using [γ-


Telomere length is a predictor for a number of common age related diseases and is a heritable trait.−6; and rs7235755, p = 5.5×10−6) on chromosome 18q12.2 in the same region as the VPS34/PIKC3C gene, which has been directly implicated in the pathway controlling telomere length variation in yeast.To identify new loci associated with mean leukocyte telomere length we conducted a genome wide association study of 314 075 single nucleotide polymorphisms (SNPs) and validated the results in a second cohort (n for both cohorts combined  =  2790). We identified two novel associated variants (rs2162440, p = 2.6×10These results provide new insights into the pathways regulating telomere homeostasis in humans. Telomeres are nucleoprotein structures capping and protecting the ends of chromosomes. Because of the “end replication problem”,3et alBICD1 gene. To date, none of these findings have been replicated, possibly due to difficulties in measuring this trait in a large number of samples and due to lack of high correlation between the methods used to measure telomere length.There is wide inter-individual variability in telomere length at birth and at subsequent ages. Both twin studies and intra-familial correlation analysis have identified a genetic influence from 40% to 80%) on telomere length variation.0% to 80%Genome-wide association (GWA) analysis is a powerful tool for unlocking the genetic basis of complex traits and has recently provided novel insights into the genetic architecture of many common diseases and traits.We conducted a two stages GWA study on 2790 individuals from the UK Adult Twin Register , in whicLeucocyte telomere length (LTL) was derived by using Southern blot analysis in duplicate to measure the mean terminal restriction fragment.Genomic DNA was subjected to SNP genotyping via the Infinium assay , using three fully compatible BeadChip microarrays , according to the manufacturer’s protocols.−4, and 725 SNPs with minor allele frequencies <1%. We also removed subjects where genotyping failed for >2% of SNPs. We retained for the analysis 98.7% (314 075) of all available SNPs. Statistical analysis was carried out with MERLIN (version 1.1.2)12We excluded 733 SNPs that had a low call rate (⩽90%), 2704 SNPs that had Hardy–Weinberg p values <10−6). We also identified 28 SNPs with a p value of ⩽10−4 and 316 SNPs with a p value of ⩽10−3. We visually inspected all the signal intensity plots of these SNPs and excluded the markers that had been miscalled (11.3%).In the discovery sample (n = 1625) the strongest association was recorded for rs7374458 on chromosome 3 (5.20×10−3 that were ≈100 Kb from other associated SNPs (p⩽1.0×10−2). Following these criteria, we identified 15 associated loci including a total of 41 SNPs with the p values for the lead SNPs ranging from 5.20×10−6 to 9.7×10−4  using Sequenom iPLEX  technology. Because the replication cohort included both males and females, LTL values were adjusted for both gender and age. After adjustment the trait was again normally distributed. To control for multiple testing, we used an SNP spectral decomposition method proposed by NyholtSince the discovery cohort included only females, we also performed a gender specific analysis on the replication population in order to test if the genetic variants may be associated with telomere lengths only for females. The result showed that for both SNPs the direction of the trend was consistent between genders in the replication cohort (rs2162440: −100 (44) base pairs (bp) for females and −140 (70) bp for males; rs7235755 −94 (42) bp for females and −138 (71) bp for males) and between females of the two cohorts (rs2162440: −104 (29) bp for female in discovery and −100 (44) bp for females in replication; rs7235755 −104 (28) bp for female in discovery and −94 (42) bp for females in replication). Although borderline , p values were statistically significant for both SNPs in both genders in the replication cohort .−6 (rs2162440) and 5.50×10−6 (rs7235755). Our analysis also indicated that the G alleles of both SNPs were associated with shorter telomeres (−106 (22) bp for rs2162440 and −103 (22) bp for rs7235755), extrapolating to an approximate 5 years of telomere erosion based on estimates of loss with age.The joint analysis of genotyped data from the two cohorts yielded combined p values of 2.60×10Although our results are unlikely to be artefacts because the identified SNPs were replicated in two independent cohorts, we do believe that our power for identifying association was reduced by the known limitations of the measurement technique.BRUNOL4, NM_020180) and VPS34  genes. The identified SNPs (or another variant present in the LD block) might be influencing the expression of either transcript through long range control, as has been demonstrated for other genes.BRUNOL4 and 4.3 Mb away from VPS34. BRUNOL4 is a member of the CELF/Bruno-like family, which encodes proteins bearing highly conserved RNA recognition motif. RNA binding proteins are important elements that control normal cell functions, regulating events such as RNA processing, mRNA transport, stability and translation. VPS34 is a component of the phosphoinositide (PI) 3 kinase family which includes proteins that regulate several aspect of the cell physiology.VPS34 yeast orthologue (Vps34) has been directly involved in the pathway which regulates telomere length variation.18According to NCBI build 36, the associated polymorphisms map to a 48 Kb LD block within a gene desert, between the Bruno-like 4 (In conclusion, we provide evidence from two independent cohorts for a new locus on chromosome 18q12.2 associated with short telomere length in humans. These data provide new insights into the likely pathways and mechanisms regulating telomere length in humans.


NF-κB is a prototypic stress-responsive transcription factor that acts within a complex regulatory network. The signaling dynamics of endogenous NF-κB in single cells remain poorly understood. To examine real time dynamics in living cells, we monitored NF-κB activities at multiple timescales using GFP-p65 knock-in mouse embryonic fibroblasts. Oscillations in NF-κB were sustained in most cells, with several cycles of transient nuclear translocation after TNF-α stimulation. Mathematical modeling suggests that NF-κB oscillations are selected over other non-oscillatory dynamics by fine-tuning the relative strengths of feedback loops like IκBα. The ability of NF-κB to scan and interact with the genome in vivo remained remarkably constant from early to late cycles, as observed by fluorescence recovery after photobleaching (FRAP). Perturbation of long-term NF-κB oscillations interfered with its short-term interaction with chromatin and balanced transcriptional output, as predicted by the mathematical model. We propose that negative feedback loops do not simply terminate signaling, but rather promote oscillations of NF-κB in the nucleus, and these oscillations are functionally advantageous. In mammalian cells, various signals such as hormones, cytokines, and cell-cell interfaces elicit changes in gene expression mediated by inducible transcription factors. Many latent transcription factors also induce expression of genes that provide feedback loops upon their signaling pathways. The negative feedback genes are generally thought to functionally terminate the signaling action of the transcription factor. These feedback loops also create the potential for the transcription factor activity to oscillate between active and inactive states over hours Nfkbia gene which codes for IκBα. The re-synthesis of IκBα acts as strong negative feedback, causing the inactivation of NF-κB and its re-localization to the cytoplasm. However, previous biochemical and genetic investigations have indicated that the propensity to oscillate, driven by induction of IκBα, is counteracted by mechanisms that modulate and dampen the oscillation NF-κB is a classical example of a transcription factor subject to negative feedback loops. NF-κB regulates numerous cell signaling processes and its activity is controlled in part by the level of nuclear translocation. In resting cells, the predominant dimer p65∶p50 exists mostly as a cytoplasmic complex bound to its inhibitor IκB proteins. Numerous upstream signals induce degradation of the IκB proteins following phosphorylation by the IκB kinase complex (IKK). This release from latency in the cytoplasm allows active NF-κB to translocate into the nucleus and activate expression of target genes, including several feedback genes Here, we report single-cell observations that show the prevalence of sustained oscillations in endogenous NF-κB localization. Using instant perturbations of the system and real-time monitoring, we show that the network dynamics can be altered as predicted by a mathematical model. We also observe a previously unsuspected nucleus-to-cytoplasm transport of active NF-κB. Through computational modeling and experimental evaluation, we explore the signaling roles of distinct NF-κB dynamics that occur in multiple timescales. Finally, we find that the fast interaction of NF-κB with chromatin remains the same from first to later cycles of nuclear localization, and the recurrent cycles contribute productively to the transcriptional output of the system.To observe real time dynamics, we monitored the localization of NF-κB in single living cells, which are the basic unit for pathway operations and are the definitive context for probing pathway dynamics unambiguously. We used fibroblasts derived from knock-in mice where the endogenous p65 gene was replaced by GFP-p65  [12]. ThTo gain systems-level insight on the parameters that influence the observed NF-κB behavior in different cells after TNF-α stimulation, we explored variability and robustness properties of a minimal mathematical model that captures some essential pathway components see  [13]. PaOur computer simulations revealed several possible patterns of response for the NF-κB network after activation . DistincOur multi-parameter variation analysis also shows a pattern with non-oscillating, one-peak responses (cluster 2), which is similar to the experimentally observed profile of non-oscillating cells  with its own kinetics, and cell-to-cell heterogeneity is invariably significant. Similarly, genetically disrupted cells settle into altered steady states long before the experiments by compensatory mechanisms, complicating model-based comparison of mutant behavior to that of wild-type. Thus, we chose to use nearly instantaneous inhibitors that interfere with key steps in the NF-κB network, using Leptomycin B (LMB), an inhibitor of nuclear protein export, and Cycloheximide (CHX), an inhibitor of protein synthesis.5-fold at t = 0; An example decomposition plot for TNF-α alone is shown in LMB blocks nuclear export of p65 into the cytoplasm, an enabling mechanism behind NF-κB oscillation. When cells are simultaneously treated with TNF-α and LMB, a single pulse of free nuclear NF-κB is expected to be followed by the formation of the NF-κB∶IκBα complex in the nucleus. This is also predicted by model simulations , and also all nuclear import rates were reduced to zero when nuclear NF-κB level is maximal (at t = 90 min). If there was no export, blockade of nuclear import at this time would not have any effect on the nuclear p65 level. The simulation results showed gradual efflux of p65 into the cytoplasm that is appreciable within minutes for a wide range of parameter values . This waWe then experimentally validated this somewhat neglected possibility by fluorescence loss in photobleaching (FLIP) . When ceThe extent of FLIP in the presence of IκBα  was slightly faster than that observed in cells treated with TNF-α and CHX, which suggests that p65 export is indeed enhanced by IκBα . As a neTaken together these results demonstrate that the nucleocytoplasmic shuttling of NF-κB is bi-directional and does not require oscillations or IκBα. This implies that NF-κB shuttles continuously, sampling the full intracellular environment, with or without concurrent oscillations in the level of nuclear p65.We predicted that the functional consequences of CHX and LMB perturbations would be extremely different on p65 activity, despite the similar effect on its nuclear localization. LMB would render NF-κB bound to IκBα, thus unable to bind chromatin, whereas CHX would maintain its ability to bind chromatin. To address this experimentally and compare perturbed conditions with the unperturbed behavior, we first evaluated features of the distinct p65 cycles in the nucleus using an assay with the temporal precision to resolve the asynchronous cycles in single cells. We note that a functional transcription factor scans the genome by binding and dissociating to chromatin repeatedly, and by diffusing within the nucleus We used a non-invasive optimized line FRAP technique In contrast, when oscillations were blocked by either LMB or CHX, intranuclear p65 mobility measured by line FRAP was progressively altered with time. LMB induced faster mobility of GFP-p65, which is consistent with unproductive interaction of p65 with the chromatin  18∼24 hrs before imaging and the medium was changed to have 2% FBS a few hours before imaging. Reagents were from the following sources: TNF-α , Cycloheximide , Leptomycin B . Because both LMB and CHX affect numerous cellular factors that are not specific to NF-κB, we attempted to minimize pleiotropic effects and cytotoxicity by eliminating pre-incubation and by using low drug concentrations (5 nM LMB and 2.5 µg/ml CHX).2. Time lapse images were acquired at 5 min intervals with a single frame per stage position using 1% of excitation at 489 nm, 63X Plan-Apochromat oil objective (1.4 NA), 0.5 zoom, and a 495 nm long pass filter. Focus was automatically corrected before each time point by a customized the Zeiss Multitime autofocus macro. TNF-α was treated at the second time point by a gentle injection through a tubing. Acquired LSM files were exported to 16 bit TIFF files for further analysis.Live cell imaging of GFP-p65 knock-in MEF was performed using Zeiss LSM 5 Live with an incubation system where cells were stably maintained at 37°C with humidified 5% CO16 bit image files were segmented, quantified, and tracked by a custom written MATLAB program that utilizes its Image Processing toolbox. In each time lapse field, we excluded from analysis the cells that moved out of view, died, overlapped with other cells, or divided during the time course. Cell boundaries were identified first automatically by intensity-thresholding then by manual correction. Nuclear boundaries were all manually drawn. The same cells were tracked from the previous time point by finding cells with the closest cell centroids. Background subtracted intensities were used to calculate the ratio of mean nuclear intensity to mean total cellular intensity.Each time course profile was analyzed by Fast Fourier Transform and the corresponding periodogram was generated to reveal any periodic components. A time course was considered to represent an oscillating p65 if the periodogram had one global maximum or if there was a sharp local maximum at a period less than 5 hours whose value was greater than 70% of the global max. The latter case corresponded to time course profiles where there was a global profile as well as an oscillating component. We are also exploring an alternative method borrowed from audio signal processing, to look for finer temporal components. Some examples are shown in The delay differential equations (DDE) model in 2). The Leica XT-FRAP wizard was used to program the acquisition protocol. Acquisition settings: 16-bit images, 512×512 pixels (59.52 µm×59.52 µm), line size 512×1 pixel, pixel size 116.48 nm, zoom 4x, pinhole Airy 1, 25 Hz with mono-directional scanning and line average = 1; scanning time 40 msec/line. Laser power: For imaging, 5% of the 488 nm single laser line; for bleaching, 100% of the 488, 496, and 514 nm laser lines. Length of bleached line segment: 5 µm. Time course: 20 line scans for pre-bleaching (0.8 s), 5 for bleaching (0.2 s), and 500 for post bleaching (20 s).Line FRAP experiments were performed on a Leica SP5 AOBS confocal equipped with a ArgonPlus Ar-ion laser , a HCX PL Apo CS 63x oil immersion objective/1.4 NA, and a humidified thermostatic chamber  using a region in the nucleus as bleaching reference and one on the glass slide (out of the cytoplasm) for the background see . ExperimStatistical significance of separation between different condition groups was assessed by a permutation test based on a 1000 random reshuffling of group labels. P values were calculated from the number of random incidents with mean T statistic greater than the observed. The analysis was implemented in R (‘compareTwoGrowthCurves’ from the library statmod).FLIP experiments have been performed with the same set-up as for FRAP.The Leica FRAP wizard was used to program the acquisition protocol. Acquisition settings: 16-bit images, 512×512 pixels (79.4 µm×79.4 µm), pixel size 155 nm, zoom 3x, pinhole Airy 1, 400 Hz in Fly-mode; scanning time 1.317 sec/frame. Laser power: for imaging, 5% of the 488 nm single laser line; for bleaching, 100% of the 488 nm laser line. Bleached region: a round area of 8 µm in diameter in the cytoplasm. Time course: 5 scans for pre-bleaching and 60 scans of bleaching every 10 seconds. Cells were stimulated with TNF-α alone or in co-treatment with LMB or CHX as described above and FLIP acquisitions started 25 minutes later. Data were singly normalized as described by Phair et al. (2004) using a second unbleached cell as “bleaching during imaging” reference and one on the glass slide (out of the cytoplasm) for the background. Experiments were repeated three times.p65 knock-in fibroblasts were cultured in 35 mm dishes and treated as described in Figure S1Time lapse imaging data quantification. (A) The example shows a time lapse image with boundaries identified by our image segmentation. (B) The quantification of the complete time lapse data for the same field of cells in (A). Cells were treated with 10 ng/ml TNF-α and imaged overnight. The number on top right denotes the cell label from (A). The time lapse movie is provided as (0.58 MB DOC)Click here for additional data file.Figure S2Mathematical modeling and simulations for the core NF-κB network dynamics. (A) The 9-variable, 18-parameter delay differential equations model was adapted from [Sung et al. 2004 Mol. Pharm.] with the addition of a term that represents the post-stimulus attenuation of IKK activity (‘neg IKK’ for the equation for IKK). For simulating TNF-α treatment, we used stimulus input k(t) which was fixed for our parameter variations to produce a step-like activation of IKK at t = 0. We note here some simplifications implemented in the model. We combined certain multiple biochemical reactions into single terms. The parameter neg represents the rate of inactivation of IKK by multiple mechanisms. The model also simplifies the induced synthesis of IκBα which in fact comprises of transcription, RNA processing and transport, translation and protein folding. All of these are lumped into a single synthesis term with a delay. Similarly, the catalyzed degradation of IκBα initiated by IKK (rate r1) represents a complex series of reactions including phosphorylation at two serines, ubiquitination, and degradation. Such signal induced degradation is distinguished from the basal degradation of free or NF-κB bound IκBα that occur with rate dg1 or dg2. Notation for the molecule concentrations as model variables: NF = NF-κB I = IκBα IKK = the active IKK complex A colon between two variables indicates a complex of the corresponding proteins, and variables with subscript ‘n’ denote nuclear species. (B) The table describes all the molecular processes that are represented in the model and the reference values for the corresponding parameters. All values are from [Hoffmann et al 2002 Science] except for the following: tau, neg, and s are parameters for simplifying terms that represent multiple biochemical processes. Therefore, their values were estimated arbitrarily so that the TNF-α response profile from these reference parameter values is qualitatively similar to that in [Hoffmann et al 2002 Science]. In fact, we did not assume that any of the parameter values were appropriate for our cells, and instead relied on extensive parameter variations to look for all possible behaviors of the dynamical system from different parameter values. (C) The example below illustrates typical plots from model simulations for TNF-α induced p65 oscillations. The graphs show the time courses of free, IκBα-complexed, and total p65 in the nucleus, as predicted by the model. The red curves indicate the three p65 species in the nucleus from a single run of simulation. (D) The time courses of free active IKK for the eight clusters are shown below. Clusters 1 through 8 correspond to those in (3.14 MB DOC)Click here for additional data file.Figure S3Magnitudes of first peak response. The magnitude of first peak response was defined as the maximum nuclear p65 level within 1 hr after TNF-α treatment for each time lapse profile. N is the number of cells for each condition. Error bars are S.D. *** indicates a significant difference from TNF-α with p = 10-4 (Student t test).(0.10 MB DOC)Click here for additional data file.Figure S4Control FLIP experiments for the nucleus-to-cytoplasm flux of free NF-κB in (0.38 MB DOC)Click here for additional data file.Figure S5Application of an alternative signal processing algorithm to representative GFP-p65 time course profiles. We tested an analysis method that is widely used in audio signal processing, against various oscillatory profiles of GFP-p65 time courses in response to TNF-α. First, each time course data y (t)  was adjusted by baseline subtraction and normalization: y_signal (t) = (y(t) - min y(t))/(max y(t) - min y(t)). Zeros were padded to extend toward negative t values. The y_signal (t) was further processed by Hanning window to produce y_hanning (t). Fast Fourier Transform was then applied to y_hanning and its Bode plot in log scales was generated. The plots in the left column show y_signal (t) as solid curves and y_hanning (t) as dashed. The corresponding Bode plots are shown in the right column. Although this analysis may show fine spectral features of the temporal profiles, it seems less obvious how to classify patterns from Bode plots. Nonetheless, at least the dominant periodic components of p65 seem to be revealed by either this method or our original analysis method in (0.26 MB DOC)Click here for additional data file.Video S1Live cell imaging of NF-κB dynamics after TNF-α activation. The time lapse data corresponds to (11.04 MB MOV)Click here for additional data file.Video S2A single cell time series of both the images and the quantified nuclear p65 level. The cell on the left of the initial image frame is followed after TNF-α stimulation.(11.76 MB MOV)Click here for additional data file.


The 2009 influenza A(H1N1) pandemic has generated thousands of articles and news items. However, finding relevant scientific articles in such rapidly developing health crises is a major challenge which, in turn, can affect decision-makers' ability to utilise up-to-date findings and ultimately shape public health interventions. This study set out to show the impact that the inconsistent naming of the pandemic can have on retrieving relevant scientific articles in PubMed/MEDLINE.We first formulated a PubMed search algorithm covering different names of the influenza pandemic and simulated the results that it would have retrieved from weekly searches for relevant new records during the first 10 weeks of the pandemic. To assess the impact of failing to include every term in this search, we then conducted the same searches but omitted in turn “h1n1,” “swine,” “influenza” and “flu” from the search string, and compared the results to those for the full string.On average, our core search string identified 44.3 potentially relevant new records at the end of each week. Of these, we determined that an average of 27.8 records were relevant. When we excluded one term from the string, the percentage of records missed out of the total number of relevant records averaged 18.7% for omitting “h1n1,” 13.6% for “swine,” 17.5% for “influenza,” and 20.6% for “flu.”Due to inconsistent naming, while searching for scientific material about rapidly evolving situations such as the influenza A(H1N1) pandemic, there is a risk that one will miss relevant articles. To address this problem, the international scientific community should agree on nomenclature and the specific name to be used earlier, and the National Library of Medicine in the US could index potentially relevant materials faster and allow publishers to add alert tags to such materials. The 2009 pandemic of influenza A(H1N1), first known as swine flu, was initially detected in humans in Mexico in April. Within weeks it had reached the United States and then Europe. As of 10 January 2010, more than 208 countries and overseas territories or communities have reported laboratory confirmed cases. While most cases have been mild, there have been at least 13,500 deaths In addition to being featured in tens of thousands of newspaper and magazine articles, the influenza A(H1N1) pandemic has been well covered in the scientific literature. For the scientific community to react swiftly and effectively to such a pandemic, it is crucial that all the relevant research and communications published in scientific journals reach experts as quickly as possible.Colleagues, peers and professional networks are of great importance in meeting the experts' information needs http://www.pubmed.gov) is a likely first choice for people seeking to monitor medical research on a given topic. PubMed also includes a database of additional material, much of it newly published. Articles that have not yet been added to the MEDLINE database, where everything is indexed with medical subject headings (MeSH), appear in PubMed as soon as publishers provide citation data for published tables of contents, including titles, authors, and in most cases abstracts MEDLINE covers over 16 million records of articles published in more than 5,000 international journals in the fields of biomedicine and health. As a complementary interface for searching MEDLINE, PubMed . They consist of sets of descriptors arranged in a hierarchical structure that permits searching at various levels of specificity not detect them.Articles on the influenza A(H1N1) pandemic are likely to be assigned the MeSH “Influenza A Virus, H1N1 Subtype” as well as “Disease outbreaks.” Combining these two MeSH is expected to generate a highly precise search result. However, if MeSH have not yet been assigned to the articles of interest, PubMed queries that only use MeSH will In a pandemic situation, where rapid retrieval of newly published material is essential, identifying articles that have not yet been assigned MeSH poses a significant challenge. It can only be done by searching for the terms used in the citation data provided by the publisher and added to the PubMed database. It is critical to choose terms that maximise the retrieval of records that may be relevant for the work of health experts and policy-makers.Since the first outbreak in April 2009, a variety of names have been associated with this virus, e.g. swine flu, swine-origin influenza virus (S-OIV), Mexico flu, novel influenza virus, influenza A/H1N1, influenza A(H1N1), H1N1 2009, H1N1/09 and, most recently, pandemic (H1N1) 2009, the term adopted by the World Health Organization (WHO). This variety characterises articles published in scientific journals as well as the popular press.In this study, we demonstrate the pitfalls of inconsistent naming and the effects it can have for health experts on obtaining relevant scientific information, and we will suggest several strategies to help address the problem.In order to assess the impact of failing to include relevant terms when conducting a search in PubMed, we first developed a search algorithm to simulate searches carried out every week during the first 10 weeks of the pandemic. Such a simulation was necessary to exclude records that would be returned by a search today due to subsequent MeSH indexing, but would not be returned by searches when the pandemic was first unfolding. The searches used here were carried out at the end of August 2009.Based on our own monitoring of scientific articles and news media, as well as consultations with influenza experts, primarily from WHO, we identified five terms as relevant and likely to be present in the records of articles on the pandemic: “h1n1,” “swine,” “pandemic,” “epidemic,” and “outbreak.” We considered the last four relevant only if the term “influenza” or “flu” was also present in the database's record of the article. The following steps accordingly made up our core PubMed search string:#A h1n1#B swine OR pandemic OR epidemic OR outbreak#C influenza OR flu#D #A OR (#B AND #C)Because of PubMed's automatic term-mapping feature, certain terms are “translated” when searches are carried out h1n1“h1n1”[all]swine“Swine”[mh] OR “swine”[all] OR “sus scrofa”[mh] OR  OR “sus scrofa”[all]pandemic“Disease Outbreaks”[mh] OR  OR “disease outbreaks”[all] OR “pandemic”[all]epidemic“Disease Outbreaks”[mh] OR  OR “disease outbreaks”[all] OR “epidemic”[all]outbreak“Disease Outbreaks”[mh] OR  OR “disease outbreaks”[all] OR “outbreak”[all]influenza“Influenza, Human”[mh] OR  OR “human influenza”[all] OR “influenza”[all]flu“Influenza, Human”[mh] OR  OR “human influenza”[all] OR “flu”[all]This translation ensures that records indexed under the included MeSH “Disease Outbreaks,” “Influenza, Human” or “Swine” are identified even if they do not contain any of the actual terms used in the search algorithm. It is worth noting that the MeSH “Influenza, Human,” which both “influenza” and “flu” translate into, does not include the MeSH “Influenza A Virus, H1N1 Subtype” as these MeSH are placed in two separate branches of the vocabulary's hierarchical structure. However, including the term “h1n1”[all] or “influenza”[all] will return records assigned the latter MeSH because it contains both “h1n1” and “influenza.”Using the core search string, we built additional strings to simulate a search carried out at the end of a given week for relevant materials that had been added to PubMed during that week. We used weeks that ran from Monday to Sunday, beginning with the last week of April 2009, meaning that Week 1 was 27 April–3 May 2009, Week 2 was 4–10 May 2009, and so on.To find all the records available at the end of Week 1, we first used PubMed to identify all records returned by the core search string:#1 h1n1 OR ((swine OR pandemic OR epidemic OR outbreak) AND (influenza OR flu))We then limited the records to the ones entered into PubMed during Week 1:#2 2009/04/27:2009/05/03[edat]#3 #1 AND #2For the next part of the algorithm, we looked for records with a MeSH date see  between #4 2009/04/27:2009/05/03[mhda]#5 #3 AND #4after Week 1, i.e. later than 3 May 2009:Then we isolated the records that have been assigned MeSH #6 2009/05/04:2099/12/31[mhda]#7 #3 AND #6To these we applied the core search string as if no MeSH had been assigned to the records, to reflect the delay in assigning such headings. In a typical search conducted at the time, almost all the database fields would have been searched, including MeSH  OR pandemic[tiab] OR epidemic[tiab] OR outbreak[tiab] OR (disease[tiab] AND outbreaks[tiab])) AND (influenza[tiab] OR flu[tiab] OR (influenza[tiab] AND human[tiab])))#9 #7 AND #8Finally, we joined the results of our search for records that have not yet been assigned MeSH or where this happened during Week 1 with the records assigned MeSH after Week 1:#10 #5 OR #9The end result simulates a search carried out at the end of the last day of Week 1, i.e. 3 May 2009, for potentially relevant publications added to the PubMed database during the previous week.By reviewing the titles and available abstracts of all the potentially relevant records returned by Step 10, we identified the ones relevant to the 2009 influenza A(H1N1) pandemic. We conducted this review individually, and then the two of us conferred on which records to classify as relevant.We included as relevant articles accepted for publication or published before the first outbreak was known if they were on subjects such as influenza vaccine development, oseltamivir (Tamiflu) resistance, or general influenza pandemic preparedness. We excluded articles primarily on influenza A(H5N1) (avian influenza) if they contained no obvious linkages to one of these subjects.Nature and Science are two examples of journals publishing substantive news reports of vitally important and sometimes controversial developments, often with data We considered all types of publications for classification as relevant, including publications categorised by the NLM as “news,” which covers announcements, statements of new data, reports of recent events, and other matters of interest to the field of science. Then we incorporated the ID of every relevant record in a single search string. This query produced a list of the relevant records that would have been found if the core search string (#1) was used in a PubMed search at the end of a given week, e.g. at the close of 3 May 2009, for articles added to PubMed during that week.#11 19408352[pmid] OR 19407756[pmid] OR 19407739[pmid]…To show how many relevant records would not have been identified if one of the key terms – “h1n1,” “swine,” “influenza,” or “flu” – were left out of the search string, we carried out a search that was similar to the above but excluded each term in turn, and then we compared the results. The following example excludes “h1n1”:#12 (swine OR pandemic OR epidemic OR outbreak) AND (influenza OR flu)#13 (swine[tiab] OR (sus[tiab] AND scrofa[tiab]) OR pandemic[tiab] OR epidemic[tiab] OR outbreak[tiab] OR (disease[tiab] AND outbreaks[tiab])) AND (influenza[tiab] OR flu[tiab] OR (influenza[tiab] AND human[tiab]))#14 (#2 AND #4 AND #12) OR (#2 AND #4 AND #13)#15 (#10 NOT #14) AND #11We carried out similar searches that excluded the other key terms in turn. For “swine,” the term itself was excluded as well as traces produced by the automatic translation, (“swine”[tiab] OR (“sus”[tiab] AND “scrofa”[tiab])). For “influenza” and for “flu,” we only excluded the term itself, since in both cases the translation adds (“influenza”[tiab] AND “human”[tiab]) to the search string.As a tool for additional analysis of parts of the results we extracted the publication types assigned to the records. We divided the publication types relevant for this study into two categories:Publication category A: case report, clinical trial, comparative study, evaluation study, journal article, multicenter study, research support and review;Publication category B: comment, congress report, editorial, historical article, interview, letter, news and newspaper article.Records can be assigned one or more publication types. This is not necessarily done at the same time as MeSH are added.To calculate the proportion of relevant records missed by each partial search, we first tallied the number of records identified at two different steps of the search process . Out of these, we found an average of 27.8 records (range 20–57 records) to be relevant.When we excluded one of the terms from the search string, the average percentage of relevant records that we missed was 18.7% (range 0.0%–36.8%) for “h1n1,” 13.6% (range 3.5%–23.8%) for “swine,” 17.5% (range 4.8%–37.0%) for influenza, and 20.6% (range 6.9%–36.0%) for “flu.”Out of the total number of relevant records that would be missed in the 10 simulated searches, 75% were category A publications when we omitted “h1n1,” 23% for “swine,” 88% for “influenza” and “29%” for “flu” (data not shown in tables); 59% of the missed relevant records did not include an abstract. This was the case for only 29% of the relevant records that were never missed when a term was omitted.This study set out to show the impact that the inconsistent naming of a disease, such as influenza A(H1N1), has for health experts when searching for scientific articles in PubMed and MEDLINE. In turn, this can affect researchers' ability to communicate up-to-date findings to decision-makers and ultimately shape public health interventions. It demonstrates that an average of between 13.6% and 20.6% of the relevant articles are not captured if just one keyword is left out of the search string. As a result, it is unlikely that all important research will reach the researchers, practitioners, and decision-makers who can utilise it.Our focus in this study was on scientific articles. It is well known that translating the findings of such studies into actionable messages for decision-makers is a complicated task The Lancet (http://www.thelancet.com/H1N1-flu), The New England Journal of Medicine (http://h1n1.nejm.org), BMJ (http://pandemicflu.bmj.com) and Public Library of Science (http://www.ploscurrents.org/influenza) have all developed online resource centres to help users find scientific information about the pandemic, including publications. The scientific community should warmly welcome such initiatives. However, these information retrieval resources have limited journal coverage compared to PubMed/MEDLINE.Developing a search strategy that will identify all relevant articles published in scientific journals is impractical http://www.pubmed.gov). As of 21 January 2010, this search string was:The NLM has also developed a search string that is slightly different from the one used in this study, but with the same purpose: to identify records on the pandemic recently added to PubMed. It is featured on the PubMed homepage ((swine OR h1n1) AND (flu OR influenza OR virus OR outbreak OR pandemic) Comparing its results with those generated by our core search string (Step 1) reveals that the NLM string misses several relevant records see .all the journals in PubMed. As such it is clearly not an option. Moreover, since MEDLINE indexing can take several months, many journal issues from the study period are still not indexed in the database and would thus require physical inspection.The principal limitation of this study is the difficulty of validating the results. It can be done in part by “hand-searching” all available journals during the 10-week study period. Yet that would involve individually searching not only the journals that returned results for our core search string, but http://www.citeulike.org) and Delicious (http://www.del.icio.us), where users can add tags of their own choice to their papers and links.There are several ways to improve the success of PubMed searches for pandemic information. First, the NLM could provide journal publishers with the possibility of including a special alert tag when they upload a new citation to the PubMed database. This tag would indicate that the cited item includes information about a rapidly evolving situation such as a pandemic. If these tags were used consistently, they would greatly facilitate such searches. The tags could then be revised or removed when MeSH are added. To our knowledge, this approach has not previously been tested in the context of health related bibliographic databases. However, a similar concept is widely used for information sharing on Internet services like CiteULike  pandemic http://www.euro.who.int/influenza/ah1n1) underscores the importance of carefully and consistently translating key pandemic terms into languages other than English. Although not relevant for the PubMed database, this issue is important for searching the WHO site and other web sites in foreign languages.Fourth, our own experience in creating and maintaining the influenza A(H1N1) web site for the WHO Regional Office for Europe  feeds provided by most publishers and PubMed itself. This technology can be utilised in several ways to enhance the timeliness and retrieval of research updates  pandemic, it is highly probable that they will not retrieve all the relevant articles. Our study demonstrated that a search string that does not include a combination of terms that covers several of the names used for the pandemic will miss relevant publications indexed in PubMed/MEDLINE. Leaving out just one term from a search can result in missing as much as a third of the relevant articles at a given point in time. These findings can have significant implications for the communication and utilisation of pandemic information.There are two main ways to remedy this deficiency. The first is to agree on a name earlier and clearly communicate it (and any subsequent changes) to the scientific community and media. The World Health Organization is perhaps best placed to lead this effort. The second solution is for the National Library of Medicine to implement faster indexing of publications that relate to a rapidly unfolding health crisis, as well as to provide publishers the possibility of adding alert tags for such articles.Box S1Search tags used for searching PubMed(0.03 MB DOC)Click here for additional data file.Box S2The WHO Regional Office for Europe RSS feed on influenza A(H1N1)(0.03 MB DOC)Click here for additional data file.


Micromonospora marina, was found to induce profound perturbations of the cell cycle. On both LoVo and SW620 human colon cancer cell lines, thiocoraline caused an arrest in G1 phase of the cell cycle and a decrease in the rate of S phase progression towards G2/M phases, as assessed by using bromodeoxyuridine/DNA biparametric flow cytometric analysis. Thiocoraline does not inhibit DNA-topoisomerase II enzymes in vitro, nor does it induce DNA breakage in cells exposed to effective drug concentrations. The cell cycle effects observed after exposure to thiocoraline appear related to the inhibition of DNA replication. By using a primer extension assay it was found that thiocoraline inhibited DNA elongation by DNA polymerase α at concentrations that inhibited cell cycle progression and clonogenicity. These studies indicate that the new anticancer drug thiocoraline probably acts by inhibiting DNA polymerase α activity. © 1999 Cancer Research CampaignThiocoraline, a new anticancer agent derived from the marine actinomycete