KAT2B, PSEN1 (underexpressed) and CDH2 (overexpressed).These three identified genes may provide new insights into the abnormal hematopoietic process observed in Leukemia as these genes are involved in Notch signaling and cell adhesion processes. It is evident that experimental validation of the protein interactors in leukemic cells could help in the identification of new diagnostic markers for leukemia.Notch signaling plays a critical role in cell fate determination and maintenance of progenitors in many developmental systems. Notch receptors have been shown to be expressed on hematopoietic progenitor cells as well as to various degrees in peripheral blood T and B lymphocytes, monocytes, and neutrophils. Our aim was to understand the protein interaction network, using Notch1 protein name as query in STRING database and we generated a model to assess the significance of Notch1 associated proteins in Acute Lymphoblastic Leukemia (ALL). We further analyzed the expression levels of the genes encoding hub proteins, using Oncomine database, to determine their significance in leukemogenesis. Of the forty two hub genes, we observed that sixteen genes were underexpressed and eleven genes were overexpressed in T-cell Acute Lymphoblastic samples in comparison to their expression levels in normal cells. Of these, we found three novel genes which have not been reported earlier-  Leukemia is an aggressive blood cancer, predominately diagnosed in children. It occurs when one lymphoblast, an immature white blood cell, turns malignant, multiplying uncontrollably and spreading rapidly throughout the body. If left untreated, the disease can be fatal in a few weeks. Unregulated proliferation of lymphoblasts is associated with deregulation of cell division and differentiation pathways. One of the key pathways involved in the regulation of cell differentiation and proliferation is the Notch signaling pathway and deregulation of the genes in this pathway may lead to uncontrolled proliferation observed in cancers such as Leukemias. Cancer related events are not just one gene signaling/defects, but multiple events which turn on the cells in synchrony to produce tumors, like clonal expansion. Therefore our intention was to understand the signaling network of Notch gene to understand how it promotes leukemogenesis which is not known until now.Drosophila and Caenorhabditis elegans, is involved in the regulation of many developmental cell fate decisions. In Drosophilia, Notch interaction with its cell-bound ligands, Delta and Serrate, initiates an intercellular signaling pathway which plays a central role in development. In humans though homologues of the Notch-ligands have been identified, precise interactions and interplay between the ligands and the Notch homologues remain to be determined [The Notch signaling pathway, first described in termined .Notch1 gene under the transcriptional control of the T-cell receptor β locus in a T-cell acute lymphoblastic leukemia [Notch1 gene. This observation led to further studies regarding the role of Notch in leukemia development. A report by Rosati et al. [Notch1 and Notch2 signaling in B-cell chronic lymphocytic leukemia (B-CLL) and suggested a different mechanism for Notch activation than that observed by Weng et al. [Mammalian Notch was first identified in 1991 in a translocation that brought the leukemia . The rolleukemia , who demi et al.  implicatg et al. . Yet, a To decipher the associated protein interactors with Notch1 we designed a protocol, as described here, to record the observed interaction with other molecules which can help decipher which interactions could be associated with proliferative mechanisms. Further, we tried to assess the importance of the most highly connected proteins from this study by analyzing their clusters using bioinformatics database STRING  which wahttp://string-db.org/ [Homo sapiens. We grew the interaction network to obtain 201 protein interactors. The database further allows the user to delineate interactions based on three levels of confidence scores- low (0.4 which represents 20%), medium (0.7 more than 50%) and high (0.9 greater than 75%). This score indicates the probability that a link predicted between two proteins is replicated in the KEGG database. To refine the quality of the NOTCH1 network, we selected only those interactions to be displayed that have a confidence score greater than 0.9 which indicates that the predicted network is highly reliable.To infer the interactions of NOTCH1 with other proteins, we used STRING database v9. STRING  is a daA better understanding of the network proteins and their interactions was obtained by grouping molecules which share some degree of similarity in terms of functional association. STRING database allows the user the choice of two clustering algorithms- MCL (Markov clustering) and k-Means Clustering. Further, the database allows the user to define the number of clusters. In the current study, we applied k-means clustering algorithm  as it ishttp://bioinfo.vanderbilt.edu/webgestalt/) [P-value of 0.05 and a minimum of two genes per category were used as a cut-off to obtain significant enrichments.Enrichment analysis for the protein interactors in this network was performed using WebGestalt, WEB-based GEne SeT AnaLysis Toolkit  , which iThe Notch1protein-protein interaction network was downloaded as an xml file from STRING database which contains data in Protein Standards Initiative (PSI) format. This file was imported into Cytoscape software  which is-γ, wherein γ is the degree exponent [Degree distribution: The degree of a node in a biological network is the number of interactions it forms. The degree distribution P(k) indicates the probability that a given node has exactly k interactions. For scale free networks the degree distribution follows power law i.e P(k)∼kexponent .Characteristic path length: The characteristic path length is the expected distance between two nodes .Diameter: The diameter of the network is the maximum of the shortest distance between two nodes .Clustering coefficient: Clustering coefficient indicates the propensity of nodes to cluster to form groups .Further, to assess which genes in the network form hubs, cytoHubba plugin  was utilIn order to assess the importance of these genes in Acute Lymphoblastic Leukemia, we analyzed the difference in the expression levels of the genes encoding these proteins in leukemic cells when compared to normal cells. Oncomine database is a comprehensive cancer microarray expression database wherein the expression data from the various studies are log-transformed and median-centered for each array . The datThe biological network derived from STRING database using Notch1 as query was observed to be a dense network comprising 201 interacting proteins. The most highly connected protein interactions seemed to be situated at the centre while Notch1 and its interacting proteins were observed to be located at the periphery. A few of the other protein interactors were also located at the periphery .The gene ontology (GO) enrichment analysis results were represented in the form of a directed acyclic graph with the most significant terms highlighted in red. The biological process GO graph is shown in Supplementary Figure S1. Some of the important biological processes in which the genes encoding these proteins participate include signaling pathways such as Wnt and Notch, regulation of cell differentiation, cell fate commitment and regulation of metabolic processes. KEGG Pathway enrichment analysis revealed the proteins were involved in many pathways, important among them being Notch, TGF-beta, ErbB, MAPK, Jak- STAT, Wnt, T cell receptor and B cell receptor pathways. Some of the important pathways are shown in To infer a better understanding of the interactions, the network was divided into ten clusters using k-means clustering option provided by the STRING database . The cluCluster 1 contains proteins such as cadherins and cadherin associated proteins that are active in the process of cell adhesion. The biggest cluster, cluster 2, was centered around CTNNB1 molecule and contains proteins with diverse functions such as regulation of cell adhesion, Wnt signaling, cell proliferation, essential cell cycle proteins like CCND1,CCND2 and growth factors.The protein interactors in cluster 3 are key participants of the Wnt signaling process. Wnt signaling has been reported to play an essential role in the hematopoietic development process. The fourth cluster is made of proteins that are important to TGF-beta mediated signaling process. This signaling is involved in processes such as proliferation, apoptosis, differentiation and migration, which are necessary for normal growth and development of an organism.Some of the proteins in cluster 5 such as DLL1, DNER, and PSEN1 are important in Notch mediated signaling pathway. The other proteins involved in this process were adhesion proteins transport proteins and some others that were associated with gamma secretase complex.The Notch1 cluster, which forms the seventh cluster, is a tightly connected cluster of 32 molecules. This cluster included molecules that were a common part of the Notch signaling network such as Notch binding ligands JAG1, JAG2, DLL3; activators of Notch pathway such as ADAM17; FBXW7 which are involved in the degradation of intracellular Notch; transcriptional coactivators of Notch such as MAML1-3; downstream effector of Notch signaling such as HEY1; targets such as HES1 and HES5 and transcriptional regulators such as IKZF1.The molecules in the above cluster seem to be highly connected to cluster 6, which comprises proteins such as RBPJ , Notch 2 and 3, DLL4 that are also integral parts of the Notch signaling pathway.The protein interactors of cluster 8 are involved in functions such as cell migration, protein transport and cell growth and development.Cluster 9 had fewer proteins of the PI3K- PI3K–PTEN signaling network pathway such as PIK3R1, AKT and the FOXO family of proteins. MDM2 and NFKB1 proteins are also part of this cluster. Most of the proteins in this cluster are involved in regulation of apoptotic mechanisms. MDM2 is an essential regulator of p53, and plays a vital role in p53 mediated apoptosis. NFKB1 plays an important role in various processes including cell differentiation, cell growth and apoptosis processes. Hence, the molecules in this cluster are crucial for the proper functioning of the cell cycle process and for differentiation.Cluster 10 is composed of transcriptional regulators such as MYC, EP300, JUN, KAT2B that act as activators and transcription factors and are essential for the proper expression of several genes.-γ) of node degree distribution with a degree exponent of 0.856 and R2 of 0.737, where R2 indicates the fitness of data points to the curve. The characteristic path length was calculated by the software to be 2.342 and the network diameter was found by the software to be 4.The clustering coefficient, which is a measure of cluster forming ability of a particular node, was found to be 0.520 [Analysis of the Network revealed that the Notch1 protein interaction network is a small world scale free network which follows power law (P(k)∼ kbe 0.520 .KAT2B, CDH2 and PSEN1 which have important biological significance in the hematopoiesis process due to their role in Notch signaling (KAT2B and PSEN1) and cell adhesion (CDH2) and their role in leukemogenesis has yet to be investigated. Three other genes from our study - SMAD4, CTNNA1, RAC1-have been reported in earlier studies but their role in T-cell leukemogenesis has not yet been elucidated.The hub genes obtained from degree ranking method and betweenness method were compared to derive genes which are common to both and hence may play a role in the disease process. Forty two genes  were obsNotch1,Notch2, Notch3, Notch4), plays a key role in regulating many developmental differentiation processes including hematopoiesis during embryogenesis, T cell lineage commitment and maturation in the thymus and Marginal zone B cell development in the spleen [Notch 1 gene is located on the long (q) arm of chromosome 9 at position 34.3 (The Notch family of genes, which in Mammals include the four transmembrane Notch receptor homologs (e spleen . The Note spleen . These ge spleen , 32. Lige spleen . Notch pion 34.3 .Notch1 consists of two key domains- PEST and the HD domains. Mutations in the PEST domain lead to deletion of this region, which normally regulates NICD degradation and hence leads to increased NOTCH1 activity. Mutations in the N- and C-terminal of the HD domain lead to ligand-independent Notch signaling. Thus alterations in this gene and its protein product lead to severe implications in the biological processes in which it functions [Notch1 has been implicated in the pathogenesis of Leukemias. These alterations also affect the Notch signaling network leading to uncontrolled proliferations and disruptive differentiation processes which are characteristic of leukemias. Also, the genes/proteins involved in Notch signaling and other interactors of Notch may contribute to increased severity of disease process through deregulation of other pathways in which they participate. Among the Leukemias, Notch1 deregulation is more commonly observed in T-cell Acute Lymphoblastic Leukemia which may be due to the importance of Notch signaling pathway in T cell development and proliferation of committed T-lineage progenitor cells [The Notch homologs and their ligands have been reported to be expressed in bone marrow, fetal liver, thymus, stromal cells indicating that they play an important role in the hematopoietic process . Notch1 unctions . Mutatioor cells .Notch1 prevent functioning of domains that are essential to prevent spontaneous activation of Notch. These mutations lead to increase in Notch signaling which in turn leads to deregulation of the signaling pathway. Mutations in FBXW7 also promote increase in Notch signaling by supporting the stability of activated Notch1 [Studies have reported that mutations in d Notch1 . Palomerd Notch1  have repThe Notch protein sits like a trigger spanning the cell membrane, with part of it inside and part outside. Ligand binding to the extracellular domain induces proteolytic cleavage with the release of the intracellular domain which enters the nucleus to alter gene expression  Figure .Drosophila and C. elegans [The Notch cascade involves the transmission of Notch signal to the cell nucleus with the help of the various Notch proteins, their ligands and intracellular proteins. The Notch/Lin-12/Glp-1 receptor family has also been reported to be implicated in cell fate specification during development in Palomero et al.  showed tIn our study we present a different picture of Notch interacting players. We have observed that, of the clusters obtained, three clusters seem to be involved in mechanisms leading to neoplastic transformation. Two of the clusters are mainly involved in the Notch signaling network and hence alterations in the proteins in the cluster would affect differentiation and proliferation processes. The Notch 1 protein is also associated with molecules in the ninth cluster, many of whose proteins are involved in the PI3K-PTENpathway. This cluster consists of proteins that play a crucial role in control of cellular proliferation. Alterations in the proteins of this cluster and also changes brought about in Notch1 or its signaling network could cause deregulation of the proliferation processes leading to increased and uncontrolled proliferative signals, defects in differentiation process and to a block in the apoptotic process. Notch signaling has also been reported to participate in antiapoptotic mechanisms through p53 and NFκB pathway –58. DereThe Notch1 cluster is also connected to cluster 4 through SMAD3, which along with the other proteins in this cluster, plays an important role in TGF-beta mediated signaling which helps in the regulation of cell cycle and apoptosis processes. Alterations in Notch1 might affect expression of SMAD3, hindering the cell cycle process and resulting in uncontrolled proliferation.Many proteins in this network also participate in Wnt mediated regulation of hematopoietic development. Recent studies have reported that upregulation of this signaling pathway was observed in most of the Leukemias , highligIn this study, we observed that the protein interactors associated with disease seem to cluster together and to interact closely with other clusters that might also be implicated in disease pathogenesis. This observation is similar to reports from other studies wherein proteins involved in a particular disease tend to form a closely associated network . We obseOur study analyzed the protein interaction network data to identify hub proteins. We identified 50 hub proteins, through degree and betweenness ranking methods, each of which had numerous interactions within the network. On analysis of the expression levels of the genes encoding the common hub proteins, we observed that the genes that were associated with regulation of apoptosis and that have crucial tumor suppressor functions were underexpressed, which is characteristic of Leukemia and other cancers. We also found that the genes involved in various biological processes such as cell cycle, proliferation and gene expression were overexpressed. Through this analysis, we have identified three proteins- KAT2B, PSEN1 and CDH2- which are important hub proteins and whose expression levels were deregulated in T-cell Leukemic samples in the studies by Andersson et al.  and HafeKAT2B is a transcriptional coactivator that plays a role in transcriptional regulation and inhibition of cell cycle progression. PSEN1 is involved in the proteolytic processing of membrane proteins such as Notch, TrkB, and APLP2 and whose expression has been reported in lymphocytes . KAT2B aIn interaction networks, hubs are usually molecules that have the highest number of interactions in the network. Hub genes are thought to play crucial roles in cell processes and hence possess biological significance . SeveralJonson and Bates  have repSMAD4 [CTNNA1 [RAC1 [Expression of some of the other genes such as SMAD4 , CTNNA1  [CTNNA1 , RAC1 [2A1 [RAC1  has beenNFKB1 was observed to be underexpressed. This was also reported by Chang et al. [NFKB1 promoter by the TAL1 oncoprotein. The underexpression of CCND1 and RAC1, which are generally overexpressed in neoplasms, in the datasets under study might indicate that these genes were not associated with leukemogenesis in the patient samples used for the expression study. Although most cancers show high expression levels of VEGF, in some cancers such as benign forms of melanoma [VEGFA are associated with better prognosis. The underexpression of VEGFA in the T-ALL datasets examined may also be indicative of favorable prognosis in the T-ALL patients. Gottardo et al. [Notch2 in their analysis of T-ALL and they associate this lowered expression to adverse outcome in the patients.In the expression datasets analyzed, g et al.  and theymelanoma  and in cmelanoma  lower exo et al.  also repSMAD2 has been reported, in a few cancers such as Pancreatic cancer and Gastric cancer, an overexpression of this protein has been observed [SMAD2, an increase in TGFβ may lead to a concomitant increase in SMAD2 expression and thus contributes to increased cell proliferation through TGFβ receptor signaling pathway [Though in majority of the cancers underexpression of tumor suppressor observed , 75. ResSMAD4 is also a major tumor suppressor which is generally downregulated in cancers. Studies have reported SMAD4 overexpression in Rhabdomyosarcoma cells and suggest that this upregulation is through TGFβ1 [gh TGFβ1 . A similThus, this study identified three unreported genes in leukemogenesis.KAT2B, PSEN1 and CDH2- which encode hub proteins and investigating their role in leukemogenesis might help in development of new therapeutic measures. It is predicted that this study could serve as a repository for selection of prognostic and therapeutic moieties involved in Notch mediated leukemia.The Notch intercellular signaling pathway is conserved evolutionarily and regulates interactions between adjacent cells. Protein interaction studies through network biology help provide an insight into possible disease pathways. The interactors associated with disease proteins might play key roles in the mechanism of disease and hence a study of protein interactions is essential towards understanding disease etiology. In our study, we observed that the Notch signaling proteins are closely associated functionally with proteins that are crucial in cell cycle, cell differentiation and apoptosis processes. Our study of the network of Notch1 protein interactors has highlighted the possible interactions through which Notch signaling might lead to uncontrolled proliferation of cells leading to neoplastic transformation resulting in Leukemia. The Notch1 interaction network shows that the protein interactors of Notch1 play an equally crucial and specific role in Notch mediated signaling. These interactions could signify that therapeutic approaches concentrating on specific Notch mediated targets and proteins that associate with Notch and help in its control of cell proliferation, might prove as effective targets towards preventing proliferation of leukemic cells. We have identified three genes-Click here for additional data file.


Among two peptide fragments Aβ42 is the primary species thought to be responsible for the neurotoxicity and amyloid plaque formation that lead to memory and cognitive defects in Alzheimer’s disease (AD). AD is a ravaging neurodegenerative disorder for which no disease-modifying treatment is currently available. Inhibition of BACE-1 is expected to stop amyloid plaque formation and emerged as an interesting and attractive therapeutic target for AD.Beta-site amyloid precursor protein cleaving enzyme (BACE-1) is a single-membrane protein belongs to the aspartyl protease class of catabolic enzymes. This enzyme involved in the processing of the amyloid precursor protein (APP). The cleavage of APP by BACE-1 is the rate-limiting step in the amyloid cascade leading to the production of two peptide fragments Aβ3D QSAR Pharmacophore Generation module available in Discovery studio. The hypothesis was validated by four different methods and the best hypothesis was utilized in database screening of four chemical databases like Maybridge, Chembridge, NCI and Asinex. The retrieved hit compounds were subjected to molecular docking study using GOLD 4.1 program.Ligand-based computational approach was used to identify the molecular chemical features required for the inhibition of BACE-1 enzyme. A training set of 20 compounds with known experimental activity was used to generate pharmacophore hypotheses using Among ten generated pharmacophore hypotheses, Hypo 1 was chosen as best pharmacophore hypothesis. Hypo 1 consists of one hydrogen bond donor, one positive ionizable, one ring aromatic and two hydrophobic features with high correlation coefficient of 0.977, highest cost difference of 121.98 bits and lowest RMSD value of 0.804. Hypo 1 was validated using Fischer randomization method, test set with a correlation coefficient of 0.917, leave-one-out method and decoy set with a goodness of hit score of 0.76. The validated Hypo 1 was used as a 3D query in database screening and retrieved 773 compounds with the estimated activity value <100 nM. These hits were docked into the active site of BACE-1 and further refined based on molecular interactions with the essential amino acids and good GOLD fitness score.The best pharmacophore hypothesis, Hypo 1, with high predictive ability contains chemical features required for the effective inhibition of BACE-1. Using Hypo 1, we have identified two compounds with diverse chemical scaffolds as potential virtual leads which, as such or upon further optimization, can be used in the designing of new BACE-1 inhibitors. Beta-site amyloid precursor protein cleaving enzyme BACE-1), also known as β-secretase, memapsin-2, or Aspartyl protease-2, is a single-membrane protein belongs to the aspartyl protease class of catabolic enzyme. This is one of the enzymes responsible for the sequential proteolysis of amyloid precursor protein (APP) , also kn. It is cin silico screening to identify hits that are highly varied in chemical nature. The retrieved hits were subsequently subjected to a well-defined refining procedure based on estimated activity values, drug-likeness prediction and further by molecular docking study. The identified hits can further be utilized in designing novel and potent BACE-1 inhibitors.The main aim of our approach, which is discussed in this study is different than the transition state mimetic approach, is to develop an accurate and efficient method for discovering potent BACE-1 inhibitors. A pharmacophore hypothesis was generated based on key structural features of compounds with BACE-1 inhibitory activity. It provides a rational hypothetical representation of the most important chemical features responsible for activity. Herein, a ligand-based 3D pharmacophore hypothesis for BACE-1 inhibitors was constructed based on the structure-activity relationship observed in a set of known BACE-1 inhibitors. The resulted pharmacophore hypotheses were validated by test set, Fischer randomization, leave-one-out, and decoy set methods. The validated pharmacophore hypothesis has been used in In a computerized pharmacophore generation process the accurate choice of the training set is a key issue. The built pharmacophore hypothesis can be as good as the input data information. The following criteria should be considered during the selection of data set in order to achieve a significant pharmacophore hypothesis. (1) All compounds used in the training set have to bind to the same receptor in roughly the same fashion. Compounds having more binding interaction with the receptor are more active than those with fewer; (2) the data set must be widely populated covering an activity range of at least 4 orders of magnitude; (3) the most active compounds should inevitably be included in the training set and (4) all biologically relevant data should be obtained by homogenous procedures . Every i50 value of the training set compounds was taken into account, the inhibitory activity values of the training set compounds span over a range of four orders of magnitude, from 4 nM to 37 000 nM. The chemical structure and experimental activity of the training set compounds are shown in Figure By taking these criteria into account, we have collected a total number of 320 BACE-1 inhibitors from various literature resources -25 and aDiverse Conformation Generation protocol implemented in DS was used to generate conformations using the Best conformation model generation method with CHARMM force field and Poling algorithm to ensure the energy-minimized conformation for each compound. The parameters like maximum number of 250 conformers, the ‘best conformational analysis’ method, and an energy threshold of 20 kcal/mol above the global energy minimum were chosen during conformation generation.Prior to the generation of pharmacophore hypotheses, the training set compounds, which were converted to 3D structure, were used to generate diverse conformations. 3D QSAR Pharmacophore Generation protocol of DS tries to generate hypotheses with features common amongst active molecules and do not reflect the inactive molecules of the training set. The training set compounds were predicted for their inherent chemical features using Feature Mapping protocol implemented in DS. During pharmacophore hypothesis generation a minimum of 4 and a maximum of 5 pharmacophoric features like hydrogen bond acceptor (HBA), hydrogen bond donor (HBD), positive ionizable (PI), ring aromatic (RA) and hydrophobic (HY) were included. These features were selected based on the feature mapping results. All parameters were set to their default values except uncertainty value, which has been changed to 2 instead of 3. An uncertainty value of 2 was more convenient for our dataset because the activity values of the training set spanned exactly the required 4 orders of magnitude; this choice has been further confirmed by preliminary calculations and by other literature evidence [The training set comprises of 20 compounds was used in pharmacophore hypothesis generation. The HypoGen algorithm available in evidence . The uncWith the full range of training set compounds from active to inactive the pharmacophore hypotheses were generated by HypoGen algorithm implemented in DS. This algorithm constructs and ranks the pharmacophore hypotheses that correlate best between 3D spatial arrangement of features in a given training set compounds and their respective experimental activities. This process is accomplished in three steps: the constructive phase, the subtractive phase and the optimization phase .The constructive phase identifies the hypotheses that are common amongst the active compounds. HypoGen enumerates all possible pharmacophore configurations using all combinations of pharmacophore features for each of the conformations of the most active compound. In order to consider the left over most active compounds the hypotheses must fit a minimum subset of its features. Hence, a large database of pharmacophore configurations will be generated at the end of the constructive phase.The subtractive phase will remove the pharmacophore configurations that are present in the least active compounds. All compounds whose activity is by default 3.5 orders of magnitude less than that of the most active compound are considered to represent the least active compounds. The value 3.5 is adjustable depending on the activity range of the training set. The optimization phase improves the hypothesis score. These scores of the generated hypotheses depend on the errors in activity estimation from regression and complexity. The optimization involves a variation of features and/or locations to optimize activity prediction via a simulated annealing approach. The total cost parameter will be calculated for every new hypothesis. The HypoGen will quit and reports the 10 top-scoring hypotheses when there is no improvement in the hypothesis score.The quality of a pharmacophore hypothesis is best determined by two theoretical cost calculations, which are represented in bit units . One is The total cost of any pharmacophore hypothesis should always be close to the fixed cost and away from the null cost to be the significant model. The cost difference between fixed and null cost values should be larger for a meaningful pharmacophore hypothesis. A value of 40-60 bits in a pharmacophore hypothesis indicates that it has 75-90% probability of representing a true correlation in the data.The hypotheses are also evaluated based on other cost components. The cost value for every individual hypothesis is the summation of three cost components: the error cost (E), the weight cost (W) and the configuration cost (C). The error cost is the value represents the root-mean-squared difference (RMSD) between experimental and estimated activity value of the training set compounds. The weight cost is a value that increases in a Gaussian form as this function weights in a model deviate from the ideal value of two. The configuration cost or entropy cost measures the entropy of the hypothesis space. If the input training set compounds are too multiplex, e.g. because of too flexible training set compounds, this will result in an effusive number of hypotheses as an outcome of the subtractive phase. This configuration cost should always be less than a maximum value of 17 . The corThe generated pharmacophore hypothesis was validated using test set, Fischer randomization, decoy set and leave-one-out methods.Diverse Conformation Generation protocol in DS. The compounds associated with their conformations were subsequently carried out for pharmacophore mapping using Ligand Pharmacophore Mapping protocol with Best/Flexible Search option available in DS.A total of 40 compounds with experimental activity data were selected as test set compounds. This method is used to elucidate whether the generated pharmacophore hypothesis is proficient to predict the activities of the compounds other than training set and classify them correctly in their activity scale. The conformation generation for test set compounds was carried out in a similar way like training set compounds using x represents the total number of hypotheses having a total cost value lower than the original hypothesis, and y represents the total number of HypoGen runs i.e. initial and random runs. The confidence level was set to 99%, where 99 random spreadsheets (random hypotheses) were generated. During the pharmacophore generation, if the randomized data set results in similar or better cost values, RMSD and correlation, it means that the original hypothesis have been generated by chance.The main purpose of this validation is to verify whether there is a strong correlation existing between the chemical structure and biological activities of compounds. This generates pharmacophore hypotheses by randomizing the activity data of the training set compounds with the same features and parameters used to generate the original pharmacophore hypothesis. The statistical significance is calculated using the following formula: Significance = 100 (1-(1+x/y)), where Ligand Pharmacophore Mapping protocol available in DS. A set of statistical parameters [An external database containing BACE-1 active and inactive compounds was used to evaluate the discriminative ability of Hypo 1 in the separation of active compounds from the inactive compounds. The database was developed using a total of 453 compounds containing 206 actives and 247 inactives. All the compounds were collected from published literature including binding database -25,31. Trameters  like Ht,The pharmacophore hypothesis is cross validated by leave-one-out method. In this method, one compound is left in the generation of a new pharmacophore model and its affinity is predicted using that new model. The model building and estimation cycle is repeated until each compound was left out once . This teLigand Pharmacophore Mapping protocol implemented in DS with Best/Flexible Search option. The retrieved compounds were filtered by restricting the estimated activity value less than 100 nM and the obtained compounds were further refined using molecular docking study.The validated pharmacophore hypothesis, Hypo 1, was used as a 3D query for screening four different chemical databases. The purpose of this screening is to retrieve novel and potential leads suitable for further development. The chemical databases used were Maybridge, Chembridge, NCI and Asinex. Conformers were generated for each molecule in the database using best conformer generation method that allows a maximum energy of 15 kcal/mol above that of the most stable conformation. The database screening was carried out using Pharmacophore modeling normally firmly associated with docking procedure, which in a first step flexibly aligns the ligand molecule into a rigid macromolecule environment and then estimates the tightness of the interaction by different scoring functions . The DocFeature Mapping protocol resulted in HBA, HBD, RA, PI and HY features. Selecting these features, the pharmacophore generation run was performed along with diverse conformers of training set molecules generated as described in methods section. Ten top-scored pharmacophore hypotheses were generated and in order to choose the best one and also to give an idea about the statistical significance, the pharmacophore hypotheses were subjected to cost analysis. The results of top ten pharmacophore hypotheses and their statistical parameters are given in Table We have used the HypoGen algorithm implemented in DS in order to quantitatively correlate the chemical structure of BACE-1 inhibitors to their biological activity. The training set of 20 compounds . The fixed cost of the run was 74.77 bits, which was well separated from the null cost of 203.22 bits and close to the total cost of 81.24 bits. The large difference (128.45 bits) observed between the fixed cost and null cost value indicates that Hypo 1 has more than 90% statistical significance to be a significant model. All the 10 hypotheses were subjected to further evaluation for their capability to predict the activity of the training set compounds. Configuration cost or entropy value must be less than 17 for which a value of 15.59 was obtained in this study. All hypotheses have scored RMSD values lower than 1.5 and ranging from 0.804 to 1.111, this characterization further emphasizing the good predictive quality of these hypotheses. Based on the rule to select a hypothesis with a lowest total cost, high correlation coefficient, large cost difference and significantly low RMSD value, Hypo 1 gave the best statistical values among other hypotheses. Hence, Hypo 1 with one HBD, one PI, one RA and two HY was chosen as the best hypothesis for further analysis. The inter-feature distance constraints were observed for this five-featured pharmacophore hypothesis (Hypo 1) , active , moderately active  and inactive . The estimated activity values of training set compounds based on Hypo 1 and the corresponding error values are calculated , active , moderately active  and inactive . A total of 11 out of 12 active (+++) compounds were estimated correctly as active, but 1 compound was estimated as most active (++++). Interestingly all the six most active (++++) compounds were estimated correctly as most active (++++). Total of twelve active (+++) compounds were estimated correctly as active. Out of thirteen moderately active (++) compounds only one compound was over estimated as active (+++), and 3 compounds were under estimated as inactive (+), whereas nine compounds were estimated correctly as moderately active. Among nine inactive (+) compounds one was over estimated as moderately active (++) compound whereas eight were estimated as inactive compounds. A total of 2 inactive (+) compounds out of 9 were over estimated as moderately active (++) whereas 7 were correctly estimated as inactive compounds. These results suggested that Hypo 1 has a good agreement with the experimental data and able to predict the activities of a wide variety of BACE-1 inhibitors.A total of 40 compounds structurally different from the training set compounds were selected as test set. The test set compounds were prepared in the same way training set compounds were prepared. The top-scored 10 hypotheses was regressed against 40 test set compounds and calculated the correlation coefficient values ranging from 0.917 to 0.859  required to be generated to achieve a confidence level of 99%. The results of top 10 random spreadsheets along with Hypo 1 are presented in Table t). The results of GH score and E-value calculation are given in Table The Hypo 1 was further validated using an external database for its ability to select BACE-1 inhibitors. This database contains a total (D) of 453 compounds including 206 active (A) compounds. Using Hypo 1, this database screening was carried out, 230 compounds were retrieved as hits  is not depending only on one particular compound. If the activity of each left-out compound is correctly estimated by the corresponding one-missing hypothesis then the test is positive. The feature composition of the pharmacophore, the value of correlation coefficient and the quality of the estimated activity of the left-out compound were used as measures for the assessment of the statistical test. By leaving each one of the 20 training set compounds according to this method, 20 new hypotheses were generated. As a result we did not obtain any meaningful differences between Hypo1 and each hypothesis resulting from the leave-one-out method. This result gives more confidence on Hypo 1 that it does not depend on one particular compound in the training set.The validated pharmacophore hypothesis, Hypo1, was used as a 3D structural query for retrieving compounds from chemical databases including MayBridge (59 652 compounds), Chembridge (50 000 compounds), NCI (238 819 compounds) and Asinex (213 462 compounds). As a result of first screening 11 578, 590, 5096 and 63 265 compounds were retrieved from Maybridge, Chembridge, NCI and Asinex respectively. Since the active site of BACE-1 is larger in size, the experimentally known most active inhibitors are also larger in size and violate the first rule of Lipinski’s rule of five. Hence, the retrieved hit compounds were filtered based only on the estimated activity values calculated by Hypo 1. The activity range for the most active compounds is <100 nM. Finally 773 compounds were selected by restricting the minimum estimated activity to <100 nM.To further refine the retrieved hits and also to remove the false positives, these 773 compounds along with the 20 training set compounds were docked into the active site of BACE-1 using GOLD 4.1 program. There are number of crystal structures for BACE-ligand complexes are available in PDB. The crystal structure of BACE-SC7 (PDB ID: 2QP8) complex was taken based on its high resolution. The GOLD fitness score was calculated for all the 793 compounds, it distinguishes molecules based on their interacting ability. The GOLD fitness score for the most active compound in the training set was 53.035. The compounds for further analysis were selected based on the ligand conformations which can satisfy the necessary interactions at the active site and scoring GOLD fitness score greater than 60. Finally 20 compounds from Maybridge and 15 compounds from Asinex have shown the required interaction with BACE-1 as well as good GOLD fitness scores. The compounds with the same chemical scaffolds were filtered carefully based on the molecular interactions observed at the active site. Finally, two compounds with different scaffolds one from Maybridge (RJC01726) and one from Asinex (Asnx-2) were selected as representative compounds. The binding mode of the final hits and the most active Compound 1 in the training set are shown in Figure Ligplot program [ciFinder search [PubChem search [The hydrophobic interactions of the final hits compounds were observed using  program . The novr search  and PubCm search .3D QSAR Pharmacophore Generation protocol available in DS 2.5. The best quantitative pharmacophore model, Hypo 1, was characterized by the highest cost difference (121.98), best correlation coefficient (0.977), lowest total cost value (81.24) and lowest RMSD (0.804). The fixed cost and null cost values were 74.77 and 203.22 bits, respectively. Hypo1 consisted of one HBD, one PI, one RA and two HY features. Hypo1 was further validated by test set, Fischer randomization test, leave-one-out, and decoy set methods. The test set containing 40 compounds was used in investigating the predictive ability of Hypo1 and resulted with a correlation coefficient of 0.917. Other validation methods also have provided reliable results on the strength of Hypo 1. This validated Hypo1 was used as a 3D query in database screening. The database hit compounds were subsequently subjected to filtering by estimated activity value. To further refine the retrieved hits the 793 compounds along with training set were carried out for molecular docking studies. The molecular docking result of all compounds was analyzed based on the GOLD fitness score, binding modes and molecular interactions with essential active site residues. Finally, two hits, namely, RJC01726 and Asnx-2 of different scaffolds with GOLD fitness score of 68.362 and 63.053, respectively, and interactions with important active site residues were chosen as lead candidates. These compounds as such and on further optimization can be used as potential leads in designing new BACE-1 inhibitors.A chemical feature based 3D pharmacophore hypotheses of BACE-1 inhibitors have been developed using The authors declare that they have no competing interests.SJ and ST equally involved in designing the work, analyzing the results and writing the manuscript. SS formatted and corrected the manuscript. KWL supervised the work and edited the manuscript. All four authors have read and approved the manuscript.Experimental and estimated IC50 values of the test set compounds based on the pharmacophore hypothesis ‘Hypo 1’.).Click here for file


In particular, the repetitive activation of GABAA receptors produces a use-dependent decrease (rundown) of the evoked currents (IGABA), which is markedly enhanced in the hippocampus and cortex of drug-resistant mTLE patients. This phenomenon has been also observed in the pilocarpine model, where the increased IGABA rundown is observed in the hippocampus at the time of the first spontaneous seizure, then extends to the cortex and remains constant in the chronic phase of the disease. Here, we examined the sensitivity of IGABA to pharmacological modulation. We focused on the antiepileptic agent levetiracetam (LEV) and on the neurotrophin brain-derived neurotrophic factor (BDNF), which were previously reported to attenuate mTLE-induced increased rundown in the chronic human tissue. In the pilocarpine model, BDNF displayed a paramount effect, decreasing rundown in the hippocampus at the time of the first seizure, as well as in the hippocampus and cortex in the chronic period. In contrast, LEV did not affect rundown in the hippocampus, but attenuated it in the cortex. Interestingly, this effect of LEV was also observed on the still unaltered rundown observed in the cortex at the time of the first spontaneous seizure. These data suggest that the sensitivity of GABAA receptors to pharmacological interventions undergoes changes during the natural history of mTLE, implicating that the site of seizure initiation and the timing of treatment may highly affect the therapeutic outcome.The pharmacological treatment of mesial temporal lobe epilepsy (mTLE), the most common epileptic syndrome in adults, is still unsatisfactory, as one-third of the patients are or become refractory to antiepileptic agents. Refractoriness may depend upon drug-induced alterations, but the disease  A receptors from epileptic tissue (hippocampus and neocortex) become less responsive to repeated activation (as detected by current rundown) than those from healthy tissue , becomes detectable in the hippocampus at the time of the first spontaneous seizure and may depend upon alteration in the molecular composition of the GABAA receptor  is the most common form of epilepsy of adulthood. In mTLE an initial “epileptogenic event”  is often identifiable which is followed, after a latent period of weeks to years, by the spontaneous occurrence of seizures. Multiple pathological and physio-pathological alterations have been identified that may be responsible for the transformation of a normal brain in an epileptic one . In party tissue . This usreceptor .per se may also implicate alterations in the responsiveness to pharmacological agents. Identifying these disease-induced alterations in the response to drugs may provide the basis for more effective treatment strategies in the different phases of mTLE.Once spontaneous seizures begin to occur and the diagnosis of epilepsy is made, the disease often continues to progress, with increasing severity of seizures; neurological decline and appearance of co-morbidities; development of resistance to pharmacological treatments . Many stIGABA) observed in epileptic tissue at different stages of experimental mTLE, namely at the time of the first spontaneous seizure and in the chronic period. We employed two structurally unrelated agents, levetiracetam (LEV) and brain-derived neurotrophic factor (BDNF), because both have been previously demonstrated to be capable of reducing the increased IGABA rundown in the human and also in the rat epileptic brain  were used for all experiments. Animals were housed under standard conditions: constant temperature (22–24°C) and humidity (55–65%), 12-h dark–light cycle, and free access to food and water. All efforts were made to minimize animal suffering. Procedures involving animals and their care were carried out in accordance with European Community and national laws and policies .Pilocarpine was administered i.p. (300 mg/kg), and behavior was observed for several hours thereafter. Within the first hour after injection, all animals developed seizures evolving into recurrent generalized convulsions . SE was interrupted 3 h after onset by administration of diazepam (10 mg/kg i.p). The animals were then assigned to two experimental groups representing different phases of the natural history of the disease: a subgroup was sacrificed 6 h after the first spontaneous seizure; the other subgroup was sacrificed 1 month after SE, i.e., in the chronic period when animals were experiencing an average of 5.3 ± 1.2 spontaneous seizures per day.Seizures were assessed by 24/24-h, 7/7-day video monitoring, performed using a digital video surveillance system DSS1000 . Recording electrodes were implanted in the hippocampus and cortex for identification of the first spontaneous seizure [continuous video-EEG  monitoring from day 4 after SE until the day of the first spontaneous seizure]. EEG seizure were categorized as paroxysmal activity of high frequency (>5 Hz) characterized by a >3-fold amplitude increment over baseline . SeizureXenopus laevis oocytes and injection procedures were as previously described in detail  perfused continuously (9–10 ml/min) with oocyte’s Ringer solution (OR) at room temperature (20–22°C). OR had the following composition (in mM): NaCl 82.5, KCl 2.5, CaCl2 2.5, MgCl2 1, 4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid (HEPES) 5, pH 7.4 (adjusted using NaOH). GABA current rundown was defined as the decrease (in percentage) of the current peak amplitude after six 10-s applications of 1 mM GABA at 40-s intervals .Membranes were prepared from the hippocampus and the fronto-temporal cortex. Preparation of n detail . Brieflyntervals . The fas2O and stored as frozen stock solutions (100 mM). BDNF (Sigma) was dissolved in H2O, stored as frozen stock solutions (50 μg/ml). Both LEV and BDNF were diluted to working concentrations shortly before the experiments and applied to oocytes for 2 h. In all experiments the holding potential was -60 mV. In some experiments, 3 h washout with OR was performed before initiation of a new rundown protocol.Levetiracetam was dissolved in HFigure 1 were analyzed for fitting a single exponential curve; data in Figures 2 and 3 were statistically analyzed using analysis of variance (ANOVA) and post hoc the Holm–Sidak test .All drugs were purchased from Sigma except GABA, which was purchased from Tocris (UK). Data in IGABA currents had variable amplitudes: some were as large as -250 nA, others as small as -10 nA. These currents exhibited rundown after repetitive GABA applications: IGABA elicited by the sixth GABA application fell to 69.4 ± 3 and 68.6 ± 4% of the one elicited by the first GABA application in oocytes injected with cortical and hippocampal membranes, respectively . As previously described  but not in the cortex . Again consistent with previous reports  and in the cortex  of in membranes prepared from chronic animals . This current rundown was not accompanied by a significant change in current decay and it was partially reversible after 15–20 min of washout (not shown), as previously shown in human brain tissue  and in the chronic epilepsy group . Moreover, BDNF abolished the increased IGABA rundown in oocytes injected with cortical membranes from chronic animals (61 ± 2%), whereas it did not influence the small IGABA rundown in first seizure animals .Application of the neurotrophic factor BDNF abolished the increase in Figures B), but significantly attenuated it in the epileptic cortex . It is noteworthy that BDNF and LEV shared the same effect in decreasing IGABA rundown only in the cortex from chronic animals. This effect was not linked to a change in the current decay .The pattern of LEV effects dramatically differed from the one of BDNF. LEV (1 μM) did not affect rundown in the hippocampus, neither in control nor in epileptic tissues  the neurotrophic factor BDNF abolishes this increased rundown in the hippocampus at the time of the first seizure, as well as in the hippocampus and cortex in the chronic period; (3) LEV does not affect rundown in the hippocampus, but attenuates it in the cortex. Below, we will discuss the possible mechanisms by which BDNF and LEV may affect IGABA current rundown, that is, use-dependent GABAA receptor desensitization; we will propose mechanisms that may underlie the alterations in rundown intensity and sensitivity to drugs during the progression of epilepsy; we will examine the implications of these findings.We found here that: (1) an increased A receptor rundown in all epileptic tissue that exhibit it, hippocampus or cortex, but does not affect rundown in normal tissue  hippocampus and cortex.The effects of BDNF in epilepsy are still controversial . WhereasA receptor subunit composition  virevidence . Moreove rundown .A receptor malfunction phosphorylating GABA subunits whose expression is altered in epilepsy, like the α 1 and the α 4, but also the δ or the γ2. Expression of the δ subunit has been reported to be consistently reduced in granule cell dendrites  and PKA-mediated phosphorylation of the ROMK1 channels  may both contribute to the anti-epileptic effects of LEV, which would therefore include both a pre-synaptic (SV2A) and a post-synaptic (PKC- and PKA-dependent) component. Of course the latter and not the former may be implicated in the effect on IGABA current rundown observed in this study.Levetiracetam is a widely used antiepileptic drug that also has utility in migraine prophylaxis . DespiteA receptor rundown in the hippocampus, whereas it reduces it in the epileptic neocortex even when it is not yet increased by the disease progression, i.e., at the time of the first spontaneous seizure. A working hypothesis may be that this is due to phosphorylation of one or more GABAA subunits differentially expressed between the epileptic cortex and hippocampus. These subunit(s) should be expressed even before increased rundown is detectable in the cortex and should be different from the one that is putatively targeted by BDNF. Ad hoc studies should be performed to challenge this hypothesis. In any event, it is noteworthy that, in a previous work in human mTLE, LEV did not affect subicular GABAA receptors whereas it profoundly influenced the cortical ones  that are specific to the epileptic cortex. Moreover, LEV reduces rundown in the cortex even before it is increased in the chronic epileptic period, whereas BDNF can only abolish disease-associated increases in IGABA rundown in the epileptic hippocampus and cortex at different stages of experimental mTLE. The data suggest that the sensitivity of GABAA receptors to pharmacological interventions undergoes changes during the natural history of mTLE, implicating that site of seizure initiation and the timing of treatment may highly affect the therapeutic outcome. Further studies will be needed to better validate this hypothesis and to characterize its mechanism. These will include testing other drugs for their ability to modulate rundown in the different regions and at the different time-points, as well as analyzing the alterations in GABA receptor subunit composition during epilepsy development and correlating it with rundown. Importantly, part of these experiments is amenable to verification in the human tissue. If successful, these studies may lead to new and more effective therapies.In this study, we challenged the hypothesis that the disease progression affects drug responsiveness by examining the sensitivity to pharmacological treatments of the increased The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Transcranial direct current stimulation (tDCS) of the primary motor cortex (M1) has beneficial effects on motor performance and motor learning in healthy subjects and is emerging as a promising tool for motor neurorehabilitation. Applying tDCS concurrently with a motor task has recently been found to be more effective than applying stimulation before the motor task. This study extends this finding to examine whether such task-concurrent stimulation further enhances motor learning on a dual M1 montage. Twenty healthy, right-handed subjects received anodal tDCS to the right M1, dual tDCS  and sham tDCS in a repeated-measures design. Stimulation was applied for 10 mins at 1.5 mA during an explicit motor learning task. Response times (RT) and accuracy were measured at baseline, during, directly after and 15 mins after stimulation. Motor cortical excitability was recorded from both hemispheres before and after stimulation using single-pulse transcranial magnetic stimulation. Task-concurrent stimulation with a dual M1 montage significantly reduced RTs by 23% as early as with the onset of stimulation (p<0.01) with this effect increasing to 30% at the final measurement. Polarity-specific changes in cortical excitability were observed with MEPs significantly reduced by 12% in the left M1 and increased by 69% in the right M1. Performance improvement occurred earliest in the dual M1 condition with a stable and lasting effect. Unilateral anodal stimulation resulted only in trendwise improvement when compared to sham. Therefore, task-concurrent dual M1 stimulation is most suited for obtaining the desired neuromodulatory effects of tDCS in explicit motor learning.  Transcranial direct current stimulation (tDCS) is a non-invasive stimulation technique that induces robust excitability changes in the human motor cortex ,2. RecenTDCS of the primary motor cortex (M1) is an important target for motor learning and rehabilitation , as M1 iHowever, recent research demonstrated that bilaterally stimulating both motor cortices simultaneously was more effective at improving performance at a finger-sequencing task than the conventional unilateral montage . Vines eHowever, despite the clinical effectiveness of dual M1 stimulation, there are neuronal compensation mechanisms in the lesioned brain as well as post-injury differences across the patient population . An undeSince tDCS modulates and does not induce neuronal firing, performing a behavioural task concurrently would enhance the stimulation-induced differences in cortical excitability. In line with this, Stagg et al.  reported Therefore, the present study aimed to establish how the dual M1 electrode montage benefits from task-concurrent stimulation. Anodal tDCS to the right M1, dual M1 stimulation and sham stimulation were applied during an explicit motor learning task and performance was compared between the conditions before, during, immediately after and 15 mins after the end of stimulation. We tested the hypothesis that dual M1 stimulation would improve finger-sequencing coordination more than unilateral anodal stimulation and compared both against the sham stimulation condition. Motor cortical excitability was measured before and after the intervention to elucidate possible differences between the conditions and we examined whether it is possible to achieve bidirectional simultaneous modulation of cortical excitability in the dual stimulation condition.  Subjects provided written informed consent and the experimental protocol was performed in accordance with the Declaration of Helsinki and approved by the Faculty of Health Sciences Research Ethics Committee, Trinity College Dublin, Ireland.Twenty healthy young adults  participated in this study. They showed no signs of any medical or neurological disease or intake of any CNS-active medication as evaluated by a medical questionnaire that was examined by a health practitioner. All were right handed, as determined by the Edinburgh Handedness Inventory .  TDCS was delivered through two saline-soaked, sponge electrodes using a battery-driven, constant-current stimulator . The electrodes were held in place with use of a MindCap .2) was fixed over the right M1, corresponding to position C4 of the 10-20 EEG system, centred over the ‘hot-spot’ of the resting first dorsal interosseus (FDI) hand muscle as determined by single-pulse TMS. In the dual condition, the cathode (25 cm2) was placed over the FDI hand area of the left M1, corresponding to position C3, also determined by single-pulse TMS. In the anodal condition, the cathode (35 cm2) was fixed as a reference electrode contralaterally at the fronto-orbital region or F3 of the 10-20 EEG system. This position for the return electrode has been shown to be functionally ineffective in experimental designs [In all conditions, the anode  and applied for 10 mins. For sham stimulation, current flow increased gradually over a 5s interval reaching the designated 1.5 mA to mimic the initial sensation of real tDCS and the stimulation was then ramped down after 10s and decreased gradually over a 5s interval, so that a conditioning effect on cortical excitability was not induced.Current intensity was 1.5 mA  of the resting FDI were recorded after stimulation of their motor-cortical representational fields by single-pulse TMS. Electromyography (EMG) signals were amplified, band-pass filtered (10 to 50 Hz) and sampled at 1,000 Hz, using conductive adhesive Ag/AgCl electrodes  in a belly tendon montage. EMGs were recorded through an Octal BioAmp . The peak-to-peak amplitudes of the MEPs were detected by an AD-Instrument custom-written script .TMS was applied using a Magstim Rapid stimulator  connected to a figure of eight coil (70mm). The coil was positioned tangentially on the scalp with the handle pointing backward at an angle of 45° to the midline. It was placed over the contralateral motor cortex at the optimal site for stimulating the FDI. This optimum coil position (‘hot-spot’) was deﬁned as the position where TMS consistently resulted in the largest mean MEP amplitude. A tight-fitting Lycra cap was placed on the subjects’ head to mark the optimal coil position for TMS. Once placed, the cap and head were marked such as to be able to fit it in the same place when re-fitting the cap after tDCS. The resting motor threshold (RMT) was defined as the minimum TMS intensity, which achieved peak-to-peak MEP amplitude of 50-100µV in the resting FDI muscle in 3 out of 6 stimulations. Twenty MEPs  at an intensity that was 120% RMT were acquired from each hemisphere at the three time points PRE, POST1 and POST2 (see section 2.2). The hemisphere that was tested first was randomised and counterbalanced for every measurement.The experiment was run in Psychopy, an open-source experimental-control software package . SubjectInstructions for a single trial were to memorise the six-sequence numerical pattern made up of the numbers 6-9 and repeat the movements of this key sequence six times as quickly and as accurately as possible with the corresponding buttons on the keyboard with their left hand . During memorising, the four consecutive boxes labelled ‘6’, ‘7’, ‘8’, ‘9’ were always displayed at the centre of the screen and these were highlighted with an asterisk one at a time for 500 ms. Subjects were shown the pattern twice before a ‘Go’ command signalled them to start the finger response. Reaction time (RT) was recorded from the appearance of the ‘go’ signal and RT was recorded for every individual button press. After the sixth repetition of the pattern, the instructions “Hit ‘S’ for next sequence” appear on the screen and the subject would initiate the next trial. There were 12 trials in total and the total duration of the task was 6 min on average. All responses were recorded during a trial and so it was any 36 button presses of either 6, 7, 8 or 9 that would be counted before initiating the next trial, as opposed to 36 ‘correct’ button presses. In order to maintain fluency of the finger movements, subjects were instructed to keep going with the pattern, even if a number was ‘missed’ rather than start from the beginning of the sequence. At the start of every experimental session, subjects carried out two trials (corresponding to two sequences) as practice trials, but these were the only practice trials in the entire session such as to not miss potential immediate effects following tDCS intervention.The sequences were chosen to be of varying difficulty, in terms of use of the less dextrous little and ring finger (e.g. 679769 vs. 879698) and in terms of ease of memorising the sequence (e.g. 879698 vs. 986987). Sequences never included consecutives of the same numbers and an effort was made to equalise the ratio of digit presses across sequences. The motor task always used the same 12 sequences, however, there were 3 versions of the task, in which the order of the sequences was randomised. Subjects would use one version of the task per experimental session, so that each subject used each version once, this was pseudo-randomised and counterbalanced.A sham-controlled, repeated-measures design was carried out. Each subject participated in three testing sessions, during which they received anodal, dual or sham tDCS. The order of condition was randomised and best care was taken to counterbalance carefully, but the condition order combinations DAS and SAD  were applied four times whereas all other combinations  were applied three times. The order of condition was into account in statistical analyses. All stimulation sessions were separated by at least 1 week.Motor cortical excitability and performance on the explicit motor learning task were measured at several time points 1. The bAt the end of every experimental session, the subjects completed a visual analogue scale to rate (from 1-7) their current level of fatigue, concentration and perceived pain during the tDCS stimulation. Motor performance on the explicit motor learning task was quantified by the average reaction times (RTs) of every button press and by the per cent accuracy. The standard deviation of RTs for every block was calculated as an index of variability of RTs. For statistical analyses, the mean RTs and the mean accuracy were analysed for every block and compared against each other. Normality was tested using the Shapiro-Wilk test of normality. Where motor parameters were not normally distributed, they were found to be normally distributed in log-space and were transformed logarithmically before statistical analyses. The MEP data were transformed using square root transformation to obtain normal distribution before statistical analyses. The data shown in the tables and figures are untransformed values. Where the assumption of sphericity was not met, the Greenhouse-Geisser correction was applied. All data are reported as mean ± standard deviation (SD).Statistical analyses were performed separately for each via a two-way repeated-measures  analysis of variance (ANOVA). Due to the considerable practice effects of the motor task, the order of conditions from every subject will be entered as a covariate in an additional analysis of covariance (ANCOVA) for the mean RTs. For the changes in motor cortical excitability, the MEP amplitudes for each subject were averaged for every block and a two-way repeated-measures ANOVA  was conducted for each hemisphere separately . All statistical analyses were performed using SPSS v.20 .  All 20 subjects completed the 3 experimental sessions. The motor performance data is depicted in F3,57=0.5, p>0.05; partialη2 = 0.02), no main effect of stimulation condition  and no significant interaction . Wrong answers were not excluded in this task, because a missed or a wrong button press still gives insight into the fluidity of finger coordination in repeating the numerical sequence. There was very little fluctuation of accuracy across stimulation condition and thus incorrect answers are evenly distributed across blocks: there was no main effect of time , but no main effect of stimulation condition . However, there was a significant interaction between time and stimulation condition , suggesting that learning rates varied between stimulation conditions. Subsequent planned ANOVAs were performed to individually contrast each stimulation condition. These contrasts revealed that the differences lay between the dual and the sham condition at the time points DURING , POST1  and POST2  when compared to baseline (PRE) as well as at DURING  and POST1  when compared to POST2. This means that the reduction of RTs by 23% in the dual condition during stimulation was significantly different to the reduction of 9% in the sham condition, with this effect increasing to 30% at POST2 compared with an 11% reduction in RTs in the sham condition at POST2.There was a significant overall decrease of RTs over time . All other time comparisons revealed non-significant results. This means that only the final performance measurement in the anodal condition was significantly different to baseline with a reduction of RTs by 20% when compared to the sham condition. The anodal and dual conditions were significantly different at POST2 when compared to baseline , but not at any of the levels .The changes in motor task performance in the anodal condition were significantly different to sham condition at POST2 when compared to baseline  at any of the levels of the variables. There was, however, a significant interaction between condition order and the main effect of stimulation condition  and the planned contrasts revealed that they significantly interact at the level anodal vs. sham . Because of this, the anodal condition was no longer significantly different to sham at any of the time point comparisons, except with near-significance at POST1 when compared to baseline . The anodal and dual condition were not significantly different at any of the levels . After removing the effect of condition order, the dual condition is still significantly different from the sham condition, but due to the stricter experimental control from the ANCOVA on fewer levels than in the ANOVA: dual and sham were significantly different at DURING , POST1  and POST2  when compared to baseline. There was no significant 3-way interaction between condition order, time and stimulation condition at any of the levels of the variables .In order to control for the practice effects associated with the motor task, the order of conditions every subject underwent was entered as a covariate into the additional ANCOVA. There was no significant interaction between the condition order and the main effect of time .There was no significant difference in the self-rated visual analogue scale for fatigue, concentration and perceived pain  and no main effect of time . There was a significant interaction between the stimulation condition and time . Subsequent planned ANOVAs were performed and contrasting the anodal with the dual condition showed that the mean MEP amplitudes from the dual condition were significantly lower than those from the anodal condition by 31% at POST1  and by 22% at POST2  when compared to baseline (PRE), but not when compared to each other . The measured MEP in the dual condition were also considerably lower than the mean amplitudes measured in the sham condition by 27% at POST1  and by 17% at POST2  when compared to baseline (PRE).For the left hemisphere, these revealed no main effect of stimulation condition , however, directly contrasting each level of the variables revealed that the dual condition was significantly different from the sham condition , which means that motor cortical excitability was, overall, significantly higher in the dual than in the sham condition. The main effect of time was significant , which was circumscribed to differences in the levels POST1  and POST2  as compared to baseline (PRE). Following a significant interaction of stimulation condition and time , further comparisons showed that mean MEP amplitudes from the dual condition were significantly higher than those from sham by 53% at POST1  and by 83% at POST2: F1,19=25.77, p<0.001; partialη2 = 0.58) when compared to baseline. The increase of MEPs by 12% in the dual condition from POST1 to POST2 was also found to be significantly different to sham . Contrasting the anodal and the dual condition revealed significantly higher average MEP amplitudes in the dual condition by 29% at POST2  but not at POST1  as compared to baseline and considerably higher average MEP amplitudes in the dual condition at POST1 vs. POST2 with near-significance . When compared to sham, the MEP amplitudes measured in the right hemisphere were significantly higher in the anodal condition than those from the sham condition by 39% at POST1  and by 41% at POST2  as compared to baseline. For the right hemisphere, there was no significant main effect of stimulation condition  were significantly reduced by 23% in the dual condition compared with a 9% reduction in the sham condition (p<0.01). This RT reduction increased to 30% at the final measurement, compared with an 11% reduction in the sham condition at the same time point. There was a clear trend for an effect in the anodal condition (RTs were 20% lower at the last measurement), but this did not reach significance compared to sham see 2. PreviOur results confirm that administering tDCS during an explicit motor learning task is beneficial to performance in healthy subjects. The literature suggests this is above that provided by stimulation prior to task execution -26,31. TDuring motor learning, synaptic efficacy in the motor cortex is enhanced, driven by Hebbian synaptic modifications and mediated by long-term potentiation (LTP) and NMDA-specific glutamate receptor function. There is increasing evidence suggesting that the lasting effects of tDCS also involve LTP-like synaptic mechanisms -34 and NA more mechanistic explanation for the timing-dependent interaction has previously been attributed to metaplasticity, which is essentially a regulatory mechanism that prevents destabilisation of the existing cortical network from dynamic cortical changes such as LTP. As outlined by Kuo et al. , an exciIn the present study, the dual M1 electrode montage was associated with early and lasting performance improvement. Notably, the blocks with the most decreased reaction times were also associated with the lowest standard deviations, suggesting a stable effect and low inter-subject variability for this condition see 1. Such It has been speculated that the measurable improvement in motor performance associated with dual M1 stimulation is mediated by inhibitory interhemispheric projections. Here, the direct excitability- and performance-enhancing effect of the anodal current is amplified by the reduced inhibitory projections coming from the homologous left M1 due to the positioning of the cathodal return electrode. In other words, the motor cortex of the motor-performing hand is additionally released from the normal amount of inhibition originating from its counterpart in the other hemisphere. Studies reporting an increase in motor skill acquisition in the ipsilateral hand following unilateral cathodal stimulation mechanistically support the role of interhemispheric projections in corticomotor functioning ,40.Two separate studies, both using resting-state fMRI to investigate the changes in functional connectivity between the primary motor cortices induced by dual tDCS, report a decrease in interhemispheric projections in the unilateral-anodal and the dual condition, but only the dual condition was associated with increases in intracortical projections ,14. In aConsistent with previous research, we observed a simultaneous modulation of cortical excitability in different directions in the dual stimulation condition. That is, the average MEP amplitudes were found to be statistically lower than sham in the left hemisphere and statistically higher than sham in the right hemisphere. This is in keeping with findings reported by Williams et al. , who empAccording to the gating principle of plasticity, the increased neuronal firing rates induced by the motor task are further amplified by the excitability-enhancing transcranial stimulation, resulting in increased postsynaptic strength . What foLooking at In conclusion, this study demonstrates that active tDCS applied concurrently to a finger-sequencing coordination task is effective at improving the rate of learning compared with sham stimulation. The bilateral stimulation of both motor cortices is more appropriate than the conventional unilateral anodal stimulation, as motor learning was significantly greater in this condition compared with sham stimulation. In the dual M1 condition, it was also possible to obtain simultaneous modulation of cortical excitability in different directions in the two motor cortices and, further, excitability in the area under the anode was significantly enhanced in the dual compared to the unilateral anodal stimulation condition. Thus, task-concurrent dual M1 stimulation is very effective at determining the desired neuromodulatory effects of tDCS. Optimising these stimulation parameters has important implications for the rehabilitative approaches that are being developed.


Acer rubrum L. using magnetic resonance imaging (MRI). MRI is one of the very few techniques that can provide direct non-destructive observations of the water content within opaque biological materials at a micrometer resolution. Thus, it has been used to determine temporal dynamics and water distributions within xylem tissue. In this study, we found good agreement between MRI measures of pixel brightness to assess xylem liquid water content and the percent loss in hydraulic conductivity (PLC) in response to water stress . These data provide strong support that pixel brightness is well correlated to PLC and can be used as a proxy of PLC even when single vessels cannot be resolved on the image. Pressure induced embolism in moderately stressed plants resulted in initial drop of pixel brightness. This drop was followed by brightness gain over 100 min following pressure application suggesting that plants can restore water content in stem after induced embolism. This recovery was limited only to current-year wood ring; older wood did not show signs of recovery within the length of experiment (16 h). In vivo MRI observations of the xylem of moderately stressed (~-0.5 MPa) A. rubrum stems revealed evidence of a spontaneous embolism formation followed by rapid refilling (~30 min). Spontaneous (not induced) embolism formation was observed only once, despite over 60 h of continuous MRI observations made on several plants. Thus this observation provide evidence for the presence of naturally occurring embolism-refilling cycle in A. rubrum, but it is impossible to infer any conclusions in relation to its frequency in nature.We report results of an analysis of embolism formation and subsequent refilling observed in stems of  There is widespread agreement that negative hydrostatic pressures make water transport in the xylem intrinsically vulnerable to cavitation . In ordein vivo water content in the stems on Vitis vinifera L. plants  that allows one to view the liquid (ice) content within the xylem of stems that were rapidly frozen in liquid nitrogen . This cr. plants . CollectAcer rubrum L.) and tulip trees (Liriodendron tulipifera L.) using a double staining method (Vitis spp.) was observed to only occur when transpiration had been stopped (Laurus nobilis L. and A. negundo L. only refilled embolisms over prolonged recovery times of 24 h and only when water stress levels were significantly reduced (Populus trichocarpa L.) was dependent on the level of water stress. Their study showed faster recovery (less than 2 h) in moderately stressed trees compared to much longer recovery times (more than 20 h) in trees exposed to severe stress. The difference in recovery rates was observed despite the fact that stem water potentials increased in both cases within 1 h  and x-ray tomography show only very slow recovery in species with large vessels: Vitis spp. with a repetition time/echo time (TR/TE) = 980/45 ms. The T2W-3DFT data provided good free water versus air contrast. Images were acquired with a 256 × 128 × 128 matrix and then zero-filled to 512 × 256 × 256 before Fourier transformation, yielding a final isotropic resolution of approximately 50 μm. The imaging time was approximately 90 s per image with 90 s resting time between images.Study was conducted on To compare MRI analysis of xylem water content to stem hydraulic conductivity, we used ~2-m-long leafy branches that were collected from seven trees (15–20 years old) growing in the field at Harvard Forest. Several leaves on each branch were placed into sealed plastic bags and covered in aluminum foil the evening before collecting branches at predawn the next day. After excising branches in the air, they were allowed to continue to transpire (in the shade) until the loss of water from the uncovered leaves reduced covered leaf water potentials to values that were needed to generate a vulnerability to embolism response curve. Covered, branch equilibrated leaf water potentials were measured using a pressure chamber system. The balancing pressure required to squeeze water to the excised petiole surface was determined and used to estimate stem water potentials. Following dehydration, each branch was labeled and was double bagged in large black plastic bags. Wet paper towels lined the two-bag layers to reduce evaporation and to allow the branch water potentials to equilibrate within each sample. These branches were then shipped from Harvard Forest, Petersham, MA to the MRI facility in Carnegie Mellon University in Pittsburgh, PA.2O and 1:1 mixture of DI H2O and D2O (volumetric). Pixel brightness ranged in images from black  to white , and these values corresponded to increasing concentration of unbound water that was present in the voxel (volumetric picture element 50 μm × 50 μm × 1000 μm) and were used for analysis of xylem water content .Prior to MRI measurements, leaf water potentials were re-measured using the same pressure chamber system to determine equilibrated water potentials of the branch samples. For each sample, a long portion of the stem was excised under water first and then two 5-cm-long stem segments were subsequently excised underwater from the current extension growth (number of sample tested 25). One of the excised stem samples was used to determine the PLC using classical hydraulic pressure-flow methods. The other excised sample was used for the determination of the water content using MRI. The MRI sample was tightly wrapped in parafilm to further reduce desiccation during the measurement. After MRI imaging was complete, a post-processing image registration algorithm was applied to the data to correct for physical translations of the stem in the image field of view over the measurement time . Image brightness was adjusted for all images using two control glass tubes filled with DI HTo determine the potential for spontaneous embolism formation in moderately stressed stems, undisturbed 3-year-old plants were fitted through the magnet bore using the same strategy as described above. Each plant was left in the magnet for 10–15 h and images were taken every 3min . Images were acquired using a multi-slice gradient-echo sequence with TR/TE = 75/5 ms and a 512 × 256 × 256 matrix size, in-plane resolution of 50 μm × 50 μm and 1 mm thick slices. Images were simultaneously collected from five slices separated by 2 mm distance and thus covering a total of 15 mm of stem length. Data were analyzed using Matlab 12 (MathWorks). During the 10- to 15-h observation period, plants were not subjected to any experimental treatments or any disturbance. They were maintained at an average leaf water potential of about -0.5 MPa. Total time of observation equaled 60 h.A. rubrum could recover from artificially induced embolism.Long-term MRI observations were followed by an air-injection experiment to determine the temporal dynamics of artificially induced embolism in intact plants. Prior to attaching a pressure collar near the base of the main stem of each plant a small incision was made to allow pressurized gas to penetrate the xylem of the plants during air-injection . Each ofFigure 1). We found that stress of -2.51 MPa was required to reduce the xylem hydraulic conductance of the xylem of current-year extension growth of A. rubrum plants by 50% (P50), determined from hydraulic methods. The equivalent 50% loss of average pixel brightness in MRI images was determined to be -2.70 MPa. We also observed similarities in the shape of the vulnerability to embolism curves obtained from both hydraulic methods and MRI image analysis and no statistical difference between estimates of EC50 (Table 1). These results provided assurance that pixel brightness was a good proxy for analysis of stem water content and for estimating changes in stem hydraulic conductance due to embolism formation .Comparative analysis of xylem water content from MRI images and stem hydraulic measurements were made on current-year extension growth. Analysis was made on branches exposed to varying levels of water stress to assess the relationship of pixel brightness measured with MRI to changes in stem hydraulics. Pixel brightness measured with MRI is related to the amount of free water in the sample. In plant tissues, this would be the water that can freely move and is not bound within cellular walls. The generated MRI-based “vulnerability” curve was found to be similar to the PLC curve measured using hydraulic methods  current-year extension growth (one xylem ring) and (2) 1-year-old stems (two xylem rings). As expected, air injection treatments, that created a 5.0-MPa gas/water interface pressure differential at the bordered pit level, resulted in the loss of pixel brightness and was interpreted as a drop in the water content in the stem and formation of embolism. In 1-year-old stems, embolism formed in both the older  and in the current-year xylem (outer ring). The loss of water content determined from decreased pixel brightness in the older ring was found to be much more pronounced . We did not observe any signs of brightness recovery over a 10-h measurement period in the older ring. However, the initial loss of water content in current-year xylem recovered within 2 h from induction of embolism . In two other instances, only sections of the current extension growth of the stem were observed with the MRI, and we found that the initial drop of water content due to air-injection induced embolism was followed by recovery within a 1- to 2-h period . The spike in brightness during the air injection process reflects the movement of water in the xylem caused by the water being replaced with the air that is being forced into the xylem .The temporal and spatial dynamics of embolism were measured using MRI on intact, well hydrated A. rubrum plants undergo “natural” spontaneous embolism formation within their xylem on plants exposed to moderate levels of water stress . In four out of the five plants, we observed no dramatic changes in pixel brightness. We observed small levels of brightness flickering in some pixels that appeared across the xylem tissue. These brightness changes were considered to be potential artifacts or possibly changes in water content of the xylem fibers. Apart from these small flickering, we observedone large a spontaneous occurrence of a drop in pixel brightness in one plant. This event had a similar change in pixel brightness as observed in stems that were injected with air, suggesting that it was a large embolism event. The event started in the current-year xylem and it spread along the perimeter of the last year xylem annual growth ring. At its maximum, the embolized areacovered 1/3 of the stem perimeter. The change in pixel brightness along the stem length (15 mm) occurred simultaneously, i.e., faster than the 180 s time interval between consecutive images. The radial spread of the embolism was slower and took several minutes to move along the stem perimeter. The drop in pixel brightens (or embolism event) was followed by a rapid increase in brightness, implying that unbound “free water” was moving back to the embolized section. It took 20 min for the pixel brightness to return to near initial level. It should also be noted that the reappearance of water was not instantaneous along the stems length and that the stem cross sections being monitored with the MRI returned to their initial pixel brightness at different times . Thus we suggest that low resolution MRI analysis can be successfully used to determine dynamic changes of stem hydraulic properties even when one cannot resolve single vessels. This opens venue to in vivo analysis of hydraulic dynamics of trees with small vessels that were shown to undergo embolism-refilling cycles . The application of pressurized air into the stem resulted in water loss from the xylem in both the current and in the older growth ring (as seen by loss of average pixel brightness). Following depressurization, we observed refilling (within 1 h), but only in the current-year xylem. The older, inner wood ring remained embolized despite monitoring the stem in the MRI for more than 12 h post air-injection treatment. These data provides insight into the functional differences between current (new) and older wood. The current-year xylem in maple has been shown to be the least vulnerable part of the xylem to embolism formation . During the 60 h of MRI observations, we were only able to observe one embolism formation event. However, we feel confident that this one observation placed in the context of other available data  was followed by the restoration of pixel brightness (refilling) to pre-embolism conditions in the affected area with 30 min. The spread of this naturally occurring embolism event was limited only to current-year xylem. The circumferential progress  occurred over several minutes  while vertical occurrence (3 cm distance) was instantaneous . Analysis of refilling showed no directionality and water seemed to occur in many separated image voxels across entire embolized volume.In this report, we also describe continuous observations of the water status of the xylem of data see  providesin vivo time-lapse observation of embolism/refilling cycles using MRI highlights important considerations for our current understanding of xylem function, i.e., that cavitation might be an everyday event in the stems of transpiring plants at a frequency that is related to tension of sap in the xylem. As our data show, the restoration of water content in the affected stem occurred relatively quickly, but one can expect that the effectiveness of refilling would decrease with increasing levels of water stress. This would eventually lead to a situation when embolisms may not be removed by refilling, and that embolisms may accumulate faster than they can be refilled, resulting in an increase of non-functional conduits . This stands in contrast to the observation of refilling of embolized vessels in Vitis spp. separated by 1.5 mm distances are shown.Click here for additional data file.