Metallic atomically-thin layered silicon epitaxially grown on silicene/ZrB2

Using low energy electron diffraction (LEED) and scanning tunnelling microscopy (STM), we observe a new two-dimensional (2D) silicon crystal that is formed by depositing additional Si atoms onto spontaneously-formed epitaxial silicene on a ZrB2 thin film. From scanning tunnelling spectroscopy (STS) studies, we find that this atomically-thin layered silicon has distinctly different electronic properties. Angle resolved photoelectron spectroscopy (ARPES) reveals that, in sharp contrast to epitaxial silicene, the layered silicon exhibits significantly enhanced density of states at the Fermi level resulting from newly formed metallic bands. The 2D growth of this material could allow for direct contacting to the silicene surface and demonstrates the dramatic changes in electronic structure that can occur by the addition of even a single monolayer amount of material in 2D systems

FAK acts as a suppressor of RTK-MAP kinase signalling in Drosophila melanogaster epithelia and human cancer cells

Receptor Tyrosine Kinases (RTKs) and Focal Adhesion Kinase (FAK) regulate multiple signalling pathways, including mitogen-activated protein (MAP) kinase pathway. FAK interacts with several RTKs but little is known about how FAK regulates their downstream signalling. Here we investigated how FAK regulates signalling resulting from the overexpression of the RTKs RET and EGFR. FAK suppressed RTKs signalling in Drosophila melanogaster epithelia by impairing MAPK pathway. This regulation was also observed in MDA-MB-231 human breast cancer cells, suggesting it is a conserved phenomenon in humans. Mechanistically, FAK reduced receptor recycling into the plasma membrane, which resulted in lower MAPK activation. Conversely, increasing the membrane pool of the receptor increased MAPK pathway signalling. FAK is widely considered as a therapeutic target in cancer biology; however, it also has tumour suppressor properties in some contexts. Therefore, the FAK-mediated negative regulation of RTK/MAPK signalling described here may have potential implications in the designing of therapy strategies for RTK-driven tumours

Analytic Markovian Rates for Generalized Protein Structure Evolution

A general understanding of the complex phenomenon of protein evolution requires the accurate description of the constraints that define the sub-space of proteins with mutations that do not appreciably reduce the fitness of the organism. Such constraints can have multiple origins, in this work we present a model for constrained evolutionary trajectories represented by a Markovian process throughout a set of protein-like structures artificially constructed to be topological intermediates between the structure of two natural occurring proteins. The number and type of intermediate steps defines how constrained the total evolutionary process is. By using a coarse-grained representation for the protein structures, we derive an analytic formulation of the transition rates between each of the intermediate structures. The results indicate that compact structures with a high number of hydrogen bonds are more probable and have a higher likelihood to arise during evolution. Knowledge of the transition rates allows for the study of complex evolutionary pathways represented by trajectories through a set of intermediate structures

Properties of Graphene: A Theoretical Perspective

In this review, we provide an in-depth description of the physics of monolayer and bilayer graphene from a theorist's perspective. We discuss the physical properties of graphene in an external magnetic field, reflecting the chiral nature of the quasiparticles near the Dirac point with a Landau level at zero energy. We address the unique integer quantum Hall effects, the role of electron correlations, and the recent observation of the fractional quantum Hall effect in the monolayer graphene. The quantum Hall effect in bilayer graphene is fundamentally different from that of a monolayer, reflecting the unique band structure of this system. The theory of transport in the absence of an external magnetic field is discussed in detail, along with the role of disorder studied in various theoretical models. We highlight the differences and similarities between monolayer and bilayer graphene, and focus on thermodynamic properties such as the compressibility, the plasmon spectra, the weak localization correction, quantum Hall effect, and optical properties. Confinement of electrons in graphene is nontrivial due to Klein tunneling. We review various theoretical and experimental studies of quantum confined structures made from graphene. The band structure of graphene nanoribbons and the role of the sublattice symmetry, edge geometry and the size of the nanoribbon on the electronic and magnetic properties are very active areas of research, and a detailed review of these topics is presented. Also, the effects of substrate interactions, adsorbed atoms, lattice defects and doping on the band structure of finite-sized graphene systems are discussed. We also include a brief description of graphane -- gapped material obtained from graphene by attaching hydrogen atoms to each carbon atom in the lattice.Comment: 189 pages. submitted in Advances in Physic

Integrating Statistical Predictions and Experimental Verifications for Enhancing Protein-Chemical Interaction Predictions in Virtual Screening

Predictions of interactions between target proteins and potential leads are of great benefit in the drug discovery process. We present a comprehensively applicable statistical prediction method for interactions between any proteins and chemical compounds, which requires only protein sequence data and chemical structure data and utilizes the statistical learning method of support vector machines. In order to realize reasonable comprehensive predictions which can involve many false positives, we propose two approaches for reduction of false positives: (i) efficient use of multiple statistical prediction models in the framework of two-layer SVM and (ii) reasonable design of the negative data to construct statistical prediction models. In two-layer SVM, outputs produced by the first-layer SVM models, which are constructed with different negative samples and reflect different aspects of classifications, are utilized as inputs to the second-layer SVM. In order to design negative data which produce fewer false positive predictions, we iteratively construct SVM models or classification boundaries from positive and tentative negative samples and select additional negative sample candidates according to pre-determined rules. Moreover, in order to fully utilize the advantages of statistical learning methods, we propose a strategy to effectively feedback experimental results to computational predictions with consideration of biological effects of interest. We show the usefulness of our approach in predicting potential ligands binding to human androgen receptors from more than 19 million chemical compounds and verifying these predictions by in vitro binding. Moreover, we utilize this experimental validation as feedback to enhance subsequent computational predictions, and experimentally validate these predictions again. This efficient procedure of the iteration of the in silico prediction and in vitro or in vivo experimental verifications with the sufficient feedback enabled us to identify novel ligand candidates which were distant from known ligands in the chemical space

Fast Homozygosity Mapping and Identification of a Zebrafish ENU-Induced Mutation by Whole-Genome Sequencing

Forward genetics using zebrafish is a powerful tool for studying vertebrate development through large-scale mutagenesis. Nonetheless, the identification of the molecular lesion is still laborious and involves time-consuming genetic mapping. Here, we show that high-throughput sequencing of the whole zebrafish genome can directly locate the interval carrying the causative mutation and at the same time pinpoint the molecular lesion. The feasibility of this approach was validated by sequencing the m1045 mutant line that displays a severe hypoplasia of the exocrine pancreas. We generated 13 Gb of sequence, equivalent to an eightfold genomic coverage, from a pool of 50 mutant embryos obtained from a map-cross between the AB mutant carrier and the WIK polymorphic strain. The chromosomal region carrying the causal mutation was localized based on its unique property to display high levels of homozygosity among sequence reads as it derives exclusively from the initial AB mutated allele. We developed an algorithm identifying such a region by calculating a homozygosity score along all chromosomes. This highlighted an 8-Mb window on chromosome 5 with a score close to 1 in the m1045 mutants. The sequence analysis of all genes within this interval revealed a nonsense mutation in the snapc4 gene. Knockdown experiments confirmed the assertion that snapc4 is the gene whose mutation leads to exocrine pancreas hypoplasia. In conclusion, this study constitutes a proof-of-concept that whole-genome sequencing is a fast and effective alternative to the classical positional cloning strategies in zebrafish

Transcriptome-Wide Binding Sites for Components of the Saccharomyces cerevisiae Non-Poly(A) Termination Pathway: Nrd1, Nab3, and Sen1

RNA polymerase II synthesizes a diverse set of transcripts including both protein-coding and non-coding RNAs. One major difference between these two classes of transcripts is the mechanism of termination. Messenger RNA transcripts terminate downstream of the coding region in a process that is coupled to cleavage and polyadenylation reactions. Non-coding transcripts like Saccharomyces cerevisiae snoRNAs terminate in a process that requires the RNA–binding proteins Nrd1, Nab3, and Sen1. We report here the transcriptome-wide distribution of these termination factors. These data sets derived from in vivo protein–RNA cross-linking provide high-resolution definition of non-poly(A) terminators, identify novel genes regulated by attenuation of nascent transcripts close to the promoter, and demonstrate the widespread occurrence of Nrd1-bound 3′ antisense transcripts on genes that are poorly expressed. In addition, we show that Sen1 does not cross-link efficiently to many expected non-coding RNAs but does cross-link to the 3′ end of most pre–mRNA transcripts, suggesting an extensive role in mRNA 3′ end formation and/or termination

Specific Binding of the Pathogenic Prion Isoform: Development and Characterization of a Humanized Single-Chain Variable Antibody Fragment

Murine monoclonal antibody V5B2 which specifically recognizes the pathogenic form of the prion protein represents a potentially valuable tool in diagnostics or therapy of prion diseases. As murine antibodies elicit immune response in human, only modified forms can be used for therapeutic applications. We humanized a single-chain V5B2 antibody using variable domain resurfacing approach guided by computer modelling. Design based on sequence alignments and computer modelling resulted in a humanized version bearing 13 mutations compared to initial murine scFv. The humanized scFv was expressed in a dedicated bacterial system and purified by metal-affinity chromatography. Unaltered binding affinity to the original antigen was demonstrated by ELISA and maintained binding specificity was proved by Western blotting and immunohistochemistry. Since monoclonal antibodies against prion protein can antagonize prion propagation, humanized scFv specific for the pathogenic form of the prion protein might become a potential therapeutic reagent

Altered Insulin Receptor Signalling and β-Cell Cycle Dynamics in Type 2 Diabetes Mellitus

Insulin resistance, reduced β-cell mass, and hyperglucagonemia are consistent features in type 2 diabetes mellitus (T2DM). We used pancreas and islets from humans with T2DM to examine the regulation of insulin signaling and cell-cycle control of islet cells. We observed reduced β-cell mass and increased α-cell mass in the Type 2 diabetic pancreas. Confocal microscopy, real-time PCR and western blotting analyses revealed increased expression of PCNA and down-regulation of p27-Kip1 and altered expression of insulin receptors, insulin receptor substrate-2 and phosphorylated BAD. To investigate the mechanisms underlying these findings, we examined a mouse model of insulin resistance in β-cells – which also exhibits reduced β-cell mass, the β-cell-specific insulin receptor knockout (βIRKO). Freshly isolated islets and β-cell lines derived from βIRKO mice exhibited poor cell-cycle progression, nuclear restriction of FoxO1 and reduced expression of cell-cycle proteins favoring growth arrest. Re-expression of insulin receptors in βIRKO β-cells reversed the defects and promoted cell cycle progression and proliferation implying a role for insulin-signaling in β-cell growth. These data provide evidence that human β- and α-cells can enter the cell-cycle, but proliferation of β-cells in T2DM fails due to G1-to-S phase arrest secondary to defective insulin signaling. Activation of insulin signaling, FoxO1 and proteins in β-cell-cycle progression are attractive therapeutic targets to enhance β-cell regeneration in the treatment of T2DM

Drug Off-Target Effects Predicted Using Structural Analysis in the Context of a Metabolic Network Model

Recent advances in structural bioinformatics have enabled the prediction of protein-drug off-targets based on their ligand binding sites. Concurrent developments in systems biology allow for prediction of the functional effects of system perturbations using large-scale network models. Integration of these two capabilities provides a framework for evaluating metabolic drug response phenotypes in silico. This combined approach was applied to investigate the hypertensive side effect of the cholesteryl ester transfer protein inhibitor torcetrapib in the context of human renal function. A metabolic kidney model was generated in which to simulate drug treatment. Causal drug off-targets were predicted that have previously been observed to impact renal function in gene-deficient patients and may play a role in the adverse side effects observed in clinical trials. Genetic risk factors for drug treatment were also predicted that correspond to both characterized and unknown renal metabolic disorders as well as cryptic genetic deficiencies that are not expected to exhibit a renal disorder phenotype except under drug treatment. This study represents a novel integration of structural and systems biology and a first step towards computational systems medicine. The methodology introduced herein has important implications for drug development and personalized medicine