The enzymes LSD1 and Set1A cooperate with the viral protein HBx to establish an active hepatitis B viral chromatin state

Indexación: Web of ScienceWith about 350 million people chronically infected around the world hepatitis B is a major health problem. Template for progeny HBV synthesis is the viral genome, organized as a minichromosome (cccDNA) inside the hepatocyte nucleus. How viral cccDNA gene expression is regulated by its chromatin structure; more importantly, how the modulation of this structure impacts on viral gene expression remains elusive. Here, we found that the enzyme SetDB1 contributes to setting up a repressed cccDNA chromatin state. This repressive state is activated by the histone lysine demethylase-1 (LSD1). Consistently, inhibiting or reducing LSD1 levels led to repression of viral gene expression. This correlates with the transcriptionally repressive mark H3K9 methylation and reduction on the activating marks H3 acetylation and H3K4 methylation on viral promoters. Investigating the importance of viral proteins we found that LSD1 recruitment to viral promoters was dependent on the viral transactivator protein HBx. Moreover, the histone methyltransferase Set1A and HBx are simultaneously bound to the core promoter, and Set1A expression correlates with cccDNA H3K4 methylation. Our results shed light on the mechanisms of HBV regulation mediated by the cccDNA chromatin structure, offering new therapeutic targets to develop drugs for the treatment of chronically infected HBV patients.http://www.nature.com/articles/srep2590

Genetic diversity of Brazilian isolates of feline immunodeficiency virus

We isolated Feline immunodeficiency virus (FIV) from three adult domestic cats, originating from two open shelters in Brazil. Viruses were isolated from PBMC following co-cultivation with the feline T-lymphoblastoid cell line MYA-1. All amplified env gene products were cloned directly into pGL8MYA. The nucleic acid sequences of seven clones were determined and then compared with those of previously described isolates. The sequences of all of the Brazilian virus clones were distinct and phylogenetic analysis revealed that all belong to subtype B. Three variants isolated from one cat and two variants were isolated from each of the two other cats, indicating that intrahost diversity has the potential to pose problems for the treatment and diagnosis of FIV infection

Brain energy metabolism: A roadmap for future research

Although we have learned much about how the brain fuels its functions over the last decades, there remains much still to discover in an organ that is so complex. This article lays out major gaps in our knowledge of interrelationships between brain metabolism and brain function, including biochemical, cellular, and subcellular aspects of functional metabolism and its imaging in adult brain, as well as during development, aging, and disease. The focus is on unknowns in metabolism of major brain substrates and associated transporters, the roles of insulin and of lipid droplets, the emerging role of metabolism in microglia, mysteries about the major brain cofactor and signaling molecule NAD+, as well as unsolved problems underlying brain metabolism in pathologies such as traumatic brain injury, epilepsy, and metabolic downregulation during hibernation. It describes our current level of understanding of these facets of brain energy metabolism as well as a roadmap for future research

Comparison of phylogenetic trees through alignment of embedded evolutionary distances

<p>Abstract</p> <p>Background</p> <p>The understanding of evolutionary relationships is a fundamental aspect of modern biology, with the phylogenetic tree being a primary tool for describing these associations. However, comparison of trees for the purpose of assessing similarity and the quantification of various biological processes remains a significant challenge.</p> <p>Results</p> <p>We describe a novel approach for the comparison of phylogenetic distance information based on the alignment of representative high-dimensional embeddings (xCEED: Comparison of Embedded Evolutionary Distances). The xCEED methodology, which utilizes multidimensional scaling and Procrustes-related superimposition approaches, provides the ability to measure the global similarity between trees as well as incongruities between them. We demonstrate the application of this approach to the prediction of coevolving protein interactions and demonstrate its improved performance over the mirrortree, tol-mirrortree, phylogenetic vector projection, and partial correlation approaches. Furthermore, we show its applicability to both the detection of horizontal gene transfer events as well as its potential use in the prediction of interaction specificity between a pair of multigene families.</p> <p>Conclusions</p> <p>These approaches provide additional tools for the study of phylogenetic trees and associated evolutionary processes. Source code is available at <url>http://gomezlab.bme.unc.edu/tools</url>.</p

Analysis of AML genes in dysregulated molecular networks

<p>Abstract</p> <p>Background</p> <p>Identifying disease causing genes and understanding their molecular mechanisms are essential to developing effective therapeutics. Thus, several computational methods have been proposed to prioritize candidate disease genes by integrating different data types, including sequence information, biomedical literature, and pathway information. Recently, molecular interaction networks have been incorporated to predict disease genes, but most of those methods do not utilize invaluable disease-specific information available in mRNA expression profiles of patient samples.</p> <p>Results</p> <p>Through the integration of protein-protein interaction networks and gene expression profiles of acute myeloid leukemia (AML) patients, we identified subnetworks of interacting proteins dysregulated in AML and characterized known mutation genes causally implicated to AML embedded in the subnetworks. The analysis shows that the set of extracted subnetworks is a reservoir rich in AML genes reflecting key leukemogenic processes such as myeloid differentiation.</p> <p>Conclusion</p> <p>We showed that the integrative approach both utilizing gene expression profiles and molecular networks could identify AML causing genes most of which were not detectable with gene expression analysis alone due to the minor changes in mRNA level.</p

Phosphorylation State-Dependent Interactions of Hepadnavirus Core Protein with Host Factors

Dynamic phosphorylation and dephosphorylation of the hepadnavirus core protein C-terminal domain (CTD) are required for multiple steps of the viral life cycle. It remains unknown how the CTD phosphorylation state may modulate core protein functions but phosphorylation state-dependent viral or host interactions may play a role. In an attempt to identify host factors that may interact differentially with the core protein depending on its CTD phosphorylation state, pulldown assays were performed using the CTD of the duck hepatitis B virus (DHBV) and human hepatitis B virus (HBV) core protein, either with wild type (WT) sequences or with alanine or aspartic acid substitutions at the phosphorylation sites. Two host proteins, B23 and I2PP2A, were found to interact preferentially with the alanine-substituted CTD. Furthermore, the WT CTD became competent to interact with the host proteins upon dephosphorylation. Intriguingly, the binding site on the DHBV CTD for both B23 and I2PP2A was mapped to a region upstream of the phosphorylation sites even though B23 or I2PP2A binding to this site was clearly modulated by the phosphorylation state of the downstream and non-overlapping sequences. Together, these results demonstrate a novel mode of phosphorylation-regulated protein-protein interaction and provide new insights into virus-host interactions

Discovering functional linkages and uncharacterized cellular pathways using phylogenetic profile comparisons: a comprehensive assessment

<p>Abstract</p> <p>Background</p> <p>A widely-used approach for discovering functional and physical interactions among proteins involves phylogenetic profile comparisons (PPCs). Here, proteins with similar profiles are inferred to be functionally related under the assumption that proteins involved in the same metabolic pathway or cellular system are likely to have been co-inherited during evolution.</p> <p>Results</p> <p>Our experimentation with <it>E. coli </it>and yeast proteins with 16 different carefully composed reference sets of genomes revealed that the phyletic patterns of proteins in prokaryotes alone could be adequate enough to make reasonably accurate functional linkage predictions. A slight improvement in performance is observed on adding few eukaryotes into the reference set, but a noticeable drop-off in performance is observed with increased number of eukaryotes. Inclusion of most parasitic, pathogenic or vertebrate genomes and multiple strains of the same species into the reference set do not necessarily contribute to an improved sensitivity or accuracy. Interestingly, we also found that evolutionary histories of individual pathways have a significant affect on the performance of the PPC approach with respect to a particular reference set. For example, to accurately predict functional links in carbohydrate or lipid metabolism, a reference set solely composed of prokaryotic (or bacterial) genomes performed among the best compared to one composed of genomes from all three super-kingdoms; this is in contrast to predicting functional links in translation for which a reference set composed of prokaryotic (or bacterial) genomes performed the worst. We also demonstrate that the widely used random null model to quantify the statistical significance of profile similarity is incomplete, which could result in an increased number of false-positives.</p> <p>Conclusion</p> <p>Contrary to previous proposals, it is not merely the number of genomes but a careful selection of informative genomes in the reference set that influences the prediction accuracy of the PPC approach. We note that the predictive power of the PPC approach, especially in eukaryotes, is heavily influenced by the primary endosymbiosis and subsequent bacterial contributions. The over-representation of parasitic unicellular eukaryotes and vertebrates additionally make eukaryotes less useful in the reference sets. Reference sets composed of highly non-redundant set of genomes from all three super-kingdoms fare better with pathways showing considerable vertical inheritance and strong conservation (e.g. translation apparatus), while reference sets solely composed of prokaryotic genomes fare better for more variable pathways like carbohydrate metabolism. Differential performance of the PPC approach on various pathways, and a weak positive correlation between functional and profile similarities suggest that caution should be exercised while interpreting functional linkages inferred from genome-wide large-scale profile comparisons using a single reference set.</p

Nature of protein family signatures: Insights from singular value analysis of position-specific scoring matrices

Position-specific scoring matrices (PSSMs) are useful for detecting weak homology in protein sequence analysis, and they are thought to contain some essential signatures of the protein families. In order to elucidate what kind of ingredients constitute such family-specific signatures, we apply singular value decomposition to a set of PSSMs and examine the properties of dominant right and left singular vectors. The first right singular vectors were correlated with various amino acid indices including relative mutability, amino acid composition in protein interior, hydropathy, or turn propensity, depending on proteins. A significant correlation between the first left singular vector and a measure of site conservation was observed. It is shown that the contribution of the first singular component to the PSSMs act to disfavor potentially but falsely functionally important residues at conserved sites. The second right singular vectors were highly correlated with hydrophobicity scales, and the corresponding left singular vectors with contact numbers of protein structures. It is suggested that sequence alignment with a PSSM is essentially equivalent to threading supplemented with functional information. The presented method may be used to separate functionally important sites from structurally important ones, and thus it may be a useful tool for predicting protein functions.Comment: 22 pages, 7 figures, 4 table

Scoring docking conformations using predicted protein interfaces

BACKGROUND: Since proteins function by interacting with other molecules, analysis of protein-protein interactions is essential for comprehending biological processes. Whereas understanding of atomic interactions within a complex is especially useful for drug design, limitations of experimental techniques have restricted their practical use. Despite progress in docking predictions, there is still room for improvement. In this study, we contribute to this topic by proposing T-PioDock, a framework for detection of a native-like docked complex 3D structure. T-PioDock supports the identification of near-native conformations from 3D models that docking software produced by scoring those models using binding interfaces predicted by the interface predictor, Template based Protein Interface Prediction (T-PIP). RESULTS: First, exhaustive evaluation of interface predictors demonstrates that T-PIP, whose predictions are customised to target complexity, is a state-of-the-art method. Second, comparative study between T-PioDock and other state-of-the-art scoring methods establishes T-PioDock as the best performing approach. Moreover, there is good correlation between T-PioDock performance and quality of docking models, which suggests that progress in docking will lead to even better results at recognising near-native conformations. CONCLUSION: Accurate identification of near-native conformations remains a challenging task. Although availability of 3D complexes will benefit from template-based methods such as T-PioDock, we have identified specific limitations which need to be addressed. First, docking software are still not able to produce native like models for every target. Second, current interface predictors do not explicitly consider pairwise residue interactions between proteins and their interacting partners which leaves ambiguity when assessing quality of complex conformations