HIV subtype and drug resistance patterns among drug naïve persons in Jos, Nigeria

To determine HIV-1 subtypes and antiretroviral drug resistance mutations for 16 infected, pregnant women in Jos, Nigeria, part of pol (1040 bp) was amplified from patient PBMC DNA, sequenced andanalyzed. Eight of the samples were subtype G, three were CRF02_AG and 2 were unique recombinant forms (URF) between G and CRF02_AG. The remaining consisted of 3 different strains: one was subtypeC, and the other 2 were unrelated URF. Nearly full-length genome sequences were completed for 6 of the strains: 4 subtype G and 2 CRF02_AG. In the 14 drug-naïve subjects, no primary resistance-associated mutations were found, but secondary mutations were identified in 7 different codons of the gene coding for protease: PR K20I, M36I, L63A/P/V, V82I, L10M/I and I93L. In addition, the K238R mutation was identified in the reverse transcriptase gene of 3 viruses. The PR K20I and M36I mutations occurred in all of the strains, and the L10M and V82I mutations occurred only in subtype G. The mutation, I93L, was carried by subtype C viruses. Two of the women that had prior niverapine treatment, had primary resistance-associated mutations, RT M184V and K103N, archived in their proviral DNA several months after treatment cessation. The study reports a predominance of clade G and CRF02_AG, and provides many more examples of nearly full-length genome sequences for subtype G viruses from Nigeria. The ubiquitous presence of PI secondary resistance-associated mutations, as well as primary resistanceassociatedmutations in 2 previously treated women, underscores the need to ensure adherence compliance to treatment

ATLASGAL: 3-mm class I methanol masers in high-mass star formation regions

We analyzed the 3-mm wavelength spectral line survey of 408 ATLASGAL clumps observed with the IRAM 30m-telescope, focusing on the class I methanol masers with frequencies near 84, 95 and 104.3 GHz. We detect narrow, maser-like features towards 54, 100 and 4 sources in the maser lines near 84, 95 and 104.3 GHz, respectively. Among them, fifty 84 GHz masers, twenty nine 95 GHz masers and four rare 104.3 GHz masers are new discoveries. The new detections increase the number of known 104.3 GHz masers from 5 to 9. The 95 GHz class I methanol maser is generally stronger than the 84 GHz maser counterpart. We find 9 sources showing class I methanol masers but no SiO emission, indicating that class I methanol masers might be the only signpost of protostellar outflow activity in extremely embedded objects at the earliest evolutionary stage. Class I methanol masers that are associated with sources that show SiO line wings are more numerous and stronger than those without such wings. The total integrated intensity of class I methanol masers is well correlated with the integrated intensity and velocity coverage of the SiO (2--1) emission. The properties of class I methanol masers are positively correlated with the bolometric luminosity, clump mass, peak H$_2$ column density of their associated clumps but uncorrelated with the luminosity-to-mass ratio, dust temperature, and mean H$_2$ volume density. We suggest that the properties of class I masers are related to shocks traced by SiO. Based on our observations, we conclude that class I methanol masers at 84 and 95 GHz can trace a similar evolutionary stage as H$_2$O maser, and appear prior to 6.7 and 12.2 GHz methanol and OH masers. Despite their small number, the 104.3 GHz class I masers appear to trace a short and more evolved stage compared to the other class I masers. [abridged]Comment: 23 pages, 27 figures, 8 tables, accepted for publication in A&

The structural basis for the specificity of pyridinylimidazole inhibitors of p38 MAP kinase

AbstractBackground: The p38 mitogen-activated protein (MAP) kinase regulates signal transduction in response to environmental stress. Pyridinylimidazole compounds are specific inhibitors of p38 MAP kinase that block the production of the cytokines interleukin-1 β and tumor necrosis factor α, and they are effective in animal models of arthritis, bone resorption and endotoxin shock. These compounds have been useful probes for studying the physiological functions of the p38-mediated MAP kinase pathway.Results: We report the crystal structure of a novel pyridinylimidazole compound complexed with p38 MAP kinase, and we demonstrate that this compound binds to the same site on the kinase as does ATP. Mutagenesis showed that a single residue difference between p38 MAP kinase and other MAP kinases is sufficient to confer selectivity among pyridinylimidazole compounds.Conclusions: Our results reveal how pyridinylimidazole compounds are potent and selective inhibitors of p38 MAP kinase but not other MAP kinases. It should now be possible to design other specific inhibitors of activated p38 MAP kinase using the structure of the nonphosphorylated enzyme

Nuclear Ground State Observables and QCD Scaling in a Refined Relativistic Point Coupling Model

We present results obtained in the calculation of nuclear ground state properties in relativistic Hartree approximation using a Lagrangian whose QCD-scaled coupling constants are all natural (dimensionless and of order 1). Our model consists of four-, six-, and eight-fermion point couplings (contact interactions) together with derivative terms representing, respectively, two-, three-, and four-body forces and the finite ranges of the corresponding mesonic interactions. The coupling constants have been determined in a self-consistent procedure that solves the model equations for representative nuclei simultaneously in a generalized nonlinear least-squares adjustment algorithm. The extracted coupling constants allow us to predict ground state properties of a much larger set of even-even nuclei to good accuracy. The fact that the extracted coupling constants are all natural leads to the conclusion that QCD scaling and chiral symmetry apply to finite nuclei.Comment: 44 pages, 13 figures, 9 tables, REVTEX, accepted for publication in Phys. Rev.

Fission barriers and asymmetric ground states in the relativistic mean field theory

The symmetric and asymmetric fission path for 240Pu, 232Th, and 226Ra is investigated within the relativistic mean field model. Standard parametrizations which are well fitted to nuclear ground state properties are found to deliver reasonable qualitative and quantitative features of fission, comparable to similar nonrelativstic calculations. Furthermore, stable octupole deformations in the ground states of Radium isotopes are investigated. They are found in a series of isotopes, qualitatively in agreement with nonrelativistic models. But the quantitative details differ amongst the models and between the various relativsitic parametrizations.Comment: 30 pages RevTeX, 7 tables, 12 low resolution Gif figures (high resolution PostScript versions are available at http://www.th.physik.uni-frankfurt.de/~bender/nucl_struct_publications.html or at ftp://th.physik.uni-frankfurt.de/pub/bender

Non-targeting siRNA induces NPGPx expression to cooperate with exoribonuclease XRN2 for releasing the stress

Short interfering RNAs (siRNAs) target specific mRNAs for their degradation mediated by RNA-induced silencing complex (RISC). Persistent activation of siRNA-RISC frequently leads to non-targeting toxicity. However, how cells mediate this stress remains elusive. In this communication, we found that the presence of non-targeting siRNA selectively induced the expression of an endoplasmic reticulum (ER)-resident protein, non-selenocysteine containing phospholipid hydroperoxide glutathione peroxidase (NPGPx), but not other ER-stress proteins including GRP78, Calnexin and XBP1. Cells suffering from constant non-targeting siRNA stress grew slower and prolonged G1 phase, while NPGPx-depleted cells accumulated mature non-targeting siRNA and underwent apoptosis. Upon the stress, NPGPx covalently bound to exoribonuclease XRN2, facilitating XRN2 to remove accumulated non-targeting siRNA. These results suggest that NPGPx serves as a novel responder to non-targeting siRNA-induced stress in facilitating XRN2 to release the non-targeting siRNA accumulation

Semantic Similarity for Automatic Classification of Chemical Compounds

With the increasing amount of data made available in the chemical field, there is a strong need for systems capable of comparing and classifying chemical compounds in an efficient and effective way. The best approaches existing today are based on the structure-activity relationship premise, which states that biological activity of a molecule is strongly related to its structural or physicochemical properties. This work presents a novel approach to the automatic classification of chemical compounds by integrating semantic similarity with existing structural comparison methods. Our approach was assessed based on the Matthews Correlation Coefficient for the prediction, and achieved values of 0.810 when used as a prediction of blood-brain barrier permeability, 0.694 for P-glycoprotein substrate, and 0.673 for estrogen receptor binding activity. These results expose a significant improvement over the currently existing methods, whose best performances were 0.628, 0.591, and 0.647 respectively. It was demonstrated that the integration of semantic similarity is a feasible and effective way to improve existing chemical compound classification systems. Among other possible uses, this tool helps the study of the evolution of metabolic pathways, the study of the correlation of metabolic networks with properties of those networks, or the improvement of ontologies that represent chemical information

Spatial chemical distance based on atomic property fields

Similarity of compound chemical structures often leads to close pharmacological profiles, including binding to the same protein targets. The opposite, however, is not always true, as distinct chemical scaffolds can exhibit similar pharmacology as well. Therefore, relying on chemical similarity to known binders in search for novel chemicals targeting the same protein artificially narrows down the results and makes lead hopping impossible. In this study we attempt to design a compound similarity/distance measure that better captures structural aspects of their pharmacology and molecular interactions. The measure is based on our recently published method for compound spatial alignment with atomic property fields as a generalized 3D pharmacophoric potential. We optimized contributions of different atomic properties for better discrimination of compound pairs with the same pharmacology from those with different pharmacology using Partial Least Squares regression. Our proposed similarity measure was then tested for its ability to discriminate pharmacologically similar pairs from decoys on a large diverse dataset of 115 protein–ligand complexes. Compared to 2D Tanimoto and Shape Tanimoto approaches, our new approach led to improvement in the area under the receiver operating characteristic curve values in 66 and 58% of domains respectively. The improvement was particularly high for the previously problematic cases (weak performance of the 2D Tanimoto and Shape Tanimoto measures) with original AUC values below 0.8. In fact for these cases we obtained improvement in 86% of domains compare to 2D Tanimoto measure and 85% compare to Shape Tanimoto measure. The proposed spatial chemical distance measure can be used in virtual ligand screening

IDSS: deformation invariant signatures for molecular shape comparison

<p>Abstract</p> <p>Background</p> <p>Many molecules of interest are flexible and undergo significant shape deformation as part of their function, but most existing methods of molecular shape comparison (MSC) treat them as rigid bodies, which may lead to incorrect measure of the shape similarity of flexible molecules.</p> <p>Results</p> <p>To address the issue we introduce a new shape descriptor, called Inner Distance Shape Signature (IDSS), for describing the 3D shapes of flexible molecules. The inner distance is defined as the length of the shortest path between landmark points within the molecular shape, and it reflects well the molecular structure and deformation without explicit decomposition. Our IDSS is stored as a histogram which is a probability distribution of inner distances between all sample point pairs on the molecular surface. We show that IDSS is insensitive to shape deformation of flexible molecules and more effective at capturing molecular structures than traditional shape descriptors. Our approach reduces the 3D shape comparison problem of flexible molecules to the comparison of IDSS histograms.</p> <p>Conclusion</p> <p>The proposed algorithm is robust and does not require any prior knowledge of the flexible regions. We demonstrate the effectiveness of IDSS within a molecular search engine application for a benchmark containing abundant conformational changes of molecules. Such comparisons in several thousands per second can be carried out. The presented IDSS method can be considered as an alternative and complementary tool for the existing methods for rigid MSC. The binary executable program for Windows platform and database are available from <url>https://engineering.purdue.edu/PRECISE/IDSS</url>.</p

Visualization of proteomics data using R and bioconductor.

Data visualization plays a key role in high-throughput biology. It is an essential tool for data exploration allowing to shed light on data structure and patterns of interest. Visualization is also of paramount importance as a form of communicating data to a broad audience. Here, we provided a short overview of the application of the R software to the visualization of proteomics data. We present a summary of R's plotting systems and how they are used to visualize and understand raw and processed MS-based proteomics data.LG was supported by the European Union 7th Framework Program (PRIME-XS project, grant agreement number 262067) and a BBSRC Strategic Longer and Larger grant (Award BB/L002817/1). LMB was supported by a BBSRC Tools and Resources Development Fund (Award BB/K00137X/1). TN was supported by a ERASMUS Placement scholarship.This is the final published version of the article. It was originally published in Proteomics (PROTEOMICS Special Issue: Proteomics Data Visualisation Volume 15, Issue 8, pages 1375–1389, April 2015. DOI: 10.1002/pmic.201400392). The final version is available at http://onlinelibrary.wiley.com/doi/10.1002/pmic.201400392/abstract