Longitudinal Analysis of CCR5 and CXCR4 Usage in a Cohort of Antiretroviral Therapy-Na?ve Subjects with Progressive HIV-1 Subtype C Infection

HIV-1 subtype C (C-HIV) is responsible for most HIV-1 cases worldwide. Although the pathogenesis of C-HIV is thought to predominantly involve CCR5-restricted (R5) strains, we do not have a firm understanding of how frequently CXCR4-using (X4 and R5X4) variants emerge in subjects with progressive C-HIV infection. Nor do we completely understand the molecular determinants of coreceptor switching by C-HIV variants. Here, we characterized a panel of HIV-1 envelope glycoproteins (Envs) (n = 300) cloned sequentially from plasma of 21 antiretroviral therapy (ART)-naïve subjects who experienced progression from chronic to advanced stages of C-HIV infection, and show that CXCR4-using C-HIV variants emerged in only one individual. Mutagenesis studies and structural models suggest that the evolution of R5 to X4 variants in this subject principally involved acquisition of an “Ile-Gly” insertion in the gp120 V3 loop and replacement of the V3 “Gly-Pro-Gly” crown with a “Gly-Arg-Gly” motif, but that the accumulation of additional gp120 “scaffold” mutations was required for these V3 loop changes to confer functional effects. In this context, either of the V3 loop changes could confer possible transitional R5X4 phenotypes, but when present together they completely abolished CCR5 usage and conferred the X4 phenotype. Our results show that the emergence of CXCR4-using strains is rare in this cohort of untreated individuals with advanced C-HIV infection. In the subject where X4 variants did emerge, alterations in the gp120 V3 loop were necessary but not sufficient to confer CXCR4 usage.     

Genome wide association mapping and candidate gene analysis for pod shatter resistance in Brassica juncea and its progenitor species

Molecular Biology Reports - We investigated phenotypic variations for pod shattering, pod length and number of seeds per pod in large germplasm collections of Brassica juncea (2n?=?36;...     

Molecular analysis of the bacterial microbiome in the forestomach fluid from the dromedary camel (Camelus dromedarius)

Rumen microorganisms play an important role in ruminant digestion and absorption of nutrients and have great potential applications in the field of rumen adjusting, food fermentation and biomass utilization etc. In order to investigate the composition of microorganisms in the rumen of camel (Camelus dromedarius), this study delves in the microbial diversity by culture-independent approach. It includes comparison of rumen samples investigated in the present study to other currently available metagenomes to reveal potential differences in rumen microbial systems. Pyrosequencing based metagenomics was applied to analyze phylogenetic and metabolic profiles by MG-RAST, a web based tool. Pyrosequencing of camel rumen sample yielded 8,979,755 nucleotides assembled to 41,905 sequence reads with an average read length of 214 nucleotides. Taxonomic analysis of metagenomic reads indicated Bacteroidetes (55.5 %), Firmicutes (22.7 %) and Proteobacteria (9.2 %) phyla as predominant camel rumen taxa. At a finer phylogenetic resolution, Bacteroides species dominated the camel rumen metagenome. Functional analysis revealed that clustering-based subsystem and carbohydrate metabolism were the most abundant SEED subsystem representing 17 and 13 % of camel metagenome, respectively. A high taxonomic and functional similarity of camel rumen was found with the cow metagenome which is not surprising given the fact that both are mammalian herbivores with similar digestive tract structures and functions. Combined pyrosequencing approach and subsystems-based annotations available in the SEED database allowed us access to understand the metabolic potential of these microbiomes. Altogether, these data suggest that agricultural and animal husbandry practices can impose significant selective pressures on the rumen microbiota regardless of rumen type. The present study provides a baseline for understanding the complexity of camel rumen microbial ecology while also highlighting striking similarities and differences when compared to other animal gastrointestinal environments.     

Fibrillation of human serum albumin shows nonspecific coordination on stoichiometric increment of Copper(II)

Protein aggregation is related to a series of pathological disorders the main cause of which are the fibrillar species generated during the process. Human serum albumin (HSA) undergoes rapid fibrillation in the presence of Cu(II) at pH 7.4 in 60% ethanol after 6-h incubation (～65?°C) followed by room temperature incubation. Here, we have investigated the effect of a stoichiometric variation of Cu(II) on the self-assembly of HSA using Congo red and thioflavin T dye-binding studies, circular dichroism spectroscopy, Fourier transform infrared spectroscopy, electron paramagnetic resonance spectroscopy, fluorescence microscopy and transmission electron microscopy. The simulation of EPR spectra suggests that with the increment in Cu(II) ion concentration, there is a change in ligand field coordination. Kinetic parameters indicate reduced cooperativity that may be related to the nonspecific coordination on increment of Cu(II) concentration. Cu(II) is also able to direct the accumulation of a large number of fibers along with a formation of dense fibrillar network which is evident from microscopic images.     

Cyclooxygenase-2 dependent metabolism of 20-HETE increases adiposity and adipocyte enlargement in mesenchymal stem cell-derived adipocytes

20-Hydroxy-5,8,11,14-eicosatetraenoic acid (20-HETE), a product of the cytochrome P450 (CYP)-catalyzed ω-hydroxylation of arachidonic acid, induces oxidative stress and, in clinical studies, is associated with increased body mass index (BMI) and the metabolic syndrome. This study was designed to examine the effects of exogenous 20-HETE on mesenchymal stem cell (MSC)-derived adipocytes. The expression levels of CYP4A11 and CYP4F2 (major 20-HETE synthases in humans) in MSCs decreased during adipocyte differentiation; however, exogenous administration of 20-HETE (0.1–1 μM) increased adipogenesis in a dose-dependent manner in these cells (P < 0.05). The inability of a 20-HETE analog to reproduce these effects suggested the involvement of a metabolic product of 20-HETE in mediating its pro-adipogenic effects. A cyclooxygenase (COX)-1 selective inhibitor enhanced, whereas a COX-2 selective or a dual COX-1/2 inhibitor attenuated adipogenesis induced by 20-HETE. The COX-derived metabolite of 20-HETE, 20-OH-PGE₂, enhanced adipogenesis and lipid accumulation in MSCs. The pro-adipogenic effects of 20-HETE and 20-OH-PGE₂ resulted in the increased expression of the adipogenic regulators PPARγ and β-catenin in MSC-derived adipocytes. Taken together we show for the first time that 20-HETE-derived COX-2-dependent 20-OH-PGE₂ enhances mature inflamed adipocyte hypertrophy in MSC undergoing adipogenic differentiation.     

Design and synthesis of (4E)-4-(4-substitutedbenzylideneamino)-3-substituted-2,3-dihydro-2-thioxothiazole-5-carbonitrile as novel A2A receptor antagonists

Novel 2-thioxothiazole derivatives (6–19) as potential adenosine A_2A receptor (A_2AR) antagonists were synthesized. The strong interaction of the compounds (6–19) with A_2AR in docking study was confirmed by high binding affinity with human A_2AR expressed in HEK293T cells using radioligand-binding assay. The compound 19 demonstrated very high selectivity for A_2AR as compared to standard A_2AR antagonist SCH58261. Decrease in A_2AR-coupled release of endogenous cAMP in treated HEK293T cells demonstrated in vitro A_2AR antagonist potential of the compound 19. Attenuation in haloperidol-induced impairment (catalepsy) in Swiss albino male mice pre-treated with compound 19 is evocative to explore its prospective in therapy of PD.     

Residue-level prediction of DNA-binding sites and its application on DNA-binding protein predictions

Protein-DNA interactions are crucial to many cellular activities such as expression-control and DNA-repair. These interactions between amino acids and nucleotides are highly specific and any aberrance at the binding site can render the interaction completely incompetent. In this study, we have three aims focusing on DNA-binding residues on the protein surface: to develop an automated approach for fast and reliable recognition of DNA-binding sites; to improve the prediction by distance-dependent refinement; use these predictions to identify DNA-binding proteins. We use a support vector machines (SVM)-based approach to harness the features of the DNA-binding residues to distinguish them from non-binding residues. Features used for distinction include the residue's identity, charge, solvent accessibility, average potential, the secondary structure it is embedded in, neighboring residues, and location in a cationic patch. These features collected from 50 proteins are used to train SVM. Testing is then performed on another set of 37 proteins, much larger than any testing set used in previous studies. The testing set has no more than 20% sequence identity not only among its pairs, but also with the proteins in the training set, thus removing any undesired redundancy due to homology. This set also has proteins with an unseen DNA-binding structural class not present in the training set. With the above features, an accuracy of 66% with balanced sensitivity and specificity is achieved without relying on homology or evolutionary information. We then develop a post-processing scheme to improve the prediction using the relative location of the predicted residues. Balanced success is then achieved with average sensitivity, specificity and accuracy pegged at 71.3%, 69.3% and 70.5%, respectively. Average net prediction is also around 70%. Finally, we show that the number of predicted DNA-binding residues can be used to differentiate DNA-binding proteins from non-DNA-binding proteins with an accuracy of 78%. Results presented here demonstrate that machine-learning can be applied to automated identification of DNA-binding residues and that the success rate can be ameliorated as more features are added. Such functional site prediction protocols can be useful in guiding consequent works such as site-directed mutagenesis and macromolecular docking.