Protease Cleavage Leads to Formation of Mature Trimer Interface in HIV-1 Capsid

During retrovirus particle maturation, the assembled Gag polyprotein is cleaved by the viral protease into matrix (MA), capsid (CA), and nucleocapsid (NC) proteins. To form the mature viral capsid, CA rearranges, resulting in a lattice composed of hexameric and pentameric CA units. Recent structural studies of assembled HIV-1 CA revealed several inter-subunit interfaces in the capsid lattice, including a three-fold interhexamer interface that is critical for proper capsid stability. Although a general architecture of immature particles has been provided by cryo-electron tomographic studies, the structural details of the immature particle and the maturation pathway remain unknown. Here, we used cryo-electron microscopy (cryoEM) to determine the structure of tubular assemblies of the HIV-1 CA-SP1-NC protein. Relative to the mature assembled CA structure, we observed a marked conformational difference in the position of the CA-CTD relative to the NTD in the CA-SP1-NC assembly, involving the flexible hinge connecting the two domains. This difference was verified via engineered disulfide crosslinking, revealing that inter-hexamer contacts, in particular those at the pseudo three-fold axis, are altered in the CA-SP1-NC assemblies compared to the CA assemblies. Results from crosslinking analyses of mature and immature HIV-1 particles containing the same Cys substitutions in the Gag protein are consistent with these findings. We further show that cleavage of preassembled CA-SP1-NC by HIV-1 protease in vitro leads to release of SP1 and NC without disassembly of the lattice. Collectively, our results indicate that the proteolytic cleavage of Gag leads to a structural reorganization of the polypeptide and creates the three-fold interhexamer interface, important for the formation of infectious HIV-1 particles. © 2012 Meng et al

Genome-wide association study for intramuscular fat content in Chinese Lulai black pigs

Objective Intramuscular fat (IMF) content plays an important role in meat quality. Identification of single nucleotide polymorphisms (SNPs) and genes related to pig IMF, especially using pig populations with high IMF content variation, can help to establish novel molecular breeding tools for optimizing IMF in pork and unveil the mechanisms that underlie fat metabolism. Methods We collected muscle samples of 453 Chinese Lulai black pigs, measured IMF content by Soxhlet petroleum-ether extraction method, and genotyped genome-wide SNPs using GeneSeek Genomic Profiler Porcine HD BeadChip. Then a genome-wide association study was performed using a linear mixed model implemented in the GEMMA software. Results A total of 43 SNPs were identified to be significantly associated with IMF content by the cutoff p<0.001. Among these significant SNPs, the greatest number of SNPs (n = 19) were detected on Chr.9, and two linkage disequilibrium blocks were formed among them. Additionally, 17 significant SNPs are mapped to previously reported quantitative trait loci (QTLs) of IMF and confirmed previous QTLs studies. Forty-two annotated genes centering these significant SNPs were obtained from Ensembl database. Overrepresentation test of pathways and gene ontology (GO) terms revealed some enriched reactome pathways and GO terms, which mainly involved regulation of basic material transport, energy metabolic process and signaling pathway. Conclusion These findings improve our understanding of the genetic architecture of IMF content in pork and facilitate the follow-up study of fine-mapping genes that influence fat deposition in muscle

Effcts of Vitamin C on A549 Cell Proliferation, Apoptosis and Expressions of Caspase, Survivin

Background and objective It was proven that Vitamin C could inhibit the growth of many types of tumors as an antioxidant. The aim of this study is to explore role of Vitamin C in proliferation and apoptosis of lung carcinoma cell line A549 and the underlying mechanism. Methods A549 cells were cultured in vitro and incubated with Vitamin C. The cell viability was measured by growth curve and clonogentic assay. Flow cytometry was used to analyze cell cycle and detect apoptosis. The levels of expression of Caspase-3 mRNA and Survivin mRNA were detected by RT-PCR. Results Vitamin C of 400 μg/mL, 4 mg/mL significantly inhibited the growth of A549 cell lines (P=0.024, P=0.015, respectively). Flow cytometry showed that the cells major stagnation stayed in the G0/G1 and S phase and the apoptotic rate increased with time prolonged. Vitamin C signifiantly up-ragulated the expression of Caspase-3 mRNA, but had no effect on Survivin mRNA. Conclusion Vitamin C can inhibit the proliferation of A549, block A549 cells in G0/G1 and S phase, and induce apoptosis of A549 cells. Apotosis occurred by up-ragulated the expressionof Caspase-3

SVM classification model in depression recognition based on mutation PSO parameter optimization

At present, the clinical diagnosis of depression is mainly through structured interviews by psychiatrists, which is lack of objective diagnostic methods, so it causes the higher rate of misdiagnosis. In this paper, a method of depression recognition based on SVM and particle swarm optimization algorithm mutation is proposed. To address on the problem that particle swarm optimization (PSO) algorithm easily trap in local optima, we propose a feedback mutation PSO algorithm (FBPSO) to balance the local search and global exploration ability, so that the parameters of the classification model is optimal. We compared different PSO mutation algorithms about classification accuracy for depression, and found the classification accuracy of support vector machine (SVM) classifier based on feedback mutation PSO algorithm is the highest. Our study promotes important reference value for establishing auxiliary diagnostic used in depression recognition of clinical diagnosis

SVM classification model in depression recognition based on mutation PSO parameter optimization

At present, the clinical diagnosis of depression is mainly through structured interviews by psychiatrists, which is lack of objective diagnostic methods, so it causes the higher rate of misdiagnosis. In this paper, a method of depression recognition based on SVM and particle swarm optimization algorithm mutation is proposed. To address on the problem that particle swarm optimization (PSO) algorithm easily trap in local optima, we propose a feedback mutation PSO algorithm (FBPSO) to balance the local search and global exploration ability, so that the parameters of the classification model is optimal. We compared different PSO mutation algorithms about classification accuracy for depression, and found the classification accuracy of support vector machine (SVM) classifier based on feedback mutation PSO algorithm is the highest. Our study promotes important reference value for establishing auxiliary diagnostic used in depression recognition of clinical diagnosis