Anti-atherosclerotic effects of sirolimus on human vascular smooth muscle cells

Sirolimus is a potent immunosuppressive agent and has an anti-atherosclerotic effect through its anti-proliferative property. The present study was undertaken to investigate the effect of sirolimus on intracellular cholesterol homeostasis in human vascular smooth muscle cells (VSMCs) in the presence of inflammatory cytokine IL-1 beta. We explored the effect of sirolimus on the lipid accumulation of VSMCs in the presence of IL-1 beta, using Oil Red O staining and quantitative measurement of intracellular cholesterol. The effect of sirolimus on the gene and protein expression of lipoprotein receptors and ATP binding cassettes (ABCA1 and ABCG1) was examined by real-time PCR and Western blotting, respectively. Furthermore, the effect of sirolimus on cholesterol efflux from VSMCs in the presence or absence of IL-1 beta was also investigated using [(3)H] cholesterol efflux. Finally, we examined the effect of sirolimus on the production of inflammatory cytokines in VSMCs using ELISA. Sirolimus reduced intracellular lipid accumulation in VSMCs mediated by IL-1 beta possibly due to the reduction of expression of low-density lipoprotein (LDL) and very low-density lipoprotein (VLDL) receptors. Sirolimus increased cholesterol efflux from VSMCs and overrode the suppression of cholesterol efflux induced by IL-1 beta. Sirolimus also increased ABCA1 and ABCG1 genes expression, even in the presence of IL-1 beta. We further confirmed that sirolimus inhibited mRNA and protein expression of inflammatory cytokines IL-6, tumor necrosis factor-alpha, IL-8, and monocyte chemoattractant protein-1. Inhibition of lipid uptake together with increasing cholesterol efflux and the inhibition of inflammatory cytokines are all important aspects of the anti-atherosclerotic effects of sirolimus on VSMCs.     

Prediction of protein secondary structure content for the twilight zone sequences

Secondary protein structure carries information about local structural arrangements, which include three major conformations: alpha-helices, beta-strands, and coils. Significant majority of successful methods for prediction of the secondary structure is based on multiple sequence alignment. However, multiple alignment fails to provide accurate results when a sequence comes from the twilight zone, that is, it is characterized by low (<30%) homology. To this end, we propose a novel method for prediction of secondary structure content through comprehensive sequence representation, called PSSC-core. The method uses a multiple linear regression model and introduces a comprehensive feature-based sequence representation to predict amount of helices and strands for sequences from the twilight zone. The PSSC-core method was tested and compared with two other state-of-the-art prediction methods on a set of 2187 twilight zone sequences. The results indicate that our method provides better predictions for both helix and strand content. The PSSC-core is shown to provide statistically significantly better results when compared with the competing methods, reducing the prediction error by 5-7% for helix and 7-9% for strand content predictions. The proposed feature-based sequence representation uses a comprehensive set of physicochemical properties that are custom-designed for each of the helix and strand content predictions. It includes composition and composition moment vectors, frequency of tetra-peptides associated with helical and strand conformations, various property-based groups like exchange groups, chemical groups of the side chains and hydrophobic group, auto-correlations based on hydrophobicity, side-chain masses, hydropathy, and conformational patterns for beta-sheets. The PSSC-core method provides an alternative for predicting the secondary structure content that can be used to validate and constrain results of other structure prediction methods. At the same time, it also provides useful insight into design of successful protein sequence representations that can be used in developing new methods related to prediction of different aspects of the secondary protein structure.     

Prediction of flexible/rigid regions from protein sequences using k-spaced amino acid pairs

Background

Traditionally, it is believed that the native structure of a protein corresponds to a global minimum of its free energy. However, with the growing number of known tertiary (3D) protein structures, researchers have discovered that some proteins can alter their structures in response to a change in their surroundings or with the help of other proteins or ligands. Such structural shifts play a crucial role with respect to the protein function. To this end, we propose a machine learning method for the prediction of the flexible/rigid regions of proteins (referred to as FlexRP); the method is based on a novel sequence representation and feature selection. Knowledge of the flexible/rigid regions may provide insights into the protein folding process and the 3D structure prediction.

Results

The flexible/rigid regions were defined based on a dataset, which includes protein sequences that have multiple experimental structures, and which was previously used to study the structural conservation of proteins. Sequences drawn from this dataset were represented based on feature sets that were proposed in prior research, such as PSI-BLAST profiles, composition vector and binary sequence encoding, and a newly proposed representation based on frequencies of k-spaced amino acid pairs. These representations were processed by feature selection to reduce the dimensionality. Several machine learning methods for the prediction of flexible/rigid regions and two recently proposed methods for the prediction of conformational changes and unstructured regions were compared with the proposed method. The FlexRP method, which applies Logistic Regression and collocation-based representation with 95 features, obtained 79.5% accuracy. The two runner-up methods, which apply the same sequence representation and Support Vector Machines (SVM) and Naïve Bayes classifiers, obtained 79.2% and 78.4% accuracy, respectively. The remaining considered methods are characterized by accuracies below 70%. Finally, the Naïve Bayes method is shown to provide the highest sensitivity for the prediction of flexible regions, while FlexRP and SVM give the highest sensitivity for rigid regions.

Conclusion

A new sequence representation that uses k-spaced amino acid pairs is shown to be the most efficient in the prediction of the flexible/rigid regions of protein sequences. The proposed FlexRP method provides the highest prediction accuracy of about 80%. The experimental tests show that the FlexRP and SVM methods achieved high overall accuracy and the highest sensitivity for rigid regions, while the best quality of the predictions for flexible regions is achieved by the Naïve Bayes method.     

Electroacupuncture Ameliorates Cognitive Impairment Through the Inhibition of NLRP3 Inflammasome Activation by Regulating Melatonin-Mediated Mitophagy in Stroke Rats

Neurochemical Research - Previous studies found that electroacupuncture (EA) at the Shenting (DU24) and Baihui (DU20) acupoints alleviates cognitive impairment in cerebral...     

Identifying core gene modules in glioblastoma based on multilayer factor-mediated dysfunctional regulatory networks through integrating multi-dimensional genomic data

The driver genetic aberrations collectively regulate core cellular processes underlying cancer development. However, identifying the modules of driver genetic alterations and characterizing their functional mechanisms are still major challenges for cancer studies. Here, we developed an integrative multi-omics method CMDD to identify the driver modules and their affecting dysregulated genes through characterizing genetic alteration-induced dysregulated networks. Applied to glioblastoma (GBM), the CMDD identified a core gene module of 17 genes, including seven known GBM drivers, and their dysregulated genes. The module showed significant association with shorter survival of GBM. When classifying driver genes in the module into two gene sets according to their genetic alteration patterns, we found that one gene set directly participated in the glioma pathway, while the other indirectly regulated the glioma pathway, mostly, via their dysregulated genes. Both of the two gene sets were significant contributors to survival and helpful for classifying GBM subtypes, suggesting their critical roles in GBM pathogenesis. Also, by applying the CMDD to other six cancers, we identified some novel core modules associated with overall survival of patients. Together, these results demonstrate integrative multi-omics data can identify driver modules and uncover their dysregulated genes, which is useful for interpreting cancer genome.     

Red Soils Harbor Diverse Culturable Actinomycetes That Are Promising Sources of Novel Secondary Metabolites

Red soils, which are widely distributed in tropical and subtropical regions of southern China, are characterized by low organic carbon, high content of iron oxides, and acidity and, hence, are likely to be ideal habitats for acidophilic actinomycetes. However, the diversity and biosynthetic potential of actinomycetes in such habitats are underexplored. Here, a total of 600 actinomycete strains were isolated from red soils collected in Jiangxi Province in southeast China. 16S rRNA gene sequence analysis revealed a high diversity of the isolates, which were distributed into 26 genera, 10 families, and 7 orders within the class Actinobacteria; these taxa contained at least 49 phylotypes that are likely to represent new species within 15 genera. The isolates showed good physiological potentials for biosynthesis and biocontrol. Chemical screening of 107 semirandomly selected isolates spanning 20 genera revealed the presence of at least 193 secondary metabolites from 52 isolates, of which 125 compounds from 39 isolates of 12 genera were putatively novel. Macrolides, polyethers, diketopiperazines, and siderophores accounted for most of the known compounds. The structures of six novel compounds were elucidated, two of which had a unique skeleton and represented characteristic secondary metabolites of a putative novel Streptomyces phylotype. These results demonstrate that red soils are rich reservoirs for diverse culturable actinomycetes, notably members of the families Streptomycetaceae, Pseudonocardiaceae, and Streptosporangiaceae, with the capacity to synthesize novel bioactive compounds.     

A Genomic View of Lactobacilli and Pediococci Demonstrates that Phylogeny Matches Ecology and Physiology

Lactobacilli are used widely in food, feed, and health applications. The taxonomy of the genus Lactobacillus, however, is confounded by the apparent lack of physiological markers for phylogenetic groups of lactobacilli and the unclear relationships between the diverse phylogenetic groups. This study used the core and pan-genomes of 174 type strains of Lactobacillus and Pediococcus to establish phylogenetic relationships and to identify metabolic properties differentiating phylogenetic groups. The core genome phylogenetic tree separated homofermentative lactobacilli and pediococci from heterofermentative lactobacilli. Aldolase and phosphofructokinase were generally present in homofermentative but not in heterofermentative lactobacilli; a two-domain alcohol dehydrogenase and mannitol dehydrogenase were present in most heterofermentative lactobacilli but absent in most homofermentative organisms. Other genes were predominantly present in homofermentative lactobacilli (pyruvate formate lyase) or heterofermentative lactobacilli (lactaldehyde dehydrogenase and glycerol dehydratase). Cluster analysis of the phylogenomic tree and the average nucleotide identity grouped the genus Lactobacillus sensu lato into 24 phylogenetic groups, including pediococci, with stable intra- and intergroup relationships. Individual groups may be differentiated by characteristic metabolic properties. The link between phylogeny and physiology that is proposed in this study facilitates future studies on the ecology, physiology, and industrial applications of lactobacilli.