Transcriptome analysis reveals physiological characteristics required for magnetosome formation in Magnetospirillum gryphiswaldense MSR-1.

Magnetosome synthesis ability of Magnetospirillum gryphiswaldense MSR-1 in an autofermentor can be precisely controlled through strict control of dissolved oxygen concentration. In this study, using transcriptome data we discovered gene transcriptional differences and compared physiological characteristics of MSR-1 cells cultured under aerobic (high-oxygen) and micro-aerobic (low-oxygen) conditions. The results showed that 77 genes were up-regulated and 95 genes were down-regulated significantly under micro-aerobic situation. These genes were involved primarily in the categories of cell metabolism, transport, regulation and unknown-function proteins. The nutrient transport and physiological metabolism were slowed down under micro-aerobic condition, whereas dissimilatory denitrification pathways were activated and it may supplemental energy was made available for magnetosome synthesis. The result suggested that the genes of magnetosome membrane proteins (Mam and Mms) are not directly regulated by oxygen level, or are constitutively expressed. A proposed regulatory network of differentially expressed genes reflects the complexity of physiological metabolism in MSR-1, and suggests that some yet-unknown functional proteins play important roles such as ferric iron uptake and transport during magnetosome synthesis. The transcriptome data provides a holistic view of the responses of MSR-1 cells to differing oxygen levels. This approach will give new insights into general principles of magnetosome formation.

Prediction of outer membrane proteins by combining the position- and composition-based features of sequence profiles.

Locating the transmembrane regions of outer membrane proteins (OMPs) is highly important for deciphering their biological functions at both molecular and cellular levels. Here, we propose a novel method to predict the transmembrane regions of OMPs by employing the position- and composition-based features of sequence profiles. Furthermore, a simple probability-based prediction model, which is estimated by the secondary structures of structurally known OMPs, is also developed. Considering that these two methods are both effective and well complementary, we integrate them into a method called TransOMP, which is also capable of identifying OMPs. Furthermore, we develop an OMP identification measure I_CScore by considering transmembrane regions by TransOMP and secondary structural topology by SSEA-OMP. Our methods were benchmarked against state-of-the-art methods and assessed in the genome of Escherichia coli. Benchmark results confirmed that our methods were reliable and useful. Meanwhile, we constructed an OMP prediction web server, which can be used for OMP identification, transmembrane region location, and 3D model building.

hCKSAAP_UbSite: improved prediction of human ubiquitination sites by exploiting amino acid pattern and properties.

As one of the most common post-translational modifications, ubiquitination regulates the quantity and function of a variety of proteins. Experimental and clinical investigations have also suggested the crucial roles of ubiquitination in several human diseases. The complicated sequence context of human ubiquitination sites revealed by proteomic studies highlights the need of developing effective computational strategies to predict human ubiquitination sites. Here we report the establishment of a novel human-specific ubiquitination site predictor through the integration of multiple complementary classifiers. Firstly, a Support Vector Machine (SVM) classier was constructed based on the composition of k-spaced amino acid pairs (CKSAAP) encoding, which has been utilized in our previous yeast ubiquitination site predictor. To further exploit the pattern and properties of the ubiquitination sites and their flanking residues, three additional SVM classifiers were constructed using the binary amino acid encoding, the AAindex physicochemical property encoding and the protein aggregation propensity encoding, respectively. Through an integration that relied on logistic regression, the resulting predictor termed hCKSAAP_UbSite achieved an area under ROC curve (AUC) of 0.770 in 5-fold cross-validation test on a class-balanced training dataset. When tested on a class-balanced independent testing dataset that contains 3419 ubiquitination sites, hCKSAAP_UbSite has also achieved a robust performance with an AUC of 0.757. Specifically, it has consistently performed better than the predictor using the CKSAAP encoding alone and two other publicly available predictors which are not human-specific. Given its promising performance in our large-scale datasets, hCKSAAP_UbSite has been made publicly available at our server (http://protein.cau.edu.cn/cksaap_ubsite/).

Two distinct sites are essential for virulent infection and support of variant satellite RNA replication in spontaneous beet black scorch virus variants.

Spontaneous point mutations of virus genomes are important in RNA virus evolution and often result in modifications of their biological properties. Spontaneous variants of beet black scorch virus (BBSV) and its satellite (sat) RNA were generated from cDNA clones by serial propagation in Chenopodium amaranticolor and Nicotiana benthamiana. Inoculation with recombinant RNAs synthesized in vitro revealed BBSV variants with divergent infectious phenotypes that affected either symptom expression or replication of satRNA variants. Sequence alignments showed a correlation between the phenotypes and distinct BBSV genomic loci in the 3'UTR or in the domain encoding the viral replicase. Comparative analysis between a virulent variant, BBSV-m294, and the wild-type (wt) BBSV by site-directed mutagenesis indicated that a single-nucleotide substitution of a uridine to a guanine at nt 3477 in the 3'UTR was responsible for significant increases in viral pathogenicity. Gain-of-function analyses demonstrated that the ability of the BBSV variants to support replication of variant satRNAs was mainly determined by aa 516 in the P82 replicase. In this case, an arginine substitution for a glutamine residue was essential for high levels of replication, and alterations of other residues surrounding position 516 in the wtBBSV isolate led to only minor phenotypic effects. These results provide evidence that divergence of virus functions affecting pathogenicity and supporting parasitic replication can be determined by a single genetic site, either a nucleotide or an amino acid. The results suggest that complex interactions occur between virus and associated satRNAs during virus evolution.

N-[2-(5,5-dimethyl-1,3-dioxane-2-yl)ethyl]amino acids: their synthesis, anti-inflammatory evaluation and QSAR analysis.

Developing novel anti-inflammatory drugs is increasingly important in modern pharmaceutical industry. In this work, the reactions of both amino acids and their methylesters with 3-(5,5-dimethyl-1,3-dioxane-2-yl)propanal (2) were performed to either directly provide the goal products N-[2-(5,5-dimethyl-1,3-dioxane-2-yl)ethyl]amino acids (4a-s) in 9-65% yields or provide the intermediates N-[2-(5,5-dimethyl-1,3-dioxane-2-yl)ethyl]amino acid methylesters (3a-s) in 78-87% yields. The saponification of 3a-s provided 4a-s in 80-89% yields. Using a xylene-induced ear edema model, the anti-inflammatory activities of these newly synthesized anti-inflammatory agents were evaluated. The results indicated that comparing to the vehicle control 17 out of 4a-s significantly inhibited the development of inflammation in mice (p<0.01). In particular, eight out of 4a-s exhibited an even higher anti-inflammatory activity than the standard reference drug aspirin (p<0.05-0.01). A QSAR analysis was performed by use of the molecular descriptors generated from e-dragon software. The predictive accuracy of the established QSAR model implies that it may be promising for screening the new derivatives of 2-position amino acid substituted 1,3-dioxanes as potential anti-inflammatory agents.