RNAi-mediated silencing of the Bmi-1 gene causes growth inhibition and enhances doxorubicin-induced apoptosis in MCF-7 cells

The oncogene Bmi-1 is a member of the Polycomb group gene family. Its expression is found to be greatly increased in a number of malignant tumors including breast cancer. This could suggest Bmi-1 as a potent therapeutic target. In this study, RNAi was introduced to down-regulate the expression of Bmi-1 in a highly malignant breast adenocarcinoma cell line, MCF-7. A thorough study of the biological behavior and chemosensitivity changes of the MCF-7 cells was carried out in context to the therapeutic potential of Bmi-1. The results obtained indicated that siRNA targeting of Bmi-1 could lead to an efficient and specific inhibition of endogenous Bmi-1 activity. The mRNA and protein expression of Bmi-1 were determined by RT-PCR and Western blot, respectively. Furthermore, silencing of Bmi-1 resulted in a drastic inhibition of the growth of MCF-7 cells as well as G(1) /S phase transition. The number of target cells was found to increase in phase G (0) /G (1) and decrease in the S phase, but no increase in the basal level of apoptosis was noticed. On the other hand, a reduction in the expression of cyclin D1 and an increase in the expression of p21 were also noticed. Silencing of Bmi-1 made the MCF-7 cells more sensitive to the chemotherapeutic agent doxorubicin and induced a significantly higher percentage of apoptotic cells. Here, we report on a study regarding the RNAi-mediated silencing of the Bmi-1 gene in breast cancer.     

Crystal structure of a methyltransferase from a no-known-vector Flavivirus

Presently known flaviviruses belong to three major evolutionary branches: tick-borne viruses, mosquito-borne viruses and viruses with no known vector. Here we present the crystal structure of the Yokose virus methyltransferase at 1.7 Å resolution, the first structure of a methyltransferase of a Flavivirus with no known vector. Structural comparison of three methyltransferases representative of each of the Flavivirus branches shows that fold and structures are closely conserved, most differences being related to surface loops flexibility. Analysis of the conserved residues throughout all the sequenced flaviviral methyltransferases reveals that, besides the central cleft hosting the substrate and cofactor binding sites, a second, almost continuous, patch is conserved and points away from active site towards the back of the protein. The high level of structural conservation in this region could be functional for the methyltransferase/RNA interaction and stabilization of the ensuing complex.     

From microarrays to mechanisms of brain development and function

Microarray technology provides a powerful approach to understand complex biological systems. The most common application of microarray technology is to document gene expression profiles of all genes within a genome in response to specific conditions such as disease, drug application, or genotype. One result of this technology is the ability to ascribe activities to genes with unknown functions - such rationale is the basis behind ‘functional genomics’. This approach is particularly well-suited to studies of the brain because roughly one third to one half of all genes in vertebrate genomes are expressed in the brain. However, less than half of such genes have any defined function. While a large number of studies have applied microarray technology to the brain, few studies have followed up the expression profiling approach with functional characterization of the genes identified. In this review, I highlight recent research that reflects the initial promise of functional genomics in the brain. I focus on neural differentiation with particular emphasis on synapse development.     

The interstrand amino acid pairs play a significant role in determining the parallel or antiparallel orientation of β-strands

It is widely considered that it is not appropriate to treat β-pairs in isolation, since other secondary structural models (such as helices, coils), protein topology and protein tertiary structures would limit β-strand pairing. However, to understand the underlying mechanisms of β-sheet formation, studies ought to be performed separately on more concrete aspects. In this study, we focus on the parallel or antiparallel orientation of β-strands. First, statistical analysis was performed on the relative frequencies of the interstrand amino acid pairs within parallel and antiparallel β-strands. Consequently, features were extracted by singular value decomposition from the statistical results. By using the support vector machine to distinguish the features extracted from the two types of β-strands, high accuracy was achieved (up to 99.4%). This suggests that the interstrand amino acid pairs play a significant role in determining the parallel or antiparallel orientation of β-strands. These results may provide useful information for developing other useful algorithms to examine to the β-strand folding pathways, and could eventually lead to protein structure predictions.     

Extraction of RNA from Ca-alginate-encapsulated yeast for transcriptional profiling

We have developed a method for preparing high-quality total RNA from Ca-alginate-encapsulated Saccharomyces cerevisiae that is suitable for microarray analysis. Encapsulated cells were harvested from immobilized cell reactors and flash-frozen in liquid nitrogen. Following low-temperature mechanical disruption, cells were freed from Ca-alginate by reverse ionotropic gelation and purified by centrifugation, and then total RNA was extracted using hot acid phenol. The yield and quality of the RNA were consistently high; the RNA was free of contaminating alginate, and in microarray analysis it performed as well as RNA isolated from planktonic cells.     

Structural Alphabets for Protein Structure Classification: A Comparison Study

Finding structural similarities between proteins often helps reveal shared functionality, which otherwise might not be detected by native sequence information alone. Such similarity is usually detected and quantified by protein structure alignment. Determining the optimal alignment between two protein structures, however, remains a hard problem. An alternative approach is to approximate each three-dimensional protein structure using a sequence of motifs derived from a structural alphabet. Using this approach, structure comparison is performed by comparing the corresponding motif sequences or structural sequences. In this article, we measure the performance of such alphabets in the context of the protein structure classification problem. We consider both local and global structural sequences. Each letter of a local structural sequence corresponds to the best matching fragment to the corresponding local segment of the protein structure. The global structural sequence is designed to generate the best possible complete chain that matches the full protein structure. We use an alphabet of 20 letters, corresponding to a library of 20 motifs or protein fragments having four residues. We show that the global structural sequences approximate well the native structures of proteins, with an average coordinate root mean square of 0.69 Å over 2225 test proteins. The approximation is best for all α-proteins, while relatively poorer for all β-proteins. We then test the performance of four different sequence representations of proteins (their native sequence, the sequence of their secondary-structure elements, and the local and global structural sequences based on our fragment library) with different classifiers in their ability to classify proteins that belong to five distinct folds of CATH. Without surprise, the primary sequence alone performs poorly as a structure classifier. We show that addition of either secondary-structure information or local information from the structural sequence considerably improves the classification accuracy. The two fragment-based sequences perform better than the secondary-structure sequence but not well enough at this stage to be a viable alternative to more computationally intensive methods based on protein structure alignment.     

Prediction of Interacting Protein Pairs from Sequence Using a Bayesian Method

With the development of bioinformatics, more and more protein sequence information has become available. Meanwhile, the number of known protein–protein interactions (PPIs) is still very limited. In this article, we propose a new method for predicting interacting protein pairs using a Bayesian method based on a new feature representation. We trained our model using data on 6,459 PPI pairs from the yeast Saccharomyces cerevisiae core subset. Using six species of DIP database, our model demonstrates an average prediction accuracy of 93.67%. The result showed that our method is superior to other methods in both computing time and prediction accuracy.     

Automated classification of bird and amphibian calls using machine learning: A comparison of methods

We compared the ability of three machine learning algorithms (linear discriminant analysis, decision tree, and support vector machines) to automate the classification of calls of nine frogs and three bird species. In addition, we tested two ways of characterizing each call to train/test the system. Calls were characterized with four standard call variables (minimum and maximum frequencies, call duration and maximum power) or eleven variables that included three standard call variables (minimum and maximum frequencies, call duration) and a coarse representation of call structure (frequency of maximum power in eight segments of the call). A total of 10,061 isolated calls were used to train/test the system. The average true positive rates for the three methods were: 94.95% for support vector machine (0.94% average false positive rate), 89.20% for decision tree (1.25% average false positive rate) and 71.45% for linear discriminant analysis (1.98% average false positive rate). There was no statistical difference in classification accuracy based on 4 or 11 call variables, but this efficient data reduction technique in conjunction with the high classification accuracy of the SVM is a promising combination for automated species identification by sound. By combining automated digital recording systems with our automated classification technique, we can greatly increase the temporal and spatial coverage of biodiversity data collection.     

A Modified Ant Colony Optimization Algorithm for Tumor Marker Gene Selection

Microarray data are often extremely asymmetric in dimensionality,such as thousands or even tens of thousands of genes but only a few hundreds of samples or less.Such extreme asymmetry between the dimensionality of genes and samples can lead to inaccurate diagnosis of disease in clinic.Therefore,it has been shown that selecting a small set of marker genes can lead to improved classification accuracy.In this paper,a simple modified ant colony optimization (ACO) algorithm is proposed to select tumor-related ma...     

An Anthropomorphic Robot Hand Developed Based on Underactuated Mechanism and Controlled by EMG Signals

When developing a humanoid myo-control hand,not only the mechanical structure should be considered to afford a highdexterity,but also the myoelectric (electromyography,EMG) control capability should be taken into account to fully accomplishthe actuation tasks.This paper presents a novel humanoid robotic myocontrol hand (AR hand Ⅲ) which adopted an underac-tuated mechanism and a forearm myocontrol EMG method.The AR hand Ⅲ has five fingers and 15 joints,and actuated by threeembedded motors.Underactuation can be found within each finger and between the rest three fingers (the middle finger,the ringfinger and the little finger) when the hand is grasping objects.For the EMG control,two specific methods are proposed:thethree-fingered hand gesture configuration of the AR hand Ⅲ and a pattern classification method of EMG signals based on astatistical learning algorithm-Support Vector Machine (SVM).Eighteen active hand gestures of a testee are recognized ef-fectively,which can be directly mapped into the motions of AR hand Ⅲ.An on-line EMG control scheme is established basedon two different decision functions:one is for the discrimination between the idle and active modes,the other is for the recog-nition of the active modes.As a result,the AR hand Ⅲ can swiftly follow the gesture instructions of the testee with a time delayless than 100 ms.