hnRNP E1 and E2 have distinct roles in modulating HIV-1 gene expression

Pre-mRNA processing, including 5' end capping, splicing, and 3' end cleavage/polyadenylation, are events coordinated by transcription that can influence the subsequent export and translation of mRNAs. Coordination of RNA processing is crucial in retroviruses such as HIV-1, where inefficient splicing and the export of intron-containing RNAs are required for expression of the full complement of viral proteins. RNA processing can be affected by both viral and cellular proteins, and in this study we demonstrate that a member of the hnRNP E family of proteins can modulate HIV-1 RNA metabolism and expression. We show that hnRNP E1/E2 are able to interact with the ESS3a element of the bipartite ESS in tat/rev exon 3 of HIV-1 and that modulation of hnRNP E1 expression alters HIV-1 structural protein synthesis. Overexpression of hnRNP E1 leads to a reduction in Rev, achieved in part through a decrease in rev mRNA levels. However, the reduction in Rev levels cannot fully account for the effect of hnRNP E1, suggesting that hmRNP E1 might also act to suppress viral RNA translation. Deletion mutagenesis determined that the C-terminal end of hnRNP E1 was required for the reduction in Rev expression and that replacing this portion of hnRNP E1 with that of hnRNP E2, despite the high degree of conservation, could not rescue the loss of function

Predictions of RNA secondary structure by combining homologous sequence information

Motivation: Secondary structure prediction of RNA sequences is an important problem. There have been progresses in this area, but the accuracy of prediction from an RNA sequence is still limited. In many cases, however, homologous RNA sequences are available with the target RNA sequence whose secondary structure is to be predicted

Improving the accuracy of predicting secondary structure for aligned RNA sequences

Considerable attention has been focused on predicting the secondary structure for aligned RNA sequences since it is useful not only for improving the limiting accuracy of conventional secondary structure prediction but also for finding non-coding RNAs in genomic sequences. Although there exist many algorithms of predicting secondary structure for aligned RNA sequences, further improvement of the accuracy is still awaited. In this article, toward improving the accuracy, a theoretical classification of state-of-the-art algorithms of predicting secondary structure for aligned RNA sequences is presented. The classification is based on the viewpoint of maximum expected accuracy (MEA), which has been successfully applied in various problems in bioinformatics. The classification reveals several disadvantages of the current algorithms but we propose an improvement of a previously introduced algorithm (CentroidAlifold). Finally, computational experiments strongly support the theoretical classification and indicate that the improved CentroidAlifold substantially outperforms other algorithms

Prediction of RNA secondary structure by maximizing pseudo-expected accuracy

<p>Abstract</p> <p>Background</p> <p>Recent studies have revealed the importance of considering the entire distribution of possible secondary structures in RNA secondary structure predictions; therefore, a new type of estimator is proposed including the maximum expected accuracy (MEA) estimator. The MEA-based estimators have been designed to maximize the expected accuracy of the base-pairs and have achieved the highest level of accuracy. Those methods, however, do not give the single best prediction of the structure, but employ parameters to control the trade-off between the sensitivity and the positive predictive value (PPV). It is unclear what parameter value we should use, and even the well-trained default parameter value does not, in general, give the best result in popular accuracy measures to each RNA sequence.</p> <p>Results</p> <p>Instead of using the expected values of the popular accuracy measures for RNA secondary structure prediction, which is difficult to be calculated, the <it>pseudo</it>-expected accuracy, which can easily be computed from base-pairing probabilities, is introduced. It is shown that the pseudo-expected accuracy is a good approximation in terms of sensitivity, PPV, MCC, or F-score. The pseudo-expected accuracy can be approximately maximized for each RNA sequence by stochastic sampling. It is also shown that well-balanced secondary structures between sensitivity and PPV can be predicted with a small computational overhead by combining the pseudo-expected accuracy of MCC or F-score with the γ-centroid estimator.</p> <p>Conclusions</p> <p>This study gives not only a method for predicting the secondary structure that balances between sensitivity and PPV, but also a general method for approximately maximizing the (pseudo-)expected accuracy with respect to various evaluation measures including MCC and F-score.</p

Directed acyclic graph kernels for structural RNA analysis

<p>Abstract</p> <p>Background</p> <p>Recent discoveries of a large variety of important roles for non-coding RNAs (ncRNAs) have been reported by numerous researchers. In order to analyze ncRNAs by kernel methods including support vector machines, we propose stem kernels as an extension of string kernels for measuring the similarities between two RNA sequences from the viewpoint of secondary structures. However, applying stem kernels directly to large data sets of ncRNAs is impractical due to their computational complexity.</p> <p>Results</p> <p>We have developed a new technique based on directed acyclic graphs (DAGs) derived from base-pairing probability matrices of RNA sequences that significantly increases the computation speed of stem kernels. Furthermore, we propose profile-profile stem kernels for multiple alignments of RNA sequences which utilize base-pairing probability matrices for multiple alignments instead of those for individual sequences. Our kernels outperformed the existing methods with respect to the detection of known ncRNAs and kernel hierarchical clustering.</p> <p>Conclusion</p> <p>Stem kernels can be utilized as a reliable similarity measure of structural RNAs, and can be used in various kernel-based applications.</p

Low-Dimensional Semiconducting Scintillators

In recent times, many researchers have been developing devices that exceed the conventional capabilities by constructing a low-dimensional structure and exploiting its unique and advantageous properties. These are entirely different from those of the conventional materials due to quantum confinement effects, and their applications are called "nanotechnology". This chapter shows that a demonstration of the "nanotechnology" in the development of scintillators, a kind of ionizing radiation sensor, that can simultaneously achieve both a quick response and a large signal output, which has been difficult by using conventional bulk materials.\nThe second section explains the physics of semiconducting scintillators including the thermal quenching (TQ) that prevents the operation at room temperature (RT). In the third section, a quantum confinement system (QCS) is introduced to solve this problem, and the physical effects are described. In the fourth section, low-dimensional (LD) semiconducting scintillators are demonstrated to show the possibilities of developing them with very short decay time constants and practical emission efficiency at RT. The fifth section contains the summary with a guide for the development of new LD semiconducting systems that will be an important field in the development of inorganic scintillators in the following decade

A novel function for Sam68: Enhancement of HIV-1 RNA 3′ end processing

Both cis elements and host cell proteins can significantly affect HIV-1 RNA processing and viral gene expression. Previously, we determined that the exon splicing silencer (ESS3) within the terminal exon of HIV-1 not only reduces use of the adjacent 3′ splice site but also prevents Rev-induced export of the unspliced viral RNA to the cytoplasm. In this report, we demonstrate that loss of unspliced viral RNA export is correlated with the inhibition of 3′ end processing by the ESS3. Furthermore, we find that the host factor Sam68, a stimulator of HIV-1 protein expression, is able to reverse the block to viral RNA export mediated by the ESS3. The reversal is associated with a stimulation of 3′ end processing of the unspliced viral RNA. Our findings identify a novel activity for the ESS3 and Sam68 in regulating HIV-1 RNA polyadenylation. Furthermore, the observations provide an explanation for how Sam68, an exclusively nuclear protein, modulates cytoplasmic utilization of the affected RNAs. Our finding that Sam68 is also able to enhance 3′ end processing of a heterologous RNA raises the possibility that it may play a similar role in regulating host gene expression