Refinement of the spectra of exon usage by combined effects of extracellular stimulus and intracellular factors

Finely tuned differential expression of alternative splice variants contributes to important physiological processes such as the fine-tuning of electrical firing or hearing frequencies; yet the underlying molecular basis for the expression control is not clear. The inclusion levels of four depolarization-regulated alternative exons were measured by RT-PCR in GH₃ pituitary cells under different conditions of stimulation and/or RNA interference of splicing factors. The usage of the exons was reduced by membrane depolarization to various extents and was differentially modulated by the knock-down of splicing factors hnRNP L, L-like, I (PTBP1) or K or their combinations. A spectrum of each exon's level was produced under six knock-down conditions and was significantly shifted by depolarization. When all these conditions were considered together, a more refined or expanded spectrum of exon usage was obtained for each of the four exons. As a proof of principle for the molecular basis of the fine-tuning of exon usage, we show in the cases of hnRNP L and LL that their differential effects through the same element or different combinations of RNA sequences by the same factor hnRNP L are critical. The results thus demonstrate that the combined effect of varying extracellular stimuli and intracellular factors/RNA sequences refines or expands the spectra of endogenous exon usage, likely contributing to the fine-tuning of cellular properties.     

Identifying functional miRNA-mRNA regulatory modules with correspondence latent dirichlet allocation

MOTIVATION: MicroRNAs (miRNAs) are small non-coding RNAs that cause mRNA degradation and translational inhibition. They are important regulators of development and cellular homeostasis through their control of diverse processes. Recently, great efforts have been made to elucidate their regulatory mechanism, but the functions of most miRNAs and their precise regulatory mechanisms remain elusive. With more and more matched expression profiles of miRNAs and mRNAs having been made available, it is of great interest to utilize both expression profiles to discover the functional regulatory networks of miRNAs and their target mRNAs for potential biological processes that they may participate in. RESULTS: We present a probabilistic graphical model to discover functional miRNA regulatory modules at potential biological levels by integrating heterogeneous datasets, including expression profiles of miRNAs and mRNAs, with or without the prior target binding information. We applied this model to a mouse mammary dataset. It effectively captured several biological process specific modules involving miRNAs and their target mRNAs. Furthermore, without using prior target binding information, the identified miRNAs and mRNAs in each module show a large proportion of overlap with predicted miRNA target relationships, suggesting that expression profiles are crucial for both target identification and discovery of regulatory modules.     

Evolutionarily emerged G tracts between the polypyrimidine tract and 3′ AG are splicing silencers enriched in genes involved in cancer

Background

The 3′ splice site (SS) at the end of pre-mRNA introns has a consensus sequence (Y)_nNYAG for constitutive splicing of mammalian genes. Deviation from this consensus could change or interrupt the usage of the splice site leading to alternative or aberrant splicing, which could affect normal cell function or even the development of diseases. We have shown that the position “N” can be replaced by a CA-rich RNA element called CaRRE1 to regulate the alternative splicing of a group of genes.

Results

Taking it a step further, we searched the human genome for purine-rich elements between the -3 and -10 positions of the 3′ splice sites of annotated introns. This identified several thousand such 3′SS; more than a thousand of them contain at least one copy of G tract. These sites deviate significantly from the consensus of constitutive splice sites and are highly associated with alterative splicing events, particularly alternative 3′ splice and intron retention. We show by mutagenesis analysis and RNA interference that the G tracts are splicing silencers and a group of the associated exons are controlled by the G tract binding proteins hnRNP H/F. Species comparison of a group of the 3′SS among vertebrates suggests that most (~87%) of the G tracts emerged in ancestors of mammals during evolution. Moreover, the host genes are most significantly associated with cancer.

Conclusion

We call these elements together with CaRRE1 regulatory RNA elements between the Py and 3′AG (REPA). The emergence of REPA in this highly constrained region indicates that this location has been remarkably permissive for the emergence of de novo regulatory RNA elements, even purine-rich motifs, in a large group of mammalian genes during evolution. This evolutionary change controls alternative splicing, likely to diversify proteomes for particular cellular functions.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-1143) contains supplementary material, which is available to authorized users.     

Control of alternative pre-mRNA splicing by Ca(++) signals

Alternative pre-mRNA splicing is a common way of gene expression regulation in metazoans. The selective use of specific exons can be modulated in response to various manipulations that alter Ca(++) signals, particularly in neurons. A number of splicing factors have also been found to be controlled by Ca(++) signals. Moreover, pre-mRNA elements have been identified that are essential and sufficient to mediate Ca(++)-regulated splicing, providing model systems for dissecting the involved molecular components. In neurons, this regulation likely contributes to the fine-tuning of neuronal properties.     

Combined feature selection and cancer prognosis using support vector machine regression

Prognostic prediction is important in medical domain, because it can be used to select an appropriate treatment for a patient by predicting the patient's clinical outcomes. For high-dimensional data, a normal prognostic method undergoes two steps: feature selection and prognosis analysis. Recently, the L?-L?-norm Support Vector Machine (L?-L? SVM) has been developed as an effective classification technique and shown good classification performance with automatic feature selection. In this paper, we extend L?-L? SVM for regression analysis with automatic feature selection. We further improve the L?-L? SVM for prognostic prediction by utilizing the information of censored data as constraints. We design an efficient solution to the new optimization problem. The proposed method is compared with other seven prognostic prediction methods on three realworld data sets. The experimental results show that the proposed method performs consistently better than the medium performance. It is more efficient than other algorithms with the similar performance.