Genome-wide identification and predictive modeling of lincRNAs polyadenylation in cancer genome

Emerging Roles of RNA 3'-end Cleavage and Polyadenylation in Pathogenesis, Diagnosis and Therapy of Human Disorders

A crucial feature of gene expression involves RNA processing to produce 3′ ends through a process termed 3′ end cleavage and polyadenylation (CPA). This ensures the nascent RNA molecule can exit the nucleus and be translated to ultimately give rise to a protein which can execute a function. Further, alternative polyadenylation (APA) can produce distinct transcript isoforms, profoundly expanding the complexity of the transcriptome. CPA is carried out by multi-component protein complexes interacting with multiple RNA motifs and is tightly coupled to transcription, other steps of RNA processing, and even epigenetic modifications. CPA and APA contribute to the maintenance of a multitude of diverse physiological processes. It is therefore not surprising that disruptions of CPA and APA can lead to devastating disorders. Here, we review potential CPA and APA mechanisms involving both loss and gain of function that can have tremendous impacts on health and disease. Ultimately we highlight the emerging diagnostic and therapeutic potential CPA and APA offer.

Identifying Cancer Targets Based on Machine Learning Methods via Chou's 5-steps Rule and General Pseudo Components

In recent years, the successful implementation of human genome project has made people realize that genetic, environmental and lifestyle factors should be combined together to study cancer due to the complexity and various forms of the disease. The increasing availability and growth rate of 'big data' derived from various omics, opens a new window for study and therapy of cancer. In this paper, we will introduce the application of machine learning methods in handling cancer big data including the use of artificial neural networks, support vector machines, ensemble learning and naïve Bayes classifiers.

Stellaris® RNA Fluorescence In Situ Hybridization for the Simultaneous Detection of Immature and Mature Long Noncoding RNAs in Adherent Cells

RNA fluorescence in situ hybridization (FISH), long an indispensable tool for the detection and localization of RNA, is becoming an increasingly important complement to other gene expression analysis methods. Especially important for long noncoding RNAs (lncRNAs), RNA FISH adds the ability to distinguish between primary and mature lncRNA transcripts and thus to segregate the site of synthesis from the site of action.We detail a streamlined RNA FISH protocol for the simultaneous imaging of multiple primary and mature mRNA and lncRNA gene products and RNA variants in fixed mammalian cells. The technique makes use of fluorescently pre-labeled, short DNA oligonucleotides (circa 20 nucleotides in length), pooled into sets of up to 48 individual probes. The overall binding of multiple oligonucleotides to the same RNA target results in fluorescent signals that reveal clusters of RNAs or single RNA molecules as punctate spots without the need for enzymatic signal amplification. Visualization of these punctate signals, through the use of wide-field fluorescence microscopy, enables the counting of single transcripts down to one copy per cell. Additionally, by using probe sets with spectrally distinct fluorophores, multiplex analysis of gene-specific RNAs, or RNA variants, can be achieved. The presented examples illustrate how this method can add temporospatial information between the transcription event and both the location and the endurance of the mature lncRNA. We also briefly discuss post-processing of images and spot counting to demonstrate the capabilities of this method for the statistical analysis of RNA molecules per cell. This information can be utilized to determine both overall gene expression levels and cell-to-cell gene expression variation.

Alternative Polyadenylation: Another Foe in Cancer

Advancements in sequencing and transcriptome analysis methods have led to seminal discoveries that have begun to unravel the complexity of cancer. These studies are paving the way toward the development of improved diagnostics, prognostic predictions, and targeted treatment options. However, it is clear that pieces of the cancer puzzle are still missing. In an effort to have a more comprehensive understanding of the development and progression of cancer, we have come to appreciate the value of the noncoding regions of our genomes, partly due to the discovery of miRNAs and their significance in gene regulation. Interestingly, the miRNA-mRNA interactions are not solely dependent on variations in miRNA levels. Instead, the majority of genes harbor multiple polyadenylation signals on their 3' UTRs (untranslated regions) that can be differentially selected on the basis of the physiologic state of cells, resulting in alternative 3' UTR isoforms. Deregulation of alternative polyadenylation (APA) has increasing interest in cancer research, because APA generates mRNA 3' UTR isoforms with potentially different stabilities, subcellular localizations, translation efficiencies, and functions. This review focuses on the link between APA and cancer and discusses the mechanisms as well as the tools available for investigating APA events in cancer. Overall, detection of deregulated APA-generated isoforms in cancer may implicate some proto-oncogene activation cases of unknown causes and may help the discovery of novel cases; thus, contributing to a better understanding of molecular mechanisms of cancer. Mol Cancer Res; 14(6); 507-17. ©2016 AACR.

Genome wide discovery of long intergenic non-coding RNAs in Diamondback moth (Plutella xylostella) and their expression in insecticide resistant strains

Long non-coding RNAs (lncRNAs) play important roles in genomic imprinting, cancer, differentiation and regulation of gene expression. Here, we identified 3844 long intergenic ncRNAs (lincRNA) in Plutella xylostella, which is a notorious pest of cruciferous plants that has developed field resistance to all classes of insecticides, including Bacillus thuringiensis (Bt) endotoxins. Further, we found that some of those lincRNAs may potentially serve as precursors for the production of small ncRNAs. We found 280 and 350 lincRNAs that are differentially expressed in Chlorpyrifos and Fipronil resistant larvae. A survey on P. xylostella midgut transcriptome data from Bt-resistant populations revealed 59 altered lincRNA in two resistant strains compared with the susceptible population. We validated the transcript levels of a number of putative lincRNAs in deltamethrin-resistant larvae that were exposed to deltamethrin, which indicated that this group of lincRNAs might be involved in the response to xenobiotics in this insect. To functionally characterize DBM lincRNAs, gene ontology (GO) enrichment of their associated protein-coding genes was extracted and showed over representation of protein, DNA and RNA binding GO terms. The data presented here will facilitate future studies to unravel the function of lincRNAs in insecticide resistance or the response to xenobiotics of eukaryotic cells.

An improved poly(A) motifs recognition method based on decision level fusion

Polyadenylation is the process of addition of poly(A) tail to mRNA 3' ends. Identification of motifs controlling polyadenylation plays an essential role in improving genome annotation accuracy and better understanding of the mechanisms governing gene regulation. The bioinformatics methods used for poly(A) motifs recognition have demonstrated that information extracted from sequences surrounding the candidate motifs can differentiate true motifs from the false ones greatly. However, these methods depend on either domain features or string kernels. To date, methods combining information from different sources have not been found yet. Here, we proposed an improved poly(A) motifs recognition method by combing different sources based on decision level fusion. First of all, two novel prediction methods was proposed based on support vector machine (SVM): one method is achieved by using the domain-specific features and principle component analysis (PCA) method to eliminate the redundancy (PCA-SVM); the other method is based on Oligo string kernel (Oligo-SVM). Then we proposed a novel machine-learning method for poly(A) motif prediction by marrying four poly(A) motifs recognition methods, including two state-of-the-art methods (Random Forest (RF) and HMM-SVM), and two novel proposed methods (PCA-SVM and Oligo-SVM). A decision level information fusion method was employed to combine the decision values of different classifiers by applying the DS evidence theory. We evaluated our method on a comprehensive poly(A) dataset that consists of 14,740 samples on 12 variants of poly(A) motifs and 2750 samples containing none of these motifs. Our method has achieved accuracy up to 86.13%. Compared with the four classifiers, our evidence theory based method reduces the average error rate by about 30%, 27%, 26% and 16%, respectively. The experimental results suggest that the proposed method is more effective for poly(A) motif recognition.