SPLICE-q: a Python tool for genome-wide quantification of splicing efficiency.

BACKGROUND: Introns are generally removed from primary transcripts to form mature RNA molecules in a post-transcriptional process called splicing. An efficient splicing of primary transcripts is an essential step in gene expression and its misregulation is related to numerous human diseases. Thus, to better understand the dynamics of this process and the perturbations that might be caused by aberrant transcript processing it is important to quantify splicing efficiency. RESULTS: Here, we introduce SPLICE-q, a fast and user-friendly Python tool for genome-wide SPLICing Efficiency quantification. It supports studies focusing on the implications of splicing efficiency in transcript processing dynamics. SPLICE-q uses aligned reads from strand-specific RNA-seq to quantify splicing efficiency for each intron individually and allows the user to select different levels of restrictiveness concerning the introns' overlap with other genomic elements such as exons of other genes. We applied SPLICE-q to globally assess the dynamics of intron excision in yeast and human nascent RNA-seq. We also show its application using total RNA-seq from a patient-matched prostate cancer sample. CONCLUSIONS: Our analyses illustrate that SPLICE-q is suitable to detect a progressive increase of splicing efficiency throughout a time course of nascent RNA-seq and it might be useful when it comes to understanding cancer progression beyond mere gene expression levels. SPLICE-q is available at: https://github.com/vrmelo/SPLICE-q.

LncRNA-OIS1 regulates DPP4 activation to modulate senescence induced by RAS.

Oncogene-induced senescence (OIS), provoked in response to oncogenic activation, is considered an important tumor suppressor mechanism. Long non-coding RNAs (lncRNAs) are transcripts longer than 200 nt without a protein-coding capacity. Functional studies showed that deregulated lncRNA expression promote tumorigenesis and metastasis and that lncRNAs may exhibit tumor-suppressive and oncogenic function. Here, we first identified lncRNAs that were differentially expressed between senescent and non-senescent human fibroblast cells. Using RNA interference, we performed a loss-function screen targeting the differentially expressed lncRNAs, and identified lncRNA-OIS1 (lncRNA#32, AC008063.3 or ENSG00000233397) as a lncRNA required for OIS. Knockdown of lncRNA-OIS1 triggered bypass of senescence, higher proliferation rate, lower abundance of the cell-cycle inhibitor CDKN1A and high expression of cell-cycle-associated genes. Subcellular inspection of lncRNA-OIS1 indicated nuclear and cytosolic localization in both normal culture conditions as well as following oncogene induction. Interestingly, silencing lncRNA-OIS1 diminished the senescent-associated induction of a nearby gene (Dipeptidyl Peptidase 4, DPP4) with established role in tumor suppression. Intriguingly, similar to lncRNA-OIS1, silencing DPP4 caused senescence bypass, and ectopic expression of DPP4 in lncRNA-OIS1 knockdown cells restored the senescent phenotype. Thus, our data indicate that lncRNA-OIS1 links oncogenic induction and senescence with the activation of the tumor suppressor DPP4.

Microprocessor dynamics shows co- and post-transcriptional processing of pri-miRNAs.

miRNAs are small regulatory RNAs involved in the regulation of translation of target transcripts. miRNA biogenesis is a multistep process starting with the cleavage of the primary miRNA transcript in the nucleus by the Microprocessor complex. Endogenous processing of pri-miRNAs is challenging to study and the in vivo kinetics of this process is not known. Here, we present a method for determining the processing kinetics of pri-miRNAs within intact cells over time, using a pulse-chase approach to label transcribed RNA during 15 min, and follow the processing within a 1-hour window after labeling with bromouridine. We show that pri-miRNAs exhibit different processing kinetics ranging from fast over intermediate to slow processing, and we provide evidence that pri-miRNA processing can occur both cotranscriptionally and post-transcriptionally.

A new microRNA target prediction tool identifies a novel interaction of a putative miRNA with CCND2.

Computational methods for miRNA target prediction vary in the algorithm used; and while one can state opinions about the strengths or weaknesses of each particular algorithm, the fact of the matter is that they fall substantially short of capturing the full detail of physical, temporal and spatial requirements of miRNA::target-mRNA interactions. Here, we introduce a novel miRNA target prediction tool called Targetprofiler that utilizes a probabilistic learning algorithm in the form of a hidden Markov model trained on experimentally verified miRNA targets. Using a large scale protein downregulation data set we validate our method and compare its performance to existing tools. We find that Targetprofiler exhibits greater correlation between computational predictions and protein downregulation and predicts experimentally verified miRNA targets more accurately than three other tools. Concurrently, we use primer extension to identify the mature sequence of a novel miRNA gene recently identified within a cancer associated genomic region and use Targetprofiler to predict its potential targets. Experimental verification of the ability of this small RNA molecule to regulate the expression of CCND2, a gene with documented oncogenic activity, confirms its functional role as a miRNA. These findings highlight the competitive advantage of our tool and its efficacy in extracting biologically significant results.

Metabolic Pulse-Chase RNA Labeling for pri-miRNA Processing Dynamics.

miRNA biogenesis is a multistep process starting with the cleavage of the primary miRNA transcript in the nucleus by the microprocessor complex. The pri-miRNA processing kinetics has a high impact on the final regulative role of the mature miRNAs on the expression of their target transcripts. Thus studying the in vivo kinetics of the miRNA biogenesis could give more insights into the contribution of each individual miRNA on regulation of gene expression. Here, we describe step by step a method to determine the processing kinetics of pri-miRNAs in vivo, using a pulse-chase approach that can be used in downstream applications such as qPCR or deep sequencing. We explain in detail the various aspects of this approach that can be applied to different mammalian cell types. The nature of this protocol allows the in vivo study of pri-miRNA processing kinetics in cells treated with different conditions, mutants, and/or cancer cell lines under physiological conditions.

Inhibiting Pri-miRNA Processing with Target Site Blockers.

The pri-miRNA processing is important for the final regulatory role of miRNAs on the expression of their target transcripts. The processing variability between pri-miRNAs can determine the final miRNA abundance better than primary transcription itself. Thus studying the in vivo pri-miRNA biogenesis could give more insights into the contribution of each individual miRNA on regulation of gene expression. Interfering processing of a specific pri-miRNA has been challenging due to the nature of the current RNA interfence methods. Here, we describe step by step a method to arrest processing of specific pri-miRNAs in vivo using LNA microRNA Target Site Blockers. We explain in detail the various aspects of this approach that can easily be applied to different mammalian cell types. The nature of this protocol allows the in vivo study of pri-miRNA processing and processing kinetics in cells treated with different conditions, mutants, and/or cancer cell lines under physiological conditions.

Transient N-6-Methyladenosine Transcriptome Sequencing Reveals a Regulatory Role of m6A in Splicing Efficiency.

Splicing efficiency varies among transcripts, and tight control of splicing kinetics is crucial for coordinated gene expression. N-6-methyladenosine (m6A) is the most abundant RNA modification and is involved in regulation of RNA biogenesis and function. The impact of m6A on regulation of RNA splicing kinetics is unknown. Here, we provide a time-resolved high-resolution assessment of m6A on nascent RNA transcripts and unveil its importance for the control of RNA splicing kinetics. We find that early co-transcriptional m6A deposition near splice junctions promotes fast splicing, while m6A modifications in introns are associated with long, slowly processed introns and alternative splicing events. In conclusion, we show that early m6A deposition specifies the fate of transcripts regarding splicing kinetics and alternative splicing.

Long ncRNA A-ROD activates its target gene DKK1 at its release from chromatin.

Long ncRNAs are often enriched in the nucleus and at chromatin, but whether their dissociation from chromatin is important for their role in transcription regulation is unclear. Here, we group long ncRNAs using epigenetic marks, expression and strength of chromosomal interactions; we find that long ncRNAs transcribed from loci engaged in strong long-range chromosomal interactions are less abundant at chromatin, suggesting the release from chromatin as a crucial functional aspect of long ncRNAs in transcription regulation of their target genes. To gain mechanistic insight into this, we functionally validate the long ncRNA A-ROD, which enhances DKK1 transcription via its nascent spliced released form. Our data provide evidence that the regulatory interaction requires dissociation of A-ROD from chromatin, with target specificity ensured within the pre-established chromosomal proximity. We propose that the post-transcriptional release of a subset of long ncRNAs from the chromatin-associated template plays an important role in their function as transcription regulators.

Prediction of miRNA targets.

Computational methods for miRNA target prediction are currently undergoing extensive review and evaluation. There is still a great need for improvement of these tools and bioinformatics approaches are looking towards high-throughput experiments in order to validate predictions. The combination of large-scale techniques with computational tools will not only provide greater credence to computational predictions but also lead to the better understanding of specific biological questions. Current miRNA target prediction tools utilize probabilistic learning algorithms, machine learning methods and even empirical biologically defined rules in order to build models based on experimentally verified miRNA targets. Large-scale protein downregulation assays and next-generation sequencing (NGS) are now being used to validate methodologies and compare the performance of existing tools. Tools that exhibit greater correlation between computational predictions and protein downregulation or RNA downregulation are considered the state of the art. Moreover, efficiency in prediction of miRNA targets that are concurrently verified experimentally provides additional validity to computational predictions and further highlights the competitive advantage of specific tools and their efficacy in extracting biologically significant results. In this review paper, we discuss the computational methods for miRNA target prediction and provide a detailed comparison of methodologies and features utilized by each specific tool. Moreover, we provide an overview of current state-of-the-art high-throughput methods used in miRNA target prediction.

Finding cancer-associated miRNAs: methods and tools.

Changes in the structure and/or the expression of protein coding genes were thought to be the major cause of cancer for many decades. The recent discovery of non-coding RNA (ncRNA) transcripts (i.e., microRNAs) suggests that the molecular biology of cancer is far more complex. MicroRNAs (miRNAs) have been under investigation due to their involvement in carcinogenesis, often taking up roles of tumor suppressors or oncogenes. Due to the slow nature of experimental identification of miRNA genes, computational procedures have been applied as a valuable complement to cloning. Numerous computational tools, implemented to recognize the features of miRNA biogenesis, have resulted in the prediction of novel miRNA genes. Computational approaches provide clues as to which are the dominant features that characterize these regulatory units and furthermore act by narrowing down the search space making experimental verification faster and cheaper. In combination with large scale, high throughput methods, such as deep sequencing, computational methods have aided in the discovery of putative molecular signatures of miRNA deregulation in human tumors. This review focuses on existing computational methods for identifying miRNA genes, provides an overview of the methodology undertaken by these tools, and underlies their contribution towards unraveling the role of miRNAs in cancer.