Combining DGE and RNA-sequencing data to identify new polyA+ non-coding transcripts in the human genome

Crosstalk between lncRNAs in the apoptotic pathway and therapeutic targets in cancer

The assertion that a significant portion of the mammalian genome has not been translated and that non-coding RNA accounts for over half of polyadenylate RNA have received much attention. In recent years, increasing evidence proposes non-coding RNAs (ncRNAs) as new regulators of various cellular processes, including cancer progression and nerve damage. Apoptosis is a type of programmed cell death critical for homeostasis and tissue development. Cancer cells often have inhibited apoptotic pathways. It has recently been demonstrated that up/down-regulation of various lncRNAs in certain types of tumors shapes cancer cells' response to apoptotic stimuli. This review discusses the most recent studies on lncRNAs and apoptosis in healthy and cancer cells. In addition, the role of lncRNAs as novel targets for cancer therapy is reviewed here. Finally, since it has been shown that lncRNA expression is associated with specific types of cancer, the potential for using lncRNAs as biomarkers is also discussed.

GAS5‑mediated regulation of cell signaling (Review)

In recent years, an increasing number of long non-coding RNAs (lncRNAs) have been discovered using microarrays and nucleic acid sequencing technology. LncRNAs exert crucial biological functions by regulating signaling pathways. In particular, the lncRNA growth arrest-specific transcript 5 (GAS5) has been documented to serve a crucial role in numerous signaling pathways. This article discusses the latest developments in the association between GAS5 and microRNA (miRNA), p53, mTOR, glucocorticoid response element (GRE) and AKT in order to investigate the roles served by GAS5. miRNAs can activate related signaling pathways and GAS5 can combine with miRNA to regulate related signaling pathways. GAS5 may regulate p53 expression via derivation of snoRNA, but the underlying mechanism requires further investigation. GAS5 overxpresion reduces the expression level of mTOR, which is induced by inhibiting miR-106a-5p expression. GAS5 is a sponge of GR, and serves a role in controlling and maintaining glucocorticoid sensitivity and drug resistance via competitive combination with GR. GAS5 can interact with miRNAs, such as miR-21 and miR-532-5p, to regulate the expression of AKT signaling pathway, affecting cell survival and apoptosis. Collectively, the data indicate that GAS5 serves a key role in the miRNA, p53, mTOR, GRE, and AKT signaling pathways. GAS5 regulates complex intracellular signaling pathways primarily through three modes of action, all of which are interrelated: Signal, decoy and guide. In the present article, latest developments in the association between GAS5 and a number of cellular signaling pathways are discussed to examine the tumor suppressive role of GAS5.

A Novel Integrative Approach for Non-coding RNA Classification Based on Deep Learning

Background:

Molecular biomarkers show new ways to understand many disease processes. Noncoding RNAs as biomarkers play a crucial role in several cellular activities, which are highly correlated to many human diseases especially cancer. The classification and the identification of ncRNAs have become a critical issue due to their application, such as biomarkers in many human diseases.

Objective:

Most existing computational tools for ncRNA classification are mainly used for classifying only one type of ncRNA. They are based on structural information or specific known features. Furthermore, these tools suffer from a lack of significant and validated features. Therefore, the performance of these methods is not always satisfactory.

Methods:

We propose a novel approach named imCnC for ncRNA classification based on multisource deep learning, which integrates several data sources such as genomic and epigenomic data to identify several ncRNA types. Also, we propose an optimization technique to visualize the extracted features pattern from the multisource CNN model to measure the epigenomics features of each ncRNA type.

Results:

The computational results using a dataset of 16 human ncRNA classes downloaded from RFAM show that imCnC outperforms the existing tools. Indeed, imCnC achieved an accuracy of 94,18%. In addition, our method enables to discover new ncRNA features using an optimization technique to measure and visualize the features pattern of the imCnC classifier.

Long Noncoding RNA Discovery in Cardiovascular Disease: Decoding Form to Function

Despite significant improvements during the past 3 decades, cardiovascular disease remains a leading worldwide health epidemic. The recent identification of a fascinating group of mediators known as long noncoding RNAs (lncRNAs) has provided a wealth of new biology to explore for cardiovascular risk mitigation. lncRNAs are expressed in a highly context-specific fashion, and multiple lines of evidence implicated them in diverse biological processes. Indeed, abnormalities of lncRNAs have been directly linked with human ailments, including cardiovascular biology and disease. Of particular interest to the cardiovascular research community, dysregulation in lncRNA regulatory circuits have been associated with cardiac pathological hypertrophy, vascular disease, cell fate programming and development, atherosclerosis, dyslipidemia, and metabolic syndrome. Although techniques in interrogating noncoding RNAs are rapidly evolving, a major challenge in studying lncRNAs remains navigating through multiple technical constraints. In this review, we provide a road map for lncRNA discovery and interrogation in biological systems relevant to cardiovascular disease and highlight approaches to decipher their modes of action.

The 'TranSeq' 3'-end sequencing method for high-throughput transcriptomics and gene space refinement in plant genomes

High-throughput RNA sequencing has proven invaluable not only to explore gene expression but also for both gene prediction and genome annotation. However, RNA sequencing, carried out on tens or even hundreds of samples, requires easy and cost-effective sample preparation methods using minute RNA amounts. Here, we present TranSeq, a high-throughput 3'-end sequencing procedure that requires 10- to 20-fold fewer sequence reads than the current transcriptomics procedures. TranSeq significantly reduces costs and allows a greater increase in size of sample sets analyzed in a single experiment. Moreover, in comparison with other 3'-end sequencing methods reported to date, we demonstrate here the reliability and immediate applicability of TranSeq and show that it not only provides accurate transcriptome profiles but also produces precise expression measurements of specific gene family members possessing high sequence similarity. This is difficult to achieve in standard RNA-seq methods, in which sequence reads cover the entire transcript. Furthermore, mapping TranSeq reads to the reference tomato genome facilitated the annotation of new transcripts improving >45% of the existing gene models. Hence, we anticipate that using TranSeq will boost large-scale transcriptome assays and increase the spatial and temporal resolution of gene expression data, in both model and non-model plant species. Moreover, as already performed for tomato (ITAG3.0; www.solgenomics.net), we strongly advocate its integration into current and future genome annotations.

RNA-Seq Analysis to Detect Abnormal Fusion Transcripts Linked to Chromothripsis

RNA-Seq approach enables the detection and characterization of fusion or chimeric transcript associated to complex genome rearrangement. Until now, these events are classically identified at DNA level.Here we describe a complete procedure including a novel way of analyzing reads that combines genomic locations and local coverage to directly infer chimeric junctions with a high sensitivity and specificity, allowing identification of different classes of chimeric RNA events. We also recommend the best practices for the bioinformatics analysis and describe the experimental process for RNA validation using real-time PCR and sequencing.

The role of long non-coding RNAs in rheumatic diseases

Long non-coding RNAs (lncRNAs) have emerged as key epigenetic regulators that govern gene expression and influence multiple biological processes. Accumulating evidence demonstrates that lncRNAs have critical roles in immune cell development and function. In this Review, the molecular mechanisms of gene expression regulation by lncRNAs are described and current knowledge of the role of lncRNAs in immune regulation and inflammation are presented, highlighting strategies for defining the roles of lncRNAs in the pathogenesis of multiple rheumatic diseases. Finally, research progress in understanding the role of lncRNAs in rheumatic diseases is discussed.

New chimeric RNAs in acute myeloid leukemia

Background: High-throughput next generation sequencing (NGS) technologies enable the detection of biomarkers used for tumor classification, disease monitoring and cancer therapy. Whole-transcriptome analysis using RNA-seq is important, not only as a means of understanding the mechanisms responsible for complex diseases but also to efficiently identify novel genes/exons, splice isoforms, RNA editing, allele-specific mutations, differential gene expression and fusion-transcripts or chimeric RNA (chRNA).

Methods: We used Crac, a tool that uses genomic locations and local coverage to classify biological events and directly infer splice and chimeric junctions within a single read. Crac’s algorithm extracts transcriptional chimeric events irrespective of annotation with a high sensitivity, and CracTools was used to aggregate, annotate and filter the chRNA reads. The selected chRNA candidates were validated by real time PCR and sequencing.  In order to check the tumor specific expression of chRNA, we analyzed a publicly available dataset using a new tag search approach.

Results:  We present data related to acute myeloid leukemia (AML) RNA-seq analysis. We highlight novel biological cases of chRNA, in addition to previously well characterized leukemia chRNA. We have identified and validated 17 chRNAs among 3 AML patients: 10 from an AML patient with a translocation between chromosomes 15 and 17 (AML-t(15;17), 4  from patient with normal karyotype (AML-NK) 3 from a patient with chromosomal 16 inversion (AML-inv16). The new fusion transcripts can be classified into four groups according to the exon organization.

Conclusions:  All groups suggest complex but distinct synthesis mechanisms involving either collinear exons of different genes, non-collinear exons, or exons of different chromosomes. Finally, we check tumor-specific expression in a larger RNA-seq AML cohort and identify new AML biomarkers that could improve diagnosis and prognosis of AML.

A Review on Recent Computational Methods for Predicting Noncoding RNAs

Noncoding RNAs (ncRNAs) play important roles in various cellular activities and diseases. In this paper, we presented a comprehensive review on computational methods for ncRNA prediction, which are generally grouped into four categories: (1) homology-based methods, that is, comparative methods involving evolutionarily conserved RNA sequences and structures, (2) de novo methods using RNA sequence and structure features, (3) transcriptional sequencing and assembling based methods, that is, methods designed for single and pair-ended reads generated from next-generation RNA sequencing, and (4) RNA family specific methods, for example, methods specific for microRNAs and long noncoding RNAs. In the end, we summarized the advantages and limitations of these methods and pointed out a few possible future directions for ncRNA prediction. In conclusion, many computational methods have been demonstrated to be effective in predicting ncRNAs for further experimental validation. They are critical in reducing the huge number of potential ncRNAs and pointing the community to high confidence candidates. In the future, high efficient mapping technology and more intrinsic sequence features (e.g., motif and k-mer frequencies) and structure features (e.g., minimum free energy, conserved stem-loop, or graph structures) are suggested to be combined with the next- and third-generation sequencing platforms to improve ncRNA prediction.

Long non-coding RNAs in human early embryonic development and their potential in ART

BACKGROUND

Human long non-coding RNAs (lncRNAs) are an emerging category of transcripts with increasingly documented functional roles during development. LncRNAs and roles during human early embryo development have recently begun to be unravelled.

OBJECTIVE AND RATIONALE

This review summarizes the most recent knowledge on lncRNAs and focuses on their expression patterns and role during early human embryo development and in pluripotent stem cells (PSCs). Public mRNA sequencing (mRNA-seq) data were used to illustrate these expression signatures.

SEARCH METHODS

The PubMed and EMBASE databases were first interrogated using specific terms, such as 'lncRNAs', to get an extensive overview on lncRNAs up to February 2016, and then using 'human lncRNAs' and 'embryo', 'development', or 'PSCs' to focus on lncRNAs involved in human embryo development or in PSC.Recently published RNA-seq data from human oocytes and pre-implantation embryos (including single-cell data), PSC and a panel of normal and malignant adult tissues were used to describe the specific expression patterns of some lncRNAs in early human embryos.

OUTCOMES

The existence and the crucial role of lncRNAs in many important biological phenomena in each branch of the life tree are now well documented. The number of identified lncRNAs is rapidly increasing and has already outnumbered that of protein-coding genes. Unlike small non-coding RNAs, a variety of mechanisms of action have been proposed for lncRNAs. The functional role of lncRNAs has been demonstrated in many biological and developmental processes, including cell pluripotency induction, X-inactivation or gene imprinting. Analysis of RNA-seq data highlights that lncRNA abundance changes significantly during human early embryonic development. This suggests that lncRNAs could represent candidate biomarkers for developing non-invasive tests for oocyte or embryo quality. Finally, some of these lncRNAs are also expressed in human cancer tissues, suggesting that reactivation of an embryonic lncRNA program may contribute to human malignancies.

WIDER IMPLICATIONS

LncRNAs are emerging potential key players in gene expression regulation. Analysis of RNA-seq data from human pre-implantation embryos identified lncRNA signatures that are specific to this critical step. We anticipate that further studies will show that these new transcripts are major regulators of embryo development. These findings might also be used to develop new tests/treatments for improving the pregnancy success rate in IVF procedures or for regenerative medicine applications involving PSC.

Characterization of the genome and transcriptome of the blue tit Cyanistes caeruleus: polymorphisms, sex-biased expression and selection signals

Decoding genomic sequences and determining their variation within populations has potential to reveal adaptive processes and unravel the genetic basis of ecologically relevant trait variation within a species. The blue tit Cyanistes caeruleus--a long-time ecological model species--has been used to investigate fitness consequences of variation in mating and reproductive behaviour. However, very little is known about the underlying genetic changes due to natural and sexual selection in the genome of this songbird. As a step to bridge this gap, we assembled the first draft genome of a single blue tit, mapped the transcriptome of five females and five males to this reference, identified genomewide variants and performed sex-differential expression analysis in the gonads, brain and other tissues. In the gonads, we found a high number of sex-biased genes, and of those, a similar proportion were sex-limited (genes only expressed in one sex) in males and females. However, in the brain, the proportion of female-limited genes within the female-biased gene category (82%) was substantially higher than the proportion of male-limited genes within the male-biased category (6%). This suggests a predominant on-off switching mechanism for the female-limited genes. In addition, most male-biased genes were located on the Z-chromosome, indicating incomplete dosage compensation for the male-biased genes. We called more than 500,000 SNPs from the RNA-seq data. Heterozygote detection in the single reference individual was highly congruent between DNA-seq and RNA-seq calling. Using information from these polymorphisms, we identified potential selection signals in the genome. We list candidate genes which can be used for further sequencing and detailed selection studies, including genes potentially related to meiotic drive evolution. A public genome browser of the blue tit with the described information is available at http://public-genomes-ngs.molgen.mpg.de.

Digital gene expression approach over multiple RNA-Seq data sets to detect neoblast transcriptional changes in Schmidtea mediterranea

BACKGROUND

The freshwater planarian Schmidtea mediterranea is recognised as a valuable model for research into adult stem cells and regeneration. With the advent of the high-throughput sequencing technologies, it has become feasible to undertake detailed transcriptional analysis of its unique stem cell population, the neoblasts. Nonetheless, a reliable reference for this type of studies is still lacking.

RESULTS

Taking advantage of digital gene expression (DGE) sequencing technology we compare all the available transcriptomes for S. mediterranea and improve their annotation. These results are accessible via web for the community of researchers. Using the quantitative nature of DGE, we describe the transcriptional profile of neoblasts and present 42 new neoblast genes, including several cancer-related genes and transcription factors. Furthermore, we describe in detail the Smed-meis-like gene and the three Nuclear Factor Y subunits Smed-nf-YA, Smed-nf-YB-2 and Smed-nf-YC.

CONCLUSIONS

DGE is a valuable tool for gene discovery, quantification and annotation. The application of DGE in S. mediterranea confirms the planarian stem cells or neoblasts as a complex population of pluripotent and multipotent cells regulated by a mixture of transcription factors and cancer-related genes.

The Landscape of long noncoding RNA classification

Advances in the depth and quality of transcriptome sequencing have revealed many new classes of long noncoding RNAs (lncRNAs). lncRNA classification has mushroomed to accommodate these new findings, even though the real dimensions and complexity of the noncoding transcriptome remain unknown. Although evidence of functionality of specific lncRNAs continues to accumulate, conflicting, confusing, and overlapping terminology has fostered ambiguity and lack of clarity in the field in general. The lack of fundamental conceptual unambiguous classification framework results in a number of challenges in the annotation and interpretation of noncoding transcriptome data. It also might undermine integration of the new genomic methods and datasets in an effort to unravel the function of lncRNA. Here, we review existing lncRNA classifications, nomenclature, and terminology. Then, we describe the conceptual guidelines that have emerged for their classification and functional annotation based on expanding and more comprehensive use of large systems biology-based datasets.