Differentially Expressed Long Non-Coding RNAs Were Predicted to Be Involved in the Control of Signaling Pathways in Pediatric Astrocytoma

Expression changes for long non-coding RNAs (lncRNAs) have been identified in adult glioblastoma multiforme (GBM) and in a mixture of adult and pediatric astrocytoma. Since adult and pediatric astrocytomas are molecularly different, the mixture of both could mask specific features in each. We determined the global expression patterns of lncRNAs and messenger RNA (mRNAs) in pediatric astrocytoma of different histological grades. Transcript expression changes were determined with an HTA 2.0 array. lncRNA interactions with microRNAs and mRNAs were predicted by using an algorithm and the LncTar tool, respectively. Interactomes were constructed with the HIPPIE database and visualized with the Cytoscape platform. The array showed expression changes in 156 and 207 lncRNAs in tumors (versus the control) and in pediatric GBM (versus low-grade astrocytoma), respectively. Predictions identified lncRNAs that have putative microRNA binding sites, which might suggest that they function as sponges in these tumors. Also, lncRNAs were shown to interact with many mRNAs, such as Pleckstrin homology-like domain, family A, member 1 (PHLDA1) and sulfatase 2 (SULF2). For example, qPCR found long intergenic non-coding RNA regulator of reprogramming (linc-RoR) expression levels upregulated in pediatric GBM when they were compared with control tissues or with low-grade tumors. Meanwhile, PHLDA1 and ELAV-like RNA binding protein 1 (ELAV1) showed expression changes in tumors relative to the control. Our data showed many lncRNAs with expression changes in pediatric astrocytoma, which might be involved in the regulation of different signaling pathways.

Identification of Aedes aegypti Long Intergenic Non-coding RNAs and Their Association with Wolbachia and Dengue Virus Infection

Long intergenic non-coding RNAs (lincRNAs) are appearing as an important class of regulatory RNAs with a variety of biological functions. The aim of this study was to identify the lincRNA profile in the dengue vector Aedes aegypti and evaluate their potential role in host-pathogen interaction. The majority of previous RNA-Seq transcriptome studies in Ae. aegypti have focused on the expression pattern of annotated protein coding genes under different biological conditions. Here, we used 35 publically available RNA-Seq datasets with relatively high depth to screen the Ae. aegypti genome for lincRNA discovery. This led to the identification of 3,482 putative lincRNAs. These lincRNA genes displayed a slightly lower GC content and shorter transcript lengths compared to protein-encoding genes. Ae. aegypti lincRNAs also demonstrate low evolutionary sequence conservation even among closely related species such as Culex quinquefasciatus and Anopheles gambiae. We examined their expression in dengue virus serotype 2 (DENV-2) and Wolbachia infected and non-infected adult mosquitoes and Aa20 cells. The results revealed that DENV-2 infection increased the abundance of a number of host lincRNAs, from which some suppress viral replication in mosquito cells. RNAi-mediated silencing of lincRNA_1317 led to enhancement in viral replication, which possibly indicates its potential involvement in the host anti-viral defense. A number of lincRNAs were also differentially expressed in Wolbachia-infected mosquitoes. The results will facilitate future studies to unravel the function of lncRNAs in insects and may prove to be beneficial in developing new ways to control vectors or inhibit replication of viruses in them.

LncRNA ontology: inferring lncRNA functions based on chromatin states and expression patterns

Accumulating evidences suggest that long non-coding RNAs (lncRNAs) perform important functions. Genome-wide chromatin-states area rich source of information about cellular state, yielding insights beyond what is typically obtained by transcriptome profiling. We propose an integrative method for genome-wide functional predictions of lncRNAs by combining chromatin states data with gene expression patterns. We first validated the method using protein-coding genes with known function annotations. Our validation results indicated that our integrative method performs better than co-expression analysis, and is accurate across different conditions. Next, by applying the integrative model genome-wide, we predicted the probable functions for more than 97% of human lncRNAs. The putative functions inferred by our method match with previously annotated by the targets of lncRNAs. Moreover, the linkage from the cellular processes influenced by cancer-associated lncRNAs to the cancer hallmarks provided a “lncRNA point-of-view” on tumor biology. Our approach provides a functional annotation of the lncRNAs, which we developed into a web-based application, LncRNA Ontology, to provide visualization, analysis, and downloading of lncRNA putative functions.

BmncRNAdb: a comprehensive database of non-coding RNAs in the silkworm, Bombyx mori

BACKGROUND

Long non-coding RNAs (lncRNAs) may play critical roles in a wide range of developmental processes of higher organisms. Recently, lncRNAs have been widely identified across eukaryotes and many databases of lncRNAs have been developed for human, mouse, fruit fly, etc. However, there is rare information about them in the only completely domesticated insect, silkworm (Bombyx mori).

DESCRIPTION

In this study, we systematically scanned lncRNAs using the available silkworm RNA-seq data and public unigenes. Finally, we identified and collected 6281 lncRNAs in the silkworm. Besides, we also collected 1986 microRNAs (miRNAs) from previous studies. Then, we organized them into a comprehensive and web-based database, BmncRNAdb. This database offers a user-friendly interface for data browse and online analysis as well as the three online tools for users to predict the target genes of lncRNA or miRNA.

CONCLUSIONS

We have systematically identified and collected the silkworm lncRNAs and constructed a comprehensive database of the silkworm lncRNAs and miRNAs. This work gives a glimpse into lncRNAs of the silkworm and lays foundations for the ncRNAs study of the silkworm and other insects in the future. The BmncRNAdb is freely available at http://gene.cqu.edu.cn/BmncRNAdb/index.php .

Long non-coding RNAs are major contributors to transcriptome changes in sunflower meiocytes with different recombination rates

BACKGROUND

Meiosis is a form of specialized cell division that marks the transition from diploid meiocyte to haploid gamete, and provides an opportunity for genetic reassortment through recombination. Experimental data indicates that, relative to their wild ancestors, cultivated sunflower varieties show a higher recombination rate during meiosis. To better understand the molecular basis for this difference, we compared gene expression in male sunflower meiocytes in prophase I isolated from a domesticated line, a wild relative, and a F1 hybrid of the two.

RESULTS

Of the genes that showed differential expression between the wild and domesticated genotypes, 63.62 % could not be identified as protein-coding genes, and of these genes, 70.98 % passed stringent filters to be classified as long non-coding RNAs (lncRNAs). Compared to the sunflower somatic transcriptome, meiocytes express a higher proportion of lncRNAs, and the majority of genes with exclusive expression in meiocytes were lncRNAs. Around 40 % of the lncRNAs showed sequence similarity with small RNAs (sRNA), while 1.53 % were predicted to be sunflower natural antisense transcripts (NATs), and 9.18 % contained transposable elements (TE). We identified 6895 lncRNAs that are exclusively expressed in meiocytes, these lncRNAs appear to have higher conservation, a greater degree of differential expression, a higher proportion of sRNA similarity, and higher TE content relative to lncRNAs that are also expressed in the somatic transcriptome.

CONCLUSIONS

lncRNAs play important roles in plant meiosis and may participate in chromatin modification processes, although other regulatory functions cannot be excluded. lncRNAs could also be related to the different recombination rates seen for domesticated and wild sunflowers.

A novel long non-coding RNA CYP4B1-PS1-001 regulates proliferation and fibrosis in diabetic nephropathy

Diabetic nephropathy is an important microvascular complication of diabetes, and the incidence of end-stage renal disease caused by it are rising annually. Long non-coding RNAs (lncRNAs) are widely regarded to associate with the occurrence and development of various diseases; however, the relationship between lncRNAs and diabetic nephropathy remains largely unknown. This work studied the effect of lncRNAs on diabetic nephropathy pathogenesis. LncRNA microarrays were initially used to detect lncRNAs with altered expression in three cases of kidney tissue from db/db mice with diabetic nephropathy. LncRNAs with differential expression (>2-fold) could be considered candidates. Particularly, CYP4B1-PS1-001 was significantly downregulated in response to early diabetic nephropathy in vitro and in vivo, while overexpression of CYP4B1-PS1-001 inhibited proliferation and fibrosis of mesangial cells. Overall, our data indicate the potential role of CYP4B1-PS1-001 in the proliferation and fibrosis of mice mesangial cells as the prominent features during early stage of diabetic nephropathy, which extend the relationship between lncRNAs and diabetic nephropathy, and may provide a potential therapeutic target and molecular biomarker for the disease.

lncRNA-RNA Interactions across the Human Transcriptome

Long non-coding RNAs (lncRNAs) represent a numerous class of non-protein coding transcripts longer than 200 nucleotides. There is possibility that a fraction of lncRNAs are not functional and represent mere transcriptional noise but a growing body of evidence shows they are engaged in a plethora of molecular functions and contribute considerably to the observed diversification of eukaryotic transcriptomes and proteomes. Still, however, only ca. 1% of lncRNAs have well established functions and much remains to be done towards decipherment of their biological roles. One of the least studied aspects of lncRNAs biology is their engagement in gene expression regulation through RNA-RNA interactions. By hybridizing with mate RNA molecules, lncRNAs could potentially participate in modulation of pre-mRNA splicing, RNA editing, mRNA stability control, translation activation, or abrogation of miRNA-induced repression. Here, we implemented a similarity-search based method for transcriptome-wide identification of RNA-RNA interactions, which enabled us to find 18,871,097 lncRNA-RNA base-pairings in human. Further analyses showed that the interactions could be involved in processing, stability control and functions of 57,303 transcripts. An extensive use of RNA-Seq data provided support for approximately one third of the interactions, at least in terms of the two RNA components being co-expressed. The results suggest that lncRNA-RNA interactions are broadly used to regulate and diversify the human transcriptome.

The RNA world in the 21st century-a systems approach to finding non-coding keys to clinical questions

There was evidence that RNAs are a functionally rich class of molecules not only since the arrival of the next-generation sequencing technology. Non-coding RNAs (ncRNA) could be the key to accelerated diagnosis and enhanced prediction of disease and therapy outcomes as well as the design of advanced therapeutic strategies to overcome yet unsatisfactory approaches.In this review, we discuss the state of the art in RNA systems biology with focus on the application in the systems biomedicine field. We propose guidelines for analysing the role of microRNAs and long non-coding RNAs in human pathologies. We introduce RNA expression profiling and network approaches for the identification of stable and effective RNomics-based biomarkers, providing insights into the role of ncRNAs in disease regulation. Towards this, we discuss ways to model the dynamics of gene regulatory networks and signalling pathways that involve ncRNAs. We also describe data resources and computational methods for finding putative mechanisms of action of ncRNAs. Finally, we discuss avenues for the computer-aided design of novel RNA-based therapeutics.