Circular RNAs are new players in regulation of post transcriptional gene expression. Animal genomes express many circular RNAs from diverse genomic locations. A recent study has validated a fairly large number of circular RNAs in human, mouse, and nematode. Circular RNAs play a crucial role in fine tuning the level of miRNA mediated regulation of gene expression by sequestering the miRNAs. Their interaction with disease associated miRNAs indicates that circular RNAs are important for disease regulation. In this paper we studied the potential association of circular RNAs (circRNA) with human diseases in two different ways. Firstly, the interactions of circRNAs with disease associated miRNAs were identified, following which the likelihood of a circRNA being associated with a disease was calculated. For the miRNAs associated with individual diseases, we constructed a network of predicted interactions between the miRNAs and protein coding, long non-coding and circular RNA genes. We carried out gene ontology (GO) enrichment analysis on the set of protein coding genes in the miRNA- circRNA interactome of individual diseases to check the enrichment of genes associated with particular biological processes. Secondly, disease associated SNPs were mapped on circRNA loci, and Argonaute (Ago) interaction sites on circular RNAs were identified. We compiled a database of disease-circRNA association in Circ2Traits (http://gyanxet-beta.com/circdb/), the first comprehensive knowledgebase of potential association of circular RNAs with diseases in human.

Competing endogenous RNA, ceRNA, vie with messenger RNAs (mRNAs) for microRNAs (miRNAs) with shared miRNAs responses elements (MREs) and act as modulator of miRNA by influencing the available level of miRNA. It has recently been discovered that, apart from protein-coding ceRNAs, pseudogenes, long noncoding RNAs (lncRNAs), and circular RNAs act as miRNA “sponges” by sharing common MRE, inhibiting normal miRNA targeting activity on mRNA. These MRE sharing elements form the posttranscriptional ceRNA network to regulate mRNA expression. ceRNAs are widely implicated in many biological processes. Recent studies have identified ceRNAs associated with a number of diseases including cancer. This brief review focuses on the molecular mechanism of ceRNA as part of the complex post-transcriptional regulatory circuit in cell and the impact of ceRNAs in development and disease.

Long noncoding RNA (lncRNA) influences post-transcriptional regulation by interfering with the microRNA (miRNA) pathways, acting as competing endogenous RNA (ceRNA). These lncRNAs have miRNA responsive elements (MRE) in them, and control endogenous miRNAs available for binding with their target mRNAs, thus reducing the repression of these mRNAs. ln Ce DB provides a database of human lncRNAs (from GENCODE 19 version) that can potentially act as ceRNAs. The putative mRNA targets of human miRNAs and the targets mapped to AGO clipped regions are collected from TargetScan and StarBase respectively. The lncRNA targets of human miRNAs (up to GENCODE 11) are downloaded from miRCode database. miRNA targets on the rest of the GENCODE 19 lncRNAs are predicted by our algorithm for finding seed-matched target sites. These putative miRNA-lncRNA interactions are mapped to the Ago interacting regions within lncRNAs. To find out the likelihood of an lncRNA-mRNA pair for actually being ceRNA we take recourse to two methods. First, a ceRNA score is calculated from the ratio of the number of shared MREs between the pair with the total number of MREs of the individual candidate gene. Second, the P-value for each ceRNA pair is determined by hypergeometric test using the number of shared miRNAs between the ceRNA pair against the number of miRNAs interacting with the individual RNAs. Typically, in a pair of RNAs being targeted by common miRNA(s), there should be a correlation of expression so that the increase in level of one ceRNA results in the increased level of the other ceRNA. Near-equimolar concentration of the competing RNAs is associated with more profound ceRNA effect. In lnCeDB one can not only browse for lncRNA-mRNA pairs having common targeting miRNAs, but also compare the expression of the pair in 22 human tissues to estimate the chances of the pair for actually being ceRNAs. Availability: Downloadable freely from http://gyanxet-beta.com/lncedb/.

Maintenance of the pluripotent state or differentiation of the pluripotent state into any germ layer depends on the factors that orchestrate expression of thousands of genes through epigenetic, transcriptional, and posttranscriptional regulation. Long noncoding RNAs (lncRNAs) are implicated in the complex molecular circuitry in the developmental processes. The ENCODE project has opened up new avenues for studying these lncRNA transcripts with the availability of new datasets for lncRNA annotation and regulation. Expression studies identified hundreds of long noncoding RNAs differentially expressed in the pluripotent state, and many of these lncRNAs are found to control the pluripotency and stemness in embryonic and induced pluripotent stem cells or, in the reverse way, promote differentiation of pluripotent cells. They are generally transcriptionally activated or repressed by pluripotency-associated transcription factors and function as molecular mediators of gene expression that determine the pluripotent state of the cell. They can act as molecular scaffolds or guides for the chromatin-modifying complexes to direct them to bind into specific genomic loci to impart a repressive or activating effect on gene expression, or they can transcriptionally or post-transcriptionally regulate gene expression by diverse molecular mechanisms. This review focuses on recent findings on the regulatory role of lncRNAs in two main aspects of pluripotency, namely, self renewal and differentiation into any lineage, and elucidates the underlying molecular mechanisms that are being uncovered lately.

Microglia cells in the brain play essential role during Japanese Encephalitis Virus (JEV) infection and may lead to change in microRNA (miRNA) and mRNA profile. These changes may together control disease outcome. Using Affymetrix microarray platform, we profiled cellular miRNA and mRNA expression at multiple time points during viral infection in human microglial (CHME3) cells. In silico analysis of microarray data revealed a phased pattern of miRNAs expression, associated with JEV replication and provided unique signatures of infection. Target prediction and pathway enrichment analysis identified anti correlation between differentially expressed miRNA and the gene expression at multiple time point which ultimately affected diverse signaling pathways including Notch signaling pathways in microglia. Activation of Notch pathway during JEV infection was demonstrated in vitro and in vivo. The expression of a subset of miRNAs that target multiple genes in Notch signaling pathways were suppressed and their overexpression could affect JEV induced immune response. Further analysis provided evidence for the possible presence of cellular competing endogenous RNA (ceRNA) associated with innate immune response. Collectively, our data provide a uniquely comprehensive view of the changes in the host miRNAs induced by JEV during cellular infection and identify Notch pathway in modulating microglia mediated inflammation.

Supplementary data are available at Bioinformatics online.

Long noncoding RNAs (lncRNAs) are emerging as key molecules in regulating many biological processes and have been implicated in development and disease pathogenesis. Biomarkers of cancer and normal tissue response to treatment are of great interest in precision medicine, as well as in public health and medical management, such as for assessment of radiation injury after an accidental or intentional exposure. Circulating and functional RNAs, including microRNAs (miRNAs) and lncRNAs, in whole blood and other body fluids are potential valuable candidates as biomarkers. Early prediction of possible acute, intermediate and delayed effects of radiation exposure enables timely therapeutic interventions. To address whether long noncoding RNAs (lncRNAs) could serve as biomarkers for radiation biodosimetry we performed whole genome transcriptome analysis in a mouse model after whole-body irradiation. Differential lncRNA expression patterns were evaluated at 16, 24 and 48 h postirradiation in total RNA isolated from whole blood of mice exposed to 1, 2, 4, 8 and 12 Gy of X rays. Sham-irradiated animals served as controls. Significant alterations in the expression patterns of lncRNAs were observed after different radiation doses at the various time points. We identified several radiation-induced lncRNAs known for DNA damage response as well as immune response. Long noncoding RNA targets of tumor protein 53 (P53), Trp53cor1, Dino, Pvt1 and Tug1 and an upstream regulator of p53, Meg3, were altered in response to radiation. Gm14005 ( Morrbid) and Tmevpg1 were regulated by radiation across all time points and doses. These two lncRNAs have important potential as blood-based radiation biomarkers; Gm14005 ( Morrbid) has recently been shown to play a key role in inflammatory response, while Tmevpg1 has been implicated in the regulation of interferon gamma. Precise molecular biomarkers, likely involving a diverse group of inducible molecules, will not only enable the development and effective use of medical countermeasures but may also be used to detect and circumvent or mitigate normal tissue injury in cancer radiotherapy.

To elucidate the role of immune cell infiltration as a prognostic signature in solid tumors, we analyzed immune-function-related genes from four publicly available single-cell RNA-Seq data sets and twenty bulk tumor RNA-Seq data sets from The Cancer Genome Atlas (TCGA). Unsupervised clustering of pan-cancer transcriptomic signature showed two major immune function types: one related to NK-, T-, and B-cell functions and the other related to monocyte, macrophage, dendritic cell, and Toll-like receptor functions. Kaplan–Meier analysis showed differential prognosis of these two groups, dependent on the cancer type. Our analysis of TCGA solid tumors with an elastic net model identified 155 genes associated with disease-free survival in different tumor types with varied influence across different cancer types. With this gene set, we computed cancer-specific prognostic immune score models for individual cancer types that predicted disease-free and overall survival. Validation of our model on available published data of immune checkpoint blockade therapies on melanoma, kidney renal cell carcinoma, non-small cell lung cancer, gastric cancer and bladder cancer confirmed that cancer-specific higher immune scores are associated with response to immunotherapy. Our analysis provides a comprehensive map of cancer-specific immune-related prognostic gene sets that are associated with immunotherapy response.