Distinct patterns of epigenetic marks and transcription factor binding sites across promoters of sense-intronic long noncoding RNAs

Novel insights on the positive correlation between sense and antisense pairs on gene expression

A considerable proportion of the eukaryotic genome undergoes transcription, leading to the generation of noncoding RNA molecules that lack protein-coding information and are not subjected to translation. These noncoding RNAs (ncRNAs) are well recognized to have essential roles in several biological processes. Long noncoding RNAs (lncRNAs) represent the most extensive category of ncRNAs found in the human genome. Much research has focused on investigating the roles of cis-acting lncRNAs in the regulation of specific target gene expression. In the majority of instances, the regulation of sense gene expression by its corresponding antisense pair occurs in a negative (discordant) manner, resulting in the suppression of the target genes. The notion that a negative correlation exists between sense and antisense pairings is, however, not universally valid. In fact, several recent studies have reported a positive relationship between corresponding cis antisense pairs within plants, budding yeast, and mammalian cancer cells. The positive (concordant) correlation between anti-sense and sense transcripts leads to an increase in the level of the sense transcript within the same genomic loci. In addition, mechanisms such as altering chromatin structure, the formation of R loops, and the recruitment of transcription factors can either enhance transcription or stabilize sense transcripts through their antisense pairs. The primary objective of this work is to provide a comprehensive understanding of both aspects of antisense regulation, specifically focusing on the positive correlation between sense and antisense transcripts in the context of eukaryotic gene expression, including its implications towards cancer progression. This article is categorized under: RNA Processing > 3' End Processing Regulatory RNAs/RNAi/Riboswitches > Regulatory RNAs.

Expression Signatures of Long Noncoding RNAs in Left Ventricular Noncompaction

Long noncoding RNAs have gained widespread attention in recent years for their crucial role in biological regulation. They have been implicated in a range of developmental processes and diseases including cancer, cardiovascular, and neuronal diseases. However, the role of long noncoding RNAs (lncRNAs) in left ventricular noncompaction (LVNC) has not been explored. In this study, we investigated the expression levels of lncRNAs in the blood of LVNC patients and healthy subjects to identify differentially expressed lncRNA that develop LVNC specific biomarkers and targets for developing therapies using biological pathways. We used Agilent Human lncRNA array that contains both updated lncRNAs and mRNAs probes. We identified 1,568 upregulated and 1,141 downregulated (log fold-change > 2.0) lncRNAs that are differentially expressed between LVNC and the control group. Among them, RP11-1100L3.7 and XLOC_002730 are the most upregulated and downregulated lncRNAs. Using quantitative real-time reverse transcription polymerase chain reaction (RT-QPCR), we confirmed the differential expression of three top upregulated and downregulated lncRNAs along with two other randomly picked lncRNAs. Gene Ontology (GO) and KEGG pathways analysis with these differentially expressed lncRNAs provide insight into the cellular pathway leading to LVNC pathogenesis. We also identified 1,066 upregulated and 1,017 downregulated mRNAs. Gene set enrichment analysis (GSEA) showed that G2M, Estrogen, and inflammatory pathways are enriched in differentially expressed genes (DEG). We also identified miRNA targets for these differentially expressed genes. In this study, we first report the use of LncRNA microarray to understand the pathogenesis of LVNC and to identify several lncRNA and genes and their targets as potential biomarkers.

The prognostic value of an autophagy-related lncRNA signature in hepatocellular carcinoma

Background

lncRNA may be involved in the occurrence, metastasis, and chemical reaction of hepatocellular carcinoma (HCC) through various pathways associated with autophagy. Therefore, it is urgent to reveal more autophagy-related lncRNAs, explore these lncRNAs’ clinical significance, and find new targeted treatment strategies.

Methods

The corresponding data of HCC patients and autophagy genes were obtained from the TCGA database, and the human autophagy database respectively. Based on the co-expression and Cox regression analysis to construct prognostic prediction signature.

Results

Finally, a signature containing seven autophagy-related lncRNAs (PRRT3-AS1, RP11-479G22.8, RP11-73M18.8, LINC01138, CTD-2510F5.4, CTC-297N7.9, RP11-324I22.4) was constructed. Based on the risk score of signature, Overall survival (OS) curves show that the OS of high-risk patients is significantly lower than that of low-risk patients (P = 2.292e−10), and the prognostic prediction accuracy of risk score (AUC = 0.786) is significantly higher than that of ALBI (0.532), child_pugh (0.573), AFP (0.5751), and AJCC_stage (0.631). Moreover, multivariate Cox analysis and Nomogram of risk score are indicated that the 1-year and 3-year survival rates of patients are obviously accuracy by the combined analysis of the risk score, child_pugh, age, M_stage, and Grade (The AUC of 1- and 3-years are 0.87, and 0.855). Remarkably, the 7 autophagy-related lncRNAs may participate in Spliceosome, Cell cycle, RNA transport, DNA replication, and mRNA surveillance pathway and be related to the biological process of RNA splicing and mRNA splicing.

Conclusion

In conclusion, the 7 autophagy-related lncRNAs might be promising prognostic and therapeutic targets for HCC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04123-6.

CNN-Peaks: ChIP-Seq peak detection pipeline using convolutional neural networks that imitate human visual inspection

ChIP-seq is one of the core experimental resources available to understand genome-wide epigenetic interactions and identify the functional elements associated with diseases. The analysis of ChIP-seq data is important but poses a difficult computational challenge, due to the presence of irregular noise and bias on various levels. Although many peak-calling methods have been developed, the current computational tools still require, in some cases, human manual inspection using data visualization. However, the huge volumes of ChIP-seq data make it almost impossible for human researchers to manually uncover all the peaks. Recently developed convolutional neural networks (CNN), which are capable of achieving human-like classification accuracy, can be applied to this challenging problem. In this study, we design a novel supervised learning approach for identifying ChIP-seq peaks using CNNs, and integrate it into a software pipeline called CNN-Peaks. We use data labeled by human researchers who annotate the presence or absence of peaks in some genomic segments, as training data for our model. The trained model is then applied to predict peaks in previously unseen genomic segments from multiple ChIP-seq datasets including benchmark datasets commonly used for validation of peak calling methods. We observe a performance superior to that of previous methods.

Identification and characterization of long non-coding RNAs involved in embryo development of Ginkgo biloba

Long non-coding RNAs (lncRNAs) are important regulatory factors for plant growth and development. Despite this, little is known about the regulatory interactions of lncRNAs with mRNA during embryo development. Here, we used a bioinformatics genome-wide approach to identify lncRNAs involved in embryo development of Ginkgo biloba, based on RNA sequencing datasets from G. biloba embryos during early, middle, late developmental stages. In total, 2326 lncRNAs were identified in the G. biloba embryos, of which 1307 and 1019 could be classified as long intergenic non-coding RNAs and antisense lncRNAs, respectively. Among them, a total of 657 differentially expressed lncRNAs were identified in the different developmental stages of the G. biloba embryos. Based on the functional annotation of potential target genes of lncRNAs, 50, 33, and 76 lncRNAs were predicted to target genes involved in plant hormone signal transduction, plant hormone biosynthesis, and circadian rhythm regulation, respectively. A lncRNA (17)-miRNA (25)-PCgene (52) network was constructed for the G. biloba embryo. Three lncRNAs (lnc000823, lnc002072, lnc000866) were predicted as target mimics of miR159, which targeted two transcription factors with variety of functions, Gb_11536 (MYB33) and Gb_23921 (MYB101). The data generated in this study provide a better understanding of the roles of lncRNAs in embryo development of G. biloba and plants in general.

Non-Coding Transcriptome Maps across Twenty Tissues of the Korean Black Chicken, Yeonsan Ogye

Yeonsan Ogye is a rare Korean domestic chicken breed whose entire body, including feathers and skin, has a unique black coloring. Although some protein-coding genes related to this unique feature have been examined, non-coding elements have not been widely investigated. Thus, we evaluated coding and non-coding transcriptome expression and identified long non-coding RNAs functionally linked to protein-coding genes in Ogye. High-throughput RNA sequencing and DNA methylation sequencing were performed to profile the expression of 14,264 Ogye protein-coding and 6900 long non-coding RNA (lncRNA) genes and detect DNA methylation in 20 different tissues of an individual Ogye. Approximately 75% of Ogye lncRNAs and 45% of protein-coding genes showed tissue-specific expression. For some genes, tissue-specific expression levels were inversely correlated with DNA methylation levels in their promoters. Approximately 39% of tissue-specific lncRNAs displayed functional associations with proximal or distal protein-coding genes. Heat shock transcription factor 2-associated lncRNAs appeared to be functionally linked to protein-coding genes specifically expressed in black skin tissues, more syntenically conserved in mammals, and differentially expressed in black relative to in white tissues. Pending experimental validation, our findings increase the understanding of how the non-coding genome regulates unique phenotypes and can be used for future genomic breeding of chickens.