Revealing protein-lncRNA interaction

Predicting RNA-protein binding sites and motifs through combining local and global deep convolutional neural networks

Motivation

RNA-binding proteins (RBPs) take over 5-10% of the eukaryotic proteome and play key roles in many biological processes, e.g. gene regulation. Experimental detection of RBP binding sites is still time-intensive and high-costly. Instead, computational prediction of the RBP binding sites using patterns learned from existing annotation knowledge is a fast approach. From the biological point of view, the local structure context derived from local sequences will be recognized by specific RBPs. However, in computational modeling using deep learning, to our best knowledge, only global representations of entire RNA sequences are employed. So far, the local sequence information is ignored in the deep model construction process.

Results

In this study, we present a computational method iDeepE to predict RNA-protein binding sites from RNA sequences by combining global and local convolutional neural networks (CNNs). For the global CNN, we pad the RNA sequences into the same length. For the local CNN, we split a RNA sequence into multiple overlapping fixed-length subsequences, where each subsequence is a signal channel of the whole sequence. Next, we train deep CNNs for multiple subsequences and the padded sequences to learn high-level features, respectively. Finally, the outputs from local and global CNNs are combined to improve the prediction. iDeepE demonstrates a better performance over state-of-the-art methods on two large-scale datasets derived from CLIP-seq. We also find that the local CNN runs 1.8 times faster than the global CNN with comparable performance when using GPUs. Our results show that iDeepE has captured experimentally verified binding motifs.

Availability and implementation

https://github.com/xypan1232/iDeepE.

Supplementary information

Supplementary data are available at Bioinformatics online.

LncRNAs as Regulators of Autophagy and Drug Resistance in Colorectal Cancer

Colorectal cancer (CRC) is a common malignancy with 1. 8 million cases in 2018. Autophagy helps to maintain an adequate cancer microenvironment in order to provide nutritional supplement under adverse conditions such as starvation and hypoxia. Additionally, most of the cases of CRC are unresponsive to chemotherapy, representing a significant challenge for cancer therapy. Recently, autophagy induced by therapy has been shown as a unique mechanism of resistance to anticancer drugs. In this regard, long non-coding RNAs (lncRNAs) analysis are important for cancer detection, progression, diagnosis, therapy response, and prognostic values. With increasing development of quantitative detection techniques, lncRNAs derived from patients' non-invasive samples (i.e., blood, stools, and urine) has become into a novel approach in precision oncology. Tumorspecific GAS5, HOTAIR, H19, and MALAT are novels CRC related lncRNAs detected in patients. Nonetheless, the effect and mechanism of lncRNAs in cancer autophagy and chemoresistance have not been extensively characterized. Chemoresistance and autophagy are relevant for cancer treatment and lncRNAs play a pivotal role in resistance acquisition for several drugs. LncRNAs such as HAGLROS, KCNQ1OT1, and H19 are examples of lncRNAs related to chemoresistance leaded by autophagy. Finally, clinical implications of lncRNAs in CRC are relevant, since they have been associated with tumor differentiation, tumor size, histological grade, histological types, Dukes staging, degree of differentiation, lymph node metastasis, distant metastasis, recurrent free survival, and overall survival (OS).

Deep Learning in the Study of Protein-Related Interactions

Protein-related interaction prediction is critical to understanding life processes, biological functions, and mechanisms of drug action. Experimental methods used to determine proteinrelated interactions have always been costly and inefficient. In recent years, advances in biological and medical technology have provided us with explosive biological and physiological data, and deep learning-based algorithms have shown great promise in extracting features and learning patterns from complex data. At present, deep learning in protein research has emerged. In this review, we provide an introductory overview of the deep neural network theory and its unique properties. Mainly focused on the application of this technology in protein-related interactions prediction over the past five years, including protein-protein interactions prediction, protein-RNA\DNA, Protein- drug interactions prediction, and others. Finally, we discuss some of the challenges that deep learning currently faces.

Diagnostic and prognostic potential of tissue and circulating long non-coding RNAs in colorectal tumors

Long non-coding RNAs (lncRNAs) are members of the non-protein coding RNA family longer than 200 nucleotides. They participate in the regulation of gene and protein expression influencing apoptosis, cell proliferation and immune responses, thereby playing a critical role in the development and progression of various cancers, including colorectal cancer (CRC). As CRC is one of the most frequently diagnosed malignancies worldwide with high mortality, its screening and early detection are crucial, so the identification of disease-specific biomarkers is necessary. LncRNAs are promising candidates as they are involved in carcinogenesis, and certain lncRNAs (e.g., CCAT1, CRNDE, CRCAL1-4) show altered expression in adenomas, making them potential early diagnostic markers. In addition to being useful as tissue-specific markers, analysis of circulating lncRNAs (e.g., CCAT1, CCAT2, BLACAT1, CRNDE, NEAT1, UCA1) in peripheral blood offers the possibility to establish minimally invasive, liquid biopsy-based diagnostic tests. This review article aims to describe the origin, structure, and functions of lncRNAs and to discuss their contribution to CRC development. Moreover, our purpose is to summarise lncRNAs showing altered expression levels during tumor formation in both colon tissue and plasma/serum samples and to demonstrate their clinical implications as diagnostic or prognostic biomarkers for CRC.

RISE: a database of RNA interactome from sequencing experiments

We present RISE (http://rise.zhanglab.net), a database of RNA Interactome from Sequencing Experiments. RNA-RNA interactions (RRIs) are essential for RNA regulation and function. RISE provides a comprehensive collection of RRIs that mainly come from recent transcriptome-wide sequencing-based experiments like PARIS, SPLASH, LIGR-seq, and MARIO, as well as targeted studies like RIA-seq, RAP-RNA and CLASH. It also includes interactions aggregated from other primary databases and publications. The RISE database currently contains 328,811 RNA-RNA interactions mainly in human, mouse and yeast. While most existing RNA databases mainly contain interactions of miRNA targeting, notably, more than half of the RRIs in RISE are among mRNA and long non-coding RNAs. We compared different RRI datasets in RISE and found limited overlaps in interactions resolved by different techniques and in different cell lines. It may suggest technology preference and also dynamic natures of RRIs. We also analyzed the basic features of the human and mouse RRI networks and found that they tend to be scale-free, small-world, hierarchical and modular. The analysis may nominate important RNAs or RRIs for further investigation. Finally, RISE provides a Circos plot and several table views for integrative visualization, with extensive molecular and functional annotations to facilitate exploration of biological functions for any RRI of interest.

Long non-coding RNA DRHC inhibits the proliferation of cancer cells in triple negative breast cancer by downregulating long non-coding RNA HOTAIR

Long non-coding RNA (lncRNA) downregulated in hepatocellular carcinoma (DRHC) is a tumor suppressor in liver cancer. However, the role of this lncRNA in breast cancer has not been investigated. The present study revealed that lncRNA DRHC was downregulated and lncRNA Hox transcript antisense RNA (HOTAIR) was upregulated in tumor tissues compared with adjacent healthy tissues in patients with triple negative breast cancer (TNBC). Expression levels of lncRNA DRHC and lncRNA HOTAIR were negatively correlated in tumor tissues but not in adjacent healthy tissues. The lncRNA DRHC expression level was correlated with tumor size but not tumor metastasis. In vitro overexpression of lncRNA DRHC in TNBC cell lines resulted in decreased expression of lncRNA HOTAIR; however, lncRNA HOTAIR overexpression did not affect the expression level of lncRNA DRHC. Overexpression of lncRNA DRHC inhibited, while overexpression of lncRNA HOTAIR promoted the proliferation of the TNBC cell lines. In addition, lncRNA HOTAIR overexpression attenuated the inhibitory effects of lncRNA DRHC overexpression on cancer cell proliferation. The results obtained in the current study suggested that lncRNA DRHC may inhibit the proliferation of TNBC cells by downregulating the expression of lncRNA HOTAIR.

Network Diffusion Approach to Predict LncRNA Disease Associations Using Multi-Type Biological Networks: LION

Recently, long-non-coding RNAs (lncRNAs) have attracted attention because of their emerging role in many important biological mechanisms. The accumulating evidence indicates that the dysregulation of lncRNAs is associated with complex diseases. However, only a few lncRNA-disease associations have been experimentally validated and therefore, predicting potential lncRNAs that are associated with diseases become an important task. Current computational approaches often use known lncRNA-disease associations to predict potential lncRNA-disease links. In this work, we exploited the topology of multi-level networks to propose the LncRNA rankIng by NetwOrk DiffusioN (LION) approach to identify lncRNA-disease associations. The multi-level complex network consisted of lncRNA-protein, protein–protein interactions, and protein-disease associations. We applied the network diffusion algorithm of LION to predict the lncRNA-disease associations within the multi-level network. LION achieved an AUC value of 96.8% for cardiovascular diseases, 91.9% for cancer, and 90.2% for neurological diseases by using experimentally verified lncRNAs associated with diseases. Furthermore, compared to a similar approach (TPGLDA), LION performed better for cardiovascular diseases and cancer. Given the versatile role played by lncRNAs in different biological mechanisms that are perturbed in diseases, LION’s accurate prediction of lncRNA-disease associations helps in ranking lncRNAs that could function as potential biomarkers and potential drug targets.

Comprehensive evaluation of deep learning architectures for prediction of DNA/RNA sequence binding specificities

MOTIVATION

Deep learning architectures have recently demonstrated their power in predicting DNA- and RNA-binding specificity. Existing methods fall into three classes: Some are based on convolutional neural networks (CNNs), others use recurrent neural networks (RNNs) and others rely on hybrid architectures combining CNNs and RNNs. However, based on existing studies the relative merit of the various architectures remains unclear.

RESULTS

In this study we present a systematic exploration of deep learning architectures for predicting DNA- and RNA-binding specificity. For this purpose, we present deepRAM, an end-to-end deep learning tool that provides an implementation of a wide selection of architectures; its fully automatic model selection procedure allows us to perform a fair and unbiased comparison of deep learning architectures. We find that deeper more complex architectures provide a clear advantage with sufficient training data, and that hybrid CNN/RNN architectures outperform other methods in terms of accuracy. Our work provides guidelines that can assist the practitioner in choosing an appropriate network architecture, and provides insight on the difference between the models learned by convolutional and recurrent networks. In particular, we find that although recurrent networks improve model accuracy, this comes at the expense of a loss in the interpretability of the features learned by the model.

AVAILABILITY AND IMPLEMENTATION

The source code for deepRAM is available at https://github.com/MedChaabane/deepRAM.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Long noncoding RNA SNHG14 facilitates colorectal cancer metastasis through targeting EZH2-regulated EPHA7

Accumulating evidence suggested the participation of long noncoding RNAs (lncRNAs) in regulating various biological processes so as to affecting cancer progression. However, the functional role of most lncRNAs in colorectal carcer (CRC) is still largely covered. In the present study, we disclosed SNHG14 as a carcinogene in CRC development, as it was low-expressed in normal colon tissues but markedly upregulated in CRC cell lines. Besides, SNHG14 contributed to CRC cell proliferation, motility and EMT in vitro, and inhibition of it confined CRC tumor growth and liver metastasis in vivo. Next, the mechanistic investigations confirmed that SNHG14-promoted CRC progression was mediated by EPHA7, which was negatively regulated by SNHG14 in CRC via an EZH2-dependent way. Importantly, EZH2 was proved as a transcription factor of EPHA7 and functioned as a repressor in EPHA7 transcription by enhancing methylation on EPHA7 promoter. Meanwhile, SNHG14 increased EZH2 expression in CRC via stabilizing its mRNA by interacting with FUS, and via freeing its mRNA from miR-186-5p-induced silence. All in all, our observations demonstrated that SNHG14 serves as a facilitator in CRC through targeting EZH2-repressed EPHA7 by enhancing EZH2 via recruiting FUS and absorbing miR-186-5p, indicating a promising new road for CRC diagnosis and treatment.

Detecting the long non‑coding RNA signature related to spinal cord ependymal tumor subtype using a genome‑wide methylome analysis approach

Ependymoma is a type of intramedullary tumor that tends to occur in the adult spinal cord. Ependymoma affects the nervous system and has significant impacts on the quality of life, and it may lead to mortality. Previous studies have performed molecular classification of spinal cord ependymal tumors at the DNA methylation level. However, the DNA methylation status of non-coding regions in spinal cord ependymal tumors remains unclear. In the present study, a genome-wide methylome method was used to characterize the DNA methylation landscape of long non-coding RNAs (lncRNAs) in spinal cord ependymal tumor samples. The present study identified lncRNA signatures associated with tumor subtypes based on the methylation status of lncRNA promoters. The present results suggested that the identified lncRNA signatures were associated with cancer- or nervous system-related protein-coding genes. The majority of the identified lncRNAs was hypomethylated, and may have a role in spinal cord development. The present findings suggested that detection of tumor subtype-specific lncRNAs may facilitate the identification of novel diagnostic and therapeutic strategies to treat patients with spinal cord ependymal tumor.

STAT3-induced long noncoding RNA LINC00668 promotes migration and invasion of non-small cell lung cancer via the miR-193a/KLF7 axis

Long noncoding RNAs (lncRNAs) have been demonstrated to play significant roles in non-small cell lung cancer (NSCLC) progression. Recently, a newly identified lncRNA, LncRNA LINC00668 (LINC00668), was reported to be involved in the regulation of progression of several tumors. However, the expression pattern and biological function of LINC00668 in NSCLC remains largely unclear. In this study, we found that LINC00668 expression was significantly up-regulated in both NSCLC tissues and cell lines. we also showed that LINC00668 upregulation was induced by transcription factor STAT3. Clinical investigation demonstrated that high expression level of LINC00668 was associated with advanced TNM stage, histological grade and lymph node metastasis. Moreover, multivariate analysis confirmed LINC00668 expression level to be an independent prognostic indicator for overall survival of NSCLC patients. Functional assays indicated that knockdown of LINC00668 suppressed NSCLC cells proliferation, migration and invasion, and promoted apoptosis. Mechanistic studies indicated that LINC00668 is a direct target of miR-193a, leading to down-regulation in the expression of its target gene KLF7. Our findings suggested that STAT3-induced LINC00668 contributed to NSCLC progression through upregulating KLF7 expression by sponging miR-193a, and may serve as a prognostic biomarker and a potential target for NSCLC.

lncRNA SNHG16 is associated with proliferation and poor prognosis of pediatric neuroblastoma

Neuroblastoma (NB) is one of the most common extracranial solid tumors in children, which has complex molecular mechanisms. Increasing evidence has suggested that long noncoding RNAs (lncRNAs) account for NB pathogenesis. However, the function of small nucleolar RNA host gene 16 (SNHG16) in NB is currently unclear. In the present study, publically available data and clinical specimens were employed to verify the expression of SNHG16 in NB. Colony formation, real‑time cell proliferation and migration assays were performed to demonstrate the status of cellular proliferation and migration. Flow cytometry was used to examine cell cycle progression in SH‑SY5Y cells, and acridine orange/ethidium bromide staining and caspase‑3/7 activity measurements were applied to study cell apoptosis. To explore the underlying mechanism of SNHG16 function, an online database was used to identify potential RNA‑binding proteins that bind SNHG16. The expression of SNHG16 was revealed to be in line with the clinical staging of NB, and high SNHG16 expression was positively associated with poor clinical outcome. Furthermore, SNHG16 silencing inhibited cell proliferation, repressed migration, and induced cell cycle arrest at the G0/G1 phase in SH‑SY5Y cells. Additionally, apoptosis was undetectable in SH‑SY5Y cells following SNHG16 silencing. Bioinformatics analysis revealed that SNHG16 regulated cell proliferation in NB through transcriptional and translational pathways. These results suggested that SNHG16 may serve important roles in the development and progression of NB, and could represent a potential target for NB therapy.

Long noncoding RNA FEZF1-AS1 promotes the motility of esophageal squamous cell carcinoma through Wnt/β-catenin pathway

Background: Long noncoding RNAs (lncRNAs), a class of noncoding RNA nucleotides >200 bp, has been demonstrated to play vital role in the development of cancer. FEZ family zinc finger 1 antisense RNA 1 (FEZF1-AS1) has been reported as an lncRNA which acts as a tumor-promoting effect in some cancers. However, the role of it in esophageal squamous cell carcinoma (ESCC) and its potential regulatory mechanism was unclear now. Methods: qRT-PCR was used to detect the levels of FEZF1-AS1 and mRNA CTNNB1 (β-catenin) in ESCC tissues and cells. Cell transfection experiments were used to knock down or overexpress the level of FEZF1-AS1 in EC1 and EC9706 cell lines. WST-1 assays, cell cycle assays, scratch wound assays, migration, and invasion assays were used to evaluate the function of FEZF1-AS1 in ESCC progression. Results: FEZF1-AS1 was remarkably upregulated in ESCC tissues and cell lines. Silencing of FEZF1-AS1 significantly inhibited the migration and invasion of ESCC cells, while overexpression of FEZF1-AS1 notably accelerated ESCC migration and invasion. Meanwhile, the levels of FEZF1-AS1 had no effect on ESCC cell proliferation and cell cycle. We also found that β-catenin was upregulated in ESCC tissues, and the level of it was positively correlated with the expression of FEZF1-AS1. Silencing of FEZF1-AS1 could decrease the mRNA and protein level of β-catenin, while overexpression FEZF1-AS1 could lead to the contrary. Conclusion: Our results suggested that the expression of lncRNA FEZF1-AS1 played an important role in ESCC progression, especially the motility of the tumor. FEZF1-AS1 may provide us with a new sight for ESCC treatment.

Knockdown of lncRNA HOTAIR sensitizes breast cancer cells to ionizing radiation through activating miR-218

Radiotherapy is a major therapeutic strategy for breast cancer, while cancer radioresistance remains an obstacle for the successful control of the tumor. Novel radiosensitizing targets are to be developed to overcome radioresistance. Recently, long non-coding RNAs (lncRNAs) were proved to play critical roles in cancer progression. Among all, lncRNA HOTAIR was found to participate in cancer metastasis and chemoresistance. In the present study, we aimed to investigate the radiosensitizing effects of targeting HOTAIR and the underlying mechanism. Our data showed that HOTAIR (HOX antisense intergenic RNA) was up-regulated in breast cancer cells and tissues, and the expression of HOTAIR increased following irradiation. Knockdown of HOTAIR inhibited cell survival and increased cell apoptosis in response to ionizing radiation. Moreover, compared with control group, radiation induced more DNA damage and cell cycle arrest in HOTAIR knockdown cells. Finally, we found that the radiosentizing effects of HOTAIR were related to the up-regulation of miR-218, a ceRNA of HOTAIR. In conclusion, our finding showed that HOTAIR inhibition sensitizes breast cancer cells to ionizing radiation, induced severe DNA damage and activated apoptosis pathways, suggesting a possible role of HOTAIR as a novel target for breast cancer radiosensitization.

The juvenility-associated long noncoding RNA Gm14230 maintains cellular juvenescence

Juvenile animals possess distinct properties that are missing in adults. These properties include capabilities for higher growth, faster wound healing, plasticity and regeneration. However, the molecular mechanisms underlying these juvenile physiological properties are not fully understood. To obtain insight into the distinctiveness of juveniles from adults at the molecular level, we assessed long noncoding RNAs (lncRNAs) that are highly expressed selectively in juvenile cells. The noncoding elements of the transcriptome were investigated in hepatocytes and cardiomyocytes isolated from juvenile and adult mice. Here, we identified 62 juvenility-associated lncRNAs (JAlncs), which are selectively expressed in both hepatocytes and cardiomyocytes from juvenile mice. Among these common (shared) JAlncs, Gm14230 is evolutionarily conserved and is essential for cellular juvenescence. Loss of Gm14230 impairs cell growth and causes cellular senescence. Gm14230 safeguards cellular juvenescence through recruiting the histone methyltransferase Ezh2 to Tgif2, thereby repressing the functional role of Tgif2 in cellular senescence. Thus, we identify Gm14230 as a juvenility-selective lncRNA required to maintain cellular juvenescence.

Knockdown of lncRNA LEF1-AS1 inhibited the progression of oral squamous cell carcinoma (OSCC) via Hippo signaling pathway

It is verified that long non-coding RNAs (lncRNAs) play crucial roles in various cancers. LncRNA LEF1-AS1 is a reported oncogene in colorectal cancer and glioblastoma. In this study, we unveiled that LEF1-AS1 markedly increased in oral squamous cell carcinoma (OSCC) tissues and cell lines. Besides, OSCC patients with high levels of LEF1-AS1 were apt to poor prognosis. Functionally, LEF1-AS1 knockdown inhibited cell survival, proliferation and migration, whereas enhanced cell apoptosis and induced G0/G1 cell cycle arrest in vitro. Consistently, LEF1-AS1 silence hindered tumor growth in vivo. Moreover, LEF1-AS1 inhibition stimulated the activation of Hippo signaling pathway through directly interacting with LATS1. Furtherly, we disclosed that LEF1-AS1 silence abolished the interaction of LEF1-AS1 with LATS1 while enhanced the binding of LATS1 to MOB, therefore promoting YAP phosphorylation but impairing YAP1 nuclear translocation. Additionally, we demonstrated that LEF1-AS1 regulated YAP1 translocation via a LATS1-dependent manner. Furthermore, we also uncovered that YAP1 overexpression abolished the suppressive impact of LEF1-AS1 repression on the biological processes of OSCC cells. In a word, we concluded that LEF1-AS1 served an oncogenic part in OSCC through suppressing Hippo signaling pathway by interacting with LATS1, suggesting the therapeutic and prognostic potential of LEF1-AS1 in OSCC.

Long noncoding RNA PURPL promotes cell proliferation in liver cancer by regulating p53

Emerging evidence suggests that long noncoding RNAs (lncRNAs) serve a key role in malignant transformation, tumor progression and metastasis. Increased expression of lncRNA p53 upregulated regulator of P53 levels (PURPL) has been reported to promote tumorigenicity in colorectal cancer; however, the role and potential mechanisms of PURPL in the development of liver cancer remain unclear. We employed reverse transcription‑quantitative polymerase chain reaction to detect PURPL and p53 mRNA expression in liver cancer tissues and cell lines. Cell Counting Kit‑8 and colony‑forming assays were used to examine the cell proliferation; whereas, flow cytometry was applied to detect apoptosis and cell cycle progression. p53 expression was detected by western blotting. The results revealed that PURPL was significantly upregulated in liver cancer tissues compared with in paracancerous tissues, and was associated with tumor differentiation stage and tumor size. PURPL was also upregulated in various liver cancer cell lines. Silencing of PURPL inhibited liver cancer cells proliferation, blocked cell cycle progression, and promoted apoptosis. Most importantly, PURPL expression was negatively correlated with p53 mRNA expression. In summary, lncRNA‑PURPL was proposed to promote cell proliferation in liver cancer by regulating the p53 gene. As such, it could serve as a potential therapeutic target for the diagnosis and treatment of liver cancer.

KATZLGO: Large-Scale Prediction of LncRNA Functions by Using the KATZ Measure Based on Multiple Networks

Aggregating evidences have shown that long non-coding RNAs (lncRNAs) generally play key roles in cellular biological processes such as epigenetic regulation, gene expression regulation at transcriptional and post-transcriptional levels, cell differentiation, and others. However, most lncRNAs have not been functionally characterized. There is an urgent need to develop computational approaches for function annotation of increasing available lncRNAs. In this article, we propose a global network-based method, KATZLGO, to predict the functions of human lncRNAs at large scale. A global network is constructed by integrating three heterogeneous networks: lncRNA-lncRNA similarity network, lncRNA-protein association network, and protein-protein interaction network. The KATZ measure is then employed to calculate similarities between lncRNAs and proteins in the global network. We annotate lncRNAs with Gene Ontology (GO) terms of their neighboring protein-coding genes based on the KATZ similarity scores. The performance of KATZLGO is evaluated on a manually annotated lncRNA benchmark and a protein-coding gene benchmark with known function annotations. KATZLGO significantly outperforms state-of-the-art computational method both in maximum F-measure and coverage. Furthermore, we apply KATZLGO to predict functions of human lncRNAs and successfully map 12,318 human lncRNA genes to GO terms.

RREB1-induced upregulation of the lncRNA AGAP2-AS1 regulates the proliferation and migration of pancreatic cancer partly through suppressing ANKRD1 and ANGPTL4

Long noncoding RNAs (lncRNAs) have been reported to be involved in a variety of human diseases, including cancers. However, their mechanisms have not yet been fully elucidated. We investigated lncRNA changes that may be associated with pancreatic cancer (PC) by analyzing published microarray data, and identified AGAP2-AS1 as a relatively overexpressed lncRNA in PC tissues. qRT-PCR assays were performed to examine expression levels of AGAP2-AS1. MTT assays, colony formation assays, and EdU assays were used to determine the proliferative capacity of cells. Flow cytometry and TUNEL assays were used to study the regulation of AGAP2-AS1 in the cell cycle and apoptosis. Transwell experiments were used to study changes in cell invasion and metastasis, and a nude mouse model was established to assess the effects of AGAP2-AS1 on tumorigenesis in vivo. RNA sequencing was performed to probe AGAP2-AS1-related pathways. Subcellular fractionation and FISH assays were used to determine the distribution of AGAP2-AS1 in PC cells, and RIP and ChIP were used to determine the molecular mechanism of AGAP2-AS1-mediated regulation of potential target genes. Increased expression of AGAP2-AS1 was associated with tumor size and pathological stage progression in patients with PC. RREB1 was found to activate transcription of AGAP2-AS1 in PC cells. AGAP2-AS1 affected proliferation, apoptosis, cycle arrest, invasion, and metastasis of PC cells in vitro, and AGAP2-AS1 regulated PC proliferation in vivo. Furthermore, AGAP2-AS1 epigenetically inhibited the expression of ANKRD1 and ANGPTL4 by recruiting zeste homolog 2 (EZH2), thereby promoting PC proliferation and metastasis. In summary, our data show that RREB1-induced upregulation of AGAP2-AS1 regulates cell proliferation and migration in PC partly through suppressing ANKRD1 and ANGPTL4 by recruiting EZH2. AGAP2-AS1 represents a potential target for the diagnosis and treatment of PC in the future.

Construction of Complex Features for Computational Predicting ncRNA-Protein Interaction

Non-coding RNA (ncRNA) plays important roles in many critical regulation processes. Many ncRNAs perform their regulatory functions by the form of RNA-protein complexes. Therefore, identifying the interaction between ncRNA and protein is fundamental to understand functions of ncRNA. Under pressures from expensive cost of experimental techniques, developing an accuracy computational predictive model has become an indispensable way to identify ncRNA-protein interaction. A powerful predicting model of ncRNA-protein interaction needs a good feature set of characterizing the interaction. In this paper, a novel method is put forward to generate complex features for characterizing ncRNA-protein interaction (named CFRP). To obtain a comprehensive description of ncRNA-protein interaction, complex features are generated by non-linear transformations from the traditional k-mer features of ncRNA and protein sequences. To further reduce the dimensions of complex features, a group of discriminative features are selected by random forest. To validate the performances of the proposed method, a series of experiments are carried on several widely-used public datasets. Compared with the traditional k-mer features, the CFRP complex features can boost the performances of ncRNA-protein interaction prediction model. Meanwhile, the CFRP-based prediction model is compared with several state-of-the-art methods, and the results show that the proposed method achieves better performances than the others in term of the evaluation metrics. In conclusion, the complex features generated by CFRP are beneficial for building a powerful predicting model of ncRNA-protein interaction.

Multiple Partial Regularized Nonnegative Matrix Factorization for Predicting Ontological Functions of lncRNAs

Long non-coding RNAs (LncRNA) are critical regulators for biological processes, which are highly related to complex diseases. Even though the next generation sequence technology facilitates the discovery of a great number of lncRNAs, the knowledge about the functions of lncRNAs is limited. Thus, it is promising to predict the functions of lncRNAs, which shed light on revealing the mechanisms of complex diseases. The current algorithms predict the functions of lncRNA by using the features of protein-coding genes. Generally speaking, these algorithms fuse heterogeneous genomic data to construct lncRNA-gene associations via a linear combination, which cannot fully characterize the function-lncRNA relations. To overcome this issue, we present an nonnegative matrix factorization algorithm with multiple partial regularization (aka MPrNMF) to predict the functions of lncRNAs without fusing the heterogeneous genomic data. In details, for each type of genomic data, we construct the lncRNA-gene associations, resulting in multiple associations. The proposed method integrates separately them via regularization strategy, rather than fuse them into a single type of associations. The results demonstrate that the proposed algorithm outperforms state-of-the-art methods based network-analysis. The model and algorithm provide an effective way to explore the functions of lncRNAs.

New Insights into the Interplay between Non-Coding RNAs and RNA-Binding Protein HnRNPK in Regulating Cellular Functions

The emerging data indicates that non-coding RNAs (ncRNAs) epresent more than the “junk sequences” of the genome. Both miRNAs and long non-coding RNAs (lncRNAs) are involved in fundamental biological processes, and their deregulation may lead to oncogenesis and other diseases. As an important RNA-binding protein (RBP), heterogeneous nuclear ribonucleoprotein K (hnRNPK) is known to regulate gene expression through the RNA-binding domain involved in various pathways, such as transcription, splicing, and translation. HnRNPK is a highly conserved gene that is abundantly expressed in mammalian cells. The interaction of hnRNPK and ncRNAs defines the novel way through which ncRNAs affect the expression of protein-coding genes and form autoregulatory feedback loops. This review summarizes the interactions of hnRNPK and ncRNAs in regulating gene expression at transcriptional and post-transcriptional levels or by changing the genomic structure, highlighting their involvement in carcinogenesis, glucose metabolism, stem cell differentiation, virus infection and other cellular functions. Drawing connections between such discoveries might provide novel targets to control the biological outputs of cells in response to different stimuli.

Long non-coding RNAs influence the transcriptome in pulmonary arterial hypertension: the role of PAXIP1-AS1

In idiopathic pulmonary arterial hypertension (IPAH), global transcriptional changes induce a smooth muscle cell phenotype characterised by excessive proliferation, migration, and apoptosis resistance. Long non‐coding RNAs (lncRNAs) are key regulators of cellular function. Using a compartment‐specific transcriptional profiling approach, we sought to investigate the link between transcriptional reprogramming by lncRNAs and the maladaptive smooth muscle cell phenotype in IPAH. Transcriptional profiling of small remodelled arteries from 18 IPAH patients and 17 controls revealed global perturbations in metabolic, neuronal, proliferative, and immunological processes. We demonstrated an IPAH‐specific lncRNA expression profile and identified the lncRNA PAXIP1‐AS1 as highly abundant. Comparative transcriptomic analysis and functional assays revealed an intrinsic role for PAXIP1‐AS1 in orchestrating the hyperproliferative and migratory actions of IPAH smooth muscle cells. Further, we showed that PAXIP1‐AS1 mechanistically interferes with the focal adhesion axis via regulation of expression and phosphorylation of its downstream target paxillin. Overall, we show that changes in the lncRNA transcriptome contribute to the disease‐specific transcriptional landscape in IPAH. Our results suggest that lncRNAs, such as PAXIP1‐AS1, can modulate smooth muscle cell function by affecting multiple IPAH‐specific transcriptional programmes. © 2018 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland.

Long non-coding RNAs in prostate cancer: Biological and clinical implications

Prostate cancer (PCa) is a major health issue in the Western world. Current clinical imperatives for this disease include better stratification of indolent versus aggressive disease to enable improved patient management, as well as the identification of more effective therapies for the prevention and treatment of metastatic and therapy-resistant PCa. The advent of next-generation transcriptomics led to the identification of an important class of molecules, long non-coding RNAs (lncRNAs). LncRNAs have critical functions in normal physiology, but their dysregulation has also been implicated in the development and progression of a variety of cancers, including PCa. Importantly, a subset of lncRNAs are highly prostate-specific, suggesting potential for utility as both biomarkers and therapeutic targets. In this review, we summarise the biology of lncRNAs and their mechanisms of action in the development and progression of prostate cancer. Additionally, we cast a critical eye over the potential for this class of molecules to impact on clinical practice.

An Overview of Methodologies in Studying lncRNAs in the High-Throughput Era: When Acronyms ATTACK!

The discovery of pervasive transcription in eukaryotic genomes provided one of many surprising (and perhaps most surprising) findings of the genomic era and led to the uncovering of a large number of previously unstudied transcriptional events. This pervasive transcription leads to the production of large numbers of noncoding RNAs (ncRNAs) and thus opened the window to study these diverse, abundant transcripts of unclear relevance and unknown function. Since that discovery, recent advances in high-throughput sequencing technologies have identified a large collection of ncRNAs, from microRNAs to long noncoding RNAs (lncRNAs). Subsequent discoveries have shown that many lncRNAs play important roles in various eukaryotic processes; these discoveries have profoundly altered our understanding of the regulation of eukaryotic gene expression. Although the identification of ncRNAs has become a standard experimental approach, the functional characterization of these diverse ncRNAs remains a major challenge. In this chapter, we highlight recent progress in the methods to identify lncRNAs and the techniques to study the molecular function of these lncRNAs and the application of these techniques to the study of plant lncRNAs.

RNA-Binding Proteomics Reveals MATR3 Interacting with lncRNA SNHG1 To Enhance Neuroblastoma Progression

The interaction of long noncoding RNAs (lncRNAs) with one or more RNA-binding proteins (RBPs) is important to a plethora of cellular and physiological processes. The lncRNA SNHG1 was reported to be aberrantly expressed and associated with poor patient prognosis in several cancers including neuroblastoma. However, the interacting RBPs and biological functions associated with SNHG1 in neuroblastoma remain unknown. In this study, we identified 283, 31, and 164 SNHG1-interacting proteins in SK-N-BE(2)C, SK-N-DZ, and SK-N-AS neuroblastoma cells, respectively, using a RNA-protein pull-down assay coupled with liquid chromatography-tandem mass spectrometry (LC-MS/MS). Twenty-four SNHG1-interacting RBPs were identified in common from these three neuroblastoma cell lines. RBPs MATR3, YBX1, and HNRNPL have the binding sites for SNHG1 predicted by DeepBind motif analysis. Furthermore, the direct binding of MATR3 with SNHG1 was validated by Western blot and confirmed by RNA immunoprecipitation assay (RIP). Coexpression analysis revealed that the expression of SNHG1 is positively correlated with MATR3 ( P = 3.402 × 10^-13). The high expression of MATR3 is associated with poor event-free survival ( P = 0.00711) and overall survival ( P = 0.00064). Biological functions such as ribonucleoprotein complex biogenesis, RNA processing, and RNA splicing are significantly enriched and in common between SNHG1 and MATR3. In conclusion, we identified MATR3 as binding to SNHG1 and the interaction might be involved in splicing events that enhance neuroblastoma progression.

Long non‑coding RNA MLK7‑AS1 promotes proliferation in human colorectal cancer via downregulation of p21 expression

Current studies have highlighted long non‑coding RNAs (lncRNAs) as critical regulators in various cancers, including colorectal cancer (CRC). By utilizing publicly available data from The Cancer Genome Atlas dataset, MLK7 antisense RNA 1 (MLK7‑AS1) was identified as a novel lncRNA that correlated with CRC progression. The results of reverse transcription‑quantitative polymerase chain reaction (RT‑qPCR) revealed a significant upregulation of MLK7‑AS1 in both CRC tissue samples and cell lines. In addition, a positive correlation was observed between increased MLK7‑AS1 expression and several clinicopathological factors in patients with CRC. Importantly, MLK7‑AS1 knockdown suppressed CRC cell proliferation and promoted G1/G0 phase arrest and apoptosis in vitro, whereas MLK7‑AS1 overexpression exhibited opposite effects. Consistently, decreased MLK7‑AS1 expression inhibited tumor growth in vivo. Furthermore, RT‑qPCR and western blot assays revealed that p21 may be a potential downstream target of MLK7‑AS1. To the best of the authors' knowledge, this is the first study to report that MLK7‑AS1 has potential as a biomarker and may promote proliferation in CRC partially through downregulating p21 expression.

Dual functions for OVAAL in initiation of RAF/MEK/ERK prosurvival signals and evasion of p27-mediated cellular senescence

Here, we report that the long noncoding RNA (lncRNA) ovarian adenocarcinoma-amplified lncRNA (OVAAL) is a mediator of cancer cell resistance, counteracting the effects of apoptosis-inducing agents acting through both the extrinsic and intrinsic pathways. Building upon previous reports associating OVAAL amplification with ovarian and endometrial cancers, we now show that OVAAL overexpression occurs during the pathogenesis of colorectal cancer and melanoma. Mechanistically, our findings also establish that OVAAL expression more generally contributes a prosurvival role to cancer cells under steady-state conditions. OVAAL accomplishes these actions utilizing distinct functional modalities: one promoting activation of RAF/MEK/ERK signaling and the other blocking cell entry into senescence. Our study demonstrates that expression of a single OVAAL in cancer cells drives two distinct but coordinated actions contributing to cancer pathology.

Long noncoding RNAs (lncRNAs) function through a diverse array of mechanisms that are not presently fully understood. Here, we sought to find lncRNAs differentially regulated in cancer cells resistant to either TNF-related apoptosis-inducing ligand (TRAIL) or the Mcl-1 inhibitor UMI-77, agents that act through the extrinsic and intrinsic apoptotic pathways, respectively. This work identified a commonly up-regulated lncRNA, ovarian adenocarcinoma-amplified lncRNA (OVAAL), that conferred apoptotic resistance in multiple cancer types. Analysis of clinical samples revealed OVAAL expression was significantly increased in colorectal cancers and melanoma in comparison to the corresponding normal tissues. Functional investigations showed that OVAAL depletion significantly inhibited cancer cell proliferation and retarded tumor xenograft growth. Mechanically, OVAAL physically interacted with serine/threonine-protein kinase 3 (STK3), which, in turn, enhanced the binding between STK3 and Raf-1. The ternary complex OVAAL/STK3/Raf-1 enhanced the activation of the RAF protooncogene serine/threonine-protein kinase (RAF)/mitogen-activated protein kinase kinase 1 (MEK)/ERK signaling cascade, thus promoting c-Myc–mediated cell proliferation and Mcl-1–mediated cell survival. On the other hand, depletion of OVAAL triggered cellular senescence through polypyrimidine tract-binding protein 1 (PTBP1)–mediated p27 expression, which was regulated by competitive binding between OVAAL and p27 mRNA to PTBP1. Additionally, c-Myc was demonstrated to drive OVAAL transcription, indicating a positive feedback loop between c-Myc and OVAAL in controlling tumor growth. Taken together, these results reveal that OVAAL contributes to the survival of cancer cells through dual mechanisms controlling RAF/MEK/ERK signaling and p27-mediated cell senescence.

Mass Spectrometry-Based Proteomics to Unveil the Non-coding RNA World

The interaction between non-coding RNAs (ncRNAs) and proteins is crucial for the stability, localization and function of the different classes of ncRNAs. Although ncRNAs, when embedded in various ribonucleoprotein (RNP) complexes, control the fundamental processes of gene expression, their biological functions and mechanisms of action are still largely unexplored. Mass Spectrometry (MS)-based proteomics has emerged as powerful tool to study the ncRNA world: on the one hand, by identifying the proteins interacting with distinct ncRNAs; on the other hand, by measuring the impact of ncRNAs on global protein levels. Here, we will first provide a concise overview on the basic principles of MS-based proteomics for systematic protein identification and quantification; then, we will recapitulate the main approaches that have been implemented for the screening of ncRNA interactors and the dissection of ncRNA-protein complex composition. Finally, we will describe examples of various proteomics strategies developed to characterize the effect of ncRNAs on gene expression, with a focus on the systematic identification of microRNA (miRNA) targets.

Accurate prediction of protein-lncRNA interactions by diffusion and HeteSim features across heterogeneous network

Background

Identifying the interactions between proteins and long non-coding RNAs (lncRNAs) is of great importance to decipher the functional mechanisms of lncRNAs. However, current experimental techniques for detection of lncRNA-protein interactions are limited and inefficient. Many methods have been proposed to predict protein-lncRNA interactions, but few studies make use of the topological information of heterogenous biological networks associated with the lncRNAs.

Results

In this work, we propose a novel approach, PLIPCOM, using two groups of network features to detect protein-lncRNA interactions. In particular, diffusion features and HeteSim features are extracted from protein-lncRNA heterogenous network, and then combined to build the prediction model using the Gradient Tree Boosting (GTB) algorithm. Our study highlights that the topological features of the heterogeneous network are crucial for predicting protein-lncRNA interactions. The cross-validation experiments on the benchmark dataset show that PLIPCOM method substantially outperformed previous state-of-the-art approaches in predicting protein-lncRNA interactions. We also prove the robustness of the proposed method on three unbalanced data sets. Moreover, our case studies demonstrate that our method is effective and reliable in predicting the interactions between lncRNAs and proteins.

Availability

The source code and supporting files are publicly available at: http://denglab.org/PLIPCOM/.

Long noncoding RNA NEAT1, regulated by LIN28B, promotes cell proliferation and migration through sponging miR-506 in high-grade serous ovarian cancer

The aberrant expression of long noncoding RNAs (lncRNAs) has been reported frequently in specific cancers, including high-grade serous ovarian cancer (HGSOC). The purpose of the present study was to explore the clinical significance and underlying mechanisms of a significantly dysregulated lncRNA (NEAT1) in HGSOC. Our results showed that elevated NEAT1 expression in human HGSOC specimens correlated with a poor prognosis. Functional experiments demonstrated that knockdown of NEAT1 significantly prohibited ovarian cancer cell proliferation and invasion in vitro and restrained tumor growth in vivo. LIN28B was identified by bioinformatics analysis along with experimental evidence as a direct actor that enhanced NEAT1 stability. A rescue functional assay confirmed that the LIN28B/NEAT1 axis contributed to oncogenic functions in ovarian cancer cells. Moreover, gene expression profile data and dual luciferase reporter assay results demonstrated that NEAT1 functioned as a competing endogenous RNA (ceRNA) for miR-506 to promote cell proliferation and migration. Taken together, our results showed that NEAT1, stabilized by LIN28B, promoted HGSOC progression by sponging miR-506. Thus, NEAT1 can be regarded as a vital diagnostic biomarker for HGSOC and a therapeutic target.

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.

3D genomic regulation of lncRNA and Xist in X chromosome

Long noncoding RNAs (lncRNAs) act as important regulators in cardiovascular diseases, neural degenerative disease, or cancers, by localizing and spreading across chromatins. lncRNA can regulate the 3D architecture of the enhancer cluster at the target gene locus, relevant to analogous lncRNA-protein coding gene pairs. X inactive specific transcript (Xist) plays a critical role in the process and biological function of lncRNAs. The lncRNA Jpx, Xist activator, is a nonprotein-coding RNA transcribed from a gene within the X-inactivation center and acts as a numerator element to control X-chromosome number and activate Xist transcription by interacting with CCCTC-binding factor. Up-regulated lncRNA Xist initiates X chromosome inactivation process and attracts specific chromatin modifiers. A number of chromatin-modified factors interact with lncRNAs modify 3D genome architecture and mediate Xist function in embryo development. Thus, the regulation of lncRNAs in 3D genome progresses is the key mechanism of Xist, as a therapeutic potential for Xist associated diseases.

A Novel lncRNA, LINC00460, Affects Cell Proliferation and Apoptosis by Regulating KLF2 and CUL4A Expression in Colorectal Cancer

Emerging evidence has proven that long noncoding RNAs (lncRNAs) play important roles in human colorectal cancer (CRC) biology, although few lncRNAs have been characterized in CRC. Therefore, the functional significance of lncRNAs in the malignant progression of CRC still needs to be further explored. In this study, through analyzing TCGA RNA sequencing data and other publicly available microarray data, we found a novel lncRNA, LINC00460, whose expression was significantly upregulated in CRC tissues compared to adjacent normal tissues. Consistently, real-time qPCR results also verified that LINC00460 was overexpressed in CRC tissues and cells. Furthermore, high LINC00460 expression levels in CRC specimens were correlated with larger tumor size, advanced tumor stage, lymph node metastasis and shorter overall survival. In vitro and in vivo assays of LINC00460 alterations revealed a complex integrated phenotype affecting cell growth and apoptosis. Mechanistically, LINC00460 repressed Krüppel-like factor 2 (KLF2) transcription by binding to enhancer of zeste homolog 2 (EZH2). LINC00460 also functioned as a molecular sponge for miR-149-5p, antagonizing its ability to repress cullin 4A (CUL4A) protein translation. Taken together, our findings support a model in which the LINC00460/EZH2/KLF2 and LINC00460/miR-149-5p/CUL4A crosstalk serve as critical effectors in CRC tumorigenesis and progression, suggesting new therapeutic directions in CRC.

Prediction of RNA-protein sequence and structure binding preferences using deep convolutional and recurrent neural networks

BACKGROUND

RNA regulation is significantly dependent on its binding protein partner, known as the RNA-binding proteins (RBPs). Unfortunately, the binding preferences for most RBPs are still not well characterized. Interdependencies between sequence and secondary structure specificities is challenging for both predicting RBP binding sites and accurate sequence and structure motifs detection.

RESULTS

In this study, we propose a deep learning-based method, iDeepS, to simultaneously identify the binding sequence and structure motifs from RNA sequences using convolutional neural networks (CNNs) and a bidirectional long short term memory network (BLSTM). We first perform one-hot encoding for both the sequence and predicted secondary structure, to enable subsequent convolution operations. To reveal the hidden binding knowledge from the observed sequences, the CNNs are applied to learn the abstract features. Considering the close relationship between sequence and predicted structures, we use the BLSTM to capture possible long range dependencies between binding sequence and structure motifs identified by the CNNs. Finally, the learned weighted representations are fed into a classification layer to predict the RBP binding sites. We evaluated iDeepS on verified RBP binding sites derived from large-scale representative CLIP-seq datasets. The results demonstrate that iDeepS can reliably predict the RBP binding sites on RNAs, and outperforms the state-of-the-art methods. An important advantage compared to other methods is that iDeepS can automatically extract both binding sequence and structure motifs, which will improve our understanding of the mechanisms of binding specificities of RBPs.

CONCLUSION

Our study shows that the iDeepS method identifies the sequence and structure motifs to accurately predict RBP binding sites. iDeepS is available at https://github.com/xypan1232/iDeepS .

Chromatin Landscape Distinguishes the Genomic Loci of Hundreds of Androgen-Receptor-Associated LincRNAs From the Loci of Non-associated LincRNAs

Cell signaling events triggered by androgen hormone in prostate cells is dependent on activation of the androgen receptor (AR) transcription factor. Androgen hormone binding to AR promotes its displacement from the cytoplasm to the nucleus and AR binding to DNA motifs, thus inducing activatory and inhibitory transcriptional programs through a complex regulatory mechanism not yet fully understood. In this work, we performed RNA-seq deep-sequencing of LNCaP prostate cancer cells and found over 7000 expressed long intergenic non-coding RNAs (lincRNAs), of which ∼4000 are novel lincRNAs, and 258 lincRNAs have their expression activated by androgen. Immunoprecipitation of AR, followed by large-scale sequencing of co-immunoprecipitated RNAs (RIP-Seq) has identified in the LNCaP cell line a total of 619 lincRNAs that were significantly enriched (FDR < 10%, DESeq2) in the anti-Androgen Receptor (antiAR) fraction in relation to the control fraction (non-specific IgG), and we named them Androgen-Receptor-Associated lincRNAs (ARA-lincRNAs). A genome-wide analysis showed that protein-coding gene neighbors to ARA-lincRNAs had a significantly higher androgen-induced change in expression than protein-coding genes neighboring lincRNAs not associated to AR. To find relevant epigenetic signatures enriched at the ARA-lincRNAs’ transcription start sites (TSSs) we used a machine learning approach and identified that the ARA-lincRNA genomic loci in LNCaP cells are significantly enriched with epigenetic marks that are characteristic of in cis enhancer RNA regulators, and that the H3K27ac mark of active enhancers is conspicuously enriched at the TSS of ARA-lincRNAs adjacent to androgen-activated protein-coding genes. In addition, LNCaP topologically associating domains (TADs) that comprise chromatin regions with ARA-lincRNAs exhibit transcription factor contents, epigenetic marks and gene transcriptional activities that are significantly different from TADs not containing ARA-lincRNAs. This work highlights the possible involvement of hundreds of lincRNAs working in synergy with the AR on the genome-wide androgen-induced gene regulatory program in prostate cells.

IMP1 regulates UCA1-mediated cell invasion through facilitating UCA1 decay and decreasing the sponge effect of UCA1 for miR-122-5p

BACKGROUND

Long noncoding RNAs (LncRNAs) represent a class of widespread and diverse endogenous RNAs that can posttranscriptionally regulate gene expression through the interaction with RNA-binding proteins and micro RNAs (miRNAs). Here, we report that in breast carcinoma cells, the insulin-like growth factor 2 messenger RNA binding protein (IMP1) binds to lncRNA urethral carcinoma-associated 1 (UCA1) and suppresses the UCA1-induced invasive phenotype.

METHODS

RT-qPCR and RNA sequence assays were used to investigate the expression of UCA1 and miRNAs in breast cancer cells in response to IMP1 expression. The role of IMP1-UCA1 interaction in cell invasion was demonstrated by transwell analysis through loss-of-function and gain-of-function effects. RNA pull-down and RNA binding protein immunoprecipitation (RIP) were performed to confirm the molecular interactions of IMP1-UCA1 and UCA1-miR-122-5p involved in breast cancer cells.

RESULTS

In breast cancer cells, IMP1 interacts with UCA1 via the "ACACCC" motifs within UCA1 and destabilizes UCA1 through the recruitment of CCR4-NOT1 deadenylase complex. Meanwhile, binding of IMP1 prevents the association of miR-122-5p with UCA1, thereby shifting the availability of miR-122-5p from UCA1 to the target mRNAs and reducing the UCA1-mediated cell invasion. Accordingly, either IMP1 silencing or UCA1 overexpression resulted in reduced levels of free miR-122-5p within the cytoplasm, affecting miR-122-5p in regulating its target mRNAs.

CONCLUSIONS

Our study provides initial evidence that interaction between IMP1 and UCA1 enhances UCA1 decay and competes for miR-122-5p binding, leading to the liberation of miR-122-5p activity and the reduction of cell invasiveness.

Probing the functions of long non-coding RNAs by exploiting the topology of global association and interaction network

Long non-coding RNAs (lncRNAs) are involved in many biological processes, such as immune response, development, differentiation and gene imprinting and are associated with diseases and cancers. But the functions of the vast majority of lncRNAs are still unknown. Predicting the biological functions of lncRNAs is one of the key challenges in the post-genomic era. In our work, We first build a global network including a lncRNA similarity network, a lncRNA-protein association network and a protein-protein interaction network according to the expressions and interactions, then extract the topological feature vectors of the global network. Using these features, we present an SVM-based machine learning approach, PLNRGO, to annotate human lncRNAs. In PLNRGO, we construct a training data set according to the proteins with GO annotations and train a binary classifier for each GO term. We assess the performance of PLNRGO on our manually annotated lncRNA benchmark and a protein-coding gene benchmark with known functional annotations. As a result, the performance of our method is significantly better than that of other state-of-the-art methods in terms of maximum F-measure and coverage.

The Role of Long Non-Coding RNAs in Hepatocarcinogenesis

Whole-transcriptome analyses have revealed that a large proportion of the human genome is transcribed in non-protein-coding transcripts, designated as long non-coding RNAs (lncRNAs). Rather than being “transcriptional noise”, increasing evidence indicates that lncRNAs are key players in the regulation of many biological processes, including transcription, post-translational modification and inhibition and chromatin remodeling. Indeed, lncRNAs are widely dysregulated in human cancers, including hepatocellular carcinoma (HCC). Functional studies are beginning to provide insights into the role of oncogenic and tumor suppressive lncRNAs in the regulation of cell proliferation and motility, as well as oncogenic and metastatic potential in HCC. A better understanding of the molecular mechanisms and the complex network of interactions in which lncRNAs are involved could reveal novel diagnostic and prognostic biomarkers. Crucially, it may provide novel therapeutic opportunities to add to the currently limited number of therapeutic options for HCC patients. In this review, we summarize the current status of the field, with a focus on the best characterized dysregulated lncRNAs in HCC.

Technological Developments in lncRNA Biology

It is estimated that more than 90% of the mammalian genome is transcribed as non-coding RNAs. Recent evidences have established that these non-coding transcripts are not junk or just transcriptional noise, but they do serve important biological purpose. One of the rapidly expanding fields of this class of transcripts is the regulatory lncRNAs, which had been a major challenge in terms of their molecular functions and mechanisms of action. The emergence of high-throughput technologies and the development in various conventional approaches have led to the expansion of the lncRNA world. The combination of multidisciplinary approaches has proven to be essential to unravel the complexity of their regulatory networks and helped establish the importance of their existence. Here, we review the current methodologies available for discovering and investigating functions of long non-coding RNAs (lncRNAs) and focus on the powerful technological advancement available to specifically address their functional importance.

Knockdown of pseudogene derived from lncRNA DUXAP10 inhibits cell proliferation, migration, invasion, and promotes apoptosis in pancreatic cancer

Current evidence suggests that pseudogene derived lncRNAs may be important players in human cancer progression. Our previous study showed that DUXAP10 could promote cell proliferation in colorectal cancer. However, the clinical significance and potential role of DUXAP10 in human pancreatic cancer (PC) has not been uncovered. In this study, we found that DUXAP10 was overexpressed in PC tissues compared with normal tissues. DUXAP10 expression was significantly higher in patients with an advanced TNM stage and positive lymph node metastasis. Bioinformatic analysis showed that cell cycle progression was increased in patients with high DUXAP10 expression. In vitro and in vivo assays of DUXAP10 alterations revealed a complex integrated phenotype affecting cell growth, apoptosis, migration, and invasion. Mechanistic studies revealed that DUXAP10 has a crucial role in G2/M arrest. We further showed that DUXAP10 regulated PC cell proliferation through interact with RNA-binding protein EZH2 and LSD1. Overall, our findings indicates that DUXAP10 is an oncogenic lncRNA that promotes PC proliferation and metastasis.

Methods for Identification of Protein-RNA Interaction

The importance of RNA-protein interactions in regulation of mRNA and non-coding RNA function is increasingly appreciated. With the development of next generation high-throughput sequencing technologies, a variety of methods have been proposed to comprehensively identify RNA-protein interactions. In this chapter, we discussed the traditional and state-of-the-art technologies that were used to study RNA-protein interaction, including experimental and computational methods. To help highlight the biological significance of RNA-protein interaction in complex diseases, online resources on RNA-protein interactions were briefly discussed. Finally, we discussed the interaction among noncoding RNAs (such as long noncoding RNAs and microRNAs) and proteins, as well as the dysregulation of RNA-protein interaction in complex diseases. These summarization will ultimately provide a more complete picture for understanding of the function of RNA-protein interactions, including how these interaction assembled and how they modulate cellular function in complex diseases.