Wei Wu

NONCODEv4: exploring the world of long non-coding RNA genes

NONCODE (http://www.bioinfo.org/noncode/) is an integrated knowledge database dedicated to non-coding RNAs (excluding tRNAs and rRNAs). Non-coding RNAs (ncRNAs) have been implied in diseases and identified to play important roles in various biological processes. Since NONCODE version 3.0 was released 2 years ago, discovery of novel ncRNAs has been promoted by high-throughput RNA sequencing (RNA-Seq). In this update of NONCODE, we expand the ncRNA data set by collection of newly identified ncRNAs from literature published in the last 2 years and integration of the latest version of RefSeq and Ensembl. Particularly, the number of long non-coding RNA (lncRNA) has increased sharply from 73 327 to 210 831. Owing to similar alternative splicing pattern to mRNAs, the concept of lncRNA genes was put forward to help systematic understanding of lncRNAs. The 56 018 and 46 475 lncRNA genes were generated from 95 135 and 67 628 lncRNAs for human and mouse, respectively. Additionally, we present expression profile of lncRNA genes by graphs based on public RNA-seq data for human and mouse, as well as predict functions of these lncRNA genes. The improvements brought to the database also include an incorporation of an ID conversion tool from RefSeq or Ensembl ID to NONCODE ID and a service of lncRNA identification. NONCODE is also accessible through http://www.noncode.org/.

NONCODE 2016: an informative and valuable data source of long non-coding RNAs

NONCODE (http://www.bioinfo.org/noncode/) is an interactive database that aims to present the most complete collection and annotation of non-coding RNAs, especially long non-coding RNAs (lncRNAs). The recently reduced cost of RNA sequencing has produced an explosion of newly identified data. Revolutionary third-generation sequencing methods have also contributed to more accurate annotations. Accumulative experimental data also provides more comprehensive knowledge of lncRNA functions. In this update, NONCODE has added six new species, bringing the total to 16 species altogether. The lncRNAs in NONCODE have increased from 210 831 to 527,336. For human and mouse, the lncRNA numbers are 167,150 and 130,558, respectively. NONCODE 2016 has also introduced three important new features: (i) conservation annotation; (ii) the relationships between lncRNAs and diseases; and (iii) an interface to choose high-quality datasets through predicted scores, literature support and long-read sequencing method support. NONCODE is also accessible through http://www.noncode.org/.

NPInter v2.0: an updated database of ncRNA interactions

NPInter (http://www.bioinfo.org/NPInter) is a database that integrates experimentally verified functional interactions between noncoding RNAs (excluding tRNAs and rRNAs) and other biomolecules (proteins, RNAs and genomic DNAs). Extensive studies on ncRNA interactions have shown that ncRNAs could act as part of enzymatic or structural complexes, gene regulators or other functional elements. With the development of high-throughput biotechnology, such as cross-linking immunoprecipitation and high-throughput sequencing (CLIP-seq), the number of known ncRNA interactions, especially those formed by protein binding, has grown rapidly in recent years. In this work, we updated NPInter to version 2.0 by collecting ncRNA interactions from recent literature and related databases, expanding the number of entries to 201 107 covering 18 species. In addition, NPInter v2.0 incorporated a service for the BLAST alignment search as well as visualization of interactions.

NPInter v3.0: an upgraded database of noncoding RNA-associated interactions

Despite the fact that a large quantity of noncoding RNAs (ncRNAs) have been identified, their functions remain unclear. To enable researchers to have a better understanding of ncRNAs’ functions, we updated the NPInter database to version 3.0, which contains experimentally verified interactions between ncRNAs (excluding tRNAs and rRNAs), especially long noncoding RNAs (lncRNAs) and other biomolecules (proteins, mRNAs, miRNAs and genomic DNAs). In NPInter v3.0, interactions pertaining to ncRNAs are not only manually curated from scientific literature but also curated from high-throughput technologies. In addition, we also curated lncRNA–miRNA interactions from in silico predictions supported by AGO CLIP-seq data. When compared with NPInter v2.0, the interactions are more informative (with additional information on tissues or cell lines, binding sites, conservation, co-expression values and other features) and more organized (with divisions on data sets by data sources, tissues or cell lines, experiments and other criteria). NPInter v3.0 expands the data set to 491,416 interactions in 188 tissues (or cell lines) from 68 kinds of experimental technologies. NPInter v3.0 also improves the user interface and adds new web services, including a local UCSC Genome Browser to visualize binding sites. Additionally, NPInter v3.0 defined a high-confidence set of interactions and predicted the functions of lncRNAs in human and mouse based on the interactions curated in the database. NPInter v3.0 is available at http://www.bioinfo.org/NPInter/. Database URL: http://www.bioinfo.org/NPInter/

Transcriptional profiling analysis and functional prediction of long noncoding RNAs in cancer.

Long noncoding RNAs (lncRNAs), which are noncoding RNAs (ncRNAs) with length more than 200 nucleotides (nt), have been demonstrated to be involved in various types of cancer. Consequently, it has been frequently discussed that lncRNAs with aberrant expression in cancer serve as potential diagnostic biomarkers and therapeutic targets. However, one major challenge of developing cancer biomarkers is tumor heterogeneity which means that tumor cells show different cellular morphology, metastatic potential as well as gene expression. In this study, a custom designed microarray platform covering both mRNAs and lncRNAs was applied to tumor tissues of gastric, colon, liver and lung. 316 and 157 differentially expressed (DE-) protein coding genes and lncRNAs common to these four types of cancer were identified respectively. Besides, the functional roles of common DE-lncRNAs were inferred based on their expression and genomic position correlation with mRNAs. Moreover, mRNAs and lncRNAs with tissue specificity were also identified, suggesting their particular roles with regard to specific biogenesis and functions of different organs. Based on the large-scale survey of mRNAs and lncRNAs in four types of cancer, this study may offer new biomarkers common or specific for various types of cancer.

Mesenchymal Stem Cells Promote Hepatocarcinogenesis via lncRNA–MUF Interaction with ANXA2 and miR-34a

Accumulating evidence suggests that cancer-associated mesenchymal stem cells (MSC) contribute to the development and metastasis of hepatocellular carcinoma (HCC). Aberrant expression of long noncoding RNAs (lncRNA) has been associated with these processes but cellular mechanisms are obscure. In this study, we report that HCC-associated mesenchymal stem cells (HCC-MSC) promote epithelial-mesenchymal transition (EMT) and liver tumorigenesis. We identified a novel lncRNA that we termed lncRNA-MUF (MSC-upregulated factor) that is highly expressed in HCC tissues and correlated with poor prognosis. Depleting lncRNA-MUF in HCC cells repressed EMT and inhibited their tumorigenic potential. Conversely, lncRNA-MUF overexpression accelerated EMT and malignant capacity. Mechanistic investigations showed that lncRNA-MUF bound Annexin A2 (ANXA2) and activated Wnt/β-catenin signaling and EMT. Furthermore, lncRNA-MUF acted as a competing endogenous RNA for miR-34a, leading to Snail1 upregulation and EMT activation. Collectively, our findings establish a lncRNA-mediated process in MSC that facilitates hepatocarcinogenesis, with potential implications for therapeutic targeting. Cancer Res; 77(23); 6704-16. ©2017 AACR.

The long noncoding RNA ASNR regulates degradation of Bcl-2 mRNA through its interaction with AUF1

The identification and characterization of long non-coding RNAs (lncRNAs) in diverse biological processes has recently developed rapidly. The large amounts of non-coding RNAs scale consistent with developmental complexity in eukaryotes, indicating that most of these transcripts may have functions in the regulation of biological processes and disorder in the organisms. In particular, Understanding of the overall biological significance of lncRNAs in cancers still remains limited. Here, we found a nuclear-retained lncRNA, termed Lnc_ASNR (apoptosis suppressing-noncoding RNA), which serves as a repressor of apoptosis. Lnc_ASNR was discovered in a set of microarray data derived from four kinds of tumor and adjacent normal tissue samples, and displayed significant up-regulation in the tumor tissues. Using an RNA-pull down assay, we found that Lnc_ASNR interacted with the protein ARE/poly (U)-binding/degradation factor 1(AUF1), which is reported to promote rapid degradation of the Bcl-2 mRNA, an inhibitor of apoptosis. Lnc_ASNR binds to AUFI in nucleus, decreasing the cytoplasmic proportion of AUF1 which targets the B-cell lymphoma-2 (Bcl-2) mRNA. Taken together, the overall effect of Lnc_ASNR expression is thus a decrease in cell apoptosis indicating that Lnc_ASNR may play a vital role in tumorigenesis and carcinogenesis.

LncKdm2b controls self‐renewal of embryonic stem cells via activating expression of transcription factor Zbtb3

Divergent long noncoding RNAs (lncRNAs) represent a major lncRNA biotype in mouse and human genomes. The biological and molecular functions of the divergent lncRNAs remain largely unknown. Here, we show that lncKdm2b, a divergent lncRNA for Kdm2b gene, is conserved among five mammalian species and highly expressed in embryonic stem cells (ESCs) and early embryos. LncKdm2b knockout impairs ESC self‐renewal and causes early embryonic lethality. LncKdm2b can activate Zbtb3 by promoting the assembly and ATPase activity of Snf2‐related CREBBP activator protein (SRCAP) complex in trans. Zbtb3 potentiates the ESC self‐renewal in a Nanog‐dependent manner. Finally, Zbtb3 deficiency impairs the ESC self‐renewal and early embryonic development. Therefore, our findings reveal that lncRNAs may represent an additional layer of the regulation of ESC self‐renewal and early embryogenesis.

RNA-splicing factor SART3 regulates translesion DNA synthesis

Abstract Translesion DNA synthesis (TLS) is one mode of DNA damage tolerance that uses specialized DNA polymerases to replicate damaged DNA. DNA polymerase η (Polη) is well known to facilitate TLS across ultraviolet (UV) irradiation and mutations in POLH are implicated in skin carcinogenesis. However, the basis for recruitment of Polη to stalled replication forks is not completely understood. In this study, we used an affinity purification approach to isolate a Polη-containing complex and have identified SART3, a pre-mRNA splicing factor, as a critical regulator to modulate the recruitment of Polη and its partner RAD18 after UV exposure. We show that SART3 interacts with Polη and RAD18 via its C-terminus. Moreover, SART3 can form homodimers to promote the Polη/RAD18 interaction and PCNA monoubiquitination, a key event in TLS. Depletion of SART3 also impairs UV-induced single-stranded DNA (ssDNA) generation and RPA focus formation, resulting in an impaired Polη recruitment and a higher mutation frequency and hypersensitivity after UV treatment. Notably, we found that several SART3 missense mutations in cancer samples lessen its stimulatory effect on PCNA monoubiquitination. Collectively, our findings establish SART3 as a novel Polη/RAD18 association regulator that protects cells from UV-induced DNA damage, which functions in a RNA binding-independent fashion.

Ennet: exert enhaner-only somatic mutations to discover potential cancer-driving biological networks

Whole genome sequencing technology has facilitated the discovery of a large number of somatic mutations in enhancers (SMEs), whereas the utility of SMEs in tumorigenesis has not been fully explored. Here we present Ennet, a method to comprehensively investigate SMEs enriched networks (SME-networks) in cancer by integrating SMEs, enhancer-gene interactions and gene-gene interactions. Using Ennet, we performed a pan-cancer analysis in 2004 samples from 8 cancer types and found many well-known cancer drivers were involved in SME-networks, including ESR1, SMAD3, MYC, EGFR, BCL2 and PAX5 et al. Meanwhile, Ennet also identified many new networks with less characterization but have potentially important roles in cancer, including the biggest SME-network in medulloblastoma (MB), which contains genes enriched in the glutamate receptor and nervous development pathways. Interestingly, SME-networks are specific across cancer types, and the vast majority of the genes identified by Ennet have few mutations in gene bodies. Collectively, our work shows that using enhancer-only somatic mutations can be an effective way to discover potential cancer-driving networks. Ennet provides a new perspective to explore new mechanisms for tumor progression from SMEs.Whole genome sequencing technology has facilitated the discovery of a large number of somatic mutations in enhancers (SMEs), whereas the utility of SMEs in tumorigenesis has not been fully explored. Here we present Ennet, a method to comprehensively investigate SMEs enriched networks (SME-networks) in cancer by integrating SMEs, enhancer-gene interactions and gene-gene interactions. Using Ennet, we performed a pan-cancer analysis in 2004 samples from 8 cancer types and found many well-known cancer drivers were involved in the SME-networks, including ESR1, SMAD3, MYC, EGFR, BCL2 and PAX5. Meanwhile, Ennet also identified many new networks with less characterization but have potentially important roles in cancer, including a large SME-network in medulloblastoma (MB), which contains genes enriched in the glutamate receptor and neural development pathways. Interestingly, SME-networks are specific across cancer types, and the vast majority of the genes identified by Ennet have few mutations in gene bodies. Collectively, our work suggests that using enhancer-only somatic mutations can be an effective way to discover potential cancer-driving networks. Ennet provides a new perspective to explore new mechanisms for tumor progression from SMEs.