Huaxia Luo

Long Noncoding RNA ADINR Regulates Adipogenesis by Transcriptionally Activating C/EBPα

Summary C/EBPα is a critical transcriptional regulator of adipogenesis. How C/EBPα transcription is itself regulated is poorly understood, however, and remains a key question that needs to be addressed for a complete understanding of adipogenic development. Here, we identify a lncRNA, ADINR (adipogenic differentiation induced noncoding RNA), transcribed from a position ∼450 bp upstream of the C/EBPα gene, that orchestrates C/EBPα transcription in vivo. Depletion of ADINR leads to a severe adipogenic defect that is rescued by overexpression of C/EBPα. Moreover, we reveal that ADINR RNA specifically binds to PA1 and recruits MLL3/4 histone methyl-transferase complexes so as to increase H3K4me3 and decrease H3K27me3 histone modification in the C/EBPα locus during adipogenesis. These results show that ADINR plays important roles in regulating the differentiation of human mesenchymal stem cells into adipocytes by modulating C/EBPα in cis.

Functional Characterization of Long Noncoding RNA Lnc_bc060912 in Human Lung Carcinoma Cells.

Long noncoding RNAs (lncRNAs) are pervasively transcribed in the human genome. Recent studies suggest that the involvement of lncRNAs in human diseases could be far more prevalent than previously appreciated. Here we have identified a lncRNA termed Lnc_bc060912 whose expression is increased in human lung and other tumors. Lnc_bc060912 is 1.2 kb in length and is composed of two exons. The expression of Lnc_bc060912 was repressed by p53. Lnc_bc060912 suppressed cell apoptosis. Using a recently developed method for RNA-pulldown with formaldehyde cross-linking, we found that Lnc_bc060912 interacted with the two DNA damage repair proteins PARP1 and NPM1. Together, these results suggest that Lnc_bc060912, via PARP1 and NPM1, affects cell apoptosis and may play important roles in tumorigenesis and cancer progression.

Distinct MicroRNA Subcellular Size and Expression Patterns in Human Cancer Cells

Introduction. Small noncoding RNAs have important regulatory functions in different cell pathways. It is believed that most of them mainly play role in gene post-transcriptional regulation in the cytoplasm. Recent evidence suggests miRNA and siRNA activity in the nucleus. Here, we show distinct genome-wide sub-cellular localization distribution profiles of small noncoding RNAs in human breast cancer cells. Methods. We separated breast cancer cell nuclei from cytoplasm, and identified small RNA sequences using a high-throughput sequencing platform. To determine the relationship between miRNA sub-cellular distribution and cancer progression, we used microarray analysis to examine the miRNA expression levels in nucleus and cytoplasm of three human cell lines, one normal breast cell line and two breast cancer cell lines. Logistic regression and SVM were used for further analysis. Results. The sub-cellular distribution of small noncoding RNAs shows that numerous miRNAs and their isoforms (isomiR) not only locate to the cytoplasm but also appeare in the nucleus. Subsequent microarray analyses indicated that the miRNA nuclear-cytoplasmic-ratio is a significant characteristic of different cancer cell lines. Conclusions. Our results indicate that the sub-cellular distribution is important for miRNA function, and that the characterization of the small RNAs sub-cellular localizome may contribute to cancer research and diagnosis.

BioCircos.js: an interactive Circos JavaScript library for biological data visualization on web applications

UNLABELLED We here present BioCircos.js, an interactive and lightweight JavaScript library especially for biological data interactive visualization. BioCircos.js facilitates the development of web-based applications for circular visualization of various biological data, such as genomic features, genetic variations, gene expression and biomolecular interactions. AVAILABILITY AND IMPLEMENTATION BioCircos.js and its manual are freely available online at http://bioinfo.ibp.ac.cn/biocircos/ CONTACT rschen@ibp.ac.cn SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

The long noncoding RNA SNHG1 promotes tumor growth through regulating transcription of both local and distal genes

Increasing evidence indicates that long noncoding RNAs (lncRNAs) have important roles in various physiological processes and dysfunction of lncRNAs could be a prevalent cause in human diseases. Here we functionally characterized the nuclear-enriched lncRNA SNHG1, which is highly expressed in multiple types of cancer. We also provide evidence that SNHG1 promotes cancer cell growth by regulating gene expression both in cis and in trans. SNHG1 was involved in the AKT signaling pathway as it promotes the neighboring transcription of the protein-coding gene SLC3A2 in cis by binding the Mediator complex to facilitate the establishment of enhancer–promoter interaction. In trans, SNHG1 directly interacted with central domain of FUBP1 and antagonize the binding of FBP-interacting repressor to FUBP1, thereby coordinating the expression of the oncogene MYC. Collectively, our findings demonstrate that lncRNA SNHG1 can function both in cis and in trans with distinct mechanisms to regulate transcription, promoting tumorigenesis and cancer progression.

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.

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.

Dynamic-BM: multispecies Dynamic BodyMap database from temporal RNA-seq data.

Biological processes, especially developmental processes, are often dynamic. Previous BodyMap projects for human and mouse have provided researchers with portals to tissue-specific gene expression, but these efforts have not included dynamic gene expression patterns. Over the past few years, substantial progress in our understanding of the molecular mechanisms of protein-coding and long noncoding RNA (lncRNA) genes in development processes has been achieved through numerous time series RNA sequencing (RNA-seq) studies. However, none of the existing databases focuses on these time series data, thus rendering the exploration of dynamic gene expression patterns inconvenient. Here, we present Dynamic BodyMap (Dynamic-BM), a database for temporal gene expression profiles, obtained from 2203 time series of RNA-seq samples, covering >25 tissues from five species. Dynamic-BM has a user-friendly Web interface designed for browsing and searching the dynamic expression pattern of genes from different sources. It is an open resource for efficient data exploration, providing dynamic expression profiles of both protein-coding genes and lncRNAs to facilitate the generation of new hypotheses in developmental biology research. Additionally, Dynamic-BM includes a literature-based knowledgebase for lncRNAs associated with tissue development and a list of manually selected lncRNA candidates that may be involved in tissue development. Dynamic-BM is available at http://bioinfo.ibp.ac.cn/Dynamic-BM.