Sanghyuk Lee

MicroRNA genes are transcribed by RNA polymerase II

MicroRNAs (miRNAs) constitute a large family of noncoding RNAs that function as guide molecules in diverse gene silencing pathways. Current efforts are focused on the regulatory function of miRNAs, while little is known about how these unusual genes themselves are regulated. Here we present the first direct evidence that miRNA genes are transcribed by RNA polymerase II (pol II). The primary miRNA transcripts (pri‐miRNAs) contain cap structures as well as poly(A) tails, which are the unique properties of class II gene transcripts. The treatment of human cells with α‐amanitin decreased the level of pri‐miRNAs at a concentration that selectively inhibits pol II activity. Furthermore, chromatin immunoprecipitation analyses show that pol II is physically associated with a miRNA promoter. We also describe, for the first time, the detailed structure of a miRNA gene by determining the promoter and the terminator of mir‐23a∼27a∼24‐2. These data indicate that pol II is the main, if not the only, RNA polymerase for miRNA gene transcription. Our study offers a basis for understanding the structure and regulation of miRNA genes.

p53 and microRNA-34 are suppressors of canonical Wnt signaling.

The tumor suppressor p53 activates miRNA-34 to inhibit Wnt signaling and colorectal cancer cell invasiveness. p53 Activates MicroRNA-34 to Inhibit Wnt Signaling The tumor suppressor p53 is missing or nonfunctional in many cancers, whereas the canonical Wnt signaling pathway is frequently activated. Here, Kim et al. show that p53 restrained Wnt signaling during Xenopus development, whereas loss of p53 function led to aberrant activation of the canonical Wnt signaling pathway, with microRNA-34 (miR-34) providing the connection between the two. They found that p53 stimulated production of miR-34, which, in turn, targeted key genes in the Wnt signaling pathway. Analyses of gene expression data sets indicated that loss of p53 or miR-34 function was associated with activation of Wnt signaling in human cancers; moreover, loss of p53 function increased Wnt signaling in colon cancer cells in vitro. In p53-mutant colon cancer cells, miR-34 attenuated Wnt signaling and decreased the invasiveness of these cells in vitro. Thus, the p53–miR-34–Wnt pathway appears to be crucial not only during development but also for p53’s tumor suppressor function. Although loss of p53 function and activation of canonical Wnt signaling cascades are frequently coupled in cancer, the links between these two pathways remain unclear. We report that p53 transactivated microRNA-34 (miR-34), which consequently suppressed the transcriptional activity of β-catenin–T cell factor and lymphoid enhancer factor (TCF/LEF) complexes by targeting the untranslated regions (UTRs) of a set of conserved targets in a network of genes encoding elements of the Wnt pathway. Loss of p53 function increased canonical Wnt signaling by alleviating miR-34–specific interactions with target UTRs, and miR-34 depletion relieved p53-mediated Wnt repression. Gene expression signatures reflecting the status of β-catenin–TCF/LEF transcriptional activity in breast cancer and pediatric neuroblastoma patients were correlated with p53 and miR-34 functional status. Loss of p53 or miR-34 contributed to neoplastic progression by triggering the Wnt-dependent, tissue-invasive activity of colorectal cancer cells. Further, during development, miR-34 interactions with the β-catenin UTR affected Xenopus body axis polarity and the expression of Wnt-dependent patterning genes. These data provide insight into the mechanisms by which a p53–miR-34 network restrains canonical Wnt signaling cascades in developing organisms and human cancer.

Accurate quantification of transcriptome from RNA-Seq data by effective length normalization

We propose a novel, efficient and intuitive approach of estimating mRNA abundances from the whole transcriptome shotgun sequencing (RNA-Seq) data. Our method, NEUMA (Normalization by Expected Uniquely Mappable Area), is based on effective length normalization using uniquely mappable areas of gene and mRNA isoform models. Using the known transcriptome sequence model such as RefSeq, NEUMA pre-computes the numbers of all possible gene-wise and isoform-wise informative reads: the former being sequences mapped to all mRNA isoforms of a single gene exclusively and the latter uniquely mapped to a single mRNA isoform. The results are used to estimate the effective length of genes and transcripts, taking experimental distributions of fragment size into consideration. Quantitative RT–PCR based on 27 randomly selected genes in two human cell lines and computer simulation experiments demonstrated superior accuracy of NEUMA over other recently developed methods. NEUMA covers a large proportion of genes and mRNA isoforms and offers a measure of consistency (‘consistency coefficient’) for each gene between an independently measured gene-wise level and the sum of the isoform levels. NEUMA is applicable to both paired-end and single-end RNA-Seq data. We propose that NEUMA could make a standard method in quantifying gene transcript levels from RNA-Seq data.

lncRNAtor: a comprehensive resource for functional investigation of long non-coding RNAs

MOTIVATION A number of long non-coding RNAs (lncRNAs) have been identified by deep sequencing methods, but their molecular and cellular functions are known only for a limited number of lncRNAs. Current databases on lncRNAs are mostly for cataloging purpose without providing in-depth information required to infer functions. A comprehensive resource on lncRNA function is an immediate need. RESULTS We present a database for functional investigation of lncRNAs that encompasses annotation, sequence analysis, gene expression, protein binding and phylogenetic conservation. We have compiled lncRNAs for six species (human, mouse, zebrafish, fruit fly, worm and yeast) from ENSEMBL, HGNC, MGI and lncRNAdb. Each lncRNA was analyzed for coding potential and phylogenetic conservation in different lineages. Gene expression data of 208 RNA-Seq studies (4995 samples), collected from GEO, ENCODE, modENCODE and TCGA databases, were used to provide expression profiles in various tissues, diseases and developmental stages. Importantly, we analyzed RNA-Seq data to identify coexpressed mRNAs that would provide ample insights on lncRNA functions. The resulting gene list can be subject to enrichment analysis such as Gene Ontology or KEGG pathways. Furthermore, we compiled protein-lncRNA interactions by collecting and analyzing publicly available CLIP-seq or PAR-CLIP sequencing data. Finally, we explored evolutionarily conserved lncRNAs with correlated expression between human and six other organisms to identify functional lncRNAs. The whole contents are provided in a user-friendly web interface. AVAILABILITY AND IMPLEMENTATION lncRNAtor is available at http://lncrnator.ewha.ac.kr/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

A High-Dimensional, Deep-Sequencing Study of Lung Adenocarcinoma in Female Never-Smokers

Background Deep sequencing techniques provide a remarkable opportunity for comprehensive understanding of tumorigenesis at the molecular level. As omics studies become popular, integrative approaches need to be developed to move from a simple cataloguing of mutations and changes in gene expression to dissecting the molecular nature of carcinogenesis at the systemic level and understanding the complex networks that lead to cancer development. Results Here, we describe a high-throughput, multi-dimensional sequencing study of primary lung adenocarcinoma tumors and adjacent normal tissues of six Korean female never-smoker patients. Our data encompass results from exome-seq, RNA-seq, small RNA-seq, and MeDIP-seq. We identified and validated novel genetic aberrations, including 47 somatic mutations and 19 fusion transcripts. One of the fusions involves the c-RET gene, which was recently reported to form fusion genes that may function as drivers of carcinogenesis in lung cancer patients. We also characterized gene expression profiles, which we integrated with genomic aberrations and gene regulations into functional networks. The most prominent gene network module that emerged indicates that disturbances in G2/M transition and mitotic progression are causally linked to tumorigenesis in these patients. Also, results from the analysis strongly suggest that several novel microRNA-target interactions represent key regulatory elements of the gene network. Conclusions Our study not only provides an overview of the alterations occurring in lung adenocarcinoma at multiple levels from genome to transcriptome and epigenome, but also offers a model for integrative genomics analysis and proposes potential target pathways for the control of lung adenocarcinoma.

ChimerDB 2.0—a knowledgebase for fusion genes updated

Chromosome translocations and gene fusions are frequent events in the human genome and have been found to cause diverse types of tumor. ChimerDB is a knowledgebase of fusion genes identified from bioinformatics analysis of transcript sequences in the GenBank and various other public resources such as the Sanger cancer genome project (CGP), OMIM, PubMed and the Mitelman’s database. In this updated version, we significantly modified the algorithm of identifying fusion transcripts. Specifically, the new algorithm is more sensitive and has detected 2699 fusion transcripts with high confidence. Furthermore, it can identify interchromosomal translocations as well as the intrachromosomal deletions or inversions of large DNA segments. Importantly, results from the analysis of next-generation sequencing data in the short read archives are incorporated as well. We updated and integrated all contents (GenBank, Sanger CGP, OMIM, PubMed publications and the Mitelman’s database), and the user-interface has been improved to support diverse types of searches and to enhance the user convenience especially in browsing PubMed articles. We also developed a new alignment viewer that should facilitate examining reliability of fusion transcripts and inferring functional significance. We expect ChimerDB 2.0, available at http://ercsb.ewha.ac.kr/fusiongene, to be a valuable tool in identifying biomarkers and drug targets.

ECgene: an alternative splicing database update

ECgene () was developed to provide functional annotation for alternatively spliced genes. The applications encompass the genome-based transcript modeling for alternative splicing (AS), domain analysis with Gene Ontology (GO) annotation and expression analysis based on the EST and SAGE data. We have expanded the ECgenes AS modeling and EST clustering to nine organisms for which sufficient EST data are available in the GenBank. As for the human genome, we have also introduced several new applications to analyze differential expression. ECprofiler is an ontology-based candidate gene search system that allows users to select an arbitrary combination of gene expression pattern and GO functional categories. DEGEST is a database of differentially expressed genes and isoforms based on the EST information. Importantly, gene expression is analyzed at three distinctive levels—gene, isoform and exon levels. The user interfaces for functional and expression analyses have been substantially improved. ASviewer is a dedicated java application that visualizes the transcript structure and functional features of alternatively spliced variants. The SAGE part of the expression module provides many additional features including SNP, differential expression and alternative tag positions.

miRGator v2.0 : an integrated system for functional investigation of microRNAs

miRGator is an integrated database of microRNA (miRNA)-associated gene expression, target prediction, disease association and genomic annotation, which aims to facilitate functional investigation of miRNAs. The recent version of miRGator v2.0 contains information about (i) human miRNA expression profiles under various experimental conditions, (ii) paired expression profiles of both mRNAs and miRNAs, (iii) gene expression profiles under miRNA-perturbation (e.g. miRNA knockout and overexpression), (iv) known/predicted miRNA targets and (v) miRNA-disease associations. In total, >8000 miRNA expression profiles, ∼300 miRNA-perturbed gene expression profiles and ∼2000 mRNA expression profiles are compiled with manually curated annotations on disease, tissue type and perturbation. By integrating these data sets, a series of novel associations (miRNA–miRNA, miRNA–disease and miRNA–target) is extracted via shared features. For example, differentially expressed genes (DEGs) after miRNA knockout were systematically compared against miRNA targets. Likewise, differentially expressed miRNAs (DEmiRs) were compared with disease-associated miRNAs. Additionally, miRNA expression and disease-phenotype profiles revealed miRNA pairs whose expression was regulated in parallel in various experimental and disease conditions. Complex associations are readily accessible using an interactive network visualization interface. The miRGator v2.0 serves as a reference database to investigate miRNA expression and function (http://miRGator.kobic.re.kr).

Variants Affecting Exon Skipping Contribute to Complex Traits

DNA variants that affect alternative splicing and the relative quantities of different gene transcripts have been shown to be risk alleles for some Mendelian diseases. However, for complex traits characterized by a low odds ratio for any single contributing variant, very few studies have investigated the contribution of splicing variants. The overarching goal of this study is to discover and characterize the role that variants affecting alternative splicing may play in the genetic etiology of complex traits, which include a significant number of the common human diseases. Specifically, we hypothesize that single nucleotide polymorphisms (SNPs) in splicing regulatory elements can be characterized in silico to identify variants affecting splicing, and that these variants may contribute to the etiology of complex diseases as well as the inter-individual variability in the ratios of alternative transcripts. We leverage high-throughput expression profiling to 1) experimentally validate our in silico predictions of skipped exons and 2) characterize the molecular role of intronic genetic variations in alternative splicing events in the context of complex human traits and diseases. We propose that intronic SNPs play a role as genetic regulators within splicing regulatory elements and show that their associated exon skipping events can affect protein domains and structure. We find that SNPs we would predict to affect exon skipping are enriched among the set of SNPs reported to be associated with complex human traits.

A simplified homology-model builder toward highly protein-like structures: An inspection of restraining potentials

A homology model builder using simple restraining potentials based on spline‐interpolated quadratic functions is developed and interfaced with CHARMM package. The continuity and stability of the potential function were validated, and the parameters were optimized using the CASP7 targets. The performance of the model builder was benchmarked to the Modeller program using the template‐based modeling targets in CASP9. The benchmark results show that, while our builder yields the structures with slightly lower packing, backbone, and template modeling scores, our models show much better protein‐like scores in terms of normalized discrete optimized protein energy, dipolar distance‐scaled finite‐ideal gas reference, Molprobity clash, Ramachandran appearance Z‐score, and rotamer Z‐score. As our model builder is interfaced with CHARMM, it is advantageous to directly use other CHARMM functionality and energy functions to refine the model structures or to use the models for other computational studies using CHARMM.