TY - JOUR T1 - MicroRNA promoter analysis. JF - Methods Mol Biol Y1 - 2010 A1 - Megraw, Molly A1 - Hatzigeorgiou, Artemis G KW - MicroRNAs KW - Promoter Regions, Genetic KW - Transcription Factors AB -

In this chapter, we present a brief overview of current knowledge about the promoters of plant microRNAs (miRNAs), and provide a step-by-step guide for predicting plant miRNA promoter elements using known transcription factor binding motifs. The approach to promoter element prediction is based on a carefully constructed collection of Positional Weight Matrices (PWMs) for known transcription factors (TFs) in Arabidopsis. A key concept of the method is to use scoring thresholds for potential binding sites that are appropriate to each individual transcription factor. While the procedure can be applied to search for Transcription Factor Binding Sites (TFBSs) in any pol-II promoter region, it is particularly practical for the case of plant miRNA promoters where upstream sequence regions and binding sites are not readily available in existing databases. The majority of the material described in this chapter is available for download at http://microrna.gr.

[Link to Tools and Supplementary Materials]

VL - 592 ER - TY - JOUR T1 - miRGen 2.0: a database of microRNA genomic information and regulation. JF - Nucleic Acids Res Y1 - 2010 A1 - Alexiou, Panagiotis A1 - Vergoulis, Thanasis A1 - Gleditzsch, Martin A1 - Prekas, George A1 - Dalamagas, Theodore A1 - Megraw, Molly A1 - Grosse, Ivo A1 - Sellis, Timos A1 - Hatzigeorgiou, Artemis G KW - 3' Untranslated Regions KW - Algorithms KW - Animals KW - Cell Line, Tumor KW - Computational Biology KW - Databases, Genetic KW - Databases, Nucleic Acid KW - Humans KW - Information Storage and Retrieval KW - Internet KW - Mice KW - MicroRNAs KW - Polymorphism, Single Nucleotide KW - Software KW - Transcription Factors AB -

MicroRNAs are small, non-protein coding RNA molecules known to regulate the expression of genes by binding to the 3'UTR region of mRNAs. MicroRNAs are produced from longer transcripts which can code for more than one mature miRNAs. miRGen 2.0 is a database that aims to provide comprehensive information about the position of human and mouse microRNA coding transcripts and their regulation by transcription factors, including a unique compilation of both predicted and experimentally supported data. Expression profiles of microRNAs in several tissues and cell lines, single nucleotide polymorphism locations, microRNA target prediction on protein coding genes and mapping of miRNA targets of co-regulated miRNAs on biological pathways are also integrated into the database and user interface. The miRGen database will be continuously maintained and freely available at http://www.microrna.gr/mirgen/.

VL - 38 IS - Database issue ER - TY - JOUR T1 - A transcription factor affinity-based code for mammalian transcription initiation. JF - Genome Res Y1 - 2009 A1 - Megraw, Molly A1 - Pereira, Fernando A1 - Jensen, Shane T A1 - Ohler, Uwe A1 - Hatzigeorgiou, Artemis G KW - Base Composition KW - Databases, Genetic KW - DNA KW - Gene Expression Regulation KW - Genome, Human KW - Humans KW - Promoter Regions, Genetic KW - RNA Polymerase II KW - TATA Box KW - Transcription Factors KW - Transcription Initiation Site KW - Transcription, Genetic AB -

The recent arrival of large-scale cap analysis of gene expression (CAGE) data sets in mammals provides a wealth of quantitative information on coding and noncoding RNA polymerase II transcription start sites (TSS). Genome-wide CAGE studies reveal that a large fraction of TSS exhibit peaks where the vast majority of associated tags map to a particular location ( approximately 45%), whereas other active regions contain a broader distribution of initiation events. The presence of a strong single peak suggests that transcription at these locations may be mediated by position-specific sequence features. We therefore propose a new model for single-peaked TSS based solely on known transcription factors (TFs) and their respective regions of positional enrichment. This probabilistic model leads to near-perfect classification results in cross-validation (auROC = 0.98), and performance in genomic scans demonstrates that TSS prediction with both high accuracy and spatial resolution is achievable for a specific but large subgroup of mammalian promoters. The interpretable model structure suggests a DNA code in which canonical sequence features such as TATA-box, Initiator, and GC content do play a significant role, but many additional TFs show distinct spatial biases with respect to TSS location and are important contributors to the accurate prediction of single-peak transcription initiation sites. The model structure also reveals that CAGE tag clusters distal from annotated gene starts have distinct characteristics compared to those close to gene 5'-ends. Using this high-resolution single-peak model, we predict TSS for approximately 70% of mammalian microRNAs based on currently available data.

[Links to Tools and Supplementary Materials]

VL - 19 IS - 4 ER - TY - JOUR T1 - Frequency and fate of microRNA editing in human brain. JF - Nucleic Acids Res Y1 - 2008 A1 - Kawahara, Yukio A1 - Megraw, Molly A1 - Kreider, Edward A1 - Iizasa, Hisashi A1 - Valente, Louis A1 - Hatzigeorgiou, Artemis G A1 - Nishikura, Kazuko KW - Adenosine KW - Adenosine Deaminase KW - Animals KW - Base Sequence KW - Brain KW - Humans KW - Inosine KW - Mice KW - MicroRNAs KW - Molecular Sequence Data KW - RNA Editing KW - RNA Precursors KW - RNA Processing, Post-Transcriptional KW - RNA-Binding Proteins AB -

Primary transcripts of certain microRNA (miRNA) genes (pri-miRNAs) are subject to RNA editing that converts adenosine to inosine (A-->I RNA editing). However, the frequency of the pri-miRNA editing and the fate of edited pri-miRNAs remain largely to be determined. Examination of already known pri-miRNA editing sites indicated that adenosine residues of the UAG triplet sequence might be edited more frequently. In the present study, therefore, we conducted a large-scale survey of human pri-miRNAs containing the UAG triplet sequence. By direct sequencing of RT-PCR products corresponding to pri-miRNAs, we examined 209 pri-miRNAs and identified 43 UAG and also 43 non-UAG editing sites in 47 pri-miRNAs, which were highly edited in human brain. In vitro miRNA processing assay using recombinant Drosha-DGCR8 and Dicer-TRBP (the human immuno deficiency virus transactivating response RNA-binding protein) complexes revealed that a majority of pri-miRNA editing is likely to interfere with the miRNA processing steps. In addition, four new edited miRNAs with altered seed sequences were identified by targeted cloning and sequencing of the miRNAs that would be processed from edited pri-miRNAs. Our studies predict that approximately 16% of human pri-miRNAs are subject to A-->I editing and, thus, miRNA editing could have a large impact on the miRNA-mediated gene silencing.

VL - 36 IS - 16 ER - TY - JOUR T1 - Genomic and epigenetic alterations deregulate microRNA expression in human epithelial ovarian cancer. JF - Proc Natl Acad Sci U S A Y1 - 2008 A1 - Zhang, Lin A1 - Volinia, Stefano A1 - Bonome, Tomas A1 - Calin, George Adrian A1 - Greshock, Joel A1 - Yang, Nuo A1 - Liu, Chang-Gong A1 - Giannakakis, Antonis A1 - Alexiou, Pangiotis A1 - Hasegawa, Kosei A1 - Johnstone, Cameron N A1 - Megraw, Molly S A1 - Adams, Sarah A1 - Lassus, Heini A1 - Huang, Jia A1 - Kaur, Sippy A1 - Liang, Shun A1 - Sethupathy, Praveen A1 - Leminen, Arto A1 - Simossis, Victor A A1 - Sandaltzopoulos, Raphael A1 - Naomoto, Yoshio A1 - Katsaros, Dionyssios A1 - Gimotty, Phyllis A A1 - DeMichele, Angela A1 - Huang, Qihong A1 - Bützow, Ralf A1 - Rustgi, Anil K A1 - Weber, Barbara L A1 - Birrer, Michael J A1 - Hatzigeorgiou, Artemis G A1 - Croce, Carlo M A1 - Coukos, George KW - DNA, Neoplasm KW - Down-Regulation KW - Epigenesis, Genetic KW - Epithelial Cells KW - Female KW - Gene Expression Profiling KW - Gene Expression Regulation, Neoplastic KW - Genome, Human KW - Humans KW - MicroRNAs KW - Neoplasm Staging KW - Ovarian Neoplasms KW - Ribonuclease III KW - RNA, Messenger KW - Survival Analysis AB -

MicroRNAs (miRNAs) are an abundant class of small noncoding RNAs that function as negative gene regulators. miRNA deregulation is involved in the initiation and progression of human cancer; however, the underlying mechanism and its contributions to genome-wide transcriptional changes in cancer are still largely unknown. We studied miRNA deregulation in human epithelial ovarian cancer by integrative genomic approach, including miRNA microarray (n = 106), array-based comparative genomic hybridization (n = 109), cDNA microarray (n = 76), and tissue array (n = 504). miRNA expression is markedly down-regulated in malignant transformation and tumor progression. Genomic copy number loss and epigenetic silencing, respectively, may account for the down-regulation of approximately 15% and at least approximately 36% of miRNAs in advanced ovarian tumors and miRNA down-regulation contributes to a genome-wide transcriptional deregulation. Last, eight miRNAs located in the chromosome 14 miRNA cluster (Dlk1-Gtl2 domain) were identified as potential tumor suppressor genes. Therefore, our results suggest that miRNAs may offer new biomarkers and therapeutic targets in epithelial ovarian cancer.

VL - 105 IS - 19 ER - TY - JOUR T1 - miRGen: a database for the study of animal microRNA genomic organization and function. JF - Nucleic Acids Res Y1 - 2007 A1 - Megraw, Molly A1 - Sethupathy, Praveen A1 - Corda, Benoit A1 - Hatzigeorgiou, Artemis G KW - Animals KW - Data Interpretation, Statistical KW - Databases, Nucleic Acid KW - Genomics KW - Humans KW - Internet KW - Mice KW - MicroRNAs KW - Rats KW - User-Computer Interface AB -

miRGen is an integrated database of (i) positional relationships between animal miRNAs and genomic annotation sets and (ii) animal miRNA targets according to combinations of widely used target prediction programs. A major goal of the database is the study of the relationship between miRNA genomic organization and miRNA function. This is made possible by three integrated and user friendly interfaces. The Genomics interface allows the user to explore where whole-genome collections of miRNAs are located with respect to UCSC genome browser annotation sets such as Known Genes, Refseq Genes, Genscan predicted genes, CpG islands and pseudogenes. These miRNAs are connected through the Targets interface to their experimentally supported target genes from TarBase, as well as computationally predicted target genes from optimized intersections and unions of several widely used mammalian target prediction programs. Finally, the Clusters interface provides predicted miRNA clusters at any given inter-miRNA distance and provides specific functional information on the targets of miRNAs within each cluster. All of these unique features of miRGen are designed to facilitate investigations into miRNA genomic organization, co-transcription and targeting. miRGen can be freely accessed at http://www.diana.pcbi.upenn.edu/miRGen.

VL - 35 IS - Database issue ER - TY - JOUR T1 - A guide through present computational approaches for the identification of mammalian microRNA targets. JF - Nat Methods Y1 - 2006 A1 - Sethupathy, Praveen A1 - Megraw, Molly A1 - Hatzigeorgiou, Artemis G KW - 3' Untranslated Regions KW - 5' Untranslated Regions KW - Animals KW - Computational Biology KW - Gene Targeting KW - Humans KW - MicroRNAs KW - Predictive Value of Tests KW - RNA, Messenger KW - Sensitivity and Specificity KW - Software AB -

Computational microRNA (miRNA) target prediction is a field in flux. Here we present a guide through five widely used mammalian target prediction programs. We include an analysis of the performance of these individual programs and of various combinations of these programs. For this analysis we compiled several benchmark data sets of experimentally supported miRNA-target gene interactions. Based on the results, we provide a discussion on the status of target prediction and also suggest a stepwise approach toward predicting and selecting miRNA targets for experimental testing.

VL - 3 IS - 11 ER - TY - JOUR T1 - MicroRNA promoter element discovery in Arabidopsis. JF - RNA Y1 - 2006 A1 - Megraw, Molly A1 - Baev, Vesselin A1 - Rusinov, Ventsislav A1 - Jensen, Shane T A1 - Kalantidis, Kriton A1 - Hatzigeorgiou, Artemis G KW - Arabidopsis KW - Base Sequence KW - Binding Sites KW - Databases, Genetic KW - Feedback, Physiological KW - Genes, Plant KW - MicroRNAs KW - Promoter Regions, Genetic KW - TATA Box KW - Transcription Factors KW - Transcription Initiation Site AB -

In this study we present a method of identifying Arabidopsis miRNA promoter elements using known transcription factor binding motifs. We provide a comparative analysis of the representation of these elements in miRNA promoters, protein-coding gene promoters, and random genomic sequences. We report five transcription factor (TF) binding motifs that show evidence of overrepresentation in miRNA promoter regions relative to the promoter regions of protein-coding genes. This investigation is based on the analysis of 800-nucleotide regions upstream of 63 experimentally verified Transcription Start Sites (TSS) for miRNA primary transcripts in Arabidopsis. While the TATA-box binding motif was also previously reported by Xie and colleagues, the transcription factors AtMYC2, ARF, SORLREP3, and LFY are identified for the first time as overrepresented binding motifs in miRNA promoters.

VL - 12 IS - 9 ER -