TY - JOUR T1 - Arabidopsis bioinformatics resources: The current state, challenges, and priorities for the future JF - Plant Direct Y1 - 2019 A1 - Colleen Doherty A1 - Joanna Friesner A1 - Brian Gregory A1 - Ann Loraine A1 - Molly Megraw A1 - Nicholas Provart A1 - R Keith Slotkin A1 - Chris Town A1 - Sarah M Assmann A1 - Michael Axtell A1 - Tanya Berardini A1 - Sixue Chen A1 - Malia Gehan A1 - Eva Huala A1 - Pankaj Jaiswal A1 - Stephen Larson A1 - Song Li A1 - Sean May A1 - Todd Michael A1 - Chris Pires A1 - Chris Topp A1 - Justin Walley A1 - Eve Wurtele VL - 3 UR - https://onlinelibrary.wiley.com/doi/full/10.1002/pld3.109 IS - 1 ER - TY - JOUR T1 - PlantSimLab-a modeling and simulation web tool for plant biologists JF - BMC Bioinformatics Y1 - 2019 A1 - S Ha A1 - E Dimitrova A1 - Stefan Hoops A1 - D Altarawy A1 - M Ansariola A1 - D Deb A1 - J Glazebrook A1 - R Hillmer A1 - H Shahin A1 - F Katagiri A1 - J McDowell A1 - M Megraw A1 - J Setubal A1 - BM Tyler A1 - Reinhard Laubenbacher VL - 20 UR - https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-019-3094-9 IS - 1 ER - TY - JOUR T1 - Establishment of Expression in the SHORTROOT-SCARECROW Transcriptional Cascade through Opposing Activities of Both Activators and Repressors JF - Dev Cell Y1 - 2016 A1 - Sparks, E. E. A1 - Drapek, C. A1 - Gaudinier, A. A1 - Li, S. A1 - Ansariola, M. A1 - Shen, N. A1 - Hennacy, J. H. A1 - Zhang, J. A1 - Turco, G. A1 - Petricka, J. J. A1 - Foret, J. A1 - Hartemink, A. J. A1 - Gordan, R. A1 - Megraw, M. A1 - Brady, S. M. A1 - Benfey, P. N. KW - Arabidopsis Proteins/ genetics/ metabolism KW - Arabidopsis/ genetics/growth & development/ metabolism KW - Computer Simulation KW - Gene Expression Regulation, Plant KW - Gene Regulatory Networks KW - Genes, Plant KW - Genes, Reporter KW - Genes, Synthetic KW - Models, Genetic KW - Plant Roots/cytology/metabolism KW - Plants, Genetically Modified KW - Promoter Regions, Genetic KW - Repressor Proteins/genetics/metabolism KW - Trans-Activators/genetics/metabolism KW - Transcription Factors/ genetics/ metabolism KW - Two-Hybrid System Techniques AB -

Tissue-specific gene expression is often thought to arise from spatially restricted transcriptional cascades. However, it is unclear how expression is established at the top of these cascades in the absence of pre-existing specificity. We generated a transcriptional network to explore how transcription factor expression is established in the Arabidopsis thaliana root ground tissue. Regulators of the SHORTROOT-SCARECROW transcriptional cascade were validated in planta. At the top of this cascade, we identified both activators and repressors of SHORTROOT. The aggregate spatial expression of these regulators is not sufficient to predict transcriptional specificity. Instead, modeling, transcriptional reporters, and synthetic promoters support a mechanism whereby expression at the top of the SHORTROOT-SCARECROW cascade is established through opposing activities of activators and repressors.

VL - 39 SN - 1878-1551 (Electronic)1534-5807 (Linking) UR - https://doi.org/10.1016/j.devcel.2016.09.031 JO - Developmental cell ER - TY - JOUR T1 - Paired-end analysis of transcription start sites in Arabidopsis reveals plant-specific promoter signatures. JF - Plant Cell Y1 - 2014 A1 - Morton, Taj A1 - Petricka, Jalean A1 - Corcoran, David L A1 - Li, Song A1 - Winter, Cara M A1 - Carda, Alexa A1 - Benfey, Philip N A1 - Ohler, Uwe A1 - Megraw, Molly KW - Arabidopsis KW - Arabidopsis Proteins KW - Binding Sites KW - Cluster Analysis KW - DNA, Plant KW - Gene Expression Regulation, Plant KW - Genome, Plant KW - Models, Genetic KW - Nucleotide Motifs KW - Plant Roots KW - Promoter Regions, Genetic KW - RNA, Messenger KW - RNA, Plant KW - Sequence Analysis, DNA KW - Species Specificity KW - TATA Box KW - Transcription Factors KW - Transcription Initiation Site AB -

Understanding plant gene promoter architecture has long been a challenge due to the lack of relevant large-scale data sets and analysis methods. Here, we present a publicly available, large-scale transcription start site (TSS) data set in plants using a high-resolution method for analysis of 5' ends of mRNA transcripts. Our data set is produced using the paired-end analysis of transcription start sites (PEAT) protocol, providing millions of TSS locations from wild-type Columbia-0 Arabidopsis thaliana whole root samples. Using this data set, we grouped TSS reads into "TSS tag clusters" and categorized clusters into three spatial initiation patterns: narrow peak, broad with peak, and weak peak. We then designed a machine learning model that predicts the presence of TSS tag clusters with outstanding sensitivity and specificity for all three initiation patterns. We used this model to analyze the transcription factor binding site content of promoters exhibiting these initiation patterns. In contrast to the canonical notions of TATA-containing and more broad "TATA-less" promoters, the model shows that, in plants, the vast majority of transcription start sites are TATA free and are defined by a large compendium of known DNA sequence binding elements. We present results on the usage of these elements and provide our Plant PEAT Peaks (3PEAT) model that predicts the presence of TSSs directly from sequence.

[Link to Additional Data and Supplementary Materials]

VL - 26 IS - 7 ER - TY - JOUR T1 - A stele-enriched gene regulatory network in the Arabidopsis root. JF - Mol Syst Biol Y1 - 2011 A1 - Brady, Siobhan M A1 - Zhang, Lifang A1 - Megraw, Molly A1 - Martinez, Natalia J A1 - Jiang, Eric A1 - Yi, Charles S A1 - Liu, Weilin A1 - Zeng, Anna A1 - Taylor-Teeples, Mallorie A1 - Kim, Dahae A1 - Ahnert, Sebastian A1 - Ohler, Uwe A1 - Ware, Doreen A1 - Walhout, Albertha J M A1 - Benfey, Philip N KW - Arabidopsis KW - Arabidopsis Proteins KW - Gene Expression Profiling KW - Gene Regulatory Networks KW - MicroRNAs KW - Plant Roots KW - Reproducibility of Results KW - Systems Biology KW - Transcription Factors KW - Two-Hybrid System Techniques AB -

Tightly controlled gene expression is a hallmark of multicellular development and is accomplished by transcription factors (TFs) and microRNAs (miRNAs). Although many studies have focused on identifying downstream targets of these molecules, less is known about the factors that regulate their differential expression. We used data from high spatial resolution gene expression experiments and yeast one-hybrid (Y1H) and two-hybrid (Y2H) assays to delineate a subset of interactions occurring within a gene regulatory network (GRN) that determines tissue-specific TF and miRNA expression in plants. We find that upstream TFs are expressed in more diverse cell types than their targets and that promoters that are bound by a relatively large number of TFs correspond to key developmental regulators. The regulatory consequence of many TFs for their target was experimentally determined using genetic analysis. Remarkably, molecular phenotypes were identified for 65% of the TFs, but morphological phenotypes were associated with only 16%. This indicates that the GRN is robust, and that gene expression changes may be canalized or buffered.

VL - 7 ER - TY - JOUR T1 - Editing of Epstein-Barr virus-encoded BART6 microRNAs controls their dicer targeting and consequently affects viral latency. JF - J Biol Chem Y1 - 2010 A1 - Iizasa, Hisashi A1 - Wulff, Bjorn-Erik A1 - Alla, Nageswara R A1 - Maragkakis, Manolis A1 - Megraw, Molly A1 - Hatzigeorgiou, Artemis A1 - Iwakiri, Dai A1 - Takada, Kenzo A1 - Wiedmer, Andreas A1 - Showe, Louise A1 - Lieberman, Paul A1 - Nishikura, Kazuko KW - Cell Line, Tumor KW - Epstein-Barr Virus Infections KW - Epstein-Barr Virus Nuclear Antigens KW - Gene Silencing KW - Herpesvirus 4, Human KW - Humans KW - Immediate-Early Proteins KW - MicroRNAs KW - Ribonuclease III KW - RNA Editing KW - RNA, Viral KW - Trans-Activators KW - Viral Proteins KW - Virus Latency AB -

Certain primary transcripts of miRNA (pri-microRNAs) undergo RNA editing that converts adenosine to inosine. The Epstein-Barr virus (EBV) genome encodes multiple microRNA genes of its own. Here we report that primary transcripts of ebv-miR-BART6 (pri-miR-BART6) are edited in latently EBV-infected cells. Editing of wild-type pri-miR-BART6 RNAs dramatically reduced loading of miR-BART6-5p RNAs onto the microRNA-induced silencing complex. Editing of a mutation-containing pri-miR-BART6 found in Daudi Burkitt lymphoma and nasopharyngeal carcinoma C666-1 cell lines suppressed processing of miR-BART6 RNAs. Most importantly, miR-BART6-5p RNAs silence Dicer through multiple target sites located in the 3'-UTR of Dicer mRNA. The significance of miR-BART6 was further investigated in cells in various stages of latency. We found that miR-BART6-5p RNAs suppress the EBNA2 viral oncogene required for transition from immunologically less responsive type I and type II latency to the more immunoreactive type III latency as well as Zta and Rta viral proteins essential for lytic replication, revealing the regulatory function of miR-BART6 in EBV infection and latency. Mutation and A-to-I editing appear to be adaptive mechanisms that antagonize miR-BART6 activities.

VL - 285 IS - 43 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 - microRNAs exhibit high frequency genomic alterations in human cancer. JF - Proc Natl Acad Sci U S A Y1 - 2006 A1 - Zhang, Lin A1 - Huang, Jia A1 - Yang, Nuo A1 - Greshock, Joel A1 - Megraw, Molly S A1 - Giannakakis, Antonis A1 - Liang, Shun A1 - Naylor, Tara L A1 - Barchetti, Andrea A1 - Ward, Michelle R A1 - Yao, George A1 - Medina, Angelica A1 - O'brien-Jenkins, Ann A1 - Katsaros, Dionyssios A1 - Hatzigeorgiou, Artemis A1 - Gimotty, Phyllis A A1 - Weber, Barbara L A1 - Coukos, George KW - Breast Neoplasms KW - Female KW - Gene Dosage KW - Gene Expression Profiling KW - Humans KW - MicroRNAs KW - Neoplasms KW - Nucleic Acid Hybridization KW - Oligonucleotide Array Sequence Analysis KW - Ovarian Neoplasms KW - Statistics as Topic AB -

MicroRNAs (miRNAs) are endogenous noncoding RNAs, which negatively regulate gene expression. To determine genomewide miRNA DNA copy number abnormalities in cancer, 283 known human miRNA genes were analyzed by high-resolution array-based comparative genomic hybridization in 227 human ovarian cancer, breast cancer, and melanoma specimens. A high proportion of genomic loci containing miRNA genes exhibited DNA copy number alterations in ovarian cancer (37.1%), breast cancer (72.8%), and melanoma (85.9%), where copy number alterations observed in >15% tumors were considered significant for each miRNA gene. We identified 41 miRNA genes with gene copy number changes that were shared among the three cancer types (26 with gains and 15 with losses) as well as miRNA genes with copy number changes that were unique to each tumor type. Importantly, we show that miRNA copy changes correlate with miRNA expression. Finally, we identified high frequency copy number abnormalities of Dicer1, Argonaute2, and other miRNA-associated genes in breast and ovarian cancer as well as melanoma. These findings support the notion that copy number alterations of miRNAs and their regulatory genes are highly prevalent in cancer and may account partly for the frequent miRNA gene deregulation reported in several tumor types.

VL - 103 IS - 24 ER -