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1.  3′LIFE: a functional assay to detect miRNA targets in high-throughput 
Nucleic Acids Research  2014;42(17):e132.
MicroRNAs (miRNAs) are short non-coding RNAs that regulate gene output at the post-transcriptional level by targeting degenerate elements primarily in 3′untranslated regions (3′UTRs) of mRNAs. Individual miRNAs can regulate networks of hundreds of genes, yet for the majority of miRNAs few, if any, targets are known. Misexpression of miRNAs is also a major contributor to cancer progression, thus there is a critical need to validate miRNA targets in high-throughput to understand miRNAs' contribution to tumorigenesis. Here we introduce a novel high-throughput assay to detect miRNA targets in 3′UTRs, called Luminescent Identification of Functional Elements in 3′UTRs (3′LIFE). We demonstrate the feasibility of 3′LIFE using a data set of 275 human 3′UTRs and two cancer-relevant miRNAs, let-7c and miR-10b, and compare our results to alternative methods to detect miRNA targets throughout the genome. We identify a large number of novel gene targets for these miRNAs, with only 32% of hits being bioinformatically predicted and 27% directed by non-canonical interactions. Functional analysis of target genes reveals consistent roles for each miRNA as either a tumor suppressor (let-7c) or oncogenic miRNA (miR-10b), and preferentially target multiple genes within regulatory networks, suggesting 3′LIFE is a rapid and sensitive method to detect miRNA targets in high-throughput.
doi:10.1093/nar/gku626
PMCID: PMC4176154  PMID: 25074381
2.  Integrative Analysis of the Caenorhabditis elegans Genome by the modENCODE Project 
Gerstein, Mark B. | Lu, Zhi John | Van Nostrand, Eric L. | Cheng, Chao | Arshinoff, Bradley I. | Liu, Tao | Yip, Kevin Y. | Robilotto, Rebecca | Rechtsteiner, Andreas | Ikegami, Kohta | Alves, Pedro | Chateigner, Aurelien | Perry, Marc | Morris, Mitzi | Auerbach, Raymond K. | Feng, Xin | Leng, Jing | Vielle, Anne | Niu, Wei | Rhrissorrakrai, Kahn | Agarwal, Ashish | Alexander, Roger P. | Barber, Galt | Brdlik, Cathleen M. | Brennan, Jennifer | Brouillet, Jeremy Jean | Carr, Adrian | Cheung, Ming-Sin | Clawson, Hiram | Contrino, Sergio | Dannenberg, Luke O. | Dernburg, Abby F. | Desai, Arshad | Dick, Lindsay | Dosé, Andréa C. | Du, Jiang | Egelhofer, Thea | Ercan, Sevinc | Euskirchen, Ghia | Ewing, Brent | Feingold, Elise A. | Gassmann, Reto | Good, Peter J. | Green, Phil | Gullier, Francois | Gutwein, Michelle | Guyer, Mark S. | Habegger, Lukas | Han, Ting | Henikoff, Jorja G. | Henz, Stefan R. | Hinrichs, Angie | Holster, Heather | Hyman, Tony | Iniguez, A. Leo | Janette, Judith | Jensen, Morten | Kato, Masaomi | Kent, W. James | Kephart, Ellen | Khivansara, Vishal | Khurana, Ekta | Kim, John K. | Kolasinska-Zwierz, Paulina | Lai, Eric C. | Latorre, Isabel | Leahey, Amber | Lewis, Suzanna | Lloyd, Paul | Lochovsky, Lucas | Lowdon, Rebecca F. | Lubling, Yaniv | Lyne, Rachel | MacCoss, Michael | Mackowiak, Sebastian D. | Mangone, Marco | McKay, Sheldon | Mecenas, Desirea | Merrihew, Gennifer | Miller, David M. | Muroyama, Andrew | Murray, John I. | Ooi, Siew-Loon | Pham, Hoang | Phippen, Taryn | Preston, Elicia A. | Rajewsky, Nikolaus | Rätsch, Gunnar | Rosenbaum, Heidi | Rozowsky, Joel | Rutherford, Kim | Ruzanov, Peter | Sarov, Mihail | Sasidharan, Rajkumar | Sboner, Andrea | Scheid, Paul | Segal, Eran | Shin, Hyunjin | Shou, Chong | Slack, Frank J. | Slightam, Cindie | Smith, Richard | Spencer, William C. | Stinson, E. O. | Taing, Scott | Takasaki, Teruaki | Vafeados, Dionne | Voronina, Ksenia | Wang, Guilin | Washington, Nicole L. | Whittle, Christina M. | Wu, Beijing | Yan, Koon-Kiu | Zeller, Georg | Zha, Zheng | Zhong, Mei | Zhou, Xingliang | Ahringer, Julie | Strome, Susan | Gunsalus, Kristin C. | Micklem, Gos | Liu, X. Shirley | Reinke, Valerie | Kim, Stuart K. | Hillier, LaDeana W. | Henikoff, Steven | Piano, Fabio | Snyder, Michael | Stein, Lincoln | Lieb, Jason D. | Waterston, Robert H.
Science (New York, N.Y.)  2010;330(6012):1775-1787.
We systematically generated large-scale data sets to improve genome annotation for the nematode Caenorhabditis elegans, a key model organism. These data sets include transcriptome profiling across a developmental time course, genome-wide identification of transcription factor–binding sites, and maps of chromatin organization. From this, we created more complete and accurate gene models, including alternative splice forms and candidate noncoding RNAs. We constructed hierarchical networks of transcription factor–binding and microRNA interactions and discovered chromosomal locations bound by an unusually large number of transcription factors. Different patterns of chromatin composition and histone modification were revealed between chromosome arms and centers, with similarly prominent differences between autosomes and the X chromosome. Integrating data types, we built statistical models relating chromatin, transcription factor binding, and gene expression. Overall, our analyses ascribed putative functions to most of the conserved genome.
doi:10.1126/science.1196914
PMCID: PMC3142569  PMID: 21177976
3.  The Landscape of C. elegans 3′UTRs 
Science (New York, N.Y.)  2010;329(5990):432-435.
Three-prime untranslated regions (3′UTRs) of metazoan messenger RNAs (mRNAs) contain numerous regulatory elements, yet remain largely uncharacterized. Using polyA capture, 3′ rapid amplification of complementary DNA (cDNA) ends, full-length cDNAs, and RNA-seq, we defined ∼26,000 distinct 3′UTRs in Caenorhabditis elegans for ∼85% of the 18,328 experimentally supported protein-coding genes and revised ∼40% of gene models. Alternative 3′UTR isoforms are frequent, often differentially expressed during development. Average 3′UTR length decreases with animal age. Surprisingly, no polyadenylation signal (PAS) was detected for 13% of polyadenylation sites, predominantly among shorter alternative isoforms. Trans-spliced (versus non–trans-spliced) mRNAs possess longer 3′UTRs and frequently contain no PAS or variant PAS. We identified conserved 3′UTR motifs, isoform-specific predicted microRNA target sites, and polyadenylation of most histone genes. Our data reveal a rich complexity of 3′UTRs, both genome-wide and throughout development.
doi:10.1126/science.1191244
PMCID: PMC3142571  PMID: 20522740
4.  Role of the HCF-1 Basic Region in Sustaining Cell Proliferation 
PLoS ONE  2010;5(2):e9020.
Background
The human herpes simplex virus-associated host cell factor 1 (HCF-1) is a conserved human transcriptional co-regulator that links positive and negative histone modifying activities with sequence-specific DNA-binding transcription factors. It is synthesized as a 2035 amino acid precursor that is cleaved to generate an amino- (HCF-1N) terminal subunit, which promotes G1-to-S phase progression, and a carboxy- (HCF-1C) terminal subunit, which controls multiple aspects of cell division during M phase. The HCF-1N subunit contains a Kelch domain that tethers HCF-1 to sequence-specific DNA-binding transcription factors, and a poorly characterized so called “Basic” region (owing to a high ratio of basic vs. acidic amino acids) that is required for cell proliferation and has been shown to associate with the Sin3 histone deacetylase (HDAC) component. Here we studied the role of the Basic region in cell proliferation and G1-to-S phase transition assays.
Methodology/Principal Findings
Surprisingly, much like the transcriptional activation domains of sequence-specific DNA-binding transcription factors, there is no unique sequence within the Basic region required for promoting cell proliferation or G1-to-S phase transition. Indeed, the ability to promote these activities is size dependent such that the shorter the Basic region segment the less activity observed. We find, however, that the Basic region requirements for promoting cell proliferation in a temperature-sensitive tsBN67 cell assay are more stringent than for G1-to-S phase progression in an HCF-1 siRNA-depletion HeLa-cell assay. Thus, either half of the Basic region alone can support G1-to-S phase progression but not cell proliferation effectively in these assays. Nevertheless, the Basic region displays considerable structural plasticity because each half is able to promote cell proliferation when duplicated in tandem. Consistent with a potential role in promoting cell-cycle progression, the Sin3a HDAC component can associate independently with either half of the Basic region fused to the HCF-1 Kelch domain.
Conclusions/Significance
While conserved, the HCF-1 Basic region displays striking structural flexibility for controlling cell proliferation.
doi:10.1371/journal.pone.0009020
PMCID: PMC2814863  PMID: 20126307
5.  UTRome.org: a platform for 3′UTR biology in C. elegans 
Nucleic Acids Research  2007;36(Database issue):D57-D62.
Three-prime untranslated regions (3′UTRs) are widely recognized as important post-transcriptional regulatory regions of mRNAs. RNA-binding proteins and small non-coding RNAs such as microRNAs (miRNAs) bind to functional elements within 3′UTRs to influence mRNA stability, translation and localization. These interactions play many important roles in development, metabolism and disease. However, even in the most well-annotated metazoan genomes, 3′UTRs and their functional elements are not well defined. Comprehensive and accurate genome-wide annotation of 3′UTRs and their functional elements is thus critical. We have developed an open-access database, available at http://www.UTRome.org, to provide a rich and comprehensive resource for 3′UTR biology in the well-characterized, experimentally tractable model system Caenorhabditis elegans. UTRome.org combines data from public repositories and a large-scale effort we are undertaking to characterize 3′UTRs and their functional elements in C. elegans, including 3′UTR sequences, graphical displays, predicted and validated functional elements, secondary structure predictions and detailed data from our cloning pipeline. UTRome.org will grow substantially over time to encompass individual 3′UTR isoforms for the majority of genes, new and revised functional elements, and in vivo data on 3′UTR function as they become available. The UTRome database thus represents a powerful tool to better understand the biology of 3′UTRs.
doi:10.1093/nar/gkm946
PMCID: PMC2238901  PMID: 17986455

Results 1-5 (5)