Publications by Year: 2014

Vempati UD, Chung C, Mader C, Koleti A, Datar N, Vidovic D, Wrobel D, Erickson S, Muhlich JL, Berriz G, Benes CH, Subramanian A, Pillai A, Shamu CE, Schurer SC. Metadata Standard and Data Exchange Specifications to Describe, Model, and Integrate Complex and Diverse High-Throughput Screening Data from the Library of Integrated Network-based Cellular Signatures (LINCS) . J Biomol Screen. 2014;19:803-816.
Starkey M, Lepine F, Maura D, Bandyopadhaya A, Lesic B, He J, Kitao T, Righi V, Milot S, Tzika A, Rahme L. Identification of Anti-virulence Compounds That Disrupt Quorum-Sensing Regulated Acute and Persistent pathogenicity. PLoS Pathog 2014;10(8):e1004321.
Online GESS. 2014;
Baell J, Walters MA. Chemistry: Chemical con artists foil drug discovery. Nature 2014;513(7519):481-3.
Yilmazel B, Hu Y, Sigoillot F, Smith JA, Shamu CE, Perrimon N, Mohr SE. Online GESS: prediction of miRNA-like off-target effects in large-scale RNAi screen data by seed region analysis. BMC Bioinformatics 2014;15:192.Abstract

BACKGROUND: RNA interference (RNAi) is an effective and important tool used to study gene function. For large-scale screens, RNAi is used to systematically down-regulate genes of interest and analyze their roles in a biological process. However, RNAi is associated with off-target effects (OTEs), including microRNA (miRNA)-like OTEs. The contribution of reagent-specific OTEs to RNAi screen data sets can be significant. In addition, the post-screen validation process is time and labor intensive. Thus, the availability of robust approaches to identify candidate off-targeted transcripts would be beneficial. RESULTS: Significant efforts have been made to eliminate false positive results attributable to sequence-specific OTEs associated with RNAi. These approaches have included improved algorithms for RNAi reagent design, incorporation of chemical modifications into siRNAs, and the use of various bioinformatics strategies to identify possible OTEs in screen results. Genome-wide Enrichment of Seed Sequence matches (GESS) was developed to identify potential off-targeted transcripts in large-scale screen data by seed-region analysis. Here, we introduce a user-friendly web application that provides researchers a relatively quick and easy way to perform GESS analysis on data from human or mouse cell-based screens using short interfering RNAs (siRNAs) or short hairpin RNAs (shRNAs), as well as for Drosophila screens using shRNAs. Online GESS relies on up-to-date transcript sequence annotations for human and mouse genes extracted from NCBI Reference Sequence (RefSeq) and Drosophila genes from FlyBase. The tool also accommodates analysis with user-provided reference sequence files. CONCLUSION: Online GESS provides a straightforward user interface for genome-wide seed region analysis for human, mouse and Drosophila RNAi screen data. With the tool, users can either use a built-in database or provide a database of transcripts for analysis. This makes it possible to analyze RNAi data from any organism for which the user can provide transcript sequences.

Gaun V, Patchen B, Volovertz J, Zhen AW, Andreev A, Pollastri MP, Fraenkel PG. A chemical screen identifies small molecules that regulate hepcidin expression. Blood Cells Mol Dis 2014;53(4):231-40.
Image and Data Analysis Core (IDAC) at HMS. 2014;
Beauchemin C, Moerke NJ, Faloon P, Kaye KM. Assay Development and High-Throughput Screening for Inhibitors of Kaposi’s Sarcoma-Associated Herpesvirus N-Terminal Latency-Associated Nuclear Antigen Binding to Nucleosomes. J Biomol Screen. 2014;19:947-958.
Chamoun-Emanuelli AM, Pecheur EI, Chen Z. Benzhydrylpiperazine compounds inhibit cholesterol-dependent cellular entry of hepatitis C Virus. Antiviral Res 2014;109:141-8.
Herschhorn A, Gu C, Espy N, Richard J, Finzi A, Sodroski JG. A broad HIV-1 inhibitor blocks envelope glycoprotein transitions critical for entry. Nat Chem Biol 2014;10(10):845-52.
Matlack KE, Tardiff DF, Narayan P, Hamamichi S, Caldwell KA, Caldwell GA, Lindquist S. Clioquinol promotes the degradation of metal-dependent amyloid-β (Aβ) oligomers to restore endocytosis and ameliorate Aβ toxicity. Proc Natl Acad Sci USA. 2014;111(11):4013-8.
Sharma M, Coen DM. Comparison of effects of inhibitors of viral and cellular protein kinases on human cytomegalovirus disruption of nuclear lamina and nuclear egress. J Virol 2014;88(18):10982-5.
Chiaraviglio L, Kirby JE. Evaluation of Impermeant, DNA-Binding Dye Fluorescence as a Real-Time Readout of Eukaryotic Cell Toxicity in a High Throughput Screening Format. Assay Drug Dev Technol 2014;12(4):219-28.
Rajamuthiah R, Fuchs BB, Jayamani E, Kim Y, Larkins-Ford J, Conery A, Ausubel FM, Mylonakis E. Whole animal automated platform for drug discovery against multi-drug resistant Staphylococcus aureus. PLoS One. 2014;9(2):e89189.
Johnston SJ, Shamu SE, Smith JA. Automation considerations for RNAi library formatting and high throughput transfection [Internet]. 2014; Frontiers in RNAi. eBook, eISBN:978-1-60805-940-9. Chapter 2 Pages 21-39.
Olive AJ, Haff MG, Emanuele MJ, Sack LM, Barker JR, Elledge SJ, Starnbach MN. Chlamydia trachomatis-induced alterations in the host cell proteome are required for intracellular growth [Internet]. 2014; Cell host & microbe 15, 113-24.
Ganem NJ, Cornils H, Chiu SY, O’Rourke KP, Arnaud J, Yimlamai D, Théry M, Camargo FD, Pellman D. Cytokinesis failure triggers hippo tumor suppressor pathway activation [Internet]. 2014; Cell 158, 833-48.
Mohseni M, Sun J, Lau A, Curtis S, Goldsmith J, Fox VL, Wei C, Frazier M, Samson O, Wong KK, Kim C, Camargo FD. A genetic screen identifies an LKB1-MARK signalling axis controlling the Hippo-YAP pathway [Internet]. 2014; Nat Cell Biol Jan;16(1):108-17.
Lee AS, Burdeinick-Kerr R, Whelan SP. A genome-wide siRNA screen identifies host factors required for vesicular stomatitis virus infection [Internet]. 2014; J Virol. Aug 1;88(15):8355-8360.
Tata A, Stoppel DC, Hong S, Ben-Zvi A, Xie T, Gu C. An image-based RNAi screen identifies SH3BP1 as a key effector of Semaphorin 3E-PlexinD1 signaling [Internet]. J Cell Biol 2014;May 26;205(4):573-90. J Cell Biol. May 26;205(4):573-590.