A genetic interaction map of cell cycle regulators

• Verfasst von: Billmann, Maximilian [VerfasserIn]
• Verfasst von: Boutros, Michael [VerfasserIn]
• Titel: A genetic interaction map of cell cycle regulators
• Verf.angabe: Maximilian Billmann, Thomas Horn, Bernd Fischer, Thomas Sandmann, Wolfgang Huber, and Michael Boutros
• E-Jahr: 2016
• Jahr: February 24, 2016
• Umfang: 11 S.
• Fussnoten: Gesehen am 07.11.2019
• Titel Quelle: Enthalten in: Molecular biology of the cell
• Ort Quelle: Bethesda, Md. : American Society for Cell Biology, 1992
• Jahr Quelle: 2016
• Band/Heft Quelle: 27(2016), 8, Seite 1397-1407
• ISSN Quelle: 1939-4586
• DOI: doi:10.1091/mbc.E15-07-0467
• Datenträger: Online-Ressource
• Sprache: eng
• K10plus-PPN: 1681411628

Abstract

Cell-based RNA interference (RNAi) is a powerful approach to screen for modulators of many cellular processes. However, resulting candidate gene lists from cell-based assays comprise diverse effectors, both direct and indirect, and further dissecting their functions can be challenging. Here we screened a genome-wide RNAi library for modulators of mitosis and cytokinesis in Drosophila S2 cells. The screen identified many previously known genes as well as modulators that have previously not been connected to cell cycle control. We then characterized ∼300 candidate modifiers further by genetic interaction analysis using double RNAi and a multiparametric, imaging-based assay. We found that analyzing cell cycle-relevant phenotypes increased the sensitivity for associating novel gene function. Genetic interaction maps based on mitotic index and nuclear size grouped candidates into known regulatory complexes of mitosis or cytokinesis, respectively, and predicted previously uncharacterized components of known processes. For example, we confirmed a role for the Drosophila CCR4 mRNA processing complex component l(2)NC136 during the mitotic exit. Our results show that the combination of genome-scale RNAi screening and genetic interaction analysis using process-directed phenotypes provides a powerful two-step approach to assigning components to specific pathways and complexes.