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09:34
5-Ethynyl-2'-Deoxyuridine/Phospho-Histone H3 Dual-Labeling Protocol for Cell Cycle Progression Analysis in Drosophila Neural Stem Cells
Published on: May 4, 2021
A protein network-guided screen for cell cycle regulators in Drosophila
Stephen T Guest1, Jingkai Yu, Dongmei Liu
1Center for Molecular Medicine and Genetics, Wayne State University School of Medicine, Detroit, Michigan, 48201, USA.
BMC Systems Biology
|May 10, 2011
Summary
RNAi screens often have low overlap due to false positives and negatives. Protein networks help identify true cell cycle regulators, improving screen accuracy and revealing key complexes involved in cell cycle progression.
Area of Science:
- Cell Biology
- Genetics
- Bioinformatics
Background:
- Large-scale RNAi screens are crucial for identifying genes regulating cellular processes.
- Low overlap between similar screens suggests high false positive/negative rates in RNAi studies.
- Existing screens provide a basis for re-examination and validation.
Purpose of the Study:
- To re-evaluate genes from previous RNAi screens for cell cycle regulation.
- To identify and minimize false positives and false negatives in RNAi screening data.
- To leverage protein interaction networks for discovering novel cell cycle regulators.
Main Methods:
- Re-screening of genes identified in prior RNAi cell cycle screens.
- Utilizing protein interaction networks to select candidate genes for re-analysis.
- Combinatorial RNAi to investigate the roles of protein complexes in cell cycle progression.
Main Results:
- Confirmed many original phenotypes and identified numerous false positives.
- Discovered novel cell cycle regulators by re-screening genes prioritized with network data.
- Identified validated, high-confidence regulators of cell cycle and cell survival.
- Found that eukaryotic translation initiation factor 3 (eIF3), COP9 signalosome, and proteasome lid complexes are involved in G1/S transition.
Conclusions:
- False positives and negatives significantly impact RNAi screen overlap.
- Protein network data effectively reduces errors and enhances the identification of gene regulators.
- A high-confidence set of cell cycle and cell survival regulators has been identified.
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