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A protein network-guided screen for cell cycle regulators in Drosophila.

Guest ST, Yu J, Liu D, Hines JA, Kashat MA, Finley RL - BMC Syst Biol (2011)

Bottom Line: We demonstrate cell cycle phenotypes for a significant number of these genes and show that the protein interaction network is an efficient predictor of new cell cycle regulators.Our results also show that protein network data can be used to minimize false negatives and false positives and to more efficiently identify comprehensive sets of regulators for a process.Finally, our data provides a high confidence set of genes that are likely to play key roles in regulating the cell cycle or cell survival.

View Article: PubMed Central - HTML - PubMed

Affiliation: Center for Molecular Medicine and Genetics, Wayne State University School of Medicine, Detroit, Michigan, 48201, USA.

ABSTRACT

Background: Large-scale RNAi-based screens are playing a critical role in defining sets of genes that regulate specific cellular processes. Numerous screens have been completed and in some cases more than one screen has examined the same cellular process, enabling a direct comparison of the genes identified in separate screens. Surprisingly, the overlap observed between the results of similar screens is low, suggesting that RNAi screens have relatively high levels of false positives, false negatives, or both.

Results: We re-examined genes that were identified in two previous RNAi-based cell cycle screens to identify potential false positives and false negatives. We were able to confirm many of the originally observed phenotypes and to reveal many likely false positives. To identify potential false negatives from the previous screens, we used protein interaction networks to select genes for re-screening. We demonstrate cell cycle phenotypes for a significant number of these genes and show that the protein interaction network is an efficient predictor of new cell cycle regulators. Combining our results with the results of the previous screens identified a group of validated, high-confidence cell cycle/cell survival regulators. Examination of the subset of genes from this group that regulate the G1/S cell cycle transition revealed the presence of multiple members of three structurally related protein complexes: the eukaryotic translation initiation factor 3 (eIF3) complex, the COP9 signalosome, and the proteasome lid. Using a combinatorial RNAi approach, we show that while all three of these complexes are required for Cdk2/Cyclin E activity, the eIF3 complex is specifically required for some other step that limits the G1/S cell cycle transition.

Conclusions: Our results show that false positives and false negatives each play a significant role in the lack of overlap that is observed between similar large-scale RNAi-based screens. Our results also show that protein network data can be used to minimize false negatives and false positives and to more efficiently identify comprehensive sets of regulators for a process. Finally, our data provides a high confidence set of genes that are likely to play key roles in regulating the cell cycle or cell survival.

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RNAi directed against members of the eIF3 protein complex does not affect Cyclin E expression but does affect Cyclin E-associated kinase activity. (A) Western blot for Cyclin E expression in whole cell extracts from S2R+ cells treated with the indicated dsRNAs. (B) Kinase activity on histone H1 of Cyclin E immunoprecipitates from S2R+ cells treated with the indicated dsRNAs. (C) Kinase activity on histone H1 of Cyclin E immunoprecipitates from S2R+ cells treated with dsRNA targeting the indicated members of the COP9 signalosome, proteasome lid or eIF3 protein complex in combination with either dsRNA targeting GFP or Dacapo.
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Figure 6: RNAi directed against members of the eIF3 protein complex does not affect Cyclin E expression but does affect Cyclin E-associated kinase activity. (A) Western blot for Cyclin E expression in whole cell extracts from S2R+ cells treated with the indicated dsRNAs. (B) Kinase activity on histone H1 of Cyclin E immunoprecipitates from S2R+ cells treated with the indicated dsRNAs. (C) Kinase activity on histone H1 of Cyclin E immunoprecipitates from S2R+ cells treated with dsRNA targeting the indicated members of the COP9 signalosome, proteasome lid or eIF3 protein complex in combination with either dsRNA targeting GFP or Dacapo.

Mentions: Activation of CycE transcription, increased CycE protein expression, and the activation of Cdk2 by CycE are all required for cells to progress from G1 into S phase [87,105]. eIF3 is a large, multi-subunit complex that has been shown to play a key role in regulating mRNA translation and thus gene expression [106]. One possible mechanism by which eIF3 could be required for G1/S is that eIF3 may be required for CycE translation. To explore this possibility, we treated cultured cells with dsRNA targeting eIF3 complex subunits and determined the effect that this had on CycE expression levels. As expected, treating cells with dsRNA targeting CycE transcripts results in a significant reduction in CycE protein levels (Figure 6A). However, treatment of cells with dsRNA targeting eIF3 subunits had no significant effect on CycE protein levels (Figure 6A). This result suggests that the increase in cells with G1 DNA content following RNAi targeting eIF3 subunits is not the result of reduced expression of CycE.


A protein network-guided screen for cell cycle regulators in Drosophila.

Guest ST, Yu J, Liu D, Hines JA, Kashat MA, Finley RL - BMC Syst Biol (2011)

RNAi directed against members of the eIF3 protein complex does not affect Cyclin E expression but does affect Cyclin E-associated kinase activity. (A) Western blot for Cyclin E expression in whole cell extracts from S2R+ cells treated with the indicated dsRNAs. (B) Kinase activity on histone H1 of Cyclin E immunoprecipitates from S2R+ cells treated with the indicated dsRNAs. (C) Kinase activity on histone H1 of Cyclin E immunoprecipitates from S2R+ cells treated with dsRNA targeting the indicated members of the COP9 signalosome, proteasome lid or eIF3 protein complex in combination with either dsRNA targeting GFP or Dacapo.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3113730&req=5

Figure 6: RNAi directed against members of the eIF3 protein complex does not affect Cyclin E expression but does affect Cyclin E-associated kinase activity. (A) Western blot for Cyclin E expression in whole cell extracts from S2R+ cells treated with the indicated dsRNAs. (B) Kinase activity on histone H1 of Cyclin E immunoprecipitates from S2R+ cells treated with the indicated dsRNAs. (C) Kinase activity on histone H1 of Cyclin E immunoprecipitates from S2R+ cells treated with dsRNA targeting the indicated members of the COP9 signalosome, proteasome lid or eIF3 protein complex in combination with either dsRNA targeting GFP or Dacapo.
Mentions: Activation of CycE transcription, increased CycE protein expression, and the activation of Cdk2 by CycE are all required for cells to progress from G1 into S phase [87,105]. eIF3 is a large, multi-subunit complex that has been shown to play a key role in regulating mRNA translation and thus gene expression [106]. One possible mechanism by which eIF3 could be required for G1/S is that eIF3 may be required for CycE translation. To explore this possibility, we treated cultured cells with dsRNA targeting eIF3 complex subunits and determined the effect that this had on CycE expression levels. As expected, treating cells with dsRNA targeting CycE transcripts results in a significant reduction in CycE protein levels (Figure 6A). However, treatment of cells with dsRNA targeting eIF3 subunits had no significant effect on CycE protein levels (Figure 6A). This result suggests that the increase in cells with G1 DNA content following RNAi targeting eIF3 subunits is not the result of reduced expression of CycE.

Bottom Line: We demonstrate cell cycle phenotypes for a significant number of these genes and show that the protein interaction network is an efficient predictor of new cell cycle regulators.Our results also show that protein network data can be used to minimize false negatives and false positives and to more efficiently identify comprehensive sets of regulators for a process.Finally, our data provides a high confidence set of genes that are likely to play key roles in regulating the cell cycle or cell survival.

View Article: PubMed Central - HTML - PubMed

Affiliation: Center for Molecular Medicine and Genetics, Wayne State University School of Medicine, Detroit, Michigan, 48201, USA.

ABSTRACT

Background: Large-scale RNAi-based screens are playing a critical role in defining sets of genes that regulate specific cellular processes. Numerous screens have been completed and in some cases more than one screen has examined the same cellular process, enabling a direct comparison of the genes identified in separate screens. Surprisingly, the overlap observed between the results of similar screens is low, suggesting that RNAi screens have relatively high levels of false positives, false negatives, or both.

Results: We re-examined genes that were identified in two previous RNAi-based cell cycle screens to identify potential false positives and false negatives. We were able to confirm many of the originally observed phenotypes and to reveal many likely false positives. To identify potential false negatives from the previous screens, we used protein interaction networks to select genes for re-screening. We demonstrate cell cycle phenotypes for a significant number of these genes and show that the protein interaction network is an efficient predictor of new cell cycle regulators. Combining our results with the results of the previous screens identified a group of validated, high-confidence cell cycle/cell survival regulators. Examination of the subset of genes from this group that regulate the G1/S cell cycle transition revealed the presence of multiple members of three structurally related protein complexes: the eukaryotic translation initiation factor 3 (eIF3) complex, the COP9 signalosome, and the proteasome lid. Using a combinatorial RNAi approach, we show that while all three of these complexes are required for Cdk2/Cyclin E activity, the eIF3 complex is specifically required for some other step that limits the G1/S cell cycle transition.

Conclusions: Our results show that false positives and false negatives each play a significant role in the lack of overlap that is observed between similar large-scale RNAi-based screens. Our results also show that protein network data can be used to minimize false negatives and false positives and to more efficiently identify comprehensive sets of regulators for a process. Finally, our data provides a high confidence set of genes that are likely to play key roles in regulating the cell cycle or cell survival.

Show MeSH