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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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A virtual protein-protein interaction screen. (A) 642 Drosophila putative cell cycle regulators were used as baits to query DroID, the Drosophila Interactions Database. Baits included hits from the two previous RNAi-based screens for cell cycle regulators along with a set of additional genes annotated as being involved in the cell cycle. The original interaction map was filtered to remove low confidence interactions (see Methods). The filtered map includes 473 of the bait proteins (red nodes), and 1843 interactors (blue nodes). 94.8% of the proteins are connected into one large network. (B) A subnetwork from (A) involving six members of the COP9 signalosome protein complex that was previously identified as a regulator of the G1/S transition. Signalosome components are shown as triangles while their interaction partners are circles. Proteins that were used as baits in the virtual protein-protein interaction screen are shown in red while their interaction partners are shown in blue.
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Figure 1: A virtual protein-protein interaction screen. (A) 642 Drosophila putative cell cycle regulators were used as baits to query DroID, the Drosophila Interactions Database. Baits included hits from the two previous RNAi-based screens for cell cycle regulators along with a set of additional genes annotated as being involved in the cell cycle. The original interaction map was filtered to remove low confidence interactions (see Methods). The filtered map includes 473 of the bait proteins (red nodes), and 1843 interactors (blue nodes). 94.8% of the proteins are connected into one large network. (B) A subnetwork from (A) involving six members of the COP9 signalosome protein complex that was previously identified as a regulator of the G1/S transition. Signalosome components are shown as triangles while their interaction partners are circles. Proteins that were used as baits in the virtual protein-protein interaction screen are shown in red while their interaction partners are shown in blue.

Mentions: The results of two large-scale, RNAi-based screens in cultured Drosophila cells have identified genes that are potential regulators of the cell cycle [60,61]. We set out to provide independent confirmation of the identified regulators, and to identify potential false negatives from the previous screens. To identify a subset of the negative genes that were likely to be enriched for cell cycle regulators, we performed a virtual protein-protein interaction screen to find proteins that interact with known or suspected cell cycle regulators. The bait proteins that we used for the virtual screen were proteins identified as potential cell cycle regulators in the two published RNAi-based screens as well as all genes annotated with a Gene Ontology (GO) biological process [62] of "cell cycle" (see Methods). These 642 bait proteins were used to query DroID, the Drosophila Interactions Database [63,64] to identify 5,008 potential protein interaction partners (Additional File 1). We filtered this data (see Methods) to obtain a higher confidence set that consisted of 1,843 interaction partners for the 642 bait proteins (Figure 1A and Additional File 1). We hypothesized that the interaction partners of the baits would be enriched for cell cycle regulators relative to random proteins. In support of this, analysis of both the filtered and unfiltered protein network showed that the bait proteins interact with each other much more than would be expected for a random group of proteins (p-value < 10-82) (Additional File 2A). The high level of connectivity between bait proteins is also evident from the size of the maximally connected component subnetwork for the baits, which was found to be significantly larger than for equally sized random sets of proteins (p-value < 10-18) (Additional File 2B). This analysis demonstrates that within the protein interaction data that we screened, cell cycle regulators frequently interact with each other. It also supports the hypothesis that proteins that interact with the bait proteins that we used in the virtual screen may be enriched for novel cell cycle regulators that were false negatives in the previous screens. Figure 1B shows a subset of the interaction map data involving 6 members of the COP9 signalosome protein complex that was identified as a regulator of the G1/S transition in one of the previous screens [60]. As expected for a protein complex, there are a number of interactions that connect COP9 signalosome subunits to each other in the map. There are also a number of interactions between COP9 signalosome subunits and non-complex members. These interactors potentially function in conjunction with the COP9 signalosome to regulate cell cycle progression and represent possible false negatives from previous screens.


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)

A virtual protein-protein interaction screen. (A) 642 Drosophila putative cell cycle regulators were used as baits to query DroID, the Drosophila Interactions Database. Baits included hits from the two previous RNAi-based screens for cell cycle regulators along with a set of additional genes annotated as being involved in the cell cycle. The original interaction map was filtered to remove low confidence interactions (see Methods). The filtered map includes 473 of the bait proteins (red nodes), and 1843 interactors (blue nodes). 94.8% of the proteins are connected into one large network. (B) A subnetwork from (A) involving six members of the COP9 signalosome protein complex that was previously identified as a regulator of the G1/S transition. Signalosome components are shown as triangles while their interaction partners are circles. Proteins that were used as baits in the virtual protein-protein interaction screen are shown in red while their interaction partners are shown in blue.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 1: A virtual protein-protein interaction screen. (A) 642 Drosophila putative cell cycle regulators were used as baits to query DroID, the Drosophila Interactions Database. Baits included hits from the two previous RNAi-based screens for cell cycle regulators along with a set of additional genes annotated as being involved in the cell cycle. The original interaction map was filtered to remove low confidence interactions (see Methods). The filtered map includes 473 of the bait proteins (red nodes), and 1843 interactors (blue nodes). 94.8% of the proteins are connected into one large network. (B) A subnetwork from (A) involving six members of the COP9 signalosome protein complex that was previously identified as a regulator of the G1/S transition. Signalosome components are shown as triangles while their interaction partners are circles. Proteins that were used as baits in the virtual protein-protein interaction screen are shown in red while their interaction partners are shown in blue.
Mentions: The results of two large-scale, RNAi-based screens in cultured Drosophila cells have identified genes that are potential regulators of the cell cycle [60,61]. We set out to provide independent confirmation of the identified regulators, and to identify potential false negatives from the previous screens. To identify a subset of the negative genes that were likely to be enriched for cell cycle regulators, we performed a virtual protein-protein interaction screen to find proteins that interact with known or suspected cell cycle regulators. The bait proteins that we used for the virtual screen were proteins identified as potential cell cycle regulators in the two published RNAi-based screens as well as all genes annotated with a Gene Ontology (GO) biological process [62] of "cell cycle" (see Methods). These 642 bait proteins were used to query DroID, the Drosophila Interactions Database [63,64] to identify 5,008 potential protein interaction partners (Additional File 1). We filtered this data (see Methods) to obtain a higher confidence set that consisted of 1,843 interaction partners for the 642 bait proteins (Figure 1A and Additional File 1). We hypothesized that the interaction partners of the baits would be enriched for cell cycle regulators relative to random proteins. In support of this, analysis of both the filtered and unfiltered protein network showed that the bait proteins interact with each other much more than would be expected for a random group of proteins (p-value < 10-82) (Additional File 2A). The high level of connectivity between bait proteins is also evident from the size of the maximally connected component subnetwork for the baits, which was found to be significantly larger than for equally sized random sets of proteins (p-value < 10-18) (Additional File 2B). This analysis demonstrates that within the protein interaction data that we screened, cell cycle regulators frequently interact with each other. It also supports the hypothesis that proteins that interact with the bait proteins that we used in the virtual screen may be enriched for novel cell cycle regulators that were false negatives in the previous screens. Figure 1B shows a subset of the interaction map data involving 6 members of the COP9 signalosome protein complex that was identified as a regulator of the G1/S transition in one of the previous screens [60]. As expected for a protein complex, there are a number of interactions that connect COP9 signalosome subunits to each other in the map. There are also a number of interactions between COP9 signalosome subunits and non-complex members. These interactors potentially function in conjunction with the COP9 signalosome to regulate cell cycle progression and represent possible false negatives from previous screens.

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