A Global Protein Kinase and Phosphatase Interaction Network in Yeast

Centre for Systems Biology, Samuel Lunenfeld Research Institute, 600 University Avenue, Toronto, Ontario, M5G 1X5, Canada.
Science (Impact Factor: 33.61). 05/2010; 328(5981):1043-6. DOI: 10.1126/science.1176495
Source: PubMed


The interactions of protein kinases and phosphatases with their regulatory subunits and substrates underpin cellular regulation.
We identified a kinase and phosphatase interaction (KPI) network of 1844 interactions in budding yeast by mass spectrometric
analysis of protein complexes. The KPI network contained many dense local regions of interactions that suggested new functions.
Notably, the cell cycle phosphatase Cdc14 associated with multiple kinases that revealed roles for Cdc14 in mitogen-activated
protein kinase signaling, the DNA damage response, and metabolism, whereas interactions of the target of rapamycin complex
1 (TORC1) uncovered new effector kinases in nitrogen and carbon metabolism. An extensive backbone of kinase-kinase interactions
cross-connects the proteome and may serve to coordinate diverse cellular responses.

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    • "Rsp5 contains 59 possible sites of phosphorylation which match consensus sequences recognized by protein kinase A (PKA), protein kinase C, casein kinase I and several other kinases (Net.phos2.0 at Phosphorylation of several sites of Rsp5 has been found in studies of the yeast phosphoproteome (Beltrao et al., 2009; Bodenmiller et al., 2008; Breitkreutz et al., 2010; Gnad et al., 2009; Holt et al., 2009; Soufi et al., 2009; Yachie et al., 2011), but the significance of these phosphorylation is still unknown. In the network of interactions of kinases and phosphatases, Rsp5 is placed in PKA interactome with the Tpk1, catalytic subunit of PKA, as an interacting partner and in the target of rapamycin complex 1 (TORC1) kinase interactome as a partner of downstream effector, Npr1 kinase (Breitkreutz et al., 2010). Moreover, it has been shown that Rsp5 can be phosphorylated in vitro by Tpk1 and several other kinases (Ptacek et al., 2005). "
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    ABSTRACT: Rsp5 ubiquitin ligase belongs to the Nedd4 family of proteins, which affect a wide variety of processes in the cell. Here we document that Rsp5 shows several phosphorylated variants of different mobility and the migration of the phosphorylated forms of Rsp5 was faster for the tpk1Δ tpk3Δ mutant devoid of two alternative catalytic subunits of protein kinase A (PKA), indicating that PKA possibly phosphorylates Rsp5 in vivo. We demonstrated by immunoprecipitation and Western blot analysis of GFP-HA-Rsp5 protein using the anti-phospho PKA substrate antibody that Rsp5 is phosphorylated in PKA sites. Rsp5 contains the sequence 758-RRFTIE-763 with consensus RRXS/T in the catalytic HECT domain and four other sites with consensus RXXS/T, which might be phosphorylated by PKA. The strain bearing the T761D substitution in Rsp5 which mimics phosphorylation grew more slowly at 28°C and did not grow at 37°C, and showed defects in pre-tRNA processing and protein sorting. The rsp5-T761D strain also demonstrated a reduced ability to form colonies, an increase in the level of reactive oxygen species (ROS) and hypersensitivity to ROS-generating agents. These results indicate that PKA may downregulate many functions of Rsp5, possibly affecting its activity. Rsp5 is found in the cytoplasm, nucleus, multivesicular body and cortical patches. The rsp5-T761D mutation led to a strongly increased cortical localization while rsp5-T761A caused mutant Rsp5 to locate more efficiently in internal spots. Rsp5-T761A protein was phosphorylated less efficiently in PKA sites under specific growth conditions. Our data suggests that Rsp5 may be phosphorylated by PKA at position T761 and that this regulation is important for its localization and function.
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    • "Our assumption that Swi4 can act alone, in the absence of Swi6, is consistent with the synthetic lethality of the swi4∆ swi6∆ double mutant (Koch et al., 1993; Nasmyth and Dirick, 1991). Bck2 is likely involved in Swi4 activity, as it can bind Swi4 and bck2∆ swi6∆ cells are dead (Breitkreutz et al., 2010; Wijnen and Futcher, 1999). Moreover, Whi5 seems to be unable to bind to Swi4 without Swi6 (Costanzo et al., 2004). "
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    ABSTRACT: The cell cycle is composed of bistable molecular switches that govern the transitions between gap phases (G1 and G2) and the phases in which DNA is replicated (S) and partitioned between daughter cells (M). Many molecular details of the budding yeast G1-S transition (START) have been elucidated in recent years, especially with regard to its switch-like behavior due to positive feedback mechanisms. These results led us to reevaluate and expand a previous mathematical model of the yeast cell cycle (Chen et al. 2004. Mol Biol Cell 15:3841). The new model incorporates Whi3 inhibition of Cln3 activity, Whi5 inhibition of SBF and MBF transcription factors, and feedback inhibition of Whi5 by G1-S cyclins. We tested the accuracy of the model by simulating various mutants not described in the literature. We then constructed these novel mutant strains, and compared their observed phenotypes to the model's simulations. The experimental results reported here led to further changes of the model, which will be fully described in a later publication. Our study demonstrates the advantages of combining model design, simulation and testing in a coordinated effort to better understand a complex biological network. © 2015 by The American Society for Cell Biology.
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    • "De novo and data-driven quantitative models were able to cover only a few signaling interactions and therefore had limited predictive power (Nelander et al., 2008; Bender et al., 2011; Klinger et al., 2013; Oates et al., 2014). Qualitative or discrete models can cover more interactions but typically lack the capability of generating quantitative predictions (Saez-Rodriguez et al., 2009; Breitkreutz et al., 2010; Saez-Rodriguez et al., 2011). Detailed physicochemical models derived using generic biochemical kinetics data can be quite comprehensive and quantitative but typically lack sufficient cell-type specificity required for translationally useful predictions (Chen et al., 2009). "
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    ABSTRACT: Resistance to targeted cancer therapies is an important clinical problem. The discovery of anti-resistance drug combinations is challenging as resistance can arise by diverse escape mechanisms. To address this challenge, we improved and applied the experimental-computational perturbation biology method. Using statistical inference, we build network models from high-throughput measurements of molecular and phenotypic responses to combinatorial targeted perturbations. The models are computationally executed to predict the effects of thousands of untested perturbations. In RAF-inhibitor resistant melanoma cells, we measured 143 proteomic/phenotypic entities under 89 perturbation conditions and predicted c-Myc as an effective therapeutic co-target with BRAF or MEK. Experiments using the BET bromodomain inhibitor JQ1 affecting the level of c-Myc protein and protein kinase inhibitors targeting the ERK pathway confirmed the prediction. In conclusion, we propose an anti-cancer strategy of co-targeting a specific upstream alteration and a general downstream point of vulnerability to prevent or overcome resistance to targeted drugs. DOI: 10.7554/eLife.04640.001
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