Evaluation of the lower protein limit in the budding yeast Saccharomyces cerevisiae using TIPI-gTOW
© Sasabe et al.; licensee BioMed Central Ltd. 2014
Received: 23 May 2013
Accepted: 27 December 2013
Published: 7 January 2014
Identifying permissible limits of intracellular parameters such as protein expression provides important information for examining robustness. In this study, we used the TEV protease-mediated induction of protein instability (TIPI) in combination with the genetic Tug-of-War (gTOW) to develop a method to measure the lower limit of protein level. We first tested the feasibility of this method using ADE2 as a marker and then analyzed some cell cycle regulators to reveal genetic interactions.
Using TIPI-gTOW, we successfully constructed a strain in which GFP-TDegFAde2 was expressed at the lower limit, just sufficient to support cellular growth under the -Ade condition by accelerating degradation by TEV protease. We also succeeded in constructing a strain in which the minimal level of GFP-TDegFCdc20 was expressed by TIPI-gTOW. Using this strain, we studied genetic interactions between cell cycle regulators and CDC20, and the result was highly consistent with the previously identified interactions. Comparison of the experimental data with predictions of a mathematical model revealed some interactions that were not implemented into the current model.
TIPI-gTOW is useful for estimating changes in the lower limit of a protein under different conditions, such as different genetic backgrounds and environments. TIPI-gTOW is also useful for analyzing genetic interactions of essential genes whose deletion mutants cannot be obtained.
Effective functioning of cellular systems requires optimal expression of individual proteins [1–3]. On the other hand, cellular systems are generally robust against changes in protein expression [4–6]. Identifying permissible limits of intracellular parameters such as protein expression provides important information for examining robustness [4–6].
We previously developed a method designated genetic Tug-Of-War (gTOW), by which we can measure the limit of overexpression of a target protein [7, 8]. In gTOW, we clone a target gene with its native promoter into a 2 μ-based plasmid harboring leu2d. Under leucine-lacking (-Leu) conditions, the copy number of the plasmid reaches >100 copies because of the selection bias driven by leu2d. If the target protein expression reaches the upper limit, i.e., essential cellular functions come to a halt when the level of protein exceeds a certain limit, the gene/plasmid copy number must decrease to below the upper limit. In gTOW, we thus increase the expression of a protein to a limit by increasing the copy number and then determine the upper limit by measuring the corresponding copy number of the gene. Using gTOW, we previously measured the copy number limits for overexpression of 30 cell cycle regulatory genes in the budding yeast Saccharomyces cerevisiae and studied the robustness of the cell cycle and mechanisms that can improve the robustness of the cell cycle [7, 9, 10]. We recently measured the copy number limits of all yeast genes and showed that the yeast cellular system is robust against >100-fold increase in the copy numbers of each of >80% genes . In that analysis, we also identified 115 “dosage-sensitive genes” whose copy number limits were <10 .
While gTOW is a method to measure the upper limit of protein expression, determination of the lower limit is still a question. The commonly used gene-deletion experiments reduce the expression of a target protein to 0. Gene-deletion experiments for all genes in S. cerevisiae have revealed that about 20% genes are essential for cellular growth under normal conditions ; other genes are essential for cellular growth under specific conditions such as specific environmental conditions or in combination with other gene knockouts [13, 14]. Each of these proteins must have a minimal requirement level (i.e., the lower limit) to support cellular growth under each of these conditions. At present, there is no effective method to assess the lower expression limit of a target protein.
To assess the lower limit, we have to gradually reduce the expression of a protein and then measure the lower limit by some methods. To reduce the expression of a gene/protein, we need to reduce the production rate of the transcript, increase the degradation rate of the transcript, decrease the production rate of the protein, or increase the degradation rate of the protein. To identify the lower limit of expression, we would need to specifically decrease/increase the production/degradation rate of the target and evaluate the limit that would halt cellular function. Experimental methods that could potentially be used for this purpose in S. cerevisiae are as follows: (1) decreasing the expression rate of the transcript using a repressible promoter , (2) decreasing the transcription rate or increasing the degradation rate using RNA interference (RNAi) , (3) increasing the degradation of mRNA by the decreased abundance by mRNA perturbation (DAmP) method [17, 18], (4) increasing the rate of degradation of the transcript by recruiting the RNA-degrading enzyme to the 3′ region of the transcript , (5) increasing the rate of degradation of the protein using inducible degrons [20, 21], (6) increasing the rate of degradation of the protein using the TEV protease-mediated induction of protein instability (TIPI) method [22, 23].
In this study, we used TIPI in combination with gTOW to develop a method to measure the lower limit of protein expression. In TIPI, a target protein is expressed with a TEV protease-induced degron, an N-terminal sequence containing a cleavage site for the site-specific TEV protease. Cleavage of the protein at this site by the TEV protease triggers the exposure of the “N-degron” , which triggers the rapid degradation of the target protein through the N-end rule pathway. We chose TIPI because, theoretically, we can increase the degradation rate of the target protein by increasing the expression of TEV protease using the gTOW scheme with a plasmid encoding TEV. We can then evaluate the lower limit of the target protein expression by measuring the copy number of the TEV plasmid. We first tested the feasibility of this method using ADE2 as a marker and then analyzed some cell cycle regulators to reveal genetic interactions.
Results and discussion
Establishment of TIPI-gTOW using ADE2 as a marker
To ensure the success of this approach, we need to adjust the expression of the following: (1) the target protein (determined by the production and degradation rate) to be in the range required to support the growth of the cell (not too high and not too low) and (2) the TEV protease to be in the appropriate range to induce degradation in order to detect the lower limit of the target protein expression. Taxis et al. used the CYC1 promoter and ADH1 promoter to express target proteins and the GAL1 promoter to induce TEV protease expression [22, 23]. In some cases, they could not observe expected lethal phenotypes, probably because the induced degradation of the target proteins by the TEV protease was insufficient to reduce the protein expression to their lower limits [22, 23].
We used constitutive promoters to increase the expression of TEV protease in accordance with the increase in gene copy number. We first used the S. cerevisiae TEF1 promoter to express TEV protease, but the strong expression of TEV protease from the TEF1 promoter on the high-copy gTOW plasmid in itself caused a cellular growth defect (data not shown). We currently do no know what causes this toxicity. We then used the CUP1 promoter. In the culture conditions we used (0.25 μM Cu2+), the CUP1 mRNA level was about 10% of the TEF1 mRNA level (data not shown). Expression of the TEV protease from the CUP1 promoter did not show any cellular growth defect, even when the gene copy number exceeded 100 (data not shown). We used an efficient version of the p14–TEV fusion protein (p14*–TEV+) [22, 23]; here, we designated this “pTEV+.” We also designated the gTOW plasmid encoding the pTEV+ expressed from the CUP1 promoter as the “TEV plasmid.”
TIPI-gTOW experiments of cell cycle regulators
We next selected 3 essential cell cycle regulators as target genes to test if TIPI-gTOW was a feasible approach to assess their lower limits. We constructed the GFP-TDegF constructs for CDC15, CDC20, and CDC28 and integrated these into their chromosomal loci. At first, we used the full-length CDC19 promoter (729 bp) to express the cell cycle proteins but could not obtain a CDC20 construct, probably because strong expression of CDC20 is toxic . We then constructed a series of deletions of the CDC19 promoter to reduce its expression (Additional file 1: Figure S1). The GFP expression from promoters with a length of 500 bp or less was markedly lower than that from promoters exceeding 500 bp (Additional file 1: Figures S1B and S1C). This reduction might be due to the deletion of a Tye7 binding site located at -523 bp to -516 bp (Additional file 1: Figure S1A). We thus used the full-length CDC19 promoter (729 bp), the CDC19 -600 promoter (600 bp), and the CDC19 -500 promoter (500 bp) to construct the GFP-TDegF target.
Reduction in the protein expression of GFP-TDegFCdc28 by TEV protease was confirmed by western blotting with anti-GFP antibody (Figure 3D). The GFP-TDegFCdc28 protein expressed from the CDC19 -500 promoter seemed to be too low to be detected by western blotting (Figure 3D). Simultaneously, we could not detect any GFP-TDegFCdc20 protein expressed from the CDC19-500 promoter (data not shown).
We further analyzed the TEV protease dependent cleavage and reduction of GFP-TDegFCdc28 by western blotting with anti-Cdc28 antibody (Figure 3E). The protein levels of GFP-TDegFCdc28 in YSM005 (P_CDC19 -600 -GFP- TDegF CDC28) were highly reduced in the presence of the TEV plasmid compared with the vector control, and the level was lower under the –Ura–Leu condition, where the expression of pTEV+ increased, than the –Ura condition. Cleaved products (degFCdc28) were also observed in the presence of the TEV plasmid. The summed levels of the GFP-TDegFCdc28 and degFCdc28 were comparable with the Cdc28 levels expressed in BY4741 (wild type). These results confirmed that the target protein level was actually reduced using TIPI-gTOW. We also tired to detect Cdc20 by western blotting using anti-Cdc20 antibodies (sc-6730 and sc-6731, Santa Cruz biotechnology). However, we could not detect any signal, probably because the expression level of Cdc20 is too low to detect with these antibodies.
Estimation of changes in the lower limit of CDC20 in gene-deletion strains
Copy numbers of the vector and the TEV plasmid in CDC20 TIPI-gTOW in the gene-deletion strains
Vector copy number*
TEV plasmid copy number*
Relative change in copy number
In addition, we observed significant correlations in the copy numbers of the vector and the TEV plasmid under both -Ura (r = 0.61) and -Ura-Leu (r = 7.0) conditions (Additional file 1: Figure S2). We thus considered that it was not appropriate to simply estimate the lower limit of GFP-TDegFCdc20 from the copy number of the TEV plasmid in each deletion strain. Therefore, we calculated the ratio of the copy number of the TEV plasmid to that of the vector in each deletion strain and compared the ratio with that of the wild type to estimate the relative change in TEV plasmid copy number (see Methods for details).
Other strains, such as mad2∆, cin8∆, and mbp1∆, showed positive relative copy number change values, indicating that the copy numbers of the TEV plasmids were higher than those in the wild type; thus, the lower limits of GFP-TDegFCdc20 decreased in these strains. These deleted genes are therefore involved in inhibition of Cdc20, e.g., by inhibition of Cdc20 production or Cdc20 activity or degradation of Cdc20 (Additional file 1: Figure S4). These genes could be dosage suppressors of the toxicity of Cdc20 overexpression.
Known genetic interactions with CDC20 mutation listed in the Saccharomyces Genome Database*
Genetic interaction with the cdc20 Mutant
Temperature sensitivity of cdc20-1(ts) is rescued by bub2 deletion.
cdc20-1(ts) shows synthetic lethality with cdh1 deletion.
In the absence of CIN8, overexpression of CDC20 is lethal.
Growth defect of cdc20_tsq368 increases with clb2 deletion.
cdc20-1(ts) shows synthetic lethality with lte1 deletion.
cdc20 blocks the premature degradation of Pds1 observed in a mad2 mutant.
cdc20-1(ts) shows synthetic lethality with sic1 deletion.
Among genetic interactions obtained in this study, the difference between CLN1 and CLN2 was surprising because they are known to have heavily overlapped functions . Previous analysis, however, showed that CLB4 has opposite genetic interactions with CLN1 and CLN2. Our results might indicate that these genes have different functions in some contexts.
Comparison of experimental data with predictions of a simulation model is useful for evaluating the predictive ability of the mathematical model and for speculating about molecular mechanisms generating negative/positive genetic interactions [7, 9, 10]. An integrative mathematical model of the budding yeast cell cycle was developed previously , and we have modified this model using gTOW data . Using the “stabilization model” published in the reference , we here measured how much the degradation parameter of Cdc20 could be increased (i.e., the upper limit of kd20) in each of the gene-deletion models and compared the upper limit of kd20 with that in the wild type to obtain the relative change in the upper limit of kd20 (see Methods for details). This analysis is considered to represent the process of TIPI-gTOW, namely indirectly assessing the lower limits of Cdc20 in the presence of gene deletions, by measuring how much degradation can increase without halting the cell cycle. The simulation result is shown in Additional file 1: Table S2. Figure 4B shows the comparison of the results of TIPI-gTOW and those of the simulation. Deletion strains, such as mad2Δ, cdh1Δ, and others, showed good agreements between TIPI-gTOW and simulation data; however, some genes such as sic1Δ, swi5Δ, and mbp1Δ showed almost opposite changes, suggesting absence of some regulatory steps in the model.
In this study, we tried to develop a method for estimating the lower limit of protein expression by combining the TIPI and gTOW methods. Using TIPI-gTOW, we successfully constructed a strain in which GFP-TDegFAde2 was expressed at the lower limit, just sufficient to support cellular growth under the -Ade condition by accelerating degradation by TEV protease.
We also succeeded in constructing a strain in which the minimal level of GFP-TDegFCdc20 was expressed by TIPI-gTOW. Using this strain, we studied genetic interactions between cell cycle regulators and CDC20. We concluded that TIPI-gTOW is useful for estimating changes in the lower limit of a protein under different conditions, such as different genetic backgrounds and environments. TIPI-gTOW is useful for analyzing genetic interactions of essential genes whose deletion mutants cannot be obtained. One important characteristic of TIPI-gTOW is that it is useful for identifying both negative and positive genetic interactions, as shown in Figure 4. The synthetic genetic array is a potent high-throughput approach to identifying genetic interactions , which now allows quantitative analyses  and can also applied to essential genes . TIPI-gTOW developed in this study may be an alternative method to study genetic interactions in detail.
The conventional gTOW method developed previously uses a gene with a native promoter and terminator as a unit to increase protein expression and measures the copy number limit to estimate the “copy number limit for protein overexpression” [7, 8, 10, 11]. The advantage of gTOW is that we can indirectly estimate by how much a target protein can be overexpressed over the native level by simply measuring the copy number of the gTOW plasmid. In TIPI-gTOW, however, we first have to modify the native copy of the target gene, which changes the native expression, activity, and stability of the target protein. In addition, the aim of the procedure is to reduce the expression of the target protein, which makes it difficult to quantitate the lower limit of the target protein by western blotting when this level is under the detection limit. In fact, we have failed to detect the protein levels of Ade2 and Cdc20 by western blotting using their specific antibodies (data not shown). We thus believe that TIPI-gTOW is a method by which a cellular condition can be constructed with the target protein expressed at the lower limit still supporting cellular function. Thus, it is useful for identifying genetic interactions as described above, although it does not measure the absolute lower limit of the target protein.
To use TIPI-gTOW, the optimal expression for the target GFP-TDegF protein must be determined so that the level is within the range of detection of the lower limit by increasing the TEV plasmid. Taxis and Knop developed a series of TDeg constructs with different promoters and N-degrons of various strengths , which will be useful to systematically determine the optimal expression.
Previous studies on haploinsufficiency and dosage compensation suggested that most of yeast proteins are expressed at least twice more than the levels required in diploid cells [37, 38]. If we could detect the lower limit protein levels under the TIPI-gTOW experiments, we might be able to argue about this issue. We failed to detect some proteins using western blotting with specific antibodies as described above. Fusing the target proteins with more sensitive proves might be useful.
Yeast strains and growth conditions
Yeast strains used in this study
MAT a, his3Δ1, leu2Δ0, met15Δ0, ura3Δ0
ade2::hphMX-P_CDC19-GFP- TDegF ADE2 in BY4741
cdc15::hphMX-P_CDC19 -500 -GFP- TDegF CDC15 in BY4741
cdc20::hphMX-P_CDC19 -500 -GFP- TDegF CDC20 in BY4741
cdc28::hphMX-P_CDC19 -500 -GFP- TDegF CDC28 in BY4741
cdc28::hphMX-P_CDC19 -600 -GFP- TDegF CDC28 in BY4741
Plasmids used in this study
Plasmids used in this study
2 μ ori, URA3, leu2d; plasmid for gTOW experiment
2 μ ori, HIS3
hph-MX4, P_CDC19-GFP- TDegF ADE2 in pRS423ks
P_CUP1-P14*–TEV + in pSBI40
P_CDC19 -500 instead of P_CDC19 in pSS1002
P_CDC19 -600 instead of P_CDC19 in pSS1002
Copy number determination of plasmids
The copy number of a plasmid in a cell was measured as described previously  with some modifications. Cells of each yeast strain with pSS1006 (the TEV plasmid) or pSBI40 (the empty vector plasmid) were grown for 50 h in 200 μL SC medium without relevant amino acids. The cells were corrected and suspended into 50 μL of Zymolyase solution (10 mM Na-phosphate [pH 7.5], 1.2 M sorbitol, and 2.5 mg/ml Zymolyase 100 T (Nacalai tesque)). The cell suspension was incubated for 15 min at 37°C, and then incubated for 10 min at 100°C. After removing the cell debris by centrifugation, the supernatant was used as the total DNA solution. Two real-time PCRs were performed using 2 μL of the DNA solution as templates with LEU2 and LEU3 primer sets (primer sequences are listed in Additional file 1: Table S3), using LightCycler® 480 SYBR Green I Master with the LightCycler® 480 system (Roche). The LEU2 and LEU3 primer sets were used to quantify the LEU2 DNA from the plasmid and the LEU3 DNA from the genome, respectively. The plasmid copy number per haploid genome was estimated by comparing the relative amount of the LEU2 DNA and the LEU3 DNA as follows: , where Cp_LEU2 and Cp_LEU3 are the crossing points of PCR amplifications using the LEU2 and LEU3 primer sets, respectively.
The relative copy number change of the TEV plasmid was calculated as follows: Relative copy number change = (ΔC T /C V - wtC T /wtC V )/(wtC T /wtC V ), where ΔC T is the average copy number of the TEV plasmid in each knockout strain, ΔC V is the average copy number of the empty vector plasmid in each knockout strain, wtC T is the average copy number of the TEV plasmid in the wild-type strain, and wtC V is the average copy number of the empty vector plasmid in the wild-type strain.
Cells were cultivated in SC medium, and the proteins were extracted as described previously . The proteins were separated using NuPAGE® 4-12% Bis-Tris gels, and transferred them onto a PVDF membrane using iBlot® gel transfer system (Life Technologies), then the target proteins were detected using their specific antibodies. For the detection of GFP, TEV protease, and Cdc28, antibodies 1814460 (Roche), PAB19931 (Abnova), and sc-6709 (Santa Cruz Biotechnology) were used, respectively. For the second antibodies, Histofine® simple stain MAX PO (MULTI) and Histofine® simple stain MAX PO (G)(Nichirei) were used. Coomassie® G-250 (SimplyBlue™ SafeStain, Life Technologies) was used to stain loading controls.
Numerical simulations were performed using Matlab version 7.3.0 with the “stabilization model” described previously . The rate of degradation of kd20 was gradually increased until the cell cycle simulation yielded the result “dead.” The relative degradation rate change was calculated as follows: Relative degradation rate change = (ΔUL kd20 - wtUL kd20 )/wtUL kd20 , where ΔUL kd20 is the upper limit of kd20 in a deletion mutant model and wtUL kd20 is that in the wild-type model.
The authors would like to thank Ayako Chino (Okayama University) for various discussions related to this study, and Dr. Christof Taxis (Philipps-Universität, Marburg, Germany) for kindly providing us with the TIPI plasmids (pDS5, pDS41). This work was supported by the Special Coordination Fund for Promoting Science and Technology, Grants-in-Aid for Scientific Research (B). The funding agencies were not involved in study design, data collection and analysis, decision to publish, or manuscript preparation.
- Zaslaver A, Mayo AE, Rosenberg R, Bashkin P, Sberro H, Tsalyuk M, Surette MG, Alon U: Just-in-time transcription program in metabolic pathways. Nat Genet. 2004, 36: 486-491. 10.1038/ng1348.View ArticlePubMedGoogle Scholar
- Dekel E, Alon U: Optimality and evolutionary tuning of the expression level of a protein. Nature. 2005, 436: 588-592. 10.1038/nature03842.View ArticlePubMedGoogle Scholar
- Wagner A: Energy constraints on the evolution of gene expression. Mol Biol Evol. 2005, 22: 1365-1374. 10.1093/molbev/msi126.View ArticlePubMedGoogle Scholar
- Alon U, Surette MG, Barkai N, Leibler S: Robustness in bacterial chemotaxis. Nature. 1999, 397: 168-171. 10.1038/16483.View ArticlePubMedGoogle Scholar
- Little JW, Shepley DP, Wert DW: Robustness of a gene regulatory circuit. EMBO J. 1999, 18: 4299-4307. 10.1093/emboj/18.15.4299.PubMed CentralView ArticlePubMedGoogle Scholar
- von Dassow G, Meir E, Munro EM, Odell GM: The segment polarity network is a robust developmental module. Nature. 2000, 406: 188-192. 10.1038/35018085.View ArticlePubMedGoogle Scholar
- Moriya H, Shimizu-Yoshida Y, Kitano H: In vivo robustness analysis of cell division cycle genes in Saccharomyces cerevisiae. PLoS Genet. 2006, 2: e111-10.1371/journal.pgen.0020111.PubMed CentralView ArticlePubMedGoogle Scholar
- Moriya H, Makanae K, Watanabe K, Chino A, Shimizu-Yoshida Y: Robustness analysis of cellular systems using the genetic tug-of-war method. Mol Biosyst. 2012, 8: 2513-2522. 10.1039/c2mb25100k.View ArticlePubMedGoogle Scholar
- Kaizu K, Moriya H, Kitano H: Fragilities caused by dosage imbalance in regulation of the budding yeast cell cycle. PLoS Genet. 2010, 6: e1000919-10.1371/journal.pgen.1000919.PubMed CentralView ArticlePubMedGoogle Scholar
- Moriya H, Chino A, Kapuy O, Csikász-Nagy A, Novák B: Overexpression limits of fission yeast cell-cycle regulators in vivo and in silico. Mol Syst Biol. 2011, 7: 556-PubMed CentralView ArticlePubMedGoogle Scholar
- Makanae K, Kintaka R, Makino T, Kitano H, Moriya H: Identification of dosage-sensitive genes in Saccharomyces cerevisiae using the genetic tug-of-war method. Genome Res. 2013, 23: 300-311. 10.1101/gr.146662.112.PubMed CentralView ArticlePubMedGoogle Scholar
- Giaever G, Chu AM, Ni L, Connelly C, Riles L, Véronneau S, Dow S, Lucau-Danila A, Anderson K, André B: Functional profiling of the Saccharomyces cerevisiae genome. Nature. 2002, 418: 387-391. 10.1038/nature00935.View ArticlePubMedGoogle Scholar
- Costanzo M, Baryshnikova A, Bellay J, Kim Y, Spear ED, Sevier CS, Ding H, Koh JL, Toufighi K, Mostafavi S: The genetic landscape of a cell. Science. 2010, 327: 425-431. 10.1126/science.1180823.View ArticlePubMedGoogle Scholar
- Hillenmeyer ME, Ericson E, Davis RW, Nislow C, Koller D, Giaever G: Systematic analysis of genome-wide fitness data in yeast reveals novel gene function and drug action. Genome Biol. 2010, 11: R30-10.1186/gb-2010-11-3-r30.PubMed CentralView ArticlePubMedGoogle Scholar
- Mnaimneh S, Davierwala AP, Haynes J, Moffat J, Peng WT, Zhang W, Yang X, Pootoolal J, Chua G, Lopez A: Exploration of essential gene functions via titratable promoter alleles. Cell. 2004, 118: 31-44. 10.1016/j.cell.2004.06.013.View ArticlePubMedGoogle Scholar
- Drinnenberg IA, Weinberg DE, Xie KT, Mower JP, Wolfe KH, Fink GR, Bartel DP: RNAi in budding yeast. Science. 2009, 326: 544-550. 10.1126/science.1176945.PubMed CentralView ArticlePubMedGoogle Scholar
- Breslow DK, Cameron DM, Collins SR, Schuldiner M, Stewart-Ornstein J, Newman HW, Braun S, Madhani HD, Krogan NJ, Weissman JS: A comprehensive strategy enabling high-resolution functional analysis of the yeast genome. Nat Methods. 2008, 5: 711-718. 10.1038/nmeth.1234.PubMed CentralView ArticlePubMedGoogle Scholar
- Schuldiner M, Collins SR, Thompson NJ, Denic V, Bhamidipati A, Punna T, Ihmels J, Andrews B, Boone C, Greenblatt JF: Exploration of the function and organization of the yeast early secretory pathway through an epistatic miniarray profile. Cell. 2005, 123: 507-519. 10.1016/j.cell.2005.08.031.View ArticlePubMedGoogle Scholar
- Finoux AL, Séraphin B: In vivo targeting of the yeast Pop2 deadenylase subunit to reporter transcripts induces their rapid degradation and generates new decay intermediates. J Biol Chem. 2006, 281: 25940-25947. 10.1074/jbc.M600132200.View ArticlePubMedGoogle Scholar
- Kanemaki M, Sanchez-Diaz A, Gambus A, Labib K: Functional proteomic identification of DNA replication proteins by induced proteolysis in vivo. Nature. 2003, 423: 720-724. 10.1038/nature01692.View ArticlePubMedGoogle Scholar
- Nishimura K, Fukagawa T, Takisawa H, Kakimoto T, Kanemaki M: An auxin-based degron system for the rapid depletion of proteins in nonplant cells. Nat Methods. 2009, 6: 917-922. 10.1038/nmeth.1401.View ArticlePubMedGoogle Scholar
- Taxis C, Stier G, Spadaccini R, Knop M: Efficient protein depletion by genetically controlled deprotection of a dormant N-degron. Mol Syst Biol. 2009, 5: 267-PubMed CentralView ArticlePubMedGoogle Scholar
- Taxis C, Knop M: TIPI: TEV protease-mediated induction of protein instability. Methods Mol Biol. 2012, 832: 611-626. 10.1007/978-1-61779-474-2_43.View ArticlePubMedGoogle Scholar
- Varshavsky A: The N-end rule pathway of protein degradation. Genes Cells. 1997, 2: 13-28. 10.1046/j.1365-2443.1997.1020301.x.View ArticlePubMedGoogle Scholar
- Ugolini S, Bruschi CV: The red/white colony color assay in the yeast Saccharomyces cerevisiae: epistatic growth advantage of white ade8-18, ade2 cells over red ade2 cells. Curr Genet. 1996, 30: 485-492. 10.1007/s002940050160.View ArticlePubMedGoogle Scholar
- Stevenson LF, Kennedy BK, Harlow E: A large-scale overexpression screen in Saccharomyces cerevisiae identifies previously uncharacterized cell cycle genes. Proc Natl Acad Sci USA. 2001, 98: 3946-3951. 10.1073/pnas.051013498.PubMed CentralView ArticlePubMedGoogle Scholar
- Tavormina PA, Burke DJ: Cell cycle arrest in cdc20 mutants of Saccharomyces cerevisiae is independent of Ndc10p and kinetochore function but requires a subset of spindle checkpoint genes. Genetics. 1998, 148: 1701-1713.PubMed CentralPubMedGoogle Scholar
- Lin YY, Qi Y, Lu JY, Pan X, Yuan DS, Zhao Y, Bader JS, Boeke JD: A comprehensive synthetic genetic interaction network governing yeast histone acetylation and deacetylation. Genes Dev. 2008, 22: 2062-2074. 10.1101/gad.1679508.PubMed CentralView ArticlePubMedGoogle Scholar
- Schott EJ, Hoyt MA: Dominant alleles of Saccharomyces cerevisiae CDC20 reveal its role in promoting anaphase. Genetics. 1998, 148: 599-610.PubMed CentralPubMedGoogle Scholar
- Tsuchiya D, Gonzalez C, Lacefield S: The spindle checkpoint protein Mad2 regulates APC/C activity during prometaphase and metaphase of meiosis I in Saccharomyces cerevisiae. Mol Biol Cell. 2011, 22: 2848-2861. 10.1091/mbc.E11-04-0378.PubMed CentralView ArticlePubMedGoogle Scholar
- Lew DJ, Weinert T, Pringle JR: Cell cycle control in Saccharomyces cerevisiae. The Molecular and Cellular Biology of the Yeast Saccharomyces: Cell Cycle and Cell Biology. Edited by: Pringle JR, Broach JR, Jones EW. 1997, Cold Spring Harbor, NY: Cold Spring Harbor Laboratory Press, 607-695.Google Scholar
- Bandyopadhyay S, Mehta M, Kuo D, Sung MK, Chuang R, Jaehnig EJ, Bodenmiller B, Licon K, Copeland W, Shales M: Rewiring of genetic networks in response to DNA damage. Science. 2010, 330: 1385-1389. 10.1126/science.1195618.PubMed CentralView ArticlePubMedGoogle Scholar
- Chen KC, Calzone L, Csikasz-Nagy A, Cross FR, Novak B, Tyson JJ: Integrative analysis of cell cycle control in budding yeast. Mol Biol Cell. 2004, 15: 3841-3862. 10.1091/mbc.E03-11-0794.PubMed CentralView ArticlePubMedGoogle Scholar
- Baryshnikova A, Costanzo M, Dixon S, Vizeacoumar FJ, Myers CL, Andrews B, Boone C: Synthetic genetic array (SGA) analysis in Saccharomyces cerevisiae and Schizosaccharomyces pombe. Methods Enzymol. 2010, 470: 145-179.View ArticlePubMedGoogle Scholar
- Baryshnikova A, Costanzo M, Kim Y, Ding H, Koh J, Toufighi K, Youn JY, Ou J, San Luis BJ, Bandyopadhyay S: Quantitative analysis of fitness and genetic interactions in yeast on a genome scale. Nat Methods. 2010, 7: 1017-1024. 10.1038/nmeth.1534.PubMed CentralView ArticlePubMedGoogle Scholar
- Li Z, Vizeacoumar FJ, Bahr S, Li J, Warringer J, Vizeacoumar FS, Min R, Vandersluis B, Bellay J, Devit M: Systematic exploration of essential yeast gene function with temperature-sensitive mutants. Nat Biotechnol. 2011, 29: 361-367. 10.1038/nbt.1832.PubMed CentralView ArticlePubMedGoogle Scholar
- Deutschbauer AM, Jaramillo DF, Proctor M, Kumm J, Hillenmeyer ME, Davis RW, Nislow C, Giaever G: Mechanisms of haploinsufficiency revealed by genome-wide profiling in yeast. Genetics. 2005, 169: 1915-1925. 10.1534/genetics.104.036871.PubMed CentralView ArticlePubMedGoogle Scholar
- Springer M, Weissman JS, Kirschner MW: A general lack of compensation for gene dosage in yeast. Mol Syst Biol. 2010, 6: 368-PubMed CentralView ArticlePubMedGoogle Scholar
- Brachmann CB, Davies A, Cost GJ, Caputo E, Li J, Hieter P, Boeke JD: Designer deletion strains derived from Saccharomyces cerevisiae S288C: a useful set of strains and plasmids for PCR-mediated gene disruption and other applications. Yeast. 1998, 14: 115-132. 10.1002/(SICI)1097-0061(19980130)14:2<115::AID-YEA204>3.0.CO;2-2.View ArticlePubMedGoogle Scholar
- Amberg DC, Burke D, Strathern JN: Methods in Yeast Genetics: A Cold Spring Harbor Laboratory Course Manual. 2005, New York: Cold Spring Harbor Laboratory PressGoogle Scholar
- Goldstein AL, McCusker JH: Three new dominant drug resistance cassettes for gene disruption in Saccharomyces cerevisiae. Yeast. 1999, 15: 1541-1553. 10.1002/(SICI)1097-0061(199910)15:14<1541::AID-YEA476>3.0.CO;2-K.View ArticlePubMedGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.