Open Collections

UBC Faculty Research and Publications

Complex regulation of Hsf1-Skn7 activities by the catalytic subunits of PKA in Saccharomyces cerevisiae:… Pérez-Landero, Sergio; Sandoval-Motta, Santiago; Martínez-Anaya, Claudia; Yang, Runying; Folch-Mallol, Jorge L; Martínez, Luz M; Ventura, Larissa; Guillén-Navarro, Karina; Aldana-González, Maximino; Nieto-Sotelo, Jorge Jul 27, 2015

Your browser doesn't seem to have a PDF viewer, please download the PDF to view this item.

Item Metadata


52383-12918_2015_Article_185.pdf [ 1.72MB ]
JSON: 52383-1.0074665.json
JSON-LD: 52383-1.0074665-ld.json
RDF/XML (Pretty): 52383-1.0074665-rdf.xml
RDF/JSON: 52383-1.0074665-rdf.json
Turtle: 52383-1.0074665-turtle.txt
N-Triples: 52383-1.0074665-rdf-ntriples.txt
Original Record: 52383-1.0074665-source.json
Full Text

Full Text

RESEARCH ARTICLE Open AccessComplex regulation of Hsf1-Skn7 activitiesby the catalytic subunits of PKA inSaccharomyces cerevisiae: experimental andcomputational evidencesSergio Pérez-Landero1†, Santiago Sandoval-Motta2†, Claudia Martínez-Anaya3, Runying Yang4,Jorge Luis Folch-Mallol5, Luz María Martínez3, Larissa Ventura6, Karina Guillén-Navarro7,Maximino Aldana-González2 and Jorge Nieto-Sotelo1*AbstractBackground: The cAMP-dependent protein kinase regulatory network (PKA-RN) regulates metabolism, memory,learning, development, and response to stress. Previous models of this network considered the catalytic subunits (CS)as a single entity, overlooking their functional individualities. Furthermore, PKA-RN dynamics are often measuredthrough cAMP levels in nutrient-depleted cells shortly after being fed with glucose, dismissing downstream physiologicalprocesses.Results: Here we show that temperature stress, along with deletion of PKA-RN genes, significantly affectedHSE-dependent gene expression and the dynamics of the PKA-RN in cells growing in exponential phase.Our genetic analysis revealed complex regulatory interactions between the CS that influenced the inhibitionof Hsf1/Skn7 transcription factors. Accordingly, we found new roles in growth control and stress responsefor Hsf1/Skn7 when PKA activity was low (cdc25Δ cells). Experimental results were used to propose an interaction schemefor the PKA-RN and to build an extension of a classic synchronous discrete modeling framework. Our computationalmodel reproduced the experimental data and predicted complex interactions between the CS and theexistence of a repressor of Hsf1/Skn7 that is activated by the CS. Additional genetic analysis identified Ssa1and Ssa2 chaperones as such repressors. Further modeling of the new data foresaw a third repressor ofHsf1/Skn7, active only in theabsence of Tpk2. By averaging the network state over all its attractors, a goodquantitative agreement between computational and experimental results was obtained, as the averagesreflected more accurately the population measurements.Conclusions: The assumption of PKA being one molecular entity has hindered the study of a wide rangeof behaviors. Additionally, the dynamics of HSE-dependent gene expression cannot be simulated accuratelyby considering the activity of single PKA-RN components (i.e., cAMP, individual CS, Bcy1, etc.). We showthat the differential roles of the CS are essential to understand the dynamics of the PKA-RN and its targets.Our systems level approach, which combined experimental results with theoretical modeling, unveils the relevance ofthe interaction scheme for the CS and offers quantitative predictions for several scenarios (WT vs. mutants in PKA-RNgenes and growth at optimal temperature vs. heat shock).Keywords: Yeast, Signal transduction, Hsf1 function, Skn7 function, Windowed discrete model* Correspondence:†Equal contributors1Instituto de Biología, Universidad Nacional Autónoma de México, 04510México, D.F., MexicoFull list of author information is available at the end of the article© 2015 Pérez-Landero et al. This is an Open Access article distributed under the terms of the Creative Commons AttributionLicense (, which permits unrestricted use, distribution, and reproduction in anymedium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated.Pérez-Landero et al. BMC Systems Biology  (2015) 9:42 DOI 10.1186/s12918-015-0185-8BackgroundThe cyclic AMP (cAMP)-dependent protein kinase (PKA)regulatory network (PKA-RN) is found in protozoa, ani-mals, algae, and fungi. It plays a central role in the controlof different inter- and intra- cellular processes such as me-tabolism, cell proliferation, stress response, and develop-ment [57, 84]. In yeast, the PKA-RN is also involved in thecontrol of growth in response to nutrient conditions, whichin turn are known to influence the stress response [84].In S. cerevisiae, the PKA holoenzyme forms an inactivetetramer composed of two regulatory subunits (2 x Bcy1)[85] and two out of three CS (Tpk1, Tpk2, or Tpk3) [86].When intracellular concentrations of cAMP increase, Bcy1binds cAMP, promoting the activation by release of the CS.Two proteins, Gpa2 and Ras2, regulate adenylate cyclase,which catalyzes the synthesis of cAMP [14, 43, 84]. Forma-tion of a Ras2⋅GTP complex [22], the active state of Ras2,requires the GDP-GTP exchange activity of Cdc25 [6].Moreover, the intracellular concentrations of cAMP arealso controlled by the phosphodiesterases Pde1 and Pde2[84]. The low affinity phosphodiesterase Pde1 reducescAMP levels in nutrient depleted cells soon after glucoseaddition [55], whereas the high affinity phosphodiesterasePde2 lowers cAMP levels during the exponential and sta-tionary phases of growth [62]. It is thought that the activityof the pathway increases at high levels of glucose (or otherrapidly fermentable sugars) and declines when the cells de-plete the sugars, or when entering stationary phase [84].Therefore, the PKA activity is influenced by the amount offermentable sugars and by the growth phase of the culture.The growth phase of yeast liquid cultures impactstheir level of thermotolerance. For instance, during theexponential phase cells are stress sensitive, whereas dur-ing the stationary phase they develop stress resistance[67, 91]. This behavior has been studied using geneticanalysis. Stress resistance is explained through a reducedactivity of the Ras-cAMP branch of the pathway (suchas in cdc25Δ, cyr1 or ras1 ras2ts strains). These mutantsgrow slowly and show elevated basal thermotoleranceduring exponential phase [23, 25, 42, 64, 83]. In contrast,mutants with high PKA activity, such as ira2, pde2, bcy1or RAS2val19, are very sensitive to temperature stress[19, 55, 62, 84, 87]. In exponential phase, basal thermo-tolerance is negatively regulated by the Rim15 proteinkinase [67]. However, acclimation to high temperaturesduring the exponential phase requires the concertedaction of Hsf1 and Msn2/Msn4 transcription factors,and chromatin remodeling complexes such as SWI/SNF[3, 18]. These factors allow the rapid transcription ofgenes encoding stress proteins involved in preventionand repair of damages caused by stress [3, 19, 33].Hsf1 transcription factor is encoded by a single gene[76] and shows high affinity for the heat shock elements(HSE), found in the promoters of the heat shock genes[21]. The essentiality of Hsf1 indicates that — besidesbeing important for the response to carbon starvation aswell as heat, osmotic, and oxidative stress — it also playsimportant functions in normal growth [3, 4, 76, 81]. Thewidespread functions regulated by Hsf1 explain its bind-ing to a large number of promoters (about 3 % of thegenes in the yeast genome). Among the functions of itstargets are: protein folding, degradation, trafficking, cellintegrity maintenance, transport, signaling, and tran-scription [33]. Hsf1 contains DNA binding and trimeri-zation domains and is hyper-phosphorylated in serineand threonine residues in response to heat and oxidativestress [39, 76], modifications that activate its transcrip-tional activity [39, 76]. The PKA constitutively repressesthe activity of Hsf1, thereby inhibiting the expression ofsmall heat shock protein genes [20]. It is documentedthat, in this regulation, the CS of PKA do not interactdirectly with Hsf1 [20]. Moreover, when the activity ofthe PKA is low, such as during glucose starvation, Hsf1is phosphorylated and activated by Yak1 and Rim15 ki-nases [50, 51]. However, the factors that mediate the regu-lation of Hsf1 by the PKA in glucose-rich media, and inresponse to heat shock, are still unknown. In addition toHsf1, Skn7 also recognizes HSE elements [66] and is partof a two-component system required for the signaling ofthe hypo-osmotic stress and the oxidative stress pathways[49, 82]. Previous reports have shown that the activity ofSkn7 during the oxidative stress response is negatively-regulated by the PKA-RN [10].Recently, it has become evident that results based onlyin experimental approaches, and the static models de-rived from them, are not sufficient to fully understandthe complex dynamics of a cellular system. Rather, theintegration of experimental data with dynamical model-ing has expanded our current knowledge of the cell byenabling the prediction of hidden cellular behaviors.Thus, computational modeling is becoming an indis-pensable tool to comprehend the organization of bio-logical systems [48, 80] and the analysis of the dynamicsof the PKA-RN is no exception. Some studies of thePKA-RN considered only its core components and fo-cused on the feed-back regulation of cAMP levels thatnutrient-depleted cells display during the short-term re-sponse (i.e., seconds) to a pulse of glucose [8, 63, 93].More recently, PKA-RN models simulate long-termgrowth (i.e., hours) in glucose [28] and evaluate targetsdownstream of the PKA [24, 29]. However, in all thesemodels the activity of the three CS (Tpk1, Tpk2, andTpk3) is considered as a single entity, assumption thatmight be correct in certain scenarios. Nonetheless, inmost situations, this assumption could be misleading, asit is known that each CS has unique target specificities[65]. Furthermore, the CS regulate certain physiologicalprocesses in an antagonistic fashion [61, 68, 69],Pérez-Landero et al. BMC Systems Biology  (2015) 9:42 Page 2 of 17complicating even more the prediction of the dynamicsof the PKA-RN.In this work, we performed a genetic analysis of theCdc25-Ras2 branch and some downstream componentsof the PKA-RN (Fig. 1). We then incorporated these re-sults into a dynamic computational model, to furtherunderstand the mechanistic nature of the network. HSE-dependent gene expression was chosen as the end prod-uct of the PKA-RN and the performance of cells grow-ing exponentially in glucose-rich media, both at optimaltemperature and in response to heat shock, was evalu-ated. We tested how the different PKA subunits (regula-tory and each CS) interacted with each other. Novelinteractions, in addition to those already known, are de-scribed. Computational modeling of the PKA-RN wasperformed by extending the well-established “discretedynamics modeling framework” [1, 38, 71, 90] in orderto take into account the fact that gene expressionmeasurements of batch cultures average out individualexpression patterns. We named this extension, the Win-dowed Discrete Model (WDM) because it averages overa given time window the discrete values of the networkelements in a given attractor, and weighs this average bythe size of the corresponding basin of attraction. Thisprocess incorporates the whole set of steady states ofthe network and captures the inherent averages inFig. 1 Scheme for the development of a dynamic computational model for the simulation of the regulation of HSE-dependent gene expression bythe PKA-RN. A PKA-RN composed of 15 elements was simulated in a dynamic computational model (see Additional file 3: Supplementary Information).In this scheme Cdc25, Ras2, Cyr1, Tpk1, Tpk3, Hsf1, and Skn7 act as positive regulators. Bcy1, an unidentified repressor of Hsf1/Skn7 (Repressor X), cAMP,and heat shock act as repressors. Interestingly, in this scheme Ssa1, Ssa2, and Tpk2 interactions are complex acting both as activators and as repressors.The experimental evidence that accounts for the activities and interactions of the components is described in the Background and Results anddiscussion sectionsPérez-Landero et al. BMC Systems Biology  (2015) 9:42 Page 3 of 17population measurements. Although discrete dynamicmodels are intended to describe expression patterns atthe single-cell level, our approach allowed us to makequantitative predictions of gene expression patternstaken at the population level for both WT and mutantstrains. Furthermore, we showed that when the popula-tion average inherent to batch cultures was implementedin the WDM, the results were similar regardless of theuse of synchronous or asynchronous updating of thenetwork elements.Our genetic analysis showed that the PKA-RN con-trols HSE-dependent gene expression via Hsf1/Skn7transcription factors. Modeling the control of Hsf1-Skn7by the PKA-RN predicted the existence of a repressorconnecting the CS with Hsf1-Skn7 and encouraged newgenetic analyses that proved that Ssa1 and Ssa2 chaper-ones repress Hsf1/Skn7 when activated by the CS ofPKA. Additionally, novel functions of Skn7 and of theC-terminal domain of Hsf1, such as growth control,thermotolerance, and resistance to H2O2, were revealedwhenever the activity of PKA was low. Our model alsopredicted the existence of a still unidentified third re-pressor of Hsf1/Skn7, active only in the absence of Tpk2.The WDM explained and predicted HSE-dependent geneexpression in WT and mutant strains with and withouthigh temperature stress. We believe that our WDM ofthe PKA-RN can be useful to simulate other biologicalprocesses where the CS of PKA show similar antagonisticinteractions, such as in the control of pseudohyphalgrowth or iron uptake [61, 68, 69].Without further adaptations, the WDM is, to ourknowledge, the first suitable tool based on discrete dy-namics that can be used to simulate data obtained frompopulation level measurements (batch cultures), despiteof their known heterogeneity at the physiological andgene expression levels [23, 40, 53].Results and discussionAnalysis of gene expression and dynamical modeling ofthe PKA-RN was performed during exponential growth.Measurements were taken under optimal temperatureand after a heat shock at 39 °C (see Methods). The regu-lation of stress gene expression depends on complextranscriptional mechanisms. For example, in S. cerevisiaeMsn2, Msn4, Hsf1, Yap1, and eight additional transcrip-tion factors contribute to the transcription of heat shockgenes [95]. The PKA-RN also controls stress gene ex-pression by inhibiting the activity of Msn2, Msn4, Hsf1,Yap1, and Skn7 [10, 20, 37, 75]. Because of this complex-ity, we decided to focus on the transcription factors Hsf1and Skn7 in WT and PKA-RN deletion mutants bymeasuring the activity of an HSE-CYC1-lacZ reportergene construct to test their in vivo activity (seeMethods), as reported before [4, 47, 50, 58]. In ourhands, this reporter showed no activity in the absence ofthe HSE and its activity did not correlate with the plas-mid copy number in the different strains analyzed (seeMethods). Because the effect on HSE-dependent expres-sion by deletions in PKA-RN genes is dependent on thegenetic background ([17, 56], and our unpublished data),all mutants used in this work were derivatives of thesame laboratory strain (W303). Previous studies haveshown that in W303, the expression of several stressgenes such as HSP104, TPS1, CTT1, GPD1, HSP12, andHSP26 are inhibited by PKA [20, 23] and, in the case ofHSP12 and HSP26, their inhibition by PKA is mediatedthrough Hsf1 [20].Cdc25 positively regulates HSE-dependent geneexpressionCDC25 deletion caused strong alterations in two well-known PKA-regulated processes: growth rate (decreased)and basal thermotolerance (increased) (Additional file 1:Table S1). HSE-driven β-galactosidase activity at 25 °Cwas 3.7-fold higher in cdc25Δ cells than in the WTstrain (Fig. 2b). After heat shock, the WT strain in-creased the reporter activity 2.3-fold relative to the25 °C condition. In cdc25Δ cells, β-galactosidase activ-ity remained unchanged at both temperatures; note-worthy these levels were significantly higher than inthe WT at 39 °C. These results indicate that CDC25down-regulates HSE-dependent gene expression in WTcells and they are consistent with previous findingsshowing that PKA inhibits Hsf1 activity [20].Hsf1 and Skn7 mediate the high basal thermotoleranceand constitutive HSE-dependent gene expression incdc25Δ cellsBoth Hsf1 and Skn7 transcription factors recognize HSEs[66, 76]. Therefore, we separately evaluated their contri-butions to the constitutively-elevated HSE-dependentexpression in cdc25Δ cells. An Hsf1 lacking 250 residuesat the C-terminal domain (hsf1-ΔCTA) was used insteadof a full deletion of the ORF, because the function ofHSF1 is essential [60, 76]. At 25 °C the β-galactosidaseactivity in the hsf1-ΔCTA strain equated that of theWT but, unlike the WT, after a heat shock at 39 °Cits β-galactosidase activity did not increase (Fig. 2a).This confirms that the C-terminal activation domain isrequired to elevate Hsf1 transcriptional activity in re-sponse to heat shock [60]. Furthermore, β-galactosidaselevels in the double mutant hsf1-ΔCTA cdc25Δ de-creased significantly compared to the single cdc25Δmutant, both at 25 °C and after heat shock, supportingthe idea that Cdc25 regulates Hsf1 (Fig. 2a). Accord-ingly, the basal thermotolerance (15 %) of hsf1-ΔCTAcdc25Δ cells decreased relative to the cdc25Δ singlemutant (70 %) (Additional file 1: Table S2). AlthoughPérez-Landero et al. BMC Systems Biology  (2015) 9:42 Page 4 of 17basal thermotolerance of hsf1-ΔCTA mutant was simi-lar to the WT strain, its duplication time at 25 °C in-creased slightly (Additional file 1: Table S2). We alsofound that deletion of the C-terminal domain of Hsf1suppressed the lack of growth of cdc25Δ cells in acet-ate or galactose at 25 °C. We suggest that the C-terminaldomain of Hsf1 plays a negative role in the control ofgrowth in non-fermentable media under conditions of lowPKA activity. In yeast, humans and in Arabidopsis, Hsp70interacts with the C-terminal activation domain of Hsf1inhibiting its transcriptional activity [4, 45, 73]. We predictthat the transcriptional activity and the growth-promotingpotential of the full-length Hsf1, when the cell is underunder low PKA conditions, could be re-established bydeletion of genes encoding Hsp70.In WT cells, HSE-dependent expression increased at thebeginning of the post-diauxic phase (Fig. 3). This observa-tion agrees with the decline of PKA activity at this stage[84]. A similar pattern was observed in cdc25Δ cells, al-though their initial activity was already very high. Interest-ingly, the β-galactosidase activity in the double mutanthsf1-ΔCTA cdc25Δ was smaller than the activity in cdc25Δcells, remaining constant during the exponential and post-diauxic phases. This indicates that the CTA domain of Hsf1is required for maximal activity in low PKA cells. Unex-pectedly, β-galactosidase levels in the hsf1-ΔCTA strain de-clined steadily as the culture advanced from exponential tothe post-diauxic phase (Fig. 3). These observations reinforcethe idea that Hsf1 activity is essential to enter the post-diauxic phase at optimal temperatures. Thus, the C-terminal domain of Hsf1 plays four novel functional rolesat 25 °C when PKA activity is low: i) increases basal ther-motolerance (Additional file 1: Table S3), ii) increases HSE-dependent gene expression (Figs. 2a and 3), iii) causesgrowth arrest in acetate, iv) causes growth arrest in galact-ose. These functions of the C-terminal domain of Hsf1were not previously described [60, 76].To analyze the contribution of Skn7, the double mu-tant skn7Δ cdc25Δ was also transformed with reporterplasmid pRY016. The β-galactosidase activity in skn7Δcdc25Δ cells at 25 °C or after heat shock was lower thanthat of cdc25Δ cells (Fig. 2b). In contrast to cdc25Δ hsf1-Fig. 2 De-repression of HSE-dependent gene expression in cdc25Δcells is dependent on both Hsf1 and Skn7 activities. Strainstransformed with reporter plasmid pRY016 (2 μ) were grown in SDmedium at 25 °C until mid-exponential phase and treated atdifferent temperatures as described in Methods section. Values arereported as β-galactosidase specific activity (nmol of hydrolyzedONPG min−1 mg−1 protein) and are the average and standarddeviation of at least three independent experiments. Bars that donot share at least a common letter differ significantly (P < 0.05).Strains assayed were: a WT (W303-6B), hsf1-ΔCTA (LM020), cdc25Δ(SL5001), and hsf1-ΔCTA cdc25Δ (SL6001). b WT (W303-6B), skn7Δ(SE1000), cdc25Δ (SL5001), and cdc25Δ skn7Δ (SL4001)Pérez-Landero et al. BMC Systems Biology  (2015) 9:42 Page 5 of 17ΔCTA cells, β-galactosidase activity increased upon heatshock at 39 °C. However, this increase was not statisti-cally significant (Fig. 2b). This indicates that, in thecdc25Δ strain, HSE-dependent expression is reliant onSkn7 for optimal temperature growth to a greater extentthan after a heat shock. Furthermore, the basal thermo-tolerance and the duplication time of cdc25Δ skn7Δ cellsdecreased relative to cdc25Δ cells (Additional file 1:Table S2), while the inhibition of growth at 36 °C and inacetate or galactose as sole carbon sources at 25 °C weresuppressed by SKN7 deletion. In agreement with the in-volvement of Skn7 in the oxidative stress response [49],we observed that resistance of cdc25Δ cells to H2O2 de-creased by deletion of SKN7 (data not shown). The activityof the reporter gene in the single skn7Δ mutant was similarto the WTat 25 °C and after a heat shock at 39 °C (Fig. 2b).Together, these results indicate that, in cells growing at op-timal temperature or when their PKA activity is low, Skn7is required to achieve maximal basal thermotolerance andHSE-dependent gene expression. The contribution of Skn7to the elevated HSE-dependent gene expression in responseto heat shock was only marginal (Fig. 2b). Thus, heat induc-tion of HSE-dependent gene expression in cells with low orhigh PKA activity depends mostly on Hsf1. However, wefound that Skn7 plays new roles in other cellular processesat low PKA activity: i) inhibits growth at 25 °C, ii) It isrequired for H2O2 resistance, iii) causes growth arrest inglucose at 36 °C, iv) causes growth arrest in acetate at25 °C, v) causes growth arrest in galactose at 25 °C.Ras2 also regulates HSE-dependent gene expressionRas2 is a positive regulator of the PKA-RN acting down-stream of Cdc25. In a RAS2 deletion mutant, basalthermotolerance was 120-fold higher than in the WTstrain [P = 0.002] (Additional file 1: Table S1). This dif-ference was consistent with a constitutively elevatedHSE-dependent gene expression at 25 °C (Fig. 4). Growthrate of the RAS2 mutant was similar to the WT strain(Additional file 1: Table S1). The increased thermotoleranceof CDC25 and RAS2 single mutants (Additional file 1:Table S1) confirmed that their PKA activity decreased.Fig. 3 Increase of HSE-dependent gene expression, during the post-diauxic phase of liquid cultures at 25 °C, requires Hsf1 activity. Strainscontaining plasmid pRY016 were grown in SD medium at 25 °C and aliquots were taken at the indicated culture densities (OD600). Data shownrepresent the average and standard deviation of at least three independent experiments. β-galactosidase specific activities are reported as in Fig. 2.Bars that do not share at least a common letter differ significantly (P < 0.05). Strains assayed were: WT (W303-6B), hsf1-ΔCTA (LM020), hsf1-ΔCTAcdc25Δ (SL6001), and cdc25Δ (SL5001)Fig. 4 Effect of RAS2, and BCY1 deletions on HSE-dependent geneexpression. Strains were transformed with plasmid pRY016 (2 μ)containing an HSE-CYC1-lacZ reporter gene. Growth and temperaturetreatments were performed as described in Methods section. Datashown represent the average and standard deviation of at leastthree independent experiments. β-galactosidase specific activitiesare reported as in Fig. 2. Bars that do not share at least a commonletter differ significantly (P < 0.05). Strains assayed were: WT (W303-1a),ras2Δ (Wras2Δ) and bcy1Δ (CM0095)Pérez-Landero et al. BMC Systems Biology  (2015) 9:42 Page 6 of 17However, the growth rate diminished only in the CDC25,but not in the RAS2 mutant. This finding indicatesthat the control of basal thermotolerance is more sen-sitive to a low PKA cellular activity than duplicationtime is.Deletion of BCY1 represses the HSE-dependent geneexpressionTo evaluate whether cells with high PKA activity alteredHSE-dependent gene expression in the opposite way tomutants with low PKA activity, such as cdc25Δ andras2Δ, a deletion mutant of BCY1 was studied. Indeed,HSE-dependent expression was repressed in bcy1Δ cellsrelative to the WT strain at 25 °C and after heat shockat 39 °C (Fig. 4). Consistent with these results, dupli-cation time decreased in the bcy1Δ mutant, while basalthermotolerance remained the same as in the WTstrain (Additional file 1: Table S1). Induced thermotol-erance decreased dramatically in bcy1Δ cells (0.22 ±0.4 % in the mutant vs. 72 ± 12 % in the WT with aP = < 0.001). Moreover, cell viability in bcy1Δ cells wasvery poor, in agreement with previous results [85].Tpk1 and Tpk3 inhibit HSE-dependent gene expression inthe absence of Tpk2To explore the possible differences between the CS ofPKA, we first analyzed HSE-dependent expression insingle TPK gene deletion mutants. In tpk1Δ cells HSE-dependent expression was slightly reduced at 39 °C butnot at 25 °C when compared to the WT (Fig. 5). Intpk3Δ cells HSE-dependent expression was not affected.Interestingly, HSE-dependent expression in the tpk2Δmutant was highly repressed both at 25 °C and 39 °C.The basal thermotolerance of the three single mutantswas similar to the WT strain (Additional file 1: TableS3). Duplication times of tpk2Δ or tpk3Δ mutants weresimilar to the WT strain. However, the tpk1Δ mutantshowed a slower growth rate (Additional file 1: TableS3). Induced thermotolerance was reduced relative toWT in tpk1Δ and tpk2Δ mutants, but not in tpk3Δ.These results suggested that each CS plays a differentrole in the control of HSE-dependent gene expression,growth, and in basal- and induced-thermotolerance. Inorder to analyze the role of individual Tpk’s, double TPKdeletion mutants were studied. The β-galactosidase ac-tivities of tpk1Δ tpk3Δ cells growing at 25 °C or afterheat shock at 39 °C were similar to their isogenicWT strain (Fig. 5). However, its basal thermotoleranceand duplication time increased relative to the WTstrain (Additional file 1: Table S3). In contrast, the β-galactosidase activities at 25 and 39 °C in cells con-taining only Tpk1 (tpk2Δ tpk3Δ) or Tpk3 (tpk1Δ tpk2Δ)were very low (Fig. 5), whereas their basal thermotoler-ance and duplication time were similar to the WT. How-ever, the level of induced thermotolerance of tpk1Δtpk2Δ was lower [P = 0.05] than in WT cells (Additionalfile 1: Table S3). In tpk2Δ tpk3Δ and tpk1Δ tpk3Δ cells,the induced thermotolerance levels were similar to theWT cells, supporting the idea that Tpk3 and Tpk1hyper-repress the HSE-dependent gene expression whenFig. 5 Effect of TPK gene deletions on HSE-dependent gene expression. Strains were transformed with plasmid pRY016 (2 μ) containing an HSE-CYC1-lacZ reporter gene. Growth and temperature treatments were performed as described in Methods section. Values are reported as β-galactosidase specificactivity (nmol of hydrolyzed ONPG min−1 mg−1 protein) and are the average and standard deviation of at least three independent experiments. Bars thatdo not share at least a common letter differ significantly (P< 0.05). Strains assayed were: WT (W303-1a), tpk1Δ (KG712), tpk2Δ (KG604), tpk3Δ (KS580), tpk2Δtpk3Δ (KS590), tpk1Δ tpk3Δ (KS700), tpk1Δ tpk2Δ (KS710), tpk2Δ:: TPK2 tpk3Δ (KS590-URA3-TPK2)Pérez-Landero et al. BMC Systems Biology  (2015) 9:42 Page 7 of 17acting as the sole PKA CS, and that Tpk3 represses theinduced thermotolerance if acting as sole PKA CS. Theseresults confirm the hypothesis that the activities of theCS are not redundant for the control of HSE-dependentgene expression, growth, basal or induced thermotoler-ance. Also, these findings imply that Tpk2 activity antag-onizes Tpk1 and Tpk3 action, as has been suggested byother studies on the control of iron uptake and pseudo-hyphal growth [61, 68, 69].Heat shock gene transcript levels are reduced when Tpk3is the only CSTo learn more about the strong repressing activity ofTpk3 upon Hsf1, when Tpk1 and Tpk2 are absent, westudied the levels of several stress genes within the con-text of their natural promoters. As shown in Additionalfile 2: Figure S1, expression of the heat shock genesHSP104, HSP82, SSA3, HSP26, and HSP12 at 25 °C wasreduced in the tpk1Δ tpk2Δ mutant relative to the WTstrain. This result is consistent with the low level of in-duced thermotolerance displayed by the tpk1Δ tpk2Δmutant (Additional file 1: Table S3). Transformation oftpk1Δ tpk2Δ cells with TPK2 in a CEN plasmid did notcomplement fully the HSE-dependent gene expression atWT levels (data not shown), most likely because TPK2gene copy number per cell was not 1, but 2.8 copies/cell.Transformation of the tpk1Δ tpk2Δ cells with TPK2 in a2 μ plasmid was toxic to the cell, explaining the surpris-ingly low copy number in the surviving cells (1.7 copies/cell).Tpk2 antagonizes the activity of Tpk1To further test the hypothesis that the loss of TPK2 inthe tpk2Δ tpk3Δ double mutant causes repression ofHSE-dependent gene expression, the TPK2 gene wasreturned to the tpk2Δ tpk3Δ double mutant using thedelitto perfetto technique (see Methods) [30, 79], res-toring the native copy number of the gene. This modifi-cation (tpk2Δ::TPK2 tpk3Δ) returned HSE-dependentexpression to WT levels (Fig. 5), supporting the ideathat Tpk2 antagonizes the activity of Tpk1 on HSE-dependent expression.Catalytic activity of PKA in extracts from TPK mutantsWe hypothesized that antagonism between Tpk2 andthe other CS (Tpk1 and Tpk3) was due to drasticchanges in the total PKA activity of the cell. Accord-ingly, we could expect that the total PKA activity inTPK2 mutants (tpk2Δ, tpk1Δ tpk2Δ, and tpk2Δ tpk3Δ)would be high, whereas in the WT, tpk1Δ, tpk3Δ, andtpk1Δ tpk3Δ mutants the PKA activity would be low.After addition of cAMP, PKA activity in extracts frommutants tpk1Δ, tpk3Δ, and tpk1Δ tpk3Δ was similar tothe WT (Additional file 2: Figure S2). On the contrary,cAMP-dependent PKA activity decreased in tpk2Δ,tpk1Δ tpk2Δ, and tpk3Δ mutants. These results indicatethat HSE-dependent expression is not a simple reflectionof the overall PKA activity within the cell. Alternatively,one could also propose that deletion of a given TPKgene reduced the PKA activity in the cell in a propor-tional manner to its abundance in the WT. It is estab-lished that during exponential growth in liquid culturesyeasts contain a large proportion of Tpk1, followed byTpk2, and Tpk3 being the one with the lowest abun-dance [88]. Thus, elimination of TPK1 and/or TPK2should diminish dramatically the PKA activity in the cell.This was the case for TPK2 deletions but not for TPK1deletions (Additional file 2: Figure S2), indicating againthat deletion of a given TPK gene does not influencearithmetically the overall PKA activity in the cell. There-fore, dynamic mechanisms seem to define the final PKAactivity in the WT and in a given TPK mutant (interac-tions between CS, compartmentalization, stability, etc.).Ssa1 and Ssa2 mediate the inhibition of HSE-dependentgene expressionOur initial computational model assumed that the regu-lation of Hsf1/Skn7 by the CS was direct. However,under this design, predicted and experimental HSE-activities for several PKA-RN mutants gave contrastingresults. Complete agreement between experimental andcomputational data was not achieved until a negativeregulator was placed as an intermediary between the CSand Hsf1/Skn7 (see Fig. 1 and the following subsection).This idea was in accordance with previous findings dem-onstrating that the CS's do not interact directly withHsf1 [20]. Therefore, we considered Hsp70 chaperonesas putative intermediate inhibitors, because they arewell-known negative regulators of Hsf1. Yeast mutantswith decreased Hsp70 levels increase the expression ofHsps, enhance thermotolerance, and grow slowly. Add-itionally, these phenotypes are suppressed by a mutation inHSF1 that decreases its DNA binding affinity [13, 34, 92].These observations and others from both mammalsand yeast reinforce a model that includes an auto-regulatory loop in which Hsp70 represses Hsf1 activity[4, 12, 94]. Moreover, Ssa1 positively controls the PKA-RN by stabilizing Cdc25 at optimal temperatures [26]and, under stress, the Cdc25-Hsp70 complex dissociatesleading to a loss of Cdc25 levels and a decrease in the ac-tivity of the PKA pathway [26]. Our experiments revealedthat deletion of SSA2 increased HSE-dependent gene ex-pression (Fig. 6). Deletion of SSA1 did not affect HSE-dependent gene expression significantly, indicating thatSSA2 suffices for maintaining WT activity. Deletion ofboth SSA1 and SSA2 largely increased the reporter acti-vity, uncovering the contribution of both Hsp70 genesas repressors of HSE-dependent gene expression in WTPérez-Landero et al. BMC Systems Biology  (2015) 9:42 Page 8 of 17cells. Interestingly, deletion of SSA1 or SSA2 in a tpk2Δbackground did not suppress the strong repression ofHSE-dependent gene expression characteristic of thetpk2Δ single mutant (Fig. 6). However, the phenotype ofthe tpk2Δ mutant was suppressed in the triple mutantssa1Δ ssa2Δ tpk2Δ, as its HSE-dependent expression washigher than in tpk2Δ cells (at 25 °C and 39 °C), similar tothat of the ssa1Δ and the WT at 25 °C, and lower com-pared to ssa1Δ and the WT at 39 °C. These results impli-cated Ssa1 and Ssa2 not only as mediators of the strongrepression of HSE-dependent gene expression, but alsosuggest the existence of an additional repressor of Hsf1/Skn7, active in the absence of Tpk2.The dynamical model of the PKA-RN revealed anadditional negative regulator of Hsf1To thoroughly understand the implications of our obser-vations we constructed a discrete dynamical model ofthe PKA-RN based both on our results and in the litera-ture [4, 10, 19, 20, 23, 26, 60, 61, 66, 68, 69, 76, 78, 84,89, 94]. As described in the Methods section, we haveused an extension of a synchronous discrete modelingframework, as this type of modeling is known to accur-ately predict the behavior of several biological networks.One of the advantages of the discrete framework is thatit only requires knowledge about the regulatory natureof the interactions involved, contrary to reaction-kineticdifferential equations that require the precise valuesfor all the kinetic parameters and cooperativity expo-nents of the network elements. For a detailed reviewof the advantages and disadvantages of discrete andBoolean models compared to other frameworks consult[1, 38, 71, 90].Briefly, our model consists on N elements {σ1, σ2,…,σN} whose dynamical states take integer values rangingfrom 0 to mi, where mi is the maximum level of activity(or level of expression) for element σi. Usually only twolevels of activity are implemented: either the node is ac-tive (σi = 1) or it is inactive (σi = 0). However, often thefunctionality of a given node depends on whether it hasa low, mild or high level of activity [9] and the binarydescription is not enough. This is the case here, as ourexperiments indicate that some nodes of the TPK-RNrequire distinction of up to six levels of activity (seeAdditional file 3: Sections 3 and 4 in the SupplementaryInformation). Additionally, as currently there is no infor-mation about the time scales implicated in the dynamicsof the PKA-RN elements, for graphing we used a syn-chronous updating scheme (see Methods).For each network (we will consider WT, tpk1Δ, etc., asdifferent networks) we sampled about 10 % of thecomplete set of initial conditions (which consists ofmore than 4 billion points) looking for steady states ofexpression (attractors) (see Additional file 4: Text S1).As several initial conditions may fall into the same at-tractor, we define the size of the basin of attraction Bk asthe number of initial conditions that fall into attractor K.Our extension of this traditional modeling frameworkconsists in two simple modifications. First we averagedthe level of expression for each element over a time win-dow whose length equaled the attractor period. Thisgave us a single continuous value Aik for each element σiin the kth attractor. Then, to better represent the experi-mental measurements from liquid batch cultures wherea single average expression level is obtained, we averagedthe quantities Aik over all the attractors of the network,weighted by the size of the corresponding basin of at-traction (see Methods). Thus, contrary to other studies[9, 46, 52], we avoided discarding any attractor reachedby the network deeming it as “non-biologically relevant”.From now on, we will refer to this extension as theWindowed Discrete Model (WDM). This statisticaltreatment of data is supported by experimental studiesshowing that individual yeast cells in batch cultures ex-hibit different cell cycle phases, physiological states, andgene expression patterns that result in a heterogeneouspopulation [23, 40, 53]. With this procedure, we wereable to make a direct and semi-quantitative comparisonbetween the model predictions and the experimentalFig. 6 A role for SSA1 and SSA2 in the repression of HSE-dependentgene expression. Strains transformed with reporter plasmid pRY016were grown in SD medium at 25 °C until mid-exponential phaseand treated at different temperatures as described in Methodssection. Data shown represent the average and standard deviationof at least three independent experiments. β-galactosidase specificactivities are reported as in Fig. 2. Bars that do not share at least acommon letter differ significantly (P < 0.05). Strains assayed were:WT (W303-1a), tpk2Δ (KG604), ssa1Δ (S001), ssa1Δ tpk2Δ (S002),ssa2Δ (SL622), ssa2Δ tpk2Δ (SL623), ssa1Δ ssa2Δ (SL625), ssa1Δ ssa2Δtpk2Δ (SL708)Pérez-Landero et al. BMC Systems Biology  (2015) 9:42 Page 9 of 17measurements. The WT interaction network consideredis shown in Fig. 1 and the logic rules governing the dy-namics of the system are presented on the Supplemen-tary Information (see Additional file 3: Section 3).The modeled PKA-RN starts with the Cdc25-Rasbranch. Cdc25 abundance and function are dependanton the activity of the Hsp70 chaperones (Ssa1 and Ssa2)[26]. Under optimal temperature and nutrients condi-tions, Cdc25 acts as the positive regulator of Ras2 activ-ity [6, 22], which in turn activates Cyr1 (adenylatecyclase) [43]. The product of Cyr1, cAMP, negativelyregulates the inhibition imposed by Bcy1 upon the CSTpk1, Tpk2, and Tpk3 [84]. The CS were modeled as amodule showing antagonism, as our results (Figs. 1 and5) and those from others have suggested [61, 68, 69].We propose that Tpk2 activity inhibits the activation ofSsa1 and Ssa2 by the Tpk1 and Tpk3 subunits. The im-plication for this interaction is that, in a WT backgroundwhere the three CS are active, only the activity of Tpk2is effective in activating Ssa1 and Ssa2 chaperones. Themechanistic basis for this antagonism remains to be stud-ied. A systematic study of yeast kinases, made in vitro,showed that some CS have as substrates other CS. In par-ticular, Tpk1 phosphorylates Tpk2 and Tpk3; Tpk3 phos-phorylates Tpk2; and Tpk2 phosphorylates Tpk3 [65]. Itremains to be seen whether the antagonism between theCS is caused by their mutual phosphorylation or whether itoccurs via other indirect mechanisms.As mentioned above (Fig. 6), the inhibition of the HSE-dependent expression by the PKA-RN requires the activa-tion, by the TPKs, of an inhibitor of Hsf1 and Skn7. Ssa1and Ssa2 (Hsp70 proteins) were introduced into the modelas repressors of the HSE-dependent expression [4, 78](Fig. 6). Moreover, based on the expression levels of thetriple mutant ssa1Δ ssa2Δ tpk2Δ (Fig. 6), we included athird repressor of Hsf1/Skn7 that gets activated exclusivelywhen Tpk1 and Tpk3 become the only CS (i.e., when Tpk2is absent or at minimum levels). We believe that a veryplausible candidate for such a repressor could be Hsp90,given that Hsp90 binds to Hsf1 [59, 96] and its deletion in-creases HSE-dependent expression [16]. Moreover, Tpk1and Tpk3 phosphorylate Hsp82 (Hsp90) in vitro [65]; al-though the functional significance of this phosphorylationis unknown. It is plausible that the binding of Hsp90 toHsf1 could be enhanced upon phosphorylation by Tpk1 orTpk3, but this needs to be addressed experimentally. Simi-larly, Tpk1 and Tpk3 could enhance the repression of Hsf1by other members of the Hsp70 family, such as Ssb1 orSsb2, as it is known that Ssb1 and Ssb2 form complexeswith Hsf1 and deletion of their genes also increases HSE-dependent expression [4]. However, more work is neededto identify the third repressor that is unleashed in the ab-sence of Tpk2. In any case, it is important to stress thatonly by including the three repressors (Ssa1, Ssa2, and theputative third repressor), the experimental measurementscould be reproduced by the model.Quantitative comparison between theoretical andexperimental results corroborates the proposedregulatory interactionsTo validate the simulations of our model, we comparedthe HSE-dependent expression results obtained compu-tationally and those obtained experimentally in a num-ber of mutant strains. Population measurements werereported as the ratio (strain expression level)/(WT ex-pression level) and are presented in Additional file 1:Table S4. Figure 7 shows that the results obtained withthe WDM closely resembled the experimental resultsobtained for all strains. The great concordance betweentheory and experiment suggests that the novel interac-tions proposed here for the PKA-RN are very likely true.Additionally, we also implemented several asynchronousupdating schemes and the results that we obtained forthe population expression level were almost identical re-gardless of the synchronicity or asynchronicity of the up-dating scheme (Additional file 2: Figure S3). This featureis quite relevant because, for a particular network (sin-gle-cell level) the use of asynchronous updating cansignificantly change the dynamical attractors of the net-work [15, 36] to the point that random asynchronousupdating has been called inadequate in some scenarios[15]. We present the structure of the attractor landscapefor the 25 °C WT network using synchronous updating(Additional file 2: Figures S5 and S6). As this exampleshows, different basins of attraction varying in size canbe visualized. The WDM takes this fact into account tosimulate subgroups of cells that might correspond to thedifferent basins of attraction.In addition to the population measurements, we presentsimulations for the temporal dynamics of Bcy1, cAMP,HSE-lacZ, and Tpk3 that, presumably, could be valid forsingle-cell measurements (Fig. 8). Each curve represents asimulation corresponding to a different strain (WT, ssa1Δssa2Δ, tpk2Δ, and tpk1Δ tpk3Δ) starting from a random ini-tial condition. At time t0, an increase in the temperaturewas simulated by turning on the heat shock node. In theabsence of Ssa1 and Ssa2 (Fig. 8, red lines), the levels ofHSE-lacZ activity and Bcy1 increased dramatically, whilethe levels of cAMP and Tpk3 were very low. In the absenceof Ssa1 and Ssa2, the dynamics of Tpk1 and Tpk2 wereidentical to Tpk3 (data not shown). The particular temporaldynamics observed in these simulations (oscillatory behav-ior, spikes, etc.) remain to be experimentally confirmedthrough the use of single-cell measurements. Nonethelessthe predictions reported in Fig. 8 fit well the experimentaldata showing that ssa1Δ ssa2Δ mutants are constitutivelyresistant to high temperature and display elevated produc-tion of Hsp’s and slow growth rates [34]. Deletion of TPK2Pérez-Landero et al. BMC Systems Biology  (2015) 9:42 Page 10 of 17also decreased the expression of HSE-lacZ with respect tothe WT, but more conspicuously at 39 °C than at 25 °C,consistent with the lower induced thermotolerance level inthis mutant (Additional file 1: Table S3).ConclusionsOur results clarified the control of Hsf1 and Skn7 by thePKA-RN, demonstrating that in the W303 strain, PKArepresses Hsf1 and also Skn7 via Ssa1, Ssa2 and a thirdunidentified repressor. No single component of thePKA-RN could be used to predict accurately theexperimental levels of downstream targets (i.e., HSE-dependent gene expression) in all the situations studied.Instead, modeling of the PKA-RN showed that the ob-served experimental dynamics arose from the complexinteractions of the network making it necessary toanalyze the system as a whole. It remains to be unveiledthe exact molecular mechanisms by which the PKA CSinhibit Hsf1 and Skn7 activities. Our results indicate thatsuch mechanism must exist and it depends on Ssa1,Ssa2, and at least a third unidentified repressor. DuringPKA-RN controlled processes, such as pseudohyphalgrowth and iron uptake, the PKA CS display similar an-tagonistic relationships to those observed during thecontrol of HSE-dependent gene expression. Tpk2, butnot Tpk1 or Tpk3, is required for the induction of pseu-dohyphal growth and for the inhibition of genes involvedin iron uptake [61, 68, 69]. Additionally, the fact thatvarious updating and averaging schemes produced es-sentially the same results is quite interesting (Additionalfile 2: Figure S3), as this means that the WDM reallycaptured the population average in batch cultures re-gardless of the specific updating scheme. To our know-ledge, this is the first model with this property. Thus,with simple modifications, it can pave the way for theanalysis of many other cellular responses at the popula-tion level apart from the PKA-RN.MethodsMedia and growth conditionsYeast cells were grown at 25 °C in media prepared aspreviously described in [23], unless otherwise indicated.Duplication times of the strains were also calculated asdescribed in [23].Strains and plasmidsAll strains employed are described in Additional file 1:Table S5. Strains with identical auxotrophies and othergenetic markers were used in all experiments to avoidphenotypic differences due to marker effects. Integrativegene-disruption cassette kanMX6 [31, 54] was used togenerate strains with full disruptions in CDC25, RAS2,BCY1, SKN7, TPK1, TPK2, TPK3, SSA1, SSA2, or in theC-terminal transcriptional activation domain of Hsf1(hsf1-ΔCTA) in strains W303-1a, W303-6B, JF3100 orJF3000. Transformants were selected on YPDA mediumplus 300 μg ml−1 of geneticin. The correct insertion ofthe cassette on each mutant was verified by PCR.Reporter plasmid pRY016 (HSE-CYC1-lacZ) was gen-erated by annealing oligonucleotides, HSEA and HSEB(Additional file 1: Table S6). Self-ligation products wereseparated by electrophoresis in agarose gels and theband corresponding to the dimer was eluted from thegel. The protruding ends were filled-in using the Klenowenzyme and ligated to BglII adapters for cloning into theFig. 7 Comparison between experimental data and predictions bythe WDM. Comparison between experimental and theoreticalmeasurements for HSE-lacZ activity. Values are given as averageratios between strain expression and WT expression at 25 °C, makingthe average expression ratio of the WT strain at 25 °C equal to one.Theoretical and experimental values show similar quantitativebehavior across strains. Moreover, since the theoretical values are nolonger discrete, subtle differences occurring experimentally arereproduced also by the model. a Ratios at 25 °C, b ratios after a heatshock at 39 °CPérez-Landero et al. BMC Systems Biology  (2015) 9:42 Page 11 of 17BglII site of plasmid pLGΔBS. Plasmid pLGΔBS is a highcopy number 2 μ vector with a CYC1-lacZ fusion, lack-ing UAS and derived from the pLG669Z [35]. Theresulting plasmid, pRY016, contained nine 5 bp units ofthe HSE consensus sequence nGAAn [21] arranged inboth, sense and anti-sense orientations. To assess thedependency of β-galactosidase activity on the HSEspresent in pRY016, the W303 WT strain and severalPKA-RN mutants, we transformed them with a pRY016-derivative plasmid expressing the same CYC1-lacZ genefusion but lacking HSEs. The β-galactosidase activity at25 °C or after a heat shock at 39 °C in all strains wasnegligible (10 to 30 units) indicating that enzyme expres-sion using pRY016 is indeed HSE-dependent. To investi-gate whether the levels of β-galactosidase activity in thestrains used in this work were influenced by differencesin the copy number of the reporter plasmid, pRY016copy number was measured in all strains. The correl-ation coefficient of pRY016 plasmid copy number and β-galactosidase activity (Pearson´s = 0.35341449) was notsignificant (P = 0.0765) (Additional file 2: Figure S4).Therefore, the activity of the pRY016 reporter seems tobe a reflection of actual changes in HSE-dependent geneexpression influenced by the mutations and not by plas-mid copy number.Stress tolerance assaysBasal thermotolerance was measured as described [23].To determine induced-thermotolerance, cultures wereexposed at 39 °C for 60 min prior to a 50 °C heat shockfor 20 min. For both basal and induced thermotolerance,aliquots of each culture were taken before and immedi-ately after the 50 °C treatment, and dilutions were platedon solid YPD to measure cell viability by colony count-ing. Thermotolerance levels are expressed as the per-centage of the number of colonies after a heat shockdivided by the number of colonies in the control sample.Biochemical analysisβ-galactosidase assays were performed from exponen-tially grown cultures (OD600 between 0.4-0.6) in SDmedia as described [70]. β-galactosidase specific activityis expressed as nmol of hydrolyzed ONPG min−1 mg−1protein. To measure the response to a heat shock, cellswere treated at 39 °C for 1 or 2 h as described [60, 77].Genetic techniques and nucleic acid manipulationsDNA manipulations and genetic techniques were per-formed according to Sambrook, Fritsch & Maniatis [72]and Guthrie & Fink [32], respectively. DNA sequencingwas performed at the Unit for DNA Synthesis and Se-quencing of the Instituto de Biotecnología. Yeast trans-formation was performed following the methodpresented in [27].RNA isolation and northern blot analysesTotal RNA was isolated from exponentially-growing cellsin SD medium at 25 °C by the method of Collart andFig. 8 Single-cell predictions of the temporal dynamics for Bcy1, cAMP, HSE-lacZ, and Tpk3 in WT and three mutants. Temporal dynamics for fourselected nodes: Bcy1 (a), cAMP (b), HSE-lacZ (c), and Tpk3 (d). Line colors correspond to different strains. Simulations were made starting from arandom initial condition for each strain. Expression and time are given in arbitrary units. Background color represents the temperature of theculture: 25 °C (blue) and 39 °C (pink)Pérez-Landero et al. BMC Systems Biology  (2015) 9:42 Page 12 of 17Oliviero [11]. Aliquots (10 μg) of total RNA were separatedby electrophoresis on 1 · 2 % (w/v) agarose gels containingformaldehyde, transferred to IMMOBILON-NY+ mem-branes (Millipore) and hybridized as described by themanufacturer. The 3 · 0 kbp BamHI fragment of clone pYS-Gal104 (courtesy of Dr. Susan Lindquist) was used as DNAprobe to detect HSP104 transcripts. Gene-specific DNAprobes for HSP82, SSA3, HSP26, HSP12, and ACT1 wereamplified by PCR. Primer pairs used during PCR were:FSHSP82 and RSHSP82 for HSP82; fc-ssa3 and rc-ssa3 forSSA3; HSP26-F and HSP26-R for HSP26; HSP12-F andHSP12-R for HSP12; ACT1-1 and ACT1-2 for ACT1(Additional file 1: Table S5). Estimation of band intensitiesof autoradiograms was performed by image analysis withNIH Image 1.62 software. Data was normalized to accountfor differences between samples in actual total-RNA loading.Estimation of plasmid copy number in yeast strainsStrains were grown under similar conditions to those ofβ-galactosidase assays. Southern blots of total genomicDNA were digested with PstI and hybridized to the340 bp PstI–ScaI fragment of plasmid pRS3 encodingthe N-terminus of Ura3. Copy number of pRY016 wasestimated as the ratio of plasmid/genome URA3 signal.Band intensities of autoradiograms were measured withNIH Image 1.62 software.Complementation of strain tpk2Δ tpk3Δ by reintroductionof TPK2 geneComplementation of strain KS590 (tpk2Δ::loxP tpk3Δ::loxP),was carried by a protocol based on the delitto perfetto tech-nique [30, 79]. First, the URA3 gene was amplified by PCRusing plasmid pRS306 [74] as template. OligonucleotidesFTPK2-URA3 and RTPK2URA3 contained 40 bp of se-quence flanking each side of the tpk2Δ::loxP chromosomaldeletion followed by URA3 flanking sequences. The PCRproduct obtained was transformed by homologous recom-bination into strain KS590 to get strain KS590-URA3(tpk2Δ::URA3 tpk3Δ::loxP). Finally, URA3 gene in strainKS590-URA3 was evicted by interchanging TPK2 using theproduct of a PCR reaction that amplified TPK2 with TPK2-Ucl and TPK2-Lcl oligonucleotides. The resulting strain,KS590-URA3-TPK2 (tpk2Δ::TPK2 tpk3Δ::loxP), was selectedby resistance to FOA at 1 mg/ml [7]. TPK2 gene was re-amplified by PCR from KS590-URA3-TPK2 to select candi-dates with the correct DNA sequence.Measurement of cAMP-dependent PKA activityCells were cultured in 50 ml of SD medium at an OD600 of0.4. After centrifugation, the pellet was washed in coldmiliQ water and centrifuged once more. The washed pelletwas frozen in liquid N2. Cells were broken with a mortarand pestle under liquid N2 and resuspended in extractionbuffer (50 mM Tris pH 7.4, 20 mM β-mercaptoetanol,0.5 mM PMSF, and 4 mg/ml COMPLETE™, a mixture ofprotease inhibitors [Roche, cat. no. 11697498001]). Thetotal protein extract was centrifuged twice and the finalsupernatant was saved. Total protein was estimated by theBradford method [5]. Finally, aliquots containing 4 mg ofprotein were assayed for PKA activity according to thePep Tag® protocol (PROMEGA, cat. no. V5340). Activitywas assayed in the presence or absence of 1 μM cAMP.Only extracts from a bcy1Δ mutant, used as a control,showed PKA activity in the absence of exogenous cAMP.WTand TPK mutants showed total dependency on cAMPfor PKA activity.Statistical analysisAll experiments were conducted at least three times.Comparisons between given pairs were analyzed usingthe two-tailed T Student test. Pairs of data were consid-ered significantly different only when P < 0.05. For mul-tiple comparisons, data were subjected to analysis ofvariance (ANOVA), and differences between the meanswere compared by Tukey (one-way) or Bonferroni (two-way) post-tests. Treatments were considered as statisti-cally different to the control when P ≤ 0.05. Prism 5.0software package was used.The Windowed Discrete ModelThe dynamic model consists of a network of 15 nodesrepresenting the regulatory interactions of the PKA-RNshown in Fig. 1. Each node acquires a set of discrete valuesthat represent the level of expression of the correspondingnetwork element. Like many other discrete models available[2, 9, 52], ours focuses on the functional state of expression(or activation) of the network components, rather than ontheir exact concentrations. These functional states of ex-pression are modeled trough discrete variables that take afinite number of values. To capture the various levels of ex-pression observed experimentally for the HSE-CYC1-lacZreporter, the number of functional states for each node wasdetermined by the maximum number of statistically-significant different groups of ß-galactosidase activity dis-played experimentally by the whole panel of WT and PKA-RN mutant strains during exponential phase (Figs. 2, 4, 5,and 6). Our final model consisted on two binary nodes, oneternary, four four-valued, one five-valued and seven six-valued elements. This gives a total of Ω = 4,299,816,960possible dynamical states for the network.As in the standard Kauffman model [44], the networkdynamics is given by the simultaneous updating of allthe network elements according to the equation 1:σn t þ 1ð Þ ¼ Fn σ1n tð Þ; σ2n tð Þ;⋯; σknn tð Þ where σn(t) represents the state of the nth element ofthe network at time t, σ1n; σ2n;⋯; σknn are the knPérez-Landero et al. BMC Systems Biology  (2015) 9:42 Page 13 of 17regulators of σn and Fn(⋅) is a discrete function (alsoknown as a logical rule) that determines the state ofσn in terms of the states of its regulators. This func-tion Fn(⋅) is constructed according to experimentalevidence regarding the regulatory interactions (activa-tor or inhibitor) for each node. All the functions Fn(⋅)for the PKA-RN are listed in the Supplementary In-formation (see Additional file 3: Section 3).Since each variable acquires a finite number of states,there are also a finite number of possible dynamical config-urations for the entire network, ranging from the confi-guration in which all the nodes are inactive, to theconfiguration in which all the nodes have reached theirmaximum values of activation. Once the dynamics fromany of these possible configurations starts, successive itera-tions of Eq. (1) will make the network traverse through aseries of states until a periodic pattern of activity is reached.This periodic activity is known as an attractor, and for eachnetwork several attractors might exist. Which attractor thenetwork falls into depends on the initial condition the net-work starts from. The set of all the initial conditions thateventually fall into the same attractor is known as the basinof attraction. It has been previously shown that attractorsrepresent the stable patterns of activity of the real biologicalsystem, and the basins of attraction correspond to the dif-ferent ways to reach these stable states [41]. Nonetheless, adirect comparison of an attractor to HSE-dependent ex-pression levels might not be so straightforward, as attrac-tors may be often composed by several states (cyclicattractors) and experimental gene expression is oftenpresented as a single value (e.g. β-galactosidase activity).Moreover, experimental measures of gene expression arecommonly taken from a population of cells, which makesthe final measurement an average. For this reason we havedeveloped the WDM, where the state of each element ofthe network is represented by its average expression over atime window. In our model, the length of the window (L)for each realization corresponds to the length of the at-tractor reached. Although other sizes can be used withsimilar results, sizes bigger than the length of the attractorare not convenient as they tend to flatten the dynamics.Additionally, since a network can have more than oneattractor, we have calculated a weighted average usingthe entire set of attractors (N) for each network. Thus,we define the average expression level of σn as:σn ¼XNa¼1ωaXLaτ¼1σn t0 þ τð ÞLa0@1Aawhere N is the number of different attractors and the ex-ternal sum is carried out over all the attractors. The par-ameter ωa is the fractional size of the basin of attractionof the ath attractor (∑a = 1Νωa = 1). The internal sum iscarried out over the La states of the ath attractor, and t0is a transient time long enough as to guarantee that thesystem has reached the attractor.This simple modification, apart from allowing an eas-ier comparison between the model and experimentaldata, resembles the way in which experimental data isgathered for gene expression in batch cultures, wheretraditionally measurements of the level of expressionrepresent the population average, as cells in the popula-tion are at different stages of a stable pattern of gene ex-pression (unless synchronization is enforced).To simulate deletions in our numerical experiments,we just kept the value of the deleted node equal to zerothroughout the dynamics, which represents the completeabsence of that node.Elevated temperatures increase the number of targets ofthe Hsp70s, reducing their positive interaction over Cdc25[26] and the inhibition of Hsf1 [4, 89]. Therefore, heatshock (HS) was introduced into the model as a node ofthe PKA-RN that affects the functional state of Ssa1 andSsa2. Its logical function corresponds to a positive autoregulation (see Additional file 3: Supplementary Informa-tion, Section 3). This means that whenever this node is ac-tive (which corresponds to the 39 °C condition), itremained active all the time. By contrast, the 25 °C condi-tion is represented by inactivating the HS node and keep-ing it inactivated throughout the simulation time.Availability of supporting dataAll supporting data are included as additional files.Additional filesAdditional file 1: Table S1. Growth and thermotolerance of WT yeaststrains and mutants deleted in CDC25, RAS2 or BCY1. Table S2.Contribution of Hsf1 and Skn7 to the elevated thermotolerance and slowgrowth rate of the cdc25Δ mutant. Table S3. Growth and thermotoleranceof WT or mutant yeast strains deleted in TPK1, TPK2 or TPK3. Table S4.Relative β-galactosidase activity values for all strains used in this work.Table S5. Strains used in this study.Additional file 2: Figure S1. Correlation analysis between pRY016plasmid copy number and HSE-dependent gene expression. Figure S2.Levels of HSP transcripts in WT and tpk2Δ tpk3Δ mutant. Figure S3.Comparison of different updating schemes. Figure S4. cAMP-dependentPKA activity in extracts of WT and TPK deletion mutants. Figure S5.Reduced network used to compute the structure of the attractor landscape.Figure S6. Structure of the attractor landscape displayed by the WTreduced network shown in Figure S5C.Additional file 3: Section 1. Synchronous and Asynchronous UpdatingSchemes. Section 2. Basins of attraction. Section 3. Assumptions andlogical functions for the Windowed Discrete Model. Section 4. LogicTables and discrete regulatory functions in the WT at 25 °C.Additional file 4: Text S1. The attractors for several simulated strainsare presented here.AbbreviationsPKA: cAMP-dependent protein kinase; PKA-RN: cAMP-dependent proteinkinase regulatory network; CS: Catalytic subunits of PKA.Pérez-Landero et al. BMC Systems Biology  (2015) 9:42 Page 14 of 17Competing interestsThe authors declare that they have no competing interests.Authors' contributionsSP-L, CM-A, RY, JLF-M, LMM, LV, and KG carried all the experimental work.SS and MA developed the computational model after discussions of theexperimental work with SP-L and JN-S. JN-S conceived, designed, andcoordinated the project. SP-L, SS, MA, and JN-S wrote the manuscript.All authors read and approved the final manuscript.Authors’ informationSergio Pérez-Landero, Runying Yang, Jorge Luis Folch-Mallol, Larissa Ventura,Karina Guillén-Navarro and Jorge Nieto-Sotelo: This work initiated whenthese authors formerly worked at Instituto de Biotecnología, UniversidadNacional Autónoma de México, 62210 Cuernavaca, Morelos, Mexico.AcknowledgementsThis work was supported by grants from Dirección General de Apoyo al PersonalAcadémico de la UNAM (IN209599 and IN226506-3) and CONACYT-México(3461-N9310, 25303-N, 129471 and C01-39935) to J.N.-S. We thank A. Brands,F. R. Cross, F. Estruch, R. Gaxiola, T.-H. D. Ho, H. Nierlich, D.J. Stillman, andJ. Thevelein for kindly providing plasmids or yeast strains. P. Gaytán and E. Lópezassisted on oligonucleotide synthesis. J. Yañez helped on DNA sequencing andA. Martínez, J. M. Hurtado, and A. Ocadiz on computer support. J. L. Gama,M. de Jesús Sánchez, M. Ortíz and B. L. Arroyo-Flores provided technical help.C. Martínez-Anaya, R. Yang, and K. Guillén-Navarro thank Consejo Técnico de laInvestigación Científica de la UNAM for postdoctoral fellowships. S. Pérez-Landero and L. Ventura received graduate student fellowships fromConsejo Nacional de Ciencia y Tecnología – México (CONACYT-México) andwere students of the Programa de Posgrado en Ciencias Bioquímicas de laUNAM. M. Aldana thanks Fundación Marcos Moshinsky for a fellowship. SSMacknowledges the PDCB-UNAM, CCG-UNAM and CONACyT-Mexico forsupport though a PhD fellowship (269040/220732). We thank Gladys Cassab,Alicia González, and Brandon Gout for critically reading this manuscript.Author details1Instituto de Biología, Universidad Nacional Autónoma de México, 04510México, D.F., Mexico. 2Instituto de Ciencias Físicas, Universidad NacionalAutónoma de México, 62210 Cuernavaca, Morelos, Mexico. 3Instituto deBiotecnología, Universidad Nacional Autónoma de México, 62210Cuernavaca, Morelos, Mexico. 4Present Address: Department ofAnesthesiology, Pharmacology & Therapeutics, The University of BritishColumbia, Vancouver V6T 1Z4, BC, Canada. 5Present Address: Centro deInvestigación en Biotecnología, Universidad Autónoma del Estado deMorelos, 62209 Cuernavaca, Mor., Mexico. 6Present Address: Grupo La FloridaMéxico, Tlalnepantla 54170, Edo. de Méx., Mexico. 7Present Address: ElColegio de la Frontera Sur, 30700 Tapachula, Chis., Mexico.Received: 30 October 2014 Accepted: 30 June 2015References1. Albert R. Boolean modeling of genetic regulatory networks. ComplexNetworks Lecture Notes in Physiscs. 2004;650:459–81.2. Albert R, Othmer HG. The topology of the regulatory interactions predictsthe expression pattern of the Drosophila segment polarity genes. J TheorBiol. 2003;223:1–18.3. Amoros M, Estruch F. Hsf1p and Msn2/4p cooperate in the expression ofSaccharomyces cerevisiae genes HSP26 and HSP104 in a gene- and stresstype-dependent manner. Mol Microbiol. 2001;39:1523–32.4. Bonner JJ, Carlson T, Fackenthal DL, Paddock D, Storey K, Lea K. Complexregulation of the yeast heat shock transcription factor. Mol Biol Cell.2000;11:1739–51.5. Bradford M. A rapid and sensitive method for the quantitation ofmicrogram quantities of protein utilizing the principle of protein-dyebinding. Anal Biochem. 1976;72:248–54.6. Broek D, Toda T, Michaeli T, Levin L, Birchmeier C, Zoller M, et al. TheS. cerevisiae CDC25 gene product regulates the RAS/adenylate cyclasepathway. Cell. 1987;48:789–99.7. Burke D, Dawson D, Stearns T. Methods in Yeast Genetics: a Cold SpringHarbor Laboratory Manual. Plainview, NY: Cold Spring Harbor LaboratoryPress; 2000.8. Cazzaniga P, Pescini D, Besozi D, Mauri G, Colombo S, Martegani E.Modeling and stochastic simulation of the Ras/cAMP/PKA pathway in theyeast Saccharomyces cerevisiae evidences a key regulatory function forintracellular guanine nucleotides pools. J Biotechnol. 2008;133:377–85.9. Chaos A, Aldana M, Espinosa-Soto C, García Ponce de León B, Garay ArroyoA, Alvarez-Buylla ER. From Genes to Flower Pattern and Evolution: DynamicModels of Gene Regulatory Networks. J Plant Growth Regul. 2006;25:278–89.10. Charizanis C, Juhnke H, Krems B, Entian KD. The oxidative stress responsemediated via Pos9/Skn7 is negatively regulated by the Ras/PKA pathway inSaccharomyces cerevisiae. Mol Gen Genet. 1999;261:740–52.11. Collart MA, Oliviero S: Preparation of yeast RNA. In Current Protocols inMolecular Biology. Edited by Ausubel FM, Brent R, Kingston RE, Moore DD,Seidman JG, Smith JA, Struhl K. New York, NY; Wiley; 1993:pp. 13.12. Vol. 2.12. Craig EA, Gross CA. Is hsp70 the cellular thermometer? Trends Biochem Sci.1991;16:135–40.13. Craig EA, Jacobsen K. Mutations of the heat inducible 70 kilodalton genesof yeast confer temperature sensitive growth. Cell. 1984;38:841–9.14. Dhar R, Nieto A, Koller R, Defeo-Jones D, Scolnick EM. Nucleotide sequenceof two rasH related-genes isolated from the yeast Saccharomyces cerevisiae.Nucleic Acids Res. 1984;12:3611–8.15. Di Paolo EA. Rhythmic and non-rhythmic attractors in asynchronous randomBoolean networks. BioSystems. 2001;59:185–95.16. Duina AA, Kalton HM, Gaber RF. Requirement for Hsp90 and a CyP-40-typecyclophilin in negative regulation of the heat shock response. J Biol Chem.1998;273:18974–8.17. Engerlberg D, Zandi E, Parker CS, Karin M. The yeast and mammalian Raspathways control transcription of heat shock genes independently of heatshock transcription factor. Mol Cell Biol. 1994;14:4929–37.18. Erkina TY, Tschetter PA, Erkine AM. Different requirements of the SWI/SNFcomplex for robust nucleosome displacement at promoters of heat shockfactor and Msn2- and Msn4-regulated heat shock genes. Mol Cell Biol.2008;28:1207–12217.19. Estruch F. Stress-controlled transcription factors, stress-induced genes andstress tolerance in budding yeast. FEMS Microbiol Rev. 2000;24:469–86.20. Ferguson SB, Anderson ES, Harshaw RB, Thate T, Craig NL, Nelson HCM. Proteinkinase A regulates constitutive expression of small heat-shock genes in anMsn2/4p-independent and Hsf1p-dependent manner in Saccharomycescerevisiae. Genetics. 2005;169:1203–14.21. Fernandes M, Xiao H, Lis JT. Fine structure analyses of the Drosophila andSaccharomyces heat shock factor-heat shock element interactions. NucleicAcids Res. 1994;22:167–73.22. Field J, Broek D, Kataoka T, Wigler M. Guanine nucleotide activation of, andcompetition between, RAS proteins from Saccharomyces cerevisiae. Mol CellBiol. 1987;7:2128–33.23. Folch-Mallol JL, Martínez LM, Casas SJ, Yang R, Martínez-Anaya C, López L,et al. New roles for CDC25 in growth control, galactose regulation andcellular differentiation in Saccharomyces cerevisiae. Microbiology.2004;150:2865–79.24. Garmendia-Torres C, Goldbeter A, Jacquet M. Nucleocytoplasmic oscillationsof the transcription factor Msn2: evidence for periodic PKA activation. CurrBiol. 2007;17:1044–9.25. Garrett S, Broach J. Loss of Ras activity in Saccharomyces cerevisiae issuppressed by disruptions of a new kinase gene, YAKI, whose product mayact downstream of the cAMP-dependent protein kinase. Genes Dev.1989;3:1336–48.26. Geymonat M, Wang L, Garreau H, Jacquet M. Ssa1p chaperone interactswith the guanine nucleotide exchange factor of ras Cdc25p and controlsthe cAMP pathway in Saccharomyces cerevisiae. Mol Microbiol.1998;30:855–64.27. Gietz RD, Woods RA. Transformation of yeast by lithium acetate/single-stranded carrier DNA/polyethylene glycol method. MethodsEnzymol. 2002;350:87–96.28. Gonzalez K, Kayikçi Ö, Schaeffer DG, Magwene PM. Modeling mutantphenotypes and oscillatory dynamics in the Saccharomyces cerevisiae cAMP-PKA pathway. BMC Systems Biology. 2013;7:40. doi:10.1186/1752-0509-7-40.29. Gonze D, Jacquet M, Goldbeter A. Stochastic modelling ofnucleocytoplasmic oscillations of the transcription factor Msn2 in yeast.J R Soc Interface. 2008;5:S95–S109.Pérez-Landero et al. BMC Systems Biology  (2015) 9:42 Page 15 of 1730. Gray M, Piccirillo S, Honigberg SM. Two-step method for constructingunmarked insertions, deletions and allele substitutions in the yeastgenome. FEMS Microbiol Lett. 2005;248:31–6.31. Guldener U, Heck S, Fielder T, Beinhauer J, Hegemann JH. A new efficientgene disruption cassette for repeated use in budding yeast. Nucleic AcidsRes. 1996;24:2519–24.32. Guthrie C, Fink GR. Guide to Yeast Genetics and Molecular Biology. NewYork: Academic Press; 1991.33. Hahn JS, Hu Z, Thiele DJ, Iyer VR. Genome-wide analysis of the biology of stressresponses through heat shock transcription factor. Mol Cell Biol. 2004;24:5249–56.34. Halladay JT, Craig EA. A heat shock transcription factor with reduced activitysupresses a yeast HSP70 mutant. Mol Cell Biol. 1995;15:4890–7.35. Harshman KD, Moye-Rowley WS, Parker CS. Transcriptional activation by theSV40 AP-1 recognition element in yeast is mediated by a factor similar toAP-1 that is distinct from GCN4. Cell. 1988;53:321–30.36. Harvey I, Bossomaier T: Time out of joint: attractors in asynchronousrandom Boolean networks. In Proceedings of the Fourth European Conferenceon Artificial Life. Edited by Husbands P, Harvey I. Cambridge, MA; MIT Press;1997: pp. 67–75.37. Hasan R, Leroy C, Isnard AD, Labarre J, Boy-Marcotte E, Toledano MB. Thecontrol of the yeast H2O2 response by the Msn2/4 transcription factors. MolMicrobiol. 2002;45:233–41.38. Helikar T, Kochi N, Konvalina J, Rogers JA. Boolean modeling of biochemicalnetworks. The Open Bioinformatics Journal. 2011;5:16–25.39. Høj A, Jakobsen BK. A short element required for turning off heat shocktranscription factor: evidence that phosphorylation enhances deactivation.EMBO J. 1994;13:2617–24.40. Holland SL, Reader T, Dyer PS, Avery SV. Phenotypic heterogeneity is aselected trait in natural yeast populations subject to environmental stress.Environ Microbiol. 2014;16:1729–40.41. Huang S, Eichler G, Bar-Yam Y, Ingber DE. Cell fates as high-dimensionalattractor states of a complex gene regulatory network. Phys Rev Lett.2005;94:128701–4.42. Iida H. Multistress resistance of Saccharomyces cerevisiae is generated byinsertion of retrotransposon Ty into the 5' coding region of the adenylatecyclase gene. Mol Cell Biol. 1988;8:5555–60.43. Kataoka T, Broek D, Wigler M. DNA sequence and characterization of theS. cerevisiae gene encoding adenylate cyclase. Cell. 1985;43:493–505.44. Kauffman S. Metabolic stability and epigenesis in randomly constructedgenetic nets. J Theoret Biol. 1969;22:437–67.45. Kim B-H, Schöffl F. Interaction between Arabidopsis heat shock transcriptionfactor 1 and 70 kDa heat shock proteins. J Experiment Bot. 2002;53:371–5.46. Kim J, Vandamme D, Kim J-R, Garcia Munoz A, Cho K-H. Robustness andEvolvability of the Human Signaling Network. PLoS Comput Biol. 2014;10(7),e1003763. doi:10.1371/journal.pcbi.1003763.47. Kirk N, Piper PW. The determinants of heat-shock element-directed lacZexpression in Saccharomyces cerevisiae. Yeast. 1991;7:539–46.48. Klipp E, Liebermeister W, Wierling C, Kowald A, Lehrach H, Herwig R.Systems Biology: A Textbook. Wiley-VCH; 2009.49. Krems B, Charizanis C, Entian KD. The response regulator-like protein Pos9/Skn7 of Saccharomyces cerevisiae is involved in oxidative stress resistance.Curr Genet. 1996;29:327–34.50. Lee P, Cho B-R, Joo H-S, Hahn J-S. Yeast Yak1 kinase, a bridge between PKAand stress-responsive transcription factors, Hsf1 and Msn2/Msn4. MolMicrobiol. 2008;70:882–95.51. Lee P, Kim MS, Paik S-M, Choi S-H, Cho B-R, Hahn J-S. Rim15-dependentactivation of Hsf1 and Msn2/4 transcription factors by direct phosphorylationin Saccharomyces cerevisiae. FEBS Letters. 2013;587:3648–55.52. Li F, Long T, Lu Y, Ouyang Q, Tang C. The yeast cell-cycle network is robustlydesigned. Proc Natl Acad Sci USA. 2004;101:4781–6.53. Lidstrom ME, Konopka MC. The role of physiological heterogeneity inmicrobial population behavior. Nat Chem Biol. 2010;6:705–12.54. Longtine MS, Mckenzie III A, Demarini DJ, Shah NG, Wach A, Brachat A, et al.Additional modules for versatile and economical PCR-based gene deletionand modification in Saccharomyces cerevisiae. Yeast. 1998;14:953–61.55. Ma P, Wera S, Van Dijck P, Thevelein JM. The PDE1-encoded low-affinityphosphodiesterase in the yeast Saccharomyces cerevisiae has a specific functionin controlling agonist-induced cAMP signaling. Mol Biol Cell. 1999;10:91–104.56. Marchler G, Schuller C, Adam G, Ruis H. A Saccharomyces cerevisiae UASelement controlled by protein kinase A activates transcription in responseto a variety of stress conditions. EMBO J. 1993;12:1997–2003.57. Milenkovic L, Scott MP. Not lost in space: trafficking in theHedgehog signalling pathway. Sci Signal 2010, 3 p. pe14doi:10.1126/scisignal.3117pe14.58. Mollapour M, Tsutsumi S, Donnelly AC, Beebe K, Tokita MJ, Lee MJ, et al.Swe1Wee1-dependent tyrosine phosphorylation of Hsp90 regulates distinctfacets of chaperone function. Mol Cell. 2010;37:333–43.59. Nadeau K, Das A, Walsh CT. Hsp90 chaperonins possess ATPase activity andbind heat shock transcription factors and peptidyl prolyl isomerases. J BiolChem. 1993;268:1479–87.60. Nieto-Sotelo J, Wiederrecht G, Okuda A, Parker CS. The yeast heat shocktranscription factor contains a transcriptional activation domain whoseactivity is repressed under nonshock conditions. Cell. 1990;62:807–17.61. Pan X, Heitman J. Protein kinase A operates a molecular switch thatgoverns yeast pseudohyphal differentiation. Mol Cell Biol. 2002;22:3981–93.62. Park JI, Grant CM, Dawes IW. The high-affinity cAMP phosphodiesteraseof Saccharomyces cerevisiae is the major determinant of cAMP levels instationary phase: involvement of different branches of the Ras-cyclicAMP pathway in stress responses. Biochem Biophys Res Commun.2005;327:311–9.63. Pescini D, Cazzaniga P, Besozzi D, Mauri G, Amigoni L, Colombo S, et al.Simulations of the Ras/cAMP/PKA pathway in budding yeast highlights theestablishment of stable oscillatory states. Biotecnol Adv. 2012;30:99–107.64. Pringle JR, Hartwell LH: The Saccharomyces cerevisiae cell cycle. In Themolecular biology of the yeast Saccharomyces. Edited by Strathern J, Jones E,Broach J. Cold Spring Harbor, NY: Cold Spring Harbor Laboratory Press;1982:97–142. vol. Life cycle and inheritance.65. Ptacek J, Devgan G, Michaud G, Zhu H, Zhu X, Fasolo J, et al. Global analysisof protein phosphorylation in yeast. Nature. 2005;438:679–84.66. Raitt DC, Johnson AL, Erkine AM, Makino K, Morgan B, Gross DS, et al. TheSkn7 response regulator of Saccharomyces cerevisiae interacts with Hsf1 invivo and is required for the induction of heat shock genes by oxidativestress. Mol Biol Cell. 2000;11:2335–47.67. Reinders A, Burckert N, Boller T, Wiemken AT, De Virgilio C. Saccharomycescerevisiae cAMP-dependent protein kinase controls entry into stationaryphase through the Rim15p protein kinase. Genes Dev. 1998;12:2943–55.68. Robertson LS, Causton HC, Young RA, Fink GR. The yeast A kinasesdifferentially regulate iron uptake and respiratory function. Proc Natl AcadSci USA. 2000;97:5984–8.69. Robertson LS, Fink GR. The three yeast A kinases have specific signalingfunctions in pseudohyphal growth. Proc Natl Acad Sci USA. 1998;95:13783–7.70. Rose M, Botstein D. Construction and use of gene fusions to lacZ(beta-galactosidase) that are expressed in yeast. Methods Enzymol.1983;101:167–80.71. Saadatpour A, Albert R. Boolean modeling of biological regulatory networks:A methodology tutorial. Methods. 2013;62:3–12.72. Sambrook J, Fritsch EF, Maniatis T. Molecular Cloning: a Laboratory Manual. ColdSpring Harbor, NY: Cold Spring Harbor Laboratory Press; 2nd edition; 1989.73. Shi Y, Mosser DD, Morimoto RI. Molecular chaperones as HSF1-specifictranscriptional repressors. Genes Dev. 1998;12:654–66.74. Sikorski RS, Hieter P. A system of shuttle vectors and yeast host strains designedfor efficient manipulation of DNA in Saccharomyces cerevisiae. Genetics.1989;122:19–27.75. Smith A, Ward MP, Garrett S. Yeast PKA represses Msn2p/Msn4p-dependentgene expression to regulate growth, stress response and glycogenaccumulation. EMBO J. 1998;17:3556–64.76. Sorger PK. Heat shock factor and the heat shock response. Cell. 1991;65:363–6.77. Sorger PK, Pelham HR. Purification and characterization of a heat-shockelement binding protein from yeast. EMBO J. 1987;6:3035–41.78. Stone DE, Craig EA. Self regulation of 70-kilodalton heat shock proteins inSaccharomyces cerevisiae. Mol Cell Biol. 1990;10:1622–32.79. Storici F, Lewis LK, Resnick MA. In vivo site-directed mutagenesis usingoligonucleotides. Nat Biotechnol. 2001;19:773–6.80. Szallasi Z, Stelling J, Periwal V. System Modeling in Cellular Biology: FromConcepts to Nuts and Bolts. The MIT Press; 2006.81. Tamai KT, Liu X, Silar P, Sosinowski T, Thiele DJ. Heat shock transcriptionfactor activates yeast metallothionein gene expression in response to heatand glucose starvation via distinct signalling pathways. Mol Cell Biol.1994;14:8155–65.82. Tao W, Deschenes RJ, Fassler JS. Intracellular glycerol levels modulate theactivity of Sln1p, a Saccharomyces cerevisiae two-component regulator. J BiolChem. 1999;274:360–7.Pérez-Landero et al. BMC Systems Biology  (2015) 9:42 Page 16 of 1783. Tatchell K. RAS genes and growth control in Saccharomyces cerevisiae.J Bacteriol. 1986;166:364–7.84. Thevelein JM, De Winde JH. Novel sensing mechanisms and targets for thecAMP-protein kinase A pathway in the yeast Saccharomyces cerevisiae. MolMicrobiol. 1999;33:904–18.85. Toda T, Cameron S, Sass P, Zoller M, Scott JD, McMullen B, et al. Cloningand characterization of BCY1, a locus encoding a regulatory subunit of thecyclic AMP-dependent protein kinase in Saccharomyces cerevisiae. Mol CellBiol. 1987;7:1371–7.86. Toda T, Cameron S, Sass P, Zoller M, Wigler M. Three different genes inS. cerevisiae encode the catalytic subunits of the cAMP-dependent proteinkinase. Cell. 1987;50:277–87.87. Toda T, Uno I, Ishikawa T, Powers S, Kataoka T, Broek D, et al. In yeast, RASproteins are controlling elements of adenylate cyclase. Cell. 1985;40:27–36.88. Tudisca V, Recouvreux V, Moreno S, Boy-Marcotte E, Jacquet M, Portela P.Differential localization to cytoplasm, nucleus or P-bodies of yeast PKAsubunits under different growth conditions. Eur J Cell Biol. 2010;89:339–48.89. Vabulas RM, Raychaudhuri S, Hayer-Hartl M, Hartl FU. Protein folding in thecytoplasm and the heat shock response. Cold Spring Harb Perspect Biol.2010;2:a004390.90. Wang R-S, Saadatpour A, Albert R. Boolean modeling in systems biology: anoverview of methodology and applications. Phys Biol. 2012;9:055001.91. Werner-Washburne M, Braun E, Johnston GC, Singer RA. Stationary phase inthe yeast Saccharomyces cerevisiae. Microbiol Rev. 1993;57:383–401.92. Werner-Washburne M, Stone DE, Craig EA. Complex interactions amongmembers of an essential subfamily of hsp70 genes in Saccharomycescerevisiae. Mol Cell Biol. 1987;7:2568–77.93. Williamson T, Schwartz JM, Kell DB, Stateva L. Deterministic mathematicalmodels of the cAMP pathway in Saccharomyces cerevisiae. BMC SystemsBiology. 2009;3:70. doi:10.1186/1752-0509-3-70.94. Wu C. Heat shock transcription factors: structure and regulation. Annu RevCell Dev Biol. 1995;11:441–69.95. Wu WS, Li WH. Identifying gene regulatory modules of heat shock responsein yeast. BMC Genomics. 2008;9:439. doi:10.1186/1471-2164-9-439.96. Zou J, Guo Y, Guettouche T, Smith DF, Voellmy R. Repression of heat shocktranscription factor HSF1 activation by HSP90 (HSP90 complex) that forms astress-sensitive complex with HSF1. Cell. 1998;94:471–80.Submit your next manuscript to BioMed Centraland take full advantage of: • Convenient online submission• Thorough peer review• No space constraints or color figure charges• Immediate publication on acceptance• Inclusion in PubMed, CAS, Scopus and Google Scholar• Research which is freely available for redistributionSubmit your manuscript atérez-Landero et al. BMC Systems Biology  (2015) 9:42 Page 17 of 17


Citation Scheme:


Citations by CSL (citeproc-js)

Usage Statistics



Customize your widget with the following options, then copy and paste the code below into the HTML of your page to embed this item in your website.
                            <div id="ubcOpenCollectionsWidgetDisplay">
                            <script id="ubcOpenCollectionsWidget"
                            async >
IIIF logo Our image viewer uses the IIIF 2.0 standard. To load this item in other compatible viewers, use this url:


Related Items