Physiological and Transcriptional Responses of Anaerobic Cultures of Saccharomyces cerevisiae Subjected to Diurnal Cycles

Marit Hebly,a,b,c Dick de Ridder,b,d* Erik A. F. de Hulster,a,b Pilar de la Torre Cortes,a,b Jack T. Pronk,a,b,c Pascale Daran-Lapujadea,b,c a Industrial Microbiology Section, Department of Biotechnology, Delft University of Technology, Delft, The Netherlands ; Kluyver Centre for Genomics of Industrial Downloaded from Fermentation, Delft, The Netherlandsb; Netherlands Consortium for Systems Biology, Amsterdam, The Netherlandsc; Delft Bioinformatics Lab, Department of Intelligent Systems, Delft University of Technology, Delft, The Netherlandsd

Diurnal temperature cycling is an intrinsic characteristic of many exposed microbial ecosystems. However, its influence on yeast physiology and the yeast transcriptome has not been studied in detail. In this study, 24-h sinusoidal temperature cycles, oscillat- ing between 12°C and 30°C, were imposed on anaerobic, glucose-limited chemostat cultures of Saccharomyces cerevisiae. After three diurnal temperature cycles (DTC), concentrations of glucose and extracellular metabolites as well as CO2 production rates

showed regular, reproducible circadian rhythms. DTC also led to waves of transcriptional activation and repression, which in- http://aem.asm.org/ volved one-sixth of the yeast genome. A substantial fraction of these DTC-responsive genes appeared to respond primarily to changes in the glucose concentration. Elimination of known glucose-responsive genes revealed an overrepresentation of previ- ously identified temperature-responsive genes as well as genes involved in the cell cycle and de novo purine biosynthesis. In- depth analysis demonstrated that DTC led to a partial synchronization of the cell cycle of the yeast populations in chemostat cultures, which was lost upon release from DTC. Comparison of DTC results with data from steady-state cultures showed that the 24-h DTC was sufficiently slow to allow S. cerevisiae chemostat cultures to acclimate their transcriptome and physiology at the DTC temperature maximum and to approach acclimation at the DTC temperature minimum. Furthermore, this comparison and literature data on growth rate-dependent cell cycle distribution indicated that cell cycle synchronization was most ␮ ␮ on February 17, 2015 by BIBLIOTHEEK TU DELFT likely an effect of imposed fluctuations of the relative growth rate ( / max) rather than a direct effect of temperature.

emperature directly influences the kinetics of all biochemical atures (2, 3). It is therefore unclear whether 24-h cycles enable full Treactions and many cellular processes (1, 2). Within their tem- temperature acclimation or whether they confront microorgan- perature range for growth, many microorganisms adapt their isms with a “continuous temperature shock” scenario. metabolic networks and biomass composition to optimize their The impact of temperature on physiology has been extensively growth rate and viability in response to changing . studied in Saccharomyces cerevisiae, a mesophilic microorganism Classical examples of temperature adaptation include modifica- with an optimum growth temperature of circa 33°C (14, 15), par- tions of membrane fluidity (3, 4) and the expression of shock ticularly because several of its industrial applications, such as proteins that assist in protein folding at high temperatures (5, 6). brewing, wine making, and frozen-dough applications, involve studies on microbial temperature responses are suboptimal temperatures (16–19). Temperature ranges for based largely on two experimental systems. Acclimation, i.e., the growth are strain dependent, but most S. cerevisiae strains are able result of complete physiological adaptation to a given tempera- to grow at temperatures between 4°C and 40°C (15). Natural iso- ture, has been studied both in batch cultures and in lates of S. cerevisiae are typically associated with exposed environ- (7–9). In cold shock and heat shock experiments, the responses of ments, e.g., the bark of oak trees (20, 21). To understand the phys- microorganisms to sudden upshifts or downshifts in temperature iology of this yeast in its natural environment, it is relevant to are studied, typically over a time period ranging from a few min- study its physiology under circadian temperature fluctuations. utes to a few hours (3, 10–13). Furthermore, such studies may contribute to our understanding In natural environments, microorganisms are subjected to sev- eral types of temperature dynamics. In exposed environments, one of the dominant aspects of temperature dynamics is related to Received 7 March 2014 Accepted 5 May 2014 circadian changes, with higher temperatures during the day and Published ahead of print 9 May 2014 lower temperatures during the night. These 24-h temperature cy- Editor: A. A. Brakhage cles, which are superimposed on seasonal changes, raise a number Address correspondence to Pascale Daran-Lapujade, of interesting questions related to microbial temperature adapta- [email protected] tion that have hitherto hardly been addressed in microbial physi- * Present address: Dick de Ridder, Bioinformatics Group, Wageningen University, ology. For many microorganisms, the time constants of circadian Wageningen, The Netherlands. temperature changes are in the same order of magnitude as their Supplemental material for this article may be found at http://dx.doi.org/10.1128 generation times. Moreover, the rate at which microorganisms /AEM.00785-14. can adapt their physiology to changes in temperature is itself likely Copyright © 2014, American Society for Microbiology. All Rights Reserved. to be strongly temperature dependent, for instance, because rates doi:10.1128/AEM.00785-14 of transcription and translation decrease strongly at low temper-

July 2014 Volume 80 Number 14 Applied and Environmental Microbiology p. 4433–4449 aem.asm.org 4433 Hebly et al. of how evolution under circadian temperature cycles has shaped the physiology and the genome of this important industrial mi- croorganism. Previous work on temperature responses of S. cerevisiae in- cluded many studies on heat and cold shock responses (11, 22–28) and on batch cultures grown at different temperatures (29) and analyses with steady-state chemostat cultures (14, 30, 31). In the latter studies, detailed physiological analysis of the cultures was combined with genome-wide expression analysis, thereby provid-

ing inclusive descriptions of fully temperature-acclimated S. Downloaded from cerevisiae cultures. The aim of this study is to investigate the impact of diurnal temperature cycles (DTC) on S. cerevisiae and to assess the extent to which these responses can be predicted from steady-state analyses. To this end, we used a continuous-cultivation setup, in which yeast was grown under controlled conditions and subjected to 24-h sinusoidal temperature cycles. This system was recently FIG 1 Diurnal temperature profile. The temperature profile imposed on che- mostat cultures of S. cerevisiae was derived from a Web-accessible air temper-

used to specifically investigate the impact of temperature dynam- http://aem.asm.org/ ature database (http://www.infoclimat.fr/recherche/sphinx.php?qϭ05%2F08 ics on yeast glycolysis, based on integrated modeling and experi- %2F2007). The open symbols are the temperatures measured in Strasbourg, mental analysis of the in vivo kinetics of glycolytic enzymes (32). France, on 5 August 2007, and the solid line indicates the programmed sinu- Rather than focusing on a single metabolic pathway, the present soidal function, T (°C) ϭ 21 ϩ 9 sin{[t (h) ϩ 6] ␲/12}. study investigates the overall physiology and transcriptome of S. cerevisiae during circadian temperature cycles fluctuating between 12°C and 30°C. Data from cultures grown under this dynamic off-gas with an NGA 2000 analyzer (Rosemount Analytical, Orrville, OH, USA). When the CO2 percentage in the off-gas dropped below 0.05%, temperature regime are compared with data from fully acclimated indicating depletion of glucose, a computer-controlled peristaltic pump chemostat cultures. removed 0.9 liters of culture, leaving approximately 0.10 liters as the in- oculum for the next batch (36). Specific growth rates at 12°C and 30°C on February 17, 2015 by BIBLIOTHEEK TU DELFT MATERIALS AND METHODS were calculated based on biomass dry weight measurements from samples Strain and media. The prototrophic haploid yeast strain Saccharomyces taken during the fourth and fifth cycles. cerevisiae CEN.PK113-7D (MATa)(33, 34) was used in this study. The Diurnal temperature experiments. After three volume changes at a medium used for chemostat and sequential batch cultivation contained constant temperature of 30°C without significant changes in biomass dry Ϫ1 Ϫ1 Ϫ1 ϭ ϩ 0.3 g · liter (NH4)2SO4, 0.3 g · liter KH2PO4, 0.5 g · liter MgSO4 · weight, a preprogrammed sinusoidal temperature profile, T (°C) 21 Ϫ1 Ϫ1 ϩ ␲ 7H2O, 3.0 g · liter NH4H2PO4, and 25 g · liter glucose. The anaerobic 9 sin{[t (h) 6] /12}, was started. This profile was designed to mimic a growth factors Tween 80 and ergosterol (0.42 g · literϪ1 and 10 mg · real-life circadian temperature cycle recorded in nature (Fig. 1). Samples literϪ1, respectively), trace elements, and vitamin solution (both 1 ml · were taken during the fifth and/or sixth temperature cycle. To minimize literϪ1) were added to the medium as described previously (35). The disturbance, sampling volumes did not exceed 5% of the reactor volume medium was supplemented with 0.15 g · literϪ1 silicone antifoam (anti- during a single temperature cycle, and intervals of at least 3 h were main- foam C; Sigma-Aldrich, St. Louis, MO, USA). tained between sampling points. Chemostat cultivation. Anaerobic glucose-limited chemostat cul- Analytical methods. Gas analysis and analyses of biomass dry weight, tures were grown in 2-liter laboratory bioreactors (Applikon, Schiedam, total cell concentration, cell size, and residual glucose and extracellular The Netherlands) with a working volume of 1.4 liters and at a dilution rate and intracellular metabolite concentrations in chemostat cultures were of 0.03 hϪ1. The pH was controlled at 5.0 by the automatic addition of 2 M performed as described previously (32, 37). Samples for the analysis of the KOH, and the stirrer speed was kept at 800 rpm. To ensure anaerobic intracellular storage carbohydrates trehalose and glycogen were taken in conditions, bioreactors and medium vessels were continuously sparged duplicate from two independent replicate cultures. By means of a rapid Ϫ1 with pure nitrogen (N2) gas at flow rates of 0.7 liters · min and circa 7 sampling setup (32), circa 2.2 ml of broth was rapidly quenched in 5 ml of ml · minϪ1, respectively, and equipped with Norprene tubing (Saint- cold (Ϫ40°C) pure . The exact sampling weight was measured, Gobain Performance Plastics, Courbevoie, France) and Viton O-rings and samples were washed once with 5 ml of cold (Ϫ40°C) pure methanol. (Eriks, Alkmaar, The Netherlands). Active temperature regulation was The pellet was stored at Ϫ80°C until analysis of glycogen and trehalose performed and measured online by connecting a resistance concentrations according to a previously reported procedure (38). Glu- thermometer, placed in a socket in the bioreactor, to an RE304 low-tem- cose concentrations were determined with an Enzyplus D-glucose kit ϩ perature (Lauda, Lauda-Königshofen, Germany). Steady- (EZS781 ; BioControl Systems, Bellevue, WA, USA). state sampling of cultures grown at a constant temperature of 12°C or Prediction of the biomass specific glucose consumption rate during 30°C was performed when biomass dry weight, metabolite levels, and DTC. The biomass specific glucose consumption rate, qS (mmol glucose · Ϫ Ϫ carbon dioxide production rates differed by Ͻ5% for at least three con- g [dry weight] 1 ·h 1), was calculated by using equations 1 and 2. The secutive volume changes. amount of biomass, MX (grams), in the reactor did not change substan- Ϫ Cultivation in sequential batch reactors. The use of sequential batch tially during DTC; therefore, a constant value of 1.95 g · liter 1 was used reactors (SBRs), instead of single batches inoculated from standardized (see Fig. 3C). Changes in the amount of substrate in the culture broth, shake-flask-grown precultures, has been shown to enhance the reproduc- dM Ϫ S (mmol · h 1), were calculated based on changes in residual glucose ibility of specific growth rate measurements (29, 36). SBR experiments at dt 12°C and 30°C were performed under conditions identical to those for concentrations (CS) per 3-h interval. Subsequently, the rate of glucose chemostat cultivation, with a working volume of 1 liter. The first batch consumption, rateS, was calculated with equation 2, in which CS,in is the was inoculated with a shake flask preculture grown overnight (37). substrate concentration in the feed (mmol · literϪ1) and ⌽ is the medium Ϫ1 Growth was constantly monitored via online CO2 measurements in the flow rate (liters · h ).

4434 aem.asm.org Applied and Environmental Microbiology Yeast Dynamics during Diurnal Temperature Cycles

ϭ qS rateS ⁄ MX (1) numbers GSE3821 and GSE8187, respectively [http://www.ncbi.nlm.nih .gov/geo]) and uploaded into Genedata Expressionist Pro (v3.1). k-means dM clustering of the 215 and 195 genes in the two data sets was performed s ϭ ϩ⌽ Ϫ rateS ͑CS,in CS͒ (2) separately as described above (see Fig. S1 in the supplemental material). dt Steady-state chemostat cultures at constant temperatures of 12°C and Budding index. For determination of the budding index (BI), i.e., the 30°C (independent duplicate cultures at both temperatures) were sam- percentage of cells carrying a bud, multiple microscopic slides were pre- pled for microarray analysis as described above, thereby expanding the pared from each independent sample, and multiple pictures of each slide total array set to 17 arrays. Genes that were differentially expressed be- were taken by phase-contrast with an Imager-D1 tween two conditions (e.g., pairwise comparison between cultures at equipped with an AxioCam MR camera (Carl Zeiss, Oberkochen, Ger- steady state at 12°C and those at steady state at 30°C) were identified by ϫ many), using an EC Plan-Neofluar 40 /0.75 Ph 2 M27 objective (Carl first removing probe intensity background (52) and quantile normalizing Downloaded from Zeiss, Oberkochen, Germany). To ensure unbiased analysis, photographs intensity levels (53) as in robust multichip analysis (RMA). Subsequently, were blindly labeled and randomly shuffled before counting. At least 200 array groups (two biologically independent arrays per group) that corre- cells were counted for each sample point and used to determine the bud- sponded to the sampling point were compared based on the perfect- ding index. match probe intensity levels only (52), by performing a per-probe-set Flow cytometry analysis. Cell cycle phase distribution in yeast popu- two-way analysis of variance (with the factors “probe” and “sampling lations was estimated with a flow cytometry-based method. A sample of point”). Adjustment of P values for multiple testing was done with Šidák ϫ 6 culture broth equivalent to circa 1.6 10 cells was taken from the bio- step-down adjustment (54). Differences with adjusted P values of Ͻ0.05 ϫ reactor and centrifuged (5 min at 4,700 g). The pellet was washed once and a fold difference of 2 or higher were considered significant.

with cold phosphate buffer (3.3 mM NaH2PO4, 6.7 mM Na2HPO4, 130 Principal component analysis. For each sampling point during DTC, http://aem.asm.org/ mM NaCl, 0.2 mM EDTA) (39), vortexed briefly, centrifuged again (5 an average expression profile was calculated based on the two biologically ϫ ␮ min at 4,700 g), and suspended in 800 l 70% ethanol while vortexing. independent arrays. This set was extended with average expression pro- ␮ After the addition of another 800 l 70% ethanol, fixed cells were stored at files of steady-state samples at 12°C and 30°C. Principal component anal- 4°C until further staining and analysis. Staining of the cells with Sytox ysis (55) was performed, and samples were projected onto the first two green nucleic acid stain (catalog number S7020; Invitrogen) was per- components. formed as described previously (40). Samples were analyzed on a Microarray data accession number. The complete data set (17 arrays) FACSCalibur instrument (BD Biosciences, Franklin Lakes, NJ, USA) was deposited at the Gene Expression Omnibus database (http://www equipped with an argon at 488 nm. The cell cycle phase distribution .ncbi.nlm.nih.gov/geo) under accession number GSE55372. for each sample was estimated by using the histogram modeler tool from the free software CyFlogic (version 1.2.1; CyFlo Ltd., Turku, Finland). on February 17, 2015 by BIBLIOTHEEK TU DELFT Microarray and transcriptome analyses. Samples for microarray RESULTS analysis were taken during the fifth and sixth temperature cycles from two Rhythmic, reproducible changes in glucose fermentation dur- independent duplicate cultures. To minimize disturbance, the sampling ing diurnal temperature cycles. To study diurnal temperature volume was kept within 5% of the reactor volume during 24 h. For the last cycles (DTC) under controlled laboratory conditions, sinusoidal time point, one additional analytical duplicate sample was taken, resulting temperature variations with a 24-h period were imposed on an- in a complete data set of 13 arrays. The sampling procedure (quenching in aerobic, glucose-limited chemostat cultures of S. cerevisiae nitrogen), processing of the samples, RNA isolation, and microar- CEN.PK113-7D. The temperature profile was designed to closely ray analysis with the use of the GeneChip Yeast Genome S98 array (Af- resemble a natural 24-h temperature cycle ranging between 30°C fymetrix, Santa Clara, CA, USA) were performed as described previously ␮ (41). Microarray data acquisition (using a target value for global scaling of and 12°C (Fig. 1). The maximum specific growth rates ( max)ofS. 240), elimination of insignificant variation, and extraction of the 6,383 cerevisiae CEN.PK113-7D at 30°C and 12°C were estimated in yeast open reading frames (42) were done as described previously (43). sequential batch reactor setups (see Materials and Methods) un- Sample points from the fifth and sixth temperature cycles were combined, der conditions identical to those of the chemostat cultures but in ␮ Ϯ resulting in six sample points covering one temperature cycle (at temper- the presence of excess glucose. At 30°C, the max was 0.310 atures of 30°C, 21°C, 14.6°C, 12°C, 21°C, and 27.4°C). The expression data 0.002 hϪ1, while at 12°C, it was only 0.048 Ϯ 0.001 hϪ1. To prevent from these 6 time points were subjected to time course differential expres- washout of the chemostat cultures during the temperature mini- sion analysis by empirical analysis of digital gene expression data in R mum of the DTC, the dilution rate in the DTC chemostat cultures Ͻ Ϫ (EDGE, v1.1.291) (43, 44). All genes with a P value of 0.002 were con- was set at 0.03 h 1. sidered to be significantly changed (1,102 genes) and were subsequently During a series of consecutive DTC, the biomass concentration k-means clustered using the k-means clustering algorithm of Genedata Expressionist Pro (v3.1) that uses positive correlation as distance metric in the chemostat cultures remained essentially constant (Fig. 2). (43), resulting in six clusters. The clusters were examined by the use of a However, CO2 production revealed a clear cyclic variation in fer- hypergeometric distribution test (45) for enrichments of functional cate- mentative activity (Fig. 2). As the temperature decreased from gories and transcription factor (TF) binding (46), as described previously 30°C to 12°C during the first half of the 24-h cycles, the CO2 (43, 47). If only a secondary or tertiary category, as defined by the Munich production rate decreased by 35%. The subsequent temperature

Information Center for Protein Sequences (MIPS) database, was en- increase was accompanied by an acceleration of the CO2 produc- riched, this category was shown, and Gene Ontology (GO) leaf categories tion rate, after which it reached its initial level. The residual glu- were shown if they identified a category that was not enriched in MIPS. cose concentration also markedly fluctuated during the DTC (Fig. DTC-specific genes were examined by a hypergeometric distribution test 2). For both CO2 and residual glucose, the amplitude of this fluc- for enrichments of a number of custom-made categories consisting of cell tuation decreased as yeast cells acclimated to the DTC and became cycle phase marker genes (according to references 48 and 49). To distinguish between glucose responses and temperature responses, steady and reproducible after three cycles (Fig. 2). the transcriptome data set was compared with data from previous studies A closer inspection of the fifth and sixth temperature cycles on glucose-dependent transcriptional responses of S. cerevisiae CEN. revealed that the residual glucose concentration was inversely cor- PK113-7D (50, 51). Primary data (.CEL files) from those previous studies related with temperature (Fig. 3A), reaching values of 2.64 Ϯ 0.07 were retrieved from the Gene Expression Omnibus database (accession mM and 0.20 Ϯ 0.01 mM at 12°C and 30°C, respectively. Interest-

July 2014 Volume 80 Number 14 aem.asm.org 4435 Hebly et al. Downloaded from http://aem.asm.org/ on February 17, 2015 by BIBLIOTHEEK TU DELFT

FIG 2 Adaptation of S. cerevisiae cultivated in anaerobic glucose-limited cultures to diurnal temperature profiles. (Top) Applied temperature cycles over time (dashed line) and average biomass concentrations (Œ) with standard deviations measured at the highest (30°C) and lowest (12°C) temperatures during seven Ϯ consecutive cycles. (Bottom) Percentage of CO2 in the off-gas measured continuously (with averages represented by the black line and averages standard deviations depicted by the gray lines). The dashed line indicates the steady-state CO2 percentage at 30°C. The dots represent the average residual glucose concentrations with standard deviations that were measured at the highest (30°C) and lowest (12°C) temperatures during seven consecutive temperature cycles. For all data, the averages and standard deviations for three independent replicates are represented. ingly, the glucose concentration profiles were not as symmetrical formed, covering six time points during the 24-h temperature as the imposed temperature profile. Instead, glucose concentra- cycle (Fig. 4). In contrast to the relatively mild physiological im- tions decreased much faster when the temperature increased after pact of the DTC, microarray analysis revealed major reprogram- passing the temperature minimum than they increased as the tem- ming of gene expression. Using stringent statistical criteria (P perature minimum was approached. This asymmetry was also vis- value of Ͻ0.002), a set of 1,102 genes (representing 17% of the S. ible in the off-gas CO2 profile (Fig. 3B), in which a slow decelera- cerevisiae genome) showed changes in transcript levels. Clustering tion of CO2 production during the temperature decrease was of these genes according to their transcript profiles revealed pat- followed by a sharp acceleration when the temperature increased terns of transcriptional induction and repression that either pos- again. After “overshooting,” the CO2 profile resumed its initial itively or negatively correlated with the temperature profiles (Fig. level before the temperature maximum was reached. 4). Transcript levels of 498 genes peaked at the lowest temperature Biomass concentration, measured as culture dry weight, re- point in the cycle (clusters 1, 2, and 3) (Fig. 4), while transcript mained constant at a value of 1.95 g · literϪ1, with a variation of levels of 384 genes (clusters 4 and 5) (Fig. 4) decreased with de- Ͻ5% during the DTC (Fig. 3C). The concentrations of extracel- creasing temperature and reached their lowest transcript level at lular metabolites (ethanol, glycerol, lactate, succinate, and pyru- the temperature minimum. The expression levels of 220 genes in vate) (Fig. 3D to G) were largely unaffected by the cyclic temper- cluster 6 were hardly affected when the temperature decreased ature variation, with the notable exception of acetate, the from 30°C to 12°C but were strongly increased as the temperature concentration of which rhythmically varied by circa 70% (Fig. increased again, reaching the highest level before the temperature 3H). However, the concentration of acetate was very low com- maximum was reached, after which expression levels returned to pared to those of ethanol and glycerol and represented Ͻ0.24% of their values at 30°C. the organic metabolites detected in culture supernatants. While transcript levels of genes in clusters 1 and 2 were in- Substantial transcriptional reprogramming during DTC. To versely correlated with temperature, they could be differentiated obtain an overview of the cellular responses of S. cerevisiae to by the rates at which their transcript levels changed. Transcript diurnal temperature cycles, a transcriptome analysis was per- levels of genes in cluster 1 increased faster with decreasing tem-

4436 aem.asm.org Applied and Environmental Microbiology Yeast Dynamics during Diurnal Temperature Cycles Downloaded from http://aem.asm.org/

FIG 3 Physiological characterization of S. cerevisiae cultivated under anaerobic glucose-limited conditions during a diurnal temperature cycle. In all panels, the dashed line illustrates the applied temperature profile over time. (A) Residual glucose concentration ( ) and biomass specific glucose uptake rate (qS) (dotted Ϯ line). (B) CO2 percentage in the off-gas (the solid black line represents the average of 13 independent cultures, and the gray solid lines depicts the averages standard deviations). (C) Culture dry weight. (D) Ethanol concentration. (E) Glycerol concentration. (F) Lactate concentration (o) and succinate concentration (छ). (G) Pyruvate concentration. (H) Acetate concentration. Data points represent the average concentrations and error bars represent the standard deviations of at least seven independent culture replicates. on February 17, 2015 by BIBLIOTHEEK TU DELFT peratures between 21°C and 12°C than did those in cluster 2, while 21°C. This decrease was followed by an increase in expression the expression levels of genes in cluster 2 displayed a steeper de- levels between 21°C and 30°C, returning to the initial expression crease with increasing temperatures between 12°C and 21°C. Al- values. These differences in transcriptional regulation between though transcript profiles of genes in cluster 3 resembled those of clusters 1, 2, and 3 were mirrored in the diversity of the overrep- genes in cluster 2, cluster 3 was characterized by a steeper decrease resented functional categories in these three clusters (Table 1). of transcript levels as the temperature increased from 12°C to Cluster 1 (54 genes) was specifically enriched for genes involved in

FIG 4 Transcriptional reprogramming of S. cerevisiae grown under anaerobic glucose-limited conditions during a diurnal temperature cycle. Based on their different time-dependent transcript profiles, the 1,102 genes that were considered to be significantly changed after time course analysis (P value of Ͻ0.002) (see Materials and Methods) were assigned to six clusters. The expression level of each gene was mean normalized and subsequently averaged over the two independent culture replicates. The dots represent the averages of these averaged expression values for all genes in the respective cluster. The gray lines represent the averages Ϯ standard deviations. The dashed line illustrates the applied temperature profile over time.

July 2014 Volume 80 Number 14 aem.asm.org 4437 TABLE 1 Functional enrichment analysisa No. of significantly changed genes in Cluster (no. of genes) and enriched functional category/total profile Enriched functional category (MIPS functional category) no. of genes in functional category P value 1 (54) Cell wall (42.01) 8/215 4.0 ϫ 10Ϫ4 Downloaded from 2 (146) Mbp1 14/165 2.5 ϫ 10Ϫ5 Ino4 6/33 8.6 ϫ 10Ϫ5 Swi6 12/160 2.7 ϫ 10Ϫ4 Swi4 11/144 4.2 ϫ 10Ϫ4 Ino2 5/31 6.1 ϫ 10Ϫ4 http://aem.asm.org/

3 (298) Protein synthesis (12) 108/511 3.8 ϫ 10Ϫ46 Transcription (11) 113/1,036 1.5 ϫ 10Ϫ20 Protein with binding function or cofactor requirement (16) 81/1,049 1.1 ϫ 10Ϫ6 Fhl1 48/208 2.0 ϫ 10Ϫ21 Rap1 27/145 4.0 ϫ 10Ϫ10 Sfp1 14/50 3.5 ϫ 10Ϫ8 on February 17, 2015 by BIBLIOTHEEK TU DELFT

4 (51) Bas1 4/39 2.4 ϫ 10Ϫ4 Gcn4 7/182 5.5 ϫ 10Ϫ4

5 (333) Energy (2) 80/360 4.1 ϫ 10Ϫ31 Mitochondrion (42.16) 31/170 5.5 ϫ 10Ϫ10 C compound and carbohydrate metabolism (1.05) 53/510 6.0 ϫ 10Ϫ7 Transported compounds (substrates) (20.01) 56/585 3.9 ϫ 10Ϫ6 Fe/S binding (16.21.08) 4/5 3.5 ϫ 10Ϫ5 Protein folding and stabilization (14.01) 15/95 1.0 ϫ 10Ϫ4 Mitochondrial transport (20.09.04) 15/100 1.9 ϫ 10Ϫ4 Hap4 19/57 2.7 ϫ 10Ϫ11 Hsf1 15/55 7.5 ϫ 10Ϫ8 Hap3 9/26 3.6 ϫ 10Ϫ6 Cin5 24/180 1.8 ϫ 10Ϫ5 Ume6 17/131 4.2 ϫ 10Ϫ4

6 (220) Metabolism (1) 87/1,530 1.3 ϫ 10Ϫ7 Cell rescue, defense, and virulence (32) 41/558 2.0 ϫ 10Ϫ6 Lysosomal and vacuolar protein degradation (13.13.04) 7/27 2.6 ϫ 10Ϫ5 Energy (2) 27/360 9.7 ϫ 10Ϫ5 Msn2 15/122 1.7 ϫ 10Ϫ5 Msn4 14/121 6.3 ϫ 10Ϫ5

a Each cluster was tested for overrepresentation of genes belonging to functional categories (see Data Set S1 in the supplemental material). Enrichment for MIPS categories is indicated in lightface type, and enrichment for targets of transcription factors is indicated in boldface type. P values indicate the probability of finding an enrichment by chance.

4438 aem.asm.org Applied and Environmental Microbiology Yeast Dynamics during Diurnal Temperature Cycles cell wall organization (8 genes [P value of 4.0 ϫ 10Ϫ4]). Cluster 2 A close comparison of the DTC data set and the two previously (146 genes) was enriched for targets of the transcriptional activa- reported data sets revealed that among the 498 genes whose tran- tor Ino2/Ino4 (P values of 6.1 ϫ 10Ϫ4 and 8.6 ϫ 10Ϫ5, respec- scription was upregulated during the phases of the DTC when the tively), involved in the induction of phospholipid biosynthesis residual glucose concentration was high (clusters 1, 2, and 3), over (56). Cluster 2 was enriched for targets of the transcription factors half (283 genes) (Fig. 5A) were also upregulated in the studies by Swi4, Swi6, and Mbp1 (11, 12, and 14 genes [P values of 5.1 ϫ Kresnowati et al. and Van den Brink et al. Similarly, about two- 10Ϫ4, 3.3 ϫ 10Ϫ4, and 2.7 ϫ 10Ϫ5], respectively), involved in cell thirds of the 604 genes downregulated at high residual glucose cycle progression. Cluster 3 (298 genes) showed a very strong concentrations in the DTC (clusters 4, 5, and 6) were also identi- overrepresentation of genes involved in protein synthesis, more fied as being glucose repressed in the two previously reported data specifically in ribosome biogenesis and rRNA processing (93 genes sets (409 genes) (Fig. 5B). These 692 “glucose-responsive” genes Downloaded from [P value of 4.3 ϫ 10Ϫ49] and 73 genes [P value of 2.8 ϫ 10Ϫ53], were not uniformly distributed over the six clusters (Fig. 5C) and respectively). Consistent with these , cluster 3 also belonged to functional categories previously associated with re- contained a large number of ribosomal protein genes, whose tran- sponses to glucose excess. scription is regulated by the transcription factors Fhl1, Rap1, and To separate indirect transcriptome responses to changes in the Sfp1. glucose concentration from temperature-specific responses, the Genes in clusters 4 and 5, which were characterized by a down- 692 glucose-responsive genes were removed from the set of DTC- regulation of expression at low temperatures, differed mostly in responsive genes. This resulted in a smaller set of 410 genes their response to increasing temperatures (Fig. 4). While the ex- (Fig. 5A to C). This set of 410 genes was, however, still strongly http://aem.asm.org/ pression of genes in cluster 4 gradually resumed toward their ex- enriched for categories that are associated with changes in glucose pression levels at 30°C, genes in cluster 5 exhibited a slight over- concentrations (e.g., protein synthesis) (Fig. 5D; see also Data Set shooting of their transcript levels before resuming their initial S2 in the supplemental material). To further narrow down the expression level at 30°C. As observed for clusters 2 and 3, this data set and more precisely define the set of DTC-responsive subtle difference in temperature responses between clusters 4 and genes, the data sets of Kresnowati et al. and Van den Brink et al. 5 coincided with substantial differences in the overrepresentation were further mined. As those two studies employed rather strin- of functional categories. Cluster 5 was strongly enriched for genes gent statistical criteria, their complete data sets were used. The Ϫ31 profiles of the 410 genes identified in the present study were com- involved in energy metabolism (80 genes [P value of 4.1 ϫ 10 ]) pared with the profiles of the corresponding genes in the studies by on February 17, 2015 by BIBLIOTHEEK TU DELFT and more specifically in energy conservation via respiration and Ϫ Kresnowati et al. and Van den Brink et al. (see Materials and energy reserves (38 genes [P value of 4.2 ϫ 10 19] and 12 genes [P Ϫ Methods; see also Fig. S1 in the supplemental material). Profiles value of 1.3 ϫ 10 5], respectively). Conversely, few categories among these 410 genes that responded to glucose in these pub- were enriched among the 51 genes in cluster 4, covering targets of Ϫ lished data sets were removed from the DTC data set (see Fig. S2 in Bas1 (involved in one-carbon metabolism) (P value of 2.4 ϫ 10 4) the supplemental material), resulting in a final data set of 253 and an enrichment for targets of the Gcn4 transcription factor (7 Ϫ genes that were considered to be DTC specific. As anticipated, this genes [P value of 5.5 ϫ 10 4]) involved in the regulation of amino additional data processing successfully filtered out genes involved acid biosynthesis. In cluster 6 (220 genes), various cellular func- in protein synthesis (see Table S1 in the supplemental material). tions and cell protection mechanisms were overrepresented, in- The DTC-specific genes belonged to six main cellular processes cluding the subcategories stress response (37 genes [P value of and were spread across the different clusters (Table 2; see also Data ϫ Ϫ7 5.3 10 ]), lysosomal and vacuolar protein degradation (7 Set S3 in the supplemental material): lipid metabolism (more spe- ϫ Ϫ5 genes [P value of 2.6 10 ]), and metabolism of energy reserves cifically phospholipid metabolism), endoplasmic reticulum (ER)- ϫ Ϫ5 (9 genes [P value of 6.9 10 ]). to-Golgi transport, RNA polymerase III transcription, one-car- Predominant response to cyclic variations in residual glu- bon metabolic processes, amino acid metabolism, and cell cycle cose concentrations. Cyclic variation of residual glucose concen- progression. trations was one of the most pronounced phenomena observed Cyclic variations in cell cycle distribution among the yeast when yeast cultures were exposed to DTC. The presence of glu- population. Among the genes that responded to DTC in an cose, the preferred carbon source of S. cerevisiae, in culture media apparently glucose-independent manner were several targets is well known to trigger a wide array of transcriptional responses of the transcription factors Swi4, Swi6, and Mbp1 (Table 2). in a concentration-dependent manner (57). Previous studies in- These three transcription factors form complexes (Swi4p- vestigated the transcriptional responses of S. cerevisiae supplied Swi6p forms the SBF complex and Swi6p-Mbp1p forms the with low and high concentrations of glucose. For instance, MBF complex) that play an active role in cell cycle control by

Kresnowati et al. and Van den Brink et al., using the same yeast regulating transcription during the transition from G1 to S strain as the one used in the present study, identified the response phase of the cell cycle (58). Measurement of cell number and of aerobic glucose-limited cultures of S. cerevisiae to a sudden size during the course of a temperature cycle revealed a 22% increase of the glucose concentration (50, 51). The sets of genes decrease in cell number and a concomitant 16% increase in cell found to be differentially expressed in these two studies largely size during half of the cycle when the temperature decreased, overlap and together cover the transcriptional response to glucose followed by an increase in cell number and a drop in cell size 15 excess, such as increased expression levels of genes related to ribo- h after the temperature maximum, when the temperature in- some biogenesis and rRNA processing and repression of genes creased to a value above 15°C (Fig. 6A). These data suggested involved in energy metabolism and (storage) carbohydrate me- that the yeast population, or rather a fraction of the popula- tabolism (50, 51). These responses were also identified in the pres- tion, was arresting cell division as the temperature decreased ent work. while still growing in size. Furthermore, the data indicated that

July 2014 Volume 80 Number 14 aem.asm.org 4439 Hebly et al. Downloaded from http://aem.asm.org/ on February 17, 2015 by BIBLIOTHEEK TU DELFT

FIG 5 Discrimination of DTC-specific genes from glucose-responsive genes. (A) Genes in clusters 1, 2, and 3 were pooled (498 genes) and compared to sets of genes identified previously by Kresnowati et al. (589 genes) and Van den Brink et al. (607 genes) to be upregulated upon glucose addition to glucose-limited cultures. P values indicate enrichment for genes identified as being glucose responsive (gray area in the Venn diagram). (B) Genes in clusters 4, 5, and 6 were pooled (604 genes) and compared to sets of genes identified previously by Kresnowati et al. (565 genes) and Van den Brink et al. (1,316 genes) to be downregulated upon glucose addition to glucose-limited cultures. P values indicate enrichment for genes identified as being glucose responsive (gray area in the Venn diagram). (C) For each cluster, the number of genes that were considered to be glucose responsive is indicated in italics, and the number of genes per cluster that showed significantly changed expression in this study only (410 genes in total) is indicated in boldface type. (D) Functional enrichment analysis of the 410 genes excluded from the glucose-responsive genes. MIPS categories are indicated in lightface type, the Gene Ontology category is recognizable by the GO identification number, and enrichment for targets of transcription factors is indicated in boldface type. P values indicate the probability of finding an enrichment by chance. cell division resumed as the temperature increased again. In temperature increased (Fig. 6B). Accordingly, analysis of cell agreement with cell size and cell number measurements, both cycle phase marker genes (48, 49) revealed an enrichment for microscopic and flow cytometric analyses (see Fig. S3 in the genes involved in the S/G2 phase of the cell cycle (Fig. 6C)at supplemental material) consistently demonstrated an accumu- 12°C, while as the temperature increased again, a concerted lation of budded cells in the G2/M phase of circa 70% of the upregulation of genes involved in early G1 and M/G1 cell cycle yeast population at 12°C and subsequent bud release as the phases was observed (Fig. 6C).

4440 aem.asm.org Applied and Environmental Microbiology Yeast Dynamics during Diurnal Temperature Cycles

TABLE 2 Functional enrichment analysis of DTC-specific genesa No. of significantly changed genes Enriched functional category(ies) in enriched functional category/ (MIPS functional category or GO total no. of genes in functional Cluster category) Genes category P value 1 None

2 ER-to-Golgi transport ERP2, ERP1, YIP3, RER1, SHR3, 6/72 8.1 ϫ 10Ϫ5 ( CHS7 Membrane lipid metabolism PLB2, DPM1, CDS1, CST26, 6/83 1.8 ϫ 10Ϫ4 Downloaded from (01.06.02) CHO1, OPI1 RNA polymerase III transcriptional NHP6A, NHP6B, YBR090C 3/13 2.7 ϫ 10Ϫ4 preinitiation complex assembly (GO:0070898) Swi4 MNN5, CIS3, PCL1, SVS1, HTA1/ 8/144 9.4 ϫ 10Ϫ5 HTA2, PCL2, YHP1, GIC1 Swi6 MNN5, CIS3, PCL1, NRM1, SVS1, 8/160 2.0 ϫ 10Ϫ4 PCL2, YHP1, GIC1 Mbp1 SEN34, MNN5, SEC14, PCL1, 8/165 2.4 ϫ 10Ϫ4 http://aem.asm.org/ NRM1, HTA1/HTA2, YHP1, GIC1 Ino2 FAS2, CDS1, KNH1, OPI1 4/31 2.5 ϫ 10Ϫ4 Ino4 YIP3, FAS2, CDS1, CHO1 4/33 3.2 ϫ 10Ϫ4 Tec1 MNN5, PCL1, SVS1, PCL2, CHS7 5/64 4.5 ϫ 10Ϫ4

3 None

4 Degradation of glycine GCV2, GCV1 2/5 2.7 ϫ 10Ϫ4

( on February 17, 2015 by BIBLIOTHEEK TU DELFT C-1 compound catabolism GCV2, GCV1 2/6 4.1 ϫ 10Ϫ4 ( Bas1 MTD1, ADE17, GCV2, GCV1 4/39 4.8 ϫ 10Ϫ5 Gcn4 SNO1, GCV2, HIS3, ICY2, YMC1, 6/182 3.4 ϫ 10Ϫ4 GNP1

5 Metabolism of arginine ARG7, ARG1, CPA1 3/20 4.4 ϫ 10Ϫ4 ( Arg81 ARG1, CPA1, CUP9 3/22 5.9 ϫ 10Ϫ4

6 Opi1 DAK1, YOP1 2/23 1.7 ϫ 10Ϫ4 a Each cluster was tested for overrepresentation of genes belonging to various functional categories. MIPS categories are indicated in lightface type, Gene Ontology categories are recognizable by the GO identification number, and enrichment for targets of transcription factors is indicated in boldface type. P values indicate the probability of finding an enrichment by chance.

Storage carbohydrate accumulation dynamics during DTC. phosphate and glucose-6-phosphate, precursors of UDP-glu- Metabolism of the storage carbohydrates glycogen and trehalose cose, displayed an inverse correlation with the applied temper- in S. cerevisiae is known to be affected by temperature (22, 59–61). ature profile (Fig. 7B). Accumulation of reserve carbohydrates During DTC, the intracellular concentrations of both glycogen could therefore not be correlated directly to intracellular me- and trehalose oscillated. After the temperature maximum, the tabolite levels in their synthetic pathways. Expression of most concentrations of glycogen and trehalose initially decreased (Fig. genes involved in both synthesis and degradation of glycogen 7A). Six hours after the temperature maximum, the profiles of and trehalose during DTC displayed similar expression these two carbohydrates then diverged. The glycogen concentra- profiles (Fig. 7C). Typically, their expression levels decreased tion increased again after 6 h, while the temperature still de- as the temperature decreased, and the profiles seemed to follow creased, and kept on increasing until the temperature maximum the residual glucose concentration, with a sharp increase in the was reached again. Conversely, the trehalose concentration con- expression level when the glucose concentration dramatically tinued to decrease as the temperature decreased and increased dropped by Ͼ80% between 15 and 18 h after the temperature back to its original level only during the 6 h before the temperature maximum. maximum. During one DTC, approximately 60% of the net gly- Differential physiological and transcriptome responses of S. cogen and trehalose content was mobilized and resynthesized. cerevisiae during DTC and acclimation to low temperature. To Levels of UDP-glucose and trehalose-6-phosphate, direct investigate to what extent S. cerevisiae is able to fully acclimatize its precursors of glycogen and trehalose, respectively, were hardly physiology and transcriptome to the DTC dynamics imposed altered during DTC (Fig. 7B). On the other hand, glucose-1- upon the chemostat cultures, S. cerevisiae was grown with the

July 2014 Volume 80 Number 14 aem.asm.org 4441 Hebly et al. Downloaded from http://aem.asm.org/ on February 17, 2015 by BIBLIOTHEEK TU DELFT

FIG 6 Cyclic variation in cell cycle distribution and cell cycle-regulated genes during anaerobic cultivation of S. cerevisiae under diurnal temperature cycles. (A) Œ ᮀ Cell size ( ) and cell concentration ( ). (B) Budding index ( ) and percentage of cells in the G2/M phase of the cell cycle based on flow cytometric analysis ( ). The bar illustrates the cell cycle distribution of the population, with light gray indicating the majority population in late G1 phase, medium gray indicating the majority population in S/G2/M phase, and dark gray indicating the majority population in early G1 phase. (C) Identification of cell cycle marker genes based on publically available data sets (46, 48, 49). a/b* indicates the number of significantly changed genes in the enriched custom-made category/total number of genes in the custom-made category. P values indicate the probability of finding an enrichment by chance. (D) BI () after switching a culture subjected to diurnal temperature cycles back to a fixed temperature of 30°C (black dashed line). Time zero corresponds to the switch. Gray symbols illustrate the BI under diurnal temperature cycles. Error bars represent standard errors of the means of independent duplicate cultures. In all panels, the dashed gray line illustrates the tem- perature profile. same experimental setup as was used for the DTC experiments transcriptome data showed that transcript levels at 30°C during (glucose-limited anaerobic chemostats, with a dilution rate of 0.03 DTC closely resembled the 30°C steady-state expression levels hϪ1) but at a constant temperature of either 30°C or 12°C. The (Fig. 8). Only 94 genes were differentially expressed between values for physiological characteristics (biomass yield, specific steady-state and DTC cultures at 30°C. Gene expression levels at uptake and production rates, and residual glucose concentration) steady state at 12°C could, however, clearly be distinguished from at 30°C and 12°C during DTC closely resembled the steady-state the levels at 12°C during DTC by PCA (Fig. 8). Pairwise compar- values, with the exception of glycogen contents (Table 3). While ison of transcript levels at 12°C and 30°C during DTC and in trehalose levels were consistently lower at 12°C for both DTC and steady-state cultures showed that the response to temperature steady-state cultures, at low temperatures in steady-state cultures, during DTC (1,061 genes) involved twice as many genes as the the level of glycogen accumulation was higher than that during temperature response to acclimation in steady-state cultures (521 DTC (Table 3). genes [same P value threshold of 0.05 for both steady-state and The cell size and budding index (BI) responded similarly in the DTC data analyses]) (Fig. 9A). However, 44% of the genes that two systems, with a slight increase in cell size and a strong increase were upregulated and 48% of the genes that were downregulated in the BI at 12°C. The BI at 30°C during DTC indicated that 28% Ϯ in steady-state cultures at low temperature responded similarly

1% of the population was in the G2/M phase, in comparison with during DTC (99 upregulated genes and 145 downregulated genes) 30% Ϯ 3% in steady-state chemostat cultures grown at 30°C. At (Fig. 9B). Genes involved in ribosome biogenesis and targets of 12°C during DTC, 72% Ϯ 6% of the yeast population was in the Rme1 (indirectly involved in cell cycle control via activation of Ϯ G2/M phase, which was close to the BI of 65% 2% measured at Cln2 [62]) were upregulated at low temperature, while genes in- the same temperature in steady-state chemostat cultures. volved in the stress response (and particularly targets of Hsf1), Principal component analysis (PCA) of DTC and steady-state protein folding, stabilization, and amino acid transport were

4442 aem.asm.org Applied and Environmental Microbiology Yeast Dynamics during Diurnal Temperature Cycles

downregulated at low temperature. Genes related to phospholipid biosynthesis and the cell cycle also showed a similar response to temperature in DTC and steady-state cultures (see Data Set S4 in the supplemental material). For most of these genes, the magni- tudes of the temperature response were very similar during DTC and steady-state cultures (Fig. 9C). A few genes (6 genes whose expressions were upregulated and 14 genes whose expressions were downregulated at low temperature) did not reach the tran- script level corresponding to low-temperature acclimation during

DTC (Fig. 9C). Many genes (393 upregulated and 424 downregu- Downloaded from lated) were specifically responding to the dynamic temperature profiles and not to acclimation. The proteins encoded by those genes were involved mostly in rRNA processing and energy me- tabolism, categories that were associated with the variation in residual glucose concentrations during DTC. Of the 277 temper- ature-responsive genes that were specifically identified in steady- state chemostat cultures, many were involved in transcription ac-

tivation, transport of metal ions, regulation of nucleotide http://aem.asm.org/ metabolism, and the cell cycle (mostly targets of Swi5, a protein

that activates transcription of genes expressed at the M/G1 phase boundary and in G1 phase [63]) (see Data Set S4 in the supple- mental material).

DISCUSSION Diurnal temperature cycling and growth kinetics. Pronounced, rhythmic variations of the residual glucose concentration were

among the most prominent phenomena observed during physio- on February 17, 2015 by BIBLIOTHEEK TU DELFT logical analysis of anaerobic, glucose-limited chemostat cultures of S. cerevisiae subjected to DTC. In steady-state glucose-limited

chemostat cultures, the residual glucose concentration (CS)isde- fined by the dilution rate (D) (0.03 hϪ1 in this work, which in steady-state chemostats equals the specific growth rate, ␮), the maximum specific growth rate under the experimental conditions ␮ ( max), and the microorganism’s substrate saturation constant for glucose (KS). An empirical relation between these variables was first proposed by Monod (64). C ␮ϭ␮ S max · ϩ (3) KS CS While ␮ did not substantially change during DTC, experimentally ␮ determined max values at 12°C and 30°C differed by almost 1 order of magnitude (0.310 Ϯ 0.002 hϪ1 and 0.048 Ϯ 0.001 hϪ1,

respectively). Assuming that KS is not affected by temperature (32), the Monod equation predicts residual glucose concentra- tions, at a specific growth rate of 0.03 hϪ1, of 1.8 mM and 0.1 mM at 12°C and 30°C, respectively. These values closely correspond to the residual concentrations measured at 12°C and 30°C in the DTC experiments (Fig. 2). These observations can be generalized:

assuming Monod kinetics and temperature insensitivity of KS,in any nutrient-limited microbial culture subjected to (diurnal) temperature cycles, irrespective of the identity of the microorgan-

phosphate (T6P) (green diamonds). Dotted lines and open symbols in panels A and B represent extrapolated data. (C) Expression profiles of genes coding FIG 7 Storage carbohydrate metabolism in S. cerevisiae during diurnal tem- for enzymes involved in storage carbohydrate metabolism. (D) Overlay of perature cycles. (A) Intracellular glycogen (green circles) and trehalose (blue variables relevant for the interpretation of the reserve carbohydrate dynamics,

squares) concentrations during DTC. (B) Intracellular metabolite concentra- including the residual glucose concentration (blue triangles), qS (dotted red tions of key metabolites in storage carbohydrate metabolism, including line), budding index (filled circles), temperature (solid red line), and cell cycle glucose-1-phosphate (G1P) (orange triangles), glucose-6-phosphate (G6P) distribution (color bar) (Fig. 6). In all panels, error bars represent the standard (purple squares), UDP-glucose (UDP-gluc) (red circles), and trehalose-6- errors of the means of at least two independent duplicate cultures.

July 2014 Volume 80 Number 14 aem.asm.org 4443 Hebly et al. 1.8 3.3 0.2 6.4 Ϯ Ϯ Ϯ Ϯ 0.14 65 0.11 30 0.040.05 28 72 Ϯ Ϯ Ϯ Ϯ 4.4 3.6 4.0 4.4 Cell size ( ␮ m) BI (%) at 30°C and 12°C, S q Downloaded from ) Ϫ 1 0.4 0.3 0.6 0.2 Ϯ Ϯ Ϯ Ϯ 2.8 29 14.5 7.7 Trehalose concn (mg glucose equivalent · g [dry weight] ) 5.7 Ϫ 1 3.6 Ϯ 0.2 0.7 http://aem.asm.org/ Ϯ Ϯ Ϯ FIG 8 Principal component analysis of DTC and steady-state transcriptome

Glycogen concn (mg glucose equivalent · g [dry weight] data. The plot shows the average expression levels of all genes at each sampling point projected onto the first and second principal components (PC1 and PC2). , sampling points during DTC; , 12°C steady-state (SS) sampling Œ 0.04 121.1 0.03 38 0.010.07 65 50.5 point; , 30°C steady-state sampling point. . Ϯ Ϯ Ϯ Ϯ Ϫ 1.5 h to 1.5 h and 10.5 to 13.5 h, corresponding to the Ϫ 1 Residual glucose concn (mM)

ism or the growth-limiting substrate, CS should be expected to

0.4 2.1 fluctuate. Indeed, in a previous study on DTC in glucose-limited 2.2 0.2 Ϯ on February 17, 2015 by BIBLIOTHEEK TU DELFT Ϯ cultures, CS was identified as a key parameter in determining the Carbon recovery (%) dynamics of yeast glycolysis (32). These fluctuations complicate

a data interpretation of DTC experiments, as nutrient-independent

Ϫ 1 biological responses to DTC may be obscured by cellular re- values of the time intervals of S

q sponses to changes in CS. Nutrient-independent responses may be 0.02 101 0.10 95 0.120.11 ND ND 0.2 2.6 (mmol · g ) Ϯ Ϯ Ϯ Ϯ identified by simultaneously studying DTC in cultures with dif- Ϫ 1 CO2 3.4 3.7 q 3.9 3.0 ·h [dry weight] ferent growth-limiting nutrients and subsequently looking for common responses. This approach has been successfully applied to study transcriptional responses to macronutrient limitation

) and oxygen availability in steady-state chemostat cultures (31, 42,

Ϫ 1 65); however, its application to DTC studies would require many ·h

Fig. 3A . The intermediate time-consuming and expensive chemostat experiments. As an al- Ϫ 1 (mmol · g [dry 0.01 0.22 ternative approach, we sought to remove glucose-specific re- Ϯ Ϯ EtOH q ND ND weight] sponses from the DTC transcriptome data by comparison with previously reported data sets (50, 51) that capture the effect of

) glucose on the S. cerevisiae transcriptome. Although those previ-

Ϫ 1 ous studies assessed the impact of glucose in different experimen- ·h grown in glucose-limited anaerobic chemostats 0.01 2.8 0.15 3.2 tal contexts (aeration, length of exposure to glucose, and/or range Ϫ 1 c c Ϯ Ϯ of glucose concentrations), circa half of the genes that showed a (mmol · g [dry S Ϫ 1.8 Ϫ 2.1 q Ϫ 2.13 Ϫ 1.96 weight] response to glucose in both previous studies showed a similar response in the DTC cultures (Fig. 5A and B). This correspon- S. cerevisiae

) dence demonstrated that the impact of fluctuating glucose con-

Ϫ 1 centrations extends beyond glycolysis and has a major impact on 0.001 0.004 the yeast transcriptome in DTC chemostats. Ϯ Ϯ b b (g glucose · g Transcriptional responses to DTC: beyond glucose. Removal standard errors of the mean of at least two independent replicates. SS, steady state; EtOH, ethyl alcohol; ND, not determined. sx Y [dry weight] Ϯ of previously described glucose-responsive genes from the DTC- responsive set of transcripts facilitated interpretation of the tran- scriptome data. However, it may have eliminated genes that tran- 30 0.08 12 0.09 12 0.09 Temp (°C) 30 0.08 scriptionally respond to DTC in glucose-dependent as well as glucose-independent manners. Consistent with temperature-de-

Physiological characteristics of pendent remodeling of membrane composition (16, 66, 67), tran- script levels of genes involved in fatty acid metabolism and, in particular, phospholipid synthesis positively correlated with tem- Values represent the averages The biomass yield during DTC was calculated by using the biomass specific glucose consumption rate listed and a specific growth rate of 0.03 h The profile of the biomass specific glucose consumption rate during DTC is shown in DTC respectively, are shown. TABLE 3 Experimental condition a b c SS perature during DTC (Table 2). Cyclic variations of extracellular

4444 aem.asm.org Applied and Environmental Microbiology Yeast Dynamics during Diurnal Temperature Cycles Downloaded from http://aem.asm.org/ on February 17, 2015 by BIBLIOTHEEK TU DELFT

FIG 9 Comparative transcriptomics of the temperature responses during acclimation and DTC. (A) Two pairwise comparisons between transcript levels at 30°C and those at 12°C were performed. Shown are data for one comparison of transcript levels at 30°C at steady state (orange filled circle) versus 12°C at steady state (open light blue circle) and one comparison of 30°C during DTC (red filled circle) versus 12°C during DTC (open dark blue circle). The gray dots represent the sampling points for microarray analysis. (B) The sets of temperature-responsive genes from acclimated and DTC cultures were subsequently compared after pooling of the genes according to their temperature response (left, upregulated at 12°C; right, downregulated at 12°C). (C) Genes that displayed similar temperature responses in both acclimated and DTC cultures were clustered according to the magnitude of their changes in expression levels, and box plots illustrate the mean-normalized expression level per cluster. The change in expression levels of 83 genes was more pronounced during DTC than in acclimated cultures (top), the majority of the genes (141 genes) responded to temperature during DTC and acclimation with similar magnitudes (middle), and 20 genes did not reach full acclimation expression levels at 12°C during DTC (bottom).

July 2014 Volume 80 Number 14 aem.asm.org 4445 Hebly et al.

acetate concentrations (Fig. 3H) may be related to the role of down more strongly than other phases of the cell cycle. However, acetyl coenzyme A (acetyl-CoA) as a key precursor for fatty acid from a previous study on batch cultures of S. cerevisiae, it was de- synthesis (16, 68). Genes involved in ER-to-Golgi transport were duced that temperature had only a minor impact on the budding upregulated as the temperature decreased during DTC (Table 2). index (81). Conversely, chemostat studies performed at a constant Since the yeast plasma membrane and cell wall composition are temperature of 30°C revealed a strong impact of the specific both affected by temperature (66, 69), this response may reflect growth rate on the budding index and cell cycle distribution (43, the need for continuous replacement of membrane and cell wall 82, 83). These seemingly contradictory observations can be recon- components. Intracellular membrane trafficking is also sensitive ciled if cell cycle distribution in S. cerevisiae is primarily correlated to temperature in mammalian cells (70–72), and in a genome- not with temperature or the absolute specific growth rate but in- ␮ ␮ wide screen for sensitivity to high pressure and low temperature, stead with the relative specific growth rate ( / max) under a given Downloaded from yeast mutants affected in membrane trafficking showed decreased set of cultivation conditions. This would imply that in batch cul- ␮Ϸ␮ fitness (73). A possible temperature-dependent alteration of the tures ( max) grown at different temperatures, no impact on cell wall during DTC is further supported by the overrepresenta- cell cycle distribution is expected, as observed previously by tion of target genes of the transcription factors Swi4 and Swi6, Vanoni et al. (81). Conversely, variation of ␮ at a constant tem- ␮ which are known to play an important role in the regulation of perature (i.e., at constant max)(43, 82, 83) or temperature-im- ␮ ␮ genes required for cell wall remodeling (69), among the DTC- posed variation of max at constant (this study) should affect the responsive transcripts. cell cycle distribution. Our results with DTC and steady-state che- ϭ Ϫ1 ␮ ␮ Genes involved in arginine synthesis (ARG7, ARG1, and CPA1) mostat cultures (D 0.03 h ), in which the / max varied from http://aem.asm.org/ and glycine degradation (GCV2 and GCV1) showed decreased approximately 0.1 at 30°C to 0.63 at 12°C, are in full agreement transcript levels when temperature was decreased during DTC. with this hypothesis. Regulated by the transcription factors Arg81 and Bas1, respec- Dynamics of storage carbohydrate content during diurnal tively, both these processes are also controlled by Gcn4 (74). How- temperature cycles. Intracellular pools of glycogen and trehalose, ever, despite a positive correlation of GCN4 expression with tem- the two major storage carbohydrates in S. cerevisiae, displayed perature (18, 75), only a few of its target genes (74) displayed intriguing dynamics during the DTC. In S. cerevisiae, levels of temperature-dependent expression. Genes involved in de novo these two glucose polymers are affected by many parameters, purine synthesis, a process regulated by Bas1 and Gcn4 (74), were including nutrient availability (84) (and thereby growth rate [43,

also overrepresented among genes whose expression levels were 82]), temperature, and cell cycle progression (reviewed in refer- on February 17, 2015 by BIBLIOTHEEK TU DELFT reduced at 12°C. An upregulation of these genes upon a relief of S. ences 59 and 85). Interpretation of the dynamics of storage carbo- cerevisiae cultures from glucose limitation was previously corre- hydrate pools during DTC is complicated due to the strong vari- lated with a strong decrease in intracellular adenine nucleotide ations of all three of these parameters. Cold shock (23) and full concentrations (50). However, the adenine nucleotide concentra- acclimation (Table 3) have been shown to affect storage carbohy- tion remained constant during the DTC (see Fig. S4 in the supple- drate accumulation. However, no direct correlations of intracel- mental material). The fluctuations of the expression of genes in- lular contents of glycogen and trehalose with temperature were volved in de novo purine biosynthesis may therefore reveal a observed during DTC, and it is likely that temperature per se was regulation specific to dynamic temperature conditions. not the primary trigger for the reserve carbohydrate dynamics Cell cycle synchronization during DTC: temperature or rel- during DTC. ative specific growth rate? Rhythmic variations of the budding Under slow-growth, aerobic, glucose-limited conditions, S.

index, flow cytometric analysis, and transcription of cell cycle- cerevisiae stores glycogen and trehalose in G1 phase and quickly responsive genes all indicated a partial synchronization of the mobilizes them in late G1 phase to provide metabolic energy for yeast cell cycle during the DTC experiments. In this respect, it is budding (77). During DTC, mobilization of reserve carbohy- relevant to note that at the dilution rate used for chemostat culti- drates coincided with a phase of the temperature cycle where the ϭ Ϫ1 ϭ vation (D 0.03 h ), the average biomass doubling time (td majority of the population was in late G1 phase (Fig. 7D). This ln2/␮ϭln2/D) of 23.1 h closely corresponded to the length of the is consistent with the notion that during DTC, reserve 24-h temperature cycle, which might have facilitated partial syn- carbohydrate dynamics were governed primarily by cell cycle pro- chronization. Rapid loss of synchronization was observed upon gression. However, this interpretation is likely to be an oversim- release from DTC (Fig. 6D). This observation showed that under plification, since the dynamics of trehalose and glycogen clearly the experimental conditions, DTC caused an externally imposed differed, probably reflecting the involvement of different signaling cell cycle synchronization rather than “entrainment” of an endog- pathways. Resolving the mechanism by which storage carbohy- enous synchronization mechanism (e.g., by a yeast circadian clock drate metabolism is regulated during DTC will require detailed [76], for which there is no molecular evidence at present). Loss of analysis of culture heterogeneity, especially with respect to cell synchronization upon release from DTC in anaerobic chemostat cycle progression. cultures also represents a marked difference from the cell cycle- Yeast adaptation to diurnal temperature variation approaches related spontaneous and sustained metabolic oscillations that are full acclimation. At the temperature extremes (12°C and 30°C) frequently observed in aerobic, glucose-limited chemostat cul- during DTC, the physiology of anaerobic S. cerevisiae chemostat tures of S. cerevisiae (77–80). cultures resembled that of steady-state chemostat cultures that

A much higher incidence of G2/M cells at 12°C than at 30°C were fully acclimatized to these two temperatures (Table 3). At was observed not only during DTC but also in nonsynchronized, 30°C, transcript profiles of steady-state and DTC cultures could steady-state chemostat cultures grown at the same dilution rate hardly be discriminated by principal component analysis (Fig. 8). (Table 3). A possible interpretation of these observations is that at While a larger difference was observed at 12°C, low-temperature-

low temperatures, progression through the G2/M phase is slowed responsive genes were involved in similar cellular functions under

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