Physiological and transcriptional responses of anaerobic chemostat cultures of Saccharomyces cerevisiae subjected to diurnal temperature cycles.

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

Updated information and services can be found at: http://aem.asm.org/content/80/14/4433 These include: SUPPLEMENTAL MATERIAL REFERENCES

CONTENT ALERTS

Supplemental material This article cites 86 articles, 39 of which can be accessed free at: http://aem.asm.org/content/80/14/4433#ref-list-1 Receive: RSS Feeds, eTOCs, free email alerts (when new articles cite this article), more»

Information about commercial reprint orders: http://journals.asm.org/site/misc/reprints.xhtml To subscribe to to another ASM Journal go to: http://journals.asm.org/site/subscriptions/

Downloaded from http://aem.asm.org/ on October 17, 2014 by Rensselaer Libraries

Marit Hebly, Dick de Ridder, Erik A. F. de Hulster, Pilar de la Torre Cortes, Jack T. Pronk and Pascale Daran-Lapujade Appl. Environ. Microbiol. 2014, 80(14):4433. DOI: 10.1128/AEM.00785-14. Published Ahead of Print 9 May 2014.

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

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, oscillating 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 involved 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 previously identified temperature-responsive genes as well as genes involved in the cell cycle and de novo purine biosynthesis. Indepth 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 phase distribution indicated that cell cycle synchronization was most likely an effect of imposed fluctuations of the relative growth rate (␮/␮max) rather than a direct effect of temperature.

T

emperature directly influences the kinetics of all biochemical reactions and many cellular processes (1, 2). Within their temperature range for growth, many microorganisms adapt their metabolic networks and biomass composition to optimize their growth rate and viability in response to changing temperatures. Classical examples of temperature adaptation include modifications of membrane fluidity (3, 4) and the expression of heat shock proteins that assist in protein folding at high temperatures (5, 6). Laboratory studies on microbial temperature responses are based largely on two experimental systems. Acclimation, i.e., the result of complete physiological adaptation to a given temperature, has been studied both in batch cultures and in chemostats (7–9). In cold shock and heat shock experiments, the responses of microorganisms to sudden upshifts or downshifts in temperature are studied, typically over a time period ranging from a few minutes to a few hours (3, 10–13). In natural environments, microorganisms are subjected to several types of temperature dynamics. In exposed environments, one of the dominant aspects of temperature dynamics is related to circadian changes, with higher temperatures during the day and lower temperatures during the night. These 24-h temperature cycles, which are superimposed on seasonal changes, raise a number of interesting questions related to microbial temperature adaptation that have hitherto hardly been addressed in microbial physiology. For many microorganisms, the time constants of circadian temperature changes are in the same order of magnitude as their generation times. Moreover, the rate at which microorganisms can adapt their physiology to changes in temperature is itself likely to be strongly temperature dependent, for instance, because rates of transcription and translation decrease strongly at low temper-

July 2014 Volume 80 Number 14

atures (2, 3). It is therefore unclear whether 24-h cycles enable full temperature acclimation or whether they confront microorganisms with a “continuous temperature shock” scenario. The impact of temperature on physiology has been extensively studied in Saccharomyces cerevisiae, a mesophilic microorganism with an optimum growth temperature of circa 33°C (14, 15), particularly because several of its industrial applications, such as brewing, wine making, and frozen-dough applications, involve suboptimal temperatures (16–19). Temperature ranges for growth are strain dependent, but most S. cerevisiae strains are able to grow at temperatures between 4°C and 40°C (15). Natural isolates of S. cerevisiae are typically associated with exposed environments, e.g., the bark of oak trees (20, 21). To understand the physiology of this yeast in its natural environment, it is relevant to study its physiology under circadian temperature fluctuations. Furthermore, such studies may contribute to our understanding

Received 7 March 2014 Accepted 5 May 2014 Published ahead of print 9 May 2014 Editor: A. A. Brakhage Address correspondence to Pascale Daran-Lapujade, [email protected]. * Present address: Dick de Ridder, Bioinformatics Group, Wageningen University, Wageningen, The Netherlands. Supplemental material for this article may be found at http://dx.doi.org/10.1128 /AEM.00785-14. Copyright © 2014, American Society for Microbiology. All Rights Reserved. doi:10.1128/AEM.00785-14

Applied and Environmental Microbiology

p. 4433– 4449

aem.asm.org

4433


Industrial Microbiology Section, Department of Biotechnology, Delft University of Technology, Delft, The Netherlandsa; Kluyver Centre for Genomics of Industrial 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

Hebly et al.

MATERIALS AND METHODS Strain and media. The prototrophic haploid yeast strain Saccharomyces cerevisiae CEN.PK113-7D (MATa) (33, 34) was used in this study. The medium used for chemostat and sequential batch cultivation contained 0.3 g · liter⫺1 (NH4)2SO4, 0.3 g · liter⫺1 KH2PO4, 0.5 g · liter⫺1 MgSO4 · 7H2O, 3.0 g · liter⫺1 NH4H2PO4, and 25 g · liter⫺1 glucose. The anaerobic growth factors Tween 80 and ergosterol (0.42 g · liter⫺1 and 10 mg · liter⫺1, respectively), trace elements, and vitamin solution (both 1 ml · liter⫺1) were added to the medium as described previously (35). The medium was supplemented with 0.15 g · liter⫺1 silicone antifoam (antifoam C; Sigma-Aldrich, St. Louis, MO, USA). Chemostat cultivation. Anaerobic glucose-limited chemostat cultures were grown in 2-liter laboratory bioreactors (Applikon, Schiedam, The Netherlands) with a working volume of 1.4 liters and at a dilution rate of 0.03 h⫺1. The pH was controlled at 5.0 by the automatic addition of 2 M KOH, and the stirrer speed was kept at 800 rpm. To ensure anaerobic conditions, bioreactors and medium vessels were continuously sparged with pure nitrogen (N2) gas at flow rates of 0.7 liters · min⫺1 and circa 7 ml · min⫺1, respectively, and equipped with Norprene tubing (SaintGobain Performance Plastics, Courbevoie, France) and Viton O-rings (Eriks, Alkmaar, The Netherlands). Active temperature regulation was performed and measured online by connecting a platinum resistance thermometer, placed in a socket in the bioreactor, to an RE304 low-temperature thermostat (Lauda, Lauda-Königshofen, Germany). Steadystate sampling of cultures grown at a constant temperature of 12°C or 30°C was performed when biomass dry weight, metabolite levels, and carbon dioxide production rates differed by ⬍5% for at least three consecutive volume changes. Cultivation in sequential batch reactors. The use of sequential batch reactors (SBRs), instead of single batches inoculated from standardized shake-flask-grown precultures, has been shown to enhance the reproducibility of specific growth rate measurements (29, 36). SBR experiments at 12°C and 30°C were performed under conditions identical to those for chemostat cultivation, with a working volume of 1 liter. The first batch was inoculated with a shake flask preculture grown overnight (37). Growth was constantly monitored via online CO2 measurements in the

4434

aem.asm.org

FIG 1 Diurnal temperature profile. The temperature profile imposed on chemostat cultures of S. cerevisiae was derived from a Web-accessible air temperature database (http://www.infoclimat.fr/recherche/sphinx.php?q⫽05%2F08 %2F2007). The open symbols are the temperatures measured in Strasbourg, France, on 5 August 2007, and the solid line indicates the programmed sinusoidal function, T (°C) ⫽ 21 ⫹ 9 sin{[t (h) ⫹ 6] ␲/12}. off-gas with an NGA 2000 analyzer (Rosemount Analytical, Orrville, OH, USA). When the CO2 percentage in the off-gas dropped below 0.05%, indicating depletion of glucose, a computer-controlled peristaltic pump removed 0.9 liters of culture, leaving approximately 0.10 liters as the inoculum for the next batch (36). Specific growth rates at 12°C and 30°C were calculated based on biomass dry weight measurements from samples taken during the fourth and fifth cycles. Diurnal temperature experiments. After three volume changes at a constant temperature of 30°C without significant changes in biomass dry weight, a preprogrammed sinusoidal temperature profile, T (°C) ⫽ 21 ⫹ 9 sin{[t (h) ⫹ 6] ␲/12}, was started. This profile was designed to mimic a real-life circadian temperature cycle recorded in nature (Fig. 1). Samples were taken during the fifth and/or sixth temperature cycle. To minimize disturbance, sampling volumes did not exceed 5% of the reactor volume during a single temperature cycle, and intervals of at least 3 h were maintained between sampling points. Analytical methods. Gas analysis and analyses of biomass dry weight, total cell concentration, cell size, and residual glucose and extracellular and intracellular metabolite concentrations in chemostat cultures were performed as described previously (32, 37). Samples for the analysis of the intracellular storage carbohydrates trehalose and glycogen were taken in duplicate from two independent replicate cultures. By means of a rapid sampling setup (32), circa 2.2 ml of broth was rapidly quenched in 5 ml of cold (⫺40°C) pure methanol. The exact sampling weight was measured, and samples were washed once with 5 ml of cold (⫺40°C) pure methanol. The pellet was stored at ⫺80°C until analysis of glycogen and trehalose concentrations according to a previously reported procedure (38). Glucose concentrations were determined with an Enzyplus D-glucose kit (EZS781⫹; BioControl Systems, Bellevue, WA, USA). Prediction of the biomass specific glucose consumption rate during DTC. The biomass specific glucose consumption rate, qS (mmol glucose · g [dry weight]⫺1 · h⫺1), was calculated by using equations 1 and 2. The amount of biomass, MX (grams), in the reactor did not change substantially during DTC; therefore, a constant value of 1.95 g · liter⫺1 was used (see Fig. 3C). Changes in the amount of substrate in the culture broth, dMS (mmol · h⫺1), were calculated based on changes in residual glucose dt concentrations (CS) per 3-h interval. Subsequently, the rate of glucose consumption, rateS, was calculated with equation 2, in which CS,in is the substrate concentration in the feed (mmol · liter⫺1) and ⌽ is the medium flow rate (liters · h⫺1).



of how evolution under circadian temperature cycles has shaped the physiology and the genome of this important industrial microorganism. Previous work on temperature responses of S. cerevisiae included 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 providing inclusive descriptions of fully temperature-acclimated S. 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 used to specifically investigate the impact of temperature dynamics on yeast glycolysis, based on integrated modeling and experimental analysis of the in vivo kinetics of glycolytic enzymes (32). Rather than focusing on a single metabolic pathway, the present 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 temperature regime are compared with data from fully acclimated chemostat cultures.

Yeast Dynamics during Diurnal Temperature Cycles

qS ⫽ rateS ⁄ MX

(1)

dMs ⫽ rateS ⫹ ⌽ 共CS,in ⫺ CS兲 dt

(2)


RESULTS

Rhythmic, reproducible changes in glucose fermentation during diurnal temperature cycles. To study diurnal temperature cycles (DTC) under controlled laboratory conditions, sinusoidal temperature variations with a 24-h period were imposed on anaerobic, glucose-limited chemostat cultures of S. cerevisiae CEN.PK113-7D. The temperature profile was designed to closely resemble a natural 24-h temperature cycle ranging between 30°C and 12°C (Fig. 1). The maximum specific growth rates (␮max) of S. cerevisiae CEN.PK113-7D at 30°C and 12°C were estimated in sequential batch reactor setups (see Materials and Methods) under conditions identical to those of the chemostat cultures but in the presence of excess glucose. At 30°C, the ␮max was 0.310 ⫾ 0.002 h⫺1, while at 12°C, it was only 0.048 ⫾ 0.001 h⫺1. To prevent washout of the chemostat cultures during the temperature minimum of the DTC, the dilution rate in the DTC chemostat cultures was set at 0.03 h⫺1. During a series of consecutive DTC, the biomass concentration in the chemostat cultures remained essentially constant (Fig. 2). However, CO2 production revealed a clear cyclic variation in fermentative activity (Fig. 2). As the temperature decreased from 30°C to 12°C during the first half of the 24-h cycles, the CO2 production rate decreased by 35%. The subsequent temperature increase was accompanied by an acceleration of the CO2 production rate, after which it reached its initial level. The residual glucose concentration also markedly fluctuated during the DTC (Fig. 2). For both CO2 and residual glucose, the amplitude of this fluctuation decreased as yeast cells acclimated to the DTC and became steady and reproducible after three cycles (Fig. 2). A closer inspection of the fifth and sixth temperature cycles revealed that the residual glucose concentration was inversely correlated with temperature (Fig. 3A), reaching values of 2.64 ⫾ 0.07 mM and 0.20 ⫾ 0.01 mM at 12°C and 30°C, respectively. Interest-

aem.asm.org 4435


Budding index. For determination of the budding index (BI), i.e., the percentage of cells carrying a bud, multiple microscopic slides were prepared from each independent sample, and multiple pictures of each slide were taken by phase-contrast microscopy with an Imager-D1 microscope equipped with an AxioCam MR camera (Carl Zeiss, Oberkochen, Germany), using an EC Plan-Neofluar 40⫻/0.75 Ph 2 M27 objective (Carl Zeiss, Oberkochen, Germany). To ensure unbiased analysis, photographs were blindly labeled and randomly shuffled before counting. At least 200 cells were counted for each sample point and used to determine the budding index. Flow cytometry analysis. Cell cycle phase distribution in yeast populations was estimated with a flow cytometry-based method. A sample of culture broth equivalent to circa 1.6 ⫻ 106 cells was taken from the bioreactor and centrifuged (5 min at 4,700 ⫻ g). The pellet was washed once with cold phosphate buffer (3.3 mM NaH2PO4, 6.7 mM Na2HPO4, 130 mM NaCl, 0.2 mM EDTA) (39), vortexed briefly, centrifuged again (5 min at 4,700 ⫻ g), and suspended in 800 ␮l 70% ethanol while vortexing. After the addition of another 800 ␮l 70% ethanol, fixed cells were stored at 4°C until further staining and analysis. Staining of the cells with Sytox green nucleic acid stain (catalog number S7020; Invitrogen) was performed as described previously (40). Samples were analyzed on a FACSCalibur instrument (BD Biosciences, Franklin Lakes, NJ, USA) equipped with an argon laser at 488 nm. The cell cycle phase distribution for each sample was estimated by using the histogram modeler tool from the free software CyFlogic (version 1.2.1; CyFlo Ltd., Turku, Finland). Microarray and transcriptome analyses. Samples for microarray analysis were taken during the fifth and sixth temperature cycles from two independent duplicate cultures. To minimize disturbance, the sampling volume was kept within 5% of the reactor volume during 24 h. For the last time point, one additional analytical duplicate sample was taken, resulting in a complete data set of 13 arrays. The sampling procedure (quenching in liquid nitrogen), processing of the samples, RNA isolation, and microarray analysis with the use of the GeneChip Yeast Genome S98 array (Affymetrix, Santa Clara, CA, USA) were performed as described previously (41). Microarray data acquisition (using a target value for global scaling of 240), elimination of insignificant variation, and extraction of the 6,383 yeast open reading frames (42) were done as described previously (43). Sample points from the fifth and sixth temperature cycles were combined, resulting in six sample points covering one temperature cycle (at temperatures of 30°C, 21°C, 14.6°C, 12°C, 21°C, and 27.4°C). The expression data from these 6 time points were subjected to time course differential expression analysis by empirical analysis of digital gene expression data in R (EDGE, v1.1.291) (43, 44). All genes with a P value of ⬍0.002 were considered to be significantly changed (1,102 genes) and were subsequently k-means clustered using the k-means clustering algorithm of Genedata Expressionist Pro (v3.1) that uses positive correlation as distance metric (43), resulting in six clusters. The clusters were examined by the use of a hypergeometric distribution test (45) for enrichments of functional categories and transcription factor (TF) binding (46), as described previously (43, 47). If only a secondary or tertiary category, as defined by the Munich Information Center for Protein Sequences (MIPS) database, was enriched, this category was shown, and Gene Ontology (GO) leaf categories were shown if they identified a category that was not enriched in MIPS. DTC-specific genes were examined by a hypergeometric distribution test for enrichments of a number of custom-made categories consisting of cell cycle phase marker genes (according to references 48 and 49). To distinguish between glucose responses and temperature responses, the transcriptome data set was compared with data from previous studies on glucose-dependent transcriptional responses of S. cerevisiae CEN. PK113-7D (50, 51). Primary data (.CEL files) from those previous studies were retrieved from the Gene Expression Omnibus database (accession

numbers GSE3821 and GSE8187, respectively [http://www.ncbi.nlm.nih .gov/geo]) and uploaded into Genedata Expressionist Pro (v3.1). k-means clustering of the 215 and 195 genes in the two data sets was performed separately as described above (see Fig. S1 in the supplemental material). Steady-state chemostat cultures at constant temperatures of 12°C and 30°C (independent duplicate cultures at both temperatures) were sampled for microarray analysis as described above, thereby expanding the total array set to 17 arrays. Genes that were differentially expressed between two conditions (e.g., pairwise comparison between cultures at steady state at 12°C and those at steady state at 30°C) were identified by first removing probe intensity background (52) and quantile normalizing intensity levels (53) as in robust multichip analysis (RMA). Subsequently, array groups (two biologically independent arrays per group) that corresponded to the sampling point were compared based on the perfectmatch probe intensity levels only (52), by performing a per-probe-set two-way analysis of variance (with the factors “probe” and “sampling point”). Adjustment of P values for multiple testing was done with Šidák step-down adjustment (54). Differences with adjusted P values of ⬍0.05 and a fold difference of 2 or higher were considered significant. Principal component analysis. For each sampling point during DTC, an average expression profile was calculated based on the two biologically independent arrays. This set was extended with average expression profiles of steady-state samples at 12°C and 30°C. Principal component analysis (55) was performed, and samples were projected onto the first two components. Microarray data accession number. The complete data set (17 arrays) was deposited at the Gene Expression Omnibus database (http://www .ncbi.nlm.nih.gov/geo) under accession number GSE55372.

Hebly et al.

(dashed line) and average biomass concentrations (Œ) with standard deviations measured at the highest (30°C) and lowest (12°C) temperatures during seven 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 as the imposed temperature profile. Instead, glucose concentrations decreased much faster when the temperature increased after passing the temperature minimum than they increased as the temperature minimum was approached. This asymmetry was also visible in the off-gas CO2 profile (Fig. 3B), in which a slow deceleration of CO2 production during the temperature decrease was followed by a sharp acceleration when the temperature increased again. After “overshooting,” the CO2 profile resumed its initial level before the temperature maximum was reached. Biomass concentration, measured as culture dry weight, remained constant at a value of 1.95 g · liter⫺1, with a variation of ⬍5% during the DTC (Fig. 3C). The concentrations of extracellular metabolites (ethanol, glycerol, lactate, succinate, and pyruvate) (Fig. 3D to G) were largely unaffected by the cyclic temperature variation, with the notable exception of acetate, the concentration of which rhythmically varied by circa 70% (Fig. 3H). However, the concentration of acetate was very low compared to those of ethanol and glycerol and represented ⬍0.24% of the organic metabolites detected in culture supernatants. Substantial transcriptional reprogramming during DTC. To obtain an overview of the cellular responses of S. cerevisiae to diurnal temperature cycles, a transcriptome analysis was per-

4436

aem.asm.org

formed, covering six time points during the 24-h temperature cycle (Fig. 4). In contrast to the relatively mild physiological impact of the DTC, microarray analysis revealed major reprogramming of gene expression. Using stringent statistical criteria (P value of ⬍0.002), a set of 1,102 genes (representing 17% of the S. cerevisiae genome) showed changes in transcript levels. Clustering of these genes according to their transcript profiles revealed patterns of transcriptional induction and repression that either positively or negatively correlated with the temperature profiles (Fig. 4). Transcript levels of 498 genes peaked at the lowest temperature point in the cycle (clusters 1, 2, and 3) (Fig. 4), while transcript levels of 384 genes (clusters 4 and 5) (Fig. 4) decreased with decreasing temperature and reached their lowest transcript level at the temperature minimum. The expression levels of 220 genes in cluster 6 were hardly affected when the temperature decreased from 30°C to 12°C but were strongly increased as the temperature increased again, reaching the highest level before the temperature maximum was reached, after which expression levels returned to their values at 30°C. While transcript levels of genes in clusters 1 and 2 were inversely correlated with temperature, they could be differentiated by the rates at which their transcript levels changed. Transcript levels of genes in cluster 1 increased faster with decreasing tem-



FIG 2 Adaptation of S. cerevisiae cultivated in anaerobic glucose-limited cultures to diurnal temperature profiles. (Top) Applied temperature cycles over time


dashed line illustrates the applied temperature profile over time. (A) Residual glucose concentration () and biomass specific glucose uptake rate (qS) (dotted 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 (〫). (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.

peratures between 21°C and 12°C than did those in cluster 2, while the expression levels of genes in cluster 2 displayed a steeper decrease with increasing temperatures between 12°C and 21°C. Although transcript profiles of genes in cluster 3 resembled those of genes in cluster 2, cluster 3 was characterized by a steeper decrease of transcript levels as the temperature increased from 12°C to

21°C. This decrease was followed by an increase in expression levels between 21°C and 30°C, returning to the initial expression values. These differences in transcriptional regulation between clusters 1, 2, and 3 were mirrored in the diversity of the overrepresented functional categories in these three clusters (Table 1). 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 ⬍0.002) (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 ⫾ standard deviations. The dashed line illustrates the applied temperature profile over time.


aem.asm.org 4437


FIG 3 Physiological characterization of S. cerevisiae cultivated under anaerobic glucose-limited conditions during a diurnal temperature cycle. In all panels, the

TABLE 1 Functional enrichment analysisa

P value

Cell wall (42.01)

8/215

4.0 ⫻ 10⫺4

2 (146)

Mbp1 Ino4 Swi6 Swi4 Ino2

14/165 6/33 12/160 11/144 5/31

2.5 ⫻ 10⫺5 8.6 ⫻ 10⫺5 2.7 ⫻ 10⫺4 4.2 ⫻ 10⫺4 6.1 ⫻ 10⫺4

3 (298)

Protein synthesis (12) Transcription (11) Protein with binding function or cofactor requirement (16) Fhl1 Rap1 Sfp1

108/511 113/1,036 81/1,049 48/208 27/145 14/50

3.8 ⫻ 10⫺46 1.5 ⫻ 10⫺20 1.1 ⫻ 10⫺6 2.0 ⫻ 10⫺21 4.0 ⫻ 10⫺10 3.5 ⫻ 10⫺8

4 (51)

Bas1 Gcn4

4/39 7/182

2.4 ⫻ 10⫺4 5.5 ⫻ 10⫺4

5 (333)

Energy (2) Mitochondrion (42.16) C compound and carbohydrate metabolism (1.05) Transported compounds (substrates) (20.01) Fe/S binding (16.21.08) Protein folding and stabilization (14.01) Mitochondrial transport (20.09.04) Hap4 Hsf1 Hap3 Cin5 Ume6

80/360 31/170 53/510 56/585 4/5 15/95 15/100 19/57 15/55 9/26 24/180 17/131

4.1 ⫻ 10⫺31 5.5 ⫻ 10⫺10 6.0 ⫻ 10⫺7 3.9 ⫻ 10⫺6 3.5 ⫻ 10⫺5 1.0 ⫻ 10⫺4 1.9 ⫻ 10⫺4 2.7 ⫻ 10⫺11 7.5 ⫻ 10⫺8 3.6 ⫻ 10⫺6 1.8 ⫻ 10⫺5 4.2 ⫻ 10⫺4

6 (220)

Metabolism (1) Cell rescue, defense, and virulence (32) Lysosomal and vacuolar protein degradation (13.13.04) Energy (2) Msn2 Msn4

87/1,530 41/558 7/27 27/360 15/122 14/121

1.3 ⫻ 10⫺7 2.0 ⫻ 10⫺6 2.6 ⫻ 10⫺5 9.7 ⫻ 10⫺5 1.7 ⫻ 10⫺5 6.3 ⫻ 10⫺5

1 (54)

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



Enriched functional category (MIPS functional category)

No. of significantly changed genes in enriched functional category/total no. of genes in functional category

Cluster (no. of genes) and profile



A close comparison of the DTC data set and the two previously reported data sets revealed that among the 498 genes whose transcription was upregulated during the phases of the DTC when the residual glucose concentration was high (clusters 1, 2, and 3), over half (283 genes) (Fig. 5A) were also upregulated in the studies by Kresnowati et al. and Van den Brink et al. Similarly, about twothirds of the 604 genes downregulated at high residual glucose concentrations in the DTC (clusters 4, 5, and 6) were also identified as being glucose repressed in the two previously reported data sets (409 genes) (Fig. 5B). These 692 “glucose-responsive” genes were not uniformly distributed over the six clusters (Fig. 5C) and belonged to functional categories previously associated with responses to glucose excess. To separate indirect transcriptome responses to changes in the glucose concentration from temperature-specific responses, the 692 glucose-responsive genes were removed from the set of DTCresponsive genes. This resulted in a smaller set of 410 genes (Fig. 5A to C). This set of 410 genes was, however, still strongly enriched for categories that are associated with changes in glucose concentrations (e.g., protein synthesis) (Fig. 5D; see also Data Set S2 in the supplemental material). To further narrow down the data set and more precisely define the set of DTC-responsive genes, the data sets of Kresnowati et al. and Van den Brink et al. were further mined. As those two studies employed rather stringent statistical criteria, their complete data sets were used. The profiles of the 410 genes identified in the present study were compared with the profiles of the corresponding genes in the studies by Kresnowati et al. and Van den Brink et al. (see Materials and Methods; see also Fig. S1 in the supplemental material). Profiles among these 410 genes that responded to glucose in these published data sets were removed from the DTC data set (see Fig. S2 in the supplemental material), resulting in a final data set of 253 genes that were considered to be DTC specific. As anticipated, this additional data processing successfully filtered out genes involved in protein synthesis (see Table S1 in the supplemental material). The DTC-specific genes belonged to six main cellular processes and were spread across the different clusters (Table 2; see also Data Set S3 in the supplemental material): lipid metabolism (more specifically phospholipid metabolism), endoplasmic reticulum (ER)to-Golgi transport, RNA polymerase III transcription, one-carbon metabolic processes, amino acid metabolism, and cell cycle progression. Cyclic variations in cell cycle distribution among the yeast population. Among the genes that responded to DTC in an apparently glucose-independent manner were several targets of the transcription factors Swi4, Swi6, and Mbp1 (Table 2). These three transcription factors form complexes (Swi4pSwi6p forms the SBF complex and Swi6p-Mbp1p forms the MBF complex) that play an active role in cell cycle control by regulating transcription during the transition from G1 to S phase of the cell cycle (58). Measurement of cell number and size during the course of a temperature cycle revealed a 22% decrease in cell number and a concomitant 16% increase in cell size during half of the cycle when the temperature decreased, followed by an increase in cell number and a drop in cell size 15 h after the temperature maximum, when the temperature increased to a value above 15°C (Fig. 6A). These data suggested that the yeast population, or rather a fraction of the population, was arresting cell division as the temperature decreased while still growing in size. Furthermore, the data indicated that

aem.asm.org 4439


cell wall organization (8 genes [P value of 4.0 ⫻ 10⫺4]). Cluster 2 (146 genes) was enriched for targets of the transcriptional activator Ino2/Ino4 (P values of 6.1 ⫻ 10⫺4 and 8.6 ⫻ 10⫺5, respectively), involved in the induction of phospholipid biosynthesis (56). Cluster 2 was enriched for targets of the transcription factors Swi4, Swi6, and Mbp1 (11, 12, and 14 genes [P values of 5.1 ⫻ 10⫺4, 3.3 ⫻ 10⫺4, and 2.7 ⫻ 10⫺5], respectively), involved in cell cycle progression. Cluster 3 (298 genes) showed a very strong overrepresentation of genes involved in protein synthesis, more specifically in ribosome biogenesis and rRNA processing (93 genes [P value of 4.3 ⫻ 10⫺49] and 73 genes [P value of 2.8 ⫻ 10⫺53], respectively). Consistent with these observations, cluster 3 also contained a large number of ribosomal protein genes, whose transcription is regulated by the transcription factors Fhl1, Rap1, and Sfp1. Genes in clusters 4 and 5, which were characterized by a downregulation of expression at low temperatures, differed mostly in their response to increasing temperatures (Fig. 4). While the expression of genes in cluster 4 gradually resumed toward their expression levels at 30°C, genes in cluster 5 exhibited a slight overshooting of their transcript levels before resuming their initial expression level at 30°C. As observed for clusters 2 and 3, this subtle difference in temperature responses between clusters 4 and 5 coincided with substantial differences in the overrepresentation of functional categories. Cluster 5 was strongly enriched for genes involved in energy metabolism (80 genes [P value of 4.1 ⫻ 10⫺31]) and more specifically in energy conservation via respiration and energy reserves (38 genes [P value of 4.2 ⫻ 10⫺19] and 12 genes [P value of 1.3 ⫻ 10⫺5], respectively). Conversely, few categories were enriched among the 51 genes in cluster 4, covering targets of Bas1 (involved in one-carbon metabolism) (P value of 2.4 ⫻ 10⫺4) and an enrichment for targets of the Gcn4 transcription factor (7 genes [P value of 5.5 ⫻ 10⫺4]) involved in the regulation of amino acid biosynthesis. In cluster 6 (220 genes), various cellular functions and cell protection mechanisms were overrepresented, including the subcategories stress response (37 genes [P value of 5.3 ⫻ 10⫺7]), lysosomal and vacuolar protein degradation (7 genes [P value of 2.6 ⫻ 10⫺5]), and metabolism of energy reserves (9 genes [P value of 6.9 ⫻ 10⫺5]). Predominant response to cyclic variations in residual glucose concentrations. Cyclic variation of residual glucose concentrations was one of the most pronounced phenomena observed when yeast cultures were exposed to DTC. The presence of glucose, the preferred carbon source of S. cerevisiae, in culture media is well known to trigger a wide array of transcriptional responses in a concentration-dependent manner (57). Previous studies investigated the transcriptional responses of S. cerevisiae supplied with low and high concentrations of glucose. For instance, Kresnowati et al. and Van den Brink et al., using the same yeast strain as the one used in the present study, identified the response of aerobic glucose-limited cultures of S. cerevisiae to a sudden increase of the glucose concentration (50, 51). The sets of genes found to be differentially expressed in these two studies largely overlap and together cover the transcriptional response to glucose excess, such as increased expression levels of genes related to ribosome biogenesis and rRNA processing and repression of genes involved in energy metabolism and (storage) carbohydrate metabolism (50, 51). These responses were also identified in the present work.

Hebly et al.

cell division resumed as the temperature increased again. In agreement with cell size and cell number measurements, both microscopic and flow cytometric analyses (see Fig. S3 in the supplemental material) consistently demonstrated an accumulation of budded cells in the G2/M phase of circa 70% of the yeast population at 12°C and subsequent bud release as the

4440

aem.asm.org

temperature increased (Fig. 6B). Accordingly, analysis of cell cycle phase marker genes (48, 49) revealed an enrichment for genes involved in the S/G2 phase of the cell cycle (Fig. 6C) at 12°C, while as the temperature increased again, a concerted upregulation of genes involved in early G1 and M/G1 cell cycle phases was observed (Fig. 6C).



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.


TABLE 2 Functional enrichment analysis of DTC-specific genesa

1

None

2

ER-to-Golgi transport (20.09.07.03) Membrane lipid metabolism (01.06.02) RNA polymerase III transcriptional preinitiation complex assembly (GO:0070898) Swi4 Swi6 Mbp1

Ino2 Ino4 Tec1 3

None

4

Degradation of glycine (01.01.09.01.02) C-1 compound catabolism (01.05.05.07) Bas1 Gcn4

5

6

Genes

ERP2, ERP1, YIP3, RER1, SHR3, CHS7 PLB2, DPM1, CDS1, CST26, CHO1, OPI1 NHP6A, NHP6B, YBR090C

No. of significantly changed genes in enriched functional category/ total no. of genes in functional category

P value

6/72

8.1 ⫻ 10⫺5

6/83

1.8 ⫻ 10⫺4

3/13

2.7 ⫻ 10⫺4

8/144

9.4 ⫻ 10⫺5

8/160

2.0 ⫻ 10⫺4

MNN5, CIS3, PCL1, SVS1, HTA1/ HTA2, PCL2, YHP1, GIC1 MNN5, CIS3, PCL1, NRM1, SVS1, PCL2, YHP1, GIC1 SEN34, MNN5, SEC14, PCL1, NRM1, HTA1/HTA2, YHP1, GIC1 FAS2, CDS1, KNH1, OPI1 YIP3, FAS2, CDS1, CHO1 MNN5, PCL1, SVS1, PCL2, CHS7

8/165

2.4 ⫻ 10⫺4

4/31 4/33 5/64

2.5 ⫻ 10⫺4 3.2 ⫻ 10⫺4 4.5 ⫻ 10⫺4

GCV2, GCV1

2/5

2.7 ⫻ 10⫺4

GCV2, GCV1

2/6

4.1 ⫻ 10⫺4

MTD1, ADE17, GCV2, GCV1 SNO1, GCV2, HIS3, ICY2, YMC1, GNP1

4/39 6/182

4.8 ⫻ 10⫺5 3.4 ⫻ 10⫺4

Metabolism of arginine (01.01.03.05) Arg81

ARG7, ARG1, CPA1

3/20

4.4 ⫻ 10⫺4

ARG1, CPA1, CUP9

3/22

5.9 ⫻ 10⫺4

Opi1

DAK1, YOP1

2/23

1.7 ⫻ 10⫺4

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. Metabolism of the storage carbohydrates glycogen and trehalose in S. cerevisiae is known to be affected by temperature (22, 59–61). During DTC, the intracellular concentrations of both glycogen and trehalose oscillated. After the temperature maximum, the concentrations of glycogen and trehalose initially decreased (Fig. 7A). Six hours after the temperature maximum, the profiles of these two carbohydrates then diverged. The glycogen concentration increased again after 6 h, while the temperature still decreased, and kept on increasing until the temperature maximum was reached again. Conversely, the trehalose concentration continued to decrease as the temperature decreased and increased back to its original level only during the 6 h before the temperature maximum. During one DTC, approximately 60% of the net glycogen and trehalose content was mobilized and resynthesized. Levels of UDP-glucose and trehalose-6-phosphate, direct precursors of glycogen and trehalose, respectively, were hardly altered during DTC (Fig. 7B). On the other hand, glucose-1-


phosphate and glucose-6-phosphate, precursors of UDP-glucose, displayed an inverse correlation with the applied temperature profile (Fig. 7B). Accumulation of reserve carbohydrates could therefore not be correlated directly to intracellular metabolite levels in their synthetic pathways. Expression of most genes involved in both synthesis and degradation of glycogen and trehalose during DTC displayed similar expression profiles (Fig. 7C). Typically, their expression levels decreased as the temperature decreased, and the profiles seemed to follow the residual glucose concentration, with a sharp increase in the expression level when the glucose concentration dramatically dropped by ⬎80% between 15 and 18 h after the temperature maximum. Differential physiological and transcriptome responses of S. cerevisiae during DTC and acclimation to low temperature. To investigate to what extent S. cerevisiae is able to fully acclimatize its physiology and transcriptome to the DTC dynamics imposed upon the chemostat cultures, S. cerevisiae was grown with the

aem.asm.org 4441


Cluster

Enriched functional category(ies) (MIPS functional category or GO category)

Hebly et al.

same experimental setup as was used for the DTC experiments (glucose-limited anaerobic chemostats, with a dilution rate of 0.03 h⫺1) but at a constant temperature of either 30°C or 12°C. The values for physiological characteristics (biomass yield, specific uptake and production rates, and residual glucose concentration) at 30°C and 12°C during DTC closely resembled the steady-state values, with the exception of glycogen contents (Table 3). While trehalose levels were consistently lower at 12°C for both DTC and steady-state cultures, at low temperatures in steady-state cultures, the level of glycogen accumulation was higher than that during DTC (Table 3). The cell size and budding index (BI) responded similarly in the two systems, with a slight increase in cell size and a strong increase in the BI at 12°C. The BI at 30°C during DTC indicated that 28% ⫾ 1% of the population was in the G2/M phase, in comparison with 30% ⫾ 3% in steady-state chemostat cultures grown at 30°C. At 12°C during DTC, 72% ⫾ 6% of the yeast population was in the G2/M phase, which was close to the BI of 65% ⫾ 2% measured at the same temperature in steady-state chemostat cultures. Principal component analysis (PCA) of DTC and steady-state

4442

aem.asm.org

transcriptome data showed that transcript levels at 30°C during DTC closely resembled the 30°C steady-state expression levels (Fig. 8). Only 94 genes were differentially expressed between steady-state and DTC cultures at 30°C. Gene expression levels at steady state at 12°C could, however, clearly be distinguished from the levels at 12°C during DTC by PCA (Fig. 8). Pairwise comparison of transcript levels at 12°C and 30°C during DTC and in steady-state cultures showed that the response to temperature during DTC (1,061 genes) involved twice as many genes as the temperature response to acclimation in steady-state cultures (521 genes [same P value threshold of 0.05 for both steady-state and DTC data analyses]) (Fig. 9A). However, 44% of the genes that were upregulated and 48% of the genes that were downregulated in steady-state cultures at low temperature responded similarly during DTC (99 upregulated genes and 145 downregulated genes) (Fig. 9B). Genes involved in ribosome biogenesis and targets of Rme1 (indirectly involved in cell cycle control via activation of Cln2 [62]) were upregulated at low temperature, while genes involved in the stress response (and particularly targets of Hsf1), protein folding, stabilization, and amino acid transport were



FIG 6 Cyclic variation in cell cycle distribution and cell cycle-regulated genes during anaerobic cultivation of S. cerevisiae under diurnal temperature cycles. (A) Cell size (Œ) and cell concentration (). (B) Budding index () and percentage of cells in the G2/M phase of the cell cycle based on flow cytometric analysis (䊐). 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 temperature profile.


DISCUSSION

Diurnal temperature cycling and growth kinetics. Pronounced, rhythmic variations of the residual glucose concentration were among the most prominent phenomena observed during physiological 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) is defined by the dilution rate (D) (0.03 h⫺1 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). CS (3) KS ⫹ C S 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 ⫾ 0.002 h⫺1 and 0.048 ⫾ 0.001 h⫺1, respectively). Assuming that KS is not affected by temperature (32), the Monod equation predicts residual glucose concentrations, at a specific growth rate of 0.03 h⫺1, 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␮ ⫽ ␮max ·

FIG 7 Storage carbohydrate metabolism in S. cerevisiae during diurnal temperature cycles. (A) Intracellular glycogen (green circles) and trehalose (blue squares) concentrations during DTC. (B) Intracellular metabolite concentrations of key metabolites in storage carbohydrate metabolism, including glucose-1-phosphate (G1P) (orange triangles), glucose-6-phosphate (G6P) (purple squares), UDP-glucose (UDP-gluc) (red circles), and trehalose-6-


phosphate (T6P) (green diamonds). Dotted lines and open symbols in panels A and B represent extrapolated data. (C) Expression profiles of genes coding for enzymes involved in storage carbohydrate metabolism. (D) Overlay of variables relevant for the interpretation of the reserve carbohydrate dynamics, including the residual glucose concentration (blue triangles), qS (dotted red line), budding index (filled circles), temperature (solid red line), and cell cycle distribution (color bar) (Fig. 6). In all panels, error bars represent the standard errors of the means of at least two independent duplicate cultures.

aem.asm.org 4443


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 magnitudes 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 transcript level corresponding to low-temperature acclimation during DTC (Fig. 9C). Many genes (393 upregulated and 424 downregulated) 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 metabolism, categories that were associated with the variation in residual glucose concentrations during DTC. Of the 277 temperature-responsive genes that were specifically identified in steadystate chemostat cultures, many were involved in transcription activation, transport of metal ions, regulation of nucleotide 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 supplemental material).

b

a

Values represent the averages ⫾ standard errors of the mean of at least two independent replicates. SS, steady state; EtOH, ethyl alcohol; ND, not determined. 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⫺1. c The profile of the biomass specific glucose consumption rate during DTC is shown in Fig. 3A. The intermediate qS values of the time intervals of ⫺1.5 h to 1.5 h and 10.5 to 13.5 h, corresponding to the qS at 30°C and 12°C, respectively, are shown.

28 ⫾ 0.2 72 ⫾ 6.4 14.5 ⫾ 0.4 7.7 ⫾ 0.6 65 ⫾ 0.7 50.5 ⫾ 3.6 ND ND ⫺2.13c ⫺1.96c 30 12 DTC

0.08b 0.09b

3.9 ⫾ 0.12 3.0 ⫾ 0.11

ND ND

0.2 ⫾ 0.01 2.6 ⫾ 0.07

4.0 ⫾ 0.04 4.4 ⫾ 0.05

30 ⫾ 3.3 65 ⫾ 1.8 3.6 ⫾ 0.11 4.4 ⫾ 0.14 29 ⫾ 0.2 2.8 ⫾ 0.3 38 ⫾ 0.2 121.1 ⫾ 5.7 0.2 ⫾ 0.03 2.1 ⫾ 0.04 3.7 ⫾ 0.10 3.4 ⫾ 0.02 3.2 ⫾ 0.22 2.8 ⫾ 0.01 ⫺2.1 ⫾ 0.15 ⫺1.8 ⫾ 0.01 0.08 ⫾ 0.004 0.09 ⫾ 0.001 30 12 SS

qS (mmol · g [dry weight]⫺1 · h⫺1) Temp (°C) Experimental condition

Ysx (g glucose · g [dry weight]⫺1)

qEtOH (mmol · g [dry weight]⫺1 · h⫺1)

95 ⫾ 2.2 101 ⫾ 0.4

Trehalose concn (mg glucose equivalent · g [dry weight]⫺1) Glycogen concn (mg glucose equivalent · g [dry weight]⫺1) Carbon recovery (%) aem.asm.org

qCO2 (mmol · g [dry weight]⫺1 · h⫺1)

TABLE 3 Physiological characteristics of S. cerevisiae grown in glucose-limited anaerobic chemostatsa 4444

FIG 8 Principal component analysis of DTC and steady-state transcriptome 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 point; Œ, 30°C steady-state sampling point.

ism or the growth-limiting substrate, CS should be expected to fluctuate. Indeed, in a previous study on DTC in glucose-limited cultures, CS was identified as a key parameter in determining the dynamics of yeast glycolysis (32). These fluctuations complicate data interpretation of DTC experiments, as nutrient-independent biological responses to DTC may be obscured by cellular responses to changes in CS. Nutrient-independent responses may be identified by simultaneously studying DTC in cultures with different 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, 65); however, its application to DTC studies would require many time-consuming and expensive chemostat experiments. As an alternative approach, we sought to remove glucose-specific responses 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 previous studies assessed the impact of glucose in different experimental contexts (aeration, length of exposure to glucose, and/or range of glucose concentrations), circa half of the genes that showed a response to glucose in both previous studies showed a similar response in the DTC cultures (Fig. 5A and B). This correspondence demonstrated that the impact of fluctuating glucose concentrations extends beyond glycolysis and has a major impact on the yeast transcriptome in DTC chemostats. Transcriptional responses to DTC: beyond glucose. Removal of previously described glucose-responsive genes from the DTCresponsive set of transcripts facilitated interpretation of the transcriptome data. However, it may have eliminated genes that transcriptionally respond to DTC in glucose-dependent as well as glucose-independent manners. Consistent with temperature-dependent remodeling of membrane composition (16, 66, 67), transcript levels of genes involved in fatty acid metabolism and, in particular, phospholipid synthesis positively correlated with temperature during DTC (Table 2). Cyclic variations of extracellular



Residual glucose concn (mM)

Cell size (␮m)

BI (%)

Hebly et al.


Downloaded from http://aem.asm.org/ on October 17, 2014 by Rensselaer Libraries 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).


aem.asm.org 4445

Hebly et al.

4446

aem.asm.org

down more strongly than other phases of the cell cycle. However, from a previous study on batch cultures of S. cerevisiae, it was deduced that temperature had only a minor impact on the budding index (81). Conversely, chemostat studies performed at a constant temperature of 30°C revealed a strong impact of the specific growth rate on the budding index and cell cycle distribution (43, 82, 83). These seemingly contradictory observations can be reconciled if cell cycle distribution in S. cerevisiae is primarily correlated not with temperature or the absolute specific growth rate but instead with the relative specific growth rate (␮/␮max) under a given set of cultivation conditions. This would imply that in batch cultures (␮ ⬇ ␮max) grown at different temperatures, no impact on cell cycle distribution is expected, as observed previously by Vanoni et al. (81). Conversely, variation of ␮ at a constant temperature (i.e., at constant ␮max) (43, 82, 83) or temperature-imposed variation of ␮max at constant ␮ (this study) should affect the cell cycle distribution. Our results with DTC and steady-state chemostat cultures (D ⫽ 0.03 h⫺1), in which the ␮/␮max varied from approximately 0.1 at 30°C to 0.63 at 12°C, are in full agreement with this hypothesis. Dynamics of storage carbohydrate content during diurnal temperature cycles. Intracellular pools of glycogen and trehalose, the two major storage carbohydrates in S. cerevisiae, displayed intriguing dynamics during the DTC. In S. cerevisiae, levels of these two glucose polymers are affected by many parameters, including nutrient availability (84) (and thereby growth rate [43, 82]), temperature, and cell cycle progression (reviewed in references 59 and 85). Interpretation of the dynamics of storage carbohydrate pools during DTC is complicated due to the strong variations of all three of these parameters. Cold shock (23) and full acclimation (Table 3) have been shown to affect storage carbohydrate accumulation. However, no direct correlations of intracellular contents of glycogen and trehalose with temperature were observed during DTC, and it is likely that temperature per se was not the primary trigger for the reserve carbohydrate dynamics during DTC. Under slow-growth, aerobic, glucose-limited conditions, S. cerevisiae stores glycogen and trehalose in G1 phase and quickly mobilizes them in late G1 phase to provide metabolic energy for budding (77). During DTC, mobilization of reserve carbohydrates coincided with a phase of the temperature cycle where the majority of the population was in late G1 phase (Fig. 7D). This observation is consistent with the notion that during DTC, reserve carbohydrate dynamics were governed primarily by cell cycle progression. However, this interpretation is likely to be an oversimplification, since the dynamics of trehalose and glycogen clearly differed, probably reflecting the involvement of different signaling pathways. Resolving the mechanism by which storage carbohydrate metabolism is regulated during DTC will require detailed analysis of culture heterogeneity, especially with respect to cell cycle progression. Yeast adaptation to diurnal temperature variation approaches full acclimation. At the temperature extremes (12°C and 30°C) during DTC, the physiology of anaerobic S. cerevisiae chemostat cultures resembled that of steady-state chemostat cultures that were fully acclimatized to these two temperatures (Table 3). At 30°C, transcript profiles of steady-state and DTC cultures could hardly be discriminated by principal component analysis (Fig. 8). While a larger difference was observed at 12°C, low-temperatureresponsive genes were involved in similar cellular functions under



acetate concentrations (Fig. 3H) may be related to the role of acetyl coenzyme A (acetyl-CoA) as a key precursor for fatty acid synthesis (16, 68). Genes involved in ER-to-Golgi transport were upregulated as the temperature decreased during DTC (Table 2). Since the yeast plasma membrane and cell wall composition are both affected by temperature (66, 69), this response may reflect the need for continuous replacement of membrane and cell wall components. Intracellular membrane trafficking is also sensitive to temperature in mammalian cells (70–72), and in a genomewide screen for sensitivity to high pressure and low temperature, yeast mutants affected in membrane trafficking showed decreased fitness (73). A possible temperature-dependent alteration of the cell wall during DTC is further supported by the overrepresentation of target genes of the transcription factors Swi4 and Swi6, which are known to play an important role in the regulation of genes required for cell wall remodeling (69), among the DTCresponsive transcripts. Genes involved in arginine synthesis (ARG7, ARG1, and CPA1) and glycine degradation (GCV2 and GCV1) showed decreased transcript levels when temperature was decreased during DTC. Regulated by the transcription factors Arg81 and Bas1, respectively, both these processes are also controlled by Gcn4 (74). However, despite a positive correlation of GCN4 expression with temperature (18, 75), only a few of its target genes (74) displayed temperature-dependent expression. Genes involved in de novo purine synthesis, a process regulated by Bas1 and Gcn4 (74), were also overrepresented among genes whose expression levels were reduced at 12°C. An upregulation of these genes upon a relief of S. cerevisiae cultures from glucose limitation was previously correlated with a strong decrease in intracellular adenine nucleotide concentrations (50). However, the adenine nucleotide concentration remained constant during the DTC (see Fig. S4 in the supplemental material). The fluctuations of the expression of genes involved in de novo purine biosynthesis may therefore reveal a regulation specific to dynamic temperature conditions. Cell cycle synchronization during DTC: temperature or relative specific growth rate? Rhythmic variations of the budding index, flow cytometric analysis, and transcription of cell cycleresponsive genes all indicated a partial synchronization of the yeast cell cycle during the DTC experiments. In this respect, it is relevant to note that at the dilution rate used for chemostat cultivation (D ⫽ 0.03 h⫺1), the average biomass doubling time (td ⫽ ln2/␮ ⫽ ln2/D) of 23.1 h closely corresponded to the length of the 24-h temperature cycle, which might have facilitated partial synchronization. Rapid loss of synchronization was observed upon release from DTC (Fig. 6D). This observation showed that under the experimental conditions, DTC caused an externally imposed cell cycle synchronization rather than “entrainment” of an endogenous synchronization mechanism (e.g., by a yeast circadian clock [76], for which there is no molecular evidence at present). Loss of synchronization upon release from DTC in anaerobic chemostat cultures also represents a marked difference from the cell cyclerelated spontaneous and sustained metabolic oscillations that are frequently observed in aerobic, glucose-limited chemostat cultures of S. cerevisiae (77–80). A much higher incidence of G2/M cells at 12°C than at 30°C was observed not only during DTC but also in nonsynchronized, steady-state chemostat cultures grown at the same dilution rate (Table 3). A possible interpretation of these observations is that at low temperatures, progression through the G2/M phase is slowed


ACKNOWLEDGMENTS We give special thanks to Danilo Porro and Frank Rosenzweig and their coworkers for their help with the flow cytometric analysis. We thank Marinka Almering, Markus Bisschops, Sebastiaan de Bruin, Xavier Hakkaart, Marijke Luttik, Mathijs Martens, and Tim Vos for technical assistance. This project was carried out within the research programs of the Kluyver Centre for Genomics of Industrial Fermentation and the Nether-


lands Consortium for Systems Biology, which are both sponsored by the Netherlands Genomics Initiative.

REFERENCES 1. Madigan MT, Martinko JM. 2006. Brock biology of microorganisms. Prentice Hall, Upper Saddle River, NJ. 2. Feller G, Gerday C. 2003. Psychrophilic enzymes: hot topics in cold adaptation. Nat. Rev. Microbiol. 1:200 –208. http://dx.doi.org/10.1038 /nrmicro773. 3. Thieringer HA, Jones PG, Inouye M. 1998. Cold shock and adaptation. Bioessays 20:49 –57. 4. Mansilla MC, Cybulski LE, Albanesi D, de Mendoza D. 2004. Control of membrane lipid fluidity by molecular thermosensors. J. Bacteriol. 186: 6681– 6688. http://dx.doi.org/10.1128/JB.186.20.6681-6688.2004. 5. Schumann W. 2009. Temperature sensors of eubacteria. Adv. Appl. Microbiol. 67:213–256. http://dx.doi.org/10.1016/S0065-2164(08)01007-1. 6. Gasch AP, Werner-Washburne M. 2002. The genomics of yeast responses to environmental stress and starvation. Funct. Integr. Genomics 2:181–192. http://dx.doi.org/10.1007/s10142-002-0058-2. 7. Dragosits M, Stadlmann J, Albiol J, Baumann K, Maurer M, Gasser B, Sauer M, Altmann F, Ferrer P, Mattanovich D. 2009. The effect of temperature on the proteome of recombinant Pichia pastoris. J. Proteome Res. 8:1380 –1392. http://dx.doi.org/10.1021/pr8007623. 8. Gunnigle E, McCay P, Fuszard M, Botting CH, Abram F, O’Flaherty V. 2013. A functional approach to uncover the low-temperature adaptation strategies of the archaeon Methanosarcina barkeri. Appl. Environ. Microbiol. 79:4210 – 4219. http://dx.doi.org/10.1128/AEM.03787-12. 9. Marr AG, Ingraham JL. 1962. Effect of temperature on the composition of fatty acids in Escherichia coli. J. Bacteriol. 84:1260 –1267. 10. Jones PG, Inouye M. 1994. The cold-shock response—a hot topic. Mol. Microbiol. 11:811– 818. http://dx.doi.org/10.1111/j.1365-2958 .1994.tb00359.x. 11. Al-Fageeh MB, Smales CM. 2006. Control and regulation of the cellular responses to cold shock: the responses in yeast and mammalian systems. Biochem. J. 397:247–259. http://dx.doi.org/10.1042/BJ20060166. 12. Lindquist S. 1986. The heat-shock response. Annu. Rev. Biochem. 55: 1151–1191. http://dx.doi.org/10.1146/annurev.bi.55.070186.005443. 13. Klinkert B, Narberhaus F. 2009. Microbial thermosensors. Cell. Mol. Life Sci. 66:2661–2676. http://dx.doi.org/10.1007/s00018-009-0041-3. 14. Postmus J, Canelas AB, Bouwman J, Bakker BM, van Gulik WM, de Mattos MJT, Brul S, Smits GJ. 2008. Quantitative analysis of the high temperature-induced glycolytic flux increase in Saccharomyces cerevisiae reveals dominant metabolic regulation. J. Biol. Chem. 283:23524 –23532. http://dx.doi.org/10.1074/jbc.M802908200. 15. Salvado Z, Arroyo-Lopez FN, Guillamon JM, Salazar G, Querol A, Barrio E. 2011. Temperature adaptation markedly determines evolution within the genus Saccharomyces. Appl. Environ. Microbiol. 77:2292–2302. http://dx.doi.org/10.1128/AEM.01861-10. 16. Redon M, Guillamon JM, Mas A, Rozes N. 2011. Effect of growth temperature on yeast lipid composition and alcoholic fermentation at low temperature. Eur. Food Res. Technol. 232:517–527. http://dx.doi.org/10 .1007/s00217-010-1415-3. 17. Lodolo EJ, Kock JL, Axcell BC, Brooks M. 2008. The yeast Saccharomyces cerevisiae—the main character in beer brewing. FEMS Yeast Res. 8:1018 – 1036. http://dx.doi.org/10.1111/j.1567-1364.2008.00433.x. 18. Pizarro FJ, Jewett MC, Nielsen J, Agosin E. 2008. Growth temperature exerts differential physiological and transcriptional responses in laboratory and wine strains of Saccharomyces cerevisiae. Appl. Environ. Microbiol. 74:6358 – 6368. http://dx.doi.org/10.1128/AEM.00602-08. 19. Sasano Y, Haitani Y, Hashida K, Oshiro S, Shima J, Takagi H. 2013. Improvement of fermentation ability under baking-associated stress conditions by altering the POG1 gene expression in baker’s yeast. Int. J. Food Microbiol. 165:241–245. http://dx.doi.org/10.1016/j.ijfoodmicro.2013.05 .015. 20. Gonçalves P, Valerio E, Correia C, de Almeida JM, Sampaio JP. 2011. Evidence for divergent evolution of growth temperature preference in sympatric Saccharomyces species. PLoS One 6:e20739. http://dx.doi.org /10.1371/journal.pone.0020739. 21. Hittinger CT. 2013. Saccharomyces diversity and evolution: a budding model genus. Trends Genet. 29:309 –317. http://dx.doi.org/10.1016/j.tig .2013.01.002. 22. Murata Y, Homma T, Kitagawa E, Momose Y, Sato MS, Odani M,

aem.asm.org 4447


steady-state and DTC regimes (lipid biosynthesis, cell cycle, stress response, arginine metabolism, and various cellular functions). The response to dynamic temperature fluctuations could therefore largely be predicted from acclimation experiments. Two categories of genes specifically responded to DTC, i.e., ER-to-Golgi transport and de novo purine biosynthesis, suggesting that the dynamic nature of the temperature profile rather than the temperature triggered this response. Comparison of the DTC-specific transcriptome response to the transcriptome response to instantaneous cold shock revealed that only very few genes showed a similar transcriptional response to these two dynamic temperature profiles. Only 10 genes were downregulated during DTC as well as in three previously reported cold shock studies (22, 23, 61) (MTD1, GCV2, HCH1, AQR1, DSE4, ICY2, FUI1, GCV1, GNP1, and PCL5), and 8 genes were upregulated (TOS4, IZH1, IZH4, KNH1, BIO2, PAU10, YLR225C, and YHR138C). The consistent downregulation of genes involved in de novo purine biosynthesis (represented by GCV1 and GCV2) in all these dynamic studies, but not during experiments at a fixed temperature, suggests a role for this cellular function during dynamic temperature profiles. IZH1 and IZH4, which are both involved in intracellular zinc ion homeostasis, were upregulated at low temperature during DTC and cold shock. Interestingly, comparison of the transcriptome response to cold shock with the transcriptome response during acclimation to low temperatures also identified genes involved in zinc ion transport. This suggests a relationship between zinc homeostasis and temperature. This hypothesis is supported by a previous report on temperature-dependent accumulation of Zn2⫹ by S. cerevisiae (77, 86) and the welldocumented role of zinc in regulation of membrane fluidity (87). In summary, growth kinetics of anaerobic, glucose-limited chemostat cultures of S. cerevisiae subjected to DTC could be described by simple Monod kinetics. Rhythmic changes of the residual glucose concentration, which are inevitable consequences of these kinetics, had a strong impact on the yeast transcriptome during DTC. The physiology and transcriptome during DTC resembled those of steady-state, temperature-acclimated chemostat cultures but showed a minor overlap in yeast cells exposed to instantaneous (“cold shock”) temperature changes. A partial, externally imposed synchronization of the yeast cell cycle in DTC chemostat cultures was not caused primarily by fluctuations of the temperature. Instead, comparison with data reported in the literature suggested that fluctuations in the relative specific growth rate (␮/␮max) caused by DTC underlie the observed synchronization. In the present study, the specific growth rate of the cultures was kept virtually constant. In natural environments, the specific growth rate can vary during DTC. To capture such growth dynamics, future studies may be based on alternative (semi)continuous cultivation setups such as repeated batch cultures, fed-batch cultures, or auxostats (32, 88).

Hebly et al.

23. 24. 25.

27. 28.

29.

30.

31.

32.

33. 34.

35.

36.

37.

38. 39. 40.

4448

aem.asm.org

41.

42.

43.

44.

45.

46.

47.

48.

49.

50.

51.

52.

53.

54. 55. 56.

57.

ding yeast cell cycle. Cell Cycle 1:132–136. http://dx.doi.org/10.4161/cc.1 .2.114. Mendes F, Sieuwerts S, de Hulster EAF, Almering MJ, Luttik MAH, Pronk JT, Smid EJ, Bron PA, Daran-Lapujade P. 2013. Transcriptomebased characterization of interactions between Saccharomyces cerevisiae and Lactobacillus delbrueckii subsp. bulgaricus in lactose-grown chemostat cocultures. Appl. Environ. Microbiol. 79:5949 –5961. http://dx.doi.org/10 .1128/AEM.01115-13. Boer VM, de Winde JH, Pronk JT, Piper MD. 2003. The genome-wide transcriptional responses of Saccharomyces cerevisiae grown on glucose in aerobic chemostat cultures limited for carbon, nitrogen, phosphorus, or sulfur. J. Biol. Chem. 278:3265–3274. http://dx.doi.org/10.1074/jbc .M209759200. Boender LG, van Maris AJA, de Hulster EAF, Almering MJ, van der Klei IJ, Veenhuis M, de Winde JH, Pronk JT, Daran-Lapujade P. 2011. Cellular responses of Saccharomyces cerevisiae at near-zero growth rates: transcriptome analysis of anaerobic retentostat cultures. FEMS Yeast Res. 11:603– 620. http://dx.doi.org/10.1111/j.1567-1364.2011.00750.x. Storey JD, Xiao W, Leek JT, Tompkins RG, Davis RW. 2005. Significance analysis of time course microarray experiments. Proc. Natl. Acad. Sci. U. S. A. 102:12837–12842. http://dx.doi.org/10.1073/pnas .0504609102. Knijnenburg TA, de Winde JH, Daran JM, Daran-Lapujade P, Pronk JT, Reinders MJ, Wessels LF. 2007. Exploiting combinatorial cultivation conditions to infer transcriptional regulation. BMC Genomics 8:25. http: //dx.doi.org/10.1186/1471-2164-8-25. Harbison CT, Gordon DB, Lee TI, Rinaldi NJ, Macisaac KD, Danford TW, Hannett NM, Tagne JB, Reynolds DB, Yoo J, Jennings EG, Zeitlinger J, Pokholok DK, Kellis M, Rolfe PA, Takusagawa KT, Lander ES, Gifford DK, Fraenkel E, Young RA. 2004. Transcriptional regulatory code of a eukaryotic genome. Nature 431:99 –104. http://dx.doi.org/10 .1038/nature02800. Bisschops MMM, Zwartjens P, Keuter SGF, Pronk JT, Daran-Lapujade P. 2014. To divide or not to divide: a key role of Rim15 in calorie-restricted yeast cultures. Biochim. Biophys. Acta 1843:1020 –1030. http://dx.doi.org /10.1016/j.bbamcr.2014.01.026. Cho RJ, Campbell MJ, Winzeler EA, Steinmetz L, Conway A, Wodicka L, Wolfsberg TG, Gabrielian AE, Landsman D, Lockhart DJ, Davis RW. 1998. A genome-wide transcriptional analysis of the mitotic cell cycle. Mol. Cell 2:65–73. http://dx.doi.org/10.1016/S1097-2765(00)80114-8. Spellman PT, Sherlock G, Zhang MQ, Iyer VR, Anders K, Eisen MB, Brown PO, Botstein D, Futcher B. 1998. Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. Mol. Biol. Cell 9:3273–3297. http://dx.doi.org/10 .1091/mbc.9.12.3273. Kresnowati MT, van Winden WA, Almering MJ, ten Pierick A, Ras C, Knijnenburg TA, Daran-Lapujade P, Pronk JT, Heijnen JJ, Daran JM. 2006. When transcriptome meets metabolome: fast cellular responses of yeast to sudden relief of glucose limitation. Mol. Syst. Biol. 2:49. http://dx .doi.org/10.1038/msb4100083. Van den Brink J, Daran-Lapujade P, Pronk JT, de Winde JH. 2008. New insights into the Saccharomyces cerevisiae fermentation switch: dynamic transcriptional response to anaerobicity and glucose-excess. BMC Genomics 9:100. http://dx.doi.org/10.1186/1471-2164-9-100. Irizarry RA, Hobbs B, Collin F, Beazer-Barclay YD, Antonellis KJ, Scherf U, Speed TP. 2003. Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics 4:249 – 264. http://dx.doi.org/10.1093/biostatistics/4.2.249. Bolstad BM, Irizarry RA, Astrand M, Speed TP. 2003. A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics 19:185–193. http://dx.doi.org/10 .1093/bioinformatics/19.2.185. Ge YC, Dudoit S, Speed TP. 2003. Resampling-based multiple testing for microarray data analysis. Test 12:1-77. http://dx.doi.org/10.1007 /BF02595811. Pearson K. 1901. On lines and planes of closest fit to systems of points in space. Philos. Mag. 2:559 –572. http://dx.doi.org/10.1080/14786440109 462720. Redon M, Borrull A, Lopez M, Salvado Z, Cordero R, Mas A, Guillamon JM, Rozes N. 2012. Effect of low temperature upon vitality of Saccharomyces cerevisiae phospholipid mutants. Yeast 29:443– 452. http://dx .doi.org/10.1002/yea.2924. Gancedo JM. 2008. The early steps of glucose signalling in yeast. FEMS



26.

Shimizu H, Hasegawa-Mizusawa M, Matsumoto R, Mizukami S, Fujita K, Parveen M, Komatsu Y, Iwahashi H. 2006. Genome-wide expression analysis of yeast response during exposure to 4°C. Extremophiles 10:117– 128. http://dx.doi.org/10.1007/s00792-005-0480-1. Schade B, Jansen G, Whiteway M, Entian KD, Thomas DY. 2004. Cold adaptation in budding yeast. Mol. Biol. Cell 15:5492–5502. http://dx.doi .org/10.1091/mbc.E04-03-0167. Morano KA, Grant CM, Moye-Rowley WS. 2012. The response to heat shock and oxidative stress in Saccharomyces cerevisiae. Genetics 190:1157– 1195. http://dx.doi.org/10.1534/genetics.111.128033. Hofmann S, Cherkasova V, Bankhead P, Bukau B, Stoecklin G. 2012. Translation suppression promotes stress granule formation and cell survival in response to cold shock. Mol. Biol. Cell 23:3786 –3800. http://dx .doi.org/10.1091/mbc.E12-04-0296. Aguilera J, Randez-Gil F, Prieto JA. 2007. Cold response in Saccharomyces cerevisiae: new functions for old mechanisms. FEMS Microbiol. Rev. 31:327–341. http://dx.doi.org/10.1111/j.1574-6976.2007.00066.x. Leach MD, Cowen LE. 2013. Surviving the heat of the moment: a fungal pathogens perspective. PLoS Pathog. 9:e1003163. http://dx.doi.org/10 .1371/journal.ppat.1003163. Tulha J, Lima A, Lucas C, Ferreira C. 2010. Saccharomyces cerevisiae glycerol/H⫹ symporter Stl1p is essential for cold/near-freeze and freeze stress adaptation. A simple recipe with high biotechnological potential is given. Microb. Cell Fact. 9:82. http://dx.doi.org/10.1186/1475-2859-9-82. Cruz AL, Verbon AJ, Geurink LJ, Verheijen PJ, Heijnen JJ, van Gulik WM. 2012. Use of sequential-batch fermentations to characterize the impact of mild hypothermic temperatures on the anaerobic stoichiometry and kinetics of Saccharomyces cerevisiae. Biotechnol. Bioeng. 109:1735– 1744. http://dx.doi.org/10.1002/bit.24454. Tai SL, Daran-Lapujade P, Luttik MAH, Walsh MC, Diderich JA, Krijger GC, van Gulik WM, Pronk JT, Daran JM. 2007. Control of the glycolytic flux in Saccharomyces cerevisiae grown at low temperature—a multi-level analysis in anaerobic chemostat cultures. J. Biol. Chem. 282: 10243–10251. http://dx.doi.org/10.1074/jbc.M610845200. Tai SL, Daran-Lapujade P, Walsh MC, Pronk JT, Daran JM. 2007. Acclimation of Saccharomyces cerevisiae to low temperature: a chemostatbased transcriptome analysis. Mol. Biol. Cell 18:5100 –5112. http://dx.doi .org/10.1091/mbc.E07-02-0131. Cruz AL, Hebly M, Duong GH, Wahl SA, Pronk JT, Heijnen JJ, Daran-Lapujade P, van Gulik WM. 2012. Similar temperature dependencies of glycolytic enzymes: an evolutionary adaptation to temperature dynamics? BMC Syst. Biol. 6:151. http://dx.doi.org/10.1186/1752-0509-6 -151. Entian KD, Kötter P. 2007. Yeast genetic strain and plasmid collections, p 629 – 666. In Stansfield I, Stark MJR (ed), Yeast gene analysis, 2nd ed, vol 36. Academic Press, San Diego, CA. Nijkamp JF, van den Broek M, Datema E, de Kok S, Bosman L, Luttik MAH, Daran-Lapujade P, Vongsangnak W, Nielsen J, Heijne WH, Klaassen P, Paddon CJ, Platt D, Kötter P, van Ham RC, Reinders MJ, Pronk JT, de Ridder D, Daran JM. 2012. De novo sequencing, assembly and analysis of the genome of the laboratory strain Saccharomyces cerevisiae CEN.PK113-7D, a model for modern industrial biotechnology. Microb. Cell Fact. 11:36. http://dx.doi.org/10.1186/1475-2859-11-36. Verduyn C, Postma E, Scheffers WA, Van Dijken JP. 1990. Physiology of Saccharomyces cerevisiae in anaerobic glucose-limited chemostat cultures. J. Gen. Microbiol. 136:395– 403. http://dx.doi.org/10.1099 /00221287-136-3-395. Abbott DA, Suir E, Duong GH, de Hulster EAF, Pronk JT, van Maris AJA. 2009. Catalase overexpression reduces lactic acid-induced oxidative stress in Saccharomyces cerevisiae. Appl. Environ. Microbiol. 75:2320 – 2325. http://dx.doi.org/10.1128/AEM.00009-09. Boender LG, de Hulster EAF, van Maris AJA, Daran-Lapujade P, Pronk JT. 2009. Quantitative physiology of Saccharomyces cerevisiae at near-zero specific growth rates. Appl. Environ. Microbiol. 75:5607–5614. http://dx .doi.org/10.1128/AEM.00429-09. Parrou JL, François J. 1997. A simplified procedure for a rapid and reliable assay of both glycogen and trehalose in whole yeast cells. Anal. Biochem. 248:186 –188. http://dx.doi.org/10.1006/abio.1997.2138. Porro D, Brambilla L, Alberghina L. 2003. Glucose metabolism and cell size in continuous cultures of Saccharomyces cerevisiae. FEMS Microbiol. Lett. 229:165–171. http://dx.doi.org/10.1016/S0378-1097(03)00815-2. Haase SB, Reed SI. 2002. Improved flow cytometric analysis of the bud-


58. 59.

60.

62.

63. 64. 65.

66.

67.

68.

69.

70. 71.

72. 73.


74.

75.

76.

77. 78. 79.

80.

81. 82.

83.

84.

85.

86. 87.

88.

strategies using a yeast deletion library. Genetics 178:851– 872. http://dx .doi.org/10.1534/genetics.107.083063. Natarajan K, Meyer MR, Jackson BM, Slade D, Roberts C, Hinnebusch AG, Marton MJ. 2001. Transcriptional profiling shows that Gcn4p is a master regulator of gene expression during amino acid starvation in yeast. Mol. Cell. Biol. 21:4347– 4368. http://dx.doi.org/10.1128/MCB.21.13.4347-4368.2001. Beltran G, Rozes N, Mas A, Guillamon JM. 2007. Effect of lowtemperature fermentation on yeast nitrogen metabolism. World J. Microbiol. Biotechnol. 23:809 – 815. http://dx.doi.org/10.1007/s11274-006 -9302-6. Eelderink-Chen Z, Mazzotta G, Sturre M, Bosman J, Roenneberg T, Merrow M. 2010. A circadian clock in Saccharomyces cerevisiae. Proc. Natl. Acad. Sci. U. S. A. 107:2043–2047. http://dx.doi.org/10.1073/pnas .0907902107. Futcher B. 2006. Metabolic cycle, cell cycle, and the finishing kick to Start. Genome Biol. 7:107. http://dx.doi.org/10.1186/gb-2006-7-4-107. Tu BP, Kudlicki A, Rowicka M, McKnight SL. 2005. Logic of the yeast metabolic cycle: temporal compartmentalization of cellular processes. Science 310:1152–1158. http://dx.doi.org/10.1126/science.1120499. Klevecz RR, Bolen J, Forrest G, Murray DB. 2004. A genomewide oscillation in transcription gates DNA replication and cell cycle. Proc. Natl. Acad. Sci. U. S. A. 101:1200 –1205. http://dx.doi.org/10.1073/pnas .0306490101. Silverman SJ, Petti AA, Slavov N, Parsons L, Briehof R, Thiberge SY, Zenklusen D, Gandhi SJ, Larson DR, Singer RH, Botstein D. 2010. Metabolic cycling in single yeast cells from unsynchronized steady-state populations limited on glucose or phosphate. Proc. Natl. Acad. Sci. U. S. A. 107:6946 – 6951. http://dx.doi.org/10.1073/pnas.1002422107. Vanoni M, Vai M, Frascotti G. 1984. Effects of temperature on the yeast cell cycle analyzed by flow cytometry. Cytometry 5:530 –533. http://dx.doi .org/10.1002/cyto.990050515. Sillje HHW, ter Schure EG, Rommens AJM, Huls PG, Woldringh CL, Verkleij AJ, Boonstra J, Verrips CT. 1997. Effects of different carbon fluxes on G1 phase duration, cyclin expression, and reserve carbohydrate metabolism in Saccharomyces cerevisiae. J. Bacteriol. 179:6560 – 6565. Brauer MJ, Huttenhower C, Airoldi EM, Rosenstein R, Matese JC, Gresham D, Boer VM, Troyanskaya OG, Botstein D. 2008. Coordination of growth rate, cell cycle, stress response, and metabolic activity in yeast. Mol. Biol. Cell 19:352–367. http://dx.doi.org/10.1091/mbc.E07-08 -0779. Hazelwood LA, Walsh MC, Luttik MAH, Daran-Lapujade P, Pronk JT, Daran JM. 2009. Identity of the growth-limiting nutrient strongly affects storage carbohydrate accumulation in anaerobic chemostat cultures of Saccharomyces cerevisiae. Appl. Environ. Microbiol. 75:6876 – 6885. http: //dx.doi.org/10.1128/AEM.01464-09. Wilson WA, Roach PJ, Montero M, Baroja-Fernandez E, Munoz FJ, Eydallin G, Viale AM, Pozueta-Romero J. 2010. Regulation of glycogen metabolism in yeast and bacteria. FEMS Microbiol. Rev. 34:952–985. http: //dx.doi.org/10.1111/j.1574-6976.2010.00220.x. Mowll JL, Gadd GM. 1983. Zinc uptake and toxicity in the yeasts Sporobolomyces roseus and Saccharomyces cerevisiae. J. Gen. Microbiol. 129:3421– 3425. de Nicola R, Hazelwood LA, de Hulster EAF, Walsh MC, Knijnenburg TA, Reinders MJ, Walker GM, Pronk JT, Daran JM, Daran-Lapujade P. 2007. Physiological and transcriptional responses of Saccharomyces cerevisiae to zinc limitation in chemostat cultures. Appl. Environ. Microbiol. 73:7680 –7692. http://dx.doi.org/10.1128/AEM.01445-07. Kasemets K, Nisamedtinov I, Laht TM, Abner K, Paalme T. 2007. Growth characteristics of Saccharomyces cerevisiae S288C in changing environmental conditions: auxo-accelerostat study. Antonie Van Leeuwenhoek 92:109 –128. http://dx.doi.org/10.1007/s10482-007-9141-y.

aem.asm.org 4449


61.

Microbiol. Rev. 32:673–704. http://dx.doi.org/10.1111/j.1574-6976.2008 .00117.x. Iyer VR, Horak CE, Scafe CS, Botstein D, Snyder M, Brown PO. 2001. Genomic binding sites of the yeast cell-cycle transcription factors SBF and MBF. Nature 409:533–538. http://dx.doi.org/10.1038/35054095. François J, Walther T, Parrou JL. 2012. Genetics and regulation of glycogen and trehalose metabolism in Saccharomyces cerevisiae, p 29 –55. In Liu ZL (ed), Microbial stress tolerance for biofuels, 22nd ed. Springer, Berlin, Germany. Panadero J, Pallotti C, Rodriguez-Vargas S, Randez-Gil F, Prieto JA. 2006. A downshift in temperature activates the high osmolarity glycerol (HOG) pathway, which determines freeze tolerance in Saccharomyces cerevisiae. J. Biol. Chem. 281:4638 – 4645. http://dx.doi.org/10.1074/jbc .M512736200. Sahara T, Goda T, Ohgiya S. 2002. Comprehensive expression analysis of time-dependent genetic responses in yeast cells to low temperature. J. Biol. Chem. 277:50015–50021. http://dx.doi.org/10.1074/jbc.M209258200. Frenz LM, Johnson AL, Johnston LH. 2001. Rme1, which controls CLN2 expression in Saccharomyces cerevisiae, is a nuclear protein that is cell cycle regulated. Mol. Genet. Genomics 266:374 –384. http://dx.doi.org/10.1007 /s004380100515. Knapp D, Bhoite L, Stillman DJ, Nasmyth K. 1996. The transcription factor Swi5 regulates expression of the cyclin kinase inhibitor p40SIC1. Mol. Cell. Biol. 16:5701–5707. Monod J. 1949. The growth of bacterial cultures. Annu. Rev. Microbiol. 3:371–394. http://dx.doi.org/10.1146/annurev.mi.03.100149.002103. Ter Linde JJ, Liang H, Davis RW, Steensma HY, Van Dijken JP, Pronk JT. 1999. Genome-wide transcriptional analysis of aerobic and anaerobic chemostat cultures of Saccharomyces cerevisiae. J. Bacteriol. 181:7409 – 7413. Henderson CM, Zeno WF, Lerno LA, Longo ML, Block DE. 2013. Fermentation temperature modulates phosphatidylethanolamine and phosphatidylinositol levels in the cell membrane of Saccharomyces cerevisiae. Appl. Environ. Microbiol. 79:5345–5356. http://dx.doi.org/10.1128 /AEM.01144-13. Tronchoni J, Rozes N, Querol A, Guillamon JM. 2012. Lipid composition of wine strains of Saccharomyces kudriavzevii and Saccharomyces cerevisiae grown at low temperature. Int. J. Food Microbiol. 155:191–198. http://dx.doi.org/10.1016/j.ijfoodmicro.2012.02.004. Kozak BU, van Rossum HM, Benjamin KR, Wu L, Daran JM, Pronk JT, van Maris AJA. 2014. Replacement of the Saccharomyces cerevisiae acetylCoA synthetases by alternative pathways for cytosolic acetyl-CoA synthesis. Metab. Eng. 21:46 –59. http://dx.doi.org/10.1016/j.ymben.2013.11 .005. Corcoles-Saez I, Ballester-Tomas L, de la Torre-Ruiz MA, Prieto JA, Randez-Gil F. 2012. Low temperature highlights the functional role of the cell wall integrity pathway in the regulation of growth in Saccharomyces cerevisiae. Biochem. J. 446:477– 488. http://dx.doi.org/10.1042 /BJ20120634. Kuismanen E, Saraste J. 1989. Low temperature-induced transport blocks as tools to manipulate membrane traffic. Methods Cell Biol. 32: 257–274. http://dx.doi.org/10.1016/S0091-679X(08)61174-7. Martinez-Alonso E, Ballesta J, Martinez-Menarguez JA. 2007. Lowtemperature-induced Golgi tubules are transient membranes enriched in molecules regulating intra-Golgi transport. Traffic 8:359 –368. http://dx .doi.org/10.1111/j.1600-0854.2006.00533.x. Hanson PK, Grant AM, Nichols JW. 2002. NBD-labeled phosphatidylcholine enters the yeast vacuole via the pre-vacuolar compartment. J. Cell Sci. 115:2725–2733. http://jcs.biologists.org/content/115/13/2725.long. Abe F, Minegishi H. 2008. Global screening of genes essential for growth in high-pressure and cold environments: searching for basic adaptive

Physiology of Saccharomyces cerevisiae in anaerobic glucose-limited chemostat cultures.

Chemostat studies of microsomal enzyme induction in Saccharomyces cerevisiae.

Growth-rate dependency of de novo resveratrol production in chemostat cultures of an engineered Saccharomyces cerevisiae strain.

Physiological effects of an antimycotic azasterol on cultures of Saccharomyces cerevisiae.

Physiological responses to acid stress by Saccharomyces cerevisiae when applying high initial cell density.

threonine biosynthetic pathway of Saccharomyces cerevisiae.

Transcriptional regulatory elements of the RAS2 gene of Saccharomyces cerevisiae.

Physiological role of glucose-phosphorylating enzymes in Saccharomyces cerevisiae.

Transcriptional commitment of mitochondrial RNA polymerase from Saccharomyces cerevisiae.

Transcriptional regulation of SSA3, an HSP70 gene from Saccharomyces cerevisiae.

tRNA thiolation links translation to stress responses in Saccharomyces cerevisiae.

Properties of the transcriptional enhancer in Saccharomyces cerevisiae telomeres.

Physiological function of superoxide dismutase in glucose-limited chemostat cultures of Escherichia coli.

A temperature sensitive mitochondrial mutation of Saccharomyces cerevisiae.

Transcriptional responses of olive flounder (Paralichthys olivaceus) to low temperature.

Temperature-dependent regulation of rDNA condensation in Saccharomyces cerevisiae.

Ty element-induced temperature-sensitive mutations of Saccharomyces cerevisiae.

The chemical nature of phenolic compounds determines their toxicity and induces distinct physiological responses in Saccharomyces cerevisiae in lignocellulose hydrolysates.

Evolutionary Engineering in Chemostat Cultures for Improved Maltotriose Fermentation Kinetics in Saccharomyces pastorianus Lager Brewing Yeast.

Influence of temperature and nutrient strength on the susceptibility of Saccharomyces cerevisiae to heavy metals.

Proteome adaptation of Saccharomyces cerevisiae to severe calorie restriction in Retentostat cultures.

Differential responses of invasive and native plants to warming with simulated changes in diurnal temperature ranges.

Change from homo- to heterolactic fermentation by Streptococcus lactis resulting from glucose limitation in anaerobic chemostat cultures.

Physiological response of Saccharomyces cerevisiae to weak acids present in lignocellulosic hydrolysate.