Environmental systems biology of cold-tolerant phenotype in Saccharomyces species adapted to grow at different temperatures

Abstract

Temperature is one of the leading factors that drive adaptation of organisms and ecosystems. Remarkably, many closely related species share the same habitat because of their different temporal or micro-spatial thermal adaptation. In this study, we seek to find the underlying molecular mechanisms of the cold-tolerant phenotype of closely related yeast species adapted to grow at different temperatures, namely S. kudriavzevii CA111 (cryo-tolerant) and S. cerevisiae 96.2 (thermo-tolerant). Using two different systems approaches, i. thermodynamic-based analysis of a genome-scale metabolic model of S. cerevisiae and ii. large-scale competition experiment of the yeast heterozygote mutant collection, genes and pathways important for the growth at low temperature were identified. In particular, defects in lipid metabolism, oxidoreductase and vitamin pathways affected yeast fitness at cold. Combining the data from both studies, a list of candidate genes was generated and mutants for two predicted cold-favouring genes, GUT2 and ADH3, were created in two natural isolates. Compared with the parental strains, these mutants showed lower fitness at cold temperatures, with S. kudriavzevii displaying the strongest defect. Strikingly, in S. kudriavzevii, these mutations also significantly improve the growth at warm temperatures. In addition, overexpression of ADH3 in S. cerevisiae increased its fitness at cold. These results suggest that temperature-induced redox imbalances could be compensated by increased glycerol accumulation or production of cytosolic acetaldehyde through the deletion of GUT2 or ADH3, respectively.

Introduction

Environmental change is a key driving force for the adaptation of species. However, we are a long way from understanding the effects of specific species–environment interactions on the evolution of organisms and ecological systems. Temperature has a major influence on the activity and performance of ecosystems and has been identified as one of two predominant factors governing biomass production, with the other being body size (Enquist et al. 2003). There are many examples in nature where closely related species coexist in the same environmental niche due to thermo-adaptation. Examples include, but are not limited to, the temporal difference in the development of Ambystoma salamander larvae in Florida (Keen et al. 1984); depth and rate of burrowing of Laternula bivalves in Singaporean mangroves (Lai et al. 2011) and the foraging schedules of Myrmecia ants in Australia (Jayatilaka et al. 2011). In each of these cases, it has been hypothesized that the different species have adapted to a specific temperature to avoid competition with the closely related species. The bivalves and the ants have adapted to circadian temperature cycles and the salamander larvae to seasonal temperatures.

The overall effect of environmental temperature on an organism phenotype has not been explicitly linked to a precise intracellular biochemical or metabolic process. In a complex biological network, a systems biology approach may help to identify key pathways and functional modules related to a particular phenotype, helping to advance towards a predictive model of biological processes (Woolf et al. 2010). Currently, computational researchers have used temperature-based modelling techniques to study the feasibility and directionality of biochemical reactions (Henry et al. 2006), but no effort has been carried out to associate temperature fluctuations to adaptation processes in vivo.

Yeast represents an established experimental system for which many genome-scale metabolic models have been constructed, mostly for Saccharomyces cerevisiae (Duarte et al. 2004; Herrgard et al. 2008; Mo et al. 2009). Saccharomyces cerevisiae is the most widely studied member of the Saccharomyces clade which also inclu-des S. paradoxus, S. cariocanus, S. mikatae, S. kudriavzevii, S. arboriculus, S. uvarum, S. eubayanus, S. bayanus and S. pastorianus. This sister group have high genetic similarity and are ubiquitous, but display different phenotypes (Sampaio & Goncalves 2008; Arroyo-Lopez et al. 2009). For example, S. cerevisiae, S. paradoxus and S. mikatae grow at an optimal temperature of 30 °C, while S. uvarum and S. pastorianus, species associated with wine and larger fermentation processes respectively, are considered cryo-tolerant, growing well at low temperatures of 10–12 °C. It has been shown that, across a number of isolates from the same Saccharomyces species, the optimal, maximal and minimal growth temperatures were consistent (Salvadó et al. 2011).

A study of the distribution of the yeast population on Mediterranean oaks has also established the co-occurrence on these trees of both S. cerevisiae and the cold-tolerant species S. kudriavzevii (Sampaio & Goncalves 2008). This sympatric association is likely to be caused by different growth temperature preferences of the two yeast species, with S. kudriavzevii better adapted to cold conditions. In fact, the average optimal growth temperature for S. cerevisiae is 32 °C, whereas S. kudriavzevii displays an optimal fitness around 24 °C (Salvadó et al. 2011).

These yeast isolates are a good experimental model for studying temperature-dependent phenotype as they live in sympatry (i.e. ecological niche is kept constant), but display a different thermo-growth profile. It is therefore possible to discriminate and compare pathways and genes involved in the cold tolerance trait. Several studies have integrated computational modelling with yeast experimental data at the genome scale (King et al. 2004; Schwartz et al. 2007; Feist et al. 2010; Smallbone et al. 2010), and other literature is available on the inclusion of thermodynamics, although mostly in bacteria (Beard et al. 2002; Henry et al. 2007; Fleming et al. 2010; Jol et al. 2012). However, none of these models currently include thermodynamic data and growth comparisons at different temperatures.

In this study, a systems biology approach has been taken to identify the genetic mechanisms underlying the cold phenotype in yeast species, adapted to grow at different temperatures. We have widened the scope of metabolic-coupled thermodynamic analysis by testing how the free energy of the reactions within a S. cerevisiae metabolic model changes with temperature. Also, a genome-scale yeast deletion collection competition experiment at low temperature was carried out to find processes that aid cold acclimation (Fig.1). Based on these large-scale studies, we selected two candidate genes, ADH3 and GUT2, to investigate their phenotype in Saccharomyces cerevisiae 96.2 and Saccharomyces kudriavzevii CA111 strains, which have optimal growth temperatures of 32 and 24 °C, respectively. Knockout and expression studies carried out in these yeasts revealed significant fitness changes in the thermo-growth profiles, indicating the disrupted genes are important for cold tolerance in these natural isolates.

Figure 1.

A flowchart of the strategy used in this study. Starting with a list of ΔG of formation of metabolites calculated using a Mavrovouniotis style group contribution method and a range of metabolite concentrations, we analysed the reactions within the iMM904 Saccharomyces cerevisiae metabolic model for their thermodynamic properties. We also carried out completion experiments of the yeast deletion collection in three different media types and calculated the change in mutant levels through SOLiD sequencing. The combination of these experiments led to a list of predicted cold-favouring reaction genes that can be verified in natural isolates.

Overall, this study integrates genomewide screening, thermodynamic analyses and experimental validations in natural yeast species to understand cold tolerance. This represents the first effort to study temperature-dependent phenotypes in a systems biology fashion and to extrapolate laboratory settings to a natural system in an attempt to address both the need to perform rigorously controlled experiments and to tackle ecologically relevant issues.

Materials and methods

Thermodynamic analysis tool

A Java program was written to calculate thermodynamic data for reactions within an SBML model, called the Thermodynamic Analysis Tool (ThAT). For each reaction in the given metabolic model, a standard Gibbs free energy of reaction () as well as a temperature-dependant ΔG_r was calculated for either 303.15K (30 °C) or 278.15K (5 °C). To calculate ΔG_r, two data sets were used, the first was a list of ΔG_f from metabolites found in Escherichia coli, from the supplementary material of Jankowski et al. (Jankowski et al. 2008). However, not all metabolites have an associated ΔG_f as some of them have generic radical groups and other side chains that are not uniquely characterized. The other list was a range of metabolite concentrations for those metabolites with measured values (Albe et al. 1990). Where no data were available an estimate of upper and lower metabolite concentration was used by selecting the second lowest and second highest metabolite concentrations from Albe et al. which gave values that reflected realistic biological conditions while taking into account possible outliers. The low and high concentration bounds chosen were 0.00001 and 0.02 m. The ΔG_f data were in k cal/mol, so a value of 1.9858775 k cal/mol was used for the gas constant (R); temperatures were expressed in Kelvin.

In this study, the Saccharomyces cerevisiae metabolic reconstruction model iMM904 was used (Mo et al. 2009). This model is compartmentalized and covers 904 genes and 1412 reactions. Approximately 22% of the reactions in the model could not have their ΔG_r calculated as they contained one or more metabolite that lacks an associated ΔG_f value; these reactions were classified as ‘undetermined’ for the thermodynamic analysis.

Strains used in this study

Saccharomyces cerevisiae heterozygous yeast deletion collection based on BY4743 (MATa/α his3Δ1/his3Δ1 leu2Δ0/leu2Δ0 LYS2/lys2Δ0 met15Δ0/MET15 ura3Δ0/ura3Δ0), S. cerevisiae 96.2 natural isolate and S. kudriavzevii natural isolate. Both these isolated from Quercus ilex bark in Castellón, Spain (courtesy of E. Barrio).

Genome profiling

A set of continuous culture experiments were carried out as described by (Delneri 2011). Small volume parallel fermentation vessels were used as chemostat system (DASGIP technology, an Eppendorf company, Jülich, Germany). Three media were used: rich medium (YPD), F1 Nitrogen-limited medium and F1 carbon-limited medium, as described in Table S1 (Supporting information) (Delneri 2011). The medium was pumped into an autoclaved 20 l barrel through a Sartopore 2 150 filter, 0.2 μm (Sartorius). A 0.5 l sample of the medium was pumped into clean autoclaved bottles for testing for contamination (5 ml of medium was put in a universal tube and incubated overnight at 30 °C) and for filling the chemostat vessels (120 ml each).

The pool of heterozygous BY4743 diploid S. cerevisiae mutants containing ∼300 cells per deletant strain was used to inoculate the chemostat's vessels. The vessels were kept in an incubator at 16 °C. The culture was grown in batch for up to 3 days (2 days for rich YPD medium and 3 days for the two F1 limited medium) before switching to continuous conditions. A pump was used to introduce fresh media into the vessels at a constant rate with a dilution rate 0.05/h (∼2 generations a day). Another line was used to pump out any surplus media, and it was mechanically regulated by the volume of the culture which remains constant throughout the experiment. A third pump fed 1 m KHO into the vessels to maintain the pH at the set value of 4.5 and was computationally controlled.

The experiment was carried out for 2 weeks after switching to continuous culture so that final sample contained a population of cells grown for ∼28 generations after the batch phase. A sample of the initial culture (used to inoculate all the flasks) was kept, and then for each experiment, samples were taken at the batch phase, at the beginning of the steady-state phase and then every other day until the last day when all remaining culture was collected. Samples from the vessels were taken via the waste tubes so that the culture volume was kept constant and the steady state was not disturbed.

The number of strains present in the population, and therefore the effect that each mutation have on the organism fitness, was evaluated via the ‘Bar-seq’ method using SOLiD platform and bioinformatics analysis (Smith et al. 2009). DNA was extracted from samples using the Promega Wizard DNA extraction kit, and for each condition (rich medium, carbon-limited medium and nitrogen-limited medium), two biological replicates were extracted. The upstream tags were amplified using the SOLiD primers and prepared for SOLiD sequencing, as described by Smith et al. (Smith et al. 2010). The PCR products were cleaned up using the Qiagen PCR clean up kit and then quantified using a fluorescence assay designed to detect double stranded DNA (Quant-iT™ PicoGreen® dsDNA Reagent and Kit, Invitrogen). Each library was diluted to 10 μg/ml and equal volumes of each pooled together. The pool was run on a 10% TBE polyacrylamide gel (Invitrogen, Paisley, UK) to check the length of the PCR products. The sequencing results were then tabulated normalized and compared with the original pool values, for further details, see Data S1 (Data analysis of competition experiment, Supporting information).

Creation of sympatric mutants

A sympatric pair of natural isolate yeasts, S. cerevisiae 96.2 and S. kudriavzevii CA111, were used for temperature fitness assays (courtesy of Eladio Barrio). Heterozygote mutants were created separately in the two yeast backgrounds according to Gietz et al. (Daniel Gietz & Woods 2002). The knockout was achieved by inserting a PCR-created kanMX cassette into the yeast genome (Wach et al. 1994), and the oligonucleotides used for the deletions are listed in the Table S2 (Supporting information). For S. kudriavzevii CA111, the heat shock was carried out at 37 °C rather than 42 °C, and the incubation was carried out at 27 °C. To create homozygote mutants, the heterozygote deletant strains were sporulated for either 2–3 days (S. cerevisiae 96.2) or 7–10 days (S. kudriavzevii CA111). The tetrads were then dissected using a micromanipulator and left to self-fertilize to create the homozygote strains, which were tested by PCR to check that both copies of the gene of interest were removed (the designed checking oligonucleotides are listed in Table S2, Supporting information).

Creation of overexpression mutants

ADH3 and GUT2 were TA cloned into pCR™2.1 Vector (Invitrogen) using primers detailed in Table S2 (Supporting information). The vectors were transformed into One Shot® INVαF’ Chemically Competent E. coli (Invitrogen). The genes were then cloned into pRS315 (GUT2) and pRS316 (ADH3) backbones containing the TDH3 constitutive promoter using suitable restriction enzymes (a list of plasmids used in this work can be found in Table S3, Supporting information). These plasmids along with the empty vectors were transformed individually into BY4743.

Temperature assay experiments

For the fitness studies, the strains of interest were grown overnight and then transferred to a 96-well microplate with an initial OD₅₉₅ of 0.1. A FluroStarOptima bioscreen machine (BMG Labtech, Offenberg, Germany) was used to score the growth curves at OD₅₉₅. The experiments were carried out at both 12 and 30 °C in the chemical defined limited media F1, Table S1 (Supporting information) (Delneri 2011). For the colder temperatures, the bioscreen was kept in a cooling incubator set at 12 °C. S. kudriavzevii fitness assays at 12 and 30 °C and S. cerevisiae fitness assays at 12 °C were carried out over 72 h while the S. cerevisiae fitness assay at 30 °C was carried out over 48 h, since it reached stationary phase earlier. For all experiments, three biological (different transformants colonies for the same mutation) and three technical replicates (different cultures of the same transformant) were used, and the error bars for these growth curves are the standard deviation of the replicates from the mean growth curve.

Measurement of genetic expression

Real-time PCR was used to quantify gene expression of AHD3 and GUT2 in both S. cerevisiae 96.2 and S. kudriavzevii CA111 at both 30 and 12 °C. Each species was grown to mid-log-phase and the total RNA extracted using RNeasy kit (Qiagen, Hilden, Germany). Real-time PCR was conducted using iScript™ One-Step RT-PCR Kit with SYBR® Green from Biorad and analysed on a Biorad CFX Connext Real-Time System (Biorad, Hemel Hempstead, UK). Expression levels were normalized to ACT1 values corresponding to the relevant species and temperature to obtain relative expression data.

Glycerol assay

To quantify extracellular glycerol, S. kudriavzevii CA111 wild type and S. kudriavzevii CA111 ΔYIL155c/ΔYIL155c were grown in F1 media at 12 °C until the stationary phase was reached. Samples of the media were filter sterilized to remove the yeast cells and a UV-assay kit was used to measure glycerol levels (R-Biopharm, Darmstadt, Germany).

Results

Whole-genome analysis of S. cerevisiae thermodynamics

Thermodynamics Analysis Tool (ThAT)

An initial investigation into temperature-dependant ΔG was conducted to compare the Gibbs–Helmholtz equation (Equation 1) and the quotient rule equation (Equation 2). The Gibbs–Helmholtz equation relies on the availability of measured ΔH values; however, only 3% of the 877 metabolites in the iMM904 genome-scale model have a trusted ΔG value (Alberty 2003). Thus, only a very limited number of reactions could have their temperature-dependant ΔH calculated. This is not suitable for a comprehensive coverage of the genome-scale network of yeast.

1

where ΔG_T is the Gibbs energy at the new temperature, is the standard Gibbs free energy at 297.15K, T is new temperature, T₀ is the standard temperature and ΔH⁰ is the standard enthalpy.

2

where v_i is the stoichiometric coefficient of metabolite i, ΔG_fi is the Gibbs free energy of formation of metabolite i, R is the gas constant, T is the temperature and x_i the concentration of metabolite i.

The quotient rule is dependent on temperature and metabolite concentrations where again, there is a limited amount of experimental data to inform this equation. Using the BioNumbers database (Milo et al. 2010), it was possible to assign metabolite concentration ranges for 4% of the 877 metabolites in the model (Albe et al. 1990). Nevertheless, an estimate of the range of concentrations can be used for the remaining metabolites (Henry et al. 2006).

We investigated varying the concentrations of individual components of each reaction and calculated ΔG_r values for all possible combinations of metabolite concentrations. However, we hypothesized that it would be biologically unfavourable to have large gaps between concentrations of metabolites participating to the same reaction, with the exception of ubiquitous metabolites such as ATP, and that such conditions would be unlikely at steady states in vivo. Thus, to calculate a range of possible ΔG_r values for a reaction, the metabolite concentrations were fixed at eit-her the high or the low concentration level for all metabolites.

Most of the ΔG_r calculated using the Gibbs–Helmholtz equation lie within or close to the range calculated using metabolite activity bounds at both temperatures tested (Tables S4 and S5, Supporting information). For this study, it was important to calculate ΔG_r for as many reactions within the S. cerevisiae metabolite network as possible. Thus, the quotient equation (Equation 2) was used as the data needed to calculate ΔG_r has a wider coverage, and the range of ΔG_r covered by considering lower/upper concentration bounds was found to encompass variations due to the enthalpy. The analysis tool designed to analyse thermodynamics of a metabolic model in this fashion was named Thermodynamic Analysis Tool (ThAT).

Application of ThAT to metabolic reactions

The ΔG_rvalues calculated at 5 °C were compared with values calculated at 30 °C (Table S6, Supporting information) for all 1,412 reactions in the Saccharomyces cerevisiae metabolic network. In principle, all reactions are reversible, and a change in temperature may cause a change of reaction direction, thus directionality of a reaction was not considered when assigning temperature favourability. A cold-favoring reaction was defined as a reaction whose products or substrates are more energetically favoured at a colder temperature than at a warmer one. Using the ΔG_r data, a reaction was deemed cold favouring if the absolute value of ΔG_r at 5 °C minus the absolute value of ΔG_r at 30 °C was >0 (Table S5, Supporting information). This means that when both values of ΔG are positive, it is the backward reaction that is cold favouring since the substrates, rather than the products, are more energetically favourable. This definition is used as a qualitative measure to classify reactions as cold favouring, warm favouring or undecided; we did not used it to quantify how the gene may respond to temperature, as there is no direct quantitative relation between ΔG_r and reaction rate.

As the ThAT calculates an upper and lower bound for the ΔG_r value it was necessary to consider both when determining temperature preference. Each reaction was analysed for cold favouritism at high and low metabolite concentration. Only reactions that preferred cold at both metabolite conditions were considered fully cold favouring. By only considering reactions as cold favouring, if they meet the requirements in both high and low concentration, the classification is rigorous and eliminates cases where the difference due to temperature change is too small.

From the S. cerevisiae iMM904 model, 46 reactions were classified as cold favouring, a list detailing these reactions can be found in Table1. We compared the list of predicted cold-favoring genes to published genomic expression data of S. cerevisiae under heat shock at 37 °C (Gasch et al. 2000). The expression of each gene was measured by Gasch et al. at 5, 10, 15, 20, 30, 40 and 60 min after the cells were shocked using microarrays.

Table 1.

List of cold-favouring reactions predicted by the thermodynamic analysis

Reactions	ORFs	Gene names	Pathways	EC numbers
Adenylosuccinate lyase: 2-[5-amino-1-(5-phospho-D-ribosyl)imidazole-4-carboxamido]succinate -> fumarate	YLR359w	ADE13	Purine and pyrimidine biosynthesis	4.3.2.2
Adenylosuccinate lyase: 1-2-Dicarboxyethyl.AMP ->fummate
Alcohol dehydrogenase: forward rxn: ethanol->acetaldehyde	YMR303c	ADH2	Pyruvate metabolism	1.1.1.1
Alcohol dehydrogenase: reverse rxn: acetaldehyde->ethanol	YOL086c YGL256w YBR145w	ADH1 ADH4 ADH5	Pyruvate metabolism	1.1.1.1
Alcohol dehydrogenase: reverse rxn: acetaldehyde->ethanol	YMR083w	ADH3	Pyruvate metabolism	1.1.1.1
Alcohol dehydrogenase: ethanol	YDL168w	SFA1	Pyruvate metabolism	1.1.1.1
Adenosine monophosphate deaminase	YML035c	AMD1	Purine and pyrimidine biosynthesis
Biotin acetyl-CoA carboxylase ligase	YDL141w	BPL1	Pantothenate and CoA biosynthesis	6.3.4.15
Chorismate synthase	YGL148w	ARO2	Tyrosine, tryptophan and phenylalanine metabolism	4.2.3.5
Chorismate pyruvate lyase	Unspecified in model	Quinone b	Unspecified
Cystathionine b lyase	YFR055w	CYS1	Methionine metabolism	4.4.1.8
Cystathionine b lyase: peroxisomal	YGL184w	STR3	Methionine metabolism	4.4.1.8
3 dehydroquinate synthase	YDR127w	ARO1	Tyrosine tryptophan and phenylalanine metabolism	4.2.3.4
Fatty acid: CoA ligase: tetradecanoate	YOR317w YIL009w YMR246w	FAA1 FAA3 FAA4	Fatty acid biosynthesis	6.2.1.3
Fatty acid: CoA ligase: tetradecanoate: peroxisomal	YER015w	FAA2	Fatty acid biosynthesis	6.2.1.3
Fructose bisphosphate aldolase	YKL060c	FBA1	Glycolysis and gluconeogenesis	4.1.2.13
D Fructose 1 phosphate D glyceraldehyde 3 phosphate lyase
Sedoheptulose 1,7-bisphosphate D glyceraldehyde 3-phosphate lyase
FMN adenylyltransferase	YDL045c	FAD1	Riboflavin metabolism	2.7.7.2
FMN adenylyltransferase: mitochondrial
Glycerol 3 phosphate dehydrogenase: FAD: mitochondrial	YIL155c	GUT2	Glycerolipid metabolism	1.1.99.5
Glycogen phosphorylase	YPR160w	GPH1	Alternate carbon metabolism	2.4.1.1
Glutamate dehydrogenase: NAD	YDL215c	GDH2	Glutamate metabolism	1.4.1.2
GTP diphosphohydrolase	YER005w	YND1	Nucleotide salvage pathway	3.6.1.5
IMP dehydrogenase	YHR216w YLR432w YAR075w YML056c YAR073w	IMD2 IMD3 IMD5 IMD4 IMD1	Purine and pyrimidine biosynthesis	1.1.1.205
Malic enzyme: NAD: mitochondrial	YKL029c	MAE1	Anaplerotic reactions	1.1.1.38
Sulphate adenylyltransferase	YJR010w	MET3	Cysteine metabolism	2.7.7.4
Methylenetetrahydrofolate dehydrogenase: NAD	YKR080w	MTD1	Folate metabolism	1.5.1.5
Cytochrome P450 lanosterol 14 alpha demethylase: NAD	YNL111c YHR007c YKL150w YIL043c YNL111c YHR007c	CYB5 ERG11 MCR1 CBR1 CYB5 ERG11	Sterol metabolism	1.14.14.1
Methylenetetrahydrofolate dehydrogenase: NAD	YKR080w	MTD1	Folate metabolism	1.5.1.5
Nucleoside diphosphatase: GDP	YER005w	YND1	Nucleotide salvage pathway	3.6.1.6
Nucleoside diphosphatase: UDP
UTP diphosphohydrolase
Nucleoside diphosphatase: GDP: Golgi	YEL042w	GDA1	Nucleotide salvage pathway	3.6.1.6
Nucleoside diphosphatase: dGDP
Nicotinate nucleotide adenylyltransferase	YLR328w	NMA1	NAD biosynthesis	2.7.7.18
Nicotinate nucleotide adenylyltransferase: mitochondrial
Nucleoside triphosphatase: GTP	Unspecified in the model	Nucleotide salvage pathway	3.6.1.15
Nucleoside triphosphatase: dGTP		Nucleotide salvage pathway	3.6.1.15
Phosphor-ribosyl-formyl-glycinamidine synthase	YGR061c	ADE6	Purine and pyrimidine biosynthesis	6.3.5.3
Pantetheine phosphate adenylyltransferase	Unspecified in the model	Pantothenate and CoA biosynthesis	2.7.7.3
Pantetheine phosphate adenylyltransferase		Pantothenate and CoA biosynthesis	2.7.7.3
D1 pyrroline 5 carboxylate dehydrogenase: mitochondrial	YHR037w	PUT2	Glutamate metabolism	1.5.1.12
L serine deaminase	YCL064c YIL168w	CHA1 SDL1	Glycine and serine metabolism	4.3.1.17
L allo Threonine Aldolase	YEL046c	GLY1	Threonine and lysine metabolism	4.1.2.5
Threonine aldolase

A χ² test for differences was used to compare the percentage of genes that were up (a fold change of over 0.5) or down (a fold change of less than −0.5) regulated in the heat-shock data set by Gasch et al. with our list of cold-favoring genes predicted by ThAT. Our cold-favoring genes were significantly under-represented among the upregulated genes (P-value <0.05) and significantly overrepresented among the downregulated genes (P-value <0.001) in the heat-shock data set (Gasch et al. 2000). Our warm favouring gene list showed the opposite trend. These values support the hypothesis that the cold predicted genes would be downregulated in heat shock and the warm predicted genes would be upregulated.

Based on the thermodynamic prediction, a Gene Ontology (for details see Data S1, Supporting information) analysis of potential cold-favouring genes was carried out using the online tool DAVID (da Huang et al. 2009a,b) using the iMM904 gene list as the background. This tool identified 16 significantly (P-value <0.05) enriched functional categories, shown in Table2. NAD-related genes were the most significant category with a P-value of 2.58E-09. The next most enriched functional categories were alcohol metabolism, purine biosynthesis and oxidoreductase. The list also included lipid metabolism and fatty acid metabolism that are essential for membrane fluidity, a well-known factor involved in temperature acclimation.

Table 2.

Gene Ontology analysis of the cold-favouring genes predicted by the thermodynamic analysis

Functional categories	P-value
NAD	2.58E-09
Alcohol Metabolism	1.34E-05
Purine Biosynthesis	8.23E-05
Oxidoreductase	1.40E-04
CBS Domain	7.19E-04
GMP Biosynthesis	7.19E-04
Metalloprotein	8.53E-04
Potassium	5.35E-03
Metal-Binding	6.55E-03
Zinc	7.54E-03
Purine Nucleotide Biosynthesis	9.88E-03
Fatty Acid Metabolism	1.28E-02
Lipid Metabolism	2.00E-02
Lyase	4.30E-02

Genome profiling of the heterozygote yeast deletion collection at cold temperature

Continuous culture experiments using the BY4743 diploid heterozygote S. cerevisiae mutant collection were carried out in three media types at 16 °C to identify genes that affect competitive growth at cold temperature. If a gene is a key player in controlling the flux through a specific pathway, it is expected that lowering its dosage from 2 to 1 will impair the cell growth compared with other genes which have low control (Delneri et al. 2008). Therefore, the competition data can be used to identify genes involved in regulating the metabolic flux, revealing pathways that are important for growth at cold temperature.

Overall, 265 genes were found to be haploinsufficient (at least 1.5-fold decrease in copy number) in all three media conditions but only 39 were haploproficient (at least 1.5-fold increase in copy number). The number of uniquely haplo insufficient and haploproficient genes is summarized in Fig.2, and the full list of fold-change values are listed in Table S8 (Supporting information). In rich media, approximately 6% of genes were haploproficient, whereas in carbon-limited and nitrogen-limited conditions ∼13 and ∼24% were haploproficient, respectively. There was a larger overlap of haploinsufficient genes between carbon-limited and YPD condition compared with the other media. Overall, in nitrogen-limited conditions, there were half of the haploinsufficient genes detected compared with the other condition. This large difference between the nitrogen-limited conditions and the other media types is also shown in the correlations between overall numbers of barcode counts (Fig.3). The rich media and carbon-limited conditions showed the highest correlation coefficient of 0.81 (Fig.3A), which is comparable with the variation between biological replicates that have an average correlation coefficient of 0.8 (example plots are shown in Fig. S1, Supporting information). The nitrogen-limited data showed less similarity with rich media and carbon-limited media, with a correlation coefficient of 0.56 and 0.58, respectively (Fig.3B,C).

Figure 2.

Venn diagrams to show the overlap of the unique genes that were haplo insufficient (A) or haploproficient (B) in the 16 °C genome profiling in rich media (YPD), carbon limited of nitrogen limited.

Figure 3.

Scatter plots barcode count data from final steady-state samples. A comparison of rich media with carbon-limited media (A) or nitrogen-limited media (B) as well as a comparison of the two limited conditions (C). The panels also include the correlation coefficient of the corresponding data sets.

Gene ontology (GO) analysis (for details, see Data S1, Supporting information) was carried out on the list of genes whose mutants were significantly affected by the cold temperature in all three media conditions (Table3). The analysis showed that lipid biosynthesis genes were significantly enriched (P-value <0.05). Oxidoreductase-related reactions were also found to be significantly enriched, and this was consistent with the GO analysis of the gene list predicted by the thermodynamic analysis.

Table 3.

Gene Ontology of mutants affected by cold temperature identified by the genome profiling experiment

Functional categories	P value
Isopeptide Bond	7.86e-04
UBL Conjugation	5.43e-03
Transport	5.63e-03
Oxidoreductase	5.81e-03
Protein Phosphatase	8.83e-03
Membrane Protein	1.03e-02
Nucleus	1.28e-02
Zinc Finger	2.00e-02
Riboflavin Biosynthesis	2.14e-02
DNA Repair	2.55e-02
FMN	2.62e-02
Iron	2.66e-02
DNA Binding	2.75e-02
NAD	3.27e-02
Protein Transport	3.27e-02
Monooxygenase	3.44e-02
Chromosomal Protein	3.57e-02
Nucleotide Binding	3.63e-02
Homodimer	3.64e-02
mRNA Transport	3.81e-02
P-Loop	4.03e-02

We found that vitamin metabolism was overrepresented among the genes affected by cold temperature. For example, riboflavin (vitamin B2) biosynthesis was one of the vitamin pathways that contained a significant number of genes that affected fitness in cold conditions. As vitamins are coregulators for a wide range of reactions, this may indicate that more processes are important in cryo-tolerant species.

Growth assay of Saccharomyces cerevisiae 96.2 and Saccharomyces kudriavzevii CA111 ADH3 and GUT2 mutants

The list of cold-favouring reactions from the thermodynamic analysis was combined with the fold-change data from the competition experiment to identify genes that were significantly haploinsufficient in two or more media conditions and were never haploproficient in any condition (Table4). The genes that had the largest control over the metabolic flux in the three media types at 16 °C were GUT2 (YIL155c) and ADH3 (YMR083w). Both genes have orthologs in S. kudriavzevii, and we expected that mutation in any of them would have a larger deleterious effect in this cold-tolerant species compared with S. cerevisiae 96.2. Heterozygote and homozygote gene knockout strains of GUT2 and ADH3 were created in the natural isolate S. cerevisiae 96.2 and S. kudriavzevii CA111, and the phenotypic fitness assays were performed using a microplate reader scoring the yeasts growth both at 30 and 12 °C. The fitness of the mutants was quantified by calculating the area under the growth curve (Norris et al. 2013) and compared with the corresponding value of the parental strain (Table5).

Table 4.

List of genes displaying the highest fitness impairment at 16 °C after combining both thermodynamic and genome screening data

ORFs	Fold change of cell growth (log2)
	YPD	Carbon	Nitrogen
ADH3	−1.82628	−0.56671	−0.08505
GUT2	*	−1.09477	−0.95155
NMA	−0.91595	−0.96021	0.184467
YND1	−0.86889	−0.56397	0.374916
ADH5	−0.80525	−0.57228	−0.05321
SFA1	−0.53395	−0.66071	−0.00589
GPH1	−0.05416	−0.7147	−0.62748

^*Data not available due to technical issues.

Table 5.

List of ratios of area under the growth curve between the ADH3 and GUT2 mutants compared with their respective wild types

Strain	Ratio to wild-type parent at 12 °C	Ratio to wild type at 30 °C
S. cerevisiae 96.2	1.00	1.00
S. cerevisaie ADH3/ΔADH3	0.73	0.99
S. cerevisaie ΔADH3/ΔADH3	0.64	1.02
S. cerevisaie GUT2/ΔGUT2	1.01	1.08
S. cerevisaie Δ GUT2/ΔGUT2	0.87	1.05
S. kudriavzevii CA111	1.00	1.00
S. kudriavzevii CA111 ADH3/ΔADH3	0.72	2.19
S. kudriavzevii CA111 ΔADH3/ΔADH3	0.71	1.75
S. kudriavzevii CA111 GUT2/ΔGUT2	0.91	2.73
S. kudriavzevii CA111 ΔGUT2/ΔGUT2	0.57	1.71

At 30 °C, when compared to the wild type, S. cerevisiae 96.2 GUT2 heterozygote and homozygote mutants showed a small advantage in the lag phase and in the final biomass, respectively, (Fig.4, Panel A). When considering the overall fitness (i.e. lag phase shift, growth rate and final biomass), the difference between mutants and wild type was minimal (Table5). At 12 °C, only the S. cerevisiae 96.2 GUT2 homozygote mutant showed a drop in overall growth (Fig.4, Panel B), while both heterozygote and homozygote ADH3 mutants had a greater fitness loss than GUT2 mutants (Table5 and Fig.4, Panel D).

Figure 4.

Growth curves of S. cerevisiae 96.2 mutants in F1 media at different temperatures. GUT2 mutants grown at 30 °C (A) and 12 °C (B) and ADH3 mutants grown at 30 °C (C) and 12 °C (D) are shown. For each mutant, three biological and three technical replicas were analysed. 49 independent data points were plotted for mutants grown at 30 °C (Panel A and C), while 73 data points were plotted for mutants grown at 12 °C (Panel B and D). The error bars represent the standard deviation from the average.

All mutant strains created in S. kudriavzevii CA111 background (heterozygotes and homozygotes mutants for both ADH3 and GUT2) showed a drop in fitness at 12 °C (Fig.5, Panel B and D, Table5); however, the GUT2 deletion strains had a much greater growth disadvantage compared with the ADH3 mutants. Both heterozygote and homozygote ADH3 deletion strains grew at about the same rate, showing a comparable fitness loss (Fig.5).

Figure 5.

Growth curves of Saccharomyces kudriavzevii CA111 mutants in F1 media at different temperatures. GUT2 mutants grown at 30 °C (A) and 12 °C (B) and ADH3 mutants grown at 30 °C (C) and 12 °C (D) are shown. For each mutant, three biological and three technical replicas were analysed, and 73 data points were plotted. The error bars represent the standard deviation from the average.

Strikingly, at 30 °C, all the S. kudriavzevii CA111 mutants grew remarkably better than the wild-type parent (Fig.5, panel A and C). The ADH3 and GUT2 homozygote mutants had a smaller fitness advantage compared with the herterozygotes, suggesting that the complete removal of the gene may interfere with other biological functions in the cell.

Overall, these data not only confirm that, by removing genes associated with predicted cold-favoring reactions, yeast fitness is decreased at low temperature. Moreover, deletion of ADH3 and GUT2 conferred resistance to S. kudriavzevii CA111 at higher temperatures (Figs4 and 5). By knocking down the cold-favouring genes, this species increased its ability to grow at 30 °C reaching a fitness comparable to its thermo-tolerant closely related species.

Expression of ADH3 and GUT2 genes in Saccharomyces natural isolates

The genome profiling study identified genes whose dosage is important for maintaining the cold phenotype. It is therefore possible that different levels of expression of ADH3 and GUT2 in S. kudriavzevii CA111 and S. cerevisiae 96.2 are partially responsible for cryo-tolerance. We would expect that both ADH3 and GUT2 are expressed at higher level in the cold-tolerant species, and we quantitatively measured the level of mRNA for these two genes via real-time PCR in both natural isolates grown at 30 and 12 °C. We found that ADH3 had extremely low expression at 30 °C in S. cerevisiae 96.2, due to glucose repression (Young & Pilgrim 1985), while in S. kudriavzevii CA111 showed a 14-fold increase in expression at 30 °C compared with S. cerevisiae 96.2 (Fig.6A). Interestingly, these data suggest that an efficient glucose repression mechanism for ADH3 is not present in S. kudriavzevii CA111. At 12 °C, a similar profile was seen for ADH3, which was more expressed in S. kudriavzevii CA111 than in S. cerevisiae 96.2. GUT2 expression was also higher in S. kudriavzevii CA111 at both temperatures, although to a lesser extent than ADH3 (Fig.6). To investigate whether overexpression of ADH3 and GUT2 could increase growth at cold, we cloned ADH3 and GUT2 genes into pRS315 and pRS316 backbone plasmids containing the strong constitutive promoter, TDH3 (detailed in Table S3, Supporting information). The overexpression vectors were transformed into the wild-type S. cerevisiae BY4743 strain, and fitness profiles at 30 and 12 °C were scored.

Figure 6.

Relative expression of GUT2 and ADH3 in Saccharomyces cerevisiae 96.2 and S. kudriavzevii CA111 at 30 °C (A) and 12 °C (B). For each gene, two biological and three technical replicas were analysed. The error bars represent the standard deviation from the average.

At 30 °C, the strain overexpressing ADH3 showed no significant phenotypic change; however, at 12 °C, the overall fitness increased by 15% (Table6 and Fig. S2, Supporting information). This result indicates that while overexpression of ADH3 does not confer any advantage at 30 °C, it does have a positive effect on the overall growth at cold. The GUT2 overexpression strains was not significantly different than the wild type (less than one standard deviation difference), at either temperature (Table6 and Fig. S2, Supporting information). These expression-based data show that both GUT2 and ADH3 are more highly expressed in S. kudriavzevii CA111 when compared to S. cerevisiae 96.2 and that ADH3 is not efficiently repressed by glucose in S. kudriavzevii. Moreover, overexpression of ADH3 in S. cerevisiae increases its cryo-tolerance.

Table 6.

List of ratios of area under the growth curve for overexpression mutants of ADH3 and GUT2 measured at 30 and 12 °C compared with their wild-type parental strains. ^*not significant due to noisy data

Strain	Ratio to wild-type S. cerevisiae at 12 °C	Ratio to wild-type S. cerevisiae at 30 °C
BY4743 ADH control	1.00	1.00
BY4743 + ADH3	1.15	0.99
BY4743 GUT2 control	1.00	1.00
BY4743 + GUT2	0.88*	1.00

Discussion

In this study, a novel approach was undertaken to identify genes that alter the thermoprofiles of Saccharomyces cerevisiae and Saccharomyces kudriavzevii. By integrating thermodynamic modelling with a metabolic yeast reconstruction, 46 potential cold-favouring reactions were identified. Previous work has used thermodynamic analysis to identify reactions that are energetically unfeasible at a given temperature (Henry et al. 2006; Jol et al. 2012), and others have used various temperatures on a specific pathway (Cruz et al. 2012), but none have considered multiple temperatures on the genome scale. The list of potential cold-favoring reactions contained a number of genes that were related to mitochondrial, fatty acid and lipid metabolisms, all of which have previously been documented to be important pathways for temperature adaptation in other organisms (Vigh et al. 1998; Los & Murata 2004; Schade et al. 2004). In addition, using ThAT, we identified oxidoreductase reactions as important for growth at cold temperature.

Comparison studies between our list of predicted genes with available heat-shock transcriptome data (Gasch et al. 2000) showed that a significant number of cold-favouring genes were downregulated during heat shock, while genes predicted to be warm favouring by ThAT were significantly upregulated in heat shock. These results suggest that our thermodynamic analysis can identify genes important for regulation of cell growth at nonoptimal temperature.

Gene ontology (GO) analysis of haploinsufficient and haploproficient genes, identified through the large-scale competition experiment at 16 °C, revealed an enrichment of pathways similar to those identified by the thermodynamic analysis, in particular lipid biosynthesis- and oxidoreductase-related reactions.

In addition, vitamin metabolism was also a highly enriched GO category. For example, the riboflavin biosynthetic pathway was clearly affected by the cold conditions. Riboflavin is used to synthesize flavin mononucleotide (FMN) and flavin adenine dinucleotide (FAD) which are cofactors that are readily oxidized or reduced for enzymatic purposes (Ghisla & Massey 1989). A disruption to the riboflavin pathway may cause a change in the production of FAD and FMN possibly leading to a redox imbalance, which in turn causes decreased fitness. According to the Kyoto Encyclopaedia of Genes and Genomes database (KEGG) (Kanehisa 2000), there are nine riboflavin genes that are present in S. cerevisiae, seven of which were found haploinsufficient in our genomic screen in YPD (YBL033C, YDR487C, YOL143C, YBR153W, YOL066C, YBR256C and YDL045C). Vitamins may have a role in growth at cold temperatures through their regulation of fatty acids. For example, vitamin H (biotin), essential for S. cerevisiae growth, is a cofactor for the carboxylase family, and the acetyl-coA carboxylase gene (ACC1) is a biotin mediated step at the beginning of the fatty acid biosynthesis pathway. Interestingly, in our genomic screen at 16 °C, the ACC1 mutant had a striking decrease in fitness in both YPD and carbon-limited medium (no change detected in nitrogen-limited condition), while in previous competition experiments at 30 °C, the ACC1 mutant showed no fitness defect in carbon-limited medium and a slightly but significant gain of fitness in the nitrogen-limited condition (Delneri et al. 2008). Taken together, these results indicate that the biotin mediated step is crucial for the yeast fitness at lower temperatures.

Saccharomyces kudriavzevii is a closely related species of S. cerevisiae which evolved an optimal growth temperature 8 °C lower than that of S. cerevisiae, thus it may favour reactions and pathways whose products are more stable or more energetically favourable at colder temperatures. This implies that genes coding for enzymes catalysing these reactions have become more important to this yeast's survival than those that have a similar function in S. cerevisiae. Two potential cold adaptation genes, ADH3 and GUT2, were identified from genome-scale analyses and hetero zygous and homozygous mutants for these genes were created in the two natural yeast isolates, S. cerevisiae 96.2 and S. kudriavzevii CA111. ADH3 mutants had a similar effect on the fitness in both S. cerevisiae and S. kudriavzevii background at 12 °C showing a decrease in fitness compared with respective wild natural isolates. The GUT2 mutants in a S. cerevisiae 96.2 background displayed little fitness defect; however, in the S. kudriavzevii background, there was a significant impairment of fitness at 12 °C. In addition to this, overexpression of ADH3 improved cryo-tolerance of S. cerevisiae but overexpression GUT2 showed no significant effect.

Saccharomyces cerevisiae has a tightly regulated fermentation pathway with a number of alcohol dehydrogenases being glucose repressed, such as ADH2 and ADH3 (Ciriacy 1975; Young & Pilgrim 1985). Saccharomyces kudriavzevii on the other hand does not seem to have such an effective repression mechanism for ADH3 (Fig.6), which could be due to its preference for the glycerol metabolism, an important pathway associated with cold tolerance (Hayashi & Maeda 2006; Panadero et al. 2006).

Saccharomyces kudriavzevii and S. cerevisiae both have very different glycerol production profiles with S. kudriavzevii producing a much larger concentration of glycerol at cold temperatures than S. cerevisiae (Arroyo-López et al. 2010; Oliveira et al. 2014). In the mitochondria, the product of GUT2 oxidizes glycerol-3-phosphate into dihydroxyacetone, which can enter glycolysis. Therefore, disruption of this gene will hinder glycerol utilization. It is possible that glycerol does not only protect S. kudriavzevii, but is also a preferred carbon source at cold temperature. We have performed a preliminary experiment measuring extracellular glycerol produced by S. kudriavzevii CA111 ΔGUT2/ΔGUT2 mutant. Despite the lower fitness of the mutant, we found that there was an accumulation of extracellular glycerol compared with S. kudriavzevii CA111, probably due to the inability to break down glycerol as nutrient (Fig. S3, Supporting information).

The most unexpected result of this work was the dramatic increase in fitness at 30 °C of the S. kudriavzevii GUT2 and ADH3 mutants when compared to the wild-type strain (Fig.5, Panel A and C). The fitness advantage of the homozygote strains was not as large as their heterozygote counterparts, perhaps because the full removal of the gene may affect several other functions in the cell. As both genes are associated with redox, it could be that their deletion is affecting the strains natural redox balance.

Redox balancing is important for homeostasis within S. cerevisiae, and stresses such as ethanol and heat shock are known to disrupt that balance (Piper 1995). During ethanol shock, a much lower concentration of NADH is observed, which indicates that these cells are subjected to a redox imbalance (Vriesekoop et al. 2007). Thus, S. cerevisiae increases glycerol production during ethanol stress to increase NAD⁺ levels (Vriesekoop et al. 2009). Studies have shown that at 38 °C there is a large increase of glycerol-3-phosphate when compared to concentration levels measured at 30 °C (Postmus et al. 2008). In heat-shock conditions, it was shown that S. cerevisiae produces excess glycerol, a fact exploited by vintners wishing to increase glycerol concentration in wines (Omori et al. 1996; Berovic & Herga 2007). These responses could indicate that heat shock, like ethanol stress, may cause a redox imbalance that can be reversed by increasing glycerol production.

The cold acclimation screen highlighted a number of other redox-related genetic knockouts that had significantly decreased or increased fitness, including the two we selected. A number of genes that were haploinsufficient over all three conditions were related to the conversion of reduced NADH or NADPH to the oxidized form, similarly to ADH3, for example, mitochondrial 3-oxoacyl-[acyl-carrier-protein] reductase (OAR1); diaminohydroxyphoshoribosylaminopyrimidine deaminase (RIB7); quinone reductase (LOT6). All these mutants may have a decreased NAD⁺ level causing a slower growth rate.

Conclusions

The use of a genome-scale metabolic reconstruction of yeast metabolism combined with thermodynamic analysis enabled the predictions for metabolic genes associated with cold acclimation. In addition, a screen of the heterozygote yeast deletion collection at 16 °C gave an indication as to which mutations are advantageous or disadvantageous in different media conditions. From these two data sets, ADH3 and GUT2 were highlighted as having a strong effect on temperature phenotype and were selected for further genetic studies in different yeast strains and species.

Fitness assays and knockout studies of ADH3 and GUT2 mutants suggested that these genes are responsible for the maintenance of the cold-tolerant phenotype. In S. kudriavzevii CA111, the disruption of these genes caused a decrease of cryo-tolerance and a dramatic increase of fitness at warm temperature. Both ADH3 and GUT2 are more highly expressed in S. kudriavzevii CA111 than in S. cerevisiae 96.2, and overexpression of ADH3 in S. cerevisiae increases its ability to grow at cold temperature. It can be speculated that these mutants correct a redox imbalance caused by the temperature stress by increasing either glycerol production or cytosolic acetylaldehyde.

Our approach and results provide support for the use of a systems biology framework to studying species adaptation to environmental changes and show that such models can yield testable predictions leading to new biological discoveries.

Conflict of interests

The authors declare no conflict of interests.

Glossary

D.D. and J.M.S.

conceived and supervised the research.

C.M.P.

carried out both computational and experimental work.

C.M.P., J.M.S. and D.D.

analysed data and wrote the manuscript. All authors read and approved the final manuscript.

Data accessibility

All required data sets (thermodynamic and genome profiling data) are included in the supporting information.

Supporting Information

Additional supporting information may be found in the online version of this article.

Figure S1. Scatter plot of the biological replicates of three time points in different media from the genomewide screen.

Figure S2. Growth curve of the overexpression of GUT2 (panels A and B) and ADH3 (panels C and D).

Figure S3. Extracellular glycerol levels measured at 12 °C and normalized to growth.

Table S1. Description of F1, Carbon and Nitrogen limited media.

Table S2. List of species specific chimeric knock-out, checking and Real-time primers for ADH3 (YMR083w) and GUT2 (YIL155c) in S. cerevisiae and S. kudriavzevii.

Table S3. List of plasmids used in this study.

Table S4. A comparison of ΔG of reactions calculated at 30°C using different thermodynamic equations. R_HEX1 is hexokinase-D glucoseATP; R_SUCRe is sucrose hydrolyzing enzyme; R_ADNK1 is Adenosine kinase and R_RBK is Deoxyribokinase.

Table S5. A comparison of ΔG of reactions calculated at 5°C using different thermodynamic equations. R_HEX1 is hexokinase-D glucoseATP; R_SUCRe is sucrose hydrolyzing enzyme; R_ADNK1 is Adenosine kinase and R_RBK is Deoxyribokinase.

Table S6. Complete list of ΔG of reactions in the iMM904 S. cerevisiae model at standard, 5°C and 30°C.

Table S7. Comparison of ΔG of reaction calculated at low and high metabolite concentration for 5°C and 30°C.

Table S8. A list of fold change data for the genome profiling carried out at cold temperature.

Data S1. Materials and methods.