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Originally published In Press as doi:10.1074/jbc.M410573200 on October 20, 2004

J. Biol. Chem., Vol. 280, Issue 1, 437-447, January 7, 2005
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Two-dimensional Transcriptome Analysis in Chemostat Cultures

COMBINATORIAL EFFECTS OF OXYGEN AVAILABILITY AND MACRONUTRIENT LIMITATION IN SACCHAROMYCES CEREVISIAE*{boxs}

Siew Leng Tai{ddagger}, Viktor M. Boer{ddagger}, Pascale Daran-Lapujade{ddagger}, Michael C. Walsh§, Johannes H. de Winde{ddagger}, Jean-Marc Daran{ddagger}||, and Jack T. Pronk{ddagger}

From the {ddagger}Kluyver Laboratory of Biotechnology, Delft University of Technology, Julianalaan 67, 2628BC Delft, The Netherlands, §Heineken Technical Services, Research and Development, Burgemeester Smeetsweg 1, 2380BB Zoeterwoude, The Netherlands, and DSM Life Sciences, Division of Bakery Ingredients, Technology Cluster, 2600MA Delft, The Netherlands

Received for publication, September 14, 2004 , and in revised form, October 20, 2004.


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 EXPERIMENTAL PROCEDURES
 RESULTS
 DISCUSSION
 REFERENCES
 
Genome-wide analysis of transcriptional regulation is generally studied by determining sets of "signature transcripts" that are up- or down-regulated relative to a reference situation when a single culture parameter or genetic modification is changed. This approach is especially relevant for defining small subsets of transcripts for use in high throughput, cost-effective diagnostic analyses. However, this approach may overlook the simultaneous control of transcription by more than one environmental parameter. This study represents the first quantitative assessment of the impact of transcriptional cross-regulation by different environmental parameters. As a model, we compared the response of aerobic as well as anaerobic chemostat cultures of the yeast Saccharomyces cerevisiae to growth limitation by four different macronutrients (carbon, nitrogen, phosphorus, and sulfur). The identity of the growth-limiting nutrient was shown to have a strong impact on the sets of transcripts that responded to oxygen availability and vice versa. We concluded that identification of reliable signature transcripts for specific environmental parameters can be obtained only by combining transcriptome data sets obtained under several sets of reference conditions. Furthermore, the two-dimensional approach to transcriptome analysis is a valuable new tool to study the interaction of different transcriptional regulation systems.


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
EXPERIMENTAL PROCEDURES
 RESULTS
 DISCUSSION
 REFERENCES
 
Recent rapid developments in DNA microarray technology have had a strong impact in research on the yeast Saccharomyces cerevisiae, an important industrial microorganism and model eukaryote. With the ability to study genome-wide transcriptome expression in a single microarray, large on-line transcriptome data bases obtained from different mutants and under a wide range of cultivation conditions have become available as research tools (Gene Expression Omnibus (1) and Yeast Microarray Global Viewer (2)).

Transcript profiles contain a wealth of information that may be applied in several ways for fundamental and applied research. When clear correlations are established between cultivation conditions and transcription of subsets of genes, such correlations can be used to guide functional analysis studies of genes with as yet unknown biological functions. Furthermore, correlation of expression data with sequences of upstream regulatory elements can be applied to unravel the intricate networks of transcriptional regulation (3). In industrial biotechnology, one of the key applications of DNA microarrays lies in diagnosing industrial fermentation processes. If transcriptional responses can be directly correlated to important parameters such as nutritional status of industrial microorganisms or to the stresses to which they are exposed in industrial processes, transcriptome analysis can provide invaluable information for process optimization (4, 5). For such diagnostic purposes, it would be preferable to construct small, cost-effective microarrays that contain a limited number of "signature transcripts." Such signature transcripts should respond uniquely to a single chemical or physical parameter that is relevant for the industrial process under study. This approach is analogous to the application of small diagnostic arrays used in clinical research for the rapid typing of tumors (6).

Hitherto, most transcriptome studies with S. cerevisiae have been done in shake-flask cultures (7, 8). In such cultures, it is not possible to control a number of important cultivation conditions (dissolved oxygen concentration, metabolite concentrations, pH, etc.). Therefore, shake-flask cultivation by definition involves a continuously changing environment. Consequently, interpretation of transcriptome data from shake-flask cultivation is likely to be complicated by differences in specific growth rate, carbon catabolite repression, nitrogen catabolite repression, product accumulation, acidification, etc.

Chemostat cultivation offers a number of advantages for studies with DNA microarrays because it enables cultivation of microorganisms under tightly defined environmental conditions. In a chemostat, culture broth (including biomass) is continuously replaced by fresh medium at a fixed and accurately determined dilution rate (D, h-1). When the dilution rate is lower than the maximal specific growth rate of the microorganism (µmax,h-1), a steady-state situation will be established in which the specific growth rate equals the dilution rate (µ = D). In such a steady-state chemostat culture, µ is controlled by the (low) residual concentration of a single growth-limiting nutrient. The option to accurately control and manipulate individual culture parameters (including medium composition, nature of the growth-limiting nutrient, pH, temperature, and µ) under steady-state conditions makes chemostats excellent tools for studies on genome-wide transcriptional regulation. Indeed, a recent interlaboratory comparison of transcriptome data obtained in chemostat cultures demonstrated that the accuracy and reproducibility of this approach are superior to those obtained in previous studies with shake-flask cultures (9).

Chemostat cultures have recently been applied to study genome-wide transcriptional responses of S. cerevisiae to carbon-limited growth on different carbon sources (10); to nutrient limitation for carbon, nitrogen, phosphorus, and sulfur (4); to starvation (11); to the presence and absence of oxygen (9, 12); and to oxidative stress responses (13). In each of these studies, attempts were made to vary a single cultivation parameter while keeping all other parameters constant. This "one-dimensional" approach resulted in sets of signature transcripts that, within the experimental context, responded uniquely to a single cultivation parameter (e.g. uniquely up-regulated under nitrogen limitation, uniquely down-regulated during growth on ethanol). There is an important potential pitfall in this approach, as it does not include the possibility that expression of genes is simultaneously controlled by two or more environmental parameters. Such dual or multiple control would complicate the identification of signature transcripts and the interpretation of diagnostic transcriptome analysis.

So far, there have been no dedicated studies to investigate and quantify the way in which different transcriptional regulation responses overlap and interact. The goal of this study was to study this interaction by analyzing genome-wide transcriptional responses to four different nutrient limitation regimes under aerobic and anaerobic conditions in chemostat cultures of S. cerevisiae. This "two-dimensional" approach resulted in a new robust set of "anaerobic" and "aerobic" signature transcripts for S. cerevisiae as well as the refinement of previous reports on nutrient-responsive genes. Moreover, the identification of genes regulated both by nutrient and oxygen availability provided new insight in cross-regulated network and hierarchy in the control of gene expression. These newly defined sets of signature genes were subjected to in silico promoter analysis to identify consensus regulatory elements.


   EXPERIMENTAL PROCEDURES
TOP
 ABSTRACT
 INTRODUCTION
 EXPERIMENTAL PROCEDURES
RESULTS
 DISCUSSION
 REFERENCES
 
Strain and Growth Conditions—The S. cerevisiae prototrophic haploid reference strain CEN.PK113-7D (MATa) (14) was grown at 30 °C in 2-liter chemostats (Applikon) with a working volume of 1.0 liter as described (15). Cultures were fed with a defined synthetic medium that limited growth by carbon, nitrogen, phosphorus, or sulfur with all other growth requirements in excess and at a constant residual concentration (4). The dilution rate was set at 0.10 h-1. The pH was measured on-line and kept constant at 5.0 by the automatic addition of 2 M KOH using an Applikon ADI 1030 Biocontroller, and the stirrer speed was set at 800 rpm. Anaerobic conditions were maintained by sparging the medium reservoir and the fermentor with pure nitrogen gas (0.5 liter min-1). Furthermore, Norprene tubing and butyl septa were used to minimize oxygen diffusion into the anaerobic cultures (16). The off-gas was cooled by a condenser connected to a cryostat set at 2 °C. Oxygen and carbon dioxide were measured off-line with an NGA 2000 Rosemount gas analyzer. Steady-state samples were taken after ~10–14 volume changes to avoid strain adaptation due to long-term cultivation (17, 18). Biomass dry weight, metabolite, dissolved oxygen, and gas profiles were constant over at least three volume changes prior to sampling for RNA extraction.

Growth Media—The synthetic medium composition was based on that described (19). In all chemostats except for carbon, the residual glucose concentration was targeted to 17 g liter-1 to sustain glucose repression at the same level. For anaerobic cultivations, the reservoir medium was supplemented with the anaerobic growth factors Tween 80 and ergosterol as described previously (20). These media contained the following components: for carbon-limited cultivation, 5.0 g liter-1 (NH4)2SO4, 3.0 g liter-1 KH2PO4, 0.5 g liter-1 MgSO4·7H2O, and 25 g liter-1 glucose; for nitrogen-limited cultivation, 0.65 g liter-1 (NH4)2SO4, 5.75 g liter-1 K2SO4, 3.0 g liter-1 KH2PO4, 0.5 g liter-1 MgSO4·7H2O, and 46 g liter-1 glucose; for phosphorus-limited cultivation, 5.0 g liter-1 (NH4)2SO4, 1.9 g liter-1 K2SO4, 0.12 g liter-1 KH2PO4, 0.5 g liter-1 MgSO4·7H2O, and 66 g liter-1 glucose; and for sulfur-limited cultivation, 4.0 g liter-1 NH4Cl, 0.05 g liter-1 MgSO4·7H2O, 3.0 g liter-1 KH2PO4, 0.4 g liter-1 MgCl2, and 59 g liter-1 glucose. The medium composition for the aerobic chemostat cultures was as described previously (4).

Analytical Methods—Culture supernatants were obtained after centrifugation of samples from the chemostats. For the purpose of glucose determination and carbon recovery, culture supernatants and media were analyzed by high performance liquid chromatography on an AMINEX HPX-87H ion exchange column using 5 mM H2SO4 as the mobile phase. Residual ammonium, phosphate, and sulfate concentrations were determined using cuvette tests from DRLANGE (Düsseldorf, Germany). Culture dry weights were determined via filtration as described by Postma et al. (21).

Microarray Analysis—Sampling of cells from chemostats, probe preparation, and hybridization to Affymetrix Genechip® microarrays were performed as described previously (9). The results for each growth condition were derived from three independently cultured replicates.

Data Acquisition and Analysis—Acquisition and quantification of array images and data filtering were performed using Affymetrix Microarray Suite Version 5.0, MicroDB Version 3.0, and Data Mining Tool Version 3.0. Before comparison, all arrays were globally scaled to a target value of 150 using the average signal from all gene features using Microarray Suite Version 5.0. To eliminate insignificant variations, genes with values below 12 were set to 12 as described (9). From the 9335 transcript features on the YG-S98 arrays, a filter was applied to extract 6383 yeast open reading frames, of which there were 6084 different genes. This discrepancy was due to several genes being represented more than once when suboptimal probe sets were used in the array design. To represent the variation in triplicate measurements, the coefficient of variation (S.D. divided by the mean) was calculated as described previously by Boer et al. (4).

For additional statistical analyses, Microsoft Excel running the significance analysis of microarrays (SAM Version 1.12) add-in was used (22) for pairwise comparisons. Genes were considered as being changed in expression if they were called significantly changed using SAM (expected median false discovery rate of 1%) by at least 2-fold from each other condition. Hierarchical clustering of the obtained sets of significantly changed expression levels was subsequently performed using Genespring Version 6.1 (Silicon Genetics).

Promoter analysis was performed using the web-based software Regulatory Sequence Analysis (RSA) Tools (23). The promoters (from -800 to -1) of each set of co-regulated genes were analyzed for over-represented hexanucleotides. When hexanucleotide sequences shared largely common sequences, they were aligned to form longer conserved elements. All of the individual promoter sequences contributing to these elements were then aligned, and the redundant elements were determined by counting the base representation at each position. The relative abundance of these redundant elements was determined from a new enquiry of the co-regulated gene promoters and the entire set of yeast promoters in the genome. The gene annotation was made according to the Comprehensive Yeast Genome Database at the Munich Information Center for Protein Sequence (MIPS; available at mips.gsf.de/genre/proj/yeast/index.jsp) (24), the Saccharomyces Genome Database (available at www.yeastgenome.org/) (25) and the Yeast Proteome Database at Incyte (available at www.incyte.com/).


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 EXPERIMENTAL PROCEDURES
 RESULTS
DISCUSSION
 REFERENCES
 
Experimental Design and Physiology of S. cerevisiae in Aerobic and Anaerobic Macronutrient-limited Chemostat Cultures—To investigate the impact of transcriptional cross-regulation on the identification of signature transcripts, we designed a two-dimensional experimental approach (Fig. 1). Four nutrient limitation regimes (carbon, nitrogen, sulfur, and phosphorus) were studied. In one set of experiments, the four nutrient limitation regimes were studied in aerobic chemostat cultures. A second set of experiments was performed under the same nutrient limitation regimes, but in anaerobic chemostat cultures. The resulting set of eight fermentation conditions, each analyzed in three independent replicate cultures, enabled the identification of genes with a specific transcriptional response to one parameter only (e.g. induced under anaerobic conditions irrespective of the macronutrient limitation regime). Furthermore, genes that transcriptionally responded to multiple parameters were identified (e.g. induced under anaerobic conditions only when growth was limited by the carbon source).



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  		FIG. 1.
Two-dimensional experimental approach. The experimental design for two-dimensional transcriptome analysis is shown. Each corner of the cube represents a chemostat condition. The upper horizontal surface represents the four aerobic macronutrient limitation regimes (carbon, nitrogen, phosphorus, and sulfur). The lower horizontal surface represents the same macronutrient limitation regimes analyzed under anaerobic conditions. The arrows indicate the pairwise comparisons included in the present two-dimensional transcriptome analysis.

 
To minimize experimental "noise," the compositions of the growth media were designed such that residual concentrations of non-growth-limiting nutrients were essentially the same in all chemostat cultures (Table I). Control experiments confirmed that the concentrations of the growth-limiting nutrients were below the detection limit of the respective assay procedures (Table I). The option to control the steady-state concentrations of limiting and excess nutrients concertedly is a unique feature of chemostat cultivations.


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  		TABLE I
Nutrient concentrations and physiological parameters of chemostat cultures used in this study Unless indicated otherwise, values represent the mean ± S.D. of data from three independent steady-state chemostat cultivations. RQ, respiratory quotient (qCO2/qO2); BD, below detection limit of assay; NA, not applicable.

 
The physiological parameters of the eight cultivation conditions are reported in Table I. Under aerobic conditions, only the glucose-limited cultures exhibited a completely respiratory glucose metabolism, without production of ethanol. This resulted in a respiratory quotient close to unity (Table I). Conversely, the aerobic cultures that were not limited by glucose exhibited a respirofermentative glucose metabolism, with simultaneous ethanol production and oxygen consumption (respiratory quotient of >1). In the anaerobic chemostat cultures, alcoholic fermentation was the sole mode of glucose dissimilation, as no oxygen was available for respiration. The ATP yield from alcoholic fermentation is much lower than that from respiratory glucose dissimilation (20), thus explaining the lower biomass yield on glucose of the anaerobic cultivations. The biomass yield on glucose in glucose-limited cultures was higher than in the non-glucose-limited cultures (Table I). Under aerobic conditions, this can be partially explained by the involvement of alcoholic fermentation in the latter cultures. However, a reduction of the biomass yield in non-glucose-limited cultures was also observed under anaerobic conditions (Table I). This may be related to the induction of energy-dependent transport systems during nitrogen-, phosphorus-, and sulfur-limited growth (26).

Microarray Reproducibility, Global Transcriptome Responses, and Data Analysis—To obtain statistically robust, reproducible transcriptome data sets (9), independent triplicate chemostat cultivations and oligonucleotide DNA microarrays were carried out for each of the eight cultivation conditions. The average coefficient of variation for the triplicate transcriptome analyses (4, 9) for each of the eight conditions was <0.21, except for the anaerobic glucose-limited chemostats (coefficient of variation of 0.27). The level of the ACT1 transcript, a common loading standard for conventional Northern analysis, did vary by <13% over the eight growth conditions (Supplemental Table 1).

The eight different cultivation conditions would, in principle, allow for 56 different pairwise comparisons. In this study, we restricted analysis of the data to pairwise comparisons between cultivation conditions that differed in a single cultivation parameter only. Ultimately, this left 28 pairwise comparisons. Four of these were pairwise comparisons between aerobic and anaerobic cultures grown under the same macronutrient limitation regime (Fig. 1, vertical arrows). An additional 24 pairwise comparisons involved all possible combinations of the four macronutrient limitation regimes under either aerobic or anaerobic conditions (Fig. 1, horizontal surfaces).

Each pairwise comparison defined a set of genes that were significantly up- or down-regulated (-fold change of >2 with a false discovery rate of 1%; see "Experimental Procedures"). In total, 3169 genes (52% of the genome) exhibited a significantly different transcript level in at least one of the 28 pairwise comparisons. 2542 genes (42%) of the genome did not exhibit a significant difference in transcript level in any of the pairwise comparisons. The remaining 373 transcripts (representing 6% of the S. cerevisiae genome) remained below the detection limit under all eight conditions investigated (Fig. 2 and Supplemental Table 2).



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  		FIG. 2.
Global transcriptional responses to growth in aerobic and anaerobic macronutrient-limited chemostats. The genome-wide transcript profiles of S. cerevisiae grown under different oxygen availability conditions and limitations (lim) for carbon, nitrogen, phosphorus, or sulfur are compared, and the classes of expression profiles are scored. About half of the predicted genome (48%) was either unchanged or not measurable across all eight conditions. The remaining significantly changed genes (3169) were categorized into oxygen-responsive genes (155); genes that responded to macronutrient limitation under solely aerobic conditions (333), solely anaerobic conditions (302), and irrespective of the presence of oxygen (152); and genes with a more complex transcription profile.

 
Transcripts that showed a consistent difference in the aerobic/anaerobic comparisons under all four macronutrient limitation regimes were identified by combining the four relevant pairwise comparisons (Fig. 1, vertical arrows). This set of consistently oxygen-responsive genes contained 155 genes (2.6% of the genome) (Fig. 3A and Supplemental Table 3).



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  		FIG. 3.
Data mining strategy: dissection of the transcriptome response with respect to nutrient limitation and oxygen availability. A, Venn diagram of signature anaerobic genes. Red and green represent up-regulation and down-regulation, respectively, under anaerobic conditions. Each of the four circles corresponds to a cluster of genes that showed a transcriptional response to oxygen availability under one of the four macronutrient limitation regimes. The overlap of the four clusters represents genes that showed a consistent response to oxygen availability irrespective of the nutrient limitation regime. Lim Ae, limited aerobic; Lim Anae, limited anaerobic. B, Venn diagram of macronutrient limitation-responsive genes. The diagram shows pairwise transcriptome comparisons (see Fig. 1) of each macronutrient limitation regime against the other three macronutrient limitation regimes for aerobic and anaerobic cultures. Each circle represents the cluster of genes up-regulated (red) or down-regulated (green) for the reference macronutrient limitation. Sets I and V contain the genes that showed a consistent response to each of the four macronutrient limitation regimes in the three pairwise comparisons under aerobic and anaerobic conditions, respectively. Combination of sets I and V yielded three new subsets of macronutrient limitation-responsive genes. Set III represents signature genes that showed a consistent response to each of the macronutrient limitation regimes under aerobic and anaerobic conditions. Sets II and IV represent genes whose transcriptional response to a single macronutrient limitation regime was specific for aerobic and anaerobic conditions, respectively.

 
To investigate transcriptional responses to macronutrient limitation, we first identified transcripts that responded to a single nutrient limitation regime under either aerobic or anaerobic conditions (sets I and V) (Fig. 3B). Combination of sets I and V for each of the four macronutrient limitation regimes yielded a subset of transcripts that showed a consistent response to macronutrient limitation irrespective of oxygen availability (set III, 152 genes) (Fig. 3B and Supplemental Table 4). In addition, this comparison yielded two sets of genes that showed a transcriptional response only to one of the four nutrient limitation conditions under either aerobic conditions (set II, 333 genes) (Fig. 3B and Supplemental Table 5) or anaerobic conditions (set IV, 302 genes) (Fig. 3B and Supplemental Table 6). The data analysis approach described above enabled us to dissect the S. cerevisiae genome clusters of genes that showed either a consistent, robust response to oxygen availability or macronutrient limitation or, alternatively, a more complex dual-parameter transcriptional regulation.

Signature Genes with a Consistent Transcriptional Response to Oxygen Availability or Macronutrient Limitation—Ten clusters of genes that were identified showed a specific and consistent response to anaerobiosis, glucose limitation, nitrogen limitation, phosphorus limitation, or sulfur limitation (Fig. 4). In five of these clusters, the transcriptional response was defined as "up-regulated" under the conditions indicated; in the other five clusters, the transcriptional response was defined as "down-regulated." This terminology does not imply any mechanism of regulation. For example, down-regulation under nutrient limitation might, mechanistically, represent up-regulation under conditions of nutrient excess. In our discussion of these "consistent response" genes, we will restrict ourselves to a detailed analysis of the anaerobically up-regulated genes and some specific observations on the macronutrient limitation-responsive genes.



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  		FIG. 4.
Signature genes that respond to a single environmental parameter. The three independent transcriptome data sets for each condition were averaged and then compared. Green (relatively low expression) and red (relatively high expression) squares are used to represent the transcription profiles of genes deemed specifically changed. The signature genes were sorted by functional categories according to the Comprehensive Yeast Genome Database (24) and the Saccharomyces Genome Database (25).

 
Anaerobically Up-regulated Genes—Based on a statistically robust, two-laboratory transcriptome analysis of glucose-limited chemostat cultures of S. cerevisiae, Piper et al. (9) identified 877 transcripts that were differentially expressed in anaerobic and aerobic cultures. These genes were distributed in 133 anaerobically up-regulated and 744 anaerobically down-regulated genes. In our two-dimensional approach, the transcriptional response to oxygen availability of 722 of these genes (82%) depended on the macronutrient limitation regime, and only 155 genes showed a consistent response to anaerobiosis under all four macronutrient limitation regimes (65 up-regulated and 90 down-regulated) (Fig. 3A).

Of the 65 anaerobically up-regulated genes, 20 have an as yet poorly defined or unknown biological function. The 45 genes with known function were distributed over the functional categories as follows: metabolism and energy (21 genes), transport (four genes), cell rescue and defense (11 genes), protein synthesis (three genes), and cell wall and organization (six genes) according to the MIPS Database (Fig. 4) (24). A closer inspection reflected the biosynthetic role of molecular oxygen in S. cerevisiae (27). Under anaerobic conditions, S. cerevisiae is not capable of de novo biosynthesis of sterols and unsaturated fatty acids, and therefore, these compounds are required as growth factors under anaerobic conditions (28, 29). Although the anaerobic chemostat cultures were supplied with ergosterol and oleate, 22 of the consistently anaerobically up-regulated genes have been implicated in or associated with sterol or lipid metabolism. Of these genes, UPC2 and SUT1 are transcription factors for sterol uptake in yeast, and PDR11 and AUS1 (members of the ABC membrane transporter family) have been shown to be involved in sterol uptake for anaerobic growth (30, 31). 13 members of the seripauperin family of possible cell wall mannoproteins (DAN1, DAN2, DAN3, DAN4, TIR1, TIR2, TIR3, TIR4, PAU1, PAU3, PAU4, PAU5, and PAU6) that were consistently up-regulated in anaerobic cultures encode mannoproteins. These important determinants of cell wall permeability during anaerobiosis (32) may be involved in sterol uptake, as recently shown for DAN1 (30). The MGA2 gene product regulates the transcription of OLE1, which is involved in the biosynthesis of unsaturated fatty acids (33). HES1, ARE1, YSR3, and PLB2 encode a putative oxysterol-binding protein, an acyl-CoA acetyltransferase, a putative regulator of sphingo-lipid metabolism, and phospholipase B2, respectively (34–38). In addition to genes involved in sterol and fatty acid metabolism, COX5B and HEM13 displayed a consistent up-regulation in all anaerobic cultures. COX5B encodes the "anoxic subunit" of cytochrome c oxidase, which is proposed to be involved in oxygen sensing (39). HEM13 encodes a cytosolic coproporphyrinogen III oxidase and has been described as the first, molecular oxygen-dependent and rate-controlling step of heme biosynthesis (27).

As a further approach to assess the biological significance of the consistent transcriptional responses identified via the two-dimensional approach, we analyzed the enrichment of regulatory motifs in promoter sequences of the oxygen-responsive genes (Figs. 3A and 4 and Table II). Four over-represented sequences were recovered from the 65 anaerobically up-regulated gene promoter regions (Table II). At least one of the two overlapping sequences (TCGTwyAG or CCTCGTwy) was recovered from 34 genes (52%) in the cluster. These sequences are similar to the previously described binding site for Upc2p (CGTTT) (40), a transcription factor whose structural gene itself was consistently up-regulated in the anaerobic cultures. 17 genes (26%) share the element ATTGTTC, which is the known binding site for the anaerobic transcription factor Rox1p (41). We also identified a new motif (AAGGCAC) within this cluster of genes for which no DNA-binding protein has yet been identified. The Upc2p and AAGGCAC motifs showed a remarkable coincidence in the promoters of 12 genes of the cluster (Fig. 5). In the upstream regions of these genes, the Upc2p-binding site is present at -450 to -380, and the AAGGCAC element is present at -360 to -300 (Fig. 5). The conservation of both the distance to the coding region and the distance between the elements strongly suggests biological relevance. 70% of the promoter sequences of the genes that were consistently up-regulated in the anaerobic cultures contain at least one of the three elements discussed above.


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  		TABLE II
Gene coverage of over-represented sequences retrieved from promoters of co-regulated genes Unless stated otherwise, elements were counted present in a gene promoter only if they occurred at least twice. NS, no significant patterns retrieved by RSA Tools.

 



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  		FIG. 5.
Localization of the consensus binding site in promoter sequences of up-regulated genes in the absence of oxygen irrespective of nutrient limitation. The promoter regions of genes from -800 to -1 were based on the sequences obtained from RSA Tools (23). {blacktriangleup}, Upc2p consensus sequence TCGTwyAG; {blacktriangledown}, Upc2p consensus sequence TCGTwyAG found on the Crich stand; +, Upc2p consensus sequence CCTCGThh; {blacksquare}, Rox1p consensus sequence (ATTGTTC); {square}, AAGGCAC consensus sequence. Consensus sequences on both strands are indicated. The open reading frames on the Watson strand and on the Crick strand are indicated by D and R, respectively. a Redundant nucleotides are as follows: r = A or G; y = C or T; s = G or C; k = G or T; m = A or C; w = A or T; d = A, G, or T; and h = A, C, or T.

 
Transcriptional Responses to Macronutrient Limitation: Genes Up-regulated upon Phosphate Limitation—The four clusters of genes that were consistently (under aerobic as well as anaerobic conditions) up-regulated in response to growth limitation by a single macronutrient share some conserved features. These involve induction of high affinity uptake systems for the limiting macronutrient, excretion of nutrient-scavenging enzymes to the extracellular medium, induction of systems for mobilization and utilization of intracellular reserves, and induction of systems for transport and assimilation of alternative sources of the limiting nutrient (Fig. 4). This is exemplified by the transcriptional response to phosphorus limitation.

Previous comparison identified 62 up-regulated signature transcripts for aerobic phosphate-limited growth (4). Introducing a second dimension (anaerobic phosphate limitation) resulted in a 50% decrease in the genes composing this cluster. Indeed, 31 genes showed a consistent up-regulation relative to the other macronutrient limitation regimes in aerobic and anaerobic phosphate-limited chemostat cultures. Among these genes, seven are involved in transport, 14 in metabolism, one in protein fate, and one in transcription, and eight have an as yet unknown function according the MIPS Database (24) and the Saccharomyces Genome Database (25). 23 of these phosphate limitation-induced genes (74%) could be directly related to phosphorus metabolism. All seven genes classified in the transport category were associated with phosphate transport (PHO84, high affinity inorganic phosphate/proton symporter; PHO89, high affinity sodium-dependent phosphate transporter (42); PHO86, protein associated with the phosphate transport complex (43); GIT1, glycerophosphoinositol transporter belonging to the major facilitator superfamily (44); and VTC1, VTC3, and VTC4, subunits of the vacuolar membrane polyphosphate transporter complex (45)). Of the remaining genes in this cluster, several are involved in phosphate mobilization: PHO11 and PHO3 encode phosphatases; HOR2 encodes a glycerol-3-phosphate phosphatase (46, 47); INM1 encodes an inositol monophosphatase (48); YNL217W encodes a putative metallophosphatase (49); YPL110C encodes a putative glycerophosphoryl-diester phosphodiesterase; DDP1 encodes a diadenosine-hexaphosphate hydrolase (50); PLB3 encodes phospholipase B (38); and PYK2 encodes a glucose-repressed pyruvate kinase (51). The proteins encoded by PHM6 and PHM8 are likely to encode proteins involved in phosphate metabolism (45) as well, and their promoter regions exhibit a Pho4p-binding site. PHO81 and SPL2 are presumed inhibitors of the Pho80p-Pho85p cyclin-dependent protein-kinase complex and positive regulators of phosphate-related genes (52). Furthermore, KCS1, an inositol-1,2,3,4,5,6-hexaphosphate kinase involved in inositol metabolism (53), was up-regulated. The remaining eight genes in the cluster (25%) could not be directly associated with phosphate metabolism. Interestingly, two of these genes are involved in transcriptional regulation: ZAP1 encodes a zinc-responsive transcriptional activator (54), and MAF1 encodes a putative repressor of RNA polymerase III transcription and a common component of multiple signaling pathways in S. cerevisiae that sense changes in the cellular environment (55).

In silico promoter analysis of the genes that were consistently up-regulated upon phosphate limitation revealed an over-represented mACGTGs motif (present in 58% of the genes in the cluster as opposed to 3% in the S. cerevisiae genome). This sequence shows strong similarity to the CACGTG consensus sequence for the binding site of Pho4p (56), the main transcription factor required for expression of the phosphate-related genes (Table II).

Transcriptional Cross-regulation Identified by Two-dimensional Transcriptome Analysis—By combining the transcriptional responses to (an)aerobiosis in cultures subjected to four different macronutrient limitation regimes, it was possible to identify gene clusters that were subjected to transcriptional regulation by two environmental parameters. Identification of such clusters is not possible in conventional one-dimensional pairwise comparisons between cultivation conditions. Eight such clusters (sets II and IV) (Fig. 3B) could be assigned. To explore the biological significance of defining these clusters, we will discuss one of these clusters in more detail.

Of the 428 genes that showed a transcriptional response to carbon limitation in our analysis (sets II–IV) (Fig. 3B), only 33 genes showed a consistent response to carbon limitation irrespective of the availability of oxygen (set III) (Fig. 3B). 193 genes (set II) (Fig. 3B) showed a significant transcriptional response only under aerobic conditions. Of the remaining 202 genes (set IV) (Fig. 3B), which responded only to carbon limitation in the anaerobic cultures, 167 genes were down-regulated in anaerobic carbon-limited cultures, and 35 genes were up-regulated.

Of the 35 genes that were uniquely up-regulated in anaerobic carbon-limited chemostat cultures at the level of transcription, 21 genes are related to mitochondrial function (Fig. 6, upper panel), even though glucose dissimilation in these cultures was completely fermentative. 15 of these mitochondrial function-related genes are involved in oxidative phosphorylation and respiration: QCR2, QCR6, QCR7, and RIP1 as core subunits of ubiquinol-cytochrome c reductase (complex III); COX4, COX5A, COX6, COX8, COX12, and COX13 as core subunits of cytochrome c oxidase (complex IV); ATP4, ATP15, and ATP20 as core subunits of the F0 subunit of the mitochondrial ATP synthase; INH1 as the inhibitory subunit of the mitochondrial ATP synthase; and finally, CYC1 as the predominant aerobic isoform of cytochrome c. In addition, three of the four subunits of succinate dehydrogenase (SDH1, SDH2, and SDH4) were significantly up-regulated in the anaerobic carbon-limited cultures. DLD1 encodes mitochondrial D-lactate-ferricytochrome c oxidoreductase (57); MAM33 encodes a mitochondrial protein required for normal respiratory growth (58); and NDE1 encodes a mitochondrial, cytosolically directed NADH dehydrogenase (59). The remaining 14 genes of the 35 genes of the discussed cluster were composed of two hexose transporter genes (HXT16 and HXT17), five genes encoding ribosomal proteins (RSP10A, RPS25A, RPP1B, RPL4A, and RPL9A), and seven genes belonging to different metabolic routes (SUT1, OSH7, AGP1, IMD2, YLR089C, YAR075W, and GPA1) (Fig. 6, middle panel).



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  		FIG. 6.
Hierarchical regulatory control of gene expression. Upper panel, normalized transcript profiles of mitochondrial function-related genes that clustered with a set of genes that showed a specific transcriptional up-regulation upon carbon limitation in anaerobic cultures. The transcript level across the four conditions for each gene was normalized relative to mean and variance. The data represent the normalized abundance of all transcripts for each cultivation condition, as well as the normalized average abundance of all transcripts of the cluster (thick black line). Error bars represent S.D. Middle panel, normalized transcript profiles of genes belonging to the cluster described for A that do not have a (known) relation to mitochondrial function. The data are presented as described for the upper panel. Lower panel, transcript profiles of the structural genes for the components of the Hap2/3/4/5p complex. Error bars represent S.D. {blacksquare}, HAP2; {circ}, HAP3; •, HAP4; {square}, HAP5.

 
The low mRNA levels of these genes in anaerobic chemostat cultures that were limited by nitrogen, phosphorus, or sulfur and thus had a high residual glucose concentration strongly suggest that glucose catabolite plays an important role in their transcriptional regulation. Conversely, under aerobic conditions, the identity of the growth-limiting nutrient did not significantly affect transcription. In fact, closer inspection indicated that, in the aerobic cultures, high transcript levels were observed in all four macronutrient limitation regimes (Fig. 6, upper panel). Furthermore, also in the aerobic cultures, the combined expression patterns of these genes suggest a moderate induction under glucose-limited conditions (Fig. 6, upper panel). However, the statistical criteria used for the definition of the cluster did not identify this induction as significant.

A simple verbal model to explain these observations is that, for this particular subset of S. cerevisiae genes, induction by oxygen supersedes glucose catabolite repression. It is beyond the scope of this work to analyze the molecular basis for this apparent hierarchy in transcriptional regulation. However, several genes of this cluster such as DLD1, QCR2, QCR7, and CYC1 are known targets of the Hap2/3/4/5p complex (Fig. 6, upper panel) (57, 60–62). In silico promoter analysis of the 35 genes of this subset revealed a significant over-representation (3-fold) of the ACCAATnA sequence, which overlaps the CCAAT core of the Hap2/3/4/5p-binding site. Furthermore, the transcript level of HAP4, known as the regulatory subunit of the Hap2/3/4/5p complex, correlated with the expression pattern within this subset of genes (Fig. 6, lower panel). Interestingly, HAP4 expression is reported to be glucose-repressible, being up-regulated after the diauxic shift and during growth on respiratory carbon sources (63). Further research is required to investigate which factors, in addition to regulation by the Hap2/3/4/5p complex, are involved in oxygen regulation of these genes and which factors determine the relative impact of glucose repression and oxygen induction.


   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 EXPERIMENTAL PROCEDURES
 RESULTS
 DISCUSSION
REFERENCES
 
DNA Microarrays as a Diagnostic Tool for Biotechnology—A detailed understanding of the environmental stimuli to which microorganisms are exposed in industrial fermentation processes is invaluable for rational design and optimization of such processes. DNA microarrays provide an interface that allows, in principle, the use of the microorganisms themselves as the ultimate "biosensor." An unequivocal coupling between relevant environmental parameters and transcriptional responses is essential for this application of DNA microarrays. An important concept in this context is that of the signature transcript, a transcript whose levels specifically increase (or decrease) in response to a single environmental stimulus.

This study indicates that, in general, robust signature transcripts cannot be identified by varying the process parameter of interest against a single constant experimental background (one-dimensional transcriptome analysis). Instead, identification of robust signature transcripts requires that transcriptional responses to an environmental parameter be analyzed against multiple experimental backgrounds. For example, the sets of signature transcripts for (an)aerobiosis and growth limitation by four macronutrients that were previously established in one-dimensional transcriptome comparisons (4, 9, 11, 12) were considerably reduced in size by the two-dimensional approach followed in this study. Chemostat cultivation is an indispensable tool for this combinatorial approach, as, in contrast to batch cultivation, it allows the manipulation of individual culture parameters while other relevant parameters, including the specific growth rate, are kept constant (4, 9, 11, 13).

Although this study covers only a minute fraction of the staggering diversity of environmental conditions to which S. cerevisiae may be exposed in nature and in industry, it clearly demonstrates the complexity of transcriptional regulation. In real life, transcriptional responses of cells are influenced by hundreds of extracellular signals. The interplay of these signals results in a multidimensional space in which each possible combination of signals results in a unique transcriptome. It is therefore to be anticipated that the number of robust signature transcripts will decrease further when, in addition to nutrient limitation and oxygen availability, other chemical or physical process parameters are included.

The significance of the combinatorial nature of the regulation of gene expression extends beyond S. cerevisiae and industrial biotechnology. For example, in the medical field, it is to be expected that the transcriptional profiles coupled to a disease or pharmacological efficacy will be equally sensitive to other stimuli and variance received by the cells. Although, in a statistical sense, such effects may be averaged out when the identification of disease-correlated signature transcripts is based on large numbers of healthy and ill individuals (64), this does not exclude a strong impact of transcriptional "cross-talk" in individual patients that have been exposed to special circumstances.

Unraveling Transcriptional Regulation—Despite the combinatorial nature of transcriptional regulation, identification of unequivocal signature transcripts should be possible when mechanisms of transcriptional regulation are fully understood. Ideally, signature transcripts should be encoded by genes that respond to a single transcriptional regulator protein whose expression and activity are uniquely dependent on a single environmental stimulus. Identification of such genes and regulators requires detailed knowledge of the regulons and recognition sequences of all relevant transcriptional regulators. Such knowledge is also essential for rational and predictable reprogramming of transcriptional regulation to improve the performance of industrial microorganisms.

Even for well studied organisms like S. cerevisiae, the physiological roles of many transcriptional regulators, as well as the sequence motifs they recognize, remain to be identified. The two-dimensional chemostat-based approach proposed in this study provides a powerful new tool for unraveling transcriptional regulation networks. This is exemplified by the enrichment of regulatory motifs in the consistently anaerobically induced transcripts (Table II). Clearly, regulation by known transcriptional regulators (relief of ROX1 repression and transcriptional activation by UPC2) (30, 65, 66) is not sufficient to account for the transcriptional response of all 65 genes that were consistently up-regulated under anaerobic conditions. Indeed, our study strongly suggests that at least a third factor, which recognizes an AAGGCAC motif, is involved in transcriptional regulation by oxygen availability. This motif had gone unnoticed in a previous one-dimensional aerobic/anaerobic comparison (12). In general, a combinatorial analysis of the transcriptional responses to environmental stimuli is likely to increase enrichment of relevant regulatory elements and facilitate their identification.

The approach used in this study also allows statements on the hierarchy of transcriptional regulation. This is exemplified by a subset of genes related to mitochondrial function. Under anaerobic conditions, these genes were regulated primarily by glucose repression/derepression. However, under aerobic conditions, a high transcript level was observed even under excess glucose conditions. Together, these data indicate that, in the aerobic cultures, oxygen regulation supersedes glucose repression (Fig. 6). By expanding data sets and combining them with an in silico analysis of promoter structure, combinatorial analysis of transcriptomes can accelerate the unraveling of transcriptional regulation networks.

Functional Analysis—Assigning physiological functions to "unknown function" genes still poses a major challenge in the post-genomic era. By identifying groups of genes that appear to be coexpressed (67), DNA microarrays can guide functional analysis. Indeed, many studies have correlated mRNA levels to cultivation conditions. However, even when chemostat cultivation is used to change only a single environmental parameter, pairwise comparisons characteristically lead to large numbers of target genes, complicating functional analysis (4, 9, 10, 12). Moreover, in a recent study on the genome-wide transcriptional responses to low temperature (68), a very poor correlation was observed between transcriptional responses of genes and the phenotype of the corresponding null mutants at low temperature.

Compared with previous one-dimensional studies, the combinatorial approach followed in this study led to a clear enrichment in our "robust response sets" of (i) genes with known function related to the environmental status under study (Fig. 4) and/or (ii) genes with relevant regulatory elements (Table II). By implication, also the unknown function genes found in the corresponding data sets are more likely to have a direct functional relationship to the corresponding nutritional/environmental status. We are currently testing this hypothesis for the subset of genes that showed a consistent up-regulation under anaerobic conditions.

Among the robust response signature genes identified in this study, 38% do not have a clearly established biological function (Fig. 4). It is noteworthy that some of these (YJL118C, YAR069C, and YGR190C) belong to a group of open reading frames for which it has recently been proposed that they should be discarded from the yeast genome directory based on genomic comparison of S. cerevisiae, Saccharomyces bayanus, Saccharomyces mikatae, and Saccharomyces paradoxus (69). The observation that three of these genes showed a consistent response to phosphate limitation (YJL118C and YAR069C) or nitrogen limitation (YGR190C) strongly suggests they are bona fide, biologically relevant genes.

Provided that yeast strains and cultivation procedures are standardized, DNA microarray analysis of chemostat cultures is well reproducible in different laboratories (9). We propose that a multi-laboratory effort to build an extensive, chemostat-based, "multidimensional" gene expression data base is an invaluable research tool for functional analysis of the S. cerevisiae genome and for yeast system biology. Obviously, such a data base should not necessarily be confined to transcriptome data, but could also cover other levels of information.


   FOOTNOTES
 
The complete data set has been submitted to the Genome Expression Omnibus Database (available at www.ncbi.nlm.nih.gov/geo/) under series GSE1723 [NCBI GEO] .

* The work performed in the Kluyver Centre for Genomics of Industrial Fermentation was supported by the Netherlands Genomics Initiative. The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked "advertisement" in accordance with 18 U.S.C. Section 1734 solely to indicate this fact. Back

{boxs} The on-line version of this article (available at http://www.jbc.org) contains Supplemental Tables 1–6. Back

|| To whom correspondence should be addressed. Tel.: 31-15-278-2412; Fax: 31-15-278-2355; E-mail: j.m.daran{at}tnw.tudelft.nl.



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