The Genome-wide Transcriptional Responses of Saccharomyces cerevisiae Grown on Glucose in Aerobic Chemostat Cultures Limited for Carbon, Nitrogen, Phosphorus, or Sulfur
2003; Elsevier BV; Volume: 278; Issue: 5 Linguagem: Inglês
10.1074/jbc.m209759200
ISSN1083-351X
AutoresViktor M. Boer, Johannes H. de Winde, Jack T. Pronk, Matthew D. W. Piper,
Tópico(s)Viral Infectious Diseases and Gene Expression in Insects
ResumoProfiles of genome-wide transcriptional events for a given environmental condition can be of importance in the diagnosis of poorly defined environments. To identify clusters of genes constituting such diagnostic profiles, we characterized the specific transcriptional responses of Saccharomyces cerevisiaeto growth limitation by carbon, nitrogen, phosphorus, or sulfur. Microarray experiments were performed using cells growing in steady-state conditions in chemostat cultures at the same dilution rate. This enabled us to study the effects of one particular limitation while other growth parameters (pH, temperature, dissolved oxygen tension) remained constant. Furthermore, the composition of the media fed to the cultures was altered so that the concentrations of excess nutrients were comparable between experimental conditions. In total, 1881 transcripts (31% of the annotated genome) were significantly changed between at least two growth conditions. Of those, 484 were significantly higher or lower in one limitation only. The functional annotations of these genes indicated cellular metabolism was altered to meet the growth requirements for nutrient-limited growth. Furthermore, we identified responses for several active transcription factors with a role in nutrient assimilation. Finally, 51 genes were identified that showed 10-fold higher or lower expression in a single condition only. The transcription of these genes can be used as indicators for the characterization of nutrient-limited growth conditions and provide information for metabolic engineering strategies. Profiles of genome-wide transcriptional events for a given environmental condition can be of importance in the diagnosis of poorly defined environments. To identify clusters of genes constituting such diagnostic profiles, we characterized the specific transcriptional responses of Saccharomyces cerevisiaeto growth limitation by carbon, nitrogen, phosphorus, or sulfur. Microarray experiments were performed using cells growing in steady-state conditions in chemostat cultures at the same dilution rate. This enabled us to study the effects of one particular limitation while other growth parameters (pH, temperature, dissolved oxygen tension) remained constant. Furthermore, the composition of the media fed to the cultures was altered so that the concentrations of excess nutrients were comparable between experimental conditions. In total, 1881 transcripts (31% of the annotated genome) were significantly changed between at least two growth conditions. Of those, 484 were significantly higher or lower in one limitation only. The functional annotations of these genes indicated cellular metabolism was altered to meet the growth requirements for nutrient-limited growth. Furthermore, we identified responses for several active transcription factors with a role in nutrient assimilation. Finally, 51 genes were identified that showed 10-fold higher or lower expression in a single condition only. The transcription of these genes can be used as indicators for the characterization of nutrient-limited growth conditions and provide information for metabolic engineering strategies. Growth of microorganisms in their natural environment and in many industrial applications is often limited by nutrient availability (1Pretorius I.S. Yeast. 2000; 16: 675-729Google Scholar,2Attfield P.V. Nat. Biotechnol. 1997; 15: 1351-1357Google Scholar). In these situations the specific growth rate of the organism is determined by the low (non-saturating) concentration of a single nutrient. For example, in the industrial production of bakers' yeast sugar-limited, aerobic cultivation at relatively low specific growth rates is essential to achieve high biomass yields. On the other hand processes such as beer fermentation occur at high concentrations of fermentable sugars and are limited by other nutrients (e.g.oxygen, nitrogen). As a result the yeast's metabolic activities are altered. This situation is different from nutrient starvation in which the absence of a nutritional component is often the cause of stress responses that result in growth arrest or cell death (3Werner-Washburne M. Braun E.L. Crawford M.E. Peck V.M. Mol. Microbiol. 1996; 19: 1159-1166Google Scholar, 4Gasch A.P. Werner-Washburne M. Funct. Integr. Genomics. 2002; 2: 181-192Google Scholar). In the laboratory, cultivation of microorganisms is predominantly performed in shake-flasks, in which all relevant nutrients are at least initially present in excess. During the course of batch cultivation the physical and chemical environment constantly changes, which directly affects the specific growth rate and the regulation of many metabolic processes (5Causton H.C. Ren B. Koh S.S. Harbison C.T. Kanin E. Jennings E.G. Lee T.I. True H.L. Lander E.S. Young R.A. Mol. Biol. Cell. 2001; 12: 323-337Google Scholar). With the use of chemostat cultures, it is possible to study steady-state physiological adaptations to nutrient-limited growth. The medium that is continuously fed into the culture can be designed such that growth is limited by a single, defined nutrient, whereas all other nutrients remain present in excess. In conjunction with this continuous feed of fresh media into the vessel, waste media and cells are removed at the same rate. This results in a constant dilution rate (h−1) which in steady-state cultures is equal to the specific growth rate μ (6Harder W. Kuenen J.G. J. Appl. Bacteriol. 1977; 43: 1-24Google Scholar). This offers the unique possibility to study metabolism and its regulation at a fixed and constant specific growth rate under tightly defined nutritional conditions. Microorganisms have evolved a multitude of strategies to cope with nutrient limitations. Low, growth-limiting amounts of important nutrients often lead to the induction of high affinity transport systems and/or metabolic systems that allow more efficient incorporation of the nutrients into biomass constituents. A classical example is the high affinity glutamine synthetase/glutamine oxoglutatrate aminotransferase system from Klebsiella pneumoniae (formerly Aerobacter aerogenes) that can replace glutamate dehydrogenase as the primary ammonia-assimilating enzyme system during ammonia-limited growth (7Tempest D.W. Meers J.L. Brown C.M. Biochem. J. 1970; 117: 405-407Google Scholar). In other cases, the final biomass composition itself has a reduced content of the growth-limiting nutrient. For example, in Saccharomyces cerevisiae and Escherichia coli, it has been observed that the amino acid composition of the subset of structural enzymes used in the assimilation of sulfur, carbon, or nitrogen have a reduced content of the respective element compared with their average content in the predicted proteome (8Baudouin-Cornu P. Surdin-Kerjan Y. Marliere P. Thomas D. Science. 2001; 293: 297-300Google Scholar). The physiological responses of microorganisms to different nutrient limitation regimes can be regulated at various levels. At the level of transcription, DNA microarrays allow the accurate, genome-wide mapping of regulatory responses. With few exceptions (9Ter Linde J.J.M. Liang H. Davis R.W. Steensma H.Y. van Dijken J.P. Pronk J.T. J. Bacteriol. 1999; 181: 7409-7413Google Scholar, 10Ter Linde J.J.M. Steensma H.Y. Yeast. 2002; 19: 825-840Google Scholar, 11Hayes A. Zhang N., Wu, J. Butler P.R. Hauser N.C. Hoheisel J.D. Lim F.L. Sharrocks A.D. Oliver S.G. Methods. 2002; 26: 281-290Google Scholar, 12Piper M.D.W. Daran-Lapujade P. Bro C. Regenberg B. Knudsen S. Nielsen J. Pronk J.T. J. Biol. Chem. 2002; 277: 37001-37008Google Scholar) DNA microarray analyses have been performed using cells grown in shake flasks. With the use of chemostats, detailed analyses of the transcriptional responses of S. cerevisiae to nutrient limitations may aid in the development of new, DNA array-based approaches for diagnosis of industrial fermentation processes. Furthermore, such studies provide valuable information for the functional analysis of genes whose encoded protein has no known or only poorly defined function. However, as yet there have been no systematic investigations into the impact on genome-wide transcriptional regulation of different nutrient limitation regimes in chemostat cultures. The main chemical elements that are assimilated into yeast biomass are carbon, hydrogen, oxygen, nitrogen, sulfur, and phosphorus (13Lange H.C. Heijnen J.J. Biotechnol. Bioeng. 2001; 75: 334-344Google Scholar). Previous work from our laboratory already addressed the impact of oxygen supply on the transcriptome of S. cerevisiae (9Ter Linde J.J.M. Liang H. Davis R.W. Steensma H.Y. van Dijken J.P. Pronk J.T. J. Bacteriol. 1999; 181: 7409-7413Google Scholar, 12Piper M.D.W. Daran-Lapujade P. Bro C. Regenberg B. Knudsen S. Nielsen J. Pronk J.T. J. Biol. Chem. 2002; 277: 37001-37008Google Scholar). The aim of the present study was to determine which of the currently recognized genes of S. cerevisiae have uniquely higher or lower expression when growth is limited for each of the macro nutrients, carbon, nitrogen, sulfur, or phosphorus. To this end, a wild type strain of S. cerevisiae was grown on glucose under strictly defined conditions in aerobic, nutrient-limited chemostat cultures at a constant growth rate. These experiments revealed the transcriptional differences that contribute to altered yeast metabolism and so can serve as molecular identifiers to diagnose the status of nutrient-limited fermentations or refine metabolic engineering strategies. The complete data set is available for download at www.nutrient-limited.bt.tudelft.nl. Wild type S. cerevisiae strain CEN.PK113–7D (MAT a) (14van Dijken J.P. Bauer J. Brambilla L. Duboc P. Francois J.M. Gancedo C. Giuseppin M.L.F. Heinen J.J. Hoare M. Lange H.C. Madden E.A. Niederberger P. Nielsen J. Parrou J.L. Petit T. Porro D. Reuss M. van Riel N. Rizzi M. Steensma H.Y. Verrips C.T. Vindelov J. Pronk J.T. Enzyme Microb. Technol.. 2000; 26: 706-714Google Scholar) was grown at 30 °C in 2-liter chemostats (Applikon) with a working volume of 1.0 liter as described in van den Berg et al.(15van den Berg M.A. de Jong-Gubbels P. Kortland C.J. van Dijken J.P. Pronk J.T. Steensma H.Y. J. Biol. Chem. 1996; 271: 28953-28959Google Scholar). Cultures were fed with a defined mineral medium that limited growth by either carbon, nitrogen, phosphorus, or sulfur with all other growth requirements in excess and at a constant residual concentration. The dilution rate was set at 0.10 h−1. The pH was measured online and kept constant at 5.0 by the automatic addition of 2m KOH with the use of an Applikon ADI 1030 bio controller. Stirrer speed was 800 rpm, and the airflow was 0.5 liters·min−1. Dissolved oxygen tension was measured on line with an Ingold model 34-100-3002 probe and was above 50% of air saturation. The off-gas was cooled by a condenser connected to a cryostat set at 2 °C, and oxygen and carbon dioxide were measured off line with an ADC 7000 gas analyzer. Steady-state samples were taken after ∼10–14 volume changes to avoid strain adaptation due to long term cultivation (16Ferea T.L. Botstein D. Brown P.O. Rosenzweig R.F. Proc. Natl. Acad. Sci. U. S. A. 1999; 96: 9721-9726Google Scholar). Dry weight, metabolite, dissolved oxygen and gas profiles had to be constant over at least 3 volume changes before sampling for RNA extraction. The defined mineral medium composition was based on that described by Verduyn et al. (17Verduyn C. Postma E. Scheffers W. van Dijken J. Yeast. 1992; 8: 501-517Google Scholar). In all limitations except for carbon, the residual glucose concentration was targeted to 17 g·liter−1 to sustain glucose repression at the same level. For each limitation, the medium contained the following components (per liter). For carbon-limited, the composition was 5.0 g of (NH4)2SO4, 3.0 g of KH2PO4, 0.5 g of MgSO4·7H2O, and 7.5 g of glucose. For nitrogen-limited, the composition was 1.0 g of (NH4)2SO4, 5.3 g of K2SO4, 3.0 g of KH2PO4, 0.5 g of MgSO4·7H2O, and 59 g of glucose. For phosphorus-limited, the composition was 5.0 g of (NH4)2SO4, 1.9 g of K2SO4, 0.12 g of KH2PO4, 0.5 g of MgSO4·7H2O, and 59 g of glucose . For sulfur-limited, the composition was 4.0 g of NH4Cl, 0.05 g of MgSO4·7H2O, 3.0 g of KH2PO4, 0.4g of MgCl2, and 42 g of glucose. Culture supernatants were obtained after centrifugation of samples from the chemostats or by a rapid sampling technique using steel balls precooled to −20 °C. 1M. R. Mashego, W. M. van Gulik, J. L. Vinke, and J. J. Heijnen (2002) Biotechnol. Bioeng., in press. For the purpose of glucose determination and carbon recovery, culture supernatants and media were analyzed by high performance liquid chromatography fitted with an AMINEX HPX-87H ion exchange column using 5 mm H2SO4 as the mobile phase. Residual glucose in the glucose-limited chemostats was determined enzymatically using a commercial glucose determination kit from Roche Molecular Biochemicals. Ammonium concentrations were determined by a modified method of the Boehringer ureum test. Phosphate and sulfate were determined with the use of cuvette tests from DRLANGE (Düsseldorf, Germany). Culture dry weights were determined via filtration as described by Postma et al. (19Postma E. Verduyn C. Scheffers W.A. van Dijken J.P. Appl. Environ. Microbiol. 1989; 55: 468-477Google Scholar). Sampling of cells from chemostats, probe preparation, and hybridization to Affymetrix GeneChip® microarrays was performed as described previously (12Piper M.D.W. Daran-Lapujade P. Bro C. Regenberg B. Knudsen S. Nielsen J. Pronk J.T. J. Biol. Chem. 2002; 277: 37001-37008Google Scholar). The results for each growth condition were derived from three independently cultured replicates. Acquisition and quantification of array images and data filtering were performed using the Affymetrix software packages Microarray Suite v5.0, MicroDB v3.0, and Data Mining Tool v3.0. For further statistical analyses, Microsoft Excel Significance Analysis of Microarrays (SAM; v1.12) add-in was used (20Tusher V.G. Tibshirani R. Chu G. Proc. Natl Acad. Sci. U. S. A. 2001; 98: 5116-5121Google Scholar). The data representation used in Figs. 2 and 3 were generated using the mean and variance normalize function of the software J-Express v2.1.Figure 3The transcript profiles and identities of the genes that were specifically up- or down-regulated in each of the four limitations. The average of three replicate genome-wide transcript profiles were averaged for each condition 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 full data set containing all transcript abundance measurements as well as those for the eight categories of changes can be found at www.nutrient-limited.bt.tudelft.nl.View Large Image Figure ViewerDownload (PPT) Before comparison, all arrays were globally scaled to a target value of 150 using the average signal from all gene features using Microarray Suite v5.0. 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 (standard deviation divided by the mean) was first calculated for each transcript. When the genes were ordered by increasing average signal, the average coefficient of variation displayed a sharp increase for the 900 genes with the lowest abundance. The average coefficient of variation for the remaining 5483 signals was used to represent the average error for each condition (for further explanation and use of these values, see Piper et al. 12). Because the lowest 900 transcripts were unable to be reliably measured, their level was set to a value of 12 (see “Lowest measurable level” in Table II) for the comparison analyses.Table IISummary of microarray experiment quality parameters for each growth limitationCulture limiting nutrientAverage coefficient of variation2-aRepresents the average of the coefficient of variation (S.D. divided by the mean) for all genes except the 900 genes with the lowest mean expression.ACT1 2-bEncoding actin; average signal and standard deviation from probe set “5392_at” comprised of 16 probe pairs found within 400 nucleotides of the 3′ end of the open reading frame.Lowest measurable signal2-cCorresponds to the signal from the open reading frame with the lowest reliably detectable abundance.Carbon0.182489 ± 8112 ± 6Nitrogen0.142265 ± 10612 ± 3Phosphorus0.212314 ± 26613 ± 3Sulfur0.132172 ± 24912 ± 32-a Represents the average of the coefficient of variation (S.D. divided by the mean) for all genes except the 900 genes with the lowest mean expression.2-b Encoding actin; average signal and standard deviation from probe set “5392_at” comprised of 16 probe pairs found within 400 nucleotides of the 3′ end of the open reading frame.2-c Corresponds to the signal from the open reading frame with the lowest reliably detectable abundance. Open table in a new tab Clusters of expression profiles were identified from all possible pairwise comparisons of the four data sets. A transcript fell into one of the eight expression clusters (significantly higher or lower in only one condition) if it was called significantly changed using Significance Analysis of Microarrays (expected median false positive rate of 1%) by at least 2-fold from each other condition. In our experience, these criteria establish a data set able to be reproduced by an independent laboratory (12Piper M.D.W. Daran-Lapujade P. Bro C. Regenberg B. Knudsen S. Nielsen J. Pronk J.T. J. Biol. Chem. 2002; 277: 37001-37008Google Scholar). Promoter analyses were performed using the web-based software Regulatory Sequence Analysis Tools (bioinformatics.bmc.uu.se/∼jvanheld/rsa-tools) (21van Helden J. Andre B. Collado-Vides J. Yeast. 2000; 16: 177-187Google Scholar). The promoters (from −800 to −50) 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 the individual promoter sequences contributing to these elements were then aligned, and redundant elements were determined by counting the base representation at each position. The relative abundance of these redundant elements was then determined from a new enquiry of the co-regulated gene promoters and the entire set of yeast promoters in the genome. Metabolic changes, mediated partly by transcriptional regulation, are required for successful adaptation to environmental changes. Here we have measured the genome-wide transcriptional responses of S. cerevisiae to four different macronutrient limitations during steady-state growth in chemostats. To verify that each of the nutrient limitations was appropriately achieved, we measured the concentrations of nutrients in the culture supernatants (Table I). This showed that when a nutrient was growth-limiting, its residual concentration was below the detection limit, whereas each other nutrient was in excess. Furthermore, the concentrations of each excess nutrient were comparable between cultures. This ability to control the concentrations of excess nutrients is a unique feature of chemostat cultivation and one that is especially important for ammonia and glucose in light of their impact on transcriptional regulation via sensors of extracellular nutrients (22Forsberg H. Ljungdahl P.O. Curr. Genet. 2001; 40: 91-109Google Scholar). Indeed, from work in our laboratory beyond the scope of this paper, we have noted systematic alterations in global transcription as a result of differences in excess extracellular glucose concentrations (data not shown).Table INutrient concentrations and physiological parameters of cultures used in this studyGrowth-limiting nutrientResidual nutrient measurementsPhysiological parametersGlucoseNH4+PO42−SO42−YSX(g/g)1-aYield of biomass (g/g of glucose consumed).qglucose1-bmmol of glucose consumed/g of biomass/h.qethanol1-cmmol of ethanol produced/g of biomass/h.qO21-dmmol of oxygen consumed/g of biomass/h.qCO21-emmol of carbon dioxide produced/g of biomass/h.RQ1-fRQ, respiratory quotient (qCO2/qO2).Carbon recoveryg/litermmmmmm%Carbon0.014 ± 0.00358.2 ± 1.319.8 ± 0.638.61-gSingle measurement.0.49 ± 0.011.1 ± 0.00.0 ± 0.02.8 ± 0.32.8 ± 0.31.0 ± 0.098 ± 3Nitrogen16.7 ± 1.0BD1-hBD, below detection limit of assay.18.6 ± 1.01-iAverage of two measurements.40.7 ± 1.01-iAverage of two measurements.0.09 ± 0.005.8 ± 0.18.0 ± 0.12.7 ± 0.112.1 ± 0.24.5 ± 0.296 ± 1Phosphorus18.1 ± 1.054.3 ± 0.0BD47.5 ± 1.00.09 ± 0.006.1 ± 0.27.8 ± 0.14.0 ± 0.113.5 ± 0.23.4 ± 0.095 ± 2Sulfur17.4 ± 0.653.7 ± 2.418.4 ± 0.2BD0.14 ± 0.003.8 ± 0.14.4 ± 0.13.0 ± 0.08.0 ± 0.82.7 ± 0.296 ± 1Except where indicated, data represent the average and S.D. of three separate chemostat steady states.1-a Yield of biomass (g/g of glucose consumed).1-b mmol of glucose consumed/g of biomass/h.1-c mmol of ethanol produced/g of biomass/h.1-d mmol of oxygen consumed/g of biomass/h.1-e mmol of carbon dioxide produced/g of biomass/h.1-f RQ, respiratory quotient (qCO2/qO2).1-g Single measurement.1-h BD, below detection limit of assay.1-i Average of two measurements. Open table in a new tab Except where indicated, data represent the average and S.D. of three separate chemostat steady states. For each culture, the rates of glucose and oxygen consumption as well as carbon dioxide and ethanol evolution were determined (Table I). In the glucose-limited culture, no ethanol was produced, and cells grew with a biomass yield on glucose of 0.5 g·g−1, reflecting complete respiratory catabolism. This is typical of steady-state growth of S. cerevisiae strain CEN.PK113–7D under glucose limitation at dilution rates below 0.3 h−1 (23Diderich J.A. Raamsdonk L.M. Kuiper A. Kruckeberg A.L. Berden J.A. Teixeira D.M. van Dam K. FEMS Yeast Res. 2002; 2: 165-172Google Scholar). The three nutrient-limited cultures containing residual glucose exhibited mixed respiratory (indicated by high q O2) and fermentative glucose catabolism, indicating that excess glucose in the medium does not fully repress respiration. Similarly high rates of oxygen consumption in the presence of excess glucose have been noted previously (24Larsson C. von Stockar U. Marison I. Gustafsson L. J. Bacteriol. 1993; 175: 4809-4816Google Scholar). For the transcriptome analyses, the variation for each condition was measured from the three independent array replicates performed (TableII). The average coefficient of variation was no more than 0.21 and for sulfur limitation was as low as 0.13, reflecting the high reproducibility between replicate arrays. Furthermore, the level of the ACT1 transcript and the signal from the gene with lowest measurable expression were both unchanged between culture conditions. This indicated that the transcriptomes from each condition were similar in their overall magnitude of expression, thus supporting the use of global scaling for these comparisons. In total 1881 genes (31% of the genome) had altered expression levels in at least one condition, whereas 3558 (58%) were unchanged, and 645 (11%) remained below reliable detection in all four conditions (Fig. 1). It is not surprising that such a large proportion of the genome was altered across our experiments since the four limiting nutrients are major constituents of biomass. For each nutrient it is believed that metabolic changes occur in the cell that result in both sparing of accessible nutrients and initiation of methods to make alternative forms of that nutrient available. In both cases, this requires the differential regulation of many genes involved in transport and metabolism of the required compounds. Among the changes observed, a division was made to separate the genes that had a significantly higher or lower abundance in only one condition when compared with each other limitations and those with more complex regulatory patterns (Fig. 1). Within the former class, there were four regulatory patterns that showed higher expression under only one limitation and four patterns that showed lower expression in only one limitation (Fig. 2). We rationalize that these eight patterns of expression should be the most informative for promoter and functional analysis studies as well as forming the basis for the list of transcripts that could be used as specific molecular identifiers to characterize specific growth limitations. Because our aim was to define transcripts that could be used for this purpose, we chose to only concentrate on the 484 open reading frames that fell into these eight classes. Coordinated regulation of global transcription is driven by the action of transcription factors that generally act once bound to short elements in gene promoters. Searching the promoters of co-regulated genes for short sequences that are over-represented can identify these elements. We analyzed the genes from the eight regulatory classes defined above using the web-based tool RSAT (21van Helden J. Andre B. Collado-Vides J. Yeast. 2000; 16: 177-187Google Scholar). Because our classifications selected for genes that were differentially regulated under only one of the nutrient limitations tested, each group should have been enriched for the regulatory elements that are specifically required for an appropriate response. Several significantly over-represented elements were recovered from each group of genes except for those that had specifically lower expression under nitrogen or phosphorus limitation (TableIII). Because it is known that many transcriptionally active elements have an enhanced effect when present in more than one copy (for example, see Ref. 25Ozsarac N. Straffon M.J. Dalton H.E. Dawes I.W. Mol. Cell. Biol. 1997; 17: 1152-1159Google Scholar), the proportion of gene promoters in each subgroup with at least two elements was compared with the proportion found in all promoters of the genome (Table III). For each regulatory profile in which a known transcriptional regulator is thought to act, its corresponding binding sequence was found among the multiple elements recovered. In addition, several unknown elements were found that could not be associated with the binding of known transcription factors. To define unique transcriptional responses to distinct nutrient limitations, only the known elements are discussed further below.Table IIIGene coverage of over-represented sequences retrieved from the promoters of co-regulated genesRegulatory clusterPromoter element3-aRedundant nucleotides are given by: r = A or G, y = C or T, s = G or C, w = A or T, k = G or T, m = A or C, b = C, G, or T, d = A, G, or T; h = A, C, or T; n = A, C, G, or T.Putative-binding proteinGene coverageGenome coverage3-bRelative to 6451 open reading frame upstream promoters in the yeast genome according to RSA Tools.Ref.ForwardReverse%%Specifically higher in C limitationdCCCCdhdhGGGGhMiglp5728(28Griggs D.W. Johnston M. Mol. Cell. Biol. 1993; 13: 4999-5009Google Scholar)drCGGCTAGCCGyh?154wCTCCATGGAGsAdrlp2216(29Taylor W.E. Young E.T. Proc. Natl. Acad. Sci. U. S. A. 1990; 87: 4098-4102Google Scholar)mwCCGGGGCCsk?2515dnnCCGCCGCGGCGGnnhUme6p21(30Strich R. Surosky R.T. Steber C. Dubois E. Messenguy F. Esposito R.E. Genes Dev. 1994; 8: 796-810Google Scholar)Specifically lower in C limitationrGAAAAATTTTTCyHxk2p/Med8p6138(32de la Cera T. Herrero P. Moreno-Herrero F. Chaves R.S. Moreno F. J. Mol. Biol. 2002; 319: 703-714Google Scholar)GAAGAATTCTTC?4234dnyTCGAGAnddhhnTCTCGArnh?81Specifically higher in N limitationwGATAAsWTTATCsGln3p/Gat1p/Dal80p/Gzf3p539(33Bysani N. Daugherty J.R. Cooper T.G. J. Bacteriol. 1991; 173: 4977-4982Google Scholar)dnCAGCAATTGCTGng?227Specifically lower in N limitationNS3-cNS, no significant patterns retrieved by the algorithm employed by RSA Tools.Specifically higher in P limitationmACGTGbvCTCGTKPho4p395(38Ogawa N. Saitoh H. Miura K. Magbanua J.P. Bun-ya M. Harashima S. Oshima Y. Mol. Gen. Genet. 1995; 249: 406-416Google Scholar)GCAGCAnnddhhnnTGCTGC?113sdTGGAdnhdnhTCCAhw?4638Specifically lower in P limitationNSSpecifically higher in S limitationhGCCACATGTGGCdCbflp/Met4p/Met28p253(41Kuras L. Cherest H. Surdin-Kerjan Y. Thomas D. EMBO J. 1996; 15: 2519-2529Google Scholar)dCACGTGAhdTCACGTGhMet31p/Met32p132(42Blaiseau P.L. Isnard A.D. Surdin-Kerjan Y. Thomas D. Mol. Cell. Biol. 1997; 17: 3640-3648Google Scholar)hACAGwkbsCTGTd?4933Specifically lower in S limitationAGGGGCCCCTMsn2p/Msn4p5614(43Martinez-Pastor M.T. Marchler G. Schuller C. Marchler-Bauer A. Ruis H. Estruch F. EMBO J. 1996; 15: 2227-2235Scopus (865) Google Scholar)CCGCGCGCGCGG?81GGmACmkGTkCC?5225Elements were counted present in a gene promoter only if they occurred at least twice.3-a Redundant nucleotides are given by: r = A or G, y = C or T, s = G or C, w = A or T, k = G or T, m = A or C, b = C, G, or T, d = A, G, or T; h = A, C, or T; n = A, C, G, or T.3-b Relative to 6451 open reading frame upstream promoters in the yeast genome according to RSA Tools.3-c NS, no significant patterns retrieved by the algorithm employed by RSA Tools. Open table in a new tab Elements were counted present in a gene promoter only if they occurred at least twice. S. cerevisiae has the ability to use several different carbohydrate molecules as its sole source of carbon and energy. The carbon source that is most directly incorporated into central metabolism is glucose, which is often referred to as a preferred car
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