Functional genomics of beer-related physiological processes in yeast

Lucie Anne Hazelwood 2009

Functional genomics of beer-related physiological processes in yeast

Proefschrift ter verkrijging van de graad van doctor aan de Technische Universiteit Delft, op gezag van de Rector Magnificus prof. dr. ir. J.T. Fokkema voorzitter van het College voor Promoties in het openbaar te verdedigen op donderdag 26 november 2009 te 10:00 uur

door

Lucie Anne HAZELWOOD

Master of Science in biological engineering and applied microbiology Ecole Supérieure d’Ingénieurs de Luminy, Marseille, Frankrijk geboren te Levallois Perret, Frankrijk

Dit proefschrift is goedgekeurd door de promotor: Prof. dr. J.T. Pronk Copromoter: Dr. J-M.G. Daran

Samenstelling promotiecommissie: Rector Magnificus Prof. dr. J.T. Pronk Dr. J-M.G. Daran Prof. dr. J.H. de Winde Prof. S. Brul Prof. dr. M.C. Kielland-Brandt Prof. dr. L. Olsson Dr. M.C. Walsh Prof. dr. J.J. Heijnen

voorzitter Technische Universiteit Delft, promotor Technische Universiteit Delft, copromotor Technische Universiteit Delft Universiteit van Amsterdam Technical University Denmark Chalmers University of Technology, Sweden Heineken Supply Chain, Zoeterwoude Technische Universiteit Delft, reservelid

The studies presented in this thesis were performed at the Industrial Microbiology Section, Department of Biotechnology, Delft University of Technology, The Netherlands. The printing of this thesis was sponsored by the TUDelft and Heineken Supply Chain, Zoeterwoude, The Netherlands. The research was financially supported by Heineken Supply Chain, Zoeterwoude. The Industrial Microbiology section is part of the Kluyver Center for Genomics of Industrial Fermentation, which is supported by the Netherlands Genomics Initiative.

ISBN 978-94-90370-02-2

Contents

Chapter 1

General Introduction

7

Chapter 2

The Ehrlich pathway for fusel alcohol production: a century of research on Saccharomyces cerevisiae metabolism

37

Chapter 3

A new physiological role for Pdr12p in Saccharomyces cerevisiae: export of aromatic and branched-chain fusel acids produced in amino acid catabolism

57

Chapter 4

Physiological and transcriptional responses of Saccharomyces cerevisiae to zinc limitation in chemostat culture

75

Chapter 5

Identity of the growth-limiting nutrient strongly affects storage carbohydrate accumulation in anaerobic chemostat cultures of Saccharomyces cerevisiae

109

Chapter 6

Resistance to hop iso-α-acids in yeast involves active export and vacuolar sequestration

137

Summary

167

Samenvatting

170

Curriculum vitae

173

List of publications

174

Acknowledgements

175

Chapter 1

General introduction

Chapter 1

1. Yeast in the brewing industry A historical perspective The natural habitats of Saccharomyces cerevisiae are fruits and berries of wild plants but throughout human civilization, Saccharomyces yeasts have been domesticated through the making of wine, bread and beer (74). In Europe, the art of beer brewing was developed by monks in monasteries who studied ancient Egyptian texts and improved their techniques and recipes (111). Fresh water from rivers, lakes and canals was often undrinkable in those days, but if converted to beer it yielded a healthy, nutritional and tasty beverage. In the Netherlands, the number of breweries increased steadily over the centuries, and by 1600, towns with a good water supply (e.g. Amersfoort, Delft, Haarlem and Gouda) counted more than 100 breweries. In Delft, the quality of the water supplied by the canals was exceptionally good, which resulted in a rapid expansion of the number of breweries. By 1600 there were about 200 small breweries along the Oude Delft, each employing just a few men brewing beer in a brew kettle (Fig. 1) (111).

Fig. 1.1: Beer brewing in the middle ages (picture from Epko J. Bult, with kind permission from Aad van der Hoeven (111))

In Bavaria, to protect consumers from beer brewed with low quality ingredients (rice or corn adjuncts) and to avoid competition for grain with bakers and set price standards, Duke Wilhem IV declared the ‘Bavarian Purity Law’, or Reinheitsgebot (in 1516), where only barley, hops and water could be used as ingredients for beer brewing. This is the oldest law concerning regulation of food products and although restricted to Germany, it has had great impact on breweries all over Europe.

8

General introduction

Until the end of the 18th century, very few technological developments were introduced in the brewing process. Brewers focused especially on learning to select the finest ingredients for beer production. The biggest and most important technological advances in beer brewing came with the industrial revolution in the 19th century (13). Chemistry and biology were becoming true sciences and were being used to elucidate the modifications that took place during beer brewing. Individual yeast cells had already been observed under a microscope as early as 1680 by the Delft-based scientist Antonie van Leeuwenhoek (Fig. 2) (100), but their identification as living organisms and the elucidation of their role in converting fermentable sugars to ethanol was only achieved by Pasteur in the second half of the 19th century (82).

Fig. 1.2: Yeast globules as depicted in 1680 by Antonie van Leeuwenhoek (116)

In the following years, one of the most important milestones in beer brewing was the pure culturing of brewing yeast. The common practice was to inoculate wort with spent yeast from the preceding fermentation which resulted in little control over the quality of the final product. In 1883, Emil Christian Hansen of the Carlsberg Laboratory devised a method for using single-cell cultures of yeast in beer production (41). His attention had been drawn to the frequent occurrence of a bitter taste and bad odor in Carlsberg beers. He cultured a number of pure yeast strains from the brewery and was able to attribute the problem to one of them while another gave consistently good beer (7). Hansen’s method proved to be of major importance in standardizing yeasts for reliable brewing practice, in particular by making it easier to use strains that did not produce off-flavors. His method of obtaining pure cultures was subsequently applied to the production of other types of yeast (e.g. baker’s yeast) (7) and as a result, yeast was propagated industrially quite early in the 20th century. This also accelerated scientific research on yeast.

9

Chapter 1

An overview of the brewing process The beer brewing process is described in the flow sheet in Fig. 3. Briefly, beer brewing requires three ingredients: water, malt and hops (66). The hop plant, Humulus lupulus, has an exceptionally high content of secondary metabolites, the so-called α-acids (27). Malt is obtained from the barley grain which has been partly germinated to produce α- and β- amylases. After milling, the malt is mixed with water in the ‘mash tun’ (Fig. 3) where high temperatures activate the α- and βamylases. Starch is extracted from the malt grain and broken down to fermentable sugars (glucose, maltose, maltotriose) and dextrins by the concerted action of the αand β-amylases. After separation of the liquid sweet wort from the spent grains, the mixture is transferred to the brew kettle (Fig. 3) where the hops are added. The wort is boiled for 60 to 90 minutes to arrest the enzyme activity, sterilize the wort, precipitate the insoluble proteins, and hasten certain desirable chemical changes. In particular, the bitter resins (or α-acids) of the hop are extracted and isomerized to iso-α-acids which impart the typical bitter flavor of beer. They also have the practical properties of inhibiting growth of contaminating microbes (e.g. lactic acid bacteria) (96). The hopped wort is subsequently cooled and prepared for fermentation. MALT YEAST Mill

HOPS

WATER

Mash tun

Mash

Brew kettle

filter

MASHING

Wort

Fermentation Filtering tank

cooling

Conditioning tank

WORT BOILING FERMENTATION

Spent grains for cattle feed

Fig. 1.3: Flow sheet of the brewing process

10

Yeast extract for food industry

Bottling Pasteurization

General introduction

The taxonomic classification described by Vaughan-Martini and Martini (117), and retained by most brewers, makes a distinction between two different groups of industrial brewer’s yeast: lager yeasts and ale yeasts. Ale yeasts, also known as ‘top-fermenting yeast’, are classified as S. cerevisiae and are mostly used for the production of specialty beers. The fermentation temperatures are relatively high (between 15 ºC and 20 ºC) and ale yeasts flocculate to the top of the fermenting medium. Lager yeasts are classified as S. pastorianus, but can also be referred to as S. carlsbergensis. Lager yeasts are used for the production of pilsner type beers, they ferment at lower temperatures than ale yeasts (10 ºC to 15 ºC), and flocculate to the bottom of the vessel, enabling their easy separation from the broth at the end of the fermentation. At present, lager beer constitutes more than 90 % of the global beer production and as a result, beer-related research focuses mostly on lager brewing yeasts (13, 66). Beer fermentations are generally aerated in the beginning to enable the synthesis of sterols and unsaturated fatty acids by the brewing yeast (4, 5) but the quality of the final product is mainly determined during the anaerobic phase thereafter (66). The fermentation process is normally carried out at low temperature (generally between 10 and 15 ºC) where yeast growth is decreased and the duration of alcoholic fermentation is maximized. Also, fewer fusel alcohols and greater proportions of acetate and ethyl esters are produced at lower temperatures, resulting in the formation of less off-flavor compounds (66). During the active, primary fermentation, the yeast cells ferment the sugars (mainly glucose, maltose and maltotriose) to ethanol and carbon dioxide. This coincides with uptake of nitrogen-containing compounds and a fall in the pH of the fermenting wort. During the subsequent secondary fermentation, the yeast cells start to flocculate and the beer is left to mature. Maturation involves important chemical and biochemical changes that have great influence on the overall flavor of the final product, such as the conversion of diacetyl (12), the undesirable butterscotch flavor compound. The total fermentation usually takes approximately two weeks, after which the beer is filtered, pasteurized and bottled. It is common practice to harvest the yeast (cropping) from the fermentation vessel and store it for a short period of time at low temperature before re-inoculating (re-pitching) the culture into a fresh batch of wort. Depending on the individual strain or the quality of the ‘cropped slurry’, the same yeast culture can be reused 8 to 10 times (66).

11

Chapter 1

Factors affecting yeast fermentation performance Many factors are known to affect the yeast performance in beer fermentation. These factors are dependent not only on the quality of the raw ingredients used for brewing, but also on the manner in which they have been processed to produce the final wort used as substrate for beer fermentation. In addition, the geometry, design and size of the fermentation vessels themselves have much influence on the yeast’s performance and physiology (93, 113, 112). These brewing and engineering aspects are beyond the scope of this thesis and will therefore not be discussed in the following sections. Two main factors that have a direct impact on yeast metabolism and physiology will be briefly discussed in the following paragraphs: the nutritional composition of wort and aspects of yeast storage and handling. ™ Wort composition The hopped wort that is used as the substrate for beer fermentation has a very complex composition. Carbon sources are available in large quantities (Table 1) with maltose and maltotriose being the most abundant (48). Dextrins or α-glucans are not fermented by brewing yeasts, but they contribute to the body and mouth feel of the beer (13). Efficient fermentation requires the rapid and complete utilization of the fermentable sugars in wort (13, 48, 66). Regulatory mechanisms in yeast ensure that sugars are consumed in an ordered way from a medium containing sugars mixtures (carbon catabolite repression) (32). Generally, when half of the glucose present in the wort has been taken up, the yeast will consume the α-glucosides maltose and maltotriose, with a slower uptake rate for maltotriose than for maltose (125). A common problem in the brewing industry is a high concentration of yeast fermentable extract in the finished beer, with maltotriose being the main residual sugar (83, 104). Incomplete fermentation leads to a lower ethanol yield, a beer with higher carbohydrate levels and potentially an atypical flavor profile (127). The limiting factor for maltose and maltotriose metabolism is widely believed to be the transport of these sugars across the plasma membrane by specific sugar transport proteins (26). Therefore, fermentation of maltotriose has received much attention leading to the identification of several lager strain specific genes encoding maltotriose transporters (29).

12

General introduction Table 1.1: Average carbohydrate levels in wort from an all-malt source expressed as a percentage of wort solids (48)

Carbohydrate Maltose Maltotriose Sucrose Monosaccharides Dextrins Others

% of wort 43.9 13.6 4.2 9.5 21.2 5

Description Disaccharide of two glucose units Trisaccharide of three glucose units Disaccharide of glucose and fructose Glucose, fructose, galactose, mannose Amylopectins, α-glucans β-glucans, pentoses

Worts generally contain low concentrations of free amino nitrogen (FAN; amino acids and small peptides of one to three units available to yeast in wort (13)). In addition, the availability of each amino acid is largely dependent on the type of raw materials used for wort preparation (13). Besides their role in supporting growth, some amino acids are the precursors for important (off)-flavor compounds and their availability has a strong impact on the overall flavor balance of the final product (43). Just like the wort sugars, amino acids are taken up by yeast in a sequential order during the fermentation (Table 2) (53). The amino acids that are taken up gradually throughout the fermentation such as the branched-chain amino acids (leucine, valine, isoleuncine), aromatic amino acids (phenylalanine, tyrosine) or sulfurcontaining amino acids (methionine) are catabolised via the Ehrlich pathway (31). In this pathway, the carbon skeletons of deaminated amino acids are converted to fusel alcohols, which not only impart flavor themselves, but are also the important precursors for the synthesis of key flavor-active esters (75). In the valine and isoleucine biosynthesis pathways, intermediates such as α-acetohydroxy acids are excreted into the growth media and chemically converted to the undesired butterscotch-flavor compounds, the vicinal diketones (diacetyl and 2,3pentonedione) (13). In the later phase of the fermentation, these undesirable compounds must be metabolized by yeast reductases to form 2,3-butanediol from diacetyl and 2,3-pentanediol from 2,3-pentanedione, which are much less flavor active than their precursors (13). Thus, the overall flavor of beer is strongly related to the amino acid uptake and metabolism during the fermentation. The now widely used high-gravity brewing methods, which consist of supplementing wort with sugar syrup adjuncts (92), result in a change of the carbon-to-nitrogen ratio which presents additional challenges in maintaining positive flavor profiles (84).

13

Chapter 1 Table 1.2: Order of uptake of amino acids from wort by brewer’s yeast (53)

Group A Immediate Arginine Asparagine Aspartate Glutamate Lysine Threonine

Group B Gradual Histidine Isoleucine Leucine Methionine Valine

Group C After a lag Alanine Ammonia Glycine Phenylalanine Tryptophan Tyrosine

Group D Only after 60 hours Proline

The fermentation medium also contains other components such as the hop isoα-acids or malt-derived polyphenols that may directly or indirectly affect yeast performance (48). Hop iso-α-acids are efficient preservatives for beer as they inhibit growth of many microorganisms. Certain species of lactic acid bacteria have developed resistance to hop iso-α-acids and can cause the beer to spoil (96). While the resistance mechanisms of lactic acid bacteria to hop iso-α-acids have been studied in many laboratories, the impact of these compounds on yeast physiology and performance has not yet been investigated in detail. Bioavailability of trace elements such as zinc is another important issue in the complex chemical matrix represented by wort. Hop iso-α-acids and malt-derived polyphenols can readily chelate Zn resulting in its inaccessibility to yeast (63). Zn in wort originates from barley malt and is extracted during the mashing procedure (48, 63) but the Zn concentration can vary as it is dependent on the crop quality and is partly removed from wort during filtration. Generally, when Zn levels fall below 0.1 mg.l-1 this may result in so-called ‘stuck’ or ‘sluggish’ fermentations. These ‘stuck’ fermentations are characterized by an inability to take up maltose from the media, a decreased stress resistance and imbalanced flavor formation (14). The metabolic and/or regulatory processes in yeast cells that underlie such retarded fermentations are incompletely understood. ™ Yeast storage and handling Starter cultures of yeast strains are usually produced in large-scale fed-batch reactors and subsequently dried by lyophilisation for long-term storage (13). Freezedrying, or lyophilisation of brewer’s yeast has long been practiced, the method is relatively simple and can be operated at low running costs (56). Furthermore, the ease of handling, storage and transport of yeast when dried make this an attractive option (57), especially for brewing companies with worldwide production facilities. Upon arrival in the brewery, the yeast culture must be propagated by sequential cultivation in increasing volumes of sterile wort until sufficient yeast is obtained to ‘pitch’ (inoculate) the first production scale fermentation (13). Although the optimal 14

General introduction

growth temperature of most brewing yeasts is 25-30 ºC (36), this temperature is extremely different from that of beer fermentation and is rarely chosen for propagation. Commonly, temperatures around 20 ºC are used in the first vessel followed by a gradual reduction in temperature at each subsequent stage with the terminal propagation being performed at the same temperature as the first beer fermentation (72). Acclimation of yeast to low temperatures is therefore an important trait in the selection of brewing strains. During the interval between ‘cropping’ (harvesting) from one fermenter and re-pitching into the next, yeast is stored at low temperature (generally around 2 ºC to 4 ºC), usually as a slurry (70). With regard to yeast physiology, the storage phase is a period of starvation that can only be prolonged for a certain length of time. In order to survive periods of starvation, the yeast relies on its endogenous carbohydrate reserves glycogen and trehalose (13). The storage carbohydrate glycogen is accumulated during mid-fermentation and utilized during storage (89). The extent of glycogen accumulation during fermentation is an important determinant of the ability of yeast to survive the storage periods. However, an excessive breakdown of glycogen during this period is likely to result in a poor glycogen content of the pitching yeast which has a negative impact on the following fermentation. Indeed, Pickerell et al (85) observed that if the glycogen content of the pitching yeast fell below a critical level (15 % of the dry weight in this case, but largely strain dependent), the performance of yeast in the subsequent fermentation was impaired. In another report, McCaig & Bendiak (73) observed that pitching yeast stored at temperatures of 5 ºC or below dissimilated little glycogen and maintained high viability. However, similar yeast stored at higher temperatures for the same period showed a progressive reduction in viability and decline in glycogen reserves (73). This correlated with increasing impaired fermentation performance when the yeast was pitched into wort. In addition to decreased viability caused by inadequate storage conditions, serial re-pitching in itself may lead to a deterioration of the physiological state of the yeast which will result in aberrant fermentations, observable as poor yeast growth or extended lag phase, slow fermentation rate, poor flocculation and/or poor flavor development (99).

15

Chapter 1

2. Functional genomics of Saccharomyces cerevisiae Since the release of the sequence of the entire genome of the S. cerevisiae laboratory strain S288C in 1996, fundamental and application-oriented yeast genetic research has greatly accelerated. The experimental accessibility and wealth of biochemical knowledge on this organism has also engaged yeast as a model eukaryotic organism for molecular and cell biology studies. For example, yeast has been applied for identifying genes related to human diseases (16, 33, 122) and for unravelling gene functions in bacteria, flies, worms and humans (105). Furthermore, the biotechnological applications of S. cerevisiae have gone far beyond traditional beer, wine and bread making. S. cerevisiae is not only used for the production of ethanol and CO2; the extensive physiological knowledge on this organism combined with the ability to produce foreign proteins make S. cerevisiae an attractive candidate for the production of various heterologous products (e.g. insulin (58), lactic acid (87) or carotenoids (124)). In the food industry, the implementation of genetically modified organisms is still limited due to consumer acceptance issues. Nevertheless, in the past few years, the Food and Drug Administration (FDA) has granted GRAS status (Generally Regarded As Safe) to two genetically engineered wine yeast strains ML01 (49) and ECMo01 (21) (GRAS Notice numbers GRN 000120 and GRN 000175 respectively) which might indicate that public view is slowly changing towards acceptation of the potential benefits of molecular science for food related applications. The yeast genome consists of approximately 6300 open reading frames (ORFs) spread over 16 chromosomes (19, 37). With the help of advanced technologies for analyzing gene function and expression, the proteins encoded by 4757 S. cerevisiae genes have now been assigned a biochemical function, while 1038 ORFs still remain uncharacterized, and 812 ORFs are dubious (www.yeastgenome.org, February 2009). The availability of the genome sequence has given rise to many functional genomics tools, some of which will be discussed in the following paragraphs.

16

General introduction

Targeted gene modification by homologous recombination Gene disruption has become a routine and indispensable tool for the molecular geneticist that allows the rapid correlation of genes with physiological processes. The precise deletion of S. cerevisiae genes can be efficiently accomplished using polymerase chain reaction (PCR)-mediated gene disruption strategy that exploits the high rate of homologous recombination in this yeast (8, 68, 118). The primer design for construction of the deletion ‘cassette’ is crucial for specific deletion of a gene from start to stop codon. As shown in Fig. 4, homologous recombination takes place and the targeted gene can be precisely deleted and replaced by mitotic recombination with the KanMX deletion ‘cassette’. In addition, two distinct 20nucleotide sequences can be inserted up- and down- stream of the gene deleted and serve as ‘molecular bar codes’ to uniquely identify each deletion mutant. Ampr

pUG6 loxP KanMX loxP loxP KanMX loxP

PCR

ORF

Integration loxP KanMX loxP

Transformation loxP KanMX loxP

Fig. 4: Gene disruption using the loxP–kanMX–loxP disruption cassette (39). For a gene disruption two oligonucleotides are used that carry at their 3’-end a segment (arrow) homologous to sequences left and right of the loxP–kanMX–loxP module on plasmid pUG6 and at their 5’-end a segment (gray box) homologous to the ORF to be disrupted. After transformation, homologous recombination takes place and the gene to be deleted is replaced by the loxP-kanMXloxP cassette.

To take full advantage of this targeted gene deletion approach and create a nearly complete collection of mutants of S. cerevisiae, an international consortium was organized, which generated deletion strains for all annotated yeast genes (120) covering 96 % of the genome. The collection is available in four different haploid strain backgrounds that differ by mating type and auxotrophies (BY4730, BY4739, BY4741 and BY4742). The homozygous and heterozygous diploid mutant collections are also available resulting in the possibility to include all essential genes in a phenotypic screen (by analysis of tetrads after sporulation of the diploid mutant) (102). The availability of such a library has enabled screening of the collection for a

17

Chapter 1

wide range of phenotypes and has greatly contributed to the functional annotation of the genome (3, 55, 71, 98). Furthermore, the bar codes allow large number of deletion strains to be pooled together and analyzed in competitive growth assays (by quantitative real-time PCR) which increases the sensitivity, the accuracy and the speed at which growth defects can be detected compared to conventional methods (35, 120). For example, such a competitive growth approach has been employed to identify genes involved in the DNA damage response (42), or the identification of genes involved in high-flux control (28) or to assess the contribution of an increased transcript level to the fitness of a strain (109). This simple method of targeted modification by homologous recombination has also been applied to many other types of genetic modifications. For example, fusion products for protein purification (His-tags: poly-histidine tag) or visualization (GFP: green fluorescent protein) can be easily achieved by specific primer design at the Cor N-terminal region of the target gene (51). In addition, the same strategy can be used for overexpression studies by simple promoter replacement (51). As is the case for the knock-out collection, fusion of genes to GFP or His-tags has also been performed on the genome-wide scale, and these strains are available in the form of whole-genome collections (65, 95). In addition, the existence of a large number of dominant selection markers (for instance genes encoding for resistance to geneticin, hygromycin, phleomycin or neouthricin) makes the combination of all types of genetic modifications within the same strain simple and straightforward (51). Two recombinant baker’s and brewer’s yeast strains received official approval by the British Government in 1990 (2) and 1994 (40) but neither of them has been used commercially (1). The fear that drug-resistance genes contained in GM (Genetically Modified) microorganisms can be transferred to certain closely related pathogenic bacteria and fungi (such as Candida albicans, Cryptococcus nerformans and Aspergillus fumigatus) prevents their commercial use. Therefore, recovery of drug-resistance markers from GM microorganisms by the Cre–loxP recombination system (39) represents important progress for implementation of such strains in food microbiology (even though a 34 bp loxP sequence is left behind in the modified DNA sequence). In the past few years, the term ‘self-cloning’ has been attributed to genetically modified yeast strains that do not contain any foreign DNA. The issue is relevant because the Japanese government decided that a ‘self-cloning sake yeast’ is not covered by regulations over GM organisms (46, 78). Self-cloning covers a growing class of GM wine yeasts that are under development to enhance or improve the flavor of wines and beer. For example, yeast genes encoding enzymes synthesizing or degrading esters such as the alcohol acetyl transferase (47) or the ethanol hexanoyl transferase (67) are targets for overexpression by promoter replacement. 18

General introduction

Other genomics tools The study of the transcriptome (i.e., the full complement of messenger RNAs in the cell) has become the most accessible tool for genome-wide analysis in yeast. Several micro-array techniques are now in use, ranging from genomic libraries or full-length cDNAs spotted on glass slides to oligonucleotide micro-arrays. Because of their higher combined specificity and sensitivity, oligonucleotide micro-arrays have become the standard in quantitative transcriptomics (23). The two most commonly used approaches for oligonucleotide micro-array analysis can be distinguished based on the number of dyes (fluorescent probes) used for detection. In experiments with double-dye arrays, cDNA derived from two different biological samples is fluorescently labeled with either Cy3 (green) or Cy5 (red) dye. The two cDNA pools are mixed and hybridized on a glass slide (Fig. 5). This approach enables only direct comparison of two conditions in each experimental context which complicates experimental design and increases the cost of quantitative comparisons that involve three or more conditions (121). Affymetrix arrays, which are used throughout this thesis, are based on single-dye DNA-micro-array technology (22). Transcript levels of yeast genes are analyzed by comparing hybridization of cRNA to an array-borne probe set comprising 20–40 gene-specific, 25-mer oligonucleotides for each yeast gene (Fig. 5). Half of these probes are ‘perfect match’ probes that correspond exactly to the gene sequence. The remaining ‘mismatch’ probes carry a substitution of the central nucleotide (13th) of the oligomer, thus strongly reducing specific binding of cRNA for the corresponding gene and providing an internal control for non-specific binding. In contrast to the approach with double-dye arrays, a single sample is hybridized on each array thus facilitating comparison of a broad range of biological samples.

19

Chapter 1

Fig. 1.5: Diagram of typical single-dye (A) and double-dye (B) micro-array experiments. 1, total RNA extraction from biomass samples. 2, cDNA synthesis. 3, in vitro transcription (cRNA synthesis). 4, metal-induced cRNA fragmentation. 5, hybridization of the prepared targets. 6, staining with streptavidin-phycoerythrin. 7, washing. 8, scanning. 9, mixing Cy3, Cy5-labeled cDNA samples. Figure obtained from (23) with kind permission from Elsevier.

Recent manufacturing developments have enabled the development of ‘tiling’ arrays on which the entire genome is represented by overlapping probes; the coverage of the entire genome is achieved with a 5 bp resolution (38). In addition to transcriptome analysis, applications of tiling arrays include discovery of hitherto unknown transcripts (25, 54, 126), mapping of single-nucleotide polymorphisms (38) and mapping of sites for protein/DNA interaction in chromatin immunoprecipitation (ChIP) experiments (80). Other ‘-omics’-type techniques aim to provide for example a quantitative analysis of all proteins (proteomics) or all intracellular low molecular weight metabolites (metabolomics) in microbial cultures. Proteomics and metabolomics are still to a large extent under development, but will no doubt become increasingly important in the near future. In addition to the advances in ‘–omics’ type techniques, the sequencing of the yeast genome has given rise to three important yeast genome data-bases: the Saccharomyces Genome Database (SGD) (19), the Yeast Proteome Database (YDP) (20) and the Comprehensive Yeast genome Database produced by the Munich Information Center for Protein Sequences (MIPS) (76).

20

General introduction

These three genome databases have differences in structure and format, but their overall missions are similar: to provide the researcher with a complete list of yeast genes and proteins. They contain detailed information on each gene and encoded protein (function, localization, regulation, interactions) with links to all relevant literature and other broader databases (such as SwissProt, Entrez, NCBI or Prosite). SGD and YPD also have bioinformatics tools enabling sequence alignments or analysis and display of published micro-array datasets. The MIPS database aims essentially to categorize genes in functional groups based on protein function. These three databases are widely used in combination with other bioinformatics tools, such as the search of regulatory sequences in promoters (RSAT (114, 115), MEME (6)) or the assessment of enrichment of specific functional categories (FunSpec (94)) within a group of co-regulated genes. The SGD, YPD and MIPS databases contribute greatly to the high rate at which yeast genomics research is advancing today.

Chemostat-based micro-array analysis of yeast A large fraction of the transcriptome data available for S. cerevisiae has been obtained in shake flask fermentations. In such cultures, growth occurs at the maximum specific growth rate possible under the experimental conditions (µmax, h-1). Many parameters (such as nutrient and product concentrations, dissolved oxygen or biomass concentration) change over time and between cultures. In contrast, chemostat fermentations enable the cultivation of microorganisms under constant physico-chemical conditions and at a fixed specific growth rate (23). Since specific growth rate itself has a strong impact on transcriptional regulation, controlling the growth rate is an important element in the experimental design of transcriptome studies. Especially in the field of ‘systems biology’, which requires high-quality and high-information-density analysis at different information levels (‘omes’), chemostat cultivation has become increasingly popular (24, 62, 64, 86). One drawback of chemostat fermentations in this specific context is that prolonged operation may lead to evolutionary changes, which are likely to obscure the interpretation of the results (15, 52, 119). Hence, sampling from a chemostat culture should always be performed within a certain time frame, for example 10 to 14 generations after starting the continuous feed.

21

Chapter 1 Specific growth rate (zero growth, 0.03 h-1, 0.05 h-1, 0.1 h-1, 0.2 h-1)

Nutrient limitation

pH

(Carbon, Nitrogen, Sulfur, Phosphorus, Fe, Zn)

Carbon source (Glucose, maltose, galactose, ethanol, acetate)

(pH 3.5, pH 5.0, pH 6.5)

Chemostat based transcriptomics

Temperature (12 ºC, 30ºC)

Carbon dioxide stress

Nitrogen source (NH4, leucine, methionine, phenylalanine, asparagine, proline)

Organic acid stress (Acetate, benzoate, propionate, sorbate, lactate)

Sulfur source

Tolerance to plant secondary metabolites

(Methionine, sulfate)

Hop iso-α-acids

Oxygen availability (aerobic, anaerobic)

Fig. 1.6: Chemostat-based transcriptomics at TU Delft, Industrial Microbiology. The categories shown are areas that have been studied so far.

The strict control of growth conditions in chemostats provides an ideal platform for reproducible transcriptome analysis. In recent years, the Industrial Microbiology section of the Delft University of Technology has developed a platform that combines the Affymetrix Genechip® technology for DNA micro-array analysis with chemostat fermentation of S. cerevisiae (23). The central approach consists in the design of sets of chemostat experiments that enable the researcher to ‘isolate’ the transcriptional responses to individual process parameters or genetic interventions while minimizing changes in other parameters. This approach can be applied to wild-type as well as genetically engineered strains. Fig. 6 shows the range of parameters that have been investigated in Delft. Apart from the applicability of micro-arrays in the functional annotation of the genome and understanding the regulation of gene expression, chemostat-based transcriptomics can also be used as diagnostic tool for industrial fermentations. Especially in the case of beer fermentation, where the wort composition is complex and has great influence on the flavor profile of the finished product, measurements of global transcriptional activity may provide an analytical tool to diagnose the quality of the yeast’s industrial environment.

22

General introduction

Functional genomics of lager brewing strains Lager yeast strains have been propagated over many hundreds of years in breweries and have acquired the unique ability to survive the severe environmental conditions that are encountered during brewing (such as high osmotic and hydrostatic pressure, low temperatures, and high CO2 and ethanol concentrations). The genotype of the particular strain of yeast is critical to the outcome of fermentation as the spectrum of flavor-active metabolites produced is as much determined by the yeast strain itself as by the conditions established during fermentation (13). For this reason, utilizing functional genomics tools like transcriptomics offer an attractive option for the improvement of lager brewing yeast strains. The genomes of lager yeasts are complex, as they are tetraploid (four copies of each chromosome) or even aneuploid (different copy numbers for individual chromosomes) and consist of a hybrid of pure and mixed genetic lines of the Saccharomyces genus (61). Several studies have aimed to identify the noncerevisiae parent and although this matter is still under debate, S. bayanus and S. uvarum are the most likely candidates (17, 60, 90, 110, 123). In contrast to the situation in haploid laboratory yeast strains, classic genetic studies in lager yeasts have been hampered due to their inability to sporulate and produce viable tetrads. Furthermore, their polyploid nature makes targeted gene alterations and selection of recessive mutants cumbersome. As a result, in comparison to the well-annotated genome of S. cerevisiae S288C, molecular and genomic knowledge and understanding of lager brewing yeasts remains incomplete. It has been recently reported that the sequencing of a lager strain Weihenstephan Nr. 34/70 (WS34/70) was completed (61, 79). Analysis of the genome sequence confirmed the hybrid nature of lager strains and shed light on the chromosomal structure of lager brewing yeast strains. Three types of chromosomes were identified corresponding to the cerevisiae-like, the non-cerevisiae-like and chimera-type chromosomes (Fig. 7). The authors found that the WS34/70 strain contains at least eight chimerical chromosomes and specific chromosomal breakpoints between both genetic lines could be identified (Fig. 7). Some of these breakpoints were found within ORFs suggesting that the hybrid nature of lager strains exists even at the gene level. Hopefully, the release of this sequence will give rise to the production of DNA microarrays specific to the genome of a lager strain. In addition, the genome sequence of a haploid wine strain has been released thus setting the stage for sequencing of industrial yeast strains (11).

23

Chapter 1

Fig. 1.7: Putative chromosomal structure of lager brewing strain Weihenstephan Nr.34 (34/70). The breakpoints between cerevisiae-like (Sc) and non-cerevisiae-like (non-Sc) DNA in chromosomes are shown as constriction (e.g. in chromosome III). Figure obtained from (61) with kind permission from Springer Science and Business Media.

Until a few years ago, whole genome sequencing was performed by ‘shot gun sequencing’ (77). The method consists of creating random DNA fragments of approximately 1 kb that are amplified in vivo in a bacterial host. After amplification, each fragment can be sequenced by the Sanger method, otherwise known as the chain termination method (88, 97, 101). Although this method yields a low error rate on relatively long sequence reads (1 kb), the in vivo amplification step is lengthy and labor intensive. Since 2005, three next-generation sequencing technologies have been introduced to the market (‘454 technology’ from Roche Applied Science, ‘Illumina/Solexa’ from Illumina and ‘ABI/SOLiD’ from Applied Biosystems) and have had great impact on genomic research (77). In these methods, the random DNA fragments can be amplified in vitro within a few hours by ‘emulsion PCR’ or ‘solid phase bridge amplification’. In addition, massive parallel sequencing technologies such as pyrosequencing, reversible terminator sequencing or sequencing by ligation have replaced the traditional Sanger method. Although the next generation sequencing technologies generate much shorter read lengths (30 to 200 bp), the possibility to perform multiple overlapping reads greatly increases the genome coverage and hence the sequencing accuracy (44). Considering the rapid decrease in sequencing costs and time, the number of genome sequences of industrial yeast strains is very likely to increase rapidly in the near future.

24

General introduction

Despite the lack of hitherto publicly accessible genome sequence for lager strains, several attempts to study the transcriptome of brewing strains have been carried out. Most studies to date have employed the S. cerevisiae S288C genome sequence as template for micro-array studies (10, 50, 81), which although rich in information, has the major disadvantage of not being able to distinguish between the cerevisiae-like and the non-cerevisiae-like transcripts. An elegant alternative strategy has been to design a ‘multispecies micro-array’ based on the genome sequences of the laboratory strains S. cerevisiae S288C and S. bayanus var. uvarum CBS7001 (30). This ‘multi species array’ can be utilized in comparative genomic or transcriptomic type studies. For example, comparative genomics of a large number of brewing strains by hybridizing DNA on the ‘multispecies micro-array’ has indicated that strains selected from similar geographic locations displayed similarities in their chromosomal structures (30).

3. Scope of this thesis The PhD project described in this thesis is part of a larger initiative to assess the applicability of genome information and genomics techniques for beer-related processes in yeast. In the first phase of the project, chemostat-based micro-array analysis was carried out on a laboratory strain (CEN.PK 113-7D) of S. cerevisiae and the response to a range of cultivation conditions that are relevant to beer fermentation was investigated (anaerobicity (106, 109), low temperature (108, 107) and nitrogen source (9)). The work described in this thesis represents the second phase of the project which aims to tackle nutritional conditions that are more typically encountered in beer fermentation. Because no genome sequence of a lager strain was yet available at the onset of this project, and to make optimal used of the compendium of chemostat-based transcriptome data compiled in Delft (59), this research is continued with the CEN.PK 113-7D strain. Identification of genes involved in nitrogen metabolism and understanding their regulation is essential to control flavor formation. In the first phase of this beerrelated project, the metabolism of a number of amino acids (asparagine, leucine, phenylalanine, methionine and proline) as sole nitrogen source was studied by transcriptome analysis of aerobic, glucose-limited chemostat cultures (9). This dataset has been valuable for the functional annotation of genes encoding the different enzymes of the Ehrlich pathway (e.g. ARO10). Chapter 2 gives a centenary review on the Ehrlich pathway, which was first proposed as early as 1907, but for which the confirmation of the individual steps and identification of the genes encoding the enzymes has only been achieved in the last decades. In Chapter 3, the role of the 25

Chapter 1

plasma membrane transporter Pdr12 in exporting fusel acids derived from the Ehrlich pathway was investigated. A common problem occurring in beer fermentation is caused by a decreased fermentation rate when Zn levels are insufficient (14) but the direct measurement of Zn in wort does not always enable an accurate prediction of its bioavailability (63). As a result, research focuses on the identification of molecular markers of Zn limitation using expression profiling by micro-arrays. Most studies published to date have been performed in batch or shake flask cultures (45, 69) where many parameters, including specific growth rate and oxygen availability, are variable over time and between cultures, thus obscuring the interpretation of the results (18, 45, 69, 91). In the experiments described in Chapter 4, S. cerevisiae CEN.PK 113-7D was grown in Zn-limited aerobic and anaerobic chemostat cultures at a fixed specific growth rate of 0.10 h-1. A combinatorial approach was employed where the transcriptome of Zn-limited cultures were compared to those obtained under carbon and nitrogen limitations (in the presence and absence of oxygen). This study enabled to pinpoint genes that specifically responded to Zn limitation irrespective of the aeration mode and provided us with leads as to the physiological consequences of growing yeast under Zn-limitation. A major conclusion from Chapter 4 was that Zn limited growth resulted in a transcriptional downregulation of genes involved in storage carbohydrate metabolism. Measurement of intracellular glycogen and trehalose confirmed the lack of reserve carbohydrate biosynthesis under Zn-limiting conditions. The relevance of this finding for beer fermentation is high and the molecular basis underlying the regulation of reserve carbohydrate in response to nutrient availability was investigated further in Chapter 5. By extending the chemostat studies to a broader range of nutrient limitations (carbon, nitrogen, sulfur, phosphorus and Zn), we show that dissecting growth rate from nutrient limitation is essential in understanding the regulation of reserve carbohydrate metabolism. In Chapter 6, we set out to characterize the resistance mechanisms of S. cerevisiae to hop iso-α-acids. Although the hop plant finds its main application in the brewery, recent experimental research has indicated an anti-cancer potential of selected hop-derived compounds (reviewed in (34) and in (103)). However, the molecular mechanisms that underlie these effects and possible mechanisms of hop acid tolerance and toxicity in eukaryotic cells remain largely unknown. In Chapter 6, a combination of two genome-wide screening approaches was successfully used to identify candidate genes involved in hop acid tolerance. Actual involvement of these genes in tolerance to hop acids was then investigated by physiological characterization of selected deletion mutants.

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General introduction

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Chapter 1 93. Renger, R. S., 1991, PhD dissertation. Carbon dioxide and its relevance to largescale brewery fermentation. Delft University of Technology 94. Robinson, M. D., J. Grigull, N. Mohammad, and T. R. Hughes. 2002. FunSpec: a web-based cluster interpreter for yeast. BMC. Bioinformatics. 3:35. 95. Ross-Macdonald, P., P. S. Coelho, T. Roemer, S. Agarwal, A. Kumar, R. Jansen, K. H. Cheung, A. Sheehan, D. Symoniatis, L. Umansky, M. Heidtman, F. K. Nelson, H. Iwasaki, K. Hager, M. Gerstein, P. Miller, G. S. Roeder, and M. Snyder. 1999. Large-scale analysis of the yeast genome by transposon tagging and gene disruption. Nature 402:413-418. 96. Sakamoto, K. and W. N. Konings. 2003. Beer spoilage bacteria and hop resistance. Int. J. Food Microbiol. 89:105-124. 97. Sanger, F., S. Nicklen, and A. R. Coulson. 1977. DNA sequencing with chainterminating inhibitors. Proc. Natl. Acad. Sci. U. S. A 74:5463-5467. 98. Scherens, B. and A. Goffeau. 2004. The uses of genome-wide yeast mutant collections. Genome Biol. 5:229. 99. Smart, K. 2003. Brewing yeast fermentation performance, Smart K., Blackwell Publishing, Oxoford, UK. 100. Smit, P. and J. Heniger. 1975. Antoni van Leeuwenhoek (1632-1723) and the discovery of bacteria. Antonie Van Leeuwenhoek 41:219-228. 101. Smith, L. M., J. Z. Sanders, R. J. Kaiser, P. Hughes, C. Dodd, C. R. Connell, C. Heiner, S. B. Kent, and L. E. Hood. 1986. Fluorescence detection in automated DNA sequence analysis. Nature 321:674-679. 102. Snoek, I. S. and H. Y. Steensma. 2006. Why does Kluyveromyces lactis not grow under anaerobic conditions? Comparison of essential anaerobic genes of Saccharomyces cerevisiae with the Kluyveromyces lactis genome. FEMS Yeast Res. 6:393-403. 103. Stevens, J. F., C. L. Mirand, D. R. Buhler, and M. L. Deinzer. 1998. Chemistry and biology of hop flavonoids. J. Am. Soc. Brew. Chem. 56:136-145. 104. Stewart, G. G., I. Russell, and A. M. Sills. 1983. Factors that control the utilization of wort carbohydrates by yeast. Tech. Q. Master Brew. Assoc. Am. 20:1-8. 105. Suter, B., D. Auerbach, and I. Stagljar. 2006. Yeast-based functional genomics and proteomics technologies: the first 15 years and beyond. Biotechniques 40:625644. 106. Tai, S. L., V. M. Boer, P. Daran-Lapujade, M. C. Walsh, J. H. de Winde, J. M. Daran, and J. T. Pronk. 2005. Two-dimensional transcriptome analysis in chemostat cultures. Combinatorial effects of oxygen availability and macronutrient limitation in Saccharomyces cerevisiae. J. Biol. Chem. 280:437-447. 107. Tai, S. L., P. Daran-Lapujade, M. A. Luttik, M. C. Walsh, J. A. Diderich, G. C. Krijger, W. M. van Gulik, J. T. Pronk, and J. M. Daran. 2007. Control of the

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General introduction glycolytic flux in Saccharomyces cerevisiae grown at low temperature: a multi-level analysis in anaerobic chemostat cultures. J. Biol. Chem. 282:10243-10251. 108. Tai, S. L., P. Daran-Lapujade, M. C. Walsh, J. T. Pronk, and J. M. Daran. 2007. Acclimation of Saccharomyces cerevisiae to low temperature: a chemostat-based transcriptome analysis. Mol. Biol. Cell 18:5100-5112. 109. Tai, S. L., I. Snoek, M. A. Luttik, M. J. Almering, M. C. Walsh, J. T. Pronk, and J. M. Daran. 2007. Correlation between transcript profiles and fitness of deletion mutants in anaerobic chemostat cultures of Saccharomyces cerevisiae. Microbiology 153:877-886. 110. Tamai, Y., T. Momma, H. Yoshimoto, and Y. Kaneko. 1998. Co-existence of two types of chromosome in the bottom fermenting yeast, Saccharomyces pastorianus. Yeast 14:923-933. 111. van der Hoeven, A. 2007. Delft, stad met een vergeten bierhistorie, Delft, The Netherlands. 112. van Hamersveld, E. H., R. G. van der Lans, P. J. Caulet, and K. C. Luyben. 1998. Modeling brewers' yeast flocculation. Biotechnol. Bioeng. 57:330-341. 113. van Hamersveld, E. H., R. G. van der Lans, and K. C. Luyben. 1997. Quantification of brewers' yeast flocculation in a stirred tank: Effect of physical parameters on flocculation. Biotechnol. Bioeng. 56:190-200. 114. van Helden, J., B. Andre, and J. Collado-Vides. 1998. Extracting regulatory sites from the upstream region of yeast genes by computational analysis of oligonucleotide frequencies. J. Mol. Biol. 281:827-842. 115. van Helden, J., A. F. Rios, and J. Collado-Vides. 2000. Discovering regulatory elements in non-coding sequences by analysis of spaced dyads. Nucleic Acids Res. 28:1808-1818. 116. van Leeuwenhoek, A. 1948. Letter of June 14th, 1680 addressed to T. Gale, p. 243-267. In The collected letters of Antoni van Leeuwenhoek, Swets & Zeitlinger LTD, Amsterdam, The Netherlands. 117. Vaughan-Martini, A. and A. Martini. 1987. Three newly delimited species of Saccharomyces sensu stricto. Antonie Van Leeuwenhoek 53:77-84. 118. Wach, A., A. Brachat, R. Pohlmann, and P. Philippsen. 1994. New heterologous modules for classical or PCR-based gene disruptions in Saccharomyces cerevisiae. Yeast 10:1793-1808. 119. Weikert, C., U. Sauer, and J. E. Bailey. 1997. Use of a glycerol-limited, long-term chemostat for isolation of Escherichia coli mutants with improved physiological properties. Microbiology 143 ( Pt 5):1567-1574. 120. Winzeler, E. A., D. D. Shoemaker, A. Astromoff, H. Liang, K. Anderson, B. Andre, R. Bangham, R. Benito, J. D. Boeke, H. Bussey, A. M. Chu, C. Connelly, K. Davis, F. Dietrich, S. W. Dow, M. El Bakkoury, F. Foury, S. H. Friend, E. Gentalen, G. Giaever, J. H. Hegemann, T. Jones, M. Laub, H. Liao, N. Liebundguth, D. J. Lockhart, A. Lucau-Danila, M. Lussier, N. M'Rabet, P.

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Chapter 1 Menard, M. Mittmann, C. Pai, C. Rebischung, J. L. Revuelta, L. Riles, C. J. Roberts, P. Ross-Macdonald, B. Scherens, M. Snyder, S. Sookhai-Mahadeo, R. K. Storms, S. Veronneau, M. Voet, G. Volckaert, T. R. Ward, R. Wysocki, G. S. Yen, K. Yu, K. Zimmermann, P. Philippsen, M. Johnston, and R. W. Davis. 1999. Functional characterization of the S. cerevisiae genome by gene deletion and parallel analysis. Science 285:901-906. 121. Wolber, P. K., P. J. Collins, A. B. Lucas, A. De Witte, and K. W. Shannon. 2006. The Agilent in situ-synthesized microarray platform. Methods Enzymol. 410:28-57. 122. Wolfe, D. M. and D. A. Pearce. 2006. Channeling studies in yeast: yeast as a model for channelopathies? Neuromolecular. Med. 8:279-306. 123. Yamagishi, H. and T. Ogata. 1999. Chromosomal structures of bottom fermenting yeasts. Syst. Appl. Microbiol. 22:341-353. 124. Yamano, S., T. Ishii, M. Nakagawa, H. Ikenaga, and N. Misawa. 1994. Metabolic engineering for production of beta-carotene and lycopene in Saccharomyces cerevisiae. Biosci. Biotechnol. Biochem. 58:1112-1114. 125. Zastrow, C. R., C. Hollatz, P. S. de Araujo, and B. U. Stambuk. 2001. Maltotriose fermentation by Saccharomyces cerevisiae. J. Ind. Microbiol. Biotechnol. 27:34-38. 126. Zhang, Z., J. R. Hesselberth, and S. Fields. 2007. Genome-wide identification of spliced introns using a tiling microarray. Genome Res. 17:503-509. 127. Zheng, X., T. D'Amore, I. Russell, and G. G. Stewart. 1994. Factors influencing maltotriose utilization during brewery wort fermentations. J. Am. Soc. Brew. Chem. 52:41-47.

36

Chapter 2

The Ehrlich pathway for fusel alcohol production: a century of research on yeast metabolism

Lucie A. Hazelwood, Jean-Marc Daran, Antonius J.A. van Maris, Jack T. Pronk and J. Richard Dickinson

Applied and Environmental Microbiology, 2008, Vol. 74, No. 8, 2259 – 66

Chapter 2

Introduction Saccharomyces cerevisiae has been used for at least eight millennia in the production of alcoholic beverages (41). Along with ethanol and carbon dioxide, fermenting cultures of this yeast produce many low-molecular-weight flavor compounds. These alcohols, aldehydes, organic acids, esters, organic sulfides and carbonyl compounds have a strong impact on product quality. Indeed, the subtle aroma balance of these compounds in fermented foods and beverages is often used as an organoleptic fingerprint for specific products and brands (42). Food fermentation by yeast and lactic acid bacteria is accompanied by formation of the aliphatic and aromatic alcohols known as 'fusel alcohols'. Fusel oil, which derives its name from the German word ‘Fusel’ (bad liquor) is obtained during distillation of spirits and is enriched in these higher alcohols. While fusel alcohols at high concentrations impart off-flavors, low concentrations of these compounds and their esters make an essential contribution to flavor and aroma of fermented foods and beverages. Fusel alcohols are derived from amino acid catabolism via a pathway that was first proposed a century ago by Ehrlich (13). Amino acids represent the major source of the assimilable nitrogen in wort and grape must, and these are taken up by yeast in a sequential manner (23, 32). Amino acids that are assimilated by the Ehrlich pathway (valine, leucine, isoleucine, methionine and phenylalanine) are taken up slowly throughout the fermentation time (32). After the initial transamination reaction (Fig. 2.1), the resulting α-keto-acid cannot be redirected into central carbon metabolism. Before α-keto-acids are excreted into the growth media, yeast cells convert them into fusel alcohols or acids via the Ehrlich pathway. Current scientific interest in the Ehrlich pathway is supported by increased demands of natural flavor compounds such as isoamyl alcohol and 2-phenylethanol, which can be produced from amino acids in yeast-based bioconversion processes (14) as well as by the need to control flavor profiles in fermented food products. The goal of this paper is to present a concise centenary overview of the biochemistry, molecular biology and physiology of this important pathway in S. cerevisiae.

Early history of the Ehrlich pathway The turn of the 20th century saw the establishment of many biochemical principles. In 1877 Kühne proposed the notion of enzymes (62) and in 1893 Ostwald proved them to be catalysts (46, 47). The following year (1894) Fischer (15) demonstrated the ‘lock and key’ relationship between enzymes and substrates. Alcoholic fermentation in yeast cell extracts was described by Büchner in 1897 (3). In 1902 Fisher (16) and Hofmeister (24) demonstrated that proteins are polypeptides. It was

38

The Ehrlich pathway for fusel alcohol production

in this background of accomplished chemistry and a growing knowledge of biochemistry that the German biochemist Felix Ehrlich (1877-1942) worked. In 1904, after isolating and characterizing isoleucine, Ehrlich noted the structural similarities between this amino acid and active amyl alcohol, and between leucine and isoamyl alcohol. These observations led him to investigate whether these fusel alcohols were derived from the amino acids (13). Indeed, the supplementation of yeast fermentations with either leucine or isoleucine led to increased production of fusel alcohols. Ehrlich then proposed (13) that the amino acids were split by a ‘hydrating’ enzyme activity to form the corresponding fusel alcohols, along with carbon dioxide and ammonia. Ammonia, which was not detected in these experiments, was assumed to be incorporated into yeast protein. In 1911, Neubauer and Fromherz proposed a modified metabolic scheme (44) that represents the ‘Ehrlich Pathway’ as it is still viewed today (Fig. 2.1). As the first intermediate of the pathway, they proposed an α-keto acid, which can subsequently be decarboxylated to an aldehyde, which was then reduced to the fusel alcohol. The acceptor of the amino group from the starting amino acid was not known and neither were the exact natures of any of the individual enzyme-catalyzed steps. Subsequent work by Lampitt (36), Yamada (67 - 69) and Thorne (58, 59) confirmed that all fusel alcohols produced by yeast can be derived from amino acid catabolism. An overview of the Ehrlich pathway intermediates and products is presented in Table 2.1.

39

Chapter 2 Amino acid Transamination

ARO8 ARO9 BAT2/TWT2 BAT1/TWT1

2-oxoglutarate glutamate α-keto acid

Decarboxylation

CO2

Reduction ALD1 ALD2 ALD3 ALD4 ALD5 ALD6

ARO10 PDC1 PDC5 PDC6

‘fusel aldehyde’ NAD+

NADH, H+

NADH, H+

‘fusel acid’ in

NAD+

‘fusel alcohol’

Oxidation ADH1, ADH2, ADH3, ADH4, ADH5, ADH6, SFA1, AAD3, AAD4, AAD6, AAD10, AAD14, AAD15, AAD16, YCR105W, YPL088W

ATP Export

PDR12 ADP

‘fusel acid’ out

Fig. 2.1: The Ehrlich pathway. Catabolism of branched-chain amino acids (leucine, valine and isoleucine), aromatic amino acids (phenylalanine, tyrosine and trytophan) and the sulphurcontaining amino acid (methionine) leads to the formation of fusel acids and fusel alcohols. The biosynthetic routes towards these amino acids are also indicated.

Identification and confirmation of the key enzymic steps in the Ehrlich pathway In the 1950s and 1960s, Sentheshanmuganathan studied conversion of tyrosine to tyrosol. He demonstrated that cell extracts of S. cerevisiae transferred the amino groups from aspartate, isoleucine, leucine, methionine, norleucine, phenylalanine, tryptophan and tyrosine to α-ketoglutarate and hence that the first step of the Ehrlich pathway could be catalysed by an aminotransferase activity (54, 55). He also demonstrated the decarboxylation of p-hydroxy 3-phenylpyruvate to p-hydroxy 3phenylacetaldehyde and the NADH-dependent reduction of the latter compound to tyrosol. This finding established a key reaction sequence for the Ehrlich pathway: transaminase, decarboxylase, alcohol dehydrogenase. While, until the late 1990s, most experimental observations were compatible with the existence of the Ehrlich pathway, its in vivo activity was not proven. For example, experiments in which radioactively labeled amino acids were converted

40

The Ehrlich pathway for fusel alcohol production

into radioactively labeled fusel alcohols did not rule out alternative intermediates and enzymes. Yeast molecular biology raised further questions on the identity of the enzymes involved in the Ehrlich pathway, as many candidate enzymes and genes were identified for each of the reaction steps. This raised the possibility that different isozymes might be involved under different environmental and/or nutritional conditions. Final experimental verification of the role of the Ehrlich pathway in amino acid catabolism was obtained through 13C labeling studies. After growth of S. cerevisiae under normal physiological conditions in minimal media with individual amino acids specifically-labeled with 13C as sole nitrogen source, 13C NMR spectroscopy was employed to identify the metabolic sequences involved. Since the fate of individual atoms in each intermediate from amino acid to end product could be followed, the Ehrlich pathway was proved for the first time in the catabolism of leucine, valine, isoleucine, phenylalanine, tryptophan and methionine to the corresponding fusel alcohols (6 – 8, 10, 50). Further confirmation of a key in vivo involvement of the Ehrlich pathway and the non-participation of other routes was obtained by testing specific mutants in which potential alternative metabolic pathways were blocked (6 - 8, 10). During studies of the catabolism of [2-13C]valine to isobutanol labeled at the expected C-1 position, formation of isoamyl alcohol labeled at C-2 was observed (7). This result was shown to be due to the entry of 13C-labeled α-ketoisovalerate, formed by valine deamination, into the leucine biosynthetic pathway, thus giving rise to labeled isoamyl alcohol (Fig. 2.1, (7)). This indicates a mixing of catabolic and biosynthetic pathways for these branched-chain amino acids via the shared intermediate α-ketoisovalerate. The Ehrlich pathway for methionine catabolism represents a special case. A conventional Ehrlich pathway involves transamination to α-keto-γ-(methylthio)butyrate (α-KMBA), decarboxylation to β-(methylthio)propionaldehyde (often called ‘methional’) and then reduction to methionol. In addition, a demethiolase activity acts on both methionine and α-KMBA to produce methanethiol and α-ketobutyrate (50). Whether this reaction occurs spontaneously or enzymatically is still unknown.

41

42 traditional

α-keto-isocaproate α-keto-isovalerate α-keto-methylvalerate 3-phenyl pyruvate p-hydroxyphenylpyruvate 3-indole pyruvate α-keto-γ-(methylthio)butyrate

4-methyl-2-oxopentanoate 3-methyl-2-oxobutanoate 3-methyl-2-oxopentanoate 3-phenyl-2-oxo propanoate 3-(4-hydroxy-phenyl) 2oxo-- propanoate 3-(indol-3-yl) 2-oxopropanoate 4-methyl-thio-2oxobutanoate

Leu

Val

Ile

Phe

Tyr

Trp

Met

α-keto acid systematic

Amino acid

3-methylthio propanal

2-(indol-3-yl)-ethanal

2-(4-hydroxy-phenyl) ethanal

2-phenyl ethanal

2-methyl butanal

2-methyl propanal

3-methyl butanal

systematic

methional

3-indole acetaldehyde

p-hydroxyphenylacetaldehyde

2-phenyl-acetaldehyde

methylvaleraldehyde

isobutanal or isovaleraldehyde

isocaproaldehyde

traditional

‘Fusel aldehyde’

3-methylthio-propanol

2-(indol-3-yl)-ethanol

2-(4-hydroxy-phenyl) ethanol

2-phenyl ethanol

2-methyl butanol

2-methyl propanol

3-methyl butanol

systematic

traditional

methionol

tryptophol

p-hydroxy phenylethanol or tyrosol

-

active amyl alcohol

isobutanol

isoamyl alcohol

Fusel alcohol

3-methylthio propanoate

2-(indol-3-yl)ethanoate

2-(4-hydroxy-phenyl) ethanaoate

2-phenyl ethanoate

2-methyl butanoate

2-methyl propanoate

3-methyl butanoate

systematic

Isobutyrate

isovalerate

traditional

-

-

p-hydroxy phenylacetate

2-phenylacetate

methyl valerate

‘Fusel acid’

Table 2.1: Ehrlich pathway intermediates With the terms ‘fusel aldehyde’ we indicate the aldehyde that is formed after decarboxylation of the indicated, amino-acid-derived α-keto acids. Similarly, the term ‘fusel’ alcohols and acids are used to indicate the compounds that are formed by reduction or oxidation of these fusel aldehydes to the corresponding alcohol or organic acid.

Chapter 2

The Ehrlich pathway for fusel alcohol production

Initial transamination reaction Four S. cerevisiae proteins have been implicated in the initial transamination step of the Ehrlich pathway (Fig. 2.1). Twt1p (also known as Bat1p or Eca39p) is the mitochondrial branched-chain amino acid aminotransferase and Twt2p (Bat2p, Eca40p) the cytosolic isozyme. The mitochondrial isozyme is highly expressed during exponential growth in batch cultures and repressed during stationary phase, whilst the cytosolic isozyme has the opposite expression pattern. The genes encoding both enzymes were cloned by two independent groups in 1996 (11, 34). Eden et al. (12) showed that an eca40 (twt2) mutation drastically reduced the production of isobutanol. Decreased production was also seen for active amyl alcohol and isoamyl alcohol (37, 53, 70), although the results also indicated involvement of a Twt2p-independent transaminase activity in the formation of these alcohols. Aro8p and Aro9p were initially characterized as the aromatic amino acid aminotransferases I and II, respectively (31). Urrestarazu et al. (60) demonstrated that Aro8p also exhibits in vitro activity with the amino donors methionine, αaminoadipate and leucine and with phenylpyruvate as amino acceptor and, reversibly, with their α-keto acid analogues as amino acceptors and phenylalanine as the amino donor. Aro9p also had broader substrate specificity than originally described (60). Apparently, Aro8p and Aro9p act as broad-substrate specificity amino acid transaminases in the Ehrlich pathway. This notion is supported by genome-wide expression profiling ARO9 and TWT2 were consistently upregulated when S. cerevisiae was grown in glucose-limited chemostat with phenylalanine, methionine or leucine as sole nitrogen source as compared to cultures grown with ammonia, proline and asparagine, three nitrogen sources whose catabolism does not involve the Ehrlich pathway (2) (Fig. 2.2).

Decarboxylation step The irreversible decarboxylation step, which commits 2-oxo acids to the Ehrlich pathway, was initially attributed to pyruvate decarboxylase (5, 55, 65). However, the yeast genome was shown to harbor no fewer than 5 genes that show sequence similarity with thiamine-diphosphate dependent decarboxylase genes. Three of these (PDC1, PDC5 and PDC6) encode pyruvate decarboxylases (25 – 28, 52, 57) while ARO10 and THI3 represent alternative candidates for Ehrlich-pathway decarboxylases (8, 10, 63).

43

NH 4 As n Pr o Ph e Le u Me t

Chapter 2

Transaminases BAT1 BAT2 ARO9 ARO8 Decarboxylases ARO10 PDC1 PDC5 PDC6 THI3 Alcohol dehydrogenases ADH1 ADH2 ADH3 ADH4 ADH5 ADH6 YCR105W SFA1 AAD3 AAD4 AAD6 AAD10 AAD14 AAD15 AAD16 YPL088W

Fig. 2.2: Heatmap and identities of the genes that might encode enzymes involved in the Ehrlich pathway (color version at end of book). The figure represents the transcript levels of genes in aerobic, glucose-limited chemostat cultures of Saccharomyces cerevisiae CEN.PK113-7D grown with various nitrogen sources ((NH4)2SO4 NH4, asparagine ASN, proline PRO, phenylalanine PHE, leucine LEU and methionine MET) (2). Transcript levels were determined with Affymetrix Genechips YG-S98 and represent the average of three independent experiments.

Adehyde dehydrogenases ALD5 ALD6 ALD4 ALD3 ALD2 ALD1 Transport PDR12

Null mutants in the 5 structural genes encoding (putative) thiaminediphosphate (TPP) dependent decarboxylases (pdc1, pdc5, pdc6, aro10 and thi3), both singly and in combination, have been studied to identify whether specific enzymes catalyze the decarboxylation of the individual α-keto acid intermediates. This research led to the initial conclusion that the major decarboxylase involved in leucine catabolism is encoded by THI3 (8). In valine catabolism, any one of the three isozymes Pdc1p, Pdc5p, Pdc6p will decarboxylate α-ketoisovalerate (7). However, the conclusion from the leucine and valine experiments were drawn while the decarboxylase encoded by the ARO10 (YDR380w) gene was not yet known and consequently could not be quantitatively assessed. In isoleucine catabolism any one

44

The Ehrlich pathway for fusel alcohol production

of the family of decarboxylases encoded by PDC1, PDC5, PDC6, ARO10 or THI3 was found to be sufficient for the conversion of isoleucine to active amyl alcohol (6). THI3 decarboxylase has no role in catabolism of the aromatic amino acids phenylalanine and tyrosine: in a thi3 null mutant, the decarboxylation of 3phenylpyruvate and 3-indolepyruvate was apparently accomplished by any one of the remaining four decarboxylases (10) while in methionine catabolism, the decarboxylation of α-keto-γ-(methylthio)-butyrate (‘α-KMBA’) was effected specifically by Aro10p (50, 63). In chemostat cultures grown with either leucine, methionine or phenylalanine as the sole nitrogen source, fusel acids and alcohols derived from amino acids other than the nitrogen source were produced in significant amounts (63). An α-keto acid decarboxylase activity for a broad range of substrates was measured in cell extracts of these cultures and suggested the involvement of a common, broad specificity decarboxylase activity. ARO10 was the only decarboxylase gene whose transcript profile correlated strongly with α-keto acid decarboxylase activity in the chemostat cultures (2, 63, 64) (Fig. 2.2). A comprehensive characterization of Arop10p using a combination of genetic, physiological and biochemical approaches (63) confirmed that Aro10p is a broad substrate-specificity decarboxylase. These experiments also demonstrated the existence of an Aro10p-independent α-keto acid decarboxylase activity (64), which required the functional alleles of both THI3 and at least one of the pyruvate decarboxylase genes PDC1, PDC5 or PDC6. Transcriptome analysis and decarboxylase activity measurements on an S. cerevisiae aro10∆ strain, a double aro10∆ thi3∆ deletion strain and a quadruple pdc1,5,6,aro10∆ mutant strains grown in carbon–limited chemostat with phenylalanine as nitrogen source indicated that: i) PDC5 is strongly upregulated in an aro10∆ background (Fig. 2.3) and also encodes a broad-substrate α-keto acid decarboxylase (Jean-Marc Daran, unpublished observation), ii) PDC5 expression depends on the presence of THI3 (Fig. 2.3), and iii) in contrast to a strain expressing ARO10 only (pdc1,5,6, thi3∆), expression of THI3 only (pdc1,5,6,aro10∆) did not result in any α-keto acid decarboxylase activity (63, 64). THI3 has recently been demonstrated to be involved in regulation of thiamine homeostasis in S. cerevisiae (43, 45), which further suggests that its role in the Ehrlich pathway may be regulatory rather than catalytic. While Pdc1, Pdc5, Pdc6 and Aro10 are cytosolic, Thi3 has been observed to be localized in both the cytosol and the nucleus (43, 45), which would support further its role in regulatory function. A systematic investigation of the catalytic properties of all five (putative) TPP-dependent decarboxylases (Aro10p, Thi3p, Pdc1p, Pdc5p, Pdc6p) is essential for a final resolution of the substrate specificity of these key enzymes in the Ehrlich pathway.

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Fig. 2.3: Expression profiling of the five TPP-dependent decarboxylases in various mutants strains grown at a dilution rate of 0.10 h-1 in carbon–limited chemostat (color version at end of book). Expression values represent the mean ± standard deviation of data from at least two independent genechip (Affymetrix YG-S98) of samples issued from different steady-state chemostat cultivations. 1- CENPK113-7D grown in carbon–limited chemostat with (NH4)2SO4 as nitrogen source (64). 2CENPK113-7D grown in carbon–limited chemostat with phenylalanine as nitrogen source (64). 3- aro10∆ strain grown in carbon–limited chemostat with phenylalanine as nitrogen source (64). 4- aro10∆ thi3∆ strain grown in carbon–limited chemostat with phenylalanine as nitrogen source (64). 5- pdc1,5,6, aro10∆ strain grown in carbon–limited chemostat with phenylalanine as nitrogen source (64). Corresponding microarray data have been deposited to the Genome Omnibus Expression Database (http://www.ncbi.nlm.nih.gov/geo/) series accession number GSE9590 (1).

In lactic acid bacteria, catabolism of branched-chain, aromatic and sulfurcontaining amino acids also involves transamination followed by a decarboxylation reaction. The fate of the aldehyde seems to be species dependent but in Lactococcus lactis a gene called KcdA has been identified and characterized. Similar to the yeast Aro10p gene, KcdA encodes a broad substrate α-keto acid decarboxylase that exhibits the highest activity on branched-chain α-keto acids (56).

Reduction or oxidation of ‘fusel aldehydes’ In its classical description, the final step of the Ehrlich pathway is the reduction of the fusel aldehyde (Table 2.1) formed in the decarboxylation step by an alcohol dehydrogenase. Indeed, in glucose-grown batch cultures, the amino acids that can be converted via the Ehrlich pathway are almost entirely converted to fusel alcohol and formation of ‘fusel acid’ via oxidation of the aldehydes plays only a minor role (6 - 8, 10).

46

The Ehrlich pathway for fusel alcohol production

Recent studies have shown that the balance between oxidation and reduction of the fusel aldehydes depends strongly on cultivation conditions. In aerobic glucose-limited chemostat cultures grown with various amino acids (leucine, methionine and phenylalanine) as sole nitrogen sources, the amino acids are predominantly converted to fusel acids and only very low concentrations of fusel alcohols are formed (2, 63, 64). In such cultures, the biomass yield on glucose is 40 % lower than in cultures grown on other nitrogen sources, thus implying that a high energy drain is associated with the formation of fusel acids (2). Given the relatively high hydrophobicity of these weak organic acids, it seems probable that ATP dissipation occurs through classical (29, 49) weak organic acid uncoupling of the plasma membrane pH gradient. A key role of the cellular redox status in determining the ratio of fusel acid to fusel alcohol production is consistent with the observations discussed above. In glucose-grown batch cultures of S. cerevisiae, growth is predominantly fermentative, and when phenylalanine is the sole nitrogen source it is converted into a mixture of 90 % fusel alcohol (phenylethanol) and less than 10 % fusel acid (phenylacetate). Furthermore, fully fermentative dissimilation of glucose under anaerobic conditions (in batch or chemostat) results in the almost complete conversion of phenylalanine to phenylethanol (63, 64). Especially under anaerobic conditions, the reduction step of the Ehrlich pathway may have a relevant impact on overall cellular redox metabolism. In S. cerevisiae, excess NADH formed during anaerobic growth can be oxidized by glycerol-3-phosphate dehydrogenase to yield glycerol (48, 61). However, glycerol formation from glucose requires an ATP and is thus energetically unfavorable. Amino acid catabolism via the Ehrlich pathway may provide an alternative, energyefficient means for NADH regeneration. Although this possible role of the Ehrlich pathway has not been systematically investigated, nitrogen-limited, respirofermentative cultures of S. cerevisiae produced less glycerol when valine instead of ammonia was supplied as the nitrogen source (5). Determining the molecular identity of the oxidoreductases involved in the Ehrlich pathway represents a challenge. The S. cerevisiae genome harbors 16 alcohol dehydrogenases, six aldehyde dehydrogenases and at least two other broad-spectrum reductases that catalyze pyridine nucleotide-dependent interconversion of aldehydes and alcohols (Fig. 2.1). Known roles of the alcohol dehdrogenases are reviewed in (10). Using strains containing all possible combinations of mutations affecting the seven AAD genes (putative aryl alcohol dehydrogenases), five ADH genes and SFA1, Dickinson et al. showed that the final step of the Ehrlich pathway (fusel alcohol formation) can be catalyzed by any one of 47

Chapter 2

the ethanol dehydrogenases (Adh1p, Adh2p, Adh3p, Adh4p, Adh5p) or by Sfa1p (formaldehyde dehydrogenase) (10). In addition, the NADPH-utilizing aldehyde reductases encoded by YPR1 and GRE2 have been shown to have activity towards 2-methylbutyraldehyde and isovaleraldehyde respectively (17, 20). Ascribing precise roles to aldo-ketose reductases is problematical in many organisms (yeast included) due to overlapping specificities and the presence of other related enzymes. Transcriptome data of Saccharomyces cerevisiae grown in glucose-limited chemostat with phenylalanine, methionine or leucine as sole nitrogen source were not discriminative, as none of the 16 alcohol dehydrogenases and the six aldehyde dehydrogenases transcript profiles could correlate the presence of fusel alcohols and acids (2) (Fig. 2.2).

Export of fusel alcohols and fusel acids The mechanism by which fusel alcohols are exported from the cells into the culture medium remains unknown. So far, there are no data that link this process to any known membrane transporter in S. cerevisiae but considering their water-octanol partition coefficients (ranging from 0.76 to 1.36), it is conceivable that export occurs by simple passive diffusion across the lipid bilayer (38). Conversely, export of fusel acids has recently been shown to involve at least one plasma membrane transporter. Expression of PDR12, an ATP-dependent transporter which is known to be involved in the export of several weak organic acid food preservations (e.g. benzoate, sorbate (29)) was strongly increased in chemostat cultures utilising leucine, methionine or phenylalanine as sole nitrogen sources (2). Phenotypic analysis of a pdr12∆ mutant confirmed the role of Pdr12p in export of fusel acids formed by catabolism of leucine, isoleucine, valine, phenylalanine and tryptophan via the Ehrlich pathway (21). The involvement of an ATP-dependent exporter is fully consistent with the reduced biomass yields observed in chemostat culture grown on amino acids as the sole nitrogen source that lead to the formation of uncoupling agents (2) (Fig. 2.2).

Regulation of the Ehrlich pathway Although regulation of many of the individual genes discussed above has already been investigated, these studies did not take into account the context of the Ehrlich pathway. Iraqui et al. identified ARO80 as the transcriptional activator involved in the induction of the ARO9 and ARO10 transaminase and decarboxylase genes by the aromatic amino acids tryptophan, phenylalanine or tyrosine. A 36-bp up-stream activating sequence was shown to be necessary and sufficient to promote transcriptional induction by aromatic amino acids (30). ChIP on chip (Chromatin Immuno Precipitation) experiments confirmed the binding of Aro80p to a 48

The Ehrlich pathway for fusel alcohol production

WRCCGWSATTTRCCG motif uniquely present in the ARO10 and ARO9 promoters (19, 39). It remains unclear whether the strong induction of ARO9 and ARO10 observed in chemostat cultures grown with the Ehrlich pathway precursors leucine and methionine as nitrogen sources (2) is also mediated by Aro80p. As discussed above, the PDR12-encoded plasma membrane transporter has recently been shown to export branched-chain and aromatic fusel acids (21). The transcriptional induction of PDR12 by the non-physiological substrates benzoate and sorbate is mediated by War1p, an activator that occupies a cis-acting element in the promoter of PDR12 and becomes active upon phosphorylation (35). A recent genetic screen identified a WAR1 mutant allele carrying 3 amino acid changes. One of them proved to be essential for the phosphorylation and binding of War1p to its target sequence. The absence of phosphorylation of War1p-42 eliminated PDR12 induction and led to a hypersensitivity to sorbate (18). It is as yet unclear whether War1p-mediated induction is also the (sole) mechanism for transcriptional induction of PDR12 by endogenously produced fusel acids. In the chemostat-based transcriptome studies by Boer et al. (2), 30 additional genes were shown to follow the transcriptional profile of ARO9, ARO10 and PDR12 (i.e., a strong transcriptional upregulation during growth with amino acid precursors of the Ehrlich pathway as the nitrogen source). However, none of the genes implicated in the Ehrlich pathway (Fig. 2.1), with the exception of the three mentioned above, were found among the set of 30 genes. This coordinate induction suggests that a general regulatory mechanism participates in the regulation of the Ehrlich pathway. As shown by Boer et al. (2), growth on phenylalanine, leucine and methionine led to relief of Nitrogen Catabolite Repression (NCR) mediated regulation. We therefore cannot exclude the involvement of GATA factor-mediated transcriptional regulation of the Ehrlich pathway. Transcriptional control may not be the only level for regulation of the Ehrlich pathway. The overexpression of the ARO10 decarboxylase gene from a strong constitutive promoter did not result in increased 3-phenylpyruvate decarboxylase activity during growth on a medium with glucose and ammonium sulfate (63). However, replacement of either the ammonium sulfate by phenylalanine, or of glucose by ethanol, led to clear increase of 3-phenylpyruvate decarboxylase activity (63). These observations suggest that functional expression of the ARO10 gene is regulated at a posttranscriptional level in a carbon- and nitrogen-source-dependent manner. Further research should address the question whether this regulation involves post-translational modification of the Aro10p protein and/or additional factors.

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Fusel alcohols and acids as signal molecules: a role of the Ehrlich pathway in the control of cellular morphology Growth in the presence of fusel alcohols such as isoamyl alcohol (derived from isoleucine) induces pseudohyphal growth (9, 33). These pronounced morphological modifications are accompanied by an increase in the specific activity of succinate dehydrogenase and an increase of the chitin content (33). These effects are not restricted to isoamyl alcohol, as other reports have shown that for S. cerevisiae, the addition of exogeneous 2-phenylethanol (4) or 2-indole acetate (also known as the plant hormone auxin) (51) affects morphogenesis and induces invasive growth. These effects indicate a quorum-signaling pathway, which links the environmental sensing of autosignaling molecules and morphogenesis. No common regulatory circuit has been identified so far. While 2-phenylethanol stimulated morphological modifications by inducing the expression of FLO11 by a Tpk2p-dependent mechanism (4), transcriptional regulation to exposure to 2-indole acetate involved Yap1p as a key mediator of that response (51). Similarly, in the human fungal pathogens Candida albicans and Candida dubliniensis, phenylethanol and isoamylalcohol produced during growth stimulate biofilm formation (40). Based on these reports it thus seems that one of the functions of the Ehrlich pathway is to form quorum sensing compounds that induce differentiation and participate in the adaptation of yeast cells to environmental changes. However, thermodynamics of the Ehrlich pathway and more precisely of the transaminase reactions may provide more insight and propose a new biological role for the Ehrlich pathway.

Thermodynamics of the Ehrlich pathway: a key role during nitrogenlimited growth? The equilibrium constant of the transamination reactions is often close to unity, like for instance for the phenylalanine/3-phenylpyruvate αketoglutarate/glutamate aminotransferase reaction (estimated data from reference (22)). Under physiological conditions, such as those studied by Wu et al. (66), the concentration of the substrates of this reaction, in this example α-ketoglutarate (36 μmol l-1) and phenylalanine (52 μmol l-1), are often much lower than that of the desired product glutamate (5850 μmol l-1). Since this transamination reaction has an essential role under conditions where nitrogen concentrations are low, simply increasing the substrate concentration is often not feasible. To solve this thermodynamic problem, cells could use active transport to accumulate the amino acids intracellularly, but this also has its limitation given the often low solubility of these compounds in water. The only remaining solution is then to maintain very low concentrations of the fourth compound involved in this reaction: 3-phenylpyruvate. 50

The Ehrlich pathway for fusel alcohol production

The transaminating reaction is followed by decarboxylation, which is generally characterized by a strongly negative Gibbs free energy change (22)). Consequently, when branched-chain, aromatic or sulfur containing amino acids are the nitrogen source, the decarboxylation reaction contributes to low intracellular α-keto acid concentrations, thereby pulling the transaminating reactions towards complete utilization of the nitrogen-donating amino acids.

Outlook Branched-chain, aromatic and sulfur-containing amino acids that are available in malt wort and grape must are important precursors for (off)-flavor formation. In the past decade, many efforts have contributed to a better understanding of the genes involved in this pathway and how their expression is regulated. A desirable trait in beer and wine fermentation is to achieve high branched-chain and aromatic fusel alcohol production in combination with low off-flavors (e.g. methionol). Our growing understanding of the key-components of the Ehrlich pathway and their regulation will serve to design strains exhibiting specific flavor profiles in foodstuffs as well as for metabolic engineering of yeast strains for the production of individual Ehrlich pathway products. Acknowledgement: This work was financially supported by the board of the Delft University of Technology, DSM and the Dutch Ministry of Economic Affairs (NWOCW project 99601).

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The Ehrlich pathway for fusel alcohol production 52. Schaaff, I., J. B. Green, D. Gozalbo, and S. Hohmann. 1989. A deletion of the PDC1 gene for pyruvate decarboxylase of yeast causes a different phenotype than previously isolated point mutations. Curr. Genet. 15:75-81. 53. Schoondermark-Stolk, S. A., M. Tabernero, J. Chapman, E. G. Ter Schure, C. T. Verrips, A. J. Verkleij, and J. Boonstra. 2005. Bat2p is essential in Saccharomyces cerevisiae for fusel alcohol production on the non-fermentable carbon source ethanol. FEMS Yeast Res. 5:757-766. 54. Sentheshanmuganathan, S. 1956. The formation of tyrosol (2-p-hydroxyethanol) from tyrosine by Saccharomyces cerevisiae. Biochem. J. 64:37P-38P. 55. Sentheshanmuganathan, S. 1960. The mechanism of formation of higher alcohols from amino acids by Saccharomyces cerevisiae. Biochem. J. 74:568-576. 56. Smit, G., B. A. Smit, and W. J. Engels. 2005. Flavour formation by lactic acid bacteria and biochemical flavour profiling of cheese products. FEMS Microbiol. Rev. 29:591-610. 57. Ter Schure, E. G., M. T. Flikweert, J. P. van Dijken, J. T. Pronk, and C. T. Verrips. 1998. Pyruvate decarboxylase catalyzes decarboxylation of branched-chain 2-oxo acids but is not essential for fusel alcohol production by Saccharomyces cerevisiae. Appl. Environ. Microbiol. 64:1303-1307. 58. Thorne, R. S. W. 1937. The assimilation of nitrogen from amino acids by yeast. J. Inst. Brew. 43:288293. 59. Thorne, R. S. W. 1941. The growth and fermentation of a strain of S. cerevisiae with amino acids as nutrients. J. Inst. Brew. 47:255-272. 60. Urrestarazu, A., S. Vissers, I. Iraqui, and M. Grenson. 1998. Phenylalanine- and tyrosineauxotrophic mutants of Saccharomyces cerevisiae impaired in transamination. Mol. Gen. Genet. 257:230-237. 61. van Dijken, J. P., B. E. van den, J. J. Hermans, L. R. de Miranda, and W. A. Scheffers. 1986. Alcoholic fermentation by 'non-fermentative' yeasts. Yeast 2:123-127. 62. von Ewald, A. and W. Kühne. 1877. Über einen neuen Bestandtheil des Nervensystems. Verhl. Naturhist. -Med. Ver. Heidelberg 1:194-198. 63. Vuralhan, Z., M. A. Luttik, S. L. Tai, V. M. Boer, M. A. Morais, D. Schipper, M. J. Almering, P. Kotter, J. R. Dickinson, J. M. Daran, and J. T. Pronk. 2005. Physiological characterization of the ARO10-dependent, broad-substrate-specificity 2-oxo acid decarboxylase activity of Saccharomyces cerevisiae. Appl. Environ. Microbiol. 71:3276-3284. 64. Vuralhan, Z., M. A. Morais, S. L. Tai, M. D. Piper, and J. T. Pronk. 2003. Identification and characterization of phenylpyruvate decarboxylase genes in Saccharomyces cerevisiae. Appl. Environ. Microbiol. 69:4534-4541. 65. Watson, T. G. and J. S. Hough. 1969. Conversion of α-keto-isocaproic acid to iso-amyl alcohol by yeast pyruvate decarboxylase and alcohol dehydrogenase. J. Inst. Brew. 75:359-363. 66. Wu, L., M. R. Mashego, A. M. Proell, J. L. Vinke, C. Ras, J. van Dam, W. A. van Winden, W. M. van Gulik, and J. J. Heijnen. 2006. In vivo kinetics of primary metabolism in Saccharomyces cerevisiae studied through prolonged chemostat cultivation. Metab Eng 8:160-171. 67. Yamada, M. 1932. Decomposition of amino acids by yeast I. Nippon Nogei Kagaku Kaishi 8:428-432. 68. Yamada, M. 1932. Decomposition of amino acids by yeast II. Nippon Nogei Kagaku Kaishi 8:498-505. 69. Yamada, M. 1932. Decomposition of amino acids by yeast III. Nippon Nogei Kagaku Kaishi 8:506-

55

Chapter 2 508. 70. Yoshimoto, H., T. Fukushige, T. Yonezawa, and H. Sone. 2002. Genetic and physiological analysis of branched-chain alcohols and isoamyl acetate production in Saccharomyces cerevisiae. Appl. Microbiol. Biotechnol. 59:501-508.

56

Chapter 3

A new physiological role for Pdr12p in Saccharomyces cerevisiae: export of aromatic and branched-chain organic acids produced in amino acid catabolism

Lucie A. Hazelwood, Siew Leng Tai, Viktor M. Boer, Johannes H. de Winde, Jack T. Pronk and Jean Marc Daran

FEMS Yeast Research, 2006, Vol. 6, 937 – 45

Chapter 3

Abstract Saccharomyces cerevisiae can use a broad range of compounds as sole nitrogen source. Many amino acids such as leucine, tyrosine, phenylalanine and methionine, are utilized through the Ehrlich pathway. The fusel acids and alcohols produced from this pathway, along with their derived esters, are important contributors to beer and wine flavor. It is unknown how these compounds are exported from the cell. Analysis of nitrogen-sourcedependent transcript profiles via microarray analysis of glucose-limited, aerobic chemostat cultures revealed a common upregulation of PDR12 in cultures grown with leucine, methionine or phenylalanine as sole nitrogen source. PDR12 encodes an ABC transporter involved in weak organic acid resistance, which, has hitherto been studied in the context of resistance to exogenous organic acids. The hypothesis that PDR12 is involved in export of natural products of amino acid catabolism was evaluated by analyzing the phenotype of null mutants in PDR12 or in WAR1, its positive transcriptional regulator. The hypersensitivity of the pdr12∆ and war1∆ strains for some of these compounds indicates that Pdr12p is involved in export of the fusel acids, but not the fusel alcohols derived from leucine, isoleucine, valine phenylalanine and tryptophan.

58

New physiological role for Pdr12 in fusel acid export

Introduction Saccharomyces cerevisiae is capable of using a wide range of nitrogen compounds (1, 16) as sole nitrogen source. For most amino acids, the amine group is entered into central nitrogen metabolism via transamination, thereby releasing the carbon skeleton as a 2-oxo acid. The 2-oxo acids derived from some amino acids, such as pyruvate derived from alanine, directly enter central metabolism. Other amino acids, such as the branchedchain and aromatic amino acids, yield 2-oxo acids that cannot be used as carbon source. These compounds are further catabolized through the socalled Ehrlich pathway, in which the 2-oxo acid is decarboxylated to the corresponding aldehyde (9, 33, 32). Depending on the redox status of the cell (i.e. oxygen availability), the latter is either oxidized to a carboxylic acid (“fusel acids”) or reduced to an alcohol (“fusel alcohols”) (33). These fusel acids and alcohols, along with their derived esters, are important contributors to beer and wine flavor. It is unclear how fusel acids and alcohols are exported from the yeast cell. The S. cerevisiae genome harbors 28 open reading frames that encode putative ATP binding cassette (ABC) transporters (7, 24) (Table 1). The best-characterized ABC transporters are those involved in multidrug resistance or pleiotropic drug resistance (PDR). Piper et al. (26) identified PDR12 as the ABC transporter required for the development of weak organic acid resistance. For example, addition of sorbate to yeast cells resulted in a dramatic induction of Pdr12p in the plasma membrane. The treatment of wild-type and pdr12∆ strains with sorbic acid, coupled with a fluorescein extrusion assay and microscope experiments, demonstrated that cells devoid of Pdr12p were incapable of exporting fluorescein (13). Screening for a weak acid hypersensitivity phenotype subsequently led to the identification of WAR1 (Weak Acid Resistance), a positive regulator of PDR12 expression (15). Promoter deletion analysis indicated that War1p recognizes and occupies a cis-acting weak acid response element (WARE) in the presence and in the absence of stress. It was suggested that weak acid stress triggers phosphorylation and activation of War1p (15). Furthermore, Pdr12p has been shown to transport different sorts of molecules ranging from linear C3 to C10 carboxylic acid (26) to multicyclic compounds such as caffeine (23) and fluorescein (13). However, until recently, research on PDR12 has principally focused on its involvement in resistance to exogenously added weak acids and in particular food preservatives like sorbic and benzoic acids (6, 12, 13).

59

Chapter 3

Export systems such as Pdr12p contribute to the tolerance of yeasts involved in food spoilage to weak organic acids (21). In solution, weak organic acids exist in a dynamic pH-dependent equilibrium between their undissociated and anionic states. An acidic pH favors the undissociated state, and thus a stronger microbial action because the uncharged acid can diffuse through the plasma membrane. Once inside the cell, it encounters a more neutral intracellular pH and a significant fraction dissociates releasing protons and anions. Protons and anions cannot cross the membrane, resulting in perturbation of intracellular pH homoeostasis and accumulation of toxic anions. We have studied regulation of nitrogen metabolism by a transcriptome comparison of S. cerevisiae in glucose-limited, aerobic chemostats grown on different sole nitrogen sources. These studies revealed a common transcriptional upregulation of PDR12 in cultures grown with leucine, methionine or phenylalanine as sole nitrogen source, relative to cultures grown on ammonium, proline and asparagine. From this nitrogen-source-dependent transcriptional regulation, we hypothesize that Pdr12p is involved in the export of catabolic products (i.e. fusel acids) derived from amino acid catabolism. If correct, this would establish a new, clear role of Pdr12p that is directly linked to the S. cerevisiae metabolic network. To test this hypothesis we analyzed the sensitivity of pdr12 and war1 null mutants to a set of relevant compounds.

60

New physiological role for Pdr12 in fusel acid export

Table 3.1: Pyrophosphate bond hydrolysis-driven active transporter ATP-binding cassette (ABC) (MIPS FUNCAT (20.03.25 ABC transporters)) in Saccharomyces cerevisiaae. (7, 20, 24) FUNCAT catalog database http://mips.gsf.de/proj/funcatDB/search_main_frame.html 1

PDR18 is absent in Saccharomyces cerevisiae CEN.PK113-7D strain as shown by comparative genome hybridization and restriction fragment length polymorphism analysis (4)

Systematic ORF name

Gene name

Description according to MIPS FUNCAT (20.03.25 ABC transporters)

YCR011C

ADP1

'Half-size' ABC transporter - unknown function

YDR011W

SNQ2

'Full-size' ABC transporter involved in multidrug resistance

YDR091C

RLI1

Protein promoting preinitiation complex assembly

YDR135C

YCF1

Vacuolar 'full-size' ABC transporter responsible for vacuolar sequestration of glutathione-S-conjugates

YDR406W

PDR15

ABC transporter of the plasma membrane

YER036C

ARB1

ATP-binding cassette protein involved in ribosome biogenesis

YGR281W

YOR1

'Full-size' ABC transporter involved in tolerance to toxic organic anions

YHL035C

VMR1

Multidrug resistance protein, member of the ABC family

YIL013W

PDR11

'Full-size' ABC transporter involved in sterol uptake

YIL121W

QDR2

Multidrug transporter, function as a quinidine resistance determinant

YKL188C

PXA2

'Half-size' ABC transporter required for import of long-chain fatty-acids into peroxisomes

YKL209C

STE6

Full-size ABC transporter responsible for export of the a factor mating pheromone

YKR103W

NFT1

New Full-length MRP-type transporter

YKR104W

Similarity to multidrug resistance proteins

YLL015W

BPT1

ABC type transmembrane transporter of MRP/CFTR family, vacuolar membrane

YLL048C

YBT1

Vacuolar, 'full-size' ABC protein transporting bile acids

YLR188C

MDL1

'Half-size' ABC transporter, mitochondrial

YML116W

ATR1

Putative substrate-H+ antiporter conferring resistance to aminotriazole

YMR301C

ATM1

Mitochondrial inner membrane, 'half-size' ABC transporter involved in the maturation of cytosolic ironsulfur (Fe/S) cluster-containing proteins

YNR070W

PDR181

Strong similarity to ABC transporter involved in multidrug resistance Snq2p

YOL075C

Similarity to A. gambiae ABC protein

YOL158C

ENB1

Transporter of the siderophore enterobactin

YOR011W

AUS1

Transporter of the ATP-binding cassette family, involved in uptake of sterols and anaerobic growth

YOR153W

PDR5

'Full-size' ABC transporter involved in multidrug resistance

YOR328W

PDR10

'Full-size' ABC transporter homologous to the Pdr5p multidrug resistance protein - unknown function

YPL058C

PDR12

'Full-size' ABC transporter of the plasma membrane acting as a weak organic acids extrusion pump

YPL147W

PXA1

'Half-size' ABC transporter required for import of long-chain fatty-acids into peroxisomes

YPL270W

MDL2

'Half-size' ABC transporter, mitochondrial

61

Chapter 3

Material and methods Strains and media. The strains used in this study are listed in Table 2. CEN.PK 113-7D was used as prototrophic reference strain. Both knockout strains (pdr12∆ and war1∆) were constructed in this genetic background. Strains were constructed by using standard yeast media and genetic techniques (2). The kanamycin resistance cassette was amplified by using the pUG6 vector as template (11). Strains were routinely grown at 30 ºC on complete media (YPD, YPE) or defined 1.5 % agar synthetic media (SMA) (30). Table 3.2: S. cerevisiae strains used in this study. a

Institut für Mikrobiologie der J.W. Goethe Universität, Marie-Curie-Strasse 9; Building N250 D-60439, Frankfurt Germany. Strain

Genotype

Source or reference

CEN.PK 113-7D

MATa MAL2-8c SUC2

P. Köttera

IMK050

MATa MAL2-8c SUC2 pdr12∆

This study

IMK052

MATa MAL2-8c SUC2 war1∆

This study

Shake flask cultivation. Growth rate experiments were performed in 500-ml flasks containing 100-ml of medium, which were incubated at 30 ºC on an orbital shaker set at 200 rpm. The composition of the synthetic medium (SM) was the following: 20 g/l glucose, 5 g/l (NH4)2SO4, 6 g/l KH2PO4, 0.5 g/l MgSO4, trace elements and vitamins solution (30). The pH of the medium was adjusted to 4, 5 or 6 and the media were sterilized by autoclaving. Glucose was autoclaved separately. Vitamins and fusel acids or alcohols were filter sterilized and supplemented to the media. Growth of various strains was monitored by optical density (OD) measurements at 660 nm. Chemostat cultivation. Carbon-limited steady-state chemostat cultures of S. cerevisiae CEN.PK113-7D strain were grown on synthetic medium as described in (30), containing 7.5 g of glucose l-1 keeping molar carbon equivalence constant at 0.25 M, and either 5.0 g l-1 (NH4)2SO4, 5.0 g l-1 of L-phenylalanine (Vuralhan et al., 2003), 10 g l-1 L-leucine, 11.3 g l-1 L-methionine, 5.0 g.l-1 asparagine, or 8.8 g.l-1 proline as the sole nitrogen source. The absence of (NH4)2SO4 was compensated by the addition of equimolar amounts of K2SO4 when phenylalanine, leucine, methionine, asparagine and proline was used as the only nitrogen source. Plate assays. Fusel acid susceptibility of yeast strains was tested by spotting 10 µl of serial dilutions of exponentially growing cultures onto YPD (Yeast Extract, 5 g.l-1; Bactopeptone, 10 g.l-1; D-glucose, 20 g.l-1, pH 4.5) plates, supplemented individually with the indicated compounds (Table 3). For the fusel acid derived from tryptophan (indole acetate), ethanol was used as carbon source instead of glucose in order to overcome practical problems caused by insolubility of this compound in water. The plates were incubated at 30oC and growth was scored after a maximum of 72 h. On ethanol the plates were scored after a maximum of 120 h.

62

New physiological role for Pdr12 in fusel acid export

Table 3.3: Fusel acids and alcohols used in this study. Amino acid

Fusel acid

Fusel alcohol

Leucine

3-methyl butanoate

3-methyl butanol

Isoleucine

2-methyl butanoate

2-methyl butanol

Valine

2-methyl propanoate

2-methyl propanol

Phenylalanine

Phenylacetic acid

Phenylethanol

Tryptophan

3-indole acetic acid

3-indole ethanol

Tyrosine

4-hydroxy phenylacetic acid

4-hydroxy phenylethanol

Microarray analysis. DNA microarray analyses were performed with the S98 Yeast GeneChip® arrays from Affymetrix as previously described in (5, 25). Cells were transferred directly from chemostats into liquid nitrogen, and processed according to the manufacturer’s instructions (Affymetrix technical manual, Affymetrix, Santa Clara, CA.). Data analyses were performed with the Affymetrix software packages: Microarray Suite v5.0, MicroDB v3.0 and Data Mining Tool v3.0. The Significance Analysis of Microarrays (SAM version 1.12) (29) add-in to Microsoft Excel was used for comparisons of replicate array experiments. The transcriptome data of ammonium sulfate and phenylalanine grown S. cerevisiae CEN.PK113-7D are available at www.bt.tudelft.nl/nitrogen-source (33).

63

Chapter 3

Results Transcript levels of PDR12 in S. cerevisiae CEN.PK113-7D grown on various nitrogen sources Transcriptome analysis of S. cerevisiae CEN.PK113-7D grown in aerobic carbon-limited chemostats with leucine, phenylalanine or even methionine as sole N-source revealed a consistent upregulation of the PDR12 expression level relative to cultures grown with ammonium sulfate, asparagine or proline. This upregulation coincides with the formation in the chemostat cultures of fusel alcohols and acids derived from the respective amino acids used as nitrogen source and concomitantly from other amino acids whose catabolism involves the Ehrlich pathway. For example, phenylethanol and phenylacetate, catabolites of phenylalanine, were detected in significant amount in supernatant from leucine and methionine grown cultures (32). Of the known 28 ABC transporters (24), PDR12 was the only member to be significantly (SAM (29) with a false discovery rate (FDR) of 1%) and positively (Fold-changeAA vs NH4 >2) upregulated in the leucine, methionine and phenylalanine chemostat cultures (Table 4). The nitrogensource-dependent transcript profile observed for PDR12 was highly similar to that of ARO10 which encodes a broad substrate 2-oxo acid decarboxylase (Table 4) (32). The level of the ACT1 transcript, a common loading standard for conventional Northern analysis (22), varied by less than 12% over the six tested growth conditions (Table 4).

64

ENB1 AUS1 PDR5 PDR10 PXA1 MDL2

YOL158C

YOR011W

YOR153W

YOR328W

YPL147W

YPL270W

YOL075C

58.6 ± 2 637.4 ± 74 55.3 ± 6 111.7 ± 22 80.6 ± 9 116.3 ± 14 146.4 ± 25

47.7 ± 16

42.3 ± 8 92.5 ± 24 70.6 ± 17 92.3 ± 15 117.9 ± 28

2.0 ± 2

3.5 ± 1

333.3 ± 48

74.4 ± 12

74.5 ± 7

PDR181

406.8 ± 22

108.6 ± 25

YNR070W

169.1 ± 12

179.8 ± 49

MDL1

YLR188W ATR1

496.6 ± 56

361.8 ± 12

YBT1

YLL048C

ATM1

78.7 ± 6

70.4 ± 15

BPT1

YLL015W

YMR301C

25.1 ± 4

25.0 ± 6

YKR104W

YML116W

6.8 ± 1

7.3 ± 2

NFT1

YKR103W

61.5 ± 10 115.4 ± 24

76.8 ± 8

STE6

YKL209C

63.5 ± 13

PXA2

YKL188C

32.2 ± 9

34.0 ± 6

QDR2

YIL121W

29.4 ± 8

25.2 ± 6

PDR11

YIL013C

33.7 ± 3

27.8 ± 11

VMR1

YHL035C

33.5 ± 5

46.2 ± 10

YOR1

YGR281W

126.0 ± 19

107.3 ± 13

ARB1

64.3 ± 9

YER036C

109.9 ± 10

99.4 ± 16 69.2 ± 21

YCF1 PDR15

YDR135C

YDR406W

232.8 ± 17

212.7 ± 54

RLI1

219.0 ± 24

123.7 ± 27

YDR091C

121.8 ± 10

102.9 ± 11

ADP1 SNQ2

YCR011C

YDR011W

1657.3 ± 37

1933.3 ± 264

PDR12

1045.9 ± 168

1996.3 ± 202

YPL058C

1904.6 ± 129

2566.6 ± 453

ACT1 ARO10

LEU

PHE

YFL039C

Gene name

YDR380W

Systematic name

65

84.1 ± 4

32.8 ± 7

69.0 ± 12

101.1 ± 23

38.5 ± 3

576.4 ± 85

63.1 ± 17

2.2 ± 2

53.9 ± 3

300.9 ± 25

163.2 ± 18

473.9 ± 59

68.6 ± 13

31.3 ± 5

10.9 ± 1

183.4 ± 4

31.5 ± 2

81.7 ± 6

16.9 ± 3

22.6 ± 3

61.2 ± 13

163.5 ± 22

75.9 ± 11

125.0 ± 13

306.8 ± 22

223.2 ± 13

147.2 ± 22

1601.9 ± 108

896.3 ± 71

2157.5 ± 87

MET

82.1 ± 24

127.6 ± 25

307.0 ± 71

259.1 ± 28

53.6 ± 11

207.2 ± 21

29.4 ± 16

1.1 ± 0

59.6 ± 6

214.1 ± 18

190.3 ± 20

302.3 ± 23

107.9 ± 16

24.7 ± 3

7.3 ± 3

68.2 ± 4

79.3 ± 12

17.1 ± 4

34.9 ± 4

26.3 ± 4

66.8 ± 2

153.0 ± 18

72.1 ± 13

117.8 ± 9

255.6 ± 33

285.1 ± 8

157.6 ± 7

439.3 ± 149

37.2 ± 7

2294.8 ± 164

PRO

Nitrogen sources

106.2 ± 7

149.9 ± 23

265.1 ± 42

304.3 ± 11

50.8 ± 3

375.8 ± 59

44.3 ± 21

1.3 ± 0

78.4 ± 3

178.9 ± 39

166.5 ± 17

311.3 ± 26

113.1 ± 12

20.8 ± 7

7.2 ± 1

60.3 ± 11

82.7 ± 13

18.3 ± 2

28.9 ± 3

28.2 ± 9

67.1 ± 8

151.5 ± 22

67.9 ± 10

125.3 ± 16

252.2 ± 60

303.8 ± 27

122.7 ± 20

79.6 ± 44

60.9 ± 10

2418.1 ± 96

ASN

95.0 ± 24

136.8 ± 13

269.9 ± 60

219.1 ± 53

40.2 ± 3

366.0 ± 51

32.4 ± 6

2.6 ± 1

42.5 ± 20

239.8 ± 37

181.9 ± 27

278.8 ± 32

86.1 ± 18

22.5 ± 5

9.3 ± 2

69.6 ± 16

84.3 ± 18

27.2 ± 2

25.7 ± 5

19.5 ± 6

57.1 ± 5

135.4 ± 13

55.9 ± 3

113.2 ± 11

377.1 ± 74

285.8 ± 40

106.9 ± 24

620.3 ± 67

68.9 ± 6

2234.3 ± 56

(NH4)2SO4(NH3)

1.2

-1.5

-3.8

-2.4

1.1

-1.1

1.5

1.4

1.8

-2.2

-1.0

1.3

-1.2

1.1

-1.3

1.1

-1.3

1.2

-1.0

1.4

-1.2

-1.3

1.2

-1.1

-1.8

-2.3

-1.0

3.1

29.0

1.1

PHE vs NH3

1.5

-1.2

-3.4

-2.0

1.4

1.7

1.8

-1.3

1.8

1.7

-1.1

1.8

-1.1

1.1

-1.4

1.7

-1.4

1.2

1.1

1.7

-1.7

-1.1

1.2

-1.0

-1.6

-1.3

1.1

2.7

15.2

-1.2

LEU vs NH3

-1.1

-4.2

-3.9

-2.2

-1.0

1.6

1.9

-1.1

1.3

1.3

-1.1

1.7

-1.3

1.4

1.2

2.6

-2.7

3.0

-1.5

1.2

1.1

1.2

1.4

1.1

-1.2

-1.3

1.4

2.6

13.0

-1.0

MET vs NH3

Fold-change

-1.2

-1.1

1.1

1.2

1.3

-1.8

-1.1

-2.3

1.4

-1.1

1.0

1.1

1.3

1.1

-1.3

-1.0

-1.1

-1.6

1.4

1.4

1.2

1.1

1.3

1.0

-1.5

-1.0

1.5

-1.4

-1.9

-1.1

PRO vs NH3

1.1

1.1

-1.0

1.4

1.3

1.0

1.4

-2.0

1.8

-1.3

-1.1

1.1

1.3

-1.1

-1.3

-1.2

-1.0

-1.5

1.1

1.4

1.2

1.1

1.2

1.1

-1.5

1.1

1.1

-7.8

-1.1

-1.2

ASN vs NH3

PDR18 is absent in S. cerevisiae CEN.PK113-7D strain as shown by comparative genome hybridization (CGH) and restriction fragment length polymorphism (RFLP) analysis (Daran-Lapujade et al., 2003).

1

Table 3.4: Transcript levels of all ABC transporter genes and of the ARO10 2-oxo acid decarboxylase gene in aerobic, glucose-limited chemostat cultures of S. cerevisiae CEN.PK113-7D grown with different amino acids as the sole nitrogen source. Transcript levels were determined with Affymetrix Gene Chips. Data represent the average ± standard deviation of three independent chemostat cultures. The ACT1 transcript is included as a reference.

New physiological role for Pdr12 in fusel acid export

Chapter 3

A pdr12∆ strain displays hypersensitivity to fusel acids but not fusel alcohols To test the hypothesis that PDR12 is involved in export of fusel acids and/or fusel alcohols, a PDR12 knockout mutant was constructed. Subsequently, sensitivity to a series of amino acid-derived catabolites was compared by spotting the pdr12∆ mutant strain and its isogenic reference strain CEN.PK113-7D on glucose complex medium (YPD, pH 4.5) plates supplemented with different concentrations of fusel acids (Figures 1 and 2) and alcohols (Figure 3). This analysis revealed that the pdr12∆ strain displayed a clearly increased sensitivity to the fusel acids derived from leucine, isoleucine, valine, phenylalanine and tryptophan (Figures 1 and 2). While, all fusel acids tested affected the growth of the reference strain CEN.PK113-7D to some extent, the susceptibility of the pdr12∆ strain to these compounds was exacerbated. 8 mM phenylacetate, 6 mM indole acetate, 10 mM 3-methyl butanoate, 4 mM 2-methyl butanoate or 6 mM 2methyl propanoate inhibited growth of the pdr12∆ strain while the reference strain, CEN.PK113-7D, was still able to grow.

Figure 3.1: Succeptibility of S. cerevisiae strains CEN.PK113-7D (a), pdr12∆ (b) and war1∆ (c) to aromatic fusel acids (phenylacetic acid, 4-hydroxy phenylacetic acid and indole acetate). The strains were plated on YPD supplemented with different concentrations of the respective acid. The plates were incubated at 30oC and scored after a maximum of 72 h. For the InAc plates, ethanol replaced glucose and the plates were incubated no longer than 120 h.

66

New physiological role for Pdr12 in fusel acid export

Even a 100mM concentration of tyrosine-derived fusel acid, 4hydroxy phenylacetate, did not affect growth of either the reference CEN.PK113-7D or the pdr12∆ mutant. This marked difference with the phenylalanine-derived catabolic phenylacetate is probably due to the strongly reduced hydrophobicity caused by the 4-hydroxyl group (10). Although this can adequately explain the absence of growth inhibition with 4-hydroxy phenylacetate, it does not rule out the possibility that Pdr12p in vivo might export 4-hydroxy phenylacetate.

Figure 3.2: Succeptibility of S. cerevisiae strains CEN.PK113-7D (a), pdr12∆ (b) and war1∆ (c) to branched-chain fusel acids (2-methyl butanoate, 3-methyl butanoate and 2-methyl propanoate). The strains were plated on YPD supplemented with different concentrations of the respective acid. The plates were incubated at 30oC and scored after a maximum of 72 h.

The induction of PDR12 under sorbic acid stress is known to be transcriptionally regulated by transcription factor War1p (15). In order to check whether, in a context of fusel acid stress, PDR12 expression is also induced by WAR1, a war1∆ strain was constructed and tested under the same conditions as the pdr12∆ strain. Indeed, both strains shared the same fusel acid sensitivity profiles (Figures 1 and 2), indicating that PDR12 expression is also controlled by WAR1 in this experimental context. These results also provide a confirmation that the increase fusel acid sensitivity of the pdr12∆ strain was indeed due to the absence of PDR12 rather than to the remote possibility of an unintended second site mutation. In this respect it should be mentioned that the classical control of reintroducing the wild type allele of PDR12 is compromised by its uncommon length (more than 4.5-kb) rendering the genetic manipulation extremely difficult (7).

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Figure 3.3: Succeptibility of S. cerevisiae strains CEN.PK113-7D (a), pdr12∆ (b) and war1∆ (c) to fusel alcohols (phenylethanol, 4-hydroxy phenylethanol and isoamyl alcohol). The strains were plated on YPD supplemented with different concentrations of the respective alcohol. The plates were incubated at 30oC and scored after a maximum of 72h.

None of the three strains (the isogenic reference, pdr12∆ and war1∆ mutants) were sensitive to the fusel alcohols tested (Figure 3). For the branched-chain fusel alcohols, concentrations of up to 100 mM failed to inhibit growth. Phenylethanol had a slight toxic effect, as the strains only tolerated up to 20 mM of this compound. However, no difference in phenylethanol tolerance was observed between the isogenic reference and knockout strains. Phenylacetate growth inhibition of pdr12∆ mutant is pH dependent For a quantification of the effect of phenylacetate on the growth, shake flask cultures of CEN.PK113-7D and its pdr12∆ derivative were performed at pH 6, on a glucose synthetic medium supplemented with 0, 2, 4 or 6 mM of phenylacetate. Determination of the growth rate confirmed the plate assay results (Table 5). Although the increasing concentration of phenylacetate affected the reference strain growth caused by classical weak acid uncoupling, the relative growth rate of the pdr12∆ (expressed as the ratio of the µpdr12 /µCENPK113-7D) clearly showed the involvement of this ABC transporter in the resistance process. It is expected that this sensitivity is pH-dependent (31). Indeed, the sensitivity of the pdr12 mutant strain to phenylacetate (pKa=4.28 at 18oC) (34) was exacerbated when the pH of the cultures was decreased below 6 (Table 6). Besides a strong reduction in specific growth rate (Table 6), the pdr12∆ strain exhibited an extended lag phase compared to the reference strain.

68

New physiological role for Pdr12 in fusel acid export Table 3.5: Maximum specific growth rates (µmax, h-1) of CEN.PK113-7D and pdr12∆ (IMK050) strains in shake-flask cultures grown on glucose synthetic medium (initial pH 6), in the presence of different concentrations of phenylacetic acid (PAA).

Strain

0 mM PAA

2 mM PAA

4 mM PAA

6 mM PAA

CEN.PK113-7D IMK050 (pdr12Δ) µIMK50/µCENPK113-7D

0.40 ± 0.00 0.40 ± 0.01 0.99

0.34 ± 0.01 0.29 ± 0.02 0.85

0.26 ± 0.00 0.15 ± 0.00 0.59

0.19 ± 0.01 No growth 1 NA

Table 3.6: Maximum specific growth rates (µmax, h-1) of CEN.PK113-7D and pdr12∆ (IMK050) strains in shake-flask cultures grown on glucose synthetic medium in presence of 4mM of phenylacetic acid (PAA) at different initial pH.

Strain

pH 6

pH 5

pH 4

CEN.PK113-7D IMK050 (pdr12Δ) µIMK50/µCEN.PK113-7D

0.26 ± 0.00 0.15 ± 0.00 0.59

0.24 ± 0.01 No growth 1 NA

0.20 ± 0.01 No growth NA1

Data are the average ± mean deviation of assays from two independent shake flask cultures. µIMK50 / µCENpk113-7D represents the relative growth rate of the pdr12∆ strain (IMK050) compared to CEN.PK113-7D. 1

Not applicable

69

Chapter 3

Discussion Previous reports have implicated Pdr12p in the export of a wide range of compounds ranging from multicyclic molecules such as fluorescein (13) and caffeine (23) to linear C3 to C7 carboxylic acids including the food preservatives sorbate and benzoate (26). However, with the possible exception of propionate (12, 19), none of the hitherto described substrates of Pdr12p are products of yeast metabolism. Our results indicate that Pdr12p is involved in the export of fusel acids, but not of fusel alcohols from the cytoplasm of the cell. This role is most relevant during growth at low pH with (a mixture of) amino acid(s) as nitrogen source. It directly links Pdr12p to the S. cerevisiae metabolic network and extends its physiological role beyond that of a defense mechanism against exogenous organic acids.

Figure 3.4: Update of the Ehrlich pathway in S. cerevisiae

In accordance with its involvement in export of several fusel acids, Pdr12p may be considered as an integral part of the Ehrlich pathway for amino acid catabolism. We recently demonstrated that an Aro10-dependent decarboxylase activity with broad substrate specificity is involved in the catabolism of branched chain and aromatic amino acids as well as

70

New physiological role for Pdr12 in fusel acid export

methionine. The present study shows that similar broad substrate specificity exists for the fusel acid export mechanism (Figure 4). The molecular nature and substrate specificity of the oxidoreductases involved in the formation of fusel alcohols and acids remain to be fully elucidated. However, the presence of one of the alcohol dehydrogenases encoded by ADH1, ADH2, ADH3, ADH4, ADH5, or SFA1 is sufficient for the final conversion step towards long chain and complex alcohol formation (8). Additionally, the biochemical characteristics of the alcohol dehydrogenases encoded by ADH6 (YMR318C) (17) and ADH7 (YCR105W) (18) are compatible with their involvement in the reduction of aldehyde precursors to fusel alcohols. Conversely, aryl alcohol dehydrogenase activity encoded by the seven AAD genes is not essential for the formation of the fusel alcohols (8). The involvement in the aldehyde oxidation process, of the five aldehyde dehydrogenases (Ald2p, Ald3p, Ald4p, Ald5p and Ald6p) still has to be investigated. Transcriptome analysis of sorbate stressed S. cerevisiae (28) revealed that, along with PDR12, ARO9 (aromatic amino acid transaminase) and ARO10 were upregulated. However, in contrast to PDR12, the latter two genes did not show WAR1-dependent regulated expression (28), but are known to be controlled by the Aro80p transcriptional activator (14). This suggests a possible interaction of the signal transduction pathway involved in nitrogen regulation and weak acid stress responses that merits further investigation. Fusel alcohols and acids are not solely byproducts of amino-acid catabolism with interesting aroma properties. In Candida albicans the tyrosine-derived fusel alcohol tyrosol is an autoregulatory molecule with important implication on the dynamics of the growth and morphology (3). Moreover, addition of low concentration indole acetic acid (Table 3) (IAA, a key plant hormone (35) induces invasive growth in S. cerevisiae (27). Interestingly, S. cerevisiae can itself produce indole acetate. In this context, export of endogenous, tryptophan-derived IAA via Pdr12p may play a key role in cell-cell communication, enabling coordinated morphological changes of yeast populations in response to environmental changes.

Acknowledgement: The research group of J.T.P. is part of the Kluyver Centre for Genomics of Industrial Fermentation, which is supported by the Netherlands Genomics Initiative. This work was financially supported by the board of the Delft University of Technology, DSM Life Science Products and the Dutch Ministry of Economic Affairs (NWO-CW project 99601). 71

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Reference List 1. Barnett, J. A., R. W. Payne, and D. Yarrow. 1983. Yeasts: characteristics and identification, Cambridge, University Press, Cambridge. 2. Burke, D., D. Dawson, and T. Stearns. 2000. Methods in yeast genetics: Edition 2000, Cold Spring Harbor Laboratory Press, New York. 3. Chen, H., M. Fujita, Q. Feng, J. Clardy, and G. R. Fink. 2004. Tyrosol is a quorum-sensing molecule in Candida albicans. Proc. Natl. Acad. Sci. U. S. A 101:5048-5052. 4. Daran-Lapujade, P., J. M. Daran, P. Kotter, T. Petit, M. D. Piper, and J. T. Pronk. 2003. Comparative genotyping of the Saccharomyces cerevisiae laboratory strains S288C and CEN.PK113-7D using oligonucleotide microarrays. FEMS Yeast Res. 4:259-269. 5. Daran-Lapujade, P., M. L. Jansen, J. M. Daran, W. van Gulik, J. H. de Winde, and J. T. Pronk. 2004. Role of transcriptional regulation in controlling fluxes in central carbon metabolism of Saccharomyces cerevisiae. A chemostat culture study. J. Biol. Chem. 279:9125-9138. 6. de Nobel, H., L. Lawrie, S. Brul, F. Klis, M. Davis, H. Alloush, and P. Coote. 2001. Parallel and comparative analysis of the proteome and transcriptome of sorbic acid-stressed Saccharomyces cerevisiae. Yeast 18:1413-1428. 7. Decottignies, A. and A. Goffeau. 1997. Complete inventory of the yeast ABC proteins. Nat. Genet. 15:137-145. 8. Dickinson, J. R., L. E. Salgado, and M. J. Hewlins. 2003. The catabolism of amino acids to long chain and complex alcohols in Saccharomyces cerevisiae. J. Biol. Chem. 278:8028-8034. 9. Ehrlich, F. 1907. Über die Bedingungen der Fuselölbildung und über ihren Zusammenhang mit dem Eiweissaufbau der Hefe. Ber. Dtsch. Chem. Ges. 40:1027-1047. 10. Gracin, S. and A. C. Rasmuson. 2002. Solubility of phenylacetic acid, phydroxyphenylacetic acid, p-aminophenylacetic acid, p-hydroxybenzoic acid and ibuprofen in pure solvents. J Chem Eng Data 47:1379-1383. 11. Güldener, U., S. Heck, T. Fielder, J. Beinhauer, and J. H. Hegemann. 1996. A new efficient gene disruption cassette for repeated use in budding yeast. Nucleic Acids Res. 24:2519-2524. 12. Hatzixanthis, K., M. Mollapour, I. Seymour, B. E. Bauer, G. Krapf, C. Schuller, K. Kuchler, and P. W. Piper. 2003. Moderately lipophilic carboxylate compounds are the selective inducers of the Saccharomyces cerevisiae Pdr12p ATP-binding cassette transporter. Yeast 20:575-585. 13. Holyoak, C. D., D. Bracey, P. W. Piper, K. Kuchler, and P. J. Coote. 1999. The Saccharomyces cerevisiae weak-acid-inducible ABC transporter Pdr12

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transports fluorescein and preservative anions from the cytosol by an energydependent mechanism. J. Bacteriol. 181:4644-4652. 14. Iraqui, I., S. Vissers, B. Andre, and A. Urrestarazu. 1999. Transcriptional induction by aromatic amino acids in Saccharomyces cerevisiae. Mol. Cell Biol. 19:3360-3371. 15. Kren, A., Y. M. Mamnun, B. E. Bauer, C. Schuller, H. Wolfger, K. Hatzixanthis, M. Mollapour, C. Gregori, P. Piper, and K. Kuchler. 2003. War1p, a novel transcription factor controlling weak acid stress response in yeast. Mol. Cell Biol. 23:1775-1785. 16. Large, P. J. 1986. Degradation of organic nitrogen compounds by yeasts. Yeast 2:1-34. 17. Larroy, C., M. R. Fernandez, E. Gonzalez, X. Pares, and J. A. Biosca. 2002. Characterization of the Saccharomyces cerevisiae YMR318C (ADH6) gene product as a broad specificity NADPH-dependent alcohol dehydrogenase: relevance in aldehyde reduction. Biochem. J 361:163-172. 18. Larroy, C., X. Pares, and J. A. Biosca. 2002. Characterization of a Saccharomyces cerevisiae NADP(H)-dependent alcohol dehydrogenase (ADHVII), a member of the cinnamyl alcohol dehydrogenase family. Eur. J Biochem. 269:5738-5745. 19. Locher, G., U. Hahnemann, B. Sonnleitner, and A. Fiechter. 1993. Automatic bioprocess control. 4. A prototype batch of Saccharomyces cerevisiae. J Biotechnol. 29:57-74. 20. Mewes, H. W., K. Albermann, K. Heumann, S. Liebl, and F. Pfeiffer. 1997. MIPS: a database for protein sequences, homology data and yeast genome information. Nucleic Acids Res. 25:28-30. 21. Mollapour, M. and P. W. Piper. 2001. spoilage yeast Zygosaccharomyces bailii Saccharomyces cerevisiae, but also the and benzoate, two major weak organic 42:919-930.

The ZbYME2 gene from the food confers not only YME2 functions in capacity for catabolism of sorbate acid preservatives. Mol. Microbiol.

22. Ng, R. and J. Abelson. 1980. Isolation and sequence of the gene for actin in Saccharomyces cerevisiae. Proc. Natl. Acad. Sci. U. S. A 77:3912-3916. 23. Parsons, A. B., R. L. Brost, H. Ding, Z. Li, C. Zhang, B. Sheikh, G. W. Brown, P. M. Kane, T. R. Hughes, and C. Boone. 2004. Integration of chemical-genetic and genetic interaction data links bioactive compounds to cellular target pathways. Nat. Biotechnol. 22:62-69. 24. Paulsen, I. T., M. K. Sliwinski, B. Nelissen, A. Goffeau, and M. H. Saier, Jr. 1998. Unified inventory of established and putative transporters encoded within the complete genome of Saccharomyces cerevisiae. FEBS Lett. 430:116-125. 25. Piper, M. D., P. Daran-Lapujade, C. Bro, B. Regenberg, S. Knudsen, J. Nielsen, and J. T. Pronk. 2002. Reproducibility of oligonucleotide microarray

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transcriptome analyses. An interlaboratory comparison using chemostat cultures of Saccharomyces cerevisiae. J. Biol. Chem. 277:37001-37008. 26. Piper, P., Y. Mahe, S. Thompson, R. Pandjaitan, C. Holyoak, R. Egner, M. Muhlbauer, P. Coote, and K. Kuchler. 1998. The pdr12 ABC transporter is required for the development of weak organic acid resistance in yeast. EMBO J 17:4257-4265. 27. Prusty, R., P. Grisafi, and G. R. Fink. 2004. The plant hormone indoleacetic acid induces invasive growth in Saccharomyces cerevisiae. Proc. Natl. Acad. Sci. U. S. A 101:4153-4157. 28. Schuller, C., Y. M. Mamnun, M. Mollapour, G. Krapf, M. Schuster, B. E. Bauer, P. W. Piper, and K. Kuchler. 2004. Global phenotypic analysis and transcriptional profiling defines the weak acid stress response regulon in Saccharomyces cerevisiae. Mol. Biol. Cell 15:706-720. 29. Tusher, V. G., R. Tibshirani, and G. Chu. 2001. Significance analysis of microarrays applied to the ionizing radiation response. Proc. Natl. Acad. Sci. U. S. A 98:5116-5121. 30. Verduyn, C., E. Postma, W. A. Scheffers, and J. P. van Dijken. 1990. Physiology of Saccharomyces cerevisiae in anaerobic glucose-limited chemostat cultures. J Gen. Microbiol. 136:395-403. 31. Verduyn, C., E. Postma, W. A. Scheffers, and J. P. van Dijken. 1992. Effect of benzoic acid on metabolic fluxes in yeasts: a continuous-culture study on the regulation of respiration and alcoholic fermentation. Yeast 8:501517. 32. Vuralhan, Z., M. A. Luttik, S. L. Tai, V. M. Boer, M. A. Morais, D. Schipper, M. J. Almering, P. Kotter, J. R. Dickinson, J. M. Daran, and J. T. Pronk. 2005. Physiological characterization of the ARO10-dependent, broadsubstrate-specificity 2-oxo acid decarboxylase activity of Saccharomyces cerevisiae. Appl. Environ. Microbiol. 71:3276-3284. 33. Vuralhan, Z., M. A. Morais, S. L. Tai, M. D. Piper, and J. T. Pronk. 2003. Identification and characterization of phenylpyruvate decarboxylase genes in Saccharomyces cerevisiae. Appl. Environ. Microbiol. 69:4534-4541. 34. Weast, R. C. and S. M. Selby. 1966. Handbook of chemistry and physics, The chemical rubber publishing company, Cleveland, OH. 35. Woodward, A. W. and B. Bartel. 2005. Auxin: regulation, action, and interaction. Ann. Bot. (Lond) 95:707-735.

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Physiological and transcriptional responses of Saccharomyces cerevisiae to zinc limitation in chemostat cultures

Raffaele De Nicola§, Lucie A. Hazelwood§, Erik A. F. De Hulster, Michael C. Walsh, Theo A. Knijnenburg, Marcel J.T. Reinders, Graeme M. Walker, Jack T. Pronk, Jean-Marc Daran, Pascale Daran-Lapujade §

Both authors contributed equally to this work

Applied and Environmental Microbiology, Vol. 73, No. 23, 7680 – 92

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Abstract Transcriptional responses of Saccharomyces cerevisiae to Zn availability were investigated at a fixed specific growth rate under limiting and abundant Zn concentrations in chemostat culture. To investigate the context-dependency of this transcriptional response and eliminate growth rate-dependent variations in transcription, yeast was grown under several chemostat regimes resulting in various carbon (glucose), nitrogen (ammonium), zinc and oxygen supplies. A robust set of genes that responded consistently to Zn limitation was identified and enabled the definition of the Zn-specific Zap1 regulon comprising of 26 genes and characterized by a broader ZRE consensus (MHHAACCBYNMRGGT) than so far described. Most surprising was the Zn-dependent regulation of genes involved in storage carbohydrate metabolism. Their concerted downregulation was physiologically relevant as revealed by a substantial decrease in glycogen and trehalose cellular content under Zn limitation. An unexpectedly large amount of genes were synergistically or antagonistically regulated by oxygen and Zn availability. This combinatorial regulation suggested a more prominent involvement of Zn in mitochondrial biogenesis and function than hitherto identified.

76

Yeast transcriptional response to Zn limitation

Introduction Zinc is a cofactor of many proteins and is indispensable for their catalytic activity and/or structural stability. Zn is also a ubiquitous component of enzymes involved in transcription and of the Zn finger proteins that regulate gene expression (11). In the yeast Saccharomyces cerevisiae, zinc is estimated to be required for the function of nearly 3% of the proteome (11). Besides its involvement in protein structure and function (73, 53), interaction of zinc with lipids contributes to regulation of membrane fluidity (7) and its interaction with nucleic acids helps to prevent deleterious radical reactions (5). Deficiency of this essential trace element can have severe consequences. For example, in beer fermentation, zinc depletion in wort leads to ‘sluggish’ fermentation and thus to deterioration of beer quality (34). While accurate monitoring of the zinc concentration in such industrial fermentations is important, formation of complexes with polyphenols, proteins and other compounds (41) implies that the concentration of zinc per se does not always accurately predict its bioavailability to yeast. Excess zinc is toxic. It can compete with other metal ions for the active sites of enzymes or intracellular transport proteins (26, 37, 57, 54, 65). For this reason, organisms have evolved mechanisms that tightly control intracellular zinc levels. Zinc homeostasis in yeast can be mediated via i) control of zinc uptake, ii) storage of zinc in vacuoles, iii) intracellular binding of zinc by metallothioneins and iv) efflux of zinc from the cells. In S. cerevisiae, various proteins involved in zinc uptake and storage have been identified in the last decade. Zinc uptake across the plasma membrane mainly occurs via the transporters Zrt1p and Zrt2p (83, 84). Fet4p and Pho84p, low-affinity and broad substrate range transporters of heavy metals, can also transport zinc (79). Zinc storage occurs in the vacuole and transport of zinc into this compartment is mediated by Cot1p and Zrc1p (46, 58) while release of zinc from vacuolar storages is mediated by Zrt3p (51, 52). Msc2p (46) and Yke4p (43) are implicated in transport of Zn into the lumen of the endoplasmic reticulum and perhaps an additional organelle involved in the secretory pathway. The genes encoding these transporters are transcriptionally induced by Zap1p (Zinc Activated Protein) under conditions of zinc limitation or deficiency (85). Contrary to the situation in mammalian cells, no plasma membrane transporter dedicated to zinc export from yeast cells has been identified so far (63). Two cytosolic metallothioneins (Cup1-1p and Cup1-2p) involved in copper chelation can

77

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also bind zinc (80). However, the expression of these proteins is not zincdependent, and involvement in zinc detoxification has not yet been demonstrated (80). In order to better define the Zap1p regulon, Lyons and co-workers analyzed the genome-wide transcriptional response of a S. cerevisiae Zap1 mutant strain and a control strain to zinc abundance or depletion (50). A combinatorial analysis identified a subset of 46 zinc-responsive genes whose expression was reduced in the Zap1p mutant and that possessed a Zinc-Responsive Element (ZRE, 5’-ACCYYNAAGGT-3’). Among the members of this updated defined Zap1p regulon were the wellcharacterized plasma membrane, vacuolar and endoplasmic reticulum zinc transporters. However, involvement of many of the proposed Zap1p targets in zinc homeostasis was difficult to interpret and, as suggested by the authors, may be due to contribution of factors other than zinc depletion. Indeed, these experiments were performed in shake flask in which the growth conditions cannot be strictly monitored and maintained at constant level as the pH, the dissolved oxygen and nutrient concentrations change during growth. Furthermore, zinc depletion and ZAP1 deletions are bound to reduce the specific growth rate as compared to zinc sufficient cultures of a wild-type strain. The regulation of gene expression is therefore affected not only by the difference in growth conditions but also by the specific growth rate (66). This variation in gene regulation can obscure the interpretation of the results. The goal of the present study was to investigate physiological and transcriptional responses of S. cerevisiae to zinc limitation, while minimizing the impact of secondary effects of zinc limitation. To this end, S. cerevisiae was grown at a fixed specific growth rate, oxygen availability, temperature and pH under zinc limitation in chemostat cultures. Comparing the transcriptome of zinc-limited cultures to those of carbon and nitrogen limited cultures identified sets of genes that responded uniquely to zinc limitation. Furthermore, these cultures were grown both in the presence and the complete absence of oxygen, in order to identify genes that are subjected to combinatorial control by oxygen and zinc availability.

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Yeast transcriptional response to Zn limitation

Material and methods Yeast strain and maintenance. The haploid prototrophic S. cerevisiae strain CEN.PK 113-7D (MATa) was obtained from Dr. P. Kötter, Frankfurt, Germany. Zincdepleted cells were obtained by four serial transfers of yeast cells in shake flasks containing synthetic medium (77) from which zinc was omitted, and subsequently mixed with glycerol (final concentration 20%), aliquoted and stored at -80 °C. Minimizing Zn contamination of culture vessels. To minimize zinc contamination, all glassware (including shake flasks for pre-cultivation), tubing and fermenters were subjected to an overnight soak in 2 % nitric acid, followed by two washes with deionised water, one wash with 0.1 M EDTA and four further washes with deionised water. Media for chemostat cultivation. The synthetic medium composition was based on that described by Verduyn (77). The modifications introduced for carbon, nitrogen and zinc limited growth are listed in Table 1. In all chemostats except for those limited by carbon, the residual glucose concentration was targeted to 17 g/l (95 mM) in order to have the same degree of glucose repression (Table 2). Under anaerobic glucose-limited conditions, the glucose concentration was increased to compensate for a low biomass yield. The decreased sulfate concentration (resulting from the reduced (NH4)2SO4 concentration under nitrogen limitation) was compensated by K2SO4 addition. The zinc replete cultures (carbon and nitrogenlimited) contained excess zinc concentration, but at sub-toxic levels (36). In anaerobic zinc-limited cultures, a minute zinc contamination (probably leaking from the metal fermenter parts) was enough to sustain growth. Conversely aerobic zinclimited cultures could not grow at a dilution rate of 0.10 h-1 without the addition of zinc as 0.05 µM zinc sulfate. For anaerobic cultivations, the reservoir medium was supplemented with the anaerobic growth factors Tween-80 and ergosterol (76). Table 4.1: Composition of the media used to perform carbon, nitrogen and zinc limitation under aerobic and anaerobic environment. Numbers in bold indicate the modifications introduced to the synthetic media described by Verduyn (77) in order to obtain the relevant nutrient limitations.

Aerobic Anaerobic

Limiting nutrient Carbon Nitrogen Zinc Carbon Nitrogen Zinc

Glucose g/l 7.5 59 66 25 46 58

(NH4)2SO4 g/l 5 1 5 5 1 5

K2SO4 g/l 5.3 5.3 -

ZnSO4·7H2O mg/l 4.5 4.5 0.014 4.5 4.5 0

Chemostat cultivation. Zinc-depleted pre-cultures were obtained by inoculating shake flasks that contained 100 ml zinc-free synthetic medium with zinc-depleted cells (obtained as described above). After overnight cultivation, these zinc depleted precultures were inoculated in 2-liter fermenters (Applikon) with a working volume of 1 l (74). Chemostat cultures were fed with synthetic medium (as described in the

79

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previous section) that limited growth by carbon, nitrogen or zinc with all other growth requirements in excess and at constant residual concentration (9). The dilution rate was set at 0.10 h-1. Cultures were assumed to be in steady-state when, after at least five volume changes, culture dry-weight, glucose concentration, carbon-dioxide production rate and oxygen consumption rate varied by less than 2 % during one additional volume change (21). Steady-state samples were taken after 10 generations at the latest to avoid strain adaptation due to long-term cultivation (35). Each cultivation condition was performed in triplicate. 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. The stirrer speed was set at 800 rpm. Anaerobic conditions were maintained by sparging the medium reservoir (0.05 liter.min-1) and the fermenter (0.5 liter.min-1) with pure nitrogen gas. Norprene tubing and butyl rubber septa were used to minimize oxygen diffusion into the anaerobic cultures (78). 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 Rosemont gas analyzer. Analytical methods. Culture supernatants were obtained after centrifugation of samples from the chemostats. For the purpose of glucose determination and carbon recovery, culture supernatant and media were analyzed by high performance liquid chromatography (HPLC) on an AMINEX HPX-87H ion exchange column using 5 mM H2SO4 as the mobile phase. Culture dry weights were determined via filtration as described by Postma et al. (64). Trehalose and glycogen measurements were adapted according to François et al .(22). Trehalose was determined in triplicate measurements for each chemostat. Glycogen was determined in duplicate for each chemostat. Glucose was determined using the UV-method based on Roche kit no. 0716251. Microarray analysis. Sampling of cells from chemostats and total RNA extraction was performed as previously described (1). Probe preparation and hybridization to Affymetrix Genechip® microarrays were performed following Affymetrix instructions. The one-cycle eukaryotic target labeling assay was used, starting with 15 µg of total RNA. The quality of total RNA, cDNA, cRNA and fragmented cRNA were checked using the Agilent Bioanalyzer 2100 (Agilent Technologies). Results for each growth condition were derived from three independent culture replicates. Transcriptomics data acquisition and statistical analysis. Acquisition and quantification of array images and data filtering were performed using Affymetrix GeneChip® Operating Software version 1.2. Before comparison, all arrays were globally scaled to a target value of 150 using the average signal from all gene features using GeneChip® Operating Software (GCOS), version 1.2. To eliminate insignificant variations, genes with expression values below 12 were set to 12 as previously described (9). To detect genes that exhibited differential expression in at least one of the experimental conditions, an in-house version of SAM (Significance Analysis of Microarrays) (72) was employed using the multiclass setting. Genes with a Q-value below the median FDR (false discovery rate) of 1.5·10-4 were considered differentially expressed. Transcript data can be downloaded from GEO under the

80

Yeast transcriptional response to Zn limitation

following series accession numbers: zinc-limited chemostats GSE8035; carbonlimited chemostats GSE8088 and GSE5326; nitrogen-limited chemostats GSE8089. Grouping of genes into modules. The continuous expression levels of all (1500) differentially expressed genes were discretized, as described in Knijnenburg et al. (39). Resultantly, each gene is represented by a discretized expression pattern of length six, indicating whether the gene is not differentially expressed (0), upregulated (1) or down-regulated (-1) under each of the six cultivation conditions. For example, a gene that has the following discretized expression pattern: C-Ana, 0; NAna, 1; Zn-Ana, 0; C-Aer, 0; N-Aer, 0; Zn-Aer, -1, where ‘Ana’ means anaerobic and ‘Aer’ means aerobic, is up-regulated when grown anaerobically under a nitrogen limitation (N-Ana) and down-regulated when grown aerobically under a zinc limitation (Zn-Aer), while the four other conditions do not exhibit differential expression. Genes are grouped into modules based on this discretized representation by imposing certain constraints on the discretized expression pattern of a gene in order for it to be part of a particular module. For example, a module could be formed by grouping all genes that have a higher discretized expression level under the zinc limitation, when compared to the other two limitations, both for aerobic and anaerobic growth. This approach provides a coherent and meaningful way to create modules of genes, since the expression behavior of the genes in a module is directly related to the cultivation conditions, allowing for a straightforward interpretation. In our study, six modules were created. The exact constraints on the discretized expression pattern of a gene to be included in one of the six modules are found in the Appendix. Table 3 gives a short verbal description for each of the modules. Hypergeometric tests. The six modules were consulted for enrichment in functional annotation and significant transcription factor (TF) binding. To test for significant relations the hypergeometric test was employed. In the case of the TF binding data, the largest available TF binding dataset for yeast in its most conservative setting (highest binding confidence) was used (29). This dataset, which originally indicates the number of binding sites for each of 102 TFs in the promoter region of each gene, was binarized, such that the data indicates whether a TF can bind a gene (upstream) or not. Then, the hypergeometric test assesses if a TF (or a TF pair) can bind the promoter region of the genes in a module much more frequently than in a randomly selected set of genes. In case of the employed gene annotation information (MIPS (56) and KEGG (38)) it assesses if the number of genes in a module that belongs to a particular functional category is much larger than would be expected by chance. The P-value cut-off to decide whether a relation is significant is P ≤ 1/(ncnx), where nc is the number of modules and nx is the number of TFs (or TF pairs) or the number of MIPS or KEGG annotation categories. This adjustment for multiple testing, corresponds with a per comparison error rate (PCER) of one (24). Motif discovery. The promoters (from -800 to -1) of the genes in each module were analyzed for over-represented regulatory motifs using the web-based software MEME (2). The p-value cut-off to consider a motif significant was 10-4. Other

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parameter settings included a motif width from 6 to 15 nucleotides, that could be repeated any number of times. Comparison with the transcriptome study from Lyons and co-workers. The data from the Lyons and co-workers (50) were downloaded from http://genomewww.stanford.edu/zinc/rawdata.html. As this website only provides raw data, the array data were processed following the instructions described in their publication and 496 genes that were up-regulated in response to zinc depletion were thus isolated. The slightly larger size of this gene set compared to the one isolated by Lyons et al. (458 genes) probably results from a few differences in data handling.

Results Establishing Zn-limited chemostat cultures of S. cerevisiae While macronutrient limitation in chemostats can be achieved in a straightforward manner, establishing micronutrient limitation still presents an experimental challenge. This holds especially for metals (Zn, Fe, Cu) that are present in laboratory equipment and that can sustain growth at extremely low concentrations (typically in the µM range). Despite thorough and repeated washing steps and use of high-grade medium components, we did not achieve completely Zn-free cultivation conditions, presumably due to Zn leakage from the metal parts (fermenter lid, pipes and connections). This contamination was sufficient to allow for anaerobic Znlimited growth at a steady-state biomass concentration of 2.5 g.l-1. However, 0.05 μM ZnSO4 had to be added to the Zn-deficient medium to enable aerobic Zn-limited growth (steady-state biomass concentration 4.2 g.l-1). Addition of 15 µM Zn to anaerobic and aerobic Zn-limited cultures resulted in a large increase of the biomass concentration, thus confirming that growth was solely limited by Zn availability (data not shown). The Zn content of biomass from Zn-limited cultures was up to five-fold lower than that of carbon- and nitrogen-limited cultures (Table 2). Consistent with a higher Zn requirement for aerobic cultivation, the Zn content of biomass from aerobic Zn-limited cultures was two-fold higher than that of anaerobic Zn-limited cultures (Table 2). Since genes encoding Zn transporters were not differentially transcribed in the presence and absence of oxygen, this difference is unlikely to be due to a different affinity for Zn uptake.

82

ANAEROBIC

AEROBIC

0.49 ± 0.00

2.36 ± 0.4

0.9 ± 0.2 2.1 ± 0.4 2.74 ± 1.3 0.52 ± 0.03

102.4 ± 6.4 BD 100.8 ± 8.6 110.4 ± 3.9

Zn C N Zn

0.07 ± 0.00

0.07 ± 0.00

0.10 ± 0.00 0.09 ± 0.00

2.74 ± 1.3

0.09 ± 0.00

YSXc

Zn in biomassb

92.7 ± 5.5

BD

Residual glucose (mM)

N

C

Growth condition

- 8.4 ± 0.0

- 8.4 ± 0.0

- 6.0 ± 0.0

- 5.3 ± 0.0

- 5.8 ± 0.1

- 1.1 ± 0.0

qglucose

13.7 ± 0.2

13.5 ± 0.6

9.6 ± 0.1

8.1 ± 0.2

8.0 ± 0.1

0.0 ± 0.0

qEthanol

1.09 ± 0.01

0.76 ± 0.04

0.81 ± 0.06

0.08 ± 0.01

0.08 ± 0.01

BD

qGlycerol

0.16 ± 0.02

0.06 ± 0.05

0.01 ± 0.00

0.07 ± 0.01

0.03 ± 0.01

BD

qAcetate

qO2

NA

NA

NA

- 2.8 ± 0.0

- 2.7 ± 0.1

- 2.8 ± 0.3

Specific consumption and production ratesd

15.5 ± 0.5

14.8 ± 0.3

10.3 ± 0.4

12.3 ± 0.2

12.1 ± 0.2

2.8 ± 0.3

qCO2

NA

NA

NA

4.5 ± 0.1

4.5 ± 0.2

1.0± 0.0

RQe

100 ± 1

101 ± 2

101 ± 2

105 ± 2

96 ± 1

98 ± 3

Carbon recovery %

The dilution rate for the cultures was 0.10 h-1. Values are means standard deviations. NA: not applicable; ND: not determined; BD: below detection b Zn in biomass is expressed as g dry weight · g glucose-1 c Specific consumption rates (expressed as mmol·g dry weight-1·h-1) of glucose and oxygen and specific production rates of ethanol, glycerol, acetate and carbon dioxide d The respiratory quotient (RQ) was qCO2/qO2

a

Table 4.2: Physiological characteristics of CEN.PK113-7D grown in aerobic and anaerobic carbon-, nitrogen-, or zinclimited chemostat culturesa

Yeast transcriptional response to Zn limitation

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Chapter 4

Physiology of Zn, glucose- and ammonia-limited chemostat cultures Zn-limited cultures were grown at a high residual glucose concentration. Comparison of their physiology and transcriptome with those of glucoselimited cultures will therefore also identify changes caused by the different glucose concentrations in the cultures. Therefore, nitrogen-limited cultures, grown at the same residual glucose concentration as the Zn-limited cultures, were included as an additional reference situation. The combination of three nutrient limitations under aerobic and anaerobic conditions resulted in six unique physiological situations (Table 2). Only in the carbon-limited aerobic cultures, a completely respiratory sugar metabolism was observed, resulting in a high biomass yield on glucose (Table 2). In the anaerobic cultures, glucose metabolism was fully fermentative, the main products of glucose dissimilation being ethanol and carbon dioxide. Finally, in glucose-sufficient (i.e. N- or Zn-limited) aerobic cultures, a mixed respiro-fermentative metabolism was observed. The Znlimited cultures strongly resembled the nitrogen-limited cultures with respect to biomass yields and rates of product formation. Even under anaerobic conditions, the biomass yield on glucose of these glucose sufficient cultures was lower than that of glucose-limited cultures, indicating a partial uncoupling of dissimilation and biomass formation under these ‘energy excess’ conditions. The only notable difference was a slightly higher specific rate of acetate and glycerol production in the Zn-limited cultures, which may be related to a reduced in vivo activity of Zn-dependent alcohol dehydrogenases.

84

Yeast transcriptional response to Zn limitation

Table 4.3: Clustering of the zinc-responsive genes a

Expression pattern of genes of each module along with their averaged expression and standard deviation. Here, the expression levels of each gene are normalized to have zero mean and unit variance. (Y axis: normalized expression, X axis: culture condition, from left to right: anaerobic carbon, nitrogen, zinc limitation, and aerobic counterparts). b Each data-set was analyzed individually for enrichment of TF binding and functional categories as described in the Material and Methods. TCA, tricarboxylic acid.

Module (number of genes)

1 (93)

Number in Description

Genes upregulated under the zinc limitation regardless of aeration.

2 (40)

Genes downregulated under the zinc limitation regardless of aeration.

3 (77)

Genes upregulated only under anaerobic zinc limitation

4 (36)

Genes downregulated only under anaerobic zinc limitation

5 (119)

6 (16)

Expression patterna

Over-represented categoriesb

p-value

genome

module

TF: Zap1p MIPS: heavy metal ion transport (Cu, Fe, etc.)

8 52

6 7

2.2.10-10 8.7.10-6

TF: Msn2 MIPS: C-compound and carbohydrate metabolism Metabolism of energy reserves (e.g. glycogen, trehalose) Metabolism of Leu and Val KEGG: Val, Leu and Ile biosynthesis Panthotenate and CoA biosynthesis

65 508 53 7 16 10

11 3 5 3 3 3

6.8.10-4 2.0.10-4 1.7.10-5 7.8.10-6 1.2.10-4 2.7.10-5

TF: Hsf1 Skn7 MIPS: Homeostasis of cations

133 156 162

7 8 9

1.0.10-3 5.4.10-4 1.3.10-4

MIPS: proteasomal degradation (ubiquitin/proteasamol pathway) KEGG: TCA cycle Proteasome

191 30 34

7 3 4

7.7.10-5 6.0.10-4 3.5.10-5

TF: Yap7

158

12

3.4.10-5

MIPS: METABOLISM

1526

11

1.8.10-4

Genes upregulated only under aerobic zinc limitation

Genes downregulated only under aerobic zinc limitation

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Chapter 4

Overall transcriptional responses to Zn limitation For all six culture conditions described above, microarray analysis was performed on three independent replicate cultures. Statistical analysis (see Material and Methods section) identified 1500 genes that were differentially transcribed in at least one cultivation condition. 381 of these genes responded specifically to Zn-limited growth. Of these Zn-responsive genes, 81 proteins do not yet have an assigned cellular function. The 381 Znresponsive genes were subjected to a further analysis to identify combinatorial effects of Zn and oxygen availability (Table 3). A majority of the genes that showed a transcriptional response to Zn-limitation (248 genes, Modules 3-6 in Table 3) did so in an oxygen-dependent manner. The remainder (133 genes, Modules 1-2 in Table 3) of the Zn-responsive genes showed a consistent response to Zn limitation that was independent of oxygen availability. The identity and transcript levels of the genes contained in the six modules are available in Supplementary Material 1 (http://aem.asm.org/cgi/content/full/73/23/7680/DC1). Below, we will analyze these sets of Zn-responsive genes for over-representation of genes involved in specific functional categories and/or controlled by specific transcription factors (see Material and Methods section).

Zinc homeostasis and the Zap1p regulon The MIPS functional category ‘heavy metal transport’ was over-represented among the 93 genes that were transcriptionally up-regulated in response to Zn limitation irrespective of oxygen availability (Table 3, Module 1). Of the seven genes belonging to this category found in Module 1, six are directly involved in Zn homeostasis. ZRT1, encoding the plasma-membrane highaffinity Zn transporter, was strongly induced (average fold-change of 13, Table 4). Transcript levels of ZRT2, ZRT3, ZRC1 and ZRG17, involved in Zn transport and homeostasis, were also increased but to a lesser extent than those of ZRT1 (fold-changes ranging from two to seven). FET4 (upregulated 3 to 43 fold under Zn limitation) encodes a protein involved in iron transport that has been demonstrated to also be a physiologically relevant Zn carrier (79). The comparison of aerobic and anaerobic cultures confirmed the previously described combinatorial regulation of FET4 by Zn and oxygen availability (79). In addition, a clear hierarchy was observed: while FET4 was strongly regulated by oxygen availability under Zn sufficient conditions (79), its transcript level in Zn-limited cultures was consistently

86

Yeast transcriptional response to Zn limitation

high regardless of oxygen supply (Fig. 2). The transcriptional regulation of these six genes was in agreement with previous studies (30, 50), and so was the up-regulation of ZAP1, the transcriptional activator of these six transporters (8 to 20 fold increase relative to Zn-sufficient cultures). FRE1, which also belongs to the ‘heavy metal transport’ category, encodes a protein specifically involved in ferric iron transport (25, 82). FRE1 does not contain a ZRE and its increased transcript levels under Zn-limited conditions suggest an indirect effect.

Figure 4.1: Consensus ZRE sequence identified by MEME using Module 1 as input (color version at end of book)

Previous reports have investigated the role of MSC2 in Zn transport into the endoplasmic reticulum (19, 46) and have found that mutations in the latter affect the cellular distribution of zinc (46). In our study, MSC2 was not found among the genes that were transcriptionally induced under Zn limitation. Instead, its transcript levels remained low under the conditions tested. Consistent with this observation, transcription of MSC2 was not affected in a zap1 mutant (50). ZRG17 encodes a protein that has been proposed to act as a complex with Msc2p (18, 46). The promoter of ZRG17 does contain a ZRE and its transcript levels were increased in Zn-limited cultures, suggesting that this protein could be the regulatory subunit of the complex. In an attempt to further define the Zap1p regulon, the promoter regions of the 93 genes that showed a robust, oxygen-independent response to Zn limitation (Module 1, Table 3) were searched for overrepresented motifs. The web-based software MEME (2), which enables unbiased probability-based motif discovery, identified 26 genes with a 15nucleotide motif that strongly resembled the previously published ZRE Zap1p-binding consensus sequence (Figure 1, Table 4). In agreement with previous reports on Zap1p regulation, all six Zn transporters in Module 1, as

87

Chapter 4

well as ZAP1 itself, harbored this element. Twelve additional genes (Table 4) have been previously proven or proposed to be Zap1p targets. An additional 7 genes that harbored the 15-nucleotide motif had not previously been implicated as Zap1p targets (50) (Table 4). The detailed ZRE sequences and positions are listed in Supplementary Material 2 (http://aem.asm.org/cgi/content/full/73/23/7680/DC1). Table 4.4: Identity and expression levels of the genes from module 1 consistently upregulated in response to zinc limitation and containing ZRE sequencesa. a

Genes indicated in bold were also part of the ZRE regulon defined by Lyons and co-workers (50) Transcript level

Gene

Description

ZAP1

Zn-responsive TF

Number of ZREs 1

Anaerobic

Aerobic

C

N

Zn

C

N

Zn

23.2

41.5

479.9

33.8

43.5

356.6 1867.5

ZRT1

High affinity Zn transporter

3

175.6

286.0

2004.1

68.3

240.6

ZRT2

Low affinity Zn transporter

2

108.6

124.0

739.4

102.8

162.8

551.5

ZRT3

Vacuolar Zn efflux

1

236.9

257.2

1665.5

313.4

282.9

1708.5

ZRC1

Vacuolar Zn influx

1

237.8

367.3

712.2

261.3

337.8

811.6

ZRG17

Putative Zn transporter

1

84.7

91.9

446.3

142.6

97.9

527.4

FET4

Low affinity Fe transporter

1

235.0

223.8

743.3

12

89.9

443.5

ADH4

Alcohol dehydrogenase

3

153.3

206.3

2889.8

76.6

118.1

2872.1

HOR2

Glycerol-P phosphatase

1

45.1

63.3

141.3

97.3

84.9

198.5

DPP1

DAGPP phosphatase

1

307.3

517.8

1019.4

294.4

636.8

1233.3

URA10

Pyrimidine biosynthesis

1

18.7

25.1

81.3

30.4

16.7

50.9

FLO11

Cell surface flocculin

1

1150.8

1293.3

1935.4

42

60.2

2105.0

ZPS1

Cell surface mannoprotein

2

74.7

225.8

3385.4

139.5

119

3349.2

MNT2

Mannosyl transferase

3

18.1

12

51.6

12

12

40.9

KTR6

Mannosyl transferase

1

449.7

388.4

537.8

314.0

253.5

620.4

MCD4

Transferase required for GPI anchor synthesis

1

337.0

323.3

996.5

232.2

279.2

1190.3

ZIP1

Synaptonemal complex

1

12

12

46.2

12.5

12

24.6

KTI12

tRNA modification

1

157.9

134.1

218.4

120.3

121.5

278.3

VTC3

Vacuolar transporter chaperone

1

66.4

95.7

175.0

51.1

85.8

272.8

TEX1

TREX complex

1

29.4

29.9

91.5

25.1

24.4

177.9

MUP1

Methionine transporter

1

69.6

38.7

636.5

128.8

211.9

912.5

YNL254C

Unknown

1

22.2

27.5

342.0

17.4

32.3

354.6

YER130C

Unknown

1

32.2

34.5

179.1

30.7

28.1

66.8

ICY2

Unknown

2

290.5

204.4

1939.2

568.3

143.8

1436.1

VEL1

Unknown – similar to YOR387C

3

12

12

1047.3

12

12

858.6

YOR387C

Unknown – similar to VEL1

3

12

12

2803.0

12

12

2612.0

88

Yeast transcriptional response to Zn limitation

Comparison with previous Zn-related transcriptome studies Two previous transcriptome studies investigated yeast adaptation to Zn depletion in batch cultures of an industrial (30) and a laboratory strain of S. cerevisiae (50). Using maltose-grown cultures, Higgins et al. observed a down-regulation of maltose-permease and maltase genes (MAL12, MAL32 and MAL31) in Zn-depleted cultures. In the present study, growth on glucose resulted in the absence of MAL gene transcripts, thus masking transcriptional responses of these genes to Zn availability. Lyons et al. identified a Zap1p regulon consisting of 46 genes by comparing the transcriptional responses to Zn depletion of a zap1Δ mutant and its parental strain. Three of these 46 genes (COS2, COS4 and COS6) were not represented on the microarrays used in our study. Of the remaining 43 genes, 25 showed increased transcript levels in Zn-limited chemostat cultures (Fig. 2A). The large majority of these (21 genes) were consistently induced in response to Zn limitation irrespective of oxygen availability (Module 1, Table 3; Fig. 2A). MEME failed to identify a ZRE sequence in 3 of these 21 genes (RAD27, YJL132W and YOL131W), which are therefore absent from Table 4. Four genes from the Zap1p regulon defined by Lyons and co-workers (IZH1, IZH2, NRG2 and PST1) were found in Module 3 (Table 3), indicating that their transcription was induced under Zn limitation but only when oxygen was absent. Their identification by Lyons et al. may have been caused by the poor oxygen transfer characteristics of shake flask cultures (68, 28, 55). Two additional genes (ADE17 and GPG1) identified as Zap1p-targets by Lyons et al. were up-regulated in Zn-limited chemostat cultures, however their expression resulted from an intricate regulation by Zn, glucose and oxygen availability. Both genes responded to zinc-limitation under aerobic and anaerobic conditions. However, they also responded to limiting glucose supply but this response was oxygen specific; while ADE17 was up-regulated under glucose-limitation in the presence of oxygen, GPG1 expression increased under glucose limited anaerobic growth. The remaining 16 of the 43 genes identified as Zap1p targets by Lyons et al. and included on our microarrays did not respond to Zn availability in our chemostat study.

89

Chapter 4 A (Zap1 regulon)

B (genome-wide comparison) Lyons et al.

Lyons et al.

Modules 1, 3 & 5

21 420 Module 3

4

21

0

73

Module 1

0 73

72 ADH4, DPP1, FET4, ICY2, MCD4, MNT2, RAD27, URA10, YGL258W, YJL132W, YNL254C, YOL131W, YOR387C, ZAP1, ZIP1, ZPS1, ZRC1, ZRG17, ZRT1, ZRT2, ZRT3

IZH1, IZH2, PST1, NRG2

2.5 Normalized expression level

2.5 1.5 0.5 -0.5 -1.5

1.5 0.5 -0.5 -1.5

Zn -a e

N -a e

Cae

na e Zn -A

na e

-A na e N

Zn -a e

N -a e

CA

N

Cae

-2.5

-A na e Zn -A na e

-2.5

CA na e

Normalized expression level

Module 1: 36 genes Module 3: 26 genes Module 5: 11 genes

Figure 4.2: Venn diagram of chemostat based transcriptome data in comparison with data obtained by Lyons and co-workers. A: Zap1p-regulon (Modules 1 and 3 in comparison with 46 genes from Lyons and coworkers). B: genome-wide comparison (Modules 1, 3 and 5 in comparison with all upregulated genes from Lyons and co-workers).

Eight potential Zap1p-targets identified in the present study (Table 4) were not found in the study of Lyons et al.. However, of these 8 genes, HOR2 and TEX1 were found to be transcriptionally induced by Zn depletion in their study. Furthermore, Zap1p was shown to bind TEX1 on ChIP on chip experiments (29). Seven genes (HOR2, FLO11, KTR6, KTI12, VTC3, MUP1 and YER130C; Table 4) are here for the first time proposed to be Zap1p targets. HOR2 encodes a glycerol-3-phosphate phosphatase involved in glycerol biosynthesis (62), which may account for the slightly, but significantly (Student’s t-Test, p-value < 0.05) elevated glycerol production observed under zinc-limited growth. VTC3 encodes a vacuolar transport chaperone involved in inorganic ion transport (13). Although it has been shown to be involved in polyphosphate transport, it may also participate in vacuolar Zn transport (61). Alternatively, Zn may be involved in polyphosphate accumulation or react with polyphosphates. Like the previously identified Zap1p target MNT2 (50) (Table 4), KTR6 encodes a mannosyl transferase involved in glycosylation of cell wall proteins (49). It

90

214

Yeast transcriptional response to Zn limitation

can be speculated that they play a role in mannosylation of Zn-scavenging cell wall proteins. For instance ZPS1, a Zap1p target also up-regulated under Zn limitation (Table 4), encodes a cell wall mannoprotein with high similarity to Zn metalloproteinases from filamentous fungi (44, 50). The yeast cell wall, and more specifically mannoproteins, has been shown to fix a substantial fraction of the cellular zinc (15). Zinc fixation by mannoproteins may represent an efficient mechanism to scavenge low zinc concentrations (59). The up-regulation of mannoproteins such as ZPS1 under zinc limitation would support this zinc scavenging function of the cell wall. The consistent up-regulation of FLO11, KTI12, MUP1 and YER130C in Zn-limited cultures and the presence of a ZRE-like motif in their promoters suggest that the encoded proteins have some as yet unknown role under Zn-limited conditions, too. For example, Flo11p is known to play an essential role in biofilm formation, filamentation and invasive growth (47). In addition, studies on Candida albicans have demonstrated that dimorphic switching from budding growth to mycelium formation is regulated by zinc (69, 4). However, in the present study, we did not observe any difference in morphology between the different culture conditions. When the 289 genes in Modules 1, 3 and 5 that were induced under Zn limitation in chemostat cultures, either in an oxygen-dependent or in an oxygen-independent manner, were compared to the 493 genes that were induced upon Zn depletion in shake flasks (50), 73 genes overlapped between the two studies. These were for the most part clustered in Modules 1 and 3 (Fig. 2B, Supplementary Material 3, http://aem.asm.org/cgi/content/full/73/23/7680/DC1). Only a small overlap was observed with Module 5 (representing only 8% of the genes in this module), which includes genes that are only induced by Zn limitation under aerobic conditions. As mentioned above, this small overlap may reflect a limiting oxygen supply in the shake flask studies. Transcriptional regulation of structural genes for zinc-dependent proteins S. cerevisiae contains multiple alcohol dehydrogenases. While the enzymes encoded by ADH1, 2, 3 and 5 all require Zn as a cofactor, Adh4p uses Mg. ADH4 has been shown to be regulated by Zap1p, while expression of the Zn-requiring isoenzymes has been reported to be decreased upon Zn depletion (presumably via Rap1p) (8). In agreement with earlier findings, ADH4 was strongly up-regulated in response to Zn limitation irrespective of

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the aeration conditions. Transcript levels of other, Zn-dependent alcohol dehydrogenase genes were either unchanged or reduced. In addition to alcohol dehydrogenases, many other yeast proteins use Zn as structural component or cofactor. Regalla and Lyons (54, 65) separated the Zn dependent protein in two distinct classes, i) the proteins that use zinc in a catalytic capacity (105 genes) and ii) the proteins with a structural Zn binding domain (360 genes). Of 105 S. cerevisiae proteins that use Zn as a cofactor (54, 65), none of the structural genes were found to be transcriptionally regulated in response to Zn availability in chemostat cultures (with the clear exception of alcohol dehydrogenases). On the other hand, out of the 360 S. cerevisiae proteins that contain a structural Zn binding domain, 16 genes were up-regulated in response to Zn limitation (Modules 1, 2 and 5) while 7 were down-regulated (Modules 2, 4 and 6). Most of these Zn-responsive genes encoded proteins that have a function in nucleic acid binding (transcription factors, chromatin reorganizing activity, mRNA binding). The two homologous transcription factors Met31p and Met32p that induce the expression of genes involved in methionine biosynthesis were only affected by Zn availability in the presence of oxygen. While MET32 expression increased two-fold, MET31 expression decreased two-fold. These changes in gene expression probably resulted in modifications of the transcriptional regulation of these transcriptional activators as their target genes displayed a slightly higher expression under conditions of aerobic Zn limitation. This antagonistic regulation of MET31 and MET32 remains difficult to relate to Zn supply as both proteins contain two Zn finger domains and do not have a different Zn content. In agreement with previous reports (81), SOD1, which encodes the cytosolic Zn-Cu superoxide dismutase, showed a two fold reduction of its transcript level under conditions of low Zn supply. However, SOD2, which encodes mitochondrial manganese-containing superoxide dismutase, did not show an increased transcript level in Zn-limited cultures. In fact, SOD2, which was only transcribed in aerobic cultures, was also down-regulated by ca. two-fold under Zn limitation. As proposed previously (81), reduced expression of superoxide dismutase may affect resistance to oxidative stress. A more direct involvement of zinc in oxidative stress resistance was previously suggested via the transcriptional regulation of TSA1, encoding a Zn-dependent peroxiredoxin, by Zap1p (81). Unfortunately, in our experiments TSA1 expression was independent of zinc and oxygen availability. This difference with earlier work may be attributed either to the

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difference between complete Zn depletion (81) and Zn-limited growth (this study) or to a different strain background. However, close scrutiny of the transcript levels revealed no oxidative stress response (AAD3, AAD6, AAD10, AAD14, AAD15, ATR1, CCP1, GTT2, GRE2, LYS20, OYE2, OYE3, TRR1, TRX2, YDR453C, YLR460C, YNL134C, YMR318C and YML131W) (40). Although the transcript levels of both SOD1 and SOD2 were reduced, their levels (748 and 295 respectively under aerobic zinclimitation) may still be high enough to enable efficient processing of ROS and thereby to prevent oxidative stress. Finally, while we cannot exclude the possibility that Zn sparing and/or mobilization mechanisms occurs at (a) post-transcriptional level(s), these results indicate that a general ‘Zn sparing’ regulation at the transcriptional level is most probably absent in S. cerevisiae. The exceptions of alcohol dehydrogenase and superoxide dismutase may be related to the relative abundance of these proteins and their pivotal role in fermentative and respiratory metabolism, respectively. Combinatorial response of mitochondrial function to oxygen and zinc availability Aerobic Zn-limitation of S. cerevisiae resulted in the up-regulation of 119 genes and the down-regulation of 16 genes (Table 3, Modules 5 and 6). However, hypergeometric distribution analysis did not reveal clear trends in the identity and function of these oxygen-responsive proteins. In order to better investigate the potential synergetic effects between oxygen and zinc availability, different discretized patterns were considered. As described in Fig. 3 for the aerobically up-regulated genes, the applied constraints selected genes for which the expression under carbon limitation was unaffected by oxygen, the expression under nitrogen limitation was also oxygen-insensitive, but for which the response to zinc limitation was oxygen-dependent. 196 genes respecting these constraints were identified, 130 being up-regulated in the presence of oxygen in a Zn-dependent manner and 66 down-regulated (given in Supplementary Material 4 http://aem.asm.org/cgi/content/full/73/23/7680/DC1). Fisher’s exact statistics was then applied to search for over-representation of genes involved in specific functional categories and/or controlled by specific transcription factors. While no enrichment was found within the genes that were down-regulated, the module containing the up-regulated showed interesting trends. This module was characterized by enrichment for two functional categories: ‘respiration’ (10 genes) and ‘mitochondrial biogenesis’

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0.5

-0.5

- li

C-

m

-1.5 Zn

Zn-Ae > Zn-Ana C-Ae =C-Ana N-Ae = N-Ana

1.5

lim

Zinc-dependent oxygen response (130 genes)

2.5

lim

Module 5 (119 genes) Aerobically up-regulated

Zn-Ae > C-Ana Zn-Ae > N-Ana Zn-Ae > Zn-Ana Zn-Ae > C-Ae Zn-Ae > N-Ae

N-

Constraints

Difference in normalised expression between aerobic and anaerobic cultures

(14 genes). The category of ‘respiration’ comprised genes encoding various subunits of the Fo (ATP4, ATP14, ATP18 and ATP20) and F1 (ATP3 and ATP15) domains of mitochondrial ATP synthase (16) but also COX23, COX14, MAM33 and MBA1 involved in the assembly of respiratory complexes in mitochondria (3, 27, 60, 67). The relation between Zn availability and these proteins remains unclear, although cytochrome c oxydase activity has been shown to be inhibited by Zn. Most of the genes in the ‘mitochondrial biogenesis’ category encoded mitochondrial ribosomal proteins (MRPL10, MRPL11, MRPL37, MNP1, RSM19 and MRPS16), but also MSS116, a gene involved in the splicing of mitochondrial group I and II introns (33). Finally, also TIM10/MRS11 responded synergistically to Zn and oxygen availability. TIM10 encodes a protein involved in the translocation of mitochondrial proteins from the cytoplasm to the mitochondria. For instance Aac1p and Aac2p, encoding ADP/ATP mitochondrial carrier cannot be translocated in a tim10 mutant (75). This translocation process, also identified in plant (6), requires Zn (48). The present study reveals a more important role for Zn in mitochondrial function and biogenesis than so far assumed. Although still not clearly understood this role could, at least in part, explain the higher Zn requirement for cells grown in the presence of oxygen, condition where mitochondria are essential for respiration.

Nutrient limitation

Figure 4.3: Combinatorial regulation of gene expression by Zn and oxygen availability: constraints for the selection of the discretized patterns and average expression profile of these selected patterns. Only the oxygen-induced genes are represented. Error bars indicate standard deviations. Lim, limited.

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Zn limitation and storage carbohydrate metabolism Genes from both glycogen biosynthesis (GSY2, GAC1, GLC3) and degradation pathways (GDB1, GPH1) were down-regulated by up to 22-fold in Zn-limited chemostat cultures, regardless of oxygen availability (Fig. 4A). Several additional genes involved in glycogen metabolism that did not pass the very stringent statistical test used in genome-wide analysis, displayed a decreased expression upon closer inspection (Fig. 4A). To investigate whether these transcriptional modifications resulted in phenotypic differences, glycogen contents were analyzed in the chemostat cultures on which the transcriptome analyses had been performed (Fig. 4B). Indeed, glycogen accumulation was strongly (10 to 20-fold) reduced in Zn-limited cultures. Genes involved in glycogen metabolism are known to be transcriptionally regulated in response to a wide variety of environmental conditions and signaling pathways (20) (temperature, nutrient supply, oxidative stress). This regulation is mediated by the general environmental stress response (ESR) and HOG pathways (22, 23). However, no other target genes of these signaling pathways were found to be differentially transcribed in response to Zn limitation. This suggests that the regulation of glycogen metabolism by Zn occurs via another, hitherto unknown signal transduction mechanism. Several genes involved in trehalose metabolism were also significantly down-regulated in Zn limited cultures (PGM1, PGM2, TPS1, TPS2 and TPS3, see Fig. 4A). These down-regulations coincided with substantially lower trehalose biomass contents, an effect that was most pronounced in aerobic cultures (Fig. 4B). These results clearly demonstrated, for the first time the impact of Zn availability on reserve carbohydrate accumulation. As the genes involved in glycogen and trehalose metabolism do not contain ZREs, their transcriptional regulation is unlikely to be directly mediated by Zap1p. In addition, their down-regulation probably occurs via a STRE-independent mechanism (we did not find overrepresented STRE in the promoters of down-regulated genes). Among the above-mentioned Zap1p regulon, YER130C, encoding a protein of unknown function containing two tandem Zn-finger domains, was upregulated under zinc limitation. This putative transcription factor may be involved in a Zap1p-dependent regulation of genes involved in trehalose and glycogen and is an interesting candidate for further functional analysis.

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A. HXK1 HXK2 GLK1

Glucose

UTP

ATP

Glucose-1-P -1-

UGP1

ADP

Glucose-6-P -6-P

PGM1 PGM2

Glucose-6- -P

PPi

UDP-Glucose -Glucose

(Glycogen)n-1

Legend: fold changes UDP

+1.5 to 2.0 -1.5 to +1.5 -1.5 to -3 -3 to -6 < -6

TPS1 TPS3 TSL1

Trehalose-6-P - -

GLG1 GLG2 GLC3 GSY1 GSY2 GAC1

TPS2

Trehalose NTH1 NTH2 ATH1

((Glycogen)n

GPH1

GDB1

Glucose-1-P

Glucose

B. Expression profile of genes involved in glycogen metabolism

Expression profile of genes involved in trehalose metabolism

-2

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PGM2 PGM1 TPS1 TPS3 ATH1 UGP1 TSL1 NTH1 NTH2 Trehalose

Normalized expression

30

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35

Trehalose (mgequiv glucose .gDW-1)

40 1

2

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45

Glycogen (mgequiv glucose.gDW-1

2

Figure 4.4: A: Glycogen and trehalose metabolism in S. cerevisiae. Genes indicated in green are clustered in Module 2 (color version at end of book). The four boxes indicate the following fold-changes from left to right: zinc vs carbon anaerobic, zinc vs nitrogen anaerobic, zinc vs carbon aerobic, zinc vs nitrogen aerobic. Intensities of fold changes are indicated by the colour map in the legend. B: normalized expression profile of genes involved in glycogen and trehalose metabolism and intracellular glycogen and trehalose concentrations (color version at end of book).

Discussion Analysis of Zn limitation in chemostat cultures The unique option of chemostat cultures to control specific growth rate prevented occurrence of specific-growth-rate-related responses. For example, in a previous study in batch cultures of S. cerevisiae (30), the observed down-regulation of ribosomal proteins in low-Zn cultures is likely to have been caused by a decrease in specific growth rate rather than directly by Zn depletion. The use of different aeration regimes showed that yeast responses to Zn limitation are strongly context dependent. This notwithstanding, a set of genes was identified whose specific transcriptional regulation by Zn availability was independent of the oxygen supply. This enabled us to propose a more precise definition of the Zap1p regulon. Most of these 26

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Yeast transcriptional response to Zn limitation

potential Zap1p targets overlapped with those proposed in a previous batch-cultivation study (50). The present study demonstrated that responses of several of the previously identified putative Zap1p targets were not Zn-specific. Instead, they were synergistically or antagonistically regulated by carbon, nitrogen and/or oxygen supply. As compared to the transcriptional responses observed in chemostat cultures under other nutrient limitations (9, 10, 14, 70), transcriptional responses to Zn limitation were strikingly pleiotropic. Genes involved in a large variety of cellular functions, apparently unrelated to Zn availability, showed marked differences to Zn limitation. Statistical analysis of co-regulated genes identified only a very limited number of overrepresented functional categories or DNA binding proteins, with the clear exception of the Zap1p regulon. These observations suggest that the only direct effect of Zn limitation on transcriptional regulation is mediated by Zap1p. Although no concerted transcriptional regulation was observed for genes encoding proteins that contain Zn as a catalytic or structural component, Zn availability is likely to influence the in vivo activity of such proteins, many of which are transcription factors. The apparently ‘scattered’ transcriptional responses to Zn limitation may further be due to the fact that new roles of Zn in yeast physiology continue to be discovered. For instance, the involvement of Zn in protein translocation by the Tim10p/Tim9p complex has only been recently revealed (48).

Effects of Zn limitation on storage carbohydrate accumulation: a possible cause for stuck fermentations in beer fermentation? Zn used by yeast during the beer fermentation process comes from barley malt and is extracted during the mashing procedure (starch conversion and extraction). However, Zn content varies largely between fermentations as its concentration is dependent on the crop quality (32) and is partly removed from wort during lautering (41) or wort separation. Insufficient Zn supply during brewing results in ‘sluggish’ fermentations characterized by a slow fermentation rate (12). The metabolic and/or regulatory processes in yeast cells that underlie such retarded fermentations are incompletely understood. Yeast crops are commonly re-used four to ten times for inoculating succeeding brews and are generally stored around 2°C under starvation (54). Under such conditions, high reserve carbohydrates contents have been shown to be critical for the survival and recovery of metabolic

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activity of yeast (54). To our knowledge, no published study has investigated how storage carbohydrate metabolism might be affected by Zn deficiency. This present study demonstrates for the first time that Zn limitation causes a strong transcriptional down-regulation of genes involved in reserve carbohydrate accumulation. The physiological relevance of this response was verified by analysis of intracellular glycogen and trehalose contents, which were strongly reduced in Zn limited cultures. Comparative studies with nitrogen-limited cultures showed that the decreased accumulation of storage carbohydrates was specific for Zn limitation and not merely a consequence of glucose-excess conditions. Furthermore, this effect was independent of the aeration of the cultures and the expression profiles of several genes involved in reserve carbohydrate metabolism perfectly matched the profile of trehalose and glycogen accumulation (Figure 3B). While our hypothesis remains to be tested under brewing conditions and with brewing strains of S. cerevisiae, it seems highly probable that the fermentation performance of Zn-limited brewers’ yeast will be strongly compromised. Additionally, follow-up research should focus on the molecular mechanisms that link reserve carbohydrate metabolism and Zn availability.

Potential implication of Zn-limitation for flavor formation Another consequence of limiting Zn supply during the course of beer fermentation might be related to flavor formation. Indeed, three genes involved in the biosynthesis of the branched-chain amino acids leucine, valine and isoleucine (ILV2, ILV3 and BAT2) were consistently downregulated under Zn-limited growth, both in the presence and in the absence of oxygen (Table 3). The flux through the branched chain amino acids synthetic pathways has been shown to have a positive impact on desirable flavor compound production, such as isoamyl acetate and isobutyl acetate (45) and Zn supplementation to wort results in increased production of the acetate esters of higher alcohols (31). The present data suggests that this effect of zinc availability on flavor formation may be mediated by the transcriptional regulation of ILV2, ILV3 and BAT2. Maintaining a sufficiently high zinc level during beer fermentation is clearly critical to maintain the desired balance between several flavor compounds.

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Signature transcripts for diagnosing Zn bio-availability in industrial media In complex industrial fermentation media such as wort or other plant biomass hydrolysates, Zn can form complexes with several medium components, thereby reducing its bioavailability for yeast (41, 34, 42). This limits the relevance of chemical analyses of the Zn content to test the bioavailability of zinc in wort and other industrial media. Addition of Zn in the form of salt or trub is a common practice to prevent Zn depletion during the brewing process (71). Especially in beer brewing, this is not risk-free as excess Zn leads to the modification of flavor compound formation (17). Molecular markers can be used to monitor fermentation processes through transcript profiling (30). For such diagnostic purposes, it would be preferable to construct small, cost-effective microarrays that contain a limited number of ‘signature transcripts’. A prerequisite of these signature transcripts is that they are specific to one environmental parameter and show a robust response in various environmental (process) contexts. Comparison of multiple chemostat regimes enabled the identification such Zn-specific signature transcripts. For instance, ZAP1 and ZRT1 would be very good signature transcripts. Also YOR387C and YGL258W, encoding proteins that have not been characterized yet and that have been previously proposed as potential signature transcripts for Zn depletion (50, 30), were specifically and consistently induced under Zn limitation in chemostat cultures. Conversely NRG2 and PST1, potential Zap1p-targets (50) were here shown to be also regulated by oxygen availability and are therefore not recommended for diagnostic purposes. Acknowledgements The research group of J. T. Pronk is part of the Kluyver Centre for Genomics of Industrial Fermentation, which is supported by The Netherlands Genomics Initiative. Raffaele De Nicola would like to thank FEMS for the scholarship that allowed him to support his stay in Delft during the execution of this work.

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Appendix Constraints imposed to group zinc responsive genes into modules. Let the discretized expression pattern of a gene be denoted by vector x of length six. The values of the elements of x can either be 0 (no differential expression), 1 (upregulated) or -1 (downregulated). The elements of x correspond to the cultivation conditions as follows: C-Ana for x(1), N-Ana for x(2), Zn-Ana for x(3), C-Aer for CAer, N-Aer for x(5), and Zn-Aer for x(6). In Table A1, we state the constraints on x that must be satisfied in order for a gene to be part of a particular module. Note that all constraints must be met to suffice. Table A1: Constraints on the discretized expression patterns of a gene to be included in one of the six modules Module 1 (upregulated regardless of aeration)

Constraints x(3)>x(1), x(3)>x(2), x(3)>x(4), x(3)>x(5), x(6)>x(1), x(6)>x(2), x(6)>x(4), x(6)>x(5)

2 (downregulated regardless of aeration)

x(3)