Strain improvement strategies

Since the beginning of beer making, all the way to the present, brewing strains have been used continuously, being passed down from batch to batch. These strains are highly adapted to wort and beer and are not very amenable to classical strain improvement strategies.That is, trying to select strains, either spontaneously or following mutagenesis, with improved fermentative, flavor-producing, or other relevant properties, is not an easy proposition. In addition, whereas laboratory strains are usually diploid and capable of sporulating and forming haploid spores or asci, brewing strains are polyploid, containing multiple alleles for many genes. Brewing strains also sporulate poorly.Thus, even mating and hybridization techniques, the standard means for modifying yeast in the laboratory, are often unsuccessful in brewing strains. Moreover, changes in one trait often have pleitropic effects and lead to undesirable changes in other performance characteristics.

Although strain improvement programs still rely on these classical approaches, the ability to target specific genes or traits is now possible using recombinant DNA and genetic engineering techniques.However, such approaches

Of all the microorganisms used in food and beverage fermentations, Saccharomyces cerevisiae was the first whose genome was sequenced (Goffeau et al., 1996). The sequenced strain (S288C) is considered a lab strain, as opposed to the industrial strains used for ale or lager man-ufacture.Although there is only limited sequence data for the latter strains, genome structure analysis has revealed significant differences between the laboratory and brewing strains.

Most importantly, whereas most lab strain genomes exist as diploids (i.e., containing two copies of each chromosome), beer yeasts are poly- or alloploidal, containing multiple chromosomal copies (i.e., more than one "genome" per cell). Moreover, it appears that the genome of the lager yeast, Saccharomyces pastorianus (or Saccharomyces carlsbergensis), is a hybrid, consisting of one genome that evolved from an S288C-like strain of S. cerevisiae and another whose ancestral progenitor is still unknown (Casaregola et al., 2001). Despite these differences, however, an incredible amount of practical information has been generated from the original sequence data, as well as that subsequently made available via the web-based Saccharomyces Genome Database (SGD, located at www.yeastgenome.org).

The S. cerevisiae S288C genome consists of sixteen chromosomes and a total of 12,068 kilobases of DNA.Based on the original computer predictions,there were 5,885 genes encoding for proteins and another 455 that encode for ribosomal, small nuclear, and transfer RNA molecules. However, a more recent analysis of the S. cerevisiae genome indicates that there are probably about 500 fewer genes for a revised total of 5,538 (Kellis et al., 2003).Also, the genomes of three other species of Saccharomyces (Saccharomyces paradoxus, Saccharomyces mikatae, and Saccharomyces bayanus), as well as the related yeast, Schizosaccharomyces pombe, have been sequenced, providing even more information on the structure and function of yeast genomes (Kellis et al., 2003;Wood et al., 2002). Numerous computational tools for comparing these sequences with those obtained from industrial strains have been described and are now available via the SGD (Christie et al., 2004).

While genome information can predict the genes that are present in the chromosome(s) of an organism, it is often more informative to identify genes that are actually transcribed.The so-called transcriptome can be determined using DNA macro- or microarray technology. It also may be interesting to determine how temporal gene expression is influenced by environmental conditions or growth phase. For example, in the case of beer strains of S. cerevisiae, one might wish to identify genes expressed during growth on maltose or that are induced when ethanol is present. In fact, it is possible to monitor gene expression throughout the entire beer fermentation process,

Box 9—10. Beer-omics (Continued)

generating an expression profile of all the proteins produced at any given time during the course of the fermentation (the proteome, commonly determined by two-dimensional gel elec-trophoresis).

Finally,the quantitative, global collection of small metabolic products (the metabolome) that appears in the medium during beer manufacture (as measured by NMR, mass spectroscopy, or other analytical methods) provides yet another molecular picture of the beer making process. Several recent reports provide excellent examples of how "omic" approaches can be used to gain a better understanding of the genetic, cellular, and metabolic events that occur during the beer fermentation.

Beer transcriptomics

As reported in two independent investigations, one using a macroarray representing 6,084 ORFs (Olesen et al., 2002) and the other using a microarray representing 6,300 ORFs (James et al., 2003), the gene expression patterns of brewing yeasts (lager strains) change during growth in wort. However, the number of differentially-expressed genes was relatively modest (about 20% of the total ORFs). Furthermore, functions for the majority of the induced or repressed genes could not be assigned.

It should also be noted that the mRNA used in these studies was from beer strains and that the arrays had been derived from a lab strain of S. cerevisiae.Thus, there may have been induced or repressed genes that would not hybridize with any of the orfomers on the arrays. Still, in both studies, there were several interesting results. For example, there was a significant increase in the expression of genes that encode for putative enzymes involved in sterol and lipid biosyn-thesis.This is consistent with the well-known observation that yeast cells synthesize sterols and membrane unsaturated fatty acids following aeration of the wort just prior to the pitching step. Similarly, there was a marked increase in the expression of mitochondrial- and peroxisome-related genes, which may reflect the necessity of anabolic pathways to generate products required for anaerobic growth.

Not surprisingly, genes encoding for glycolytic enzymes were also induced after inoculation of the yeast into the wort. However, the most unexpected result was the finding by James et al. (2003) that glycolytic genes were subsequently repressed after about forty-eight to seventy-two hours and respiration genes were activated. In additionJames et al.,(2003) showed that protein synthesis and most stress response genes were repressed after the first day of fermentation, despite the increase in the ethanol concentration. Based on these collective reports, the lager yeast transcriptome clearly changes during the beer fermentation, reflecting the catabolic and anabolic adjustments the cell evidently makes in response to chemical and physical changes in the growth environment.

Beer proteomics

The yeast transcriptome, as described above, provides a means of identifying the mRNA transcripts that are produced at a given time during growth. In contrast, the proteome represents the complete set of actual functional gene products (i.e.,the proteins) that are synthesized during growth. Proteome maps of industrial lager and other yeast strains during growth in synthetic medium revealed that about 1,200 polypeptides are produced, although many appear to be duplications (Joubert et al., 2000).There is also a high degree of similarity between the proteins produced by lager strains and lab strains, supporting the notion that lager strains are hybrids and contain a genome derived from S. cerevisiae. However, as many as thirty-two other proteins are made by lager yeasts that do not appear in the S. cerevisiae proteome (Joubert et al., 2001).Analysis of these non-S. cerevisiae proteins by peptide mass fingerprinting and mass spectroscopy techniques revealed that many are involved in maltose metabolism, glycolytic pathways, and production of ethanol.

Box 9—10. Beer-omics (Continued)

In another recent report, induction of protein synthesis by lager yeasts was studied during the lag and early log phase of growth (Brejning et al., 2005). Interestingly, the induced protein expression pattern differed from the gene expression patterns (at least for the genes investigated), indicating that post-transcriptional regulation was involved.Among the early expressed proteins were those involved in amino acid and protein synthesis, glycerol metabolism, glycoly-sis, and ergosterol biosynthesis. Moreover, the expression profile for cells grown in minimal medium was consistent with those grown under brewing conditions.

Beer metabolomics

It would be understatement to suggest that wide variations exist in the chemical composition of different beers. After all, differences in composition account, to a large extent, for the many different types of beer that are produced around the world. Still, determining on a quantitative basis what the actual chemical composition is for a given beer can provide considerable information regarding the brewing process, yeast metabolism, and overall beer quality.This chemical profile, or the metabolome, which can be determined by principal component analysis, has been shown to distinguish between ales and lagers, as well as between different manufactured beers (Duarte et al., 2002; Duarte et al., 2004). For example, the presence or absence of particular beer constituents containing aliphatic and aromatic regions (as determined by NMR spectra), were associated with ales or lagers.

References

Brejning,J., N.Arneborg, and L.Jespersen. 2005. Identification of genes and proteins induced during the lag and early exponential phase of growth of lager brewing yeasts. J.Appl. Microbiol. 98:261-271. Casaregola, S., H.-V Nguyen, G. Lapathitis,A. Kotyk, and C. Gaillardin. 2001.Analysis of the constitution of the beer yeast genome by PCR, sequencing and subtelomeric sequence hybridization. Int. J. Syst. Evol. Microbiol. 51:1607-1618.

Christie, K.R., S.Weng, R. Balakrishnan,M.C. Costanzo, and 19 other authors. 2004.Saccharomyces Genome Database (SGD) provides tools to identify and analyze sequences from Saccharomyces cerevisiae and related sequences from other organisms. Nucl.Acids Res. 32 (Database issue):D311-D314. Duarte, I.,A. Barros, P.S. Belton, R. Righelato, M. Spraul, E. Humpfer, and A.M. Gil. 2002. High-resolution nuclear magnetic resonance spectroscopy and multivariate analysis for the characterization of beer. J. Agric. Food Chem. 50:2475-2481. Duarte, I.F.,A. Barros, C.Almeida, M. Spraul, and A.M. Gil. 2004. Multivariate analysis of NMR and FTIR data as a potential tool for the quality control of beer.J.Agric. Food Chem. 52:1031-1038. Goffeau,A., B.G. Barrell, H. Bussey, R.W. Davis, B. Dujon, H. Feldmann, F. Galibert, J.D. Hoheisel, C.Jacq, M. Johnston, E.J. Louis, H.W. Mewes,Y. Murakami, P Philippsen, H.Tettelin, and S.G. Oliver. 1996. Life with 6000 genes. Science 274:546-567. James,T.C., S. Campbell, D. Donnelly, and U. Bond. 2003 Transcription profile of brewery yeast under fermentation conditions. J.Appl. Microbiol. 94:432-448. Joubert, R., P. Brignon, C. Lehmann, C. Monribot, F. Gendre, and H. Boucherie. 2000.Two-dimensional gel analysis of the proteome of lager brewing yeast.Yeast 16:511-522. Joubert, R.,J.-M. Strub, S. Zugmeyer, D. Kobi, N. Carte,A.V Dorsselaer, H. Boucherie, and L.Jaquet-Gutfreund. 2001. Identification by mass spectrometry of two-dimensional gel electrophoresis-separated proteins extracted from lager brewing yeast. Electrophoresis 22:2969-2982. Kellis, M., N. Patterson, M. Endrizzi, B. Birren, and E.S. Lander. 2003. Sequencing and comparison of yeast species to identify genes and regulatory elements. Nature 423:241-254. Olesen, K.,T. Felding, C. Gjermansen, and J. Hansen. 2002.The dynamics of the Saccharomyces carlbergen-sis brewing yeast transcriptome during a production-scale lager yeast fermentation. FEMS Yeast Res. 2:563-573.

Wood, V, R. Gwilliam, M.-A. Rajandream, M. Lyne, and 130 other authors. 2002.The genome sequence of Schizosaccharomyces pombe. Nature 415:871-880.

must be carefully considered, due to the public perception (often supported by regulatory authorities) that genetically modified organisms pose risks to the consumer or the environment. Rather, it seems that commercial applications can still be realized, but will require the use of more benign techniques (i.e., no foreign DNA used, no antibiotic resistance markers) for strain construction.

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