AFLP fingerprinting for analysis of yeast genetic variation

Lopes, Miguel de Barros; Rainieri, Sandra; Henschke, Paul A.; Langridge, Peter

doi:10.1099/00207713-49-2-915

Cited by 106 publications

(64 citation statements)

References 32 publications

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“…Intraspecies identification of Brettanomyces/Dekkera yeasts has not been frequently reported and some of the first techniques that have been described used random amplified polymorphic DNA (RAPD-PCR) and amplified fragment length polymorphisms (AFLPs) (de Barros Lopes et al, 1999;Mitrakul et al, 1999). Genetically different strains of D. bruxellensis wine isolates were revealed from different vintages and exhibited different chromosomes (three or four) and consequently different chromosomal fingerprints (Mitrakul et al, 1999).…”

Section: Genetic Diversity and Techniques For Strain Discriminationmentioning

confidence: 99%

Significance of Brettanomyces and Dekkera during Winemaking: A Synoptic Review

Oelofse

Pretorius

Toit

2016

SAJEV

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Wine comprises a complex microbial ecology of opportunistic microorganisms, some of which could potentially induce spoilage and result in consequent economic losses under uncontrolled conditions. Yeasts of the genus Brettanomyces, or its teleomorph Dekkera, have been indicated to affect the chemical composition of the must and wine by producing various metabolites that are detrimental to the organoleptic properties of the final product. These yeasts can persist throughout the harsh winemaking process and have in recent years become a major oenological concern worldwide. This literature review summarises the main research focus areas on yeasts of the genera Brettanomyces and Dekkera in wine. Specific attention is given to the spoilage compounds produced, the methods of detection and isolation from the winemaking environment and the factors for controlling and managing Brettanomyces spoilage.

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Section: Genetic Diversity and Techniques For Strain Discriminationmentioning

confidence: 99%

Significance of Brettanomyces and Dekkera during Winemaking: A Synoptic Review

Oelofse

Pretorius

Toit

2016

SAJEV

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“…Traditional approaches include examining colony morphology on plates (27), the ability of yeasts to metabolize melibiose (lager strains can do so due to their elaboration of an ␣-galactosidase, whereas ale strains cannot [28]), temperature tolerance (29), flocculation tests (2), behavior in smallscale fermenters (30,31), and oxygen requirements (32,33). Latterly, the emphasis has been on DNA-based techniques, including restriction fragment length polymorphism analysis (34), PCR (35,36), karyotyping (11), and amplified fragment length polymorphism analysis (37). Additionally, pyrolysis mass spectroscopy (38), Fourier transform infrared spectroscopy (39), fatty acid methyl ester profiling (40), and protein fingerprinting (41) are other possibilities.…”

Section: Typing Of Yeastmentioning

confidence: 99%

The Microbiology of Malting and Brewing

Bokulich

Bamforth

2013

Microbiol Mol Biol Rev

267

206

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SUMMARY Brewing beer involves microbial activity at every stage, from raw material production and malting to stability in the package. Most of these activities are desirable, as beer is the result of a traditional food fermentation, but others represent threats to the quality of the final product and must be controlled actively through careful management, the daily task of maltsters and brewers globally. This review collates current knowledge relevant to the biology of brewing yeast, fermentation management, and the microbial ecology of beer and brewing.

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“…Other reports indicate that lab strains are not exceptionally diverged from other subpopulations (de Barros Lopes et al 1999;Winzeler et al 2003;Ben-Ari et al 2005;Fay and Benavides 2005;Aa et al 2006), although certain S288C alleles were almost certainly selected in the lab (Liu et al 1996;Bonhivers et al 1998). By contrast, the vineyard isolate RM has the fastest rate of protein evolution genomewide, and nonsynonymous changes are enriched in other strains where these strains share recent ancestry with RM.…”

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confidence: 99%

Genomewide Evolutionary Rates in Laboratory and Wild Yeast

2006

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As wild organisms adapt to the laboratory environment, they become less relevant as biological models. It has been suggested that a commonly used S. cerevisiae strain has rapidly accumulated mutations in the lab. We report a low-to-intermediate rate of protein evolution in this strain relative to wild isolates. W HEN introduced into the lab, wild organisms often undergo selection for easier growth. This adaptation, and loss of selective pressures normally present in the wild, may have wide-ranging effects, such that the biology of a lab organism may no longer reflect that of wild populations. This concern has arisen in the recent literature for Saccharomyces cerevisiae (Liu et al. 1996;Bonhivers et al. 1998;Yvert et al. 2003;Deutschbauer and Davis 2005;Dunn et al. 2005;Gu et al. 2005;Qin and Lu 2006). The S288C yeast strain was bred in the 1950s from wild and commercial strains (Mortimer and Johnston 1986) and passed into use as a common lab strain. In a recent comparison of S288C with the clinical strain YJM789 (Gu et al. 2005), isolated in 1989(Tawfik et al. 1989, the phylogenetic lineage to the lab strain exhibited faster protein evolution. One interpretation of this result is that S288C accumulated more mutations during its longer tenure in the lab. Here we revisit this hypothesis in the context of a third strain, the vineyard isolate RM11-1a, which was introduced into the lab in 1996 (Torok et al. 1996;Brem et al. 2002).To parallel previous calculations (Gu et al. 2005), we first analyzed protein evolution in yeast strain pairs. We obtained ORF alignments of S288C, RM11-1a (hereafter RM), YJM789 (hereafter YJM), and S. paradoxus orthologs (Ronald et al. 2005) and eliminated frameshifts, for a total of 4162 genes. We reasoned that most assumptions of molecular evolution methods would be as valid here as when species are compared, and the approximation of constant generation time may be better in this case. As such, for each pair of S. cerevisiae strains plus S. paradoxus, we used PAML (Yang 1997) to infer maximum-likelihood branch lengths for the star tree describing each gene, assuming an independent evolutionary rate for each lineage. We then used nonsynonymous and synonymous changes in inferred trees to estimate genomewide evolutionary rates for each lineage as described (Chimpanzee Sequencing and Analysis Consortium 2005; Gu et al. 2005). The results are shown in Table 1. As expected, in a comparison of S288C and YJM, the lineage to the lab strain had a faster evolutionary rate. However, in other comparisons, the rate for the RM lineage was faster still (Table 1). Thus, the vineyard strain bears the strongest signature of rapid protein evolution.To confirm this, we sought to analyze the three S. cerevisiae strains and S. paradoxus simultaneously. As the genealogy of these genomes varies between loci (Ruderfer et al. 2006), we modeled each gene separately. For each gene, we inferred branch lengths, assuming independent evolutionary rates on all branches, fixing in turn each of the first three topo...

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AFLP fingerprinting for analysis of yeast genetic variation

Cited by 106 publications

References 32 publications

Significance of Brettanomyces and Dekkera during Winemaking: A Synoptic Review

Significance of Brettanomyces and Dekkera during Winemaking: A Synoptic Review

The Microbiology of Malting and Brewing

Genomewide Evolutionary Rates in Laboratory and Wild Yeast

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