Survey of molecular methods for the typing of wine yeast strains

Schuller, Dorit Elisabeth; Valero, Eva; Dequin, Sylvie; Casal, Margarida

doi:10.1016/s0378-1097(03)00928-5

Cited by 139 publications

(158 citation statements)

References 31 publications

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“…Numerous methods have been used to assay genomic variation in yeast and determine relationships between strains, and also used to infer strain origins and history (e.g., Schuller et al 2004;Legras et al 2005). Such studies include comparative analyses of microsatellites (Legras et al 2007;Franco-Duarte et al 2009;Muller and McCusker 2009b;Richards et al 2009), mini-and megasatellites (Richard and Dujon 2006;Rolland et al 2010), copy number variation using aCGH (Pérez-Ortín et al 2002;Infante et al 2003;Winzeler et al 2003;Dunn et al 2005;Carreto et al 2008;Kvitek et al 2008), and polymorphisms detected by tiling arrays (Schacherer et al 2009), as well as the use of multispecies 131-gene taxonomic microarrays (Muller and McCusker 2009a) and Multi Locus Sequence Typing (MLST) (Fay and Benavides 2005a,b;Ayoub et al 2006;Vigentini et al 2009).…”

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confidence: 99%

Analysis of the Saccharomyces cerevisiae pan-genome reveals a pool of copy number variants distributed in diverse yeast strains from differing industrial environments

Dunn¹,

Richter²,

Kvitek³

et al. 2012

Genome Res.

205

182

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Although the budding yeast Saccharomyces cerevisiae is arguably one of the most well-studied organisms on earth, the genome-wide variation within this species—i.e., its “pan-genome”—has been less explored. We created a multispecies microarray platform containing probes covering the genomes of several Saccharomyces species: S. cerevisiae, including regions not found in the standard laboratory S288c strain, as well as the mitochondrial and 2-μm circle genomes–plus S. paradoxus, S. mikatae, S. kudriavzevii, S. uvarum, S. kluyveri, and S. castellii. We performed array-Comparative Genomic Hybridization (aCGH) on 83 different S. cerevisiae strains collected across a wide range of habitats; of these, 69 were commercial wine strains, while the remaining 14 were from a diverse set of other industrial and natural environments. We observed interspecific hybridization events, introgression events, and pervasive copy number variation (CNV) in all but a few of the strains. These CNVs were distributed throughout the strains such that they did not produce any clear phylogeny, suggesting extensive mating in both industrial and wild strains. To validate our results and to determine whether apparently similar introgressions and CNVs were identical by descent or recurrent, we also performed whole-genome sequencing on nine of these strains. These data may help pinpoint genomic regions involved in adaptation to different industrial milieus, as well as shed light on the course of domestication of S. cerevisiae.

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mentioning

confidence: 99%

Analysis of the Saccharomyces cerevisiae pan-genome reveals a pool of copy number variants distributed in diverse yeast strains from differing industrial environments

Dunn¹,

Richter²,

Kvitek³

et al. 2012

Genome Res.

205

182

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“…Strains with identical mtDNA RFLP patterns were grouped and one representative strain was further characterised by analysis of 10 S. cerevisiae specific microsatellite loci [4,5]. The equivalent discriminatory power of mtDNA RFLP and microsatellite analysis has been previously reported [6].…”

Section: Molecular Molecular Identification Identificationmentioning

confidence: 86%

Bioinformatic approaches for the genetic and phenotypic characterization of a Saccharomyces cerevisiae wine yeast collection

et al. 2008

Self Cite

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The objective of the present study was to compare genetic and phenotypic variation of 103 Saccharomyces cerevisiae strains isolated from winemaking environments. We used bioinformatics approaches to identify genetically similary strains with specific phenotypes and to estimate a strain's biotechnological potential. A S. cerevisiae collection, comprising 440 strains that were obtained from winemaking environments in Portugal has been constituted during the last years. All strains were genetically characterized by a set of eleven highly polymorphic microsatellites and showed unique allelic combinations. Using neural networks, a subset of 103 genetically most diverse strains was chosen for phenotypic analysis, that included growth in synthetic must media at various temperatures, utilization of carbon sources (glucose, ribose, arabinose, xylose, saccharose, galactose, rafinose, maltose, glycerol, potassium acetate and pyruvic acid), growth in ethanol containing media, evaluation of osmotic and oxidative stress resistance, H2S production and utilization of different nitrogen sources. Using supervised data mining approaches we have found that genotype represented with presence/absence of eleven microsatellites relates well with geographical location (performance evaluation using leave-out-out technique resulted in high performance scores; e.g., area under ROC curve was above 0.8 for a number of standard machine learning approaches tested). To find relations between phenotypes and genotypes, we used a two-step approach which first hierarchically clusters the strains according to their phenotype, and then tests if the resulting sub-clusters are identifiable using strain’s genetic data. Several groups of strains with similar phenotype profiles and common features in genotype were identified this way, and they are subject to further investigations. Financially supported by the programs POCI 2010 (FEDER/FCT, POCTI/AGR/56102/2004) and AGRO (ENOSAFE, Nº 762).

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“…The proposed method can be very useful to mark industrial strains in general, and especially those wine strains where it is difficult to differentiate them with the currently available methods (Schuller et al, 2004). Furthermore, it would be possible to construct different 23S RNA versions, thus making it possible to have different strains with different tags.…”

Section: Discussionmentioning

confidence: 99%

“…chromosome separation by pulsed-field electrophoresis, restriction fragment length polymorphism (RFLP) analysis of mitochondrial DNA (mt DNA); polymerase chain reaction (PCR) analysis of delta (δ) sequences number and location; or genotyping of microsatellite or simple sequence repeat loci (Schuller et al, 2004, and references therein). Other techniques also available for differentiation among enological S. cerevisiae strains are PCR analysis of introns in mitochondrial gene COX1 (Lopez et al, 2003) or PCR-temperature gradient gel electrophoresis (Manzano et al, 2005).…”

Section: Introductionmentioning

confidence: 99%

23S RNA‐derived replicon as a ‘molecular tag’ for monitoring inoculated wine yeast strains

Esteban

Rodríguez-Cousiño

2008

Yeast

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In this study we have developed a useful method to identify a particular yeast strain within a mixture of strains during must fermentation, based on the presence or absence of a stable genetic element derived from Saccharomyces cerevisiae 23S RNA autonomous replicon. 23S RNA is a natural virus-like RNA replicon present in some S. cerevisiae strains, which encodes only its own RNA-dependent RNA polymerase named p104. A modified version of 23S RNA (23S-tagged RNA) was generated after transformation of S. cerevisiae wine strains with a launching plasmid, where six nucleotides were changed in the 23S RNA cDNA sequence without modifying the amino acid sequence of p104 RNA polymerase. Once generated, the 23S-tagged RNA can replicate autonomously (without the plasmid), is very stable, is present in high copy number in stationary phase or nitrogen-starved cells and confers no phenotype to the host, like the endogenous 23S RNA replicon. However, it can be distinguished from endogenous 23S RNA by reverse transcription followed by polymerase chain reaction (RT-PCR) with specific oligonucleotide primers. 23S RNA-derived replicon can be used to tag wine yeast strains in order to monitor easily their prevalence over endogenous strains during wine fermentation.

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Survey of molecular methods for the typing of wine yeast strains

Cited by 139 publications

References 31 publications

Analysis of the Saccharomyces cerevisiae pan-genome reveals a pool of copy number variants distributed in diverse yeast strains from differing industrial environments

Analysis of the Saccharomyces cerevisiae pan-genome reveals a pool of copy number variants distributed in diverse yeast strains from differing industrial environments

Bioinformatic approaches for the genetic and phenotypic characterization of a Saccharomyces cerevisiae wine yeast collection

23S RNA‐derived replicon as a ‘molecular tag’ for monitoring inoculated wine yeast strains

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