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      Population genomics of domestic and wild yeasts

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          Abstract

          Since the completion of the genome sequence of Saccharomyces cerevisiae in 1996 1, 2, there has been an exponential increase in complete genome sequences accompanied by great advances in our understanding of genome evolution. Although little is known about the natural and life histories of yeasts in the wild, there are an increasing number of studies looking at ecological and geographic distributions 3, 4, population structure 5- 8, and sexual versus asexual reproduction 9, 10. Less well understood at the whole genome level are the evolutionary processes acting within populations and species leading to adaptation to different environments, phenotypic differences and reproductive isolation. Here we present one- to four-fold or more coverage of the genome sequences of over seventy isolates of the baker's yeast, S. cerevisiae, and its closest relative, S. paradoxus. We examine variation in gene content, SNPs, indels, copy numbers and transposable elements. We find that phenotypic variation broadly correlates with global genome-wide phylogenetic relationships. Interestingly, S. paradoxus populations are well delineated along geographic boundaries while the variation among worldwide S. cerevisiae isolates shows less differentiation and is comparable to a single S. paradoxus population. Rather than one or two domestication events leading to the extant baker's yeasts, the population structure of S. cerevisiae consists of a few well-defined geographically isolated lineages and many different mosaics of these lineages, supporting the idea that human influence provided the opportunity for cross-breeding and production of new combinations of pre-existing variation.

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          Sequencing and comparison of yeast species to identify genes and regulatory elements.

          Identifying the functional elements encoded in a genome is one of the principal challenges in modern biology. Comparative genomics should offer a powerful, general approach. Here, we present a comparative analysis of the yeast Saccharomyces cerevisiae based on high-quality draft sequences of three related species (S. paradoxus, S. mikatae and S. bayanus). We first aligned the genomes and characterized their evolution, defining the regions and mechanisms of change. We then developed methods for direct identification of genes and regulatory motifs. The gene analysis yielded a major revision to the yeast gene catalogue, affecting approximately 15% of all genes and reducing the total count by about 500 genes. The motif analysis automatically identified 72 genome-wide elements, including most known regulatory motifs and numerous new motifs. We inferred a putative function for most of these motifs, and provided insights into their combinatorial interactions. The results have implications for genome analysis of diverse organisms, including the human.
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            The selection-mutation-drift theory of synonymous codon usage.

            M Bulmer (1991)
            It is argued that the bias in synonymous codon usage observed in unicellular organisms is due to a balance between the forces of selection and mutation in a finite population, with greater bias in highly expressed genes reflecting stronger selection for efficiency of translation. A population genetic model is developed taking into account population size and selective differences between synonymous codons. A biochemical model is then developed to predict the magnitude of selective differences between synonymous codons in unicellular organisms in which growth rate (or possibly growth yield) can be equated with fitness. Selection can arise from differences in either the speed or the accuracy of translation. A model for the effect of speed of translation on fitness is considered in detail, a similar model for accuracy more briefly. The model is successful in predicting a difference in the degree of bias at the beginning than in the rest of the gene under some circumstances, as observed in Escherichia coli, but grossly overestimates the amount of bias expected. Possible reasons for this discrepancy are discussed.
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              High-resolution mapping of meiotic crossovers and non-crossovers in yeast.

              Meiotic recombination has a central role in the evolution of sexually reproducing organisms. The two recombination outcomes, crossover and non-crossover, increase genetic diversity, but have the potential to homogenize alleles by gene conversion. Whereas crossover rates vary considerably across the genome, non-crossovers and gene conversions have only been identified in a handful of loci. To examine recombination genome wide and at high spatial resolution, we generated maps of crossovers, crossover-associated gene conversion and non-crossover gene conversion using dense genetic marker data collected from all four products of fifty-six yeast (Saccharomyces cerevisiae) meioses. Our maps reveal differences in the distributions of crossovers and non-crossovers, showing more regions where either crossovers or non-crossovers are favoured than expected by chance. Furthermore, we detect evidence for interference between crossovers and non-crossovers, a phenomenon previously only known to occur between crossovers. Up to 1% of the genome of each meiotic product is subject to gene conversion in a single meiosis, with detectable bias towards GC nucleotides. To our knowledge the maps represent the first high-resolution, genome-wide characterization of the multiple outcomes of recombination in any organism. In addition, because non-crossover hotspots create holes of reduced linkage within haplotype blocks, our results stress the need to incorporate non-crossovers into genetic linkage analysis.
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                Author and article information

                Journal
                0410462
                6011
                Nature
                Nature
                Nature
                0028-0836
                1476-4687
                18 March 2009
                11 February 2009
                19 March 2009
                19 September 2009
                : 458
                : 7236
                : 337-341
                Affiliations
                [1 ]Institute of Genetics, Queen's Medical Centre, University of Nottingham, Nottingham NG7 2UH, UK
                [2 ]Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1HH, UK
                [3 ]Department of Cell & Systems Biology, University of Toronto, Canada, M5S 2J4
                [4 ]Department of Cell and Molecular Biology, Lundberg Laboratory, University of Gothenburg, Medicinaregatan 9c, 41390 Gothenburg, Sweden
                [5 ]National Collection of Yeast Cultures, Institute of Food Research, Norwich Research Park, Colney, Norwich NR4 7UA, UK
                [6 ]Division of Biology, Imperial College London, Silwood Park, Ascot, Berks., SL5 7PY, UK
                [7 ]Faculty of Life Sciences, University of Manchester, Manchester M13 9PT, UK
                [8 ]Department of Physics, Massachusetts Institute of Technology, Cambridge MA 02139, USA
                Author notes
                [*]

                These authors contributed equally to this work

                [‡]

                Present address: School of Biological Sciences, University of Liverpool, Liverpool, LG9 3BX

                Author Contributions R.D. and E.J.L. conceived and designed the project. G.L. selected and manipulated yeast strains and extracted DNA samples. M.J., M.A.Q., I.G., S.S., F.S. performed the subcloning and sequencing. D.M.C. did the reference comparison and assembly of the sequences. D.M.C. and G.L. coordinated the collection of data. D.M.C. and R.D. performed much of the global analysis, which was the basis for specific analyses performed by the rest. A.M.M. did the selection studies. E.J.L., G.L. D.M.C., L.B. did the population structure and novel genes analysis. C.M.B. and D.B. performed the analysis of Ty elements abundance. S.A.J., R.P.D., M.J.T.O., A.V. and I.N.R. analysed the rDNA. A.B., V.K. and I.J.T. did the sequence variation and recombination analyses. A.M.M. and A.N.N.B. created a BLAST server. J.W. and A.B. generated the phenomics data. E.J.L. and G.L. wrote the paper, coordinating everyone's contributions.

                Correspondence and requests for materials should be addressed to R. D. ( rd@ 123456sanger.ac.uk ) or E. J. L. ( ed.louis@ 123456nottingham.ac.uk ).
                Article
                UKMS4241
                10.1038/nature07743
                2659681
                19212322
                5e3cf48b-450d-42df-a9cc-80a02ac8d1d8
                History
                Funding
                Funded by: Wellcome Trust :
                Award ID: 084507 || WT
                Funded by: Wellcome Trust :
                Award ID: 067008 || WT
                Funded by: Biotechnology and Biological Sciences Research Council :
                Award ID: G10415 || BB_
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