Domestication and Divergence of Saccharomyces cerevisiae Beer Yeasts

There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

Summary

Whereas domestication of livestock, pets, and crops is well documented, it is still unclear to what extent microbes associated with the production of food have also undergone human selection and where the plethora of industrial strains originates from. Here, we present the genomes and phenomes of 157 industrial Saccharomyces cerevisiae yeasts. Our analyses reveal that today’s industrial yeasts can be divided into five sublineages that are genetically and phenotypically separated from wild strains and originate from only a few ancestors through complex patterns of domestication and local divergence. Large-scale phenotyping and genome analysis further show strong industry-specific selection for stress tolerance, sugar utilization, and flavor production, while the sexual cycle and other phenotypes related to survival in nature show decay, particularly in beer yeasts. Together, these results shed light on the origins, evolutionary history, and phenotypic diversity of industrial yeasts and provide a resource for further selection of superior strains.

PaperClip

Graphical Abstract

Highlights

•

We sequenced and phenotyped 157 S. cerevisiae yeasts
•

Present-day industrial yeasts originate from only a few domesticated ancestors
•

Beer yeasts show strong genetic and phenotypic hallmarks of domestication
•

Domestication of industrial yeasts predates microbe discovery

Abstract

The history and domestication of yeast used for making beer and other types of alcohol are revealed through genomic and phenotypic analyses.

Related collections

Most cited references 44

Record: found
Abstract: found
Article: not found

Population genomics of domestic and wild yeasts

Gianni Liti, David Carter, Alan M. Moses … (2009)

Since the completion of the genome sequence of Saccharomyces cerevisiae in 19961,2, 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 distributions3,4, population structure5-8, and sexual versus asexual reproduction9,10. 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.

0 comments Cited 624 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: not found

Improving the accuracy of demographic and molecular clock model comparison while accommodating phylogenetic uncertainty.

Guy Baele, Philippe Lemey, Trevor Bedford … (2012)

Recent developments in marginal likelihood estimation for model selection in the field of Bayesian phylogenetics and molecular evolution have emphasized the poor performance of the harmonic mean estimator (HME). Although these studies have shown the merits of new approaches applied to standard normally distributed examples and small real-world data sets, not much is currently known concerning the performance and computational issues of these methods when fitting complex evolutionary and population genetic models to empirical real-world data sets. Further, these approaches have not yet seen widespread application in the field due to the lack of implementations of these computationally demanding techniques in commonly used phylogenetic packages. We here investigate the performance of some of these new marginal likelihood estimators, specifically, path sampling (PS) and stepping-stone (SS) sampling for comparing models of demographic change and relaxed molecular clocks, using synthetic data and real-world examples for which unexpected inferences were made using the HME. Given the drastically increased computational demands of PS and SS sampling, we also investigate a posterior simulation-based analogue of Akaike's information criterion (AIC) through Markov chain Monte Carlo (MCMC), a model comparison approach that shares with the HME the appealing feature of having a low computational overhead over the original MCMC analysis. We confirm that the HME systematically overestimates the marginal likelihood and fails to yield reliable model classification and show that the AICM performs better and may be a useful initial evaluation of model choice but that it is also, to a lesser degree, unreliable. We show that PS and SS sampling substantially outperform these estimators and adjust the conclusions made concerning previous analyses for the three real-world data sets that we reanalyzed. The methods used in this article are now available in BEAST, a powerful user-friendly software package to perform Bayesian evolutionary analyses.

0 comments Cited 530 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: found

Is Open Access

PopGenome: An Efficient Swiss Army Knife for Population Genomic Analyses in R

Bastian Pfeifer, Ulrich Wittelsbürger, Sebastián Ramos-Onsins … (2014)

Although many computer programs can perform population genetics calculations, they are typically limited in the analyses and data input formats they offer; few applications can process the large data sets produced by whole-genome resequencing projects. Furthermore, there is no coherent framework for the easy integration of new statistics into existing pipelines, hindering the development and application of new population genetics and genomics approaches. Here, we present PopGenome, a population genomics package for the R software environment (a de facto standard for statistical analyses). PopGenome can efficiently process genome-scale data as well as large sets of individual loci. It reads DNA alignments and single-nucleotide polymorphism (SNP) data sets in most common formats, including those used by the HapMap, 1000 human genomes, and 1001 Arabidopsis genomes projects. PopGenome also reads associated annotation files in GFF format, enabling users to easily define regions or classify SNPs based on their annotation; all analyses can also be applied to sliding windows. PopGenome offers a wide range of diverse population genetics analyses, including neutrality tests as well as statistics for population differentiation, linkage disequilibrium, and recombination. PopGenome is linked to Hudson’s MS and Ewing’s MSMS programs to assess statistical significance based on coalescent simulations. PopGenome’s integration in R facilitates effortless and reproducible downstream analyses as well as the production of publication-quality graphics. Developers can easily incorporate new analyses methods into the PopGenome framework. PopGenome and R are freely available from CRAN (http://cran.r-project.org/) for all major operating systems under the GNU General Public License.

0 comments Cited 433 times – based on 0 reviews      Review now

Bookmark

All references

Author and article information

Contributors

Steven Maere

Kevin J. Verstrepen

Journal

Journal ID (nlm-ta): Cell

Journal ID (iso-abbrev): Cell

Title: Cell

Publisher: Cell Press

ISSN (Print): 0092-8674

ISSN (Electronic): 1097-4172

Publication date PMC-release: 08 September 2016

Publication date (Print): 08 September 2016

Volume: 166

Issue: 6

Pages: 1397-1410.e16

Affiliations

[1 ]Laboratory for Genetics and Genomics, Centre of Microbial and Plant Genetics (CMPG), KU Leuven, Kasteelpark Arenberg 22, 3001 Leuven, Belgium

[2 ]Laboratory for Systems Biology, VIB, Bio-Incubator, Gaston Geenslaan 1, 3001 Leuven, Belgium

[3 ]Department of Plant Systems Biology, VIB, 9052 Gent, Belgium

[4 ]Department of Plant Biotechnology and Bioinformatics, Ghent University, 9052 Gent, Belgium

[5 ]White Labs, 9495 Candida Street, San Diego, CA 92126, USA

[6 ]Synthetic Genomics, 11149 North Torrey Pines Road, La Jolla, CA 92037, USA

[7 ]Department of Microbiology and Immunology, Rega Institute, KU Leuven, 3000 Leuven, Belgium

[8 ]Encinitas Brewing Science, 141 Rodney Avenue, Encinitas, CA 92024, USA

[9 ]Illumina, 5200 Illumina Way, San Diego, CA 92122, USA

[10 ]Biological & Popular Culture (BioPop), 2205 Faraday Avenue, Suite E, Carlsbad, CA 92008, USA

Author notes

[∗ ]Corresponding author steven.maere@ 123456psb.vib-ugent.be

[∗∗ ]Corresponding author kevin.verstrepen@ 123456biw.vib-kuleuven.be

[11]

Co-first author

[12]

Lead Contact

Article

Publisher ID: S0092-8674(16)31071-6

DOI: 10.1016/j.cell.2016.08.020

PMC ID: 5018251

PubMed ID: 27610566

SO-VID: 091fd30f-ffa4-40cb-940c-c720badb03dd

License:

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

History

Date received : 24 March 2016

Date revision received : 8 June 2016

Date accepted : 8 August 2016

Comments

Comment on this article

scite_

Cited by 243

See all cited by

Most referenced authors 1,348

See all reference authors

- Version 1
- Version 1

Domestication and Divergence of Saccharomyces cerevisiae Beer Yeasts

Read this article at

Summary

PaperClip

Graphical Abstract

Highlights

Abstract

Related collections

Resource Identification

Most cited references 44

Population genomics of domestic and wild yeasts

Improving the accuracy of demographic and molecular clock model comparison while accommodating phylogenetic uncertainty.

PopGenome: An Efficient Swiss Army Knife for Population Genomic Analyses in R

Author and article information

Contributors

Journal

Affiliations

Author notes

Article

History

Categories

Comments

Comment on this article

Similar content 73

Cited by 243

Most referenced authors 1,348