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Comparative Genomics and Evolutionary Genetics of Yeast Carbon Metabolism

  • Zhenguo Lin
  • Wen-Hsiung LiEmail author
Chapter
  • 1.8k Downloads

Abstract

Yeasts display highly diversified physiological characteristics. The most distinct physiological character in many yeast species is their special sugar metabolism. In particular, the baker’s yeast Sacharomyces cerevisiae and its relatives predominantly ferment sugars into ethanol even in the presence of oxygen, which is known as Crabtree effect or aerobic fermentation. It has been postulated that this unusual carbohydrate metabolism provides these yeasts selective advantages in sugar-rich environments. However, it has long been a mystery as to genetically how these yeasts evolved a predominantly fermentative lifestyle. The rapid accumulation of genomic, transcriptomic, and epigenetic data in many yeast species in recent years has greatly increased our understanding of the genetic basis and molecular mechanism for the diversified sugar metabolisms among yeasts. In this chapter, we provide a review of recent comparative genomics and evolutionary studies related to the metabolisms of glucose and galactose, whose metabolic pathways have been extensively studied in yeasts. A series of studies suggested that the evolution of aerobic fermentation involved many different factors, including increases in copy numbers of genes involved in glucose transport, glycolysis and ethanol production; sequence divergence; and transcriptional reprogramming of genes involved in mitochondrial functions through changes of cis-regulatory elements and promoter structures. It has also been found that the different abilities among yeasts to use galactose is strongly correlated with the presence of the galactose pathway genes in their genomes. These studies revealed that the adaptation of yeasts to specific niches has greatly shaped the genomic content and the regulatory program.

Keywords

Yeast Species Codon Usage Bias Whole Genome Duplication Aerobic Fermentation Whole Genome Duplication Event 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

We thank Ken Wolfe and Chris Hittinger for their valuable comments.

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© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.Department of Ecology and Evolutionary BiologyRice UniversityHoustonUSA
  2. 2.Biodiversity Research CenterAcademia SinicaTaipeiTaiwan
  3. 3.Department of Ecology and EvolutionUniversity of ChicagoChicagoUSA

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