RESUMO
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.
Assuntos
Cerveja/microbiologia , Microbiologia Industrial , Filogenia , Saccharomyces cerevisiae/classificação , Saccharomyces cerevisiae/fisiologia , Variações do Número de Cópias de DNA/genética , Genes Fúngicos/genética , Variação Genética , Genoma Fúngico/genética , Viabilidade Microbiana/genética , Fenótipo , Ploidias , Saccharomyces cerevisiae/genética , Seleção GenéticaRESUMO
The direct reprogramming of adult skin fibroblasts to neurons is thought to be controlled by a small set of interacting gene regulators. Here, we investigate how the interaction dynamics between these regulating factors coordinate cellular decision making in direct neuronal reprogramming. We put forward a quantitative model of the governing gene regulatory system, supported by measurements of mRNA expression. We found that nPTB needs to feed back into the direct neural conversion network most likely via PTB in order to accurately capture quantitative gene interaction dynamics and correctly predict the outcome of various overexpression and knockdown experiments. This was experimentally validated by nPTB knockdown leading to successful neural conversion. We also proposed a novel analytical technique to dissect system behaviour and reveal the influence of individual factors on resulting gene expression. Overall, we demonstrate that computational analysis is a powerful tool for understanding the mechanisms of direct (neuronal) reprogramming, paving the way for future models that can help improve cell conversion strategies.
Assuntos
Técnicas de Reprogramação Celular/métodos , Reprogramação Celular/fisiologia , Proteína de Ligação a Regiões Ricas em Polipirimidinas/fisiologia , Idoso , Reprogramação Celular/genética , Biologia Computacional/métodos , Feminino , Fibroblastos/metabolismo , Expressão Gênica/genética , Regulação da Expressão Gênica/genética , Redes Reguladoras de Genes/genética , Humanos , Pessoa de Meia-Idade , Modelos Teóricos , Proteínas do Tecido Nervoso/metabolismo , Neurônios/metabolismo , Proteína de Ligação a Regiões Ricas em Polipirimidinas/metabolismo , Cultura Primária de Células , Processos Estocásticos , Fatores de Transcrição/metabolismoRESUMO
The use of variable-length genomes in evolutionary computation has applications in optimisation when the size of the search space is unknown, and provides a unique environment to study the evolutionary dynamics of genome structure. Here, we revisit crossover for linear genomes of variable length, identifying two crucial attributes of successful recombination algorithms: the ability to retain homologous structure, and to reshuffle variant information. We introduce direct measures of these properties-homology score and linkage score-and use them to review existing crossover algorithms, as well as two novel ones. In addition, we measure the performance of these crossover methods on three different benchmark problems, and find that variable-length genomes out-perform fixed-length variants in all three cases. Our homology and linkage scores successfully explain the difference in performance between different crossover methods, providing a simple and insightful framework for crossover in a variable-length setting.