葡萄酒
生物
适应性进化
酿酒酵母
谱系(遗传)
进化生物学
酵母
选择(遗传算法)
领域
质量(理念)
生化工程
计算生物学
酿酒酵母
计算机科学
遗传学
工程类
人工智能
基因
地理
食品科学
哲学
考古
认识论
作者
Payam Ghiaci,Paula Jouhten,Nikolay Martyushenko,Helena Roca-Mesa,Jennifer Vázquez,Dimitrios Konstantinidis,Simon Stenberg,Sergej Andrejev,Kristina Grkovska,Albert Mas,Gemma Beltran,Eivind Almaas,Kiran Raosaheb Patil,Jonas Warringer
标识
DOI:10.1101/2022.04.18.488345
摘要
ABSTRACT Adaptive Laboratory Evolution (ALE) of microbes can improve the efficiency of sustainable industrial processes important to the global economy, but chance and genetic background effects often lead to suboptimal outcomes. Here we report an ALE platform to circumvent these flaws through parallelized clonal evolution at an unprecedented scale. Using this platform, we clonally evolved 10 ^4 yeast populations in parallel from many strains for eight desired wine production traits. Expansions of both ALE replicates and lineage numbers broadened the evolutionary search spectrum and increased the chances of evolving improved wine yeasts unencumbered by unwanted side effects. ALE gains often coincided with distinct aneuploidies and the emergence of semi-predictable side effects that were characteristic of each selection niche. Many high performing ALE strains retained their desired traits upon transfer to industrial conditions and produced high quality wine. Overall, our ALE platform brings evolutionary engineering into the realm of high throughput science and opens opportunities for rapidly optimizing microbes for use in many industrial sectors which otherwise could take many years to accomplish.
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