Have you tried turning it off and on again? Oscillating selection to enhance fitness-landscape traversal in adaptive laboratory evolution experiments

IF 3.7 Q2 BIOTECHNOLOGY & APPLIED MICROBIOLOGY
Alexander C. Carpenter , Adam M. Feist , Fergus S.M. Harrison , Ian T. Paulsen , Thomas C. Williams
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引用次数: 2

Abstract

Adaptive Laboratory Evolution (ALE) is a powerful tool for engineering and understanding microbial physiology. ALE relies on the selection and enrichment of mutations that enable survival or faster growth under a selective condition imposed by the experimental setup. Phenotypic fitness landscapes are often underpinned by complex genotypes involving multiple genes, with combinatorial positive and negative effects on fitness. Such genotype relationships result in mutational fitness landscapes with multiple local fitness maxima and valleys. Traversing local maxima to find a global maximum often requires an individual or sub-population of cells to traverse fitness valleys. Traversing involves gaining mutations that are not adaptive for a given local maximum but are necessary to ‘peak shift’ to another local maximum, or eventually a global maximum. Despite these relatively well understood evolutionary principles, and the combinatorial genotypes that underlie most metabolic phenotypes, the majority of applied ALE experiments are conducted using constant selection pressures. The use of constant pressure can result in populations becoming trapped within local maxima, and often precludes the attainment of optimum phenotypes associated with global maxima. Here, we argue that oscillating selection pressures is an easily accessible mechanism for traversing fitness landscapes in ALE experiments, and provide theoretical and practical frameworks for implementation.

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你试过把它关掉再打开吗?自适应实验室进化实验中振荡选择增强适应度景观遍历
适应性实验室进化(ALE)是工程和理解微生物生理学的强大工具。ALE依赖于突变的选择和富集,这些突变能够在实验设置施加的选择性条件下存活或更快地生长。表型适应度景观通常由涉及多个基因的复杂基因型支撑,对适应度有积极和消极的组合影响。这种基因型关系导致了具有多个局部适应度最大值和谷的变异适应度景观。遍历局部最大值以找到全局最大值通常需要单个或子群体的细胞来遍历适应度谷。遍历涉及获得突变,这些突变对给定的局部最大值不自适应,但对于“峰移”到另一个局部最大值或最终全局最大值是必要的。尽管有这些相对广为人知的进化原理,以及大多数代谢表型背后的组合基因型,但大多数应用ALE实验都是使用恒定的选择压力进行的。恒定压力的使用可能导致种群被困在局部最大值内,并且通常妨碍实现与全局最大值相关的最佳表型。在这里,我们认为振荡选择压力是ALE实验中遍历适应度景观的一种容易获得的机制,并为实现提供了理论和实践框架。
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来源期刊
Metabolic Engineering Communications
Metabolic Engineering Communications Medicine-Endocrinology, Diabetes and Metabolism
CiteScore
13.30
自引率
1.90%
发文量
22
审稿时长
18 weeks
期刊介绍: Metabolic Engineering Communications, a companion title to Metabolic Engineering (MBE), is devoted to publishing original research in the areas of metabolic engineering, synthetic biology, computational biology and systems biology for problems related to metabolism and the engineering of metabolism for the production of fuels, chemicals, and pharmaceuticals. The journal will carry articles on the design, construction, and analysis of biological systems ranging from pathway components to biological complexes and genomes (including genomic, analytical and bioinformatics methods) in suitable host cells to allow them to produce novel compounds of industrial and medical interest. Demonstrations of regulatory designs and synthetic circuits that alter the performance of biochemical pathways and cellular processes will also be presented. Metabolic Engineering Communications complements MBE by publishing articles that are either shorter than those published in the full journal, or which describe key elements of larger metabolic engineering efforts.
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