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频繁、微小的瓶颈最大限度地提高了微生物适应的速度。

Frequent, infinitesimal bottlenecks maximize the rate of microbial adaptation.

机构信息

School of the Environment, The University of Queensland, Queensland 4072, Australia.

出版信息

Genetics. 2023 Dec 6;225(4). doi: 10.1093/genetics/iyad185.

Abstract

Serial passaging is a fundamental technique in experimental evolution. The choice of bottleneck severity and frequency poses a dilemma: longer growth periods allow beneficial mutants to arise and grow over more generations, but simultaneously necessitate more severe bottlenecks with a higher risk of those same mutations being lost. Short growth periods require less severe bottlenecks, but come at the cost of less time between transfers for beneficial mutations to establish. The standard laboratory protocol of 24-h growth cycles with severe bottlenecking has logistical advantages for the experimenter but limited theoretical justification. Here we demonstrate that contrary to standard practice, the rate of adaptive evolution is maximized when bottlenecks are frequent and small, indeed infinitesimally so in the limit of continuous culture. This result derives from revising key assumptions underpinning previous theoretical work, notably changing the metric of optimization from adaptation per serial transfer to per experiment runtime. We also show that adding resource constraints and clonal interference to the model leaves the qualitative results unchanged. Implementing these findings will require liquid-handling robots to perform frequent bottlenecks, or chemostats for continuous culture. Further innovation in and adoption of these technologies has the potential to accelerate the rate of discovery in experimental evolution.

摘要

连续传代是实验进化中的一项基本技术。瓶颈严重程度和频率的选择带来了一个困境:较长的生长周期允许有益突变体在更多代中出现和生长,但同时需要更严重的瓶颈,从而增加了这些相同突变丢失的风险。较短的生长周期需要不那么严重的瓶颈,但代价是在转移之间,有益突变体建立的时间更少。标准的实验室方案是 24 小时生长周期和严重的瓶颈,这对实验者来说具有后勤优势,但理论上的合理性有限。在这里,我们证明与标准做法相反,当瓶颈频繁且较小时,适应性进化的速度达到最大化,实际上在连续培养的极限下是无穷小的。这一结果源于对先前理论工作的关键假设进行了修正,特别是将优化的度量标准从每一次连续转移的适应性改变为每次实验的运行时间。我们还表明,在模型中添加资源限制和克隆干扰不会改变定性结果。实施这些发现将需要液体处理机器人来执行频繁的瓶颈,或连续培养的恒化器。这些技术的进一步创新和采用有可能加速实验进化中的发现速度。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7d7e/10697810/d9ff31e57213/iyad185f1.jpg

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