Survival Probability of Beneficial Mutations in Bacterial Batch Culture
Survival Probability of Beneficial Mutations in Bacterial Batch Culture
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DOI：
10.1534/genetics.114.172890
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发表时间：
2015-05-01
期刊：
影响因子：
3.3
通讯作者：
Zhu, Anna Dai
中科院分区：
文献类型：
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作者：
Wahl, Lindi M.;Zhu, Anna Dai
罕见有益突变的生存可能对生物体的生命史和受突变影响的性状极其敏感。鉴于分批培养中的细菌作为适应研究模型系统的巨大影响，了解这些群体中有益突变的生存概率非常重要。在这里，我们为批量培养中的细菌群体开发了一个生活史模型，并通过对特定性状（滞后时间、裂变时间、活力和稳定期时间）的影响来预测突变的存活率，从而提高适应度。我们发现，如果在培养物生长开始时创始群体中存在有益突变，那么降低子代细胞死亡率的突变最有可能在漂移中存活下来。相反，在生长过程中从头发生的突变中，那些延迟稳定期开始的突变最有可能存活下来。我们的模型预测瓶颈之间大约五倍的人口增长将优化所有四种类型的有益突变的发生和生存。这种预测对其他模型参数相对不敏感，例如滞后时间、裂变时间或种群死亡率。我们进一步估计，比这个最佳预测更严重的瓶颈会大大减少适应性突变的发生和生存。
The survival of rare beneficial mutations can be extremely sensitive to the organism's life history and the trait affected by the mutation. Given the tremendous impact of bacteria in batch culture as a model system for the study of adaptation, it is important to understand the survival probability of beneficial mutations in these populations. Here we develop a life-history model for bacterial populations in batch culture and predict the survival of mutations that increase fitness through their effects on specific traits: lag time, fission time, viability, and the timing of stationary phase. We find that if beneficial mutations are present in the founding population at the beginning of culture growth, mutations that reduce the mortality of daughter cells are the most likely to survive drift. In contrast, of mutations that occur de novo during growth, those that delay the onset of stationary phase are the most likely to survive. Our model predicts that approximately fivefold population growth between bottlenecks will optimize the occurrence and survival of beneficial mutations of all four types. This prediction is relatively insensitive to other model parameters, such as the lag time, fission time, or mortality rate of the population. We further estimate that bottlenecks that are more severe than this optimal prediction substantially reduce the occurrence and survival of adaptive mutations.