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Wide lag time distributions break a trade-off between reproduction and survival in bacteria.
Proceedings of the National Academy of Sciences of the United States of America ( IF 11.1 ) Pub Date : 2020-08-04 , DOI: 10.1073/pnas.2003331117
Stefany Moreno-Gámez 1, 2, 3 , Daniel J Kiviet 2, 4 , Clément Vulin 2, 4, 5 , Susan Schlegel 2, 4, 6 , Kim Schlegel 2, 4 , G Sander van Doorn 3 , Martin Ackermann 2, 4
Affiliation  

Many microorganisms face a fundamental trade-off between reproduction and survival: Rapid growth boosts population size but makes microorganisms sensitive to external stressors. Here, we show that starved bacteria encountering new resources can break this trade-off by evolving phenotypic heterogeneity in lag time. We quantify the distribution of single-cell lag times of populations of starved Escherichia coli and show that population growth after starvation is primarily determined by the cells with shortest lag due to the exponential nature of bacterial population dynamics. As a consequence, cells with long lag times have no substantial effect on population growth resumption. However, we observe that these cells provide tolerance to stressors such as antibiotics. This allows an isogenic population to break the trade-off between reproduction and survival. We support this argument with an evolutionary model which shows that bacteria evolve wide lag time distributions when both rapid growth resumption and survival under stressful conditions are under selection. Our results can explain the prevalence of antibiotic tolerance by lag and demonstrate that the benefits of phenotypic heterogeneity in fluctuating environments are particularly high when minorities with extreme phenotypes dominate population dynamics.



中文翻译:

较宽的滞后时间分布打破了细菌繁殖与存活之间的权衡。

许多微生物在繁殖和生存之间面临着根本的权衡:快速增长会增加种群数量,但会使微生物对外部压力敏感。在这里,我们表明,遇到新资源的饥饿细菌可以通过在滞后时间内进化表型异质性来打破这种折衷。我们量化饥饿的大肠杆菌群体的单细胞滞后时间的分布结果表明,由于细菌种群动态的指数特性,饥饿后种群的增长主要由滞后最短的细胞决定。结果,具有较长滞后时间的细胞对种群增长恢复没有实质影响。但是,我们观察到这些细胞对诸如抗生素等应激源具有耐受性。这使同基因种群打破了繁殖与生存之间的权衡。我们支持这一论点与这表明细菌进化宽滞后时间分布当两个快速增长的恢复和生存压力的条件下,正在选择中进化模型。

更新日期:2020-08-05
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