Noise and Epigenetic Inheritance of Single-Cell Division Times Influence Population Fitness.
Noise and Epigenetic Inheritance of Single-Cell Division Times Influence Population Fitness.
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DOI：
10.1016/j.cub.2016.03.010
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发表时间：
2016-05-09
期刊：
影响因子：
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通讯作者：
Verstrepen KJ
中科院分区：
文献类型：
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作者：
Cerulus B;New AM;Pougach K;Verstrepen KJ
生物噪声的适应性效应仍不清楚。例如，即使在克隆微生物种群中，单个细胞也以不同的速度生长。虽然已知个体的平均生长速度会影响种群水平的适应度，但尚不清楚生长速度异质性本身如何或是否受到自然选择的影响。在这里，我们表明，嘈杂的单细胞分裂时间可以显着影响人口水平的增长率。利用延时显微镜测量数千个单个S.通过对不同遗传和环境背景的酿酒酵母细胞进行比较，我们发现克隆个体之间单个细胞分裂时间的长度可能存在很大差异，并且亚系通常表现出分裂时间的表观遗传。通过将这些实验测量与数学建模相结合，我们发现，对于给定的平均分裂时间，增加分裂时间的异质性和表观遗传性会增加种群增长率。此外，我们证明了单细胞分裂时间的异质性和表观遗传可以与分解代谢基因表达的变化联系起来。总之，我们的研究结果揭示了嘈杂的单细胞行为的变化如何通过独立于平均值变化所引起的影响的动力学直接影响适应性。这些结果不仅可以更好地理解微生物的适应性，而且有助于更准确地预测其他克隆种群（如肿瘤）的适应性。
The fitness effect of biological noise remains unclear. For example, even within clonal microbial populations, individual cells grow at different speeds. Although it is known that the individuals’ mean growth speed can affect population-level fitness, it is unclear how or whether growth speed heterogeneity itself is subject to natural selection. Here, we show that noisy single-cell division times can significantly affect population-level growth rate. Using time-lapse microscopy to measure the division times of thousands of individual S. cerevisiae cells across different genetic and environmental backgrounds, we find that the length of individual cells’ division times can vary substantially between clonal individuals and that sublineages often show epigenetic inheritance of division times. By combining these experimental measurements with mathematical modeling, we find that, for a given mean division time, increasing heterogeneity and epigenetic inheritance of division times increases the population growth rate. Furthermore, we demonstrate that the heterogeneity and epigenetic inheritance of single-cell division times can be linked with variation in the expression of catabolic genes. Taken together, our results reveal how a change in noisy single-cell behaviors can directly influence fitness through dynamics that operate independently of effects caused by changes to the mean. These results not only allow a better understanding of microbial fitness but also help to more accurately predict fitness in other clonal populations, such as tumors.