Abreu Clare I, Mathur Shaili, Petrov Dmitri A
Department of Biology, Stanford University; Stanford CA, USA.
bioRxiv. 2023 Sep 21:2023.09.14.557739. doi: 10.1101/2023.09.14.557739.
Evolution in a static environment, such as a laboratory setting with constant and uniform conditions, often proceeds via large-effect beneficial mutations that may become maladaptive in other environments. Conversely, natural settings require populations to endure environmental fluctuations. A sensible assumption is that the fitness of a lineage in a fluctuating environment is the time-average of its fitness over the sequence of static conditions it encounters. However, transitions between conditions may pose entirely new challenges, which could cause deviations from this time-average. To test this, we tracked hundreds of thousands of barcoded yeast lineages evolving in static and fluctuating conditions and subsequently isolated 900 mutants for pooled fitness assays in 15 environments. We find that fitness in fluctuating environments indeed often deviates from the expectation based on static components, leading to fitness non-additivity. Moreover, closer examination reveals that fitness in one component of a fluctuating environment is often strongly influenced by the previous component. We show that this environmental memory is especially common for mutants with high variance in fitness across tested environments, even if the components of the focal fluctuating environment are excluded from this variance. We employ a simple mathematical model and whole-genome sequencing to propose mechanisms underlying this effect, including lag time evolution and sensing mutations. Our results demonstrate that environmental fluctuations have large impacts on fitness and suggest that variance in static environments can explain these impacts.
在静态环境中进化,比如在条件恒定且均匀的实验室环境中,通常是通过具有显著效应的有益突变来进行的,而这些突变在其他环境中可能会变得不适应。相反,自然环境要求种群忍受环境波动。一个合理的假设是,一个谱系在波动环境中的适应度是其在遇到的一系列静态条件下适应度的时间平均值。然而,条件之间的转变可能带来全新的挑战,这可能导致与这个时间平均值产生偏差。为了验证这一点,我们追踪了数十万在静态和波动条件下进化的条形码酵母谱系,随后分离出900个突变体,用于在15种环境中进行混合适应度测定。我们发现,波动环境中的适应度确实常常偏离基于静态成分的预期,导致适应度的非可加性。此外,进一步研究发现,波动环境中某一成分的适应度往往受到前一成分的强烈影响。我们表明,这种环境记忆对于在测试环境中适应度方差较高的突变体尤为常见,即使目标波动环境的成分被排除在该方差之外。我们采用一个简单的数学模型和全基因组测序来提出这种效应背后的机制,包括滞后时间进化和传感突变。我们的结果表明,环境波动对适应度有很大影响,并表明静态环境中的方差可以解释这些影响。