高通量实验中微生物适合度的量化方式被系统梳理
Quantifying microbial fitness in high-throughput experiments
研究系统梳理了高通量微生物突变体适合度实验中的统计定义问题，指出不同定义会让结果难以比较。作者把可能的适合度统计归为三个选择：相对丰度的编码方式（如 log 或 logit 变换）、测量相对丰度变化的时间尺度，以及批量竞争实验中参考亚群的选择（例如使用 DNA 条形码突变体的实验）。在 Saccharomyces cerevisiae 和 Escherichia coli 中，这些不同选择会显著改变对突变体适合度的解读，影响适合度效应的大小、上位效应是否出现，甚至改变突变体之间的适合度排序，从而干扰对演化动态和基因功能的预测。作者据此强调，一致的适合度定义是实验结果可重复的前提。
来源：eLife · doi.org
Few concepts are as central to evolution as is fitness, and yet the quantification of fitness is often ambiguous. In particular, high-throughput experiments to measure mutant fitness in microbes are increasingly common, but vary widely in their definitions of fitness, which makes their results difficult to compare. What are the consequences of these different fitness statistics, and is there a best way to quantify fitness in a given context? Here, we systematize the set of possible fitness statistics according to the following three choices: (1) the encoding of relative abundance (e.g., transforming by a log or logit function), (2) the time scale over which to measure the change in relative abundance, and (3) the choice of a reference subpopulation for calculating relative fitness in bulk competition experiments, such as those using DNA-barcoded mutants. We show that these different choices can lead to significantly different interpretations of mutant fitness in Saccharomyces cerevisiae and Escherichia coli , affecting the magnitude of fitness effects, the apparent presence of epistasis, and even the fitness ranking across mutants. This can confound predictions for evolutionary dynamics and gene functions. Altogether, our results demonstrate the importance of consistent fitness definitions for reproducible results across experiments.