Estimating the transfer rates of bacterial plasmids with an adapted Luria-Delbrück fluctuation analysis.
Estimating the transfer rates of bacterial plasmids with an adapted Luria-Delbrück fluctuation analysis.
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用Luria-Delbrück波动分析法估算细菌质粒的转移率。
DOI：
10.1371/journal.pbio.3001732
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发表时间：
2022-07
期刊：
影响因子：
9.8
通讯作者：
Kerr, Benjamin
中科院分区：
文献类型：
--
作者：
Kosterlitz, Olivia;Tirado, Adamaris Muniz;Wate, Claire;Elg, Clint;Bozic, Ivana;Top, Eva M.;Kerr, Benjamin
为了增加我们对接合质粒的生态和进化的基本了解，我们需要可靠地估计它们在细菌细胞之间的转移率。当前测量传输速率的分析基于确定性建模框架。然而，这些测定中的某些细胞数量可能非常小，因此依赖这些数量的估计容易产生噪音。在这里，我们采用不同的方法来估计质粒转移率，该方法明确包含了这种噪音。受到 Luria 和 Delbrück 经典波动分析的启发，我们的方法基于随机建模框架。除了捕获质粒缀合的随机性质之外，我们的新方法 Luria-Delbrück 方法（“LDM”）还可用于多种细菌系统，包括当前方法不准确的情况。一个值得注意的例子涉及不同菌株或物种之间的质粒转移，其中一种类型的细胞捐赠质粒的速率不等于另一种细胞类型捐赠的速率。这些速率的不对称有可能使当前的转移估计产生偏差或受到限制，从而限制我们估计微生物群落转移的能力。相比之下，LDM 通过避免对测定中每个群体的生长和转移率的限制性假设来克服传统方法的障碍。通过随机模拟和实验，我们表明，与最广泛使用的方法相比，LDM 在估计传输速率方面具有较高的准确度和精确度，这可以产生与 LDM 估计值相差几个数量级的估计值。质粒转移通常可以在重要的临床病原体之间传播耐药性。这项研究表明，广泛使用的方法可能会导致质粒转移率的估计出现几个数量级的偏差，并提出了一种受经典 Luria-Delbrück 方法启发的新方法，用于准确评估这一基本速率参数
To increase our basic understanding of the ecology and evolution of conjugative plasmids, we need reliable estimates of their rate of transfer between bacterial cells. Current assays to measure transfer rate are based on deterministic modeling frameworks. However, some cell numbers in these assays can be very small, making estimates that rely on these numbers prone to noise. Here, we take a different approach to estimate plasmid transfer rate, which explicitly embraces this noise. Inspired by the classic fluctuation analysis of Luria and Delbrück, our method is grounded in a stochastic modeling framework. In addition to capturing the random nature of plasmid conjugation, our new methodology, the Luria–Delbrück method (“LDM”), can be used on a diverse set of bacterial systems, including cases for which current approaches are inaccurate. A notable example involves plasmid transfer between different strains or species where the rate that one type of cell donates the plasmid is not equal to the rate at which the other cell type donates. Asymmetry in these rates has the potential to bias or constrain current transfer estimates, thereby limiting our capabilities for estimating transfer in microbial communities. In contrast, the LDM overcomes obstacles of traditional methods by avoiding restrictive assumptions about growth and transfer rates for each population within the assay. Using stochastic simulations and experiments, we show that the LDM has high accuracy and precision for estimation of transfer rates compared to the most widely used methods, which can produce estimates that differ from the LDM estimate by orders of magnitude. Plasmid transfer can often spread resistance between important clinical pathogens. This study shows that widely used methods can lead to biased estimates of plasmid transfer rate by several orders of magnitude, and presents a new approach, inspired by the classic Luria-Delbrück approach, for accurately assessing this fundamental rate parameter
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