Adaptive landscapes of antibiotic resistance: population size and 'survival-of-the-flattest'.

抗生素耐药性的适应性景观：人口规模和“最平坦的生存”。

• 批准号: BB/M020975/1
• 负责人: Christopher Knight
• 金额: $ 40.88万
• 依托单位: University of Manchester
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2015
• 资助国家: 英国
• 起止时间: 2015 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FM020975%2F1
• 关键词: Adaptive; landscapes; antibiotic; resistance; population

Spontaneous mutation altering genetic sequences is a key engine of evolution. However, if the rate of mutations exceeds a 'critical mutation rate', changes occur too frequently for natural selection to maintain the population's genetic makeup. For example, one can think of mutation as moving offspring away from their parents in a 'fitness landscape' where peaks are genetic sequences with high fitness. If there is too much mutation (above a critical mutation rate), selection may be able to keep a population on a broad fitness peak, but not on a narrow one, so-called 'survival-of-the-flattest'. Such critical mutation rates are generally believed to be much higher than those seen in typical biological organisms. However, we have recently discovered that, in computer simulations, critical mutation rates are much lower in very small populations (<100 individuals). This suggests that survival-of-the-flattest could be occurring within the normal range of biological mutation rates.This proposal therefore aims to test whether survival-of-the-flattest really does occur in biology, using antibiotic resistance evolution as a test case. This requires an integrated, multi-disciplinary approach, conducting decisive wet-lab experiments, aligning computer simulations with biology and developing a rigorous and predictive theoretical framework that relates findings to mathematical understanding. Our first objective will be to characterise the fitness landscapes of bacteria resistant to different antibiotics to determine whether appropriate distinctions exist in practice between narrow and broad fitness peaks. Here we will focus on environments containing antibiotics, but will use experimental evolution (at high and minimal population sizes) with complete genome sequencing to consider mutations across the genome. Our second Objective is to develop computer simulations and theory to align with the biology. Specifically we need to relate our findings about critical mutation rates in simulations to existing theory. We then need to move both theory and simulation towards more biologically realistic assumptions. For instance we shall test more realistic fitness landscapes, particularly those determined empirically through our first Objective. Our final Objective is rigorously to test the coherence of the theory, simulation and biological experiment with each other. In particular we shall use experimental evolution at small population sizes to test whether we can detect the effect of critical mutation rates as predicted and further characterise the experimentally evolved strains to test the consistency of biological mechanisms involved.Rigorously unpicking this novel population size effect in the evolution of antibiotic resistance could have broad impacts. Antibiotic resistant bacteria inevitably appear by mutation with a small population size and bacteria resistant to one antibiotic will be knocked down to small population sizes by another. The hypotheses we shall test here will determine whether the maintenance of particular antibiotic resistances at such small population sizes, could depend on the details of the different fitness landscapes imposed by different antibiotics in a theoretically predictable way - potentially crucial information in combatting antimicrobial resistance. In sum this work will closely link experimental approaches to evolution in biology, simulation and theory to determine if when and how survival-of-the-flattest impinges on the increasingly critical issue of antibiotic resistance evolution.

改变基因序列的自发突变是进化的关键引擎。然而，如果突变率超过“临界突变率”，变化发生得太频繁，自然选择无法维持种群的遗传组成。例如，人们可以将突变视为在“适应度景观”中将后代从父母身边移开，其中峰值是具有高适应度的基因序列。如果有太多的突变（超过临界突变率），选择可能能够使种群保持在一个广泛的适应度峰值上，但不能保持在一个狭窄的峰值上，即所谓的“最平坦者生存”。这种临界突变率通常被认为比在典型的生物有机体中看到的要高得多。然而，我们最近发现，在计算机模拟中，临界突变率在非常小的群体（<100个个体）中要低得多。这表明，平坦生存试验可能发生在生物突变率的正常范围内，因此，这项提议旨在测试平坦生存试验是否真的发生在生物学中，使用抗生素耐药性进化作为测试案例。这需要一个综合的，多学科的方法，进行决定性的湿实验室实验，调整计算机模拟与生物学和开发一个严格的和预测性的理论框架，将发现与数学理解。我们的第一个目标将是确定对不同抗生素具有耐药性的细菌的适应性景观，以确定在实践中窄适应性峰和宽适应性峰之间是否存在适当的区别。在这里，我们将重点关注含有抗生素的环境，但将使用实验进化（在高和最小的群体大小）与完整的基因组测序来考虑整个基因组的突变。我们的第二个目标是开发计算机模拟和理论，以符合生物学。具体来说，我们需要将我们在模拟中关于临界突变率的发现与现有理论联系起来。然后，我们需要将理论和模拟都转向更符合生物学现实的假设。例如，我们将测试更现实的适应度景观，特别是那些通过我们的第一个目标经验确定的景观。我们的最终目标是严格测试理论，模拟和生物实验的一致性。特别是，我们将在小种群规模下使用实验进化来测试我们是否可以检测到预测的临界突变率的影响，并进一步验证实验进化的菌株以测试所涉及的生物学机制的一致性。抗生素耐药性细菌不可避免地会出现突变，种群规模较小，对一种抗生素耐药的细菌会被另一种抗生素击倒，种群规模较小。我们将在这里测试的假设将确定在如此小的种群规模下维持特定抗生素耐药性是否可能取决于不同抗生素以理论上可预测的方式施加的不同适应度景观的细节-这可能是对抗抗生素耐药性的关键信息。总之，这项工作将把实验方法与生物学、模拟和理论中的进化紧密联系起来，以确定最平的生存何时以及如何影响抗生素耐药性进化这一日益关键的问题。

项目成果

Critical Mutation Rate has an Exponential Dependence on Population Size for Eukaryotic-length Genomes with Crossover.

• DOI: 10.1038/s41598-017-14628-x
• 发表时间: 2017-11-14
• 期刊: Scientific reports
• 影响因子: 4.6
• 作者: Aston E;Channon A;Belavkin RV;Gifford DR;Krašovec R;Knight CG
• 通讯作者: Knight CG

Measuring Microbial Mutation Rates with the Fluctuation Assay

用波动测定法测量微生物突变率

• DOI: 10.3791/60406-v
• 发表时间: 2019
• 期刊: Journal of Visualized Experiments
• 作者: Knight C

Opposing effects of final population density and stress on Escherichia coli mutation rate.

• DOI: 10.1038/s41396-018-0237-3
• 发表时间: 2018-12
• 期刊: The ISME journal
• 作者: Krašovec R;Richards H;Gifford DR;Belavkin RV;Channon A;Aston E;McBain AJ;Knight CG
• 通讯作者: Knight CG

Critical mutation rate has an exponential dependence on population size for eukaryotic-length genomes

对于真核长度基因组，临界突变率与种群大小呈指数依赖性

• 发表时间: 2016
• 期刊: Proceedings of the Artificial Life Conference 2016, ALIFE 2016
• 作者: Aston E.

Monotonicity of fitness landscapes and mutation rate control.

• DOI: 10.1007/s00285-016-0995-3
• 发表时间: 2016-12
• 期刊: JOURNAL OF MATHEMATICAL BIOLOGY
• 影响因子: 1.9
• 作者: Belavkin, Roman V.;Channon, Alastair;Aston, Elizabeth;Aston, John;Krasovec, Rok;Knight, Christopher G.
• 通讯作者: Knight, Christopher G.