抗微生物治疗的成功受到耐药性演变的威胁。种群遗传模型是减轻这种威胁的重要工具。然而,大多数这样的模型考虑通过单个突变步骤出现抗性。这里,我们收集了实验证据,证明耐药性进化遵循两种模式:(i)单个突变,这提供了很大的阻力优势,或(ii)多个突变,每个人都会带来小小的好处,结合起来产生高水平的抗性。使用随机建模,然后,我们调查了这两种模式在各种治疗下对治疗失败和种群多样性的影响。我们发现,如果需要两个以上的突变,耐药性的进化将受到很大限制,并且这种限制的程度取决于药物类型和药代动力学特征的组合。Further,如果多个突变是必要的,适应性治疗,这只会抑制细菌种群,比积极治疗更长时间地延迟由于抵抗而导致的治疗失败,旨在根除。
抗生素耐药性的上升正威胁着我们治疗细菌感染的能力。细菌通常通过在治疗期间获得新的基因突变来进化抗性。了解耐药性如何在细菌群体中出现和传播对于防止抗生素药物失效至关重要。数学模型是探索细菌如何对抗生素作出反应和评估耐药性风险的有用工具。通常,这些模型只考虑细菌获得一种遗传突变,使它们几乎不受治疗的情况。但是,在自然界中,这不是唯一的可能性。尽管一些突变确实会给细菌带来高水平的耐药性,许多其他药物只提供少量的保护。如果这些突变在同一细菌细胞中积累,它们的作用可以结合起来,使菌株高度抵抗治疗。但目前尚不清楚多个突变的出现如何影响治疗失败的风险和细菌种群的多样性。为了回答这个问题,Igleretal.设计了一个数学模型,其中每个细菌能够在治疗期间多次突变。该模型显示,如果一个突变本身提供了高水平的抗性,细菌存活治疗的风险非常高。但是,如果需要两个以上的突变才能达到高水平的抗性,风险下降到几乎没有。Igleretal.还发现,细菌进化出足够高的耐药性的机会受到所用抗生素类型和药物衰减速度的影响。低水平的抗性突变,调整治疗以维持可接受数量的敏感细菌作为(少量)耐药细菌的竞争者,比尝试一次杀死所有细菌更有效地延缓治疗失败。这些发现表明,根据低水平和高水平抗性突变的比例调整用于细菌感染的治疗策略可以减缓抗性的演变。为了在现实世界中应用这些模型,测量单个突变赋予的抗性水平将是重要的。这里使用的模型类型还可以预测其他抵抗治疗的疾病的反应,比如癌症。
The success of antimicrobial treatment is threatened by the evolution of drug resistance. Population genetic models are an important tool in mitigating that threat. However, most such models consider resistance emergence via a single mutational step. Here, we assembled experimental evidence that drug resistance evolution follows two patterns: (i) a single mutation, which provides a large resistance benefit, or (ii) multiple mutations, each conferring a small benefit, which combine to yield high-level resistance. Using stochastic modeling, we then investigated the consequences of these two patterns for treatment failure and population diversity under various treatments. We find that resistance evolution is substantially limited if more than two mutations are required and that the extent of this limitation depends on the combination of drug type and pharmacokinetic profile. Further, if multiple mutations are necessary, adaptive treatment, which only suppresses the bacterial population, delays treatment failure due to resistance for a longer time than aggressive treatment, which aims at eradication.
The rise in antibiotic resistance is threatening our ability to treat bacterial infections. Bacteria often evolve resistance by acquiring new genetic mutations during the treatment period. Understanding how resistance emerges and spreads through a bacterial population is crucial to prevent antibiotic drugs from failing. Mathematical models are a useful tool for exploring how bacteria will respond to antibiotics and assessing the risk of resistance. Usually, these models only consider instances where bacteria acquire one genetic mutation that makes them virtually impervious to treatment. But, in nature, this is not the only possibility. Although some mutations do give bacteria a high level of resistance, numerous others only provide small amounts of protection against the drug. If these mutations accumulate in the same bacterial cell, their effects can combine to make the strain highly resistant to treatment. But it was unclear how the emergence of multiple mutations affects the risk of treatment failure and the diversity of the bacterial population. To answer this question, Igler et al. devised a mathematical model in which each bacterium is able to mutate multiple times during the treatment period. The model revealed that if one mutation provides a high level of resistance on its own, the risk of bacteria surviving treatment is very high. But, if it takes more than two mutations to achieve a high level of resistance, the risk drops to almost nothing. Igler et al. also found that the chance of bacteria evolving high enough resistance is affected by the type of antibiotics used and how fast the drug decays. With low-level resistance mutations, adapting treatment to maintain an acceptable number of sensitive bacteria as competitors for (a small number of) resistant bacteria was more effective at delaying treatment failure than trying to kill all the bacteria at once. These findings suggest that adjusting the treatment strategy used for bacterial infections according to the proportion of low- and high-level resistance mutations could slow down the evolution of resistance. To apply these models in the real world, it will be important to measure the level of resistance conferred by single mutations. The type of models used here could also predict the response of other diseases that resist treatment, such as cancer.