Sub-inhibitory antibiotic treatment selects for enhanced metabolic efficiency
Sub-inhibitory antibiotic treatment selects for enhanced metabolic efficiency
复制标题
DOI：
10.1128/spectrum.03241-23
复制
发表时间：
2024-01-16
影响因子：
3.7
通讯作者：
Lopatkin，Allison J.
中科院分区：
文献类型：
--
作者：
Aduru，Sai Varun;Szenkiel，Karolina;Lopatkin，Allison J.
细菌生长和代谢率往往密切相关。然而，在抗生素选择中，这种关系出现了一个悖论：当细菌处于代谢休眠状态时，抗生素的功效会下降，而抗生素会选择在治疗期间生长最快的耐药细胞。也就是说，与直觉相反，抗生素的选择倾向于生长快但代谢慢的细菌。尽管存在这种明显的矛盾，但抗生素耐药细胞的历史特征主要是在生长的背景下，而代谢方面类似变化的程度相对未知。在这里，我们观察到先前进化的抗生素耐药菌株在生长和代谢之间表现出独特的关系，即营养利用变得更有效，而不管生长速度如何。为了更好地理解这一意想不到的现象，我们使用了一个简化的模型来模拟细菌群体通过连续的瓶颈事件适应亚抑制性抗生素选择。模拟预测亚抑制杀菌抗生素浓度可以选择提高代谢效率，这是根据营养利用来定义的：即使在没有处理的情况下，适应药物的细胞也能够在利用较少底物的情况下获得相同的生物量。此外，模拟预测，恢复代谢效率将使耐药细菌重新敏感，表现出代谢依赖的耐药性；我们利用大肠杆菌在卡比西林治疗下的适应性实验室进化证实了这一结果。总之，这些结果表明，在抗生素治疗过程中，代谢效率受到直接选择压力，进化背景的差异可能决定了不同抗生素的疗效和相应的再致敏方法。抗生素耐药病原体的持续出现，加上药物发现管道的停滞，凸显了更好地了解抗生素耐药性潜在进化机制的迫切需要。为此，细菌生长和代谢率通常密切相关，耐药细胞在历史上只在生长的背景下被表征。然而，在抗生素的选择中，抗生素反而倾向于生长快、代谢慢的细胞。通过数学建模和实验的综合方法，本研究因此解决了抗生素选择是否驱动代谢变化的重要知识差距，这些变化补充和/或独立作用于抗生素耐药性表型。
Bacterial growth and metabolic rates are often closely related. However, under antibiotic selection, a paradox in this relationship arises: antibiotic efficacy decreases when bacteria are metabolically dormant, yet antibiotics select for resistant cells that grow fastest during treatment. That is, antibiotic selection counterintuitively favors bacteria with fast growth but slow metabolism. Despite this apparent contradiction, antibiotic resistant cells have historically been characterized primarily in the context of growth, whereas the extent of analogous changes in metabolism is comparatively unknown. Here, we observed that previously evolved antibiotic-resistant strains exhibited a unique relationship between growth and metabolism whereby nutrient utilization became more efficient, regardless of the growth rate. To better understand this unexpected phenomenon, we used a simplified model to simulate bacterial populations adapting to sub-inhibitory antibiotic selection through successive bottlenecking events. Simulations predicted that sub-inhibitory bactericidal antibiotic concentrations could select for enhanced metabolic efficiency, defined based on nutrient utilization: drug-adapted cells are able to achieve the same biomass while utilizing less substrate, even in the absence of treatment. Moreover, simulations predicted that restoring metabolic efficiency would re-sensitize resistant bacteria exhibiting metabolic-dependent resistance; we confirmed this result using adaptive laboratory evolutions ofEscherichia coliunder carbenicillin treatment. Overall, these results indicate that metabolic efficiency is under direct selective pressure during antibiotic treatment and that differences in evolutionary context may determine both the efficacy of different antibiotics and corresponding re-sensitization approaches.IMPORTANCEThe sustained emergence of antibiotic-resistant pathogens combined with the stalled drug discovery pipelines highlights the critical need to better understand the underlying evolution mechanisms of antibiotic resistance. To this end, bacterial growth and metabolic rates are often closely related, and resistant cells have historically been characterized exclusively in the context of growth. However, under antibiotic selection, antibiotics counterintuitively favor cells with fast growth, and slow metabolism. Through an integrated approach of mathematical modeling and experiments, this study thereby addresses the significant knowledge gap of whether antibiotic selection drives changes in metabolism that complement, and/or act independently, of antibiotic resistance phenotypes.