Department of Chemical Engineering, University of Rochester, Rochester, New York, USA.
Department of Biology, Barnard College, New York, New York, USA.
Microbiol Spectr. 2024 Feb 6;12(2):e0324123. doi: 10.1128/spectrum.03241-23. Epub 2024 Jan 16.
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 of under 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.
细菌的生长和代谢率通常密切相关。然而，在抗生素选择下，这种关系出现了一个悖论：当细菌处于代谢休眠状态时，抗生素的疗效会降低，但抗生素会选择在治疗过程中生长最快的耐药细胞。也就是说，抗生素选择出人意料地有利于生长速度快但代谢速度慢的细菌。尽管存在这种明显的矛盾，但历史上对抗生素耐药细胞的特征主要是在生长的背景下进行的，而代谢方面类似变化的程度则相对未知。在这里，我们观察到，先前进化出的抗生素耐药菌株表现出一种独特的生长和代谢关系，即营养物质的利用变得更加高效，而不管生长速度如何。为了更好地理解这一意外现象，我们使用简化模型模拟了细菌种群通过连续瓶颈事件适应亚抑菌抗生素选择的情况。模拟预测，亚抑菌杀菌抗生素浓度可以选择增强代谢效率，这是基于营养物质的利用来定义的：适应药物的细胞能够在不使用药物的情况下利用更少的基质达到相同的生物量。此外，模拟预测，恢复代谢效率将使表现出代谢依赖性耐药的耐药细菌重新敏感；我们使用在卡那西林处理下的适应性实验室进化实验证实了这一结果。总的来说，这些结果表明，代谢效率在抗生素治疗过程中受到直接的选择性压力，而进化背景的差异可能决定不同抗生素的疗效和相应的重新敏感方法。
重要性
抗生素耐药性病原体的持续出现以及停滞不前的药物发现管道突出表明，迫切需要更好地了解抗生素耐药性的潜在进化机制。为此，细菌的生长和代谢率通常密切相关，而耐药细胞的历史特征仅在生长背景下进行。然而，在抗生素选择下，抗生素出人意料地有利于生长速度快、代谢速度慢的细胞。通过数学建模和实验的综合方法，本研究解决了一个重要的知识空白，即抗生素选择是否会导致代谢变化，这些变化是否与抗生素耐药表型互补，以及/或独立于抗生素耐药表型。
