大肠杆菌反复暴露于氨苄西林抗生素下的抗药性单细胞表型特征。

IF 5 2区 生物学 Q1 MICROBIOLOGY mSystems Pub Date : 2024-07-23 Epub Date: 2024-06-26 DOI:10.1128/msystems.00256-24
Silvia Kollerová, Lionel Jouvet, Julia Smelková, Sara Zunk-Parras, Alexandro Rodríguez-Rojas, Ulrich K Steiner
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引用次数: 0

摘要

抗生素的非遗传表型耐药性给抗生素疗法带来了挑战。这种表型抗药性的特征对遗传抗药性的进化具有影响。表型耐药性的形式多种多样,但与遗传耐药性相比,对表型耐药性特征的探索仍然较少。在这里,我们增加了表型抗性大肠杆菌细胞单细胞特征的新组合,并通过暴露于不同水平的氨苄西林抗生素,将其与亲代群体易感细胞的特征进行比较。与预期不同的是,我们没有发现表型抗性细胞具有通常描述的停止生长或接近生长的特征。我们发现,与接触氨苄西林抗生素前的生长率相比,表型抗性细胞的生长率降低了约 50%。生长速度的降低是抗生素暴露后的延迟变化,表明这是一种诱导反应,而不是随机切换,也不是像通常描述的那样是由预定状态引起的。表型抗性细胞在氨苄西林暴露下的存活率最高,而与预期相反的是,不仅快速生长的细胞在氨苄西林的诱导下死亡率很高,生长受阻的细胞也是如此。我们的研究结果支持表型抗性的多种模式,我们揭示了与基因固定抗性进化增强相关的抗性细胞特征,这支持了表型抗性细胞在基因抗性进化中的作用未得到充分重视的说法。更好地了解表型耐药性将有利于通过开发和吸收有效的抗表型耐药性策略来对抗遗传耐药性:抗生素耐药性是现代医学面临的一大挑战。除了抗生素的遗传抗药性外,不可遗传的表型抗药性也可能在抗生素抗药性的进化过程中发挥关键作用。我们利用高度可控的微流体系统,对反复接触抗生素的单细胞进行了表征。常见的抗生素疗法可能会产生抗生素暴露波动。这些耐药细胞的表型特征与之前描述的耐药表型不同,凸显了耐药模式的多样性。我们发现的耐药细胞的表型特征还意味着,这些细胞可能为基因耐药提供了垫脚石,从而导致治疗失败。
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Phenotypic resistant single-cell characteristics under recurring ampicillin antibiotic exposure in Escherichia coli.

Non-heritable, phenotypic drug resistance toward antibiotics challenges antibiotic therapies. Characteristics of such phenotypic resistance have implications for the evolution of heritable resistance. Diverse forms of phenotypic resistance have been described, but phenotypic resistance characteristics remain less explored than genetic resistance. Here, we add novel combinations of single-cell characteristics of phenotypic resistant E. coli cells and compare those to characteristics of susceptible cells of the parental population by exposure to different levels of recurrent ampicillin antibiotic. Contrasting expectations, we did not find commonly described characteristics of phenotypic resistant cells that arrest growth or near growth. We find that under ampicillin exposure, phenotypic resistant cells reduced their growth rate by about 50% compared to growth rates prior to antibiotic exposure. The growth reduction is a delayed alteration to antibiotic exposure, suggesting an induced response and not a stochastic switch or caused by a predetermined state as frequently described. Phenotypic resistant cells exhibiting constant slowed growth survived best under ampicillin exposure and, contrary to expectations, not only fast-growing cells suffered high mortality triggered by ampicillin but also growth-arrested cells. Our findings support diverse modes of phenotypic resistance, and we revealed resistant cell characteristics that have been associated with enhanced genetically fixed resistance evolution, which supports claims of an underappreciated role of phenotypic resistant cells toward genetic resistance evolution. A better understanding of phenotypic resistance will benefit combatting genetic resistance by developing and engulfing effective anti-phenotypic resistance strategies.

Importance: Antibiotic resistance is a major challenge for modern medicine. Aside from genetic resistance to antibiotics, phenotypic resistance that is not heritable might play a crucial role for the evolution of antibiotic resistance. Using a highly controlled microfluidic system, we characterize single cells under recurrent exposure to antibiotics. Fluctuating antibiotic exposure is likely experienced under common antibiotic therapies. These phenotypic resistant cell characteristics differ from previously described phenotypic resistance, highlighting the diversity of modes of resistance. The phenotypic characteristics of resistant cells we identify also imply that such cells might provide a stepping stone toward genetic resistance, thereby causing treatment failure.

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来源期刊
mSystems
mSystems Biochemistry, Genetics and Molecular Biology-Biochemistry
CiteScore
10.50
自引率
3.10%
发文量
308
审稿时长
13 weeks
期刊介绍: mSystems™ will publish preeminent work that stems from applying technologies for high-throughput analyses to achieve insights into the metabolic and regulatory systems at the scale of both the single cell and microbial communities. The scope of mSystems™ encompasses all important biological and biochemical findings drawn from analyses of large data sets, as well as new computational approaches for deriving these insights. mSystems™ will welcome submissions from researchers who focus on the microbiome, genomics, metagenomics, transcriptomics, metabolomics, proteomics, glycomics, bioinformatics, and computational microbiology. mSystems™ will provide streamlined decisions, while carrying on ASM''s tradition of rigorous peer review.
期刊最新文献
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