Artemova Tatiana, Gerardin Ylaine, Dudley Carmel, Vega Nicole M, Gore Jeff
Department of Physics, Massachusetts Institute of Technology, Cambridge, MA, USA.
Department of Systems Biology, Harvard Medical School, Boston, MA, USA.
Mol Syst Biol. 2015 Jul 29;11(7):822. doi: 10.15252/msb.20145888.
Bacterial antibiotic resistance is typically quantified by the minimum inhibitory concentration (MIC), which is defined as the minimal concentration of antibiotic that inhibits bacterial growth starting from a standard cell density. However, when antibiotic resistance is mediated by degradation, the collective inactivation of antibiotic by the bacterial population can cause the measured MIC to depend strongly on the initial cell density. In cases where this inoculum effect is strong, the relationship between MIC and bacterial fitness in the antibiotic is not well defined. Here, we demonstrate that the resistance of a single, isolated cell-which we call the single-cell MIC (scMIC)-provides a superior metric for quantifying antibiotic resistance. Unlike the MIC, we find that the scMIC predicts the direction of selection and also specifies the antibiotic concentration at which selection begins to favor new mutants. Understanding the cooperative nature of bacterial growth in antibiotics is therefore essential in predicting the evolution of antibiotic resistance.
细菌对抗生素的耐药性通常通过最低抑菌浓度（MIC）来量化，最低抑菌浓度定义为从标准细胞密度开始抑制细菌生长的抗生素的最小浓度。然而，当抗生素耐药性由降解介导时，细菌群体对抗生素的集体失活会导致测得的最低抑菌浓度强烈依赖于初始细胞密度。在这种接种物效应很强的情况下，最低抑菌浓度与抗生素中细菌适应性之间的关系并不明确。在这里，我们证明单个分离细胞的耐药性——我们称之为单细胞最低抑菌浓度（scMIC）——为量化抗生素耐药性提供了一个更好的指标。与最低抑菌浓度不同，我们发现单细胞最低抑菌浓度可以预测选择方向，还能确定选择开始有利于新突变体的抗生素浓度。因此，了解抗生素中细菌生长的协同性质对于预测抗生素耐药性的演变至关重要。
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