基因与环境相互作用产生的极端拮抗作用
Extreme antagonism arising from gene-environment interactions
摘要：
生物系统中的拮抗性相互作用，当一种干扰减弱另一种干扰的作用时发生，通常被解释为两种干扰影响相同细胞途径或功能的证据。然而，这种解释忽略了极端的对抗性相互作用，其中有害的扰动补偿了由于先前扰动而损失的功能。在这里，我们报告了基因与环境的相互作用，涉及基因突变，这些基因突变在允许的环境中是有害的，但在限制生长的特定环境中是有益的。这些极端的拮抗相互作用构成了基因-环境类似物的合成救援先前观察到的基因-基因相互作用。我们的方法使用两个独立的自适应进化步骤来解决缺乏实验方法来系统地识别这种极端的相互作用。我们将该方法应用于大肠杆菌，通过连续地将其适应于不含和含抗生素利福平的确定的葡萄糖培养基。该方法确定了多个突变，这些突变在利福平存在的情况下是有益的，而在利福平不存在的情况下是有害的。转录分析表明，拮抗适应性突变抑制一个严格的响应样转录程序，而非拮抗突变具有相反的转录谱。我们的方法代表了系统表征极端拮抗基因-药物相互作用的一步，可用于确定针对抗生素耐药性选择的靶点。
英文摘要：
Antagonistic interactions in biological systems, which occur when one perturbation blunts the effect of another, are typically interpreted as evidence that the two perturbations impact the same cellular pathway or function. Yet, this interpretation ignores extreme antagonistic interactions wherein an otherwise deleterious perturbation compensates for the function lost due to a prior perturbation. Here, we report on gene-environment interactions involving genetic mutations that are deleterious in a permissive environment but beneficial in a specific environment that restricts growth. These extreme antagonistic interactions constitute gene-environment analogs of synthetic rescues previously observed for gene-gene interactions. Our approach uses two independent adaptive evolution steps to address the lack of experimental methods to systematically identify such extreme interactions. We apply the approach to Escherichia coli by successively adapting it to defined glucose media without and with the antibiotic rifampicin. The approach identified multiple mutations that are beneficial in the presence of rifampicin and deleterious in its absence. The analysis of transcription shows that the antagonistic adaptive mutations repress a stringent response-like transcriptional program, while non-antagonistic mutations have an opposite transcriptional profile. Our approach represents a step toward the systematic characterization of extreme antagonistic gene-drug interactions, which can be used to identify targets to select against antibiotic resistance.
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