Mechanisms underlying bacterial sensitivity to host-targeted drugs

肠道微生物群是人类正常发育和维持健康的关键。然而， 微生物组的完整性可能会受到针对某些目标的抗菌素的消费的严重影响 它的微生物成员。这种选择压力会引发物种组成的变化，最终 最终导致对健康有害的微生物群失调。最近的药物筛查显示，四分之一的人 针对宿主的药物，不是作为抗生素开出的，在生理上有很强的抗菌活性 浓度。然而，尽管许多宿主靶向药物的细菌毒性得到了广泛的认可，但靶向 这些药物在细菌细胞中的作用目前大多还不清楚。鉴于这一现象的规模，涉及 数百种广泛使用的处方药，迫切需要开发一种高通量方法来 确定潜在的细菌毒性的途径和过程。这种系统的理解将揭示 哪些宿主靶向药物与已知类别的抗生素药物相似，如果延长治疗时间可以 无意中导致了多药耐药性。此外，宿主靶向药物的分类按 细菌毒性机制可以通过尚未开发的抗生素发现潜在的新靶点。 我们的总体目标是研究宿主靶向毒性的细菌途径和过程。 并揭示可以推动对它们的进化耐药性的机制。我们将使用埃希里希菌 Coli作为模型系统，并将利用我们开发的池基因筛查方法来定位所有 调节多种药物毒性的单基因敲除。我们将筛选117种宿主靶向药物 我们已经确定对大肠杆菌有抑制作用，并补充了142种标准抗生素(包括 已知目标)。加在一起，这些药物敏感性的系统测量(3680个基因敲除菌株X 269种药物)将允许我们通过计算推断出绘制药物-药物相似性的网络图 击倒应变灵敏度。这个网络将被用来注释宿主靶向药物的细菌靶标 无论是银幕大片的身份，还是药物与注释充分的抗生素的相似性。我们将补充 通过实验室进化实验进行筛选，并将确定药物下出现的耐药机制 选择。这种方法将通过揭示更多的 对相同药物的耐药机制(例如，功能获得突变)。最后，我们将调查 进化菌株和自然分离的大肠杆菌的附带耐药性，并将评估是否适应宿主- 靶向药物存在风险，因为无意中选择了多药耐药性和抗生素耐药性。建议数 这项工作将首次系统地将细菌敏感性的机制映射到宿主靶标 并将发现长期服用特定宿主靶向药物是否会增加 出现对特定抗生素的交叉耐药性或多重耐药。

The gut microbiome is key for proper human development and for maintenance of health. However, microbiome integrity can be severely impacted by consumption of antimicrobial xenobiotics that target some of its microbial members. Such selection pressure can trigger shifts in its species composition and ultimately culminate in microbiome dysbiosis that is detrimental to health. Recent drug screens revealed that a quarter of host-targeted drugs, that are not prescribed as antibiotics, have potent antimicrobial activity at physiological concentrations. Yet, while the bacterial toxicity of many host-targeted drugs is widely appreciated, the targets of these drugs in bacterial cells remain mostly unknown. Given the scale of this phenomenon, involving hundreds of widely prescribed drugs, there is a critical need to develop a high-throughput approach for identifying the pathways and processes underlying bacterial toxicity. Such systematic understanding will reveal which host-targeted drugs resemble known categories of antibiotic drugs and if prolonged treatment can unintentionally contribute to multi-drug antibiotic resistance. Moreover, classification of host-targeted drugs by mechanism of bacterial toxicity can uncover potentially new targets for by yet-to-be developed antibiotics. Our overall goal is to investigate the bacterial pathways and processes underlying the toxicity of host-targeted drugs and reveal the mechanisms that can propel evolved resistance against them. We will use Escherichia coli as a model system and will leverage on a pooled genetic screening approach we developed to map all single-gene knockouts that modulate toxicity across multiple drugs. We will screen 117 host-targeted drugs that we already identified as inhibitory for E. coli and a complementary set of 142 standard antibiotics (with known targets). Taken together, these systematic measurements of drug sensitivity (3,680 knockout strains X 269 drugs) will allow us to computationally infer a network graph mapping drug-drug resemblance by profiles of knockout strain sensitivity. This network will be used to annotate the bacterial targets of host-targeted drugs by both the identity of screen hits and by the drug similarity to well-annotated antibiotics. We will complement the screen with lab evolution experiments and will identify the resistance mechanisms that emerge under drug selection. This approach will expand our mechanistic understanding of drug targets by uncovering additional resistance mechanisms against the same drugs (e.g., gain-of-function mutations). Lastly, we will investigate collateral drug resistance in evolved strains and natural E. coli isolates and will evaluate if adaptation to host- targeted drugs poses a risk by unintentionally selecting for multi-drug and antibiotics resistance. The proposed work will, for the first time, systematically map the mechanisms underlying bacterial sensitivity to host-targeted drugs and will uncover if chronic administration of specific host-targeted drugs increases the likelihood for emergence of cross-resistance against specific antibiotics or multi-drug resistance.