Mechanisms underlying bacterial sensitivity to host-targeted drugs

细菌对宿主靶向药物敏感的机制

• 批准号: 10642874
• 负责人: Amir Mitchell
• 金额: $ 53.45万
• 依托单位: UNIV OF MASSACHUSETTS MED SCH WORCESTER
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-06-10 至 2027-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10642874
• 关键词: Antibiotic Resistance; Antibiotic Therapy; Antibiotics; Bacteria; Biological Models; Categories; Cells; Chronic; Classification; Complement; Consumption; Development; Drug Modelings; Drug Prescriptions; Drug Screening; Drug Targeting; Drug resistance; Escherichia coli; Evolution; Future; Genetic Screening; Genome; Goals; Graph; Growth; Health; Human Development; Human Microbiome; Intervention; Knock-out; Maintenance; Maps; Measurement; Measures; Multi-Drug Resistance; Mutation; Pathway interactions; Pharmaceutical Preparations; Physiological; Post-Translational Protein Processing; Probability; Process; Resistance; Resistance profile; Risk; Testing; Time; Toxic effect; Work; Xenobiotics; antimicrobial; computerized tools; drug mechanism; drug sensitivity; dysbiosis; experimental study; gain of function; gain of function mutation; gut microbiome; knockout gene; loss of function; mathematical model; member; microbial; microbiome; pressure; resistance mechanism; resistant strain; screening

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.

肠道微生物组是人类正常发育和维持健康的关键。然而，在这方面， 微生物组的完整性可能会受到针对某些微生物的抗微生物外源性物质的消费的严重影响。 微生物成员。这种选择压力可能会引发物种组成的变化， 最终导致对健康有害的微生物群失调。最近的药物筛选显示，四分之一的 不作为抗生素处方的宿主靶向药物在生理学上具有有效的抗微生物活性， 浓度的然而，尽管许多靶向宿主的药物的细菌毒性得到了广泛的认可，但靶向宿主的药物仍然存在。 这些药物在细菌细胞中的作用仍然是未知的。鉴于这种现象的规模， 数百种广泛使用的处方药，迫切需要开发一种高通量方法， 确定细菌毒性的潜在途径和过程。这种系统的理解将揭示 哪些宿主靶向药物类似于已知类别的抗生素药物，如果长期治疗， 无意中导致多药抗生素耐药性。此外，通过以下方式对宿主靶向药物进行分类： 细菌毒性的机制可以揭示尚未开发的抗生素的潜在新靶点。 我们的总体目标是研究细菌途径和过程的毒性宿主靶向 并揭示了可以促进对它们的进化抗性的机制。我们将使用埃希氏菌 大肠杆菌作为模型系统，并将利用我们开发的汇集遗传筛选方法来绘制所有 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 我们已经鉴定出对大肠杆菌有抑制作用大肠杆菌和一套互补的142种标准抗生素（ 已知目标）。总之，这些药物敏感性的系统测量（3，680个敲除菌株X 269种药物）将允许我们通过计算推断出一个网络图，该网络图通过药物的分布图来映射药物-药物相似性。 敲除菌株敏感性。该网络将用于注释宿主靶向药物的细菌靶标， 筛选命中的同一性以及与注释良好的抗生素的药物相似性。我们将补充 通过实验室进化实验进行筛选，并将确定在药物作用下出现的耐药机制。 选择.这种方法将通过揭示额外的药物靶点来扩大我们对药物靶点的机制理解。 对相同药物的抗性机制（例如，功能获得性突变）。最后，我们将调查 进化株和天然E.大肠杆菌分离株，并将评估是否适应宿主- 有针对性的药物由于无意中选择对多种药物和抗生素产生耐药性而带来风险。拟议 这项工作将首次系统地绘制细菌对宿主靶向药物敏感性的机制。 如果长期服用特定的宿主靶向药物会增加 出现对特定抗生素的交叉耐药性或多重耐药性。