Single-cell characterization of antibiotic-induced heteroresistance

抗生素诱导的异质抗性的单细胞表征

• 批准号: 10317120
• 负责人: Saeed F Tavazoie
• 金额: $ 24.3万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-12-10 至 2023-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10317120
• 关键词: Address; Adjuvant; Affect; Aminoglycosides; Antibiotic Resistance; Antibiotic Therapy; Antibiotics; Antimicrobial Resistance; Bacteria; CRISPR interference; Cells; Complement; Complex; Development; Discipline; Discrimination; Drug Targeting; Dyes; Effectiveness; Escherichia coli; Essential Genes; Etiology; Eukaryota; Exhibits; Expression Profiling; Foundations; Future; Gene Expression; Gene Expression Profile; Genes; Genetic Transcription; Genome; Genomic approach; Heterogeneity; Intermediate resistance; Investigation; Libraries; Masks; Messenger RNA; Metabolic; Methods; Multi-Drug Resistance; Nature; Pathway interactions; Pharmaceutical Preparations; Population; Predisposition; Proteomics; Public Health; Quinolones; RNA; Research; Resistance; Resolution; Staphylococcus aureus; Sum; System; Systems Biology; Technology; Testing; Variant; Work; antimicrobial; bacterial community; beta-Lactams; cost; drug candidate; functional genomics; gene discovery; gene repression; genome-wide; improved; in situ sequencing; microbial community; novel; overexpression; prevent; promoter; response; screening; single-cell RNA sequencing; small molecule inhibitor; tool; transcriptome; transcriptome sequencing

Project Summary Antimicrobial resistance (AMR) is one of the biggest threats to public health. The complex heterogeneous nature of bacterial communities poses a fundamental challenge in understanding the mechanisms of AMR. Even genetically homogenous bacterial populations can exhibit differential susceptibility to antibiotics, a phenomenon known as antibiotic heteroresistance. Pre-existing variation in gene expression states is a fundamentally important mechanism that underlies heteroresistance. Also, it has been shown that antibiotics themselves could induce transcriptional responses in a small subpopulation of cells that protect them from drug attack. Remarkably, studies have shown that repressing these responses with small molecule inhibitors leads to a substantial reduction of multidrug resistance. These findings highlight how understanding transcriptional heterogeneity could be the foundation for development of novel effective antimicrobial strategies. However, systematic investigation of how transcriptional heterogeneity affects antibiotic sensitivity has been lacking, due to unavailability of suitable tools and approaches. Recent work has clearly demonstrated the utility of high- throughput single-cell RNA sequencing (scRNA-seq) technology to explore gene expression states of eukaryotes. However, comparable tools for bacteria do not exist due to numerous challenges. We have recently overcome these challenges by developing Prokaryotic Expression-profiling by Tagging RNA In Situ and sequencing (PETRI-seq), a low-cost, high-throughput, prokaryotic scRNA-seq technology. PETRI-seq can capture single-cell bacterial transcriptomes with high purity and low capture bias, enabling robust discrimination of transcriptional states of various subpopulations including those that represent as rare as 0.05% of the population. Here, we propose strategies to further improve the sensitivity of PETRI-seq, and apply it to profile the heterogeneous transcriptional responses of isogenic Escherichia coli to antibiotic challenge at single-cell resolution. Using three different classes of antibiotics, we will study how different antibiotics cause cells to differentiate into subpopulations with distinct transcriptional states. We will study how these transcriptional states change over the course of antibiotic treatment and contribute to survival. Finally, we propose to determine which transcriptional states induced by antibiotics are important for survival. Utilizing two functionally-complementing screening platforms – systematic over-expression and CRISPR interference, we will interrogate how the expression of every gene in the E. coli genome affects antibiotic sensitivity. We will validate the discovered genes and pathways whose expression enhance survival, and determine whether their inhibition potentiates the effect of antibiotics and prevents resistance. In sum, we expect that the combination of our scRNA-seq and functional genomic strategies will reveal novel transcriptional determinants of antibiotic resistance in small subpopulations that have been masked by previous bulk methods. These resistance determinants will constitute promising candidate drug targets for maximizing the efficacy of current antibiotics.

项目概要 抗菌素耐药性（AMR）是对公共健康的最大威胁之一。复杂的异质性 细菌群落的研究对理解 AMR 机制提出了根本性挑战。甚至 遗传同质的细菌群体可能对抗生素表现出不同的敏感性，这是一种现象 称为抗生素异质耐药性。基因表达状态中预先存在的变异从根本上来说是一种 异质抗性的重要机制。此外，研究表明抗生素本身也可以 在一小部分细胞中诱导转录反应，保护它们免受药物攻击。 值得注意的是，研究表明，用小分子抑制剂抑制这些反应会导致 显着降低多重耐药性。这些发现强调了如何理解转录 异质性可以成为开发新型有效抗菌策略的基础。然而， 由于转录异质性如何影响抗生素敏感性，目前还缺乏系统研究 缺乏合适的工具和方法。最近的工作清楚地证明了高 高通量单细胞 RNA 测序 (scRNA-seq) 技术可探索基因表达状态 真核生物。然而，由于面临众多挑战，不存在类似的细菌工具。我们最近有 通过原位标记 RNA 开发原核表达谱来克服这些挑战 测序（PETRI-seq），一种低成本、高通量的原核 scRNA-seq 技术。 PETRI-seq 可以 以高纯度和低捕获偏差捕获单细胞细菌转录组，从而实现强大的区分 各种亚群的转录状态，包括那些仅占 0.05% 的罕见亚群 人口。在这里，我们提出了进一步提高 PETRI-seq 灵敏度的策略，并将其应用于分析 同基因大肠杆菌对单细胞抗生素攻击的异质转录反应 解决。使用三种不同类别的抗生素，我们将研究不同的抗生素如何导致细胞 分化成具有不同转录状态的亚群。我们将研究这些转录状态如何 改变抗生素治疗过程并有助于生存。最后，我们建议确定哪些 抗生素诱导的转录状态对于生存很重要。利用两个功能互补的 筛选平台——系统性过度表达和CRISPR干扰，我们将探究如何 大肠杆菌基因组中每个基因的表达都会影响抗生素敏感性。我们将验证发现的基因 以及表达增强存活的途径，并确定它们的抑制是否会增强效果 抗生素并防止耐药性。总之，我们期望 scRNA-seq 和功能性的结合 基因组策略将揭示小亚群抗生素耐药性的新转录决定因素 已被以前的批量方法掩盖。这些耐药性决定因素将构成有希望的 最大化现有抗生素功效的候选药物靶点。