Single-cell characterization of antibiotic-induced heteroresistance

项目摘要 抗生素耐药性（AMR）是对公共卫生的最大威胁之一。复杂的异质性 细菌群落的变化对了解AMR的机制提出了根本性的挑战。甚至 遗传上同质的细菌群体可以表现出对抗生素的不同敏感性，这是一种现象， 称为抗生素异源耐药性。基因表达状态的预先存在的变异是一个基本的 重要的机制，基础异质性。此外，已经表明抗生素本身可以 在一小群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， 多药耐药性显著降低。这些发现强调了理解转录 异质性可能是开发新的有效抗菌策略的基础。然而，在这方面， 转录异质性如何影响抗生素敏感性的系统研究一直缺乏， 没有合适的工具和方法。最近的工作已经清楚地表明了高- 通过单细胞RNA测序（scRNA-seq）技术探索基因表达状态， 真核生物然而，由于许多挑战，细菌的类似工具并不存在。我们最近 通过原位标记RNA开发Proximal表达谱克服了这些挑战， 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，实现稳健的区分 转录状态的各种亚群，包括那些代表罕见的0.05%， 人口在这里，我们提出了进一步提高PETRI-seq灵敏度的策略，并将其应用于轮廓 等基因大肠杆菌单细胞对抗生素攻击的异质性转录反应 分辨率使用三种不同类型的抗生素，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 在抗生素治疗过程中发生变化，并有助于生存。最后，我们建议确定 由抗生素诱导的转录状态对于存活是重要的。利用两个功能互补的 筛选平台-系统性过表达和CRISPR干扰，我们将询问 在E.大肠杆菌基因组影响抗生素敏感性。我们将验证发现的基因 以及其表达增强存活的途径，并确定它们的抑制是否会增强效应 抗生素和防止耐药性。总之，我们希望我们的scRNA-seq和功能性的组合能够在更短的时间内完成。 基因组策略将揭示小亚群中抗生素耐药性的新转录决定因素 被以前的批量方法掩盖了。这些抗性决定因素将构成有希望的 候选药物靶点以最大化当前抗生素的功效。

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.