Single-cell characterization of antibiotic-induced heteroresistance

项目摘要 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的机制构成了一个根本的挑战。连 基因同源的细菌群体可能对抗生素表现出不同的敏感性，这一现象 这被称为抗生素异耐药。基因表达状态的预先存在的变异是一个根本的 构成异质电阻的重要机制。此外，研究还表明，抗生素本身可以 在一小部分细胞中诱导转录反应，以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 大幅度减少多药耐药性。这些发现强调了理解转录是如何 异质性可能是开发新的有效抗微生物策略的基础。然而， 关于转录异质性如何影响抗生素敏感性的系统研究一直缺乏， 由于没有合适的工具和方法。最近的研究已经清楚地证明了高密度的 吞吐量单细胞RNA测序(scRNA-seq)技术研究细胞的基因表达状态 真核生物。然而，由于面临许多挑战，目前还不存在针对细菌的可比工具。我们最近做了 通过原位标记RNA和开发原核表达谱来克服这些挑战 测序技术是一种低成本、高通量的原核生物scRNA-seq技术。PETRI-SEQ可以 捕获高纯度和低捕获偏差的单细胞细菌转录本，实现强大的鉴别力 不同亚群的转录状态，包括那些代表0.05%的罕见的 人口。在此，我们提出了进一步提高PETRI-SEQ灵敏度的策略，并将其应用于Profile 单细胞条件下同基因大肠杆菌对抗生素攻击的异质性转录反应 决议。使用三种不同的抗生素，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是如何 在抗生素治疗过程中发生变化，并有助于生存。最后，我们建议确定哪一个 抗生素诱导的转录状态对生存很重要。利用两种功能互补 筛选平台-系统性过度表达和CRISPR干扰，我们将询问 大肠杆菌基因组中每一个基因的表达都会影响抗生素的敏感性。我们将验证已发现的基因 以及其表达提高存活率的途径，并确定它们的抑制是否增强了这一效应 抗菌药物和防止抗药性。总而言之，我们期望我们的scRNA-seq和功能性的组合 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 已被以前的Bulk方法屏蔽的。这些抗性决定因素将构成有希望的 最大限度提高现有抗生素疗效的候选药物靶点。

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.