A blind source separation approach for deconvolution of bulk transcriptional data leads to early detection of ATF cell-states in complex bacterial populations, in vitro and in vivo

用于批量转录数据去卷积的盲源分离方法可以在体外和体内早期检测复杂细菌群体中的 ATF 细胞状态

• 批准号: 10703357
• 负责人: Tim van Opijnen
• 金额: $ 84.95万
• 依托单位: BROAD INSTITUTE, INC.
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-12 至 2026-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10703357
• 关键词: Affect; Algorithms; Antibiotic Resistance; Antibiotic Therapy; Antibiotic susceptibility; Antibiotics; Antimicrobial Resistance; Bacteria; Cells; Clinical; Communicable Diseases; Complex; Custom; Data; Data Set; Detection; Development; Diagnostic; Disease; Drug resistance; Early Diagnosis; Entropy; Epigenetic Process; Exposure to; Failure; Frequencies; Future; Genes; Genetic Transcription; Goals; Immune system; Immunocompromised Host; Immunotherapeutic agent; In Vitro; Infection; Intermediate resistance; Link; Machine Learning; Maintenance; Malignant Neoplasms; Maps; Measurement; Methods; Minority; Modeling; Mus; Mutation; Pathway interactions; Patients; Pharmaceutical Preparations; Phenotype; Physicians; Population; Predisposition; Resistance; Sampling; Serum; Source; Speed; Stress; Technology; Testing; Time; Tissues; Treatment Failure; Tumor Tissue; Validation; Work; blind; cancer cell; cancer type; clinical diagnostics; design; diagnostic assay; diagnostic strategy; experience; experimental study; improved; in vivo; machine learning algorithm; magnetic beads; nano-string; nanopore; novel diagnostics; pressure; prevent; reconstitution; resistance mutation; response; single-cell RNA sequencing; targeted treatment; technology development; tool; transcriptome sequencing; treatment strategy

SUMMARY – PROJECT 3 Transient bacterial cell-states including tolerance, persistence and hetero-resistance (HR) are harbingers of antibiotic treatment failure (ATF) and enablers of antibiotic resistance. Importantly, they are missed in any currently employed diagnostic assay or antibiotic susceptibility tests. Intriguingly, in the treatment of different types of cancer, physicians are often confronted with similar treatment failure issues. It turns out that these epigenetic cell-states create extended opportunities for high-level resistance mutations to emerge. Moreover, due to the phenotype’s transience, they themselves can directly drive the re-emergence of the (susceptible) population after drug pressure subsides. While these cell-states are increasingly recognized as drivers that sit at the root of treatment failure, new strategies are emerging to specifically identify, track and target them. To achieve such highly targeted treatment, approaches are developed that map out the composition of complex cancer tissue, for instance through single cell RNA-Seq (scRNA-Seq), or computational deconvolution of bulk RNA-Seq data. While, scRNA-Seq on bacteria remains technically challenging we found that by modifying existing tools, specific bacterial cell-states can be identified in complex bacterial populations. However, the capabilities of current tools are limited, and through the implementation of state-of-the-art machine learning algorithms there is much room for improvement. Moreover, ATF cell-states are poorly characterized, making it currently impossible to effectively define them. Herein, 3 aims are pursued to develop an approach that, based on bulk RNA-Seq data, dissects a complex bacterial population into its separate cell-states, and calculates their frequencies and MICs. In Aim 1 a large and diverse temporal RNA-Seq dataset is generated by following a wide variety of strains and species while they are exposed to antibiotics and a subset of the population switches to an ATF cell state. In Aim 2 a blind source separation algorithm is explored to design a state-of-the-art machine learning tool that deconvolves bulk RNA-Seq data from a complex bacterial population into the cell-states and their frequencies that make up the population. Moreover, by reconstituting each cell-state’s expression profile we enable transcriptional entropy calculations and thereby cell-state specific MIC predictions. In Aim 3 the approach is validated by retrospectively predicting the presence of ATF cell-states in patient samples. Finally, the model’s applicability is extended to bulk dual RNA-Seq data from host and bacterium, and validated on patient serum samples. This project therefore not only informs on how ATF cell-states develop and are maintained in a population, but also creates a path towards the development of diagnostics that can detect them in an active infection. Combined with the collateral sensitivities from Project 2 this could eventually enable linking detection to targeted treatment decisions.

摘要 - 项目3 瞬态细菌细胞态，包括耐受性，持久性和异抗性（HR） 抗生素治疗衰竭（ATF）和抗生素耐药性的推动因素。重要的是，他们错过了任何 目前使用诊断测定或抗生素易感性测试。有趣的是，在治疗不同 癌症类型，医生通常面临类似的治疗失败问题。事实证明这些 表观遗传细胞态创造了扩展的机会，可以使高级抗性突变出现。而且， 由于表型的瞬间，它们本身可以直接推动（易感性）的重新出现 药物压力后的种群消退。虽然这些细胞状态越来越被认为是坐着的驱动程序 在治疗失败的根源上，正在出现新策略，以专门识别，跟踪和针对它们。到 达到这种高度针对性的治疗，开发了方法来绘制复杂的组成 癌组织，例如通过单细胞RNA-seq（SCRNA-SEQ）或大量计算反卷积 RNA-seq数据。而细菌上的scrna-seq在技术上仍然受到挑战，我们发现通过修改 现有的工具，特定细菌细胞园可以在复杂的细菌群体中鉴定。但是， 当前工具的功能是有限的，并且通过实施最先进的机器学习 算法有很大的改进空间。此外，ATF细胞园的特征很差，使其成为 目前无法有效定义它们。在此，追求3个目标来开发一种基于的方法 在散装RNA-seq数据上，将复杂的细菌种群剖析到其单独的细胞状态，并计算其 频率和麦克风。在目标1中 当它们暴露于抗生素时，各种菌株和物种的种群转换为一部分 ATF细胞状态。在AIM 2中，探索了盲目分离算法来设计最先进的机器 从复杂细菌群体中散布的RNA-seq数据的学习工具， 他们构成人口的频率。此外，通过重新建立每个单元状态的表达式配置文件 我们启用转录熵计算，从而启用细胞状态特定的MIC预测。在目标3中 通过回顾性预测患者样品中ATF细胞园的存在来验证方法。最后， 该模型的适用性扩展到宿主和细菌的批量双RNA-seq数据，并在 患者血清样品。因此，该项目不仅告知ATF细胞态如何发展和是 维持在人群中，但也为可以检测到诊断的发展创造了一条途径 在主动感染中。结合项目2的附带敏感性，这最终可以链接 检测有针对性的治疗决策。