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 瞬时细菌细胞状态，包括耐受性、持久性和异源抗性（HR）是 抗生素治疗失败（ATF）和抗生素耐药性的促成因素。重要的是，它们在任何 目前使用的诊断测定或抗生素敏感性测试。有趣的是，在对待不同的 对于不同类型的癌症，医生经常面临类似的治疗失败问题。原来这些 表观遗传细胞状态为高水平抗性突变的出现创造了延长的机会。此外，委员会认为， 由于表型的短暂性，它们本身可以直接驱动（易感） 毒品压力消退后的人口。虽然这些细胞状态越来越多地被认为是 在治疗失败的根源上，正在出现新的战略，以具体查明、跟踪和针对这些问题。到 为了实现这种高度靶向的治疗，开发了绘制复合物的组成的方法， 癌组织，例如通过单细胞RNA-Seq（scRNA-Seq），或通过大体积的计算去卷积， RNA-Seq数据。虽然细菌上的scRNA-Seq在技术上仍然具有挑战性，但我们发现，通过修饰 利用现有的工具，可以在复杂的细菌群体中鉴定特定的细菌细胞状态。但 当前工具的能力有限，通过实施最先进的机器学习， 算法还有很大的改进空间。此外，ATF细胞状态的特征很差， 目前还无法对其进行有效的定义。在此，追求3个目标，以开发一种方法， 在大量的RNA-Seq数据上，将复杂的细菌种群解剖成其单独的细胞状态，并计算它们的 在Aim 1中，通过遵循广泛的 各种菌株和物种，而他们暴露于抗生素和一个子集的人口切换到一个 ATF细胞状态。在目标2中，探索盲源分离算法以设计最先进的机器 学习工具，将来自复杂细菌群体的大量RNA-Seq数据解卷积为细胞状态， 他们的频率构成了人口。此外，通过重建每个细胞状态的表达谱， 我们实现了转录熵计算，从而实现了细胞状态特异性MIC预测。在Aim 3中， 通过回顾性预测患者样品中ATF细胞状态的存在来验证该方法。最后， 该模型的适用性扩展到来自宿主和细菌的大量双RNA-Seq数据，并在 患者血清样本。因此，该项目不仅告知ATF细胞状态如何发展， 在人群中维持，而且还为开发可以检测它们的诊断方法创造了一条道路 在一个活跃的感染。结合项目2的附带敏感性，这最终可以实现联系 检测到有针对性的治疗决策。