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、持久性和异质抗性 (HR)，是细菌细胞出现耐药性的先兆。 重要的是，任何抗生素治疗失败（ATF）和抗生素耐药性的促成因素都被忽视了。 有趣的是，目前在不同的治疗中采用诊断测定或抗生素敏感性测试。 对于不同类型的癌症，医生经常面临类似的治疗失败问题。 表观遗传细胞状态为高水平耐药突变的出现创造了更多机会。 由于表型的短暂性，它们本身可以直接驱动（易感）的重新出现 尽管这些细胞状态越来越被认为是药物压力消退后的驱动因素。 针对治疗失败的根源，正在出现专门识别、跟踪和瞄准治疗失败的新策略。 为了实现如此高度针对性的治疗，人们开发了一些方法来绘制复杂的成分 癌症组织，例如通过单细胞 RNA-Seq (scRNA-Seq) 或批量计算反卷积 RNA-Seq 数据虽然，细菌的 scRNA-Seq 在技术上仍然具有挑战性，但我们发现通过修改 利用现有的工具，可以在复杂的细菌群体中识别特定的细菌细胞状态。 当前工具的功能有限，通过实施最先进的机器学习 算法还有很大的改进空间，而且 ATF 细胞状态的表征很差。 目前还无法有效地定义它们。在此，我们追求 3 个目标，以开发一种基于该方法的方法。 基于大量 RNA-Seq 数据，将复杂的细菌群体分解为单独的细胞状态，并计算它们的 在目标 1 中，通过跟踪广泛的数据生成一个大型且多样化的时间 RNA-Seq 数据集。 多种菌株和物种在接触抗生素时并且一部分人群转而使用抗生素 ATF 细胞状态。在目标 2 中，探索了盲源分离算法来设计最先进的机器。 学习工具，可将复杂细菌群体的大量 RNA-Seq 数据解卷积为细胞状态， 此外，通过重建每个细胞状态的表达谱来确定它们组成群体的频率。 在目标 3 中，我们启用转录熵计算，从而实现细胞状态特定的 MIC 预测。 最后，通过回顾性预测患者样本中 ATF 细胞状态的存在来验证该方法。 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 因此，该项目不仅揭示了 ATF 细胞状态的发展过程，而且还揭示了 ATF 细胞状态的变化。 维持在人群中，同时也为开发能够检测它们的诊断方法开辟了道路 结合项目 2 的附带敏感性，这最终可以实现链接。 检测以制定有针对性的治疗决策。