Engineering a diagnostic platform for rapid breath-based respiratory pathogen identification and treatment monitoring

项目摘要 急性呼吸道感染(ARI)由许多细菌、病毒和真菌病原体和 需要病原体鉴定才能实施正确的治疗。然而，由于标准中的滞后时间， 生化分析和药敏试验，临床医生已经开始依赖广谱， 导致耐药性和病人死亡的经验性治疗。这个项目的目标是 开发一种呼吸测试，用于ARI的快速病原体识别和治疗监测，以促进更多 立即在诊所进行针对性的治疗。目前的建议集中在细菌病原体鉴定上。 未来的目标是扩大到病毒和真菌病原体。底物裂解试验目前用于查询 细菌蛋白水解酶活性，并可用于快速和准确地将细菌分类到物种水平。 利用Bhatia实验室的蛋白酶反应纳米传感器平台，这项提议的目标是开发 可吸入的多路复合纳米传感器，在感染时将挥发性记者释放到呼吸中- 肺中的相关蛋白酶。从那里，我们可以为常见的病原体生成呼气“指纹” 呼吸机相关性肺炎(VAP)(铜绿假单胞菌、金黄色葡萄球菌、肺炎克雷伯菌、大肠杆菌、S. 肺炎和流感嗜血杆菌)。此外，抗菌药物开始使用后蛋白分解活性的变化 治疗时可用来产生“良好反应”和“不良反应”的呼吸信号，以便更及时 药物疗效评价。为此，本项目的具体目标如下：(1)建立一个易变的- 多肽底物条码系统(2)构建和验证可吸入的多路蛋白酶系统 用于病原体鉴定的纳米传感器以及(3)研究多重蛋白酶纳米传感器用于 监测对抗生素治疗的反应。目标1将通过确定不稳定的记者候选人来完成 它可以附着在多肽底物上，而不会对切割动力学、蛋白酶专一性 和呼吸信号。一旦确定，反复无常的记者将被标记为同位素，以创建一个具有 类似的波动只是在质量上有所不同。对宿主和病原体蛋白水解酶具有正向敏感性的多肽 然后在目标2中通过筛选针对细菌培养上清液的肽库和 感染小鼠的细支气管肺泡灌洗。然后使用设计的VOC质量标签对多肽进行条形码 在目标1中，然后通过附着到纳米颗粒核心来配制成可吸入的纳米传感器。由此产生的 纳米传感器面板将通过气管内注射给感染六种VAP之一的小鼠 病原体。将使用机器学习来生成统计分类器，以基于以下条件识别病原体 记者呼吸水平，并将被用来进一步识别记者签名的好和差的反应 抗生素治疗。成功完成这些目标将产生一个诊断平台，该平台可能 扩展以快速识别包括病毒和真菌在内的呼吸道病原体的详尽清单 病原体。

Project Summary Acute respiratory infections (ARIs) are caused by a number of bacterial, viral, and fungal pathogens and pathogen identification is needed to administer the correct treatment. However, due to the lagtime in standard biochemical assays and antimicrobial susceptibility testing, clinicians have come to depend on broad-spectrum, empirical treatment which contributes to both drug resistance and patient death. The goal of this project is to develop a breath test for rapid pathogen identification and treatment monitoring in ARI to facilitate more immediate, targeted treatment in the clinic. The current proposal is focused on bacterial pathogen identification with future goals to expand to viral and fungal pathogens. Substrate cleavage assays are currently used to query bacterial protease activity and can be used to rapidly and accurately classify bacteria down to the species level. Leveraging the protease-responsive nanosensor platform in the Bhatia lab, the goal of this proposal is to develop inhalable multiplexed nanosensors that release volatile reporters into the breath in response to infection- associated proteases in the lung. From there, we can generate breath “fingerprints” for pathogens common in ARIs such as ventilator-associated pneumonia (VAP) (P. aeruginosa, S. aureus, K. pneumoniae, E. coli, S. pneumoniae, and H. influenzae). Furthermore, changes in proteolytic activity after the start of antimicrobial treatment can be used to generate a “good response” and “poor response” breath signature for more timely evaluation of drug efficacy. To this end, the specific aims of this project are the following: (1) establish a volatile- barcoding system for peptide substrates (2) build and validate an inhalable multiplexed system of protease nanosensors for pathogen identification and (3) investigate use of multiplexed protease nanosensors for monitoring response to antibiotic treatment. Aim 1 will be completed by identifying volatile reporter candidates that can be attached to peptide substrates without deleterious effects on cleavage kinetics, protease specificity, and breath signal. Once identified, volatile reporters will be isotope-labeled to create a panel of reporters with similar volatility differing only by mass. Peptides with orthogonal susceptibility to host and pathogen proteases will then be identified in Aim 2 by screening a peptide library against bacterial culture supernatants and bronchioalveolar lavage from infected mice. Peptides will then be barcoded using the VOC mass labels designed in Aim 1 and then formulated into inhalable nanosensors by attachment to a nanoparticle core. The resulting nanosensor panel will be delivered via intratracheal injection into mice infected with one of the six VAP pathogens. Machine learning will be used to generate a statistical classifier to identify pathogens based on reporter levels in breath and will be used to further identify reporter signatures for good and poor response to antibiotic treatment. Successful completion of these aims would result in a diagnostic platform that can potentially be expanded for rapid identification of an exhaustive list of respiratory pathogens, including viral and fungal pathogens.