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

设计一个诊断平台，用于基于呼吸的呼吸道病原体快速识别和治疗监测

• 批准号: 9805608
• 负责人: LESLIE CHAN
• 金额: $ 8.75万
• 依托单位: MASSACHUSETTS INSTITUTE OF TECHNOLOGY
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-07-01 至 2021-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9805608
• 关键词: Acute respiratory infection; Aftercare; Animals; Antibiotic Therapy; Antibiotics; Antimicrobial susceptibility; Bacteria; Bacterial Pneumonia; Biochemical; Biological Assay; Blinded; Blood; Breath Tests; Breathalyzer Tests; Bronchoalveolar Lavage; Cessation of life; Chemicals; Classification; Clinic; Clinical; Data; Detection; Diagnosis; Diagnostic; Diagnostic tests; Disease; Drug Evaluation; Drug resistance; Early Diagnosis; Engineering; Enzymes; Escherichia coli; Esters; Fingerprint; Fluorocarbons; Future; Goals; Haemophilus influenzae; Hydroxyl Radical; Immune response; In Vitro; Infection; Inhalation; Injections; Isotope Labeling; Kinetics; Klebsiella pneumonia bacterium; Label; Leukocyte Elastase; Libraries; Ligands; Liposomes; Lung; Machine Learning; Mass Spectrum Analysis; Measures; Methods; Monitor; Mus; Patients; Peptide Hydrolases; Peptide Library; Peptides; Pharmaceutical Preparations; Pharmacotherapy; Predisposition; Property; Pseudomonas aeruginosa; Randomized; Reporter; Research Personnel; Resistance; Respiratory Tract Infections; Sensitivity and Specificity; Signal Transduction; Specificity; Sputum; Staphylococcus aureus; Streptococcus pneumoniae; Survival Rate; System; Testing; Time; Tissues; Validation; Work; antimicrobial; antimicrobial drug; base; biomaterial compatibility; classification algorithm; cohort; design; drug efficacy; exhaustion; extracellular; in vivo; mouse model; nanoparticle; nanosensors; outcome forecast; pathogen; pathogenic bacteria; pathogenic fungus; pathogenic virus; portability; pressure; prevent; random forest; resistant strain; respiratory; response; screening; sensor; targeted treatment; thioester; urinary; vapor; ventilator-associated pneumonia; volatile organic compound

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.

项目概要 急性呼吸道感染 (ARIs) 由多种细菌、病毒和真菌病原体引起， 需要进行病原体鉴定才能进行正确的治疗。但由于标准的滞后 生化测定和抗菌药物敏感性测试，临床医生已经开始依赖广谱、 经验性治疗会导致耐药性和患者死亡。该项目的目标是 开发呼吸测试，用于快速识别 ARI 病原体并监测治疗，以促进更多 在诊所立即进行有针对性的治疗。当前提案的重点是细菌病原体鉴定 未来的目标是扩展到病毒和真菌病原体。目前使用底物裂解测定来查询 细菌蛋白酶活性，可用于快速准确地将细菌分类到物种水平。 利用 Bhatia 实验室的蛋白酶响应纳米传感器平台，该提案的目标是开发 可吸入的多重纳米传感器，可将挥发性报告物释放到呼吸中以应对感染- 肺中的相关蛋白酶。从那里，我们可以生成常见病原体的呼吸“指纹” ARI，例如呼吸机相关性肺炎 (VAP)（铜绿假单胞菌、金黄色葡萄球菌、肺炎克雷伯菌、大肠杆菌、金黄色葡萄球菌） 肺炎链球菌和流感嗜血杆菌）。此外，开始抗菌后蛋白水解活性的变化 治疗可用于产生“良好反应”和“不良反应”呼吸特征，以便更及时地进行治疗。 评价药物疗效。为此，本项目的具体目标如下：（1）建立一个不稳定的- 肽底物条形码系统 (2) 构建并验证可吸入多重蛋白酶系统 用于病原体识别的纳米传感器和（3）研究多重蛋白酶纳米传感器的用途 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 可以附着在肽底物上，而不会对切割动力学、蛋白酶特异性、 和呼吸信号。一旦确定，挥发性记者将被同位素标记，以创建一个记者小组 类似的挥发性仅在质量上有所不同。对宿主和病原体蛋白酶具有正交敏感性的肽 然后将在目标 2 中通过针对细菌培养物上清液筛选肽库来进行鉴定，并且 受感染小鼠的支气管肺泡灌洗液。然后使用设计的 VOC 质量标签对肽进行条形码标记 目标 1，然后通过附着到纳米颗粒核心配制为可吸入纳米传感器。由此产生的 纳米传感器面板将通过气管内注射输送到感染六种 VAP 之一的小鼠体内 病原体。机器学习将用于生成统计分类器，以识别病原体 记者呼吸水平，并将用于进一步识别记者签名的良好和不良反应 抗生素治疗。成功完成这些目标将产生一个诊断平台，该平台有可能 扩大以快速识别呼吸道病原体的详尽清单，包括病毒和真菌 病原体。