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.

项目摘要 是由多种双臂，病毒和真菌病原体引起的， 需要病原体鉴定来进行正确的治疗。 生化测定和抗菌敏感性测试，临床医生已经依赖广谱， 经验信任既有助于耐药性和患者死亡。 为ARI中的快速病原体鉴定和治疗监测制定呼气测试，以促进更多 诊所的立即靶向治疗。 将未来的目标扩展到病毒和真菌病原体。 细菌蛋白酶活性，可以将贝克氏菌的分类为物种水平。 利用Bhatia实验室中的蛋白酶响应性纳米传感器平台，其目的是开发 可吸入的多路复用纳米传感器，这些纳米传感器响应感染而释放挥发性记者的呼吸 肺中的相关蛋白酶。 ARIS，例如呼吸机相关肺炎（VAP） 肺炎和流感的H.）。 治疗可用于产生“良好的反应”和“不良反应”呼吸签名，以及时 对药物疗效的评估。 用于肽底物的条形码系统（2）构建并验证可吸入的蛋白酶的多重多路复用系统 用于病原体鉴定的纳米传感器和（3）研究多重蛋白酶纳米传感器fors的使用 监测对抗生素治疗的反应。 可以连接到肽底物上，而不会对裂解动力学，蛋白酶特异性，蛋白酶特异性有害影响， 和呼吸信号。 相似的波动率仅由质量。肽具有正交敏感性和病原体的质量。 然后，ScreenStide库在AIM 2中识别出针对Bacture培养物上清液和 感染小鼠的支气管肺泡灌洗。 在AIM 1和通过附着在纳米颗粒芯上的吸入纳米传感器中。 纳米传感器面板将通过气管内气管内输送到感染了六个VAP之一的小鼠中 病原体。 记者呼吸中的水平，并将用于削减识别记者的签名签名签名，并恢复 这些目的的抗生素文本将导致一个诊断平台，该平台可以稳定 扩展以快速识别详尽的呼吸道病原体清单，包括病毒和真菌 病原体。