MS Diagnostic Bacterial Identification Library

MS 诊断细菌鉴定库

• 批准号: 10116273
• 负责人: Robert K Ernst
• 金额: $ 46.35万
• 依托单位: UNIVERSITY OF MARYLAND BALTIMORE
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-03-01 至 2025-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10116273
• 关键词: Address; Animal Model; Antibiotics; Antimicrobial Resistance; Bacteria; Bacterial Infections; Bacterial Proteins; Bar Codes; Biological; Biological Assay; Blood; Cardiolipins; Cell Culture Techniques; Cells; Cessation of life; Chemicals; Clinical; Clinical Microbiology; Colistin; Communicable Diseases; Complex; Computer software; Data; Detection; Development; Diagnostic; Escherichia coli; Event; Face; Failure; Feces; Financial Hardship; Funding; General Hospitals; Glycerophospholipids; Glycolipids; Grant; Growth; Health care facility; Health system; Healthcare; Hour; Individual; Infection; Intensive Care; Ions; Laboratories; Laboratory Research; Length of Stay; Libraries; Lipid A; Lipids; Liquid substance; MALDI-TOF Mass Spectrometry; Machine Learning; Mass Spectrum Analysis; Membrane; Membrane Lipids; Methodology; Methods; Microbe; Minor; Modeling; Morbidity - disease rate; Mycoses; Organism; Patients; Pattern; Peer Review; Phenotype; Process; Proteins; Protocols documentation; Publications; Rivers; Sampling; Sepsis; Solid; Specimen; Speed; Sphingolipids; Sterols; Structure; Technology; Time; Urinary tract infection; Urine; Work; accurate diagnosis; antimicrobial; base; biodefense; chemical fingerprinting; chemotherapy; clinically relevant; combat; cost; design; detection limit; diagnostic platform; experimental study; feature extraction; fungus; global health; improved; innovation; lipoteichoic acid; microbial; mortality; new technology; novel; novel diagnostics; novel therapeutics; pathogen; pathogenic fungus; point of care; rapid diagnosis; resistant strain; simulation; software development; stool sample; tandem mass spectrometry; tool; ward; wound

PROJECT SUMMARY Infectious diseases have a substantial global health impact. Clinicians need rapid and accurate diagnoses of infections to direct patient treatment and improve antibiotic stewardship, but current methodologies face severe limitations in this regard. In the first funding cycle of our MPI grant “GM111066 - MS diagnostic bacterial identification library,” we produced a novel diagnostic platform in which microbial membrane glycolipids analyzed by mass spectrometry represent chemical “fingerprints” that were then used to differentiate Gram- negative and –positive and fungal isolates after mono- or poly-microbial growth in standard laboratory medias or complex biological (urine, blood bottles, and would effluent). In the second funding cycle, we aim to improve the diagnostic as discussed below. At the start this project, it had not been previously shown that bacterial or fungal membrane lipids could provide a unique chemical signature or barcode that could be used for reliable pathogen identification. The fact that these lipids (Gram-: LPS/lipid A, Gram+: Lipoteichoic acid/cardiolipin, Fungi: glycerophospholipids, sphingolipids, and sterols) are present in high abundance (~106 copies per cell) makes them easily extractable with a single rapid LPS-based protocol (less than 60 minutes from sample to MS identification). Importantly, for clinical use, we successfully used our platform to solve these four major unmet needs from the protein-based phenotyping approach: 1) removed the need for growth prior to MS analysis, 2) identification of bacterial and fungal isolates with a single extraction protocol, 3) identification directly from complex biological fluids, including urine, BAL fluid, wound effluent, and blood bottles, and 4) antimicrobial resistant strains could be distinguished from the related susceptible strain. Finally, based on our thirteen peer-reviewed publications from the first funding period and extensive preliminary data, we believe we have proven our highly innovative original hypothesis and even advanced it past the original aims by using a design of experiment (DOE) process to allow identification in under an hour direct from specimen. In the second funding cycle, we propose to further innovate by i) using DOE to improve limit of detection (LOD) from 106 to 103 which is the threshold for urinary tract infections; ii) extend the assay to direct analysis of urine and stool samples without culture; iii) develop machine learning approaches to improve identification of individual bacteria from polymicrobial infections; iv) expand detection of antimicrobial resistance beyond colistin; v) develop a method for identification and structure analysis of lipids isolated from 100-1000 cells; and vi) vastly expand our ability to identify pathogenic fungi, which are a growing healthcare issue, and Gram- positive organisms.

项目摘要 传染病对全球健康产生重大影响。临床医生需要快速准确的诊断， 感染，以指导患者治疗和改善抗生素管理，但目前的方法面临严重的 在这方面的限制。在我们的MPI赠款“GM 111066- MS诊断细菌”的第一个资助周期中， 鉴定库，”我们产生了一种新的诊断平台，其中微生物膜糖脂 质谱分析代表化学“指纹”，然后用来区分革兰氏， 在标准实验室培养基中单一或多种微生物生长后，阴性和阳性以及真菌分离株 或复杂的生物（尿液、血瓶和废液）。在第二个融资周期，我们的目标是改善 诊断如下所述。 在这个项目开始时，以前没有显示细菌或真菌的膜脂可以 提供可用于可靠病原体鉴定的独特化学特征或条形码。的事实 这些脂质（革兰氏-：LPS/脂质A，革兰氏+：脂磷壁酸/心磷脂，真菌：甘油磷脂， 鞘脂和甾醇）以高丰度（每个细胞约106个拷贝）存在，使其易于提取 使用基于LPS的单一快速方案（从样品到MS鉴定不到60分钟）。重要的是对 在临床应用中，我们成功地利用我们的平台解决了基于蛋白质的 表型分型方法：1）在MS分析之前不需要生长，2）鉴定细菌和 用单一提取方案进行真菌分离，3）直接从复杂生物流体中鉴定， 包括尿液、BAL液、伤口流出物和血瓶，以及4）抗菌药物耐药菌株可能 与相关的敏感菌株相区别。最后，根据我们的13篇同行评议的出版物， 第一个融资期和广泛的初步数据，我们相信我们已经证明了我们的高度创新 原始假设，甚至通过使用实验设计（DOE）过程将其推进到原始目标之外 一小时内就能从标本上鉴定出身份 在第二个资助周期，我们建议进一步创新，i）使用DOE来提高检测限 (LOD)从106到103，这是尿路感染的阈值; ii）将测定扩展到直接分析 未经培养的尿液和粪便样本; iii）开发机器学习方法以改善识别 iv）扩大对抗菌素耐药性的检测范围， v）开发用于鉴定和结构分析从100-1000个细胞分离的脂质的方法;和 vi）极大地扩展我们识别致病真菌的能力，这是一个日益严重的医疗保健问题， 阳性微生物