MS Diagnostic Bacterial Identification Library

MS 诊断细菌鉴定库

• 批准号: 10356152
• 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/10356152
• 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 传染病具有重大的全球健康影响。临床医生需要快速准确的诊断 引导患者治疗并改善抗生素管理的感染，但是当前的方法面临严重 这方面的局限性。在我们的MPI赠款的第一个融资周期中，“ GM111066 -MS诊断细菌 识别库，“我们产生了一个新型的诊断平台，其中微生物膜糖脂 通过质谱法分析代表化学“指纹”，然后用来区分革兰氏阴性 标准实验室培养基中单或多微晶生长后的负和阳性和真菌分离株 或复杂的生物学（尿液，血液瓶，会受益）。在第二个融资周期中，我们旨在改善 如下所述的诊断。 一开始这个项目，以前尚未证明细菌或真菌膜脂质可以 提供可用于可靠病原体鉴定的独特化学特征或条形码。事实 这些脂质（gram-：lps/lipid a，gram+：lipoteichoic Acid/cardiolipin，真菌：甘油磷脂， 鞘脂脂质和立体声）以高丰度（每个单元格〜106份）存在，使其易于提取 具有基于单一的快速LPS协议（从样本到MS识别少于60分钟）。重要的是，是 临床用途，我们成功地使用了我们的平台来解决基于蛋白质的这四个主要未满足的需求 表型方法：1）在MS分析之前消除了生长的需求，2）鉴定细菌和 具有单个提取方案的真菌分离株，3）直接从复杂的生物流体鉴定， 包括尿液，BAL液，伤口有效和血液瓶，以及4）抗菌抗菌菌株可能是 与相关的易感性菌株区分开。最后，根据我们的13个同行评审的出版物 第一个资金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性 原始假设，甚至通过使用实验设计（DOE）过程来超越原始目标 允许直接从标本直接识别标识。 在第二个融资周期中，我们建议通过i）使用doe来进一步创新来提高检测的极限 （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 没有培养的尿液和粪便样品； iii）开发机器学习方法以改善识别 来自多数菌感染的个体细菌； iv）扩大抗菌耐药性的检测 colistin; v）开发一种从100-1000个细胞分离的脂质鉴定和结构分析的方法；和 vi）极大地扩展了我们识别致病真菌的能力，这是一个日益增长的医疗保健问题，并掌握 积极的生物。