MS Diagnostic Bacterial Identification Library

MS 诊断细菌鉴定库

基本信息

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
项目类别：
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

项目摘要

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.

项目概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。感染指导患者治疗并改善抗生素管理，但目前的方法面临着严峻的挑战在我们的 MPI 拨款“GM111066 - MS 诊断细菌”的第一个资助周期中。鉴定库”，我们制作了一个新颖的诊断平台，其中微生物膜糖脂通过质谱分析代表化学“指纹”，然后用于区分革兰氏阴性菌标准实验室培养基中单一或多微生物生长后的阴性和阳性以及真菌分离株或复杂的生物（尿液、血瓶和废水）在第二个资助周期中，我们的目标是改进。诊断如下所述。在该项目开始时，之前并未表明细菌或真菌膜脂可以提供可用于可靠的病原体识别的独特化学特征或条形码。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）的含量很高（每个细胞约 106 个拷贝），因此很容易提取重要的是，使用基于 LPS 的单一快速方案（从样品到 MS 鉴定不到 60 分钟）。临床使用中，我们成功地利用我们的平台解决了基于蛋白质的四大未满足的需求表型分析方法：1) 消除了 MS 分析之前生长的需要，2) 细菌和细菌的鉴定使用单一提取方案分离真菌，3) 直接从复杂的生物液体中进行鉴定，包括尿液、BAL 液、伤口流出物和血瓶，4) 可能存在抗菌药物耐药菌株最后，根据我们的十三篇同行评审出版物进行区分。第一个资助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最初的假设，甚至通过使用实验设计 (DOE) 过程将其超越最初的目标可以在一小时内直接从样本中进行鉴定。在第二个资助周期中，我们建议通过 i) 使用 DOE 来进一步创新，以提高检测限 (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) 开发机器学习方法来改进识别多种微生物感染中的单个细菌；iv) 将抗菌药物耐药性的检测范围扩大到其他范围 v) 开发一种从 100-1000 个细胞中分离的脂质的鉴定和结构分析方法； vi) 极大地扩展了我们识别病原真菌的能力，这是一个日益严重的医疗保健问题，而革兰氏阴性菌阳性生物体。