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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.

项目总结