MS Diagnostic Bacterial Identification Library

项目总结 传染病对全球健康有重大影响。临床医生需要快速而准确的诊断 感染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MPI赠款的第一个资金周期中，GM111066-MS诊断细菌 我们开发了一种新的诊断平台，在该平台中微生物膜糖脂 用质谱仪分析代表化学“指纹”，然后用这些指纹来区分革兰氏 在标准实验室培养基中单微生物或多微生物生长后的阴性和阳性以及真菌分离物 或复杂的生物(尿液、血瓶，并会流出)。在第二个资金周期中，我们的目标是改善 诊断如下所述。 在这个项目开始的时候，以前还没有证明细菌或真菌的膜脂可以 提供可用于可靠病原体识别的唯一化学签名或条形码。事实是 这些脂类(革兰氏：脂多糖/脂A，革兰氏：脂磷壁酸/心磷脂，真菌：甘油磷脂， 神经鞘脂脂和甾醇)含量很高(每个细胞约106个拷贝)，因此很容易提取 使用单一的基于内毒素的快速方案(从样品到MS鉴定不到60分钟)。重要的是，对于 临床应用，我们成功地利用我们的平台解决了来自蛋白质基础的这四大未得到满足的需求 表型方法：1)不需要在MS分析之前生长，2)鉴定细菌和 单一提取方案的真菌分离物，3)直接从复杂的生物液中鉴定， 包括尿液、BAL液、伤口分泌物和血瓶，以及4)抗菌素耐药菌株 区别于相关的敏感菌株。最后，根据我们的13份同行评议的出版物， 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)过程将其提升到超出最初的目标 允许在不到一小时的时间内直接从样本中识别。 在第二个资金周期中，我们建议进一步创新：1)使用DOE来提高检测极限 (LOD)从106到103，这是尿路感染的阈值；ii)将检测扩展到直接分析 未经培养的尿液和粪便样本；三)开发机器学习方法以改进识别 从多个微生物感染中分离出单个细菌；四)扩大抗菌素耐药性的检测范围 粘菌素；v)开发一种从100-1000个细胞中分离的脂类的鉴定和结构分析方法；以及 六)极大地扩大我们识别病原真菌的能力，这是一个日益严重的医疗问题，而葛兰素史克 阳性生物体。

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.