Microbial lipidomics

脂质是微生物细胞膜的重要组成部分，在微生物细胞与其环境之间形成界面。脂质的生物活性依赖于其结构（构效关系; SAR）。微生物显示出大量的脂质结构修饰，这些修饰改变了外膜电荷和流动性，并使它们能够在暴露于抗菌剂、渗透压和温度变化时存活。结构也不利于宿主免疫受体识别脂质。因此，当建立感染时，一些病原体转向产生具有低炎症潜力的脂质分子。为了更好地定义脂质SAR，我们将开发允许分析脂质结构而无需体外培养的方法，已知体外培养会导致影响功能的结构变化。因此，需要准确反映体内存在的脂质结构。该提案的目标是开发研究微生物脂质的新技术，因为它们存在于它们的原生环境中，并培养高素质的专业人员在质谱（MS）和相关的工具表征微生物脂质。我们将专注于以下四个5年目标，这将提高对微生物脂质SAR的认识，并在此过程中培训HQP。HQP将接受以下特定领域的培训：（1）提高基于MS的微生物脂质分析的灵敏度。基于微流体的分离和浓缩技术将用于直接从复杂的生物样品中鉴定微生物脂质。我们还将探索nanoPOTS（用于微量样品的一锅纳米液滴处理），以实现单细胞脂质分析。(2)通过添加来自水生、陆地和临床环境的脂质，扩展我们现有的微生物脂质库（BACLIB）。(3)优化传输模式基质辅助激光解吸/电离质谱成像（MALDI-MSI），用于在单细胞水平或接近单细胞水平原位检测微生物脂质。(4)开发有助于微生物脂质表征的计算模型。将开发基于人工智能（AI）的模型，以帮助微生物脂质结构表征。我们还将采用计算机模型来预测宿主受体与微生物脂质的结合，并使用这些预测来创建脂质活性谱。 该提案的短期目标是培训HQP，他们将开发技术，允许直接从环境和临床标本中分配微生物脂质结构，而无需培养。这些目标符合我们的长期目标，即了解微生物脂质的SAR-了解微生物如何适应不同环境以及它们如何引起宿主反应的关键-并利用这些知识提高诊断特异性和针对性预防和治疗从生物膜到感染的微生物疾病。

Lipids are essential components of microbial cell membranes, creating an interface between the microbial cell and its environment. Biological activities of lipids are dependent on their structure (Structure Activity Relationship; SAR). Microbes display a vast array of lipid structure modifications that alter the outer membrane charge and fluidity, and enable them to survive exposure to antimicrobials, and osmolality and temperature shifts. Structure is also detrimental to recognition of lipids by the host immune receptors. Some pathogens therefore switch to production of lipid molecules with low inflammatory potential when establishing infection. In order to better define lipid SARs, we will develop methods that allow analysis of lipid structures without need to culture in vitro, which is known to result in changes to structure that impact function. Thus lipid structures that accurately reflect those present in vivo are needed. The goal of this proposal is to develop novel techniques for studying microbial lipids as they exist in their native environments, and to train highly qualified personnel specialized in mass spectrometry (MS) and allied tools for characterization of microbial lipids. We will focus on the following four 5-year objectives that will improve knowledge on SAR of microbial lipids and in doing so train HQP. HQP will be trained in the following specific areas: (1) Increase sensitivity of MS-based microbial lipids analysis. Microfluidics-based separation and concentration techniques will be employed to identify microbial lipids directly from complex biological samples. We will also explore nanoPOTS (nanodroplet processing in one pot for trace samples) to progress towards single cell lipid analysis. (2) Expand our existing library of microbial lipids (BACLIB) by addition of lipids from aquatic, terrestrial and clinical environments. (3) Optimize transmission-mode Matrix Assisted Laser Desorption/Ionization Mass Spectrometry Imaging (MALDI-MSI) for detection of microbial lipids in situ at or near the single cell level. (4) Develop computational models that aid microbial lipid characterization. Artificial intelligence (AI)-based models that will aid microbial lipid structure characterization will be developed. We will also employ in silico models to predict host receptor binding to microbial lipids and use these predictions to create lipid activity profiles. The short-term goals of this proposal are focused on training HQP who will develop technologies that allow microbial lipid structure assignments directly from environmental and clinical specimens without need for culture. These goals are in-line with our long-term objective to understand SAR of microbial lipids - a key to understanding how microbes adapt to distinct environments and how they elicit host response - and to use this knowledge to improve diagnostic specificity and targeted prevention and treatment of microbial maladies from biofilms to infections.