Microbial lipidomics

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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.