Lipopolysaccharides are unique bacterial lipids and components of the outer membrane of Gram-negative bacteria. Lipopolysaccharides in the outer membrane provide membrane integrity, a mechanism for interaction between bacteria and other surfaces, and resistance to different antibiotics. They are also potential factors in the pathogenicity of Gram-negative bacteria. The three main parts that compose lipopolysaccharides are the membrane anchor, the core oligosaccharide, and the O-polysaccharide. Specifically, the membrane anchor, called Lipid A, can vary in structure, saccharide composition, and size among bacterial species and even individual strains. Its unique structure contributes to the outer membrane's properties, resistance to antibiotics, and activation of a pro-inflammatory response.
The central core of Lipid A is a glucosamine disaccharide with three to seven acyl chains (ester or amide bonds) attached to the N-acetylglucosamine sugars. Most Gram-negative bacterial strains exhibit some variation in Lipid A structure, including differences in the length and number of aliphatic chains and the location of acyl groups. Furthermore, Lipid A can vary in degrees of phosphorylation and the presence of substituents, such as phosphoethanolamine and 4-amino-4-deoxy-L-arabinose. Additionally, saturated, unsaturated, hydroxylated, and branched fatty acids have been described as constituents of the molecule, and these fatty acids can vary in number, acyl chain length, and degree of acylation.
Due to this structural complexity, analyzing Lipid A presents numerous challenges. Currently, the main methods for elucidating Lipid A structure are NMR and mass spectrometry. While mass spectrometry cannot provide absolute structural information about the target, the general components of the molecule can be detected and studied, allowing for comparative profiles. However, even this limited interpretation of the data requires manual annotation of full scan and fragmentation spectra, which strongly limits throughput.
To the best of our knowledge, no automatic tool currently exists to annotate Lipid A in mass spectrometry data. Only a few individual Lipid A species can be found in online databases and there is no consistent nomenclature that describes the variations in the structure of this complex group of molecules. Currently, individual Lipid A species are named after the bacteria in which they are found.
Here, we present an innovative approach to address the challenge of identifying and annotating Lipid A features from Liquid Chromatography-Mass Spectrometry and data-dependent tandem mass spectrometry measurements. We created a library containing the structures of 100 published Lipid A from a variety of biological backgrounds and used this library to identify unique structural features, such as the natural isotopic pattern, to identify additional and previously undescribed Lipid A structures in untargeted datasets. In addition, we established a comprehensive system to categorize Lipid A structures based on their structural characteristics, such as the number of hydroxyl fatty acids and fatty acid esters of hydroxy fatty acids, which will facilitate future annotation, interpretation, and comparison. Lastly, we developed a Python-based script that predicts putative Lipid A structures based on their accurate mass and natural isotope distribution.
The combination of our literature-based in-house library, comprehensive sorting system, and unique script for structural prediction will significantly advance Lipid A research.