Double bond localization in unsaturated rhamnolipid precursors 3-(3-hydroxyalkanoyloxy)alkanoic acids by liquid chromatography-mass spectrometry applying online Paternò-Büchi reaction.

Lipids are biomolecules with a broad variety of chemical structures, which renders them essential not only for various biological functions but also interestingly for biotechnological applications. Rhamnolipids are microbial glycolipids with surface-active properties and are widely used biosurfactants. They are composed of one or two L-rhamnoses and up to three hydroxy fatty acids. Their biosynthetic precursors are 3-hydroxy(alkanoyloxy)alkanoic acids (HAAs). The latter are also present in cell supernatants as complex mixtures and are extensively studied for their potential to replace synthetically derived surfactants. The carbon chain lengths of HAAs determine their physical properties, such as their abilities to foam and emulsify, and their critical micelle concentration. Despite growing biotechnological interest, methods for structural elucidation are limited and often rely on hydrolysis and analysis of free hydroxy fatty acids losing the connectivity information. Therefore, a high-performance liquid chromatography-mass spectrometry method was developed for comprehensive structural characterization of intact HAAs. Information is provided on chain length and number of double bonds in each hydroxy fatty acid and their linkage by tandem mass spectrometry (MS/MS). Post-column photochemical derivatization by online Paternὸ-Büchi reaction and MS/MS fragmentation experiments generated diagnostic fragments allowing structural characterization down to the double bond position level. Furthermore, the presented experiments demonstrate a powerful approach for structure elucidation of complex lipids by tailored fragmentation.

Lipid Species Annotation at Double Bond Position Level with Custom Databases by Extension of the MZmine 2 Open-Source Software Package.

In recent years, proprietary and open-source bioinformatics software tools have been developed for the identification of lipids in complex biological samples based on high-resolution mass spectrometry data. These existent software tools often rely on publicly available lipid databases, such as LIPID MAPS, which, in some cases, only contain a limited number of lipid species for a specific lipid class. Other software solutions implement their own lipid species databases, which are often confined regarding implemented lipid classes, such as phospholipids. To address these drawbacks, we provide an extension of the widely used open-source metabolomics software MZmine 2, which enables the annotation of detected chromatographic features as lipid species. The extension is designed for straightforward generation of a custom database for selected lipid classes. Furthermore, each lipid's sum formula of the created database can be rapidly modified to search for derivatization products, oxidation products, in-source fragments, or adducts. The versatility will be exemplified by a liquid chromatography-high resolution mass spectrometry data set with postcolumn Paternò-Büchi derivatization. The derivatization reaction was performed to pinpoint the double bond positions in diacylglyceryltrimethylhomoserine lipid species in a lipid extract of a green algae ( Chlamydomonas reinhardtii) sample. The developed Lipid Search module extension of MZmine 2 supports the identification of lipids as far as double bond position level.

Three-dimensional Kendrick mass plots as a tool for graphical lipid identification.

RATIONALE: The rising field of lipidomics strongly relies on the identification of lipids in complex matrices. Recent technical advances regarding liquid chromatography (LC) and high-resolution mass spectrometry (HRMS) enable the mapping of the lipidome of an organism with short data acquisition times. However, interpretation and evaluation of resulting multidimensional datasets are challenging and this is still the bottleneck regarding overall analysis times. METHODS: A novel adaption of Kendrick mass plot analysis is presented for a rapid and accurate analysis of lipids in complex matrices. Separation of lipids by their respective head group was achieved via hydrophilic interaction liquid chromatography (HILIC) coupled to HRMS. The resulting LC/HRMS datasets are processed to a list of chromatographically separated features by applying an optimized MZmine 2 workflow. All features are plotted in a three-dimensional Kendrick mass plot, which allows a fast identification of present lipid classes, based on equidistant features with fitting retention times and the same Kendrick mass defect. Suspected lipid classes are used for exact mass database matching to annotate features. A second three-dimensional Kendrick mass plot of annotated features of a single lipid class helps to reveal potential database mismatches, resulting in a curated list of identified lipid species. RESULTS: The use of the novel adaption of the Kendrick mass plot has accelerated the identification of the relevant lipid species in the green alga Chlamydomonas reinhardtii. A total of 106 species were identified within the lipid classes: phosphatidylserine, phosphatidylethanolamine, phosphatidylglycerol, phosphatidylinositol, monogalactosyldiacylglycerol, digalactosyldiacylglycerol, and sulfoquinovosyldiacylglycerol. CONCLUSIONS: This work shows how the addition of chromatographic information, i.e. the retention time, to a classical two-dimensional Kendrick mass plot enables rapid and accurate analysis of LC/HRMS datasets, exemplified on a green alga (C. reinhardtii) sample. Three-dimensional Kendrick mass plots have improved lipid class identification and fast spotting of falsely annotated lipid species.