LiPydomics: A Python Package for Comprehensive Prediction of Lipid Collision Cross Sections and Retention Times and Analysis of Ion Mobility-Mass Spectrometry-Based Lipidomics Data

Benchmarking of Trapped Ion Mobility Spectrometry in Differentiating Plasmalogens from Other Ether Lipids in Lipidomics Experiments

Trapped Ion Mobility Spectrometry (TIMS) has demonstrated promising potential as a powerful discriminating method when coupled with mass spectrometry, enhancing the precision of feature annotation. Such a technique is particularly valuable for lipids, where a large number of isobaric but structurally distinct molecular species often coexist within the same sample matrix. In this study, we explored the potential of ion mobility for ether lipid isomer differentiation. Mammalian ether phospholipids are characterized by a fatty alcohol residue at the sn-1 position of their glycerol backbone. They can make up to 20% of the total phospholipid mass and are present in a broad range of tissues. There they are, for example, crucial for nervous system function, membrane homeostasis, and inter- as well as intracellular signaling. Molecular ether lipid species are difficult to distinguish analytically, as they occur as 1-O-alkyl and 1-O-alkenyl subclasses, with the latter being also known as plasmalogens. Isomeric ether lipid pairs can be separated with reversed-phase chromatography. However, their precise identification remains challenging due to the lack of clear internal reference points, inherent to the nature of lipid profiles and the lack of sufficient commercially available standard substances. Here, we demonstrate─with focus on phosphatidylethanolamines─that ion mobility measurements allow to discriminate between the ether lipid subclasses through distinct differences in their gas phase geometries. This approach offers significant advantages as it does not depend on potential retention time differences between different chromatographic systems. However, the current resolution in the ion mobility dimension is not sufficient to baseline separate 1-O-alkyl and 1-O-alkenyl isobars, and the observed differences are not yet accurately represented in existing collision cross section databases. Despite these challenges, the predictable properties of the ion mobility behavior of ether lipid species can significantly support their accurate annotation and hold promise for future advancements in lipid research.

Deep MALDI-MS spatial omics guided by quantum cascade laser mid-infrared imaging microscopy

In spatial'omics, highly confident molecular identifications are indispensable for the investigation of complex biology and for spatial biomarker discovery. However, current mass spectrometry imaging (MSI)-based spatial 'omics must compromise between data acquisition speed and biochemical profiling depth. Here, we introduce fast, label-free quantum cascade laser mid-infrared imaging microscopy (QCL-MIR imaging) to guide MSI to high-interest tissue regions as small as kidney glomeruli, cultured multicellular spheroid cores or single motor neurons. Focusing on smaller tissue areas enables extensive spatial lipid identifications by on-tissue tandem-MS employing imaging parallel reaction monitoring-Parallel Accumulation-Serial Fragmentation (iprm-PASEF). QCL-MIR imaging-guided MSI allowed for unequivocal on-tissue elucidation of 157 sulfatides selectively accumulating in kidneys of arylsulfatase A-deficient mice used as ground truth concept and provided chemical rationales for improvements to ion mobility prediction algorithms. Using this workflow, we characterized sclerotic spinal cord lesions in mice with experimental autoimmune encephalomyelitis (EAE), a model of multiple sclerosis, and identified upregulation of inflammation-related ceramide-1-phosphate and ceramide phosphatidylethanolamine as markers of white matter lipid remodeling. Taken together, widely applicable and fast QCL-MIR imaging-based guidance of MSI ensures that more time is available for exploration and validation of new biology by default on-tissue tandem-MS analysis.

Rapid MALDI-MS/MS-Based Profiling of Lipid A Species from Gram-Negative Bacteria Utilizing Trapped Ion Mobility Spectrometry and mzmine

Lipid A, a crucial component of lipopolysaccharides (LPS), plays a pivotal role in the pathogenesis of Gram-negative bacteria. Lipid A patterns are recognized by mammals and can induce immunostimulatory effects. However, the outcome of the interaction is highly dependent on the chemical composition of individual lipid A species. The diversity of potential fatty acyl and polar headgroup combinations in this complex saccharolipid presents a significant analytical challenge. Current mass spectrometry (MS)-based lipid A methods are focused on either direct matrix-assisted laser desorption/ionization (MALDI)-MS screening or comprehensive structural elucidation by tandem mass spectrometry (MS/MS) hyphenated with separation techniques. In this study, we developed an alternative workflow for rapid lipid A profiling covering the entire analysis pipeline from sample preparation to data analysis. This workflow is based on microextraction and subsequent MALDI-MS/MS analysis of uropathogenic Escherichia coli utilizing trapped ion mobility spectrometry (TIMS), followed by mzmine data processing. The additional TIMS dimension served for enhanced sensitivity, selectivity, and structural elucidation through mobility-resolved fragmentation via parallel accumulation-serial fragmentation (PASEF) in parallel reaction monitoring (prm)-mode. Furthermore, mzmine enabled automated MS/MS acquisition by adapting the spatial ion mobility-scheduled exhaustive fragmentation (SIMSEF) strategy for MALDI spot analysis. It also facilitated robust lipid A annotation through a newly developed extension of the rule-based lipid annotation module, allowing for the custom generation of lipid classes, including specific fragmentation rules. In this study, the first publication of lipid A species' collision cross section (CCS) values is reported, which will enhance high-confidence lipid A annotation in future studies.

Membrane Lipids Augment Cell Envelope Stress Signaling via the MadRS System to Defend Against Antimicrobial Peptides and Antibiotics in Enterococcus faecalis

Enterococci have evolved resistance mechanisms to protect their cell envelopes against bacteriocins and host cationic antimicrobial peptides (CAMPs) produced in the gastrointestinal environment. Activation of the membrane stress response has also been tied to resistance to the lipopeptide antibiotic daptomycin. However, the actual effectors mediating resistance have not been elucidated. Here, we show that the MadRS (formerly YxdJK) membrane antimicrobial peptide defense system controls a network of genes, including a previously uncharacterized 3-gene operon (madEFG) that protects the Enterococcus faecalis cell envelope from antimicrobial peptides. Constitutive activation of the system confers protection against CAMPs and daptomycin in the absence of a functional LiaFSR system and leads to persistence of cardiac microlesions in vivo. Moreover, changes in the lipid cell membrane environment alter CAMP susceptibility and expression of the MadRS system. Thus, we provide a framework supporting a multilayered envelope defense mechanism for resistance and survival coupled to virulence.

Molecular basis of cell membrane adaptation in daptomycin-resistant Enterococcus faecalis

Daptomycin is a last-resort lipopeptide antibiotic that disrupts cell membrane (CM) and peptidoglycan homeostasis. Enterococcus faecalis has developed a sophisticated mechanism to avoid daptomycin killing by redistributing CM anionic phospholipids away from the septum. The CM changes are orchestrated by a 3-component regulatory system, designated LiaFSR, with a possible contribution of cardiolipin synthase (Cls). However, the mechanism by which LiaFSR controls the CM response and the role of Cls are unknown. Here, we show that cardiolipin synthase activity is essential for anionic phospholipid redistribution and daptomycin resistance since deletion of the 2 genes (cls1 and cls2) encoding Cls abolished CM remodeling. We identified LiaY, a transmembrane protein regulated by LiaFSR, and Cls1 as important mediators of CM remodeling required for redistribution of anionic phospholipid microdomains. Together, our insights provide a mechanistic framework on the enterococcal response to cell envelope antibiotics that could be exploited therapeutically.

Acetyl-CoA carboxylase inhibition increases retinal pigment epithelial cell fatty acid flux and restricts apolipoprotein efflux

Lipid-rich deposits called drusen accumulate under the retinal pigment epithelium (RPE) in the eyes of patients with age-related macular degeneration and Sorsby's fundus dystrophy (SFD). Drusen may contribute to photoreceptor degeneration in these blinding diseases. Stimulating β-oxidation of fatty acids could decrease the availability of lipid with which RPE cells generate drusen. Inhibitors of acetyl-CoA carboxylase (ACC) stimulate β-oxidation and diminish lipid accumulation in fatty liver disease. In this report, we test the hypothesis that an ACC inhibitor, Firsocostat, can diminish lipid deposition by RPE cells. We probed metabolism and cellular function in mouse RPE-choroid tissue and human RPE cells. We used 13C6-glucose, 13C16-palmitate, and gas chromatography-linked mass spectrometry to monitor effects of Firsocostat on glycolytic, Krebs cycle, and fatty acid metabolism. We quantified lipid abundance, apolipoprotein E levels, and vascular endothelial growth factor release using liquid chromatography-mass spectrometry, ELISAs, and immunostaining. RPE barrier function was assessed by trans-epithelial electrical resistance (TEER). Firsocostat-mediated ACC inhibition increases β-oxidation, decreases intracellular lipid levels, diminishes lipoprotein release, and increases TEER. When human serum or outer segments are used to stimulate lipoprotein release, fewer lipoproteins are released in the presence of Firsocostat. In a culture model of SFD, Firsocostat stimulates fatty acid oxidation, increases TEER, and decreases apolipoprotein E release. We conclude that Firsocostat remodels RPE metabolism and can limit lipid deposition. This suggests that ACC inhibition could be an effective strategy for diminishing pathologic drusen in the eyes of patients with age-related macular degeneration or SFD.

Several common methods of making vesicles (except an emulsion method) capture intended lipid ratios

Researchers choose different methods of making giant unilamellar vesicles to satisfy different constraints of their experimental designs. A challenge that arises when researchers use a variety of methods is that each method may produce vesicles with a different average lipid ratio, even if all experiments use lipids from a common stock mixture. Here, we use mass spectrometry to investigate ratios of lipids in vesicle solutions made by five common methods: electroformation on indium tin oxide slides, electroformation on platinum wires, gentle hydration, emulsion transfer, and extrusion. We made vesicles from either five-component or binary mixtures of lipids chosen to span a wide range of physical properties: di(18:1)PC, di(16:0)PC, di(18:1)PG, di(12:0)PE, and cholesterol. For a mixture of all five of these lipids, ITO electroformation, Pt electroformation, gentle hydration, and extrusion methods result in only minor shifts in lipid ratios (≤5 mol %) relative to a common stock solution. In contrast, emulsion transfer results in ∼80% less cholesterol than expected from the stock solution, which is counterbalanced by a surprising overabundance of saturated PC-lipid relative to all other phospholipids. Experiments using binary mixtures of saturated and unsaturated PC-lipids and cholesterol largely support results from the five-component mixture. In general, our results imply that experiments that increment lipid ratios in small steps will produce data that are highly sensitive to the technique used and to sample-to-sample variations. For example, sample-to-sample variations are ∼±2 mol % for five-component vesicles produced by a single technique. In contrast, experiments that explore larger increments in lipid ratio or that seek to explain general trends and new phenomena will be less sensitive to sample-to-sample variation and the method used.

Untargeted Lipidomics Analysis to Discover Lipid Profiles and Biomarkers of Rabbit Acne Model and Reveal Action Mechanism of Isotretinoin

Background

Acne vulgaris (AV), a chronic inflammatory pilosebaceous disorder, affects 80-90% of teenagers. This study aimed to discover lipid profiles and biomarkers of the rabbit ear acne model, and investigate the mechanism of isotretinoin in treating acne at the lipid level.

Methods

Untargeted lipidomic analysis using ultra-high performance liquid chromatography system (UHPLC) coupled to q-extraction plus was performed to identify skin lipid metabolites in blank control (groups C), model group (group M) and isotretinoin group (group T). Multivariate statistical analysis was used to process the lipidomics data.

Results

A total of 43 lipid classes comprising 6989 lipid species were identified from the mass spectrometry data. The orthogonal partial least squares discriminant analysis (OPLS-DA) model demonstrated significant separation in skin lipidomic profiles between group M and group C. With variable influence on projection (VIP) > 1.0 and P-value < 0.05, 299 significantly different lipid metabolites were identified. These lipid metabolites consisted mainly of ceramides (Cer) (53.85%), phosphatidylethanolamines (PE) (9.03%), phosphatidylcholines (PC)(5.35%), and sphingomyelin (SM)(4.01%). Combining with AUC ≥ 0.9 as the elected criteria, Cer (d18;1_24:0), zymosterol (ZyE)(33:5), Cer (t43:1), ZyE (33:6), ZyE (24:7), and ZyE (35:6) have "high" accuracy. Isotretinoin treatment normalized 25 lipid metabolites in the acne model.

Conclusion

Our findings provide new insights into the role of lipid metabolism in the pathogenesis of acne and the action mechanism of isotretinoin.

Hyphenation of Liquid Chromatography and Trapped Ion Mobility - Mass Spectrometry for Characterization of Isomeric Phosphatidylethanolamines with Focus on N-Acylated Species

In prior research, hydrophilic interaction liquid chromatography coupled to tandem mass spectrometry (HILIC-MS/MS) has demonstrated applicability for characterizing regioisomers in lipidomics studies, including phosphatidylglycerols (PG) and bis(monoacyl)glycerophosphates (BMP). However, there are other lipid regioisomers, such as phosphatidylethanolamines (PE) and lyso-N-acyl-PE (LNAPE), that have not been studied as extensively. Therefore, hyphenated mass spectrometric methods are needed to investigate PE and LNAPE regioisomers individually. The asymmetric structure of LNAPE favors isomeric species, which can result in coelution and chimeric MS/MS spectra. One way to address the challenge of chimeric MS/MS spectra is through mobility-resolved fragmentation using trapped ion mobility spectrometry (TIMS). Therefore, we developed a multidimensional HILIC-TIMS-MS/MS approach for the structural characterization of isomeric phosphatidylethanolamines in both negative and positive ionization modes. The study revealed the complementary fragmentation pattern and ion mobility behavior of LNAPE in both ionization modes, which was confirmed by a self-synthesized LNAPE standard. With this knowledge, a distinction of regioisomeric PE and LNAPE was achieved in human plasma samples. Furthermore, regioisomeric LNAPE species containing at least one unsaturated fatty acid were noted to exhibit a change in collision cross-section in positive ionization mode, leading to a lipid characterization with respect to fatty acyl positional level. Similar mobility behavior was also observed for the biological LNAPE precursor N-acyl-PE (NAPE). Application of this approach to plasma and cereal samples demonstrated its effectiveness in regioisomeric LNAPE and NAPE species' elucidation.

Several common methods of making vesicles (except an emulsion method) capture intended lipid ratios

Researchers choose different methods of making giant unilamellar vesicles in order to satisfy different constraints of their experimental designs. A challenge of using a variety of methods is that each may produce vesicles of different lipid compositions, even if all vesicles are made from a common stock mixture. Here, we use mass spectrometry to investigate ratios of lipids in vesicles made by five common methods: electroformation on indium tin oxide slides, electroformation on platinum wires, gentle hydration, emulsion transfer, and extrusion. We made vesicles from either 5-component or binary mixtures of lipids chosen to span a wide range of physical properties: di(18:1)PC, di(16:0)PC, di(18:1)PG, di(12:0)PE, and cholesterol. For a mixture of all five of these lipids, ITO electroformation, Pt electroformation, gentle hydration, and extrusion methods result in only minor shifts (≤ 5 mol%) in lipid ratios of vesicles relative to a common stock solution. In contrast, emulsion transfer results in ∼80% less cholesterol than expected from the stock solution, which is counterbalanced by a surprising overabundance of saturated PC-lipid relative to all other phospholipids. Experiments using binary mixtures of some of the lipids largely support results from the 5-component mixture. Exact values of lipid ratios variations likely depend on the details of each method, so a broader conclusion is that experiments that increment lipid ratios in small steps will be highly sensitive to the method of lipid formation and to sample-to-sample variations, which are low (roughly ±2 mol% in the 5-component mixture and either scale proportionally with increasing mole fraction or remain low). Experiments that increment lipid ratios in larger steps or that seek to explain general trends or new phenomena will be less sensitive to the method used.

SIGNIFICANCE STATEMENT

Small changes to the amounts and types of lipids in membranes can drastically affect the membrane's behavior. Unfortunately, it is unknown whether (or to what extent) different methods of making vesicles alter the ratios of lipids in membranes, even when identical stock solutions are used. This presents challenges for researchers when comparing data with colleagues who use different methods. Here, we measure ratios of lipid types in vesicle membranes produced by five methods. We assess each method's reproducibility and compare resulting vesicle compositions across methods. In doing so, we provide a quantitative basis that the scientific community can use to estimate whether differences between their results can be simply attributed to differences between methods or to sample-to-sample variations.

Development and Application of a Multidimensional Database for the Detection of Quaternary Ammonium Compounds and Their Phase I Hepatic Metabolites in Humans

The COVID-19 pandemic has led to significantly increased human exposure to the widely used disinfectants quaternary ammonium compounds (QACs). Xenobiotic metabolism serves a critical role in the clearance of environmental molecules, yet limited data are available on the routes of QAC metabolism or metabolite levels in humans. To address this gap and to advance QAC biomonitoring capabilities, we analyzed 19 commonly used QACs and their phase I metabolites by liquid chromatography-ion mobility-tandem mass spectrometry (LC-IM-MS/MS). In vitro generation of QAC metabolites by human liver microsomes produced a series of oxidized metabolites, with metabolism generally occurring on the alkyl chain group, as supported by MS/MS fragmentation. Discernible trends were observed in the gas-phase IM behavior of QAC metabolites, which, despite their increased mass, displayed smaller collision cross-section (CCS) values than those of their respective parent compounds. We then constructed a multidimensional reference SQLite database consisting of m/z, CCS, retention time (rt), and MS/MS spectra for 19 parent QACs and 81 QAC metabolites. Using this database, we confidently identified 13 parent QACs and 35 metabolites in de-identified human fecal samples. This is the first study to integrate in vitro metabolite biosynthesis with LC-IM-MS/MS for the simultaneous monitoring of parent QACs and their metabolites in humans.

A supervised machine-learning approach for the efficient development of a multi method (LC-MS) for a large number of drugs and subsets thereof: focus on oral antitumor agents

OBJECTIVES

Accumulating evidence argues for a more widespread use of therapeutic drug monitoring (TDM) to support individualized medicine, especially for therapies where toxicity and efficacy are critical issues, such as in oncology. However, development of TDM assays struggles to keep pace with the rapid introduction of new drugs. Therefore, novel approaches for faster assay development are needed that also allow effortless inclusion of newly approved drugs as well as customization to smaller subsets if scientific or clinical situations require.

METHODS

We applied and evaluated two machine-learning approaches i.e., a regression-based approach and an artificial neural network (ANN) to retention time (RT) prediction for efficient development of a liquid chromatography mass spectrometry (LC-MS) method quantifying 73 oral antitumor drugs (OADs) and five active metabolites. Individual steps included training, evaluation, comparison, and application of the superior approach to RT prediction, followed by stipulation of the optimal gradient.

RESULTS

Both approaches showed excellent results for RT prediction (mean difference ± standard deviation: 2.08 % ± 9.44 % ANN; 1.78 % ± 1.93 % regression-based approach). Using the regression-based approach, the optimum gradient (4.91 % MeOH/min) was predicted with a total run time of 17.92 min. The associated method was fully validated following FDA and EMA guidelines. Exemplary modification and application of the regression-based approach to a subset of 14 uro-oncological agents resulted in a considerably shortened run time of 9.29 min.

CONCLUSIONS

Using a regression-based approach, a multi drug LC-MS assay for RT prediction was efficiently developed, which can be easily expanded to newly approved OADs and customized to smaller subsets if required.

Retention time prediction and MRM validation reinforce the biomarker identification of LC-MS based phospholipidomics

Dysfunctional lipid metabolism plays a crucial role in the development and progression of various diseases. Accurate measurement of lipidomes can help uncover the complex interactions between genes, proteins, and lipids in health and diseases. The prediction of retention time (RT) has become increasingly important in both targeted and untargeted metabolomics. However, the potential impact of RT prediction on targeted LC-MS based lipidomics is still not fully understood. Herein, we propose a simplified workflow for predicting RT in phospholipidomics. Our approach involves utilizing the fatty acyl chain length or carbon-carbon double bond (DB) number in combination with multiple reaction monitoring (MRM) validation. We found that our model's predictive capacity for RT was comparable to that of a publicly accessible program (QSRR Automator). Additionally, MRM validation helped in further mitigating the interference in signal recognition. Using this developed workflow, we conducted phospholipidomics of sorafenib resistant hepatocellular carcinoma (HCC) cell lines, namely MHCC97H and Hep3B. Our findings revealed an abundance of monounsaturated fatty acyl (MUFA) or polyunsaturated fatty acyl (PUFA) phospholipids in these cell lines after developing drug resistance. In both cell lines, a total of 29 lipids were found to be co-upregulated and 5 lipids were co-downregulated. Further validation was conducted on seven of the upregulated lipids using an independent dataset, which demonstrates the potential for translation of the established workflow or the lipid biomarkers.

Elucidating the impact of bacterial lipases, human serum albumin, and FASII inhibition on the utilization of exogenous fatty acids by Staphylococcus aureus

IMPORTANCE

Incorporation of host-derived exogenous fatty acids (eFAs), particularly unsaturated fatty acids (UFAs), by Staphylococcus aureus could affect the bacterial membrane fluidity and susceptibility to antimicrobials. In this work, we found that glycerol ester hydrolase (Geh) is the primary lipase hydrolyzing cholesteryl esters and, to a lesser extent, triglycerides and that human serum albumin (HSA) could serve as a buffer of eFAs, where low levels of HSA facilitate the utilization of eFAs but high levels of HSA inhibit it. The fact that the type II fatty acid synthesis (FASII) inhibitor, AFN-1252, leads to an increase in UFA content even in the absence of eFA suggests that membrane property modulation is part of its mechanism of action. Thus, Geh and/or the FASII system look to be promising targets to enhance S. aureus killing in a host environment by restricting eFA utilization or modulating membrane properties, respectively.

MALDI-IM-MS Imaging of Brain Sterols and Lipids in a Mouse Model of Smith-Lemli-Opitz Syndrome

Smith-Lemli-Opitz syndrome (SLOS) is a neurodevelopmental disorder caused by genetic mutations in the DHCR7 gene, encoding the enzyme 3β-hydroxysterol-Δ7-reductase (DHCR7) that catalyzes the last step of cholesterol synthesis. The resulting deficiency in cholesterol and accumulation of its precursor, 7-dehydrocholesterol (7-DHC), have a profound impact on brain development, which manifests as developmental delay, cognitive impairment, and behavioral deficits. To understand how the brain regions are differentially affected by the defective Dhcr7, we aim to map the regional distribution of sterols and other lipids in neonatal brains from a Dhcr7-KO mouse model of SLOS, using mass spectrometry imaging (MSI). MSI enables spatial localization of biomolecules in situ on the surface of a tissue section, which is particularly useful for mapping the changes that occur within a metabolic disorder such as SLOS, and in an anatomically complex organ such as the brain. In this work, using MALDI-ion mobility (IM)-MSI, we successfully determined the regional distribution of features that correspond to cholesterol, 7-DHC/desmosterol, and the precursor of desmosterol, 7-dehydrodesmosterol, in WT and Dhcr7-KO mice. Interestingly, we also observed m/z values that match the major oxysterol metabolites of 7-DHC (DHCEO and hydroxy-7-DHC), which displayed similar patterns as 7-DHC. We then identified brain lipids using m/z and CCS at the Lipid Species-level and curated a database of MALDIIM-MS-derived lipid CCS values. Subsequent statistical analysis of regions-of-interest allowed us to identify differentially expressed lipids between Dhcr7-KO and WT brains, which could contribute to defects in myelination, neurogenesis, neuroinflammation, and learning and memory in SLOS.

High-throughput analysis of lipidomic phenotypes of methicillin-resistant Staphylococcus aureus by coupling in situ 96-well cultivation and HILIC-ion mobility-mass spectrometry

Antimicrobial resistance is a major threat to human health as resistant pathogens spread globally, and the development of new antimicrobials is slow. Since many antimicrobials function by targeting cell wall and membrane components, high-throughput lipidomics for bacterial phenotyping is of high interest for researchers to unveil lipid-mediated pathways when dealing with a large number of lab-selected or clinical strains. However, current practice for lipidomic analysis requires the cultivation of bacteria on a large scale, which does not replicate the growth conditions for high-throughput bioassays that are normally carried out in 96-well plates, such as susceptibility tests, growth curve measurements, and biofilm quantitation. Analysis of bacteria grown under the same condition as other bioassays would better inform the differences in susceptibility and other biological metrics. In this work, a high-throughput method for cultivation and lipidomic analysis of antimicrobial-resistant bacteria was developed for standard 96-well plates exemplified by methicillin-resistant Staphylococcus aureus (MRSA). By combining a 30-mm liquid chromatography (LC) column with ion mobility (IM) separation, elution time could be dramatically shortened to 3.6 min for a single LC run without losing major lipid features. Peak capacity was largely rescued by the addition of the IM dimension. Through multi-linear calibration, the deviation of retention time can be limited to within 5%, making database-based automatic lipid identification feasible. This high-throughput method was further validated by characterizing the lipidomic phenotypes of antimicrobial-resistant mutants derived from the MRSA strain, W308, grown in a 96-well plate.

Permethylation and Metal Adduction: A Toolbox for the Improved Characterization of Glycolipids with Cyclic Ion Mobility Separations Coupled to Mass Spectrometry

Lipids are an important class of molecules involved in various biological functions but remain difficult to characterize through mass-spectrometry-based methods because of their many possible isomers. Glycolipids, specifically, play important roles in cell signaling but display an even greater level of isomeric heterogeneity as compared to other lipid classes stemming from the introduction of a carbohydrate and its corresponding linkage position and α/β anomericity at the headgroup. While liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) remains the gold standard technique in lipidomics, it is still unable to characterize all isomeric species, thus presenting the need for new, orthogonal, methodologies. Ion mobility spectrometry-mass spectrometry (IMS-MS) can provide an additional dimension of information that supplements LC-MS/MS workflows, but has seen little use for glycolipid analyses. Herein, we present an analytical toolbox that enables the characterization of various glycolipid isomer sets using high-resolution cyclic ion mobility separations coupled with mass spectrometry (cIMS-MS). Specifically, we utilized a combination of both permethylation and metal adduction to fully resolve isomeric sphingolipids and ceramides with our cIMS-MS platform. We also introduce a new metric that can enable comparing peak-to-peak resolution across varying cIMS-MS pathlengths. Overall, we envision that our presented methodologies are highly amenable to existing LC-MS/MS-based workflows and can also have broad utility toward other omics-based analyses.

Tracking the Metabolic Fate of Exogenous Arachidonic Acid in Ferroptosis Using Dual-Isotope Labeling Lipidomics

Lipid metabolism is implicated in a variety of diseases, including cancer, cell death, and inflammation, but lipidomics has proven to be challenging due to the vast structural diversity over a narrow range of mass and polarity of lipids. Isotope labeling is often used in metabolomics studies to follow the metabolism of exogenously added labeled compounds because they can be differentiated from endogenous compounds by the mass shift associated with the label. The application of isotope labeling to lipidomics has also been explored as a method to track the metabolism of lipids in various disease states. However, it can be difficult to differentiate a single isotopically labeled lipid from the rest of the lipidome due to the variety of endogenous lipids present over the same mass range. Here we report the development of a dual-isotope deuterium labeling method to track the metabolic fate of exogenous polyunsaturated fatty acids, e.g., arachidonic acid, in the context of ferroptosis using hydrophilic interaction-ion mobility-mass spectrometry (HILIC-IM-MS). Ferroptosis is a type of cell death that is dependent on lipid peroxidation. The use of two isotope labels rather than one enables the identification of labeled species by a signature doublet peak in the resulting mass spectra. A Python-based software, D-Tracer, was developed to efficiently extract metabolites with dual-isotope labels. The labeled species were then identified with LiPydomics based on their retention times, collision cross section, and m/z values. Changes in exogenous AA incorporation in the absence and presence of a ferroptosis inducer were elucidated.

Collision cross section measurement and prediction methods in omics

Omics studies such as metabolomics, lipidomics, and proteomics have become important for understanding the mechanisms in living organisms. However, the compounds detected are structurally different and contain isomers, with each structure or isomer leading to a different result in terms of the role they play in the cell or tissue in the organism. Therefore, it is important to detect, characterize, and elucidate the structures of these compounds. Liquid chromatography and mass spectrometry have been utilized for decades in the structure elucidation of key compounds. While prediction models of parameters (such as retention time and fragmentation pattern) have also been developed for these separation techniques, they have some limitations. Moreover, ion mobility has become one of the most promising techniques to give a fingerprint to these compounds by determining their collision cross section (CCS) values, which reflect their shape and size. Obtaining accurate CCS enables its use as a filter for potential analyte structures. These CCS values can be measured experimentally using calibrant-independent and calibrant-dependent approaches. Identification of compounds based on experimental CCS values in untargeted analysis typically requires CCS references from standards, which are currently limited and, if available, would require a large amount of time for experimental measurements. Therefore, researchers use theoretical tools to predict CCS values for untargeted and targeted analysis. In this review, an overview of the different methods for the experimental and theoretical estimation of CCS values is given where theoretical prediction tools include computational and machine modeling type approaches. Moreover, the limitations of the current experimental and theoretical approaches and their potential mitigation methods were discussed.

Molecular Basis of Cell Membrane Adaptation in Daptomycin-Resistant Enterococcus faecalis

Daptomycin is a last-resort lipopeptide antibiotic that disrupts cell membrane (CM) and peptidoglycan homeostasis. Enterococcus faecalis has developed a sophisticated mechanism to avoid daptomycin killing by re-distributing CM anionic phospholipids away from the septum. The CM changes are orchestrated by a three-component regulatory system, designated LiaFSR, with a possible contribution of cardiolipin synthase (Cls). However, the mechanism by which LiaFSR controls the CM response and the role of Cls are unknown. Here, we show that cardiolipin synthase activity is essential for anionic phospholipid redistribution and daptomycin resistance since deletion of the two genes ( cls1 and cls2 ) encoding Cls abolished CM remodeling. We identified LiaY, a transmembrane protein regulated by LiaFSR, as an important mediator of CM remodeling required for re-distribution of anionic phospholipid microdomains via interactions with Cls1. Together, our insights provide a mechanistic framework on the enterococcal response to cell envelope antibiotics that could be exploited therapeutically.

DBLiPro: A Database for Lipids and Proteins in Human Lipid Metabolism

To help researchers in the field of biology, medicine, chemistry, and materials science to use lipidomic data conveniently, there is an urgent need to develop a platform that provides a systematic knowledgebase of human lipid metabolism and lipidome-centric omics analysis tools. DBLiPro is a user-friendly webserver allowing for access to human metabolism-related lipids and proteins knowledge database and an interactive bioinformatics integrative analysis workflow for lipidomics, transcriptomics, and proteomics data. In DBLiPro, there are 3109 lipid-associated proteins (LAPs) and 2098 lipid metabolites in the knowledge base section, which were obtained from Uniprot, Kyoto Encyclopedia of Genes and Genomes (KEGG) and were further annotated by information from other public resources in the knowledge base section, such as RaftProt and PubChem. DBLiPro offers a step-by-step interactive analysis workflow for lipidomics, transcriptomics, proteomics, and their integrating multi-omics analysis focusing on the human lipid metabolism. In summary, DBLiPro is capable of helping users discover key molecules (lipids and proteins) in human lipid metabolism and investigate lipid-protein functions underlying mechanisms based on their own omics data. The DBLiPro is freely available at http://lipid.cloudna.cn/home.

Elucidating the Impact of Bacterial Lipases, Human Serum Albumin, and FASII Inhibition on the Utilization of Exogenous Fatty Acids by Staphylococcus aureus

Staphylococcus aureus only synthesizes straight-chain or branched-chain saturated fatty acids (SCFAs or BCFAs) via the type II fatty acid synthesis (FASII) pathway, but as a highly adaptive pathogen, S. aureus can also utilize host-derived exogenous fatty acids (eFAs), including SCFAs and unsaturated fatty acids (UFAs). S. aureus secretes three lipases, Geh, sal1, and SAUSA300_0641, which could perform the function of releasing fatty acids from host lipids. Once released, the FAs are phosphorylated by the fatty acid kinase, FakA, and incorporated into the bacterial lipids. In this study, we determined the substrate specificity of S. aureus secreted lipases, the effect of human serum albumin (HSA) on eFA incorporation, and the effect of FASII inhibitor, AFN-1252, on eFA incorporation using comprehensive lipidomics. When grown with major donors of fatty acids, cholesteryl esters (CEs) and triglycerides (TGs), Geh was found to be the primary lipase responsible for hydrolyzing CEs, but other lipases could compensate for the function of Geh in hydrolyzing TGs. Lipidomics showed that eFAs were incorporated into all major S. aureus lipid classes and that fatty acid-containing HSA can serve as a source of eFAs. Furthermore, S. aureus grown with UFAs displayed decreased membrane fluidity and increased production of reactive oxygen species (ROS). Exposure to AFN-1252 enhanced UFAs in the bacterial membrane, even without a source of eFAs, indicating a FASII pathway modification. Thus, the incorporation of eFAs alters the S. aureus lipidome, membrane fluidity, and ROS formation, which could affect host-pathogen interactions and susceptibility to membrane-targeting antimicrobials.

Collision Cross Section Prediction Based on Machine Learning

Ion mobility-mass spectrometry (IM-MS) is a powerful separation technique providing an additional dimension of separation to support the enhanced separation and characterization of complex components from the tissue metabolome and medicinal herbs. The integration of machine learning (ML) with IM-MS can overcome the barrier to the lack of reference standards, promoting the creation of a large number of proprietary collision cross section (CCS) databases, which help to achieve the rapid, comprehensive, and accurate characterization of the contained chemical components. In this review, advances in CCS prediction using ML in the past 2 decades are summarized. The advantages of ion mobility-mass spectrometers and the commercially available ion mobility technologies with different principles (e.g., time dispersive, confinement and selective release, and space dispersive) are introduced and compared. The general procedures involved in CCS prediction based on ML (acquisition and optimization of the independent and dependent variables, model construction and evaluation, etc.) are highlighted. In addition, quantum chemistry, molecular dynamics, and CCS theoretical calculations are also described. Finally, the applications of CCS prediction in metabolomics, natural products, foods, and the other research fields are reflected.

Differential Contributions of Distinct Free Radical Peroxidation Mechanisms to the Induction of Ferroptosis

Ferroptosis is a form of regulated cell death driven by lipid peroxidation of polyunsaturated fatty acids (PUFAs). Lipid peroxidation can propagate through either the hydrogen-atom transfer (HAT) or peroxyl radical addition (PRA) mechanism. However, the contribution of the PRA mechanism to the induction of ferroptosis has not been studied. In this study, we aim to elucidate the relationship between the reactivity and mechanisms of lipid peroxidation and ferroptosis induction. We found that while some peroxidation-reactive lipids, such as 7-dehydrocholesterol, vitamins D3 and A, and coenzyme Q10, suppress ferroptosis, both nonconjugated and conjugated PUFAs enhanced cell death induced by RSL3, a ferroptosis inducer. Importantly, we found that conjugated PUFAs, including conjugated linolenic acid (CLA 18:3) and conjugated linoleic acid (CLA 18:2), can induce or potentiate ferroptosis much more potently than nonconjugated PUFAs. We next sought to elucidate the mechanism underlying the different ferroptosis-inducing potency of conjugated and nonconjugated PUFAs. Lipidomics revealed that conjugated and nonconjugated PUFAs are incorporated into distinct cellular lipid species. The different peroxidation mechanisms predict the formation of higher levels of reactive electrophilic aldehydes from conjugated PUFAs than nonconjugated PUFAs, which was confirmed by aldehyde-trapping and mass spectrometry. RNA sequencing revealed that protein processing in the endoplasmic reticulum and proteasome are among the most significantly upregulated pathways in cells treated with CLA 18:3, suggesting increased ER stress and activation of unfolded protein response. These results suggest that protein damage by lipid electrophiles is a key step in ferroptosis.

Evaluating Software Tools for Lipid Identification from Ion Mobility Spectrometry-Mass Spectrometry Lipidomics Data

The unambiguous identification of lipids is a critical component of lipidomics studies and greatly impacts the interpretation and significance of analyses as well as the ultimate biological understandings derived from measurements. The level of structural detail that is available for lipid identifications is largely determined by the analytical platform being used. Mass spectrometry (MS) coupled with liquid chromatography (LC) is the predominant combination of analytical techniques used for lipidomics studies, and these methods can provide fairly detailed lipid identification. More recently, ion mobility spectrometry (IMS) has begun to see greater adoption in lipidomics studies thanks to the additional dimension of separation that it provides and the added structural information that can support lipid identification. At present, relatively few software tools are available for IMS-MS lipidomics data analysis, which reflects the still limited adoption of IMS as well as the limited software support. This fact is even more pronounced for isomer identifications, such as the determination of double bond positions or integration with MS-based imaging. In this review, we survey the landscape of software tools that are available for the analysis of IMS-MS-based lipidomics data and we evaluate lipid identifications produced by these tools using open-access data sourced from the peer-reviewed lipidomics literature.

Prediction of a Large-Scale Database of Collision Cross-Section and Retention Time Using Machine Learning to Reduce False Positive Annotations in Untargeted Metabolomics

Metabolite identification in untargeted metabolomics is complex, with the risk of false positive annotations. This work aims to use machine learning to successively predict the retention time (Rt) and the collision cross-section (CCS) of an open-access database to accelerate the interpretation of metabolomic results. Standards of metabolites were tested using liquid chromatography coupled with high-resolution mass spectrometry. In CCSBase and QSRR predictor machine learning models, experimental results were used to generate predicted CCS and Rt of the Human Metabolome Database. From 542 standards, 266 and 301 compounds were detected in positive and negative electrospray ionization mode, respectively, corresponding to 380 different metabolites. CCS and Rt were then predicted using machine learning tools for almost 114,000 metabolites. R² score of the linear regression between predicted and measured data achieved 0.938 and 0.898 for CCS and Rt, respectively, demonstrating the models' reliability. A CCS and Rt index filter of mean error ± 2 standard deviations could remove most misidentifications. Its application to data generated from a toxicology study on tobacco cigarettes reduced hits by 76%. Regarding the volume of data produced by metabolomics, the practical workflow provided allows for the implementation of valuable large-scale databases to improve the biological interpretation of metabolomics data.

Evaluating LC-HRMS metabolomics data processing software using FAIR principles for research software

BACKGROUND

Liquid chromatography-high resolution mass spectrometry (LC-HRMS) is a popular approach for metabolomics data acquisition and requires many data processing software tools. The FAIR Principles - Findability, Accessibility, Interoperability, and Reusability - were proposed to promote open science and reusable data management, and to maximize the benefit obtained from contemporary and formal scholarly digital publishing. More recently, the FAIR principles were extended to include Research Software (FAIR4RS).

AIM OF REVIEW

This study facilitates open science in metabolomics by providing an implementation solution for adopting FAIR4RS in the LC-HRMS metabolomics data processing software. We believe our evaluation guidelines and results can help improve the FAIRness of research software.

KEY SCIENTIFIC CONCEPTS OF REVIEW

We evaluated 124 LC-HRMS metabolomics data processing software obtained from a systematic review and selected 61 software for detailed evaluation using FAIR4RS-related criteria, which were extracted from the literature along with internal discussions. We assigned each criterion one or more FAIR4RS categories through discussion. The minimum, median, and maximum percentages of criteria fulfillment of software were 21.6%, 47.7%, and 71.8%. Statistical analysis revealed no significant improvement in FAIRness over time. We identified four criteria covering multiple FAIR4RS categories but had a low %fulfillment: (1) No software had semantic annotation of key information; (2) only 6.3% of evaluated software were registered to Zenodo and received DOIs; (3) only 14.5% of selected software had official software containerization or virtual machine; (4) only 16.7% of evaluated software had a fully documented functions in code. According to the results, we discussed improvement strategies and future directions.

Semantic Annotation of Experimental Methods in Analytical Chemistry

A major obstacle for reusing and integrating existing data is finding the data that is most relevant in a given context. The primary metadata resource is the scientific literature describing the experiments that produced the data. To stimulate the development of natural language processing methods for extracting this information from articles, we have manually annotated 100 recent open access publications in Analytical Chemistry as semantic graphs. We focused on articles mentioning mass spectrometry in their experimental sections, as we are particularly interested in the topic, which is also within the domain of several ontologies and controlled vocabularies. The resulting gold standard dataset is publicly available and directly applicable to validating automated methods for retrieving this metadata from the literature. In the process, we also made a number of observations on the structure and description of experiments and open access publication in this journal.

Isomer and Conformer-Specific Mass Distribution-Based Isotopic Shifts in High-Resolution Cyclic Ion Mobility Separations

Herein, we present the use of mass distribution-based isotopic shifts in high-resolution cyclic ion mobility spectrometry-mass spectrometry (cIMS-MS)-based separations to characterize various isomeric species as well as conformers. Specifically, by using the observed relative arrival time values for the isotopologues found in the isotopic envelope after long pathlength cIMS-MS separations, we were able to distinguish dibromoaniline, dichloroaniline, and quaternary ammonium salt isomers, as well as a pair of 25-hydroxyvitamin D3 conformers based on their respective mass distribution-based shifts. Our observed shifts were highly reproducible and broadly applied to the isotopologues of various atoms (i.e., Cl, Br, and C). Additionally, through a control experiment, we determined that such shifts are indeed pathlength-independent, thus demonstrating that our presented methodology could be readily extended to other high-resolution IMS-MS platforms. These results are the first characterization of conformers using mass distribution-based IMS-MS shifts, as well as the first use of a commercial cIMS-MS platform to characterize isomers via their mass distribution-based shifts. We anticipate that our methodology will have broad applicability for biological analytes and that mass distribution-based shifts could potentially act as an added dimension of analysis in existing IMS-MS workflows in omics-based research. Specifically, we envision that the development of a database of these mass distribution-based shifts could, for example, enable the identification of unknown metabolites in complex matrices.

A Current Encyclopedia of Bioinformatics Tools, Data Formats and Resources for Mass Spectrometry Lipidomics

Mass spectrometry is a widely used technology to identify and quantify biomolecules such as lipids, metabolites and proteins necessary for biomedical research. In this study, we catalogued freely available software tools, libraries, databases, repositories and resources that support lipidomics data analysis and determined the scope of currently used analytical technologies. Because of the tremendous importance of data interoperability, we assessed the support of standardized data formats in mass spectrometric (MS)-based lipidomics workflows. We included tools in our comparison that support targeted as well as untargeted analysis using direct infusion/shotgun (DI-MS), liquid chromatography-mass spectrometry, ion mobility or MS imaging approaches on MS1 and potentially higher MS levels. As a result, we determined that the Human Proteome Organization-Proteomics Standards Initiative standard data formats, mzML and mzTab-M, are already supported by a substantial number of recent software tools. We further discuss how mzTab-M can serve as a bridge between data acquisition and lipid bioinformatics tools for interpretation, capturing their output and transmitting rich annotated data for downstream processing. However, we identified several challenges of currently available tools and standards. Potential areas for improvement were: adaptation of common nomenclature and standardized reporting to enable high throughput lipidomics and improve its data handling. Finally, we suggest specific areas where tools and repositories need to improve to become FAIRer.

Evolution of Enterococcus faecium in Response to a Combination of Daptomycin and Fosfomycin Reveals Distinct and Diverse Adaptive Strategies

Infections caused by vancomycin-resistant Enterococcus faecium (VREfm) are an important public health threat. VREfm isolates have become increasingly resistant to the front-line antibiotic daptomycin (DAP). As such, the use of DAP combination therapies with other antibiotics like fosfomycin (FOS) has received increased attention. Antibiotic combinations could extend the efficacy of currently available antibiotics and potentially delay the onset of further resistance. We investigated the potential for E. faecium HOU503, a clinical VREfm isolate that is DAP and FOS susceptible, to develop resistance to a DAP-FOS combination. Of particular interest was whether the genetic drivers for DAP-FOS resistance might be epistatic and, thus, potentially decrease the efficacy of a combinatorial approach in either inhibiting VREfm or in delaying the onset of resistance. We show that resistance to DAP-FOS could be achieved by independent mutations to proteins responsible for cell wall synthesis for FOS and in altering membrane dynamics for DAP. However, we did not observe genetic drivers that exhibited substantial cross-drug epistasis that could undermine the DAP-FOS combination. Of interest was that FOS resistance in HOU503 was largely mediated by changes in phosphoenolpyruvate (PEP) flux as a result of mutations in pyruvate kinase (pyk). Increasing PEP flux could be a readily accessible mechanism for FOS resistance in many pathogens. Importantly, we show that HOU503 was able to develop DAP resistance through a variety of biochemical mechanisms and was able to employ different adaptive strategies. Finally, we showed that the addition of FOS can prolong the efficacy of DAP and slow down DAP resistance in vitro.

Recent developments in data acquisition, treatment and analysis with ion mobility-mass spectrometry for lipidomics

Lipids are involved in many biological processes and their study is constantly increasing. To identify a lipid among thousand requires of reliable methods and techniques. Ion Mobility (IM) can be coupled with Mass Spectrometry (MS) to increase analytical selectivity in lipid analysis of lipids. IM-MS has experienced an enormous development in several aspects, including instrumentation, sensitivity, amount of information collected and lipid identification capabilities. This review summarizes the latest developments in IM-MS analyses for lipidomics and focusses on the current acquisition modes in IM-MS, the approaches for the pre-treatment of the acquired data and the subsequent data analysis. Methods and tools for the calculation of Collision Cross Section (CCS) values of analytes are also reviewed. CCS values are commonly studied to support the identification of lipids, providing a quasi-orthogonal property that increases the confidence level in the annotation of compounds and can be matched in CCS databases. The information contained in this review might be of help to new users of IM-MS to decide the adequate instrumentation and software to perform IM-MS experiments for lipid analyses, but also for other experienced researchers that can reconsider their routines and protocols. This article is protected by copyright. All rights reserved.

Improving confidence in lipidomic annotations by incorporating empirical ion mobility regression analysis and chemical class prediction

MOTIVATION

Mass spectrometry-based untargeted lipidomics aims to globally characterize the lipids and lipid-like molecules in biological systems. Ion mobility increases coverage and confidence by offering an additional dimension of separation and a highly reproducible metric for feature annotation, the collision cross-section (CCS).

RESULTS

We present a data processing workflow to increase confidence in molecular class annotations based on CCS values. This approach uses class-specific regression models built from a standardized CCS repository (the Unified CCS Compendium) in a parallel scheme that combines a new annotation filtering approach with a machine learning class prediction strategy. In a proof-of-concept study using murine brain lipid extracts, 883 lipids were assigned higher confidence identifications using the filtering approach, which reduced the tentative candidate lists by over 50% on average. An additional 192 unannotated compounds were assigned a predicted chemical class.

AVAILABILITY AND IMPLEMENTATION

All relevant source code is available at https://github.com/McLeanResearchGroup/CCS-filter.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Temporal changes in the brain lipidome during neurodevelopment of Smith-Lemli-Opitz syndrome mice

Neurodevelopment is an intricately orchestrated program of cellular events that occurs with tight temporal and spatial regulation. While it is known that the development and proper functioning of the brain, which is the second most lipid rich organ behind adipose tissue, greatly rely on lipid metabolism and signaling, the temporal lipidomic changes that occur throughout the course of neurodevelopment have not been investigated. Smith-Lemli-Opitz syndrome is a metabolic disorder caused by genetic mutations in the DHCR7 gene, leading to defective 3β-hydroxysterol-Δ⁷-reductase (DHCR7), the enzyme that catalyzes the last step of the Kandutsch-Russell pathway of cholesterol synthesis. Due to the close regulatory relationship between sterol and lipid homeostasis, we hypothesize that altered or dysregulated lipid metabolism beyond the primary defect of cholesterol biosynthesis is present in the pathophysiology of SLOS. Herein, we applied our HILIC-IM-MS method and LiPydomics Python package to streamline an untargeted lipidomics analysis of developing mouse brains in both wild-type and Dhcr7-KO mice, identifying lipids at Level 3 (lipid species level: lipid class/subclass and fatty acid sum composition). We compared relative lipid abundances throughout development, from embryonic day 12.5 to postnatal day 0 and determined differentially expressed brain lipids between wild-type and Dhcr7-KO mice at specific developmental time points, revealing lipid metabolic pathways that are affected in SLOS beyond the cholesterol biosynthesis pathway, such as glycerolipid, glycerophospholipid, and sphingolipid metabolism. Implications of the altered lipid metabolic pathways in SLOS pathophysiology are discussed.

Least absolute shrinkage and selection operator-based prediction of collision cross section values for ion mobility mass spectrometric analysis of lipids

A least absolute shrinkage and selection operator (LASSO)-based prediction method was developed for the prediction of lipids’ CCS values.

Recent advances in the mass spectrometric profiling of bacterial lipids

Exploring the lipids of bacteria presents a predicament that may not be broadly recognized in a field dominated by the biology and biochemistry of eukaryotic - and especially, mammalian - lipids. Bacteria make multifarious metabolites that contain fatty acyl chains of unusual length and unsaturation attached to assorted headgroups, including sugars and fatty alcohols. Lipid profiling approaches developed for eukaryotic lipids often fail to detect, resolve, or identify bacterial lipids due to their wide range of polarities (including very hydrophobic species) and diverse positional and stereochemical variations. Global lipid profiling, or lipidomics, of bacteria has thus developed as a separate mission with methodological and scientific considerations tailored to the biology of these organisms. In this review, we summarize findings primarily from the last three years that exemplify recent advances and continuing challenges to learning about bacterial lipids.

Synergy Between Beta-Lactams and Lipo-, Glyco-, and Lipoglycopeptides, Is Independent of the Seesaw Effect in Methicillin-Resistant Staphylococcus aureus

Methicillin-resistant S. aureus (MRSA) are resistant to beta-lactams, but synergistic activity between beta-lactams and glycopeptides/lipopeptides is common. Many have attributed this synergy to the beta-lactam-glycopeptide seesaw effect; however, this association has not been rigorously tested. The objective of this study was to determine whether the seesaw effect is necessary for synergy and to measure the impact of beta-lactam exposure on lipid metabolism. We selected for three isogenic strains with reduced susceptibility to vancomycin, daptomycin, and dalbavancin by serial passaging the MRSA strain N315. We used whole genome sequencing to identify genetic variants that emerged and tested for synergy between vancomycin, daptomycin, or dalbavancin in combination with 6 beta-lactams with variable affinity for staphylococcal penicillin binding proteins (PBPs), including nafcillin, meropenem, ceftriaxone, ceftaroline, cephalexin, and cefoxitin, using time-kills. We observed that the seesaw effect with each beta-lactam was variable and the emergence of the seesaw effect for a particular beta-lactam was not necessary for synergy between that beta-lactam and vancomycin, daptomycin, or dalbavancin. Synergy was more commonly observed with vancomycin and daptomycin based combinations than dalbavancin in time-kills. Among the beta-lactams, cefoxitin and nafcillin were the most likely to exhibit synergy using the concentrations tested, while cephalexin was the least likely to exhibit synergy. Synergy was more common among the resistant mutants than the parent strain. Interestingly N315-D1 and N315-DAL0.5 both had mutations in vraTSR and walKR despite their differences in the seesaw effect. Lipidomic analysis of all strains exposed to individual beta-lactams at subinhibitory concentrations suggested that in general, the abundance of cardiolipins (CLs) and most free fatty acids (FFAs) positively correlated with the presence of synergistic effects while abundance of phosphatidylglycerols (PGs) and lysylPGs mostly negatively correlated with synergistic effects. In conclusion, the beta-lactam-glycopeptide seesaw effect and beta-lactam-glycopeptide synergy are distinct phenomena. This suggests that the emergence of the seesaw effect may not have clinical importance in terms of predicting synergy. Further work is warranted to characterize strains that don't exhibit beta-lactam synergy to identify which strains should be targeted with combination therapy and which ones cannot and to further investigate the potential role of CLs in mediating synergy.

New software tools, databases, and resources in metabolomics: updates from 2020

BACKGROUND

Precision medicine, space exploration, drug discovery to characterization of dark chemical space of habitats and organisms, metabolomics takes a centre stage in providing answers to diverse biological, biomedical, and environmental questions. With technological advances in mass-spectrometry and spectroscopy platforms that aid in generation of information rich datasets that are complex big-data, data analytics tend to co-evolve to match the pace of analytical instrumentation. Software tools, resources, databases, and solutions help in harnessing the concealed information in the generated data for eventual translational success.

AIM OF THE REVIEW

In this review, ~ 85 metabolomics software resources, packages, tools, databases, and other utilities that appeared in 2020 are introduced to the research community.

KEY SCIENTIFIC CONCEPTS OF REVIEW

In Table 1 the computational dependencies and downloadable links of the tools are provided, and the resources are categorized based on their utility. The review aims to keep the community of metabolomics researchers updated with all the resources developed in 2020 at a collated avenue, in line with efforts form 2015 onwards to help them find these at one place for further referencing and use.