Lipidomic consequences of phospholipid synthesis defects in Escherichia coli revealed by HILIC-ion mobility-mass spectrometry

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.

From big data to big insights: statistical and bioinformatic approaches for exploring the lipidome

The goal of lipidomic studies is to provide a broad characterization of cellular lipids present and changing in a sample of interest. Recent lipidomic research has significantly contributed to revealing the multifaceted roles that lipids play in fundamental cellular processes, including signaling, energy storage, and structural support. Furthermore, these findings have shed light on how lipids dynamically respond to various perturbations. Continued advancement in analytical techniques has also led to improved abilities to detect and identify novel lipid species, resulting in increasingly large datasets. Statistical analysis of these datasets can be challenging not only because of their vast size, but also because of the highly correlated data structure that exists due to many lipids belonging to the same metabolic or regulatory pathways. Interpretation of these lipidomic datasets is also hindered by a lack of current biological knowledge for the individual lipids. These limitations can therefore make lipidomic data analysis a daunting task. To address these difficulties and shed light on opportunities and also weaknesses in current tools, we have assembled this review. Here, we illustrate common statistical approaches for finding patterns in lipidomic datasets, including univariate hypothesis testing, unsupervised clustering, supervised classification modeling, and deep learning approaches. We then describe various bioinformatic tools often used to biologically contextualize results of interest. Overall, this review provides a framework for guiding lipidomic data analysis to promote a greater assessment of lipidomic results, while understanding potential advantages and weaknesses along the way.

Revolutions in Lipid Isomer Resolution: Application of Ultrahigh-Resolution Ion Mobility to Reveal Lipid Diversity

Many families of lipid isomers remain unresolved by contemporary liquid chromatography-mass spectrometry approaches, leading to a significant underestimation of the structural diversity within the lipidome. While ion mobility coupled to mass spectrometry has provided an additional dimension of lipid isomer resolution, some isomers require a resolving power beyond the capabilities of conventional platforms. Here, we present the application of high-resolution traveling-wave ion mobility for the separation of lipid isomers that differ in (i) the location of a single carbon-carbon double bond, (ii) the stereochemistry of the double bond (cis or trans), or, for glycerolipids, (iii) the relative substitution of acyl chains on the glycerol backbone (sn-position). Collisional activation following mobility separation allowed identification of the carbon-carbon double-bond position and sn-position, enabling confident interpretation of variations in mobility peak abundance. To demonstrate the applicability of this method, double-bond and sn-position isomers of an abundant phosphatidylcholine composition were resolved in extracts from a prostate cancer cell line and identified by comparison to pure isomer reference standards, revealing the presence of up to six isomers. These findings suggest that ultrahigh-resolution ion mobility has broad potential for isomer-resolved lipidomics and is attractive to consider for future integration with other modes of ion activation, thereby bringing together advanced orthogonal separations and structure elucidation to provide a more complete picture of the lipidome.

Do Nanodisc Assembly Conditions Affect Natural Lipid Uptake?

Lipids play critical roles in modulating membrane protein structure, interactions, and activity. Nanodiscs provide a tunable membrane mimetic that can model these endogenous protein-lipid interactions in a nanoscale lipid bilayer. However, most studies of membrane proteins with nanodiscs use simple synthetic lipids that lack the headgroup and fatty acyl diversity of natural extracts. Prior research has successfully used natural lipid extracts in nanodiscs that more accurately mimic natural environments, but it is not clear how nanodisc assembly may bias the incorporated lipid profiles. Here, we applied lipidomics to investigate how nanodisc assembly conditions affect the profile of natural lipids in nanodiscs. Specifically, we tested the effects of assembly temperature, nanodisc size, and lipidome extract complexity. Globally, our analysis demonstrates that the lipids profiles are largely unaffected by nanodisc assembly conditions. However, a few notable changes emerged within individual lipids and lipid classes, such as a differential incorporation of cardiolipin and phosphatidylglycerol lipids from the E. coli polar lipid extract at different temperatures. Conversely, some classes of brain lipids were affected by nanodisc size at higher temperatures. Collectively, these data enable the application of nanodiscs to study protein-lipid interactions in complex lipid environments.

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.

A sustainable and regioselective synthesis of Hemi-bis(monoacylglycero)phosphates and bis(diacylglycero)phosphates

A sustainable and green approach was developed for the scalable synthesis of uncommon naturally occurring phospholipid species, Hemi-bis(monoacylglycero)phosphates (Hemi-BMPs) and bis(diacylglycero)phosphates (BDPs) via the phospholipase D (PLD) mediated transphosphatidylation. PLD from Streptomyces sp. showed great substrate promiscuity for both phospholipids from different biological sources, and alcohol donors with diverse regiochemistry; monoacylglycerols with diverse fatty acyl structures (C12-C22), affording 74-92 wt% yields in 2 h. Experimental results demonstrated that the reaction rate is rather independent of phosphatidyls but to a large extent governed by the size, shape and regiolocation of fatty acyls incorporated on the glycerol backbone, particularly for the regio-isomers of bulky diacylglycerols (Sn-1,3 or Sn-1,2), which displays great diversity. In addition, a plausible mechanism is proposed based on molecular simulations for an elaborated explanation of the reaction thermodynamic and kinetic favorability toward the synthesis of Hemi-BMPs and BDPs.

Construction of a Bacterial Lipidomics Analytical Platform: Pilot Validation with Bovine Paratuberculosis Serum

Lipidomics analyses of bacteria offer the potential to detect and monitor infections in a host since many bacterial lipids are not present in mammals. To evaluate this omics approach, we first built a database of bacterial lipids for representative Gram-positive and Gram-negative bacteria. Our lipidomics analysis of the reference bacteria involved high-resolution mass spectrometry and electrospray ionization with less than a 1.0 ppm mass error. The lipidomics profiles of bacterial cultures clearly distinguished between Gram-positive and Gram-negative bacteria. In the case of bovine paratuberculosis (PTB) serum, we monitored two unique bacterial lipids that we also monitored in Mycobacterium avian subspecies PTB. These were PDIM-B C82, a phthiodiolone dimycocerosate, and the trehalose monomycolate hTMM 28:1, constituents of the bacterial cell envelope in mycolic-containing bacteria. The next step will be to determine if lipidomics can detect subclinical PTB infections which can last 2-to-4 years in bovine PTB. Our data further suggest that it will be worthwhile to continue building our bacterial lipidomics database and investigate the further utility of this approach in other infections of veterinary and human clinical interest.

Strain-Level Discrimination of Bacteria by Liquid Chromatography and Paper Spray Ion Mobility Mass Spectrometry

Determining bacterial identity at the strain level is critical for public health to enable proper medical treatments and reduce antibiotic resistance. Herein, we used liquid chromatography, ion mobility, and tandem MS (LC-IM-MS/MS) to distinguish Escherichia coli (E. coli) strains. Numerical multivariate statistics (principal component analysis, followed by linear discriminant analysis) showed the capability of this method to perform strain-level discrimination with prediction rates of 96.1% and 100% utilizing the negative and positive ion information, respectively. The tandem MS and LC separation proved effective in discriminating diagnostic lipid isomers in the negative mode, while IM separation was more effective in resolving lipid conformational biomarkers in the positive ion mode. Because of the clinical importance of early detection for rapid medical intervention, a faster technique, paper spray (PS)-IM-MS/MS, was used to discriminate the E. coli strains. The achieved prediction rates of the analysis of E. coli strains by PS-IM-MS/MS were 62.5% and 73.5% in the negative and positive ion modes, respectively. The strategy of numerical data fusion of negative and positive ion data increased the classification rates of PS-IM-MS/MS to 80.5%. Lipid isomers and conformers were detected, which served as strain-indicating biomarkers. The two complementary multidimensional techniques revealed biochemical differences between the E. coli strains confirming the results obtained from comparative genomic analysis. Moreover, the results suggest that PS-IM-MS/MS is a rapid, highly selective, and sensitive method for discriminating bacterial strains in environmental and food samples.

A rapid single-phase extraction for polar staphylococcal lipids

The lipid membrane is gaining appreciation as a critical factor in the emergence of antibiotic resistance, both for antibiotics that target lipid synthesis or the membrane directly and for cell-wall-targeting antibiotics. The methods used to study the emergence of antibiotic resistance in vitro can generate a large number of samples that may be low in volume and in cell density. As in eukaryotic/mammalian lipidomics, two-phase liquid-liquid extractions are the most commonly used approach to recover lipids from bacteria. The need to separate the lipid layer is cumbersome for high-throughput applications and can be a source of poor reproducibility or contaminant introduction. While several single-phase extractions have been proposed for serum, tissue, and eukaryotic cells, there have been far fewer efforts to adapt or develop such methods for bacteria lipidomics. Here, we describe a simple, single-phase lipid extraction method based on methanol, acetonitrile, and water-the MAW method. The merits of the MAW method are evaluated against the Bligh & Dyer (B&D) method for the recovery of the major membrane lipids (phosphatidylglycerols, diglycosyldiacylglycerols, and lysyl-phosphatidylglycerols) in the Gram-positive pathogen Staphylococcus aureus. We demonstrate that the MAW method achieves recoveries that are comparable to that of the B&D extraction (≥ 85% for PG 15:0/d₇-18:1). The benefits of the MAW method enable the detection of lipids from lower amounts of bacteria than the B&D method (0.57 vs 0.74 McFarlands for PG 32:0, respectively) and is easily scaled down to microplate volumes to facilitate high-throughput studies of bacterial lipids.

Transient Complexity of E. coli Lipidome Is Explained by Fatty Acyl Synthesis and Cyclopropanation

In the case of many bacteria, such as Escherichia coli, the composition of lipid molecules, termed the lipidome, temporally adapts to different environmental conditions and thus modifies membrane properties to permit growth and survival. Details of the relationship between the environment and lipidome composition are lacking, particularly for growing cultures under either favourable or under stress conditions. Here, we highlight compositional lipidome changes by describing the dynamics of molecular species throughout culture-growth phases. We show a steady cyclopropanation of fatty acyl chains, which acts as a driver for lipid diversity. There is a bias for the cyclopropanation of shorter fatty acyl chains (FA 16:1) over longer ones (FA 18:1), which likely reflects a thermodynamic phenomenon. Additionally, we observe a nearly two-fold increase in saturated fatty acyl chains in response to the presence of ampicillin and chloramphenicol, with consequences for membrane fluidity and elasticity, and ultimately bacterial stress tolerance. Our study provides the detailed quantitative lipidome composition of three E. coli strains across culture-growth phases and at the level of the fatty acyl chains and provides a general reference for phospholipid composition changes in response to perturbations. Thus, lipidome diversity is largely transient and the consequence of lipid synthesis and cyclopropanation.

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.

Lipidomics Profiling of HepG2 Cells and Interference by Mycotoxins Based on UPLC-TOF-IMS

Discovering the fungus-infected or mycotoxin-contaminated biomarkers is significant for systems biology since the metabolites in biological samples have significant polarity differences in both stochastic gene expression and microenvironmental change. Here, we aim to establish a comprehensive method for a lipidome by ion mobility mass spectrometry (IMS) merged with chemometrics to accurately find out the more scientific markers of cell interference by mycotoxins and for pathogenesis exploration and drug development. The differences in the abundances of several small molecules found in these metabolites were explored through multivariate statistical analysis, including principal component analysis (PCA) and orthogonal partial least-squares discriminant analysis (OPLS-DA), to further screen biomarkers. Good applicability and predictability were demonstrated by R²(X) and Q² (R² = 0.959, Q² = 0.999). Five compounds with m/z values of 512.502 8, 540.5343, 722.525 8, 787.667 5, and 813.683 0 were selected as markers, and four of them were further confirmed by chemical standards (i.e., MSMS of m/z 813.683 0 covering m/z 86.0978, 125.0008, 184.0745, and 185.0781). In summary, we demonstrated the integration of UPLC-TOF-IMS and the chemometrics approach to elucidate identified biomarkers, which also provides a new way of thinking for covering lipid biomarkers or prognostic indicators for mycotoxins.

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.

Structures and functions of the gut microbial lipidome

Microbial lipids provide signals that are responsible for maintaining host health and controlling disease. The differences in the structures of microbial lipids have been shown to alter receptor selectivity and agonist/antagonist activity. Advanced lipidomics is an emerging field that helps to elucidate the complex bacterial lipid diversity. The use of cutting-edge technologies is expected to lead to the discovery of new functional metabolites involved in host homeostasis. This review aims to describe recent updates on functional lipid metabolites derived from gut microbiota, their structure-activity relationships, and advanced lipidomics technologies.

A Novel Approach to Characterize the Lipidome of Marine Archaeon Nitrosopumilus maritimus by Ion Mobility Mass Spectrometry

Archaea are differentiated from the other two domains of life by their biomolecular characteristics. One such characteristic is the unique structure and composition of their lipids. Characterization of the whole set of lipids in a biological system (the lipidome) remains technologically challenging. This is because the lipidome is innately complex, and not all lipid species are extractable, separable, or ionizable by a single analytical method. Furthermore, lipids are structurally and chemically diverse. Many lipids are isobaric or isomeric and often indistinguishable by the measurement of mass or even their fragmentation spectra. Here we developed a novel analytical protocol based on liquid chromatography ion mobility mass spectrometry to enhance the coverage of the lipidome and characterize the conformations of archaeal lipids by their collision cross-sections (CCSs). The measurements of ion mobility revealed the gas-phase ion chemistry of representative archaeal lipids and provided further insights into their attributions to the adaptability of archaea to environmental stresses. A comprehensive characterization of the lipidome of mesophilic marine thaumarchaeon, Nitrosopumilus maritimus (strain SCM1) revealed potentially an unreported phosphate- and sulfate-containing lipid candidate by negative ionization analysis. It was the first time that experimentally derived CCS values of archaeal lipids were reported. Discrimination of crenarchaeol and its proposed stereoisomer was, however, not achieved with the resolving power of the SYNAPT G2 ion mobility system, and a high-resolution ion mobility system may be required for future work. Structural and spectral libraries of archaeal lipids were constructed in non-vendor-specific formats and are being made available to the community to promote research of Archaea by lipidomics.

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.

HutW from Vibrio cholerae Is an Anaerobic Heme-Degrading Enzyme with Unique Functional Properties

Increasing antibiotic resistance, and a growing recognition of the importance of the human microbiome, demand that new therapeutic targets be identified. Characterization of metabolic pathways that are unique to enteric pathogens represents a promising approach. Iron is often the rate-limiting factor for growth, and Vibrio cholerae, the causative agent of cholera, has been shown to contain numerous genes that function in the acquisition of iron from the environment. Included in this arsenal of genes are operons dedicated to obtaining iron from heme and heme-containing proteins. Given the persistence of cholera, an important outstanding question is whether V. cholerae is capable of anaerobic heme degradation as was recently reported for enterohemorrhagic Escherichia coli O157:H7. In this work, we demonstrate that HutW from V. cholerae is a radical S-adenosylmethionine methyl transferase involved in the anaerobic opening of the porphyrin ring of heme. However, in contrast to the enzyme ChuW, found in enterohemorrhagic E. coli O157:H7, there are notable differences in the mechanism and products of the HutW reaction. Of particular interest are data that demonstrate HutW will catalyze ring opening as well as tetrapyrrole reduction and can utilize reduced nicotinamide adenine dinucleotide phosphate as an electron source. The biochemical and biophysical properties of HutW are presented, and the evolutionary implications are discussed.

Lipidomic Analysis of Roseobacters of the Pelagic RCA Cluster and Their Response to Phosphorus Limitation

The marine roseobacter-clade affiliated cluster (RCA) represents one of the most abundant groups of bacterioplankton in the global oceans, particularly in temperate and sub-polar regions. They play a key role in the biogeochemical cycling of various elements and are important players in oceanic climate-active trace gas metabolism. In contrast to copiotrophic roseobacter counterparts such as Ruegeria pomeroyi DSS-3 and Phaeobacter sp. MED193, RCA bacteria are truly pelagic and have smaller genomes. We have previously shown that RCA bacteria do not appear to encode the PlcP-mediated lipid remodeling pathway, whereby marine heterotrophic bacteria remodel their membrane lipid composition in response to phosphorus (P) stress by substituting membrane glycerophospholipids with alternative glycolipids or betaine lipids. In this study, we report lipidomic analysis of six RCA isolates. In addition to the commonly found glycerophospholipids such as phosphatidylglycerol (PG) and phosphatidylethanolamine (PE), RCA bacteria synthesize a relatively uncommon phospholipid, acylphosphatidylglycerol, which is not found in copiotrophic roseobacters. Instead, like the abundant SAR11 clade, RCA bacteria upregulate ornithine lipid biosynthesis in response to P stress, suggesting a key role of this aminolipid in the adaptation of marine heterotrophs to oceanic nutrient limitation.

Untargeted Lipidomics Analysis of the Cyanobacterium Synechocystis sp. PCC 6803: Lipid Composition Variation in Response to Alternative Cultivation Setups and to Gene Deletion

Cyanobacteria play an important role in several ecological environments, and they are widely accepted to be the ancestors of chloroplasts in modern plants and green algae. Cyanobacteria have become attractive models for metabolic engineering, with the goal of exploring them as microbial cell factories. However, the study of cyanobacterial lipids’ composition and variation, and the assessment of the lipids’ functional and structural roles have been largely overlooked. Here, we aimed at expanding the cyanobacterial lipidomic analytical pipeline by using an untargeted lipidomics approach. Thus, the lipid composition variation of the model cyanobacterium Synechocystis sp. PCC 6803 was investigated in response to both alternative cultivation setups and gene deletion. This approach allowed for detecting differences in total lipid content, alterations in fatty-acid unsaturation level, and adjustments of specific lipid species among the identified lipid classes. The employed method also revealed that the cultivation setup tested in this work induced a deeper alteration of the cyanobacterial cell lipidome than the deletion of a gene that results in a dramatic increase in the release of lipid-rich outer membrane vesicles. This study further highlights how growth conditions must be carefully selected when cyanobacteria are to be engineered and/or scaled-up for lipid or fatty acids production.

Cationic π-Conjugated Polyelectrolyte Shows Antimicrobial Activity by Causing Lipid Loss and Lowering Elastic Modulus of Bacteria

Cationic, π-conjugated oligo-/polyelectrolytes (CCOEs/CCPEs) have shown great potential as antimicrobial materials to fight against antibiotic resistance. In this work, we treated wild-type and ampicillin-resistant (amp-resistant) Escherichia coli (E. coli) with a promising cationic, π-conjugated polyelectrolyte (P1) with a phenylene-based backbone and investigated the resulting morphological, mechanical, and compositional changes of the outer membrane of bacteria in great detail. The cationic quaternary amine groups of P1 led to electrostatic interactions with negatively charged moieties within the outer membrane of bacteria. Using atomic force microscopy (AFM), high-resolution transmission electron microscopy (TEM), we showed that due to this treatment, the bacterial outer membrane became rougher, decreased in stiffness/elastic modulus (AFM nanoindentation), formed blebs, and released vesicles near the cells. These evidences, in addition to increased staining of the P1-treated cell membrane by lipophilic dye Nile Red (confocal laser scanning microscopy (CLSM)), suggested loosening/disruption of packing of the outer cell envelope and release and exposure of lipid-based components. Lipidomics and fatty acid analysis confirmed a significant loss of phosphate-based outer membrane lipids and fatty acids, some of which are critically needed to maintain cell wall integrity and mechanical strength. Lipidomics and UV-vis analysis also confirmed that the extracellular vesicles released upon treatment (AFM) are composed of lipids and cationic P1. Such surface alterations (vesicle/bleb formation) and release of lipids/fatty acids upon treatment were effective enough to inhibit further growth of E. coli cells without completely disintegrating the cells and have been known as a defense mechanism of the cells against cationic antimicrobial agents.

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

Comprehensive profiling of lipid species in a biological sample, or lipidomics, is a valuable approach to elucidating disease pathogenesis and identifying biomarkers. Currently, a typical lipidomics experiment may track hundreds to thousands of individual lipid species. However, drawing biological conclusions requires multiple steps of data processing to enrich significantly altered features and confident identification of these features. Existing solutions for these data analysis challenges (i.e., multivariate statistics and lipid identification) involve performing various steps using different software applications, which imposes a practical limitation and potentially a negative impact on reproducibility. Hydrophilic interaction liquid chromatography-ion mobility-mass spectrometry (HILIC-IM-MS) has shown advantages in separating lipids through orthogonal dimensions. However, there are still gaps in the coverage of lipid classes in the literature. To enable reproducible and efficient analysis of HILIC-IM-MS lipidomics data, we developed an open-source Python package, LiPydomics, which enables performing statistical and multivariate analyses ("stats" module), generating informative plots ("plotting" module), identifying lipid species at different confidence levels ("identification" module), and carrying out all functions using a user-friendly text-based interface ("interactive" module). To support lipid identification, we assembled a comprehensive experimental database of m/z and CCS of 45 lipid classes with 23 classes containing HILIC retention times. Prediction models for CCS and HILIC retention time for 22 and 23 lipid classes, respectively, were trained using the large experimental data set, which enabled the generation of a large predicted lipid database with 145,388 entries. Finally, we demonstrated the utility of the Python package using Staphylococcus aureus strains that are resistant to various antimicrobials.

Breaking Down Structural Diversity for Comprehensive Prediction of Ion-Neutral Collision Cross Sections

Identification of unknowns is a bottleneck for large-scale untargeted analyses like metabolomics or drug metabolite identification. Ion mobility-mass spectrometry (IM-MS) provides rapid two-dimensional separation of ions based on their mobility through a neutral buffer gas. The mobility of an ion is related to its collision cross section (CCS) with the buffer gas, a physical property that is determined by the size and shape of the ion. This structural dependency makes CCS a promising characteristic for compound identification, but this utility is limited by the availability of high-quality reference CCS values. CCS prediction using machine learning (ML) has recently shown promise in the field, but accurate and broadly applicable models are still lacking. Here we present a novel ML approach that employs a comprehensive collection of CCS values covering a wide range of chemical space. Using this diverse database, we identified the structural characteristics, represented by molecular quantum numbers (MQNs), that contribute to variance in CCS and assessed the performance of a variety of ML algorithms in predicting CCS. We found that by breaking down the chemical structural diversity using unsupervised clustering based on the MQNs, specific and accurate prediction models for each cluster can be trained, which showed superior performance than a single model trained with all data. Using this approach, we have robustly trained and characterized a CCS prediction model with high accuracy on diverse chemical structures. An all-in-one web interface (https://CCSbase.net) was built for querying the CCS database and accessing the predictive model to support unknown compound identifications.

Characterization of the Impact of Drug Metabolism on the Gas-Phase Structures of Drugs Using Ion Mobility-Mass Spectrometry

Conventional strategies for drug metabolite identification employ a combination of liquid chromatography-mass spectrometry (LC-MS), which offers higher throughput but provides limited structural information, and nuclear magnetic resonance spectroscopy, which can achieves the most definitive identification but lacks throughput. Ion mobility-mass spectrometry (IM-MS) is a rapid, two-dimensional analysis that separates ions on the basis of their gas-phase size and shape (reflected by collision cross section, CCS) and their mass-to-charge (m/z) ratios. The rapid nature of IM separation combined with the structural information provided by CCS make IM-MS a promising technique for obtaining more structural information on drug metabolites without sacrificing analytical throughput. Here, we present an in vitro biosynthesis coupled with IM-MS strategy for rapid generation and analysis of drug metabolites. Drug metabolites were generated in vitro using pooled subcellular fractions derived from human liver and analyzed using a rapid flow injection-IM-MS method. We measured CCS values for 19 parent drugs and their 37 metabolites generated in vitro (78 values in total), representing a wide variety of metabolic modifications. Post-IM fragmentation and computational modeling were used to support metabolite identifications and explore the structural characteristics driving behaviors observed in IM separation. Overall, we found the effects of metabolic modifications on the gas-phase structures of the metabolites to be highly dependent upon the structural characteristics of the parent compounds and the specific position of the modification. This in vitro biosynthesis coupled with rapid IM-MS analysis workflow represents a promising platform for rapid and high-confidence identification of drug metabolites, applicable at a large scale.