Recent additions and access to a multidimensional lipidomic database containing liquid chromatography, ion mobility spectrometry, and tandem mass spectrometry information.

The importance of lipids in biology continues to grow with their recent linkages to more diseases and conditions, microbiome fluctuations, and environmental exposures. These associations have motivated researchers to evaluate lipidomic changes in numerous matrices and studies. Lipidomic analyses, however, present numerous challenges as lipid species have broad chemistries that require different extraction methods and instrumental analyses to evaluate and separate their many isomers and isobars. Increasing knowledge about different lipid characteristics is therefore crucial for improving their separation and identification. Here, we present a multidimensional database for lipids analyzed on a platform combining reversed-phase liquid chromatography, drift tube ion mobility spectrometry, collision-induced dissociation, and mass spectrometry (RPLC-DTIMS-CID-MS). This platform and the different separation characteristics it provides enables more confident lipid annotations when compared to traditional tandem mass spectrometry platforms, especially when analyzing highly isomeric molecules such as lipids. This database expands on our previous publication containing only human plasma and bronchoalveolar lavage fluid lipids and provides experimental RPLC retention times, IMS collision cross section (CCS) values, and m/z information for 877 unique lipids from additional biofluids and tissues. Specifically, the database contains 1504 precursor [M + H]+, [M + NH4]+, [M + Na]+, [M-H]-, [M-2H]2-, [M + HCOO]-, and [M + CH3COO]- ion species and their associated CID fragments which are commonly targeted in clinical and environmental studies, in addition to being present in the chloroform layer of Folch extractions. Furthermore, this multidimensional RPLC-DTIMS-CID-MS database spans 5 lipid categories (fatty acids, sterols, sphingolipids, glycerolipids, and glycerophospholipids) and 24 lipid classes. We have also created a webpage (tarheels.live/bakerlab/databases/) to enhance the accessibility of this resource which will be populated regularly with new lipids as we identify additional species and integrate novel standards.

Deciphering ApoE Genotype-Driven Proteomic and Lipidomic Alterations in Alzheimer's Disease Across Distinct Brain Regions.

Alzheimer's disease (AD) is a neurodegenerative disease with a complex etiology influenced by confounding factors such as genetic polymorphisms, age, sex, and race. Traditionally, AD research has not prioritized these influences, resulting in dramatically skewed cohorts such as three times the number of Apolipoprotein E (APOE) ε4-allele carriers in AD relative to healthy cohorts. Thus, the resulting molecular changes in AD have previously been complicated by the influence of apolipoprotein E disparities. To explore how apolipoprotein E polymorphism influences AD progression, 62 post-mortem patients consisting of 33 AD and 29 controls (Ctrl) were studied to balance the number of ε4-allele carriers and facilitate a molecular comparison of the apolipoprotein E genotype. Lipid and protein perturbations were assessed across AD diagnosed brains compared to Ctrl brains, ε4 allele carriers (APOE4+ for those carrying 1 or 2 ε4s and APOE4- for non-ε4 carriers), and differences in ε3ε3 and ε3ε4 Ctrl brains across two brain regions (frontal cortex (FCX) and cerebellum (CBM)). The region-specific influences of apolipoprotein E on AD mechanisms showcased mitochondrial dysfunction and cell proteostasis at the core of AD pathophysiology in the post-mortem brains, indicating these two processes may be influenced by genotypic differences and brain morphology.

Identifying Membrane Protein-Lipid Interactions with Lipidomic Lipid Exchange-Mass Spectrometry.

Lipids can play important roles in modulating membrane protein structure and function. However, it is challenging to identify natural lipids bound to membrane proteins in complex bilayers. Here, we developed lipidomic lipid exchange-mass spectrometry (LX-MS) to study the lipid affinity for membrane proteins on a lipidomic scale. We first mix membrane protein nanodiscs with empty nanodiscs that have no embedded membrane proteins. After allowing lipids to passively exchange between the two populations, we separate the two types of nanodiscs and perform lipidomic analysis on each with liquid chromatography and MS. Enrichment of lipids in the membrane protein nanodiscs reveals the affinity of individual lipids for binding the target membrane protein. We apply this approach to study three membrane proteins. With the Escherichia coli ammonium transporter AmtB and aquaporin AqpZ in nanodiscs with E. coli polar lipid extracts, we detected binding of cardiolipin and phosphatidyl-glycerol lipids to the proteins. With the acetylcholine receptor in nanodiscs with brain polar lipid extracts, we discovered a complex set of lipid interactions that depended on the head group and tail composition. Overall, lipidomic LX-MS provides a detailed understanding of the lipid-binding affinity and thermodynamics for membrane proteins in complex bilayers and provides a unique perspective on the chemical environment surrounding membrane proteins.

Combining native mass spectrometry and lipidomics to uncover specific membrane protein-lipid interactions from natural lipid sources.

While it is known that lipids play an essential role in regulating membrane protein structure and function, it remains challenging to identify specific protein-lipid interactions. Here, we present an innovative approach that combines native mass spectrometry (MS) and lipidomics to identify lipids retained by membrane proteins from natural lipid extracts. Our results reveal that the bacterial ammonia channel (AmtB) enriches specific cardiolipin (CDL) and phosphatidylethanolamine (PE) from natural headgroup extracts. When the two extracts are mixed, AmtB retains more species, wherein selectivity is tuned to bias headgroup selection. Using a series of natural headgroup extracts, we show TRAAK, a two-pore domain K+ channel (K2P), retains specific acyl chains that is independent of the headgroup. A brain polar lipid extract was then combined with the K2Ps, TRAAK and TREK2, to understand lipid specificity. More than a hundred lipids demonstrated affinity for each protein, and both channels were found to retain specific fatty acids and lysophospholipids known to stimulate channel activity, even after several column washes. Natural lipid extracts provide the unique opportunity to not only present natural lipid diversity to purified membrane proteins but also identify lipids that may be important for membrane protein structure and function.

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.

Impacts of Gestational FireMaster 550 Exposure on the Neonatal Cortex Are Sex Specific and Largely Attributable to the Organophosphate Esters.

INTRODUCTION: Flame retardants (FRs) are common bodily and environmental pollutants, creating concern about their potential toxicity. We and others have found that the commercial mixture FireMaster® 550 (FM 550) or its individual brominated (BFR) and organophosphate ester (OPFR) components are potential developmental neurotoxicants. Using Wistar rats, we previously reported that developmental exposure to FM 550 or its component classes produced sex- and compound-specific effects on adult socioemotional behaviors. The underlying mechanisms driving the behavioral phenotypes are unknown. METHODS: To further mechanistic understanding, here we conducted transcriptomics in parallel with a novel lipidomics approach using cortical tissues from newborn siblings of the rats in the published behavioral study. Inclusion of lipid composition is significant because it is rarely examined in developmental neurotoxicity studies. Pups were gestationally exposed via oral dosing to the dam to FM 550 or the BFR or OPFR components at environmentally relevant doses. RESULTS: The neonatal cortex was highly sexually dimorphic in lipid and transcriptome composition, and males were more significantly impacted by FR exposure. Multiple adverse modes of action for the BFRs and OPFRs on neurodevelopment were identified, with the OPFRs being more disruptive than the BFRs via multiple mechanisms including dysregulation of mitochondrial function and disruption of cholinergic and glutamatergic systems. Disrupted mitochondrial function by environmental factors has been linked to a higher risk of autism spectrum disorders and neurodegenerative disorders. Impacted lipid classes included ceramides, sphingomyelins, and triacylglycerides. Robust ceramide upregulation in the OPFR females could suggest a heightened risk of brain metabolic disease. CONCLUSIONS: This study reveals multiple mechanisms by which the components of a common FR mixture are developmentally neurotoxic and that the OPFRs may be the compounds of greatest concern.

Combining Micropunch Histology and Multidimensional Lipidomic Measurements for In-Depth Tissue Mapping.

While decades of technical and analytical advancements have been utilized to discover novel lipid species, increase speciation, and evaluate localized lipid dysregulation at subtissue, cellular, and subcellular levels, many challenges still exist. One limitation is that the acquisition of both in-depth spatial information and comprehensive lipid speciation is extremely difficult, especially when biological material is limited or lipids are at low abundance. In neuroscience, for example, it is often desired to focus on only one brain region or subregion, which can be well under a square millimeter for rodents. Herein, we evaluate a micropunch histology method where cortical brain tissue at 2.0, 1.25, 1.0, 0.75, 0.5, and 0.25 mm diameter sizes and 1 mm depth was collected and analyzed with multidimensional liquid chromatography, ion mobility spectrometry, collision induced dissociation, and mass spectrometry (LC-IMS-CID-MS) measurements. Lipid extraction was optimized for the small sample sizes, and assessment of lipidome coverage for the 2.0 to 0.25 mm diameter sizes showed a decline from 304 to 198 lipid identifications as validated by all 4 analysis dimensions (~35% loss in coverage for ~88% less tissue). While losses were observed, the ~200 lipids and estimated 4630 neurons contained within the 0.25 punch still provided in-depth characterization of the small tissue region. Furthermore, while localization routinely achieved by mass spectrometry imaging (MSI) and single cell analyses is greater, this diameter is sufficiently small to isolate subcortical, hypothalamic, and other brain regions in adult rats, thereby increasing the coverage of lipids within relevant spatial windows without sacrificing speciation. Therefore, micropunch histology enables in-depth, region-specific lipid evaluations, an approach that will prove beneficial to a variety of lipidomic applications.

Cupric Ions Selectively Modulate TRAAK-Phosphatidylserine Interactions.

TRAAK and TREK2 are two-pore domain K+ (K2P) channels and are modulated by diverse factors including temperature, membrane stretching, and lipids, such as phosphatidic acid. In addition, copper and zinc, both of which are essential for life, are known to regulate TREK2 and a number of other ion channels. However, the role of ions in the association of lipids with integral membrane proteins is poorly understood. Here, we discover cupric ions selectively modulate the binding of phosphatidylserine (PS) to TRAAK but not TREK2. Other divalent cations (Ca2+, Mg2+, and Zn2+) bind both channels but have no impact on binding PS and other lipids. Additionally, TRAAK binds more avidly to Cu2+ and Zn2+ than TREK2. In the presence of Cu2+, TRAAK binds similarly to PS with different acyl chains, indicating a crucial role of the serine headgroup in coordinating Cu2+. High-resolution native mass spectrometry (MS) enables the determination of equilibrium binding constants for distinct Cu2+-bound stoichiometries and uncovered the highest coupling factor corresponds to a 1:1 PS-to-Cu2+ ratio. Interestingly, the next three highest coupling factors had a ∼1.5:1 PS-to-Cu2+ ratio. Our findings bring forth the role of cupric ions as an essential cofactor in selective TRAAK-PS interactions.

Assessing the Fate of Dissolved Organic Compounds in Landfill Leachate and Wastewater Treatment Systems.

Landfill leachate and municipal wastewater are major sources of chemical pollutants that contaminate our drinking water sources. Evaluating the dissolved organic chemical composition in wastewater treatment plants is therefore essential to understand how the discharge impacts the environment, wildlife, and human health. In this study, we utilized a nontargeted analysis method coupling liquid chromatography and tandem mass spectrometry (LC-MS/MS) to analyze chemical features at different points along two landfill leachate treatment plants (LLTPs) and two municipal wastewater treatment plants (WWTPs) in the Southeastern United States. Significant feature differences were observed for the WWTPs where activated sludge clarification was employed versus the LLTPs utilizing reverse osmosis. Specifically, even though both LLTPs had the largest number of features in their influent water, their effluent following reverse osmosis yielded a lower number of features than the WWTPs. Additionally, the clarification processes of each WWTP exhibited different efficiencies as chemical disinfection removed more features than UV disinfection. Feature identification was then made using the LC, MS, and MS/MS information. Analysis of the identified molecules showed that lipids were the most effectively removed from all plants, while alkaloid and organic nitrogen compounds were the most recalcitrant.

Multiomic Big Data Analysis Challenges: Increasing Confidence in the Interpretation of Artificial Intelligence Assessments.

The need for holistic molecular measurements to better understand disease initiation, development, diagnosis, and therapy has led to an increasing number of multiomic analyses. The wealth of information available from multiomic assessments, however, requires both the evaluation and interpretation of extremely large data sets, limiting analysis throughput and ease of adoption. Computational methods utilizing artificial intelligence (AI) provide the most promising way to address these challenges, yet despite the conceptual benefits of AI and its successful application in singular omic studies, the widespread use of AI in multiomic studies remains limited. Here, we discuss present and future capabilities of AI techniques in multiomic studies while introducing analytical checks and balances to validate the computational conclusions.

Structural-based connectivity and omic phenotype evaluations (SCOPE): a cheminformatics toolbox for investigating lipidomic changes in complex systems.

Since its inception, the main goal of the lipidomics field has been to characterize lipid species and their respective biological roles. However, difficulties in both full speciation and biological interpretation have rendered these objectives extremely challenging and as a result, limited our understanding of lipid mechanisms and dysregulation. While mass spectrometry-based advancements have significantly increased the ability to identify lipid species, less progress has been made surrounding biological interpretations. We have therefore developed a Structural-based Connectivity and Omic Phenotype Evaluations (SCOPE) cheminformatics toolbox to aid in these evaluations. SCOPE enables the assessment and visualization of two main lipidomic associations: structure/biological connections and metadata linkages either separately or in tandem. To assess structure and biological relationships, SCOPE utilizes key lipid structural moieties such as head group and fatty acyl composition and links them to their respective biological relationships through hierarchical clustering and grouped heatmaps. Metadata arising from phenotypic and environmental factors such as age and diet is then correlated with the lipid structures and/or biological relationships, utilizing Toxicological Prioritization Index (ToxPi) software. Here, SCOPE is demonstrated for various applications from environmental studies to clinical assessments to showcase new biological connections not previously observed with other techniques.

Unveiling molecular signatures of preeclampsia and gestational diabetes mellitus with multi-omics and innovative cheminformatics visualization tools.

To fully enable the development of diagnostic tools and progressive pharmaceutical drugs, it is imperative to understand the molecular changes occurring before and during disease onset and progression. Systems biology assessments utilizing multi-omic analyses (e.g. the combination of proteomics, lipidomics, genomics, etc.) have shown enormous value in determining molecules prevalent in diseases and their associated mechanisms. Herein, we utilized multi-omic evaluations, multi-dimensional analysis methods, and new cheminformatics-based visualization tools to provide an in depth understanding of the molecular changes taking place in preeclampsia (PRE) and gestational diabetes mellitus (GDM) patients. Since PRE and GDM are two prevalent pregnancy complications that result in adverse health effects for both the mother and fetus during pregnancy and later in life, a better understanding of each is essential. The multi-omic evaluations performed here provide new insight into the end-stage molecular profiles of each disease, thereby supplying information potentially crucial for earlier diagnosis and treatments.

From Prevention to Disease Perturbations: A Multi-Omic Assessment of Exercise and Myocardial Infarctions.

While a molecular assessment of the perturbations and injury arising from diseases is essential in their diagnosis and treatment, understanding changes due to preventative strategies is also imperative. Currently, complex diseases such as cardiovascular disease (CVD), the leading cause of death worldwide, suffer from a limited understanding of how the molecular mechanisms taking place following preventive measures (e.g., exercise) differ from changes occurring due to the injuries caused from the disease (e.g., myocardial infarction (MI)). Therefore, this manuscript assesses lipidomic changes before and one hour after exercise treadmill testing (ETT) and before and one hour after a planned myocardial infarction (PMI) in two separate patient cohorts. Strikingly, unique lipidomic perturbations were observed for these events, as could be expected from their vastly different stresses on the body. The lipidomic results were then combined with previously published metabolomic characterizations of the same patients. This integration provides complementary insights into the exercise and PMI events, thereby giving a more holistic understanding of the molecular changes associated with each.

Utilizing Drift Tube Ion Mobility Spectrometry for the Evaluation of Metabolites and Xenobiotics.

Metabolites and xenobiotics are small molecules with a molecular weight that often falls below 600 Da. Over the last few decades, multiple small molecule databases have been curated listing structures, masses, and fragmentation spectra possible in metabolomic and exposomic measurements. To date only a small portion of the spectra in these databases are experimentally derived due to the high expense of obtaining, synthesizing, and analyzing standards. A vast majority of spectra have thus been created using theoretical programs to fit the available experimental data. The errors associated with theoretical data have however caused problems with current small molecule identifications, and accurate quantitation as searching the databases using just one or two analysis dimensions (i.e., chromatography retention times and mass spectrometry (MS) m/z values) results in numerous annotations for each experimental feature. Additional analysis dimensions are therefore needed to better annotate and identify small molecules. Drift tube ion mobility spectrometry coupled with MS (DTIMS-MS) is a promising technique to address this challenge as it is able to perform rapid structural evaluations of small molecules in complex matrices by assessing the collision cross section values for each in addition to their m/z values. The use of IMS in conjunction with other separation techniques such as gas or liquid chromatography and MS has therefore enabled more accurate identifications for the small molecules present in complex biological and environmental samples. Here, we present a review of relevant parameter considerations for DTIMS application with emphasis on xenobiotics and metabolomics isomer separations.