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Lipidomic Analysis Reveals Systemic Alterations in Servicemen Exposed to Repeated Occupational Low-Level Blast Waves

INTRODUCTION

Occupational exposure to blast is a prevalent risk experienced by military personnel. While low-level exposure may not manifest immediate signs of illness, prolonged and repetitive exposure may result in neurophysiological dysfunction. Such repeated exposure to occupational blasts has been linked to structural and functional modifications in the brain, adversely affecting the performance of servicemen in the field. These neurological changes can give rise to symptoms resembling concussion and contribute to the development of post-traumatic stress disorder.

MATERIALS AND METHODS

To understand long-term effects of blast exposure, the study was conducted to assess memory function, serum circulatory protein and lipid biomarkers, and associated concussive symptomology in servicemen. Concussion-like symptoms were assessed using the Rivermead Post-Concussion Symptoms Questionnaire (RPSQ) along with memory function using PGI memory scale. The serum protein biomarkers were quantified using a sandwich ELISA assay, and the serum lipid profile was measured using liquid chromatography-mass spectrometer.

RESULTS

The findings revealed that repeated low-level blast exposure resulted in impaired memory function, accompanied by elevated levels of serum neurofilament light chain (neuroaxonal injury) and C-reactive protein. Furthermore, alterations in the lipid profile were observed, with an increase in lipid species associated with immune activation. These changes collectively point to systemic inflammation, neuronal injury, and memory dysfunction as pathological characteristics of repeated low-level blast exposure.

CONCLUSION

The results of our preliminary investigation offer valuable insights for further large-scale study and provide a guiding principle that necessitates a suitable mitigation approach to safeguard the health of personnel against blast overpressure.

Assessment of the effects of cadmium, samarium and gadolinium on the blue mussel (Mytilus edulis): A biochemical, transcriptomic and metabolomic approach

Improving the understanding of how chemicals affect on organisms and assessing the associated environmental risks is of major interest in environmental studies. This can be achieved by using complementary approaches based on the study of the molecular responses of organisms. Because of the known chemical pressures on the environment, regulations on the content of some chemicals, such as cadmium, have been mostly completed. In contrast, the environmental toxicity of rare earth elements (REEs), which are widely used in industry, has only recently begun to receive attention. Here, we investigated the effects of cadmium, and two REEs, samarium and gadolinium, on marine mussels under laboratory exposures. We found that after an 8-day exposure at 500 µg/L, the metals were bioaccumulated by the mussels. Furthermore, samarium and gadolinium affected two oxidative stress biomarkers, GST and SOD. Lipidomic analysis showed that lipid content was modulated by the REEs, but not by cadmium. Interestingly, several compounds belonging to the phosphoinositide metabolism were more abundant, suggesting a pro-mitotic or cell survival response, while a higher abundance of cardiolipins after samarium exposure suggested an alteration of mitochondrial activity. Moreover, depending on the tissue and the metal considered, transcriptional analyses revealed an effect on metallothionein, hsp70/90, energy metabolism enzymes, as well as pro-mitotic transcript accumulation. Thus, this study sheds a new light on metal toxicity and in particularl REEs by highlighting the accumulation and toxicity of cadmium, samarium and gadolinium at 500 µg/L at different molecular levels, from gene expression to the lipidome of blue mussels.

Glial Biologist's Guide to Mass Spectrometry-Based Lipidomics: A Tutorial From Sample Preparation to Data Analysis

Neurological diseases are associated with disruptions in the brain lipidome that are becoming central to disease pathogenesis. Traditionally perceived as static structural support in membranes, lipids are now known to be actively involved in cellular signaling, energy metabolism, and other cellular activities involving membrane curvature, fluidity, fusion or fission. Glia are critical in the development, health, and function of the brain, and glial regulation plays a major role in disease. The major pathways of glial dysregulation related to function are associated with downstream products of metabolism including lipids. Taking advantage of significant innovations and technical advancements in instrumentation, lipidomics has emerged as a popular omics discipline, serving as the prevailing approach to comprehensively define metabolic alterations associated with organismal development, damage or disease. A key technological platform for lipidomics studies is mass spectrometry (MS), as it affords large-scale profiling of complex biological samples. However, as MS-based techniques are often refined and advanced, the relative comfort level among biologists with this instrumentation has not followed suit. In this review, we aim to highlight the importance of the study of glial lipids and to provide a concise record of best practices and steps for MS-based lipidomics. Specifically, we outline procedures for glia lipidomics workflows ranging from sample collection and extraction to mass spectrometric analysis to data interpretation. To ensure these approaches are more accessible, this tutorial aims to familiarize glia biologists with sample handling and analysis techniques for MS-based lipidomics, and to guide non-experts toward generating high quality lipidomics data.

Exploring natural product biosynthesis in plants with mass spectrometry imaging

The biosynthesis of natural products (NPs) is a complex dynamic spatial and temporal process that requires the collaboration of multiple disciplines to explore the underlying mechanisms. Mass spectrometry imaging (MSI) is a powerful technique for studying NPs due to its high molecular coverage and sensitivity without the need for labeling. To date, many analysts still use MSI primarily for visualizing the distribution of NPs in heterogeneous tissues, although studies have proved that it can provide crucial insights into the specialized spatial metabolic process of NPs. In this review we strive to bring awareness of the importance of MSI, and we advocate further exploitation of the spatial information obtained from MSI to establish metabolite-gene expression relationships.

Development and thorough evaluation of a multi-omics sample preparation workflow for comprehensive LC-MS/MS-based metabolomics, lipidomics and proteomics datasets

The importance of sample preparation selection if often overlooked particularly for untargeted multi-omics approaches that gained popularity in recent years. To minimize issues with sample heterogeneity and additional freeze-thaw cycles during sample splitting, multiple -omics datasets (e.g. metabolomics, lipidomics and proteomics) should ideally be generated from the same set of samples. For sample extraction, commonly biphasic organic solvent systems are used that require extensive multi-step protocols. Individual studies have recently also started to investigate monophasic (all-in-one) extraction procedures. The aim of the current study was to develop and systematically compare ten different mono- and biphasic extraction solvent mixtures for their potential to aid in the most comprehensive metabolomics, lipidomics and proteomics datasets. As the focus was on human postmortem tissue samples (muscle and liver tissue), four tissue homogenization parameters were also evaluated. Untargeted liquid chromatography mass spectrometry-based metabolomics, lipidomic and proteomics methods were utilized along with 1D sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) and bicinchoninic acid (BCA) assay results. Optimal homogenization was found to be achieved by bead-homogenizing 20 mg of muscle or liver tissue with 200 μL (1:10 ratio) Water:Methanol (1:2) using 3 × 30 s pulses. The supernatant of the homogenate was further extracted. Comprehensive ranking, taking nine different processing parameters into account, showed that the monophasic extraction solvents, overall, showed better scores compared to the biphasic solvent systems, despite their recommendation for one or all of the -omics extractions. The optimal extraction solvent was found to be Methanol:Acetone (9:1), resulting in the most comprehensive metabolomics, lipidomics and proteomics datasets, showing the potential to be automated, hence, allowing for high-throughput analysis of samples and opening the door for comprehensive multi-omics results from routine clinical cases in the future.

Early pregnancy serum PFAS are associated with alterations in the maternal lipidome

Per- and polyfluoroalkyl substances (PFAS) have been detected in the blood of humans and animals worldwide. Exposure to some PFAS are associated with multiple adverse pregnancy outcomes. Existing literature has identified a strong association with PFAS exposure and metabolic dysfunction in humans, including modification of lipid metabolism. Using a subset of the Michigan Mother-Infant Pairs cohort (n = 95), this study investigated associations between first trimester plasma levels of PFAS and maternal lipids and metabolites in the first trimester (T1), at the time of delivery (T3), and in the infant cord blood (CB) using untargeted shotgun lipidomics and metabolomics. Identifying PFAS-induced alterations in the maternal lipid- or metabolome at specific timepoints may help elucidate windows of susceptibility to adverse pregnancy outcomes. Out of 9 PFAS measured, 7 were detected in at least 20% of samples and were used for further analyses. PFOS and PFHxS were measured at the highest concentrations with medians of 5.76 ng/mL and 3.33 ng/mL, respectively. PFOA, PFNA, and PFDA had lower measured values with medians of <1.2 ng/mL. PFHxS concentrations were positively associated with monounsaturated sphingomyelins (SMs) in T1 maternal plasma in adjusted models, determined by an adjusted p-value (q) < 0.1. PFHxS was positively associated with saturated and polyunsaturated SMs and inversely associated with saturated diacylglycerols in T1. Following metabolite-specific analysis, two mono-unsaturated diacylglycerols with carbon chain lengths of 32 and 35 were inversely associated with PFHxS in T1. In T3, only the association between PFHxS and SMs remained, but was attenuated. In addition, PFDA was associated with an increase in polyunsaturated plasmenyl-phosphatidylethanolamines in T3. No associations were identified between PFAS and infant cord blood lipids. Continued research into PFAS associated disruptions in lipid metabolism at sensitive stages of gestation may provide insight into the mechanisms that lead to adverse birth and pregnancy outcomes.

Predicting Tandem Mass Spectra of Small Molecules Using Graph Embedding of Precursor-Product Ion Pair Graph

Liquid chromatography-mass spectrometry (LC-MS)-based metabolomics identification relies heavily on high-quality MS/MS data; MS/MS prediction is a good way to address this issue. However, the accuracy of the prediction, resolution, and correlation with chemical structures have not been well-solved. In this study, we have developed a MS/MS prediction method, PPGB-MS2, which transforms the MS/MS prediction into fragment intensity prediction, and the concept of precursor-product ion pair graph bags (PPGBs) was introduced to represent fragments, achieving uniform representation of precursor and product ion structures and MS/MS fragmentation information. The chemical structure information is kept before it is incorporated into machine learning models. Due to the PPGB representation, graph neural networks (GNNs) can be utilized to achieve MS/MS fragment intensity prediction. The system was trained and evaluated using [M+H]+ and [M-H]- data acquired by an Agilent QTOF 6530 in the NIST 20 tandem MS database. Results demonstrated that the average cosine similarity is 0.71 in the test set, which is higher than classical MS/MS prediction methods. PPGB-MS2 also achieves high-resolution MS/MS prediction due to its effective management of the correspondence between fragments and structures.

Nuclear lipids in chromatin regulation: Biological roles, experimental approaches and existing challenges

Lipids are crucial for various cellular functions. Besides the storage of energy equivalents, these include forming membrane bilayers and serving as signaling molecules. While significant progress has been made in the comprehension of the molecular and cellular biology of lipids, their functions in the cell nucleus remain poorly understood. The main role of the eukaryotic cell nucleus is to provide an environment for the storage and regulation of chromatin which is a complex of DNA, histones, and associated proteins. Recent studies suggest that nuclear lipids play a role in chromatin regulation and epigenetics. Here, we discuss various experimental methods in lipid-chromatin research, including biophysical, structural, and cell biology approaches, pointing out their strengths and weaknesses. We take the view that nuclear lipids have a far more widespread impact on chromatin than is currently acknowledged. This gap in comprehension is mostly due to existing experimental challenges in the study of lipid-chromatin biology. Several new, interdisciplinary approaches are discussed that could aid in elucidating the roles of nuclear lipids in chromatin regulation and gene expression.

Exploring environmental modifiers of LRRK2-associated Parkinson's disease penetrance: An exposomics and metagenomics pilot study on household dust

Pathogenic variants in the Leucine-rich repeat kinase 2 (LRRK2) gene are a primary monogenic cause of Parkinson's disease (PD). However, the likelihood of developing PD with inherited LRRK2 pathogenic variants differs (a phenomenon known as "reduced penetrance"), with factors including age and geographic region, highlighting a potential role for lifestyle and environmental factors in disease onset. To investigate this, household dust samples from four different groups of individuals were analyzed using metabolomics/exposomics and metagenomics approaches: PD+/LRRK2+ (PD patients with pathogenic LRRK2 variants; n = 11), PD-/LRRK2+ (individuals with pathogenic LRRK2 variants but without PD diagnosis; n = 8), iPD (PD of unknown cause; n = 11), and a matched, healthy control group (n = 11). The dust was complemented with metabolomics and lipidomics of matched serum samples, where available. A total of 1,003 chemicals and 163 metagenomic operational taxonomic units (mOTUs) were identified in the dust samples, of which ninety chemicals and ten mOTUs were statistically significant (ANOVA p-value < 0.05). Reduced levels of 2-benzothiazolesulfonic acid (BThSO3) were found in the PD-/LRRK2+ group compared to the PD+/LRRK2+ . Among the significant chemicals tentatively identified in dust, two are hazardous chemical replacements: Bisphenol S (BPS), and perfluorobutane sulfonic acid (PFBuS). Furthermore, various lipids were found altered in serum including different lysophosphatidylethanolamines (LPEs), and lysophosphatidylcholines (LPCs), some with higher levels in the PD+/LRRK2+ group compared to the control group. A cellular study on isogenic neurons generated from a PD+/LRRK2+ patient demonstrated that BPS negatively impacts mitochondrial function, which is implicated in PD pathogenesis. This pilot study demonstrates how non-target metabolomics/exposomics analysis of indoor dust samples complemented with metagenomics can prioritize relevant chemicals that may be potential modifiers of LRRK2 penetrance.

From Omics to Multi-Omics: A Review of Advantages and Tradeoffs

Bioinformatics is a rapidly evolving field charged with cataloging, disseminating, and analyzing biological data. Bioinformatics started with genomics, but while genomics focuses more narrowly on the genes comprising a genome, bioinformatics now encompasses a much broader range of omics technologies. Overcoming barriers of scale and effort that plagued earlier sequencing methods, bioinformatics adopted an ambitious strategy involving high-throughput and highly automated assays. However, as the list of omics technologies continues to grow, the field of bioinformatics has changed in two fundamental ways. Despite enormous success in expanding our understanding of the biological world, the failure of bulk methods to account for biologically important variability among cells of the same or different type has led to a major shift toward single-cell and spatially resolved omics methods, which attempt to disentangle the conflicting signals contained in heterogeneous samples by examining individual cells or cell clusters. The second major shift has been the attempt to integrate two or more different classes of omics data in a single multimodal analysis to identify patterns that bridge biological layers. For example, unraveling the cause of disease may reveal a metabolite deficiency caused by the failure of an enzyme to be phosphorylated because a gene is not expressed due to aberrant methylation as a result of a rare germline variant. Conclusions: There is a fine line between superficial understanding and analysis paralysis, but like a detective novel, multi-omics increasingly provides the clues we need, if only we are able to see them.

Development of LC-FAIMS-MS and its application to lipidomics study of Acinetobacter baumannii infection

The recent advances in mass spectrometry (MS) technologies have enabled comprehensive lipid profiling in biological samples. However, the robustness and efficiency of MS-based lipidomics is compromised by the complexity of biological samples. High-field asymmetric waveform ion mobility spectrometry (FAIMS) is a technology that can continuously transmit one type of ion, independent of the mass-to-charge ratio. Here we present the development and application of LC-FAIMS-MS/MS-based platform for untargeted lipidomics. We used 3 optimally balanced compensation voltages, i.e., 29 V, 34 V and 39 V, to analyze all subclasses of glycerophospholipids. The reproducibility of the method was evaluated using reference standards. The reproducibility of retention times ranged from 0.9% to 1.5% RSD; whereas RSD values of 5%-10% were observed for peak areas. More importantly, the coupling of a FAIMS device can significantly improve the robustness and efficiency. We exploited this NPLC-FAIMS-HRMS to analyze the serum lipid profiles in mice infected intranasally with Acinetobacter baumannii. The temporal profiles of serum lipids after A. baumannii inoculation were obtained for 4 h, 8 h, and 24 h. We found that nearly all ether PC and ether PE lipids were significantly decreased 8 h after inoculation. The resultant volcano plot illustrated the distribution of 28 increased and 28 decreased lipid species in mouse sera 24 h after inoculation. We also found that a single ether PE composition can comprise multiple isomeric structures, and the relative abundance of each isomer could be quantified using the newly developed NPLC-FAIMS-PRM method. We have demonstrated that the proposed LC-FAIMS-MS is a valuable platform for lipidomics.

Metabolomics-driven approaches for identifying therapeutic targets in drug discovery

Identification of therapeutic targets can directly elucidate the mechanism and effect of drug therapy, which is a central step in drug development. The disconnect between protein targets and phenotypes under complex mechanisms hampers comprehensive target understanding. Metabolomics, as a systems biology tool that captures phenotypic changes induced by exogenous compounds, has emerged as a valuable approach for target identification. A comprehensive overview was provided in this review to illustrate the principles and advantages of metabolomics, delving into the application of metabolomics in target identification. This review outlines various metabolomics-based methods, such as dose-response metabolomics, stable isotope-resolved metabolomics, and multiomics, which identify key enzymes and metabolic pathways affected by exogenous substances through dose-dependent metabolite-drug interactions. Emerging techniques, including single-cell metabolomics, artificial intelligence, and mass spectrometry imaging, are also explored for their potential to enhance target discovery. The review emphasizes metabolomics' critical role in advancing our understanding of disease mechanisms and accelerating targeted drug development, while acknowledging current challenges in the field.

Combining metabolomics and machine learning to discover biomarkers for early-stage breast cancer diagnosis

There is an urgent need for better biomarkers for the detection of early-stage breast cancer. Utilizing untargeted metabolomics and lipidomics in conjunction with advanced data mining approaches for metabolism-centric biomarker discovery and validation may enhance the identification and validation of novel biomarkers for breast cancer screening. In this study, we employed a multimodal omics approach to identify and validate potential biomarkers capable of differentiating between patients with breast cancer and those with benign tumors. Our findings indicated that ether-linked phosphatidylcholine exhibited a significant difference between invasive ductal carcinoma and benign tumors, including cases with inconsistent mammography results. We observed alterations in numerous lipid species, including sphingomyelin, triacylglycerol, and free fatty acids, in the breast cancer group. Furthermore, we identified several dysregulated hydrophilic metabolites in breast cancer, such as glutamate, glycochenodeoxycholate, and dimethyluric acid. Through robust multivariate receiver operating characteristic analysis utilizing machine learning models, either linear support vector machines or random forest models, we successfully distinguished between cancerous and benign cases with promising outcomes. These results emphasize the potential of metabolic biomarkers to complement other criteria in breast cancer screening. Future studies are essential to further validate the metabolic biomarkers identified in our study and to develop assays for clinical applications.

Differences in honeybee queen pheromones revealed by LC-MS/MS: Reassessing the honest signal hypothesis

The honest signal hypothesis posits that social insect queens emit pheromonal signals that convey information about fecundity, which workers use to make decisions around investing in direct or indirect fitness. We used liquid chromatography-tandem mass spectrometry to measure honeybee (Apis mellifera) queen retinue pheromone (QRP) in relation to age, laying status, and acceptance using a protocol that enables concurrent metabolomic and lipidomic analyses. Older queens produced higher levels of the QRP components 9-R-hydroxydec-2(E)-enoic acid (9(R)-HDA), linolenic acid (LEA), and 4-hydroxy-3-methoxyphenylethanol (HVA) compared to younger queens, with HVA also correlating with ovary mass. However, ovary mass was not an influential metric for worker decision-making around queen acceptance; therefore, the relationship between HVA and ovary mass is merely an "honest signal" of a non-influential metric. Parallel metabolomic and lipidomic analyses showed that samples cluster according to queen age and mating status, but not ovary mass, revealing many other physiological changes occurring in the queen's early life.

In-Depth Chemical Analysis of the Brain Extracellular Space Using In Vivo Microdialysis with Liquid Chromatography-Tandem Mass Spectrometry

Metabolomic analysis of samples acquired in vivo from the brain extracellular space by microdialysis sampling can provide insights into chemical underpinnings of a given brain state and how it changes over time. Small sample volumes and low physiological concentrations have limited the identification of compounds from this compartment, so at present, we have scant knowledge of its composition. As a result, most in vivo measurements have limited depth of analysis. Here, we describe an approach to (1) identify hundreds of compounds in brain dialysate and (2) routinely detect many of these compounds in 5 μL microdialysis samples to enable deep monitoring of brain chemistry in time-resolved studies. Dialysate samples collected over 12 h were concentrated 10-fold and then analyzed using liquid chromatography with iterative tandem mass spectrometry (LC-MS/MS). Using this approach on dialysate from the rat striatum with both reversed-phase and hydrophilic interaction liquid chromatography yielded 479 unique compound identifications. 60% of the identified compounds could be detected in 5 μL of dialysate without further concentration using a single 20 min LC-MS analysis, showing that once identified, most compounds can be detected using small sample volumes and shorter analysis times compatible with routine in vivo monitoring. To detect more neurochemicals, LC-MS analysis of dialysate derivatized with light and isotopically labeled benzoyl chloride was employed. 872 nondegenerate benzoylated features were detected with this approach, including most small-molecule neurotransmitters and several metabolites involved in dopamine metabolism. This strategy allows deeper annotation of the brain extracellular space than previously possible and provides a launching point for defining the chemistry underlying brain states.

Microbiome-derived metabolites in early to mid-pregnancy and risk of gestational diabetes: a metabolome-wide association study

BACKGROUND

Pre-diagnostic disturbances in the microbiome-derived metabolome have been associated with an increased risk of diabetes in non-pregnant populations. However, the roles of microbiome-derived metabolites, the end-products of microbial metabolism, in gestational diabetes (GDM) remain understudied. We examined the prospective association of microbiome-derived metabolites in early to mid-pregnancy with GDM risk in a diverse population.

METHODS

We conducted a prospective discovery and validation study, including a case-control sample of 91 GDM and 180 non-GDM individuals within the multi-racial/ethnic The Pregnancy Environment and Lifestyle Study (PETALS) as the discovery set, a random sample from the PETALS (42 GDM, 372 non-GDM) as validation set 1, and a case-control sample (35 GDM, 70 non-GDM) from the Gestational Weight Gain and Optimal Wellness randomized controlled trial as validation set 2. We measured untargeted fasting serum metabolomics at gestational weeks (GW) 10-13 and 16-19 by gas chromatography/time-of-flight mass spectrometry (TOF-MS), liquid chromatography (LC)/quadrupole TOF-MS, and hydrophilic interaction LC/quadrupole TOF-MS. GDM was diagnosed using the 3-h, 100-g oral glucose tolerance test according to the Carpenter-Coustan criteria around GW 24-28.

RESULTS

Among 1362 annotated compounds, we identified 140 of gut microbiome metabolism origin. Multivariate enrichment analysis illustrated that carbocyclic acids and branched-chain amino acid clusters at GW 10-13 and the unsaturated fatty acids cluster at GW 16-19 were positively associated with GDM risk (FDR < 0.05). At GW 10-13, the prediction model that combined conventional risk factors and LASSO-selected microbiome-derived metabolites significantly outperformed the model with only conventional risk factors including fasting glucose (discovery AUC: 0.884 vs. 0.691; validation 1: 0.945 vs. 0.731; validation 2: 0.987 vs. 0.717; all P < 0.01). At GW 16-19, similar results were observed (discovery AUC: 0.802 vs. 0.691, P < 0.01; validation 1: 0.826 vs. 0.780; P = 0.10).

CONCLUSIONS

Dysbiosis in microbiome-derived metabolites is present early in pregnancy among individuals progressing to GDM.

Hepatic stearoyl-CoA desaturase-1 deficiency induces fibrosis and hepatocellular carcinoma-related gene activation under a high carbohydrate low fat diet

Stearoyl-CoA desaturase-1 (SCD1) is a pivotal enzyme in lipogenesis, which catalyzes the synthesis of monounsaturated fatty acids (MUFA) from saturated fatty acids, whose ablation downregulates lipid synthesis, preventing steatosis and obesity. Yet deletion of SCD1 promotes hepatic inflammation and endoplasmic reticulum stress, raising the question of whether hepatic SCD1 deficiency promotes further liver damage, including fibrosis. To delineate whether SCD1 deficiency predisposes the liver to fibrosis, cirrhosis, and hepatocellular carcinoma (HCC), we employed in vivo SCD1 deficient global and liver-specific mouse models fed a high carbohydrate low-fat diet and in vitro established AML12 mouse cells. The absence of liver SCD1 remarkably increased the saturation of liver lipid species, as indicated by lipidomic analysis, and led to hepatic fibrosis. Consistently, SCD1 deficiency promoted hepatic gene expression related to fibrosis, cirrhosis, and HCC. Deletion of SCD1 increased the circulating levels of Osteopontin, known to be increased in fibrosis, and alpha-fetoprotein, often used as an early marker and a prognostic marker for patients with HCC. De novo lipogenesis or dietary supplementation of oleate, an SCD1-generated MUFA, restored the gene expression related to fibrosis, cirrhosis, and HCC. Although SCD1 deficient mice are protected against obesity and fatty liver, our results show that MUFA deprivation results in liver injury, including fibrosis, thus providing novel insights between MUFA insufficiency and pathways leading to fibrosis, cirrhosis, and HCC under lean non-steatotic conditions.

Efficient lipidomic approach for the discovery of lipid ligands for immune receptors by combining LC-HRMS/MS analysis with fractionation and reporter cell assay

C-type lectin receptors (CLRs), which are pattern recognition receptors responsible for triggering innate immune responses, recognize damaged self-components and immunostimulatory lipids from pathogenic bacteria; however, several of their ligands remain unknown. Here, we propose a new analytical platform combining liquid chromatography-high-resolution tandem mass spectrometry with microfractionation capability (LC-FRC-HRMS/MS) and a reporter cell assay for sensitive activity measurements to develop an efficient methodology for searching for lipid ligands of CLR from microbial trace samples (crude cell extracts of approximately 5 mg dry cell/mL). We also developed an in-house lipidomic library containing accurate mass and fragmentation patterns of more than 10,000 lipid molecules predicted in silico for 90 lipid subclasses and 35 acyl side chain fatty acids. Using the developed LC-FRC-HRMS/MS system, the lipid extracts of Helicobacter pylori were separated and fractionated, and HRMS and HRMS/MS spectra were obtained simultaneously. The fractionated lipid extract samples in 96-well plates were thereafter subjected to reporter cell assays using nuclear factor of activated T cells (NFAT)-green fluorescent protein (GFP) reporter cells expressing mouse or human macrophage-inducible C-type lectin (Mincle). A total of 102 lipid molecules from all fractions were annotated using an in-house lipidomic library. Furthermore, a fraction that exhibited significant activity in the NFAT-GFP reporter cell assay contained α-cholesteryl glucoside, a type of glycolipid, which was successfully identified as a lipid ligand molecule for Mincle. Our analytical platform has the potential to be a useful tool for efficient discovery of lipid ligands for immunoreceptors.

A comprehensive IDA and SWATH-DIA Lipidomics and Metabolomics dataset: SARS-CoV-2 case control study

A significant hurdle in untargeted lipid/metabolomics research lies in the absence of reliable, cross-validated spectral libraries, leading to a considerable portion of LC-MS features being labeled as unknowns. Despite continuous advancement in annotation tools and libraries, it is important to safeguard, publish and share acquired data through public repositories. Embracing this trend of data sharing not only promotes efficient resource utilization but also paves the way for future repurposing and in-depth analysis; ultimately advancing our comprehension of Covid-19 and other diseases. In this work, we generated an extensive MS-dataset of 39 Covid-19 infected patients versus age- and gender-matched 39 healthy controls. We implemented state of the art acquisition techniques including IDA and SWATH-DIA to ensure a thorough insight in the lipidome and metabolome, ensuring a repurposable dataset.

Differences in Prediagnostic Serum Metabolomic and Lipidomic Profiles Between Cirrhosis Patients with and without Incident Hepatocellular Carcinoma

Background

Early detection of hepatocellular carcinoma (HCC) is crucial for improving patient outcomes, but we lack robust clinical biomarkers. This study aimed to identify a metabolite and/or lipid panel for early HCC detection.

Methods

We developed a high-resolution liquid chromatography mass spectrometry (LC-MS)-based profiling platform and evaluated differences in the global metabolome and lipidome between 28 pre-diagnostic serum samples from patients with cirrhosis who subsequently developed HCC (cases) and 30 samples from patients with cirrhosis and no HCC (controls). We linked differentially expressed metabolites and lipids to their associated genes, proteins, and transcriptomic signatures in publicly available datasets. We used machine learning models to identify a minimal panel to distinguish between cases and controls.

Results

Among cases compared with controls, 124 metabolites and 246 lipids were upregulated, while 208 metabolites and 73 lipids were downregulated. The top upregulated metabolites were glycoursodeoxycholic acid, 5-methyltetrahydrofolic acid, octanoyl-coenzyme A, and glycocholic acid. Elevated lipids comprised glycerol lipids, cardiolipin, and phosphatidylethanolamine, whereas suppressed lipids included oxidized phosphatidylcholine and lysophospholipids. There was an overlap between differentially expressed metabolites and lipids and previously published transcriptomic signatures, illustrating an association with liver disease severity. A panel of 12 metabolites that distinguished between cases and controls with an area under the receiver operating curve of 0.98 for the support vector machine (interquartile range, 0.9-1).

Conclusion

Using prediagnostic serum samples, we identified a promising metabolites panel that accurately identifies patients with cirrhosis who progressed to HCC. Further validation of this panel is required.

Environmental Temperature Variation Affects Brain Lipid Composition in Adult Zebrafish (Danio rerio)

This study delves deeper into the impact of environmental temperature variations on the nervous system in teleost fish. Previous research has demonstrated that exposing adult zebrafish (Danio rerio) to 18 °C and 34 °C for 4 or 21 days induces behavioural changes compared to fish kept at a control temperature of 26 °C, suggesting alterations in the nervous system. Subsequent studies revealed that these temperature conditions also modify brain protein expression, indicating potential neurotoxic effects. The primary aim of this work was to investigate the effects of prolonged exposure (21 days) to 18 °C or 34 °C on the brain lipidomes of adult zebrafish compared to a control temperature. Analysis of the brain lipidome highlighted significant alteration in the relative abundances of specific lipid molecules at 18 °C and 34 °C, confirming distinct effects induced by both tested temperatures. Exposure to 18 °C resulted in an increase in levels of phospholipids, such as phosphatidylethanolamine, alongside a general reduction in levels of sphingolipids, including sphingomyelin. Conversely, exposure to 34 °C produced more pronounced effects, with increases in levels of phosphatidylethanolamine and those of various sphingolipids such as ceramide, gangliosides, and sphingomyelin, alongside a reduction in levels of ether phospholipids, including lysophosphatidylethanolamine ether, phosphatidylethanolamine ether, and phosphatidylglycerol ether, as well as levels of glycolipids like monogalactosyldiacylglycerol. These results, when integrated with existing proteomic and behavioural data, offer new insights into the effects of thermal variations on the nervous system in teleost fish. Specifically, our proteomic and lipidomic findings suggest that elevated temperatures may disrupt mitochondrial function, increase neuronal susceptibility to oxidative stress and cytotoxicity, alter axonal myelination, impair nerve impulse transmission, hinder synapse function and neurotransmitter release, and potentially lead to increased neuronal death. These findings are particularly relevant in the fields of cell biology, neurobiology, and ecotoxicology, especially in the context of global warming.

Investigating the effects of dexamethasone on pulmonary surfactant lipids based on lipidomics studies

Dexamethasone, a glucocorticoid commonly used in pediatric patients, has potent anti-inflammatory and immunosuppressive properties. However, it is associated with side effects such as reduced lung function and decreased immunity. Pulmonary surfactant lipids are closely linked to lung disease and play a role in reducing surface tension, immune response and antiviral activity. The dysregulation of lipid metabolism is closely associated with lung disease. Hence, untargeted lipidomics may be instrumental in elucidating the effects of dexamethasone on pulmonary surfactant lipids. We obtained surfactant lipid samples from the bronchoalveolar lavage fluid of young mice injected subcutaneously with dexamethasone and conducted a comprehensive lipidomic analysis, comparing them with a control group. We observed a decrease in lipids, such as phosphatidylcholine, phosphatidylglycerol and phosphatidylethanolamine, and an increase in ceramide, fatty acid, diacylglycerol and monoglyceride, which may impact lung health. This study revealed the influence of dexamethasone on pulmonary surfactant lipids, offering new insights into adverse reactions in clinical settings.

Integrating Lipidomics, Metabolomics, and Network Pharmacology to Reveal the Mechanism of Cannabidiol against Inflammation in High-Fat, High-Cholesterol Diet-Induced Mice

Inflammation plays a critical role in the development of numerous diseases. Cannabidiol (CBD), found in hemp, exhibits significant pharmacological activities. Accumulating evidence suggests that CBD has anti-inflammatory and cardiovascular protection effects, but the potential mechanisms require further exploration. In this study, we aimed to reveal the mechanisms of CBD against high-fat, high-cholesterol (HFC) diet-induced inflammation combining metabolomics with network pharmacology. First, plasma lipidomics results indicated that oxidized lipids could serve as potential biomarkers for HFC diet-induced inflammation, and CBD reversed the elevated levels of oxidized lipids. The HFC diet was also found to enhance intestinal permeability, facilitating the entry of lipopolysaccharides (LPSs) into the circulatory system and subsequently increasing systemic inflammation. Additionally, cell metabolomic results indicated that CBD could reverse 10 important differential metabolites in LPS-induced RAW 264.7 cells. Using network pharmacology, we identified 49 core targets, and enrichment analysis revealed that arachidonic acid was the most significantly affected by CBD, which was closely associated with inflammation. Further integrated analysis focused on three key targets, including PTGS2, ALOX5, and ALOX15. Molecular docking showed high affinities between key targets and CBD, and qPCR further demonstrated that CBD could reverse the mRNA expression of these key targets in RAW 264.7 cells. Collectively, this finding integrates lipidomics and metabolomics with network pharmacology to elucidate the anti-inflammatory effects of CBD and validates key therapeutic targets.

Challenges in Lipidomics Biomarker Identification: Avoiding the Pitfalls and Improving Reproducibility

Identification of features with high levels of confidence in liquid chromatography-mass spectrometry (LC-MS) lipidomics research is an essential part of biomarker discovery, but existing software platforms can give inconsistent results, even from identical spectral data. This poses a clear challenge for reproducibility in biomarker identification. In this work, we illustrate the reproducibility gap for two open-access lipidomics platforms, MS DIAL and Lipostar, finding just 14.0% identification agreement when analyzing identical LC-MS spectra using default settings. Whilst the software platforms performed more consistently using fragmentation data, agreement was still only 36.1% for MS2 spectra. This highlights the critical importance of validation across positive and negative LC-MS modes, as well as the manual curation of spectra and lipidomics software outputs, in order to reduce identification errors caused by closely related lipids and co-elution issues. This curation process can be supplemented by data-driven outlier detection in assessing spectral outputs, which is demonstrated here using a novel machine learning approach based on support vector machine regression combined with leave-one-out cross-validation. These steps are essential to reduce the frequency of false positive identifications and close the reproducibility gap, including between software platforms, which, for downstream users such as bioinformaticians and clinicians, can be an underappreciated source of biomarker identification errors.

Sensory Neurons Release Cardioprotective Factors in an In Vitro Ischemia Model

Sensory neurons densely innervate the myocardium. The role of their sensing and response to acute and prolonged ischemia is largely unclear. In a cellular model of ischemia-reperfusion injury, the presence of sensory neurons increases cardiomyocyte survival. Here, after the exclusion of classical neurotransmitter release, and measurement of cytokine release, we modified the experiment from a direct co-culture of primary murine cardiomyocytes and sensory neurons to a transfer of the supernatant. Sensory neurons were exposed to ischemia and the resulting conditioned supernatant was transferred onto cardiomyocytes. This approach largely increased the tolerance of cardiomyocytes to ischemia and reperfusion. Towards the identification of the mechanism, it was demonstrated that after ten-fold dilution, the conditioned solution lost its protective effect. The effect remained after removal of extracellular vesicles by ultracentrifugation, and was not affected by exposure to protease activity, and fractionation pointed towards a hydrophilic agent. Solutions conditioned by HEK293t cells or 3T3 fibroblasts also increase cardiomyocyte survival, but to a lower degree. A metabolomic search identified 64 at least two-fold changed metabolites and lipids. Many of these could be identified and are involved in essential cellular functions. In the presented model for ischemia-reperfusion, sensory neurons secrete one or more cardioprotective substances that can improve cardiomyocyte survival.

Phosphate availability conditions caspofungin tolerance, capsule attachment and titan cell formation in Cryptococcus neoformans

There is an urgent need for new antifungal drugs to treat invasive fungal diseases. Unfortunately, the echinocandin drugs that are fungicidal against other important fungal pathogens are ineffective against Cryptococcus neoformans, the causative agent of life-threatening meningoencephalitis in immunocompromised people. Contributing mechanisms for echinocandin tolerance are emerging with connections to calcineurin signaling, the cell wall, and membrane composition. In this context, we discovered that a defect in phosphate uptake impairs the tolerance of C. neoformans to the echinocandin caspofungin. Our previous analysis of mutants lacking three high affinity phosphate transporters revealed reduced elaboration of the polysaccharide capsule and attenuated virulence in mice. We investigated the underlying mechanisms and found that loss of the transporters and altered phosphate availability influences the cell wall and membrane composition. These changes contribute to the shedding of capsule polysaccharide thus explaining the reduced size of capsules on mutants lacking the phosphate transporters. We also found an influence of the calcineurin pathway including calcium sensitivity and an involvement of the endoplasmic reticulum in the response to phosphate limitation. Furthermore, we identified membrane and lipid composition changes consistent with the role of phosphate in phospholipid biosynthesis and with previous studies implicating membrane integrity in caspofungin tolerance. Finally, we discovered a contribution of phosphate to titan cell formation, a cell type that displays modified cell wall and capsule composition. Overall, our analysis reinforces the importance of phosphate as a regulator of cell wall and membrane composition with implications for capsule attachment and antifungal drug susceptibility.

QC-GN2oMS2: a Graph Neural Net for High Resolution Mass Spectra Prediction

Predicting the mass spectrum of a molecular ion is often accomplished via three generalized approaches: rules-based methods for bond breaking, deep learning, or quantum chemical (QC) modeling. Rules-based approaches are often limited by the conditions for different chemical subspaces and perform poorly under chemical regimes with few defined rules. QC modeling is theoretically robust but requires significant amounts of computational time to produce a spectrum for a given target. Among deep learning techniques, graph neural networks (GNNs) have performed better than previous work with fingerprint-based neural networks in mass spectra prediction. To explore this technique further, we investigate the effects of including quantum chemically derived information as edge features in the GNN to increase predictive accuracy. The models we investigated include categorical bond order, bond force constants derived from extended tight-binding (xTB) quantum chemistry, and acyclic bond dissociation energies. We evaluated these models against a control GNN with no edge features in the input graphs. Bond dissociation enthalpies yielded the best improvement with a cosine similarity score of 0.462 relative to the baseline model (0.437). In this work we also apply dynamic graph attention which improves performance on benchmark problems and supports the inclusion of edge features. Between implementations, we investigate the nature of the molecular embedding for spectra prediction and discuss the recognition of fragment topographies in distinct chemistries for further development in tandem mass spectrometry prediction.

Monitoring Epoxidized Soybean Oil Degradation Using Liquid Chromatography-Mass Spectrometry and In Silico Spectral Libraries

Epoxidized soybean oil (ESO) is routinely used as a bioderived plasticizer and stabilizer in polyvinyl chloride (PVC), as it prolongs material integrity during dehydrochlorination. During this process, the epoxide moieties of ESO are progressively converted to chlorohydrins, which amplify ESO's inherent structural complexity. Past characterization efforts utilized separation-mass spectrometry (MS) analysis of the hydrolyzed acyl chains to simplify the complexity. However, this approach significantly increases the complexity of sample preparation and cannot directly monitor the chlorination of individual ESO species during aging. Here, we present a comprehensive LC-MS/MS data acquisition and in silico spectral library identification workflow optimized for intact ESO byproduct analysis. Detailed MS/MS fragmentation rules derived from synthesized standards were coupled with improved fragment ion intensity modeling capabilities to generate a high-fidelity spectral library for rapid ESO byproduct identification. Identification confidence was further bolstered by using retention time modeling to filter spurious MS/MS matches. Finally, we paired this informatic approach with an optimized extraction procedure and reversed-phase separation to generate a detailed timeline of more than 400 ESO species and byproducts during PVC thermal aging. These developments significantly improve our ability to detect, characterize, and understand ESO degradation in complex PVC formulations with new levels of molecular resolution.

Studying the Intrinsic Reactivity of Chromanes by Gas-Phase Infrared Spectroscopy

Tandem mass spectrometry is routinely used for the structural analysis of organic molecules, but many fragmentation reactions are not well understood. Because several potential structures can correspond to a measured mass, the assignment of product ions is ambiguous using mass spectrometry alone. Here, we combine mass spectrometry with high-resolution gas-phase infrared spectroscopy and computational chemistry tools to identify product ion structures and derive collision-induced fragmentation mechanisms of the chromane derivatives Trolox and Methyltrolox. We find that protonated Trolox and Methyltrolox fragment identically via dehydration and decarbonylation, while deprotonated ions display substantially diverging reactivities. For deprotonated Methyltrolox, we observe unusual radical fragmentation reactions and suggest a [1,2]-Wittig rearrangement involving aryl migration in the gas phase. Overall, the combined experimental and theoretical approach presented here revealed complex proton dynamics and intramolecular rearrangement reactions, which expand our understanding on structure-reactivity relationships of isolated molecules in different protonation states.

Tonic type 2 immunity is a critical tissue checkpoint controlling autoimmunity in the skin

Immunoregulatory mechanisms established in the lymphoid organs are vital for preventing autoimmunity. However, the presence of similar mechanisms in non-lymphoid tissues remains unclear. Through transcriptomic and lipidomic analyses, we find a negative association between psoriasis and fatty acid metabolism, as well as Th2 signature. Homeostatic expression of liver X receptor (LXR) and peroxisome proliferator-activated receptor gamma (PPARγ) is essential for maintaining fatty acid metabolism and for conferring resistance to psoriasis in mice. Perturbation of signal transducer and activator of transcription 6 (STAT6) diminishes the homeostatic levels of LXR and PPARγ. Furthermore, mice lacking STAT6, interleukin 4 receptor alpha (IL-4Rα), or IL-13, but not IL-4, exhibit increased susceptibility to psoriasis. Under steady state, innate lymphoid cells (ILCs) are the primary producers of IL-13. In human skin, inhibiting tonic type 2 immunity exacerbates psoriasis-like inflammation and IL-17A, while activating LXR or PPARγ inhibits them. Hence, we propose that tonic type 2 immunity, driven by IL-13-producing ILCs, represents a crucial tissue checkpoint that represses autoimmunity and maintains lipid homeostasis in the skin.

High-Resolution Mass Spectrometry for Human Exposomics: Expanding Chemical Space Coverage

In the modern "omics" era, measurement of the human exposome is a critical missing link between genetic drivers and disease outcomes. High-resolution mass spectrometry (HRMS), routinely used in proteomics and metabolomics, has emerged as a leading technology to broadly profile chemical exposure agents and related biomolecules for accurate mass measurement, high sensitivity, rapid data acquisition, and increased resolution of chemical space. Non-targeted approaches are increasingly accessible, supporting a shift from conventional hypothesis-driven, quantitation-centric targeted analyses toward data-driven, hypothesis-generating chemical exposome-wide profiling. However, HRMS-based exposomics encounters unique challenges. New analytical and computational infrastructures are needed to expand the analysis coverage through streamlined, scalable, and harmonized workflows and data pipelines that permit longitudinal chemical exposome tracking, retrospective validation, and multi-omics integration for meaningful health-oriented inferences. In this article, we survey the literature on state-of-the-art HRMS-based technologies, review current analytical workflows and informatic pipelines, and provide an up-to-date reference on exposomic approaches for chemists, toxicologists, epidemiologists, care providers, and stakeholders in health sciences and medicine. We propose efforts to benchmark fit-for-purpose platforms for expanding coverage of chemical space, including gas/liquid chromatography-HRMS (GC-HRMS and LC-HRMS), and discuss opportunities, challenges, and strategies to advance the burgeoning field of the exposome.

PANAMA-enabled high-sensitivity dual nanoflow LC-MS metabolomics and proteomics analysis

High-sensitivity nanoflow liquid chromatography (nLC) is seldom employed in untargeted metabolomics because current sample preparation techniques are inefficient at preventing nanocapillary column performance degradation. Here, we describe an nLC-based tandem mass spectrometry workflow that enables seamless joint analysis and integration of metabolomics (including lipidomics) and proteomics from the same samples without instrument duplication. This workflow is based on a robust solid-phase micro-extraction step for routine sample cleanup and bioactive molecule enrichment. Our method, termed proteomic and nanoflow metabolomic analysis (PANAMA), improves compound resolution and detection sensitivity without compromising the depth of coverage as compared with existing widely used analytical procedures. Notably, PANAMA can be applied to a broad array of specimens, including biofluids, cell lines, and tissue samples. It generates high-quality, information-rich metabolite-protein datasets while bypassing the need for specialized instrumentation.

Resolving multi-image spatial lipidomic responses to inhaled toxicants by machine learning

Regional responses to inhaled toxicants are essential to understand the pathogenesis of lung disease under exposure to air pollution. We evaluated the effect of combined allergen sensitization and ozone exposure on eliciting spatial differences in lipid distribution in the mouse lung that may contribute to ozone-induced exacerbations in asthma. Lung lobes from male and female BALB/c mice were cryosectioned and acquired by high resolution mass spectrometry imaging (MSI). Processed MSI peak annotations were validated by LC-MS/MS data from scraped tissue slides and microdissected lung tissue. Images were normalized and segmented into clusters. Interestingly, segmented clusters overlapped with stained serial tissue sections, enabling statistical analysis across biological replicates for morphologically relevant lung regions. Spatially distinct lipids had higher overall degree of unsaturated fatty acids in distal lung regions compared to proximal regions. Furthermore, the airway and alveolar epithelium exhibited significantly decreased sphingolipid and glycerophospholipid abundance in females, but not in males. We demonstrate the potential role of lipid saturation in healthy lung function and highlight sex differences in regional lung lipid distribution following ozone exposure. Our study provides a framework for future MSI experiments capable of relative quantification across biological replicates and expansion to multiple sample types, including human tissue.

MetaboAnalyst 6.0: towards a unified platform for metabolomics data processing, analysis and interpretation

We introduce MetaboAnalyst version 6.0 as a unified platform for processing, analyzing, and interpreting data from targeted as well as untargeted metabolomics studies using liquid chromatography - mass spectrometry (LC-MS). The two main objectives in developing version 6.0 are to support tandem MS (MS2) data processing and annotation, as well as to support the analysis of data from exposomics studies and related experiments. Key features of MetaboAnalyst 6.0 include: (i) a significantly enhanced Spectra Processing module with support for MS2 data and the asari algorithm; (ii) a MS2 Peak Annotation module based on comprehensive MS2 reference databases with fragment-level annotation; (iii) a new Statistical Analysis module dedicated for handling complex study design with multiple factors or phenotypic descriptors; (iv) a Causal Analysis module for estimating metabolite - phenotype causal relations based on two-sample Mendelian randomization, and (v) a Dose-Response Analysis module for benchmark dose calculations. In addition, we have also improved MetaboAnalyst's visualization functions, updated its compound database and metabolite sets, and significantly expanded its pathway analysis support to around 130 species. MetaboAnalyst 6.0 is freely available at https://www.metaboanalyst.ca.

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.

An exploratory study on lipidomic profiles in a cohort of individuals with posttraumatic stress disorder

Posttraumatic stress disorder (PTSD) can develop after trauma exposure. Some studies report that women develop PTSD at twice the rate of men, despite greater trauma exposure in men. Lipids and their metabolites (lipidome) regulate a myriad of key biological processes and pathways such as membrane integrity, oxidative stress, and neuroinflammation in the brain by maintaining neuronal connectivity and homeostasis. In this study, we analyzed the lipidome of 40 adults with PTSD and 40 trauma-exposed non-PTSD individuals (n = 20/sex/condition; 19-39 years old). Plasma samples were analyzed for lipidomics using Quadrupole Time-of-Flight (QToF) mass spectrometry. Additionally, ~ 90 measures were collected, on sleep, and mental and physical health indices. Poorer sleep quality was associated with greater PTSD severity in both sexes. The lipidomics analysis identified a total of 348 quantifiable known lipid metabolites and 1951 lipid metabolites that are yet unknown; known metabolites were part of 13 lipid subclasses. After adjusting for BMI and sleep quality, in women with PTSD, only one lipid subclass, phosphatidylethanolamine (PE) was altered, whereas, in men with PTSD, 9 out of 13 subclasses were altered compared to non-PTSD women and men, respectively. Severe PTSD was associated with 22% and 5% of altered lipid metabolites in men and women, respectively. Of the changed metabolites, only 0.5% measures (2 PEs and cholesterol) were common between women and men with PTSD. Several sphingomyelins, PEs, ceramides, and triglycerides were increased in men with severe PTSD. The correlations between triglycerides and ceramide metabolites with cholesterol metabolites and systolic blood pressure were dependent upon sex and PTSD status. Alterations in triglycerides and ceramides are linked with cardiac health and metabolic function in humans. Thus, disturbed sleep and higher body mass may have contributed to changes in the lipidome found in PTSD.

Non-targeted mass spectrometry and feature-based molecular networking for determination of branched fatty acid esters of hydroxy fatty acids in milk

Branched fatty acid esters of hydroxy fatty acids (FAHFAs) represent trace lipids with significant natural biological functions. While exogenous FAHFAs have been extensively studied, research on FAHFAs in milk remains limited, constraining our grasp of their nutritional roles. This study introduces a non-targeted mass spectrometry approach combined with chemical networking of spectral fragmentation patterns to uncover FAHFAs. Through meticulous sample handling and comparisons of various data acquisition and processing modes, we validate the method's superiority, identifying twice as many FAHFAs compared to alternative techniques. This validated method was then applied to different milk samples, revealing 45 chemical signals associated with known and potential FAHFAs, alongside findings of 66 ceramide/hexosylceramide (Cer/HexCer), 48 phosphatidyl ethanolamine/lyso phosphatidyl ethanolamine (PE/LPE), 21 phosphatidylcholine/lysophosphatidylcholine (PC/LPC), 16 phosphatidylinositol (PI), 7 phosphatidylserine (PS), and 11 sphingomyelin (SM) compounds. This study expands our understanding of the FAHFA family in milk and provides a fast and convenient method for identifying FAHFAs.

Unveiling the "hidden quality" of the walnut pellicle: a precious source of bioactive lipids

Tree nut consumption has been widely associated with various health benefits, with walnuts, in particular, being linked with improved cardiovascular and neurological health. These benefits have been attributed to walnuts' vast array of phenolic antioxidants and abundant polyunsaturated fatty acids. However, recent studies have revealed unexpected clinical outcomes related to walnut consumption, which cannot be explained simply with the aforementioned molecular hallmarks. With the goal of discovering potential molecular sources of these unexplained clinical outcomes, an exploratory untargeted metabolomics analysis of the isolated walnut pellicle was conducted. This analysis revealed a myriad of unusual lipids, including oxylipins and endocannabinoids. These lipid classes, which are likely present in the pellicle to enhance the seeds' defenses due to their antimicrobial properties, also have known potent bioactivities as mammalian signaling molecules and homeostatic regulators. Given the potential value of this tissue for human health, with respect to its "bioactive" lipid fraction, we sought to quantify the amounts of these compounds in pellicle-enriched waste by-products of mechanized walnut processing in California. An impressive repertoire of these compounds was revealed in these matrices, and in notably significant concentrations. This discovery establishes these low-value agriculture wastes promising candidates for valorization and translation into high-value, health-promoting products; as these molecules represent a potential explanation for the unexpected clinical outcomes of walnut consumption. This "hidden quality" of the walnut pellicle may encourage further consumption of walnuts, and walnut industries may benefit from a revaluation of abundant pellicle-enriched waste streams, leading to increased sustainability and profitability through waste upcycling.

Longitudinal changes in DNA methylation during the onset of islet autoimmunity differentiate between reversion versus progression of islet autoimmunity

Background

Type 1 diabetes (T1D) is preceded by a heterogenous pre-clinical phase, islet autoimmunity (IA). We aimed to identify pre vs. post-IA seroconversion (SV) changes in DNAm that differed across three IA progression phenotypes, those who lose autoantibodies (reverters), progress to clinical T1D (progressors), or maintain autoantibody levels (maintainers).

Methods

This epigenome-wide association study (EWAS) included longitudinal DNAm measurements in blood (Illumina 450K and EPIC) from participants in Diabetes Autoimmunity Study in the Young (DAISY) who developed IA, one or more islet autoantibodies on at least two consecutive visits. We compared reverters - individuals who sero-reverted, negative for all autoantibodies on at least two consecutive visits and did not develop T1D (n=41); maintainers - continued to test positive for autoantibodies but did not develop T1D (n=60); progressors - developed clinical T1D (n=42). DNAm data were measured before (pre-SV visit) and after IA (post-SV visit). Linear mixed models were used to test for differences in pre- vs post-SV changes in DNAm across the three groups. Linear mixed models were also used to test for group differences in average DNAm. Cell proportions, age, and sex were adjusted for in all models. Median follow-up across all participants was 15.5 yrs. (interquartile range (IQR): 10.8-18.7).

Results

The median age at the pre-SV visit was 2.2 yrs. (IQR: 0.8-5.3) in progressors, compared to 6.0 yrs. (IQR: 1.3-8.4) in reverters, and 5.7 yrs. (IQR: 1.4-9.7) in maintainers. Median time between the visits was similar in reverters 1.4 yrs. (IQR: 1-1.9), maintainers 1.3 yrs. (IQR: 1.0-2.0), and progressors 1.8 yrs. (IQR: 1.0-2.0). Changes in DNAm, pre- vs post-SV, differed across the groups at one site (cg16066195) and 11 regions. Average DNAm (mean of pre- and post-SV) differed across 22 regions.

Conclusion

Differentially changing DNAm regions were located in genomic areas related to beta cell function, immune cell differentiation, and immune cell function.

Cholesterol inhibits assembly and activation of the EphA2 receptor

The receptor tyrosine kinase EphA2 drives cancer malignancy by facilitating metastasis. EphA2 can be found in different self-assembly states: as a monomer, dimer, and oligomer. However, our understanding remains limited regarding which EphA2 state is responsible for driving pro-metastatic signaling. To address this limitation, we have developed SiMPull-POP, a single-molecule method for accurate quantification of membrane protein self-assembly. Our experiments revealed that a reduction of plasma membrane cholesterol strongly promoted EphA2 self-assembly. Indeed, low cholesterol caused a similar effect to the EphA2 ligand ephrinA1-Fc. These results indicate that cholesterol inhibits EphA2 assembly. Phosphorylation studies in different cell lines revealed that low cholesterol increased phospho-serine levels, the signature of oncogenic signaling. Investigation of the mechanism that cholesterol uses to inhibit the assembly and activity of EphA2 indicate an in-trans effect, where EphA2 is phosphorylated by protein kinase A downstream of beta-adrenergic receptor activity, which cholesterol also inhibits. Our study not only provides new mechanistic insights on EphA2 oncogenic function, but also suggests that cholesterol acts as a molecular safeguard mechanism that prevents uncontrolled self-assembly and activation of EphA2.

The manganese transporter SLC39A8 links alkaline ceramidase 1 to inflammatory bowel disease

The metal ion transporter SLC39A8 is associated with physiological traits and diseases, including blood manganese (Mn) levels and inflammatory bowel diseases (IBD). The mechanisms by which SLC39A8 controls Mn homeostasis and epithelial integrity remain elusive. Here, we generate Slc39a8 intestinal epithelial cell-specific-knockout (Slc39a8-IEC KO) mice, which display markedly decreased Mn levels in blood and most organs. Radiotracer studies reveal impaired intestinal absorption of dietary Mn in Slc39a8-IEC KO mice. SLC39A8 is localized to the apical membrane and mediates 54Mn uptake in intestinal organoid monolayer cultures. Unbiased transcriptomic analysis identifies alkaline ceramidase 1 (ACER1), a key enzyme in sphingolipid metabolism, as a potential therapeutic target for SLC39A8-associated IBDs. Importantly, treatment with an ACER1 inhibitor attenuates colitis in Slc39a8-IEC KO mice by remedying barrier dysfunction. Our results highlight the essential roles of SLC39A8 in intestinal Mn absorption and epithelial integrity and offer a therapeutic target for IBD associated with impaired Mn homeostasis.

Pulmonary maternal immune activation does not cross the placenta but leads to fetal metabolic adaptation

The fetal development of organs and functions is vulnerable to perturbation by maternal inflammation which may increase susceptibility to disorders after birth. Because it is not well understood how the placenta and fetus respond to acute lung- inflammation, we characterize the response to maternal pulmonary lipopolysaccharide exposure across 24 h in maternal and fetal organs using multi-omics, imaging and integrative analyses. Unlike maternal organs, which mount strong inflammatory immune responses, the placenta upregulates immuno-modulatory genes, in particular the IL-6 signaling suppressor Socs3. Similarly, we observe no immune response in the fetal liver, which instead displays metabolic changes, including increases in lipids containing docosahexaenoic acid, crucial for fetal brain development. The maternal liver and plasma display similar metabolic alterations, potentially increasing bioavailability of docosahexaenoic acid for the mother and fetus. Thus, our integrated temporal analysis shows that systemic inflammation in the mother leads to a metabolic perturbation in the fetus.

Non-Targeted PFAS Suspect Screening and Quantification of Drinking Water Samples Collected through Community Engaged Research in North Carolina's Cape Fear River Basin

A community engaged research (CER) approach was used to provide an exposure assessment of poly- and perfluorinated (PFAS) compounds in North Carolina residential drinking water. Working in concert with community partners, who acted as liaisons to local residents, samples were collected by North Carolina residents from three different locations along the Cape Fear River basin: upper, middle, and lower areas of the river. Residents collected either drinking water samples from their homes or recreational water samples from near their residence that were then submitted by the community partners for PFAS analysis. All samples were processed using weak anion exchange (WAX) solid phase extraction and analyzed using a non-targeted suspect screening approach as well as a quantitative approach that included a panel of 45 PFAS analytes, several of which are specific to chemical industries near the collection site locations. The non-targeted approach, which utilized a suspect screening list (obtained from EPA CompTox database) identified several PFAS compounds at a level two confidence rating (Schymanski scale); compounds identified included a fluorinated insecticide, a fluorinated herbicide, a PFAS used in polymer chemistry, and another that is used in battery production. Notably, at several locations, PFOA (39.8 ng/L) and PFOS (205.3 ng/L) were at levels that exceeded the mandatory EPA maximum contaminant level (MCL) of 4 ng/L. Additionally, several sites had detectable levels of PFAS that are unique to a local chemical manufacturer. These findings were communicated back to the community partners who then disseminated this information to the local residents to help empower and aid in making decisions for reducing their PFAS exposure.

Investigation of the Effects of Labware Contamination on Mass Spectrometry-Based Human Serum Lipidome Analysis

Polypropylene microcentrifuge tubes (MCTs) are increasingly used in lipidome sample preparation. In the absence of a comprehensive study evaluating ramifications of plasticware utilization in mass spectrometry-based lipidomic analyses, we conducted a systematic analysis to elucidate potential negative effects ascribable to labware contamination in serum lipidomics. During serum lipid extractions, tested glassware introduced 24 labware contaminants. In contrast, Eppendorf polypropylene MCTs contributed 485 contaminant features, many of which could be erroneously putatively identified as lipids via their m/z values. Eppendorf MCTs contamination engendered severe ion-suppression of 40 low abundance serum lipids, while generating mild to modest lipid ion-suppression across a multitude of higher abundance coeluting lipids. Less compatible polypropylene MCTs from an alternative manufacturer introduced a staggering 2,949 contaminant m/z values, severely affecting 75 coeluting serum lipids and causing more frequent and pronounced ion-suppression instances. Furthermore, by performing serum extractions with varied initial volumes, it was ascertained that labware-induced lipid ion-suppression is a dynamic phenomenon, contingent on both lipid and labware contaminant concentrations where low-abundance lipids are disproportionately impacted by coelutes of suppressive contaminants. In addition to lipid ion-suppression, the identification and quantification of 7 fatty acid endogenous serum lipids were compromised by the leaching of structurally identical surfactants from MCTs. MCTs artificially introduced 10 additional primary amides extraneous to serum samples. Utmost caution is imperative in interpreting data concerning primary amides and fatty acids when employing plastic labware. Through this investigation, we aspire to elevate awareness regarding the pernicious impact of labware contamination on lipidome analysis.

Excipient-related impurities in liposome drug products

Liposomes are widely used in the pharmaceutical industry as drug delivery systems to increase the efficacy and reduce the off-target toxicity of active pharmaceutical ingredients (APIs). The liposomes are more complex drug delivery systems than the traditional dosage forms, and phospholipids and cholesterol are the major structural excipients. These two excipients undergo hydrolysis and/or oxidation during liposome preparation and storage, resulting in lipids hydrolyzed products (LHPs) and cholesterol oxidation products (COPs) in the final liposomal formulations. These excipient-related impurities at elevated concentrations may affect liposome stability and exert biological functions. This review focuses on LHPs and COPs, two major categories of excipient-related impurities in the liposomal formulations, and discusses factors affecting their formation, and analytical methods to determine these excipient-related impurities.

Network pharmacology combined with lipidomics to reveal the regulatory effects and mechanisms of Kangzao granules in the hypothalamus of rats with central precocious puberty

Central precocious puberty (CPP) is a prevalent endocrine disorder that primarily affects children, specifically females, and is associated with various physical and psychological complications. Although Kangzao granules (KZG) are efficacious in managing CPP, the underlying mechanisms remain unclear. Therefore, this study aimed to elucidate the therapeutic mechanisms of KZG using network pharmacology, molecular docking, pharmacodynamics, and pathway validation. A putative compound-target-pathway network was constructed using Cytoscape, before KEGG and Gene Ontology enrichment analyses were conducted. Moreover, molecular docking was performed using AutoDockTools. Quality control of the 10 key components of KZG was carried out using UHPLC-ESI/LTQ-Orbitrap-MS/MS, and hypothalamic lipids were analyzed using UHPLC-Q-Exactive Orbitrap MS/MS. In total, 87 bioactive compounds that targeting 110 core proteins to alleviate CPP were identified in KZG. Lipidomic analysis revealed 18 differential lipids among the CPP, KZG, and control groups, wherein fatty acids were significantly reduced in the model group; however, these changes were effectively counteracted by KZG treatment. Molecular docking analysis revealed a strong binding affinity between flavonoids and RAC-alpha serine/threonine-protein kinase (AKT) when docked into the crystal structure. Moreover, a substantial disruption in lipid metabolism was observed in the model group; however, treatment with KZG efficiently reversed these alterations. Furthermore, the phosphoinositide 3-kinase/AKT signaling pathway was identified as a pivotal regulator of hypothalamic lipid metabolism regulator. Overall, this study highlights the effectiveness of a multidisciplinary approach that combines network pharmacology, lipidomics, molecular docking, and experimental validation in the elucidation of the therapeutic mechanisms of KZG in CPP treatment.

Data-dependent and -independent acquisition lipidomics analysis reveals the tissue-dependent effect of metformin on lipid metabolism

INTRODUCTION

Despite the well-recognized health benefits, the mechanisms and site of action of metformin remains elusive. Metformin-induced global lipidomic changes in plasma of animal models and human subjects have been reported. However, there is a lack of systemic evaluation of metformin-induced lipidomic changes in different tissues. Metformin uptake requires active transporters such as organic cation transporters (OCTs), and hence, it is anticipated that metformin actions are tissue-dependent. In this study, we aim to characterize metformin effects in non-diabetic male mice with a special focus on lipidomics analysis. The findings from this study will help us to better understand the cell-autonomous (direct actions in target cells) or non-cell-autonomous (indirect actions in target cells) mechanisms of metformin and provide insights into the development of more potent yet safe drugs targeting a particular organ instead of systemic metabolism for metabolic regulations without major side effects.

OBJECTIVES

To characterize metformin-induced lipidomic alterations in different tissues of non-diabetic male mice and further identify lipids affected by metformin through cell-autonomous or systemic mechanisms based on the correlation between lipid alterations in tissues and the corresponding in-tissue metformin concentrations.

METHODS

A dual extraction method involving 80% methanol followed by MTBE (methyl tert-butyl ether) extraction enables the analysis of free fatty acids, polar metabolites, and lipids. Extracts from tissues and plasma of male mice treated with or without metformin in drinking water for 12 days were analyzed using HILIC chromatography coupled to Q Exactive Plus mass spectrometer or reversed-phase liquid chromatography coupled to MS/MS scan workflow (hybrid mode) on LC-Orbitrap Exploris 480 mass spectrometer using biologically relevant lipids-containing inclusion list for data-independent acquisition (DIA), named as BRI-DIA workflow followed by data-dependent acquisition (DDA), to maximum the coverage of lipids and minimize the negative effect of stochasticity of precursor selection on experimental consistency and reproducibility.

RESULTS

Lipidomics analysis of 6 mouse tissues and plasma allowed a systemic evaluation of lipidomic changes induced by metformin in different tissues. We observed that (1) the degrees of lipidomic changes induced by metformin treatment overly correlated with tissue concentrations of metformin; (2) the impact on lysophosphatidylcholine (lysoPC) and cardiolipins was positively correlated with tissue concentrations of metformin, while neutral lipids such as triglycerides did not correlate with the corresponding tissue metformin concentrations; (3) increase of intestinal tricarboxylic acid (TCA) cycle intermediates after metformin treatment.

CONCLUSION

The data collected in this study from non-diabetic mice with 12-day metformin treatment suggest that the overall metabolic effect of metformin is positively correlated with tissue concentrations and the effect on individual lipid subclass is via both cell-autonomous mechanisms (cardiolipins and lysoPC) and non-cell-autonomous mechanisms (triglycerides).

Changes in fecal lipidome after treatment with ivacaftor without changes in microbiome or bile acids

BACKGROUND

Alterations in gastrointestinal health are prominent manifestations of cystic fibrosis (CF) and can independently impact pulmonary function. Ivacaftor has been associated with robust improvements in pulmonary function and weight gain, but less is known about the impact of ivacaftor on the fecal microbiome, lipidome, and bile acids.

METHODS

Stool samples from 18 patients with CF and gating mutations (ages 6-61 years, 13 pancreatic insufficient) were analyzed for fecal microbiome and lipidome composition as well as bile acid concentrations at baseline and after 3 months of treatment with ivacaftor. Microbiome composition was also assessed in a healthy reference cohort.

RESULTS

Alpha and beta diversity of the microbiome were different between CF and reference cohort at baseline, but no treatment effect was seen in the CF cohort between baseline and 3 months. Seven lipids increased with treatment. No differences were seen in bile acid concentrations after treatment in CF. At baseline, 403 lipids and unconjugated bile acids were different between pancreatic insufficient (PI-CF) and sufficient (PS-CF) groups and 107 lipids were different between PI-CF and PS-CF after 3 months of treatment.

CONCLUSIONS

The composition and diversity of the fecal microbiome were different in CF as compared to a healthy reference, and did not change after 3 months of ivacaftor. We detected modest differences in the fecal lipidome with treatment. Differences in lipid and bile acid profiles between PS-CF and PI-CF were attenuated after 3 months of treatment.

MetaboAnalystR 4.0: a unified LC-MS workflow for global metabolomics

The wide applications of liquid chromatography - mass spectrometry (LC-MS) in untargeted metabolomics demand an easy-to-use, comprehensive computational workflow to support efficient and reproducible data analysis. However, current tools were primarily developed to perform specific tasks in LC-MS based metabolomics data analysis. Here we introduce MetaboAnalystR 4.0 as a streamlined pipeline covering raw spectra processing, compound identification, statistical analysis, and functional interpretation. The key features of MetaboAnalystR 4.0 includes an auto-optimized feature detection and quantification algorithm for LC-MS1 spectra processing, efficient MS2 spectra deconvolution and compound identification for data-dependent or data-independent acquisition, and more accurate functional interpretation through integrated spectral annotation. Comprehensive validation studies using LC-MS1 and MS2 spectra obtained from standards mixtures, dilution series and clinical metabolomics samples have shown its excellent performance across a wide range of common tasks such as peak picking, spectral deconvolution, and compound identification with good computing efficiency. Together with its existing statistical analysis utilities, MetaboAnalystR 4.0 represents a significant step toward a unified, end-to-end workflow for LC-MS based global metabolomics in the open-source R environment.

Diazobutanone-assisted isobaric labelling of phospholipids and sulfated glycolipids enables multiplexed quantitative lipidomics using tandem mass spectrometry

Mass spectrometry-based quantitative lipidomics is an emerging field aiming to uncover the intricate relationships between lipidomes and disease development. However, quantifying lipidomes comprehensively in a high-throughput manner remains challenging owing to the diverse lipid structures. Here we propose a diazobutanone-assisted isobaric labelling strategy as a rapid and robust platform for multiplexed quantitative lipidomics across a broad range of lipid classes, including various phospholipids and glycolipids. The diazobutanone reagent is designed to conjugate with phosphodiester or sulfate groups, while accommodating various functional groups on different lipid classes, enabling subsequent isobaric labelling for high-throughput multiplexed quantitation. Our method demonstrates excellent performance in terms of labelling efficiency, detection sensitivity, quantitative accuracy and broad applicability to various biological samples. Finally, we performed a six-plex quantification analysis of lipid extracts from lean and obese mouse livers. In total, we identified and quantified 246 phospholipids in a high-throughput manner, revealing lipidomic changes that may be associated with obesity in mice.