A lipidome atlas in MS-DIAL 4

Tricky Isomers—The Evolution of Analytical Strategies to Characterize Plasmalogens and Plasmanyl Ether Lipids

Typically, glycerophospholipids are represented with two esterified fatty acids. However, by up to 20%, a significant proportion of this lipid class carries an ether-linked fatty alcohol side chain at the sn-1 position, generally referred to as ether lipids, which shape their specific physicochemical properties. Among those, plasmalogens represent a distinct subgroup characterized by an sn-1 vinyl-ether double bond. The total loss of ether lipids in severe peroxisomal defects such as rhizomelic chondrodysplasia punctata indicates their crucial contribution to diverse cellular functions. An aberrant ether lipid metabolism has also been reported in multifactorial conditions including Alzheimer’s disease. Understanding the underlying pathological implications is hampered by the still unclear exact functional spectrum of ether lipids, especially in regard to the differentiation between the individual contributions of plasmalogens (plasmenyl lipids) and their non-vinyl-ether lipid (plasmanyl) counterparts. A primary reason for this is that exact identification and quantification of plasmalogens and other ether lipids poses a challenging and usually labor-intensive task. Diverse analytical methods for the detection of plasmalogens have been developed. Liquid chromatography–tandem mass spectrometry is increasingly used to resolve complex lipid mixtures, and with optimized parameters and specialized fragmentation strategies, discrimination between ethers and plasmalogens is feasible. In this review, we recapitulate historic and current methodologies for the recognition and quantification of these important lipids and will discuss developments in this field that can contribute to the characterization of plasmalogens in high structural detail.

VAP-A and its binding partner CERT drive biogenesis of RNA-containing extracellular vesicles at ER membrane contact sites

RNA transfer via extracellular vesicles (EVs) influences cell phenotypes; however, lack of information regarding biogenesis of RNA-containing EVs has limited progress in the field. Here, we identify endoplasmic reticulum membrane contact sites (ER MCSs) as platforms for the generation of RNA-containing EVs. We identify a subpopulation of small EVs that is highly enriched in RNA and regulated by the ER MCS linker protein VAP-A. Functionally, VAP-A-regulated EVs are critical for miR-100 transfer between cells and in vivo tumor formation. Lipid analysis of VAP-A-knockdown EVs revealed reductions in the EV biogenesis lipid ceramide. Knockdown of the VAP-A-binding ceramide transfer protein CERT led to similar defects in EV RNA content. Imaging experiments revealed that VAP-A promotes luminal filling of multivesicular bodies (MVBs), CERT localizes to MVBs, and the ceramide-generating enzyme neutral sphingomyelinase 2 colocalizes with VAP-A-positive ER. We propose that ceramide transfer via VAP-A-CERT linkages drives the biogenesis of a select RNA-containing EV population.

Multi-Site Observational Study to Assess Biomarkers for Susceptibility or Resilience to Chronic Pain: The Acute to Chronic Pain Signatures (A2CPS) Study Protocol

Chronic pain has become a global health problem contributing to years lived with disability and reduced quality of life. Advances in the clinical management of chronic pain have been limited due to incomplete understanding of the multiple risk factors and molecular mechanisms that contribute to the development of chronic pain. The Acute to Chronic Pain Signatures (A2CPS) Program aims to characterize the predictive nature of biomarkers (brain imaging, high-throughput molecular screening techniques, or "omics," quantitative sensory testing, patient-reported outcome assessments and functional assessments) to identify individuals who will develop chronic pain following surgical intervention. The A2CPS is a multisite observational study investigating biomarkers and collective biosignatures (a combination of several individual biomarkers) that predict susceptibility or resilience to the development of chronic pain following knee arthroplasty and thoracic surgery. This manuscript provides an overview of data collection methods and procedures designed to standardize data collection across multiple clinical sites and institutions. Pain-related biomarkers are evaluated before surgery and up to 3 months after surgery for use as predictors of patient reported outcomes 6 months after surgery. The dataset from this prospective observational study will be available for researchers internal and external to the A2CPS Consortium to advance understanding of the transition from acute to chronic postsurgical pain.

Guide to Metabolomics Analysis: A Bioinformatics Workflow

Metabolomics is an emerging field that quantifies numerous metabolites systematically. The key purpose of metabolomics is to identify the metabolites corresponding to each biological phenotype, and then provide an analysis of the mechanisms involved. Although metabolomics is important to understand the involved biological phenomena, the approach's ability to obtain an exhaustive description of the processes is limited. Thus, an analysis-integrated metabolomics, transcriptomics, proteomics, and other omics approach is recommended. Such integration of different omics data requires specialized statistical and bioinformatics software. This review focuses on the steps involved in metabolomics research and summarizes several main tools for metabolomics analyses. We also outline the most abnormal metabolic pathways in several cancers and diseases, and discuss the importance of multi-omics integration algorithms. Overall, our goal is to summarize the current metabolomics analysis workflow and its main analysis software to provide useful insights for researchers to establish a preferable pipeline of metabolomics or multi-omics analysis.

Pre-Operative Assessment of Micronutrients, Amino Acids, Phospholipids and Oxidative Stress in Bariatric Surgery Candidates

Obesity has been linked to lower concentrations of fat-soluble micronutrients and higher concentrations of oxidative stress markers as well as an altered metabolism of branched chain amino acids and phospholipids. In the context of morbid obesity, the aim of this study was to investigate whether and to which extent plasma status of micronutrients, amino acids, phospholipids and oxidative stress differs between morbidly obese (n = 23) and non-obese patients (n = 13). In addition to plasma, malondialdehyde, retinol, cholesterol and triglycerides were assessed in visceral and subcutaneous adipose tissue in both groups. Plasma γ-tocopherol was significantly lower (p < 0.011) in the obese group while other fat-soluble micronutrients showed no statistically significant differences between both groups. Branched-chain amino acids (all p < 0.008) and lysine (p < 0.006) were significantly higher in morbidly obese patients compared to the control group. Malondialdehyde concentrations in both visceral (p < 0.016) and subcutaneous (p < 0.002) adipose tissue were significantly higher in the morbidly obese group while plasma markers of oxidative stress showed no significant differences between both groups. Significantly lower plasma concentrations of phosphatidylcholine, phosphatidylethanolamine, lyso-phosphatidylethanolamine (all p < 0.05) and their corresponding ether-linked analogs were observed, which were all reduced in obese participants compared to the control group. Pre-operative assessment of micronutrients in patients undergoing bariatric surgery is recommended for early identification of patients who might be at higher risk to develop a severe micronutrient deficiency post-surgery. Assessment of plasma BCAAs and phospholipids in obese patients might help to differentiate between metabolic healthy patients and those with metabolic disorders.

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

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

Plasma Quantitative Lipid Profiles: Identification of CarnitineC18:1-OH, CarnitineC18:2-OH and FFA (20:1) as Novel Biomarkers for Pre-warning and Prognosis in Acute Myocardial Infarction

This study was aimed to determine the association between potential plasma lipid biomarkers and early screening and prognosis of Acute myocardial infarction (AMI). In the present study, a total of 795 differentially expressed lipid metabolites were detected based on ultra-performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS). Out of these metabolites, 25 lipid metabolites were identified which showed specifical expression in the AMI group compared with the healthy control (HC) group and unstable angina (UA) group. Then, we applied the least absolute shrinkage and selection operator (LASSO) and support vector machine-recursive feature elimination (SVM-RFE) methods to obtain three lipid molecules, including CarnitineC18:1-OH, CarnitineC18:2-OH and FFA (20:1). The three lipid metabolites and the diagnostic model exhibited well predictive ability in discriminating between AMI patients and UA patients in both the discovery and validation sets with an area under the curve (AUC) of 0.9. Univariate and multivariate logistic regression analyses indicated that the three lipid metabolites may serve as potential biomarkers for diagnosing AMI. A subsequent 1-year follow-up analysis indicated that the three lipid biomarkers also had prominent performance in predicting re-admission of patients with AMI due to cardiovascular events. In summary, we used quantitative lipid technology to delineate the characteristics of lipid metabolism in patients with AMI, and identified potential early diagnosis biomarkers of AMI via machine learning approach.

Non-targeted analysis for organic components of microplastic leachates

Organic components of microplastic leachates were investigated in an integrated non-targeted analysis study that included statistical analysis on leachates generated under different leaching scenarios. Leaching experiments were undertaken with simulated gastric fluid (SGF), river water, and seawater with common polymer types, including polyethylene, polypropylene, polyvinyl chloride, polyethylene terephthalate, and polyester fabrics comprising both raw and recycled materials. Totals of 111.0 ± 26.7, 98.5 ± 20.3, and 53.5 ± 4.7 different features were tentatively identified as compounds in SGF, freshwater, and seawater leachates, respectively, of which 5 compounds were confirmed by reference standards. The leaching capacities of the media were compared, and the clusters of structurally related features leached in the same medium were studied. For leachates generated from raw and recycled plastics, volcano plots and Pearson's Chi-squared tests were used to identify characteristic features. More characteristic features (3-20) had an average intensity across all recycled plastics that were significantly higher (p < 0.05) than that (1-3) of raw plastics under different conditions. The results indicate that gastric solution is more likely to leach components from microplastics, and there exists the difference of leachate's organic composition between raw and recycled materials, providing new insights into understanding microplastic environmental effects.

Data mining of natural hazard biomarkers and metabolites with integrated metabolomic tools

Data mining was one of the most important challenges in natural product analysis and biomarker discovery. In this work, we proposed an integrated data analysis protocol for natural products annotation and identification in data-dependent acquisition. Firstly, natural products and structure-related compounds could be identified by comparing mass spectrum behavior with commercial standard. Secondly, diagnostic fragmentation filtering (DFF) function in MZmine (http://mzmine.github.io/) was investigated for screening specific conjugation compounds with the same neutral loss. Thirdly, we present feature-based molecular networking (FBMN) in GNPS (https://gnps.ucsd.edu/) as a chromatographic feature detection and alignment tool. In addition, FBMN could enable natural products analysis based on molecular networks. This proposed integrated protocol should facilitate metabolomic data mining and biomarker discovery.

Bisphenol S exposure induces intestinal inflammation: An integrated metabolomic and transcriptomic study

As a typical substitute for bisphenol A (BPA), bisphenol S (BPS) is raising concerns due to the potential adverse effects on human health. Limit evidence is available to understand the toxicity of BPS to the digestive system, especially for intestine. In this study, we aimed to investigate the potential effects and underlying mechanisms of BPS exposure on human colon mucosal epithelial cells (NCM460). Our results showed that BPS exposure significantly increased the production of pro-inflammatory cytokines, including tumor necrosis factor-α (TNF-α), interferon-γ (IFN-γ) and interleukin-17A (IL-17A). The tight junctions of the cells has been destroyed by BPS exposure, which was characterized by a down-regulation of the tight junction proteins (Claudin1 and zonula occluden 1 (ZO1)). A multi-omics study explored the underlying mechanisms based on the metabolomic and transcriptomic responses. A variety of neurotransmitters increased significantly after exposure to BPS. The top enriched pathway was "glutamatergic synapse", which was activated by BPS exposure, resulting in the up-regulation of l-glutamine. Links were observed among the altered metabolites, genes and cytokines. Our results indicate that exposure to BPS may disturb the balance of gut-brain axis, leading to the production of inflammatory cytokines and the destruction of tight junction in NCM460 cells. It provides new clue for the development of intestinal inflammation in terms of the environmental pollutants.

New Cytotoxic Secondary Metabolites from Two Deep-Sea-Derived Fungi and the Co-Culture Impact on the Secondary Metabolic Patterns

In this study, chemical profiles for two co-existing deep-sea-derived Penicillium fungal strains were thoroughly investigated. Two new compounds and 11 known compounds were identified from Penicillium sp. LXY140-R, while one new compound and 12 known compounds were isolated from Penicillium sp. LXY140-3. Their structures were elucidated by extensive 1D and 2D NMR experiments, which were supported by HR-ESI-MS data. The antiproliferative activities of all isolates against HCT-116, A549 and Bel-7402 cell lines were also evaluated. Compounds 2, 5, 6, 10 and 13 showed potent antiproliferative activity. To reveal the metabolic relationship of the two strains, we conducted co-culture experiments to discover cross-talk molecules by a device that allows only small molecule to communicate. Extensive HPLC/MS² experiments were applied to identify the disturbance of the chemical profiles within the synthetic Penicillium-Penicillium community. The fungal strain LXY140-R was found to accumulate mono or multiple-acetylation derivatives of deoxynivalenol (DON) sesquiterpenes as responsible molecules by the disturbance of the metabolites produced by the LXY140-3 strain.

Multi-Omics Approach Reveals Dysregulation of Protein Phosphorylation Correlated with Lipid Metabolism in Mouse Non-Alcoholic Fatty Liver

Obesity caused by overnutrition is a major risk factor for non-alcoholic fatty liver disease (NAFLD). Several lipid intermediates such as fatty acids, glycerophospholipids and sphingolipids are implicated in NAFLD, but detailed characterization of lipids and their functional links to proteome and phosphoproteome remain to be elucidated. To characterize this complex molecular relationship, we used a multi-omics approach by conducting comparative proteomic, phoshoproteomic and lipidomic analyses of high fat (HFD) and low fat (LFD) diet fed mice livers. We quantified 2447 proteins and 1339 phosphoproteins containing 1650 class I phosphosites, of which 669 phosphosites were significantly different between HFD and LFD mice livers. We detected alterations of proteins associated with cellular metabolic processes such as small molecule catabolic process, monocarboxylic acid, long- and medium-chain fatty acid, and ketone body metabolic processes, and peroxisome organization. We observed a significant downregulation of protein phosphorylation in HFD fed mice liver in general. Untargeted lipidomics identified upregulation of triacylglycerols, glycerolipids and ether glycerophosphocholines and downregulation of glycerophospholipids, such as lysoglycerophospholipids, as well as ceramides and acylcarnitines. Analysis of differentially regulated phosphosites revealed phosphorylation dependent deregulation of insulin signaling as well as lipogenic and lipolytic pathways during HFD induced obesity. Thus, this study reveals a molecular connection between decreased protein phosphorylation and lipolysis, as well as lipid-mediated signaling in diet-induced obesity.

Adverse Effects of Arsenic Uptake in Rice Metabolome and Lipidome Revealed by Untargeted Liquid Chromatography Coupled to Mass Spectrometry (LC-MS) and Regions of Interest Multivariate Curve Resolution

Rice crops are especially vulnerable to arsenic exposure compared to other cereal crops because flooding growing conditions facilitates its uptake. Besides, there are still many unknown questions about arsenic’s mode of action in rice. Here, we apply two untargeted approaches using liquid chromatography coupled to mass spectrometry (LC-MS) to unravel the effects on rice lipidome and metabolome in the early stages of growth. The exposure is evaluated through two different treatments, watering with arsenic-contaminated water and soil containing arsenic. The combination of regions of interest (ROI) and multivariate curve resolution (MCR) strategies in the ROIMCR data analyses workflow is proposed and complemented with other multivariate analyses such as partial least square discriminant analysis (PLS-DA) for the identification of potential markers of arsenic exposure and toxicity effects. The results of this study showed that rice metabolome (and lipidome) in root tissues seemed to be more affected by the watering and soil treatment. In contrast, aerial tissues alterations were accentuated by the arsenic dose, rather than with the watering and soil treatment itself. Up to a hundred lipids and 40 metabolites were significantly altered due to arsenic exposure. Major metabolic alterations were found in glycerophospholipids, glycerolipids, and amino acid-related pathways.

Metabolic Signature of Ethanol-Induced Hepatotoxicity in HepaRG Cells by Liquid Chromatography-Mass Spectrometry-Based Untargeted Metabolomics

Alcoholic liver disease is highly prevalent but poorly identified and characterized, leading to knowledge gaps, which impairs early diagnosis. Excessive alcohol consumption is known to alter lipid metabolism, followed by progressive intracellular lipid accumulation, resulting in alcoholic fatty liver disease. In this study, HepaRG cells were exposed to ethanol at IC₁₀ and 1/10 IC₁₀ for 24 and 48 h. Metabolic alterations were investigated intra- and extracellularly with liquid chromatography-high-resolution mass spectrometry. Ion mobility was added as an extra separation dimension for untargeted lipidomics to improve annotation confidence. Distinctive patterns between exposed and control cells were consistently observed, with intracellular upregulation of di- and triglycerides, downregulation of phosphatidylcholines and phosphatidylethanolamines, sphingomyelins, and S-adenosylmethionine, among others. Several intracellular metabolic patterns could be related to changes in the extracellular environment, such as increased intracellular hydrolysis of sphingomyelins, leading to increased phosphorylcholine secretion. Carnitines showed alterations depending on the size of their carbon chain, which highlights the interplay between β-oxidation in mitochondria and peroxisomes. Potential new biomarkers of ethanol-induced hepatotoxicity have been observed, such as ceramides with a sphingadienine backbone, octanoylcarnitine, creatine, acetylcholine, and ethoxylated phosphorylcholine. The combination of the metabolic fingerprint and footprint enabled a comprehensive investigation of the pathophysiology behind ethanol-induced hepatotoxicity.

LipidOne: user-friendly lipidomic data analysis tool for a deeper interpretation in a systems biology scenario

SUMMARY

LC/MS-based analysis techniques combined with specialized lipid tool allow for the qualitative and quantitative determination of thousands of lipid molecules. Some recent bioinformatics tools have been developed to study changes in the lipid profile in case-control experiments and correlate these changes to different enzyme activity or gene expression. However, the existing tools have the limitation to treat only the assembled lipid molecules. In reality, each individual molecule can be considered as an assembly of smaller parts, often called building blocks. These are the result of a myriad of biochemical synthesis and transformation processes that, from a systems biology perspective, should not be ignored. Here, we present LipidOne, a new lipidomic tool which highlights all qualitative and quantitative changes in lipid building blocks both among all detected lipid classes and among experimental groups. Thanks to LipidOne, even differences in lipid building blocks can now be linked to the activity of specific classes of enzymes, transcripts and genes.

AVAILABILITY AND IMPLEMENTATION

LipidOne software is freely available at www.dcbb.unipg.it/LipidOne and https://github.com/matteogiulietti/LipidOne.

CONTACT

roberto.pellegrino@unipg.it.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

A Preprocessing Tool for Enhanced Ion Mobility-Mass Spectrometry-Based Omics Workflows

The ability to improve the data quality of ion mobility-mass spectrometry (IM-MS) measurements is of great importance for enabling modular and efficient computational workflows and gaining better qualitative and quantitative insights from complex biological and environmental samples. We developed the PNNL PreProcessor, a standalone and user-friendly software housing various algorithmic implementations to generate new MS-files with enhanced signal quality and in the same instrument format. Different experimental approaches are supported for IM-MS based on Drift-Tube (DT) and Structures for Lossless Ion Manipulations (SLIM), including liquid chromatography (LC) and infusion analyses. The algorithms extend the dynamic range of the detection system, while reducing file sizes for faster and memory-efficient downstream processing. Specifically, multidimensional smoothing improves peak shapes of poorly defined low-abundance signals, and saturation repair reconstructs the intensity profile of high-abundance peaks from various analyte types. Other functionalities are data compression and interpolation, IM demultiplexing, noise filtering by low intensity threshold and spike removal, and exporting of acquisition metadata. Several advantages of the tool are illustrated, including an increase of 19.4% in lipid annotations and a two-times faster processing of LC-DT IM-MS data-independent acquisition spectra from a complex lipid extract of a standard human plasma sample. The software is freely available at https://omics.pnl.gov/software/pnnl-preprocessor.

A protocol for investigating lipidomic dysregulation and discovering lipid biomarkers from human serums

Lipids play important roles in various human diseases. Disease-associated lipid dysregulation and biomarkers could provide molecular clues for diagnosis, pathogenesis, and therapy. This protocol provides a step-by-step workflow to investigate lipid dysregulation and discover biomarkers in human serum samples by liquid chromatography-mass spectrometry (LC-MS)-based lipidomics and machine learning analysis. The workflow includes project design, serum collection, sample preparation, data acquisition, data processing, and machine learning analysis.

For complete details on the use and execution of this profile, please refer to Hao et al. (2021).

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LC-MS based lipidomics for large-scale profiling of human serum samples

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Identification and quantitation of serum lipids based on MS-DIAL

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An effective feature selection approach for discovery of biomarkers

Structures and functions of the gut microbial lipidome

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

LPS-induced lipid alterations in microglia revealed by MALDI mass spectrometry-based cell fingerprinting in neuroinflammation studies

Pathological microglia activation can promote neuroinflammation in many neurodegenerative diseases, and it has therefore emerged as a potential therapeutic target. Increasing evidence suggests alterations in lipid metabolism as modulators and indicators in microglia activation and its effector functions. Yet, how lipid dynamics in activated microglia is affected by inflammatory stimuli demands additional investigation to allow development of more effective therapies. Here, we report an extensive matrix-assisted laser desorption/ionization (MALDI) mass spectrometry (MS) whole cell fingerprinting workflow to investigate inflammation-associated lipid patterns in SIM-A9 microglial cells. By combining a platform of three synergistic MALDI MS technologies we could detect substantial differences in lipid profiles of lipopolysaccharide (LPS)- stimulated and unstimulated microglia-like cells leading to the identification of 21 potential inflammation-associated lipid markers. LPS-induced lipids in SIM-A9 microglial cells include phosphatidylcholines, lysophosphatidylcholines (LysoPC), sphingolipids, diacylglycerols and triacylglycerols. Moreover, MALDI MS-based cell lipid fingerprinting of LPS-stimulated SIM-A9 microglial cells pre-treated with the non-selective histone deacetylase inhibitor suberoylanilide hydroxamic acid revealed specific modulation of LPS-induced-glycerolipids and LysoPC(18:0) with a significant reduction of microglial inflammation response. Our study introduces MALDI MS as a complementary technology for fast and label-free investigation of stimulus-dependent changes in lipid patterns and their modulation by pharmaceutical agents.

Equivalent Carbon Number and Interclass Retention Time Conversion Enhance Lipid Identification in Untargeted Clinical Lipidomics

Chromatography is often used as a method for reducing sample complexity prior to analysis by mass spectrometry, and the use of retention time (RT) is becoming increasingly popular to add valuable supporting information in lipid identification. The RT of lipids with the same headgroup in reversed-phase separation can be predicted using the equivalent carbon number (ECN) model. This model describes the effects of acyl chain length and degree of saturation on lipid RT. For the first time, we have found a robust correlation in the chromatographic separation of lipids with different headgroups that share the same fatty acid motive. This relationship can be exploited to perform interclass RT conversion (IC-RTC) by building a model from RT measurements from lipid standards that allows the prediction of RT of one lipid subclass based on another. Here, we utilize ECN modeling and IC-RTC to build a glycerophospholipid RT library with 517 entries based on 136 tandem mass spectrometry-characterized lipid RTs from NIST SRM-1950 plasma and lipid standards. The library was tested on a patient cohort undergoing coronary artery bypass grafting surgery (n = 37). A total of 156 unique circulating glycerophospholipids were identified, of which 52 (1 LPG, 24 PE, 5 PG, 18 PI, and 9 PS) were detected with IC-RTC, thereby demonstrating the utility of this technique for the identification of lipid species not found in commercial standards.

Comparative metabolomics with Metaboseek reveals functions of a conserved fat metabolism pathway in C. elegans

Untargeted metabolomics via high-resolution mass spectrometry can reveal more than 100,000 molecular features in a single sample, many of which may represent unidentified metabolites, posing significant challenges to data analysis. We here introduce Metaboseek, an open-source analysis platform designed for untargeted comparative metabolomics and demonstrate its utility by uncovering biosynthetic functions of a conserved fat metabolism pathway, α-oxidation, using C. elegans as a model. Metaboseek integrates modules for molecular feature detection, statistics, molecular formula prediction, and fragmentation analysis, which uncovers more than 200 previously uncharacterized α-oxidation-dependent metabolites in an untargeted comparison of wildtype and α-oxidation-defective hacl-1 mutants. The identified metabolites support the predicted enzymatic function of HACL-1 and reveal that α-oxidation participates in metabolism of endogenous β-methyl-branched fatty acids and food-derived cyclopropane lipids. Our results showcase compound discovery and feature annotation at scale via untargeted comparative metabolomics applied to a conserved primary metabolic pathway and suggest a model for the metabolism of cyclopropane lipids.

Untargeted mass spectrometry-based metabolomics can reveal new biochemistry, but data analysis is challenging. Here, the authors develop Metaboseek, an open-source software that facilitates metabolite discovery, and apply it to characterize fatty acid alpha-oxidation in C. elegans.

Recent Advances in Understanding of Alzheimer's Disease Progression through Mass Spectrometry-Based Metabolomics

Alzheimer's disease (AD) is the leading cause of dementia in the aging population, but despite extensive research, there is no consensus on the biological cause of AD. While AD research is dominated by protein/peptide-centric research based on the amyloid hypothesis, a theory that designates dysfunction in beta-amyloid production, accumulation, or disposal as the primary cause of AD, many studies focus on metabolomics as a means of understanding the biological processes behind AD progression. In this review, we discuss mass spectrometry (MS)-based AD metabolomics studies, including sample type and preparation, mass spectrometry specifications, and data analysis, as well as biological insights gleaned from these studies, with the hope of informing future AD metabolomic studies.

Comparative Evaluation of Plasma Metabolomic Data from Multiple Laboratories

In mass spectrometry-based metabolomics, the differences in the analytical results from different laboratories/machines are an issue to be considered because various types of machines are used in each laboratory. Moreover, the analytical methods are unique to each laboratory. It is important to understand the reality of inter-laboratory differences in metabolomics. Therefore, we have evaluated whether the differences in analytical methods, with the exception sample pretreatment and including metabolite extraction, are involved in the inter-laboratory differences or not. In this study, nine facilities are evaluated for inter-laboratory comparisons of metabolomic analysis. Identical dried samples prepared from human and mouse plasma are distributed to each laboratory, and the metabolites are measured without the pretreatment that is unique to each laboratory. In these measurements, hydrophilic and hydrophobic metabolites are analyzed using 11 and 7 analytical methods, respectively. The metabolomic data acquired at each laboratory are integrated, and the differences in the metabolomic data from the laboratories are evaluated. No substantial difference in the relative quantitative data (human/mouse) for a little less than 50% of the detected metabolites is observed, and the hydrophilic metabolites have fewer differences between the laboratories compared with hydrophobic metabolites. From evaluating selected quantitatively guaranteed metabolites, the proportion of metabolites without the inter-laboratory differences is observed to be slightly high. It is difficult to resolve the inter-laboratory differences in metabolomics because all laboratories cannot prepare the same analytical environments. However, the results from this study indicate that the inter-laboratory differences in metabolomic data are due to measurement and data analysis rather than sample preparation, which will facilitate the understanding of the problems in metabolomics studies involving multiple laboratories.

Gowda SG, Ikeda K, Arita M, Murakami M. Group IIA secreted phospholipase A2 controls skin carcinogenesis and psoriasis by shaping the gut microbiota

Besides promoting inflammation by mobilizing lipid mediators, group IIA secreted phospholipase A₂ (sPLA₂-IIA) prevents bacterial infection by degrading bacterial membranes. Here, we show that, despite the restricted intestinal expression of sPLA₂-IIA in BALB/c mice, its genetic deletion leads to amelioration of cancer and exacerbation of psoriasis in distal skin. Intestinal expression of sPLA₂-IIA is reduced after treatment with antibiotics or under germ-free conditions, suggesting its upregulation by gut microbiota. Metagenome, transcriptome, and metabolome analyses have revealed that sPLA₂-IIA deficiency alters the gut microbiota, accompanied by notable changes in the intestinal expression of genes related to immunity and metabolism, as well as in the levels of various blood metabolites and fecal bacterial lipids, suggesting that sPLA₂-IIA contributes to shaping of the gut microbiota. The skin phenotypes in Pla2g2a^–/– mice are lost (a) when they are cohoused with littermate WT mice, resulting in the mixing of the microbiota between the genotypes, or (b) when they are housed in a more stringent pathogen-free facility, where Pla2g2a expression in WT mice is low and the gut microbial compositions in both genotypes are nearly identical. Thus, our results highlight a potentially new aspect of sPLA₂-IIA as a modulator of gut microbiota, perturbation of which affects distal skin responses.

Antioxidant Role and Cardiolipin Remodeling by Redox-Activated Mitochondrial Ca2+-Independent Phospholipase A2γ in the Brain

Mitochondrial Ca²⁺-independent phospholipase A₂γ (iPLA₂γ/PNPLA8) was previously shown to be directly activated by H₂O₂ and release free fatty acids (FAs) for FA-dependent H⁺ transport mediated by the adenine nucleotide translocase (ANT) or uncoupling protein 2 (UCP2). The resulting mild mitochondrial uncoupling and consequent partial attenuation of mitochondrial superoxide production lead to an antioxidant effect. However, the antioxidant role of iPLA₂γ in the brain is not completely understood. Here, using wild-type and iPLA₂γ-KO mice, we demonstrate the ability of tert-butylhydroperoxide (TBHP) to activate iPLA₂γ in isolated brain mitochondria, with consequent liberation of FAs and lysophospholipids. The liberated FA caused an increase in respiratory rate, which was fully inhibited by carboxyatractyloside (CATR), a specific inhibitor of ANT. Employing detailed lipidomic analysis, we also demonstrate a typical cleavage pattern for TBHP-activated iPLA₂γ, reflecting cleavage of glycerophospholipids from both sn-1 and sn-2 positions releasing saturated FAs, monoenoic FAs, and predominant polyunsaturated FAs. The acute antioxidant role of iPLA₂γ-released FAs is supported by monitoring both intramitochondrial superoxide and extramitochondrial H₂O₂ release. We also show that iPLA₂γ-KO mice were more sensitive to stimulation by pro-inflammatory lipopolysaccharide, as reflected by the concomitant increase in protein carbonyls in the brain and pro-inflammatory IL-6 release in the serum. These data support the antioxidant and anti-inflammatory role of iPLA₂γ in vivo. Our data also reveal a substantial decrease of several high molecular weight cardiolipin (CL) species and accumulation of low molecular weight CL species in brain mitochondria of iPLA₂γ-KO mice. Collectively, our results support a key role of iPLA₂γ in the remodeling of lower molecular weight immature cardiolipins with predominantly saturated acyl chains to high molecular weight mature cardiolipins with highly unsaturated PUFA acyl chains, typical for the brain.

Lifespan prolonging mechanisms and insulin upregulation without fat accumulation in long-lived reproductives of a higher termite

Kings and queens of eusocial termites can live for decades, while queens sustain a nearly maximal fertility. To investigate the molecular mechanisms underlying their long lifespan, we carried out transcriptomics, lipidomics and metabolomics in Macrotermes natalensis on sterile short-lived workers, long-lived kings and five stages spanning twenty years of adult queen maturation. Reproductives share gene expression differences from workers in agreement with a reduction of several aging-related processes, involving upregulation of DNA damage repair and mitochondrial functions. Anti-oxidant gene expression is downregulated, while peroxidability of membranes in queens decreases. Against expectations, we observed an upregulated gene expression in fat bodies of reproductives of several components of the IIS pathway, including an insulin-like peptide, Ilp9. This pattern does not lead to deleterious fat storage in physogastric queens, while simple sugars dominate in their hemolymph and large amounts of resources are allocated towards oogenesis. Our findings support the notion that all processes causing aging need to be addressed simultaneously in order to prevent it.

Metabolite discovery: Biochemistry's scientific driver

Metabolite identification represents a major challenge, and opportunity, for biochemistry. The collective characterization and quantification of metabolites in living organisms, with its many successes, represents a major biochemical knowledgebase and the foundation of metabolism's rebirth in the 21^st century; yet, characterizing newly observed metabolites has been an enduring obstacle. Crystallography and NMR spectroscopy have been of extraordinary importance, although their applicability in resolving metabolism's fine structure has been restricted by their intrinsic requirement of sufficient and sufficiently pure materials. Mass spectrometry has been a key technology, especially when coupled with high-performance separation technologies and emerging informatic and database solutions. Even more so, the collective of artificial intelligence technologies are rapidly evolving to help solve the metabolite characterization conundrum. This perspective describes this challenge, how it was historically addressed, and how metabolomics is evolving to address it today and in the future.

Metabolome-wide association study of serum exogenous chemical residues in a cohort with 5 major chronic diseases

BACKGROUND

Chronic diseases have become main killers affecting the health of human, and environmental pollution is a major health risk factor that cannot be ignored. It has been reported that exogenous chemical residues including pesticides, herbicides, fungicides, veterinary drugs and persistent organic pollutants are associated with chronic diseases. However, the evidence for their relationship is equivocal and the underlying mechanisms are unclear.

OBJECTIVES

We aim to investigate the linkages between serum exogenous chemical residues and 5 main chronic diseases including obesity, hyperuricemia, hypertension, diabetes and dyslipidemia, and further reveal the metabolic perturbations of chronic diseases related to exogenous chemical residue exposure, then gain potential mechanism insight at the metabolic level.

METHODS

LC-MS-based targeted and nontargeted methods were respectively performed to quantify exogenous chemical residues and acquire metabolic profiling of 496 serum samples from chronic disease patients. Non-parametric test, correlation and regression analyses were carried out to investigate the association between exogenous chemical residues and chronic diseases. Metabolome-wide association study combined with the meeting-in-the-middle strategy and mediation analysis was performed to reveal and explain exposure-related metabolic disturbances and their risk to chronic diseases.

RESULTS

In the association analysis of 106 serum exogenous chemical residues and 5 chronic diseases, positive associations of serum perfluoroalkyl substances (PFASs) with hyperuricemia were discovered while other associations were not significant. 240 exposure markers of PFASs and 84 disease markers of hyperuricemia were found, and 47 of them were overlapped and considered as putative effective markers. Serum uric acid, amino acids, cholesterol, carnitines, fatty acids, glycerides, glycerophospholipids, ceramides, and a part of sphingolipids were positively correlated with PFASs and associated with increased risk for hyperuricemia. Creatine, creatinine, glyceryl monooleate, phosphatidylcholine 36:6, phosphatidylethanolamine 40:6, cholesterol and sphingolipid 36:1;2O were significant markers which mediated the associations of the residues with hyperuricemia.

CONCLUSIONS

Our study demonstrated a significantly positive association between PFASs exposure and hyperuricemia. The most significant metabolic abnormality was lipid metabolism which not only was positively associated with PFASs, but also increased the risk of hyperuricemia.

Sample Preparation, Data Acquisition, and Data Analysis for 15N-Labeled and Nonlabeled Monoterpene Indole Alkaloids in Catharanthus roseus

Recent approaches developed in metabolomics using liquid chromatography-tandem mass spectrometry (LC-MS/MS) enabled us to assign a part of specialized metabolites in plants. However, the approaches are not good enough for the rest of the metabolites, which are still unknown. To characterize the unknown metabolites, more appropriate and precise approaches need to be developed. Here, a procedure to analyze ¹⁵N-labeled and nonlabeled LC-MS/MS data for identification of monoterpene indole alkaloids was developed.

Multiomic Metabolic Enrichment Network Analysis Reveals Metabolite-Protein Physical Interaction Subnetworks Altered in Cancer

Metabolism is recognized as an important driver of cancer progression and other complex diseases, but global metabolite profiling remains a challenge. Protein expression profiling is often a poor proxy since existing pathway enrichment models provide an incomplete mapping between the proteome and metabolism. To overcome these gaps, we introduce multiomic metabolic enrichment network analysis (MOMENTA), an integrative multiomic data analysis framework for more accurately deducing metabolic pathway changes from proteomics data alone in a gene set analysis context by leveraging protein interaction networks to extend annotated metabolic models. We apply MOMENTA to proteomic data from diverse cancer cell lines and human tumors to demonstrate its utility at revealing variation in metabolic pathway activity across cancer types, which we verify using independent metabolomics measurements. The novel metabolic networks we uncover in breast cancer and other tumors are linked to clinical outcomes, underscoring the pathophysiological relevance of the findings.

Strategies for structure elucidation of small molecules based on LC–MS/MS data from complex biological samples

LC–MS/MS is a major analytical platform for metabolomics, which has become a recent hotspot in the research fields of life and environmental sciences. By contrast, structure elucidation of small molecules based on LC–MS/MS data remains a major challenge in the chemical and biological interpretation of untargeted metabolomics datasets. In recent years, several strategies for structure elucidation using LC–MS/MS data from complex biological samples have been proposed, these strategies can be simply categorized into two types, one based on structure annotation of mass spectra and for the other on retention time prediction. These strategies have helped many scientists conduct research in metabolite-related fields and are indispensable for the development of future tools. Here, we summarized the characteristics of the current tools and strategies for structure elucidation of small molecules based on LC–MS/MS data, and further discussed the directions and perspectives to improve the power of the tools or strategies for structure elucidation.

Applications of lipidomics in marine organisms: Progresses, challenges and future perspectives

Marine ecosystems comprise a high diversity of life forms, such as algae, invertebrates, and vertebrates. These organisms have adapted their physiology according to the conditions of the environments in which...

The lipid environment modulates cardiolipin and phospholipid constitution in wild type and tafazzin-deficient cells

Deficiency of the transacylase tafazzin due to loss of function variants in the X-chromosomal TAFAZZIN gene causes Barth syndrome (BTHS) with severe neonatal or infantile cardiomyopathy, neutropenia, myopathy, and short stature. The condition is characterized by drastic changes in the composition of cardiolipins, a mitochondria-specific class of phospholipids. Studies examining the impact of tafazzin deficiency on the metabolism of other phospholipids have so far generated inhomogeneous and partly conflicting results. Recent studies showed that the cardiolipin composition in cells and different murine tissues is highly dependent on the surrounding lipid environment. In order to study the relevance of different lipid states and tafazzin function for cardiolipin and phospholipid homeostasis we conducted systematic modulation experiments in a CRISPR/Cas9 knock-out model for BTHS. We found that-irrespective of tafazzin function-the composition of cardiolipins strongly depends on the nutritionally available lipid pool. Tafazzin deficiency causes a consistent shift towards cardiolipin species with more saturated and shorter acyl chains. Interestingly, the typical biochemical BTHS phenotype in phospholipid profiles of HEK 293T TAZ knock-out cells strongly depends on the cellular lipid context. In response to altered nutritional lipid compositions, we measured more pronounced changes on phospholipids that were largely masked under standard cell culturing conditions, therewith giving a possible explanation for the conflicting results reported so far on BTHS lipid phenotypes.

Research Techniques Made Simple: Lipidomic Analysis in Skin Research

Although lipids are crucial molecules for cell structure, metabolism, and signaling in most organs, they have additional specific functions in the skin. Lipids are required for the maintenance and regulation of the epidermal barrier, physical properties of the skin, and defense against microbes. Analysis of the lipidome-the totality of lipids-is of similar complexity to those of proteomics or other omics, with lipid structures ranging from simple, linear, to highly complex structures. In addition, the ordering and chemical modifications of lipids have consequences on their biological function, especially in the skin. Recent advances in analytic capability (usually with mass spectrometry), bioinformatic processing, and integration with other dermatological big data have allowed researchers to increasingly understand the roles of specific lipid species in skin biology. In this paper, we review the techniques used to analyze skin lipidomics and epilipidomics.

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

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

Evolution of the Ergot Alkaloid Biosynthetic Gene Cluster Results in Divergent Mycotoxin Profiles in Claviceps purpurea Sclerotia

Research into ergot alkaloid production in major cereal cash crops is crucial for furthering our understanding of the potential toxicological impacts of Claviceps purpurea upon Canadian agriculture and to ensure consumer safety. An untargeted metabolomics approach profiling extracts of C. purpurea sclerotia from four different grain crops separated the C. purpurea strains into two distinct metabolomic classes based on ergot alkaloid content. Variances in C. purpurea alkaloid profiles were correlated to genetic differences within the lpsA gene of the ergot alkaloid biosynthetic gene cluster from previously published genomes and from newly sequenced, long-read genome assemblies of Canadian strains. Based on gene cluster composition and unique polymorphisms, we hypothesize that the alkaloid content of C. purpurea sclerotia is currently undergoing adaptation. The patterns of lpsA gene diversity described in this small subset of Canadian strains provides a remarkable framework for understanding accelerated evolution of ergot alkaloid production in Claviceps purpurea.

Characterization of epidermal bladder cells in Chenopodium quinoa

Chenopodium quinoa (quinoa) is considered a superfood with its favourable nutrient composition and being gluten free. Quinoa has high tolerance to abiotic stresses, such as salinity, water deficit (drought) and cold. The tolerance mechanisms are yet to be elucidated. Quinoa has epidermal bladder cells (EBCs) that densely cover the shoot surface, particularly the younger parts of the plant. Here, we report on the EBC's primary and secondary metabolomes, as well as the lipidome in control conditions and in response to abiotic stresses. EBCs were isolated from plants after cold, heat, high-light, water deficit and salt treatments. We used untargeted gas chromatography-mass spectrometry (GC-MS) to analyse metabolites and untargeted and targeted liquid chromatography-MS (LC-MS) for lipids and secondary metabolite analyses. We identified 64 primary metabolites, including sugars, organic acids and amino acids, 19 secondary metabolites, including phenolic compounds, betanin and saponins and 240 lipids categorized in five groups including glycerolipids and phospholipids. We found only few changes in the metabolic composition of EBCs in response to abiotic stresses; these were metabolites related with heat, cold and high-light treatments but not salt stress. Na⁺ concentrations were low in EBCs with all treatments and approximately two orders of magnitude lower than K⁺ concentrations.

Drug-Induced Lysosomal Impairment Is Associated with the Release of Extracellular Vesicles Carrying Autophagy Markers

Amiodarone is a cationic amphiphilic drug used as an antiarrhythmic agent. It induces phospholipidosis, i.e., the accumulation of phospholipids within organelles of the endosomal-lysosomal system. Extracellular vesicles (EVs) are membrane-enclosed structures released by any type of cell and retrieved in every fluid of the body. EVs have been initially identified as a system to dispose cell waste, but they are also considered to be an additional manner to transmit intercellular signals. To understand the role of EVs in drug-induced phospholipidosis, we investigated EVs release in amiodarone-treated HEK-293 cells engineered to produce fluorescently labelled EVs. We observed that amiodarone induces the release of a higher number of EVs, mostly of a large/medium size. EVs released upon amiodarone treatment do not display significant morphological changes or altered size distribution, but they show a dose-dependent increase in autophagy associated markers, indicating a higher release of EVs with an autophagosome-like phenotype. Large/medium EVs also show a higher content of phospholipids. Drugs inducing lysosomal impairment such as chloroquine and bafilomycin A1 similarly prompt a higher release of EVs enriched in autophagy markers. This result suggests a mechanism associated with amiodarone-induced lysosomal impairment more than a connection with the accumulation of specific undigested substrates. Moreover, the implementation of the lysosomal function by overexpressing TFEB, a master gene regulator of lysosomal biogenesis, prevents the amiodarone-induced release of EVs, suggesting that this could be a feasible target to attenuate drug-induced abnormalities.

Mass Spectrometry-Based Technology and Workflows for Studying the Chemistry of Fungal Endophyte Derived Bioactive Compounds

Bioactive compounds have gained substantial attention in research and have conferred great advancements in the industrial and pharmacological fields. Highly diverse fungi and their metabolome serve as a big platform to be explored for their diverse bioactive compounds. Omics tools coupled with bioinformatics, statistical, and well-developed algorithm tools have elucidated immense knowledge about fungal endophyte derived bioactive compounds. Further, these compounds are subjected to chromatography-gas chromatography and liquid chromatography (LC), spectroscopy-nuclear magnetic resonance (NMR), and "soft ionization" technique-matrix-assisted laser desorption/ionization-time-of-flight (MALDI-TOF) based analytical techniques for structural characterization. The mass spectrometry (MS)-based approach, being highly sensitive, reproducible, and reliable, produces quick and high-profile identification. Coupling these techniques with MS has resulted in a descriptive account of the identification and quantification of fungal endophyte derived bioactive compounds. This paper emphasizes the workflows of the above-mentioned techniques, their advancement, and future directions to study the unraveled area of chemistry of fungal endophyte-derived bioactive compounds.

Advancements in capturing and mining mass spectrometry data are transforming natural products research

Covering: 2016 up to 2021Mass spectrometry (MS) is an essential technology in natural products research with MS fragmentation (MS/MS) approaches becoming a key tool. Recent advancements in MS yield dense metabolomics datasets which have been, conventionally, used by individual labs for individual projects; however, a shift is brewing. The movement towards open MS data (and other structural characterization data) and accessible data mining tools is emerging in natural products research. Over the past 5 years, this movement has rapidly expanded and evolved with no slowdown in sight; the capabilities of today vastly exceed those of 5 years ago. Herein, we address the analysis of individual datasets, a situation we are calling the '2021 status quo', and the emergent framework to systematically capture sample information (metadata) and perform repository-scale analyses. We evaluate public data deposition, discuss the challenges of working in the repository scale, highlight the challenges of metadata capture and provide illustrative examples of the power of utilizing repository data and the tools that enable it. We conclude that the advancements in MS data collection must be met with advancements in how we utilize data; therefore, we argue that open data and data mining is the next evolution in obtaining the maximum potential in natural products research.

Antibacterial Mechanism of Cinnamaldehyde: Modulation of Biosynthesis of Phosphatidylethanolamine and Phosphatidylglycerol in Staphylococcus aureus and Escherichia coli

Cinnamaldehyde is a natural antimicrobial food preservative. Previous studies have suggested that cinnamaldehyde interacts with the cell membrane, but the molecular targets of cinnamaldehyde action on foodborne pathogens are still unclear. In this study, the structural changes of Staphylococcus aureus and Escherichia coli cells were observed after cinnamaldehyde treatment. Then, quantitative real-time polymerase chain reaction (PCR) and parallel reaction monitoring were used for determining the effects of cinnamaldehyde treatment of these bacteria on the expression of genes and proteins associated with glycerophospholipid biosynthesis. Changes in fatty acids (raw materials for the biosynthesis of glycerophospholipids) and glycerophospholipids in S. aureus and E. coli after cinnamaldehyde treatment were analyzed to confirm the results of gene and protein expression experiments. Cinnamaldehyde regulated the glycerophospholipid biosynthesis pathways of these foodborne pathogens, mainly targeting phosphatidylglycerol and phosphatidylethanolamine, which resulted in the disruption of cell membrane integrity.

Effect of intravenous ghrelin administration, combined with alcohol, on circulating metabolome in heavy drinking individuals with alcohol use disorder

BACKGROUND

Ghrelin may influence several alcohol-related behaviors in animals and humans by modulating central and/or peripheral biological pathways. The aim of this exploratory analysis was to investigate associations between ghrelin administration and the human circulating metabolome during alcohol exposure in nontreatment seeking, heavy drinking individuals with alcohol use disorder (AUD).

METHODS

We used serum samples from a randomized, crossover, double-blind, placebo-controlled human laboratory study with intravenous (IV) ghrelin or placebo infusion in two experiments. During each session, participants received a loading dose (3 µg/kg) followed by continuous infusion (16.9 ng/kg/min) of acyl ghrelin or placebo. The first experiment included an IV alcohol self-administration (IV-ASA) session and the second experiment included an IV alcohol clamp (IV-AC) session, both with the counterbalanced infusion of ghrelin or placebo. Serum metabolite profiles were analyzed from repeated blood samples collected during each session.

RESULTS

In both experiments, ghrelin infusion was associated with an altered serum metabolite profile, including significantly increased levels of cortisol (IV-ASA q-value = 0.0003 and IV-AC q < 0.0001), corticosterone (IV-ASA q = 0.0202 and IV-AC q < 0.0001), and glycochenodeoxycholic acid (IV-ASA q = 0.0375 and IV-AC q = 0.0013). In the IV-ASA experiment, ghrelin infusion increased levels of cortisone (q = 0.0352) and fatty acids 18:1 (q = 0.0406) and 18:3 (q = 0.0320). Moreover, in the IV-AC experiment, ghrelin infusion significantly increased levels of glycocholic acid (q < 0.0001) and phenylalanine (q = 0.0458).

CONCLUSION

IV ghrelin infusion, combined with IV alcohol administration, was associated with increases in the circulating metabolite levels of corticosteroids and glycine-conjugated bile acids, among other changes. Further research is needed to understand the role that metabolomic changes play in the complex interaction between ghrelin and alcohol.

The SGLT2-inhibitor dapagliflozin improves neutropenia and neutrophil dysfunction in a mouse model of the inherited metabolic disorder GSDIb

Glycogen Storage Disease type 1b (GSDIb) is a genetic disorder with long term severe complications. Accumulation of the glucose analog 1,5-anhydroglucitol-6-phosphate (1,5AG6P) in neutrophils inhibits the phosphorylation of glucose in these cells, causing neutropenia and neutrophil dysfunctions. This condition leads to serious infections and inflammatory bowel disease (IBD) in GSDIb patients. We show here that dapagliflozin, an inhibitor of the renal sodium-glucose co-transporter-2 (SGLT2), improves neutrophil function in an inducible mouse model of GSDIb by reducing 1,5AG6P accumulation in myeloid cells.

The Hitchhiker's Guide to Untargeted Lipidomics Analysis: Practical Guidelines

Lipidomics is a newly emerged discipline involving the identification and quantification of thousands of lipids. As a part of the omics field, lipidomics has shown rapid growth both in the number of studies and in the size of lipidome datasets, thus, requiring specific and efficient data analysis approaches. This paper aims to provide guidelines for analyzing and interpreting lipidome data obtained using untargeted methods that rely on liquid chromatography coupled with mass spectrometry (LC-MS) to detect and measure the intensities of lipid compounds. We present a state-of-the-art untargeted LC-MS workflow for lipidomics, from study design to annotation of lipid features, focusing on practical, rather than theoretical, approaches for data analysis, and we outline possible applications of untargeted lipidomics for biological studies. We provide a detailed R notebook designed specifically for untargeted lipidome LC-MS data analysis, which is based on xcms software.

Metabolomics and complementary techniques to investigate the plant phytochemical cosmos

Covering: up to 2021Plants and their associated microbial communities are known to produce millions of metabolites, a majority of which are still not characterized and are speculated to possess novel bioactive properties. In addition to their role in plant physiology, these metabolites are also relevant as existing and next-generation medicine candidates. Elucidation of the plant metabolite diversity is thus valuable for the successful exploitation of natural resources for humankind. Herein, we present a comprehensive review on recent metabolomics approaches to illuminate molecular networks in plants, including chemical isolation and enzymatic production as well as the modern metabolomics approaches such as stable isotope labeling, ultrahigh-resolution mass spectrometry, metabolome imaging (spatial metabolomics), single-cell analysis, cheminformatics, and computational mass spectrometry. Mass spectrometry-based strategies to characterize plant metabolomes through metabolite identification and annotation are described in detail. We also highlight the use of phytochemical genomics to mine genes associated with specialized metabolites' biosynthesis. Understanding the metabolic diversity through biotechnological advances is fundamental to elucidate the functions of the plant-derived specialized metabolome.

Recommendations for good practice in MS-based lipidomics

In the last 2 decades, lipidomics has become one of the fastest expanding scientific disciplines in biomedical research. With an increasing number of new research groups to the field, it is even more important to design guidelines for assuring high standards of data quality. The Lipidomics Standards Initiative is a community-based endeavor for the coordination of development of these best practice guidelines in lipidomics and is embedded within the International Lipidomics Society. It is the intention of this review to highlight the most quality-relevant aspects of the lipidomics workflow, including preanalytics, sample preparation, MS, and lipid species identification and quantitation. Furthermore, this review just does not only highlights examples of best practice but also sheds light on strengths, drawbacks, and pitfalls in the lipidomic analysis workflow. While this review is neither designed to be a step-by-step protocol by itself nor dedicated to a specific application of lipidomics, it should nevertheless provide the interested reader with links and original publications to obtain a comprehensive overview concerning the state-of-the-art practices in the field.

Defining NASH from a Multi-Omics Systems Biology Perspective

Non-alcoholic steatohepatitis (NASH) is a chronic liver disease affecting up to 6.5% of the general population. There is no simple definition of NASH, and the molecular mechanism underlying disease pathogenesis remains elusive. Studies applying single omics technologies have enabled a better understanding of the molecular profiles associated with steatosis and hepatic inflammation—the commonly accepted histologic features for diagnosing NASH, as well as the discovery of novel candidate biomarkers. Multi-omics analysis holds great potential to uncover new insights into disease mechanism through integrating multiple layers of molecular information. Despite the technical and computational challenges associated with such efforts, a few pioneering studies have successfully applied multi-omics technologies to investigate NASH. Here, we review the most recent technological developments in mass spectrometry (MS)-based proteomics, metabolomics, and lipidomics. We summarize multi-omics studies and emerging omics biomarkers in NASH and highlight the biological insights gained through these integrated analyses.