LipiDex: An Integrated Software Package for High-Confidence Lipid Identification

Proteomics, lipidomics, metabolomics and 16S DNA sequencing of dental plaque from patients with diabetes and periodontal disease

Oral microbiome influences human health, specifically prediabetes and type 2 diabetes (Pre-DM/DM) and periodontal diseases (PDs), through complex microbial interactions. To explore these relations, we performed 16S rDNA sequencing, metabolomics, lipidomics, and proteomics analyses on supragingival dental plaque collected from individuals with Pre-DM/DM (n = 39), Pre-DM/DM and PD (n = 37), PD alone (n = 11), or neither (n = 10). We identified on average 2790 operational taxonomic units and 2025 microbial and host proteins per sample and quantified 110 metabolites and 415 lipids. Plaque samples from Pre-DM/DM patients contained higher abundance of Fusobacterium and Tannerella than plaques from metabolically healthy patients. Phosphatidylcholines, plasmenyl phosphatidylcholines, ceramides containing non-OH fatty acids, and host proteins related to actin filament rearrangement were elevated in plaques from PD versus non-PD samples. Cross-omic correlation analysis enabled the detection of a strong association between Lautropia and monomethyl phosphatidylethanolamine (PE-NMe), which is striking because synthesis of PE-NMe is uncommon in oral bacteria. Lipidomics analysis of in vitro cultures of Lautropia mirabilis confirmed the synthesis of PE-NMe by the bacteria. This comprehensive analysis revealed a novel microbial metabolic pathway and significant associations of host-derived proteins with PD.

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Patients with periodontal disease or diabetes have unique microbial dysbiosis.

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Proteomics and 16S data provide complementary information about microbial diversity.

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Cross-omic correlation reveals host signatures associated with periodontal disease.

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Multi-omic data lead to finding about microbially synthesized lipids.

Metabolomic analysis to discriminate drug-induced liver injury (DILI) phenotypes

Drug-induced liver injury (DILI) is an adverse toxic hepatic clinical reaction associated to the administration of a drug that can occur both at early clinical stages of drug development, as well after normal clinical usage of approved drugs. Because of its unpredictability and clinical relevance, it is of medical concern. Three DILI phenotypes (hepatocellular, cholestatic, and mixed) are currently recognized, based on serum alanine aminotransferase (ALT) and alkaline phosphatase (ALP) values. However, this classification lacks accuracy to distinguish among the many intermediate mixed types, or even to estimate the magnitude and progression of the injury. It was found desirable to have additional elements for better evaluation criteria of DILI. With this aim, we have examined the serum metabolomic changes occurring in 79 DILI patients recruited and monitored using established clinical criteria, along the course of the disease and until recovery. Results revealed that free and conjugated bile acids, and glycerophospholipids were among the most relevant metabolite classes for DILI phenotype characterization. Using an ensemble of PLS-DA models, metabolomic information was integrated into a ternary diagram to display the disease phenotype, the severity of the liver damage, and its progression. The modeling implemented and the use of such compiled information in an easily understandable and visual manner facilitates a straightforward DILI phenotyping and allow to monitor its progression and recovery prediction, usefully complementing the concise information drawn out by the ALT and ALP classification.

Aircraft noise exposure drives the activation of white blood cells and induces microvascular dysfunction in mice

Epidemiological studies showed that traffic noise has a dose-dependent association with increased cardiovascular morbidity and mortality. Whether microvascular dysfunction contributes significantly to the cardiovascular health effects by noise exposure remains to be established. The connection of inflammation and immune cell interaction with microvascular damage and functional impairment is also not well characterized. Male C57BL/6J mice or gp91phox^−/y mice with genetic deletion of the phagocytic NADPH oxidase catalytic subunit (gp91phox or NOX-2) were used at the age of 8 weeks, randomly instrumented with dorsal skinfold chambers and exposed or not exposed to aircraft noise for 4 days. Proteomic analysis (using mass spectrometry) revealed a pro-inflammatory phenotype induced by noise exposure that was less pronounced in noise-exposed gp91phox^−/y mice. Using in vivo fluorescence microscopy, we found a higher number of adhesive leukocytes in noise-exposed wild type mice. Dorsal microvascular diameter (by trend), red blood cell velocity, and segmental blood flow were also decreased by noise exposure indicating microvascular constriction. All adverse effects on functional parameters were normalized or improved at least by trend in noise-exposed gp91phox^−/y mice. Noise exposure also induced endothelial dysfunction in cerebral microvessels, which was associated with higher oxidative stress burden and inflammation, as measured using video microscopy. We here establish a link between a pro-inflammatory phenotype of plasma, activation of circulating leukocytes and microvascular dysfunction in mice exposed to aircraft noise. The phagocytic NADPH oxidase was identified as a central player in the underlying pathophysiological mechanisms.

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Noise exposure induces a pro-thrombo-inflammatory phenotype in mouse plasma.

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Aircraft noise increases leukocyte-endothelium interactions in dorsal microvessels.

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Noise decreases segmental blood flow/red blood cell velocity in dorsal microvessels.

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Noise increases cerebral microvascular dysfunction and oxidative stress.

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Nox2 deficiency (gp91phox^-/y) improves noise-induced adverse effects.

Interpreting the lipidome: bioinformatic approaches to embrace the complexity

BACKGROUND

Improvements in mass spectrometry (MS) technologies coupled with bioinformatics developments have allowed considerable advancement in the measurement and interpretation of lipidomics data in recent years. Since research areas employing lipidomics are rapidly increasing, there is a great need for bioinformatic tools that capture and utilize the complexity of the data. Currently, the diversity and complexity within the lipidome is often concealed by summing over or averaging individual lipids up to (sub)class-based descriptors, losing valuable information about biological function and interactions with other distinct lipids molecules, proteins and/or metabolites.

AIM OF REVIEW

To address this gap in knowledge, novel bioinformatics methods are needed to improve identification, quantification, integration and interpretation of lipidomics data. The purpose of this mini-review is to summarize exemplary methods to explore the complexity of the lipidome.

KEY SCIENTIFIC CONCEPTS OF REVIEW

Here we describe six approaches that capture three core focus areas for lipidomics: (1) lipidome annotation including a resolvable database identifier, (2) interpretation via pathway- and enrichment-based methods, and (3) understanding complex interactions to emphasize specific steps in the analytical process and highlight challenges in analyses associated with the complexity of lipidome data.

HIGH RESOLUTION MASS SPECTROMETRY IN LIPIDOMICS

The boost of research output in lipidomics during the last decade is tightly linked to improved instrumentation in mass spectrometry. Associated with this trend is the shift from low resolution-toward high-resolution lipidomics platforms. This review article summarizes the state of the art in the lipidomics field with a particular focus on the merits of high mass resolution. Following some theoretical considerations on the benefits of high mass resolution in lipidomics, it starts with a historical perspective on lipid analysis by sector instruments and moves further to today's instrumental approaches, including shotgun lipidomics, liquid chromatography-mass spectrometry, matrix-assisted laser desorption ionization-time-of-flight, and imaging lipidomics. Subsequently, several data processing and data analysis software packages are critically evaluated with all their pros and cons. Finally, this article emphasizes the importance and necessity of quality standards as the field evolves from its pioneering phase into a mature and robust omics technology and lists various initiatives for improving the applicability of lipidomics. © 2020 The Authors. Mass Spectrometry Reviews published by John Wiley & Sons Ltd. Mass Spec Rev.

The molecular and metabolic program by which white adipocytes adapt to cool physiologic temperatures

Although visceral adipocytes located within the body's central core are maintained at approximately 37°C, adipocytes within bone marrow, subcutaneous, and dermal depots are found primarily within the peripheral shell and generally exist at cooler temperatures. Responses of brown and beige/brite adipocytes to cold stress are well studied; however, comparatively little is known about mechanisms by which white adipocytes adapt to temperatures below 37°C. Here, we report that adaptation of cultured adipocytes to 31°C, the temperature at which distal marrow adipose tissues and subcutaneous adipose tissues often reside, increases anabolic and catabolic lipid metabolism, and elevates oxygen consumption. Cool adipocytes rely less on glucose and more on pyruvate, glutamine, and, especially, fatty acids as energy sources. Exposure of cultured adipocytes and gluteal white adipose tissue (WAT) to cool temperatures activates a shared program of gene expression. Cool temperatures induce stearoyl-CoA desaturase-1 (SCD1) expression and monounsaturated lipid levels in cultured adipocytes and distal bone marrow adipose tissues (BMATs), and SCD1 activity is required for acquisition of maximal oxygen consumption at 31°C.

Multi-Omic Single-Shot Technology for Integrated Proteome and Lipidome Analysis

Mass spectrometry (MS) serves as the centerpiece technology for proteome, lipidome, and metabolome analysis. To gain a better understanding of the multifaceted networks of myriad regulatory layers in complex organisms, integration of different multiomic layers is increasingly performed, including joint extraction methods of diverse biomolecular classes and comprehensive data analyses of different omics. Despite the versatility of MS systems, fractured methodology drives nearly all MS laboratories to specialize in analysis of a single ome at the exclusion of the others. Although liquid chromatography-mass spectrometry (LC-MS) analysis is similar for different biomolecular classes, the integration on the instrument level is lagging behind. The recent advancements in high flow proteomics enable us to take a first step towards integration of protein and lipid analysis. Here, we describe a technology to achieve broad and deep coverage of multiple molecular classes simultaneously through multi-omic single-shot technology (MOST), requiring only one column, one LC-MS instrument, and a simplified workflow. MOST achieved great robustness and reproducibility. Its application to a Saccharomyces cerevisiae study consisting of 20 conditions revealed 2842 protein groups and 325 lipids and potential molecular relationships.

[Analysis of phosphatidylcholines alterations in human glioblastoma multiform tissues ex vivo]

Significant metabolism alteration is accompanying the cell malignization process. Energy metabolism disturbance leads to the activation of de novo synthesis and beta-oxidation processes of lipids and fatty acids in a cancer cell, which becomes an indicator of pathological processes inside the cell. The majority of studies dealing with lipid metabolism alterations in glial tumors are performed using the cell lines in vitro or animal models. However, such conditions do not entirely represent the physiological conditions of cell growth or possible cells natural variability. This work presents the results of the data obtained by applying ambient mass spectrometry to human glioblastoma multiform tissues. By analyzing a relatively large cohort of primary and secondary glioblastoma samples, we identify the alterations in cells lipid composition, which accompanied the development of grade IV brain tumors. We demonstrate that primary glioblastomas, as well as ones developed from astrocytomas, are enriched with mono- and diunsaturated phosphatidylcholines (PC 26:1, 30:2, 32:1, 32:2, 34:1, 34:2). Simultaneously, the saturated and polyunsaturated phosphatidylcholines and phosphatidylethanolamines decrease. These alterations are obviously linked to the availability of the polyunsaturated fatty acids and activation of the de novo lipid synthesis and beta-oxidation pathways under the anaerobic conditions in the tumor core.

Large-Scale Multi-omic Analysis of COVID-19 Severity

We performed RNA-seq and high-resolution mass spectrometry on 128 blood samples from COVID-19-positive and COVID-19-negative patients with diverse disease severities and outcomes. Quantified transcripts, proteins, metabolites, and lipids were associated with clinical outcomes in a curated relational database, uniquely enabling systems analysis and cross-ome correlations to molecules and patient prognoses. We mapped 219 molecular features with high significance to COVID-19 status and severity, many of which were involved in complement activation, dysregulated lipid transport, and neutrophil activation. We identified sets of covarying molecules, e.g., protein gelsolin and metabolite citrate or plasmalogens and apolipoproteins, offering pathophysiological insights and therapeutic suggestions. The observed dysregulation of platelet function, blood coagulation, acute phase response, and endotheliopathy further illuminated the unique COVID-19 phenotype. We present a web-based tool (covid-omics.app) enabling interactive exploration of our compendium and illustrate its utility through a machine learning approach for prediction of COVID-19 severity.

Toward Rapid Screening of Liver Grafts at the Operating Room Using Mid-infrared Spectroscopy

The estimation of steatosis in a liver graft is mandatory prior to liver transplantation, as the risk of graft failure increases with the level of infiltrated fat. However, the assessment of liver steatosis before transplantation is typically based on a qualitative or semiquantitative characterization by visual inspection and palpation and histological analysis. Thus, there is an unmet need for transplantation surgeons to have access to a diagnostic tool enabling an in situ fast classification of grafts prior to extraction. In this study, we have assessed an attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopic method compatible with the requirements of an operation room for the evaluation of the lipid contents in human livers. A set of 20 human liver biopsies obtained from organs intended for transplantation were analyzed by expert pathologists, ATR-FTIR spectroscopy, lipid biochemical analysis, and UPLC-ESI(+/-)TOFMS for lipidomic profiling. Comparative analysis of multisource data showed strong correlations between ATR-FTIR, clinical, and lipidomic information. Results show that ATR-FTIR captures a global picture of the lipid composition of the liver, along with information for the quantification of the triradylglycerol content in liver biopsies. Although the methodology performance needs to be further validated, results support the applicability of ATR-FTIR for the in situ determination of the grade of liver steatosis at the operation room as a fast, quantitative method, as an alternative to the qualitative and subjective pathological examination.

Isolation and Metabolic Assessment of Cancer Cell Mitochondria

Mitochondrial metabolism plays an essential role in various biological processes of cancer cells. Herein, we established an experimental procedure for the metabolic assessment of mitochondria in cancer cells. We examined procedures for mitochondrial isolation coupled with various mitochondrial extraction buffers in three major cancer cell lines (PANC1, A549, and MDA-MB-231) and identified a potentially optimal and generalized approach. The purity of the mitochondrial fraction isolated by the selected protocol was verified using specific protein markers of cellular components, and the ultrastructure of the isolated mitochondria was also analyzed by transmission electron microscopy. The isolation procedure, involving a bead beater for cell lysis, a modified sucrose buffer, and differential centrifugation, appeared to be a suitable method for the extraction of mitochondria from cancer cells. Electron micrographs indicated an intact two-layer membrane and inner structures of mitochondria isolated by this procedure. Metabolomic and lipidomic analyses were conducted to examine the metabolic phenotypes of the mitochondria-enriched fractions and associated bulk cancer cells. A total of 44 metabolites, including malate and succinate, occurred at significantly higher levels in the mitochondrial fractions, whereas 51 metabolites, including citrate, oxaloacetate, and fumarate of the Krebs cycle and the oncometabolites glutamine and glutamate, were reduced in mitochondria compared to that in the corresponding bulk cells of PANC1. Similar patterns were observed in mitochondria and bulk cells of MDA-MB-231 and A549 cell lines. A clear difference between the lipid profiles of bulk PANC1, MDA-MB-231, and A549 and corresponding mitochondrial fractions of these cell lines was detected by principal component analysis. In conclusion, we developed an experimental procedure for a large-scale metabolic assessment for suborganelle metabolic profiling and multiple omics data integration in cancer cells with broad applications.

Structures of a Complete Human V-ATPase Reveal Mechanisms of Its Assembly

Vesicular- or vacuolar-type adenosine triphosphatases (V-ATPases) are ATP-driven proton pumps comprised of a cytoplasmic V₁ complex for ATP hydrolysis and a membrane-embedded V_o complex for proton transfer. They play important roles in acidification of intracellular vesicles, organelles, and the extracellular milieu in eukaryotes. Here, we report cryoelectron microscopy structures of human V-ATPase in three rotational states at up to 2.9-Å resolution. Aided by mass spectrometry, we build all known protein subunits with associated N-linked glycans and identify glycolipids and phospholipids in the V_o complex. We define ATP6AP1 as a structural hub for V_o complex assembly because it connects to multiple V_o subunits and phospholipids in the c-ring. The glycolipids and the glycosylated V_o subunits form a luminal glycan coat critical for V-ATPase folding, localization, and stability. This study identifies mechanisms of V-ATPase assembly and biogenesis that rely on the integrated roles of ATP6AP1, glycans, and lipids.

Analysis of the Association between Fatigue and the Plasma Lipidomic Profile of Inflammatory Bowel Disease Patients

Inflammatory bowel disease (IBD) is a chronic, relapsing noninfectious inflammatory condition of the intestinal tract with two main phenotypes, ulcerative colitis (UC) and Crohn's disease (CD), and globally increasing incidence and prevalence. Nearly 80% of the IBD patients with active disease and 50% of those with inactive disease suffer fatigue with significant impairment of their quality of life. Fatigue has been associated with multiple factors in IBD patients but, in most cases, no direct cause can be identified, and risk factors in clinically quiescent IBD are contradictory. Furthermore, as the assessment of fatigue is subjective, there is an unmet clinical need for fatigue biomarkers. In this explorative study, we analyzed the plasma lipidomic profiles of 47 quiescent UC and CD patients (23 fatigued, 24 nonfatigued) using ultraperformance liquid chromatography-time-of-flight mass spectrometry (UPLC-TOFMS). The results showed changes in lipids associated with fatigue and IBD. Significantly decreased levels of phosphatidylcholines, plasmanyls, sphingomyelins, lysophosphatidylcholines, phosphatidylethanolamines, phosphatidylinositols, phosphatidylserines, and eicosanoids were observed in patients with fatigue. Network and metabolic pathway analysis indicated a dysregulation of the arachidonic acid and glycerophospholipid metabolisms and the sphingolipid pathway. The protein-metabolite interaction network showed interactions between functionally related metabolites and proteins, displaying 40 disease-associated hidden proteins including ABDH4, GLTP, and LCAT.

A large-scale genome-lipid association map guides lipid identification

Despite the crucial roles of lipids in metabolism, we are still at the early stages of comprehensively annotating lipid species and their genetic basis. Mass spectrometry-based discovery lipidomics offers the potential to globally survey lipids and their relative abundances in various biological samples. To discover the genetics of lipid features obtained through high-resolution liquid chromatography-tandem mass spectrometry, we analysed liver and plasma from 384 diversity outbred mice, and quantified 3,283 molecular features. These features were mapped to 5,622 lipid quantitative trait loci and compiled into a public web resource termed LipidGenie. The data are cross-referenced to the human genome and offer a bridge between genetic associations in humans and mice. Harnessing this resource, we used genome-lipid association data as an additional aid to identify a number of lipids, for example gangliosides through their association with B4galnt1, and found evidence for a group of sex-specific phosphatidylcholines through their shared locus. Finally, LipidGenie's ability to query either mass or gene-centric terms suggests acyl-chain-specific functions for proteins of the ABHD family.

Automated Annotation of Sphingolipids Including Accurate Identification of Hydroxylation Sites Using MSn Data

Sphingolipids constitute a heterogeneous lipid category that is involved in many key cellular functions. For high-throughput analyses of sphingolipids, tandem mass spectrometry (MS/MS) is the method of choice, offering sufficient sensitivity, structural information, and quantitative precision for detecting hundreds to thousands of species simultaneously. While glycerolipids and phospholipids are predominantly non-hydroxylated, sphingolipids are typically dihydroxylated. However, species containing one or three hydroxylation sites can be detected frequently. This variability in the number of hydroxylation sites on the sphingolipid long-chain base and the fatty acyl moiety produces many more isobaric species and fragments than for other lipid categories. Due to this complexity, the automated annotation of sphingolipid species is challenging, and incorrect annotations are common. In this study, we present an extension of the Lipid Data Analyzer (LDA) "decision rule set" concept that considers the structural characteristics that are specific for this lipid category. To address the challenges inherent to automated annotation of sphingolipid structures from MS/MS data, we first developed decision rule sets using spectra from authentic standards and then tested the applicability on biological samples including murine brain and human plasma. A benchmark test based on the murine brain samples revealed a highly improved annotation quality as measured by sensitivity and reliability. The results of this benchmark test combined with the easy extensibility of the software to other (sphingo)lipid classes and the capability to detect and correctly annotate novel sphingolipid species make LDA broadly applicable to automated sphingolipid analysis, especially in high-throughput settings.

The Impact of Plasma Membrane Lipid Composition on Flagellum-Mediated Adhesion of Enterohemorrhagic Escherichia coli

Enterohemorrhagic Escherichia coli (EHEC) O157:H7 is a major cause of foodborne gastrointestinal illness. The adhesion of EHEC to host tissues is the first step enabling bacterial colonization. Adhesins such as fimbriae and flagella mediate this process. Here, we studied the interaction of the bacterial flagellum with the host cell's plasma membrane using giant unilamellar vesicles (GUVs) as a biologically relevant model. Cultured cell lines contain many different molecular components, including proteins and glycoproteins. In contrast, with GUVs, we can characterize the bacterial mode of interaction solely with a defined lipid part of the cell membrane. Bacterial adhesion on GUVs was dependent on the presence of the flagellar filament and its motility. By testing different phospholipid head groups, the nature of the fatty acid chains, or the liposome curvature, we found that lipid packing is a key parameter to enable bacterial adhesion. Using HT-29 cells grown in the presence of polyunsaturated fatty acid (α-linolenic acid) or saturated fatty acid (palmitic acid), we found that α-linolenic acid reduced adhesion of wild-type EHEC but not of a nonflagellated mutant. Finally, our results reveal that the presence of flagella is advantageous for the bacteria to bind to lipid rafts. We speculate that polyunsaturated fatty acids prevent flagellar adhesion on membrane bilayers and play a clear role for optimal host colonization. Flagellum-mediated adhesion to plasma membranes has broad implications for host-pathogen interactions.IMPORTANCE Bacterial adhesion is a crucial step to allow bacteria to colonize their hosts, invade tissues, and form biofilm. Enterohemorrhagic Escherichia coli O157:H7 is a human pathogen and the causative agent of diarrhea and hemorrhagic colitis. Here, we use biomimetic membrane models and cell lines to decipher the impact of lipid content of the plasma membrane on enterohemorrhagic E. coli flagellum-mediated adhesion. Our findings provide evidence that polyunsaturated fatty acid (α-linolenic acid) inhibits E. coli flagellar adhesion to the plasma membrane in a mechanism separate from its antimicrobial and anti-inflammatory functions. In addition, we confirm that cholesterol-enriched lipid microdomains, often called lipid rafts, are important in bacterial adhesion. These findings demonstrate that plasma membrane adhesion via bacterial flagella play a significant role for an important human pathogen. This mechanism represents a promising target for the development of novel antiadhesion therapies.

Metabolomic and Lipidomic Analysis of Bone Marrow Derived Macrophages

Macrophages are highly plastic immune cells that are capable of adopting a wide array of functional phenotypes in response to environmental stimuli. The changes in macrophage function are often supported and regulated by changes in cellular metabolism. Capturing a comprehensive picture of metabolism is vital for understanding the role of metabolic rewiring in the immune response. Here we present a method for systematically quantifying the abundance of metabolites and lipids in primary murine bone marrow derived macrophages (BMDMs). This method simultaneously extracts polar metabolites and lipids from BMDMs using a rapid two-phase extraction procedure. The polar metabolite fraction and lipid fraction are subsequently analyzed by separate liquid chromatography-mass spectrometry (LC-MS) methods for optimized coverage and quantification. This allows for a comprehensive characterization of cellular metabolism that can be used to understand the impact of a variety of environmental stimuli on macrophage metabolism and function.

Large-scale Multi-omic Analysis of COVID-19 Severity

We performed RNA-Seq and high-resolution mass spectrometry on 128 blood samples from COVID-19 positive and negative patients with diverse disease severities. Over 17,000 transcripts, proteins, metabolites, and lipids were quantified and associated with clinical outcomes in a curated relational database, uniquely enabling systems analysis and cross-ome correlations to molecules and patient prognoses. We mapped 219 molecular features with high significance to COVID-19 status and severity, many involved in complement activation, dysregulated lipid transport, and neutrophil activation. We identified sets of covarying molecules, e.g., protein gelsolin and metabolite citrate or plasmalogens and apolipoproteins, offering pathophysiological insights and therapeutic suggestions. The observed dysregulation of platelet function, blood coagulation, acute phase response, and endotheliopathy further illuminated the unique COVID-19 phenotype. We present a web-based tool (covid-omics.app) enabling interactive exploration of our compendium and illustrate its utility through a comparative analysis with published data and a machine learning approach for prediction of COVID-19 severity.

A lipidome atlas in MS-DIAL 4

We present Mass Spectrometry-Data Independent Analysis software version 4 (MS-DIAL 4), a comprehensive lipidome atlas with retention time, collision cross-section and tandem mass spectrometry information. We formulated mass spectral fragmentations of lipids across 117 lipid subclasses and included ion mobility tandem mass spectrometry. Using human, murine, algal and plant biological samples, we annotated and semiquantified 8,051 lipids using MS-DIAL 4 with a 1-2% estimated false discovery rate. MS-DIAL 4 helps standardize lipidomics data and discover lipid pathways.

Lipid Annotation by Combination of UHPLC-HRMS (MS), Molecular Networking, and Retention Time Prediction: Application to a Lipidomic Study of In Vitro Models of Dry Eye Disease

Annotation of lipids in untargeted lipidomic analysis remains challenging and a systematic approach needs to be developed to organize important datasets with the help of bioinformatic tools. For this purpose, we combined tandem mass spectrometry-based molecular networking with retention time (t_R) prediction to annotate phospholipid and sphingolipid species. Sixty-five standard compounds were used to establish the fragmentation rules of each lipid class studied and to define the parameters governing their chromatographic behavior. Molecular networks (MNs) were generated through the GNPS platform using a lipid standards mixture and applied to lipidomic study of an in vitro model of dry eye disease, i.e., human corneal epithelial (HCE) cells exposed to hyperosmolarity (HO). These MNs led to the annotation of more than 150 unique phospholipid and sphingolipid species in the HCE cells. This annotation was reinforced by comparing theoretical to experimental t_R values. This lipidomic study highlighted changes in 54 lipids following HO exposure of corneal cells, some of them being involved in inflammatory responses. The MN approach coupled to t_R prediction thus appears as a suitable and robust tool for the discovery of lipids involved in relevant biological processes.

2,4-dienoyl-CoA reductase regulates lipid homeostasis in treatment-resistant prostate cancer

Despite the clinical success of Androgen Receptor (AR)-targeted therapies, reactivation of AR signalling remains the main driver of castration-resistant prostate cancer (CRPC) progression. In this study, we perform a comprehensive unbiased characterisation of LNCaP cells chronically exposed to multiple AR inhibitors (ARI). Combined proteomics and metabolomics analyses implicate an acquired metabolic phenotype common in ARI-resistant cells and associated with perturbed glucose and lipid metabolism. To exploit this phenotype, we delineate a subset of proteins consistently associated with ARI resistance and highlight mitochondrial 2,4-dienoyl-CoA reductase (DECR1), an auxiliary enzyme of beta-oxidation, as a clinically relevant biomarker for CRPC. Mechanistically, DECR1 participates in redox homeostasis by controlling the balance between saturated and unsaturated phospholipids. DECR1 knockout induces ER stress and sensitises CRPC cells to ferroptosis. In vivo, DECR1 deletion impairs lipid metabolism and reduces CRPC tumour growth, emphasizing the importance of DECR1 in the development of treatment resistance.

Urine metabolomic analysis for monitoring internal load in professional football players

INTRODUCTION

The design of training programs for football players is not straightforward due to intra- and inter-individual variability that leads to different physiological responses under similar training loads.

OBJECTIVE

To study the association between the external load, defined by variables obtained using electronic performance tracking systems (EPTS), and the urinary metabolome as a surrogate of the metabolic adaptation to training.

METHODS

Urine metabolic and EPTS data from 80 professional football players collected in an observational longitudinal study were analyzed by ultra-performance liquid chromatography coupled to electrospray ionization quadrupole time-of-flight mass spectrometry and assessed by partial least squares (PLS) regression.

RESULTS

PLS models identified steroid hormone metabolites, hypoxanthine metabolites, acetylated amino acids, intermediates in phenylalanine metabolism, tyrosine, tryptophan metabolites, and riboflavin among the most relevant variables associated with external load. Metabolic network analysis identified enriched pathways including steroid hormone biosynthesis and metabolism of tyrosine and tryptophan. The ratio of players showing a deviation from the PLS model of adaptation to exercise was higher among those who suffered a muscular lesion compared to those who did not.

CONCLUSIONS

There was a significant association between the external load and the urinary metabolic profile, with alteration of biochemical pathways associated with long-term adaptation to training. Future studies should focus on the validation of these findings and the development of metabolic models to identify professional football players at risk of developing muscular injuries.

Comparing Targeted vs. Untargeted MS2 Data-Dependent Acquisition for Peak Annotation in LC-MS Metabolomics

One of the most widely used strategies for metabolite annotation in untargeted LCMS is based on the analysis of MSⁿ spectra acquired using data-dependent acquisition (DDA), where precursor ions are sequentially selected from MS scans based on user-selected criteria. However, the number of MSⁿ spectra that can be acquired during a chromatogram is limited and a trade-off between analytical speed, sensitivity and coverage must be ensured. In this research, we compare four different strategies for automated MS² DDA, which can be easily implemented in the frame of standard QA/QC workflows for untargeted LC–MS. These strategies consist of (i) DDA in the MS working range; (ii) iterated DDA split into several m/z intervals; (iii) dynamic iterated DDA of (pre)selected potentially informative features; and (iv) dynamic iterated DDA of (pre)annotated metabolic features using a reference database. Their performance was assessed using the analysis of human milk samples as model example by comparing the percentage of LC–MS features selected as the precursor ion for MS², the number, and class of annotated features, the speed and confidence of feature annotation, and the number of LC runs required.

Lipid Annotator: Towards Accurate Annotation in Non-Targeted Liquid Chromatography High-Resolution Tandem Mass Spectrometry (LC-HRMS/MS) Lipidomics Using A Rapid and User-Friendly Software

Lipidomics has great promise in various applications; however, a major bottleneck in lipidomics is the accurate and comprehensive annotation of high-resolution tandem mass spectral data. While the number of available lipidomics software has drastically increased over the past five years, the reduction of false positives and the realization of obtaining structurally accurate annotations remains a significant challenge. We introduce Lipid Annotator, which is a user-friendly software for lipidomic analysis of data collected by liquid chromatography high-resolution tandem mass spectrometry (LC-HRMS/MS). We validate annotation accuracy against lipid standards and other lipidomics software. Lipid Annotator was integrated into a workflow applying an iterative exclusion MS/MS acquisition strategy to National Institute of Standards and Technology (NIST) SRM 1950 Metabolites in Frozen Human Plasma using reverse phase LC-HRMS/MS. Lipid Annotator, LipidMatch, and MS-DIAL produced consensus annotations at the level of lipid class for 98% and 96% of features detected in positive and negative mode, respectively. Lipid Annotator provides percentages of fatty acyl constituent species and employs scoring algorithms based on probability theory, which is less subjective than the tolerance and weighted match scores commonly used by available software. Lipid Annotator enables analysis of large sample cohorts and improves data-processing throughput as compared to previous lipidomics software.

Trapped ion mobility spectrometry and PASEF enable in-depth lipidomics from minimal sample amounts

A comprehensive characterization of the lipidome from limited starting material remains very challenging. Here we report a high-sensitivity lipidomics workflow based on nanoflow liquid chromatography and trapped ion mobility spectrometry (TIMS). Taking advantage of parallel accumulation–serial fragmentation (PASEF), we fragment on average 15 precursors in each of 100 ms TIMS scans, while maintaining the full mobility resolution of co-eluting isomers. The acquisition speed of over 100 Hz allows us to obtain MS/MS spectra of the vast majority of isotope patterns. Analyzing 1 µL of human plasma, PASEF increases the number of identified lipids more than three times over standard TIMS-MS/MS, achieving attomole sensitivity. Building on high intra- and inter-laboratory precision and accuracy of TIMS collisional cross sections (CCS), we compile 1856 lipid CCS values from plasma, liver and cancer cells. Our study establishes PASEF in lipid analysis and paves the way for sensitive, ion mobility-enhanced lipidomics in four dimensions.

Trapped ion mobility (TIMS)-mass spectrometry with parallel accumulation-serial fragmentation (PASEF) facilitates high-sensitivity proteomics experiments. Here, the authors expand TIMS and PASEF to small molecules and demonstrate fast and comprehensive lipidomics of low biological sample amounts.

Methods of the Analysis of Oxylipins in Biological Samples

Oxylipins are derivatives of polyunsaturated fatty acids and due to their important and diverse functions in the body, they have become a popular subject of studies. The main challenge for researchers is their low stability and often very low concentration in samples. Therefore, in recent years there have been developments in the extraction and analysis methods of oxylipins. New approaches in extraction methods were described in our previous review. In turn, the old analysis methods have been replaced by new approaches based on mass spectrometry (MS) coupled with liquid chromatography (LC) and gas chromatography (GC), and the best of these methods allow hundreds of oxylipins to be quantitatively identified. This review presents comparative and comprehensive information on the progress of various methods used by various authors to achieve the best results in the analysis of oxylipins in biological samples.

Lipidomics from sample preparation to data analysis: a primer

Lipids are amongst the most important organic compounds in living organisms, where they serve as building blocks for cellular membranes as well as energy storage and signaling molecules. Lipidomics is the science of the large-scale determination of individual lipid species, and the underlying analytical technology that is used to identify and quantify the lipidome is generally mass spectrometry (MS). This review article provides an overview of the crucial steps in MS-based lipidomics workflows, including sample preparation, either liquid–liquid or solid-phase extraction, derivatization, chromatography, ion-mobility spectrometry, MS, and data processing by various software packages. The associated concepts are discussed from a technical perspective as well as in terms of their application. Furthermore, this article sheds light on recent advances in the technology used in this field and its current limitations. Particular emphasis is placed on data quality assurance and adequate data reporting; some of the most common pitfalls in lipidomics are discussed, along with how to circumvent them.

Accelerating Lipidomic Method Development through in Silico Simulation

Judicious selection of mass spectrometry (MS) acquisition parameters is essential for effectively profiling the broad diversity and dynamic range of biomolecules. Typically, acquisition parameters are individually optimized to maximally characterize analytes from each new sample matrix. This time-consuming process often ignores the synergistic relationship between MS method parameters, producing suboptimal results. Here we detail the creation of an algorithm which accurately simulates LC-MS/MS lipidomic data acquisition performance for a benchtop quadrupole-Orbitrap MS system. By coupling this simulation tool with a genetic algorithm for constrained parameter optimization, we demonstrate the efficient identification of LC-MS/MS method parameter sets individually suited for specific sample matrices. Finally, we utilize the in silico simulation to examine how continued developments in MS acquisition speed and sensitivity will further increase the power of MS lipidomics as a vital tool for impactful biochemical analysis.

A Mitochondrial Specific Antioxidant Reverses Metabolic Dysfunction and Fatty Liver Induced by Maternal Cigarette Smoke in Mice

Maternal smoking leads to glucose and lipid metabolic disorders and hepatic damage in the offspring, potentially due to mitochondrial oxidative stress. Mitoquinone mesylate (MitoQ) is a mitochondrial targeted antioxidant with high bioavailability. This study aimed to examine the impact of maternal cigarette smoke exposure (SE) on offspring’s metabolic profile and hepatic damage, and whether maternal MitoQ supplementation during gestation can affect these changes. Female Balb/c mice (eight weeks) were either exposed to air or SE for six weeks prior to mating and throughout gestation and lactation. A subset of the SE dams were supplied with MitoQ in the drinking water (500 µmol/L) during gestation and lactation. Intraperitoneal glucose tolerance test was performed in the male offspring at 12 weeks and the livers and plasma were collected at 13 weeks. Maternal SE induced glucose intolerance, hepatic steatosis, mitochondrial oxidative stress and related damage in the adult offspring. Maternal MitoQ supplementation reduced hepatic mitochondrial oxidative stress and improved markers of mitophagy and mitochondrial biogenesis. This may restore hepatic mitochondrial health and was associated with an amelioration of glucose intolerance, hepatic steatosis and pathological changes induced by maternal SE. MitoQ supplementation may potentially prevent metabolic dysfunction and hepatic pathology induced by intrauterine SE.

Hybrid SWATH/MS and HR-SRM/MS acquisition for phospholipidomics using QUAL/QUANT data processing

A hybrid SWATH/MS and HR-SRM/MS acquisition approach using multiple unit mass windows and 100 u precursor selection windows has been developed to interface with a chromatographic lipid class separation. The method allows for the simultaneous monitoring of sum compositions in MS1 and up to 48 lipids in MS2 per lipid class. A total of 240 lipid sum compositions from five phospholipid classes could be monitored in MS2 (HR-SRM/MS) while there was no limitation in the number of analytes in MS1 (HR-SIM/MS). On average, 92 lipid sum compositions and 75 lipid species could be quantified in human plasma samples. The robustness and precision of the workflow has been assessed using technical triplicates of the subject samples. Lipid identification was improved using a combined qualitative and quantitative data processing based on prediction instead of library search. Lipid class specific extracted ion currents of precursors and the corresponding molecular species fragments were extracted based on the information obtained from lipid building blocks and a combinatorial strategy. The SWATH/MS approach with the post-acquisition processing is not limited to the analyzed phospholipid classes and can be applied to other analytes and samples of interest. Graphical abstract.

Mapping Lipid Fragmentation for Tailored Mass Spectral Libraries

Libraries of simulated lipid fragmentation spectra enable the identification of hundreds of unique lipids from complex lipid extracts, even when the corresponding lipid reference standards do not exist. Often, these in silico libraries are generated through expert annotation of spectra to extract and model fragmentation rules common to a given lipid class. Although useful for a given sample source or instrumental platform, the time-consuming nature of this approach renders it impractical for the growing array of dissociation techniques and instrument platforms. Here, we introduce Library Forge, a unique algorithm capable of deriving lipid fragment mass-to-charge (m/z) and intensity patterns directly from high-resolution experimental spectra with minimal user input. Library Forge exploits the modular construction of lipids to generate m/z transformed spectra in silico which reveal the underlying fragmentation pathways common to a given lipid class. By learning these fragmentation patterns directly from observed spectra, the algorithm increases lipid spectral matching confidence while reducing spectral library development time from days to minutes. We embed the algorithm within the preexisting lipid analysis architecture of LipiDex to integrate automated and robust library generation within a comprehensive LC-MS/MS lipidomics workflow. Graphical Abstract.

An Isoprene Lipid-Binding Protein Promotes Eukaryotic Coenzyme Q Biosynthesis

The biosynthesis of coenzyme Q presents a paradigm for how cells surmount hydrophobic barriers in lipid biology. In eukaryotes, CoQ precursors-among nature's most hydrophobic molecules-must somehow be presented to a series of enzymes peripherally associated with the mitochondrial inner membrane. Here, we reveal that this process relies on custom lipid-binding properties of COQ9. We show that COQ9 repurposes the bacterial TetR fold to bind aromatic isoprenes with high specificity, including CoQ intermediates that likely reside entirely within the bilayer. We reveal a process by which COQ9 associates with cardiolipin-rich membranes and warps the membrane surface to access this cargo. Finally, we identify a molecular interface between COQ9 and the hydroxylase COQ7, motivating a model whereby COQ9 presents intermediates directly to CoQ enzymes. Overall, our results provide a mechanism for how a lipid-binding protein might access, select, and deliver specific cargo from a membrane to promote biosynthesis.

LipidMS: An R Package for Lipid Annotation in Untargeted Liquid Chromatography-Data Independent Acquisition-Mass Spectrometry Lipidomics

High resolution LC-MS untargeted lipidomics using data independent acquisition (DIA) has the potential to increase lipidome coverage, as it enables the continuous and unbiased acquisition of all eluting ions. However, the loss of the link between the precursor and the product ions combined with the high dimensionality of DIA data sets hinder accurate feature annotation. Here, we present LipidMS, an R package aimed to confidently identify lipid species in untargeted LC-DIA-MS. To this end, LipidMS combines a coelution score, which links precursor and fragment ions with fragmentation and intensity rules. Depending on the MS evidence reached by the identification function survey, LipidMS provides three levels of structural annotations: (i) "subclass level", e.g., PG(34:1); (ii) "fatty acyl level", e.g., PG(16:0_18:1); and (iii) "fatty acyl position level", e.g., PG(16:0/18:1). The comparison of LipidMS with freely available data dependent acquisition (DDA) and DIA identification tools showed that LipidMS provides significantly more accurate and structural informative lipid identifications. Finally, to exemplify the utility of LipidMS, we investigated the lipidomic serum profile of patients diagnosed with nonalcoholic steatohepatitis (NASH), which is the progressive form of nonalcoholic fatty liver disease, a disorder underlying a strong lipid dysregulation. As previously published, a significant decrease in lysophosphatidylcholines, phosphatidylcholines and cholesterol esters and an increase in phosphatidylethanolamines were observed in NASH patients. Remarkably, LipidMS allowed the identification of a new set of lipids that may be used for NASH diagnosis. Altogether, LipidMS has been validated as a tool to assist lipid identification in the LC-DIA-MS untargeted analysis of complex biological samples.

Tools and resources for metabolomics research community: A 2017-2018 update

The scale at which MS- and NMR-based platforms generate metabolomics datasets for both research, core, and clinical facilities to address challenges in the various sciences-ranging from biomedical to agricultural-is underappreciated. Thus, metabolomics efforts spanning microbe, environment, plant, animal, and human systems have led to continual and concomitant growth of in silico resources for analysis and interpretation of these datasets. These software tools, resources, and databases drive the field forward to help keep pace with the amount of data being generated and the sophisticated and diverse analytical platforms that are being used to generate these metabolomics datasets. To address challenges in data preprocessing, metabolite annotation, statistical interrogation, visualization, interpretation, and integration, the metabolomics and informatics research community comes up with hundreds of tools every year. The purpose of the present review is to provide a brief and useful summary of more than 95 metabolomics tools, software, and databases that were either developed or significantly improved during 2017-2018. We hope to see this review help readers, developers, and researchers to obtain informed access to these thorough lists of resources for further improvisation, implementation, and application in due course of time.