Lipidomic data analysis: Tutorial, practical guidelines and applications

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

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

Lipidomics-driven drug discovery and delivery strategies in glioblastoma

With few viable treatment options, glioblastoma (GBM) is still one of the most aggressive and deadly types of brain cancer. Recent developments in lipidomics have demonstrated the potential of lipid metabolism as a therapeutic target in GBM. The thorough examination of lipids in biological systems, or lipidomics, is essential to comprehending the changed lipid profiles found in GBM, which are linked to the tumor's ability to grow, survive, and resist treatment. The use of lipidomics in drug delivery and discovery is examined in this study, focusing on how it may be used to find new biomarkers, create multi-target directed ligands, and improve drug delivery systems. We also cover the use of FDA-approved medications, clinical trials that use lipid-targeted medicines, and the integration of lipidomics with other omics technologies. This study emphasizes lipidomics as a possible tool in developing more effective treatment methods for GBM by exploring various lipid-centric techniques.

Effects of Combined Low-Dose Spironolactone Plus Vitamin E versus Vitamin E Monotherapy on Lipidomic Profile in Non-Alcoholic Fatty Liver Disease: A Post Hoc Analysis of a Randomized Controlled Trial

Background/Objectives: Lipid dysmetabolism seems to contribute to the development and progression of nonalcoholic fatty liver disease (NAFLD). Our aim was to compare serum lipidomic profile between patients with NAFLD having received monotherapy with vitamin E (400 IU/d) and those having received combination therapy with vitamin E (400 IU/d) and low-dose spironolactone (25 mg/d) for 52 weeks. Methods: This was a post hoc study of a randomized controlled trial (NCT01147523). Serum lipidomic analysis was performed in vitamin E monotherapy group (n = 15) and spironolactone plus vitamin E combination therapy group (n = 12). We employed an untargeted liquid chromatography-mass spectrometry lipid profiling approach in positive and negative ionization mode. Results: Univariate analysis revealed 36 lipid molecules statistically different between groups in positive mode and seven molecules in negative mode. Multivariate analysis in negative mode identified six lipid molecules that remained robustly different between groups. After adjustment for potential confounders, including gender, omega-3 supplementation, leptin concentration and homeostasis model assessment-insulin resistance (HOMA-IR), four lipid molecules remained significant between groups: FA 20:5, SM 34:2;O2, SM 42:3;O2 and CE 22:6, all being higher in the combination treatment group. Conclusions: The combination of spironolactone with vitamin E led to higher circulating levels of four lipid molecules than vitamin E monotherapy, after adjustment for potential confounders. Owing to very limited relevant data, we could not support that these changes in lipid molecules may be beneficial or not for the progression of NAFLD. Thus, mechanistic studies are warranted to clarify the potential clinical significance of these findings.

Combinatory data-independent acquisition and parallel reaction monitoring method for revealing the lipid metabolism biomarkers of coronary heart disease and its comorbidities

Disorders of lipid metabolism are a common cause of coronary heart disease (CHD) and its comorbidities. In this study, ultra-performance liquid chromatography-high-resolution mass spectrometry in data-independent acquisition (DIA) mode was applied to collect abundant tandem mass spectrometry data, which provided valuable information for lipid annotation. For the lipid isomers that could not be completely separated by chromatography, parallel reaction monitoring (PRM) mode was used for quantification. A total of 223 plasma lipid metabolites were annotated, and 116 of them were identified for their fatty acyl chain composition and location. In addition, 152 plasma lipids in patients with CHD and its comorbidities were quantitatively analyzed. Multivariate statistical analysis and metabolic pathway analysis demonstrated that glycerophospholipid and sphingolipid metabolism deserved more attention for CHD. This study proposed a method combining DIA and PRM for high-throughput characterization of plasma lipids. The results also improved our understanding of metabolic disorders of CHD and its comorbidities, which can provide valuable suggestions for medical intervention.

Comprehensive analysis of phospholipid in milk and their biological roles as nutrients and biomarkers

Phospholipids (PL) have garnered significant attention due to their physiological activities. Milk and other dairy products are important dietary sources for humans and have been extensively used to analyze the presence of PL by various analytical techniques. In this paper, the analysis techniques of PL were reviewed with the eight trigrams of phospholipidomics and a comprehensive fingerprint of 1295 PLs covering 8 subclasses in milk and other dairy products, especially. Technology is the primary productive force. Based on phospholipidomics technology, we further review the relationship between the composition of PL and factors that may be involved in processing and experimental operation, and emphasized the significance of the biological role played by PL in dietary supplements and biomarkers (production, processing and clinical research), and providing the future research directions.

LiLA: lipid lung-based ATLAS built through a comprehensive workflow designed for an accurate lipid annotation

Accurate lipid annotation is crucial for understanding the role of lipids in health and disease and identifying therapeutic targets. However, annotating the wide variety of lipid species in biological samples remains challenging in untargeted lipidomic studies. In this work, we present a lipid annotation workflow based on LC-MS and MS/MS strategies, the combination of four bioinformatic tools, and a decision tree to support the accurate annotation and semi-quantification of the lipid species present in lung tissue from control mice. The proposed workflow allowed us to generate a lipid lung-based ATLAS (LiLA), which was then employed to unveil the lipidomic signatures of the Mycobacterium tuberculosis infection at two different time points for a deeper understanding of the disease progression. This workflow, combined with manual inspection strategies of MS/MS data, can enhance the annotation process for lipidomic studies and guide the generation of sample-specific lipidome maps. LiLA serves as a freely available data resource that can be employed in future studies to address lipidomic alterations in mice lung tissue.

Blood Proteome Profiling Reveals Biomarkers and Pathway Alterations in Fragile X PM at Risk for Developing FXTAS

Fragile X-associated Tremor/Ataxia Syndrome (FXTAS) is a neurodegenerative disorder associated with the FMR1 premutation. Currently, it is not possible to determine when and if individual premutation carriers will develop FXTAS. Thus, with the aim to identify biomarkers for early diagnosis, development, and progression of FXTAS, along with associated dysregulated pathways, we performed blood proteomic profiling of premutation carriers (PM) who, as part of an ongoing longitudinal study, emerged into two distinct groups: those who developed symptoms of FXTAS (converters, CON) over time (at subsequent visits) and those who did not (non-converters, NCON). We compared these groups to age-matched healthy controls (HC). We assessed CGG repeat allele size by Southern blot and PCR analysis. The proteomic profile was obtained by liquid chromatography mass spectrometry (LC-MS/MS). We identified several significantly differentiated proteins between HC and the PM groups at Visit 1 (V1), Visit 2 (V2), and between the visits. We further reported the dysregulated protein pathways, including sphingolipid and amino acid metabolism. Our findings are in agreement with previous studies showing that pathways involved in mitochondrial bioenergetics, as observed in other neurodegenerative disorders, are significantly altered and appear to contribute to the development of FXTAS. Lastly, we compared the blood proteome of the PM who developed FXTAS over time with the CSF proteome of the FXTAS patients recently reported and found eight significantly differentially expressed proteins in common. To our knowledge, this is the first report of longitudinal proteomic profiling and the identification of unique biomarkers and dysregulated protein pathways in FXTAS.

A Critical Overview of HPLC-MS-Based Lipidomics in Determining Triacylglycerol and Phospholipid in Foods

With the current advancement in mass spectrometry (MS)-based lipidomics, the knowledge of lipidomes and their diverse roles has greatly increased, enabling a deeper understanding of the action of bioactive lipid molecules in plant- and animal-based foods. This review provides in-depth information on the practical use of MS techniques in lipidomics, including lipid extraction, adduct formation, MS analysis, data processing, statistical analysis, and bioinformatics. Moreover, this contribution demonstrates the effectiveness of MS-based lipidomics for identifying and quantifying diverse lipid species, especially triacylglycerols and phospholipids, in foods. Further, it summarizes the wide applications of MS-based lipidomics in food science, such as for assessing food processing methods, detecting food adulteration, and measuring lipid oxidation in foods. Thus, MS-based lipidomics may be a useful method for identifying the action of individual lipid species in foods.

Schisandra lignans ameliorate nonalcoholic steatohepatitis by regulating aberrant metabolism of phosphatidylethanolamines

Nonalcoholic steatohepatitis (NASH) is a spectrum of chronic liver disease characterized by hepatic lipid metabolism disorder. Recent reports emphasized the contribution of triglyceride and diglyceride accumulation to NASH, while the other lipids associated with the NASH pathogenesis remained unexplored. The specific purpose of our study was to explore a novel pathogenesis and treatment strategy of NASH via profiling the metabolic characteristics of lipids. Herein, multi-omics techniques based on LC-Q-TOF/MS, LC-MS/MS and MS imaging were developed and used to screen the action targets related to NASH progress and treatment. A methionine and choline deficient (MCD) diet-induced mouse model of NASH was then constructed, and Schisandra lignans extract (SLE) was applied to alleviate hepatic damage by regulating the lipid metabolism-related enzymes CES2A and CYP4A14. Hepatic lipidomics indicated that MCD-diet led to aberrant accumulation of phosphatidylethanolamines (PEs), and SLE could significantly reduce the accumulation of intrahepatic PEs. Notably, exogenous PE (18:0/18:1) was proved to significantly aggravate the mitochondrial damage and hepatocyte apoptosis. Supplementing PE (18:0/18:1) also deteriorated the NASH progress by up regulating intrahepatic proinflammatory and fibrotic factors, while PE synthase inhibitor exerted a prominent hepatoprotective role. The current work provides new insights into the relationship between PE metabolism and the pathogenesis of NASH.

Plasma lipid metabolites as potential biomarkers for identifying individuals at risk of obesity-induced metabolic complications

Lipidomics studies have indicated an association between obesity and lipid metabolism dysfunction. This study aimed to evaluate and compare cardiometabolic risk factors, and the lipidomic profile in adults and older people. A cross-sectional study was conducted with 72 individuals, divided into two sex and age-matched groups: obese (body mass index-BMI ≥ 30 kg/m2; n = 36) and non-obese (BMI < 30 kg/m2; n = 36). The lipidomic profiles were evaluated in plasma using 1H nuclear magnetic resonance (1H-NMR) spectroscopy. Obese individuals had higher waist circumference (p < 0.001), visceral adiposity index (p = 0.029), homeostatic model assessment insulin resistance (HOMA-IR) (p = 0.010), and triacylglycerols (TAG) levels (p = 0.018). 1H-NMR analysis identified higher amounts of saturated lipid metabolite fragments, lower levels of unsaturated lipids, and some phosphatidylcholine species in the obese group. Two powerful machine learning (ML) models-k-nearest neighbors (kNN) and XGBoost (XGB) were employed to characterize the lipidomic profile of obese individuals. The results revealed metabolic alterations associated with obesity in the NMR signals. The models achieved high accuracy of 86% and 81%, respectively. The feature importance analysis identified signal at 1.50-1.60 ppm (-CO-CH2-CH2-, Cholesterol and fatty acid in TAG, Phospholipids) to have the highest importance in the two models.

Untargeted Lipidomics and Chemometric Tools for the Characterization and Discrimination of Irradiated Camembert Cheese Analyzed by UHPLC-Q-Orbitrap-MS

In this work, an investigation using UHPLC-Q-Orbitrap-MS and multivariate statistics was conducted to obtain the lipid fingerprint of Camembert cheese and to explore its correlated variation with respect to X-ray irradiation treatment. A total of 479 lipids, categorized into 16 different lipid subclasses, were measured. Furthermore, the identification of oxidized lipids was carried out to better understand the possible phenomena of lipid oxidation related to this technological process. The results confirm that the lipidomic approach adopted is effective in implementing the knowledge of the effects of X-ray irradiation on food and evaluating its safety aspects. Furthermore, Partial Least Squares-Discriminant Analysis (PLS-DA) and Linear Discriminant Analysis (LDA) were applied showing high discriminating ability with excellent values of accuracy, specificity and sensitivity. Through the PLS-DA and LDA models, it was possible to select 40 and 24 lipids, respectively, including 3 ceramides (Cer), 1 hexosyl ceramide (HexCer), 1 lysophosphatidylcholine (LPC), 1 lysophosphatidylethanolamine (LPE), 3 phosphatidic acids (PA), 4 phosphatidylcholines (PC), 10 phosphatidylethanolamines (PE), 5 phosphatidylinositols (PI), 2 phosphatidylserines (PS), 3 diacylglycerols (DG) and 9 oxidized triacylglycerols (OxTG) as potential markers of treatment useful in food safety control plans.

Lipidomic profiling of the Brazilian yellow scorpion venom: new insights into inflammatory responses following Tityus serrulatus envenomation

Due to the high prevalence and clinical relevance, scorpionism is a critical public health issue in several Brazilian regions. Tityus serrulatus, commonly known as the Brazilian yellow scorpion, is the most venomous genus found in Brazilian fauna and associated with severe clinical manifestations such as localized pain, hypertension, sweating, tachycardia and complex hyperinflammatory responses. In general, T. serrulatus venom contains a complex mixture of active compounds, including proteins, peptides, and amino acids. Although knowledge of the protein fractions of scorpion venom is available, venom lipid components are not yet comprehensively known. The aim of the present study was to determine and characterize the lipid constituents/profile of the T. serratus venom utilizing liquid chromatography coupled with high-resolution mass spectrometry. Lipid species (164 in total) belonging to 3 different lipid categories, glycerophospholipids, sphingolipids, and glycerolipids, were identified. A further search on MetaCore/MetaDrug platform, which is based upon a manually curated database of molecular interactions, molecular pathways, gene-disease associations, chemical metabolism, and toxicity information, exhibited several metabolic pathways for 24 of previously identified lipid species, including activation of nuclear factor kappa B and oxidative stress pathways. Further several bioactive compounds, such as plasmalogens, lyso-platelet-activating factors, and sphingomyelins, associated with systemic responses triggered by T. serrulatus envenomation were detected. Finally, lipidomic data presented provide advanced and valuable information to better comprehend the mechanisms underlying the complex pathophysiology induced by T. serrulatus envenomation.

Chewing the fat: How lipidomics is changing our understanding of human health and disease in 2022

Lipids are biological molecules that play vital roles in all living organisms. They perform many cellular functions, such as 1) forming cellular and subcellular membranes, 2) storing and using energy, and 3) serving as chemical messengers during intra- and inter-cellular signal transduction. The large-scale study of the pathways and networks of cellular lipids in biological systems is called "lipidomics" and is one of the fastest-growing omics technologies of the last two decades. With state-of-the-art mass spectrometry instrumentation and sophisticated data handling, clinical studies show how human lipid composition changes in health and disease, thereby making it a valuable medium to collect for clinical applications, such as disease diagnostics, therapeutic decision-making, and drug development. This review gives a comprehensive overview of current workflows used in clinical research, from sample collection and preparation to data and clinical interpretations. This is followed by an appraisal of applications in 2022 and a perspective on the exciting future of clinical lipidomics.

Influence of culture conditions on the secretome of mesenchymal stem cells derived from feline adipose tissue: Proteomics approach

This study aimed to describe the secretome of mesenchymal stem cells derived from feline adipose tissue (AD-MSCs) and compare the effects of different culture conditions on AD-MSC proteomics using a shotgun approach. Adipose tissue was collected from 5 female cats and prepared to culture. Conditioned media was collected at third passage, in which the cells were cultured under 4 conditions, normoxia with fetal bovine serum (N + FBS), hypoxia with FBS (H + FBS), normoxia without FBS (N - FBS), and hypoxia without FBS (H - FBS). Then, the secretome was concentrated and prepared for proteomic approaches. Secretomes cultured with FBS-free medium had more than twice identified proteins in comparison with the secretomes cultured with FBS. In contrast, hypoxic conditions did not increase protein amount and affected only a small proteome fraction. Relevant proteins were related to the extracellular matrix promoting environmental modulation, influencing cell signaling pathways, and providing a suitable environment for cell proliferation and maintenance. Moreover, other proteins were also related to cell adhesion, migration and morphogenesis. Culture conditions can influence protein abundance in AD-MSC secretome, and can give also more specificity to cell and cell-free treatments for different diseases.

Practical and comparative application of efficient data reduction - Multivariate curve resolution

The term 'Big Data' has recently attracted much attention in science. Working with big data sets can be both challenging and rewarding. The complexity and big data sets make the analysis difficult to deal with, and the increasing volume of data sets requires the development of new practical methods for their handling. In this contribution, we explored the efficient data reduction-multivariate curve resolution (EDR-MCR) strategy based on the convex hull theory for quantitative and qualitative analysis of large chemical data sets. For the quantitative example, the potential of the EDR-MCR method for selecting a representative calibration set was investigated, and the results were compared with the widely used Kennard-Stone (KS) algorithm. The EDR-MCR strategy strongly limits the number of calibration samples with a high potency of prediction performance. The priority of EDR-MCR over KS is its ability to find informative variables and eliminate redundant features. Moreover, the EDR-MCR strategy was also applied for the qualitative analysis of a large-scale metabolomic data set. The comparable analysis results of EDR-MCR with the region of interest (ROI) method confirmed the ability of this method for quantitative analysis of big mass spectrophotometer data sets.

Guiding the choice of informatics software and tools for lipidomics research applications

Progress in mass spectrometry lipidomics has led to a rapid proliferation of studies across biology and biomedicine. These generate extremely large raw datasets requiring sophisticated solutions to support automated data processing. To address this, numerous software tools have been developed and tailored for specific tasks. However, for researchers, deciding which approach best suits their application relies on ad hoc testing, which is inefficient and time consuming. Here we first review the data processing pipeline, summarizing the scope of available tools. Next, to support researchers, LIPID MAPS provides an interactive online portal listing open-access tools with a graphical user interface. This guides users towards appropriate solutions within major areas in data processing, including (1) lipid-oriented databases, (2) mass spectrometry data repositories, (3) analysis of targeted lipidomics datasets, (4) lipid identification and (5) quantification from untargeted lipidomics datasets, (6) statistical analysis and visualization, and (7) data integration solutions. Detailed descriptions of functions and requirements are provided to guide customized data analysis workflows.

Lipidomics reveals alterations of lipid composition and molecular nutrition in irradiated marble beef

Electron beam irradiation can effectively inhibit microbial growth, but the changes of lipid during irradiation have not been comprehensively analyzed in marble beef. Here, UHPLC-MS/MS was used to detect lipids changes of irradiated marble beef. A total of 1032 lipids were identified and classified into 3 lipid classes and 8 subclasses in irradiated marble beef. 9 lipid biomarkers were screened with increasing irradiation dose. 122 differential lipids were generated and involved in 4 metabolic pathways included Glycerophospholipid metabolism, Linoleic acid metabolism, alpha-Linolenic acid metabolism and Arachidonic acid metabolism though PC(18:0/14:0), PE(16:0/16:0) and PE(18:0/16:0) in irradiated. Our results showed that irradiation had effect on the lipid of marbled beef, but the increase of irradiation dose from 2.5 kGy to 4.5 kGy had little effect on lipids. These results help us to understand the dynamic changes of irradiated meat lipids and lay a foundation for the application of irradiation in meat preservation.

Nutritional lipidomics for the characterization of lipids in food

Lipids represent one out of three major macronutrient classes in the human diet. It is estimated to account for about 15-20% of the total dietary intake. Triacylglycerides comprise the majority of them, estimated 90-95%. Other lipid classes include free fatty acids, phospholipids, cholesterol, and plant sterols as minor components. Various methods are used for the characterization of nutritional lipids, however, lipidomics approaches become increasingly attractive for this purpose due to their wide coverage, comprehensiveness and holistic view on composition. In this chapter, analytical methodologies and workflows utilized for lipidomics profiling of food samples are outlined with focus on mass spectrometry-based assays. The chapter describes common lipid extraction protocols, the distinct instrumental mass-spectrometry based analytical platforms for data acquisition, chromatographic and ion-mobility spectrometry methods for lipid separation, briefly mentions alternative methods such as gas chromatography for fatty acid profiling and mass spectrometry imaging. Critical issues of important steps of lipidomics workflows such as structural annotation and identification, quantification and quality assurance are discussed as well. Applications reported over the period of the last 5years are summarized covering the discovery of new lipids in foodstuff, differential profiling approaches for comparing samples from different origin, species, varieties, cultivars and breeds, and for food processing quality control. Lipidomics as a powerful tool for personalized nutrition and nutritional intervention studies is briefly discussed as well. It is expected that this field is significantly growing in the near future and this chapter gives a short insight into the power of nutritional lipidomics approaches.

ISG15 Is a Novel Regulator of Lipid Metabolism during Vaccinia Virus Infection

Interferon-stimulated gene 15 (ISG15) is a 15-kDa ubiquitin-like modifier that binds to target proteins in a process termed ISGylation. ISG15, first described as an antiviral molecule against many viruses, participates in numerous cellular processes, from immune modulation to the regulation of genome stability. Interestingly, the role of ISG15 as a regulator of cell metabolism has recently gained strength. We previously described ISG15 as a regulator of mitochondrial functions in bone marrow-derived macrophages (BMDMs) in the context of Vaccinia virus (VACV) infection. Here, we demonstrate that ISG15 regulates lipid metabolism in BMDMs and that ISG15 is necessary to modulate the impact of VACV infection on lipid metabolism. We show that Isg15-/- BMDMs demonstrate alterations in the levels of several key proteins of lipid metabolism that result in differences in the lipid profile compared with Isg15+/+ (wild-type [WT]) BMDMs. Specifically, Isg15-/- BMDMs present reduced levels of neutral lipids, reflected by decreased lipid droplet number. These alterations are linked to increased levels of lipases and are independent of enhanced fatty acid oxidation (FAO). Moreover, we demonstrate that VACV causes a dysregulation in the proteomes of BMDMs and alterations in the lipid content of these cells, which appear exacerbated in Isg15-/- BMDMs. Such metabolic changes are likely caused by increased expression of the metabolic regulators peroxisome proliferator-activated receptor-γ (PPARγ) and PPARγ coactivator-1α (PGC-1α). In summary, our results highlight that ISG15 controls BMDM lipid metabolism during viral infections, suggesting that ISG15 is an important host factor to restrain VACV impact on cell metabolism. IMPORTANCE The functions of ISG15 are continuously expanding, and growing evidence supports its role as a relevant modulator of cell metabolism. In this work, we highlight how the absence of ISG15 impacts macrophage lipid metabolism in the context of viral infections and how poxviruses modulate metabolism to ensure successful replication. Our results open the door to new advances in the comprehension of macrophage immunometabolism and the interaction between VACV and the host.

Role of lipidomics in assessing the functional lipid composition in breast milk

Breast milk is the ideal source of nutrients for infants in early life. Lipids represent 2–5% of the total breast milk composition and are a major energy source providing 50% of an infant’s energy intake. Functional lipids are an emerging class of lipids in breast milk mediating several different biological functions, health, and developmental outcome. Lipidomics is an emerging field that studies the structure and function of lipidome. It provides the ability to identify new signaling molecules, mechanisms underlying physiological activities, and possible biomarkers for early diagnosis and prognosis of diseases, thus laying the foundation for individualized, targeted, and precise nutritional management strategies. This emerging technique can be useful to study the major role of functional lipids in breast milk in several dimensions. Functional lipids are consumed with daily food intake; however, they have physiological benefits reported to reduce the risk of disease. Functional lipids are a new area of interest in lipidomics, but very little is known of the functional lipidome in human breast milk. In this review, we focus on the role of lipidomics in assessing functional lipid composition in breast milk and how lipid bioinformatics, a newly emerging branch in this field, can help to determine the mechanisms by which breast milk affects newborn health.

Interep: An R Package for High-Dimensional Interaction Analysis of the Repeated Measurement Data

We introduce interep, an R package for interaction analysis of repeated measurement data with high-dimensional main and interaction effects. In G × E interaction studies, the forms of environmental factors play a critical role in determining how structured sparsity should be imposed in the high-dimensional scenario to identify important effects. Zhou et al. (2019) (PMID: 31816972) proposed a longitudinal penalization method to select main and interaction effects corresponding to the individual and group structure, respectively, which requires a mixture of individual and group level penalties. The R package interep implements generalized estimating equation (GEE)-based penalization methods with this sparsity assumption. Moreover, alternative methods have also been implemented in the package. These alternative methods merely select effects on an individual level and ignore the group-level interaction structure. In this software article, we first introduce the statistical methodology corresponding to the penalized GEE methods implemented in the package. Next, we present the usage of the core and supporting functions, which is followed by a simulation example with R codes and annotations. The R package interep is available at The Comprehensive R Archive Network (CRAN).

Probing Herbicide Toxicity to Algae (Selenastrum capricornutum) by Lipid Profiling with Machine Learning and Microchip/MALDI-TOF Mass Spectrometry

Matrix-assisted laser desorption ionization mass spectrometry (MALDI-MS)-based lipid profiling is a powerful method to study the cytotoxicity of chemical exposure to microorganisms at the single cell level. We report here a combined approach of machine learning (ML) and microchip-based MALDI-time of flight (TOF) mass spectrometry to investigate the cytotoxic effect of herbicides on algae through single cell lipid profiling. Algal species Selenastrum capricornutum was chosen as the target system, and its exposure to different doses of common chemical herbicides and the resulting cytotoxic behaviors under various stress conditions were characterized. A lipid library for S. capricornutum has been established with 63 identified lipids that include glycosyldiacylglycerols and triacylglycerols. We demonstrated that major alternations occurred for lipids with functional groups of digalactosyldiacylglycerol (DGDG), triacylglycerol (TAG), and monogalactosyldiacylglycerol (MGDG). DGDG was shown to decrease upon exposure to herbicides of norflurazon and atrazine, while some MGDG and TAG lipids would increase for norflurazon. Compared to other algae, S. capricornutum was more strongly impacted by norflurazon than atrazine while the latter was observed to have a greater effect on C. reinhardtii. Machine learning algorithms have been applied to improve the classification of herbicide impact and help identify lipid species affected by the chemical exposure. A total of 69 machine learning models were trained and tested for the identification of ideal algorithms in the classification process, in which flexible discriminant analysis and support vector machine model were found to be the most accurate and consistent. The ML algorithms accurately differentiated herbicide impact and have identified cytotoxic differences that were previously hidden. The results suggest that herbicides express toxicity among different algae likely on the basis of metabolic differences. The ML-assisted method proves to be highly effective and can provide an advanced technological platform for probing cytotoxicity for bacterial species and in metabolic pathway analysis.

Integrated Metabolomics and Lipidomics Reveal High Accumulation of Glycerophospholipids in Human Astrocytes under the Lipotoxic Effect of Palmitic Acid and Tibolone Protection

Lipotoxicity is a metabolic condition resulting from the accumulation of free fatty acids in non-adipose tissues which involves a series of pathological responses triggered after chronic exposure to high levels of fatty acids, severely detrimental to cellular homeostasis and viability. In brain, lipotoxicity affects both neurons and other cell types, notably astrocytes, leading to neurodegenerative processes, such as Alzheimer (AD) and Parkinson diseases (PD). In this study, we performed for the first time, a whole lipidomic characterization of Normal Human Astrocytes cultures exposed to toxic concentrations of palmitic acid and the protective compound tibolone, to establish and identify the set of potential metabolites that are modulated under these experimental treatments. The study covered 3843 features involved in the exo- and endo-metabolome extracts obtained from astrocytes with the mentioned treatments. Through multivariate statistical analysis such as PCA (principal component analysis), partial least squares (PLS-DA), clustering analysis, and machine learning enrichment analysis, it was possible to determine the specific metabolites that were affected by palmitic acid insult, such as phosphoethanolamines, phosphoserines phosphocholines and glycerophosphocholines, with their respective metabolic pathways impact. Moreover, our results suggest the importance of tibolone in the generation of neuroprotective metabolites by astrocytes and may be relevant to the development of neurodegenerative processes.

Targeted Lipidomics Analysis of Oxylipids in Hazelnut Oil during Storage by Liquid Chromatography Coupled to Tandem Mass Spectrometry

Hazelnut oil is a high-grade edible oil with high nutritional value and unique taste. However, it is prone to oxidative degradation during storage. Herein, we used liquid chromatography coupled to tandem mass spectrometry to carry out a lipidomics analysis of the storage process of hazelnut oil. A total of 41 triacylglycerols and 12 oxylipids were determined. The contents of all oxylipids increased significantly after storage (p < 0.05). The oxylipid accumulation of hazelnut oil during storage was clarified for the first time. Nine significantly different oxylipids were further screened out. It was considered that the 15th day of storage is the dividing point. In addition, the lipoxygenase-catalyzed oxidation may be the major contributor to lipid oxidation of hazelnut oil. This study provides a new insight and theoretical basis to explore the storage oxidation mechanism of hazelnut oil and take quality control measures.

Effects of chronic crude oil exposure on the fitness of polar cod (Boreogadus saida) through changes in growth, energy reserves and survival

Climate models predict extended periods with sea-ice free Arctic waters during the next decade, which will allow more shipping activity and easier access to petroleum resources. Increased industrial activities raise concerns about the biological effects of accidental petroleum release on key species of the Arctic marine ecosystem, such as the polar cod (Boreogadus saida). This study examines effects on physiological traits related to the fitness of adult polar cod, such as growth, survival, and lipid parameters. Fish were exposed to environmentally-relevant crude oil doses through their diet over an 8-month period, concurrent with reproductive development. In liver tissue, lipid class composition differed between treatments while in gonad tissue, lipid class composition varied between sexes, but not treatments. Crude oil did not affect growth and survival, which indicated that polar cod were relatively robust to dietary crude oil exposure at doses tested (0.11-1.14 μg crude oil/g fish/day) in this study.

A Comprehensive Review of Lipidomics and Its Application to Assess Food Obtained from Farm Animals

Lipids are one of the major macronutrients essential for adequate growth and maintenance of human health. Their structure is not only complex but also diverse, which makes systematic and holistic analyses challenging; consequently, little is known regarding the relationship between phenotype and mechanism of action. In recent years, rapid advancements have been made in the fields of lipidomics and bioinformatics. In comparison with traditional approaches, mass spectrometry-based lipidomics can rapidly identify as well as quantify >1,000 lipid species at the same time, facilitating comprehensive, robust analyses of lipids in tissues, cells, and body fluids. Accordingly, lipidomics is now being widely applied in various fields, particularly food and nutrition science. In this review, we discuss lipid classification, extraction techniques, and detection and analysis using lipidomics. We also cover how lipidomics is being used to assess food obtained from livestock and poultry. The information included herein should serve as a reference to determine how to characterize lipids in animal food samples, enhancing our understanding of the application of lipidomics in the field in animal husbandry.

Integrative Lipidomics and Metabolomics for System-Level Understanding of the Metabolic Syndrome in Long-Term Treated HIV-Infected Individuals

People living with HIV (PLWH) require life-long anti-retroviral treatment and often present with comorbidities such as metabolic syndrome (MetS). Systematic lipidomic characterization and its association with the metabolism are currently missing. We included 100 PLWH with MetS and 100 without MetS from the Copenhagen Comorbidity in HIV Infection (COCOMO) cohort to examine whether and how lipidome profiles are associated with MetS in PLWH. We combined several standard biostatistical, machine learning, and network analysis techniques to investigate the lipidome systematically and comprehensively and its association with clinical parameters. Additionally, we generated weighted lipid-metabolite networks to understand the relationship between lipidomic profiles with those metabolites associated with MetS in PLWH. The lipidomic dataset consisted of 917 lipid species including 602 glycerolipids, 228 glycerophospholipids, 61 sphingolipids, and 26 steroids. With a consensus approach using four different statistical and machine learning methods, we observed 13 differentially abundant lipids between PLWH without MetS and PLWH with MetS, which mainly belongs to diacylglyceride (DAG, n = 2) and triacylglyceride (TAG, n = 11). The comprehensive network integration of the lipidomics and metabolomics data suggested interactions between specific glycerolipids' structural composition patterns and key metabolites involved in glutamate metabolism. Further integration of the clinical data with metabolomics and lipidomics resulted in the association of visceral adipose tissue (VAT) and exposure to earlier generations of antiretroviral therapy (ART). Our integrative omics data indicated disruption of glutamate and fatty acid metabolism, suggesting their involvement in the pathogenesis of PLWH with MetS. Alterations in the lipid homeostasis and glutaminolysis need clinical interventions to prevent accelerated aging in PLWH with MetS.

Lipidomic analysis of single and combined effects of polyethylene microplastics and polychlorinated biphenyls on human hepatoma cells

Microplastics are an emerging environmental issue as a result of their ubiquity, persistence, and intrinsic toxic potential. In addition, their ability to sorb and transport a wide variety of environmental pollutants (i.e. "Trojan Horse" effect) exerts significant adverse impacts upon ecosystems. The toxicological evaluation of the single and combined effects produced by polyethylene microplastics and two polychlorinated biphenyl congeners was performed on the human hepatoma cell line HepG2 by cell viability assessment and an untargeted lipidomic study. The cell lethality evaluation evinced that MPs did not induce relevant cell lethality at any of the concentration range tested, while both PCBs presented a hormetic behavior. The lipidomic analysis suggested that both single PCB exposures induced significant lipidomic changes, especially for glycerophospholipids and glycerolipids. In contrast, for MPs single exposure, the most remarkable change was the substantial enhancement of triglyceride content. Regarding combined exposures, results showed that MPs could induce even more harmful effects than those produced intrinsically as a result of desorbing previously sorbed toxic pollutants. To the best of our knowledge, this is the first study assessing the toxicity of microplastics and their possible "Trojan Horse" effect by applying an untargeted lipidomic methodology.

Integrated Lipidomic and Transcriptomic Analysis Reveals Lipid Metabolism in Foxtail Millet (Setaria italica)

Foxtail millet (Setaria italica) as the main traditional crop in China, is rich in many kinds of high quality fatty acids (FAs). In this study, Ultra-high performance liquid chromatography-time-of-flight-tandem mass spectrometer (UHPLC-Q-TOF-MS/MS) was used to determine the lipids of JG35 and JG39. A total of 2,633 lipid molecules and 31 lipid subclasses were identified, mainly including thirteen kinds of glycerophospholipids (GP), eleven kinds of glycerolipids (GL), four kinds of sphingolipids (SP), two kinds of fatty acyls (FA) and one kind of sterol (ST). Among them JG35 had higher contents of diacylglycerols (DG) and ceramides (Cer), while triacylglycerols, phosphatidyl ethanolamine, phosphatidic acid, sterol, fatty acyls and pardiolipin (TG, PE, PA, ST, FA and CL) were higher in JG39. Meantime, the correlation analysis of lipidomics and transcriptomics was used to map the main differential lipid metabolism pathways of foxtail millet. The results shown that a differentially expressed genes (DEGs) of FATA/B for the synthesis of FA was highly expressed in JG35, and the related genes for the synthesis DG (ACCase, KAS, HAD, KCS, LACS and GAPT), TG (DGAT and PDAT) and CL (CLS) were highly expressed in JG39. The results of this study will provide a theoretical basis for the future study of lipidomics, improvement of lipid quality directionally and breeding of idiosyncratic quality varieties in foxtail millet.

Visually guided preprocessing of bioanalytical laboratory data using an interactive R notebook (pguIMP)

The evaluation of pharmacological data using machine learning requires high data quality. Therefore, data preprocessing, that is, cleaning analytical laboratory errors, replacing missing values or outliers, and transforming data adequately before actual data analysis, is crucial. Because current tools available for this purpose often require programming skills, preprocessing tools with graphical user interfaces that can be used interactively are needed. In collaboration between data scientists and experts in bioanalytical diagnostics, a graphical software package for data preprocessing called pguIMP is proposed, which contains a fixed sequence of preprocessing steps to enable reproducible interactive data preprocessing. As an R-based package, it also allows direct integration into this data science environment without requiring any programming knowledge. The implementation of contemporary data processing methods, including machine-learning-based imputation techniques, ensures the generation of corrected and cleaned bioanalytical data sets that preserve data structures such as clusters better than is possible with classical methods. This was evaluated on bioanalytical data sets from lipidomics and drug research using k-nearest-neighbors-based imputation followed by k-means clustering and density-based spatial clustering of applications with noise. The R package provides a Shiny-based web interface designed to be easy to use for non-data analysis experts. It is demonstrated that the spectrum of methods provided is suitable as a standard pipeline for preprocessing bioanalytical data in biomedical research domains. The R package pguIMP is freely available at the comprehensive R archive network (https://cran.r-project.org/web/packages/pguIMP/index.html).

Conserved fatty acid profiles and lipid metabolic pathways in a tropical reef fish exposed to ocean warming - An adaptation mechanism of tolerant species?

Climate warming is causing rapid spatial expansion of ocean warm pools from equatorial latitudes towards the subtropics. Sedentary coral reef inhabitants in affected areas will thus be trapped in high temperature regimes, which may become the "new normal". In this study, we used clownfish Amphiprion ocellaris as model organism to study reef fish mechanisms of thermal adaptation and determine how high temperature affects multiple lipid aspects that influence physiology and thermal tolerance. We exposed juvenile fish to two different experimental conditions, implemented over 28 days: average tropical water temperatures (26 °C, control) or average warm pool temperatures (30 °C). We then performed several analyses on fish muscle and liver tissues: i) total lipid content (%), ii) lipid peroxides, iii) fatty acid profiles, iv) lipid metabolic pathways, and v) weight as body condition metric. Results showed that lipid storage capacity in A. ocellaris was not affected by elevated temperature, even in the presence of lipid peroxides in both tissues assessed. Additionally, fatty acid profiles were unresponsive to elevated temperature, and lipid metabolic networks were consequently well conserved. Consistent with these results, we did not observe changes in fish weight at elevated temperature. There were, however, differences in fatty acid profiles between tissue types and over time. Liver showed enhanced α-linolenic and linoleic acid metabolism, which is an important pathway in stress response signaling and modulation on environmental changes. Temporal oscillations in fatty acid profiles are most likely related to intrinsic factors such as growth, which leads to the mobilization of energetic reserves between different tissues throughout time according to organism needs. Based on these results, we propose that the stability of fatty acid profiles and lipid metabolic pathways may be an important thermal adaptation feature of fish exposed to warming environments.

Lipidomic Profiling of Algae with Microarray MALDI-MS toward Ecotoxicological Monitoring of Herbicide Exposure

Misuse of agrochemicals has a long-lasting negative impact on aquatic systems. Mismanagement of herbicides in agri-food sectors is often linked to a simultaneous decline in the health of downstream waterways. However, monitoring the herbicide levels in these areas is a laborious task, and modern analytical approaches, such as solid-phase extraction-liquid chromatography-mass spectrometry (SPE-LC-MS) and enzyme-linked immunosorbent assay, are low-throughput and require significant sample preparation. We report here the use of microchip technology in combination with matrix-assisted laser desorption ionization mass spectrometry (MALDI-MS) for the assessment of the ecotoxicological effect of agrochemicals on aquatic species at the single-cell level. This approach quantifies the fluctuations in lipid content in sentinel organisms and targets the microalga, Chlamydomonas reinhardtii (C. reinhardtii), as the model system. Specifically, we investigated the cytotoxicity of three herbicides (atrazine, clomazone, and norflurazon) on C. reinhardtii by analyzing the lipid component variation upon assorted herbicide exposure. Lipidomic profiling reveals a significantly altered lipid content at >EC₅₀ in atrazine-exposed cells. The response for norflurazon showed similar trends but diminished in magnitude, while the result for clomazone was near muted. At lower herbicide concentrations, digalactosyldiacylglycerols showed a rapid decrease in abundance, while several other lipids displayed a moderate increase. The microchip-based MALDI technique demonstrates the ability to achieve lipidomic profiling of aquatic species exposed to different stressors, proving effective for high-throughput screening and single-cell analysis in ecotoxicity studies.

Analysis of the uterine lumen in fertility-classified heifers: I. Glucose, prostaglandins, and lipids†

Survival and growth of the bovine conceptus (embryo and associated extraembryonic membranes) are dependent on endometrial secretions or histotroph found in the uterine lumen. Previously, serial embryo transfer was used to classify heifers as high fertile (HF), subfertile (SF), or infertile (IF). Here, we investigated specific histotroph components [glucose, prostaglandins (PGs), and lipids] in the uterine lumen of day 17 pregnant and open fertility-classified heifers. Concentrations of glucose in the uterine lumen were increased by pregnancy but did not differ among fertility-classified heifers. Differences in expression of genes encoding glucose transporters and involved with glycolysis and gluconeogenesis were observed between conceptuses collected from HF and SF heifers. In the uterine lumen, PGE2 and PGF2α were increased by pregnancy, and HF heifers had higher concentrations of PGE2, PGF2α, and 6-keto-PFG1α than SF heifers. Differences were found in expression of genes regulating PG signaling, arachidonic acid metabolism, and peroxisome proliferator-activated receptor signaling among conceptuses and endometrium from fertility-classified heifers. Lipidomics was conducted exclusively in samples from HF heifers, and phosphatidylcholine was the main lipid class that increased in the uterine lumen by pregnancy. Expression of several lipid metabolism genes differed between HF and SF conceptuses, and a number of fatty acids were differentially abundant in the uterine lumen of pregnant HF and SF heifers. These results support the ideas that uterine luminal histotroph impacts conceptus survival and programs its development and is a facet of dysregulated conceptus-endometrial interactions that result in loss of the conceptus in SF cattle during the implantation period of pregnancy establishment.

Effects of Obesity and Diabetes on Sperm Cell Proteomics in Rats

Infertility caused by male factors is potentially associated with metabolic disorders such as obesity and/or diabetes. This experimental study was conducted in a male rodent model to assess the effects of different diseases on semen quality and sperm proteomics. Ten Wistar rats were used for each treatment. Rats were fed commercial food provided controllably to the control group and the diabetic group, and a hypercaloric diet supplemented with 5% sucrose in water was provided ad libitum to the obese group for 38 weeks. Diabetes was induced with 35 mg/kg streptozotocin. After euthanasia, testicles, spermatozoa, fat, and blood (serum) samples were collected. Spermatozoa were evaluated for quality and subjected to proteomics analyses. Histology and cytology of the testis, and serum leptin, adiponectin, interleukin 8 (IL-8), blood glucose, and testosterone levels, were also assessed. Body weight, retroperitoneal and testicular fat, and the Lee index were also measured. Obesity and diabetes were induced. The diabetic group showed noticeable changes in spermatogenesis and sperm quality. The mass spectrometry proteomics data have been deposited in Mendeley Data (doi: 10.17632/rfp7kfjcsd.5). Fifteen proteins varied in abundance between groups, especially proteins related to energy production and structural function of the spermatozoa, suggesting disturbances in energy production with a subsequent alteration in sperm motility in both groups, but with a compensatory response in the obese group.

Untargeted lipidomic approach in studying pinot noir wine lipids and predicting wine origin

An untargeted lipidomic profiling approach based on ultra - performance liquid chromatography - time-of-flight tandem mass spectrometry (UPLC-TOF-MS/MS) was successfully used to study the origin of commercial Pinot noir wines. The total wine lipids were extracted using a modified Bligh-Dyer method. In all wine samples, the total lipids were less than 0.1% (w/w) of wine. The wines analyzed consisted of 222 lipids from 11 different classes. 48 commercial Pinot noir wine samples were collected from producers in Burgundy, California, Oregon, and New Zealand. Lipidomic data was studied using advanced multivariate analysis methods, random forest, k-nearest neighbor (k-NN), and linear discriminant analysis. The overall classification accuracy was 97.5% for random forest and 90% for k-NN. Wine lipids showed a strong potential for classifying wines by origin, with the top 58 lipids contributing to the discrimination. This information could potentially be used for further study of the impacts of lipids on wine characteristics and authenticity.

Proteomics Approach of Rapamycin Anti-Tumoral Effect on Primary and Metastatic Canine Mammary Tumor Cells In Vitro

Rapamycin is an antifungal drug with antitumor activity and acts inhibiting the mTOR complex. Due to drug antitumor potential, the aim of this study was to evaluate its effect on a preclinical model of primary mammary gland tumors and their metastases from female dogs. Four cell lines from our cell bank, two from primary canine mammary tumors (UNESP-CM1, UNESP-CM60) and two metastases (UNESP-MM1, and UNESP-MM4) were cultured in vitro and investigated for rapamycin IC₅₀. Then, cell lines were treated with rapamycin IC₅₀ dose and mRNA and protein were extracted in treated and non-treated cells to perform AKT, mTOR, PTEN and 4EBP1 gene expression and global proteomics by mass spectrometry. MTT assay demonstrated rapamycin IC₅₀ dose for all different tumor cells between 2 and 10 μM. RT-qPCR from cultured cells, control versus treated group and primary tumor cells versus metastatic tumor cells, did not shown statistical differences. In proteomics were found 273 proteins in all groups, and after data normalization 49 and 92 proteins were used for statistical analysis for comparisons between control versus rapamycin treatment groups, and metastasis versus primary tumor versus metastasis rapamycin versus primary tumor rapamycin, respectively. Considering the two statistical analysis, four proteins, phosphoglycerate mutase, malate dehydrogenase, l-lactate dehydrogenase and nucleolin were found in decreased abundance in the rapamycin group and they are related with cellular metabolic processes and enhanced tumor malignant behavior. Two proteins, dihydrolipoamide dehydrogenase and superoxide dismutase, also related with metabolic processes, were found in higher abundance in rapamycin group and are associated with apoptosis. The results suggested that rapamycin was able to inhibit cell growth of mammary gland tumor and metastatic tumors cells in vitro, however, concentrations needed to reach the IC₅₀ were higher when compared to other studies.

Lipidomics reveals multiple stressor effects (temperature × mitochondrial toxicant) in the zebrafish embryo toxicity test

Aquatic organisms are exposed to multiple stressors in the environment, including contaminants and rising temperatures due to climate change. The objective of this study was to characterize the effect of increased temperature on chemical-induced toxicity and lipid profiles during embryonic development and hatch in fish. This is important because temperature and many environmental chemicals modulate cellular metabolism and lipids, both of which play integral roles for normal embryonic development. As such, we employed the zebrafish embryo toxicity test for multiple stressor exposures, using the mitochondrial toxicant 2,4-Dinitrophenol (DNP; 6-30 μM) in conjunction with different temperature treatments (28 °C and 33 °C). We found a positive relationship between temperature and lethality at lower DNP concentrations, suggesting temperature stress can increase toxicant sensitivity. Next, we used LC-MS/MS for lipidomics following exposure to sublethal stressor combinations. It was determined that temperature stress at 33 °C augmented DNP-induced effects on the lipidome, including the upregulation of bioactive lipids involved in apoptosis (e.g., ceramides). These data reveal potential implications for climate change and sensitivity to environmental pollution and demonstrate the utility of lipidomics to characterize metabolic pathways underlying toxicity. Data such as these are expected to advance adverse outcome pathways by establishing multiple stressor networks that include intermediate lipid responses.

Mass Spectrometry-Based Shotgun Lipidomics for Cancer Research

Shotgun lipidomics is an analytical approach for large-scale and systematic analysis of the composition, structure, and quantity of cellular lipids directly from lipid extracts of biological samples by mass spectrometry. This approach possesses advantages of high throughput and quantitative accuracy, especially in absolute quantification. As cancer research deepens at the level of quantitative biology and metabolomics, the demand for lipidomics approaches such as shotgun lipidomics is becoming greater. In this chapter, the principles, approaches, and some applications of shotgun lipidomics for cancer research are overviewed.

Systems biology approaches to study lipidomes in health and disease

Lipids have many important biological roles, such as energy storage sources, structural components of plasma membranes and as intermediates in metabolic and signaling pathways. Lipid metabolism is under tight homeostatic control, exhibiting spatial and dynamic complexity at multiple levels. Consequently, lipid-related disturbances play important roles in the pathogenesis of most of the common diseases. Lipidomics, defined as the study of lipidomes in biological systems, has emerged as a rapidly-growing field. Due to the chemical and functional diversity of lipids, the application of a systems biology approach is essential if one is to address lipid functionality at different physiological levels. In parallel with analytical advances to measure lipids in biological matrices, the field of computational lipidomics has been rapidly advancing, enabling modeling of lipidomes in their pathway, spatial and dynamic contexts. This review focuses on recent progress in systems biology approaches to study lipids in health and disease, with specific emphasis on methodological advances and biomedical applications.

Mass spectrometry-based lipidomics in food science and nutritional health: A comprehensive review

With the advance in science and technology as well as the improvement of living standards, the function of food is no longer just to meet the needs of survival. Food science and its associated nutritional health issues have been increasingly debated. Lipids, as complex metabolites, play a key role both in food and human health. Taking advantages of mass spectrometry (MS) by combining its high sensitivity and accuracy with extensive selective determination of all lipid classes, MS-based lipidomics has been employed to resolve the conundrum of addressing both qualitative and quantitative aspects of high-abundance and low-abundance lipids in complex food matrices. In this review, we systematically summarize current applications of MS-based lipidomics in food field. First, common MS-based lipidomics procedures are described. Second, the applications of MS-based lipidomics in food science, including lipid composition characterization, adulteration, traceability, and other issues, are discussed. Third, the application of MS-based lipidomics for nutritional health covering the influence of food on health and disease is introduced. Finally, future research trends and challenges are proposed. MS-based lipidomics plays an important role in the field of food science, promoting continuous development of food science and integration of food knowledge with other disciplines. New methods of MS-based lipidomics have been developed to improve accuracy and sensitivity of lipid analysis in food samples. These developments offer the possibility to fully characterize lipids in food samples, identify novel functional lipids, and better understand the role of food in promoting healt.

HILIC-ESI-FTMS with All Ion Fragmentation (AIF) Scans as a Tool for Fast Lipidome Investigations

Lipidomics suffers from the lack of fast and reproducible tools to obtain both structural information on intact phospholipids (PL) and fatty acyl chain composition. Hydrophilic interaction liquid chromatography with electrospray ionization coupled to an orbital-trap Fourier-transform analyzer operating using all ion fragmentation mode (HILIC-ESI-FTMS-AIF MS) is seemingly a valuable resource in this respect. Here, accurate m/z values, HILIC retention times and AIF MS scan data were combined for PL assignment in standard mixtures or real lipid extracts. AIF scans in both positive and negative ESI mode, achieved using collisional induced dissociation for fragmentation, were applied to identify both the head-group of each PL class and the fatty acyl chains, respectively. An advantage of the AIF approach was the concurrent collection of tandem MS-like data, enabling the identification of linked fatty acyl chains of precursor phospholipids through the corresponding carboxylate anions. To illustrate the ability of AIF in the field of lipidomics, two different types of real samples, i.e., the lipid extracts obtained from human plasma and dermal fibroblasts, were examined. Using AIF scans, a total of 253 intact lipid species and 18 fatty acids across 4 lipid classes were recognized in plasma samples, while FA C20:3 was confirmed as the fatty acyl chain belonging to phosphatidylinositol, PI 38:3, which was found to be down-regulated in fibroblast samples of Parkinson's disease patients.

Nutrient availability affects the polar lipidome of Halimione portulacoides leaves cultured in hydroponics

Halophytes are increasingly regarded as suitable extractive species and co-products for coastal Integrated Multi-Trophic Aquaculture (IMTA) and studying their lipidome is a valid means towards their economic valorization. Halimione portulacoides (L.) Aellen edible leaves are rich in functional lipids with nutraceutical and pharmaceutical relevance and the present study aimed to investigate the extent to which its lipidome remains unchanged under a range of dissolved inorganic nitrogen (N) and phosphorus (P) concentrations typical of aquaculture effluents. Lipidomics analysis, done by hydrophilic interaction liquid chromatography coupled to high resolution mass spectrometry, identified 175 lipid species in the lipid extract of leaves: 140 phospholipids (PLs) and 35 glycolipids (GLs). Plants irrigated with a saline solution with 20-100 mg DIN-N L-1 and 3-15.5 mg DIP-P L-1 under a 1-week hydraulic retention time displayed a relatively stable lipidome. At lower concentrations (6 mg DIN-N L-1 and 0.8 mg DIP-P L-1), plants exhibited less PLs and GLs per unit of leaves dry weight and the GLs fraction of the lipidome changed significantly. This study reveals the importance of analyzing the lipidomic profile of halophytes under different nutritional regimens in order to establish nutrient-limitation thresholds and assure production conditions that deliver a final product with a consistent lipid profile.

Differential Toxicity of Alkylphenols in JEG-3 Human Placental Cells: Alteration of P450 Aromatase and Cell Lipid Composition

Alkylphenols (APs) are a diverse class of chemicals that can cross the placental barrier and interfere with embryonic and fetal development. This work investigates the comparative toxicity, ability to inhibit aromatase activity, and to alter the lipid composition of 10 alkylphenols in the human placenta choriocarcinoma cell line JEG-3. Among the selected APs, 4-dodecylphenol (DP), 4-heptylphenol (HP), and 4-cumylphenol (CP) showed the highest cytotoxicity (EC50: 18-65 µM). Aromatase inhibition was closely related to the hydrophobicity of APs. HP significantly induced the generation of reactive oxygen species (ROS) (43-fold), inhibited placental aromatase activity (IC50: 41 µM), and induced a general dose-dependent depletion of polyunsaturated lipids (10-20 µM), which were attributed to high levels of oxidative stress. In contrast, 2,4,6-tri-tert-butylphenol (TTBP) significantly induced the intracellular accumulation of triacylglycerides (TGs), whereas DP increased the synthesis of phosphatidylcholines (PCs) and TGs at the expense of diacylglycerides (DGs). Overall, this study evidences the different modes of action of alkylphenols in human placental JEG-3 cells, describes differential lipidomic fingerprints, and highlights DP, HP, CP, and TTBP as the ones that caused the most harmful effects.

"Lipidomics": Mass spectrometric and chemometric analyses of lipids

Lipids are ubiquitous in the human organism and play essential roles as components of cell membranes and hormones, for energy storage or as mediators of cell signaling pathways. As crucial mediators of the human metabolism, lipids are also involved in metabolic diseases, cardiovascular and renal diseases, cancer and/or hepatological and neurological disorders. With rapidly growing evidence supporting the impact of lipids on both the genesis and progression of these diseases as well as patient wellbeing, the characterization of the human lipidome has gained high interest and importance in life sciences and clinical diagnostics within the last 15 years. This is mostly due to technically advanced molecular identification and quantification methods, mainly based on mass spectrometry. Mass spectrometry has become one of the most powerful tools for the identification of lipids. New lipidic mediators or biomarkers of diseases can be analysed by state-of-the art mass spectrometry techniques supported by sophisticated bioinformatics and biostatistics. The lipidomic approach has developed dramatically in the realm of life sciences and clinical diagnostics due to the available mass spectrometric methods and in particular due to the adaptation of biostatistical methods in recent years. Therefore, the current knowledge of lipid extraction methods, mass-spectrometric approaches, biostatistical data analysis, including workflows for the interpretation of lipidomic high-throughput data, are reviewed in this manuscript.

Penalized Variable Selection for Lipid-Environment Interactions in a Longitudinal Lipidomics Study

Lipid species are critical components of eukaryotic membranes. They play key roles in many biological processes such as signal transduction, cell homeostasis, and energy storage. Investigations of lipid-environment interactions, in addition to the lipid and environment main effects, have important implications in understanding the lipid metabolism and related changes in phenotype. In this study, we developed a novel penalized variable selection method to identify important lipid-environment interactions in a longitudinal lipidomics study. An efficient Newton-Raphson based algorithm was proposed within the generalized estimating equation (GEE) framework. We conducted extensive simulation studies to demonstrate the superior performance of our method over alternatives, in terms of both identification accuracy and prediction performance. As weight control via dietary calorie restriction and exercise has been demonstrated to prevent cancer in a variety of studies, analysis of the high-dimensional lipid datasets collected using 60 mice from the skin cancer prevention study identified meaningful markers that provide fresh insight into the underlying mechanism of cancer preventive effects.