Metabolomics meets lipidomics: Assessing the small molecule component of metabolism

Electron-activated dissociation (EAD) for the complementary annotation of metabolites and lipids through data-dependent acquisition analysis and feature-based molecular networking, applied to the sentinel amphipod Gammarus fossarum

The past decades have marked the rise of metabolomics and lipidomics as the -omics sciences which reflect the most phenotypes in living systems. Mass spectrometry-based approaches are acknowledged for both quantification and identification of molecular signatures, the latter relying primarily on fragmentation spectra interpretation. However, the high structural diversity of biological small molecules poses a considerable challenge in compound annotation. Feature-based molecular networking (FBMN) combined with database searches currently sets the gold standard for annotation of large datasets. Nevertheless, FBMN is usually based on collision-induced dissociation (CID) data, which may lead to unsatisfying information. The use of alternative fragmentation methods, such as electron-activated dissociation (EAD), is undergoing a re-evaluation for the annotation of small molecules, as it gives access to additional fragmentation routes. In this study, we apply the performances of data-dependent acquisition mass spectrometry (DDA-MS) under CID and EAD fragmentation along with FBMN construction, to perform extensive compound annotation in the crude extracts of the freshwater sentinel organism Gammarus fossarum. We discuss the analytical aspects of the use of the two fragmentation modes, perform a general comparison of the information delivered, and compare the CID and EAD fragmentation pathways for specific classes of compounds, including previously unstudied species. In addition, we discuss the potential use of FBMN constructed with EAD fragmentation spectra to improve lipid annotation, compared to the classic CID-based networks. Our approach has enabled higher confidence annotations and finer structure characterization of 823 features, including both metabolites and lipids detected in G. fossarum extracts.

Blood-Derived Lipid and Metabolite Biomarkers in Cardiovascular Research from Clinical Studies: A Recent Update

The primary prevention, early detection, and treatment of cardiovascular disease (CVD) have been long-standing scientific research goals worldwide. In the past decades, traditional blood lipid profiles have been routinely used in clinical practice to estimate the risk of CVDs such as atherosclerotic cardiovascular disease (ASCVD) and as treatment targets for the primary prevention of adverse cardiac events. These blood lipid panel tests often fail to fully predict all CVD risks and thus need to be improved. A comprehensive analysis of molecular species of lipids and metabolites (defined as lipidomics and metabolomics, respectively) can provide molecular insights into the pathophysiology of the disease and could serve as diagnostic and prognostic indicators of disease. Mass spectrometry (MS) and nuclear magnetic resonance (NMR)-based lipidomics and metabolomics analysis have been increasingly used to study the metabolic changes that occur during CVD pathogenesis. In this review, we provide an overview of various MS-based platforms and approaches that are commonly used in lipidomics and metabolomics workflows. This review summarizes the lipids and metabolites in human plasma/serum that have recently (from 2018 to December 2022) been identified as promising CVD biomarkers. In addition, this review describes the potential pathophysiological mechanisms associated with candidate CVD biomarkers. Future studies focused on these potential biomarkers and pathways will provide mechanistic clues of CVD pathogenesis and thus help with the risk assessment, diagnosis, and treatment of CVD.

Prenatal exposures to endocrine disrupting chemicals: The role of multi-omics in understanding toxicity

Endocrine disrupting chemicals (EDCs) are a diverse group of toxicants detected in populations globally. Prenatal EDC exposures impact birth and childhood outcomes. EDCs work through persistent changes at the molecular, cellular, and organ level. Molecular and biochemical signals or 'omics' can be measured at various functional levels - including the epigenome, transcriptome, proteome, metabolome, and the microbiome. In this narrative review, we introduce each omics and give examples of associations with prenatal EDC exposures. There is substantial research on epigenomic modifications in offspring exposed to EDCs during gestation, and a growing number of studies evaluating the transcriptome, proteome, metabolome, or microbiome in response to these exposures. Multi-omics, integrating data across omics layers, may improve understanding of disrupted function pathways related to early life exposures. We highlight several data integration methods to consider in multi-omics studies. Information from multi-omics can improve understanding of the biological processes and mechanisms underlying prenatal EDC toxicity.

Proteomics and Metabolomics in Biomedicine

The technological advances of recent years have significantly enhanced medical discoveries [...].

The Impact of Abnormal Lipid Metabolism on the Occurrence Risk of Idiopathic Pulmonary Arterial Hypertension

The aim was to determine whether lipid molecules can be used as potential biomarkers for idiopathic pulmonary arterial hypertension (IPAH), providing important reference value for early diagnosis and treatment. Liquid chromatography-mass spectrometry-based lipidomic assays allow for the simultaneous detection of a large number of lipids. In this study, lipid profiling was performed on plasma samples from 69 IPAH patients and 30 healthy controls to compare the levels of lipid molecules in the 2 groups of patients, and Cox regression analysis was used to identify meaningful metrics, along with receiver operator characteristic curves to assess the ability of the lipid molecules to predict the risk of disease in patients. Among the 14 lipid subclasses tested, 12 lipid levels were significantly higher in IPAH patients than in healthy controls. Free fatty acids (FFA) and monoacylglycerol (MAG) were significantly different between IPAH patients and healthy controls. Logistic regression analysis showed that FFA (OR: 1.239, 95%CI: 1.101, 1.394, p < 0.0001) and MAG (OR: 3.711, 95%CI: 2.214, 6.221, p < 0.001) were independent predictors of IPAH development. Among the lipid subclasses, FFA and MAG have potential as biomarkers for predicting the pathogenesis of IPAH, which may improve the early diagnosis of IPAH.

Integrative multi-omics and systems bioinformatics in translational neuroscience: A data mining perspective

Bioinformatic analysis of large and complex omics datasets has become increasingly useful in modern day biology by providing a great depth of information, with its application to neuroscience termed neuroinformatics. Data mining of omics datasets has enabled the generation of new hypotheses based on differentially regulated biological molecules associated with disease mechanisms, which can be tested experimentally for improved diagnostic and therapeutic targeting of neurodegenerative diseases. Importantly, integrating multi-omics data using a systems bioinformatics approach will advance the understanding of the layered and interactive network of biological regulation that exchanges systemic knowledge to facilitate the development of a comprehensive human brain profile. In this review, we first summarize data mining studies utilizing datasets from the individual type of omics analysis, including epigenetics/epigenomics, transcriptomics, proteomics, metabolomics, lipidomics, and spatial omics, pertaining to Alzheimer's disease, Parkinson's disease, and multiple sclerosis. We then discuss multi-omics integration approaches, including independent biological integration and unsupervised integration methods, for more intuitive and informative interpretation of the biological data obtained across different omics layers. We further assess studies that integrate multi-omics in data mining which provide convoluted biological insights and offer proof-of-concept proposition towards systems bioinformatics in the reconstruction of brain networks. Finally, we recommend a combination of high dimensional bioinformatics analysis with experimental validation to achieve translational neuroscience applications including biomarker discovery, therapeutic development, and elucidation of disease mechanisms. We conclude by providing future perspectives and opportunities in applying integrative multi-omics and systems bioinformatics to achieve precision phenotyping of neurodegenerative diseases and towards personalized medicine.

Exploration of the underlying mechanisms of isoniazid/rifampicin-induced liver injury in mice using an integrated proteomics and metabolomics approach

The hepatotoxic mechanism resulting from coadministration of isoniazid (INH) and rifampicin (RIF) are complex and studies remain inconclusive. To systematically explore the underlying mechanisms, an integrated mass-based untargeted metabolomics and label-free quantitative proteomics approach was used to clarify the mechanism of INH/RIF-induced liver injury. Thirty male mice were randomly divided into three groups: control (receiving orally administered vehicle solution), INH (150 mg/kg) + RIF (300 mg/kg) orally administered for either 7 or 14 days, respectively. Serum was collected for the analysis of biochemical parameters and liver samples were obtained for mass spectrum-based proteomics, metabolomics, and lipidomics analysis. Overall, 511 proteins, 31 metabolites, and 23 lipids were dysregulated and identified, and disordered biological pathways were identified. The network of integrated multiomics showed that glucose, lipid, and amino acid metabolism as well as energy metabolism were mainly dysregulated and led to oxidative stress, inflammation, liver steatosis, and cell death induced by INH and RIF. Coadministration of INH and RIF can induce liver injury by oxidative stress, inflammation, liver steatosis, and cell death, and the reduction in glutathione levels may play a critical role in these systematic changes and warrants further study.

The Plasma Oxylipin Signature Provides a Deep Phenotyping of Metabolic Syndrome Complementary to the Clinical Criteria

Metabolic syndrome (MetS) is a complex condition encompassing a constellation of cardiometabolic abnormalities. Oxylipins are a superfamily of lipid mediators regulating many cardiometabolic functions. Plasma oxylipin signature could provide a new clinical tool to enhance the phenotyping of MetS pathophysiology. A high-throughput validated mass spectrometry method, allowing for the quantitative profiling of over 130 oxylipins, was applied to identify and validate the oxylipin signature of MetS in two independent nested case/control studies involving 476 participants. We identified an oxylipin signature of MetS (coined OxyScore), including 23 oxylipins and having high performances in classification and replicability (cross-validated AUC_ROC of 89%, 95% CI: 85–93% and 78%, 95% CI: 72–85% in the Discovery and Replication studies, respectively). Correlation analysis and comparison with a classification model incorporating the MetS criteria showed that the oxylipin signature brings consistent and complementary information to the clinical criteria. Being linked with the regulation of various biological processes, the candidate oxylipins provide an integrative phenotyping of MetS regarding the activation and/or negative feedback regulation of crucial molecular pathways. This may help identify patients at higher risk of cardiometabolic diseases. The oxylipin signature of patients with metabolic syndrome enhances MetS phenotyping and may ultimately help to better stratify the risk of cardiometabolic diseases.

A validated and optimized method for separation and quantification of total fatty acids by gas chromatography-ion trap mass spectrometry in human plasma

Fatty acids (FAs) are associated with many physiological functions of tissues, and their alteration has been linked with tissue-specific or systemic diseases. The current situation warrants us to have a sensitive and specific method for analysis of total FAs simultaneously from the biological fluid so that the risk prediction, diagnosis or prognosis of the disease can be made effectively. Because of greater sensitivity and resolution, a method of gas chromatography-ion trap mass spectrometry (GC-IT/MS) has been optimized and validated to quantify simultaneously 19 total FAs levels in plasma and compared with GC-triple quadrupole mass spectrometry. FAs have been transesterified by methanolic acetyl chloride to fatty acid methyl esters (FAMEs). A 65 min GC method separated all 19 FAMEs. The calibration curve had good linearity up to 313-922 μM with a correlation coefficient between 0.9882 and 0.9998. The LODs and LOQs of FAMEs were in the range of 0.63 to 9.55 and 2.12 to 31.8 μM, respectively. The method has recovery up to 144 %, stability at 4 °C for 48 h and one freeze-thaw cycle, and good intra-day and inter-day precision. The optimized method has been used to quantify plasma total FAs in type 2 diabetes mellitus patients with and without acute coronary syndrome. Though a significant difference has been found between IT/MS and triple quadrupole mass spectrometry, the GC-IT/MS can help to quantify total FAs in the clinical setting.

Metabolomics of Human Semen: A Review of Different Analytical Methods to Unravel Biomarkers for Male Fertility Disorders

Background: Human life without sperm is not possible. Therefore, it is alarming that the fertilizing ability of human spermatozoa is continuously decreasing. The reasons for that are widely unknown, but there is hope that metabolomics-based investigations may be able to contribute to overcoming this problem. This review summarizes the attempts made so far. Methods: We will discuss liquid chromatography–mass spectrometry (LC-MS), gas chromatography (GC), infrared (IR) and Raman as well as nuclear magnetic resonance (NMR) spectroscopy. Almost all available studies apply one of these methods. Results: Depending on the methodology used, different compounds can be detected, which is (in combination with sophisticated methods of bioinformatics) helpful to estimate the state of the sperm. Often, but not in all cases, there is a correlation with clinical parameters such as the sperm mobility. Conclusions: LC-MS detects the highest number of metabolites and can be considered as the method of choice. Unfortunately, the reproducibility of some studies is poor, and, thus, further improvements of the study designs are needed to overcome this problem. Additionally, a stronger focus on the biochemical consequences of the altered metabolite concentrations is also required.

Untargeted metabolomics analysis of omeprazole-enhanced chemosensitivity to cisplatin in mice with non-small cell lung cancer

Drug resistance of tumors remains a major barrier in cisplatin (CDDP)-based chemotherapy. Omeprazole (OME) is often utilized during chemotherapy to alleviate gastrointestinal symptoms. In a previous investigation, we demonstrated a protective effect of OME against CDDP-induced kidney injury. To further establish whether OME could enhance chemosensitivity to CDDP and the underlying mechanisms, an in vivo tumor-bearing mouse model with CDDP-resistant A549 non-small cell lung cancer (A549/CDDP) was established in the current study. A high-performance liquid chromatography-time of flight mass spectrometry (HPLC-TOF/MS)-based untargeted metabolomics approach for tumor tissue and serum was employed to explore the mechanisms underlying the enhanced therapeutic effects of co-administration of CDDP and OME. Notably, tumor weights of mice in the CDDP + OME group were significantly decreased compared with those treated with CDDP alone. HE and TUNEL staining revealed more significant apoptosis of tumor cells in the group co-administered CDDP + OME relative to CDDP alone. Overexpression of multidrug resistance-associated protein 2 in CDDP-resistant tumors was significantly reversed upon treatment with CDDP + OME. PCA score plots of the groups co-treated with CDDP + OME were clearly separated from those treated with CDDP alone in metabolomics analysis for tumor and serum samples, clearly suggesting that co-administration of OME enhances the antitumor effect of CDDP. Subsequently, 10 and 7 metabolites in CDDP + OME group with significant changes in tumor and serum compared with CDDP group, respectively, were identified. Pathway analysis both in tumor and serum samples revealed regulation of the metabolism of purines, several amino acids and riboflavin in enhanced chemotherapy with both OME and CDDP. The collective findings provide beneficial novel insights into drug-drug interactions, which could improve the application of CDDP in clinical practice.

Metabolomics as an emerging tool to study plant–microbe interactions

In natural environments, interaction between plant roots and microorganisms are common. These interactions between microbial species and plants inhabited by them are being studied using various techniques. Metabolomics research based on mass spectrometric techniques is one of the crucial approaches that underpins system biology and relies on precision instrument analysis. In the last decade, this emerging field has received extensive attention. It provides a qualitative and quantitative approach for determining the mechanisms of symbiosis of bacteria and fungi with plants and also helps to elucidate the tolerance mechanisms of host plants against various abiotic stresses. However, this -omics application and its tools in plant–microbe interaction studies is still underutilized compared with genomic and transcriptomic methods. Therefore, it is crucial to bring this field forward to bear on the study of plant resistance and susceptibility. This review describes the current status of methods and progress in metabolomics applications for plant–microbe interaction studies discussing current challenges and future prospects.

Towards a comprehensive characterisation of the human internal chemical exposome: Challenges and perspectives

The holistic characterisation of the human internal chemical exposome using high-resolution mass spectrometry (HRMS) would be a step forward to investigate the environmental ætiology of chronic diseases with an unprecedented precision. HRMS-based methods are currently operational to reproducibly profile thousands of endogenous metabolites as well as externally-derived chemicals and their biotransformation products in a large number of biological samples from human cohorts. These approaches provide a solid ground for the discovery of unrecognised biomarkers of exposure and metabolic effects associated with many chronic diseases. Nevertheless, some limitations remain and have to be overcome so that chemical exposomics can provide unbiased detection of chemical exposures affecting disease susceptibility in epidemiological studies. Some of these limitations include (i) the lack of versatility of analytical techniques to capture the wide diversity of chemicals; (ii) the lack of analytical sensitivity that prevents the detection of exogenous (and endogenous) chemicals occurring at (ultra) trace levels from restricted sample amounts, and (iii) the lack of automation of the annotation/identification process. In this article, we discuss a number of technological and methodological limitations hindering applications of HRMS-based methods and propose initial steps to push towards a more comprehensive characterisation of the internal chemical exposome. We also discuss other challenges including the need for harmonisation and the difficulty inherent in assessing the dynamic nature of the internal chemical exposome, as well as the need for establishing a strong international collaboration, high level networking, and sustainable research infrastructure. A great amount of research, technological development and innovative bio-informatics tools are still needed to profile and characterise the "invisible" (not profiled), "hidden" (not detected) and "dark" (not annotated) components of the internal chemical exposome and concerted efforts across numerous research fields are paramount.

Sex-specific alterations in NAD+ metabolism in 3xTg Alzheimer's disease mouse brain assessed by quantitative targeted LC-MS

Levels of nicotinamide adenine dinucleotide (NAD+) are known to decline with age and have been associated with impaired mitochondrial function leading to neurodegeneration, a key facet of Alzheimer's disease (AD). NAD+synthesis is sustained via tryptophan-kynurenine (Trp-Kyn) pathway as de novo synthesis route, and salvage pathways dependent on the availability of nicotinic acid and nicotinamide. While being currently investigated as a multifactorial disease with a strong metabolic component, AD remains without curative treatment and important sex differences were reported in relation to disease onset and progression. The aim of this study was to reveal the potential deregulation of NAD+metabolism in AD with the direct analysis of NAD+precursors in the mouse brain tissue (wild type (WT) versus triple transgenic (3xTg) AD), using a sex-balanced design. To this end, we developed a quantitative liquid chromatography-tandem mass spectrometry (LC-MS/MS) method, which allowed for the measurement of the full spectrum of NAD+precursors and intermediates in all three pathways. In brain tissue of mice with developed AD symptoms, a decrease in kynurenine (Kyn) versus increase in kynurenic acid (KA) levels were observed in both sexes with a significantly higher increment of KA in males. These alterations in Trp-Kyn pathway might be a consequence of neuroinflammation and a compensatory production of neuroprotective kynurenic acid. In the NAD+ salvage pathway, significantly lower levels of nicotinamide mononucleotide (NMN) were measured in the AD brain of males and females. Depletion of NMN implies the deregulation of salvage pathway critical for maintaining optimal NAD+ levels and mitochondrial and neuronal function.

A guide to interrogating immunometabolism

The metabolic charts memorized in early biochemistry courses, and then later forgotten, have come back to haunt many immunologists with new recognition of the importance of these pathways. Metabolites and the activity of metabolic pathways drive energy production, macromolecule synthesis, intracellular signalling, post-translational modifications and cell survival. Immunologists who identify a metabolic phenotype in their system are often left wondering where to begin and what does it mean? Here, we provide a framework for navigating and selecting the appropriate biochemical techniques to explore immunometabolism. We offer recommendations for initial approaches to develop and test metabolic hypotheses and how to avoid common mistakes. We then discuss how to take things to the next level with metabolomic approaches, such as isotope tracing and genetic approaches. By proposing strategies and evaluating the strengths and weaknesses of different methodologies, we aim to provide insight, note important considerations and discuss ways to avoid common misconceptions. Furthermore, we highlight recent studies demonstrating the power of these metabolic approaches to uncover the role of metabolism in immunology. By following the framework in this Review, neophytes and seasoned investigators alike can venture into the emerging realm of cellular metabolism and immunity with confidence and rigour.

Mapping the distribution of double bond location isomers in lipids across mouse tissues

Lipids are highly diverse and essential biomolecules in all living systems. As lipid homeostasis is often perturbed in metabolic diseases, these molecules can serve as both biomarkers and drug targets. The development of modern mass spectrometry (MS) provided the platform for large-scale lipidomic studies at the level of molecular species. Traditionally, more detailed structural information, such as the C[double bond, length as m-dash]C location, was mostly assumed instead of properly measured, though the specific isomers were indicated as potential biomarkers of cancers or cardiovascular diseases. Recent C[double bond, length as m-dash]C localization methods, including the Paternò-Büchi (PB) reaction, have shown the prevalent and heterogeneous distribution of C[double bond, length as m-dash]C location in lipids across tissues. Mapping the lipidome of model animals at the level of C[double bond, length as m-dash]C position would increase the understanding of the metabolism and function of lipid isomers, facilitating clinical research. In this study, we employed an online PB reaction on a liquid chromatography-high resolution MS platform to map C[double bond, length as m-dash]C location isomers in five different murine tissues. We analyzed phosphatidylcholines, phosphatidylethanolamines, and sphingomyelins; we relatively quantified and mapped the distribution of ∼30 groups of co-existing isomers, characterized by different chain lengths and degrees of unsaturation. More specifically, we performed relative quantitation of four isomers of the C16:1 fatty acyl, which included rarely reported n-10 and n-5 species besides n-9 and n-7 isomers. We showed a small variation of the isomers' relative composition among individual animals (<20%) but significant differences across different lipid species and mouse tissues. Our results provided an initial database to map alternative lipid metabolic pathways at the tissue level.