Systems level studies of mammalian metabolomes: the roles of mass spectrometry and nuclear magnetic resonance spectroscopy

Accuracy of COVID-19 diagnostic tests via infrared spectroscopy: A systematic review and meta-analysis

This study aims to synthesize the evidence on the accuracy parameters of COVID-19 diagnosis methods using infrared spectroscopy (FTIR). A systematic review with searches in PubMed and Embase was performed (September 2023). Studies reporting data on test specificity, sensitivity, true positive, true negative, false positive, and false negative using different human samples were included. Meta-analysis of accuracy estimates with 95 % confidence intervals and area under the ROC Curve (AUC) were conducted (Meta-Disc 1.4.7). Seventeen studies were included - all of them highlighted regions 650-1800 cm-1 and 2300-3900 cm-1 as most important for diagnosing COVID-19. The FTIR technique presented high sensitivity [0.912 (95 %CI, 0.878-0.939), especially in vaccinated [0.959 (CI95 %, 0.908-0.987)] compared to unvaccinated [0.625 (CI95 %, 0.584-0.664)] individuals for COVID-19. Overall specificity was also high [0.886 (95 %CI, 0.855-0.912), with increased rates in vaccinated [0.884 (CI95 %, 0.819-0.932)] than in unvaccinated [0.667 (CI95 %, 0.629-0.704)] patients. These findings reveal that FTIR is an accurate technique for detecting SARS-CoV-2 infection in different biological matrices with advantages including low cost, rapid and environmentally friendly with minimal preparation analyses. This could lead to an easy implementation of this technique in practice as a screening tool for patients with suspected COVID-19, especially in low-income countries.

Proteomics and metabolomics analyses of mechanism underlying bovine sperm cryoinjury

BACKGROUND

The cryoinjury of semen during cryopreservation reduces sperm motility, constraining the application of artificial insemination (AI) in bovine reproduction. Some fertility markers, related to sperm motility before and after freezing have been identified. However, little is known about the biological mechanism through which freezing reduces sperm motility. This study investigated the selective effects of cryoinjury on high-motility sperm (HMS) and low-motility sperm (LMS) in frozen-thawed from the perspectives of reactive oxygen species (ROS), mitochondrial membrane potential (MMP), and ATP levels. The molecular mechanism of decreased sperm motility caused by cryoinjury was explored through a joint analysis of 4D-label free quantitative proteomics and non-targeted metabolomics.

RESULTS

The results indicate that low levels of ROS and high degrees of MMP and ATP play a critical role in the survival of HMS during the freezing process. The sperm samples from the frozen-thawed HMS and LMS were analysed for proteomics and metabolomics, 2,465 proteins and 4,135 metabolites were detected in bovine sperm samples. In contrast to LMS, HMS have 106 proteins and 106 metabolites with high abundance expression, and 79 proteins and 223 metabolites with low abundance expression. Proteomics and metabolomics data exhibit that highly expressed antioxidant enzymes and metabolites in HMS can maintain sperm motility by regulating the ROS produced during freezing to prevent sperm from oxidative stress and apoptosis. Furthermore, the KEGG analysis of differential proteins and metabolites during the freezing process implies that the significant enrichment of glycolysis and cAMP in HMS can guarantee energy supply.

CONCLUSIONS

The results provided that during the process of bovine sperm freezing, highly expressed antioxidant enzymes can regulate the reactive oxygen species levels to avoid oxidative stress and the glycolysis signalling pathway ensures ATP production can sustain frozen-thawed sperm motility.

Quantitative Blood Serum IVDr NMR Spectroscopy in Clinical Metabolomics of Cancer, Neurodegeneration, and Internal Medicine

Despite more than two decades of metabolomics having joined the "omics" scenery, to date only a few novel blood metabolite biomarkers have found their way into the clinic. This is changing now by massive large-scale population metabolic phenotyping for both healthy and disease cohorts. Here, nuclear magnetic resonance (NMR) spectroscopy is a method of choice, as typical blood serum markers can be easily quantified and by knowledge of precise reference concentrations, more and more NMR-amenable biomarkers are established, moving NMR from research to clinical application. Besides customized approaches, to date two major commercial platforms have evolved based on either 600 MHz (14.1 Tesla) or 500 MHz (11.7 Tesla) high-field NMR systems. This chapter provides an introduction into the field of quantitative in vitro diagnostics research (IVDr) NMR at 600 MHz and its application within clinical research of cancer, neurodegeneration, and internal medicine.

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.

Rapid and Robust Workflows Using Different Ionization, Computation, and Visualization Approaches for Spatial Metabolome Profiling of Microbial Natural Products in Pseudoalteromonas

Ambient mass spectrometry (MS) technologies have been applied to spatial metabolomic profiling of various samples in an attempt to both increase analysis speed and reduce the length of sample preparation. Recent studies, however, have focused on improving the spatial resolution of ambient approaches. Finer resolution requires greater analysis times and commensurate computing power for more sophisticated data analysis algorithms and larger data sets. Higher resolution provides a more detailed molecular picture of the sample; however, for some applications, this is not required. A liquid microjunction surface sampling probe (LMJ-SSP) based MS platform combined with unsupervised multivariant analysis based hyperspectral visualization is demonstrated for the metabolomic analysis of marine bacteria from the genus Pseudoalteromonas to create a rapid and robust spatial profiling workflow for microbial natural product screening. In our study, metabolomic profiles of different Pseudoalteromonas species are quickly acquired without any sample preparation and distinguished by unsupervised multivariant analysis. Our robust platform is capable of automated direct sampling of microbes cultured on agar without clogging. Hyperspectral visualization-based rapid spatial profiling provides adequate spatial metabolite information on microbial samples through red-green-blue (RGB) color annotation. Both static and temporal metabolome differences can be visualized by straightforward color differences and differentiating m/z values identified afterward. Through this approach, novel analogues and their potential biosynthetic pathways are discovered by applying results from the spatial navigation to chromatography-based metabolome annotation. In this current research, LMJ-SSP is shown to be a robust and rapid spatial profiling method. Unsupervised multivariant analysis based hyperspectral visualization is proven straightforward for facile/rapid data interpretation. The combination of direct analysis and innovative data visualization forms a powerful tool to aid the identification/interpretation of interesting compounds from conventional metabolomics analysis.

Precision neurology

Over the past several decades, high-resolution brain imaging, blood and cerebrospinal fluid analyses, and other advanced technologies have changed diagnosis from an exercise depending primarily on the history and physical examination to a computer- and online resource-aided process that relies on larger and larger quantities of data. In addition, randomized controlled trials (RCT) at a population level have led to many new drugs and devices to treat neurological disease, including disease-modifying therapies. We are now at a crossroads. Combinatorially profound increases in data about individuals has led to an alternative to population-based RCTs. Genotyping and comprehensive "deep" phenotyping can sort individuals into smaller groups, enabling precise medical decisions at a personal level. In neurology, precision medicine that includes prediction, prevention and personalization requires that genomic and phenomic information further incorporate imaging and behavioral data. In this article, we review the genomic, phenomic, and computational aspects of precision medicine for neurology. After defining biological markers, we discuss some applications of these "-omic" and neuroimaging measures, and then outline the role of computation and ultimately brain simulation. We conclude the article with a discussion of the relation between precision medicine and value-based care.

Recent advances associated with cardiometabolic remodeling in diabetes-induced heart failure

Diabetes mellitus (DM) is characterized by chronic hyperglycemia, and despite intensive glycemic control, the risk of heart failure in patients with diabetes remains high. Diabetes-induced heart failure (DHF) presents a unique metabolic challenge, driven by significant alterations in cardiac substrate metabolism, including increased reliance on fatty acid oxidation, reduced glucose utilization, and impaired mitochondrial function. These metabolic alterations lead to oxidative stress, lipotoxicity, and energy deficits, contributing to the progression of heart failure. Emerging research has identified novel mechanisms involved in the metabolic remodeling of diabetic hearts, such as autophagy dysregulation, epigenetic modifications, polyamine regulation, and branched-chain amino acid (BCAA) metabolism. These processes exacerbate mitochondrial dysfunction and metabolic inflexibility, further impairing cardiac function. Therapeutic interventions targeting these pathways-such as enhancing glucose oxidation, modulating fatty acid metabolism, and optimizing ketone body utilization-show promise in restoring metabolic homeostasis and improving cardiac outcomes. This review explores the key molecular mechanisms driving metabolic remodeling in diabetic hearts, highlights advanced methodologies, and presents the latest therapeutic strategies for mitigating the progression of DHF. Understanding these emerging pathways offers new opportunities to develop targeted therapies that address the root metabolic causes of heart failure in diabetes.

Advance computational tools for multiomics data learning

The burgeoning field of bioinformatics has seen a surge in computational tools tailored for omics data analysis driven by the heterogeneous and high-dimensional nature of omics data. In biomedical and plant science research multi-omics data has become pivotal for predictive analytics in the era of big data necessitating sophisticated computational methodologies. This review explores a diverse array of computational approaches which play crucial role in processing, normalizing, integrating, and analyzing omics data. Notable methods such similarity-based methods, network-based approaches, correlation-based methods, Bayesian methods, fusion-based methods and multivariate techniques among others are discussed in detail, each offering unique functionalities to address the complexities of multi-omics data. Furthermore, this review underscores the significance of computational tools in advancing our understanding of data and their transformative impact on research.

Metabolite Biomarkers for Early Detection of Pancreatic Ductal Adenocarcinoma: A Systematic Review

Pancreatic ductal adenocarcinoma (PDAC) remains one of the most lethal malignancies, with a poor prognosis. This poor prognosis is largely attributed to a late-stage diagnosis. Recent advancements in metabolomics have emerged as a promising avenue for biomarker discovery in PDAC. This systematic review evaluates the potential of metabolite biomarkers for early detection of PDAC. Four studies meeting the inclusion criteria were analyzed, encompassing experimental, case-control, and prospective cohort designs. Key findings include the identification of distinct metabolic subtypes in PDAC with varying sensitivities to metabolic inhibitors. A biomarker signature comprising nine metabolites plus CA19-9 showed high accuracy in distinguishing PDAC from chronic pancreatitis, outperforming CA19-9 alone. Another study identified a five-metabolite signature demonstrating high diagnostic accuracy for pancreatic cancer, differentiating it from type 2 diabetes mellitus. A two-metabolite model (isoleucine and adrenic acid) showed superior performance in detecting stage-I PDAC compared to CA19-9. These studies consistently demonstrate altered metabolic pathways in PDAC patients compared to healthy controls and those with benign pancreatic conditions. Integrating metabolomic data with other molecular profiling approaches has become a powerful strategy for improving diagnostic accuracy. However, challenges remain, including the influence of confounding factors, the need for large-scale validation studies, and the standardization of metabolomic methods. The potential of artificial intelligence in interpreting complex metabolomic data offers promising avenues for future research. This review highlights the significant potential of metabolite biomarkers in early PDAC detection while emphasizing the need for further validation and refinement of these approaches.

Personalized Medicine Approach to Proteomics and Metabolomics of Cytochrome P450 Enzymes: A Narrative Review

Cytochrome P450 enzymes (CYPs) represent a diverse family of heme-thiolate proteins involved in the metabolism of a wide range of endogenous compounds and xenobiotics. In recent years, proteomics and metabolomics have been used to obtain a comprehensive insight into the role of CYPs in health and disease aspects. The objective of the present work is to better understand the status of proteomics and metabolomics in CYP research in optimizing therapeutics and patient safety from a personalized medicine approach. The literature used in this narrative review was procured by electronic search of PubMed, Medline, Embase, and Google Scholar databases. The following keywords were used in combination to identify related literature: "proteomics," "metabolomics," "cytochrome P450," "drug metabolism," "disease conditions," "proteome," "liquid chromatography-mass spectrometry," "integration," "metabolites," "pathological conditions." We reviewed studies that utilized proteomics and metabolomics approaches to explore the multifaceted roles of CYPs in identifying disease markers and determining the contribution of CYP enzymes in developing treatment strategies. The applications of various cutting-edge analytical techniques, including liquid chromatography-mass spectrometry, nuclear magnetic resonance, and bioinformatics analyses in CYP proteomics and metabolomics studies, have been highlighted. The identification of CYP enzymes through metabolomics and/or proteomics in various disease conditions provides key information in the diagnostic and therapeutic landscape. Leveraging both proteomics and metabolomics presents a powerful approach for an exhaustive exploration of the multifaceted roles played by CYP enzymes in personalized medicine. Proteomics and metabolomics have enabled researchers to unravel the complex connection between CYP enzymes and metabolic markers associated with specific diseases. As technology and methodologies evolve, an integrated approach promises to further elucidate the role of CYPs in human health and disease, potentially ushering in a new era of personalized medicine.

Formation and retention of aroma compounds in pigeons roasted by circulating non-fried roast technique by means of UHPLC-HRMS and GC-O-MS

Lipids are key aroma contributors in meat products. However, the role of different lipids in the presence of aroma compounds in roasted pigeons has not been studied. The formation of aroma compounds and lipids during the circulating non-fried roasting of pigeons was investigated. The results presented that 18 aroma compounds, including 5-methy-2,3-diethylpyrazine, were identified as key aroma compounds. A total of 6324 lipids were classed into 47 categories, such as phosphatidylcholine (PC), phosphatidylethanolamine (PE), and triglyceride (TG). Nine lipids, containing PA(P-20:0/22:4(7Z,10Z,13Z,16Z)) and LPC 16:0-SN1, showed promise as potential biomarkers for discriminating differential pigeons using OPLS-DA. PC (13.76%), TG (13.58%), and their products were major lipids, among which TG 16:0 16:0 18:2, LPC 18:2-SN1, and PC 18:1_18:1 played a crucial role in the presence of aroma compounds. Interestingly, the linoleic acid, an important aroma contributor, was predominantly bonded to the sn-2 position of phospholipid and sn-3 position of neutral lipids.

Unveiling the gut microbiota blueprint of schizophrenia: a multilevel omics approach

Background

Schizophrenia is a persistent incurable mental disorder and is characterized by the manifestation of negative emotions and behaviors with anxiety and depression, fear and insecurity, self-harm and social withdrawal. The intricate molecular mechanisms underlying this phenomenon remain largely elusive. Accumulating evidence points towards the gut microbiota exerting an influence on brain function via the gut-brain axis, potentially contributing to the development of schizophrenia. Therefore, the objective of this study is to delineate the gut microbial composition and metabolic profile of fecal samples from individuals with schizophrenia.

Methods

Liquid chromatography-mass spectrometry (LC-MS) and 16S ribosomal RNA (16S rRNA) gene sequencing were employed to analyze fecal metabolites and gut microbiota profiles in a cohort of 29 patients diagnosed with schizophrenia and 30 normal controls. The microbial composition of fecal samples was determined through the 16S rRNA gene sequencing, and microbial α-diversity and β-diversity indices were calculated. Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were performed to analyze the distribution of samples. The metabolites and gut microbiota exhibiting differential expression were identified through the application of biological variance criteria. Co-occurrence analysis of bacteria and metabolites was conducted using the spearman's rank correlation coefficient and visualized in a circular layout with the Cytoscape software.

Results

The findings of the study indicated a lack of substantial evidence supporting significant disparities in α-diversity and β-diversity between individuals with schizophrenia and normal controls. In terms of metabolomics, a discernible pattern in sample distribution between the two groups was observed. Our analysis has revealed 30 bacterial species and 45 fecal metabolites that exhibited notable differences in abundance between individuals diagnosed with schizophrenia and normal controls. These alterations in multilevel omics have led to the development of a co-expression network associated with schizophrenia. The perturbed microbial genes and fecal metabolites consistently demonstrated associations with amino acid and lipid metabolism, which play essential roles in regulating the central nervous system.

Conclusion

Our results offered profound insights into the impact of imbalanced gut microbiota and metabolism on brain function in individuals with schizophrenia.

Glutathione Attenuates Copper Levels and Alleviates Hepatic Injury in TX Mice

Wilson's disease (WD) is an inherited disorder that is characterized by abnormal copper metabolism, and treatment of this condition in the clinic focuses on promoting copper ion excretion. Glutathione (GSH) is a tripeptide compound whose active group is a sulfhydryl group, which is involved in many important biochemical reactions. Thus, the antioxidant and integrative detoxification effects of GSH have attracted attention. Whether GSH promotes copper ion excretion and reduces oxidative stress to alleviate WD-related liver injury is the focus of this study. Here, we used toxic milk (TX) mice as a model to study WD, and we treated these mice with GSH. We observed that GSH was effective at promoting copper excretion by TX mice. In addition, GSH has been shown to be effective in attenuating liver injury, including improving the structure and morphology of stem tissue and reducing hepatocyte necrosis. The effects of GSH on hepatic oxidative stress were determined by measuring catalase, malondialdehyde and total superoxide dismutase. The results showed that GSH could increase hepatic antioxidant enzyme activities, reduce lipid peroxidation levels and attenuate liver injury. In conclusion, GSH may exert its hepatic benefits by promoting copper ion excretion and preventing oxidative stress.

[Research progress of metabolomics in children with irritable bowel syndrome]

Irritable bowel syndrome (IBS) is a common functional gastrointestinal disorder characterized by symptoms such as abdominal pain, diarrhea, constipation, and indigestion. Given its unclear etiology and pathogenesis, and the absence of specific biomarkers, clinical diagnosis and treatment of IBS continue to pose significant challenges. In recent years, metabolomics technology, known for its non-invasive, high-throughput, high-precision, and highly reproducible features, has been widely applied in the diagnosis, treatment, and prognosis of various diseases. Therefore, metabolomics technology is expected to offer novel insights and methodologies for the biological mechanism research, diagnosis, and treatment of IBS. This article reviews recent advancements in the application of metabolomics to IBS, exploring its potential value in the clinical diagnosis and treatment of children with this condition.

Techniques, Databases and Software Used for Studying Polar Metabolites and Lipids of Gastrointestinal Parasites

Gastrointestinal parasites (GIPs) are organisms known to have coevolved for millennia with their mammalian hosts. These parasites produce small molecules, peptides, and proteins to evade or fight their hosts' immune systems and also to protect their host for their own survival/coexistence. The small molecules include polar compounds, amino acids, lipids, and carbohydrates. Metabolomics and lipidomics are emerging fields of research that have recently been applied to study helminth infections, host-parasite interactions and biochemicals of GIPs. This review comprehensively discusses metabolomics and lipidomics studies of the small molecules of GIPs, providing insights into the available tools and techniques, databases, and analytical software. Most metabolomics and lipidomics investigations employed LC-MS, MS or MS/MS, NMR, or a combination thereof. Recent advancements in artificial intelligence (AI)-assisted software tools and databases have propelled parasitomics forward, offering new avenues to explore host-parasite interactions, immunomodulation, and the intricacies of parasitism. As our understanding of AI technologies and their utilisation continue to expand, it promises to unveil novel perspectives and enrich the knowledge of these complex host-parasite relationships.

Assessing inorganic nanoparticle toxicity through omics approaches

In the last two decades, the development of nanotechnology has resulted in inorganic nanoparticles playing crucial roles in key industries, ranging from healthcare to energy technologies. For instance, gold and silver nanoparticles are widely used in rapid COVID-19 and flu tests, titania and zinc oxide nanoparticles are commonly found in cosmetic products, and superparamagnetic iron oxide nanoparticles have been clinically exploited as contrast agents and anti-anemia medicines. As a result, human exposure to nanomaterials is continuously increasing, raising concerns about their potential adverse health effects. Historically, the study of nanoparticle toxicity has largely relied on macroscopic observations obtained in different in vitro and in vivo models, resulting in readouts such as median lethal dose, biodistribution profile, and/or histopathological assessment. In recent years, omics methodologies, including transcriptomics, epigenomics, proteomics, metabolomics, and lipidomics, are increasingly used to characterize the biological interactions of nanomaterials, providing a better and broader understanding of their impact and mechanisms of toxicity. These approaches have been able to identify important genes and gene products that mediate toxicological effects, as well as endogenous functions and pathways dysregulated by nanoparticles. Omics methods improve our understanding of nanoparticle biology, and unravel mechanistic insights into nanomedicine-based therapies. This review aims to provide a deeper understanding and new perspectives of omics approaches to characterize the toxicity and biological interactions of inorganic nanoparticles, and improve the safety of nanoparticle applications.

Unveil Intrahepatic Cholangiocarcinoma Heterogeneity through the Lens of Omics and Multi-Omics Approaches

Intrahepatic cholangiocarcinoma (iCCA) is recognized worldwide as the second leading cause of morbidity and mortality among primary liver cancers, showing a continuously increasing incidence rate in recent years. iCCA aggressiveness is revealed through its rapid and silent intrahepatic expansion and spread through the lymphatic system leading to late diagnosis and poor prognoses. Multi-omics studies have aggregated information derived from single-omics data, providing a more comprehensive understanding of the phenomena being studied. These approaches are gradually becoming powerful tools for investigating the intricate pathobiology of iCCA, facilitating the correlation between molecular signature and phenotypic manifestation. Consequently, preliminary stratifications of iCCA patients have been proposed according to their "omics" features opening the possibility of identifying potential biomarkers for early diagnosis and developing new therapies based on personalized medicine (PM). The focus of this review is to provide new and advanced insight into the molecular pathobiology of the iCCA, starting from single- to the latest multi-omics approaches, paving the way for translating new basic research into therapeutic practices.

Metabolomics studies in common multifactorial eye disorders: a review of biomarker discovery for age-related macular degeneration, glaucoma, diabetic retinopathy and myopia

Introduction

Multifactorial Eye disorders are a significant public health concern and have a huge impact on quality of life. The pathophysiological mechanisms underlying these eye disorders were not completely understood since functional and low-throughput biological tests were used. By identifying biomarkers linked to eye disorders, metabolomics enables early identification, tracking of the course of the disease, and personalized treatment.

Methods

The electronic databases of PubMed, Scopus, PsycINFO, and Web of Science were searched for research related to Age-Related macular degeneration (AMD), glaucoma, myopia, and diabetic retinopathy (DR). The search was conducted in August 2023. The number of cases and controls, the study's design, the analytical methods used, and the results of the metabolomics analysis were all extracted. Using the QUADOMICS tool, the quality of the studies included was evaluated, and metabolic pathways were examined for distinct metabolic profiles. We used MetaboAnalyst 5.0 to undertake pathway analysis of differential metabolites.

Results

Metabolomics studies included in this review consisted of 36 human studies (5 Age-related macular degeneration, 10 Glaucoma, 13 Diabetic retinopathy, and 8 Myopia). The most networked metabolites in AMD include glycine and adenosine monophosphate, while methionine, lysine, alanine, glyoxylic acid, and cysteine were identified in glaucoma. Furthermore, in myopia, glycerol, glutamic acid, pyruvic acid, glycine, cysteine, and oxoglutaric acid constituted significant metabolites, while glycerol, glutamic acid, lysine, citric acid, alanine, and serotonin are highly networked metabolites in cases of diabetic retinopathy. The common top metabolic pathways significantly enriched and associated with AMD, glaucoma, DR, and myopia were arginine and proline metabolism, methionine metabolism, glycine and serine metabolism, urea cycle metabolism, and purine metabolism.

Conclusion

This review recapitulates potential metabolic biomarkers, networks and pathways in AMD, glaucoma, DR, and myopia, providing new clues to elucidate disease mechanisms and therapeutic targets. The emergence of advanced metabolomics techniques has significantly enhanced the capability of metabolic profiling and provides novel perspectives on the metabolism and underlying pathogenesis of these multifactorial eye conditions. The advancement of metabolomics is anticipated to foster a deeper comprehension of disease etiology, facilitate the identification of novel therapeutic targets, and usher in an era of personalized medicine in eye research.

Multiscale metabolomics techniques: Insights into neuroscience research

The field of metabolomics examines the overall composition and dynamic patterns of metabolites in living organisms. The primary methods used in metabolomics include liquid chromatography (LC), nuclear magnetic resonance (NMR), and mass spectrometry (MS) analysis. These methods enable the identification and examination of metabolite types and contents within organisms, as well as modifications to metabolic pathways and their connection to the emergence of diseases. Research in metabolomics has extensive value in basic and applied sciences. The field of metabolomics is growing quickly, with the majority of studies concentrating on biomedicine, particularly early disease diagnosis, therapeutic management of human diseases, and mechanistic knowledge of biochemical processes. Multiscale metabolomics is an approach that integrates metabolomics techniques at various scales, including the holistic, tissue, cellular, and organelle scales, to enable more thorough and in-depth studies of metabolic processes in organisms. Multiscale metabolomics can be combined with methods from systems biology and bioinformatics. In recent years, multiscale metabolomics approaches have become increasingly important in neuroscience research due to the nervous system's high metabolic demands. Multiscale metabolomics can offer novel concepts and approaches for the diagnosis, treatment, and development of medication for neurological illnesses in addition to a more thorough understanding of brain metabolism and nervous system function. In this review, we summarize the use of multiscale metabolomics techniques in neuroscience, address the promise and constraints of these techniques, and provide an overview of the metabolome and its applications in neuroscience.

An exercise physiologist's guide to metabolomics

The field of exercise physiology has undergone significant technological advancements since the pioneering works of exercise physiologists in the early to mid-20th century. Historically, the ability to detect metabolites in biofluids from exercising participants was limited to single-metabolite analyses. However, the rise of metabolomics, a discipline focused on the comprehensive analysis of metabolites within a biological system, has facilitated a more intricate understanding of metabolic pathways and networks in exercise. This review explores some of the pivotal technological and bioinformatic advancements that have propelled metabolomics to the forefront of exercise physiology research. Metabolomics offers a unique 'fingerprint' of cellular activity, offering a broader spectrum than traditional single-metabolite assays. Techniques, including mass spectrometry and nuclear magnetic resonance spectroscopy, have significantly improved the speed and sensitivity of metabolite analysis. Nonetheless, challenges persist, including study design and data interpretation issues. This review aims to serve as a guide for exercise physiologists to facilitate better research design, data analysis and interpretation within metabolomics. The potential of metabolomics in bridging the gap between genotype and phenotype is emphasised, underscoring the critical importance of careful study design and the selection of appropriate metabolomics techniques. Furthermore, the paper highlights the need to deeply understand the broader scientific context to discern meaningful metabolic changes. The emerging field of fluxomics, which seeks to quantify metabolic reaction rates, is also introduced as a promising avenue for future research.

Untargeted Metabolomic Analysis Reveals Plasma Differences between Mares with Endometritis and Healthy Ones

The aim of this study was to explore alterations in plasma metabolites among mares afflicted with endometritis. Mares were divided into two groups, namely, the equine endometritis group (n = 8) and the healthy control group (n = 8), which included four pregnant and four non-pregnant mares, using a combination of clinical assessment and laboratory confirmation. Plasma samples from both groups of mares were analyzed through untargeted liquid chromatography-tandem mass spectrometry (LC-MS/MS) metabolomics. A total of 28 differentially abundant metabolites were identified by screening and identifying differentially abundant metabolites and analyzing the pathway enrichment of differentially. Ten metabolites were identified as potential biomarkers for the diagnosis of endometritis in mares. Among them, seven exhibited a decrease in the endometritis groups, including hexadecanedioic acid, oleoyl ethanolamide (OEA), [fahydroxy(18:0)]12_13-dihydroxy-9z-octa (12,13-diHOME), deoxycholic acid 3-glucuronide (DCA-3G), 2-oxindole, and (+/-)9-HPODE, and 13(S)-HOTRE. On the other hand, three metabolites, adenosine 5'-monophosphate (AMP), 5-hydroxy-dl-tryptophan (5-HTP), and l-formylkynurenine, demonstrated an increase. These substances primarily participate in the metabolism of tryptophan and linolenic acid, as well as fat and energy. In conclusion, metabolomics revealed differentially abundant metabolite changes in patients with mare endometritis. These specific metabolites can be used as potential biomarkers for the non-invasive diagnosis of mare endometritis.

A Look into Ocular Diseases: The Pivotal Role of Omics Sciences in Ophthalmology Research

Precision medicine is a new medical approach which considers both population characteristics and individual variability to provide customized healthcare. The transition from traditional reactive medicine to personalized medicine is based on a biomarker-driven process and a deep knowledge of biological mechanisms according to which the development of diseases occurs. In this context, the advancements in high-throughput omics technologies represent a unique opportunity to discover novel biomarkers and to provide an unbiased picture of the biological system. One of the medical fields in which omics science has started to be recently applied is that of ophthalmology. Ocular diseases are very common, and some of them could be highly disabling, thus leading to vision loss and blindness. The pathogenic mechanism of most ocular diseases may be dependent on various genetic and environmental factors, whose effect has not been yet completely understood. In this context, large-scale omics approaches are fundamental to have a comprehensive evaluation of the whole system and represent an essential tool for the development of novel therapies. This Review summarizes the recent advancements in omics science applied to ophthalmology in the last ten years, in particular by focusing on proteomics, metabolomics and lipidomics applications from an analytical perspective. The role of high-efficiency separation techniques coupled to (high-resolution) mass spectrometry ((HR)MS) is also discussed, as well as the impact of sampling, sample preparation and data analysis as integrating parts of the analytical workflow.

Metabolic biomarkers in irritable bowel syndrome diagnosis

Irritable bowel syndrome (IBS) is a chronic gastrointestinal (GI) disorder characterized by altered bowel habits and abdominal discomfort during defecation. It significantly impacts life quality and work productivity for those affected. Global data suggests a slightly higher prevalence in females than in males. Today, unambiguous diagnosis of IBS remains challenging due to the absence of a specific biochemical, histopathological, or radiological test. Current diagnosis relies heavily on thorough symptom evaluation. Efforts by the Rome committees have established standardized diagnostic criteria (Rome I-IV), improving consistency and clinical applicability. Recent studies in this framework, seem to have successfully employed metabolomics techniques to identify distinct metabolite profiles in breath and stool samples of IBS patients, differentiating them from healthy controls and those with other functional GI disorders, such as inflammatory bowel disease (IBD). Building on this success, researchers are investigating the presence of similar metabolites in easily accessible biofluids such as urine, potentially offering a less invasive diagnostic approach. Accordingly, this review focuses on key metabolites specifically detected in IBS patients' biological specimens, with a focus on urinary metabolites, using various methods, particularly mass spectrometry (MS)-based techniques, including gas chromatography-MS (GC-MS), liquid chromatography-tandem MS (LC-MS/MS), and capillary electrophoresis-MS (CE-MS) metabolomics assays. These findings may make provision for a new set of non-invasive biomarkers for IBS diagnosis and management.

Untargeted metabolomics reveal signatures of a healthy lifestyle

This cross-sectional study investigated differences in the plasma metabolome in two groups of adults that were of similar age but varied markedly in body composition and dietary and physical activity patterns. Study participants included 52 adults in the lifestyle group (LIFE) (28 males, 24 females) and 52 in the control group (CON) (27 males, 25 females). The results using an extensive untargeted ultra high-performance liquid chromatography-high resolution mass spectrometry (UHPLC-HRMS) metabolomics analysis with 10,535 metabolite peaks identified 486 important metabolites (variable influence on projections scores of VIP ≥ 1) and 16 significantly enriched metabolic pathways that differentiated LIFE and CON groups. A novel metabolite signature of positive lifestyle habits emerged from this analysis highlighted by lower plasma levels of numerous bile acids, an amino acid profile characterized by higher histidine and lower glutamic acid, glutamine, β-alanine, phenylalanine, tyrosine, and proline, an elevated vitamin D status, higher levels of beneficial fatty acids and gut microbiome catabolism metabolites from plant substrates, and reduced levels of N-glycan degradation metabolites and environmental contaminants. This study established that the plasma metabolome is strongly associated with body composition and lifestyle habits. The robust lifestyle metabolite signature identified in this study is consistent with an improved life expectancy and a reduced risk for chronic disease.

The effects of Atractylodes macrocephala extract BZEP self-microemulsion based on gut-liver axis HDL/LPS signaling pathway to ameliorate metabolic dysfunction-associated fatty liver disease in rats

OBJECTIVES

To elucidate the therapeutic effects and mechanisms of Atractylodes macrocephala extract crystallize (BZEP) and BZEP self-microemulsion (BZEPWR) on metabolic dysfunction-associated fatty liver disease (MAFLD) induced by "high sugar, high fat, and excessive alcohol consumption" based on the gut-liver axis HDL/LPS signaling pathway.

METHODS

In this study, BZEP and BZEPWR were obtained via isolation, purification, and microemulsification. Furthermore, an anthropomorphic MAFLD rat model of "high sugar, high fat, and excessive alcohol consumption" was established. The therapeutic effects of BZEPWR and BZEP on the model rats were evaluated in terms of liver function, lipid metabolism (especially HDL-C), serum antioxidant indexes, and liver and intestinal pathophysiology. To determine the lipoproteins in the serum sample, the amplitudes of a plurality of NMR spectra were derived via deconvolution of the composite methyl signal envelope to yield HDL-C subclass concentrations. The changes in intestinal flora were detected via 16 S rRNA gene sequencing. In addition, the gut-liver axis HDL/LPS signaling pathway was validated using immunohistochemistry, immunofluorescence, and western blot.

RESULTS

The findings established that BZEPWR and BZEP improved animal signs, serum levels of liver enzymes (ALT and AST), lipid metabolism (TC, TG, HDL-C, and LDL-C), and antioxidant indexes (GSH, SOD, and ROS). In addition, pathological damage to the liver, colon, and ileum was ameliorated, and the intestinal barrier function of the model rats was restored. At the genus level, BZEPWR and BZEP exerted positive effects on beneficial bacteria, such as Lactobacillus and norank_f__Muribaculaceae, and inhibitory effects on harmful bacteria, such as unclassified_f__Lachnospiraceae and Blautia. Twenty HDL-C subspecies were detected, and their levels were differentially increased in both BZEPWR and BZEP groups, with BZEPWR exhibiting a stronger elevating effect on specific HDL-C subspecies. Also, the gut-liver axis HDL/LPS signaling pathway was studied, which indicated that BZEPWR and BZEP significantly increased the expressions of ABCA1, LXR, occludin, and claudin-1 proteins in the gut and serum levels of HDL-C. Concomitantly, the levels of LPS in the serum and TLR4, Myd88, and NF-κB proteins in the liver were decreased.

CONCLUSION

BZEPWR and BZEP exert restorative and reversal effects on the pathophysiological damage to the gut-liver axis in MAFLD rats, and the therapeutic mechanism may be related to the regulation of the intestinal flora and the HDL/LPS signaling pathway.

Identification of novel biomarkers in the early diagnosis of malignant melanoma by untargeted liquid chromatography coupled to high-resolution mass spectrometry-based metabolomics: a pilot study

BACKGROUND

Malignant melanoma (MM) is a highly aggressive form of skin cancer whose incidence continues to rise worldwide. If diagnosed at an early stage, it has an excellent prognosis, but mortality increases significantly at advanced stages after distant spread. Unfortunately, early detection of aggressive melanoma remains a challenge.

OBJECTIVES

To identify novel blood-circulating biomarkers that may be useful in the diagnosis of MM to guide patient counselling and appropriate disease management.

METHODS

In this study, 105 serum samples from 26 healthy patients and 79 with MM were analysed using an untargeted approach by liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS) to compare the metabolomic profiles of both conditions. Resulting data were subjected to both univariate and multivariate statistical analysis to select robust biomarkers. The classification model obtained from this analysis was further validated with an independent cohort of 12 patients with stage I MM.

RESULTS

We successfully identified several lipidic metabolites differentially expressed in patients with stage I MM vs. healthy controls. Three of these metabolites were used to develop a classification model, which exhibited exceptional precision (0.92) and accuracy (0.94) when validated on an independent sample.

CONCLUSIONS

These results demonstrate that metabolomics using LC-HRMS is a powerful tool to identify and quantify metabolites in bodily fluids that could serve as potential early diagnostic markers for MM.

Metabolite signatures associated with microRNA miR-143-3p serve as drivers of poor lung function trajectories in childhood asthma

BACKGROUND

Lung function trajectories (LFTs) have been shown to be an important measure of long-term health in asthma. While there is a growing body of metabolomic studies on asthma status and other phenotypes, there are no prospective studies of the relationship between metabolomics and LFTs or their genomic determinants.

METHODS

We utilized ordinal logistic regression to identify plasma metabolite principal components associated with four previously-published LFTs in children from the Childhood Asthma Management Program (CAMP) (n = 660). The top significant metabolite principal component (PCLF) was evaluated in an independent cross-sectional child cohort, the Genetic Epidemiology of Asthma in Costa Rica Study (GACRS) (n = 1151) and evaluated for association with spirometric measures. Using meta-analysis of CAMP and GACRS, we identified associations between PCLF and microRNA, and SNPs in their target genes. Statistical significance was determined using an false discovery rate-adjusted Q-value.

FINDINGS

The top metabolite principal component, PCLF, was significantly associated with better LFTs after multiple-testing correction (Q-value = 0.03). PCLF is composed of the urea cycle, caffeine, corticosteroid, carnitine, and potential microbial (secondary bile acid, tryptophan, linoleate, histidine metabolism) metabolites. Higher levels of PCLF were also associated with increases in lung function measures and decreased circulating neutrophil percentage in both CAMP and GACRS. PCLF was also significantly associated with microRNA miR-143-3p, and SNPs in three miR-143-3p target genes; CCZ1 (P-value = 2.6 × 10-5), SLC8A1 (P-value = 3.9 × 10-5); and TENM4 (P-value = 4.9 × 10-5).

INTERPRETATION

This study reveals associations between metabolites, miR-143-3p and LFTs in children with asthma, offering insights into asthma physiology and possible interventions to enhance lung function and long-term health.

FUNDING

Molecular data for CAMP and GACRS via the Trans-Omics in Precision Medicine (TOPMed) program was supported by the National Heart, Lung, and Blood Institute (NHLBI).

Biomolecular Actions by Intestinal Endotoxemia in Metabolic Syndrome

Metabolic syndrome (MetS) is a combination of metabolic disorders that concurrently act as factors promoting systemic pathologies such as atherosclerosis or diabetes mellitus. It is now believed to encompass six main interacting conditions: visceral fat, imbalance of lipids (dyslipidemia), hypertension, insulin resistance (with or without impairing both glucose tolerance and fasting blood sugar), and inflammation. In the last 10 years, there has been a progressive interest through scientific research investigations conducted in the field of metabolomics, confirming a trend to evaluate the role of the metabolome, particularly the intestinal one. The intestinal microbiota (IM) is crucial due to the diversity of microorganisms and their abundance. Consequently, IM dysbiosis and its derivate toxic metabolites have been correlated with MetS. By intervening in these two factors (dysbiosis and consequently the metabolome), we can potentially prevent or slow down the clinical effects of the MetS process. This, in turn, may mitigate dysregulations of intestinal microbiota axes, such as the lung axis, thereby potentially alleviating the negative impact on respiratory pathology, such as the chronic obstructive pulmonary disease. However, the biomolecular mechanisms through which the IM influences the host's metabolism via a dysbiosis metabolome in both normal and pathological conditions are still unclear. In this study, we seek to provide a description of the knowledge to date of the IM and its metabolome and the factors that influence it. Furthermore, we analyze the interactions between the functions of the IM and the pathophysiology of major metabolic diseases via local and systemic metabolome's relate endotoxemia.

Modeling blood metabolite homeostatic levels reduces sample heterogeneity across cohorts

Blood metabolite levels are affected by numerous factors, including preanalytical factors such as collection methods and geographical sites. These perturbations have caused deleterious consequences for many metabolomics studies and represent a major challenge in the metabolomics field. It is important to understand these factors and develop models to reduce their perturbations. However, to date, the lack of suitable mathematical models for blood metabolite levels under homeostasis has hindered progress. In this study, we develop quantitative models of blood metabolite levels in healthy adults based on multisite sample cohorts that mimic the current challenge. Five cohorts of samples obtained across four geographically distinct sites were investigated, focusing on approximately 50 metabolites that were quantified using 1H NMR spectroscopy. More than one-third of the variation in these metabolite profiles is due to cross-cohort variation. A dramatic reduction in the variation of metabolite levels (90%), especially their site-to-site variation (95%), was achieved by modeling each metabolite using demographic and clinical factors and especially other metabolites, as observed in the top principal components. The results also reveal that several metabolites contribute disproportionately to such variation, which could be explained by their association with biological pathways including biosynthesis and degradation. The study demonstrates an intriguing network effect of metabolites that can be utilized to better define homeostatic metabolite levels, which may have implications for improved health monitoring. As an example of the potential utility of the approach, we show that modeling gender-related metabolic differences retains the interesting variance while reducing unwanted (site-related) variance.

Large-Scale Population-Based Studies of Blood Metabolome and Brain Health

Metabolomics technologies enable the quantification of multiple metabolomic measures simultaneously, which provides novel insights into molecular aspects of human health and disease. In large-scale, population-based studies, blood is often the preferred biospecimen. Circulating metabolome may relate to brain health either by affecting or reflecting brain metabolism. Peripheral metabolites may act at or cross the blood-brain barrier and, subsequently, influence brain metabolism, or they may reflect brain metabolism if similar pathways are engaged. Peripheral metabolites may also include those penetrating the circulation from the brain, indicating, for example, brain damage. Most brain health-related metabolomics studies have been conducted in the context of neurodegenerative disorders and cognition, but some studies have also focused on neuroimaging markers of these disorders. Moreover, several metabolomics studies of neurodevelopmental disorders have been performed. Here, we provide a brief background on the types of blood metabolites commonly assessed, and we review the literature describing the relationships between human blood metabolome (n > 50 metabolites) and brain health reported in large-scale studies (n > 500 individuals).

Mass Spectrometry-Based Metabolomics Multi-platform for Alzheimer's Disease Research

The integration of complementary analytical platforms is nowadays the most common strategy for comprehensive metabolomics analysis of complex biological systems. In this chapter, we describe methods and tips for the application of a mass spectrometry multi-platform in Alzheimer's disease research, based on the combination of direct mass spectrometry and orthogonal hyphenated approaches, namely, reversed-phase ultrahigh-performance liquid chromatography and gas chromatography. These procedures have been optimized for the analysis of multiple biological samples from human patients and transgenic animal models, including blood serum, various brain regions (e.g., hippocampus, cortex, cerebellum, striatum, olfactory bulbs), and other peripheral organs (e.g., liver, kidney, spleen, thymus).

Polyurea-magnetic hierarchical porous composites for profiling of anionic metabolites

Combining powerful adsorption capacity, simple preparation, rapid separation as well as superior stability and recyclability, a polyurea-magnetic hierarchical porous composite has been prepared. It demonstrates efficient physisorption for anionic metabolites in less than one minute and is promising for application to the analysis of a broad range of anionic metabolites in complex matrices.

Omics insights into the responses to dietary selenium

Selenium is a well-known health-relevant element related with cancer chemoprevention, neuroprotective roles, beneficial in diabetes, and in several infectious diseases, among others. It is naturally present in some foods, but deficiency in people led to the production of nutraceuticals, supplements, and functional food enriched in this element. There is a U-shaped link between selenium levels and health and a narrow range between toxic and essential levels, and thus, supplementation should be performed carefully. Omics methodologies have become valuable approaches to delve into the responses of dietary selenium in mammals that allowed a deeper knowledge about the metabolism of this element as well as its biological role. In this review, we discuss omics approaches from the workflows to their applications that has been previously used to deep insight into the metabolism of dietary selenium. There is a special focus on selenoproteins, metabolomics responses in blood and tissues (e.g., brain, reproductive organs, etc.) as well as the impact on gut microbiota and its metabolites profile. Thus, we mainly reviewed heteroatom-tagged proteomics, metallomics, metabolomics, and metataxonomics, usually combined with transcriptomics, genomics, and other molecular methods.

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.

Multitargeted Internal Calibration for the Quantification of Chronic Kidney Disease-Related Endogenous Metabolites Using Liquid Chromatography-Mass Spectrometry

Accurate quantitative analysis in liquid chromatography-mass spectrometry (LC-MS) benefits from calibration curves generated in the same matrix as the study sample. In the case of endogenous compound quantification, as no blank matrix exists, the multitargeted internal calibration (MTIC) is an attractive and straightforward approach to avoid the need for extensive matrix similarity evaluation. Its principle is to take advantage of stable isotope labeled (SIL) standards as internal calibrants to simultaneously quantify authentic analytes using a within sample calibration. An MTIC workflow was developed for the simultaneous quantification of metabolites related to chronic kidney disease (CKD) using a volumetric microsampling device to collect 20 μL of serum or plasma, followed by a single-step extraction with acetonitrile/water and liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis. Since a single concentration of internal calibrant is necessary to calculate the study sample concentration, the instrument response function was investigated to determine the best SIL concentration. After validation, the trueness of 16 endogenous analytes in authentic human serum ranged from 72.2 to 116.0%, the repeatability from 1.9 to 11.3%, and the intermediate precision ranged overall from 2.1 to 15.4%. The proposed approach was applied to plasma samples collected from healthy control participants and two patient groups diagnosed with CKD. Results confirmed substantial concentration differences between groups for several analytes, including indoxyl sulfate and cortisone, as well as metabolite enrichment in the kynurenine and indole pathways. Multitargeted methodologies represent a major step toward rapid and straightforward LC-MS/MS absolute quantification of endogenous biomarkers, which could change the paradigm of MS use in clinical laboratories.

Recent Advances and Perspectives in Relation to the Metabolomics-Based Study of Diabetic Retinopathy

Diabetic retinopathy (DR), a prevalent microvascular complication of diabetes, is a major cause of acquired blindness in adults. Currently, a clinical diagnosis of DR primarily relies on fundus fluorescein angiography, with a limited availability of effective biomarkers. Metabolomics, a discipline dedicated to scrutinizing the response of various metabolites within living organisms, has shown noteworthy advancements in uncovering metabolic disorders and identifying key metabolites associated with DR in recent years. Consequently, this review aims to present the latest advancements in metabolomics techniques and comprehensively discuss the principal metabolic outcomes derived from analyzing blood, vitreous humor, aqueous humor, urine, and fecal samples.

Search for serum biomarkers in patients with bipolar disorder and major depressive disorder using metabolome analysis

Objective

Bipolar disorder (BD) and major depressive disorder (MDD) are two common psychiatric disorders. Due to the overlapping clinical symptoms and the lack of objective diagnostic biomarkers, bipolar disorder (BD) is easily misdiagnosed as major depressive disorder (MDD), which in turn affects treatment decisions and prognosis. This study aimed to investigate biomarkers that could be used to differentiate BD from MDD.

Methods

Nuclear magnetic resonance (NMR) spectroscopy was performed to assess serum metabolic profiles in depressed patients with BD (n = 59), patients with MDD (n = 14), and healthy controls (n = 10). Data was analyzed using partial least squares discriminant analysis, orthogonal partial least squares discriminant analysis and t-tests. Different metabolites (VIP > 1 and p < 0.05) were identified and further analyzed using Metabo Analyst 5.0 to identify relevant metabolic pathways.

Results

The metabolic phenotypes of the BD and MDD groups were significantly different from those of the healthy controls, and there were different metabolite differences between them. In the BD group, the levels of 3-hydroxybutyric acid, n-acetyl glycoprotein, β-glucose, pantothenic acid, mannose, glycerol, and lipids were significantly higher than those in the healthy control group, and the levels of lactate and acetoacetate were significantly lower than those in the healthy control group. In the MDD group, the levels of 3-hydroxybutyric acid, n-acetyl glycoprotein, pyruvate, choline, acetoacetic acid, and lipids were significantly higher than those of healthy controls, and the levels of acetic acid and glycerol were significantly lower than those of healthy controls.

Conclusion

Glycerolipid metabolism is significantly involved in BD and MDD. Pyruvate metabolism is significantly involved in MDD. Pyruvate, choline, and acetate may be potential biomarkers for MDD to distinguish from BD, and pantothenic acid may be a potential biomarker for BD to distinguish from MDD.

Salivary metabolomic profile associated with cariogenic risk in children

OBJECTIVES

To identify the metabolomic differences in the saliva of healthy children versus children with active carious lesions and to estimate the predictive capacity of a model based on the salivary metabolomic profile.

METHODS

A study of cases (n = 31) and controls (n = 37) was designed for children aged between 6 and 12 (mean age of the cases: 8.9; controls: 8.7). The said children attended public health centers in Valencia, Spain. Intraoral examinations were performed by a single examiner using ICDAS II diagnostic criteria. Unstimulated total saliva samples were analyzed by nuclear magnetic resonance (NMR) spectroscopy.

RESULTS

The dft index for cases was 2.84 while it was 0.19 for the control group, the DMFT index was 1.13 and 0.11, respectively. The predictive model generated by the multivariate PLS-DA analysis projects a separation between the cases and the controls on the score chart with a predictive capacity and generating an area under the curve of 0.71. The metabolites: 3-methyl-2-oxovalerate, 3-hydroxybutyrate, lactate, acetone, citrate, ornithine, ethanolamine, taurine, proline, glycine, mannose, glucose, 1-6-Anhydro-β-d-glucose and citraconate, are those that show greater significance in the model. In the controls, glycine (Cohen's d = 0.430) and glucose (Cohen's d = 0.560) present higher means compared to the cases. On the contrary, taurine (Cohen's d= -0.474) and mannose (Cohen's d= -0.456) show higher means in cases compared to controls.

CONCLUSIONS

Our findings show a difference in the salivary metabolomic profiles, specifically in the groups of saccharides and amino acids, suggesting an association of these with the level of caries risk.

CLINICAL SIGNIFICANCE

The results reported in the present study reinforce the use of salivary metabolomics as a research method for the search for salivary biomarkers that allow the evaluation of caries risk in patients. Furthermore, it brings us closer to a personalized medicine that will help in dental caries prevention strategies.

Characteristics of Cancer Epidemiology Studies That Employ Metabolomics: A Scoping Review

An increasing number of cancer epidemiology studies use metabolomics assays. This scoping review characterizes trends in the literature in terms of study design, population characteristics, and metabolomics approaches and identifies opportunities for future growth and improvement. We searched PubMed/MEDLINE, Embase, Scopus, and Web of Science: Core Collection databases and included research articles that used metabolomics to primarily study cancer, contained a minimum of 100 cases in each main analysis stratum, used an epidemiologic study design, and were published in English from 1998 to June 2021. A total of 2,048 articles were screened, of which 314 full texts were further assessed resulting in 77 included articles. The most well-studied cancers were colorectal (19.5%), prostate (19.5%), and breast (19.5%). Most studies used a nested case-control design to estimate associations between individual metabolites and cancer risk and a liquid chromatography-tandem mass spectrometry untargeted or semi-targeted approach to measure metabolites in blood. Studies were geographically diverse, including countries in Asia, Europe, and North America; 27.3% of studies reported on participant race, the majority reporting White participants. Most studies (70.2%) included fewer than 300 cancer cases in their main analysis. This scoping review identified key areas for improvement, including needs for standardized race and ethnicity reporting, more diverse study populations, and larger studies.

Sexual Dimorphism of the Mouse Plasma Metabolome Is Associated with Phenotypes of 30 Gene Knockout Lines

Although metabolic alterations are observed in many monogenic and complex genetic disorders, the impact of most mammalian genes on cellular metabolism remains unknown. Understanding the effect of mouse gene dysfunction on metabolism can inform the functions of their human orthologues. We investigated the effect of loss-of-function mutations in 30 unique gene knockout (KO) lines on plasma metabolites, including genes coding for structural proteins (11 of 30), metabolic pathway enzymes (12 of 30) and protein kinases (7 of 30). Steroids, bile acids, oxylipins, primary metabolites, biogenic amines and complex lipids were analyzed with dedicated mass spectrometry platforms, yielding 827 identified metabolites in male and female KO mice and wildtype (WT) controls. Twenty-two percent of 23,698 KO versus WT comparison tests showed significant genotype effects on plasma metabolites. Fifty-six percent of identified metabolites were significantly different between the sexes in WT mice. Many of these metabolites were also found to have sexually dimorphic changes in KO lines. We used plasma metabolites to complement phenotype information exemplified for Dhfr, Idh1, Mfap4, Nek2, Npc2, Phyh and Sra1. The association of plasma metabolites with IMPC phenotypes showed dramatic sexual dimorphism in wildtype mice. We demonstrate how to link metabolomics to genotypes and (disease) phenotypes. Sex must be considered as critical factor in the biological interpretation of gene functions.

Metabolic profiles reflect weight loss maintenance and the composition of diet after very-low-energy diet

BACKGROUND & AIMS

Diet and weight loss affect circulating metabolome. However, metabolite profiles induced by different weight loss maintenance diets and underlying longer term weight loss maintenance remain unknown. Herein, we investigated after-weight-loss metabolic signatures of two isocaloric 24-wk weight maintenance diets differing in satiety value due to dietary fibre, protein and fat contents and identified metabolite features that associated with successful weight loss maintenance.

METHODS

Non-targeted LC-MS metabolomics approach was used to analyse plasma metabolites of 79 women and men (mean age ± SD 49.7 ± 9.0 years; BMI 34.2 ± 2.5 kg/m2) participating in a weight management study. Participants underwent a 7-week very-low-energy diet (VLED) and were thereafter randomised into two groups for a 24-week weight maintenance phase. Higher satiety food (HSF) group consumed high-fibre, high-protein, and low-fat products, while lower satiety food (LSF) group consumed isocaloric low-fibre products with average protein and fat content as a part of their weight maintenance diets. Plasma metabolites were analysed before the VLED and before and after the weight maintenance phase. Metabolite features discriminating HSF and LSF groups were annotated. We also analysed metabolite features that discriminated participants who maintained ≥10% weight loss (HWM) and participants who maintained <10% weight loss (LWM) at the end of the study, irrespective of the diet. Finally, we assessed robust linear regression between metabolite features and anthropometric and food group variables.

RESULTS

We annotated 126 metabolites that discriminated the HSF and LSF groups and HWM and LWM groups (p < 0.05). Compared to LSF, the HSF group had lower levels of several amino acids, e.g. glutamine, arginine, and glycine, short-, medium- and long-chain acylcarnitines (CARs), odd- and even-chain lysoglycerophospholipids, and higher levels of fatty amides. Compared to LWM, the HWM group in general showed higher levels of glycerophospholipids with a saturated long-chain and a C20:4 fatty acid tail, and unsaturated free fatty acids (FFAs). Changes in several saturated odd- and even-chain LPCs and LPEs and fatty amides were associated with the intake of many food groups, particularly grain and dairy products. Increase in several (lyso)glycerophospholipids was associated with decrease in body weight and adiposity. Increased short- and medium-chain CARs were related to decreased body fat-free mass.

CONCLUSIONS

Our results show that isocaloric weight maintenance diets differing in dietary fibre, protein, and fat content affected amino acid and lipid metabolism. Increased abundances of several phospholipid species and FFAs were related with greater weight loss maintenance. Our findings indicate common and distinct metabolites for weight and dietary related variables in the context of weight reduction and weight management. The study was registered in isrctn.org with identifier 67529475.

Unlocking the secrets of the microbiome: exploring the dynamic microbial interplay with humans through metabolomics and their manipulation for synthetic biology applications

Metabolomics is a powerful research discovery tool with the potential to measure hundreds to low thousands of metabolites. In this review, we discuss the application of GC-MS and LC-MS in discovery-based metabolomics research, we define metabolomics workflows and we highlight considerations that need to be addressed in order to generate robust and reproducible data. We stress that metabolomics is now routinely applied across the biological sciences to study microbiomes from relatively simple microbial systems to their complex interactions within consortia in the host and the environment and highlight this in a range of biological species and mammalian systems including humans. However, challenges do still exist that need to be overcome to maximise the potential for metabolomics to help us understanding biological systems. To demonstrate the potential of the approach we discuss the application of metabolomics in two broad research areas: (1) synthetic biology to increase the production of high-value fine chemicals and reduction in secondary by-products and (2) gut microbial interaction with the human host. While burgeoning in importance, the latter is still in its infancy and will benefit from the development of tools to detangle host-gut-microbial interactions and their impact on human health and diseases.

DiffN Selection of Tandem Mass Spectrometry Precursors

Current data-dependent acquisition (DDA) approaches select precursor ions for tandem mass spectrometry (MS/MS) characterization based on their absolute intensity, known as a TopN approach. Low-abundance species may not be identified as biomarkers in a TopN approach. Herein, a new DDA approach is proposed, DiffN, which uses the relative differential intensity of ions between two samples to selectively target species undergoing the largest fold changes for MS/MS. Using a dual nano-electrospray (nESI) ionization source which allows samples contained in separate capillaries to be analyzed in parallel, the DiffN approach was developed and validated with well-defined lipid extracts. A dual nESI source and DiffN DDA approach was applied to quantify the differences in lipid abundance between two colorectal cancer cell lines. The SW480 and SW620 lines represent a matched pair from the same patient: the SW480 cells from a primary tumor and the SW620 cells from a metastatic lesion. A comparison of TopN and DiffN DDA approaches on these cancer cell samples highlights the ability of DiffN to increase the likelihood of biomarker discovery and the decreased probability of TopN to efficiently select lipid species that undergo large fold changes. The ability of the DiffN approach to efficiently select precursor ions of interest makes it a strong candidate for lipidomic analyses. This DiffN DDA approach may also apply to other molecule classes (e.g., other metabolites or proteins) that are amenable to shotgun analyses.

Insight into the mechanisms of therapeutic hypothermia for asphyxia cardiac arrest using a comprehensive approach of GC-MS/MS and UPLC-Q-TOF-MS/MS based on serum metabolomics

Cardiac arrest (CA) is a severe worldwide health problem. Therapeutic hypothermia is widely used to reduce the cardiac injury and improve the neurological outcomes after CA. However, a few studies have reported the changes of serum metabolic characteristics after CA. The healthy male New Zealand Rabbits successfully resuscitated from 10-min asphyxia-induced CA were divided randomly into the normothermia (NT) group and mild therapeutic hypothermia (HT) group. The sham group underwent sham-operation. Survival was recorded and neurological deficit score (NDS) was assessed. The serum non-targeted metabolomics were detected using ultra-high-performance liquid chromatography-quadrupole time-of-flight tandem mass spectrometry (UPLC-Q-TOF-MS/MS) and gas chromatography tandem mass spectrometry (GC-MS/MS) at 15 min, 3 h, 6 h and 24 h after return of spontaneous circulation (ROSC). Our study showed that the heart rate (HR) significantly slowed down during 0.5-6 h post ROSC, consistent with the decreasing trend of body temperature in the HT group. Compared with the NT group, the levels of Lac and PCO2 at 24 h post ROSC were lower, while a significant increase in PO2 level at 24 h post ROSC was observed in the HT group. The survival rate of the HT group was significantly higher than that of the NT group, and NDS scores were remarkably increased at 24 h post ROSC in the NT group. Significant differences in metabolic profiles at 15 min, 3 h, 6 h and 24 h post ROSC were observed among the Sham, NT and HT groups. The differential metabolites detected by UPLC-Q-TOF-MS/MS and GC-MS/MS were screened for further study between every two groups (NT vs sham, HT vs sham and HT vs NT) at 15 min, 3 h, 6 h and 24 h post ROSC. Phenylalanine metabolism, alanine, aspartate and glutamate metabolism and tricarboxylic acid (TCA) cycle were enriched in NT vs sham, HT vs sham and HT vs NT respectively. Our study demonstrated that therapeutic hypothermia improves the survival and neurological outcomes in rabbit model of cardiac arrest, and firstly represents the dynamic metabolic changes in the hypothermia therapy for CA by comprehensive UPLC-Q-TOF-MS/MS- and GC-MS/MS-based metabolomics.

Metabolomics and its applications in assisted reproductive technology

Metabolomics, an emerging omics technology developed in the post-gene age, is an important part of systems biology. It interprets the pathophysiological state of the subject by quantitatively describing the dynamic changes of metabolites through analytical methods, mainly mass spectrometry (MS) and nuclear magnetic resonance (NMR). Assisted reproductive technology (ART) is a method used to manipulate sperm, oocytes, and embryos to achieve conception. Recently, several studies have reported that metabolomics methods can be used to measure metabolites in ART samples; these metabolites can be used to evaluate the quality of gametes and embryos. This article reviews the progress of research on metabolomics and the application of this technology in the field of ART, thus providing a reference for research and development directions in the future.

Interpretable Machine Learning on Metabolomics Data Reveals Biomarkers for Parkinson's Disease

The use of machine learning (ML) with metabolomics provides opportunities for the early diagnosis of disease. However, the accuracy of ML and extent of information obtained from metabolomics can be limited owing to challenges associated with interpreting disease prediction models and analyzing many chemical features with abundances that are correlated and "noisy". Here, we report an interpretable neural network (NN) framework to accurately predict disease and identify significant biomarkers using whole metabolomics data sets without a priori feature selection. The performance of the NN approach for predicting Parkinson's disease (PD) from blood plasma metabolomics data is significantly higher than other ML methods with a mean area under the curve of >0.995. PD-specific markers that predate clinical PD diagnosis and contribute significantly to early disease prediction were identified including an exogenous polyfluoroalkyl substance. It is anticipated that this accurate and interpretable NN-based approach can improve diagnostic performance for many diseases using metabolomics and other untargeted 'omics methods.

Metabolomics in viral hepatitis: advances and review

Viral hepatitis is a major worldwide public health issue, affecting hundreds of millions of people and causing substantial morbidity and mortality. The majority of the worldwide burden of viral hepatitis is caused by five biologically unrelated hepatotropic viruses: hepatitis A virus (HAV), hepatitis B virus (HBV), hepatitis C virus (HCV), hepatitis D virus (HDV), and hepatitis E virus (HEV). Metabolomics is an emerging technology that uses qualitative and quantitative analysis of easily accessible samples to provide information of the metabolic levels of biological systems and changes in metabolic and related regulatory pathways. Alterations in glucose, lipid, and amino acid levels are involved in glycolysis, the tricarboxylic acid cycle, the pentose phosphate pathway, and amino acid metabolism. These changes in metabolites and metabolic pathways are associated with the pathogenesis and medication mechanism of viral hepatitis and related diseases. Additionally, differential metabolites can be utilized as biomarkers for diagnosis, prognosis, and therapeutic responses. In this review, we present a thorough overview of developments in metabolomics for viral hepatitis.

Early sex-dependent differences in metabolic profiles of overweight and adiposity in young children: a cross-sectional analysis

BACKGROUND

Childhood obesity is a global health concern and can lead to lifetime cardiometabolic disease. New advances in metabolomics can provide biochemical insights into the early development of obesity, so we aimed to characterize serum metabolites associated with overweight and adiposity in early childhood and to stratify associations by sex.

METHODS

Nontargeted metabolite profiling was conducted in the Canadian CHILD birth cohort (discovery cohort) at age 5 years (n = 900) by multisegment injection-capillary electrophoresis-mass spectrometry. Clinical outcome was defined using novel combined measures of overweight (WHO-standardized body mass index ≥ 85th percentile) and/or adiposity (waist circumference ≥ 90th percentile). Associations between circulating metabolites and child overweight/adiposity (binary and continuous outcomes) were determined by multivariable linear and logistic regression, adjusting for covariates and false discovery rate, and by subsequent sex-stratified analysis. Replication was assessed in an independent replication cohort called FAMILY at age 5 years (n = 456).

RESULTS

In the discovery cohort, each standard deviation (SD) increment of branched-chain and aromatic amino acids, glutamic acid, threonine, and oxoproline was associated with 20-28% increased odds of overweight/adiposity, whereas each SD increment of the glutamine/glutamic acid ratio was associated with 20% decreased odds. All associations were significant in females but not in males in sex-stratified analyses, except for oxoproline that was not significant in either subgroup. Similar outcomes were confirmed in the replication cohort, where associations of aromatic amino acids, leucine, glutamic acid, and the glutamine/glutamic acid ratio with childhood overweight/adiposity were independently replicated.

CONCLUSIONS

Our findings show the utility of combining measures of both overweight and adiposity in young children. Childhood overweight/adiposity at age 5 years has a specific serum metabolic phenotype, with the profile being more prominent in females compared to males.

Mass Sportrometry: An annual look back at applications of mass spectrometry in sport and exercise science

Research in sport and exercise science (SES) is reliant on robust analyses of biomarker measurements to assist with the interpretation of physiological outcomes. Mass spectrometry (MS) is an analytical approach capable of highly sensitive, specific, precise, and accurate analyses of a range of biomolecules, many of which are of interest in SES including, but not limited to, endogenous metabolites, exogenously administered compounds (e.g. supplements), mineral ions, and circulating/tissue proteins. This annual review provides a summary of the applications of MS across studies investigating aspects related to sport or exercise in manuscripts published, or currently in press, in 2022. In total, 93 publications are included and categorized according to their methodologies including targeted analyses, metabolomics, lipidomics, proteomics, and isotope ratio/elemental MS. The advantageous analytical opportunities afforded by MS technologies are discussed across a selection of relevant articles. In addition, considerations for the future of MS in SES, including the need to improve the reporting of assay characteristics and validation data, are discussed, alongside the recommendation for selected current methods to be superseded by MS-based approaches where appropriate. The review identifies that a targeted, mostly quantitative, approach is the most commonly applied MS approach within SES, although there has also been a keen interest in the use of 'omics' to perform hypothesis-generating research studies. Nonetheless, MS is not commonplace in SES at this time, but its use to expand, and possibly improve, the analytical options should be continually considered to exploit the benefits of analytical chemistry in exercise/sports-based research. Overall, it is exciting to see the gradually increasing adoption of MS in SES and it is expected that the number, and quality, of MS-based assays in SES will increase over time, with the potential for 2023 to further establish this technique within the field.

Multi-omics insights and therapeutic implications in polycystic ovary syndrome: a review

Polycystic ovary syndrome (PCOS) is a common gynecological disease that causes adverse effects in women in their reproductive phase. Nonetheless, the molecular mechanisms remain unclear. Over the last decade, sequencing and omics approaches have advanced at an increased pace. Omics initiatives have come to the forefront of biomedical research by presenting the significance of biological functions and processes. Thus, multi-omics profiling has yielded important insights into understanding the biology of PCOS by identifying potential biomarkers and therapeutic targets. Multi-omics platforms provide high-throughput data to leverage the molecular mechanisms and pathways involving genetic alteration, epigenetic regulation, transcriptional regulation, protein interaction, and metabolic alterations in PCOS. The purpose of this review is to outline the prospects of multi-omics technologies in PCOS research by revealing novel biomarkers and therapeutic targets. Finally, we address the knowledge gaps and emerging treatment strategies for the management of PCOS. Future PCOS research in multi-omics at the single-cell level may enhance diagnostic and treatment options.