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

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.

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.

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).

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).

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.

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.

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.

Amino acid analysis as a method of discovering biomarkers for diagnosis of diabetes and its complications

Diabetes mellitus (DM) is a severe chronic diseases with a global prevalence of 9%, leading to poor health and high health care costs, and is a direct cause of millions of deaths each year. The rising epidemic of diabetes and its complications, such as retinal and peripheral nerve disease, is a huge burden globally. A better understanding of the molecular pathways involved in the development and progression of diabetes and its complications can facilitate individualized prevention and treatment. High diabetes mellitus incidence rate is caused mainly by lack of non-invasive and reliable methods for early diagnosis, such as plasma biomarkers. The incidence of diabetes and its complications in the world still grows so it is crucial to develop a new, faster, high specificity and more sensitive diagnostic technologies. With the advancement of analytical techniques, metabolomics can identify and quantify multiple biomarkers simultaneously in a high-throughput manner, and effective biomarkers can greatly improve the efficiency of diabetes and its complications. By providing information on potential metabolic pathways, metabolomics can further define the mechanisms underlying the progression of diabetes and its complications, help identify potential therapeutic targets, and improve the prevention and management of T2D and its complications. The application of amino acid metabolomics in epidemiological studies has identified new biomarkers of diabetes mellitus (DM) and its complications, such as branched-chain amino acids, phenylalanine and arginine metabolites. This study focused on the analysis of metabolic amino acid profiling as a method for identifying biomarkers for the detection and screening of diabetes and its complications. The results presented are all from recent studies, and in all cases analyzed, there were significant changes in the amino acid profile of patients in the experimental group compared to the control group. This study demonstrates the potential of amino acid profiles as a detection method for diabetes and its complications.

Demographic, Health and Lifestyle Factors Associated with the Metabolome in Older Women

Demographic and clinical factors influence the metabolome. The discovery and validation of disease biomarkers are often challenged by potential confounding effects from such factors. To address this challenge, we investigated the magnitude of the correlation between serum and urine metabolites and demographic and clinical parameters in a well-characterized observational cohort of 444 post-menopausal women participating in the Women’s Health Initiative (WHI). Using LC-MS and lipidomics, we measured 157 aqueous metabolites and 756 lipid species across 13 lipid classes in serum, along with 195 metabolites detected by GC-MS and NMR in urine and evaluated their correlations with 29 potential disease risk factors, including demographic, dietary and lifestyle factors, and medication use. After controlling for multiple testing (FDR < 0.01), we found that log-transformed metabolites were mainly associated with age, BMI, alcohol intake, race, sample storage time (urine only), and dietary supplement use. Statistically significant correlations were in the absolute range of 0.2–0.6, with the majority falling below 0.4. Incorporation of important potential confounding factors in metabolite and disease association analyses may lead to improved statistical power as well as reduced false discovery rates in a variety of data analysis settings.

The resting serum metabolome in response to short-term sprint interval training

PURPOSE

To investigate the response of a targeted fraction of (168 metabolites) of the resting serum metabolome to 9 sessions of sprint interval training (SIT).

METHODS

Thirty-four recreationally active males provided resting blood samples before (baseline) and 48-72 h after (post) a short-term (9 sessions) cycle ergometer-based SIT intervention. A targeted analysis of 168 metabolites was performed on serum using liquid chromatography mass spectrometry (LC-MS). 160 distinct metabolites were identified and combined with 4 calculated metabolite sums and 3 calculated metabolite ratios creating a panel of 167 individual factors. Data were analysed using principal component analysis and univariate testing of all factors classified into 5 metabolite subgroups.

RESULTS

SIT improved anaerobic capacity measured by average power output during a Wingate test (p < 0.01; mean difference = 38 W, 95% confidence interval [26, 51]) and aerobic capacity measured by average power output in a 20 min cycling test (p < 0.01; 17 W [12, 23]). Limited separation was discernible in the targeted serum metabolome between baseline and post-intervention when projected on the first and second principal component(s). However, univariate testing identified 11 fatty acids that had lower concentrations (false discovery rate < 0.05) in post-intervention samples.

CONCLUSIONS

These findings demonstrate that this short-term SIT intervention had limited effect on the serum metabolome at rest, but a subfraction of fatty acids are potentially sensitive to short-term exercise training.

Closing the gap between in vivo and in vitro omics: using QA/QC to strengthen ex vivo NMR metabolomics

Metabolomics aims to achieve a global quantitation of the pool of metabolites within a biological system. Importantly, metabolite concentrations serve as a sensitive marker of both genomic and phenotypic changes in response to both internal and external stimuli. NMR spectroscopy greatly aids in the understanding of both in vitro and in vivo physiological systems and in the identification of diagnostic and therapeutic biomarkers. Accordingly, NMR is widely utilized in metabolomics and fluxomics studies due to its limited requirements for sample preparation and chromatography, its non-destructive and quantitative nature, its utility in the structural elucidation of unknown compounds, and, importantly, its versatility in the analysis of in vitro, in vivo, and ex vivo samples. This review provides an overview of the strengths and limitations of in vitro and in vivo experiments for translational research and discusses how ex vivo studies may overcome these weaknesses to facilitate the extrapolation of in vitro insights to an in vivo system. The application of NMR-based metabolomics to ex vivo samples, tissues, and biofluids can provide essential information that is close to a living system (in vivo) with sensitivity and resolution comparable to those of in vitro studies. The success of this extrapolation process is critically dependent on high-quality and reproducible data. Thus, the incorporation of robust quality assurance and quality control checks into the experimental design and execution of NMR-based metabolomics experiments will ensure the successful extrapolation of ex vivo studies to benefit translational medicine.

Utilizing Amniotic Fluid Metabolomics to Monitor Fetal Well-Being: A Narrative Review of the Literature

Fetal and perinatal periods are critical phases for long-term development. Early diagnosis of maternal complications is challenging due to the great complexity of these conditions. In recent years, amniotic fluid has risen in a prominent position in the latest efforts to describe and characterize prenatal development. Amniotic fluid may provide real-time information on fetal development and metabolism throughout pregnancy as substances from the placenta, fetal skin, lungs, gastric fluid, and urine are transferred between the mother and the fetus. Applying metabolomics to monitor fetal well-being, in such a context, could help in the understanding, diagnosis, and treatment of these conditions and is a promising area of research. This review shines a spotlight on recent amniotic fluid metabolomics studies and their methods as an interesting tool for the assessment of many conditions and the identification of biomarkers. Platforms in use, such as proton nuclear magnetic resonance (¹H NMR) and ultra-high-performance liquid chromatography (UHPLC), have different merits, and a combinatorial approach could be valuable. Metabolomics may also be used in the quest for habitual diet-induced metabolic signals in amniotic fluid. Finally, analysis of amniotic fluid can provide information on exposure to exogenous substances by detecting the exact levels of metabolites carried to the fetus and associated metabolic effects.

A metabolomics approach to the validation of predictive metabolites and phenotypic expression in non-alcoholic fatty liver disease

AIMS

Nonalcoholic fatty liver disease (NAFLD) is becoming more common and severe. Individuals with NAFLD have an altered composition of gut- microbial metabolites. We used metabolomics profiling to identify microbial metabolites that could indicate gut-liver metabolic severity. Noninvasive biomarkers are required for NAFLD, especially for patients at high risk of disease progression.

MAIN METHODS

We compared the stool metabolomes, untargeted metabolomics, and clinical data of 80 patients. Patients with nonalcoholic fatty liver (NAFL: n = 16), nonalcoholic steatohepatitis (NASH: n = 26), and cirrhosis (n = 19) and healthy control individuals (HC: n = 19) were enrolled. The identified metabolites in NAFLD were evaluated by multivariate statistical analysis and metabolic pathotypic expression. Gas chromatography-mass spectrometry (GC-MS) and liquid chromatography coupled to time-of-flight-mass spectrometry (LC-TOF-MS) were used to analyze metabolites.

KEY FINDINGS

Untargeted metabolomics was used to identify and quantify 103 metabolites. Principal component analysis (PCA) was used to assess the metabolic discrimination of NAFL, NASH, and cirrhosis. Short-chain fatty acids (SCFA) levels were significantly lower in NAFLD patients, including those of acetate (p = 0.03), butyrate (p = 0.0008), and propionate. The stool cholic acid (p = 0.001) level was significantly increased in NAFLD patients. Palmitoylcarnitine and l-carnitine levels were significantly increased in NASH and cirrhosis patients. The phenotypic expression of these metabolites was linked to β-oxidation.

SIGNIFICANCE

We demonstrated a distinct metabolome profile in NAFLD patients with NAFL, NASH, and cirrhosis. We also discovered that the expression of certain metabolites and metabolic pathways was linked to NAFLD.

Status of Metabolomic Measurement for Insights in Alzheimer's Disease Progression-What Is Missing?

Alzheimer's disease (AD) is an aging-related neurodegenerative disease, leading to the progressive loss of memory and other cognitive functions. As there is still no cure for AD, the growth in the number of susceptible individuals represents a major emerging threat to public health. Currently, the pathogenesis and etiology of AD remain poorly understood, while no efficient treatments are available to slow down the degenerative effects of AD. Metabolomics allows the study of biochemical alterations in pathological processes which may be involved in AD progression and to discover new therapeutic targets. In this review, we summarized and analyzed the results from studies on metabolomics analysis performed in biological samples of AD subjects and AD animal models. Then this information was analyzed by using MetaboAnalyst to find the disturbed pathways among different sample types in human and animal models at different disease stages. We discuss the underlying biochemical mechanisms involved, and the extent to which they could impact the specific hallmarks of AD. Then we identify gaps and challenges and provide recommendations for future metabolomics approaches to better understand AD pathogenesis.

Integration of omics studies indicates that species-dependent molecular mechanisms govern male fertility

BACKGROUND

Comparative and comprehensive omics studies have recently been conducted to provide a comprehensive understanding of the biological mechanisms underlying infertility. However, because these huge omics datasets often contain irrelevant information, editing strategies for summarizing and filtering the data are necessary prerequisite steps for identifying biomarkers of male fertility. Here, we attempted to integrate omics data from spermatozoa with normal and below-normal fertility from boars and bulls, including transcriptomic, proteomic, and metabolomic data. Pathway enrichment analysis was conducted and visualized using g:Profiler, Cytoscape, EnrichmentMap, and AutoAnnotation to determine fertility-related biological functions according to species.

RESULTS

In particular, gamete production and protein biogenesis-associated pathways were enriched in bull spermatozoa with below-normal fertility, whereas mitochondrial-associated metabolic pathways were enriched in boar spermatozoa with normal fertility. These results indicate that below-normal fertility may be determined by aberrant regulation of protein synthesis during spermatogenesis, and the modulation of reactive oxygen species generation to maintain capacitation and the acrosome reaction governs boar sperm fertility.

CONCLUSION

Overall, this approach demonstrated that distinct molecular pathways drive sperm fertility in mammals in a species-dependent manner. Moreover, we anticipate that searching for species-specific signaling pathways may aid in the discovery of fertility-related biomarkers within large omics datasets.

A proposed framework to evaluate the quality and reliability of targeted metabolomics assays from the UK Consortium on Metabolic Phenotyping (MAP/UK)

Targeted metabolite assays that measure tens or hundreds of pre-selected metabolites, typically using liquid chromatography-mass spectrometry, are increasingly being developed and applied to metabolic phenotyping studies. These are used both as standalone phenotyping methods and for the validation of putative metabolic biomarkers obtained from untargeted metabolomics studies. However, there are no widely accepted standards in the scientific community for ensuring reliability of the development and validation of targeted metabolite assays (referred to here as 'targeted metabolomics'). Most current practices attempt to adopt, with modifications, the strict guidance provided by drug regulatory authorities for analytical methods designed largely for measuring drugs and other xenobiotic analytes. Here, the regulatory guidance provided by the European Medicines Agency, US Food and Drug Administration and International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use are summarized. In this Perspective, we have adapted these guidelines and propose a less onerous 'tiered' approach to evaluate the reliability of a wide range of metabolomics analyses, addressing the need for community-accepted, harmonized guidelines for tiers other than full validation. This 'fit-for-purpose' tiered approach comprises four levels-discovery, screening, qualification and validation-and is discussed in the context of a range of targeted and untargeted metabolomics assays. Issues arising with targeted multiplexed metabolomics assays, and how these might be addressed, are considered. Furthermore, guidance is provided to assist the community with selecting the appropriate degree of reliability for a series of well-defined applications of metabolomics.

Pre-analytical aspects in metabolomics of human biofluids - sample collection, handling, transport, and storage

Metabolomics is the field of omics research that offers valuable insights into the complex composition of biological samples. It has found wide application in clinical diagnostics, disease investigation, therapy prediction, monitoring of treatment efficiency, drug discovery, or in-depth analysis of sample composition. A suitable study design constitutes the fundamental requirements to ensure robust and reliable results from the study data. The study design process should include a careful selection of conditions for each experimental step, from sample collection to data analysis. The pre-analytical variability that can introduce bias to the subsequent analytical process, decrease the outcome reliability, and confuse the final results of the metabolomics research, should also be considered. Herein, we provide key information regarding the pre-analytical variables affecting the metabolomics studies of biological fluids that are the most desirable type of biological samples. Our work offers a practical review that can serve and guide metabolomics pre-analytical design. It indicates pre-analytical factors, which can introduce artificial data variation and should be identified and understood during experimental design (through literature overview or analytical experiments).

Metabolomics in pediatric lower respiratory tract infections and sepsis: a literature review

Lower respiratory tract infections (LRTIs) are a leading cause of morbidity and mortality in children. The ability of healthcare providers to diagnose and prognose LRTIs in the pediatric population remains a challenge, as children can present with similar clinical features regardless of the underlying pathogen or ultimate severity. Metabolomics, the large-scale analysis of metabolites and metabolic pathways offers new tools and insights that may aid in diagnosing and predicting the outcomes of LRTIs in children. This review highlights the latest literature on the clinical utility of metabolomics in providing care for children with bronchiolitis, pneumonia, COVID-19, and sepsis. IMPACT: This article summarizes current metabolomics approaches to diagnosing and predicting the course of pediatric lower respiratory infections. This article highlights the limitations to current metabolomics research and highlights future directions for the field.

The role of exercise and hypoxia on glucose transport and regulation

Muscle glucose transport activity increases with an acute bout of exercise, a process that is accomplished by the translocation of glucose transporters to the plasma membrane. This process remains intact in the skeletal muscle of individuals with insulin resistance and type 2 diabetes mellitus (T2DM). Exercise training is, therefore, an important cornerstone in the management of individuals with T2DM. However, the acute systemic glucose responses to carbohydrate ingestion are often augmented during the early recovery period from exercise, despite increased glucose uptake into skeletal muscle. Accordingly, the first aim of this review is to summarize the knowledge associated with insulin action and glucose uptake in skeletal muscle and apply these to explain the disparate responses between systemic and localized glucose responses post-exercise. Herein, the importance of muscle glycogen depletion and the key glucoregulatory hormones will be discussed. Glucose uptake can also be stimulated independently by hypoxia; therefore, hypoxic training presents as an emerging method for enhancing the effects of exercise on glucose regulation. Thus, the second aim of this review is to discuss the potential for systemic hypoxia to enhance the effects of exercise on glucose regulation.

Metabolomics-based study of potential biomarkers of sepsis

The purpose of our study was to explore potential characteristic biomarkers in patients with sepsis. Peripheral blood specimens from sepsis patients and normal human volunteers were processed by liquid chromatography-mass spectrometry-based analysis. Outlier data were excluded by principal component analysis and orthogonal partial least squares-discriminant analysis using the metabolomics R software package metaX and MetaboAnalyst 5.0 ( https://www.metaboanalyst.ca/home.xhtml ) online analysis software, and differential metabolite counts were identified by using volcano and heatmaps. The obtained differential metabolites were combined with KEGG (Kyoto Gene and Kyoto Encyclopedia) analysis to screen out potential core differential metabolites, and ROC curves were drawn to analyze the changes in serum metabolites in sepsis patients and to explore the potential value of the metabolites in the diagnosis of sepsis patients. By metabolomic analysis, nine differential metabolites were screened for their significance in guiding the diagnosis and differential diagnosis of sepsis namely: 3-phenyl lactic acid, N-phenylacetylglutamine, phenylethylamine, traumatin, xanthine, methyl jasmonate, indole, l-tryptophan and 1107116. In this study, nine metabolites were finally screened based on metabolomic analysis and used as potential characteristic biomarkers for the diagnosis of sepsis.

Metabolomics as a potential tool for monitoring patients with aneurysmal subarachnoid hemorrhage

Metabolomics has evolved into a particularly useful tool to study interactions between metabolites and serves as an aid in unraveling the complexity of entire metabolomes. Nonetheless, it is increasingly viewed as a methodology with practical applications in the clinical setting, where identifying and quantifying biomarkers of interest could prove useful for diagnostics. Starting from a concise overview of the most prominent analytical techniques employed in metabolomics, herein we present a review of its application in studies of brain metabolism and cerebrovascular diseases, paying most attention to its uses in researching aneurysmal subarachnoid hemorrhage. Both animal models and human studies are considered, and metabolites identified as potential biomarkers are highlighted.

Metabolomics and NMR

The purpose of this manuscript will be to convince the reader to dive deeper into NMR spectroscopy and prevent the technique from being just another "black-box" in the lab. We will try to concisely highlight interesting topics and supply additional references for further exploration at each stage. The advantages of delving into the technique will be shown. The secondary objective, i.e., avoiding common problems before starting, will hopefully then become clear. Lastly, we will emphasize the spectrometer information needed for manuscript reporting to allow reproduction of results and confirm findings.

Metabolites as extracellular vesicle cargo in health, cancer, pleural effusion, and cardiovascular diseases: An emerging field of study to diagnostic and therapeutic purposes

Extracellular vesicles (EVs) are highly diverse nanoscale membrane-bound structures released from different cell types into the extracellular environment. They play essential functions in cell signaling by transporting their cargo, such as proteins, RNA, DNA, lipids, metabolites, and small molecules, to recipient cells. It has recently been shown that EVs might modulate carcinogenesis by delivering cargo to recipient cells. Furthermore, recent discoveries revealed that changes in plasma-derived EV levels and cargo in subjects with metabolic diseases were documented by many researchers, suggesting that EVs might be a promising source of disease biomarkers. One of the cargos of EVs that has recently attracted the most attention is metabolites. The metabolome of these vesicles introduces a plethora of disease indicators; hence, examining the metabolomics of EVs detected in human biofluids would be an effective approach. On the other hand, metabolites have various roles in biological systems, including the production of energies, synthesizing macromolecules, and serving as signaling molecules and hormones. Metabolome rewiring in cancer and stromal cells is a characteristic of malignancy, but the current understanding of how this affects the metabolite composition and activity of tumor-derived EVs remains in its infancy. Since new findings and studies in the field of exosome biology and metabolism are constantly being published, it is likely that diagnostic and treatment techniques, including the use of exosome metabolites, will be launched in the coming years. Recent years have seen increased interest in the EV metabolome as a possible source for biomarker development. However, our understanding of the role of these molecules in health and disease is still immature. In this work, we have provided the latest findings regarding the role of metabolites as EV cargoes in the pathophysiology of diseases, including cancer, pleural effusion (PE), and cardiovascular disease (CVD). We also discussed the significance of metabolites as EV cargoes of microbiota and their role in host-microbe interaction. In addition, the latest findings on metabolites in the form of EV cargoes as biomarkers for disease diagnosis and treatment are presented in this study.

Semen quality and seminal plasma metabolites in male rabbits (Oryctolagus cuniculus) under heat stress

Heat stress causes infertility in male rabbits in summer. This study was conducted to determine the effects of heat stress on semen quality and seminal plasma metabolites of male rabbits. To achieve these objectives, the temperature and humidity index (THI) was used to determine the stress state of male rabbits during different months, thereby the rabbits were divided into heat stress and no heat stress groups. The quality of the semen and the biochemical indices of seminal plasma were then analyzed. Next the plasma metabolites of rabbits in both groups were evaluated using the ultra-high performance liquid chromatography-mass spectroscopy (UPLC-MS)/MS technique. Our results showed that the THI value of the rabbit housing in May was 20.94 (no heat stress). The THI value of the housing in August was 29.10 (heat stress group, n = 10). Compared with the non-heat stress group, the sperm motility, density, and pH in the heat stress group (n = 10) were significantly decreased (P < 0.01); the semen volume decreased significantly (P < 0.05); and the sperm malformation rate increased significantly (P < 0.01). The number of grade A sperm significantly decreased, while the numbers of B and C grade sperm significantly increased (P < 0.01). The total sperm output (TSO), total motile sperm (TMS), and total functional sperm fraction (TFSF) decreased significantly (P < 0.01). Heat stress protein 70 (HSP70) and acid phosphatase (ACP) in the seminal plasma of rabbits in the heat stress group (n = 20) were significantly increased (P < 0.01). Seminal plasma testosterone (T), α-glucosidase (α-Glu), and fructose decreased significantly (P < 0.01). The concentrations of Mg²⁺ (P < 0.05), Na⁺ (P < 0.01), and K⁺ (P < 0.01) in metal ions were significantly decreased. These findings indicated that heat stress severely affected the quality of the male rabbit semen. Furthermore, UPLC-MS/MS technology was used to analyze the seminal plasma samples of rabbits in the heat stress group and non-heat stress group (n = 9 for each group). In total, 346 metabolites were identified, with variable importance in project (VIP) > 1.0, fold change (FC) > 1.5 or < 0.667, and P < 0.05 as the threshold. A total of 71 differential metabolites were matched, including stearic acid, betaine, arachidonic acid, L-malic acid, and indole. The Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of differential metabolites revealed 51 metabolic pathways, including synthesis and degradation of ketones, serine and threonine metabolism, tryptophan metabolism, and the citric acid cycle. Our study has shown that the sperm motility, sperm pH value, and sperm density of male rabbits decreased significantly under heat stress, and the sperm malformation rate increased significantly. Furthermore, the quality of semen was shown to deteriorate and the energy metabolism pathway was disturbed. These findings provide a theoretical reference for alleviating the adaptive heat stress in male rabbits.