Plasma metabolomic profiles enhance precision medicine for volunteers of normal health. Proc Natl Acad Sci U S A. 2015;112:E4901-E4910. [PMID: 26283345 DOI: 10.1073/pnas.1508425112]

Reference Intervals of Serum Metabolites and Lipids of a Healthy Chinese Population Determined by Liquid Chromatography-Mass Spectrometry

Background: Metabolomics serves as a very useful tool for elucidating disease mechanisms and identifying biomarkers. Establishing reference intervals (RIs) of metabolites in a healthy population is crucial to the application of metabolomics in life sciences and clinics. Methods: We enrolled 615 healthy Chinese adults aged between 21 and 85 years. Their health status was ascertained through clinical examinations, biochemical parameters, and medical history. Targeted metabolomics and lipidomics analyses were applied to quantify 705 metabolites and lipids in the serum, establishing RIs and investigating the effect of sex and age on the metabolome and lipidome. Results: This study is the first large-scale effort in China to establish RIs for metabolites in the apparently healthy population. We found that most of the sex-related metabolites, including amino acids, acyl-carnitines and triacylglycerols, had higher concentrations in males, while the other sex-related lipids showed higher concentrations in females. Most of the age-related metabolites increased with age, including those associated with protein synthesis, nitric oxide synthesis, energy metabolism, and lipid metabolism. Conclusions: This study gives the reference intervals of the healthy Chinese metabolome and lipidome and their relationship with sex and age, which facilitates life sciences and precision medicine, especially for disease research and biomarker discovery.

Exploring Diagnostic Markers and Therapeutic Targets in Parkinson's Disease: A Comprehensive 1H-NMR Metabolomic Analysis - Systematic Review

Parkinson's disease (PD) affects millions of people globally. Accurate early diagnosis remains a challenge due to the lack of specific biomarkers. This systematic review explores the potential of 1H-NMR metabolomics in identifying diagnostic markers and therapeutic targets for PD. A comprehensive analysis was conducted across databases such as Scopus, Web of Science, PubMed, and Embase, focusing on studies that utilized 1H-NMR spectroscopy to profile metabolites associated with PD progression. The review identifies key metabolites-glutamate, taurine, myo-inositol, glutamine, and creatine-that play critical roles in the pathophysiology of PD. Glutamate, linked to excitotoxicity and neuronal degeneration, emerges as a prominent target for therapeutic intervention, while taurine is associated with oxidative stress. Myo-inositol, a key regulator of autophagy, underscores the biochemical dysregulation associated with PD, similar to glutamine and glutamate. Creatine's role in neuronal energy metabolism suggests potential avenues for treatment focused on energy supplementation. The reproducibility of metabolite findings varied, indicating the complexity of PD's metabolomic landscape. Despite challenges in consistency, these metabolites hold promise as biomarkers for diagnosing PD and tracking disease progression. The review underscores the need for further validation of these markers and their integration with other omics technologies to enhance PD management. By identifying key metabolic pathways, this study opens new directions for personalized medicine, offering potential therapeutic targets to slow disease progression and improve patient outcomes.

Benchmarking feature selection and feature extraction methods to improve the performances of machine-learning algorithms for patient classification using metabolomics biomedical data

Objective

Classification tasks are an open challenge in the field of biomedicine. While several machine-learning techniques exist to accomplish this objective, several peculiarities associated with biomedical data, especially when it comes to omics measurements, prevent their use or good performance achievements. Omics approaches aim to understand a complex biological system through systematic analysis of its content at the molecular level. On the other hand, omics data are heterogeneous, sparse and affected by the classical "curse of dimensionality" problem, i.e. having much fewer observation, samples (n) than omics features (p). Furthermore, a major problem with multi-omics data is the imbalance either at the class or feature level. The objective of this work is to study whether feature extraction and/or feature selection techniques can improve the performances of classification machine-learning algorithms on omics measurements.

Methods

Among all omics, metabolomics has emerged as a powerful tool in cancer research, facilitating a deeper understanding of the complex metabolic landscape associated with tumorigenesis and tumor progression. Thus, we selected three publicly available metabolomics datasets, and we applied several feature extraction techniques both linear and non-linear, coupled or not with feature selection methods, and evaluated the performances regarding patient classification in the different configurations for the three datasets.

Results

We provide general workflow and guidelines on when to use those techniques depending on the characteristics of the data available. To further test the extension of our approach to other omics data, we have included a transcriptomics and a proteomics data. Overall, for all datasets, we showed that applying supervised feature selection improves the performances of feature extraction methods for classification purposes. Scripts used to perform all analyses are available at: https://github.com/Plant-Net/Metabolomic_project/.

Streamlining Phenotype Classification and Highlighting Feature Candidates: A Screening Method for Non-Targeted Ion Mobility Spectrometry-Mass Spectrometry (IMS-MS) Data

Nontargeted analysis (NTA) is increasingly utilized for its ability to identify key molecular features beyond known targets in complex samples. NTA is particularly advantageous in exploratory studies aimed at identifying phenotype-associated features or molecules able to classify various sample types. However, implementing NTA involves extensive data analyses and labor-intensive annotations. To address these limitations, we developed a rapid data screening capability compatible with NTA data collected on a liquid chromatography, ion mobility spectrometry, and mass spectrometry (LC-IMS-MS) platform that allows for sample classification while highlighting potential features of interest. Specifically, this method aggregates the thousands of IMS-MS spectra collected across the LC space for each sample and collapses the LC dimension, resulting in a single summed IMS-MS spectrum for screening. The summed IMS-MS spectra are then analyzed with a bootstrapped Lasso technique to identify key regions or coordinates for phenotype classification via support vector machines. Molecular annotations are then performed by examining the features present in the selected coordinates, highlighting potential molecular candidates. To demonstrate this summed IMS-MS screening approach, we applied it to clinical plasma lipidomic NTA data and exposomic NTA data from water sites with varying contaminant levels. Distinguishing coordinates were observed in both studies, enabling the evaluation of phenotypic molecular annotations and resulting in screening models capable of classifying samples with up to a 25% increase in accuracy compared to models using annotated data.

Metabolomic characterization of human glioblastomas and patient plasma: a pilot study

Background

Glioblastoma (GBM) is a clinically challenging primary brain tumor with poor survival outcome despite surgical resection and intensive chemoradiation. The metabolic heterogeneity of GBM can become biomarkers for treatment response, resistance, and outcome prediction. The aim of the study is to investigate metabolic distinctions between primary and recurrent GBM tissue and patient plasma to establish feasibility for metabolic profiling.

Methods

A single-center cohort study analyzed tissue and blood samples from 15 patients with GBM using untargeted metabolomic/lipidomic assays. Metabolomic, lipidomic, and biogenic amine analyses were conducted on GBM tissue and patient plasma at diagnosis and recurrence using untargeted mass spectrometry. The study utilized a small but longitudinally collected cohort to evaluate alteration in metabolites, lipids, and biogenic amines between specimens at diagnosis and recurrence.

Results

Exploratory analysis revealed significant alteration in metabolites, lipids, and biogenic amines between diagnostic and recurrent states in both tumor and plasma specimens. Notable metabolites differed at recurrence, including N-alpha-methylhistamine, glycerol-3-phosphate, phosphocholine, and succinic acid in tissue, and indole-3-acetate, and urea in plasma. Principal component analysis revealed distinct metabolomic profiles between tumor tissue and patient plasma. Distinct metabolic profiles were observed in GBM tissue and patient plasma at recurrence, demonstrating the feasibility of using metabolomic methodologies for longitudinal studies. One patient exhibited a unique tumor resistance signature at diagnosis, possibly indicating a high-risk metabolomic phenotype.

Conclusions

In this small cohort, the findings suggest the potential of metabolomic signatures of GBM tissue and patient plasma for risk stratification, outcome prediction, and the development of novel adjuvant metabolic-targeting therapies. The findings suggest metabolic discrepancies at diagnosis and recurrence in tissue and plasma, highlighting potential implications for evaluation of clinical response. The identification of significant changes in metabolite abundance emphasizes the need for larger studies using targeted metabolomics to validate and further explore these profiles.

Analysis of plasma metabolomes from 11 309 subjects in five population-based cohorts

Plasma metabolomics holds potential for precision medicine, but limited information is available to compare the performance of such methods across multiple cohorts. We compared plasma metabolite profiles after an overnight fast in 11,309 participants of five population-based Swedish cohorts (50-80 years, 52% women). Metabolite profiles were uniformly generated at a core laboratory (Metabolon Inc.) with untargeted liquid chromatography mass spectrometry and a comprehensive reference library. Analysis of a second sample obtained one year later was conducted in a subset. Of 1629 detected metabolites, 1074 (66%) were detected in all cohorts while only 10% were unique to one cohort, most of which were xenobiotics or uncharacterized. The major classes were lipids (28%), xenobiotics (22%), amino acids (14%), and uncharacterized (19%). The most abundant plasma metabolome components were the major dietary fatty acids and amino acids, glucose, lactate and creatinine. Most metabolites displayed a log-normal distribution. Temporal variability was generally similar to clinical chemistry analytes but more pronounced for xenobiotics. Extensive metabolite-metabolite correlations were observed but mainly restricted to within each class. Metabolites were broadly associated with clinical factors, particularly body mass index, sex and renal function. Collectively, our findings inform the conduct and interpretation of metabolite association and precision medicine studies.

Serotonin Transporter Deficiency Induces Metabolic Alterations in the Ileal Mucosa

Serotonin transporter (SERT) deficiency has been implicated in metabolic syndrome, intestinal inflammation, and microbial dysbiosis. Interestingly, changes in microbiome metabolic capacity and several alterations in host gene expression, including lipid metabolism, were previously observed in SERT-/- mice ileal mucosa. However, the precise host or microbial metabolites altered by SERT deficiency that may contribute to the pleiotropic phenotype of SERT KO mice are not yet understood. This study investigated the hypothesis that SERT deficiency impacts lipid and microbial metabolite abundances in the ileal mucosa, where SERT is highly expressed. Ileal mucosal metabolomics was performed by Metabolon on wild-type (WT) and homozygous SERT knockout (KO) mice. Fluorescent-activated cell sorting (FACS) was utilized to measure immune cell populations in ileal lamina propria to assess immunomodulatory effects caused by SERT deficiency. SERT KO mice exhibited a unique ileal mucosal metabolomic signature, with the most differentially altered metabolites being lipids. Such changes included increased diacylglycerols and decreased monoacylglycerols in the ileal mucosa of SERT KO mice compared to WT mice. Further, the ileal mucosa of SERT KO mice exhibited several changes in microbial-related metabolites known to play roles in intestinal inflammation and insulin resistance. SERT KO mice also had a significant reduction in the abundance of ileal group 3 innate lymphoid cells (ILC3). In conclusion, SERT deficiency induces complex alterations in the ileal mucosal environment, indicating potential links between serotonergic signaling, gut microbiota, mucosal immunity, intestinal inflammation, and metabolic syndrome.

Mechanistic insights into inositol-mediated rumen function promotion and metabolic alteration using in vitro and in vivo models

Inositol is a bioactive factor that is widely found in nature; however, there are few studies on its use in ruminant nutrition. This study investigated the effects of different inositol doses and fermentation times on rumen fermentation and microbial diversity, as well as the levels of rumen and blood metabolites in sheep. Rumen fermentation parameters, microbial diversity, and metabolites after different inositol doses were determined in vitro. According to the in vitro results, six small-tailed Han sheep fitted with permanent rumen fistulas were used in a 3 × 3 Latin square feeding experiment where inositol was injected into the rumen twice a day and rumen fluid and blood samples were collected. The in vitro results showed that inositol could increase in vitro dry matter digestibility, in vitro crude protein digestibility, NH3-N, acetic acid, propionic acid, and rumen microbial diversity and affect rumen metabolic pathways (p < 0.05). The feeding experiment results showed that inositol increased the blood concentration of high-density lipoprotein and IgG, IgM, and IL-4 levels. The rumen microbial composition was significantly affected (p < 0.05). Differential metabolites in the rumen were mainly involved in ABC transporters, biotin metabolism, and phenylalanine metabolism, whereas those in the blood were mainly involved in arginine biosynthesis and glutathione and tyrosine metabolism. In conclusion, inositol improves rumen function, affects rumen microorganisms and rumen and blood metabolites and may reduce inflammation, improving animal health.

Metabolic Profiling of Aromatic Compounds

Metabolic profiling is a powerful modern tool in searching for novel biomarkers and indicators of normal or pathological processes in the body [...].

Integrated metabolomics and proteomics analysis of plasma lipid metabolism in Parkinson's disease

INTRODUCTION

Metabolomics and proteomics are two growing fields of science which may shed light on the molecular mechanisms that contribute to neurodegenerative diseases. Studies focusing on these aspects can reveal specific metabolites and proteins that can halt or reverse the progressive neurodegenerative process leading to dopaminergic cell death in the brain.

AREAS COVERED

In this article, an overview of the current status of metabolomic and proteomic profiling in the neurodegenerative disease such as Parkinson's disease (PD) is presented. We discuss the importance of state-of-the-art metabolomics and proteomics using advanced analytical methodologies and their potential for discovering new biomarkers in PD. We critically review the research to date, highlighting how metabolomics and proteomics can have an important impact on early disease diagnosis, future therapy development and the identification of new biomarkers. Finally, we will discuss interactions between lipids and α-synuclein (SNCA) and also consider the role of SNCA in lipid metabolism.

EXPERT OPINION

Metabolomic and proteomic studies contribute to understanding the biological basis of PD pathogenesis, identifying potential biomarkers and introducing new therapeutic strategies. The complexity and multifactorial nature of this disease requires a comprehensive approach, which can be achieved by integrating just these two omic studies.

An interactive web application for exploring human plasma and fibroblast metabolomics data from patients with inborn errors of metabolism

Metabolomic profiling is instrumental in understanding the systemic and cellular impact of inborn errors of metabolism (IEMs), monogenic disorders caused by pathogenic genomic variants in genes involved in metabolism. This study encompasses untargeted metabolomics analysis of plasma from 474 individuals and fibroblasts from 67 subjects, incorporating healthy controls, patients with 65 different monogenic diseases, and numerous undiagnosed cases. We introduce a web application designed for the in-depth exploration of this extensive metabolomics database. The application offers a user-friendly interface for data review, download, and detailed analysis of metabolic deviations linked to IEMs at the level of individual patients or groups of patients with the same diagnosis. It also provides interactive tools for investigating metabolic relationships and offers comparative analyses of plasma and fibroblast profiles. This tool emphasizes the metabolic interplay within and across biological matrices, enriching our understanding of metabolic regulation in health and disease. As a resource, the application provides broad utility in research, offering novel insights into metabolic pathways and their alterations in various disorders.

Identification of Serum Biomarkers of Ischemic Stroke in a Hypertensive Population Based on Metabolomics and Lipidomics

Hypertensive individuals are at a high risk of stroke, and thus, prevention of stroke in hypertensive patients is essential. Metabolomics and lipidomics can be used to identify diagnostic biomarkers and conduct early assessments of stroke risk in hypertensive populations. In this study, serum samples were collected from 30 hypertensive ischemic stroke (IS), 30 matched hypertensive and 30 matched healthy participants. Metabolomics and lipidomics analyses were conducted via liquid chromatography-tandem mass spectrometry, and the data were analyzed using multivariate and univariate statistical methods. A random forest algorithm and binary logistic regression were used to screen the biomarkers and establish diagnostic model. We detected 21 differential metabolites and 38 differential lipids between the hypertensive IS and healthy group. Moreover, we found 18 differential metabolites and 31 differential lipids between the hypertensive IS and hypertension group. In particular, the following seven metabolites or lipids distinguished the hypertensive IS from the healthy group: 4-hydroxyphenylpyruvic acid, cafestol, phosphatidylethanolamine (PE) (18:0p/18:2), PE (16:0e/20:4), (O-acyI)-1-hydroxy fatty acid (36:3), PE (16:0p/20:3) and PE (18:1p/18:2) (rep). The following seven biomarkers distinguished the hypertensive IS from the hypertension group: diglyceride (DG) (20:1/18:2), PE (18:0p/18:2), PE (16:0e/22:5), phosphatidylcholine (40:7), dimethylphosphatidylethanolamine (50:3), DG (18:1/18:2), and 4-hydroxyphenylpyruvic acid. The aforementioned panels had good diagnostic and predictive ability for hypertensive IS. Our study determines the metabolomic and lipidomic profiles of hypertensive IS patients and thereby identifies potential biomarkers of the presence of IS in hypertensive populations.

Host metabolome predicts the severity and onset of acute toxicities induced by CAR T-cell therapy

Anti-CD19 chimeric antigen receptor (CAR) T-cell therapy is a highly effective treatment option for patients with relapsed/refractory large B-cell lymphoma. However, widespread use is deterred by the development of clinically significant acute inflammatory toxicities, including cytokine release syndrome (CRS) and immune effector cell-associated neurotoxicity syndrome (ICANS), that induce significant morbidity and require close monitoring. Identification of host biochemical signatures that predict the severity and time-to-onset of CRS and ICANS may assist patient stratification to enable timely mitigation strategies. Here, we report pretreatment host metabolites that are associated with CRS and ICANS induced by axicabtagene ciloleucel or tisagenlecleucel therapy. Both untargeted metabolomics analysis and validation using targeted assays revealed a significant association between the abundance of specific pretreatment biochemical entities and an increased risk and/or onset of clinically significant CRS (q < .1) and ICANS (q < .25). Higher pretreatment levels of plasma glucose and lower levels of cholesterol and glutamate were associated with a faster onset of CRS. In contrast, low baseline levels of the amino acids proline and glycine and the secondary bile acid isoursodeoxycholate were significantly correlated with clinically significant CRS. Lower concentration of the amino acid hydroxyproline was associated with higher grade and faster onset of ICANS, whereas low glutamine was negatively correlated with faster development of ICANS. Overall, our data indicate that the pretreatment host metabolome has biomarker potential in determining the risk of clinically significant CRS and ICANS, and may be useful in risk stratification of patients before anti-CD19 CAR T-cell therapy.

Role of untargeted omics biomarkers of exposure and effect for tobacco research

Tobacco research remains a clear priority to improve individual and population health, and has recently become more complex with emerging combustible and noncombustible tobacco products. The use of omics methods in prevention and cessation studies are intended to identify new biomarkers for risk, compared risks related to other products and never use, and compliance for cessation and reinitation. to assess the relative effects of tobacco products to each other. They are important for the prediction of reinitiation of tobacco use and relapse prevention. In the research setting, both technical and clinical validation is required, which presents a number of complexities in the omics methodologies from biospecimen collection and sample preparation to data collection and analysis. When the results identify differences in omics features, networks or pathways, it is unclear if the results are toxic effects, a healthy response to a toxic exposure or neither. The use of surrogate biospecimens (e.g., urine, blood, sputum or nasal) may or may not reflect target organs such as the lung or bladder. This review describes the approaches for the use of omics in tobacco research and provides examples of prior studies, along with the strengths and limitations of the various methods. To date, there is little consistency in results, likely due to small number of studies, limitations in study size, the variability in the analytic platforms and bioinformatic pipelines, differences in biospecimen collection and/or human subject study design. Given the demonstrated value for the use of omics in clinical medicine, it is anticipated that the use in tobacco research will be similarly productive.

Metabolomics: Perspectives on Clinical Employment in Autism Spectrum Disorder

Precision medicine is imminent, and metabolomics is one of the main actors on stage. We summarize and discuss the current literature on the clinical application of metabolomic techniques as a possible tool to improve early diagnosis of autism spectrum disorder (ASD), to define clinical phenotypes and to identify co-occurring medical conditions. A review of the current literature was carried out after PubMed, Medline and Google Scholar were consulted. A total of 37 articles published in the period 2010-2022 was included. Selected studies involve as a whole 2079 individuals diagnosed with ASD (1625 males, 394 females; mean age of 10, 9 years), 51 with other psychiatric comorbidities (developmental delays), 182 at-risk individuals (siblings, those with genetic conditions) and 1530 healthy controls (TD). Metabolomics, reflecting the interplay between genetics and environment, represents an innovative and promising technique to approach ASD. The metabotype may mirror the clinical heterogeneity of an autistic condition; several metabolites can be expressions of dysregulated metabolic pathways thus liable of leading to clinical profiles. However, the employment of metabolomic analyses in clinical practice is far from being introduced, which means there is a need for further studies for the full transition of metabolomics from clinical research to clinical diagnostic routine.

Mucosal Metabolomic Signatures in Chronic Colitis: Novel Insights into the Pathophysiology of Inflammatory Bowel Disease

Inflammatory bowel diseases (IBD) involve complex interactions among genetic factors, aberrant immune activation, and gut microbial dysbiosis. While metabolomic studies have focused on feces and serum, fewer investigations have examined the intestinal mucosa despite its crucial role in metabolite absorption and transport. The goals of this study were twofold: to test the hypothesis that gut microbial dysbiosis from chronic intestinal inflammation leads to mucosal metabolic alterations suitable for therapeutic targeting, and to address gaps in metabolomic studies of intestinal inflammation that have overlooked the mucosal metabolome. The chronic DSS colitis was induced for five weeks in 7-9-week-old wild-type C57BL/6J male mice followed by microbial profiling with targeted 16srRNA sequencing service. Mucosal metabolite measurements were performed by Metabolon (Morrisville, NC). The data were analyzed using the bioinformatic tools Pathview, MetOrigin, and Metaboanalyst. The novel findings demonstrated increases in several host- and microbe-derived purine, pyrimidine, endocannabinoid, and ceramide metabolites in colitis. Origin analysis revealed that microbial-related tryptophan metabolites kynurenine, anthranilate, 5-hydroxyindoleacetate, and C-glycosyltryptophan were significantly increased in colon mucosa during chronic inflammation and strongly correlated with disease activity. These findings offer new insights into the pathophysiology of IBD and provide novel potential targets for microbial-based therapeutics.

Plasma metabolic fingerprints for large-scale screening and personalized risk stratification of metabolic syndrome

Direct diagnosis and accurate assessment of metabolic syndrome (MetS) allow for prompt clinical interventions. However, traditional diagnostic strategies overlook the complex heterogeneity of MetS. Here, we perform metabolomic analysis in 13,554 participants from the natural cohort and identify 26 hub plasma metabolic fingerprints (PMFs) associated with MetS and its early identification (pre-MetS). By leveraging machine-learning algorithms, we develop robust diagnostic models for pre-MetS and MetS with convincing performance through independent validation. We utilize these PMFs to assess the relative contributions of the four major MetS risk factors in the general population, ranked as follows: hyperglycemia, hypertension, dyslipidemia, and obesity. Furthermore, we devise a personalized three-dimensional plasma metabolic risk (PMR) stratification, revealing three distinct risk patterns. In summary, our study offers effective screening tools for identifying pre-MetS and MetS patients in the general community, while defining the heterogeneous risk stratification of metabolic phenotypes in real-world settings.

The Impact of Acute Nutritional Interventions on the Plasma Proteome

CONTEXT

Humans respond profoundly to changes in diet, while nutrition and environment have a great impact on population health. It is therefore important to deeply characterize the human nutritional responses.

OBJECTIVE

Endocrine parameters and the metabolome of human plasma are rapidly responding to acute nutritional interventions such as caloric restriction or a glucose challenge. It is less well understood whether the plasma proteome would be equally dynamic, and whether it could be a source of corresponding biomarkers.

METHODS

We used high-throughput mass spectrometry to determine changes in the plasma proteome of i) 10 healthy, young, male individuals in response to 2 days of acute caloric restriction followed by refeeding; ii) 200 individuals of the Ely epidemiological study before and after a glucose tolerance test at 4 time points (0, 30, 60, 120 minutes); and iii) 200 random individuals from the Generation Scotland study. We compared the proteomic changes detected with metabolome data and endocrine parameters.

RESULTS

Both caloric restriction and the glucose challenge substantially impacted the plasma proteome. Proteins responded across individuals or in an individual-specific manner. We identified nutrient-responsive plasma proteins that correlate with changes in the metabolome, as well as with endocrine parameters. In particular, our study highlights the role of apolipoprotein C1 (APOC1), a small, understudied apolipoprotein that was affected by caloric restriction and dominated the response to glucose consumption and differed in abundance between individuals with and without type 2 diabetes.

CONCLUSION

Our study identifies APOC1 as a dominant nutritional responder in humans and highlights the interdependency of acute nutritional response proteins and the endocrine system.

Application of novel AI-based algorithms to biobank data: uncovering of new features and linear relationships

We independently analyzed two large public domain datasets that contain ¹H-NMR spectral data from lung cancer and sex studies. The biobanks were sourced from the Karlsruhe Metabolomics and Nutrition (KarMeN) study and Bayesian Automated Metabolite Analyzer for NMR data (BATMAN) study. Our approach of applying novel artificial intelligence (AI)-based algorithms to NMR is an attempt to globalize metabolomics and demonstrate its clinical applications. The intention of this study was to analyze the resulting spectra in the biobanks via AI application to demonstrate its clinical applications. This technique enables metabolite mapping in areas of localized enrichment as a measure of true activity while also allowing for the accurate categorization of phenotypes.

Effect of Genetically Reduced Maternal Myostatin on Late Gestation Maternal, Fetal, and Placental Metabolomes in Mice

Myostatin (gene symbol: Mstn) is an autocrine and paracrine inhibitor of muscle growth. Pregnant mice with genetically reduced levels of myostatin give birth to offspring with greater adult muscle mass and bone biomechanical strength. However, maternal myostatin is not detectable in fetal circulations. Fetal growth is dependent on the maternal environment, and the provisioning of nutrients and growth factors by the placenta. Thus, this study examined the effect of reduced maternal myostatin on maternal and fetal serum metabolomes, as well as the placental metabolome. Fetal and maternal serum metabolomes were highly distinct, which is consistent with the role of the placenta in creating a specific fetal nutrient environment. There was no effect from myostatin on maternal glucose tolerance or fasting insulin. In comparisons between pregnant control and Mstn+/- mice, there were more significantly different metabolite concentrations in fetal serum, at 50, than in the mother's serum at 33, confirming the effect of maternal myostatin reduction on the fetal metabolic milieu. Polyamines, lysophospholipids, fatty acid oxidation, and vitamin C, in fetal serum, were all affected by maternal myostatin reduction.

Untargeted Metabolomic Analysis of Sjögren-Larsson Syndrome Reveals a Distinctive Pattern of Multiple Disrupted Biochemical Pathways

Sjögren-Larsson syndrome (SLS) is a rare inherited neurocutaneous disease characterized by ichthyosis, spastic diplegia or tetraplegia, intellectual disability and a distinctive retinopathy. SLS is caused by bi-allelic mutations in ALDH3A2, which codes for fatty aldehyde dehydrogenase (FALDH) and results in abnormal lipid metabolism. The biochemical abnormalities in SLS are not completely known, and the pathogenic mechanisms leading to symptoms are still unclear. To search for pathways that are perturbed in SLS, we performed untargeted metabolomic screening in 20 SLS subjects along with age- and sex-matched controls. Of 823 identified metabolites in plasma, 121 (14.7%) quantitatively differed in the overall SLS cohort from controls; 77 metabolites were decreased and 44 increased. Pathway analysis pointed to disrupted metabolism of sphingolipids, sterols, bile acids, glycogen, purines and certain amino acids such as tryptophan, aspartate and phenylalanine. Random forest analysis identified a unique metabolomic profile that had a predictive accuracy of 100% for discriminating SLS from controls. These results provide new insight into the abnormal biochemical pathways that likely contribute to disease in SLS and may constitute a biomarker panel for diagnosis and future therapeutic studies.

Impact of acute stress on murine metabolomics and metabolic flux

Plasma metabolite concentrations and labeling enrichments are common measures of organismal metabolism. In mice, blood is often collected by tail snip sampling. Here, we systematically examined the effect of such sampling, relative to gold-standard sampling from an in-dwelling arterial catheter, on plasma metabolomics and stable isotope tracing. We find marked differences between the arterial and tail circulating metabolome, which arise from two major factors: handling stress and sampling site, whose effects were deconvoluted by taking a second arterial sample immediately after tail snip. Pyruvate and lactate were the most stress-sensitive plasma metabolites, rising ~14 and ~5-fold. Both acute handling stress and adrenergic agonists induce extensive, immediate production of lactate, and modest production of many other circulating metabolites, and we provide a reference set of mouse circulatory turnover fluxes with noninvasive arterial sampling to avoid such artifacts. Even in the absence of stress, lactate remains the highest flux circulating metabolite on a molar basis, and most glucose flux into the TCA cycle in fasted mice flows through circulating lactate. Thus, lactate is both a central player in unstressed mammalian metabolism and strongly produced in response to acute stress.

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.

Diagnostic metabolomic profiling of Parkinson's disease biospecimens

BACKGROUND

Reliable and sensitive biomarkers are needed for enhancing and predicting Parkinson's disease (PD) diagnosis.

OBJECTIVE

To investigate comprehensive metabolomic profiling of biochemicals in CSF and serum for determining diagnostic biomarkers of PD.

METHODS

Fifty subjects, symptomatic with PD for ≥5 years, were matched to 50 healthy controls (HCs). We used ultrahigh-performance liquid chromatography linked to tandem mass spectrometry (UHPLC-MS/MS) for measuring relative concentrations of ≤1.5 kDalton biochemicals. A reference library created from authentic standards facilitated chemical identifications. Analytes underwent univariate analysis for PD association, with false discovery rate-adjusted p-value (≤0.05) determinations. Multivariate analysis (for identifying a panel of biochemicals discriminating PD from HCs) used several biostatistical methods, including logistic LASSO regression.

RESULTS

Comparing PD and HCs, strong differentiation was achieved from CSF but not serum specimens. With univariate analysis, 21 CSF compounds exhibited significant differential concentrations. Logistic LASSO regression led to selection of 23 biochemicals (11 shared with those determined by the univariate analysis). The selected compounds, as a group, distinguished PD from HCs, with Area-Under-the-Receiver-Operating-Characteristic (ROC) curve of 0.897. With optimal cutoff, logistic LASSO achieved 100% sensitivity and 96% specificity (and positive and negative predictive values of 96% and 100%). Ten-fold cross-validation gave 84% sensitivity and 82% specificity (and 82% positive and 84% negative predictive values). From the logistic LASSO-chosen regression model, 2 polyamine metabolites (N-acetylcadaverine and N-acetylputrescine) were chosen and had the highest fold-changes in comparing PD to HCs. Another chosen biochemical, acisoga (N-(3-acetamidopropyl)pyrrolidine-2-one), also is a polyamine metabolism derivative.

CONCLUSIONS

UHPLC-MS/MS assays provided a metabolomic signature highly predictive of PD. These findings provide further evidence for involvement of polyamine pathways in the neurodegeneration of PD.

Serum metabolic traits reveal therapeutic toxicities and responses of neoadjuvant chemoradiotherapy in patients with rectal cancer

Neoadjuvant chemoradiotherapy (nCRT) has become the standard treatment for patients with locally advanced rectal cancer (LARC). Therapeutic efficacy of nCRT is significantly affected by treatment-induced diarrhea and hematologic toxicities. Metabolic alternations in cancer therapy are key determinants to therapeutic toxicities and responses, but exploration in large-scale clinical studies remains limited. Here, we analyze 743 serum samples from 165 LARC patients recruited in a phase III clinical study using untargeted metabolomics and identify responsive metabolic traits over the course of nCRT. Pre-therapeutic serum metabolites successfully predict the chances of diarrhea and hematologic toxicities during nCRT. Particularly, levels of acyl carnitines are linked to sex disparity in nCRT-induced diarrhea. Finally, we show that differences in phenylalanine metabolism and essential amino acid metabolism may underlie distinct therapeutic responses of nCRT. This study illustrates the metabolic dynamics over the course of nCRT and provides potential to guide personalized nCRT treatment using responsive metabolic traits.

Independent Validation and Assay Standardization of Improved Metabolic Biomarker Signature to Differentiate Pancreatic Ductal Adenocarcinoma From Chronic Pancreatitis

BACKGROUND & AIMS

Pancreatic ductal adenocarcinoma cancer (PDAC) is a highly lethal malignancy requiring efficient detection when the primary tumor is still resectable. We previously developed the MxPancreasScore comprising 9 analytes and serum carbohydrate antigen 19-9 (CA19-9), achieving an accuracy of 90.6%. The necessity for 5 different analytical platforms and multiple analytical runs, however, hindered clinical applicability. We therefore aimed to develop a simpler single-analytical run, single-platform diagnostic signature.

METHODS

We evaluated 941 patients (PDAC, 356; chronic pancreatitis [CP], 304; nonpancreatic disease, 281) in 3 multicenter independent tests, and identification (ID) and validation cohort 1 (VD1) and 2 (VD2) were evaluated. Targeted quantitative plasma metabolite analysis was performed on a liquid chromatography-tandem mass spectrometry platform. A machine learning-aided algorithm identified an improved (i-Metabolic) and minimalistic metabolic (m-Metabolic) signatures, and compared them for performance.

RESULTS

The i-Metabolic Signature, (12 analytes plus CA19-9) distinguished PDAC from CP with area under the curve (95% confidence interval) of 97.2% (97.1%-97.3%), 93.5% (93.4%-93.7%), and 92.2% (92.1%-92.3%) in the ID, VD1, and VD2 cohorts, respectively. In the VD2 cohort, the m-Metabolic signature (4 analytes plus CA19-9) discriminated PDAC from CP with a sensitivity of 77.3% and specificity of 89.6%, with an overall accuracy of 82.4%. For the subset of 45 patients with PDAC with resectable stages IA-IIB tumors, the sensitivity, specificity, and accuracy were 73.2%, 89.6%, and 82.7%, respectively; for those with detectable CA19-9 >2 U/mL, 81.6%, 88.7%, and 84.5%, respectively; and for those with CA19-9 <37 U/mL, 39.7%, 94.1%, and 76.3%, respectively.

CONCLUSIONS

The single-platform, single-run, m-Metabolic signature of just 4 metabolites used in combination with serum CA19-9 levels is an innovative accurate diagnostic tool for PDAC at the time of clinical presentation, warranting further large-scale evaluation.

Role of Intestinal Microbes in Chronic Liver Diseases

With the recent availability and upgrading of many emerging intestinal microbes sequencing technologies, our research on intestinal microbes is changing rapidly. A variety of investigations have found that intestinal microbes are essential for immune system regulation and energy metabolism homeostasis, which impacts many critical organs. The liver is the first organ to be traversed by the intestinal portal vein, and there is a strong bidirectional link between the liver and intestine. Many intestinal factors, such as intestinal microbes, bacterial composition, and intestinal bacterial metabolites, are deeply involved in liver homeostasis. Intestinal microbial dysbiosis and increased intestinal permeability are associated with the pathogenesis of many chronic liver diseases, such as alcoholic fatty liver disease (AFLD), non-alcoholic fatty liver disease (NAFLD), non-alcoholic steatohepatitis (NASH), chronic hepatitis B (CHB), chronic hepatitis C (CHC), autoimmune liver disease (AIH) and the development of hepatocellular carcinoma (HCC). Intestinal permeability and dysbacteriosis often lead to Lipopolysaccharide (LPS) and metabolites entering in serum. Then, Toll-like receptors activation in the liver induces the exposure of the intestine and liver to many small molecules with pro-inflammatory properties. And all of these eventually result in various liver diseases. In this paper, we have discussed the current evidence on the role of various intestinal microbes in different chronic liver diseases. As well as potential new therapeutic approaches are proposed in this review, such as antibiotics, probiotics, and prebiotics, which may have an improvement in liver diseases.

Role of insulin resistance and the gut microbiome on urine oxalate excretion in ob/ob mice

Ob/ob mice have recently emerged as a model for obesity-related hyperoxaluria as they are obese and excrete more urine oxalate compared to wild type mice. Ob/ob mice are deficient of leptin and develop obesity with hyperphagia and hyperinsulinemia. We hypothesized that insulin resistance and the gut microbiome contribute to hyperoxaluria in ob/ob mice. We developed a new liquid chromatography-mass spectrometry assay for urine oxalate and first compared urine oxalate excretion in ob/ob mice before and after ablation of intestinal bacteria with a standard antibiotic cocktail. We then compared urine oxalate excretion in ob/ob mice before and after leptin replacement or pioglitazone treatment, two maneuvers that reduce insulin resistance in ob/ob mice. Ob/ob mice excreted more oxalate into the urine in a 24-h period compared to wild type mice, but antibiotic, leptin, or pioglitazone treatment did not change urine oxalate excretion in ob/ob mice. Unexpectedly, we found that when food intake was carefully matched between ob/ob and wild type mice, the amount of 24-h urine oxalate excretion did not differ between the two mouse strains, suggesting that ob/ob mice excrete more urine oxalate because of hyperphagia. Since the level of urine oxalate excretion in wild type mice in our study was higher than those reported in prior studies, future work will be needed to standardize the measurement of urine oxalate and to define the range of urine oxalate excretion in wild type mice so that accurate and valid comparisons can be made between wild type mice and ob/ob mice or other mouse models.

Challenges in Metabolomics-Based Tests, Biomarkers Revealed by Metabolomic Analysis, and the Promise of the Application of Metabolomics in Precision Medicine

Metabolomics helps identify metabolites to characterize/refine perturbations of biological pathways in living organisms. Pre-analytical, analytical, and post-analytical limitations that have hampered a wide implementation of metabolomics have been addressed. Several potential biomarkers originating from current targeted metabolomics-based approaches have been discovered. Precision medicine argues for algorithms to classify individuals based on susceptibility to disease, and/or by response to specific treatments. It also argues for a prevention-based health system. Because of its ability to explore gene-environment interactions, metabolomics is expected to be critical to personalize diagnosis and treatment. Stringent guidelines have been applied from the very beginning to design studies to acquire the information currently employed in precision medicine and precision prevention approaches. Large, prospective, expensive and time-consuming studies are now mandatory to validate old, and discover new, metabolomics-based biomarkers with high chances of translation into precision medicine. Metabolites from studies on saliva, sweat, breath, semen, feces, amniotic, cerebrospinal, and broncho-alveolar fluid are predicted to be needed to refine information from plasma and serum metabolome. In addition, a multi-omics data analysis system is predicted to be needed for omics-based precision medicine approaches. Omics-based approaches for the progress of precision medicine and prevention are expected to raise ethical issues.

Explore potential disease related metabolites based on latent factor model

Background

In biological systems, metabolomics can not only contribute to the discovery of metabolic signatures for disease diagnosis, but is very helpful to illustrate the underlying molecular disease-causing mechanism. Therefore, identification of disease-related metabolites is of great significance for comprehensively understanding the pathogenesis of diseases and improving clinical medicine.

Results

In the paper, we propose a disease and literature driven metabolism prediction model (DLMPM) to identify the potential associations between metabolites and diseases based on latent factor model. We build the disease glossary with disease terms from different databases and an association matrix based on the mapping between diseases and metabolites. The similarity of diseases and metabolites is used to complete the association matrix. Finally, we predict potential associations between metabolites and diseases based on the matrix decomposition method. In total, 1,406 direct associations between diseases and metabolites are found. There are 119,206 unknown associations between diseases and metabolites predicted with a coverage rate of 80.88%. Subsequently, we extract training sets and testing sets based on data increment from the database of disease-related metabolites and assess the performance of DLMPM on 19 diseases. As a result, DLMPM is proven to be successful in predicting potential metabolic signatures for human diseases with an average AUC value of 82.33%.

Conclusion

In this paper, a computational model is proposed for exploring metabolite-disease pairs and has good performance in predicting potential metabolites related to diseases through adequate validation. The results show that DLMPM has a better performance in prioritizing candidate diseases-related metabolites compared with the previous methods and would be helpful for researchers to reveal more information about human diseases.

Hollow Cobalt Oxide/Carbon Hybrids Aid Metabolic Encoding for Active Systemic Lupus Erythematosus during Pregnancy

A noninvasive, easy operation, and accurate diagnostic protocol is highly demanded to assess systemic lupus erythematosus (SLE) activity during pregnancy, promising real-time activity monitoring during the whole gestational period to reduce adverse pregnancy outcomes. Here, machine learning of serum metabolic fingerprints (SMFs) is developed to assess the SLE activity for pregnant women. The SMFs are directly extracted through a hollow-cobalt oxide/carbon (Co₃ O₄ /C)-composite-assisted laser desorption/ionization mass spectrometer (LDI MS) platform. The Co₃ O₄ /C composite owns enhanced light absorption, size-selective trapping, and better charge-hole separation, enabling improved ionization efficiency and selectivity for LDI MS detection toward small molecules. Metabolic fingerprints are collected from ≈0.1 µL serum within 1 s without enrichment and encoded by the optimized elastic net algorithm. The averaged area under the curve (AUC) value in the differentiation of active SLE from inactive SLE and healthy controls reaches 0.985 and 0.990, respectively. Further, a simplified panel based on four identified metabolites is built to distinguish SLE flares in pregnant women with the highest AUC value of 0.875 for the blind test. This work sets an accurate and practical protocol for SLE activity assessment during pregnancy, promoting precision diagnosis of disease status transitions in clinics.

Dietary Omega-3 Polyunsaturated Fatty-Acid Supplementation Upregulates Protective Cellular Pathways in Patients with Type 2 Diabetes Exhibiting Improvement in Painful Diabetic Neuropathy

Background: Omega-3 polyunsaturated fatty acids (PUFAs) have been proposed to improve chronic neuroinflammatory diseases in peripheral and central nervous systems. For instance, docosahexaenoic acid (DHA) protects nerve cells from noxious stimuli in vitro and in vivo. Recent reports link PUFA supplementation to improving painful diabetic neuropathy (pDN) symptoms, but cellular mechanisms responsible for this therapeutic effect are not well understood. The objective of this study is to identify distinct cellular pathways elicited by dietary omega-3 PUFA supplementation in patients with type 2 diabetes mellitus (T2DM) affected by pDN. Methods: Forty volunteers diagnosed with type 2 diabetes were enrolled in the “En Balance-PLUS” diabetes education study. The volunteers participated in weekly lifestyle/nutrition education and daily supplementation with 1000 mg DHA and 200 mg eicosapentaenoic acid. The Short-Form McGill Pain Questionnaire validated clinical determination of baseline and post-intervention pain complaints. Laboratory and untargeted metabolomics analyses were conducted using blood plasma collected at baseline and after three months of participation in the dietary regimen. The metabolomics data were analyzed using random forest, hierarchical clustering, ingenuity pathway analysis, and metabolic pathway mapping. Results: The data show that metabolites involved in oxidative stress and glutathione production shifted significantly to a more anti-inflammatory state post supplementation. Example of these metabolites include cystathionine (+90%), S-methylmethionine (+9%), glycine cysteine-glutathione disulfide (+157%) cysteinylglycine (+19%), glutamate (−11%), glycine (+11%), and arginine (+13.4%). In addition, the levels of phospholipids associated with improved membrane fluidity such as linoleoyl-docosahexaenoyl-glycerol (18:2/22:6) (+253%) were significantly increased. Ingenuity pathway analysis suggested several key bio functions associated with omega-3 PUFA supplementation such as formation of reactive oxygen species (p = 4.38 × 10−4, z-score = −1.96), peroxidation of lipids (p = 2.24 × 10−5, z-score = −1.944), Ca2+ transport (p = 1.55 × 10−4, z-score = −1.969), excitation of neurons (p = 1.07 ×10−4, z-score = −1.091), and concentration of glutathione (p = 3.06 × 10−4, z-score = 1.974). Conclusion: The reduction of pro-inflammatory and oxidative stress pathways following dietary omega-3 PUFA supplementation is consistent with the promising role of these fatty acids in reducing adverse symptoms associated with neuroinflammatory diseases and painful neuropathy.

Finger sweat analysis enables short interval metabolic biomonitoring in humans

Metabolic biomonitoring in humans is typically based on the sampling of blood, plasma or urine. Although established in the clinical routine, these sampling procedures are often associated with a variety of compliance issues, which are impeding time-course studies. Here, we show that the metabolic profiling of the minute amounts of sweat sampled from fingertips addresses this challenge. Sweat sampling from fingertips is non-invasive, robust and can be accomplished repeatedly by untrained personnel. The sweat matrix represents a rich source for metabolic phenotyping. We confirm the feasibility of short interval sampling of sweat from the fingertips in time-course studies involving the consumption of coffee or the ingestion of a caffeine capsule after a fasting interval, in which we successfully monitor all known caffeine metabolites as well as endogenous metabolic responses. Fluctuations in the rate of sweat production are accounted for by mathematical modelling to reveal individual rates of caffeine uptake, metabolism and clearance. To conclude, metabotyping using sweat from fingertips combined with mathematical network modelling shows promise for broad applications in precision medicine by enabling the assessment of dynamic metabolic patterns, which may overcome the limitations of purely compositional biomarkers.

Multi 'omic data integration: A review of concepts, considerations, and approaches

The application of 'omic techniques including, but not limited to genomics/metagenomics, transcriptomics/meta-transcriptomics, proteomics/meta-proteomics, and metabolomics to generate multiple datasets from a single sample have facilitated hypothesis generation leading to the identification of biological, molecular and ecological functions and mechanisms, as well as associations and correlations. Despite their power and promise, a variety of challenges must be considered in the successful design and execution of a multi-omics study. In this review, various 'omic technologies applicable to single- and meta-organisms (i.e., host + microbiome) are described, and considerations for sample collection, storage and processing prior to data generation and analysis, as well as approaches to data storage, dissemination and analysis are discussed. Finally, case studies are included as examples of multi-omic applications providing novel insights and a more holistic understanding of biological processes.

Metabolomics Approaches for the Comprehensive Evaluation of Fermented Foods: A Review

Fermentation is an important process that can provide new flavors and nutritional and functional foods, to deal with changing consumer preferences. Fermented foods have complex chemical components that can modulate unique qualitative properties. Consequently, monitoring the small molecular metabolites in fermented food is critical to clarify its qualitative properties and help deliver personalized nutrition. In recent years, the application of metabolomics to nutrition research of fermented foods has expanded. In this review, we examine the application of metabolomics technologies in food, with a primary focus on the different analytical approaches suitable for food metabolomics and discuss the advantages and disadvantages of these approaches. In addition, we summarize emerging studies applying metabolomics in the comprehensive analysis of the flavor, nutrition, function, and safety of fermented foods, as well as emphasize the applicability of metabolomics in characterizing the qualitative properties of fermented foods.

Effects of dexmedetomidine, propofol, sevoflurane and S-ketamine on the human metabolome: A randomised trial using nuclear magnetic resonance spectroscopy

BACKGROUND

Pharmacometabolomics uses large-scale data capturing methods to uncover drug-induced shifts in the metabolic profile. The specific effects of anaesthetics on the human metabolome are largely unknown.

OBJECTIVE

We aimed to discover whether exposure to routinely used anaesthetics have an acute effect on the human metabolic profile.

DESIGN

Randomised, open-label, controlled, parallel group, phase IV clinical drug trial.

SETTING

The study was conducted at Turku PET Centre, University of Turku, Finland, 2016 to 2017.

PARTICIPANTS

One hundred and sixty healthy male volunteers were recruited. The metabolomic data of 159 were evaluable.

INTERVENTIONS

Volunteers were randomised to receive a 1-h exposure to equipotent doses (EC50 for verbal command) of dexmedetomidine (1.5 ng ml-1; n = 40), propofol (1.7 μg ml-1; n = 40), sevoflurane (0.9% end-tidal; n = 39), S-ketamine (0.75 μg ml-1; n = 20) or placebo (n = 20).

MAIN OUTCOME MEASURES

Metabolite subgroups of apolipoproteins and lipoproteins, cholesterol, glycerides and phospholipids, fatty acids, glycolysis, amino acids, ketone bodies, creatinine and albumin and the inflammatory marker GlycA, were analysed with nuclear magnetic resonance spectroscopy from arterial blood samples collected at baseline, after anaesthetic administration and 70 min postanaesthesia.

RESULTS

All metabolite subgroups were affected. Statistically significant changes vs. placebo were observed in 11.0, 41.3, 0.65 and 3.9% of the 155 analytes in the dexmedetomidine, propofol, sevoflurane and S-ketamine groups, respectively. Dexmedetomidine increased glucose, decreased ketone bodies and affected lipoproteins and apolipoproteins. Propofol altered lipoproteins, fatty acids, glycerides and phospholipids and slightly increased inflammatory marker glycoprotein acetylation. Sevoflurane was relatively inert. S-ketamine increased glucose and lactate, whereas branched chain amino acids and tyrosine decreased.

CONCLUSION

A 1-h exposure to moderate doses of routinely used anaesthetics led to significant and characteristic alterations in the metabolic profile. Dexmedetomidine-induced alterations mirror α2-adrenoceptor agonism. Propofol emulsion altered the lipid profile. The inertness of sevoflurane might prove useful in vulnerable patients. S-ketamine induced amino acid alterations might be linked to its suggested antidepressive properties.

TRIAL REGISTRATION

ClinicalTrials.gov identifier: NCT02624401. URL: https://clinicaltrials.gov/ct2/show/NCT02624401.

Fatty acid metabolism and colon cancer protection by dietary methyl donor restriction

INTRODUCTION

A methyl donor depleted (MDD) diet dramatically suppresses intestinal tumor development in Apc-mutant mice, but the mechanism of this prevention is not entirely clear.

OBJECTIVES

We sought to gain insight into the mechanisms of cancer suppression by the MDD diet and to identify biomarkers of cancer risk reduction.

METHODS

A plasma metabolomic analysis was performed on Apc^Δ14/+ mice maintained on either a methyl donor sufficient (MDS) diet or the protective MDD diet. A group of MDS animals was also pair-fed with the MDD mice to normalize caloric intake, and another group was shifted from an MDD to MDS diet to determine the durability of the metabolic changes.

RESULTS

In addition to the anticipated changes in folate one-carbon metabolites, plasma metabolites related to fatty acid metabolism were generally decreased by the MDD diet, including carnitine, acylcarnitines, and fatty acids. Some fatty acid selectivity was observed; the levels of cancer-promoting arachidonic acid and 2-hydroxyglutarate were decreased by the MDD diet, whereas eicosapentaenoic acid (EPA) levels were increased. Machine-learning elastic net analysis revealed a positive association between the fatty acid-related compounds azelate and 7-hydroxycholesterol and tumor development, and a negative correlation with succinate and β-sitosterol.

CONCLUSION

Methyl donor restriction causes dramatic changes in systemic fatty acid metabolism. Regulating fatty acid metabolism through methyl donor restriction favorably effects fatty acid profiles to achieve cancer protection.

Metabolomic and transcriptomic analysis reveals endogenous substrates and metabolic adaptation in rats lacking Abcg2 and Abcb1a transporters

Abcg2/Bcrp and Abcb1a/Pgp are xenobiotic efflux transporters limiting substrate permeability in the gastrointestinal system and brain, and increasing renal and hepatic drug clearance. The systemic impact of Bcrp and Pgp ablation on metabolic homeostasis of endogenous substrates is incompletely understood. We performed untargeted metabolomics of cerebrospinal fluid (CSF) and plasma, transcriptomics of brain, liver and kidney from male Sprague Dawley rats (WT) and Bcrp/Pgp double knock-out (dKO) rats, and integrated metabolomic/transcriptomic analysis to identify putative substrates and perturbations in canonical metabolic pathways. A predictive Bayesian machine learning model was used to predict in silico those metabolites with greater substrate-like features for either transporters. The CSF and plasma levels of 169 metabolites, nutrients, signaling molecules, antioxidants and lipids were significantly altered in dKO rats, compared to WT rats. These metabolite changes suggested alterations in histidine, branched chain amino acid, purine and pyrimidine metabolism in the dKO rats. Levels of methylated and sulfated metabolites and some primary bile acids were increased in dKO CSF or plasma. Elevated uric acid levels appeared to be a primary driver of changes in purine and pyrimidine biosynthesis. Alterations in Bcrp/Pgp dKO CSF levels of antioxidants, precursors of neurotransmitters, and uric acid suggests the transporters may contribute to the regulation of a healthy central nervous system in rats. Microbiome-generated metabolites were found to be elevated in dKO rat plasma and CSF. The altered dKO metabolome appeared to cause compensatory transcriptional change in urate biosynthesis and response to lipopolysaccharide in brain, oxidation-reduction processes and response to oxidative stress and porphyrin biosynthesis in kidney, and circadian rhythm genes in liver. These findings present insight into endogenous functions of Bcrp and Pgp, the impact that transporter substrates, inhibitors or polymorphisms may have on metabolism, how transporter inhibition could rewire drug sensitivity indirectly through metabolic changes, and identify functional Bcrp biomarkers.

Applying metabolomics to veterinary pharmacology and therapeutics

Metabolomics is the large-scale study of low-molecular-weight substances in a biological system in a given physiological state at a given time point. Metabolomics can be applied to identify predictors of inter-individual variability in drug response, provide clinicians with data useful for decision-making processes in drug selection, and inform about the pharmacokinetics and pharmacodynamics of a drug. It is, therefore, an exceptional approach for gaining new understanding effects in the field of comparative veterinary pharmacology. However, the incorporation of metabolomics into veterinary pharmacology and toxicology is not yet widespread, and this is probably, at least in part, a result of its highly multidisciplinary nature. This article reviews the potential applications of metabolomics in veterinary pharmacology and therapeutics. It integrates key concepts for designing metabolomics studies and analyzing and interpreting metabolomics data, providing solid foundations for applying metabolomics to the study of drugs in all veterinary species.

Plasma Metabolic Profiling of Pediatric Sepsis in a Chinese Cohort

Sepsis represents one of the most pressing problems in pediatrics, characterized by pathogenic bacteria invading the blood, growing and multiplying in the blood circulation, and ultimately causing severe infections. Most children with sepsis have a rapid disease onset and frequently exhibit sudden high fever or first chills. Here we performed comprehensive metabolomic profiling of plasma samples collected from pediatric sepsis patients to identify specific metabolic alterations associated with these patients (n = 84, designated as case subjects) as compared to healthy cohorts (n = 59, designated as control subjects). Diagnostic models were constructed using MetaboAnalyst, R packages, and multiple statistical methods, such as orthogonal partial least squares-discriminant analysis, principal component analysis, volcano plotting, and one-way ANOVA. Our study revealed a panel of metabolites responsible for the discrimination between case and control subjects with a high predictive value of prognosis. Moreover, significantly altered metabolites in sepsis survivors versus deceased patients (non-survivors) were identified as those involved in amino acids, fatty acids, and carbohydrates metabolism. Nine metabolites including organic acids and fatty acids were also identified with significantly higher abundance in sepsis patients with related microbes, implicating greater potentials to distinguish bacterial species using metabolomic analysis than blood culture. Pathway enrichment analysis further revealed that fatty acid metabolism might play an important role in the pathogenesis of sepsis.

Metabolomic analysis of uremic pruritus in patients on hemodialysis

Pruritus is a common debilitating symptom experienced by hemodialysis patients. Treatment is difficult because the cause of uremic pruritus is not known. This study addressed the hypothesis that pruritus is caused by solutes that accumulate in the plasma when the kidneys fail. We sought to identify solutes responsible for uremic pruritus using metabolomic analysis to compare the plasma of hemodialysis patients with severe pruritus versus mild/no pruritus. Pruritus severity in hemodialysis patients was assessed using a 100-mm visual analogue scale (VAS), with severe pruritus defined as >70 mm and mild/no pruritus defined as <10 mm. Twelve patients with severe pruritus (Itch) and 24 patients with mild/no pruritus (No Itch) were included. Pre-treatment plasma and plasma ultrafiltrate were analyzed using an established metabolomic platform (Metabolon, Inc.). To identify solutes associated with pruritus, we compared the average peak area of each solute in the Itch patients to that of the No Itch patients using the false discovery rate (q value) and principal component analysis. Dialysis vintage, Kt/V_urea, and serum levels of calcium, phosphorus, PTH, albumin, ferritin, and hemoglobin were similar in the Itch and No Itch patients. Metabolomic analysis identified 1,548 solutes of which 609 were classified as uremic. No difference in the plasma or plasma ultrafiltrate levels of any solute or group of solutes was found between the Itch and No Itch patients. Metabolomic analysis of hemodialysis patients did not reveal any solutes associated with pruritus. A limitation of metabolomic analysis is that the solute of interest may not be included in the metabolomic platform’s chemical library. A role for uremic solutes in pruritus remains to be established.

Preventing and Treating Neurological Disorders with the Flavonol Fisetin

Neurological disorders, including neurodegenerative diseases, have a significant negative impact on both patients and society at large. Since the prevalence of most of these disorders increases with age, the consequences for our aging population are only going to grow. It is now acknowledged that neurological disorders are multi-factorial involving disruptions in multiple cellular systems. While each disorder has specific initiating mechanisms and pathologies, certain common pathways appear to be involved in most, if not all, neurological disorders. Thus, it is becoming increasingly important to identify compounds that can modulate the multiple pathways that contribute to disease development or progression. One of these compounds is the flavonol fisetin. Fisetin has now been shown in preclinical models to be effective at preventing the development and/or progression of multiple neurological disorders including Alzheimer’s disease, Parkinson’s disease, Huntington’s disease, amyotrophic lateral sclerosis, stroke (both ischemic and hemorrhagic) and traumatic brain injury as well as to reduce age-associated changes in the brain. These beneficial effects stem from its actions on multiple pathways associated with the different neurological disorders. These actions include its well characterized anti-inflammatory and anti-oxidant effects as well as more recently described effects on the regulated cell death oxytosis/ferroptosis pathway, the gut microbiome and its senolytic activity. Therefore, the growing body of pre-clinical data, along with fisetin’s ability to modulate a large number of pathways associated with brain dysfunction, strongly suggest that it would be worthwhile to pursue its therapeutic effects in humans.

Overview of Experimental Methods and Study Design in Metabolomics, and Statistical and Pathway Considerations

Metabolomics has become a powerful tool in biological and clinical investigations. This chapter reviews the technological basis of metabolomics and the considerations in answering biomedical questions. The workflow of metabolomics is explained in the sequence of data processing, quality control, metabolite annotation, statistical analysis, pathway analysis, and multi-omics integration. Reproducibility in both sample analysis and data analysis is key to the scientific progress, and the recommendation is made on reporting standards in publications. This chapter explains the technical aspects of metabolomics in the context of systems biology and applications to human health.

Comparative metabolome analysis provides new insights into increased larval mortality under seawater acidification in the sea urchin Strongylocentrotus intermedius

Mortality and metabolic responses of four-armed larvae of Strongylocentrotus intermedius under CO₂-induced seawater acidification were investigated. Gametes of S. intermedius were fertilized and developed to the four-armed larval stage in either current natural seawater pH levels (as Control; pH = 7.99 ± 0.01) or laboratory-controlled acidified conditions (OA₁: ΔpH = -0.3 units; OA₂: ΔpH = -0.4 units; OA₃: ΔpH = -0.5 units) according to the predictions of the Intergovernmental Panel on Climate Change (IPCC). The degrees of spicule exposure and asymmetry and mortality of four-armed larvae of S. intermedius were observed; each had a significant linearly increasing trend as the seawater pH level decreased. Comparative metabolome analysis identified a total of 87 significantly differentially expressed metabolites (SDMs, UP: 57, DOWN: 30) in OA-treated groups compared with the control group. Twenty-three SDMs, including carnitine, lysophosphatidylcholine (LPC) 18:3, lysophosphatidyl ethanolamine (LPE) 16:1, glutathione (GSH) and L-ascorbate, exhibited a linear increasing trend with decreasing seawater pH. Nine SDMs exhibited a linear decreasing trend as the seawater pH declined, including hypoxanthine, guanine and thymidine. Among all SDMs, we further mined 48 potential metabolite biomarkers responding to seawater acidification in four-armed larvae of S. intermedius. These potential metabolite biomarkers were mainly enriched in five pathways: glycerophospholipid metabolism, glutathione metabolism, purine metabolism, pyrimidine metabolism and the tricarboxylic acid cycle (TCA cycle). Our results will enrich our knowledge of the molecular mechanisms employed by sea urchins in response to CO₂-induced seawater acidification.

A rapid GC method coupled with quadrupole or time of flight mass spectrometry for metabolomics analysis

Gas chromatography-mass spectrometry (GC-MS) is an ideal tool for analyzing the intermediates of tricarboxylic acid cycle and glycolysis, sugars, organic acids and amino acids, etc. High-throughput metabolomics methods are required by large-scale clinical researches, and time of flight mass spectrometry (TOF MS) having fast scanning rate is preferable for rapid GC. Quadrupole MS (qMS) instruments have 95% market share, and their potential in rapid metabolomics is worth being studied. In this work, a within 15-min GC program was established and matched by qMS scanning for plasma metabolome analysis after N-methyl-N-(trimethylsilyl)-trifluoroacetamide derivatization. Compared to the longer-time program GC-qMS method, the rapid GC-qMS method had nearly no metabolome information loss, and it had excellent profile performance in repeatability, intra-day and inter-day precision, sampling range, linearity and extraction recovery. Compared to TOF MS, qMS achieved similar results in investigating lung cancer serum metabolic disruptions. Partial least squares-discriminant analysis revealed that the two datasets acquired by qMS and TOF MS had very similar model parameters, and most of top ranked differential metabolites were the same. This study provides a rapid and economical GC-qMS metabolomics method for researchers. Still, MS having faster scanning rate and higher sensitivity are recommended, if possible, to detect more small peaks and some co-eluted peaks.

Capillary ultrahigh-pressure liquid chromatography-mass spectrometry for fast and high resolution metabolomics separations

LC-MS is an important tool for metabolomics due its high sensitivity and broad metabolite coverage. The goal of improving resolution and decreasing analysis time in HPLC has led to the use of 5 - 15 cm long columns packed with 1.7 - 1.9 µm particles requiring pressures of 8 - 12 kpsi. We report on the potential for capillary LC-MS based metabolomics utilizing porous C18 particles down to 1.1 µm diameter and columns up to 50 cm long with an operating pressure of 35 kpsi. Our experiments show that it is possible to pack columns with 1.1 µm porous particles to provide predicted improvements in separation time and efficiency. Using kinetic plots to guide the choice of column length and particle size, we packed 50 cm long columns with 1.7 µm particles and 20 cm long columns with 1.1 µm particles, which should produce equivalent performance in shorter times. Columns were tested by performing isocratic and gradient LC-MS analyses of small molecule metabolites and extracts from plasma. These columns provided approximately 100,000 theoretical plates for metabolite standards and peak capacities over 500 in 100 min for a complex plasma extract with robust interfacing to MS. To generate a given peak capacity, the 1.1 µm particles in 20 cm columns required roughly 75% of the time as 1.7 µm particles in 50 cm columns with both operated at 35 kpsi. The 1.1 µm particle packed columns generated a given peak capacity nearly 3 times faster than 1.7 µm particles in 15 cm columns operated at ~10 kpsi. This latter condition represents commercial state of the art for capillary LC. To consider practical benefits for metabolomics, the effect of different LC-MS variables on mass spectral feature detection was evaluated. Lower flow rates (down to 700 nL/min) and larger injection volumes (up to 1 µL) increased the features detected with modest loss in separation performance. The results demonstrate the potential for fast and high resolution separations for metabolomics using 1.1 µm particles operated at 35 kpsi for capillary LC-MS.

Rapid Computer-Aided Diagnosis of Stroke by Serum Metabolic Fingerprint Based Multi-Modal Recognition

Stroke is a leading cause of mortality and disability worldwide, expected to result in 61 million disability-adjusted life-years in 2020. Rapid diagnostics is the core of stroke management for early prevention and medical treatment. Serum metabolic fingerprints (SMFs) reflect underlying disease progression, predictive of patient phenotypes. Deep learning (DL) encoding SMFs with clinical indexes outperforms single biomarkers, while posing challenges with poor prediction to interpret by feature selection. Herein, rapid computer-aided diagnosis of stroke is performed using SMF based multi-modal recognition by DL, to combine adaptive machine learning with a novel feature selection approach. SMFs are extracted by nano-assisted laser desorption/ionization mass spectrometry (LDI MS), consuming 100 nL of serum in seconds. A multi-modal recognition is constructed by integrating SMFs and clinical indexes with an enhanced area under curve (AUC) up to 0.845 for stroke screening, compared to single-modal diagnosis by only SMFs or clinical indexes. The prediction of DL is addressed by selecting 20 key metabolite features with differential regulation through a saliency map approach, shedding light on the molecular mechanisms in stroke. The approach highlights the emerging role of DL in precision medicine and suggests an expanding utility for computational analysis of SMFs in stroke screening.

Distinct Fecal and Plasma Metabolites in Children with Autism Spectrum Disorders and Their Modulation after Microbiota Transfer Therapy

Despite the prevalence of autism and its extensive impact on our society, no U.S. Food and Drug Administration-approved treatment is available for this complex neurobiological disorder. Based on mounting evidences that support a link between autism and the gut microbiome, we previously performed a pioneering open-label clinical trial using intensive fecal microbiota transplant. The therapy significantly improved gastrointestinal and behavioral symptoms. Comprehensive metabolomic measurements in this study showed that children with autism spectrum disorder (ASD) had different levels of many plasma metabolites at baseline compared to those in typically developing children. Microbiota transfer therapy (MTT) had a systemic effect, resulting in substantial changes in plasma metabolites, driving a number of metabolites to be more similar to those from typically developing children. Our results provide evidence that changes in metabolites are one mechanism of the gut-brain connection mediated by the gut microbiota and offer plausible clinical evidence for a promising autism treatment and biomarkers.

Accumulating evidence has strengthened a link between dysbiotic gut microbiota and autism. Fecal microbiota transplant (FMT) is a promising therapy to repair dysbiotic gut microbiota. We previously performed intensive FMT called microbiota transfer therapy (MTT) for children with autism spectrum disorders (ASD) and observed a substantial improvement of gastrointestinal and behavioral symptoms. We also reported modulation of the gut microbiome toward a healthy one. In this study, we report comprehensive metabolite profiles from plasma and fecal samples of the children who participated in the MTT trial. With 619 plasma metabolites detected, we found that the autism group had distinctive metabolic profiles at baseline. Eight metabolites (nicotinamide riboside, IMP, iminodiacetate, methylsuccinate, galactonate, valylglycine, sarcosine, and leucylglycine) were significantly lower in the ASD group at baseline, while caprylate and heptanoate were significantly higher in the ASD group. MTT drove global shifts in plasma profiles across various metabolic features, including nicotinate/nicotinamide and purine metabolism. In contrast, for 669 fecal metabolites detected, when correcting for multiple hypotheses, no metabolite was significantly different at baseline. Although not statistically significant, p-cresol sulfate was relatively higher in the ASD group at baseline, and after MTT, the levels decreased and were similar to levels in typically developing (TD) controls. p-Cresol sulfate levels were inversely correlated with Desulfovibrio, suggesting a potential role of Desulfovibrio on p-cresol sulfate modulation. Further studies of metabolites in a larger ASD cohort, before and after MTT, are warranted, as well as clinical trials of other therapies to address the metabolic changes which MTT was not able to correct.

IMPORTANCE Despite the prevalence of autism and its extensive impact on our society, no U.S. Food and Drug Administration-approved treatment is available for this complex neurobiological disorder. Based on mounting evidences that support a link between autism and the gut microbiome, we previously performed a pioneering open-label clinical trial using intensive fecal microbiota transplant. The therapy significantly improved gastrointestinal and behavioral symptoms. Comprehensive metabolomic measurements in this study showed that children with autism spectrum disorder (ASD) had different levels of many plasma metabolites at baseline compared to those in typically developing children. Microbiota transfer therapy (MTT) had a systemic effect, resulting in substantial changes in plasma metabolites, driving a number of metabolites to be more similar to those from typically developing children. Our results provide evidence that changes in metabolites are one mechanism of the gut-brain connection mediated by the gut microbiota and offer plausible clinical evidence for a promising autism treatment and biomarkers.

Practicing precision medicine with intelligently integrative clinical and multi-omics data analysis

Precision medicine aims to empower clinicians to predict the most appropriate course of action for patients with complex diseases like cancer, diabetes, cardiomyopathy, and COVID-19. With a progressive interpretation of the clinical, molecular, and genomic factors at play in diseases, more effective and personalized medical treatments are anticipated for many disorders. Understanding patient’s metabolomics and genetic make-up in conjunction with clinical data will significantly lead to determining predisposition, diagnostic, prognostic, and predictive biomarkers and paths ultimately providing optimal and personalized care for diverse, and targeted chronic and acute diseases. In clinical settings, we need to timely model clinical and multi-omics data to find statistical patterns across millions of features to identify underlying biologic pathways, modifiable risk factors, and actionable information that support early detection and prevention of complex disorders, and development of new therapies for better patient care. It is important to calculate quantitative phenotype measurements, evaluate variants in unique genes and interpret using ACMG guidelines, find frequency of pathogenic and likely pathogenic variants without disease indicators, and observe autosomal recessive carriers with a phenotype manifestation in metabolome. Next, ensuring security to reconcile noise, we need to build and train machine-learning prognostic models to meaningfully process multisource heterogeneous data to identify high-risk rare variants and make medically relevant predictions. The goal, today, is to facilitate implementation of mainstream precision medicine to improve the traditional symptom-driven practice of medicine, and allow earlier interventions using predictive diagnostics and tailoring better-personalized treatments. We strongly recommend automated implementation of cutting-edge technologies, utilizing machine learning (ML) and artificial intelligence (AI) approaches for the multimodal data aggregation, multifactor examination, development of knowledgebase of clinical predictors for decision support, and best strategies for dealing with relevant ethical issues.