Clinical metabolomics paves the way towards future healthcare strategies

Insight into the mechanism and toxicology of nitrofurantoin: a metabolomics approach

Safety and effectiveness are the two ends of the balance in drug development that needs to be evaluated. The biotransformation of drugs within a living organism could potentiate biochemical insults in the tissue and compromise the safety of drugs. Nitrofurantoin (NFT) is a cheap clinical antibiotic with a wide array of activities against gram-positive and gram-negative organisms. The NFT scaffold has been utilized to develop other derivates or analogues in the quest to repurpose drugs against other infectious diseases. Several techniques were developed over the years to study the mechanism of NFT metabolism and toxicity, such as voltammetry, chromatographic analysis, protein precipitation, liquid-liquid extraction, etc. Due to limitations in these methods, the mechanism of NFT biotransformation in the cell is poorly understood. Metabolomics has been adopted in drug metabolism to understand the mechanism of drug toxicity and could provide a solution to overcome the limitations of current techniques to determine mechanisms of toxicity. Unfortunately, little or no information regarding the metabolomics approach in NFT metabolism and toxicity is available. Hence, this review highlights the metabolomic techniques that can be adopted in NFT metabolism and toxicological studies to encourage the research community to widely adopt and utilize metabolomics in understanding NFT's metabolism and toxicity.

Chicoric Acid Effectively Mitigated Dextran Sulfate Sodium (DSS)-Induced Colitis in BALB/c Mice by Modulating the Gut Microbiota and Fecal Metabolites

Chicoric acid (CA) has been reported to exhibit biological activities; it remains unclear, however, whether CA could regulate colitis via modulation of the gut microbiota and metabolites. This study aimed to assess CA's impact on dextran sulfate sodium (DSS)-induced colitis, the gut microbiota, and metabolites. Mice were induced with 2.5% DSS to develop colitis over a 7-day period. CA was administered intragastrically one week prior to DSS treatment and continued for 14 days. The microbial composition in the stool was determined using 16S rRNA sequencing, while non-targeted metabolomics was employed to analyze the metabolic profiles of each mouse group. The results show that CA effectively alleviated colitis, as evidenced by an increased colon length, lowered disease activity index (DAI) and histological scores, and decreased tumor necrosis factor-α (TNF-α) and interleukin-6 (IL-6) expression levels. CA intervention restored the structure of gut microbiota. Specifically, it decreased the abundance of Bacteroidetes and Cyanobacteria at the phylum level and Bacteroides, Rosiarcus, and unclassified Xanthobacteraceae at the genus level, and increased the abundance of unclassified Lachnospiraceae at the genus level. Metabolomic analysis revealed that CA supplementation reversed the up-regulation of asymmetric dimethylarginine, N-glycolylneuraminic acid, and N-acetylneuraminic acid, as well as the down-regulation of phloroglucinol, thiamine, 4-methyl-5-thiazoleethanol, lithocholic acid, and oxymatrine induced by DSS. Our current research provides scientific evidence for developing CA into an anti-colitis functional food ingredient. Further clinical trials are warranted to elucidate the efficacy and mechanism of CA in treating human inflammatory bowel disease (IBD).

Optimising a urinary extraction method for non-targeted GC-MS metabolomics

Urine is ideal for non-targeted metabolomics, providing valuable insights into normal and pathological cellular processes. Optimal extraction is critical since non-targeted metabolomics aims to analyse various compound classes. Here, we optimised a low-volume urine preparation procedure for non-targeted GC-MS. Five extraction methods (four organic acid [OA] extraction variations and a "direct analysis" [DA] approach) were assessed based on repeatability, metabolome coverage, and metabolite recovery. The DA method exhibited superior repeatability, and achieved the highest metabolome coverage, detecting 91 unique metabolites from multiple compound classes comparatively. Conversely, OA methods may not be suitable for all non-targeted metabolomics applications due to their bias toward a specific compound class. In accordance, the OA methods demonstrated limitations, with lower compound recovery and a higher percentage of undetected compounds. The DA method was further improved by incorporating an additional drying step between two-step derivatization but did not benefit from urease sample pre-treatment. Overall, this study establishes an improved low-volume urine preparation approach for future non-targeted urine metabolomics applications using GC-MS. Our findings contribute to advancing the field of metabolomics and enable efficient, comprehensive analysis of urinary metabolites, which could facilitate more accurate disease diagnosis or biomarker discovery.

Comprehensive brain tissue metabolomics and biological network technology to decipher the mechanism of hydrogen-rich water on Radiation-induced cognitive impairment in rats

BACKGROUND

Hydrogen-rich water (HRW) has been shown to prevent cognitive impairment caused by ionizing radiation. This study aimed to investigate the pharmacological effects and mechanisms of HRW on ionizing radiation by coupling the brain metabolomics and biological target network methods.

METHODS AND RESULTS

HRW significantly improves the cognitive impairment in rats exposed to ionizing radiation. Based on metabolomics and biological network results, we identified 54 differential metabolites and 93 target genes. The KEGG pathway indicates that glutathione metabolism, ascorbic acid and aldehyde acid metabolism, pentose and glucuronic acid interconversion, and glycerophospholipid metabolism play important roles in ionizing radiation therapy.

CONCLUSION

Our study has systematically elucidated the molecular mechanism of HRW against ionizing radiation, which can be mediated by modulating targets, pathways and metabolite levels. This provides a new perspective for identifying the underlying pharmacological mechanism of HRW.

Correlations for untargeted GC × GC-HRTOF-MS metabolomics of colorectal cancer

INTRODUCTION

Modern comprehensive instrumentations provide an unprecedented coverage of complex matrices in the form of high-dimensional, information rich data sets.

OBJECTIVES

In addition to the usual biomarker research that focuses on the detection of the studied condition, we aimed to define a proper strategy to conduct a correlation analysis on an untargeted colorectal cancer case study with a data set of 102 variables corresponding to metabolites obtained from serum samples analyzed with comprehensive two-dimensional gas chromatography coupled to high-resolution time-of-flight mass spectrometry (GC × GC-HRTOF-MS). Indeed, the strength of association existing between the metabolites contains potentially valuable information about the molecular mechanisms involved and the underlying metabolic network associated to a global perturbation, at no additional analytical effort.

METHODS

Following Anscombe's quartet, we took particular attention to four main aspects. First, the presence of non-linear relationships through the comparison of parametric and non-parametric correlation coefficients: Pearson's r, Spearman's rho, Kendall's tau and Goodman-Kruskal's gamma. Second, the visual control of the detected associations through scatterplots and their associated regressions and angles. Third, the effect and handling of atypical samples and values. Fourth, the role of the precision of the data on the attribution of the ranks through the presence of ties.

RESULTS

Kendall's tau was found the method of choice for the data set at hand. Its application highlighted 17 correlations significantly altered in the active state of colorectal cancer (CRC) in comparison to matched healthy controls (HC), from which 10 were specific to this state in comparison to the remission one (R-CRC) investigated on distinct patients. 15 metabolites involved in the correlations of interest, on the 25 unique ones obtained, were annotated (Metabolomics Standards Initiative level 2).

CONCLUSIONS

The metabolites highlighted could be used to better understand the pathology. The systematic investigation of the methodological aspects that we expose allows to implement correlation analysis to various fields and many specific cases.

Crosstalk between COVID-19 Infection and Kidney Diseases: A Review on the Metabolomic Approaches

The novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes COVID-19, a respiratory disorder. Various organ injuries have been reported in response to this virus, including kidney injury and, in particular, kidney tubular injury. It has been discovered that infection with the virus does not only cause new kidney disease but also increases treatment difficulty and mortality rates in people with kidney diseases. In individuals hospitalized with COVID-19, urinary metabolites from several metabolic pathways are used to distinguish between patients with acute kidney injury (AKI) and those without. This review summarizes the pathogenesis, pathophysiology, treatment strategies, and role of metabolomics in relation to AKI in COVID-19 patients. Metabolomics is likely to play a greater role in predicting outcomes for patients with kidney disease and COVID-19 with varying levels of severity in the near future as data on metabolic profiles expand rapidly. Here, we also discuss the correlation between COVID-19 and kidney diseases and the available metabolomics approaches.

A Comprehensive Mass Spectrometry-Based Workflow for Clinical Metabolomics Cohort Studies

As a comprehensive analysis of all metabolites in a biological system, metabolomics is being widely applied in various clinical/health areas for disease prediction, diagnosis, and prognosis. However, challenges remain in dealing with the metabolomic complexity, massive data, metabolite identification, intra- and inter-individual variation, and reproducibility, which largely limit its widespread implementation. This study provided a comprehensive workflow for clinical metabolomics, including sample collection and preparation, mass spectrometry (MS) data acquisition, and data processing and analysis. Sample collection from multiple clinical sites was strictly carried out with standardized operation procedures (SOP). During data acquisition, three types of quality control (QC) samples were set for respective MS platforms (GC-MS, LC-MS polar, and LC-MS lipid) to assess the MS performance, facilitate metabolite identification, and eliminate contamination. Compounds annotation and identification were implemented with commercial software and in-house-developed PAppLine^TM and Ulib^MS library. The batch effects were removed using a deep learning model method (NormAE). Potential biomarkers identification was performed with tree-based modeling algorithms including random forest, AdaBoost, and XGBoost. The modeling performance was evaluated using the F1 score based on a 10-times repeated trial for each. Finally, a sub-cohort case study validated the reliability of the entire workflow.

Exploration of the Gut-Brain Axis through Metabolomics Identifies Serum Propionic Acid Associated with Higher Cognitive Decline in Older Persons

The gut microbiome is involved in nutrient metabolism and produces metabolites that, via the gut−brain axis, signal to the brain and influence cognition. Human studies have so far had limited success in identifying early metabolic alterations linked to cognitive aging, likely due to limitations in metabolite coverage or follow-ups. Older persons from the Three-City population-based cohort who had not been diagnosed with dementia at the time of blood sampling were included, and repeated measures of cognition over 12 subsequent years were collected. Using a targeted metabolomics platform, we identified 72 circulating gut-derived metabolites in a case−control study on cognitive decline, nested within the cohort (discovery n = 418; validation n = 420). Higher serum levels of propionic acid, a short-chain fatty acid, were associated with increased odds of cognitive decline (OR for 1 SD = 1.40 (95% CI 1.11, 1.75) for discovery and 1.26 (1.02, 1.55) for validation). Additional analyses suggested mediation by hypercholesterolemia and diabetes. Propionic acid strongly correlated with blood glucose (r = 0.79) and with intakes of meat and cheese (r > 0.15), but not fiber (r = 0.04), suggesting a minor role of prebiotic foods per se, but a possible link to processed foods, in which propionic acid is a common preservative. The adverse impact of propionic acid on metabolism and cognition deserves further investigation.

Combined urine metabolomics and 16S rDNA sequencing analyses reveals physiological mechanism underlying decline in natural mating behavior of captive giant pandas

The decline in natural mating behavior is the primary reason underlying in the poor population growth of captive giant pandas. However, the influencing factors and underlying mechanisms remain unclear to data. It is speculated that the decline in natural mating behavior could be related to the psychological stress caused by captivity, which restricts their free choice of mates. In order to test this hypothesis, we performed urinary metabolomics analysis using Ultra-High-Performance Liquid Chromatography-Mass Spectrometry (UHPLC/-MS) combined with 16S rDNA sequencing for exploring the physiological mechanism underlying the decline in the natural mating behavior of captive giant panda. The results demonstrated that the decline in mating ability could be related to abnormalities in arginine biosynthesis and neurotransmitter synthesis. Additionally, the relative abundance of bacteria from the Firmicutes, Proteobacteria, and Actinobacteria phyla and the Acinetobacter, Weissella, and Pseudomonas genus was significantly reduced in the group with low natural mating behavior. These findings imply that the inhibition of arginine synthesis induced by environmental changes could be related to the poor libido and failure of mate selection in captive giant pandas during the breeding period. The results also demonstrate the relationship between the altered urinary microbes and metabolites related to arginine and neurotransmitter synthesis. These findings may aid in understanding the mechanism underlying environment-induced mate selection in captive giant pandas and propose a novel strategy for determining the sexual desire of giant pandas based on urinary microbes. The method would be of great significance in improving the natural reproductive success rate of captive giant pandas.

Metabolomics Markers of COVID-19 Are Dependent on Collection Wave

The effect of COVID-19 infection on the human metabolome has been widely reported, but to date all such studies have focused on a single wave of infection. COVID-19 has generated numerous waves of disease with different clinical presentations, and therefore it is pertinent to explore whether metabolic disturbance changes accordingly, to gain a better understanding of its impact on host metabolism and enable better treatments. This work used a targeted metabolomics platform (Biocrates Life Sciences) to analyze the serum of 164 hospitalized patients, 123 with confirmed positive COVID-19 RT-PCR tests and 41 providing negative tests, across two waves of infection. Seven COVID-19-positive patients also provided longitudinal samples 2-7 months after infection. Changes to metabolites and lipids between positive and negative patients were found to be dependent on collection wave. A machine learning model identified six metabolites that were robust in diagnosing positive patients across both waves of infection: TG (22:1_32:5), TG (18:0_36:3), glutamic acid (Glu), glycolithocholic acid (GLCA), aspartic acid (Asp) and methionine sulfoxide (Met-SO), with an accuracy of 91%. Although some metabolites (TG (18:0_36:3) and Asp) returned to normal after infection, glutamic acid was still dysregulated in the longitudinal samples. This work demonstrates, for the first time, that metabolic dysregulation has partially changed over the course of the pandemic, reflecting changes in variants, clinical presentation and treatment regimes. It also shows that some metabolic changes are robust across waves, and these can differentiate COVID-19-positive individuals from controls in a hospital setting. This research also supports the hypothesis that some metabolic pathways are disrupted several months after COVID-19 infection.

Lung cancer survival prediction and biomarker identification with an ensemble machine learning analysis of tumor core biopsy metabolomic data

INTRODUCTION

While prediction of short versus long term survival from lung cancer is clinically relevant in the context of patient management and therapy selection, it has proven difficult to identify reliable biomarkers of survival. Metabolomic markers from tumor core biopsies have been shown to reflect cancer metabolic dysregulation and hold prognostic value.

OBJECTIVES

Implement and validate a novel ensemble machine learning approach to evaluate survival based on metabolomic biomarkers from tumor core biopsies.

METHODS

Data were obtained from tumor core biopsies evaluated with high-resolution 2DLC-MS/MS. Unlike biofluid samples, analysis of tumor tissue is expected to accurately reflect the cancer metabolism and its impact on patient survival. A comprehensive suite of machine learning algorithms were trained as base learners and then combined into a stacked-ensemble meta-learner for predicting "short" versus "long" survival on an external validation cohort. An ensemble method of feature selection was employed to find a reliable set of biomarkers with potential clinical utility.

RESULTS

Overall survival (OS) is predicted in external validation cohort with AUROC_TEST of 0.881 with support vector machine meta learner model, while progression-free survival (PFS) is predicted with AUROC_TEST of 0.833 with boosted logistic regression meta learner model, outperforming a nomogram using covariate data (staging, age, sex, treatment vs. non-treatment) as predictors. Increased relative abundance of guanine, choline, and creatine corresponded with shorter OS, while increased leucine and tryptophan corresponded with shorter PFS. In patients that expired, N6,N6,N6-Trimethyl-L-lysine, L-pyrogluatmic acid, and benzoic acid were increased while cystine, methionine sulfoxide and histamine were decreased. In patients with progression, itaconic acid, pyruvate, and malonic acid were increased.

CONCLUSION

This study demonstrates the feasibility of an ensemble machine learning approach to accurately predict patient survival from tumor core biopsy metabolomic data.

Lung cancer metabolomic data from tumor core biopsies enables risk-score calculation for progression-free and overall survival

INTRODUCTION

Metabolomics has emerged as a powerful method to provide insight into cancer progression, including separating patients into low- and high-risk groups for overall (OS) and progression-free survival (PFS). However, survival prediction based mainly on metabolites obtained from biofluids remains elusive.

OBJECTIVES

This proof-of-concept study evaluates metabolites as biomarkers obtained directly from tumor core biopsies along with covariates age, sex, pathological stage at diagnosis (I/II vs. III/VI), histological subtype, and treatment vs. no treatment to risk stratify lung cancer patients in terms of OS and PFS.

METHODS

Tumor core biopsy samples obtained during routine lung cancer patient care at the University of Louisville Hospital and Norton Hospital were evaluated with high-resolution 2DLC-MS/MS, and the data were analyzed by Kaplan-Meier survival analysis and Cox proportional hazards regression. A linear equation was developed to stratify patients into low and high risk groups based on log-transformed intensities of key metabolites. Sparse partial least squares discriminant analysis (SPLS-DA) was performed to predict OS and PFS events.

RESULTS

Univariable Cox proportional hazards regression model coefficients divided by the standard errors were used as weight coefficients multiplied by log-transformed metabolite intensity, then summed to generate a risk score for each patient. Risk scores based on 10 metabolites for OS and 5 metabolites for PFS were significant predictors of survival. Risk scores were validated with SPLS-DA classification model (AUROC 0.868 for OS and AUROC 0.755 for PFS, when combined with covariates).

CONCLUSION

Metabolomic analysis of lung tumor core biopsies has the potential to differentiate patients into low- and high-risk groups based on OS and PFS events and probability.

Clinical metabolomics for inborn errors of metabolism

Metabolism is a highly regulated process that provides nutrients to cells and essential building blocks for the synthesis of protein, DNA and other macromolecules. In healthy biological systems, metabolism maintains a steady state in which the concentrations of metabolites are relatively constant yet are subject to metabolic demands and environmental stimuli. Rare genetic disorders, such as inborn errors of metabolism (IEM), cause defects in regulatory enzymes or proteins leading to metabolic pathway disruption and metabolite accumulation or deficiency. Traditionally, the laboratory diagnosis of IEMs has been limited to analytical methods that target specific metabolites such as amino acids and acyl carnitines. This approach is effective as a screening method for the most common IEM disorders but lacks the comprehensive coverage of metabolites that is necessary to identify rare disorders that present with nonspecific clinical symptoms. Fortunately, advancements in technology and data analytics has introduced a new field of study called metabolomics which has allowed scientists to perform comprehensive metabolite profiling of biological systems to provide insight into mechanism of action and gene function. Since metabolomics seeks to measure all small molecule metabolites in a biological specimen, it provides an innovative approach to evaluating disease in patients with rare genetic disorders. In this review we provide insight into the appropriate application of metabolomics in clinical settings. We discuss the advantages and limitations of the method and provide details related to the technology, data analytics and statistical modeling required for metabolomic profiling of patients with IEMs.

NMR-based Metabolomics and Fatty Acid Profiles to Unravel Biomarkers in Preclinical Animal Models of Compulsive Behavior

Compulsivity is a key manifestation of inhibitory control deficit and a cardinal symptom of psychopathological conditions such as obsessive-compulsive and attention-deficit hyperactivity disorders, in which metabolic alterations have raised attention as putative biomarkers for early identification. The present study assessed the metabolic profile in a preclinical model of a compulsive phenotype of rats. We used the schedule-induced polydipsia (SIP) method to classify male Wistar rats into high drinkers (HDs) or low drinkers (LDs) according to their compulsive drinking rate developed by exposure to a fixed-time 60 s (FT-60) schedule of reinforcement with water available ad libitum during 20 sessions. Before and after SIP, blood samples were collected for subsequent serum analysis by nuclear magnetic resonance spectroscopy coupled to multivariate analysis. Although no differences existed in the pre-SIP set, the compulsive drinking behavior induced remarkable metabolic alterations: HD rats selected by SIP exhibited a hyperlipidemic, hypoglycemic, and hyperglutaminergic profile compared with their low-compulsive counterparts. Interestingly, these alterations were not attributable to the mere exposure to reward pellets because a control experiment did not show differences between HDs and LDs after 20 sessions of pellet consumption without intermittent reinforcement. Our results shed light toward the implication of dietary and metabolic factors underpinning the vulnerability to compulsive behaviors.

Cerebrospinal Fluid Metabolome in Parkinson's Disease and Multiple System Atrophy

Parkinson’s disease (PD) and multiple system atrophy (MSA) belong to the neurodegenerative group of synucleinopathies; differential diagnosis between PD and MSA is difficult, especially at early stages, owing to their clinical and biological similarities. Thus, there is a pressing need to identify metabolic biomarkers for these diseases. The metabolic profile of the cerebrospinal fluid (CSF) is reported to be altered in PD and MSA; however, the altered metabolites remain unclear. We created a single network with altered metabolites in PD and MSA based on the literature and assessed biological functions, including metabolic disorders of the nervous system, inflammation, concentration of ATP, and neurological disorder, through bioinformatics methods. Our in-silico prediction-based metabolic networks are consistent with Parkinsonism events. Although metabolomics approaches provide a more quantitative understanding of biochemical events underlying the symptoms of PD and MSA, limitations persist in covering molecules related to neurodegenerative disease pathways. Thus, omics data, such as proteomics and microRNA, help understand the altered metabolomes mechanism. In particular, integrated omics and machine learning approaches will be helpful to elucidate the pathological mechanisms of PD and MSA. This review discusses the altered metabolites between PD and MSA in the CSF and omics approaches to discover diagnostic biomarkers.

Metabolomics for personalized medicine: the input of analytical chemistry from biomarker discovery to point-of-care tests

Metabolomics refers to the large-scale detection, quantification, and analysis of small molecules (metabolites) in biological media. Although metabolomics, alone or combined with other omics data, has already demonstrated its relevance for patient stratification in the frame of research projects and clinical studies, much remains to be done to move this approach to the clinical practice. This is especially true in the perspective of being applied to personalized/precision medicine, which aims at stratifying patients according to their risk of developing diseases, and tailoring medical treatments of patients according to individual characteristics in order to improve their efficacy and limit their toxicity. In this review article, we discuss the main challenges linked to analytical chemistry that need to be addressed to foster the implementation of metabolomics in the clinics and the use of the data produced by this approach in personalized medicine. First of all, there are already well-known issues related to untargeted metabolomics workflows at the levels of data production (lack of standardization), metabolite identification (small proportion of annotated features and identified metabolites), and data processing (from automatic detection of features to multi-omic data integration) that hamper the inter-operability and reusability of metabolomics data. Furthermore, the outputs of metabolomics workflows are complex molecular signatures of few tens of metabolites, often with small abundance variations, and obtained with expensive laboratory equipment. It is thus necessary to simplify these molecular signatures so that they can be produced and used in the field. This last point, which is still poorly addressed by the metabolomics community, may be crucial in a near future with the increased availability of molecular signatures of medical relevance and the increased societal demand for participatory medicine.

Metabolomics in the Diagnosis and Prognosis of COVID-19

Coronavirus disease 2019 (COVID-19) pandemic triggered an unprecedented global effort in developing rapid and inexpensive diagnostic and prognostic tools. Since the genome of SARS-CoV-2 was uncovered, detection of viral RNA by RT-qPCR has played the most significant role in preventing the spread of the virus through early detection and tracing of suspected COVID-19 cases and through screening of at-risk population. However, a large number of alternative test methods based on SARS-CoV-2 RNA or proteins or host factors associated with SARS-CoV-2 infection have been developed and evaluated. The application of metabolomics in infectious disease diagnostics is an evolving area of science that was boosted by the urgency of COVID-19 pandemic. Metabolomics approaches that rely on the analysis of volatile organic compounds exhaled by COVID-19 patients hold promise for applications in a large-scale screening of population in point-of-care (POC) setting. On the other hand, successful application of mass-spectrometry to detect specific spectral signatures associated with COVID-19 in nasopharyngeal swab specimens may significantly save the cost and turnaround time of COVID-19 testing in the diagnostic microbiology and virology laboratories. Active research is also ongoing on the discovery of potential metabolomics-based prognostic markers for the disease that can be applied to serum or plasma specimens. Several metabolic pathways related to amino acid, lipid and energy metabolism were found to be affected by severe disease with COVID-19. In particular, tryptophan metabolism via the kynurenine pathway were persistently dysregulated in several independent studies, suggesting the roles of several metabolites of this pathway such as tryptophan, kynurenine and 3-hydroxykynurenine as potential prognostic markers of the disease. However, standardization of the test methods and large-scale clinical validation are necessary before these tests can be applied in a clinical setting. With rapidly expanding data on the metabolic profiles of COVID-19 patients with varying degrees of severity, it is likely that metabolomics will play an important role in near future in predicting the outcome of the disease with a greater degree of certainty.

Optimization of Sample Preparation for Metabolomics Exploration of Urine, Feces, Blood and Saliva in Humans Using Combined NMR and UHPLC-HRMS Platforms

Currently, most clinical studies in metabolomics only consider a single type of sample such as urine, plasma, or feces and use a single analytical platform, either NMR or MS. Although some studies have already investigated metabolomics data from multiple fluids, the information is limited to a unique analytical platform. On the other hand, clinical studies investigating the human metabolome that combine multi-analytical platforms have focused on a single biofluid. Combining data from multiple sample types for one patient using a multimodal analytical approach (NMR and MS) should extend the metabolome coverage. Pre-analytical and analytical phases are time consuming. These steps need to be improved in order to move into clinical studies that deal with a large number of patient samples. Our study describes a standard operating procedure for biological specimens (urine, blood, saliva, and feces) using multiple platforms (¹H-NMR, RP-UHPLC-MS, and HILIC-UHPLC-MS). Each sample type follows a unique sample preparation procedure for analysis on a multi-platform basis. Our method was evaluated for its robustness and was able to generate a representative metabolic map.

NMR-Based Metabolomics Approach to Explore Brain Metabolic Changes Induced by Prenatal Exposure to Autism-Inducing Chemicals

NMR offers the unique potential to holistically screen hundreds of metabolites and has already proved to be a powerful technique able to provide a global picture of a wide range of metabolic processes underlying complex and multifactorial diseases, such as neurodegenerative and neurodevelopmental diseases. The aim of this study was to apply an NMR-based metabolomics approach to explore brain metabolic changes in both male and female rats induced by prenatal exposure to two chemicals associated with autism disorders-the organophosphorus pesticide chlorpyrifos (CPF) and the antiepileptic drug valproic acid (VPA)-at different postnatal ages. Depending on the age and on the brain region (hippocampus and cerebellum), several metabolites were shown to be significantly affected by exposure to both compounds. The evaluation of the spectral profiles revealed that the nervous-system-specific metabolite N-acetylaspartate (NAA), amino acid neurotransmitters (e.g., glutamate, glutamine, GABA, glycine), pyroglutamic acid, unsaturated fatty acids, and choline-based compounds are discriminant biomarkers. Additionally, metabolic changes varied as a function of age, but importantly not of sex.

Proteomics and metabolomics of HIV-associated neurocognitive disorders: A systematic review

HIV-associated neurocognitive disorders (HAND) are common features of the effect of human immunodeficiency virus (HIV)-1 within the central nervous system (CNS). The underlying neuropathophysiology of HAND is incompletely known. Furthermore, there are no markers to effectively predict or stratify the risk of HAND. Recent advancements in the fields of proteomics and metabolomics have shown promise in addressing these concerns, however, it is not clear if these approaches may provide new insight into pathways and markers related to HAND. We therefore conducted a systematic review of studies using proteomic and/or metabolomic approaches in the aim of identifying pathways or markers associated with neurocognitive impairment in people living with HIV (PLWH). Thirteen studies were eligible, including 11 proteomic and 2 metabolomic investigations of HIV-positive clinical samples (cerebrospinal fluid (CSF), brain tissue, and serum). Across varying profiling techniques and sample types, the majority of studies found an association of markers with neurocognitive function in PLWH. These included metabolic marker myo-inositol and proteomic markers superoxide dismutase, gelsolin, afamin, sphingomyelin, and ceramide. Certain markers were found to be dysregulated across various sample types. Afamin and gelsolin overlapped in studies of blood and CSF and sphingomyelin and ceramide overlapped in studies of CSF and brain tissue. The association of these markers with neurocognitive functioning may indicate the activity of certain pathways, potentially those related to the underlying neuropathophysiology of HAND.

MetaboAge DB: a repository of known ageing-related changes in the human metabolome

Accumulating metabolomics data is starting to become extremely useful in understanding the ageing process, by providing a snapshot into the metabolic state of tissues and organs, at different ages. Molecular studies of such metabolic variations during “normal” ageing can hence guide lifestyle changes and/or medical interventions aimed at improving healthspan and perhaps even lifespan. In this work, we present MetaboAge, a freely accessible database which hosts ageing-related metabolite changes, occurring in healthy individuals. Data is automatically filtered and then manually curated from scientific articles reporting statistically significant associations of human metabolite variations or correlations with ageing. Up to date, MetaboAge contains 408 metabolites annotated with their biological and chemical information, and more than 1515 ageing-related variations, graphically represented on the website grouped by validation methods, sex and age-groups. The MetaboAge database aims to continually structure the expanding information from the field of metabolomics in relation to ageing, thus making it more accessible for further research in gerontology.

Assessing Urinary Metabolomics in Giant Pandas Using Chromatography/Mass Spectrometry: Pregnancy-Related Changes in the Metabolome

Giant pandas represent one of the most endangered species worldwide, and their reproductive capacity is extremely low. They have a relatively long gestational period, mainly because embryo implantation is delayed. Giant panda cubs comprise only a small proportion of the mother's body weight, making it difficult to determine whether a giant panda is pregnant. Timely determination of pregnancy contributes to the efficient breeding and management of giant pandas. Meanwhile, metabolomics studies the metabolic composition of biological samples, which can reflect metabolic functions in cells, tissues, and organisms. This work explored the urinary metabolites of giant pandas during pregnancy. A sample of 8 female pandas was selected. Differences in metabolite levels in giant panda urine samples were analyzed via ultra-high-performance liquid chromatography/mass spectrometry comparing pregnancy to anoestrus. Pattern recognition techniques, including partial least squares-discriminant analysis and orthogonal partial least squares-discriminant analysis, were used to analyze multiple parameters of the data. Compared with the results during anoestrus, multivariate statistical analysis of results obtained from the same pandas being pregnant identified 16 differential metabolites in the positive-ion mode and 43 differential metabolites in the negative-ion mode. The levels of tryptophan, choline, kynurenic acid, uric acid, indole-3-acetaldehyde, taurine, and betaine were higher in samples during pregnancy, whereas those of xanthurenic acid and S-adenosylhomocysteine were lower. Amino acid metabolism, lipid metabolism, and organic acid production differed significantly between anoestrus and pregnancy. Our results provide new insights into metabolic changes in the urine of giant pandas during pregnancy, and the differential levels of metabolites in urine provide a basis for determining pregnancy in giant pandas. Understanding these metabolic changes could be helpful for managing pregnant pandas to provide proper nutrients to their fetuses.

Application of model systems to study adaptive responses of Mycobacterium tuberculosis during infection and disease

Tuberculosis (TB) claims more human lives than any other infectious organism. The lethal synergy between TB-HIV infection and the rapid emergence of drug resistant strains has created a global public health threat that requires urgent attention. Mycobacterium tuberculosis, the causative agent of TB is an exquisitely well-adapted human pathogen, displaying the ability to promptly remodel metabolism when encountering stressful environments during pathogenesis. A careful study of the mechanisms that enable this adaptation will enhance the understanding of key aspects related to the microbiology of TB disease. However, these efforts require microbiological model systems that mimic host conditions in the laboratory. Herein, we describe several in vitro model systems that generate non-replicating and differentially culturable mycobacteria. The changes that occur in the metabolism of M. tuberculosis in some of these models and how these relate to those reported for human TB disease are discussed. We describe mechanisms that tubercle bacteria use to resuscitate from these non-replicating conditions, together with phenotypic heterogeneity in terms of culturabiliy of M. tuberculosis in sputum. Transcriptional changes in M. tuberculosis that allow for adaptation of the organism to the lung environment are also summarized. Finally, given the emerging importance of the microbiome in various infectious diseases, we provide a description of how the lung and gut microbiome affect susceptibility to TB infection and response to treatment. Consideration of these collective aspects will enhance the understanding of basic metabolism, physiology, drug tolerance and persistence in M. tuberculosis to enable development of new therapeutic interventions.

Metabolomics toward personalized medicine

Metabolomics, which is the metabolites profiling in biological matrices, is a key tool for biomarker discovery and personalized medicine and has great potential to elucidate the ultimate product of the genomic processes. Over the last decade, metabolomics studies have identified several relevant biomarkers involved in complex clinical phenotypes using diverse biological systems. Most diseases result in signature metabolic profiles that reflect the sums of external and internal cellular activities. Metabolomics has a major role in clinical practice as it represents >95% of the workload in clinical laboratories worldwide. Many of these metabolites require different analytical platforms, such as Nuclear Magnetic Resonance (NMR), Mass Spectrometry (MS), and Ultra Performance Liquid Chromatography (UPLC), while many clinically relevant metabolites are still not routinely amenable to detection using currently available assays. Combining metabolomics with genomics, transcriptomics, and proteomics studies will result in a significantly improved understanding of the disease mechanisms and the pathophysiology of the target clinical phenotype. This comprehensive approach will represent a major step forward toward providing precision medical care, in which individual is accounted for variability in genes, environment, and personal lifestyle. In this review, we compare and evaluate the metabolomics strategies and studies that focus on the discovery of biomarkers that have "personalized" diagnostic, prognostic, and therapeutic value, validated for monitoring disease progression and responses to various management regimens.

The application of metabolomics toward pulmonary tuberculosis research

In the quest to identify novel biomarkers for pulmonary tuberculosis (TB), high-throughput systems biology approaches such as metabolomics has become increasingly widespread. Such biomarkers have not only successfully been used for better disease characterization, but have also provided new insights toward the future development of improved diagnostic and therapeutic approaches. In this review, we give a summary of the metabolomics studies done to date, with a specific focus on those investigating various aspects of pulmonary TB, and the infectious agent responsible, Mycobacterium tuberculosis. These studies, done on a variety of sample matrices, including bacteriological culture, sputum, blood, urine, tissue, and breath, are discussed in terms of their intended research outcomes or future clinical applications. Additionally, a summary of the research model, sample cohort, analytical apparatus and statistical methods used for biomarker identification in each of these studies, is provided.

Fatty Acid Metabolome Extraction from Mycobacterial Cells for GC-MS Metabolomics Analysis

Metabolomics is becoming an increasingly popular research tool for identifying new biomarkers, which can, among other applications, be applied to elucidate various microbial growth and virulence mechanisms. Since the lipid composition of numerous microorganisms are unique and characteristic of the particular species, and in many instances also associated with several of their growth and virulence features, we developed a method for extracting the total free fatty acid metabolome from mycobacterial cells, in order to better characterize these using a gas chromatography-mass spectrometry (GC-MS) metabolomics approach. The described method can be considered an optimized Bligh-Dyer approach, since it uses the traditional solvents; chloroform, methanol and water, in a ratio of 1:2:1. However, due to the robust cell walls associated with mycobacteria, and many other microorganisms, the method was adapted to include a step which allows for the physical disruption of the cells using a vibration mill, which dramatically increases the efficiency of this approach. Hereafter, the organic phase is collected, dried, and methylated (as a derivatization step), prior to GC-MS analyses.

Metabolomics in the clinic: A review of the shared and unique features of untargeted metabolomics for clinical research and clinical testing

Metabolomics is the untargeted measurement of the metabolome, which is composed of the complement of small molecules detected in a biological sample. As such, metabolomic analysis produces a global biochemical phenotype. It is a technology that has been utilized in the research setting for over a decade. The metabolome is directly linked to and is influenced by genetics, epigenetics, environmental factors, and the microbiome-all of which affect health. Metabolomics can be applied to human clinical diagnostics and to other fields such as veterinary medicine, nutrition, exercise, physiology, agriculture/plant biochemistry, and toxicology. Applications of metabolomics in clinical testing are emerging, but several aspects of its use as a clinical test differ from applications focused on research or biomarker discovery and need to be considered for metabolomics clinical test data to have optimum impact, be meaningful, and be used responsibly. In this review, we deconstruct aspects and challenges of metabolomics for clinical testing by illustrating the significance of test design, accurate and precise data acquisition, quality control, data processing, n-of-1 comparison to a reference population, and biochemical pathway analysis. We describe how metabolomics technology is integral to defining individual biochemical phenotypes, elaborates on human health and disease, and fits within the precision medicine landscape. Finally, we conclude by outlining some future steps needed to bring metabolomics into the clinical space and to be recognized by the broader medical and regulatory fields.

A targeted metabolomics approach for clinical diagnosis of inborn errors of metabolism

Metabolome, the ultimate functional product of the genome, can be studied through identification and quantification of small molecules. The global metabolome influences the individual phenotype through clinical and environmental interventions. Metabolomics has become an integral part of clinical research and allowed for another dimension of better understanding of disease pathophysiology and mechanism. More than 95% of the clinical biochemistry laboratory routine workload is based on small molecular identification, which can potentially be analyzed through metabolomics. However, multiple challenges in clinical metabolomics impact the entire workflow and data quality, thus the biological interpretation needs to be standardized for a reproducible outcome. Herein, we introduce the establishment of a comprehensive targeted metabolomics method for a panel of 220 clinically relevant metabolites using Liquid chromatography-tandem mass spectrometry (LC-MS/MS) standardized for clinical research. The sensitivity, reproducibility and molecular stability of each targeted metabolite (amino acids, organic acids, acylcarnitines, sugars, bile acids, neurotransmitters, polyamines, and hormones) were assessed under multiple experimental conditions. The metabolic tissue distribution was determined in various rat organs. Furthermore, the method was validated in dry blood spot (DBS) samples collected from patients known to have various inborn errors of metabolism (IEMs). Using this approach, our panel appears to be sensitive and robust as it demonstrated differential and unique metabolic profiles in various rat tissues. Also, as a prospective screening method, this panel of diverse metabolites has the ability to identify patients with a wide range of IEMs who otherwise may need multiple, time-consuming and expensive biochemical assays causing a delay in clinical management.

1H NMR-Based Metabonomic Study of Functional Dyspepsia in Stressed Rats Treated with Chinese Medicine Weikangning

1H NMR-based metabolic profiling combined with multivariate data analysis was used to explore the metabolic phenotype of functional dyspepsia (FD) in stressed rats and evaluate the intervention effects of the Chinese medicine Weikangning (WKN). After a 7-day period of model establishment, a 14-day drug administration schedule was conducted in a WKN-treated group of rats, with the model and normal control groups serving as negative controls. Based on 1H NMR spectra of urine and serum from rats, PCA, PLS-DA, and OPLS-DA were performed to identify changing metabolic profiles. According to the key metabolites determined by OPLS-DA, alterations in energy metabolism, stress-related metabolism, and gut microbiota were found in FD model rats after stress stimulation, and these alterations were restored to normal after WKN administration. This study may provide new insights into the relationship between FD and psychological stress and assist in research into the metabolic mechanisms involved in Chinese medicine.

Probiotic Bifidobacterium longum NCC3001 Reduces Depression Scores and Alters Brain Activity: A Pilot Study in Patients With Irritable Bowel Syndrome

BACKGROUND & AIMS

Probiotics can reduce symptoms of irritable bowel syndrome (IBS), but little is known about their effects on psychiatric comorbidities. We performed a prospective study to evaluate the effects of Bifidobacterium longum NCC3001 (BL) on anxiety and depression in patients with IBS.

METHODS

We performed a randomized, double-blind, placebo-controlled study of 44 adults with IBS and diarrhea or a mixed-stool pattern (based on Rome III criteria) and mild to moderate anxiety and/or depression (based on the Hospital Anxiety and Depression scale) at McMaster University in Canada, from March 2011 to May 2014. At the screening visit, clinical history and symptoms were assessed and blood samples were collected. Patients were then randomly assigned to groups and given daily BL (n = 22) or placebo (n = 22) for 6 weeks. At weeks 0, 6, and 10, we determined patients' levels of anxiety and depression, IBS symptoms, quality of life, and somatization using validated questionnaires. At weeks 0 and 6, stool, urine and blood samples were collected, and functional magnetic resonance imaging (fMRI) test was performed. We assessed brain activation patterns, fecal microbiota, urine metabolome profiles, serum markers of inflammation, neurotransmitters, and neurotrophin levels.

RESULTS

At week 6, 14 of 22 patients in the BL group had reduction in depression scores of 2 points or more on the Hospital Anxiety and Depression scale, vs 7 of 22 patients in the placebo group (P = .04). BL had no significant effect on anxiety or IBS symptoms. Patients in the BL group had a mean increase in quality of life score compared with the placebo group. The fMRI analysis showed that BL reduced responses to negative emotional stimuli in multiple brain areas, including amygdala and fronto-limbic regions, compared with placebo. The groups had similar fecal microbiota profiles, serum markers of inflammation, and levels of neurotrophins and neurotransmitters, but the BL group had reduced urine levels of methylamines and aromatic amino acids metabolites. At week 10, depression scores were reduced in patients given BL vs placebo.

CONCLUSION

In a placebo-controlled trial, we found that the probiotic BL reduces depression but not anxiety scores and increases quality of life in patients with IBS. These improvements were associated with changes in brain activation patterns that indicate that this probiotic reduces limbic reactivity. ClinicalTrials.gov no. NCT01276626.

UPLC-MS for metabolomics: a giant step forward in support of pharmaceutical research

Metabolomics is a relatively new and rapidly growing area of post-genomic biological research. As use of metabolomics technology grows throughout the spectrum of drug discovery and development, and its applications broaden, its impact is expanding dramatically. This review seeks to provide the reader with a brief history of the development of metabolomics, its significance and strategies for conducting metabolomics studies. The most widely used analytical tools for metabolomics: NMR, LC-MS and GC-MS, are discussed along with considerations for their use. Herein, we will show how metabolomics can assist in pharmaceutical research studies, such as pharmacology and toxicology, and discuss some examples of the importance of metabolomics analysis in research and development.

Blood type biochemistry and human disease

Associations between blood type and disease have been studied since the early 1900s when researchers determined that antibodies and antigens are inherited. In the 1950s, the chemical identification of the carbohydrate structure of surface antigens led to the understanding of biosynthetic pathways. The blood type is defined by oligosaccharide structures, which are specific to the antigens, thus, blood group antigens are secondary gene products, while the primary gene products are various glycosyltransferase enzymes that attach the sugar molecules to the oligosaccharide chain. Blood group antigens are found on red blood cells, platelets, leukocytes, plasma proteins, certain tissues, and various cell surface enzymes, and also exist in soluble form in body secretions such as breast milk, seminal fluid, saliva, sweat, gastric secretions, urine, and amniotic fluid. Recent advances in technology, biochemistry, and genetics have clarified the functional classifications of human blood group antigens, the structure of the A, B, H, and Lewis determinants and the enzymes that produce them, and the association of blood group antigens with disease risks. Further research to identify differences in the biochemical composition of blood group antigens, and the relationship to risks for disease, can be important for the identification of targets for the development of nutritional intervention strategies, or the identification of druggable targets. WIREs Syst Biol Med 2016, 8:517-535. doi: 10.1002/wsbm.1355 For further resources related to this article, please visit the WIREs website.

High-throughput and simultaneous quantitative analysis of homocysteine-methionine cycle metabolites and co-factors in blood plasma and cerebrospinal fluid by isotope dilution LC-MS/MS

The methionine cycle is a key pathway contributing to the regulation of human health, with well-established involvement in cardiovascular diseases and cognitive function. Changes in one-carbon cycle metabolites have also been associated with mild cognitive decline, vascular dementia, and Alzheimer's disease. Today, there is no single analytical method to monitor both metabolites and co-factors of the methionine cycle. To address this limitation, we here report for the first time a new method for the simultaneous quantitation of 17 metabolites in the methionine cycle, which are homocysteic acid, taurine, serine, cysteine, glycine, homocysteine, riboflavin, methionine, pyridoxine, cystathionine, pyridoxamine, S-adenosylhomocysteine, S-adenosylmethionine, betaine, choline, dimethylglycine, and 5-methyltetrahydrofolic acid. This multianalyte method, developed using ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS), provides a highly accurate and precise quantitation of these 17 metabolites for both plasma and cerebrospinal fluid metabolite monitoring. The method requires a simple sample preparation, which, combined with a short chromatographic run time, ensures a high sample throughput. This analytical strategy will thus provide a novel metabolomics approach to be employed in large-scale observational and intervention studies. We expect such a robust method to be particularly relevant for broad and deep molecular phenotyping of individuals in relation to their nutritional requirements, health monitoring, and disease risk management.

Urine metabolome profiling of immune-mediated inflammatory diseases

BACKGROUND

Immune-mediated inflammatory diseases (IMIDs) are a group of complex and prevalent diseases where disease diagnostic and activity monitoring is highly challenging. The determination of the metabolite profiles of biological samples is becoming a powerful approach to identify new biomarkers of clinical utility. In order to identify new metabolite biomarkers of diagnosis and disease activity, we have performed the first large-scale profiling of the urine metabolome of the six most prevalent IMIDs: rheumatoid arthritis, psoriatic arthritis, psoriasis, systemic lupus erythematosus, Crohn's disease, and ulcerative colitis.

METHODS

Using nuclear magnetic resonance, we analyzed the urine metabolome in a discovery cohort of 1210 patients and 100 controls. Within each IMID, two patient subgroups were recruited representing extreme disease activity (very high vs. very low). Metabolite association analysis with disease diagnosis and disease activity was performed using multivariate linear regression in order to control for the effects of clinical, epidemiological, or technical variability. After multiple test correction, the most significant metabolite biomarkers were validated in an independent cohort of 1200 patients and 200 controls.

RESULTS

In the discovery cohort, we identified 28 significant associations between urine metabolite levels and disease diagnosis and three significant metabolite associations with disease activity (P FDR < 0.05). Using the validation cohort, we validated 26 of the diagnostic associations and all three metabolite associations with disease activity (P FDR < 0.05). Combining all diagnostic biomarkers using multivariate classifiers we obtained a good disease prediction accuracy in all IMIDs and particularly high in inflammatory bowel diseases. Several of the associated metabolites were found to be commonly altered in multiple IMIDs, some of which can be considered as hub biomarkers. The analysis of the metabolic reactions connecting the IMID-associated metabolites showed an over-representation of citric acid cycle, phenylalanine, and glycine-serine metabolism pathways.

CONCLUSIONS

This study shows that urine is a source of biomarkers of clinical utility in IMIDs. We have found that IMIDs show similar metabolic changes, particularly between clinically similar diseases and we have found, for the first time, the presence of hub metabolites. These findings represent an important step in the development of more efficient and less invasive diagnostic and disease monitoring methods in IMIDs.

Personalized Medicine in Ocular Fibrosis: Myth or Future Biomarkers

Significance: Fibrosis-related events play a part in the pathogenesis or failure of treatment of virtually all the blinding diseases around the world, and also account for over 40% of all deaths. It is well established that the eye and other tissues of some group of patients, for example Afro-Caribbean people, scar worse than others. However, there is a current lack of reliable biomarkers to stratify the risk of scarring and postsurgical fibrosis in the eye.

Recent Advances: Recent studies using genomics, proteomics, metabolomics, clinical phenotyping, and high-resolution in vivo imaging techniques have revealed potential novel biomarkers to identify and stratify patients at risk of scarring in different fibrotic eye diseases.

Critical Issues: Most of the studies, to date, have been done in animals or small cohorts of patients and future research is needed to validate these results in large longitudinal human studies. Detailed clinical phenotyping and effective biobanking of patient tissues will also be critical for future biomarker research in ocular fibrosis.

Future Directions: The ability to predict the risk of scarring and to tailor the antifibrotic treatment regimen to each individual patient will be an extremely useful tool clinically to prevent undertreating, or exposing patients to unnecessary treatments with potential side effects. An exciting future prospect will be to use new advances in genotyping, namely next-generation whole genome sequencing like RNA-Seq, to develop a customized gene chip in ocular fibrosis. Successful translation of future biomarkers to benefit patient care will also ultimately require a strong collaboration between academics, pharmaceutical, and biotech companies.

Gut Microbiota Profiling: Metabolomics Based Approach to Unravel Compounds Affecting Human Health

The gut microbiota is composed of a huge number of different bacteria, that produce a large amount of compounds playing a key role in microbe selection and in the construction of a metabolic signaling network. The microbial activities are affected by environmental stimuli leading to the generation of a wide number of compounds, that influence the host metabolome and human health. Indeed, metabolite profiles related to the gut microbiota can offer deep insights on the impact of lifestyle and dietary factors on chronic and acute diseases. Metagenomics, metaproteomics and metabolomics are some of the meta-omics approaches to study the modulation of the gut microbiota. Metabolomic research applied to biofluids allows to: define the metabolic profile; identify and quantify classes and compounds of interest; characterize small molecules produced by intestinal microbes; and define the biochemical pathways of metabolites. Mass spectrometry and nuclear magnetic resonance spectroscopy are the principal technologies applied to metabolomics in terms of coverage, sensitivity and quantification. Moreover, the use of biostatistics and mathematical approaches coupled with metabolomics play a key role in the extraction of biologically meaningful information from wide datasets. Metabolomic studies in gut microbiota-related research have increased, focusing on the generation of novel biomarkers, which could lead to the development of mechanistic hypotheses potentially applicable to the development of nutritional and personalized therapies.

Gut Microbiota Profiling: Metabolomics Based Approach to Unravel Compounds Affecting Human Health

The gut microbiota is composed of a huge number of different bacteria, that produce a large amount of compounds playing a key role in microbe selection and in the construction of a metabolic signaling network. The microbial activities are affected by environmental stimuli leading to the generation of a wide number of compounds, that influence the host metabolome and human health. Indeed, metabolite profiles related to the gut microbiota can offer deep insights on the impact of lifestyle and dietary factors on chronic and acute diseases. Metagenomics, metaproteomics and metabolomics are some of the meta-omics approaches to study the modulation of the gut microbiota. Metabolomic research applied to biofluids allows to: define the metabolic profile; identify and quantify classes and compounds of interest; characterize small molecules produced by intestinal microbes; and define the biochemical pathways of metabolites. Mass spectrometry and nuclear magnetic resonance spectroscopy are the principal technologies applied to metabolomics in terms of coverage, sensitivity and quantification. Moreover, the use of biostatistics and mathematical approaches coupled with metabolomics play a key role in the extraction of biologically meaningful information from wide datasets. Metabolomic studies in gut microbiota-related research have increased, focusing on the generation of novel biomarkers, which could lead to the development of mechanistic hypotheses potentially applicable to the development of nutritional and personalized therapies.

Gut Microbiota Profiling: Metabolomics Based Approach to Unravel Compounds Affecting Human Health

The gut microbiota is composed of a huge number of different bacteria, that produce a large amount of compounds playing a key role in microbe selection and in the construction of a metabolic signaling network. The microbial activities are affected by environmental stimuli leading to the generation of a wide number of compounds, that influence the host metabolome and human health. Indeed, metabolite profiles related to the gut microbiota can offer deep insights on the impact of lifestyle and dietary factors on chronic and acute diseases. Metagenomics, metaproteomics and metabolomics are some of the meta-omics approaches to study the modulation of the gut microbiota. Metabolomic research applied to biofluids allows to: define the metabolic profile; identify and quantify classes and compounds of interest; characterize small molecules produced by intestinal microbes; and define the biochemical pathways of metabolites. Mass spectrometry and nuclear magnetic resonance spectroscopy are the principal technologies applied to metabolomics in terms of coverage, sensitivity and quantification. Moreover, the use of biostatistics and mathematical approaches coupled with metabolomics play a key role in the extraction of biologically meaningful information from wide datasets. Metabolomic studies in gut microbiota-related research have increased, focusing on the generation of novel biomarkers, which could lead to the development of mechanistic hypotheses potentially applicable to the development of nutritional and personalized therapies.

Metabolomics of autism spectrum disorders: early insights regarding mammalian-microbial cometabolites

INTRODUCTION

Autism spectrum disorders (ASD) are a group of neurodevelopmental disorders consisting of delayed or impaired language development and difficulties in social interactions. The very high degree of phenotypic heterogeneity in ASD originates from the interaction between environmental risk factors and susceptible genetic loci, leading to epigenetic DNA methylation. Advances in system biology are becoming strategic for implementing knowledge on the ASD aetiology and for the early diagnosis of the disease after birth.

AREAS COVERED

We overhauled the value of either targeted or untargeted metabolomics studies in autism for identifying the most relevant metabolic pathways and key metabolites implicated in the disease, with special emphasis to mammalian-microbial metabolites. The most discriminant metabolites in ASD belong to amino acid metabolism, antioxidant status, nicotinic acid metabolism, and mitochondrial metabolism. Expert commentary: Most published studies point out the role of metabolites derived from the gut microbiota: they can modulate the behavioral phenotype of the autistic children, greatly influencing host metabolic pathways and the immune system, shaping the individual susceptibility to the disease. Pitfalls and caveats in metabolomics results across studies have been additionally recognized and discussed leading to the conclusion that metabolomics studies in ASD are far to be definitive and univocal.

Metabolomics: Applications and Promise in Mycobacterial Disease

Until recently, the study of mycobacterial diseases was trapped in culture-based technology that is more than a century old. The use of nucleic acid amplification is changing this, and powerful new technologies are on the horizon. Metabolomics, which is the study of sets of metabolites of both the bacteria and host, is being used to clarify mechanisms of disease, and can identify changes leading to better diagnosis, treatment, and prognostication of mycobacterial diseases. Metabolomic profiles are arrays of biochemical products of genes in their environment. These complex patterns are biomarkers that can allow a more complete understanding of cell function, dysfunction, and perturbation than genomics or proteomics. Metabolomics could herald sweeping advances in personalized medicine and clinical trial design, but the challenges in metabolomics are also great. Measured metabolite concentrations vary with the timing within a condition, the intrinsic biology, the instruments, and the sample preparation. Metabolism profoundly changes with age, sex, variations in gut microbial flora, and lifestyle. Validation of biomarkers is complicated by measurement accuracy, selectivity, linearity, reproducibility, robustness, and limits of detection. The statistical challenges include analysis, interpretation, and description of the vast amount of data generated. Despite these drawbacks, metabolomics provides great opportunity and the potential to understand and manage mycobacterial diseases.

Diagnostics and Resistance Profiling of Bacterial Pathogens

Worldwide infectious disease is one of the leading causes of death. Despite improvements in technology and healthcare services, morbidity and mortality due to infections have remained unchanged over the past few decades. The high and increasing rate of antibiotic resistance is further aggravating the situation. Growing resistance hampers the use of conventional antibiotics, and substantial higher mortality rates are reported in patients given ineffective empiric therapy mainly due to resistance to the agents used. These infections cause suffering, incapacity, and death and impose an enormous financial burden on both healthcare systems and on society in general. The accelerating development of multidrug resistance is one of the greatest diagnostic and therapeutic challenges to modern medicine. The lack of new antibiotic options underscores the need for optimization of current diagnostics, therapies, and prevention of the spread of multidrug-resistant organisms. The so-called -omics technologies (genomics, transcriptomics, proteomics, and metabolomics) have yielded large-scale datasets that advanced the search for biomarkers of infectious diseases in the last decade. One can imagine that in the future the implementation of biomarker-driven molecular test systems will transform diagnostics of infectious diseases and will significantly accelerate the identification of the bacterial pathogens at the infected host site. Furthermore, molecular tests based on the identification of markers of antibiotic resistance will dramatically change resistance profiling. The replacement of culturing methods by molecular test systems for early diagnosis will provide the basis not only for a prompt and targeted therapy, but also for a much more effective stewardship of antibiotic agents and a reduction of the spread of multidrug resistance as well as the appearance of new antibiotic resistances.

Metabolomics in Newborns

Metabolomics is the quantitative analysis of a large number of low molecular weight metabolites that are intermediate or final products of all the metabolic pathways in a living organism. Any metabolic profiles detectable in a human biological fluid are caused by the interaction between gene expression and the environment. The metabolomics approach offers the possibility to identify variations in metabolite profile that can be used to discriminate disease. This is particularly important for neonatal and pediatric studies especially for severe ill patient diagnosis and early identification. This property is of a great clinical importance in view of the newer definitions of health and disease. This review emphasizes the workflow of a typical metabolomics study and summarizes the latest results obtained in neonatal studies with particular interest in prematurity, intrauterine growth retardation, inborn errors of metabolism, perinatal asphyxia, sepsis, necrotizing enterocolitis, kidney disease, bronchopulmonary dysplasia, and cardiac malformation and dysfunction.

Pharmacometabolomics of l-carnitine treatment response phenotypes in patients with septic shock

RATIONALE

Sepsis therapeutics have a poor history of success in clinical trials, due in part to the heterogeneity of enrolled patients. Pharmacometabolomics could differentiate drug response phenotypes and permit a precision medicine approach to sepsis.

OBJECTIVES

To use existing serum samples from the phase 1 clinical trial of l-carnitine treatment for severe sepsis to metabolically phenotype l-carnitine responders and nonresponders.

METHODS

Serum samples collected before (T0) and after completion of the infusion (T24, T48) from patients randomized to either l-carnitine (12 g) or placebo for the treatment of vasopressor-dependent septic shock were assayed by untargeted (1)H-nuclear magnetic resonance metabolomics. The normalized, quantified metabolite data sets of l-carnitine- and placebo-treated patients at each time point were compared by analysis of variance with post-hoc testing for multiple comparisons. Pathway analysis was performed to statistically rank metabolic networks.

MEASUREMENTS AND MAIN RESULTS

Thirty-eight metabolites were identified in all samples. Concentrations of 3-hydroxybutyrate, acetoacetate, and 3-hydroxyisovalerate were different at T0 and over time in l-carnitine-treated survivors versus nonsurvivors. Pathway analysis of pretreatment metabolites revealed that synthesis and degradation of ketone bodies had the greatest impact in differentiating l-carnitine treatment response. Analysis of all patients based on pretreatment 3-hydroxybutyrate concentration yielded distinct phenotypes. Using the T0 median 3-hydroxybutyrate level (153 μM), patients were categorized as either high or low ketone. l-Carnitine-treated low-ketone patients had greater use of carnitine as evidenced by lower post-treatment l-carnitine levels. The l-carnitine responders also had faster resolution of vasopressor requirement and a trend toward a greater improvement in mortality at 1 year (P = 0.038) compared with patients with higher 3-hydroxybutyrate.

CONCLUSIONS

The results of this preliminary study, which were not readily apparent from the parent clinical trial, show a unique metabolite profile of l-carnitine responders and introduce pharmacometabolomics as a viable strategy for informing l-carnitine responsiveness. The approach taken in this study represents a concrete example for the application of precision medicine to sepsis therapeutics that warrants further study.

Metabolomic applications in nutritional research: a perspective

Metabolomics focuses on the global study of metabolites in cells, tissues and biofluids. Analytical technologies such as nuclear magnetic resonance (NMR) spectroscopy and hyphenated mass spectrometry (MS) combined with advanced multivariate statistical methods allow us to study perturbations in metabolism. The close link between metabolism and nutrition has seen the application of metabolomics in nutritional research increase in recent times. Such applications can be divided into three main categories, namely (1) the area of dietary biomarker identification, (2) diet-related diseases and (3) nutritional interventions. The present perspective gives an overview of these applications and an outlook to the future.