Biomarker Discovery and Translation in Metabolomics

A Microenvironment-responsive small-molecule probe and application in quick acute myocardial infarction imaging

Acute myocardial infarction (AMI) patients are at an elevated risk for life-threatening myocardial ischemia/reperfusion injury. Early-stage nonradioactive and noninvasive diagnosis of AMI is imperative for the subsequent disease treatment, yet it presents substantial challenges. After AMI, the myocardium typically exhibits elevated levels of peroxynitrite (ONOO-), constituting a distinct microenvironmental feature. In this context, the near-infrared imaging probe (BBEB) is employed to precisely delineate the boundaries of AMI lesions with a high level of sensitivity and specificity by monitoring endogenous ONOO-. This probe allows for the early detection of myocardial damage at cellular and animal levels, providing exceptional temporal and spatial resolution. Notably, BBEB enables visualization of ONOO- level alterations during AMI treatment incorporating antioxidant drugs. Overall, BBEB can rapidly and accurately visualize myocardial injury, particularly in the early stages, and can further facilitate antioxidant drug screening.

Untargeted Metabolomic Profiling Reveals Differentially Expressed Serum Metabolites and Pathways in Type 2 Diabetes Patients with and without Cognitive Decline: A Cross-Sectional Study

Diabetes is recognized as a risk factor for cognitive decline, but the underlying mechanisms remain elusive. We aimed to identify the metabolic pathways altered in diabetes-associated cognitive decline (DACD) using untargeted metabolomics. We conducted liquid chromatography-mass spectrometry-based untargeted metabolomics to profile serum metabolite levels in 100 patients with type 2 diabetes (T2D) (54 without and 46 with DACD). Multivariate statistical tools were used to identify the differentially expressed metabolites (DEMs), and enrichment and pathways analyses were used to identify the signaling pathways associated with the DEMs. The receiver operating characteristic (ROC) analysis was employed to assess the diagnostic accuracy of a set of metabolites. We identified twenty DEMs, seven up- and thirteen downregulated in the DACD vs. DM group. Chemometric analysis revealed distinct clustering between the two groups. Metabolite set enrichment analysis found significant enrichment in various metabolite sets, including galactose metabolism, arginine and unsaturated fatty acid biosynthesis, citrate cycle, fructose and mannose, alanine, aspartate, and glutamate metabolism. Pathway analysis identified six significantly altered pathways, including arginine and unsaturated fatty acid biosynthesis, and the metabolism of the citrate cycle, alanine, aspartate, glutamate, a-linolenic acid, and glycerophospholipids. Classifier models with AUC-ROC > 90% were developed using individual metabolites or a combination of individual metabolites and metabolite ratios. Our study provides evidence of perturbations in multiple metabolic pathways in patients with DACD. The distinct DEMs identified in this study hold promise as diagnostic biomarkers for DACD patients.

Metabolomic profiling implicates mitochondrial and immune dysfunction in disease syndromes of the critically endangered black rhinoceros (Diceros bicornis)

The critically endangered black rhinoceros (Diceros bicornis; black rhino) experiences extinction threats from poaching in-situ. The ex-situ population, which serves as a genetic reservoir against impending extinction threats, experiences its own threats to survival related to several disease syndromes not typically observed among their wild counterparts. We performed an untargeted metabolomic analysis of serum from 30 ex-situ housed black rhinos (Eastern black rhino, EBR, n = 14 animals; Southern black rhino, SBR, n = 16 animals) and analyzed differences in metabolite profiles between subspecies, sex, and health status (healthy n = 13 vs. diseased n = 14). Of the 636 metabolites detected, several were differentially (fold change > 1.5; p < 0.05) expressed between EBR vs. SBR (40 metabolites), female vs. male (36 metabolites), and healthy vs. diseased (22 metabolites). Results suggest dysregulation of propanoate, amino acid metabolism, and bile acid biosynthesis in the subspecies and sex comparisons. Assessment of healthy versus diseased rhinos indicates involvement of arachidonic acid metabolism, bile acid biosynthesis, and the pentose phosphate pathway in animals exhibiting inflammatory disease syndromes. This study represents the first systematic characterization of the circulating serum metabolome in the black rhinoceros. Findings further implicate mitochondrial and immune dysfunction as key contributors for the diverse disease syndromes reported in ex-situ managed black rhinos.

Systematic Review: Urine Biomarker Discovery for Inflammatory Bowel Disease Diagnosis

Inflammatory bowel diseases (IBDs) are chronic, heterogeneous, and inflammatory conditions mainly affecting the gastrointestinal tract. Currently, endoscopy is the gold standard test for assessing mucosal activity and healing in clinical practice; however, it is a costly, time-consuming, invasive, and uncomfortable procedure for the patients. Therefore, there is an urgent need for sensitive, specific, fast and non-invasive biomarkers for the diagnosis of IBD in medical research. Urine is an excellent biofluid for discovering biomarkers because it is non-invasive to sample. In this review, we aimed to summarize proteomics and metabolomics studies performed in both animal models of IBD and humans that identify urinary biomarkers for IBD diagnosis. Future large-scale multi-omics studies should be conducted in collaboration with clinicians, researchers, and industry to make progress toward the development of sensitive and specific diagnostic biomarkers, thereby making personalized medicine possible.

Biomarkers as Biomedical Bioindicators: Approaches and Techniques for the Detection, Analysis, and Validation of Novel Biomarkers of Diseases

A biomarker is any measurable biological moiety that can be assessed and measured as a potential index of either normal or abnormal pathophysiology or pharmacological responses to some treatment regimen. Every tissue in the body has a distinct biomolecular make-up, which is known as its biomarkers, which possess particular features, viz., the levels or activities (the ability of a gene or protein to carry out a particular body function) of a gene, protein, or other biomolecules. A biomarker refers to some feature that can be objectively quantified by various biochemical samples and evaluates the exposure of an organism to normal or pathological procedures or their response to some drug interventions. An in-depth and comprehensive realization of the significance of these biomarkers becomes quite important for the efficient diagnosis of diseases and for providing the appropriate directions in case of multiple drug choices being presently available, which can benefit any patient. Presently, advancements in omics technologies have opened up new possibilities to obtain novel biomarkers of different types, employing genomic strategies, epigenetics, metabolomics, transcriptomics, lipid-based analysis, protein studies, etc. Particular biomarkers for specific diseases, their prognostic capabilities, and responses to therapeutic paradigms have been applied for screening of various normal healthy, as well as diseased, tissue or serum samples, and act as appreciable tools in pharmacology and therapeutics, etc. In this review, we have summarized various biomarker types, their classification, and monitoring and detection methods and strategies. Various analytical techniques and approaches of biomarkers have also been described along with various clinically applicable biomarker sensing techniques which have been developed in the recent past. A section has also been dedicated to the latest trends in the formulation and designing of nanotechnology-based biomarker sensing and detection developments in this field.

Metabolomics of infectious diseases in the era of personalized medicine

Infectious diseases continue to be a major cause of morbidity and mortality worldwide. Diseases cause perturbation of the host's immune system provoking a response that involves genes, proteins and metabolites. While genes are regulated by epigenetic or other host factors, proteins can undergo post-translational modification to enable/modify function. As a result, it is difficult to correlate the disease phenotype based solely on genetic and proteomic information only. Metabolites, however, can provide direct information on the biochemical activity during diseased state. Therefore, metabolites may, potentially, represent a phenotypic signature of a diseased state. Measuring and assessing metabolites in large scale falls under the omics technology known as "metabolomics". Comprehensive and/or specific metabolic profiling in biological fluids can be used as biomarkers of disease diagnosis. In addition, metabolomics together with genomics can be used to differentiate patients with differential treatment response and development of host targeted therapy instead of pathogen targeted therapy where pathogens are more prone to mutation and lead to antimicrobial resistance. Thus, metabolomics can be used for patient stratification, personalized drug formulation and disease control and management. Currently, several therapeutics and in vitro diagnostics kits have been approved by US Food and Drug Administration (FDA) for personalized treatment and diagnosis of infectious diseases. However, the actual number of therapeutics or diagnostics kits required for tailored treatment is limited as metabolomics and personalized medicine require the involvement of personnel from multidisciplinary fields ranging from technological development, bioscience, bioinformatics, biostatistics, clinicians, and biotechnology companies. Given the significance of metabolomics, in this review, we discussed different aspects of metabolomics particularly potentials of metabolomics as diagnostic biomarkers and use of small molecules for host targeted treatment for infectious diseases, and their scopes and challenges in personalized medicine.

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

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

Urine Metabolite of Mice with Orientia tsutsugamushi Infection

Scrub typhus is an acute febrile, mite-borne disease endemic to the Asia-Pacific region. In South Korea, it is a seasonal disease that occurs frequently in the autumn, and its incidence has increased steadily. In this study, we used a liquid chromatography and flow injection analysis-tandem mass spectrometry-based targeted urine metabolomics approach to evaluate the host response to Orientia tsutsugamushi infection. Balb/c mice were infected with O. tsutsugamushi Boryong, and their urine metabolite profile was examined. Metabolites that differed significantly between the experimental groups were identified using the Kruskal-Wallis test. Sixty-five differential metabolites were identified. The principal metabolite classes were acylcarnitines, glycerophospholipids, biogenic amines, and amino acids. An ingenuity pathway analysis revealed that several toxic (cardiotoxic, hepatotoxic, and nephrotoxic) metabolites are induced by scrub typhus infection. This is the first report of urinary metabolite biomarkers of scrub typhus infection and it enhances our understanding of the metabolic pathways involved.

Current Role of Modern Chromatography with Mass Spectrometry and Nuclear Magnetic Resonance Spectroscopy in the Investigation of Biomarkers of Endometriosis

Endometriosis has a wide range of clinical manifestations, and the disease course is unpredictable, making the diagnosis a challenging task. Despite significant advances in the pathophysiology of endometriosis and various proposed theories, the exact etiology is not fully understood and is still unknown. The most commonly used biomarker of endometriosis is CA-125, however, it is nonspecific and is applied for cancers diagnosis. Therefore, the development of reliable noninvasive diagnostic tests for the early diagnosis of endometriosis remains one of the top priorities. Omics technologies are very promising approaches for constructing diagnostic models and biomarker discovery. Their use can greatly facilitate the study of such a complex disease as endometriosis. Nowadays, powerful analytical platforms commonly used in omics, such as gas and liquid chromatography with mass spectrometry and nuclear magnetic resonance (NMR) spectroscopy, have proven to be a promising tools for biomarker discovery. The aim of this review is to summarize the various features of the analytical approaches, practical challenges and features of gas and liquid chromatography with MS and NMR spectroscopy (including sample processing protocols, technological advancements, and methodology) used for profiling of metabolites, lipids, peptides and proteins in physiological fluids and tissues from patients with endometriosis. In addition, this report devotes special attention to the issue of how comprehensive analyses of these profiles can effectively contribute to the study of endometriosis. The search query included reports published between 2012 and 2022 years in PubMed, Web-of-Science, SCOPUS, Science Direct.

The Integration of Metabolomics with Other Omics: Insights into Understanding Prostate Cancer

Our understanding of prostate cancer (PCa) has shifted from solely caused by a few genetic aberrations to a combination of complex biochemical dysregulations with the prostate metabolome at its core. The role of metabolomics in analyzing the pathophysiology of PCa is indispensable. However, to fully elucidate real-time complex dysregulation in prostate cells, an integrated approach based on metabolomics and other omics is warranted. Individually, genomics, transcriptomics, and proteomics are robust, but they are not enough to achieve a holistic view of PCa tumorigenesis. This review is the first of its kind to focus solely on the integration of metabolomics with multi-omic platforms in PCa research, including a detailed emphasis on the metabolomic profile of PCa. The authors intend to provide researchers in the field with a comprehensive knowledge base in PCa metabolomics and offer perspectives on overcoming limitations of the tool to guide future point-of-care applications.

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.

Metabolomics of oxidative stress: Nrf2 independent depletion of NAD or increases of sugar alcohols

Nrf2 encodes a transcription factor best known for regulating the expression of antioxidant and detoxification genes. Recent evidence suggested that Nrf2 mediates metabolic reprogramming in cancer cells. However, the role of Nrf2 in the biochemical metabolism of cardiac cells has not been studied. Using LC-MS/MS-based metabolomics, we addressed whether knocking out the Nrf2 gene in AC16 human cardiomyocytes affects metabolic reprogramming by oxidative stress. Profiling the basal level metabolites showed an elevated pentose phosphate pathway and increased levels of sugar alcohols, sorbitol, L-arabitol, xylitol and xylonic acid, in Nrf2 KO cells. With sublethal levels of oxidative stress, depletion of NAD, an increase of GDP and elevation of sugar alcohols, sorbitol and dulcitol, were detected in parent wild type (WT) cells. Knocking out Nrf2 did not affect these changes. Biochemical assays confirmed depletion of NAD in WT and Nrf2 KO cells due to H₂O₂ treatment. These data support that although Nrf2 deficiency caused baseline activation of the pentose phosphate pathway and sugar alcohol synthesis, a brief exposure to none-lethal doses of H₂O₂ caused NAD depletion in an Nrf2 independent manner. Loss of NAD may contribute to oxidative stress associated cell degeneration as observed with aging, diabetes and heart failure.

Noninvasive monitoring of evolving urinary metabolic patterns in neonatal encephalopathy

BACKGROUND

Infants with moderate and severe neonatal encephalopathy (NE) frequently suffer from long-term adverse outcomes. We hypothesize that the urinary metabolome of newborns with NE reflects the evolution of injury patterns observed with magnetic resonance imaging (MRI).

METHODS

Eligible patients were newborn infants with perinatal asphyxia evolving to NE and qualifying for therapeutic hypothermia (TH) included in the HYPOTOP trial. MRI was employed for characterizing brain injury. Urine samples of 55 infants were collected before, during, and after TH. Metabolic profiles of samples were recorded employing three complementary mass spectrometry-based assays, and the alteration of detected metabolic features between groups was assessed.

RESULTS

The longitudinal assessment revealed significant perturbations of the urinary metabolome. After 24 h of TH, a stable disease pattern evolved characterized by the alterations of 4-8% of metabolic features related to lipid metabolism, metabolism of cofactors and vitamins, glycan biosynthesis and metabolism, amino acid metabolism, and nucleotide metabolism. Characteristic metabolomic fingerprints were observed for different MRI injury patterns.

CONCLUSIONS

This study shows the potential of urinary metabolic profiles for the noninvasive monitoring of brain injury of infants with NE during TH.

IMPACT

A comprehensive approach for the study of the urinary metabolome was employed involving a semi-targeted capillary electrophoresis-time-of-flight mass spectrometry (TOFMS) assay, an untargeted ultra-performance liquid chromatography (UPLC)-quadrupole TOFMS assay, and a targeted UPLC-tandem MS-based method for the quantification of amino acids. The longitudinal study of the urinary metabolome identified dynamic metabolic changes between birth and until 96 h after the initiation of TH. The identification of altered metabolic pathways in newborns with pathologic MRI outcomes might offer the possibility of developing noninvasive monitoring approaches for personalized adjustment of the treatment and for supporting early outcome prediction.

Knowledge gaps in late-onset neonatal sepsis in preterm neonates: a roadmap for future research

Late-onset neonatal sepsis (LONS) remains an important threat to the health of preterm neonates in the neonatal intensive care unit. Strategies to optimize care for preterm neonates with LONS are likely to improve survival and long-term neurocognitive outcomes. However, many important questions on how to improve the prevention, early detection, and therapy for LONS in preterm neonates remain unanswered. This review identifies important knowledge gaps in the management of LONS and describe possible methods and technologies that can be used to resolve these knowledge gaps. The availability of computational medicine and hypothesis-free-omics approaches give way to building bedside feedback tools to guide clinicians in personalized management of LONS. Despite advances in technology, implementation in clinical practice is largely lacking although such tools would help clinicians to optimize many aspects of the management of LONS. We outline which steps are needed to get possible research findings implemented on the neonatal intensive care unit and provide a roadmap for future research initiatives. IMPACT: This review identifies knowledge gaps in prevention, early detection, antibiotic, and additional therapy of late-onset neonatal sepsis in preterm neonates and provides a roadmap for future research efforts. Research opportunities are addressed, which could provide the means to fill knowledge gaps and the steps that need to be made before possible clinical use. Methods to personalize medicine and technologies feasible for bedside clinical use are described.

Common and diet-specific metabolic pathways underlying residual feed intake in fattening Charolais yearling bulls

Residual feed intake (RFI) is one of the preferred traits for feed efficiency animal breeding. However, RFI measurement is expensive and time-consuming and animal ranking may depend on the nature of the diets. We aimed to explore RFI plasma biomarkers and to unravel the underlying metabolic pathways in yearling bulls fed either a corn-silage diet rich in starch (corn diet) or a grass-silage diet rich in fiber (grass diet). Forty-eight extreme RFI animals (Low-RFI, n = 24, versus High-RFI, n = 24, balanced per diet) were selected from a population of 364 Charolais bulls and their plasma was subjected to a targeted LC-MS metabolomic approach together with classical metabolite and hormonal plasma analyses. Greater lean body mass and nitrogen use efficiency, and lower protein turnover were identified as common mechanisms underlying RFI irrespective of the diet. On the other hand, greater adiposity and plasma concentrations of branched-chain amino acids (BCAA) together with lower insulin sensitivity in High-RFI animals were only observed with corn diet. Conversely, greater plasma concentrations of BCAA and total triglycerides, but similar insulin concentrations were noted in efficient RFI cattle with grass diet. Our data suggest that there are diet-specific mechanisms explaining RFI differences in fattening Charolais yearling bulls.

Classification and biomarker identification of prostate tissue from TRAMP mice with hyperpolarized 13C-SIRA

Hyperpolarized ¹³C isotope resolved spectroscopy boosts NMR signal intensity, which improves signal detection and allows metabolic fluxes to be analyzed. Such hyperpolarized flux data may offer new approaches to tissue classification and biomarker identification that could be translated in vivo. Here we used hyperpolarized stable isotope resolved analysis (SIRA) to measure metabolite specific ¹³C isotopic enrichments in the central carbon metabolism of mouse prostate. Prostate and tumor tissue samples were acquired from transgenic adenocarcinomas of the mouse prostate (TRAMP) mice. Before euthanasia, mice were injected with [U-¹³C]glucose intraperitoneally (i.p.). Polar metabolite extracts were prepared, and hyperpolarized 1D-¹³C NMR spectra were obtained from normal prostate (n = 19) and cancer tissue (n = 19) samples. Binary classification and feature analysis was performed to make a separation model and to investigate differences between samples originating from normal and cancerous prostate tissue, respectively. Hyperpolarized experiments were carried out according to a standardized protocol, which showed a high repeatability (CV = 15%) and an average linewidth in the 1D-¹³C NMR spectra of 2 ± 0.5 Hz. The resolution of the hyperpolarized 1D-¹³C spectra was high with little signal overlap in the carbonyl region and metabolite identification was easily accomplished. A discrimination with 95% success rate could be made between samples originating from TRAMP mice prostate and tumor tissue based on isotopomers from uniquely identified metabolites. Hyperpolarized ¹³C-SIRA allowed detailed metabolic information to be obtained from tissue specimens. The positional information of ¹³C isotopic enrichments lead to easily interpreted features responsible for high predictive classification of tissue types. This analytical approach has matured, and the robust experimental protocols currently available allow systematic tracking of metabolite flux ex vivo.

Expanding Urinary Metabolite Annotation through Integrated Mass Spectral Similarity Networking

The urine metabolome constitutes a rich source of functional information reflecting physiological states that are influenced by distinct conditions and biological stresses, such as responses to drug treatments or disease manifestations. Although global liquid chromatography-mass spectrometry (MS) profiling provides the most comprehensive measurement of metabolites in complex biological samples, annotation remains a challenge, and computational approaches are necessary to translate the molecular composition into biological knowledge. Here, we investigated the use of tandem MS-based enhanced molecular networks (MolNetEnhancer) to improve the metabolite annotation of urine extracts. The samples (n = 10) were analyzed by hydrophilic interaction chromatography-quadrupole time-of-flight mass spectrometry in both electrospray ionization (ESI) modes. Consistent with other common data preprocessing software, the use of Progenesis QI led to the annotation of up to 20 metabolites based on MS2 library searches, showing a high fragmentation score (cosine similarity ≥ 0.7), that is, ∼2% of mass features containing MS2 spectra. Molecular networking based on library matching resulted in the annotation of up to 62 urinary compounds. Using a combination of unsupervised substructure discovery (MS2LDA), the in silico tool network annotation propagation (NAP), and ClassyFire chemical ontology, embedded in a multilayered molecular network by MolNetEnhancer, we were able to expand the chemical characterization to ∼50% of the data set. The integrative approach led to the annotation of 275 compounds at the metabolomics standards initiative (MSI) confidence level 2, as well as 459 and 578 urinary metabolites (MSI level 3) in both negative and positive ESI modes, respectively. The exhaustive MS2-based annotation outperformed similar studies applied to larger cohorts while offering the discovery of metabolites not identified by the MS2 library search. This is the first work that effectively integrates orthogonal annotation methods and MS2-based fragmentation studies to improve metabolite annotation in urine samples.

Metabolic Profiling of Neocortical Tissue Discriminates Alzheimer's Disease from Mild Cognitive Impairment, High Pathology Controls, and Normal Controls

Alzheimer's disease (AD) is the most common cause of dementia, accounting for an estimated 60-80% of cases, and is the sixth-leading cause of death in the United States. While considerable advancements have been made in the clinical care of AD, it remains a complicated disorder that can be difficult to identify definitively in its earliest stages. Recently, mass spectrometry (MS)-based metabolomics has shown significant potential for elucidation of disease mechanisms and identification of therapeutic targets as well diagnostic and prognostic markers that may be useful in resolving some of the difficulties affecting clinical AD studies, such as effective stratification. In this study, complementary gas chromatography- and liquid chromatography-MS platforms were used to detect and monitor 2080 metabolites and features in 48 postmortem tissue samples harvested from the superior frontal gyrus of male and female subjects. Samples were taken from four groups: 12 normal control (NC) patients, 12 cognitively normal subjects characterized as high pathology controls (HPC), 12 subjects with nonspecific mild cognitive impairment (MCI), and 12 subjects with AD. Multivariate statistics informed the construction and cross-validation (p < 0.01) of partial least squares-discriminant analysis (PLS-DA) models defined by a nine-metabolite panel of disease markers (lauric acid, stearic acid, myristic acid, palmitic acid, palmitoleic acid, and four unidentified mass spectral features). Receiver operating characteristic analysis showed high predictive accuracy of the resulting PLS-DA models for discrimination of NC (97%), HPC (92%), MCI (∼96%), and AD (∼96%) groups. Pathway analysis revealed significant disturbances in lysine degradation, fatty acid metabolism, and the degradation of branched-chain amino acids. Network analysis showed significant enrichment of 11 enzymes, predominantly within the mitochondria. The results expand basic knowledge of the metabolome related to AD and reveal pathways that can be targeted therapeutically. This study also provides a promising basis for the development of larger multisite projects to validate these candidate markers in readily available biospecimens such as blood to enable the effective screening, rapid diagnosis, accurate surveillance, and therapeutic monitoring of AD. All raw mass spectrometry data have been deposited to MassIVE (data set identifier MSV000087165).

Metabolic responses in neonatal sepsis-A systematic review of human metabolomic studies

AIM

To systematically review human metabolomic studies investigating metabolic responses in septic neonates.

METHODS

A systematic literature search was performed in the databases MEDLINE, EMBASE and Cochrane library up to the 1st of January 2021. We included studies that assessed neonatal sepsis and the following outcomes; (1) change in the metabolism compared to healthy neonates and/or (2) metabolomics compared to traditional diagnostic tools of neonatal sepsis. The screened abstracts were independently considered for eligibility by two researchers.

PROSPERO ID

CRD42020164454.

RESULTS

The search identified in total 762 articles. Fifteen articles were assessed for eligibility. Four studies were included, with totally 78 neonates. The studies used different diagnostic criteria and had between 1 and 16 sepsis cases. All studies with bacterial sepsis found alterations in the glucose and lactate metabolism, reflecting possible redistribution of glucose consumption from mitochondrial oxidative phosphorylation to the lactate and pentose phosphate pathway. We also found signs of increased oxidative stress and fatty acid oxidation in sepsis cases.

CONCLUSION

We found signs of metabolomic signatures in neonatal sepsis. This may lead to better understanding of sepsis pathophysiology and detection of new candidate biomarkers. Results should be validated in large-scale multicentre studies.

Altered metabolic pathways in a transgenic mouse model suggest mechanistic role of amyloid precursor protein overexpression in Alzheimer's disease

INTRODUCTION

The mechanistic role of amyloid precursor protein (APP) in Alzheimer's disease (AD) remains unclear.

OBJECTIVES

Here, we aimed to identify alterations in cerebral metabolites and metabolic pathways in cortex, hippocampus and serum samples from Tg2576 mice, a widely used mouse model of AD.

METHODS

Metabolomic profilings using liquid chromatography-mass spectrometry were performed and analysed with MetaboAnalyst and weighted correlation network analysis (WGCNA).

RESULTS

Expressions of 11 metabolites in cortex, including hydroxyphenyllactate-linked to oxidative stress-and phosphatidylserine-lipid metabolism-were significantly different between Tg2576 and WT mice (false discovery rate < 0.05). Four metabolic pathways from cortex, including glycerophospholipid metabolism and pyrimidine metabolism, and one pathway (sulphur metabolism) from hippocampus, were significantly enriched in Tg2576 mice. Network analysis identified five pathways, including alanine, aspartate and glutamate metabolism, and mitochondria electron transport chain, that were significantly correlated with AD genotype.

CONCLUSIONS

Changes in metabolite concentrations and metabolic pathways are present in the early stage of APP pathology, and may be important for AD development and progression.

Metabolomics Analysis of Viral Therapeutics

Virotherapy, enabled by recent advances in the transdisciplinary field of biotechnology, has emerged as a powerful tool for use in anticancer treatment, gene therapy, immunotherapy, etc. Examining the effects of viruses and virus-derived immune-modulating therapeutics is of great fundamental and clinical interest. Here we describe a sample preparation protocol for metabolite extraction from virus-infected tissue, in addition to liquid chromatography-mass spectrometry conditions essential for subsequent analysis. This metabolomics approach delivers highly sensitive and specific metabolite information on various biospecimens. Such an approach may be adopted to monitor biological changes in over 30 relevant metabolic pathways in response to viral infection and also viral therapeutics.

Advances and Perspectives in Prostate Cancer Biomarker Discovery in the Last 5 Years through Tissue and Urine Metabolomics

Prostate cancer (PCa) is the second most diagnosed cancer in men worldwide. For its screening, serum prostate specific antigen (PSA) test has been largely performed over the past decade, despite its lack of accuracy and inability to distinguish indolent from aggressive disease. Metabolomics has been widely applied in cancer biomarker discovery due to the well-known metabolic reprogramming characteristic of cancer cells. Most of the metabolomic studies have reported alterations in urine of PCa patients due its noninvasive collection, but the analysis of prostate tissue metabolome is an ideal approach to disclose specific modifications in PCa development. This review aims to summarize and discuss the most recent findings from tissue and urine metabolomic studies applied to PCa biomarker discovery. Eighteen metabolites were found consistently altered in PCa tissue among different studies, including alanine, arginine, uracil, glutamate, fumarate, and citrate. Urine metabolomic studies also showed consistency in the dysregulation of 15 metabolites and, interestingly, alterations in the levels of valine, taurine, leucine and citrate were found in common between urine and tissue studies. These findings unveil that the impact of PCa development in human metabolome may offer a promising strategy to find novel biomarkers for PCa diagnosis.

Biodiscovery of Potential Antibacterial Diagnostic Metabolites from the Endolichenic Fungus Xylaria venustula Using LC-MS-Based Metabolomics

Simple Summary

In this study, we determined the bioactivities and chemical natures of three species of lichen Usnea and their associated endolichenic fungi (ELF) through metabolomics. We found significant differences in the antibacterial activities and the metabolites produced by the host lichen and its ELF, with the latter targeting a wider scope of organisms. We also discovered potential key metabolites produced by ELF that are yet to be reported. This study shows the application of metabolomics in rapidly identifying bioactive metabolites that are of significance in the discovery of new drugs.

Abstract

Three species of the lichen Usnea (U. baileyi (Stirt.) Zahlbr., U. bismolliuscula Zahlbr. and U. pectinata Stirt.) and nine associated endolichenic fungi (ELF) were evaluated using a metabolomics approach. All investigated lichen crude extracts afforded antibacterial activity against Staphylococcus aureus (minimum inhibitory concentration (MIC): 0.0625 mg/mL), but none was observed against Escherichia coli, while the ELF extract Xylaria venustula was found to be the most active against S. aureus (MIC: 2.5 mg/mL) and E. coli (MIC: 5 mg/mL). X. venustula was fractionated and tested for to determine its antibacterial activity. Fractions XvFr1 to 5 displayed bioactivities against both test bacteria. Selected crude extracts and fractions were subjected to metabolomics analyses using high-resolution LC–MS. Multivariate analyses showed the presence of five secondary metabolites unique to bioactive fractions XvFr1 to 3, which were identified as responsible for the antibacterial activity of X. venustula. The p-values of these metabolites were at the margin of significance level, with methyl xylariate C (P_60) being the most significant. However, their high variable importance of projection (VIP) scores (>5) suggest these metabolites are potential diagnostic metabolites for X. venustula for “dual” bioactivity against S. aureus and E. coli. The statistical models also showed the distinctiveness of metabolites produced by lichens and ELF, thus supporting our hypotheses of ELF functionality similar to plant endophytes.

Metabolomics: small molecules that matter more

Metabolomics, an analytical study with high-throughput profiling, helps to understand interactions within a biological system. Small molecules, called metabolites or metabolomes with the size of <1500 Da, depict the status of a biological system in a different manner. Currently, we are in need to globally analyze the metabolome and the pathways involved in healthy, as well as diseased conditions, for possible therapeutic applications. Metabolome analysis has revealed high-abundance molecules during different conditions such as diet, environmental stress, microbiota, and disease and treatment states. As a result, it is hard to understand the complete and stable network of metabolites of a biological system. This review helps readers know the available techniques to study metabolomics in addition to other major omics such as genomics, transcriptomics, and proteomics. This review also discusses the metabolomics in various pathological conditions and the importance of metabolomics in therapeutic applications.

NMR-Based Metabolomics

Nuclear magnetic resonance (NMR) spectroscopy is a major analytical method used in the growing field of metabolomics. Although NMR is relatively less sensitive than mass spectrometry, this analytical platform has numerous characteristics including its high reproducibility and quantitative abilities, its nonselective and noninvasive nature, and the ability to identify unknown metabolites in complex mixtures and trace the downstream products of isotope labeled substrates ex vivo, in vivo, or in vitro. Metabolomic analysis of highly complex biological mixtures has benefitted from the advances in both NMR data acquisition and analysis methods. Although metabolomics applications span a wide range of disciplines, a majority has focused on understanding, preventing, diagnosing, and managing human diseases. This chapter describes NMR-based methods relevant to the rapidly expanding metabolomics field.

Exploratory and confirmatory analysis to investigate the presence of vaginal metabolome expression of microbial invasion of the amniotic cavity in women with preterm labor using high-performance liquid chromatography

BACKGROUND

Although the influence of microbial invasion of the amniotic cavity on the development of spontaneous preterm delivery is unquestionable, the use of an invasive procedure to diagnose the status of an infection limits its clinical translation.

OBJECTIVE

This study aimed to use exploratory and confirmatory analyses to investigate the presence of vaginal metabolome expression of microbial invasion of the amniotic cavity in women diagnosed as having preterm labor using high-performance liquid chromatography.

STUDY DESIGN

In 140 women with singleton pregnancies and a diagnosis of preterm labor at <34 weeks' gestation, we analyzed vaginal amino acid concentrations using high-performance liquid chromatography. Vaginal samples were collected shortly after the amniocentesis performed at admission to rule out microbial invasion of the amniotic cavity. Data were normalized for the median of all the amino acid concentrations evaluated. Microbial invasion of the amniotic cavity was defined as a positive aerobic or anaerobic amniotic fluid culture for the presence of bacteria or yeast or Ureaplasma species or Mycoplasma hominis in the mycoplasma culture or a positive polymerase chain reaction result for 16S rRNA gene sequence. Exploratory analysis was performed in half of the sample and confirmatory analysis in the other half. We compared vaginal amino acid concentrations between women with and without microbial invasion of the amniotic cavity in both cohorts. The area under the curve with 95% confidence interval values were calculated for vaginal amino acids with significant differences.

RESULTS

In the exploratory cohort (2014-2015), 17 of 76 women (22.3%) had microbial invasion of the amniotic cavity compared with 14 of 72 (19.4%) in the confirmatory cohort (2016-2017). In the exploratory cohort, we found significantly higher amino acid concentrations of vaginal taurine, lysine, and cysteine and significantly lower concentrations of vaginal glutamate, aspartate, and the aspartate to asparagine ratio. These significant differences were confirmed in the confirmatory cohort. The area under the curve of these vaginal amino acids to predict microbial invasion of the amniotic cavity ranged between 0.72 and 0.79, with cysteine being the amino acid with the best performance with an area under the curve of 0.79 (95% confidence interval, 0.71-0.88).

CONCLUSION

We found the vaginal metabolome expression of microbial invasion of the amniotic cavity in women with preterm labor and intact membranes. These findings might open the possibility to develop noninvasive diagnostic tools of microbial invasion of the amniotic cavity with the aim of selecting women who would most likely benefit from an amniocentesis for this indication.

Metabolomics in chronic pain research

BACKGROUND AND OBJECTIVE

Metabolomics deals with the identification and quantification of small molecules (metabolites) in biological samples. As metabolite levels can reflect normal or altered metabolic pathways, their measurement provides information to improve the understanding, diagnosis and management of diseases. Despite its immense potential, metabolomics applications to pain research have been sparse. This paper describes current metabolomics techniques, reviews published human metabolomics pain research and compares successful metabolomics research in other areas of medicine with the goal of highlighting opportunities offered by metabolomics to advance pain medicine.

DATABASES AND DATA TREATMENT

Non-systematic review.

RESULTS

Our search identified 19 studies that adopted a metabolomics approach in: fibromyalgia (7), chronic widespread pain (4), other musculoskeletal pain conditions (5), neuropathic pain (1), complex regional pain syndrome (1) and pelvic pain (1). The studies used either mass spectrometry or nuclear magnetic resonance. Most are characterized by small sample sizes. Some consistency has been found for alterations in glutamate and testosterone metabolism, and metabolic imbalances caused by the gut microbiome.

CONCLUSIONS

Metabolomics research in chronic pain is in its infancy. Most studies are at the pilot stage. Metabolomics research has been successful in other areas of medicine. These achievements should motivate investigators to expand metabolomics research to improve the understanding of the basic mechanisms of human pain, as well as provide tools to diagnose, predict and monitor chronic pain conditions. Metabolomics research can lead to the identification of biomarkers to support the development and testing of treatments, thereby facilitating personalized pain medicine.

2,2',4,4'-tetrabromodiphenyl ether (BDE-47) induces wide metabolic changes including attenuated mitochondrial function and enhanced glycolysis in PC12 cells

Polybrominated diphenyl ethers (PBDEs) are extensively used as brominated flame retardants in various factory products. As environmental pollutants, the adverse effects of PBDEs on human health have been receiving considerable attention. However, the precise fundamental mechanisms of toxicity induced by PBDEs are still not fully understood. In this study, the mechanism of cytotoxicity induced by 2,2',4,4'-tetrabromodiphenyl ether (BDE-47) was investigated by combining Seahorse XFp analysis and mass spectrometry-based metabolomics and flux approaches in PC12 cells, one of the most widely used neuron-like cell lines for investigating cytotoxic effects. The Seahorse results suggest that BDE-47 significantly attenuated mitochondrial respiration and enhanced glycolysis in PC12 cells. Additionally, metabolomics results revealed the reduction of TCA metabolites such as citrate, succinate, aconitate, malate, fumarate, and glutamate after BDE-47 exposure. Metabolic flux analysis showed that BDE-47 exposure reduced the oxidative metabolic capacity of mitochondria in PC12 cells. Furthermore, various altered metabolites were found in multiple metabolic pathways, especially in glycine-serine-threonine metabolism and glutathione metabolism. A total of 17 metabolic features were determined in order to distinguish potentially disturbed metabolite markers of BDE-47 exposure. Our findings provide possible biomarkers of cytotoxic effects induced by BDE-47 exposure, and elicit a deeper understanding of the intramolecular mechanisms that could be used in further studies to validate the potential neurotoxicity of PBDEs in vivo. Based on our results, therapeutic approaches targeting mitochondrial function and the glycolysis pathway may be a promising direction against PBDE exposure.

Biomolecules and Electrochemical Tools in Chronic Non-Communicable Disease Surveillance: A Systematic Review

Over recent three decades, the electrochemical techniques have become widely used in biological identification and detection, because it presents optimum features for efficient and sensitive molecular detection of organic compounds, being able to trace quantities with a minimum of reagents and sample manipulation. Given these special features, electrochemical techniques are regularly exploited in disease diagnosis and monitoring. Specifically, amperometric electrochemical analysis has proven to be quite suitable for the detection of physiological biomarkers in monitoring health conditions, as well as toward the control of reactive oxygen species released in the course of oxidative burst during inflammatory events. Besides, electrochemical detection techniques involve a simple and swift assessment that provides a low detection-limit for most of the molecules enclosed biological fluids and related to non-transmittable morbidities.

Nuclear magnetic resonance-based metabolomics analysis and characteristics of beef in different fattening periods

Beef quality is influenced by the fattening period. Therefore, meat metabolomics profiles from the different fattening periods (e.g., short-term vs. long-term) were analyzed for identify potential indicators using nuclear magnetic resonance. Additionally, blood, free fatty acid, sensory, and mineral compositions in Korean steers were determined. Blood, free fatty acid, and mineral concentrations showed significant differences between short-term and long-term groups that were fed different diets. However, there were no sensory differences in the two fattening groups. Additionally, the metabolic profiles of meats were clearly separated based on multivariate orthogonal partial least square discriminant analysis. Six metabolites of variable importance in the projection plot were identified and showed high sensitivity as candidate markers for meat characteristics. In particular, lactate, carnosine, and creatine could be directly linked to scientific indicators of the fattening stage (31 vs. 28 mo) of meat. Our findings suggest that the metabolomics approach could be a powerful method for the detection of novel signatures underlying the managing period of beef.

Novel biomarkers for lysosomal storage disorders: Metabolomic and proteomic approaches

Lysosomal storage disorders (LSDs) are characterized by the accumulation of specific disease substrates inside the lysosomes of various cells, eventually leading to the deterioration of cellular function and multisystem organ damage. With the continuous discovery and validation of novel and advanced therapies for most LSDs, there is an urgent need to discover more versatile and clinically relevant biomarkers. The utility of these biomarkers should ideally extend beyond the screening and diagnosis of LSDs to the evaluation of disease severity and monitoring of therapy. Metabolomic and proteomic approaches provide the means to the discovery and validation of such novel biomarkers. This is achieved mainly through the application of various mass spectrometric techniques to common and easily accessible biological samples, such as plasma, urine and dried blood spots. In this review, we tried to summarize the complexity of the lysosomal disorders phenotypes, their current diagnostic and therapeutic approaches, the various techniques supporting metabolomic and proteomic studies and finally we tried to explore the newly discovered biomarkers for most LSDs and their reported clinical values.

Mapping Metabolite and ICD-10 Associations

The search for novel metabolic biomarkers is intense but has had limited practical outcomes for medicine. Part of the problem is that we lack knowledge of how different comorbidities influence biomarkers’ performance. In this study, 49 metabolites were measured by targeted LC/MS protocols in the serum of 1011 volunteers. Their performance as potential biomarkers was evaluated by the area under the curve of receiver operator characteristics (AUC-ROC) for 105 diagnosis codes or code groups from the 10th revision of the international classification of diseases (ICD-10). Additionally, the interferences between diagnosis codes were investigated. The highest AUC-ROC values for individual metabolites and ICD-10 code combinations reached a moderate (0.7) range. Most metabolites that were found to be potential markers remained so independently of the control group composition or comorbidities. The precise value of the AUC-ROC, however, could vary depending on the comorbidities. Moreover, networks of metabolite and disease associations were built in order to map diseases, which may interfere with metabolic biomarker research on other diseases.

Distinguishing NASH Histological Severity Using a Multiplatform Metabolomics Approach

Nonalcoholic fatty liver disease (NAFLD) is categorized based on histological severity into nonalcoholic fatty liver (NAFL) or nonalcoholic steatohepatitis (NASH). We used a multiplatform metabolomics approach to identify metabolite markers and metabolic pathways that distinguish NAFL from early NASH and advanced NASH. We analyzed fasting serum samples from 57 prospectively-recruited patients with histologically-proven NAFLD, including 12 with NAFL, 31 with early NASH and 14 with advanced NASH. Metabolite profiling was performed using a combination of liquid chromatography-mass spectrometry (LC-MS) and nuclear magnetic resonance (NMR) spectroscopy analyzed with multivariate statistical and pathway analysis tools. We targeted 237 metabolites of which 158 were quantified. Multivariate analysis uncovered metabolite profile clusters for patients with NAFL, early NASH, and advanced NASH. Also, multiple individual metabolites were associated with histological severity, most notably spermidine which was more than 2-fold lower in advanced fibrosis vs. early fibrosis, in advanced NASH vs. NAFL and in advanced NASH vs. early NASH, suggesting that spermidine exercises a protective effect against development of fibrosing NASH. Furthermore, the results also showed metabolic pathway perturbations between early-NASH and advanced-NASH. In conclusion, using a combination of two reliable analytical platforms (LC-MS and NMR spectroscopy) we identified individual metabolites, metabolite clusters and metabolic pathways that were significantly different between NAFL, early-NASH, and advanced-NASH. These differences provide mechanistic insights as well as potentially important metabolic biomarker candidates that may noninvasively distinguish patients with NAFL, early-NASH, and advanced-NASH. The associations of spermidine levels with less advanced histology merit further assessment of the potential protective effects of spermidine in NAFLD.

Identification of serum biomarkers for active pulmonary tuberculosis using a targeted metabolomics approach

Although tuberculosis (TB) is a severe health problem worldwide, the current diagnostic methods are far from optimal. Metabolomics is increasingly being used in the study of infectious diseases. We performed metabolome profiling to identify potential biomarkers in patients with active TB. Serum samples from 21 patients with active pulmonary TB, 20 subjects with latent TB infection (LTBI), and 28 healthy controls were analyzed using liquid chromatography-tandem mass spectrometry (LC-MS/MS) followed by multivariate and univariate analyses. Metabolic profiles indicated higher serum levels of glutamate, sulfoxy methionine, and aspartate and lower serum levels of glutamine, methionine, and asparagine in active TB patients than in LTBI subjects or healthy controls. The ratios between metabolically related partners (glutamate/glutamine, sulfoxy methionine/methionine, and aspartate/asparagine) were also elevated in the active TB group. There was no significant difference in the serum concentration of these metabolites according to the disease extent or risk of relapse in active TB patients. Novel serum biomarkers such as glutamate, sulfoxy methionine, aspartate, glutamine, methionine, and asparagine are potentially useful for adjunctive, rapid, and noninvasive pulmonary TB diagnosis.

Urinary chemical fingerprint left behind by repeated NSAID administration: Discovery of putative biomarkers using artificial intelligence

Prediction and early detection of kidney damage induced by nonsteroidal anti-inflammatories (NSAIDs) would provide the best chances of maximizing the anti-inflammatory effects while minimizing the risk of kidney damage. Unfortunately, biomarkers for detecting NSAID-induced kidney damage in cats remain to be discovered. To identify potential urinary biomarkers for monitoring NSAID-based treatments, we applied an untargeted metabolomics approach to urine collected from cats treated repeatedly with meloxicam or saline for up to 17 days. Applying multivariate analysis, this study identified a panel of seven metabolites that discriminate meloxicam treated from saline treated cats. Combining artificial intelligence machine learning algorithms and an independent testing urinary metabolome data set from cats with meloxicam-induced kidney damage, a panel of metabolites was identified and validated. The panel of metabolites including tryptophan, tyrosine, taurine, threonic acid, pseudouridine, xylitol and lyxitol, successfully distinguish meloxicam-treated and saline-treated cats with up to 75–100% sensitivity and specificity. This panel of urinary metabolites may prove a useful and non-invasive diagnostic tool for monitoring potential NSAID induced kidney injury in feline patients and may act as the framework for identifying urine biomarkers of NSAID induced injury in other species.

Comparative metabolomics implicates threitol as a fungal signal supporting colonization of Armillaria luteobubalina on eucalypt roots

Armillaria root rot is a fungal disease that affects a wide range of trees and crops around the world. Despite being a widespread disease, little is known about the plant molecular responses towards the pathogenic fungi at the early phase of their interaction. With recent research highlighting the vital roles of metabolites in plant root-microbe interactions, we sought to explore the presymbiotic metabolite responses of Eucalyptus grandis seedlings towards Armillaria luteobuablina, a necrotrophic pathogen native to Australia. Using a metabolite profiling approach, we have identified threitol as one of the key metabolite responses in E. grandis root tips specific to A. luteobubalina that were not induced by three other species of soil-borne microbes of different lifestyle strategies (a mutualist, a commensalist, and a hemi-biotrophic pathogen). Using isotope labelling, threitol detected in the Armillaria-treated root tips was found to be largely derived from the fungal pathogen. Exogenous application of d-threitol promoted microbial colonization of E. grandis and triggered hormonal responses in root cells. Together, our results support a role of threitol as an important metabolite signal during eucalypt-Armillaria interaction prior to infection thus advancing our mechanistic understanding on the earliest stage of Armillaria disease development. Comparative metabolomics of eucalypt roots interacting with a range of fungal lifestyles identified threitol enrichment as a specific characteristic of Armillaria pathogenesis. Our findings suggest that threitol acts as one of the earliest fungal signals promoting Armillaria colonization of roots.

Microenvironment-Responsive Small-Molecule Probe for Pulmonary Fibrosis Detection

Pulmonary fibrosis (PF) is a fatal disease with increasing prevalence. Nonradioactive and noninvasive diagnosis of PF at an early stage can improve the prognosis but represents a daunting challenge. Up-regulation of nitric oxide (NO) is a typical microenvironmental feature of PF. Here, we report a small-molecule probe, PNO1, that can fluorogenically sense this microenvironmental feature for PF diagnosis. We demonstrate that PNO1 fluorescence is 6-fold higher in PF-diseased mice lungs than in normal-control groups. In addition to this in vivo result, PNO1 can also be applied in vitro to detect PF-diseased cells and ex vivo to detect PF-diseased tissues from clinical patients. These results highlight PNO1 as a complement to the traditional immunostaining-based methods for PF detection to facilitate quick screening for anti-PF drug candidates.

Metabolomics and Biomarker Discovery in Traumatic Brain Injury

Traumatic brain injury (TBI) is one of the leading causes of disability and mortality worldwide. The TBI pathogenesis can induce broad pathophysiological consequences and clinical outcomes attributed to the complexity of the brain. Thus, the diagnosis and prognosis are important issues for the management of mild, moderate, and severe forms of TBI. Metabolomics of readily accessible biofluids is a promising tool for establishing more useful and reliable biomarkers of TBI than using clinical findings alone. Metabolites are an integral part of all biochemical and pathophysiological pathways. Metabolomic processes respond to the internal and external stimuli resulting in an alteration of metabolite concentrations. Current high-throughput and highly sensitive analytical tools are capable of detecting and quantifying small concentrations of metabolites, allowing one to measure metabolite alterations after a pathological event when compared to a normal state or a different pathological process. Further, these metabolic biomarkers could be used for the assessment of injury severity, discovery of mechanisms of injury, and defining structural damage in the brain in TBI. Metabolic biomarkers can also be used for the prediction of outcome, monitoring treatment response, in the assessment of or prognosis of post-injury recovery, and potentially in the use of neuroplasticity procedures. Metabolomics can also enhance our understanding of the pathophysiological mechanisms of TBI, both in primary and secondary injury. Thus, this review presents the promising application of metabolomics for the assessment of TBI as a stand-alone platform or in association with proteomics in the clinical setting.

Dimethylguanidino Valerate: A Lifestyle-Related Metabolite Associated With Future Coronary Artery Disease and Cardiovascular Mortality

Background

Identification of lifestyle modifiable metabolic pathways related to cardiometabolic disease risk is essential for improvement of primary prevention in susceptible individuals. It was recently shown that plasma dimethylguanidino valerate (DMGV) levels are associated with incident type 2 diabetes mellitus. Our aims were to investigate whether plasma DMGV is related to risk of future coronary artery disease and with cardiovascular mortality and to replicate the association with type 2 diabetes mellitus and pinpoint candidate lifestyle interventions susceptible to modulate DMGV levels.

Methods and Results

Plasma DMGV levels were measured using liquid chromatography‐mass spectrometry in a total of 5768 participants from the MDC (Malmö Diet and Cancer Study—Cardiovascular Cohort), MPP (Malmö Preventive Project), and MOS (Malmö Offspring Study). Dietary intake assessment was performed in the MOS. Baseline levels of DMGV associated with incident coronary artery disease in both the MDC (hazard ratio=1.29; CI=1.16–1.43; P<0.001) and MPP (odds ratio=1.25; CI=1.08–1.44; P=2.4e‐3). In the MDC, DMGV was associated with cardiovascular mortality and incident coronary artery disease, independently of traditional risk factors. Furthermore, the association between DMGV and incident type 2 diabetes mellitus was replicated in both the MDC (hazard ratio=1.83; CI=1.63–2.05; P<0.001) and MPP (odds ratio=1.65; CI=1.38–1.98; P<0.001). Intake of sugar‐sweetened beverages was associated with increased levels of DMGV, whereas intake of vegetables and level of physical activity was associated with lower DMGV.

Conclusions

We discovered novel independent associations between plasma DMGV and incident coronary artery disease and cardiovascular mortality, while replicating the previously reported association with incident type 2 diabetes mellitus. Additionally, strong associations with sugar‐sweetened beverages, vegetable intake, and physical activity suggest the potential to modify DMGV levels using lifestyle interventions.

Mass Spectrometry-Based Non-targeted Metabolic Profiling for Disease Detection: Recent Developments

Mass spectrometry (MS) plays an important role in seeking biomarkers for disease detection. High-quality quantitative data is needed for accurate analysis of metabolic perturbations in patients. This article describes recent developments in MS-based non-targeted metabolomics research with applications to the detection of several major common human diseases, focusing on study cohorts, MS platforms utilized, statistical analyses and discriminant metabolite identification. Potential disease biomarkers recently discovered for type 2 diabetes, cardiovascular disease, hepatocellular carcinoma, breast cancer and prostate cancer through metabolomics are summarized, and limitations are discussed.

Plasma metabolomic profiling of amino acids and polar lipids in Iranian obese adults

BACKGROUND

Obesity, widely recognized as a serious health concern, is characterized by profoundly altered metabolism. However, the intermediate metabolites involved in this change remain largely unknown.

OBJECTIVE

We conducted targeted metabolomics profiling to identify moieties associated with adult obesity.

METHODS

In this case-control study of Iranian adults, 200 obese patients were compared with 100 controls based on 104 metabolites profiled by a targeted metabolomic approach using liquid chromatography coupled to triple quadrupole mass spectrometry (LC-MS/MS). The analysis comprised acylcarnitines, diacyl-phosphatidylcholines (PCaa), acyl-alkyl-phosphatidylcholines (PCae), sphingomyelins (SM), lyso-phospholipids (LPC) and amino acids. We performed multivariable linear regression to identify metabolites associated with obesity, adjusting for age, sex, total energy intake, total physical activity, smoking, and alcohol consumption. The Bonferroni correction was used to adjust for multiple testing.

RESULTS

A pattern of 19 metabolites was significantly associated with obesity. Branched chain amino acids, alanine, glutamic acid, proline, tyrosine LPCa C16:1, PCaa C32:1, PCaa C32:2 and PCaa C38:3 were positively, while serine, asparagine, LPCa C18:1, LPCa C18:2, LPCe C18:0, PCae C34:3, PCae C38:4 and PCae C40:6 were negatively associated with obesity (all p < 0.00048).

CONCLUSIONS

A metabolomic profile containing 9 amino acids and 10 polar lipids may serve as a potential biomarker of adult obesity. Further studies are warranted to replicate these findings as well as investigate potential changes in this profile after weight reduction.

Recent Advances and Challenges in Steroid Metabolomics for Biomarker Discovery

BACKGROUND

Steroid hormones belong to a group of low-molecular weight compounds which are responsible for maintenance of various body functions, thus, their accurate assessment is crucial for evaluation of biosynthetic defects. The development of reliable methods allowing disease diagnosis is essential to improve early detection of various disorders connected with altered steroidogenesis. Currently, the field of metabolomics offers several improvements in terms of sensitivity and specificity of the diagnostic methods when opposed to classical diagnostic approaches. The combination of hyphenated techniques and pattern recognition methods allows to carry out a comprehensive assessment of the slightest alterations in steroid metabolic pathways and can be applied as a tool for biomarker discovery.

METHODS

We have performed an extensive literature search applying various bibliographic databases for peer-reviewed articles concentrating on the applications of hyphenated techniques and pattern recognition methods incorporated into the steroid metabolomic approach for biomarker discovery.

RESULTS

The review discusses strengths, challenges and recent developments in steroidbased metabolomics. We present methods of sample collection and preparation, methods of separation and detection of steroid hormones in biological material, data analysis, and interpretation as well as examples of applications of steroid metabolomics for biomarker discovery (cancer, mental and central nervous system disorders, endocrine diseases, monitoring of drug therapy and doping control).

CONCLUSION

Information presented in this review will be valuable to anyone interested in the application of metabolomics for biomarker discovery with a special emphasis on disorders of steroid hormone synthesis and metabolism.

Metabolomic Insights into Human Arboviral Infections: Dengue, Chikungunya, and Zika Viruses

The global burden of arboviral diseases and the limited success in controlling them calls for innovative methods to understand arbovirus infections. Metabolomics has been applied to detect alterations in host physiology during infection. This approach relies on mass spectrometry or nuclear magnetic resonance spectroscopy to evaluate how perturbations in biological systems alter metabolic pathways, allowing for differentiation of closely related conditions. Because viruses heavily depend on host resources and pathways, they present unique challenges for characterizing metabolic changes. Here, we review the literature on metabolomics of arboviruses and focus on the interpretation of identified molecular features. Metabolomics has revealed biomarkers that differentiate disease states and outcomes, and has shown similarities in metabolic alterations caused by different viruses (e.g., lipid metabolism). Researchers investigating such metabolomic alterations aim to better understand host⁻virus dynamics, identify diagnostically useful molecular features, discern perturbed pathways for therapeutics, and guide further biochemical research. This review focuses on lessons derived from metabolomics studies on samples from arbovirus-infected humans.