Development of isotope labeling liquid chromatography mass spectrometry for mouse urine metabolomics: quantitative metabolomic study of transgenic mice related to Alzheimer's disease

Stereoselective Amine-omics Using Heavy Atom Isotope Labeled l- and d-Marfey's Reagents

Biological amines and amino acids play essential roles in many biochemical processes. The chemical complexity of biological samples is challenging, and the selective identification and quantification of amines and amino acid stereoisomers would be very useful for amine-focused "amino-omics" studies. Many amines and amino acids are chiral, and their stereoisomers cannot be resolved on achiral media without chiral derivatization. In prior studies, we demonstrated the use of Marfey's reagent─a chiral derivatization reagent for amines and phenolic OH groups─for the LC-MS/MS resolution and quantification of amines and amino acid stereoisomers. In this study, a heavy atom isotope labeled Marfey's reagent approach for the stereoselective detection and quantification of amines and amino acids was developed. Heavy (13C2) l-Marfey's (Hl-Mar) and heavy (2H3) d-Marfey's (Hd-Mar) were synthesized from 13C2-l-Ala and 2H3-d-Ala, respectively. Both light and heavy Marfey's reagents were used to derivatize standard amine mixtures, which were analyzed by LC-QToF-HRMS. Aligned peak lists were comparatively analyzed by light vs heavy Mar mass differences to identify mono-, di-, and tri-Marfey's adducts and then by the retention time difference between l- and d-Mar derivatives to identify stereoisomers. This approach was then applied to identify achiral and chiral amine and amino acid components in a methicillin-resistant Staphylococcus aureus (MRSA) extract. This approach shows high analytical selectivity and reproducibility.

Analyses of plasma metabolites using a high performance four-channel CIL LC-MS method and identification of metabolites associated with enteric methane emissions in beef cattle

Reducing enteric methane (one greenhouse gas) emissions from beef cattle not only can be beneficial in reducing global warming, but also improve efficiency of nutrient utilization in the production system. However, direct measurement of enteric methane emissions on individual cattle is difficult and expensive. The objective of this study was to detect plasma metabolites that are associated with enteric methane emissions in beef cattle. Average enteric methane emissions (CH4) per day (AVG_DAILYCH4) for each individual cattle were measured using the GreenFeed emission monitoring (GEM) unit system, and beef cattle with divergent AVG_DAILYCH4 from Angus (n = 10 for the low CH4 group and 9 for the high CH4 group), Charolais (n = 10 for low and 10 for = high), and Kinsella Composite (n = 10 for low and 10 for high) populations were used for plasma metabolite quantification and metabolite-CH4 association analyses. Blood samples of these cattle were collected near the end of the GEM system tests and a high performance four-channel chemical isotope labeling (CIL) liquid chromatography (LC) mass spectrometer (MS) method was applied to identify and quantify concentrations of metabolites. The four-channel CIL LC-MS method detected 4235 metabolites, of which 1105 were found to be significantly associated with AVG_DAILYCH4 by a t-test, while 1305 were significantly associated with AVG_DAILYCH4 by a regression analysis at p<0.05. Both the results of the t-test and regression analysis revealed that metabolites that were associated with enteric methane emissions in beef cattle were largely breed-specific whereas 4.29% to 6.39% CH4 associated metabolites were common across the three breed populations and 11.07% to 19.08% were common between two breed populations. Pathway analyses of the CH4 associated metabolites identified top enriched molecular processes for each breed population, including arginine and proline metabolism, arginine biosynthesis, butanoate metabolism, and glutathione metabolism for Angus; beta-alanine metabolism, pyruvate metabolism, glycolysis / gluconeogenesis, and citrate cycle (TCA cycle) for Charolais; phenylalanine, tyrosine and tryptophan biosynthesis, phenylalanine metabolism, arginine biosynthesis, and arginine and proline metabolism for Kinsella Composite. The detected CH4 associated metabolites and enriched molecular processes will help understand biological mechanisms of enteric methane emissions in beef cattle. The detected CH4 associated plasma metabolites will also provide valuable resources to further characterize the metabolites and verify their utility as biomarkers for selection of cattle with reduced methane emissions.

LC/MS-based discrimination between plasma and urine metabolomic changes following exposure to ultraviolet radiation by using data modelling

INTRODUCTION

This study sought to compare between metabolomic changes of human urine and plasma to investigate which one can be used as best tool to identify metabolomic profiling and novel biomarkers associated to the potential effects of ultraviolet (UV) radiation.

METHOD

A pilot study of metabolomic patterns of human plasma and urine samples from four adult healthy individuals at before (S1) and after (S2) exposure (UV) and non-exposure (UC) were carried out by using liquid chromatography-mass spectrometry (LC-MS).

RESULTS

The best results which were obtained by normalizing the metabolites to their mean output underwent to principal components analysis (PCA) and Orthogonal Partial least squares-discriminant analysis (OPLS-DA) to separate pre-from post-of exposure and non-exposure of UV. This separation by data modeling was clear in urine samples unlike plasma samples. In addition to overview of the scores plots, the variance predicted-Q2 (Cum), variance explained-R2X (Cum) and p-value of the cross-validated ANOVA score of PCA and OPLS-DA models indicated to this clear separation. Q2 (Cum) and R2X (Cum) values of PCA model for urine samples were 0.908 and 0.982, respectively, and OPLS-DA model values were 1.0 and 0.914, respectively. While these values in plasma samples were Q2 = 0.429 and R2X = 0.660 for PCA model and Q2 = 0.983 and R2X = 0.944 for OPLS-DA model. LC-MS metabolomic analysis showed the changes in numerous metabolic pathways including: amino acid, lipids, peptides, xenobiotics biodegradation, carbohydrates, nucleotides, Co-factors and vitamins which may contribute to the evaluation of the effects associated with UV sunlight exposure.

CONCLUSIONS

The results of pilot study indicate that pre and post-exposure UV metabolomics screening of urine samples may be the best tool than plasma samples and a potential approach to predict the metabolomic changes due to UV exposure. Additional future work may shed light on the application of available metabolomic approaches to explore potential predictive markers to determine the impacts of UV sunlight.

Chronic treatment with baicalein alleviates behavioural disorders and improves cerebral blood flow via reverting metabolic abnormalities in a J20 transgenic mouse model of Alzheimer's disease

Baicalein (BE) has both antioxidant and anti-inflammatory effects. It has also been reported able to improve cerebral blood circulation in brain ischemic injury. However, its chronic efficacy and metabolomics in Alzheimer's disease (AD) remain unknown. In this study, BE at 80 mg/kg was administrated through the oral route in J20 AD transgenic mice aged from aged 4 months to aged 10 months. Metabolic- and neurobehavioural phenotyping was done before and after 6 months' treatment to evaluate the drug efficacy and the relevant mechanisms. Meanwhile, molecular docking was used to study the binding affinity of BE and poly (ADP-ribose) polymerase-1 (PARP-1) which is related to neuronal injury. The open field test showed that BE could suppress hyperactivity in J20 mice and increase the frequency of the target quadrant crossing in the Morris Water Maze test. More importantly, BE restored cerebral blood flow back to the normal level after the chronic treatment. A ¹H NMR-based metabolomics study showed that BE treatment could restore the tricarboxylic acid cycle in plasma. And such a treatment could suppress oxidative stress, inhibit neuroinflammation, alleviate mitochondrial dysfunction, improve neurotransmission, and restore amino homeostasis via starch and sucrose metabolism and glycolipid metabolism in the cortex and hippocampus, which could affect the behavioural and cerebral blood flow. These findings showed that BE is a potential therapeutic agent for AD.

A urine and serum metabolomics study of gastroesophageal reflux disease in TCM syndrome differentiation using UPLC-Q-TOF/MS

Gastroesophageal reflux disease (GERD) is a common, chronic and complex upper gastrointestinal disease. In Traditional Chinese medicine (TCM) theory, GERD is classified into two main types: stagnant heat of liver and stomach (SHLS) and deficient cold of spleen and stomach (DCSS). The discovery and evaluation of potential biomarkers for different syndrome types of GERD may contribute to comprehend specific molecular mechanism and identify new targets for diagnosis and appropriate management. In our study, 60 subjects including 40 GERD patients (20 SHLS and 20 DCSS) and 20 healthy controls were recruited, and the serum and urine metabolic profiles from untargeted liquid chromatography coupled to mass spectrometry (LC-MS) metabolomics approach were obtained. Finally 38 biomarkers associated with disease were identified and 9 metabolic pathways were enriched. The most enriched pathways were amino acid metabolism, steroid hormone biosynthesis, glycerophospholipid metabolism, sphingolipid metabolism and TCA cycle. According to the area under curve (AUC) value, we propose a cohort of three metabolites from urine and serum samples as promising biomarkers for TCM syndrome differentiation of GERD, which are prolylhydroxyproline, glycitein-4'-O-glucuronide, capsianoside I in urine and neuAcalpha2-3Galbeta-Cer (d18:1/16:0), sphinganine, arachidonic acid in serum. The cumulative AUC value of merged biomarkers in urine and serum was 0.979 (95%CI 0.927-1) and 0.842 (95%CI 0.704-0.980), respectively. The results indicated that LC-MS based metabolomic profiling method might be an effective and promising tool on further pathogenesis discovering of GERD. The findings provided new strategy for the diagnosis of GERD TCM syndrome differentiation in clinic.

Based on Multi-Activity Integrated Strategy to Screening, Characterization and Quantification of Bioactive Compounds from Red Wine

According to French Paradox, red wine was famous for the potential effects on coronary heart disease (CHD), but the specific compounds against CHD were unclear. Therefore, screening and characterization of bioactive compounds from red wine was extremely necessary. In this paper, the multi-activity integrated strategy was developed and validated to screen, identify and quantify active compounds from red wine by using ultra high performance liquid chromatography-fraction collector (UHPLC-FC), ultra fast liquid chromatography-quadrupole-time-of-flight/mass spectrometry (UFLC-Q-TOF/MS) and bioactive analysis. UHPLC-FC was employed to separate and collect the components from red wine, which was further identified by UFLC-Q-TOF/MS to acquire their structural information. Furthermore, the active fractions were tested for antioxidant activity, inhibitory activity against thrombin and lipase activities in vitro by the activity screening kit. As the results, there were 37 fractions had antioxidant activity, 22 fractions had thrombin inhibitory activity and 28 fractions had lipase inhibitory activity. Finally, 77 active components from red wine were screened and 12 ingredients out of them were selected for quantification based on the integration of multi-activity. Collectively, the multi-activity integrated strategy was helpful for the rapid and effective discovery of bioactive components, which provided reference for exploring the health care function of food.

Alterations in Urine Metabolomics Following Sport-Related Concussion: A 1H NMR-Based Analysis

Objective: Millions of sport-related concussions (SRC) occur annually in North America, and current diagnosis of concussion is based largely on clinical evaluations. The objective of this study was to determine whether urinary metabolites are significantly altered post-SRC compared to pre-injury.

Setting: Outpatient sports medicine clinic.

Participants: Twenty-six male youth sport participants.

Methods: Urine was analyzed pre-injury and after SRC by ¹H NMR spectroscopy. Data were analyzed using multivariate statistics, pairwise t-test, and metabolic pathway analysis. Variable importance analysis based on random variable combination (VIAVC) was applied to the entire data set and resulted in a panel of 18 features. Partial least square discriminant analysis was performed exploring the separation between pre-injury and post-SRC groups. Pathway topography analysis was completed to identify biological pathway involvement. Spearman correlations provide support for the relationships between symptom burden and length of return to play and quantifiable metabolic changes in the human urinary metabolome.

Results: Phenylalanine and 3-indoxysulfate were upregulated, while citrate, propylene glycol, 1-methylhistidine, 3-methylhistidine, anserine, and carnosine were downregulated following SRC. A receiver operator curve (ROC) tool constructed using the 18-feature classifier had an area under the curve (AUC) of 0.887. A pairwise t-test found an additional 19 altered features, 7 of which overlapped with the VIAVC analysis. Pathway topology analysis indicated that aminoacyl-tRNA biosynthesis and beta-alanine metabolism were the two pathways most significantly changed. There was a significant positive correlation between post-SRC 2-hydroxybutyrate and the length of return to play (ρ = 0.482, p = 0.02) as well as the number of symptoms and post-SRC lactose (ρ = 0.422, p = 0.036).

Conclusion: We found that ¹H NMR metabolomic urinary analysis can identify a set of metabolites that can correctly classify SRC with an accuracy of 81.6%, suggesting potential for a more objective method of characterizing SRC. Correlations to both the number of symptoms and length of return to play indicated that 2-hydroxybutyrate and lactose may have potential applications as biomarkers for sport-related concussion.

Clinical Insights into Mitochondrial Neurodevelopmental and Neurodegenerative Disorders: Their Biosignatures from Mass Spectrometry-Based Metabolomics

Mitochondria are dynamic multitask organelles that function as hubs for many metabolic pathways. They produce most ATP via the oxidative phosphorylation pathway, a critical pathway that the brain relies on its energy need associated with its numerous functions, such as synaptic homeostasis and plasticity. Therefore, mitochondrial dysfunction is a prevalent pathological hallmark of many neurodevelopmental and neurodegenerative disorders resulting in altered neurometabolic coupling. With the advent of mass spectrometry (MS) technology, MS-based metabolomics provides an emerging mechanistic understanding of their global and dynamic metabolic signatures. In this review, we discuss the pathogenetic causes of mitochondrial metabolic disorders and the recent MS-based metabolomic advances on their metabolomic remodeling. We conclude by exploring the MS-based metabolomic functional insights into their biosignatures to improve diagnostic platforms, stratify patients, and design novel targeted therapeutic strategies.

Mechanistic Insights into Alzheimer's Disease Unveiled through the Investigation of Disturbances in Central Metabolites and Metabolic Pathways

Hydrophilic metabolites are closely involved in multiple primary metabolic pathways and, consequently, play an essential role in the onset and progression of multifactorial human disorders, such as Alzheimer’s disease. This review article provides a comprehensive revision of the literature published on the use of mass spectrometry-based metabolomics platforms for approaching the central metabolome in Alzheimer’s disease research, including direct mass spectrometry, gas chromatography-mass spectrometry, hydrophilic interaction liquid chromatography-mass spectrometry, and capillary electrophoresis-mass spectrometry. Overall, mounting evidence points to profound disturbances that affect a multitude of central metabolic pathways, such as the energy-related metabolism, the urea cycle, the homeostasis of amino acids, fatty acids and nucleotides, neurotransmission, and others.

Urinary metabolic phenotyping for Alzheimer's disease

Finding early disease markers using non-invasive and widely available methods is essential to develop a successful therapy for Alzheimer’s Disease. Few studies to date have examined urine, the most readily available biofluid. Here we report the largest study to date using comprehensive metabolic phenotyping platforms (NMR spectroscopy and UHPLC-MS) to probe the urinary metabolome in-depth in people with Alzheimer’s Disease and Mild Cognitive Impairment. Feature reduction was performed using metabolomic Quantitative Trait Loci, resulting in the list of metabolites associated with the genetic variants. This approach helps accuracy in identification of disease states and provides a route to a plausible mechanistic link to pathological processes. Using these mQTLs we built a Random Forests model, which not only correctly discriminates between people with Alzheimer’s Disease and age-matched controls, but also between individuals with Mild Cognitive Impairment who were later diagnosed with Alzheimer’s Disease and those who were not. Further annotation of top-ranking metabolic features nominated by the trained model revealed the involvement of cholesterol-derived metabolites and small-molecules that were linked to Alzheimer’s pathology in previous studies.

Targeted Metabolic Profiling of Urine Highlights a Potential Biomarker Panel for the Diagnosis of Alzheimer's Disease and Mild Cognitive Impairment: A Pilot Study

The lack of sensitive and specific biomarkers for the early detection of mild cognitive impairment (MCI) and Alzheimer’s disease (AD) is a major hurdle to improving patient management. A targeted, quantitative metabolomics approach using both ¹H NMR and mass spectrometry was employed to investigate the performance of urine metabolites as potential biomarkers for MCI and AD. Correlation-based feature selection (CFS) and least absolute shrinkage and selection operator (LASSO) methods were used to develop biomarker panels tested using support vector machine (SVM) and logistic regression models for diagnosis of each disease state. Metabolic changes were investigated to identify which biochemical pathways were perturbed as a direct result of MCI and AD in urine. Using SVM, we developed a model with 94% sensitivity, 78% specificity, and 78% AUC to distinguish healthy controls from AD sufferers. Using logistic regression, we developed a model with 85% sensitivity, 86% specificity, and an AUC of 82% for AD diagnosis as compared to cognitively healthy controls. Further, we identified 11 urinary metabolites that were significantly altered to include glucose, guanidinoacetate, urocanate, hippuric acid, cytosine, 2- and 3-hydroxyisovalerate, 2-ketoisovalerate, tryptophan, trimethylamine N oxide, and malonate in AD patients, which are also capable of diagnosing MCI, with a sensitivity value of 76%, specificity of 75%, and accuracy of 81% as compared to healthy controls. This pilot study suggests that urine metabolomics may be useful for developing a test capable of diagnosing and distinguishing MCI and AD from cognitively healthy controls.

Baicalein protects PC12 cells from Aβ25-35-induced cytotoxicity via inhibition of apoptosis and metabolic disorders

AIMS

This study aimed to explore the protective effects and possible mechanisms of baicalein on Aβ_25-35-induced toxicity.

MAIN METHODS

Thioflavin-T (Th-T) dye was used to determine the effects of baicalein on Aβ_25-35 aggregation in vitro. PC12 cells were stimulated with Aβ_25-35, then the effects of baicalein on apoptosis, mitochondrial membrane potential (MMP), adenosine triphosphate (ATP), mitochondrial respiratory complex I, reactive oxygen species (ROS) and nitric oxide (NO) levels were determined. Moreover, LC-MS metabolomics approach was used to detect metabolic changes induced by baicalein in Aβ_25-35-injured PC12 cells.

KEY FINDINGS

The results showed that baicalein could inhibit the aggregation of Aβ_25-35 in vitro. Furthermore, pretreatment with baicalein significantly prevented Aβ_25-35-induced cell apoptosis, as manifested by increasing the levels of MMP, ATP and mitochondrial respiratory complex I, decreasing the contents of ROS and NO. LC-MS metabolomics revealed that baicalein can regulate 5 metabolites, mainly involving two metabolic pathways, arginine and proline metabolism, nicotinate and nicotinamide metabolism.

SIGNIFICANCE

Our study revealed that baicalein has a protective effect on Aβ_25-35-induced neurotoxicity in PC12 cells, which may be related to inhibition of apoptosis and metabolic disorders.

Insulin resistance and metabonomics analysis of fatty liver haemorrhagic syndrome in laying hens induced by a high-energy low-protein diet

Fatty liver haemorrhagic syndrome (FLHS) is a widespread metabolic disease in laying hens that causes a decrease in egg production and even death. Insulin resistance is a major contributor to the pathogenesis of nonalcoholic fatty liver disease. However, the relationship between FLHS and the insulin resistance mechanisms underlying FLHS is not well elucidated. Therefore, we established an FLHS model induced by feeding a high-energy low-protein diet. In the current study, we found that the fasting glucose and insulin concentrations were elevated in the FLHS group compared with the control group during the experimental period. The results of the oral glucose tolerance test (OGTT) and insulin sensitivity test (IST) showed a high level of insulin resistance in the FLHS model. InsR, 4EBP-1, Glut-1 and Glut-3 mRNA expression were decreased, and TOR, S6K1, and FOXO1 were elevated (P < 0.05). Metabolomic analysis with GC/MS identified 46 differentially expressed metabolites between these two groups, and of these, 14 kinds of metabolism molecules and 32 kinds of small metabolism molecules were decreased (P < 0.05). Further investigation showed that glucose, lipid and amino acid metabolism blocks in the progression of FLHS by GO functional and pathway analysis. Overall, these results suggest that insulin resistance participated in FLHS; comprehensively, metabolites participated in the dysregulated biological process.

Metabolomic and Immunological Profiling of Respiratory Syncytial Virus Infection after Intranasal Immunization with a Subunit Vaccine Candidate

Respiratory syncytial virus (RSV) is a significant cause of mortality and morbidity in infants, the elderly, immunocompromised individuals, and patients with congenital heart diseases. Despite extensive efforts, a vaccine against RSV is still not available. We have previously reported the development of a subunit vaccine (ΔF/TriAdj) composed of a truncated version of the fusion protein (ΔF) and a polymer-based combination adjuvant (TriAdj). We compared inflammatory responses of ΔF/TriAdj-vaccinated and unvaccinated mice following intranasal challenge with RSV. Rapid and early inflammatory responses were observed in lung samples from both groups but modulated in the vaccinated group 7 days after the viral challenge. The underlying mechanism of action of ΔF/TriAdj was further studied through LC-MS-based metabolomic profiling by using ¹²C- or ¹³C-dansyl labeling for the amine/phenol submetabolome. RSV infection predominantly affected the amino acid biosynthesis pathways and urea cycle, whereas ΔF/TriAdj modulated the concentrations of almost all of the altered metabolites. Tryptophan metabolites were significantly affected, including indole, l-kynurenine, xanthurenic acid, serotonin, 5-hydroxyindoleacetic acid, and 6-hydroxymelatonin. The results from the present study provide further mechanistic insights into the mode of action of this RSV vaccine candidate and have important implications in the design of metabolic therapeutic interventions.

Predictive Role of Urinary Metabolic Profile for Abnormal MRI Score in Preterm Neonates

Background and Objective

Early identification of neonates at risk for brain injury is important to start appropriate intervention. Urinary metabolomics is a source of potential, noninvasive biomarkers of brain disease. We studied the urinary metabolic profile at 2 and 10 days in preterm neonates with normal/mild and moderate/severe MRI abnormalities at term equivalent age.

Methods

Urine samples were collected at two and 10 days after birth in 30 extremely preterm infants and analyzed using proton magnetic resonance spectroscopy. A 3 T MRI was performed at term equivalent age, and images were scored for white matter (WM), cortical grey matter (cGM), deep GM, and cerebellar abnormalities. Infants were divided in two groups: normal/mild and moderately/severely abnormal MRI scores.

Results

No significant clustering was seen between normal/mild and moderate/severe MRI scores for all regions at both time points. The ROC curves distinguished neonates at 2 and 10 days who later developed a markedly less mature cGM score from the others (2 d: area under the curve (AUC) = 0.72, specificity (SP) = 65%, sensitivity (SE) = 75% and 10 d: AUC = 0.80, SP = 78%, SE = 80%) and a moderately to severely abnormal WM score (2 d: AUC = 0.71, specificity (SP) = 80%, sensitivity (SE) = 72% and 10 d: AUC = 0.69, SP = 64%, SE = 89%).

Conclusions

Early urinary spectra of preterm infants were able to discriminate metabolic profiles in patients with moderately/severely abnormal cGM and WM scores at term equivalent age. Urine spectra are promising for early identification of neonates at risk of brain damage and allow understanding of the pathogenesis of altered brain development.

Next-generation biomarker discovery in Alzheimer's disease using metabolomics - from animal to human studies

Alzheimer's disease (AD) is a complex disease driven mainly by neuronal loss due to accumulation of intracellular neurofibrillary tangles and amyloid β aggregates in the brain. The diagnosis of AD currently relies on clinical symptoms while the disease can only be confirmed at autopsy. The few available biomarkers allowing for diagnosis are typically detected many years after the onset of the disease. New diagnostic approaches, particularly in easily-accessible biofluids, are essential. By providing an exhaustive information of the phenotype, metabolomics is an ideal approach for identification of new biomarkers. This review investigates the current position of metabolomics in the field of AD research, focusing on animal and human studies, and discusses the improvements carried out over the past decade.

Integrated work-flow for quantitative metabolome profiling of plants, Peucedani Radix as a case

Universal acquisition of reliable information regarding the qualitative and quantitative properties of complicated matrices is the premise for the success of metabolomics study. Liquid chromatography-mass spectrometry (LC-MS) is now serving as a workhorse for metabolomics; however, LC-MS-based non-targeted metabolomics is suffering from some shortcomings, even some cutting-edge techniques have been introduced. Aiming to tackle, to some extent, the drawbacks of the conventional approaches, such as redundant information, detector saturation, low sensitivity, and inconstant signal number among different runs, herein, a novel and flexible work-flow consisting of three progressive steps was proposed to profile in depth the quantitative metabolome of plants. The roots of Peucedanum praeruptorum Dunn (Peucedani Radix, PR) that are rich in various coumarin isomers, were employed as a case study to verify the applicability. First, offline two dimensional LC-MS was utilized for in-depth detection of metabolites in a pooled PR extract namely universal metabolome standard (UMS). Second, mass fragmentation rules, notably concerning angular-type pyranocoumarins that are the primary chemical homologues in PR, and available databases were integrated for signal assignment and structural annotation. Third, optimum collision energy (OCE) as well as ion transition for multiple monitoring reaction measurement was online optimized with a reference compound-free strategy for each annotated component and large-scale relative quantification of all annotated components was accomplished by plotting calibration curves via serially diluting UMS. It is worthwhile to highlight that the potential of OCE for isomer discrimination was described and the linearity ranges of those primary ingredients were extended by suppressing their responses. The integrated workflow is expected to be qualified as a promising pipeline to clarify the quantitative metabolome of plants because it could not only holistically provide qualitative information, but also straightforwardly generate accurate quantitative dataset.

Analysis of liposoluble carboxylic acids metabolome in human serum by stable isotope labeling coupled with liquid chromatography-mass spectrometry

Fatty acids (FAs) are groups of liposoluble carboxylic acids (LCAs) and play important roles in various physiological processes. Abnormal contents or changes of FAs are associated with a series of diseases. Here we developed a strategy with stable isotope labeling combined with liquid chromatography-tandem mass spectrometry (IL-LC-MS) analysis for comprehensive profiling and relative quantitation of LCAs in human serum. In this strategy, a pair of isotope labeling reagents (2-dimethylaminoethylamine (DMED)) and d4-2-dimethylaminoethylamine (d4-DMED) were employed to selectively label carboxyl groups of LCAs. The DMED and d4-DMED labeled products can lose four characteristic neutral fragments of 45 and 49Da or 63 and 67Da in collision-induced dissociation. Therefore, quadruple neutral loss scan (QNLS) mode was established and used for non-targeted profiling of LCAs. The peak pairs of DMED and d4-DMED labeling with the same retention time, intensity and characteristic mass differences were extracted from the two NLS spectra respectively, and assigned as potential LCA candidates. Using this strategy, 241 LCA candidates were discovered in the human serum; 156 carboxylic acid compounds could be determined by searching HMDB and METLIN databases (FAs are over 90%) and 21 of these LCAs were successfully identified by standards. Subsequently, a modified pseudo-targeted method with multiple reaction monitoring (MRM) detection mode was developed and used for relative quantification of LCAs in human serum from type 2 diabetes mellitus (T2DM) patients and healthy controls. As a result, 81 LCAs were found to have significant difference between T2DM patients and healthy controls. Taken together, the isotope labeling combined with tandem mass spectrometry analysis demonstrated to be a powerful strategy for identification and quantification of LCA compounds in serum samples.

Methods used to increase the comprehensive coverage of urinary and plasma metabolomes by MS

Metabolomics, focusing on comprehensive analysis of all the metabolites in a biological system, provides a direct signature of biochemical activity. Using emerging technologies in MS, it is possible to simultaneously and rapidly analyze thousands of metabolites. However, due to the chemical and physical diversity of metabolites, it is difficult to acquire a comprehensive and reliable profiling of the whole metabolome. Here, we summarize the state of the art in metabolomics research, focusing on efforts to provide a more comprehensive metabolome coverage via improvements in two fundamental processes: sample preparation and MS analysis. Additionally, the reliable analysis is also highlighted via the combinations of multiple methods (e.g., targeted and untargeted approaches), and analytical quality control and calibration methods.

Urinary Biomarkers of Brain Diseases

Biomarkers are the measurable changes associated with a physiological or pathophysiological process. Unlike blood, urine is not subject to homeostatic mechanisms. Therefore, greater fluctuations could occur in urine than in blood, better reflecting the changes in human body. The roadmap of urine biomarker era was proposed. Although urine analysis has been attempted for clinical diagnosis, and urine has been monitored during the progression of many diseases, particularly urinary system diseases, whether urine can reflect brain disease status remains uncertain. As some biomarkers of brain diseases can be detected in the body fluids such as cerebrospinal fluid and blood, there is a possibility that urine also contain biomarkers of brain diseases. This review summarizes the clues of brain diseases reflected in the urine proteome and metabolome.

NMR analysis of the human saliva metabolome distinguishes dementia patients from matched controls

A procedure for NMR analysis of the human saliva metabolome, that can differentiate dementia patients from matched controls and identify discriminatory metabolites, is described.