Urine metabolomics

Optimization and validation of metabolomics methods for feline urine and serum towards application in veterinary medicine

BACKGROUND

Metabolomics is an emerging and powerful technology that offers a comprehensive view of an organism's physiological status. Although widely applied in human medicine, it is only recently making its introduction in veterinary medicine. As a result, validated metabolomics protocols in feline medicine are lacking at the moment. Since biological interpretation of metabolomics data can be misled by the extraction method used, species and matrix-specific optimized and validated metabolomic protocols are sorely needed.

RESULTS

Systematic optimization was performed using fractional factorial experiments for both serum (n = 57) and urine (n = 24), evaluating dilution for both matrices, and aliquot and solvent volume, protein precipitation time and temperature for serum. For the targeted (n = 76) and untargeted (n = 1949) validation of serum respectively, excellent instrumental, intra-assay and inter-day precision were observed (CV ≤ 15% or 30%, respectively). Linearity deemed sufficient both targeted and untargeted (R2 ≥ 0.99 or 0.90, respectively). An appropriate targeted recovery between 70 and 130% was achieved. For the targeted (n = 69) and untargeted (n = 2348) validation of the urinary protocol, excellent instrumental and intra-assay precision were obtained (CV ≤ 15% or 30%, respectively). Subsequently, the discriminative ability of our metabolomics methods was confirmed for feline chronic kidney disease (CKD) by univariate statistics (n = 41 significant metabolites for serum, and n = 55 for urine, p-value<0.05) and validated OPLS-DA models (R2(Y) > 0.95, Q2(Y) > 0.65, p-value<0.001 for both matrices).

SIGNIFICANCE

This study is the first to present an optimized and validated wholistic metabolomics methods for feline serum and urine using ultra-high performance liquid chromatography coupled to quadrupole-Orbitrap high-resolution mass spectrometry. This robust methodology opens avenues for biomarker panel selection and a deeper understanding of feline CKD pathophysiology and other feline applications.

Metabolic biomarkers in irritable bowel syndrome diagnosis

Irritable bowel syndrome (IBS) is a chronic gastrointestinal (GI) disorder characterized by altered bowel habits and abdominal discomfort during defecation. It significantly impacts life quality and work productivity for those affected. Global data suggests a slightly higher prevalence in females than in males. Today, unambiguous diagnosis of IBS remains challenging due to the absence of a specific biochemical, histopathological, or radiological test. Current diagnosis relies heavily on thorough symptom evaluation. Efforts by the Rome committees have established standardized diagnostic criteria (Rome I-IV), improving consistency and clinical applicability. Recent studies in this framework, seem to have successfully employed metabolomics techniques to identify distinct metabolite profiles in breath and stool samples of IBS patients, differentiating them from healthy controls and those with other functional GI disorders, such as inflammatory bowel disease (IBD). Building on this success, researchers are investigating the presence of similar metabolites in easily accessible biofluids such as urine, potentially offering a less invasive diagnostic approach. Accordingly, this review focuses on key metabolites specifically detected in IBS patients' biological specimens, with a focus on urinary metabolites, using various methods, particularly mass spectrometry (MS)-based techniques, including gas chromatography-MS (GC-MS), liquid chromatography-tandem MS (LC-MS/MS), and capillary electrophoresis-MS (CE-MS) metabolomics assays. These findings may make provision for a new set of non-invasive biomarkers for IBS diagnosis and management.

Urine metabolic profile in rats with arterial hypertension of different genesis

The diversity of pathogenetic mechanisms underlying arterial hypertension leads to the necessity to devise a personalized approach to the diagnosis and treatment of the disease. Metabolomics is one of the promising methods for personalized medicine, as it provides a comprehensive understanding of the physiological processes occurring in the body. The metabolome is a set of low-molecular substances available for detection in a sample and representing intermediate and final products of cell metabolism. Changes in the content and ratio of metabolites in the sample mark the corresponding pathogenetic mechanisms by highlighting them, which is especially important for such a multifactorial disease as arterial hypertension. To identify metabolomic markers for hypertensive conditions of different origins, three forms of arterial hypertension (AH) were studied: rats with hereditary AH (ISIAH rat strain); rats with AH induced by L-NAME administration (a model of endothelial dysfunction with impaired NO production); rats with AH caused by the administration of deoxycorticosterone in combination with salt loading (hormone-dependent form - DOCA-salt AH). WAG rats were used as normotensive controls. 24-hour urine samples were collected from all animals and analyzed by quantitative NMR spectroscopy for metabolic profiling. Then, potential metabolomic markers for the studied forms of hypertensive conditions were identified using multivariate statistics. Analysis of the data obtained showed that hereditary stress-induced arterial hypertension in ISIAH rats was characterized by a decrease in the following urine metabolites: nicotinamide and 1-methylnicotinamide (markers of inflammatory processes), N- acetylglutamate (nitric oxide cycle), isobutyrate and methyl acetoacetate (gut microbiota). Pharmacologically induced forms of hypertension (the L-NAME and DOCA+NaCl groups) do not share metabolomic markers with hereditary AH. They are differentiated by N,N-dimethylglycine (both groups), choline (the L-NAME group) and 1-methylnicotinamide (the group of rats with DOCA-salt hypertension).

Characterizing poorly controlled type 2 diabetes using 1H-NMR metabolomics

INTRODUCTION

The prevalence of type 2 diabetes has surged to epidemic proportions and despite treatment administration/adherence, some individuals experience poorly controlled diabetes. While existing literature explores metabolic changes in type 2 diabetes, understanding metabolic derangement in poorly controlled cases remains limited.

OBJECTIVE

This investigation aimed to characterize the urine metabolome of poorly controlled type 2 diabetes in a South African cohort.

METHOD

Using an untargeted proton nuclear magnetic resonance metabolomics approach, urine samples from 15 poorly controlled type 2 diabetes patients and 25 healthy controls were analyzed and statistically compared to identify differentiating metabolites.

RESULTS

The poorly controlled type 2 diabetes patients were characterized by elevated concentrations of various metabolites associated with changes to the macro-fuel pathways (including carbohydrate metabolism, ketogenesis, proteolysis, and the tricarboxylic acid cycle), autophagy and/or apoptosis, an uncontrolled diet, and kidney and liver damage.

CONCLUSION

These results indicate that inhibited cellular glucose uptake in poorly controlled type 2 diabetes significantly affects energy-producing pathways, leading to apoptosis and/or autophagy, ultimately contributing to kidney and mild liver damage. The study also suggests poor dietary compliance as a cause of the patient's uncontrolled glycemic state. Collectively these findings offer a first-time comprehensive overview of urine metabolic changes in poorly controlled type 2 diabetes and its association with secondary diseases, offering potential insights for more targeted treatment strategies to prevent disease progression, treatment efficacy, and diet/treatment compliance.

Potential Metabolite Biomarkers of Multiple Sclerosis from Multiple Biofluids

Multiple sclerosis (MS) is a chronic and progressive neurological disorder without a cure, but early intervention can slow disease progression and improve the quality of life for MS patients. Obtaining an accurate diagnosis for MS is an arduous and error-prone task that requires a combination of a detailed medical history, a comprehensive neurological exam, clinical tests such as magnetic resonance imaging, and the exclusion of other possible diseases. A simple and definitive biofluid test for MS does not exist, but is highly desirable. To address this need, we employed NMR-based metabolomics to identify potentially unique metabolite biomarkers of MS from a cohort of age and sex-matched samples of cerebrospinal fluid (CSF), serum, and urine from 206 progressive MS (PMS) patients, 46 relapsing-remitting MS (RRMS) patients, and 99 healthy volunteers without a MS diagnosis. We identified 32 metabolites in CSF that varied between the control and PMS patients. Utilizing patient-matched serum samples, we were able to further identify 31 serum metabolites that may serve as biomarkers for PMS patients. Lastly, we identified 14 urine metabolites associated with PMS. All potential biomarkers are associated with metabolic processes linked to the pathology of MS, such as demyelination and neuronal damage. Four metabolites with identical profiles across all three biofluids were discovered, which demonstrate their potential value as cross-biofluid markers of PMS. We further present a case for using metabolic profiles from PMS patients to delineate biomarkers of RRMS. Specifically, three metabolites exhibited a variation from healthy volunteers without MS through RRMS and PMS patients. The consistency of metabolite changes across multiple biofluids, combined with the reliability of a receiver operating characteristic classification, may provide a rapid diagnostic test for MS.

Urinary metabolite biomarkers of pregnancy complications associated with maternal exposure to particulate matter

Particulate matter 2.5 (PM2.5) is associated with reproductive health and adverse pregnancy outcomes. However, studies evaluating biological markers of PM2.5 are lacking, and identifying biomarkers for estimating prenatal exposure to prevent pregnancy complications is essential. Therefore, we aimed to explore urine metabolites that are easy to measure as biomarkers of exposure. In this matched case-control study based on the PM2.5 exposure, 30 high PM2.5 group (>15 μg/m3) and 30 low PM2.5 group (<15 μg/m3) were selected from air pollution on pregnancy outcome (APPO) cohort study. We used a time-weighted average model to estimate individual PM exposure, which used indoor PM2.5 and outdoor PM2.5 concentrations by atmospheric measurement network based on residential addresses. Clinical characteristics and urine samples were collected from participants during the second trimester of pregnancy. Urine metabolites were quantitatively measured using gas chromatography-mass spectrometry following multistep chemical derivatization. Statistical analyses were conducted using SPSS version 21 and MetaboAnalyst 5.0. Small for gestational age and gestational diabetes (GDM) were significantly increased in the high PM2.5 group, respectively (P = 0.042, and 0.022). Fifteen metabolites showed significant differences between the two groups (P < 0.05). Subsequent pathway enrichment revealed that four pathways, including pentose and glucuronate interconversion with three pentose sugars (ribose, arabinose, and xylose; P < 0.05). The concentration of ribose increased preterm births (PTB) and GDM (P = 0.044 and 0.049, respectively), and the arabinose concentration showed a tendency to increase in PTB (P = 0.044). Therefore, we identified urinary pentose metabolites as biomarkers of PM2.5 and confirmed the possibility of their relationship with pregnancy complications.

Multi-omics analysis on the mechanism of the effect of Isatis leaf on the growth performance of fattening sheep

Introduction

This study evaluated the effects of Isatis Leaf (ISL) on the growth performance, gastrointestinal tissue morphology, rumen and intestinal microbiota, rumen, serum and urine metabolites, and rumen epithelial tissue transcriptome of fattening sheep.

Methods

Twelve 3.5-month-old healthy fattening sheep were randomly divided into two groups, each with 6 replicates, and fed with basal diet (CON) and basal diet supplemented with 80 g/kg ISL for 2.5 months. Gastrointestinal tract was collected for histological analysis, rumen fluid and feces were subjected to metagenomic analysis, rumen fluid, serum, and urine for metabolomics analysis, and rumen epithelial tissue for transcriptomics analysis.

Results

The results showed that in the ISL group, the average daily gain and average daily feed intake of fattening sheep were significantly lower than those of the CON group (P < 0.05), and the rumen ammonia nitrogen level was significantly higher than that of the CON group (P < 0.01). The thickness of the reticulum and abomasum muscle layer was significantly increased (P < 0.05). At the genus level, the addition of ISL modified the composition of rumen and fecal microorganisms, and the relative abundance of Methanobrevibacter and Centipeda was significantly upregulated in rumen microorganisms, The relative abundance of Butyrivibrio, Saccharofermentans, Mogibacterium, and Pirellula was significantly downregulated (P < 0.05). In fecal microorganisms, the relative abundance of Papillibacter, Pseudoflavonifractor, Butyricicoccus, Anaerovorax, and Methanocorpusculum was significantly upregulated, while the relative abundance of Roseburia, Coprococcus, Clostridium XVIII, Butyrivibrio, Parasutterella, Macellibacteroides, and Porphyromonas was significantly downregulated (P < 0.05). There were 164, 107, and 77 different metabolites in the rumen, serum, and urine between the ISL and CON groups (P < 0.05). The differential metabolic pathways mainly included thiamine metabolism, niacin and nicotinamide metabolism, vitamin B6 metabolism, taurine and taurine metabolism, beta-Alanine metabolism and riboflavin metabolism. These metabolic pathways were mainly involved in the regulation of energy metabolism and immune function in fattening sheep. Transcriptome sequencing showed that differentially expressed genes were mainly enriched in cellular physiological processes, development, and immune regulation.

Conclusion

In summary, the addition of ISL to the diet had the effect of increasing rumen ammonia nitrogen levels, regulating gastrointestinal microbiota, promoting body fat metabolism, and enhancing immunity in fattening sheep.

Unveiling the Dichotomy of Urinary Proteins: Diagnostic Insights into Breast and Prostate Cancer and Their Roles

This review covers the diagnostic potential of urinary biomarkers, shedding light on their linkage to cancer progression. Urinary biomarkers offer non-invasive avenues for detecting cancers, potentially bypassing the invasiveness of biopsies. The investigation focuses primarily on breast and prostate cancers due to their prevalence among women and men, respectively. The intricate interplay of urinary proteins is explored, revealing a landscape where proteins exhibit context-dependent behaviors. The review highlights the potential impact of physical activity on urinary proteins, suggesting its influence on tumorigenic behaviors. Exercise-conditioned urine may emerge as a potential diagnostic biomarker source. Furthermore, treatment effects, notably after lumpectomy and prostatectomy, induce shifts in the urinary proteome, indicating therapeutic impacts rather than activating oncogenic signaling. The review suggests further investigations into the double-sided, context-dependent nature of urinary proteins, the potential role of post-translational modifications (PTM), and the integration of non-protein markers like mRNA and metabolites. It also discusses a linkage of urinary proteomes with secretomes from induced tumor-suppressing cells (iTSCs). Despite challenges like cancer heterogeneity and sample variability due to age, diet, and comorbidities, harnessing urinary proteins and proteoforms may hold promise for advancing our understanding of cancer progressions, as well as the diagnostic and therapeutic role of urinary proteins.

The role of the host-microbiome and metabolomics in sarcoidosis

Sarcoidosis is a complex inflammatory fibrotic disease that affects multiple organ systems. It is characterized by the infiltration of lymphocytes and mononuclear phagocytes, which form non-caseating granulomas in affected organs. The lungs and intrathoracic lymph nodes are the most commonly affected organs. The underlying cause of sarcoidosis is unknown, but it is believed to occur in genetically predisposed individuals who are exposed to pathogenic organisms, environmental contaminants, or self and non-self-antigens. Recent research has suggested that the microbiome may play a role in the development of respiratory conditions, including sarcoidosis. Additionally, metabolomic studies have identified potential biomarkers for monitoring sarcoidosis progression. This review will focus on recent microbiome and metabolomic findings in sarcoidosis, with the goal of shedding light on the pathogenesis and possible diagnostic and therapeutic approaches.

Metabolomics analysis of visceral leishmaniasis based on urine of golden hamsters

BACKGROUND

Leishmaniasis is one of the most neglected tropical diseases and is spread mainly in impoverished regions of the world. Although many studies have focused on the host's response to Leishmania invasion, relatively less is known about the complex processes at the metabolic level, especially the metabolic alterations in the infected hosts.

METHODS

In this study, we conducted metabolomics analysis on the urine of golden hamsters in the presence or absence of visceral leishmaniasis (VL) using the ultra-performance liquid chromatography (UPLC) system tandem high-resolution mass spectrometer (HRMS). The metabolic characteristics of urine samples, along with the histopathological change and the parasite burden of liver and spleen tissues, were detected at 4 and 12 weeks post infection (WPI), respectively.

RESULTS

Amino acid metabolism was extensively affected at both stages of VL progression. Meanwhile, there were also distinct metabolic features at different stages. At 4 WPI, the significantly affected metabolic pathways involved alanine, aspartate and glutamate metabolism, the pentose phosphate pathway (PPP), histidine metabolism, tryptophan metabolism and tyrosine metabolism. At 12 WPI, the markedly enriched metabolic pathways were almost concentrated on amino acid metabolism, including tyrosine metabolism, taurine and hypotaurine metabolism and tryptophan metabolism. The dysregulated metabolites and metabolic pathways at 12 WPI were obviously less than those at 4 WPI. In addition, seven metabolites that were dysregulated at both stages through partial least squares-discriminant analysis (PLS-DA) and receiver-operating characteristic (ROC) tests were screened to be of diagnostic potential. The combination of these metabolites as a potential biomarker panel showed satisfactory performance in distinguishing infection groups from control groups as well as among different stages of infection.

CONCLUSION

Our findings could provide valuable information for further understanding of the host response to Leishmania infection from the aspect of the urine metabolome. The proposed urine biomarker panel could help in the development of a novel approach for the diagnosis and prognosis of VL.

The Urine Metabolome of R6/2 and zQ175DN Huntington's Disease Mouse Models

Huntington's disease (HD) is caused by the expansion of a polyglutamine (polyQ)-encoding tract in exon 1 of the huntingtin gene to greater than 35 CAG repeats. It typically has a disease course lasting 15-20 years, and there are currently no disease-modifying therapies available. Thus, there is a need for faithful mouse models of HD to use in preclinical studies of disease mechanisms, target validation, and therapeutic compound testing. A large variety of mouse models of HD were generated, none of which fully recapitulate human disease, complicating the selection of appropriate models for preclinical studies. Here, we present the urinary liquid chromatography-high-resolution mass spectrometry analysis employed to identify metabolic alterations in transgenic R6/2 and zQ175DN knock-in mice. In R6/2 mice, the perturbation of the corticosterone metabolism and the accumulation of pyrraline, indicative of the development of insulin resistance and the impairment of pheromone excretion, were observed. Differently from R6/2, zQ175DN mice showed the accumulation of oxidative stress metabolites. Both genotypes showed alterations in the tryptophan metabolism. This approach aims to improve our understanding of the molecular mechanisms involved in HD neuropathology, facilitating the selection of appropriate mouse models for preclinical studies. It also aims to identify potential biomarkers specific to HD.

Early Perturbations in Red Blood Cells in Response to Murine Malarial Parasite Infection: Proof-of-Concept 1H NMR Metabolomic Study

BACKGROUND

The major focus of metabolomics research has been confined to the readily available biofluids-urine and blood serum. However, red blood cells (RBCs) are also readily available, and may be a source of a wealth of information on vertebrates. However, the comprehensive metabolomic characterization of RBCs is minimal although they exhibit perturbations in various physiological states. RBCs act as the host of malarial parasites during the symptomatic stage. Thus, understanding the changes in RBC metabolism during infection is crucial for a better understanding of disease progression.

METHODS

The metabolome of normal RBCs obtained from Swiss mice was investigated using 1H NMR spectroscopy. Several 1 and 2-dimensional 1H NMR experiments were employed for this purpose. The information from this study was used to investigate the changes in the RBC metabolome during the early stage of infection (~1% infected RBCs) by Plasmodium bergheii ANKA.

RESULTS

We identified over 40 metabolites in RBCs. Several of these metabolites were quantitated using 1H NMR spectroscopy. The results indicate changes in the choline/membrane components and other metabolites during the early stage of malaria.

CONCLUSIONS

The paper reports the comprehensive characterization of the metabolome of mouse RBCs. Changes during the early stage of malarial infection suggest significant metabolic alteration, even at low parasite content (~1%).

GENERAL SIGNIFICANCE

This study should be of use in maximizing the amount of information available from metabolomic experiments on the cellular components of blood. The technique can be directly applied to real-time investigation of infectious diseases that target RBCs.

Urine myo-inositol as a novel prognostic biomarker for diabetic kidney disease: a targeted metabolomics study using nuclear magnetic resonance

BACKGROUND

As a leading cause of chronic kidney disease, clinical demand for noninvasive biomarkers of diabetic kidney disease (DKD) beyond proteinuria is increasing. Metabolomics is a popular method to identify mechanisms and biomarkers. We investigated urinary targeted metabolomics in DKD patients.

METHODS

We conducted a targeted metabolomics study of 26 urinary metabolites in consecutive patients with DKD stage 1 to 5 (n = 208) and healthy controls (n = 26). The relationships between estimated glomerular filtration rate (eGFR) or urine protein-creatinine ratio (UPCR) and metabolites were evaluated. Multivariate Cox analysis was used to estimate relationships between urinary metabolites and the target outcome, end-stage renal disease (ESRD). C statistics and time-dependent receiver operating characteristics (ROC) were used to assess diagnostic validity.

RESULTS

During a median 4.5 years of follow-up, 103 patients (44.0%) progressed to ESRD and 65 (27.8%) died. The median fold changes of nine metabolites belonged to monosaccharide and tricarboxylic acid (TCA) cycle metabolites tended to increase with DKD stage. Myo-inositol, choline, and citrates were correlated with eGFR and choline, while mannose and myo-inositol were correlated with UPCR. Elevated urinary monosaccharide and TCA cycle metabolites showed associations with increased morality and ESRD progression. The predictive power of ESRD progression was high, in the order of choline, myo-inositol, and citrate. Although urinary metabolites alone were less predictive than serum creatinine or UPCR, myo-inositol had additive effect with serum creatinine and UPCR. In time-dependent ROC, myo-inositol was more predictive than UPCR of 1-year ESRD progression prediction.

CONCLUSION

Myo-inositol can be used as an additive biomarker of ESRD progression in DKD.

Wearable Sensor for Continuous Sweat Biomarker Monitoring

In recent years, wearable sensors have enabled the unique mode of real-time and noninvasive monitoring to develop rapidly in medical care, sports, and other fields. Sweat contains a wide range of biomarkers such as metabolites, electrolytes, and various hormones. Combined with wearable technology, sweat can reflect human fatigue, disease, mental stress, dehydration, and so on. This paper comprehensively describes the analysis of sweat components such as glucose, lactic acid, electrolytes, pH, cortisol, vitamins, ethanol, and drugs by wearable sensing technology, and the application of sweat wearable devices in glasses, patches, fabrics, tattoos, and paper. The development trend of sweat wearable devices is prospected. It is believed that if the sweat collection, air permeability, biocompatibility, sensing array construction, continuous monitoring, self-healing technology, power consumption, real-time data transmission, specific recognition, and other problems of the wearable sweat sensor are solved, we can provide the wearer with important information about their health level in the true sense.

Sample Processing Considerations for Protein Stability Studies of Low Concentration Biofluid Samples using Differential Scanning Calorimetry

Background:

The analysis of biofluid samples with low protein content (e.g., urine or saliva) can be challenging for downstream analysis methods with limited sensitivity. To circumvent this problem, sample processing methods are employed to increase the protein concentration in analyzed samples. However, for some techniques, like differential scanning calorimetry (DSC) that characterizes thermally-induced unfolding of biomolecules, sample processing must not affect native protein structure and stability.

Methods:

We evaluated centrifugal concentration and stirred cell ultrafiltration, two common methods of sample concentration characterized by a low risk of protein denaturation, with the goal of establishing a protocol for DSC analysis of low concentration biospecimens.

Results:

Our studies indicate that both methods can affect protein stability assessed by DSC and, even after optimization of several parameters, the obtained DSC profile (thermogram) suggested that sample processing affects the structure or intermolecular interactions of component proteins contributing to altered thermal stability detectable by DSC. We also found a relationship between changes in thermograms and low protein concentration, indicating that diluting biospecimens to concentrations below 0.1 mg/mL can perturb the intermolecular environment and affect the structure of proteins present in the solution.

Conclusions:

Dilution of samples below 0.1 mg/mL, as well as concentration of samples with low protein content, resulted in affected thermogram shapes suggesting changes in protein stability. This should be taken into account when concentrating dilute samples or employing techniques that lower the protein concentration (e.g., fractionation), when downstream applications include techniques, such as DSC, that require the preservation of native protein forms.

Multi-Omic Profiling of Multi-Biosamples Reveals the Role of Amino Acid and Nucleotide Metabolism in Endometrial Cancer

Background

Endometrial cancer (EC) is one of the most common gynecological cancers. The traditional diagnosis of EC relies on histopathology, which, however, is invasive and may arouse tumor spread. There have been many studies aiming to find the metabolomic biomarkers of EC to improve the early diagnosis of cancer in a non-invasive or minimally invasive way, which can also provide valuable information for understanding the disease. However, most of these studies only analyze a single type of sample by metabolomics, and cannot provide a comprehensive view of the altered metabolism in EC patients. Our study tries to gain a pathway-based view of multiple types of samples for understanding metabolomic disorders in EC by combining metabolomics and proteomics.

Methods

Forty-four EC patients and forty-three controls were recruited for the research. We collected endometrial tissue, urine, and intrauterine brushing samples. Untargeted metabolomics and untargeted proteomics were both performed on the endometrial tissue samples, while only untargeted metabolomics was performed on the urine and intrauterine brushing samples.

Results

By integrating the differential metabolites and proteins between EC patients and controls detected in the endometrial tissue samples, we identified several EC-related significant pathways, such as amino acid metabolism and nucleotide metabolism. The significance of these pathways and the potential of metabolite biomarker-based diagnosis were then further verified by using urine and intrauterine brushing samples. It was found that the regulation of metabolites involved in the significant pathways showed similar trends in the intrauterine brushings and the endometrial tissue samples, while opposite trends in the urine and the endometrial tissue samples.

Conclusions

With multi-omics characterization of multi-biosamples, the metabolomic changes related to EC are illustrated in a pathway-based way. The network of altered metabolites and related proteins provides a comprehensive view of altered metabolism in the endometrial tissue samples. The verification of these critical pathways by using urine and intrauterine brushing samples provides evidence for the possible non-invasive or minimally invasive biopsy for EC diagnosis in the future.

How previous treatment changes the metabolomic profile in patients with metastatic breast cancer

Purpose

Metabolites are in the spotlight of attention as promising novel breast cancer biomarkers. However, no study has been conducted concerning changes in the metabolomics profile of metastatic breast cancer patients according to previous therapy.

Methods

We performed a retrospective, single-center, nonrandomized, partially blinded, treatment-based study. Metastatic breast cancer (MBC) patients were enrolled between 03/2010 and 09/2016 at the beginning of a new systemic therapy. The endogenous metabolites in the plasma samples were analyzed using the AbsoluteIDQ^® p180 Kit (Biocrates Life Sciences AG, Innsbruck) a targeted, quality and quantitative-controlled metabolomics approach. The statistical analysis was performed using R package, version 3.3.1. ANOVA was used to statistically assess age differences within groups. Furthermore, we analyzed the CTC status of the patients using the CellSearch^™ assay.

Results

We included 178 patients in our study. Upon dividing the study population according to therapy before study inclusion, we found the following: 4 patients had received no therapy, 165 chemotherapy, and 135 anti-hormonal therapy, 30 with anti-Her2 therapy and 38 had received treatment with bevacizumab. Two metabolites were found to be significantly different, depending on the further therapy of the patients: methionine and serine. Whereas methionine levels were higher in the blood of patients who received an anti-Her2-therapy, serine was lower in patients with endocrine therapy only.

Conclusion

We identified two metabolites for which concentrations differed significantly depending on previous therapies, which could help to choose the next therapy in patients who have already received numerous different treatments.

Therapeutic Effect and Mechanism of Si-Miao-Yong-An-Tang on Thromboangiitis Obliterans Based on the Urine Metabolomics Approach

Si-Miao-Yong-An-Tang (SMYAT) is a classic prescription for the treatment of thromboangiitis obliterans (TAO). However, the effect and mechanism are still unclear. This experiment aims to evaluate the therapeutic effect and mechanism of SMYAT on sodium laurate solution induced thromboangiitis obliterans model rats using urine metabolomics. The therapeutic effect of SMYAT was evaluated by histopathology, hemorheology and other indexes. The urine metabolomic method, principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were used for clustering group and discriminant analysis to screen urine differential metabolic biomarkers, and explore new insight into pathophysiological mechanisms of SMYAT in the treatment of TAO. SMYAT has significant antithrombotic and anti-inflammatory effects, according to the results of urine metabolomic analysis, and regulate the metabolic profile of TAO rats, and its return profile is close to the state of control group. Through metabolomics technology, a total of 35 urine biomarkers of TAO model were characterized. Among them, SMYAT treatment can regulate 22 core biomarkers, such as normetanephrine and 4-pyridoxic acid. It is found that the therapeutic effect of SMYAT is closely related to the tyrosine metabolism, vitamin B6 metabolism and cysteine and methionine metabolism. It preliminarily explored the therapeutic mechanism of SMYAT, and provided a scientific basis for the application of SMYAT.

Interactions between the intestinal microbiota and epigenome in individuals with autism spectrum disorder

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by variable impairment of cognitive function and interpersonal relationships. Furthermore, some individuals with ASD have gastrointestinal disorders that have been correlated with impairments in intestinal microbiota. Gut microbiota are important not only for intestinal health, but also for many other functions including food digestion, energy production, immune system regulation, and, according to current data, behavior. Disruption of the indigenous microbiota, microbial dysbiosis (imbalance between microorganisms present in the gut), overgrowth of potentially pathogenic microorganisms, a less diverse microbiome, or lower levels of beneficial bacteria in children with ASD can affect behavior. Metabolome analysis in children with ASD has identified perturbations in multiple metabolic pathways that might be associated with cognitive functions. Recent studies have shown that the intestinal microbiome provides environmental signals that can modify host response to stimuli by modifying the host epigenome, which affects DNA methylation, histone modification, and non-coding RNAs. The most studied microbiota-produced epigenetic modifiers are short-chain fatty acids, although other products of intestinal microbiota might also cause epigenetic modifications in the host's DNA. Here we review evidence suggesting that epigenetic alterations caused by modification of gene expression play an important role in understanding ASD.

Serum Glycine Levels Are Associated With Cortical Bone Properties and Fracture Risk in Men

CONTEXT

In a recent study a pattern of 27 metabolites, including serum glycine, associated with bone mineral density (BMD).

OBJECTIVE

To investigate associations for serum and urinary glycine levels with BMD, bone microstructure, and fracture risk in men.

METHODS

In the population-based Osteoporotic Fractures in Men (MrOS) Sweden study (men, 69-81 years) serum glycine and BMD were measured at baseline (n = 965) and 5-year follow-up (n = 546). Cortical and trabecular bone parameters of the distal tibia were measured at follow-up using high-resolution peripheral quantitative computed tomography. Urinary (n = 2682) glycine was analyzed at baseline. X-ray-validated fractures (n = 594) were ascertained during a median follow-up of 9.6 years. Associations were evaluated using linear regression (bone parameters) or Cox regression (fractures).

RESULTS

Circulating glycine levels were inversely associated with femoral neck (FN)-BMD. A meta-analysis (n = 7543) combining MrOS Sweden data with data from 3 other cohorts confirmed a robust inverse association between serum glycine levels and FN-BMD (P = 7.7 × 10-9). Serum glycine was inversely associated with the bone strength parameter failure load in the distal tibia (P = 0.002), mainly as a consequence of an inverse association with cortical cross-sectional area and a direct association with cortical porosity. Both serum and urinary glycine levels predicted major osteoporotic fractures (serum: hazard ratio [HR] per SD increase = 1.22, 95% CI, 1.05-1.43; urine: HR = 1.13, 95% CI, 1.02-1.24). These fracture associations were only marginally reduced in models adjusted by FRAX with BMD.

CONCLUSIONS

Serum and urinary glycine are indirectly associated with FN-BMD and cortical bone strength, and directly associated with fracture risk in men.

Metabolomic Approaches to Investigate the Effect of Metformin: An Overview

Metformin is the first-line antidiabetic drug that is widely used in the treatment of type 2 diabetes mellitus (T2DM). Even though the various therapeutic potential of metformin treatment has been reported, as well as the improvement of insulin sensitivity and glucose homeostasis, the mechanisms underlying those benefits are still not fully understood. In order to explain the beneficial effects on metformin treatment, various metabolomics analyses have been applied to investigate the metabolic alterations in response to metformin treatment, and significant systemic metabolome changes were observed in biofluid, tissues, and cells. In this review, we compare the latest metabolomic research including clinical trials, animal models, and in vitro studies comprehensively to understand the overall changes of metabolome on metformin treatment.

Self-Reported Medication Use and Urinary Drug Metabolites in the German Chronic Kidney Disease (GCKD) Study

BACKGROUND

Polypharmacy is common among patients with CKD, but little is known about the urinary excretion of many drugs and their metabolites among patients with CKD.

METHODS

To evaluate self-reported medication use in relation to urine drug metabolite levels in a large cohort of patients with CKD, the German Chronic Kidney Disease study, we ascertained self-reported use of 158 substances and 41 medication groups, and coded active ingredients according to the Anatomical Therapeutic Chemical Classification System. We used a nontargeted mass spectrometry-based approach to quantify metabolites in urine; calculated specificity, sensitivity, and accuracy of medication use and corresponding metabolite measurements; and used multivariable regression models to evaluate associations and prescription patterns.

RESULTS

Among 4885 participants, there were 108 medication-drug metabolite pairs on the basis of reported medication use and 78 drug metabolites. Accuracy was excellent for measurements of 36 individual substances in which the unchanged drug was measured in urine (median, 98.5%; range, 61.1%-100%). For 66 pairs of substances and their related drug metabolites, median measurement-based specificity and sensitivity were 99.2% (range, 84.0%-100%) and 71.7% (range, 1.2%-100%), respectively. Commonly prescribed medications for hypertension and cardiovascular risk reduction-including angiotensin II receptor blockers, calcium channel blockers, and metoprolol-showed high sensitivity and specificity. Although self-reported use of prescribed analgesics (acetaminophen, ibuprofen) was <3% each, drug metabolite levels indicated higher usage (acetaminophen, 10%-26%; ibuprofen, 10%-18%).

CONCLUSIONS

This comprehensive screen of associations between urine drug metabolite levels and self-reported medication use supports the use of pharmacometabolomics to assess medication adherence and prescription patterns in persons with CKD, and indicates under-reported use of medications available over the counter, such as analgesics.

Metabolomic Profiling of Human Urine Samples Using LC-TIMS-QTOF Mass Spectrometry

The identification of metabolites in biological samples is challenging due to their chemical and structural diversity. Ion mobility spectrometry (IMS) separates ionized molecules based on their mobility in a carrier buffer gas giving information about the ionic shape by measuring the rotationally averaged collision cross-section (CCS) value. This orthogonal descriptor, in combination with the m/z, isotopic pattern distribution, and MS/MS spectrum, has the potential to improve the identification of molecular molecules in complex mixtures. Urine metabolomics can reveal metabolic differences, which arise as a result of a specific disease or in response to therapeutic intervention. It is, however, complicated by the presence of metabolic breakdown products derived from a wide range of lifestyle and diet-related byproducts, many of which are poorly characterized. In this study, we explore the use of trapped ion mobility spectrometry (TIMS) via LC parallel accumulation with serial fragmentation (PASEF) for urine metabolomics. A total of 362 urine metabolites were characterized from 80 urine samples collected from healthy volunteers using untargeted metabolomics employing HILIC and RP chromatography. Additionally, three analytes (Trp, Phe, and Tyr) were selected for targeted quantification. Both the untargeted and targeted data was highly reproducible and reported CCS measurements for identified metabolites were robust in the presence of the urine matrix. A comparison of CCS values among different laboratories was also conducted, showing less than 1.3% ΔCCS values across different platforms. This is the first report of a human urine metabolite database compiled with CCS values experimentally acquired using an LC-PASEF TIMS-qTOF platform.

Individual variability in human urinary metabolites identifies age-related, body mass index-related, and sex-related biomarkers

Background

Metabolites present in human urine can be influenced by individual physiological parameters (e.g., body mass index [BMI], age, and sex). Observation of altered metabolites concentrations could provide insight into underlying disease pathology, disease prognosis and diagnosis, and facilitate discovery of novel biomarkers.

Methods

Quantitative metabolomics analysis in the urine of 183 healthy individuals was performed based on high‐resolution liquid chromatography–mass spectrometry (LC–MS). Coefficients of variation were obtained for 109 urine metabolites of all the 183 human healthy subjects.

Results

Three urine metabolites (such as dehydroepiandrosterone sulfate, acetaminophen glucuronide, and p‐anisic acid) with CV₁₈₃ > 0.3, for which metabolomics studies have been scarce, are considered highly variable here. We identified 30 age‐related metabolites, 18 BMI‐related metabolites, and 42 sex‐related metabolites. Among the identified metabolites, three metabolites were found to be associated with all three physiological parameters (age, BMI, and sex), which included dehydroepiandrosterone sulfate, 3‐methylcrotonylglycine and N‐acetyl‐aspartic acid. Pearson's coefficients demonstrated that some age‐, BMI‐, and sex‐related compounds are strongly correlated, suggesting that age, BMI, and sex could affect them concomitantly.

Conclusion

Metabolic differences between distinct physiological statuses were found to be related to several metabolic pathways (such as the caffeine metabolism, the amino acid metabolism, and the carbohydrate metabolism), and these findings may be key for the discovery of new diagnostics and treatments as well as new understandings on the mechanisms of some related diseases.

Machine Learning-Enabled Renal Cell Carcinoma Status Prediction Using Multiplatform Urine-Based Metabolomics

Renal cell carcinoma (RCC) is diagnosed through expensive cross-sectional imaging, frequently followed by renal mass biopsy, which is not only invasive but also prone to sampling errors. Hence, there is a critical need for a noninvasive diagnostic assay. RCC exhibits altered cellular metabolism combined with the close proximity of the tumor(s) to the urine in the kidney, suggesting that urine metabolomic profiling is an excellent choice for assay development. Here, we acquired liquid chromatography-mass spectrometry (LC-MS) and nuclear magnetic resonance (NMR) data followed by the use of machine learning (ML) to discover candidate metabolomic panels for RCC. The study cohort consisted of 105 RCC patients and 179 controls separated into two subcohorts: the model cohort and the test cohort. Univariate, wrapper, and embedded methods were used to select discriminatory features using the model cohort. Three ML techniques, each with different induction biases, were used for training and hyperparameter tuning. Assessment of RCC status prediction was evaluated using the test cohort with the selected biomarkers and the optimally tuned ML algorithms. A seven-metabolite panel predicted RCC in the test cohort with 88% accuracy, 94% sensitivity, 85% specificity, and 0.98 AUC. Metabolomics Workbench Study IDs are ST001705 and ST001706.

Sweat metabolome and proteome: Recent trends in analytical advances and potential biological functions

Metabolome and proteome profiling of biofluids, e.g., urine, plasma, has generated vast and ever-increasing amounts of knowledge over the last few decades. Paradoxically, omics analyses of sweat, one of the most readily available human biofluids, have lagged behind. This review capitalizes on the current knowledge and state of the art analytical advances of sweat metabolomics and proteomics. Moreover, current applications of sweat omics such as the discovery of disease biomarkers and monitoring athletic performance are also presented in this review. Another area of emerging knowledge that has been highlighted herein lies in the role of skin host-microbiome interactions in shaping the sweat metabolite-protein profiles. Discussion of future research directions describes the need to have a better grasp of sweat chemicals and to better understand how they function as aided by advances in omics tools. Overall, the role of sweat as an information-rich biofluid that could complement the exploration of the skin metabolome/proteome is emphasized.

Comprehensive Isotope Ratio Metabolomics: Gas chromatography Isotope Ratio Mass Spectrometry of urinary metabolites and exhaled breath

We have developed an analytical procedure to measure the carbon isotopic composition of multiple compounds even when there is a partial overlap in the chromatographic profiles and applied this procedure to measure the carbon isotopic composition of different metabolites in human urine and exhaled breath. Method development and validation was performed with CRM IAEA-600 caffeine after calibration of the reference CO₂ gas using a mixture of certified undecane, pentadecane and eicosane δ(¹³C) standards. The alternative data treatment procedure included the correction of time-lag between Faraday cup amplifiers (44 ms at mass 45 and -160 ms at mass 46), the calculation and correction of chromatographic isotope effects on each peak (isotope shifts) and the calculation of the isotope ratio for each compound using the linear regression slope procedure with data only at the top of the chromatographic peak. In that way, partial chromatographic overlap between different metabolites can be tolerated (resolution equal or higher than 1). The reproducibility (SD) of the carbon isotope composition of 93 metabolites in human urine (n = 8) from one volunteer was typically better than 0.5 δ(¹³C) (range 0.1-2.0 δ(¹³C), median 0.4 δ(¹³C)). The method was applied to follow the carbon isotope composition of different metabolites in human urine and exhaled breath after the oral administration of 100 mg of universally labelled ¹³C-glucose to another human volunteer. It was demonstrated that isotopically labelled compounds could be detected in both samples even 2 h after administration. So, the developed methodology can be applied to multiple types of samples containing a large number of partially overlapping analytes including environmental applications, anti-doping control or metabolomics studies, including the use of enriched isotope tracers.

Urinary metabolomic analysis to identify potential markers for the diagnosis of tuberculosis and latent tuberculosis

Tuberculosis (TB) is a serious infectious disease with high infection and mortality rates. 5%-10% of the latent tuberculosis infections (LTBI) are likely to develop into active TB, and there are currently no clinical biomarkers that can distinguish between LTBI, active TB and other non-tuberculosis populations. Therefore, it is necessary to develop rapid diagnostic methods for active TB and LTBI. In this study, urinary metabolome of 30 active TB samples and the same number of LTBI and non-TB control samples were identified and analyzed by UPLC-Q Exactive MS. In total, 3744 metabolite components were obtained in ESI- mode and 4086 in ESI + mode. Orthogonal partial least square discriminant analysis (OPLS-DA) and hierarchical cluster analysis (HCA) showed that there were significant differences among LTBI, active TB and non-TB. Six differential metabolites were screened in positive and negative mode, 3-hexenoic acid, glutathione (GSH), glycochenodeoxycholate-3-sulfate, N-[4'-hydroxy-(E)-cinnamoyl]-l-aspartic acid, deoxyribose 5-phosphate and histamine. The overlapping pathways differential metabolites involved were mainly related to immune regulation and urea cycle. The results showed that the urine metabolism of TB patients was disordered and many metabolic pathways changed. Multivariate statistical analysis revealed that GSH and histamine were selected as potential molecular markers, with area under curve of receiver operating characteristic curve over 0.75. Among the multiple differential metabolites, GSH and histamine changed to varying degrees in active TB, LTBI and the non-TB control group. The levels of GSH and histamine in 48 urinary samples were measured by ELISA in validation phase, and the result in our study provided the potential for non-invasive biomarkers of TB.

Metabolomics Signatures of Aging: Recent Advances

Metabolomics is the latest state-of-the-art omics technology that provides a comprehensive quantitative profile of metabolites. The metabolites are the cellular end products of metabolic reactions that explain the ultimate response to genomic, transcriptomic, proteomic, or environmental changes. Aging is a natural inevitable process characterized by a time-dependent decline of various physiological and metabolic functions and are dominated collectively by genetics, proteomics, metabolomics, environmental factors, diet, and lifestyle. The precise mechanism of the aging process is unclear, but the metabolomics has the potential to add significant insight by providing a detailed metabolite profile and altered metabolomic functions with age. Although the application of metabolomics to aging research is still relatively new, extensive attempts have been made to understand the biology of aging through a quantitative metabolite profile. This review summarises recent developments and up-to-date information on metabolomics studies in aging research with a major emphasis on aging biomarkers in less invasive biofluids. The importance of an integrative approach that combines multi-omics data to understand the complex aging process is discussed. Despite various innovations in metabolomics and metabolite associated with redox homeostasis, central energy pathways, lipid metabolism, and amino acid, a major challenge remains to provide conclusive aging biomarkers.

Reliability of urinary charged metabolite concentrations in a large-scale cohort study using capillary electrophoresis-mass spectrometry

Currently, large-scale cohort studies for metabolome analysis have been launched globally. However, only a few studies have evaluated the reliability of urinary metabolome analysis. This study aimed to establish the reliability of urinary metabolomic profiling in cohort studies. In the Tsuruoka Metabolomics Cohort Study, 123 charged metabolites were identified and routinely quantified using capillary electrophoresis-mass spectrometry (CE-MS). We evaluated approximately 750 quality control (QC) samples and 6,720 participants’ spot urine samples. We calculated inter- and intra-batch coefficients of variation in the QC and participant samples and technical intraclass correlation coefficients (ICC). A correlation of metabolite concentrations between spot and 24-h urine samples obtained from 32 sub-cohort participants was also evaluated. The coefficient of variation (CV) was less than 20% for 87 metabolites (70.7%) and 20–30% for 19 metabolites (15.4%) in the QC samples. There was less than 20% inter-batch CV for 106 metabolites (86.2%). Most urinary metabolites would have reliability for measurement. The 96 metabolites (78.0%) was above 0.75 for the estimated ICC, and those might be useful for epidemiological analysis. Among individuals, the Pearson correlation coefficient of 24-h and spot urine was more than 70% for 59 of the 99 metabolites. These results show that the profiling of charged metabolites using CE-MS in morning spot human urine is suitable for epidemiological metabolomics studies.

Functional Metabolomics Analysis Elucidating the Metabolic Biomarker and Key Pathway Change Associated With the Chronic Glomerulonephritis and Revealing Action Mechanism of Rhein

Chronic glomerulonephritis (CGN) as the culprit of kidney failure can increase the mortality of critically ill patients and seriously threatens people’s health all over the world. This study using metabolomics strategy is to reveal the potential therapeutic mechanism-related targets to evaluate the effects of rhein (RH) on CGN rats. Changes of serum metabolites and pathways were analyzed by non-targeted metabolomic method based on liquid chromatography-mass spectrometry (LC-MS) combined with ingenuity pathway analysis. In addition, the levels of biochemical indicators were also detected. A total of 25 potential biomarkers were identified to express serum metabolic turbulence in CGN animal model, and then 16 biomarkers were regulated by RH trending to the normal states. From metabolite enrichment and pathway analysis, pharmacological activity of RH on CGN were mainly involved in six vital metabolic pathways including phenylalanine, tyrosine and tryptophan biosynthesis, phenylalanine metabolism, arachidonic acid metabolism, tricarboxylic acid cycle (TCA cycle), alanine, aspartate, and glutamate metabolism, arginine and proline metabolism. It suggested CGN treatment with RH, which may be mediated via interference with metabolic pathway such as amino acid metabolism, arachidonic acid metabolism, and TCA cycle to regulating inflammation, oxidation response and immune regulation against CGN. It showed that metabolomics method offer deeply insight into the therapeutic mechanisms of natural product.

A combined LC-MS and NMR approach to reveal metabolic changes in the hemolymph of honeybees infected by the gut parasite Nosema ceranae

Nosema ceranae is an emerging and invasive gut pathogen in Apis mellifera and is considered as a factor contributing to the decline of honeybee populations. Here, we used a combined LC-MS and NMR approach to reveal the metabolomics changes in the hemolymph of honeybees infected by this obligate intracellular parasite. For metabolic profiling, hemolymph samples were collected from both uninfected and N. ceranae-infected bees at two time points, 2 days and 10 days after the experimental infection of emergent bees. Hemolymph samples were individually analyzed by LC-MS, whereas each NMR spectrum was obtained from a pool of three hemolymphs. Multivariate statistical PLS-DA models clearly showed that the age of bees was the parameter with the strongest effect on the metabolite profiles. Interestingly, a total of 15 biomarkers were accurately identified and were assigned as candidate biomarkers representative of infection alone or combined effect of age and infection. These biomarkers included carbohydrates (α/β glucose, α/β fructose and hexosamine), amino acids (histidine and proline), dipeptides (Glu-Thr, Cys-Cys and γ-Glu-Leu/Ile), metabolites involved in lipid metabolism (choline, glycerophosphocholine and O-phosphorylethanolamine) and a polyamine compound (spermidine). Our study demonstrated that this untargeted metabolomics-based approach may be useful for a better understanding of pathophysiological mechanisms of the honeybee infection by N. ceranae.

High-Throughput Metabolomics Method for Discovering Metabolic Biomarkers and Pathways to Reveal Effects and Molecular Mechanism of Ethanol Extract From Epimedium Against Osteoporosis

Metabolomics is an effective strategy to explore the molecular mechanism of herbal medicine. Epimedium, a traditional Chinese herb from the Epimedium brevicornu Maxim., has a therapeutic effect on osteoporosis (OP), however the molecular mechanism of the anti-OP effect is uncle\ar. Therefore, we investigated the pharmacological effect and action mechanism of ethanol extract of epimedium (Ext-epi) onOP rat model. The serum of OP rats was analyzed utilized UPLC-Q-TOF/MS metabolomics, and the potential biomarkers were screened and identified using multivariate data analysis systems and network databases. To further appraise the influence of Ext-epi on biological markers and metabolic pathways, and reveal the potential mechanism of Ext-epi on OP treatment. The results showed that 46 potential biomarkers were screened out and after intervention with Ext-epi extracts solution, 16 potential biomarkers were significantly recalled. Further pathway experiments showed that key pathway analysis include sarachidonic acid metabolism, glycerolphospholipid metabolism as potential targets which is related with the efficacy of Ext-epi protect against OP. These results explain the correlation between metabolites and molecular mechanisms, which is of great significance for understanding the intervention of Ext-epi on OP. In short, based on UPLC-Q-TOF/MS metabolomics may provide effective strategies for understanding the pathogenesis of diseases and evaluating the intervention effect of natural products.

Metabolomic Biomarkers for Detection, Prognosis and Identifying Recurrence in Endometrial Cancer

Metabolic reprogramming is increasingly recognised as one of the defining hallmarks of tumorigenesis. There is compelling evidence to suggest that endometrial cancer develops and progresses in the context of profound metabolic dysfunction. Whilst the incidence of endometrial cancer continues to rise in parallel with the global epidemic of obesity, there are, as yet, no validated biomarkers that can aid risk prediction, early detection, prognostic evaluation or surveillance. Advances in high-throughput technologies have, in recent times, shown promise for biomarker discovery based on genomic, transcriptomic, proteomic and metabolomic platforms. Metabolomics, the large-scale study of metabolites, deals with the downstream products of the other omics technologies and thus best reflects the human phenotype. This review aims to provide a summary and critical synthesis of the existing literature with the ultimate goal of identifying the most promising metabolite biomarkers that can augment current endometrial cancer diagnostic, prognostic and recurrence surveillance strategies. Identified metabolites and their biochemical pathways are discussed in the context of what we know about endometrial carcinogenesis and their potential clinical utility is evaluated. Finally, we underscore the challenges inherent in metabolomic biomarker discovery and validation and provide fresh perspectives and directions for future endometrial cancer biomarker research.

Study on Urinary Candidate Metabolome for the Early Detection of Breast Cancer

A metabolomic study for determination of certain urinary metabolomes, 1-methyladenosine (1-MA), 1-methylguanosine (1-MG), and 8-hydroxy-2' deoxyguanosine (8-OHdG) in urine specimens of breast cancer patients. The accuracy of these metabolites and their combined score with cancer antigen 15-3 (CA15-3) was developed to improve the early detection of breast cancer. This study recruited 52 healthy individuals, 47 benign breast tumors, and 167 malignant breast tumor patients. Urine samples were handled to adjust the creatinine concentrations to 8 mg/dL (0.7 mmol/L) and analyzed using GC-MS to detect and quantify the selected urinary metabolomes in urine samples of all participants. The accuracy of individual urinary metabolomes and their combination with CA15-3 were evaluated using multivariate statistical analysis. The cutoff value of CA15-3 was 32.5 U/mL. Cutoff values of 1-MA, 1-MG, and 8-OHdG were 2.19, 2.1, and 7.3 µmol/mmol creatinine, respectively. The concentrations of 1-MA, 1-MG, and 8-OHdG were significantly higher in breast cancer patients, especially in the early-stage. The combination of three urinary metabolomes with CA15-3 improves the diagnostic sensitivity of breast cancer. For the combined score, the area under the curve (AUC) value of combined score ranged from 0.820 to 0.950, with high accuracy, ranged from 77.0 to 95.5%. The most significant AUC (0.973), sensitivity (90.1%), selectivity (94.0%) was recorded at comparing the healthy control with the early-stage of malignant breast cancer. In conclusion, the combination of three urinary metabolomes with serum CA15-3 improves the diagnostic sensitivity of breast cancer.

The key points in the pre-analytical procedures of blood and urine samples in metabolomics studies

BACKGROUND

Metabolomics provides measurement of numerous metabolites in human samples, which can be a useful tool in clinical research. Blood and urine are regarded as preferred subjects of study because of their minimally invasive collection and simple preprocessing methods. Adhering to standard operating procedures is an essential factor in ensuring excellent sample quality and reliable results.

AIM OF REVIEW

In this review, we summarize the studies about the impacts of various preprocessing factors on metabolomics studies involving clinical blood and urine samples in order to provide guidance for sample collection and preprocessing.

KEY SCIENTIFIC CONCEPTS OF REVIEW

Clinical information is important for sample grouping and data analysis which deserves attention before sample collection. Plasma and serum as well as urine samples are appropriate for metabolomics analysis. Collection tubes, hemolysis, delay at room temperature, and freeze-thaw cycles may affect metabolic profiles of blood samples. Collection time, time between sampling and examination, contamination, normalization strategies, and storage conditions may alter analysis results of urine samples. Taking these collection and preprocessing factors into account, this review provides suggestions of standard sample preprocessing.

Human urine 1H NMR metabolomics reveals alterations of protein and carbohydrate metabolism when comparing habitual Average Danish diet vs. healthy New Nordic diet

OBJECTIVES

The aim of this study was to investigate the alteration of the human urine metabolome by means of diet and to compare the metabolic effects of the nutritionally healthy New Nordic Diet (NND) with an Average Danish Diet (ADD). The NND was designed a decade ago by scientists and chefs, based on local and sustainable foods, including fish, shellfish, vegetables, roots, fruit, and berries. The NND has been proven to lower blood pressure, reduce glycemia, and lead to weight loss.

METHODS

The human urine metabolome was measured by untargeted proton nuclear magnetic resonance spectroscopy in samples from 142 centrally obese Danes (20-66 years old), randomized to consume the ADD or the NND. The resulting metabolomics data was processed and analyzed using advanced multivariate data analysis methods to reveal effects related to the design factors, including diet, season, sex, and changes in body weight.

RESULTS

Exploration of the nuclear magnetic resonance profiles revealed unique metabolite markers reflecting changes in protein and carbohydrate metabolism between the two diets. Glycine betaine, glucose, trimethylamine N-oxide and creatinine were increased in urine of the individuals following the NND compared with the ADD population, whereas relative concentrations of tartrate, dimethyl sulfone, and propylene glycol were decreased. Propylene glycol had a strong association with the homeostatic model assessment for insulin resistance in the NND group. The food intake biomarkers found in this study confirm the importance of these as tools for nutritional research.

CONCLUSIONS

Findings from this study provided new insights into the effects of a healthy diet on glycemia, reduction of inflammation, and weight loss among obese individuals, and alteration of the gut microbiota metabolism.

Metabolomics approach based on utra-performance liquid chromatography coupled to mass spectrometry with chemometrics methods for high-throughput analysis of metabolite biomarkers to explore the abnormal metabolic pathways associated with myocardial dysfunction

Ultra-performance liquid chromatography/mass spectrometry-based metabolomics can been used for discovery of metabolite biomarkers to explore the metabolic pathway of diseases. Identification of metabolic pathways is key to understanding the pathogenesis and mechanism of disease. Myocardial dysfunction induced by sepsis (SMD) is a severe complication of septic shock and represents major causes of death in intensive care units; however its pathological mechanism is still not clear. In this study, ultrahigh-pressure liquid chromatography with mass spectrometry-based metabolomics with chemometrics anaylsis and multivariate pattern recognition analysis were used to detect urinary metabolic profile changes in a lipopolysaccharide-induced SMD mouse model. Multivariate statistical analysis including principal component analysis and orthogonapartial least squares discriminant analysis for the discrimination of SMD was conducted to identify potential biomarkers. A total of 19 differential metabolites were discovered by high-resolution mass spectrometry-based urinary metabolomics strategy. The altered biochemical pathways based on these metabolites showed that tyrosine metabolism, phenylalanine metabolism, ubiquinone biosynthesis and vitamin B6 metabolism were closely connected to the pathological processes of SMD. Consequently, integrated chemometric analyses of these metabolic pathways are necessary to extract information for the discovery of novel insights into the pathogenesis of disease.

Towards Raman spectroscopy of urine as screening tool

For the screening purposes urine is an especially attractive biofluid, since it offers easy and noninvasive sample collection and provides a snapshot of the whole metabolic status of the organism, which may change under different pathological conditions. Raman spectroscopy (RS) has the potential to monitor these changes and utilize them for disease diagnostics. The current study utilizes mouse models aiming to compare the feasibility of the urine based RS combined with chemometrics for diagnosing kidney diseases directly influencing urine composition and respiratory tract diseases having no direct connection to urine formation. The diagnostic models for included diseases were built using principal component analysis with linear discriminant analysis and validated with a leave-one-mouse-out cross-validation approach. Considering kidney disorders, the accuracy of 100% was obtained in discrimination between sick and healthy mice, as well as between two different kidney diseases. For asthma and invasive pulmonary aspergillosis achieved accuracies were noticeably lower, being, respectively, 77.27% and 78.57%. In conclusion, our results suggest that RS of urine samples not only provides a solution for a rapid, sensitive and noninvasive diagnosis of kidney disorders, but also holds some promises for the screening of nonurinary tract diseases.

High-throughput liquid chromatography mass-spectrometry-driven lipidomics discover metabolic biomarkers and pathways as promising targets to reveal the therapeutic effects of the Shenqi pill

Lipidomics, a branch of metabonomics, could provide a powerful technique for discovery of lipid molecules to reveal disease status and drug efficacy.

Exploring the metabolic biomarkers and pathway changes in crucian under carbonate alkalinity exposure using high-throughput metabolomics analysis based on UPLC-ESI-QTOF-MS

The aims of this study is to explore the metabolomic biomarker and pathway changes in crucian under carbonate alkalinity exposures using high-throughput metabolomics analysis based on ultra-performance liquid chromatography-electrospray ionization-quadrupole time of flight-tandem mass spectrometry (UPLC-ESI-QTOF-MS) for carrying out adaptive evolution of fish in environmental exposures and understanding molecular physiological mechanisms of saline–alkali tolerance in fishes. Under 60 day exposure management, the UPLC-ESI-QTOF-MS technology, coupled with a pattern recognition approach and metabolic pathway analysis, was utilized to give insight into the metabolic biomarker and pathway changes. In addition, biochemical parameters in response to carbonate alkalinity in fish were detected for chronic impairment evaluation. A total of twenty-seven endogenous metabolites were identified to distinguish the biochemical changes in fish in clean water under exposure to different concentrations of carbonate alkalinity (CA); these mainly involved amino acid synthesis and metabolism, arachidonic acid metabolism, glyoxylate and dicarboxylate metabolism, pyruvate metabolism and the citrate cycle (TCA cycle). Compared with the control group, CA exposure increased the level of blood ammonia; TP; ALB; Gln in the liver and gills; GS; urea in blood, the liver and gills; CREA; CPS; Glu and LDH; and decreased the level of weight gain rate, oxygen consumption, discharge rate of ammonia, SOD, CAT, ALT, AST and Na⁺/K⁺-ATPase. At low concentrations, CA can change the normal metabolism of fish in terms of changing the osmotic pressure regulation capacity, antioxidant capacity, ammonia metabolism and liver and kidney function to adapt to the CA exposure environment. As the concentration of CA increases, various metabolic processes in crucian are inhibited, causing chronic damage to the body. The results show that the metabolomic strategy is a potentially powerful tool for identifying the mechanisms in response to different environmental exposomes and offers precious information about the chronic response of fish to CA.

We explore the metabolic biomarker and pathway changes accompanying the adaptive evolution of crucian subjected to carbonate alkalinity exposure, using UPLC-ESI-QTOF-MS, in order to understand the molecular physiological mechanisms of saline–alkali tolerance.

Metabolomics-driven identification of perturbations in amino acid and sphingolipid metabolism as therapeutic targets in a rat model of anorexia nervosa disease using chemometric analysis and a multivariate analysis platform

It is important to explore novel therapeutic targets and develop an effective strategy for the treatment of anorexia nervosa. In this work, serum samples were analyzed using ultra-performance liquid chromatography coupled with quadrupole time-of flight mass spectrometry (UPLC/Q-TOF MS) coupled with chemometric analysis and multivariate analysis to obtain the metabolites and their corresponding pathways. In addition, knock-in and knock-down of the key enzyme in vivo was performed to verify the reliability of the obtained metabolic pathway, which is closely associated with the anorexia nervosa pathomechanism and the potential targets. There were significant differences in the biochemical parameters between the model group and the control group. A total of 26 potential biomarkers were identified to resolve the difference between the control and model rats, which were closely related to amino acid metabolism, sphingolipid metabolism, arachidonic acid metabolism, the citrate cycle, and so forth. According to the ingenuity pathway analysis, we further elucidated the relationship between the gene, protein, and metabolite alteration in anorexia nervosa, which are involved in cellular compromise, lipid metabolism, small molecule biochemistry, cell signaling, molecular transport, nucleic acid metabolism, cell morphology, cellular function and maintenance. Arginosuccinate synthetase (ASS) deficiency was accompanied by a significant downregulation of the β-endorphin and ghrelin in the animal models. The metabolites and pathways obtained using the metabolomics strategy may provide valuable information for the early treatment for anorexia nervosa.

It is important to explore novel therapeutic targets and develop an effective strategy for the treatment of anorexia nervosa.

Functional metabolomics using UPLC-Q/TOF-MS combined with ingenuity pathway analysis as a promising strategy for evaluating the efficacy and discovering amino acid metabolism as a potential therapeutic mechanism-related target for geniposide against alcoholic liver disease

Metabolomics has been used as a strategy to evaluate the efficacy of and potential targets for natural products.

Current Understanding of Methamphetamine-Associated Metabolic Changes Revealed by the Metabolomics Approach

Metabolomics is a powerful tool used in the description of metabolic system perturbations caused by diseases or abnormal conditions, and it usually involves qualitative and/or quantitative metabolome determination, accompanied by bioinformatics assessment. Methamphetamine is a psychostimulant with serious abuse potential and due to the absence of effective pharmacotherapy and a high recurrence potential, methamphetamine addiction is a grave issue. Moreover, its addiction mechanisms remain unclear, probably due to the lack of experimental models that reflect personal genetic variances and environmental factors determining drug addiction occurrence. The metabolic approach is only recently being used to study the metabolic effects induced by a variety of methamphetamine exposure statuses, in order to investigate metabolic disturbances related to the adverse effects and discover potential methamphetamine addiction biomarkers. To provide a critical overview of methamphetamine-associated metabolic changes revealed in recent years using the metabolomics approach, we discussed methamphetamine toxicity, applications of metabolomics in drug abuse and addiction studies, biological samples used in metabolomics, and previous studies on metabolic alterations in a variety of biological samples—including the brain, hair, serum, plasma, and urine—following methamphetamine exposure in animal studies. Metabolic alterations observed in animal brain and other biological samples after methamphetamine exposure were associated with neuronal and energy metabolism disruptions. This review highlights the significance of further metabolomics studies in the area of methamphetamine addiction research. These findings will contribute to a better understanding of metabolic changes induced by methamphetamine addiction progress and to the design of further studies targeting the discovery of methamphetamine addiction biomarkers and therapeutic targets.

Metabolomic signatures and microbial community profiling of depressive rat model induced by adrenocorticotrophic hormone

Background

Adrenocorticotrophic hormone (ACTH)-treatment rat model has been utilized as a widely accepted model of treatment-resistant depression. Metabolomic signatures represent the pathophysiological phenotype of diseases. Recent studies in gut microbiota and metabolomics analysis revealed the dramatic role of microbiome in psychoneurological system diseases, but still, the mechanisms underlying gut microbiome–host interaction remain unclear.

Methods

Male Wistar rats were s.c. injection of ACTH fragment 1–24 for 14 days to induce treatment-resistant depression. Depression-related behavioral tests, analysis of serum monoamine neurotransmitters and hypothalamic–pituitary–adrenal (HPA) axis-related hormones were determined for assessment of ACTH-induced depression rat model. A gas chromatography-time-of-flight mass spectrometer based urinary metabolomic signatures integrated 16S rRNA sequence analysis based gut microbial profiling was performed, as well as Spearman’s correlation coefficient analysis was used to manifest the covariation between the differential urinary metabolites and gut microbiota of genus level.

Results

Chronic injection of ACTH-induced depression-like phenotype (increased immobility time in forced swimming test and tail suspension test) was accompanied by peripheral serotonin down-regulation and HPA axis overactivation (ACTH and corticosterone up-regulation). Urinary metabolomics analysis indicated that pyruvic acid, l-threonine, mannitol, d-gluconic acid, 4-hydroxybenzoic acid, d-arabitol, myo-inositol and ascorbic acid levels were reduced in ACTH-treated rats’ urine, while hippurate level was elevated. In addition, microbial community profiling revealed bacterial enrichment (e.g. Ruminococcus, Klebsiella) and reduction (e.g. Akkermansia, Lactobacillus) in the ACTH-induced depression rat model. Correlation analysis showed that Akkermansia and Lactobacillus were closely relevant to metabolites myo-inositol and hippurate, which were included in host inositol phosphate metabolism, and phenylalanine, tyrosine and tryptophan biosynthesis.

Conclusions

Depression rat model induced by ACTH is associated with disturbance of pyruvate metabolism, ascorbate and aldarate metabolism, inositol phosphate metabolism, glycine, serine and threonine metabolism, and glycolysis or gluconeogenesis, as well as changes in microbial community structure. Gut microbiota may participate in the mediation of systemic metabolomic changes in ACTH-induced depression model. Therefore, integrated metabolomic signatures and gut microbial community profiling would provide a basis for further studies on the pathogenesis of depression.

Electronic supplementary material

The online version of this article (10.1186/s12967-019-1970-8) contains supplementary material, which is available to authorized users.

Untargeted Metabolite Profiling of Adipose Tissue in Hyperlipidemia Rats Exposed to Hawthorn Ethanol Extracts

The study aimed to explore the metabolic changes of adipose tissue of hyperlipidemia rats with hawthorn ethanol extracts (HEE) consumption by a high-throughput metabolomics approach. HEE were mainly composed of chlorogenic acid, hyperoside, isoquercitrin, rutin, vitexin, quercetin, and apigenin by HPLC analysis. HEE administration significantly lowered levels of the total cholesterols, triglyceride and low-density lipoprotein cholesterol as compared to the high-fat diet model. Gas chromatography-mass spectrometry was used to identify adipose metabolite profiles. Numerous endogenous molecules were altered by high-fat diet and restored following intervention of HEE. Metabolites elevated in adipose, including l-threonine, aspartic acid, glutamine, mannose, inositol and oleic acid, were detected after HEE consumption. Fifteen metabolites were identified as potential biomarkers of hyperlipidemia. Pathway analysis showed that most of the discriminant metabolites were included in fatty acid biosynthesis, galactose metabolism, biosynthesis of unsaturated fatty acids, arginine and proline metabolism, alanine, aspartate and glutamate metabolism, glycerolipid metabolism and steroid biosynthesis. These metabolites and metabolic networks we found offer new insights into exploring the molecular mechanisms of lipid-lowering of hawthorn ethanol extracts on adipose tissue of rats. PRACTICAL APPLICATION: There was a very high proportion of hyperlipidemia in China. Hawthorn is attracting increasing attention owing to their health benefits, low toxicity, effectiveness and might be suitable for long-term use.

Evaluation of sample preparation protocols for quantitative NMR-based metabolomics

INTRODUCTION

Quantification of metabolites in biological fluids and tissues by NMR spectroscopy is challenged by the presence of abundant macromolecules and lipoproteins in samples, which give broad signals in the NMR spectra. To improve the quality of NMR spectra the different protocols for protein and lipid removal from the sample are used.

OBJECTIVES

This work is aimed at the evaluation of the effectiveness of various methods of purification of blood serum from proteins and lipids for ¹H NMR metabolomic profiling.

METHODS

The advantages and limitations of different methods of the sample preparation for NMR-based quantitative metabolomics have been compared, including ultrafiltration, methanol and ethanol extractions with and without additional lipid removal, and methanol-chloroform extraction.

RESULTS

The concentrations of 30 abundant metabolites extracted from human blood serum have been measured. It is found that ultrafiltration provides the best lipid removal, but causes significant and inhomogeneous metabolite losses. Ethanol and methanol extractions demonstrate similar performance with the minimal metabolite losses, and are ideal for fluids and tissues with low lipid content. The additional purification of alcohol extracts from lipids allows for the significant improving of NMR spectra, but causes additional metabolite losses.

CONCLUSIONS

The methanol-chloroform extraction seems to be an optimal method for tissues with the high lipid content, providing a satisfactory lipid removal and low metabolite losses. The ultrafiltration leads to large losses of metabolites (up to 60%) and for this reason is not suitable for quantitative analysis.