Integrated ionization approach for RRLC-MS/MS-based metabonomics: finding potential biomarkers for lung cancer

Metabolomics and cellular altered pathways in cancer biology: A review

Cancer is a deadly disease that affects a cell's metabolism and surrounding tissues. Understanding the fundamental mechanisms of metabolic alterations in cancer cells would assist in developing cancer treatment targets and approaches. From this perspective, metabolomics is a great analytical tool to clarify the mechanisms of cancer therapy as well as a useful tool to investigate cancer from a distinct viewpoint. It is a powerful emerging technology that detects up to thousands of molecules in tissues and biofluids. Like other "-omics" technologies, metabolomics involves the comprehensive investigation of micromolecule metabolites and can reveal important details about the cancer state that is otherwise not apparent. Recent developments in metabolomics technologies have made it possible to investigate cancer metabolism in greater depth and comprehend how cancer cells utilize metabolic pathways to make the amino acids, nucleotides, and lipids required for tumorigenesis. These new technologies have made it possible to learn more about cancer metabolism. Here, we review the cellular and systemic effects of cancer and cancer treatments on metabolism. The current study provides an overview of metabolomics, emphasizing the current technologies and their use in clinical and translational research settings.

Hierarchical superstructure aerogels for in situ biofluid metabolomics

High-throughput biofluid metabolomics analysis for screening life-threatening diseases is urgently needed. However, the high salt content of biofluid samples, which introduces severe interference, can greatly limit the analysis throughput. Here, a new 3-D interconnected hierarchical superstructure, namely a "plasmonic gold-on-silica (Au/SiO2) double-layered aerogel", integrating distinctive features of an upper plasmonic gold aerogel with a lower inert silica aerogel was successfully developed to achieve in situ separation and storage of inorganic salts in the silica aerogel, parallel enrichment of metabolites on the surface of the functionalized gold aerogel, and direct desorption/ionization of enriched metabolites by the photo-excited gold aerogel for rapid, sensitive, and comprehensive metabolomics analysis of human serum/urine samples. By integrating all these unique advantages into the hierarchical aerogel, multifunctional properties were introduced in the SALDI substrate to enable its effective utilization in clinical metabolomics for the discovery of reliable metabolic biomarkers to achieve unambiguous differentiation of early and advanced-stage lung cancer patients from healthy individuals. This study provides insight into the design and application of superstructured nanomaterials for in situ separation, storage, and photoexcitation of multi-components in complex biofluid samples for sensitive analysis.

Integration of transcriptome and metabolome reveals the accumulation of related metabolites and gene regulation networks during quinoa seed development

Quinoa seeds are gluten- and cholesterol-free, contain all amino acids required by the human body, have a high protein content, provide endocrine regulation, protein supplementation, and cardiovascular protection effects. However, metabolite accumulation and transcriptional regulatory networks in quinoa seed development are not well understood. Four key stages of seed development in Dianli-3260 and Dianli-557 were thus analyzed and 849 metabolites were identified, among which sugars, amino acids, and lipids were key for developmental processes, and their accumulation showed a gradual decrease. Transcriptome analysis identified 40,345 genes, of which 20,917 were differential between the M and F phases, including 8279 and 12,638 up- and down-regulated genes, respectively. Grain development processes were mainly enriched in galactose metabolism, pentose and glucuronate interconversions, the biosynthesis of amino acids, and carbon metabolism pathways, in which raffinose, phosphoenolpyruvate, series and other metabolites are significantly enriched, gene-LOC110689372, Gene-LOC110710556 and gene-LOC110714584 are significantly expressed, and these metabolites and genes play an important role in carbohydrate metabolism, lipid and Amino acid synthesis of quinoa. This study provides a theoretical basis to expand our understanding of the molecular and metabolic development of quinoa grains.

Identification of changes in bile composition in pancreaticobiliary reflux based on liquid chromatography/mass spectrometry metabolomics

INTRODUCTION

Pancreaticobiliary reflux (PBR) can induce gallstone formation; however, its pathogenic mechanism remains unclear. In this study, we explored the mechanism of PBR by the non-targeted metabolomic analysis of bile in patients with PBR.

OBJECTIVE

The aim of this study was to investigate the pathogenic mechanism in PBR by the non-targeted metabolomic analysis of bile collected during surgery.

METHODS

Sixty patients who underwent gallstone surgery at our center from December 2020 to May 2021 were enrolled in the study. According to the level of bile amylase, 30 patients with increased bile amylase ( > 110 U/L) were classified into the PBR group, and the remaining 30 patients were classified into the control group (≤ 110 U/L). The metabolomic analysis of bile was performed.

RESULTS

The orthogonal projections to latent structure-discriminant analysis of liquid chromatography mass spectrometry showed significant differences in bile components between the PBR and control groups, and 40 metabolites were screened by variable importance for the projection value (VIP > 1). The levels of phosphatidylcholine (PC) and PC (20:3(8Z,11Z,14Z)/14:0) decreased significantly, whereas the levels of lysoPC (16:1(9z)/0:0), lysoPC (15:0), lysoPC (16:0), palmitic acid, arachidonic acid, leucine, methionine, L-tyrosine, and phenylalanine increased.

CONCLUSIONS

Significant differences in bile metabolites were observed between the PBR and control groups. Changes in amino acids and lipid metabolites may be related to stone formation and mucosal inflammation.

DT-109 ameliorates nonalcoholic steatohepatitis in nonhuman primates

Nonalcoholic steatohepatitis (NASH) prevalence is rising with no pharmacotherapy approved. A major hurdle in NASH drug development is the poor translatability of preclinical studies to safe/effective clinical outcomes, and recent failures highlight a need to identify new targetable pathways. Dysregulated glycine metabolism has emerged as a causative factor and therapeutic target in NASH. Here, we report that the tripeptide DT-109 (Gly-Gly-Leu) dose-dependently attenuates steatohepatitis and fibrosis in mice. To enhance the probability of successful translation, we developed a nonhuman primate model that histologically and transcriptionally mimics human NASH. Applying a multiomics approach combining transcriptomics, proteomics, metabolomics, and metagenomics, we found that DT-109 reverses hepatic steatosis and prevents fibrosis progression in nonhuman primates, not only by stimulating fatty acid degradation and glutathione formation, as found in mice, but also by modulating microbial bile acid metabolism. Our studies describe a highly translatable NASH model and highlight the need for clinical evaluation of DT-109.

Urinary Biomarkers and Point-of-Care Urinalysis Devices for Early Diagnosis and Management of Disease: A Review

Biosensing and microfluidics technologies are transforming diagnostic medicine by accurately detecting biomolecules in biological samples. Urine is a promising biological fluid for diagnostics due to its noninvasive collection and wide range of diagnostic biomarkers. Point-of-care urinalysis, which integrates biosensing and microfluidics, has the potential to bring affordable and rapid diagnostics into the home to continuing monitoring, but challenges still remain. As such, this review aims to provide an overview of biomarkers that are or could be used to diagnose and monitor diseases, including cancer, cardiovascular diseases, kidney diseases, and neurodegenerative disorders, such as Alzheimer’s disease. Additionally, the different materials and techniques for the fabrication of microfluidic structures along with the biosensing technologies often used to detect and quantify biological molecules and organisms are reviewed. Ultimately, this review discusses the current state of point-of-care urinalysis devices and highlights the potential of these technologies to improve patient outcomes. Traditional point-of-care urinalysis devices require the manual collection of urine, which may be unpleasant, cumbersome, or prone to errors. To overcome this issue, the toilet itself can be used as an alternative specimen collection and urinalysis device. This review then presents several smart toilet systems and incorporated sanitary devices for this purpose.

A novel nomogram model combining CT texture features and urine energy metabolism to differentiate single benign from malignant pulmonary nodule

Objective

To investigate a novel diagnostic model for benign and malignant pulmonary nodule diagnosis based on radiomic and clinical features, including urine energy metabolism index.

Methods

A total of 107 pulmonary nodules were prospectively recruited and pathologically confirmed as malignant in 86 cases and benign in 21 cases. A chest CT scan and urine energy metabolism test were performed in all cases. A nomogram model was established in combination with radiomic and clinical features, including urine energy metabolism levels. The nomogram model was compared with the radiomic model and the clinical feature model alone to test its diagnostic validity, and receiver operating characteristic (ROC) curves were plotted to assess diagnostic validity.

Results

The nomogram was established using a logistic regression algorithm to combine radiomic features and clinical characteristics including urine energy metabolism results. The predictive performance of the nomogram was evaluated using the area under the ROC and calibration curve, which showed the best performance, area under the curve (AUC) = 0.982, 95% CI = 0.940-1.000, compared to clinical and radiomic models in the testing cohort. The clinical benefit of the model was assessed using the decision curve analysis (DCA) and using the nomogram for benign and malignant pulmonary nodules, and preoperative prediction of benign and malignant pulmonary nodules using nomograms showed better clinical benefit.

Conclusion

This study shows that a coupled model combining CT imaging features and clinical features (including urine energy metabolism) in combination with the nomogram model has higher diagnostic performance than the radiomic and clinical models alone, suggesting that the combination of both methods is more advantageous in identifying benign and malignant pulmonary nodules.

Integrative Analysis of the Metabolome and Transcriptome Provides Insights into the Mechanisms of Flavonoid Biosynthesis in Quinoa Seeds at Different Developmental Stages

Quinoa (Chenopodium quinoa Willd.) is a crop with high nutritional and health benefits. Quinoa seeds are rich in flavonoid compounds; however, the mechanisms behind quinoa flavonoid biosynthesis remain unclear. We independently selected the high-generation quinoa strain ‘Dianli-3260′, and used its seeds at the filling, milk ripening, wax ripening, and mature stages for extensive targeted metabolome analysis combined with joint transcriptome analysis. The results showed that the molecular mechanism of flavonoid biosynthesis in quinoa seeds was mainly concentrated in two pathways: “flavonoid biosynthesis pathway” and “flavone and flavonol biosynthesis pathway”. Totally, 154 flavonoid-related metabolites, mainly flavones and flavonols, were detected in the four development stages. Moreover, 39,738 genes were annotated with KEGG functions, and most structural genes of flavonoid biosynthesis were differentially expressed during grain development. We analyzed the differential flavonoid metabolites and transcriptome changes between the four development stages of quinoa seeds and found that 11 differential flavonoid metabolites and 22 differential genes were the key factors for the difference in flavonoid biosynthesis. This study provides important information on the mechanisms underlying quinoa flavonoid biosynthesis, the screening of potential quinoa flavonoid biosynthesis regulation target genes, and the development of quinoa products.

Metabolomics in cancer research and emerging applications in clinical oncology

Cancer has myriad effects on metabolism that include both rewiring of intracellular metabolism to enable cancer cells to proliferate inappropriately and adapt to the tumor microenvironment, and changes in normal tissue metabolism. With the recognition that fluorodeoxyglucose-positron emission tomography imaging is an important tool for the management of many cancers, other metabolites in biological samples have been in the spotlight for cancer diagnosis, monitoring, and therapy. Metabolomics is the global analysis of small molecule metabolites that like other -omics technologies can provide critical information about the cancer state that are otherwise not apparent. Here, the authors review how cancer and cancer therapies interact with metabolism at the cellular and systemic levels. An overview of metabolomics is provided with a focus on currently available technologies and how they have been applied in the clinical and translational research setting. The authors also discuss how metabolomics could be further leveraged in the future to improve the management of patients with cancer.

Spatially Resolved Metabolomics Based on Air-Flow-Assisted Desorption Electrospray Ionization-Mass Spectrometry Imaging Reveals Region-Specific Metabolic Alterations in Diabetic Encephalopathy

Spatially resolved metabolic profiling of brain is vital for elucidating tissue-specific molecular histology and pathology underlying diabetic encephalopathy (DE). In this study, a spatially resolved metabolomic method based on air-flow-assisted desorption electrospray ionization-mass spectrometry imaging (AFADESI-MSI) was developed for investigating the region-specific metabolic disturbances in the brain of DE model rats induced by a high-fat diet in combination with streptozotocin administration. A total of 19 discriminating metabolites associated with glycolysis and the pentose phosphate pathway (PPP); the glutamate/gamma aminobutyric acid-glutamine cycle and tricarboxylic acid cycle; nucleotide metabolism; lipid metabolism; carnitine homeostasis; and taurine, ascorbic acid, histidine, and choline metabolism were identified and located in the brains of the diabetic rats simultaneously for the first time. The results indicated that increased glycolytic and PPP activity; dysfunction of mitochondrial metabolism; dysregulation of adenosinergic, glutamatergic, dopaminergic, cholinergic, and histaminergic systems; disorder of osmotic regulation and antioxidant system; and disorder of lipid metabolism occur in a region-specific fashion in the brains of DE rats. Thus, this study provides valuable information regarding the molecular pathological signature of DE. These findings also underline the high potential of AFADESI-MSI for applications in various central nervous system diseases.

Ratiometric fluorescence nanoprobe for monitoring of intracellular temperature and tyrosine based on a dual emissive carbon dots/gold nanohybrid

A novel dual-emission nitrogen doped carbon dots/gold nanohybrid (NCDs-Au) was designed for specific and sensitive ratiometric detection of intracellular temperature and tyrosine. In this probe, a reductive NCDs was successfully prepared with the use of natural biomass Dendrobium officinale as precursor. The new prepared NCDs acted as both reducers and stabilizers to synthesize a novel NCDs-Au nanohybrid by a facile one-step procedure along with a quantum yield of 14.3%. The prepared nanoprobe showed characteristic fluorescence emissions of NCDs and Au NCs with single-wavelength excitation. Notably, the nanoprobe shows an interesting wavelength-dependent dual response to temperature (448 nm) and tyrosine (660 nm), enabling the two targets to be detected proportionally. As an effective temperature sensor, the nanoprobe exhibited good temperature-dependent fluorescence with a sensational linear response from 5 to 75 °C. In addition, the sensor has a linear response toward tyrosine in the range of 0.5-175 μM with a detection limit of 0.19 μM. Moreover, the fluorescent nanoprobe was successfully applied to ratiometricly monitor the variation of temperature or tyrosine level in cells because of the low cytotoxicity, chemical stability and excellent fluorescence properties. These results suggested that the nanoprobe here has provided the possibility for rapidly biosensing with the acceptable selectivity and sensitivity.

Biomarkers in Early Diagnosis and Early Stage Lung Cancer: The Clinician's Point of View

Starting from the work of Ulivi and colleagues, we aim to summarize the research area of biomarkers for early diagnosis and early stage lung cancer.

Advances in lung cancer biomarkers: The role of (metal-) metabolites and selenoproteins

Lung cancer (LC) is the second most common cause of death in men after prostate cancer, and the third most recurrent type of tumor in women after breast and colon cancers. Unfortunately, when LC symptoms begin to appear, the disease is already in an advanced stage and the survival rate only reaches 2%. Thus, there is an urgent need for early diagnosis of LC using specific biomarkers, as well as effective therapies and strategies against LC. On the other hand, the influence of metals on more than 50% of proteins is responsible for their catalytic properties or structure, and their presence in molecules is determined in many cases by the genome. Research has shown that redox metal dysregulation could be the basis for the onset and progression of LC disease. Moreover, metals can interact between them through antagonistic, synergistic and competitive mechanisms, and for this reason metals ratios and correlations in LC should be explored. One of the most studied antagonists against the toxic action of metals is selenium, which plays key roles in medicine, especially related to selenoproteins. The study of potential biomarkers able to diagnose the disease in early stage is conditioned by the development of new analytical methodologies. In this sense, omic methodologies like metallomics, proteomics and metabolomics can greatly assist in the discovery of biomarkers for LC early diagnosis.

Effect of Huangqin Tang on Urine Metabolic Profile in Rats with Ulcerative Colitis Based on UPLC-Q-Exactive Orbitrap MS

As a classic prescription, Huangqin Tang (HQT) has been widely applied to treat ulcerative colitis (UC), although its pharmacological mechanisms are not clear. In this study, urine metabolomics was first analysed to explore the therapeutic mechanisms of HQT in UC rats induced by TNBS. We identified 28 potential biomarkers affected by HQT that might cause changes in urine metabolism in UC rats, mapped the network of metabolic pathways, and revealed how HQT affects metabolism of UC rats. The results showed that UC affects amino acid metabolism and biosynthesis of unsaturated fatty acids and impairs the tricarboxylic acid cycle (TCA cycle). UC induced inflammatory and gastrointestinal reactions by inhibiting the transport of fatty acids and disrupting amino acid metabolism. HQT plays key roles via regulating the level of biomarkers in the metabolism of amino acids, lipids, and so on, normalizing metabolic disorders. In addition, histopathology and other bioinformatics analysis further confirm that HQT altered UC rat physiology and pathology, ultimately affecting metabolic function of UC rats.

Cancer metabolomic markers in urine: evidence, techniques and recommendations

Urinary tests have been used as noninvasive, cost-effective tools for screening, diagnosis and monitoring of diseases since ancient times. As we progress through the 21st century, modern analytical platforms have enabled effective measurement of metabolites, with promising results for both a deeper understanding of cancer pathophysiology and, ultimately, clinical translation. The first study to measure metabolomic urinary cancer biomarkers using NMR and mass spectrometry (MS) was published in 2006 and, since then, these techniques have been used to detect cancers of the urological system (kidney, prostate and bladder) and nonurological tumours including those of the breast, ovary, lung, liver, gastrointestinal tract, pancreas, bone and blood. This growing field warrants an assessment of the current status of research developments and recommendations to help systematize future research.

Untargeted metabolomics reveals novel serum biomarker of renal damage in rheumatoid arthritis

Rheumatoid arthritis (RA) is a chronic progressive disease, it often involves kidney, lung, heart, and other systems.Renal damage is quite common in RA. Exploring of biomarkers of renal damage in the course of RA progression is of significant importance for disease diagnosis and treatment. We use type II Collagen-Induced Arthritis(CIA) Model. Serums were collected at the 4th, 6th, 8th, and 10th week after the first immunization. An untargeted metabonomic strategy based on UPLC-Q/TOF/MS with support vector machine(SVM) was developed to discover the biomarkers in the rats' serum samples between the RA stage(4-6 weeks in RA model, at which time the kidneys are not affected) and renal damage in RA stage(8-10 weeks in RA model, and the kidneys are affected). Principal component analysis (PCA) and orthogonal partial least squares-discriminant analysis (OPLS-DA) were used to analyze the metabolic profiles of rat serum. The support vector machine (SVM) method was used to screen the specific markers of renal damage in RA. Following multivariate statistical and integration analysis, 5 specific markers of renal damage in RA were screened and found. After the analysis of these metabolites, pentose and glucuronate interconversions are closely related to the pathogenesis of RA renal damage. The present study first use untargeted dmetabonomics combined with the pathological features in the different phases of CIA model rats. This will provide a basis for the choice of treatment drugs for patients with RA who may be complicated by renal damage.

Targeted metabolomics for serum amino acids and acylcarnitines in patients with lung cancer

Lung cancer is one of the most prevalent types of cancer, but accurate diagnosis remains a challenge. The aim of the present study was to create a model using amino acids and acylcarnitines for lung cancer screening. Serum samples were obtained from two groups of patients with lung cancer recruited in 2015 (including 40 patients and 100 matched controls) and 2017 (including 17 patients and 30 matched controls). Using a metabolomics method, 21 metabolites (13 types of amino acids and 8 types of acylcarnitines) were measured using liquid chromatography-tandem mass spectrometry. Data (from the 2015 and 2017 data sets) were analysed using a Mann-Whitney U test, Student's t-test, Welch's F test, receiver-operator characteristic curve or logistic regression in order to investigate the potential biomarkers. Six metabolites (glycine, valine, methionine, citrulline, arginine and C16-carnitine) were indicated to be involved in distinguishing patients with lung cancer from healthy controls. The six discriminating metabolites from the 2017 data set were further analysed using Partial least squares-discriminant analysis (PLS-DA). The PLS-DA model was verified using Spearman's correlation analysis and receiver operating characteristic curve analysis. These results demonstrated that the PLS-DA model using the six metabolites (glycine, valine, methionine, citrulline, arginine and C16-carnitine) had a strong ability to identify lung cancer. Therefore, the PLS-DA model using glycine, valine, methionine, citrulline, arginine and C16-carnitine may become a novel screening tool in patients with lung cancer.

Serum Metabolomics Study of Nonsmoking Female Patients with Non-Small Cell Lung Cancer Using Gas Chromatography-Mass Spectrometry

The incidence of nonsmoking female patients with non-small cell lung cancer (NSCLC) has increased in recent decades; however, the pathogenesis of patients is unclear, and early diagnosis biomarkers are in urgent need. In this study, 136 nonsmoking female subjects (65 patients with NSCLC, 6 patients with benign lung tumors, and 65 healthy controls) were enrolled, and their metabolic profiling was investigated by using pseudotargeted gas chromatography-mass spectrometry. A total of 56 annotated metabolites were found and verified to be significantly different in nonsmoking females with NSCLC compared with the control. The metabolic profiling was featured by disturbed energy metabolism, amino acid metabolism, oxidative stress, lipid metabolism, and so on. Cysteine, serine, and 1-monooleoylglycerol were defined as the biomarker panel for the diagnosis of NSCLC patients. 98.5 and 91.4% of subjects were correctly distinguished in the discovery and validation sets, respectively. The biomarker panel was also useful for the diagnosis of in situ malignancy patients, with an accuracy of 97.7 and 97.8% in the discovery and validation sets, respectively. The study provides a biomarker panel for the auxiliary diagnosis of nonsmoking females with NSCLC.

Untargeted metabolomics profiles delineate metabolic alterations in mouse plasma during lung carcinoma development using UPLC-QTOF/MS in MSE mode

In this work, an untargeted metabolomic method based on ultra-high-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-QTOF/MS) in MSE (E represents collision energy) mode was exploited to determine the dynamic metabolic alterations in the plasma of male C57BL/6 mice during the onset and development of lung carcinoma. Plasma samples were collected from control and model mice (male C57BL/6 mice experimentally inoculated with the Lewis lung carcinoma cells) at 7 and 14 days post-inoculation (DPI). As a result, 15 dysregulated metabolites, including cholesterol sulphate, tiglylcarnitine, 1-palmitoylglycerophosphoinositol, 2-stearoylglycerophosphoinositol, stearoylcarnitine, PC(20:2(11Z,14Z)/16:0), PC(22:4(7Z,10Z,13Z,16Z)/14:0), PC(22:5(7Z,10Z,13Z,16Z,19Z)/14:0), PC(22:6(4Z,7Z,10Z,13Z,16Z,19Z)/16:0), 12,20-Dioxo-leukotriene B4, sphingosine 1-phosphate(d19:1-P), sphingomyelin(d18:0/16:1(9Z)), lysoPC(16:0), lysoPC(18:0) and lysoPC(20:4(5Z,8Z,11Z,14Z)), were identified in the plasma of model mice with xenografts at both 7 and 14 DPI. All the altered metabolites associated with the onset and development of lung carcinoma were involved in the metabolism of glycerophospholipid, fatty acid, sphingolipid and arachidonic acid. The feasible utility of these endogenous biomarkers as potential diagnostic indicators was validated through receiver operating characteristic curve analysis. Collectively, these findings provide a systematic view of metabolic changes linked to the onset and development of lung carcinoma.

Metabolomics of Hydrazine-Induced Hepatotoxicity in Rats for Discovering Potential Biomarkers

Metabolic pathway disturbances associated with drug-induced liver injury remain unsatisfactorily characterized. Diagnostic biomarkers for hepatotoxicity have been used to minimize drug-induced liver injury and to increase the clinical safety. A metabolomics strategy using rapid-resolution liquid chromatography/tandem mass spectrometry (RRLC-MS/MS) analyses and multivariate statistics was implemented to identify potential biomarkers for hydrazine-induced hepatotoxicity. The global serum and urine metabolomics of 30 hydrazine-treated rats at 24 or 48 h postdosing and 24 healthy rats were characterized by a metabolomics approach. Multivariate statistical data analyses and receiver operating characteristic (ROC) curves were performed to identify the most significantly altered metabolites. The 16 most significant potential biomarkers were identified to be closely related to hydrazine-induced liver injury. The combination of these biomarkers had an area under the curve (AUC) > 0.85, with 100% specificity and sensitivity, respectively. This high-quality classification group included amino acids and their derivatives, glutathione metabolites, vitamins, fatty acids, intermediates of pyrimidine metabolism, and lipids. Additionally, metabolomics pathway analyses confirmed that phenylalanine, tyrosine, and tryptophan biosynthesis as well as tyrosine metabolism had great interactions with hydrazine-induced liver injury in rats. These discriminating metabolites might be useful in understanding the pathogenesis mechanisms of liver injury and provide good prospects for drug-induced liver injury diagnosis clinically.

Targeting human urinary metabolome by LC-MS/MS: a review

Urine is a biological matrix that contains hundreds of metabolic end products which constitute the urinary metabolome. The development and advances on LC-MS/MS have revolutionized the analytical study of biomolecules by enabling their accurate identification and quantification in an unprecedented manner. Nowadays, LC-MS/MS is helping to unveil the complexity of urine metabolome, and the results obtained have multiple biomedical applications. This review focuses on the targeted LC-MS/MS analysis of the urine metabolome. In the first part, we describe general considerations (from sample collection to quantitation) required for a proper targeted metabolic analysis. In the second part, we address the urinary analysis and recent applications of four relevant families: amino acids, catecholamines, lipids and steroids.

Next-generation metabolomics in lung cancer diagnosis, treatment and precision medicine: mini review

Lung cancer is the leading cause of cancer-related death. Next-generation metabolomics is becoming a powerful emerging technology for studying the systems biology and chemistry of health and disease. This mini review summarized the main platforms of next-generation metabolomics and its main applications in lung cancer including early diagnosis, pathogenesis, classifications and precision medicine. The period covers between 2009 and August, 2017. The major issues and future directions of metabolomics in lung cancer research and clinical applications were also discussed.

LC-MS-based metabolomics reveals metabolic signatures related to glioma stem-like cell self-renewal and differentiation

A metabolomic study of three glioma cell lines with different stemness were conducted. The specific metabolite signatures associated with SLC self-renewal and differentiation were characterized.

Metabolomics, a Powerful Tool for Agricultural Research

Metabolomics, which is based mainly on nuclear magnetic resonance (NMR), gas-chromatography (GC) or liquid-chromatography (LC) coupled to mass spectrometry (MS) analytical technologies to systematically acquire the qualitative and quantitative information of low-molecular-mass endogenous metabolites, provides a direct snapshot of the physiological condition in biological samples. As complements to transcriptomics and proteomics, it has played pivotal roles in agricultural and food science research. In this review, we discuss the capacities of NMR, GC/LC-MS in the acquisition of plant metabolome, and address the potential promise and diverse applications of metabolomics, particularly lipidomics, to investigate the responses of Arabidopsis thaliana, a primary plant model for agricultural research, to environmental stressors including heat, freezing, drought, and salinity.

Global metabolomics reveals potential urinary biomarkers of esophageal squamous cell carcinoma for diagnosis and staging

We performed a metabolomics study using liquid chromatography-mass spectrometry (LC-MS) combined with multivariate data analysis (MVDA) to discriminate global urine profiles in urine samples from esophageal squamous cell carcinoma (ESCC) patients and healthy controls (NC). Our work evaluated the feasibility of employing urine metabolomics for the diagnosis and staging of ESCC. The satisfactory classification between the healthy controls and ESCC patients was obtained using the MVDA model, and obvious classification of early-stage and advanced-stage patients was also observed. The results suggest that the combination of LC-MS analysis and MVDA may have potential applications for ESCC diagnosis and staging. We then conducted LC-MS/MS experiments to identify the potential biomarkers with large contributions to the discrimination. A total of 83 potential diagnostic biomarkers for ESCC were screened out, and 19 potential biomarkers were identified; the variations between the differences in staging using these potential biomarkers were further analyzed. These biomarkers may not be unique to ESCCs, but instead result from any malignant disease. To further elucidate the pathophysiology of ESCC, we studied related metabolic pathways and found that ESCC is associated with perturbations of fatty acid β-oxidation and the metabolism of amino acids, purines, and pyrimidines.

Potential Metabolic Biomarkers to Identify Interstitial Lung Abnormalities

Determining sensitive biomarkers in the peripheral blood to identify interstitial lung abnormalities (ILAs) is essential for the simple early diagnosis of ILAs. This study aimed to determine serum metabolic biomarkers of ILAs and the corresponding pathogenesis. Three groups of subjects undergoing health screening, including healthy subjects, subjects with ILAs, and subjects who were healthy initially and with ILAs one year later (Healthy→ILAs), were recruited for this study. The metabolic profiles of all of the subjects’ serum were analyzed by liquid chromatography quadruple time-of-flight mass spectrometry. The metabolic characteristics of the ILAs subjects were discovered, and the corresponding biomarkers were predicted. The metabolomic data from the Healthy→ILAs subjects were collected for further verification. The results indicated that five serum metabolite alterations (up-regulated phosphatidylcholine, phosphatidic acid, betaine aldehyde and phosphatidylethanolamine, as well as down-regulated 1-acylglycerophosphocholine) were sensitive and reliable biomarkers for identifying ILAs. Perturbation of the corresponding biological pathways (RhoA signaling, mTOR/P70S6K signaling and phospholipase C signaling) might be at least partially responsible for the pathogenesis of ILAs. This study may provide a good template for determining the early diagnostic markers of subclinical disease status and for obtaining a better understanding of their pathogenesis.

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.

Integrated Metabolomics Study of the Milk of Heat-stressed Lactating Dairy Cows

Heat stress (HS) damages the global dairy industry by reducing milk yields and quality, harming health, and damaging the reproduction of dairy cows, causing huge economic losses each year. However, an understanding of the physiological mechanism of HS lactating dairy cows remains elusive. Here, a metabolomics study using LC-MS and ¹H NMR spectroscopy was performed to analyze the metabolomic differences in the milk between HS-free and HS dairy cows, and discover diagnostic biomarkers and changes in the metabolic pathway. A total of 53 discriminating metabolites were significantly up- or down-regulated in the HS group compared with the HS-free group (P < 0.05). These biomarkers were involved in pathways of carbohydrate, amino acid, lipid, and gut microbiome-derived metabolism. Comparing these potential biomarkers with previously identified HS candidate biomarkers in plasma, significant correlations between the levels of lactate, pyruvate, creatine, acetone, β-hydroxybutyrate, trimethylamine, oleic acid, linoleic acid, lysophosphatidylcholine 16:0, and phosphatidylcholine 42:2 in milk and plasma were found, indicating that the blood-milk barrier became leaky and the levels of these 10 biomarkers in milk can reflect HS-induced metabolomic alterations in blood. These novel findings can support more in-depth research to elucidate the milk-based changes in metabolic pathways in HS lactating dairy cows.

Progress in the development of volatile exhaled breath signatures of lung cancer

RATIONALE

Volatile organic compounds present in the exhaled breath have shown promise as biomarkers of lung cancer. Advances in colorimetric sensor array technology, breath collection methods, and clinical phenotyping may lead to the development of a more accurate breath biomarker.

OBJECTIVES

Perform a discovery-level assessment of the accuracy of a colorimetric sensor array-based volatile breath biomarker.

METHODS

Subjects with biopsy-confirmed untreated lung cancer, and others at risk for developing lung cancer, performed tidal breathing into a breath collection instrument designed to expose a colorimetric sensor array to the alveolar portion of the breath. Random forest models were built from the sensor output of 70% of the study subjects and were tested against the remaining 30%. Models were developed to separate cancer and subgroups from control, and to characterize the cancer. Additional models were developed after matching the clinical phenotypes of cancer and control subjects.

MEASUREMENTS AND MAIN RESULTS

Ninety-seven subjects with lung cancer and 182 control subjects participated. The accuracies, reported as C-statistics, for models of cancer and subgroups versus control ranged from 0.794 to 0.861. The accuracy was improved by developing models for cancer and control groups selected through propensity matching for clinical variables. A model built using only subjects from the largest available clinical subgroup (49 subjects) had a C-statistic of 0.982. Models developed and tested to characterize cancer histology, and to compare early- with late-stage cancer, had C-statistics of 0.881-0.960.

CONCLUSIONS

The colorimetric sensor array signature of exhaled breath volatile organic compounds was capable of distinguishing patients with lung cancer from clinically relevant control subjects in a discovery level trial. The incorporation of clinical phenotypes into the further development of this biomarker may optimize its accuracy.

Metabolic changes in rat urine after acute paraquat poisoning and discriminated by support vector machine

Paraquat is quick-acting and non-selective, killing green plant tissue on contact; it is also toxic to human beings and animals. In this study, we developed a urine metabonomic method by gas chromatography-mass spectrometry to evaluate the effect of acute paraquat poisoning on rats. Pattern recognition analysis, including both partial least squares discriminate analysis and principal component analysis revealed that acute paraquat poisoning induced metabolic perturbations. Compared with the control group, the levels of benzeneacetic acid and hexadecanoic acid of the acute paraquat poisoning group (intragastric administration 36 mg/kg) increased, while the levels of butanedioic acid, pentanedioic acid, altronic acid decreased. Based on these urinary metabolomics data, support vector machine was applied to discriminate the metabolomic change of paraquat groups from the control group, which achieved 100% classification accuracy. In conclusion, metabonomic method combined with support vector machine can be used as a useful diagnostic tool in paraquat-poisoned rats.

Enzymatic-induced upconversion photoinduced electron transfer for sensing tyrosine in human serum

This paper reports a novel nanosensor for tyrosine based on photoinduced electron-transfer (PET) between NaYF4:Yb, Tm upconversion nanoparticles (UCNPs) and melanin-like polymers. Melanin-like films were obtained from catalytic oxidation of tyrosine by tyrosinase, and deposited on the surface of UCNPs, and then quenched the fluorescence of UCNPs. Under the optimized conditions, the fluorescence quenching of UCNPs showed a good linear response to tyrosine concentration in the range of 0.8-100 μΜ with a detection limit of 1.1 μΜ. Meanwhile, it showed good sensitivity, stability and has been successfully applied to the detection of tyrosine in human serum.

The strengths and weaknesses of NMR spectroscopy and mass spectrometry with particular focus on metabolomics research

Mass spectrometry (MS) and nuclear magnetic resonance (NMR) have evolved as the most common techniques in metabolomics studies, and each brings its own advantages and limitations. Unlike MS spectrometry, NMR spectroscopy is quantitative and does not require extra steps for sample preparation, such as separation or derivatization. Although the sensitivity of NMR spectroscopy has increased enormously and improvements continue to emerge steadily, this remains a weak point for NMR compared with MS. MS-based metabolomics provides an excellent approach that can offer a combined sensitivity and selectivity platform for metabolomics research. Moreover, different MS approaches such as different ionization techniques and mass analyzer technology can be used in order to increase the number of metabolites that can be detected. In this chapter, the advantages, limitations, strengths, and weaknesses of NMR and MS as tools applicable to metabolomics research are highlighted.

Data from identification of diagnostic biomarkers and metabolic pathway shifts of heat-stressed lactating dairy cows

Controlling heat stress (HS) is a global challenge for the dairy industry. In this work, an integrated metabolomics and lipidomics approach using (1)H nuclear magnetic resonance (NMR) and ultra-fast LC-MS in combination with multivariate analyses was employed to investigate the discrimination of plasma metabolic profiles between HS-free and HS lactating dairy cows. Here we provide the information about the acquiring and processing of raw data obtained by (1)H NMR and LC-MS techniques. The data of present study are related to the research article "Identification of diagnostic biomarkers and metabolic pathway shifts of heat-stressed lactating dairy cows" in the Journal of Proteomics (Tian et al., J. Proteomics, (2015), doi:10.1016/j.jprot.2015.04.014).

Human metabolic responses to chronic environmental polycyclic aromatic hydrocarbon exposure by a metabolomic approach

The toxicities of polycyclic aromatic hydrocarbons (PAHs) have been extensively explored due to their carcinogenic and mutagenic potency; however, little is known about the metabolic responses to chronic environmental PAH exposure among the general population. In the present study, 566 healthy volunteers were dichotomized into exposed and control groups to investigate PAH-induced perturbations in the metabolic profiles. Nine urine PAH metabolites were measured by a sensitive LC-MS/MS method to comprehensively evaluate the PAH exposure level of each individual, and the metabolic profiles were characterized via a LC-MS-based metabolomic approach. PAH exposure was correlated to its metabolic outcomes by linear and logistic regression analyses. Metabolites related to amino acid, purine, lipid, and glucuronic acid metabolism were significantly changed in the exposed group. 1-Hydroxyphenanthrene and dodecadienylcarnitine have potential as sensitive and reliable biomarkers for PAH exposure and its metabolic outcomes, respectively, in the general population. These findings generally support the hypothesis that environmental PAH exposure causes oxidative stress-related effects in humans. The current study provides new insight into the early molecular events induced by PAH exposure in the actual environment.

Identification of diagnostic biomarkers and metabolic pathway shifts of heat-stressed lactating dairy cows

Controlling heat stress (HS) is a global challenge for the dairy industry. However, simple and reliable biomarkers that aid the diagnoses of HS-induced metabolic disorders have not yet been identified. In this work, an integrated metabolomic and lipidomic approach using (1)H nuclear magnetic resonance and ultra-fast LC-MS was employed to investigate the discrimination of plasma metabolic profiles between HS-free and HS lactating dairy cows. Targeted detection using LC-MS in multiple reaction monitoring mode was used to verify the reliability of the metabolites as biomarker candidates. Overall, 41 metabolites were identified as candidates for lactating dairy cows exposed to HS, among which 13 metabolites, including trimethylamine, glucose, lactate, betaine, creatine, pyruvate, acetoacetate, acetone, β-hydroxybutyrate, C16 sphinganine, lysophosphatidylcholine (18:0), phosphatidylcholine (16:0/14:0), and arachidonic acid, had high sensitivity and specificity in diagnosing HS status, and are likely to be the potential biomarkers of HS dairy cows. All of these potentially diagnostic biomarkers were involved in carbohydrate, amino acid, lipid, or gut microbiome-derived metabolism, indicating that HS affected the metabolic pathways in lactating dairy cows. Further research is warranted to evaluate these biomarkers in practical applications and to elucidate the physiological mechanisms of HS-induced metabolic disorders.

BIOLOGICAL SIGNIFICANCE

Heat stress (HS) annually causes huge losses to global dairy industry, including animal performance decrease, metabolic disorder and health problem. So far, physiological mechanisms underlying HS of dairy cows still remain elusive. To our best knowledge, this is the first attempt to elucidate the HS-induced metabolic disorders of dairy cows using integrated (1)H NMR and LC-MS-based metabolic study. The results not only provided potential diagnostic biomarkers for HS lactating dairy cows, but also significantly explored the related physiological mechanisms of metabolic pathway shifts induced by HS environment. This work offers comprehensive insights into the global metabolic alterations of dairy cows exposed to HS and provides a new perspective for further study.

A new strategy for surface modification of polysulfone membrane by in situ imprinted sol–gel method for the selective separation and screening ofl-Tyrosine as a lung cancer biomarker

Schematic representation of surface modification of polysulfone membrane byin situMIP sol–gel process.

The early diagnosis and monitoring of squamous cell carcinoma via saliva metabolomics

Early diagnosis of oral squamous cell carcinoma (OSCC) is an attractive strategy to increase survival rate of patient. An integrated separation approach of reversed phase liquid chromatography and hydrophilic interaction chromatography combining with time of flight mass spectrometer has been firstly developed for performing global saliva metabonomics analysis for early diagnosis of OSCC. This approach was designed to overcome the limitations of a single chromatographic method due to different polarity of endogenous metabolites. As a result, 14 potential salivary metabolites were identified. Eight biomarkers up-regulated in OSCC patients are compared with control and six down-regulated groups. Receiver operating characteristic analysis was exploited to evaluate the diagnostic power of the candidate biomarkers, and related metabolic pathways have also been studied. Five salivary biomarkers (propionylcholine, N-Acetyl-L-phenylalanine, sphinganine, phytosphingosine, and S-carboxymethyl-L-cysteine) in combination yielded satisfactory accuracy (AUC = 0.997), sensitivity (100%), and specificity (96.7%) in distinguishing early stage of OSCC from the control. In this study, a comprehensive saliva metabonomics analysis for identifying potential biomarkers to early diagnose OSCC is successfully demonstrated, which has the advantages of non-invasive, simple, reliable, and low-cost. These novel metabolic biomarkers have obvious clinical utility that will help to diagnose OSCC at its early stage.