The development of plasma pseudotargeted GC-MS metabolic profiling and its application in bladder cancer

Targeting Metabolic Reprogramming in Bladder Cancer Immunotherapy: A Precision Medicine Approach

Bladder cancer, as a highly heterogeneous malignant tumor of the urinary system, is significantly affected by tumor metabolic reprogramming in its response to immunotherapy. This review systematically elaborates on the molecular mechanisms of abnormal glucose and lipid metabolism in the bladder cancer microenvironment and immune escape, and discusses precision treatment strategies based on metabolic regulation. In the future, it will be necessary to combine spatiotemporal omics and artificial intelligence technologies to construct a multi-target intervention system for the metabolic-immune interaction network, promoting a paradigm shift in precision treatment for bladder cancer.

Metabolomics for the diagnosis of bladder cancer: A systematic review

Objective

Metabolomics has been extensively utilized in bladder cancer (BCa) research, employing mass spectrometry and nuclear magnetic resonance spectroscopy to compare various variables (tissues, serum, blood, and urine). This study aimed to identify potential biomarkers for early BCa diagnosis.

Methods

A search strategy was designed to identify clinical trials, descriptive and analytical observational studies from databases such as Medline, Embase, Cochrane Central Register of Controlled Trials, and Latin American and Caribbean Literature in Health Sciences. Inclusion criteria comprised studies involving BCa tissue, serum, blood, or urine profiling using widely adopted metabolomics techniques like mass spectrometry and nuclear magnetic resonance. Primary outcomes included description of metabolites and metabolomics profiling in BCa patients and the association of metabolites and metabolomics profiling with BCa diagnosis compared to control patients. The risk of bias was assessed using the Quality Assessment of Studies of Diagnostic Accuracy.

Results

The search strategy yielded 2832 studies, of which 30 case-control studies were included. Urine was predominantly used as the primary sample for metabolite identification. Risk of bias was often unclear inpatient selection, blinding of the index test, and reference standard assessment, but no applicability concerns were observed. Metabolites and metabolomics profiles associated with BCa diagnosis were identified in glucose, amino acids, nucleotides, lipids, and aldehydes metabolism.

Conclusion

The identified metabolites in urine included citric acid, valine, tryptophan, taurine, aspartic acid, uridine, ribose, phosphocholine, and carnitine. Tissue samples exhibited elevated levels of lactic acid, amino acids, and lipids. Consistent findings across tissue, urine, and serum samples revealed downregulation of citric acid and upregulation of lactic acid, valine, tryptophan, taurine, glutamine, aspartic acid, uridine, ribose, and phosphocholine.

Unmasking the Metabolite Signature of Bladder Cancer: A Systematic Review

Bladder cancer (BCa) research relying on Omics approaches has increased over the last few decades, improving the understanding of BCa pathology and contributing to a better molecular classification of BCa subtypes. To gain further insight into the molecular profile underlying the development of BCa, a systematic literature search was performed in PubMed until November 2023, following the PRISMA guidelines. This search enabled the identification of 25 experimental studies using mass spectrometry or nuclear magnetic resonance-based approaches to characterize the metabolite signature associated with BCa. A total of 1562 metabolites were identified to be altered by BCa in different types of samples. Urine samples displayed a higher likelihood of containing metabolites that are also present in bladder tumor tissue and cell line cultures. The data from these comparisons suggest that increased concentrations of L-isoleucine, L-carnitine, oleamide, palmitamide, arachidonic acid and glycoursodeoxycholic acid and decreased content of deoxycytidine, 5-aminolevulinic acid and pantothenic acid should be considered components of a BCa metabolome signature. Overall, molecular profiling of biological samples by metabolomics is a promising approach to identifying potential biomarkers for early diagnosis of different BCa subtypes. However, future studies are needed to understand its biological significance in the context of BCa and to validate its clinical application.

Warburg effect enhanced by AKR1B10 promotes acquired resistance to pemetrexed in lung cancer-derived brain metastasis

BACKGROUND

Resistance to pemetrexed (PEM), a rare chemotherapeutic agent that can efficiently cross the blood-brain barrier, limits the therapeutic efficacy for patients with lung cancer brain metastasis (BM). Aldo-keto reductase family 1 B10 (AKR1B10) was recently found to be elevated in lung cancer BM. The link between AKR1B10 and BM-acquired PEM is unknown.

METHODS

PEM drug-sensitivity was assessed in the preclinical BM model of PC9 lung adenocarcinoma cells and the BM cells with or without AKR1B10 interference in vitro and in vivo. Metabolic reprogramming of BM attributed to AKR1B10 was identified by chromatography-mass spectrometry (GC-MS) metabolomics, and the mechanism of how AKR1B10 mediates PEM chemoresistance via a way of modified metabolism was revealed by RNA sequencing as well as further molecular biology experimental approaches.

RESULTS

The lung cancer brain metastatic subpopulation cells (PC9-BrM3) exhibited significant resistance to PEM and silencing AKR1B10 in PC9-BrM3 increased the PEM sensitivity in vitro and in vivo. Metabolic profiling revealed that AKR1B10 prominently facilitated the Warburg metabolism characterized by the overproduction of lactate. Glycolysis regulated by AKR1B10 is vital for the resistance to PEM. In mechanism, AKR1B10 promoted glycolysis by regulating the expression of lactate dehydrogenase (LDHA) and the increased lactate, acts as a precursor that stimulates histone lactylation (H4K12la), activated the transcription of CCNB1 and accelerated the DNA replication and cell cycle.

CONCLUSIONS

Our finding demonstrates that AKR1B10/glycolysis/H4K12la/CCNB1 promotes acquired PEM chemoresistance in lung cancer BM, providing novel strategies to sensitize PEM response in the treatment of lung cancer patients suffering from BM.

Paclitaxel Induces Neurotoxicity by Disrupting Tricarboxylic Acid Cycle Metabolic Balance in the Mouse Hippocampus

Objective

It is well known that paclitaxel (PTX)-induced neurotoxicity seriously affects the quality of life of patients and is the main reason for reducing the dose of chemotherapy or even stopping chemotherapy. The current data are limited, and further information is required for practice and verification. The aims of this study were to clarify the molecular mechanism underlying PTX-induced neurotoxicity by combining in vivo and in vitro metabolomics studies and provide new targets for the prevention and treatment of PTX-induced neurotoxicity.

Methods

In the in vivo study, a PTX-induced neurotoxicity mouse model was established by intraperitoneal injection of PTX (6 mg/kg every three days) for two consecutive weeks. After verification by water maze tests and HE staining of pathological sections, hippocampal metabolites were measured and the differential metabolites and related metabolic pathways were identified by multivariate statistical analysis. In the in vitro study, we investigated the effects of PTX on mouse hippocampal neuron cells, assessing the concentration and time of administration by MTT assays. After modeling, the relevant metabolites in the TCA cycle were quantified by targeted metabolomics using stable isotope labeling. Finally, the key enzymes of the TCA cycle in tissues and cells were verified by RT-PCR.

Results

Administration of PTX to model mice resulted in neurological damage, shown by both water-maze tests and hippocampal tissue sections. Twenty-four metabolites and five associated metabolic pathways were found to differ significantly between the hippocampal tissues of the model and control groups. These included metabolites and pathways related to the TCA cycle and pyruvate metabolism. Metabolomics analysis using stable isotope labeling showed significant changes in metabolites associated with the TCA cycle compared with the control group (P < 0.05). Finally, RT-PCR verified that the expression of key enzymes in the TCA cycle was changed to different degrees in both hippocampal tissues and cells.

Conclusion

Our results showed that PTX neurotoxicity in hippocampal tissue and neuron cells was associated with inhibition of the TCA cycle. This inhibition leads to brain insufficiency and impaired metabolism, resulting in various neurotoxic symptoms.

Metabolomic and elemental profiling of blood serum in bladder cancer

Bladder cancer (BC) is one of the most frequently diagnosed types of urinary cancer. Despite advances in treatment methods, no specific biomarkers are currently in use. Targeted and untargeted profiling of metabolites and elements of human blood serum from 100 BC patients and the same number of normal controls (NCs), with external validation, was attempted using three analytical methods, i.e., nuclear magnetic resonance, gold and silver-109 nanoparticle-based laser desorption/ionization mass spectrometry (LDI-MS), and inductively coupled plasma optical emission spectrometry (ICP-OES). All results were subjected to multivariate statistical analysis. Four potential serum biomarkers of BC, namely, isobutyrate, pyroglutamate, choline, and acetate, were quantified with proton nuclear magnetic resonance, which had excellent predictive ability as judged by the area under the curve (AUC) value of 0.999. Two elements, Li and Fe, were also found to distinguish between cancer and control samples, as judged from ICP-OES data and AUC of 0.807 (in validation set). Twenty-five putatively identified compounds, mostly related to glycans and lipids, differentiated BC from NCs, as detected using LDI-MS. Five serum metabolites were found to discriminate between tumor grades and nine metabolites between tumor stages. The results from three different analytical platforms demonstrate that the identified distinct serum metabolites and metal elements have potential to be used for noninvasive detection, staging, and grading of BC.

Untargeted ultra-high-resolution mass spectrometry metabolomic profiling of blood serum in bladder cancer

Bladder cancer (BC) is a common urological cancer of high mortality and recurrence rates. Currently, cystoscopy is performed as standard examination for the diagnosis and subsequent monitoring for recurrence of the patients. Frequent expensive and invasive procedures may deterrent patients from regular follow-up screening, therefore it is important to look for new non-invasive methods to aid in the detection of recurrent and/or primary BC. In this study, ultra-high-performance liquid chromatography coupled with ultra-high-resolution mass spectrometry was employed for non-targeted metabolomic profiling of 200 human serum samples to identify biochemical signatures that differentiate BC from non-cancer controls (NCs). Univariate and multivariate statistical analyses with external validation revealed twenty-seven metabolites that differentiate between BC patients from NCs. Abundances of these metabolites displayed statistically significant differences in two independent training and validation sets. Twenty-three serum metabolites were also found to be distinguishing between low- and high-grade of BC patients and controls. Thirty-seven serum metabolites were found to differentiate between different stages of BC. The results suggest that measurement of serum metabolites may provide more facile and less invasive diagnostic methodology for detection of bladder cancer and recurrent disease management.

LC-MS metabolomics of urine reveals distinct profiles for non-muscle-invasive and muscle-invasive bladder cancer

PURPOSE

Bladder cancer (BC) is among the most frequent malignancies worldwide. Novel non-invasive markers are needed to diagnose and stage BC with more accuracy than invasive procedures like cystoscopy. To date, no study has identified urine metabolites characteristic of all BC stages. To discover novel urine metabolomic profiles to diagnose and stage non-muscle-invasive (NMIBC) and muscle-invasive (MIBC) patients using mass spectrometry-based metabolomics.

METHODS

We prospectively recruited 198 BC patients and 98 age- and sex-matched healthy volunteers without evidence of renal or bladder condition confirmed by ultrasound, from whom we collected a first morning urine sample (before surgery in patients). In a discovery stage, an untargeted metabolomic analysis was conducted in urine samples of a selection of 64 BC patients (19 TaG1, 11 TaG3, 20 T1G3, 12 T2G3, 1 T2G2, 1 T3G3) and 20 controls to identify dysregulated metabolites. Next, after exhaustive multivariate analysis, confirmed dysregulated metabolites were validated in an independent cohort of 134 BC patients (19 TaG1, 62 TaG2, 9 TaG3, 15 T1G2, 16 T1G3, 4 T2G2, 9 T2G3) and 78 controls.

RESULTS

We validated p-cresol glucuronide as potential diagnostic biomarker for BC patients compared to controls (AUC = 0.79). For NMIBC, p-cresol glucuronide was valuable as staging biomarker (AUC = 0.803). And among NMIBCs, p-coumaric acid may be a potential specific staging biomarker for the TaG1 NMIBC; however, future validation experiments should be conducted once the precise version of the standard is commercially available. Remarkably, for MIBC we validated spermine as potential specific staging biomarker (AUC = 0.882).

CONCLUSION

Ours is the first metabolomics study conducted in urine of a thoroughly characterized cohort comprising all stages of NMIBC, MIBC and healthy controls in which we identified non-invasive diagnostic and staging biomarkers. These may improve BC management, thus reducing the use of current harmful diagnostic techniques.

Biomarkers of Metabolomics in Inflammatory Bowel Disease and Damp-Heat Syndrome: A Preliminary Study

Aims

This study aims to investigate the potential biomarkers of inflammatory bowel disease (IBD) and IBD with damp-heat syndrome (IBD-DH) by metabolomics.

Methods

Plasma and urine samples were collected from 15 healthy controls and 30 IBD patients, including 15 IBD-DH and 15 IBD with spleen deficiency syndrome (IBD-SD), which was coded as SF8G and SF70 according to the International Classification of Diseases Eleventh Revision (ICD-11) issued by World Health Organization. Pseudotargeted metabolomics method was used based on ultra-high-performance liquid chromatography-high-resolution mass spectrometry and triple-quadrupole mass spectrometry.

Results

Under the condition of false discovery rate (FDR) < 0.05, variable importance projection (VIP) > 1.0, and fold change (FC) > 1.5 or < 2/3, we found 57 plasma differential metabolites and 20 urinary differential metabolites in IBD. Then, with area under the curve (AUC) ≥ 0.85 and FC ≥ 2 or ≤ 0.3, 11 potential biomarkers were identified, such as acylcarnitine (ACar 20:4, ACar 18:1, and ACar 20:3), 3-indoleacetic acid, hippuric acid, and dehydroepiandrosterone sulfate, which is related to intestinal microbiota and immune response. However, less obvious differences were observed in IBD-DH when compared with IBD-SD. Under the condition of FDR < 0.2, VIP >1.0, and FC > 1.5 or < 2/3, we identified 16 plasma differential metabolites. In urine samples, IBD-DH and IBD-SD had the same metabolite pattern. With AUC ≥ 0.80, 7 differential plasma metabolites, mainly glycerophospholipids, were identified in IBD-DH. Kyoto Encyclopedia of Genes and Genomes analysis indicated that metabolic pathways, such as citrate cycle and amino acids metabolism, were mainly responsible for the distinction between IBD and healthy controls, whereas glycerophospholipid metabolism perturbation was not only a manifestation of IBD but also an important pathway to distinguish two subtypes defined by traditional medicine, IBD-DH and IBD-SD.

Conclusion

In this study, we found that several metabolites of aromatic acids and lipid derivatives could act as potential biomarkers to discriminate IBD from healthy controls. Glycerophospholipids metabolites might be used to differentiate IBD-DH from IBD-SD.

Metabolomic Approaches for Detection and Identification of Biomarkers and Altered Pathways in Bladder Cancer

Metabolomic analysis has proven to be a useful tool in biomarker discovery and the molecular classification of cancers. In order to find new biomarkers, and to better understand its pathological behavior, bladder cancer also has been studied using a metabolomics approach. In this article, we review the literature on metabolomic studies of bladder cancer, focusing on the different available samples (urine, blood, tissue samples) used to perform the studies and their relative findings. Moreover, the multi-omic approach in bladder cancer research has found novel insights into its metabolic behavior, providing excellent start-points for new diagnostic and therapeutic strategies. Metabolomics data analysis can lead to the discovery of a “signature pathway” associated with the progression of bladder cancer; this aspect could be potentially valuable in predictions of clinical outcomes and the introduction of new treatments. However, further studies are needed to give stronger evidence and to make these tools feasible for use in clinical practice.

Proteomic Profiling of Plasma-Derived Biomarkers in Patients with Bladder Cancer: A Step towards Clinical Translation

Background: Bladder cancer is a life-threatening disease and a major cause of cancer-associated complications. The main challenges confronted during the clinical management of bladder cancer are associated with recurrence and disease progression to the muscle-invasive phenotype. Improved early detection of the disease is of paramount importance to prevent disease progression and improve survival. Hence, novel clinically applicable biomarkers for early detection are warranted. Methods: In the current study, a comparative proteomic approach was undertaken using plasma samples to identify protein biomarkers associated with the muscle-invasive phenotype of bladder carcinoma. Isolated plasma proteins were depleted, DIGE-labeled, then subjected to conventional 2D electrophoresis followed by mass spectrometry for identification of differentially expressed proteins. Western blot was used for data validation. Results: Fourteen differentially expressed proteins with statistically significant changes in abundance between the cancer group and control group were identified. Three differentially expressed proteins were selected for validation, among which apolipoprotein A1 exhibited high specificity and sensitivity (AUC = 0.906). Ingenuity pathway analysis identified IFN-γ and TNF-α as the main signaling hub for the differentially regulated proteins. Conclusion: Our findings provide additional insight into understanding bladder cancer pathogenesis. Our data identified potential non-invasive plasma-derived biomarker proteins that merit additional investigation to validate its clinical usefulness to prevent bladder cancer progression.

Glutathione peroxidase 4-dependent glutathione high-consumption drives acquired platinum chemoresistance in lung cancer-derived brain metastasis

BACKGROUND

Platinum-based chemotherapy is effective in inducing shrinkage of primary lung cancer lesions; however, it shows finite therapeutic efficacy in patients suffering from brain metastasis (BM). The intrinsic changes of BM cells, which contribute to the poor results remain unknown.

METHODS

Platinum drug-sensitivity was assessed by utilizing a preclinical BM model of PC9 lung adenocarcinoma cells in vitro and in vivo. High consumption of glutathione (GSH) and two associated upregulated proteins (GPX4 and GSTM1) in BM were identified by integrated metabolomics and proteomics in cell lines and verified by clinical serum sample. Gain-of-function and rescue experiments were implemented to reveal the impact and mechanism of GPX4 and GSTM1 on the chemosensitivity in BM. The interaction between GPX4 and GSTM1 was examined by immunoblotting and immunoprecipitation. The mechanism of upregulation of GPX4 was further uncovered by luciferase reporter assay, immunoprecipitation, and electrophoretic mobility shift assay.

RESULTS

The derivative brain metastatic subpopulations (PC9-BrMs) of parental cells PC9 developed obvious resistance to platinum. Radically altered profiles of BM metabolism and protein expression compared with primary lung cancer cells were described and GPX4 and GSTM1 were identified as being responsible for the high consumption of GSH, leading to decreased chemosensitivity by negatively regulating ferroptosis. Besides, GSTM1 was found regulated by GPX4, which was transcriptionally activated by the Wnt/NR2F2 signaling axis in BM.

CONCLUSIONS

Collectively, our findings demonstrated that Wnt/NR2F2/GPX4 promoted acquired chemoresistance by suppressing ferroptosis with high consumption of GSH. GPX4 inhibitor was found to augment the anticancer effect of platinum drugs in lung cancer BM, providing novel strategies for lung cancer patients with BM.

Epigenomic and Metabolomic Integration Reveals Dynamic Metabolic Regulation in Bladder Cancer

Bladder cancer (BC) represents a clinical, social, and economic challenge due to tumor-intrinsic characteristics, limitations of diagnostic techniques and a lack of personalized treatments. In the last decade, the use of liquid biopsy has grown as a non-invasive approach to characterize tumors. Moreover, the emergence of omics has increased our knowledge of cancer biology and identified critical BC biomarkers. The rewiring between epigenetics and metabolism has been closely linked to tumor phenotype. Chromatin remodelers interact with each other to control gene silencing in BC, but also with stress-inducible factors or oncogenic signaling cascades to regulate metabolic reprogramming towards glycolysis, the pentose phosphate pathway, and lipogenesis. Concurrently, one-carbon metabolism supplies methyl groups to histone and DNA methyltransferases, leading to the hypermethylation and silencing of suppressor genes in BC. Conversely, α-KG and acetyl-CoA enhance the activity of histone demethylases and acetyl transferases, increasing gene expression, while succinate and fumarate have an inhibitory role. This review is the first to analyze the interplay between epigenome, metabolome and cell signaling pathways in BC, and shows how their regulation contributes to tumor development and progression. Moreover, it summarizes non-invasive biomarkers that could be applied in clinical practice to improve diagnosis, monitoring, prognosis and the therapeutic options in BC.

Prognosis prediction of hepatocellular carcinoma after surgical resection based on serum metabolic profiling from gas chromatography-mass spectrometry

Comprehensive prognostic risk prediction of hepatocellular carcinoma (HCC) after surgical treatment is particularly important for guiding clinical decision-making and improving postoperative survival. Hence, we aimed to build prognostic models based on serum metabolomics data, and assess the prognostic risk of HCC within 5 years after surgical resection. A pseudotargeted gas chromatography-mass spectrometry (GC-MS)-based metabolomics method was applied to analyze serum profiling of 78 HCC patients. Important metabolic features with discriminant ability were identified by a novel network-based metabolic feature selection method based on combinational significance index (N-CSI). Subsequently, phenylalanine and galactose were further identified to be relevant with mortality by the Cox regression analysis, while galactose and tyrosine were associated with recurrence and metastasis. Two models to predict risk of mortality (risk score of overall survival, RS_OS) and risk of recurrence and metastasis (risk score of disease-free survival, RS_DFS) were generated based on two panels of metabolites, respectively, which present favorable ability to predict prognosis of HCC, especially when combined with clinical staging system. The performance of models was further validated in an external independent cohort from 91 HCC patients. This study demonstrated that metabolomics is a powerful tool for risk screening of HCC prognosis.

Differential metabolic network construction for personalized medicine: Study of type 2 diabetes mellitus patients' response to gliclazide-modified-release-treated

Individual variation in genetic and environmental factors can cause the differences in metabolic phenotypes, which may have an effect on drug responses of patients. Deep exploration of patients' responses to therapeutic agents is a crucial and urgent event in the personalized treatment study. Using machine learning methods for the discovery of suitability evaluation biomarkers can provide deep insight into the mechanism of disease therapy and facilitate the development of personalized medicine. To find important metabolic network signals for the prediction of patients' drug responses, a novel method referred to as differential metabolic network construction (DMNC) was proposed. In DMNC, concentration changes in metabolite ratios between different pathological states are measured to construct differential metabolic networks, which can be used to advance clinical decision-making. In this study, DMNC was applied to characterize type 2 diabetes mellitus (T2DM) patients' responses against gliclazide modified-release (MR) therapy. Two T2DM metabolomics datasets from different batches of subjects treated by gliclazide MR were analyzed in depth. A network biomarker was defined to assess the patients' suitability for gliclazide MR. It can be effective in the prediction of significant responders from nonsignificant responders, achieving area under the curve values of 0.893 and 1.000 for the discovery and validation sets, respectively. Compared with the metabolites selected by the other methods, the network biomarker selected by DMNC was more stable and precise to reflect the metabolic responses in patients to gliclazide MR therapy, thereby contributing for the personalized medicine of T2DM patients. The better performance of DMNC validated its potential for the identification of network biomarkers to characterize the responses against therapeutic treatments and provide valuable information for personalized medicine.

A rapid GC method coupled with quadrupole or time of flight mass spectrometry for metabolomics analysis

Gas chromatography-mass spectrometry (GC-MS) is an ideal tool for analyzing the intermediates of tricarboxylic acid cycle and glycolysis, sugars, organic acids and amino acids, etc. High-throughput metabolomics methods are required by large-scale clinical researches, and time of flight mass spectrometry (TOF MS) having fast scanning rate is preferable for rapid GC. Quadrupole MS (qMS) instruments have 95% market share, and their potential in rapid metabolomics is worth being studied. In this work, a within 15-min GC program was established and matched by qMS scanning for plasma metabolome analysis after N-methyl-N-(trimethylsilyl)-trifluoroacetamide derivatization. Compared to the longer-time program GC-qMS method, the rapid GC-qMS method had nearly no metabolome information loss, and it had excellent profile performance in repeatability, intra-day and inter-day precision, sampling range, linearity and extraction recovery. Compared to TOF MS, qMS achieved similar results in investigating lung cancer serum metabolic disruptions. Partial least squares-discriminant analysis revealed that the two datasets acquired by qMS and TOF MS had very similar model parameters, and most of top ranked differential metabolites were the same. This study provides a rapid and economical GC-qMS metabolomics method for researchers. Still, MS having faster scanning rate and higher sensitivity are recommended, if possible, to detect more small peaks and some co-eluted peaks.

Metabolic Modulation and Potential Biomarkers of the Prognosis Identification for Severe Aortic Stenosis after TAVR by a Metabolomics Study

Objectives

To investigate the metabolic profile in patients with aortic stenosis (AS) after transcatheter aortic valve replacement (TAVR) and explore the potential biomarkers to predict prognosis after TAVR based on metabolomics.

Methods and Results

Fifty-nine consecutive AS patients were prospectively recruited. Blood samples from the ascending aorta, coronary sinus, and peripheral vein at before and after TAVR were collected, respectively. Liquid chromatography-mass spectrometry and gas chromatography-mass spectrometry were performed to analyze the metabolic profile before and after TAVR. Influential metabolites were identified by integrating the univariate test, multivariate analysis, and weighted gene coexpression network analysis (WGCNA) algorithm. PLS-DA analysis revealed a significant extremely early (within 30 minutes after TAVR) alterations of metabolites in the ascending aorta, coronary sinus, and peripheral vein. The early (within 7 days after TAVR) changed metabolites in the peripheral vein were involved in purine metabolism, primary bile acid biosynthesis, glycerolipid metabolism, amino sugar and nucleotide sugar metabolism, one carbon pool by folate and alanine, and the aspartate and glutamate metabolism pathway. We used volcano plots to find that the cardiac-specific changed metabolites were enriched to the sphingolipid metabolism pathway after TAVR. Besides, WGCNA algorithm was performed to reveal that arginine and proline metabolites could reflect left ventricle regression to some extent.

Conclusion

This is the first study to reveal systemic and cardiac metabolites changed significantly in patients with AS after TAVR. Some altered metabolites involved in the arginine and proline metabolism pathway in the peripheral vein could predict left ventricle regression, which merited further study.

Serum Metabolomics for Biomarker Screening of Esophageal Squamous Cell Carcinoma and Esophageal Squamous Dysplasia Using Gas Chromatography-Mass Spectrometry

BACKGROUND

Esophageal squamous cell carcinoma (ESCC) is one of the most common malignancies with poor diagnosis. Esophageal squamous dysplasia (ESD) is considered as an immediate precancerous lesion of ESCC. Lack of biomarkers for discriminating ESCC and ESD from healthy subjects limits the early diagnosis and treatment of ESCC. Therefore, a serum metabolomic strategy was conducted to identify and validate potential metabolic markers for the screening of ESCC and ESD subjects.

METHODS

A total of 74 patients with ESCC, 72 patients with ESD, and 75 normal control (NC) subjects were enrolled in this study. Gas chromatography-mass spectrometry was used to acquire serum metabolic profiles. Pathway analysis was conducted to uncover the fluctuated metabolic pathways during ESCC. Multivariate analyses were used to screen and validate the biomarkers.

RESULTS

ESCC, ESD, and NC subjects revealed progressively altered metabolic profiles, in which amino acids globally increased, while fatty acids decreased in ESCCs compared with the control groups. Pathway analysis demonstrated the activated biosynthesis of amino acids and inhibited desaturation of saturated fatty acids. The panel constructed with propanoic acid, linoleic acid, glycerol-3-phosphate, and l-glutamine showed the area under the curve (AUC), sensitivity, and specificity of 0.817, 0.75, and 0.74, respectively, in the discrimination of ESCC/ESD patients from NC subjects. The panel constructed by propanoic acid, l-leucine, and hydroxyproline revealed the AUC, sensitivity, and specificity of 0.819, 0.76, and 0.72, respectively, in the discrimination of ESD from NC subjects. The combination of hypoxanthine, 2-ketoisocaproic acid, l-glutamate, and l-aspartate showed the AUC, sensitivity, and specificity of 0.818, 0.83, and 0.74, respectively, in the discrimination of ESCC patients from ESD subjects.

CONCLUSIONS

Our study revealed the systematic landscape for metabolic alterations in sera of ESD and ESCC patients. The defined metabolite markers showed reasonable performance in the discrimination of ESCC and ESD patients, and may provide helpful reference for clinicians and biologists.

A Urine Metabonomics Study of Rat Bladder Cancer by Combining Gas Chromatography-Mass Spectrometry with Random Forest Algorithm

A urine metabolomics study based on gas chromatography-mass spectrometry (GC-MS) and multivariate statistical analysis was applied to distinguish rat bladder cancer. Urine samples with different stages were collected from animal models, i.e., the early stage, medium stage, and advanced stage of the bladder cancer model group and healthy group. After resolving urea with urease, the urine samples were extracted with methanol and, then, derived with N, O-Bis(trimethylsilyl) trifluoroacetamide and trimethylchlorosilane (BSTFA + TMCS, 99 : 1, v/v), before analyzed by GC-MS. Three classification models, i.e., healthy control vs. early- and middle-stage groups, healthy control vs. advanced-stage group, and early- and middle-stage groups vs. advanced-stage group, were established to analyze these experimental data by using Random Forests (RF) algorithm, respectively. The classification results showed that combining random forest algorithm with metabolites characters, the differences caused by the progress of disease could be effectively exhibited. Our results showed that glyceric acid, 2, 3-dihydroxybutanoic acid, N-(oxohexyl)-glycine, and D-turanose had higher contributions in classification of different groups. The pathway analysis results showed that these metabolites had relationships with starch and sucrose, glycine, serine, threonine, and galactose metabolism. Our study results suggested that urine metabolomics was an effective approach for disease diagnosis.

Gas chromatography-mass spectrometry untargeted profiling of non-Hodgkin's lymphoma urinary metabolite markers

Non-Hodgkin's lymphoma (NHL) is a cancer of the lymphatic system where the lymphoid and hematopoietic tissues are infiltrated by malignant neoplasms of B, T, and natural killer lymphocytes. Effective and less invasive methods for NHL screening are urgently needed. Herein, we report an untargeted gas chromatography-mass spectrometry (GC-MS) method to investigate metabolic changes in non-volatile derivatized compounds from urine samples of NHL patients (N = 15) and compare them to healthy controls (N = 34). Uni- and multivariate data analysis showed 18 endogenous metabolites, including amino acids and their metabolites, sugars, small organic acids, and vitamins, as statistically significant for group differentiation. A receiver operating characteristic curve (ROC) generated from a support vector machine (SVM) algorithm-based model achieved 0.998 of predictive accuracy, displaying the potential and relevance of GC-MS-detected urinary non-volatile compounds for predictive purposes. Furthermore, a specific panel of key metabolites was also evaluated, showing similar results. All in all, our results indicate that this robust GC-MS method is an effective screening tool for NHL diagnosis and it is able to highlight different pathways of the disease. Graphical Abstract.

Development of a plasma pseudotargeted metabolomics method based on ultra-high-performance liquid chromatography-mass spectrometry

Untargeted methods are typically used in the detection and discovery of small organic compounds in metabolomics research, and ultra-high-performance liquid chromatography-high-resolution mass spectrometry (UHPLC-HRMS) is one of the most commonly used platforms for untargeted metabolomics. Although they are non-biased and have high coverage, untargeted approaches suffer from unsatisfying repeatability and a requirement for complex data processing. Targeted metabolomics based on triple-quadrupole mass spectrometry (TQMS) could be a complementary tool because of its high sensitivity, high specificity and excellent quantification ability. However, it is usually applicable to known compounds: compounds whose identities are known and/or are expected to be present in the analyzed samples. Pseudotargeted metabolomics merges the advantages of untargeted and targeted metabolomics and can act as an alternative to the untargeted method. Here, we describe a detailed protocol of pseudotargeted metabolomics using UHPLC-TQMS. An in-depth, untargeted metabolomics experiment involving multiple UHPLC-HRMS runs with MS at different collision energies (both positive and negative) is performed using a mixture obtained using small amounts of the analyzed samples. XCMS, CAMERA and Multiple Reaction Monitoring (MRM)-Ion Pair Finder are used to find and annotate peaks and choose transitions that will be used to analyze the real samples. A set of internal standards is used to correct for variations in retention time. High coverage and high-performance quantitative analysis can be realized. The entire protocol takes ~5 d to complete and enables the simultaneously semiquantitative analysis of 800-1,300 metabolites.

An improved pseudotargeted GC-MS/MS-based metabolomics method and its application in radiation-induced hepatic injury in a rat model

The liver is the pivotal metabolic organ primarily responsible for metabolic activities, detoxification and regulation of carbohydrate, protein, amino acid, and lipid metabolism. However, very little is known about the complicated pathophysiologic mechanisms of liver injury result from ionizing radiation exposure. Therefore, a pseudotargeted metabolomics approach based on gas chromatography-tandem mass spectrometry with selected reaction monitoring (GC-MS-SRM) was developed to study metabolic alterations of liver tissues in radiation-induced hepatic injury. The pseudotargeted GC-MS-SRM method was validated with satisfactory analytical characteristics in terms of precision, linearity, sensitivity and recovery. Compared to the SIM-based approach, the SRM scanning method had mildly better precision, higher sensitivity, and wider linear ranges. A total of 37 differential metabolites associated with radiation-induced hepatic injury were identified using the GC-MS-SRM metabolomics method. Global metabolic clustering analysis showed that amino acids, carbohydrates, unsaturated fatty acids, organic acids, metabolites associated with pyrimidine metabolism, ubiquinone biosynthesis and oxidative phosphorylation appeared significantly declined after high dose irradiation exposure, whereas metabolites related to lysine catabolism, glycerolipid metabolism and glutathione metabolism presented the opposite behavior. These changes indicate energy deficiency, antioxidant defense damage, accumulation of ammonia and lipid oxidation of liver tissues in response to radiation exposure. It is shown that the developed pseudotargeted method based on GC-MS-SRM is a useful tool for metabolomics study.

Metabolomics markers in Neurology: current knowledge and future perspectives for therapeutic targeting

INTRODUCTION

Metabolomics is an emerging approach providing new insights into the metabolic changes and underlying mechanisms involved in the pathogenesis of neurological disorders.

AREAS COVERED

Here, the authors present an overview of the current knowledge of metabolic profiling (metabolomics) to provide critical insight on the role of biochemical markers and metabolic alterations in neurological diseases.

EXPERT OPINION

Elucidation of characteristic metabolic alterations in neurological disorders is crucial for a better understanding of their pathogenesis, and for identifying potential biomarkers and drug targets. Nevertheless, discrepancies in diagnostic criteria, sample handling protocols, and analytical methods still affect the generalizability of current study results.

LC-MS-Based Plasma Metabolomics and Lipidomics Analyses for Differential Diagnosis of Bladder Cancer and Renal Cell Carcinoma

Bladder cancer (BC) and Renal cell carcinoma(RCC) are the two most frequent genitourinary cancers in China. In this study, a comprehensive liquid chromatography-mass spectrometry (LC-MS) based method, which utilizes both plasma metabolomics and lipidomics platform, has been carried out to discriminate the global plasma profiles of 64 patients with BC, 74 patients with RCC, and 141 healthy controls. Apparent separation was observed between cancer (BC and RCC) plasma samples and controls. The area under the receiving operator characteristic curve (AUC) was 0.985 and 0.993 by plasma metabolomics and lipidomics, respectively (external validation group: AUC was 0.944 and 0.976, respectively). Combined plasma metabolomics and lipidomics showed good predictive ability with an AUC of 1 (external validation group: AUC = 0.99). Then, separation was observed between the BC and RCC samples. The AUC was 0.862, 0.853 and 0.939, respectively, by plasma metabolomics, lipidomics and combined metabolomics and lipidomics (external validation group: AUC was 0.802, 0.898, and 0.942, respectively). Furthermore, we also found eight metabolites that showed good predictive ability for BC, RCC and control discrimination. This study indicated that plasma metabolomics and lipidomics may be effective for BC, RCC and control discrimination, and combined plasma metabolomics and lipidomics showed better predictive performance. This study would provide a reference for BC and RCC biomarker discovery, not only for early detection and screening, but also for differential diagnosis.

Switching from Fatty Acid Oxidation to Glycolysis Improves the Outcome of Acute-On-Chronic Liver Failure

Acute-on-chronic liver failure (ACLF) has a high mortality rate. Metabolic reprogramming is an important mechanism for cell survival. Herein, the metabolic patterns of ACLF patients are analyzed. An in vitro model of ACLF is established using Chang liver cells under hyperammonemia and hypoxia. A randomized clinical trial (ChiCTR-OPC-15006839) is performed with patients receiving L-ornithine and L-aspartate (LOLA) daily intravenously (LOLA group) and trimetazidine (TMZ) tid orally (TMZ group) based on conventional treatment (control group). The primary end point is 90-day overall survival, and overall survival is the secondary end point. By analyzing metabolic profiles in liver tissue samples from hepatitis B virus (HBV)-related ACLF patients and the controls, the metabolic characteristics of HBV-related ACLF patients are identified: inhibited glycolysis, tricarboxylic acid cycle and urea cycle, and enhanced fatty acid oxidation (FAO) and glutamine anaplerosis. These effects are mainly attributed to hyperammonemia and hypoxia. Further in vitro study reveals that switching from FAO to glycolysis could improve hepatocyte survival in the hyperammonemic and hypoxic microenvironment. Importantly, this randomized clinical trial confirms that inhibiting FAO using TMZ improves the prognosis of patients with HBV-related ACLF. In conclusion, this study provides a practical strategy for targeting metabolic reprogramming using TMZ to improve the survival of patients with HBV-related ACLF.

Metabolic profiling of acromegaly using a GC-MS-based nontargeted metabolomic approach

PURPOSE

Acromegaly is a rare disease caused by chronic hypersecretion of growth hormone, which leads to multiple comorbidities and reduced life expectancy. The objective of this study was to characterize the serum metabolic profiles of acromegaly patients and identify metabolic biomarkers using metabolomics.

METHODS

Twenty-nine active acromegaly patients and age- and sex-matched normal controls were recruited. Serum samples were collected, and serum metabolites were analyzed using gas chromatography-mass spectrometry coupled with a series of multivariate statistical analyses.

RESULTS

The orthogonal projections to latent structures-discriminate analysis (OPLS-DA) model identified and validated significant metabolic differences between individuals with acromegaly and normal controls (R²Y = 0.908 and Q²Y = 0.601). Compared with normal controls, acromegaly patients had elevated levels of 5-aminovaleric acid, glyceric acid, L-dithiothreitol, dihydrocoumarin, N-acetyl-L-glutamic acid, gluconic acid, and monoolein (P < 0.05) and reduced serum levels of D-erythronolactone, taurine, carbamoyl-aspartic acid, and mucic acid (P < 0.01). Furthermore, glyceric acid and taurine possessed higher area under the receiver operating characteristic curve values (AUC values, 0.914 and 0.931, respectively), suggesting an excellent clinical ability to distinguish acromegaly patients from normal controls. Pathway analysis revealed that the pentose phosphate pathway and the taurine and hypotaurine metabolic pathway are significant pathways (P = 0.002 and 0.004, respectively).

CONCLUSIONS

Metabolic activity is significantly altered in the serum of individuals with active acromegaly. Glyceric acid and taurine may be considered potential biomarkers for distinguishing acromegaly patients from normal controls.

Improving diagnosis of genitourinary cancers: Biomarker discovery strategies through mass spectrometry-based metabolomics

The genitourinary oncology field needs integration of results from basic science, epidemiological studies, clinical and translational research to improve the current methods for diagnosis. MS-based metabolomics can be transformative for disease diagnosis and contribute to global health parity. Metabolite panels are promising to translate metabolomic findings into the clinics, changing the current diagnosis paradigm based on single biomarker analysis. This review article describes capabilities of the MS-based oncometabolomics field for improving kidney, prostate, and bladder cancer detection, early diagnosis, risk stratification, and outcome. Published works are critically discussed based on the study design; type and number of samples analyzed; data quality assessment through quality assurance and quality control practices; data analysis workflows; confidence levels reported for identified metabolites; validation attempts; the overlap of discriminant metabolites for the different genitourinary cancers; and the translation capability of findings into clinical settings. Ongoing challenges are discussed, and future directions are delineated.

Metabolic profiling reveals distinct metabolic alterations in different subtypes of pituitary adenomas and confers therapeutic targets

BACKGROUND

Pituitary adenomas are common brain tumors. Although transsphenoidal surgery are able to achieve extensive tumor removal, the rate of recurrence ranges from 5 to 20% depending on the different subtype. Further understanding of these tumors is needed to develop novel strategies to improve the prognosis of patients. But their metabolic characteristics are largely unknown.

METHODS

We used metabolomic, transcriptomic, and proteomic approaches to systematically investigate eight subtypes of pituitary adenomas and normal pituitary glands. By blocking IDH2, we investigate IDH2 play an inhibitory role in GH tumor cell growth and tumor secretion.

RESULTS

We found that all of the pituitary adenomas displayed downregulated glucose metabolism and glycolysis compared to normal tissues. Together with the differences in amino acids and fatty acids, we categorized these tumors into three clusters. We then re-established the reprogrammed metabolic flux in pituitary adenomas based on multiomic analyses. Take growth hormone-secreting pituitary adenomas as an example, we revealed that IDH2 is a key player in the reprogrammed metabolism of such tumors. By blocking IDH2, we confirmed that IDH2 is a potential target for the inhibition of tumor cell growth and tumor secretion.

CONCLUSIONS

Our study first uncovered the metabolic landscape of pituitary adenomas and demonstrated a possible way to inhibit tumor growth by regulating aberrant metabolism.

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.

Recent advances in the metabolomic study of bladder cancer

INTRODUCTION

Metabolomics is a chemical process, involving the characterization of metabolites and cellular metabolism. Recent studies indicate that numerous metabolic pathways are altered in bladder cancer (BLCA), providing potential targets for improved detection and possible therapeutic intervention. We review recent advances in metabolomics related to BLCA and identify various metabolites that may serve as potential biomarkers for BLCA. Areas covered: In this review, we describe the latest advances in defining the BLCA metabolome and discuss the possible clinical utility of metabolic alterations in BLCA tissues, serum, and urine. In addition, we focus on the metabolic alterations associated with tobacco smoke and racial disparity in BLCA. Expert commentary: Metabolomics is a powerful tool which can shed new light on BLCA development and behavior. Key metabolites may serve as possible markers of BLCA. However, prospective validation will be needed to incorporate these markers into clinical care.

Discovering biomarkers in bladder cancer by metabolomics

It has become increasingly clear that the development of cancer, a multifactorial disease, cannot be explained by a single molecule or gene mutation. As a new discipline, metabolomics focuses on the body's metabolite changes, and attempts to find differences to explain the development of cancer; it has proven to be effective and credible. Metabolic studies of bladder cancer (BCa) lag behind those of other tumors. This review systematically outlines the specific process of metabolomics and the use of metabolomics in BCa studies in recent years. We have reviewed the in vitro cell line, bladder tumor tissue and biofluid (urine, plasma and serum) studies used in metabolomics analyses of BCa. The advantages and drawbacks of the use of different samples were compared. Based on the available studies, we have further described the aberrant metabolic pathways of BCa and have suggested some metabolites that may be potential biomarkers for BCa detection.

Ion-Pair Selection Method for Pseudotargeted Metabolomics Based on SWATH MS Acquisition and Its Application in Differential Metabolite Discovery of Type 2 Diabetes

The pseudotargeted metabolomics method integrates advantages of nontargeted and targeted analysis because it can acquire data of metabolites in the multireaction monitoring (MRM) mode of mass spectrometry (MS) without needing standards. The key is the ion-pair information collection from samples to be analyzed. It is well-known that sequential windowed acquisition of all theoretical Fragment ion (SWATH) MS mode can acquire MS2 information to a maximum extent. To expediently acquire as many ion-pairs as possible with optimal collision energy (CE), an ion-pair selection approach based on SWATH MS acquisition with variable isolation windows was developed in this study. Initially, nontargeted acquisition of all metabolites information in plasma Standard Reference Material (SRM 1950) was performed by ultra high-performance liquid chromatography (UHPLC)-quadrupole time-of-flight (Q-TOF) MS platform with three CEs. With the help of software tool, the ion-pairs of unique metabolites were gained. Then they were validated in scheduled MRM coupled with UHPLC. After removing false positive, the ion-pairs with an optimal CE was integrated. A total of 1373 unique metabolite ion-pairs were obtained at positive ion mode. And repeatability of the established pseudotargeted approach was evaluated by intraday and interday precision. The results demonstrated the method was stable, reliable, and suitable for metabolomics study. As an application example, alterations of serum metabolites in Type 2 diabetes were investigated by using the established method. This work provides a pseudotargeted ion-pair selection method based on SWATH MS acquisition with the characters of increased metabolite coverage, suitable CE, and convenient processing.

Nuclear magnetic resonance spectroscopy as a new approach for improvement of early diagnosis and risk stratification of prostate cancer

Prostate cancer (PCa) is the second most common male cancer worldwide and the fifth leading cause of death from cancer in men. Early detection and risk stratification is the most effective way to improve the survival of PCa patients. Current PCa biomarkers lack sufficient sensitivity and specificity to cancer. Metabolite biomarkers are evolving as a new diagnostic tool. This review is aimed to evaluate the potential of metabolite biomarkers for early detection, risk assessment, and monitoring of PCa. Of the 154 identified publications, 27 and 38 were original papers on urine and serum metabolomics, respectively. Nuclear magnetic resonance (NMR) is a promising method for measuring concentrations of metabolites in complex samples with good reproducibility, high sensitivity, and simple sample processing. Especially urine-based NMR metabolomics has the potential to be a cost-efficient method for the early detection of PCa, risk stratification, and monitoring treatment efficacy.

A review of validated biomarkers obtained through metabolomics

INTRODUCTION

Studying changes in the whole set of small molecules, final products of biochemical reactions in living systems or metabolites, is extremely appealing because they represent the best approach to identifying what occurs in an organism when samples are collected. However, their usefulness as potential biomarkers is limited by discoveries obtained in small groups without proper validation or even confirmation of the chemical structure. Areas covered: During the past 5 years, more than 900 papers have been published on metabolomics for biomarker discovery, but the numbers are much lower when some criteria of validation are applied. In total, 102 papers have been included in this review. The most frequent disease areas in which these markers have been discovered include the following: cancer, diabetes, and related diseases and neurodegenerative, cardiovascular, autoimmune, liver, and kidney diseases. Expert commentary: Metabolomics has been demonstrated as rapidly growing due to the improvements in instrumentation, mainly mass spectrometry, and data mining software. For application in the clinic, the results should be validated in different stages, from analytical validation to validation in independent sets of samples, using thousands of samples from different sources.

Enhancement of mitochondrial biogenesis and paradoxical inhibition of lactate dehydrogenase mediated by 14-3-3η in oncocytomas

Oncocytomas represent a subset of benign pituitary adenomas that are characterized by significant mitochondrial hyperplasia. Mitochondria are key organelles for energy generation and metabolic intermediate production for biosynthesis in tumour cells, so understanding the mechanism underlying mitochondrial biogenesis and its impact on cellular metabolism in oncocytoma is vital. Here, we studied surgically resected pituitary oncocytomas by using multi-omic analyses. Whole-exome sequencing did not reveal any nuclear mutations, but identified several somatic mutations of mitochondrial DNA, and dysfunctional respiratory complex I. Metabolomic analysis suggested that oxidative phosphorylation was reduced within individual mitochondria, and that there was no reciprocal increase in glycolytic activity. Interestingly, we found a reduction in the cellular lactate level and reduced expression of lactate dehydrogenase A (LDHA), which contributed to mitochondrial biogenesis in an in vitro cell model. It is of note that the hypoxia-response signalling pathway was not upregulated in pituitary oncocytomas, thereby failing to enhance glycolysis. Proteomic analysis showed that 14-3-3η was exclusively overexpressed in oncocytomas, and that 14-3-3η was capable of inhibiting glycolysis, leading to mitochondrial biogenesis in the presence of rotenone. In particular, 14-3-3η inhibited LDHA by direct interaction in the setting of complex I dysfunction, highlighting the role of 14-3-3η overexpression and inefficient oxidative phosphorylation in oncocytoma mitochondrial biogenesis. These findings deepen our understanding of the metabolic changes that occur within oncocytomas, and shine a light on the mechanism of mitochondrial biogenesis, providing a novel perspective on metabolic adaptation in tumour cells. © 2018 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland.

An integrated strategy to improve data acquisition and metabolite identification by time-staggered ion lists in UHPLC/Q-TOF MS-based metabolomics

The narrow linear range and the limited scan time of the given ion make the quantification of the features challenging in liquid chromatography-mass spectrometry (LC-MS)-based untargeted metabolomics with the full-scan mode. And metabolite identification is another bottleneck of untargeted analysis owing to the difficulty of acquiring MS/MS information of most metabolites detected. In this study, an integrated workflow was proposed using the newly established multiple ion monitoring mode with time-staggered ion lists (tsMIM) and target-directed data-dependent acquisition with time-staggered ion lists (tsDDA) to improve data acquisition and metabolite identification in UHPLC/Q-TOF MS-based untargeted metabolomics. Compared to the conventional untargeted metabolomics, the proprosed workflow exhibited the better repeatability before and after data normalization. After selecting features with the significant change by statistical analysis, MS/MS information of all these features can be obtained by tsDDA analysis to facilitate metabolite identification. Using time-staggered ion lists, the workflow is more sensitive in data acquisition, especially for the low-abundant features. Moreover, the metabolites with low abundance tend to be wrongly integrated and triggered by full scan-based untargeted analysis with MS^E acquisition mode, which can be greatly improved by the proposed workflow. The integrated workflow was also successfully applied to discover serum biosignatures for the genetic modification of fat-1 in mice, which indicated its practicability and great potential in future metabolomics studies.

Current and future perspectives of functional metabolomics in disease studies-A review

Functional metabolomics is a new concept, which studies the functions of metabolites and related enzymes focused on metabolomics. It overcomes the shortcomings of traditional discovery metabolomics of mainly relying on literatures for biological interpretation. Functional metabolomics has many advantages. Firstly, the functional roles of metabolites and related metabolic enzymes are focused. Secondly, the in vivo and in vitro experiments are conducted to validate the metabolomics findings, therefore, increasing the reliability of metabolomics study and producing the new knowledge. Thirdly, functional metabolomics can be used by biologists to investigate functions of metabolites, and related genes and proteins. In this review, we summarize the analytical, biological and clinical platforms used in functional metabolomics studies. Recent progresses of functional metabolomics in cancer, metabolic diseases and biological phenotyping are reviewed, and future development is also predicted. Because of the tremendous advantages of functional metabolomics, it will have a bright future.

Integration of Proteomics and Metabolomics Revealed Metabolite-Protein Networks in ACTH-Secreting Pituitary Adenoma

An effective treatment for the management of adrenocorticotropic hormone-secreting pituitary adenomas (ACTH-PA) is currently lacking, although surgery is a treatment option. We have integrated information obtained at the metabolomic and proteomic levels to identify critical networks and signaling pathways that may play important roles in the metabolic regulation of ACTH-PA and therefore hopefully represent potential therapeutic targets. Six ACTH-PAs and seven normal pituitary glands were investigated via gas chromatography-mass spectrometry (GC-MS) analysis for metabolomics. Five ACTH-PAs and five normal pituitary glands were subjected to proteomics analysis via nano liquid chromatography tandem-mass spectrometry (nanoLC-MS/MS). The joint pathway analysis and network analysis was performed using MetaboAnalyst 3.0. software. There were significant differences of metabolites and protein expression levels between the ACTH-PAs and normal pituitary glands. A proteomic analysis identified 417 differentially expressed proteins that were significantly enriched in the Myc signaling pathway. The protein-metabolite joint pathway analysis showed that differentially expressed proteins and metabolites were significantly enriched in glycolysis/gluconeogenesis, pyruvate metabolism, citrate cycle (TCA cycle), and the fatty acid metabolism pathway in ACTH-PA. The protein-metabolite molecular interaction network identified from the metabolomics and proteomics investigation resulted in four subnetworks. Ten nodes in subnetwork 1 were the most significantly enriched in cell amino acid metabolism and pyrimidine nucleotide metabolism. Additionally, the metabolite-gene-disease interaction network established nine subnetworks. Ninety-two nodes in subnetwork 1 were the most significantly enriched in carboxylic acid metabolism and organic acid metabolism. The present study clarified the pathway networks that function in ACTH-PA. Our results demonstrated the presence of downregulated glycolysis and fatty acid synthesis in this tumor type. We also revealed that the Myc signaling pathway significantly participated in the metabolic changes and tumorigenesis of ACTH-PA. This data may provide biomarkers for ACTH-PA diagnosis and monitoring, and could also lead to the development of novel strategies for treating pituitary adenomas.

Discovery and validation of potential urinary biomarkers for bladder cancer diagnosis using a pseudotargeted GC-MS metabolomics method

Bladder cancer (BC) is the second most prevalent malignancy in the urinary system and is associated with significant mortality; thus, there is an urgent need for novel noninvasive diagnostic biomarkers. A urinary pseudotargeted method based on gas chromatography-mass spectrometry was developed and validated for a BC metabolomics study. The method exhibited good repeatability, intraday and interday precision, linearity and metabolome coverage. A total of 76 differential metabolites were defined in the discovery sample set, 58 of which were verified using an independent validation urine set. The verified differential metabolites revealed that energy metabolism, anabolic metabolism and cell redox states were disordered in BC. Based on a binary logistic regression analysis, a four-biomarker panel was defined for the diagnosis of BC. The area under the receiving operator characteristic curve was 0.885 with 88.0% sensitivity and 85.7% specificity in the discovery set and 0.804 with 78.0% sensitivity and 70.3% specificity in the validation set. The combinatorial biomarker panel was also useful for the early diagnosis of BC. This approach can be used to discriminate non-muscle invasive and low-grade BCs from healthy controls with satisfactory sensitivity and specificity. The results show that the developed urinary metabolomics method can be employed to effectively screen noninvasive biomarkers.